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526202

PCOGR: Phylogenetic COG ranking as an online tool to judge the specificity of COGs with respect to freely definable groups of organisms

Background The rapidly increasing number of completely sequenced genomes led to the establishment of the COG-database which, based on sequence homologies, assigns similar proteins from different organisms to clusters of orthologous groups (COGs). There are several bioinformatic studies that made use of this database to determine (hyper)thermophile-specific proteins by searching for COGs containing (almost) exclusively proteins from (hyper)thermophilic genomes. However, public software to perform individually definable group-specific searches is not available. Results The tool described here exactly fills this gap. The software is accessible at and is linked to the COG-database. The user can freely define two groups of organisms by selecting for each of the (current) 66 organisms to belong either to groupA, to the reference groupB or to be ignored by the algorithm. Then, for all COGs a specificity index is calculated with respect to the specificity to groupA, i. e. high scoring COGs contain proteins from the most of groupA organisms while proteins from the most organisms assigned to groupB are absent. In addition to ranking all COGs according to the user defined specificity criteria, a graphical visualization shows the distribution of all COGs by displaying their abundance as a function of their specificity indexes. Conclusions This software allows detecting COGs specific to a predefined group of organisms. All COGs are ranked in the order of their specificity and a graphical visualization allows recognizing (i) the presence and abundance of such COGs and (ii) the phylogenetic relationship between groupA- and groupB-organisms. The software also allows detecting putative protein-protein interactions, novel enzymes involved in only partially known biochemical pathways, and alternate enzymes originated by convergent evolution.

Background The COG-database has become a powerful tool in the field of comparative genomics. The construction of this data base is based on sequence homologies of proteins from different completely sequenced genomes. Highly homologous proteins are assigned to clusters of orthologous groups (COGs) [ 1 , 2 ]. Each of the COGs consists of individual proteins or groups of orthologs from at least 3 lineages and thus corresponds to a conserved domain. The COG collection currently consists of 138,458 proteins, which form 4,873 COGs and comprise 75% of the 185,505 (predicted) proteins encoded in 66 genomes of unicellular organisms [ 3 ]. In addition, the database now includes KOGs containing the clusters of seven eukaryotic genomes. The COG database is an ideal source to search for proteins specific to a certain group of organisms. Several such surveys aimed at finding (hyper)thermophile-specific proteins that made use of the COG-database are published. For instance, Forterre detected reverse gyrase as the only hyperthermophile-specific protein [ 4 ]. In addition, a survey to find specific genes important for hyperthermophily [ 5 ] and a study identifying thermophile-specific proteins [ 6 ] are published. However, those studies used rather nonflexible tools designed for other purposes [ 7 ] or software especially written and not accessible for the public. To overcome these issues, a more flexible software-tool is needed that allows defining the group of organisms individually for which specific COGs can be searched. Here we describe phylogenetic COG ranking (PCOGR), a platform independent software tool capable to rank all COGs with respect to a freely definable group of organisms versus a group of reference organisms. Implementation PCOGR is written in PHP (v.4.3.3) including the domxml (v.20020815) plugin and runs on an openBSD (v.3.4) operating system at dmz.uni-wh.de in an apache (v.1.3.28) web-server environment. In addition, at the clients-side, HTML, javascript, and CSS are used. Phylogenetic COG ranking (PCOGR) is an online-tool to analyze the microbial COG, or after clicking "Switch to PKOGR", to analyze the eukaryotic KOG database. PCOGR provides a means for determining the specificity of each COG with respect to the presence of sequences from organisms belonging to a predefined group (groupA) versus the absence of sequences from organisms belonging to a second predefined reference group (groupB). For that purpose, each of the organisms can be assigned to one of the two groups or defined to be ignored by the analysis. The software then calculates a specificity index S for every individual COG. The highest ranking COGs (large S) contain sequences from the most groupA-organisms whereas the most sequences from groupB-organisms are absent. To process S for each individual COG, the algorithm starts at S = 0, adds a constant A for each groupA-organism and subtracts a constant B for each groupB-organism being present in the COG under analysis with A = A tot /B tot and B = B tot /A tot where A tot is the total number of organisms belonging to groupA and B tot is the total number of organisms belonging to groupB. After all COGs have been processed in this way, all S-values are scaled to values between 0 and 1. Then, all COGs are output in the order of their specificity indexes S. In addition, a graphical representation shows the number of COGs as a function of their S-values in discrete intervals. The total number of intervals to be displayed can be specified by the user (default = 40 for PCOGR and 7 for PKOGR). A Javascript-mouseover info box intuitively explains all functions of the graphical user interface of PCOGR. Furthermore, additional information about both, organisms and output COGs, are available by the implementation of links to Figure 1 , 2 , and 3 show screenshots of the parameter input and output sections, respectively. Results and discussion PCOGR allows detecting group-specific proteins by both ranking all COGs and graphically showing their distribution over their specificity indexes. The graphical representations can be interpreted as follows: If the two predefined groups are rather related, one expects a single peak in the middle of the graph, i. e. there are little or no proteins specific to one of the groups resulting in a specificity value of around 0.5 for most COGs. In contrast, if the two groups are rather distant, further maxima, either on the left, the right or on both sides become visible, i. e. there are group-specific proteins with S-values around 1 and/or S-values around 0. Even two single organisms can be compared by assigning the first to groupA, the second to groupB and ignoring all other organisms. For instance comparing the closely related Escherichia coli strains O157:H7 EDL933 and O157:H7 results in a prominent single peak in the middle of the graph whereas two further peaks on the edges become visible if two more distant organisms e. g. Aquifex aeolicus and Saccharomyces cerevisiae are compared. Distance and relationship may be interpreted either in phylogenetic or in physiologic terms. To demonstrate that physiologic relevant differences in protein distributions indeed can be detected by PCOGR, two parameter-presets are selectable: (i) a specificity ranking of hyperthermophile-specific versus non-thermophile-specific proteins as published by Makarova et al. [ 5 ] and of thermophile-specific versus non-thermophile-specific proteins as described by Klinger et al. [ 6 ]. For the ranking according to Makarova et al., optimum growth temperatures of corresponding organisms belonging to groupA are all above 80°C and all other organisms are assigned to groupB. For the specificity ranking according to Klinger et al., the optimum growth temperature needed for an organism to be assigned to groupA is above 55°C instead of 80°C. The user will notice that for the two presets, there are two additional peaks, the first corresponding to COGs containing (hyper)thermophile-specific proteins, and the second peak corresponding to COGs containing mesophile-specific proteins. A further attractive potential of PCOGR lies in the easy way to detect novel protein-protein interactions since physically interacting proteins should phylogenetically similarly be distributed [ 8 ]. Thus, if the phylogenetic pattern for a putative interacting protein target is known, a ranking with this pattern as the input will result in a ranking of potentially interacting candidates. To simplify such a procedure, the phylogenetic pattern of a certain COG defined by the user can automatically be assigned as the preset of a subsequent ranking. As an example, we performed a ranking choosing the phylogenetic pattern of COG2025 (electron transfer flavoprotein, alpha subunit). This ranking resulted in only two high-scoring outputs (specificity value S = 1): COG2025 (the target) and COG2086 (electron transfer flavoprotein, beta subunit) which is shown by x-ray crystallography to build a complex with the alpha subunit [ 9 ]. All following proteins have specificity values below 0.9 indicating the suitability of such a search for protein-protein interactions. Not only protein-protein interactions can be detected but also enzymes involved in the same biochemical pathway as a certain target enzyme [ 8 ]. This possibility may be useful to find the biochemical function of yet uncharacterized proteins given that one or more catalysts of the same pathway are already characterized. For example, a search performed with the phylogenetic pattern of COG0135 (phosphoribosylanthranilate isomerase), an enzyme involved in the biosynthesis of L-tryptophan, results in four (COG0135, COG0159, COG0547, and COG0134) of the five enzymes involved in tryptophan biosynthesis at the top four places of the ranking. The beta subunit of tryptophan synthase is the only missing enzyme also involved in this pathway. A closer look reveals that this protein is assigned to two instead of one COGs (COG0133: rank 29 and COG1350: rank 1770). The latter COG is annotated as "predicted alternative tryptophan synthase beta-subunit (paralog of TrpB)". This double assignment may explain the absence of the beta subunit of tryptophan synthase from high-scoring proteins of the ranking. Another attractive use of PCOGR can be to look for an alternative enzyme form catalyzing the same reaction but originated by non orthologous gene displacement (NOGD). Occurrence of NOGD in essential functions can be explored systematically by detecting complementary, rather than identical or similar, phylogenetic patterns [ 10 ]. A ranking performed with COG0588 (phosphoglycerate mutase 1) indeed resulted in COG3635 (predicted phosphoglycerate mutase, AP superfamily) at the seventh last rank (rank 4867 out of 4873) demonstrating that PCOGR is also well suited for such a purpose. Conclusions With the online availability of PCOGR researchers can perform their own individual searches for group-specific proteins. This will not only allow a deeper insight into phylogenetic relationships of organisms or groups of organisms but also help to detect new highly group-specific proteins worth for isolation and further biochemical characterization. In addition, novel protein-protein interactions could be detected in silico, and this tool is also suitable to assign proteins of unknown function to partially known biochemical pathways. A further application lies in the search of alternate enzymes originated by convergent evolution. Availability and requirements Project name: Phylogenetic COG ranking (PCOGR) Project home page: Operating system(s): Platform independent Programming language: PHP, javascript, CSS and HTML Other requirements: Web-browser capable to execute javascript License: GNU General Public License Any restrictions to use by non-academics: Contact authors Authors' contributions FM carried out the software development and programming work. MK conceived of the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526202.xml

524373

The fallacy of enrolling only high-risk subjects in cancer prevention trials: Is there a "free lunch"?

Background There is a common belief that most cancer prevention trials should be restricted to high-risk subjects in order to increase statistical power. This strategy is appropriate if the ultimate target population is subjects at the same high-risk. However if the target population is the general population, three assumptions may underlie the decision to enroll high-risk subject instead of average-risk subjects from the general population: higher statistical power for the same sample size, lower costs for the same power and type I error, and a correct ratio of benefits to harms. We critically investigate the plausibility of these assumptions. Methods We considered each assumption in the context of a simple example. We investigated statistical power for fixed sample size when the investigators assume that relative risk is invariant over risk group, but when, in reality, risk difference is invariant over risk groups. We investigated possible costs when a trial of high-risk subjects has the same power and type I error as a larger trial of average-risk subjects from the general population. We investigated the ratios of benefit to harms when extrapolating from high-risk to average-risk subjects. Results Appearances here are misleading. First, the increase in statistical power with a trial of high-risk subjects rather than the same number of average-risk subjects from the general population assumes that the relative risk is the same for high-risk and average-risk subjects. However, if the absolute risk difference rather than the relative risk were the same, the power can be less with the high-risk subjects. In the analysis of data from a cancer prevention trial, we found that invariance of absolute risk difference over risk groups was nearly as plausible as invariance of relative risk over risk groups. Therefore a priori assumptions of constant relative risk across risk groups are not robust, limiting extrapolation of estimates of benefit to the general population. Second, a trial of high-risk subjects may cost more than a larger trial of average risk subjects with the same power and type I error because of additional recruitment and diagnostic testing to identify high-risk subjects. Third, the ratio of benefits to harms may be more favorable in high-risk persons than in average-risk persons in the general population, which means that extrapolating this ratio to the general population would be misleading. Thus there is no free lunch when using a trial of high-risk subjects to extrapolate results to the general population. Conclusion Unless the intervention is targeted to only high-risk subjects, cancer prevention trials should be implemented in the general population.

Background Some prevention trials are restricted to high-risk subjects. If the investigators are only interested in the effects of the intervention on subjects at increased risk [ 1 ] or if the study is designed to be a preliminary investigation in preparation for a definitive study in the general population, we think this restriction is reasonable. However some investigators who are interested in studying the effect of the intervention in the general population may be tempted to design a "definitive" study to estimate the effect of the intervention in a high-risk group. Some investigators may believe that a trial of high-risk subjects would have greater power than a trial of the same size among average-risk subjects. Some examples of this type of thinking can be found in papers on risk prediction models [ 2 , 3 ]. Some investigators may believe that a trial of high-risk subjects with the same power as a trial of average-risk subjects would have lower costs than a trial of average-risk subjects. Some investigators may believe the ratio of benefits to harms can be correctly extrapolated from high-risk to average-risk subjects. Although the rationales for these various beliefs are related, they involve some distinct underlying assumptions that are important to critically examine. Methods and results Possibly lower statistical power To crystallize our thinking about statistical power, we consider the following simple hypothetical and realistic example. Investigators want to estimate the effect of intervention in the general population, so they first consider designing a randomized trial among the general at-risk population. Suppose they anticipate that the cumulative probability of incident cancer over the course of the study is p C = .02 in the control arm and p I = .01 in the study arm, and they believe that the difference in probabilities is clinically significant. Also suppose that due to the limited availability of the intervention, they can enroll at most n = 2000 study participants in each arm. The investigators compute power using the following standard formula [ 1 ] setting the two-sided type I error at .05, where NormalCDF is the cumulative distribution function for a normal distribution with mean 0 and variance 1, Δ is the anticipated difference one wants to detect, n is the sample size per arm, se Null is the standard error under the null hypothesis, and se Alt is the standard error under the alternative hypothesis. Let p = ( p C + p I )/2. As discussed in [ 1 ], for a study designed to estimate the absolute risk difference, the statistic of interest is , so For a study designed to estimate the relative risk, the statistic of interest is , so Applying these formulas to the above example and substituting either (2) or (3) into (1), the investigators obtain a power of .74 based on the absolute risk difference statistic and a power .76 based on a relative risk statistic [see Additional file 1 ]. Suppose the investigators think this power is too low. To increase power they propose to restrict the study to a high-risk group in which the probability of cancer is .04. Also suppose the investigators make the typical assumption that if the intervention yields a relative risk of .5 in the general population, it would also yield a relative risk of .5 in the high-risk group. Applying (1–3) with high risk subjects for whom p C = .04 and p I = .02 with n = 2000, the investigators compute a power of .96 using either the absolute risk difference or relative risk. Because the power is higher using high-risk subjects, the investigators plan the study for a high-risk population and will generalize the results to the general population. Is there a free lunch? An underlying assumption in this example is that the relative risk is invariant between the general population and the high-risk group. There is no free lunch because the impact of violating this assumption could be substantial. For example, suppose instead that the absolute risk difference is invariant between the general population and the high risk group. Under this scenario the absolute risk difference in the general population is .01, so the absolute risk difference in the high-risk group is also .01. In this case for p C = .04, p I = .03, and n = 2000, the power (computed using either absolute risk difference or relative risk statistics) for the trial of high-risk subjects is only .41. The decreased power in a high risk group under a constant risk difference model is not surprising: if the risk difference p C - p I is the same, but p I is increasing, the variances, p C (1 - p C )/ n and p I (1 - p I )/ n , will increase as p C increases up to .5, which will reduce the power. A crucial issue is whether or not the absolute risk difference or the relative risk is likely invariant between average-risk subjects in the general population and high-risk subjects. The answer depends on the cancer, the interventions, and the biology. To gain some appreciation of this issue, we analyzed published data (summarized in Table 1 ) from a prevention trial of particular interest to us, a study of tamoxifen for the prevention of breast cancer [ 5 ]. Rather than limit the analysis to one particular high-risk group, we investigated subjects at various levels of risk defined separately by three variables: age, predicted risk, (the five-year risk of cancer based on the Gail model [ 3 ]), and family risk. We fit four models separately to each variable: Table 1 Data from a cancer prevention trial for investigating assumptions of constant risk difference and relative risk when risk groups change. Placebo group Tamoxifen group Variable risk group cancer at risk cancer at risk age at entry 1 ≤ 49 68 10149 38 10045 2 50–59 50 7912 25 8040 3 >60 57 7719 26 7782 predicted risk 1 ≤ 2.00% 35 6318 13 6311 2 2.01–3.01% 42 8108 29 8262 3 3.01–5.00% 43 7313 27 6959 4 ≤ 5.01% 55 4142 20 4425 family risk 1 0 38 5891 17 5724 2 1 90 15000 46 15182 3 2 37 4263 20 4211 4 3 10 729 6 855 Cancer is invasive breast cancer. Predicted risk is the 5-year predicted risk. Family risk is number of first degree relatives with breast cancer. Data are from Table 5 of [5] with number at risk computed by dividing number of breast cancers by reported breast cancer rate. constant risk difference, where δ is the risk difference that is constant over groups; varying risk difference, where δ i is the risk difference that varies over groups; constant relative risk, where β is the relative risk that is constant over groups; varying relative risk, where β is the relative risk that varies over groups. We obtained maximum likelihood estimates of δ , δ i , β , and β i using a Newton-Raphson procedure [see Additional file 2 ]. To investigate the plausibility of the constant relative risk and constant risk difference models in this example, we plotted the estimates of δ , δ i , β , and β i along with confidence intervals (Figure 1 ). In the top row of Figure 1 we plotted points corresponding to with (100 - 5/ k ) % confidence intervals and horizontal lines for with 95% confidence intervals. We also presented the p-values corresponding to twice the difference in log-likelihoods for Varying RD versus Constant RD . Similarly, in the bottom row of Figure 1 , we plotted points corresponding to with (100 - 5/ k )% confidence intervals and horizontal lines for with 95% confidence intervals. We also presented the p-value corresponding to twice the difference in log-likelihoods for Varying RR versus Constant RR . Out of 6 p-values (3 risk factors × 2 statistics) only one, for absolute risk difference under the risk factor of predicted risk had a small p-value (and the p-value of .01 would not be significant at the .05 level under a Bonferroni adjustment of .05/6). Based on these p-values and inspection of Figure 1 , the models Constant RD and Constant RR are both plausible, especially for age and family risk. Figure 1 Data from the tamoxifen prevention trial. See text for a description of groups. Horizontal lines are estimates and 95% confidence intervals for model for constant absolute risk difference per 1000 (RD) or relative risk (RR). P-values correspond to likelihood ratio tests comparing the models with varying and constant risk difference or relative risks. The trial designer does not know the true state of nature. If Constant RD is the true state of nature, the power will be lower in the high-risk group than the general population. However if Constant RR is the true state of nature, the power will be greater in the high-risk group than the general population. Thus there is high probability that the power could be reduced when studying high-risk subjects than when studying the general population. Therefore, there is no free lunch in terms of lowering statistical power. Possibly increased costs Even if the model is correct (namely p C and p I are correctly chosen), the smaller trial of high-risk subjects may be more expensive than the larger trial of average-risk subjects from the general population. Consider the following two trials with a power of .90 and a one-sided type I error of .05. In the trial of high-risk subjects p C = .04 and p I = .02, and in the trial of average-risk subjects, p C = .02 and p I = .01. Suppose the statistic of interest is the absolute risk difference. To obtain sample size for each randomization group we use the standard sample size formula [ 4 ], where p = ( p C + p I )/2, 1.644485 is the z-statistics corresponding to the 95th percentile of the normal distribution (for a one-sided type I error of .05) and 1.28155 is the z-statistics corresponding to the 90th percentile (for a power of .90). Based on (4), the sample size for a trial using average-risk subjects from the general population study is 2529 per group and the sample size for a trial of high-risk subjects is 1244 per group. Let C R denote the cost of recruitment per subject and C I denote the cost of intervention and follow-up per subject averaged over the two randomization groups . Suppose high risk subjects comprise a fraction f of the general population. The total cost of the trial for average-risk subjects from the general populations is C general = 2( C R 2529 + C I 2529),    (5) and the total cost of the trial for high-risk subjects is C high-risk = 2( C R 1244/ f + C I 1244).    (6) where the factor of 2 is for the two randomization groups. The condition for the trial of high-risk subjects to cost more than the trial of average-risk subjects (namely C high-risk > C general ) is when 1244/ f - 2529 > 0. If f = .20, the trial of high-risk subjects will cost more than the trial of average-risk subjects if C R / C I > .34. If f = .10, the trial of high-risk subjects will cost more than the trial of average-risk subjects if C R / C I > .13. In many cancer prevention trials the above values of C R / C I are likely. For example, diagnostic testing to identify high-risk smokers can include expensive airway pulmonary function tests or bronchoscopy. In the future, more trials will likely involve expensive genetic testing of subjects [ 5 ] with costs ranging from $350 to almost $3,000 per test according to recent information from Myriad Genetic Laboratories. As part of a sensitivity analysis related to genetic testing of subjects prior to enrollment in a trial, Baker and Freedman [ 5 ] considered values of .1, .5, and 1 for ratios similar to C R / C I . Even without diagnostic testing, the costs of obtaining high-risk subjects can be substantial. If f = .10, the initial recruitment will require ten times the number of people as for a trial of average-risk subjects from the general population. This increased recruitment would likely require higher advertising costs and increased overhead costs from the inclusion of additional institutions. One additional consideration is how noncompliance and contamination affect the intent-to-treat analysis. If noncompliance and contamination can be anticipated, the investigator can correspondingly adjust the sample size and costs. Mathematically the effect of noncompliance and contamination is to change the values of p C and p I in (4), which would then affect (5) and (6). In some settings, investigators may anticipate that high-risk subjects are more likely to comply with the intervention than average-risk subjects. To compensate for the anticipated increased compliance, study designers could reduce the sample size which would lower costs. However, in other situations, investigators may anticipate that subjects found to be at high-risk on a diagnostic test would likely seek the best therapy outside of the trial rather than chance randomization to standard or experimental therapy. To compensate for the anticipated dilution in treatment effect, investigators would need to increase the sample size which would increase the costs. For the above reasons even if the probabilities under the alternative hypothesis are correctly specified, some trials of high-risk subjects may be more expensive than larger trials of average-risk subjects with the same power and type I error. Possibly misleading ratio of benefits to harms When there is strong evidence prior to the trial of a high probability of harmful side effects due to the intervention, one would want to restrict the intervention to high-risk subjects. Otherwise, some investigators may be tempted to estimate the ratio of benefit to harms in the trial of high-risk subjects and extrapolate the ratio to average risk subjects. Unfortunately, even if the assumption of constant relative risk over risk categories were true, extrapolating the benefit-harm ratio from a high risk group to the general population could be misleading. Suppose that in a randomized trial involving average-risk subjects from the general population the probability of cancer is .02 in the control arm and .01 in the study arm. Also suppose that relative risk is same in the general population as in the high-risk group, so that in a randomized trial involving a high-risk group, the probability of cancer is .04 in the control arm and .02 in the study arm. Furthermore, suppose that the probability of harmful side effects is the same for high-risk subjects as for average-risk subjects in the general population, namely .015 in the control arm and .025 in the study arm. Based on these results, for every 1000 high-risk persons who receive the intervention, (.04 - .02) 1000 = 20 will benefit from the intervention and (.025 - .015) 1000 = 10 will be harmed by side effects, yielding a benefit-harm ratio of 20:10 = 2:1. Similarly for every 1000 average-risk person who receive the intervention, (.02 - .01) 1000 = 10 will benefit from the intervention and (.025 - .015) 1000 = 10 will be harmed by side effects yielding a benefit-harm ratio of 10:10 = 1:1. In this example it would be incorrect to extrapolate the high benefit-harm ratio estimated from the high-risk group to the general population for whom the benefit-harm ratio is much lower. For many cancer prevention interventions, the ratio of life-threatening disease avoided to life threatening harms would be favorable in the high-risk group but not favorable when extrapolated to the general population. Conclusion There is no "free lunch" when using high-risk subjects in prevention trials design to make inference about the general population. Using high risk subjects instead of average-risk subjects from the general population may lower statistical power, increase costs, and yield a misleading ratio of benefit to harms than actually the case. Given the substantial costs of definitive randomized trials in cancer prevention, and the importance of accurately assessing the balance of benefit and harm when treating healthy and asymptomatic people, it is therefore important to conduct trials in the actual target population rather than try to conduct them in high-risk populations with the plan to extrapolate results to the general population. Competing Interests The authors declare that they have no competing interests. Authors' contributions SGB wrote the initial draft, and BSK and DC made valuable improvements. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Supplementary Material Additional File 1 Appendix A, worked-out calculations of power. Click here for file Additional File 2 Appendix B, likelihood formulations Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524373.xml

545955

Antitumor effects of two bisdioxopiperazines against two experimental lung cancer models in vivo

Background Probimane (Pro), an anti-cancer agent originating in China, was derived from razoxane (ICRF-159, Raz), a drug created in Britain, specifically targeting at cancer metastasis and as a cardioprotectant of anthrocyclines. Pro and Raz are bisdioxopiperazine compounds. In this work, we evaluated the anti-tumor and anti-metastatic effects of Pro and Raz in vivo against two lung tumor models, one of murine origin (Lewis lung carcinoma, LLC) and one of human origin (LAX-83). Results After determining the lethal dosage of Pro and Raz, we assessed and compared the inhibitory effects of Pro and Raz against primary tumor growth and metastatic occurrences of LLC at the dosage of LD 5 . Pro and Raz were active against primary tumor growth and significantly inhibited pulmonary metastasis of LLC at same dose-ranges (inhibitory rates > 90 %). Both Raz and Pro were effective in 1, 5, and 9 day administration schedules. Three different schedules of Raz and Pro were effective against the primary tumor growth of LLC (35–50 %). The synergistic anticancer effect of Raz with bleomycin (Ble) (from 41.3 % to 73.3 %) was more obvious than those with daunorubicin (Dau) (from 33.1 % to 56.3 %) in the LLC tumor model. Pro was also seen to have synergistic anti-cancer effects with Ble in the LLC model. Both Raz and Pro inhibited the growth of LAX 83 in a statistically significant manner. Conclusions These data suggest that both Raz and Pro may have anti-tumor potentiality and Raz and Pro have combinative effects with Ble or Dau. The potential targets of bisdioxopiperazines may include lung cancers, especially on tumor metastasis. The anti-cancer effects of Raz and Pro can be increased with the help of other anticancer drugs.

Background Razoxane (ICRF-159) ( Raz ), first developed in UK, was the earliest agent against spontaneous metastasis for the murine model (Lewis lung carcinoma) in 1969 [ 1 ]. A large volume of papers and projects have been published in the utilities and mechanisms of Raz for anticancer actions, like assisting radiotherapy, [ 2 ] overcoming multi-drug resistance (MDR) of daunorubicin and doxorubicin [ 3 ], inhibiting topoisomerase II [ 4 ] and so on. More importantly, Raz , as a cardioprotectant of anthrocyclines, has been licensed in 28 countries in 4 continents. Since morpholine groups in some structures were reported to be responsible for cytotoxic or modulative actions on tumors, an anticancer agent, probimane [1,2-bis (N 4 -morpholine-3, 5-dioxopeprazine-1-yl) propane; AT-2153, Pro] was synthesized by introducing two morpholine groups into Raz in China.[ 5 ]. Raz and Pro belong to bisdiopiperazines . Like Raz , Pro also exhibits anti-tumor activity both in vivo and in vitro against experimental tumor models in a small scale investigation [ 6 , 7 ] and limited clinical data showed that Pro could inhibit human malignant lymphoma even for those resistant to other anticancer drugs [ 8 ]. Pro exhibits the same pharmacological effects as Raz , like detoxication of Adriamycin ( ADR ) induced cardiotoxicities, and synergism with ADR against tumors [ 9 , 10 ]. We have found some novel biological effects of Pro , like inhibiting the activity of calmodulin ( CaM ), a cell-signal regulator, which can explain anticancer actions and the combined cytotoxic effect of Pro and ADR [ 11 ]. Pro was also shown to inhibit lipoperoxidation ( LPO ) of erythrocytes [ 12 ], influence tumor sialic acid synthesis [ 13 ] and inhibit the binding of fibrinogen to leukemia cells [ 14 ]. Lung cancer is the No 1 killer among all categories of cancers in urban areas in China and many Western countries. The high mortality rate of lung cancer can easily be caused by inducing multi-drug resistance ( MDR ) and by high metastatic occurrence in clinics [ 15 ]. Since we assume that Pro , like Raz may possess useful therapeutic potentialities, we evaluated in vivo the chemotherapeutical parameters of Pro and Raz for lung cancer of both murine and human origins. Results Lethal toxicity of Pro and Raz in mice The lethal dosage of Pro and Raz is tabulated in Table 1 . Since the toxicity of Pro and Raz seemed to lack sex specificity in mice, we were able to combine their numbers for LD 50 and LD 5 calculations. We used the approximate dosage of LD 5 of Pro (60 mg/kg ip × 7) and Raz (20 mg/kg ip × 7) as equitoxic dosages for further treatment studies. Table 1 The subacute toxicity of Pro and Raz in mice: Mouse survival was observed for 1 month. The numbers of mice in each group were 20 for each of the 5 dosages of a single agent. Drugs Protocols LD 5 mg/kg LD 50 mg/kg Probimane ip × 10 66 121 Razoxane ip × 10 23 53 Antitumor and antimetastatic effects of Pro and Raz on LLC Antitumor and antimetastatic effects of Pro and Raz on LLC are tabulated in Table 2 and Table 3 . Pro and Raz at equitoxic dosages (LD 5 ) showed a noticeable anticancer effect on primary tumor growth (inhibitory rates, approximately 30–45 %), and significantly inhibited the formation of tumor metastases (inhibitory rates on pulmonary metastasis > 90 %, P < 0.001). Primary tumor growth of LLC was inhibited more by Pro (48 %) than by Raz (40.3%) in a 20 day trial, whereas the inhibition of Pro (35.7%) was slightly less than that of Raz (40 %) on an 11 day trial. Pro seems to be more persistent than Raz in inhibiting primary tumor growth of LLC . Antitumor effects of bisdioxopiperazines for different schedules and in combination with other anticancer drugs Antitumor effects of Raz and Pro on LLC are included in Table 4 , 5 , 6 . We evaluated 1, 5 and 9 day administration schedules in our study. We found that Raz and Pro were effective in a statistically significant manner with the 3 injection schedule of the 1, 5 and 9 day administrations on LLC . If we administered Raz to tumor-bearing mice once on day 1, 5 and 9, there was no difference between treatment and vehicle control. Antitumor effects of Raz in combination with Ble on LLC (73.3 %) were better than those in combination with Dau (56.3 %) (Table 5 and Table 6 ). Pro also showed synergistic effects in combination with Ble (Table 7 ). Table 2 The influence of Pro and Raz on primary tumor of LLC (using Student T-test): Route: ip × 7 daily. Experiment term was 11 days. * P < 0.05 (treatment vs vehicle control). The numbers of mice were 30 for the control group and 20 for each treatment group. 100 % survival was observed in each group. Compounds Dosage mg/kg/d Body weight (g) Tumor weight (g) Tumor inhibition% Control -- 23.3/24.4 2.80 ± 0.04 -- Razoxane 20 23.3/23.4 1.61 ± 0.03* 40.0 Probimane 30 23.4/21.6 1.91 ± 0.03* 32.1 Probimane 60 23.3/23.8 1.80 ± 0.03* 35.7 Table 3 The influence of Pro and Raz on primary and metastatic tumor of LLC: PTI (%) – Primary tumor inhibition. MFCPM – metastatic foci count per mouse. Route: ip × 7 every 2 days. Experiment term was 20 days, * P < 0.001(treatment vs vehicle control). The numbers of mice were 30 for both control group and each treatment group. 100 % survival was observed in each group. Compounds Dosage mg/kg/d Body weigh (g) PTI(%) MFCPM Control --- 22.8/21.4 -- 30.9 ± 7.3 Razoxane 20 22.7/21.5 40.3 1.2 ± 0.5* Probimane 30 23.3/22.5 42.0 1.5 ± 0.5* Probimane 60 23.3/20.3 48.0 1.0 ± 0.2* Table 4 Antitumor effects of bisdioxopiperazines of different schedules on Lewis lung carcinoma: *Administration every 3 hours, 16 mice were included in each testing group. **p < 0.05 (treatment vs control), Experimental term was 11 days Compounds Dosage Schedule Tumor weight Tumor inhibition mg/kg 1, 5, 9 administrations (g) % Control -- -- 2.36 ± 0.05 Razoxane 80 1 time a day 2.49 ± 0.05 -5.5 Razoxane 40 1 time a day 2.32 ± 0.07 1.7 Razoxane 20 1 time a day 2.80 ± 0.06 -18.6 Razoxane 10 3 times a day* 1.51 ± 0.04** 36.0 Probimane 20 3 time a day* 1.19 ± 0.05** 49.6 Table 5 Antitumor effects of Raz on Lewis lung carcinoma in combination with daunorubicin: *Administration every 3 hours. Experimental term was 11 days Compounds Dosage Schedule Tumor weight (g) Tumor inhibitions mg/kg 1, 5, and 9 administrations % Control 2.34 ± 0.05 Razoxane (Raz) 10 3 times a day* 1.57 ± 0.05 32.9 Daunorubicin (Dau) 2 1 time a day 1.10 ± 0.04 53.0 Raz + Dau 10 + 2 3 times/1 time a day 1.02 ± 0.04 56.4 Table 6 Antitumor effects of Raz on Lewis lung carcinoma in combination with bleomycin: * Administrate every 3 hours in one day. ** p < 0.01 (treatment vs vehicle control). Experimental term was 11 days Compounds Dosage Schedule Tumor weight Tumor Inhibition mg/kg 1, 5, and 9 administration (g) % Control -- -- 2.46 ± 0.06 Razoxane (Raz) 10 3 times a day* 1.44 ± 0.07 41.5 Bleomycin (Ble) 15 1 time a day 1.50 ± 0.06 39.0 Raz + Ble 10 + 15 3 times + 1 time a day 0.66 ± 0.05** 73.2** Table 7 Antitumor effects of Pro on Lewis lung carcinoma in combination with daunorubicin or bleomycin: *Administration every 3 hours. Experimental term was 11 days Compounds Dosage Schedule Body weight Tumor weight (g) Tumor inhibitions mg/Kg 1, 5, and 9 administration g % Control -- -- 20.6/21.6 2.62 ± 0.08 Pro 20 3 times a day 20.6/20.8 1.45 ± 0.07 44.6 Dau 2 1 time a day 20.6/20.0 1.14 ± 0.08 56.5 Ble 15 1 time a day 20.7/21.2 1.36 ± 0.08 48.1 Pro + Dau 20 + 2 3 times/1 time a day 20.6/20.9 1.07 ± 0.05 59.2 Pro + Ble 20 + 15 3 times/1 time a day 20.7/19.8 0.59 ± 0.04 77.5 Antitumor activity of Pro and Raz on LAX-83 The experiments showed that LAX-83 was sensitive to Raz (40–60 mgKg -1 , ip × 5) and Pro (80–100 mgKg -1 ip × 5) with inhibitory rates of 25–32 % and 55–60 % respectively (P < 0.01 vs control). CTX , as a positive anticancer drug (40 mgKg -1 ip × 5), exhibited antitumor activities against the growth of LAX-83 with an inhibitory rate of 84 %. Obvious necrosis in tumor tissues was observed by histological evaluation of CTX and Pro treatment groups, but Pro showed larger vacuoles than CTX . Drug inhibition on tumor volumes were calculated and outlined in Table 8 . We have tested the 5 most commonly used anticancer drugs – cyclophosphamide (CTX), 5-fluoruoracil (5-Fu), methotrexate (MTX), cisplatin (DDP) and vincristine (VCR) (Table 9 ). In the LAX-83 model, CTX has been shown to be the most effective one. The anticancer effect of Pro was the same or better than those of MTX, DDP and as well as 5-Fu against LAX-83 tumor growth. Table 8 Antitumor activities of Pro and Raz on human tumor LAX-83 using subrenal capsule assay: Route: ip × 5 daily from the day after surgery. * P < 0.05, ** P < 0.001 (treatment vs vehicle control). Experiment was completed within 7 days. Tumor volume = 1/2 × width 2 × length (using T-test) Compounds Dosage mg/kg/d No mice Body weight (g) Tumor volume (mm 3 ) Inhibition% Control --- 16 19.2/21.0 39.8 ± 3.2 -- Razoxane 40 12 20.8/21.5 29.7 ± 3.0* 25 Razoxane 60 12 19.8/18.8 27.2 ± 2.8* 32 Probimane 80 12 20.0/19.6 18.0 ± 2.6** 55 Probimane 100 12 20.0/20.0 15.8 ± 2.6** 60 Cyclophosphamide 40 12 21.0/20.9 6.4 ± 2.0** 84 Table 9 Antitumor activities of anticancer drugs on human tumor LAX-83 using subrenal capsule assay: Route: ip × 5 daily from the day after surgery. * P < 0.05, ** P < 0.001 (treatment vs vehicle control). Experiment was completed within 7 days. Tumor volume = 1/2 × width 2 × length (using T-test) Compounds Dosage mg/kg/d No mice Body weight (g) Tumor volume (mm 3 ) Inhibition% Control --- 16 20.9/22.5 29.7 ± 3.2 -- Methotrexate 1.5 12 21.2/21.9 27.4 ± 3.0 7.7 Cis-platin 1.5 12 22.8/21.7 16.6 ± 2.6** 44.1 5-fluoruoracil 37.5 12 21.7/21.4 12.8 ± 2.6** 57.5 Cyclophosphamide 30.0 12 21.0/20.9 5.8 ± 2.3** 80.5 Vincristine 0.3 12 20.8/20.8 7.6 ± 2.2** 74.4 Discussion Explanations of anticancer and antimetastatic mechanisms of bisdioxopiperazines are now inconclusive. The present explanation for the anticancer mechanisms of Raz has been attributed to antiangiogenesis and topoisomerase II inhibition.[ 16 ] Since the antimetastatic activities of Raz and Pro were much stronger than those actions against primary tumor growth, this special targeting on metastasis ought to be more useful in clinical cancer treatment. Raz and Pro show typical characteristics of antiangiogenesis agents, which target small nodule of tumors. Meanwhile, recent reports on drugs targeting angiogenesis indicate that most anti-vascular drugs have low or even no effects on most cancers when they are used alone in clinics, but they show synergistic effects in combination with other anticancer drugs. [ 17 , 18 ] Our study shows synergistic anticancer actions of Raz and Pro with Ble or Dau basing on this theory. Previous work showed that Pro and Raz could reduce the cardiotoxicity of anthrocycline ,[ 1 , 9 , 10 ] so we may reasonably deduce that they can also reduce the cytotoxicity of anthrocyclines . The data in our study suggests that the synergistic effects of Raz with anthrocyclines are present, but not as potent as those with Ble . Since we have tested the antitumor activity of clinically available anticancer drugs (CTX, 5-Fu, MTX, DDP and VCR) against LAX-83, CTX being the best one, two bisdioxopiperazines studied on this work show overall similar anticancer effective as commonly used drugs. Although the anticancer effects of CTX and VCR are better than those of Pro, for other commonly used drugs, such as DDP, MTX and 5-Fu, the antitumor effects are no better than those of Pro. Since the antitumor effects of MTX and DDP are even less effective than those of Pro and Raz , we suggest that anticancer effects of Pro and Raz are within the effective anticancer ranges of commonly available anticancer drugs. The other useful property of Pro is that it is the most water-soluble among the bisdioxopiperazines . Most bisdioxopiperazines are less water-soluble and given orally in clinics. Although oral administration is easy for patients, bioavailability varies from patient to patient. For some patients who have a poor absorption of bisdioxopiperazines in oral administration, Pro can be injected iv to maintain stable drug levels. Our previous work showed that Pro could strongly accumulate in tumor tissue while Pro levels in other tissues decrease rapidly [ 19 ]. Presently, a stereo-isomer of Raz , (dexrazoxane, ICRF-187 ), a water-soluble Raz, is being reinvestigated and has aroused the interests of clinical oncologists. Phase III clinical studies are currently underway in the US. More importantly, ICRF-187 was licensed in 28 countries in 4 continents. This work shows a noticeable inhibition of Pro and Raz on lung cancers and suggests possible usage of Raz and Pro on lung cancer in clinics. Conclusions The advantages of bisdioxopiperazines in clinical treatment of lung cancers are as follows: (i) Pro and Raz can inhibit the growth of lung cancers, with and without the help of other anticancer drugs, like Dau and Ble ; (ii) like Raz , Pro strongly inhibits spontaneous pulmonary metastasis of LLC ; (iii) since Pro can inhibit CaM [ 11 ], a calcium activated protein that's associated with MDR and metastatic phenotypes, synergistic anticancer effects of Pro and Raz can be expected in combination with other anti-cancer drugs, like Dau or Ble . Now, new concepts of the relationship between tumor metastasis and MDR in cancers have been stated,[ 20 ] whereas bisdioxopiperazines can inhibit both tumor metastasis and MDR . As a counterpart of Raz , Pro might be of interest and have chemotherapeutic potential in clinics. Methods Drugs and animals Cyclophosphomide ( CTX ), daunorubicin ( Dau ) and bleomycin ( Ble ), 5-fluororacil (5-Fu), vincristine (VCR), cisplatin (DDP), methotrexate (MTX) were purchased from Shanghai Pharmaceutical Company. Pro and Raz were prepared by Department of Medicinal Chemistry, Shanghai Institute of Materia Medica, Chinese Academy of Sciences. C57BL/6J and Kun-Min strain mice were purchased from Shanghai Center of Laboratory Animal Breeding, Chinese Academy of Sciences. Nude mice (Swiss-DF), taken from Roswell Park Memorial Institute, USA, were bred in Shanghai Institute of Materia Medica, Chinese Academy of Sciences under a specific pathogen free condition. Human pulmonary adenocarcinoma xenograft ( LAX-83 )[ 21 ] and Lewis lung carcinoma ( LLC ) were serially transplanted in this laboratory. All animal experiments were conducted in compliance with the Guidelines for the Care and Use of Research Animals, NIH, established by Washington University's Animal Studies Committee. Bouin's solution consists of water saturated with picric acid: formaldehyde: glacial acetic acid (75: 20: 5, v/v/v). Lethal dosage determination in mice Mice of Kun-Min strain (equal amount of male and female) were ip injected with Pro and Raz daily for 10 successive days. The deaths of mice were counted after 1 month. Lethal dosage of agents was calculated by Random Probity tests . Antitumor and antimetastatic studies of LLC C57BL/6J mice were implanted sc with LLC (2 × 10 6 cells) from donor mice. The mice were injected intraperitoneally with drugs daily or every two days for 7 injections. On day 11 or day 20, mice were sacrificed, and locally growing tumors were separated from skin and muscles and weighed, and lungs of host mice were placed into a Bouin's solution for 24 h, and then the lung samples were submerged into a solution of 95 % alcohol for 24 h. Finally, the numbers of extruding metastatic foci in lungs were counted. Antitumor actions of different schedules and in combinations with different drugs C57BL/6J mice were implanted sc with LLC (2 × 10 6 cells) from donor mice. Mice were injected intraperitoneally with drugs on day 1, 5, 9. Single injection or 3 injections every 3 hours were used. Tumors were separated and weighed on day 11. Antitumor activity study of human tumors Nude mice were inoculated with LAX-83 under the renal capsule (SRC method).[ 22 ] Nude mice were injected intraperitoneally with drugs daily during next five days after inoculation of LAX-83 . Then nude mice were sacrificed, and their kidneys were taken out for measurement of tumor sizes using a stereomicroscope a week after transplantation. Tumor volume was calculated as 1/2(ab 2 ) where a and b are their major and minor axes of the lump. Kidneys with tumors were paraffin-embedded, sliced and hematoxylin dyed. The tumor tissues were then observed from a light microscope. Statistical analysis Student's t-test was used to assess the differences between control and drug treatment groups of above methods. List of abbreviation used are Pro, probimane; Raz, razoxane; CaM, calmodulin; LPO, lipoperoxidation; Dau, daunorubicin; Ble, bleomycin; LLC, Lewis lung carcinoma, LAX-83; a lung adenocarcinoma xenograft; ADR, adriamycin; Author's contribution The experimental design was made by Bin Xu and Da-Yong Lu. Experiments were performed by Da-Yong Lu (anticancer activity tests) The manuscript was written by Da-Yong Lu, and Jian Ding. Figure 1 Structural formulas of razoxane and probimane

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Perceived personal, social and environmental barriers to weight maintenance among young women: A community survey

Background Young women are a group at high risk of weight gain. This study examined a range of perceived personal, social and environmental barriers to physical activity and healthy eating for weight maintenance among young women, and how these varied by socioeconomic status (SES), overweight status and domestic situation. Methods In October-December 2001, a total of 445 women aged 18–32 years, selected randomly from the Australian electoral roll, completed a mailed self-report survey that included questions on 11 barriers to physical activity and 11 barriers to healthy eating (relating to personal, social and environmental factors). Height, weight and socio-demographic details were also obtained. Statistical analyses were conducted mid-2003. Results The most common perceived barriers to physical activity and healthy eating encountered by young women were related to motivation, time and cost. Women with children were particularly likely to report a lack of social support as an important barrier to physical activity, and lack of social support and time as important barriers to healthy eating. Perceived barriers did not differ by SES or overweight status. Conclusions Health promotion strategies aimed at preventing weight gain should take into account the specific perceived barriers to physical activity and healthy eating faced by women in this age group, particularly lack of motivation, lack of time, and cost. Strategies targeting perceived lack of time and lack of social support are particularly required for young women with children.

Introduction In many developed countries, overweight and obesity have reached epidemic proportions [ 1 - 8 ]. One group at particular risk of weight gain and the development of obesity is young women[ 2 , 9 , 10 ]. In the US, for example, one study that tracked weight in a large population sample over a 10-year period found that major weight gain (increased body mass index (BMI) > 5 kg/m 2 ) was twice as common in women (5.3%) as in men (2.3%) [ 2 ]. A recent study of almost 9,000 women aged 18–23 years in Australia showed that 41% of the sample gained more than 5% of their BMI baseline over a four-year period (1996–2000) [ 9 ]. This risk of weight gain and the development of obesity places young women at increased risk of a range of chronic medical conditions and diseases, such as hypertension, type-2 diabetes, cardiovascular disease, and certain cancers [ 11 ]. In an effort to reverse the current global epidemic of overweight and obesity, strategies to promote increased physical activity and to encourage healthy eating have been promoted in many countries [ 12 - 15 ]. In Australia, for instance, individuals are encouraged to consume diets that are low in fat, high in fibre and rich in fruits and vegetables[ 13 ], and to participate in at least 30-minutes of moderate-intensity activities at least five days/week [ 12 ]. Despite such efforts, many young women do not meet the current physical activity recommendations [ 16 ] and their diets are less than optimal. For example, mean daily intakes of fruits and vegetables fall well below recommended levels [ 17 ] and 50% of young Australian women are consuming at least one takeaway meal per week, which is likely to be high in energy density [ 9 ]. Poor compliance with dietary and physical activity guidelines is not unique to Australia [ 18 - 20 ]. In addition, recent work we have conducted suggests that many young women do not consider the kinds of lifestyle changes that are being recommended as feasible for them in the context of their daily lives [ 21 ]. An understanding of the perceived barriers faced by young women in achieving healthy lifestyle changes is therefore important. Most existing studies examining perceived barriers to physical activity and healthy eating have focused on the general population,[ 18 , 22 - 25 ]. with few specifically considering the perceived barriers experienced by those at particular risk of weight gain, such as young women. However, the perceived barriers faced by young women are likely to differ from those faced by other groups, such as by men or older women. For example, a study in the USA showed that women more frequently report 'tiredness' and 'time' as significant perceived barriers to healthy habits than do men, and that this may be partly attributable to their domestic situation [ 25 ]. In addition, young women are more likely than older women to experience particular life events (e.g. leaving family homes, starting work, entering a marital or de facto relationship, and becoming mothers) that may influence their physical activity and dietary habits [ 26 , 27 ]. As well as perceiving different barriers to those faced by other groups in the population, the perceived barriers to increasing physical activity and improving diet that young women face may vary according to their social and personal circumstances. For example, having children is likely to impact on a women's ability to adopt healthy habits [ 21 , 28 , 29 ]. In addition, persons of lower socioeconomic status (SES) may have poorer access to parks, walking or jogging trails, and gym equipment than those of higher SES [ 25 ]. Access to good quality, inexpensive healthy foods has also been reported to be more limited among persons of low SES; for instance, the cost of healthy foods has been reported to be greater for those living in deprived areas. [ 30 , 31 ]. A number of studies have suggested that a lack of knowledge is a greater barrier to eating a healthy diet among those of lower education level [ 22 , 23 ]. Being overweight can also be perceived as a significant barrier to physical activity [ 32 ]. However, whether or not these factors are perceived as barriers to physical activity and healthy eating among young women is unknown. In order to develop appropriate and effective obesity prevention strategies for young women it is important to understand the barriers they perceive in attempting to control their weight. The aim of this study was to examine perceptions of a range of personal, social and environmental barriers to physical activity and healthy eating, specifically related to weight maintenance, among young women, and how these vary by domestic situation, SES and overweight status. Methods Participants A total of 445 women provided data for this study. Initially, a sample of 1200 women aged 18–32 years was selected from the Australian Electoral Roll using a stratified random sampling procedure, with strata based on the number of eligible cases in each of the eight States/Territories of Australia. As voting is compulsory for Australian adults, the electoral roll provides a complete record of population data on Australian residents aged 18 years and over. Excluding those who had moved and left no forwarding address, the study achieved a response rate of 41% (462 women participated), which is comparable to response rates reported in similar postal surveys with this age group [ 33 , 34 ]. Data from 17 women who were pregnant were excluded. The socio-demographic characteristics of the sample are reported in full elsewhere [ 21 ]. Briefly, 42% of the respondents were tertiary-educated. Half of the women were married and one in three had at least one child. One in three respondents was classified as overweight or obese. The socio-demographic profile of the sample was comparable to that of women of similar age (18–44 y) who participated in the most recent (2001) Australian National Health Survey [ 35 ]. Procedures A questionnaire was developed and pilot-tested with a convenience sample of 10 women in the same age group as participants. The questionnaire, a study description, an invitation to participate, a consent form and a reply-paid envelope for returns were mailed to the study sample of women in October 2001. Non-responders were sent a reminder postcard two weeks later and a second reminder with replacement questionnaire a further three weeks later. Measures The participants completed the following questions. Socio-demographic background The socio-demographic questions included domestic situation (household composition) and education. Domestic situation was assessed by asking 'Who lives with you?' with response options: No-one , I live alone ; Partner / spouse ; Own children ; someone else's children ; parents ; brothers / sisters ; Other adult relatives ; and Other adults who are not family members . This was subsequently re-categorized as living with parental family; living alone/share 'flatting'; living with partner (no children); or living with children (including those living with partner and child/ren, and single mothers). Education level (highest level of schooling: still at school , primary school , some high school , completed high school , technical / trade school certificate / apprenticeship , or University / tertiary qualification ) was subsequently categorized as tertiary educated or not tertiary educated and used as an indicator of SES. Body weight Women were asked to self-report their height and weight and this information was used to calculate body mass index (BMI = weight (kg)/height (m 2 )). Self-reported height and weight have been shown to provide a reasonably valid measure of actual height and weight for the purpose of investigating relationships in epidemiological studies [ 36 ]. Women were categorised as overweight (BMI ≥ 25) or not overweight (BMI < 25) [ 11 ]. Perceived barriers to weight maintenance Young women's perceptions of barriers to weight maintenance were assessed using 22 items. Participants were asked 'How important are the following as barriers to you keeping your weight at the level you want?' The complete list of barrier items is included in Tables 1 and 2 . These items were based on a review of the literature investigating barriers to weight maintenance behaviours in other population groups [ 22 - 25 ]. There were two sets of perceived barriers assessed, those related to physical activity and those to healthy eating. For each set of questions, participants were asked about access to information; motivation; enjoyment; skills; partner support and children's support (where relevant); friends' support; access; cost; time due to job demands; and time due to family commitments as possible barriers. Response options for all barrier items were: Not a barrier ; A somewhat important barrier ; A very important barrier ; Not applicable . For analyses, responses Not applicable and Not a barrier were combined. Table 1 Perceived barriers to physical activity (N = 445) Barriers to physical activity Factor loadings Not a barrier (%) A somewhat important barrier (%) A very important barrier (%) Factor 1: Personal barriers to physical activity (Eigenvalue = 4.21, 38% variance, Cronbach's alpha = 0.76) Do not have the motivation to do physical activity, exercise or sport 0.58 26 34 40 Not enjoying physical activity, exercise or sport 0.80 57 25 18 Do not have the skills to do physical activity, exercise or sport 0.70 81 14 5 Factor 2: Social support barriers to physical activity (Eigenvalue = 1.13, 10% variance, Cronbach's alpha = 0.68) No partner's support to be physically active 0.80 78 13 9 No children's support to be physically active 0.82 94 4 2 No friends' support to be physically active 0.57 84 11 5 Factor 3: Environmental barriers to physical activity (Eigenvalue = 1.22, 11% variance, Cronbach's alpha = 0.71) Do not have enough information about how to increase physical activity 0.75 83 12 5 Not having access to places to do physical activity, exercise or sport 0.57 66 23 11 Not being able to find physical activity facilities that are inexpensive 0.70 49 29 22 Not having the time to be physically active because of job 0.76 42 29 29 Not having the time to be physically active because of family commitments 0.68 63 22 15 Table 2 Perceived barriers to healthy eating (N = 445) Barriers to healthy eating Factor loadings Not a barrier (%) A somewhat important barrier (%) A very important barrier (%) Factor 4: Personal and environmental barriers to healthy eating (Eigenvalue = 4.61, 42% variance, Cronbach's alpha = 0.83) Do not have enough information about a healthy diet 0.70 72 17 11 Do not have the motivation to eat a healthy diet 0.70 34 41 25 Do not enjoy eating healthy foods 0.80 64 26 10 Do not have the skills to plan, shop for, prepare or cook healthy foods 0.70 73 19 8 Do not have access to healthy foods 0.65 80 16 4 Not able to buy healthy foods that are inexpensive 0.60 60 27 13 Factor 5: Social and environmental barriers to healthy eating (Eigenvalue = 1.23, 11% variance, Cronbach's alpha = 0.72) No partner's support to eat a healthy diet 0.76 79 13 8 No children's support to eat a healthy diet 0.80 97 2 1 No friends' support to eat a healthy diet 0.57 83 12 5 Not having time to prepare or eat healthy foods because of job 0.47 57 23 20 Not having time to prepare or eat healthy foods because of family commitment 0.55 77 15 8 Most important perceived barriers In order to ascertain women's perceptions of the single most important barrier to physical activity and healthy eating (which may not have been included in the list of barriers developed by the researchers), participants were asked the following two open-ended questions: 'What is the one thing that makes it hardest for you to be physically active?' and 'What is the one thing that makes it hardest for you to eat a healthy diet?' Statistical Analyses Analyses were conducted mid-2003, using SPSS version 11.0.0 statistical software. [ 37 ]. Initially, descriptive analyses were performed to describe the proportion of women rating each of the items as not a barrier, a somewhat important barrier or a very important barrier. Content analyses of the open-ended questions were undertaken to identify main recurring themes. Two separate exploratory factor analyses using SPSS FACTOR were performed with the 11 barriers to physical activity and the 11 barriers to healthy eating, to identify underlying patterns of relationships among individual items, and to reduce and simplify the items in order to facilitate subsequent analyses. Principal components analysis with varimax rotation (since factors were not correlated) was used. For any cross-loading items (i.e. items that had loadings of greater than 0.4 on more than one factor), only the higher loading was taken into account when calculating final factor scores. Inter-item reliability for each factor was assessed by Cronbach's α coefficients. Kaiser's measure of sampling adequacy was used to confirm the appropriateness of factor analysis [ 38 ]. Standardized factor scores were computed for each factor, with a large positive score representing more important barriers and a large negative score, less important barriers. Analysis of variance or t-tests were performed separately for each of the standardized factor scores to investigate differences in perceived barriers to physical activity and healthy eating with regard to domestic situation, SES and overweight status. Results Perceived barriers to physical activity Table 1 presents the proportions of women reporting each of the perceived barriers to physical activity. The main barriers reported by young women related to motivation, time and cost. Combining the response categories 'somewhat important' and 'very important', 74% of the sample reported lack of motivation – 'not having the motivation to do physical activity, exercise or sport', time (58%) – 'not having time to be physically active because of my job,' and cost (51%) – 'not being able to find physical activity facilities that are inexpensive' – as common barriers to physical activity. Lack of time due to work commitments (reported by 58%) was more commonly reported than lack of time due to family commitments (37%), perhaps due to the relatively small proportion (30%) of young women in this study with at least one child. Less common perceived barriers to physical activity included lack of information, skills, partners' and children's support, and friends' support. Perceived barriers to healthy eating Table 2 presents perceived barriers to healthy eating. As with physical activity, lack of motivation (66%), lack of time due to job commitments (43%), and cost (inability to buy healthy foods that are inexpensive: 40%) were common perceived barriers. Less commonly reported barriers included lack of information, skills and friends', partners' and children's support, and access. As with physical activity, lack of time related to job demands (reported by 43%) was more common than lack of time due to family commitment (23%). The most important perceived barriers to physical activity and healthy eating Consistent with women's responses to the closed-ended questions, the most important perceived barriers to physical activity reported in response to the open-ended questions were lack of time due to work, study or family commitments (78%), lack of motivation (37%) and childcare issues (25%). The most important perceived barriers to healthy eating related to taste (24%); lack of time (21%); lack of motivation (13%); and the perception that healthy foods are inconvenient or expensive (13%). Factor analysis of perceived barriers to weight maintenance The factor analysis of the perceived barriers to physical activity revealed three interpretable factors (Table 1 ) with eigenvalues greater than one. These factors together explained 60% of the total variance. Two items – 'not having access to places to do physical activity, exercise or sport' and 'not having friends' support to be physically active' – cross-loaded on two factors and these items were included only on factors on which each item showed the largest loading. The Cronbach's α coefficients for the three factors ranged from 0.68 to 0.76, indicating moderate internal reliability. Provisional names were assigned for these three factors: 'personal barriers', 'social support barriers' and 'environmental barriers'. The items included as personal barriers to physical activity were related to motivation, enjoyment, and skill. Social support barriers encompassed lack of support from family and friends; and environmental barriers related to information, access, cost, and time. The principal components analysis of the 11 barriers to healthy eating resulted in two distinct interpretable factors with eigenvalues greater than one (Table 2 ). The Cronbach's α coefficients for the two factors were 0.72 and 0.83, indicating moderate to good internal reliability. Together the two factors explained 53% of the total variance. Provisional names were assigned to these factors: 'personal and environmental barriers' and 'social and environmental barriers'. Personal and environmental barriers to healthy eating included motivation, enjoyment, skills, information, cost, and access. Social and environmental barriers were related to lack of support from family and friends and time constraints. Associations of domestic situation, education and overweight status with perceived barriers Mean factor scores did not vary according to women's overweight status or SES. Mean factor scores did differ significantly by domestic situation for two factors: social support barriers to physical activity and social and environmental barriers to healthy eating (see Table 3 ). Compared with women living in other domestic situations, women with children had the lowest score on the social support for physical activity factor, suggesting that lack of support from partners, children and friends was a more important perceived barrier to physical activity for these women. This group also had the lowest score on social and environmental barriers to healthy eating factor, suggesting that lack of social support and insufficient time were more important perceived barriers to healthy eating among women with children than among other women. Conversely, young women who lived with their parents had the highest scores on these factors, indicating the relative lack of importance of social support for physical activity, and social and environmental barriers to healthy eating, for this group. Table 3 Mean standardized factor scores on weight maintenance by domestic situation a Factor Domestic situation Parents Alone/ Share Partner Children p Personal barriers to physical activity 0.08 0.22 -0.09 -0.07 0.12 Social support for physical activity -0.37 -0.14 -0.06 0.55 .000 Environmental barriers to physical activity 0.13 0.12 0.03 -0.18 0.11 Personal and environmental barriers to healthy eating -0.04 0.23 0.02 -0.04 0.25 Social and environmental barriers to healthy eating -0.30 -0.16 -0.06 0.49 .000 a . A large positive score represents more important barriers; a large negative score, less important barriers. Discussion This study suggests that a lack of motivation, time constraints due to work, and cost issues are the key perceived barriers to maintaining weight faced by young women. Overall these findings support other research that has examined barriers to physical activity and healthy eating [ 18 , 22 , 25 , 39 ]. However, the present study is unique in providing an insight into the relative importance of a range of personal, social and environmental factors as perceived barriers to weight maintenance among young women, a high risk group for weight gain. Findings showed that young women tended to rate personal factors as key perceived barriers to physical activity and healthy eating, followed by environmental factors, with social factors rated as less important. While the environment is likely to be an important source of influence on obesity-related behaviours [ 40 ], these findings highlight that efforts to prevent obesity should not ignore the central role of cognitive factors. Given the striking similarities in the types of barriers reported to impede physical activity, and the perceived barriers to healthy eating, findings also suggest that there may be potential economies of scale in health promotion programs aimed at preventing weight gain among young women. For example, strategies aimed at boosting motivation for healthy behaviour may help to promote both increased physical activity and healthy eating simultaneously. While motivating young healthy women to adopt healthy eating and physical activity behaviors is likely to be challenging, recent intervention research suggests that motivationally-tailored interventions may be more successful that other approaches (e.g. based on social-cognitive theory) in promoting physical activity and healthy eating [ 41 , 42 ]. It is noteworthy that perceived barriers to weight maintenance did not vary by socio-economic status or overweight status in this sample of women. In contrast, previous research has shown that overweight men and women face a number of perceived physical activity barriers [ 32 ]. Similarly, given that diet varies by socio-economic status [ 43 , 44 ] we expected that women of lower socio-economic status would be more likely to experience barriers to eating a healthy diet. Previous studies also suggest that persons of low SES often live in areas where the cost of food is greater, and access to healthy foods is poorer [ 30 , 31 ]. The reasons for the difference between the present results and earlier findings are unclear. It may be, however, that in this sample of relatively young women, many were still acquiring their education, and hence any SES differences in perceived barriers to healthy behaviours were not yet established. Compared to other young women, those living with children were the most likely to report lack of social support for physical activity, and lack of support and time for healthy eating, as key perceived barriers to maintaining their weight. Young women who lived with their parents were the least likely to perceive these to be barriers to weight maintenance. These findings are consistent with those of previous studies showing that getting married and having children are associated with decreased physical activity and greater weight gain [ 21 , 26 ]. Any weight gain prevention program targeting women with children should incorporate a focus on enlisting social support for both physical activity, and shopping for and preparing healthy foods. In a previous study with the same sample, we reported that while the majority of the women were in a healthy weight range (51%) or overweight/obese (31%), 18% of the women were underweight [ 21 ]. It should be acknowledged that some women in this sample, particularly those who were underweight, may have been trying to gain weight. One limitation of the present study was that the questions assessing perceived barriers to weight maintenance did not distinguish women trying to keep their weight down, from those trying to keep their weight up, and interpretation of the questions on perceived barriers may have been slightly different between these groups. However, attempts to gain weight are relatively uncommon among young women [ 45 ], and hence this is likely to have affected only a small proportion of the sample. A second limitation of this study is that the barriers were not assessed objectively, but rather through self-reports (ie perceived barriers). Nonetheless, it is important to consider women's perceptions of factors hindering their efforts to engage in healthy behaviours, since objective barriers may be perceived differently by different women (e.g., poor access to a gym may be viewed as less of a barrier to physical activity among a woman who walks for exercise than one who prefers aerobics). Finally, although the study achieved a somewhat modest response rate, the sample was selected from a nationally representative sampling frame and the socio-demographic profile of women was comparable to that of similarly-aged women in the wider population [ 35 ]. Conclusions The findings of this study highlight the need for health promotion strategies that provide increased motivation, support and skills to enable young women to shop and prepare healthy, quick and inexpensive meals. Similarly, the findings suggest a need to promote more time-efficient physical activity alternatives. Additional strategies that recognize the perceived barriers to physical activity and healthy eating faced by young women with children are particularly required. Competing interests The authors declare that they have no competing interests. Authors' contributions SA conducted the literature review, final statistical analyses and early drafts of the results and conclusions sections. KB and DC conceived the study, design and measures, collected the data, coordinated the analyses and participated in the write-up of all sections. NW conducted preliminary analyses and drafting of early results. VI contributed to drafting the final manuscript.

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Imbalance in the health workforce

Imbalance in the health workforce is a major concern in both developed and developing countries. It is a complex issue that encompasses a wide range of possible situations. This paper aims to contribute not only to a better understanding of the issues related to imbalance through a critical review of its definition and nature, but also to the development of an analytical framework. The framework emphasizes the number and types of factors affecting health workforce imbalances, and facilitates the development of policy tools and their assessment. Moreover, to facilitate comparisons between health workforce imbalances, a typology of imbalances is proposed that differentiates between profession/specialty imbalances, geographical imbalances, institutional and services imbalances and gender imbalances.

Introduction Imbalance in the health workforce is a major challenge for health policy-makers, since human resources – the different kinds of clinical and non-clinical staff who make each individual and public health intervention happen – are the most important of the health system's inputs [ 1 ]. Imbalance is not a new issue, as nursing shortages were reported in hospitals in the United States of America as early as 1915 [ 2 ]. It remains a major concern to this day, reported in both developed and developing countries and for most of the health care professions. Although imbalance in the health workforce is an important issue for policy-makers, various elements contribute to obscuring policy development. First, many reports of shortages are not borne out by the evidence. Rosenfeld and Moses [ 3 ] show that an overwhelming majority of newspapers, journals and newsletter articles describing the nursing situation in the United States presume the existence of a shortage. They found that even in those areas where concrete evidence of a shortage was not available, the term "nursing shortage" still appeared. Second, the notion of shortage is a relative one: what is considered a nursing shortage in Europe would probably be viewed differently from an African perspective. Finally, imbalances are of different types and their impact on the health care system varies. In consequence, there is a general need to critically review the imbalance issue. The objective of this paper is to contribute to a better understanding of the issues related to imbalance through a critical review of its definition and nature and the development of an analytical framework. Definition There are various approaches to defining imbalances [ 4 ]. From an economic perspective, a skill imbalance (shortage/surplus) occurs when the quantity of a given skill supplied by the workforce and the quantity demanded by employers diverge at the existing market conditions [ 5 ]. Labour market supplies and demands for occupational skills fluctuate continuously, so at times there will be imbalances in the labour market. In other words, a shortage/surplus is the result of a disequilibrium between the demand and supply for labour. In contrast, non-economic definitions are usually normative, i.e. there is a shortage of labour relative to defined norms [ 6 ]. In the case of health personnel, these definitions are based either on a value judgement – for instance, how much care people should receive – or on a professional determination – such as deciding what is the appropriate number of physicians for the general population. Nature One of the key questions regarding imbalances is how long these last: Is the imbalance temporary or permanent? In a competitive labour market, we should expect most imbalances to be resolved over time. Imbalances will tend to disappear faster the greater the reaction speed and also the greater the elasticity of supply (or demand) [ 7 ]. This type of imbalance (shortage or surplus) is defined as dynamic. In contrast, a static imbalance occurs because supply does not increase or decrease; market equilibrium is therefore not achieved. For instance, wage adjustments may respond slowly to shifts in demand or supply as a result of institutional and regulatory arrangements, imperfect market competition (monopoly, monopsony) and wage-control policies. Another example is physicians' education: because of the length of time required to educate physicians, changes in available supply take a long time to react significantly. Lack of information on the state of the various labour markets can also be a factor in the speed of market adjustment. To make proper labour market decisions, households and firms must be informed of the existing market conditions across markets. They must therefore know what wages are paid and the nature and location of job openings and available workers. Moreover, we should also differentiate between qualitative and quantitative imbalance. In a tight labour market, employers might not find the ideal candidate, but will still recruit someone. Under these conditions, the issue is the quality of job candidates rather than the quantity of people willing and able to do the job [ 8 ]. From the employers' perspective, a shortage of workers exists; from the job-market perspective, the existence of a shortage could be questioned because the jobs are filled. One negative hidden impact of a qualitative shortage is the number of positions that are filled with ineffective individuals [ 9 ]. A conceptual framework To better understand the role of factors affecting health workforce imbalances and to facilitate the development of policy tools, a conceptual framework is presented in this section. Introduction Factors affecting health workforce imbalances are numerous and complex, but focusing on crucial elements should permit insight into the issue of health workforce imbalances. The framework is depicted in Fig. 1 and contains six main components: the demand for health labour, the supply of health labour, the health care system, policies, resources and "global" factors. Figure 1 Framework for imbalance of human resources for health Central to this framework are the demand for and supply of health labour. Also included in the framework is the health care system, and in particular, some of its features that are likely to have an impact on health workforce imbalances. Policies constitute another crucial element of the framework. In effect, health policies but also non-health-oriented policies can have an impact on health workforce imbalances. The framework also incorporates financial, physical and knowledge resources that contribute to model the health workforce.Finally, "global" factors such as economic, sociodemographic, political, geographical and cultural factors are included. These elements contribute directly or indirectly to shaping and transforming the entire society and hence the health workforce. The demand for health personnel The first element of the framework to be examined is the demand for health personnel. The demand for health personnel can be considered as a derived demand for health services. Accordingly, we should consider factors determining the demand for health services. Personal characteristics – such as health needs, cultural and sociodemographic characteristics – and economic factors play an important role. It has often been proposed that the planning of human resources for health be based solely on estimates of health needs in the population [ 10 ]. However, relying only on the concept of need is difficult, because it can be defined either broadly or restrictedly and accordingly lead to a perception of either systematic shortage or surplus. Health needs is only one of the factors affecting the demand for health personnel. Several studies have attempted to estimate the impact of economic factors on the demand for health care. In particular in the United States, studies have attempted to estimate price and income elasticities of demand for medical services [ 11 - 13 ]. Measurements of price or income elasticities make it possible to evaluate the impact of a change in price or income on the demand for health care. Most studies reported elasticities in the range between 0.0 and -1.0, indicating that consumers tend to be responsive to price changes but that the degree of price sensitivity is not very large compared to that for many other goods and services [ 14 ]. Another element influencing the demand for health care is the value of a patient's time, such as travel time and waiting time. Acton [ 15 ] found that in the United States, elasticity of demand with respect to travel time ranged between -0.6 and -1, meaning that a 10% increase in travel time would induce a reduction of 6%-10% in the demand for health care. Other factors affect the demand for health labour. In particular, some specific features of the health care system and its features, policies, resources and environmental factors do have an impact on the demand for health labour. Their respective role will further discussed later. The supply of human resources for health After reviewing factors affecting the demand for health labour, we shall now turn to those affecting the supply of the health workforce. In particular, we shall consider the following elements: factors affecting the choice for a health professional training/education, participation in the health labour market and migration. Education/professional training choice The availability of a renewed health workforce, as well as the type of profession and specialty chosen by individuals, is a major concern for health decision-makers. These issues are of particular relevance, especially since the number of younger people, predominantly women, choosing a nursing career is declining in some countries and since in professional training/education, individuals' choices do not always match the absorptive capacity of the market. From an economic perspective, the decision to undertake professional training/education is considered an investment decision. To emphasize the essential similarities of these investments to other kinds of investments, economists refer to them as investment in human capital [ 16 ]. Since investment decisions usually deliver payoffs over time, we must consider the entire stream of costs and benefits. The expected returns on human capital investments are a higher level of earnings, greater job satisfaction over one's lifetime and a greater appreciation of non-market activities and interests. Based on the human capital approach, rate of return on education can be estimated. An average rate of return that is high and rising for a given profession will attract more individuals to that profession. On the other hand, a lower and decreasing average rate of return will discourage individuals from choosing that profession. Nowak and Preston [ 17 ], using the human capital approach, found that Australian nurses are poorly paid in comparison to other female professionals. The declining interest in nursing can be partly explained by the expansion of career opportunities in traditionally male-dominated occupations over the last three decades that entail a higher rate of return [ 18 ]. The number of young women entering the registered-nurse workforce has declined because many women who would have entered nursing in the past – particularly those with high academic ability – are now entering managerial and professional occupations that used to be traditionally male. Besides the human capital approach, the choice of a profession can also be explained by sociopsychological factors. For instance, individuals may choose a profession because it is highly valued by the society or for family tradition. In the health sector, the satisfaction afforded by caring for people and assisting them to improve their health is an important element used by nursing schools to attract new enrollees. In the light of this approach, the decline in the number of individuals choosing nursing as a career might also be explained by the fact that this profession is now less socially valued than before [ 19 , 20 ]. Participation in the labour market The economic theory of the decision to work views the decision as a choice concerning how people spend their time. Individuals face a trade-off between labour and leisure. They decide how much of their time to spend working for pay or participating in leisure activities, the latter being activities that are not work-related. An issue that has drawn a lot of attention recently is the impact of wage increases on labour participation, in particular for nurses. In the short term, higher wages can have at least two effects on the labour supply of current qualified nurses: first, qualified nurses who are working in other occupations may return to nursing activities; second, nurses now in practice may respond by working more hours. In the long run, higher wages in nursing relative to other occupations make nursing an attractive profession and will draw more people into nurse training programmes. In their literature review of wage elasticity of nursing labour supply, Antonazzo et al. [ 21 ] and Chiha and Link [ 22 ] found that most of the studies indicate a positive relationship, although not a strong one, between wages and labour supply. Accordingly, increases in nursing wages are unlikely to cause significant increases in labour participation. A literature review on the women's workforce undertaken by Killingsworth and Heckman [ 23 ] indicated that in addition to wage rate, women's participation is responsive to changes in unearned income, spouse's wage and having children (particularly of pre-school age). Another aspect of labour supply decisions that has been investigated by Philips [ 24 ] is the costs associated with entering the nursing labour market (such as costs of child care and housework). The elasticity of participation with respect to changes in working costs was evaluated at -0.67 for all nurses. This suggests that a subsidy leading to a decrease of 10% in these costs would increase the participation of nurses by 6.7%. Moreover, hospitals are also using a variety of strategies to recruit new staff. A survey of hospitals in the United States shows that richer benefits, such as health insurance and vacation time, are the most common incentives used. In addition, hospitals may offer other recruitment and retention benefits, such as tuition reimbursement, flexible hours and signing bonuses based on experience or length of commitment [ 25 ]. Many countries, but particularly developed ones, use such incentives to recruit new staff. Economic factors also play a role in physician's participation in the labour market, as demonstrated by the impact of cost-containment policies in Canada, where most provincial governments have implemented an assortment of controls of health care expenses. Threshold reductions were introduced, so that fees payable to individual physicians were reduced as billing exceeded an agreed threshold. As a consequence, physicians who had billed at the threshold level chose to take leaves of absence rather than receive a level of reimbursement they considered inadequate [ 26 ]. When health personnel choose an alternative or additional occupation, this is likely to have consequences on health labour supply. In developing countries, and particularly in Africa, attempts to reform the health care sector have frequently failed to respond to the aspirations of staff concerning remuneration and working conditions. Salaries are often inadequate and may be paid late, and health workers try to solve their financial problems in a variety of ways [ 27 ]. Private practice is only one of the many survival strategies that health personnel use to supplement their income and increase their job satisfaction. Teaching, attending training courses, supervision activities, research, trade and agriculture are some of these alternative strategies [ 28 ]. Labour market exit Parker and Rickam [ 29 ] examined the economic determinants of the labour force withdrawal of registered nurses in the United States, i.e. nurses leaving the profession to pursue a non-nursing occupation and employed nurses withdrawing from the labour force. Their results suggest that a significant number of registered nurses withdraw, at least temporarily, from the labour force. Among the significant elements influencing the withdrawal decision are the wage rate, other family income, presence of children and full-time/part-time work status. Increasing registered nurses' wages and working full-time is expected to reduce the probability of labour force withdrawal, whereas higher education levels, age and other family income increase the probability of labour force withdrawal. The relative importance of wage is also emphasized by studies investigating job satisfaction. There is support in the empirical literature for the existence of job dissatisfaction among nurses, and the link between job dissatisfaction and job exit [ 30 , 31 ]. In the United States the most important factors in nurses' resignation were, in order of importance: workload, staffing, time with patients, flexible scheduling, respect from nursing administration, increasing nursing knowledge, promotion opportunities, work stimulation, salary and decision-making. These studies suggest that salary is just one of the reasons why nurses are quitting. The relative importance of wage is confirmed by Shields and Ward [ 32 ]. Their results suggest that dissatisfaction with promotion and training opportunities has a stronger impact than workload or pay. Migration Migration of health personnel can have a serious impact on the supply of human resources in health, because it may exacerbate health personnel imbalances in "sending" countries. It is suggested that migration is an "individual, spontaneous and voluntary act" that is motivated by the perceived net gain of migrating – that is, the gain will offset the tangible and intangible costs of moving [ 33 ]. Decisions to migrate are often a family strategy to produce a better income and improve survival chances [ 34 ]. The reality for many health workers in developing countries is to be underpaid, poorly motivated and increasingly dissatisfied and sceptical [ 35 ]. The relevance of motivation to migration is self-evident. There can be little doubt that for many health workers an improvement in pay and conditions will act as an incentive to stay in the country. Improved pensions, child care, educational opportunities and recognition are also known to be important [ 36 - 38 ]. In Ghana it is estimated that only 191 of the 489 doctors who graduated between 1985 and 1994 were still working in the country in 1997 [ 39 ]. Health system characteristics As the health workforce is part of the health care system, we shall also consider features of the health care system that are likely to have an impact on the demand and supply of health labour. In particular, we shall examine market failures, the diversity of stakeholders, the supply-demand adjustment time lag and hospitals' potential monopsony power. Market failures From an economic perspective, the health care market is characterized by market failures – that is, the assumptions for perfect competition are violated. From a societal perspective, in the presence of market failures such as externalities – imperfect knowledge, asymmetry of information and uncertainty – market mechanisms lead to a non-optimal demand and/or supply in health services. In other words, shortages and surpluses are likely to result from the health care market. Most markets are characterized by market failures, but what is unique to the health services market is the extent of these market failures [ 40 ]. Governments try to correct health care market failures through policy interventions. A classic example of public intervention in the presence of a positive externality is the introduction of a policy of mandatory vaccination. However, implementing such policies is sometimes difficult and may result in only partial correction of the market failures. Stakeholders The health care system is characterized by a wide range of institutional stakeholders involved in shaping human resources for health, all of whom may have different objectives [ 41 , 42 ]. The objectives of a union or professional association do not necessarily coincide, for example, with those of a government ministry, a hospital manager or the central government. Unions/professional associations seek to increase their members' market power, employment and income [ 43 ], whereas the ministry of finance will want more budget equilibrium and will favour measures to limit health care expenditures. In the case of Mozambique, whereas the policy of employing national professionals by cooperation agencies has met with warm support from national cadres, its effect on the health sector is problematic [ 44 ]. The prospect of immediate financial gains puts pressure on qualified professionals to leave their posts within the Mozambique National Health Service to take up management or consultant positions. The substantial investment in their training is therefore producing dubious direct returns to the National Health Service. More seriously perhaps, the presence of donor-paid jobs outside the health sector (as programme coordinators, researchers, etc.) is creating pressure on the Ministry of Health itself, exacerbating the imbalances in the National Health Service and creating incentives for trained Mozambicans to leave the public sector. Time lag Moreover, adjustments between the demand and supply for health personnel may take a long time. In the health care field; the time lag between education and practising may be quite substantial. To obtain licensure to practise medicine requires lengthy education and training, and the long lag time between a changed student intake and a change in supply has been noted [ 45 ]: supply adjustment for physicians is not immediate, but takes a long time. Hospitals' potential monopsony power A single entity that is the sole purchaser of labour is a monopsony . One example is the potential monopsony power of hospitals in hiring nurses or the ministry of health in hiring the health workforce. The amount of labour demanded will influence the price the monopsonist must pay for it. In contrast to the situation in a competitive market, the monopsony is a price maker, not a price taker. Monopsony results in lower wages and lower employment of nurses compared to a competitive market. A number of studies have tested whether or not hospitals possess monopsony power with respect to nurses, and the results are contradictory. Sullivan [ 46 ] and Staiger et al. [ 47 ] concluded that hospitals have a substantial degree of monopsony power. In contrast, Hirsch and Schumacher [ 48 ] did not find empirical support for the monopsony model. Nurses' wages were found not to be related to hospital density and to decrease rather than increase with respect to labour market size. Provider power/monopoly In contrast, providers' power may enable the latter to restrict the supply of human resources for health. Seldon, Jung and Cavazos [ 49 ] suggest that physicians in the United States have market power through such avenues as restricting supply and price-fixing. In France, trade unions are granted an institutional role at establishment level [ 50 ]. In India and Sri Lanka, a clear constraint to support-services contracting was the inability to counter the power of the public service unions in dictating employment terms and conditions [ 51 ]. The varying degree of homogeneity of the different professional groups may also explain their relative success in maintaining a monopoly of practice. In Iceland, one of the factors that contributed to breaking the professional monopoly of pharmacists was division within the profession [ 52 ]. Regulations The type off regulation associated with a profession plays an important role regarding the supply of members of a profession. Regulation has, by tradition, been achieved through a combination of direct government regulation and, to a large extent, through rules adopted by professional associations, whose self-regulatory powers enable them to establish both entry requirements and rules regarding professional conduct [ 53 ]. Such barriers to entry exist in particular for doctors, but also in other health professions, such as dentistry. Some argue that these barriers constitute a means to limit entry into the profession, and hence maintain high incomes. Muzondo and Pazderka [ 54 ] established, for Canadian professional licensing restrictions, a relationship between different variables of self-regulation and higher income. Seldon et al. [ 55 ] suggest that physicians in the United States have market power through such sources as restricting supply and price-fixing. However, the proponents of self-regulation claim that these barriers are a means to provide health care of quality and to protect patients from incompetent providers. In contrast, although most countries have a professional nursing association, nurses tend to have limited power to regulate entry to the profession. This could be associated with a large diversity of specialist groups in nursing failing to unite on issues related to professional regulation [ 56 ]. Health and non-health policies Health and non-health policies contribute to shaping the health care system and have an influence on the demand and supply of health labour. Health policy can be defined as a formal statement or procedure within institutions (notably government) that defines priorities and the parameters for action in response to health needs, available resources and other political pressures. Health policy is often enacted through legislation or other forms of rule-making that create regulations and incentives for providing health services and programmes and access to them. For instance, the decision to introduce or expand health insurance coverage is likely to have an impact on the demand for health services. This is illustrated by the RAND Health Insurance Experiment, a controlled experiment that increased knowledge about the effect of different insurance copayments on use of medical services. Insurance copayments ranged from zero to 95%. The RAND study concluded that as the co-insurance rose, overall use and expenditure fell for adults and children combined [ 57 ]. Non-health policies reflect state interventions in areas such as employment, education and regional development that contribute to shaping the health workforce. These policies do not directly address health issues, but have an indirect impact on such issues. In France, a controversial new regulation was introduced that reduced the workweek to a maximum of 35 hours in an attempt both to create hundreds of thousands of new jobs and to achieve greater flexibility in the labour force. Unions responded by demanding the creation of more posts in public hospitals. Financial, physical and knowledge resources Financial, physical and knowledge resources are crucial to any type of health care workforce. The level of resources attributed to the health care system, and how these resources are used, will have a significant impact on health workforce issues. In terms of financial resources, human resources account for a high proportion of national budgets assigned to the health sector [ 58 ]. Health expenditure claims an increasingly important share of the gross domestic product and, in most countries, wage costs (salaries, bonuses and other payments) are estimated to account for between 65% and 80% of the renewable health system expenditure [ 59 , 60 ]. Physical resources include human resources within the health sector and other sectors; buildings and engineering services such as sanitation, water and heating systems for community use and for the use of medical care institutions; and equipment and supplies. Finally, the health workforce is also constrained by its human capital. This human capital can be associated with the qualification and education of the health workforce. Education of the health workforce is the systematic instruction, schooling or training given in preparation for work. Global factors Economic, sociodemographic, cultural, and geographical factors contribute to shaping and transforming society and hence have a direct or indirect impact on health workforce issues. From an economic perspective, for instance, there is evidence of a correlation between the level of economic development of a country and its level of human resources for health. Countries with higher GDP per capita are said to spend more on health care than countries with lower income, as demonstrated by cross-sectional studies, [ 61 ] and hence would also tend to have larger health workforces. Moreover, both the demand and supply are likely to be affected by sociodemographic elements such as the age distribution of the population. On the demand side, the ageing of the population is giving rise to an increase in the demand for health services and health personnel, especially nurses for home care. On the supply side, the ageing of the health workforce, and in particular of nurses, has serious implications for the future of the nursing labour market. For example, the Institute of Medicine noted that older registered nurses have a reduced capacity to perform certain tasks [ 62 ]. It was found that between 1983 and 1998 the average age of practising registered nurses increased by more than four years, from 37.4 to 41.9 years [ 63 ]. In contrast, the average age of the United States workforce as a whole increased by less than two years during the same period. Furthermore, the proportion of the registered-nurse workforce younger than 30 years decreased from 30.3% to 12.1% during this period. Geographical and cultural factors also play a role in determining the demand and supply of human resources. Geographical characteristics affect the organization of health services delivery. For instance, a country with many islands or with isolated population groups will face particular challenges in terms of health workforce issues. Similarly, significant climatic changes are likely to give rise to changes in health needs, which in turn will call for changes in health services and in the health workforce. Finally, both cultural and political values also affect the demand for and supply of human resources for health. Health workforce imbalances: a typology This section considers a typology of imbalances, and differentiates between the following: • Profession/specialty imbalances: Under this category, we consider imbalance in the various health professions, such as doctors or nurses, as well as shortages within a profession, e.g. shortage of one type of specialists. • Geographical imbalances: These are disparities between urban and rural regions and poor and rich regions. • Institutional and services imbalances: These are differences in health workforce supply between health care facilities, as well as between services. • Gender imbalances: These are disparities in female/male representation in the health workforce. Profession/specialty imbalances Imbalances have been reported for almost all health professions, and in particular for nurses. The United States General Accounting Office [ 64 ] reports a nursing shortage. However, the nursing shortage has not been institution-wide but is concentrated in specialty care areas, particularly intensive care units and operating rooms [ 65 ]. The shortage of registered nurses in intensive care units is explained in part by the sharp decline in the number of younger registered nurses, whom intensive care units have historically attracted. Shortages in operating rooms probably reflect that many registered nurses who work in this setting are reaching the age when they are beginning to reduce their hours worked or are retiring altogether. Major variations occur in the number of health care workers per capita population and in the skill mix employed across countries, as depicted in Fig. 2 . The nurse/doctor ratio varies widely from one country to another, as shown in Fig. 2 . The nurse/doctor skill mix is important and may have consequences for the respective tasks of nurses and doctors [ 66 ]. It is also interesting to note that these variations are taking place among countries with a relatively similar economic development level. Figure 2 Distribution of physicians, nurses, midwives, dentists and pharmacists in selected countries. WHO data, 2000. Geographical imbalances Virtually all countries suffer from a geographical maldistribution of human resources for health, and the primary area of concern is usually the physician workforce [ 67 ]. In both industrialized and developing countries, urban areas almost invariably have a substantially higher concentration of physicians than rural areas. Understandably, most health care professionals prefer to settle in urban areas, which offer opportunities for professional development as well as education and other amenities for themselves and their families. But it is in the rural and remote areas, especially in the developing countries, that most severe public health problems are found. The geographical maldistribution of doctors has been the object of particular attention. In general there is a higher concentration of general practitioners in the inner suburbs of the metropolitan areas. According to the Australian Medical Workforce Advisory Committee [ 68 ], the reasons for high concentration of general practitioners in inner city areas are: • historical • lifestyle-related: access to amenities • spouse/husband-related: greater employment opportunities • child-related: better access to secondary and tertiary education services • professional, family and social ties and professional ambitions. The geographical distribution of health care personnel is an important issue in many countries. Managua, the capital of Nicaragua, contains one-fifth of the country's population but around half of the available health personnel [ 69 ]. In Bangladesh, most of the doctors (35%) and nurses (30%) in health services are located in four metropolitan districts where only 14.5% of the population lives [ 70 ]. This concentration pattern is characteristic of developing countries. In Indonesia the geographical distribution of physicians is a particular concern, since Indonesia's vast size and difficult geography present a tremendous challenge to health service delivery [ 71 ]. It is difficult to place doctors in remote islands or mountain or forest locations with few amenities, no opportunities for private practice, and poor communications with the rest of the country. To improve the geographical distribution of physicians, governments often have used combinations of compulsory service and incentives. So far, there is virtually no country in the world that has solved the problem of a rural/urban imbalance of the physician workforce [ 67 ]. This does not necessarily mean that policies and programmes designed to reduce the imbalance have had no effect. For example, Thailand has successfully begun to stem the migration of health professionals from rural to urban areas and from public to private facilities with a range of strong financial incentives [ 72 ]. Institutional and services imbalances Institutional imbalances occur when some health care facilities have too many staff because of prestige, working conditions, ability to generate additional income, or other situation-specific factors, while others are understaffed [ 73 ]. Institutions such as magnet hospitals, for example, are hospitals characterized by adequate to excellent staffing, low turnover, rich nursing skill mix and greater job satisfaction, among other factors, even in the face of a general health personnel shortage [ 74 ]. Imbalance between the types of health services provided may also arise. In particular, we can consider the issue of curative versus preventive care. In effect, it has been estimated that most diseases (80%) and accidents are preventable through known methodologies, yet at present there is an imbalance in the funding of medical research, with only 1%-2% going to prevention and 98%-99% spent on curative approaches [ 75 ]. This imbalance in funding raises the question of a health workforce imbalance between preventive and curative care. Gender imbalances In many countries, women still tend to concentrate in the lower-status health occupations and to be a minority among more highly trained professionals and managers. In Bangladesh, the distribution by gender of the health workforce shows that the total proportion of women accounts for little more than one-fifth in health services [ 76 ]. The distribution of women by occupational category is biased in favour of nurses. Women are very poorly represented in other categories, such as dentists, medical assistants, pharmacists, managers/trainers and doctors. The underrepresentation of women in managerial and decision-making positions may lead to less attention to and poorer understanding of the problems specific to women and the particularities of their utilization patterns [ 77 ]. Female general practitioners have been shown to practise differently from males, managing different types of medical conditions, with some differences due to patient mix and patient selectivity, and others inherent in the sex of physician. In some more traditional areas, some women will not seek care for themselves or even for their children because they do not have access to a female provider [ 76 ]. Discussion This framework can be used to assess policy reforms and their impact on health workforce imbalances; it also provides a common framework for cross-country comparisons. This framework emphasizes the number and type of factors affecting health workforce imbalances, illustrating the complexity of this issue. From a policy perspective, it is particularly interesting to identify factors that policy-makers can influence in order to remedy imbalance problems. Various monetary and non-monetary incentives are used to influence the supply and/or demand for the health workforce. For example, subsidies, grants and scholarships are examples of incentives that can be used to attract more nursing students, whereas wage increases, additional benefits and working hours flexibility are examples of commonly used incentives to attract or retain the health workforce. The numerous factors and actors involved in the health workforce imbalance issue call for a coherent health workforce vision and policy. In that context, health planning plays an important role since it contributes to shaping the health care system. Moreover, since from a societal perspective market mechanisms alone do not allow an adequate demand/supply of health personnel to be reached, public interventions such as human resources planning are a means to correct for market failures. Health planning involves a time horizon. Forecasting the future number of health personnel needed and developing policies to meet such figures are common to any health care system. Physicians represent the profession for which more planning effort has been expended to achieve a workforce of appropriate size than for any other health profession. Countries' desire to meet population health needs and to avoid social welfare losses resulting from a shortage or an oversupply are factors explaining, to a large extent, the importance attributed to planning in the context of public health policies. The policy implications of forecasting either a shortage or a surplus of health care personnel are different, and hence attempts at projections must be rigorous. For instance, referring to previous studies predicting significant surpluses, Cooper [ 78 ] notes that such large surpluses have not occurred so far, because of a decrease in physician work effort. Factors such as age, sex and lifestyle contributed to this evolution. As a result of forecasted physician surpluses, various policy recommendations have been formulated. The United States Institute of Medicine [ 79 ] published a report recommending, among other things, that there be no new medical schools, that existing schools should not increase their class size and that the number of first-year residency positions should be reduced. The Pew Health Professions Commission Report [ 80 ] issued a report recommending more severe steps, such as the closing of some medical schools and tightening the visa process for international medical graduates. This framework also apprehends the different types of imbalances. This is important since the choice of a policy will also depend on the type of imbalance. Significant disparities in human resources for health between health occupation, regions, gender or health services are recognized as classic problems of imbalance. However, the question of a public/private imbalance is more debatable. One the one hand, we can argue that for equity and access, a health care system should have a strong public component. On the other hand, we can imagine a private-sector oriented health care system with mechanisms to ensure access to the poor. Conclusion In an attempt to contribute to a better understanding of imbalances in the health workforce, this paper has discussed a framework for human resources for health and proposed a typology of imbalances. Although the term "imbalance" is commonly used with respect to the health workforce, it is clear that imbalance in the health workforce encompasses a wide range of possible situations and is a complex issue. The use of a framework should facilitate the development of policy tools and their assessment. Competing interests None declared. Authors' contributions All authors participated in writing the original text and read and approved the final manuscript.

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Comparison of the NEI-VFQ and OSDI questionnaires in patients with Sjögren's syndrome-related dry eye

Background To examine the associations between vision-targeted health-related quality of life (VT-HRQ) and ocular surface parameters in patients with Sjögren's syndrome, a systemic autoimmune disease characterized by dry eye and dry mouth. Methods Forty-two patients fulfilling European / American diagnostic criteria for Sjögren's syndrome underwent Schirmer testing without anesthesia, ocular surface vital dye staining; and measurement of tear film breakup time (TBUT). Subjects were administered the Ocular Surface Disease Index (OSDI) and the 25-item National Eye Institute Vision Functioning Questionnaire (NEI-VFQ). Main outcome measures included ocular surface parameters, OSDI subscales describing ocular discomfort (OSDI-symptoms), vision-related function (OSDI-function), and environmental triggers, and NEI-VFQ subscales. Results Participants (aged 31–81 y; 95% female) all had moderate to severe dry eye. Associations of OSDI subscales with the ocular parameters were modest (Spearman r (ρ) < 0.22) and not statistically significant. Associations of NEI-VFQ subscales with the ocular parameters reached borderline significance for the near vision subscale with TBUT (ρ = 0.32, p = .05) and for the distance vision subscale with van Bijsterveld score (ρ = 0.33, p = .04). The strongest associations of the two questionnaires were for: ocular pain and mental function with OSDI-symptoms (ρ = 0.60 and 0.45, respectively); and general vision, ocular pain, mental function, role function, and driving with OSDI-function (ρ = 0.60, 0.50, 0.61, 0.64, 0.57, and 0.67, respectively). Conclusions Associations between conventional objective measures of dry eye and VT-HRQ were modest. The generic NEI-VFQ was similar to the disease-specific OSDI in its ability to measure the impact of Sjögren's syndrome-related dry eye on VT-HRQ.

Background Dry eye is a common disorder of the ocular surface and tear film and is estimated to affect from 2% to over 15% of persons in surveyed populations, depending on the definition used [ 1 - 6 ]. Symptoms of dry eye are a major reason to seek ophthalmic care: a study by Nelson and co-workers found that 1.3% of Medicare patients had a primary diagnosis of keratoconjunctivitis sicca or dry eye [ 7 ]. Dry eye can range from mild to severe disease; although the majority of patients with dry eye experience ocular discomfort without serious vision-threatening sequelae, severe dry eye can compromise corneal integrity by causing epithelial defects, stromal infiltration, and ulceration, and can result in visually significant scarring [ 8 ]. Moderate to severe dry eye disease can adversely affect performance of visually demanding tasks due to pain and impaired vision [ 9 ]. In addition, corneal surface irregularity due to epithelial desiccation, quantified by using corneal topography, can decrease visual acuity [ 10 ]. Patient-reported measurements used to evaluate the specific impact of eye disease and vision on symptoms (discomfort), functioning (the ability to carry out activities in daily life), and perceptions (concern about one's health) are referred to as vision-targeted health-related quality of life (VT-HRQ) instruments. Valid and reliable measurements of VT-HRQ have become essential to the assessment of disease status and treatment effectiveness in ocular disease [ 11 ]. There are two general categories of VT-HRQ instruments: generic, which are designed to be used for a broad spectrum of visual disorders and ocular disease; and disease-specific, which are tailored toward particular aspects of a specific ocular disorder. In general, disease-specific instruments tend to be more sensitive than generic ones in detecting VT-HRQ impairments [ 12 ]; however, generic instruments allow comparisons across more diverse populations and diseases [ 13 ]. In addition, generic instruments may be able to capture additional aspects of systemic disease, related to the ocular disorder in question, providing a broader characterization of health-related quality of life [ 14 , 15 ]. There is therefore no clear-cut basis in a given study or population for choosing a generic versus a disease-specific measure: if possible, both should be utilized to determine whether one or the other is more consistent with clinical indicators, or if one appears to obtain additional, relevant information on patient status [ 16 ]. However, it may be the case that weak-to-moderate associations between clinical indicators and quality-of-life measures indicates that the VT-HRQ measure is capturing elements of disease above and beyond those that can be measured clinically (for example, visual acuity may be good but a patient may have problems with functioning related to problems with contrast sensitivity or glare disability). Again, depending on the characterization of the disease desired and the goal of the study, a researcher might choose an instrument that either is or is not strongly correlated with clinical signs. The measurement of the impact of dry eye on a patient's daily life, particularly symptoms of discomfort, is a critical aspect of characterizing the disease [ 17 ]. Despite the fact that most studies have found weak or no correlations between symptoms and signs of dry eye [ 18 - 20 ], symptoms are often the motivation for seeking eye care and are therefore a critical outcome measure when assessing treatment effect [ 7 ], and hence are increasingly used as a surrogate for ocular surface disease in many epidemiologic studies. Indeed, recent studies have focused on developing more robust ways of measuring patient-reported symptoms of dry eye [ 21 - 23 ]. The Ocular Surface Disease Index (OSDI) © [ 24 ] was developed to quantify the specific impact of dry eye on VT-HRQ. Sjögren's Syndrome is an autoimmune systemic disease characterized by dry mouth and dry eye signs and symptoms [ 25 , 26 ]. Its manifestations include fatigue, arthritis, neuropathy, and pulmonary and renal disease. Histopathologic evidence of salivary gland inflammation and the presence of serum autoantibodies SSA or SSB are important diagnostic features of the disease [ 27 ]. Sjögren's Syndrome has been stated to be the second most common autoimmune disease, ranking between rheumatoid arthritis and systemic lupus erythematosus [ 27 ]. In the U.S., it is estimated that between 1 and 4 million persons (approximately 1–2 in 200) have Sjögren's Syndrome [ 28 ]. Prevalence estimates for other countries range from 0.3 to 4.8% [ 29 ]. Female gender and older age are known risk factors for Sjögren's syndrome [ 30 ]. A wide range of studies have assessed the ocular manifestations of Sjögren's syndrome [ 31 - 33 ]; however, assessment of symptoms and quality of life have been limited and, in most cases, generic measures of well-being, psychological distress, and fatigue without ocular dimensions have been employed [ 34 - 40 ]. Further, while there are many published studies of VT-HRQ in mild to moderate dry eye, there are few publications on VT-HRQ in Sjögren's syndrome, which is characterized by dry eye causing significant ocular irritation as well as systemic disease factors that could have their own additional significant impact on VT-HRQ. Our purpose in this study was to examine VT-HRQ in patients with primary Sjögren's syndrome, using a generic and a dry-eye-disease-specific instrument. We examined the associations of ocular surface parameters with the VT-HRQ scores, hypothesizing that the disease-specific instrument would be more closely related than the generic to the clinical markers of disease. We also examined the association of the generic and disease-specific VT-HRQ scores with each other. Methods The study protocol was approved by the National Eye Institute Internal Review Board. All patients completed an informed consent prior to examination. Consecutive patients with diagnosed primary Sjögren's syndrome were recruited from the NIH Clinical Center, Bethesda, MD. The diagnosis of primary Sjögren's syndrome was based on European-American criteria, which requires at least four of the following six features: signs and symptoms of dry eye and of dry mouth, histopathologic evidence of inflammation on minor salivary gland biopsy, and positive anti-Ro or anti-La antibodies. Before the clinical examination, a trained interviewer administered two questionnaires (described further below) to measure VT-HRQ to each patient. The subsequent clinical examination included a comprehensive anterior segment evaluation, including slit lamp biomicroscopy, evaluation of lid margin thickness and hyperemia, conjunctival erythema, chemosis, tear film debris and mucus, and extent of meibomian gland plugging. Tests of tear function and ocular surface status were performed as described below. The OSDI [ 24 ] (provided by Allergan, Inc. Irvine, CA) was used to quantify the specific impact of dry eye on VT-HRQ. This disease-specific questionnaire includes three subscales: ocular discomfort (OSDI-symptoms), which includes symptoms such as gritty or painful eyes; functioning (OSDI-function), which measures limitation in performance of common activities such as reading and working on a computer; and environmental triggers (OSDI-triggers), which measures the impact of environmental triggers, such as wind or drafts, on dry eye symptoms. The questions are asked with reference to a one-week recall period. Possible responses refer to the frequency of the disturbance: none of the time, some of the time, half of the time, most of the time, or all of the time. Responses to the OSDI were scored using the methods described by the authors [ 24 ]. Subscale scores were computed for OSDI-symptoms, OSDI-function, and OSDI-triggers, as well as an overall averaged score. OSDI subscale scores can range from 0 to 100, with higher scores indicating more problems or symptoms. However, we subtracted the OSDI overall and subscale scores from 100, so that lower scores would indicate more problems or symptoms. The 25-item NEI Visual Function Questionnaire (NEI-VFQ) [ 41 , 42 ] is a non-disease-specific (i.e., "generic") instrument designed to measure the impact of ocular disorders on VT-HRQ. Depending on the item, responses to the NEI-VFQ pertain to either frequency or severity of a symptom or functioning problem. A recall period is not specified in the questionnaire. Responses to the NEI-VFQ were scored using the methods described by the authors [ 43 ]. Subscale scores for general vision, ocular pain, near vision, distance vision, social functioning, mental functioning, role functioning, dependency, driving, color vision, and peripheral vision, as well as an overall score, were computed. The NEI-VFQ scores can range from 0–100, with lower scores indicating more problems or symptoms. Schirmer tests of tear production without and with anesthesia were performed by inserting a Schirmer tear test sterile strip (35 mm, Alcon Laboratories, Inc, Fort Worth, TX) into the inferior fornix, at the junction of the middle and lateral third of the lower eyelid margin, for 5 minutes with the eyes closed. The extent of wetting was measured by referring to the ruler provided by the manufacturer on the envelope containing the strips. Possible scores range from 0 to 35 mm, with lower scores indicating greater abnormality in tear production. This test was repeated after instillation of topical anesthetic, 0.5% proparacaine [ 44 ]. A Schirmer without anesthesia score of ≤ 5 mm in at least one eye is one required element of dry eye, as defined by the European-American Sjögren's syndrome diagnostic criteria [ 45 ]. The assessment of ocular surface damage was performed by a cornea specialist using vital dye staining with 2% unpreserved sodium fluorescein and then 5% lissamine green dye. The corneal, temporal, and nasal regions of the conjunctiva were scored individually from 0–5 (for fluorescein) and 0–5 (for lissamine green) using the Oxford grading scheme [ 46 ]. The Oxford score was derived by adding the scores for corneal fluorescein and nasal plus temporal conjunctival lissamine green staining. Total Oxford score could range from 0–15. The van Bijsterveld score [ 47 ] (VB) was assessed using lissamine green staining of the cornea (0–3) and conjunctiva (0–3). Total VB score could range from 0–9. For all staining tests, higher scores indicate worse ocular surface damage. Tear film stability was assessed using fluorescein tear film breakup time (TBUT). Five microliters of 2% sodium fluorescein was instilled into the inferior fornix and the patient was asked to blink several times. Using the cobalt blue filter and slit lamp biomicroscopy, the duration of time required for the first area of tear film breakup after a complete blink was determined. If the TBUT was less than 10 seconds, the test was repeated for a total of 3 values and the average was calculated. For analysis, for each individual, the maximum (worse) score for the two eyes was used for Oxford score and VB, and the minimum (worse) score for the two eyes was used for Schirmer with and without anesthesia and for TBUT. TBUT values greater than or equal to 10 seconds [ 48 ] were coded as 10 (normal) and < 10 seconds was defined as abnormal. Schirmer without anesthesia score result of ≤ 5 mm or VB ≥ 4 were used as objective evidence of dry eye, following the European / American criteria for the diagnosis of dry eye for Sjogren's syndrome [ 49 ]. Hypotheses of specific associations were formulated based on the areas and domains assessed by the two VT-HRQ instruments. Scatterplots and Spearman's correlation coefficient (ρ) [ 50 ] were used to examine associations between pairs of variables. Multiple linear regression [ 51 ] was used to assess the strength of association between pairs of variables while adjusting for confounders (e.g., age). Results Characteristics of participants A total of 42 patients, 40 female and 2 male, were included in this study. The average age was 55 years (range, 31–81 y). Most (81%) were of European descent. Visual acuity in the better eye was 20/20 or better for 68% of the patients; the remainder had 20/25 or better in the better eye, except for one patient who was 20/32 in both eyes. Ocular examination (Table 1 ) showed that, on average, the participants suffered from moderate to severe dry eye: mean Oxford score was 7.2, mean VB score was 5.3. Average Schirmer without anesthesia score was 4.8 mm, with nearly all (79%) having scores less than 10 mm and the majority (59%) having scores less than 5 mm. Mean TBUT was 2.9 seconds, with nearly all (87%) having scores less than 5 seconds. Table 1 Characteristics of participants (n = 42) Mean, sd [range] N (%) Age (y) 54.9 (12.7) [31–81] Ethnicity European-derived 34 (81%) African-derived 3 (7%) Other 5 (12%) Gender Female 40 (95%) Male 2 (5%) Visual acuity* 20/20 + OU 18 (44%) 20/20+, better eye 10 (24%) 20/25+, better eye 12 (29%) <20/25, better eye 1 (2%) Vital dye staining Oxford score 7.2 (3.4) [1–14] -- 5+ -- 34 (81%) Van Bijsterveld score** 5.3 (2.7) [0–9] -- 4+ -- 28 (74%) Tear production Tear film break-up time (s)** 2.9 (1.7) [1–8] -- < 5 sec -- 33 (87%) Schirmer without anesthesia (mm) 4.9 (5.4) [0–20] -- 0–5 -- 25 (60%) 5-<10 -- 8 (19%) 10+ -- 9 (21%) Meibomian gland disease** None -- 10 (26%) 1 -- 8 (21%) 2+ -- 20 (53%) European-American dry eye criteria -- 37 (90%) *One person had missing visual acuity information; **Four persons had missing information for some components of the clinical examination Association of OSDI © with ocular surface parameters OSDI scores (all subtracted from 100) indicated moderate problems with symptoms, functioning, and adverse environmental conditions. Mean OSDI-symptoms score was 62.5, mean OSDI-function score was 78.2, and mean OSDI-triggers score was 60.2. However, some patients had no problems with these areas: 12% reported no problems with irritation symptoms, 21% reported no problems with functioning, and 24% had no problems with environmental triggers. Associations of the OSDI subscale and overall scores with ocular surface parameters (Oxford score, VB, TBUT, and Schirmer score with and without anesthesia) are shown in 2 . In general, no substantive associations were found, except for visual functioning with TBUT (r = 0.22), and none of the observed associations reached statistical significance. Median scores on OSDI were compared between normal/abnormal categories of ocular surface variables (Schirmer without anesthesia score < 5, 5-<10, versus 10+; TFB < 5 versus > = 5; VB < 4 versus 4+, Oxford score < 5 versus 5+; European-American criteria, yes versus no). Considerable overlap in the distributions between categories was observed for all subscales, with no significant differences in median values (data not shown). Table 2 Association of OSDI (scores subtracted from 100) with ocular surface parameters (Spearman ρ) Oxford score van Bijsterveld score Tear film breakup time Schirmer without anesthesia score Schirmer with anesthesia score OSDI Mean (sd); % floor Symptoms 62.5 (25.7); 12% 0.02 0.16 -0.10 -0.04 0.02 Visual function 78.2 (21.4); 21% 0.15 0.17 0.22 0.12 0.05 Environmental triggers 60.2 (34.3); 24% -0.01 0.13 -0.02 0.04 0.12 Overall 70.0 (20.2); 10% 0.07 0.19 0.06 0.04 0.08 Association of NEI-VFQ with ocular surface parameters Overall, scores on the NEI-VFQ subscales tended to be high. Average scores for near and distance vision, social and mental functioning, dependency, driving, and peripheral vision were over 80, and a substantial percentage reported no problems at all with any of the items on the subscale: 26% for near vision, 24% for distance vision, 83% for social functioning, 17% for mental functioning, 74% for dependency, 38% for driving, and 79% for peripheral vision. The subscale indicating the most impairment was the ocular pain subscale, with a mean score of 66.7. Associations of the NEI-VFQ subscale and overall scores with ocular surface parameters (Oxford score, VB, TBUT, and Schirmer score with and without anesthesia) are shown in Table 3 . Overall, associations were weak to moderate, and none attained statistical significance. General vision showed moderate correlations with Oxford score, VB, and TBUT scores (r values from 0.20–0.27). Ocular pain showed a moderate correlation with TBUT (r = 0.23) and Schirmer with anesthesia score (r = 0.22). Near vision was associated with VB (r = .20) and to a greater extent with TBUT (r = 0.32). Distance vision showed moderate associations with Oxford score, TBUT, and Schirmer with anesthesia score (r values from 0.21 – 0.26) and a stronger association with VB (r = 0.33). Social functioning was moderately associated with VB (r = .24). Role functioning was associated with Schirmer scores both with and without anesthesia, more strongly so with Schirmer with anesthesia score (r = 0.31). Dependency was associated with TBUT (r = .29) and Schirmer with anesthesia score (r = .21). An anomalous finding was that peripheral vision showed moderate association with VB score (r = .29). Mental functioning and driving showed no associations with any of the ocular surface parameters. Median scores on NEI-VFQ scales were compared between normal/abnormal categories of ocular surface variables (Schirmer without anesthesia score < 5, versus 10+; TFB < 5 versus > = 5; VB < 4 versus 4+, Oxford score < 5 versus 5+; European-American criteria, yes versus no). Considerable overlap in the distributions between categories was observed for all subscales, with no significant differences in median values (data not shown), with the exception of the European-American criteria, where, counterintuitively, scores were higher (better) for near vision for those with dry eye (45.8) than for those without (83.7; p = .03). However, only 4 patients were in the "no dry eye" category, so this result may be the consequence of unstable small sample size. Table 3 Association of NEI-VFQ with ocular surface parameters (Spearman ρ) Oxford score van Bijsterveld score Tear film breakup time Schirmer without anesthesia score Schirmer with anesthesia score NEI-VFQ Mean (sd); % floor General vision 78.6 (12.8); 14% 0.22 0.20 0.27 -0.04 0.08 Ocular pain 66.7 (22.2); 12% 0.06 0.06 0.23 -0.06 0.22 Near vision 80.4 (19.4); 26% 0.18 0.20 0.32 -0.02 0.16 Distance vision 80.2 (18.4); 24% 0.25 0.33 0.26 -0.04 0.21 Social function 96.1 (11.2); 83% 0.14 0.24 0.15 -0.07 -0.09 Mental function 83.1 (17.5); 17% 0.15 0.18 0.19 -0.10 0.17 Role function 73.2 (25.4); 29% 0.07 -0.02 0.16 0.22 0.31 Dependency 94.4 (10.5); 74% -0.09 -0.04 0.29 0.03 0.21 Driving 84.9 (15.5); 38% -0.02 0.04 0.19 -0.07 -0.06 Peripheral vision 91.7 (19.6); 79% 0.11 0.29 0.15 -0.15 -0.13 Overall 83.6 (12.8); 2% 0.19 0.20 0.24 -0.04 0.19 Association of OSDI © with NEI-VFQ subscales In general, stronger associations were observed between subscales of the OSDI and NEI-VFQ (Table 4 ) than between ocular surface parameters and either the OSDI or the NEI-VFQ. Because of the large number of potential comparisons, we restrict discussion to associations that were hypothesized based on clinical plausibility. To test whether the overall (i.e., combined) OSDI and NEI-VFQ scales were related, we examined their linear relationship (Figure 1 ). Indeed, the association of these scales was strong (r = 0.61) and remained statistically significant after age adjustment. We hypothesized that the OSDI-symptoms subscale and the NEI-VFQ ocular pain subscale should show strong association, and in fact this was observed (r = 0.60, p < .001 after adjustment for age). A scatterplot of the data is shown in Figure 2 . We also hypothesized that the OSDI-triggers measure should be associated with the NEI-VFQ ocular pain subscale. This association was moderate (r = 0.46, Figure 3 ) and did not remain statistically significant after age adjustment. The OSDI-function subscale measures a domain that has theoretical overlap with the NEI-VFQ subscales for general, near, and distance vision, as well as driving, so we hypothesized that these correlations should also be relatively strong. This was true in particular for general vision (r = 0.60, Figure 4 ) and driving (r = 0.57, Figure 7 ), both of which remained highly statistically significant after adjustment for age (p < .001). The correlations of OSDI-function with NEI-VFQ near and distance vision were not as strong (0.45, Figures 5 and 6 ) and were not statistically significant after adjusting for age. Table 4 Associations of OSDI © subscales (subtracted from 100) with NEI-VFQ subscales (Spearman ρ). OSDI Symptoms OSDI Visual function OSDI Environmental triggers OSDI Overall NEI-VFQ General vision 0.34 0.60*† 0.28 0.51* Ocular pain 0.60*† 0.50* 0.46† 0.62* Near vision 0.08 0.46† 0.23 0.33 Distance vision 0.37 0.45† 0.27 0.46 Social function 0.16 0.26 0.17 0.22 Mental function 0.45* 0.61* 0.20 0.53* Role function 0.19 0.64* 0.33 0.48* Dependency 0.17 0.42* 0.17 0.33 Driving 0.28 0.57*† 0.33 0.48* Peripheral vision 0.18 0.02 0.04 0.14 Overall 0.43 0.67* 0.37 0.61*† †Associations hypothesized at the start of the study; *statistically significant after age adjustment (p < 0.001) Figure 1 Association between OSDI (scores subtracted from 100) and NEI-VFQ overall scales. Spearman ρ: 0.61*. Figure 2 Association between OSDI ocular discomfort subscale (scores subtracted from 100) and NEI-VFQ ocular pain subscale. Spearman ρ: 0.60* Figure 3 Association between OSDI environmental triggers subscale (scores subtracted from 100) and NEI-VFQ ocular pain subscale. Spearman ρ: 0.46. Figure 4 Association between OSDI visual function subscale (scores subtracted from 100) and NEI-VFQ general vision subscale. Spearman ρ: 0.61. Figure 7 Association between OSDI visual function subscale (scores subtracted from 100) and NEI-VFQ driving subscale. Spearman ρ: 0.57. Figure 5 Association between OSDI visual function subscale (scores subtracted from 100) and NEI-VFQ near vision subscale. Spearman ρ: 0.46. Figure 6 Association between OSDI visual function subscale (scores subtracted from 100) and NEI-VFQ distance vision subscale. Spearman ρ: 0.45. Table 4 shows that, in fact, several other significant associations not conjectured in our original hypotheses were observed. In particular, the OSDI-function subscale, in addition to the associations hypothesized above, showed substantial and statistically significant associations with ocular pain (r = 0.50), mental function (r = 0.61), role function (r = 0.64), and dependency (r = 0.42). The OSDI-symptoms subscale showed a moderate and statistically significant association with NEI-VFQ mental health (r = 0.45). The overall OSDI scale showed significant associations with NEI-VFQ general vision (r = 0.51, ocular pain (r = 0.62), mental and role functioning (r = 0.53 and 0.48, respectively), and driving (r = 0.61). Discussion We compared subscale scores for an ocular surface disease-specific instrument (OSDI) with a generic VT-HRQ instrument (NEI-VFQ-25) in patients with a systemic autoimmune disease associated with moderate to severe dry eye. We found that patients with primary Sjögren's syndrome had OSDI scores (mean, 30, before subtraction from 100) similar to those previously published [ 24 ] for moderate to severe dry eye patients (mean score was 36 for severe cases). Despite the fact that all of our patients had Sjögren's syndrome, with moderate to severe dry eye, we found that correlations of ocular surface parameters with VT-HRQ (i.e., patient-reported) parameters tended to be weak or nonexistent, consistent with several other studies demonstrating poor correlations between signs and symptoms of dry eye [ 18 - 20 ]. Indeed, contrary to our expectations, NEI-VFQ correlations with objective ocular surface parameters tended to be higher than those of OSDI, although all were relatively modest (all < 0.35) and none reached statistical significance. One explanation could be that the nature of the items for each of these instruments is quite different. The OSDI queries the frequency of a symptom or difficulty with an activity, over a one week recall period. The NEI-VFQ incorporates questions both the frequency and intensity of symptoms and their impact on activities, with no specified recall period. Perhaps this added element of capturing both the frequency and intensity of a symptom or impact accounts for some of the differences we found. For subscales that are similar, agreement was higher but still moderate, possibly due to differences in the nature of the questions or response options. The OSDI is targeted to assess how much the symptoms of dry eye affect the patient's current status (i.e., in the past week), whereas the NEI-VFQ may be more suited to capturing the overall impact of a chronic ocular disease on VT-HRQ. In this group of primary Sjögren's syndrome patients, associations between subscales of the NEI-VFQ and OSDI were moderate to strong (< 0.70) and in hypothesized directions. Significant associations were seen between OSDI and NEI-VFQ overall scales; OSDI-symptoms and NEI-VFQ ocular pain; and OSDI-function and NEI-VFQ general vision and driving. This suggests that both instruments are capturing important aspects of VT-HRQ. It is not surprising that the highest correlations were observed between subscales with similar domains, which serves to validate the use of alternate methodologies. On the other hand, it is counter-intuitive that the generic and disease-specific instruments appeared similar (or that the generic seemed to do a little better) with respect to their association with objectively measured clinical signs of dry eye, as the NEI-VFQ was designed to capture broader aspects of VT-HRQ. For the NEI-VFQ, we found moderate correlations (greater than 0.3) of distance vision with VB and near vision with TBUT. This was surprising, as one may have expected that subscales measuring ocular discomfort or pain (i.e., more disease-specific for dry eye) would have the strongest correlations with clinical measures of dry eye. Clinical signs of dry eye include measures of tear production, ocular surface staining, and tear film break-up; visual acuity and other aspects of visual function are not generally as widely used. However, some investigators have reported that visual acuity in dry eye patients is correlated with decreased spatial contrast sensitivity [ 52 ] and is functionally reduced with sustained eye opening due to increased surface irregularity which can be detected with corneal topography [ 53 , 10 ], which could explain our finding of moderate associations of ocular surface measures with near and distance vision. It has been proposed [ 10 ] that "subtle visual disturbance" is an important reason for dry eye patients to seek care. Indeed, improvement in blurred vision symptoms was one of the most frequently reported benefits of topical cyclosporine treatment for dry eye in a large, multicenter clinical trial [ 54 ]. The impact of the quality of vision or functional visual acuity on VT-HRQ has not been a focus of studies of the subjective aspects of dry eye. Our data indicate that the impact of dry eye on VT-HRQ is only partially accounted for by ocular pain in patients with severe dry eye, such as in Sjogren's syndrome. Would we expect the associations to be different in Sjögren's patients? Sjögren's syndrome is an autoimmune exocrinopathy and effects of its systemic nature and chronicity on dry eye may have been more readily captured by the NEI-VFQ's ability to measure both frequency and intensity of problems with VT-HRQ. In contrast, although the OSDI includes items to measure function, responses are limited to the frequency of problems. Because the type of dry eye in Sjögren's syndrome is more likely to be severe, and all patients in our study had Sjögren's-related dry eye, we speculated that somewhat stronger associations between signs and symptoms might be observed. On the other hand, ocular surface inflammation and decreased corneal sensation are features of severe dry eye which might alter a patient's perception of symptoms of ocular irritation and might be the cause of weaker correlations between signs and symptoms [ 48 , 55 ]. Indeed, reduced corneal sensation could provide inadequate feedback through the ophthalmic nerve to the central nervous system, resulting in less efferent stimulation to the lacrimal gland with reduced tear production and promotion of a vicious cycle. In addition, meibomian gland dysfunction plays a key role in dry eye in Sjögren's syndrome [ 56 ]. Therefore, aqueous and evaporative tear deficiency may combine to produce a particularly diseased ocular surface. Conclusions In addition to clinical signs, it is important to include assessments of VT-HRQ and visual function to fully characterize the impact of dry eye on health status. The correlation between signs and VT-HRQ are modest at best, indicating that VT-HRQ is capturing an additional component of disease that is not captured by the clinical assessment. This does not necessarily mean that the measures of VT-HRQ or the methods of detecting clinical signs are deficient, but rather that VT-HRQ is an additional element of the overall impact of this disease process on affected individuals. Furthermore, in diseases with systemic manifestations, such as Sjögren's syndrome, that may have an influence on quality of life independent of dry eye symptoms, appropriate tests of VT-HRQ are critical to completely characterize quality of life in these patients. It may also be valuable to explore possible differences in associations of clinical signs with VT-HRQ in patient populations with different manifestations or causes of dry eye. List of abbreviations VT-HRQ: Vision-targeted health-related quality of life; TBUT: Tearfilm breakup time; OSDI: Ocular Surface Disease Index; NEI-VFQ: National Eye Institute Visual Function Questionnaire; VB: van Bijsterveld Authors' contributions SV helped to design the study and performed all analyses and took the lead in writing the manuscript. LG performed the patient interviews and assisted with data analyses. GFR provided advice on statistical methods and presentation of the results. JA conceived and helped to design the study and assisted with writing the manuscript.

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527875

Statistical design considerations for pilot studies transitioning therapies from the bench to the bedside

Pilot studies are often used to transition therapies developed using animal models to a clinical setting. Frequently, the focus of such trials is on estimating the safety in terms of the occurrence of certain adverse events. With relatively small sample sizes, the probability of observing even relatively common events is low; however, inference on the true underlying event rate is still necessary even when no events of interest are observed. The exact upper limit to the event rate is derived and illustrated graphically. In addition, the simple algebraic expression for the confidence bound is seen to be useful in the context of planning studies.

Introduction In the translational research setting, statisticians often assist in the planning and analysis of pilot studies. While pilot studies may vary in the fundamental objectives, many are designed to explore the safety profile of a drug or a procedure [ 1 , 2 ]. Often before applying a new therapy to large groups of patients, a small, non-comparative study is used to estimate the safety profile of the therapy using relatively few patients. This type of investigation is typically encountered in the authors' experiences as collaborating biostatisticians at our General Clinical Research Center as well as developing applications addressing the National Institutes on Health Roadmap Initiative . In the context of pilot studies, traditional levels of α (the Type I error rate) and β (the Type II error rate) may be inappropriate since the objective of the research is not to provide definitive support for one treatment over another [ 3 ]. For example, the null hypothesis in a single arm pilot study might be that the tested intervention produces a safety profile equal to a known standard therapy. A Type I error (rejecting the null hypothesis when it is false) in the context of this preliminary investigation would encourage additional examination of the treatment in a new clinical trial. This is in contrast to a Type I error in a Phase III/IV clinical trial in which the error could result in widespread exposure of an ineffective treatment. Allowing for a less stringent Type I error rate is critical when trying to transition therapies from the animal models to clinical practice since it identifies a greater pool of potential therapies that could undergo additional research in humans. Similarly, power (1 - β ) is of less practical importance in a single arm, non-comparative (or historically controlled) pilot study since the results would almost always require confirmation in a controlled trial setting. Shih et al [ 4 ] extend the deviations from traditional hypothesis-driven analyses to suggest preliminary investigations should focus on observing responses at the subject level rather than testing a treatment's estimated mean response. In the section that follows, we will relate these notions under the context of safety data analysis and provide interpretations that can be used for sample size considerations. Methods For ease of presentation, assume the pilot study will involve n independent patients for which the probability of the adverse event of interest is π , where 0 < π < 1. A 100 × (1 - α )% confidence interval is to be generated for π and an estimate of the sample size, n , is desired. Denote X as the number of patients sampled who experience the adverse event of interest. Then, the probability of observing x events in n subjects follows the usual binomial distribution. Namely, Denote π u as the upper limit of the exact one-sided 100 × (1 - α )% confidence interval for the unknown proportion, π [ 5 ]. Then π u is the value such that A special case of the binomial distribution occurs when zero events of interest are observed. In pilot studies with relatively few patients, this is of practical concern and warrants particular attention. When zero events are realized (i.e., x = 0), equation (1) reduces to (1 - π u ) n = α . Accordingly, the upper limit of a one-sided 100 × (1 - α )% confidence interval for π is π u = 1 - α 1/ n .     (2) The resulting 100 × (1 - α )% one-sided confidence interval is (0, 1 - α 1/ n ). Graphically, one can represent this interval on a plot of π against n as illustrated in Figure 1 for α = 0.05, 0.10 and 0.25. As the figure illustrates, for relatively small sample sizes, there is a large amount of uncertainty in the true value of π . It is critical to convey this uncertainty in the findings and to guard against inferring a potential treatment is harmless when no adverse effects of interest are observed with limited data. Louis [ 6 ] also cautioned the clinical observation of zero false negatives in the context of diagnostic testing stating that zero false negatives may generate unreasonable optimism regarding the rate, particularly for smaller sample sizes. Figure 1 Upper limit of the 100 × (1 - α )% one-sided confidence interval for the true underlying adverse event rate, π , for increasing sample sizes when zero events of interests are observed Furthermore, one can consider using (2) in other clinically important manners. For instance, an investigator may be planning a pilot study and want to know how large it would need to be to infer with 100 × (1 - α )% confidence that the true rate did not exceed a pre-specified π , say π 0 , given that zero adverse events were observed. Using (2), it follows that: To illustrate the utility of this solution, consider the following example. Ototoxicity is well documented with increasing doses of cisplatin, a platinum-containing antitumoral drug that is known to be effective against a variety of solid tumors. It is of clinical interest to identify augmentative therapies that can alleviate some of the cell death since up to 31% of patients receiving initial doses of 50 mg/m 2 cisplatin are expected to have irreversible hearing loss [ 7 , 8 ]. Therefore, it is desirable to rule out potential treatments not consistent with this rate of hearing loss before considering more conclusive testing. Using equation (3), we would conclude that the augmentative therapy has a hearing loss rate less than 0.31, at the 90% confidence level, if a total of 7 patients are recruited and all 7 do not experience ototoxicity. Therefore, an initial sample size of 7 patients would be sufficient to identify augmentative therapies, such as heat shock or antioxidant supplements, that demonstrate preliminary efficacy in humans. In the event one or more ototoxic events are observed, then the results in relationship to the historical rate (31% in this example) may not be statistically different. The results of several of these pilot studies could then be used to rank-order potential therapies thereby proving an empirically justified approach to therapy development. Conclusions In translational research, it is common to explore the adverse event profile of a new regimen. In this note, we illustrate how a simple expression has utility for the generation of confidence intervals when zero events are observed. A more comprehensive and methodological treatment of inference with zero events can be found in Carter and Woolson [ 9 ], and Winkler et al [ 10 ], which treats the issue from a Bayesian statistical viewpoint. This commentary and related works have implications as a practical finding for the interpretation of clinical trial safety data and offer clinicians advice on the range of adverse event rates that can be thought to be consistent with the observation of zero events. The presented formula offers more flexibility than the "rule of 3" approximation [ 11 ] since it allows for the specification of significance levels other than α = 0.05. The ability to choose the significance level might be important when designing or interpreting preliminary data obtained from a pilot study. In summary, small sample sizes and a focus on safety are often associated with translational research, and the statistical approaches to these studies may need to deviate from traditional, hypothesis-driven designs. Competing interests The author(s) declare that they have no competing interests. Authors' Contributions RC and RW contributed to the conceptualization, writing and editing of this manuscript.

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545941

Differential regulation of Aβ42-induced neuronal C1q synthesis and microglial activation

Expression of C1q, an early component of the classical complement pathway, has been shown to be induced in neurons in hippocampal slices, following accumulation of exogenous Aβ42. Microglial activation was also detected by surface marker expression and cytokine production. To determine whether C1q induction was correlated with intraneuronal Aβ and/or microglial activation, D-(-)-2-amino-5-phosphonovaleric acid (APV, an NMDA receptor antagonist) and glycine-arginine-glycine-aspartic acid-serine-proline peptide (RGD, an integrin receptor antagonist), which blocks and enhances Aβ42 uptake, respectively, were assessed for their effect on neuronal C1q synthesis and microglial activation. APV inhibited, and RGD enhanced, microglial activation and neuronal C1q expression. However, addition of Aβ10–20 to slice cultures significantly reduced Aβ42 uptake and microglial activation, but did not alter the Aβ42-induced neuronal C1q expression. Furthermore, Aβ10–20 alone triggered C1q production in neurons, demonstrating that neither neuronal Aβ42 accumulation, nor microglial activation is required for neuronal C1q upregulation. These data are compatible with the hypothesis that multiple receptors are involved in Aβ injury and signaling in neurons. Some lead to neuronal C1q induction, whereas other(s) lead to intraneuronal accumulation of Aβ and/or stimulation of microglia.

Introduction Alzheimer's disease (AD) is the most common form of dementia in the elderly. Its main pathological features include extracellular amyloid beta (Aβ) deposition in plaques, neurofibrillary tangles (composed of hyperphosphorylated tau protein) in neurons, progressive loss of synapses and cortical/hippocampal neurons, and upregulation of inflammatory components including activated microglia and astrocytes and complement activation [ 1 ]. Although the contribution of abnormal phosphorylation and assembly of tau to AD dementia remains a focus of investigation, therapies that interfere with Aβ production, enhance its degradation, or cause its clearance from the central nervous system (CNS) have been the center of many studies in search of a cure for this disease. Microglial cells, when activated, are believed to be responsible for much of the Aβ clearance through receptor-mediated phagocytosis [ 2 , 3 ]. Upon activation, microglia acquire features more characteristic of macrophages, including high phagocytic activity, increased expression of leukocyte common antigen (CD45), major histocompatibility complex (MHC) class II and costimulatory molecules B7, and secretion of proinflammatory substances [ 4 ]. In addition, phagocytic microglia also participate in the removal of degenerating neurons and synapses as well as Aβ deposits ([ 5 ], and reviewed in [ 6 ]). Thus, while some microglial functions are beneficial, the destructive effects of the production of toxins (such as nitric oxide, superoxide) and proinflammatory cytokines by activated microglia apparently overcome the protective functions in the chronic stage of neuroinflammation [ 7 , 8 ]. In vitro studies have shown both protection and toxicity contributed by microglia in response to Aβ depending on the state of activation of microglia [ 9 , 10 ]. Correlative studies on AD patients and animal models of AD strongly suggest that accumulation of reactive microglia at sites of Aβ deposition contributes significantly to neuronal degeneration [ 3 , 11 ], although decreased microglia have been reported to be associated with both lowered and enhanced neurodegeneration in transgenic animals [ 12 , 13 ]. Aβ itself is believed to initiate the accumulation and activation of microglia. However, recent reports provide evidence for neuron-microglial interactions in regulating CNS inflammation [ 14 ]. Nevertheless, the molecular mechanisms responsible for activation and regulation of microglia remain to be defined. Complement proteins have been shown to be associated with Aβ plaques in AD brains, specifically those plaques containing the fibrillar form of the Aβ peptide [ 11 ]. Complement proteins are elevated in neurodegenerative diseases like AD, Parkinson's disease, and Huntington's disease as well as more restricted degenerative diseases such macular degeneration and prion disease [ 11 , 15 - 18 ]. Microglia, astrocytes, and neurons in the CNS can produce most of the complement proteins upon stimulation. C1q, a subcomponent of C1, can directly bind to fibrillar Aβ and activate complement pathways [ 19 ], contributing to CNS inflammation [ 13 ]. In addition, C1q has been reported to be synthesized by neurons in several neurodegenerative diseases and animal injury models, generally as an early response to injury [ 20 - 23 ], possibly prior to the synthesis of other complement components. Interestingly, C1q and, upon complement activation, C3 also can bind to apoptotic cells and blebs and promote ingestion of those dying cells [ 24 - 26 ]. Elevated levels of apoptotic markers are present in AD brain tissue suggesting that many neurons undergo apoptosis in AD [ 27 - 29 ]. Excess glutamate, an excitatory neurotransmitter released from injured neurons and synapses, is one of the major factors that perturb calcium homeostasis and induce apoptosis in neurons [ 30 ]. Thus, it is reasonable to hypothesize that neuronal expression of C1q, as an early injury response, may serve a potentially beneficial role of facilitating the removal of apoptotic neurons or neuronal blebs [ 31 ] in diseases thereby preventing excess glutamate release, excitotoxicity, and the subsequent additional apoptosis. We have previously reported that in rat hippocampal slice cultures treated with exogenous Aβ42, C1q expression was detected in pyramidal neurons following the internalization of Aβ peptide. This upregulation of neuronal C1q could be a response to injury from Aβ that would facilitate removal of dying cells. Concurrently, microglial activation was prominent upon Aβ treatment. In the present study, the relationship of Aβ-induced neuronal C1q production to microglia activation and Aβ uptake in slice cultures was investigated. Materials and methods Materials Aβ 1–42, obtained from Dr. C. Glabe (UC, Irvine), was synthesized as previously described [ 32 ]. Aβ 10–20 was purchased from California Peptide Research (Napa, CA). Lyophilized (in 10 mM HCl) Aβ peptides were solubilized in H 2 O and subsequently N-2-hydroxyethylpiperazine-N'-2-ethanesulfonic acid (HEPES) was added to make a final concentration of 10 mM HEPES, 500 μM peptide. This solution was immediately diluted in serum-free medium and added to slices. Glycine-arginine-glycine-aspartic acid-serine-proline (RGD) peptide was purchased from Calbiochem (San Diego, CA). D-(-)-2-amino-5-phosphonovaleric acid (APV) was purchased from Sigma (St. Louis, MO). Both compounds were dissolved in sterile Hanks' balanced salt solution (HBSS) without glucose at 0.2 M and 5 mM, respectively, before diluted in serum-free medium. Antibodies used in experiments are listed in Table 1 ; RT-PCR primers, synthesized by Integrated DNA Technologies (Coralville, IA), are listed in Table 2 . All other reagents were from Sigma unless otherwise noted. Table 1 Antibodies used in immunohistochemistry. antibody/antigen concentration source anti-rat C1q 2 μg/ml M. Wing, Cambridge, UK OX-42 (CD11b/c) 5 μg/ml BD/PharMingen, San Diego, CA ED-1 3 μg/ml Chemicon, Temecula, CA anti-CD45 0.5 μg/ml Serotec Inc, Raleigh, NC 4G8 (Aβ) 1 μg/ml Signet Pathology Systems, Dedham, MA 6E10 (Aβ) 0.5 μg/ml Signet Pathology Systems Table 2 PCR primers and cycling conditions for RT-PCR assay. Gene Primer sequences Denaturation Annealing Extension cycle Ref C1qB 5'-cgactatgcccaaaacacct-3' 5'-ggaaaagcagaaagccagtg-3' 94°C 1 min 60°C 1 min30 sec 72°C 2 min 35 [61] MCSF 5'-ccgttgacagaggtgaacc-3' 5'-tccacttgtagaacaggaggc-3' 92°C 30 sec 58°C 1 min 72°C 1 min30 sec 35 [62] CD40 5'-cgctatggggctgcttgttgacag-3' 5'-gacggtatcagtggtctcagtggc-3' 94°C 30 sec 58°C30 sec 72°C 1 min 30 [63] β-actin 5'-ggaaatcgtgcgtgacatta-3' 5'-gatagagccaccaatccaca-3' 94°C 30 sec 60°C30 sec 72°C 1 min 25 [61] IL-8 5'-gactgttgtggcccgtgag-3' 5'-ccgtcaagctctggatgttct-3' 94°C 1 min 56°C 1 min 72°C 1 min 39 [64] Slice cultures Hippocampal slice cultures were prepared according to the method of Stoppini et al [ 33 ] and as described in Fan and Tenner [ 34 ]. All experimental procedures were carried out under protocols approved by the University of California Irvine Institutional Animal Care and Use Committee. Slices prepared from hippocampi dissected from 10d-old Sprague Dawley rat pups (Charles River Laboratories, Inc., Wilmington, MA) were kept in culture for 10 to 11 days before treatment started. All reagents were added to serum-free medium (with 100 mg/L transferrin and 500 mg/L heat-treated bovine serum albumin) which was equilibrated at 37°C, 5% CO 2 before addition to the slices. Aβ 1–42 or Aβ 10–20 was added to slice cultures as described previously [ 34 ]. Briefly, peptide was added to cultures in serum-free medium at 10 or 30 μM. After 7 hours, the peptide was diluted with the addition of an equal amount of medium containing 20% heat-inactivated horse serum. Fresh peptide was applied for each day of treatment. Controls were treated the same way except without peptide. RGD or APV was added to the slice cultures at the same time as Aβ 42. Immunohistochemistry At the end of the treatment period, media was removed, the slices were washed with serum-free media and subjected to trypsinization as previously described [ 34 ] for 15 minutes at 4°C to remove cell surface associated, but not internalized, Aβ. After washing, slices were fixed and cut into 20 μm sections for immunohistochemistry or extracted for protein or RNA analysis as described in Fan and Tenner [ 34 ]. Primary antibodies (anti-Aβ antibody 4G8 or 6E10; rabbit anti rat C1q antibody; CD45 (leukocyte common antigen, microglia), OX42 (CD11b/c, microglia), or ED1 (rat microglia/macrophage marker), or their corresponding control IgGs were applied at concentrations listed in Table 1 , followed by biotinylated secondary antibody (Vector Labs, Burlingame, CA) and finally FITC- or Cy3-conjugated streptavidin (Jackson ImmunoResearch Laboratories, West Grove, PA). Slides were examined on an Axiovert 200 inverted microscope (Carl Zeiss Light Microscopy, Göttingen, Germany) with AxioCam (Zeiss) digital camera controlled by AxioVision program (Zeiss). Images (of the entire CA1-CA2 region of hippocampus) were analyzed with KS 300 analysis program (Zeiss) to obtain the percentage area occupied by positive immunostaining in a given field. ELISA Slices were homogenized in ice-cold extraction buffer (10 mM triethanolamine, pH 7.4, 1 mM CaCl 2 , 1 mM MgCl 2 , 0.15 M NaCl, 0.3% NP-40) containing protease inhibitors pepstatin (2 μg/ml), leupeptin (10 μg/ml), aprotinin (10 μg/ml), and PMSF (1 mM). Protein concentration was determined by BCA assay (Pierce, Rockford, IL) using BSA provided for the standard curve. An ELISA for rat C1q was adapted from Tenner and Volkin [ 35 ] with some modifications as previously described [ 34 ]. RNA preparation and RT-PCR Total RNA from cultures was isolated using the Trizol reagent (Life Technologies, Grand Island, NY) according to the manufacturer's instructions. RNA was treated with RNase-free DNase (Fisher, Pittsburgh, PA) to remove genomic DNA contamination. Each RNA sample was extracted from 3 to 5 hippocampal organotypic slices in the same culture insert. The reverse transcription (RT) reaction conditions were 42°C for 50 min, 70°C for 15 min. Tubes were then centrifuged briefly and held at 4°C. Primer sequences and PCR conditions are listed in Table 2 . PCR products were electrophoresed in 2% agarose gel in TAE buffer and visualized with ethidium bromide luminescence. To test for differences in total RNA concentration among samples, mRNA level for rat β-actin were also determined by RT-PCR. Results were quantified using NIH image software [ 36 ] by measuring DNA band intensity from digital images taken on GelDoc (BIO-RAD) with Quantity One program. Results NMDA receptor antagonist APV inhibits Aβ42 uptake and Aβ42-induced microglial activation and neuronal C1q production We have previously reported that C1q was detected in cells positive for neuronal markers and that microglial cells were activated in slices following Aβ42 ingestion [ 34 ]. Lynch and colleagues have shown that APV, a specific NMDA glutamate receptor antagonist, was able to block Aβ42 uptake by hippocampal neurons in slice cultures [ 37 ]. This provided a mechanism to down-modulate the Aβ42 internalization and test the effect on induction of C1q synthesis in neurons. Slices were treated with no peptide, 50 μM APV, 30 μM Aβ42, or 30 μM Aβ42 + 50 μM APV for 3 days with fresh reagents added daily. Cultures were collected and processed as described in Materials and Methods. Similar to reported previously, addition of exogenous Aβ42 resulted in Aβ uptake by hippocampal neurons, induction of C1q synthesis in neurons, and activation of microglial cells (Figure 1d, e, f compared with 1a, b, c ). As anticipated, Aβ42 uptake in neurons detected by both 4G8 (Figure 1g ) and 6E10 (data not shown) was inhibited by APV co-treatment. Neuronal C1q immunoreactivity was also inhibited when APV was added to Aβ42 treated slices (Figure 1h ). Aβ42-triggered microglial activation, assessed by upregulation of antigens detected by anti-CD45 (Figure 1i vs. 1f ), OX42 and ED1 (data not shown) was also fully diminished by APV. To quantify the immunohistochemistry results, images were taken from the entire CA1-CA2 region of each immunostained hippocampal section and averaged. Image analysis further substantiated the reduction in Aβ uptake, C1q synthesis and microglial activation (Figure 1j ). C1q gene expression at mRNA and protein levels was also assessed by RT-PCR and ELISA, respectively. Results showed decrease of C1q mRNA and protein in slice extracts treated with 30 μM Aβ42 + APV, compared to 30 μM Aβ42 alone (Figure 2a and 2b , n = 2). Figure 1 APV inhibited Aβ uptake, neuronal C1q production, and microglial activation. Slices were treated with no peptide (a, b, c), 30 μM Aβ 42 (d, e, f), or 30 μM Aβ 42 + 50 μM APV (g, h, i) for 3 days with fresh reagents added daily. Immunohistochemistry for Aβ (4G8, a, d, g), C1q (anti-rat C1q, b, e, h), and microglia (CD45, c, f, i) was performed on fixed and sectioned slices. Scale bar = 50 μm. Results are representative of three separately performed experiments. j. Immunoreactivity of Aβ (open bar), C1q (black bar), or CD45 (striped bar) was quantified as described in Materials and Methods. Values are the mean ± SD (error bars) from images taken from 8 slices (2 sections per slice) in 3 independent experiments (* p < 0.0001 compared to Aβ, Anova single factor test). Figure 2 Inhibition of Aβ-induced C1q synthesis by APV. a. C1q and β-actin mRNAs were assessed by RT-PCR in slices after 3 days of no peptide, 30 μM Aβ, or 30 μM Aβ + 50 μM APV treatment. Results are from one experiment representative of two independent experiments. b. Slices were treated with no peptide (open bar), 30 μM Aβ (black bar), or 30 μM Aβ + 50 μM APV (striped bar) daily for 3 days. 3 or 4 slices that had received same treatment were pooled, extracted and proteins analyzed by ELISA. Data are presented as percentage of control in ng C1q/mg total protein (mean ± SD of three independent experiments, **p = 0.01 compared to Aβ, one-tailed paired t-test). Integrin receptor antagonist GRGDSP (RGD) peptide enhances Aβ42 uptake and Aβ42-induced microglial activation and neuronal C1q expression It has been shown that an integrin receptor antagonist peptide, GRGDSP (RGD), can enhance Aβ ingestion by neurons in hippocampal slice cultures [ 37 ]. Therefore, we adopted this experimental manipulation as an alternative approach to modulate the level of Aβ uptake in neurons and assess the correlation between Aβ ingestion and neuronal C1q expression. Slices were treated with no peptide, 2 mM RGD, 10 μM Aβ42, or 10 μM Aβ42 + 2 mM RGD for 3 days with fresh peptides added daily. At the end of treatments, slices were collected and processed. Addition of RGD peptide by itself did not result in neuronal C1q induction or microglial activation (CD45) compared to no treatment control, as assessed by immunostaining (data not shown). While greater ingestion was seen at 30 μM (Figure 1d, e, f ), addition of 10 μM Aβ shows detectable Aβ ingestion, C1q expression, and microglial activation (Figure 3d, e, f compared with 3a, b, c ). The lower concentration of Aβ was chosen for these experiments to ensure the detection of potentiation of uptake (vs. a saturation of uptake at higher Aβ42 concentrations). When RGD was provided in addition to 10 μM Aβ42, Aβ immunoreactivity in neurons with antibody 4G8 (Figure 3g vs. 3d ) and 6E10 (similar results, data not shown), neuronal C1q expression (Figure 3h vs. 3e ), and CD45 (Figure 3i vs. 3f ) upregulation in microglia triggered by Aβ42, were significantly enhanced. Enhanced microglial activation was also detected with OX42 and ED1 antibodies (data not shown). Quantification by image analysis (Figure 3j ) definitively demonstrated that the increased accumulation of Aβ in neurons, microglial activation, and induction of neuronal C1q synthesis in the presence of RGD. RT-PCR (Figure 4a ) and ELISA (Figure 4b ) further demonstrated that both mRNA and protein expression of C1q was enhanced by RGD. Thus, under the conditions tested, both neuronal C1q synthesis and microglial activation are coordinately affected when the internalization of Aβ is modulated negatively by APV or positively by RGD. Figure 3 RGD enhanced Aβ uptake, neuronal C1q expression, and microglial activation. Hippocampal slices were treated with no peptide (a, b, c), 10 μM Aβ 42 (d, e, f), or 10 μM Aβ 42 + 2 mM RGD (g, h, i) for 3 days with fresh peptides added daily. Immunohistochemistry for Aβ (4G8, a, d, g), C1q (anti-rat C1q, b, e, h), and microglia (CD45, c, f, i) was performed on fixed slice sections. Scale bar = 50 μm. Results are representative of three separately performed experiments. j. Immunoreactivities of Aβ (open bar), C1q (black bar), or CD45 (striped bar) were quantified as described in Materials and Methods. Values are the mean ± SD (error bars) from images taken from 8 slices (2 sections per slice) in 3 independent experiments (* p < 0.0001, compared to Aβ, Anova single factor test). Figure 4 Enhancement of Aβ-induced C1q synthesis by RGD. a. C1q and β-actin mRNAs were assessed by RT-PCR in slices after 3 days of no peptide, 10 μM Aβ, or 10 μM Aβ + 2 mM RGD treatment. Results are from one experiment representative of two independent experiments. b. Slices were treated with no peptide (open bar), 10 μM Aβ (black bar), or 10 μM Aβ + 2 mM RGD (striped bar) daily for 3 days. 3 or 4 slices that had received same treatment were pooled, extracted and proteins analyzed by ELISA. Data are presented as percentage of control in ng C1q/mg total protein (mean ± SD of three independent experiments, **p = 0.06 compared to Aβ, one-tailed paired t-test). Aβ10–20 blocks Aβ42 induced microglial activation but triggers C1q synthesis in hippocampal neurons Data reported by Giulian et al suggests that residues 13–16, the HHQK domain in human Aβ peptide, mediate Aβ-microglia interaction [ 38 ]. To investigate the effect of HHQK peptides in this slice culture system, rat hippocampal slices were treated with no peptide, 10 μM Aβ42, 10 μM Aβ42 + 30 μM Aβ10–20, or 30 μM Aβ10–20 for 3 days with fresh peptides added daily. Sections were immunostained for Aβ, C1q, and microglia. Aβ immunoreactivity was significantly reduced in the Aβ42 +Aβ10–20 treated tissues compared to the Aβ42 alone treatment (Figure 5g vs. 5d ). Aβ10–20 alone-treated slices lacked detectable immunopositive cells with either 4G8 or 6E10 anti-Aβ antibody (Figure 5j and data not shown). Furthermore, as anticipated [ 38 ], when Aβ10–20 was present, microglial activation by Aβ42 as assessed by level of CD45, OX42, and ED1, was significantly reduced (Figure 5i vs. 5f and data not shown). Image analysis confirmed the inhibition of Aβ uptake (Figure 5m , open bars) and microglial activation (Figure 5m , striped bars) by the HHQK-containing Aβ10–20 peptide. However, production of C1q in neurons treated with Aβ42 was not inhibited by Aβ10–20 (Figure 5h vs. 5e ). In fact, with Aβ10–20 alone, neurons were induced to express C1q to a similar level as Aβ42 (Figure 5k ). The sustained C1q induction by Aβ10–20 was confirmed by RT-PCR for C1q with mRNAs extracted from slices (Figure 6a ). Figure 5 Aβ10–20 blocked Aβ42 uptake, microglial activation, but not neuronal C1q induction. Slices were treated with no peptide (a, b, c), 10 μM Aβ 42 (d, e, f), 10 μM Aβ 42 + 30 μM Aβ 10–20 (g, h, i) or 30 μM Aβ 10–20 (j, k, l) for 3 days with fresh peptides added daily. Immunohistochemistry for Aβ (4G8, a, d, g, j), C1q (anti-rat C1q, b, e, h, k), and microglia (CD45, c, f, i, l) was performed on fixed and sectioned slices. Results are representative of three independent experiments. Scale bar = 50 μm. m. Immunoreactivities of Aβ (open bar), C1q (black bar), or CD45 (striped bar) were quantified as described in Materials and Methods. Values are the mean ± SD (error bars) from images taken from 8 slices (2 sections per slice) in 3 independent experiments. Microglial activation by Aβ42 was significantly inhibited by Aβ10–20 (* p < 0.0001, compared to either Aβ42 + Aβ10–20 or Aβ10–20, Anova single factor test). Figure 6 a. Aβ10–20 inhibited Aβ42-induced C1q and CD40 mRNA elevation, but not that of MCSF. C1q, MCSF, CD40, and β-actin mRNAs were assessed by RT-PCR in slices treated for 3 days with no peptide, 10 μM Aβ 42, 30 μM Aβ 10–20, or 10 μM Aβ 42 + 30 μM Aβ 10–20. Results are from one experiment representative of two independent experiments. b. APV blocked MCSF, CD40, and IL-8 mRNA induction triggered by Aβ42. RT-PCR for MCSF, CD40, IL-8, and β-actin were performed on RNA extracted from slices treated with no peptide (control), 30 μM Aβ 42, or 30 μM Aβ42 + 50 μM APV for 3 days. Results are from one experiment representative of two separate experiments. CD40, IL-8, and MCSF mRNAs are induced by Aβ42 and differentially regulated by Aβ10–20 and APV It is known that activated microglia cells can produce pro-inflammatory cytokines, chemokines, and nitric oxide, as well as higher expression of co-stimulatory molecules like CD40 and B7 [ 39 ]. Many of those proteins have been shown to be upregulated in microglia stimulated by Aβ in cell culture and in vivo [ 40 ]. Semi-quantitative reverse transcriptase PCR technique was used to determine how certain inducible activation products were modified in slice cultures stimulated with exogenous Aβ42 and in the presence of Aβ10–20 or APV. Rat slices were treated with 30 μM Aβ42 +/-APV or 10 μM Aβ42 +/- 30 μM Aβ10–20 for 3 days before mRNAs were extracted from tissues. LPS, was added at 150 ng/ml for 24 hr, served as positive control, with positive detection for all molecules tested (data not shown). RT-PCR revealed that mRNAs for CD40 and IL-8 were enhanced in Aβ treated slice cultures relative to the control after 3 days (Figure 6a and 6b ). Both Aβ10–20 and APV inhibited Aβ42-triggered upregulation of CD40 (Figure 6a and 6b ), consistent with the inhibition of microglial activation by both Aβ10–20 and APV assessed by immunohistochemistry. APV also blocked Aβ42-induced IL-8 expression (Figure 6b ), as did Aβ10–20 (data not shown). Macrophage-colony stimulating factor (MCSF), a proinflammatory mediator for microglial proliferation and activation, has been shown to be expressed by neurons upon Aβ stimulation [ 41 ]. The expression of MCSF was induced in slice culture by Aβ treatment by Day 3 (Figure 6a and 6b ) and this increase was blocked by the presence of APV (Figure 6b ). In contrast, Aβ10–20 did not alter the Aβ42-triggered MCSF induction (Figure 6a ), suggesting that MCSF may be required for microglial activation, but alone is not sufficient to induce that activation. Discussion Previously, it has been shown that Aβ is taken up by pyramidal neurons in hippocampal slice culture and that the synthesis of complement protein C1q is induced in neurons [ 34 ]. Here we demonstrate that blocking of Aβ42 accumulation in neurons by NMDA receptor antagonist APV and increasing Aβ42 ingestion by integrin antagonist RGD is accompanied by inhibition and elevation in neuronal C1q expression, respectively. However, Aβ10–20, which markedly inhibits Aβ42 accumulation in pyramidal neurons, does not have any inhibitory effect on neuronal C1q expression. Thus, intraneuronal accumulation of Aβ is not necessary for Aβ-mediated induction of neuronal C1q synthesis. Since Aβ10–20 alone can induce a level of C1q expression in neurons comparable to Aβ42, it is hypothesized that amino acids 10–20 in Aβ peptide contain the sequence that is recognized by at least one Aβ receptor. It was reported by Giulian et al. that the HHQK domain (residues 13–16) in Aβ is critical for Aβ-microglia interaction and activation of microglia, as they demonstrated that small peptides containing HHQK suppress microglial activation and Aβ-induced microglial mediated neurotoxicity [ 38 ]. We have previously reported that rat Aβ42, which differs in 3 amino acids from human Aβ42, including 2 in the 10–20 region and 1 in the HHQK domain, was internalized and accumulated in neurons but failed to induce neuronal C1q expression [ 34 ]. This is consistent with the hypothesis that a specific Aβ interaction (either neuronal or microglial), presumably via the HHQK region of the Aβ peptide, but not intracellular Aβ accumulation, can lead to neuronal C1q induction in hippocampal neurons. Neurons are the major type of cells that accumulate exogenous Aβ in slice cultures. Microglial activation, as assessed by CD45, OX42, and ED1, was increased with enhanced neuronal Aβ42 uptake and inhibited when Aβ42 uptake was blocked by APV or Aβ10–20 in this slice culture system. These data would be consistent with a model in which neurons, upon internalization of Aβ peptide, secrete molecules to modulate microglial activation [ 14 , 41 , 42 ] (Figure 7 , large arrows). Synthesis and release of those molecules may require the intracellular accumulation of Aβ since blocking intraneuronal Aβ accumulation always blocked microglial activation. The finding that treatment with Aβ10–20 alone did not result in intraneuronal Aβ immunoreactivity or microglial activation, while rat Aβ42, which did accumulate within neurons, induced activation of microglial cells, is consistent with this hypothesis. It should be noted that an absence of Aβ immunoreactivity in Aβ10–20 treated slices does not exclude the possibility that Aβ10–20 was ingested but soon degraded by cells, and thus accumulation of Aβ rather than ingestion alone may be necessary to induce secretion of microglia activating molecules from neurons. Giulian et al. reported that the HHQK region alone was not able to activate microglia [ 38 ]. Thus, Aβ10–20 might block microglial activation by competing with Aβ42 for direct microglial binding, as well as by blocking uptake and accumulation of Aβ in neurons. Figure 7 Model of Aβ interaction with neurons and microglia in slice cultures. Exogenous Aβ peptide interacts with neuronal receptors leads to at least two separate consequences, in one of which C1q expression is upregulated in neurons. A second receptor mediates the secretion of certain modulatory molecules, which lead to microglial activation involving the expression of CD45, CR3, CD40, and IL-8. This does not exclude the direct interactions of Aβ with receptor(s) on microglia that may also contribute to microglial activation. Activated glial cells, especially microglia, are major players in the neuroinflammation seen in of Alzheimer's disease [ 43 ]. Microglial cells can be activated by Aβ and produce proinflammatory cytokines, nitric oxide, superoxide, and other potentially neurotoxic substances in vitro , although the state of differentiation/ activation of microglia and the presence of other modulating molecules is known to influence this stimulation [ 7 , 9 , 43 ]. "Activated" microglia also become more phagocytic and can partially ingest and degrade amyloid deposits in brain. This leads many to hypothesize that there are multiple subsets of "activated" microglia, each primed to function in a specific but distinct way [ 5 , 43 ]. In hippocampal slice cultures, we and others have shown that Aβ42 triggered microglial activation as assessed by immunohistochemical detection of CR3 (OX42), and cathepsin D [ 34 , 37 ]. Several chemokines, including macrophage inflammatory protein-1 (MIP-1α, MIP-1β), monocyte chemotactic protein (MCP-1), and interleukin 8 (IL-8), have been reported to increase in Alzheimer's disease patients or cell cultures treated with Aβ [ 44 , 45 ]. CD40, a co-stimulatory molecule, is also upregulated in Aβ-treated microglia [ 10 ]. In this study, similar to reports of cultured microglia, immunoreactivity of CD45 was found increased on microglia in Aβ42 treated slice cultures, and CD40 and IL-8 messenger RNAs were elevated after Aβ42 exposure. As expected, CD40 and IL-8 mRNA induction was blocked whenever immunohistochemistry analysis showed the inhibition of microglial activation. [We did not observe change in MIP-1α, 1β mRNAs in slice culture with Aβ42 treatment, and MCP-1 was too low to be detected with or without Aβ stimulation although it was detectable in LPS treated slices (data not shown).] The data presented thus far suggest the hypothesis that neurons, upon uptake and accumulation of Aβ, release certain substances that activate microglia. One possible candidate of those neuron-produced substances is MCSF, which has been reported to be induced in neuronal cultures upon Aβ stimulation [ 41 , 46 ], and is known to be able to trigger microglial activation [ 47 ]. Indeed, MCSF mRNA was found to increase after 3 days of Aβ treatment (Figure 6a and 6b ). The diminished MCSF signal with the addition of APV and coordinate lack of microglial activation is consistent with a proposed role of activating microglia by MCSF produced by stimulated neurons. However, in the presence of Aβ10–20, MCSF induction was unaltered, though microglial activation was inhibited. Thus, MCSF alone does not lead to the upregulation of the above-mentioned microglial activation markers. In this organotypic slice culture, no significant neuronal damage was observed in 3 day treatment with Aβ at concentrations that have been reported to cause neurotoxicity in cell cultures. One possible explanation is that the peptide has to penetrate the astrocyte layer surrounding the tissue to reach the multiple layers of neurons. Thus, the effective concentration of Aβ on neurons is certainly much lower than the added concentration. Aβ failing to induce neurotoxicity in slices to the same extent as in cell cultures may also indicate the loss of certain protective mechanisms in isolated cells. A distinct advantage of the slice culture model is that the tissue contains all of the cell types present in brain, the cells are all at the same developmental stage, and cells may communicate in similar fashion as in vivo . Our data demonstrating distinct pathways for the induction of neuronal C1q and the activation of microglial by amyloid peptides suggest the involvement of multiple Aβ receptors on multiple cell types in response to Aβ (Figure 7 , model) and possibly in Alzheimer's disease progression. This multiple-receptor mechanism is supported by reports suggesting many proteins/complexes can mediate the Aβ interaction with cells [ 48 ]. These include, but not limited to, the alpha7nicotinic acetylcholine receptor (alpha7nAChR), the P75 neurotrophin receptor (P75NTR) on neurons, the scavenger receptors and heparan sulfate proteoglycans on microglia, as well as receptor for advanced glycosylation end-products (RAGE) and integrins on both neurons and microglia (Figure 7 ). Several signaling pathways have been implicated in specific Aβ-receptor interactions [ 49 - 51 ]. However, it is not known which receptors are required for induction of C1q in neurons. In addition, as of yet the function of neuronal C1q has not been determined. Previous reports from our lab have shown that C1q is associated with hippocampal neurons in AD cases but not normal brain [ 52 ], and the fact that it is synthesized by the neurons has been documented by others [ 23 , 53 ]. In addition, C1q was prominently expressed in a preclinical case of AD (significant diffuse amyloid deposits, with no plaque associated C1q, and no obvious cognitive disorder) and is expressed in other situations of "stress" or injury in the brain [ 54 - 58 ]. Indeed, overexpression of human cyclooxygenase-2 in mice leads to C1q synthesis in neurons and inhibition of COX-2 activity abrogates C1q induction. These data suggest that in addition to the facilitation of phagocytosis by microglia [ 59 , 60 ] (particularly of dead cells or neuronal blebs), the induction of C1q may be an early response of neurons to injury or regulation of an inflammatory response, consistent with a role in the progression of neurodegeneration in AD. Whether and how the neuronal C1q production affects the survival of neurons is still under investigation. Identifying the receptors responsible for neuronal C1q induction may be informative in understanding the role of C1q in neurons in injury and disease. Conclusions In summary, induction of C1q expression in hippocampal neurons by exogenous Aβ42 is dependent upon specific cellular interactions with Aβ peptide that require HHQK region-containing sequence, but does not require intraneuronal accumulation of Aβ or microglial activation. Thus, induction of neuronal C1q synthesis may be an early response to injury to facilitate clearance of damaged cells, while modulating inflammation and perhaps facilitating repair. Microglial activation in slice culture involves the induction of CD45, CD40, CR3, and IL-8, which correlates with intraneuronal accumulation of Aβ, indicating contribution of factors released by neurons upon Aβ exposure. MCSF may be one of those stimulatory factors, though by itself MCSF cannot fully activate microglia. Removal of Aβ to prevent deposition and of cellular debris to avoid excitotoxicity would be a beneficial role of microglial activation in AD. However, activated microglia also produce substances that are neurotoxic. Therefore, the goal of modulating the inflammatory response in neurodegenerative diseases like AD is to enhance the phagocytic function of glial cells and inhibit the production of proinflammatory molecules. Being able to distinguish in the slice system C1q expression (which has been shown to facilitate phagocytosis of apoptotic cells in other systems [ 24 ]) from microglial activation suggests a plausible approach to reach that goal in vivo . List of abbreviations Aβ: amyloid beta; AD: Alzheimer's disease; APV: D-(-)-2-amino-5-phosphonovaleric acid; BSA: bovine serum albumin; GRGDSP (RGD): glycine-arginine-glycine-aspartic acid-serine-proline; HBSS: Hanks' balanced salt solution; HEPES: N-2-hydroxyethylpiperazine-N'-2-ethanesulfonic acid; MCSF: macrophage colony stimulating factor; NMDA: N-methyl-D-aspartic acid; PMSF: phenylmethylsulfonylfluoride; TAE: triethanolamine. Competing interests The author(s) declare that they have no competing interests. Authors' contributions RF cultured and processed the tissue, performed all experiments (immunohistochemistry, ELISA, PCR and others), analyzed the data, and drafted the manuscript. AJT contributed to the design of the study, guided data interpretation and presentation and edited the manuscript.

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Expression pattern and regulation of genes differ between fibroblasts of adhesion and normal human peritoneum

Background Injury to the peritoneum during surgery is followed by a healing process that frequently results in the attachment of adjacent organs by a fibrous mass, referred commonly as adhesions. Because injuries to the peritoneum during surgery are inevitable, it is imperative that we understand the mechanisms of adhesion formation to prevent its occurrence. This requires thorough understanding of the molecular sequence that results in the attachment of injured peritoneum and the development of fibrous tissue. Recent data show that fibroblasts from the injured peritoneum may play a critical role in the formation of adhesion tissues. Therefore, identifying changes in gene expression pattern in the peritoneal fibroblasts during the process may provide clues to the mechanisms by which adhesion develop. Methods In this study, we compared expression patterns of larger number of genes in the fibroblasts isolated from adhesion and normal human peritoneum using gene filters. Contributions of TGF-beta1 and hypoxia in the altered expression of specific genes were also examined using a semiquantitative RT-PCR technique. Results Results show that several genes are differentially expressed between fibroblasts of normal and adhesion peritoneum and that the peritoneal fibroblast may acquire a different phenotype during adhesion formation. Genes that are differentially expressed between normal and adhesion fibroblasts encode molecules involved in cell adhesion, proliferation, differentiation, migration and factors regulating cytokines, transcription, translation and protein/vesicle trafficking. Conclusions Our data substantiate that adhesion formation is a multigenic phenomenon and not all changes in gene expression pattern between normal and adhesion fibroblasts are the function of TGF-beta1 and hypoxia that are known to influence adhesion formation. Analysis of the gene expression data in the perspective of known functions of genes connote to additional targets that may be manipulated to inhibit adhesion development.

Background Peritoneal adhesions resulting from surgical injury are often associated with pelvic pain, bowel obstruction and infertility [ 1 ]. Epidemiological studies conclude that 30 to 35% of all hospital readmissions are associated with adhesion associated complications, of which 4.5 to 5.1% are directly related to adhesions [ 2 ]. Mechanisms of adhesion formations are not completely known. It is also not clear why adhesion form in some patients and not in others. Therefore, deciphering genetic components that signal adhesion formation may help diagnose adhesion-prone patients prior to surgery. Needless to mention that such information will facilitate finding ways to prevent post-surgical adhesion formation. Parietal and visceral peritoneum that surfaces the intraperitoneal organs is covered by a layer of squamous epithelial cells, the mesothelium. The submesothelial layer consists of fibroblasts, macrophages and blood vessels. Surgical abrasion to the peritoneum releases mesothelial cells, macrophages, fibroblasts, and blood containing cytokines and several cell types at the site of injury. Coagulation of blood creates a fibrinous mass between injured surfaces. In some patients fibrinolysis of clot followed by proliferation of mesothelial cells covers the wound. In others, failure of fibrinolysis followed by proliferation and migration of fibroblasts into the proteinous mass generates fibrous tissues of adhesion. Consequently, the process of adhesion formation include inflammatory response, fibrin deposition, cell-proliferation, -differentiation, -migration, -death, angiogenesis, extra cellular matrix (ECM) turnover regulated by cytokines, hypoxia, genetic and epigenetic factors [ 3 ]. Recent studies illustrate roles of peritoneal fibroblasts in adhesion development [ 4 - 10 ]. It is also proposed that fibroblasts from the chronic wounds migrate into the fibrin deposit; secrete ECM proteins causing wound contraction and scar formation [ 11 ]. The migration of fibroblasts may be coordinated by TGF-β1 mediated interactions of integrin receptors [ 10 ] with the RGD sequence of the fibrin, fibrinogen and fibronectin at the fibrin clot [ 12 ]. Additional cytokines and the hypoxic condition at the site of injury may also influence peritoneal fibroblasts to attain a phenotype supporting formation of adhesion tissue. This change in the phenotype of fibroblasts may be induced by changes in expression pattern of several genes during the process of adhesion development. Therefore, identifying differences in the global gene expression pattern between normal and adhesion fibroblasts may provide additional clues to the mechanisms by which normal fibroblasts attain the adhesive, proliferating and migratory phenotype required for the formation of fibrous tissues of adhesions. In the present study, we compared gene expression patterns between adhesion and normal peritoneal fibroblasts using GF211 gene filters (Research genetics) containing 4325 randomly selected known genes. Furthermore, we confirmed the expression pattern of genes of interest by a semiquantitative RT-PCR method and examined possible contribution/s of TGF-β1 and hypoxia in the transformation of normal peritoneal fibroblasts into an adhesion phenotype. Methods Peritoneal-tissue collection, fibroblast-isolation and culture Tissues were collected at the initiation of surgery and after the entry into the abdominal cavity of female patients (25–50 years) undergoing laparatomy for pelvic pain as described earlier [ 4 ]. All patients gave informed written consent for the tissue collection, which was conducted under a protocol approved by the Wayne State University Institutional Review Board. Normal parietal peritoneal tissues were collected from these patients from the anterior abdominal wall, approximately midway between the umbilicus and symphyses pubis, and lateral to the midline incision. Peritoneal tissues from adhesions, that were at least 3 inches away from the site of normal tissue collections, were also collected from the same patient. The peritoneal fibroblasts were isolated and separated from mesothelial cells by a differential centrifugation procedure that is briefly described earlier [ 4 ]. The isolation of fibroblasts from mesothelial cells were also verified by the RT-PCR detection of Collagen type I, Matrix metalloproteinase-2 (MMP-2) and Transforming growth factor-β3 (TGF-β3) [ 13 - 15 ]. The primary cultures were maintained in a humidified incubator (37°C, 5% CO 2 ) for 3 days in DMEM (Life Tech.) supplemented with 10% fetal bovine serum (Life Tech.) and antibiotics (Penicillin and Streptomycin 50 U/ml; Life Tech.). The monolayer of cells were passaged in trypsin-EDTA solution (Life Tech.). Cells at 3–5 passages were cultured in serum free medium in 75 cm 2 flasks (Fisher Scientific, Pittsburgh, PA) to 75% confluency prior to studies. Gene expression pattern in the fibroblasts from adhesion and normal peritoneum Total RNA was isolated from monolayer of fibroblasts at 12 h of culture in serum free medium using Trizol reagent (Invitrogen Inc.). Human Gene Filters (GF211; Research Genetics, Inc., Huntsville, AL) containing 4325 known human cDNA spots were used for the identification of differentially expressed genes between adhesion and normal fibroblasts from human peritoneum. Method suggested by the manufacturer was strictly followed. In brief, 10 μg of total RNA from monolayer cultures of fibroblasts were subjected to cDNA synthesis in presence of 10 μl 33 dCTP (10 mCi/ml; ICN Radiochemicals, Irvin, CA). Radiolabeled cDNA was separated from the free nucleotides using Bio-Spin 6 chromatography column (Bio-Rad Laboratories). Gene filters were labeled as adhesion or normal fibroblasts and washed with 0.5% sodium dodecyl sulfate (SDS) prior to prehybridization. Individual membrane was transferred to separate roller tubes of the hybridization oven (Fisher Scientific, Inc., Pittsburg, PA), each containing MicroHyb hybridization solution (Research Genetics) supplemented with Human Cot-1 DNA (Life Technology) and Poly dA (Research Genetics). The membranes were rotated at 10 RPM and at 42°C for 2 h. Radio-labeled cDNA prepared from adhesion and normal fibroblasts total RNA was denatured by heating in a boiling water bath for 3 min. The denatured probes were then injected into the prehybridization solution containing respective membrane. The membranes were hybridized with respective probe for 18 h at 42°C. The hybridization solution was then replaced with washing solution (2 × SSC containing 1% SDS). The temperature of the oven was raised to 50°C and RPM of rotors was increased to 15. Membranes were washed for 20 minutes when washing solution was replaced with a batch of fresh and prewarmed (50°C) washing solution. Washing was continued for additional 20 min. A third wash was performed with 0.5 × SSC solution containing 1% SDS at 55°C for 15 minutes. Membranes with cDNA spots facing up were covered with Saran wrap and exposed to phosphor screen (Kodak) for overnight. The screen was scanned with a Phosphor Imager (Storm System; Amersham Biosciences Corp., Piscataway, NJ). After acquisition of signal intensities from the normal and adhesion fibroblasts of one patient, filters were stripped according to protocol and subjected to gene filter experiments with the RNA samples from a second patient and images were scanned. Tiff images obtained from normal and adhesion fibroblasts of two patients were analyzed using Pathway 4 software (Research Genetics) for the identification of differentially expressed genes between the normal and adhesion fibroblasts of each patient. Relative abundance of selected genes in the fibroblasts from adhesion and normal peritoneum Steady-state levels of mRNA of selected genes that are known to have a role in cellular adhesion, proliferation, migration, apoptosis and demonstrating different expression levels between adhesion and normal fibroblasts in the gene filter experiments were verified further by a previously described semiquantitative RT-PCR method [ 16 ]. Total RNA (1 μg) from the monolayer culture of adhesion or normal fibroblasts was subjected to reverse transcription as described earlier. Complementary DNA (100 ng) was subjected to PCR amplification of the cDNA of interests in a 25 μl reaction mixture containing 50 mM Tris-HCl (pH 8.4), 50 mM KCl, 2.5 mM MgCl 2 , 0.2 mM dNTP, 0.5 U Taq Polymerase (all from Life Technology, BRL) and 1 μM each of sense and antisense primers. Primer sequences were determined using Primer3 software from the Internet . The control primers ( sense 5'-ggaggttcgaagacgatcag-3' and antisense 5'-cgctgagccagtcagtgtag-3') were expected to provide an amplicon of 509 bp from human 18S ribosomal subunit cDNA (gi: 337376). Accession numbers of genes of interests are provided in Table 2 and nucleotide sequences of primers and expected size amplicons are provided in the Table 3. Each PCR cycle consisted of a hot start at 95°C for 1 min, followed by melting at 95°C for 30 sec, annealing at 58°C for 1 min and extension at 72°C for 1 min. At the end an extension reaction at 72°C for 10 min was performed. Table 2 Genes differentially expressed in the adhesion fibroblasts and known to have roles in cell-adhesion, -proliferation, -migration, -differentiation and -death. Accession Number Definition Fold Change Functions P1 P2 * Increased in Intensity gi:17986276 Collagen, type IV alpha 2 2.4 2.7 See Discussion gi:4506760 S100 calcium-binding protein A10 2.3 2.7 See Discussion gi:6679055 Nidogen 2 6.4 7.1 See Discussion gi:14250074 Transmembrane 4 superfamily member 1 3.7 2.6 See Discussion gi:4758081 Chondroitin sulfate proteoglycan 2 3.4 3.2 See Discussion gi:187538 Metallothionein 1E 4.3 4.0 See Discussion gi:4336324 Small membrane protein 1 2.4 2.6 Cell viability [54] gi:17738299 Cyclin-dependent kinase inhibitor 2A 2.0 2.0 Cell proliferation [55] gi:16359382 Nuclear receptor subfamily 4, group A 1.6 2.1 Antagonizes TNF-α induced apoptosis [56] gi:40353726 Synaptopodin 2.9 2.4 Actin cytoskeleton dynamics [57] gi:23398519 Vasodilator-stimulated phosphoprotein 1.5 2.1 Enhances actin based cell motility, Cytoskeltal dynamics [58] gi:28329 α-Smooth muscle actin 3.0 3.2 Myofibroblast transformation [44] gi:14574570 Bcl-2 related gene bfl-1 1.6 1.4 Anti apoptotic; inhibitor of p53 induced apoptosis gi:796812 p53 tumor suppressor 1.5 1.6 Cell cycle arrest and apoptosis [52] Decreased in Intensity gi:184522 Insulin-like growth factor binding protein 3 3.2 2.3 See Discussion gi:4504618 Insulin-like growth factor binding protein 7 2.3 2.0 Growth suppressing factor [59] gi:28610153 Interleukin 8 3.2 2.6 Inhibits fibroblast migration, delays wound healing, reduces wound contraction [60] gi:4504982 Lectin, galactoside-binding, soluble 3 [galectin) 3.0 3.0 Tumor-suppressive and pro apoptotic [61] gi:12803916 Gap junction protein, beta 1, [Connexin 32) 1.8 2.2 Tumor suppressive and Proapoptotic [62] gi:14589894 Cadherin 5, type 2, VE-cadherin [vascular] 2.3 1.7 Down regulation associates with tumor metastasis, Initiates endothelial-mesenchymal transdifferentiation [63] gi: 16198356 Lactotransferrin 2.2 2.1 Inhibits growth of malignant tumors. Elevated by high level of estrogen [64] gi:21619838 Lipocalin 2, Oncogene 24p3 3.3 2.5 Proapoptotic [65] gi: 23273645 Calponin 1, basic, Smooth muscle cell 1.7 2.5 Inhibits smooth muscle cell contraction and Tumor Suppressive [66] gi:40225461 RAP1A, member of RAS oncogene family 1.8 1.6 Inhibits cell proliferation [67] gi:4507112 Synuclein-gamma 1.5 1.3 Expression reduced in carcinoma [68] * Adhesion/Normal peritoneal fibroblasts values of gene expression intensity from patient 1 (P1) and 2 (P2). Table 3 PCR primers, amplicon size and expression ratios of genes between adhesion and normal peritoneal fibroblasts Transcripts Primer Sequence (5' to 3') Amplicon size (base pairs) Adhesion/Normal Ratio Gene Filter* Adhesion/Normal Ratio (RT-PCR)** 18S Ribosomal Subunit Sense ggaggttcgaagacgatcag 509 (No spot) 0.9 Antisense cgctgagccagtcagtgtag Collagen type IV alpha 2 chain(COL4A2) Sense caccatgcccttcctgtact 351 2.6 2.3 Antisense ttgcattcgatgaatggtgt S100 Calcium binding protein A10 (S100A10) Sense cacaccaaaatgccatctca 389 2.5 2.1 Antisense cttctatgggggaagctgtg Nidogen 2 (NID2) Sense gcttacgaggaggtcaaacg 500 6.8 2.9 Antisense ttcacccggaaggtattcag Transmembrane 4 superfamily member 1 (TM4SF1) Sense tcgcggctaatattttgctt 500 3.2 1.9 Antisense gcctccaagcactccattta Chondoitin sulfate proteoglycan 2 (CSPG2) Sense gaaccaaattatggggcaga 400 3.3 3.0 Antisense ctcccaatccttcgtcgata Insulin-like growth factor binding protein 3 precursor (IGFBP3) Sense gggtaggcacgttgtaggaa 603 -2.8 -2.8 Antisese gtgaggctggctaagaatgc Metallothionine (hMT-Ie) Sense cagagggtctctgggtttca 400 4.2 3.3 Antisense gccccatgtcctctcactaa * Average intensity of Adhesion/Normal peritoneal fibroblast gene expression data from patient 1(P1) and 2 (P2) presented in Table 2. Minus (-) sign indicate fold decrease in intensity in the adhesion fibroblasts. ** Ratios of Adhesion/Normal mean values from 4 patients. Table 4 Expression profiles of genes in the adhesion vs. normal peritoneal fibroblasts and the effects of TGF-β1 or hypoxia on the expression level of genes in the normal peritoneal fibroblasts Transcripts Adhesion/Normal fibroblasts (Gene Filter & RT-PCR) TGF-β1 Effects (RT-PCR) Hypoxia Effects (RT-PCR) COL4A2 ↑ ↑ ↑ S100A10 ↑ ↑ ↑ NID2 ↑ — ND TM4SF1 ↑ — ND CSPG2 ↑ ↑ — IGFBP3 ↓ — ND hMT-Ie ↑ ↑ ↑ ↑ = Up regulation (p < 0.05); ↓ = Down Regulation (p < 0.05); — = No Change ND = Not determined Initially cDNA of interests were amplified from normal peritoneal fibroblasts at different (25 to 35) PCR cycles. PCR products were subjected to agarose gel electrophoresis. Molecular weight marker (100 bp DNA ladder; Life Technology) were also loaded in adjacent lanes. DNA in the gel were stained with 1:10,000 dilution of SYBR Green I dye (Molecular Probes, Inc., Eugene, OR) and photographed using a DC 120 Kodak digital camera (Eastman Kodak, Rochester, NY) for the verification of size and analysis of band intensity using Image J software . Band intensities were plotted to determine the linearity of PCR reactions for the amplification of target transcripts. Target cDNA were amplified by PCR from normal and peritoneal fibroblasts at specific PCR cycle within its linear range of amplification. Total RNA samples from normal and adhesion fibroblasts of 4 patients (included RNA from normal and adhesion fibroblasts of two patients used for the gene filter experiments) were used for the RT-PCR experiments. Optical densities obtained from amplicons of 4 patients (1 normal and 1 adhesion fibroblast RNA sample per patient) were used to derive mean ± standard error of mean values representing relative levels of each mRNA species in normal and adhesion fibroblasts. Effects of TGF-β 1 or hypoxia on gene expression pattern Effects of TGF-β1 or hypoxic conditions on the steady state levels of specific gene transcripts in the normal peritoneal fibroblasts were also studied to examine the possibility of adhesion causing factors potentiating the gene expression pattern in the normal fibroblasts similar to adhesion fibroblasts. Normal peritoneal fibroblasts were cultured in six well culture plates (FALCON). When confluent, monolayer of cells in culture were exposed to 1 ng/ml TGF-β 1 (Sigma Chemical Company, St. Louis, MO) or hypoxia (2% Oxygen) for 24 h. Control plates were cultured for the same duration in absence of TGF-β1 or hypoxia. Total RNA was isolated from the control, TGF-β1 and hypoxia treated cells and subjected to RT-PCR reactions as described above to determine relative levels of 18S ribosomal subunit and gene specific transcripts in the control and treated cells. RT-PCR experiments were conducted twice with the normal peritoneal fibroblasts isolated from 3 patients to have six control, six TGF-β1 treated and six hypoxia exposed amplicons. This included normal fibroblasts from one new patient and two patients that were used exclusively for RT-PCR experiments for the confirmation of gene array data. Optical densities of amplicons from six control or treated cells per mRNA species were used to derive the mean ± standard error of mean values for comparison. Statistical analysis Band intensity value of each RT-PCR experiment (normal, adhesion or treated fibroblasts) was used to derive Mean ± Standard error of Mean using Statview 4.5 software (Abacus Concepts, Berkley, CA). Differences between Means were tested for significance by one-way analysis of variance with the specific post hoc test using the same software to compare differences in the steady state levels of different mRNA species. Results Expression pattern of genes between adhesion and normal peritoneal fibroblasts Hybridization intensities of radio labeled cDNA from normal or adhesion fibroblasts from both the patients were different when analyzed using Pathway software. Comparison of hybridization intensities from individual gene spots between normal and adhesion fibroblast RNA (Figure 1 ) demonstrated that the expression levels of ~4% genes were >1.5 fold different. BLAST search of the accession number of genes from the list provided by the manufacturer showed that genes with altered expression level between normal and adhesion fibroblasts are reported to be involved in cell adhesion and migration; transformation, transcription, translation and growth factors as well as cytokines and signaling molecules. Figure 1 Images depicting radioactive signals from GF211 filters hybridized with radiolabeled cDNA. Gene filters were hybridized with 33 P labeled cDNA from normal peritoneal fibroblasts or fibroblasts from adhesion tissue. Unbound signals were washed and relative radioactive signal intensities were detected using a Phosphoroimager as described in the Methods. A. Tiff images of radioactive signals from individual gene spots of filters hybridized with normal (above) and adhesion fibroblasts, both isolated from Patient 1. B. Scatter plot showing signal intensities from normal peritoneal (Intensity I) and adhesion (Intensity II) fibroblasts. Dotted lines indicate a two fold changes in hybridization intensities from the median (solid line). Gene filter data from two patients showed similar expression pattern of collagen type 1 (alpha 2), Collagen type III (alpha 1), fibronectin 1, Matrix metalloproteinase-1 (MMP-1), Transforming Growth Factor beta-1 (TGF-β1), TGF-β2 and tissue plasminogen activator as reported earlier using multiplex PCR technique (Table- 1 ). Signal intensities representing TGF-β3 (gi:22531293), TGF-β III Receptor (gi:26251745), VEGF-A (gi:2565322), VEGF-B (gi:39725673) and VEGF-C (gi:19924300) expression levels were respectively 1.6, 1.5, 1.9, 1.3 and 1.3 fold (average values from two patients) lower in the adhesion compared to normal fibroblasts. No spots for antiapoptotic bcl-2 and proapoptotic bax were present in GF211 filters. Signal intensities representing anti apoptotic molecule bcl-2 related gene bfl-1 (gi: 14574570) and pro-apoptotic molecule p53 (gi:796812) were higher (Table 2 ) in adhesion compared to normal fibroblasts. Expression levels of proapoptotic molecule bad (gi: 14670387) and bak1 (gi: 33457353) were not different between normal and adhesion fibroblasts. A list of additional genes that are differentially expressed between normal and adhesion fibroblasts and known to be involved in apoptosis as well as cell adhesion, proliferation and migration are listed in Table 2 . Table 1 Ratios of signal intensities from adhesion and normal peritoneal fibroblasts detected from gene filters representing relative expression level of genes in patient 1 (P1) and 2 (P2). Gene Accession Number P1 (A/N) P2 (A/N) Reference* Collagen Type I (alpha 2) gi:48762933 1.4 1.5 [4,15,53] Collagen Type III (alpha 1) gi:15149480 2.0 1.7 [15] Fibronectin 1 gi:53791222 1.5 1.2 [4,15] MMP-1 gi:13027798 1.6 1.4 [4] TGF-β1 gi:10863872 1.4 1.7 [4,15] TGF-β2 gi:339549 1.5 1.3 [4] tPA gi:2161467 -1.5 -2.0 [8] Minus (-) sign represents lower signal intensity in adhesion (A) compared to normal (N) fibroblasts (gene filter data) * Citations reporting expression levels of respective genes in fibroblasts from normal human peritoneum and adhesion using multiplex PCR technique Semiquantitative RT-PCR experiments (Figure 2 ) conducted to verify expression pattern of specific genes from the gene filter experiments that were not studied earlier in the peritoneal fibroblasts confirmed higher expression (p < 0.05) of Collagen type IV (alpha 2) chain (COL4A2), S100 Calcium binding protein A10 (S100A10), Nidogen 2 (NID2), Transmembrane 4 superfamily member 1 (TM4SF1), Chondroitin sulfate proteoglycan 2 (CSPG2) and Metallothioneine (hMT-Ie) in adhesion compared to normal fibroblasts. The semiquantitative RT-PCR experiments also confirmed lower expression levels of Insulin-like growth factor binding protein 3 precursor (IGFBP3) mRNA in the adhesion compared to normal peritoneal fibroblasts. Transcript levels of 18S ribosomal subunit estimated by RT-PCR method was not significantly different between fibroblasts isolated from normal and adhesion peritoneum (Figure 2 and Table 3 ). Figure 2 Relative abundance of specific mRNA species in the normal and adhesion fibroblasts. Genes differentially expressed between the normal and adhesion fibroblasts, as identified by gene filter experiments, were amplified by the RT-PCR technique at 26 PCR cycle. PCR products (20 μl) were subjected to electrophoresis, stained with fluorescent dye, photographed and optical density determined as described in Methods. A : Representative gel showing amplicons from normal (odd lane numbers) and adhesion (even lane numbers) fibroblasts. Lanes 1 & 2, 3 & 4; 5 & 6; 7 & 8; 9 & 10 and 11 & 12 show RT-PCR products from COL4A2; NID2; CSPG2; S100A10; 18S ribosomal subunit and TM4SF1 mRNA respectively. Lanes 13 & 14; 15 & 16 and 17 & 18 show RT-PCR products from 18S ribosomal subunit, IGFBP3 precursor and MET-1e mRNA respectively. L: Lanes loaded each with 7 μl of 100 bp DNA ladder. The 600 bp band of the ladder is shown by arrow head. B . Histogram showing mean and standard error of mean values of optical densities derived from amplicons of specific genes (x axis) from normal (empty bars) and adhesion (filled bars) fibroblasts isolated from 4 patients as described in Methods. *Significantly different ( p < 0.05) between normal and adhesion fibroblasts. Effects of TGF-β1 or hypoxia on the expression levels of specific genes in the normal peritoneal fibroblasts Exposure to TGF-β 1 or hypoxic conditions for 24 h altered expression levels of specific genes in the normal peritoneal fibroblasts as evidenced by semiquantitative RT-PCR. Transcript levels of COL4A2, S100A10, CSPG2 and hMT-Ie were up regulated by TGF-β1 in the normal peritoneal fibroblasts (Figure 3 ), whereas transcript levels of NID2, TM4SF1, and IGFBP3 were not altered by TGF-β1 treatment. Hypoxic conditions elevated expression levels of COL4A2, S100A10 and hMT-Ie transcripts in the normal peritoneal fibroblasts (Figure 4 ). Transcript levels of CSPG2 were not significantly altered by hypoxia. Figure 3 Effects of TGF-β1 on the steady state levels of specific mRNA species in normal peritoneal fibroblasts. Normal peritoneal fibroblasts were cultured for 24 h in absence or presence of TGF-β1 and total RNA from cells were examined for the steady-state levels of different mRNA species by semiquantitative RT-PCR technique as described in Methods. A . Representative gels showing amplicons generated by RT-PCR from specific gene transcripts (denoted on the left of the panel) from control (lanes 1, 2 and 3) and TGF-β1 (lanes 4, 5 and 6) treated cells. Complementary DNA for all genes except IGFBP3 precursor was amplified at 26 PCR cycles. IGFBP3 precursor transcripts were amplified at 25 cycles. L Lane loaded with 100 bp DNA ladder. B Histogram showing mean and standard errors of mean of optical densities from amplicons representing specific mRNA species (x axis). The RT-PCR experiments were conducted twice from normal and peritoneal isolated from 3 patients to obtain OD values of six amplicons from control (empty bars) or treated (shaded bars) fibroblasts statistical analysis. * Significantly different from control conditions at p < 0.05. Figure 4 Effects of hypoxia on the steady state levels of specific genes in normal peritoneal fibroblasts. Normal peritoneal fibroblasts were cultured for 24 h in normoxic and hypoxic conditions and total RNA from cells were examined to determine the steady state levels of specific transcripts as described in Methods. Complementary DNA for all genes was amplified at 26 PCR cycles. Histogram showing mean and standard errors of mean of optical densities of amplicons representing specific mRNA species (x axis) from control (empty bars) or hypoxia exposed cells (shaded bars) from 3 patients. The RT-PCR experiments were conducted twice to obtain OD values of six amplicons from normoxic or hypoxic fibroblasts for statistical analysis. Images of gels with amplicons from cells treated with hypoxia are not shown. * Significantly different from control conditions at p < 0.05. Discussion We present evidence that the expression pattern of large number of genes differ between the fibroblasts isolated from adhesion tissues and normal human peritoneal supporting the notion that adhesion fibroblasts may attain a different phenotype following peritoneal injury. Genes that displayed altered expression levels in this transition included those involved in cell proliferation, differentiation, signaling molecules, transcription and translation factors, proteolysis and cytokines. Results indicate that fibroblasts from adhesion tissue may perceive and respond to external and internal cues differently than those residing in normal human peritoneum. We attempted to decipher the functional consequences of altered gene expression pattern in the adhesion fibroblast to further elucidate the mechanism of adhesion formation and point to additional ways adhesion development may be restrained. Expression pattern of genes in the fibroblasts from normal and pathological sites are shown to be different also in earlier studies [ 17 ]. More relevant to the present study are the reports [ 4 , 8 ] on the mRNA levels of human type I collagen (alpha 2), fibronectin 1, MMP-1, TIMP-1, TGF-β1, TGF-β2, IL-10, PAI-1, tPA and COX-2 in adhesion and normal peritoneal fibroblasts from humans estimated by multiplex PCR technique. Gene filter data from two patients also showed similar pattern of collagen, type 1 (alpha 2), fibronectin 1, MMP-1, TGF-β1, TGF-β2 and tPA mRNA levels in the normal and adhesion fibroblasts (Table 1 ). Expression pattern of TIMP-1, IL-10, PAI-1, COX-2 in the adhesion and normal peritoneal fibroblasts as reported earlier [ 4 , 8 , 9 ] could not be verified by gene filter experiments because GF211 filters do not have spots representing these genes. Even so, similarities in the expression pattern of many genes between two patients (Tables 1–3) and those reported earlier using multiplex PCR technique [ 4 , 8 ] validate our findings. The semiquantitative RT-PCR experiments conducted to verify expression pattern of specific genes recorded from gene filter experiments show that mRNA levels of COL4A2, S100A10, nidogen-2, TM4SF1, CSPG2, MT-1e and IGFBP3 precursor indeed differ between normal and adhesion fibroblasts. Even though expression levels of these transcripts were significantly different between normal and adhesion fibroblasts, only minor variations in the optical densities of amplicons were recorded within normal or adhesion tissues of patients of different age groups. This indicate that age dependent differences in the expression levels of genes in the fibroblasts from normal or adhesion tissues may tend to attain a relatively similar expression levels when in culture. Despite the fact that our study focused on the steady state levels of mRNA species and not on translation or posttranslational events, analysis of the functional consequences of altered expression of encoded proteins from the literature as referred below indicated that changes in the pool of these mRNA species may lead to the transformation of normal peritoneal fibroblasts into a specialized phenotype during the healing process. COL4A2 is a major structure-defining component in all basement membranes [ 18 ] and forms a framework for the ordered aggregation of additional molecules like laminin, heparin sulphate proteoglycans, and nidogen [ 19 ]. Relatively higher levels of COL4A2 observed in the adhesion fibroblasts may enhance synthesis of basement membrane in the tissues of adhesions. As COL4A2 gene is up regulated during malignant transformation and tumor vessel proliferation [ 20 ], it is anticipated that up regulated levels of COL4A2 in the adhesion fibroblasts may aid to the formation of adhesion tissue by increasing proliferation of adhesion fibroblasts and supporting new vessel formation for the nourishments of growing tissue. S100A10 proteins interact with Annexin A2 forming a heterotetrameric structure AIIt; that dock into the cell membrane promoting F-actin reorganization and cell migration [ 21 ]. AIIt also colocalizes with uPAR and plasminogen in the cells [ 22 ]. Heightened levels of S100A10 may enhance migration of adhesion fibroblasts by changing F-actin dynamics and influencing Cathepsin B and plasminogen machinery [ 23 ]. S100A10 also interacts with cytosolic phospholipase A2, inhibits its activity and decreases synthesis of archidonic acid [ 24 ]. Therefore, increase in S100A10 levels in the adhesion fibroblasts may deplete intracellular levels of archidonic acid and Prostaglandin E2 (PGE2) that are known to inhibit cell proliferation, collagen I synthesis, contraction of ECM and fibroblast migration [ 25 ]. Nidogen-2 (entactin-2) interacts with laminin1 P1, collagen I, collagen IV, perlecan and fibulin-2 in the extracellular space and stabilizes the basement membrane. It also interacts with α6β1 and α3β1 integrin receptors on cells [ 26 ]. Relatively higher levels of nidogen-2 secreted by adhesion fibroblasts in the extracellular space may strengthen the basement membrane and enhance integrin mediated adhesion and migration of fibroblasts into the growing tissue of adhesion. TM4SF molecules (tetraspanins) play important roles in cell migration and in the generation of complexes with integrins functionally relevant for cell motility, tumor progression and wound healing [ 27 ]. It is proposed that tetraspanins can influence cell migration by (i) modulating integrin signaling and integrin-mediated reorganization of the cortical actin cytoskeleton; (ii) regulating compartmentalization of integrins on the cell surface or (iii) directing intracellular trafficking and recycling of integrins [ 27 ]. Therefore, heightened intercalation of TM4SF1 in the cell surface of adhesion fibroblasts may facilitate their integrin-mediated migration into the developing tissues of adhesion. Versicans (CSPG2) are also known to influence α4β1 and α2β1 integrin mediated invasion of melanoma cells [ 28 ]. Higher CSPG2 in the fibroblasts of adhesion tissues may assist in the integrin-CSPG2 mediated migration of peritoneal fibroblasts to the site of injury and increase the number of fibroblasts by enhancing proliferation and decreasing apoptosis as evidenced in other cell types [ 28 , 29 ]. Veriscan interacts with hyaluronan and CD44 and increase the viscoelastic nature of the pericellular matrix, creating a highly malleable extracellular environment that supports a cell-shape change necessary for cell proliferation and migration [ 30 ]. Because MT-1e transcripts are detected in cell types that have undergone myoepithelial differentiation [ 31 ], significant differences in the MT-1e mRNA levels between adhesion and normal peritoneal fibroblasts indicate that fibroblasts in the adhesions are at different state of differentiation compared to normal peritoneum. Molecules including IL-1; IL-6, TNF-α, EGF, bFGF, glucocorticoids, LPS, and estrogen that promote post surgical adhesion formation [ 32 - 34 ] directly or indirectly increase MT-1 transcripts and proteins in several tissues and cell types [ 35 ]. Therefore, it is likely that these molecules may increase adhesion formation by augmenting MT-IE levels which in turn may increase proliferation, reduce cell death and confer invasiveness of adhesion fibroblasts [ 36 ]. Contrary to increase in the above mentioned mRNA species in the adhesion fibroblasts, steady state levels of IGFBP3 precursor transcript were found to be lower. Because IGFBP-3 is known to inhibit cell growth by sequestering IGF, its decreased level may enhance proliferation of adhesion fibroblasts [ 37 ]. Reduced levels of IGFBP3 mRNA are reported in the tumorigenic cells [ 38 ]. Therefore, reported lower incidence of pelvic adhesion formation in the primates on anti-estrogenic therapy [ 32 ] could be due to the antiproliferative effects of anti-estrogens mediated in part, by IGFBP-3 [ 39 ]. IGFBP-3 also induces growth inhibition and apoptosis [ 40 ]. Decrease in the levels of IGFBP-3 in the adhesion fibroblasts may promote adhesion development both by increasing proliferation and reducing apoptosis at the site of injury. Our attempts to examine the regulatory roles of TGF-β1 and hypoxia, factors known to promote adhesion development [ 3 ], on the expression pattern of specific genes show that not all changes in the gene expression pattern between the normal and adhesion fibroblast are the function of these factors (Figure 3 and 4 ; Table 4 ). Our data show that while mRNA levels of COL4A2, S100A10 and MT-1e are elevated by both TGF-β1 and hypoxia in the human peritoneal fibroblasts, the mRNA levels of only CSPG2 is influenced by TGF-β1. Moreover, transcript levels of nidogen-2, TM4SF1 and IGFBP3 mRNA were not influenced by TGF-β1. Based on these results we hypothesize that genes that are not influenced by TGF-β1 and hypoxia in the peritoneal fibroblasts may be influenced by factors such as interleukins and TNF-α that are also known to play role in adhesion formation. Alternately, TGF-β1 and/or hypoxia may influence actions of these genes at the post transcriptional level without altering transcript levels. TGF-β1 induced up regulation of integrin α5, αv and α6 subunits in the normal human peritoneal fibroblasts without altering mRNA levels [ 10 ] is consistent with this possibility. It is also possible that TGF-β1 and hypoxia may alter expression of these genes in mesothelial and other cell types following peritoneal injury. Likewise lower levels of VEGF transcripts in adhesion fibroblasts may be compensated by its higher levels in other cell type required for angiogenesis during adhesion formation [ 3 ]. Detected lower intensity of VEGF-A isoform in the adhesion fibroblasts may also be due to the fact that spots representing this isoform do not distinguish different VEGF-A splice variants that are known to be up or down regulated during adhesion formation [ 16 ]. It is known that a new phenotype of fibroblasts is induced during wound healing. These fibroblasts, termed- myofibroblasts, express higher levels of α-smooth muscle actin and vinculin-containing fibronexus adhesion complexes [ 41 ]. Fibroblasts isolated from adhesion tissues express higher levels of α-smooth muscle actin transcripts compared to normal peritoneal fibroblast (Table- 2 ) [ 42 ] and TGF-β1 induces formation of adhesion complex in these cells [ 10 ]. These observations in addition to the known roles of TGF-β in the development of post surgical adhesions [ 43 ] and transformation of fibroblasts into smooth muscle α-actin expressing myofibroblasts [ 44 ] imply that this cytokine may influence transformation of normal fibroblasts into a phenotype similar to myofibroblasts in the developing tissues of adhesion. Therefore, hindering this transformation may reduce adhesion formation. For instance, augmenting E prostanoid 2 (EP2) receptor pathways may be a way to reduce the incidence of adhesion formation because prostaglandin E 2 (PGE 2 ) is shown to inhibit TGF-β1 induced expression of α-SMA, production of Collagen I and the transformation of fibroblasts to myofibroblasts via EP2 signaling [ 45 ]. Additionally, adhesion formation may be reduced by P311 ( PTZ17 ) and Interferon γ treatments, which inhibits TGF-β1 induced myofibroblast transformation [ 46 , 47 ]. During the course of normal wound healing, myofibroblasts disappear, possibly by apoptosis [ 48 ]. In contrast, when there is abnormal wound healing, myofibroblasts persist [ 49 ]. Data obtained in our study also indicate that adhesion fibroblasts may resist apoptosis due to anti apoptotic effects mediated by increased hMet1-e and CSPG2 levels and down regulation of IGFBP3. They may also attain a high proliferating nature due to up regulation of S100A10 and CSPG2 genes, and down regulation of IGFBP3 (Table 2 ). Higher proliferating and reduced apoptotic nature of adhesion fibroblasts derived from altered ratio of bcl-2 and bax expression is suggested in an earlier study [ 5 ]. It is apparent now that higher proliferative and reduced apoptotic nature of adhesion fibroblasts in human as reported earlier [ 5 ] could also derive from altered expression of hMet1-e, CSPG2, S100A10, CSPG2, IGFBP3, and the Bfl-1 that inhibits p53-induced apoptosis and is induced by cytokines TNF-α and IL-1β [ 50 ]. This altered phenotype of adhesion fibroblasts, acquired during the healing process, may lead to the accumulation of extra number of cells at the site of peritoneal injury resulting fibrosis and scar formation. Of note, one of the pivotal differences between wounds that proceed to normal scar compared with those that develop hypertrophic scars or fibrosis may be a lack of or reduced cell death [ 51 ]. Therefore, excess fibroblasts at the site of peritoneal wound healing may divert the normal process of healing towards fibrosis and adhesion. The elevated levels of p53 in the adhesion fibroblasts during this disarray, as evident from the gene filter data (Table 2 ), may guard against its transition towards malignancy [ 52 ]. Conclusions It is evident from our study that steady state levels of several genes are different between adhesion and normal peritoneal fibroblasts in human and that adhesion development may be a function of several genes. Changes in the functional interdependence of these genes at the site of injury may transform normal peritoneal fibroblast into cell type/s with altered phenotype. These cells- designated as adhesion fibroblasts may mimic previously known myofibroblasts and are highly proliferative. These cells resist apoptosis and secrete ECM molecules to renovate basement membrane. With changed expression pattern of cell surface molecules these cells may respond to intracellular signaling for migration over the fibrin clot. This altered nature of adhesion fibroblasts therefore may play a major role in the formation of the fibrous mass of adhesion-tissue that bridges adjacent and injured peritoneum. Blocking changes in the expression or function of genes necessary for this transformation of normal peritoneal fibroblasts may curtail adhesion formation. This could be achieved by the application of PGE 2 , EP 2 blockers, interferon γ, P311 and applying measures to induce apoptosis in the peritoneal fibroblast at the site of injury. The obvious question – "how to maintain apoptosis at a desired level for normal peritoneal healing?" however, remains to be answered. List of Abbreviations Collagen type IV (alpha 2) chain (COL4A2), Nidogen 2 (NID2), Chondroitin sulfate proteoglycan 2 (CSPG2), S100 Calcium binding protein A10 (S100A10), Transmembrane 4 superfamily member 1 (TM4SF1), Metallothioneine (hMT-Ie), Insulin-like growth factor binding protein 3 precursor (IGFBP3), Transforming growth factor (TGF), Prostaglandin E2 (PGE2), Urokinase Plasminogen activator receptor (uPAR), Annexin 2 and S100A10 complex (AIIt), tissue Plasminogen Activator (tPA), Plasminogen Activator Inhibitor (PAI), Cyclooxigenase (COX), Matrix metalloproteinase (MMP), Tissue Inhibitor of Metalloproteinase (TIMP), Interferon γ (IFN-γ), IL (Interleukin). Authors' Contributions GMS and MPD were responsible for the isolation of peritoneal fibroblasts from normal peritoneum and adhesion tissues as well as establishing hypoxia chambers. MPD provided patient information and valuable suggestions during writing the manuscript. UKR performed microarray and semiquantitative RT-PCR experiments, analyzed the data and wrote the manuscript.

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535530

A trial design for evaluation of empiric programming of implantable cardioverter defibrillators to improve patient management

The delivery of implantable cardioverter defibrillator (ICD) therapy is sophisticated and requires the programming of over 100 settings. Physicians tailor these settings with the intention of optimizing ICD therapeutic efficacy, but the usefulness of this approach has not been studied and is unknown. Empiric programming of settings such as anti-tachycardia pacing (ATP) has been demonstrated to be effective, but an empiric approach to programming all VT/VF detection and therapy settings has not been studied. A single standardized empiric programming regimen was developed based on key strategies with the intention of restricting shock delivery to circumstances when it is the only effective and appropriate therapy. The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of empiric to physician tailored programming for VT/VF detection and therapy in a broad group of about 900 dual chamber ICD patients. The trial will provide a better understanding of how particular programming strategies impact the quantity of shocks delivered and facilitate optimization of complex ICD programming.

Background Over the past decade ICD implantation has become increasingly straightforward, yet ICD programming and follow up has become more complex due to device feature and capability enhancements. While sophisticated algorithms provide high sensitivity and improved specificity of arrhythmia detection, allowing delivery of necessary effective therapy with minimization of inappropriate defibrillation shocks, detection and therapy of ventricular tachycardia (VT) / ventricular fibrillation (VF) still requires programming about 100 settings [ 1 - 3 ]. Good programming choices are crucial as they relate to patient acceptance of ICD therapy. It has been found that patients who receive multiple shocks have greater difficulty adjusting to the ICD implant. These patients may become anxious or depressed, especially if a prior history of these ailments exists [ 4 ]. Reducing shocks delivered to the patient would improve overall patient management. To date, there is no proven consensus on how to use information about the patient's complex diseases to program the ICD, and usually little is known about the patient's spontaneous VT rates, their risk of syncope, or therapies to effectively terminate spontaneous ventricular arrhythmias. Furthermore, ICD indications have dramatically changed within the last five years. Physicians may retain old programming habits even with enhanced devices or expanding patient indications, which may result in sub-optimal detection and therapy, such as unnecessary shocks for faster VT, supraventricular tachycardia (SVT), and non-sustained VT. Physicians often adjust many programmable settings that may benefit the patient. For example, physicians may prescribe patient-specific regimens for anti-tachycardia pacing (ATP) or shock energies based on lab testing. While one would expect this tailoring of programming to improve outcomes, it has never been studied. Empiric programming has been shown to be effective for subsets of ICD settings, including subsets of dual chamber detection and ATP therapies [ 3 , 5 - 10 ]. Whether this holds true for comprehensive programming of VT/VF detection and therapy for all ICD patients is unknown. A proven optimal programming approach would be useful for simplifying therapy prescription, improving therapy outcomes, reducing inadvertent programming errors, and overall reducing shock-related morbidity. The EMPIRIC trial has been designed to evaluate a standardized empiric programming regimen by testing the hypothesis stated below. The EMPIRIC trial outcome will provide an understanding of how programming strategies impact defibrillation shock delivery in ICD therapy. EMPIRIC Trial Hypothesis This trial tests the hypothesis that the shock related morbidity of ICD therapy is similar whether patients are treated with a standardized empiric programming regimen for VT/VF detection and therapy or with a patient-specific physician tailored approach. Indices of Shock Morbidity Only sustained VT/VF that cannot be painlessly terminated should result in shock therapy and it is unusual for supraventricular arrhythmias (SVT) to require shock therapy. Shock morbidity is related to the number and frequency of shocks that patients receive and therefore morbidity is reduced if shocks are delivered only when necessary for effective arrhythmia termination. Thus, indices that address shock morbidity should reflect both the frequency and appropriateness of shocks for VT/VF and SVT. Shock morbidity is quantifiable by determination of the following: ♦ proportion of true VT/VF episodes that are shocked ♦ proportion of true SVT episodes that are shocked ♦ time to first shock (VT/VF or SVT) ♦ time to first VT/VF shock ♦ time to first SVT shock These parameters are used to define the Empiric Trial's main objectives. Empiric Trial Objectives The primary objective is to demonstrate that the proportion of shocked VT/VF episodes and the proportion of shocked SVT episodes in a population whose ICDs are programmed using a standardized regimen for VT/VF detection and therapy, is either similar to or less than the same proportion in a similar population whose ICDs are programmed using a physician-tailored approach. This primary objective was chosen to independently evaluate the effects of programming on both appropriate and inappropriate ICD shocks (which are likely to have different implications for patient management). The advantage of this approach is that it focuses on frequency of shock delivery while also allowing an assessment of their appropriateness. However, this assessment could be confounded by a disproportionate number of SVT events in the two study groups. For example, an abundance of non-shocked SVT events in the physician-tailored arm, despite a greater incidence of inappropriate SVT shock therapies in that arm, nevertheless would result in the proportion of SVT episodes shocked being similar in the two arms. The analysis is also heavily dependent on the electrogram data stored in the ICDs. Given the electrogram storage capability of ICDs, differing rates of electrogram storage might occur between study arms or between VT/VF and SVT episodes that may skew the amount of data available for analysis. Therefore, the key secondary endpoint in this study is considered to be the time to delivery of first shock therapy in any given patient. This endpoint offers the advantage that it enables patient cross over to occur between the study arms without endpoint compromise and it is a clinically robust indicator of patient shock-related morbidity. Furthermore, its analysis is not influenced by the appropriateness or otherwise of a shock therapy and therefore cannot be confounded by differential occurrence of non-shocked SVT events in the study arms. Other secondary endpoints will further evaluate the impact of the standardized programming regimen on patients by an assessment of detection performance, health care utilization, shock impact on device longevity, and "true VT/VF" episode durations. EMPIRIC Trial Protocol Design The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of empiric to physician tailored programming. About 900 patients were enrolled worldwide at 52 centers from August 2002 to October 2003. Each patient will be followed for approximately one year. The inclusion criteria require patients to meet all of the following conditions: 1. Indicated for an ICD according to internationally accepted criteria. 2. Willing to sign informed consent or offer a legal representative who can provide consent. 3. Achieved a 10 Joule safety margin at implant. Patients are excluded if they: 1. Have permanent atrial fibrillation (AF). 2. Had a previous ICD. 3. Have a medical condition that precludes the testing required by the protocol or limited trial participation. 4. Have a life expectancy less than one year. 5. Are unable to complete follow-ups at the trial center. 6. Are enrolled or participating in another clinical trial. Randomization Patients receiving a Marquis DR ICD are randomized to one of the two programming approaches after meeting a 10 J safety margin. In order to control for physician practice between the two treatment arms, randomization is stratified by treatment center. Further, since the incidence and prevalence of spontaneous VT/VF and SVT among primary prevention patients is not well known, randomization is also stratified by ICD indication (secondary vs. primary). A secondary indication includes patients with a history of spontaneous sustained VT/VF or syncope with suspected VT. A primary prevention indication includes all other patients. Programming Approaches The physician tailored approach is based on the standard practice of each physician. All VT/VF programming may be tailored to the patient except that VT detection must be turned to 'On' or 'Monitor' to record episodes of slower VT. The empiric standardized regimen is based on various programming strategies to reduce shocks. In this arm, initial device settings are fixed (see Table 1 ), with the exception of the VT detection interval, which can be set slower than 150 bpm when clinically necessary. Table 1 Empiric Arm Programming Detection Interval Beats To Detect Redetect Therapies VF On 300 ms 18/24 9/12 9/12 30 J × 6 FVT via VF 240 ms NA Burst (1 sequence), 30 J × 5 VT On ≥ 400 ms* 16 12 Burst (2), Ramp (1), 20 J, 30 J × 3 SVT Criteria On: AF/Afl, Sinus Tach (1:1 VT-ST Boundary = 66%), SVT Limit = 300 ms Burst ATP: 8 intervals, R-S1 = 88%, 20 ms decrement Ramp ATP: 8 intervals, R-S1 = 81%, 10 ms decrement VT/VF detection and therapy programming changes are permitted at follow-up in both arms only when medically justified. These changes must be documented, and are reviewed throughout the study. Data Collection Patients are followed for a 12-month period, with required clinic visits at 3, 6 and 12 months. Data collection includes: VT/VF and SVT episodes, device programming, medical justifications for VT/VF programming changes, cardiovascular medication, adverse device events, P and R wave measurements, and cardiovascular-related hospitalizations. Study Design Challenges A challenge of the study design is the possibility that physician practice could become biased by in-trial experience, causing physician practice to gravitate towards the empiric standardized regimen. This might occur if empiric programming is perceived to be efficacious, particularly with respect to management of rapid ventricular tachycardia by pace termination. Collection of pre-trial programming practices provides the capacity to evaluate potential "treatment drift". This result will be reported. Additionally, in an effort to prevent drifting or possible physician bias to programming in the physician tailored arm, a weekly comparison of programming status and initial implant programming will be assessed through device interrogation information. Any programming changes made must be supported by a medical justification with a basis of event-related occurrences (i.e. system- or procedure-related adverse events, spontaneous episodes, or inappropriate shocks). In order to protect protocol design integrity, reprogramming will be encouraged for non-justified programming deviations. In this manner the initial treatment strategies are tested using an intention-to-treat analysis with characterization of programming changes. Empiric Arm Programming Strategies The empiric arm standardized programming regimen is based on the following key strategies to reduce shocks. 1) Strategies to reduce shocks for VT/VF • Multiple ATP attempts for VT≤ 200 bpm: Three sequences of ATP will be attempted for rhythms with ventricular rates ≤ 200 bpm. Empiric ATP has been shown to terminate ≥ 90% of VTs in the VT zone [ 5 - 10 ]. Furthermore, induced VTs do not predict spontaneous VT cycle length, morphology, or therapy efficacy [ 11 ]. Three sequences will be attempted for rates up to 200 bpm because the average rate of fast VTs was 199 bpm in the PainFREE Rx1 study, where the FVT zone was 188 – 250 bpm, and more ATP provided incremental shock reductions[ 6 ]. ATP will be used in all patients because even cardiac arrest patients have been shown to have VTs [ 5 , 12 - 14 ]. • ATP for VTs 201 – 250 bpm: One sequence of ATP will be delivered for fast VTs (FVT) using the FVT via VF zone, which maintains sensitivity to polymorphic VT (PVT) and VF and delivers ATP if the 8 beats prior to FVT detection are ≤ 250 bpm. Approximately 81% of ICD detected VF is monomorphic VT (MVT). MVT can be pace-terminated approximately 75% of the time with one sequence of ATP, without increased risk of syncope or acceleration [ 6 , 7 , 15 ]. • Longer detection duration: The VF initial beats to detect will be set to 18 of 24. Shorter beats to detect are often programmed by physicians, but may increase the unnecessary shocks for non-sustained VT and for SVTs. At least 25% of ICD-detected VF is non-sustained VT/VF [ 15 - 17 ]. • High Output 1 st VF and FVT Shock: A 30 Joule energy will be used for the first VF and FVT shock. This will allow additional time for spontaneous conversions that frequently occur. A higher shock energy may also improve 1 st shock success and therefore reduce the need for multiple shocks within an episode. The LESS study found no difference in 1 st shock success with 31 J versus DFT++, however it analyzed all VT/VF faster than 200 bpm [ 18 ] ATP should terminate a majority of these rhythms and for that reason the benefit of empiric high-energy shocks for polymorphic VT (PVT)/VF or after a failed ATP is unknown. The primary reason some physicians program lower energy 1 st shocks is due to concerns about syncope. Several recent studies have shown very low syncope rates [ 6 , 19 ] Furthermore, charge times are much faster and more stable over the life of the device than in older ICDs. For instance, the Medtronic Marquis DR 30 Joule charge time is 5.9 and 7.5 seconds at beginning and end of life, respectively [ 20 ]. 2) Strategies to Reduce Shocks for SVTs and Sensing Issues • Empiric SVT Criteria: The PR logic criteria of AF/A. Flutter and Sinus Tach will be programmed 'On' in all patients. These criteria have been shown to have a relative VT/VF sensitivity of 100% and a positive predictive value to 88.4% [ 3 ]. • SVT Criteria applied to faster rates: The SVT limit and VF rate cut-off will be increased to 200 bpm in all patients to provide SVT discrimination at faster rates. Two of the top five reasons for inappropriate detections in the GEM DR Study (933 patients) were a ventricular rate during AF in VF zone and a SVT cycle length faster than programmed SVT limit [ 3 ]. • Avoid detecting 1:1 SVTs with Long PRs as VT: 1:1 SVTs with long PR intervals accounted for 38% of inappropriate detections in the Gem DR (7271) Clinical Study [ 3 ]. A retrospective analysis found that changing the 1:1 VT-ST boundary programmable parameter from 50% to 66% might eliminate 32% of all inappropriate detections. The downside to this approach is that it may result in a 0.8% rate of VT/VF misclassification or delay [ 21 ]. • Longer detection duration: VF initial beats to detect will be set to 18 of 24. Shorter beats to detect may result in more unnecessary shocks for SVTs or ventricular over-sensing. • ATP attempts: In addition to terminating ventricular arrhythmias without shocks, ATP should eliminate some inappropriate shocks when inappropriate detections occur by terminating SVTs or slowing conduction. The VT rate cut-off is one of the most important ICD settings because it can result in untreated symptomatic VT if set too fast, however it can result in unnecessary therapies for non-sustained VT, SVTs, or sensing issues, if set too slow. Reports have shown that some secondary prevention patients have significant symptoms for VTs outside treated zones [ 22 ]. The VT cut-off in the empiric arm is set to ≤ 150 bpm to err on the side of treating VTs and to advance the understanding of the incidence of slower VTs in all patient populations. The optimal VT rate cut-off may need to be set according to the patient's presenting conditions at implant (e.g., faster cut-off in primary prevention patients). Statistical Considerations The primary endpoint is the proportion of true episodes that are shocked during the 12-month follow-up period. The standardized empiric programming regimen will be considered non-inferior to the physician tailored programming approach if both the proportion of shocked VT/VF episodes and the proportion of shocked SVT episodes are no more than 10 percentage points greater in the empiric arm than the physician tailored arm. The chosen margin 10 percent is considered clinically important. It is assumed that 24% of patients will have at least one true VT/VF episode and 33% of patients will have at least one true SVT episode during the 12-month follow-up period. Based on unpublished data from other Medtronic trials, the within-patient correlation coefficient for multiple episodes is assumed to be 0.3. Assuming a similar distribution of episode counts per patient as observed in these previous trials and a shock rate of 30% and 14% for VT/VF and SVT episodes respectively, a total of 900 patients (450 in each arm) will give at least 80% power for the VT/VF hypothesis and 90% power for the SVT hypothesis, each tested at the significance level 0.05. The critical secondary endpoint, time to first shock therapy, will be analyzed using the Cox proportional hazards model for 1) any VT/VF or SVT, 2) true VT/VF only and 3) true SVT only. The empiric programming approach will be considered non-inferior if the upper confidence limit for the hazard ratio is less than 1.5. Other Planned Analyses To better understand the changing ICD patient populations, we will investigate whether or not the proportion of appropriate and inappropriate shocks delivered is related to the following baseline characteristics: main indication for implant (especially spontaneous sustained monomorphic VT), left ventricular ejection fraction, CAD status, history of Atrial Tach/Atrial Fib/Atrial Flutter, NYHA classification, use of amiodarone, sotalol, or beta-blockers, and inducibility for VT/VF. In addition, to facilitate understanding of the optimal programmable settings for various patient sub-groups, we will consider the impact of programmable settings on outcomes. In particular, we will examine the "treated cut-off" (TC), which is the VT detection cut-off if VT detection is 'On' or the VF detection cut-off if VT detection is 'Off' or 'Monitor'. Outcomes in patients with a faster TC (physician tailored arm) will be compared to patients with slower TC (either physician tailored arm or empiric arm). Other programmable settings that will be investigated include the number of beats to detect VF and the number of ATP attempts based at various rates (e.g., <175 bpm, 175–200 bpm, >200 bpm). The types of arrhythmias, median ventricular cycle length, and therapies delivered will also be characterized relative to the patient's conditions and programming. Furthermore, the incidence of slower VTs in patients without a history of spontaneous, sustained monomorphic VT will be characterized. Conclusions and Trial Impact The EMPIRIC trial is a worldwide, multi-center, prospective, one-to-one randomized comparison of shock- related morbidity in a population of about 900 ICD patients whose ICD therapy is determined either by a standardized programming regimen or by physician tailored programming of VT/VF detection and therapy. Shock-related morbidity is assessed by a primary objective that compares between study arms the proportion of VT/VF episodes that are shocked and the proportion of SVT episodes that are shocked, and by a key secondary endpoint that compares to time to first shock therapy. ICD patient populations have rapidly changed within the last five years but little has been published on optimal programming for the emerging patient subsets (e.g., primary prevention). Therefore a standardized regimen of parameters is used in this trial for all patient populations. Today's patient population is quite diverse, so a slightly more sophisticated programming approach may be necessary (e.g. change VT cut-off based on main ICD indication) or perhaps complex physician tailoring is critical to reducing shocks. The EMPIRIC trial will characterize the shock morbidity of a single empiric programming approach compared to patient-specific, physician tailored programming. Empiric programming may be an acceptable strategy if it achieves equivalence with physician tailored programming. The EMPIRIC trial results will also provide a better understanding of how particular programming strategies impact the frequency of shocks delivered and will facilitate a way to optimize complex ICD programming. Competing Interests 1. Have you received reimbursements, fees, funding, or salary from an organization that may in any way gain or lose financially from the publication of this paper in the past five years, or is such an organization financing the article-processing charge for this article? Dr. Morgan: Yes, Medtronic has paid me honoraria. Dr. Sterns: Yes, I am a paid investigator in several Medtronic clinical trials and key investigator in the present trial. I understand that Medtronic is paying for the processing fee for this article. Dr. Wilkoff: Yes, Medtronic, Guidant, St. Jude Medical Hanson, Ousdigian, and Otterness: Yes, Employees of Medtronic. 2. Have you held any stocks or shares in an organization that may in any way gain or lose financially from the publication of this paper? Dr. Morgan and Dr. Sterns and Dr. Wilkoff: No Hanson, Ousdigian, and Otterness: Yes, own Medtronic stock. 3. Do you have any other financial competing interests? Dr. Morgan and Dr. Sterns and Dr. Wilkoff: and Hanson and Ousdigian and Otterness: No. 4. Are there any non-financial competing interests you would like to declare in relation to this paper? Dr. Morgan and Dr. Sterns and Dr. Wilkoff: and Hanson and Ousdigian and Otterness: No. Authors' Contributions All 6 authors contributed to the study design and writing of this manuscript.

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545969

Receptor and secreted targets of Wnt-1/β-catenin signalling in mouse mammary epithelial cells

Background Deregulation of the Wnt/ β-catenin signal transduction pathway has been implicated in the pathogenesis of tumours in the mammary gland, colon and other tissues. Mutations in components of this pathway result in β-catenin stabilization and accumulation, and the aberrant modulation of β-catenin/TCF target genes. Such alterations in the cellular transcriptional profile are believed to underlie the pathogenesis of these cancers. We have sought to identify novel target genes of this pathway in mouse mammary epithelial cells. Methods Gene expression microarray analysis of mouse mammary epithelial cells inducibly expressing a constitutively active mutant of β-catenin was used to identify target genes of this pathway. Results The differential expression in response to ΔNβ-catenin for five putative target genes, Autotaxin, Extracellular Matrix Protein 1 (Ecm1), CD14, Hypoxia-inducible gene 2 (Hig2) and Receptor Activity Modifying Protein 3 (RAMP3), was independently validated by northern blotting. Each of these genes encodes either a receptor or a secreted protein, modulation of which may underlie the interactions between Wnt/β-catenin tumour cells and between the tumour and its microenvironment. One of these genes, Hig2 , previously shown to be induced by both hypoxia and glucose deprivation in human cervical carcinoma cells, was strongly repressed upon ΔNβ-catenin induction. The predicted N-terminus of Hig2 contains a putative signal peptide suggesting it might be secreted. Consistent with this, a Hig2-EGFP fusion protein was able to enter the secretory pathway and was detected in conditioned medium. Mutation of critical residues in the putative signal sequence abolished its secretion. The expression of human HIG2 was examined in a panel of human tumours and was found to be significantly downregulated in kidney tumours compared to normal adjacent tissue. Conclusions HIG2 represents a novel non-cell autonomous target of the Wnt pathway which is potentially involved in human cancer.

Background The Wnt/β-catenin signal transduction pathway plays a central role in metazoan development, controlling such diverse processes as cell growth, proliferation and organogenesis [ 1 ]. Wnt-1 is the prototypic member of this large family of secreted glycoproteins and was originally identified as a gene insertionally activated by mouse mammary tumour virus [ 2 ]. Wnt-1 is one of a number of Wnt family members which act to control the cellular level of β-catenin. Wnt proteins bind seven-pass transmembrane receptors of the Frizzled family, and a signal is transduced via Dishevelled to a complex which contains the Adenomatous Polyposis Coli (APC), Axin and Glycogen Synthase Kinase-3β (GSK-3β) proteins [ 3 , 4 ]. This signal antagonizes the phosphorylation of β-catenin by GSK-3β. There are four phosphorylation sites in the N-terminus of β-catenin which, in the absence of Wnt signal, are phosphorylated by Casein Kinase I alpha and GSK-3β [ 5 , 6 ]. This phosphorylation leads to the ubiquitination and subsequent proteasomal degradation of β-catenin [ 7 ]. Inhibition of β-catenin phosphorylation by Wnt signalling leads to the accumulation of β-catenin which forms a bipartite complex with members of the TCF/LEF transcription factor family and activates the transcription of target genes, a process which is regulated by multiple interacting factors [ 8 ]. Overexpression of Wnt-1 in the mammary glands of transgenic mice leads to extensive hyperplasia and tumorigenesis [ 9 ]. APC was identified as the tumour suppressor gene mutated in the hereditary colorectal cancer syndrome, Familial Adenomatous Polyposis [ 10 , 11 ]. Mutations in Axin and β- catenin have also been detected in tumours of the colon and other tissues [ 12 ]. Deregulation of this pathway appears to be play a contributory role in a significant proportion of human tumours of epithelial origin and hence, the identification of effector genes of this pathway is an important step towards the elucidation of the mechanisms involved. Many of the Wnt targets thus far identified are cell-cycle regulators [ 13 , 14 ] and transcription factors [ 15 - 20 ], and function in a cell-autonomous manner, providing insight into the mechanisms by which tumour cells deregulate proliferation and inhibit apoptosis. Tumours are complex organs composed of tumour cells, stromal fibroblasts, endothelial cells and cells of the immune system; and reciprocal interactions between these cell types in the tumour microenvironment are necessary for tumour growth [ 21 , 22 ]. Here we postulate that proteins secreted by Wnt/β-catenin tumour cells and receptors expressed by these cells may play roles in mediating interactions between neighbouring tumour cells or between tumour cells and their microenvironment. Consequently, in this study we have focussed our attention on identifying novel genes encoding receptors and secreted proteins. Methods Cell culture All reagents were purchased from Sigma unless otherwise noted. HC11 mouse mammary epithelial cells were cultured in 5% CO 2 at 37°C in RPMI 1640, supplemented with 10% Foetal Bovine Serum, 2 mM L-glutamine, 2.5 μg/ml insulin, 5 ng/ml epidermal growth factor and 50 μg/ml gentamycin [ 23 ]. HC11- lacZ and HC11-Δ N β- catenin cells were routinely cultured in 2 μg/ml tetracycline to repress transcription of the tetracycline-regulated transgene. HEK293 and MDCK cells were grown in DMEM supplemented with 10% Foetal Bovine Serum. The HC11- lacZ and HC11-Δ N β- catenin cell lines were generated by infecting the cells with an ecotropic retrovirus (TRE-tTA) in which the tTA cDNA is under the control of a tetracycline responsive promoter. Consequently, tTA expression is minimal in the presence of tetracycline and, upon tetracycline withdrawal, tTA activates its own transcription in an autoregulatory manner [ 24 ]. HC11 cells expressing tTA were subsequently infected with ecotropic retroviruses derived from RevTRE (Clontech) which directed the expression of either β-galactosidase or ΔNβ-catenin in a tetracycline dependent manner. Bosc23 cells were used to produce ecotropic retroviruses [ 25 ]. Cells were transiently transfected with the appropriate retroviral construct and the supernatant was collected 48 hours post-transfection. Polybrene was added to a final concentration of 5 μg/ml and the supernatant was added to HC11 cells for 24 hours. HC11 cells were then subjected to antibiotic selection using either 250 μg/ml G418 or 200 μg/ml hygromycin B as appropriate. RNA isolation Cell monolayers were washed twice in ice-cold Phosphate Buffered Saline and lysed by addition of Trizol (Invitrogen). Total RNA was isolated according to the manufacturer's instructions. PolyA+ RNA was purified from total RNA using Oligotex (Qiagen) according to the manufacturer's instructions. Northern blotting 10 μg of total RNA from each cell line was fractionated on a denaturing formaldehyde agarose gel and transferred to a positively charged nylon membrane (Hybond N+, Amersham Pharmacia Biotech) in 10x SSC. Membranes were prehybridised for four hours in 50% (v/v) formamide, 5X SSPE, 2X Denhardt's reagent, 0.1% (w/v) SDS and 100 μg/ml denatured herring sperm DNA. Radiolabelled probes were prepared from PCR-amplified cDNA clones using the Rediprime II kit (Amersham Pharmacia Biotech) according to the manufacturer's instructions. EST sequences corresponding to the coding sequence of the genes-of-interest were identified by BLAST [ 26 ] and obtained from the I.M.A.G.E. consortium through the UK Human Genome Mapping Project Resource Centre (Hinxton, UK). ESTs bearing the following I.M.A.G.E. cloneIDs were used: Autotaxin – 533819; CD14 – 2936787; Ecm1 717050; Hig2 – 367488; Ramp3 – 615797, HIG2 4366895). Following overnight hybridisation with the labelled probe, the membranes were washed twice in 1X SSC, 0.1% (w/v) SDS at room temperature for 20 mins, and twice in 0.2X SSC, 0.1% (w/v) SDS at 68°C for 10 mins and exposed to film at -80°C for 48 hours. Bound probe was quantitated using a phosphorimager (Molecular Dynamics). Western blotting Cell monolayers were rinsed twice with ice-cold Phosphate Buffered Saline and total cell lysates were prepared by scraping cells into a minimal volume of 50 mM Tris. HCl pH 7.5, 150 mM NaCl, 0.5% NP40 and Complete protease inhibitor cocktail (Roche). Aliquots containing 80 μg protein from each sample were analysed by SDS-PAGE [ 27 ], and transferred electrophoretically to a PVDF membrane. Mouse monoclonal antibodies were used to detect tTA (Clontech), β-catenin (Transduction Laboratories) and EGFP (Santa Cruz Biotechnology). Samples of conditioned medium were concentrated 12-fold using Microcon YM-10 centrifugal filter units (Millipore) prior to analysis. Construction of plasmids A BgIII fragment containing the lacZ cDNA was excised from the CMV- lacZ construct (a gift of Trevor Dale) and sub-cloned into BamHI digested RevTRE to make RevTRE- lacZ . A plasmid containing a myc-tagged ΔNβ-catenin was obtained from Hans Clevers. The myc-tagged ΔNβ-catenin was excised with KpnI and NotI and the ends were blunted, and subcloned into HpaI digested RevTRE to make RevTRE-Δ N β- catenin . The mouse Hig2 open reading frame was amplified by PCR from I.M.A.G.E. cDNA clone 367488 using the primers TTTACTAGTAGGAGCTGGGCACCGTCGCC and TTTTACCGGTGCCTGCACTCCTCGGGATGGATGG. The PCR product was digested with AgeI and SpeI and subcloned into the AgeI and NheI sites in pEGFP-C1 (Clontech) to make the Hig2-EGFP fusion gene. Site directed mutagenesis was carried out by the method of Sawano and Miyawaki (2000) [ 28 ]. The primer TGCTGAACCTCGAGGAGCTGGGCATCATG was used to make the Hig2-EGFP(Y8V9/D8D9) mutant. Transient transfections Transient transfections were performed using Lipofectamine (Invitrogen) according to the manufacturer's instructions. Briefly, 1.5 × 10 5 cells were plated in 3.5 cm wells on the day prior to transfection. Each well was transfected with a total of 0.9 μg DNA under serum-free conditions for six hours, after which the cells were washed and incubated for a further 48 hours before assaying expression. β-galactosidase activity assay For the tetracycline dose response curve, 5000 HC11- lacZ cells for each condition, were cultured in triplicate in 96 well plates for 72 hours, and beta-galactosidase activity was determined as previously described [ 24 ]. Results Generation of HC11- lacZ and HC11-Δ N β- catenin cell lines Stable cell lines were generated in which either ΔNβ-catenin or β-galactosidase was expressed in a tetracycline dependent manner. These cell lines were established using a novel autoregulatory system in which the expression level of the tetracycline transactivator (tTA) protein is minimised during routine culture and is induced upon withdrawal of tetracycline with concomitant upregulation of the transgene-of-interest [ 24 ]. This strategy helps to minimise deleterious effects due to tTA toxicity. A dose-response analysis for the HC11- lacZ cell line is shown in Figure 1A . β-galactosidase expression is effectively repressed at tetracycline concentrations in excess of 20 ng/ml and is strongly induced in the absence of tetracycline. The N-terminal truncation mutant of β-catenin can be detected by western blotting by both its myc-epitope tag and an anti-β-catenin antibody (Figure 1B ). tTA expression is detectable only in the absence of tetracycline demonstrating the autoregulatory nature of this system. Microarray analysis Transgene expression was induced in HC11- lacZ and HC11-Δ N β- catenin cells by withdrawal of tetracycline for 72 hours. Total RNA was isolated, from which mRNA was purified. cDNAs were labelled and hybridized to an 8962 element Incyte mouse GEM1 cDNA microarray (Incyte Genomics, Palo Alto, CA). These data are provided as supplementary material (See Additional file 1 ). Among those genes upregulated were two genes shown by other workers to be transcriptional targets of this pathway – Fibronectin [ 29 ] and Autotaxin [ 30 ] (data not shown) – suggesting that our model of Wnt/β-catenin signalling deregulation results in the activation of a set of target genes which overlaps, at least partially, with pathway targets in other cell lines. The microarray experiment described here was performed only once but differential expression was repeatedly validated by northern blotting from independent samples for the genes discussed here. Validation of targets Five genes were selected for further study – Extracellular Matrix Protein 1 ( Ecm1 ), Autotaxin , Receptor Activity Modifying Protein 3 ( Ramp3 ), Cd14 and Hypoxia Inducible Gene 2 ( Hig2 ). Each putative target gene was initially subjected to a secondary screen by Northern blotting to confirm the differential expression in response to ΔNβ-catenin (Figure 2 ). RNA samples used for Northern blotting were from independent induction experiments to those used for microarray analysis, thus demonstrating repeatedly by two distinct methods that the transcript levels of these genes are altered in cells overexpressing ΔNβ-catenin. The expression level of each of the transcripts was quantitated using a phosphorimager and normalised to the expression of Gapdh mRNA in the samples. The data in Figure 2 represent film exposure times ranging between 24 and 72 hours. Quantitations were performed using short (one hour or less) exposures to a phosphorimager screen, such that the signal intensity was not saturating. Molecular cloning of mouse Hig2 Hypoxia Inducible Gene 2 encodes a 63 amino acid polypeptide and was one of several genes identified in a screen for genes regulated by hypoxia in a human cervical epithelial cell line [ 31 ]. HIG2 shares no sequence similarity with other known proteins. In order to facilitate the functional analysis of this gene, ESTs were identified which encoded mouse and rat Hig2, and the sequences of chimpanzee and baboon were inferred from genomic sequence data. A multiple alignment of the inferred amino acid sequences shows that these polypeptides are highly similar (Fig 3A ). Analysis of these sequences using a Kyte-Doolittle hydrophobicity plot showed that the N-termini of these proteins contain a series of hydrophobic amino acids (Fig 3B ). This region of hydrophobicity was reminiscent of a signal peptide and sequence analysis using the signal peptide prediction program, SignalP [ 32 , 33 ] supported this possibility. Hig2 has an N-terminal signal peptide and is secreted To investigate the subcellular localisation of Hig2, a Hig2 - EGFP fusion gene was constructed and expressed in both HC11 and Madin-Darby Canine Kidney (MDCK) cells by transient transfection (Figure 4a and 4c ). In both cell lines, Hig2-EGFP is localised to large round vesicle-like structures in the cytoplasm. Similar observations were made in HEK-293 cells (data not shown). The fluorescence was detected predominantly around the periphery of these structures suggesting that they do not consist of solid masses of aggregated protein. When two aspartate residues were introduced to the putative signal peptide by site-directed mutagenesis, Hig2(Y8V9/D8D9)EGFP, this distinctive subcellular localization was abolished (Fig 4B and 4D ). These large structures did not colocalize with either markers of mitochondria (pDsRed2-mito, Clontech), nor lipid droplets (Nile Red, Molecular Probes) nor with markers of endosomes or lysosomes (pulse-chase analysis with TRITC-dextran); data not shown. However, in live HC11 cells transfected with Hig2-EGFP, observations at high magnification revealed that the cytoplasm of these cells contained many very small solid green vesicles moving along the cytoskeleton. These vesicles were approx 1/100 the size of the large vesicles shown in Fig 4A and 4C , and were not observed in cells transfected with either Hig2(Y8V9/D8D9)EGFP or EGFP alone. The rapidity of this motion in live cells, even at room temperature, precluded capture of these images but suggested the possibility that measurable amounts of secreted Hig2-EGFP might be found in the culture medium. HEK-293 cells were chosen as they could be transfected at high efficiency (approx 80%), the presence of the green transport vesicles was confirmed, and 48 hours after transfection samples of total cell lysate and conditioned medium were analysed by western blotting. Secreted Hig2-EGFP was detected in the conditioned medium of Hig2-EGFP cells, but not Hig2(Y8V9/D8D9)EGFP cells (Fig 5 ). Multiple bands were detected in cell lysates for both Hig2-EGFP fusion proteins: whether these represent artifactual degradation products or physiologically relevant biological entities is as yet unknown. Such multiple banding has also been observed with other EGFP-fusion proteins targeted to the secretory pathway (Amphiregulin-EGFP, PK unpublished observations). At least one of the bands may result from internal translation initiation at the consensus Kozak initiation sequence of pEGFP-C1 which is located between the Hig2 and EGFP open reading frames. EGFP was also detected in the conditioned medium. This is consistent with previous reports of GFP secretion via a non-classical Brefeldin A-insensitive pathway [ 34 ]. In this study, several cell lines are described (including HEK293) in which wild-type GFP is released from the cell without passing through the golgi apparatus. Thus, it is formally possible that, instead of its secretion being directed by the putative signal peptide, HIG2-EGFP might be released from the cell via this pathway in a manner specifically dependent on the EGFP moiety. The presence of post-translational modifications acquired during endoplasmic reticulum/golgi apparatus mediated secretion would exclude the latter hypothesis. The altered mobility of HIG-2-EGFP in the medium suggested that it might be glycosylated, however the mobility was not changed by treatment with the glycosidase PNGaseF, suggesting that this secreted protein is not glycosylated (data not shown). Previous studies using GFP fused to a signal peptide directing entry into the ER demonstrated that, in this redox environment, the cysteine residues of GFP form intermolecular disulphide bridges which result in oligomerization of GFP molecules [ 35 ]. Oligomers of Hig2-EGFP were detected (Fig 5 , black arrowheads) but no oligomerization of EGFP was observed. Hig2 itself does not contain cysteine residues, thus the oligomerization is mediated by the EGFP domains. These data are consistent with HIG2-EGFP entry into the classical secretory pathway. Collectively, these data demonstrate that Hig2 contains a functional N-terminal signal peptide and is likely a secreted protein. Expression of HIG2 in human tumours To investigate the relevance of HIG2 in human tumours, the expression level of this gene was examined in 68 tumour cDNA samples compared to normal adjacent tissue from the same patients using a Matched Tumour/Normal cDNA blot (Clontech) (Figure 6A ). The levels of HIG2 were approximately similar in most of the tumour types examined but were strongly and consistently downregulated in most of the cases of kidney and stomach tumours analysed. These data suggest that the downregulation of HIG2 observed upon deregulated β-catenin signalling in vitro may be of clinical relevance in human tumours. Discussion cDNA microarray analysis of the transcriptional changes resulting from overexpression of a constitutively active β-catenin revealed a panel of putative target genes of the Wnt/β-catenin pathway in mouse mammary epithelial cells. This differential expression was confirmed by Northern blotting in five cases. Autotaxin was originally identified as a secreted enzyme with potent motility stimulating activity [ 36 ] and has both pyrophosphatase and phosphodiesterase activity [ 37 ]. Transplantation experiments in athymic mice showed that ras-transformed NIH-3T3 fibroblasts became significantly more tumorigenic, invasive and metastatic when transfected with Autotaxin [ 38 ], and purified recombinant Autotaxin has potent angiogenic activity in vivo [ 39 ]. Autotaxin has been shown to be regulated by both Wnt-1 and retinoic acid [ 30 ]. Autotaxin has been shown to have lysophospholipase activity and the effects of Autotaxin on tumour cell motility are mediated by its conversion of lysophosphatidylcholine to lysophosphatidic acid (LPA), a potent signalling molecule [ 40 , 41 ]. Extracellular Matrix Protein 1 was first identified as a novel 85 KDa protein secreted by a mouse osteogenic stromal cell line [ 42 ]. In situ hybridisation showed that Ecm1 was strongly expressed in most newly formed blood vessels and experiments using purified recombinant Ecm1 showed that it could increase the proliferation rate of vascular endothelial cells in vitro and also stimulate angiogenesis in vivo . The ability to induce de novo angiogenesis is an absolute requirement for tumours to grow beyond a size which can be readily perfused by oxygen and nutrients from the interstitial fluid. ECM1 is overexpressed in many epithelial tumours including 73% of breast tumours analyzed [ 43 ]. Homozygous loss-of-function mutations in the human ECM1 gene were recently identified by linkage analysis as the causative mutations behind Lipoid Proteinosis, a rare autosomal recessive disorder characterized by hyaline deposition in the skin, mucosae and viscera [ 44 ]. The identification of Autotaxin and Ecm1 as genes upregulated by activation of this pathway, together with VEGF [ 45 ] suggests that deregulation of Wnt/β-catenin signalling during tumour initiation and progression may be one of the factors which promotes tumour angiogenesis. CD14 , which can function as both a receptor and a secreted protein, was downregulated upon ΔNβ-catenin expression. CD14 is a glycosyl-phosphatidylinositol-linked cell surface protein, preferentially expressed in monocytes, where it acts as a receptor for Lipopolysaccharide Binding Protein:Lipopolysaccharide complexes [ 46 ]. Soluble CD14 (sCD14) is also expressed in mammary epithelial cells in vitro and has been detected in human milk where it is postulated to play a role in neonatal immunity [ 47 ], and is strongly upregulated in mammary luminal epithelial cells in vivo at the onset of involution [ 48 ]. Receptor Activity Modifying Protein 3 ( RAMP3 ) was downregulated upon ΔNβ-catenin induction and is one of three members of the RAMP family. These proteins are involved in mediating the cellular response to the neuropeptides calcitonin, calcitonin gene related peptide, amylin and adrenomedullin. The RAMP family members function as chaperones for the seven transmembrane domain G-protein coupled receptors for these neuropeptides, shuttling the receptor to the cell surface and altering receptor glycosylation. The ligand binding phenotype of the receptor is dependent on the RAMP family member with which it is associated [ 49 ]. RAMP3-Calcitonin Receptor (CR) heterodimers form a functional receptor for amylin [ 50 ], and RAMP3-Calcitonin-Receptor-Like-Receptor (CRLR) heterodimers act as an adrenomedullin receptor [ 51 ]. Expression of both CR and CRLR was detected in HC11 by RT-PCR (data not shown) suggesting that functional receptor-RAMP complexes are present in this cell line. Adrenomedullin, the ligand for the CRLR/RAMP3 receptor dimer, functions as a growth factor in several human tumour cell lines [ 52 ], in addition to promoting angiogenesis in vivo [ 53 ] via CRLR/RAMP3 and CRLR/RAMP2 receptor dimers [ 54 , 55 ]. Hypoxia - inducible gene 2 ( Hig2 ) was one of several genes identified in a representational difference analysis screen for genes regulated by hypoxia in a human cervical epithelial cell line. The human gene encodes a 63 amino acid polypeptide of unknown function [ 31 ]. Expression of mouse Hig2 was downregulated in HC11 cells overexpressing ΔNβ-catenin. The identification of a group of mammalian orthologues revealed a well conserved hydrophobic region in the N-terminus, reminiscent of a signal peptide. A Hig2-EGFP fusion protein entered the secretory pathway and was detected in conditioned medium of transfected cells. The introduction of a pair of charged amino acids into the hydrophobic region abolished secretion, lending support to the hypothesis that this region contains a functional signal peptide. The nature of the large vesicular structures observed in Hig2-EGFP overexpressing cells is as yet unclear. Mammary epithelial cells are known to contain membrane-enclosed lipid droplets, as well as a variety of vesicular compartments involved in the secretion of casein, citrate, lactose and calcium [ 56 ], however the presence of these vesicles in MDCK and HEK293 cells argues that they are not mammary specific. Indeed, co-localization experiments suggest that these structures are neither mitochondria, lysosomes, endosomes nor lipid droplets. Given the demonstration that Hig2 is secreted, these structure most likely correspond to overexpressed Hig2-EGFP in transit through the endoplasmic reticulum and golgi apparatus. As no antibody is available against Hig2, it was not possible to investigate the localisation of the endogenous protein, but these data represent a useful initial step in the functional characterisation of this gene. Analysis of the expression of HIG2 using a matched Tumour/Normal tissue cDNA array showed that HIG2 is widely expressed. In most cases, the levels of HIG2 in the tumours and the associated normal tissue controls were similar. HIG2 was, however, strongly and consistently downregulated in the majority of the kidney and stomach tumours analysed. This represents an significant validation of our in vitro findings in human tumours, and suggests that HIG2 may exert a tumour suppressive effect in vivo . Human HIG2 is located on 7q32.2, a commonly deleted region in several tumour types, most prominently leukaemias and lymphomas [ 57 ]. Deletion analysis of 7q in a panel of patients with Splenic Lymphoma with Villous Lymphocytes by Catovsky and colleagues suggests that a critical tumour suppressor is located on 7q32 [ 58 ]. Conclusions The identification of this panel of candidate target genes for this clinically important signal transduction pathway adds to those identified by other workers in a variety of model systems and suggests that, as well as promoting tumour cell proliferation and survival in a cell autonomous manner, this activation of this pathway is likely to have a series of non-cell autonomous effects. Here we have focussed on the identification of Wnt/β-catenin target genes that are either secreted signalling molecules or receptors. It is likely that such targets are involved in mediating autocrine proliferation, promotion of angiogenesis and the mediation of reciprocal communication between Wnt/β-catenin tumours and their microenvironmental milieu. Competing interests The authors declare that they have no competing interests. Authors' contributions PK carried out all of the experimental procedures and drafted the manuscript. PK, TE and AA contributed to the design of the study. All authors read and approved the final version of this manuscript. Pre-publication history The pre-publication history for this paper can be accessed here: Supplementary Material Additional File 1 cDNA microarray dataset: HC11 ΔNβ-catenin v. HC11 lacZ. This file contains the dataset from the Incyte GEM1 cDNA microarray comparison between HC11 cells overexpressing ΔNβ-catenin (Probe 1) and β-galactosidase (Probe 2). This file may be easily imported into MS Excel, Genespring or other microarray analysis software to facilitate further analysis. Click here for file

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548281

PCR cloning of a histone H1 gene from Anopheles stephensi mosquito cells: comparison of the protein sequence with histone H1-like, C-terminal extensions on mosquito ribosomal protein S6

Background In Aedes and Anopheles mosquitoes, ribosomal protein RPS6 has an unusual C-terminal extension that resembles histone H1 proteins. To explore homology between a mosquito H1 histone and the RPS6 tail, we took advantage of the Anopheles gambiae genome database to clone a histone H1 gene from an Anopheles stephensi mosquito cell line. Results We designed specific primers based on RPS6 and histone H1 alignments to recover an Anopheles stephensi histone H1 corresponding to a conceptual An. gambiae protein, with 92% identity. Southern blots suggested that Anopheles stephensi histone H1 gene has multiple variants, as is also the case for histone H1 proteins in Chironomid flies. Conclusions Histone H1 proteins from Anopheles stephensi and Anopheles gambiae mosquitoes share 92% identity to each other, but only 50% identity to a Drosophila homolog. In a phylogenetic analysis, Anopheles , Chironomus and Drosophila histone H1 proteins cluster separately from the histone H1-like, C-terminal tails on RPS6 in Aedes and Anopheles mosquitoes. These observations suggest that the resemblance between histone H1 and the C-terminal extensions on mosquito RPS6 has been maintained by convergent evolution.

Background Ribosomal protein (RP) S6 is a phosphorylated protein that resides on the small subunit of eukaryotic ribosomes. Phosphorylation occurs on a cluster of five serine residues near the C-terminal end of the protein. Although details remain unclear, the phosphorylation state of RPS6 is believed to influence translational efficiency of some mRNAs [ 1 ], possibly mediated by direct contact between RPS6 and the 28S rRNA in the large subunit. RPS6 has also been implicated in ribosome biogenesis, and is thought to play a conserved role in the initiation of protein synthesis [ 2 ]. In Aedes aegypti and Aedes albopictus mosquitoes, the RPS6 protein is ~17 kDa larger than its Drosophila homolog, and on polyacrylamide gels, it migrates as the largest protein from the small ribosomal subunit. Ae. aegypti and Ae. albopictus RPS6 cDNAs encode an approximately 100 amino acid extension at the C-terminal end of the protein. The extension is particularly rich in lysine, alanine and glutamic acid, and most closely resembles the sequence of histone H1 proteins from diverse sources [ 3 ]. Because RPS6 is thought to have regulatory function(s) in a variety of cell signaling pathways [ 2 ], we were surprised to uncover this difference between mosquito and Drosophila RPS6 proteins. We have recently shown that RPS6 protein isolated from ribosomal subunits retains its histone H1-like tail [ 4 ]. Thus, unlike the case with the ubiquitinated ribosomal protein S27a in the rat [ 5 ], the histone tail is not removed from the mosquito ribosomal protein prior to ribosome assembly. RpS6 cDNA from an Anopheles stephensi cell line encodes an approximately 170 amino acid histone H1-like C-terminal extension, and in silico analysis reveals a similar modification encoded by the rpS6 gene in Anopheles gambiae . In both Aedes and Anopheles mosquitoes, the C-terminal extension was completely encoded in Exon 3, directly contiguous with upstream open reading frame encoding the series of serines that may be phosphorylated [ 4 ]. Anopheline mosquitoes are believed to be ancestral to the Culicidae, which includes the genera Aedes and Culex [ 6 ]. Thus, to a first approximation, we infer that the longer tail in Anopheles mosquitoes represents the ancestral state, and that the RPS6 tail has been lost in the higher Diptera, which include D. melanogaster . Although mosquito RPS6 tails in general resemble histone H1 proteins, their divergence between Aedes and Anopheles mosquitoes was high, relative to the conventional portion of the RPS6 coding sequence. Because histone H1 is the most variable of the histone proteins, and functions as a linker, rather than as a component of the histone octamer, we set out to clone a cDNA encoding a bona fide histone H1 protein from an An. stephensi cell line. In a phylogenetic comparison, the An. stephensi histone H1 protein clusters with homologs from Drosophila and Chironomus , rather than with RPS6 histone H1-like tails from mosquitoes. These results indicate that the histone H1-like tails on mosquito RPS6 proteins are evolving independently of conspecific histone H1 proteins. Results Design of PCR primers The gene encoding Drosophila melanogaster histone H1 spans 1204 nucleotides, and encodes a 256 amino acid protein in a single exon [ 7 ]. There is a single recorded His1 allele in Drosophila [ 8 ], while multiple histone H1 variants have been described in Chironomid flies [ 9 - 11 ]. When the deduced sequence of the Drosophila histone H1 protein (Accession NM_058232) was compared to the Anopheles gambiae genome using the program BLAST [ 12 ] on the NCBI website (National Center for Biotechnology Information; ), we obtained 5 accessions with E values ranging from 3e-35 to 8e-43, distributed on mosquito chromosomes 2 and 3. Upon further examination, we noted that XP_314184 and XP_314186 (chromosome 2) corresponded to the same protein. Two additional histone H1 candidates (XP_311486 and XP_309451) were encoded on chromosome 3. These three conceptual Anopheles proteins shared 70–80% identity to one another, and about 50% identity to the Drosophila H1 protein sequence. In the EST-other database, we found a single uninformative match to an unidentified An. gambiae entry (dbEST id = 11236311), with the relatively modest E value of 0.055. Histone H1 sequences from Aedes mosquitoes are not yet in existing databases. The 50% identity between Drosophila and Anopheles histone H1 proteins was relatively low, compared to approximately 80% amino acid identity between Drosophila and Anopheles RPS6, exclusive of the histone-H1-like tail in the mosquito protein. The Drosophila H1 histone was also ~50% identical to that from Chironomus thummi , a fly closely related to mosquitoes in the infraorder/superfamily Culicomorpha [ 13 ]. To design primers that would amplify a histone H1 gene, and not the histone H1-like tail in mosquito rpS6 , we aligned one of the An. gambiae H1 candidate proteins (XP_311486) to a histone H1 protein from C. thummi , and examined the alignment for precise matches (Fig. 1A ) that did not match well in a separate alignment of the An. gambiae histone H1 protein with the An. gambiae RPS6 tail (Fig. 1B ). The forward primer (F1) corresponded to amino acids PKKPKKP in An. gambiae , and a reverse primer (R1) corresponded to residues AAKKPKA (Fig. 2 ). Figure 1 Primer design. To design primers, we aligned an An. gambiae putative histone H1 candidate XP_311486 (Panel A, top) with a histone H1 protein (Q07134; Panel A, bottom) from C. thummi . Boxed residues were chosen for design of primers, according to the An. gambiae nucleotide sequence. Panel B shows these primer residues aligned between the An. stephensi RPS6 tail (top), and the putative Anopheles gambiae histone H1 (bottom). Vertical bars designate identities. Figure 2 Sequence of An. stephensi histone H1 gene. The positions of internal primers F1 and R1, and primers F2 and R2 are designated by arrows. The ATG start codon and TAA stop codon are boxed. Recovery of An. stephensi histone H1 gene We used F1 and R1 primers with Hin dIII-digested genomic DNA from An. stephensi cells to obtain an approximately 450 bp PCR product, which was sequenced and verified to encode a histone H1 protein. The 5-end of the gene, which extended 81 nucleotides upstream of the ATG start codon, was obtained using primer R1 with the GeneRacer kit (Invitrogen, Carlsbad, CA), with total RNA as the template. The absence of a poly(A) tail on histone mRNAs required an unconventional strategy to obtain the 3'-end of the coding sequence. First, we used Hin dIII-digested genomic DNA template, with a primer based entirely on the 3'-UTR of An. gambiae XP_314184, without success. When we designed a second primer (R2, in Fig. 2 ) extending from the 3'-UTR through the TAA stop codon and into the coding region, we obtained the 3'-end of the coding sequence. Finally, primers F2 and R2 (Fig. 2 ) were used to verify the complete nucleotide sequence. Southern blots with An. stephensi genomic DNA The likelihood that the mosquito genome contains multiple histone H1 gene variants is consistent with the multiple H1 variants that have been described in Chironomus [ 9 - 11 ] and eight histone H1 subtypes that have been described in mammals [ 14 , 15 ]. When we used the An. stephensi cDNA to probe Southern blots of genomic DNA digested with various restriction enzymes with 6 bp recognition sites, most enzymes gave multiple bands, with the notable exception of Bam HI, which hybridized to a single band longer than 10 kb (Fig. 3 ). Based on the observation that D. melanogaster H1, H2A, H2B, H3 and H4 histone genes are organized in approximately one hundred 5 kb repeats per haploid genome [ 16 ], the large Bam HI fragment from An. stephensi may be a starting point for recovery of a complete cluster of the An. stephensi histone gene family. Figure 3 Southern blot of An. stephensi genomic DNA hybridized to the An. stephensi histone H1 probe. DNA was digested with Bam HI (B), Eco RI (E), Hin dIII (H) and Pvu I (P). Positions of size markers are shown at right. The An. stephensi nucleotide sequence (GenBank accession # AY672907) matched An. gambiae histone H1 candidates on chromosomes 2 and 3 with an E value of 0.0. In addition, 6 unmapped sites also had E values of 0.0. A final two sites had E values of 4e-170 and 3e-127. The deduced An. stephensi protein sequence was 92% identical to An. gambiae protein XP_314184 on chromosome 2 (Fig. 4A ). A similar level of identity was obtained with An. gambiae XP_309451 on chromosome 3, but the alignment required introduction of a 58 amino acid gap in the shorter (190 residue) deduced Anopheles gambiae protein (not shown). Identity with An. gambiae XP-311486 was 79%. Based on these criteria, we have cloned the An. stephensi homolog of An. gambiae XP_314184. Figure 4 Comparison of mosquito histone H1 proteins and RPS6 histone H1-like tails. Panel A shows the alignment of the experimentally-determined An. stephensi histone H1 amino acid sequence, compared to An. gambiae conceptual protein XP_314184. Panel B shows a phylogram produced in PAUP* by neighbor joining, with the nematode C. elegans histone H1-like protein 2 (AAM44399) designated as the outgroup. The alignment includes histone H1 proteins from various Diptera, and the known histone H1-like tails on mosquito RPS6. Values on the horizontal lines indicate branch lengths, defined as the fraction of substitutions between the nodes that define the branch. Bootstrap values based on 1000 replicates are shown within circles. A single tree with identical topology was obtained with the optimality criterion set to parsimony. Comparisons of histone H1 proteins with mosquito RPS6 C-terminal extensions The identity between Drosophila and Anopheles (or Drosophila and Chironomus ) histone H1 proteins was only 50%. This divergence undoubtedly reflects the ~250 million years [ 6 ] separating Nematoceran from Cyclorrhaphan diptera. In this study, we were interested in comparing mosquito histone H1 proteins to the histone H1-like tails of mosquito RPS6. Fig. 4B shows a neighbor-joining analysis in which we compared protein sequences from Aedes and Anopheles RPS6 histone H1-like tails, exclusive of the conventional RPS6 protein sequence, with histone H1 proteins from the nematode Caenorhabditis elegans (AAM44399), the closely-related flies Chironomus thummi (Q07134) and Chironomus tentans (AAB62239), Drosophila , and the Anopheles gambiae and Anopheles stephensi homologs (Fig. 4A ). With the C. elegans sequence designated as the outgroup, the phylogram shows that the RPS6 tails cluster into a distinct group relative to the Dipteran histone H1 proteins. Circled values indicate bootstrap values based on 1000 replicates. When the analysis was repeated with the optimality criterion set to parsimony, we obtained a tree with the same topology, with the 77% value shown in Fig. 4B reduced to 59%, and the 97% value reduced to 94%. The 100% values remained unchanged. In an alignment of mosquito RPS6 tails with the Anopheles H1 histones (Fig. 5 ), we note that while some degree of identity covers the entire histone H1 protein, the C-terminal half of the H1 histone has a higher proportion of identities to the RPS6 tail, as indicated by the distribution of consensus residues. Within the RPS6 tails, however, the boxed motifs:VAKK(D/E)A, KKEVKK, AAPA, KKEAPKRKPE, KG(D/E)ASAAK(E/D) are shared by all four mosquitoes. In contrast, the additional amino acids in the Anopheles RPS6 tails, which are represented by gaps in the Aedes sequences (Fig. 5 ), did not show regions of homology with Anopheles histone H1. Figure 5 Alignment of mosquito RPS6 tails with mosquito histone H1 proteins. Angam (CAD89874), An. gambiae ; Anstep (AY237124), An. stephensi ; Aealbo (Q9U762), Ae. albopictus ; Aeaegy (Q9U761), Ae. aegypti . The alignment was produced with ClustalX (version 1.83), using default settings. Indicators of consensus residues are shown below the alignment. Boxes in the top four entries indicate identities (aside from D, E substitutions) shared by the mosquito RPS6 tails. Discussion An important rationale for cloning an An. stephensi histone H1 was to compare its sequence to the histone H1-like tails on mosquito RPS6 ribosomal proteins. Our choice of an Anopheles histone H1 was based on the existing database for An. gambiae , the observation that the tail in Anopheles RPS6 is nearly twice as long as that in Aedes RPS6 proteins [ 4 ], and evidence that the genus Anopheles is ancestral to Aedes [ 6 ]. Because putative homologies to Drosophila histone H1 protein could be recovered as conceptual translation products from the An. gambiae database, we used these sequences to design primers that would discriminate between an An. stephensi histone H1 gene, and the histone H1-like extension in An. stephensi RPS6. Because the Drosophila gene was encoded in a single exon, and the histone message was unlikely to be polyadenylated [ 14 ], we used genomic DNA from An. stephensi as a template for our PCR reaction. The gene we recovered had more than 90% identity to XP_314184 in An. gambiae . The proteins differed in length by a single amino acid residue, and showed 92 % identity. When we analyzed RPS6 tails and histone H1 genes, we found that the Dipteran histone H1 proteins and the RPS6 tails each fell into distinct groups, suggesting that in present-day mosquitoes, these proteins are evolving independently. Although these data are consistent with the possibility that present-day histone H1 proteins and the histone H1-like tails on mosquito RPS6 protein share a common ancestral gene, the histone tails seem to be evolving independently in the two mosquito genera, and have changed more rapidly than the conventional portion of mosquito RPS6 proteins. Because RPS6 is considered an important functional component of the ribosome, it seems surprising that a histone H1-like tail occurs at the C-terminal end of this particular protein. However, histone H1-like tails have been reported at the N-terminus of Drosophila melanogaster ribosomal proteins L22 and L23a [ 17 ]. The An. gambiae homolog of D. melanogaster L23a also contains an N-terminal histone-like extension. The N-terminal tails of Drosophila L22 and L23a were found in an effort to identify proteins that interact with poly (ADP-ribose) polymerase (PARP). In future studies, we plan to explore whether the histone H1-like tail undergoes posttranslational modification, and whether it plays a functional role in ribosome biogenesis, perhaps through the activity of PARP. Experimental procedures Mosquito cells and culture conditions We used the ASE-IV Anopheles stephensi mosquito cell line [ 18 ], which was adapted to Eagle's minimal medium, supplemented with non-essential amino acids, glutamine and 5% heat-inactivated fetal bovine serum [ 19 ]. This formulation is called E-5 medium. Genomic DNA preparation Cells grown as suspended vesicles for 4 to 5 days in twenty 60 mm plates were collected by centrifugation, and the cell pellet was washed twice with phosphate-buffered saline (PBS; [ 20 ]). The cell pellet was resuspended in 20 ml lysis buffer (10 mM Tris-HCl, pH 7.5, 10 mM EDTA, 200 μg/ml proteinase K), and SDS was added to a final concentration of 0.5%. The lysate was incubated at 37°C overnight. NaCl was added to a final concentration of 0.4 M, and the DNA was extracted once with 20 ml phenol, twice with an equal volume of phenol:chloroform (1:1), and twice with an equal volume of chloroform. Two volumes of ethanol were added, and DNA was spooled onto a clean glass rod. The DNA was dried, and dissolved in 10 ml of TE (10 mM Tris-HCl, pH 8.0, containing 1 mM EDTA) at 37°C. RNase A was added to a final concentration of 200 μg/ml and incubated at 37°C for 4 hours. DNA was phenol extracted, ethanol precipitated and dissolved in TE as described above. DNA amplification by PCR Genomic DNA (0.4 mg) was digested with Hin dIII (Promega) at 37°C overnight. Enzyme was removed by phenol:choloroform extraction, and the DNA was recovered by precipitation with ethanol and dissolved in TE. Digested DNA (100 ng) was used as template for the PCR reaction, which contained 1X PCR buffer, 1.5 mM MgCl 2 , 0.2 mM of each of the four dNTPs, 0.4 μM of primer F1 (5'CCG AAG AAG CCG AAG AAG CCC) and R1 (5'TGC TTT CGG CTT CTT GGC AGC) and 2.5 units of Taq DNA polymerase (Promega, Madison, WI). PCR was performed with an initial denaturation at 94°C for 2 minutes. The next 35 cycles included 94°C denaturation for 45 sec, 55°C annealing for 1 minute, and 72°C extension for 1 minute. The reaction was terminated by a final elongation cycle at 72°C for 2 minutes. The PCR product was recovered from a 0.9% agarose gel, purified using Ultra-Clean 15 (MO Bio Laboratories Inc., Solana Beach, CA) and cloned into PGEM T-Easy vector (Promega). The 3'-end of the gene was obtained in a similar manner, using primers R2 (Fig. 2 ) and F1. Amplifying the 5'-end of the cDNA Total RNA was recovered from ASE-IV cells by guanidine isothiocyanate extraction and cesium chloride centrifugation as described by Davis et al. [ 21 ]. The final RNA pellet was dissolved in DEPC-treated water and stored at -70°C. RNA (1 μg) was used with the GeneRacer kit (Invitrogen) to obtain the 5' end of the mRNA, using primer R1 as the reverse primer. Programs and accession numbers The analysis in Fig. 4A was produced using the Genetics Computer Group (GCG; Madison, WI) program "gap". The tree in Fig. 4B and the alignment in Fig. 5 were produced by an alignment of amino acid residues using default parameters of Clustal X (version 1.83) [ 22 ]. The tree was created in PAUP* [ 23 ], with the C. elegans H1 protein designated as an outgroup. The An. stephensi histone H1 sequence has GenBank accession # AY672907. Authors' contributions YZ did the experimental work, AMF helped with experimental design and manuscript preparation. Both authors read and approved the final manuscript.

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545799

A Drosophila protein-interaction map centered on cell-cycle regulators

A Drosophila protein-protein interaction map was constructed using the LexA system, complementing a previous map using the GAL4 system and adding many new interactions.

Background Protein-protein interactions have an essential role in a wide variety of biological processes. A wealth of data has emerged to show that most proteins function within networks of interacting proteins, and that many of these networks have been conserved throughout evolution. Although some of these networks constitute stable multi-protein complexes while others are more dynamic, they are all built from specific binary interactions between individual proteins. Maps depicting the possible binary interactions among proteins can therefore provide clues not only about the functions of individual proteins but also about the structure and function of entire protein networks and biological systems. One of the most powerful technologies used in recent years for mapping binary protein interactions is the yeast two-hybrid system [ 1 ]. In a yeast two-hybrid assay, the two proteins to be tested for interaction are expressed with amino-terminal fusion moieties in the yeast Saccharomyces cerevisiae . One protein is fused to a DNA-binding domain (BD) and the other is fused to a transcription activation domain (AD). An interaction between the two proteins results in activation of reporter genes that have upstream binding sites for the BD. To map interactions among large sets of proteins, the BD and AD expression vectors are placed initially into different haploid yeast strains of opposite mating types. Pairs of BD and AD fused proteins can then be tested for interaction by mating the appropriate pair of yeast strains and assaying reporter activity in the resulting diploid cells [ 2 ]. Large arrays of AD and BD strains representing, for example, most of the proteins encoded by a genome, have been constructed and used to systematically detect binary interactions [ 3 - 6 ]. Most large-scale screens have used such arrays in a library-screening approach in which the BD strains are individually mated with a library containing all of the AD strains pooled together. After plating the diploids from each mating onto medium that selects for expression of the reporters, the specific interacting AD-fused proteins are determined by obtaining a sequence tag from the AD vector in each colony. High-throughput two-hybrid screens have been used to map interactions among proteins from bacteria, viruses, yeast, and most recently, Caenorhabditis elegans and Drosophila melanogaster [ 4 - 10 ]. Analyses of the interaction maps generated from these screens have shown that they are useful for predicting protein function and for elaborating biological pathways, but the analyses have also revealed several shortcomings in the data [ 11 - 13 ]. One problem is that the interaction maps include many false positives - interactions that do not occur in vivo . Unfortunately, this is a common feature of all high-throughput methods for generating interaction data, including affinity purification of protein complexes and computational methods to predict protein interactions [ 11 - 14 ]. A solution to this problem has been suggested by several studies that have shown that the interactions detected by two or more different high-throughput methods are significantly enriched for true positives relative to those detected by only one approach [ 11 - 13 ]. Thus it has become clear that the most useful protein-interaction maps will be those derived from combinations of cross-validating datasets. A second shortcoming of the large-scale screens has been the high rate of false negatives, or missed interactions. This is evident from comparing the high-throughput data with reference data collected from published low-throughout studies. Such comparisons with two-hybrid maps from yeast [ 13 ] and C. elegans [ 5 ], for example, have shown that the high-throughput data rarely covers more than 13% of the reference data, implying that only about 13% of all interactions are being detected. The finding that different large datasets show very little overlap, despite having similar rates of true positives, supports the conclusion that high-throughput screens are far from saturating [ 10 , 12 ]. For example, three separate screening strategies were used to detect hundreds of interactions among the approximately 6,000 yeast proteins, and yet only six interactions were found in all three screens [ 10 ]. These results suggest that many more interactions might be detected simply by performing additional screening, or by applying different screening strategies to the same proteins. In addition, anecdotal evidence has suggested that the use of two-hybrid systems based on different fusion moieties may broaden the types of protein interactions that can be detected. In one study, for example, screens performed using the same proteins fused to either the LexA BD or the Gal4 BD produced only partially overlapping results, and each system detected biologically significant interactions missed by the other [ 15 ]. Thus, the application of different two-hybrid systems and different screening strategies to a proteome would be expected to provide more comprehensive datasets than would any single screen. We set out to map interactions among the approximately 14,000 predicted Drosophila proteins by using two different yeast two-hybrid systems (LexA- and Gal4-based) and different screening strategies. Results from the screens using the Gal4 system have already been published [ 6 ]. In that study, Giot et al . successfully amplified 12,278 Drosophila open reading frames (ORFs) and subcloned a majority of them into the Gal4 BD and Gal4 AD expression vectors by recombination in yeast. They screened the arrays using a library-screening approach and detected 20,405 interactions involving 7,048 proteins. To extend these results we subcloned the same amplified Drosophila ORFs into vectors for use in the LexA-based two-hybrid system, and constructed arrays of BD and AD yeast strains for high-throughput screening. Our expectation was that maps generated with these arrays would include interactions missed in previous screens, and would also partially overlap the Gal4 map, providing opportunities for cross-validation. Initially, we screened for interactions involving proteins that are primarily known or suspected to be cell-cycle regulators. We chose cell-cycle proteins as a starting point for our interaction map because cell-cycle regulatory systems are known to be highly conserved in eukaryotes, and because previous results have suggested that the cell-cycle regulatory network is centrally located within larger cellular networks [ 16 ]. This is most evident from examination of the large interaction maps that have been generated for yeast proteins using yeast two-hybrid and other methods. Within these maps there are more interactions between proteins that are annotated with the same function (for example, 'Pol II transcription', 'cell polarity', 'cell-cycle control') than between proteins with different functions, as expected for a map depicting actual functional connections between proteins. Interestingly, however, certain functional groups have more inter-function interactions than others. Proteins annotated as 'cell-cycle control', in particular, were frequently connected to proteins from a wide range of other functional groups, suggesting that the process of cell-cycle control is integrated with many other cellular processes [ 16 ]. Thus, we set out to further elaborate the cell-cycle regulatory network by identifying new proteins that may belong to it, and new connections to other cellular networks. Results Construction of an extensive protein interaction map centered on cell-cycle regulators by high-throughput two-hybrid screening We used the same set of 12,278 amplified Drosophila full-length ORFs from the Gal4 project [ 6 ] to generate yeast arrays for use in a modified LexA-based two-hybrid system (see Materials and methods). In the LexA system the BD is LexA and the AD is B42, an 89-amino-acid domain from Escherichia coli that fortuitously activates transcription in yeast [ 17 ]. In the version that we used, both fusion moieties are expressed from promoters that are repressed in glucose so that their expression can be repressed during construction and amplification of the arrays [ 18 ]. Previous results have shown that this prevents the loss of genes encoding proteins that are toxic to yeast, and that interactions involving such proteins can be detected by inducing their expression only on the final indicator media [ 18 , 19 ]. The ORFs were subcloned into the two vectors by recombination in yeast as previously described [ 3 , 6 ], and the yeast transformants were arrayed in a 96-well format. The resulting BD and AD arrays each have approximately 12,000 yeast strains, over 85% of which have a full-length Drosophila ORF insert (see Materials and methods). For all strains involved in an interaction reported here, the plasmid was isolated and the insert was sequenced to verify the identity of the ORF. As a first step toward generating a LexA-based protein-interaction map, we chose 152 BD-fused proteins that were either known or homologous to regulators of the cell cycle or DNA damage repair (see Additional data file 2). We used all 152 proteins as 'baits' to screen the 12,000-member AD array. We used a pooled mating approach [ 19 ] in which individual BD bait strains are first mated with pools of 96 AD strains. For pools that are positive with a particular BD, the corresponding 96 AD strains are then mated with that BD in an array format to identify the particular interacting AD protein(s). We had previously shown that this approach is very sensitive and allows detection of interactions involving proteins that are toxic to yeast or BD fused proteins that activate transcription on their own [ 19 ]. Moreover, the final assay in this approach is a highly reproducible one-on-one assay between an AD and a BD strain, in which the reporter gene activities are recorded to provide a semi-quantitative measure of the interaction. Using this approach we detected 1,641 reproducible interactions involving 93 of the bait proteins. We also performed library screening [ 6 ] with a subset of the 152 baits that did not activate the reporter genes on their own. This resulted in the detection of 173 additional interactions with 57 bait proteins. Thirty-nine interactions were found by both approaches, and these involved 21 of the 44 BD genes active in both approaches. There were 95 BD genes for which interaction data was obtained by the pooled mating approach, and 59 active BD genes in the library screening approach. The average number of interactions was 18 per BD gene in the pooled mating data, while the library screening data had an average of only four interactions per active BD gene. The average level of reporter activation for the 39 interactions that were detected in both screens was significantly higher than the average of all interactions (see Additional data file 3), suggesting that the weaker interactions are more likely to be missed by one screen or another, even though they are reproducible once detected. Altogether we detected interactions with 106 of the 152 baits, which resulted in a protein-interaction map with 1,814 unique interactions among the products of 488 genes (see Additional data file 3). The map includes interactions that were already known or that could be predicted from known orthologous or paralogous interactions (see below). The map also includes a large number of novel interactions, including many involving functionally unclassified proteins. Evaluation of the LexA-based protein interaction map As is common with data derived from high-throughput screens, the number of novel interactions detected was large, making direct in vivo experimental verification impracticable. Thus, we set out to assess the quality of the data by examining the topology of the interaction map, by looking for enrichment of genes with certain functions, and by comparing the LexA map with other datasets. First we examined the topology of the interaction map, because recent studies have shown that cellular protein networks have certain topological features that correlate with biological function [ 20 ]. In our interaction map, the number of interactions per protein ( k ) varies over a broad range (from 1 to 84) and the distribution of proteins with k interactions follows a power law, similar to previously described protein networks [ 6 , 21 ]. Most (98%) of the proteins in the map are linked together into a single network component by direct or indirect interactions (Figure 1a ). The network has a small-world topology [ 22 ], characterized by a relatively short average distance between any two proteins (Table 1 ) and highly interconnected clusters of proteins. Removal of the most highly connected proteins from the map does not significantly fragment the network, indicating that the interconnectivity is not simply due to the most promiscuously interacting proteins (Figure 1b ). In other interaction maps generated with randomly selected baits, proteins with related functions tend to be clustered into regions that are more highly interconnected than is typical for the map as a whole [ 5 , 6 , 16 ]. Moreover, interactions within more highly interconnected regions of a protein-interaction map tend to be enriched for true positives [ 6 , 23 - 25 ]. Thus, the overall topology of the interaction map that we generated is consistent with that of other protein networks, and in particular, with the expectation for a network enriched for functionally related proteins. Next we assessed the list of proteins in the interaction map to look for enrichment of proteins or pairs of proteins with particular functions. An interaction map with a high rate of biologically relevant interactions should have a high frequency of interactions between pairs of proteins previously thought to be involved in the same biological process. Among the 488 proteins in the map, 153 have been annotated with a putative biological function using the Gene Ontology (GO) classification system [ 26 , 27 ]. Because we used a set of BD fusions enriched for cell-cycle and DNA metabolic functions, we expected to see similar enrichments in the list of interacting AD fusions, as well as more interactions between genes with these functions. Both of these expectations are borne out. In the list of BD genes, both cell-cycle and DNA metabolism functions are enriched approximately 17-fold compared to similarly sized lists of randomly selected proteins ( P < 0.00002). In the AD list, these two functions are enriched four- and threefold, respectively (Table 2 ). The frequency with which interactions occur between pairs of proteins annotated for DNA metabolism is five times more than expected by chance; similarly, cell-cycle genes interact with each other six times more frequently than expected ( P < 0.001). Thus, the enrichment for proteins and pairs of interacting proteins annotated with the same function suggests that many of the novel interactions will be biologically significant. It also suggests that the map will be useful for predicting the functions of novel proteins on the basis of their connections with proteins having known functions, as described for other interaction maps [ 16 , 28 ]. Comparison of the Drosophila protein-interaction maps Direct comparison of the LexA cell-cycle map with the Gal4 data revealed that only 28 interactions were found in common between the two screens (Table 1 ). Moreover, more than a quarter of the proteins in the LexA map were absent from the Gal4 proteome-wide map. Among the 106 baits that had interactions in the LexA map, for example, 60 failed to yield interactions in the Gal4 proteome-wide map, even though all but six of these were successfully cloned in the Gal4 arrays [ 6 ] (see Additional data file 6). Similarly, 46 of the 152 LexA baits that we used failed to yield interactions from our work, yet 14 of these had interactions in the Gal4 map. Thus, the lack of overlap between the two datasets is partly due to their unique abilities to detect interactions with specific proteins. Nevertheless, for the 347 proteins common to both maps, the two screens combined to detect 1428 interactions, and yet only 28 of these were in both datasets. This indicates that the two screens detected mostly unique interactions even among the same set of proteins. Comparison with a set of approximately 2,000 interactions recently generated in an independent two-hybrid screen [ 29 ] showed only three interactions in common with our data, in part because only eight of the same bait proteins were used successfully in both screens. Although only 28 interactions were found in both the Gal4 map and our map, this rate of overlap is significantly greater than expected by chance ( p < 10 -6 ; Table 1 ). To show this, we generated 10 6 random networks having the same BD proteins, total interactions and topology as the LexA map, and found that none of these random maps shared more than two interactions in common with the Gal4 map. To assess the relative quality of the 28 common interactions we used the confidence scores assigned to them by Giot et al . [ 6 ]. They used a statistical model to assign confidence scores (from 0 to 1), such that interactions with higher scores are more likely to be biologically relevant than those with lower scores. The average confidence scores of the 28 interactions in common with our LexA data (0.63), was higher than the average for all 20,439 Gal4 interactions (0.34), or for random samplings of 28 Gal4 interactions (0.32; P < 0.0001), indicating that the overlap of the two datasets is significantly enriched for biologically relevant interactions. Thus, the detection of interactions by both systems could be used as an additional measure of reliability. The surprisingly small number of common interactions, however, severely limits the opportunities for cross-validation, and suggests that both datasets are far from comprehensive. An alternative explanation for the small proportion of common interactions is the possible presence of a large number of false positives in one or both datasets. The estimation of false-positive rates is challenging, in part because it is difficult to prove that an interaction does not occur under all in vivo conditions, and also because the number of potential false positives is enormous. Nevertheless, the relative rates of false positives between two datasets can be inferred by comparing their estimated rates of true positives [ 11 - 13 ]. To compare true-positive rates between the LexA and Gal4 datasets, we looked for their overlap with several datasets that are thought to be enriched for biologically relevant interactions (Table 3 ). These include a reference set of published interactions involving the proteins that were used as baits in both the LexA and Gal4 screens; interactions between the Drosophila orthologs of interacting yeast or worm proteins (orthologous interactions or 'interlogs' [ 30 , 31 ]); and between proteins encoded by genes known to interact genetically, which are more likely to physically interact than random pairs of proteins [ 32 , 33 ]. As expected, the overlap with these datasets is enriched for higher confidence interactions. The average confidence scores for the Gal4 interactions in common with the yeast interlogs, worm interlogs and Drosophila genetic interactions are 0.63, 0.68 and 0.80, respectively, substantially higher than the average confidence scores for all Gal4 interactions (0.34). This supports the notion that these datasets are enriched for true-positive interactions relative to randomly selected pairs of proteins. We found that the fractions of LexA- and Gal4-derived interactions that overlap with these datasets are similar (Table 3 ). For example, 25 (1.4%) of the 1814 LexA interactions and 294 (1.4%) of the 20,439 Gal4 interactions have yeast interlogs. This suggests that the LexA and Gal4 two-hybrid datasets have similar percentages of true positives, and thus similar rates of false positives. They also appear to have similar rates of false negatives, which may be over 80% if calculation is based on the lack of overlap with published interactions (Table 3 ). This supports the explanation that the main reason for the lack of overlap between the datasets is that neither is a comprehensive representation of the interactome, and suggests that a large number of interactions remain to be detected. Biologically informative interactions Further inspection of the LexA cell-cycle interaction map revealed biologically informative interactions and additional insights for interpreting high-throughput two-hybrid data. For example, we expected to observe interactions between cyclins and cyclin-dependent kinases (Cdks), which have been shown to interact by a number of assays. Our interaction map includes six proteins having greater than 40% sequence identity to Cdk1 (also known as Cdc2). A map of all the interactions involving these proteins reveals that they are multiply connected with several cyclins (Figure 2 ). For example, all of the known cyclins in the map interacted with at least two of the Cdk family members. The map includes 20 interactions between five Cdks and six known cyclins plus one uncharacterized protein, CG14939, which has sequence similarity to cyclins. Only one of these interactions (Cdc2c-CycJ) is known to occur in vivo [ 34 ], and several others are thought not to occur in vivo (for example Cdc2-CycE [ 35 ]). Similarly, the Gal4 interaction map has three Cdk-cyclin interactions [ 6 ], including one known to occur in vivo (Cdk4-CycD) and two that do not occur in vivo [ 35 ]. Thus, while some of these interactions are false positives in the strictest sense, the data is informative nevertheless, as it clearly demonstrates a high incidence of paralogous interactions - where pairs of interacting proteins each have paralogs, some combinations of which also interact in vivo . Such patterns are consistent with potential interactions between members of different protein families, even though they do not reveal the precise pair of proteins that interact in vivo . This class of informative false positives may be common in two-hybrid data where the interaction is assayed out of biological context. Experimentally reproducible interactions, whether or not they occur in vivo , can be used to discover interacting protein motifs or domains [ 6 , 36 ]. They can also suggest functional relationships between protein families and guide experiments to establish the actual in vivo interactions and functions of specific pairs of interacting proteins. The Cdk subgraph also illustrates that proteins with similar interaction profiles may have related functions or structural features. To look for other groups of proteins having similar interaction profiles we used a hierarchical clustering algorithm to cluster BD and AD fusion proteins according to their interactions (see Materials and methods). The resulting clustergram reveals several groups of proteins with similar interaction profiles (Figure 3 ). One of the most prominent clusters (Figure 3 , circled in blue) includes three related proteins involved in ubiquitin-mediated proteolysis, SkpA, SkpB and SkpC. Skp proteins are known to interact with F-box proteins, which act as adaptors between ubiquitin ligases, known as SCF (Skp-Cullin-F-box) complexes, and proteins to be targeted for destruction by ubiquitin-mediated proteolysis [ 37 ]. A map of the interactions involving the Skp proteins shows a group of 21 AD proteins that each interact with two or three of the Skp proteins (Figure 4 ). This group is highly enriched for F-box proteins, including 13 of the 15 F-box proteins in the AD list; the other two F-box proteins interacted with only one Skp (Figure 4 ). Several of the interactions in common with the Gal4 data are also in the Skp cluster, and 12 out of 16 of these involve proteins that interact with two or more Skp proteins. Thus, the Skp cluster provides another example of how proteins with similar interaction profiles may be structurally or functionally related, and how such clusters may be enriched for biologically relevant interactions. This is consistent with previous results showing that protein pairs often have related functions if they have a significantly larger number of common interacting partners than expected by chance [ 24 , 38 ]. These groups of proteins are likely to be part of more extensive functional clusters that could be identified by more sophisticated topological analyses (for example [ 39 - 44 ]. Maps showing several other major clusters derived from the cluster-gram are shown in Additional data file 7. The interaction profile data is statistically confirmed by domain-pairing data, which shows that certain pairs of domains are found within interacting pairs of proteins more frequently than expected by chance (Table 4 ). These include the Skp domain and F-box pair, the protein kinase and cyclin domains, and several less obvious pairings. For example, the cyclin and kinase domains are observed to be associated with various zinc-finger and homeodomain proteins, and the kinase domain with a number of nucleic-acid metabolism domains (Table 4 ). A similar analysis of the Gal4 data, performed by Giot et al . [ 6 ], revealed a number of significant domain pairings, including the Skp/F-box and the kinase/cyclin pairs and several others found in the LexA dataset. Therefore, although the number of proteins in the LexA dataset is relatively small, domain associations are observed in the data, demonstrating that a high-density interaction map, with a high average number of interactions per protein, provides insight into patterns of domain interactions that is equally valuable as that obtained from a proteome-wide map. Discussion Proteome-wide maps depicting the binary interactions among proteins provide starting points for understanding protein function, the structure and function of protein complexes, and for mapping biological pathways and regulatory networks. High-throughput approaches have begun to generate large protein-interaction maps that have proved useful for functional studies, but are also often plagued by high rates of false positives and false negatives. Several analyses have shown that the set of interactions detected by more than one high-throughout approach is enriched for biologically relevant interactions, suggesting that the application of multiple screens to the same set of proteins results in higher-confidence, cross-validated interactions [ 11 - 13 ]. Such cross-validation has been limited, however, by the lack of overlap among high-throughput datasets. Here we describe initial efforts to complement a recently published Drosophila protein interaction map that was generated using the Gal4 yeast two-hybrid system [ 6 ]. We constructed yeast arrays for use in the LexA-based two-hybrid system by subcloning approximately 12,000 Drosophila ORFs, using the same PCR amplification products used in the Gal4 project, into the LexA two-hybrid vectors. Initially, we used a novel pooled mating approach [ 19 ] to screen one of the 12,000-member arrays with 152 bait proteins related to cell cycle regulators. By using both a different screening approach and a different two-hybrid system, we expected to increase coverage and to validate some of the interactions detected by the Gal4 screens. The level of coverage for a high-throughput screen can be estimated by determining the percentage of a reference dataset that was detected; reference sets have been derived from published low-throughput experiments, for example, which are considered to have relatively low false-positive rates. High-throughput two-hybrid data for yeast and C. elegans proteins were shown to cover only about 10-13% of the corresponding reference datasets [ 5 , 10 , 13 ]. Two factors may contribute to this lack of coverage. First, some interactions cannot be detected using the yeast two-hybrid system, even though they could be detected in low-throughput studies using other methods. Examples include interactions that depend on certain post-translational modifications, that require a free amino terminus or that involve membrane proteins. Second, high-throughput yeast two-hybrid screens often fail to test all possible combinations of interactions; in other words, the screens are not saturating or complete. Although the relative contribution of these two factors is difficult to estimate, results from screens to map interactions among yeast proteins suggest that the major reason for the lack of coverage is that the screens are incomplete. Complete screens would identify all interactions that could possibly be detected by a given method; ideally therefore, two complete screens using the same method would identify all the same interactions. However, the rate of overlap among the different yeast proteome screens is low, even though they used very similar two-hybrid systems. Moreover, the overlap between screens is not significantly greater than the rate at which they overlap any reference set [ 4 , 10 ]. This is true even when only higher-confidence interactions are considered; for example, two large interaction screens of yeast proteins detected 39% and 65% of a higher-confidence dataset, respectively, but only 11% of the reference set was detected by both screens [ 12 ]. These results indicate that the lack of coverage in high-throughput two-hybrid data is largely due to incomplete screening, and that significantly larger datasets than those currently available will be needed before different datasets can be used to cross-validate interactions. The rates of coverage and completeness from our high-throughput two-hybrid screening with Drosophila proteins are consistent with those for the yeast proteins. We used the LexA system to detect 1,814 reproducible interactions to complement the 20,439 interactions previously detected in a proteome-wide screen using the Gal4 system [ 6 ]. The overlap between the LexA and Gal4 screens is less than 2% of each dataset, whereas their overlap with a reference set was 17% and 14%, respectively, and only 2% of the reference set was detected by both screens (Table 2 ). Taken together, these results suggest that, like the yeast interaction data, both Drosophila datasets are far from complete and that many more interactions could be detected by additional two-hybrid screening. The actual number of interactions that might be detected by complete two-hybrid screening might be roughly estimated from the partially overlapping datasets, as was performed for accurate estimation of the number of genes in the human genome [ 45 , 46 ]. In this approach, the overlap of two subsets, given that one subset is a homogeneous random sample of the whole, is sufficient to estimate the size of the whole. To make such an estimate with high-throughput two-hybrid data, however, it is necessary to first filter out false positives, as they are mostly different for the two datasets, as suggested by the fact that the nonoverlapping data has a lower rate of true positives than the overlapping data. Giot et al . estimated that at least 11% of the Gal4 interactions are likely to be biologically relevant, based on the prediction accuracy of their statistical model [ 6 ]. We found by comparison with other datasets that the rates of true positives are not substantially different between the LexA and Gal4 data (Table 3 ). Thus, if we use 11% as the minimal rate of true positives in each dataset, we obtain 200 true interactions from the LexA screen and 2,248 from the Gal4 screens. If we further assume that all of the 28 common interactions are true positives, we can estimate that complete screens should be able to detect around 16,000 true positive interactions (200 × 2,248/28). If each screening approach has a false-positive rate of 89%, then around 150,000 interactions from each approach would be required in order to create complete, cross-validating datasets, where the overlap would be comprised of true positives. This estimate is highly sensitive to both the frequency of true positives in the two datasets, and the number of positives in the overlap between the datasets; for example, if true-positive frequency is underestimated by only twofold, there will be four times as many interactions. False-positive interactions have been classified as technical or biological [ 5 ]. A technical false positive is an artifact of the particular interaction assay, and the two proteins involved do not actually interact under any setting. A biological false positive is one in which the two proteins genuinely and reproducibly interact in a particular assay, but the interaction does not take place in a biological setting; for example, the interacting proteins may never be temporally or spatially co-localized in vivo . Using the approach described here, the interactions are shown to be reproducible during the one-on-one two-hybrid assays that are used to record reporter activity scores, suggesting that we have minimized the frequency of technical false positives. We suggest that the biological false positives might be further classified as informative and non-informative. Informative false positives are interactions that do not occur in vivo , but that nevertheless have some biological basis for being detected and are potentially useful for guiding future experiments. In our data, for example, the Cdk and Skp proteins each interact with a different group of targets, which in turn interact with multiple Cdk or Skp proteins. From this data alone, we would accurately predict that Cdk proteins interact with cyclins, and that Skp proteins interact with F-box proteins, even though only some of the specific combinations are true in vivo partners. Similarly, from analysis of domain pairs in the LexA dataset, other patterns are evident, such as homeobox domains being associated with both protein kinase and cyclin domains (Table 4 ). Additional information or experimentation would be needed to determine which of the specific paralogous interactions function in vivo . Co-affinity purification, for example, might be used to directly test all possible pairs of paralogous interactions implied by the two-hybrid map. Alternatively, the genes encoding each possible pair of proteins could be examined for correlated expression patterns, for example, to suggest more likely pairs or to exclude pairs that are not coexpressed. Conclusions We used high-throughput screening to detect 1,814 protein interactions involving many proteins with cell-cycle and related functions. The resulting interaction map is similar in quality to other large interaction maps and is predominated by previously unidentified interactions. The majority of the proteins in the map have not been assigned a biological function, and the map provides a first clue about the potential functions of these proteins by connecting them with characterized proteins or pathways. High-throughput interaction data such as this should allow researchers to quickly identify possible patterns of protein interactions for use in selecting additional functional assays to perform on their gene(s) of interest. This narrows down the number of potential assays necessary to establish function for a given gene from hundreds to just a handful; conversely, when studying a specific function, such as the cell cycle, interaction data can identify which few genes, selected from thousands, may have a role in the process. Just as the sequencing of various genomes has not allowed unambiguous ascription of biological function to the majority of the identified genes, mapping of an interactome by high-throughput methods does not allow final assignment of interaction capacity or of higher functionality to a protein. This requires additional experiments, guided by these and other high-throughput data. The results presented here show that extending and combining different two-hybrid datasets will allow further refinement of the selection of functional analyses to be performed for each protein of the proteome. Materials and methods Plasmids and strains Yeast two-hybrid vectors used are related to those originally described for the LexA system [ 17 ]. The vector for expressing amino-terminal LexA DNA-binding domain (BD) fusions was pHZ5-NRT, which expresses fusions from the regulated MAL62 promoter [ 18 ]. The vector for expressing amino-terminal activation domain (AD) fusions from the GAL1 promoter was pJZ4-NRT, which was constructed from pJG4-5 [ 17 ] by replacing the ADH1 terminator with the CYC1 terminator and inserting the 5' and 3' recombination tags (5RT1 and 3RT1 [ 18 ]) into the cloning site downstream from the AD coding region. Construction details can be found in Additional data file 1. Maps and sequences are available at [ 47 ]. Yeast ( S. cerevisiae ) strain RFY231 (MAT trp1 :: hisG his3 ura3-1 leu2 ::3Lexop- LEU2 ) and RFY206 (Mat a his3Δ200 leu2-3 lys2Δ201 ura3-52 trp1Δ :: hisG ) were previously described [ 2 , 48 ]. RFY206 containing the lacZ reporter plasmid pSH18-34 [ 49 ] is referred to here as strain Y309. Yeast two-hybrid arrays Two yeast arrays were constructed by homologous recombination (gap repair) in yeast [ 3 ]. We began with the 13,393 unique PCR products, which were generated using gene-specific primer pairs corresponding to the predicted Drosophila ORFs, from ATG to stop codon, described in Giot et al . [ 6 ]. For the AD array, we co-transformed RFY231 with each PCR product along with pJZ4-NRT that had been linearized with Eco RI and Bam HI, and selected recombinants on glucose minimal media lacking tryptophan. Five colonies from each transformation were picked and combined into a well of a 96-well plate. For the BD array, we co-transformed Y309 with each PCR product along with pHZ5-NRT that had been linearized with Eco RI and Bam HI, and selected recombinants on glucose minimal medium lacking histidine and uracil. BD clones used in the screens and AD clones showing positive interactions were sequenced to verify the ORF identities. See Additional data files for details. Two-hybrid screening The BD fused proteins used as baits in our screens are listed in Additional data file 2. The AD array was screened using a two-phase pooled mating approach [ 19 ]. First, pools containing the 96 AD strains from each plate in the AD array were constructed by scraping strains grown on agar plates, dispersing in 15% glycerol, and aliquoting into a 96-well format; the 142 pools, representing approximately 13,000 AD strains, were arrayed on two 96-well plates. In the first phase, individual BD strains were mated with the 142 AD pools by dispensing 5-μl volumes of each culture onto YPD plates using a Biomek FX robot (Beckman Coulter). After 2 days growth at 30°C, yeast were replicated to medium selective for diploids, which have the AD, BD and lacZ reporter plasmids, and containing both galactose and maltose to induce expression of the AD and BD fusions, respectively. The plates also lacked leucine to assay for expression of the LEU2 reporter, and contained X-Gal (40 μg/ml) to assay for expression of lacZ . These plates were photographed after 5 days at 30°C and interactions were scored as described [ 19 ]. In the second phase of screening, single BD strains were mated with the appropriate panel(s) of 93 AD strains corresponding to the pools that were positive in the first phase. The LEU2 and lacZ reporters were assayed on separate plates: growth on plates lacking leucine was scored from 0 (no growth) to 3 (heavy growth); the extent of blue on the X-Gal plates was scored from 0 (white) to 5 (dark blue). After re-testing interactions (see Additional data files) the AD plasmids from interacting AD strains were rescued in bacteria and clones were sequenced to verify insert identity. Cloned plasmids were then reintroduced into RFY231 and used in all possible combinations of one-on-one mating operations with the appropriate BD strains to repeat the interaction assay a third time. The same set of BDs was also used to screen a pool of all approximately 13,000 AD strains using a library screening approach as described in the Additional data files. All interaction data from both screens are listed in Additional data file 3 and are also available at [ 47 , 50 ] and at IntAct [ 51 ] in the Proteomics Standards Initiative - Molecular Interactions (PSI-MI) standard exchange format [ 52 ]. Data analysis The interaction profiles for the BD fused proteins and AD fused proteins were independently clustered and are plotted in Figure 3 using Genespring software (Silicon Genetics). Protein-interaction map graphs in Figures 1 , 2 and 4 and Additional data file 7 were drawn with a program developed by Lana Pacifico (L. Pacifico, F. Fotouhi and R.L.F., unpublished work) available at [ 47 ]. To determine Drosophila interlogs of yeast or worm interactions, a list of Drosophila proteins belonging to eukaryotic clusters of orthologous groups (KOGs) [ 53 ] was obtained from the National Center for Biotechnology Information (NCBI) [ 54 ]. Each fly protein was assigned one or more KOG IDs, based on the cluster(s) to which it belongs. A list of interactions among yeast ( S. cerevisiae ) proteins, derived mostly from high-throughput yeast two-hybrid screens [ 4 , 55 ] and from the determination of proteins in precipitated complexes [ 56 , 57 ], was obtained from the Comprehensive Yeast Genome Database [ 58 , 59 ]. For the interactions determined by precipitation of complexes, two lists were generated. One list includes the binary interactions between the bait protein and every protein that was co-precipitated, but not between the precipitated proteins (hub and spoke model). The second list included all possible binary interactions among the members of a complex (matrix model). The lists were each used to generate a list of interactions between KOG pairs, which in turn was used to generate a list of potential interactions between pairs of Drosophila proteins belonging to those KOGs. Similarly, Drosophila -worm ( C. elegans ) interlogs were determined using the list of interactions between worm proteins determined by high-throughput yeast two-hybrid screening [ 5 ]. Drosophila genetic interactions were obtained from Flybase [ 27 , 60 ]. To compare the two-hybrid data with other datasets we generated random interaction maps having the same BD proteins, total interactions and topological properties as the LexA or Gal4 data. The AD clones in each interaction list were indexed, an array of the same number of genes as the AD clones was randomly fetched from the Drosophila Release 3.1 genome [ 61 ] and these genes were used to replace the original AD clones at the same indexed positions. Fifty thousand such random networks were generated for each two-hybrid dataset, and then compared with the yeast interlogs, worm interlogs, and genetic interactions to determine the amount of overlap expected by chance. P values represented the number of times that the observed number of overlapping interactions was detected in 50,000 iterations of a random network, divided by 50,000. In most cases P < 0.0002 (see Additional data file 6). Additional methods are in Additional data file 1. To compare the number of common interactions between the LexA and Gal4 maps with the number expected by chance, we generated 10 6 random LexA maps and found that they never contained more than two interactions in common with the Gal4 map; thus, the P -value for the 28 common interactions is significantly less than 10 -6 . Additional data files The following additional data are available with the online version of this paper. Additional data file 1 contains Supplementary materials and methods; Additional data file 2 contains Supplementary Table 1, BD 'baits' used in the LexA screens; Additional data file 3 contains Supplementary Table 2, Interactions detected in the LexA screens; Additional data file 4 contains Supplementary Table 3, Enrichment of Gene Ontology classes, complete list; Additional data file 5 contains Supplementary Table 4, Enrichment of Domain pairs, complete list; Additional data file 6 contains Supplementary Table 5, P -values for overlap among datasets, and Supplementary Table 6, Interactions from the LexA and Gal4 screens that successfully used the same BD bait proteins; Additional data file 7 is a PDF containing Supplementary Figure 1, Interaction maps of other clusters; Additional data file 8 is a PDF containing Supplementary Figure 2, Proteins clustered by interaction profile; Additional data file 9 contains the legends to Supplementary Figures 1 and 2. Supplementary Material Additional data file 1 Supplementary Materials and methods Click here for additional data file Additional data file 2 Supplementary Table 1: BD 'baits' used in the LexA screens Click here for additional data file Additional data file 3 Supplementary Table 2: Interactions detected in the LexA screens Click here for additional data file Additional data file 4 Supplementary Table 3: Enrichment of Gene Ontology classes (the complete list) Click here for additional data file Additional data file 5 Supplementary Table 4: Enrichment of Domain pairs (the complete list) Click here for additional data file Additional data file 6 Supplementary Table 5: P -values for overlap among datasetsa nd Supplementary Table 6: Interactions from the LexA and Gal4 screens that successfully used the same BD bait proteins Click here for additional data file Additional data file 7 Supplementary Figure 1: Interaction maps of other clusters Click here for additional data file Additional data file 8 Supplementary Figure 2: Proteins clustered by interaction profile Click here for additional data file Additional data file 9 The legends to Supplementary Figures 1 and 2 Click here for additional data file

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DNA Display I. Sequence-Encoded Routing of DNA Populations

Recently reported technologies for DNA-directed organic synthesis and for DNA computing rely on routing DNA populations through complex networks. The reduction of these ideas to practice has been limited by a lack of practical experimental tools. Here we describe a modular design for DNA routing genes, and routing machinery made from oligonucleotides and commercially available chromatography resins. The routing machinery partitions nanomole quantities of DNA into physically distinct subpools based on sequence. Partitioning steps can be iterated indefinitely, with worst-case yields of 85% per step. These techniques facilitate DNA-programmed chemical synthesis, and thus enable a materials biology that could revolutionize drug discovery.

Introduction Subsequent to the discovery of DNA as the information-carrying blueprint for biopolymer assembly, the possibility has existed for its utilization to program molecular processes devised by man. DNA is an attractive material for several reasons. It provides very high information density: a micromolar solution of thousand-base DNA fragments can store 10 6 bits per femtoliter. The information is amplifiable, so that a single molecule can be copied to produce a measurable quantity of nucleic acid. A large collection of enzymatic tools (e.g., polymerases, helicases, recombinases, and restriction enzymes) and man-made tools (e.g., oligonucleotide synthesizers, thermal cyclers, and purification kits) exist to manipulate DNA. Several technologies take advantage of these facts. For example, patterned DNA fragments have been used to direct self-assembly of nucleic acid objects ( Seeman 2003 ), to follow the fate of cells in complex populations ( Shoemaker et al. 1996 ), to localize substrates and catalysts for “lab on a chip” experiments ( Winssinger et al. 2002 ), and for DNA computing ( Braich et al. 2002 ). More recently, the idea has been advanced that patterned DNAs could be used to direct small-molecule synthesis ( Harbury and Halpin 2000 ; Gartner and Liu 2001 ), providing a genetic code for organic chemistry. A fundamental difficulty in using DNA to program molecular events is transducing the information contained within a nucleic acid sequence into a corresponding physical outcome. One general scheme to link DNA identity to a downstream process relies on sequence-specific partitioning. This self-separation is accomplished straightforwardly by hybridization of DNA molecules to immobilized oligonucleotides. Once spatially separated, the different pools of nucleic acid can be subjected to different processing steps. Thus, the sequence of a DNA fragment determines its fate. For multistep procedures, sequential hybridizations to multiple subsequences within a DNA molecule are required. Iterative partitioning of DNA molecules is equivalent to routing the molecules through a network, with each sequence taking a unique path. Routing small quantities of DNA requires high-yielding, high-fidelity, and repeatable preparative hybridization. Although a vast literature exists on DNA hybridization for analytical purposes, literature on preparative applications, where DNA must be recovered after hybridization, is quite limited. Major precedents include RNA purification over polyrA binding resins ( Aviv and Leder 1972 ) or tRNA binding resins ( Tsurui et al. 1994 ), and an electrophoresis-based selection procedure used in DNA computing ( Kenney et al. 1998 ). However, none of these methods is suitable for routing, either because they are not efficient, not repeatable, or difficult to interface with a downstream physical outcome. Here we present a practical method to autoroute DNA libraries through multiple decision points of a tree-type network, making DNA-programmed assembly processes possible. Results Our experimental scheme is illustrated in Figure 1 . A population of DNA “genes” consisting of catenated coding positions is constructed. Defined sets of “codon” sequences exist at the first position (a 1 , b 1 , and so on), second position (a 2 , b 2 , and so on), and subsequent positions. Codons present in one coding position are mutually exclusive of the codons at any other position. The identity of the first codon determines the fate of the gene at the first decision point of the network, the second codon at the second decision point, and so forth. Figure 1 Routing DNA through Networks (A) Structure of a simplified nine-member routing gene library. The ssDNA consists of 20-base noncoding regions (black lines Z 1 –Z 4 ) and 20-base coding positions (colored bars [a,b,c] 1–3 ). All library members contain the same four DNA sequences at the four noncoding regions. At each of the three coding positions, three mutually exclusive codons, (a,b,c) n , are present for a total of twenty-seven different routing genes. Resin beads coated with an oligonucleotide complementary to one codon (anticodon beads; gray ball at left) capture by hybridization ssDNAs containing the corresponding codon. (B) To travel through the network, the ssDNA library starts on one or multiple DEAE columns (black column on left) and is hybridized to a set of anticodon columns (red, green, and blue columns) corresponding to the set of codons in the first coding position. The genes are thus physically partitioned into subpools based on sequence identity and can be processed accordingly. Each subpool is subsequently transferred to a distinct DEAE column, completing the first step through the network. The hybridization splitting, processing, and transfer are repeated for all subsequent coding regions. After completion of the final step, the library is concentrated on a reverse-phase column (RP; black-and-white column on right) and eluted for solution manipulation. Genes are “read” by hybridization to a set of anticodon columns. Each anticodon column displays an oligonucleotide complementary to one codon sequence, and a complete set of columns comprises the complements to all codons at a single coding position. Genes bind to the columns by codon–anticodon base pairing, and are thus partitioned. To read a subsequent coding position, the genes are first transferred to a nonspecific DNA binding resin, regenerating an unhy-bridized state. This DNA is then hybridized to a new set of anticodon columns. By a series of such reading cycles, the sequence of a gene guides it through the network. DNA Routing Genes For DNA routing genes, we adopted a modular design adaptable to networks of varying depth and width. We chose codons consisting of 20 bases, catenated to neighboring codons through 20-base noncoding regions ( Figure 1 ). To prevent aberrant codon–anticodon pairing, all sequences were taken from a set of more than 10,000 distinct 20mers that do not crosshybridize in microarray experiments ( Giaever et al. 2002 ). The work reported here utilized 340-base fragments that specified routes through a tree with eight hierarchical levels and ten branches per level. Each of the 10 8 unique genes contained routing instructions for eight decision points. Construction of the gene library proceeded in two stages ( Figure 2 A). Initially, 160 40-base oligonucleotides comprising a codon and an adjacent noncoding region were synthesized. We assembled sets of 16 of these 40mers (for example, the oligonucleotides corresponding to codons a 1 , a 2 , . . . , a 8 ) into ten 340-base genes (“all a,” “all b,” etc.). The ten different genes were subcloned. Eight 60-base segments were then PCR amplified from an equimolar mixture of the parental plasmids. Each segment consisted of a coding position and two adjacent noncoding regions. The eight degenerate products were spliced together into 340-base fragments by primerless PCR, thus producing a library of 10 8 complexity. In principle, collective assembly of the 160 40mer oligonucleotides would have created the library in one step. In practice, the two-stage approach provided better control over codon distributions in the final gene population. Figure 2 Construction and Diversification of Routing Gene Populations (A) Overlapping complementary oligonucleotides that span an entire gene (for example [Z–a] 1–8 and a 1–8 ′–Z 2–9 ′) were assembled into full gene products (“all a,” “all b,” etc.) by primerless PCR and subcloned. Equivalent amounts of the ten resulting plasmids (a 1–8 , … , j 1–8 ) were mixed and used as template for eight separate PCR reactions with noncoding region primer pairs (Z i /Z i +1 ′) that flanked a single coding position. The eight degenerate PCR products (Z n −x n − Z n +1 ) were assembled into a library of 10 8 different genes by primerless PCR (right). (B) To generate ssDNA, a T7 promoter (pT7) was appended to the 3′ end of the double strand DNA library. The minus strand of the library was transcribed using T7 RNA Polymerase (T7 RNAP), and reverse transcribed from a Z 1 primer using MMLV Reverse Transcriptase (MMLV RT) in a coupled reaction. The resulting DNA/RNA heteroduplex was treated with sodium hydroxide to hydrolyze the RNA, providing ssDNA. The noncoding regions play an instrumental role in the construction and handling of genes. By providing conserved crossover points, they facilitate the modular generation of highly complex populations from a small number of starting oligonucleotides. For the same reason, the noncoding regions make it possible to diversify existing gene sets by recombination. The noncoding regions also place codons in the correct coding position, ensuring that all genes incorporate one codon per branch point of a network. The existence of a well-defined “reading frame” results from the fact that anticodon columns only hybridize to DNA subsequences at a specified coding position, and not to codons elsewhere in the gene. To obtain hybridization-capable nucleic acid, the duplex DNA genes must be converted to a single-stranded form. (Possibly, duplex DNA hybridization to oligonucleotides through D-loops could be driven by RecA and ATP [ Shortle et al. 1980 ]). A number of approaches for generating single-stranded DNA (ssDNA) have been described (for example Nikiforov et al. 1994 ; Williams and Bartel 1995 ; Pagratis 1996 ; Ball and Curran 1997 ), but we found most of them unsuitable for large-scale work. A modified nucleic-acid-sequence-based amplification protocol ( Compton 1991 ) ultimately proved most expedient. Thus, we appended a T7 polymerase promoter to duplex DNA routing genes by PCR amplification with appropriate primers ( Figure 2 B). This material was used as the substrate for a coupled transcription/reverse-transcription reaction to generate DNA/RNA heteroduplexes. Hydrolysis of the RNA strand of the heteroduplexes with sodium hydroxide provided nanomole quantities of high-quality single-stranded DNA. Oligonucleotide Hybridization Chromatography Synthesis of anticodon columns involves covalent attachment of oligonucleotides to a solid phase. Thiol-containing and amine-containing oligonucleotide modification reagents are commercially available, and either should facilitate coupling to an appropriately activated resin. However, pilot experiments indicated that amide linkages were more easily prepared than thioether linkages. The deprotection protocols for oligonucleotide-linked sulfhydryl moieties were more complex than for amine moieties, and sulfhydryl-modified oligonucleotides were prone to oxidation and general loss during manipulation steps. Amine-modified oligonucleotides were easier to work with and were thus used for production of anticodon columns. It proved necessary to desalt crude oligonucleotides over reverse-phase cartridges before coupling. As candidate solid phases, we tested commercially available chromatography resins made of polystyrene (Magnapore macroporous chloromethylpolystyrene beads, Argogel-NH2, epoxide-activated Poros 50 OH), methacrylate (Ultralink Biosupport Mediumand Iodoacetyl), and agarose ( N -hydroxysuccinimide[NHS]-activated Sepharose, carbonyl diimidazole-activated Sephacryl S-1000). 20-base modified anticodon oligonucleotides were coupled to the resins. Quantification by reverse-phase chromatography of the uncoupled oligonucleotide remaining in solution provided a measure of reaction progress ( Figure 3 ). An underivatized ten-base oligonucleotide was included in all coupling reactions to control for nonspecific loss of nucleic acid. Figure 3 Anticodon Column Synthesis (A) Jeffamine 1500 (compound 1) was reacted with glutaric anhydride, and the singly acylated linker (compound 2) was purified over a HiTrap SP column. Purified compound 2 was coupled to NHS-activated Sepharose (gray ball). Treatment of the linkered resin compound 3 with TBTU/NHS, and subsequent incubation with a 5′-amino modified oligonucleotide (NH 2 -DNA), completed the synthesis of an anticodon column. (B) Refractive index FPLC chromatograms of PEG compounds 1 and 2 before and after purification by cation-exchange chromatography. Linker compound 1 migrates as a bisamine (green trace) while compound 2 migrates as a monoamine (red trace). (C) HPLC chromatograms of a 5′-aminated 20-base oligonucleotide (NH 2 -20mer) and a nonaminated ten-base oligonucleotide control (10mer) incubated with TBTU/NHS activated resin compound 3. Chromatograms of the starting material (black) and supernatant after 12 h (red) are shown. An unknown side-product of the coupling reaction (NH 2 -20mer side-product) is labeled. To test hybridization properties, 50 μl columns of the derivatized resins were loaded with 1 nmol each of a complementary 20-base oligonucleotide and a noncomplementary ten-base oligonucleotide by cyclical flow in high-salt buffer. After column washing, bound oligonucleotides were eluted with deionized water. The specificity and efficiency of hybridization were evaluated by high performance liquid chromatography (HPLC) analysis of the load, flow-through, and elute fractions ( Figure 4 ). By this assay, none of the initial resins functioned for preparative DNA fractionation, either because they failed to bind DNA well (Argogel-NH2, epoxide-activated Poros 50 OH, NHS-activated Sepharose, and Biosupport Medium) or because they bound DNA without sequence specificity (Magnapore beads and Iodoacetyl). Figure 4 Linker Effects on Hybridization The hybridization to anticodon columns of a ten-base noncomplementary oligonucleotide and a 20-base complementary oligonucleotide was analyzed by HPLC. Chromatograms of the hybridization load (blue), flow-through (red), and elute (black) are shown. (A) shows the anticodon column that was synthesized by coupling the anticodon oligonucleotide directly to NHS-activated Sepharose. (B) shows the anticodon column that was synthesized by coupling the anticodon oligonucleotide to NHS-activated Sepharose through a PEG linker. Following an observation that long polyethylene glycol (PEG) linkers dramatically improve hybridization to DNA on polypropylene surfaces ( Shchepinov et al. 1997 ), we synthesized an approximately 100-atom modified PEG spacer to sit between the resin and the anticodon oligonucleotide (see Figure 3 ). The synthetic scheme utilized inexpensive, commercially available reagents and ion-exchange chromatography for purification. Efforts to attach the spacer to Biosupport Medium were unsuccessful, but the spacer coupled readily to NHS-activated Sepharose and to carbonyl diimidazole-activated Sephacryl S-1000. The linkered Sephacryl and Sepharose materials immobilized amine-modified oligonucleotides to final densities of approximately 90 nmol per milliliter of resin ( Figure 3 C). Anticodon columns containing 50 μl of either resin efficiently and reversibly hybridized to 1 nmol of a complementary 20-base oligonucleotide, while exhibiting unmeasurable binding to a noncomplementary ten-base oligonucleotide ( Figure 4 ). Subsequent experiments were carried out with the Sepharose-based resin. The hybridization columns proved extremely robust, withstanding over 30 hybridization cycles, treatment with 10 mM sodium hydroxide, and exposure to dimethylformamide (DMF) without a detectable decrease in perfor-mance. Fidelity of Routing We next investigated how buffer conditions and temperature influenced the accuracy and yield of 340-base ssDNA hybridization to anticodon columns. For these experiments, a single radiolabeled DNA gene was diluted 10-fold into an excess (50 pmol) of an unlabeled routing gene library, and loaded onto a 250-μl diethylaminoethyl (DEAE) Sepharose column. The DEAE Sepharose column was placed in a closed 3-ml fluid circuit containing ten anticodon columns, of which only one complemented a codon within the radiolabeled DNA. Hybridization buffer was pumped over the system in a direction that placed the complementary anticodon column distal to the DEAE Sepharose column ( Figure 5 , left). After hybridization, the flow-through was collected, and bound nucleic acid was eluted off of each column in the system. The quantity of radiolabeled DNA present in each fraction was determined by scintillation counting. Figure 5 Cyclical Multistep Routing (Left) Genes are transferred from the “ n− 1” step DEAE columns to the “ n ” step anticodon columns by connecting all columns in series and cyclically pumping a high-salt buffer through the system with a peristaltic pump (gray box) for 1 h at 70 °C and 1 h at 46 °C. (Right) Genes are transferred from an “ n ” step anticodon column to an “ n ” step DEAE column by connecting the two columns in series and cyclically pumping 50% DMF through the system for 1 h at 45 °C. Arrows indicate the direction of flow. By varying the temperature (25 °C to 70 °C), salt identity (sodium chloride, lithium chloride, or tetramethylammonium chloride) and salt concentration (10 mM to 2 M) of the hybridization buffer, we determined that 1.5 M sodium chloride in a phosphate buffered solution with pH 6.5 at 45 °C provided the most robust hybridization behavior over multiple codon sequences. In addition, an initial high temperature step (70 °C) and the presence of a DEAE column inline proved critical to achieving uniformly high yields ( Table 1 ). The high temperature and DEAE column may serve to break up structures in the DNA genes that inhibit association with anticodon columns. Consistent with previous microarray data ( Shoemaker et al. 1996 ), addition of 20-base oligonucleotides complementary to the noncoding regions improved hybridization efficiency. The hybridization kinetics were fast, approaching equilibrium to within 5% in less than an hour at 46 °C. Using optimal hybridization conditions, 90% or more of the input radiolabeled DNA was routed to the correct anticodon column irrespective of the sequence pair used. Table 1 340-Base ssDNA Hybridization Efficiencies and Specificities A radiolabeled “all b” gene was hybridized to anticodon columns corresponding to one coding region (see Figure 5 , left). The fraction of input radiolabel recovered from each component of the system is reported, as measured by scintillation counting. Cold DNA: an unlabeled library of 10 6 genes was added to the hybridization reaction in 10-fold excess over radiolabeled DNA. 70 °C Step: hybridization was carried out at 70 °C for 1 h before cooling to 45 °C. FT: radiolabel recovered from hybridization flow-through. DEAE: radiolabel recovered from an inline DEAE column. a′–j′: radiolabel recovered from the specified anticodon column or pair of anticodon columns. Lost: input radiolabel not recovered from any component Serial Multistep Routing In order to route a DNA fragment through successive levels of a hierarchical tree, multiple hybridization steps are required. DNA from parental anticodon columns must be isolated and hybridized to anticodon columns corresponding to the daughter-node branches. The manipulations must be highly efficient to ensure good routing yields through trees with many levels. We investigated several schemes for accomplishing iterative hybridizations. Our initial strategies utilized a multi-step procedure with three columns (anticodon, DEAE, and reverse-phase), linear transfer formats, and centrifugal evaporation. These three-column strategies did not prove to be high-yielding. We eventually observed that efficient iterative hybridizations could be accomplished with only two columns, using cyclic column-to-column transfers. Thus, a parental anticodon column with bound DNA was placed in a liquid circuit with a 250-μl DEAE-Sepharose column ( Figure 5 , right). A 50% DMF solution was pumped over the system, breaking interactions between the anticodon column and bound DNA, and promoting the binding of free DNA to the anion-exchange resin. (DNA bound to DEAE columns can be conveniently interfaced with an encoded process, such as covalent transformation by solid-phase organic chemistry [ Halpin et al. 2004 ]). Subsequently, the DEAE-Sepharose column with bound DNA was placed in a liquid circuit with a set of anticodon columns corresponding to the branches of the daughter node. As described above, a high-salt buffer was pumped cyclically over the system to elute DNA from the anion-exchange resin, and to promote hybridization to the new set of anticodon columns. The two reciprocal transfers constitute one hybridization cycle and can be repeated indefinitely. The iterative hybridizations proceed with very high DNA recoveries (greater than 95% for anticodon to DEAE and greater than 90% for DEAE to anticodon) for several reasons. First, the columns “see” a large volume of liquid flow in the cyclic format, although the total volume of buffers used is small. Second, because the transfers are column-to-column, losses associated with manipulation of dilute DNA solutions do not occur. The two-column strategy makes it practical to iterate successive hybridizations, with worst-case overall yields of 0.85 n for n hybridizations. The final requirement was to isolate DNA as a concentrated, salt-free aqueous solution upon completion of routing. For this purpose, DNA bound to anticodon columns was eluted with a small volume of an (ethylenedinitrilo)tetraacetic acid (EDTA) solution and precipitated. Alternatively, DNA bound to DEAE-Sepharose columns was transferred to a reverse-phase cartridge by cyclically pumping a high-salt buffer over the two columns in series. DNA on the reverse-phase cartridge was then washed with deionized water, eluted in acetonitrile/water, concentrated by evaporation, and desalted over a microcentrifuge gel-filtration column. Discussion Several technical improvements to our protocols are possible. The hybridization conditions could be further optimized to increase yields and shorten times, perhaps by the addition of proteins such as Escherichia coli SSB or RecA ( Nielson and Mathur 1995 ). For procedures involving multiple generations of a gene population, a T7 promoter must be appended repeatedly to the library. Incorporation of an RNAZ module into the routing genes would eliminate this step by providing a permanent T7 promoter ( Breaker et al. 1994 ). Conceivably, ssDNA production could be rendered unnecessary by using peptide nucleic acids as capture oligonucleotides or as complements to the noncoding regions. Peptide nucleic acids have been reported to invade DNA duplexes, forming more stable heteroduplexes ( Kuhn et al. 2002 ). Routing of DNA populations provides a general way to exploit DNA as a programming medium. For example, a routing approach has been utilized to compute solutions to the traveling salesman problem ( Braich et al. 2002 ). In order to obtain the answer, it was necessary to isolate DNA fragments containing a defined set of subsequences through iterated hybridizations. By increasing the speed and yield of such isolation steps, the tools described here should aid DNA computing advances. The preparative hybridization protocols will also facilitate purification of defined genomic sequences and primary mRNA transcripts for the study of nucleic acid modifications, and for the analysis of adjunct proteins. One advantage of iterated hybridization in this context is that it increases the overall specificity of purification, in much the same way that successive amplifications with nested primers increase the specificity of PCR reactions. The technique could also be applied to the isolation of nucleoprotein complexes, such as telomerase, that have been tagged with nucleic acid epitopes. Finally, the fates of individual molecules undergoing a process of covalent assembly can be programmed by routing. For example, the protocols presented here were used to direct the split-and-pool synthesis of a combinatorial chemistry library ( Halpin and Harbury 2004 ). That work involved routing genes through a tree with six levels and ten branches per level. In order to program large libraries of very low-molecular-weight compounds, routing through shallow trees with thousands of branches per level will be required. Adaptation of the anticodon columns to a microarray format would achieve this goal in a practical manner. Such massively parallel DNA-directed chemistry has the potential to revolutionize modern drug discovery. Materials and Methods Materials. O , O′ -bis(3-aminopropyl) polyethylene glycol of average molecular weight 1500 Da (compound #14535-F, also called Jeffamine 1500) and all other chemicals and solvents were purchased from Sigma-Aldrich (St. Louis, Missouri, United States). DEAE columns were prepared by pipetting approximately 250 μl of DEAE Sepharose Fast Flow resin (#17-0709-01; Amersham Biosciences, Little Chalfont, United Kingdom) into empty TWIST column housings (#20-0030; Glen Research, Sterling, Virginia, United States). DNA library assembly. 160 40mer oligonucleotides were synthesized using standard phosphoramidite chemistry. The 5′ 20 bases of each oligonucleotide consisted of a noncoding region sequence, while the 3′ 20 bases consisted of a codon sequence. Oligonucleotides were purified by electrophoresis on 15% denaturing acrylamide gels. The oligonucleotides were divided into ten sets exclusive to a-type codons (a 1 –a 8 ), b-type codons (b 1 –b 8 ), and so forth. Each set of 16 oligonucleotides was assembled into a 340-base fragment by primerless PCR ( Stemmer et al. 1995 ). Assembly reactions contained 1 μl Vent DNA Polymerase (#0254; New England Biolabs, Beverly, Massachusetts, United States), 1X Vent buffer, 250 μM each dNTP, and 0.1–10 pmol of each oligonucleotide, and were run for 20 to 35 cycles (unless otherwise noted, PCR reactions had a volume of 100 μl). In a second PCR step, the assembly products were amplified from 1 μl of the assembly reaction using 0.2 nmol of each end primer. The amplified genes were purified on 2% NuSieve (#50081; FMC BioProducts, Rockland, Maryland, United States) agarose gels and subcloned between the SphI and EcoRI sites of the pET24A plasmid (#70769-3; Novagen, Madison, Wisconsin, United States). To construct a full library, the ten plasmids were mixed in equal proportions and used as template for eight PCR reactions. The primers were Z 1 /Z 2 ′ for the first reaction, Z 2 /Z 3 ′ for the second reaction, and so forth. The resulting 60-base-pair products were purified on 3% NuSieve agarose gels. Following quantification by densitometry, 120 ng of each fragment was used in a single 50-μl, ten-cycle primerless PCR reaction to assemble a library. The assembly products were amplified using 1 μl of the assembly reaction as template and 0.2 nmol of each end primer. The final library was subcloned, and 36 isolates were sequenced to verify the presence of the expected codon distribution at each coding position. Preparation of ssDNA. ssDNA was generated using a modified NASBA reaction ( Compton 1991 ). Duplex DNA template (1–10 pmol) was transcribed/reverse-transcribed in a 200-μl reaction containing 1 nmol primer, 40 mM Tris (pH 8.3), 20 mM magnesium dichloride, 40 mM potassium chloride, 10% DMSO, 5 mM DTT, 0.1 mg/ml BSA, 3.5 mM each rNTP, 2.5 mM each dNTP, 1000 U MMLV RNAseH minus reverse transcriptase (for example #M3682; Promega, Madison, Wisconsin, United States), 100 U T7 RNA Polymerase (for example Promega #P2075), and 2 U of pyrophosphatase (New England Biolabs #MO296). To prepare radiolabeled ssDNA, a primer kinased with γ- 33 P ATP was used. Reactions were incubated for 12 h at 42 °C. Following the enzymatic step, RNA was hydrolyzed by addition of sodium hydroxide to 100 mM and heating of the reaction tube for 2 min at 100 °C. The solution was subsequently neutralized with acetic acid and spun in a benchtop microfuge at 16,000 g for 2 min to remove precipitated material. The supernatant was transferred to a fresh tube, brought to 50 mM EDTA, and ethanol precipitated. ssDNA product was purified by electrophoresis on 4% denaturing acrylamide gels. Excised gel bands were crushed and rotated overnight in 3–6 ml 5 mM Tris (pH 8.0), 500 μM EDTA, and 500 μM EGTA. Acrylamide was removed by spin column filtration, and the solution volume was reduced to 800 μl by centrifugal evaporation. Samples were phenol/chlorofom extracted, ethanol precipitated, and resuspended in water. Purification of bisamine linker (compound 1). The crude Jeffamine material was purified by fast protein liquid chromatography cation-exchange chromatography over a 5-ml Hi-Trap SP column (Amersham Biosciences #17-1152-01). In early work, 1-ml batches of a 250-mg/ml aqueous solution were loaded onto the column in 50 mM acetic acid, washed with load buffer, and bumped off with 1 M lithium chloride. Subsequently, we developed a gradient protocol. The material was loaded in water, and the product was eluted with a linear water–hydrogen-chloride gradient (0–30 mM hydrogen chlo-ride over 15 column volumes) at 6 °C monitored by refractive index detection (RID-10A; Shimadzu, Tokyo, Japan). After every fifth injection, the column was washed with 1.5 M sodium chloride to remove a yellow residue and was then reequilibrated in deionized water. Pooled fractions of the bisamine peak were brought to pH 10 by addition of solid sodium carbonate, and the purified Jeffamine product (compound 1) was extracted into methylene chloride. The combined organic layers were dried over sodium sulfate, and solvent was removed by rotary evaporation. Yields of the pale yellow solid were 40% based on the weight of crude starting material. Synthesis of amine-acid linker (compound 2) One mole equivalent of 1.5 M glutaric anhydride in dioxane was added to a briskly stirred 250-mg/ml aqueous solution of purified Jeffamine (compound 1). After 30 min, the crude reaction product was injected in 1-ml batches over a 5-ml Hi-Trap SP column and eluted as described above. Pooled fractions of the monoamine peak were brought to pH 7 by addition of solid sodium bicarbonate, and the purified linker product (compound 2) was extracted into methylene chloride, dried over sodium sulfate, and obtained as a pale yellow solid by rotary evaporation. Yields were 35% to 50% based on the weight of purified Jeffamine starting material. A compound similar to the purified linker compound 2 is commercially available (#0Z2W0F02; Nektar Therapeutics, San Carlos, California, United States). Synthesis of linkered resin (compound 3). To prepare resin compound 3, compound 1 or compound 2 was dissolved at 300 mg/ml in DMF/200 mM DIEA. The linker solution was incubated with one volume equivalent of drained NHS-activated Sepharose (Amersham Biosciences #17-0906-01). The suspension was rotated at 37 °C for 72 h, washed over a plastic frit with DMF to remove excess linker, and incubated with 1 M ethanolamine in DMF for an additional 12 h at 37 °C. The product resin was washed and stored at 6 °C. Resins coupled to compound 1 were further treated by incubation with an equal volume of DMF containing 100 mM glutaric anhydride and 15 mM pyridine at 37 °C under rotation for 48 h. Resin activation (compound 4). Typically, TBTU (320 mg) and NHS (115 mg) were dissolved in 4 ml of DMF/500 mM DIEA, and drained compound 3 (1 ml) was added. The suspension was rotated at 37 °C for 1 h. Product resin was washed with the following sequence (20 ml of each): ethyl acetate, tetrahydrofuran, ethanol, water, 5 M sodium chloride, water, and DMF. Resin activation was performed just prior to oligonucleotide coupling. Construction of anticodon columns Twenty-base capture oligonucleotides were synthesized using standard phosphoramidite chemistry, with the addition of a C12-methoxytritylamine modifier at the 5′-end (Glen Research #10-1912). Following ammonia cleavage and drying, the oligonucleotides were desalted over C18 Sep-Pak cartridges (#WAT020515; Waters Corporation, Milford, Massachusetts, United States). Purification proceeded according to the manufacturer's instructions, but a deionized water wash was inserted before the final elution step to remove residual TEAA. Coupling reactions of oligonucleotides to resin were carried out in low-binding 0.65-ml microcentrifuge tubes (#11300; Sorenson Bioscience, Salt Lake City, Utah, United States). Ten nanomoles of a capture oligonucleotide and 10 nmol of a nonaminated 10mer control oligonucleotide in a 40-μl aqueous solution were mixed with 160 μl of DMF/200 mM DIEA. Fifty microliters of drained resin compound 4 was added, and the suspension was rotated at 37 °C for 12 h. Reaction progress was monitored by HPLC. Supernatant aliquots (20 μl) were injected onto a 4.6-mm × 25-cm Varian Microsorb-MV 300-5 C18 column (#R0086203C5; Varian, Palo Alto, California, United States) and eluted with a linear water–acetonitrile gradient (0%–45% acetonitrile in five column volumes) in the presence of 0.1 M TEAA (pH 5.2) at 50 °C. After 12 h, the resin was pelleted by centrifugation at 100 g for 1 min, supernatant was removed, and the resin was incubated with 1 M ethanolamine in DMF for an additional 12 h at 37 °C. The derivatized resins were loaded into empty DNA synthesis column housings (#CL-1502-1; Biosearch Technologies, Novato, California, United States). Oligonucleotide hybridization. Hybridization was performed in a closed system consisting of an anticodon column, male tapered luer couplers (Biosearch Technologies #CL-1504-1), capillary tubing (Amersham Biosciences #19-7477-01), silicone tubing (#8060-0020; Nalgene Labware, Rochester, New York, United States), tubing connectors (Amersham Biosciences #19-2150-01, #18-1003-68, and #18-1027-62), and a peristaltic pump (Amersham Biosciences #18-1110-91). Approximately 1 ml of hybridization buffer (60 mM sodium phosphate (pH 6.5), 1.5 M sodium chloride, 10 mM EDTA, and 0.005% Triton X-100) containing 400 pmol of a complementary 20-base oligonucleotide and 400 pmol of a noncomplementary ten-base oligonucleotide was cyclically pumped through the system at 0.5 ml/min for 1 h in a 46-°C water bath. DNA was eluted off the anticodon column with 4 ml of 1 mM EDTA (pH 8.0) and 0.005% Triton X-100 heated to 80 °C. Flow-through and elute fractions were analyzed by HPLC as described above (0%–18% acetonitrile in five column volumes). ssDNA hybridization. ssDNA was loaded onto a DEAE-Sepharose column as described ( Halpin et al. 2004 ). Anticodon columns were connected in series to the DEAE column using male tapered luer couplers, capillary tubing, silicone tubing, and tubing connectors. Approximately 3 ml of hybridization buffer containing 1 nmol of each oligonucleotide complementary to the noncoding regions was cyclically pumped over the system at 0.5 ml/min for 1 h at 70 °C, 10 min at 37 °C, and 1 h in a 46-°C water bath within a 37-°C room. Hybridized DNA was transferred back to a fresh DEAE column, or eluted with 4 ml of 1 mM EDTA (pH 8.0) and 0.005% Triton X-100 heated to 80 °C. For analysis purposes, the hybridization flow-through, the DEAE resin, and the anticodon column elutes were mixed with 10 ml of scintillation cocktail (Bio-safe 2, Research Products International, Mount Prospect, Illinois, United States) and shaken vigorously. Counting was performed using the 35 S preset channel of a scintillation counter (Beckman Instruments, Fullerton, California, United States). Anticodon to DEAE DNA transfer. DEAE and anticodon columns were connected in series using male tapered luer couplers, 3.16-mm manifold tubing (#39-628; Rainin, Oakland, California, United States), and tygon tubing 3603. Using a peristaltic pump (Minpuls2; Gilson, Middleton, Wisconsin, United States), approximately 7 ml of a 50% DMF solution was flowed cyclically over the columns at 3 ml/min for either 1 h at 45 °C or 12 h at 25 °C. Endpoint isolation of DNA. DEAE columns were connected in series to C8 SepPak columns (Waters #WAT036775) using male tapered luer couplers, tygon tubing 3603, and tubing connectors. Approximately 6 ml of 50 mM ethanolamine (pH 10.0), 1.5 M sodium chloride, 1 mM EDTA, and 0.005% Triton X-100 was cyclically pumped over the columns at 1 ml/min for 1 h at 50 °C. The Sep-Pak columns were then washed with 12 ml of 100 mM TEAA (pH 6.5) followed by 12 ml of water. ssDNA was eluted from the Sep-Pak column with 4 ml of 50% acetonitrile heated to 80 °C. Samples were concentrated by centrifugal evaporation to a volume of approximately 30 μl and desalted over G25 Sephadex spin columns (Sigma-Aldrich #G-25-150).

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539322

Disentangling Sub-Millisecond Processes within an Auditory Transduction Chain

Every sensation begins with the conversion of a sensory stimulus into the response of a receptor neuron. Typically, this involves a sequence of multiple biophysical processes that cannot all be monitored directly. In this work, we present an approach that is based on analyzing different stimuli that cause the same final output, here defined as the probability of the receptor neuron to fire a single action potential. Comparing such iso-response stimuli within the framework of nonlinear cascade models allows us to extract the characteristics of individual signal-processing steps with a temporal resolution much finer than the trial-to-trial variability of the measured output spike times. Applied to insect auditory receptor cells, the technique reveals the sub-millisecond dynamics of the eardrum vibration and of the electrical potential and yields a quantitative four-step cascade model. The model accounts for the tuning properties of this class of neurons and explains their high temporal resolution under natural stimulation. Owing to its simplicity and generality, the presented method is readily applicable to other nonlinear cascades and a large variety of signal-processing systems.

Introduction Animals and human beings rely on accurate information about their external environment and internal state for proper behavioral reactions. This vital requirement has led to a large variety of highly sophisticated sensory systems [ 1 ]. A common feature, though, is the step-by-step conversion of the incoming signal through multiple sequential transformations. In auditory systems, for example, air-pressure fluctuations induce oscillations of mechanical resonators such as the eardrums, basilar membranes, and hair sensilla [ 2 , 3 , 4 , 5 ]. These oscillations cause the opening of mechanosensory ion channels in auditory receptor cells [ 6 , 7 , 8 ]. The resulting electrical currents change the cells' membrane potentials. This, in turn, activates voltage-dependent ion channels that eventually trigger action potentials, which are passed to higher brain areas for further information processing ( Figure 1 ). Each processing step induces a transformation of the stimulus representation that may include rectification, saturation, and temporal filtering. In the mammalian ear, this processing sequence is extended by nonlinear mechanical amplification and feedback [ 9 ], which influence the individual processing steps. Similar multi-step sequences of biophysical or biochemical transduction processes underlie the proper function of all sensory and many other signaling systems. Figure 1 Sequential Processing in the Auditory Transduction Chain A sequence of several steps transforms an incident sound wave into a neural spike response. (1) Mechanical coupling. The acoustic stimulus induces vibrations of a mechanical membrane (basilar or tympanic membrane). (2) Mechanosensory transduction. The deflections cause the opening of mechanosensory ion channels in the membrane of a receptor neuron. Many details of this transduction process are still unknown. The depicted schematic coupling follows the gating-spring model proposed for mechanosensory transduction in hair cells [ 43 ]. (3) Electrical integration. The electrical charge due to the transmembrane current accumulates at the cell membrane. (4) Spike generation. Action potentials are triggered by voltage-dependent currents. Each of these four steps transforms the signal in a specific way, which may be nearly linear (as for the eardrum response) or strongly nonlinear (as for spike generation, which is subject to thresholding and saturation). In general, the illustrated steps may contain further sub-processes such as cochlear amplification or synaptic transmission between hair cells and auditory nerve fibers. For the auditory periphery of locusts investigated in the present study, this schematic picture resembles anatomical findings [ 18 ], which reveal that the receptor neurons are directly attached to the eardrum and that they send their action potentials down the auditory nerve without any further relay stations. We here show that it is possible to extract fine temporal details of individual processes within such signal-processing chains from observing the output activity alone. This progress results from a new method that extends an experimental strategy well known from measuring threshold curves in neurobiology [ 10 ] or applying equivalence criteria in psychophysics [ 11 ]: varying stimulus parameters such that the investigated pathway, cell, or system stays at a constant level of output activity. The key to the new method is to compare different stimuli within these measured iso-response sets in such a way that single processing steps can be dissociated. A cascade model is used as a mathematical framework to infer the salient features of the individual processes. This allows us to quantitatively characterize the signal-processing dynamics even under in vivo conditions. Unlike many classical approaches of systems identification, the method is not based on temporal correlations between the input and output; hence, the time resolution of the method is not limited by the output precision of the system under study. In a spike-based analysis of neural response properties, this allows us to assess the dynamical features of the involved processes with considerably higher resolution than suggested by the spike jitter. A particularly fine temporal resolution is needed to analyze signal processing in auditory systems that solve complex tasks such as sound localization, echolocation, and acoustic communication [ 12 , 13 , 14 , 15 ]. Here, even single receptor cells display extraordinary sub-millisecond precision [ 14 , 16 , 17 ], with the underlying signal-processing steps involving yet shorter time scales. How these individual steps operate over short times and eventually allow such remarkable precision is largely unknown because of the high vulnerability of the auditory periphery. This calls for methods based on neurophysiological measurements from a remote downstream location such as the auditory nerve, so that the mechanical structures of the ear remain intact. As a suitable model system to study signal processing in the ear, we chose the auditory periphery of the locust ( Locusta migratoria ). Its anatomy is well characterized [ 18 ], and the auditory nerve is easily accessible for electrophysiological recordings. The nerve contains the axons of the receptor cells. These can be roughly divided into two groups according to their frequency of maximum sensitivity, which lies near 5 kHz for low-frequency receptor cells and around 15 kHz for high-frequency receptor cells. The mechanical structure of the locust system is simpler than that of mammals, as the receptor cells are directly attached to the tympanic membrane, the animal's eardrum. Also, in contrast to the signal amplification in the vertebrate cochlea, there are no known feedback loops, a circumstance which facilitates the modeling. General features of mechanoreceptors, on the other hand, are surprisingly similar across species and are also shared by hair cells in the mammalian inner ear [ 8 ]. Results To analyze signal processing in the locust ear, we performed intracellular recordings in vivo from single receptor-cell axons in the auditory nerve. The stimuli consisted of two short clicks. The clicks were sound-pressure pulses with peak amplitudes A 1 and A 2 , respectively, and were separated by a short time interval, Δ t ( Figure 2 A; see also Figure S1 for microphone recordings). For such stimuli, the receptor cell fired at most one action potential per double click; stimulus intensity hardly influenced spike timing, but strongly affected spike probability, as shown in Figure 2 B. The response strength may thus be described by the probability that a spike occurs within a certain time window after the two clicks. Figure 2 Receptor Neuron Responses for Two-Click Stimuli (A) Stimulus parameters. Acoustic stimuli consisted of two short clicks with amplitudes A 1 and A 2 , respectively, separated by a peak-to-peak interval Δ t . The clicks were triangular and had a total width of 20 μs. The peak-to-peak interval was generally less than 1.5 ms. (B) Raster plots of spike responses. Spike times obtained from a single receptor neuron with four different peak intensities (83–86 dB SPL) are shown for 30 runs each. For the different intensities, both click amplitudes were varied while their ratio was kept fixed, with intensity values referring to the larger click amplitude. The inter-click interval in this example was 40 μs. The values of p denote the measured spike probabilities. The inset displays spike times from the strongest sound stimulus at higher magnification. All spikes fall in a temporal window between 4.5 and 5.5 ms after stimulation. Spike times were recorded with a temporal resolution of 0.1 ms. These data illustrate that the response of the receptor cell is well described by the occurrence probability of a single spike in a rather broad time window, for example, between 3 and 10 ms after stimulus presentation. As is often observed for these receptor cells, there is virtually no spontaneous activity. For fixed time interval Δ t, an iso-response set consists of those combinations of A 1 and A 2 that lead to the same predefined spike probability p . Since the spike probability increases with the click amplitudes, A 1 and A 2 can easily be tuned during an experiment to yield the desired value of p (see Materials and Methods ). The tuning scheme was applied for stimulus patterns with different relative sizes of the two clicks, so that a multitude of different combinations of A 1 and A 2 corresponding to the same p was obtained. Rapid online analysis of the neural responses and automatic feedback to the stimulus generator made it possible to apply this scheme despite the time limitations of the in vivo experiments. Figure 3 shows typical examples of such iso-response sets, measured for different time intervals Δ t . For each of the three cells displayed, two distinct values of Δ t were used. The sets can be used to identify stimulus parameters that govern signal processing at a particular time scale. Most importantly, the iso-response sets exhibit specific shapes that vary systematically with Δ t . For short intervals (below approximately 60 μs), the sets generally lie on straight lines, at least for low-frequency receptor cells. High-frequency receptor cells do not display straight lines even at the smallest Δ t used in the experiment (40 μs) for reasons that will become apparent later. For long intervals (between approximately 400 and 800 μs, depending on the cell), the iso-response sets fall onto nearly circular curves. Note that in Figure 3 C, the iso-response set for Δ t = 500 μs deviates from the symmetry between A 1 and A 2 . In Figure 3 D, the inter-click interval of Δ t = 120 μs fell in neither of the two regimes discussed above, and the corresponding iso-response set shows a particularly bulged shape. Recordings from a total of eight cells agree with the observations from the three examples displayed in Figure 3 . Figure 3 Measurements of Iso-Response Sets and Identification of Relevant Stimulus Parameters (A) Acoustic stimuli. The stimuli consisted of two short clicks with amplitudes A 1 and A 2 that were separated by a peak-to-peak interval Δ t , here shown for Δ t = 40 μs (upper trace) and Δ t = 750 μs (lower trace). (B–D) Examples of iso-response sets from three receptor cells. Here, as throughout the paper, iso-response sets correspond to a spike probability of 70%. Each panel shows iso-response sets from a single receptor cell for two different values of Δ t, one smaller than 100 μs (filled circles) and one larger (open squares). The solid lines denote fits to the data of either straight lines or circles. The values for Δ t used in the experiments are indicated in the respective panels. All error measures display 95% confidence intervals. For the short intervals, the data are well fitted by straight lines ( A 1 + A 2 = constant). For the long intervals in (B) and (C), circles ( A 1 2 + A 2 2 = constant) yield good fits; a slight asymmetry is clearly visible in (C). The data for the intermediate inter-click interval Δ t = 120 μs in (D) are not well fitted by either of these shapes. Here, the measured points are connected by a dashed line for visual guidance. Note that in (B) the overall sensitivity of the neuron seems to have changed; the intersections of the straight line and the circle with the x- and y-axis do not match exactly although the stimulus in these cases is the same, a single click. The reason may be either a slow adaptation process or a slight rundown of the recording over the experimental time of around 30 min. However, this does not account for the more prominent differences in shape of the two iso-response sets. These examples demonstrate that on different time scales, different stimulus parameters are relevant for the transduction process, the amplitude A of a sound stimulus for short times and its energy A 2 for long times. The two prominent shapes of the iso-response sets—straight lines and circles—reflect two different processing steps in the auditory transduction chain. A straight line implies that the linear sum, A 1 + A 2 , of both click amplitudes determines the spike probability and demonstrates that the sound pressure is most likely the relevant stimulus parameter. Such linear summation of the pressure on short time scales is not surprising, considering the mechanical properties of the eardrum; owing to its mechanical inertia, rapidly following stimuli can be expected to superimpose. This interpretation is in agreement with laser-interferometric and stroboscopic observations of the eardrum, which have demonstrated that it reacts approximately linearly to increases in sound pressure [ 3 , 19 ]. For the longer intervals, on the other hand, the iso-response sets are circles to good approximation, indicating that the quadratic sum, or A 1 2 + A 2 2 , now determines the spike probability. It follows that the sound energy, which is proportional to the squared pressure, is the relevant stimulus parameter on this time scale. This quadratic summation represents a fundamentally different way of stimulus integration from that of the linear summation on short time scales and indicates the involvement of a different biophysical process. A process that can mediate stimulus integration over longer intervals is the accumulation of electrical charge at the neural membrane. According to this explanation, the electrical potential induced by a click is proportional to the click's energy; contributions from consecutive clicks are then summed approximately linearly because of the passive membrane properties. This is in accordance with earlier investigations for stationary sound signals that revealed an energy dependence of the neurons' firing rate [ 20 ]. We conclude that in between the mechanical vibration of the eardrum and the accumulation of electrical charge at the neural membrane, there is a squaring of the transmitted signal. This squaring may be attributed to the core process of mechanosensory transduction, i.e., the opening of ion channels by the mechanical stimulus. The above findings motivate the following mathematical model, which describes how a stimulus consisting of two sound clicks is transformed into a spike probability. Within the model, a single click of amplitude A generates a vibration of the tympanum with strength X = c 1 ·A, i.e., linear in the amplitude with a proportionality constant c 1 . This mechanical vibration leads to a membrane potential, whose effect on the generation of the spike some time T after the click is given by J = c 2 · X 2 = c 2 · ( c 1 ·A ) 2 , i.e., quadratic in the amplitude with an additional proportionality constant c 2 . The square follows from the circular shape of the iso-response sets for longer time scales, which indicated that a quadratic operation must take place before the accumulation of charge at the neural membrane. Finally, the spike probability p is given by a yet unknown function p = g ( J ). As J is the relevant quantity determining spike probability, we also refer to it as “effective stimulus intensity.” The model contains a freedom of scaling; any proportionality constants in J can be absorbed into the function g ( J ). To simplify the notation, we thus set c 1 = c 2 = 1 and obtain X = A for the strength of the mechanical vibration and J = X 2 = A 2 for the effective stimulus intensity in response to a single click. Note that in this picture, the mechanical vibration and the membrane potential are each captured by a single quantity that does not describe the time course of the corresponding processes, but rather their integrated strength in response to a click. In general, the conversion of the mechanical vibration into a membrane potential as well as the spike generation are dynamical processes that do not happen at a single moment in time. For simplicity, however, one may think of X as describing the velocity of the mechanical vibration immediately after the click and J as capturing the membrane potential at the time of spike generation. For the two-click stimulus with amplitudes A 1 and A 2 , respectively, we choose the first click to be small enough so that it does not lead to a spike by itself. The measured action potential is thus elicited at some time T after the second click. To derive the model equation for this experimental situation, we divide the time from the first click to spike generation into the period between the two clicks and the period following the second click. Let us start by focusing on the inter-click interval. After the first click, the mechanical vibration has the strength X 1 = A 1 . However, how much electrical charge accumulates during the inter-click interval to influence spike generation at time T after the second click depends on the length Δ t of the inter-click interval. This effect is incorporated by a Δ t -dependent scaling factor Q (Δ t ) into the model and results in a first contribution from the first click to spike generation given by J 1 = A 1 2 ·Q (Δ t ). Since Q (Δ t ) denotes the effect of the first click within the inter-click interval only, it should vanish in the limit of very small Δ t . Let us now consider the remaining time before spike generation. After the second click, the mechanical vibration is due to a superposition of both clicks. For short inter-click intervals, the straight iso-response lines suggest a simple addition of the two click amplitudes; in general, however, the contribution of the first click to the membrane vibration after the second click will again depend on the inter-click interval Δ t . This is modeled by a scaling factor L (Δ t ), i.e., the vibration after the second click has a strength X 2 = A 1 · L (Δ t ) + A 2 . Accordingly, the effect of the two-click vibration on the membrane potential at time T after the second click is J 2 = ( X 2 ) 2 = ( A 1 · L (Δ t ) + A 2 ) 2 . For very small Δ t, L (Δ t ) should approach unity to account for the equal contribution of both clicks for vanishing inter-click intervals. The total effective stimulus intensity is then given by This quantity determines the spike probability p via the relation p = g ( J ). How does this model explain the particular shapes of the iso-response sets in Figure 3 ? The linear and the circular iso-response sets apparently correspond to the two special cases: (1) L (Δ t ) = 1 and Q (Δ t ) = 0 (straight line) and (2) L (Δ t ) = 0 and Q (Δ t ) = 1 (circle). We can therefore regard equation 1 as a minimal model incorporating linear as well as quadratic summation, as suggested by the measured iso-response sets. Based on the experimental data, we expect that the first case is approximately fulfilled for small Δ t and the second case in some range of larger Δ t . In our biophysical interpretation, the first case means that the two clicks are added at the tympanic membrane ( L (Δ t ) ≈ 1), but the short interval between the two clicks prevents a substantial accumulation of charge from the first click alone ( Q (Δ t ) ≈ 0), as already discussed above. The second case may be found for Δ t long enough that the mechanical vibration has already decayed ( L (Δ t ) ≈ 0). The two clicks are then individually squared, i.e., they independently lead to two transduction currents. The currents add up if the time constant of the neural membrane is significantly longer than the inter-click interval ( Q (Δ t ) ≈ 1). In the two limiting cases, equation 1 is symmetric with respect to A 1 and A 2 , reflecting the symmetry of, e.g., the data in Figure 3 B. However, for values of Δ t where neither of the two cases is strictly fulfilled, this symmetry of the iso-response sets will be distorted, as is noticable for the longer Δ t in Figure 3 C. Other sets of values for L (Δ t ) and Q (Δ t ) may lead to very different iso-response shapes, as in Figure 3 D. Equation 1 presents a self-contained model for click stimuli and is sufficient to analyze the temporal characteristics of the individual steps. It can be interpreted as a signal-processing cascade that contains two summation processes, one linear in the click amplitudes and one quadratic. For click stimuli, the functions L (Δ t ) and Q (Δ t ) are thus filter functions associated with the linear and quadratic summation, respectively. Despite the simple structure of the model, the filters L (Δ t ) and Q (Δ t ) can be expected to retain the salient features of the underlying biophysical processes such as frequency content and integration time. In Protocol S1 , we show that equation 1 can be obtained in an a posteriori calculation from a generalized cascade model and that this derivation leads to an interpretation of L (Δ t ) as the velocity of the mechanical vibration and of Q (Δ t ), at least for large enough Δ t, as the time course of the membrane potential following a click. In this generalized model, the input signal is an arbitrary sound pressure wave A ( t ), and the effective stimulus intensity is a continuous function of time, J ( t ), which is given by Here, the input A ( t ) is first convolved with a temporal filter, l ( τ ), the result is squared and subsequently convolved with a second filter, q ( τ ), as depicted in Figure 4 . The filters l ( τ ) and q ( τ ) have characteristics similar to the click-version filters L (Δ t ) and Q (Δ t ), but are not identical to them. Their relations follow from the calculation in Protocol S1 . As we here focus on click stimuli, we will use the simpler equation 1 to evaluate the temporal structures of L (Δ t ) and Q (Δ t ). Figure 4 Generalized Cascade Model of the Auditory Transduction Chain The model is composed of a sequence containing two linear temporal filters, l ( τ ) and q ( t ), and two static nonlinear transformations, namely a quadratic nonlinearity and an output nonlinearity g˜ (·), which may differ from the nonlinearity g (·) of the click-stimulus model (see Protocol S1 ). First, the stimulus A ( t ) is convolved with the filter l ( τ ) (linear integration). Second, the result is squared (nonlinear transformation). Third, the result of the previous step is convolved with the filter q ( τ ), yielding the effective stimulus intensity J ( t ) (linear integration). Fourth, a final transformation g˜ of J ( t ) (nonlinear transformation) determines the response, which in this generalized model is the time-dependent firing rate r ( t ). The model thus corresponds to an LNLN cascade. This abstract structure directly follows the sequential configuration of the biophysical processing steps shown in Figure 1 . Note that we interpret equation 1 to yield the spike probability after the second click. If the first click is large and the second small, however, the first click alone may account for some of the observed spikes; clearly this is the case when the second click vanishes. This is not captured by equation 1 , and one might expect that, for large values of A 1 , these additional spikes lead to measured values of A 2 that are slightly smaller than expected for a circular iso-response set. The data in Figure 3 , however, suggest that this effect is small and not picked up by our experiment. Nevertheless, for the following quantitative study, we will keep the first click always on a level where the click by itself does not contribute substantially to the spike probability. The previous experiment showed that the separate effects of the two summation processes can be discerned for short and long time intervals. For intermediate Δ t, however, their dynamics may largely overlap. Is it nevertheless possible to design an experiment that directly reveals the whole time course of the mechanical vibration L (Δ t ) and the electrical integration Q (Δ t )? This would provide a parameter-free description of both processes and advance the quantitative understanding of the auditory transduction dynamics. To reach this goal, we again measure iso-response sets. As before, we exploit that for fixed Δ t, any pair of click amplitudes ( B 1 , B 2 ) should result in the same spike probability p as the pair ( A 1 , A 2 ) as soon as J ( A 1 , A 2 ) = J ( B 1 , B 2 ). It is this straightforward relation that allows us to determine both L (Δ t ) and Q (Δ t ) independently of each other. In fact, some appropriate set of measurements that fulfill the iso-response relation is all that is needed to calculate L (Δ t ) and Q (Δ t ). Illustrating this concept, we now proceed with a particularly suited choice of stimulus patterns, which keeps the mathematical requirements for the calculation at a minimum. For each Δ t, we measure two different iso-response stimuli, and as a key feature, one of these has a “negative” second click, i.e., a sound-pressure pulse pointing in the opposite direction as the first click, as depicted in Figure 5 A. Mathematically, this choice of stimulus patterns leads to two simple equations for the two unknowns L (Δ t ) and Q (Δ t ), which can be solved explicitly, as explained in Materials and Methods . By repeating such double measurements for different values of Δ t, the whole time course of L (Δ t ) and Q (Δ t ) is obtained. Figure 5 Temporal Structure of the Mechanical Oscillation and Electrical Integration (A) Stimulus patterns. Two clicks were presented, separated by a time interval Δ t . The first click (amplitude A 1 ) was held constant throughout this experiment. The second click was presented in the same direction as the first click (solid line, amplitude A 2 ) or in the opposite (“negative”) direction (dashed line, amplitude à 2 ). The click amplitudes A 2 and à 2 were adjusted to fall in the desired iso-response set. (B–G) Mechanical oscillation and electrical integration of a high-frequency (B and E) and two low-frequency (C and F, and D and G, respectively) receptor neurons. (B–D) Time course of the eardrum vibration. The individual values (circles) were calculated from the measured values of A 2 and à 2 for each Δ t . The results are compared with a theoretical curve from a damped harmonic oscillator (solid line) with fundamental frequency f and decay time constant τ dec fitted to the data. (E–G) Time course of the electrical integration process. The measured data are compared to an exponential fit (solid line) with a time constant τ int . Figure 5 shows examples of L (Δ t ) and Q (Δ t ) for three different cells. L (Δ t ) displays strong oscillatory components, as was observed for all cells. This property presumably reflects the eardrum's oscillation at the attachment site of the receptor cell. The detailed temporal structure of L (Δ t ) now allows us to investigate the salient features of this oscillation. To quantify our findings, we fit a damped harmonic oscillation to the measured data for L (Δ t ) and extract the fundamental frequency as well as the decay time constant. We can use these values to predict the neuron's characteristic frequency (the frequency of highest sensitivity) and the width of its frequency-tuning curve. Figure 6 shows the comparison of these predictions with traditional measurements of the tuning curves for all 12 cells measured under this experimental paradigm with sufficient sampling to extract L (Δ t ). The remarkable agreement confirms that the new analysis faithfully extracts the relevant, cell-specific properties of the transduction sequence. The correspondence between the tuning characteristics and the filter L (Δ t ) also explains why high-frequency receptor cells do not feature straight lines for their iso-response sets even at the shortest inter-click interval (40 μs) used in the experiment. For those cells, L (Δ t ) decays rapidly, thus not allowing access to the region where L (Δ t ) ≈ 1. Figure 6 Predictions of Tuning Characteristics (A) Tuning curves for the same two cells as in Figure 5 B and 5 E, and 5C and 5F, respectively. The data show the intensity required to drive a receptor cell at a firing rate of 150 Hz for different sound frequencies in the range of 1 to 40 kHz. The characteristic frequency f CF is determined as the minimum of the tuning curve, and the tuning width Δ f 3dB as the width of the curve 3 dB above the minimum value. (B) Comparison of the predicted and measured characteristic frequency and the tuning width. The predictions were obtained from the fundamental frequency and decay time constant of the measured filter L (Δ t ); the measured values are taken from the tuning curves as in (A) ( n = 12). The encircled data points correspond to the three examples shown in Figure 5 . The width of the tuning curves is notoriously difficult to assess quantitatively, as it depends sensitively on an accurate determination of the intensity minimum of the tuning curve. This contributes strongly to the differences of the tuning-width values. The short initial rise phase of the measured Q (Δ t ) in Figure 5 E and 5 F illustrates the rapid buildup of the membrane potential after a click. The exponential decay following this phase suggests that the accumulated electrical charge decays over time owing to a leak conductance. Previously, the time constant could not be measured because of difficulties in obtaining recordings from the somata or dendrites of the auditory receptor cells. Using our new method, we find time constants in the range of 200 to 800 μs. These values are small compared to time constants in more central parts of the nervous system, reflect the high demand for temporal resolution in the auditory periphery, and explain the high coding efficiency of the investigated receptor neurons under natural stimulation [ 21 ]. In most of our recordings, the temporal extent of the filter L (Δ t ) was considerably smaller than that of Q (Δ t ). This usually leads to a region around a Δ t of 400–800 μs, depending on the specific cell, where L (Δ t ) ≈ 0 and Q (Δ t ) is still near unity. These findings correspond to the circular iso-response sets of the initial experiment. Towards very small Δ t, on the other hand, the data show that Q (Δ t ) usually decreases strongly. As explained earlier, this is expected from the linear iso-response sets, and it is observed exemplarily in the data shown in Figure 5 E and 5 F. In addition, the first few 100 μs of the data may show considerable fluctuations of Q (Δ t ) for some recordings, as in Figure 5 G. Different effects may influence this early phase of Q (Δ t ). (1) The electrical potential might be shaped by further dynamics in addition to the low-pass properties of the neural membrane, such as inactivation of the transduction channels or electrical resonances as found in some hair cells [ 6 ]. (2) The fluctuations could reflect the oscillatory influx of current following from the oscillation of the eardrum. In other words, the low-pass filtering of the neural membrane may not be strong enough to quench all oscillatory components of the transduction currents. The resulting effect on the filter Q (Δ t )—though too small to be picked up reliably by the present experiments—can be observed in simulations of the processing cascade, see Figure S2 . At present, we cannot distinguish between these two interpretations. More detailed future experiments, however, may allow a quantitative test of these hypotheses. Measuring the mechanical and electrical response dynamics, L (Δ t ) and Q (Δ t ), completes the model. In order to test its validity and suitability to make quantitative predictions, we investigated the model's performance on a different class of stimuli, namely combinations of three short clicks. Having measured the required values for L (Δ t ) and Q (Δ t ) with two-click stimuli as in the previous experiment (see Figure 5 ), we now ask the following question: if we keep the first two clicks small enough that they do not lead to a spike response, can we predict the size of the third click required to reach a given spike probability? We can use the measured values of L (Δ t ) and Q (Δ t ) to calculate these predictions and experimentally test them by performing a series of three-click iso-response measurements. This experiment was performed on three different cells; one cell featured an unusually high response variability, and results from the other two cells are shown in Figure 7 . The agreement between the predicted and the true click amplitudes shows that the model yields quantitatively accurate results. Figure 7 Model Predictions for Three-Click Stimuli (A) Stimulus patterns. The stimuli consisted of three clicks with amplitudes A 1 , A 2 , and A 3 that were separated by time intervals Δ t 1 and Δ t 2 , respectively. The second and third clicks were either given in the same or opposite (“negative”) direction as the first click. A 1 and A 2 were set equal and held constant, and A 3 was adjusted to yield a spike probability of 70%. The following pairs of time intervals (Δ t 1 , Δ t 2 ) were applied: (100 μs, 100 μs), (100 μs, 200 μs), and (200 μs, 100 μs). (B and C) Predicted and measured amplitudes of the third click for two different cells. Predictions were made after L (Δ t ) and Q (Δ t ) had been measured with two-click experiments such as in Figure 5 . The comparison between predicted and measured values for A 3 therefore contains no free parameters. The model equation for three-click stimuli is presented in Materials and Methods . As demonstrated by these data, the model allows quantitatively accurate predictions. Discussion We have presented a novel technique to disambiguate single processing steps within a larger sensory transduction sequence and to analyze their detailed temporal structures. Our approach is based on measuring particular iso-response sets, i.e., sets of stimuli that yield the same final output, and on specific quantitative comparisons of such stimuli to dissociate the individual processes. For the investigated auditory transduction chain in the locust ear, this strategy led to a precise characterization of two consecutive temporal integration processes, which we interpret as the mechanical resonance of the eardrum and the electrical integration of the attached receptor neuron. The method revealed new details of these processes with a resolution far below 1 ms. The results for the time course of the mechanical resonance agree with traditional measurements of tuning curves and show the decay of the oscillation with a temporal precision much higher than expected from the jitter of the measured output signal, the spikes. The time constants of the electrical integration that were extracted from the data had not been accessible by other means. The analysis resulted in a four-step model of auditory transduction in locusts. The model comprises a series of two linear filters and two nonlinear transformations. The quadratic nonlinearity that separates the two linear filters suggests that the mechanosensory transduction can be described by an energy-integration mechanism, as the squared amplitude corresponds to the oscillation energy of the tympanum. This quadratic form was derived from the circular shape of the iso-response sets for longer time scales Δ t and is in accordance with the energy-integration model that was found to capture the sound-intensity encoding of stationary sound signals in these cells [ 20 ]. Furthermore, the direct current component of the membrane potential in hair cells is also proportional to sound energy [ 22 ], and in psychoacoustic experiments, energy integration accounts for hearing thresholds [ 23 , 24 , 25 , 26 ]. However, a recent analysis of response latencies in auditory nerve fibers and auditory cortex neurons in cats suggests an integration of the pressure envelope for determining thresholds [ 27 ]. This effect may be attributable to the synapse between the hair cell and the auditory nerve fiber in the mammalian ear. In the locust ear, this synapse does not exist, as the fibers are formed by the axons of the receptor neurons themselves. Although the quadratic nonlinearity is fully consistent with our data, there is a second possibility within the general cascade model, equation 2 , namely, squaring after rectification. From a biophysical point of view, this would be expected if the mechanosensory ion channels can only open in one direction. Based on the current data, we cannot distinguish between these two possibilities. As the two scenarios should lead to slightly different response characteristics, future high-resolution experiments should be able to resolve this question. The linear filters L (Δ t ) and Q (Δ t ) were interpreted as the mechanical oscillation of the tympanum and the electrical integration at the neural membrane. Their oscillatory and exponential decay characteristics, respectively, support this view. In principle, however, other processes may well contribute to these characteristics, e.g., electrical resonances as seen in hair cells of the turtle and bullfrog [ 6 , 28 ]. These electrical amplification processes would be expected to influence the filter Q (Δ t ), but our data generally provide little evidence for such effects. Deviations from the exponential decay characteristics in Q (Δ t ) may in part be attributable to the oscillatory influx of charge resulting from the tympanic vibration. This may lead to a small oscillatory component in the early phase of the filter (cf. Protocol S1 ; Figure S2 ). The mechanical coupling in the first step of our model is linear. This is in accordance with mechanical investigations of the tympanum using laser interferometry [ 3 ] and stroboscopic measurements [ 19 ]. As the short clicks used in our study produce reliable spiking responses only at high sound pressure, however, we cannot exclude the influence of nonlinear coupling at low sound pressure, which has been hypothesized on the basis of distortion-product otoacoustic emissions [ 29 ]. In addition, the mechanical properties of the tympanum seem to change slightly under prolonged stimulation and give rise to mechanical adaptation effects with time scales in the 100-ms range [ 30 ]. Spike-frequency adaptation also adds a nontrivial feedback term to the minimal feedforward model of Figure 4 . Similarly, specific potassium currents and sodium-current inactivation induced by sub-threshold membrane potential fluctuations may complicate the transduction dynamics for more general inputs, but do not leave a signature in the present click-stimulus data. The model was quantitatively investigated by using combinations of short clicks. The particular structure of these stimuli allowed a fairly simple mathematical treatment. The derivation of equation 1 relied on capturing the mechanical vibration and the membrane potential, respectively, by single quantities in each time period following a click. This was possible because of the expected stereotypic evolution of the dynamic variables during the “silent phases” between and after the clicks. A generalization to arbitrary acoustic stimuli would require a more elaborate model in the form of equation 2 as well as extensions that account for neural refractoriness and adaptation. Besides its applicability under in vivo conditions, the presented framework has several advantageous properties. First, the method effectively decouples temporal resolution on the input side from temporal precision on the output side by focusing on spike probabilities. In all our measurements, for example, spike latencies varied by about 1 ms within a single recording set owing to cell-intrinsic noise (see Figure 2 ). Still, we were able to probe the system with a resolution down to a few microseconds. This would not have been possible using classical techniques such as poststimulus time histograms, reverse correlation, and Wiener-series analysis. All these methods are intrinsically limited by the width of spike-time jitter and thus cannot capture the fine temporal details of rapid transduction processes. With our method, the resolution is limited only by the precision with which the sensory input can be applied. For the investigated system, the achievable temporal resolution thus increases by at least two orders of magnitude. Second, the method is robust against moderate levels of spontaneous output activity, as this affects all stimuli within one iso-response set in the same way. Methods that require measurements at different response levels, on the other hand, are likely to be systematically affected because the same internal noise level may have a different influence at different levels of output activity. Finally, in many input–output systems, the last stage of processing can be described by a monotonic nonlinearity. Here, this is the relation between the effective stimulus intensity J and the spike probability p = g ( J ), which includes thresholding and saturation. By always comparing stimuli that yield the same output activity, our analysis is independent of the actual shape of g ( J ). Preceding integration steps may thus be analyzed without any need to model g ( J ). This feature is independent of the specific output measure and applies to spike probabilities, firing rates, or any other continuous output variable. Let us also note that the method does not require that the time scales of the individual processes be well separated. For the studied receptor cells, mechanical damping was on average about two times faster than electrical integration, and even for cells with almost identical time constants, iso-response measurements led to high-quality data and reliable parameter fits. Nor is the method limited to particularly simple nonlinearities. All that is needed are solid assessments of the iso-response sets. Mathematically, it is straightforward to substitute some or all of the analytical treatments of this work by numerical approaches, if required by the complexity of the identified signal-processing steps. This extension allows one to use a general parametrization of the full processing chain when the nonlinear transformation cannot be estimated from iso-response sets at large and small Δ t . Instead, performing more than the two measurements at each intermediate Δ t in the second experiment (see Figure 5 ) will provide additional information that can be exploited to improve the numerical estimates of the nonlinearity. As in many other approaches of nonlinear systems identification, the development of a quantitative model relies on the prior determination of the appropriate cascade structure. Unfortunately, there is no universal technique for doing so. In many cases, intuition is required to find suitable models, which should eventually be tested by their predictive power. In the present case, the findings of characteristic shapes of the iso-response sets gave a clear signature of two distinct linear filters with a sandwiched quadratic nonlinearity. In addition, this structure was supported by its amenability to straightforward biophysical interpretation. Generalizing our results, specific iso-response sets may aid structure identification in conjunction with a priori anatomical and physiological knowledge. Once the cascade structure is established, the individual constituents can be quantitatively evaluated by specific comparisons of iso-response stimuli. Comparing responses to clicks in positive and negative directions as in this study is in essence similar to the approach used by Gold and Pumphrey [ 31 ], who evaluated the perceptual difference between short sine tones with coherent phase relations and sine tones that contained phase-inverted parts in order to estimate the temporal extent of the cochlear filters. A yet open problem is the inclusion of feedback components. The present approach relies on the feedforward nature of the system to disentangle the individual processing steps. In particular cases, however, the iso-response approach may also aid in separating feedforward and feedback contributions, namely, when the feedback depends purely on the last stage of the processing cascade [ 30 ]. In this situation, iso-response measurements lead to a constant feedback contribution, and the analysis of the feedforward components may be carried out as in the present case. The experiment may then be repeated for different output levels to map out the feedback characteristics. The feedforward model that we have proposed here for the auditory transduction chain has the form of an LNLN (where “L” stands for linear and “N” stands for nonlinear) cascade, composed of two linear temporal integrations and two nonlinear static transformations [ 32 ]. Similar signal-processing sequences combining linear filters and nonlinear transformations are ubiquitous at all levels of biological organization, from molecular pathways for gene regulation to large-scale relay structures in sensory systems. In neuroscience, applications range from the sensory periphery, including frog hair cells [ 33 ], insect tactile neurons [ 34 ], and the mammalian retina [ 35 , 36 , 37 ], over complex cells in visual cortex [ 38 , 39 ], to psychophysics [ 40 ]. These studies are restricted to models that contain a single nonlinear transformation, corresponding to NL, LN, or LNL cascades [ 32 , 41 ]. An extension of these analyses was presented by French et al. [ 42 ], who derived an NLN cascade for fly photoreceptors. Complementary to the correlation techniques underlying the parameter estimations in those models, the method presented in this work provides a new way of quantitatively evaluating and testing cascade models. The increased complexity of the LNLN cascade identified in the present case was made accessible by invoking particular iso-response measurements, and a higher temporal resolution was achieved by focusing on how spike probabilities depend on the temporal stimulus structure instead of relying on temporal correlations between stimulus and response. Our experimental technique will be most easily applicable to systems whose signal processing resembles the cascade structure investigated here. The general concept of combining different measurements from within one iso-response set covers, however, a much larger range of systems. With increasingly available high-speed computer power for online analysis and stimulus generation, this framework therefore seems well suited to solve challenging process-identification tasks in many signal-processing systems. Materials and Methods Electrophysiology We performed intracellular recordings from axons of receptor neurons in the auditory nerve of adult Locusta migratoria . Details of the preparation, stimulus presentation, and data acquisition are described elsewhere [ 20 ]. In short, the animal was waxed to a Peltier element; head, legs, wings, and intestines were removed, and the auditory nerves, which are located in the first abdominal segment, were exposed. Recordings were obtained with standard glass microelectrodes (borosilicate, GC100F-10, Harvard Apparatus, Edenbridge, United Kingdom) filled with 1 mol/l KCl, and acoustic stimuli were delivered by loudspeakers (Esotec D-260, Dynaudio, Skanderborg, Denmark, on a DCA 450 amplifier, Denon Electronic, Ratingen, Germany) ipsilateral to the recorded auditory nerve. The reliability of the sound signals used in this study was tested by playing samples of the stimuli while recording the sound at the animal's location with a high-precision microphone (40AC, G.R.A.S. Sound and Vibration, Vedbæk, Denmark, on a 2690 conditioning amplifier, Brüel and Kjær, Langen, Germany). See Figure S1 for example recordings. Spikes were detected online from the recorded voltage trace with the custom-made Online Electrophysiology Laboratory software and used for online calculation of spike probabilities and automatic tuning of the sound intensities. The measurement resolution of the timing of spikes was 0.1 ms. During the experiments, the animals were kept at a constant temperature of 30 °C by heating the Peltier element. The experimental protocol complied with German law governing animal care. Measurement of iso-response sets Since the spike probability p of the studied receptor neurons increases monotonically with stimulus intensity, parameters of iso-response stimuli corresponding to the same value of p can be obtained by a simple online algorithm that tunes the absolute stimulus intensity. For fast and reliable data acquisition, we chose p = 70%. The response latency of the neurons varied by 1–2 ms, so that spike probabilities could be assessed by counting spikes over repeated stimulus presentations in a temporal window from 3 to 10 ms after the first click. In the first set of experiments, stimulus patterns were defined by fixed ratios of A 1 and A 2 , and the tuning was achieved by adjusting the two amplitudes simultaneously. The ratios were chosen so that the angles α in the A 1 – A 2 plane given by tan α = A 2 / A 1 were equally spaced. In the second set of experiments, A 1 was kept fixed, and only A 2 was adjusted; similarly, in the three-click experiments, only A 3 was adjusted. In the following, the intensity I always refers to the peak amplitude A max of the stimulus pattern, measured in decibel sound pressure level (dB SPL), For each stimulus, the absolute intensity I 70 corresponding to a spike probability of 70% was determined online in the following way. Beginning with a value of 50 dB SPL, the intensity was raised or lowered in steps of 10 dB, depending on whether the previous intensity gave a spike probability lower or higher than 70% from five stimulus repetitions. This was continued until rough upper and lower bounds for I 70 were found. From these, a first estimate of I 70 was obtained by linear interpolation. Seven intensity values in steps of 1 dB from 3 dB below to 3 dB above this first estimate were then repeated 15 times. From the measured spike probabilities, a refined estimate of I 70 was obtained by linear regression. Nine intensities from 4 dB above to 4 dB below this value were repeated 30 times (in some experiments 40 times). The final estimate of I 70 was determined offline from fitting a sigmoidal function of the form with parameters α and β to these nine intensity-probability pairs. This relation between p and I was then inverted to find the intensity and thus the absolute values of the amplitudes that correspond to p = 0.7. Extraction of L (Δ t ) and Q (Δ t ) from iso-response sets The response functions L (Δ t ) and Q (Δ t ) can be obtained independently of each other by combining the results from different measurements within one iso-response set. Here, we derive explicit expressions based on a specific choice of stimuli that are particularly suited for our system. Two measurements are needed to obtain both L (Δ t ) and Q (Δ t ) for given time interval Δ t . Each stimulus consists of two clicks. The first click has a fixed amplitude A 1 ; the amplitude A 2 of the second click at time Δ t later is adjusted so that a predefined spike probability p is reached. For the second measurement, the experiment is then repeated with a “negative” second click, i.e., a click with an air-pressure peak in the opposite direction from the first click. The absolute value of this click amplitude is denoted by à 2 . We thus find the two pairs ( A 1, A 2 ) and ( A 1 , à 2 ) as elements of an iso-response set. Since the spike probability increases with the effective stimulus intensity J, equal spike probability p implies equal J . The two pairs ( A 1, A 2 ) and ( A 1 , à 2 ) therefore correspond to the same value of J . According to the model, equation 1 , the click amplitudes thus satisfy the two equations Setting the two right sides equal to each other, we obtain or The first solution of this mathematical equation, à 2 = − A 2 , does not correspond to a physical situation as both A 2 and à 2 denote absolute values and are therefore positive. The remaining, second solution reads Solving for L (Δ t ), we obtain Substituting L (Δ t ) from equation 10 in equation 5 or equation 6 , we find This yields with c = J / A 1 2 . As we keep A 1 and J constant throughout the experiment, this determines Q (Δ t ) up to the constant c . It can be inferred from an independent measurement with a single click: by setting A 1 = 0 in equation 5 , we see that J corresponds to the square of the single-click amplitude that yields the desired spike probability. Alternatively, c can be estimated from the saturation level of Q (Δ t ) for large Δ t, as was done in the present study. The specific form of the effective stimulus intensity, equation 1 , led to particularly simple expressions for the response functions L (Δ t ) and Q (Δ t ); see equation 10 and equation 12 , respectively. Other nonlinearities may result in more elaborate expressions or implicit equations, but this technical complication does not limit the scope of the presented approach. Data fitting The datasets for L (Δ t ) were fitted with velocity response functions of a damped harmonic oscillator where ω and δ were optimized for minimizing the total squared error. From these, the fundamental frequency f and the decay time constant τ dec were determined as f = ω /( 2π ) and τ dec = 1/ δ . A simpler fit function of the form led to essentially indistinguishable results for f and τ dec . The resonance frequency, which corresponds to the characteristic frequency, f CF , of the tuning curve, and the tuning width, Δ f 3dB , can be predicted from the fitted values of ω and δ according to the theory of harmonic oscillators: The datasets for Q (Δ t ) were fitted with an exponential decay where the parameters a, τ int , and c were adjusted. Here, only data points for Δ t > 150 μs were taken into account, as Q (Δ t ) initially shows a rising phase. The obtained value for c was used to determine the constant J / A 1 2 in equation 12 . For comparing these predicted values with measurements, the minimum and width of the tuning curves (see Figure 6 A) were determined by fitting a quadratic function to the five data points closest to the data point with smallest intensity. Model predictions for three-click stimuli For stimuli consisting of three clicks with amplitudes A 1 , A 2 , and A 3 that are separated by time intervals Δ t 1 and Δ t 2 , respectively (see Figure 7 A), an approximate equation for the effective stimulus intensity J can be derived in the following way: The first click induces a tympanic vibration proportional to A 1 and a membrane potential proportional to A 1 2 . Following the second click, the tympanic deflection has become A 1 · L (Δ t 1 ) and is augmented by A 2 . This yields a membrane potential proportional to ( A 1 · L (Δ t 1 ) + A 2 ) 2 . After the third click, the tympanic deflection has evolved to A 1 · L (Δ t 1 + Δ t 2 ) + A 2 · L (Δ t 2 ) so that the membrane potential is increased by ( A 1 · L (Δ t 1 + Δ t 2 ) + A 2 · L (Δ t 2 ) + A 3 ) 2 . Summing up the different contributions and approximating the influence of the inter-click intervals on the membrane potential by appropriate factors of Q, we find for the effective stimulus intensity The value of J for a predefined spike probability can be measured from a single-click experiment by setting A 1 = A 2 = 0 and tuning A 3 until the desired spike probability is reached. After having measured L (Δ t ) and Q (Δ t ) from two-click experiments, the above equation can be used to predict the amplitude A 3 needed to reach this predefined spike probability for any combination of A 1 , A 2 , Δ t 1 , and Δ t 2 . Supporting Information Protocol S1 General Cascade Model (50 KB PDF). Click here for additional data file. Figure S1 Examples of Click Stimuli The four panels show different examples of stimuli used in our study. Each panel illustrates the computer-generated pulse signal that drives the loud speaker (upper trace) and the resulting air-pressure fluctuations as measured with a high-precision microphone at the site of the animal's ear (lower trace). The computer-generated clicks are triangular with a total width of 20 μs. The stimuli shown are (A) a single click, (B) a double click with a peak-to-peak interval Δ t = 50 μs, (C) a double click with Δ t = 500 μs, and (D) another double click with Δ t = 500 μs whose second click points in the oppositve (“negative”) direction. The measurements of air-pressure fluctuations indicate a slight broadening of the click width and some residual vibrations, but they nevertheless present a good approximation of the sharp original pulses. (10 KB PDF). Click here for additional data file. Figure S2 Simulation and Analysis of the General Cascade Model in Response to Two-Click Stimuli The general cascade model, equation 2 in the main text, was used with filters modeled as l ( t ) = sin(2 πft )exp(− t / τ dec ) and q ( t ) = exp(− t / τ int ). The parameters were taken from the first two cells presented in detail in the main text: f = 14.5 kHz, τ dec = 100 μs, and τ int = 300 μs for Cell 1 (left column) and f = 5.1 kHz, τ dec = 154 μs, and τ int = 590 μs for Cell 2 (right column). (A and B) Responses of tympanic vibration. x ( t ) denotes the signal after application of the linear filter l ( t ), arbitrary units, for positive second click (solid line) and negative second click (dashed line). Inter-click intervals in these two shown examples were Δ t = 80 μs for Cell 1 and Δ t = 130 μs for Cell 2. (C and D) Corresponding responses of J (Δ t ). The second click was tuned so that the maximum of J (Δ t ) was equal for positive and negative second clicks. This required click amplitudes of size 1.92 and −2.49 relative to the first click for Cell 1 and 2.09 and −1.27 for Cell 2. (E–H) Filters L (Δ t ) and Q (Δ t ) extracted according to equation 1 in the main text from tuning the maximum of J (Δ t ) for many different values of Δ t (gray dots). The parameters f, τ dec , and τ int indicated in the plots were obtained by fitting a damped harmonic oscillator and an exponential function to L (Δ t ) and Q (Δ t ), respectively (black lines). The initial part of Q (Δ t ) shows small fluctuations that result from the oscillatory influx of charge following the tympanic vibrations. In (G), a magnified view of the initial section is shown in the inset. (138 KB PDF). Click here for additional data file.

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The association between clinical integration of care and transfer of veterans with acute coronary syndromes from primary care VHA hospitals

Background Few studies report on the effect of organizational factors facilitating transfer between primary and tertiary care hospitals either within an integrated health care system or outside it. In this paper, we report on the relationship between degree of clinical integration of cardiology services and transfer rates of acute coronary syndrome (ACS) patients from primary to tertiary hospitals within and outside the Veterans Health Administration (VHA) system. Methods Prospective cohort study. Transfer rates were obtained for all patients with ACS diagnoses admitted to 12 primary VHA hospitals between 1998 and 1999. Binary variables measuring clinical integration were constructed for each primary VHA hospital reflecting: presence of on-site VHA cardiologist; referral coordinator at the associated tertiary VHA hospital; and/or referral coordinator at the primary VHA hospital. We assessed the association between the integration variables and overall transfer from primary to tertiary hospitals, using random effects logistic regression, controlling for clustering at two levels and adjusting for patient characteristics. Results Three of twelve hospitals had a VHA cardiologist on site, six had a referral coordinator at the tertiary VHA hospital, and four had a referral coordinator at the primary hospital. Presence of a VHA staff cardiologist on site and a referral coordinator at the tertiary VHA hospital decreased the likelihood of any transfer (OR 0.45, 95% CI 0.27–0.77, and 0.46, p = 0.002, CI 0.27–0.78). Conversely, having a referral coordinator at the primary VHA hospital increased the likelihood of transfer (OR 6.28, CI 2.92–13.48). Conclusions Elements of clinical integration are associated with transfer, an important process in the care of ACS patients. In promoting optimal patient care, clinical integration factors should be considered in addition to patient characteristics.

Background Coronary artery disease is the leading cause of death among Americans [ 1 ]. Hospitalization for acute coronary syndromes (ACS), which includes both acute myocardial infarction (AMI) and unstable angina, is common and costly. Many patients admitted with ACS to primary hospitals (i.e. those without on-site cardiology subspecialty services, including cardiac catheterization facilities) are transferred to tertiary hospitals for cardiac catheterization and consideration of coronary revascularization. The coordination and integration between primary and tertiary hospitals has important implications for integrated health care delivery systems. The Veterans Health Administration (VHA) is one of the largest vertically integrated health care delivery systems in the United States [ 2 ]. The VHA is organized in 21 regional networks. Regionalization has been adopted by many integrated health care delivery systems, both to improve quality and to increase efficiency [ 3 - 6 ]. In most VHA regions, a single tertiary hospital is associated with one or more primary hospitals. A particular challenge in the VHA is providing access to sub-specialty cardiology services for patients hospitalized with acute coronary syndromes because primary hospitals are often geographically distant from tertiary hospitals [ 5 ]. Treatment guidelines for acute coronary syndromes [ 7 - 10 ] suggest that some diagnostic tests and therapies can be performed at most primary VHA hospitals, while others, such as cardiac catheterization and coronary revascularization, require transfer to a tertiary hospital. Well-functioning transfer processes are critical to making a policy of regionalization work. In addition, there are strong financial and organizational incentives to provide care within an integrated health care system like VHA rather than referring to non-VHA hospitals, even when this requires transfer to distant tertiary hospitals [ 11 ]. In the VHA, transfers within the system represent cost savings, while transfers out, by and large, represent cost increases. In addition to cost issues, there are also coordination of care concerns that are addressed through within-system transfer, particularly in a system with a common electronic medical record. However, the constraint on within-system transfer is that patients requiring urgent or emergent transfer to receive definitive care should be transferred to the nearest facility with capacity to provide care, even if this requires a transfer out of the system. Issues related to cost differences due to transfer within and outside integrated health care systems are most applicable in the United States, where the multiplicity of payers is a major financial concern; in other countries with integrated national health care, or single payer, systems, these issues are less relevant, although issues of care coordination may still be important. The objective of this study was to evaluate the association between structural components of clinical integration and patient transfer rates from VHA primary hospitals to tertiary hospitals, both within and outside the VHA system for patients with ACS. We hypothesized that primary VHA hospitals with structural components of clinical integration present would have a higher rate of within-system transfer of ACS patients than primary VHA hospitals lacking these components. Methods The VHA Access to Cardiology study was a prospective cohort study of 2,733 patients with a primary discharge diagnosis of either acute myocardial infarction (ICD9-CM 410.xx) or unstable angina (ICD9-CM 411.xx) discharged over a one year period (March 1, 1998 through February 28, 1999) from 24 VHA hospitals in five regions, including Minnesota and the Dakotas, the Southwest, the Rocky Mountains, the Pacific Northwest, and Southern California. Patient demographics, clinical characteristics, and specific processes of care including hospital transfer were obtained as part of the Access to Cardiology Study. All patients admitted to one of the 12 primary VHA hospitals in the study were eligible for this analysis (n = 862 out of the 2,733 in the larger Access to Cardiology study). The remaining 12 VHA Medical Centers were tertiary hospitals with cardiology services and cardiac catheterization laboratories on site. These were not the focus of the analysis reported in this paper. We excluded 107 patients because they were initially admitted to a private hospital and transferred into a primary VHA hospital. In addition, we excluded 3 patients who were transferred from one primary VHA hospital to another. Finally, 27 patients had missing data in the variable indicating prior history of congestive heart failure, which was included in the final analysis. As a result, a total of 725 patients from 12 primary VHA hospitals were included in these analyses. The study protocol was approved by the Human Subjects Committee at the University of Washington, and by Institutional Review Boards and Research and Development Committees at each participating VHA hospital. Transfer rates Patient transfer from a primary VHA hospital to a tertiary hospital (either VHA or private) was the primary outcome for this study. Secondary outcomes included both transfer from a primary VHA hospital to a tertiary VHA hospital, and transfer from a primary VHA hospital to a private (non-VHA) tertiary hospital. Transfers to a tertiary VHA hospital were considered transfers within the system, while transfers to a private hospital were considered transfers outside the system. Transfer data were available for all 725 patients in the study cohort. We constructed two binary variables for the analyses: transfer to any tertiary care hospital (yes/no), and transfer to a tertiary VHA hospital versus transfer to a private (non-VHA) hospital. Clinical integration The key independent variable for this study was clinical integration of cardiac services. We defined clinical integration [ 12 , 13 ] as the extent to which patient care services, in this case cardiology consultation services, are coordinated across the units and hospitals in the VHA providing care to cardiology patients. We measured clinical integration of cardiac services using three binary variables to indicate the presence or absence of these structural elements of clinical integration: a) a VHA staff cardiologist on-site at least episodically at the primary VHA hospital (either through a full or part time VHA staff cardiologist on site, or through periodic visits by a VHA staff cardiologist from the affiliated tertiary VHA hospital); b) a referral coordinator at the tertiary referral VHA hospital; and c) a referral coordinator at the primary VHA hospital. Referral coordinators at primary VHA hospitals are generalists, in that they facilitate referrals, transfers, and sometimes consultations for patients with many different kinds of diseases or health problems. In contrast, at tertiary VHA hospitals, referral coordinators are often associated with particularly sub-specialties, and work closely with these specialty services to provide assistance to referring hospitals and providers in determining whether transfer, referral, or consultation is advisable, and expediting the processes. These were all hospital level variables. We combined the two groups, VHA staff cardiologist on site and periodic visits by a VHA staff cardiologist, for two reasons. First, only one of the 12 primary hospitals in the sample had an on site VHA cardiologist, and the sample size in that group was too small to analyze independently. Second, in our interviews with Chiefs of Cardiology at the tertiary VHA hospitals, there was unanimity in their beliefs that either type of VHA cardiologist being available in a primary hospital produced more appropriate referrals, and improved interactions between providers at the primary hospital and the VHA tertiary cardiology service. The data used to construct these measures came from on-site interviews conducted with Chiefs of Cardiology at each of the tertiary VHA hospitals associated with the primary VHA hospitals included in this study. During on-site interviews, Chiefs of Cardiology were asked to describe all of the primary VHA hospitals that refer ACS patients to them on a regular basis, and to identify the presence or absence of each of the structural elements of clinical integration. Interviews followed a structured protocol, ensuring uniform data collection. In all cases, the Chiefs of Cardiology were able to provide detailed information about the services available at both the tertiary and primary VHA hospitals. We also asked the Chief of Cardiology about the degree of competitiveness for cardiac services in the local markets for each of the primary VHA hospitals. This was an ordinal variable, with three levels: non-competitive; moderately competitive; or highly competitive market. In all cases, the Chief of Cardiology was able to answer the questions about market competition in the primary hospital market without difficulty, indicating considerable awareness of market conditions and the impact these had on their referral base. In addition, we constructed two separate variables to control for patient distance from the primary VHA hospital to which they were initially admitted, and to control for the distance between primary and tertiary VHA hospitals. The patient distance variable was measured as the distance from the patient's home zip code centroid to the primary VHA hospital. The distance between the primary and tertiary referral VHA hospitals was measured in miles using VHA national databases. We tested different specifications of the distance variables, concluding that it was best to enter the distance between primary and tertiary VHA hospital as a continuous variable, whereas it made no difference in the results of the estimation what form we used for patient distance to primary VHA hospital. In the final analyses, it was dichotomized at greater than or equal to 100 miles – approximately two hours driving time. The patient distance variable is measured at the patient level, while the hospital distance variable is measured at the hospital level. We included several measures of patient clinical characteristics, including age 65 or over; prior history of chronic obstructive pulmonary disease, bleeding disorder (such as hemophilia or anticoagulation therapy), smoking, prior percutaneous coronary intervention (PCI), or chronic heart failure; having a "Do Not Resuscitate" order, and several measures of seriousness or urgency of condition during the index admission in the primary VHA hospital: ST segment elevation on electrocardiogram or elevated cardiac enzymes at presentation; and a composite variable indicating the presence of a serious event during admission. Presence of a serious event during admission was a binary variable taking the value "1" if at least one of the following conditions was present: angina persisting more than 24 hours after admission; hypotensive episode; heart failure during admission; cardiac arrest; or positive stress test during admission. All of these variables were abstracted from the medical record. Analyses We explored the bivariate association between clinical integration variables, distance variables, patient characteristic variables, and patient transfer using one-way analysis of variance with Scheffe correction for multiple comparisons. To construct the most parsimonious models using the full set of candidate independent variables (clinical integration variables and patient characteristics), we used backward stepwise logistic regression, beginning with all available patient clinical characteristics that have been shown to be significant in predicting mortality outcomes for ACS patients in prior studies. We eliminated variables from the model if the p-value for the variable was greater than 0.1. A number of the candidate variables, including many of the history and co-morbidity variables, were found to be insignificant, and we created a summary variable described above which included many of the highly significant variables from the index hospital admission (details available from authors). C-statistics for each of the final models ranged from 0.77 to 0.85. We used Stata SE version 8.2 for all analyses. We then investigated the relationship between clinical integration of cardiac services and transfer rates using random effects logistic regression [ 14 ], correcting for cluster sampling by hospital and region and controlling for distance and patient characteristics that reflect cardiac disease severity and therefore may affect the likelihood of transfer. Two models were estimated, one for transfer to any tertiary care hospital, and the second to estimate the conditional probability that the patient was transferred to a VHA tertiary hospital versus transfer to a non-VHA tertiary hospital, given that they were transferred. Random effects logistic regression allowed us to control for the effects of clustering on both the hospital and regional (Veterans Integrated Service Network, or VISN) level. The intra-class correlation of overall transfer with hospital and VISN jointly was 0.12 (p = 0.006), suggesting the need to control clustering at both levels. Results Among the 12 primary VHA hospitals included in the sample, the mean rate of transfer was 42% (319 of 725). Mean rate of transfer to a tertiary VHA hospital was 31% (237 of 725), and to a private hospital was 11% (82 of 725). Most patients were transferred in order to receive cardiac catheterization or coronary revascularization. In addition, 37% of patients were treated in primary VHA hospitals that were over 250 miles from their tertiary referral VHA hospital, and 18% of patients lived over 100 miles from the primary VHA hospital to which they were admitted. Three of the 12 primary care VHA hospitals had a VHA cardiologist available at least episodically on site; six had a referral coordinator at the associated tertiary center; and four had a referral coordinator at the primary VHA hospital. The distribution of these components is shown in Figure 1 . Five of the twelve hospitals had none of the three components of integration. Figure 1 Distribution of integration components across the 12 primary VHA hospitals Unadjusted associations The bivariate associations between the patient characteristic variables, clinical integration variables, and type of transfer are shown in Table 1 . All of the patient characteristics except history of chronic obstructive pulmonary disease were strongly and positively associated with transfer to a tertiary hospital. Distance between primary and tertiary VHA hospital was significantly different between the three groups, with overall transfer being associated with increased distance between the primary and tertiary VHA hospital. The degree of market competition was also significantly associated with transfer, principally to tertiary private hospitals. Each of the three individual components of integration were significantly associated with transfer from primary VHA. Table 1 Patient and facility characteristics by transfer type Variable Overall for study sample N = 755 Not transferred N = 436 Transferred to tertiary VHA hospital N = 237 Transferred to tertiary private hospital N = 82 p-value* Patient age 65 and over 58.0% 63.1% 52.3% 48.8% 0.005 Prior medical history Chronic obstructive pulmonary disease 37.1% 40.6% 31.9% 33.7% 0.067 Bleeding disorder 3.6% 2.1% 5.5% 6.2% 0.035 Smoker 31.6% 26.8% 41.8% 27.2% <0.001 Prior percutaneous coronary intervention 15.2% 11.7% 21.5% 15.8% 0.003 Chronic heart failure 23.0% 28.9% 13.1% 18.8% <0.001 Course of index hospital admission ST segment elevation on EKG 17.8% 12.8% 19.0% 39.0% <0.001 Cardiac enzymes abnormal on presentation 52.5% 52.0% 46.4% 71.3% <0.001 Do not resuscitate during hospitalization 5.3% 6.7% 2.1% 5.2% 0.039 In-hospital event** 47.3% 37.8% 62.9% 52.4% <0.001 Distance, market and integration variables Distance from patient home zip code centroid to hospital >100 miles 18.1% 15.6% 21.1% 22.0% 0.128 Distance from primary VHA to tertiary VHA hospital in miles 281 270 285 326 0.045 Degree of market competition (1 = not competitive; 3 = highly competitive) 1.74 1.82 1.57 1.79 <0.001 VHA cardiologist on site 30.6% 29.8% 36.3% 19.5% 0.015 Tertiary VHA hospital has referral coordinator 54.7% 56.4% 60.8% 30.5% <0.001 Primary VHA hospital has referral coordinator 33.0% 28.9% 43.9% 24.4% <0.001 * p-value obtained from ANOVA testing difference between means for patients not transferred, transferred to VHA tertiary hospital, or transferred to non-VHA tertiary hospital for continuous variables, chi-square test of inference for categorical variables ** Presence of at least one of the following adverse events during admission: angina persisting more than 24 hours after admission; a hypotensive episode; an episode of heart failure; cardiac arrest; or positive stress test during admission Risk-adjusted association: transfer to any tertiary care hospital Results of the random effects logistic regressions for transfer to any tertiary care hospital are shown in Table 2 . Patient factors increasing the likelihood of transfer to a tertiary hospital included being a smoker; history of chronic heart failure; ST-segment elevation on presenting electrocardiogram; in-hospital events (presence of at least one of the following events during admission: angina persisting more than 24 hours after admission; a hypotensive episode; an episode of heart failure; cardiac arrest; or positive stress test during admission); and distance from patient home to hospital more than 100 miles. Table 2 Results of random effects logistic regression of transfer to any tertiary care hospital Variable Odds ratio p-value Lower limit 95% CI Upper limit 95% CI Patient age 65 and over 0.69 0.06 0.48 1.01 Chronic obstructive pulmonary disease 0.48 <0.001 0.31 0.74 Bleeding disorder 0.68 0.04 0.47 0.98 Smoker 3.28 0.01 1.32 8.12 Prior percutaneous coronary intervention 1.30 0.18 0.89 1.91 Chronic heart failure 2.10 <0.001 1.33 3.32 ST segment elevation on presenting electrocardiogram 2.07 <0.001 1.32 3.26 Cardiac enzymes abnormal on presentation 0.92 0.65 0.64 1.31 Do not resuscitate during hospitalization 0.29 <0.001 0.12 0.65 In-hospital event* 3.14 <0.001 2.21 4.46 Distance from patient home zip code centroid to hospital >100 miles 1.71 0.02 1.08 2.70 Distance from primary VHA to tertiary VHA hospital in miles 0.998 0.03 0.997 0.999 Degree of market competition (1 = not competitive; 3 = highly competitive) 0.55 <0.001 0.41 0.73 VHA cardiologist on site 0.48 <0.001 0.29 0.79 Tertiary VHA hospital has referral coordinator 0.39 <0.001 0.23 0.69 Primary VHA hospital has referral coordinator 6.53 <0.001 3.29 12.98 * Presence of at least one of the following adverse events during admission: angina persisting more than 24 hours after admission; a hypotensive episode; an episode of heart failure; cardiac arrest; or positive stress test during admission Patient factors that decreased the likelihood of transfer to any tertiary hospital included history of chronic obstructive pulmonary disease, or bleeding disorder; and having a do not resuscitate (DNR) order during the hospital admission. In addition, the further the distance between primary and tertiary VHA, the less likely patients were to be transferred at all, and the more competitive the market for cardiac care, the less likely that the patient was transferred to a tertiary care hospital. All three components of integration were significantly associated with transfer to tertiary care, although in different directions. After adjustment for patient and other characteristics, the presence of a VHA staff cardiologist and having a referral coordinator at the tertiary VHA hospital decreased the likelihood of transfer to any tertiary care hospital. In contrast, the presence of a referral coordinator at the primary VHA hospital increased the probability of transfer to a tertiary hospital. Risk-adjusted association: transfer to tertiary VHA hospital vs. tertiary non-VHA hospital The results of this analysis are shown in Table 3 . Patient factors associated with transfer to VHA rather than private tertiary hospital included prior history of percutaneous coronary intervention, and history of chronic heart failure. Patient factors associated with transfer to private rather than VHA tertiary hospital included elevated ST-segment on presenting electrocardiogram, abnormal cardiac enzymes on presentation, and presence of a do not resuscitate order during the hospitalization. Table 3 Results of conditional random effects logistic regression of transfer toVHA tertiary care compared to private tertiary care hospital Variable Odds ratio p-value Lower limit 95% CI Upper limit 95% CI Patient age 65 and over 1.42 0.29 0.75 2.71 Chronic obstructive pulmonary disease 0.56 0.15 0.25 1.23 Bleeding disorder 1.10 0.75 0.60 2.03 Smoker 1.14 0.84 0.34 3.77 Prior percutaneous coronary intervention 3.67 <0.001 1.91 7.04 Chronic heart failure 2.05 <0.001 1.43 2.95 ST segment elevation on presenting electrocardiogram 0.27 <0.001 0.14 0.51 Cardiac enzymes abnormal on presentation 0.30 0.02 0.11 0.81 Do not resuscitate during hospitalization 0.14 <0.001 0.04 0.54 In-hospital event* 1.47 0.31 0.70 3.08 Distance from patient home zip code centroid to hospital >100 miles 2.10 0.10 0.86 5.10 Distance from primary VHA to tertiary VHA hospital in miles 1.00 0.35 0.99 1.00 Degree of market competition (1 = not competitive; 3 = highly competitive) 0.19 0.06 0.03 1.05 VHA cardiologist on site 1.17 0.85 0.23 6.06 Tertiary VHA hospital has referral coordinator 20.62 <0.001 4.50 94.47 Primary VHA hospital has referral coordinator 1.38 0.69 0.27 6.99 * Presence of at least one of the following adverse events during admission: angina persisting more than 24 hours after admission; a hypotensive episode; an episode of heart failure; cardiac arrest; or positive stress test during admission The degree of market competition was not significantly associated with transfer to VHA versus private tertiary hospital. Neither of the distance variables were associated with transfer either to VHA or non-VHA tertiary hospitals. Furthermore, only one of the individual integration variables entered separately were significantly associated with likelihood of transfer to tertiary VHA versus private hospital, and although the parameter estimate for the variable indicating presence of a referral coordinator at the tertiary hospital was large and significant, it was very imprecise (i.e. large standard error). This is probably due to the relatively small number of patients included in the estimation (N = 319) and uneven splits among hospitals, clustered by VISN. Discussion The goal of this study was to investigate the association between measures of clinical integration of care and transfer of patients with acute coronary syndromes in the VHA. In particular, we evaluated whether structural components of clinical integration, such as the presence of referral coordinators and on-site cardiologists, were associated with patient transfer within and/or outside of the VHA healthcare system. In multivariate analysis, the presence of referral coordinators located at primary care VHA hospitals increased the overall likelihood of transfer of ACS patients. In contrast, having a VHA staff cardiologist available or a referral coordinator at a tertiary VHA hospital significantly decreased the likelihood of any transfer to a tertiary care hospital. Finally, we found that only one of the three integration components, presence of a referral coordinator at the tertiary VHA hospital, was significantly associated with transfer to a tertiary VHA hospital compared to a non-VHA tertiary hospital. Our finding that referral coordinators at primary care hospitals increase the likelihood of transfer to tertiary care hospitals is consistent with prior studies demonstrating that referral coordinators increase the ease of referral and frequency of transfer [ 5 , 15 - 19 ]. Presence of a referral coordinator at the primary hospital means that a knowledgeable staff person, not a physician but usually a clinician such as a nurse, is available to coordinate and facilitate what can otherwise be a very cumbersome process of referral and transfer. This individual usually locates and communicates with tertiary care providers and facilitates paperwork and other processes required for patient transfer. However, our finding that the presence of a referral coordinator at a tertiary VHA hospital was negatively associated with transfer appears contradictory. It is possible that referral coordinators at the tertiary centers may facilitate consultation, which may, at least for lower risk patients, appropriately reduce the need for transfer. However, it is of some concern that these referral coordinators may be serving in a gatekeeper role with regard to transfer decisions. Future research should focus on the role and decision-making associated with these referral coordinators. Of note, when transfer did occur, the presence of a referral coordinator at the tertiary VHA hospital was positively associated with transfer to VHA facilities rather than non-VHA facilities. This suggests that referral coordinators may function differently with different kinds of patients, decreasing overall transfer rates but facilitating within-system transfer when transfer occurred. In general, we found that transfers to tertiary care were largely associated with patient characteristics appropriate to transfer: sicker and more urgent patients, except for those for whom more intensive care may not be indicated (e.g. DNR status), were significantly more likely to be transferred. In particular, patients with ST-segment elevation on their presenting electrocardiogram and abnormal cardiac enzymes were significantly more likely to be transferred, most likely for coronary revascularization. These patients are most likely to benefit from revascularization [ 5 , 9 ], and their higher probability of transfer suggests that appropriate triage and risk stratification took place in the primary VHA hospitals providing their care. In addition, we found that these patients were more likely to be transferred to non-VHA tertiary hospitals, presumably because these hospitals were closer to the primary VHA hospital than the affiliated tertiary VHA hospital, indicating appropriate out-of-system transfer for the most urgent patients who could benefit from rapid access to tertiary care. The finding that DNR status appears to be associated with transfer to private tertiary rather than VHA tertiary hospital may be due to small cell size, combined with other characteristics of the small number of patients with that status among those who were transferred at all (9 of 319). Distance between the patient's home and primary VHA hospital was significantly associated with increased likelihood of subsequent transfer to a tertiary care hospital. This may indicate that patients who live further from the hospital take longer to present and are therefore sicker on arrival, leading to the requirement for higher levels of care. Also of interest, distance between primary and tertiary VHA hospitals was significantly associated with a decreased likelihood of transfer, indicating that in situations where primary and tertiary VHA hospitals are further apart, primary VHA hospitals may elect to keep more ACS patients rather than transfer them at all. Future research is needed on the appropriateness of transfer of ACS patients, as it is not clear that variation in transfer based on distance between hospitals represents appropriate variation in care. The finding that cardiologist availability at the primary VHA hospitals was associated with less transfer to tertiary care hospitals may reflect that local or distant cardiology consultation was sufficient in some cases (e.g. lower risk patients) to avoid transfer. Similarly, the availability of a transfer coordinator at the tertiary VHA hospital may have provided an avenue for consultation and avoidance of transfer in some cases. Future studies are needed to define the mechanisms of association between reduced transfers and both on-site cardiology availability and tertiary hospital transfer coordinators. The findings of this study, that referral coordination is associated with transfer from primary to tertiary hospitals, but may operate differently for different types of patients, and may have one mechanism of operation within a health care system and another outside that system, have potential application outside VHA. Previous studies [ 20 ] have found that patients' access to needed services, such as revascularization after acute myocardial infarction, has a significant effect on mortality outcomes. Services such as referral coordination, which increase the likelihood that a patient will be transferred, can reduce the negative impact of receiving initial care in a hospital without specialized tertiary services, such as cardiac catheterization. These findings are potentially relevant in all health care systems where hospitals have different levels of service. Even though they are based on a relatively small patient sample size, the implications of the findings – that referral coordinators at primary hospitals increase the probability of transfer, with the link to better outcomes at tertiary centers [ 21 ] with a full range of treatment options – should spark discussion in a health care system such as VHA about recommending use of referral coordinators in primary hospitals. Limitations First, we were not able to conduct full-scale validation and reliability testing of the clinical integration measures, which would have required a larger sample of hospitals participating in the study to conduct split-sample validation. Second, we used structural, rather than process, elements of integration in this analysis. We focus on structural elements both because they are relatively easier to measure (present or not), and because in Donabedian's widely accepted model of quality in health care, structure precedes process and outcome [ 22 , 23 ]. Third, clinical integration is a complex multi-faceted construct which we captured in a relatively simplistic way. However, we wanted to see if measures that would be straightforward to implement in a health care system like the VHA, such as referral coordinators, had an impact on this key process of care. We measured other components of integration, including communication methods, provider satisfaction with communication methods, and overall perception of how well referral and consultation worked in providing care to ACS patients. Individually, these factors were not as strongly linked to the transfer process as the three structural components we present in this analysis. Fourth, because transfer is closely related to patient outcomes, especially for ACS patients [ 21 ], careful modeling of the relationship between transfer and mortality and morbidity outcomes is essential. We plan to conduct future analyses on the relationships between patient characteristics, transfer, and mortality and morbidity outcomes. In addition, it is important to note that most veterans over the age of 65 are dually eligible for Medicare as well as VHA benefits, and previous analyses have shown that a majority of veterans with acute myocardial infarction, even among those who use VHA hospitals, receive care for AMI in private hospitals [ 24 , 25 ]. This study was designed only to assess transfer of veterans who went to primary VHA hospitals for their ACS care. Conclusions We found that referral coordinators located at primary care VHA hospitals increase the overall likelihood of transfer of ACS patients. Referral coordinators at tertiary VHA hospitals and the presence of on-site cardiologists appeared to decrease the likelihood of transfer. Only one component of integration, presence of a referral coordinator at the tertiary hospital, was associated with within-system compared to out-of-system transfer. These findings have significant potential implications for the VHA. One of the goals of an integrated health care system is to maintain optimal coordination between its component parts [ 12 ]. This study demonstrates that simple structural components of care, such as a referral coordinator at either a primary or tertiary care hospital, can have an impact on a key process of care above and beyond patient characteristics. Competing interests The author(s) declare that they have no competing interests. Authors' contributions AES participated in the design and conduct of the study, conducted the analyses and wrote the manuscript. SLP participated in conducting the project, and assisted in writing the manuscript. DJM participated in writing the manuscript. NRE participated in the design and conduct of the study. NDS participated in writing the manuscript. JSR participated in the statistical analyses and co-wrote the manuscript. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

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543459

Relationship among Dexamethasone Suppression Test, personality disorders and stressful life events in clinical subtypes of major depression: An exploratory study

Background The present study aimed to investigate the relationship between dexamethasone suppression test, personality disorder, stressful life events and depression. Material Fifty patients (15 males and 35 females) aged 41.0 ± 11.4 years, suffering from Major Depression according to DSM-IV criteria entered the study. Method Diagnosis was obtained with the aid of the SCAN v 2.0 and the IPDE. Psychometric assessment included the HDRS, HAS, the Newcastle Scale (version 1965 and 1971), the Diagnostic Melancholia Scale, the Personality Deviance Scale and the GAF scale. The 1 mg DST was used. Statistical Analysis Included MANOVA, ANOVA with LSD post hoc test and chi-square test. Results Sixteen (32%) patients were non-suppressors. Eight patients without Personality Disorder (PD) (23.5%), and 5 of those with PD of cluster B (50%) were non-suppressors. Atypical patients were the subtype with the highest rate of non-suppression (42.85%). No difference between suppressors and non-suppressors was detected in any of the scales. Discussion The results of the current study suggest that pathological DST is not a core feature of major depression. They also suggest that there are more than one subtypes of depression, concerning the response to stress. It seems that the majority of depressed patients (50%) does not experience high levels of stress either in terms of self reported experience or neuroendocrine function. The rest of patients however, either experience high levels of stress, or manifest its somatic analogue (DST non-suppression) or have a very low threshold of stress tolerance, which makes them to behave in a hostile way.

Background Life events and environmental stressful factors may relate to the development of depression [ 1 - 4 ]. However, biological theories suggest that the cause of depression rely on a biochemical disturbance of the functioning of the central nervous system (CNS). The Dexamethasone Suppression Test (DST) [ 5 ] is the most known and worldwide used biological marker, its results suggest that a disorder of the HPA axis is present in at least some depressed patients [ 6 ]. DST non-suppression is of unknown aetiology, and as a test is not specific to any disease. Rather it constitutes an endocrin expression of stress. Basically, DST is reported to assess norepinephrine function. Topographically, it assesses the function of the hypothalamus and indirectly of the structures, which project to it. However, it is also supposed to be the result of an increased serotonin (5-HT) or Ach activity, or of a disturbance of the feedback to the hippocampus [ 7 ] and the hypothalamus. A debate still holds, whether some forms of depression are characterized by hypercortisolaimia or early escape from HPA tests. Possibly, DST non-suppression and hypercortisolemia are two different things [ 8 ]. The present study aimed to investigate the relationship between dexamethasone suppression test, personality disorder (PD), stressful life events and clinical manifestations of major depression. The hypothesis to test was that subtypes of depression could be identified on the basis of the presence of personality disorder (which constitutes an abnormal interpretation and response to environmental stimuli), the presence of abnormal DST results and/or hypercortisolemia (which both constitute an idiosyncratic neuroendocrine response to stress) and the presence or not of stressful life events (which trigger the above behavioral and neuroendocrine responses). The presence or not of Personality Disorder, and the response to the DST are both characteristics of the patient. Life events reflect the impact of the environment on the patient. So, life events provoke responses from the side of the patient, which are largely determined by Personality and DST response. Thus, four groups of patients can be identified and studied, according to the combination of the co-existence of DST non-suppression and personality disorder. Material Fifty (50) major depressive patients (15 males and 35 females) aged 41.0 ± 11.4 (range 21–60) years [ 9 , 10 ], took part in the study. All provided written informed consent. Fourteen of them fulfilled criteria for atypical features, 16 for melancholic features (according to DSM-IV) and 32 for somatic syndrome (according to ICD-10). Nine patients did not fulfil criteria for any specific syndrome according either classification system. Patients were in- or outpatients of the 3 rd department of psychiatry, Aristotle University of Thessaloniki, Greece. They constituted consecutive cases that fulfilled the inclusion criteria and no systemic bias exists. The SCAN v 2.0 [ 11 ] was used for the diagnosis of depression and its subtypes and the IPDE [ 12 - 14 ] was used for the diagnosis of personality disorders. Seventeen patients (34%) suffered from a personality disorder (PD). Ten of them (20%) had a cluster B PD. Concerning depressive subtypes, 5 (out of 16) melancholics (26.32%), 7 (out of 14) atypicals (50%), 9 (out of 32) patients with somatic syndrome (28.13%), and 3 (out of 9) 'undifferentiated' patients (33.33%), fulfilled criteria for PD (note: patients with PD are not 5 + 7 + 9 + 3 = 24, but only 17 as mentioned above, because there is ovelapping between depressive syndromes). No patient suffered from a paranoid, schizotypal, antisocial, dissocial, narcissistic, and avoidant PD, although individual criteria were met. No criteria belonging to the schizotypal or antisocial PDs were met. No patient fulfilled criteria for catatonic or psychotic features or for seasonal affective disorder. No patient fulfilled criteria for another DSM-IV axis-I disorder, excepting generalized anxiety disorder (N = 10) and panic disorder (N = 7). Another 5 patients had both generalized anxiety disorder and panic disorder (totally 22 patients that is 44% had some anxiety disorder). The present study did not include a normal controls group, since the aim of the study was to compare depressive subtypes between each other. Method Laboratory Testing included blood and biochemical testing, test for pregnancy, T3, T4, TSH, B 12 and folic acid. The Psychometric Assessment included the Hamilton Depression Rating Scale (HDRS), the Hamilton Anxiety Scale (HAS), the 1965 and 1971 Newcastle Depression Diagnostic Scale (1965 and 1971-NDDS) and the Diagnostic Melancholia Scale (DMS) [ 15 ] and the General Assessment of Functioning Scale (GAF) [ 16 ]. An attempt was made to assess the direction of aggression of the depressed patients, with the use of the Personality Deviance Scale (PDS) [ 17 ]. This was done mainly because the direction of aggression is considered to be a core feature for the etiopathogenesis of depression according to psychodynamic theories, but also is related to personality traits. The PDS consists from the following subscales: a. Extrapunitive Scale (ES) which consists of 1. HT: Hostile Thoughts and 2. DO: Denigratory Attitudes Toward other People. All these scales and subscales are scored in such a way that high scores denote lack of the characteristic. b. Intropunitive Scale (IS), which consists of 1. LSC: Lack of Self-Confidence and DEP: Overdependency on Others. All these scales and subscales are scored in such a way that high scores denote presence of the characteristic. c. Dominance Scale (DS) which consists of 1. MIN: Domineering Social Attitude and 2. HA: Uninhibited Hostile Acts. The MIN is scored in such a way that high scores denotes presence of the characteristic, while HA has opposite properties. Data concerning personal and family history and stressful life events a. age of onset b. presence of a recent suicide attempt c. history of such attempts d. The questionnaire of Holmes [ 18 ] was used to search for stressful life events during the last 6 months before the onset of the symptomatology. The 1 mg Dexamethasone Suppression Test (DST) protocol demands the administration of 1 mg dexamethasone per os at 23.00 of the first day, and determination of cortisol serum levels simultaneously and the next day at 16.00 and 23.00. Cortisol levels expressed in μg/dl were measured with Luminance Immunoassay (intra-essay reliability: 4.9%; inter-essay: 7.5%). Non-suppression cut-off level: 5 μg/dl. Statistical Analysis Multiple Analysis of Variance (MANOVA) was performed with DST (suppression vs. non suppression) and Personality Disorder (present vs. absent) as factors. The dependent variables list included: Age, Age of Onset, Number of previous episodes, Number of DSM-IV Criteria, Number of atypical features, Number of melancholic features, GAF, NDDS 1965, NDDS 1971, Endogenous axis of DMS, Reactive axis of DMS, Number of stressful life events, HDRS-17, HDRS-21, HDRS Depressive index, HDRS Anxiety index, HDRS Sleep index, HDRS non-specific index, HAS, HAS Somatic subscale, HAS Psychic subscale, PDS-Hostile Thoughts Scale, PDS-Denigratory Attitude Scale, PDS-Extrapunitive Scale, PDS-Low Self Confidence Scale, PDS-Overdependency by others Scale, PDS-Intropunitive Scale, PDS-Domineering Social Attitude Scale, PDS-Uninhibited Hostile Acts Scale and PDS-Dominance Scale. Afterwards, Analysis of Variance (ANOVA) with Least Significance Difference (LSD) test as post-hoc test was performed. Finally, Chi-square test was performed. PD and DST were independently placed in cross-tabulation with the presence or absence of Recent Suicide Attempt, History of Suicide Attempt, Generalized Anxiety or Panic Disorder, Melancholic Features, Atypical Features, Somatic Syndrome, 'Undifferentiated' symptomatology, Full and sustained remission, With Relapsing circumscribed episodes, Chronic Depression without full remission, Presence of Stressful life events, Family history of any mental disorder, Family history of depression in 1 st degree relatives, and Family history of depression in 2 nd degree relatives. Results Women were twice as many as men (70% versus 30%), which is not uncommon [ 19 ] and reflects the higher prevalence of depression observed in women. Sixteen out of 50 depressed patients (32%) were DST non-suppressors (NS). Eight out of 17 (47.05%) depressed patients with PD were also NS. When the patients with a coexistent personality disorder (PD) were excluded, then 8 out of 33 (24.24%) patients left, were NS. When only cluster b PDs were excluded, the respected percentage of NS climbs to 27.5% (11 out of 40). Fifty percent of Cluster b PD patients were NS (5 S and 5 NS). Six out of 14 (42.85%) atypical patients were NS, and this percentage makes this subtype the one with the highest NS percentage. No one of Chi-square tests revealed any significant findings (at p > 0.01). MANOVA results were significant both for Personality Disorder (p < 0.001) and for DST (P < 0.001) (table 1 ). Table 1 2-way MANOVA results. Both Personality disorders and DST results and their interaction produce significant results. Wilks' Lambda Rao's R df 1 df 2 p-level Factors : 1-Personality Disorder (present vs. absent) and 2-DST results (suppressors vs. non-suppressors) 1 0.02 18.26 30 12 0.000 2 0.02 20.99 30 12 0.000 12 0.01 28.42 30 12 0.000 ANOVA testing, separately for each dependent variable, revealed significant findings concerning the number of episodes, and HT, DO and HA subscales of the PDS. When PD was used as the sole factor variable, significant findings were found concerning the endogenous axis of DMS and the HDRS depressive index. The interaction of PD and DST produced significant findings concerning age, age of onset, number of atypical features, number of stressful life events, and the DO subscale of the PDS (table 2 ). Post-hoc comparisons for DST showed that NS were more endogenous (1971-NDDS and DMS endogenous axis) but with lower HDRS depressive index (p < 0.05). Post-hoc comparisons for PD characteristics showed that patients without PD had more previous episodes and less hostile thoughts (HT) and less uninhibited hostile acts (HA) (p < 0.05). The post-hoc results for the groups defined by the interaction of PD with DST are shown in table 3 . A graphical representation of these results is shown in figures 1 and 2 . Table 2 ANOVA results for each dependent variable separately (only significant results are shown. df Effect MS Effect df Error MS Error F p-level Factors : 1-Personality Disorder (present vs. absent) and 2-DST results (suppressors vs. non-suppressors) Dependent variable : age 1 1 93.29 46.00 103.25 0.90 0.347 2 1 80.23 46.00 103.25 0.78 0.383 12 1 935.13 46.00 103.25 9.06 0.004 Dependent variable : endogenous axis of DMS 1 1 9.08 46.00 8.26 1.10 0.300 2 1 78.71 46.00 8.26 9.53 0.003 12 1 21.10 46.00 8.26 2.55 0.117 Dependent variable : age of onset 1 1 71.51 46.00 117.92 0.61 0.440 2 1 82.59 46.00 117.92 0.70 0.407 12 1 750.95 46.00 117.92 6.37 0.015 Dependent variable : number of episodes 1 1 17.46 46.00 2.11 8.28 0.006 2 1 0.48 46.00 2.11 0.23 0.637 12 1 0.31 46.00 2.11 0.15 0.703 Dependent variable : number of atypical features 1 1 0.81 46.00 0.75 1.09 0.302 2 1 0.59 46.00 0.75 0.79 0.377 12 1 4.35 46.00 0.75 5.82 0.020 Dependent variable : number of stressful life events 1 1 10.45 46.00 3.27 3.20 0.080 2 1 4.87 46.00 3.27 1.49 0.229 12 1 19.51 46.00 3.27 5.97 0.018 Dependent variable : HDRS Depressive Index 1 1 1.47 46.00 7.04 0.21 0.650 2 1 44.23 46.00 7.04 6.29 0.016 12 1 4.01 46.00 7.04 0.57 0.454 Dependent variable : PDS HT subscale 1 1 76.28 41.00 9.74 7.83 0.008 2 1 4.23 41.00 9.74 0.43 0.514 12 1 10.51 41.00 9.74 1.08 0.305 Dependent variable : PDS DO subscale 1 1 44.95 41.00 10.11 4.44 0.041 2 1 10.27 41.00 10.11 1.02 0.319 12 1 40.50 41.00 10.11 4.01 0.052 Dependent variable : PDS HA subscale 1 1 97.48 41.00 13.12 7.43 0.009 2 1 7.91 41.00 13.12 0.60 0.442 12 1 30.77 41.00 13.12 2.35 0.133 Table 3 Post-hoc comparison between the four diagnostic groups determined by DST results and the presence of personality disorder concerning the continuous variables (Least Significance Difference-LSD Test). Group A Group B Group C Group D N = 25 (50%) N = 8 (16%) N = 9 (18%) N = 8 (16%) p p p p p p Mean SD Mean SD Mean SD Mean SD A/B A/C A/D B/C B/D C/D Age 44.90 9.55 34.00 10.89 33.78 8.96 40.57 11.63 0.005 0.002 0.168 0.964 0.241 0.173 Age of Onset 33.33 11.24 29.00 10.74 23.44 7.13 35.00 13.14 0.217 0.009 0.967 0.223 0.313 0.028 Number of Episodes 1.52 1.89 1.88 1.55 0.33 0.71 0.43 0.53 0.575 0.068 0.092 0.017 0.021 0.893 Number of atypical features 0.71 0.85 1.63 1.06 1.67 1.00 1.14 0.38 0.019 0.010 0.102 0.935 0.375 0.298 DMS Endogenous axis 4.33 2.29 5.88 1.89 2.11 2.52 6.57 4.28 0.217 0.032 0.155 0.004 0.754 0.018 Number of Life Events reported 2.05 0.97 2.50 2.39 4.22 2.77 2.14 1.77 0.260 0.001 0.529 0.193 0.720 0.082 HDRS depressed index 11.43 2.38 8.50 2.14 10.22 3.87 8.86 2.79 0.005 0.350 0.014 0.282 0.837 0.378 HT 19.24 2.36 19.63 2.56 17.44 3.88 15.71 4.50 0.703 0.129 0.012 0.197 0.045 0.422 DO 13.00 3.16 9.88 3.44 13.11 2.57 14.14 3.63 0.028 0.927 0.431 0.043 0.036 0.515 HA 18.86 3.61 19.75 3.28 17.44 4.90 14.71 1.25 0.548 0.385 0.007 0.279 0.002 0.175 DST baseline cortisol value (day 1, 23:00) 3.85 2.79 7.71 10.28 3.79 1.71 5.43 4.37 0.123 0.724 0.568 0.275 0.491 0.474 DST cortisol level at day 2, 16:00 1.40 1.13 6.81 7.91 1.34 0.98 4.84 5.32 0.002 0.973 0.001 0.057 0.584 0.047 DST cortisol level at day 2, 23:00 1.25 1.45 8.04 5.19 1.36 0.71 5.13 1.40 0.000 0.769 0.000 0.002 0.212 0.000 Group A: DST suppressors, no PD Group B: DST non-suppressors, no PD Group C: DST suppressors, with PD Group D : DST non-suppressors, with PD Figure 1 Histogram of the Distribution of Frequencies of Depressive Subtypes in the Four Groups Figure 2 Characteristics of the four groups (white arrows in dark background indicate that the characteristic takes its largest or lower value in the respective group in comparison to all 4. DST suppressors without PD were older, with more severe depressed mood and less atypical features (50% of patients, figure 2 , group A). DST non-suppressors without PD were hypercortisolemic, with less severe depressed mood and denigratory attitude towards others (16% of patients, figure 2 , group B). DST suppressors with PD were younger, with younger age of onset, more atypical features and less endogeneity and more stressful life events (18% of patients, figure 2 , group C). DST non-suppressors with PD had older age of onset, high endogeneity and high levels of expressed hostility (16% of patients, figure 2 , group D). Discussion The current study reports that personality disorders (PD) in depressed patients is 2.5–3 times higher in comparison to the general population. Half (47.05%) of these PD patients were also DST non-suppressors (NS). Atypical patients was the depressive subtype with the highest frequency of both personality psychopathology and DST NS. Figure 2 represents a graphical image of the intercorrelations between personality disorder, DST results and clinical manifestations. It seems that there is a circular relationship between PD, DST, age at interview, age of onset, number of episodes, reactivity to environment, hostility and depressed mood. DST results seem to be a severity marker rather than directly related to symptomatology. In patients without PD, DST NS (group B in figure 2 ) may relate to milder depressed mood, higher denigratory attitude and hostility, higher number of previous episodes and hypercortisolemia. In patients with PD, non suppression (group D in figure 2 ) was related to 'endogenous quality' of depression, and higher levels of hostility. These patients (group B) are highly hostile and perform uninhibited hostile acts, however simultaneously have lower denigratory attitude and hostile thoughts (possibly the hostility is impulsive) and older age of onset. Half of depressed patients belonged to the A group (suppressors without PD), and were characterized by the absence of atypical features. One could say that they represent a more 'formal' group of depressed patients. The rest of patients were equally distributed in the three groups (B, C and D). Groups B and C may represent two distinct types of vulnerability to stress (hypercortisolemia, DST non suppression and PD), while group D seems to represent a more severe form of depression, with an 'autonomous' hostility independent from the environment. This severe type could be considered to be the product of the accumulation of both vulnerabilities that characterize groups B and C, with the addition of a very low threshold for the tolerance of stress. Nearly 4–10% of normal persons are reported to be DST-NS. The reason for this is unknown, however it has been suggested that it is due to an underlying mood disorder or family history of affective disorder. Another explanation suggests that DST reflects in fact the degree of psychological pressure or discomfort of the subject and not a specific vulnerability or characteristic of depression. It seems that non-suppression is gradually increasing along a continuum, which has mourning outpatients on the one pole (13% NS) and severe psychotic melancholic inpatients with psychotic features and suicidal ideation on the opposite one (64% NS) [ 20 ]. In this frame, the percentage of non-suppression reported in the current study (32%) is not in contrast with the international literature, since most of patients were out-patients and 16 of them (32%) were melancholics. An important finding is the 42.85% rate of non-suppression in atypical patients. This is reported for the first time in the international literature. DST NS and hypercortisolemia may constitute two separate entities. For example, a patient may have baseline cortisol equal to 6 μg/dl, second cortisol value equal to 2.5 μg/dl and third cortisol value equal to 5.5 μg/dl and thus is classified as NS, but is not hypercorisolemic. On the contrary, a patient with baseline cortisol value equal to 10 μg/dl, second value equal to 4 μg/dl and third also equal to 4 μg/dl, is classified as NS, but is hypercorisolaimic. Kirschbaum et al [ 21 ] reported that it is possible, some normal control subjects do not manifest the hypercorisolaimic response to stressful life events when these events are repeated (habituation). They also divided responses in high and low-cortisol responses. They related the first group with low self-confidence, increased depressed mood and higher number of symptoms, and the second group with lower extraversion. Joyce et al [ 22 ] suggested that the hypercortisolaimic response is related to a tendency for dependence and extravagance. These are generally in accord with the findings of the present study. In contrast to what is widely accepted, NS is appeared to be closer to the atypical subtype. There are no direct reports in the international literature on this matter. However, the results of the study of Kocsis et al [ 23 ], in essence are in accord with the current study. Rothschild et al [ 24 ] related DST NS with increased dopamine (DA) activity. Atypical patients, on the other hand, when compared with melancholics, reported more stressful life events, relatively higher levels of anxiety and shorter brain potentials [ 25 ]. While it is not possible to interpret what is the cause and what is the effect, it is interesting that there are papers in the international literature suggesting that conditions of internal conflict increase DA activity and lead to the appearance of displacement activities, which in turn serve the lowering of the level of arousal and stabilize the system [ 26 ]. Increased appetite, food intake and weight gain (atypical features) could be attributed to such a displacement activity. From the opposite point of view, the exhaustion of DA storage is reported to increase vulnerability to stress, because the already hyperfunctioning neurons (DST non-suppression) fail to respond properly [ 27 ]. According to Tazi et al [ 26 ], behavioral analogues of the defensive mechanism of displacement seem to suppress this procedure and in this way contribute to the better copying with stressful situations. Conclusion Although the study sample of the current study is relatively small, the results suggest that there are more than one subtypes of depression, concerning the response to stress. The majority of depressed patients (50%) seems not to experience high levels of stress both in terms of self reported experience and neuroendocrine function. The rest of patients however, experience high levels of stress, either internally or have the somatic analogue of it (DST non-suppression) or have a very low threshold of stress tolerance, which makes them to behave in a hostile way. Competing interests The authors declare that they have no competing interests.

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509236

Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays

Background Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The default method in GeneChip expression microarray uses a constant factor, the scaling factor ( SF ), for every gene on an array. The SF is obtained from a trimmed average signal of the array after excluding the 2% of the probe sets with the highest and the lowest values. Results Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from SF calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd). In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal, SF showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation. Conclusions Eliminating 40% of the signal data during SF calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays.

Background The high-density oligonucleotide microarray, also known as GeneChip ® , made by Affymetrix Inc (Santa Clara, CA), has been widely used in both academic institutions and industrial companies, and is considered as the "standard" of gene expression microarrays among several platforms. A single GeneChip ® can hold more than 50,000 probe sets for every gene in human genome. A probe set is a collection of probe pairs that interrogates the same sequence, or set of sequences, and typically contains 11 probe pairs of 25-mer oligonucleotides [ 1 - 3 ]. Each pair contains the complementary sequence to the gene of interest, the so-called perfect match (PM), and a specificity control, called the Mismatch (MM) [ 3 ]. Gene expression level is obtained from the calculation of hybridization intensity to the probe pairs and is referred to as the "signal" [ 4 - 10 ]. The normalization method used in GeneChip software is called scaling and is defined as an adjustment of the average signal value of all arrays to a common value, the target signal value in order to make the data from multiple arrays comparable [ 4 , 11 ]. The purpose of data normalization is to minimize the effects of experimental and/or technical variations so that meaningful biological comparisons can be made and true biological changes can be found among multiple experiments. Several approaches have been proposed and shown to be effective and beneficial. They were mostly from studies on two-color spotted microarrays [ 12 - 19 ]. Some authors proposed normalization of the hybridization intensities, while others preferred to normalize the intensity ratios. Some used global, linear methods, while others used local, non-linear methods. Some suggested using the spike-in controls, or house-keeping genes, or invariant genes, while others preferred all the genes on the array. For GeneChip data, some have proposed different models to normalize signal values or normalize probe pair values [ 10 , 20 - 24 ]. Despite the presence of other alternatives, many biologists still use the default scaling method and consider that such method is satisfactory and is useful to identify biological alterations [ 23 , 25 , 26 ]. With the increasing awareness and usage of GeneChip technology and willingness to continue to use GeneChip software among many biologists, it is worth improving the performance or correcting the problems of the software. In this report, the author has demonstrated that in the scaling algorithm excluding 2% of the probe sets with the highest and the lowest values did not have much benefit. However, the logarithmic transformation of signal values prior to scaling proved to be the optimum normalization strategy and is strongly recommended. Results The statistical algorithm in current GeneChip software (MAS 5 and GCOS 1) for gene expression microarray data has eliminated the negative gene expression values, a problem present in earlier versions of the software [ 5 , 7 ]. It uses a robust averaging method based on the Tukey biweight function to calculate the gene expression level from the logarithm transformed hybridization data [ 3 - 5 , 11 ]. The reported data of a probe set is the antilog of the Tukey biweight mean multiplied by a SF and/or a normalization factor ( NF affy ). When both the SF and NF affy are equal to 1, there is no normalization or manipulation of original data. Both NF affy and SF are computed in virtually the same way. NF affy is calculated in comparison analysis to compare the array average of one experiment with that of a baseline experiment, while SF is obtained from the signal average of one experiment comparing with a common value, the target signal in absolute analysis [ 3 - 5 , 11 , 22 ]. The average value used in GeneChip is a trimmed average. It is not calculated from all probe sets, but from 96% of the probe sets after the 2% of the probe sets with the highest and the 2% of the lowest signals were removed. In this report, a total of 76 experiments with rat U34A GeneChip were analyzed. As shown in Table 1 , the total hybridization signals varied although all arrays were hybridized with the same amount of biotin-labeled cRNA and scanned with the same scanner of identical settings. The array of the highest hybridization intensities had 2.8 times more signals than that of the lowest. The average array signals had 25.8% variation in terms of coefficient of variation. The mean signals were significantly greater than the median signals on each array, indicating a non-normal distribution. The density plot showed a long-tailed and skewed distribution (not shown) and the average of such data is known to be sensitive to the larger values in the data set. The rat U34A GeneChip contained 8799 probe sets; hence 2% was about 176 probe sets. The sum of the 2% of the probe sets with the lowest signals accounts for less than 0.1% of the total signals (0.05% ± 0.01%, mean ± SD, n = 76) and its impact on SF calculation can be ignored. However, the sum of the 2% of the probe sets with the highest signals, the TrimTotal as used in this report, was responsible for about 40% of the total signals (from 34% to 54%, Table 1 ). The remaining 96% of the probe sets used for SF calculation, produced only about 60% of the signals. Excluding 4% of the probe sets did not reduce the variation, but rather slightly increased the variation, which in turn resulted in a wider range of SF s (Table 1 ). It was also found that the TrimTotal was highly correlated with total signal (R = 0.928), but less with medians (R = 0.536) and the mean of log signals (R = 0.643). The trimmed percentage ( Tp ) was found to be negatively associated with the median (R = 0.558, b = -1.116) and the mean of log signals (R = 0.495, b = -0.968), but not with the total signal of all probe sets. Among other approaches to global linear normalization, one can also use the median signal or the mean of logarithm transformed signals to calculate the NF. NFLogMean showed a higher correlation with NFMedian than with SF . There were larger differences between NFLogMean and SF than those between NFLogMean and NFMedian (Fig. 1 ). To test if the larger difference was a result of removing 4% of the probe sets from the calculation, another NF, the NFTrimLogMean was obtained using the same data as for SF , but with a log transformation. There is a very significant correlation between NFTrimLogMean and NFLogMean (R = 0.9998). The 4% of the probe sets that was removed from NFTrimLogMean calculation reduced the total data by only 4% after log transformation. Since it is impossible to obtain the true normalization factor, an average of the four global linear NF s mentioned above was used instead to estimate the 'true' NF. To compare them with the true NF, a score ( NFscore ) is introduced. Each NF is calculated against the respective 'true' NF to obtain its NFscore . The average NFscore (± SD) is 7.01% (± 6.24%), 4.51% (± 3.48%), 2.25%(± 2.33%) and 1.95% (± 1.61%), and the sum of NFscore is 5.33, 3.43, 1.71 and 1.48 for SF , NFMedian , NFTrimLogMean and NFLogMean , respectively (Fig. 1 ). The sum of NFscore indicated an accumulated variation from the true NF, and the larger the number, the larger the accumulated variation. An attempt to add a 5th NF obtained from the arithmetic mean of all probe sets of the array was also made to calculate and compare NFscore with each NFs, and the results showed the same conclusion (data not shown). It is fair to conclude that NFLogMean produced the least variation. Discussion Logarithmic transformation is a well-accepted approach for stabilizing variance and has become a common choice for data transformation and normalization for spotted microarrays [ 12 , 16 ]. Much improvement has been made in GeneChip microarray technology and accompanying software during the past few years. The current version of GeneChip software has improved its performance and is better than the earlier versions that used the Average Difference to express levels of gene expression [ 3 , 4 ]. However, the normalization algorithm was inherited and remains the only and default option for gene expression data processing in both MAS 5 and the newly released GeneChip Operating Software (GCOS) software. They continue to use the arithmetic mean of signals to obtain the SF in absolute analysis (single array) and the NF in comparison analysis (two arrays) [ 3 - 5 , 7 , 11 , 22 ]. It is clearly shown here that the trimmed average and the resulting SF had a larger variance than the median-based NF, or the NF based on the mean of log transformed signals. Similar results were observed in other GeneChip expression arrays, such as mouse U74A and human U133A (data not shown). Elimination of the highest and the lowest 2% of the probe set signals did not stabilize the trimmed means. When intra-array variance was reduced by 40%, this approach cannot be considered to be optimal. The logarithmic transformation of signals stabilized the variation well and made the normalization process much less dependent upon the mean and less affected by the outliers. Although simple and popular, the global linear normalization has its drawbacks, especially when the relationship among multiple experiments or genes is not linear. To address such problems, several methods have been proposed to conduct local and non-linear normalization, [ 12 , 14 - 17 , 20 , 22 , 27 ]. Data normalization is a very critical and important step for microarray data mining process. The use of different approaches to normalization may have a profound impact on the selection of differentially expressed genes and conclusions about the underlying biological processes especially when subtle biological changes are investigated [ 12 , 16 , 28 ]. Conclusions Normalization of microarray data allows direct comparison of gene expression levels among experiments. A global linear normalization, called scaling has been widely used in GeneChip microarray technology for gene expression analysis. The scaling factor ( SF ) is calculated from a trimmed average of gene expression level after excluding the 2% of the data points of the highest values and the lowest values. It is shown here that the 2% of the probe sets of the highest signals contained from 34% to 54% of the total signals. Elimination of the outliers did not reduce, but increased the variation among multiple arrays. Instead, normalization factors obtained from the mean of the log transformed signals had the best performance. Thus, the current scaling method, although widely used, is not optimal and needs further improvement. The mean of logarithm transformed signals is highly recommended to use for normalization factor calculation. Methods GeneChip experiments and data Total RNA was isolated from rat tissues or cells in Trizol reagent and purified with Qiagen Rneasy kit. cDNA was synthesized in presence of oligo(dT)24-T4 (Genset Corp, La Jolla, CA) and biotinlated UTP and CTP were used to generate biotin labeled cRNA according to the recommended protocols [ 29 ]. Rat genome microarray, U34A GeneChip (Affymetrix Inc., Santa Clara, CA) was used and hybridized with 15 μg of gel-verified fragmented cRNA. Hybridization intensity was scanned in GeneArray 2500 scanner (Agilent, Palo Alto, CA) with Microarray Suite (MAS) 5.0 software [ 4 ]. Data from a total of 76 independent GeneChip experiments were used in this study. Normalization factor (NF) Gene expression data exported from MAS 5.0 were submitted to a Perl script to calculate different normalization factors. In the scaling approach, a trimmed average signal is calculated after excluding 2% probe sets with the highest signals and 2% with the lowest signal values. The scaling factor ( SF ) is obtained using equation (1) in comparison with a chosen fixed number, called the target signal ( TS ) and is verified with the results from MAS 5.0 of the same settings [ 3 , 4 , 11 ]. SF j = TS / S TrimMeanj (1) Other normalization factors for comparison were obtained by the following: NFMedian j = TS / S med j (2) NFLogMean j = 2 nf j where i = 1..., n represents the probe sets, j = 1..., J represented the array experiments, Si is the signal of the anti-log of a robust average (Tukey biweight) of log(PM-MM) reported from MAS 5.0 [ 5 ], S med j is the median signal on the array j , S TrimMeanj is the trimmed average on array j after excluding 2% of the probe sets with the highest and the lowest signals [ 3 , 4 , 11 , 22 ]. NFMedian j is obtained by using the median signal on array j , and NFLogMean j is obtained by using the mean of log transformed signals. TS was set to 150, 38 and 38 for SF , NFMedian and NFLogMean , respectively in order to have similar NFs. In comparison with different NFs, a score, NFscore is introduced. NFscore j = ( NF j - TrueNF j )/ TrueNF j , and TrueNF j = ( SF j + NFMedian j + NFLogMean j + NFTrimLogMean j )/4, where NFTrimLogMean j , was calculated from equation (3) excluding the 2% of the probe sets with the highest and lowest signals, TrueNF j was used as a 'true' NF. Sum of . Other analysis Unless otherwise specified, logarithm transformation is carried out with the logarithm base 2. Trimmed total signal TrimTotal is the sum of the signals from the 2% of the probe sets with the highest signal values. Total signal Total is the sum of the signals of all probe sets in the array, and trimmed percentage Tp j = ( TrimTotal j / Total j ) × 100%. Abbreviations GeneChip ® is the registered trademark owned by Affymetrix Inc. PM: perfect Match; MM: mismatch; SF: scaling factor; NF: normalization factor; TS: target signal Short phrase: Normalization of GeneChip microarray data

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519025

Protein-polymer nano-machines. Towards synthetic control of biological processes

The exploitation of nature's machinery at length scales below the dimensions of a cell is an exciting challenge for biologists, chemists and physicists, while advances in our understanding of these biological motifs are now providing an opportunity to develop real single molecule devices for technological applications. Single molecule studies are already well advanced and biological molecular motors are being used to guide the design of nano-scale machines. However, controlling the specific functions of these devices in biological systems under changing conditions is difficult. In this review we describe the principles underlying the development of a molecular motor with numerous potential applications in nanotechnology and the use of specific synthetic polymers as prototypic molecular switches for control of the motor function. The molecular motor is a derivative of a TypeI Restriction-Modification (R-M) enzyme and the synthetic polymer is drawn from the class of materials that exhibit a temperature-dependent phase transition. The potential exploitation of single molecules as functional devices has been heralded as the dawn of new era in biotechnology and medicine. It is not surprising, therefore, that the efforts of numerous multidisciplinary teams [ 1 , 2 ]. have been focused in attempts to develop these systems. as machines capable of functioning at the low sub-micron and nanometre length-scales [ 3 ]. However, one of the obstacles for the practical application of single molecule devices is the lack of functional control methods in biological media, under changing conditions. In this review we describe the conceptual basis for a molecular motor (a derivative of a TypeI Restriction-Modification enzyme) with numerous potential applications in nanotechnology and the use of specific synthetic polymers as prototypic molecular switches for controlling the motor function [ 4 ].

1. Type I Restriction-Modification enzymes Type I R-M enzymes are multifunctional, multisubunit enzymes that provide bacteria with protection against infection by DNA-based bacteriophage [ 5 ] They accomplish this through a complex restriction activity that cuts the DNA at random locations, which can be extremely distal (>20 kbp) from the enzyme's recognition sequence. In fact, the enzyme is capable of two opposing functions (restriction and modification), which are controlled enzymatically through an allosteric effector (ATP) and temporally through the assembly of the holoenzyme. In addition, the R-M enzyme has a powerful ATPase activity, which is associated with DNA translocation prior to cleavage; it is this translocation process that leads to random cleavage sites. Therefore, these enzymes are unusual molecular motors that bind specifically to DNA and then move the rest of the DNA through this bound complex (Fig 1 ). Figure 1 DNA Translocation by TypeI Restriction-Modification enzyme. The yellow block represents the recognition sequence for the enzyme. The enzyme binds at this site and upon addition of ATP, DNA translocation begins. During translocation, an expanding loop is produced. Type I R-M enzymes fall into families based on complementation grouping, protein sequence similarities, gene order and related biochemical characteristics [ 6 - 8 ]. Within one sub-type (the IC family) there are three well-described members, including EcoR124I, which is the focus of our interest. This enzyme recognises the DNA sequence GAAnnnnnnRTCG [ 9 ] and is comprised of three subunits (HsdR,M,S) in a stoichiometric ratio of R 2 M 2 S [ 10 , 11 ], (Fig 2 ). However, Janscák et al . also showed that the Eco R124I R-M holoenzyme exists in equilibrium with a sub-assembly complex of stoichiometry R 1 M 2 S [ 11 ] which is unable to cleave DNA, but retains the ATPase and motor activity [ 12 ]. The HsdS subunit is responsible for DNA specificity; HsdM is required for DNA methylation (modification activity) and together they can produce an independent DNA methyltransferase (M 2 S) [ 13 , 14 ]. HsdR, along with the core MTase is absolutely required for DNA cleavage (restriction activity) and is also responsible for ATP-binding and subsequent DNA translocation. Therefore, the HsdR subunit is the motor subunit of the enzyme and this subunit is associated with helicase activity [ 15 - 18 ]. However, the precise mechanism of DNA translocation is uncertain and the true nature of the motor function has yet to be fully determined but a number of important functional units – nuclease, helicase and assembly domains have been identified within the HsdR subunit [ 19 ]. Figure 2 Schematic of the motor subunits. HsdS denotes the DNA binding subunit; HsdM – is the subunit responsible for DNA methylation and HsdR subunit, together with the core enzyme acts to restrict DNA. 2. A versatile molecular motor The motor activity of Type I R-M enzymes is the mechanism through which random DNA cleavage is accomplished. Szczelkun et al . [ 20 ] showed that cleavage only occurs in a cis fashion indicating that the motor component of the HsdR subunit is able to 'grasp' adjacent DNA and pull this DNA through the enzyme-DNA-bound complex. According to the Studier model [ 21 ] cleavage occurs when two translocating enzymes collide (Fig 3 ). However, highly efficient cleavage of circular DNA carrying only a single recognition sites for the enzyme suggests collision-based cleavage is not the whole story [ 20 , 22 ]. Figure 3 Mechanism of DNA cleavage. The enzyme subunits are represented by: green ellipse – M2S complex, green box – HsdR subunit (with ATPase and restrictase activities; C denoting cleavage site). The black line represents DNA with the yellow box denoting the recognition sequence. Arrow shows direction of DNA translocation. For more details see text. DNA translocation has been assayed in bulk solution using protein-directed displacement of a DNA triplex and the kinetics of one-dimensional motion determined. The data shows processive DNA translocation followed by collision with the triplex and oligonucleotide displacement. A linear relationship between lag duration and inter-site distance gives a translocation velocity of 400 ± 32 bp/s at 20°C. Furthermore, this can only be explained by bi-directional translocation. An endonuclease with only one of the two HsdR subunits responsible for motion could still catalyse translocation. The reaction is less processive, but can 'reset' in either direction whenever the DNA is released (Fig 4 ). Figure 4 Motor activity of type I R-M Enzyme. (a) The yellow block represents the DNA-binding (recognition) site of the enzyme, which is represented by the green object approaching from the top of the diagram and about to dock onto the recognition sequence. (b) The motor is bound to the DNA at the recognition site and begins to attach to adjacent DNA sequences. (c) The motor begins to translocate the adjacent DNA sequences through the motor/DNA complex, which remains tightly bound to the recognition sequence. (d) Translocation produces an expanding loop of positively supercoiled DNA. The motor follows the helical thread of the DNA resulting in spinning of the DNA end (illustrated by the rotation of the yellow cube). (e) When translocation reaches the end of the linear DNA it stops, resets and then the process begins again. As previously mentioned, the final step of the subunit assembly pathway of the Type I Restriction-Modification enzyme EcoR124I produces a weak endonuclease complex of stoichiometry R 2 M 2 S 1 . We have produced a hybrid HsdR subunit combining elements of the HsdR subunits of the EcoR124I and EcoprrI [ 23 - 25 ] Type I Restriction-Modification enzymes. This subunit has been shown to assemble with the EcoR124I DNA methyltransferase (MTase) to produce an active complex with low-level restriction activity. We have also assembled a hybrid REase and the data obtained show that the hybrid endonuclease (REase) containing only HsdR(prrI) is an extremely weak complex, producing primarily R 1 -complex. The availability of the hybrid REase produced from core MTase(R124I) and HsdR(prrI), which provides a stable R 1 -complex, also gives a useful molecular motor that will not cleave the DNA that it translocates. 3. Sub-cellular localisation of R-M enzymes As can be seen from the above, DNA cleavage by Type I restriction enzymes occurs by means of a very unusual, and highly energy-dependent, mechanism. Therefore, these enzymes are believed to be involved not only as a defence mechanism for the bacterial cell, but also in some types of specialised recombination system controlling the flow of genes between bacterial strains [ 26 , 27 ]. A periplasmic location would be well adapted for the restriction activity of R-M enzymes, but recombination requires a cytoplasmic location. Restriction enzymes protect the cells by cutting foreign DNA and could be assumed to be located at the cell periphery. Using immunoblotting to analyse subcellular fractions, Holubova et al. [ 28 ] detected that the subunits of the R-M enzyme were predominantly in the spheroplast extract. The HsdR and HsdM subunits were found in the membrane fraction only when co-produced with HsdS and, therefore, part of a complex enzyme, either methylase or endonuclease. Further studies have shown that the R-M enzyme is bound to the membrane via the HsdS subunit and that for some enzymes this may involve DNA [ 29 ]. 4. Uses of the EcoR124I molecular motor: polymer-protein conjugates in nanobiotechnology One of the major obstacles for the practical application of single molecule devices is the absence of control methods in biological media, where substrates or energy sources (such as ATP) are ubiquitous. Synthetic polymers offer a robust and highly flexible means by which devices based on single biological molecules can be controlled. They can also be used to link individual biomacromolecules to surfaces, package them or to control their specific functions, thus expanding the applicability of the natural molecules outside conventional biological environments. Moreover, a number of synthetic polymers have been recently developed that can potentially perform nanoscale operations in a manner identical to natural and engineered biopolymers. A key property of these materials is 'smart' behaviour, especially the ability to undergo conformational or phase changes in response to variations in temperature and/or pH. Synthetic polymers with these properties are being developed for applications ranging from microfluidic device formation, [ 30 ] through to pulsatile drug release [ 31 - 34 ], control of cell-surface interactions [ 35 - 39 ], as actuators [ 40 ] and, increasingly, as nanotechnology devices [ 41 ]. In the context of bio-nanotechnology we focus here on the uses of one particular subclass of smart materials, i.e. substituted polyacrylamides, but it should be noted that there are many more examples of synthetic polymers and engineered/modified biopolymers that exhibit responsive behaviour and new types and applications of smart materials are constantly being reported. Poly(N-isopropylacrylamide) (PNIPAm) is the prototypical smart polymer and is both readily available and of well-understood properties [ 42 ]. PNIPAm undergoes a sharp coil-globule transition in water at 32 °C, being hydrophilic below this temperature and hydrophobic above it. This temperature (the Lower Critical Solution Temperature or LCST) corresponds to the region in the phase diagram at which the enthalpic contribution of water hydrogen-bonded to the polymer chain becomes less than the entropic gain of the system as a whole and thus is largely dependent on the hydrogen-bonding capabilities of the constituent monomer units (Fig 5 ). Accordingly, the LCST of a given polymer can in principle be "tuned" as desired by variation in hydrophilic or hydrophobic co-monomer content. Figure 5 Inverse temperature solubility behavior of responsive polymers at the Lower Critical Solution Temperature (LCST). Left hand side shows hydrated polymer below LCST with entropic loss of water and chain collapse above LCST (right hand side). 4.1 Soluble PNIPAm-biopolymer conjugates Covalent attachment of single or multiple responsive polymer chains to biopolymers offers the possibility of exerting control over their biological activity as, in theory at least, the properties of the resultant polymer-biopolymer conjugate should be a simple additive function of those of the individual components. This principle is now being widely exploited in pharmaceutical development, as covalent attachment of, for example, PEG chains to therapeutic proteins has been shown to stabilize the proteins without losing their biological function [ 43 - 48 ]. Polymer-biopolymer conjugates can be prepared as monodisperse single units, or as self-assembling ensembles depending on the chemistries used for attaching the synthetic component and on the associative properties of the polymer and/or biopolymer. Furthermore, by altering the response stimulus of the synthetic polymer, and how and where it is attached to the biopolymer, the activity of the overall conjugate can be very closely regulated. These chimeric systems can thus be considered as true molecular-scale devices. Pioneering work in this area has been carried out by Hoffman, Stayton and co-workers, who engineered a mutant of cytochrome b5 such that a single cysteine introduced via site-directed mutagenesis was accessible for reaction with maleimide end-functionalised PNIPAm [ 49 ]. Since the native cytochrome b5 does not contain any cysteine residues this substitution provided a unique attachment point for the polymer. The resultant polymer-protein conjugate displayed LCST behaviour and could be reversibly precipitated from solution by variation in temperature. This approach has proved to be very versatile and a large number of polymer-biopolymer conjugates have now been prepared, incorporating biological components as diverse as antibodies, protein A, streptavidin, proteases and hydrolases [ 50 , 51 , 50 , 51 ]. The biological functions or activities of these conjugate systems were all similar to their native counterparts, but were switched on or off as a result of thermally induced polymer phase transitions. Of especial note have been the recent reports of a temperature and photochemically switchable endoglucanase, which displayed varying and opposite activities depending on whether temperature or UV/Vis illumination was used as the switch [ 52 ]. 4.2. Controllable DNA packaging and compartmentalization devices We are currently developing responsive polymers as a switch to control the EcoR124I motor function and are investigating this polymer-motor conjugate as part of an active drug delivery system. We aim for the practical demonstration of a nano-scale DNA packaging/separation and delivery system uniting the optimal features of both natural and synthetic molecules. In essence, we assemble a supramolecular device containing the molecular motor capable of binding and directionally translocating DNA through an impermeable barrier. To control the process of translocation in biological systems, where a constant supply of ATP is present, we have added to the motor subunit of EcoR124I the thermoresponsive poly(N-isopropylacrylamide) (PNIPAm), which, through its coil-globule transition, acts as a temperature-dependent switch controlling motor activity. PNIPAm copolymers with reactive end-groups are being attached to a preformed R subunit of the motor via coupling of a maleimide-tipped linker on the synthetic polymer terminus to a cysteine residue. This residue has been selected, as it is both accessible and located close to the active centre on the R subunit of the motor. The protein-polymer conjugates are stable to extensive purification and, when combined with M2S complex, the activity of this conjugate motor system is similar to the native counterpart, but can be switched on or off as a result of thermally induced polymer phase transitions [ 53 , 54 ]. Thus the conjugation of the responsive polymer to the molecular motor generates a nano-scale, switchable device (Fig 6 ), which can translocate DNA under one set of conditions (i.e. into a protective capsule or into a compartment). Conversely, in another environment (e.g. inside cells), in response to changed conditions (e.g. changed temperature, pH) the polymer switch will change its conformation, allowing ATP to power the motor, releasing DNA from capsules or compartments. Figure 6 Schematic representation of the molecular motor function controlled by a thermoresponsive polymer switch. R, M and S denote the specific motor subunits. Chain-extension of the polymer below LCST provides a steric shield blocking the active site. Chain collapse (above LCST) enables access to the active site and restoration of enzyme function. For more details see text. The conjugation of the motor with synthetic polymers brings additional advantages. One such benefit arises from the ability to functionalise the polymer side chains or terminus in a way that allows attachment of the entire complex to surfaces for sensing and device applications. Therefore, although our hybrid polymer-protein conjugate was originally aimed at gene targeting (as it has the potential to increase the delivery of intact DNA to cell nuclei and thereby increase gene expression) this system may also be used in building automated nano-chip sensors, therapeutic and diagnostic devices, where DNA itself would be a target, or where DNA might be used as a 'conveyor-belt' for attached molecules. The strength of the molecular motor has proven sufficient to disrupt most protein-DNA interactions and thus numerous processes and applications where highly localised force is required can also be envisaged. 5. Conclusions The use of synthetic polymers offers a number of possibilities, which otherwise could not be exploited or would be difficult to take advantage of, if purely biological systems were used. Moreover, the combination of the properties of molecular motors with "smart" polymers has hitherto been unexplored and represents a novel concept in nanotechnology, which could ultimately lead to a wholly new class of molecular devices. Nanoscale control of molecular transport in vitro and especially in vivo opens up a whole host of possibilities in medicine, including drug or DNA delivery (e.g. gene therapy), but also where protection of a therapeutic is required under one biological regime and release in another (e.g. prodrugs conjugated to DNA which can be released by nuclease-mediated degradation at the site of action). In addition, this system may allow the generation of switchable nanodevices and actuators, controllable by changes in the synthetic copolymer structure as well as ATP-mediated DNA motion and may pave the way for biofeedback-responsive nanosystems. It can be used for nano-scale isolation of various biochemical processes in separate compartments connected via a tightly controlled shuttle device. In essence, this concept bridges the disciplines of chemistry and biology by using a biological motor to control chemistry and a synthetic polymer to regulate biological processes. Author's contributions KF conceived the idea of using the modified R-M enzyme as a molecular motor and carried out, with co-workers, the molecular studies of the motor components, SSP carried out the polymer synthesis, polymer-motor conjugations and functional studies, CA designed and participated in the synthesis of smart polymers and DCG conceived of the study. All authors participated in study design and coordination as well as the reading and approval of the final manuscript

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543465

Modeling the HIV Epidemic in Africa

The HIV epidemic is continuing to grow, year by year. According to the Joint United Nations Programme on HIV/AIDS (UNAIDS), in 2004 there were more people living with the virus than ever before, and in the same year more people than ever before died of it. So, although in the developed world HIV/AIDS is a controllable disease, one with which a treated person might expect to have a near normal lifespan, in much of the rest of the world AIDS is still a death sentence. Despite the fact that the cost of AIDS medicine has come down to around $150 per year in the developing world—a much lower cost than previously—the drugs are still unavailable to the vast majority of patients. What is more, every infected person has the chance of infecting many others. Although huge sums of money have been poured into combating HIV/AIDS—around US$4.7 billion in 2003—UNAIDS estimates this amount is less than half of what is required by 2005, and only a quarter of what will be required by 2007, to mount a comprehensive response to AIDS in low-income and middle-income countries. One of the real dilemmas, therefore, of HIV/AIDS policy is deciding whether it is better to concentrate resources on prevention of infections or on treatment of infected individuals. Each approach has ramifications for the other, as shown by the experience in some developed countries, where an increase in availability of treatment has been accompanied by an increase in risk behavior. The best strategy is to combine prevention and treatment An analysis by Joshua Salomon and colleagues in this month's PLoS Medicine suggests that trying to concentrate on one or the other of these alternatives is a false dichotomy, and that not integrating the two approaches could have a catastrophic effect on the global toll of HIV/AIDS by 2020. In this theoretical paper the authors analyze the epidemic in sub-Saharan Africa (where three-fourths of deaths from AIDS occur). With no change in current levels of prevention and care, it is predicted that there will be 3.7 million new HIV infections and 2.6 million adults dying of AIDS in this region each year within the next two decades. The authors predicted that concentrating on prevention alone could decrease yearly infections by half, and that concentrating on treatment could decrease yearly infections by 6%. However, combining both approaches could yield substantially greater benefits than the sum of the two alone—lowering projected new infections by 74% and projected annual mortality by half. These percentages translate into 29 million new infections and 10 million deaths averted between 2004 and 2020. The challenge now is obviously how to put these policy suggestions into practice. The current World Health Organization treatment target of having three million people on antiretroviral therapy by the end of 2005 (the “3 by 5” objective) provides a yardstick for only one part of the equation. The authors comment that the mobilization of communities that will be needed to achieve the 3 by 5 objective should also be harnessed for prevention, and that those who teach prevention must also be allowed to get care for those infected. As the authors say, only by doing so “will we at last move from slogans to impact.”

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548518

"Harnessing genomics to improve health in Africa" – an executive course to support genomics policy

Background Africa in the twenty-first century is faced with a heavy burden of disease, combined with ill-equipped medical systems and underdeveloped technological capacity. A major challenge for the international community is to bring scientific and technological advances like genomics to bear on the health priorities of poorer countries. The New Partnership for Africa's Development has identified science and technology as a key platform for Africa's renewal. Recognizing the timeliness of this issue, the African Centre for Technology Studies and the University of Toronto Joint Centre for Bioethics co-organized a course on Genomics and Public Health Policy in Nairobi, Kenya, the first of a series of similar courses to take place in the developing world. This article presents the findings and recommendations that emerged from this process, recommendations which suggest that a regional approach to developing sound science and technology policies is the key to harnessing genome-related biotechnology to improve health and contribute to human development in Africa. Methods The objectives of the course were to familiarize participants with the current status and implications of genomics for health in Africa; to provide frameworks for analyzing and debating the policy and ethical questions; and to begin developing a network across different sectors by sharing perspectives and building relationships. To achieve these goals the course brought together a diverse group of stakeholders from academic research centres, the media, non-governmental, voluntary and legal organizations to stimulate multi-sectoral debate around issues of policy. Topics included scientific advances in genomics innovation systems and business models, international regulatory frameworks, as well as ethical and legal issues. Results Seven main recommendations emerged: establish a network for sustained dialogue among participants; identify champions among politicians; use the New Plan for African Development (NEPAD) as entry point onto political agenda; commission an African capacity survey in genomics-related R&D to determine areas of strength; undertake a detailed study of R&D models with demonstrated success in the developing world, i.e. China, India, Cuba, Brazil; establish seven regional research centres of excellence; and, create sustainable financing mechanisms. A concrete outcome of this intensive five-day course was the establishment of the African Genome Policy Forum, a multi-stakeholder forum to foster further discussion on policy. Conclusion With African leaders engaged in the New Partnership for Africa's Development, science and technology is well poised to play a valuable role in Africa's renewal, by contributing to economic development and to improved health. Africa's first course on Genomics and Public Health Policy aspired to contribute to the effort to bring this issue to the forefront of the policy debate, focusing on genomics through the lens of public health. The process that has led to this course has served as a model for three subsequent courses (in India, Venezuela and Oman), and the establishment of similar regional networks on genomics and policy, which could form the basis for inter-regional dialogue in the future.

Background Inequities in global health continue to be among the major challenges facing the international community [ 1 ]. Despite tremendous advances in medicine, the benefits of science and technology have yet to make a major impact on the health and quality of life of majority of the world's population. Recognizing its fundamental role as engine for development, the New Partnership for Africa's Development (NEPAD) has identified science and technology as a key platform for Africa's renewal [ 2 ]. A major challenge for Africa, and for the entire international community, is to bring scientific and technological advances to bear on the health priorities of poorer countries [ 3 , 4 ]. Africa in the twenty-first century is faced with a heavy burden of disease, combined with ill-equipped medical systems and underdeveloped technological capacity [ 5 ]. The crippling poverty in many countries in the continent contributes to the disease burden, and hampers countries' ability to address the problem adequately [ 6 ]. While Africa's response to its health challenges has varied considerably across the continent, with governments traditionally placing less emphasis on developing S&T than other sectors [ 7 ], there has been ongoing R&D activity in genomics and related fields of technology over the past several years in various parts of the region. The African Medical Research Foundation (AMREF), Africa's largest indigenous health charity, has for nearly half a century made an important contribution to addressing health challenges in Africa through partnerships with local communities, governments and donors [ 8 ]. A number of centres of excellence have emerged across the continent in recent decades, including the International Centre of Insect Physiology and Ecology (ICIPE) in Nairobi where important work has been done to uncover the role of insects in the transmission of infection , and the Institute for Molecular and Cell Biology-Africa (IMCB-A), founded in 1999 to study the molecular mechanisms of tropical infections. A further example is the new Biosciences Facility for Eastern and Central Africa that was recently launched as part of a NEPAD initiative [ 9 ]. NEPAD, which has been adopted by the United Nations General Assembly as Africa's development framework, has called "for the establishment of regional platforms with concrete actions to build and strengthen Africa's competence to harness and use new technologies for human development" [ 2 ]. Its strategy acknowledges that Africa will have to overcome considerable challenges, including creating adequate regulatory and biosafety frameworks, building scientific capacity, and developing integrated systems of innovation. In March 2002, the African Centre for Technology Studies (ACTS) and the University of Toronto Joint Centre for Bioethics (JCB) co-organized an intensive five-day Course on Genomics and Public Health Policy in Nairobi, Kenya, bringing together scientists, policy makers, journalists, lawyers and NGOs from ten African countries to discuss, collectively, the question of "How best to harness genomics to improve health in Africa?" This course was sponsored by Genome Canada, the International Development Research Centre, and the African Centre for Technology Studies, through the Norwegian Agency for Development Co-operation. The primary goal of the course was to familiarize participants with the potential of genomics and related biotechnologies to address health needs in Africa. This article presents the findings and recommendations that emerged from this process, and suggests how such courses might be more broadly employed as a method for bringing together opinion leaders to share ideas and work collectively to develop practical policy solutions. Methods The programme was planned collaboratively by the African Centre for Technology Studies and the Joint Centre for Bioethics. The basic layout of the sessions and their topics was modelled on a prior course held in Toronto, Canada in May 2002. The programme was organized in line with the objectives outlined in Table 1 . Course participants as well as session leaders were identified on the basis of recommendations from recognized experts in the region and through literature searches. Many session leaders were local experts, well placed to contextualize the "new science" of genomics within the frame of concerns and realities particular to Africa. Care was taken to select participants representing a range of interests and backgrounds, including individuals from science, economics, law, government, the press, and non-governmental organizations. Such diversity was sought in recognition of the importance of "cross-pollination" on a multifaceted topic like genomics, and consequently the need for multiple actors to be part of the building of policy, as well as mediating the dialogue between policymakers and the public. In total, 30 participants attended; the countries and the institutions they represent are listed in Table 2 . Despite concerted efforts to draw a balanced group, the participant list reveals a markedly high proportion of academics, and indeed no representatives from industry. Moreover, only three of the participants are women. The organizers covered all costs for attending the course (transportation, hotel accommodation, and meals), in order that inability to pay not be an inhibiting factor for those who wished to participate. Table 1 Objectives of the course • To familiarize participants with the current status and implications of genomics and biotechnology for health in India, and to provide information relevant to public policy • To provide frameworks for analyzing and debating the policy issues and related ethical questions, and to help understand, anticipate and possibly influence the legal and regulatory frameworks which will operate, both nationally and internationally • To begin developing an opinion leaders network across different sectors (industry, academic, government, and voluntary organizations) by sharing perspectives and building relationships Table 2 Countries and Institutions Represented African Centre for Technology Studies, Kenya African Malaria Vaccine Testing Network (AMVTN), Tanzania African Medical and Research Foundation, Kenya Centre for the Development of People (CEDEP), Uganda Chemistry Department, University of Zambia, Zambia Department of Biochemistry, University of Khartoum, Sudan Department of Epidemiology of Parasitic Disease, National School of Medicine and Pharmacy, Mali Department of Obstetrics and Gynecology, Assiut University, Egypt Department of Pathology, Makarere University, Uganda Department of Virology, University of Ibadan, Nigeria Division of Human Genetics, Faculty of Health Sciences, University of Cape Town, South Africa Dysmorphology and Alcohol Pharmacokinetics in Fetal Alcohol Syndrome, South Africa Federal Ministry of Science and Technology, Nigeria Inter-Region Economic Network (IREN), Kenya Journalist Against AIDS (JAAIDS), Nigeria Lawyer, Kenya Maternal, Child and Women's Health, Dpt. of Health, Western Cape Province, South Africa Molecular Biology Research Facility, Nelson R Mandela School of Medicine, South Africa National Council for Science and Technology, Kenya National Health Laboratory Service and Division of Human Genetics, University of Witwatersrand, South Africa School of Public Health, University of Ghana, Ghana Science and Development News, and BiotekAfrika, Kenya Science Secretary, Uganda Council for Science and Technology, Uganda The People Newspaper, Kenya Because of the diversity of the participants, no background in science was presupposed. The sessions were organized so that participants were first introduced to the subject of the "new science" of genomics, and were then instructed in areas including national innovation systems, business models, intellectual property rights, international conventions and regulatory structures, ethics, and the role of networks in facilitating dialogue, advocacy and policy making. A detailed time-table of the programme is shown in Table 3 . Presenters used overhead transparencies or presentation software such as Microsoft Powerpoint. Active participation was encouraged throughout with at least 45 minutes allotted for discussion at the end of each session, on the assumption that each participant brought considerable expertise and valuable practical experience of his or her own. The programme therefore employed a peer-learning environment in which participants could learn from each other, in addition to learning from material presented by instructors. Each participant was provided with a course reader, which included additional background material on session topics; class sessions used a variety of learning methods including lectures, discussions, case analysis, and simulations. Table 3 Agenda for the Course on Genomics and Public Health Policy in Africa. Time Day 1 Day 2 Day 3 Day 4 Day 5 9.00–10.30 Introduction Prof Abdallah Daar, Dr John Mugabe New Science I : Introduction Dr Stephen Scherer Internet-based Leader Networking: Exercise Prof Joseph D'Cruz Intellectual Property Rights I Dr Patricia Kameri-Mbote Ethics I Dr Peter Singer Group Presentations 11.00–12.30 New Science II Dr Stephen Scherer National Innovation Systems Prof Norman Clark Intellectual Property Rights II Dr Patricia Kameri-Mbote Ethics II Prof Abdallah Daar Group Presentations Continued 1.30–3.00 New Science III Prof Onesmo ole-MoiYoi Business Models Prof Joseph D'Cruz Internet-based Leader Networking: Results Prof Joseph D'Cruz Science & Innovation Policy in International Conventions Dr John Mugabe 3.30–5.00 Genomics and Global Health Dr Peter Singer Group Work Group Work Group Work Early in the course, participants were divided into small Study Teams consisting of persons with diverse backgrounds, in order to maximize complementary skills. These Study Teams were an integral part of the learning process of the programme. Sessions were intended primarily to provide input for participant Study Teams, which assembled several times during the week. Their primary task was to draw upon the course material and their own experiences to propose recommendations for policy relating to genomics and biotechnology in Africa. Presentations were made on the last day of the course, and the final sessions focused on how to take forward the ideas and proposals generated during the course. This was the first course of its kind in Africa, as well as the first of a series of planned courses on genomics policy to be held in developing countries; evaluation was therefore a key component of the programme. At the end of each day, participants were given a questionnaire to complete, in which they had an opportunity to evaluate the day's sessions. At the end of the course, participants were asked to complete a more detailed questionnaire, asking for their feedback on the overall aims and organization of the course. Results The course opened with an Introduction, where Prof. Abdallah Daar and Dr John Mugabe welcomed the participants, explained the course's objectives, and then invited each of the participants to introduce him- or herself to the rest of the group. The opening session was led by Dr Stephen Scherer, and was intended to provide a comprehensive overview of the science of genomics and its relevance to health. Several of the participants had a limited scientific background; the presentation therefore include very basic descriptions of the science involved, as well as images and a brief video, and gradually progressed to a discussion of its applications in health research and medicine, both now and in the future. This session was followed by an introduction by Prof. Onesmo ole-MoiYoi, a pioneering Kenyan scientist, to advances in genomics and molecular biology within the African context – including cutting-edge research at his institute and others on the continent, as well as the broader relevance of genomics and molecular approaches to the health of Africa's people, animals and the environment. The first day closed with a session led by Dr Peter Singer who described a five-point strategy to systematically capture the benefits of genomics for the health of citizens in developing countries, through research, capacity-strengthening, consensus-building, public engagement, and an investment fund. Examples of ongoing work by the University of Toronto's Canadian Program on Genomics and Global Health in these areas were discussed, including the results of its 2002 study to identify the most promising biotechnologies to improve health in developing countries [ 13 ]. Prof. Joseph D'Cruz opened the second day with a discussion introducing participants to new approaches to forming and expressing opinions about emerging issues using the internet. Leaders in any area are required to develop their own views about new developments in their fields, and the process of forming these views is facilitated by peer discussions. Though traditionally these processes have taken place face-to-face, the internet offers an alternative medium that allows individuals to interact with their peers in other locations at a time and pace suited to each individual's commitments, without forcing the group to reach early consensus. Prof. Norman Clark followed with a session aimed at introducing participants to the concept of 'National Systems of Innovation', a conceptual framework for analysing country-specific factors that influence innovation across sectors. Innovation is understood as processes of generating new ideas, products and production processes, as well as to processes of institutional change and development. Such frameworks can be useful in identifying and analyzing key factors affecting African countries' ability to engage effectively in biotechnology and genomics for human development. The last session of the day focused on the business life cycle of a genomic product, tracing its development from the laboratory bench to a patented invention that is exploited commercially. The session addressed the strategic issues and choices that firms face at each point in this life cycle, and used a case-study based approach to frame the issues. The last one-and-a-half-hour session of the day was devoted to group work among members of Study Teams, whose members were selected to bring diverse views and experiences to bear on their deliberations. The third day of the course was devoted primarily to the issue of intellectual property rights (IPRs). The two sessions on IPRs were led by Dr Patricia Kameri-Mbote, Kenyan lawyer and scholar. During the first of these, Dr Kameri-Mbote explained the nature and different kinds of IPR protection, and explored how these impact on biotechnology development and technology transfer. She also considered the relationship between IP protection and public health in developing countries, using specific cases that have arisen under the World Trade Organization's Agreement on Trade Related Aspects of Intellectual Property Rights (TRIPS). Positions held by different countries and scholars on IP and biotechnology transfer in health were examined, and international, regional and national intellectual property regimes were reviewed. The second session focused on the link between IP, public health and transfer of biotechnology, in addition to the ethical, social and policy implications of the "Doha Declaration" on health by WTO ministers intellectual property rights in the area of health under TRIPS. At the end of the third and fourth days, participants again met for 1.5 hours in their Study Teams to prepare their proposals. Day four of the course had a heavy focus on ethical dimensions of emerging technologies like genomics. The first session provided an overview of ethical issues related to genomics and public health policy. Prof. Abdallah Daar led this and the second session on ethics. He described the World Health Organization's draft Guiding Principles on Medical Genetics and Biotechnology document, which he co-authored and which provides a broad overview of the ethical principles in this field. During the second session, Prof. Daar and Dr. Singer led the group through a case involving benefit sharing, and introduced the Human Genome Organization's principles and statement on benefit sharing. Dr. Singer then described an ethical framework and approach to priority-setting for genomics technologies in health care institutions. The last hour of this session was devoted to providing a forum for participants to share their expertise and experiences in areas related to policy. The final session of the day was led by Dr John Mugabe, then-Director of the African Centre for Technology Studies in Nairobi, Kenya. This session introduced participants to international conventions and protocols that emerged out of the United Nations Conference on Environment and Development (UNCED), and focused on science and innovation issues covered by the Conventions on Biological Diversity and its Cartagena Protocol on Biosafety, and the International Treaty on Plant Genetic Resources for Food and Agriculture. Specific lessons were drawn for international rule-making for health equity, and emphasis was given to biotechnology, risks assessment, technology transfer, sharing benefits of global scientific and technological advances, and technical cooperation. On the last day, each of the four Study Teams presented their proposals, which addressed the overarching question of the course: "How to harness genomics and related biotechnology to improve health in Africa?" Study Teams presented one at a time; after each presentation, there was a period for questions and discussion, and afterward an opportunity to consider all proposals together in light of the host of issues raised during the course of the week. The presentations, though prepared independently by each group, demonstrated a number of common themes that tended to be organized in terms of long-term foundational issues of sustainability, and more concrete short-term issues relating to garnering political involvement. Table 5 enumerates the key recommendations that emerged from these sessions. Table 5 Recommended Action-Steps Establish a regional network to foster sustained inter-sectoral dialogue Identify champions among politicians Use the New Plan for African Development (NEPAD) as entry point onto political agenda Commission African capacity survey in genomics-related R&D to determine areas of strength Undertake a detailed study of R&D models with demonstrated success in the developing world Establish seven regional research centres of excellence Create sustainable financing mechanisms Discussion The following is a synthesis of the participants' efforts, summarizing and describing key issues that emerged from their presentations and throughout the weeks' deliberations. It includes several concrete action-steps recommended by the participants, which flow from these considerations. Creating a Platform for Ongoing Dialogue and Advocacy The course generated a great deal of enthusiasm and vigorous discussion, and there was consensus among the participants on the need to create a mechanism for capitalizing on this momentum. Course participants and faculty therefore established an e-mail-based network, the African Genome Policy Forum (AGPF) , to allow the continued exchange of ideas and the building of consensus on issues related to genomics and public health policy. The group, composed of participants from areas of government, academia, civil society and the media, was created to bring to the table the views of their respective constituencies, and inform their peers of insights gained from the course and through the network. The network may also play an advocacy role in promoting the responsible use of genomics as a tool to improve health and promote development in Africa. Concrete Action-Step 1 : Establish a regional network to foster sustained inter-sectoral dialogue On the final day of the course, it was decided that a regional network, the "African Genome Policy Forum", be established comprising all participants and session leaders; it was further agreed that the Joint Centre for Bioethics would set up a web-site, discussion board, and e-mail based platform to facilitate ongoing discussion and inter-sectoral debate on the issues and proposals raised during the course. Mobilizing Political Support The success of any major initiative requires sustained dialogue with politicians. It is important to take the time to address their legitimate concerns, by clarifying the specific relevance of genomics and its applicability within the context of their communities. A point of particular relevance is the link between technologies like genomics and Africa's development, which has been well described in a number of recent reports [ e.g . [ 6 , 10 ]]. Participants highlighted the importance of taking back to their colleagues in their respective countries and institutions the lessons drawn from the course; those participants in public office agreed to seize opportunities to raise some of issues and proposals of the course when attending relevant forums. In particular, the nascent New Partnership for Africa's Development, adopted in 2001 under the mandate of the Organisation of African Unity, was repeatedly pointed to as an opportunity to bring genomics and its relevance to health in Africa onto the political agenda. Science and technology is among NEPAD's seven priority areas; another is human development, which encompasses health [ 11 ]. Genomics provides a clear example of how these two areas – science and technology, and health – come together, and can serve as a model for considering how science and technology and health concerns can be better integrated to address the continent's economic and health needs. Concrete Action-Step 2 : Identify champions among politicians The most efficient means of garnering political support is often to go directly to the politicians themselves – those who have been supportive or outspoken of the issues in question – to put the subject before their colleagues. The course itself represented an important step in this direction, as it brought together a spectrum of stakeholders, including academics, civil society, and government officials. The course, and the subsequently established network, therefore furnished an opportunity for direct communication and dialogue among individuals with a shared vision, including policymakers in a position to "champion" the issues and proposals that emerged from the course to their colleagues and others. Concrete Action-Step 3 : Use the New Plan for African Development (NEPAD) as entry point onto political agenda NEPAD offers a possible forum to bring the subject of genomics-related biotechnology onto the political agenda, and provides a means of informing African leaders of genomics and its relevance to improving health and development in Africa. In particular, the AGPF recommends the establishment by NEPAD of an 'African Genomics Committee', which would provide a plan for utilizing genomics and other new technologies to enhance health in Africa, advocate for increased investment in S & T, target other relevant stakeholders in individual countries, educate policy makers about the need for a strong R&D base established through partnerships across Africa, and organize steering committees to identify gaps and implement strategies for improvement. Prioritizing Needs Participants agreed on the need to consider emerging technologies like genomics in light of Africa's specific health challenges, and consequently on the importance of prioritizing these and identifying strategic entry points. Infectious (including sexually transmitted) diseases, genetic and other non-communicable disorders, sanitation, nutrition, environmental pollution and loss of biodiversity were all proposed as areas requiring concerted attention, with a special emphasis on the potential for using genomics-related biotechnology to target the three biggest killers in Africa: malaria, HIV/AIDS and tuberculosis. There are already well-known African-led initiatives to apply scientific innovation to combat important health concerns, such as the Multilateral Initiative on Malaria, and the African Malaria Vaccine Testing Network (AMVTN). It will be important to build on existing success stories, and to identify gaps in terms of priority health areas receiving inadequate attention. This will help to focus efforts and to more efficiently channel limited resource, both financial and human. A regional approach, which has since been adopted by NEPAD, was proposed as a promising mechanism for harnessing existing competence to address local needs. Concrete Action-Step 4 : Commission African capacity survey in genomics-related R&D to determine areas of strength This survey would identify strategic areas of strength, such as existing centres of excellence, potential areas of improvement, and health priorities receive inadequate attention. It would also serve to identify local and national innovators, and to inform the structuring of Regional Centres of Excellence described below. Capacity Building & Public Engagement For several years, genomics has been linked with a number of high-profile, intensely controversial issues like human cloning and genetically modified organisms. While emerging technologies like genomics raise a number of important ethical and social issues that deserve careful consideration [ 12 ], a nuanced message takes account of the possibilities as well as the challenges of new approaches. Often, technological applications can complement existing, well-established health approaches [ 13 ]. Scientists, policy-makers, and the media have an important part to play in publicizing science, and pointing out its relevance to Africans in a moderate rather than hyperbolic tone [ 14 ]. Local leaders can have an important role to play, not only in reflecting the leading-edge opinions of their different constituencies to policymakers, but also by playing a role in raising awareness within their communities. A more informed public is often a more engaged public, which can effectively advocate for the development of policies that reflect legitimate concerns, while leaving space to explore promising avenues of scientific endeavour. Public engagement was seen to form part of a long-term strategy for capacity building, and raising the overall profile of science and technology in Africa. The discussions reflected a conception of capacity strengthening as intimately linked with quality education – at all levels, and across disciplines. Core to this debate among course participants was the belief that endogenous capacity must be developed in order that Africa can begin to be self-sufficient, and itself become an innovator. Participants identified the following categories as needing attention: Primary, secondary and tertiary education There is a need to introduce innovative techniques to teach science and technology in the classroom, in order to generate interest and aptitude in the subject matter from an early stage in the educational process. Besides contemporary scientific approaches, indigenous knowledge and its applications to health could also be a relevant component to include in the curriculum. Policymakers Those in a position to shape policy should be familiarized with codes of ethics pertaining to their field; moreover, they should be educated about how best to capitalize on international frameworks (e.g. WTO's Trade-Related Aspects of Intellectual Property Rights; the UN's Convention on Biological Diversity) in order to ensure that their countries benefit from such arrangements, and are not exploited. Policy makers should develop strategies for negotiating their interests collectively in international forums, when appropriate, given shared needs and values. Media There is a general need to strengthen capacity in the area of communication, in particular on increasing the level of science literacy among the media. This might include integrating journalism and science programs at the college and university levels. There is a corresponding need to improve the ability of scientists to communicate the relevance of their work to the public, and to policy-makers. ELSI There is a great need to build capacity in Africa with regard to the ethical, legal and social issues (ELSI) which inevitably accompany the emergence of new technologies. Strategies would in many cases involve sensitizing the public to issues of relevance, such as their rights as patients and participants in research (e.g. informed consent, confidentiality of patient information), encouraging dialogue about the social consequences of introducing new technologies into traditional settings, and putting frameworks in place (e.g. ethics review boards) to ensure that ethical, quality and safety standards before research is undertaken. Partnerships Along with the need to strengthen the R&D base in science and technology, participants of the course identified a related need to increase the emphasis on commercialization – not only as a tool for sparking innovation but also to permit the generation of capital necessary to sustain the industry. An important step in the process of moving toward commercialization is the forming of alliances within countries, between universities and industry, sometimes known as "cross-linking". The fruitfulness of the Africa course, where people from across sectors and sub-regions came together with a common mission, re-enforced the value and the importance of establishing cross-sectoral networks and collaborations. Networks provide a means of generating new ideas, pooling the creative energies of individuals, and exchanging advice and expertise around a particular area of focus, in this case genomics and health policy. Such networks could play an advocacy role, combining the voices and the influence of key players from diverse disciplines and sectors, to advance a common aim. Collaborations , at the level of institutions – both within and between countries and regions – would facilitate the transfer of both knowledge and technology. During the course, it was pointed out that there is a particular need to encourage linkages between universities and industry to, among other things, facilitate the move from research and development to product generation and commercialization. This could include mechanisms to facilitate relationships between universities undertaking research in biotechnology and local industries. Institutional partnerships and collaborations at all levels, including internationally, can mean the channelling of resources to common areas of focus, and pooling the relative strengths and resources of partner institutions [ 15 ]. Such collaborations require very clearly defined roles for partners, and transparency with respect to goals, prioritization of needs, funding, and mechanisms to ensure equitable access to products. Creating sustainable financing mechanisms Ensuring that the benefits of science and technology, including emerging fields like genomics, requires a long-term strategy for sustained investment. Concrete Action-Step 5 : Design proposals for obtaining sustained investment for both research and development (R&D) in genomics and related biotechnologies to improve health, and the commercialization of the products of R&D Three models were suggested The establishment of an African Science and Technology Fund , dedicated to supporting research and development in the area of health-related biotechnology, would rely upon the contribution of African governments. The establishment of an Investment Fund for genome-related biotechnologies for improving health would represent an innovative approach to obtaining capital, providing a further incentive for investors to put money into development by creating a fund that provides a return on investment, as well as furnishing funds for advancement. Such a fund might be dedicated to providing capital for the development of mature, or future, health-related technologies. Capitalizing on existing funds allocated for research related to diseases afflicting Africa, such as the WHO's Global Fund to Fight AIDS, Tuberculosis and Malaria. Genomics and biotechnology represents a powerful set of tools for health improvement, and the World Health Organization through its Genomics and World Health (2002) report has raised it as an important issue deserving international attention. It is important to use this positive emphasis to give weight to the case for the relevance of biotechnology to health in developing countries, particularly for policy makers. Research and Development (R&D) With respect to R&D, there are already areas of strength on the continent; it is crucial to identify localized expertise, and to establish linkages with centres elsewhere in the region, as well as abroad, to ensure the transfer of knowledge and of technology, and to facilitate human resource development. Infrastructure must be developed to attract qualified African researchers to remain in or to return to Africa – both to support them, technically, intellectually, and socially and to provide them with similar opportunities for creativity and growth as may be found in other locales. The Biosciences Facility, established in 2003 by NEPAD, takes up this challenge, promoting "scientific excellence by bringing together a critical mass of scientists drawn from national, regional and international institutions in state-of-the-art facilities where they can undertake cutting-edge research to help solve the most important development constraints faced by the poor in Africa" [ 9 ]. While the new Biosciences Facility is the first of network of centres of excellent focused primarily on using science to help poor farmers, it may be an appropriate model for like initiatives using a regional approach for targeting health challenges. Concrete Action-Step 6 : Undertake a detailed study of R&D models with demonstrated success in the developing world, i.e. China, India, Cuba, Brazil Developing countries in various parts of the world have proven that they too can have strong technology sectors, and make important contributions in terms of science and innovation. Their successes represent an opportunity to bring to the attention of politicians that there are countries succeeding in genomics. A detailed study of these models can provide important insights into how Africa can capitalize on the promise of genomics and biotechnology, particularly as it relates to health. In 2003, the Joint Centre for Bioethics completed a qualitative study of R&D in biotechnology in South Africa; similar studies are underway in Cuba, Egypt and China. Research of this kind could feed into more systematic efforts in the region to better understand how some developing countries, including those in Africa, have managed to develop S&T research and manufacturing capacity in the health sector. Concrete Action-Step 7 : Establish Seven Regional Research Centres of Excellence The proposed centres would be distributed across Northern, Southern, Eastern, Western and Central African sub-regions. Each centre would have its own area of focus, in terms of targeted health problems, depending on regional expertise. The Centres would not be the sole preserve of each region, but would in fact use the strengths and specializations of each region to achieve the goal of harnessing genomics to improve health in Africa . These regional centres of excellence need not preclude the existence of national centres of excellence. The Biosciences Facility is modelled on such an approach. Conclusion Analysis The course on Genomics and Public Health Policy in Africa was carefully designed, with inputs from both its Canadian and African co-organizers, to have a programme and participant profile reflecting the inter-disciplinarity of the issue being considered. Genomics cuts across S&T, environmental, development, industrial, education and health policy and generates important ethical, legal and social issues. It therefore requires a genuinely participatory and multi-stakeholder approach, as well as frank discussions about both the potential promise and perils of a relatively new science. The strength of the course, as reflected in the evaluations submitted by participants, was the rare opportunity for discussion and networking among opinion leaders from different sectors. Both during and between sessions, participants exchanged perspectives and experiences with others from different regions of the content, and from different disciplines. Senior political officials, journalists, academics, and civil society representatives worked together in Study Teams to create proposals. Discussions were lively and open, with broad participation from those in attendance. However, a weakness of the course was the absence of industry representatives, who would certainly have contributed an important and valuable point of view. The small number of women participants was also a notable disadvantage. Later courses modelled on the Nairobi offering (i.e. those in Latin America, the Eastern Mediterranean, and India) had greater success in drawing participants from industry and obtaining a better gender balance. Notably, however, the recommendations that emerged from these courses, while reflecting differences due to regional priorities and context, did not vary considerably despite the broader contribution, particularly from the private sector [ 20 ]. A major outcome of the Nairobi course, and one which had strong support from participants, was the creation of a virtual network to facilitate ongoing interaction and discussion. Within two weeks of its completion, a website was created for the course , as well as a web-based discussion board. While there was some initial activity on the discussion board, this eventually subsided, and was soon evident that this approach had failed. In an effort to revive the momentum and to solicit ideas from AGPF members about how to best move forward with the network, a short survey was sent to members asking what their needs were, both in terms of the network as well as in terms of the technical facilities at their disposal. The response rate was extremely low; however, those who provided feedback confirmed what the participation level suggested: namely, that information technology facilities in Africa are such that very few individuals, outside of some well-equipped academic or private institutions, have regular access to the internet. The web-based discussion board was, therefore, in practice a highly unsustainable option for the majority of participants. The point was also raised that it was not enough to be connected electronically; there was also a need to share a more tangible goal or project, and to have a more visible leader from within the group, to galvanize efforts and motivate continued interaction. One respondent explained that finding the time to contribute to such networks is extraordinarily difficult for many Africans, who often "wear many hats". As a result, a general interest was insufficient to justify diverting time from other tasks; a concrete, realizable goal was essential for engaging individuals who already feel over-stretched. As a consequence of these inputs, an email-based forum was established, since most AGPF members have better access to email than to the internet, and a moderator was temporarily appointed over the group. Activity on the forum improved and continues today, more than two years later, though interventions are irregular and generally extend to the sharing of information or material of interest, rather than discussions about issues. The India course on Genomics and Public Health Policy was held in January 2003, less than one year after the inaugural Nairobi effort. Based on feedback from the previous course, the questionnaire requesting feedback about participants' technical and substantive needs in relation to the creation of a network was distributed during the course, to permit the creation of a network that was much more responsive to the needs of the participants. Moderators from among the participants were nominated before the course' end and their roles clarified, to facilitate the sustainability and autonomy of the network. Later in 2003, two further courses were held in Oman and in Venezuela, both of which added a further element demonstrating the learning from the first two courses. On both occasions, the Joint Centre for Bioethics collaborated with the Regional Offices of the World Health Organization; in the first instance, with the Eastern Mediterranean office (EMRO) and in the second, with the Pan-American Health Organization (PAHO). This collaboration ensured that the recommendations of each course had an institutional structure through which they could be channelled, to reach the ear of decision-makers. EMRO and PAHO have extensive links with ministries of health within their regions, as well as with representatives from civil society and industry. This provided an opportunity for the results of the course to have a much wider impact. By contract, the impact of the Nairobi course is very much linked to the efforts of individual participants to engage with their constituencies and with the NEPAD initiative, of which one of their members is now a senior actor. The Forum developed following the Nairobi course has not provided a framework to drive action the way it was initially intended; however, it continues to provide a portal for information-sharing and dialogue. Final Remarks The executive course on Genomics and Public Health Policy in Africa was the first of its kind to be held on the continent. The response of participants indicated a tremendous enthusiasm for and interest in discussing the emerging technology of genomics and its applications for addressing the health woes of Africans. The sessions covered a spectrum of topics, from basic science, to ethics, business models and international frameworks – exemplifying the range of intersecting issues relevant to informed discussions about genomics and related policy. The course also was a demonstration of the fruitfulness of a multi-stakeholder approach. An important aim of the course was to encourage network-building and the development of meaningful interactions, as a foundation for sustained dialogue among opinion leaders. Participants were encouraged to develop independent proposals in a collaborative environment, rather than to be passive recipients of "expertise" from the session leaders. The result was a series of concrete proposals for action, and the establishment of an e-network to provide a forum for ongoing communication, discussion and elaboration of the issues and proposals raised during the course. Several participants agreed to raise the proposals and themes articulated to their colleagues; the course also generated some publicity, as journalists invited to attend and to participate actively in the meeting reported on the key issues in various media [[ 16 , 17 ]; see also [ 18 ]]. Since the completion of this course, three more offerings have taken place, one in India in collaboration with the Indian Council for Medical Research (ICMR) in January 2003, another in Oman in August 2003, and a third in Venezuela in 2004. A fourth course is being planned for a venue in South-east Asia. The Nairobi offering demonstrated clearly the receptiveness of African researchers and policy makers to such an initiative, and captured the vision of a cross-section of stakeholders around how to ensure that the new wave of scientific promise does not pass them by, or crush them in its wake, but instead is harnessed for better health and to further economic development in their region [ 19 ]. The courses in India and Oman similarly gave rise to regional e-networks [ 20 ], which may eventually be connected to form an inter-regional forum for dialogue to form a basis for the sharing of experiences and expertise across regions in the developing world. Each of the three executive course held to-date has addressed similar themes in relation to genomics and health; but each has also been adapted to the particular context and interests of the host country or region. This has partly been achieved through active collaboration between the Joint Centre for Bioethics and the host institutions. The electronic networks provide a means of generating a long-term impact, driven by participants who are empowered, in their particular capacities, to take forward the ideas shared and the proposals developed through their interaction. The Nairobi course also highlighted the importance of being proactive in soliciting suggestions from participants about creative means of virtual networking that realistically address the poor information technology infrastructure in most parts of Africa. It also was instructive in demonstrating that a network is not itself self-sustaining; it must be driven by a clear, shared vision among participants, and possibly even a concrete and realizable project. Moreover, ideally a moderator from within the group should take leadership in feeding the forum, and motivating ongoing participation. The New Partnership for Africa's Development (NEPAD) has made science and technology (including genomics and biotechnology) a key platform in its plan for economic renewal [ 2 , 9 ]. Indeed, the recommendations outlined above overlap considerably with those described in a recent document detailing the resolutions of the first science and technology workshop of NEPAD, held in February 2003 [ 2 ]. The recent establishment of the African Biosciences Facility as a centre of scientific and technological excellence in the region, is further evidence that the recommendations articulated by the AGPF reflect a more widely shared vision. There is a growing recognition in Africa, and internationally, of the role that genomics and biotechnology can play, not only in alleviating health scourges of the poor, but also in addressing some of their economic concerns. With appropriate emphasis on its health needs, incentives for meaningful partnerships, sound regulatory structures, innovation and foresight, Africa could be in a position to benefit from genomics and related fields of biotechnology. The Course and Genomics and Health Policy in Africa had as its overarching goal that of bringing together a vibrant cross-section of individuals to foster dialogue around this timely issue. The African Genome Policy Forum works to build on this foundation, to sustain the momentum of the course, and to fulfill some of the participants' proposed goals. Perhaps most significantly, this series of courses represents a practical and effective mechanism for drawing together a variety of actors to address an issue of recognized import, which deserves a truly inter-disciplinary approach. Moreover, it is an initiative that generates important debate, but which is ultimately focused around generating concrete proposals to inform policymaking. Competing interests The author(s) declare that they have no competing interests. Authors' contributions All authors participated in and contributed to the course. ACS drafted the manuscript. PAS and ASD conceived of the course, refined the manuscript for critical content and approved final version; and with JM, participated the course design and its coordination. AGFP members provided intellectual input, through their lively discussions and proposals during the Course on Genomics & Public Health Policy in Africa, held 4–8 March 2002. Funding The Canadian Program from Genomics and Global Health is funded by several sources listed at . This course was funded primarily by Genome Canada and the International Development Research Centre (Canada). PAS holds a Distinguished Investigator Award from the Canadian Institutes for Health Research. ASD is supported by the McLaughlin Centre for Molecular Medicine. The African Centre for Technology Studies, which hosted the course, was supported by the Norwegian Agency for Development Co-operation. Table 4 Reading materials. 1 Scherer, S.W. 2001. The Human Genome Project. Isuma: Canadian Journal of Policy Research Vol. 2, No. 3, 11–19. 2 OWENS, K., KING, M-C. 1999, Genomic views on human history. Science 286, 451–455. 3 ROSES, A.D. 2000, Pharmacogenetics and the practice of medicine. Nature 405, 857–865. 4 Nature, Human Genome Volume, Vol. 409, Feb. 2001. 5 Science, Human Genome Volume, Vol. 291 Feb. 2001. 6 Nature, Human Genome Volume, Vol. 409, Feb. 2001. 7 Science, Human Genome Volume, Vol. 291 Feb. 2001. 8 PA Singer, AS Daar (2001). Harnessing Genomics and Biotechnology to Improve Global Health Equity. Science, 294 pp87–89 9 PA Singer, AS Daar (2000). Avoiding Frankendrugs. Nature Biotechnology, 18(12) 1225. 10 Walter W. Powell (1998). "Learning from Collaboration: Knowledge and Networks in the Biotechnology and Pharmaceutical Industries". California Management Review, vol. 40 (3), Spring. 11 Calestous Juma and Norman Clark. "Technological Catch-up: Opportunities and Challenges for Developing Countries". SUPRA Occasional Paper, Research Centre for the Social Sciences, University of Edinburgh (February, 2002). 12 Von Hippel, E. 1986. Lead Users: a source of novel product concepts. Management Science, Vol. 32, No. 7, pp. 791–805. 13 OECD, 1998. National Systems of Innovation. OCED, Paris. 14 1. Stefan Thomke, Ashok Nimgade (2001). "Millenium Pharmaceuticals, Inc." Harvard Business Law Review. 24pp. 15 2. Ray A. Goldberg. "Gene Research, the Mapping of Life and the Global Economy". Harvard Business Review. 58pp. 16 Philippe Cullet. "Trips and the Human Right to Health in Developing Countries". International Environmental Law Research Centre. (See ) 17 Jean O. Lanjouw (April 2001)."A Patent Policy Proposal for Global Diseases". Yale University, Brookings Institution and the NBER 18 Hartley & Hartley. "Limitations on using existing legal doctrines in addressing changes in technology: the example of the "Fertility Fraud" cases at UC Irvine". See Hartley & Hartley Attorneys at Law (California) at 19 "Declaration on the TRIPS Agreement and Public Health" (2001). WTO Ministerial Meeting, Doha, Qatar. 20 A.S. Daar, J.-F. Mattei. Appendix 2: Draft Guiding Principles and Recommendations, with alternative suggestions, after receiving comments. Medical Genetics and Biotechnology: Implications for Public Health. December 1999, World Health Organization. 21 A.S. Daar, J.-F. Mattei. Chapter 6: The Human Genome Diversity Project. Medical Genetics and Biotechnology: Implications for Public Health. December 1999, World Health Organization. 22 A.S. Daar, J.-F. Mattei. Chapter 7: Issues Raised by Conducting Research With Indigenous and Genetically Defined Communities. Medical Genetics and Biotechnology: Implications for Public Health. December 1999, World Health Organization. 23 HUGO Ethics Committee. Statement on Benefit-Sharing. April 9, 2000. 24 B.M. Knoppers, M. Hirtle, S. Lormeau. Statement on the Principled Conduct of Genetic Research. HUGO Ethical, Legal, and Social Issues Committee Report to HUGO Council, March 1996. 25 Statement of the WHO Expert Consultation on New Developments in Human Genetics. World Health Organization, 2000. 26 PA Singer, DK Martin, M Giacomini, L Purdy (2000). Priority setting for new technologies in medicine: qualitative case study. BMJ, 321(7272):1316-8. 27 N Daniels (2000). Accountability for reasonableness. BMJ, 321(7272):1300-1. 28 DK Martin, JL Pater, PA Singer (2001). Priority-setting decisions for new cancer drugs: a qualitative case study. Lancet, 358(9294):1676-81. 29 Mugabe, J. et. al. 1996. Managing Access to Genetic Resources: Strategies for Sharing Benefits. ACTS Press, Nairobi. 30 Mugabe, J. and Clark, N. 1997. Technology Transfer and the Convention on Biological Diversity. ACTS Press, Nairobi. 31 Sanchez, V. and Juma, C. 1993. Biodiplomacy. (Chapter 1). ACTS Press, Nairobi.

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Hairy Transcriptional Repression Targets and Cofactor Recruitment in Drosophila

Members of the widely conserved Hairy/Enhancer of split family of basic Helix-Loop-Helix repressors are essential for proper Drosophila and vertebrate development and are misregulated in many cancers. While a major step forward in understanding the molecular mechanism(s) surrounding Hairy-mediated repression was made with the identification of Groucho, Drosophila C-terminal binding protein (dCtBP), and Drosophila silent information regulator 2 (dSir2) as Hairy transcriptional cofactors, the identity of Hairy target genes and the rules governing cofactor recruitment are relatively unknown. We have used the chromatin profiling method DamID to perform a global and systematic search for direct transcriptional targets for Drosophila Hairy and the genomic recruitment sites for three of its cofactors: Groucho, dCtBP, and dSir2. Each of the proteins was tethered to Escherichia coli DNA adenine methyltransferase, permitting methylation proximal to in vivo binding sites in both Drosophila Kc cells and early embryos. This approach identified 40 novel genomic targets for Hairy in Kc cells, as well as 155 loci recruiting Groucho, 107 loci recruiting dSir2, and wide genomic binding of dCtBP to 496 loci. We also adapted DamID profiling such that we could use tightly gated collections of embryos (2–6 h) and found 20 Hairy targets related to early embryogenesis. As expected of direct targets, all of the putative Hairy target genes tested show Hairy-dependent expression and have conserved consensus C-box–containing sequences that are directly bound by Hairy in vitro. The distribution of Hairy targets in both the Kc cell and embryo DamID experiments corresponds to Hairy binding sites in vivo on polytene chromosomes. Similarly, the distributions of loci recruiting each of Hairy's cofactors are detected as cofactor binding sites in vivo on polytene chromosomes. We have identified 59 putative transcriptional targets of Hairy. In addition to finding putative targets for Hairy in segmentation, we find groups of targets suggesting roles for Hairy in cell cycle, cell growth, and morphogenesis, processes that must be coordinately regulated with pattern formation. Examining the recruitment of Hairy's three characterized cofactors to their putative target genes revealed that cofactor recruitment is context-dependent. While Groucho is frequently considered to be the primary Hairy cofactor, we find here that it is associated with only a minority of Hairy targets. The majority of Hairy targets are associated with the presence of a combination of dCtBP and dSir2. Thus, the DamID chromatin profiling technique provides a systematic means of identifying transcriptional target genes and of obtaining a global view of cofactor recruitment requirements during development.

Introduction Transcriptional repression is an important feature of developmental processes, where it is necessary for establishing intricate patterns of gene expression (reviewed in Herschbach and Johnson 1993 ; Gray and Levine 1996 ; Hanna-Rose and Hansen 1996 ; Courey and Jia 2001 ; Gaston and Jayaraman 2003 ). Drosophila embryogenesis is marked by the subdivision of the embryo into progressively more precise spatial domains, achieved through the coordinated functions of both transcriptional activators and repressors (maternal→gap→pair-rule→segment polarity; for review, see Lawrence 1992 ). One such developmental repressor is the pair-rule gene hairy, which sits at a key position in the segmentation gene hierarchy: it is one of the first genes to show the reiterated periodicity that is central to the establishment of proper embryonic body plan throughout metazoa ( Ingham et al. 1985 ). During segmentation, hairy behaves genetically as a negative regulator of a downstream (secondary) pair-rule gene, fushi tarazu ( ftz ; Carroll and Scott 1986 ; Howard and Ingham 1986 ). In addition to embryonic segmentation, Hairy also regulates several other developmental processes (cf. Brown et al. 1995 ; Davis and Turner 2001 ; Myat and Andrew 2002 ). For example, during larval development, Hairy is required for proper peripheral nervous system development, where it is a negative regulator of the proneural basic Helix-Loop-Helix (bHLH) activator gene achaete ( ac; Botas et al. 1982 ; Ohsako et al. 1994 ; Van Doren et al. 1994 ). Hairy belongs to the evolutionarily conserved Hairy/Enhancer of split/Deadpan (HES) subclass of repressor bHLH proteins ( Rushlow et al. 1989 ). These proteins function throughout development as dedicated transcriptional repressors of genes necessary for cell fate decisions in processes including segmentation, myogenesis, somitogenesis, sex determination, vasculogenesis, mesoderm formation, and neurogenesis (reviewed in Fisher and Caudy 1998a ; Davis and Turner 2001 ). Misregulation of HES family members has been linked to developmental defects and oncogenesis. In Drosophila, the HES family consists of Hairy and twelve other structurally related proteins, including Deadpan and seven members of the Enhancer of split complex . All members of this repressor family possess a highly conserved bHLH domain, required for DNA binding and protein dimerization; an adjacent Orange domain, which confers specificity among family members; and a C-terminal tetrapeptide motif, WRPW, which has been shown to be necessary and sufficient for the recruitment of the corepressor Groucho. HES proteins have been shown to bind preferentially to Class C sites (CACNNG; C-box) as homodimers in vitro ( Sasai et al. 1992 ; Tietze et al. 1992 ; Oellers et al. 1994 ; Ohsako et al. 1994 ; Van Doren et al. 1994 ). The prevailing view is that Hairy functions as a promoter-bound repressor: an intact bHLH region is required for Hairy to bind to specific DNA sites, where it then recruits cofactors to mediate its activities. Indeed, ac has been shown to be a direct transcriptional target of Hairy during peripheral nervous system development ( Ohsako et al. 1994 ; Van Doren et al. 1994 ; Fisher et al. 1996 ). However, while ftz was identified as a genetic target of Hairy during segmentation, there is currently no evidence for Hairy binding directly to the ftz promoter to regulate its transcription (despite the efforts of several labs to find such an association). A common theme among DNA-bound transcriptional regulators is the recruitment of coactivators or corepressors to carry out their functions (reviewed in Mannervik et al. 1999 ; Bone and Roth 2001 ; Urnov et al. 2001 ; Jepsen and Rosenfeld 2002 ). Three such cofactors have been identified as Hairy-interacting proteins that are required for Hairy-mediated transcriptional repression: Groucho, Drosophila C-terminal binding protein (dCtBP), and Drosophila silent information regulator 2 (dSir2) ( Paroush et al. 1994 ; Poortinga et al. 1998 , Phippen et al. 2000 , Rosenberg and Parkhurst 2002 ). None of these cofactors bind DNA themselves, but they are brought to the DNA through their interaction with sequence-specific DNA binding repressors, such as Hairy. Groucho was the first cofactor shown to be required for Hairy-mediated repression, where it was shown to enhance the hairy mutant phenotype ( Paroush et al. 1994 ). Groucho, as well as its mammalian homologs collectively called TLEs (TLE1–4), share a similar overall domain structure (reviewed in Parkhurst 1998 ; Fisher and Caudy 1998b ). Groucho has been proposed to utilize a chromatin remodeling mechanism through its recruitment of Rpd3 ( Drosophila histone deacetylase 1 homolog), but the evidence for the significance of this interaction is somewhat mixed ( Chen et al. 1999 ; Mannervik and Levine 1999 ; Courey and Jia 2001 ). C-terminal binding protein (CtBP) family members are an interesting new class of transcriptional coregulators that encode nicotinamide adenine dinucleotide + –dependent (NAD + -dependent) acid dehydrogenases (reviewed in Turner and Crossley 2001 ; Chinnadurai 2002a , 2002b ; Kumar et al. 2002 ). CtBP proteins function as context-dependent cofactors: they act as either coactivators or corepressors of transcription, with distinct regions of the CtBP protein being required for activation or repression ( Nibu et al. 1998a , 1998b ; Poortinga et al. 1998 ; Phippen et al. 2000 ; Chinnadurai 2002a ). The mechanism of CtBP coactivation is not known. CtBP proteins, however, have also been postulated to use a chromatin-based mechanism when functioning as a corepressor for transcription: they can bind to histone deacetylases and have been shown to modify histones ( Sundqvist et al. 1998 ; Shi et al. 2003 ). Like its yeast homolog, dSir2 encodes NAD + -dependent histone deacetylase activity that is required for heterochromatic silencing ( Rosenberg and Parkhurst 2002 ; Newman et al. 2002 ; reviewed in Gottschling 2000 ; Imai et al. 2000 ; Denu 2003 ). While yeast silent information regulator 2 (Sir2) has been thought to function as a dedicated heterochromatic silencing factor, dSir2, and more recently the human Sir2-related protein SIRT1, have been shown to play a role in euchromatic repression by interacting with Hairy and other HES family members ( Rosenberg and Parkhurst 2002 ; Takata and Ishikawa 2003 ). dSir2 mutants are viable ( Newman et al. 2002 ) and exhibit a dominant genetic interaction with hairy, resulting in derepression of Ftz expression ( Rosenberg and Parkhurst 2002 ), suggesting that Sir2 in higher organisms plays a role in both euchromatic repression and heterochromatic silencing. The choice of cofactor recruited by a particular DNA-bound repressor has been proposed to help distinguish among the mechanisms of repression employed. Despite the importance of Hairy and other HES family proteins in many developmental regulatory processes, little is known about the number and kinds of target genes they regulate. Understanding the spectrum of direct targets will be essential to addressing mechanistic questions such as how or when different cofactors are recruited. To this end, we have used the chromatin profiling technique DamID to systematically identify direct Hairy transcriptional target genes and to obtain a global view of the cofactors Hairy recruits to the various loci at which it acts. Results Identification of Direct Hairy Transcriptional Targets in Drosophila Kc Cells Using the Chromatin Profiling Technique DamID To identify direct transcriptional targets for Hairy in vivo, we employed a powerful new chromatin profiling technique, DamID, in which E. coli DNA adenine methyltransferase (Dam) tethered to a chromatin binding protein leads to specific methylation of DNA adjacent to the protein binding/recruitment sites ( van Steensel and Henikoff 2000 ; van Steensel et al. 2001 ). We generated a functional Dam–Hairy fusion construct under the control of the heat shock promoter to use in Drosophila Kc cells (see Materials and Methods ). Since overexpression of Dam fusion constructs leads to a high level of nonspecific methylation ( van Steensel and Henikoff 2000 ), only low-level leaky expression from the uninduced heat shock promoter was used: the cells were not heat shocked. Genomic DNA was isolated from the Kc cells 24 h post transfection, and methylated DNA fragments were recovered on a sucrose gradient following digestion of the genomic DNA with the methylation-sensitive enzyme DpnI. These methylated fragments were labeled with the Cy5 (Dam–Hairy fusion protein) and Cy3 (Dam alone, a control for nonspecific binding/accessibility; van Steensel and Henikoff 2000 ) fluorochromes, then cohybridized to a Drosophila microarray chip containing approximately 6200 full-length Drosophila Gene Collection (DGC) cDNAs and ESTs (DGC Release 1; Rubin et al. 2000 ) representing roughly half of the fly cDNAs. Putative targets were identified based on the Cy5:Cy3 fluoresence ratio ( van Steensel and Henikoff 2000 ; van Steensel et al. 2001 ). The DamID chromatin profiles were generated as previously described ( van Steensel et al. 2001 ; Orian et al. 2003 ) and subjected to a series of statistical analyses to determine the statistically significant targets (see Materials and Methods ; Datasets S1 and S2 ). We identified 40 statistically significant putative direct Hairy transcriptional targets in Kc cells ( Table 1 ). For just over half of these putative Hairy targets, some genetic, molecular, or functional information exists, allowing us to divide them roughly into three functional categories: those affecting morphogenesis (e.g., egghead [egh], kayak, pointed, mae ), those affecting cell cycle or cell growth (e.g., string (stg), ImpL2, Idgf2 ), and those with unknown/unlinked functions. Unfortunately, the two previously identified Hairy targets, ftz and ac, are not present in the DGC Release 1 cDNA set used to generate our microarray chips. Table 1 Hairy Targets Identified in Kc Cells DamID was recently used to identify targets for the Drosophila Myc/Max/Mad-Mnt network of bHLH leucine zipper proteins ( Orian et al. 2003 ), which shares many structural and functional similarities with the HES network of bHLH proteins ( Gallant et al. 1996 ). Using the same Drosophila cDNA microarray chips, Orian et al. (2003) found that hundreds of binding sites are occupied by dMyc (287 targets) or dMnt (429 targets), and that their expression is modulated by dMyc in the Drosophila larva. Their study is consistent with a global role for Myc family proteins in modulating chromatin responsiveness of targets, and identified most of the transcriptional targets that had been found previously utilizing other approaches. As our current knowledge of direct Hairy transcriptional targets for comparison is minimal, we applied a higher stringency than Orian et al. (2003) when analyzing our Hairy DamID datasets so that we would reduce the likelihood of getting false positives. However, at this stringency we may be missing some bona fide Hairy targets. We compared the Hairy targets we identified with those identified for dMyc and dMnt using datasets analyzed at the higher statistical stringency ( Figure 1 ). As might be expected, there was minimal overlap of Hairy targets with those identified for the transcriptional activator dMyc (three of 40 Hairy targets) ( Figure 1 A). There was also little overlap of Hairy targets with those identified for the transcriptional repressor dMnt (nine of 40 Hairy targets) ( Figure 1 B). Even when the less stringent statistics were applied to the datasets, we did not see additional overlap (data not shown). Thus, sequence-specific DNA binding factors are exhibiting binding specificity in the DamID assay, and the 40 statistically significant putative direct Hairy transcriptional targets we identified are what might be expected for a nonglobally acting sequence-specific DNA binding developmental repressor. Figure 1 Hairy Binds to a Specific Set of Transcriptional Targets (A and B) Comparison of DamID-identified targets for Hairy with the Drosophila Myc and Mad/Mnt family proteins. Venn diagram comparing DamID-identified Hairy downstream targets in Kc cells compared to the transcriptional activator dMyc (A) and the transcriptional repressor dMnt (B). (C) Venn diagram comparing DamID-identified Hairy targets from Kc cells and embryos. Identification of Direct Hairy Transcriptional Targets in Early Embryos Using DamID Since Hairy is part of the segmentation gene transcriptional regulatory cascade, we expected to find segmentation-related transcription factors as downstream targets of Hairy. The putative Hairy targets we identified in Kc cells do not fulfill this expectation, but rather suggest roles for Hairy in cell cycle, cell growth, and morphogenesis; these putative targets are likely targets for Hairy during its other developmental roles. This could be because of cellular context (i.e., Kc cells are thought to be embryonic neuronal stem cell in origin and may reflect Hairy's later role in neurogenesis rather than segmentation), or because only half of the Drosophila cDNAs are present on the chip (and the ones responding to Hairy during segmentation are not in this subset), or because the mechanism by which Hairy acts during segmentation is different than expected. To begin distinguishing among these possibilities, we used the DamID approach to identify Hairy targets in Drosophila embryos during segmentation. Towards this aim, we generated functional transgenic flies carrying a UAS–Dam or UAS–Dam–Hairy fusion gene construct (see Materials and Methods ). As with the Kc cells, we did not drive overexpression of these Dam fusion constructs, but rather relied on the leaky expression from the minimal promoter of the pUASp vector. Genomic DNA was harvested from 2–6-h embryos (at and just after peak Hairy expression during segmentation), then used to generate probes for the microarray chips, similar to the procedure used for the Kc cells (see Materials and Methods ; Datasets S1 and S3 ). We identified 20 putative direct Hairy targets from the 2–6-h embryos, which fell into four broad functional categories: transcription factors, cell cycle or cell growth, morphogenesis, and unknown/unlinked functions ( Table 2 ). When compared to the 40 Hairy targets identified in Kc cells, we found that only one target, egh, overlapped between the datasets ( Figure 1 C). This result suggests, perhaps not surprisingly, that transcriptional targets exhibit context dependence/tissue specificity, and that the DamID approach is sensitive to developmental context/tissue specificity. Table 2 Hairy Targets Identified in Embryos (2–6 h) Taken together, the DamID profiles for Hairy targets from Kc cells and embryos identified 59 potential new direct targets of Hairy regulation. Importantly, one of the putative Hairy targets in embryos, paired (prd), is a homeobox-encoding transcription factor known to function in segmentation (cf. Baumgartner and Noll 1990 ). The Expression of Potential Target Genes Depends on Hairy Regulation In Vivo Direct Hairy targets would be expected to exhibit altered expression in a hairy mutant background compared to wild-type. For a subset of targets from both the Kc cell and embryo DamID experiments, we performed whole mount RNA in situ hybridization on wild-type and hairy mutant embryos ( hairy 7H ; Figure 2 and data not shown). For embryo targets, we examined early embryos representing the same stages used for the DamID analysis. In keeping with our primary focus on Hairy's role in segmentation, we chose as the subset of Kc target genes to examine genes known to be expressed in the embryo (but not necessarily as early as the embryo targets), since we would not expect all of the Kc cell targets to be expressed during embryogenesis. In all cases examined, the alterations in the levels, as well as spatial and temporal patterns, of putative target gene expression were consistent with derepression in a hairy mutant background ( Figure 2 ). For example, as previously described ( Lehman et al. 1999 ), segmental expression of stg is altered (expanded) in a hairy mutant background ( Figure 2 C and 2 D). Similarly, for prd, there is a failure of stripe sharpening consistent with a role for Hairy in prd repression and stripe maintenance ( Figure 2 A and 2 B; Gutjahr et al. 1993 ). Figure 2 Expression of Hairy Target Genes Is Disrupted in hairy Mutant Embryos Whole mount in situ hybridization on wild-type (A, C, E, G, I, K, and M) or hairy 7H mutant (B, D, F, H, J, L, and N) embryos with probes recognizing prd (A and B), stg (C and D), ImpL2 (E and F), mae (G and H), egh (I and J), kayak (K and L), or Idgf2 (M and N). Anterior is to the left. Dorsal is up, except in (M) and (N), which are dorsal views. hairy Exhibits Dominant Genetic Interactions withMutants Encoding Target Genes and Affects stg-lacZ Reporter Expression If Hairy is a direct regulator of a particular target gene, genetic interaction might be expected between hairy and a mutant corresponding to this putative target. Reduction of hairy dose might be expected to deregulate the expression of its target gene, resulting in increased or spatially aberrant expression of its target gene. We examined seven of the 15 Hairy targets for which mutant alleles are available for genetic interaction with hairy ( Table 3 ). In all seven cases, we observed dominant genetic interactions where a reduced number of transheterozygous progeny survive (i.e., synthetic lethality). Embryos from mothers heterozygous for either hairy ( hairy/+ ) or its target gene (i.e., prd/+ ) alone were viable. The reduction of Hairy in this target-gene-sensitized background allows inappropriate target gene regulation (i.e., target gene expression in spatial domains where it should not be expressed, with subsequent embryo lethality). Table 3 Dominant Genetic Interactions between hairy and Mutants Corresponding to Its Putative Downstream Targets a Homozygous egh 7 exhibits pupal (not embryonic) lethality For one Hairy target identified in Kc cells, stg, a series of transgenic lines have been generated in which lacZ expression is driven from different promoter fragments ( Lehman et al. 1999 ). For stg to be a direct transcriptional target of Hairy, we would expect Hairy to bind to the stg promoter. To narrow down regions of the stg promoter sensitive to Hairy, we examined the expression of four of these stg-lacZ reporter genes in hairy mutant and wild-type backgrounds. Sequence analysis of the promoter fragments for each of the four reporter genes revealed the presence of canonical Hairy binding sites in two of them (pstg β-E4.9 and pstg β-E6.4), but not the other two (pstg β-E2.2 and pstg β-E6.7). Consistent with the presence of Hairy binding sites, the lacZ expression from pstg β-E4.9 and pstg β-E6.4, but not from pstg β-E2.2 or pstg β-E6.7, was derepressed (expanded) in a hairy mutant background compared to wild-type ( Figure 3 ; data not shown). We mutated the C-box (Hairy binding site) in the pstg β-E4.9 reporter construct (CACGCG→C T CGC A ) to generate pstg β-E4.9 Δhairy . This mutation abolishes Hairy binding in vitro (see next section and Materials and Methods). Wild-type flies carrying this pstg β-E4.9 Δhairy reporter exhibit the same lacZ derepression as observed for the original pstg β-E4.9 reporter when in a hairy mutant background, indicating that the derepression is due to Hairy binding ( Figure 3 G). Figure 3 hairy Affects stg-lacZ Reporter Expression (A–F) β-galactosidase expression from the stg-lacZ reporter lines pstg β-E4.9 (A and B), pstg β-E6.4 (C and D), and pstg β-E6.7 (E and F) in wild-type (A, C, and E) and hairy mutant (B, D, and F) embryos. Note the expanded (de-repressed) lacZ expression in the hairy mutant background compared to wild-type for the E4.9 and E6.4 lines (compare [B] to [A] and [D] to [C], respectively). (G) β-galactosidase expression from the stg-lacZ reporter line pstg β-E4.9 ΔHairy (same as the reporter construct shown in [A], but with a Hairy binding site mutation) in a wild-type background. Note the expanded (de-repressed) lacZ expression (compare with [A]). Anterior is to the left. Dorsal is up in (A–D) and (G), whereas the ventral surface is shown in (E–F). Hairy Binds Directly to Target Genes Hairy has been shown to bind at Class C sequences (ggCACGCG A / C C) that contain the canonical core Hairy site (CACGCG). We searched for this consensus site within the promoter and transcribed regions of three Hairy targets: stg, egh, and prd . We identified one site in prd, three in egh, and four in the stg genomic region ( Figure 4 A). In the latter case, we focused on the site within the 4.9-kb promoter fragment, as its segmental expression was derepressed in a hairy mutant background (see above). We tested whether the identified sites are direct Hairy binding sites in electromobility shift assays (EMSAs), utilizing bacterially purified full-length Hairy protein and 32 P-labeled oligos containing the appropriate Hairy binding sites (see Materials and Methods ). The C-box within the ac promoter, a bona fide Hairy target ( Ohsako et al. 1994 ; Van Doren et al. 1994 ), served as our positive control. A slow migrating complex was observed when the ac probe was incubated with GST–Hairy protein, but not with GST alone ( Figure 4 B, compare lanes 2 and 3). This binding is specific: the complex is competed by excess unlabeled wild-type ac oligo, but not by excess mutated ac oligo ( Figure 4 B, lanes 4 and 5, respectively). Similar assays showed direct and specific binding to the sole C-box site within the prd promoter, as well as to the site within the stg 4.9-promoter region ( Figure 4 C). While an oligo containing the wild-type Hairy binding site efficiently competes with Hairy binding to the stg 4.9-promoter region in EMSAs, an oligo encoding the mutated Hairy site used in the pstg β-E4.9 Δhairy reporter is unable to compete ( Figure 4 D). Three putative sites were identified within the egh promoter. Hairy binding to these sites was differential, and can be summarized as egh1 > egh3 > egh2 ( Figure 4 E; compare lanes 3, 7, and 11). This preferential binding may reflect sequences flanking the core C-box (CACGCG; see Figure 4 A). Indeed, experiments with the related fly Enhancer of split proteins have shown that even subtle sequence changes within the core C-box or flanking sequences have dramatic consequences for the overall range of proteins that can bind in vivo ( Jennings et al. 1999 ). We have used several bioinformatics approaches to analyze Hairy target gene promoters, to determine if there are conserved sequences flanking the core Hairy binding sites or association of the Hairy binding sites with other transcription factor binding sites as defined by the TRANSFAC database that correlate with the context dependence of Hairy binding. However, we have been unable to uncover any common features of regulation, perhaps because of the relatively small sample size of Hairy targets for these types of approaches (see Materials and Methods ; data not shown). Figure 4 Binding of Hairy to Class C (C-Box) Sites in Putative Targets In Vitro (A) Schematic diagram (not to scale) of C-boxes within putative Hairy targets. C-boxes (Hairy binding sites) are denoted by white boxes, black arrows indicate transcription start sites (Ra, Rb, and Rc), ATG denotes the initiating methionine, and capital letters indicate bases matching with the Hairy consensus C-box. The distances in kilobases of the C-boxes from transcription start sites are noted in gray. (B) EMSA with either GST or GST–Hairy and the ac h/E-1 oligonucleotide. Lane 1, probe alone; lane 2, binding to probe by GST; lanes 3–5, binding to probe by GST–Hairy. In lanes 4 and 5, binding to probe by GST–Hairy was in the presence of competitor unlabeled oligos. An arrow indicates the Hairy–DNA complex; comp wt and comp mut indicate wild-type and mutated cold probes, respectively. (C) EMSA with either GST or GST–Hairy to the C-boxes within the stg and prd genes. Lanes 1–5, GST and GST–Hairy binding to the stg C-box (location: 25072658); lanes 6–10, GST and GST–Hairy binding to the prd C-box. (location: 12074032). Lane order and annotations are as in Figure 4 B. (D) EMSA with GST–Hairy to the same C-box within the stg 4.9-kb genomic fragment is not competed by the presence of mutant competitor unlabeled oligo. Lane 1, probe alone; lane 2, binding to GST; lane 3, binding to probe by GST–Hairy; lanes 4 and 5, binding to probe by GST–Hairy in the presence of wild-type and mutant competitor unlabeled oligos, respectively. (E) Differential binding to C-boxes within the egh gene. EMSA with either GST or GST–Hairy to C-boxes within the egh promoter and transcribed region. Binding to three putative C-box sites is shown: egh1 (location: 2341609), egh2 (location: 2350367), and egh3 (location: 2352168). Lanes 1, 5, and 9: probe alone; lanes 2, 6, and 10: binding to probes by GST; lanes 3, 7, and 11: binding to probes with GST–Hairy. Lanes 4, 8, and 12: binding with GST–Hairy in the presence of unlabeled wild-type competitor. C-box locations and promoter information generated using Apollo (Berkeley Drosophila Genome Project). Hairy Binds to Specific Sites on Polytene Chromosomes To confirm the genomic loci associated with Hairy in vivo, we examined binding of endogenous Hairy to third instar larval salivary gland polytene chromosomes using antibodies to Hairy ( Figure 5 ). We identified approximately 120 strongly staining sites for Hairy ( Figure 5 ). This is likely an underestimate as some bands stain more intensely than others and likely represent more than one closely spaced binding site. Hairy binding sites are, for the most part, distributed evenly along all chromosome arms ( Figures 5 and 9 A). Figure 5 Hairy Binds to Specific Loci on Polytene Chromosomes (A and B) Hairy staining (green) on third instar larval salivary gland polytene chromosome sets counterstained with DAPI (blue) to visualize the chromosomes. (C and D) Higher magnification of chromosome arms X, 3R (C) and 2L, 2R (D). Since there are a relatively small number of Hairy binding sites on the polytene chromosomes, the location of the bands can be determined cytologically with relatively high resolution. While we have not been able to unambiguously assign all of the approximately 120 binding sites cytologically, we examined whether Hairy staining corresponds to the targets identified in the Kc cells and embryo DamID experiments. There are 39 out of 40 Kc cell and 20 out of 20 embryo targets that map cytologically to regions that correspond to Hairy binding sites (e.g., Figure 6 A– 6 F). Thus, while tissue or developmental specificity appear to be lost, polytene chromosomes provide a reliable indicator for Hairy DNA binding targets. Note the presence of Hairy binding at the tip of the X chromosome, the cytological location of the direct Hairy transcriptional target ac ( Figure 6 A). Interestingly, we were unable to detect Hairy binding at position 84A, the cytological location for ftz ( Figure 6 B). Hairy binding was also detected at the cytological location for stg ( Figure 6 C) and egh ( Figure 6 D), as well as at 33C, the cytological location of prd ( Figure 6 E). Recent work established a role for Hairy in regulating salivary gland tube morphology that genetically depends, in part, on repression of huckebein (hkb), a zinc-finger-encoding transcription factor ( Myat and Andrew 2002 ). It is not yet known if Hairy's repression of hkb is direct or not. hkb is not in the DGC Release1 cDNA set used to generate our microarray chips, but we do find that one of the strong Hairy binding sites maps to 82A on polytene chromosomes, the cytological location of hkb (see Figure 6 F). Consistent with our identification of stg as a Hairy target, derepression of C-box-containing stg-lacZ reporter lines, and gel shift assays, we detect a new band of Hairy staining in chromosomes from larvae carrying the stg-lacZ (pstg β-E4.9) reporter at cytological location 1F, the transgene insertion site ( Figure 6 G– 6 J; see Materials and Methods ). Figure 6 Hairy Binds to Putative Target Loci on Polytene Chromosomes (A) Hairy binds to polytene region 1A, the location of the Hairy target, ac. (B) Hairy is not found at 84A, the cytological location for ftz. (C–F) Hairy also binds to polytene region 99A, the location of stg (C); polytene region 3A, the location of egh (D); polytene region 33C, the location of prd (E); and polytene region 82A, the location of hkb (F). (G–I) Hairy is recruited to the insertion site for the pstg βE-4.9 reporter construct (arrow in [H] and [I]). Compare to the equivalent region of wild-type X chromosomes marked by brackets in (A), (D), and (G). (J) In situ hybridization to polytene chromosomes from pstg βE-4.9 larvae showing that this line has two insertions on the X chromosome at 1F and 6C. The probe also recognizes sequences to the endogenous white locus (asterisk). Identification of Targets for Recruitment of the Transcriptional Cofactors Groucho, dCtBP, and dSir2 As with other sequence-specific DNA binding transcription factors, Hairy recruits cofactors to carry out its functions. One of the major questions in the field concerns how and when particular cofactors are recruited. It has been technically challenging to address this question with current methods such as ChIP assays, since cofactor association may be transient, unstable, or far removed from the DNA binding protein. Utilizing expression-based microarray analysis is also not easy, because of the difficulty in sorting direct from indirect interactions with such widely recruited cofactors. To circumvent these technical issues and as a first step towards understanding the rules governing Hairy cofactor recruitment, we used the DamID approach to determine if the three known Hairy cofactors, Groucho, dCtBP, and dSir2, are recruited to all or a subset of Hairy targets. We generated Dam fusions to Groucho and dCtBP (see Materials and Methods ). The Dam–dSir2 fusion construct was described previously ( van Steensel et al. 2001 ). While none of these cofactors binds DNA on its own, they are recruited to the DNA through their interaction with sequence-specific DNA binding proteins such as Hairy. Using the same procedure and statistical analyses used for the identification of Hairy targets in Kc cells (see Material and Methods; Datasets S1 and S4–S6 ), we identified 155 loci that recruit Groucho, 496 loci that recruit dCtBP, and 107 loci that recruit dSir2 in Kc cells ( Figure 7 ; Datasets S7–S9 ). Comparison for overlap between these cofactor datasets and that of Hairy from Kc cells showed that, surprisingly, only one of the putative Hairy targets we identified overlaps with Groucho recruitment ( Figure 7 A and 7 D). The majority of Hairy targets, however, overlap with dCtBP (38/40; Figure 7 B and 7 D), and most of these also overlap with dSir2 (34/40; Figure 7 C and 7 D). At present, we cannot rule out the possibility that a protein unrelated to Hairy is recruiting these cofactors to a given putative Hairy target. Interestingly, dCtBP and dSir2 appear to colocalize at loci outside the subset of putative Hairy targets (90% of dSir2 targets overlap with those of dCtBP; Figure 7 D). Figure 7 Hairy Overlaps with Cofactors Differentially (A–C) Venn diagram showing the overlap between Hairy targets and those loci also binding to the cofactors Groucho (A), dCtBP (B), and dSir2 (C). (D) Venn diagram showing combined overlaps of Hairy with its three known cofactors. Hairy Target Gene Expression Depends on Hairy Cofactor Regulation In Vivo If particular Hairy targets require specific cofactors to be appropriately regulated, we would expect their expression to be altered (deregulated) in a cofactor mutant background. We performed RNA in situ hybridization for two Hairy Kc cell targets that differentially recruit Groucho, dCtBP, and dSir2. We chose to examine the expression of two hairy targets that are expressed relatively early in the embryo since these cofactors are used in a number of developmental systems and exhibit severe morphological phenotypes when their activity is removed maternally (cf. Phippen et al. 2000 ). Consistent with a requirement for dCtBP and dSir2, stg expression is derepressed in dCtBP and dSir2, but not groucho mutant backgrounds ( Figure 8 A– 8 D). Similarly, consistent with a requirement for dCtBP alone, kayak expression is expanded in dCtBP, but not in groucho or dSir2 mutant backgrounds ( Figure 8 E– 8 H). While we cannot extrapolate the cofactor recruitment requirements from Kc cells to embryos, we used in situ hybridization as a prediction for cofactor recruitment for the embryo target, prd . We examined the expression of prd in cofactor mutant backgrounds and found that prd expression is altered in groucho and dCtBP, but not dSir2, mutant backgrounds ( Figure 8 I– 8 L), suggesting that prd may represent a minority of Hairy targets that could recruit both Groucho and dCtBP. Consistent with this finding, we find both Groucho and dCtBP staining on polytene chromosomes at the cytological location for prd (data not shown). Figure 8 Hairy Target Gene Expression Is Disrupted in the Mutant Background of the Cofactors Associated with a Particular Target Whole mount in situ hybridization on wild-type (A, E, and I), groucho germline clone (B, F, and J), dCtBP germline clone (C, G, and K), and dSir2 mutant (D, H, and L) embryos with probes recognizing stg (A–D), kayak (E–H), or prd (I–L). Anterior is to the left. Dorsal is up. The Transcriptional Cofactors Groucho, dCtBP, and dSir2 Are Recruited to Specific Sites on Polytene Chromosomes When DamID data for the three Hairy cofactors and Hairy itself are graphically projected onto chromosomes, several interesting features come to light ( Figure 9 A). For example, while Groucho and dCtBP are distributed along all the chromosomes, dSir2 shows region- and chromosome-specific binding (e.g., there are more dSir2 sites on Chromosome 2R than on Chromosome 3L). To confirm loci associated with recruitment of the different cofactors in vivo, we examined the localization of endogenous Groucho, dCtBP, and dSir2 on wild-type third instar larval salivary gland polytene chromosomes using antibodies to Groucho, dCtBP, and dSir2, respectively ( Figure 9 B– 9 D). Consistent with the relative numbers of targets identified for each of the cofactors by the DamID approach, we find many more sites for dCtBP than either Groucho or dSir2. Also consistent with our DamID findings, Groucho overlaps with Hairy at only a small number of the Hairy binding sites ( Figure 9 E), whereas dCtBP overlaps with the majority of Hairy binding sites ( Figure 9 F). Differences in distribution for the cofactors observed by DamID are reflected on the polytene staining patterns. For example, our DamID data suggest that the distal portion of Chromosome 2L has more sites for dCtBP than the proximal half of the chromosome. This observation is reflected in dCtBP recruitment on the polytene chromosomes as well ( Figure 9 F). Likewise, as predicted from the DamID data, dSir2 staining on the polytene chromosomes exhibits region-specific association in which some chromosomes and chromosomal regions exhibit a high degree of staining, while other whole chromosomes exhibit very little staining ( Figure 9 D and 9 G– 9 I; Rosenberg and Parkhurst 2002 ). Figure 9 Hairy Shows Context-Dependent Association with Its Cofactors (A) Sites of Hairy binding and Hairy cofactor recruitment based on DamID. The gray lines depict the relative position on the chromosomes of the approximately 6200 cDNAs on the microarray chip. The blue dots below the line represent Hairy binding sites while the green (Groucho), red (dCtBP), and yellow (dSir2) dots represent the positions of cofactor recruitment. (B–D) Cofactor recruitment visualized on third instar larval salivary gland chromosomes. Polytene chromosome sets stained (green) with antibodies to Groucho (B), dCtBP (C), and dSir2 (D). All chromosomes were counterstained with DAPI (blue) to visualize the DNA. (E) Higher magnification view of chromosome arms 2L and 2R costained with Groucho (red) and Hairy (green), and the merged image. (F) Higher magnification view of chromosome arm 2L costained with dCtBP (red) and Hairy (green), and the merged image, compared to the predicted DamID map. Note that both the DamID projected map and polytene chromosomes have more dCtBP recruitment sites to the left of the dashed line than to the right of the dashed line. (G) Chromosome arm 3R stained with dSir2 (green), highlighting regional specificity of dSir2 recruitment. (H and I) Higher magnification view of the distal ends of chromosome arms 2R (H) and 3L (I) from (D), stained with dSir2 (green), showing regional specificity and lack of dSir2 recruitment, respectively. Discussion We have known for almost two decades that Hairy plays a pivotal role in the segmentation hierarchy, as well as other developmental processes, but the details of Hairy action have not been easy to tease apart. An important step in understanding the molecular mechanisms surrounding Hairy-mediated repression was made with the identification of Groucho as a Hairy binding protein ( Paroush et al. 1994 ). One of the key remaining questions regarding the mechanism(s) of repression employed by Hairy concerns the identities of its direct transcriptional targets. We have employed a novel chromatin profiling approach, DamID, to effectively identify a total of 59 potentially direct Hairy targets from 2–6-h embryos and Kc cells. As expected of direct targets, these genes show Hairy-dependent expression, are detected as Hairy binding sites in vivo on polytene chromosomes, and have consensus C-box-containing sequences that are directly bound by Hairy in vitro. While the DamID approach had previously been used only in Kc cells, we found that this technique is also powerful when utilizing transgenic embryos that carry fusions of the protein of interest to the Dam methylase. As target genes are likely context dependent, the use of embryos makes it possible to choose the precise time or place of development to be examined, as well as allowing the analysis to take place in an organismal context. The 59 putative Hairy targets we identified in the embryo and Kc cell DamID experiments correspond to bands of Hairy immunostaining on polytene chromosomes, suggesting that the polytene chromosome staining faithfully represents Hairy binding. Polytene chromosomes are functionally similar in transcriptional activity and display factor/cofactor binding properties similar to chromatin of diploid interphase cells, despite their DNA endoreplication ( Hill et al. 1987 ; Andrew and Scott 1994 ; Hill and Mott 2000 ; Pile and Wassarman 2000 , 2002 ). Since the microarray chips we used contain roughly half of Drosophila cDNAs, we estimate the actual number of Hairy targets to be approximately twice that number (i.e., 118 targets). This predicted number of Hairy targets is close to the approximately 120 strongly staining sites we observe on polytene chromosomes. Of the 59 putative Hairy targets we identified in both the Kc cell and embryo DamID experiments, 58 correspond to bands of Hairy staining on the polytene chromosomes, suggesting that polytene chromosome staining is representing Hairy binding sites without regard to tissue specificity. It is not yet clear what is limiting Hairy accessibility in different tissues or why Hairy's access does not appear to be limited in salivary glands. It may be that polytene chromosome organization necessitates a looser chromatin structure or that the large number of factors that seem to be endogenously expressed in salivary glands affects accessibility. Ultimately, additional confirmation of the DamID and polytene staining correspondence will require microarray tiling chips containing overlapping genomic DNA fragments; however, such genomic DNA tiling chips are currently unavailable. Van Steensel and Henikoff (2000) showed that DNA methylation by tethered Dam spreads up to a few kilobases from the point where it is brought to the DNA. We were concerned in the beginning that we might miss Hairy targets if the DNA fragments of 2.5 kb or less that we recovered for probes were far away from the start of the transcribed region, especially since the Drosophila microarray chip we used was generated using full-length cDNAs. Indeed, as Hairy has been described as a long-range repressor ( Barolo and Levine 1997 ), it is likely to bind at a distance from the transcription start site. However, the targets we identified by DamID in both Kc cells and in embryos correspond closely to the Hairy staining pattern on polytene chromosomes. As is the case for Hairy, the distribution of DamID-identified loci that recruit the long-range repression-mediating Groucho corepressor ( Zhang and Levine 1999 ) corresponds well with the distribution of Groucho binding sites on polytene chromosomes. Our results suggest that there is a higher-order structure to the promoter that is allowing factors that bind far upstream of the transcription start site to have physical access to the transcribed region (i.e., DNA looping; reviewed in Ogata et al. 2003 ) or that Hairy does not bind as far away from the transcription start site as it has been proposed to do. Hairy Targets Hairy is needed at multiple times during development, where it has primarily been associated with the regulation of cell fate decisions. During embryonic segmentation, ftz has long been thought to be a direct Hairy target. However, the order of appearance of ftz stripes is not inversely correlated with those of Hairy, as would be expected if ftz stripes are generated by Hairy repression ( Yu and Pick 1995 ). While we were unable to assess ftz as a direct Hairy target using DamID, we did not find evidence for ftz being a direct Hairy target based on the association of Hairy with polytene chromosomes. Indeed, the evidence suggesting that ftz is a direct target of Hairy is based on timing, i.e., that there is not enough time for another factor to be involved (cf. Ish-Horowicz and Pinchin 1987 ). As the half-life of the pair-rule gene products is very short (less than 5 min; Edgar et al. 1986 ), it is possible that additional factors could be acting and that the interaction between Hairy and ftz is indirect. Interestingly, one of the Hairy targets we identified in embryos is the homeobox-containing transcriptional regulator, prd . Pair-rule genes have been split into two groups: primary pair-rule genes mediate the transition from nonperiodic to reiterated patterns via positional cues received directly from the gap genes, whereas secondary pair-rule genes take their patterning cues from the primary pair-rule genes and in turn regulate the segment polarity and homeotic gene expression. The transcriptional regulator prd was originally categorized as a secondary pair-rule gene since its expression is affected by mutations in all other known pair-rule genes. However, prd stripes were subsequently shown to require gap gene products for their establishment, and the prd locus has the modular promoter structure associated with primary pair-rule genes ( Baumgartner and Noll 1990 ; Gutjahr et al. 1993 ). Thus, prd has properties of both primary and secondary pair-rule genes and is a good candidate to directly mediate Hairy's effects on segmentation. We found that Hairy can specifically bind to C-box sequences in the prd promoter and interacts genetically with prd . Further experiments will be required to determine if Paired in turn binds to the ftz promoter, such that the order of regulation would be Hairy > prd > ftz . In addition to identifying potential targets for Hairy in segmentation, we identified targets that implicate Hairy in other processes including cell cycle, cell growth, and morphogenesis. The group of targets implicating Hairy in the regulation of morphogenesis includes: concertina, a G-alpha protein involved in regulating cell shape changes during gastrulation ( Parks and Wieschaus 1991 ); kayak, the Drosophila Fos homolog involved in morphogenetic processes such as follicle cell migration, dorsal closure, and wound healing ( Riesgo-Escovar and Hafen 1997 ; Dequier et al. 2001 ; Dobens et al. 2001 ; Ramet et al. 2002 ); pointed and mae, both of which function in the ras signaling pathway to control aspects of epithelial morphogenesis (cf. Beitel and Krasnow 2000 ; Baker et al. 2001 ; James et al. 2002 ); egh, a novel, putative secreted or transmembrane protein proposed to play a role in epithelial morphogenesis ( Goode et al. 1996 ); and Mipp1, a phosphatase required for proper tracheal development ( Ebner et al. 2002 ). Hairy has been thought to be involved mostly in the regulation of cell fate decisions. However, mosaic experiments in the eye imaginal disc have suggested that Hairy may also play a role in the regulation of cell cycle or cell growth ( Brown et al. 1995 ). Consistent with this, another group of Hairy targets implicates Hairy in the regulation of cell cycle or cell growth; this group includes stg, the Drosophila Cdc25 homolog (cf. Lehman et al. 1999 ); dacapo, a cyclin-dependent kinase inhibitor related to mammalian p27 kip1 /p21 waf1 ( Lane et al. 1996 ; Meyer et al. 2002 ); IDGF2, a member of a newly identified family of growth-promoting glycoproteins ( Kawamura et al. 1999 ); and ImpL2, a steroid-responsive gene of the secreted immunoglobulin superfamily that functions as a negative regulator of insulin signaling ( Garbe et al. 1993 ; Andersen et al. 2000 ; Montagne et al. 2001 ; Tapon et al. 2001 ; Johnston and Gallant 2002 ). Consistent with a role for Hairy in growth signaling, mammalian HES family proteins have been linked to insulin signaling ( Yamada et al. 2003 ). Since cells that are dividing or proliferating cannot simultaneously undergo the cell shape changes and cell migrations required for morphogenetic movements, Hairy may be required to transiently pause the cell cycle in a spatially and temporally defined manner, thereby allowing the cell fate decisions regulated by the transcription cascade to be completed. As Hairy is itself spatially and temporally expressed, Hairy must be only one of several genes necessary to orchestrate these processes. While much progress has been made in understanding the regulatory networks governing pattern formation, cell proliferation, and morphogenesis, and while it is clear that they must be integrated, the details surrounding their coordination have not yet been elucidated. Thus, the putative Hairy targets we identified are consistent with known processes involving Hairy and suggest that in addition to regulating pattern formation, Hairy plays a role in transiently repressing other events, perhaps in order to coordinate cell cycle events with the segmentation cascade. Further experiments will be needed to determine how these different roles for Hairy fit together. Cofactor Recruitment Corepressor recruitment is an important aspect of transcriptional repression (reviewed in Mannervik et al. 1999 ; Bone and Roth 2001 ; Mannervik 2001 ; Urnov et al. 2001 ; Jepsen and Rosenfeld 2002 ). While the sequence-specific DNA-bound repressors contribute to target specificity, the corepressors are thought to help distinguish among particular repression mechanisms to be used via alteration of their recruitment or function. For example, the Drosophila developmental factors Dorsal and T-cell factor (TCF) have been shown to function as either positive or negative regulators of transcription depending on promoter context and cofactor recruitment ( Dubnicoff et al. 1997 ; Cavallo et al. 1998 ). As each of Hairy's cofactors appears to act differently with Hairy, thereby conferring different developmental consequences, we used the DamID approach, along with polytene chromosome staining, to get our first look at the patterns of Hairy's cofactor recruitment. The numbers of loci that recruit Groucho, dCtBP, and dSir2 cofactors are consistent with the breadth of interaction they have been shown to exhibit. We identified by DamID profiling 155 loci that recruit Groucho and, as expected, found roughly twice as many sites on polytene chromosomes. Groucho was one of the first corepressors identified and shown to affect a variety of different developmental processes, suggesting that it is a widely used corepressor ( Parkhurst 1998 ; Chen and Courey 2000 ). In addition to its interaction with Hairy, Groucho was subsequently shown to mediate repression through several other classes of DNA-binding transcriptional regulators including Engrailed, Dorsal, T-cell factor, and Runt ( Aronson et al. 1997 ; Dubnicoff et al. 1997 ; Jiménez et al. 1997 ; Cavallo et al. 1998 ; Roose et al. 1998 ). Although Groucho was the first Hairy cofactor identified ( Paroush et al. 1994 ) and its interaction site is often described as Hairy's “major” repression motif ( Mannervik 2001 ), we find that it is associated with only a minority of Hairy targets in Kc cells. Groucho's dominance as a cofactor during segmentation may reflect a preference for Groucho in the reporter assays used previously to assess corepressor activity, or it may be more heavily recruited to Hairy's targets during segmentation. In the future it will be interesting to determine the loci that recruit Groucho in early embryos and, as Groucho binds a number of other repressors, which, if any, of these factors recruits Groucho as its major cofactor. CtBP was identified more recently, first on the basis of its binding to the C-terminal region of E1A, and in Drosophila by its association with the developmental repressors Hairy and Knirps (reviewed in Turner and Crossley 2001 ; Chinnadurai 2002a ). CtBP is an integral component in a variety of multiprotein transcriptional complexes. It has been shown to function as a context-dependent cofactor, having both positive and negative effects on transcriptional repression depending upon the repressor to which it is recruited. More than 40 different repressors have been shown to recruit CtBP. Consistent with this wide recruitment of CtBP, we identified 496 loci that recruit dCtBP by DamID profiling and roughly twice that many sites on polytene chromosomes. A recently reported global protein–protein interaction study showed that the binding partners for Groucho and dCtBP are largely nonoverlapping ( Giot et al. 2003 ). This, along with the near exclusivity of Groucho and dCtBP binding as assayed by DamID and polytene chromosome staining, makes it unlikely that both cofactors work together as a general rule and strengthens the possibility that the binding of each of these factors assembles different protein complexes that are, for the most part, mutually exclusive. dSir2 was only very recently identified as a corepressor for Hairy and other HES family members ( Rosenberg and Parkhurst 2002 ; Takata and Ishikawa 2003 ). We identified 107 loci that recruit dSir2 by DamID profiling and roughly twice that many sites on polytene chromosomes. Surprisingly, the distribution of loci recruiting dSir2 identified by DamID profiling, as well as dSir2′s staining on polytene chromosomes, shows regional binding specificity (see Figure 9 D and 9 G). This binding specificity may be a reflection of the different nuclear compartments that these regions of the chromosomes find themselves in (cf. Francastel et al. 2000 ; Leitch 2000 ). Sir2 has been described mostly as a protein involved in heterochromatic silencing rather than in euchromatic repression. The number of dSir2 euchromatic sites we observe is similar to that of Groucho, suggesting that euchromatic repressors (in addition to HES family members) are likely to recruit Sir2. Consistent with this, a recent report has described a role for mammalian Sir2 in repressing the muscle cell differentiation program ( Fulco et al. 2003 ). The region-specific binding of dSir2 might reflect a difference in the types of factors it can associate with, or the association of dSir2 with particular chromosomal regions or nuclear domains (cf. Spellman and Rubin 2002 ). Interestingly, dCtBP and dSir2 recruitment are largely overlapping, and this association continues outside of those loci where Hairy binds: 90% of dSir2-recruiting loci also recruit dCtBP. dCtBP and dSir2 are unique among transcriptional coregulators in that they both encode NAD + -dependent enzymatic activities. As NAD and NADH levels within the cell exist in closely regulated equilibrium (for review see Dang et al. 1997 ; Ziegler 2000 ), it is possible that dCtBP and dSir2 function as NAD/NADH redox sensors (cf. Denu 2003 ; Fjeld et al. 2003 ). In this way, the cell could use coenzyme metabolites to coordinate the transcriptional activity of differentiation-specific genes with the cellular redox state. The success of the combination of DamID profiling and polytene chromosome staining results provides a global systematic way in which to address a number of mechanistic questions concerning the rules governing cofactor recruitment. For example, it will be possible to address whether target gene location or promoter structure determines the accessibility of cofactors to specifically bound repressors or whether, conversely, the association of repressors with cofactors influences target gene choice by altering DNA binding specificity. We now have a number of direct Hairy targets and in vivo assay systems to use in future experiments addressing questions surrounding Hairy's biological functions and the precise molecular mechanisms it employs to carry out its functions. Materials and Methods DamID. To generate Dam–Hairy or Dam–dCtBP, a full-length hairy or dCtBP cDNA fragment was generated by standard PCR using primers containing a BglII 5′ site and a XbaI 3′ site, cut with BglII and XbaI, and subcloned into the BglII and XbaI sites of pNMycDam plasmid, as described previously ( van Steensel and Henikoff 2000 ). To generate Dam–Groucho, a full-length groucho cDNA fragment (minus the stop codon) was generated by standard PCR using primers containing a BamHI 5′ site and a NotI 3′ site, cut with BamHI and NotI, and subcloned into the BglII and NotI sites of pCMycDam plasmid, as described previously ( van Steensel and Henikoff 2000 ). Dam–dSir2 was described previously ( van Steensel et al. 2001 ). All four of these constructs are expressed in Kc167 cells (data not shown). Kc cell culture and transfections were performed as described previously ( Henikoff et al. 2000 ). The Kc cells were harvested 24 h posttransfection, then genomic DNA was isolated and processed for microarray hybridizations as described previously ( van Steensel et al. 2001 ). The UAS–Dam and UAS–Dam–Hairy expression constructs were made by first amplifying the Dam or Dam–Hairy open reading frames by PCR from the appropriate fusion construct described above, then cloning them into the pUASp vector ( Rørth 1998 ) as 5′KpnI-3′XbaI fragments. The resulting UAS–Dam and UAS–Dam–Hairy plasmids (500 μg/ml) were injected along with the pTURBO helper plasmid (100 μg/ml) ( Mullins et al. 1989 ) into isogenic w 1118 flies as described by Spradling (1986) . Transgenics were scored by eye color, and the insertions were mapped and balanced using standard genetic methods. These chimeric genes are properly expressed when induced with various Gal4 driver lines (e.g., Engrailed–Gal4; Brand and Perrimon 1993 ; data not shown). The Dam–Hairy fusion protein is functional because presence of the UAS–Dam–Hairy transgene, but not the UAS–Dam transgene, partially rescues the segmentation phenotype of hairy mutant embryos when induced with an actin–Gal4 driver (rescue is similar to UAS–Hairy; data not shown). As in Kc cells, induced expression of these Dam fusion constructs leads to high levels of nonspecific methylation. Therefore we utilized low-level leaky expression from the minimal promoter of the pUASp vector for these experiments. 2–6-h embryos were collected and dechorionated with 100% bleach. Approximately 500 μl of embryos were crushed in 1 ml of lysis buffer (100 mM Tris [pH 9.0], 100 mM NaCl, 100 mM EDTA, and 5% sucrose). SDS (to 0.5%) and proteinase K (to 100 μg/ml) were added immediately after homogenization, followed by incubation at 55 °C for 2 h. SDS was increased to 1.5%, followed by incubation for an additional 2–3 h. The genomic DNA was isolated and processed for microarray hybridizations essentially as described previously ( van Steensel et al. 2001 ). Drosophila microarray chips were produced in house (Genomics Shared Resource; Fred Hutchinson Cancer Research Center, Seattle, Washington, United States) for the Northwest Fly Consortium and contain approximately 6200 full-length DGC cDNAs (DGC Release 1; Rubin et al. 2000 ), as well as approximately 300 clones added by members of the Consortium. Arrays were scanned using a GenePix 4000 scanner (Axon Instruments, Union City, California, United States), and image analysis was performed using GenePix Pro 3.0. For each array, spot intensity signals were filtered and removed if the values did not exceed 3 standard deviations above the background signal in at least one channel or if the spot was flagged as questionable by the GenePix Pro software. For each spot, background-corrected ratios were natural log transformed and a median-centered normalization strategy was applied across each array. Dam–protein and Dam transfections were independently replicated three times, and the subsequent array comparisons (i.e., Dam–protein/Dam) were analyzed using CyberT ( Baldi and Long 2001 ), a Bayesian t-statistic derived for microarray analysis ( http://genomics.biochem.uci.edu/genex/cybert/ ). We employed the default window size of 101 and used a confidence value of ten in our CyberT analysis. The null hypothesis was rejected and a spot ratio was called significantly changed if p Bon ≤ 0.05, where p Bon is the Bayesian p -value adjusted for multiple hypothesis tests using the conservative Bonferroni correction. Based on prior “self versus self” DamID comparisons, we empirically determined a lower-bound ln(ratio) threshold = |0.405| as an additional significance criterion to discriminate spot intensity signals from the inherent noise in the hybridization process. For each protein analyzed, a fluor-reversed array comparison was performed and used to screen all significant calls for fluor-specific artifacts. For our analyses, we treated the small subset of replicated spots on the array independently. For those cases, both spots were required to be called significant. Reported ratio values were retransformed to log 2 as a matter of convention. The complete raw and processed datasets can be accessed at http://www.fhcrc.org/labs/parkhurst/supplementary-data/ . Flies and genetics. Flies were cultured and crossed on yeast-cornmeal-molasses-malt extract medium at 25 °C. The alleles used in this study were the following: h 7H rucuca / TM3, h 12C st e / TM3, Df(3 l) h i22 Ki roe p p /TM3, and prd 2.45.17 /CyO (D. Ish-Horowicz); FRT82B- P{ry+t7.2=PZ} CtBP 03463 ry 506 /TM3 (N. Perrimon); FRT 82B- gro E47 /TM3 ( Phippen et al. 2000 ); dSir2 5.26 /SM6 and dSir2 4.5 /SM6 ( Newman et al. 2002 ); FRT82B- ovo D1 /TM3, y w hs-FLP22, TM3/CxD, egh 7 /FM7a (#3902), ImpL2 KG02223 (#14083), mae k06602 /CyO (#10633), pnt Δ88 /TM3 (#861), and rgr KG03110 (#13770) (Bloomington Drosophila Stock Center, Indiana University, Bloomington, Indiana, United States). Details of these strains are found on FlyBase ( http://flybase.bio.indiana.edu:82/ ). stg AR2 and the stg-lacZ reporter lines (pstg β-E2.2, pstg β-E4.9, pstg β-E6.4, pstg β-E6.7) were described previously ( Lehman et al. 1999 ). The genomic locations of the Hairy binding sites in pstg β-E4.9 and pstg β-E6.4 are 25072653 and 25080219, respectively. Germline clones for dCtBP and groucho were generated as previously described ( Poortinga et al. 1998 , Phippen et al. 2000 ). The pstg-βE4.9 Δhairy transgenic flies were generated by injecting vector (500 μg/ml) along with the pTURBO helper plasmid (100 μg/ml) ( Mullins et al. 1989 ) into isogenic w 1118 flies as described by Spradling (1986) . Transgenics were scored by eye color, and the insertions were mapped and balanced using standard genetic methods. Embryo analysis. Larval cuticle preparations were prepared and analyzed as described by Wieschaus and Nüsslein-Volhard (1986) . Immunohistochemical detection of proteins in embryos was performed as described previously ( Parkhurst et al. 1990 ) using Alkaline Phosphatase–coupled secondary antibodies (Jackson Laboratory, Bar Harbor, Maine, United States) visualized with Substrate Kit II reagents (Vector Laboratories, Burlingame, California, United States). Antisera used were as follows: antiMyc (9e10, 1:100 dilution; Santa Cruz Biotechnology, Santa Cruz, California, United States). Immunohistochemical whole mount RNA in situ hybridization was performed according to the protocol of Tautz and Pfeifle (1989) . Digoxygenin-substituted probes were obtained by PCR amplification with primers to the vector just 3′ of the cDNA insert. EMSA. EMSA was carried out using either bacterially expressed GST or GST–Hairy ( full-length) proteins, similar to the procedure described by Van Doren et al. (1994) and Rosenberg and Parkhurst (2002) . Briefly, 40 fmol of 32 P-end-labeled probe of each oligo was incubated with either GST– or GST–Hairy–purified proteins (200 ng each), in a 25-μl reaction supplemented with binding buffer (5% glycerol, 20 mM HEPES [pH 7.6], 50 mM KCl, 1 mM EDTA, 1 mM DTT, and 10 ng/μl poly dI-dC) at room temperature. Where indicated, the binding was preformed in the presence of 15-fold excess of unlabeled wild-type or mutated ac competitor oligos. Following incubation, the complexes were resolved using 0.5% TBE-PAGE gels and visualized by autoradiograms. The following oligos were used (forward primers are shown): ac 5′-TAAACCGGTTGGCAGCCGGCACGCGACAGGGCCAGGTTTT-3′; egh egh1 5′-TGCGCGTCACGCGCCGTTC-3′, egh2 5′-TCATTCGCACGCGGAATCT-3′, and egh 3 5′-GCCGGACACGCGATGATGG-3′; mutated ac oligo 5′-TAAACCGGTTGGCAGCCGG G ACGCGACAGGGCCAGGTTTT-3′; mutated stg oligo 5′-TCTACCACACACAAACAC T CGC A CGCGAAAACTGGG -3′; prd 5′-AAGTGACACGCGCTCCGCT-3′; and stg 5′-AAACACACGCGCGCGAAAA-3′. Hairy binding site bioinformatics analysis. Several bioinformatics approaches were employed to analyze Hairy target gene promoters. In particular, Drosophila promoter sequences were captured using Apollo Genome Sequence and Annotation Tool ( Lewis et al. 2002 ). Match v1.0-public (BIOBASE Biological Databases, Wolfenbüttel, Germany) was used to search promoter sequences for known transcription factor binding sites using a library of mononucleotide-weighted matrices from TRANSFAC v6.0. Match v1.0-public employs the core- and matrices-matching algorithms published by Quandt et al. (1995) . Sequences were interrogated using only high-quality Drosophila transcription factor binding sites found in TRANSFAC v6.0, and the software parameters were adjusted to minimize the sum of false positives and false negatives. The number of Hairy binding sites found in target gene promoters was tallied (excluding “hits” to AT-rich regions [assigned to CF2-II, BRC-Z1, and BRC-Z4] that were ubiquitous in both the target and nontarget sequence under analysis). Using the Hairy site closest to transcription start site, the composition of transcription factor binding sites adjacent to (within 500 bp of) the Hairy site was assessed. This was also performed for non-Hairy targets selected because they contained one or more core C-box sequences. Matrices were compared that matched percentages of known Hairy targets ( egh2, egh3, prd1, ac1, and stg1 ) to C-box–containing nontargets. Chromosomes. Wild-type or pstg βE4.9 third instar larval salivary gland polytene chromosomes were prepared and stained for endogenous proteins essentially as described by Andrew and Scott (1994) . Antisera used were as follows: rat anti-Hairy polyclonal (1:50 dilution; gift of J. Reinitz; Kosman et al. 1998 ), mouse anti-Groucho monoclonal (1:40 dilution; gift of C. Delidakis; Delidakis et al. 1991 ), mouse anti-dCtBP polyclonal (1:100; Poortinga et al. 1998 ); mouse anti-dSir2 polyclonal (1:20 dilution; Rosenberg and Parkhurst 2002 ); rabbit anti-β-galactosidase polyclonal (1:1000); donkey antirat Alexa 488 (1:1000 dilution; Molecular Probes, Eugene, Oregon, United States); and goat antimouse Texas Red (1:200; Jackson ImmunoResearch Laboratories, West Grove, Pennsylvania, United States). Chromosomes were viewed on an Olympus (Tokyo, Japan) IX-70 inverted microscope equipped with a 40×/N.A. 1.35 oil immersion objective. Three-dimensional stacks were collected using the DeltaVision softWoRx acquisition software (Applied Precision, Issaquah, Washington, United States), and out-of-focus information was removed using a constrained iterative deconvolution algorithm ( Agard et al. 1989 ). The insertion site for the pstg β-E4.9 reporter line was performed as described by Pardue and Gall (1975) using DIG-substituted probes according to the protocol of Tautz and Pfeifle (1989) . Supporting Information Dataset S1 Complete List of Binding Loci for Hairy in Kc Cells and Embryos As Well As the Cofactors Groucho (Kc Cells), dCtBP (Kc Cells), and dSir2 (Kc Cells) (2.9 MB XLS). Click here for additional data file. Dataset S2 DamID Primary Binding Data for Hairy in Kc Cells (11.8 MB XLS). Click here for additional data file. Dataset S3 DamID Primary Binding Data for Hairy in Embryos (11.8 MB XLS). Click here for additional data file. Dataset S4 DamID Primary Binding Data for Groucho in Kc Cells (11.8 MB XLS). Click here for additional data file. Dataset S5 DamID Primary Binding Data for dCtBP in Kc Cells (11.8 MB XLS) Click here for additional data file. Dataset S6 DamID Primary Binding Data for dSir2 in Kc Cells (11.8 MB XLS) Click here for additional data file. Dataset S7 List of the 155 Target Loci That Recruit Groucho (Duplicates Removed) (230 KB XLS). Click here for additional data file. Dataset S8 List of the 496 Target Loci That Recruit dCtBP (Duplicates Removed) (276 KB XLS). Click here for additional data file. Dataset S9 List of the 107 Target Loci That Recruit dSir2 (Duplicates Removed) (44 KB XLS). Click here for additional data file.

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519031

Potential cellular conformations of the CCN3(NOV) protein

Aim To study the cellular distribution of CCN3(NOV) and to determine if the carboxyterminus of CCN3 is hidden or masked due to high affinity interactions with other partners. CCN3 was detected using affinity purified antibodies (anti-K19M-AF) as well as a Protein A purified anti-K19M antibodies (anti-K19M IgG) against a C-terminal 19-aminoacid peptide (K19M) of human CCN3 protein. The antibodies were applied in indirect immunofluorescence tests and immunoenzyme assays on glial tumor cell line, G59, and its CCN3-transfected variant G59/540 and the adrenocortical cell line, NCI-H295R. Results Anti-K19M-AF antibodies reacted against K19M peptide in ELISA and recognized two bands of 51 kDa and 30 kDa in H295R (adrenocortical carcinoma) cell culture supernatants by immunoblotting. H295R culture supernatants which contained CCN3 as shown by immunoblotting did not react with anti-CCN3 antibodies in liquid phase. Anti-CCN3 antibodies stained the surface membranes of non-permeabilized H295R and cytoplasm in permeabilized H295R cells. Similarly, anti-CCN3 stained surface membranes of G59/540, but did not react with G59 cells. Prominent cytoplasmic staining was observed in G59/540, as well as the cell footprints of G59/540 and H295R were strongly labeled. Conclusions The K19M-AF antibody directed against the C-terminal 19-aminoacid peptide of CCN3 recognized the secreted protein under denaturing conditions. However, the C-terminal motif of secreted CCN3 was not accessible to K19M-AF in liquid phase. These anti-CCN3 antibodies stained CCN3 protein which was localized to cytoplasmic stores, cell membranes and extracellular matrix. This would suggest that cytoplasmic and cell membrane bound CCN3 has an exposed C-terminus while secreted CCN3 has a sequestered C-terminus which could be due to interaction with other proteins or itself (dimerization). Thus the K19M-AF antibodies revealed at least two conformational states of the native CCN3 protein.

Introduction The CCN3 protein belongs to an emerging family of growth regulators referred under the CCN acronym (cysteine-rich protein, Cyr61, connective tissue growth factor, CTGF, and the nephroblastoma overexpressed gene, nov; CCN 1–3 respectively) [ 1 - 3 ]. The CCN family now comprises six identified members with properties of both positive and negative regulators of cell growth, sharing a common multimodular organization. New members of the CNN family have been described over the past few years, and recent reviews on the CCN proteins highlight their intimate involvement in a variety of key biological processes including development, angiogenesis, and cancer [ 1 - 4 ]. The CCN3 (NOV) gene had been initially characterized as an integration site for the myeloblastosis associated virus MAV [ 5 ] which induces kidney tumors resembling nephroblastoma and Wilms tumor [ 6 ]. In human and animal tumors, the expression of the CCN3 gene was found to be altered either positively or negatively [ 7 - 11 ]. Experiments performed in our laboratory have established that CCN3 is a marker of tumor differentiation in Wilms tumors [ 12 ] and several other tumor types [unpublished observations]. Furthermore, an increasing amount of results assigns growth inhibitory functions to CCN3 in several conditions ([ 7 , 8 , 13 - 15 ], Manara et al. submitted). The CCN proteins share a strikingly conserved multimodular organization with distinctive functional features [ 1 ]. From the amino to the carboxy terminus of these proteins, four modules can be recognized : an insulin-like growth factor (IGF) binding protein (IGFBP)-type motif, followed by a Von Willebrand type C (VWC) domain likely responsible for oligomerisation, a thrombospondin type 1 (TSP1) repeat, responsible for interaction with extracellular matrix proteins, and a carboxy-terminal module (CT), postulated to represent a dimerization domain, as it contains a cysteine-knot motif that is present and involved in the dimerization of several growth factors such as nerve growth factor (NGF), transforming growth factor -2 (TGF-2) and platelet derived growth factor BB (PDGFB). The multimodular structure of CCN3 and other CNN proteins raises interesting questions as to participation of each individual module in conferring the biological properties to the full length proteins. Either the biochemical functions of the individual IGFBP, VWC, TSP and CT modules are indeed conserved and in sum determine the ultimate function of the full length protein, or each module confers on the whole protein specific biological functions which may vary from the conserved function, and either substitute or add to those of individual modules. Application of the yeast two-hybrid system and co-precipitation strategies to identify proteins interacting with CCN3 has revealed that full length CCN3 interacts with several receptors, signaling molecules, and proteins of the extracellular matrix (16–19), suggesting functional involvement of CCN3 in cell signaling and adhesion regulation. Our results also established that truncated isoforms of CCN3 could bind specific targets and pointed the CT domain of CCN3 as a critical determinant for protein interaction. This led us to hypothesize that truncated isoforms of CCN3 could also modulate its biological activity (3). The question therefore arises whether different conformational states exist due to multiple protein interactions and thereby the presentation of known antigenic epitopes. In the present study we have used an immunological approach to establish the cellular distribution of CCN3 in cell lines representing adrenocortical and glioblastoma tumors and to ask whether the CT module of CCN3 exists in different conformational states depending on its involvement in protein interactions and cellular location. We now provide evidence that the CT end of CCN3 exists in more than one conformational state. Results Cell culture supernatants and cellular lysates from the H295R, G59/540, and parental G59 cell lines were electrophoresed under denaturing conditions and immunoblotted with anti-K19M IgG antibody. Immunoblot analysis revealed secreted forms of CCN3 for the H295R and G59/540 cell lines, consisting of two distinct bands at 51 kDa and 30 kDa [Figure 1A ]. The latter likely corresponded to the previously described amino-truncated CCN3 isoform [ 3 ]. Intracellular CCN3 proteins were also detected in these cell lines. However, in addition to the two bands at 48 kDa and 30 kDa, two other high molecular species reacting positively with the antibodies were also detected in the lysates [Figure 1B ]. The different sizes of these various isoforms likely results from post-translational modifications and oligomerisation of CCN3 protein. Figure 1 Western blot analysis. Representative gels illustrating the expression of CCN3 proteins in H295R, G59/540, and G59 cell lines. Conditioned medium and cellular lysates were electrophoresed under denaturing conditions and immunoblotted with anti-K19M IgG antibody. A. Starts: 1). supernatant collected form H295R cell line; 2). G59/540 transfected cells supernatant; 3). supernatant from G59 CCN3 negative cells; 4). lysate from H295R cells; 5). lysate from G59/540 cells; 6). lysate from G59 cells showing two bands at 48 kDa and 30 kDa. When tested in ELISA, pre-incubation of the anti-K19M-AF antibodies with CCN3-containing H259R supernatant did not affect the binding of anti-K19M-AF antibodies to the K19M peptide coated on microtitre plates (Figure 2 ). Under identical conditions, the absorption of K19M-AF antibodies with serial dilutions of K19M peptide showed a dose-dependent absorption pattern with 7.78 μg/ml K19M peptide, yielding a 50% reduction in the binding of anti-K19M-AF to K19M peptide coated on plates (Figure 3 ). These results suggested that the K19M-AF antibodies did not recognize the CCN3 protein in its native configuration, whereas it can be detected in the same sample after denaturation and Western blotting. Figure 2 Reactivity of affinity purified anti-K19M-AF antibodies in ELISA. Affinity chromatography fractions were tested in wells coated with: a) GST 1 μg/ml (hatched columns); b) GST-CCN3 1 μg/ml (blank columns); c) K19M peptide 1 μg/ml (black columns). Column fractions F1 to F3 contain the flow through unbound proteins while fractions F4 to F8 contain antibody bound to K19M peptide that eluted with glycine buffer pH 2.8. Figure 3 Absorption ELISA to check the binding of anti-K19M-AF to CCN3 in liquid phase. A fixed dilution of anti-K19M-AF (1/250 in 1% BSA) was mixed with a) serial dilutions of K19M peptide -- --- --- -- b) serial dilutions of H295R supernatant, positive for CCN-3 --- --- ---; c). G59 supernatant, negative for CCN-3 --- x ---- x ---; d). 1% BSA in PBS pH 7.4 --- --- --- -- and applied to wells coated with K19M peptide. Each point repsent the average of 4 determinations. When fixed and non-permebialized cells were used in cell-ELISA with anti-K19M-AF antibodies it was shown that positive reaction of the antibodies could be recorded with H295R cells which are known to synthesize and secrete CCN3 protein, while the reaction with G59 cells was in the ranges of the negative background. After permebialization of the cells the intensity of the reaction was increased but a significantly positive reaction was recorded with H295R cells (Figure 4 ). Figure 4 Detection of CCN3 expression on cultured cell lines by cell-ELISA. Cells from H295R line or glioblastoma G59 cells were treated with 1% bovine serum albumin in PBS, pH 7.4 (blank columns) or K19M-AF diluted 1/500 in 1%BSA (hatched columns). Treatment of cells: A – G59 non-permeabilized cells; B – C59 permeabilized cells; C – H295R non-permeabilized cells; D – H295R permeabilized cells. Each point repsent the average of 4 determinations. Since CCN3 was secreted by H259R cells, it was important to check whether it could bind cell surface. Evidence for this would lend support to the previous suggestion of an autocrine mechanism of control by the CCN3 protein. To explore this possibility cells from H259R, G59/540 and G59 cell lines were incubated for 1 h on ice in the presence of supernatant containing CCN3 protein and then analyzed by cell ELISA as described above. The results obtained showed that such a treatment did not increase the intensity of the reaction with anti-K19M-AF bound to the cell surface. These experiments demonstrated that H295R, and G59/540 which expressed CCN3 on the cell surface and no further absorption occurred, and the control G59/540 cells did not absorb CCN3 from the culture supernatant (data not shown). Cellular Localization of CCN3 Paraformaldehyde fixed, non-permeabilized H259R cells treated with the anti-K19M antibody (Protein A purified) exhibited immunofluorescent membrane specific staining distributed over the cell surface (Figure. 5A ) while similarly treated G59 CCN3 negative cells did not stain (not shown). The CCN3-transfected glioblastoma cell line G59/540 stained positively with a similar localization of the reaction product (Figure 5B – G540). Interestingly, since cells grown on coverslips and fixed in paraformadehyde tend to slough, we did note the presence of positively staining cell footprints, suggesting deposition of CCN3 protein in a secretable extracellular matrix (Figures 5C,5D ). After ethanol/formalin fixation and further permeabilization of the cells with 0.1% Triton X-100 the anti-K19M antibody (Protein A purified) gave an intensive granular fluorescence pattern which appeared perinuclear in a significant fraction of the cells with a similar pattern observed in H295R and G59/540 cells (Figure 5E,5F ). On the other hand, the parent G59 cell line showed a weak, but still perinuclear cytoplasmic staining (Figure 5G ). The latter may represent a smaller endogenous isoform of CCN3. Figure 5 Indirect immunofluorescence staining with the anti-K19M IgG antibody. A, B The H295R and G540 cell lines, paraformaldehyde fixed and non-permeabilized, show a similar granular like surface membrane labeling pattern; C, D, Both in H295R and G540 cultures, paraformadehyde fixed and non-permeabilized, cell footprints are positively labeled; E, F, The H295R and G540 cell lines, formalin/ethanol fixed and permeabilized show a granular cytoplasmic labelling pattern that is perinuclear localized in ER and Golgi network; G, The G59/540 parent line, formalin/ethanol fixed and permeabilized, still shows a small amount of a perinuclear granular staining pattern. In summary, the results produced using an immunological approach would suggest multiple conformations of the C-terminal end which harbors the immunogenic epitopes. Furthermore, these variations are associated with cell bound and secreted forms of CCN3. Cytoplasmic localization indicated abundant CCN3 protein localized with ER and Golgi networks. Discussion In this study we exploited the range of binding affinities present in polyclonal antibodies raised against the C-terminal peptide of CCN3 and analyzed with different immunoaffinity methods to ask whether CCN3 exists in alternate conformational forms in cell cultures and supernatants. Whereas immunocytochemistry of fixed, permeabilized and non-permeabilized cells yielded evidence of both cell surface membrane and cytoplasmic expression and topographical distribution of native CCN3, ELISA method and western immunoblot revealed different possible conformational forms of CCN3. Taken together the results of the assays suggest that native CCN3 assumes different configurations that either expose or sequester the C-terminal peptide depending on whether CCN3 is cell associated or free within the culture supernatant. When considered in the light of recent evidence indicating that CCN3 can associate with specific integrins at the cell membrane [ 23 , 24 ] the question also arises whether CCN3 associates with specific protein partners in the circulation and in the extracellular matrix produced by different cell types. In these studies we focused on two cell lines, H295R and G59, representing adrenocortical and glial tissues, significantly different in their anatomical location and microenvironment. Since CCN3 has been demonstrated in plasma [ 25 ] it is conceptually feasible that CCN3 may be secreted by well organized ectodermal, mesodermal and endodermal cell types where it is expressed [ 26 - 28 ], and then is transported through complex extracellular matrix to enter the circulation. Moreover, as CCN3 has been shown to be expressed by endothelium [ 23 , 29 ], the source of the circulating CCN3 may be restricted to endothelium. Keeping to this scenario, tissue expression of CCN3 would be restricted to regional cell types and its arena of activity relegated to the extracellular matrix and resident cells. Interestingly, we could show in vitro that CCN3 is sequestered in cell footprints representing a secreted extracellular matrix. Cell footprints can be deposited by various cell types in different arrangements and consists of extracellular matrix, including a variety of basement membrane proteins [ 30 - 32 ]. In our studies we noted a more uniform and punctuate deposition of CCN3 in the footprints suggesting possible association with regularly arranged clustered partners (e.g. integrins), yet to be determined. Localization of CCN3 to footprints is perhaps expected since it has been shown to mediate adhesion of endothelial cells [ 23 ], in turn triggering intracellular phosphorylation signaling events. This then raises the notion that proximity of CCN3 to cell surfaces could allow CCN3 to function in possible autocrine and paracrine mechanisms. Depending on the associating proteins, CCN3 would likely undergo specific conformational changes with potentially different functional outcomes. A variety of functional states may exist since CCN3 is expressed in secretable and non-secretable isoforms and contains motifs that overlap with other proteins and therein, additional binding partners [ 1 , 4 ]. Thus far the actual molecular function of native CCN3 has not been determined, although biologically it shows evidence of being able to regulate mitogenesis and motogenesis [ 3 ]. In turn, CCN3, compared to other CCN proteins, may be differentially regulated by mechanical stress [ 29 ]. As a heparan binding protein, CCN3 could associate with a large group of molecules at the cell membrane and in the extracellular matrix. Yeast two hybrid studies have indicated associations with fibulin 1C [ 16 ]. Other studies have shown CCN3 involved in calcium signaling [ 19 , 33 ], associated with Notch signaling [ 17 ], and able to trigger membrane mediated phosphorylation events [ 34 ] Some of the cellular effects of CCN3 may also be mediated by different isoforms operating at the level of the nucleus [ 35 ]. The biological significance of different conformations of CCN3 is not known. However, examples from other studies have suggested that conformational changes can occur in serum proteins due to binding of bivalent cations [ 36 ]. Since we recently reported that CCN3 may interact with Ca2+ binding proteins like fibulin-1C, modulate calcium uptake, and considering that Ca2+ binding modulates function of other protein partners such as integrins [reviewed in [ 33 ]], it is conceivable that secreted CCN3 could assume an altered conformation by binding bivalent cations, directly or indirectly, like Ca2+ present in culture media. Whether calcium or some other bivalent cation could be involved and how this could occur is still speculative as the sequence of CCN3 does not suggest any obvious cation binding properties. It is also possible that secreted CCN3 complexes with an as yet unknown partner thus sequestering the antigenic epitopes. Altered expression of CCN3 in a variety of cancers may reflect maintenance of a normal homeostatic function of the cell of origin, or may indicate requirement of specific CCN proteins for maintaining the undifferentiated tumor state. One such example where the two possibilities are not yet resolved is in Wilms tumor, where CCN3 is abundantly expressed during normal nephrogenesis and in tumors [ 12 ]. Interestingly, CCN3 was originally identified during MAV virus induction of nephroblastoma but is not a direct target of WT1, the Wilms tumor suppressor gene [ 34 ]. Thus far few mutations have been described for CCN family and none for CCN3. CCN proteins have however been associated with a variety of cancers where they can be markedly overexpressed [ 11 , 37 - 39 ]. It may be that CCN proteins are not directly involved in tumorigenesis (e.g., Wilms tumor) but rather play supporting roles or may act as a negative regulator on malignant behavior reflecting their roles as integrators of cell-cell and cell-matrix communications. Thus having antibodies that can recognize the different isoforms of CCN proteins with great specificity and in respect to specific epitopes within the domains would be invaluable for quantitation [ 40 ] and for dissecting their functions in communication signaling. Conclusions Our preliminary investigations here have revealed possible physical and functional states of native CCN3 localizing to cytoplasmic, cell membrane and extracellular matrix. Further complexity is added since shorter and larger isoforms of CCN3 can be detected using western blotting. The origin of these short forms is still not fully understood. As there do not appear to be alternate transcripts [ 1 ] this suggests post-translational processing including, in addition to variant glycosylation, phosphorylation, specific proteolytic events and sites. The C-terminal antibody recognizes these forms. The use of antibodies to other motifs in CCN3 will permit us to track the cleaved N-terminal peptide which potentially could be functionally active as it resembles IGFBP [ 1 ]. Therefore the cleavage products of CCN3 in concert with native CCN3 may also be involved in several aspects of the regulation of growth factor activity at the cell membrane or its management in extracellular repositories. Finally, cells can coordinately express a variety of CCN proteins that are closely related, for example CCN1-3 with cross-over and opposite functional effects yet bearing similar functional domains. Evidence is starting to surface that they might compete for binding partners, such as integrins, thus forming protein complexes with different biological consequences to cell behavior [ 18 , 19 ]. It will be important to understand how stoichiometric changes in CCN protein concentrations can change the behavior of cells, thus opening up opportunities for therapeutic manipulations in disease. It is obvious that there will be a necessity for antibody reagents and quantitative methodologies to enable these studies. Materials and methods Cell Lines NCI H259R (American Type Cell Collection) is a human adenocortical carcinoma cell line and was cultured in DMEM/F12 supplemented with 2.5 % Nu-serum plus ITS+ supplement (Sigma Co, St. Louis, USA). H295R cells have been characterized and were shown to secrete high levels of CCN3 protein [ 20 , 21 ]. The glioblastoma cell line (G-59) has been described previously [ 22 ]. CCN3 expressing G59/540 sublines were obtained following transfection of G59 with pCMV CCN3 plasmid and G418 selection [ 13 ]. These cell lines and their derivatives were used in cell ELISA and indirect immunofluorescence labeling experiments as described below. Antibodies Antibodies against C-terminal peptide K19M were used in these experiments after either purification by an antigen specific affinity chromatography (anti-K19M-AF) or by Protein A chromatography (anti-K19M IgGs). Antibody Affinity Purification and Characterization The K19M C-terminal peptide (KNNEAFLQELELKTTRGKM) of human CCN3 protein was coupled to CNBr activated Sepharose 4B (Pharmacia Biotech, Uppsala, Sweden) following the protocol recommended by the manufacturer. Briefly, 3 mg of peptide were dissolved in 5 ml of 0.1 M NaCO 3 pH 9.0 containing 0.5 M NaCl (coupling buffer) and added to 3.5 ml CNBr-Sepharose swelled gel and the mixture was rotated end-over-end for 2 hours at room temperature. Excess ligand was eluted with 20 ml of coupling buffer and the gel was incubated in 0.1 M Tris-HCl buffer pH 8.0 for 2 hours at room temperature. The gel was washed 5 times in cycles consisting of 20 ml of 0.1 M acetate buffer pH 4.0 followed by 20 ml of 0.1 M Tris-HCl buffer pH 8.0, each containing 0.5 M NaCl, and then packed into a PD-10 column. The rabbit anti-K19M antiserum was absorbed with 1 mg/ml human serum albumin to remove cross-reactivity with human plasma and dialyzed overnight at 4°C against phosphate buffer pH 7.0. An aliquot of 3.5 ml serum was loaded on the affinity column and the flow through and the unbound proteins were collected in 3 ml fractions followed by thorough washing of the column with the loading buffer. The K19M peptide bound antibodies were eluted with 9 ml of 0.1 M Tris-glycine buffer pH 2.8 in 3 ml fractions that were collected into test tubes containing 100 μl 1 M Tris buffer pH 8.0. All column fractions were tested by ELISA for the presence of antibodies reacting against K19M peptide and the positively reacting fractions were further purified by affinity chromatography on pre-packed HiTrap Protein A columns (Pharmacia Biotech, Uppsala, Sweden) as recommended. Affinity purified antibody preparations were further tested to determine their reactivities and specificities. The affinity purified anti-K19M-AF antibodies reacted against the K19M peptide when tested in serial dilutions in ELISA (see below). The titers of K19M-AF antibodies were comparatively lower as compared to the unfractionated K19M antiserum. This finding was not unexpected as it likely reflects the polyclonal composition of the primary rabbit antiserum and differences in the content of the specific monoclonal specificities in the antiserum. Importantly, the affinity purified antibodies recognized the K19M peptide when coated on a solid phase. K19M Peptide Enzyme-linked Immunoabsorbent Assay (ELISA) Affinity purified antibodies were titered by ELISA. Individual wells of polystyrene 96-well flat bottom plates (NUNC) were coated with 1 μg/ml of K19M peptide diluted in coating buffer (0.05 M carbonate buffer pH 9.6) by incubation overnight at 4°C. The unsaturated protein binding sites were blocked with 300 μl/well 2% BSA for 1 h at room temperature. The primary anti-K19M antiserum and affinity purified antibodies were added in serial dilutions in duplicates and the wells were incubated for 2 h at room temperature. After thorough washing the wells were incubated with goat anti-rabbit IgG serum conjugated with peroxidase (Sigma Co) diluted 1/5000 in blocking buffer for 1 h at room temperature. The bound enzyme activity was revealed by adding the enzyme substrate 0.5 mg/ml ortho-phenylenediamine in citrate buffer pH 5.0 containing 0.5 μl/ml H 2 0 2 . The enzyme reaction was stopped by addition of 50 μl/well of H 2 SO 4 and the color reaction was read at 492 nm in a MicroELISA reader. Cell Enzyme-Linked Immunoabsorbent Assay (Cell ELISA) Tumor cell lines were cultured in complete medium in 96-well flat bottom plates (Corning) to form a subconfluent monolayer and further incubated overnight in serum free medium. The cells were washed 3 times for 5 min each with phosphate buffered saline (PBS, pH 7.2) and cells were fixed by treatment with ice-cold methanol for 30 min warming to room temperature. The endogenous peroxidase activity was blocked with 3% H 2 O 2 in distilled water for 7 min at room temperature followed by 3 × 5 min washes in PBS, pH 7.2. Non-specific binding was blocked with 1% bovine serum albumin (BSA) for 1 h at room temperature. Cells were washed once in PBS and incubated with K19M-AF diluted in 1%BSA for 2 h at room temperature. After 3 × 10 min washes in PBS goat-anti rabbit IgG conjugated with peroxidase diluted 1/10000 in 1% BSA-PBS was added for 1 hour at room temperature. The cells were washed again in PBS and the bound enzyme activity was developed by adding ortho-phenylendiamine (5 mg/10 ml citrate buffer, pH 5.0) containing 5 μl of 30% H 2 O 2 . The color reaction was stopped by adding 50 μl of 10% H 2 SO 4 and the intensity was read at 492 nm in a MicroELISA reader. Gel Electrophoresis and Western blotting To prepare proteins for immunoblotting, cells were lysed in NP40 buffer (50 mM Tris hydrochloride, pH 8.0, 150 mM NaCl, 5 mM EDTA and 2% NP40) with protease inhibitors (Cocktail Tablets, complete, Roche) and phosphatase inhibitors (50 mM NaF, 2 mM sodium orthovanadate) for 30 min at 4°C. After centrifugation at 15 000 g, extracts were stored at -80°C until use. CCN3 proteins in the conditioned medium were concentrated on Heparin Sepharose (Amersham, Uppsala, Sweden) as described by Chevalier et al (1998). Briefly, supernatants were incubated overnight with heparine and then washed 4 times in PBS containing protease inhibitors. Bound CCN3 was dissociated using 2-mercaptoethanol in Laemmli buffer, boiled for 10 min and then centrifuged to keep the free protein. Heparin Sepharose concentrated samples and cellular extracts were subjected to electrophoresis under reducing conditions in 12.5% polyacrylamide gels. Separated proteins were subsequently transferred to nitrocellulose by a semi-dry blotter (LKB Biotech, Sweden) as recommended by the supplier. The nitrocellulose sheet was blocked by incubation for 1 hour at room temperature with 5% nonfat milk in PBST (PBS with 0.2% Tween 20, pH 7.4). The membrane was then incubated in the same buffer with the anti-K19M IgG (1/2000) and then washed extensively. The blots were further incubated in goat anti-rabbit IgG conjugated with peroxidase (1/10 000 in blocking solution, Sigma Co, USA) for 1 hour at room temperature. Revelation was performed using the chemoluminescence protocol and reagents (Pierce, Rockford, IL, USA). Indirect Immunofluorescence Labeling Cells were grown on alcohol flamed coverslips, rinsed in PBS and fixed in cold 70% ethanol containing 10% formalin (Sigma) for 10 min on ice, and stored in PBS. Immunofluorescence labeling was performed at room temperature. For this procedure, coverslips were placed into weighing boats [Sigma; 4.5cm × 4.5cm] maintaining cell side up. Cells were further permeabilized in 0.5% Triton X-100 in PBS for 15 min and then blocked in 5%FBS/PBS for 30 min. Anti-K19M IgGs antibodies were applied at 1:1000 dilution in 5%FBS/PBS for 1 hour with intermittent rotation, followed by 5 washes in PBS containing 0.1% Tween 20. Subsequently, the cells were incubated in anti-rabbit IgG serum conjugated with either Alexa 488 (green fluorescence) or Alexa 594 (red fluorescence), in 5%PBS/BSA for 1 hour. After final washes in PBS/Tween 20 followed by one wash in PBS cells were mounted with antifade mounting medium (Bio-Rad, France), excess liquid adsorbed with filter paper, and coverslips sealed with clear nail polish. Immunofluorescence images were captured on 400 ASA film and processed further with Adobe Photoshop (version 7.0). List of abbreviations None. Competing interests None declared. Authors' contributions SK carried the affinity chromatography and immunoenzyme experiments; HY carried out immunofluorescence labeling experiments; AMB carried out IgG purification and Western blots; BP conceived the study design and coordinated and edited the manuscript

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514529

Early endothelial dysfunction in cholesterol-fed rabbits: a non-invasive in vivo ultrasound study.

Background Endothelial function in hypercholesterolemic rabbits is usually evaluated ex vivo on isolated aortic rings. In vivo evaluation requires invasive imaging procedures that cannot be repeated serially. Aim We evaluated a non-invasive ultrasound technique to assess early endothelial function in rabbits and compare data with ex vivo measurements. Methods Twenty-four rabbits (fed with a cholesterol diet (0.5%) for 2 to 8 weeks) were given progressive infusions of acetylcholine (0.05–0.5 μg/kg/min) and their endothelial function was assessed in vivo by transcutaneous vascular ultrasound of the abdominal aorta. Ex vivo endothelial function was evaluated on isolated aortic rings and compared to in vivo data. Results Significant endothelial dysfunction was demonstrated in hypercholesterolemic animals as early as 2 weeks after beginning the cholesterol diet (aortic cross-sectional area variation: -2.9% vs. +4% for controls, p < 0.05). Unexpectedly, response to acetylcholine at 8 weeks was more variable. Endothelial function improved in 5 rabbits while 2 rabbits regained a normal endothelial function. These data corroborated well with ex vivo results. Conclusion Endothelial function can be evaluated non-invasively in vivo by transcutaneous vascular ultrasound of the abdominal aorta in the rabbit and results correlate well with ex vivo data.

Background Endothelial dysfunction occurs early in the development of atherosclerosis. Historically, evaluation of endothelial function in small animals has been performed on isolated vessel segments, or vessels exposed by surgical procedures. Very few attempts were made to develop a method of analysis of endothelium-dependent relaxation in vivo [ 1 - 5 ]. In those studies, an invasive intravascular ultrasound approach was used. Correlation between results obtained in vivo and data on isolated arteries ex vivo was never assessed. Non-invasive methods to study endothelial function in humans (e.g. ultrasound of the brachial artery) have been used for many years and have yielded an important amount of data [ 6 - 12 ]. Unfortunately this non-invasive technique has never been transposed to animal studies. The objective of the current study was to assess the reliability of transcutaneous vascular ultrasound in order to evaluate endothelial function in vivo in rabbits and to compare this non-invasive method with results obtained ex vivo on isolated aortic rings. Material and methods Materials Acetylcholine, nitroglycerin and sodium nitroprusside were from Sigma (Markham, ON, Canada). Angiotensin II and endothelin-1 peptides were acquired from Peninsula Laboratories Inc. (San Carlos, CA) Animals and Study Design Twenty-four male New Zealand White rabbits (3–4 kg body weight) were used in this study. Animals were treated in accordance to the Guide to the care and use of experimental animals published by the Canadian Council on Animal Care and the protocol was approved by the Animal Protection Committee of the Université Laval. Sixteen rabbits were divided in two groups (n = 8) and all animals were fed with standard rabbit chow supplemented with 0.5% cholesterol (w/w) (Harlan, Indianapolis, IN) for 2 or 8 weeks respectively. The other 8 animals received normal rabbit chow for eight weeks (normal controls). After 2 weeks, 8 randomly chosen cholesterol-fed rabbits were killed; the others were kept alive for an additional 6 weeks (total of 8 weeks of hypercholesterolemic diet) as for the normal control group. When animals were sacrificed abdominal and thoracic aortas were excised and immediately rinsed in freshly prepared Krebs buffer in preparation for the ex vivo experiments. Plasma samples were drawn from the marginal ear vein every week and plasma cholesterol levels were determined using a commercially available spectrophotometric assay kit (Roche Molecular Biochemicals, Saint-Laurent, Canada). In vivo evaluation of endothelial function by trans-abdominal ultrasound Ultrasound evaluation of endothelial function of the abdominal aorta was performed at baseline, 2 weeks and 8 weeks. Rabbits were sedated using midazolam (0.5 mg/kg), butorphanol (0.5 mg/kg) and ketamine (30 mg/kg) IM. Marginal ear vein and artery were cannulated for drug infusions and arterial blood pressure monitoring, respectively (Figure 1A ). Heart rate was monitored continuously throughout the procedure. The abdomen was shaved and the animal was put in dorsal decubitus (Figure 1B and 1C ). The abdominal aorta was located using two-dimensional and color Doppler ultrasound. Image settings were optimized to allow the best and clearest definition of the endothelial-blood interface (Figure 1D ). All studies were performed with a vascular 7.5 MHz probe coupled to a Sonos 5500 echograph (Phillips, Andover, MA). Figure 1 In vivo assessment of endothelial function in rabbits. Rabbits under sedation were serially infused for two minutes in the marginal ear (A) vein with the vehicle. An ink mark (Panel B) was made on the rabbit abdomen for probe location for each exam. Images (Panels C and D) of the abdominal aorta were recorded throughout the procedure. Once the imaging of the aorta was considered optimal, the animals received the following drug perfusions I.V sequentially for 2 minutes each: 1) saline at 1 ml/min; 2) acetylcholine (Ach) at 0.05 μg/ml/min and Ach at 0.5 μg/ml/min. Nitroglycerin (5 μg/ml/min) was used as positive control. Typical arterial blood pressure recordings are illustrated in Figure 2 . At the end of a drug infusion, blood pressure was allowed to come back to baseline for at least one minute before the next infusion was started. Images of the abdominal aorta were recorded continuously through the entire procedure on standard S-VHS videotape for off-line analysis. Figure 2 Representative blood pressure recordings during vehicle and drug infusions. Video sequences from the first 15 seconds (void volume) of drug infusion at baseline and between 40 to 60 seconds of drug infusion were digitized and used for analysis. Image analysis Video sequences from the first 15 seconds (void volume) of drug infusion at baseline and between 40 to 60 seconds of drug infusion were digitized (Dazzle Video Creator, Dazzle Multimedia, Fremont CA) and stored on a computer for analysis (Figure 2 ). Still frames of the aorta from both the baseline and drug infusion (n = 5 each) synchronized to the beginning of the QRS and the respiratory phase were analyzed. The maximal diameter was measured (5 beats averaged) using the SigmaScan Pro software (SSSP Science, Chicago IL). Care was taken to measure the same segment for each beat using anatomic landmarks as a guide. The mean of 5 diameters for each image of the aorta was calculated. Vessel cross-sectional area was then calculated assuming the aortic section is circular using the formula: Area = π (D/2) 2 where D is the diameter of the aorta. Area was expressed in percent of change from baseline. Inter and intra-observer variability was assessed on 10 randomly selected studies. Ex vivo endothelial function evaluation in isolated rabbit aortic rings At the end of the protocol rabbits were given a sub-lethal dose pentobarbital (25 mg/kg) and were sacrificed by exsanguination. The middle part of the descending thoracic aorta as well as the abdominal aorta were removed and dissected free of adhering fat and connective tissues. The aorta was placed in warm Krebs solution. Rings of 5 mm thickness were suspended in individual organ chambers filled with 5 ml of oxygenated Krebs (37C pH 7.4). The segments were connected to force transducers and any variations in force were recorded continuously (WIN-SMT software, PO-NE-MAH inc., Gould, Valley View, OH.). Contractile response Baseline contractile response was evaluated by a 30 to 60 minutes exposition to KCl (80 mM) where the rings were gradually stretched to a resting tension of 2 g until steady state was reached. Following this initial experiment, contractile capacity was further evaluated by exposing the rings to other vasoconstrictors. Briefly, when the rings had recovered their resting tension after the initial KCL exposure, they were exposed sequentially to cumulative concentrations of L-phenylephrine (PE, 10 -9 to 10 -5 M), angiotensin II (10 -10 to 10 -7 M) and endothelin-1 (10 -9 to 10 -6 M). Results were compared to the initial response obtained with KCL 80 mM. Relaxation response Relaxation studies were performed after a precontraction with PE (10 -6 M). Cumulative concentrations of acetylcholine (10 -9 to 3 × 10 -6 M) or sodium nitroprusside (10 -10 to 3 × 10 -5 M) were used. Sodium nitroprusside was used as a non-endothelial dependant vasodilator while acetylcholine evaluated the endothelial-dependant vasodilatation. Relaxation was expressed as a percent of change from the pre-contracted tension with PE. Statistical analysis Results are expressed as mean ± standard error of the mean (SEM). Differences between the various conditions in the in vivo endothelial function experiments were evaluated with an ANOVA for repeated measures using the Tukey's post hoc test to evaluate significance. In the in vitro study, Student t-test was used when two values were compared. Differences were considered significant when p < 0.05. Results Total cholesterol circulating levels Total cholesterol levels were measured weekly in the serum of cholesterol-fed rabbits. As illustrated in figure 3 , total cholesterol levels rose sharply after one week of hypercholesterolemia and stabilized around 20 mM from Week 3 to Week 8. Figure 3 Total cholesterol circulatory levels in rabbits fed with the cholesterol diet. Results are expressed as mean ± SEM in mmoles/l (mM). (n = 8 animals) In vivo experiments As expected, saline alone had no effect. Low doses of acetylcholine (ACh 0.05 and ACh 0.5 μg/kg/min) had only a minor and transient lowering effect on blood pressure. As illustrated in Figure 4 , both saline and ACh 0.05 infusion had no effect on abdominal aortic cross-sectional area compared to baseline in both normal (Week 0) and hypercholesterolemic (Week 2 and 8) rabbits. As expected, ACh 0.5 infusion induced a dilatation of the aorta in animals at week 0 but had a paradoxical effect (contraction) at Week 2. Interestingly, while the response was clear and homogeneous at 2 weeks, it was more heterogeneous after eight weeks of hypercholesterolemia as endothelial function had improved in 5/8 rabbits (62%) and 2/8 (25%) regained a normal endothelial function. Although a trend towards contraction was recorded overall at week 8, it did not reach statistical significance. Figure 4 In vivo assessment of endothelial function in rabbits. Change in the area of the abdominal aorta in response to acetylcholine infusions. Rabbits (n = 8) were fed with a cholesterol diet for the indicated period of time. The endothelial function was assessed before (week 0) and after 2 (week 2) and 8 (week 8) of hypercholesterolemia. Rabbits under sedation were serially infused for two minutes (saline (vehicle); 1 ml/min) in the marginal ear vein with the vehicle (left), acetylcholine (Ach) at 0.05 and at 0.5 mg/ml/min. *: P < 0.05 vs. vehicle and ¶ P < 0.05 vs Week 0. Ex vivo endothelial function measurements In order to confirm the validity of the in vivo results, we performed sections isometric contraction-relaxation experiments on isolated aortic rings. In Figure 5 are illustrated the contraction experiments using phenylephrine (Fig. 5A and 5B ), angiotensin II (Fig. 5C and 5D ) and endothelin-1 (Fig. 5E and 5F ). All results are expressed relative to a control contraction using potassium chloride (80 mM). Except for endothelin-1, responses to vasoconstrictor were similar between abdominal and thoracic portions of the rabbit aorta. Thoracic aorta was less responsive to endothelin-1 than the abdominal portion. The amplitude of this response to endothelin-1 was also clearly less in the thoracic aorta. Figure 5 Contraction to phenylephrine (PE), angiotensin II (ATII) and to endothelin-1 (ET-1) of abdominal (panels A, C and E) and thoracic (panels B, D and F) aortic rings from rabbits fed or not with a cholesterol diet for 2 or 8 weeks. Aortic rings were exposed to cumulative doses of the indicated agent. Values are presented as percentage of contraction relative to a KCl (80 mM) control contraction. Values are expressed as mean ± SEM (n = 16). * P < 0.05 vs Week 0. We then studied the endothelium-dependent relaxation using acetylcholine on our aortic rings after a pre-contraction with phenylephrine (1 μM). As illustrated in Figure 6A and 6B , abdominal and thoracic aortic ring of rabbits fed for two weeks with a cholesterol diet had a decreased vasodilatory response compared to normal rabbits (Week 0). Figure 6 Relaxation to acetylcholine (ACh) and to sodium nitroprusside (SNP) of abdominal (panels A and C) and thoracic (panels B and D) aortic rings from rabbits fed or not with a cholesterol diet for 2 or 8 weeks Aortic rings were exposed to cumulative doses of the indicated agent. Values are presented as percentage of relaxation relative to a phenylephrine precontraction (1 μM). Values are expressed as mean ± SEM (n = 8). * P < 0.05 vs Week 0. As seen in vivo , the endothelial function of the animals fed 8 weeks with the cholesterol diet was heterogeneous. In those animals hypercholesterolemia had no effect on the acetylcholine-induced relaxation of thoracic aortic rings while for the abdominal aortic sections; the response to acetylcholine was highly variable. As illustrated in Figure 6C and 6D the endothelium-independent response to sodium nitroprusside of aortic rings was normal and similar for all treatments and controls. Discussion Our results clearly show that endothelial function can be assessed non-invasively by transcutaneous ultrasound of the abdominal aorta in hypercholesterolemic rabbits. The method was easily feasible in all animals and yielded very reproducible results. We also show that this in vivo method correlates very well with the ex vivo evaluation of endothelial function on isolated aortic rings. To our knowledge, this is the first demonstration of such a comparison. Ultrasound imaging of the brachial artery in response to reactive hyperaemia has been used in many studies in humans [ 6 - 12 ]. Normal arteries dilate in response to reactive hyperaemia while arteries with an abnormal endothelial function show a decreased or absent response to this stimulus. Ultrasound evaluation of the aorta in rabbits has been performed in the past mostly to evaluate the extent of atherosclerotic plaques in response to a hypercholesterolemic diet. Intravascular ultrasound has been used to document endothelial dysfunction but has never been compared to ex vivo evaluation of endothelial function on aortic rings [ 1 - 5 ]. Our method is much less invasive for the animals than intravascular ultrasound. It is compatible with longitudinal studies requiring repeated measurements in the same animal and correlates very well with the ex vivo data [ 13 , 14 ]. The extent of endothelial dysfunction observed after 2 weeks of hypercholesterolemic diet was surprising although this parameter has not been studied very much after such a short exposure to hypercholesterolemia in rabbits [ 2 ]. The paradoxical contraction in response to acetylcholine signs the presence of endothelial dysfunction and this is confirmed in bath studies. However, the results obtained after 8 weeks were unexpected. Indeed, a significant number of animals had an improved endothelial function after 8 weeks of hypercholesterolemia compared to the two weeks data both in vivo and ex vivo . This transient improvement of endothelial function in the early phases of the atherosclerotic process has never been described before to our knowledge and the underlying mechanisms responsible for this paradoxical response need to be explored. This dysfunction may relate to an initial stress response of the aortic endothelium to hyperlipidemia then evolving with the development of atherosclerosis lesions. Conclusion Endothelial function can be evaluated non-invasively in rabbits using a standard vascular ultrasound probe by a trans-abdominal approach. Results correlate well with in vitro data. A transient improvement in endothelial function can occur after 8 weeks of hypercholesterolemia in some animals for reasons that remain unclear.

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534809

Algorithmic Self-Assembly of DNA Sierpinski Triangles

Algorithms and information, fundamental to technological and biological organization, are also an essential aspect of many elementary physical phenomena, such as molecular self-assembly. Here we report the molecular realization, using two-dimensional self-assembly of DNA tiles, of a cellular automaton whose update rule computes the binary function XOR and thus fabricates a fractal pattern—a Sierpinski triangle—as it grows. To achieve this, abstract tiles were translated into DNA tiles based on double-crossover motifs. Serving as input for the computation, long single-stranded DNA molecules were used to nucleate growth of tiles into algorithmic crystals. For both of two independent molecular realizations, atomic force microscopy revealed recognizable Sierpinski triangles containing 100–200 correct tiles. Error rates during assembly appear to range from 1% to 10%. Although imperfect, the growth of Sierpinski triangles demonstrates all the necessary mechanisms for the molecular implementation of arbitrary cellular automata. This shows that engineered DNA self-assembly can be treated as a Turing-universal biomolecular system, capable of implementing any desired algorithm for computation or construction tasks.

Introduction How is complex organization produced and maintained by physical processes? One may look to biology, where we find the most sophisticated organization of matter, often spanning more than 24 orders of magnitude from component molecules (0.1 attograms) to entire organism (100 kilograms). This organization is information-based: DNA sequences refined by evolution encode both the components and the processes that guide their development into an organism—the developmental program. For a language to describe this carefully orchestrated organization, it is tempting to turn to computer science, where the concepts of programming languages, data structures, and algorithms are used to specify complex organization of information and behavior. Indeed, the importance of universal computation for autonomous fabrication tasks was recognized in von Neumann's seminal work on self-reproducing automata, where he postulated a universal constructor that, by reading an input tape specifying an algorithm for what to build, could carry out the commands necessary to construct an arbitrary object ( von Neumann 1966 ). If algorithmic concepts can be successfully adapted to the molecular context, the algorithm would join energy and entropy as essential concepts for understanding how physical processes create order. Unfortunately, the study of molecular algorithms has been hampered by the lack of suitable physical systems on which to hone such a theory: nature provides us with elementary chemical reactions too simple to program, full-blown life too complex to use as a model system, and few systems in between. This gap may be explored by synthesizing programmable biochemical systems in vitro, where we can implement and study a variety of molecular algorithms ranging gradually from simple to complex. Biomolecular self-assembly is particularly attractive for the exploration of molecular algorithms that control nanofabrication tasks. Attesting to its power, self-assembly is used pervasively in biology to create such structures as virus capsids, microtubules, and flagella. In each case, the binding interactions between a small number of protein species is sufficient to dictate the form of the final structure, often via a complex sequence of cooperative assembly steps. This can be viewed loosely as a form of programmable nanofabrication, where the program is the set of molecular species involved. For synthetic approaches, Seeman ( 1982 , 2003 ) has demonstrated that DNA provides an alternative to protein that can be readily programmed by Watson–Crick complementarity. A seminal paper by Adleman (1994) used one-dimensional (1D) DNA self-assembly to operate as a finite-state machine, establishing the first experimental connection between DNA self-assembly and computation. This work inspired a theoretical proposal ( Winfree 1996 ) that builds on Wang's ( 1961 , 1962 ) embedding of computation in geometrical tilings to show that two-dimensional (2D) self-assembly of DNA can perform Turing-universal computation—which implies that any algorithm can in principle be embedded in, and guide, a potentially aperiodic crystallization process. In this “algorithmic self-assembly” paradigm, a set of molecular Wang tiles is viewed as the program for a particular computation or molecular fabrication task ( Reif 1999 ; Rothemund and Winfree 2000 ; Adleman et al. 2001 ). (This framework differs from previous approaches relating tiling theory to crystalline ground-states [ Radin 1985 ] in that kinetic phenomena are essential here.) Whereas 1D algorithmic self-assembly offers limited computational power ( Winfree et al. 1998b ) and has been experimentally demonstrated ( Adleman 1994 ; Mao et al. 2000 ), 2D algorithmic self-assembly offers not only new capabilities for computation and construction, but also presents a new range of physical phenomena and experimental challenges as well. A natural Turing-universal model of computation that can be implemented by 2D algorithmic self-assembly is the class of 1D cellular automata. A simple but interesting choice for the local cellular automaton rule is the exclusive–or (XOR) function: at each time step, each cell is computed as the XOR of its two neighbors. Beginning with a row of all ‘0's punctuated by a single central ‘1,' snapshots of the cellular automaton's state at successive time steps may be stacked one on top of the other to produce a space–time history identical to Pascal's triangle ( Bondarenko 1993 ) modulo 2 ( Figure 1 A, left), which is a discrete form of Sierpinski's fractal triangle ( Figure 1 A, right). To represent this cellular automaton as a tiling, each local context present in the space–time history must have a corresponding Wang tile whose shape represents the input and output occurring at that location ( Figure 1 B). Thus, we need four tiles, one for each entry of the truth table for XOR, and a linear input row representing the initial state of the cellular automaton ( Figure 1 C). Given these tiles and the input row there is a unique way to tile the upper half-plane without mismatches or missing tiles—the Sierpinski Tiling—which reproduces the cellular automaton's space–time history ( Figure 1 D). Figure 1 The XOR Cellular Automaton and Its Implementation by Tile-Based Self-Assembly (A) Left: three time steps of its execution drawn as a space–time history. Cells update synchronously according to XOR by the equation shown. Cells at even time steps are interleaved with those at odd time steps; arrows show propagation of information. Right: the Sierpinski triangle. (B) Translating the space–time history into a tiling. For each possible input pair, we generate a tile T- xy that bears the inputs represented as shapes on the lower half of each side and the output as shapes duplicated on the top half of each side. (C) The four Sierpinski rule tiles, T-00, T-11, T-01, and T-10, represent the four entries of the truth table for XOR: 0 ⊕ 0 = 0, 1 ⊕ 1 = 0, 0 ⊕ 1 = 1, and 1 ⊕ 0 = 1. Lower binding domains on the sides of tiles match input from the layer below; upper binding domains provide output to both neighbors on the layer above. Semicircular domains represent ‘0' and rectangular domains, ‘1'. Tiles that output ‘0' (T-00 and T-11) are gray, and we refer to them as ‘0' tiles. Tiles that output ‘1' (T-01 and T-10) are white and are referred to as ‘1' tiles. Initial conditions for the computation are provided by a nucleating structure (blue). Red asterisks indicate sites on the nucleating structure that bear a ‘1' binding domain; elsewhere, sites have all ‘0' binding domains. Black arrows indicate associations that would form two bonds; red arrows, associations that would form one bond. (D) Error-free growth results in the Sierpinski pattern. (E) Error-prone growth from a nucleating structure with three ‘1' domains. Red crosses indicate four mismatch errors. Whereas execution of a cellular automaton occurs perfectly and synchronously, molecular self-assembly is asynchronous and may have many types of errors. To be successful, an implementation of cellular automata by molecular tiling must address four challenges: (1) The abstract tiles must be translated into molecules (molecular tiles) that readily form 2D crystals. (2) Molecular tiles must be programmed with specific binding domains that match the logic of the chosen abstract tiles. (3) The binding of molecular tiles must be sufficiently cooperative to enforce the correct order of assembly and prevent errors. (4) Assembly of molecular tiles must occur on a specified nucleating structure, and spurious nucleation must be suppressed. These properties are necessary and sufficient for implementing not only the XOR cellular automaton, but also any other 1D cellular automaton. All four have been shown individually: several types of DNA Wang tiles have been designed and shown to grow into micron-scale 2D periodic crystals ( Winfree et al. 1998a ; Mao et al. 1999 ; LaBean et al. 2000b ); the interactions between these tiles can be programmed by sequence-specific hybridization ( Winfree et al. 1998a ; Mao et al. 2000 ); cooperative binding of multiple domains ensures specificity—the right tile attaches in the right place ( Winfree et al. 1998b ; Mao et al. 2000 ); and input to the self-assembly process can be provided by a single-stranded template ( LaBean et al. 2000a ; Yan et al. 2003a ). Here we demonstrate, via self-assembly of Sierpinski triangles, that all four challenges can be simultaneously overcome, thus establishing all the mechanisms necessary to implement arbitrary cellular automata. The Sierpinski tiling, then, gives rise to a new type of aperiodic crystal—an algorithmic crystal. Results Modeling Tile-Based Self-Assembly Preventing the types of errors mentioned above may seem impossible. For example, if a single binding domain is strong enough to hold a tile in place (red arrows in Figure 1 C), then one would expect roughly 33% of tiles to mismatch with tiles in the layer below. Simulations of self-assembly shed light on how to avoid such dire circumstances. We use two levels of abstraction that isolate and address critical issues for the design and analysis of our algorithmic self-assembly experiments. How crystal morphology and patterning can be programmed by tile design in an inherently asynchronous assembly process is addressed by the abstract Tile Assembly Model (aTAM) ( Winfree 1996 , 1998a ). To explore how physical parameters, such as tile concentration and temperature, affect crystal growth and influence error rates, we use the kinetic Tile Assembly Model (kTAM) based on reversible tile association and dissociation rates ( Winfree 1998a ). Control over the order of assembly is obtained by exploiting the cooperativity of binding. The aTAM models cooperativity via a threshold, τ, representing the number of bonds that must be made for an association event to be thermodynamically stable: a tile may be added to a crystal if at least τ binding domains match the existing crystal. Black arrows in Figure 1 C indicate four potential association events that could occur at τ = 2; red arrows indicate two additional association events that would be permitted at τ = 1, but not at τ = 2. Simulation of cellular automata is designed to work at τ = 2. Isolated tiles cannot associate at τ = 2 and so growth and computation must begin with a preformed nucleating structure ( Figure 1 C, blue) that represents the input to the computation. Importantly, at τ = 2 no tile may be added until both preceding tiles are already present, guaranteeing a deterministic outcome despite the asynchronous order of events. Thus, assembly from an input row containing a solitary ‘1' domain produces the Sierpinski triangle pattern ( Figures 1 D and 2 A) regardless of the order in which permitted associations occur. If a small number of additional τ = 1 associations are permitted to occur, then mismatches between neighboring tiles (mismatch errors) may result. In this case, or if there are several ‘1's in the input row, the resulting pattern can appear to be qualitatively different: owing to propagation of information and the linearity of XOR, the resulting pattern is the superposition of Sierpinski triangles initiated at input ‘1's and at mismatch error sites (see Figure 1 E). Figure 2 Typical kTAM Simulation Results (A) A roughly 130 × 70 subregion of an error-free templated crystal. (B) A subregion with 10 mismatch errors (0.1%), shown in red (both false ‘0's and false ‘1's). Grown at G mc = 17, G se = 8.8. Large all-zero patches near the template row are due to intact Sierpinski pattern; for simulations with these parameters, asymptotically only approximately 1% of T-00 tiles are in all-zero patches containing more than 90 tiles (referred to as large patches). (C) A subregion from a crystal grown with the T-00 and T-11 tiles at doubled concentration, on a slowly growing nucleating row. Mismatch errors (43 of them, i.e., 0.3%, during growth at G mc = 17, G se = 8.6) characteristically terminate the Sierpinski pattern at corners. Asymptotically, approximately 18% of T-00 tiles are in large patches. (D) An untemplated crystal with roughly 4000 tiles and no errors. Inset: The largest subregion of a perfect Sierpinski pattern is small. (E) An untemplated crystal with several errors, grown at G mc = 17, G se = 10.4. Note that growth in the reverse direction is more error-prone. Only approximately 1% of T-00 tiles are in large patches. (F) An untemplated crystal with few errors, grown at G mc = 17, G se = 8.6, with T-00 and T-11 at doubled stoichiometry. Note the large perfect subregion. Simulation was initiated by a preformed seed larger than the critical nucleus size (roughly 100 tiles). For these simulation parameters, approximately 25% of T-00 tiles are in large patches. According to the approximations used in Winfree (1998a) , G mc = 17 corresponds to 0.8 μM, G se = 8.5 corresponds to 41.8 °C, and G se = 10.4 corresponds to 32.7 °C. The black outline around the crystals is for clarity; it does not represent tiles. The rate at which such errors occur can be understood using the kTAM. In this model, all tiles (regardless of how well they match) may associate at a given site at a rate, r f , proportional to their concentration: r f = k [ tile ] = ke − G mc , where k is a forward rate constant and G mc > 0 is the nondimensionalized entropy lost due to association—thus it represents the monomer concentration. Dissociation rates depend on how many binding domains match correctly: a tile with b correctly matching binding domains has a dissociation rate given by r r,b = ke − bG se , where G se > 0 is the nondimensionalized free energy for a single binding domain—thus it represents the sticky end strength. G se decreases with increased temperature. Thus, if G se < G mc < 2 G se , a reaction wherein the tile matches at a single domain would have r f < r r, 1 and thus would be thermodynamically unfavorable, while a reaction wherein the tile matches at two domains would have r f > r r, 2 and thus would be thermodynamically favorable. This model is a reasonable first-order approximation for the tile-based self-assembly of single crystals. For G mc ≈ 2 G se , as G mc and G se become arbitrarily large, the τ = 2 aTAM is approximated arbitrarily well, and error rates go to zero—concomitantly, assembly speed goes to zero ( Winfree 1998a ). For ranges of G mc and G se compatible with current experimental conditions, assuming thermodynamic and kinetic parameters extrapolated from the literature of DNA duplex hybridization ( Bloomfield et al. 2000 ), this model ( Figure S1 ) predicts mismatch error rates between 0.1% and 1.0% ( Figure 2 B). The effects of non-idealities can also be explored in this model. For example, Figure 2 C shows growth when the concentrations of the T-00 and T-11 tiles are twice that of the T-01 and T-10 tiles, and the nucleating structure grows slowly from special nucleating tiles rather than being preformed. Under this condition there is a preferential association of ‘0's on the facets of the growing crystal, causing characteristic errors that terminate Sierpinski triangles at corners and result in large all-zero patches ( Figures 3 A and S2 ). The mechanism responsible for these errors appears to be preferential nucleation of T-00 tiles on all-zero facets, due to their higher concentration ( Figure S3 ). If nucleation occurs on an all-zero facet both above and below a ‘1' tile, correct growth from the ‘1' will be sandwiched between ‘0's and therefore further errors will be forced ( Figure 3 B). The further errors could be either (1) termination of the Sierpinski triangle by addition of a mismatched ‘0' tile at the corner site, or (2) sideways propagation creating a new small triangle by addition of a mismatched ‘1' tile on the facet below the corner site (arrow in Figure 3 A). Thus, slight quantitative variations in the model parameters can lead to striking qualitative differences in the observed error morphologies, which are effectively never seen under growth conditions with equimolar tile concentrations or with preformed borders (see Figure S2 ). Figure 3 Simulations with Slow Border Growth and T-00 and T-11 at Doubled Concentrations (A) A common error pattern: termination of triangles at corners. (B) An observed mechanism leading to termination or sideways extension of triangles: preferential nucleation of T-00 on facets. (C) Forward and sideways growth is deterministic: at sites presenting two binding domains, there is always a unique tile that can form exactly two bonds. Backward growth is non-deterministic: at sites where both binding domains agree (e.g., green arrows), there are two possible tiles that can make two bonds (either {T-10, T-01}, or {T-00, T-11}). At sites where the available binding domains disagree (e.g., red arrows), there is no tile that can associate to form two bonds. Since only the output type of tiles are shown, it is impossible to tell from these figures which backward growth sites present agreeing or disagreeing binding domains. The kTAM also provides insights into a second kind of error, the spontaneous 2D nucleation of untemplated crystals in the absence of the nucleating structure. For G mc ≈ 2 G se , which corresponds to the melting temperature of the crystals, such untemplated nucleation is inhibited by a kinetic barrier—the existence of a critical nucleus size ( Davey and Garside 2000 ) that decreases with increasing supersaturation. The growth rate of untemplated crystals also increases with supersaturation since their growth occurs by spontaneous 1D nucleation of a new layer of tiles on any of four facets. Via any single binding domain, there are always two tiles that can bind, so such nucleation must effectively invent a new bit of information. This bit may be propagated quickly forward or sideways (wherein tiles attach by one input and one output domain) to complete the layer without error according to the logic of XOR. Consequently, such crystals have none of the qualitative appearance of Sierpinski triangles even though they may contain no mismatched tiles (see Figure 2 D). If G se is increased, corresponding to lowering the temperature, nucleation occurs more rapidly but errors are more frequent. Backward growth, in which tiles attach to a crystal by both of their output domains, is especially error-prone since every one of these associations involves the invention of information ( Figure 3 C). Whenever two backward-growing domains meet and disagree on the information that they have invented, growth can only proceed via an error. Under fast growth conditions, significantly below the melting temperature as in Figure 2 E, this gives rise to higher error rates in the reverse growth direction. Near the melting temperature, however, this effect is insignificant. The most noticeable effect for untemplated crystals is due to the non-ideality discussed above (doubling the relative concentration of T-00 and T-11 tiles): the statistical preference for all-zero patches actually increases the frequency and size of perfect Sierpinski patterns (see Figure 2 F). These simulation studies suggest that all three difficulties (asynchronous association of tiles, mismatch errors, and untemplated nucleation) in principle can be controlled by slowing down the growth processes, making experimental investigations the appropriate next step. Design and Preparation of DNA Tiles Abstract Wang tiles are implemented as DNA tiles according to the scheme described earlier ( Winfree et al. 1998a ): each molecular Wang tile is a DNA double-crossover molecule ( Fu and Seeman 1993 ) with four sticky-ends (5-base single-stranded overhangs) that serve as the programmable binding domains. We rendered the four Sierpinski rule tiles using two types of double-crossover molecule, known as DAE-E and DAO-E molecules ( Winfree 1996 ), resulting in two independent molecular implementations ( Figure 4 , sequences are as given in Figures S4–S7 ). The DAE-E Sierpinski tile set ( Figure 4 A) consists of four molecular tiles, each composed of five strands whose sequences were designed to minimize the potential for forming alternative structures ( Seeman 1990 ), as confirmed by non-denaturing gel electrophoresis ( Figure S8 ). Figure 4 Molecular Schema (A) Top center: abstract versions of the four DAE-E Sierpinski rule tiles, VE-00, UE-11, RE-01, and SE-10, highlight their differences from the tiles in Figure 1 . The arrangement of 3′ and 5′ ends on DAE-E tiles dictates that two distinct pairs of complementary binding domains must be used for each symbol ‘0' or ‘1,' denoted here by making complementary shapes large or small. Pink legends show the mapping of shape to sticky-end sequences. Top left: a molecular diagram for VE-00 shows how each DAE-E tile is comprised of five DNA strands; small arrows point to crossovers. Top right: a diagram for RE-01 shows how hairpins are attached to ‘1' tiles to provide AFM contrast; the exact orientation of these hairpins is unknown. Below: tiles are shown assembling on a nucleating structure. The nucleating strand for the input row (blue) is generated by assembly PCR and frequently reaches lengths of more than 3 μm (200 tiles). The nucleating strand contains subsequences onto which capping strands (orange) and input tile strands assemble to form an input tile outputting ‘0' at random intervals, the nucleating strand contains a subsequence (asterisk) for a different input tile that outputs a ‘1' on one side. (B) Top center: the six DAO-E Sierpinski rule tiles: S-00, R-00, S-11, R-11, S-01, and R-01. Top left and right: molecular diagrams highlight two notable features: (1) R-type tiles output only to S-type tiles, and vice-versa, as dictated by the 3′/5′ polarity of the molecules—again requiring two distinct pairs of binding domains per symbol. (2) The indicated rotational symmetry of the DAO-E molecules allows each molecule to serve in either of two orientations; no explicit S-10 or R-10 tiles are needed. An input tile outputting a single ‘1' sticky end is shown (asterisk). Sequences are given in Figures S4–S7 . Since untemplated crystals were not expected to produce recognizable Sierpinski triangles, it was necessary to create a proper nucleating structure to provide the initial input for the algorithmic self-assembly. Previous work using DNA tiles to self-assemble an initial boundary had proven to be difficult ( Schulman et al. 2004 ), so in this work we took an alternative approach of using assembly PCR ( Stemmer et al. 1995 ) to create a long single-stranded molecule which could serve as a scaffold ( LaBean et al. 2000a ; Yan et al. 2003a ) for the assembly of a row of input tiles ( Figures 4 A and S9–S11 ). Because this nucleating strand serves as the bottom of these tiles, only four strands are needed to assemble the input tiles, and an additional capping strand is used to form a double-helix between input tiles on the nucleating strand. By doping the assembly PCR mix with a small fraction of the strands coding for an input tile outputting a single ‘1,' we ensure that each nucleating structure contains a few randomly located sites from which a Sierpinski triangle should grow. The DAO-E Sierpinski tile set ( Figure 4 B) consists of six molecular tiles, due to peculiarities of the DAO-E motif. First, consideration of the 5′ and 3′ orientation of strands—particularly the fact that the sticky ends at the top and bottom of a DAO-E tile have opposite polarity—demands that each tile binds only to “upside-down” neighbors, resulting in layers of tiles with alternating orientation, which we refer to here as R-type and S-type tiles. Furthermore, the sugar–phosphate backbone of the DAO-E tiles has a dyad symmetry axis, implying that the R-01 and S-01 tiles each can play the roles of both the T-01 and T-10 tiles. Likewise, the R-00, R-11, S-00, and S-11 tiles can each bind in two orientations in a site where both inputs match. In order for the nucleating structure for the DAO-E lattice to assemble onto a long PCR-generated nucleating strand, the tiles on the input row must be of the DAE-O variant. Further, we simplified the construction so that all nucleating strands contain the same repetitive sequence, but the input tile strands are doped with a fraction of strands containing a ‘1' sticky-end, and again the nucleating structure contains a few randomly located sites from which a Sierpinski triangle should grow. Self-Assembly of DNA Sierpinski Triangles In principle, two approaches can be taken for initiating algorithmic self-assembly of DNA tiles. In the preformed tile approach, each tile is prepared separately by mixing a stoichiometric amount of each component strand in the hybridization buffer and then annealing from 90 °C to room temperature over the course of several hours. The nucleating structure is similarly prepared by annealing the nucleating strand with input tile and capping strands. Then the rule tiles and nucleating structure are mixed together at a temperature appropriate for crystal growth. In the bulk annealing approach, the nucleating strand, the capping and input tile strands, and the strands for all rule tiles are initially mixed together and then annealed. Since, at the concentrations we use, the tiles themselves have melting temperatures between roughly 60 °C and 70 °C while the crystals have a melting temperature within a few degrees of 40 °C ( Figure S12 ), during annealing the tiles themselves will first form, and only later will the fully formed tiles assemble into crystals, presumably growing from the nucleating structure prior to overcoming the barrier to spontaneous nucleation. Both approaches work, but because of the convenience of the bulk annealing approach, all samples reported here were prepared using that method, with a final concentration of 0.2 μM each tile. After self-assembly in solution, samples are deposited onto mica and imaged by tapping mode atomic force microscopy (AFM). Results for the DAE-E tile set are shown in Figures 5 and S13–S15 . The majority of DNA crystals we observed were similar to those in Figure 5 A: in addition to many small and indistinctly formed fragments, larger crystals are typically thin and long (up to several microns) with ‘1' tiles clearly visible. Crystals consisting exclusively of VE-00 tiles (upper arrow in Figure 5 A) were particularly common; further investigation revealed that some (perhaps all) of these crystals formed as DNA tubes, and subsequently broke open and lay flat on the mica (see Figure S15 ; Rothemund et al. 2004 ). A ‘011011'-striped pattern (lower arrow in Figure 5 A) was also quite common; it can be constructed from the RE-01, SE-10, and UE-11 tiles. Growth may have been biased to form ‘011011' patterns by the depletion of VE-00 tiles, a stoichiometric disproportionation of tiles, due to growth of tubes early during annealing. Crystals that clearly grew from the nucleating structure were also apparent; Figure 5 B– 5 E show examples with particularly few errors. In several of these crystals, individual tiles could be identified and a compatible arrangement of abstract tiles (and thus errors) could be determined. Large error-free domains containing more than eight rows of perfect Sierpinski triangle were observed. In these examples, the mismatch error rate was about 2% over a wider selection of fragments, the error rate varied between 1% and 10%. We partly attribute this variation to changes in the physical conditions during annealing that result in a disproportionation of tiles. In addition to errors due to incorporation of the wrong tile, we also observed missing tiles and lattice dislocations. Frequently, as in Figure 5 E, the identity of obscured or missing tiles was deduced from the neighboring tiles by assuming correct information propagation (the imperfection often being caused by sample preparation or by interaction with the AFM tip rather than by errors during assembly). Figure 5 AFM Images of DAE-E Crystals (A) Several frequent morphologies that appear in most samples, including all-'0' (upper arrow) and ‘011011'-striped crystals (lower arrow). The all-'0' crystal may be a tube that opened upon adsorption to the mica. (B) A templated crystal. The identification of tiles in this crystal is given in Figure 1 E. Crosses indicate mismatch errors. Asterisks indicate ‘1's on the nucleating strand. (C) A crystal containing 10 rows of error-free Sierpinski triangle. A red triangle marks a lattice defect in the input row. (D) Another Sierpinski triangle, better resolved. (E) A crystal containing a perfect 19 × 6 subregion. Individual tiles can be clearly seen; three tiles are outlined in the lower left. Unfortunately, this crystal landed atop a thin sliver of DNA (lower arrow), obscuring the central columns of the Sierpinski triangle. The upper arrow indicates a 4-tile wide tube, near the point where it opens. A pentagon marks a lattice dislocation. Scale bars are 100 nm. Shown in Figures 6 and S16–S18 , the DAO-E tiles also succeeded in producing recognizable Sierpinski triangles. However, the DAO-E tiles self-assembled into considerably larger sheets than the DAE-E tiles, presumably because of the DAO-E tiles' symmetries that result in cancellation of strain and thus encourage flat sheets. Templated crystals were observed that had grown more than 70 rows ( Figure 6 A). Because the R-11 tile does not appear in an error-free templated crystal, in some experiments ( Figure 6 A) we did not include this tile; however, we observed no qualitative difference between samples prepared with and without R-11. In these crystals we almost always observed subregions with a characteristic pattern of errors that coincidentally resulted in termination of Sierpinski triangles at their corners and tops, creating large patches of zeros. Even untemplated crystals ( Figure 6 B) contained recognizable subregions of the Sierpinski pattern. These features may be explained as follows: although the DAO-E tiles were mixed with equal quantities, the R-00, S-00, R-11, and S-11 tiles can bind to any permitted site in two orientations, thus making the experimental conditions analogous to the simulations of Figure 2 C– 2 F wherein the concentration of the corresponding tiles is doubled; slow growth of the input row in the simulations may correspond to slow straightening out of the nucleating strand, which is initially a random coil (see Figure S17 ). Large crystals often have strikingly different tile distributions and error rates, as can be seen in the amalgamation of several large crystals shown in Figure 6 C and Video S1 . Again, this may be attributed to the disproportionation of tiles during annealing, or to sideways growth as the nucleating structure straightens out. Figure 6 D– 6 E shows particularly clear examples of Sierpinski triangles, averaged from several scans of the same crystal. Attempts to optimize the reaction conditions to produce Sierpinski triangles with lower error rates did not yield dramatic improvements ( Figure S18 ). Figure 6 AFM Images of DAO-E Crystals (A) A large templated crystal in a 5-tile reaction (no R-11). A single ‘1' in the input row (asterisk) initiates a Sierpinski triangle, which subsequently devolves due to errors. Mismatch errors within ‘0' domains initiate isolated Sierpinski patterns terminated by additional errors at their corners. (B) A large untemplated fragment in a 5-tile reaction (no S-11). Large triangles of ‘0's can be seen. Crystals similar to this are also seen in samples lacking the nucleating structure. (C) Several large crystals in a 6-tile reaction, some with more zeros than ones, some with more ones than zeros. It is difficult to determine whether these crystals are templated or not. (D) An average of several scans of the boxed region from (C), containing roughly 1,000 tiles and 45 errors. (E) An average of several scans of a Sierpinski triangle that initiated by a single error in a sea of zeros and terminated by three further errors (a 1% error rate for the 400 tiles here). Red crosses in (D) and (E) indicate tiles that have been identified (by eye) to be incorrect with respect to the two tiles from which they receive their input. Scale bars are 100 nm. Discussion The self-assembly of DNA Sierpinski triangles demonstrates all four features necessary for Turing-universal computation by crystallization: formation of extended crystals, programmable interactions between DNA tiles determined by sticky-end sequences, selective associations of tiles enforced by the cooperative binding of more than one sticky end, and controlled nucleation of growth initiated by a template containing input information. This tiling approach could be used to implement other cellular automaton rules. Given a set S of possible states for the memory cells and an update function f : S × S → S , one can create a set of | S | 2 tiles according to the scheme of Figure 1 B, one tile for each possible input pair. The need for binding specificity limits the number of sticky-end sequences (and hence | S |) to about 20 for the DAO-E and DAE-E tile designs used here, but this is already sufficient to implement several known universal Turing machines and cellular automata ( Lindgren and Nordahl 1990 ; Rogozhin 1996 ). A larger set of sticky-end sequences could be achieved by redesigning the DNA tile molecules to use longer sticky-ends, provided that the melting temperatures of tiles and crystals remain well separated. Thus, DNA crystallization is programmable and Turing-universal. Furthermore, for fabrication purposes, computation by self-assembly could be used to control the direction and extent of growth, thus allowing arbitrary shapes to be created efficiently ( Soloveichik and Winfree 2005 )—demonstrating that algorithmic self-assembly is not limited to the simulation of cellular automata or Turing machines. The main obstacle currently limiting attempts to compute or fabricate using algorithmic self-assembly is the presence of several types of errors. We observed lattice dislocations, a structural error; untemplated tubes and untemplated crystals, an error in the control of nucleation; and mismatched tiles, an error in the growth process. Accurate quantitative models of algorithmic self-assembly will be valuable for developing methods to control and reduce such errors. The kTAM simulations described here, while qualitatively insightful, predict mismatch error rates an order of magnitude smaller than those observed—motivating experimental measurements of errors and refinement of the model. Although it may be possible to reduce the error rates by carefully controlling the assembly conditions, a more promising route is the creation of fault-tolerant tile sets that perform the same logic ( Winfree and Bekbolatov 2004 ; Chen and Goel 2005 ; Reif et al. 2005 ; Schulman and Winfree 2005 ). For the same assembly conditions, and thus roughly the same growth rate, the kTAM predicts that these tile sets can reduce the mismatch error rates by many orders of magnitude—a conclusion likely to hold in spite of inaccuracies in the model. Self-assembly has been touted as a possible successor to photolithography, a basis for nanotechnology and a route to complexity in chemistry ( Whitesides et al. 1991 ). Algorithmic self-assembly—whether using DNA tiles as demonstrated here or using appropriately designed small molecules, proteins, or even macroscopic tiles ( Bowden et al. 1997 ; Rothemund 2000 )—extends the range of structures accessible by bottom-up fabrication techniques. For example, an abstract tile set that enumerates binary numbers—a binary counter—uses just four tiles, yet it can be used to define the size of self-assembled structures ( Rothemund and Winfree 2000 ), thus addressing the synthetic chemistry challenge of creating monodisperse particles with programmable size. Furthermore, attachment of suitable logic gates to ‘0' and ‘1' tiles would yield a demultiplexer for a RAM circuit. This and other interesting digital circuits ( Cook et al. 2004 ) might be created by using algorithmic crystals as templates for further chemical processing ( Braun et al. 1998 ; Yan et al. 2003b ). The Turing-universality of self-assembly allows theoretical insights from computer science to be applied to self-assembly. For example, many questions phrased using the aTAM—such as “Will a certain tile type, say tile type #5, ever be incorporated into the assembly?” or “Will the final assembled shape have 4-fold symmetry?”—are formally undecidable as a consequence of the undecidability of the halting problem ( Turing 1936 ; Adleman et al. 2002 ). This suggests that there exists no generally applicable method for predicting the behavior or properties of crystals. A concrete instance of this dilemma is whether quasicrystals' 5-fold symmetry and aperiodicity could arise from self-assembly. Crystallographers have argued that, if so, definitions of order based on X-ray diffraction must be modified to include the new structures ( Senechal 1995 ). The growth of Sierpinski triangles, demonstrated here, shows unequivocally that self-assembly can create aperiodic structures based on local rules. Furthermore, traditional methods of measuring order, such as X-ray diffraction, will not recognize order that exists in certain algorithmic crystals. For example, an algorithmic crystal simulating a pseudo-random number generator ( Wolfram 1986 ; Jen 1990 ; Knuth 1997 ) would appear disordered, yet each molecule would be precisely and deterministically positioned. Thus, the growth of algorithmic crystals motivates the use of algorithmic definitions of order ( Kolmogorov 1965 ; Levin 1984 ; Bennett 1995 ) that generalize crystallography ( Mackay 1975 ). Finally, we ask whether the study of algorithmic self-assembly might further our understanding of biological self-assembly. Algorithmic crystals composed of simple sugar-based tiles have appeared in science fiction as a form of life ( Egan 1995 ); indeed, the simplicity and versatility of crystalline self-assembly suggests that templating, as a basis for simple organisms ( Penrose and Penrose 1957 ; Cairns-Smith 1971 ), may be more natural than previously supposed. However, examination of self-assembly in modern organisms reveals many mechanisms beyond those considered here, including conformational changes, dissipative mechanisms such as ATP hydrolysis, and interactions with genetic regulatory networks—themselves biochemical information processors. The development of a theory of molecular algorithms that encompasses these additional mechanisms, if successful, will deepen our understanding of the complex processes found in nature, their fundamental limits, and their remarkable potential. Materials and Methods kTAM simulations Simulations described in this paper were performed with the xgrow program, written by Erik Winfree and available, along with tile sets used here, from http://www.dna.caltech.edu/SupplementaryMaterial . The xgrow program simulates the kTAM for a set of square Wang tiles (see Figure S1 ), beginning with a single seed tile. The tile set used here consists of the four Sierpinski rule tiles T-00, T-11, T-01, and T-10, augmented by three border tiles B-0, B-1, and B-B, the latter being used as the seed tile. To simulate the presence of a nucleating structure, the binding domain that joins border tiles is considered to be twice as strong as the other bonds—that is, it counts as two bonds in the sum b that determines off-rates r r,b = ke − bG se . The border row grows—simulating the long nucleating structure becoming straight—by association of border tiles at the rate r f = k [ tile ] = kS i e − G mc , where S i is the stoichiometry of border tile i relative to the concentration of the four Sierpinski rule tiles. Since we have no knowledge of how quickly DNA nucleating structures straighten in our experiments, we considered two cases: (1) A rigid or quickly straightening nucleating structure was simulated by setting S i = 4, so that near the crystal melting temperature where G mc ≈ 2 G se , the border growth is strongly favorable. This was used for Figure 2 B, where the seed tile stoichiometry was also set to zero, so that exactly one seed tile would be incorporated into the nucleating structure. (2) A floppy and slowly straightening nucleating structure was simulated by setting S i = 0.25 for the border tiles; in this case, near the melting temperature border growth requires stabilization by growth of rule tiles, resulting in faceted crystals. In combination with doubled concentrations of T-00 and T-11 ( S i = 2), this case was used for Figure 2 C, where additionally the seed tile stoichiometry was set to 0.01 so that roughly 1% of border tiles output a ‘1,' in rough agreement with the observed fraction of ‘1's within the DNA nucleating structures in our DAO-E experiments. The strong effect of these variations may be seen in Figure S2 . Slow border growth significantly increases the mismatch error rate, resulting in the information contained in the border being lost in a few layers. The primary effect of doubled T-00 and T-11 concentrations is to increase the predominance of all-zero patches in the resulting crystal; not only are all-zero patches typically larger, but all-zero information in the border is propagated more reliably. Additionally, under these conditions spontaneous nucleation almost exclusively involves an initial all-zero nucleus. Simulations confirm the preferential nucleation of T-00 tiles on all-zero facets when T-00 and T-11 concentrations are doubled ( Figure S3 ). In contrast, preferential nucleation on facets is not seen for the T-11 tile, despite its increased concentration. This is because, regardless of what information is presented on the facet, there is no way to create a layer containing more than 50% T-11 tiles and no mismatches; T-01 or T-10 tiles must intervene. Thus the nucleation rate is substantially reduced, relative to T-00 nucleation on all-zero facets. This can be assessed in simulations by measuring the probability, p(L), that a T-00 tile will be found after L layers of growth from a facet. Simulations with parameters similar to Figure 2 C (doubled T-00 and T-11 concentrations) show that p(L) ≈ 0.66 e −L/27 + 0.34 for all-zero facets, indicating strongly preferential nucleation, but for all other facets p(L) relaxes quickly to the asymptotic distribution. Simulations with parameters similar to Figure 2 B (normal T-00 and T-11 concentrations) show no preferential nucleation, as p(L) relaxes to the asymptotic distribution immediately for every facet type investigated. DNA sequence design Design of DNA Wang tiles occurs in three steps. First, the tile and lattice geometry must be determined. Here, the sizes (number of base-pairs) of each double-helical domain and sticky end, and other structural adornments such as contrast hairpins, are decided. These decisions impact the stability of each tile molecule, as the natural geometry of the DNA double-helix (10.5 bp for a full turn of B-form DNA) ( Wang 1979 ; Rhodes and Klug 1980 ) constrains, for example, the separation between crossover points to be an integral number of half-turns. For the double-crossover motif used here ( Fu and Seeman 1993 ), the acronym DAE-E refers to some of these choices at the structural level: double-crossover; antiparallel orientation of non-crossover strands at each junction; even number of half-turns (21 bp) between crossover points within each molecules; and even number of half-turns (21 bp) between nearest crossover points in two molecules joined by sticky ends. DAO-E refers to a similar set of choices, except that an odd number of half-turns (16 bp) separates the crossover points within each molecule. Where hairpin sequences were inserted for AFM contrast, we included two unpaired Ts at the bulged three-arm junctions, which has been shown to encourage stacking in the original helix domain ( Ouporov and Leontis 1995 ) without significantly affecting the rigidity of the molecule ( Li et al. 1996 ). At the second level, specific sequences must be chosen. The issue here is that we wish to prevent undesired associations between strands that might inhibit formation of the correct molecular structure. We used the heuristic principle of sequence symmetry minimization ( Seeman 1982 , 1990 ) to minimize the length and number of unintentional Watson–Crick complementary subsequences among all strands in each system (DAO-E and DAE-E). Violations that occurred within a single strand were weighed more heavily than violations between two strands; similarly, violations between strands in the same tile were weighed more heavily than violations between strands in different molecules. A simple adaptive walk algorithm was found to be effective in minimizing the violations and arriving at acceptable sequences. Sticky-end sequences were chosen with particular care to minimize the possibility of erroneous hybridization. The third level of design concerns variations. We conceptualize DNA Wang tiles as consisting of three modules: the sticky ends, the core helical regions, and adornments such as the hairpin structures that provide contrast for AFM imaging. A given double-crossover core can be given different sticky ends (reprogrammed) by replacing just one or two strands, thus allowing reuse of core designs to implement different tile sets. In our experience, the structural and thermodynamic stability of a given core is not significantly affected by changes in the sticky-end sequences. Similarly, using additional strands, a given core can be used with or without the hairpin adornments, which can be inserted at various locations. Although the hairpin adornments can affect the integrity of a DNA tile (e.g., strand dimers or other high molecular weight species), we have seldom found the undesired products to exceed 20% of the material. The core sequences for R-00 and S-00 are identical to the A and B tiles from a previous study ( Winfree et al. 1998a ). We usually give tiles names that indicate their core, sticky ends, and adornments. However, in the main text of this paper we have dispensed with the indication of these variations for clarity. For example, R-01 would more properly be called R-01n-23JC; S-01 called S-01-23JC; RE-01 called RE-01-15J; and SE-10 called SE-10-15J to specify which component strands have hairpins, and where those hairpins are. (The shorter names properly refer to unadorned tiles.) DNA tile preparation and gel electrophoresis All oligonucleotides were synthesized by standard methods (Integrated DNA Technologies), PAGE purified, and quantitated by UV absorption at 260 nm in H 2 O (purified by a Milli-Q system, Millipore, Bedford, Massachusetts, United States) based on extinction coefficients estimated using a nearest-neighbor model ( Bloomfield et al. 2000 ). DNA tiles were prepared by mixing stoichiometric quantities of each component strand in a TAE/Mg 2+ buffer, as described in Winfree et al. (1998a) . Proper formation of each of the ten DAE-E and DAO-E tile cores was confirmed in non-denaturing PAGE (10%–15% 19:1 bis:acrylamide, 3–5 h at 15 V/cm and 4C, 2 pmol complex/lane, Sybr Gold [Molecular Probes, Eugene, Oregon, United States] stained for 20–30 min, excited at 488 nm, imaged with 530 bandpass filter on a Bio-Rad [Hercules, California, United States] Molecular Imager FX Pro Plus) by observing a single major band (see Figure S8 ; typically between 5% and 20% of the total material appears in bands identified as partial products, such as incomplete tiles with missing strands). We redesigned the core sequences for one tile (R01) that initially did not form clean gel bands; the new tile (R01n) was used exclusively in this study. Most notably in DAE-E tiles, some lanes containing subsets of a tile's component strands showed ill-formed or heavy species such as dimers, but these difficulties were not pronounced in lanes containing the complete tile. Formation gels also allow us to estimate the relative accuracy of our concentration measurements: mismatches in stoichiometry would result in excess single-stranded or partial complexes. Concentrations appear to be accurate to ± 10%. This suggests that purification of tile complexes could result in cleaner self-assembly reactions and lower error rates. Synthesis of the nucleating strand The single-stranded nucleating strands were synthesized using a procedure based on Stemmer's assembly PCR ( Stemmer et al. 1995 ). In assembly PCR, a long, repetitive, double-stranded product is generated by performing PCR on a set of splints, primer-like short (typically 40 nt) oligos that are subsequences of the desired repeat sequence as shown in Figure S9 . To generate the single-stranded product needed for subsequent assembly of tiles on the nucleating strand, asymmetric PCR with primers for just one of the two complementary product strands could be used, in principle. In practice, we have found that such reactions result in more double-stranded product and little or no single-stranded product—probably because the repetitive nature of the assembly PCR product means that every 3′ end, including those of the undesired strand, may act as a primer. Thus we designed the long covalent strand of our nucleating structures to contain exclusively As, Cs, and Ts and generated single-stranded nucleating strands from the output of an assembly PCR by performing synthesis using a reaction mixture containing just dATP, dCTP, and dTTP. Although predominantly single-stranded, the output of this reaction has both single- and double-stranded material in it. We do not purify the single strands and thus double-stranded material persists in our experiments ( Figure S17 ). The splint strands for generating the DAE-E nucleating strands and the DAO-E nucleating strands are given in Figure S10 . Note that in order to have 20 base overlaps, some splint strands complement the same central three base sections of complementary splints. Assembly reactions for DAO-E and DAE-E nucleating strands were designed using slightly different principles. The improved design used for DAO-E nucleating strands is simpler: A single periodic sequence is generated. The fraction of ‘1' sites is determined by the stoichiometry of input tile strands used in subsequent self-assembly reactions—strands A4SV and A4-S00 both assemble in the input tile in the same place, but one carries a ‘1' sticky end while the other carries a ‘0' sticky end. The approach used for DAE-E nucleating strands is more complex but more powerful for generating non-trivial input patterns. By having multiple splints that can overlap a given sequence, the assembly can be directed to non-deterministically choose one of several ways to extend a sequence. Thus, assembly PCR can be used to generate any regular language ( Winfree 1998b ). In this work, we used a combination of splint strands that generates substrings of the language (NRE NUE + ) * . The fraction of NRE subsequences is controlled by the amount of SplintNREUE2 and SplintNUERE2, which mediate the transitions into and out of the NRE sequence. (Here, we used these splints at one-fifth the concentration of other splints.) The NRE input tile outputs ‘0' and ‘1,' while the NUE input tile outputs ‘0' and ‘0'. To generate a different language, or a different distribution of sequences in the same language, a new assembly PCR must be run. (The simpler design approach used for DAO-E could also be used for DAE-E nucleating structures.) For both methods, the PCR protocol has four stages, the first three for assembly PCR and the last to generate single-stranded material. PCR was performed in a Stratagene (La Jolla, California, United States) MX 4000 real-time PCR instrument using a Perkin-Elmer (Torrance, California, United States) GeneAMP XL kit that uses rTth polymerase. In stage 1, a 20 μl reaction mixture containing 1 pmol total of splints (of which there are N types) is prepared without polymerase (Mix A, per 20 μl: 1 μl of 1 μM mixed splints, 1/ N μM each; 1.6 μl of 10 mM dNTPs, 2.5 mM each; 1 μl of 25 mM magnesium acetate, 6 μl of 3.3X GeneAMP XL PCR buffer; 10 μl water). To avoid mispriming events, the splints are annealed in the reaction mixture at 37 °C for 5 min. The polymerase (0.4 μl) is added and the reaction is subjected to an initial 72 °C extension step, followed by 40 cycles (94 °C for 15 s, 40 °C for 30 s, 72 °C for 10 s + 1 s/cycle; about 2 h). In stage 2, 40 μl of new PCR Mix B (Mix A minus the splints, plus 0.4 μl of polymerase, water adjusted to 20 μl), is added to the first reaction volume and the reaction cycled for an additional 25 cycles (94 °C for 15 s, 40 °C for 30 s, 72 °C for 45 s + 1 s/cycle; about 1.5 h). In stage 3, the 60 μl reaction volume is split into three 20 μl volumes, an additional 40 μl of Mix B is added to each and an additional 20 cycles (94 °C for 15 s, 40 °C for 30 s, 72 °C for 70 s + 1 s/cycle; about 1.3 h) are performed. At this point, long double-stranded product should be formed. (We have observed that such products remain in the well of an agarose gel long after a 20 kb marker has entered the gel.) Also the dNTPs in the mixture are presumably nearly exhausted—specifically there is little dGTP left. (Any remaining dGTP will be used up early in stage 4.) In stage 4, to create single-stranded nucleating strands, 5 μl of the stage 3 product are mixed with 55 μl of fresh PCR mixture (Mix B with 1.6 μl of a mixture containing 2.5 mM each dATP, dCTP, and dTTP, rather than all four dNTPs) for additional 60 cycles of the stage 3 program (94 °C for 15 s, 40 °C for 30 s, 72 °C for 70 s + 1 s/cycle). While addition of asymmetric primers at this stage might yield more single-stranded product, a satisfactory yield of single-stranded product results without doing so. After the final PCR, the reaction mixture is extracted with phenol:chloroform:isoamyl alcohol (Sigma, St. Louis, Missouri, United States), ethanol precipitated, and resuspended in purified water; the yield was estimated by UV absorbance. Typically, three 60 μl tubes of stage 4 product were pooled in a single recovery step and DNA was resuspended in 200 μl of water. Absorbance measurements of freshly resuspended material appeared unstable, perhaps because clumps of nucleating material scatter light. Long single-stranded DNAs may be prone to hydrolysis in water or strand-breakage upon freeze–thaw. However, after storage in water at 4 °C for a year, the nucleating structure still works well (as in Figure S18 ). To check whether the output of stage 4 is suitable as a nucleating strand for self-assembly, one can estimate the binding capacity of the nucleating strand material. Figure S11 shows such a gel (non-denaturing PAGE, 5% 19:1 bis:acrylamide, 1 h, 150 V) for the DAO-E nucleating strand, examining how much of the fluorescently labeled Cy3-cpBr1 can be bound. We observe several things. First, stage 3 double-stranded material does not bind Cy3-cpBr1 well, as expected. Second, stage 4 material does bind Cy3-cpBr1 well, quantitatively absorbing the full amount (1 μl) added. Third, stage 4 material cannot absorb 2 μl of Cy3-cpBr1, giving us an estimate of the binding capacity of the nucleating strands. This is important for determining how much of the input tile strands must be added to ensure that a tile assembles onto nearly every site on the nucleating strand. Fourth, the presence of Sybr Green I during PCR does not appear to affect the quality of double or single-stranded material generated. UV melts of tiles and crystals Melting temperatures for tiles and crystals were estimated based on UV 260 melts of S-00 and R-00-23J and a mixture of both tiles ( Figure S12 ). These tiles, also used in Winfree et al. (1998a) , are identical to the R-00 and S-00 tiles for the DAO-E Sierpinski system, but with hairpins added to the R-00 tile. Individual tiles were at 0.4 μM of each component strand in TAE/Mg 2+ . The mixture of R-00-23J and S-00, which forms crystals when annealed slowly, contained each strand at 0.2μM. Melts were performed on an Aviv model 14NT-UV-VIS spectrophotometer (Aviv Instruments/Protein Solutions, Lakewood, NJ, United States), and began with preannealed samples at 15 °C, increasing to 80 °C over the course of several hours. Single-tile melts were superimposable with the reanneal from 80 °C back to 15 °C, indicating that equilibrium values were measured. Raw absorbance values were normalized. Whereas S-00 has a sharp melting transition (also seen for most other tiles lacking hairpins) near 65 °C, the R-00-23J tile has a somewhat more gradual transition, which we attribute to the presence of the hairpin. Above 40 °C, the absorbance of the mixture equals the average absorbance of the individual tiles, indicating that crystals have completely melted by that point. Prior to the crystal melting transition between 36 °C and 40 °C, there is significant noise in the measurement, presumably due to light scattering. We have not performed UV 260 melts of all tiles; however, several other DAO-E and DAE-E tiles have similar transitions between 50 °C and 70 °C. Therefore we assume that the templated and untemplated Sierpinski crystals also melt at approximately 40 °C and that at that temperature, the DNA tiles are reasonably well formed. Self-assembly reactions Self-assembly was performed by bulk annealing of all relevant rule tile, input tile, capping, and nucleating strands in a (typically) 50 μl volume of 1× TAE/Mg 2+ buffer (40 mM Tris–acetate [pH 8.0], 2 mM EDTA, 12.5 mM Mg 2+ ), annealing from 90 °C to 20 °C at a rate of 1 °C/min (taking about 1 h). Longer annealing schedules (e.g., 1 °C/min from 90 °C to 50 °C followed by 1 °C/30 min in the critical region from 50 °C to 20 °C, a total of about 15 h) did not seem to decrease the error rate or the number of untemplated tubes or crystals. DAO-E reactions contained nucleating strands sufficient to bind 0.004 μM of input tile (as estimated from binding capacity gels), 0.2 μM of each capping and input tile strand (A1S, A2, A3-nick, A4-S00, cpBr1, and 1/100 as much A4SV), and 0.2 μM of each rule tile strand (for each of the five or six tiles used). An excess of input tile strands was used to ensure complete coverage of the nucleating strand. The excess partial input tiles appeared not to significantly interfere with the self-assembly of algorithmic crystals. DAE-E reactions contained nucleating strands sufficient to bind 0.002–0.008 μM of input tile (as inferred from the estimated yield of the PCR), 0.2 μM of each capping and input tile strand (NRE1 to NRE4, NUE1 to NUE4, CapNRERE and CapNUERE), and 0.2 μM of each tile strand (for each of the four tiles used). Again, an excess of input tile strands was used to ensure complete coverage of the nucleating strand. AFM imaging AFM imaging was performed in tapping mode under TAE/Mg 2+ buffer on a Digital Instruments Nanoscope III (Veeco Metrology, Woodbury, New York, United States) equipped with a nano-Analytics Q-control III (Asylum Research, Santa Barbara, California, United States) and a vertical engage J-scanner, using the roughly 9.4 kHz resonance of the narrow 100 μM, 0.38 N/m force constant cantilever of an NP-S oxide-sharpened silicon nitride tip (Veeco Metrology). After self-assembly is complete, samples were prepared for AFM imaging by deposition of 5 μl onto a freshly cleaved mica surface (Ted Pella) attached by hot melt glue to a 15 mm metal puck; an additional 30 μl of buffer was added to both sample and cantilever (mounted in the standard tapping mode fluid cell) before the sample and fluid cell were positioned in the AFM head. The tapping amplitude setpoint, after engage, was typically 0.2–0.4 V, the drive amplitude was typically 100–150 mV, scan rates ranged from 2 to 5 Hz. Individual tiles are most clearly resolved for low amplitude setpoint and high drive amplitude values. However, under such conditions, the greatest damage is done to the sample and the hairpin labels are less distinct, sometimes disappearing entirely. Thus, to prevent damage to samples, amplitude setpoint was maximized and/or drive amplitude minimized subject to the constraint that tiles and their hairpin labels be visible. After acquisition, most images were flattened by subtracting a low-order polynomial from each scan line, or by adjusting each scan line to match intensity histograms. For some images (see Figures 6 D– 6 E and S18 , bottom), multiple scans were aligned using hand-picked fiducial marks and averaged in Matlab (The Mathworks, Inc., Natick, Massachusetts, United States). Supporting Information Figure S1 Representations and Tile Sets Used in Simulations (57 KB PDF). Click here for additional data file. Figure S2 Behavior of Simulated Crystal Growth (160 KB PDF). Click here for additional data file. Figure S3 Simulations of Growth on Large Facets (126 KB PDF). Click here for additional data file. Figure S4 DAE-E Strand Sequences (16 KB PDF). Click here for additional data file. Figure S5 DAE-E Tile Diagrams (21 KB PDF). Click here for additional data file. Figure S6 DAO-E Strand Sequences (16 KB PDF). Click here for additional data file. Figure S7 DAO-E Tile Diagrams (22 KB PDF). Click here for additional data file. Figure S8 Formation Gels for Representative DAO-E and DAE-E Tiles (244 KB PDF). Click here for additional data file. Figure S9 Using Assembly PCR to Generate Long, Repetitive, Single-Stranded DNA (22 KB PDF). Click here for additional data file. Figure S10 Assembly PCR Scheme for DAE-E and DAO-E Nucleating Strands (25 KB PDF). Click here for additional data file. Figure S11 Binding Capacity Gel for Determining Nucleating Strand Stoichiometry (53 KB PDF). Click here for additional data file. Figure S12 Melts of R-00-23J and S-00 and Their Mixture (33 KB PDF). Click here for additional data file. Figure S13 AFM Images Showing the Context and Distribution of DAE-E Crystals (234 KB PDF). Click here for additional data file. Figure S14 AFM Images Showing the Context and Distribution of DAE-E Crystals (203 KB PDF). Click here for additional data file. Figure S15 AFM Images of DAE-E Crystals and Tubes (226 KB PDF). Click here for additional data file. Figure S16 AFM Images Showing the Context and Distribution of DAO-E Crystals (256 KB PDF). Click here for additional data file. Figure S17 AFM Images of Boundary Assemblies and Untemplated DAO-E Crystals (234 KB PDF). Click here for additional data file. Figure S18 AFM Images of DAO-E Crystals Grown under Constant-Temperature, Near-Constant Concentration Conditions (146 KB PDF). Click here for additional data file. Figure S19. Compiled Figures S1–S18 This file contains Figures S1–S18 and their captions in a single file for convenient printing (1.7 MB PDF). Click here for additional data file. Video S1 Composite of 64 AFM Images Taken Sequentially at Scales from 24 μm to 24 nm Each frame is an average of three raw images. At the center is an amalgamation of many individual algorithmic crystals, each with its own characteristic pattern of tiles (e.g., mostly zero, bearing small triangles, or apparently random). While no large undamaged Sierpinski triangles were seen in this series of images, in some frames it is possible to see both double-helices within the tiles, as well as the major and minor grooves within the helices. (17.8 MB MPG). Click here for additional data file.

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555549

Secondary structure in the target as a confounding factor in synthetic oligomer microarray design

Background Secondary structure in the target is a property not usually considered in software applications for design of optimal custom oligonucleotide probes. It is frequently assumed that eliminating self-complementarity, or screening for secondary structure in the probe, is sufficient to avoid interference with hybridization by stable secondary structures in the probe binding site. Prediction and thermodynamic analysis of secondary structure formation in a genome-wide set of transcripts from Brucella suis 1330 demonstrates that the properties of the target molecule have the potential to strongly influence the rate and extent of hybridization between transcript and tethered oligonucleotide probe in a microarray experiment. Results Despite the relatively high hybridization temperatures and 1M monovalent salt imposed in the modeling process to approximate hybridization conditions used in the laboratory, we find that parts of the target molecules are likely to be inaccessible to intermolecular hybridization due to the formation of stable intramolecular secondary structure. For example, at 65°C, 28 ± 7% of the average cDNA target sequence is predicted to be inaccessible to hybridization. We also analyzed the specific binding sites of a set of 70mer probes previously designed for Brucella using a freely available oligo design software package. 21 ± 13% of the nucleotides in each probe binding site are within a double-stranded structure in over half of the folds predicted for the cDNA target at 65°C. The intramolecular structures formed are more stable and extensive when an RNA target is modeled rather than cDNA. When random shearing of the target is modeled for fragments of 200, 100 and 50 nt, an overall destabilization of secondary structure is predicted, but shearing does not eliminate secondary structure. Conclusion Secondary structure in the target is pervasive, and a significant fraction of the target is found in double stranded conformations even at high temperature. Stable structure in the target has the potential to interfere with hybridization and should be a factor in interpretation of microarray results, as well as an explicit criterion in array design. Inclusion of this property in an oligonucleotide design procedure would change the definition of an optimal oligonucleotide significantly.

Background Sequence-specific hybridization of a long single-stranded labeled DNA or RNA target molecule to shorter oligonucleotide probes is the basis of the gene expression microarray experiment. In this type of microarray experiment, gene specific probe molecules are either synthesized in situ or are printed to the microarray slide, and are either non-specifically cross-linked to the surface or are attached specifically using a method such as poly-Lysine linkers. Target molecules (most often fluorescently labeled cDNA molecules, although cRNA and aRNA are used in some protocols) hybridize transiently to the probe oligomers until they form stable double helices with their specific probes. At some point, the rate of on and off reactions reach equilibrium, and the concentration of the target in the sample solution can be calculated. Transcript abundance is assessed by the relative intensity of signal from each spot on the array. This interpretation of array data relies on the assumption that each hybridization reaction goes to completion within the timeframe of the experiment and that the behavior of all pairs of intended reaction partners in the experiment is somewhat uniform. There are three major types of DNA microarrays, which differ in the approach used for probe design: Affymetrix type microarrays [ 1 ], which assay each transcript with a distributed set of 25-mer oligonucleotides, full length cDNA microarrays, in which long cDNA molecules of lengths up to several hundred bases are crosslinked to the slide surface to probe their complement [ 2 ], and synthetic long-oligomer probe microarrays, which usually assay each transcript only once. The latter class of microarrays encompasses a variety of commercial and custom platforms, and there has yet to emerge a consensus on an optimal probe length for particular experimental designs. Oligo lengths ranging from 35 to 70 nucleotides have been shown to perform well under different conditions [ 3 - 5 ], though recent studies have shown that oligomers of up to 150 nucleotides may be desirable for assessing transcript abundance [ 6 ]. In general, the use of synthetic oligomers has been shown to result in improved data quality [ 7 , 8 ] relative to cDNA arrays, and 70mers have been shown to detect target with a sensitivity similar to that of full length cDNA probes [ 9 ]. Short probes have been promoted because they facilitate finding unique sequence matches while forming fewer, and less stable, hairpin structures and because they display more uniform hybridization behavior overall. However, the need for sensitivity and detection of transcripts in low copy number drives the use of long-oligonucleotide arrays. In this study, we have modeled the accessibility of transcripts to hybridization with 70mer oligonucleotides. A number of oligonucleotide design software packages have been published in recent years, each having design strengths in one of a number of criteria [ 10 - 14 ]. Several factors are considered by almost all microarray design software packages: in particular, the sequence specificity of the probe-target interface and the overall balance of GC content across the array. Unique regions of the target sequence are identified using sequence comparison methods; the unique regions become the search space for probe selection based on other criteria. The number of probes per sequence and location of the probe in the sequence also restrict sequence availability. A relatively uniform melting profile generally is achieved simply by selecting for probes with similar GC content and uniform or close-to-uniform length, although some design methods explicitly compute the duplex melting temperature for each candidate probe-target pair and filter unique probes to find those which match a specified range of melting temperatures. Another biophysical criterion that is sometimes applied is the elimination of probes having the ability to form stable intramolecular structures under the conditions of the experiment. This is usually done by eliminating regions of self-complementarity, although at least one design program [ 13 ] does explicitly compute the melting temperature of the most stable structure to form in the probe molecule and uses that information to filter out stable secondary structures in the probe. Few of the available array design packages explicitly consider the possible structures of the transcript-derived molecules in the sample solution and their impact on whether the microarray will provide an effective assay, although the OligoDesign web server [ 14 ] does compute this information for use in design of locked nucleic acid probes. It has been shown that a hairpin of as little as six bases in an oligonucleotide can require a 600-fold excess of the complementary strand to displace the hairpin even partially [ 15 ]. Since the target molecules are generally longer than the probe and may be of a different chemistry, it is not sufficient to conclude that their behavior will mirror that of the complementary probe. Prediction of secondary structure in a sample transcript using a standard nucleic acid secondary structure prediction algorithm (Mfold) demonstrates that while longer-range interactions are reduced at high temperatures, stable local structures persist in the transcript even at high salt concentration and high temperature (Figure 1 ). Because unimolecular reactions within the target can occur on a much shorter timescale than the diffusion-mediated, bimolecular, duplex hybridization reaction, competition for binding by intramolecular structures is expected to block the specific probe annealing sites on the target sequence in some cases and result in misinterpretation of the signal obtained from the assay if these effects are not taken into account. Figure 1 Secondary structure in a sample transcript. Circular diagrams of structure in a sample transcript (moeB homolog designated BR0004) from Brucella suis . Circular diagrams show hydrogen bonds between individual nucleotides, color-coded according to single-strandedness – the fraction of structures in which that bond is not present. Black bonds indicate 0% single-strandedness; red bonds indicate 100% single-strandedness. In order to estimate the prevalence of stable secondary structure in long target molecules, and thus the impact such structures might have on the analysis of microarray data, we have modeled secondary structure formation in mRNA transcripts of the intracellular pathogen Brucella suis . We have assessed the stability of structures formed in the transcript and the accessibility of the binding sites of optimal probes generated using commonly applied design criteria. Because random shearing of the full-length target molecule is used in some protocols, we have also modeled the effects of shearing to an average length on the prevalence of secondary structure in selected targets. Results Extent and stability of target secondary structure Our modeling results obtained for the genome-wide set of intact single-stranded DNA or RNA targets demonstrate that stable secondary structures are widespread in target mixtures from Brucella suis (Figure 2 ) and in randomly chosen transcripts from the genomes of E. coli and L. lactis . Figure 2 shows the ΔG of formation for the most stable predicted secondary structure of the full-length transcript, as a function of reaction temperature. The major energy components of the Mfold ΔG are hydrogen bond energy and base pair stacking energy. These can be assumed to have a roughly linear relationship with transcript length. In order to make energies from different-length transcripts comparable, energies were normalized by computing a per-residue folding ΔG for each transcript and then multiplying that value by the global mean target length, for all transcripts considered from all organisms, of 851 bp. Average ΔG of secondary structure formation decreases with increasing temperature, but even at 65°C, the average ΔG of secondary structure formation for a full-length transcript is -98.2 kcal/mol (-27.9 kcal/mol when modeled as cDNA), meaning that the transcript is quite stable in that structure and a considerable energy input will be required to displace or melt the remaining structure. The trend in ΔG of secondary structure formation from the high-GC genome of B. suis to the low-GC genome of L. lactis is a decrease in overall stability. The average normalized ΔG of secondary structure formation for transcripts selected from the GC-balanced genome ( E. coli ) is near 70% of the average for Brucella , while the average ΔG for transcripts from the GC-poor genome ( L. lactis ) are even lower (30% at 52°C). However, even in the most GC-poor genome, stable target secondary structure in the single-stranded target is widespread. Figure 2 Stability of transcript secondary structure in Brucella suis. Average free energy change on global secondary structure formation for Brucella suis targets, modeled as DNA or RNA. ΔG values are normalized to global mean target length. Our results demonstrate that a significant fraction of nucleotide sites in the average target mixture, whether single stranded DNA or RNA, will be found in stable secondary structure under the hybridization conditions used in oligonucleotide microarray experiments, and will be relatively inaccessible for intermolecular interactions. Figure 3 shows the percentage of nucleotides that are in a double-helical state in at least 50% of the secondary structure conformations predicted by Mfold, at various reaction temperatures. The measure of accessibility used is the fraction of structures in which a nucleotide is found in a single-stranded conformation, when all optimal and suboptimal structures predicted are considered. Figure 3 Fractional accessibility of nucleotides in the target. Fraction of the complete transcript classified as inaccessible due to the presence of stable structure in >50% of predicted conformations. Data shown are for 37, 42, 52 and 65°C simulations in Brucella suis . Extent and stability of target secondary structure Figure 4 is a plot of the average ΔG of structure formation when shearing of the target molecule is simulated by dividing the target into overlapping 200, 100, and 50mer fragments. Shearing the target into smaller fragments destabilizes secondary structure, especially at very short fragment lengths. However, shearing does not eliminate occlusion of nucleotides by secondary structure, even in the shortest fragments examined. When a DNA target is modeled at 52°C, for example, the double stranded fraction decreases by only about 30% – from 41% to 29% – when the target is simulated as sheared into 50mer fragments. However, in hybridization experiments involving low copy number targets and longer oligos, creating extremely short target fragments may reduce or eliminate the signal on the chip, because the target can not be sheared specifically to present an unbroken hybridization site for the probe, and so some fragments will be created that match the probe only partially. Figure 4 Stability of secondary structure in sheared fragments. Free energy change on secondary structure formation for the ureG-1 RNA transcript from Brucella suis . The transcript is modeled as sheared into fragments of length 200 nt, 100 nt or 50 nt; fragments are chosen starting at every 10th residue. Interference of secondary structure with the hybridization site Figure 5 shows the average percentage of nucleotides within a probe binding region in the target that are inaccessible, when different fractional accessibility cutoffs are used to classify the sites. Even when a relatively demanding criterion – double-strandedness in over 75% of optimal and suboptimal structures – is used to classify a nucleotide as inaccessible, an average of 21 ± 13% of nucleotides in the probe binding region are found in stable secondary structures at 65°C. Figure 6 shows a representative transcript and the challenge it presents to hybridization when modeled as full-length cDNA and fragments of various lengths. Figure 5 Accessibility of the probe binding site. Fraction of the average probe binding site in the Brucella genomic array that is found to be inaccessible at 37°, 42°, 52° and 65°C, for DNA or RNA target. Inaccessible sites are defined here using three different cutoffs for the fraction of structures in which the site is base-paired: 25%, 50%, and 75%. Figure 6 Structure in a binding site – full length target and sheared fragments. The position of a 70mer oligonucleotide probe (green) binding site (red dots) within a full-length optimal transcript structure, as well as examples of stable structure in 200mer and 100mer fragments which overlap the probe binding site. Corresponding ΔG values for these fragments modeled at 42° and 52°C are shown in Table 1. Discussion Lack of bioinformatics tools that incorporate experimentally validated biophysical properties of nucleic acids as a criterion for synthetic oligomer probe design is a major challenge for do-it-yourself microarray designers. One biophysical characteristic, which we predict will reduce the binding efficiency of microarray probes to their targets, is the propensity of long single-stranded DNA or RNA molecules to form stable secondary structure. 3-D structures such as hairpins and stacked regions have the potential to pre-empt target nucleotides, thus blocking regions of the target molecules from hybridizing to their intended probes. Prediction and thermodynamic analysis of secondary structure at a range of temperatures in full length target sequences, as well as in subsequences formed by in silico shearing, revealed the likely presence of stable secondary structures in both full-length target and sheared target mixtures. These structures do not convert completely to random coil with either increasing hybridization temperature, more extensive shearing, or both. These secondary structures may therefore compete with the intended target for effective probe annealing in a microarray experiment, resulting in a misinterpretation of the amount of target present in the sample. Applying target secondary structure as a criterion in array design Based on the results of this in silico experiment, secondary structure prediction in the target is being used to develop a new criterion for oligonucleotide probe design. Our results from this modeling experiment demonstrate that the implicit assumption used until now – that eliminating probe secondary structure by avoiding self-complementarity eliminates target secondary structure as well – is valid only when the target and probe are of the same length. Use of target secondary structure as an explicit criterion will allow for masking or preferentially avoiding the regions of the target sequence in which base pairs are directly involved in secondary structure formation, to eliminate these regions from the sequence for the purpose of the search for the optimal probe. In this study we have assigned accessibility scores to sites in the target sequence based only on the fraction of predicted structures within 5% of the energy optimum, in which a residue is found in a single-stranded conformation. While this measure is not too computationally intensive to compute, and can be applied to genome-scale problems using readily available software (Mfold), it is not the most physically rigorous definition of accessibility. By equally weighting each possible structure in the ensemble of optimal and suboptimal structures that a molecule can form, it is possible that secondary structure at some positions in the molecule is overcounted; bonds which form only in rare conformations are considered equal to bonds which are present in the lowest-energy structure. The program Sfold [ 16 - 18 ] assigns accessibility based on an ensemble-weighted average of secondary structure. The program RNAfold[ 19 ], part of the Vienna RNA package, implements McCaskill's partition function approach[ 20 ] to arrive at pairing probabilities for each pair of bases in the sequence, from which a summary per-base accessibility can be derived. These methods are more rigorous than MFold and we expected they might produce somewhat different results, although it has also been shown that predicted binding states from MFold optimal structures perform almost as well as SFold and RNAFold predictions when applied to molecules of known 3D structure [ 16 ]. When we compared MFold-based accessibility predictions for an individual transcript to those generated by SFold and RNAFold, we found that the difference in average predicted accessibility over an entire transcript is small. We computed accessibility for the transcript of human 1CAM-1, which has been mapped experimentally to determine its accessibility [ 21 ]. The average fractional accessibility derived from MFold results is about 3–4% greater than that predicted by RNAFold or SFold. Therefore use of this fractional accessibility measure will not impose an unnecessary constraint on the design process relative to other predictive approaches. The accessibility profiles calculated for ICAM-1 using each method are shown in Fig. 7 . In each section of the figure, antipeak locations (having lower pairing probability and therefore likely to be more accessible) can be compared to the extendable sites detected by Allawi et al [ 21 ], which are indicated by green dots at the bottom of the plot. In each prediction, there are a number of apparently correct predictions and obvious errors, and it is not clear which method is yielding the best results at the residue level. A systematic, competitive test of these predictions against solution accessibility data gathered on various experimental platforms is called for, although available data sets for validation are still rare. In the absence of such validation, the MFold accessibility predictions are sufficient to predict the scope of the secondary structure problem in a genome-based array design, even if some details of the prediction are not correct. An experimental approach will eventually be required to determine which approach best represents the conditions of the microarray experiment. Figure 7 Accessibility prediction using three common methods. Pairing probabilities computed using RNAFold (top), MFold (middle) and SFold (bottom) for the human ICAM-1 transcript. Extendable sites detected by Allawi et al [21] Loop length and other considerations In this study, we focused specifically on the DNA/RNA base pairs that are actively involved in hydrogen bond formation. We realize that other accessibility considerations will have to be added to the scoring scheme in practice. The structure of a long single stranded DNA or RNA molecule can contain many nucleotides that, while not part of a double-helical stem, remain inaccessible to hybridization due to their location inside small loops within the target secondary structure. A loop is a somewhat constrained structure as well, and the length at which it presents accessible sequence that favors hybridization has been shown to be on the order of 10 nucleotides and longer [ 22 ], while nucleotides found in shorter loops may be classifiable as inaccessible. However, there is a need for quantitative hybridization experiments that would elucidate how loops and loop-like structures in tethered long-oligo probe and target molecules affect the performance of assays, and we have chosen not to formulate a system for scoring the accessibility of single-stranded loop structures or weighting this criterion relative to the double-strandedness criterion until we have carried out some of these experiments. Development of a target secondary structure criterion for oligonucleotide array design is expected to impose restrictions on the probe selection beyond the sequence similarity and melting temperature criteria that are currently used, especially in cases where short probe length restricts the annealing temperature used in the hybridization protocol to 22–37°. In the B. suis example, use of a low annealing temperature, e.g. 42°C which is the temperature used in some published 70-mer array experiments [ 9 ], would result in only about 30% of the average transcript being accessible for intermolecular hybridization, not counting 'free' bases found in short loops in secondary structures. There will be greater design latitude for experiments carried out at higher hybridization temperatures. Recommended hybridization temperatures for long synthetic oligomer arrays may prove to be closer to 65°C, when only 50% of a typical RNA transcript or 30% of the corresponding cDNA molecule remain inaccessible. To shear or not to shear We have shown here that while shearing reduces overall ΔG of secondary structure formation for individual molecules in the target solution, shearing does not in itself eliminate formation of secondary structure in single-stranded DNA or RNA. The question of whether shearing should be used for long oligomer arrays is still an open one. While some signal may be gained by reducing the stability of secondary structure in the target molecule, random shearing by its nature creates a mixture of targets that may have substantially different affinities. For instance, in a 300 nt transcript that is targeted by a 70mer oligonucleotide, there is nearly a one in four chance that a random break in the sequence will occur within the target site for which the probe is designed. Short fragments may present a substantially different binding site, and therefore have a different binding affinity, than the full-length transcript that is considered when the probe is designed. This is illustrated in Figure 8d, where binding of a 50mer sheared fragment to a 70mer probe leaves a dangling end in the probe. A break very close to one end or the other of the target site may create a target that still binds to the probe, though with reduced affinity; a break closer to the middle of the target site may produce fragments that bind partially to the probe, competing for binding with perfect matches. The utility of experimentally validated biophysical criteria In other experimental contexts where hybridization is critical to success, the impact of secondary structure in single stranded polynucleotides on results has been recognized and is now being systematically studied (18–21). Intramolecular folding of mRNAs is so extensive that only 5–10% of most transcripts is accessible to binding of complementary nucleic acids; however the modeling of long molecules has not proven to give very accurate binding predictions [ 23 - 25 ]. In fact, array-based screens have been utilized to empirically select oligonucleotides that bind effectively to transcripts for siRNA experiments [ 23 , 26 ]. Several studies have demonstrated that, at 37°C and 0 mM Mg2+ oligonucleotides of length >20 yield good binding/RNAseH digestion at low concentrations relative to shorter oligonucleotides (30 nM vs 300 nM compared) and found that microarray binding was a good predictor of siRNA activity despite the 3' tethering and 1M NaCl used in array experiments vs siRNA experiments [ 26 ]. Systematic "scanning" of mRNA sequences with libraries of short oligos [ 27 ] has also been shown to be successful in locating sites for siRNA targeting; however, such methods are likely to become extremely expensive if applied to the large number of targets in a microarray design. We have begun to develop an experimental approach to this problem, in which structure predictions like those used in this study are experimentally evaluated to determine whether the structures we can predict using existing modeling approaches will detectably affect signal in the microarray context. Conclusion The results of the current study suggest a significant role for target secondary structure in hybridization to oligonucleotide arrays, which will warrant further investigation. Oligonucleotide probe binding sites in a significant fraction of transcripts are found in double-stranded conformations even in cases where self-complementarity was avoided during the probe design process. We find that at 52°C, for example, approximately 57% of probes designed for Brucella had binding sites in the target which were predicted to contain a stretch of unpaired bases of at least 14 nt in length; at 65°C, that fraction increased to 93%. Based on these findings we would expect that at 52°C only 57% of our probes would encounter optimal conditions for hybridization and therefore would demonstrate the expected behavior in the experiment, where intensity is expected to scale with target concentration. We predict that the remaining probes, which have shorter, or no, accessible sequences, will exhibit modified binding behavior, and we plan to conduct experiments to characterize this behavior. We have shown conclusively that avoiding self-complementarity in the probe when designing an oligonucleotide array is insufficient to eliminate secondary structure from the binding site in the target. By combining the procedure for systematic computational assessment of transcript accessibility described in this study with selective experimental validation of the impact of predicted accessibility on hybridization, we will develop a useful criterion for avoiding troublesome secondary structure when designing microarray targets. Methods Prediction and thermodynamic analysis of secondary structure was performed for all protein-coding gene transcripts predicted from 3264 CDSs in the Brucella suis 1330 genome. Brucella suis has a relatively high (57%) genomic GC content. Brucella suis was chosen for this experiment because our collaborators have previously acquired a custom synthetic oligomer microarray for this organism, developed using standard oligo array design software, and we have access to both target sequences and to a set of unique probe sequences that define the interaction sites for which expression results have been obtained by the laboratory. In order to determine whether Brucella sequences form atypical structures we randomly picked and analyzed 50 gene coding sequences from a compositionally balanced genome ( Escherichia coli ), and 50 from the GC-poor genome of the nonpathogenic AT-rich gram-positive bacterium Lactococcus lactis (35% genomic GC content). The Brucella suis genes ranged in length from 90 to 4,803 bp, with an average transcript length of 851 bp. The E. coli genes ranged in length from 140 to 2,660 bp, with an average transcript length of 792 bp. The range of GC content in the genes chosen was 37% to 57% with an average value of 50%, which is reasonably representative of the E. coli genome. The L. lactis genes chosen ranged in length from 140 to 2,730 bp, with an average transcript length of 765 bp., and ranged in GC content range from 30% to 42% with an average value of 35%. Microarray design 70-mer probes for each Brucella suis target were previously designed (Stephen Boyle, personal communication) using ArrayOligoSelector (pick70) [ 10 ]. ArrayOligoSelector uses sequence uniqueness, self-complementarity, and sequence complexity as criteria but does not explicitly evaluate ΔG of secondary structure formation for the probe. 72% of the probes designed using this method were found to contain secondary structures with melting temperatures greater than 65°C, and 10% contained secondary structures with melting temperatures greater than 80°C. The Brucella probes defined the interaction sites within the target transcripts for which structural accessibility was evaluated. Secondary structure prediction Probe and transcript secondary structure were predicted using the Mfold 3.1 software package [ 28 , 29 ]. Mfold identifies the optimal folding of a nucleic acid sequence by energy minimization and can identify suboptimal foldings within a specified energy increment of the optimum as an approach to modeling the ensemble of possible structures that a single-stranded nucleotide molecule can assume. We modeled secondary structure in the single-stranded target, modeling the target both as DNA and as RNA, at a range of temperatures which is inclusive of hybridization temperatures commonly used in microarray protocols: 37°C, 42°C, 52°C and 65°C. The modeling conditions were chosen within the allowed settings of Mfold to approximate a microarray experiment: solution conditions of 1.0 M sodium concentration and no magnesium ion were used. The free energy increment for computing suboptimal foldings, ΔΔG, was set to 5% of the computed minimum free energy. The default values of the window parameters, which control the number of structures automatically computed by Mfold 3.1, were chosen based on the sequence length. Free energy changes on formation of secondary structure were extracted from the Mfold output. Accessibility calculation Accessibility in folded single-stranded DNA or RNA has recently begun to be addressed in a few experimental studies, mainly with the goal of targeting appropriate sites for RNAi. Because the structure of single-stranded nucleotide molecules is much more dynamic than that of proteins, with each molecule likely to exist in an ensemble of structures, and because the 3D structure of these molecules is rarely known, there is not yet a consensus representational standard of per-residue accessibility for single-stranded nucleic acids. Ding et al. [ 17 , 18 ] implement probability of single-strandedness, when the weighted ensemble of likely structures is taken into account, as an accessibility criterion. However, use of their Sfold server, with batch jobs limited to 3500 bases, is not currently practical for a genome-scale survey of accessibility. Another approach to accessibility prediction is McCaskill's partition function approach [ 20 ] which can be used to compute base pair probabilities and summary pairing probability for any base. This approach is implemented in RNAFold [ 19 ], a component of the Vienna RNA package. In this study, we chose to use the less physically rigorous approximation of probability of single strandedness as a simple fraction of predicted optimal and suboptimal structures in which a residue is found to be part of a single stranded structure, as computed by Mfold. Accessibility scores derived from MFold predictions have been used in limited studies of RNA structure focused on hammerhead ribozymes[ 30 ], antisense and siRNA targeting [ 22 , 31 ] and have been shown to be predictive in cases where some experimental measure of accessibility has been made[ 32 ]. While MFold-derived accessibility scores may not be completely optimal, they have been used with reasonable success to predict accessibility in the siRNA targeting context, and so we use MFold here. Shearing simulation Random shearing of the target mixture is an approach that is often offered as a solution for the problem of target secondary structure. The actual content of a sheared mixture of DNA or RNA fragments is complex. Shearing breaks the molecule not in predictable locations, but in random locations that give rise to a distribution of fragments around an average fragment length. In order to simulate the effects of different degrees of shearing on structure formation and stability in a transcript, we picked fragments of 200, 100, or 50 bases in length, choosing the start position via a sliding window of 10 bases. Secondary structure prediction for all fragments derived from every transcript in the B. suis genome is computationally intensive and produces an extremely large amount of output. Since our initial goal was to determine how much the method would affect the number and type of secondary structures probes would be expected to bind the shearing simulation was performed for fragments derived from the 300 bp Ure-1A gene of B. suis . Secondary structure and thermodynamics were computed for each of these fragments individually. Authors' contributions VGR participated in the design of the study, carried out the simulations and analysis, and drafted the manuscript. JWW participated in the design of the study and helped to draft the manuscript. CJG conceived of the study, participated in its design, coordinated the research and analysis, and drafted the manuscript. Table 1 Stability of a sample transcript – full length target and sheared fragments Folding ΔG of target transcript and fragment molecules shown in Figure 8, at hybridization temperatures commonly used for long oligomer arrays. Molecule 2 G, kcal/mole 42°C 52°C DNA RNA DNA RNA 70-mer Probe - 6.8 N/A - 4.2 N/A Full Length Target - 85.9 - 188.4 -56.6 - 140.2 200-mer sheared Target - 25.5 - 58.6 -15.9 - 41.6 100-mer sheared Target -14.2 - 25.7 -9.6 -18.0 50-mer sheared Target (not shown) - 6.1 -10.5 - 4.2 -7.3

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The use of retroviral vectors for gene therapy-what are the risks? A review of retroviral pathogenesis and its relevance to retroviral vector-mediated gene delivery

Retroviral vector-mediated gene transfer has been central to the development of gene therapy. Retroviruses have several distinct advantages over other vectors, especially when permanent gene transfer is the preferred outcome. The most important advantage that retroviral vectors offer is their ability to transform their single stranded RNA genome into a double stranded DNA molecule that stably integrates into the target cell genome. This means that retroviral vectors can be used to permanently modify the host cell nuclear genome. Recently, retroviral vector-mediated gene transfer, as well as the broader gene therapy field, has been re-invigorated with the development of a new class of retroviral vectors which are derived from lentiviruses. These have the unique ability amongst retroviruses of being able to infect non-cycling cells. Vectors derived from lentiviruses have provided a quantum leap in technology and seemingly offer the means to achieve significant levels of gene transfer in vivo . The ability of retroviruses to integrate into the host cell chromosome also raises the possibility of insertional mutagenesis and oncogene activation. Both these phenomena are well known in the interactions of certain types of wild-type retroviruses with their hosts. However, until recently they had not been observed in replication defective retroviral vector-mediated gene transfer, either in animal models or in clinical trials. This has meant the potential disadvantages of retroviral mediated gene therapy have, until recently, been seen as largely, if not entirely, hypothetical. The recent clinical trial of γc mediated gene therapy for X-linked severe combined immunodeficiency (X-SCID) has proven the potential of retroviral mediated gene transfer for the treatment of inherited metabolic disease. However, it has also illustrated the potential dangers involved, with 2 out of 10 patients developing T cell leukemia as a consequence of the treatment. A considered review of retroviral induced pathogenesis suggests these events were qualitatively, if not quantitatively, predictable. In addition, it is clear that the probability of such events can be greatly reduced by relatively simple vector modifications, such as the use of self-inactivating vectors and vectors derived from non-oncogenic retroviruses. However, these approaches remain to be fully developed and validated. This review also suggests that, in all likelihood, there are no other major retroviral pathogenetic mechanisms that are of general relevance to replication defective retroviral vectors. These are important conclusions as they suggest that, by careful design and engineering of retroviral vectors, we can continue to use this gene transfer technology with confidence.

Background Retroviruses Retroviruses are viruses that are found throughout the animal kingdom, including in chickens, mice, cats, sheep, goats, cattle, primates, fish and humans. The first retro viruses were identified as cell free oncogenic factors in chickens. Subsequently, many of the oncogenic retroviruses have been shown to be replication defective forms that have substituted a part of their normal viral gene complement with an oncogene sequence [ 1 ]. Replication competent retroviruses also cause malignant disease, as well as a range of other pathogenic states, in a broad range of species. This includes what must be the most significant transmissible disease of humans in recent times, acquired immunodeficiency syndrome (AIDS), which is caused by the retroviruses Human Immunodeficiency Virus Types 1 and 2 (HIV-1, HIV-2). However, many retroviruses cause life-long infections and appear to be relatively, if not completely benign, in their normal host species. In mice there are retroviruses that are very closely related to strongly oncogenic retroviruses but which are not themselves oncogenic, or are only very weakly oncogenic [ 2 - 5 ]. In addition, there is a whole class of retroviruses, the spumaviruses, or foamy viruses, which do not appear to be linked to any specific pathogenic state [ 6 ]. Even the simian equivalent of HIV-1, the causative agent of AIDS, is not pathogenic in all its hosts [ 7 ]. There is also a range of endogenous retroviral sequences that are not associated with specific pathologies [ 8 ]. Vestigial forms of retroviruses also exist; these are represented by various classes of insertional elements and can constitute a significant proportion of animal genomes [ 8 ]. The retroviral virion is a spherical particle of about 80–100 nm in diameter. It is enclosed by a lipid bilayer derived from the host cell plasma membrane into which one of the retroviral gene products, the envelope protein, is inserted. The virion has considerable internal structure that is mainly comprised of the products of the viral gag gene. In addition, the virion contains two identical copies of a genomic RNA molecule (the retrovirus is then genetically haploid but can also be described as pseudo-diploid), a tRNA primer for reverse transcription as well as small amounts of the products of the viral pol gene. The virion may also include a range of other host cell derived proteins although it is unclear whether these represent a random assortment of proteins that are coincidently incorporated into the virion or whether they play some role in the viral life cycle. Both possibilities are probably true, certainly HIV-1 is known to incorporate into its virion a number of host cell proteins that play a vital role in its life cycle [ 9 , 10 ]. While the simple retroviruses have only three genes, gag , pol and env , the complex retroviruses encode a number of other proteins that are involved in regulating viral replication or the host cells response to the virus. For example, HIV-1 has six gene sequences in addition to the minimal retroviral complement of gag , pol and env . Two of these, tat and rev , encode proteins that regulate expression of the viral genome, while the other four, vpu , vif , vpr and nef , encode proteins that play multiple roles in enhancing viral replication. Retroviral life cycle It is the unique nature of the retroviral life cycle, combined with the simplicity and advantageous arrangement of the retroviral genome, which has made retroviruses so attractive as vectors for gene therapy [ 11 , 12 ]. The principal feature of the retroviral life cycle that is of interest is the ability of the retrovirus to copy its RNA genome into a double-stranded DNA form which is then efficiently and exactly integrated into the host cell genome. The integrated form is termed the provirus and it is transcribed as a normal cellular gene to produce both mRNAs encoding the various viral proteins, and the genomic RNA that is packaged into progeny virions. The genetic structure of the virus and the existence of the proviral form make it easy to manipulate retroviruses to make replication defective vectors for transfer of heterologous gene sequences. The proviral form, being DNA, can be readily isolated in standard plasmid cloning vectors and so made amenable to molecular manipulation. The genetic structure of the virus is such that the viral cis (sequences that are biologically active in the form of nucleic acids) and trans (protein coding sequences) functions (Fig. 1 ) are largely non-overlapping; indeed, as far as recombinant vectors are concerned it is possible to separate them completely, albeit at some cost in efficiency. The generation of systems capable of producing non-replication competent virus can then be achieved by placing the cis elements on a transfer vector construct and expressing the trans functions using standard recombinant plasmid expression systems (Fig. 2 ). As the genomic RNA expressed from the transfer vector construct is the only RNA molecule that carries the cis signals required for packaging into the virion, and for reverse transcription and integration, no viral genes are transferred to cells infected with the resulting virus. The resulting provirus, lacking all viral genes, is a replicative dead end and no further viral replication is possible. The nature of the retroviral replication process, where the U3 region of both the 5' and 3' LTRs of the provirus are effectively copied from the 3' LTR of the provirus in the preceding generation, also makes possible the construction of self-inactivating (SIN) vectors. With these vectors the resulting provirus contains no active retroviral derived transcriptional promoter or enhancer elements [ 13 , 14 ]. Figure 1 The cis and trans genetic functions of a retrovirus. Cis sequences (shown in black) are those that are directly active as nucleic acids, they include the 5' long terminal repeat (LTR) which, in the DNA form found in the provirus acts as a transcriptional promoter, and in the RNA (genomic) form contains sequences important for reverse transcription of the genome; the primer binding site (PBS) for first strand DNA synthesis during reverse transcription; the psi (ψ) sequence which directs packaging of the genomic RNA into the virion; the polypurine tract (ppt) which is the primer binding site for second strand DNA synthesis during reverse transcription and the 3' LTR which, in the DNA form (in the provirus) acts as a polyadenylation signal, and in the RNA (genomic) form contains sequences important for the reverse transcription process. The trans functions (shown in green) are the protein coding sequences, these are the gagpol gene, which encodes the Gag and Pol polyproteins, and the env gene that encodes the viral envelope protein. Figure 2 Separation of the cis and trans functions of a retrovirus in a recombinant, replication defective vector system. Replication defective retroviral vector systems are made by separating the cis (shown in black) and trans (shown in green) genetic functions of the virus into a vector construct, which contains the cis sequences, and helper or packaging plasmids, that encode the viral proteins (i.e. contain the trans sequences). To minimize overlap between the two components of the system heterologous transcriptional control elements (shown in red) are used to express the trans functions. Recombinant virus is made by introducing all these elements into the same cell. Only the vector transcript is incorporated into virions as this is the only RNA that contains the retroviral packaging signal (ψ). The use of a replication-defective retroviral vector to transfer gene sequences into target cells has been termed transduction , to distinguish it from the process of infection with replication competent viruses. It is theoretically possible that with most, if not all, recombinant vector systems, that recombination of the constituent parts of the system with each other, or with cellular sequences, can regenerate a replication competent retrovirus (RCR) [ 15 , 16 ]. However, the careful engineering of these systems has led to the point where they can largely be assumed to be free of such RCR. While this does not mean that screening for RCR in preparations of vector is unimportant, as there are a number of other ways in which RCR may arise, and as quality control is obviously central to the clinical use of retroviral vectors, it does mean that in practice RCR generation should no longer be a major safety issue. This means that in terms of evaluating the safety of retroviral vectors it is the direct and indirect consequences of proviral integration that are important to consider, rather than the effects of actively replicating virus. Retroviral mediated pathogenesis Retroviruses have historically been most intensively studied in animals that are either the subject of scientific experimentation (principally the laboratory mouse), or are of commercial significance (such as farmed animals such as chickens, horses, goats, cattle and fish, and pets), where they cause a number of commercially significant diseases. Indeed, the first retroviruses to be described were the oncogenic retroviruses Avian leukosis virus (ALV), and Rous sarcoma virus (RSV), which are both found in chickens. A large number of oncogenic retroviruses have now been described. These tend to cause malignant disease in a very high proportion of infected hosts. In addition, the complex retroviruses human T-cell leukemia virus (HTLV) and bovine leukemia virus (BLV) can cause leukemia in their hosts, although they do so in only a small percentage of infected individuals. The lentiviruses are also overtly pathogenic and have been shown to be the causative agent of several slow progressive diseases in animals including arthritis and encephalitis in goats, leukemia in cattle, anaemia in horses, and immunodeficiency in cats, cattle, primates and humans. The AIDS epidemic means that the lentivirus HIV-1 is now the most intensively studied retrovirus ever-incredibly, given the relative genetic simplicity of the retroviruses, there appears to be much still to learn about many aspects of HIV-1. There are also a number of viruses that cause central nervous system (CNS) pathology. For some of these, such as HIV and HTLV, CNS disease is a secondary pathology, while others are more specific in their effects. Similarly, while ALV and RSV are best known as oncogenic viruses, they are also associated with wasting syndromes. Oncogenic retroviruses The archetypal retroviral pathogen is the oncogenic retrovirus. Some of these are replication defective retroviruses that carry and express an oncogene sequence-indeed it was these retroviruses that largely allowed the concept of oncogenes to be first defined. These viruses induce cancers with relatively short latency periods. In addition, there are a large number of non-defective retroviruses that are oncogenic. These generally induce cancers after longer latency periods. HTLV and BLV and related viruses form a separate class of complex retroviruses that cause leukemia in a small percentage of infected individuals after very long latency periods. Retroviruses have also been associated with sarcomas in fish but these viruses have not been studied in great detail. Defective oncogenic retroviruses These have been described in a number of species, but have been most extensively studied in the laboratory mouse. These are replication defective, simple retroviruses in which part of the normal viral genome has been replaced with a cDNA copy of a cellular oncogene. The viral oncogene sequence often contains mutations that make the protein it encodes act in a dominant manner. The capture of a cellular oncogene by a retrovirus is an extremely rare event, the major significance of these viruses in scientific terms is that they led to the discovery of cellular oncogenes. These viruses depend on the presence of a replication competent helper virus in order to replicate and they induce cancers with relatively short latency periods. The existence of a latency period suggests that oncogene expression is, in itself, not enough to cause malignant disease, but that additional genetic events are required. The majority of the cancers caused by these retroviruses are found in the haematopoietic system although sarcomas are also common. They are also able to transform the phenotype of cells grown in culture, principally by causing cells to lose their contact inhibition. The type of malignant event caused by any one virus is determined by the nature of the oncogene expressed by the virus and by the nature of the enhancer sequences present in the long terminal repeat which control the tissue specific expression of the oncogene. Replication defective vectors obviously also have the same potential to capture oncogenes. However, the mechanism of oncogene capture by retroviruses, and its extreme rarity, means it is probably not of major relevance when considering the risk factors associated with the use of retroviral vectors for gene therapy. Non-defective oncogenic retroviruses Non-defective, replication competent retroviruses are also associated with malignant diseases. These viruses do not carry oncogene sequences. Although first discovered in the chicken they have been most extensively studied in the laboratory mouse. These viruses induce cancer by activating cellular oncogenes via a number of different mechanisms. In contrast to the oncogene carrying retroviruses, these viruses are associated with much longer latency periods. This is a reflection of the relatively low probability that proviral insertion will result in activation of an oncogene, in combination with the requirement for other genetic changes before a cancer eventuates. Although proviral integration can also result in gene inactivation, inactivation of tumour suppressor genes does not appear to be a mechanism associated with any known instances of retroviral induced malignancy. The principal routes of oncogene activation are transcriptional promotion from one of the viral LTRs, and activation of endogenous cellular promoters by the strong transcriptional enhancer elements present in the viral LTRs. In the former case the provirus must obviously integrate in the sense orientation and upstream of the relevant coding sequence. Transcription can be from either LTR [ 17 ], and may involve splicing from either the retroviral, or cryptic, splice donor sites to a splice acceptor within the gene sequence [ 17 ]. If transcription is from the 3' LTR it is usually associated with inactivating mutations in the 5' LTR [ 18 ]. Transcriptional enhancement can occur with the provirus in either orientation [ 19 ] and over relatively large distances [ 20 , 21 ]. This is by far the most common mechanism of oncogene activation. Another mechanism by which proviral integration can activate cellular oncogenes is by negation of negative regulatory elements in the oncogene or its transcript [ 22 ]. However, this is a rare phenomenon. If proviral integration is downstream of the oncogene translation initiation codon a dominant variant of the oncogene product may result [ 23 ]. Not all non-defective simple retroviruses are overtly oncogenic and the oncogenic, non-defective simple retroviruses show a spectrum of tissue specificity and oncogenic potential. Analysis of the oncogenic potential of different retroviruses has clearly shown that the major determinant of both the overall oncogenic potential of the virus, and the cell specificity of the type of cancer that results, is the viral long terminal repeat [ 24 - 27 ]. More specifically, it is the transcriptional enhancer sequences in the long terminal repeat that are the major determinant of these properties [ 28 - 33 ]. Mechanistically, this makes perfect sense. As transcriptional enhancer elements are capable of acting at a distance they will not only control transcription from the viral LTR but will also have the potential to influence transcription from promoter sequences in adjacent chromosomal genes. In contrast to oncogene activation, the oncogenic potential of some retroviruses maps to the env gene sequences. For example, the SU protein (p55) of the polycythemic strain of Friend virus binds to, and activates, the erythropoietin receptor resulting in massive erythroid proliferation and splenomegaly [ 34 ]. However, p55 does not bind to the active site of the Epo receptor and the Epo receptor is not used as the receptor for virus infection. In fact, p55 is not a functional envelope for infection and a helper virus is needed to allow the virus encoding p55 to propagate itself. In an analogous manner, the sag gene of Murine Mammary Tumour Viruses (MMTV) induces an immune response by interacting with the T-cell receptor [ 35 ]. This does not result in leukemia but facilitates the eventual induction of malignant disease in an indirect way. As the interaction between Sag and the T-cell receptor is not via the antigen binding site itself, a large proportion of the T-cell population (up to 10%) is stimulated. This, in turn, stimulates B-cells, the initial cellular target for infecting MMTV, allowing enhanced viral replication and the subsequent infection of mammary epithelial cells, the eventual site of tumour formation. Although Sag is a major determinant of the oncogenic potential of MMTV it should be noted that in the final analysis malignancy is due to oncogene activation. How HTLV [ 36 ] and BLV cause cancer is not entirely clear. Both are complex retroviruses, and in addition to the gag , pol and env genes common to all retroviruses, have two genes that encode regulatory proteins. HTLV causes adult T-cell leukemia, often after a very long latency period (two or three decades can pass between infection and emergence of malignant disease). Only a small percentage of infected individuals (about 1% for HTLV) develop cancer. Although the mechanism of disease induction is unclear it is certainly related to the clonal proliferation of infected cells in vivo . Although viral gene expression does not appear to be necessary for maintenance of the disease, evidence suggests that one of the regulatory proteins, Tax, is important in inducing the initial T cell proliferation. Given the recent development of vectors from lentiviruses, including HIV, it is worth noting that despite intense scientific scrutiny, examples of insertional mutagenesis or gene activation resulting from infection with these viruses have not been documented. However, in the case of HIV-1 the limited lifespan of most infected cells means that this observation must be interpreted with caution. In terms of replication defective retroviral vectors, the study of oncogenic retroviruses suggests that oncogene activation, via the provision of promoter or enhancer sequences, but especially the latter, will be the major risk factor for disease induction. In addition, selection of the retroviral envelope used for vector pseudotyping could also potentially play a role as could inadvertent transfer and expression of other retroviral proteins, at least for vectors developed from particular retroviruses, such as Friend virus. Retroviruses causing CNS disease Several retroviruses cause CNS disease. Some of these, such as the murine retroviruses Cas-Br-E MLV [ 37 ] and FMCF98 [ 38 ] are specifically associated with CNS pathology. For other retroviruses, such as HTLV and HIV, CNS disease is not the defining pathology induced by the virus, even though for the latter a high proportion of infected individuals will develop CNS disease. Cas-Br-E MLV infects the brain via infection of the epithelial cells of the blood-brain barrier. After these become infected they release virus directly into the CNS where it infects microglial cells, resulting in a spongiform encephalopathy. The SU (env) protein has been shown to be a major determinant of the neuropathogenesis of Cas-Br-E MLV [ 39 ] and other neuropathogenic murine retroviruses. However, the mechanisms involved have not been elucidated although receptor activation [ 40 ], analogous to that caused by the SU protein of the polycythemic strain of Friend virus, has been suggested but as yet remains unproven. HTLV causes CNS disease in only a small percentage (about 1%) of infected individuals after a latent period that can be as short as two, or as long as thirty years [ 41 ]. The development of CNS disease is not correlated with the development of ATL. For HTLV CNS disease is characterised by a vigorous inflammatory response involving T cells that causes severe demyelination in the spinal cord. Little is known about how the virus infects the CNS and what cell types are infected, or what factors influence the induction of CNS pathology. Most individuals infected with HIV have virus within the CNS and the route of infection is thought to be transmigration of infected macrophages across the blood-brain barrier. As well as allowing opportunistic infections within the CNS there is a specific condition, AIDS dementia complex (ADC), which is a direct result of HIV infection of the CNS [ 42 ]. Within the CNS HIV is found in macrophages and microglia, and causes demyelination, vacuolation and gliosis. Again, the mechanism by which HIV causes CNS pathology is not well understood. The gp120 (Env) and Tat proteins have been shown to be neurotoxic in vitro and a number of the cytokines induced by HIV infection of monocytes and macrophages also have the capacity to damage neural tissue, either directly or indirectly [ 43 ]. All of the retroviruses that cause CNS disease would appear to do so as a consequence of their active replication. In the case of HIV there is direct evidence for this-treatment of patients with antiretrovirals can significantly decrease the severity of CNS disease [ 44 ]. However, aspects of CNS pathology remain unresolved, for example HIV encephalitis persists even during highly active anti-retroviral therapy [ 45 ]. Therefore, this area of retrovirus induced pathology does not appear to be of immediate relevance to replication defective retroviral vectors. However, until the mechanisms by which some aspects of CNS pathology are induced are better understood this facet of retroviral pathogenesis cannot be entirely dismissed in terms of its relevance to the design and use of retroviral vectors. Retroviruses causing immuno-deficiencies The AIDS epidemic has brought a substantial focus to bear on the retroviruses that cause immunodeficiencies in general, and the subset of these that are lentiviruses in particular. Simple retroviruses that cause immune deficiencies in mice [ 46 ], cats [ 47 ] and primates [ 48 , 49 ] have been described. Somewhat surprisingly, the pathological mechanisms in these diseases are all different. In mice, immunodeficiency is associated with proliferation of B cells (the primary target of infection), macrophages and CD4+ T-cells, all of which are non-functional. The disease is consistent with the development of anergy after antigen driven stimulation of the immune response [ 50 ]. Expression of a mutant gag gene product, Pr60 Gag, which is not processed normally [ 51 ], is required for induction of disease. However, the pathogenetic mechanisms involved are not understood. The defect in Gag processing makes the virus replication defective and a helper virus is required for virus spread, although not for induction of disease [ 52 ]. In cats the simple retroviruses that induce immunodeficiency do so via expression of an altered SU (Env) protein. This protein is incapable of causing resistance to superinfection [ 53 ]; as a consequence repeated superinfection leads directly to T cell lysis [ 54 ] and immunodeficiency then results due to a loss of T-cell function. The lentiviruses that have been associated with immune deficiency are FIV, SIV and HIV. All appear to share a common pathogenetic mechanism where virus infection of, and replication in, T-cells directly causes cell death, T-cell depletion and immunodeficiency [ 55 ]. Cell death is caused by high levels of viral replication in infected cells, although the exact mechanism is unclear. However, it is also clear that the pathogenesis of HIV-1 infection is much more complicated than this, with a complex interaction between the virus and host being played out over time [ 56 ]. In some non-human primates, infection with SIV is usually a chronic, but largely asymptomatic, condition [ 7 ]. This is thought to reflect a host/virus balance that has evolved over a long period of time. Presumably, the human AIDS epidemic reflects a recent movement of HIV into the population with a resulting imbalance between viral pathogenicity and host defences, which, after a relatively long period of infection, is resolved in favour of the pathogen. Again, the pathogenetic mechanisms involved with these retroviruses do not have major relevance to replication defective retroviral vectors. However, the pathogenetic mechanisms involved in the murine and feline immunodeficiencies caused by simple retroviruses do reiterate the point that expression of certain retroviral gene products can induce serious pathogenetic states and that this fact may have some relevance to vector design. Lentiviruses Apart from the lentiviruses mentioned above that result in immunodeficiency, there are a number of other lentiviral-associated diseases including those caused by caprine arthritis encephalitis virus (CAEV) [ 57 ], equine infectious anemia virus (EIAV) [ 58 ] and maedi/visna virus (MVV) [ 59 ]. For CAEV and MMV viral infection of macrophages seems to induce an inflammatory response involving macrophages and CD4+ and CD8+ T cells. It is this inflammatory response that is responsible for the different aspects of the pathology associated with infection by these viruses. EIAV causes erythrocyte lysis when high titres of cell free virus are present in the circulation. There are several mechanisms involved. Direct interaction of EIAV particles and erythrocytes results in complement mediated lysis and macrophage engulfment. This interaction is probably mediated by the Env protein. In addition, the virus appears able to suppress the differentiation of erythroid precursors. Eventually, most animals become asymptomatic carriers six to twelve months after infection. For all these viruses pathology appears to be intimately linked to viral replication. Therefore, the pathological mechanisms involved are not of direct relevance to replication defective retroviral vectors. Other retrovirus induced pathologies Retroviral infection has also been shown to be the cause of wasting and osteopetrosis in birds [ 60 ] and anaemia in cats [ 61 ]. Apart from feline anaemia, where the SU (Env) protein is a major, although not the sole determinant for the determination of pathology, the disease mechanisms involved are not well understood. However, pathology is clearly dependent on sustained viral replication meaning its significance to replication defective vectors is again limited. Pathogenic potential of retroviral vectors From the known mechanisms of retroviral pathogenesis discussed above the most obvious pathogenic potential of retroviral vectors is (i) the production of a replication competent virus, and (ii) insertional mutagenesis, specifically oncogene activation. Clearly, the production of replication competent virus not only creates the potential of pathogenetic disease, but will also greatly increase the probability of insertional mutagenesis. In fact, in the one instance where a vector contaminated with a replication competent virus was administered to animals viral replication per se did not appear to have an overt pathogenetic affect, rather a T-cell lymphoma eventuated [ 62 ], presumably as a result of oncogene activation. Although these conclusions are obvious and widely acknowledged it is reassuring to know that there appear to be no retroviral pathogenetic mechanisms of general relevance to the safety, or otherwise, of retroviral vector systems that have been overlooked. While the inadvertent transfer of gag, env and other retroviral genes also has the potential of inducing a pathogenetic state this would appear to depend on the specific retroviral gene sequence in question and to not be of general significance. Even so, minimizing the inadvertent transfer of retroviral gene sequences should clearly be an objective when developing retroviral vectors, not only because of this issue but also because it will have a bearing on the likelihood of replication competent virus being produced and of an endogenous retrovirus being activated. In addition, even though oncogene capture by retroviruses is an extremely rare event, the very significant pathogenic potential of the viruses that result means that it should also be taken into consideration during the development of retroviral vector systems. For various reasons, not least of which has been the problem of achieving positive experimental outcomes, only the issue of reducing the probability of replication competent virus arising has been systematically addressed during the development of retroviral vector technology. Indeed, great care has been taken in the development of retroviral vector systems to minimise the chance of producing replication competent retroviruses [ 63 , 64 ]. However, although clear means of doing so have been described [ 13 , 14 , 65 ], the need to minimize the probability of oncogene activation has often been made secondary to the issue of efficient transgene expression [ 66 ]. This has especially been the case with oncogenic retroviral vectors where transcriptional silencing has been a major problem [ 67 ]. Replication competent virus The generation of replication competent virus has, from the very beginning, been seen as the major safety issue for retroviral vectors and this has led to a prolonged effort to develop means of minimising the probability of it arising. There are two principal ways in which replication competent virus can be produced. The first of these is through recombination of the constituent parts of the vector system (i.e. vector and helper trans function plasmids), either with themselves or with endogenous viral sequences in the cell lines used for virus production [ 15 , 16 ]; the second is by activation of an endogenous proviral sequence. The first of these issues has been addressed by (i) breakdown of helper functions onto different plasmids; (ii) manipulation of codon usage in helper plasmids; (iii) removal, or mutagenesis, of unnecessary cis sequences present in the vector; (iv) the development of SIN vectors; (v) the minimisation of homology between the separate plasmids that make up the system; and (vi) the use of cell lines that do not contain endogenous retroviral sequences with homology to the vector system [ 13 , 14 , 63 - 65 ]. Although for many vector systems each of these approaches requires further refinement, in principle, they clearly provide the basis for the construction of vector systems where the probability of replication competent virus being produced via any of these mechanisms appears to be remote. While this doesn't negate the need for appropriate quality control procedures, especially as there is still the remote probability of inadvertent activation of an endogenous retrovirus from the cell line used for virus production, it means that the major safety issue faced by those wishing to use retroviral vectors is that of insertional mutagenesis and oncogene activation. Insertional mutagenesis and oncogene activation As discussed above, oncogene activation can occur either by transcription from one of the proviral LTRs, or by activation of an endogenous promoter by provision of transcriptional enhancer elements. The transgene aside, these events would appear to depend absolutely on the presence of active transcriptional control elements in the viral LTRs as evidenced by the critical role LTR sequences play in determining the ability of most non-defective retroviruses to induce cancers, and in determining the tissue specificity of cancer induction. There is no evidence that retroviruses contain transcriptional control elements of significance in other parts of their genomes. Therefore, the main approaches to minimizing the probability of oncogene activation must be the development of vectors from non-oncogenic retroviruses, the careful development of the SIN vector principal, and careful consideration of the promoter used to drive transcription of the transgene (see below). Retroviral gene transfer The minimization of the inadvertent transfer of retroviral genes to target cells is clearly a worthwhile objective as some of these genes have direct pathogenic potential and they may also influence the probability of endogenous retroviral sequences in the target cell being activated. Generally, the principles applied to the design of vector systems in order to minimize the probability of RCR being produced will also minimize the probability of inadvertent retroviral gene transfer. However, as the production of RCR requires multiple recombination events more effort should be made to analyse the rate of transfer and expression of individual retroviral gene sequences by vector systems. It is clear that the rate of individual gene transfer is much higher than the rate of RCR generation and can occur at a significant frequency even in highly evolved systems where RCR cannot be detected [ 68 ]. This suggests that further efforts need to be made to assess and reduce the rate of transfer of retroviral genes. Oncogene capture The mechanism of oncogene capture appears to be dependent on the generation of a chimeric retroviral-oncogene transcript (69, 70). This suggests that the risk of oncogene capture will be related to the efficiency of termination/polyadenylation of the proviral transcript and that this should be considered and assessed in the process of vector development, especially as retroviral polyadenylation sequences are often relatively inefficient, perhaps reflecting the necessity for the polyadenylation signal to be inactive in the context of the 5' LTR. However, in transient virus production systems, where the transfected vector plasmid presumably remains either entirely, or almost entirely extrachromosomal, this mechanism would appear to preclude the probability of oncogene capture. In the case of stable producer cell lines there is clearly an argument for categorizing the integration site of the vector sequence and discarding any clones where this is in a known or suspected oncogene. Adverse events in animal experiments and clinical trials The adverse events that have been observed in animal experiments and clinical trials reinforce the conclusions discussed above, that replication competent virus [ 62 ] and insertional mutagenesis [ 71 , 72 ] are the two risk factors of significance in retroviral mediated gene therapy. The two known instances where insertional mutagenesis/oncogene activation has resulted from the administration of a replication defective retroviral vector suggest that, the design of the vector aside, there are additional risk factors that influence the probability of an adverse event, the most obvious of these being the specific transgene expressed from the vector [ 71 , 73 , 74 ] which in both cases is a gene capable of influencing cell growth (although in neither case can it be considered a classical oncogene). In terms of the influence of vector design it is interesting to note that in both of these instances the same vector, pMFG [ 66 ] was used [ 75 , 76 ]. This vector is derived from MoMLV, a strongly oncogenic murine retrovirus, and notably uses the viral LTR to drive expression of the transgene. In both cases the vector appears to have been chosen primarily for its ability to efficiently drive transgene expression in haematopoietic lineages without consideration that this may also select for an increased risk of oncogene activation. Given the historical difficulty of obtaining good transgene expression from MoMLV derived vectors in haematopoietic lineages, and the lack of evidence to suggesting that oncogene activation was a significant safety issue with replication defective MoMLV vectors, it is not surprising that this approach was taken. Indeed, it is generally believed that, in general, the risk of insertional mutagenesis, while poorly defined, is probably substantially lower than seen in the X-SCID trial [ 77 ] where there appear to be a number of specific secondary risk factors [ 72 - 74 ]. In the absence of such secondary risk factors it is unclear what the real risk is; given the complexity of cellular and genetic regulatory processes and networks it is also unclear how many apparently innocuous transgenes will in fact increase the risk of adverse effects when expressed in a constitutive manner. However, no adverse events have been reported for the long running ADA-SCID trial where mature T-cells were targeted [ 78 ] or in PBL and PHSC targeted gene therapy for the same condition [ 79 ], although in both cases the number of patients who have been treated is very small. In all these protocols a non-self inactivating MoMLV derived vector was used. However, even with these unknowns it is apparent that improvements in vector technology, such as the use of SIN vectors, will greatly reduce the risk, whether or not additional risk factors are present. In terms of the vector technology used on the two occasions where oncogene activation has been observed the following comments can be made: 1) The vector is derived from MoMLV and uses the LTR sequence to drive the transgene via splicing. MoMLV is a strongly oncogenic, non-defective virus that causes B-and T-cell lymphomas and leukemias in mice. As with other non-defective oncogenic retroviruses the primary determinant of its pathological properties is the long terminal repeat enhancer. MoMLV has been shown to induce oncogenesis via activation of any one of a number of different cellular genes ( Ahi1 , Bla1 , Bmi1 , Cyclin D2, Dsi1 , Emi1 , Ets1 , Evi1 , Gfi1 , c-Ha-ras, Lck , Mis2 , Mlvi2 , 3 and 4 , c- myb , c- myc , N-myc, Notch1 , Pal1 , Pim1 and 2 , prolactin receptor, Pvt1 , Tiam1 and Tpl2 ). 2) The vector LTR is used to control transcription of the transgene. In the case of the X-SCID trial there is a strong selective pressure for gene corrected cells and accordingly there will clearly be an equally strong selection for transduced T-cell clones in which the LTR is active. 3) The PHSC is notoriously difficult to transduce with oncogenic retroviral vectors and the protocol used was designed to enhance transduction by using multiple cytokines to stimulate division of PHSC. This is likely to induce many genes involved in regulating cell growth. As retroviruses preferentially integrate into active gene sequences, this would increase the number of growth regulating genes accessible as targets for provirus integration and hence promiscuous, unregulated activation. Specifically, LMO2, the oncogene activated in the X-SCID trial, is normally expressed in primitive haematopoietic cells (the target for gene transfer) but not in mature cells (80). Therefore, it will be accessible for proviral integration during the transduction process and its continuing expression in maturing T cells generated from gene corrected precursors is biologically inappropriate. The problems that occurred in this X-SCID trial, their broader relevance and possible answers, have all been reviewed from a number of aspects [ 72 , 73 , 77 ]. However, the focus has been on the biology of the system, and little attention has been paid to how technological changes in vector delivery systems and protocols might impact on the risk of insertional mutagenesis/oncogene activation. Given what is known about retroviral mediated insertional mutagenesis it is surprising that more attention has not been paid to the technology used in many of the retroviral mediated gene therapy animal studies and human trials. With hindsight, it seems that the technologies used were selected on the basis of efficacy, not safety, that is achieving adequate gene expression took preference over consideration and assessment of insertional mutagenesis. However, given the technical difficulties involved in developing a workable protocol this is not surprising, and it is a pre-occupation that was, and is, shared by all gene therapy researchers. Possible technological approaches that would appear to provide answers to these issues include: 1) The use of self-inactivating (SIN) vectors would make a major difference in that the provirus would lack all U3 enhancer sequences, negating the ability of the LTR to activate cellular genes. The vector should also not contain active splice signals. However, given the ability of SIN vectors to be repaired at a significant rate during virus production (see below) careful selection of the retrovirus used to build the vector backbone is also important if this risk is to be minimised. Clearly the construction of vectors from non-oncogenic retroviruses and the development of more effective (i.e. less prone to LTR repair) SIN vectors is warranted. If SIN vectors are to be used the transgene must be expressed from an internal promoter which must also be presumed to have the potential for oncogene activation. Therefore it would be preferable to use a promoter without highly active enhancer elements. In addition, the wisdom of incorporating matrix/scaffold attachment regions into vectors to increase expression may also be contraindicated as these sequences have long-range enhancer like properties (81). If high levels of gene product are required, consideration should be given to other means to enhance transgene expression, such as codon-optimisation of coding sequences. 2) Vectors should be developed from non-oncogenic retroviruses. The recent development of vectors from HIV-1 and other lentiviruses for unrelated reasons (predominantly their ability to transduce non-cycling cells) means that this has already happened. The Tat dependence of the HIV-1 LTR may also provide an extra measure of safety as long as Tat is not transferred along with the vector. However, the enhancing properties of the HIV-1 LTR in the presence and absence of Tat needs to be carefully defined in order to test the assumption that the HIV-1 LTR lacks the ability to trans -activate adjacent promoters. The different integration specificities of lentiviral (centrally in active gene sequences) and oncogenic (promoter adjacent in active gene sequences) retroviruses and vectors [ 82 ] also give reason to suppose that the former may be less likely to cause oncogene activation. However, this remains to be directly demonstrated. 3) The incorporation of strong transcription termination/polyadenylation signals and gene isolator sequences (83) may provide another means to reduce the possibility of adjacent genes being activated. These sequences should also reduce the probability of oncogene capture in virus producer cells. However, the incorporation of insulator sequences appears to lead to a significant loss of vector titre (84). 4) When the transgene plays a role in regulating cell growth, extra consideration should be given to using the relevant control signals from the gene in question to regulate expression of the transgene. 5) Although the PHSC is theoretically a very attractive target for gene transfer it is extremely difficult to transduce with retroviral vectors derived from oncogenic viruses such as MoMLV. Although efficient transduction of human PHSC can now be achieved this requires exposure to multiple cytokines over a relatively long culture period. The potential of new retroviral vectors derived from lentiviruses (85) and spumaviruses (86) to transduce PHSC with shorter exposure to less cytokines needs to be fully explored. 6) In general the limitations of vectors should be taken into account when designing gene therapy protocols. For example, in the case of X-SCID, it may be just as efficacious to target a more committed T-cell precursor that can be transduced more easily, and without biological manipulation using multiple cytokines. Alternatively, if the PHSC is to be targeted as highly enriched a PHSC population as possible should be used in order to expose the patient to the minimum number of transduction events compatible with the desired outcome. In the two X-SCID patients who developed T cell leukemia, molecular analysis of samples collected before the appearance of malignant disease showed the presence of >50 γc transduced T cell clones. Approximately 14 to 20 million transduced CD34 + cells were infused into these patients. Therefore, it would appear the patient is exposed to a much greater number of transduced cells than is theoretically necessary to produce the desired result. In other words, the process of generating gene corrected T cell clones by transduction of CD34 + cells is very inefficient. SIN vectors, how good are they? With hindsight SIN vectors [ 13 , 87 , 88 ] now appear likely to be one of the most important general developments in retroviral vector technology since the advent of replication defective vector systems in the 1980s. SIN vectors take advantage of the reverse transcription reaction in which the U3 region of the 3' LTR acts as the template for the U3 region in both LTRs of the provirus. As the transcriptional enhancer elements in the 3' LTR are redundant in the context of a retroviral vector construct they can theoretically be deleted without affecting vector performance. After transduction of the target cell both LTRs are deleted and are transcriptionally silent. Although this requires that an internal promoter is used to control expression of the transgene, and makes it more difficult to generate high titre stable packaging cell lines, the advantages of the approach are obvious. However, SIN vectors have not been widely used in the case of oncogenic retroviral vectors, principally because viral titres were low, because of high rates of repair of the SIN deletion [ 13 , 89 ] and because of negative effects of the SIN deletion on gene transfer efficiency [ 90 ]. Subsequently, by the use of a heterologous promoter in the 5' LTR an effective SIN vector based on spleen necrosis virus was developed [ 91 ] but this vector has not been widely utilized to date. In contrast, SIN vectors have been widely adopted in the lentiviral vector field [ 14 , 65 , 92 , 93 ] where transient expression systems are generally used to produce virus, avoiding the difficulties of making stable cell lines associated with SIN vectors. In addition, in terms of transgene expression, lentiviral SIN vectors appear to perform as well as, if not better than, vectors with an intact 3' LTR [ 93 , 94 ]. However, even with vectors with large 3' LTR deletions it is obvious that repair of the SIN deletion also occurs at a significant rate with lentiviral SIN vectors [ 14 , 92 ]. Therefore, while the concept of SIN vectors is a powerful one, further development and rigorous testing of this technology is required before it can be confidently used to address the problems of insertional mutagenesis. Conclusion The most important determinant of the safety of retroviral vectors remains ensuring they are free of replication competent retrovirus of any sort. Clearly, the technologies available for the production of vector virions would appear able to preclude the production of replication competent virus by recombination of the constituent parts of the vector system (i.e. vector and helper plasmids) with a very high degree of certainty. However, production of replication competent virus from the cell lines used for virus production remains a theoretical possibility and more work needs to be done on generic assays for replication competent retroviruses. Apart from the issue of replication competent virus, analysis of the pathologies associated with retroviruses, and the results of the X-SCID trial, demonstrate that careful attention must be paid to the ability of sequences in retroviral vectors to activate transcription of genes adjacent to proviral integration sites. Although the use of SIN vectors will greatly reduce the risk of such events, given the predilection of current SIN vectors to be repaired during virus production these vectors need to be further developed, especially for vectors derived from strongly oncogenic viruses. In addition, inadvertent transfer to, and expression in, transduced cells of gag, env (SU) and other retroviral gene sequences would appear to of relevance and needs to be specifically addressed in the development of vector systems. As both oncogenic (MoMLV derived) and lentiviral (HIV-1 derived) vectors have been shown to preferentially integrate into transcribed sequences it would appear logical that the likelihood of proviral integration near cellular genes involved in the positive regulation of cell growth would be increased in actively growing cell populations. This suggests that the use of transduction protocols that target non-cycling cells, or cells that are subjected to the minimum of stimulatory signals as is compatible with efficient gene transfer, would be greatly advantageous in terms of minimising the risk of malignant events after the stimulatory signals are removed. With hindsight, the observation of malignant events induced by replication defective MoMLV retroviral vectors is not surprising although the frequency of these events in the X-SCID gene therapy trail certainly was. The concern is that these events will now cause a significant backlash against the use of all retroviral vectors while the real message is that we need to make better use of the knowledge we now have in terms of designing vectors and gene therapy protocols. Clearly, the known oncogenic potential of MoMLV and its relationship to viral sequences has, for one reason or another, been largely ignored to date. Indeed, most of the retroviral vectors used in trials to date are based on MoMLV and contain an intact 5' LTR. While the historic reasons for this are obvious we now need to evaluate and adopt more appropriate technologies as rapidly as possible. There are several obvious conclusions to be drawn from the X-SCID trial and the results of Li et al [ 71 ]. The first is that in the absence of additional risk factors the risk of malignant events resulting from exposure to a replication defective retroviral vector is low but remains to be accurately quantified. Secondly, what constitutes an additional risk factor is hard to predict, making risk assessment difficult. However, even with these unknowns, there exist technological approaches that should greatly reduce the risks associated with retroviral mediated gene therapy. These include SIN vectors and new types of retroviral vectors (namely lentiviral vectors) that may allow simpler transduction protocols that perturb the normal state of the target cell less than current approaches. This is especially true when the PHSC is the target of gene transfer; current protocols using oncogenic retroviral vectors rely heavily on manipulating the state of the target cell by exposure to multiple cytokines over relatively long periods. These protocols are also relatively inefficient; this reflects the poor match between the target cell and properties of these vectors. In contrast, vectors derived from lentiviruses and spumaviruses appear to allow more efficient transduction of PHSC with less requirement for cytokine stimulation of the target cells [ 85 , 86 , 95 - 97 ]. It is of note that the use of lentiviral vectors may also be preferable in other ways. Not only do they have uniquely positive properties as gene therapy vectors, there is no evidence that the viruses from which they are derived are able to induce gene activation using the same mechanisms as used by non-defective oncogenic retroviruses. Regulatory authorities also have a role to play. Clinical trials are based on extensive preclinical experimentation and animal trials that take many years to complete. Clearly, the particular vector system that has been used to develop a protocol may no longer be the best to use ten years later when clinical trials become a reality. The question is, then, how can the regulation of clinical trials be made flexible enough to allow the introduction of new and improved vector technology late in the process? In conclusion, retroviral mediated gene transfer remains an extremely attractive option for gene therapy when the stable and permanent genetic modification of the target cell is optimal. However, we must take greater care, and utilise more resources, for the pro-active, rather than reactive, refinement and testing of the basic technology that is used for gene therapy and for the adoption of improved vector systems if adverse events are to be minimised.

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549606

Acquired Resistance of Lung Adenocarcinomas to Gefitinib or Erlotinib Is Associated with a Second Mutation in the EGFR Kinase Domain

Background Lung adenocarcinomas from patients who respond to the tyrosine kinase inhibitors gefitinib (Iressa) or erlotinib (Tarceva) usually harbor somatic gain-of-function mutations in exons encoding the kinase domain of the epidermal growth factor receptor (EGFR). Despite initial responses, patients eventually progress by unknown mechanisms of “acquired” resistance. Methods and Findings We show that in two of five patients with acquired resistance to gefitinib or erlotinib, progressing tumors contain, in addition to a primary drug-sensitive mutation in EGFR, a secondary mutation in exon 20, which leads to substitution of methionine for threonine at position 790 (T790M) in the kinase domain. Tumor cells from a sixth patient with a drug-sensitive EGFR mutation whose tumor progressed on adjuvant gefitinib after complete resection also contained the T790M mutation. This mutation was not detected in untreated tumor samples. Moreover, no tumors with acquired resistance had KRAS mutations, which have been associated with primary resistance to these drugs. Biochemical analyses of transfected cells and growth inhibition studies with lung cancer cell lines demonstrate that the T790M mutation confers resistance to EGFR mutants usually sensitive to either gefitinib or erlotinib. Interestingly, a mutation analogous to T790M has been observed in other kinases with acquired resistance to another kinase inhibitor, imatinib (Gleevec). Conclusion In patients with tumors bearing gefitinib- or erlotinib-sensitive EGFR mutations, resistant subclones containing an additional EGFR mutation emerge in the presence of drug. This observation should help guide the search for more effective therapy against a specific subset of lung cancers.

Introduction Somatic gain-of-function mutations in exons encoding the epidermal growth factor receptor (EGFR) tyrosine kinase domain are found in about 10% of non-small cell lung cancers (NSCLCs) from the United States [ 1 , 2 , 3 ], with higher percentages observed in east Asia [ 2 , 4 , 5 , 6 ]. Some 90% of NSCLC-associated mutations occur as either multi-nucleotide in-frame deletions in exon 19, involving elimination of four amino acids, Leu-Arg-Glu-Ala, or as a single nucleotide substitution at nucleotide 2573 (T→G) in exon 21, resulting in substitution of arginine for leucine at position 858 (L858R). Both of these mutations are associated with sensitivity to the small-molecule kinase inhibitors gefitinib or erlotinib [ 1 , 2 , 3 ]. Unfortunately, nearly all patients who experience marked improvement on these drugs eventually develop progression of disease. While KRAS mutations have been associated with some cases of primary resistance to gefitinib or erlotinib [ 7 ], mechanisms underlying “acquired” or “secondary” resistance are unknown. Acquired resistance to kinase-targeted anticancer therapy has been most extensively studied with imatinib, an inhibitor of the aberrant BCR-ABL kinase, in chronic myelogenous leukemia (CML). Mutations in the ABL kinase domain are found in 50%–90% of patients with secondary resistance to the drug (reviewed in [ 8 ]). Such mutations, which cluster in four distinct regions of the ABL kinase domain (the ATP binding loop, T315, M351, and the activation loop), interfere with binding of imatinib to ABL [ 9 , 10 , 11 ]. Crystallographic studies of various ABL mutants predict that most should remain sensitive to inhibitors that bind ABL with less stringent structural requirements. Using this insight, new small-molecule inhibitors have been identified that retain activity against the majority of imatinib-resistant BCR-ABL mutants [ 12 , 13 ]. Although imatinib inhibits different kinases in various diseases (BCR-ABL in CML, KIT or PDGFR-alpha in gastrointestinal stromal tumors [GISTs], and PDGFR-alpha in hypereosinophilic syndrome [HES]) (reviewed in [ 14 ]), some tumors that become refractory to treatment with imatinib appear to have analogous secondary mutations in the kinase-coding domain of the genes encoding these three enzymes. For example, in CML, a commonly found mutation is a C→T single nucleotide change that replaces threonine with isoleucine at position 315 (T315I) in the ABL kinase domain [ 9 , 10 , 11 ]. In GIST and HES, respectively, the analogous T670I mutation in KIT and T674I mutation in PDGFR-alpha have been associated with acquired resistance to this drug [ 15 , 16 ]. To determine whether lung cancers that acquire clinical resistance to either gefitinib or erlotinib display additional mutations in the EGFR kinase domain, we have examined the status of EGFR exons 18 to 24 in tumors from five patients who initially responded but subsequently progressed while on these drugs. These exons were also assessed in tumor cells from a sixth patient whose disease rapidly recurred while on gefitinib therapy after complete gross tumor resection. Because of the association of KRAS mutations with primary resistance to gefitinib and erlotinib [ 7 ], we also examined the status of KRAS in tumor cells from these six patients. In an effort to explain the selective advantage of cells with a newly identified “resistance” mutation in EGFR— a T790M amino acid substitution—we further characterized the drug sensitivity of putatively resistant EGFR mutants versus wild-type or drug-sensitive EGFR mutants, using both a NSCLC cell line fortuitously found to contain the T790M mutation and lysates from cells transiently transfected with wild-type and mutant EGFR cDNAs. Methods Tissue Procurement Tumor specimens, including paraffin blocks, fine needle biopsies, and pleural effusions, were obtained through protocols approved by the Institutional Review Board of Memorial Sloan-Kettering Cancer Center (protocol 92–055 [ 7 ] and protocol 04–103 [ Protocol S1 ]). All patients provided informed consent. Mutational Analyses of EGFR and KRAS in Lung Tumors Genomic DNA was extracted from tumor specimens, and primers for EGFR (exons 18–24) and KRAS2 (exon 2) analyses were as published [ 3 , 7 ]. All sequencing reactions were performed in both forward and reverse directions, and all mutations were confirmed at least twice from independent PCR isolates. A specific exon 20 mutation (T790M) was also detected by length analysis of fluorescently labeled (FAM) PCR products on a capillary electrophoresis device (ABI 3100 Avant, Applied Biosystems, Foster City, California, United States), based on a new NlaIII restriction site created by the T790M mutation (2369 C→T), using the following primers: EGFR Ex20F, 5′-FAM- CTCCCTCCAGGAAGCCTACGTGAT-3′ and EGFR Ex20R 5′- TTTGCGATCTGCACACACCA-3′. Using serially mixed dilutions of DNA from NSCLC cell lines (H1975, L858R- and T790M-positive; H-2030, EGFR wild-type) for calibration, this assay detects the presence of the T790M mutation when H1975 DNA comprises 3% or more of the total DNA tested, compared to a sensitivity of 6% for direct sequencing (data not shown). RT-PCR The following primers were used to generate EGFR cDNA fragments spanning exon 20: EGFR 2095F 5′- CCCAACCAAGCTCTCTTGAG-3′ and EGFR 2943R 5′- ATGACAAGGTAGCGCTGGGGG-3′. PCR products were ligated into plasmids using the TOPO TA-cloning kit (Invitrogen, Carlsbad, California, United States), as per manufacturer's instructions. Minipreps of DNA from individual clones were sequenced using the T7 priming site of the cloning vector. Functional Analyses of Mutant EGFRs Two numbering systems are used for EGFR. The first denotes the initiating methionine in the signal sequence as amino acid −24. The second, used here, denotes the methionine as amino acid +1. Commercial suppliers of antibodies, such as the Y1068-specific anti-phospho-EGFR, use the first nomenclature. To be consistent, we consider Y1068 as Y1092. Likewise, the T790M mutation reported here has also been called T766M. Mutations were introduced into full-length wild-type and mutant EGFR cDNAs using a QuikChange Site-Directed Mutagenesis Kit (Stratagene, La Jolla, California, United States) and cloned into expression vectors as described [ 3 ]. The following primers were used to generate the deletion (del) L747–E749;A750P mutant: forward 5′- TAAAATTCCCGTCGCTATCAAGGAGCCAACATCTCCGAAAGCCAACAAGG-3′ and reverse 5′- CCTTGTTGGCTTTCGGAGATGTTGGCTCCTTGATAGCGACGGGAATTTTA-3′. The following primers were used to introduce the T790M mutation: forward 5′- AGCTCATCATGCAGCTCAT-3′ and reverse 5′- ATGAGCTGCATGATGAGCT-3′. The L858R mutant cDNA was generated previously [ 3 ]. All mutant clones were fully re-sequenced bidirectionally to ensure that no additional mutations were introduced. Various EGFRs were transiently expressed in 293T human embryonic kidney cells as published [ 3 ]. Cells were treated with different concentrations of gefitinib or erlotinib. Immunoblotting See Methods and supplementary methods in [ 3 ] for details on cell lysis, immunoblotting, and antibody reagents. At least three independent experiments were performed for all analyses. Cell Culture The NSCLC cell lines H1650, H1975, H2030, H2347, H2444, H358, and H1734 were purchased from American Type Culture Collection (Manassas, Virginia, United States). H3255 was a gift of B. Johnson and P. Janne. Cells were grown in complete growth medium (RPMI-1640; American Type Culture Collection catalog no. 30–2001) supplemented with 10% fetal calf serum, 10 units/ml penicillin, and 10 μg/ml streptomycin) at 37 °C and 5% CO 2 . For viability studies, cells were seeded in complete growth medium in black 96-well clear bottom ViewPlates (PerkinElmer, Wellesley, Massachusetts, United States) at a density of 5,000 (H1975 and H2030) or 7,500 cells per well (H3255). Following overnight incubation, cells were grown for 24 h in the supplemented RPMI-1640 medium with 0.1% serum. Cells (in supplemented RPMI-1640 medium containing 0.1% serum) were then incubated for 48 h in the continued presence of gefitinib or erlotinib. Viability Assay Cell viability was assayed using Calcein AM (acetoxymethyl ester of Calcein, Molecular Probes, Eugene, Oregon, United States). Following incubation with gefitinib or erlotinib, monolayers were washed twice with PBS (containing calcium and magnesium) and incubated with 7.5 μmol Calcein AM in supplemented RPMI-1640 (no serum) for 30 min. Labeling medium was removed, and cells were washed three times with PBS. Calcein fluorescence (Ex, 485 nm; Em, 535 nM) was detected immediately using a Victor V multi-label plate reader (PerkinElmer). Three independent experiments were performed for each cell line; each experiment included four to eight replicates per condition. Results Case Reports We identified secondary EGFR mutations in three of six individuals whose disease progressed on either gefitinib or erlotinib ( Table 1 ). Brief case histories of these three patients are presented below. Table 1 Specimens Analyzed in This Study for Mutations in the EGFR Tyrosine Kinase Domain (Exons 18 to 24) and KRAS (Exon 2) The transbronchial biopsy in patient 1 had scant tumor cells; sequencing analysis revealed only wild-type sequence (see text) a Percent tumor cells is defined by assessment of corresponding histopathological slides n/a, not applicable Patient 1 This 63-y-old female “never smoker” (smoked less than 100 cigarettes in her lifetime) initially presented with bilateral diffuse chest opacities and a right-sided pleural effusion. Transbronchial biopsy revealed adenocarcinoma. Disease progressed on two cycles of systemic chemotherapy, after which gefitinib, 250 mg daily, was started. Comparison of chest radiographs obtained prior to starting gefitinib ( Figure S1 A, left panel) and 2 wk later ( Figure S1 A, middle panel) showed dramatic improvement. Nine months later, a chest radiograph revealed progression of disease ( Figure S1 A, right panel). Subsequently, the patient underwent a computed tomography (CT)–guided biopsy of an area in the right lung base ( Figure 1 A, left panel). Despite continued treatment with gefitinib, either with chemotherapy or at 500 mg daily, the pleural effusion recurred, 12 mo after initiating gefitinib ( Figure 1 A, right panel). Pleural fluid was obtained for molecular studies. In total, this patient had three tumor specimens available for analysis: the original lung tumor biopsy, a biopsy of the progressing lung lesion, and pleural fluid. However, re-review of the original transbronchial biopsy showed that it had scant tumor cells ( Table 1 ). Figure 1 Re-Biopsy Studies (A.) Patient 1. CT-guided biopsy of progressing lung lesions after 10 mo on gefitinib (left panel). Two months later, fluid from a right-sided pleural effusion (right panel) was collected for molecular analysis. (B) Patient 2. CT-guided biopsy of a progressing thoracic spine lesion (left panel) and fluoroscopic-guided biopsy of a progressing lung lesion (right panel). The biopsy needles are indicated by white arrows. Patient 2. This 55-y-old woman with a nine pack-year history of smoking underwent two surgical resections within 2 y (right lower and left upper lobectomies) for bronchioloalveolar carcinoma with focal invasion. Two years later, her disease recurred with bilateral pulmonary nodules and further progressed on systemic chemotherapy. Thereafter, the patient began erlotinib, 150 mg daily. A baseline CT scan of the chest demonstrated innumerable bilateral nodules ( Figure S1 B, left panel), which were markedly reduced in number and size 4 mo after treatment ( Figure S1 B, middle panel). After 14 mo of therapy, the patient's dose of erlotinib was decreased to 100 mg daily owing to fatigue. At 23 mo of treatment with erlotinib, a CT scan demonstrated an enlarging sclerotic lesion in the thoracic spine. The patient underwent CT-guided biopsy of this lesion ( Figure 1 B, left panel), and the erlotinib dose was increased to 150 mg daily. After 25 mo of treatment, she progressed within the lung ( Figure S1 B, right panel). Erlotinib was discontinued, and a fluoroscopically guided core needle biopsy was performed at a site of progressive disease in the lung ( Figure 1 B, right panel). In total, this patient had three tumor specimens available for analysis: the original resected lung tumor, the biopsy of the enlarging spinal lesion, and the biopsy of the progressing lung lesion ( Table 1 ). Patient 3 This 55-y-old female “never smoker” was treated for nearly 4.5 y with weekly paclitaxel and trastuzumab [ 17 ] for adenocarcinoma with bronchioloalveolar carcinoma features involving her left lower lobe, pleura, and mediastinal lymph nodes. Treatment was discontinued owing to fatigue. Subsequently, the patient underwent surgical resection. Because of metastatic involvement of multiple mediastinal lymph nodes and clinical features known at that time to be predictive of response to gefitinib (female, never smoker, bronchioloalveolar variant histology), she was placed on “adjuvant” gefitinib 1 mo later ( Figure S1 C, left panel). This drug was discontinued after 3 mo when she developed a new left-sided malignant pleural effusion ( Figure S1 C, middle panel). Despite drainage and systemic chemotherapy, the pleural effusion recurred 4 mo later ( Figure S1 C, right panel), at which time pleural fluid was collected for analysis. In total, this patient had two clinical specimens available for analysis: tumor from the surgical resection and pleural fluid ( Table 1 ). Patients' Tumors Contain EGFR Tyrosine Kinase Domain Mutations Associated with Sensitivity to EGFR Tyrosine Kinase Inhibitors We screened all available tumor samples from these three patients for previously described drug-sensitive EGFR mutations, by direct DNA sequencing of exons 19 and 21 [ 3 ]. Tumor samples from patient 1 showed a T→G change at nucleotide 2573, resulting in the exon 21 L858R amino acid substitution commonly observed in drug-responsive tumors. This mutation was present in the biopsy material from the progressing lung lesion ( Figure S2 A, upper panels) and from cells from the pleural effusion ( Figure S2 A, lower panels), both of which on cytopathologic examination consisted of a majority of tumor cells ( Table 1 ). Interestingly, comparisons of the tracings suggest that an increase in copy number of the mutant allele may have occurred. Specifically, while the ratio of wild-type (nucleotide T) to mutant (nucleotide G) peaks at position 2573 was approximately 1:1 or 1:2 in the lung biopsy specimen ( Figure S2 A, upper panels), sequencing of DNA from the pleural fluid cells demonstrated a dominant mutant G peak ( Figure S2 A, lower panels). Consistent with this, a single nucleotide polymorphism (SNP) noted at nucleotide 2361 (A or G) demonstrated a corresponding change in the ratios of A:G, with a 1:1 ratio in the transbronchial biopsy, and a nearly 5:1 ratio in the pleural fluid ( Figure 2 A). Notably, we did not detect the 2573 T→G mutation in the original transbronchial biopsy specimen ( Table 1 ; data not shown). As stated above, this latter specimen contained scant tumor cells, most likely fewer than needed for detection of an EGFR mutation by direct sequencing (see [ 7 ]). Figure 2 Sequencing Chromatograms with the T790M EGFR Exon 20 Mutation in Various Clinical Specimens and the NSCLC Cell Line H1975 (A–C) In all three patients—patient 1 (A), patient 2 (B), and patient 3 (C)—the secondary T790M mutation was observed only in lesions obtained after progression on either gefitinib or erlotinib. (D) Cell line H1975 contains both an exon 21 L858R mutation (upper panel) and the exon 20 T790M mutation (lower panel). The asterisks indicate a common SNP (A or G) at nucleotide 2361; the arrows indicate the mutation at nucleotide 2369 (C→T), which leads to substitution of methonine (ATG) for threonine (ACG) at position 790. In the forward direction, the mutant T peak is blue. In the reverse direction, the mutant peak is green, while the underlying blue peak represents an “echo” from the adjacent nucleotide. All three specimens from patient 2, including the original lung tumor and the two metastatic samples from bone and lung, showed an exon 19 deletion involving elimination of 11 nucleotides (2238–2248) and insertion of two nucleotides, G and C ( Figure S2 B, all panels; Table 1 ). These nucleotide changes delete amino acids L747–E749 and change amino acid 750 from alanine to proline (A750P). A del L747–E749;A750P mutation was previously reported with different nucleotide changes [ 2 ]. In all samples from patient 2, the wild-type sequence predominated at a ratio of about 3:1 over the mutant sequence. Both of the available tumor samples from patient 3 contained a deletion of 15 nucleotides (2236–2250) in exon 19 ( Table 1 ; data not shown), resulting in elimination of five amino acids (del E746–A750). This specific deletion has been previously reported [ 3 ]. The ratio of mutant to wild-type peaks was approximately 1:1 in both specimens (data not shown). Collectively, these results demonstrate that tumors from all three patients contain EGFR mutations associated with sensitivity to the tyrosine kinase inhibitors gefitinib and erlotinib. In addition, these data show that within individual patients, metastatic or recurrent lesions to the spine, lung, and pleural fluid contain the same mutations. These latter observations support the idea that relapsing and metastatic tumor cells within individuals are derived from original progenitor clones. A Secondary Missense Mutation in the EGFR Kinase Domain Detected in Lesions That Progressed while on Treatment with Either Gefitinib or Erlotinib To determine whether additional mutations in the EGFR kinase domain were associated with progression of disease in these patients, we performed direct sequencing of all of the exons (18 through 24) encoding the EGFR catalytic region in the available tumor specimens. Analysis of patient 1's pre-gefitinib specimen, which contained scant tumor cells ( Table 1 ; see above), not surprisingly showed only wild-type EGFR sequence ( Table 1 ; data not shown). However, careful analysis of the exon 20 sequence chromatograms in both forward and reverse directions from this patient's lung biopsy specimen obtained after disease progression on gefitinib demonstrated an additional small peak at nucleotide 2369, suggesting a C→T mutation ( Figure 2 A, upper panels; Table 1 ). This nucleotide change leads to substitution of methionine for threonine at position 790 (T790M). The 2369 C→T mutant peak was even more prominent in cells from the patient's pleural fluid, which were obtained after further disease progression on gefitinib ( Figure 2 A, lower panels; Table 1 ). The increase in the ratio of mutant to wild-type peaks obtained from analyses of the lung specimen and pleural fluid paralleled the increase in the ratio of the mutant G peak (leading to the L858R mutation) to the wild-type T peak at nucleotide 2573 (see above; Figure S2 A), as well as the increase in the ratio of the A:G SNP at position 2361 ( Figure 2 A). Collectively, these findings imply that the exon 20 T790M mutation was present on the same allele as the exon 21 L858R mutation, and that a subclone of cells harboring these mutations emerged during drug treatment. In patient 2, the tumor-rich sample obtained prior to treatment with erlotinib did not contain any additional mutations in the exons encoding the EGFR tyrosine kinase domain ( Figure 2 B, upper panels; Table 1 ). By contrast, her progressing bone and lung lesions contained an additional small peak at nucleotide 2369, suggesting the existence of a subclone of tumor cells with the same C→T mutation observed in patient 1 ( Figure 2 B, middle and lower panels; Table 1 ). The relative sizes of the 2369 T mutant peaks seen in these latter two samples appeared to correlate with the relative size of the corresponding peaks of the exon 19 deletion ( Figure S2 B). Interestingly, the SNP at nucleotide 2361 (A or G) was detected in specimens from patient 2 before but not after treatment with erlotinib, suggesting that one EGFR allele underwent amplification or deletion during the course of treatment ( Figure S2 B). Patient 3 showed results analogous to those of patient 2. A tumor-rich pre-treatment specimen did not demonstrate EGFR mutations other than the del E746–A750 exon 19 deletion; specifically, in exon 20, no secondary changes were detected ( Figure 2 C, upper panels; Table 1 ). However, analysis of DNA from cells in the pleural effusion that developed after treatment with gefitinib showed the C→T mutation at nucleotide 2369 in exon 20 ( Figure 2 C, lower panels; Table 1 ), corresponding to the T790M mutation described above. There was no dramatic change between the two samples in the ratio of the A:G SNP at position 2361. The mutant 2369 T peak was small, possibly because gefitinib had been discontinued in this patient for 4 mo at the time pleural fluid tumor cells were collected; thus, there was no selective advantage conferred upon cells bearing the T790M mutation. To determine whether the 2369 C→T mutation was a previously overlooked EGFR mutation found in NSCLCs, we re-reviewed exon 20 sequence tracings derived from analysis of 96 fresh-frozen resected tumors [ 3 ] and 59 paraffin-embedded tumors [ 7 ], all of which were removed from patients prior to treatment with an EGFR tyrosine kinase inhibitor. We did not detect any evidence of the T790M mutation in these 155 tumors (data not shown; see Discussion ). Collectively, our results suggest that the T790M mutation is associated with lesions that progress while on gefitinib or erlotinib. Moreover, at least in patients 1 and 2, the subclones of tumor cells bearing this mutation probably emerged between the time of initial treatment with a tyrosine kinase inhibitor and the appearance of drug resistance. In three additional patients (case histories not described here) with lung adenocarcinomas who improved but subsequently progressed on therapy with either gefitinib or erlotinib, we examined DNA from tumor specimens obtained during disease progression. In all three patients, we found EGFR mutations associated with drug sensitivity (all exon 19 deletions). However, we did not find any additional mutations in exons 18 to 24 of EGFR, including the C→T change at position 2369 (data not shown). These results imply that alternative mechanisms of acquired drug resistance exist. Patients' Progressive Tumors Lack KRAS Mutations Mutations in exon 2 of KRAS2 occur in about one-fourth of NSCLCs. Such mutations rarely, if ever, accompany EGFR mutations and are associated with primary resistance to gefitinib or erlotinib [ 7 ]. To evaluate the possibility that secondary KRAS mutations confer acquired resistance to these drugs, we performed mutational profiling of KRAS2 exon 2 from tumor specimens from patients 1 to 3, as well as the three additional patients lacking evidence of the T790M mutation. None of the specimens contained any changes in KRAS ( Table 1 ; data not shown), indicating that KRAS mutations were not responsible for drug resistance and tumor progression in these six patients. An Established NSCLC Cell Line Also Contains Both T790M and L858R Mutations We profiled the EGFR tyrosine kinase domain (exons 18 to 24) and KRAS exon 2 in eight established NSCLC lines ( Table 2 ). Surprisingly, one cell line—H1975—contained the same C→T mutation at position 2369 (T790M) as described above ( Figure 2 D, lower panel). This cell line had previously been shown by others to contain a 2573 T→G mutation in exon 21 (L858R) [ 18 ], which we confirmed ( Figure 2 D, upper panel); in addition, H1975 was reported to be more sensitive to gefitinib inhibition than other lung cancer cell lines bearing wild-type EGFR [ 18 ]. Only exons 19 and 21 were apparently examined in this published study. Table 2 Status of NSCLC Cell Lines Analyzed for EGFR Tyrosine Kinase Domain (Exons 18 to 24) and KRAS (Exon 2) Mutations See Methods for further details In our own analysis of H1975 (exons 18 to 24), the mutant 2369 T peak resulting in the T790M amino acid substitution was dominant, suggesting an increase in copy number of the mutant allele in comparison to the wild-type allele. The ratio of mutant to wild-type peaks was similar to that of the mutant 2573 G (corresponding to the L858R amino acid substitution) to wild-type T peaks ( Figure 2 D, all panels), implying that the T790M and L858R mutations were in the same amplified allele. To further investigate this possibility, we performed RT-PCR to generate cDNAs that spanned exon 20 of EGFR and included sequences from exon 19 and 21. PCR products were then cloned, and individual colonies were analyzed for EGFR mutations. Sequencing chromatograms of DNA from four of four clones showed both the 2369 C→T and 2573 T→G mutations, confirming that both mutations were in the same allele (data not shown). Other NSCLC cell lines carried either EGFR or KRAS mutations, but none had both ( Table 2 ). As reported, H3255 contained an L858R mutation [ 19 ] and H1650 contained an exon 19 deletion [ 18 ]. No other cell lines analyzed contained additional mutations in the exons encoding the EGFR tyrosine kinase domain. A Novel PCR Restriction Fragment Length Polymorphism Assay Independently Confirms the Absence or Presence of the T790M Mutation As stated above, the mutant peaks suggestive of a T790M mutation in exon 20 were small in some sequence chromatograms. To eliminate the possibility that these peaks were due to background “noise,” we sought to confirm the presence of the 2369 C→T mutation in specific samples, by developing an independent test, based on a fluorescence detection assay that takes advantage of a PCR restriction fragment length polymorphism (PCR-RFLP) generated by the specific missense mutation. After PCR amplification with exon-20-specific primers spanning nucleotide 2369, wild-type sequence contains specific NlaIII sites, which upon digestion yield a 106-bp product ( see Methods; Figure 3 A). Presence of the mutant 2369 T nucleotide creates a new NlaIII restriction digest site, yielding a slightly shorter product (97 bp), readily detected by fluorescent capillary electrophoresis. This test is about 2 -fold more sensitive than direct sequencing (see Methods; data not shown). Figure 3 A Novel PCR-RFLP Assay Independently Confirms Presence of the T790M Mutation in Exon 20 of the EGFR Kinase Domain (A) Design of the assay (see text for details). “F” designates the fluorescent label, FAM. At the bottom of this panel, the assay demonstrates with the 97-bp NlaIII cleavage product the presence of the T790M mutation in the H1975 cell line; this product is absent in H2030 DNA. The 106-bp NlaIII cleavage product is generated by digestion of wild-type EGFR . (B) The PCR-RFLP assay demonstrates that pre-drug tumor samples from the three patients lack detectable levels of the mutant 97-bp product, while specimens obtained after disease progression contain the T790M mutation. Pt, patient. We first used DNA from the H1975 cell line (which contains both T790M and L858R mutations) to confirm the specificity of the PCR-RFLP assay. As expected, analysis of these cells produced both the 97- and 106-bp fragments. By contrast, analysis of DNA from H2030 (which contains wild-type EGFR; Table 2 ) showed only the 106-bp fragment ( Figure 3 A). These data show that this test can readily indicate the absence or presence of the mutant allele in DNA samples. However, this test was only semi-quantitative, as the ratio of the mutant 97-bp product versus the wild-type 106-bp product varied in independent experiments from approximately 1:1 to 2:1. We next used this PCR-RFLP assay to assess various patient samples for the presence of the specific 2369 C→T mutation corresponding to the T790M amino acid substitution. DNA from the progressing bone and lung lesions in patient 1 produced both the 97- and 106-bp fragments, but DNA from the original lung tumor did not ( Figure 3 B). The ratio of mutant to wild-type products was higher in the cells from the pleural fluid, consistent with the higher peaks seen on the chromatograms from direct sequencing of exon 20 (see Figure 2 A). Likewise, DNA from progressive lesions from patients 2 and 3 yielded both 97- and 106-bp fragments in the PCR-RFLP assay ( Figure 3 B), whereas the pre-treatment specimens did not produce the 97-bp product. Collectively, these data from an independent assay confirm that the T790M mutation was present in progressing lesions from all three patients. We were also unable to detect the T790M mutation in any specimens from the three additional patients with acquired resistance that failed to demonstrate secondary mutations in EGFR exons 18 to 24 by direct sequencing (data not shown). Biochemical Properties of EGFR Mutants To determine how the T790M mutation would affect EGFR proteins already containing mutations associated with sensitivity to EGFR tyrosine kinase inhibitors, we introduced the specific mutation into EGFR cDNAs that encoded the exon 21 and 19 mutations found in patients 1 and 2, respectively. Corresponding proteins ([i] L858R and L858R plus T790M, [ii] del L747–E749;A750P and del L747–E749;A750P plus T790M, and [iii] wild-type EGFR and wild-type EGFR plus T790M) were then produced by transient transfection with expression vectors in 293T cells, which have very low levels of endogenous EGFR [ 3 ]. Various lysates from cells that were serum-starved and pre-treated with gefitinib or erlotinib were analyzed by immunoblotting. Amounts of total EGFR (t-EGFR) were determined using an anti-EGFR monoclonal antibody, and actin served as an indicator of relative levels of protein per sample. To assess the drug sensitivity of the various EGFR kinases in surrogate assays, we used a Y1092-phosphate-specific antibody (i.e., phospho-EGFR [p-EGFR]) to measure the levels of “autophosphorylated” Tyr-1092 on EGFR in relation to levels of t-EGFR protein. We also assessed the global pattern and levels of induced tyrosine phosphorylation of cell proteins by using a generalized anti-phosphotyrosine reagent (RC-20). Gefitinib inhibited the activity of wild-type and L858R EGFRs progressively with increasing concentrations of drug, as demonstrated by a reduction of tyrosine-phosphorylated proteins ( Figure 4 A) and a decrease in p-EGFR:t-EGFR ratios ( Figure 4 B). By contrast, wild-type and mutant EGFRs containing the T790M mutation did not display a significant change in either phosphotyrosine induction or p-EGFR:t-EGFR ratios ( Figure 4 A and 4 B). Similar results were obtained using erlotinib against wild-type and del E747–L747;A750P EGFRs in comparison to the corresponding mutants containing the T790M mutation ( Figure 4 C). These results suggest that the T790M mutation may impair the ability of gefitinib or erlotinib to inhibit EGFR tyrosine kinase activity, even in EGFR mutants (i.e., L858R or an exon 19 deletion) that are clinically associated with drug sensitivity. Figure 4 EGFR Mutants Containing the T790M Mutation Are Resistant to Inhibition by Gefitinib or Erlotinib 293T cells were transiently transfected with plasmids encoding wild-type (WT) EGFR or EGFR mutants with the following changes: T790M, L858R, L858R + T790M, del L747–E749;A750P, or del L747–E749;A750P + T790M. After 36 h, cells were serum-starved for 24 h, treated with gefitinib or erlotinib for 1 h, and then harvested for immunoblot analysis using anti-p-EGFR (Y1092), anti-t-EGFR, anti-phosphotyrosine (p-Tyr), and anti-actin antibodies as described in Methods. The EGFR T790M mutation, in conjunction with either wild-type EGFR or the drug-sensitive L858R EGFR mutant, prevents inhibition of tyrosine phosphorylation (A) or p-EGFR (B) by gefitinib. Analogously, the T790M mutation, in conjunction with the drug-responsive del L747–E749;A750P EGFR mutant, prevents inhibition of p-EGFR by erlotinib (C). Resistance of a NSCLC Cell Line Harboring Both T790M and L858R Mutations to Gefitinib or Erlotinib To further explore the functional consequences of the T790M mutation, we determined the sensitivity of various NSCLC cells lines grown in the presence of either gefitinib or erlotinib, using an assay based upon Calcein AM. Uptake and retention of this fluorogenic esterase substrate by vehicle- versus drug-treated live cells allows for a comparison of relative cell viability among cell lines [ 20 ]. The H3255 cell line, which harbors the L858R mutation and no other EGFR TK domain mutations ( Table 2 ), was sensitive to treatment with gefitinib, with an IC 50 of about 0.01 μmol ( Figure 5 ). By contrast, the H1975 cell line, which contains both L858R and T790M mutations ( Table 2 ), was approximately 100-fold less sensitive to drug, with an IC 50 of about 1 μmol ( Figure 5 ). In fact, the sensitivity of H1975 cells was more similar to that of H2030, which contains wild-type EGFR (exons 18 to 24) and mutant KRAS ( Figure 5 ). Very similar results were obtained with erlotinib ( Figure S3 ). Figure 5 Sensitivity to Gefitinib Differs Among NSCLC Cell Lines Containing Various Mutations in EGFR or KRAS The three indicated NSCLC cell lines, H3255 (L858R mutation), H1975 (both T790M and L858R mutations), and H2030 (wild-type EGFR, mutant KRAS ) (see Table 2 ), were grown in increasing concentrations of gefitinib, and the density of live cells after 48 h of treatment was measured using a Calcein AM fluorescence assay. Fluorescence in vehicle-treated cells is expressed as 100%. Results are the mean ± standard error of three independent experiments in which there were four to eight replicates of each condition. Similar results were obtained with erlotinib (see Figure S3 ). Discussion Specific mutations in the tyrosine kinase domain of EGFR are associated with sensitivity to either gefitinib or erlotinib, but mechanisms of acquired resistance have not yet been reported. Based upon analogous studies in other diseases with another kinase inhibitor, imatinib, a single amino acid substitution from threonine to methionine at position 790 in the wild-type EGFR kinase domain was predicted to lead to drug resistance, even before the association of exon 19 and 21 mutations of EGFR with drug responsiveness in NSCLC was reported. The T790M mutation was shown in vitro in the context of wild-type EGFR to confer resistance to gefitinib [ 21 ] and a related quinazoline inhibitor, PD153035 [ 22 ]. We show here, through molecular analysis of tumor material from three patients and one NSCLC cell line, as well as additional biochemical studies, that acquired clinical drug resistance to gefitinib or erlotinib is indeed associated with the T790M mutation. Importantly, we find that the T790M mutation confers drug resistance not just to wild-type EGFR but also to mutant EGFRs associated with clinical responsiveness to EGFR tyrosine kinase inhibitors [ 1 , 2 , 3 ]. Our results further demonstrate that an analogous mechanism of acquired resistance exists for imatinib and EGFR tyrosine kinase inhibitors ( Table 3 ), despite the fact that the various agents target different kinases in distinct diseases. Table 3 Analogous Mutations in Four Kinases Associated with Resistance to Kinase Inhibitors In tumors from patients not treated with either gefitinib or erlotinib, the 2369 C→T mutation (T790M) appears to be extremely rare. We have not identified this mutation in 155 tumors (see above), and among nearly 1,300 lung cancers in which analysis of EGFR exons 18 to 21 has been performed [ 1 , 2 , 3 , 4 , 5 , 6 ], only one tumor (which also harbored an L858R mutation) was reported to contain the T790M mutation. Whether the patient from which this tumor was resected had received gefitinib or erlotinib is unclear, and the report did not note an association with acquired resistance to either drug [ 5 ]. How tumor cells bearing the T790M mutation emerge within gefitinib- or erlotinib-treated patients is a matter of investigation. Subclones bearing this mutation could arise de novo during treatment . However, based upon analogous studies in CML, it is also possible that NSCLC subclones bearing this secondary mutation pre-exist within the primary tumor clone in individual patients, albeit at low frequency [ 23 ]. In either scenario, treatment with gefitinib or erlotinib subsequently allows these resistant subclones to become apparent, because most cells bearing sensitivity-conferring mutations die, while cells with the T790M mutation persist. From analysis of the crystal structure of the EGFR kinase domain bound to erlotinib, it is has been shown that the wild-type threonine residue at position 790 is located in the hydrophobic ATP-binding pocket of the catalytic region, where it forms a critical hydrogen bond with the drug [ 24 ]. The related compound, gefitinib, is predicted to interact with this threonine residue as well. Substitution of the threonine at position 790 by a larger residue like methionine would probably result in steric clash with the aromatic moieties on these two drugs [ 25 ]. By contrast, ATP would likely not depend on the accessibility of the same hydrophobic cavity and is therefore probably not affected by the incorporation of a bulky methionine side chain [ 25 ]. Consistent with this, the T790M mutation has been shown not to abrogate the catalytic activity of wild-type EGFR [ 22 ]. The T790M mutation could also affect the kinase activity or alter the substrate specificity of mutant EGFRs, such that a proliferative advantage would be conferred upon cells bearing the mutation. Consistent with this, the H1975 NSCLC cell line reported here to contain both T790M and L858R did not to our knowledge undergo any prior treatment with gefitinib or erlotinib; the doubly mutated cells must have become dominant over time through multiple passages in vitro. This scenario could explain the seemingly contradictory report by others who found the H1975 cell line to be highly sensitive to gefitinib [ 18 ]; our H1975 cells could represent a subclone that emerged over time. Analysis of earlier passages of H1975 cells for the T790M mutation would be informative in this regard. Recently, new small-molecule inhibitors have been identified that retain activity against the majority of imatinib-resistant BCR-ABL mutants. The new drugs bind to ABL in an “open” conformation, as opposed to imatinib, which binds ABL in a “closed” conformation [ 12 , 13 ]. Analogously, it may be possible to find EGFR tyrosine kinase inhibitors that bind to the EGFR kinase domain in different ways than gefitinib and erlotinib. For example, the crystal structure of another EGFR inhibitor, lapatinib (GW572016), was recently solved bound to EGFR [ 26 ]. This study revealed that the quinazoline rings of erlotinib and lapatinib interact differently with the EGFR kinase domain, suggesting that while the T790M mutation may affect inhibition by erlotinib and gefitinib, it may not affect inhibition of EGFR by compounds similar to lapatinib. To our knowledge, no NSCLC patient who initially responded to but then progressed on either gefitinib or erlotinib has yet been treated with lapatinib. In some of the patient specimens analyzed, the actual sequencing peaks demonstrating the T790M mutation were smaller than originally anticipated. These results differ from those of acquired resistance mutation in CML [ 10 ], GIST [ 15 , 27 ], and HES [ 16 ]. However, in contrast to all of these diseases, in which tumor cells are readily accessible, lung-cancer-related tumors are more difficult to access, as illustrated by the limited manner in which we were able to obtain tumor cells from various sites of disease (see Figure 1 ). Moreover, re-biopsy of patients with lung cancer is not routinely performed. The use of position emission tomography scans to identify the most metabolically active lesions for biopsy could possibly circumvent this factor in the future, as long as such lesions are resectable. Additionally, as more molecularly tailored treatment options become available for lung cancer, re-biopsy of progressive sites of disease should become a standard procedure, especially for patients on clinical trials of targeted agents. Since tumor specimens from three additional patients with acquired resistance to EGFR tyrosine kinase inhibitors did not demonstrate the T790M mutation, this specific lesion does not account for all mechanisms of acquired resistance to gefitinib or erlotinib. Given the paradigm established with imatinib, other drug-resistance mutations in EGFR, either within or outside the tyrosine kinase domain, are likely to exist. It is also possible that EGFR amplification itself plays a role in acquired resistance, since imatinib-resistant clones have been shown to lack resistance mutations but contain amplified copies of BCR-ABL [ 11 , 28 ]. Nonetheless, studies presented here provide a basis for the rational development of “second generation” kinase inhibitors for use in NSCLC. Supporting Information Figure S1 Imaging Studies from Patients 1, 2, and 3 (A) Patient 1. Serial chest radiographs from before (day 0) and during gefitinib treatment (14 d and 9 mo), demonstrating initial response and subsequent progression. (B) Patient 2. Serial CT studies of the chest before (day 0) and during erlotinib treatment (4 mo and 25 mo), demonstrating initial response and subsequent progression. (C) Patient 3. Serial chest radiographs before (day 0) and during adjuvant gefitinib treatment (3 mo), following complete resection of grossly visible disease. The left-sided pleural effusion seen at 3 mo recurred 4 mo later, at which time fluid was collected for molecular analysis. (951 KB PPT). Click here for additional data file. Figure S2 Sequencing Chromatograms with the EGFR Exon 19 and 21 Mutations Identified in Patients 1 and 2 (A) Status of EGFR exon 21 in tumor specimens from patient 1. DNA from the growing lung lesion and the pleural effusion demonstrated a heterozygous T→G mutation at position 2573, leading to the common L858R amino acid substitution. (B) All three specimens from patient 2 showed the same heterozygous exon 19 deletion, removing residues 747–749 and changing the alanine at position 750 to proline. (104 KB PPT). Click here for additional data file. Figure S3 Sensitivity to Erlotinib Differs among NSCLC Cell Lines Containing Various Mutations in EGFR or KRAS See legend for Figure 5 . (153 KB PPT). Click here for additional data file. Protocol S1 Memorial Sloan-Kettering Cancer Center IRB Protocol 04–103 (566 KB PDF). Click here for additional data file. Accession Numbers The LocusLink ( http://www.ncbi.nlm.nih.gov/LocusLink/ ) accession number for the KRAS2 sequence discussed in this paper is 3845; the GenBank ( http://www.ncbi.nlm.nih.gov/Genbank/ ) accession number for the KRAS2 sequence discussed in this paper is NT_009714.16. Reference EGFR sequence was obtained from LocusLink accession number 1956 and GenBank accession number NT_033968. Patient Summary Background Normal cells in our body have safety mechanisms that keep them from growing out of control. Tumor cells have somehow found ways around these safety mechanisms, in some cases through activating particular growth-promoting genes. One of these, the EGFR gene, is often activated in lung cancer. Two drugs, gefitinib (also known as Iressa) and erlotinib (also called Tarceva), have been developed to inhibit activated EGFR, and studies have shown that they can shrink tumors in some patients. Most patients who respond to these drugs have tumors that carry an alteration (or mutation) in the EGFR gene, which somehow makes their tumors responsive to the drugs. Why Was This Study Done? In those patients in whom the drugs work, the tumors shrink initially, but after a while they stop responding and the cancer comes back. The cancer has, as researchers describe it, become resistant to the drugs. Understanding how tumors become resistant is important to develop new and better drugs. What Did the Researchers Do? They asked patients who initially responded to erlotinib or gefitinib but then became resistant to consent to studies allowing further analysis of tumor tissue during and after drug treatment. They then re-examined the EGFR gene in these tumor samples. What Did They Find? They found that tumors from all patients carried mutations in the EGFR gene that are known to make them responsive to the drugs. In addition, three of the post-treatment tumors had an identical second mutation in their EGFR gene. Biochemical studies showed that these secondary alterations made the original drug-sensitive EGFR less sensitive to drug treatment. The numbers are small but suggest that this secondary resistance mutation could be quite common. Tumor cells from the three other patients didn't have this mutation, which suggests that there are other ways for lung cancers to become resistant to gefitinib and erlotinib. What Next? Larger studies are needed to confirm that this particular mutation is a major cause of resistance against the two drugs. It is also important to find out what causes resistance in the other cases. And knowing about this resistance mutation will help researchers to develop drugs that will work even against tumors with the mutation. More Information Online The following pages contain some information on the EGFR kinase inhibitors. U. S. Food and Drug Administration information page on Iressa (gefitinib): http://www.fda.gov/cder/drug/infopage/iressa/iressaQ&A.htm Cancer Research UK information page about erlotinib (Tarceva): http://www.cancerhelp.org.uk/help/default.asp?page=10296

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Wearable Conductive Fiber Sensors for Multi-Axis Human Joint Angle Measurements

Background The practice of continuous, long-term monitoring of human joint motion is one that finds many applications, especially in the medical and rehabilitation fields. There is a lack of acceptable devices available to perform such measurements in the field in a reliable and non-intrusive way over a long period of time. The purpose of this study was therefore to develop such a wearable joint monitoring sensor capable of continuous, day-to-day monitoring. Methods A novel technique of incorporating conductive fibers into flexible, skin-tight fabrics surrounding a joint is developed. Resistance changes across these conductive fibers are measured, and directly related to specific single or multi-axis joint angles through the use of a non-linear predictor after an initial, one-time calibration. Because these sensors are intended for multiple uses, an automated registration algorithm has been devised using a sensitivity template matched to an array of sensors spanning the joints of interest. In this way, a sensor array can be taken off and put back on an individual for multiple uses, with the sensors automatically calibrating themselves each time. Results The wearable sensors designed are comfortable, and acceptable for long-term wear in everyday settings. Results have shown the feasibility of this type of sensor, with accurate measurements of joint motion for both a single-axis knee joint and a double axis hip joint when compared to a standard goniometer used to measure joint angles. Self-registration of the sensors was found to be possible with only a few simple motions by the patient. Conclusion After preliminary experiments involving a pants sensing garment for lower body monitoring, it has been seen that this methodology is effective for monitoring joint motion of the hip and knee. This design therefore produces a robust, comfortable, truly wearable joint monitoring device.

Background Long-term measurement of human movement in the field is an important need today [ 1 ]. For many types of rehabilitation treatment, it is desirable to monitor a patient's activities of daily life continuously in the home environment, outside the artificial environment of a laboratory or doctor's office [ 2 ]. This type of monitoring is quite beneficial to the therapist, allowing a better assessment of human motor control, and tremor or functional use of a body segment, over long periods of time [ 1 ]. Evaluating a patient's daily life activities allows a more reliable assessment of a patient's disabilities, and aids in developing rehabilitation treatments and programs, as well as assessing a treatment's effectiveness [ 2 , 3 ]. In addition, the recognition of deviations in joint movement patterns is essential for rehabilitation specialists to select and implement an appropriate rehabilitation protocol for an individual [ 4 , 5 ]. Many specific medical applications benefit from the information provided by continuous human movement monitoring. To better develop and optimize total joint replacements, for instance, a detailed record of a patient's daily activities after such a replacement is required [ 6 ]. The measurement of tremor and motor activity in neurological patients has long been studied [ 7 ]. In pulmonary patients, it is often desirable to precisely quantify the amount of walking and exercise performed during daily living, since this is a fundamental goal in improving physical functioning and life quality [ 3 ]. Furthermore, physiological responses, such as changes in heart rate or blood pressure, often result from changes in body position or activity, making the assessment of posture and motion an essential issue in any type of continuous, ambulatory monitoring [ 8 ]. Presently, there is no satisfactory solution for long-term, human movement monitoring in the field. The use of video and optical motion analysis systems offer the most precise evaluation of human motion, but obviously restrict measurements to a finite volume [ 9 ]. Body mounted sensors such as accelerometers and pedometers are used for monitoring daily physical activity, but those devices are unable to detect the body posture and are often limited in reliability and applicability [ 3 , 7 ]. Even methods of self-report designed to gather information on general daily activity, such as diaries or questionnaires, are time consuming and often unreliable, especially for the elderly relying on their memory [ 3 ]. Electrogoniometers are frequently used to measure dynamic, multi-axis joint angle changes in individuals, providing continuous joint movement information. These devices, however, are not desirable for long-term monitoring of daily living, since they are exoskeletal devices that cross the joint, potentially interfering with movement. Furthermore, any shift from their original placement leads to errors in angle estimations [ 2 ]. Such commercially available goniometers can produce erratic readings once the device is detached from the patient body and put back on the same joint in a slightly different orientation. It is therefore difficult to use these goniometers at home for long periods of time. Other types of goniometric devices have been developed for measuring particular parts of the body. Electronic gloves [ 10 - 13 ], for example, can measure the hand posture accurately, but are often cumbersome to wear for long periods of time. Various types of textile fabrics with integrated sensing devices have also been devised [ 14 , 15 ]. In each of these cases, the sensing devices are traditional strain gauges, carefully attached to an article of clothing. One patented device uses conductive fabrics acting as strain gauges on a garment to emit "effects" such as light or sound based upon a wearer's movements [ 16 ]. While this is a novel wearable device, it is not designed, nor is suitable, for long-term accurate joint angle measurement. For all types of body-mounted sensors, the issues of comfort and wearability are of major importance, if a patient has to wear the monitoring device for extended periods of time. Furthermore, such home-use wearable sensors need to be put on and off every day without close supervision of a medical professional. Proper registration of the sensor is therefore a crucial requirement for deploying wearable sensors to the home environment. The goal of this paper is therefore to develop a new method for continuous monitoring of human movement by measuring single or multi-axis joint angles with a wearable sensing garment that is non-intrusive and non-cumbersome and that can be properly registered for reliable monitoring. A new method is presented here for joint monitoring using conductive fibers incorporated into comfortable, flexible fabrics. All that is needed is a one-time calibration with a standard goniometer, and a conductive fiber sensor garment is then able to continuously detect joint movement and measure specific single or multi-axis joint angles. With an array of sensors incorporated into a sensing garment, registration of the sensor occurs automatically each time the garment is worn through only a few simple motions by the wearer. This type of wearable sensor would allow extended home monitoring of a patient, and is no harder to put on than a typical article of clothing. In the following, the principle and design details of this wearable device will be presented, along with effective algorithms for allowing a patient to perform long-term, unsupervised monitoring in the home environment. Experimental feasibility tests will also be presented on a prototype wearable sensor for both single-axis and multi-axis joints. Methods Working Principle The basic principle behind the wearable sensors presented in this paper is as follows: when a particular joint on the human body moves, skin around the joint stretches, along with any clothing surrounding the joint as well. A former study by the textile industry has shown that body movements about joints require specific amounts of skin extension. Lengthwise across the knee for example, the skin stretches anywhere from 35–45% during normal joint movement [ 17 ]. When a particular joint moves, fabric around the joint will either expand or contract accordingly, assuming the fabric is form fitting to the skin, and has the necessary elastic properties. To assure comfort and freedom of body movement, stretchability of 25 to 30 percent is recommended for fabrics fitting closely to the body [ 17 ]. By incorporating conductive fibers into such a fabric surrounding a joint, the conductive materials will necessarily change length with joint movement. The electrical resistance of the conductive material will change as well, and can be directly measured and correlated to changes in the orientation of the joint. Figure 1 shows how a single conductive fiber is implemented as a sensor. One end of the conductive fiber is permanently attached to the nonconductive, form-fitting fabric substrate at point A in the figure. Along the conductive fiber, there is a wire contact point at B that is permanently stitched into the fabric. The other end of the conductive fiber, point C , is kept in tension by a coupled elastic cord, which is permanently attached to the remote side of the joint, point D . Therefore, any stretching in this coupled material will take place in the highly elastic cord, CD , and not in the conductive fiber AC . As the joint moves, the elastic cord will change length, causing the coupled conductive fiber to freely slide past the wire contact point at B that is stationary. The conductive fiber always keeps an electrical contact with this wire, but the length of conductive thread between points A and B will change as the joint rotates. The resistance, which is linearly related to length, is then measured continuously across these two points A and B . Figure 1 Sensor Design Schematic. This particular sensor arrangement shows one sensor thread running lengthwise across a single-axis knee joint. Predictor Design Consider Figure 2 . Shown here are a sensor spanning across a single axis knee joint, and a pair of sensors about a double axis hip joint. The angles of interest are labeled θ 1 , θ 2 , and θ 3 . Our goal is to estimate these joint angles based upon the output of sensors 1, 2, and 3. Figure 2 Lower Body Sensors. Schematic of three sensors positioned to measure three lower body joint angles. Preliminary experiments have shown a clear relationship between joint angle and sensor output for individual sensors about various joints of the body. Figure 3 , for instance, shows a typical set of output data from a single sensor thread across a single-axis knee joint with the output "zeroed" for a joint angle of 0°. Figure 3 Sensor Output Curve. Preliminary data showing sensor output vs. knee flexion angle. It is desired to design a filter that receives sensor signals as inputs, and predicts the joint angle(s) of interest. In the proposed method, each joint angle being monitored has a corresponding single sensor that is situated about that particular joint for maximum sensitivity, as in Figure 2 . Consider N axis sensors for measuring N joints, each consisting of a single thread sensor, as shown in Figure 2 . The simplest predictor model that can be used is a linear regression: where is the N × 1 vector of N joint angle predictions, is its bias term, y = ( y 1 … y N ) T is the N × 1 vector of corresponding sensor readings, and G and are, respectively, the N × N matrix and the N × 1 vector experimentally determined to relate the inputs and the outputs. Since there is a slight amount of curvature in the preliminary data of Figure 3 , a nonlinear predictor may be more effective. We will use a second order polynomial model where and G ' is an N × N ( N -1)/2 experimentally determined matrix. The three terms on the right hand side of the above equation can be incorporated into a homogeneous expression using augmented matrix and vector: where W and Y are W = (θ 0 G G') (5) To determine the parameter matrix W , a least squares regression is performed using m sets of experimental data from a collection of sensors on an individual patient. Let P be a N × m matrix consisting of m sets of experimentally measured joint angles, and B be a {1 + N ( N + 1)/2} × m matrix containing the corresponding sensor outputs and their quadratic terms: B = (Y (1) … Y ( m ) )     (8) The optimal regression coefficient matrix W * that minimizes the squared prediction errors is given by W * = PB T (BB T ) -1 (9) if the data are rich enough to make the matrix product BB T non-singular. The above expressions are the most general forms for N axis sensors. In practice, however, they can be reduced to a compact expression with lower orders. First the offset can be eliminated from the coefficient matrix W , if the sensor outputs are zeroed at a particular posture, e.g. the one where the extremities are fully extended. Second, although the matrix G contains off-diagonal elements representing cross couplings among multiple joints, some joints have no cross coupling with other joints. For example, the measurement of the knee joint can be performed separately from that of the hip joints. If the j -th joint is decoupled from all others, it can be treated separately as: where the offset is eliminated. Third, although multiple joints are coupled to each other having non-zero, first-order off-diagonal coefficients in matrix G , their second-order cross coupling terms, e.g. y j y k , can be negligibly small with proper design of individual sensors. In such a case, two coupled joints, say j and k , can be written as: where the offset terms have been eliminated. Thus the number of parameters to identify through calibration experiments is reduced. In consequence, the dimension of the optimal coefficient matrix must be reduced accordingly. The same calibration procedure is performed for both single axis and multiple axis cases, and need be performed only once for a specific set of sensors on an individual. Although one sensor is sufficient to capture single-axis joint motion, any misalignment of such a sensor from use to use will lead to erroneous measurements. From a practical standpoint, it is obvious that a method is needed to adjust for any shifting of a sensor about the joint that will take place from one use to the next. It is both undesirable and impractical to recalibrate the whole sensor every time the patient takes off the sensing garment and places it back again. To take care of such registration problems, an array of multiple sensor threads is used. By incorporating multiple threads in a known pattern, a template-matching algorithm can be performed to determine a sensor's offset from calibration. In this way, measurement errors due to sensor misalignment are significantly reduced. The details of this method are described in the next section. Sensor Registration for Single Axis Joints The goal in designing these wearable sensors is to create a device that is ultimately self-registering for subsequent uses after the initial one-time calibration experiments. This means that no additional equipment is needed to register the sensors for each use. Also, it is important that any procedures that are needed for self-registration are simple, and able to be preformed by the patient without supervision. To achieve these goals, a multi-thread sensor array design is presented. First, consider an array of M sensors covering a single-axis joint as shown in Figure 4(a) . Each sensor thread is separated from the adjacent sensor thread by a known, constant distance, d . This multi-thread sensor array is used to estimate a single-axis joint angle, θ j . To develop a registration procedure let us first calibrate each sensor thread individually. Let be the estimate of the j -th joint based on the i -th thread sensor given by Figure 4 Sensor Arrays. (a) Array of equidistantly spaced sensors over knee joint. (b) Array shifted by an unknown distance, α . where and is the 1 × 2 regression vector that is optimized for the i -th single-thread sensor of the j -th joint placed at a home position. Now consider the situation where the sensor array has been removed, and placed back on the joint for more measurements. The sensor array is now offset an unknown distance, α , from the original position where calibration was performed. See Figure 4(b) . Since the individual single-thread sensors in the array are equally spaced, each sensor thread is shifted from its home calibration position by the same distance α . Assuming that the individual sensor threads are identical other than being separated by a distance d , we can conclude that the pattern of the sensitivity array is a shifted version of the calibrated one, as shown in the simplified plots of Figure 5 . This reduces the self-registration problem to a type of pattern matching problem. Figure 5 Sensitivity Shifts. (a) Array of equidistantly spaced sensors over knee joint, with each sensor having unique sensitivity in this calibration position. (b) Shifting of array by an unknown distance, α , will lead to a shift in sensitivities. will no longer be the appropriate regression matrix to estimate θ j from Y j ( i ). A new, unknown vector will instead relate the sensor output to θ j : Although is unknown, each individual sensor in the array should ideally give the same estimate for the actual joint angle at any time, so that If the shifting of the sensor array were to happen in a discrete fashion, α = nd (16) where n is an integer value, it is seen that Since n is an unknown, it is desired to find an n that satisfies (15) and (17), rewritten as where n is assumed to be | n | < M - 1. Namely, the sensor array, although shifted, can still cover the joint, having an overlap with the original sensor at the home position. In the ideal, theoretical case, there will exist an integer n that can be found to exactly solve (18). Unfortunately, for practical usage, n will not be a discrete integer. Furthermore, n cannot be explicitly found since process and measurement noise will cause the sensor outputs to deviate from their "ideal" values. With the knowledge of for i = 1 ~ M , however, it is possible to find the optimal integer n that best solves (18). Let us first define the average joint angle estimate for M threads of sensor outputs for a given integer n as follows (with Y and H * reducing to scalars for the linear case): The best estimate for n is found by minimizing the average squared error between each sensor's estimate and the average estimate with respect to n (i.e. reducing the variance in the estimated angle as a function of n ): Equations (20a) and (20b) are solved for n = - M +2, - M +3, ..., M -3, M -2. The value for found from (20c) is then used in (17) to approximate each sensor's predictor regression matrix for this new offset position of the array. In the ideal discrete case, where α = n o d , n o is the discrete offset of the sensor array, = n o , and R j ( ) = 0. For the non-ideal case, where a is not a discrete multiple of d , the minimum variance is not zero, R j ( ) ≠ 0, but it will decrease as M increases, and d decreases. Creating a denser sensor array in this way leads to more accurate estimates of sensor sensitivities, which in turn leads to more accurate estimates of θ j . Furthermore, since can always be approximated using this algorithm, a one-time calibration is all that is needed for these wearable sensors to be used by a patient. The registration algorithm takes place in real time as the sensor is in use. All that is needed for a patient to begin using these sensors is to first "zero" the sensor output with the joint fully extended in the 0° position, and then freely move the joint to obtain non-zero data. This non-zero data will then allow the self-registration to take place. While registration is not needed at all times, it should be performed during initial operation until an appropriate is converged upon. Again, the denser the array of sensors used, the better the estimate obtained. Following this, the algorithm need not be performed as often, as long as the sensor array remains stationary for an individual use. To begin monitoring, it is assumed that = 0. Sensor Registration for Double Axis Joints In the double axis case, two sensor arrays are placed around a predominantly two-axis joint such as the hip. As in the single-axis case, each array contains M sensor threads equally spaced by a distance d . The j -th joint array is placed so that it is most sensitive to changes in θ j , while the k -th joint array is situated so that it is most sensitive to changes in θ k . For registration, let the patient move only one axis at a time. As illustrated in Figure 6-(a) , the patient is instructed to move axis θ 1 alone. This hip flexion/extension causes significant changes to sensor array 1, y 1 ( i ), i = 1 ~ M . Next the patient is instructed to make hip abduction/adduction ( θ 2 ) alone, which causes significant changes to sensor array 2, as shown in Figure 6-(b) . Until registration has been completed, the estimate of the joint angles is not accurate. However, it is possible to distinguish which joint, θ 1 or θ 2 , has been moved, since sensor array 1 is most sensitive to θ 1 , and sensor array 2 for θ 2 . Once the individual axis movements are detected, the same registration procedure as that of a single axis can be applied to determine the misalignment of each sensor array. Once the misalignment is determined, the corrected, optimal predictor can now be used for verifying whether the registration has been performed correctly based on individual axis movements. Figure 6 Registration Procedure for Hip Sensor Array. For registration of individual sensor arrays, the patient moves only one axis at a time (a) flexion/extension, and (b) abduction/adduction. This registration method reduces the multi-axis problem to individual single axis procedures. However, the single axis procedures do not have to be repeated for all axes, if they are tightly related. For the two hip axes in Figure 6 , a shifting of one sensor array around the body will be accompanied by a nearly identical shift in the second array. Therefore, registering one array will also register the other. In this case, it is required that a patient performs only one simple movement when first putting on the sensors – extending the joint about a single axis over a sufficient range. Results All experiments have been conducted under a protocol approved by the Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects (Approval No. 0411000960). Wearable Prototype Garment Figure 7 shows a prototype pair of spandex pants with conductive fibers incorporated into the fabric to measure lower body movement. Spandex was chosen due to its favorable qualities: very stretchable, elastic, fits closely to the skin, and is able to withstand normal body movements and return to its original shape with no permanent distortions [ 17 ]. Furthermore, it is a comfortable material, able to be worn on a daily basis since it does not restrict movement in any way. Thus it is quite suitable for this sensor design. Figure 7 Prototype Sensing Garment. Spandex pants with conductive fiber sensors for lower body monitoring. In these particular pants, an array of eleven sensors spans across the knee joint, each separated by a distance of 5 mm, and each with an unstretched length of 55 cm. The sensors threads were silver plated nylon 66 yarn, which had an impedance of approximately 3.6 Ω/cm. Single sensors span both the posterior and side of the hip as well to capture two axes of hip motion. These single sensors are not seen in the view of Figure 7 , but the locations are the same as those shown for sensors 1 and 2 in the schematic of Figure 2 . This is the sensing garment used for all experimental tests. Preliminary Experiments To get an idea of the capabilities of existing technology available for joint monitoring, tests were initially performed using a standard electrogoniometer. Figure 8 shows the set-up of the preliminary experiments. The goniometer used was a BIOPAC TSD130B Twin Axis Goniometer that consisted of two telescoping end-blocks that were taped to the side of the leg on either side of the knee joint. A strain gauge between these blocks was the device that measured the joint angle. The goniometer was used to measure knee flexion angle for two discrete positions. An untrained professional attached the goniometer to the leg, but followed the recommended attachment procedures as described by the vendor in the instruction manual. This was to simulate the knowledge of a typical patient who would be using such a device on his or her own, outside a carefully controlled setting. Figure 8 Preliminary experiments set-up. Measurements were taken from a standard electrogoniometer at Position 1 (0°) and Position 2 (50°) The goniometer was taken off and placed back on the knee joint eight separate times. Each time the goniometer was put on, the leg was extended (Position 1) and the goniometer output was set to 0°. The leg was then slowly bent to Position 2 (50°) and the goniometer output was recorded. The average rms error between the goniometer output, and the known joint angle (50°) for these tests was 3.5° with a standard deviation of 2.6°. Even with the goniometer placed on the same joint by the same person, these results illustrate the fact that slight changes in how the goniometer is attached can lead to varying measurements. It will be important to keep errors such as these in mind when the results from the conductive fiber sensor are analyzed. Having just discussed the possible errors introduced by a standard electrogoniometer, it is important to also highlight the possible errors introduced by a conductive fiber thread sensor. Consider again Figure 3 , which shows sensor output vs. knee flexion angle for one thread sensor on the pants garment when the knee was randomly swung over a large range of motion. As can be seen, there is a significant amount of variation possible in sensor output for a given joint angle. In particular, for threads over the knee joint, the average rms error between curves such as those shown in Figure 3 , and the calibrated predictor curves from (10) was approximately 3°-5° over the many tests performed. Therefore, it is noted upfront that errors will be introduced based solely on the type of measuring device being used due to hysteresis, material uncertainties, and other processes that cannot be accurately modeled. This should be kept in mind when using such a wearable device. Single Axis Results The pants sensing garment was first used to estimate single-axis knee angle measurements. For the following single-axis experiments, a rotary potentiometer firmly attached to the leg was used as a goniometer, and this was the standard for which to compare joint angles. In each experiment, the potentiometer was "zeroed" with the leg in the full extension position. A calibration was performed to find the optimal regression matrix for both the linear and nonlinear predictors, and a sequence of knee movements was then monitored with the sensors. Figure 9 shows the results of a typical sequence of these knee measurements, comparing the estimated angle from the conductive fiber sensors using the predictor models to that of the rotary potentiometer firmly attached to the leg. Figure 9 Sensor outputs – Comparison of goniometer measured knee joint angle and estimated angles from wearable conductive fiber sensor. The performance of the pants sensors can be seen to be quite good, accurately capturing the joint movement patterns over time. The average rms error between the pants sensor estimate and the potentiometer using the linear predictor was 5.4°, while that for the quadratic predictor was significantly better, at just 3.2°. It is important that these sensors are able to measure all types of motion, including higher frequency motion. To determine the frequency capabilities of the prototype fiber sensors, tests were performed where the leg was swung back and forth at different frequencies. The resulting sensor estimations, and errors when compared to the potentiometer, are summarized in Figure 11 and Table 1 respectively. Figure 11 Self-Registration Results. Joint angle measurements with sensing garment taken off and put back on before each test. Table 1 Sensor Frequency Capability Results Approximate Frequency (Hz) Average RMS Error (degrees) 0.1 3.8 0.5 6.6 1 5.5 1.5 4.9 2 7.1 Fiber sensor thread errors for various frequencies of joint motion. From these results, it is seen that the sensors are able to track the joint motion for frequencies as high as 2 Hz, but significantly larger errors result as the frequency is increased. Since most gross human motion takes place below these frequencies in a typical day, these sensors are suitable for everyday measurements, but such limitations should be considered if more accurate measurements are desired. Since these sensors are to be worn multiple times by a user, the reliability of registration is important every time the sensors are worn. Therefore, it is important that using the template-matching algorithm with an array of sensors will give an accurate registration each time the sensors are taken off and put back on. To verify this, an initial repeatability test was performed on the prototype sensor pants. The pants sensors were taken off and put back on four separate times to simulate four future uses of the sensors after an initial calibration test. The knee joint was moved over a wide range of motion in each instance. The joint angles measured by the fiber sensors for each test are shown in 11. The errors between these measurements and the potentiometer measurements are summarized in Table 2 . Table 2 Sensor Self-Registration Results Test Number Average RMS Error (degrees) 2 5.7 3 8.6 4 8.5 5 11.6 Fiber sensor thread errors for successive tests where pants have been taken off and put back on. Again, the sensors are able to capture the overall motion of the knee in each case, but appear to give less accurate results each time the pants are worn. For this reason, while a completely self-calibrating sensor is always desirable, it may be necessary to re-calibrate the sensors after many uses for more accurate measurements. Double Axis Results Figure 12 shows sensor outputs for a sequence of semi-random leg movements. In this case, output was captured from sensors y 1 and y 2 , spanning the posterior and lateral side of the hip, respectively (see Figure 2 ). In the first segment of motion, the leg was kept fully extended in the sagitall plane, and the subject performed a flexion/extension three times ( θ 1 varies, while θ 2 = 0). In the second segment, leg movement was allowed only in the frontal plane, while the subject performed an abduction/adduction movement three times ( θ 2 varies, θ 1 = 0). Figure 12 Multi-Axis Sensor Outputs. Hip sensor outputs for two distinct leg motions. In each case, the sensor spanning the axis in which the angle changes took place was the most sensitive to change, as expected. Each joint motion also produced small, but not insignificant, cross-coupling outputs in the "remote" sensors as well, showing that a single sensor output is dependent on multiple joint angles, and not one single angle. The pants sensor threads about the hip joint were then calibrated with the twin-axis goniometer. Table 3 shows the calibration matrix obtained per (9) using the predictor expression of (11). As can be seen, the first-order diagonal terms are dominant, with the cross-coupling terms significant, but not as dominant. The third and higher-order non-linearities were found to be insignificant compared to the values shown, and thus a second order predictor of the form of (11) seemed sufficient. Table 3 Calibration Matrix y 1 y 2 2.86 0.27 0.04 -0.24 1.32 3.83 -0.29 0.17 Calibrated parameter matrix for two hip sensor threads on one individual. After initial calibration, random leg movements were then monitored with the sensors. Figure 13 shows the results of a typical sequence of the resulting hip angle measurements. Again, the estimated angles from the conductive fiber sensors using both a linear and quadratic predictor are compared to that of a twin-axis goniometer. Figure 13 Hip Joint Measurement Results. Comparison of goniometer measured hip joint angles and estimated angles from wearable conductive fiber sensors: (a) Hip flexion/extension, (b) Hip abduction/adduction. The pants sensors were again able to capture the joint movement patterns over time, in this case for two axes of motion. The average rms error between the pants sensors' estimate of hip flexion angle and the goniometer's was 2.5° using the linear predictor and 2.4° using the quadratic predictor. For hip abduction, these errors were 2.1° and 1.7° respectively. In this double axis case, the differences between the linear and quadratic predictors were not very significant over the typical ranges of hip joint angles measured. Previously, the assumption was made for the double axis hip joint that both sensor arrays would be offset from their calibration position by the same amount for each use. This allowed the double axis registration to be reduced to a single axis registration. To verify this assumption, a simple experiment was performed on the pant's hip sensors. The pants were taken off and put back on ten times. Each time, the distance around the waist between the sensor thread on the side of the hip, and the sensor thread on the rear of the hip was measured (distance between Point A and B in Figure 2 ). The average distance measured on a single individual in this way was 12.5 cm, with a standard deviation of 0.1 cm. The greatest discrepancy between any of these ten measurements was 0.6 cm (Maximum was 12.8 cm, minimum was 12.2 cm), which is approximately the same distance that separated the single threads in the array over the knee joint. Therefore, slight errors may result from making this assumption, but overall these errors should not contribute much due to the small variation in this experimental data. Discussion For continuous joint monitoring, it should be noted that there are at least three fundamental sources of uncertainty in sensor output. The resistance measures across a section of conductive fiber, while ideally linearly related to length, may differ from an expected value due to the following factors: 1) movement of the fiber across the wire contact point may affect sensor output due to uncertainty in the area being contacted, and dynamic effects of the constant rubbing action; 2) although the elastic cord takes up a majority of the sensor tension, slight changes will also take place in the fiber tension as the joint is moved, and this will affect fiber resistance; and 3) different sections of even the same fibers will exhibit slightly different resistance characteristics due to the slightly inhomogeneous nature of such fibers. In spite of all these sources of uncertainty, it is still possible to accurately calibrate a set of sensors, and achieve acceptable joint measurements with minimal errors. These effects are minimized through careful selection of the particular fibers used as sensors, and in manufacturing the garment. While two specific predictor models have been presented for the calibration of a set of sensors, there are of course many more candidates that could be used as well. The linear and quadratic models used in this paper were the simplest choices, and the experimental results showed no advantage to adding more terms. Doing so only increased the computational requirements unnecessarily. This is why the models were presented as they were. A few more words should also be said about the registration algorithm. As presented, this algorithm only accounts for shifting of a set of sensors in one direction (particularly, in the "horizontal" direction). It is felt that this is appropriate due to the construction of the sensing garment. With the sensors instrumented in a "vertical" fashion, the user is responsible for visually checking that they put the garment on with no twist. This is relatively easy to do with the fibers oriented vertically. Furthermore, as long as the sensors span well beyond the local effects of skin movement around a joint, small shifts in the vertical direction will theoretically have little to no effect on the sensor output. Requiring a patient to "zero" the sensor output with all joints in the 0° position each time the garment is worn further eliminates any errors due to sensor drift. Finally, the wearability of the pants sensing garment must be addressed. What makes this sensing garment "more wearable" than existing joint measurement devices is that it is simply a pair of pants that people already wear on a regular basis. The extra sensors and wires added to these pants are compact and lightweight, almost negligible to the wearer. These sensors are easy to use, requiring much less skill and carefulness by the user, in general, than a typical goniometer. Conclusion A wearable joint movement sensor design has been presented that uses conductive fibers incorporated into a fabric that is form fitting to a joint. Resistance changes in the fibers caused by fiber movement as the joint is moved can be related to angular joint position. Using multiple fiber sensors, multi-axis joint angles can be determined, in addition to single-axis angles, after a one-time calibration procedure performed by a therapist/physician. Implementing a nonlinear predictor model, continuous joint angle measurements can be made during daily activities, with the sensor able to be taken off and put back on at any time with no need for manual recalibration. Sensor offsets due to misregistration can be accounted for through the use of a sensor array spanning the joints of interest. This allows the sensors to self-calibrate, with only a few simple motions of the patient. After preliminary experiments involving a pants sensing garment for lower body monitoring, it has been seen that this methodology is feasible for monitoring joint motion of the hip and knee. Multiple sensor arrays are used at multi-d.o.f. joints, where each sensor output is coupled to multiple joint angle changes. This design therefore produces a robust, comfortable, truly wearable joint monitoring device. This paper outlines the development of this sensor from initial idea to working prototype. Future effort is needed in developing a completely wearable, highly accurate sensor, though. This would include making the sensors wireless, and therefore "tether-free." More precise textile manufacturing techniques would also be needed to further reduce measurement errors. Competing interests The author(s) declare that they have no competing interests. Authors' contributions PTG developed the ideas discussed in this paper under the guidance of HHA. PTG carried out all experiments. Both authors read and approved the final manuscript. Figure 10 Frequency Variation Results. Joint angle estimations for various frequencies of joint motion.

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Nodular osteochondrogenic activity in soft tissue surrounding osteoma in neurogenic para osteo-arthropathy: morphological and immunohistochemical study

Background Neurogenic Para-Osteo-Arthropathy (NPOA) occurs as a consequence of central nervous system injuries or some systemic conditions. They are characterized by bone formation around the main joints. Methods In order to define some biological features of NPOAs, histological and immunohistological studies of the soft tissue surrounding osteoma and Ultrasound examination (US) of NPOA before the appearance of abnormal ossification on plain radiographs were performed. Results We have observed a great number of ossifying areas scattered in soft tissues. US examination have also shown scattered ossifying areas at the early stage of ossification. A high osteogenic activity was detected in these tissues and all the stages of the endochondral process were observed. Mesenchymal cells undergo chondrocytic differentiation to further terminal maturation with hypertrophy, which sustains mineralization followed by endochondral ossification process. Conclusion We suggest that periosteoma soft tissue reflect early stage of osteoma formation and could be a model to study the mechanism of osteoma formation and we propose a mechanism of the NPOA formation in which sympathetic dystony and altered mechanical loading induce changes which could be responsible for the cascade of cellular events leading to cartilage and bone formation.

Background Neurogenic Para Osteo-Arthropathies (NPOA) occurs in patients with brain or spinal cord injury, hemiplegias, various encephalopathies, tetanus [ 1 ] or neurological disregulation [ 2 ]. In this process, new bone named "osteoma" forms in extraskeletal areas which in normal condition do not ossify. NPOA were first described by Dejerine and Cellier [ 3 ] from observations of medullary wounded soldiers. They proposed the term NPOA, though other terms are used, such as: neurogenic osteoma, ossifying myositis in paraplegic, ectopic ossification, heterotopic ossification, etc. NPOAs have also been described as complications of many systemic diseases [ 4 ], acute pancreatitis, toxic syndromes and others [ 5 ]. The first clinical manifestations are local inflammatory signs, tumefaction and progressively limited range of motion of the involved joint region. Those appear between the second and tenth weeks after the onset of the pathological condition [ 6 ]. Despite anti-inflammatories treatment to prevent NPOA [ 7 ], excision of the newly formed bone called "osteoma", is the only known therapy. As shown by radiographic and scintigraphic observations, heterotopic bone formation evolves from an early appearance of soft tissue densification and attenuation of the muscle signal to a mineral signal [ 8 ]. After 6 months, osteoma rarely increases in amount, but some further maturation occurs. As an assumption based on the fading of technetium fixation, the lesion is supposed to be mature after 1 to 1.5 years [ 9 , 10 ]. Hence, the process of NPOA formation seems to be frozen at the time of osteoma mineralization. Very little is known about the pathophysiology of NPOA formation. Assuming such a freezing of the process of NPOA formation and an involvement of the periosteoma tissues in the reported relapses following surgery, we postulated that the periosteoma soft tissues could show some of the very early stages of the NPOA formation. We performed histological, histochemical and immunohistochemical studies of soft tissues dissected from the periphery of osteomas. We used samples of varying age lesions and searched for the main osteogenic and chondrogenic markers: alkaline phosphatase (ALP) activity, type I collagen and osteocalcin (OCN) for the bone [ 11 - 13 ], and type II collagen, sulfated and acid glycosaminoglycans, type X collagen and Vascular Endothelial Growth Factor (VEGF) for the cartilage [ 14 ]. In the light of our results, we propose a model of NPOA formation. Methods a)Specimen processing and histochemicals The 28 specimens were obtained from 27 patients undergoing orthopedic surgery for osteoma excision. NPOA's were localized on: elbows (7), hips (18) and knees (3). The time from the neurologic insult ranged from 5 months to 216 months. The initial conditions were: 11 Brain Injuries (BI), 3 Spinal Cord Injury (SCI), 1 BI plus SCI, 4 strokes, and 9 patients sustained coma of various etiology (legionellose, anoxia, toxic condition, pneumonia, suicide attempt using neuroplegic). Specimens obtained during the course of surgery, referred to in this paper as "osteoma", were immediately placed in sterile Gibco Hanks' balanced salts solution (Invitrogen, Cergy-Pontoise, France) at 4°C for transportation. The soft connective tissue was easily dissected off from the osteoma in order to exclude any part of the bony mass (Fig 1 ). The specimens were fixed in 4% paraformaldehyde in Phosphate Buffered Saline (PBS) with 0.5 M sucrose, frozen in isopentane in liquid nitrogen and stored at -86°C until embedding in OCT compound (Tissue-Tek, Sakuran Zoeterwoude, The Netherlands). Cryosectioning was performed on a Leica CM 3050 S cryostat (Leica Micro-Systems, Reil-Malmaison, France) at a thickness of 7 μm. Histochemical staining was performed according to standard protocols: Erlich's hematoxylin-eosin for general topographic staining, alcian blue pH1 for sulfated acid glycosaminoglycans, Von Kossa to show calcified areas, Oil red O to identify lipids, and Van Gieson Picro-Fuchsine for collagen distribution. The ALP-activity was demonstrated by using the Sigma procedure n°86 (Sigma Diagnostics, Saint Quentin Fallier, France). Slides were examined on a Leica DMR microscope (Leica Micro-Systems, Reuil-Malmaison, France), and pictures were recorded using a CCD colour camera with the Q Fluoro and Lida software systems (Leica Micro-Systems, Reil-Malmaison, France). Figure 1 Dissection of part of the soft tissue surrounding a piece of osteoma. b)Immunohistochemistry The anti-human monoclonal mouse antibodies against type II collagen (NeoMarkers Inc., CA, USA), OCN (Interchim, Montlucon, France), anti-human polyclonal goat antibody against type I collagen (Santacruz Biotechnology, CA, USA), anti-rat polyclonal rabbit antibody against type X collagen (Calbiochem-Novabiochem, CA, USA) and anti-human polyclonal rabbit antibody against VEGF (Santacruz Biotechnology, CA, USA) were used at 1 mg/ml. Peroxidase-conjugated goat anti-mouse was purchased from Immunovision Technologies (CA, USA), peroxidase-conjugated goat anti-rabbit from Dako (Dako Corporation, CA, USA) and peroxidase-conjugated donkey anti-goat from Santacruz Biotechnology (CA, USA). They were used at a 1/20, 1/50 and 1/100 dilution, respectively. Frozen sections post-fixed with acetone were treated with hyaluronidase (5 mg/ml in Tris Buffered Saline) one hour at 37°C. For type X collagen immunostaining slides were treated first 15 minutes at 37°C with hyaluronidase (5 mg/ml in PBS), washed two times in PBS, then 15 minutes with chondroitinase (2 U/ml in PBS), and washed two times in PBS. Immunohistochemistry was performed using ABC method. Briefly, endogenous peroxydase activity was eliminated with 0.3% H 2 O 2 until total clearing of oxygen bubbles. Non-specific protein binding was performed with 10% non-immune serum, same host as secondary antibody, in PBS with 1% Bovin Serum Albumin (Sigma, Saint Quentin Fallavier, France). Sections were then incubated with primary antibodies for 1 hour at room temperature, or 24 hours at 4°C with anti-type I collagen. Excess antibody was removed by washing the sections with PBS. Sections were incubated 1 hour with horseradish peroxydase-labeled secondary antibody diluted in PBS. 3-3'diaminobenzidine (DAB) solution (Dako Corporation, CA, USA) was then added in order to obtain staining. Sections were counter-stained with hematoxylin-eosin or nuclear red/eosin, dehydrated, and mounted with Mountex medium (Microm, France). Controls were systematically performed omitting the primary antibody. Slides were examined by light microscopy using a Leica DMR microscope (Leica Micro-Systems, Reil-Malmaison, France). c)Ultrasound (US) Examination Most of the patients were referred to Raymond Poincaré teaching hospital, at the time of surgery. US examination was performed when NPOA was clinically suspected in five patients whose rehabilitation has begun. Five hips were explored by US in two BI and three SCI. Linear 8 to 15 MHz and sectorial 4 MHz transducers (Sequoia Acuson-Siemens Erlangen) were used. A sectorial low frequency transducer was used because in the hip area NPOA can be very large and very deep especially in the gluteus area compared to the subcutaneous plane. US examination was combined with color and energy Doppler. In all cases a plain film was obtained the same day as the US examination. Results a)Histological and immunohistological studies Non mineralized connective tissues from the periphery of the osteoma were examined by light microscopy on Erlich's hematoxylin-eosin-stained sections. Several kinds of tissues appeared on the slides so the diversity of these figures deserves a systematic analysis which will be completed in the next sections. Briefly, the ground basis of our preparations was a more or less fibro-cellular connective tissue displaying sometimes edema and/or necrosis. Suffering and degenerating tissues with vacuolized myofibers, thrombotic vessels and adipose tissue were often observed (Fig 2a ). Muscular tissue underwent degeneration as shown by the vacuolisation or the disappearance of the internal eosinophily. Oil red-O stained adipocytes in the vicinity of some degenerating muscle with hyperplastic endomysium and perimysium (Fig 2b ). In these regions, ALP activity was detected in the endomysium and perimysium cells of degenerated muscles (Fig 2b , inset). Figure 2 a: Frozen section of soft tissue from a 72 months hip NPOA : Hematoxylin eosin (h&e): Thrombotic vessels (V), more or less advanced vacuolization/degeneration of muscular fibers (M), and adipous tissue (A). b: Oil Red-O staining : Degenerated muscle fibers (M) stained by eosin were embedded in a strongly hyperplastic perimysium. This structure was itself located inside an adipous tissue whose appearance and compartmentalization by endomysium-like sheets of cell layers suggests a muscular origin. Inset: ALP activity: the same area showed an intense ALP activity in some cells in hyperplastic perimysium. c: Hematoxylin eosin (h&e) staining showed hyperplastic intima and media. Morphologically normal vessels were rarely observed and winding of the vasculature was an almost constant finding. Many of the vessels were thrombotic, sometimes with hyperplastic intimae or media (Fig 2c ). Some perivascular cells showed ALP activity and seemed to migrate out from these proliferating zones (Fig 3 ). Clustered or isolated round cells with high ALP activity were also observed embedded in a high amount of collagen matrix (Fig 3 , inset). Figure 3 Frozen sections of soft tissue of an 8 months hip NPOA : Blue Alkaline Phosphatase (ALP) activity counter stained with nuclear red-eosin: perivascular cells show high ALP activity near by vessels (V) and some of these seems to migrate from these areas. Inset: Cluster and isolated rounded cells (C) with high ALP showed a more advanced stage of differentiation. These findings point to a chondrogenic or osteogenic differentiation of formerly undifferentiated mesenchymal cells from the stroma and the vessel walls. More advanced stages of cartilaginous differentiation were frequently observed in avascular areas with varying degrees of chondrocyte maturation. Morphologically recognizable columns of chondrocyte-like cells presenting a high ALP activity were observed. Moreover, we could observe all the stages of progressive chondrocyte differentiation from quiescence to chondrocyte hypertrophy/matrix mineralization and endochondral ossification (Fig 4 ). Figure 4 Frozen section of soft tissue of an 8 months hip NPOA : Blue Alkaline Phosphatase (ALP) activity counter stained with nuclear red-eosin: The successive stage of chondrocytes differentiation were observed: quiescent (Q), proliferative (P), prehypertrophic (D), hypertrophic (H) and mineralization zones. Inset: Lamellar bone deposition (L) is visualized with polarized light. A high ALP activity was also found in cells surrounding the cartilage areas undergoing mineralization and embedded in a slight envelope of woven bone with ALP positive cells (Fig 5a ). On the other hand, ALP activity of hypertrophic chondrocytes was progressively lost as mineralization occurred, thus Von Kossa staining seemed to be a negative image of ALP activity (Fig 5b ). Figure 5 Frozen sections of soft tissue of a 5 months elbow NPOA : a: Blue Alkaline Phosphatase (ALP) activity counter stained by nuclear red-eosin: A very strong ALP activity in multilayered cells surrounded a peripherically mineralized area. This area was made of woven bone (W) deposed on an hypertrophic cartilage (H) centered by prehypertrophic chondrocytes. Hypertrophic and prehypertrophic chondrocytes in the non-mineralized matrix displayed ALP activity. b: Vonkossa staining of a next section counter stained by h&e: Von Kossa stain was a negative image of the ALP activity. Osteoid matrix was deposed upon the calcified cartilage matrix and underwent mineralization. Palissade-arranged cells lined this osteoid and displayed morphological image of osteoblast-like cells (OB). c: Oscteocalcin immunolabelling of a next section of the same specimen counter stained by h&e: Osteocalcin immunoreactivity in the eosinophilic osteoid at the border of the mineralized woven bone and sligtly on osteoblast-like cells (OB) lining this zone. Slight labelling was also present in the woven bone (W), but not in the cartilage (C) areas. d: Type X collagen immunolabelling of a next section of the same specimen counter stained by h&e: Type X collagen immunoreactivity was found in the mineralized cartilage area up to the woven bone. It also streched over most of the fibrous tissue (F) surrounding the mineralized area. Inset: It was also present in hypertrophic chondrocytes and their matrix. e: Type II collagen immunolabelling of this specimen conter stain by h&e: The matrix of the columnar cartilage was immunolabelled with type II collagen antibody. Remnants of eosinophilic degenerated muscle fibers were interspersed among the cartilage. Inset: The type II colllagen immunoreactive areas displayed an heavy ALP activity. Bands of eosinophilic material stained partially by Von Kossa underlined some borders of the mineralized cartilage. These osteoid-like bands were lined by cells which morphologically appeared to be osteoblast-like cells. To confirm the osteoblastic nature of these cells, immunolabelling for OCN was performed (Fig 5c ). In front of the osteoblastic-like cells a consistent matricial OCN immunoreactivity was evident onto the eosinophilic matrix underlining the mineralized cartilage. We have also observed deposition of OCN into woven bone formed adjacent to cartilage. In order to document the collagenic composition of these tissues and confirm their osseous or cartilaginous nature, immunolabelling for type I, II and X collagens were performed. Conspicuous immunolabelling for type X collagen was observed in most of the hypertrophic chondrocytes and in their matrix. The immunoreactivity became more intense nearby the mineralization front. In addition the matrix of the fibrous and non cartilage-like tissue around some mineralized areas was unexpectedly labelled (Fig 5d ). Control samples in which primary antibody incubation was omitted were clearly negative (data not shown). Distribution of type II collagen was limited to the matrix of non hypertrophic and prehypertrophic chondrocytes with high ALP activity (Fig 5e ). Nevertheless some type II collagen immunoreactivity could sometimes be detected in the hypertrophic areas. Bone deposition was frequently observed by polarized light and confirmed by type I collagen immunostaining (Fig 6 ). Type I collagen was located in the osteoblast-containing matrix which formed and lined up along spicules of calcified cartilage. Osteocytes were trapped in the lamellar and woven bone with type I collagen immunoreactivity. Figure 6 Frozen section of soft tissue of a 24 months hip NPOA : Type I collagen immunolabelling counter stained by h&e: Type I collagen was the main constituant of the matrix in the primary osteons (OS) and non organized woven bone (W). Inset: lamellar bone deposition observed by polarized light b)Serial sections In order to determine the chronology of events at work in the described endochondral ossification, we performed serial cryosectioning of samples in which a cortical bone followed soft tissue. These samples seem to be appropriate to have all stages of osteogenesis One of the specimens appeared to contain an aponeurotic tissue which showed signs of bursitis. In a highly cellular tissue we observed a high angiogenic activity. Bundles of vessels surrounded amorphous and avascular zones (Fig 7a ). Some of these vessels expressed slight ALP activity which became more and more intense in the vicinity of the acellular areas. Then the avascular areas were replaced by nodules of cartilage with prehypertrophic and hypertrophic chondrocytes. These areas were stained by alcian blue pH1, showing chondroitin sulfate accumulation in their matrix (Fig 7b ). Figure 7 Serial sections of soft tissue of 24 months hip NPOA : a: ALP activity counter stained with nuclear red-eosin: Slide 118; Important angiogenesis encircles avascular areas. Many of these vessels express ALP activity (ALP+). b: Alcian bleu pH = 1 staining: Slide 70: chondroitin sulfate accumulation in cartilage. c: ALP activity counter stained with nuclear red-eosin: Slide 71. d: Von Kossa staining countre stained with h&e: Slide 65. At this stage a strong ALP activity was observed in the cells surrounding the cartilage zone as well as in non mineralized hypertrophic areas (Fig 7c ). Finally Von Kossa staining revealed the matrix mineralization (Fig 7d ). Immunolabelling of these sections with type II collagen antibody demonstrated a circle of prehypertrophic chondrocytes (Fig 8a ). The matrix of the fibrous tissue outside this mineralized nodule was immunoreactive to type I collagen antibody (Fig 8b ). Type X collagen recovered the nodule of hypertrophic chondrocytes and the rest of this section showing a high osteogenic activity (Fig 8c ). OCN immunolabelling revealed exactly the same zone stained by Von Kossa showing deposition of OCN on mineralized zone (Fig 8d ). As previously described OCN was detected in the osteoblast-like cells lining the newly lay down osteoid as well as in the newly formed woven bone and on areas of membranous bone formation (Fig 8e ). OCN was also observed in some cells around the vessels near by the areas of osteogenesis. Figure 8 Immunological study of serial sections : a: Type II Collagen immunolabellingcountre stained with h&e: Slide 69: Type II collagen had the same pattern of ALP activity of nodule showing prehypertrophic chondrocytes in nonmineralized zone. b: Type I collagen Immunolabelling countre stained with h&e: Slide 72; Type I collagen expression encercled the mineralized nodule. c: Type X collagen immunolabelling countre stained with h&e:Slide 73: Type X collagen was expressed by most of cells and distributed in their matrix. d: OCN Immunolabelling countre stained with h&e: Slide 64: OCN was expressed by hypertrophic chondrocytes. e: OCN immunolabelling counter stained with h&e: Activated osteoblasts (OB) and woven bone (W) are strongly labelled. Some capillaries (C) near by these areas express osteocaline too. f: VEGF immunolabelling counter stained with h&e: VEGF was expressed by some hypertrophic chondrocytes (H). Activated and non activated osteoblasts-like cells (OB) lining the cartilage express also VEGF. Inset: Clustered and isolated cells in the matrix, surrounding hypertrophic chondrocytes, which could be destinated to capillary or osteoblast formation, are also labelled by VEGF. To further confirm the process of endochondral ossification, we decided to search for VEGF expression. Immunolabelling of these tissues with VEGF monoclonal antibody, showed a labelling of the hypertrophic chondrocytes as well as an intense labelling of activated osteoblats lining the osteoid. Some clusters of rounded cells also expressed VEGF in the fibrous part of these preparations (Fig 8f ). c)Ultrasound examination and digital radiographs of suspected NPOA US examination showed a huge focal disorganization of the muscles in the area of the suspected NPOA. Normal longitudinal muscular striation disappeared and was replaced by a relatively well defined mass with a very heterogeneous echostructure. The masses ranged from six to eleven centimeters of long axis. No scattered ossified areas were detected by US at this stage. Hypervascularization was detected on Doppler examination inside and outside the NPOA tumors (Fig 9a ). Figure 9 Ultrasound and color Doppler examination and digital radiographs of suspected NPOA a: Axial US view combined with color Doppler of the anterior side of the left hip in a paraplegic patient presenting acute limitation and inflammation of this joint. The striation of the psoas iliaque muscle, normally detectable at the anterior part of the hip joint with US examination, has disappeared. A relatively well defined mass (orange arrows) is detectable at the anterior part of the left femoral head (F). This mass is very heterogeneous with mixed hypo and hyper echoic areas. Color Doppler enables visualization of vessels in the mass (red and blue Doppler signals). A mass effect is visible on the femoral vessels (top right of the view). b: Same patient, one week later, axial US view of the posterior side of the left hip. The classical zone phenomenon (ZP) is detectable with a central hypoechoic area surrounded by hyper echoic nodules with posterior attenuation(black arrows). c: Axial US examination at the same day combined with color Doppler view of the posterior side of the left hip. A posterior mass (orange arrows) is also visible in the gluteal muscles, very heterogeneous with mixed hypo and hyper echoic areas. Color Doppler reveals large vessels in the mass (red and blue Doppler signals). d: Plain radiographs of the left hip obtained the same day as first US examination: Any sign of ossification is visible while a well defined mass is detected by US examination. e: Plain radiographs of the left hip obtained two weeks after: Early anterior and posterior NPOA ossification is only slightly visible two weeks (Orange arrows) after the initial clinical signs whereas the US examination was initially positive. Classical zone phenomenon previously described in the literature [ 15 , 16 ] was visible with a central hypo echoic area surrounded by small (less than one centimeter) hyper echoic nodules with posterior attenuation (Fig 9b ). At this stage color Doppler examination showed increasing angiogenesis with the appearance of large vessels in the tumor mass (Fig 9c ). The zone phenomenon became visible on the second US examination performed one week later (Fig 9d ). The opacity of the early ossification became slightly visible on plain films only two weeks after the US detection of zone phenomenon (Fig 9e ). Discussion NPOA pathogenesis is still poorly understood, and the exact environmental and humoral conditions underlying the ossifying process are not clear. In this study we postulated that periosteoma soft tissues display interrupted early stages of osteoma formation, which could help us to understand the chronology as well as the mechanism of osteoma formation. Thus, histological and immunohistological experiments were performed on 28 specimens. Moreover, ultrasound examination of suspected NPOA tumor on five patients permitted to follow osteoma formation in the early stages before ossification. Histological studies have shown varying amount of muscle and connective tissue degeneration in which some areas underwent reorganization. Islands of cartilage, woven bone, and mature lamellar bone were a constant finding in most of specimens, whatever the estimated age of the studied lesion. The spatial organization of chondrocytes was reminiscent of the epiphyseal growth plate or of the fracture callus organization [ 17 ]. In the developmental pathway leading to skeletogenesis, undifferentiated mesenchymal cells pass sequentially through at least four differentiation stages: committed mesenchymal cells which produce type I collagen and possibly basal level of type II collagen, quiescent chondrocytes, then proliferating chondrocytes characterized by the synthesis of a large amount of type II collagen and sulfated proteoglycans, and ultimately hypertrophic chondrocytes characterized by the synthesis of type X collagen. Then these hypertrophic chondrocytes allow the mineralization of the matrix elaborated and induce vascular invasion by releasing VEGF [ 18 , 19 ]. Studies of our specimens showed the same sequence of events. These results suggest that endochondral osteogenesis is the major pathway in the NPOA bone formation. Nevertheless in view of some features suggesting bone deposition without any cartilage scaffold we cannot exclude the occurrence of membranous bone formation. Some sections showed a high expression of type X collagen in the hypertrophic chondrocytes areas, and unexpectedly in non differentiated mesenchymal tissue. As this labelling did not occur in other fibrocellular areas it was unlikely to be produced by a binding of the antibody to some other matricial component of the extra cellular matrix. It was shown, that type X collagen is not only associated with chondrocyte proliferation and hypertrophy, but also with resting chondrocytes, cells at the border of the perichondrium and resting cartilage of the fetal femoral head [ 20 ]. The finding of still degenerating muscular fibers and early chondro-osteogenesis accompanied by heavy ALP activity in large parts of the soft tissues in old lesions (till 8 years) is singular. Except when serial sectioning was performed the studied specimens were carefully dissected from the osteoma during the surgery or at the fixation time. Therefore the extra-osteoma localization of these tissues can without any doubt be assumed. In one study [ 21 ] "recent POA" was described as a kind of fibrocellular tissue including vascular stasis, overabundance of micro vessels, myolysis, edematous fibrocellular tissue, with chondrogenesis, osteogenesis and lamellar bone apposition on mineralized structures. This description of "recent POA" is perfectly in agreement with our description of the periosteoma tissues. However, we found each of these elements notwithstanding the advanced age of some lesions. Most of our specimens contained clusters of ALP positive cells in the undifferentiated fibrous connective tissue, suggesting the presence of preosteogenic cells. This fact and the presence of cartilage and bone at varying stages of maturity are indicative of a persistent chondro-osteogenic activity in these tissues. This point sounds of interest as regards to contingencies of heterotopic bone formation relapse following surgical excision. The occurrence of relapses was reported to correlate neither to the classical criteria of osteoma maturation nor to the amount of heterotopic bone left after excision nor to the age of the lesion [ 22 - 24 ]. The occurrence of relapses could be linked to the activation of the still process. It was claimed that osteoma develops in the periphery of a muscle in which some myofibers undergo degeneration [ 25 , 26 ], and that osteoma involves the muscles. However the endochondral process of bone formation described here is in agreement with the results of various bone induction experiments in muscle [ 27 - 30 ]. Moreover, US examination of suspected NPOA tumor at the early stage showed huge focal disorganisation of muscle surrounding the hip joint and disappearance of normal longitudinal muscular striation of the psoas iliaque muscle replaced by masses with heterogeneous echostructure. This finding argues in favour of an intramuscular beginning of the process. We could not set up the place and part of the muscle degeneration process in the heterotopic bone formation. Though the process described here resembles by some aspects the Fibrodysplasia Ossificans Progressiva (FOP) where endochondral ossification was demonstrated in muscle and adjacent connective tissue [ 31 , 32 ]. These reports combined with our finding of endomysium-perimysium hyperplasia in the degenerating muscles with ALP activity and US result, could suggest a role for the muscular tissue and especially mesenchymal cells from endomysium and perimysium in the setting of the heterotopic bone formation process. Vascular disorders, such as vascular disruption or compression [ 33 ] and venous stasis together with a cascade of inflammatory reactions including release of enzymes from necrotic tissues and α-adrenergic mediated vasoconstriction, lead to the formation of hypoxic zones beneath the nervous injury. On the other hand it has been shown that hypoxia promotes chondrocyte differentiation [ 34 , 35 ]. In our specimens, high amount of thrombotic vessels were indicative of the hypoxic status of these tissues. Color Doppler examination of tumor during the first clinical signs of NPOA, showed an avascular area in which some vessels appeared. These signals became more intense one week later. This tends to confirm hypoxic status of the initial lesion. This suggests that a timely defined hypoxic condition in tissue induces chondrocytes differentiation. The volume of the tumor is acquired during the very early stages of NPOA. After tumefaction there is no real change in the tumor size [ 2 ]. Moreover chondrocytes in the hypertrophic stage increase volume 10 times [ 36 ]. So the chondrocyte hypertrophy could be the cause of the tumefaction and so determines the final size of the tumor. Then, the related vascular sign occurs and color Doppler showed the first sign of increasing angiogenesis. On tissue section, VEGF immunolabelling revealed a more intense expression by osteoblasts and osteoblast-like cells, in addition to its expected expression by hypertrophic chondrocytes. VEGF induces neoangiogenesis and then endochondral ossification occurs. The restoration of normoxic conditions promote the onset of lamellar ossification and hamper any other de novo cartilaginous differentiation. US examination of the same NPOA tumor one week later showed scattered ossifying nodules. This nodular activity is in accordance with our histological finding in periosteoma soft tissue. Extense of the hypoxic area determines the size of each nodule. Areas with important hypoxia induce larger cartilage zones which could join together after ossification but small nodules remain scattered in periosteoma soft tissue. These results confirm that, periosteoma soft tissue has the same pattern of early stage of osteoma ossification, and could be a model of ossification for further studies. Moreover, NPOA occurs in neurologically deficient patients with altered mechanical loading. Mechanical loading is of pivotal importance to the development, function and repair of all tissues in the musculoskeletal system. In nonfunctional joints, as it is the case of these patients, the absence or reduction of intermittent hydrostatic pressure in the articular cartilage could permit cartilage degeneration and the progressive advance of the ossification These mechanical influence could indeed shed light on the finding that osteomas only occur near the main joints.[ 37 ] Moreover, Carter and associates have also shown that intermittently applied shear stresses (or strain energy) promote endochondral ossification and that intermittently applied hydrostatic compression inhibits or prevents cartilage degeneration and ossification. Thus, the imbalance of these forces among these patients can promote endochondral ossification of the cartilage nodules in areas of high shear (deviatoric) stresses[ 38 ]. Urist demonstrated that the induced endochondral bone is resorbed once the inductive agent has disappeared [ 30 ]. We have not seen many osteoclasts nor other multinucleated cells in ours preparations, and the literature does not report on NPOA regression except in people under 15 years of age [ 39 ]. Therefore it would be interesting to study the regulation of osteoclasts and the remodelling in such model. We therefore propose a model of the NPOA lesion formation. The sympathetic hyperactivity causes major changes in the peripheral vascular dynamics. As related some of these changes end in vascular stasis and/or thrombosis [ 2 ]. Edema follows on in the connective tissue which sustains some amount of necrosis. The trauma, subsequent neurological conditions and perhaps systemic factors [ 40 , 41 ] induce major changes in these tissues. Regenerating celles under low oxygen pressure/high dilatational hydrostatic forces [ 34 , 42 , 43 ] transmogrify themselves into chondrogenic cells. Then cartilage differentiation gets moving on until hypertrophy of the chondrocytes and cartilage matrix mineralization. The cell hyperplasia and hypertrophy of the chondrocytes could account for the solid swelling clinically noticed soon after the onset of the clinical signs of NPOA. Like they did in the developing limb, some competent cells lining the cartilage rudiments undergo the osteoblastic differentiation and lay down osteoid on the cartilage. Concomitantly the cartilage hypertrophy induces angiogenesis, osteoid deposition, and some extent of cartilage resorption. Remodelling of the mineralized cartilage and woven bone occurs. The osteoma could then control the process and inhibit any further bone formation. Some questions remain which deserve further studies. Why do NPOAs form only around the main joints? Why are they not resorbed as does any intramuscularly implanted bone graft [ 44 ]. What freezes the osteoma bone growth and the process of bone formation? Studies are ongoing in order to find some clues about the regulation of this heterotopic bone formation. Conclusion In conclusion, our results demonstrate that periosteoma soft tissues are a replica of the early stages of osteoma formation, and could be used as a model for NPOA formation. We propose also a mechanism for osteoma formation in which hypoxia is a major cause of nodular osteoinduction and chondrocyte differentiation. Combination of hypoxia and applied shear stresses induce endochodral ossification. Finally our results indicate implication of different types of mesenchymal cells in NPOA formation but US examination support specially muscular origin hypothesis. Pre-publication history The pre-publication history for this paper can be accessed here:

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526772

An evaluation of UV protection imparted by cotton fabrics dyed with natural colorants

Background The ultraviolet properties of textiles dyed with synthetic dyes have been widely reported in literature. However, no study has investigated the ultraviolet properties of natural fabrics dyed with natural colorants. This study reports the Ultraviolet Protection Factor (UPF) of cotton fabrics dyed with colorants of plant and insect origins. Methods Three cotton fabrics were dyed with three natural colorants. Fabrics were characterized with respect to fabric construction, weight, thickness and thread count. Influence of fabric characteristics on Ultraviolet Protection Factor was studied. Role of colorant concentration on the ultraviolet protection factor was examined via color strength analysis. Results A positive correlation was observed between the weight of the fabric and their UPF values. Similarly, thicker fabrics offered more protection from ultraviolet rays. Thread count appears to negatively correlate with UPF. Dyeing with natural colorants dramatically increased the protective abilities of all three fabric constructions. Additionally, within the same fabric type UPF values increased with higher depths of shade. Conclusion Dyeing cotton fabrics with natural colorants increases the ultraviolet protective abilities of the fabrics and can be considered as an effective protection against ultraviolet rays. The UPF is further enhanced with colorant of dark hues and with high concentration of the colorant in the fabric.

Background High, short-term exposure to ultraviolet radiation (UVR) from the sun causes sunburns and long-term exposure leads to skin cancer. The National Toxicology Program, U.S. Department of Health and Human Services has classified UVR as a known human carcinogen [ 1 ]. The American Cancer Society estimates that more than one million cases of skin cancer cases are diagnosed each year in the United States [ 2 ]. In 2002, an estimated 54,200 new cases of melanoma skin cancer alone were diagnosed [ 2 ]. A primary reason for the increased incidence of skin cancers is attributed to ozone depletion. Each one percent decrease in ozone concentration is predicted to increase the rate of skin cancer by two percent to five percent [ 3 ]. The United States Environmental Protection Agency estimates that ozone depletion will lead to between three and fifteen million new cases of skin cancer in the United States by the year 2075. Other reasons for the skin cancer epidemic can be traced to lifestyle changes such as excessive exposure to sunlight during leisure activities, for example, playing outdoors and swimming in the case of children and golfing and fishing in the case of adults. In the case of agricultural and other outdoor workers, exposure to the sun is an occupational hazard as they have no choice about the duration of their exposure to the sun [ 3 - 5 ]. The ultraviolet radiation (UVR) band consists of three regions: UV-A (320 to 400 nm), UV-B (290 to 320 nm), and UV-C (200 to 290 nm). UV-C is totally absorbed by the atmosphere and does not reach the earth. UV-A causes little visible reaction on the skin but has been shown to decrease the immunological response of skin cells [ 3 ]. UV-B is most responsible for the development of skin cancers [ 3 ]. Other than drastically reducing exposure to the sun, the most frequently recommended form of UV protection is the use of sunscreens, hats, and proper selection of clothing. Unfortunately, one cannot hold up a textile material to sunlight and determine how susceptible a textile is to UV rays. Even textiles which seem to be non-light transmitting may pass significant amounts of erythema-inducing UV irradiation [ 4 ]. Therefore, knowledge of the factors that contribute to the protective abilities of textiles is vital. Important factors include fiber composition, fabric construction and wet-processing history of the fabric such as color and other finishing chemicals that may have been applied to the textile material. To the author's knowledge, no study has investigated the ultraviolet properties of natural fabrics dyed with natural colorants. A plethora of previous studies have concluded that good UVR protection can be provided by synthetic fibers dyed with high concentrations of synthetic dyes. However, synthetic fibers such as polyester are hydrophobic and are generally not deemed to be comfortable for wear especially in warm weather. According to a report in America's Textile Industries [ 6 ] natural fibers are back in demand. The emergence and popularity of a more natural way of life as reflected in a return to organic farming and natural foods has now extended into textiles where the resurgence of natural fibers and natural dyes is on the increase [ 6 , 7 ]. It is hoped that data from the present study will be useful for dermatologists advising patients regarding the UV-protective properties of clothing made from natural fibers and dyed with natural colorants. In this study, cotton fabrics were dyed with three natural colorants of plant and insect origin. Fabrics were characterized with respect to fabric construction, weight, thickness and thread count. Ultraviolet Protection Factor (UPF) was measured using a labsphere ® UV-100 F Ultraviolet Transmission Analyzer. The effect of colorant concentration on the ultraviolet protection factor was examined via color strength analysis using a HunterLab ColorQuest XE ® spectrophotometer. Methods Three fabrics were chosen to cover the gamut of basic weave constructions. They were a bleached, mercerized plain weave cotton fabric, a bleached mercerized cotton twill and a desized and bleached cotton sateen. Fabric weight was measured according to ASTM Test Method D3776-96 [ 8 ]. Fabric thickness was measured according to ASTM Test Method D1777-96 [ 8 ]. Thread counts were measured according to ASTM D3775-98 [ 8 ]. Natural plant colorants used were madder ( Rubia tinctorum ) and indigo ( Indigofera tinctoria ) and the natural colorant of insect origin was cochineal ( Dactylopius coccus ). Since natural dyes do not have affinity for cellulosic fibers an alum mordant was used to impart affinity. Fabrics were mordanted prior to dyeing by treating with alum at boil for 45 minutes. The liquor ratio was 1:40 and alum concentration was 10% on weight of the fabric. After mordanting, fabric was squeezed thoroughly and dyed. Madder and cochineal dyeings were done in stainless steel canisters of an Atlas launder-ometer using 2%, 4% and 6% dye on weight of fabric. The liquor-goods ratio was 40:1. Fabrics were introduced into the dyeing solutions at room temperature. Temperature was raised to the boil and dyeing continued at the boil for 60 minutes. After dyeing, fabrics were rinsed in deionized water, washed using a non-ionic detergent and air-dried. Three replications were done for each colorant and at each dye concentration. Dyeing with indigo was done in the following manner. Indigo dye was made into a paste and solubilized using sodium hydroxide and sodium hydrosulfite. Fabrics were introduced into dyebaths containing 2%, 4% and 6% dye on weight of fabric. The liquor-goods ratio was 40:1. After thirty minutes of dyeing the fabrics were removed and oxidized by drying in air. The fabrics were then rinsed in deionized water and washed using a non-ionic detergent and dried. Direct and diffuse UV transmittance through a fabric is the crucial factor determining the UV protection of textiles [ 9 ]. Ultraviolet protection factor (UPF) is the scientific term used to indicate the amount of Ultraviolet (UV) protection provided to skin by fabric. UPF values are analogous to SPF values the only distinction being that SPF values for sunscreens are determined through human testing whereas UPF values are based on instrumental measurements [ 10 ]. UPF is defined as the ratio of the average effective UV irradiance calculated for unprotected skin to the average UV irradiance calculated for skin protected by the test fabric [ 5 , 10 ]. The higher the value, the longer a person can stay in the sun until the area of skin under the fabric becomes red [ 5 , 10 ]. An effective UVR dose (ED) for unprotected skin is calculated by convolving the incident solar spectral power distribution with the relative spectral effectiveness function and summing over the wavelength range 290-400 nm. The calculation is repeated with the spectral transmission of the fabric as an additional weighting to get the effective dose (ED m ) for the skin when it is protected. The UPF is defined as the ratio of ED to ED m and calculated as follows [ 11 ]: where: E λ = erythemal spectral effectiveness S λ = solar spectral irradiance in Wm -2 nm -1 T λ = spectral transmittance of fabric Δ λ = the bandwidth in nm λ = the wavelength in nm UPF's were measured in vitro using a labsphere ® UV-100 F Ultraviolet Transmission Analyzer according to standard AS/NZ 4399:1996 [ 12 ]. Fabrics with a UPF value in the range 15 – 24 were classified as having "Good UV Protection"; when the UPF values were between 25 and 39 fabrics were classified as having "Very Good UV Protection" and "Excellent UV Protection" classification was used when the UPF was 40 or greater [ 13 ]. In no event was a fabric assigned a UPF rating greater than 50. Measured UPF values were also correlated to the color strength of the dyed fabrics. Color strength was evaluated using K/S values generated by a HunterLab ColorQuest XE diffuse/8° spectrophotometer. K/S is a function of color depth and is represented by the equation of Kubelka and Munk (Equation 2). Higher the value of K/S greater is the color strength [ 14 , 15 ]. where R is the reflectance of the dyed fabric; K is the sorption coefficient, and S is the scattering coefficient. The spectrophotometer was standardized for a 1 inch diameter specimen viewing aperture in reflectance – specular included mode. Illuminant D65 and CIE 10-degree observer were used. During measurements, fabric samples were held flat and securely using a spring-loaded sample clamp. Three measurements were taken on each dyed fabric with the fabric rotated between measurements. Results and discussion Fabric characterization parameters and UPF values prior to dyeing are listed in Table 1 . Based on the classification parameters referenced previously the plain weave fabric and the sateen weave fabric cannot be rated as offering any degree of protection since their UPF values were less than 15. The undyed twill weave fabric with a UPF of 19.2 is rated as having Good UV Protection. The UPF values of the undyed fabrics can be explained in terms of fiber composition and fabric construction. In terms of fiber composition it is known that undyed bleached cotton, linen, acetate, and rayon fabrics afford poor protection against UV radiation [ 16 ]. Fabric construction parameters of weight and thickness show a positive correlation with UPF values. Higher the weight and thicker the fabric, higher is the degree of protection afforded by the fabric. Accordingly, the twill weave fabric with a weight of 235 g/m 2 and a thickness of 0.069 centimeters has the highest UPF value followed by the sateen weave fabric which weighed 235 g/m 2 and was 0.061 centimeters thick. The plain weave fabric with a weight of 120 g/m 2 and a thickness of 0.035 centimeters offers no protection against transmittance of UV rays. The positive correlation between fabric weight and fabric thickness with UPF values can be explained with reference to porosity. Porosity is a measure of tightness of weave and is also called as Coverfactor. Cover factor is defined as the percentage area occupied by warp and filling yarns in a given fabric area [ 4 , 17 ]. The closer the weave, the more is the percentage area occupied by the yarns and more opaque is the fabric to UV radiation. Cover factor is increased by an increase in weight per unit area. Heavier fabric minimizes UV transmission by virtue of having smaller spaces between yarns thus blocking more radiation [ 3 , 17 ]. A related variable is thickness. Thicker, denser fabrics transmit less UV radiation and have a higher cover factor [ 10 ]. The data also reveals a negative correlation between thread count and UPF. Higher the thread count, lower is the degree of protection afforded by the fabric. The plain weave fabric with a thread count of 205 had a UPF of 3.2 whereas the twill weave fabric with a thread count of 81 had a UPF of 19.2 with the sateen weave between the two with a thread count of 106 and a UPF of 13.3. A possible explanation for the negative correlation between thread count and UPF is the fact that fabrics that are thinner tend to contain finer yarns and therefore have the highest thread counts [ 10 ]. In other words thickness and thread count are inversely correlated a point substantiated by the values in Table 1 . Table 1 Fabric characterization parameters and % UV transmittance of undyed fabrics Weight, g/m 2 Thickness, cm. Thread Count (per inch) UPF UV Protection Class Plain weave 120 0.035 205 3.8 No Class Twill weave 258 0.069 81 19.2 Good Sateen weave 235 0.061 106 13.3 No Class The percent UV transmittance data in the presence and absence of colorants for the plain weave fabric is shown in Figure 1 . It is noted that since the relative erythemal spectral effectiveness is higher in the UV-B region compared to the UV-A region, the UPF values depend primarily on transmission in the UV-B region. Undyed plain weave fabric had significant transmittance and consequently a very low UPF value of 3.8. UPF values and protection categories of the plain weave fabric dyed with the different colorants are listed in Table 2 . As is evident from the transmission data and the corresponding UPF values all colorants used in the study caused a dramatic reduction in UV radiation transmission through the plain weave fabric. The increase in UPF values in the presence of colorant was especially significant for the cochineal and indigo dyed samples which were classified as having Very Good (UPF values between 25 and 39) to Excellent UV Protection (UPF values 40 or greater). Madder dyed samples could be classified as having Good UV Protection (UPF values between 15 and 24) to Very Good UV Protection. Compared to cochineal and indigo, madder is a paler color and therefore these results agree with previous data reported by Reinert et al. [ 18 ] who showed that pale colored fabrics of cotton, silk, polyamide, and polyamide/elastan gave less protection against intense UV radiation. The results also show that UPF values for colorants applied at higher concentrations gave higher UPF values. For example, the UPF of the plain weave fabric at a 2% indigo on weight of fabric was 43.1 and that increased to greater than UPF 50 at an indigo concentration of 6%. We agree with Gies et al. [ 11 ] who indicated that dyeing fabrics in deeper shades and darker colors improves sun protection properties. Thus although the studies by Reinert at al. and Gies et al. were done with synthetic dyes their conclusions seem to hold with natural colorants as well. Figure 1 UV transmission of plain weave fabric in the absence and presence of colorants. Table 2 UPF values, protection class and K/S values of plain weave fabric dyed with natural colorants at different concentrations Colorant UPF UV Protection Class K/S Plain weave 2% Madder 11.1 No Class 0.20 4% Madder 15.8 Good 0.28 6% Madder 16.6 Good 0.38 2% Cochineal 28.5 Very Good 0.63 4% Cochineal 34 Very Good 0.79 6% Cochineal 36.6 Very Good 0.99 2% Indigo 43.1 Excellent 1.78 4% Indigo > 50 Excellent 2.56 6% Indigo > 50 Excellent 3.02 The K/S values of the dyed fabrics which are a measure of color depth seem to support the claim that higher color depths increases UPF values. For example, in the case of the madder dyed samples when the K/S value increased from 0.20 to 0.38 the UPF values rose from 11.1 to 16.6. However, the relationship of K/S with UPF is limited to the same fabric type and the results cannot be generalized across fabrics of different weave structures. A primary reason for this observation is the acknowledgement that UPF values are dependent on a multitude of fabric construction factors such as pores in the fabric, thickness, and weight in addition to processing parameters such as dyeing and finishing. Another probable reason is the dependence of K/S on the absorbing properties of colorants in the visible region of the spectrum and that may not influence the absorption characteristics of colorants in the UV region. The percent UV transmittance data in the presence and absence of colorants for the twill weave fabric is shown in Figure 2 . UPF values and protection categories for the dyed twill weave fabric are shown in Tables 3 . The twill weave fabric which prior to dyeing was rated as offering Good UV Protection moved to the Excellent UV Protection classification irrespective of the colorant and the concentration of the dye used. Again, it was found that dark colors within the same fabric type transmit less UV radiation than light colors and consequently have higher UPFs. Figure 2 UV transmission of twill weave fabric in the absence and presence of colorants. Table 3 UPF values, protection class and K/S values of twill weave fabric dyed with natural colorants at different concentrations Colorant UPF UV Protection Class K/S Twill weave 2% Madder > 50 Excellent 0.27 4% Madder > 50 Excellent 0.44 6% Madder > 50 Excellent 0.59 2 % Cochineal > 50 Excellent 0.82 4% Cochineal > 50 Excellent 1.70 6% Cochineal > 50 Excellent 1.89 2% Indigo > 50 Excellent 2.33 4% Indigo > 50 Excellent 3.76 6% Indigo > 50 Excellent 4.00 Table 4 shows the UPF values and protection categories for the dyed sateen weave fabric. The percent UV transmittance data in the presence and absence of colorants for the sateen weave fabric is shown in Figure 3 . The increase in UPF values of the sateen weave dyed fabrics was dramatic in the sense that the sateen which prior to dyeing could not be rated (UPF < 15) achieved the Excellent UV Protection classification by virtue of its UPF values increasing by more than a factor of four (UPF > 50). This result was true for all colorants and at all dye concentrations. Again, as was the case with the dyed plain weave fabric, the color strength (K/S) of the cochineal and indigo dyed twill and sateen fabrics were higher than the color strength of the madder dyed fabrics conclusively establishing that indigo and cochineal colorants resulted in deeper colors on the fabrics. Table 4 UPF values, protection class and K/S values of sateen weave fabric dyed with natural colorants at different concentrations Colorant UPF UV Protection Class K/S Sateen weave 2% Madder > 50 Excellent 0.25 4% Madder > 50 Excellent 0.36 6% Madder > 50 Excellent 0.59 2% Cochineal > 50 Excellent 1.78 4% Cochineal > 50 Excellent 1.87 6% Cochineal > 50 Excellent 2.42 2% Indigo > 50 Excellent 1.66 4%Indigo > 50 Excellent 2.05 6% Indigo > 50 Excellent 2.40 Figure 3 UV transmission of sateen weave fabric in the absence and presence of colorants. Conclusions Fabric weight and thickness are important predictors of UPF values for undyed cotton fabrics. In general, it was found that increase in weight and thickness increased the UPF though the relationship was not linear. UPF of undyed fabrics was significantly enhanced by dyeing with natural colorants especially for fabrics such as the plain weave and the sateen weave fabrics that displayed no protective abilities in the undyed state. The degree of protection imparted after dyeing was a function of the concentration of the colorant in the fabric. Within the same fabric type, as the percentage depth of shade increased so did the UPF values. In addition, darker colors such as indigo provide better protection on account of higher UV absorption. Based on the results of this study it can be theorized that plain, twill or sateen weave cotton fabrics dyed with natural colorants can provide good protection against ultraviolet rays with the only condition being that either the color has to be a dark hue or the concentration of the colorant in the fabric has to be high. Competing interests The author(s) declare that they have no competing interests. Authors' contributions AKS conceived the study, carried out the dyeing and testing and drafted the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

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529441

Real-time PCR quantitation of glucocorticoid receptor alpha isoform

Background The expression of glucocorticoid-receptor (GR) seems to be a key mechanism in the regulation of glucocorticoid (GC) sensitivity and is potentially involved in cases of GC resistance or hypersensitivity. The aim of this study is to describe a method for quantitation of GR alpha isoform (GRα) expression using real-time PCR (qrt-PCR) with analytical capabilities to monitor patients, offering standard-curve reproducibility as well as intra- and inter-assay precision. Results Standard-curves were constructed by employing standardized Jurkat cell culture procedures, both for GRα and BCR (breakpoint cluster region), as a normalizing gene. We evaluated standard-curves using five different sets of cell culture passages, RNA extraction, reverse transcription, and qrt-PCR quantification. Intra-assay precision was evaluated using 12 replicates of each gene, for 2 patients, in a single experiment. Inter-assay precision was evaluated on 8 experiments, using duplicate tests of each gene for two patients. Standard-curves were reproducible, with CV (coefficient of variation) of less than 11%, and Pearson correlation coefficients above 0,990 for most comparisons. Intra-assay and inter-assay were 2% and 7%, respectively. Conclusion This is the first method for quantitation of GRα expression with technical characteristics that permit patient monitoring, in a fast, simple and robust way.

Background Glucocorticoids (GC) are a vital class of steroidal hormones that mediate profound and diverse physiological effects in vertebrates. GC are key hormones in the regulation of glucose homeostasis, but other essential functions are assigned to GC as well, such as regulatory roles in development and other metabolic pathways, stress and immune responses, neurobiology, and programmed cell death. In addition, corticosteroids are among the most widely prescribed class of drugs, primarily for their anti-inflammatory and immunosuppressive roles. They are also used in many chemotherapy regimens for leukemias and other cancers due to their critical capability to induce apoptosis. Cortisol and its synthetic derivatives act upon the glucocorticoid receptor (GR), a member of the nuclear hormone receptor superfamily of ligand-activated transcription factors. Prior to ligand binding, GR is primarily localized within the cytoplasm as an oligomeric complex composed of one receptor polypeptide, two molecules of heat shock protein 90 (hsp90), one molecule each of hsp 70, hsp 56 (immunophilin) and hsp23. When the hormone binds to the receptor, the GC-GR complex undergoes conformational changes, followed by dissociation from the hsp complex and dimerization of the GR molecules. The activated GR dimer is translocated into the nucleus and because of its high DNA affinity is able to bind to a specific DNA sequence named glucocorticoid response element (GRE), which is located in the vicinity of the target-regulated gene. The GR-GRE complex interacts with other components of the transcription apparatus to either enhance or repress the expression of the targeted gene [ 1 - 3 ]. There are several molecular mechanisms involved in glucocorticoid resistance or hypersensitivity (reviewed by Yudt, 2002) and GR expression seems to be a key one. These mechanisms are important for the regulation of cell and tissue-specific GC sensitivity, but they can be pathologically modified in clinical conditions such as AIDS, glucocorticoid-resistant asthma, rheumatoid arthritis and familial glucocorticoid resistance, among others [ 4 - 7 ]. Evaluation of GR expression in these conditions presents critical restrictions and has been limited to research protocols, partly due to analytical difficulties. Methods employed so far include ligand-binding assays, northern and western-blots and PCR. These methods could only provide qualitative or semi-quantitative information and a truly quantitative and reproducible evaluation of GR expression was still needed. In this study, we describe a quantitative real-time PCR (qrt-PCR) for GR alpha isoform (GRα) expression that is suitable for patient monitoring and can be easily reproduced. Results GRα and BCR (breakpoint cluster region) standard-curves were very stable using five different standard preparations, with maximum coefficient of variation (CV) of 10.3% observed for GRα most concentrated standard-point. GRα standard-curves presented CVs of 10.3%, 7.8%, 9.1%, 5.5% and 3.6% for the cycle-thresholds (Ct) obtained with each standard (6 to 2 logs of Jurkat cells). On both genes, standard-curves CV were greater at the most concentrated standard-points. BCR CV was smaller than those observed for GRα CV in all standard-points and all standard-curves. Standards of 6 to 1 log of Jurkat cells presented, respectively, CV of their Ct of 4.5%, 2.7%, 3.1%, 2.7%, 1.9% and 0.9%. BCR standard-curves had greater analytical sensitivity than GRα, with 10 EC/mL versus 100 EC/mL. Linear regression analysis of pairs of standard-curves demonstrated strong correlation for both genes. The smallest Pearson correlation coefficient on GRα curves was 0.982, but most of them were higher than 0.990. Two pairs of BCR curves showed Pearson correlation coefficient of 1.000 (Table 1 ). Standard-curves slopes presented a CV of 7.3% for GRα and 3.7% for BCR (mean slopes of -0.279 and -0.271, respectively). Using the t-test, we found this difference in GRα and BCR slopes not significant (t = -0.819 with 8 degrees of freedom, P = 0.437). Table 1 Evaluation of the standard-curve stability on five different sets of standard constructs. A B C D E A 1.000 0.993 0.996 0.994 0.993 1.000 1.000 0.994 0.997 0.996 B 0.993 1.000 0.987 0.981 1.000 1.000 1.000 0.994 0.997 0.996 C 0.996 0.987 1.000 0.999 0.989 0.994 0.994 1.000 0.998 0.997 D 0.994 0.981 0.999 1.000 0.982 0.997 0.997 0.998 1.000 1.000 E 0.993 1.000 0.989 0.982 1.000 0.996 0.996 0.997 1.000 1.000 Pearson's coefficients of correlation of GRα and BCR standard-curves for the same pair of experiments are expressed in the upper and lower rows, respectively. A to E refer to different sets of standard constructs, from Jurkat cell culture, viable cell count, RNA extraction, cDNA synthesis, standard dilution and qrt-PCR for GRα and BCR with calculations. Intra-assay precision was around 2% for both evaluated controls. Cases 1 and 2 presented, respectively, mean GRα-EU of 1.406 and 1.443, with SD of 0.030 and 0.027 and CV of 2.1% and 1.9%. Inter-assay precision was approximately 7% for both cases. Cases 1 and 3 presented, respectively, mean GRα-EU of 1.427 and 1.333, with SD of 0.088 and 0.104 and CV of 6.2% and 7.8%. The median GRα-EU values of case 1 were similar both in intra and inter-assay evaluations, respectively, 1.402 and 1.414 (figure 1 ). This difference was not considered significant when we applied the Mann-Whitney Rank Sum test (p = 0.847). Figure 1 Intra (A) and Inter-assay (B) precision evaluation of GRα expression Case 1 was evaluated in both situations. Bars indicate median values of GRα-EU (expression units). Intra-assay CV was 2.1% and 1.9% for case 1 and 2. Inter-assay CV was 6.2% and 7.8% (cases 1 and 3). Discussion Glucocorticoid resistance and hypersensitivity are determined by a number of factors such as intra-cellular hormone concentration, GR expression levels, GRα/GRβ heterodimerization, GR gene polymorphisms or mutations and GC-GR-protein interaction, among others [ 1 , 3 , 8 , 9 ]. One of the major factors affecting GC sensitivity seems to be the expression of its receptor. However, there were methodological difficulties regarding absolute quantitation of GR. Binding assays were initially used to evaluate GR levels, but this assay is labor-intensive (and therefore, more error-prone), it assess only GR ligand-binding properties in the cytoplasm, providing no information regarding GR in the nucleus and requires the use of radioactive materials. Western blot assays were developed to assess GR protein levels. Despite numerous efforts to make well-characterized and specific antibodies, to date there are no highly specific human monoclonal antibodies targeted to GR isoforms, preventing the development of true quantitative methods, such as ELISA [ 2 , 10 ]. Quantitative PCR approaches were difficult until qrt-PCR was developed. Quantitative real-time PCR assays are based on cycle-by-cycle fluorescence monitoring of detectable PCR products (e.g., using TaqMan probes) and analysis of amplification during the exponential phase of PCR (for a review in qrt-PCR, see Mocellin, 2003 [ 11 ]). Three groups recently described qrt-PCR techniques for GRα evaluation. DeRijk et al. developed Taqman evaluation of GRα and GRβ, however they did not describe the use of standard-curves or gene normalization [ 12 ]. Since their group was investigating GRα/GRβ ratio in the hippocampus, controls were produced using mixtures of PCR products of each isoform quantified by agarose gel densitometry. They found GRα/GRβ ratio of approximately 8300 in leukocytes and 14500 in hippocampal cells suggesting a minor physiological role for GRβ in normal cells from these tissues. Boullu-Ciocca et al. evaluated GRα and GRβ expression using real-time PCR in obese and control cases [ 13 ]. They used 18S mRNA as a normalizing gene, but no standard-curves or precision characteristics were described. Mononuclear cells from control individuals showed GRα/GRβ ratio of 32:1, much higher than that observed by DeRijk and co-workers, and 9.2 and 2.6 in patients with gluteofemoral and visceral obesity, respectively. The lack of information about test precision makes interpreting the difference observed in the GRα/GRβ ratio among the two groups very difficult. Pedersen and Vedeckis evaluated two cell lines using real-time PCR for seven different isoforms of GR (GRα, GRβ, and exon 1 splicing variants: 1A1, 1A2, 1A3, 1B, 1C), total GR (using exons 5–6) and 18S mRNA as normalizing gene [ 14 ]. Standards were plasmid constructs for each isoform. They observed a 1000-fold difference of GRα to GRβ expression levels and that the major exon 1 splicing transcripts are 1A3, 1B and 1C. Although elegantly designed, the authors conclude that their technique presents excessive variability for routine use. They estimated intra-assay CV to be 13%, inter-assay to be ca. 4%, inter-standard to be ca. 8% and overall CV of 15%. The test performance data was presented in a very succinct way and it was not possible to establish how inter-assay CV was calculated neither how inter-assay CV was smaller than intra-assay. The aim of this study was to describe and validate the reproducibility for clinical monitoring of absolute quantitation of glucocorticoid receptor alpha isoform using real-time PCR. Our approach used a standardized procedure for Jurkat cell culture to make cDNA standards for both GRα and BCR. Standard-curves were reproducible, with less than 11% CV for all standards used and Pearson correlation coefficient above 0.990 for most comparisons. Intra-assay and inter-assay precision were 2% and 7%, respectively. The BCR gene, derived from normal chromosome 22, was chosen as an appropriate endogenous control. The gene is ubiquitously expressed, has no reported pseudogenes and is a stringent control for the detection of degradation of RNA [ 15 , 16 ]. We found our approach to standard-curve construction to be an advantage of our method, since GRα and BCR were equally affected during all phases of standard construction. Another probable factor for achieving the precision reported is the similar expression levels and amplification rates of GRα and BCR. Conclusions In conclusion, we described for the first time a method for quantitation of GRα expression with technical characteristics that permit patient monitoring, in a fast, simple and robust way. Methods Standards We developed standards for real-time quantitation of GRα expression by employing standardized Jurkat (E6-1 clone, ATCC) cell culture. Jurkat cells were grown in RPMI 1640 (Gibco) supplemented with 10% FBS, 1% penicillin-streptomycin (Gibco), 5% CO 2 at 37°C. Culture medium was changed every 72 h, by centrifugation at 500 rpm for 5 min and 10 mL of fresh medium was added. Viable cells were counted in a hemocytometer (Neubauer chamber) using Trypan Blue (Sigma) and re-suspended to obtain a final density of 10 5 cells per milliliter. This procedure was repeated three times and 24 hours after the third medium exchange, cells were re-suspended to a final density of a 10 6 cells per milliliter. This strict protocol is necessary to ensure RNA extraction on the log phase of cell growing phase in order to minimize the variation of mRNA expression. Controls Three normal individuals participated in this study, in accordance with the guidelines proposed in The Declaration of Helsinki and approved by the ethics committee of our institution. Cases 1 and 2 are 30 and 32 year-old females, respectively, and case 3 is a 9 year-old boy. Heparinized blood was collected by venipuncture. After collection of 20 mL blood, mononuclear cells were separated using Histopaque-1077 (Sigma) by density gradient separation after centrifugation at 1750 rpm for 30 minutes. Cells were washed and re-suspended in PBS and 10% DMSO (Sigma) was added before freezing at -80°C. Prior to RNA extraction, DMSO was removed by PBS washing and re-suspension. RNA isolation and cDNA synthesis Total RNA was isolated from cells using guanidinium thiocyanate-chloroform extraction (Trizol, Gibco), according to the manufacture's recommendations. RNA was dissolved in DNAse-RNAse-free water (Gibco), after which the OD 260/280 was confirmed as >1.7, allowing the estimation of RNA concentration. RNA was stored at -80°C when the RT step was not immediate. cDNA was synthesized from 1 μg of total RNA, using TaqMan Reverse Transcription reagents (N808-0234, Applied Biosystems) with 1 × buffer, 5.5 mM MgCl 2 , 500 mM each dNTP, 2.5 mM random primers, 1.25 U/mL reverse transcriptase (Multiscribe reverse transcriptase) and DNAse-RNAse-free water as needed. After 10 minutes at 25°C, reaction was carried at 48°C for 30 minutes, followed by enzyme inactivation at 95°C for 5 minutes. Standard preparation Jurkat cell cDNA was serially diluted in 1:10 ratio with DNAse-RNAse-free water in order to obtain 6 logs of standards from one million cells per millimeter to ten cells per milliliter. They were numbered accordingly to their log number (6 to 1). Real-Time PCR quantitation GRα primers and probes were designed with Primer Express v.1.5 (Applied Biosystems) and both GRα and BCR (a normalizing gene) primers sets target two different exons, in order to prevent genomic amplification. BCR primers were previously used by Branford et al and our group [ 16 , 17 ]. Primers and probes used are indicated in figure 2 . Figure 2 Primer and probe hybridization sites for each transcript. The 5' end of the TaqMan probe is labeled with the reporter dye and the 3' end with the quencher dye. GRα stands for glucocorticoid receptor alpha isoform and BCR for breakpoint cluster region, used as a normalizing gene. PCR conditions were equal for both genes, using TaqMan PCR Core kit (N808-0228, Applied Biosystems). Briefly, 1 × TaqMan buffer A, 500 μM each dNTP, 4.5 mM MgCl 2 , 200 nM of each primer, 100 nM of probe, 0.025 U/μL of AmpliTaq Gold, 5 μL of cDNA and water were incubated in a total volume of 25 μL. Cycle conditions on an ABI 7700 (Applied Biosystems) were: 95°C for 10 minutes (AmpliTaq Gold activation) followed by 45 cycles of 95°C for 15 seconds (denaturation) and 60°C for 90 seconds (annealing and extension). No template controls and duplicates of each standard, for both genes, were used in each run. Results were exported to MS-Excel to perform linear regression analysis for each gene, determining a standard-curve for Log of EC (equivalent of cells) based on the average Ct of duplicates (fig. 3 ). Figure 3 Standard-curves for GRα (A) and BCR (B) in a typical experiment. In the upper part of the graph, we show the linear regression analysis equation and coefficient of correlation for log of Jurkat cell equivalents of expression and real-time PCR Ct (cycle-threshold), for each gene. The average Ct for each sample was employed in the linear regression equation in order to obtain the EC, for each gene. Then, GRα-EC was divided by BCR-EC in order to establish the GRα-EU (Expression Units). Standard-curve evaluation In order to evaluate standard-curve stability with this process, we used five different sets of cell culture passages, trypan blue viable cell counting, RNA extraction, cDNA synthesis and qrt-PCR. These standard-curves sets were named A to E. We calculated the mean, SD and CV for each standard, the CV for standard-curves slopes, as well as Pearson correlation coefficient for each pair of curves. Precision evaluation Intra-assay precision was evaluated in a single experiment that included standard-curves for each gene and 12 replicates for each gene, in samples obtained from two normal controls (1 and 2). In order to calculate GRα-EU, pairs of samples (GRα and BCR) were established according to their position on the thermocycler. Inter-assay precision was evaluated over eight different experiments, each one with its own standard-curve and duplicates of each gene, with samples obtained from two normal controls (1 and 3). Duplicates were averaged for calculation of GRα-EU. Statistical analysis Standard-curves and GRα-EU were calculated on MS-Excel 2000 for Windows. Linear regression of standard-curves pairs was calculated using SPSS 10.0 (SPSS, Chicago). For comparison of intra and inter-assay results of case 1, we employed Mann-Whitney Rank Sum test on SigmaStat v.2.03 (SPSS, Chicago), which was also used to perform the t-test. Differences of p < 0.05 were considered statistically significant. Authors' contributions MRM planned, performed and analyzed real-time PCR reactions and wrote the manuscript; CDCF did sample collection, cell culture optimization, RNA extraction and cDNA synthesis; KCM did statistical calculations and prepared figures and manuscript; NAR provided real-time PCR support; CAL planned the study, revised experimental data and the manuscript.

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A Taxonomic Search Engine: Federating taxonomic databases using web services

Background The taxonomic name of an organism is a key link between different databases that store information on that organism. However, in the absence of a single, comprehensive database of organism names, individual databases lack an easy means of checking the correctness of a name. Furthermore, the same organism may have more than one name, and the same name may apply to more than one organism. Results The Taxonomic Search Engine (TSE) is a web application written in PHP that queries multiple taxonomic databases (ITIS, Index Fungorum, IPNI, NCBI, and uBIO) and summarises the results in a consistent format. It supports "drill-down" queries to retrieve a specific record. The TSE can optionally suggest alternative spellings the user can try. It also acts as a Life Science Identifier (LSID) authority for the source taxonomic databases, providing globally unique identifiers (and associated metadata) for each name. Conclusion The Taxonomic Search Engine is available at and provides a simple demonstration of the potential of the federated approach to providing access to taxonomic names.

Background Biological taxonomy provides the central link between diverse items of information about an organism. Given the scientific name of an organism, a researcher can query a wide range of databases for information on that organism's genome, development, morphology, geographic distribution, behaviour, phylogeny, and conservation status. However, the utility of taxonomic names as keys to accessing information is hampered by several factors, notably the lack of a single authoritative list of all taxonomic names [ 1 , 2 ]. In the absence of such a list, databases that make use of taxonomic names have no ready means of validating those names. Consequently, there is no guarantee that taxonomic names stored in different databases will be mutually consistent. In the absence of a single database of names, one solution is to use a federated approach [ 3 ] where multiple, independent databases are queried. Numerous taxonomic databases exist, although each tends to have limited taxonomic and geographic scope, and the degree of interoperability among these databases varies greatly. The NIH/NIAID/Wellcome Trust Workshop on Model Organism Databases [ 4 ] defines the minimum level of interoperability as providing a FTP dump of the database contents. The only taxonomic databases currently meeting even this minimum level are the Integrated Taxonomic Information Service (ITIS) [ 5 ] and the NCBI Taxonomy [ 6 ] databases. Greater degrees of interoperability require the availability of an explicit Application Programming Interface (API) that clients can use to query the database. Each taxonomic database provider has developed their own interface which is typically aimed at a single user with a web browser. Few databases provide an API, or better still, a documented API. Taxonomic names themselves have limitations as identifiers in databases [ 7 ] due to the existence of multiple names (synonyms) for the same taxon, and the use of the same name to refer to different taxa. For example, the genus Morus applies to both an animal (the gannet) and a plant (the mulberry tree). Even species names can be identical – a species of wasp and a species of conifer both share the name Agathis montana . Hence, using names alone to link different data sources can be prone to error. As an example, at the time of writing NCBI's LinkOut feature [ 8 ] mistakenly links the catfish genus Loricaria (tax_id = 52085) to the TreeBASE [ 9 ] taxon Loricaria (TaxonID = 1305), which is a plant genus (family Compositae). To avoid ambiguity some form of identifier other than a taxonomic name needs to be employed, such as Digital Object Identifiers (DOIs) [ 10 ] or Life Science Identifiers (LSIDs) [ 11 , 12 ]. Given such an identifier a user can unambiguiously refer to a name, and at the same time discover the provenance of that name (i.e., the source database). The use of globally unique identifiers in taxonomy is in its infancy: the use of DOIs has been explored in the context of prokaryote taxonomy [ 13 ], but LSIDs have yet to be employed for taxonomic names. Instead most efforts to link taxonomic databases use URLs (e.g., Species 2000 [ 14 ]) and NCBI Linkout [ 8 ]). However link integration using URLs has serious limitations [ 15 ]. Given the lack of a central list of names, and the limitations of names as identifiers, there is a clear need for a taxonomy name service that can validate names and provide unique identifiers [ 2 ]. The SPICE project [ 16 , 17 ] has explored the utility of a federated approach to querying taxonomic databases. For each database, SPICE requires that a wrapper is installed on the computer hosting that database. This wrapper communicates natively with the local database to perform a standard set of queries. The central query engine then communicates with each instance of the wrapper using a consistent protocol (e.g., CGI). This approach places much of the burden of interoperability on the source database, which must adapt and install the SPICE wrappers. This paper describes the Taxonomic Search Engine (TSE), which takes federated approach to the problem of searching for taxonomic names. Unlike the SPICE project, the TSE relies solely on the interfaces made available by the data source. A wrapper is created for each source database, but this resides on the same machine as the TSE. In this way, no special demands are made of the source database. The TSE searches multiple databases for a name, and returns the result in a consistent format. For each name, TSE also creates a LSID, so that each name from each source database has a globally unique identifier. Implementation Source databases The TSE uses five data providers: ITIS, Index Fungorum, IPNI, uBIO, and the NCBI. ITIS The Integrated Taxonomic Information System (ITIS) [ 5 ] was established in the mid 1990's by a consortium of United States federal agencies tasked with to providing a database of taxonomic information for North American taxa. In addition to the original site in the United States [ 5 ], there is a French language version hosted by the Canadian Biodiversity Information Facility [ 18 ], and a Spanish language version hosted in Mexcio [ 19 ]. The Canadian site can serve data in XML format, and users can search for a name, or retreive details about an individual record using a simple URL API. A Document Type Definition (DTD) file for the XML format is available from the ITIS web site. ITIS provides a classification of taxonomic names (i.e., a parent-child hierarchy), and where more than one name exists for a taxon, ITIS specifies which name it regards as correct (termed the "accepted" name if the taxon is an animal, and "valid" if it is a plant). Every name in the database, regardless of taxonomic status or position in the hierarchy is assigned a unique identifier (its "taxon serial number"). The database schema is fully documented, and the entire database is available for downloading by FTP as a SQL schema with the data in delimited text files. As a consequence, ITIS is frequently used as the de facto source of taxonomic data in biodiversity informatics projects. IPNI The International Plant Names Index (IPNI) [ 20 ] combines data from three sources: Index Kewensis (Royal Botanic Gardens, Kew), the Gray Card Index (Harvard University Herbaria), and the Australian Plant Names Index (Australian National Herbarium), and contains some 1.6 million records. It provides names and associated basic bibliographical details for vascular plants. The IPNI web site provides web forms for querying the database, and data can be returned in HTML, "%" delimited text, or XML. However, the XML is a serialisation of IPNI database objects, rather than a format designed to be handled by end users. There are plans to support emerging standards, such as the Taxonomic Concept Transfer Schema [ 21 ]. IPNI aims to be a catalogue of all names that have been applied to vascular plants. However, where more than one name for a taxon exists, IPNI does not specify which name should be used, that is, it does not indicate an "accepted name" for a taxon. In this sense it is That is, it is a nomenclatural database rather than a taxonomic database. However, if two names are nomenclatural synonyms, the HTML output specifies the nature of synonymy, such as "basionym" (one name is the original name for the taxon), "nomenclatural synonym" (one or other of the names is the basionym, or the names share a basionym), or "replaced synonym" (one name has been created to replace another). IPNI provides a minimal classification, in that genera are assigned to families, but no higher-level classification is given. Index Fungorum IndexFungorum [ 22 ] is a database of over 370,000 names of fungi, primarily at species level. The database can be searched through a web interface or through a SOAP web service which returns an XML document. If more than one name exists for a fungus, Index Fungorum designates one name as the "current name." It also reports the basionym (first recorded name) for that taxon. Index Fungorum does support a detailed hierarchical classification in the form of a lineage, but higher level taxa are not assigned records in the database (unlike, for example, ITIS). In fungal taxonomy, names are often assigned to the asexual state (anamorph) of a fungus for which the sexual state (telomorph) is unknown. Names for anamorphs are flagged as such in the database. uBio The Universal Biological Indexer and Organizer (uBio) [ 23 ] is a product of the science library community, and is motivated by the information retrieval problem posed by the lack of long term stability of many taxonomic names [ 2 ]. Presently it is the single largest electronic catalogue of scientific names (1,396,868 as of 13 November 2004). In addition to a web interface uBio provides a SOAP web service which returns a nested array data structure. NCBI The NCBI Taxonomy database [ 6 ] is a curated database of the names of all organisms for which sequences have been submitted to GenBank [ 24 ]. Each taxon regardless of taxonomic level is assigned a unique identifier (the "taxid"), and the NCBI taxonomy provides a single classification for all taxa in its database. If a taxon has more than one scientific name, each name has name has the same taxid, but only one is indicated as the "scientific name" [ 25 ]. The other names are flagged as synonyms, common names, etc. The NCBI taxonomy is not intended to be an authoritative source of taxonomic information, but is a rapidly grouping database that contains many taxa that are not found in other databases. Although every sequence in NCBI is assigned to an organism, in many cases the exact identity of that organism may be unknown. Sequences obtained from environmental sampling are typically unidentified, and the number of such sequences is likely to increase with the advent of large scale environmental genomics [ 26 ]. The NCBI taxonomy database can be queried via the Entrez Utilities [ 27 ] using wither a URL or a SOAP interface. The entire database is also available for download by FTP. Architecture The basic architecture of the TSE is summarised in Fig. 1 . For each database a wrapper (implemented as a class in the PHP scripting language) is responsible for communicating with the database, using either the HTTP GET protocol (using the Net HTTP Client [ 28 ] library) or SOAP (using the NuSOAP library [ 29 ]). The wrapper takes the query string supplied by the user, and constructs a suitable query for the corresponding database, such as a URL or a SOAP call. The wrapper is also responsible for handling the response. If databases return a XML document this is transformed using an XSLT style sheet into the XML format used by TSE. Other formats such as text or SOAP data structures are converted into XML by the wrapper. Each wrapper is derived from the same base class which provides some generic routines for creating XML documents and for caching results (see next section). The wrapper class supports three methods, IsAlive , NameSearch , and GetDataForID , which must be overridden in descendant classes. The IsAlive method queries whether the data source is available. The NameSearch method queries a data source for a given string. If one or more names are found, NameSearch returns basic information about that name, including the identifier used by the data source. This identifier is used by the GetDataForID method to query the data source for more details about the name. Caching results In order to improve the responsiveness of the search engine, the results of queries to each source database are cached for 24 hours. The results of the query are stored in the format returned by the database (i.e., XML or delimited text), except for uBio where the SOAP response is serialised to disk. Approximate string matching The Taxonomic Search Engine seeks exact matches to the user supplied query. In order to accommodate spelling mistakes the web interface to the search engine supports approximate string matching using two techniques. The first employs agrep [ 30 ] to search for a match amongst a flat file list of names obtained from the ITIS and NCBI databases. Names showing no more than two character differences from the query string are returned as suggested alternative spellings. To supplement agrep, the TSE calls Google's spelling suggestion web service [ 31 ] and adds the result of that query (if any) to the list of suggested spellings. Interface The TSE has a simple web interface (Fig. 2 ). The user types in a query, and has the option to specify whether TSE should look for alternative spellings. Clicking on the "Go" button starts the search. The XML summary of the search is transformed into HTML using an XSLT transformation. The user can click on a name to get more information, including a link to the original database source for the name, and a LSID for the name. Web service The TSE has a SOAP web service that is described by a Web Services Description Language (WSDL) file available at . The service provides two operations: NameSearch which queries the source databases for a user-supplied name, and SpellingSuggestion , which suggests alternative spellings for a name. Hence users can write web service clients that can use the TSE as part of their own applications. The TSE web site provides source code for two simple clients written in perl. Life Science Identifiers A LSID is a Uniform Resource Name (URN) comprising five parts: the Network Identifier ("lsid"), the root DNS name of the issuing authority, a namespace, an object identifier, and optionally a revision id to indicate the version [ 11 ]. TSE generates LSIDs by concatenating the name of the source web server with the suffix "lsid.zoology.gla.ac.uk" to generate the authority. The namespace is the name given to the identifier in the source database, and the object identifier is the identifier used by the source database. For example, the record for Homo sapiens in the ITIS database would have the LSID: urn:lsid:itis.usda.gov.lsid.zoology.gla.ac.uk:tsn:180092 where "tsn" is the "taxonomic serial number" used by ITIS as a unique identifier for each taxonomic name, and "180092" is the tsn for Homo sapiens . The TSE uses the perl library distributed by IBM's Life Science Identifier project [ 11 ] to create a LSID authority for each of the source databases. Hence, any software that can resolve LSIDs (such as LaunchPad [ 11 ] or the BioPathways Consortium Web Resolver [ 32 ]) can view the metadata associated with an LSID generated by TSE. For ITIS this metadata is constructed by querying a local copy of the ITIS database, but for the remaining databases the LSID metadata is generated using the same combination of GET/HTTP and SOAP calls used to query the source databases by TSE (although these calls are implemented in perl). Performance evaluation The 2004 edition of the Species 2000 CD-ROM [ 14 ] was used as a source of names with which to query the TSE. This database comprises 583,469 names provided by 18 taxonomic databases, two of which (ITIS and Index Fungorum) are also source databases for TSE. In addition, uBio currently includes names from the 2003 edition of the Species 2000 CD-ROM in its database. Hence, most names in the Species 2000 list are likely to be found by TSE. To create a test dataset, 1000 names were selected at random from the Species 2000 dataset. Each name was sent to the TSE web service by a perl script which recorded the time taken for each source database to respond to the query, and whether that source database contained the name. The time recorded is from the time the query was made until the time the response was returned – post processing by the TSE is not included in the measurement. For this experiment, the cache feature was turned off so that for each query the TSE went to the external source database, rather than using a local copy of the query result. Results and discussion Performance The results of the simple performance benchmarks are shown in Table 1 . Most of the names were found in uBio (887 of the 1000 names), which is as expected given that uBio has harvested all the names in the previous (2003) edition of the Species 2000 CD-ROM. ITIS is a major contributor to both uBio and Species 2000, and just over half the names in the test set are present in ITIS. The Species 2000 CD-ROM contains some names from Index Fungorum, and none from IPNI, hence its coverage of plants and fungi is somewhat limited. That only 10% of the query names were found in the NCBI database suggests there is little overlap between the taxa being catalogued by taxonomic databases and those being sequenced. Amongst the five source databases, ITIS had the slowest median response time (0.915 seconds) and Index Fungorum was the quickest (0.132 seconds). The IPNI database was the second slowest, and occasionally took up to a minute to respond – on 20 occasions no response was obtained at all. It is difficult to generalise about these results as the performance of a data source will depend on a number of factors, such as the server hardware and software, the database design, and the load other users are placing on the system. For the five data sources currently queried, the operating systems being used include both Linux and Windows 2000, the web servers are Apache, Oracle HTTP server, and Microsoft IIS (determined by NetCraft [ 33 ]), and the database vendors include Microsoft, Oracle, and MySQL. However, it is encouraging that five such disparate systems all have a median response time of less than a second. Extensibility The TSE can be extended to handle additional data sources simply by deriving a new wrapper class from the base class. To date wrappers have only been written for data sources which can return plain text, XML, or SOAP messages. There are many more taxonomic databases that could be queried if wrappers were written to handle HTML output ("screen scraping"). However, this would make the wrapper very vulnerable to changes in web page design [ 34 ]. Of course, a change in a data source's API would also break the wrapper. This is a general problem in integrating disparate databases [ 34 ], and in the long term a better solution would be for each taxonomic database to support a standard API that services such as the TSE can query. Scalability Despite the reasonable performance of TSE, there are obvious limitations in the current design and implementation. The PHP language does not support threads, so each source database is queried sequentially. As additional source databases are added the time to complete the search will get progressively longer. If the performance of additional databases is comparable to those already being queried (Table 1 ), then each new source will add at least 0.5 – 1.0 seconds to the time required for TSE to return a result (not counting the additional overhead of pre- and post-processing the query). If the search engine is to scale to handle a large number of databases it is likely that these databases will need to be queried in parallel. Query filtering Some source databases have broad taxonomic coverage such as ITIS, NCBI, and uBio, whereas others are restricted to particular groups, such as fungi (Index Fungorum) and vascular plants (IPNI). Hence, it makes little sense to query Index Fungorum or IPNI for an animal name (especially as this will could 1–2 seconds onto the time taken to complete the search). An option to select the databases to query could be easily added to the TSE web interface. However, it would be more efficient if the TSE could determine which databases were relevant to the user's query. If the TSE knew that the query string was the name of a fungus, it could send the query to the appropriate database. In practice, however, this is problematic. In order to know what organism a name refers to the TSE would have to have access to a databases of names and their classification – the very lack of such a database is the motivation behind the TSE in the first place. Furthermore, as discussed above, the same name can apply to different organisms. A user searching using the term "Morus" might be looking for a plant name, or an animal name (or perhaps both). There is some scope for more intelligent querying, such as looking for aspects of the name that are specific to one of the codes of nomenclature (e.g., most plant family names end in "-aceae"), but any such effort needs to be done with care – for example, "Compositae" is a family of plants. Conclusion The Taxonomic Search Engine is a simple tool for querying multiple taxonomic databases. Typically, results of querying five major databases are returned in a few seconds. In addition to providing basic information about a name, the TSE acts as a LSID authority, providing globally unique identifiers for each name. The TSE provides a simple demonstration of the potential of the federated approach to providing access to taxonomic names. Availability and requirements The source code for the TSE, the web site, and the LSID authorities is available from the TSE site . System requirements TSE requires a web server and the PHP scripting language. It has been developed and tested under Red Hat Linux 8.0 with the Apache web server version 2.0.40 and PHP version 4.2.2, and Mac OS X 10.2.8 with Apache version 1.3.29 and PHP version 4.3.4. If PHP does not have the XSLT extension enabled then the user will either have to recompile PHP, or install the Sablotron toolkit [ 35 ]. The code makes use of various PHP libraries including NuSOAP [ 29 ], Net HTTP Client [ 28 ], Php.XPath [ 36 ], and phpdomxml [ 37 ]. The approximate string matching feature requires agrep to be installed (available from ), and a developer key from Google [ 31 ].

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Evolution of sexual asymmetry

Background The clear dominance of two-gender sex in recent species is a notorious puzzle of evolutionary theory. It has at least two layers: besides the most fundamental and challenging question why sex exists at all, the other part of the problem is equally perplexing but much less studied. Why do most sexual organisms use a binary mating system? Even if sex confers an evolutionary advantage (through whatever genetic mechanism), why does it manifest that advantage in two, and exactly two, genders (or mating types)? Why not just one, and why not more than two? Results Assuming that sex carries an inherent fitness advantage over pure clonal multiplication, we attempt to give a feasible solution to the problem of the evolution of dimorphic sexual asymmetry as opposed to monomorphic symmetry by using a spatial (cellular automaton) model and its non-spatial (mean-field) approximation. Based on a comparison of the spatial model to the mean-field approximation we suggest that spatial population structure must have played a significant role in the evolution of mating types, due to the largely clonal (self-aggregated) spatial distribution of gamete types, which is plausible in aquatic habitats for physical reasons, and appears to facilitate the evolution of a binary mating system. Conclusions Under broad ecological and genetic conditions the cellular automaton predicts selective removal from the population of supposedly primitive gametes that are able to mate with their own type, whereas the non-spatial model admits coexistence of the primitive type and the mating types. Thus we offer a basically ecological solution to a theoretical problem that earlier models based on random gamete encounters had failed to resolve.

Background One of the most general rules in biology seems to be that sex involves the fusion of gametes (sometimes of other specialised structures) of different type. In most taxa this sexual asymmetry is reflected in the male / female distinction between mating partners and/or between mating sex cells. This paper aims to help understand why sex is asymmetric. The primary difference between male and female is anisogamy, the differential size and mobility of gametes. Anisogamy is thought to have evolved from a more primitive condition of isogamy (for reviews see [ 1 ]; [ 2 ] see also [ 3 ]). In isogamous species without apparent male-female differentiation, like unicellular green algae (e.g. Chlamydomonas ) and fungi (e.g. yeast), the asymmetry in sexual fusion and subsequent development are regulated by a binary mating type system. Mating is only possible between cells of different mating type. Molecular analysis has revealed a remarkable and complex genetic mating type structure [ 4 , 5 ]. The two mating types in a species consist of so-called idiomorphs [ 6 ], non-homologous complexes of closely linked genes that occupy homologous positions at the same chromosomal locus. They behave as alleles in being mutually exclusive in meiotic segregation. A similar binary mating type system exists in many filamentous ascomycetous fungi [ 7 ], which however often also exhibit male / female differentiation. Only matings between individuals of different mating type are allowed. Thus in mycelia that can function both as male and as female self-mating is prevented. Mating in such species is heterothallic, that is, always between different individuals. However, many ascomycetes are homothallic, i.e. can complete the sexual cycle in a single individual. Homothallic species may lack mating types, such as Aspergillus nidulans , or may consist of individuals that are heterokaryotic for mating type (carry nuclei of both mating types) such as Podospora anserina. In the latter case sexual fusion is between different mating types at the nuclear level, but can occur within a single individual mycelium. In basidiomycetous fungi, morphological sexual differentiation is absent, but mating is regulated by complex mating systems, generating in some cases large numbers of different mating types. Also here, the mating type genes control sexual fusion and post-fusion development [ 8 ]. Again, mating cannot occur between individuals of the same mating type. In other taxa still other genetic systems exist that control sexual fusion, sometimes in addition to the male-female difference. In monoecious higher plants often self-incompatibility systems occur that effectively exclude self-mating [ 9 , 10 ]. Among ciliates, several variations on the theme of mating type differentiation exist, which are not further detailed here. All these different mating systems have one characteristic in common: mating is always asymmetric. When gender differences exist, mating involves the fusion of a male and a female cell; this may occur when the male and female functions are in different individuals, or when a single individual possesses both male and female functions. When gender differentiation is absent, mating type systems guarantee that sexual fusions are between different types. However, the absence of both gender and mating type differentiation has never been observed. This would imply symmetric sexual fusion: a species in which every sex cell could potentially fuse with any other sex cell. Because gender differences starting with anisogamy most likely evolved from pre-existing isogamy, we should consider the evolution of mating types in an isogamous species to understand why sex is asymmetric. Functional explanations of the evolution of a binary mating type system have been explored in theoretical models by [ 11 - 13 ] and [ 14 ]. These models differ in their biological assumptions. According to [ 12 ] and [ 13 ], mating types have evolved to suppress harmful conflicts between cytoplasmic elements, while [ 11 ] suggests that mating type loci have evolved in response to polymorphisms for genes involved in gamete recognition. It is still not possible to conclusively decide between the alternative biological scenario's [ 15 ]. However, all models envisage as a starting point an initially undifferentiated population in which every gamete can mate with any other gamete, and derive conditions for the evolution of two mating types that exclusively mate with each other and have lost the ability to mate with their own type. A general conclusion emerging from the models is that mating types may invade the initially undifferentiated population under fairly broad conditions, but that the removal of the undifferentiated type requires very strong selective forces. It is this latter aspect which in our view still forms a problem, because it is difficult to see why the original type should be so disadvantageous compared to the differentiated mating types. The mentioned models assume a homogeneous population in which random encounters lead to mating. However, this assumption is likely to be very unrealistic if vegetative reproduction is much more frequent than sexual reproduction, like it is in present-day protists, and if the mobility of the cells is low. Since the motion of cells or gametes in water is characterized by a Reynolds number (the ratio of the inertial forces to the viscous forces) smaller than one [ 16 ], clonally related cells will tend to remain in each others vicinity, and therefore a clonal distribution of cells and gametes is expected, rather than a well-mixed homogeneous population. This implies that mating types will have a smaller chance of finding a suitable mating partner than in a homogeneous population, since they are unable to mate within their clone, while the undifferentiated gamete type has no reduced opportunity for mating, although most matings will be intra-clonal. As shown in a theoretical study by [ 17 ] the "mating kinetics" may strongly influence the optimality of a sexual system. In order to investigate the effects of spatial population structure on the evolution of mating types, we have analysed this process in a cellular automaton model and compared the results it yields to those of the corresponding non-spatial (mean-field) approximation. Such a comparison allows precise consideration of the kinetics of gamete encounters in the model system and emphasizes the role that spatial aspects of the kinetics might have played in mating type evolution. For detailed descriptions of both models see the Methods section. Results The specific questions we address with both the mean-field model and the cellular automaton are the following: a) Are there reasonable parameter values that allow the coexistence of the mating types and the pan-sexual type? b) Under what (if any) circumstances is it possible that the mating types exclude the pan-sexual type? c) Does spatial structure play an important role in the outcome of the mating type competition system? Coexistence of the two Mating Types and the Pan-Sexual Type Numerical solutions to the mean-field model and simulations with the cellular automaton reveal that the system admits a single stable equilibrium state both in the non-spatial and in the spatial setting (see eg. Fig. 1 ). The actual equilibrium densities depend on the parameters, i.e., on the vegetative growth rates r , R , R' , the vegetative death rates d , D , D' , the germination rate g , the sex rate σ and the finess erosion rate φ in the mean-field, and the corresponding probability parameters in the cellular automaton model. Having explored a broad range of the parameter space – with straightforward constraints on the fitness parameters (birth and death rates), i.e., with D ≤ D' ≤ d < r ≤ R' ≤ R – we found that it is the strength of the inbreeding effect (the difference of D and D' and that of R' and R ) and the rate of fitness erosion φ that has the most interesting effects on coexistence. Changing the remaining parameters – the sex rate and the germination rate – within reasonable limits ( σ > 0, g > 0) does not affect the results in a qualitative sense. Figure 1 Mating type, pan-sexual and zygote abundances in time, at zero fitness erosion rate ( φ = 0)) and zero inbreeding effect ( ξ = 0). Other parameters (in all simulations): Mean-field (upper-panel): birth rate of pre-zygote cells: 0.001; birth rate of post-zygote cells: 0.0015; death rate of pre-zygote cells: 0.12; death rate of post-zygote cells: 0.08; sex rate: 0.0003; germination rate: 15.0; grid size: 90.000. Cellular automaton (lower-panel): birth probability of pre-zygote cells: 0.8; birth probability of post-zygote cells: 0.9; death probability of pre-zygote cells: 0.3; death probability of post-zygote cells: 0.2; sex probability: 0.8; germination probability: 0.8; grid size: 300 × 300 (= 90.000) See the Methods section for details. We have scaled the inbreeding effect into a single parameter ξ , defined by the equations D' and R' have been replaced by D ξ and R ξ both in the mean-field model and in the spatial simulations, with ξ changing from 0 to 1 along the "inbreeding effect" axis of the graphs in Fig. 2 and Fig. 3 . ξ = 0 represents no inbreeding effect (i.e., the vegetative cells germinated from outbred zygotes have the same fitness as those produced by inbred zygotes), and ξ > 0 means a fitness difference in favour of outbred offspring. Figure 2 Simulation results: A) mean-field: fitness erosion rate range φ : 0.0 → 20.0; inbreeding effect range ξ : 0.0 → 1.0; abundance range N : 0 → 90.000. B) cellular automaton: fitness erosion probability range φ : 0.0 → 1.0; inbreeding effect range ξ : 0.0 → 1.0; abundance range N : 0 → 90.000 Figure 3 Simulation results with 40% sex rate (sex probability) reduction in the pan-sexual strain. Scales as in Fig. 2. A) mean-field B) cellular automaton Fig. 2 shows the equilibrium densities of the mating types and the pan-sexual type, the zygotes and the empty cells across a range of the ξ – φ projection of the parameter space, for both the mean-field model (Fig. 2a ) and the cellular automaton (Fig. 2b ). It is obvious from the graphs that the sum of mating types, pan-sexual and zygote equilibrium densities (and thus the equilibrium density of empty sites) is almost unaffected by the focal parameters, but the relative frequencies of the mating types and the pan-sexual type vary across the ξ – φ plane. This applies to both the mean-field and the spatial model. Role of space Fig. 2a and Fig. 2b might look quite alike at first sight, suggesting that spatial constraints like short-range interactions and limited dispersion might not play a decisive role in the dynamics of the gamete type competition system. Upon closer inspection of the data, however, this impression turns out to be wrong. Even though the general shapes of the 3D graphs are similar for the non-spatial and the spatial model, there are important differences between them affecting mainly the persistence of the pan-sexual population. One of these differences shows up in the biologically significant case of very small ξ and φ values. In the mean-field model, at ξ = 0, that is, at no fitness advantage for outbreeding, the pan-sexual strain excludes the mating types for any positive rate of fitness erosion ( φ > 0). At φ = 0 (no fitness loss during vegetative multiplications), on the other hand, it is the mating types who win for any ξ > 0. At ξ = 0 = φ , the mating types and the pan-sexual type coexist, and the same applies to any parameter combination satisfying ξ ≠ 0 ≠ φ . Thus we can say that the non-spatial (mean-field) model allows coexistence for almost any parameter combination, except for the biologically less feasible margins of the parameter plane. It predicts in general that both the mating types and the pan-sexual type should have persisted, even if at variable relative frequencies. The cellular automaton model yields a different prediction, admitting the exclusion of the pan-sexual type, i.e., the victory of the two mating types on a considerable section of the parameter plane, including the ξ = 0 = φ point and its close (and biologically the most realistic) neighbourhood (cf. Fig. 1 ). Alternative adaptations? One might guess that in the spatial model the ultimate exclusion of the pan-sexual strain – wherever it happens – is the result of its producing too many dormant zygotes. This would mean that the pan-sexual cells are too frequently induced to become sexually competent and that the resulting high mating frequency impairs their ecological competitiveness. With this hypothesis, a logical next question to ask is: can the pan-sexual strain prevent its elimination by lowering its sensitivity to the induction of sexual competence? With modified versions of both the mean-field model and the cellular automaton we have simulated the effect of such an "adaptation" (Fig. 3 ). The only modification made to the original models was the reduction by 40 percent of the chance that a pan-sexual cell gets induced by a neighbouring gamete resulting in mating. As it is obvious from a comparison of Figs. 2 and 3 , this does not solve the problem of the pan-sexual strain – to the contrary, the chances of the mating types to displace the pan-sexual are even slightly better in the modified models for the largest part of the parameter space. In the mean-field model the relative frequency of the pan-sexual population at equilibrium is smaller almost everywhere except for small nonzero values of the inbreeding effect (compare Figs. 2a and 3a ). In the cellular automaton the pan-sexual strain does a little better for very high values of both the inbreeding effect and the fitness erosion rate, but suffers more everywhere else compared to the original model without sex rate reduction (compare Figs. 2b and 3b ). Discussion There are a few conclusions that apply to any simulation regardless of its being non-spatial or spatial. Not surprisingly, increasing the fitness advantage of outbreeding ξ favours the mating types, because all their sexual interactions produce outbred offspring, while part of the matings of pan-sexual gametes always produces inbred offspring with a smaller fitness. Less obviously, increasing the fitness erosion rate φ benefits the pan-sexual type in general, because its effective sex rate is higher: every mating attempt of a pan-sexual gamete can be successful, unlike for the mating types which refuse inbreeding. Therefore the pan-sexual type has more chance than the mating types to reset its eroded fitness to the post-zygote level through mating. The faster the fitness erosion, the more pronounced the advantage of being pan-sexual, hence the more frequent the pan-sexual strain becomes. In the mean-field model the coexistence of mating types and the pan-sexual type at ξ = 0 = φ is a spatially unrobust phenomenon. It is highly dependent on the assumption that the system is well-mixed, i.e., each cell encounters other cells of each type with a probability exactly proportional to the relative frequency of that particular type within the whole habitat. It is the breaking of this interaction symmetry in the cellular automaton that gives the mating types a definite advantage compared to the pan-sexuals, even at ξ = 0 = φ (see Fig. 1 ). The detailed mechanism is as follows: At ξ = 0 it makes no difference whether the mating is inbred or outbred, and at φ = 0 the fitness advantage once obtained in a single event of sex cannot be lost. Since dormant zygotes do not die, empty sites can only be produced by the death of vegetative cells, but the death rates are all equal and independent of gamete type, because (after a short transient period) every vegetative cell is in the post-zygote state. For the same reason the birth rates are also equal for all the vegetative cells, so the only factor that can make a difference between the cell types is the availability of empty sites: the limiting "resource" for reproduction. In the mean-field model the empty sites are equally available to any cell, so the growth rates of the pan-sexual and the mating type strains are identical in the long run, hence their coexistence. In the cellular automaton, however, each strain develops patches. The mating type strains do not have sex at all within their own patch, only at the interface with the patches of other strains. The pan-sexual strain has sex all the time everywhere in the habitat, therefore a larger part of its population is in the dormant zygote state. It is for this reason that at the interface with the mating type patches the pan-sexual strain has a smaller supply of vegetative invaders and thus a smaller chance to capture an empty site there. This results in a travelling front between a mating type patch and a pan-sexual patch and ultimately in the demise of the pan-sexual population altogether. This effect can even overcompensate a small disadvantage for the mating types arising from increasing the rate of fitness erosion φ slightly above 0, therefore the close neighbourhood of the ξ = 0 = φ point on the parameter plane belongs to the mating types as well. We think that it is exactly this mechanism that makes the mating types victorious in the spatial model at many parameter combinations that allowed for coexistence in the mean-field approximation. The elementary events at the interfaces between patches of different gamete types have a profound effect on the ultimate outcome of their competition at the larger spatial scale of the whole habitat. An alternative explanation for the difference of mean-field and cellular automaton results could be that it is the finite size effect that kills off the pan-sexual population from the spatial model at many parameter combinations. Indeed, the cellular automaton is a finite system, the margins of the state space of which are sinks, but looking at the striking difference of the behaviours of the frequency trajectories at ξ = 0 = φ for example (or anywhere else where the mating types take over) in the two models proves that it is not stochastic drift but a real dynamical trend that eliminates the pan-sexual strain in the cellular automaton (see Fig. 1 ). The equilibrium value for the pan-sexual type is so far from zero in the mean-field model and its decrease to zero so steady in the cellular automaton that drift as the cause of the difference can be safely ruled out. Moreover, if the pan-sexual strain could be drifted to extinction, so could the mating types, but in fact we have never obtained ambiguous outcomes: sufficiently long replicate simulations always yield the same result. This applies to the whole range of the parameter space. In order to explain the net effect of sex rate reduction on the fitness, and thus on the survival chances of the pan-sexual population one has to consider two different aspects. On the one hand, sex rate reduction decreases the relative fitness of the pan-sexual strain, because it decreases the frequency of both its inbred and outbred matings, the means of keeping fitness high. This negative fitness effect is most pronounced at high rates of fitness erosion φ . On the other hand, less frequent sex yields fewer zygotes, i.e., fewer dormant cells with 0 growth rate (recall that zygotes do not multiply and do not die). If the populations are viable, i.e., if they have a vegetative growth rate higher than 0, then less frequent mating (dormancy) is beneficial in terms of the average fitness of the pan-sexual population. This effect dominates at low values of φ , where the fitness advantage of sex does not vanish too fast. A comparison of Figs. 2 and 3 shows that neither these effects are strong, but both are detectable. The net influence on the mean-field model is quantitative, the size of the parameter domain of coexistence is not much affected. In the cellular automaton model the overall effect of sex rate reduction is a slightly larger domain of coexistence: the pan-sexual strain cannot exclude the mating types at high fitness erosion rates, and it is somewhat more persistent at medium values of φ . In all, it is quite obvious that sex rate reduction is not an efficient strategy for the pan-sexual strain to avoid exclusion by the mating types. There is a logical possibility that asymmetric cell fusion has evolved for other reasons than and prior to sex and has subsequently been incorporated in the evolution of a full sexual cycle (the sequence of syngamy, karyogamy and meiosis). In that case sex would have been asymmetric from the start. This speculative idea has been analysed theoretically by [ 18 ] (see also [ 19 ]). The present analysis clearly does not apply to that scenario, but implicitly explains why sexual asymmetry did not disappear once evolved. Conclusions Assuming that sexual reproduction confers some average fitness advantage compared to simple clonal multiplication, and also supposing that the more genetically different the fusing gametes are the bigger the fitness benefit of the offspring can be, we show that a population consisting of two mating types can displace a pan-sexual population which is otherwise similar to the mating types in all other respects. In the most realistic domain of its parameter space (i.e., at low rates φ of the erosion of sexually gained fitness, and very slight extra fitness benefits for heterothallic – outbred – matings, ξ ) our spatial (cellular automaton) model shows the evolution towards exclusively two mating types, whereas the non-spatial model of the same system with the same parameters predicts the coexistence of the mating types and the pan-sexuals. Thus, taking for granted that sex is profitable in evolutionary terms, we offer a basically ecological answer to the question why two mating types can be better than just one. This is, however, only a solution to half of the problem of the optimal number of mating types. Could a third, a fourth, a fifth etc. mating type invade the same system? These questions arise on a very general level in relation to the origin of sexual asymmetry, and they call for a more extended theoretical approach in the future. Methods The Mating Type Competition System The basic setup of our model is similar to that of [ 11 ]. The model organism is an aquatic unicellular 'alga' with a haplontic life cycle. Three different types of haploid cells compete for space and reproduce both vegetatively and sexually. During the periods between instances of sexual reproduction, the cells multiply vegetatively, producing genetically identical daughter cells. When entering the sexual cycle, a vegetative cell turns into a gamete that can fuse with another gamete. In their gamete stage the three types of cells differ in their mating capacities as represented by different configurations of recognition molecules on the cell surface, as shown on Fig. 4 . Figure 4 Supposed recognition molecules on the cell surface of the "pan-sexual" type (G1) and the two mating types (G2 and G3) The first gamete type G 1 is 'pan-sexual' and can mate with any potential partner including its own type, while the other two, G 2 and G 3 , are mating types, unable to mate with their own kind. Thus the system allows four kinds of matings: G 1 .G 1 , G 1 .G 2 , G 1 .G 3 and G 2 .G 3 of which only the last one involves both mating types. In this basic model we furthermore specify the following assumptions. The fitness of a vegetatively produced daughter cell is equal to (or lower than, see below) that of its parent. Sexual fusion produces a dormant zygote which upon germination gives rise to haploid vegetative cells through meiotic division, in which the parental gamete types segregate as if determined by a mendelian pair of alleles. To these meiotic products – "post-zygote" vegetative cells -a higher fitness, i.e., a higher division rate and/or a lower death rate, is attributed than to "pre-zygote" vegetative cells not having gone through a sexual cycle in the near past. That is, we assume that sexual offspring have an immediate short-term fitness advantage over asexually derived daughter cells. The actual advantage may be dependent on whether the zygote has been produced by "outbreeding" (with at least one of the gametes involved belonging to one of the two mating types) or "inbreeding" (both gametes pan-sexual). In general we may, but need not, assume that inbred zygotes yield vegetative cells of somewhat less (but still positive) fitness advantage than outbred zygotes. Note that here "outbreeding" and "inbreeding" mean mating between different and identical gamete types, respectively, i.e., we assume – without specifying the precise nature of this outbreeding advantage – that mating between different gamete types may result in fitter offspring on average than mating between cells of the same (pan-sexual) gamete type. The simplest possible genetic mechanism with this effect might be the production of recombinant offspring carrying fewer (slightly) deleterious alleles than both parental genotypes. This mechanism will be operative more often in heterotypic than in homotypic matings, because among the latter a larger proportion will involve selfing (mating between genetically (almost) identical genotypes). The fitness advantage of sexually derived vegetative cells fades away in time during successive rounds of vegetative reproduction (fitness erosion due to the accumulation of harmful mutations), but it can be re-gained through another sexual event. This means that post-zygote cells return to the pre-zygote state when they are not involved in a new sexual cycle for a sufficiently long time. As for the ecology of the system, we assume that the habitat consists of a limited amount of sites that cells can occupy, and that the three cell types are competing for these sites. Death events leave empty sites behind, which can be occupied later by new offspring. The chance of a newborn cell to settle is proportional to its division rate and the number of empty sites available. In accordance with what has been said earlier about the fitness advantages of sex, three different division rates and death rates are possible: one for pre-zygote, the second for inbred post-zygote, and the third for outbred post-zygote vegetative cells. The straightforward fitness order of these three types is: W pre - zygote < W post - zygote , inbred ≤ W post - zygote , outbred . The fusion of two gametes produces a zygote of double size compared to a gamete, and the zygote enters a dormant state with zero rates of division and death. Zygotes leave dormancy at a constant rate, giving rise to post-zygote vegetative cells which inherit the mating type of the gametes they are produced by, and gain fitness according to whether the mating was of the inbreeding or the outbreeding type. Fig. 5 is a diagram of the possible state transitions in the mating type system. The number of possible states for a site is 12 (including the empty state), according to the type of the cell occupying the site. Thus a site can be in any one of the 3 types of pre-zygote vegetative, 4 types of different zygote, 4 types of post-zygote vegetative, and the empty state. Figure 5 Box diagram of the mean-field model. Box arrows: death of vegetative cells; loop arrows: clonal division; full arrows: sexual fusion; dot-headed arrows: germination; dashed arrows: fitness erosion The Nonspatial Model Based on Fig. 5 , the mathematical formulation of the nonspatial (mean-field) model for the competitive mating type system is straightforward; the differential equations for the 12 site-states are: where x , y and p are the numbers of sites occupied by pre-zygote vegetative cells ( x and y : mating types, p : pan-sexual type), Z xy , Z xp , Z yp are the sites of outbred, and Z pp are those of inbred zygotes. Similarly, X , Y and P are sites of outbred, Q are those of inbred post-zygote vegetative cells. E is the number of empty sites within the habitat. The parameters of the model are listed and described in Table 1 . The right-hand side of the differential equations for the sites occupied by pre-zygote vegetative cells ( x , y and p ) has three terms. The first defines the vegetative fitness of the corresponding cell type (divisions and deaths under the competitive effect of all cell types present in the habitat), the second is the outflow from the pre-zygote vegetative state due to sex, and the third is the inflow due to the fitness erosion of post-zygote vegetative cells. Zygotes have no vegetative fitness; the first term in their differential equations is the inflow due to sex, the second is the outflow due to germination. Post-zygote vegetative cells have a vegetative fitness different from that of pre-zygotes (first term); they form zygotes fusing (after induction to sexual competence) with both pre- and post-zygote cells matching in mating type (second term); their fitness advantage erodes at a constant rate resulting in an outflow into the pre-zygote state (third term), and the germination of dormant zygotes maintains an inflow from the zygote states (fourth term). The number of empty sites is increased by the deaths of vegetative cells (first three terms) and decreased by the number of sites taken by newborn vegetative offspring (fourth term). The total number of sites does not change in time, so the 12 time derivatives sum up to zero. Table 1 Parameters of the non-spatial model: r pre-zygote birth rate R post-zygote birth rate (outbred) R' post-zygote birth rate (inbred) d pre-zygote death rate D post-zygote death rate (outbred) D' post-zygote death rate (inbred) σ sex rate g germination rate φ erosion rate of post-zygote fitness advantage Analytical solutions to this nonlinear model are out of question. We have chosen to find equilibria via numerical solutions, in order to be able to compare the results to those of the spatial model (see below). In all numerical calculations the initial populations were 10 pre-zygote vegetative cells of both mating types and the pan-sexual type, all other states had 0 initial abundances. The Spatial Simulation Model With assumptions as similar to the nonspatial system as possible, we have implemented a site-based (cf. [ 20 ]), spatially explicit stochastic cellular automaton model to which the nonspatial system above is a mean-field approximation. The arena of the spatial model is a set of sites arranged in a 300 × 300 square grid of toroidal topology to avoid edge effects. Each site can be occupied by any one of the 11 cell types (3 pre-zygote, 4 post-zygote vegetative types and 4 types of zygote) or it can be empty. Zygotes occupy two adjacent sites. The pattern is updated one randomly chosen site at a time, i.e., we use an asynchronous random updating algorithm. Any site chosen for update can be empty, occupied by a vegetative cell, or occupied by a zygote. We specify the algorithm for each of these cases in turn. A schematic diagram of a single step of updating is given in Fig. 6 . Figure 6 Flow chart of a single site update of the cellular automaton algorithm Empty site update After updating, an empty site can be occupied by one (and only one) of the vegetatively produced offspring of the cells in the 8 neighbouring sites (i.e., the Moore neighbourhood of the focal site), or it remains empty. Each vegetative neighbour i has a chance p i to put a daughter cell into the empty site. p i depends on the vegetative reproduction parameter β I (0 ≤ β I ≤ 1)of neighbour i . β I is the spatial analogon of r i in the mean-field model, and it takes one of three possible values depending on whether i is in the pre-zygote, the inbred or the outbred post-zygote state. Specifically, the chance of the empty site to remain empty is so the probability that the offspring of neighbour i takes the site is The rationale behind this formalism is that each neighbour attempts putting an offspring into the empty site with a probability β i , but only one of the candidate offspring survives. The chance of survival is proportional to the reproduction parameter of the mother cell. Vegetative site update Updating a site occupied by a vegetative cell may result in four possible outcomes: turn the site into the empty state (death), leave it as it was (survival maintaining fitness), change the vegetative status of the resident cell from post-zygote to pre-zygote (survival with fitness erosion), or produce a zygote (sex). The probability of a death event depends on the death probability δ of the cell occupying the site, which in turn depends on its vegetative status (pre-zygote, inbred or outbred post-zygote). With a mating partner in one of the neighbouring sites, a surviving vegetative cell may enter the sexual cycle with probability s turning itself and a randomly chosen, suitable neighbour into gametes, and mate. The result is a dormant zygote occupying the two neighbouring sites of the fused gametes. A survivor skipping sex may keep its original fitness, or – if it was a post-zygote cell – it can lose its fitness advantage with a probability f (which is the spatial analogon of the fitness erosion rate φ in the mean-field model). Zygote site update A zygote can do two things: remain dormant (with probability 1 – γ ) or germinate (with probability γ ). A germinated zygote yields two vegetative cells, the mating types of which are the same as those of the gametes which produced the zygote. The vegetative status of the cells thus obtained is post-zygote, and they can be either inbred or outbred, depending on the parental gamete type combination. The daughter cells are positioned at random into the two sites the zygote had occupied. At time 0 we have populated 2% of the sites by pre-zygote vegetative individuals of both mating types and the pan-sexual type, assigning individuals to sites at random. All other sites were empty at time 0. The simulations were run for 10.000 generations. Authors' contributions RH organized and coordinated the project, collected most of the literature and drafted parts of the manuscript. TC designed and built the models, drafted parts of the manuscript and the figures.

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529455

Clinical education of ethicists: the role of a clinical ethics fellowship

Background Although clinical ethicists are becoming more prevalent in healthcare settings, their required training and education have not been clearly delineated. Most agree that training and education are important, but their nature and delivery remain topics of debate. One option is through completion of a clinical ethics fellowship. Method In this paper, the first four fellows to complete a newly developed fellowship program discuss their experiences. They describe the goals, structure, participants and activities of the fellowship. They identify key elements for succeeding as a clinical ethicist and sustaining a clinical ethics program. They critically reflect upon the challenges faced in the program. Results The one-year fellowship provided real-time clinical opportunities that helped them to develop the necessary knowledge and skills, gain insight into the role and scope of practice of clinical ethicists and hone valuable character traits. Conclusion The fellowship enabled each of the fellows to assume confidently and competently a position as a clinical ethicist upon completion.

Background Bioethics is being integrated into healthcare settings more widely and systematically than ever before. In Canada, clinical ethicists are employed in many teaching hospitals and their presence is increasing in community hospitals and long-term care facilities. Although individuals who work in the field come from diverse backgrounds with a variety of skills and training, the roles that clinical ethicists fill have some commonalities. Most clinical ethicists serve as resource persons and engage in consultation services, research, education and policy development within a healthcare setting, as well as engage in organizational ethics activities [ 1 ]. The American Society for Bioethics and Humanities has developed a set of core competencies for health care ethics consultation [ 2 , 3 ]. The Society suggests that ethics consultants should have skills in three general areas (assessment skills, process skills, and interpersonal skills) and knowledge in nine areas. The Society suggests that competencies can be acquired through a variety of different approaches. Although there is general agreement that education and training for clinical ethicists are important, the most effective methods of delivering that training have not yet been clearly identified [ 1 , 4 - 9 ]. The fit between the education and training students receive and the ability to assume a position in bioethics upon completion has been questioned [ 9 , 10 ]. There is also debate as to whether education and training programs should become more uniform and homogenous or remain heterogeneous [ 9 , 10 ]. A clinical ethics fellowship is perceived by some to be one of the ways in which necessary core competencies can be acquired [ 2 ]. Currently, however, clinical ethics fellowship opportunities for individuals wishing to pursue a career as a clinical ethicist are relatively limited. In a fellowship, individuals are provided with real-time clinical opportunities to help them develop necessary knowledge and skills, gain insight into the role and scope of practice of clinical ethicists and hone their character over a period of time. Specifically, clinical ethics experience may assist individuals in the development of their abilities to identify and analyze ethical problems, use reasonable clinical judgment, communicate effectively, negotiate and facilitate when there is conflict, and act as a resource for healthcare professionals who are faced with the daily challenges of delivering ethical care. The University of Toronto Joint Centre for Bioethics (hereafter referred to as the JCB) developed and implemented a clinical ethics fellowship program to assist in meeting the identified need for clinical knowledge and skills [ 2 , 3 , 6 , 11 ]. In this article, a description of the fellowship is provided, including its goals, structure, participants and activities. By reflecting on their experiences, the authors, who were the program's first four participants, discuss how the clinical ethics fellowship helped prepare them to work as clinical ethicists. They identify key elements they perceive as necessary for success as a clinical ethicist and for developing an effective clinical ethics service. As well, they critically reflect upon the challenges faced as they progressed through the program. Method University of Toronto Joint Centre for Bioethics Clinical Ethics Fellowship The JCB is a collaborating centre of the World Health Organization. It was formed in 1995 and is a partnership among the University of Toronto and its affiliated hospitals. With a membership of over 160, approximately 20 of whom work full-time in bioethics, it represents the largest multi-disciplinary group of in-hospital ethicists in Canada. Its members are widely published and actively engaged in a number of locally and nationally funded ethics research projects. In addition to the clinical ethics fellowship, the JCB offers two bioethics graduate programs. The first two participants in the JCB's clinical ethics fellowship entered the program in July 2001. The second cohort of two fellows began the program in August 2002. (In September 2003, the program expanded to include three fellows, and in September 2004 grew to five fellows.) The primary purpose of the one-year fellowship program is to provide the necessary preparation individuals require for a smooth transition from academic and clinical education, training and experience to the position of clinical ethicist. The fellowship provides multi-site clinical ethics opportunities at both specialty and general hospitals, exposes fellows to a variety of multi-disciplinary approaches to clinical ethics, supports the work of the ethicists at the JCB's affiliated hospitals, and, lastly, expands and strengthens the network among clinical ethicists, both within the JCB and across Canada. To be eligible for the fellowship candidates must have a graduate degree in bioethics or a professional degree with significant bioethics training. Preference is given to candidates with previous exposure to clinical bioethics including consultation and teaching experiences. Structure of the fellowship In the first two years the program of the program, each fellow rotated through four of the JCB's eight affiliated teaching hospitals, both specialty and general. As the number of affiliated hospitals has continued to grow, so too has the number of fellows. The fellowship was structured so that each fellow was concurrently assigned to two hospitals for a six-month period of time, averaging about two days per week on site at each hospital. A minimum of one day per week was spent working at the JCB where the fellows shared well-equipped office space. This functional arrangement promoted opportunities for collaboration, reflection and mutual support among the fellows. The fellows received a monthly stipend that was sufficient for covering basic costs of living. Results Activities in the fellowship Throughout the year, fellows attended and actively participated in the weekly Wednesday meetings and case conferences of the JCB's Clinical Ethics Group, as well as the weekly seminars hosted by the JCB that were open to the university community and the public. The Clinical Ethics Group is comprised of the ethicists who work at the JCB's affiliated hospitals. The focus of these weekly meetings is to develop exemplary models of clinical ethics practice in diverse healthcare settings. Activities include research and practice collaborations, sharing of ideas and resources, strategic planning and policy discussions. Fellows actively participated as full members of the group in these meetings. For example, one of the projects that fellows worked on was the conception, development, and implementation of the Project for Examining Effectiveness of Clinical Ethics (PEECE) . PEECE was an ongoing research initiative. Its purpose was twofold: to describe the current state of affairs of clinical ethics across sites and through interviews with key stakeholders to identify benchmarks of effectiveness. Fellows participated in all aspects of the project from reviewing literature, developing a proposal, collecting and analyzing data to preparing papers for publication. Policy discussions revolved around such varied topics as sexuality in long-term care, pharmaceutical sponsorship, gift-giving in the context of professional/provider relationships and end-of-life care. During the weekly case conferences, individual ethicists bring complex and challenging cases forward for broader consultation and review. For example, in the second year of the fellowship, a pressing clinical situation arose, with an accompanying set of complex ethical questions. This was the emergence of Severe Acute Respiratory Syndrome (SARS). The weekly case conference discussions during this time period focused on ethical issues such as the professional duty to care for and treat patients, limits of confidentiality and visitor restrictions. Among the many other cases that came to the case conference were situations of conflict around end-of-life treatment and defining futility, moral distress of staff providing care in the context of serious resource limitations, elder abuse in the community, and pregnancy termination for genetic anomalies. Fellows who were involved in the cases collaborated and co-presented with the hospital ethicist. Fellows also provided background literature, developed presentation materials and other resources for the Clinical Ethics Group on specific ethical issues as requested. In addition to providing a mechanism for acquiring broader consultation on a particularly challenging and complex case, the case conferences served as a quality assurance mechanism for the affiliated hospitals. The weekly meetings and case conferences were a resource for the clinical ethicists and clinical ethics fellows to receive collegial support and networking opportunities. The weekly seminars featured local, national and international speakers on a wide range of topics. Fellows were encouraged and mentored to participate in a wide range of activities at each of the affiliated hospitals. The fellows were warmly welcomed into the various institutions by the clinical ethicists, staff and patients. Participation in the preparation and delivery of formal and informal educational activities comprised the largest element of the fellowship, and occurred on at least a weekly basis and frequently more often. Educational activities included presenting at Grand Rounds on ethics topics such as clinical ethics decision-making, moral distress, and advance care planning; leading unit-based rounds on topics such as artificial hydration and nutrition at the end-of-life; facilitating brown bag lunches on topical ethics issues; teaching segments of undergraduate and graduate programs; and developing and implementing innovative curriculum for ethics committee members. Second, case consultations were another activity in which the fellows routinely engaged. Initially, fellows participated in the preparation for case consultations and then observed the consultation process as it unfolded. They provided support for the hospital ethicists by gathering background information about the case, reviewing the relevant literature and documenting the consultation in the health record. As the Fellows progressed through the program and their skills and confidence increased, they assumed more responsibility in consultations by chairing or facilitating meetings. In addition, fellows had the opportunity mentor graduate bioethics students by including them in consultations. Throughout the fellowship, fellows received immediate feedback on the progress and outcomes of the consultations from the hospital ethicist. This debriefing opportunity was invaluable for fellows, enabling them to gain insights into the context of the case, the nature of the conflict or difficulty and the unique and recurring themes that were encountered within and across consultations. Teachable moments, individual strengths and areas for further skill and knowledge development were also identified. The number of consultations varied from one site to another, but over the course of the fellowship, each fellow was exposed to a wide variety of consultation experiences. Case consultations differed in terms of their length, from a very short 10-minute conversation to up to 6 hours in a single day with continuing follow-up over subsequent days, weeks and sometimes months. Third, fellows participated in policy and guideline development, although these activities consumed less time than educational and consultation duties. For example, one fellow developed guidelines for the administration of blood and blood products to pediatric Jehovah's Witness patients. She then took the draft to focus groups consisting of various stakeholders both internal and external to the hospital and redrafted the guidelines based on this input. Fourth, fellows participated in clinical ethics research and research ethics board activities. An example of such research was a chart audit conducted by a fellow to examine how consent and capacity issues were being addressed in a particular facility. Several practice concerns were identified and subsequently a facility-wide educational program was implemented. In addition, the fellows engaged in a variety of other scholarly activities including writing, presenting and publishing on ethics-related topics in a variety of forums, which allowed the fellows to develop a comprehensive understanding of a wide variety of strategies for building a sustainable, integrated and accountable ethics program. These experiences, which built professional knowledge, skill and confidence, laid the foundation for the fellows in developing their professional identity as clinical ethicists. Observing the hospital ethicists in action, the fellows realized that these clinical ethics roles were developed over time and with effort. This helped to shape realistic goals and expectations for the early phase of a clinical ethics career. The first fellows In July 2001, Paula Chidwick and Laurie Hardingham were the first fellows accepted into the JCB's clinical ethics fellowship program. Dr. Chidwick holds a PhD in Philosophy and prior to entering the fellowship program completed an ethics internship at Sunnybrook and Women's College Health Sciences JCB. She has taught bioethics at the University of Toronto. Laurie Hardingham is a registered nurse who has worked in a variety of healthcare settings. She has comprehensive academic education in philosophy, completing a Masters in Philosophy and doctoral course work in philosophy. She has taught philosophy and ethics at the University of Calgary and Mt. Royal College, as well as planned and coordinated the Provincial Health Ethics Network in Alberta. Karen Faith and Dianne Godkin were selected for the 2002/2003 clinical ethics fellowship program. Karen Faith completed a Masters in Science majoring in bioethics through the Collaborative Program at the Joint Centre for Bioethics and the Institute of Medical Sciences, University of Toronto. After completing her degree in bioethics, Ms. Faith was a part-time ethics consultant to several healthcare organizations. Previously, she was a social worker who worked in the area of mental health. Ms. Faith has taught at York University, Seneca College and Centennial College in Toronto. Just prior to beginning the clinical ethics fellowship, Dianne Godkin completed a PhD in Nursing. During her doctoral studies she focused on ethics and gerontology, particularly in the areas of end-of-life decision-making and advance care planning. While studying at the University of Alberta, she taught an interdisciplinary graduate course in health ethics and was an observer on a healthcare ethics committee. The objectives that the fellows set out to accomplish during the fellowship included gaining expertise in the clinical consultation process, further developing their teaching and researching skills, increasing their confidence in working through difficult ethical situations as they unfold and expanding their multi-disciplinary network of contacts. Discussion Preparing fellows to work as clinical ethicists The fellowship helped prepare the fellows to make the transition to clinical ethicists by providing real-time clinical opportunities. Although there were opportunities to attend lectures, seminars and conferences and to participate in research projects and the activities of research ethics boards, the focus of this fellowship was clinical practice. "Real-time" clinical opportunities Generally, bioethics education is largely theoretical, focusing on academic course work in philosophy and ethics, as well as other disciplines, at the graduate level. Practical clinical experiences for individuals wishing to pursue a career as a clinical ethicist have been very limited historically and offered only sporadically. In this fellowship, ethical challenges unfold and are addressed within the day-to-day experiences of hospital life. Although hypothetical or retrospective cases studied in the classroom are useful in applying theory to clinical cases, the value of experiential knowledge gained when cases are encountered in the here and now involving real people with tangible consequences cannot be overstated. One fellow recalls a case involving a family having a very difficult time coming to terms with the imminent death of a loved one. The family was adamant that "everything be done", a phrase that often is bandied about in these sorts of discussions and requires considerable exploration. In this case, "everything" was defined by the family to include CPR and admission to intensive care. The fellow attended a meeting with the family and the healthcare team to discuss the plan of care, but the patient, although capable, was too ill to attend. The fellow had not met the patient. The description of the patient by the healthcare team was completely different from that given by the family. The fellow was uncomfortable with the decisions made without directly hearing the patient's voice. It was not until the fellow met with the patient and the physician alone, that she began to understand the situation. Seeing the physical frailty, but clear thinking and comprehension of the patient fuelled her wish to see that the patient received the care that she desired. It mattered what decisions were made, the situation was no longer hypothetical, but was real and the stakes were high. Through their daily work and interactions with staff in the various hospitals, the fellows became familiar with the fast-paced clinical environment and culture, the healthcare providers' values and practices and the complexity and diversity of ethical issues. Given the unpredictability of when consultation requests would surface, fellows found themselves needing to be flexible and accommodating, often leaving writing or research activities to respond to requests for consultation. Fellows could be called to the intensive care unit, coronary care unit, emergency department, or hospital boardroom at any time and some of these consultations required an immediate response. Consultations of a less emergent nature were scheduled for a later time and often included meetings with the healthcare team, families and patients. Fellows carried a pager so that they could be reached immediately. Other learning opportunities included the following: developing and implementing an ethics program through participation in strategic planning activities; raising the profile of ethics in a hospital using a variety of networking, public relations and communication strategies; reaching out to those who questioned the value of ethics programs by establishing an ongoing presence on units that were struggling with a particular ethics issue; building trust and establishing credibility with healthcare professionals by recognizing, understanding and responding first to their most urgent needs; identifying opinion leaders in the organization and integrating them into the ethics program; building bridges with senior management; and supporting the work of ethics committees as well as other hospital committees Skill development Throughout the year, the fellows each worked with a number of clinical ethicists with varied approaches, backgrounds, training and expertise. As a result there were numerous and ongoing opportunities to develop a multiplicity of skills. Through the observation and mentoring of the clinical ethicists, fellows honed their mediation, communication and negotiating skills. They developed political, practical and conflict resolution skills in both observing and responding to conflicts pertaining to patient care decisions They learned to use wisdom or judgment, particularly in establishing credibility, gaining trust and responding to challenges regarding their role and duties. For example, when a fellow witnessed a clinical ethicist's role being challenged by a senior hospital staff member, the clinical ethicist modeled a respectful but assertive approach, demonstrating both good judgment and clarity of purpose. They acquired skills in the recognition, prevention and management of moral distress and moral residue. Many of the clinical ethicists shared personal experiences of morally distressing situations and modeled the need for broad consultation through the JCB consultation group and debriefing with colleagues as a way to cope with stress. The development of this last skill has proven invaluable as the role that moral distress and residue play in the clinical setting becomes increasingly acknowledged and better understood [ 13 , 16 ]. The skills that were nurtured and developed during the fellowship mirror the ethical assessment skills, process skills and interpersonal skills that have been identified as core competencies for ethics consultation [ 2 , 3 ] As the fellows moved through the program, they received ongoing critique of their skills. They participated in educational and practice activities to support their skill development (for example, conflict negotiation workshops). Insights into the role and scope of practice of clinical ethicists The fellows observed that the scope and practice of the clinical ethicists included four primary areas of focus: building capacity, acting as a resource, organizational ethics and scholarly work. The goals of capacity building within the organization included promoting ethical sensitivity and discernment, increasing ethics knowledge and skills and enhancing ethical behavior in the delivery of healthcare. This was accomplished through formal and informal educational activities, committee work, consultations and daily interactions with staff. As a resource, clinical ethicists were called upon to do ethics consultations, provide information and share expertise in various areas of ethical concern. Clinical ethicists' organizational ethics activities were diverse and included the development of policy, guidelines and procedures, collaborative initiatives with other departments and professionals and strategic planning. As well, all of the clinical ethicists were engaged in scholarly activities such as research, writing and publishing, presenting at conferences and teaching at universities and colleges. As a result of working with clinical ethicists in a variety of healthcare settings with different educational backgrounds, the fellowship experience offered a broad perspective on the role and scope of clinical ethics practice. Because clinical ethics is a relatively young field that continues to evolve and define itself [ 6 , 7 , 17 , 18 ], seeing and working with clinical ethicists in action, demonstrating their skills and knowledge, was instructive and assisted the fellows in developing their own professional identity and understanding of what an ethicist's role and responsibilities were and were not in the healthcare setting. The fellows learned that common misperceptions of the clinical ethicist's role included that of moral expert, judge of right and wrong, legal expert, risk manager, ethics police, ombudsperson, locus of ethics for the institution and final decision-maker [ 19 , 20 ]. Character development By observing and participating with the clinical ethicists in their daily activities the fellows identified certain important character traits for this role, such as humility, respect for others, self-knowledge, self-awareness and courage. Although other character traits were also observed, the fellows agreed that these particular traits were both necessary and desirable and thus worthy of emulation in their own practice. The fellows observed that clinical ethicists who modeled humility recognized that their role was neither that of judge nor moral expert, but as a member of the team who was able to engage in a collegial process of deliberation and ethical decision-making. As well, with humility came the recognition that one ethicist cannot be knowledgeable in all areas and that it was essential to build up a network of colleagues from different educational backgrounds with whom to consult. Similar traits such as self-knowledge and self-awareness involved the ability of the ethicist to recognize his or her strengths and limitations. The extent to which the ethicist demonstrated self-knowledge and self-awareness influenced their own self-care practices and ability to manage work demands and work related-stress and thus avoid burnout. Fellows observed ethicists maintaining an attitude of respect toward the opinions of all concerned parties; they ensured that each individual's voice was heard and his or her perspective considered. When clinical ethicists upheld an ethical position in the face of considerable opposition the fellows concluded that ethicists modeled courage. The traits deemed important by the fellows reflect many of the character traits that are considered to be prerequisites to successful healthcare ethics consultation [ 2 , 11 ]. Further contemplation on these traits by the fellows raised their own level of self-awareness and their desire and ability to integrate and exhibit these traits in their daily practice. Key elements for success Through their fellowship experiences in a variety of ethics programs at differing stages of development, the fellows recognized certain elements that appeared to contribute to an effective clinical ethics program. First, a clinical ethics program needs to be integrated throughout the organization. Integration was key in building capacity from bedside to boardroom and dispelling myths about the role of ethics and ethics programs. Embedding ethical considerations into all aspects of decision-making is achieved through an understanding of how ethics can be a resource for the staff when they face ethical dilemmas. Indicators of a well-integrated ethics program included a clear understanding of the program by staff, visibility within the organizational structure and accessibility of the ethicist to staff, patients and families. Second, a sustainable ethics program requires organizational support and a commitment through the provision of a dedicated budget for ethics including administrative support, adequate physical space and resources, as well as support for continued education. Organizational commitment can be demonstrated through a clearly defined and stable reporting structure and the clinical ethicist's participation in decision-making at the management level. Such organizational commitment allows the ethicist the resources and time to provide the services that support excellence in patient care and to help staff when faced with ethical issues. The clinical ethicist needs to have clear goals and parameters for the work and establish reasonable expectations in order to provide an effective service, reducing ethicists' moral distress and burnout. Third, clinical ethicists cannot work in isolation and need the support of a network of colleagues both within and outside of the field of ethics, especially when confronted with complex or unusual cases in new and emerging areas. One of the roles of clinical ethicists is to act at the same time as both trusted organizational insider and as an objective neutral outsider. Clinical ethicists are best able to succeed in this capacity when they develop collaborative relationships with other service providers in the healthcare settings for example, risk management, pastoral care and social work. Fellows observed that this network of support included the JCB clinical ethics group as well as key professionals knowledgeable in areas of bioethics relevant to the specialized areas of health care. For example, one clinical ethicist had particular expertise in pediatric settings and was called upon often by colleagues when an ethical challenge concerned the care of neonates or children. Fourth, the clinical ethicist's ability to see beyond the initial presenting problem was a crucial skill in the case consultation process. As the clinical ethicist entered into the situation the scope of inquiry often broadened and new and larger, and sometimes quite different, questions emerged. For example, when called in by staff for a consultation, the fellows often observed that upon discussion with the patient or family a different problem was brought to light. Fellows observed that ethicists that kept the dynamic nature of the consultation in mind usually had more successful consults. Critical reflections Christine Harrison challenges those engaged in bioethics to consider what "bioethics is " before contemplating its future [ 6 ]. The clinical ethics fellowship assisted the fellows in developing their own understanding of what clinical ethics is and the clinical ethicist's role, as well as acquiring the necessary knowledge, skills and character traits. The one-year practical learning experience in clinical ethics was perceived by the fellows as an excellent way for them to begin to understand what it means to be a clinical ethicist and to develop core competencies to succeed in that role. However, as the field is evolving quickly with new issues emerging, sometimes quite unexpectedly, it is unlikely that one would ever feel fully prepared to independently step into the position of clinical ethicist. The fellows in the second cohort learned this lesson first-hand, when Severe Acute Respiratory Syndrome (SARS) struck Toronto and dramatically transformed the work environment in the hospitals in which they served [ 12 ]. Rotations were in six-month segments with a shared work week between two hospitals, but due to SARS precautions which prohibited people from traveling between sites, fellows needed to limit their work to one hospital. Indeed, some of the fellows were not allowed into particular hospitals until infection control restrictions were lifted and were forced to continue their work from home as best they could. Even prior to SARS, fellows found that the disparate geographic location of multiple work settings made availability for consults difficult at times. Subsequently, full-time three-month block placements for fellows have been implemented at some hospital sites rather than the split workweek. After the first year of the program, a position became available for a one-year senior clinical ethics fellowship. Laurie Hardingham accepted that position, and during the senior fellowship year, she worked in one teaching hospital, concentrating on ethics consultations, increasing educational opportunities for staff and strengthening the clinical ethics program in that hospital. She was also available to mentor and advise the new first year fellows, supporting the fellowship program. The senior fellowship allowed her to develop a greater understanding of how to integrate ethics throughout an organization and develop the ability to more effectively utilize organizational structures and resources in the clinical ethics program. The hospital ethicists that the fellows assisted had many organizational commitments, were involved in numerous projects and could be called upon at a moment's notice for consultations. As the areas of focus for clinical ethics services varied significantly between hospital settings, fellows were required to review and research literature on many complex and different ethical, clinical and legal topics. To meet the demands of working in a fast-paced healthcare environment with rapidly changing needs, fellows were also faced with the challenges of being available, flexible and accommodating. Being introduced to several hospital settings at the beginning of each rotation presented the fellows with the additional tasks of quickly familiarizing themselves with and acclimatizing to new organizational rules and procedures, staff and institutional cultures. Being a fellow also brought in practical considerations such as taking leave from previously held positions, adjusting to a considerable reduction in pay and relocating to Toronto. The fellows were exposed to stylistic and theoretical differences in the way clinical ethics was practiced when working with ethicists who entered the field through diverse academic and clinical backgrounds. The potential does exist for such differences to become a barrier to learning and building trust within the clinical ethicist/fellow relationship and the fellows who experienced this learned about developing working relationships with ethicists whose priorities differed. For example, when the hospital ethicists also had responsibilities as physicians or nurses in addition to clinical ethics responsibilities, the perspectives could differ on which activities receive attention first. Therefore, it is essential that support be made available in the form of advocacy and mediation for the fellows should such a conflict arise. In this program, such support is available through the program's coordinator at the Joint Centre for Bioethics. Conclusions Not unlike the field of bioethics itself, the Joint Centre for Bioethics Clinical Ethics Fellowship program is evolving with each successive year and will ultimately be judged by how well graduates are integrated into the healthcare community and the contributions they make to the field. The fellows concur that none of them would have felt sufficiently prepared to take on the considerable responsibilities, complex role demands and inevitable moral distress that are inherent in the position of clinical ethicist without the fellowship. Participation in the fellowship was instrumental in helping the fellows develop the necessary clinical ethics skills, knowledge and character traits required for them to assume a role as a clinical ethicist in a healthcare setting. As well, through the fellowship, they cultivated a support network for the future. Since completing the fellowship, each of the first four fellows has obtained a position as a clinical ethicist in a healthcare setting. Because of their fellowship experiences, they embark on their new careers with a realistic picture of clinical ethics, demonstrated core competencies and a strong network of ethics support and expertise to draw upon in the future. Although other educational models for clinical ethicists exist, a clinical ethics fellowship that is applicable to individuals from a variety of backgrounds (i.e., not limited to clinicians or philosophers only) appears to be a viable educational option and one that ought to be further developed and more formally evaluated. List of abbreviations used JCB – The University of Toronto Joint Centre for Bioethics SARS – Severe Acute Respiratory Syndrome Competing interests The author(s) declare that they have no competing interests. Authors' contributions PC contributed substantially to the conception and design, analysis and interpretation of data, drafting of the article and revising it critically and gave final approval for the version to be published. KF contributed substantially to the conception and design, analysis and interpretation of data, drafting of the article and revising it critically and gave final approval for the version to be published. DG contributed substantially to the conception and design, analysis and interpretation of data, drafting of the article and revising it critically and gave final approval for the version to be published. LH contributed substantially to the conception and design, analysis and interpretation of data, drafting of the article and revising it critically and gave final approval for the version to be published. Pre-publication history The pre-publication history for this paper can be accessed here:

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544849

Clinical significance of BRAF mutations in metastatic melanoma

Forty to eighty percent of melanoma tumors have activating mutations in BRAF although the clinical importance of these mutations is not clear. We previously reported an analysis of BRAF mutations in metastatic melanoma samples from 68 patients. In this study, we correlated patient baseline characteristics, prognostic factors, and/or clinical outcomes with the presence of BRAF mutations. No significant differences were observed in age, gender, location of primary melanoma, stage at the diagnosis, and depth of primary tumor between patients with and without BRAF mutations. Melanomas harboring BRAF mutations were more likely to metastasize to liver (P = 0.02) and to metastasize to multiple organs (P = 0.048). Neither time to progression to stage IV nor overall survival were associated with BRAF mutations. In conclusion, we observed no significant differences in clinical characteristics or outcomes between melanomas with or without BRAF mutations. Although there was an increased frequency of liver metastasis and tendency to metastasize to multiple organs in tumors with BRAF mutations, there was no detectable effect on survival. Future prospective studies should include analysis of whether BRAF mutations in melanoma tumors correlate with an increased tendency to metastasize to liver or to multiple organs.

Introduction The mitogen-activated protein kinase (MAPK) pathway mediates cellular responses to growth signals and activation of this pathway has been shown to be critical in tumor formation, particularly in melanoma [ 1 - 3 ]. Recently, activating BRAF mutations were found with high frequency in malignant melanomas, including primary tumors and cell lines [ 4 , 5 ]. Suppression of activating BRAF mutations in cultured human melanoma cells inhibited the MAPK cascade causing growth arrest and promoting apoptosis [ 6 ], further suggesting the potential critical role of activating BRAF mutations in malignant transformation in melanoma. We have reported the analysis of BRAF mutations in a cohort of metastatic melanoma patients [ 7 ] and noted a mutation proportion of 44%. As expected from previous reports, the most frequent mutation was BRAF V599E , which was found in 40% of samples. Since little is known about the clinical implications of activating BRAF mutations in melanoma tumors, we examined whether the melanoma tumors harboring BRAF mutations in this cohort showed different clinical or biological features compared to the melanoma tumors without mutations. Materials and Methods Retrieval of Tumor Specimens and Patient Information Cryopreserved metastatic melanoma samples from 68 patients were selected from the Memorial Sloan-Kettering Cancer Center Tumor Bank. Patient demographic data were collected on the 68 patients whose tumors we had previously analyzed for BRAF mutations [ 7 ]. Data collected included: location of primary tumor, thickness, ulceration, stage of disease (according to American Joint Committee on Cancer Staging System), sites of metastasis, site of tumor biopsy, and history of and responsiveness to chemotherapy. This retrospective analysis was performed with IRB approval which determined that this was exempt research under 45 CFR 46.101.b(4). BRAF Mutations Detection BRAF (exons 11 and 15) was sequenced as previously reported [ 7 ]. For 65/68 patients, a single metastatic site was sequenced for BRAF . In three patients, two to four metastatic sites were available for sequencing. For patients with multiple specimens, we considered only the first acquisition of tissue in assigning patients to mutant or wild type categories. Clinical Correlation and Statistical Analysis The patients were first seen at MSKCC between June 1993 and April 2000. Clinical follow up was available through April, 2003. Comparisons between mutated and wild type were made using either the χ 2 test, t-test or Cochran-Armitage test to trend. Survival distributions were estimated using the Kaplan-Meier method and compared using the log-rank test. Stage IV patients were stratified into two categories: those with stage M1a or M1b (lymph nodes, soft tissues and/or lung metastasis) and those with stage M1c (all other sites). Results We studied 74 cryopreserved metastatic melanoma samples from 68 patients: 42 men and 26 women (Table 1 ). Thirty-five patients had stage III, 33 were stage IV at the time the biopsies were obtained. These samples were melanoma metastasis from the following sites: lung (9), liver (3), gastrointestinal mucosa (9), soft tissues (20), lymph nodes (31), fallopian tube and ovarian (1), and uterus (1). Of the 68 patients analyzed, 30 had mutations in BRAF , including one with mutations in both BRAF and NRAS , and 38 patients were wild type. Overall, mutations in BRAF exons 11 and 15 were detected in 30 of 68 (44%) patients. Table 1 BRAF mutations and clinical characteristics Clinical Features BRAF Status P value Mutation N = 30 (44.1%) Wild Type N = 38 (55.9%) Gender Female 11 15 0.81 Male 19 23 Age 1 Mean 63.3 57.3 0.12 Median (range) 56.5 (29–91) 65.0 (42–97) Stage at Diagnosis I 5 3 0.92 II 13 19 III 7 10 IV 4 2 Unknown 1 4 Thickness (Number available) (N = 18) (N = 22) Mean 2.98 4.83 0.29 Median (range) 1.75 (0.2, 20) 2.80 (0.4, 35) Primary Site Head/Neck 1 6 Trunk 10 11 Extremities 10 14 Ocular 1 0 Mucosal 1 0 Unknown 7 7 Response 2 CR 2 3 PR 0 2 NR 16 10 Response Rate 11% 33% 0.12 1 Age at time of biopsy used to assess BRAF sequence. 2 Response data is based on the 33 patients who received systemic therapy. Patients' age ranged from 29 to 97 years; there was no statistically significant difference in patients' age with regards to BRAF mutations (p = 0.12). Similarly, there was no difference in the distribution of primary sites and stages at diagnosis between patients with and without BRAF mutations. We noted that among the 7 melanomas arising from the head and neck region, only 1 harbored a BRAF mutation. Although there were too few of these patients for a meaningful statistical analysis, this observation is consistent with a recent report indicating that mucosal melanomas did not harbor BRAF mutations [ 8 , 9 ]. The mean thickness of primary tumor was 2.98 mm (range: 0.2, 20 mm) for patients with BRAF mutations, and 4.83 mm (range: 0.4, 35 mm) for patients without (p = 0.29). The effect of BRAF mutation on other known prognostic features of primary tumor such as the presence or absence of ulceration, regression, tumor-infiltrating lymphocytes, lymph-vascular invasion, and mitotic index could not be assessed because this information was available for only a small proportion of patients. Patients with tumors harboring BRAF mutations were more likely to have metastasis to liver compared to those without the mutations (41% and 13%, respectively; p = 0.02) (Table 2 ). Tumors with BRAF mutations were also more likely to metastasize to multiple organs (p = 0.048) (Table 3 ). Among the 51 patients who developed stage IV disease (either at the time of the biopsy or during subsequent follow up), 19 out of the 27 patients (70.4%) with BRAF mutations in their melanomas were found to have more than one metastatic site compared to only 11 of the 24 patients (37.5%) with wild type BRAF . Table 2 Correlation between BRAF mutations and number of metastasis among patients with stage IV melanoma Sites of Metastasis BRAF Status P value Mutation N = 27 (%) Wild Type N = 24 (%) Soft Tissue/Lymph Nodes/Lung only 8 (30%) 12 (50%) 0.16 Non-soft tissue site 19 (70%) 12 (50%) 0.14 Liver 11 (41%) 3 (13%) 0.02 Table 3 Association of BRAF mutations with the number of metastatic sites in patients with stage IV melanoma Number of Sites Per Patients BRAF Status P value* Mutation N = 27 (%) Wild Type N = 24 (%) 5 4 (14.8%) 0 p = 0.048 4 4 (14.8%) 3 (12.5%) 3 6 (22.2%) 5 (20.8%) 2 5 (18.5%) 3 (12.5%) 1 8 (29.6%) 13 (54.2%) * Cochran-Armitage test for trend We examined the response to systemic therapy (chemotherapy or biochemotherapy) for the 33 patients who received such treatments. For patients with BRAF mutations, 18 patients received systemic therapy of who two patients achieved complete remission (response rate 11.1%). Fifteen patients with wild-type BRAF received systemic therapy of whom three patients achieved complete remission and two achieved partial remission (response rate 33.3%) (p = 0.12). There was no statistically significant difference between time to progression to stage IV disease either from the time of diagnosis or from stage III in patients with or without BRAF mutations (data not shown). As this is a retrospective study, we cannot rule out the possibility that differences in interval assessments affected our ability to detect a difference in time to progression. On the other hand, date of death is an endpoint not affected by interval assessment times. There was no statistically significant difference between patients with BRAF mutations and those without BRAF mutations. Discussions High frequency of BRAF mutations has been reported in malignant melanoma [ 4 , 5 , 7 ], however, there has been little clinical correlation data elucidating the biological effects of these mutations in patients. We initiated this study in an attempt to address this question. The observation that BRAF mutations are common in melanocytic nevi [ 10 ] has led to the assumption that mutations in BRAF occur early in melanocytic transformation and play an important role in the initiation of malignant transformation. Recently, an alternative view has been suggested by Dong et al who confirmed the high frequency of BRAF mutations present both in nevi and later stage melanomas but found few BRAF mutations in early stage radial growth phase melanomas [ 11 ]. They interpret these findings to mean that BRAF mutations are not involved in the initiation of the majority of melanoma, but rather play a role later in progression. Since little information was available on the biological effects of activating BRAF mutations in melanoma, we analyzed the clinical characteristics of 68 melanoma patients whose tumors we had previously analyzed for BRAF [ 7 ]. We found that patients with tumors harboring a BRAF mutation were more likely to have metastasis to the liver and tended to have more organs involved with melanoma than patients without mutations. This is consistent with the idea that activating BRAF mutations affect the pattern of metastatic spread in melanoma, although we await confirmation of these findings in a prospective study. In our cohort of subjects, there were 33 patients who received systemic therapy (18 patients with BRAF mutations, 15 patients without detectable mutations). There was a trend towards lower response rates among patients with mutations, although this trend was not statistically significant and is confounded by the small number of patients, the heterogeneity of treatments these patients received, and the retrospective nature of these analyses. This is a question that deserves to be revisited in a prospective manner. Kumar and colleagues found that melanoma patients with BRAF mutations showed a statistically significant diminished duration of response to treatment compared to those without the mutations [ 12 , 13 ]. Their retrospective analysis consisted of 38 patients with metastatic melanoma (stage III or IV) who had been treated with chemoimmunotherapy (dacarbazine, vincristine, bleomycin, lomustine, and human leukocyte interferon). This cohort of patients had a surprisingly high response rate of 55%. Although the likelihood of response did not correlate with the presence of a BRAF mutation, multivariate analysis revealed that among patients who had responded, patients with BRAF mutations had a shorter duration of response compared to patients without any BRAF mutations (median 3.4 versus 9.8 months). They did not analyze the effect of BRAF mutations on the site of metastatic spread or other biological characteristics of the tumor. Houben et al. reported that the presence of BRAF mutation in a metastatic melanoma lesion was associated with a poor prognosis as measured by shortened survival [ 14 ]. In our study, we did not detect any impact on either progression free or overall survival by the presence of BRAF mutation. The patient characteristics were not reported by Houben and colleagues but they indicate that most patients had soft-tissue metastases (M1a or M1b). In contrast, most of our patients had M1c melanoma and this could account for the different findings. In three patients, multiple metastatic samples were available for analysis; in 2 of these patients, there was discordance in the presence of detectable BRAF mutations. In one patient in whom 2 lung metastasis collected over a period of one month were analyzed, one metastasis contained a BRAF V599E mutation; the other metastasis was wild-type for BRAF . In another patient, metastasis from lung, gastrointestinal (GI) tract, lymph node, and soft tissue were collected of a period of 34 months. All tumors harbored the BRAF V599E mutation except for the GI metastasis which was wild-type. It is possible that this discordance represents a problem with assay sensitivity, but we cannot rule out the possibility that there is true heterogenicity among metastasis with regard to BRAF mutations. Although this discordance among metastasis seems to contradict the observation that BRAF mutations are an early event in melanocytic nevi transformation, one possibility is that in melanomas arising from non-nevus melanocytes, BRAF mutation is a late event occurring in individual metastasis. Consistent with this, Shinozaki et al. recently reported that the incidence of BRAF mutation of primary melanoma did not correlate with Breslow thickness, and there was significantly higher frequency of BRAF mutation in metastasis than in primary melanoma, arguing that BRAF mutation maybe acquired during development of metastasis [ 15 ]. Houben also reported that in 3/22 cases, the BRAF mutational status of the primary and metastasis did not correlate [ 14 ]. This issue merits further investigation. In summary, this analysis represents the largest study to date correlating BRAF mutations and clinical outcomes in metastatic melanoma. Although we observed a statistically significant higher frequency of liver metastasis and tendency to metastasize to multiple organs in patients with BRAF mutations, there was no significant effect on survival or response to systemic therapy detected by this study. Although this analysis is limited by its retrospective nature and the relatively small number of patients, it appears unlikely from these observations that there will be a major qualitative difference in the biological behavior between melanomas with and without BRAF mutations. Larger prospective studies are required to verify these observations and to clarify other biological consequences of BRAF mutations in melanoma.

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Substituting abacavir for hyperlipidemia-associated protease inhibitors in HAART regimens improves fasting lipid profiles, maintains virologic suppression, and simplifies treatment

Background Hyperlipidemia secondary to protease inhibitors (PI) may abate by switching to anti-HIV medications without lipid effects. Method An open-label, randomized pilot study compared changes in fasting lipids and HIV-1 RNA in 104 HIV-infected adults with PI-associated hyperlipidemia (fasting serum total cholesterol >200 mg/dL) who were randomized either to a regimen in which their PI was replaced by abacavir 300 mg twice daily (n = 52) or a regimen in which their PI was continued (n = 52) for 28 weeks. All patients had undetectable viral loads (HIV-1 RNA <50 copies/mL) at baseline and were naïve to abacavir and non-nucleoside reverse transcriptase inhibitors. Results At baseline, the mean total cholesterol was 243 mg/dL, low density lipoprotein (LDL)-cholesterol 149 mg/dL, high density lipoprotein (HDL)-cholesterol 41 mg/dL, and triglycerides 310 mg/dL. Mean CD4+ cell counts were 551 and 531 cells/mm 3 in the abacavir-switch and PI-continuation arms, respectively. At week 28, the abacavir-switch arm had significantly greater least square mean reduction from baseline in total cholesterol (-42 vs -10 mg/dL, P < 0.001), LDL-cholesterol (-14 vs +5 mg/dL, P = 0.016), and triglycerides (-134 vs -36 mg/dL, P = 0.019) than the PI-continuation arm, with no differences in HDL-cholesterol (+0.2 vs +1.3 mg/dL, P = 0.583). A higher proportion of patients in the abacavir-switch arm had decreases in protocol-defined total cholesterol and triglyceride toxicity grades, whereas a smaller proportion had increases in these toxicity grades. At week 28, an intent-to treat: missing = failure analysis showed that the abacavir-switch and PI-continuation arms did not differ significantly with respect to proportion of patients maintaining HIV-1 RNA <400 or <50 copies/mL or adjusted mean change from baseline in CD4+ cell count. Two possible abacavir-related hypersensitivity reactions were reported. No significant changes in glucose, insulin, insulin resistance, C-peptide, or waist-to-hip ratios were observed in either treatment arm, nor were differences in these parameters noted between treatments. Conclusion In hyperlipidemic, antiretroviral-experienced patients with HIV-1 RNA levels <50 copies/mL and CD4+ cell counts >500 cells/mm 3 , substituting abacavir for hyperlipidemia-associated PIs in combination antiretroviral regimens improves lipid profiles and maintains virologic suppression over a 28-week period, and it simplifies treatment.

Background Protease inhibitors (PIs) used as components of highly active antiretroviral therapy (HAART) have been well documented to reduce both the morbidity and mortality associated with HIV infection [ 1 , 2 ]. However, many PI-based HAART regimens incur treatment-limiting side effects, interactions with concomitant medications, high daily pill burdens, and dietary and/or fluid requirements, making adherence to treatment a challenge [ 3 ]. Even more problematic from a long-term treatment perspective are the metabolic adverse effects, such as hyperlipidemia, insulin resistance, and lipodystrophy, which along with HIV infection itself, may constitute major risk factors for the development of coronary artery disease (CAD) [ 4 ]. The incidence of hyperlipidemia varies among PIs. The Swiss HIV Cohort Study showed that over 470 days, HIV-infected patients experienced a mean increase in total cholesterol of 77 mg/dL with ritonavir ( n = 46), 46 mg/dL with nelfinavir ( n = 21), 31 mg/dL with indinavir ( n = 26), and 4 mg/dL with non-PI-containing antiretroviral regimens ( n = 28) [ 5 ]. At the end of the study, total cholesterol exceeded 240 mg/dL in 44% of the ritonavir group, 35% of the indinavir group, 33% of the nelfinavir group, and 14% of the non-PI group. Other research has shown that some of the more recently developed PIs, including fosamprenavir and atazanavir, have a low likelihood of causing significant lipid elevation or adversely affecting the total cholesterol: high density lipoprotein (HDL) cholesterol ratio [ 6 - 8 ]. Although HIV infection itself is associated with above-normal triglyceride levels due to disease-related reduction in triglyceride clearance and increase in de-novo hepatic lipogenesis [ 9 ], some PIs exacerbate this lipid elevation by down-regulating low density lipoprotein (LDL)-receptor expression [ 10 ], interfering with the proteasome-mediated degradation of activated nuclear sterol regulatory element-binding protein-1 [ 11 ], and reducing triglyceride clearance further [ 12 ]. Because PIs have been available for less than a decade, it may be too early to confirm an association between PI usage and a more rapid onset of CAD. Epidemiological studies to date have reported varying findings regarding a PI-CAD link [ 13 - 15 ]. Until questions about such a possible link are resolved, it appears prudent to treat HIV-infected patients who have CAD risk factors with combinations of antiretroviral agents that produce maximal virologic suppression with the fewest lipid-elevating effects and metabolic adverse events. Unlike most PIs, the nucleoside reverse transcriptase inhibitor (NRTI) abacavir is administered as just one tablet twice daily with no requirements regarding extra hydration or dosing with or without meals [ 16 ]. Abacavir is unlikely to be involved in drug interactions because it does not affect CYP3A4 drug metabolism, its use does not affect lipids or metabolic parameters, and it does not cause lipodystrophy [ 16 ]. These features of abacavir have prompted its use in substituting for components of HAART regimens in order to simplify treatment and to obviate the risk of drug interactions, fat maldistribution, and metabolic complications [ 17 ]. HAART regimens containing abacavir have been shown in direct comparative trials in HIV-infected patients with a baseline viral load <100,000 copies/mL to be as effective as nelfinavir-and indinavir-containing HAART in suppressing viral load and increasing CD4+ cell counts without inducing hyperlipidemia, insulin resistance, or central adiposity [ 18 - 21 ]. In patients with a baseline viral load ≥ 100,000 copies/mL, abacavir in combination with lamivudine and zidovudine has been shown to suppress viral load and increase CD4+ cell counts as well as nelfinavir-containing HAART [ 20 ], and was as effective as indinavir-containing HAART in one trial [ 18 ] but not in another [ 21 ]. In view of these findings, we conducted a 28-week, open-label pilot study (ESS40003) to compare the changes in PI-associated hyperlipidemia (fasting serum total cholesterol >200 mg/dL) in treatment-experienced HIV-infected patients with HIV-1 RNA <50 copies/mL who either substituted abacavir for the PI component in their regimen or continued the same PI-containing regimen. Methods Patient selection Male and non-pregnant, non-lactating female outpatients were eligible for study enrollment if they were at least 18 years of age; had HIV-1 infection (documented by HIV-1 antibody enzyme-linked immunosorbent assay and confirmed by Western blot test of HIV-1 antibody, or positive HIV-1 blood culture, positive HIV serum antigen, or plasma viremia); had fasting serum total cholesterol >200 mg/dL; were stabilized on a well-tolerated PI-containing antiretroviral regimen for >3 months (either 2 NRTIs + 1 PI, or 2 NRTIs + 2 PIs if one PI was ritonavir); were naïve to abacavir and all non-nucleoside reverse transcriptase inhibitors (NNRTIs); and if their two most recently reported consecutive plasma HIV-1 RNA values were <400 copies/mL prior to screening and HIV-1 RNA was <50 copies/mL at screening. CD4+ cell counts could be of any magnitude. Patients were also eligible if their HAART regimen had changed due to intolerance (one drug substitution, such as a PI for another PI and/or an NRTI for another NRTI) >12 weeks prior to study start; or if their initial HAART regimen consisted of 2 NRTIs followed within 1 year by the addition of a PI. Patients were excluded from the study if they had genetically-related lipid disorders (familial lipoprotein lipase deficiency, apoprotein CII deficiency, type 3 hyperlipoproteinemia, hypercholesterolemia, hypertriglyceridemia, or combined hyperlipidemia); took a hypolipidemic or antidiabetic drug within 30 days of screening; had an AIDS-defining opportunistic infection or disease within 30 days of study entry; had a history of angina, anginal symptoms, and/or myocardial infarction; were substance abusers; or had a malabsorption syndrome that could interfere with absorption of the study medications. Patients provided written informed consent to participate in the study. Study design and treatment This Phase IV, parallel group, active control, randomized, open-label, multicenter trial was conducted between November 1999 and November 2001 at 44 outpatient sites in the United States. An Institutional Review Board approved the study protocol at each site. To determine study eligibility, study candidates underwent a medical history, physical examination, CDC classification, clinical chemistry, hematology, and β-human chorionic gonadotropin test (women of childbearing age only) at the screening visit within 14 days pre-study. Patients meeting entry criteria at screening were randomized to either continued therapy with their current PI-containing antiretroviral regimen or to the same regimen with abacavir (300 mg twice daily) substituted for the PI(s). Abacavir was supplied as 300-mg tablets of Ziagen ® (GlaxoSmithKline, Research Triangle Park, North Carolina). In the abacavir-switch arm, patients received both abacavir and the hyperlipidemia-associated PI with their usual two-NRTI background combination for the first 4 weeks, after which the PI component of the regimen was discontinued. Co-administration of abacavir and the PI was done during the first 4 weeks to make sure that there was virologic coverage in case a patient developed a suspected abacavir-related hypersensitivity reaction, which would have necessitated stopping abacavir. Blood was sampled for lipid, viral load, and laboratory value measurements from fasted patients at baseline, and at weeks 4, 8, 12, 20, and 28. Patients who experienced HIV-1 RNA breakthrough (defined as HIV-1 RNA values between 50 and 1000 copies/mL by Roche Amplicor Ultrasensitive Assay) during the study period could receive intensification with efavirenz 600 mg once daily. Assessment of lipids A complete fasting lipid panel was obtained from blood samples at baseline and at weeks 4, 8, 12, 20, and 28 to allow measurement of the primary study endpoint (change from baseline in fasting serum total cholesterol) and secondary study endpoints (change from baseline in LDL-cholesterol, very low density lipoprotein (VLDL)-cholesterol, high density lipoprotein (HDL)-cholesterol, and triglycerides). Apolipoproteins B and E, free fatty acids, and LDL subfractions were measured at baseline and week 28. Direct LDL-cholesterol was measured in all patients at baseline, week 4, and week 28, as well as any treatment visit at which time a fasting triglyceride >400 mg/dL was observed. Fasting serum total cholesterol and triglyceride levels were measured enzymatically by using cholesterol/HP reagent (Roche Diagnostics, Indianapolis, Indiana) and triglyceride reagent (GPO-Trender) (Roche Diagnostics, Ibid). HDL-cholesterol was measured enzymatically in the supernatant formed following centrifugation of serum mixed with a polyanion dextran sulfate/divalent magnesium solution (Roche Diagnostics, Ibid). LDL- and VLDL-cholesterol were estimated by the Friedewald equation [ 22 ]. Direct LDL-cholesterol was measured by a nonionic detergent method using alpha-cyclodextrin/4-aminoantipyrin (Roche Diagnostics, Ibid.). Apolipoprotein B was measured using the apolipoprotein B antigen-antibody reaction method employing the Beckman IMMAGE Immunochemistry System (Beckman Instrument, Brea, California), and apolipoprotein E by a phosphate buffer/anti-human apolipoprotein E method employing a Wako Apolipoprotein Calibrator (WAKO Chemicals, Richmond, Virginia). LDL subfractions were measured by electrophoresis (Pacific Biometrics, Seattle, Washington) [ 23 ], and non-esterified free fatty acids were measured by the WAKO enzymatic colorimetic method (WAKO Chemicals, Ibid.). Four protocol-defined toxicity grades for hyperlipidemia were assigned during the study for serum cholesterol (Grade 1 for values >1–1.3 times the upper limit of normal [ULN], Grade 2 for >1.3–1.6 times the ULN, Grade 3 for >1.6–2 times the ULN; and Grade 4 for >2 times the ULN) and serum triglycerides (Grade 1 for ULN-399 mg/dL, Grade 2 for 400–750 mg/dL, Grade 3 for 751–1200 mg/dL, and Grade 4 for >1200 mg/dL). Efficacy assessment Changes in HIV-1 RNA levels and CD4+ cell counts were secondary endpoints in this study. Plasma HIV-1 RNA levels were measured in blood samples at screening and at all study visits using both the Roche AMPLICOR PCR Standard 1.0 assay (lower limit of quantitation [LLOQ] 400 copies/mL) and the Roche PCR assay Amplicor HIV-1 MONITOR UltraSensitive Version 1.0 (LLOQ 50 copies/mL) (both assays from Roche Diagnostics, Branchburg, New Jersey). Virologic failure was defined as HIV-1 RNA >1000 copies/mL on two occasions at least 1 week apart. CD4+ cell counts were determined by flow cytometry at baseline, and at weeks 4, 12, 20, and 28. Safety assessment Frequency and severity of all clinical and laboratory adverse experiences were assessed at each visit. A cardiovascular disease risk factor assessment was conducted at baseline only. Body mass index (BMI) and waist-to-hip ratio (WHR) were measured at baseline and at weeks 4, 8, 12, 20 and 28. Three types of waist measurements were conducted: mid, minimal, and umbilicus. Insulin resistance measures (C-peptide and fasting insulin to glucose ratio) were assessed at baseline and at weeks 4, 8, 12, 20, and 28. Leptin and lactate were measured at baseline and week 28. Diagnosis of possible abacavir-related hypersensitivity reaction was to be considered if the following multi-organ signs and symptoms appeared in a patient following initiation of abacavir: fever, rash, gastrointestinal symptoms (nausea, vomiting, diarrhea, or abdominal pain), lethargy, or malaise with or without concomitant respiratory symptoms (dyspnea, sore throat, cough), musculoskeletal symptoms (myalgia, myolysis, arthralgia), headache, paresthesia, and edema. No rechallenge of abacavir was permitted in patients developing this syndrome. The definition and usual clinical presentation of abacavir-related hypersensitivity has been defined previously [ 24 , 25 ]. Adherence and health outcomes assessments Adherence was assessed by the Patient Medication Adherence Questionnaire-7 (PMAQ-7) [ 26 ]. The PMAQ-7 is a self-reported measure of adherence that patients completed at baseline and at weeks 4, 8, 12, 20, and 28 (or upon permanent discontinuation due to virologic failure or toxicity). In the PMAQ-7, patients were asked to indicate the number of doses of each medication they took during each of the previous 7 days. An overall regimen adherence rate was calculated at each week as the proportion of doses actually taken relative to the number of doses prescribed summed across each medication within the regimen. The PMAQ-7 also measured barriers and motivators to adherence. Health-related quality of life (QOL) was evaluated using the Medical Outcomes Study 36-item Short Form Health Survey (SF-36) [ 27 ], which patients completed at baseline and at study weeks 12 and 28. Healthcare resource utilization (total health care visits, emergency room visits, intensive care hospitalizations [nights], general ward hospitalizations [nights], outpatient clinic visits, home care visits, and long-term care visits [nights]) was assessed at weeks 4, 8, 12, 20 and 28. Statistical analysis A sample size of 80 patients per treatment arm was deemed necessary for 80% power to detect a difference of 20 mg/dL in fasting total cholesterol between groups using a two-group t -test with a two-sided significance level of 0.05, assuming a dropout rate of 20%. This sample size calculation is based on data from NZTA4002, in which the standard deviation of total cholesterol in the nelfinavir/zidovudine/lamivudine group at baseline was 39.5 mg/dL. The primary efficacy population was the intent-to-treat (ITT) population, which consisted of all patients who were randomized into the study. The safety population consisted of all randomized patients who received at least one dose of study drug. Analysis of covariance (ANCOVA) was used to compare the least squares means (LSM) of the two treatment groups. The LSM represented the mean value adjusted for the average value of the covariate from both treatment groups. LSMs of change from baseline in serum lipids (total, LDL, VLDL, and HDL-cholesterol, LDL subfractions, triglycerides, free fatty acids), apolipoprotein B, apolipoprotein E, leptin, C-peptide, glucose, insulin, and lactate were reported. The ANCOVA model included terms for treatment group, baseline value of the variable of interest, PI strata and gender. Two types of analyses were performed with HIV-1 RNA data: the ITT: observed analysis and the ITT missing equals failure analysis (ITT: M = F). In the ITT: observed analysis, only available assessments were used (no imputation for missing values), regardless of whether the patient was still receiving their original therapy. In the ITT: M = F analysis, all missing values were considered as failure. Proportions of patients with HIV-1 RNA <400 copies/mL and <50 copies/mL were compared between treatment groups with a 95% confidence interval (CI) on the difference between proportions. Differences in domain scores to the SF-36 and the PMAQ-7 were compared using the Wilcoxon rank sum test. Differences between treatment arms in the incidence of treatment-related adverse events by body system were evaluated by Fisher's Exact test. A P value of 0.05 was considered statistically significant. Results Patient characteristics and disposition Of 104 patients enrolled in the study, 52 were randomized to the abacavir-switch arm and 52 to the PI-continuation arm (Figure 1 ). Most of the patients were males (89%), and the median age was 42 years (Table 1 ). The study population was ethnically diverse; approximately one-half were Caucasian, one-quarter African American, and one-quarter Hispanic. Mean CD4+ cell counts were 551 and 531 cells/mm 3 in the abacavir-switch and PI-continuation arms, respectively. About two-thirds of the patients were HIV Category A. The abacavir-switch and PI-continuation arms were well matched with respect to mean baseline HIV-1 RNA levels (1.727 vs 1.699 log 10 copies/mL, P = 0.062), total cholesterol (244 vs 241 mg/dL), LDL-cholesterol (149 vs 149 mg/dL), VLDL-cholesterol (68 vs 56 mg/dL), HDL-cholesterol (39 vs 42 mg/dL), triglycerides (340 vs 280 mg/dL), CAD risk factors (Table 1 ), and specific PI used immediately pre-study (>80% receiving either nelfinavir or indinavir). There were no significant differences between treatment arms with respect to baseline BMI, waist-to-hip ratios or insulin resistance. During treatment, the background NRTIs used in the abacavir-switch and PI-continuation arms were similar and included most commonly the lamivudine 150 mg/zidovudine 300 mg combination tablet (Combivir ® [GlaxoSmithKline, Ibid.], 52% vs 47%), stavudine (36% vs 42%), lamivudine (32% vs 40%), and zidovudine (6% vs 2%). Figure 1 Profile of patient enrollment and discontinuations through 28 weeks of treatment. Table 1 Characteristics and disposition of the study patients (intent-to-treat population) Characteristic Abacavir-switch arm (N = 52) PI-continuation arm (N = 52) Total study population (N = 104) Age, y Median (range) 43 (23–64) 42 (25–62) 42 (23–64) Sex, No. (%) Male 46 (88) 47 (90) 93 (89) Female 6 (12) 5 (10) 11 (11) Race, No. (%) Caucasian 26 (50) 28 (54) 54 (52) African American 16 (31) 11 (21) 27 (26) Hispanic 10 (19) 13 (25) 23 (22) Mean HIV-1 RNA, log 10 copies/mL (SD) 1.73 (0.16) 1.69 (0.04) 1.71 (0.06) Mean CD4+ cell count, cells/mm 3 (SD) 551 (226) 531 (233) 541 (229) Mean weight, kg (SD) 79.3 (16.8) 80.4 (16.6) 79.8 (16.7) Mean BMI, kg/cm 2 (SD) 25.9 (4.5) 26.4 (4.9) 26.1 (4.7) Mean waist-to-hip ratio 0.94 (0.06) 0.93 (0.07) 0.94 (0.06) CDC Class, n (%) Category A 33 (63) 34 (65) 67 (64) Category B 10 (19) 11 (21) 21 (20) Category C 9 (17) 7 (13) 16 (15) Mean (SD) total cholesterol, mg/dL 244 (45) 241 (44) Mean (SD) LDL cholesterol, mg/dL 149 (34) 149 (30) Mean (SD) HDL cholesterol, mg/dL 39 (15) 42 (14) Mean (SD) triglycerides, mg/dL 340 (213) 280 (282) Coronary artery disease risk factors, n (%)* 34 (65) 26 (50) 60 (58) Age 18 (35) 12 (23) 30 (29) Family history 4 (8) 4 (8) 8 (8) Cigarette smoking 19 (37) 13 (25) 32 (31) Hypertension 5 (10) 8 (15) 13 (13) Low HDL 10 (19) 4 (8) 14 (13) Diabetes mellitus 3 (6) 0 3 (3) Antiretroviral medications taken prior to screening, n (%) Any 28 (56) 20 (44) 48 (46) NRTIs Zidovudine 16 (32) 9 (20) 25 (24) Lamivudine 12 (24) 8 (18) 20 (19) Stavudine 5 (10) 2 (4) 7 (7) Didanosine 7 (14) 0 7 (7) Zalcitabine 3 (6) 0 3 (3) NNRTIs Efavirenz 1 (2) 0 PIs Indinavir 11 (22) 8 (18) 19 (18) Nelfinavir 4 (8) 2 (4) 6 (6) Ritonavir 2 (4) 1 (2) 3 (3) Saquinavir 2 (4) 0 2 (2) PI used at screening Nelfinavir 22 (42) 21 (40) 43 (41) Indinavir 21 (40) 22 (42) 43 (41) Saquinavir SGC 6 (12) 6 (12) 12 (12) Amprenavir 2 (4) 2 (4) 4 (4) Ritonavir 1 (2) 1 (2) 2 (2) Premature withdrawal from study, n (%) 14 (27) 11 (21) 25 (24) Adverse event 7 (13) 1 (2) 8 (8) Consent withdrawn 0 9 (17) 9 (9) Lost to follow-up 1 (2) 1 (2) 2 (2) Protocol violation 2 (4) 0 2 (2) Protocol-defined virologic failure 3 (6) 0 3 (3) Other 1 (2) 0 1 (1) *Cumulative coronary artery disease risk factors in the abacavir-switch arm: 0 factors = 18; 1 factor = 16; 2 factors = 12; 3 factors = 5; 4 factors = 1. Cumulative coronary artery risk factors in the PI-continuation arm: 0 factors = 26; 1 factor = 12; 2 factors = 13; and 3 factors = 1. Abbreviations: AIDS = acquired immune deficiency syndrome; ART = antiretroviral therapy; CDC = Centers for Disease Control; HDL = high density lipoprotein; HIV-1 = human immunodeficiency virus type 1; LDL = low density lipoprotein; LSM = least squares mean; SD = standard deviation; SGC = soft gel capsule. A similar number of patients withdrew prematurely from the study for the reasons delineated in Table 1 . Comparatively more patients withdrew due to adverse events in the abacavir-switch arm (7/52 vs 1/52) and due to consent withdrawn in the PI-continuation arm (0 vs 9/52 [all withdrawals occurred after the patients learned they were not receiving abacavir]). The difference between the two treatment arms regarding number of premature withdrawals due to the sum of adverse events plus virologic failure was not statistically significant ( P = 0.144). No patient required efavirenz intensification of their treatment regimen. Changes in lipids In the ITT analysis, the abacavir-switch arm experienced greater reductions in total cholesterol (Figure 2 ) and LDL-cholesterol (Figure 3 ) than the PI-continuation arm over the entire study period, with differences between treatment arms being statistically significant from week 8 onward. At week 28, patients in the abacavir-switch arm had a significantly greater LSM decrease from baseline in total cholesterol (-42 vs -10 mg/dL; P <0.001), LDL-cholesterol (-14 vs +5 mg/dL; P = 0.016), LDL direct-cholesterol (-15 vs +1 mg/dL; P = 0.028), VLDL-cholesterol (-27 vs -7 mg/dL; P = 0.019), triglycerides (-134 vs -36 mg/dL; P = 0.019), apolipoprotein B (-23 vs -11 mg/dL; P = 0.031), and apolipoprotein E (-2 vs -1 mg/dL; P = 0.021) versus the PI-continuation arm. Over the study period, no significant differences were observed between the abacavir-switch or PI-continuation arms with respect to LDL subfractions (particle size A and major class) or HDL-cholesterol (LSM change from baseline at week 28, +0.2 vs +1.3 mg/dL, P = 0.583; LSM at week 28, 40 vs 41 mg/dL). The abacavir-switch arm showed a trend for greater LSM reduction from baseline in free fatty acids (-0.3 vs -0.2 mEq/L; P = 0.052). Figure 2 Least squares mean change from baseline in fasting serum total cholesterol. Figure 3 Least squares mean change from baseline in fasting serum LDL-cholesterol. The LSM value for total cholesterol was below the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) goal value (<200 mg/dL) in the abacavir-switch arm (198 mg/dL); however, it remained above this value in the PI-continuation arm (230 mg/dL). The LSM LDL-cholesterol level at week 28 was 133 mg/dL in the abacavir-switch arm and 153 mg/dL in the PI-continuation arm ( P = 0.016). A higher proportion of patients in the abacavir-switch arm had shifts to lower toxicity grades as compared to the PI-continuation arm, whereas a lower proportion in the abacavir-switch arm had shifts from baseline in cholesterol to more severe toxicity grades (Figure 4 ) Figure 4 Changes in cholesterol toxicity grade. Changes in serum triglycerides in the two treatment groups paralleled the changes in total cholesterol. At week 28, the LSM reduction in triglycerides was significantly greater in the abacavir-switch arm than the PI-continuation arm (-134 vs -36 mg/dL, P = 0.019) (Figure 5 ). A higher proportion of patients in the abacavir-switch arm had decreases in the triglyceride toxicity grade as compared to the PI-continuation arm, whereas a lower proportion in the abacavir-switch arm had increases in triglyceride toxicity grade (Figure 6 ). Figure 5 Least squares mean change from baseline in fasting serum triglycerides. Figure 6 Changes in triglycerides toxicity grade. Efficacy In the ITT: M = F analysis, no differences were observed between the abacavir-switch and PI-continuation treatment arms at any time during the study regarding proportions of patients achieving undetectable HIV-1 RNA. At week 28, HIV-1 RNA was <400 copies/mL in 69% [36/52] and 77% [40/52] of patients in the abacavir-switch and PI-continuation arms, respectively ( P = 0.508; 95% CI [-0.25, 0.09]), and <50 copies/mL in 62% [32/52] and 75% [39/52] of patients in these respective groups ( P = 0.206; 95% CI [-0.31, 0.04]). The ITT: observed analysis showed that HIV-1 RNA remained <400 copies/mL in ≥ 90% of patients over the entire study duration. At week 28, the proportion of patients with HIV-1 RNA <400 copies/ml was 90% [36/40] and 100% [40/40] in the abacavir-switch and PI-continuation arms, respectively( P = 0.116; 95% CI [-0.19, -0.11]) (Table 2 ). A significantly higher proportion of patients in the PI-continuation arm achieved HIV-1 RNA levels <50 copies/ml at week 28 (98% [39/40] vs. 80% [32/40]; P = 0.029; 95% CI [-0.31, -0.04]). Table 2 Proportion of patients with HIV-1 RNA <400 copies/mL and <50 copies/mL Week HIV-1 RNA <400 copies/mL, n/N (%) HIV-1 RNA <50 copies/mL, n/N (%) ITT: Observed ITT: M = F ITT: Observed ITT: M = F Abacavir PI Abacavir PI Abacavir PI Abacavir PI Baseline 52/52 (100) 52/52 (100) 52/52 (100) 52/52 (100) 42/52 (81) 47/52 (90) 42/52 (81) 47/52 (90) 4 49/49 (100) 44/45 (98) 49/52 (94) 44/52 (85) 48/49 (98) 43/45 (96) 48/52 (92) 43/52 (83) 8 39/42 (93) 42/42 (100) 39/52 (75) 42/52 (81) 38/42 (90) 40/42 (95) 38/52 (73) 40/52 (77) 12 38/41 (93) 40/40 (100) 38/52 (73) 40/52 (77) 38/41 (93) 39/40 (98) 38/52 (73) 39/52 (75) 20 38/41 (93) 40/40 (100) 38/52 (73) 40/52 (77) 32/41 (78) 38/40 (95) 32/52 (62) 38/52 (73) 28 36/40 (90) 40/40 (100) 36/52 (69) 40/52 (77) 32/40 (80) 39/40 (98) 32/52 (62) 39/52 (75) Abbreviations: ITT = intent to treat; M = F = missing equals failure analysis; PI = protease inhibitor. No differences were observed regarding time to virologic breakthrough ( P = 0.961). Three patients (6%) in the abacavir-switch arm were protocol-defined virologic failures. However, one of these patients remained on a 2-NRTI/abacavir regimen after withdrawing from the study and his viral load declined. The second of these patients was noncompliant and discontinued his antiretroviral treatment prior to the confirmation blood draw for virologic failure. The third virologic failure in the abacavir-switch arm had a history of prior dual nucleoside therapy for >1 year. At baseline, the mean CD4+ cell counts were 551 and 531 cells/mm 3 in the abacavir-switch and PI-continuation arms, respectively. Changes in CD4+ counts over the study period were similar. At week 28, the LSM CD4+ cell counts were 514 and 526 cells/mm 3 , respectively, with the LSM difference from baseline being -13 and -1 cells/mm 3 , respectively ( P = 0.597). Adherence and health outcomes assessments PMAQ-7 results showed no apparent differences between the abacavir-switch and PI-continuation arms in overall adherence at week 28 (90% vs 94%) or in the Social Support, Adaptability, Knowledge and Attitudes, and Memory and Recall domains. However, the abacavir-switch arm had significantly higher scores in the Scheduling and Timing domain for all study visits ( P ≤ 0.027 at each week) and in the Physical Effects domain for all visits ( P < 0.05) except those at weeks 4 and 28. In answer to the PMAQ-7 questions "it is easy for me to take my medicines at the time I am supposed to" and "my medicines are convenient to take", more patients in the abacavir-switch arm answered "definitely true" at week 28 (51% vs 35% and 63% vs 29%, respectively). In the SF-36, the week 28 QOL scores did not differ between treatment arms for most domains, except Vitality (in favor of the PI-continuation arm; P = 0.042) and Health (in favor of the abacavir-switch arm; P = 0.013). There were no differences between treatment arms with respect to any health care utilization parameter. Safety Substitution of PIs with abacavir in HAART regimens was generally well tolerated. No significant differences between treatment arms were observed for cardiovascular, endocrine/metabolic, hepatobiliary tract/pancreatic events, lower respiratory events, musculoskeletal, neurology, psychiatric, or skin adverse events, although more gastrointestinal ( P = 0.002) and non-site specific adverse events ( P = 0.003) were observed in the abacavir-switch arm. Treatment-related adverse events reported by >5% of patients in the abacavir-switch arm included nausea (12/50 [14%]), fatigue (6/50 [12%]), diarrhea (4/50 [8%]), and depressive disorders (3/50 [6%]). Two patients (4%) in the abacavir-switch arm experienced possible abacavir-related hypersensitivity reactions. Adverse events leading to treatment withdrawal in 7 patients in the abacavir-switch arm included possible abacavir-related hypersensitivity reaction (2), mild nausea (2), mild shortness of breath/tachycardia (1), mild disorientation (1), and combination of mild diarrhea, facial edema/numbness and malaise (1). Hyperlipidemia led to treatment withdrawal in 1 patient in the PI-continuation arm. No differences between the two treatments were observed in body weight, waist-to-hip ratio, BMI, lactate, leptin, glucose, insulin, or C-peptide. Discussion The results of this study indicate that in hyperlipidemic, virologically suppressed, immunocompetent antiretroviral-experienced patients (HIV-1 RNA <50 copies/mL, CD4+ counts >500 cells/mm 3 ), substituting abacavir for hyperlipidemia-associated PIs in HAART regimens improves lipid profiles and maintains virologic suppression over a 28-week period. The lipid findings are consistent with those of other studies in which abacavir was substituted for PIs in HAART regimens [ 28 - 33 ]. However, unlike the other studies, ESS40003 evaluated a population consisting entirely of patients who were hyperlipidemic at baseline. Also, unlike two previous studies that reported lipid changes following abacavir substitution [ 29 , 30 ], ESS40003 measured fasting lipids rather than lipids in a non-fasted state (which may confound results). Thus, true differences between abacavir and PI-containing regimens could be determined, and lipid criteria established by the NCEP ATP III could be applied [ 34 ]. In addition to monitoring changes in cholesterol and triglycerides, ESS40003 measured changes in other laboratory values known to contribute to atherogenesis (apolipoproteins B and E, LDL subfractions, and indicators of changes in lipid processing [leptin and free fatty acids]) to gain a better understanding of the extent of the improvement in lipid profile following the switch to abacavir. Substitution of hyperlipidemia-associated PIs with abacavir was expected to improve lipid profiles because results of direct comparisons of abacavir/NRTI regimens with PI/NRTI regimens (same NRTIs administered) in antiretroviral-naïve patients have shown no significant effects on lipids with abacavir/NRTI-containing regimens [ 18 - 20 ]. Cross-trial comparisons with other abacavir-switch studies regarding lipid changes are limited by differences between studies in prevalence and severity of hyperlipidemia prior to the switch, specific PIs administered, pre-existing CAD risk factors of the particular patients enrolled, and stipulated dietary and exercise restrictions. Nevertheless, some generalizations can be made between the lipid changes noted in ESS40003 and those reported in other abacavir-switch studies [ 28 - 33 ]. First, as in these other studies, decreases in total cholesterol, LDL-cholesterol, and triglycerides occurred rapidly following the switch to abacavir, with statistically significant differences noted between treatment arms within 4 weeks. Second, as would be expected, the magnitude of the reduction in total- and LDL-cholesterol and triglycerides at week 28 in our study was markedly greater than that reported in studies that included normolipidemic as well as hyperlipidemic patients. Third, as in the other studies, switching from a PI to abacavir had no significant effect on HDL-cholesterol. The small reduction in lipids observed in the PI-continuation arm may have occurred because patients were aware that their lipids were being monitored, and therefore may have exerted more self-control than usual regarding their dietary fat intake and frequency of smoking, or may have exercised more (not monitored in this study). A similar phenomenon was observed over 48 weeks in another PI-to-abacavir switch study, CNA30017 [ 29 ]. As no change in body weight, BMI, or waist-to-hip ratio was observed in our study in either group, anthropomorphic parameters were unlikely to have accounted for the decreases in lipids observed during this study. The 28-week duration of this study may have been too short to see significant changes in BMI or waist-to-hip ratios. However, in the Swiss HIV Cohort Study, substitution of abacavir for a PI was not associated with a change in waist-to-hip ratios even after 48 weeks post-switch [ 30 ]. Maintenance of virologic suppression and increases in CD4+ cell count over the 24-week period following the switch to abacavir were expected in view of the lack of change in these surrogate markers previously observed in abacavir-switch studies conducted over 48 weeks to 1 year [ 27 , 29 ]. The abacavir-containing regimen was generally well-tolerated, and the type and incidence of adverse events (notably fatigue and nausea) and rare occurrence of suspected abacavir-related hypersensitivity reactions were consistent with what has been reported in other clinical trials [ 16 ]. New adverse events in the PI-continuation arm were not expected as patients had been stabilized on this treatment for >3 months. This fact may have accounted for the comparatively lower incidence of GI adverse events in the PI-continuation arm than in the abacavir-switch arm, a finding that has not been observed in direct comparisons of abacavir with PIs (indinavir or nelfinavir) administered with the same background antiretroviral drugs [ 18 , 19 , 21 ]. Results of the PMAQ-7 indicated significantly better Scheduling and Timing scores in the abacavir-switch arm. Significant improvement in this PMAQ-7 dimension was similarly reported at 24 weeks in the PI-to-abacavir switch study, COL30305 [ 33 ]. Improved scores in this dimension may have been related to the simpler, twice-daily dosing of abacavir compared to the patients' previous PI-containing regimens. The proportion of patients reporting 100% adherence previously was shown to be higher at 24 weeks in one PI-to-abacavir switch study (COL30305; 92% vs 68%) [ 33 ] and at 48 weeks in another (CNA30017; 91% vs 76%) [ 29 ]. In these trials, better adherence with the abacavir-containing regimen was believed to be due at least in part to the relatively lower pill count and absence of special dosing requirements incurred by abacavir-containing HAART. The absence of a significant difference in overall QOL between the abacavir-switch and PI-continuation arms was not unexpected in view of the same finding being demonstrated on the SF-36 at 24 weeks in COL30305 [ 33 ]. Switching to abacavir is just one of several switch strategies that have been investigated to date in an attempt to remedy hyperlipidemia in patients receiving PI-containing HAART. It is acknowledged that the ideal candidate for this switch strategy is a patient started on triple therapy, where pre-existing abacavir resistance is unlikely [ 30 , 32 ]. Another switch strategy-replacement of hyperlipidemia-associated PIs with PIs that have a low likelihood of causing significant lipid elevation or adversely affecting the total cholesterol:HDL cholesterol ratio (fosamprenavir or atazanavir)-would be expected to improve lipid profiles in HIV-infected patients [ 6 - 8 , 35 ]. Attempts to reduce lipids by switching from a hyperlipidemia-associated PI to NNRTIs have also been investigated [ 32 , 36 - 43 ]. More favorable lipid effects appear to occur when a switch is made to nevirapine than to efavirenz [ 42 , 43 ]. This study had several limitations. As patients in this study had mean CD4+ cell counts >500 cells/mm 3 , they were highly immunocompetent and may not be representative of typical HIV-infected patients presenting to their physician with PI-associated hyperlipidemia. The study did not evaluate the influence of NRTI background drugs on lipid changes because the patients remained on the same baseline NRTIs. This could have affected the results because stavudine is known to elevate cholesterol and triglyceride levels [ 44 ], whereas zidovudine and lamivudine do not. Most studies have found that switching therapy tends to be optimally effective in patients whose viral load is fully suppressed for at least 6 months rather than the 3 months in our study [ 45 ]. This shorter pre-study time of viral suppression could have biased the virologic results against abacavir, as could allowing prior suboptimal nucleoside therapy. As we did not have information about prior NRTI combinations that many patients received pre-study, we could not assess whether these earlier NRTI combination regimens had been inadequate. Lipid data for study participants prior to their receipt of PI-containing regimens were not available to the investigators; thus, whether PIs were the cause of the patients' hyperlipidemia could not be verified. Dietary intake and physical activity assessments were not performed in this study to evaluate whether either differed between the treatment arms. Further valuable clinical information could have been acquired from this study had switch agents in addition to abacavir been used as comparators. In the only study of this type that has been conducted to date – the Nevirapine-Efavirenz-Abacavir (NEFA) Study – a significantly lower proportion of stabilized HAART recipients (HIV-1 RNA <50 copies/mL for = 6 months) switching from a PI to abacavir developed fasting plasma triglycerides >400 mg/dL and plasma cholesterol >240 mg/dL at 12 months compared to treatment groups in which patients switched from a PI to efavirenz or nevirapine [ 32 ]. Kaplan-Meier estimates of the likelihood of reaching the primary treatment end point (increase in HIV-1 RNA levels to ≥ 200 copies/mL, progression to AIDS, or death) in NEFA showed no significant differences between treatments. Median CD4+ cell counts increased above baseline similarly in all three treatment arms (by 39–50 cells/mm 3 at 12 months). However, the abacavir-switch arm had a significantly lower incidence of adverse events than the efavirenz-switch and nevirapine-switch arms (41% vs 57% and 54%, P = 0.03), a lower incidence of neuropsychiatric adverse events than the efavirenz group (9% vs 31%; Grade 3 or 4: 0.7% vs 14%), and significantly fewer cases of discontinuation of study drug due to adverse events (6% vs 17% and 17%, P = 0.01). Overall, NEFA, like our study, indicated that abacavir was an appropriate drug to substitute for PIs in HAART regimens as long as patients were virologically suppressed pre-switch and minimally antiretroviral-experienced. Our study differed from NEFA in that all participants in ESS40003 had PI-associated hyperlipidemia at baseline (NEFA included only 7–13% with triglycerides >400 mg/dL and 21–25% with total cholesterol >240 mg/dL) and because our study used a less stringent definition of virologic failure (HIV-1 RNA >1000 copies/mL on two occasions at least 1 week apart vs. HIV-1 RNA ≥ 200 copies/mL at ≥ 16 weeks, with subsequent confirmation [NEFA]). Conclusions In conclusion, in antiretroviral-experienced patients with HIV-1 RNA <50 copies/mL and CD4+ counts >500 cells/mm 3 , substituting abacavir for hyperlipidemia-associated PIs in HAART regimens improves lipid profiles and maintains virologic suppression over a 28-week period, and it simplifies treatment. Competing interests The author(s) declare that they have no competing interests. Authors' contributions JEH, PHK and VCW conceived the study design, PHK, MGS, EDJ, AR, JFO, VCW, JWS reviewed and approved study design, VCW provided the statistical methods for the study and performed the statistical analysis of the results, EH, VCW, JHW, JWS wrote, reviewed and edited the protocol, GEP drafted manuscript and evaluated lipid data previously published in antiretroviral studies described in Background and Discussion, JWF, JEH, PHK, MGS, EDJ, AR, JFO, JEH, JWF, JHW, JWS, ADS-C, VCW reviewed and edited the manuscript, ADS-C, JWF, PHK, MGS, EDJ, AR, JFO enrolled study subjects, ADS-C, JWF, JEH, JHW monitored the study, ADS-C, JWF, JEH, JWS, VCW evaluated the clinical data from the study, ADS-C set up the study at study sites and JEH contributed to secure funding. Pre-publication history The pre-publication history for this paper can be accessed here:

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423144

Ethics as Our Guide

In response to the Blackburn and Rowley essay on the President's Council on Bioethics, several thought-provoking opinions on ethical challenges in biomedical research are expressed by prominent stakeholders

Blackburn and Rowley's (2004) criticism of a report on embryonic stem cell research from the President's Council on Bioethics (2004) is puzzling. Where is the bioethics? The nub of their complaint is that some details of the report have been partisan and have distorted ‘the potential of biomedical research and the motivation of some of its researchers’. No doubt their quibbles are well-founded, as every committee report is a compromise. However, it does not follow that if the benefits of embryo stem cell research had been presented more persuasively and in greater detail, then the case for ‘non-commercial, federal, peer-reviewed funding’ would be unassailable. Such a view appears to be based squarely on a utilitarian view of the moral status of embryos: that the good flowing from destructive research outweighs the evil of embryo destruction. Far from being a neutral scientific analysis, this expresses a commitment to the proposition that biomedical progress is more important than the defence of human life. If twentieth century philosophy of science has taught us anything, it is that the aspiration to pure scientific objectivity is a dangerous illusion. Research programs always embody philosophical and moral assumptions that must be openly defended. If Blackburn and Rowley want government support for embryo stem cell research, they must justify their bioethical approach and not hide behind a smokescreen of indignation over Blackburn's unwilling departure from the Council.

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544861

Review of "Methods for testing and evaluating survey questionnaires" by S. Presser, et al

Questionnaires are, by far, the most common method of data collection in the world. There is hardly anyone who has not been asked to answer questions from one form of questionnaire or another. Familiarity with the method may, on the other hand, give the impression that they are easy to develop and that their findings are unproblematic. For those who construct questionnaires, this is a laborious, time-consuming, artful exercise which requires considerable skills and expertise. In quantitative research, the validity and reliability of the instruments are crucial to the credibility of the findings. The rigorous development and testing of questionnaires are, therefore, essential for producing valid and reliable findings. Yet, as the authors of this book point out, "most text books offer minimal, if any guidance about pretesting methods". Since the mid-1930's we have learnt a lot about how to make survey instruments, in particular questionnaires, more rigorous and user friendly. Much of this progress was achieved in the last two decades. This book represents this body of work. It reviews key research studies which have evaluated the various techniques and strategies used to enhance the validity and reliability of survey questionnaires. The idea of this 'monograph' (as the authors call it) was conceived at the Spring 1999 Questionnaire Evaluation Standards International Work Group meeting in London. The chapters evolved out of selected abstracts submitted to the International Conference on Questionnaire Development, Evaluation and Testing Methods in 2002 in South Carolina. As such they represent the latest thinking of an international array of experts on this topic. The book is divided into the following seven parts: 'cognitive interviews', 'supplements to conventional pretests', 'experiments', 'statistical modelling', 'mode of administration', 'special populations' and 'multimethod applications'. The twenty five chapters go well beyond conventional testing methods and reviews the different ways which can be used to evaluate survey questionnaires. Additionally the book sets the agenda for future research on this topic. It makes a significant contribution to the development and testing of questionnaires. The book is well sign-posted and the style is clear. I found it both very informative and interesting. It should appeal to those who construct questionnaires for the first time as well as to more experienced researchers. It is likely to become a reference text which research students, at any level, would find it hard to do without.

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423150

A Voice for Research, a Voice for Patients

In response to the Blackburn and Rowley essay on the President's Council on Bioethics, several thought-provoking opinions on ethical challenges in biomedical research are expressed by prominent stakeholders

In the very thoughtful essay “Reason as Our Guide” by Drs. Elizabeth Blackburn and Janet Rowley (2004) , the authors highlight a key concern with the reports published by the President's Council on Bioethics—the lack of credible scientific information being passed on to policy makers. Blackburn and Rowley point out many areas of the report “Monitoring Stem Cell Research” that needed correction from a scientific standpoint. While it is impossible to include every suggestion in a report that seeks to draw consensus from a large panel of members, in a heated, political debate like that surrounding embryonic stem cell research and therapeutic cloning, providing the most accurate and complete scientific information to policy makers is crucial. Unfortunately, with the recent dismissal of Dr. Blackburn from the Council, there will now be one less voice for scientific research and for the potential the research holds for curing disease and alleviating the suffering of millions. Speaking for the Coalition for the Advancement of Medical Research, our concern is not only the small number of researchers on the Council and lack of complete scientific data being shared with policy makers, but the absence of patient representation on the Council itself. With the exception of public comment periods, patient organizations have no voice in the work of the Council as it discusses issues that profoundly impact them. Now, with one less member standing up for research and thus patients, our concern grows even stronger. The Blackburn and Rowley essay also correctly points out that there is more published work on adult stem cell research because of a “paucity of funding for research using embryonic stem cells.” Despite this lack of federal and private funding, advances continue to be made—but just think of the advances we could have had if only there were a supportive federal policy that encouraged embryonic stem cell research instead of stifling it. We hope—in light of scientific advances made over the past several years and the strong support of the scientific community, including the National Institutes of Health, the Health and Human Services Department, and the National Academy of Sciences—that the President will reevaluate the current federal policy for stem cell research and consider easing the restrictions. We commend Drs. Blackburn and Rowley for trying to set the record straight in their essay, and applaud their efforts to stand up for medical research, which has the potential to benefit us all.

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526766

Identification and utilization of inter-species conserved (ISC) probesets on Affymetrix human GeneChip® platforms for the optimization of the assessment of expression patterns in non human primate (NHP) samples

Background While researchers have utilized versions of the Affymetrix human GeneChip ® for the assessment of expression patterns in non human primate (NHP) samples, there has been no comprehensive sequence analysis study undertaken to demonstrate that the probe sequences designed to detect human transcripts are reliably hybridizing with their orthologs in NHP. By aligning probe sequences with expressed sequence tags (ESTs) in NHP, inter-species conserved (ISC) probesets, which have two or more probes complementary to ESTs in NHP, were identified on human GeneChip ® platforms. The utility of human GeneChips ® for the assessment of NHP expression patterns can be effectively evaluated by analyzing the hybridization behaviour of ISC probesets. Appropriate normalization methods were identified that further improve the reliability of human GeneChips ® for interspecies (human vs NHP) comparisons. Results ISC probesets in each of the seven Affymetrix GeneChip ® platforms (U133Plus2.0, U133A, U133B, U95Av2, U95B, Focus and HuGeneFL) were identified for both monkey and chimpanzee. Expression data was generated from peripheral blood mononuclear cells (PBMCs) of 12 human and 8 monkey (Indian origin Rhesus macaque ) samples using the Focus GeneChip ® . Analysis of both qualitative detection calls and quantitative signal intensities showed that intra-species reproducibility (human vs. human or monkey vs. monkey) was much higher than interspecies reproducibility (human vs. monkey). ISC probesets exhibited higher interspecies reproducibility than the overall expressed probesets. Importantly, appropriate normalization methods could be leveraged to greatly improve interspecies correlations. The correlation coefficients between human (average of 12 samples) and monkey (average of 8 Rhesus macaque samples) are 0.725, 0.821 and 0.893 for MAS5.0 (Microarray Suite version 5.0), dChip and RMA (Robust Multi-chip Average) normalization method, respectively. Conclusion It is feasible to use Affymetrix human GeneChip ® platforms to assess the expression profiles of NHP for intra-species studies. Caution must be taken for interspecies studies since unsuitable probesets will result in spurious differentially regulated genes between human and NHP. RMA normalization method and ISC probesets are recommended for interspecies studies.

Background Microarray studies on non human primates (NHP) have been used to address viral pathogenesis [ 1 , 2 ], neurological disorders [ 3 ], development [ 4 ] and phylogenetic studies [ 5 - 7 ]. Due to the lack of species-specific microarray platforms for NHP, researchers have used GeneChip ® platforms built using human sequence information. An underlying assumption in such studies is that transcripts of humans and NHP are highly conserved, and probe sequences designed to detect human genes will detect their orthologs in NHP samples. It is estimated that chimpanzees ( Pan troglodytes ) and humans shared 98.77 % DNA similarity [ 8 ]. While this statistic is widely quoted and believed, Britten [ 9 ] reported that the divergence between humans and chimpanzees to be about 5%. Anzai and colleagues [ 10 ] compared the chimpanzee MHC region (1,750,601 bp) with the human HLA region (1,870,955 bp), and concluded that the similarity drops to 86.7% if insertions and deletions were taken into account. All these analyses are based on genomic DNA sequences; however, for microarray studies on the transcriptome, the similarity of RNA transcripts is the primary concern. A single gene does not necessarily generate a single transcript. Splicing variants are very common in the human [ 11 , 12 ], and humans and NHPs may use different splicing strategies in some genes. Therefore, it is necessary to re-assess the reliability of human GeneChips ® for NHP expression analysis. Few published studies employing human GeneChip ® platforms for NHP expression profiling have robustly addressed the quantitative aspects of cross platform performance. Vahey and colleagues [ 1 ] used the HuGeneFL GeneChip ® and demonstrated that there was no significant difference in the dynamic range of the raw fluorescence distribution for equivalent amounts of human cRNA and macaque cRNA hybridized to the chip. Chismar and colleagues [ 13 ] used the U95Av2 GeneChip ® platform and compared the expression patterns of humans with that of the rhesus macaque. They concluded that the percentage of 'present' calls observed in the transcriptome of macaque brain is lower than that of human brain, and that this is especially true for genes with lower signal intensity. Caceres and colleagues [ 5 ] used the HG-U95Av2 arrays to identify upregulated genes in the human cortex compared with those of the NHPs. Since sequence divergence could lead to an underestimation of expression levels in NHPs, they excluded 4572 probes that exhibited different hybridization behaviour between two sets of samples in order to reduce false positives. However, this analysis is solely based on probe signal intensities. A more robust way to assess the utility of human GeneChip ® platforms for the study of expression profiles in NHP is to employ a sequence analysis approach. In this study, we address the power of human GeneChip ® platforms to assess expression patterns in NHP samples by: a) identifying ISC probesets based on sequence analysis; b) assessing intra (within NHP species)- and interspecies (between NHP and human samples) reproducibility of GeneChip ® data; and c) applying appropriate normalization methods to improve interspecies reproducibility. Results and discussion Identification of ISC probesets When a probe sequence on the human GeneChip ® hybridizes with the transcriptome of a NHP, there are three possible outcomes: 1) it hybridizes with the ortholog of the NHP; 2) it cross-hybridizes with a non-ortholog transcript, or 3) it fails to hybridize due to sequence divergence. In Affymetrix GeneChip ® system, a probeset is composed of 11–20 probes and each probe is a 25-mer oligo. We identified probes on the human GeneChip that are complementary to ESTs in NHP postulating that these probes would hybridize most optimally with the transcripts of NHP. We defined a probeset as an ISC probeset if it had at least two complementary probes. The rationale used to define the criterion that defines an ISC probeset is described in the methods section. The procedure used to generate ISC probesets is shown in Figure 1 and described in methods section. ISC probesets in each of the seven Affymetrix human GeneChip ® platforms (U133Plus2.0, U133A, U133B, U95Av2, U95B, Focus and HuGeneFL) were generated for both monkey and chimpanzee. Detailed information about each ISC probeset such as probe sequence, GenBank accession and the position and degree of matching is provided in the supplemental materials ( Additional File 1 , Additional File 2 , Additional File 3 , Additional File 4 ). Table 1 displays a summary of the statistical characteristics of ISC probesets. Not surprisingly, there were more ISC probesets for monkey ( Macaca mulatta ) than for chimpanzee ( Pan troglodytes ). This is not because monkey EST sequences are more similar to human sequences than chimpanzee EST sequences, but because we have a much greater amount of EST sequences available for monkey. At the time of writing this manuscript, there were 33,474 monkey ESTs available, while there were only 6,943 ESTs available for chimpanzee. As the number of defined ESTs will increase in the future, additional ISC probesets could be identified for both monkey and chimpanzee using this method. Table 1 The number of ISC probesets in various human GeneChip ® platforms Human GeneChip ® platforms Probes / probeset Total number of probesets (genes*) The number of ISC probesets (genes) Monkey Chimpanzee HG-FL 20 7129 (5435) 1036 (891) 422 (362) HG-Focus 11 8793 (8466) 1179 (1136) 523 (511) HG-U95Av2 16 12625 (9203) 1505 (1267) 586 (511) HG-U95B 16 12620 (9948) 561 (497) 256 (236) HG-U133A 11 22283 (13624) 2676 (1991) 1102 (861) HG-U133B 11 22646 (16119) 886 (773) 406 (363) HG-U133 Plus2.0 11 54675 (29963) 3636 (2704) 1529 (1190) * Refer to the number of unique UniGene clusters. Figure 1 Algorithm for identifying ISC probesets in Affymetrix Human GeneChip ® platforms. In the Affymetrix platform, a probe is a 25-mer oligo. A set of 11–20 probes forms a probeset. An ISC probeset is defined as having at least two probes that are complementary to ESTs in NHP. A perfect probeset is the one that all of its probes are complementary to ESTs in NHP. It is not uncommon, especially in the U133Plus2.0 platform, that multiple probesets target the same gene. For example, in the U133A and the U133 Plus 2.0 GeneChip ® s, there are three probesets (217028_at, 211919_s_at and 209201_x_at) that target the gene CXCR4 at different positions in its transcript. In order to address this redundancy issue, we converted the number of probesets into the number of unique UniGene clusters based on the GeneChip ® annotation file provided by Affymetrix Website [ 18 ]. While a UniGene cluster does not necessarily correspond to a unique gene, it is a reasonable way to assess probeset redundancy. As shown in Table 1 , the Focus GeneChip ® and the U133Plus2.0 GeneChip ® have the lowest and highest frequency of redundant probesets for a given gene, respectively. The U133Plus2.0 is the most current version of human GeneChip ® from Affymetrix and covers the human genome most extensively. Figure 2 displays the distribution of probesets on the human chromosomes. The yellow bars represent the distribution of all probesets on the U133Plus2.0 platform, and the blue and red bars represent the distribution of ISC probesets for monkey and chimpanzee, respectively. As shown in Figure 2 , ISC probesets for both monkey and chimpanzee are distributed throughout the genome, from chromosome 1 to chromosome 22, including the two sex chromosomes X and Y. The percentage of ISC probesets on each chromosome is roughly proportional to that of the total probesets. Figure 2 Distribution of ISC probesets on human chromosomes. The yellow bars represent the distribution of all probesets in GeneChip ® U133Plus2.0 platform. The blue and red bars represent the distribution of ISC probesets for monkey ( Macaca mulatta ) and chimpanzee ( Pan troglodytes ), respectively. Intra- and interspecies reproducibility of detection calls The qualitative detection call (present / absent) output from MAS5.0 was the initial approach used to examine the reproducibility of GeneChip ® data observed in intra- and interspecies samples. The intra-species reproducibility is displayed in Figure 3A and 3B for human samples and monkey samples, respectively. As shown in Figure 3A , 66% of probesets showed 100% reproducibility across 12 human replicates, being either present in all samples (24%) or absent in all samples (42%). Similarly, among 8 monkey samples, 69% of probesets showed 100% reproducibility, being either present in all samples (12%) or absent in all samples (57%) (Figure 3B ). Although the percentage of absent calls in monkey samples (57%) is higher than those in human samples (42%), the detection call itself is consistent across replicates. In other words, an absent call caused by sequence divergence will be reliably repeated across monkey samples. This result suggests that it is feasible to use the human GeneChip ® for NHP intra-species studies. Figure 3 Intra-species reproducibility of detection calls. A : Reproducibility among human samples. B: Reproducibility among monkey ( Rhesus macaque ) samples. 1 P, 2 P ... 12 P represent 1, 2 ...12 present calls among all samples. Absent = no present calls in any sample. N = 12 human and 8 monkey samples. In contrast, if human GeneChip ® platforms are used to compare the expression pattern of humans with those of NHPs, care must be taken in the interpretation of data. If we consider a probeset as being expressed when 50% or more of replicates have present calls, then 3445 (2059+1386) and 2321 (2059+262) probesets are expressed in the PBMC fraction of humans and monkeys, respectively (Figure 4A ). Approximately 40% (1386/3445) of probesets being detected in human PBMCs are not detected in the monkey. Due to the close evolutionary relationship between human and monkey, one would not expect that 40% of genes expressed in human PBMCs are not expressed in monkey PBMCs. This observation suggests that a subset of human probesets failed to properly hybridize with the orthologs of monkey. Based on expression data alone, however, it is difficult to distinguish a genuine absent call from a spurious absent call resulting from sequence divergence. ISC probesets can help to distinguish spurious from genuine absent calls. As shown in Figure 4B , of 868 ISC probesets that were detected in human PBMCs, only 216 (24.9%) are not detected in monkey PBMCs. The interspecies discordance is reduced significantly for ISC probesets (Fisher's exact test p < 2.2e-16). It is important to point out that ISC probesets will significantly reduce, but not eliminate interspecies discordance as it requires only a minimum of two complementary probes. It can be postulated that a perfect probeset in which all of its probes were complementary to ESTs of NHP would provide the ultimate reduction in discordance. However, the identification of such probesets is limited by currently available sequence information. Figure 4 Interspecies reproducibility of detection calls. A: Venn diagram of the number of expressed probesets in human and monkey ( Rhesus macaque ) samples; B: Venn diagram of the number of expressed ISC probesets in human and monkey ( Rhesus macaque ) samples. Intra- and interspecies reproducibility of signal intensities To assess the intra- and interspecies reproducibility of GeneChip ® signal intensities, a matrix that contains signal intensities of 3445 expressed probesets across 20 samples (12 human and 8 monkeys) was created. Probesets that are not expressed in human PBMCs were excluded in this analysis. Pair-wise correlation coefficients were calculated for all 20 samples ( 20 C 2 = 190 combinations in total). The correlation coefficients were visualized using heat spectrum graphs where colors ranging from red to white correspond to correlation coefficients of 0.5 to 1.0, respectively. In figure 5A , the cells in the diagonal line are all white as they represent samples correlating with themselves with a correlation coefficient of 1.0. The highest correlations were found among human replicates (lower left corner), followed by monkey replicates (upper right corner). The lowest correlations were found in interspecies comparisons (bottom right corner). The means and standard deviations of human-human, monkey-monkey and human-monkey correlation coefficients are 0.92 ± 0.013, 0.85 ± 0.039 and 0.65 ± 0.044, respectively. If the low correlation coefficients of human-monkey are caused by unsuitable probesets, then ISC probesets should have higher correlation coefficients. Figure 5B displayed the correlation coefficients of the same 20 samples as Figure 5A , but limited to ISC probesets. As shown in Fig 5B , the colors are much less red than those in Figure 5A , indicating higher correlation coefficients. The means and standard deviations of correlation coefficients of ISC probesets for human-human, monkey-monkey and human-monkey are 0.95 ± 0.0094, 0.92 ± 0.023 and 0.80 ± 0.026, respectively. The greatest improvement (0.65 to 0.80) in correlation coefficients are observed in the human-monkey comparison (Figure 5A and 5B ) using the ISC probesets. This data suggests that a subset of problematic probesets interfered with interspecies comparison, and the ISC probesets could be used to improved interspecies reproducibility. Figure 5 Intra- and interspecies reproducibility of expression signal intensities. Pair-wise correlation coefficients of 20 samples (12 human and 8 monkey ( Rhesus macaque ) samples) were calculated for expressed probesets (Figure 5A) and for expressed ISC probesets (Figure 5B). Correlation coefficients are visualized using colors of a heat spectrum (red=correlation coefficient of 0.5; white = correlation coefficient of 1.0). The graphs are symmetric along the diagonal lines. The diagonal line represents samples correlating with themselves, with a correlation coefficient of 1.0 (white). The means and standard deviations of correlation coefficients of human-human, monkey-monkey and human-monkey are shown in the bottom left, upper right and bottom right of each graph, respectively. A : Correlation coefficients calculated based on all expressed probesets. B : Correlation coefficients calculated based on expressed ISC probesets. The effect of normalization methods on interspecies reproducibility Different normalization methods have been shown to significantly affect GeneChip ® data variation [ 14 - 17 ]. We compared three different normalization methods: MAS5.0, RMA [ 14 - 16 ] and dChip [ 17 ], to evaluate the effect of normalization methods on interspecies reproducibility. Both RMA and dChip methods normalize GeneChip ® data at the probe level using a non-linear algorithm while MAS5.0 normalizes data at probeset level using linear scaling. Sequence divergence usually leads to one or very few probes in a probeset being problematic while the majority of probes in that probeset may still work reasonably well. If the variation generated from these problematic probes were normalized, the interspecies reproducibility should improve. Figure 6 showed the interspecies correlation coefficients using three different normalization methods. The average signal intensities of 8 monkey samples were given on the ordinate and that of 12 human samples on the abscissa. The RMA normalization method improved interspecies reproducibility the most for both expressed probesets and ISC probesets. As shown in the Figure 6A,6B,6C , correlation coefficients for expressed probesets using MAS5.0, dChip and RMA were 0.725, 0.821 and 0.893, respectively. Similarly, in Figure 6D,6E,6F , correlation coefficients for ISC probesets using MAS5.0, dChip and RMA were 0.850, 0.879 and 0.921, respectively. For the same normalization method, ISC probesets exhibited higher correlation coefficients than those of expressed probesets (horizontal comparison such as Figure 6A vs. Figure 6D ). Use of the RMA normalization method in conjunction with the use of ISC probesets optimized the correlation coefficient between human and monkey. The resulting correlation coefficient of 0.92 is equivalent to the human-human correlation using the MAS5.0 normalization method (Figure 6F and Figure 5A ). Figure 6 The effect of normalization methods on interspecies reproducibility. A, B and C: MAS5.0, dChip and RMA normalization for expressed probesets. D,E, and F: MAS5.0, dChip and RMA normalization for expressed ISC probesets. x-axes and y-axes are average expression intensities of 12 human samples and 8 monkey ( Rhesus macaque ) samples, respectively. Conclusions This paper presents a comprehensive analysis of probe sequences and GeneChip ® expression data as applied to the derivation of meaningful expression profile data from NHP. The utility of the human Affymetrix GeneChip ® for the assessment of expression profiles in NHP depends on the experimental design and on the approach to data normalization and analysis. Our observations suggest that: 1) it is feasible to use the human GeneChip ® in the evaluation of expression profiles of NHP samples for intra-species comparisons; 2) use of ISC probesets and RMA normalization are recommended for interspecies studies; and 3) with the increasing amount of ESTs of NHP, additional ISC probesets (and perfect probesets) will be identified in the near future. Methods Sequence data source Affymetrix GeneChip probe sequences were downloaded from Affymetrix website [ 18 ]. The ESTs (Expressed Sequence Tags) of monkey ( Macaca mulatta ) and chimpanzee ( Pan troglodytes ) were downloaded from NCBI website [ 19 ]. Identification of ISC probesets Stand alone BALST program was downloaded from NCBI website [ 19 ]. Perl script was written to automatically run BLAST search between GeneChip ® probe sequences and monkey /chimpanzee EST sequences. The length of a probe sequence is always 25 nucleotides while the number of probes in a probeset varies from 11 to 20 depending on GeneChip ® platforms (see Table 1 ). A certain degree of mismatch between a probe sequence and ESTs is allowed. If a probe has at least 23 nucleotides complementary to at least one EST sequence, this probe is designated as a complementary probe. If a probeset has at least two complementary probes, we defined this probeset as an ISC probeset. If all probes of a probeset are complementary probes, this probeset is called a 'perfect' probeset. The rationale for the definition of ISC probesets is as follows: 1) since each probe is a 25-mer oligo, the probability of random matching of one probe is 4 -25 thus, the probability of random matching of two probes goes down to 4 -50 , being exponentially reduced; 2) in comparison with an RT-PCR experiment, the primer length is equivalent to our probe length, and two primers (one forward and one backward) usually generate a unique sequence in a whole genome; 3) a probe sequence on the Affymetrix GeneChip ® is a well designed sequence with a single probe hybridizing with a unique transcript in whole transcriptome; and 4) since the EST sequences in NHP are very limited so far, most of them do not cover whole transcript such that a false negative could be generated if we require all the probes in a probeset being complementary to known ESTs. In order to convert probeset IDs to UniGene IDs and map them onto chromosomes, probeset annotation files were downloaded from Affymetrix website [ 18 ]. No animals or human samples were used for the purpose of this analysis. Affymetrix datasets used in this analysis are from other approved ongoing projects in our lab. The procedure used to process these samples was previously published [ 1 ]. Briefly, peripheral blood from healthy human and NHP (Indian origin Rhesus macaque ) was collected and peripheral blood mononuclear cells (PBMCs) were separated by Histopaque-Ficoll (Sigma) gradient centrifugation. RNA preparation, Hybridization, staining and scanning of the GeneChip ® was carried out as described by Vahey et al. [ 1 ]. Animal and human samples were handled identically throughout the process. All 20 samples (12 human and 8 rhesus macaques) were hybridized to Affymetrix's HG-Focus GeneChip ® . Signal values and detection calls (present or absent) for all samples were determined by using MAS5.0 (Affymetrix Inc. Santa Clara, California). Signal values were scaled to the default target signal intensity of 500). A matrix of detection calls (present, absent and marginal) and a matrix of signal intensities for all samples across all probesets were constructed. A gene must exhibit 50% or more of 'present' calls in all samples to be considered 'expressed'. In this study, an expressed probeset in human is a probeset that has 6 or more present calls among 12 human samples. Similarly, an expressed probeset in monkey means there were 4 or more present calls among 8 monkey samples. The signal intensities output from MAS5.0 were log2 transformed. Model-based normalization was performed using dChip version 1.3 [ 17 ]. The output signal intensities were log2 transformed. RMA (Robust Multichip Average) normalization [ 14 - 16 ] was carried out using BioConductor package Affy_1.2.30 [ 20 ]. The rma() function in the package was used at its default setting, that is, 'RMA' background correction, 'quantile normalization', 'PM only model' and 'median polish summarization'. By default, the signal intensities were already log2 transformed. Intra- and interspecies correlation coefficients of signal intensities were calculated by built in function ' cor ' in statistical package R version 1.9.0. [ 21 ]. Visualization of correlation coefficients matrix was done by the function ' image '. The function ' heat.colors ' was used to create heat-spectrum (red to white) and set color scales between 0.5 (red) and 1.0 (white). Abbreviations NHP: non human primate EST: expressed sequence tag ISC: inter-species conserved RMA: robust multi-chip average MAS5.0: Microarray suite version 5.0 Authors' contributions ZW developed the original hypotheses, performed the bioinformatics analyses to test them and drafted the manuscript. MV provided critical input on design and execution of the laboratory experiments and with ZW interpreted the data sets and revised the manuscript. ML conducted all aspects of the animal handling including the harvest of well characterized primary samples. MN and AA are technical staff who extracted the nucleic acid and performed the laboratory portions of the microarray experiments. All authors read and approved the final manuscript. Supplementary Material Additional File 1 There are three worksheets in this file. Worksheet 1, 2 and 3 are ISC probesets for monkey ( Macaca mulatta ) in GeneChip ® platforms Plus2.0, U133A and U133B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e - values and matching identities, in that order. Click here for file Additional File 2 There are four worksheets in this file. Worksheet 1, 2, 3 and 4 are ISC probesets for monkey ( Macaca mulatta ) in GeneChip ® platforms Focus, FL, U95Av2 and U95B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e - values and matching identities, in that order. Click here for file Additional File 3 There are three worksheets in this file. Worksheet 1, 2 and 3 are ISC probesets for chimpanzee ( Pan troglodytes ) in GeneChip ® platforms Plus2.0, U133A and U133B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e - values and matching identities, in that order. Click here for file Additional File 4 There are four worksheets in this file. Worksheet 1, 2, 3 and 4 are ISC probesets for chimpanzee ( Pan troglodytes ) in GeneChip ® platforms Focus, FL, U95Av2 and U95B, respectively. Note: All the four additional files are multiple-sheets MS excel files. Within each worksheet of a file, the rows are ISC probesets, the columns are probeset IDs, positions of a probe (x and y coordinates in the chip), GenBank accessions of a matching EST, probe sequence, matched EST sequence and position, BLAST e - values and matching identities, in that order. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526766.xml

555575

Local therapy with CpG motifs in a murine model of allergic airway inflammation in IFN-β knock-out mice

Background CpG oligodeoxynucleotides (CpG-ODN) are capable of inducing high amounts of type I IFNs with many immunomodulatory properties. Furthermore, type-I IFNs have been proposed to play a key role in mediating effects of CpG-ODN. The precise role of IFN-β in the immunomodulatory effects of CpG-ODN is not known. Objective Here, we aimed to elucidate the role of IFN-β in the anti-allergic effect of CpG motifs. Methods We assessed the immune response in OVA-primed/OVA-challenged IFN-β knockout (-/-) mice compared to wild type (WT) control, after intranasal and systemic treatment with synthetic CpG motifs. Results Vaccination with CpG-ODN reduced the number of cells in airways of OVA-sensitized WT but not IFN-β-/- mice. Although airway eosinophilia was reduced in both treated groups, they were significantly higher in IFN-β - /- mice. Other inflammatory cells, such as lymphocytes and macrophages were enhanced in airways by CpG treatment in IFN-β-/- mice. The ratio of IFN-γ/IL-4 cytokines in airways was significantly skewed to a Th1 response in WT compared to IFN-β - /- group. In contrast, IL-4 and IgE were reduced with no differences between groups. Ag-specific T-cell proliferation, Th1-cytokines such as IFN-γ, IL-2 and also IL-12 were significantly lower in IFN-β-/- mice. Surprisingly, we discovered that intranasal treatment of mice with CpG-ODN results in mild synovitis particularly in IFN-β-/- mice. Conclusion Our results indicate that induction of Th1 response by therapy with CpG-ODN is only slightly and partially dependent on IFN-β, while IFN-β is not an absolute requirement for suppression of airway eosinophilia and IgE. Furthermore, our finding of mild synovitis is a warning for possible negative effects of CpG-ODN vaccination.

Introduction Allergic diseases are characterized by elevated serum IgE, an inflammatory reaction with increased number of eosinophils, mast cells and an adaptative immune responses orchestrated by Th2-like CD4+ memory T cells secreting an array of cytokines such as IL-4, IL-5 and IL-13. Thus, there are major efforts focused on a therapeutic treatment which will decrease the Th2 profile and/or re-direct the immune response from a Th2, IgE-mediated allergic hypersensitivity reaction towards the more favorable Th1 response. IL-12 and IFN-γ are of primary importance in modulating the Th1/Th2 balance. IFN-γ has been shown to attenuate eosinophil recruitment[ 1 ], and also inhibit the development of secondary allergic response [ 2 - 4 ]. There has also been extensive research into therapeutic use of IL-12[ 5 ]. However, difficulties with precise dosing and toxicity associated with the direct administration of these cytokines may preclude their therapeutic application. Another approach is to use natural up-regulators to elevate endogenous levels of IL-12 or IFN-γ. Many microbial products, including heat-killed bacteria and CpG motifs can up-regulate Th1 cytokines. Oligodeoxynucleotides (ODN) containing unmethylated cytosine-guanine motifs (CpG) have powerful immunomodulatory activity in human and murine lymphocytes in both Th1 and Th2 associated diseases [ 6 - 12 ]. It is believed that CpG exert their effect through antigen presenting cells by inducing cytokines such as TNF-alpha, IL-12, IL-18, and IFNs [ 9 , 13 , 14 ]. Type I IFNs have been proposed as mediators of immunomodulatory effects of CpG oligonucleotides [ 15 ]. Importantly, some studies have suggested that endogenous type I IFN might contribute to the downregulation of eosinophil infiltration in murine asthma model [ 16 ]. Furthermore, reduced inflammatory infiltration and IgE production have been shown after administration of recombinant IFN-β[ 17 , 18 ]. We have recently demonstrated that lung eosinophilic inflammatory response was exacerbated by the lack of IFN-β gene[ 19 ]. Even though it is believed that immunomodulatory effects of CpG-ODN are mediated by type I IFNs, the relative role of IFN-β has not been defined. In this report, we examined the role of IFN-β in the immune response after CpG treatment in a murine model of allergic inflammation. Our results indicate that induction of Th1 response by therapy with CpG-ODN is partially dependent on IFN-β, while IFN-β is not an absolute requirement for suppression of eosinophilia and IgE. Materials and methods Animals Groups of pathogen-free female[ 20 , 21 ] 8-10-week-old, 17-20 g, B10.RIII mice (n = 5 mice per group) were used in the experiments. IFN-β deficient mice (IFN-β-/-) were kindly provided by Dr Leanderson[ 22 ]. Genotyping of the offspring has been described before[ 23 ]. All animal care and experimentation were conducted at the animal unit of Medical Inflammation Research in Lund in accordance with the current protocols in Lund University. Induction of disease and treatment protocol Immunization and allergen challenge of the mice were carried out according to a short term allergy model protocol by Sur and colleagues [ 24 ] with slight modification. Mice were sensitized by i.p. injection on days 0 and 4 with OVA 50 μg (Sigma Chemical Co., St Louis, Mo), with 5 mg alum (Sigma Chemical Co.). At day 14 and 16 after immunization, mice were challenged with 50 μg of OVA plus 5 μg of CpG-ODN (Scandinavian Gene Synthesis AB, Köping, Sweden) delivered through the airways as intranasal drops after light anesthesia. Control mice were immunized with 5 mg alum with PBS, and challenge with PBS using the same schedule as OVA immunized mice. Our previous studies have confirmed that control mice did not show any remarkable allergy changes[ 19 ]. The ODNs were designed using published sequences[ 8 , 25 ] consisting of a single-stranded phosphorothioate-modified ODNs with 22 bases containing two CpG motifs (5'-TGACTGTGAACGTTCGAGATGA-3'), highly purified with undetectable levels of LPS (detection limit: 1 ng/mg DNA): and were dissolved in PBS with a final concentration of 1 μg/μl [ 11 ]. Mice received either 5 μg of CpG-ODN in PBS or PBS alone intranasally in conjunction with OVA challenges. On day 17 (i.e. 24 h after the last challenge) mice were assessed for lung allergic inflammatory response. In the prevention study (vaccination), mice were pretreated i.p. with 5 μg of CpG-ODN in PBS on day 0. On the same day, mice were sensitized by i.p. injection with OVA complexed with 5 mg alum (Sigma). On day 4 mice were injected i.p. OVA (50 μg) in Alum (5 mg). On days 14 and 16 after immunization mice were challenged with 50 μg of OVA delivered through the airways as intranasal drops after light anesthesia. On day 17 mice were assessed for lung allergic inflammatory response, 24 hours (h) after the last challenge. Bronchoalveolar lavage Fluid (BALF) Mice were deeply anesthetized with an ip injection of 0.2 ml avertin (20 mg/ml; 2,2,2 tribromoethanol, Sigma-Aldrich) and sacrificed 24 hours after the last OVA exposure. After thoracotomy, the trachea was cannulated and BAL was collected twice with 0.5 mL of PBS and the collected fluid was pooled. Total cell counts were determined using an automated hemocytometer (Sysmex CDA-500, Toa Medical Electronics CO., Ltd., Kobey, Japan), and the fluid was centrifuged (1.000 rpm, 4°C, 10 min). The supernatant was used to determine the airway cytokine and IgE levels contents. The cells were applied to slides using a cytospin apparatus (Auto-smear CF-12DE, Sakura Finetek Europe BV, Zoeterwoude, The Netherlands) and were stained with May-Grunwald-Giemsa staining. Eosinophils were specifically detected by histochemical staining of cyanide-resistant eosinophil peroxidase activity (CREPA) using as substrate 3,3 diaminobenzidine tetrahydrochlorhid (DAB), as described before[ 26 ]. Briefly, samples were dried overnight at room temperature and fixed with 4% paraformaldehide for 5 min and PBS for 2 min. Then, samples were incubated in PBS buffer with DAB 60%, H 2 O 2 30% and NaCN 120% for 7 min. After washing with PBS, samples were counterstained with hemtoxiline 30" and mounted with Kaiser medium (Merck, Darmstadt, Germany). Eosinophils were easily detected by its dark brown color. The slides were examined by light microscopy (×40 magnification) in a blinded fashion counting at least 400 cells per slide Allergen specific T cell proliferation At the time of sacrifice spleens were dissected and a single cell suspensions from each mouse was prepared in DMEM with glutamax I (Gibco BRL, Life Technologies), supplemented with 10% heat-inactivated fetal calf serum, 10 mmol/l HEPES, 50 mmol/l β-mercaptoethanol, 100 U/ml penicillin G, and 100 μg/ml streptomycin. Cells were cultured (5 × 10 6 /ml) in triplicates in 96-well flat-bottomed plates at 37°C, 5% CO2 in a humidified incubator. Cells were cultured in absence or presence of OVA (111 μM), CpG-ODN (1 μg/ml) or concavalin A (4 μg/ml). 3 H-thymidine (100 μCi/ml) was added 54 h later, and after a further 18 hr later incubation, a beta-scintillation counter measured incorporation. Cytokine Assays Splenocytes were isolated as described and incubated for 48 h with or without OVA (Sigma-Aldrich) (111 μM) in 48-well plates. Enzyme immunoassays were performed as described before[ 23 , 27 ] using monoclonal Ab (anti-IL-2, anti-IL-4, anti-IL-5, anti-IL-12, anti-IFN-γ (BD Pharmingen, San Diego, CA, USA) and reading by chemiluminescence (Victor ® ; 1420 Multilabel Counter © , Wallac Oy; EG & G Turku, Finland). Determination of total and OVA-specific IgE levels Mice were bled at the time of sacrifice. A sandwich ELISA (BD Pharmingen) was used to measure levels of IgG and IgE as described previously[ 28 ]. To determine OVA-specific IgE plates were incubated with OVA 10 μg/ml in PBS buffer (pH 7.'5). Procedure was the same as total IgE. Standard curve was performed with sera with known levels of specific IgE as it has been published before [ 29 ]. Briefly, real concentration of specific IgE in ng/ml of a pooled serum was determined indirectly by absorption of 50 μl of serum with either conjugated BSA in Sepharose (Pharmacia, Uppsala, Suecia) or conjugated OVA in Sepharose. Total IgE ELISA, as mentioned before, determined the level of not absorbed specific IgE. The percentage of OVA-specific IgE was calculated by reciprocal value of: (IgE not absorbed by OVA-Sepharose/IgE not absorbed by BSA-Sepharose) × 100. The result of a pool of sera from several immunized mice by this method was 402 ng/ml of OVA-specífica IgE. In next experiments this serum was used as standard pattern. For that, plates were coated with OVA (10 μg/ml) overnight 4°C and blocked with 1% BSA in PBS 1 h room temperature. The remainder steps were performed as total IgE ELISA, as described before. Flow cytometry At time of sacrifice spleens were removed and a single cell suspension was made, cells were then lysed with 0.84% NH 3 Cl 2 and washed in PBS with 1% BSA and 0.01% sodium azide. After blocking Fc receptors, using 24.G2 (from our hybridoma collection), cells were stained with the following antibodies (BD PharMingen); PE conjugated anti-B7.1 (clone 16-10A1), FITC conjugated anti-B7.2 (GL1), cytochrome conjugated anti-B220 (RA3-6B2), APC conjugated anti-Thy1.2 (53-2.1), PE conjugated anti-CD4 (H129.19), cytochrome conjugated anti-CD8 (53-6.7). The cells were then analyzed by flow cytometry FACSort (Becton Dickinson, Franklin Lakes, NJ, USA), using the BD Cell-Quest™ Pro, Version 4.0.1 software (Becton Dickinson). Three individuals per time point and group were analyzed. The program then displays the percentage of events, which express the CD86 molecule and this percentage is the compared between the groups. Clinical and Histological analysis of joints for arthritis Seventeen days post CpG-ODN or control vaccination, paws were visually assessed looking for swelling or deformation with redness in one joint, several joints or severe swelling of the entire paw and/or ankylosis[ 30 ]. Then, mice were sacrificed and paws were dissected and were fixed in 4% formaldehyde, decalcified with EDTA (for 2–3 weeks), embedded in paraffin, sectioned at 5μm and stained with hematoxylin and erythrosine. Approximately, 20–30 sections were made from each paw (2 paws per mouse, i.e. front and back paws). The sections were then evaluated blindly for pathological changes in joints (synovitis, erosion or destruction)[ 31 ]. Statistic analysis The significance of changes was evaluated using Mann-Whitney U test. Significance was assumed at p values ≤ 0.05. Results Treatment with different dose of intranasal CpG-ODN showed similar results The percentage of local eosinophils in airways was increased after immunization and challenge with OVA in BALF of WT and IFN-β-/- compared to non immunized mice. Preliminary data with different dose of CpG administered intranasally with OVA (5 μg, 10 μg or 20 μg) to both strain of mice resulted in similar reduction of percentage of infiltrating eosinophils in BALF (Table 1 ). Table 1 Eosinophils in airways with different dose of intranasal CpG-ODN Treatment Genotype Eosinophils PBS B10.RIII 0.5 % IFN-β - /- 0.7 % OVA B10.RIII 55 % IFN-β - /- 62 % OVA+CPG 5 μ B10.RIII 2.1 % IFN-β - /- 9.2 % OVA+CPG 10 μ B10.RIII 1. 9 % IFN-β - /- 9.4 % OVA+CPG 20 μ B10.RIII 1.9 % IFN-β - /- 9.0 % B10.RIII/WT (□) and IFN-β - /- (■) mice were sensitized to OVA by intraperitoneal injection and subsequently challenged with OVA either alone or with different dose of CpG-ODN by intranasal drops on days 14 and 16. Eosinophil percentage in bronchoalveolar lavage with different dose of intranasal CpG-ODN were similar in all IFN-β - /- treated mice. Treatment with CpG-ODN inhibits total number of infiltrating cells in airways in WT but not in IFN-β-/- mice The treatment with 5 μg of CpG administered intranasally with OVA resulted in significant reduction of total number of infiltrating cells in BALF in WT group while it had no effect in IFN-β-/- group (Figure 1A ). We examined the number of recruited cells in lung airways after administration of PBS, OVA or CpG-ODN plus OVA and challenge with OVA. We found that OVA nasal challenge increased significantly the number of cells recruited in airways of OVA-primed mice compared to PBS group. CpG-ODN vaccinated mice had reduced the number of cells in OVA-sensitized B10.RIII mice but not in IFN-β-/-. Figure 1 Effects of treatment with CpG-ODN on total BALF cell recruitment (A), eosinophils (B). B10.RIII/WT (□) and IFN-β - /- (■) mice were sensitized to OVA by intraperitoneal injection and subsequently challenged with OVA either alone or with CpG-ODN by intranasal drops on days 14 and 16. Cells were harvested on day 17 th . n = 5/group, * P < 0.05 vs. OVA groups. † P < 0.05 vs OVA-treated WT mice. Suppression of eosinophilia by CpG-ODN in airways is only partially dependent on IFN-β gene Next, we were interested in the effect of CpG-ODN treatment on eosinophilia. As expected, we found that OVA-sensitized/OVA-challenge WT mice had a dramatic increase in numbers of eosinophils compared with non-treated WT. Vaccination with CpG-ODN diminished dramatically the number of eosinophils in WT mice while it was only partially effective in prevention of eosinophilia in IFN-β - /- mice, and the difference between the CpG-ODN vaccinated and PBS vaccinated mice was statistically significant for both WT and IFN-β - /- (figure 1B ). IFN-γ induction in the airways by CpG-ODNs vaccination is impaired in IFN-β-/- mice We were interested in investigating if disease mediated Th2 cytokines or disease counter-acting cytokine, IFN-γ, was effected by the CpG-ODN vaccination. We observed that the level of IL-4 in BALF was reduced from 65 ± 7 pg/ml to 43 ± 6 pg/ml (33% of reduction) in WT mice and from 62 ± 8 pg/ml to 46 ± 87 pg/ml (26%) in IFN-β-/- mice respectively after CpG-ODN vaccination. The levels of IL-5 were significantly reduced in both groups with no difference between groups (figure 2A ). IFN-γ production in airways of WT mice was enhanced upon CpG-ODN vaccination and it was dependent on IFN-β gene since its induction was impaired in IFN-β-/- mice (figure 2B ). Hence, the ratio IFN-γ/IL-4 determining the Th1/Th2 ratio, was skewed to a Th1 response in both groups although much stronger in WT mice (figure 2C ). Figure 2 BALF cytokine (protein) concentrations after intranasal CpG-ODN. BALF were collected 24 h after the last challenge from each group ( n = 5/group) and cytokine levels determined by ELISA in non-immunized, OVA-challenged, and OVA-challenged/CpG-treated B10.RIII (□) and IFN-β - /- (■) mice at days 14 and 16. IL-5 (A) levels were significantly augmented after OVA challenge and diminished after CpG vaccination in both strains similarly. IFN-γ (B) was not induced in OVA/primed-OVA/challenge, but was induced after CpG vaccination. IFN-γ was stronger induced in B10.RIII than in IFN-β - /- mice. Th1/T2 ratio was stronger skewed to Th1-profile in B10.RIII than in IFN-β - /- mice. Data are given as mean ± SEM, * P < 0.05 vs. OVA groups. † P < 0.05 vs B10.RIII mice treated with CpG Vaccinated with CpG-ODN induces CD86 expression on B cells in IFN-β-/- mice In order to observe any differences between cell surface markers between IFN-β - /- and wild type mice treated with CPG-ODN or with PBS, splenocytes were analyzed by flow cytometry. We could not see any difference in T cell population, in regards to both CD4:CD8 ratio and expression of CD86 (B7.2) on T cells. However, there was a significant difference in CD86 (B7.2) expression on B cells. This difference was observed between CpG-ODN vaccinated IFN-β - /- mice and PBS control IFN-β - /- mice as well as between CpG-ODN vaccinated IFN-β - /- and CpG-ODN vaccinated wild type mice (Figure 3 ). Figure 3 Percent of expression of CD86/B7.2 on B cells in splenocytes of mice at day 17 after immunization and vaccination of ODN-CpG in IFN-β - /- mice (KO) and wild type litter-mates (WT). CpG-ODN vaccination induces mild synovitis particularly in IFN-β-/- mice Mice did not show any clinical visually deformation. While surveying the capacity of CpG-ODN vaccination to induce IFN-β in different tissues, it was noticeable that there were pathological changes in joints of some mice. Thus, we stained the paws of mice (n = 3) with hematoxylin and erythrosine and evaluated the pathologic changes in joints. Data revealed mild synovitis and pannus formation in multiple joints of CpG-ODN vaccinated mice while no control mice had any pathologic changes. Furthermore, we discovered that mice lacking IFN-β were more affected than their wild type littermates (table 2 and figure 4 ). Table 2 Histopathologic evaluation of joints for arthritis changes. Groups Vaccination CpG-ODN Control IFN-β - /- (n.1) ++ - IFN-β - /- (n.2) ++ - IFN-β - /- (n.3) + - WT (n.1) ++ - WT (n.2) - - WT (n.3) - - Hematoxylin-eosin staining of joints from four different groups of mice (IFN-β - /- and their WT littermates with CPG-ODN treatment or control) were analyzed. This revealed mild synovitis and pannus formation in 3/3 IFN-β-/- mice treated with CPG-ODN and 1/3 WT treated with CPG-ODN while no pathological changes were observed in these two non-treated groups. Figure 4 Illustration of joint synovitis after hematoxylin-eosin staining. A. It shows synovitis and pannus formation in IFN-β - /- mice treated with CPG-ODN. B. It shows no pathologic changes in a control treated IFN-β - /- mice. Cell profile in airways after vaccination withCpG-ODN The CpG-ODN vaccination reduced the number of cells in OVA-sensitized B10.RIII mice. However, the number of cells recovered in IFN-β - /- mice did not significantly change (table 3 ). ODN vaccinated mice had a slight increase in numbers of eosinophils compared with non-treated WT. CpG-ODN therapy diminished the number of eosinophils in WT mice, while it was only partially effective in prevention of eosinophilia in IFN-β - /- mice with significant differences between the CpG-ODN treated and non-treated mice in WT and IFN-β - /- (table 3 ). Similarly, vaccination with CpG-ODN showed an enhanced response of macrophages in IFN-β - /- mice compared to WT mice, but this macrophage response was similar in treated and non-treated WT mice. Lymphocyte and neutrophil response in airways of treated-IFN-β - /- mice was also significantly enhanced compared to WT mice. Table 3 Effects of vaccination with CpG-ODN (prevention study) on eosinophil and total BAL cell recruitment. Treatment Genotype Total cells Eosinophils Monocytes Lymphocytes Neutrophils PBS B10.RIII 245 ± 43 3 ± 1 232 ± 20 5 ± 1 5 ± 1 IFN-β - /- 259 ± 14 3 ± 1 242 ± 33 6 ± 1 8 ± 2 OVA B10.RIII 622 ± 37* 381 ± 43* 144 ± 17 62 ± 3* 35 ± 2* IFN-β - /- 683 ± 66* 427 ± 83* 178 ± 22 55 ± 8* 22 ± 4* OVA+CpG B10.RIII 227 ± 18† 2.7 ± 2† 142 ± 19 67 ± 4 14 ± 1 IFN-β - /- 574 ± 32 52 ± 7 † ‡ 321 ± 39† ‡ 130 ± 38† ‡ 70 ± 22† ‡ Cell types quantified in BALF were eosinophils, macrophages, lymphocytes and neutrophils and are expressed as no. of cells × 10 3 /ml. n = 5/group, * P < 0.05 vs. untreated groups. † P < 0.05 vs OVA-treated mice. ‡ P < 0.05 vs WT mice treated with CpG-ODN. OVA-treated mice and control groups. Inhibition of OVA-specific IgE in the prevention study (vaccination) by CpG-ODNs is independent of IFN-β It has been shown that systemic administration of CpG-ODN do not inhibit established IgE response while vaccination inhibits IgE production[ 32 ], however the role of INF-β was not investigated. Here, we examined what the function of IFN-β was in prevention of OVA-specific IgE in CpG-ODN vaccine. We found that CpG-ODN vaccine resulted in inhibition of OVA-Specific IgE in both WT and IFN-β-/- mice (figure 5 ). IgG2a levels were similar in both WT (118 ± 15 μg/ml) and IFN-β-/- (135 ± 25 μg/ml) mice. Figure 5 OVA-specific IgE levels in the prevention study (vaccination). B10.RIII/WT (□) and IFN-β - /- (■) were sensitized to OVA by intraperitoneal injection either OVA alone or with CpG-ODN and subsequently challenged with OVA by intranasal drops on days 14 and 16 ; control mice received PBS alone. Cells were harvested on day 17. n = 5/group, * P < 0.05 vs. OVA groups. † P < 0.05 vs OVA-treated WT mice. Allergen specific Th1 response as a result of CpG-ODN vaccination is partly impaired in the absence of IFN-β To address if splenocytes from WT and IFN-β - /- respond differently in vitro , cells from naïve mice were stimulated and cell proliferation was measured. Splenocytes from both groups, WT and IFN-β - /-, had the same proliferation levels after stimulation with concavalin A, CpG or culture media (figure 6A ). However, cells from WT immunized mice vaccinated with CpG in vivo had more cell proliferation after restimulation with OVA than IFN-β - /- immunized and CpG vaccinated mice (figure 6B ). Next we assessed whether OVA specific Th1 response, i.e. IFN-γ, IL-2 and IL-12, were affected by CpG-ODN vaccination plus OVA treatment in vivo . We found that IFN-γ, IL-12 and IL-2 were significantly lower in OVA-primed/OVA-challenge IFN-β-/- mice compared to WT mice (figure 6C ). Figure 6 Ex-vivo immune response in the prevention study (vaccination). A. In vitro stimulation of splenocytes from naïve mice with con A and CpG does not show any difference between B10.RIII (□) and IFN-β-/- mice (■). B. In vitro proliferation of OVA restimulated T cells from in vivo CpG-vaccinated OVA-primed B10.RIII (□) and IFN-β-/- mice (■). Mice were primed and challenged as in Figure 2. In vitro proliferation after recall with OVA was weaker in IFN-β-/- mice (■) than B10.RIII mice (□). C. Th-1 cytokines from supernatants after in vitro proliferation of OVA restimulated T cells in OVA-primed/CpG-vaccinated mice. IFN-γ, IL-12 and IL-2 production in supernatants from cell cultures was higher in B10.RIII than in IFN-β-/- mice. n = 5/group * P < 0.05 vs. OVA-treated B10.RIII mice. Discussion Synthetic unmethylated CG dinucleotides within particular sequence context (CpG motifs) mimic bacterial DNA, and are responsible for the immunostimulatory activity of that [ 6 ]. CpG oligonucleotides have shown to produce a strong activation of B cells[ 33 ], NK cells [ 34 ], macrophages[ 35 ] and dendritic cells[ 36 ] by a direct mechanism. However CpG have also the ability to exert activation of T cells by an indirect mechanism through via IFN-α/β [ 37 , 38 ]. Furthermore, CpG in mice results in production of inflammatory and antiinflammatory cytokines including IL-1, IL-2, IL-6, IL-18, TNF-α, type I IFN (IFN-α/β) and type II IFN (IFN-γ) [ 39 - 41 ]. Type I IFNs (IFN-α/β) have pleiomorphic effect on the immune system with activation of macrophages and stimulation of NK cells to produce IL-12, which in turn induces Th1 cell development[ 42 ]. Some of these immunostimulatory effects have been applied in animal models of several diseases including allergic disorders[ 8 , 43 - 50 ]. It have been shown that therapies using oligonucleotides containing CpG have the ability of immunomodulation with a downregulation of elevated IgE and eosinophilic inflammation in the airways, both of which are orchestrated by cytokines elaborated by Th2 cells. However, systemic administration of CpG has been reported to increase side effects, owing in part to high dose of these oligonucleotides. Systemic immunization, even with adjuvants, induces robust adaptive immune responses at systemic sites but weak in the airways, while local immunization can elicit both systemic and mucosal responses [ 51 - 53 ]. In this report, we have demonstrated that concomitant intranasal administration of low doses of CpG and the offending antigen exerted significant reduction of total number of infiltrating cells, including eosinophils in BALF (table 1 ). As mentioned before, CpG in mice results in production of several cytokines including type I IFN (IFN-α/β)[ 37 , 38 , 54 - 56 ] which have the ability to exert indirect activation of T cells [ 37 , 38 ]. IFN-β treatment, used by either oral[ 18 ] or parenteral[ 17 ] via in mice, have shown to produce an inhibition of antigen-induced bronchial inflammation and airway hyperresponsiveness [ 17 , 18 ] probably influenced by the inhibition of Th-2 airway eosinophilia by the suppressive effect on eosinopoiesis [ 57 ]. We have recently demonstrated that lung eosinophilic inflammatory response was exacerbated by the lack of IFN-β gene[ 19 ]. Even though it is believed that immunomodulatory effects of CpG-ODN may be mediated by type I IFNs [ 15 ], the relative role of IFN-β, a type I IFN, has not been defined. Here, we aimed to elucidate whether IFN-β have a key role in the anti-allergic effect of CpG motifs. Our results demonstrate that therapy with CpG-ODN prior to and after the allergen challenge resulted in significant reduction of total number of infiltrating cells, including eosinophils, in BALF in WT mice while CpG-ODN did show an enhanced response of macrophages, lymphocytes and neutrophils in airways of IFN-β-/- mice. These findings might be explained since CpG motifs in bacterial DNA can delay apoptosis of neutrophil granulocytes [ 58 ] and macrophages [ 59 ], indicating a possibility of inhibition of macrophage apoptosis by CpG and a difference of cellular responses downstream of different Toll-like receptors [ 59 ]. Another possibility might be that phosphorothioated ODNs used in our experiments might have been chemoattractants for primary macrophages[ 60 ] in the absence of IFN-β. This chemoattractant activity have been exposed as independent of CpG activity[ 60 ], since it has not been seen with phosphodiester CpG-ODNs. However, up to our knowledge this is the first reference about the influence of CpG on neutrophils. It has been shown that systemic administration of CpG-ODN do not inhibit established IgE response while vaccination inhibits IgE production[ 32 ]. We found that CpG-ODN vaccine resulted in inhibition of OVA-Specific IgE in both WT and IFN-β - /- mice (figure 5 ). These data underline that IFN-β is not required for the beneficial effect of CpG-ODN vaccine in a model of allergic inflammation. Vaccination with a single low dose of CpG-dinucleotide inhibited OVA-specific IgE production with subsequent upregulation of IgG2a in both groups. The success in inhibiting established IgE response is most likely due to the timing of the protocol where mice received CpG-ODN at the time of priming. This early intervention presumably prevents presence of IgE-plasma cells in the bone marrow as suggested earlier by Peng et al [ 32 ]. Production of the Th1 cytokine, IFN-γ, has been reported to be dependent on CpG-ODN-induced IFN-α/β as demonstrated by antibodies that block IFN-α/β[ 54 ]. Since, earlier reports target both IFN-α and β, it was unclear if one or both of these cytokines mediate the biological effects of CpG-ODN. In addition, we have recently reported that IFN-β knock out mice do not have any failing mounting a T H 1 response, measured by IFN-γ production. In contrary, IFN-γ production was significantly elevated as a result of experimental autoimmune encephalomyelitis (EAE), a T H 1-mediated disease model for multiple sclerosis. Consequently IFN-β knock out mice had more severe and chronic symptoms than their WT littermates with more extensive CNS inflammation and higher demyelination [ 23 ]. Thus, here we aimed to investigate the profile of OVA-specific Th1 cytokines after CpG-ODN vaccination in the absence of IFN-β. We found a clear reduction in Th1 response (IL-2 and IFN-γ) in IFN-β knock out mice vaccinated with CpG-ODN which was in agreement with earlier reports[ 55 ]. As Th1-promoting activity of CpG-ODN is controlled by IL-12[ 12 ], we measured the levels of IL-12 and found that production was elevated in the CpG-ODN WT group. We also found that its induction is partially under the influence of IFN-β triggered by synthetic CpG sequences. Since IFN-γ is almost undetectable in non-treated mice, at least under the conditions used in this study, the results also suggest that CpG is capable of inducing IFN-β in substantial amounts to trigger IFN-γ production. Our findings of Th1 mediated response in systemic immune response were moreover supported by the fact that IFN-γ production was also defective in the inflammatory organ measured in BALF. Moreover, our results also provide evidence that IFN-β is an important cofactor for IFN-γ production through induction of IL-12 pathway as it has been suggested by Sun et al[ 37 ] While, it is crucial to underline that IFN-β-/- mice do not have a general defect on mounting a Th1 immune response[ 23 ] therefore it is more likely that the defect in inducing a proper Th1 response in IFN-β-/- mice is due to malfunctioning IL-12 and IFN-γ induction through TLR9 pathway as a result of CPG-ODN vaccination. This might also explain the lower proliferative response of OVA-specific Th1 cells in IFN-β-/- mice reported here. Once more, it should be mentioned that IFN-β-/- mice are capable of inducing significantly higher OVA-specific T cell proliferation of Th2 character [ 19 ] which might also partly contribute to suppression of a more profound Th1 response. It has been reported that CpG-ODNs do not directly stimulate T cells, but by inducing production of IFN-γ from APCs, thus activating T cells to express CD69 and B7.2[ 9 , 37 ], while their proliferative responses are reduced[ 37 ]. It was also shown that CpG stimulate T cells by inducing APCs to synthesize IFN-I, which then act directly on T cells via IFNAR[ 37 ]. In addition, it has been suggested that production of type I IFNs by APCs is through increased availability of costimulatory signals on activated DC[ 37 , 36 ]. It has also been reported that stimulation with CpG motifs induces the changes in surface molecules of APCs[ 25 , 55 , 37 ]. However, the reduced OVA-specific Th1 response in IFN-β-/- mice is less likely to be mediated by lack of upregulation of costimulatory molecules on APCs as we have previously reported that these mice have upregulated B7.1/2 on APCs[ 19 ]. After treatment with CPG-ODN we made an interesting observation that the mice developed a mild synovitis, which to our knowledge is the first report of mucosal administration of CPG-ODN causing joint modification. Synovitis is one of the phenotype features of the experimental murine animal models of autoimmune arthritis, such as collagen-induced arthritis (CIA), which is an extensive investigated model of human rheumatoid arthritis. This model can be elicited in susceptible strains by immunization with type II collagen (CII), the major protein of articular cartilage. Assessment of disease includes visual/clinical evaluation of arthritis severity, measurement of humoral and cellular immune responses, including CII-specific antibody titers and T cell responses to CII. In these models, joints are histologically scored for the changes of inflammation including synovitis and periarticular, pannus formation, cartilage damage with marginal erosions or diffuse changes, and bone damage including resorption and periosteal proliferation[ 31 ]. It is known that unmethylated CpG-ODN are responsible for induction of arthritis triggered by bacterial DNA[ 11 , 61 - 63 ] that supports our data. Our finding that mucosal administration of CpG-ODN causes mild synovitis points out a potential hazardous side effect when using CpG-ODN as a treatment. In summary, we have demonstrated that the CpG-ODNs can partly prevent the development of eosinophilic airway inflammation and allergen specific IgE response in the absence of IFN-β, while Th1 response is defective. In addition, these results demonstrate that mucosal administration of CpG-ODN before allergen exposure could be a less harmful form of active immunotherapy in allergic diseases without impeding systemic immune responses as earlier suggested [ 8 , 51 ]. However, due to the potential of hazardous side effects, meticulous caution must be undertaken prior to considering it as a therapy in allergic asthma. Abbreviations APC: Antigen presenting cells; CpG, cytosine-guanine motifs; ODNs, oligodeoxynucleotides; DAB, 3.3 diamino benzidine tetrahydrochlorhide; BALF, bronchoalveolar lavage fluid; CREPA, (cyanide-resistant eosinophil peroxidase activity); IFNAR, type I IFN receptor; APC, antigen-presenting cells; DC, dendritic cells. Authors' contributions VM conceived of the study, participated in its design and coordination, performed the experiments and drafted the manuscript. AT carried out the analysis of flow cytometry, prepared histological samples of joints and performed the clinical and histological analysis of joints for arthritis. AT and IT generated crossing of IFN-β ko mice to B10.RIII strain of mice, genotyped, backcrossed and maintained the IFN- β-/- mouse line. VN participated in the design and coordination of the study. SI-N participated in the direction of the study, performed histological analysis of joints, as well as writing and preparing the manuscript. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555575.xml

555950

Bacillus subtilis actin-like protein MreB influences the positioning of the replication machinery and requires membrane proteins MreC/D and other actin-like proteins for proper localization

Background Bacterial actin-like proteins have been shown to perform essential functions in several aspects of cellular physiology. They affect cell growth, cell shape, chromosome segregation and polar localization of proteins, and localize as helical filaments underneath the cell membrane. Bacillus subtilis MreB and Mbl have been shown to perform dynamic motor like movements within cells, extending along helical tracks in a time scale of few seconds. Results In this work, we show that Bacillus subtilis MreB has a dual role, both in the formation of rod cell shape, and in chromosome segregation, however, its function in cell shape is distinct from that of MreC. Additionally, MreB is important for the localization of the replication machinery to the cell centre, which becomes aberrant soon after depletion of MreB. 3D image reconstructions suggest that frequently, MreB filaments consist of several discontinuous helical filaments with varying length. The localization of MreB was abnormal in cells with decondensed chromosomes, as well as during depletion of Mbl, MreBH and of the MreC/MreD proteins, which we show localize to the cell membrane. Thus, proper positioning of MreB filaments depends on and is affected by a variety of factors in the cell. Conclusion Our data provide genetic and cytological links between MreB and the membrane, as well as with other actin like proteins, and further supports the connection of MreB with the chromosome. The functional dependence on MreB of the localization of the replication machinery suggests that the replisome is not anchored at the cell centre, but is positioned in a dynamic manner.

Background Actin provides vital functions as a cytoskeletal component in eukaryotic and in prokaryotic cells. In eukaryotes, actin filaments give mechanical strength to cells in form of a dynamic cytoskeleton, and are structural fibers in muscle contraction. Additionally, actin proteins have motor like functions [ 1 - 3 ], most notably in cell migration through pushing of membranes. Motility receptors turn on WASP family proteins, which binds to and activate the Arp2/3 complex. The latter induces branching and growth of actin filaments [ 3 ]. In vitro , actin filaments can deform vesicles and thus push membranes, providing the force to elongate cellular extensions such as pseudopods [ 4 , 5 ]. Listeria monocytogenes cells move within macrophages through propelling by actin bundles that extend at only one pole of the bacterial cells, due to the ActA protein that is present at one cell pole and that induces rapid polymerisation of actin. Bacterial cells also possess several different actin-like proteins [ 6 ]. MreB is essential for cell viability, its depletion leads to a defect in chromosome segregation, and ultimately to the formation of round cells, i.e. to loss of rod cell shape. Except for plasmid encoded ParM protein, which actively partitions plasmids [ 7 ], the true mode of action and regulation of bacterial actins is still rather unclear. During the depletion of Bacillus subtilis MreB or of Mbl, the second actin ortholog, or of Caulobacter crescentus MreB, origin regions on the chromosomes fail to separate properly, leading to a severe (or in case of Mbl more moderate) segregation defect [ 8 , 9 ], likewise to overproduction of a dominant negative mreB allele in E. coli [ 10 ]. It is unclear, if actin proteins have a direct role, e.g. as an active segregation motor, or an indirect influence on the segregation of chromosomes. In support of an active role, MreB appears to be associated with the nucleoids, in contrast to Mbl [ 8 ], which is thought to be involved in the insertion of new cell wall material into the growing peptide glycan layer [ 11 ]. Plasmid encoded E. coli ParR protein binds to a specific cis site on the duplicated plasmids, which are located close to the cell centre, and induces polymerisation of the ParM actin homolog [ 12 ]. ParM filaments contain plasmids at their pole ward ends, so two oppositely orientated ParM filaments appear to push plasmids towards each cell pole [ 7 ]. On the other hand, C. crescentus MreB has been shown to also affect cell shape and the localization of cell wall synthesizing proteins [ 13 ], and to play an important role in determining the global polarity of the cell, i.e. by affecting the localization of proteins to the cell pole [ 9 ]. MreB and Mbl form helical filaments just underneath the cell membrane [ 13 - 16 ], which in B. subtilis are highly dynamic. MreB and Mbl move along helical tracks, with a speed of about 0.1 μm/s, providing potential motor like force [ 17 ]. Actin polymerises into a two stranded right handed helix through addition of ATP-bound actin monomers. Actin movement arises through growth at the barbed end of the filament, while actin is released from the pointed end following ATP hydrolysis (a process termed treadmilling). Active pushing is thought to occur through binding of actin monomers to the tip of the filament when the object moves away, thus preventing backward movement, such that the object is driven by Brownian diffusion, with the actin filament dictating a single direction (polymerisation rachett) [ 1 ]. Bacterial chromosome segregation is a highly organized process, depending on several essential protein complexes. DNA polymerase localizes to the cell centre throughout most of the cell cycle [ 18 ]. During replication, the chromosome moves through this stationary replisome, and duplicated regions are rapidly moved towards opposite cell poles [ 19 ], where they are bound and organized by the SMC complex [ 20 ]. It is still unknown if the localization of the replisome involves an anchor, or which factors are involved in the central positioning. We have investigated the role of MreB and Mbl in the positioning of the replication factory, and investigated the role of other actin like proteins and membrane proteins for the localization of MreB. We have found that B. subtilis MreC and MreD proteins localize to the cell membrane, and affect the localization of MreB, likewise to Mbl and MreBH, showing that an intricate interplay exists between actin orthologs and MreCD membrane proteins. Results MreB and MreC have distinct functions in cellular growth and in control of cell shape The depletion of MreB has been shown to lead to a strong defect in chromosome segregation, followed by a loss of rod shape, while the depletion of MreC results in a defect in cell shape, but not in a segregation defect [ 8 , 14 ]. Thus, it is clear that the chromosome segregation defect is due to the lack of MreB. However, repression of transcription of mreB may also lead to the depletion of MreC, because the mreC gene lies directly downstream of mreB , so, it was unknown if depletion of MreB also affects cell shape directly, and if so, to which extent, or if the observed defect in cell shape is due to a polar effect on mreC . Since both genes are essential [ 8 , 21 , 22 ], the lethal defect in cell shape could be caused by the loss of either gene product. To clarify this point, we depleted MreB in the presence of continued synthesis of MreC and MreD (the mreD gene lies downstream of mreC , and loss of MreD also leads to a defect in cell shape [ 8 ]). We introduced a second, IPTG inducible copy of mreCD at an ectopic site on the chromosome, which fully complemented the loss of the original mreCD genes. Depletion of mreB during continued synthesis of mreCD from the ectopic site resulted in cessation of growth (Fig. 1 , compare first tube with second and fourth), in the formation of round cells (Fig. 2A , upper panels), and in the described defect in chromosome segregation (data not shown). This finding shows that MreB has indeed a dual function, in the formation of rod shaped cells, as well as in chromosome segregation. However, depletion of MreB in the absence of ectopically expressed MreC and MreD led to a considerably different phenotype compared to continued expression of MreCD. In the absence of de novo MreCD synthesis, depletion of MreB resulted in a much more rapid growth arrest (Fig. 1 , compare second and fourth tubes with third and fifth), cell growth was abolished after about 4–5 doubling times. Moreover, round cells appeared already 2–3 doubling times after the onset of depletion (Fig. 2A , lower left panel), when cells with continued expression of MreCD still had wild type cell morphology [Fig. 2A , upper left panel, cells look indistinguishable from wild type cells (data not shown)]. Additionally, the cell shape was differentially affected during each condition. MreB depleted cells which continued to express MreCD became highly enlarged (Fig. 2A , upper middle and right panel), but were still mostly rod shaped, whereas MreBCD depleted cells became round and entirely lost rod shape (Fig. 2A , lower middle and right panel). Likewise, the depletion of MreC is followed by the rapid formation of small round cells, and an early arrest in growth [ 8 ]. Thus, the depletion of MreC has a much more immediate effect on growth compared with MreB, and MreB affects the formation of rod shaped cells in a manner distinct from MreC. Figure 1 Synthesis of MreB, MreC and MreD is continued or repressed during the exponential growth phase. Depletion of MreB in the presence of MreC and MreD leads to an arrest in growth, compared to cells with continued synthesis of all the proteins, as indicated above the tubes. Depletion of MreB, C and D results in a more rapid cessation of growth. Figure 2 Depletion of MreB in the presence of MreC and of MreD affects both cell shape and segregation of chromosomes, and affects localization of the replication factory. Fluorescence microscopy of exponentially growing Bacillus subtilis cells. A) MreB is depleted in the presence (+IPTG, upper panels) or in the absence (-IPTG, lower panels) of MreC and MreD, 2–3, 4–5 and 5–6 doubling times indicate the time after the onset of depletion. B) Localization of DnaX-CFP in wild type cells, or C) 2–3 doubling times after depletion of MreB, or D) 2–3 doubling times after depletion of Mbl. Arrowheads indicate the proper positioning of DnaX-CFP in wild type cells, and its abnormal loacalization during depletion of actin orthologs. E) Localization of GFP-MreB in wild type cells, or F) in smc mutant cells, G) localization of GFP-MreC, overlay of GFP-MreC (green) and DNA stain (red), H) localization of MreD, overlay of GFP-MreD (green) and DNA (red). White lines indicate ends of cells, white bars 2 μm. Depletion of MreB leads to the loss of mid cell localization of the replication machinery We wished to further investigate the function of MreB in chromosome partitioning. An important cell biological question is which factors are implicated in the localization of the replication machinery to the cell centre in B. subtilis and in E. coli cells. To investigate a possible role of actin like proteins in this positioning, we depleted MreB or Mbl in cells expressing DnaX-CFP, the tau subunit of the replication DNA polymerase core machinery. In wild type cells showing clear foci (92%), 67% contained a single focus that was positioned close to the cell centre (< 0.2 μm distance, Fig. 2B , and Fig 3A ), 7% had a focus >0,2 μm away from the cell centre, and 26% had two foci that were mostly located around at the cell quarter positions (300 cells have been monitored). The latter cells were the larger cells (> 2.7 to 2.8 μm), as has been described before [ 23 ]. Of note, 7% of the double-DnaX-CFP foci were present in smaller cells (<2.7 μm), indicating that the replication forks can also move apart, and come back together (under the growth conditions used, a new round of replication can only occur very late in the cell cycle). Contrarily, 3–4 hours after depletion of MreB, when most cells still retained their rod shape (which is lost after 4–5 doubling times, see above), DnaX-CFP foci were placed at irregular positions within the cells. Only 14% of the cells contained single central foci, while 86% of the foci were off centre (that is more than 0.2 μm away from the cell centre), and were present at random places on the nucleoids (Fig. 2C , Fig. 3C ). Additionally, 6% of the MreB-depleted cells contained 3 foci, which was observed in only 1% of the wild type cells, 5% contained two foci within one cell half (never found in wild type cells), and in 3% of the cells, foci were even seen close to a cell pole, which was also never found for wild type cells. However, Fig. 3C shows that in spite of the loss of mid cell positioning, DnaX-CFP foci were still mostly absent from the cell poles, which is due to the fact that there is rarely any DNA at these subcellular places (Fig. 2C ). Thus, the replication machinery persists for a long time during depletion of MreB, but is located at random sites on the nucleoids, as illustrated in Fig. 3C . Figure 3 Graphical representation of the position of the replication machinery within wild type or actin-depleted cells. The distance of DnaX-CFP foci to the nearest cell pole was measured and plotted relative to cell size. A) wild type cells, B) cells 2–3 doubling times after depletion of Mbl, C) cells 2–3 doubling times after depletion of MreB. ◇ single focus of focus closest to a pole, □ second focus, Δ third focus. The depletion of Mbl also had an effect on the positioning of the replication machinery, however, to a much milder extent compared with MreB. 3–4 doubling times after the onset of depletion, 38% of the cells contained a single, mid cell-positioned focus, and 30% two foci in each cell half (roughly at the quarter positions), whereas in 32% of the cells, the DnaX-CFP signals were more than 0.4 μm away from the cell centre (Fig. 2D ). Nevertheless, it is apparent from Fig. 3B that although the scatter of DnaX-CFP around the cell centre (and around the quarter positions) is larger in Mbl depleted cells compared with wild type cells, the replication machinery is still largely retained close to the cell centre, contrarily to MreB depleted cells. These results show that directly or indirectly, MreB has a major effect on the positioning of the replisome. MreB appears to form several discontinuous helices within each cell To obtain a more detailed view on the nature of the helical MreB filaments, Z sections were taken through the cells, and 3D image reconstruction was performed on the stacks of fluorescent images. Fig. 4 shows representative reconstructions (cells are turned around 180°, as indicated by the grey arrows, such that the MreB filaments can be seen from 15° angle turns around a 180° view), which clearly show that MreB filaments have a helical path underneath the cell membrane. However, the filaments were not continuous; rather, the cells appeared to contain a variable number of distinct, apparently unconnected filaments. The longest filaments were observed to be only little longer than a full turn around the cell diameter, (indicated by arrowheads in Fig. 4A and 4B ), while half turn and much shorter filaments were also present within the cells. Thus, MreB appears to be present as a number of unrelated, membrane-associated very short filamentous structures. However, the reconstructions do not rule out that MreB is organized into longer helices with linkers between the short fragments that are difficult to visualize. It is also apparent from Fig. 4A and 4B , that the fluorescence intensity of the filaments is different within a single cell (compare filaments indicated by arrowheads with other filaments in the respective cell), which was highly reproducible. Thus, MreB helices are heterogeneous within cells, and apparently, do not form cytoskeletal fibres extending continuously throughout the cell. These data are in agreement with our finding, that several MreB filaments or bundles of filaments rapidly move along helical tracks [ 17 ], and support our findings that these filaments form independent dynamic structures. Figure 4 3D reconstruction of stacks of Z sections taken through B. subtilis cells expressing MreB-GFP. 180° view of cells (panels are tilted 15° relative to each other as indicated by the grey arrows next to the panels). A) Horizontally turned view of two cells (ends are indicated by white lines, arrow indicates clearly visible helical filament), B) horizontal (upper panel) and vertical (lower panel) view on a single cell (white arrow indicates helical filament, grey arrow half turn filament). The cartoons indicate the rotation, the cartoon on top for the first two panels, the cartoon on the right for the third panel. All images are scaled identically, grey bar 2 μm. The localization of MreB is affected by the state of the nucleoids It has been shown that MreB is closely associated with DNA, because no helical filaments are visible in anucleate cells, in contrast to Mbl or MreBH filaments that are found in anucleate cells [ 17 ]. However, MreB filaments were present in cells containing nucleoids during depletion of Topo IV, which leads to a block in full separation of the chromosomes. To investigate, if MreB filaments might be affected by the shape of the nucleoids, we moved the GFP-MreB fusion into spo0J mutant cells, which have slightly decondensed DNA, or into smc mutant cells, in which the nucleoids are highly decondensed, and which contain less negatively supercoiled DNA compared to wild type cells [ 24 ]. Wild type cells contained different numbers of distinct MreB filaments at cellular positions that also contained DNA, but not close to the cell poles, which are devoid of DNA (Fig. 2E ). Contrarily, MreB formed somewhat abnormal long filaments in spo0J mutant cells (data not shown), and highly aberrant elongated filaments throughout smc mutant cells (Fig. 2F ), that is the filaments extended right to the cell poles, had fewer gaps than in wild type cells, and the spacing between individual turns was much shorter compared with wild type cells. These findings indicate that the formation of proper MreB filaments is influenced by the state of the chromosomes. In agreement with earlier results, GFP-MreB filaments were not observed in all of the 35 anucleate smc mutant cells monitored (forming about 15% anucleate cells [ 25 ]). Formation of MreB filaments is influenced by MreC and MreD membrane proteins, and by other actin proteins MreB is upstream of mreC and mreD genes, whose depletion leads to formation of round cells (see above, [ 8 , 22 , 26 ]). Both gene products are highly hydrophobic, and MreD is predicted to form at least 5 membrane spanning helices (data not shown). N-terminal GFP fusions to both proteins were fully functional, and showed a uniform staining of the cell membrane (Fig. 2G and 2H ). Thus, both proteins are associated with the cell membrane. Lee and Stewart have used immuno-gold labelling to show that MreC is predominantly found at the septum between cells [ 22 ]. It is clear from Fig. 2G and 2H that MreC and MreD fluorescence is highest at the septum, because two membranes are closely adjacent to each other, which is most likely the explanation for why immuno-gold labels were enriched at this location. We wished to investigate if formation of helical filaments of MreB depends on the other two actin proteins, or on MreC and MreD, which could provide membrane association of the helical filaments. We moved a gfp-mreB copy to the amylase locus under control of the hyperspank promoter that is induced by IPTG, while mbl , mreBH , mreC or mreD genes were driven by the xylose promoter that is induced by xylose (in fructose medium), and is repressed in glucose medium lacking xylose. GFP-MreB filaments were observed in 85–90% of exponentially growing cells in the presence of IPTG (Fig. 5A ). After 1–2 generation times of growth of pxyl-mreC cells in the absence of xylose, 65% of the cells contained GFP-MreB foci, rather than filaments, and only 20% of the cells showed GFP-MreB filaments, while the cell morphology was still normal (Fig. 5B ). When cells started to become round and ceased to grow after 3–4 generation times, only 15% of the cells showed MreB filaments, and after more than 6 generation times, when most cells had a cocci like morphology, only 5% contained visible GFP-MreB helices, while most cells contained GFP-MreB foci (Fig. 5C ). Depletion of MreD led to a similar albeit much less drastic phenotype (data not shown). Thus, MreC and MreD are required for the formation of proper helical filaments of MreB. Figure 5 Fluorescence microscopy of Bacillus subtilis cells expressing GFP-MreB from an ectopic site on the chromosome. A) wild type cells (helical filaments), B) 2 or C) 4 doubling times after depletion of MreC (loss of filaments), D) 2 or E) 6 doubling times after depletion of Mbl (abnormal filaments and later loss of filaments), F) 2 or G) 6 doubling times after depletion of MreBH (abnormal filaments). Grey arrows point out extended GFP-MreB filaments, and the white arrow indicates GFP-MreB foci. Grey bars 2 μm. As opposed to depletion of MreC or MreD, depletion of Mbl or of MreBH leads to a high number of cells, in which MreB filaments extended throughout the entire cell (about 40% of the cells 2 doubling times after depletion of Mbl or of MreBH, indicated by grey arrows, Fig. 5D and 5F ), or in which only foci were visible (25%, white arrow, Fig. 5F ). In Mbl depleted cells (which are bulgy and twisted [ 8 , 14 , 27 ]), only highly aberrant and weak GFP-MreB filaments were detectable (Fig. 5E ), while the more vibrio-shaped MreBH depleted cells contained highly irregular MreB filaments (Fig. 5G ). Thus, formation of proper MreB filaments is affected by Mbl and MreBH. However, even the highly abnormal MreB filaments in Mbl depleted cells are able to support cell viability, albeit at a highly reduced level ( mbl deleted cells grow extremely slowly [ 14 , 27 ]). Discussion This work provides several important conclusions on the function and localization of the B. subtilis actin ortholog MreB. Our experiments establish that MreB has a dual function, it is vital for the formation of proper rod shape of the cells, and for regular chromosome segregation. However, its function in cell shape is different from that of MreC, or of MreD. The depletion of MreC and MreD leads to rapid cessation of growth and to the formation of small round cells, whereas the sole depletion of MreB results in the formation of large oval shaped cells, and a slower occurring growth arrest. Interestingly, though, we found a connection between MreC and MreB, because during depletion of MreCD, MreB formed fewer and usually abnormally shaped helical filaments. Similar observations have recently been made in E. coli cells [ 28 ]. Our experiments show that MreC and MreD localize throughout the B. subtilis membrane, establishing a link between MreB and the membrane. We speculate that MreC and MreD might provide low affinity binding sites for MreB, such that the filaments extend underneath the membrane in a regular helical pattern. Our results also suggest a dual function for MreC, because its deletion affects the localization of MreB (which is apparently not severe enough to strongly interfere with chromosome segregation), as well as cell shape (in a manner distinct from MreB). An important, if not crucial function of MreB is the positioning of the replication machinery in B. subtilis cells. Soon after the depletion of MreB, the replisome lost its central position in the cell, before a change in cell shape was apparent. The depletion of Mbl had only a minor effect on the localization of the replisome, showing that MreB also affects the positioning of an intracellular protein assembly. Our results do not distinguish between the possibilities that the lack of MreB activity results in the loss of central localization of the replisome, which in turn leads to a segregation defect, or that a more direct defect in chromosome segregation due to the lack of MreB might cause mislocalization of the replisome. However, it is tempting to speculate that MreB could actively push DNA away from the central replisome towards opposite cell poles, and that the net result of this simultaneous pushing of ejected DNA towards opposite directions might lead to a balanced positioning of the replisome towards the cell centre, without any need for an anchor. This is in agreement with recent data showing that the replication machinery is highly mobile around the cell centre [ 29 , 30 ]. An intriguing property of bacterial actin orthologs is the formation of highly dynamic helical filaments underneath the cell membrane that for some members of this protein family are thought to extend through the entire cell length [ 14 , 16 ]. Three dimensional image reconstructions have helped to resolve the nature of the helical MreB filaments in live cells. MreB does not form a closed cytoskeleton like structure, but different forms of filaments within a single cell. These filaments can stretch along a half turn up to a full turn underneath the membrane, but are not clearly connected with each other. This is in agreement with findings showing that several MreB filaments move continuously along helical tracks [ 31 ], generating motor-like intracellular movement. We also provide evidence that the formation of MreB filaments is affected by the nature of the nucleoids, and by the other actin like proteins. In smc mutant cells, MreB filaments are abnormally spaced and extended, and to a much lesser extent in spo0J mutant cells. This further supports and extends our earlier findings that a connection exists between MreB and the nucleoids. Interestingly, smc mutant cells are elongated and frequently twisted and wider than wild type cells [ 32 ], which might be due to the effect on MreB. Likewise, the depletion of Mbl or of MreBH interfered with formation of proper MreB filaments, revealing a tight link between the three actin orthologs. It will be interesting to investigate how these proteins localize relative to each other within a single cell, and if they even physically interact with each other. Conclusion Our findings show that an intricate interplay exists between MreB, membrane associated MreC and MreD proteins, other actin orthologs, the replication machinery and the nucleoids, shedding light on the question why the depletion of MreB affects both, chromosome segregation and cell shape. What remains to be investigated are several important questions, e.g. what is the mode of interaction between MreB and the MreCD proteins or with Mbl and MreBH, and to identify the link between MreB and the nucleoids or the replisome, to distinguish between the causality of defects caused by the loss of MreB activity. Also, it will be highly revealing to identify the possible load MreB might be pushing, if its dynamic movement indeed constitutes a motor function within the prokaryotic cell. Methods Growth conditions Escherichia coli XL1-Blue (Stratagene) or B. subtilis strains were grown in Luria-Bertani (LB) rich medium supplemented with 50 μg/ml ampicillin or other antibiotics, where appropriate. For induction of the hyperspank promoter, the culture media were supplemented with 0.1 to 1 mM isopropyl-β-D-thiogalactopyranoside (IPTG). For induction of xylose promotor, glucose in S7 50 medium was exchanged for 0.5% fructose and xylose was added up to 0.5%. Constructions of plasmids Gfp mut1 including MCS was amplified from pSG1729 [ 33 ] and was cloned into pSG1164 [ 33 ] in which the gfp mut1 for C-terminal fusion had been excised using Kpn I and Spe I. The resulting plasmid pHJDS1 was used to generate N-terminal GFP fusions at the original gene locus. To obtain inducible N-terminal GFP fusion alleles of mreC or of mreD at the original locus, the 5' prime regions (350 to 500 bp) of the genes were PCR amplified and inserted in the EcoR I and Apa I sites of plasmid pHJDS1 to generate pJS14 or pJS15, respectively. To create a fusion of GFP to the N-terminus of mreB , mreC or mreD at an ectopic site on the chromosome, the entire sequences of theses genes were PCR amplified and inserted into the Eco RI and Apa I sites of plasmid pSG1729 [ 33 ] to generate pJS17, pJS19, or pJS20, respectively. To generate an IPTG inducible copy of gfp-mreB (pJS22) or of mreCD (pJS23) at the amylase locus, gfp-mreB was PCR amplified from pJS17 and mreCD from B. subtilis PY79 chromosomal DNA, and the products were cloned as Hin dIII- Sph I or as Sal I- Sph I fragments, respectively, immediately downstream of the hyperspank promotor in plasmid pDR111 (kind gift of D. Rudner, Harvard Medical School). Bacterial strains To express GFP-MreC or GFP-MreD at their original locus in Bacillus , pJS14 or pJS15 plasmids were transformed into wild type B. subtilis (PY79) selecting for chloramphenicol resistance (Cm, 5 μg/ml) to generate strains JS14 ( Pxyl-gfp-mre C) or JS15 ( Pxyl-gfp-mreD ), respectively. For GFP N-terminal fusions at the amy locus, plasmids pJS19 and pJS20 for mreC and mreD were transformed into PY79 selecting for spectinomycin resistance (spec, 25 μg/ml) to generate strains JS19 ( Pxyl-gfp-mreC::amy ) and JS20 (Pxyl-gfp-mreD::amy ), respectively. Strain JS32, in which mreB can be depleted in the presence or absence of mreCD , was created by transforming compentent JS1 cells with chromosomal DNA of JS32. To examine the subcellular localization of GFP-MreB in spo0J or in smc null cells, strain JS19 was transformed with chromosomal DNA from strains AG1468 [ 34 ] or PGΔ388 [ 25 ], generating strains JS23 ( Pxyl-gfp-mreB::amy, ΔspoOJ ) and JS24 ( Pxy-gfp-mreB::amy, smc::kan ) respectively. To be able to visualize the localisation patterns of labelled MreB helices in cells depleted of MreC, MreD, Mbl and MreBH cells, chromosomal DNA from strains JS3 ( Pxyl-mreC ), JS4 ( Pxyl-mreD ), JS2 ( Pxyl-mbl ) and JS5 ( Pxyl-mreBH ) was used to transform strain JS25 ( Phyperspank-gfp-mreB::amy )selecting for Cm and spec, generating strains JS29 ( Phyperspank-gfp-mreB::amy, Pxyl-mreC ), JS30 ( Phypespank-gfp-mreB::amy, Pxyl-mreD ), JS28 ( Phyperspank-gfp-mreB::amy, Pxyl-mbl ), and JS31 ( Phyperspank-gfp-mreB::amy, Pxyl-mreBH ). To express DnaX-CFP in MreB or Mbl depleted cells, chromosomal DNA from JS1 and JS2 was used to transform PG24 competent cells. Image acquisition For microscopic analysis, Bacillus strains were grown in S7 50 defined medium [35] complemented with 1% casamino acids. Fluorescence microscopy was performed on an Olympus AX70 microscope. Cells were mounted on agarose gel pads containing S7 50 growth medium on object slides. Images were acquired with a digital CCD camera; signal intensities and cell length were measured using the Metamorph 4.6 program (Universal Imaging Corp., USA). For and 3D reconstruction, 10 to 12 images (spacing between 0.2 to 0.38 μm) were taken through the focal plane, and processed in Metamorph 6 program. DNA was stained with 4',6-diamidino- 2-phenylindole (DAPI; final concentration 0.2 ng/ml) and membranes were stained with FM4-64 (final concentration 1 nM). Authors' contributions H J D S performed all experiments, PLG helped with 3D image reconstructions, conceived the study, and participated in its design and coordination. All authors read and approved the final manuscript.

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545216

Authors' Reply

We are pleased that Dr. Maitland and colleagues consider our data on volume status (intra- and extracellular) of Gabonese children to be important. We did not consider our children with severe malaria to have intravascular volume depletion for the following reasons. When we measured central venous pressures in a proportion of children on admission, there was no evidence of intravascular volume depletion (median [interquartile range] = 6.5 [3–7.5] cm water), and these values did not change significantly over 24 h, suggesting that our severely ill children had adequate filling pressures. Consistent with this observation, our severely ill children improved rapidly when markers of tissue hypoxia (blood lactate concentrations, tachycardia, and tachypnoea) were serially monitored and children were managed with a relatively conservative fluid replacement regimen. Interestingly, extracellular volume was not increased at admission or afterwards either. Capillary leakage, which commonly accompanies hypovolaemia associated with septic shock, was therefore unlikely to be a significant pathophysiological process in these children with malaria. There may be differences in the severe syndromes of malaria seen in different geographical locations, perhaps accounting for the clinical features attributable to compensated hypovolemic shock reported by Maitland and colleagues. Such differences can be assessed using simple and recently calibrated bioelectrical impedance analysis methodology as well as other techniques that monitor intravascular volumes. The design of optimal fluid management regimens for children with severe malaria can thus be informed not only by theoretical considerations, but also by appropriate physiological assessments.

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212698

Candidate Gene Association Study in Type 2 Diabetes Indicates a Role for Genes Involved in β-Cell Function as Well as Insulin Action

Type 2 diabetes is an increasingly common, serious metabolic disorder with a substantial inherited component. It is characterised by defects in both insulin secretion and action. Progress in identification of specific genetic variants predisposing to the disease has been limited. To complement ongoing positional cloning efforts, we have undertaken a large-scale candidate gene association study. We examined 152 SNPs in 71 candidate genes for association with diabetes status and related phenotypes in 2,134 Caucasians in a case-control study and an independent quantitative trait (QT) cohort in the United Kingdom. Polymorphisms in five of 15 genes (33%) encoding molecules known to primarily influence pancreatic β-cell function— ABCC8 (sulphonylurea receptor), KCNJ11 (KIR6.2), SLC2A2 (GLUT2), HNF4A (HNF4α), and INS (insulin)—significantly altered disease risk, and in three genes, the risk allele, haplotype, or both had a biologically consistent effect on a relevant physiological trait in the QT study. We examined 35 genes predicted to have their major influence on insulin action, and three (9%)— INSR , PIK3R1 , and SOS1 —showed significant associations with diabetes. These results confirm the genetic complexity of Type 2 diabetes and provide evidence that common variants in genes influencing pancreatic β-cell function may make a significant contribution to the inherited component of this disease. This study additionally demonstrates that the systematic examination of panels of biological candidate genes in large, well-characterised populations can be an effective complement to positional cloning approaches. The absence of large single-gene effects and the detection of multiple small effects accentuate the need for the study of larger populations in order to reliably identify the size of effect we now expect for complex diseases.

Introduction Type 2 diabetes is a serious metabolic disease associated with an increased risk of premature death and substantial disability, largely mediated through its adverse effects on the vasculature. The prevalence of the disease is increasing, and the World Health Organisation estimates suggest that by 2025 there will be 300 million affected individuals worldwide ( King et al. 1998 ). The disorder is characterised by a combination of impaired insulin secretion and insulin action, both of which precede and predict the onset of disease ( Weyer et al. 1999 ). Through its adverse impact on insulin action, obesity is a major risk factor for the disease. Although environmental factors, both post- and prenatal, play an important role in determining the risk of disease, a substantial body of evidence supports the notion that disease susceptibility is influenced by inherited factors ( Zimmet 1982 ). While the molecular basis for several uncommon Mendelian forms of Type 2 diabetes have been defined ( Vionnet et al. 1992 ; Yamagata et al. 1996a , 1996b ; Horikawa et al. 1997 ; Stoffers et al. 1997 ; Barroso et al. 1999 ; Malecki et al. 1999 ; Savage et al. 2002 ), the nature and range of allelic variants conferring susceptibility to the more common forms of this disorder remain poorly defined. Many investigators have embarked on attempts to identify diabetes susceptibility genes through genome-wide linkage-based approaches using multigenerational pedigrees and/or large numbers of affected sibpairs. Regions of significant linkage, some of which have been replicated in more than one study, have been identified. To date, however, only Calpain 10 ( CAPN10 ; LocusLink ID 11132) has emerged from such studies as a new putative diabetogene ( Horikawa et al. 2000 ). While some subsequent studies have supported a role for the CAPN10 alleles originally described as susceptibility alleles, others have found associations with different alleles and some have found no association with this gene ( Baier et al. 2000 ; Cox 2001 ; Evans et al. 2001 ; Hegele et al. 2001 ; Tsai et al. 2001 ; Daimon et al. 2002 ; Elbein et al. 2002 ; Garant et al. 2002 ). The positional cloning effort has been supplemented by a large number of studies examining specific candidate genes using a variety of methodologies, mostly of the case-control association design. Although many positive reports have emerged, few have been consistently replicated. Of these candidates, the most compelling evidence to date, generated from a meta-analysis of multiple published studies, is that a common amino acid variant in the N-terminus of the nuclear receptor peroxisome proliferator-activated receptor γ ( PPARG ; LocusLink ID 5468) confers significant protection against the development of Type 2 diabetes ( Altshuler et al. 2000 ). More recently, evidence has accumulated supporting a role for the E23K variant of KCNJ11 (LocusLink ID 3767) in Type 2 diabetes predisposition ( Hani et al. 1998 ; Gloyn et al. 2001 , 2003 ; Love-Gregory et al. 2003 ; Nielsen et al. 2003 ). Whole-genome association studies in large case-control populations may ultimately have the greatest power to detect alleles of small but significant effects on the susceptibility to common diseases such as Type 2 diabetes. As yet, however, the resource implications of such an approach are prohibitive. In the meantime, knowledge of both mammalian biology and disease pathogenesis is progressing rapidly, and it is possible to identify a large panel of known genes, the dysfunction of which might reasonably be considered likely to contribute to Type 2 diabetes. In this study we have identified 152 informative single nucleotide polymorphisms (SNPs) in 71 such genes and, using these, have examined their association with Type 2 diabetes and related intermediate phenotypes in Caucasian subjects from the United Kingdom. Results/Discussion Overall Study Design Candidate gene studies are based on selection of genes with a known or inferred biological function whose role makes it plausible that they may predispose to disease or the observed phenotype. These types of studies are similar to traditional epidemiological approaches in which an a priori hypothesis between exposure to a given factor (in this case, a genotype at a given locus) and disease is formulated. To date, most Type 2 diabetes candidate gene studies have largely lacked thoroughness and sensitivity, as they have tested a limited number of genes and variants in small populations or in populations that were poorly matched or phenotyped, frequently resulting in a lack of replication of the weak associations detected ( Altshuler et al. 2000 ). Our strategy aimed to address these problems by a unique combination of features, including comprehensive SNP discovery in a large number of candidate genes, testing of a large number of SNPs, use of two independent populations, and analysis of haplotypes in addition to individual SNPs where possible. Figure 1 illustrates the overall design of the study. On the basis of their known or putative role in glucose metabolism, 71 candidate genes were selected for study ( Table 1 ). These were subdivided into three broad groups: (1) genes primarily involved in pancreatic β-cell function; (2) genes primarily influencing insulin action and glucose metabolism in the main target tissues, muscle, liver, and fat; and (3) other genes. This group includes genes that influence processes potentially relevant to diabetes, such as energy intake, energy expenditure, and lipid metabolism. A de novo search for common SNPs was undertaken using fluorescent single-stranded conformation polymorphism (fSSCP) examination of all coding regions and splice junctions in a variety of human populations. All genes were minimally screened against 47 samples of mixed ethnicity, providing 0.99 probability of detecting variants with a minor allele frequency of 0.05. Our ‘in-house' polymorphism detection programme identified 954 SNPs in the 71 genes, with a range of allele frequencies from 0.003 to 0.50. Of the 152 SNPs chosen for further study ( Table S1 ), the great majority had a minor allele frequency of greater than 5%, but in a few instances less frequent variants were typed when the candidate gene had strong biological plausibility and there were no known polymorphisms of higher frequency at the time of SNP selection. Figure 1 Study Design Candidate genes were selected based on known or putative function. A de novo polymorphism discovery step was undertaken to identify novel variants for association studies. We selected 152 SNPs and tested them in a case-control study and a QT study. Association analysis with Type 2 diabetes was done for SNPs and haplotypes under multiple genetic models. Only SNPs and haplotypes associated with disease were evaluated for association with five diabetes-related QTs under the same model in the QT study. Table 1 Genes with SNPs Genotyped in This Study Candidate genes, identified by official HUGO Gene Nomenclature Committee symbols, are grouped by known or putative biological function, with the number of genotyped polymorphisms per gene shown Table 1 Continued The 152 SNPs were genotyped in a population-based cohort of 517 unrelated Caucasians in the United Kingdom with Type 2 diabetes and an equal number of controls with normal glycated haemoglobin (HbA1c) levels, individually matched to cases by age, sex, and geographical location. A second independent population was also genotyped for the same 152 SNPs. This consisted of 1,100 middle-aged Caucasian subjects in the United Kingdom who had been extensively and serially phenotyped for glucose tolerance and variables related to insulin secretion, insulin action, and adiposity. In the first stage of data analysis, all SNPs (and haplotypes when multiple SNPs were present at the same gene) were examined for their association with diabetes in the case-control study using multiple models of inheritance. In the second phase of analysis, all SNPs and haplotypes showing statistically significant association with diabetes status in the first phase were examined for association with glucose levels and other intermediate phenotypes in the quantitative trait (QT) study population. The intermediate phenotypes chosen for study were fasting and 2-h post-glucose load plasma glucose levels (measures of glucose tolerance), fasting insulin (a measure of insulin sensitivity), 30-min insulin incremental response (a measure of β-cell function), and body mass index (BMI) (a measure of adiposity). Power to detect an association is dependent on several factors: the frequency of the ‘predisposing' allele, genotype, or haplotype; the accepted false-positive or Type 1 error rate (α); and the odds ratio (OR) or effect size. Rarer alleles, genotypes, or haplotypes with small effects require larger sample sizes to attain the same power to detect an association, as compared to more frequent alleles or alleles with larger effects. At the time that we collected the populations and designed this study, our power calculations had shown that a sample size of 500 cases and 500 matched controls would have 80% power to detected effect sizes as small as 1.3–1.7 OR, depending on the frequency of the predisposing allele, with a 5% Type 1 error rate ( Figure 2 ). Figure 2 Power Calculations Power of the current Cambridgeshire Case-Control Study to detect associations with risk allele of varying frequencies and with a Type 1 error rate of 5%. Abbreviations: p0, frequency of the predisposing allele; chr, number of chromosomes. Graphs were plotted with the PS power and sample-size program (available at http://www.mc.vanderbilt.edu/prevmed/ps ; DuPont and Plummer 1997 ). Overview of Results of Association Studies Table S1 shows the genotype counts for all 152 SNPs in the case-control and QT studies. In the control subjects, 16 SNPs (10.6%) had a minor allele frequency below 5%; 19 (12.5%) had a minor allele frequency between 5% and 10%; and 117 (76.9%) had a minor allele frequency greater than or equal to 10%. Each variant was tested for association with disease status under several genetic models . Twenty SNPs in 11 different genes showed statistically significant association with disease status ( p < 0.05) under at least one model ( Table 2 ). The strongest statistical evidence for disease association was for genes SOS1 (LocusLink ID 6654), SLC2A2 (LocusLink ID 6514), PIK3R1 ( LocusLink ID 5295), ABCC8 and KCNJ11 (LocusLink ID 6833), and INSR (LocusLink ID 3643). Of the 29 loci with multiple SNPs, only three— HNF4A (LocusLink ID 3172) INSR , and ABCC8 – KCNJ11 —showed significant association of particular haplotypes with disease status ( Figure 3 ; Table 3 ). In only one case ( HNF4A ) was a haplotype significantly associated with disease risk (see below) when no significant association was seen with any individual SNP in that gene. Table S2 , shows the results of association studies undertaken in the QT population, further examining the SNPs that had shown significant association in the case-control study. Table 3 shows the relationship between disease-associated haplotypes at ABCC8 – KCNJ11 , HNF4A , and INSR with intermediate phenotypes in the QT study. We now consider in more detail, the data for those genes where the strongest and most consistent effects were seen. Figure 3 Genes with Haplotypes Associated with Type 2 Diabetes Genomic organization with exons (black boxes or vertical lines) and with genotyped SNPs and SNPs utilised in the haplotype reconstructions (in blue boxes) is shown. The most common haplotypes with population prevalence greater than 5% in the control population are shown, and the measure of LD ( r 2 ) is shown for a subset of the SNPs. (A) ABCC8–KCNJ11 . (B) HNF4A . (C) INSR . Table 2 Genes with Variants Significantly Associated with Type 2 Diabetes Status SNP identifiers (SNPID), OR, significance level ( p value), and genetic model are shown. p values for the additive effect are for the test for a linear trend across the genotypes, which were coded as 0 = 11, 1 = 12, 2 = 22. Allele 2 dominant refers to a combination of 12 + 22 and allele 2 recessive refers to combination of 11 + 12 Table 3 Association of ABCC8–KCNJ11 , HNF4A , and INSR Haplotypes with Diabetes and QTs For case control, OR and 95% CI of haplotypes are shown. For QT, means and 95% CI of haplotypes are shown. Means were adjusted for age and sex, but not for BMI. Abbreviations: BMI, body mass index (kg/m 2 ); PG0, fasting plasma glucose (mmol/l); 2hPG, 2-h plasma glucose (mmol/l); INS0, fasting insulin (pmol/l); Ins inc, 30-min insulin increment (pmol/mmol). Associations significant at 0.10 and below are in italics bold , with association 0.05 or below in red bold . Genes Primarily Affecting β-Cell Function ABCC8 and KCNJ11 (encoding, respectively, the sulphonylurea receptor and inwardly rectifying potassium channel KIR 6.2). The genes encoding the two molecular components of the voltage-gated potassium channel of the pancreatic β-cell are located within 4.5 kb of each other on Chromosome 11. KCNJ11 encodes the channel protein KIR6.2 and ABCC8 encodes an ATP-binding cassette (ABC) transporter-containing transmembrane protein (SUR1) that is thought to regulate the activity of the channel and that also contains the site to which sulphonylurea antidiabetic drugs bind. Three SNPs in KCNJ11 were associated with disease under multiple genetic models. The strongest statistical association in this gene was with a 3′-SNP (SNP74; OR 0.59, p = 0.0027 under recessive model for allele 2) (see Table 2 ). In ABCC8 , five SNPs were associated with disease status under multiple models; the strongest evidence for association with disease were with SNP79 and SNP81, respectively an intronic variant (OR 1.68, p = 0.0043; see Table 2 ) and a missense variant A1369S (OR 1.68, p = 0.0048; see Table 2 ). Although neither of these two SNPs was significantly associated with any trait in the QT study, two other SNPs showed effects in the QT study (see Table S2 ). SNP84 (IVS18–36) from ABCC8 , which associated with increased disease risk (OR 3.43, p = 0.0163; see Table 2 ) also associated with increased BMI (mean 28.2 kg/m 2 , 95% confidence interval [CI] [26.6, 29.9] for homozygous 22 versus 26.2 kg/m 2 , 95% CI [25.9, 26.5] for homozygous 11 and 26.3 kg/m 2 , 95% CI [25.7, 26.8] for heterozygous subjects; p = 0.016) and associated with borderline significance with higher fasting glucose (5.53 mmol/l, 95% CI [5.29, 5.77] for homozygous 22 versus 5.30 mmol/l, 95% CI [5.26, 5.35] for homozygous 11 and 5.27 mmol/l, 95% CI [5. 19, 5.35] for heterozygous subjects; p = 0.057) under a recessive model for allele 2 (see Table S2 ). SNP87 (K649), which was also significantly associated with increased disease risk (OR 3.90, p = 0.0157; see Table 2 ), also showed borderline significant association with decreased insulin secretion (23.6 pmol/mmol, 95% CI [18.6, 30.1] for homozygous 22 versus 29.8 pmol/mmol, 95% CI [28.4, 31.1] for homozygous 11 and 30.9 pmol/mmol, 95% CI [28.6, 33.4] for heterozygous subjects; p = 0.054; see Table S2 ), consistent with a role for this gene in insulin secretion. Given the close physical proximity of ABCC8 and KCNJ11 and their role in the same functional unit, we performed haplotype reconstructions with data from both genes combined (see Figure 3 A). Haplotype B was associated with increased disease risk (OR 1.46, 95% CI [1.14, 1.85]; data not shown), but did not show any significant association in the QT study (see Table 3 ). Mutations in each of these genes have been associated with familial persistent hyperinsulinaemia hypoglycaemia of infancy (PHHI), a rare disorder of glucose homeostasis characterised by up-regulated insulin secretion despite severe hypoglycaemia. In addition, evidence for association of KCNJ11 DNA variants with Type 2 diabetes has been evaluated in multiple studies, and until recently these data have been conflicting. Several recent studies have, however, suggested a role for the aminoacid variant E23K in Type 2 diabetes susceptibility ( Hani et al. 1998 ; Gloyn et al. 2001 , 2003 ; Schwanstecher and Schwanstecher 2002 ; Love-Gregory et al. 2003 ; Nielsen et al. 2003 ). In total we tested four SNPs at the KCNJ11 locus for association with disease status; of these, three were tightly linked (data not shown) and all three had a statistically significant association with disease status (see Table 2 ). In our study we replicated the effect of the E23K polymorphism in Type 2 diabetes predisposition (KK homozygous, OR 1.49, p = 0.0333; see Table 2 ) with an OR estimate in agreement with that demonstrated by the meta-analysis of Nielsen et al. (2003 ); in addition, two other KCNJ11 SNPs associated with disease risk (SNP74 and SNP76). The recent evidence from multiple studies and from meta-analysis for association of the E23K SNP with Type 2 diabetes, along with in vitro studies using cell lines expressing the E23K mutation showing an increased stimulation threshold of insulinsecretion ( Schwanstecher et al. 2002 ), suggests that E23K is the functional variant leading to increased disease risk. Given our finding of high levels of linkage disequilibrium (LD) between SNP74 and SNP76 with E23K (data not shown), we adjusted the measures of association at these sites for the E23K genotype. These data suggest that these SNPs are independently associated with diabetes ( Table 4 ). Table 4 Association of KCNJ11 and ABCC8 Variants with Type 2 Diabetes Status Adjusted for E23K Genotype SNP identifiers (SNPID), OR, significance level ( p value), and genetic model are shown. p Values for the additive effect are for the test for a linear trend across the genotypes, which were coded as 0 = 11, 1 = 12, 2 = 22. Allele 2 dominant refers to a combination of 12 + 22 and the allele 2 recessive refers to combination of 11 + 12 ABCC8 variants have been associated with Type 2 diabetes in multiple studies ( Inoue et al. 1996 ; T. Hansen et al. 1998 ; Hart et al. 1999b ). However, a recent large study failed to replicate previous associations with Type 2 diabetes ( Altshuler et al. 2000 ). In our study we found evidence for association with Type 2 diabetes in five of 16 ABCC8 SNPs tested. Owing to the physical mapping of ABCC8 in close proximity to KCNJ11 , we further investigated whether the associations at the ABCC8 locus could be completely explained through LD between ABCC8 SNPs and the E23K variant at KCNJ11 . After adjustment for E23K, two ABCC8 SNPs (SNP79 and SNP81) that were significantly associated with diabetes ( p = 0.0043 and p = 0.0048 for the recessive model; see Table 2 ) prior to adjustment were no longer significantly associated with diabetes ( p = 0.0536 and p = 0.1339 for the recessive model; see Table 4 ). However, for the remaining three SNPs (SNP84, SNP87, and SNP89), although the significance levels were reduced, they remained statistically significant ( p = 0.0401, p = 0.0214, and p = 0.0445 for the recessive model; see Table 4 ). Moreover, the OR for two of these SNPs increased to 4.36 and 3.16, respectively. This suggests that there are effects at the ABCC8 locus that are independent from the E23K KCNJ11 variant. The lowered significance levels are likely due to loss of power resulting from the adjustment. Our data and that from at least nine other independent association and linkage studies ( T. Hansen et al. 2001 ) have shown some evidence for ABCC8 involvement in Type 2 diabetes and related phenotypes. SLC2A2 (encoding GLUT2). SLC2A2 encodes the glucose transporter GLUT2, a member of the facilitative glucose transporter family that is highly expressed in pancreatic β-cells and liver. We typed six SNPs in SLC2A2 , three of which (SNP21, SNP23, and SNP24) were significantly associated with diabetes status with an OR of approximately 1.4–1.5 (see Table 2 ). The most highly significant association was with a T110I substitution (OR 1.49, p = 0.0059) under a dominant model for the minor allele. In the reduction process prior to haplotype estimations (see Materials and Methods), only one SNP (SNP21) contributed significantly to disease association. Therefore, haplotype reconstructions were not performed. In the QT study, all three disease-associated SNPs were also associated with lower levels of fasting plasma insulin. Rather surprisingly allele 2 (A) at T198, which was associated with increased disease risk, was associated with lower 2-h plasma glucose. No other significant associations with intermediate phenotypes were seen. Multiple previous studies have sought evidence for association or linkage between SLC2A2 variants and Type 2 diabetes, and most have reported negative results. However, all studies have been small and were insufficiently powered to detect effects of modest size ( Li et al. 1991 ; Baroni et al. 1992 ; Tanizawa et al. 1994 ; Moller et al. 2001 ). SLC2A2 is a highly plausible candidate gene for Type 2 diabetes, as it is a high K m transporter that regulates entry of glucose into the pancreatic β-cell, thus initiating the cascade of events leading to insulin secretion. GLUT2 is also highly expressed in the liver, where it is involved in the regulation of both glucose uptake and output. It is notable that the alleles that associated with increased diabetes risk were also all associated with lower fasting insulin levels, suggesting that these may influence basal insulin secretion. However, interpretation is complex, as (1) fasting insulin is strongly influenced by insulin sensitivity and (2) the potential risk alleles were not associated with any impairment of insulin secretion in response to a glucose load. Finally, allele 2 (A) at T198, which associated with increased risk of diabetes in the case-control study, was associated with lower 2-h glucose in the QT study. Clearly, more detailed genetic mapping combined with functional studies (which will be challenging in humans owing to the inaccessibility of the pancreatic β-cell) will be needed to identify the mechanism whereby variants in this gene influence diabetes risk. HNF4A (encoding hepatic nucleotide factor 4α). HNF4A (the MODY1 gene) encodes an orphan hormone nuclear receptor that, together with TCF1 (LocusLink ID 6927), encoding HNF1α, TCF2 (LocusLink ID 6928), encoding HNF1β, and FOXA2 (LocusLink ID 3170), encoding HNF3β, constitutes part of a network of transcription factors controlling gene expression in pancreatic β-cells, liver, and other tissues. In β-cells, these transcription factors regulate expression of the insulin gene as well as genes encoding proteins involved in glucose transport and metabolism and in mitochondrial metabolism, all of which are linked to insulin secretion ( Fajans et al. 2001 ). While no individual SNP in HNF4A was significantly associated with disease status, we identified a haplotype (haplotype B in Figure 3 B) that was significantly associated with reduced disease risk (OR 0.83, 95% CI [0.68, 1.00]; data not shown). In the QT study, this ‘reduced-risk' haplotype was significantly associated with increased insulin secretion (mean = 31.5 pmol/mmol, 95% CI [29.9, 33.3] versus 29.3 pmol/mmol, 95% CI [28.0, 30.6] for haplotype A and 30.9 pmol/mmol, 95% CI [28.1, 34.0] for haplotype C]. Carriers of this haplotype also showed a trend towards lower fasting and 2-h plasma glucose, compared to the subjects with the other haplotypes (see Table 3 ). HNF4A maps to Chromosome 20 ( Argyrokastritis et al. 1997 ) in a region that has been linked to Type 2 diabetes in multiple studies ( Bowden et al. 1997 ; Ji et al. 1997 ; Zouali et al. 1997 ; Ghosh et al. 1999 ; Klupa et al. 2000 ; Permutt et al. 2001 ). This positional information, combined with the known role of major mutations at this gene in the causation of autosomal-dominant maturity-onset diabetes of the young (MODY), has led to HNF4A being considered as a strong candidate for involvement in Type 2 diabetes. However, most studies to date have failed to identify an association between variants at this locus and disease susceptibility ( Moller et al. 1997 ; Malecki et al. 1998 ; Ghosh et al. 1999 ; Price et al. 2000 ). This study differs from all other previous reports in its examination of haplotypes, as well as in the fact that it included several SNPs not previously examined. Our findings lead us to speculate as to how a particular HNF4A haplotype might be associated with lower risk of diabetes and increased insulin secretory capacity. The fact that a multiplicity of heterozygous nonsense and missense mutations in HNF4α lead to an insulinopaenic form of MODY strongly suggests that β-cell dysfunction is sensitive to the amount of HNF4α in the β-cell and that haploinsufficiency is the likely mode of molecular pathogenesis in that condition ( Stoffel and Duncan 1997 ; Shih et al. 2000 ). It is plausible, therefore, that variants in this gene that enhance expression levels of the protein might lead to increased insulin secretory capacity and protection against diabetes. INS (encoding insulin). The INS (LocusLink ID 3630) gene encodes the hormone preproinsulin, which upon proteolytic cleavage generates mature insulin and C-peptide. We tested for association of a single SNP in the 3′-UTR (SNP72) of the insulin gene with disease status. This SNP was significantly associated with increased Type 2 diabetes risk under a recessive model for the T allele (OR 2.02, p = 0.0258) (see Table 2 ). In the QT study this SNP did not associate with any of the intermediate phenotypes studied. The insulin gene variable number tandem repeat ( INS –VNTR) has been extensively studied and is proposed to exert pleiotropic effects on birth weight and diabetes susceptibility ( Huxtable et al. 2000 ). However, evidence for this has been conflicting and a role for INS in Type 2 diabetes predisposition has not been definitively established. The data for the single SNP we tested suggest that either the insulin gene or other loci in LD may be involved in Type 2 diabetes risk. Genes Primarily Affecting Insulin Action INSR (encoding the insulin receptor). At the INSR locus of the seven SNPs genotyped, we detected a single intronic SNP (SNP131) that was significantly associated with increased disease risk (OR 1.48, p = 0.0039 for the dominant model for allele 2) (see Table 2 ). In the QT study, this SNP also had a nonsignificant association with increased 2-h glucose under a dominant model for allele 2 (see Table S2 ). Haplotype C (see Figure 3 C) for INSR was associated with increased disease risk (1.34 mmol/l, 95% CI [1.05, 1.71]; data not shown); in the QT study, there was a nonsignificant trend for subjects carrying this haplotype to have increased values for fasting glucose (5.32 mmol/l, 95% CI [5.24, 5.39] versus 5.27 mmol/l, 95% CI [5.21,5.33] for haplotype B and 5.27 mmol/l, 95% CI [5.23, 5.32] for haplotype A), 2-h glucose (6.00 mmol/l, 95% CI [5.80, 6. 20] versus 5.78 mmol/l, 95% CI [5.61, 5.96] for haplotype B and 5.87 mmol/l, 95% CI [5.75, 5.99] for haplotype A), and fasting insulin (41.8 pmol/l, 95% CI [39.2, 44.5] versus 40.4 pmol/l, 95% CI [38.4, 42.5] for haplotype B and 41.3 pmol/l, 95% CI [39.6, 43.1] for haplotype A) (see Table 3 ). A role for INSR in Type 2 diabetes and related phenotypes has long been sought. Many studies initiated over the past decade have explored the possibility that DNA variants at this locus would not only cause rare syndromes of extreme insulin resistance, but would also associate with increased Type 2 diabetes risk. In particular, the role of the Val985Met in disease predisposition has been analysed in many different populations, but the data remain inconclusive, with some studies suggesting a role for this variant ( Hart et al. 1996 , 1999b ), while others do not support this finding ( O'Rahilly et al. 1991 , 1992 ; L. Hansen et al. 1997 ). In this study we provide preliminary evidence for a role of INSR in diabetes susceptibility through genotyping of a previously untested SNP in case-control studies and via haplotype analysis using multiple SNPs in the gene. PIK3R1 and SOS1 . The gene PIK3R1, encoding the p85α regulatory subunit of the phosphatidylinositol 3-kinase, is a logical candidate gene for involvement in the development of Type 2 diabetes owing to its role in insulin signal transduction. An intronic variant, SNP42, was associated with increased disease risk under two genetic models (OR 1.41, p = 0.0090 for the allele 2 dominant and OR1.34, p = 0.0088 for the additive model; see Table 2 ). In the QT study, SNP42 was significantly associated with increased BMI and showed a borderline significance with increased fasting insulin (measure of insulin resistance) under a dominant model for allele 2 (see Table S2 ). Obesity is a major risk factor for insulin resistance, and the observed increase in BMI coupled with increased insulin resistance in carriers of allele G at SNP42 suggests that variation at this gene may be increasing Type 2 diabetes risk through impaired insulin action. Other association studies at this locus have focussed on investigating the Met326Ile variant in disease predisposition, with mostly negative results ( T. Hansen et al. 1997 , 2001 ; Kawanishi et al. 1997 ). One study did describe an association with disease status and with QTs underlying Type 2 diabetes ( Baier et al. 1998 ). However, functional data for this polymorphism have suggested that the Ile326 variant may have only minor impact on signalling events ( Baynes et al. 2000 ; Almind et al. 2002 ). Our data suggest that variation in this gene is a risk factor for the development of Type 2 diabetes, although further detailed studies will be required to elucidate the precise functional variants and mechanisms that lead to increased disease risk. The gene SOS1 ( son of sevenless homolog 1 in Drosophila ) encodes a guanine nucleotide exchange factor that functions in the transduction of signals that control cell growth and differentiation. We analysed two SNPs for association with disease status, a nonsynonymous SNP (N1011S) and an intronic variant (SNP8). While the nonsynonymous S1011 variant, which was very rare (minor allele, 0.003), did not associate with disease status, the intronic SNP was highly significantly associated with decreased disease risk (OR 0.58, p = 0.0032) (see Table 2 ), despite not showing any effects in the QT study. To our knowledge, this is the first investigation into the role of SOS1 in Type 2 diabetes risk. The data presented here suggest that further investigation into the potential role of common variants at this gene and diabetes risk is warranted. Other Genes PPARGC1 (LocusLink ID 10891) encodes a transcriptional coactivator of nuclear receptors with a critical role in regulating multiple aspects of energy metabolism, including adaptive thermogenesis ( Puigserver et al. 1998 ), mitochondrial biogenesis ( Wu et al. 1999 ), fatty acid β-oxidation ( Vega et al. 2000 ), the control of hepatic gluconeogenesis ( Herzig et al. 2001 ; Yoon et al. 2001 ), and the control of glucose uptake ( Michael et al. 2001 ). PPARGC1 SNP30 (Thr528Thr), which was associated with decreased disease risk (see Table 2 ), was rather surprisingly associated with decreased insulin secretion in the QT study (see Table S2 ). In this locus, Thr528Thr has not been previously associated with diabetes, and our data most likely reflect stochastic variation at this site. The Gly482Ser has in some studies been shown to be associated with increased Type 2 diabetes risk ( Ek et al. 2001 ), but not in others ( Hara et al. 2002 ; Lacquemant et al. 2002 ; Muller et al. 2003 ), and has additionally been associated with insulin resistance ( Hara et al. 2002 ), obesity indices in women ( Esterbauer et al. 2002 ), and mean insulin secretory response and lipid oxidation ( Muller et al. 2003 ). In our study, this allele was not associated with increased diabetes risk, but rather was associated with a lower risk of diabetes under a recessive model (OR 0.67, p = 0.0295) (see Table 2 ). The opposing results for this polymorphism and the fact that the amino acid change Gly482Ser is unlikely to be a major functional change ( Esterbauer et al. 2002 ) may indicate that the contributing functional polymorphism may be an unidentified variant in LD with the Gly482Ser. Amongst the remaining genes tested, of particular interest are the results observed in PYY (encoding polypeptide YY; LocusLink ID 5697). An intronic variant, IVS3+68, showed a significant association with increased Type 2 diabetes risk under two genetic models (OR 1.47, p = 0.0240 in the allele 2 dominant; OR 1.47, p = 0.0157 in the additive effect allele 2) (see Table 2 ), but no evidence of association with underlying traits was observed in the QT study. Early functional studies suggested an inhibitory role of PYY in glucose-stimulated insulin secretion ( Bertrand et al. 1992 ; Nieuwenhuizen et al. 1994 ), which led us to evaluate the potential role of variants at this gene in Type 2 diabetes predisposition. Recent data have shown that the peptide PYY 3–36 encoded by this gene inhibits food intake and reduces weight gain when injected in rats, while physiological infusions of PYY 3–36 in humans decreased food intake by 33% ( Batterham et al. 2002 ). Although our data do not show an association between the intronic variant SNP122 with BMI, they suggest a putative role for PYY in Type 2 diabetes predisposition. As we only tested a noncoding variant in PYY , it is possible that the association is due to other contributing variants within the gene and that a link between those and BMI is still plausible. In the genes ABCC9 (LocusLink ID 10060) and LIPC (LocusLink ID 3990), single SNPs of modest significance were associated with disease status in the case control and therefore are not discussed further here. Examination of Other Previously Reported Associations We were unable to confirm some associations observed in other studies. The PPARG Pro12Ala Pro allele has previously been shown to confer susceptibility to Type 2 diabetes, with the Ala allele providing a decreased risk ( Altshuler et al. 2000 ). Our results for this polymorphism show the same direction and magnitude of effect for Ala/Ala versus Ala/Pro and Pro/Pro genotypes (OR 0.54; derived from data in Table 2 ), but the association was not statistically significant ( p = 0.2269). The lower limit of the 95% CI for the protective effect of the Ala allele (OR 1.08, 95% CI [0.82,1.42], p = 0.583) is still consistent with the results of the meta-analysis by Altshuler et al. (2000 ). Our study was not sufficiently powered to detect the small effects expected for the predisposing Pro allele. The study only had 25.4% power to detect an OR of 1.25 for the Pro allele that occurred in 89.4% of our control population. It is also possible that the Pro12Ala variant does not affect diabetes susceptibility in this population, because of the dependence of the allele effect on environmental factors such as dietary fat composition ( Luan et al. 2001 ). In the ENPP1 (LocusLink ID 5167) gene (commonly known as PC-1 ) the K121Q polymorphism has variably been found to be both associated with increased Type 2 diabetes risk ( Gu et al. 2000 ; Rasmussen et al. 2000 ; Hegele et al. 2001 ) and with insulin resistance QTs ( Pizzuti et al. 1999 ; Gu et al. 2000 ; Rasmussen et al. 2000 ). In our study we did not find evidence for association between the K121Q polymorphism and Type 2 diabetes (OR 1.10, p = 0.5277 for the dominant model for allele 2; OR 1.07, p = 0.6290 for the additive effect for allele 2; OR 0.86, p = 0.7 496 for a recessive model for allele 2; data derived from Table 2 ). Analysis of this allele in our QT study showed that QQ individuals have higher mean BMI levels compared to carriers of the K121 allele (28.3 kg/m 2 [26.4, 30.2] versus 26.1 [25.6, 26.7] in KQ subjects, 26.3 [25.9, 26.6] in KK subjects; data not shown). PPP1R3A (protein phosphatase 1 regulatory subunit 3; LocusLink ID 5506), which encodes the muscle-specific regulatory subunit of PP1, has been investigated as a potential diabetogene. Evidence for a role of the PPP1R3A D905Y polymorphism in Type 2 diabetes risk has also been conflicting ( L. Hansen et al. 1995 , 2000; Hegele et al. 1998 ; Xia et al. 1998 ). While in this study we did not find an association between the D905Y variant and disease risk, we have previously described an association between a rare frameshift and premature stop variant with Type 2 diabetes risk under a dominant model (OR 5.03, p = 0.0110) in this population ( Savage et al. 2002 ). Concluding Remarks This study, which to our knowledge is the largest of its kind yet reported in Type 2 diabetes, has provided evidence for the existence of variants in certain key candidate genes that influence the risk of Type 2 diabetes and, in some cases, has afforded clues as to the pathophysiological mechanism whereby those effects on disease risk might be mediated. By its very nature, any study of candidate genes, however large, is restricted in scope, and it is likely that other variants (namely in regulatory regions, which we did not cover) in the genes that we have considered, as well as ones that we have not, may exist and have effects equal to or greater than those we have demonstrated. In addition, this study is not intended to be an ‘exclusion study,' as many issues that relate to coverage of any given gene, environmental risk factors, and power in our populations do not allow us to definitively assert that negative findings correspond to genes that truly do not play a role in Type 2 diabetes predisposition. The power of our study to detect small effects in uncommon variants was low. Evidence from many recent studies now suggests that in Type 2 diabetes the effects are likely in the range of OR 1.15–1.5. It is clear that much larger studies than that reported here are required for such effects ( Figure 4 ), in particular when adjusting to a lower Type 1 error rate of 0.01% to compensate for multiple testing. The significance of the associations we report have been described without adjustment for the number of tests undertaken, and thus the group of positive associations is likely to contain a proportion that is falsely positive. There is no consensus about the ideal method for adjusting the probability of an observation occurring by chance for multiple testing. The simple Bonferroni correction would constitute overadjustment because the 152 genetic markers in this study are not independent. In addition, in the false-discovery rate method ( Benjamini and Hochberg 1995 ), it is assumed that all N tests are carried out simultaneously, which may not correspond to reality if groups genotype one set of SNPs, as in this study, but then report results for additional SNPs at a later date. It is not clear whether the number of tests N should reflect the number to date or the number one might potentially undertake by continuing working through projects like these. An alternative Bayesian approach leading to a ‘genome-wide’ significance level for association, such as has been done for whole-genome linkage studies ( Lander and Kruglyak 1995 ), might be preferable. However, this also runs into difficulties. In studies that are not based on fine-mapping of linkage intervals, but rather on candidate genes selected on the basis of data from other studies, including previous reports of association, it is unclear what level of prior probability of association should be used. As a result of this uncertainty about the appropriate method of correction for multiple testing, our preferred strategy is to report the number of tests done and to encourage readers to interpret the significance tests in that light, acknowledging that the results will require replication in other cohorts. Figure 4 Size of Case-Control Study Required to Detect Small Risk Effects The number is shown of the case chromosomes (assuming an equal number of control chromosomes) required to attain 80% power to detect associations with the OR varying between 1.0 and 1.5 and with a Type 1 error rate of 0.01%. Abbreviations: p0, frequency of the predisposing allele; chr, number of chromosomes. Graphs were plotted with the PS power and sample-size program ( DuPont and Plummer 1997 ). Although we have not undertaken a formal replication of the case-control study, we have used a complimentary QT population to examine the association of the variants studied with continuously distributed measures of glucose tolerance, insulin secretion, and insulin action. This provides different information to a replication case-control study, as it may identify pathophysiological mechanisms by which the association with diabetes arises. We are cautious about putting forward particular variants as established ‘diabetogenes' and enthusiastically invite researchers to examine these candidate variants in their own particular populations. Indeed, as the genetic architecture of Type 2 diabetes may vary between populations, it is critical that such variants are examined in multiple diverse ethnic groups. As with the Pro12Ala PPARG example, it is likely that meta-analysis of several studies will be required to narrow the CIs around the point estimates of association seen in any single study. This will be especially important when the association is weak, as it is for Pro12Ala, because few individual case-control studies, including the one reported here, are currently powered to detect very small increases in risk. It is, however, important that such meta-analyses include all studies of variants examined, rather than only those that are individually published, to avoid publication bias. The associations we describe are highly biologically plausible and in many of the genes are supported by associations with multiple SNPs at the same locus. These include genes affecting both insulin secretion and insulin action. Given the importance of both insulin resistance and defective β-cell function to the pathogenesis of Type 2 diabetes, it is intriguing that we have found a disproportionate representation of genes affecting pancreatic β-cell function among those that were found to be associated with diabetes risk. This contrasts with the impact of known environmental factors and their correlates (e.g., high-fat diet, lower physical activity, obesity, and central fat distribution), all of which are thought to have their major influence on diabetes risk through impairment of insulin action. While it would be premature to put forward any definitive model for the causation of Type 2 diabetes, it is tempting to speculate that the ‘insulin resistance' component of the disease may have a substantial environmental influence modulated by polygenic effects, some of which may relate to molecules identified in this and other studies. On the other hand, the ability of the pancreatic β-cell to continue to secrete sufficient insulin to maintain life-long normoglycaemia may be more profoundly influenced by genetic factors, some of which are reported herein. It will be critical to examine the functional consequences of such variants, a task that will be particularly challenging when it comes to genes influencing human β-cell function, as it is entirely possible that this disproportionate representation of β-cell genes may be a reflection of our success in choosing diabetes genes in each of the candidate genes in the major groupings. The success of the approach presented here, although necessarily limited in scope, suggests that the systematic examination of panels of biological candidate genes in large, well-characterised populations may usefully complement positional approaches to the identification of allelic variants conferring susceptibility to complex polygenic disease. The detection of multiple small gene effects accentuates the need for larger populations in order to reliably identify the types of effects (OR 1.15–1.5) we now expect for complex diseases. Materials and Methods Methods for SNP discovery and SNP selection for genotyping. SNP discovery was performed by a high-fSSCP-based analysis, as previously described ( Thorpe et al. 1999 ). Genomic structure was determined for all genes, and primers were designed to span the exons and splice junctions. To detect common variants, genes were screened against one or more of a variety of different DNA panels, which included a 47-member multiethnic human diversity panel (comprised of 17 Europid, seven Hispanic, 13 East Asian, and ten African-American subjects), our 129-member severe insulin-resistant cohort ( Barroso et al. 1999 ), a panel of 47 European-American samples, a panel of 47 African-American samples, and in some cases a panel of 94 samples (half European and half Asian Indian). Some genes had only partially screened coding sequence and splice junctions at the time of SNP selection for genotyping. In addition, we had access to an internal database of in silico SNPs that had been validated against 100 samples. Choice of polymorphisms for further testing in association studies was not constrained by the type of variant (e.g., nonsynonymous, silent, noncoding), although higher priority was given to variants with a likely effect on protein structure and function. Polymorphisms with a minor allele frequency greater than or equal to 5% were selected for further testing in population-based studies. In some instances, polymorphisms of lower allele frequency were genotyped to examine whether lower frequency variants with high penetrance might account for some cases of polygenic disease. Polymorphisms of lower frequency were also genotyped when, at the time of selection for genotyping, no other variants of known frequency were identified in the gene to test. Populations for SNP genotyping. The Cambridgeshire Case-Control Population ( Poulton et al. 2002 ; Halsall et al. 2003 ) consists of a collection of 517 Type 2 diabetics and 517 matched controls. The cases were a random sample of Europid men and women with Type 2 diabetes, aged 47–75 years, from a population-based diabetes register in a geographically defined region in Cambridgeshire, United Kingdom. The presence of Type 2 diabetes in these subjects was defined as onset of diabetes after the age of 30 years without use of insulin therapy in the first year after diagnosis. The control subjects were individually age-, gender-, and geographical location-matched to each of the cases. Controls were not matched by BMI to cases. Potential controls that had HbA1c levels greater than 6% were excluded, as this group may contain a higher proportion of individuals with previously undiagnosed diabetics . HbA1c was assayed using high performance liquid chromatography on a BioRad Diamat 33 (Hercules, California, United States), according to the method of Standing and Taylor (1992 ). The coefficient of variation (CV) was 3.6% at the lower end of the range (mean = 4.94%) and 3.0% at the upper end (mean = 9.76%). Further details on the characteristics of the subjects are shown in Table 5 . Table 5 Study Subjects in the Cambridgeshire Case-Control Study Data are means and standard error is in parentheses The QT study population is a collection of 1,100 samples collected for the Ely Study, a prospective population-based cohort study of the aetiology and pathogenesis of Type 2 diabetes and related metabolic disorders ( Wareham et al. 1999 ). Height was measured using rigid stadiometer, and weight was measured on Seca-calibrated scales with participants in light clothing. BMI was estimated as weight (kg) divided by height (m) squared. Plasma glucose was measured in the routine National Health Service Laboratory at Addenbrooke's Hospital, using the hexokinase method ( Kunst et al. 1983 ). Plasma insulin was measured by two-site immunometric assays with either 125 I or alkaline phosphatase labels ( Sobey et al. 1989 ; Alpha et al. 1992 ). Cross-reactivity with intact proinsulin was less than 0.2% and CVs were less than 7%. Methods for genotyping. Genotyping was performed using an adaptation of the fluorescence polarisation template-directed incorporation (FP-TDI) method described by Chen et al.(1999). In short, PEP-amplified DNA samples were PCR-amplified in 8 μl reactions with primers flanking the variant site; unincorporated dNTP and remaining unused primer were degraded by exonuclease I and shrimp alkaline phosphatase at 37°C for 45 min before the enzymes were heat-inactivated at 95°C for 15 min. At the end of the reaction, the samples were held at 4°C. Single base primer extension reactions were performed as previously described ( Chen et al. 1999 ), and allele detection was performed by measuring fluorescence polarisation on an LJL Analyst fluorescent reader (Molecular Devices, Sunnyvale, California, United States). The PEP protocol was specifically developed and tested to ensure that allele bias was not introduced during the amplification process. A minimum of 12% internal replicate samples within each population (case control and QT) were included in all genotyping tests to assess genotyping accuracy. Only assays that provided 100% concordance between replicates were analysed for association. The genotyping pass rate was greater than 90% once a working assay had been established. There was an 85% success rate for an SNP to be converted into a working assay at the first attempt, with a number of failed assays recovered by designing an assay to the reverse strand. Statistical analysis. All analyses used SAS 8.02 (SAS Institute, Cary, North Carolina, United States) or Stata 7.0 (Stata Corporation, College Station, Texas, United States) statistical programs, unless otherwise stated. Agreement with Hardy–Weinberg equilibrium was tested using a χ 2 ‘goodness-of-fit' test. The disequilibrium coefficient for the controls ( r 2 ) was calculated ( Lewontin 1964 ). For the case-control study, tests for association with disease status under dominant, additive, and recessive models were undertaken using univariate logistic regression analysis. Dominance was defined in terms of allele 2 effects; in the dominant allele 2 model, homozygous subjects for allele 1 were compared with carriers of allele 2; in the recessive allele 2 model, carriers of allele 1 were compared with homozygous subjects for allele 2. In some cases, a large number of polymorphisms within a gene were typed. To reduce complexity, a subset of markers within a gene associated with diabetes status was identified using backward logistic regression. Any polymorphism that had a p value greater than 0.1 was removed from the model. The genotypes were assumed as having additive effects. p values for the additive effect are for the test for a linear trend across the genotypes, which were coded as 0 = 11, 1 = 12, 2 = 22. Where the subset consisted of more than one polymorphism within a gene, haplotype analysis was performed. To avoid possible errors due to either genotyping or the estimation process, only haplotypes that had a frequency greater than 5% were considered for further analysis. Haplotype frequencies were estimated using maximum-likelihood methods. A log-linear model embedded with the expectation-maximization algorithm was fitted to a frequency table ( Chiano and Clayton 1998 ; Mander 2001 ). Differences in haplotype distributions between the diabetic and nondiabetic groups were examined using a likelihood-ratio statistic ( Mander 2001 ). To obtain separate ORs for each haplotype, the most common haplotype was used as the reference category. CIs were obtained using a profile-likelihood approach ( Mander 2001 ). For the QT study, the distributions of fasting plasma glucose, 2-h plasma glucose, fasting plasma insulin, and insulin increment were skewed and were thus normalised by logarithmic transformation. Baseline and follow-up measurements of BMI, fasting and 2-h plasma glucose, fasting plasma insulin, and 30-min insulin increment during an oral glucose tolerance test were collected. Where two measures were available, the mean was used. Otherwise, a single measure (either baseline or follow-up) was used for further analysis. The subset of SNPs identified in the case-control study was used. In separate dominant, additive, and recessive models, adjusting for age and sex, genotype differences in these measurements were modelled using the General Linear Model procedure in the statistical package SAS. For each individual, a list of possible haplotypes and their probabilities was obtained using Snphap software ( http://www-gene.cimr.cam.ac.uk/clayton/software/ ). Haplotypes with a frequency greater than 5% were the same as those reconstructed in the case-control study. Only haplotypes that had a frequency greater than 5% and individuals that had at least one marker typed were considered for analysis. As currently haplotype analysis software cannot handle repeated measurements, the average of two measurements was used for further analysis. Associations of haplotypes (adjusted for age and sex) with the QTs were determined by cluster-weighted regression analysis, thereby taking into account nonindependent multiple observations from an individual ( Huber 1967 ; White 1980 , 1982 ). QT means and their 95% CI were estimated for each haplotype. Supporting Information Table S1 Genotype Counts and Frequencies for All SNPs Genotyped in This Study (79 KB XLS). Click here for additional data file. Table S2 Single SNP Associations with QTs (174 KB DOC). Click here for additional data file. Accession Numbers The LocusLink accession numbers discussed in this paper are 3170, 3172, 3630, 3643, 3767, 3990, 5167, 5295, 5468, 5506, 5697, 6514, 6654, 6833, 6927, 6928, 10060, 10891, and 11132.

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535929

Pharmacokinetics of quinacrine in the treatment of prion disease

Background Prion diseases are caused by the accumulation of an aberrantly folded isoform of the prion protein, designated PrP Sc . In a cell-based assay, quinacrine inhibits the conversion of normal host prion protein (PrP C ) to PrP Sc at a half-maximal concentration of 300 nM. While these data suggest that quinacrine may be beneficial in the treatment of prion disease, its penetration into brain tissue has not been extensively studied. If quinacrine penetrates brain tissue in concentrations exceeding that demonstrated for in vitro inhibition of PrP Sc , it may be useful in the treatment of prion disease. Methods Oral quinacrine at doses of 37.5 mg/kg/D and 75 mg/kg/D was administered to mice for 4 consecutive weeks. Plasma and tissue (brain, liver, spleen) samples were taken over 8 weeks: 4 weeks with treatment, and 4 weeks after treatment ended. Results Quinacrine was demonstrated to penetrate rapidly into brain tissue, achieving concentrations up to 1500 ng/g, which is several-fold greater than that demonstrated to inhibit formation of PrP Sc in cell culture. Particularly extensive distribution was observed in spleen (maximum of 100 μg/g) and liver (maximum of 400 μg/g) tissue. Conclusions The documented extensive brain tissue penetration is encouraging suggesting quinacrine might be useful in the treatment of prion disease. However, further clarification of the distribution of both intracellular and extracellular unbound quinacrine is needed. The relative importance of free quinacrine in these compartments upon the conversion of normal host prion protein (PrP C ) to PrP Sc will be critical toward its potential benefit.

Background Prion diseases, while rare, invariably result in fatal neurodegeneration. At present, no therapy has been proven to be useful in the treatment of prion disease. However, acridine and phenothiazine derivatives have been evaluated [ 1 , 2 ] using an in vitro model, and quinacrine and chlorpromazine inhibit PrP Sc formation in a scrapie-infected neuroblastoma (ScN2a) cell line [ 2 ]. Half-maximal inhibition of PrP Sc formation at effective concentrations (EC 50 ) for quinacrine was found to be 300 nM (120 μg/ml) [ 2 ]. While this concentration represents the concentration of quinacrine added to the cell culture, it does not reflect the effective intracellular quinacrine concentrations. In the past, quinacrine was used as an antiparasitic agent in the treatment of malaria and giardiasis, however, more effective, less toxic agents have since replaced this agent [ 3 ]. While quinacrine may be useful in the treatment of prion disease, its pharmacokinetics have not been extensively studied. Studies from the 1940s suggest that quinacrine is associated with extensive tissue distribution and a prolonged pharmacologic half-life [ 3 , 4 ]. However, the rate and extent of quinacrine penetration into brain tissue has not been characterized. If quinacrine administration in vivo results in brain tissue concentrations exceeding that shown to inhibit PrP Sc in vitro , it may constitute an effective therapy for prion disease. While the precise site of antiprion action is unknown (intracellular versus extracellular) adequate quinacrine penetration into brain tissue might result in an effective agent for the treatment of prion diseases. The objective of this study was to characterize the achievable plasma concentrations and associated tissue (brain, liver, spleen) penetration associated with quinacrine. Methods Animal Model The protocol was approved by the institutional animal care and use committee. The Animal Facility of the Institute for Neurodegenerative Diseases provided animal samples from 2 different strains of mice, FVB and CD1. Twenty four animals of each strain (24 FVB and 24 CD1 strain animals) were fed 37.5 mg/kg/D and 24 animals of each strain were fed 75 mg/kg/D of oral quinacrine over a 4-week period (weeks 1–4), given ad lib as a chocolate flavored liquid diet. From weeks 5–8 animals were given regular feed without quinacrine. At weekly intervals, 3 animals of each strain were euthanized and tissues were collected for analysis. Quinacrine level analysis Preparation of standard solutions Quinacrine dihydrochloride (purity: 98.6%) was purchased from Fluka, while sulfadimethoxine sodium salt (purity: 98%) which served as the internal standard (IS) was obtained from Sigma. Quinacrine stock solution was prepared as 1 mg/ml in methanol. Sulfadimethoxine stock solution was prepared as 1 mg/ml in 50% methanol. The working solutions were prepared by diluting the respective standard and control stock solutions with 50% methanol and 0.1% formic acid to 2 μg/ml and 100 ng/ml, respectively. All solutions were stored in a 4°C refrigerator in silinized brown glass containers. LC/MS/MS system and conditions The HPLC system employed a Shimadzu LC-10 AD pump and a Waters intelligent Sample Processor 717 Plus autosampler/injector. A BDS Hypersil C18 column, 50 × 4.60 mm, 5 μm particle size was directly coupled to a Micromass Quattro LC Ultima triple quadrupole tandem mass spectrometer using electrospray ionization in positive ion mode. The sample cone voltage and collision energy were 25 V and 20 eV respectively for both quinacrine and the internal standard and the source block and desolvation temperature were 100°C and 400°C, respectively. The mass scanning mode employed multiple reaction monitoring (MRM) with the singly charged quinacrine ion selected at m/z 400.5 giving a fragment ion at m/z/142.0, and the internal standard at m/z 311.0→156.0. The mobile phase consisted of CH 3 OH/H 2 O/TFA (45:55:0.05) with 1 mM ammonium formate. The flow rate was 0.8 ml/min with 1/4 split into the mass spectrometer. The injection volume was 5–10 μl with a run time of 3.5 min. Sample preparation All samples were stored at -70°C until analyzed. Each tissue sample was subjected to a specific method, as described below, for drug extraction and for the determination of concentration. All samples were analyzed by LC/MS/MS. Accuracy and precision Accuracy and precision was demonstrated throughout the working range with interday and intraday coefficient of variation and relative error <10%. Plasma extraction Each plasma sample was thawed at room temperature for 10–15 min; then 20 μl of plasma was aliquotted to a new test tube. To each tube 200 μl of 70% acetonitrile solution, containing 0.1% formic acid and 50 ng/mL of internal standard, was added. The test tubes were vortexed at high speed for 1 min and centrifuged at 10,000 rpm for 10 min. The supernatant was transferred into the autosampler for LC/MS/MS analysis. A set of standard curves with a duplicate set of quality control (QC) samples was generated for sample analysis. Brain samples Prior to the in vivo study, the stability of quinacrine in mouse brain tissue was determined. Using 3 different doses of quinacrine (4 mg/kg/d, 80 mg/kg/d, 160 mg/kg/d), mouse brain tissue was soaked in 100% methanol for 7 days. Samples were taken on days 0,1,2,3, and 7 to analyze quinacrine concentrations using LC/MS/MS. Complete equilibration was observed by Day 1 and no degradation in quinacrine was noted through day 7. Brain samples were thawed (in plastic tubes) at room temperature for 15–20 min and weighed. To each sample 0.5 ml of 0.9% NaCl was added followed by incubation at room temperature for 1 h. For the standard curve and quality control samples, a brain tissue sample from an untreated mouse was spiked with different amounts of quinacrine and incubated at room temperature for 1 h. After the addition of 100 μl of 1 μg/ml internal standard solution in 50% methanol and 0.1% formic acid, 5 ml of 100% methanol was added into each sample and soaked at 4°C for two weeks. On the last day, 200 μl was aliquotted from each brain sample and placed into the autosampler for analysis via LC/MS/MS. Liver samples Liver samples were thawed at room temperature for 15–30 min and weighed. Considering the increased size of the liver samples and that the increased connective tissue in liver samples could potentially prevent the complete distribution of quinacrine into methanol, homogenization was used for these samples. To each sample 100% methanol was added (10 ml/g of tissue) and the tissue was homogenized in ice water for 1 min at speed 3 (Tissue Tearor, model 985-370, Biospec Products, Inc). The internal standard (100 μl of 10 μg/ml in 50% methanol, 0.1% formic acid) was added to 0.2 ml of each homogenized liver sample in a glass test tube. Samples were vortexed for 1 min, centrifuged at 3000 rpm for 10 min and 20 μl of each supernatant was transferred into a new test tube. Each sample was further diluted with 4 ml of 50% methanol, vortexed, and 200 μl was transferred to the autosampler for LC/MS/MS analysis. A set of standard curve with a duplicate set of QC samples was generated for sample analysis. Spleen samples Spleen sample preparation was similar to the preparation of brain samples using a methanol soak. Prior to the in vivo study, the stability of quinacrine in mouse spleen tissue was determined. Using 3 different doses of quinacrine (4 mg/kg/d, 80 mg/kg/d, 160 mg/kg/d), mouse spleen tissue was soaked in 100% methanol for 7 days. Samples were taken on days 0, 1, 2, 3, and 7 to analyze quinacrine concentrations using LC/MS/MS. Complete equilibration took place by Day 1 and no degradation in quinacrine was observed through day 7. Samples from the in vivo analysis were soaked for 14 days at 4°C. On day 14, 50 μl was aliquotted from each spleen sample and diluted with 1 ml of 50% methanol. Each sample was vortexed for 1 min and 200 μl was transferred to the autosampler for LC/MS/MS analysis. A set of standard curves with a duplicate set of QC samples was generated for sample analysis. Results Quinacrine analysis The mass spectrum of quinacrine (Q1) and its tandem spectrum (Q3), showing the fragment ion selected for MRM, are shown in Figure 1 . Using MRM for m/z 400.5 ± 142.0 and the appropriate LC/MS/MS conditions described above, quinacrine could be selectively and sensitively detected in plasma (Fig. 2 ) and brain tissue (Fig. 3 ) after relatively simple sample preparation. In vivo studies In plasma, quinacrine concentrations with the 37.5 mg/kg/D dose ranged from 75 to 175 ng/ml for both FVB and CD1 strains mice and remained at this level during the 4-week dosing interval. Once discontinued, quinacrine was completely eliminated from plasma, with no drug detectable within one week. At 75 mg/kg/D quinacrine, plasma concentrations reached >2 × those observed with the lower dose, averaging 300 to 400 ng/ml. Quinacrine levels in brain tissue for both mice strains were determined to be substantially greater than those achieved in plasma. Similar to observations with plasma, steady-state levels in the brain with the 37.5 mg/kg/D dose were achieved by the end of the first week, averaging 400 to 600 ng/g brain tissue. Figure 4 characterizes the brain tissue levels in the FBV mice. Quinacrine was undetectable in brain tissue within a week after discontinuing treatment. With the 75 mg/kg/D dose, quinacrine levels in brain tissue were observed to be greater than 2 × those observed with the 37.5 mg/kg/D dose, averaging 1500 ng/g. After high-dose (75 mg/Kg/D) treatment ceased, quinacrine levels in brain were detected for two weeks. The analysis of liver samples showed particularly high quinacrine levels, at many times those observed in plasma and brain. At the end of the first week of treatment with 37.5 mg/kg/D, steady-state levels of 70 to 90 μg/g were achieved in liver tissue in both mice strains, which remained throughout the course of treatment. Figure 5 demonstrates the achievable liver tissue levels in FBV mice. In contrast, the 75 mg/kg/D dosing resulted in gradually increasing levels in liver tissue, rising from approximately 150 μg/g in the first week to a steady-state level of 300 to 400 μg/g by the end of the 4 th week. Similarly, spleen tissue levels in both mice strains were very elevated, averaging 5 to 10 μg/g with the 37.5 mg/kg/D dose and 40 to 100 μg/g with the 75 mg/kg/D dose. Figure 6 characterizes the spleen tissue levels of the FBV mice. Of note, in contrast with plasma and brain tissue, in which dose dependency was somewhat linear, increasing quinacrine doses in spleen and liver were associated with substantially greater tissue levels. In addition to the isolation of quinacrine, numerous metabolites were identified in all tested tissue samples (data not shown). Discussion The current study is the first comprehensive evaluation of quinacrine distribution in plasma and brain tissue. Shannon et al. evaluated the pharmacokinetics of quinacrine in the treatment of malaria [ 3 ]. Using a dog model, these researchers observed quinacrine to be concentrated in the liver and spleen, as well as muscle and lung. In the same study, humans receiving 100 mg quinacrine three times daily achieved maximal plasma concentrations of 100 ng/ml. Administration of a single intravenous dose of 2 mg/kg quinacrine in rabbits was associated with plasma levels of 10 ng/ml [ 4 ]. The quinacrine levels we observed in plasma are similar to those recorded from oral dosing for malaria [ 3 ]. We observed rapid and extensive distribution of quinacrine in brain, liver, and spleen tissue in association with much lower plasma concentrations. Although quinacrine was consistently observed one week after drug discontinuation in all tissue at the 75 mg/kg/D dose, none could be observed one week after cessation of the 37.5 mg/kg/D doses. The concentrations we report in brain in terms of concentration/gm of tissue are several-fold greater than that shown for effective antiprion activity in ScN2a cells [ 2 ]. In that in vitro study, the EC 50 for quinacrine was 300 nM, which approximates 120 ng/ml. We report here that quinacrine levels in brain tissue averaged 400 to 600 ng/g with the 37.5 mg/kg/D dose and 1500 ng/g with the 75 mg/kg/D dose, concentrations that exceed the in vitro EC 50 by 3- to 10-fold. Others have reported the antiprion function of quinacrine in vitro , including clearance of PrP Sc (5) and/or inhibition of PrP Sc formation [ 6 ]. Barret et al., who also noted the in vitro benefit from quinacrine, found the drug to be ineffective in the animal model under the conditions employed [ 1 ]. In contrast, some case reports in humans suggest quinacrine to be associated with clinical improvement, including the return of voluntary eye movement [ 7 , 8 ]. While the current study has confirmed brain tissue concentrations in excess of the reported EC 50 , it important to note critical limitations in their interpretation. Quinacrine has been determined to be highly protein-bound [ 3 ], measured at 83–90% in older studies. Additionally, the drug has been shown to be highly concentrated in white blood cells with intracellular levels ranging from 9,500–18,400 μg/L with accompanying low CSF levels (4.3–5.4 μg/L). Previous investigations confirm that it is free drug that is microbiologically active in the treatment of infection [ 9 ]. The brain tissue levels in the current investigation represent the sum total of intracellular, extracellular, protein-bound and unbound quinacrine. Highly protein-bound agents penetrate less well between plasma and tissue compartments, suggesting that quinacrine would more likely be plasma-bound. However, the results of our study strongly suggest deep brain tissue penetration of quinacrine, suggesting the possible contribution of membrane transporter proteins or other mechanisms facilitating passage of quinacrine into brain tissue. Considering the extensive intracellular white blood cell concentrations achieved with quinacrine, similar mechanisms may be responsible in actively pumping quinacrine into these cells. Consequently, it is critical to determine the actual free quinacrine concentrations in both intracellular and extracellular brain tissue and to document the relative contribution of these compartments in the pathogenesis and treatment of prion disease. Using microdialysis probes, Mindermann and colleagues [ 10 ] evaluated the penetration of rifampin into cerebral extracellular space, brain tumor, perifocal, and normal brain tissue. The findings confirmed consistent cerebral extracellular space concentrations, but remarkably different rifampin brain tissue levels, particularly concentrating in brain tumor tissue. Considering the pathogenesis of prion disease, heterogeneity of brain tissue concentrations may impact similarly the efficacy of quinacrine or other agents. Our results strongly suggest that similar microdialysis experiments take place with quinacrine to clarify the distribution characteristics of this agent. In addition to steady-state levels of quinacrine, we observed a number of quinacrine metabolites in all tissue samples studied, which raises the possibility that these metabolites may have in vitro antiprion activity similar to or greater than that of the parent compound. These tests warrant further evaluation. Conclusions Based upon its oral bioavailability, favorable tissue distribution characteristics, and in vitro activity, quinacrine may be a useful agent in the treatment of prion disease. However, more detailed tissue distribution analyses linked with the critical sites of prion action are warranted. Competing interests The author(s) declare that they have no competing interests. Authors' contributions LY, YH, ETL developed the assay, performed sample analyses, contributed to the writing of the paper. PL, GL, MB, CR developed the protocol and performed the animal model experiments. SBP conceived of the study and participated in its coordination. BJG drafted the manuscript, participated in its coordination and mentored LY. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

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529469

Symptomatic hypogammaglobulinemia in infancy and childhood – clinical outcome and in vitro immune responses

Background Symptomatic hypogammaglobulinemia in infancy and childhood (SHIC), may be an early manifestation of a primary immunodeficiency or a maturational delay in the normal production of immunoglobulins (Ig). We aimed to evaluate the natural course of SHIC and correlate in vitro lymphoproliferative and secretory responses with recovery of immunoglobulin values and clinical resolution. Methods Children, older than 1 year of age, referred to our specialist clinic because of recurrent infections and serum immunoglobulin (Ig) levels 2 SD below the mean for age, were followed for a period of 8 years. Patient with any known familial, clinical or laboratory evidence of cellular immunodeficiency or other immunodeficiency syndromes were excluded from this cohort. Evaluation at 6- to 12-months intervals continued up to 1 year after resolution of symptoms. In a subgroup of patients, in vitro lymphocyte proliferation and Ig secretion in response to mitogens was performed. Results 32 children, 24 (75%) males, 8 (25%) females, mean age 3.4 years fulfilled the inclusion criteria. Clinical presentation: ENT infections 69%, respiratory 81%, diarrhea 12.5%. During follow-up, 17 (53%) normalized serum Ig levels and were diagnosed as transient hypogammaglobulinemia of infancy (THGI). THGI patients did not differ clinically or demographically from non-transient patients, both having a benign clinical outcome. In vitro Ig secretory responses, were lower in hypogammaglobulinemic, compared to normal children and did not normalize concomitantly with serum Ig's in THGI patients. Conclusions The majority of children with SHIC in the first decade of life have THGI. Resolution of symptoms as well as normalization of Ig values may be delayed, but overall the clinical outcome is good and the clinical course benign.

Background Pediatric patients with "recurrent infections" within our area are referred to the pediatric immunology clinic in the Kaplan Medical Center. Few fulfill the clinical criteria of the immune deficiency "red flags", Table 1 , and only in a small minority, quantitative or qualitative defects in immunological function are documented. As expected, most such defects, involve the humoral immune system, the most common of the primary immune deficiencies [ 1 , 2 ]. Classically, the clinical presentation, includes a neonatal "grace" period, during which the baby is protected from infection by the presence of passively acquired maternal antibodies. As the level of these antibodies decline, the babies present at the end of the first year of life or the beginning of the second with recurrent respiratory, ENT and GI infections. The pathogens involved are mostly the "usual" bacteria, Streptococcus pneumonia , Haemophilus influenza and Staphilococcus aureus , but the infections may be of unusual severity, persistence, or frequency. Table 1 Clinical "Red Flags" for Immunodeficiency 1 Eight or more new ear infections within 1 year 2 Two or more serious sinus infections in 1 year 3 Two or more months on antibiotics with no effect 4 Two or more pneumonias within one year 5 Failure of an infant to gain weight or grow well 6 Recurrent, deep skin or organ abscesses 7 Persistent thrush in the mouth or elsewhere on the skin, after age 1 8 Need for intravenous antibiotics to clear infection 9 One or more deep-seated infections such as sepsis, meningitis or cellulitis 10 A family history of primary immune deficiency or early infant death from infection, recurrent infection, malignancy, or autoimmune disease Adapted from: Primary Immunodeficiency Diseases: A molecular and genetic approach New York, Oxford University Press, 1999. In the last 10 years, tremendous advances in the fields of molecular medicine and genetics, have made possible the definitive diagnosis of most combined immunodeficiency patients, agammaglobulinemia patients and clinical syndromes associated immunodeficiency patients, on the basis of a recognized genetic aberration leading to a protein product dysfunction [ 3 , 4 ]. Nevertheless, the diagnosis of some of the most common forms of primary immune deficiency, IgA deficiency [ 5 ], common variable immunodeficiency [ 6 ] and transient hypogammaglobulinemia of infancy (THGI), are still based on clinical criteria and the exclusion of other specific diagnoses [ 7 , 8 ]. THGI is thought to be caused by a poorly understood maturation delay in the normal production of Ig, extending the physiologic hypogammaglobulinemia of the new born beyond the first year of life [ 1 , 9 ]. Currently, there are no diagnostic tests that differentiate, on initial presentation of a young child with recurrent infections and low Ig levels, those that will spontaneously correct on follow-up from those where a primary and permanent immune deficiency will develop, except for B cell numbers below 2%, which point towards X-linked agammaglobulinemia (XLA)[ 10 ]. In this study we aimed to evaluate the natural course of disease in symptomatic hypogammaglobulinemia of infancy and correlate in vitro lymphoproliferative and secretory responses to mitogens in this population with recovery of immunoglobulin values and clinical resolution. Methods Patients Children more than 1 year of age, with recurrent infections, defined as more than three episodes of acute otitis media and/or more than one episode of acute sinusitis and/or more than one episode of pneumonia or the presence of a severe deep seated infection (meningitis, septicemia, etc.) within the last 6 months, or fulfillment of one of the "red flags" of immunodeficiency, see Table 1 , and hypogammaglobulinemia, defined as serum Ig values 2 SD below the age defined norms on two or more measurements [ 11 ], have been prospectively recruited from a cohort of children referred to our clinic because of recurrent or severe infections. Patients were seen at presentation and reevaluated periodically at 6 to 12 month intervals, up to 1 year after resolution of symptoms. All procedures were performed according to accepted ethical standards of the Institutional Review Board of Kaplan Medical Center. The parents of all the children were informed accordingly and gave their permission for participating in the study and blood sampling. Ig assessment Total serum Ig levels were measured by nephelometry (Beckman Immunochemistry Systems, IgM, IgG and IGA test, Beckman Instruments, Galway, Ireland) and serum IgG subclasses have been assayed with an immunodiffusion commercial kit (Human IgG Subclasses single dilution BINARID, Birgminham, UK). Specific antibody production was not evaluated. In vitro cell proliferation and Ig secretion Peripheral blood mononuclear cells (PBMC) were isolated from heparinized venous blood of patients and age-matched donors (children hospitalized or attending the outpatient clinic for unrelated conditions) on ficoll isopaque gradients (Sigma, St. Louis, MO, USA). Patient and normal donor cells were cultured in microtiter plates with culture medium: RPMI medium supplemented with 10% FCS (Bio-Lab, Jerusalem, Israel), 10 mM Hepes buffer, 100 U/ml penicilin 100 μg/ml streptomycin, 2 mM L-glutamine, and 100 μg/ml kanamycine (Sigma Israel), and were grown at 37°C with 7.5% CO2 in air. 5 × 10 5 cells were stimulated for four days with 0.01% w/v SAC (Calbiochem, La Jolla, CA, USA), 2.5 μg/ml PWM, 20 μg/ml E. coli: O55:B5 LPS or with 20 μg/ml PHA (Sigma, St. Louis, MO, USA). The cells were then pulsed with 1 μCi/well of [ 3 H]-Thymidine (Nuclear Research Center, Negev, Israel) and incorporated radioactivity was measured by a β scintillation counter. Proliferation was expressed as stimulation index (SI). In parallel, cell culture supernatant aliquots were harvested and Ig isotype concentrations were measured in the culture supernatants, by a solid-phase immunoassay, in Nunc- Immunoplate Maxisorp 96 wells (Nunc, Denmark). The plates were coated with goat anti-human IgM, IgG or IgA antibodies (Jackson Immunoresearch Laboratories, West Grove PA, USA). Biotinilated goat anti-human IgM, IgG or IgA (Jackson, West Grove, PA, USA) and streptavidin-alkaline phosphatase (Amersham, Buckinghamshire, England) were used for measurement of IgM, IgG and IgA respectively. Resulting yellow dye intensity was read by an ELISA reader (Microplate Auto-Reader Bio-Tek Instruments, VT, USA). Dye units were converted to immunoglobulin concentrations by extrapolation from standard curves determined by using purified myeloma proteins of known concentration in every assay. Statistical analysis We used non-parametric tests to compare the means (Kruskal Wallis test for k independent samples) and a standard analysis of variance to compare between groups. Logistic regression analysis was used for comparison of distribution of dichotomous values between the groups. Analysis was performed using SPSS for windows ver 9.0. Results 32 patients were included in the study, 24 males (75%) and 8 females (25%) with a mean age at diagnosis of 3.4 years (range 1.2 – 7.0). Clinical presentations included severe and recurrent Ear-Nose-Throat (ENT) infections – 22 patients (69%), pneumonia, bronchopneumonia or severe, recurrent upper respiratory infections – 26 patients (81%), diarrhea – 4 (12.5%) and atopy related complaints – 20 patients (63%). A positive family history of recurrent, unusual or severe infections was obtained in 7 patients (22%). Demographical and clinical data of patient cohort is summarized in Table 2 . Table 2 Clinical and Demographic Data of Patient Cohort THGI Non Transient p Demographics No of Patients 17 (53%) 15 (47%) > 0.05 Males 15 (88%) 9 (60%) > 0.05 Females 2 [30] 6 [31] > 0.05 Average age at Diagnosis 3.6 years 3.1 years > 0.05 Clinical Data ENT 10 (59%) 12 (80%) > 0.05 Respiratory 15 (88%) 11 (73%) > 0.05 GI 2 [32] 2 [33] > 0.05 Atopy 11 (65%) 9 (60%) > 0.05 Family History 4 (24%) 3 (20%) > 0.05 Diagnosis IgAD 9 (53%) 7 (48%) > 0.05 IgGD 12 (71%) 10 (68%) > 0.05 IgMD 2 [34] 3 (20%) > 0.05 Treatment Antibiotics 4 (24%) 8 (53%) > 0.05 IVIg 2 [35] 1 [36] > 0.05 Outcome No Infections 14 (83%) 13 (87%) > 0.05 Follow-up (years) Age at last follow-up 7.1 5.5 > 0.05 Length of follow-up 3.5 2.5 > 0.05 THGI – Transient Hypogammglobulinemia of Childhood Non Transient – Hypogammaglobulinemic patients who did not correct during follow-up IgAD – IgA 2SD bellow age specific norms IgGD – One or more IgG isotype 2SD bellow age specific norms IgMD – IgM 2SD bellow age specific norms Out of the initial 32 patients, 17 (53%) spontaneously corrected their Ig abnormalities. This group included 15 boys (88%) and 2 girls[ 12 ], mostly in the second or third year of life, average age at diagnosis being 3.6 years (range 1.3–9). The two groups, those with essentially transient hypogammaglobulinemia – THGI and those who did not correct their Ig values during the follow up period did not differ significantly at diagnosis, see Table 2 . All defects were partial, no patient in this group, showing a complete absence of a given Ig isotype. The clinical course was benign, only 9.4% (3/32) patients requiring IVIg (2 in the THGI group, 1 in the non corrected group). 38% (12/32) of patients received prolonged antibiotic prophylaxis (4/17 in the THGI and 8/15 in the non corrected group, p = 0.09) and resolution of clinical symptoms occurred in 84% of patients (14/17 in the THGI and 13/15 in the non corrected group). All calculated p values, non significant for comparison between the 2 groups. Comparative analysis of serum Ig isotype levels at diagnosis showed no significant difference, between the transient and non-transient group. In vitro lymphocyte proliferation and Ig secretion were measured in 9 patients (5 patients with THGI and 4 with non-corrected IGD) on one or more occasions. Lymphocytes proliferative responses to SAC, PWM and LPS showed no differences between the groups and no significant differences from childhood norms. The proliferative response to PHA was significantly increased (p < 0.005) after the correction of Ig abnormalities, overshooting normal controls (Fig 1 ). Figure 1 Lymphocyte proliferation after in-vitro mitogenic stimuli in Pediatric Hypogammaglobulinemia. SAC – Staph. Aureus Cowan. PWM – Pokeweed Mitogen PHA – Phytohemmaglutinin LPS – Lipopolysacharide Values are given as Stimulation Index (SI) mean ± 95% CI, ** p < 0.005 Quantification of the in-vitro immunoglobulin secretion in response to the various mitogens showed significant isotype and mitogen dependent variation. The IgM secretory response to PWM and LPS, was low in hypogammaglobulinemic patients. After normalization of serum Ig values, the IgM response to LPS stimulation increased, see table 3 . However, the improved response to LPS was still lower than age matched controls (p < 0.05). Table 3 Immunoglobulin Secretion from B-cells after in-vitro mitogenic stimuli in Pediatric Hypogammaglobulinemia SAC PWM LPS IgM IgG IgA IgM IgG IgA IgM IgG IgA Hypogamma 1.6 1.2 * 1.1 ** 1.1 * 1.4 * 1.3 * 1.3 * 2.0 * 1.1 * (0.6–2.6) (0.9–1.5) (1.0–1.2) (0.9–1.3) (0.9–1.9) (1.0–1.6) (1.1–1.5) (1.1–2.9) (1.0–1.2) Corrected 2.8 1.0 * 1.3 * 1.0 * 1.1 ** 1.3 * 2.2 * 1.5 * 1.2 * (1.7–3.9) (0.9–1.1) (1.0–1.6) (0.9–1.1) (1.0–1.2) (1.1–1.5) (1.4–3.0) (1.2–1.8) (1.0–1.4) Normal 3.0 2.5 2.3 2.6 3.0 2.2 4.4 5.0 3.4 (2.5–3.5) (1.9–3.1) (1.8–2.8) (2.2–3.0) (2.4–3.6) (1.7–2.7) (3.9–4.9) (3.5–6.5) (3.0–3.8) SAC – Staph. Aureus Cowan. PWM – Pokeweed Mitogen LPS – Lipopolysacharide Values are given as Secretion Index Mean and (95% confidence interval) ** p < 0.005 compared to normal controls * p < 0.05 compared to normal controls For both IgG and IgA, the response in normal children was significantly better than in either group of patients. The IgA secretion index to all 3 stimulatory mitogens was minimal in hypogammaglobulinemic patients, even after serum Ig correction and differed significantly from age matched controls (p < 0.005 for SAC, p < 0.05 for PWM, p < 0.05 for LPS), table 3 . The IgG secretion index in hypogammaglobulinemic patients was slightly better to LPS than to other mitogens (non significant) and differed significantly from age matched controls (p < 0.05 for SAC, p < 0.005 for PWM, p < 0.05 for LPS). No difference was observed on either test between THGI and non corrected patients on initial presentation. Discussion Young patients with recurrent infections represent a sizeable portion of the daily practice of all primary care pediatricians and family physicians, with parents clamoring for a solution with the accumulation of lost daycare or school days. The physician is faced with the dilemma when parental assurance will suffice, rather than initiation of a costly immunological investigation. Often, the presenting clinical signs and symptoms are insufficient for an educated diagnosis, as well as Ig levels in infants below the age of one year. The present investigation was initiated in order to try to contribute additional understanding how to differentiate between cases of primary immune deficiency and those who are not. We prospectively studied the outcome of SHIC in 32 patients during a period of 8 years, mean follow up of 3.2 years. During this time more than half corrected their Ig abnormalities. The mean follow up of the non-corrected IGD group is slightly shorter (though not statistically significant) than that of the THGI group (2.5 years and 3.5 years respectively) which leads us to speculate that some patients in the "non-transient" group may eventually correct. This is consistent with previously published reports from Dalal et al. who, after a follow up of 10 years, found that 70% of patients had complete resolution of their Ig abnormalities [ 13 ]. Interestingly, the majority of our patients were males (75%), and a higher proportion of males corrected their serum Ig (15/24 males and 2/8 females). This finding of male preponderance is not uniquely ours. In the reports of Dalal et al. [ 14 ], 24/35 (69%) and Walker et al. [ 15 ] 29/39 (74%) of patients included in the study were male. We found no readily available explanation for this phenomenon. It is impossible to estimate the prevalence of THGI in Israel, from our data, since our patients are part of a biased referral clinic population. Still, our impression is that this diagnosis is perhaps the most common of the Ig deficiencies in childhood, an impression supported by a number of publications from different groups [ 16 , 17 ]. In the paragraph on THGI in the most recent Scientific Group Report on the subject [ 1 ], the age cutoff of recovery of normal Ig synthesis capability "may be delayed for as long as 36 months". In contrast, in our series of patients, normalization to age adjusted Ig levels, has occurred in some cases at 8 or 9 years of age. These results are consistent with Dalal et al [ 18 ], who also describe resolution of THGI over the first decade of life. A more recently published series [ 19 ], 7/40 [ 20 ] of patients with THGI, still had low levels of antibodies at age 5 years. More over, the benign course of the group of patients who did not correct their Ig values during the course of our follow up, may indicate that a significant portion of these patients may actually still belong to the THGI group and will correct on subsequent follow up. These combined observations may induce a change in the classical definition and diagnostic criteria for THGI [ 21 ]. The majority of patients (84%) resolved their tendency for recurrent infections irrespective of their Ig values. Only a minority of patients required any medical intervention, 38% received antibiotic prophylaxis and only 9% intravenous Ig replacement therapy. We observed no difference in the clinical presentation or follow up of the transient group as compared to the non corrected IGD patients and resolution of serum Ig abnormality did not cause a complete clinical remission in all patients. This may be due to a residual inability to mount an adequate antibody response to specific antigen challenge, data that has not been evaluated in our series. The impaired IgG and IgA in vitro secretory responses, seen in hypogammaglobulinemic patients, even after serum Ig normalization, may be an expression of such an inability, which warrants further investigation. Atopy was a prominent associated complaint in 21/37 (57%) of our patients. Especially so in comparison with the prevalence of asthma in this age group in Israel – 7% [ 22 , 23 ] and the estimated prevalence of other atopy associated diseases, about 20%. This finding was inconsistently reported by other investigators [ 24 , 25 ]. Though no clinical evidence of T-cell functional impairment was observed in our patients (no opportunistic, fungal or chronic viral infections), previous reports [ 26 - 28 ] suggest that the apparent B cell defect may be secondary to a T cell dysfunction such as T cell cytokine disregulation. We have shown that cellular and humoral responses to mitogenic stimuli tend to be lower in SHIC patients as compared to normal children but do not differentiate THGI from patients with more persistent hypogammaglobulinemia. Lymphocyte proliferation in response to PHA is significantly increased after Ig correction in THGI, overshooting normal controls. The molecular basis of this observation is unclear. In vitro IgM secretion is less impaired than other isotypes and in THGI a definite improvement of IgM secretion with SAC and LPS stimulation occurs concomitantly with correction of serum Ig. The IgG and IgA secretion in response to mitogenic stimuli is severely impaired and does not normalize concomitantly with serum Ig, indicating a possible impairment in the isotype switching mechanism. This observation is supported by the previous long term follow-up reported by Dalal [ 29 ] where specific IgG antibody responses to polysaccharide antigen were reduced even after the resolution of the serum Ig deficiency in a large subgroup of patients with apparently resolved THGI. Conclusions THGI is a relatively common cause of symptomatic hypogammaglubulinemia in infancy in our area. Most children will spontaneously correct their Ig abnormalities during the first decade of life. Though tests of cellular or humoral stimulation index, are not as yet capable of differentiating the transient from the non-transient patients upon their presentation, significant isotype and mitogen specific variability is evident. The relative preservation of the in vitro IgM secretory response and the lack of IgA/IgG response in patients with hypogammaglobulinemia, argues for a delay in isotype switching as the molecular basis underlying the clinical entity of transient hypogammaglobulinemia of infancy. Competing interests The author(s) declare that they have no competing interests. Abbreviations SHIC – Symptomatic Hypogammaglobulinemia in Infancy and Childhood THGI – Transient Hypogammaglobulinemia of Childhood Ig – Immunoglobulin IGD – Imunoglobulin deficiency SAC – Staphylococcus aureus cowan I PWM – Pokeweed mitogen LPS – Lipopolysaccharide PHA – Phytohemagglutinin IVIg – Intravenous Immunoglobulin ENT – Ear, nose & throat Authors' contribution MIK – carried out the patient care and follow-up, was responsible for the database organization, data analysis and for manuscript coordination and writing ZT – is one of the research initiators, carried out the patient care and follow-up and contributed to the manuscript writing IA – carried out the in vitro cell proliferation and Ig secretion studies RS – carried out the in vitro cell proliferation and Ig secretion studies MS – carried out the patient care and follow-up, was responsible for the database initiation IZ – is one of the research initiators, carried out of the laboratory evaluation and contributed to the manuscript writing All authors read and approved the final manuscript Pre-publication history The pre-publication history for this paper can be accessed here:

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545202

Should an Institution That Has Commercial Rights in a New Drug or Device Be Allowed to Evaluate the Technology?

Background to the debate: In the United States, the passage of the Bayh–Dole Act in 1980 encouraged universities to license inventions for commercial development. Although this financial incentive can stimulate academic researchers to discover new drugs and devices, there is concern that the possibility of monetary reward could distort investigators' objectivity.

Ross McKinney's Viewpoint: Universities Should Be Allowed, Provided the Trial Is Approved by an External Review Board One of the principal missions of an academic health center is to advance the understanding and treatment of disease through clinical research. In this pursuit, there is a need for checks and balances. When Jesse Gelsinger, a relatively healthy young adult, died in Philadelphia during a clinical trial of a novel adenovirus-based genetic therapy for ornithine transcarbamylase deficiency, it was a tragedy [1] . In retrospect, there were many clues that there were problems with the adenovirus vector, clues that neither the investigator nor the institution pursued. Attorney Alan Milstein made the case that the investigator and institution were both blinded to these problems by their heavy financial investment in the technology, an investment worth millions of dollars [2] . Though the legal case was settled out of court, it created a de facto standard that institutions with commercial rights in a new drug or technology should not be allowed to pursue clinical trials involving that new technology. I do not believe that such a blanket prohibition is necessary. At its core, the issue revolves around conflicts of interest. In clinical research, the investigator should be primarily an advocate for the patient or volunteer. The core reason to perform clinical research is to create generalizable knowledge about a therapy, patient population, or a disease process with the long-term intent of improving human health. The interests of the patient and investigator should be fully aligned. However, most physicians in clinical research have other, more personal motivations, intermixed with the desire for progress. Successful research projects can lead to publications, promotions, grant renewals, and per case clinical trial enrollment fees. Some investigators have intellectual property rights that may have very substantial financial value if the drug or device reaches the level of approval by the United States Food and Drug Administration. These investigators stand to gain personally if the clinical trial is successful, a situation that has the potential to distort the investigator's objectivity, and may lead to a less honest relationship with study volunteers. An external IRB could provide independent oversight of the trial (Illustration: Giovanni Maki) In order to ensure that investigators are honest with potential research volunteers, the system of institutional review boards (IRBs) evolved. The IRB approves the informed consent document, which should describe the clinical experiment in a clear and dispassionate way to patients and their families. IRBs are largely made up of faculty and staff from the institution, although there are also public members and nonscientists on most IRB panels. The IRB must remain autonomous and be able to hold up or stop an investigation. There is an obligation that the IRB first and foremost think about patient rights and safety. The passage of the Bayh–Dole Act in 1980 enabled universities to license inventions for commercial development [3] . The closer to Food and Drug Administration approval the drug or technology is at the time of licensure, the more valuable it becomes. Therefore, universities have an incentive to advance the clinical development of inventions by their faculty. In this regard, they are very much like corporate sponsors of research, subject to the same Food and Drug Administration oversight as corporations. In terms of performing clinical trials using new technologies in which it has a financial interest, how is a university different from a corporate sponsor? In regard to patient safety, one primary distinction rests with the IRB. The corporate sponsor will present the research protocol to an independent commercial IRB, the university to its own IRB. Yet in both cases, there are potential conflicts of interest. The university IRB members will have a conflict of interest between the investments of their employer and the rights of the research volunteers. Independent commercial IRBs depend on pleasing corporate customers for their continued existence, and there is an unstated expectation that they will both be fast and produce rulings consistent with corporate expectations (which in most cases include a desire to do the research ethically). A university's financial conflicts could influence the conduct of the trial (Illustration: Giovanni Maki) At the university level a logical and conservative solution to the problem of institutional conflicts is to require that an IRB from outside the institution become the IRB of record when such a conflict arises. This external IRB could be either an independent commercial IRB or one of another university. The key is to grant the IRB independence and the authority to provide real oversight. There are other elements of conflicts of interest that need to be considered when the institution has a commercial interest, but most have more to do with the management of personal conflicts than institutional conflicts. The institution needs to assure the presence of an independent data safety monitoring board, thorough audits of good clinical practice, and a publications committee that will ensure submission of all meaningful study results, whether positive, negative, or neutral. Anyone subjectively evaluating patient data should be as free of conflicts as possible. These steps can be formulaically required, which should allow for performance of clinical research despite the presence of institutional commercial interests. David Korn's Viewpoint: Academic Biomedical Research Must Be Free from the Taint of Financial Compromise United States research universities, and especially their academic medical centers, have greatly benefited from their uniquely privileged status in our society. That status is rooted in public confidence and trust that these institutions and their faculties will be independent and impartial in fulfilling both their academic mission to create, transmit, and preserve knowledge, and their duty to the general society to serve as credible, trustworthy arbiters of knowledge. One important mark of this status has been the remarkably consistent generosity of public support for biomedical research. Another has been the noteworthy deference of the federal government to university autonomy, and the light hand with which the sponsoring agencies historically have overseen the conduct of university research. When federal interposition occurred, it typically responded to widely publicized episodes of research misconduct, sometimes intertwined with egregious financial self interests of investigators; these episodes legitimately questioned the effectiveness of institutional oversight. Nevertheless, regulations consistently focused more on defining the metes and bounds of the permissible than on prescriptive mandates, and their implementation was effected largely through the mechanism of “assurances”—commitments that institutions would faithfully safeguard the specified perimeters of acceptable conduct. Awardee institutions thus bear primary responsibility for assuring the credibility and integrity of federally sponsored research. Public confidence in the trustworthiness of these institutions is critical, and yet nowhere is it more fragile than in biomedical research involving human participants. That confidence eroded in the 1980s and 1990s because of reports of scientific misconduct and of individual and institutional financial self interests in clinical trials. Scathing reports from federal oversight agencies and angry congressional hearings questioned whether financially self-interested institutions could any longer be trusted to guard the welfare of research participants or the integrity of clinical research. In 2001, the Association of American Medical Colleges convened a task force to examine and make recommendations on individual and institutional financial self interests in clinical research. The task force began by recognizing four important trends over the past three decades. First, the nature and culture of academic biomedical research have changed, bringing the potential of commercial relevance even to the most fundamental of scientific discoveries. Second, there has been enormous growth in the extent and depth of interactions between research universities and industry, especially in biomedicine. Third, the public has become increasingly impatient that its extraordinary investments in research yield more effective disease preventions and therapies. Fourth, the involvement of academic researchers in the translation of their discoveries has been essential in bringing those discoveries to market and to the benefit of public health. But the task force, in its two reports, asserted that both individual and institutional financial conflicts of interest in clinical research could be problematic [ 4 , 5 ]. It recommended urgent and substantial refinement and strengthening of institutional policies and practices for monitoring, managing, and—when necessary—extinguishing such conflicts. Both reports rest on a common set of core principles. The most important is that institutions should regard all significant financial interests in research involving human participants as potentially problematic. Where such interests exist, there should be a rebuttable presumption that the concerned individual or institution should not conduct the research, absent compelling circumstances. Importantly, the task force, after intense debate, rejected categorical prohibitions lest they unintentionally impede the translation of research discoveries into tangible public benefits. The task force acknowledged that the issue of institutional financial self interests is extraordinarily complex and sensitive, since it touches the very core of institutional autonomy. But the fact that an institution has a financial interest per se should raise a strong presumption against its participation in the clinical testing of that product. Public accountability and scientific integrity require that all research results emanating from academic medicine be as free as possible from the taint of financial compromise. Adding human participants to the research mix should raise the barrier to the highest level and require compelling justification for any participation by a financially self-interested institution. The task force did not define “compelling,” believing that each institution should make that determination based on disinterested scrutiny of the facts and circumstances of each case. For example, there may exist in a given institution a unique capability, without which the proposed research involving human participants could not be conducted as effectively or safely, or at all. In these instances, the public and science deserve access to that capability, provided the necessary safeguards are put in place to mediate the conflicting interests. In all such instances, protection of scientific integrity and the welfare of research participants must remain the foremost priority of both investigator and institution. This narrow window avoids absolute prohibition while striving to prevent institutional participation where credible alternatives exist. Only by such stringent self-policing can we sustain the trustworthiness and credibility of biomedical research, researchers, and their institutions, while continuing vigorously to promote the translation of biomedical discovery for the public's benefit. Ross McKinney's Response to David Korn's Viewpoint The public has every right to expect that academic institutions are working first for the public's interest. This value is even codified in the laws granting these institutions tax-exempt status. The public also expects that new, more effective therapies will be developed swiftly as a consequence of its support for academic research. The inventor of a new technology is always more motivated to see it through to widespread use than anyone else. This motivation, which may be as simple and benign as curiosity or as easy to understand as a financial incentive, is a powerful force driving human research. This force can be disciplined and controlled by the IRB and policies on conflicts of interest. Personal investment in research is, nevertheless, an important driver of scientific progress. When society makes an unnecessarily broad assumption that nearly all research with financial implications for investigators or their institution is potentially corrupted, a brake is placed on progress. Society will be better served by establishing clear guidelines and formalizing oversight of the research process than by rigidly limiting clinical research affected by conflict of interest. As examples, clinical trials should have independent data safety monitoring boards charged to review the study design, execution, data analysis, and publication of results. The IRB system should be strengthened in its independence through the use of community members. And, to be certain that institutional conflict of interest is avoided, an IRB from outside the institution in most cases will be preferable Society wants better treatments. The fact that an inventor has mixed motives for developing a new treatment has always been acknowledged. The need to carefully manage the experimental process in human studies has always been understood. However, when rules minimize the role of inventors at academic centers, by forcing trials of their new ideas to go to outside institutions, society loses more than it gains. The incremental gain in safety is likely to be small (particularly if oversight is well established), while the decrease in speed of development will be significant. David Korn's Response to Ross McKinney's Viewpoint There are many similarities between the position espoused by Ross McKinney and my position. Most saliently, we both recognize the critically important role played by academic biomedical scientists in making discoveries and in facilitating their efficient translation into beneficial products. Neither of us proposes that academic investigators, or their institutions, should be flatly prohibited from trying to foster that translation in the presence of financial self interests. But there is an important difference. McKinney's approach for dealing with institutional conflicts of interest depends critically on the engagement of external agents to monitor closely both scientific integrity and the welfare of human participants. That, in my view, would require such deep interposition of those agents into the conduct of academic research as to be not only unprecedented but unfeasible. Beyond that, the approach falls short with respect to the maintenance of institutional trustworthiness and protection of public trust. Routine clinical assessment of technologies by financially interested institutions fosters public cynicism and distrust of the motives of academic biomedical researchers. The protective mechanisms recommended by McKinney are opaque to the public and reflect a “business as usual” image that fails fully to account for the markedly changed circumstances and perceptions of academic biomedical research. Most important, the mechanisms appear to be aimed primarily at protecting institutions' financial interests. By contrast, the American Association of Medical Colleges formulation urges that any institutional involvement in clinical research involving human participants in the presence of financial conflicts must be predicated on the presence or absence within the institution of demonstrably unique capability. This approach offers a much higher and more credible standard that aims to protect not only participant well-being and scientific integrity, but also institutional trustworthiness and public trust.

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535901

Evaluation of an inter-professional workshop to develop a psychosocial assessment and child-centred communication training programme for paediatricians in training

Background The quality of psychosocial assessment of children in consultations varies widely. One reason for this difference is the variability in effective mental health and communication training at undergraduate and post-qualification levels. In recognition of this problem, the Royal College of Paediatrics and Child Health in the United Kingdom have developed the Child in Mind Project that aims to meet this deficit in medical training. This paper describes the evaluation of a workshop that explored the experiences and expectations of health care professionals in the development of a training programme for doctors. Methods The one-day inter-professional workshop was attended by 63 participants who were invited to complete evaluation forms before and immediately after the workshop. Results The results showed that the workshop was partially successful in providing an opportunity for an inter-professional group to exchange ideas and influence the development of a significant project. Exploring the content and process of the proposed training programme and the opportunity for participants to share experiences of effective practice were valued. Participants identified that the current culture within many health care settings would be an obstacle to successful implementation of a training programme. Working within existing training structures will be essential. Areas for improvement in the workshop included clearer statement of goals at the outset and a more suitable environment for the numbers of participants. Conclusions The participants made a valuable contribution to the development of the training programme identifying specific challenges. Inter-professional collaborations are likely to result in more deliverable and relevant training programmes. Continued consultation with potential users of the programme – both trainers and trainees will be essential.

Background Childhood mental health disorders are common. A Department of Health survey of 5 to 15 year-olds in England and Wales, 5% had conduct disorder, 4% had emotional disorders and 1% rated as hyperactive [ 1 ]. However, during consultations the psychosocial assessment of children is sometimes compromised. The reasons are varied and most often reflect deficits in relevant knowledge, attitudes and skills of health care professionals. Children are often not placed at the centre of the consultation [ 2 - 7 ]. Further, health care professionals have been found to lack knowledge in pain management of children [ 8 , 9 ] and are poor at giving information to children and adolescents with cancer [ 10 ] which may lead to poor adjustment to illness and emotional problems. The consequences for children and their families can be profound. Knowledge of psychosocial and mental health problems is only part of the patient assessment process. The ability to communicate effectively with the patient is pivotal for accurate assessment [ 11 - 13 ]. A unique feature of the paediatric interview is its triadic structure. That is, consultations often involve a child, their parent and a doctor. However, research in paediatric interviewing usually deals with one dyad (parent-doctor), sometimes two dyads (parent-doctor and child-doctor) or even three dyads (parent-doctor and child-doctor and parent-child) rather than a triad of parent-doctor-child [ 14 ]. New graduates in the United Kingdom are expected to be able to "communicate clearly, sensitively and effectively with patients and their relatives" [ 15 ]. Teaching and learning about medical interviewing is now part of mainstream undergraduate medical education [ 16 ]. Acquisition of medical interviewing skills should not stop at graduation. Continuing professional development supports the maintenance and extension of patient-centred interviewing skills. Although summative assessments of interviewing skills are well established in some Royal Colleges [ 17 ], they are being incorporated in others [ 18 ]. Training programmes to support doctors in these summative assessments are developing simultaneously. In the United Kingdom there is scant evidence that medical interviewing programmes at any level consider the specialised skills required for triadic interviewing associated with paediatric consultations. However, studies from Europe and North America report educational interventions that incorporate triadic interviewing [ 19 - 21 ] while others focus on the dyad (e.g. doctor-parent; doctor-child; doctor-adolescent) [ 22 , 23 ]. In order to address these two issues, the Child in Mind Project at the Royal College of Paediatrics and Child Health (RCPCH) aims to develop psychosocial awareness and interviewing skills of paediatricians (in training). This will improve the assessment and management of psychosocial issues that effect children. To achieve this goal, a modular training programme in child and adolescent mental health is proposed. The programme will be piloted with senior house officers (SHOs) on paediatric rotations. Building additional modules into existing SHO training programmes is problematic since clinical and other commitments already consume a shrinking working week. Therefore, a key consideration in developing the training modules is to work within existing SHO training by maximising both planned and opportunistic teaching and learning in psychosocial care and interviewing skills appropriate for working with children, adolescents and their families. At an early stage in the project, the team thought it appropriate to elicit ideas and experiences of interested professionals as well as recruit individuals to help with developing the project. An open invitation to attend a one-day workshop held at the RCPCH was advertised in relevant professional newsletters for paediatricians, child psychiatrists, and child psychologists and by word of mouth in other disciplines. The invitation stated that the workshop would elicit the views of an inter-professional group interested in developing a training programme to improve psychosocial assessment and child-centred communication skills of doctors. Participants were chosen from twice the number of applicants to represent a balance of disciplines and geographical spread. Selection within these criteria was made on the order of receipt of applications. Numbers were limited by the capacity of the College facilities. This paper describes the evaluation of the inter-professional workshop in the development of the training programme. Description of workshop The opening plenary session introduced the Child in Mind Project together with the aims of the workshop (Table 1 ). These aims reflected the preliminary work the Project team had undertaken as well as their areas of content expertise. Four parallel group sessions then focused on core content and process issues for paediatric trainees: communication and interviewing skills, management of children and adolescents with recurrent aches and pains and intentional overdose. Topic experts were invited by the RCPCH to facilitate each group. The structure and methods used in the groups varied. Participants were allocated to specific sessions so that there were approximately equal numbers representing all disciplines present in each group. Table 1 Participants' ratings of the helpfulness of the sessions in meeting the aims of the workshop (n = 28) Not at all helpful Partially helpful Very helpful Plenary Session 1 14 11 Introduction, background, aims Group Sessions – Content 1. How can we teach communication & interview skills? 3 2. How can we teach communication & interview skills? 1 3 3 3. How can we teach the management of recurrent aches and pains? 2 7 4. How can we teach the management of intentional overdose? 4 2 Plenary Session 2 2 17 6 Feedback from morning sessions, planning for afternoon Group Sessions – Process 1. How can we get trainees to role-play? 4 2 2. How can we combine traditional teaching and learning methods with new technology? 2 1 3 3. How can we integrate child mental health into existing training programmes? 7 3 4. How can we assess paediatric trainees in child mental health? 1 5 Plenary Session 3 11 6 Feedback from afternoon sessions, conclusions, action Immediately after lunch, a plenary session was held in which the key issues from the morning sessions were shared with the entire group. The four parallel sessions that followed focused on core process issues in training: promoting role-play, introducing technology, integrating new with existing programmes, and assessment. The final plenary session provided an opportunity for groups to share their experiences and then a wider discussion considered key issues from both morning and afternoon sessions. An action plan was devised based on this discussion and was shared with participants providing an opportunity for participants to continue to be involved in the project. Methods All participants were invited to complete evaluation forms immediately before and after the workshop. The pre workshop form explored participants' reasons for attending, their expectations, their most important issue in relation to the workshop, their experiences in learning about communication and education, their current role, age and sex (Figure 1 ). The post workshop form was divided into two parts (Figure 2 ). The first part asked participants' about their experiences of the workshop, the most important issue that was covered, the degree to which their expectations were met, the aspects of the workshop that went well and those that could have been improved. The second part asked them to rate each session in relation to whether it was helpful in meeting the aims of the workshop. All responses were anonymous. Figure 1 Pre-Workshop Evaluation Form Figure 2 Post-Workshop Evaluation Form Results Sixty-three participants attended the workshop of whom 23 were clinical psychologists (36.5%), 18 paediatricians (28.6%), 9 psychiatrists (14.3%), 9 nurses (14.3%) and one representative from each of the following professions: social work, education, play therapy and occupational therapy (6.4%). Forty-one participants were female (65.1%) and 22 were male (34.9%). Approximately twenty participants (31.8%) left the workshop immediately prior to the closing plenary session. This was unexpected and apparently not triggered by anything more than a need to catch commuter and intercity trains. That is, the exodus seemed unrelated to the quality of the meeting. Twenty-two participants (34.9%) completed the pre workshop form and 28 completed the post workshop form (44.4%). Pre workshop evaluation Of the 22 respondents completing the pre workshop form, 17 were female (77.3%) and 5 were male (22.7%) with an age range of 34 to 58 years and mean age of 45. Although the group were inter-professional, the respondents were predominantly medical with 10 paediatricians (45.5%), 5 child and adolescent psychiatrists (22.7%), 4 clinical psychologists (18.2%), one occupational therapist (4.5%) and one educator (4.5%). Nineteen (86.4%) participants reported previous formal training in communication as part of undergraduate, post-graduate and continuing professional development. Training included theoretical and skills practice within and outside of paediatrics at fundamental (e.g. presentation skills, psychology training) and advanced levels (e.g. Balint groups, psychology training, bereavement). Eighteen participants (81.1%) reported at least some previous formal training in education. Reasons for attending the workshop Participants' reasons for attending the workshop were diverse and included a strong interest and or experience in the major themes of the workshop – assessment and management of psychosocial issues in paediatrics, paediatric interviewing and training. To enhance the "voice of the child" in paediatrics. (4) I have a long standing interest and involvement in the teaching of junior paediatricians the skills of communication, family therapy and management of behavioural and emotional issues. I have been trying to find ways of formalizing mental health training for paediatricians. (1) Because I have a very real interest in improving the awareness and training of paediatricians in the psychosocial aspects of paediatrics. (7) To participate in the development of paediatric psychological training. (17) To help develop teaching of mental health issues in childhood. (18) Having worked in the area of paediatric psychology for some years, I am particularly interested in developing the awareness of paediatricians re psychological issues. (19) To contribute to the planning of teaching paediatricians how to tackle social and emotional issues. (20) To better understand needs of paediatricians for training in psychological needs of children and families. (11) Some participants wanted to develop existing local programmes. To feedback ideas to our paediatric College/Clinical Tutor who did not get a place at the course. (12) To try and improve our in-house teaching of psychological factors in paediatrics. (13) One participant acknowledged a deficit in current training. I realize we are generally very poor at integrating psychological aspects of child and family health into the busy acute training programme. (2) Expectations of the workshop The second question asked participants what they were expecting from the workshop. Various themes emerged and included the generation of ideas for the Child in Mind project generally and specifically in the development of training materials. Participants expected to be able to exchange ideas on what and how to change existing training and there was an expressed desire not only to influence these developments but to ensure they are deliverable. To meet, share and hopefully influence colleagues. (17) To participate in putting together relevant training modules and to have a voice in the future training of paediatricians. (19) To ensure that programmes will be acceptable. To broaden ideas around what to include in communication programme. (5) To offer my experience in direct work with children, adolescents and families. (9) An understanding of where the project is so far – aims, methods, plans. A chance to contribute. (14) A second theme related to expectations of inter-professional collaboration both in the development and delivery of the training module and the third and overlapping theme focussed on the opportunity for networking. Decrease inter-professional tension and enhance collaboration. (4) Participants' perceptions of the most important issue to be addressed in the workshop reflected their different expectations. Training issues were dominant and focused on both the content and process. Content issues included thinking about ways of raising the significance of the assessment and management of psychological problems in children and adolescents together with the need to identify the child and adolescent's perspective separately to their family's and health care professionals. Developing a culture of respect for child and family. Accessing children's thoughts and feelings independently of their parents or other professionals. (4) Process issues included identifying ways to maximise existing expertise, to use limited resources efficiently, to encourage participation from paediatric trainers and trainees and to consider assessment and evaluation as integral to the training programme. Post workshop evaluation Twenty-eight (44.4%) participants completed the post workshop form. Demographic data was not collected. The "did not attend" option on the evaluation form is not included in Table 1 because participants did not use it. Instead, they indicated the parallel session that they attended by rating it. Ratings were consistent with the numbers of forms received. That is, 25 (89.2%) for plenary sessions 1 & 2 and the morning group sessions while 28 (100%) rated the afternoon group sessions and 17 (60.7%) rated plenary session 3. Using a 3-point scale from not at all, partially to completely, participants rated the helpfulness of the sessions in meeting the objectives of the workshop (Table 1 ). The majority of participants rated the sessions as at least partially helpful. The qualitative data provided insight into participants' ratings. Most important issues Respondents were asked to identify the most important issues that they thought had been addressed in the workshop. Several participants wrote of the need to change the existing culture to one in which psychosocial assessment and communication skills are valued. There was also acceptance of diversity in workplaces and the training offered therein. To ensure that the new training programme is deliverable it must be sufficiently flexible to fit within these diverse settings and that it must be evaluated. The need for training both supervisors and trainers was considered requisite for implementing any programme. Learning about the hospital paediatric culture and previous difficulties of teaching SHOs and getting the culture right. (20) The importance of mental health teaching/learning for all doctors caring for children/families. (2) Delivering training for trainers and that the child mental health programme needs to be integrated into existing paediatric training. (3) Need to address appropriate training and supervision of SHOs and for consultants to be trained first themselves. (5) The importance of introducing a general shift. The extreme inflexibility of the system as a whole. (9) The realities of teaching busy SHOs who are preoccupied with passing exams. (14) Changing culture of consultants to understand importance of training for mental health and communication skills. (24) Some participants valued the opportunity to learn about existing effective practices while others gave consideration to who should teach, how and that whatever is taught must be relevant. The importance of taking a full history and empowering SHOs to ask difficult questions, to reflect on their practice and to have supervision in order to understand what to do with the information they have gathered. (17) Introduction of video review of consultations/interactions with children and parents to paediatrics. (22) Meeting expectations Participants used a 3-point scale from not at all, partially to completely to rate the degree to which they met their expectations. Eight participants completely met their expectations (28.6%) while twenty participants (71.4%) partially met their expectations. What worked well In response to being asked what worked well in the workshop, participants identified the opportunity to exchange ideas with colleagues with different levels of experience, who work in different settings and have different professional backgrounds. The group sizes for sessions were valued since they were sufficiently small to enable several participants to express their views and large enough for diverse experiences to be shared. The plenary sessions were helpful in summarising group sessions and consolidating broad ranging issues. The enthusiasm of delegates was thought to contribute to the success of the workshop together with the relaxed atmosphere and the genuine desire of participants and organisers to change existing practices. Improvements to workshop Most participants recorded at least one response to being asked how the workshop could be improved. The single most frequently cited issue related to the venue. Groups were too large for their rooms and for two groups, their presence in the same large room impeded discussion. Other improvements included stronger facilitation in some groups to ensure all views were heard and that the discussion stayed focused. Providing delegates with basic information prior to the workshop on the aims, objectives and content of group sessions could have improved the quality of the discussions. Participants expressed a desire to attend the group sessions of their choice. One participant thought that the workshop was too rigidly organised between content and process and that this limited creativity in thinking about training. Two participants suggested including SHOs for whom the training will initially be delivered. I felt unable to contribute much of my experience and knowledge with the tight preset agenda. I was particularly wanting to discuss raising awareness of child protection issues, and working with children in complex and or chaotic home situations. Also were there many current paediatric SHOs here today. If not, there should have been. If so, could they have contributed more? (17) Sign up for preferred workshops on arrival – I don't remember what preferences I indicated but they certainly weren't the ones I was allocated. More general paediatricians – meeting seemed to be dominated by psychiatrists/psychologists (23) The workshop was planned with definite areas for discussion and a very strong split between content and process. This defined structure led to cramping of ideas (27) Maybe including some real SHOs just for the occasional reality check (14) Discussion The workshop was valuable in contributing to the development of the Child in Mind Project training programme. The content and process of the programme were explored and several issues emerged that will need serious consideration by the Child in Mind project team. These include the strongly expressed need for a change in culture within the health care system that will embrace child-centred mental health care. The magnitude of change required is uncertain but may well be extensive given evidence that a study based in general practice in The Netherlands reported that the inclusion of the child in all phases of the consultation was "limited" with parents frequently speaking for the child, the child not questioning the parent, and the GP supporting this behaviour by minimal exploration of meta-communicative behaviours. The authors described this process resulting in a dyadic emphasis as being "institutionally co-constructed" [ 24 ]. Ways to change the health care culture in the United Kingdom were not explicitly identified. However, the project teams' desire to implement the training programme in a few centres that were already enthusiastic suggests that creating centres of best practice is inherent in their approach for change. This supports theoretical approaches for effective institutional change [ 25 , 26 ]. That is, implementation commences in sites receptive to change before introducing change on a wider scale having already demonstrated positive outcomes. One outcome of the workshop was the identification of individuals willing to trial the new programme with their trainees. These individuals work in centres with different structures and functions in the health care system so will prove valuable in evaluating how deliverable the programme is in different types of settings. The inter-professional nature of the workshop was beneficial in exchanging views from different perspectives. This supports the findings of the few studies in medical curriculum development that reports this approach [ 27 ]. Most participants acknowledged the importance of continuing the consultation process although there was no attempt to agree on format. The importance of regular consultation with the principal users of the training programme – the senior house officers – will be essential to ensure that the programme is deliverable within the diverse settings in which they learn and work. Although consultation with other stakeholders (children, adolescents and their families) was not identified by this group, it is important that they are also included in the development and evaluation of the training programme. Community participation – especially of key stakeholders, is often lacking in all phases of professional education (development, implementation and evaluation). In order that the training can best meet the needs of its intended targets their voices should be considered. The medical education literature strongly supports inclusion of patient voices in all aspects of curricula development [ 28 - 30 ]. The importance of training the trainers of the programme was identified as key to success of implementation. Although agreement was not sought, there was a powerful sentiment that trainers should be inter-professional. This notion may also address cultural barriers that relate to doctors' lack of understanding of other health care professional roles by exposing them to trainers who have mental health assessment and/or communication skills expertise. The nature of support provided to trainers may vary reflecting the diverse settings in which the training programme will eventually be implemented. There appeared to be agreement that the workshop was not an appropriate forum for identifying the details of content and process of the training programme. Rather core issues were identified in psychosocial assessment, mental health and communication. Effective approaches to learning patient-centred communication skills are labour-intensive (videotaped interviews with feedback) [ 31 , 32 ] so maximising the benefits of such activities will be essential. The literature reports examples of communication skills programmes for trainee paediatricians [ 19 , 21 ] as well as other doctors and health care professionals who work with children [ 20 , 22 , 23 , 33 ] that address diverse issues. Common to many of these programmes is the use of simulated patients and parents incorporating critiquing of videotapes. This may provide valuable guidance in selecting educational methods that are effective and can be delivered in different settings. Ensuring that the training programme incorporates principles of work-based and other adult-learning approaches are essential [ 34 - 37 ]. The purpose of eliciting participants' reasons for attending and their expectations of the workshop is to help make sense of their satisfaction afterwards. Although the invitation outlined the purpose of the workshop, participants came with varied views that to some extent reflected their level of experience, their unique professional perspective and their interpretation of the information provided in the invitation. However, there was an overarching expectation that each would contribute to the development of a training programme. It is important to reflect on the reasons that only 28.6% of the participants reported that their expectations were completely met. The suggestions given for improvements offer insight into why more participants did not meet their expectations. Restating the project team's aims at the commencement of the workshop may have been helpful. Although some participants felt able to express their views others were unable to do so because of the structure of sessions, the way in which they were facilitated and the settings in which the discussions took place. Providing a more open forum for discussion may have generated different ideas. The breadth and depth of the "culture change" some participants consider essential for implementation of the training programme is extensive and is likely to have influenced their judgement as to what could be realistically achieved both in the workshop and the training programme. The physical limitations of the workshop impeded discussion in some groups. Although group sizes were thought appropriate, providing spaces in which they could work will need to be considered in future workshops. Limitations of the evaluation There are several limitations with this evaluation project some of which were beyond the control of the evaluator (DN). • Higher response rates may have improved the quality of the evaluation. It is possible that respondents differed to non-respondents which may influence the results in someway although it is difficult speculate how. • Scheduling the evaluation forms as part of the workshop may have increased response rates and may also help participants to focus on their expectations immediately before the meeting and then afterwards in considering what they achieved. • The low response rate in relation to the final plenary session may be explained by the request to complete the forms immediately after the workshop. It is possible that some participants wanted more time to reflect on their experiences. It may have been more helpful to contact participants after the workshop. • Further, the responses may not represent the diversity of opinions expressed during the workshop nor were the professional groups equally represented in the evaluation forms. For example, no nurses completed the pre workshop evaluation form. It is unclear why this was the case as all respondents were equally encouraged to complete the forms. Future evaluations of workshops attended by disparate groups may consider: • Scheduling the completion of evaluation forms into the workshop timetable • Using identifiers to link pre and post workshop evaluation forms • Following-up participants some time after the workshop to elicit their considered views Despite these methodological weaknesses, the evaluation offers useful insights to the management of an inter-professional workshop for curricula development. Conclusions The workshop provided the Child in Mind project team with valuable insight relevant to the development of a deliverable training programme in mental health and communication. This was an adequate forum in which the ideas and experiences of an interested inter-professional group could contribute although there were several ways in which this could have been improved. The diversity of the settings in which the programme will be delivered was highlighted as was the need for cultural change and support not only for trainees but the trainers themselves. Continued consultation with this inter-professional group together with broadening the consultation process to include other stakeholders may lead to the development of an effective training programme. Commencing the programme in sites with clinicians who are receptive to change of this nature is likely to influence its' success. Evaluation will continue to be essential to monitor the process. The enthusiasm of the participants needs to be harnessed to ensure that the long-term goals of the project team will be met. Competing interests The author(s) declare that they have no competing interests. Authors' contributions All authors contributed to each phase of the project although DN took a lead role in writing the paper. DN was responsible for the evaluation while ST and QS were instrumental in the development of the workshop. Pre-publication history The pre-publication history for this paper can be accessed here:

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523839

Characterization of T Lymphocytes in Chronic Obstructive Pulmonary Disease

A new study adds to the mounting evidence implicating T cells as an important component of the inflammation in chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a global epidemic of major proportions that is predicted to become the third most common cause of death and fifth most frequent cause of chronic disability by 2020. In developed countries it is mainly caused by cigarette smoking, but the reasons why only a proportion (10%–20%) of smokers develop progressive airflow limitation is currently unknown. The disease is characterized by a chronic inflammatory process predominantly in the small airways and lung parenchyma, with increased numbers of macrophages, neutrophils, and T lymphocytes [ 1 ]. The difference between smokers without COPD and smokers with COPD appears to be the intensity rather than the nature of the inflammatory process. This inflammation in the small airways is associated with fibrosis and increases with the severity of airflow limitation [ 2 ], which has led to the view that COPD represents an amplification of the normal inflammatory response to inhaled irritants such as cigarette smoke. T Lymphocytes in COPD T lymphocytes were first reported to be increased in patients with COPD by Finkelstein and colleagues, who showed a correlation between the number of T lymphocytes/mm 3 of lung and the extent of emphysema [ 3 ]. It was later shown that both CD4 + (T helper) and CD8 + (suppressor/cytotoxic) T cells were increased in the airways and lung parenchyma of patients with COPD, with a predominance of CD8 + cells [ 4 , 5 ]. This is in contrast to the findings in asthma, in which there is a predominance of CD4 + cells, which are predominantly of the T helper 2 (Th2) pattern, with increased expression of interleukin (IL)-4, IL-5, and IL-13 (see Glossary), and which are associated with an increased number of eosinophils. In smokers who develop COPD there appears to be activation of adaptive immunity, with the infiltration of CD8 + and CD4 + cells in the alveolar walls and small airways and—in patients with the most severe disease—the presence of lymphoid follicles that contain a core of B lymphocytes surrounded by T cells [ 2 ]. This activation presumably follows on from the initial and then sustained innate immune response characterized by increased numbers of macrophages and neutrophils; it may involve the migration of dendritic cells from the epithelium to the local lymph nodes and presentation of antigenic substances to T cells, resulting in clonal expansion of CD4 + and, to an even greater extent, CD8 + cells. The study by Grumelli et al. (2004) published in this issue of PLoS Medicine takes the story forward [ 6 ]. The CD4 + and CD8 + cells appear to be fully activated, as they would be after being presented with antigens, and they show predominantly a T helper 1 (Th1)/cytotoxic T 1 (Tc1) pattern, with increased expression of interferon-γ (IFN-γ) and Th1 chemokines. This is consistent with the recent demonstration of increased expression of IL-12 in bronchial biopsies of patients with COPD and activation of the transcription factor STAT-4 in T cells, subsequent STAT-4 nuclear translocation, and IFN-γ gene induction, and thus a Th1 commitment in the T cells [ 7 ]. As well as producing the cytokines IL-2 and IFN-γ, Th1 and Tc1 cells also express the chemokine receptor CXCR3 and the ligands that activate this receptor, IFN-γ inducible protein 10 (IP-10, CXCL10), monokine induced by IFN-γ (CXCL9), and IFN-inducible T cell α chemoattractant (CXCL11). There is an increase in the expression of IP-10 in the airways of patients with COPD and an increase in the number of CXCR3 + cells [ 8 ]. CXCR3 is expressed on Th1/Tc1 cells, macrophages, and epithelial cells. Release of CXCR3-activating chemokines would attract Th1 and Tc1 cells into the lungs, and these cells then release IFN-γ, which releases more CXCR3 chemoattractants. This results in a self-perpetuating loop that may lead to accumulation of activated Th1 and Tc1 cells in the peripheral lung ( Figure 1 ). Figure 1 In Emphysema, a Self-Perpetuating Loop May Lead to Accumulation of Activated Th1/Tc1T Cells in the Peripheral Lung Role of Cytotoxic T Cells It is likely that Th1 cells are the major source of IFN-γ in the lungs of patients with COPD and therefore drive and maintain the T cell response and promote an “immune inflammation” with neutrophils and macrophages. However, it is the role of Tc1 cells that is of particular interest, as these cells are cytotoxic to epithelial cells through the release of granzymes and perforins, which induce apoptosis. Increased concentrations of perforins have recently been reported in the sputum of patients with COPD [ 9 ]. In support of this idea there is an increase in the apoptosis of alveolar cells in the lungs of patients with COPD, and this is correlated with the number of CD8 + cells and the severity of emphysema [ 10 ]. T Cell Perpetuation The T cell inflammatory response appears in mild COPD but increases markedly with disease severity. It is possible that the initial immune response becomes self-perpetuating because of endogenous autoantigens resulting from inflammatory and oxidative lung injury. There are also antigens in tobacco, but the inflammatory response appears to become independent of smoking status, and there is intense inflammation even in patients who stopped smoking many years previously [ 2 ], as seen in the present study by Grumelli et al. [ 6 ]. Another possibility is that this chronic immune response is driven, or at least maintained, by chronic infection of the respiratory tract often seen in patients with severe disease, in which there is increased colonization of the lower airways. These infections could act as co-stimulators, or by antigenic mimicry or as polyclonal activators they could provide a persisting antigenic stimulus and maintain the inflammatory process. Further studies on T cell receptor usage and expression of surface markers may give further clues as to the driving mechanisms for the increased Th1 and Tc1 cells in COPD. Proteases COPD is characterized by destruction of the lung parenchyma and loss of elastin due to elastolytic enzymes, such as neutrophil elastase and certain matrix metalloproteinases (MMPs). The predominant MMP in COPD appears to be MMP9, which is released in much larger amounts from alveolar macrophages of patients with COPD than from those of smokers without the disease [ 11 ]. The study by Grumelli et al. showed that CXCR3 ligands led to the expression of the elastolytic enzyme MMP12 in alveolar macrophages and that this process was increased in the lungs of patients with COPD. This finding provides a neat link between T cells and alveolar destruction, but is discrepant with other data that have failed to show significant MMP12 release from macrophages of patients with COPD [ 11 ]. Therapeutic Implications There are currently no treatments that reduce the relentless progression of COPD, and none that have significant anti-inflammatory effects. However the recognition that an adaptive immune T cell response, most likely driven by antigens, may play an important pathophysiological role in the pathogenesis of COPD has important therapeutic implications. It is possible that T cell inhibitory strategies, such as the use of immunosuppressants, might be effective, although side effects may be a problem, and there is particular concern about increasing the risk of bacterial infection. Another approach might be to block the trafficking of Th1 and Tc1 cells to the lungs by blocking CXCR3, and there is now a search for small-molecule inhibitors of these receptors. Inhibition of IFN-γ signaling might be another approach. The mounting evidence implicating T cells, and thus an adaptive immune response, as an important component of the inflammation in COPD is overwhelming. A better understanding of the immune mechanisms involved in COPD is important, since it might lead us to new and more effective therapeutic approaches to this important disease. Glossary CD4 + (helper) T cell: T lymphocyte that enhances the inflammatory response CD8 + (cytotoxic/suppressor) T cell: T lymphocyte that suppresses the inflammatory response CXCR3: Chemokine receptor that is selectively activated by IP-10, monokine induced by IFN-γ, and IFN-inducible T cell chemoattractant Cytotoxic (Tc1) cell: T cell that is characterized by secretion of INF-γ Granzyme: Enzyme released by cytotoxic T cells Interferon-γ inducible protein 10 (IP-10, CXCL10): Chemokine of 10 kDa that selectively activates CXCR3 Interferon-inducible T cell γ chemoattractant (I-TAC, CXCL11): Chemokine that selectively activates CXCR3 Interferon-γ (IFN-γ): Protein secreted by Th1 and Tc1 cells Interleukin-4 (IL-4): Protein secreted by Th2 cells that is important in increasing IgE secretion Interleukin-5 (IL-5): Protein secreted by Th2 cells that is important for eosinophilia Interleukin-12 (IL-12): Protein secreted by antigen-presenting cells that promotes differentiation of Th1 cells Interleukin-13 (IL-13): Protein secreted by Th2 cells that is important for IgE secretion Matrix metalloproteinase (MMP): Proteolytic enzyme that degrades connective tissue MMP9, MMP12: MMPs that destroy elastin fibers Monokine induced by interferon-γ (MIG, CXCL9): Chemokine that selectively activates CXCR3 Neutrophil elastase: Enzyme released from neutrophils that destroys elastin fibers Perforin: Protein released by cytotoxic T cells that induces apoptosis STAT-4: Transcription factor specifically activated by IL-1 T helper 1 (Th1) cell: T lymphocyte that is characterized by secretion of INF-γ T helper (Th2) cell: T lymphocyte that is characterized by increased secretion of the cytokines IL-4, IL-5, and IL-13; characteristically increased in allergic inflammation

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555763

Inhibition of breathing after surfactant depletion is achieved at a higher arterial PCO2 during ventilation with liquid than with gas

Background Inhibition of phrenic nerve activity (PNA) can be achieved when alveolar ventilation is adequate and when stretching of lung tissue stimulates mechanoreceptors to inhibit inspiratory activity. During mechanical ventilation under different lung conditions, inhibition of PNA can provide a physiological setting at which ventilatory parameters can be compared and related to arterial blood gases and pH. Objective To study lung mechanics and gas exchange at inhibition of PNA during controlled gas ventilation (GV) and during partial liquid ventilation (PLV) before and after lung lavage. Methods Nine anaesthetised, mechanically ventilated young cats (age 3.8 ± 0.5 months, weight 2.3 ± 0.1 kg) (mean ± SD) were studied with stepwise increases in peak inspiratory pressure (PIP) until total inhibition of PNA was attained before lavage (with GV) and after lavage (GV and PLV). Tidal volume (V t ), PIP, oesophageal pressure and arterial blood gases were measured at inhibition of PNA. One way repeated measures analysis of variance and Student Newman Keuls-tests were used for statistical analysis. Results During GV, inhibition of PNA occurred at lower PIP, transpulmonary pressure (Ptp) and Vt before than after lung lavage. After lavage, inhibition of inspiratory activity was achieved at the same PIP, Ptp and Vt during GV and PLV, but occurred at a higher PaCO 2 during PLV. After lavage compliance at inhibition was almost the same during GV and PLV and resistance was lower during GV than during PLV. Conclusion Inhibition of inspiratory activity occurs at a higher PaCO 2 during PLV than during GV in cats with surfactant-depleted lungs. This could indicate that PLV induces better recruitment of mechanoreceptors than GV.

Background Partial liquid ventilation (PLV) combines liquid ventilation and gas ventilation (GV). Perfluorocarbon is administered to the trachea in a volume equivalent to the pulmonary functional residual capacity, and ventilation is maintained with conventional gas ventilation of the liquid-filled lung [ 1 ]. The improvement of gas exchange during PLV is mainly due to recruitment of collapsed alveoli [ 2 ], decreased physiological shunting and increased compliance [ 3 ]. During breathing of gas the rate and depth of breathing is influenced by mechanoreceptors in the lung [ 4 - 6 ], and by peripheral and central chemoreceptors, which modulate the phrenic motoneurone output representing central inspiratory activity [ 7 ]. An increase in tidal volume and flow rate during mechanical ventilation with gas results in a decrease in magnitude or duration of the phrenic nerve signal [ 8 , 9 ], with absence of that response after vagotomy [ 8 ]. It has been shown that inhibition of inspiratory activity can be achieved with air with high frequency positive pressure ventilation (HFPPV) [ 10 ] at ventilatory frequencies of 60–100/min and with different positive end-expiratory pressures (PEEP) and insufflation periods in animals [ 11 ] and in humans [ 12 ] at normo-ventilation. To achieve inhibition of phrenic nerve activity (PNA) during ventilation with air at lower ventilatory frequencies than 60, a lower arterial PCO 2 and a higher pH will be needed [ 11 ]. No studies have been presented concerning PNA during PLV, but it has been shown in studies of animals that spontaneous breathing can take place during PLV with beneficial physiological effects [ 13 , 14 ]. Inhibition of PNA can thus provide a physiological setting at which ventilatory pressures, volumes and arterial blood gases can be compared during GV and during PLV in surfactant-depleted animals. This study was therefore undertaken to determine whether inhibition of PNA can be achieved at the same airway or transpulmonary pressures during GV and PLV and to find out at what levels of arterial blood gases and pH inhibition occurs with these modes of ventilation in cats with healthy and surfactant-depleted lungs. Methods Animal Preparation Juvenile cats (n = 9; mean ± SD; age 3.8 ± 0.5 months, weight 2.3 ± 0.1 kg) were anaesthetised with chloroform, placed in a supine position and endotracheally intubated (tube 4.0 mm inner diameter). The tube was then connected to an infant ventilator (Stephanie ® , F. Stephan Biomedical Inc., Gackenbach, Germany) and the animal was placed on assist control (A/C) ventilation during the surgical procedures with the following settings: peak inspiratory pressure (PIP) 1.0 kPa, positive end-expiratory pressure (PEEP) 0.3 kPa, inspiratory time (Ti) 1 sec, respiratory rate (RR) 30/min. The right femoral vein and artery were dissected and catheters were inserted with the tip of each catheter placed in the thorax close to the heart. Anaesthesia was continued with 0.72% α-chloralose (Sigma-Aldrich Chemie GmbH, Steinheim, Germany) (50 mg/kg) and maintained at regular intervals via the venous line. A continuous infusion of 10% glucose (2/3) and 5% 0.6 M sodium bicarbonate (1/3) was given at a rate of 6.4 mL/kg/h (7.15 mg/kg/min of glucose) through the venous catheter throughout the experiment. The arterial line was used for continuous monitoring of blood pressure and intermittent determination of blood gases (Acid-Base Laboratory ABL 505 ® , Radiometer Corp., Copenhagen, Denmark). The cat's core body temperature, measured as deep rectal temperature, was maintained at 38°C by a heating blanket and an overhead warmer. A pretracheal midline incision was performed for preparation of the trachea, the oesophagus and both phrenic nerves. A tight ligature was tied around the trachea in order to prevent air leakage around the tube. An 8 French catheter with an oesophageal balloon (40 × 7.5 mm; flat frequency response up to 5 Hz) was inserted into the distal part of the oesophagus and a ligature was softly tied around the oesophagus to avoid air entrance into the stomach [ 15 ]. Both phrenic nerves were exposed and the connective sheath was removed. The intact right phrenic nerve was then placed on two platinum electrodes. For reasons of isolation the phrenic nerves, the electrodes and the dissected area were submerged in mineral oil [ 16 ]. Measurements and data collection Airflow was measured by a sensor placed between the endotracheal tube connector and the Y connector of the tubing circuit of the Stephanie ® infant ventilator. This sensor is a pneumotachometer with the dynamic properties of an original Fleisch 00 pneumotachograph, but with less dead space (0.6 ml) and resistance (1.1 kPa/l/s at 5 L/min) [ 17 ]. Airflow was calibrated with a precision flowmeter (Timeter RT 200 ® , Timeter Instrument Corporation, Lancaster, PA, USA). Airway pressure (P aw ) was measured at the connector of the endotracheal tube. Oesophageal pressure (P oes ) was recorded from the oesophageal balloon catheter by a pressure transducer (Druck Ltd. Transducer, Leicestershire, UK) and, like P aw , was calibrated with a water manometer. Arterial blood pressure and heart rate were measured using the same type of transducer (Druck Ltd. Transducer, Leicestershire, UK) connected to the arterial catheter with the tip of the catheter at the same level as the transducer. Continuous recordings of arterial blood pressure and heart rate were made with a polygraph recorder (Recorder 330P, Hellige AG, Freiburg, Germany). PNA was amplified, filtered and rectified with a Neurolog system ® (Digitimer Research Instrumentation Inc., Welwyn Garden City, Hertforshire, UK; preamplifier NL 103, AC-amplifier NL 105, filters NL 115). The rectified nerve signal was fed to a spike trigger to produce spikes of uniform amplitude (Digitimer 130 ® and Spike Trigger NL 200, Digitimer Research Instrumentation Inc., Welwyn Garden City, Hertforshire, UK) and subsequently integrated by a resistance-capacitance low-pass filter with a leak (time constant 250 ms), providing a moving time average of PNA. Monitoring of the signals was achieved by means of an oscilloscope (Tektronix Inc., Portland, Oregon, USA). Signals of airflow and P aw were obtained directly from the analogue outlets of the ventilator. Together with signals of P oes and the PNA they were transferred to an analogue-digital converter and recorded on disk at a sampling rate of 10 kHz per channel by a data acquisition system (Windaq Pro+ ® , Dataq Instruments Inc., Akron, OH, USA). Compliance and resistance values were given by the ventilator. Protocol The cats were kept ventilated with air using A/C ventilation and the ventilation was adjusted so that normal arterial blood gases were achieved. The cats were then treated with endotracheal continuous positive airway pressure with 0.3 kPa PEEP in order to monitor and record the spontaneous breathing activity of each cat. Pressure-controlled mechanical ventilation with sinusoidal inspiratory waveform was then initiated with the following settings: RR 60/min; Ti 0.33 sec; PIP 0.8 to 1.0 kPa using a PEEP of 0.5 kPa. PIP was adjusted so that blood gas values were in a normal range. The fraction of inspired oxygen was kept at 0.21. PIP was then gradually increased until rhythmic PNA disappeared. Three breaths after inhibition of PNA, data from 20 consecutive breaths were recorded and arterial blood gases were analysed. Thereafter lung lavage was performed by filling the lungs with warmed saline solution (37.5°C, 30 mL/kg) through a funnel connected to the endotracheal tube. Very gentle chest compressions were performed to allow the saline to be well distributed, before it was removed by suctioning. This procedure was repeated 7 to 8 times and mechanical ventilation was provided in between the lavage procedures. After a 30-minute period of stabilisation on mechanical ventilation (PIP/PEEP 3.0/0.5 kPa, RR 60/min, Ti 0.33 sec, FiO 2 1.0), ventilation was increased until PNA was inhibited. Airway pressures were then recorded and arterial blood gases and pH were measured again. In the next step a bolus of 30 ml/kg prewarmed (38°C) perfluorocarbon (PFC) (Perfluorodecaline ® , F2 chemicals Ltd, Preston, Lancashire, UK) was instilled into the endotracheal tube via an adapter with a side port. Instillation of PFC into the lung was performed within 10 minutes during pressure-controlled ventilation (PIP/PEEP 3.2/0.5 kPa, RR 60/min, Ti 0.33, FiO 2 1.0). Sufficient filling was ascertained by disconnecting the endotracheal tube from the ventilator circuit at the end of the filling procedure and observing to see that a meniscus was present in the endotracheal tube at end-expiration. If no meniscus could be observed prior to recording, additional PFC was instilled. After a stabilisation period of 10 minutes, the cats were studied with the same protocol during PLV as during GV, but with an FiO 2 of 1.0 and a PIP adjusted to blood gases in the normal range. Data on PNA could be recorded and the whole protocol could be completed in all nine cats. Lavage and instillation of PFC were well tolerated, with no coughing or gasping. No bradycardia or arterial hypotension occurred during the procedure. The experiments were performed at the Biomedical Centre of Uppsala University and were approved by the Uppsala University Animal Research Ethics Board (No. C224 / 0). Data Analysis and Statistics Windaq Playback ® Software (Dataq Instruments, Inc., Akron, OH, USA) was used to review the recorded signals. Analysis was done by means of Windaq Playback ® and Excel ® (Office 2000, Microsoft Corporation, USA). For statistical evaluation, Sigmastat ® (SPSS Inc, IL, USA) was used. The amplitude of the integrated PNA was monitored and inhibition of spontaneous breathing activity was defined as total disappearance of PNA, i.e. total inhibition of inspiratory activity. Gas flow, P aw and P oes were measured at peak inspiratory pressures. The airflow signal was integrated to obtain tidal volumes (V t ) at different PIPs. Transpulmonary pressure (P tp ) was calculated as Paw – Pes. Lung compliance (C L ) is given as the ratio of V t over P tp . In three cats an endotracheal tube leak of > 20% of the tidal volume was found, and in those cats no volume values were therefore calculated and consequently no compliance values can be given. After inhibition of PNA, the 20 breaths were evaluated at the three settings studied, i.e. during GV with a normal lung, and during GV and PLV after surfactant depletion. Data are presented as mean ± SD or median and 25th and 75th percentiles. One way repeated measures analysis of variance (ANOVA) or RM ANOVA on ranks was performed to test for differences between the three groups. Student-Newman-Keuls tests were applied for comparisons between two groups when a difference was detected by ANOVA. The level of significance was set at p < 0.05 in all tests. An a posteriori power analysis revealed that the study had a power of 99% to detect a difference in PIP between healthy and surfactant-depleted lungs during GV, and of 100% to detect such a difference between healthy and liquid-filled lungs (n = 9). The power values for detecting differences in tidal volume between the same groups were 98% and 61% respectively (n = 6). Results Inhibition of PNA could be achieved in all cats during GV and PLV both before and after lavage at the applied ventilatory frequency of 60/min, insufflation time 0.33% of the period time and PEEP of 0.5 kPa. Figure 1 shows examples of recordings before and at inhibition of spontaneous breathing after lavage during GV (A and B) and during PLV (C and D) in one representative cat. Figure 1 Recording before and after inhibition of breathing. Recording of airway pressure (P aw ), oesophageal pressure (P oes ) and phrenic nerve activity (PNA) before inhibition of spontaneous breathing in a representative cat after lung lavage during gas ventilation (GV) (A) and during partial liquid ventilation (PLV) (C), and at inhibition during GV after lung lavage (B) and during PLV (D). Ventilatory parameters and lung mechanics Inhibition of PNA occurred at a lower PIP (Table 1 ), a lower P tp and lower tidal volumes (Table 1 and Fig. 2 ) before lavage than after lavage. Compliance at inhibition of inspiratory activity was higher before than after lavage (Table 1 and Fig. 2 ). Resistance was lower before than after lavage during GV. Table 1 Ventilatory parameters, lung mechanics and arterial blood gases at inhibition of spontaneous breathing GV PLV before lavage after lavage after lavage p PIP (kPa) 1.3 ± 0.2 2.8 ± 0.6* 2.9 ± 0.6* *<0.001 P tp (kPa) 0.98 ± 0.2 2.36 ± 0.7 * 2.46 ± 0.6* *<0.001 V t (ml/kg) 10 ± 1.2 17 ± 2.6* 19 ± 5.6* *<0.02 C L (ml/kPa) 41.5 [34;47] 18 [16;25]* 17 [14;20]* *<0.05 Resistance (kPa/L/s) 2.58 ± 0.59 4.94 ± 0.54* 5.49 ± 0.59 *‡ *<0.001 ‡ = 0.038 pH 7.42 ± 0.05 7.38 ± 0.07 7.33 ± 0.8* * = 0.008 PaCO 2 , kPa 5.5 ± 0.9 5.2 ± 0.6 6.3 ± 1.7‡ ‡ = 0.027 PaO 2 , kPa 14.1 ± 1.8 11.0 ± 6.0 29.2 ± 17.1*‡ * = 0.01 ‡ = 0.01 BE 1.71 ± 1.47 -2.08 ± 2.97* -1.89 ± 3.95* *<0.001 * different from GV before lavage ‡ different from GV after lavage Mean ± SD; RM ANOVA and Student-Newman-Keuls Tests or RM ANOVA on ranks with 25 and 75 percentiles (for compliance values only) PIP = peak inspiratory pressure; P tp = transpulmonary pressure; V t = tidal volume; C L = lung compliance; BE = base excess Figure 2 Lung mechanics and blood gases. Multipanel figure showing (A) transpulmonary pressure; (B) tidal volume; (C) lung compliance; (D) resistance; (E) arterial pH; (F) arterial pCO 2 during gas ventilation (GV) before lavage and during GV and partial liquid ventilation after lavage in each cat (unbroken lines) and as mean (broken line). After lavage, PIP and P tp were similar at inhibition during GV and during PLV. After lavage, compliance at inhibition remained the same during GV and PLV and resistance was lower during GV than during PLV (Table 1 and Fig. 2 ). Figure 3 shows the pressure-volume loops at inhibition during GV before lavage and during GV and PLV after lavage in a representative cat. The loop obtained before lavage shows the highest compliance, whereas the loop obtained during PLV after lavage shows the highest resistance. Figure 3 Pressure-volume curves. Pressure-volume curve during (A) gas ventilation (GV) before lavage and (B) during GV and partial liquid ventilation (C) after lavage in a representative cat. Arterial blood gases Before lavage, inhibition of PNA during GV occurred at an arterial pH of 7.42, which did not differ significantly from the post lavage arterial pH at inhibition of PNA. There was no statistically significant difference in arterial pH at PNA inhibition between GV and PLV. At inhibition of PNA the arterial PCO 2 was lower during GV before lavage than after lavage, but was higher during PLV than during GV after lavage (Table 1 and Fig. 2 ). Arterial PO 2 was at a level which provided sufficient oxygenation at all settings (Table 1 ). Discussion This study shows that in cats ventilated with gas, inspiratory activity is inhibited at higher peak airway pressures and tidal volumes after lung lavage than before. In cats with surfactant-depleted lungs, inhibition of inspiratory activity occurs at about the same airway pressures and tidal volumes during GV and during PLV, but at higher arterial PCO 2 during PLV than during GV. PLV with perfluorocarbon is a method of ventilatory support introduced by Fuhrman in 1991, wherein gas is ventilated into a partially liquid (perfluorocarbon) filled lung (1). PLV has been shown to decrease the alveolar surface tension mainly in dependent parts of the lung, resulting in alveolar recruitment and reduced ventilation-perfusion mismatch, thereby improving gas exchange and lung mechanics [ 18 ]. These beneficial effects of PLV have been demonstrated not only in animal models of respiratory distress and meconium aspiration syndrome [ 19 , 20 ], but also in adults and newborn infants with severe respiratory distress syndrome [ 21 , 22 ]. In the present study a ventilatory strategy of a moderate PEEP (0.5 kPa) and positive pressure ventilation at 60/min was chosen in a model of surfactant depletion to simulate a relevant clinical situation in which lung recruitment and possibly low tidal ventilation could be promoted. The point of inhibition of PNA represents the time point at which central inspiratory activity ceases and at which all spontaneous breathing activity has disappeared completely. It has been used as a point of comparison between different ventilatory patterns [ 11 ]. Lung compliance did not differ between PLV and GV in surfactant-depleted lungs, but resistance was higher during PLV, as reported elsewhere [ 2 ]. This might not represent a real increase in resistance of the airways, but is more likely due to higher inertia of the liquid than of the gas. In this study all experiments were performed in the same order and time sequence, i.e. first GV in the healthy lung, and then GV and PLV in that order in the surfactant-depleted lung. We avoided randomisation of the order of GV and PLV in the surfactant-depleted lung, as that approach would have meant a much longer period of mechanical ventilation in the group randomised to PLV as the first part of the protocol, to allow evaporation of the perfluorocarbon. In cats with normal lungs the pulmonary stretch receptor (PSR) activity is similar during GV and PLV, indicating that there is no extensive stretching of the lung during PLV [ 15 ]. On the other hand, the impulse frequencies of PSRs are higher at the start of inspiration with PLV than with GV at the highest insufflation pressures used [ 15 ]. This might also be the case when the lung has been lavaged and surfactant-depleted. In animals with surfactant-depleted lungs, which may be partially atelectatic, mechanoreceptors in some well-ventilated areas may be active, whereas other receptors in atelectatic areas may not give any impulses. In the present study all receptors which were active during GV were also active during PLV. The study showed that during GV inhibition of PNA occurred at much higher airway pressures after than before lung lavage, but at similar arterial blood gases, findings which might be due to an altered stretch receptor input from, for example, areas that are surfactant-depleted and/or partially atelectatic. As instillation of perfluorocarbon might exert an effect similar to that of surfactant on lavaged lungs, increased mechanoreceptor discharge during PLV due to increased stretch receptor activity might explain why PNA inhibition occurs at a higher arterial PCO 2 during PLV than during GV. This possibility is supported by the finding that administration of surfactant increases the activity of mechanoreceptors in surfactant-depleted animals [ 23 ]. It is unlikely that a high arterial PO 2 during PLV influences the respiratory drive. Conclusion Higher airway pressures are needed to achieve inhibition of inspiratory activity during GV in animals with surfactant-depleted lungs than in animals with normal lungs. After surfactant depletion, inhibition of inspiratory activity during PLV occurs at about the same peak inspiratory and end-expiratory pressures and tidal volume as during GV. Inhibition of inspiratory activity occurs at a lower arterial pH and a higher arterial PCO 2 during PLV than during GV in animals with surfactant-depleted lungs, which might be explained by recruitment of pulmonary stretch receptors during PLV. This may be a reason why inhibition of spontaneous breathing is more easily achieved during PLV than during GV in animals with surfactant-depleted lungs. List Of Abbreviations PNA , phrenic nerve activity GV , gas ventilation PLV , partial liquid ventilation PIP , peak inspiratory pressure V t , tidal volume HFPPV , high frequency pressure ventilation PEEP , positive end-expiratory pressure A/C ventilation , assist/control ventilation Ti , inspiratory time RR , respiratory rate P aw , airway pressure P oes , oesophageal pressure P tp , transpulmonary pressure C L , lung compliance RM-ANOVA , one way repeated measures analysis of variance Competing Interests The authors declare that they have no competing interests. Authors' Contributions ERF participated in designing the study, was involved in the preparation and care of the animals, was responsible for the acquisition and analysis of the data and drafted the manuscript. RS participated in the design of the study, was responsible for the preparation of the animals, was involved in the acquisition and analysis of the data, and revised the manuscript. AJ participated in the design of the study, was responsible for the preparation of the animals and for the neurophysiological recordings, and revised the manuscript. AS made substantial contributions to the data collection and their interpretation, and revised the manuscript. GS conceived of the study and its design, performed the lavage and PFC instillation procedures, helped to interpret the data, and revised the manuscript. All authors read and approved the final manuscript.

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539054

Cancer in Families

The Icelandic population is now a part of a unique epidemiological study, which has involved investigating the genetic heritage of many of them. The reason that this experiment can be done is because of the remarkable records that exist in Iceland. Not only is there almost complete genealogical information dating back to the 18th century on all current (288,000) and many previous Icelanders (more than 600,000 in total), but in addition the country has an almost complete cancer registry dating from 1955. A company, deCODE Genetics, was set up to mine health-care data in Iceland, and to use it to assess the effect of genetics on health. Initially, the company attracted criticism, with some questioning the ethics of providing access to health-care data for many disease projects to a for-profit company. But the company has been supported by many Icelanders themselves, demonstrated by Icelanders donating blood samples with informed consent for research on multiple diseases, and now the project's scientific value is becoming apparent. One such analysis is the subject of a paper by Laufey Amundadottir and colleagues in this month's PLoS Medicine that assesses how much genetic factors contribute to cancer risk across the whole Icelandic population. The paper looked at 27 different types of cancers (all those with more than 200 cases) that had been registered between 1955 and 2002 and analysed the frequency of close and distant relatives also having that cancer, or another kind of cancer. Of the 27 cancers, 16 showed significant “familiality,” and for some this risk even extended to distant (that is, third- to fifth-degree) relatives. The seven cancers with the highest increased familial occurrence both in close and distant relatives were breast, prostate, stomach, lung, colon, kidney, and bladder cancers. And, interestingly, three cancers—stomach, lung, and colon cancer—were also seen more frequently in mates of patients, indicating a shared environmental risk factor. And for some cancers there was a familial association with other cancers, for example, relatives of individuals with stomach, colon, rectal, or endometrial cancer were more likely to have any of these cancers. Icelandic genetics and genealogy Cathryn Lewis, the academic editor for the paper comments on the study's strengths. “This level of family relationship and clinical diagnosis is rarely available from interviewing patients and family members. The size of the study (over 600,000 individuals, with 32,000 cancer cases) and the high quality of data enables the authors to detect subtle effects across distant relationships.” How robust are these data, and what do they mean for the biological understanding of cancer? As Lewis says, “Although the current study is impressive in its size and scope, even here, the sample size becomes an issue, with the most convincing results seen in the most common cancers.” Certainly not all the findings are surprising; some rare cancers are already known to be associated with particular genetic defects, and syndromes that predispose to multiple cancers have been described, for example, that of Hereditary Nonpolyposis Colorectal Cancer. Other associations are more intriguing—the cluster of related cancers that include prostate, kidney, and bladder could possibly have a developmental origin, since all arise from the same part of the embryo. So, by highlighting these subtle links, the study's particular value may become apparent: deciding future avenues of investigation in the complex interrelationships that interact to produce cancer.

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524171

Characterization and phylogenetic epitope mapping of CD38 ADPR cyclase in the cynomolgus macaque

Background The CD38 transmembrane glycoprotein is an ADP-ribosyl cyclase that moonlights as a receptor in cells of the immune system. Both functions are independently implicated in numerous areas related to human health. This study originated from an inherent interest in studying CD38 in the cynomolgus monkey ( Macaca fascicularis ), a species closely related to humans that also represents a cogent animal model for the biomedical analysis of CD38. Results A cDNA was isolated from cynomolgus macaque peripheral blood leukocytes and is predicted to encode a type II membrane protein of 301 amino acids with 92% identity to human CD38. Both RT-PCR-mediated cDNA cloning and genomic DNA PCR surveying were possible with heterologous human CD38 primers, demonstrating the striking conservation of CD38 in these primates. Transfection of the cDNA coincided with: (i) surface expression of cynomolgus macaque CD38 by immunofluorescence; (ii) detection of ~42 and 84 kDa proteins by Western blot and (iii) the appearance of ecto-enzymatic activity. Monoclonal antibodies were raised against the cynomolgus CD38 ectodomain and were either species-specific or cross-reactive with human CD38, in which case they were directed against a common disulfide-requiring conformational epitope that was mapped to the C-terminal disulfide loop. Conclusion This multi-faceted characterization of CD38 from cynomolgus macaque demonstrates its high genetic and biochemical similarities with human CD38 while the immunological comparison adds new insights into the dominant epitopes of the primate CD38 ectodomain. These results open new prospects for the biomedical and pharmacological investigations of this receptor-enzyme.

Background Just over a decade after being identified as a leukocyte surface antigen with receptorial activity [ 1 , 2 ], CD38 was re-classified among the ADP-ribosyl (ADPR) cyclases [ 3 , 4 ]. These are a group of related membrane-bound or soluble enzymes, comprising CD157 and Aplysia ADPR cyclase [ 5 , 6 ], which have the unique capacity to convert NAD to cyclic ADP ribose (cADPR) or nicotinic acid-adenine dinucleotide phosphate (NAADP), part of a new generation of endogenous activators of intracellular Ca 2+ release [ 6 ]. Human CD38 is a broadly expressed type II transmembrane glycoprotein of ~45 kDa in its monomeric form [ 7 ]. This consists of a short intracytoplasmic (IC) tail, a transmembrane domain and a major extracellular domain (ECD) formed by 256 of the 300 constituent amino acids of the CD38 polypeptide [ 7 ]. Homodimeric and homotetrameric forms have also been described [ 8 , 9 ] and a 3-D dimer structure obtained by homology modeling to Aplysia cyclase [ 10 ]. The CD38 ECD, where both receptor and enzymatic activities reside, harbours a 12 cysteine/6 disulfide signature common to the members of this family. According to a growing body of experimental evidence, the disulfides mediate control of the ECD conformation and function since reduction modifies CD38 enzymatic activity and homodimerization [ 11 , 12 ], and sensitivity to proteolysis and monoclonal antibody (mAb) binding [ 13 ]. The mobilization of intracellular Ca 2+ caused by the CD38/cADPR/NAADP axis has been implicated in a variety of physiological and pathological processes including insulin secretion and diabetes [ 14 ], myometrial contractility and pregnancy [ 15 ], airway smooth muscle contractility and hyperreactivity [ 16 ], vascular smooth muscle contraction [ 17 ], osteoclast activity [ 18 ], and the functions of the immune [ 19 ], renal [ 20 ] and exocrine gland [ 21 ] systems. The assortment of effects caused by CD38 ligation and transmembrane signalling is also broad though mostly described in hematopoietic cells, and ranges from lymphocyte proliferation and cytokine release [ 2 , 22 - 24 ], regulation of B and myeloid cell development and survival [ 25 - 28 ], inhibition of human immunodeficiency virus (HIV) entry [ 29 ], to induction of dendritic cell maturation [ 30 ]. In addition, ligation of human pancreatic islet cells by anti-CD38 autoantibodies induces insulin release [ 31 ]. CD38 is also a clinically useful marker of HIV infection progression [ 32 ] and therapy-requiring B-CLL [ 33 ]. In this study, we describe the molecular cloning and functional expression of CD38 from the cynomolgus macaque. In addition, with a panel of newly-raised mAbs, we comparatively analyse the macaque and human CD38 ECDs and identify new structural-functional characteristics of CD38 epitopes. Results Cloning CD38 cDNA from cynomolgus macaque Activation of human peripheral blood mononuclear cells (PBMC) with phytohemagglutinin (PHA) strongly upregulates expression of CD38 in human T lymphocytes [ 34 ]. Therefore, to isolate a CD38 cDNA, PHA-activated cynomolgus PBMC were chosen as the source of RNA for amplification by RT-PCR using primers derived from the human CD38 5' and 3' untranslated regions. The 1113 base-pair (bp) insert contained an open reading frame of 906 bp (Figure 1A ) that was 95% identical to the human CD38 sequence. The cDNA encodes a 301 amino acid (aa) polypeptide with the typical CD38 type II membrane protein structure, i.e ., a short cytosolic tail (residues 1–21), a transmembrane region (residues 22–44), and an ECD (residues 45–301) containing the signature 12-cysteine array. Alignment of the macaque and human CD38 polypeptides showed 92% identity and 94% similarity. There is complete conservation of the IC region while there are five conservative changes in the transmembrane region where macaque CD38 has one more residue than human CD38. Macaque CD38 has four potential N -linked glycosylation sites, as in human CD38; three are co-linear. Furthermore, macaque CD38 shows conservation of the 4 acidic residues (Glu 148 , Asp 149 , Asp 157 , Glu 228 ) and 2 tryptophans (Trp 127 and Trp 191 ) that play a critical role in the ADP-ribosyl cyclase/cADPR hydrolase activities of human CD38 [ 35 ]. Lys 130 is also maintained suggesting that, like human CD38, binding of ATP to this residue may lead to inhibition of the hydrolase activity [ 36 ]. Likewise, macaque CD38 conserves Arg 271 which is ADP-ribosylated in human CD38, causing inactivation [ 37 ]. Figure 1 Cynomologus macaque CD38 cDNA, promoter and 5' end of intron 1. A. Primers used to clone the cDNA are wavy-underlined. Members of the 12-cysteine array of the ectodomain are color-boxed; cysteines that pair in disulfide formation are boxed in the same color. Differences in the human CD38 amino acid sequence are indicated under the macaque sequence. The transmembrane domain is underlined and glycosylation sites indicated. The sequence accession number is AY555148. B. Nucleotide sequence of the CD38 promoter from Macaca fascicularis (MFA) (Acc. No. AY622999), Homo sapiens (HSA) (Acc. No. AF001985) and Pan troglodytes (PTA) (Acc. No. AY623001). The nucleotide sequences begin at -1 immediately upstream of the ATG initiation codon. Potential transcription regulatory motifs common to the 3 species, identified with TRANSFAC ® [62], are boxed and the relevant transcription factor indicated above. C. 5' end of intron 1 of macaque and human CD38 , Acc. No. AY623000 and AF088883, respectively. Common genomic organization and regulatory features of macaque and human CD38 genes Human CD38 has been characterized as a single-copy, 8-exon gene that spans ~70 kb [ 5 , 38 ] and not only CD38 but ADPR cyclase genes in general are highly conserved from mollusks to humans [ 39 , 40 ]. In addition, the Southern blot hybridization patterns of macaque and human genomic DNAs digested with Eco RI, Bam HI and Hind III and probed with their homologous CD38 cDNA are similar ( data not shown ), indicating that the structural organization of macaque and human CD38 is highly conserved. This was further demonstrated by the finding that the same primers previously used to amplify the 8 human CD38 exons [ 41 ] also amplified 8 CD38 exons from macaque genomic DNA. The putative macaque exons could be perfectly aligned with their human counterparts [ 5 ] in number, size and splice site of their exons (Table 1 ). All intron-exon boundaries conformed to the GT-AG rule, most 5' splice donor and 3' splice acceptor sequences of the 7 introns were identical. Table 1 Exons and introns of cynomolgus macaque CD38 Exon 5' splice donor Intron 3' splice acceptor Exon 1 ATGAGgtggg I cacagACATG 2 MetAr 79 gHisV 2 ACAGGgtaat II cttagACTCT 3 snLys 122 ThrLe 3 TTTCGgtgag III tttagAAATA 4 rPheG 168 luIle 4 GCAGGgtaag IV ttaagTTTGC 5 rgArg 196 PheAl 5 AACAGgtaac V tttagCACTT 6 AsnSe 221 rThrP 6 TCCAGgtata VI cccagAGACT 7 SerAr 251 gAspL 7 TACAGgtaat VII cacagACCTG 8 TyrAr 280 gProA Transcription of TATA-boxless human CD38 initiates at multiple start sites [ 5 , 38 ] while induction of gene expression by retinoids is controlled by a retinoic acid responsive element (RARE) at the beginning of intron 1 [ 42 ]. To identify conserved cis -regulatory sequences, ~500 bp upstream of the initiation codon ATG were amplified by PCR from genomic DNA of cynomolgus macaque but also from chimpanzee ( Pan troglodytes ), to strengthen the alignment with the human CD38 promoter sequence, and numerous conserved general and immune-related potential binding sites were found (Figure 1B ). The alignment of the 5' end of intron 1 from macaque and human CD38 shows the presence of the RARE (Figure 1C ), suggesting that this and perhaps other molecular mechanisms involved in regulating CD38 are conserved between these primates. Expression of macaque CD38 reveals an active cyclase of ~42 kDa To analyse surface expression and function of macaque CD38, the cDNA insert was subcloned into the pcDNA3.1 expression vector and a similar construct was prepared containing the human CD38 cDNA ( see Materials & Methods ). DNAs were transfected for heterologous expression in the NIH/3T3 cell line which does not express CD38 [ 43 ]. Given that cross-reactivity of OKT10 anti-human CD38 mAb has been exploited to detect CD38 in rhesus macaque hematopoietic cells [ 44 , 45 ], clones expressing cynomolgus CD38 were identified by indirect immunofluorescence (IF) with OKT10 (see below). The stable cell line expressing macaque CD38 was designated NIH/mac38, while NIH/hum38 is the human CD38-expressing cell line. The ecto-cyclase activity of macaque CD38 was evaluated by incubating the NIH/mac38 clone with nicotinamide guanine dinucleotide (NGD), an NAD analog which is converted by ADP-ribosyl cyclases such as CD38 to cyclic GDP ribose (cGDPR). Unlike cADPR, cGDPR is a stable, fluorescent end-product which can be detected in cell supernatants [ 46 ]. Increased fluorescence following incubation with NGD was detected in supernatants of macaque and human CD38 transfectants, demonstrating that macaque CD38 is enzymatically active (Figure 2A ). Figure 2 Enzymatic activity and western blot analysis of cynomolgus CD38. A. Ecto-cyclase activity was evaluated by measuring conversion of NGD to fluorescent cGDPR by NIH/mac38 (1), NIH/hum38 (2), NIH/3T3 (3), or cynomolgus macaque RBCs (4) and human RBCs (5). Each bar represents mean ± SD; (*) means significantly higher than controls ( P < 0.05, t -test, n = 3 experiments). Y axis = fluorescence emission 410 nm. B. Western blot of lysates from parental NIH/3T3 cells (NIH), NIH/hum38 (hum38) and NIH/mac38 cells (mac38) analysed by 10% SDS-PAGE under non-reducing (NR) conditions. Blots were probed with AT1 anti-human CD38 mAb. C. Western blot of parental NIH/3T3 and NIH/mac38 cell lysates analysed by 8% SDS-PAGE in non-reducing (NR) or reducing (R) conditions and probed with the indicated anti-cynomolgus CD38 mAbs. In B and C, molecular weight markers (in kDa) are indicated on the right. In human red blood cells (RBCs), CD38 is the only source of ecto-cyclase activity [ 47 ] which was also found on the surface of cynomolgus macaque RBCs (Figure 2A ), suggesting a further parallel with human CD38. To establish the approximate molecular weight of macaque CD38, SDS-PAGE and Western blot analysis were performed with lysates prepared from NIH/mac38 and NIH/hum38 cells. Blots were probed with the AT1 anti-human CD38 mAb which detected a band of ~42 kDa in both transfectants (Figure 2B ). Production of anti-macaque CD38 mAbs To raise mouse mAbs against macaque CD38, live NIH/mac38 cells were used for immunization. Four mAbs, KK1B5 (IgG1), KK4E5 (IgG2a), KK6A11 (IgG2a) and KK9H4 (IgG1) were selected for further analyses. All four anti-macaque CD38 mAbs reacted by IF with NIH/mac38 but were negative with the parental cell line (Figure 3 ). Only two mAbs (KK1B5 and KK9H4) reacted with NIH/hum38. Figure 3 Reactivity of anti-cynomolgus CD38 mAbs. Top row: Flow cytometric profiles obtained by IF of parental NIH/3T3 (grey profile) and NIH/mac38 expressing cynomolgus CD38 (white profile) with OKT10 anti-human CD38 mAb and anti-cynomolgus CD38 mAb panel. Profiles obtained by staining both cell lines with CBT3G IgG control mAb completely overlap with the grey profile. Bottom row: Flow cytometric profiles obtained with NIH/hum38 and the indicated mAbs (white profile). Grey profile is the reactivity of CBT3G. The anti-macaque CD38 mAbs were further assessed by IF for binding to cynomolgus PBMC and to SL-691 and SL-999, two cynomolgus macaque B lymphoblastoid cell lines. All four mAbs reacted with PBMC from cynomolgus macaques, indicating they recognize native cynomolgus CD38, and they strongly stained cells of the two B cell lines (Table 2 ). Table 2 Reactivity of anti-macaque CD38 mAbs Monoclonal antibodies Cell lines KK1B5 KK4E5 KK6A11 KK9H4 Control Macaque CD38 + NIH/mac38 +++ a +++ ++ +++ - SL-691 ++ ++ ++ ++ - SL-999 ++ ++ ++ ++ - PBMC ++ ++ ++ ++ - Human CD38 + NIH/hum38 ++ - - ++ - RAJI ++ - - ++ - Jurkat ++ - - ++ - PBMC ++ - - + - Control NIH/3T3 - - - - - a +++, very strong reactivity; ++, strong reactivity; +, weak reactivity; -, no reactivity. Additional Western blot analyses were carried out with the anti-macaque CD38 mAbs. MAbs KK1B5 and KK9H4 confirmed detection of a ~42 kDa band in NIH/mac38 cell lysates but also recognized a second band of ~84 kDa. (Figure 2C ). The doublet was also identified in SL-999 cynomolgus B cells ( data not shown ). Instead mAbs KK4E5 and KK6A11 only recognized a band of ~84 kDa, even in reducing conditions. No bands were detected by these mAbs in parental NIH/3T3 cells. Anti-macaque CD38 mAbs recognize either species-specific/DTT-resistant epitopes or a human cross-reactive/DTT-sensitive epitope The observation that mAbs KK4E5 and KK6A11 recognize only cynomolgus CD38 while mAbs KK1B5 and KK9H4 also recognize human CD38 indicates that the two mAb subsets are directed towards different epitopes. To evaluate the contribution of disulfides to these epitopes, mAb reactivity was assessed after treating NIH/mac38 with dithiothreitol (DTT). Reduction had no effect on binding of the species-specific mAbs (KK4E5 and KK6A11) but significantly reduced binding of mAbs KK1B5 and KK9H4 (Figure 4A ), indicating that the latter recognize a disulfide-requiring conformational epitope of the cynomolgus CD38 ECD, and predicting they should recognize a similar epitope in human CD38. The results (Figure 4B ) confirm that treatment of NIH/hum38 with DTT decreased binding of mAbs KK1B5 and KK9H4, indicating that cynomolgus macaque and human CD38 have a conformational epitope in common. Figure 4 Reactivity of anti-macaque CD38 mAbs with native and DTT-treated NIH/macCD38 and NIH/humCD38. A. Flow cytometric profile of NIH/mac38 cells treated for 45 min at 37°C with 10 mM DTT (red profile) or without DTT (black profile) and then tested for binding of anti-cynomolgus CD38 mAbs by IF. Grey profile shows reactivity of CBT3G mAb (isotype control). B. Results of IF/DTT experiments illustrated by confocal microscopy. Transfectants are indicated at the top of the panel. Left vertical triplet of images (from top to bottom): (1) reactivity of mAb KK1B5 with native cynomolgus CD38 cells stained with FITC; (2) reactivity with DTT-treated cells stained by FITC; (3) cells in plate 2 viewed by differential interference contrast (DIC). (4–6): idem mAb KK1B5 with NIH/humCD38; (7–9): idem mAb KK4E5 mAb with NIH/macCD38. C. IF/confocal microscopy of NIH/humCD38, with/without DTT. Left vertical triplet of images (from top to bottom) shows staining with mAb IB4 and FITC of control (1), DTT-treated visualized by FITC (2) and DIC (3). Right trio: results with mAb AT1 (4–6). D. Flow cytometric profiles of control (black profile) and DTT-treated (red profile) NIH/macCD38 binding by anti-human CD38 mAbs and FITC. Grey profile shows reactivity of CBT3G mAb (isotype control). Anti-human CD38 mAbs are also species-specific/DTT-resistant or cross-reactive/DTT-sensitive Reciprocal experiments were performed to see if the correlation between cross-reactivity and epitope sensitivity to DTT was also valid for a panel of 6 well-known mAbs raised against human CD38. MAbs IB4, IB6, OKT10, SUN-4B7, AT1 and HB7 were assessed for binding to native and DTT-treated NIH/hum38, and for cross-reactivity with cynomolgus CD38. Binding of mAbs IB4, IB6 and HB7 to human CD38 was unaffected by DTT and none bound cynomolgus CD38 (Figure 4C and 4D ). On the contrary, binding of mAbs OKT10, SUN-4B7 and AT1 to human CD38 was significantly reduced by DTT and all three mAbs bound cynomolgus CD38 (Figure 4D ) in a DTT-sensitive manner. The epitope recognized by cross-reactive cynomolgus anti-CD38 mAbs maps to the C-terminal disulfide loop The observation that mAbs KK1B5 and KK9H4 (raised against cynomolgus CD38) and mAbs OKT10, AT1 and SUN-4B7 (raised against human CD38) all bind native but not reduced cynomolgus and human CD38 is compatible with their binding the same epitope. A priori knowledge of the human CD38 epitope map previously established that OKT10 binding is abrogated by deletion of one or both of the C-terminal Cys residues (Cys 287 and Cys 296 ) predicted to pair in disulfide bond formation [ 48 ] and that mAbs OKT10, AT1 and SUN-4B7 mutually compete for binding to the human CD38 ECD [ 49 ]. This would position the common epitope of cynomolgus and human CD38 in the C-terminal disulfide loop formed by Cys 288 and Cys 297 in cynomolgus CD38 (Cys 287 /Cys 296 in human CD38). To test this possibility, we analysed reactivity of these mAbs with the CD38-negative MT2 human T cell line stably transfected with CD38 Δ285 , a human CD38 deletion mutant which lacks the 15 C-terminal amino acids and loses OKT10 binding [ 29 ]. IF analysis demonstrates that the 15 C-terminal amino acids of human CD38 are required for binding of mAbs KK1B5, KK9H4 and OKT10 (Figure 5 ). In contrast, mAbs SUN-4B7 and AT1 maintained binding to MT2/CD38 Δ285 ( data not shown ). These data are consistent with the presence of two close conformational epitopes in human and cynomolgus CD38: one identified by mAbs KK1B5, KK9H4 and OKT10 located in the last (6 th ) disulfide loop, the other by mAbs SUN-4B7 and AT1 mapping to the penultimate (5 th ) C-terminal disulfide loop involving Cys 254 -Cys 275 (Figure 6 ). Note that human-cynomolgus CD38 amino acid sequence identity in the 5 th loop is 20/22 amino acids, and 10/10 amino acids in the 6 th loop. Figure 5 Reactivity of macaque/human CD38 cross-reactive mAbs with MT2 Δ285 cells expressing truncated human CD38. Flow cytometric profiles obtained by IF with the indicated mAbs and MT2 T lymphoid cells stably expressing human CD38 deleted of the 15 C-terminal residues (heavy black line profile). Isotype control (light black line profile). Figure 6 Molecular model of human CD38 epitope map. A. Homology model of human CD38 derived from Aplysia ADPR cyclase 3-D model made with RasWin Molecular Graphics showing footprints of the relevant anti-macaque and anti-human CD38 mAbs. Close-up of two C-terminal disulfide loops delimited by Cys 254 -Cys 275 , and Cys 285 -Cys 296 . Position of cysteine residues and disulfide bonds are indicated in yellow. Sequence is represented by secondary structure (red, alpha helix; blue, beta strand; white, turn). B. Same model illustrating the two beta strands implicated in binding of human species-specific mAbs and the residue changes in macaque CD38 that may account for their lack of cross-reactivity. C. Model of the dimeric form of the human CD38 ECD showing where epitopes are located. Discussion In this study, we describe the molecular cloning and functional expression of the CD38 receptor/enzyme from the cynomolgus macaque. The cDNA described here presents the expected homology to human CD38 considering that the macaque-human lineages diverged some 25 million years ago [ 50 ] and their genomes are 93–95% identical. This homology was exploited in RT-PCR cloning and in our genomic PCR survey of cynomolgus CD38 . Indeed, human CD38 primers proved to be equally agile with CD38 in other members of the Primate order such as the chimpanzee, the gibbon ( Hylobates concolor ) and the rhesus monkey ( Macaca mulatta ) (MO, FS and SC, unpublished observations). The conceptual translation of the cynomolgus macaque CD38 cDNA yielded a polypeptide with the characteristic type II structure, size, catalytic core residues and 12-cysteine ECD array common to CD38 orthologs. With respect to human CD38, cynomolgus CD38 has an extra residue in the transmembrane domain but no difference was found in the IC tail, where human CD38 is reported to interact with the SH2 domain of Lck [ 51 ] in lipid rafts [ 52 ]. Cross-reactivity of anti-human CD38 mAbs was exploited in the initial part of the protein analyses although these give discrepant results in binding to leukocytes from other primates. For example, OKT10, but not Leu17 or T16, binds bone marrow from rhesus macaques [ 44 ] whereas HIT2 stains horse lymphocytes but not leukocytes from baboon, cynomolgus macaque, rhesus macaque, pig, sheep, cow, dog, cat or rabbit [ 53 ]. In addition, the behaviour of cross-reactive mAbs may not be reproducible in another species as illustrated by anti-human CD34 mAbs: 6 out of 13 mAbs cross-reacted with cynomolgus bone marrow but only 3 of these correctly identified the functional cynomolgus equivalent of the human progenitor cell in clonogenic assays [ 54 ]. To avoid similar pitfalls, we raised mAbs to macaque CD38. The apparent molecular weight of macaque and human CD38 were indistinguishable by SDS-PAGE. The macaque polypeptide weighs 34.4 kDa and has four N -linked glycosylation motifs, suggesting it is probably glycosylated, like human CD38 [ 55 ], to give rise to the 42 kDa band. We wanted to confirm this result with anti-cynomolgus CD38 mAbs but when lysates were probed with mAbs KK1B5 and KK9H4, the 42 kDa band was always accompanied by an 84 kDa band, and the doublet was also observed with lysates obtained from SL-999 macaque B cells expressing CD38 in its native milieu. The KK4E5 and KK6A11 mAbs instead detected only the 84 kDa band in transfectants, and the band was unaffected by addition of DTT. CD38 dimers and tetramers have been abundantly reported and postulated to be formed by diverse mechanisms such as intermolecular disulfides [ 12 , 51 ], non-covalent association [ 56 ] and transglutamination [ 9 ]. Therefore, our interpretation of the upper band is that it represents a CD38 dimer, possibly a non-covalently associated form compatible with lysate preparation in NP-40 detergent which stabilizes such dimers [ 56 ], or a transglutaminase-linked form. Although the data are consistent with KK4E5 and KK6A11 recognizing a unique epitope in macaque CD38 dimers, it is also possible that these mAbs have another unknown specificity and that further experiments are needed to fully characterize their specificity. The availability of the macaque CD38 amino acid sequence and its alignment with the human homolog gave a new dimension to the analysis of mAb cross-reactivity and ultimately led to a better understanding of CD38 epitopes. Macaque and human CD38 are strikingly conserved yet only two of the four anti-macaque CD38 mAbs cross-reacted, as did only three of the six anti-human CD38 mAbs in reciprocal experiments. The key finding was that a mAb's capacity to cross-react always correlated with the sensitivity of its target to reduction which, added to the prior knowledge of the human CD38 epitope map, crystal structure and active site [ 57 ], allowed us to footprint the binding sites of cross-reactive anti-CD38 mAbs. The classification of anti-primate CD38 mAbs as species-specific/DTT-resistant (type I) or cross-reactive/DTT-sensitive (type II) and directed against a conformational epitope may be of practical importance. Firstly, our results demonstrate the necessity for careful antibody selection when performing biological assays involving detection of CD38 expression in circumstances of cell membrane redox perturbation, e.g ., detection of membrane CD38 in apoptotic cells might be positive according to type I mAbs and negative by type II (Alla Egorova, personal communication). Secondly, simultaneous use of the two types of mAbs can provide information about CD38 expression (type I) while the type II mAb can give indications on its conformation. Thirdly, it is possible that potent Ca 2+ -mobilizing agonistic mAbs are more likely to be type I mAbs since this subgroup includes IB4, which is the only anti-human CD38 mAb to mobilize Ca 2+ , and NIM-R5, a rat anti-murine CD38 mAb [ 58 ] which also mobilizes Ca 2+ and whose binding to murine CD38 transfectants was not affected by DTT (EF, personal communication). Finally, autoantibodies to CD38 have been detected in diabetes and thyroiditis and it would be interesting to identify the epitopic culprits. Conclusions Some of the essential biological features of CD38 in Macaca fascicularis have been elucidated and new insights obtained about the epitopic structure of the CD38 ECD. We hope that, by providing the reagents for analysis of CD38 in the cynomolgus macaque, this study may expedite our understanding of the role of CD38 in human disease. Methods Cynomolgus CD38 cDNA cloning PBMC were isolated by Ficoll-Paque PLUS (Amersham Biosciences) centrifugation from whole blood obtained from a female cynomolgus macaque housed according to state regulations at the RBM (Ivrea, Italy). Cells were cultured in medium with 5 μg/ml PHA (Sigma-Aldrich) for 72 h, harvested and resuspended in TRIzol RNA extraction solution (Invitrogen). RT-PCR was carried out using the Titan One Tube RT-PCR system (Roche Diagnostics) with 150 ng total RNA and the gene-specific primers derived from the human CD38 sequence (GenBank accession no. D84278): forward 5'-AGT TTC AGA ACC CAG CCA-3' (corresponding to nt -84 to -66 upstream of the ATG initiation codon); reverse 5'-ATT GAC CTT ATT GTG GAG G-3' (corresponding to nt 102–121 downstream of the TGA stop codon). After RT for 50°C for 30 min, the cDNA was amplified by two rounds of PCR: 5 min at 94°C, followed by 30 (first round) or 35 (second round) cycles at 94°C for 30 s, 54°C for 30 s and 2 min at 72°C. The product was gel purified and cloned into the pGEM-T Easy vector (Promega). The inserts of three recombinant clones were analyzed by automated sequencing (Applied Biosystems). Analysis of simian genomic DNAs Cynomolgus macaque (15 samples) and chimpanzee (5 samples) genomic DNAs were obtained as described [ 59 ]. The eight exons of the cynomolgus CD38 gene were amplified by PCR (45 cycles, annealing temp 52°C) using primers known to amplify human CD38 exons [ 41 ] while the CD38 promoter of cynomolgus macaque was amplified (15 cycles annealing at 52°C, followed by 35 cycles with 0.3°C touchdown) with a forward primer from the human CD38 promoter (5'-GAA GAG GCA AGA AAA GCC-3') and reverse primer chosen from macaque CD38 exon 1 (5'-AACTCG CAG TTG GCC ATA-3'). The chimpanzee CD38 promoter was amplified with the human CD38 sequences. The 5' end of cynomolgus CD38 intron 1 was amplified (same conditions used for exon amplification but 57°C annealing and 1.5 M MgCl 2 ) with forward primer 5'-CCG TCC TGG CAC GAT GCG TCA AG-3' from macaque exon 1, and reverse primer 5'-ACA CCC TCC TCC CCT ACC ACA GG-3' taken from human CD38 intron 1. Amplicons were gel purified and analysed by automated sequencing. Alignments were performed with CLUSTALW (ExPASy, Swiss Institute of Bioinformatics). Cell lines and antibodies NIH/3T3 murine fibroblast and COS-7 monkey kidney cell lines were from the ATCC. Production of SL-691 and SL-999 cynomolgus B lymphoblastoid cell lines was previously described [ 60 ]. The mutant human CD38 Δ285 plasmid [ 48 ] was kindly provided by Dr. Toshiaki Katada (University of Tokyo, Japan) while the MT2 Δ285 transfectant was kindly provided by Dr. Umberto Dianzani (A. Avogadro University of Eastern Piedmont, Novara, Italy) [ 29 ]. Cells were maintained in RPMI 1640 medium with 10% heat-inactivated FCS, penicillin/streptomycin. The murine anti-human CD38 mAbs AT1 (hybridoma kindly provided by Dr. Jo Hilgers, BioProbe AV, Amstelveen, The Netherlands), SUN-4B7, OKT10, IB4, IB6 and HB7 were produced in-house from hybridoma culture supernatants. CBT3G, a murine anti-human CD3 mAb, was used as IgG control. F(ab') 2 goat anti-mouse Ig-FITC was used as secondary antibody (Jackson ImmunoResearch Laboratories). Expression of macaque and human CD38 cDNAs were cloned into pcDNA3.1/V5-His-TOPO expression vector (Invitrogen). Stable transfected cell lines were produced in NIH/3T3 by electroporation (250 V, 960 μF at 20°C), selected for 3–4 weeks in G418 after which isolated clones were picked and transferred to 96-well plates. Ecto-GDPR cyclase activity Ecto-GDPR cyclase activity of intact cells was determined as previously described [ 29 , 30 , 61 ]. Briefly, parental and transfected NIH/3T3 were used at 2.5 or 5 × 10 5 cells/ml in PBS. For RBCs, 420 μl packed volume were brought to 1 ml by addition of NGT buffer (0.15 M NaCl, 5 mM glucose, 10 mM Tris. Cl, pH 7.4). To 1 ml cell suspensions 10 μl 10 mM NGD (Sigma) in 20 mM Tris, pH 7.4 or 10 μl buffer (control) were added. After 30 min at 37°C, supernatants were collected after brief centrifugation. Supernatants were analysed by fluorescence spectrometer set at excitation wavelength 300 nm and emission wavelength 410 nm. Cell lines were measured in triplicate in three independent experiments; RBCs from two different animals were tested once; RBCs from humans were tested in duplicate in two independent experiments. Western blotting Cells were lysed in NP-40 buffer (150 mM NaCl, 1.0% NP-40, 50 mM Tris pH 8.0) containing protease inhibitors. Samples (20 μg protein/lane) were analysed by 8 or 10% SDS-PAGE and transferred to PVDF membranes (Bio-Rad Laboratories, Hercules, CA). Membranes were blocked in 5% milk/TBST, incubated for 2–3 h at RT with mAb supernatant with 1% milk, and incubated with horseradish peroxidase-labeled anti-mouse IgG (PerkinElmer) followed by ECL visualization. Production of anti-cynomolgus macaque CD38 mAbs BALB/c mice (Charles River Laboratories) were anaesthetized with Avertin i.p. injection and immunized by intrasplenic injection with 300 μl containing 5 × 10 5 live NIH/mac38 cells in PBS. Eight days later, mice received an i.p. boost of NIH/mac38 cells, and mouse sera tested 8 days later. The spleen of one mouse that responded to immunization was selected for fusion to P3.X63.Ag8.653 murine myeloma cell line. Hybridoma supernatants were screened for reactivity with the immunizing cells and lack of reactivity with the parental cell line. Positive hybridomas were cloned by limiting dilution. MAbs were used in the form of supernatants containing NaN 3 . Analysis of surface CD38 expression Surface expression of cynomolgus and human CD38 was determined by IF. Briefly, 2 × 10 5 cells/sample were incubated with 100 μl mAb supernatant/NaN 3 for 1 h at 4°C, and 30 min at 4°C with FITC-labeled secondary Ab. Background fluorescence was established with control IgG antibody. Cells were analysed immediately either by fluorescence microscope, FACSCalibur flow cytometer (10,000 events acquired) with CellQuest software (Becton Dickinson) or Olympus FV3000 confocal microscope with Nomarski optics for differential interference contrast (DIC) and FluoView 300 software. Modulation of surface CD38 by treatment with DTT Cells were detached with 1 mM EDTA/PBS, washed and resuspended in complete medium at a concentration of 2 × 10 6 cells/ml. One hundred μl 100 mM DTT stock was added per ml cell suspension, kept for 45 min in a 37°C incubator and washed twice with PBS/BSA/NaN 3 before analysis. Authors' contributions EF carried out cellular, biochemical and enzymatic analyses, devised the comparative epitope mapping and wrote the manuscript; MO cloned the cDNA and participated in the genomic DNA analyses; PV carried out mAb production and confocal microscopy, and participated in cellular and biochemical assays; EO carried out FACS analyses; SC participated in the genomic DNA analyses and contributed primate DNA samples; FS designed and participated in the cDNA and genomic DNA analyses; FT carried out expression analyses in macaques; FM designed and supervised mAb generation, and edited the manuscript. All authors read and approved the final manuscript. Table 3 Inter-species cross-reactivity of anti-CD38 mAbs Macaque CD38 Human CD38 MAbs native DTT native DTT Anti-macaque CD38 KK1B5 + - + - KK4E5 + + - - KK6A11 + + - - KK9H4 + - + - Anti-human CD38 IB4 - - + + IB6 - - + + HB7 - - + + AT1 + - + - OKT10 + - + - SUN-4B7 + - + -

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524171.xml

551539

A universal method for automated gene mapping

A high-throughput method for genotyping by mapping InDels. This method has been used to create fragment-length polymorphism maps for Drosophila and C. elegans .

Background For humans and model organisms, such as worms and flies, the availability of high-density sequence polymorphism maps greatly facilitates the rapid mapping and cloning of genes [ 1 - 3 ]. Key advantages of most molecular polymorphisms are the fact that they are codominant and in general phenotypically neutral. The vast majority of sequence polymorphisms are single-nucleotide polymorphisms (SNPs). The most direct approach for SNP detection is sequencing of a PCR product spanning the polymorphism, but this is too costly and labor intense for high-throughput genotyping. For this reason, several different strategies and methods have been developed in order to detect SNPs more efficiently. In general, assays can be grouped into strategies, where the nature of the SNP is determined by directly analyzing the primary PCR product and those that require a secondary assay performed on the primary amplification product [ 4 - 6 ]. An important strategy of the first group is the 5' nuclease assay, where allele-specific, dual-labeled fluorescent TaqMan probes guarantee specificity [ 7 ]. However, the need for two dual-labeled fluorescent probes, expensive specialized chemistry and specialized machinery increase the costs per assay of this approach significantly. Similarly, denaturing high-performance liquid chromatography (DHPLC) also analyses the primary amplification product [ 8 ]. This approach is based on melting differences of homo- versus heteroduplex DNA fragments under increasingly denaturing conditions and requires no specific labeling of the PCR fragments. However, conditions have to be optimized for every assay, throughput is limited and specialized equipment is required. DHPLC has been used in small-scale genotyping projects in Drosophila melanogaster [ 9 ]. Of the methods that detect the SNP in a secondary assay, restriction fragment length polymorphism (RFLP) analysis are very popular [ 10 ]. For this purpose, only those SNPs that alter a restriction site are analyzed. A great advantage of RFLP analysis is that no specialized equipment is needed and it can be carried out in every laboratory. RFLP maps recently established for Caenorhabditis elegans and Drosophila are used regularly in genotyping projects [ 2 , 3 , 11 ]. However, RFLP analysis requires significant manual input. Moreover, the use of different restriction enzymes with different reaction requirements adds another level of complexity that makes this method difficult to automate. Primer-extension-based technologies have also gained some prominence [ 12 ]. Here, a primer that anneals right next to the polymorphism is extended by one polymorphism-specific terminator nucleotide. Extension products are analyzed by size or, alternatively, by differences in the behavior of incorporated versus non-incorporated terminator nucleotides under polarized fluorescent light [ 13 ]. Swan and colleagues [ 14 ] have developed a set of fluorescence polarization-template directed incorporation (FP-TDI) assays for C. elegans . However, this approach is labor intensive and requires specialized chemistry and equipment. Using DNA microarrays, large numbers of SNPs can be analyzed in parallel, but the number of individuals that can be analyzed is low because of the high cost per chip [ 15 , 16 ]. Besides SNPs, short tandem repeats (STRs) or microsatellites represent another class of sequence polymorphisms used for genotyping [ 17 - 21 ]. STRs result in fragment length differences that are either detected on gel-based or capillary sequencers or high-resolution hydrogels (Elchrom Scientific Inc.). One advantage of STRs over SNPs is that they are highly polymorphic and are thus ideal for measuring the degree of variability in natural populations. STRs are, however, present at much lower density than SNPs and are therefore not suitable for high-resolution mapping of genes. Interestingly, a significant proportion of the currently available polymorphisms are caused by small insertions or deletions (InDels). Weber et al . [ 22 ] identified a genome-wide set of about 2,000 human InDel polymorphisms and estimated that InDels comprise at least 8% and up to 20% of all human polymorphisms. This is in line with the findings of Berger and co-workers [ 2 ] who found that 16.2% of polymorphisms in Drosophila are of the InDel type. Also, two independent studies in C. elegans found that InDels constitute between 25% and 28% of all polymorphisms [ 3 , 14 ]. In addition, those studies found that the vast majority of InDels are due to 1-2 base-pair (bp) differences (65% in Drosophila [ 2 ], 84% in C. elegans [ 3 ]). To take full advantage of this class of small InDel polymorphisms, we have developed a strategy that allows us to detect most, if not all, InDels by analyzing the lengths of primary PCR products on a capillary sequencer at single base-pair resolution. We call these assays fragment length polymorphism (FLP) assays. Importantly, this approach can easily be automated on standard robotic pipetting platforms as it involves a simple PCR reaction setup. Furthermore, allele calling is performed automatically using the Applied Biosystems GeneMapper software commonly used for genotyping STRs (Materials and methods). To demonstrate the feasibility of this strategy, we have validated 112 evenly spaced FLP assays at 3 centimorgan (cM) resolution in C. elegans (one every 0.9 megabase-pair (Mbp)) and 54 FLP assays at 4 cM resolution for the Drosophila autosomes. This set of FLP assays allows us to rapidly map mutations to small chromosomal subregions with a minimum of manual input. Furthermore, we provide a list of predicted InDels for which additional assays can be readily designed in the chromosomal subregion of interest. Those non-validated FLPs enhance the resolution of the map by a factor of 5.6 and 17.9, respectively. We show the usefulness of this approach by identifying novel alleles of previously characterized genes. In summary, we have taken advantage of a publicly available dataset to adapt a technology widely used for STR analysis to genetic mapping. Thanks to the complete automation of genotyping, this approach is considerably faster, more reliable and cheaper than previously used mapping strategies in C. elegans or Drosophila . Results and discussion Detection of fragment length polymorphisms (FLPs) To detect a FLP, the region of interest is amplified in a standard PCR reaction with one fluorescently labeled primer, and the PCR products are directly analyzed on a capillary sequencer. Fragment sizes are determined automatically relative to an internal size standard with AppliedBiosystem's GeneMapper software (for details see Materials and methods). The software then allocates fragment sizes to previously calibrated genotypes. Taq polymerase has the tendency to catalyze the addition of adenosine (A) to the 3' end of PCR products. This activity could make it difficult to achieve the single base-pair resolution required to assay all available InDels and may hamper allele-calling [ 23 ]. However, we have found that the sensitivity of a capillary sequencer and the genotyping software is sufficient to allow for unambiguous allele assignment even for 'difficult' sequences exhibiting 3' A addition. The examples shown in Figure 1a-d illustrate that robust genotyping is feasible for 1-bp InDels even when 3' A addition occurs. Another problem is the stuttering of the polymerase when it encounters poly(N) stretches. However, larger InDels are reliably detected by the software in poly(N) stretches (Figure 1f ), and in a few difficult cases visual inspection can even resolve and unambiguously assign 'stuttering' 1-bp InDels according to the location and number of peaks (Figure 1e ). Genotyping with FLP assays is extremely accurate. In a control experiment, we genotyped all 96 samples of the fly strains FRT42B and EP0755 for the 1-bp InDel 2R090 and 231 samples homo- and heterozygous for the C. elegans Bristol and Hawaii backgrounds, respectively, for the 1-bp InDel ZH5-16. 2R090 exhibits both stuttering and A addition and hence is especially difficult to resolve (see Additional data file 8). The genotype was correctly and automatically assigned by GeneMapper in all 423 assays. Thus, automated genotyping based on FLPs is sensitive down to single base-pair resolution and is extremely robust. The accuracy of FLP mapping is comparable to other methods such as TaqMan (error rate less than 1 in 2,000 [ 24 ]), minisequencing (99.5% [ 25 ]), and pyrosequencing (97.3 % [ 25 ]). C. elegans and Drosophila FLP maps In C. elegans , genetic experiments are performed almost exclusively in the background of the standard wild-type strain N2 ( C. elegans variety Bristol) [ 26 ]. For gene mapping experiments, the polymorphic strain CB4856 ( C. elegans , variety Hawaii) has proved extremely useful [ 3 ]. When compared to N2, CB4856 contains on average one SNP every 840 bp and approximately 25% of all polymorphisms are InDels [ 14 ]. Starting from the dataset previously published by Wicks et al. [ 3 ], 112 FLPs that are evenly spaced on the physical map of C. elegans were validated to date (Figure 2a ). The confirmation rate of the predicted InDels was 88% ( n = 169). Most failures to detect a FLP are probably due to original sequencing errors. The average distance between neighboring FLP assays is about 0.9 Mbp. This physical distance corresponds to about 3 cM, assuming 300 kb per map unit, and encompasses between 100 and a maximum of 500 genes (Figure 2a ). The length of the amplicons ranges from 100 to 444 bp, and the fragment length differences are between 1 and 21 bp (Additional data file 9). If necessary, another 2,454 predicted InDels are available to increase the mapping resolution down to 50 kbp on average (Additional data files 12-17). To establish a Drosophila FLP map, a set of 54 FLP assays (12 to 17 per arm of the two major autosomes) was validated from the list of polymorphisms identified by Berger et al . [ 2 ] (Figure 2b , and Additional data file 10); high-resolution X-chromosomal SNP and FLP maps have yet to be established. Similarly to C. elegans , the confirmation rate of the predicted Drosophila InDels was 80% ( n = 30). Furthermore, another 509 InDels are predicted at 248 sites for which an assay can be established to discriminate between EP and FRT strains (Additional data file 18). The validated Drosophila FLP assays were evenly spaced on the genetic map with an average distance between neighboring assays of about 4 cM, corresponding to an average resolution of 1.77 Mbp on the physical map encompassing 95,55 Mbp [ 27 , 28 ]. Taking into account the non-validated InDels, the maximal average resolution is currently 314 kb or 0.7 cM. On the left arm of chromosome 3, where the genetic map is inexact, FLPs were spaced on the physical map assuming colinearity between the two maps. The length of amplicons ranges from 99 to 365 bp, and the size difference ranges from 1 to 54 bp (Additional data file 9). Our Drosophila FLP assays are in part derived from a set of InDels of size difference 7 bp or more (termed PLPs by Berger et al . [ 2 ]). However, since 86.8% of all Drosophila InDels exhibit a length difference of one to six nucleotides [ 2 ], so far only a small subset of the available InDels has been covered. The approach presented here significantly increases the number of possible FLP assays for genotyping and offers a greater flexibility and higher resolution. FLP mapping of C. elegans genes To demonstrate the usefulness of the C. elegans FLP map, we mapped three previously characterized mutations on chromosome II that exhibit diverse phenotypes. Those were the centrally located let-23(sy1) allele that causes an 80% penetrant vulvaless phenotype [ 29 ], rol-1(e91) in the middle of the left chromosome arm, which causes the animals to roll around their body axis [ 30 ], and the unc-52(e444) mutation located at the right end of the chromosome, which results in a paralyzed phenotype [ 31 ]. Mutant hermaphrodites were crossed with CB4856 males, and wild-type F 1 cross-progeny was selected (F 1 self-progeny would exhibit a mutant phenotype). Finally, mutant self-progeny was isolated in the F 2 generation and used for genotyping (Figure 3a ). To minimize the number of PCR reactions, we pursued a two-step strategy. First, we determined chromosomal linkage by analyzing 16 individual F 2 animals (corresponding to 32 chromosomes in total) with one centrally located FLP assay per chromosome (Tier 1, Figure 2a ). This allowed us to establish clear linkage to chromosome 2 for all three mutations (Additional data file 2). Surprisingly, the rol-1(e91) mutation showed linkage to the X chromosome of N2 in addition to chromosome II. This pseudo-linkage could be due to a suppressor of the Rol phenotype present on the CB4856 X chromosome. In a second step, 48 F 2 animals for each mutation were analyzed with eight FLP assays along chromosome 2 (Tier 2, Figure 2a ). In this way, we could narrow down the three mutations to the correct chromosomal subregions (Additional data files 3-5). We used the same strategy to map the zh41 mutation that was identified in a forward genetic screen for mutants exhibiting a loss of egl-17::gfp expression in the vulval cell linage ([ 32 ] and I. Rimann and A. Hajnal, unpublished work). Analysis with Tier 1 established linkage to chromosome 1 (Figure 3b ), and Tier 2 narrowed down the candidate region to an interval of 2.2 Mbp containing 498 genes (Figure 3c ). The phenotype of zh41 animals is similar to the phenotype caused by loss-of-function mutations in lin-11 , which maps to the same interval in the center of chromosome I [ 33 ]. Like lin-11 mutants, zh41 animals exhibit a penetrant protruding vulva (Pvl) phenotype, and staining of the adherens junctions with the MH27 antibody showed defects in the formation of the vulval torroid rings (Figure 3d ) [ 33 ]. Subsequent sequencing of the lin-11 locus in zh41 animals revealed a point mutation that results in a change of leucine 274 to phenylalanine. Furthermore, zh41 failed to complement lin-11(n389) , indicating that the zh41 mutation in the lin-11 open reading frame (ORF) is responsible for the vulval phenotype. In cases where a mutation maps to an interval that contains no obvious candidate gene, we first screen for additional informative recombinants by FLP analysis and then refine the map position by extracting more FLPs from our set of non-validated InDels (Additional data files 12-17) and by genotyping existing SNPs in the candidate interval [ 3 ]. In many cases, this resolution is sufficient to identify the affected gene through RNA interference (RNAi) analysis of the genes in the corresponding interval [ 34 ]. (See Additional data file 6 for a detailed flowchart of the mapping process). In summary, FLP mapping in C. elegans allows us to rapidly map a mutation down to a small region containing, on average, 200 candidate genes by crossing a mutant strain to CB4856 and analyzing 48 F 2 animals with 300 to 500 PCR reactions. Genotyping Drosophila strains with FLP assays In contrast to the well defined genetic backgrounds used for C. elegans , zebrafish ( Danio rerio ) or Arabidopsis genetics, Drosophila strains are very heterogeneous and of ill-defined origin [ 2 , 9 , 11 ]. In this respect, gene mapping in Drosophila resembles human genetics in that standard inbred lines do not exist and the genotypes of the parental lines have to be determined first. As genome-wide polymorphism databases for reference strains are available [ 2 , 11 ], a line of interest can be crossed with two reference strains, such as EP and FRT (see below). Owing to the codominant character of sequence polymorphisms, at least one of the two respective crosses will distinguish between the mutant and the mapping chromosomes. To further facilitate mapping with our set of FLP assays, we genotyped several common laboratory lines such as two 'wild-type' yw strains for the whole set, four FRT-Minute or FRT-cell-lethal strains at the relevant autosomal arms [ 35 ], as well as the FRT and EP reference strains at both relevant autosomal arms (Figure 2b ). Surprisingly, the FRT and EP lines are largely not of FRT or EP genotype on the chromosome arm for which they have not been calibrated. Overall, we found novel alleles for 18 of the 48 assays, and in an extreme case, we even observed five different alleles in five examined strains ( 2R017 , Figure 2b ). This result further highlights the heterogeneity of Drosophila strains (see Additional data file 1 for further details on FLP calibration and fly genetics). FLP mapping in Drosophila In a genetic screen devised to isolate genes that regulate growth and are situated on chromosome 2R, we found a complementation group characterized by a mild overgrowth phenotype (Figure 4b (2), and C. Rottig and E.H., unpublished work). From a cross between allele VI.29 and EP0755 we recovered three types of recombinant chromosomes: recombinants with a crossover proximal or distal to the mutation, respectively, and double-crossovers (Figure 4a , see also Additional data file 1 for further details on the crossing scheme). The mutation could be placed 16.9 cM proximal to EP0755 and 38.7 cM distal to FRT42D. The FLPs in the recombinant flies were directly analyzed without backcrossing the recombinant chromosome into a parental strain background. DNA was prepared from recombinants by a novel high-throughput protocol (see Materials and methods). We genotyped 34 distal crossover events, 40 proximal crossovers, and eight double-crossovers. This analysis placed the mutation between markers 2R096 and 2R109 (Figure 4c ). This interval includes the tumor suppressor hippo [ 36 ], and subsequent complementation analysis confirmed VI.29 as a weak hippo allele (data not shown). Furthermore, data from this and other FLP mappings in this region allowed us to further refine the genetic map (Additional data file 11). This kind of experimental data is helpful to space new FLP assays more evenly on the genetic map should the available map turn out to be inexact. If the resolution of the validated FLP map is too low to identify a candidate gene, we further refine the map position by several approaches. First, we design novel FLP-assays in the region of interest and genotype the most informative recombinants from the first round of FLP mapping (Additional data file 18). Second, we genotype recombinants with SNPs available in the region of interest and resolve them by RFLP, sequencing or DHPLC [ 2 , 9 ]. Third, we perform complementation analysis with recently established Drosophila lines with molecularly defined deletions [ 37 , 38 ]. (See Additional data file 7 for a detailed flowchart illustrating the mapping process.) Conclusions We have developed an automated method to detect most naturally occurring InDel polymorphisms at single base-pair resolution. Since a significant fraction of polymorphisms are caused by InDels of only a few base pairs (for example, 8% to 20% in humans [ 22 ]) the resolution of the medium-density FLP maps can be greatly increased where necessary, for example during the positional cloning of genes. We are therefore continually designing new FLP assays according to our specific needs using the predicted FLPs (Additional data files 12-18). The full automation of the genotyping has three main advantages when compared to manual methods. First, the error rate (the number of wrongly assigned genotypes) is extremely low, as it was not measurable in 432 assays. Second, genotyping can be done very rapidly and at a high-throughput with little manpower. The automatic allele-calling, in particular, saves much time. As the identification of informative recombinants is usually the rate-limiting step, FLP mapping is very helpful in extracting the few relevant recombinants from a large number of samples. Third, thanks to the standardized conditions, the low error rate and the absence of a secondary assay, FLP mapping is considerably cheaper than the previously published 'manual' mapping methods [ 2 , 3 ]. Unlike other high-throughput methods like TaqMan, Pyrosequencing, DHPLC, fluorescence polarization or primer-extension assays, FLP mapping does not require any investment in specialized equipment. It can be done in any molecular biology lab with access to a sequencing facility equipped with a capillary- or gel-based system, which usually includes the genotyping software. PCR costs are marginally higher because of the use of fluorescently labeled primers, but there are no added expenses for secondary enzymatic assays. It seems likely that in most organisms the frequency of polymorphisms caused by InDels is in the same range as found in humans, C. elegans or Drosophila . For example, 7.3% of the Arabidopsis sequence polymorphisms are InDels [ 39 ]. Thus, FLP mapping can easily be adapted to any organism for which polymorphism maps have been established, as there is no conceptual difference between human, Arabidopsis , C. elegans or Drosophila FLPs. Materials and methods C. elegans and Drosophila culture techniques and alleles Culturing and crossing of C. elegans was done according to standard procedures described in [ 26 ]. C. elegans alleles used were: LG I: lin-11(zh41), lin-11(n389) ; LG II: rol-1(e91) , let-23(sy1) , unc-52(e444) . Drosophila strains and the genetic screen have been described previously [ 9 , 35 , 40 - 42 ]. Single worm DNA extraction Adult worms were collected in 10 μl lysis buffer (50 mM KCl, 10 mM Tris pH 8.2, 2.5 mM MgCl 2 , 0.45% NP-40, 0.45% Tween-20, 100 μg/ml freshly added proteinase K) and incubated for 60 min at 65°C followed by heat-inactivation of proteinase K at 95°C for 10 min. Before PCR, 90 μl double-distilled H 2 O (ddH 2 O) was added to obtain a total volume of 100 μl per lysate. Fly DNA extraction DNA from recombinant flies was extracted in bulk by squishing flies through mechanical force in a vibration mill (Retsch MM30) programmed to shake for 20 sec at 20 strokes per second [ 43 ]. Single flies were placed into wells of a 96-well format deep-well plate with each well filled with 200 μl squishing buffer (10 mM Tris-Cl pH 8.2, 1 mM EDTA, 0.2% Triton X-100, 25 mM NaCl, 200 μg/ml freshly added proteinase K) and a tungsten carbide bead (Qiagen). The deep-well plate was then sealed with a rubber mat (Eppendorf) and clamped into the vibration mill. (Tungsten carbide beads can be recycled: after an overnight incubation in 0.1 M HCl and thorough washing in ddH 2 O the beads are virtually free of contaminating DNA.) Debris was allowed to settle for about 5 min, and 50 μl of each supernatant were transferred into a 96-well PCR plate. The reactions were incubated in a thermo-cycler for 30 min at 37°C and finally for 10 min at 95°C to heat-inactivate proteinase K. Before PCR amplification, the crude DNA extracts were diluted 20-fold to reduce the concentration of proteins that might be harmful for the capillary sequencer. PCR and FLP fragment analysis Diluted single-worm lysates (2 μl samples) or single fly extracts were added to 23 μl PCR reaction mix. Final concentrations in the PCR reaction were: 0.4 μM forward/reverse primer, 0.2 mM dNTPs, 2 mM MgCl 2 , 1x PCR reaction buffer, 0.25 U EuroTaq polymerase (Euroclone). PCR reaction setup was done in 96-well plates using a Tecan Genesis pipetting robot with disposable tips. PCR was carried out in two MJR thermo-cyclers that are integrated into the robot. The current setup allows for the sequential processing of six 96-well plates at a time. Cycling parameters were 2 min 95°C, 20 sec 95°C, 20 sec 61°C (-0.5°C for each cycle), 45 sec 72°C (for 10 cycles) followed by 24 cycles of 20 sec 95°C, 20 sec 56°C, 45 sec 72°C and a 10 min 72°C final extension. Following PCR, reactions were diluted 1:100 in water, and 2 μl diluted PCR products were mixed with 10 μl HiDi formamide containing 0.025 μl LIZ500 size standard (Applied Biosystems). This dilution before analysis on the capillary sequencer is necessary to reduce signal intensity because too strong signals compromise data analysis. In addition, sample dilution reduces the risk of damaging the capillaries with proteins or lipids present in the crude lysates. The dilution was done with standard tips using the Tecan Genesis pipetting station. Carryover of fragments was prohibited by a simple wash step with H 2 O. Fragments were analyzed on an ABI3730 capillary sequencer using POP7 polymer according to standard procedures. Data were analyzed using AppliedBiosystems GeneMapper software and raw data were treated further with Microsoft Excel. Additional data files The following additional data are available with the online version of this article. Additional data file 1 contains general information on fly genetics. Further C. elegans mapping results are given in Additional data files 2 , 3 , 4 and 5 . Detailed flowcharts illustrating the FLP mapping process are shown in Additional data files 6 and 7 . Additional data file 8 contains electropherograms demonstrating the accuracy of allele-calling. Additional data files 9 and 10 contain tables of primer and sequence data of experimentally verified FLP assays in C. elegans and Drosophila , respectively. Additional data file 11 contains a table of the refined genetic distances for FLP assays on the right arm of Drosophila chromosome 2. Additional non-validated FLPs can be found in Additional data files 12 , 13 , 14 , 15 , 16 and 17 ( C. elegans ) and Additional data file 18 ( Drosophila ). Supplementary Material Additional data file 1 General information on fly genetics Click here for additional data file Additional data file 2 Proof-of-principle for chromosomal linkage with 3 known mutations on chromosome 2. Assays used to assess linkage were ZH1-01, ZH2-01, ZH3-05a, ZH4-03, ZH5-01 and ZHX-02 Click here for additional data file Additional data file 3 Mapping of let-23 to its subchromosomal region ( C. elegans ) Click here for additional data file Additional data file 4 Mapping of rol-1 to its subchromosomal region ( C. elegans ) Click here for additional data file Additional data file 5 Mapping of unc-52 to its subchromosomal region ( C. elegans ) Click here for additional data file Additional data file 6 C. elegans FLP mapping flow chart Click here for additional data file Additional data file 7 Drosophila FLP mapping flow chart Click here for additional data file Additional data file 8 Electropherograms demonstrating the accuracy of allele-calling Click here for additional data file Additional data file 9 Tables of primer and sequence data of experimentally verified FLP assays in C. elegans Click here for additional data file Additional data file 10 Tables of primer and sequence data of experimentally verified FLP assays in Drosophila Click here for additional data file Additional data file 11 A table of the refined genetic distances for FLP assays on the right arm of Drosophila chromosome 2 Click here for additional data file Additional data file 12 Additional non-validated FLPs (predicted C. elegans InDels LGI) Click here for additional data file Additional data file 13 Additional non-validated FLPs (predicted C. elegans InDels LGII) Click here for additional data file Additional data file 14 Additional non-validated FLPs (predicted C. elegans InDels LGIII) Click here for additional data file Additional data file 15 Additional non-validated FLPs (predicted C. elegans InDels LGIV) Click here for additional data file Additional data file 16 Additional non-validated FLPs (predicted C. elegans InDels LGV) Click here for additional data file Additional data file 17 Additional non-validated FLPs (predicted C. elegans InDels LGX) Click here for additional data file Additional data file 18 Additional non-validated FLPs ( Drosophila ) Click here for additional data file

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Scavenger properties of cultivated pig liver endothelial cells

Background The liver sinusoidal endothelial cells (LSEC) and Kupffer cells constitute the most powerful scavenger system in the body. Various waste macromolecules, continuously released from tissues in large quantities as a consequence of normal catabolic processes are cleared by the LSEC. In spite of the fact that pig livers are used in a wide range of experimental settings, the scavenger properties of pig LSEC has not been investigated until now. Therefore, we studied the endocytosis and intracellular transport of ligands for the five categories of endocytic receptors in LSEC. Results Endocytosis of five 125 I-labelled molecules: collagen α-chains, FITC-biotin-hyaluronan, mannan, formaldehyde-treated serum albumin (FSA), and aggregated gamma globulin (AGG) was substantial in cultured LSEC. The endocytosis was mediated via the collagen-, hyaluronan-, mannose-, scavenger-, or IgG Fc-receptors, respectively, as judged by the ability of unlabelled ligands to compete with labelled ligands for uptake. Intracellular transport was studied employing a morphological pulse-chase technique. Ninety minutes following administration of red TRITC-FSA via the jugular vein of pigs to tag LSEC lysosomes, cultures of the cells were established, and pulsed with green FITC-labelled collagen, -mannan, and -FSA. By 10 min, the FITC-ligands was located in small vesicles scattered throughout the cytoplasm, with no co-localization with the red lysosomes. By 2 h, the FITC-ligands co-localized with red lysosomes. When LSEC were pulsed with FITC-AGG and TRITC-FSA together, co-localization of the two ligands was observed following a 10 min chase. By 2 h, only partial co-localization was observed; TRITC-FSA was transported to lysosomes, whereas FITC-AGG only slowly left the endosomes. Enzyme assays showed that LSEC and Kupffer cells contained equal specific activities of hexosaminidase, aryl sulphates, acid phosphatase and acid lipase, whereas the specific activities of α-mannosidase, and glucuronidase were higher in LSEC. All enzymes measured showed considerably higher specific activities in LSEC compared to parenchymal cells. Conclusion Pig LSEC express the five following categories of high capacity endocytic receptors: scavenger-, mannose-, hyaluronan-, collagen-, and IgG Fc-receptors. In the liver, soluble ligands for these five receptors are endocytosed exclusively by LSEC. Furthermore, LSEC contains high specific activity of lysosomal enzymes needed for degradation of endocytosed material. Our observations suggest that pig LSEC have the same clearance activity as earlier described in rat LSEC.

Background Livers of pig are used in a wide range of experimental settings related to problems encountered in human medicine such as fulminant hepatic failure [ 1 , 2 ] and graft survival in liver transplantation [ 3 ]. Bioartificial liver support systems, which combine living cells of the liver in an extracorporeal circuit, have been successfully used in first clinical trials [ 4 ]. The shortage of human organs to be used for bioreactors and the lack of safe and effective human liver cell lines have resulted in pigs becoming an important hepatic cell source. Although much work based on the use of pig liver has been carried out in the past, no studies have so far focused on isolated cell populations of the liver. To overcome this lack of knowledge, we recently published a study on high yield isolation and characterization of pig liver cells [ 5 ]. This work represents a basis for in vitro studies of the cells constituting the hepatic reticulo-endothelial system: the Kupffer cells (KC) and the liver sinusoidal endothelial cells (LSEC) [ 6 ]. Together these cells represent the most powerful scavenger system of the body. Most soluble or insoluble macromolecular harmful material entering the body from the gut is transported to the liver via the portal vein and eliminated by uptake in KC and LSEC. Studies in rats have revealed that LSEC are actively engaged in blood clearance of an array of waste macromolecules of both soluble and colloidal nature. Major components of connective tissue as well as other potentially hazardous products are eliminated rapidly and almost exclusively from the blood by receptor-mediated endocytosis in LSEC [ 7 , 8 ]. The endocytic receptors of rat LSEC can be listed in five categories: (i) collagen α-chain receptor (COLLAR), (ii) hyaluronan receptor (HAR), (iii) IgG Fc receptor, (iv) mannose receptor, and (v) scavenger receptor (SR). With specificity for free α-chains of types I, II, III, IV, V, XI collagens, but not native triple helical collagen, the COLLAR function has so far only been described in LSEC [ 9 , 10 ]. The HAR is responsible for the clearance of circulating hyaluran (HA) [ 11 , 12 ]. Chondroitin sulphate, dermatan sulphate, as well as some ligands for the SR are also endocytosed via this receptor, which has been purified and characterized [ 13 , 14 ]. In both KC and LSEC the IgG Fc receptor has been shown to mediate the elimination of circulating IgG immune complexes in the liver [ 15 - 18 ]. The mannose receptor recognizes the terminal non-reducing sugar residue of the oligosaccharide (fucose, mannose or N-acetyl-glucosamine) moiety of glycoproteins [ 19 , 20 ]. First found in alveolar macrophages [ 21 ], this receptor was later functionally demonstrated on LSEC as well [ 22 ], where it performs an extremely rapid endocytosis of mannosylated glycoproteins [ 23 ]. Sharing the feature of recognizing negatively charged ligands, the SR is a rapidly growing family of receptors that have been identified in macrophages and other cell types [ 24 , 25 ]. An array of physiological and foreign ligands has been reported to be eliminated from the circulation predominantly via endocytosis in the rat LSEC SR [ 8 , 26 - 29 ], using the receptors described above. Recent observations have resulted in new insight into the LSEC SRs and a relationship between SR and the HAR. First, it was published that a rat LSEC surface protein has affinity for both HA and SR-ligands; Antibodies generated against this protein inhibited endocytosis of both HA and various SR-ligands [ 13 ]. Therefore, the name hyaluronan/scavenger receptor (HA/S-R) was coined to describe its activity [ 14 , 30 ], but it is also known as stabilin-2. It was recently reported that LSEC in knock-out mice lacking SR class A, remove SR-ligands both in vivo and in vitro as avidly as do wild-type mice [ 31 ]. These results show that LSEC, employing this unique receptor, represents the most important cellular site of uptake of blood-borne SR-ligands. We have previously shown that pig LSEC express a functionally active receptor that both in vivo and in vitro clear a SR ligand [ 5 ]. Until now this represents the only functional study that has been done on pig LSEC. Accordingly, in the present study we wanted to study if pig LSEC carry out the same avid clearance based on the use of the same endocytosis mechanisms that has been already shown for the rat. We also compared the technique of isolation of pig LSEC with the established method used to prepare isolated rat LSEC [ 32 ]. Apart from the difference in organ size, the most striking difference from rat LSEC isolation was the high number of stellate cells within the pig non-parenchymal cells (NPC) which hampered the purification of the pig LSEC. In addition to represent a functional study of pig LSEC, this study also describes a method for obtaining a high number of purified pig LSEC which is necessary for the further purification and characterisation of molecules unique for LSEC. We here show that pig LSEC, but not stellate cells, parenchymal cells (PC) or KC, express the same five categories of endocytosis receptors for soluble and colloidal ligands as reported for rat LSEC. Ligands endocytosed via these receptors are transported along the endocytic pathway and degraded. Assays carried out to measure activity of lysosomal enzymes in cells showed that several enzymes are present at higher specific activities in LSEC than in either KC or PC. Results Isolation and cultivation of liver cells The fraction of cells collected after density centrifugation in OptiPrep consisted of LSEC, stellate cells, KC, PC and a minor proportion of unidentified cells, possibly of epithelial nature. In this fraction, LSEC were most numerous, closely followed by stellate cells. Further purification, using either elutriation centrifugation or selective adherence, resulted in 80–95 % purity of LSEC. One cycle of elutriation centrifugation resulted in approximately 10 8 LSEC. Contaminants in LSEC cultures were epithelial-like cells, stellate cells, KC, and PC. Compared with rat and mice PC, the population of PC from pig are much more heterogeneous in size; consequently, pig LSEC cultures may be contaminated by small PC. Cultures of KC prepared by selective adherence were prepared by incubating the non-PC fraction, for 15 min, on glass dishes coated with glutaraldehyde-treated BSA This procedure yielded 70–80 % pure KC. The use of tissue culture plastic dishes resulted in a lower purity of KC (40–50%), with a higher proportion of LSEC. Specificity of endocytosis via different receptors in cultured LSEC Experiments were carried out to establish whether ligands known to be taken up by rat LSEC via the different categories of receptors are endocytosed with the same specificities in pig LSEC. To this end, trace amounts of 125 I-labelled AGG, FSA, collagen α-chain (COLLA), mannan and FITC-bHA were incubated with purified cultures of pig LSEC with or without the presence of excess amounts of unlabelled substances known to compete with the radiolabelled ligands for endocytosis in rat LSEC. Control experiments showed that endocytosis was substantial following 2 h incubation at 37°C with only radiolabelled ligands. Expressed as % endocytosis of total added ligand, the results were as follows: AGG, 7.1%; FSA, 42.7%; COLLA, 59.7%; mannan, 38.2%; FITC-bHA, 12.1% (Fig. 1 ). Approximately 40–50% of the endocytosed COLLA and FSA and 30% of endocytosed AGG were recovered as acid-soluble degradation products in the medium after 2 h of incubation. No acid soluble radioactive degradation products of 125 I-mannan or 125 I-FITC-bHA were released to the medium. Competition experiments showed that AGG (300 μg/ml) inhibited uptake of trace amounts of 125 I-AGG by 73% (Fig. 1A ). AGG at a concentration of 100 μg/ml was only 40% inhibitory (results not shown). FSA (100 μg/ml) inhibited uptake of trace amounts of 125 I-FSA and 125 I-FITC-bHA by 95% and 14%, respectively (Figs. 1B and 1E ); COLLA (100 μg/ml) inhibited uptake of trace amounts of 125 I-COLLA by 84% (Fig. 1C ); mannose (50 mM) inhibited uptake of trace amounts of 125 I-mannan by 96% (Fig. 1D ); and hyaluronan (100 μg/ml) inhibited uptake of trace amounts of 125 I-FITC-bHA by 62% (Fig. 1E ). Figure 1 Receptor specificity. Specificity of uptake of 125 I-AGG (A), 125 I-FSA (B), 125 I-collagen (C), 125 I-mannan (D), or 125 I-FITC-bHA (E) in LSEC in the presence of mannose (50 mM), FSA (0.1 mg/ml), COLLA (0.1 mg/ml), hyaluronan (0.1 mg/ml) and AGG (0.3 mg/ml). Uptake (cell-associated radioactivity (solid bars) plus acid soluble radioactivity in spent medium [open bars]) was measured after 120 min incubation at 37°C. Results, given as % of control, are means of three experiments, each consisting of three parallels. Error bars represent standard deviation (+SD). Endocytosis of fluorochrome-labelled ligands To study the transport from early endosomes to later endocytic compartments, late endosomes and lysosomes were prelabelled with TRITC by administering TRITC-FSA to pigs via the jugular vein, 1.5 h prior to preparation of cultures [ 33 ]. Following incubation for 6.5 h at 37°C to allow the LSEC to adapt to the in vitro conditions, cultures were pulsed for 1 h at 4°C with fluorochrome-labelled ligands, and chased after an additional 10 min or 2 h incubation at 37°C. Due to weak intensity, FITC-bHA needed to be pulsed for 20 min at 37°C in cells that were not prelabelled with TRITC-FSA, and then chased for 20 min or 2 h to obtain a detectable image of the uptake. Observation of these cultures in the fluorescence microscope revealed that following a 10 min chase, FITC-labelled FSA (Fig. 2D ), COLLA (Fig. 2F ) and mannan (Fig. 2H ) were distributed in small (green) vesicles scattered throughout the cytoplasm. Co-localization of these green vesicles with perinuclear organelles prelabelled with red TRITC-FSA could not be observed at this early chase period. FITC-bHA chased for 20 min was also found in similar small vesicles and also in larger vesicles spread throughout the cell (Fig 2J ). Furthermore, following 10 min chase, most FITC-labelled AGG and TRITC-FSA co-localized (yellow color indicates co-localization) in large ring shaped vesicles throughout the cell, but some vesicles with only FITC-AGG were observed (Fig. 2A ). After 2 h, the co-localization with TRITC-FSA was significant for FSA (Fig. 2E ), COLLA (Fig. 2G ), and mannan (Fig. 2I ), whereas FITC-bHA was found in vesicles similar to those observed after 20 min (Fig. 2K ). After this chase-period FITC-AGG and TRITC-FSA, only partly co-localized as seen in a single cell (Fig. 2B ). FITC-AGG was still present in large ring shaped vesicles, of which many still were spread throughout the cell, whereas TRITC-FSA was found in small red vesicles but also in some big ring shaped vesicles together with FITC-AGG. Interestingly, control cells chased for 2 h with only TRITC-FSA (Fig. 2C ) had all ligand concentrated in the perinuclear region in lysosomes, and no ring shaped vesicles were observed. This indicates that FITC-AGG influences the transport of TRITC-FSA to the lysosomes. Figure 2 Intracellular transport of endocytosed ligands. Cultures of LSEC were pulsed with both TRITC-FSA and FITC-AGG for 1 h at 4°C. Chasing was performed after removal of unbound ligand by washing, and transferring of the cultures to 37°C. The cultures were fixed after chase periods of 10 min or 2 h and examined in fluorescence microscope. At 10 min (A) all TRITC-FSA co-localized with FITC-AGG (yellow colour indicates co-localization) in large ring-shaped vesicles (large arrowheads), and some vesicles with only FITC-AGG were observed (small arrowheads). After 2 h (B), co-localization of FITC-AGG and TRITC-FSA in large vesicles (arrows) was observed together with big vesicles with only FITC-AGG (large arrowheads) and small vesicles with only TRITC-FSA (small arrowheads). Controls show a more perinuclear appearance of TRITC-FSA when pulsed and chased for 2 h alone (C). In other experiments, TRITC-FSA was injected intravenously 1.5 h before isolation of the cells. Following an additional 6.5 h of cultivation at 37°C cultures of LSEC were pulsed with FITC-FSA (D-E), FITC-collagen (F-G), or FITC-mannan (H-I) for 1 h at 4°C. Following a 10 min chase, the FITC-ligands were observed to appear in small vesicles (arrowheads), and did not co-localize with TRITC-FSA (D, F and H). After 2 h, the FITC-ligands were transported to perinuclear compartments that co-localized almost completely with TRITC-FSA (small arrows in E, G and I). Other cultures of LSEC were pulsed for 10 min at 37°C with FITC-bHA. Following a 20 min chase, the FITC-bHA was observed in vesicles distributed all over the cell (arrowheads in J), and a similar appearance was observed after 2 h (arrowheads in K). Occasionally, cells that did not take up TRITC-FSA in vivo , but endocytosed FITC-ligands in vitro (big arrow in I), were observed. Scale bars: 10 μm. The FITC-ligands were observed only in cells that endocytosed TRITC-FSA in vivo . In spite of the fact that nearly all cells judged as LSEC in the cultures accumulated FITC-ligands in vitro , approximately 5% of the cells had not taken up TRITC-FSA in vivo . Lysosomal enzyme activities By using sensitive fluorometric assays, we measured the activities of six different lysosomal hydrolases, and compared their specific activities in three major liver cell types LSEC, KC and PC. The results, listed in Table 2 , reveal that all the enzymes measured were present in significantly higher specific activities in LSEC as compared to PC, with LSEC:PC ratios as high as 7.5, 6.8, 4.9, 3.3 and 2.3 for hexosaminidase, glucuronidase, aryl sulphatase, acid lipase and acid phosphatase, respectively. The specific activities of α-mannosidase and glucuronidase in LSEC were significantly higher also when compared to KC. Compared to PC, all the enzymes except for acid lipase were present in significantly higher activities in KC. Table 2 Specific activities of lysosomal enzymes in parenchymal (PC), sinusoidal endothelial cells (LSEC) and Kupffer cells (KC) isolated from pig liver. LSEC KC PC α-Mannosidase (= 7) 0.76 a (0.14) 0.59 b (0,16) 0.40 c (0.08) Hexosaminidase (n = 7) 30.3 a (7.86) 29.2 a (1.70) 4.02 b (2.06) Acid lipase (n = 6) 6.66 a (2.40) 4.33 ab (2.60) 2.03 b (0.46) Acid phosphatase (n = 6) 11.2 a (3.90) 8.89 ab (2.72) 4.97 b (1.96) Glucuronidase (n = 5) 3.55 a (0.77) 2.35 b (0.53) 0.52 c (0.28) Aryl sulphatase (n = 4) 0.27 a (0.11) 0.20 a (0.05) 0.05 b (0.02) Each experiment consisted of three parallels. Values are means (SD). The letters (a, b, and c) indicate significant statistical differences between the values of the different cell types. Enzyme activities are expressed as 10 6 4-methylumbelliferyl (-mannopyranoside, -N-acetyl-glucosaminide, -oleate, -phosphate, -glucuronide, -sulfate) molecules released per min per g cell solubilisate at 37°C. (p < 0.05, ANOVA, LSD post hoc test). Discussion In spite of the fact that most researchers in the field would assume that pig LSEC perform the same important scavenger function as rat LSEC [ 34 ], no studies have been published so far to actually prove this supposition. The present study was undertaken to establish whether LSEC from pig liver have the same high scavenger capacity as has been found for LSEC from rat liver [ 7 ]. To this end pig liver LSEC were isolated and cultivated, and studied with respect to endocytic and degradative activity. Since LSEC make up just a few percent of the total liver volume [ 35 ], it is important to employ a cell marker that readily and specifically distinguishes LSEC from other types of liver cells. We found that soluble FITC- and TRITC-FSA serve this function. Although other fluorescently marked probes can be used for the same specific distinction of LSEC, FITC-FSA stands out as the optimal marker, since it is inexpensive, easy to prepare and very stable. To label LSEC in vivo , 20 mg FITC/TRITC-FSA in 20 ml physiological saline was administered via the left external jugular vein 90 min prior to perfusion of the liver with collagenase. The selective adherence steps used for the separation of KC from the NPC fraction in mice (10 min on uncoated tissue culture plastic dishes) [ 31 ] and rats (30 min on tissue culture plastic dishes coated with glutaraldehyde-fixed BSA) [ 36 ] are not useful for separation of KC from pig NPC. Glass dishes coated with GA-fixed BSA were found to be the best substrate for selective adherence of KC. After KC depletion by selective adherence on this substrate, the non-adherent NPC fraction was transferred to fibronectin-coated plastic culture dishes, incubated for 15 min to allow attachment of LSEC, and washed extensively. This procedure resulted in 90% pure LSEC. Longer incubation time or too weak washing were found to yield a higher relative number of contaminating stellate cells and small PC. Furthermore, in contrast to liver cell isolation from rat and mice, it is not feasible to remove all PC from the pig NPC suspension by neither isopycnic- or elutriation centrifugation due to the large heterogenity in both density and size of the cells. Heterogenity in density was also observed in LSEC, since the cells were found in all tested OptiPrep-layers, with densities varying from 1.038–1.086 g/ml. Moreover, LSEC were elutriated at all flow rates varying from 20–50 ml/min, indicating heterogeneity in cell size. For mass isolation of LSEC by elutriation centrifugation, we obtained purities between 80–95%, very similar to purities obtained by selective adherence. Taken together, LSEC purified by selective adherence is faster as long as a limited number of cells are needed. Elutriation centrifugation is more time-consuming, but yields a higher cell number, with up to 1.1 × 10 9 purified LSEC from one pig. The addition of the detergent Pluronic acid F-68 in the elutriation buffer eliminated clotting of cells in the elutriation chamber [ 37 ], thereby allowing a higher number of cells to be loaded per cycle. The ligands COLLA, FITC-bHA, mannan, FSA, and AGG were used to probe the endocytic activity in pig LSEC via the COLLAR, HAR, SR, mannose receptor and IgG Fc receptors, respectively. We found that all these ligands were avidly endocytosed in LSEC. Competition experiments showed that the ligands were taken up in a specific manner, via the five different categories of receptors. Moreover, morphologic pulse-chase experiments using FITC-labelled ligands to study the intracellular transport of endocytosed ligand suggested that all ligands studied, with the exception of AGG, reached lysosomal compartments within a time span of 2 h. At that time, AGG was still found in ring shaped structures which is a typical feature of early and late endosomes [ 33 , 38 ]. The finding that endocytosed AGG was degraded (albeit not as efficiently as other ligands), without reaching the lysosomal compartment, indicates that this ligand is processed differently than the other ligands studied. This phenomenon has also been observed by Løvdal et al. [ 15 ] who noted that rat LSEC and KC in vitro degraded endocytosed IgG-complexed antigen much slower than ligands for the mannose- and scavenger receptor, and that the delay was due to slow departure from early endosomes. We also observed that FITC-AGG delayed the transport of TRITC-FSA to the lysosomes when the ligands were given simultaneously. This is consistent with the observation that AGG reduces the amount of 125 I-FSA degraded even if the total amount of endocytosed FSA was not changed. Interestingly, we observed no uptake of FITC-AGG in KC. FITC-labelled ligands were seen concentrated in small spherical vesicles scattered over the entire cell body after a 10 min chase. These small vesicles are probably early endosomes reminiscent of small bristle coated vesicles that have been previously observed electron microscopically as vesicles with a diameter of 180 nm, located directly below the cell surface [ 39 ]. The vesicles containing FITC-bHA after a 10 min pulse at 37°C followed by a 20 min chase, are probably late endosomes (with diameter ranging between 800–1500 nm) as reported in similar studies in rat LSEC [ 38 , 39 ]. After a 2 h chase, the stain partly co-localized with TRITC-FSA, indicating further transport to late endosomes and lysosomes. FITC-bHA was observed in small perinuclear vesicles after a 2 h chase, and almost 30% of the endocytosed 125 I-FITC-bHA was found as low molecular weight material, demonstrating intracellular degradation (results not shown). We stress that, although practically all cultured LSEC accumulated FITC-labelled ligands in vitro , not all LSEC had taken up TRITC-FSA in vivo . We speculate that the explanation for the heterogeneous uptake in vivo was due to circulatory regulation: not all sinusoids may have allowed entrance of blood at the time of injection, thus preventing LSEC from being exposed to the injected ligand in those sinusoids. This is not an unreasonable explanation, since it is known that hepatic sinusoids may regulate blood flow by a sphincter mechanism [ 40 ]. If LSEC are an important part of the reticulo-endothelial system in the body they also need a high activity of lysosomal enzymes to degrade waste material endocytosed from the circulation. Therefore, we compared the lysosomal activity in LSEC with the metabolically very active PC and the phagocytic KC. Earlier studies in rat have revealed that KC and LSEC, as compared to PC, contain higher specific lysosomal enzyme activities [ 41 - 43 ], and we found that all enzymes measured were present in considerably higher specific activities in LSEC than in PC. The specific activities of α-mannosidase and glucuronidase in LSEC were also higher than in KC. The high specific activities of lysosomal enzymes in LSEC are compatible with the notion that these cells are true professional scavenger cells. Moreover, studies in rat have shown that LSEC may recruit lysosomal enzymes from the circulation by endocytosis via the mannose receptor [ 44 , 45 ]. This may partly explain the very high specific activity of such enzymes in these cells in the pig as well. In all our experiments we used freshly isolated cells because we believe that when cells have just been isolated from the intact organ, their in vitro scavenger properties resemble their in vivo properties. Once outside their micro-environment in the liver, rat LSEC dedifferentiate and eventually die after 2–4 days on culture dishes. During the first hours in culture only small changes in endocytic capacity of LSEC occur in so far as experiments in our laboratory have shown that pig LSEC cultivated in RPMI for 2 days retain 80% of the endocytic capacity when compared to freshly isolated cells (results not shown). Because of the short cultivation time used in our experiments, the endocytic capacity of the cells would not be influenced by mediators in the medium. But in other experiments with cultures of rat LSEC that were incubated for 18 h or more, it has been shown that inflammatory mediators like tumor necrosis factor-α and interleukin-1β enhance 2–3-fold endocytosis via the SR and mannose receptor, while COLLAR mediated endocytosis remained unaffected [ 46 ]. Also lipopolysaccharide can increase endocytosis in LSEC by stimulating the cells to release autocrine interleukin-1β. Another mediator, the nitric oxide, decreases endocytosis via the mannose receptor in rat LSEC [ 47 ]. Using interleukin-10, Knolle et al. [ 48 ] found a similar effect as that reported with nitric oxide, namely down-regulation of mannose receptor mediated endocytosis in mouse LSEC. Other potentially mediators of endocytic capacity are VEGF [ 49 , 50 ] and phorbol ester [ 51 ] which at least have been shown to improve maintenance of rat LSEC in culture. Conclusion Our results suggest that pig LSEC are functionally very similar to rat LSEC, at least in terms of clearance activity. It is therefore highly likely that LSEC in pig (as in rat) represent the major site of elimination of an array of soluble waste molecules from the circulation. Several if not all of these waste macromolecules are harmful if allowed to accumulate in the blood. The very active endocytosis in LSEC ensures that these soluble waste macromolecules are never allowed to increase above trace levels in the circulation. Methods Chemicals and animals 1,3,4,6-tetrachloro-3α,6α-diphenylglycoluril (Iodogen), carrier-free Na 125 I, and TRITC were from Pierce, Rockford, IL, USA, Institute for Energiteknikk, Norway and ICN Biomedicals Inc., OH, USA. FITC, BSA, 4-methylumbelliferyl-substrates for fluorometric assays of lysosomal enzymes, Triton X-100, mannose, and mannan was from Sigma Chemical Co, St.Louis, MO, USA. Collagenase P was from Boehringer Mannheim, Germany. Fibronectin was kindly donated by Dr. B. Hansen, University of Tromsø, Norway. Human IgG and high molecular weight hyaluronan (Healon) were from Pharmacia, Sweden. OptiPrep was from Nycomed, Norway. Human serum albumin was from Octapharma, Ziegelbrucke, Switzerland. Monoclonal mouse anti-human desmin, clone D33, was from DAKO A/S, Denmark. Monoclonal goat anti-mouse IgG, TRITC-conjugate, was from Zymed, CA USA. Two mouse monoclonal antibodies (clones 2G6 and 2B10) against porcine macrophages were kindly provided by Dr. A. Berndt, Institute of Pathology, Friedrich Schiller University, Jena, Germany. Castrated male piglets ( Sus scrofa domesticus , Norwegian strain), weighing 7–8 kg, were fasted for 18 h, drinking water ad libitum , prior to sacrifice. Animals received care according to "Guide for the Care and Use of Laboratory Animals" prepared by the National Academy of Sciences and published by the National Institutes of Health, NIH publication 86-23 revised 1985. Isolation and cultivation of liver cells The procedure for isolation of functionally intact LSEC from a single pig liver was as described [ 5 ]. Briefly, the liver was perfused with a physiological saline buffer to wash out blood cells before perfusion with a collagenase buffer to disperse the liver cells. The resulting single cell suspension was subjected to 2 × 3 min velocity centrifugation (50 g ) to pellet PC, and the resulting supernatant was concentrated by centrifugation (850 g ) for 10 min, mixed into a 21% OptiPrep density solution, and centrifuged for 30 min (3300 g ). The PC and RBC were pelleted, and a layer consisting of 6 × 10 8 -3.5 × 10 9 non-PC were recovered. For further purification, 2.0 × 10 8 non-PC in HBBS solution containing 0.3 % BSA, 0.4 mM EDTA, 200 μg/ml Pluronic acid F-68, and antibiotics were introduced into a standard elutriation chamber of a JE-6B rotor (Beckman Instruments) in a J-21-type Beckman centrifuge at a flow rate of 22 ml/min and a rotor speed of 2500 rpm. The first fraction of 100 ml was enriched in stellate cells as judged by strong staining with anti-desmin antibody. The flow rate was then increased to 35 ml/min, and 150 ml was collected. This fraction contained purified LSEC characterized by their specific accumulation of FITC-labelled formaldehyde-treated serum albumin (FSA). The cells remaining in the chamber were pumped out and discharged after the centrifuge was stopped. These were mainly KC and PC. KC were identified by the immunostaining with two anti-porcine macrophage antibodies. The average numbers of LSEC grown per cm 2 were 2.5 × 10 5 in Falcon dishes (Becton Dickinson, France). An alternative separation technique was also used: the cell suspension of non-PC after the gradient centrifugation was diluted to 4 millions/ml and seeded on glass dishes at a concentration of 5 × 10 5 /cm 2 and allowed to attach for 15 min, at 37°C. Prior to use, the glass dishes were washed in 96% ethanol, coated with bovine serum albumin and fixed in 1% glutaraldehyde for 30 min., before being extensively rinsed in distilled water. It was mainly KC that attached well to these dishes. Poorly attached cells that were detached by gentle washing together with non-adherent cells were transferred to fibronectin-coated culture dishes, incubated for another 15 min followed by thorough washing, and supplied with fresh medium to enable attachment and spreading of viable SEC. The purity of these LSEC cultures was between 80–95%. Ligands FSA was prepared by treating BSA with 10% formaldehyde in 0.2 M carbonate buffer, pH 10, for 3 days as described [ 52 ]. Aggregated gamma-globulin (AGG) was prepared by heating purified human IgG (10 mg/ml) for 30 min at 63°C [ 53 ]. Insoluble AGG was removed by centrifugation for 30 min (3300 g ). Native triple helical collagen ((Nutacon, Leimunden, The Netherlands) was denatured to single collagen α-chains (COLLA) by incubation at 60°C for 60 min. Biotinylated hyaluronan, bHA, was prepared by incubating HA with Biotin-LC-Hydrazide (Pierce, Rockford, IL, USA) and 1-ethyl-3-[3-dimethylaminopropyl] carbodiimide hydrochloride (Sigma Chemical) in a ratio allowing a maximum of 1 out of 10 carboxyl groups per HA molecule to be labelled [ 54 ]. Labelling of ligands with fluorochromes FSA, AGG, collagen, bHA and mannan dissolved in sodium carbonate buffer (0.1 M, pH 9.5) were incubated with FITC or TRITC in a ligand/dye weight ratio of 5:1, at 4°C overnight. To remove unbound dye, the solutions were dialysed against PBS. Radiolabelling procedures Macromolecular ligands (FSA, mannan, collagen, AGG and FITC-bHA) in PBS were labelled with Na 125 I employing Iodogen as the oxidizing agent [ 55 ]. Radiolabelled proteins and free iodine were separated by gel filtration on a PD-10 column (prepacked Sephadex G-25, Pharmacia, Uppsala, Sweden). The resulting specific radioactivity was 1–3 × 10 6 cpm/μg protein. To radiolabel bHA, FITC was first attached to the biotin, thereby providing an 125 I acceptor. For separation of free iodine from radiolabelled FITC-bHA, the reaction solution was dialysed against PBS, giving a final radioactivity of 0,3 × 10 6 cpm/μg HA. Receptor specificity of 125 I-labelled ligands After seeding and cultivation for 2–3 h in 24 well dishes, purified cultures of LSEC were washed and supplied with fresh RPMI 1640 medium containing 1% human serum albumin and trace amounts of one of the six 125 I-ligands (10,000–30,000 cpm per culture) and excess cold ligands. All endocytosis experiments were terminated after an incubation-period of 2 h at 37°C, by transferring the conditioned medium (200 μl), along with 500 μl PBS used for washing of the cells, to tubes containing 500 μl 20% trichloroacetic acid. Following centrifugation of the tubes, the extent of degradation was determined by measuring the radioactivity in the pellets and the supernatants, except for mannan or FITC-bHA where the trichloroacetic acid-precipitation step was omitted due to lack of degradation products being released from the cells. The 125 I was attached to the protein core in mannan and to the FITC adduct in FITC-bHA of which both accumulate in the lysosomes, since mammalian cells do not carry degradative hydrolases for these molecules. Cell-associated radioactivity was determined by measuring the amount of 125 I released by solubilizing washed cultures in 1% SDS. All experiments were carried out in triplicate. Accumulation of fluorochrome-labelled ligands TRITC-FSA (20 mg) in 20 ml physiological saline was administered via the left external jugular vein 90 min prior to liver perfusion. Prior to use, TRITC-FSA was centrifuged at high speed and sterile-filtered in order to remove aggregates which would otherwise be taken up in KC by phagocytosis. Cultures of LSEC were established on fibronectin-coated 14 mm diameter glass coverslips for 3–4 h in serum-free growth medium. The cultures were then washed in PBS, and pulsed in fresh medium with 0.1 mg/ml FITC-ligands for 1 h at 4°C. Because FITC-labelling of proteins remove positive charges, and thus may turn proteins into negatively charged ligands for the SR [ 56 ], FITC-mannan and FITC-collagen were pulsed in the presence of 0.5 mg/ml FSA to avoid binding to SR. Endocytosis of FITC-AGG was studied in cells that had not been prelabelled with TRITC-FSA. Instead, the cells were pulsed in fresh medium with both 0.1 mg/ml FITC-AGG and TRITC-FSA for 1 h, at 4°C. After removal of unbound ligand by washing with PBS, bound ligands were chased for 10 min and 120 min in fresh pre-warmed medium at 37°C. As the only fluorochrome in the cells, FITC-bHA (0.2 mg/ml) was pulsed for 10 min at 37°C before medium change, and then chased for 20 min and 2 h. Incubations were terminated by fixation in 4% formaldehyde, and the specimens examined in a Zeiss Axioplan fluorescence microscope. Micrographs were taken with a Nikon Coolpix 4500 digital camera. Assay of lysosomal enzymes Samples of PC were taken from the pellet resulting from the density centrifugation, and solubilized in 0.1 % Triton, whereas KC and LSEC samples were obtained from the solubilizates of cultures seeded on 7.5 cm 2 Falcon culture dishes. The assay conditions of the six enzymes are given in Table 1 . In all the enzyme assays, except for acid lipase (see below) aliquots of 100 μl substrate were incubated at 37°C with the appropriate amount of cell-solubilizates, and the appropriate length of time after which 2.0 ml of 0.5 M glycine/sodium hydroxide buffer pH 10.4 were added to stop the reaction and to develop the fluorochrome. To assay for acid lipase 10 μl of substrate was used, and 2.0 ml of 0.5 M Tris buffer pH 8.5 was added to stop the reaction. Table 1 Conditions of enzyme assays. Enzyme Conc. (mM) Substrate Buffer pH Incubation time (min) Solubilisate volume (μl) α-Mannosidase 2.5 4-methylumbelliferyl-α-D-mannopyranoside Phosphate/citrate (0.1 M) 4.0 120 40 Hexosaminidase 5.0 4-methylumbelliferyl-N-acetyl-β-D-glucosaminide Phosphate/citrate (0.1 M) 4.5 30 5 10 (PC) Glucuronidase 2.5 4-methylumbelliferyl-β-D-glucuronide Acetate (0.1 M) 4.5 120 20 5 (LSEC) Acid phosphatase 1.0 4-methylumbelliferyl-phosphate Acetate (0.1 M) 4.5 30 10 Aryl sulphatase 10.0 4-methylumbelliferyl-sulfate Acetate (0.5 M) 5.5 120 40 Acid lipase 0.3 4-methylumbelliferyl-oleate Acetate (0.1 M)* 4.0 60 20 *Acetate (0.1 M) + 0.1% Triton X-100 + phosphatidylcholine (100 μM) + taurodeoxycholic acid (300 μM). The fluorescence of 4-methylumbelliferone, resulting from the action of the enzymes on the various substrates, was measured in a Shimadzu RF 5000 spectrofluorometer with excitation set at 360 nm and emission at 450 nm. Enzyme activities are given in number of substrate molecules transformed min -1 ·gram protein -1 under the conditions stated above. Protein content in the samples was measured according to Lowry [ 57 ], and BSA in 0.1 % Triton was used as standard. Statistics The values are expressed as: mean (standard deviation) unless otherwise noted. We used SPSS 10.0 software package (SPSS, Chicago, IL) for statistical analysis. Analysis of variance (ANOVA) was used to test whether any statistical significance existed between the three cell populations enzyme activity followed by the LSD post hoc comparison test. Probability values of p ≤ 0.05 were considered significant for all tests applied. Authors' contributions KHE and GIN designed and carried out the experiments. KHE drafted the manuscript. AR and BS coordinated the study and contributed to the text of the manuscript.

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Yeast Prions: Protein Aggregation Is Not Enough

Although many proteins -- both damaged and normal -- have a tendency to aggregate, only some are capable of dividing and propagating. What does it take to turn a protein aggregate into an infectious prion?

Many damaged and mutant polypeptides, as well as some normal proteins, have a tendency to aggregate in cells. Some protein aggregates are capable of “dividing” and propagating in cells, leading to formation of similar aggregates in daughter cells or even in neighboring cells due to “infection.” These self-propagating protein aggregates are called prions and constitute the basis of prion diseases. The infectious agent in these diseases is an abnormal conformation of the PrP protein (PrP Sc ), which makes it protease-resistant and initiates its aggregation ( Prusiner 1998 ). The abnormal aggregated species can recruit normal soluble PrP molecules into aggregates, thus inactivating them. The aggregates of PrP Sc can proliferate within cells and be transmitted to other cells and tissues, leading to the spread of neurotoxicity. Prion Domains While so far only one prion protein is known in mammals, several prion-like proteins capable of forming self-propagating aggregates have been found in various yeast species. The common structural feature of yeast prion proteins is the so-called prion domain, characterized by the high content of glutamines (Q) and asparagines (N) ( DePace et al. 1998 ; Michelitsch and Weissman 2000 ), also known as the Q/N-rich domain. The prion domains are the major structural determinants that are solely responsible for the polypeptide aggregation and propagation of the aggregates. Interestingly, the mammalian PrP Sc is fundamentally different from yeast prions, since it lacks a Q/N-rich domain, indicating that distinct structural features are responsible for its ability to form self-propagating aggregates. The Q/N-rich domains in yeast prions are transferable in that, when fused to a heterologous polypeptide, they confer prion properties to this polypeptide. With a low probability, soluble proteins with prion domains can change conformation to form self-propagating aggregates, which can be transmitted to daughter cells ( Lindquist 1997 ) ( Figure 1 ). As with PrP Sc , yeast prions efficiently recruit soluble molecules of the same species, thus inactivating them ( Lindquist 1997 ; Chernoff 2001 ; Wickner et al. 2001 ). Also with low probability, the aggregation-prone conformation of yeast prion proteins can reverse to a soluble functional conformation. Certain yeast prion proteins, when in soluble conformation, function in important pathways; e.g., Sup35 (forming [PSI + ] prion) controls termination of translation, and Ure2 (forming [URE3 + ] prion) controls some membrane transporter systems. Aggregation of these proteins leads to phenotypes (e.g., suppression of nonsense mutations or transport defects) inherited in a non-Mendelian fashion owing to the nonchromosomal basis of the inheritance. Figure 1 Aggregation, Division, and Transfer of Prions in Yeast Inheriting Variations A remarkable feature of yeast prion proteins is their ability to produce distinct inherited “variants” of the prion. For example, [PSI + ] prion could exist in several distinct forms that suppress termination of translation to different degrees. These “variants” of yeast prions are analogous to different prion “strains” of PrP Sc , which cause versions of the disease with different incubation periods and different patterns of brain pathology. The molecular nature of distinct PrP Sc strains is determined by specific stable conformations of PrP. Similarly, “variants” of yeast prions are explained by different stable conformation states of the corresponding prion proteins ( Chien et al. 2003 ). Strict conformation requirements for aggregate formation can also explain interspecies transmission barriers, where prion domains of Sup35 derived from other yeast species cannot cause formation of [PSI + ] prion in Saccharomyces cerevisiae, in spite of a high degree of homology. This observation is very intriguing, especially in light of a recent finding that prion conformation of some proteins is required for formation of prions by the other proteins. For example, for de novo formation of [PSI + ] prion, a distinct prion [RNQ + ] should be present in a cell ( Derkatch et al. 2001 ; Osherovich and Weissman 2001 ), probably in order to cross-seed Sup35 aggregates. This is in spite of relatively limited homology between the prion domains of these proteins. The apparent contradiction between the interspecies transmission barriers of very homologous prion proteins and possible cross-seeding of aggregates by prion proteins with more limited homology represents an interesting biological problem. On the other hand, this apparent contradiction may indicate that prion formation is a more complicated process than we currently think and that it may involve many cellular factors. What Do Prions Do? Although yeast prions have been studied for almost ten years, very little is known about their biological significance. We do not know the functions of the majority of proteins that can exist as prions. Even if a function of prion proteins, such as with Sup35 or Ure2, is known, we do not understand the biological significance of their “prionization,” i.e., that they aggregate and propagate in the aggregated form. A very intriguing and unexpected finding was that formation of [PSI + ] prion causes a wide variety of phenotypic alterations, which depend on the strain background ( True and Lindquist 2000 ). In fact, comparison of yeast strains of different origin, each with and without [PSI + ] prion, showed that certain strains with [PSI + ] prion have different sensitivity to stresses and antibiotics than their non-prion derivatives, despite their genetic identity. In some strains, cells with [PSI + ] prion demonstrated better survival than their non-prion counterparts in the presence of inhibitors of translation or microtubules, heavy metals, low pH, and other deleterious conditions, which of course gives a strong advantage to the [PSI + ] cells. It is likely that some genomic mutations could be suppressed and therefore become silent when termination of translation by Sup35 is partially inactivated in [PSI + ] prion cells ( Lindquist 2000 ; True and Lindquist 2000 ). [PSI + ] could also reveal previously silent mutations or their combinations. It was hypothesized that switches between prion and non-prion forms of Sup35 enhance survival in fluctuating environments and provide a novel instrument for evolution of new traits. Q/N Does Not Necessarily a Prion Make Searching genomes of various species demonstrated that a relatively large fraction of proteins (between 0.1% and 2%) contain Q/N-rich domains ( Michelitsch and Weissman 2000 ) or polyQ or polyN sequences. These domains are often found in transcription factors, protein kinases, and components of vesicular transport. The Q/N-rich domains usually are not evolutionary conserved and their functional role is largely unknown. Some of the Q/N-rich or polyQ domains facilitate aggregation of polypeptides, especially if expanded owing to mutations. Such expansion of the polyQ domains in certain neuronal proteins could cause neurodegenerative disorders, e.g., Huntington's disease or several forms of ataxia. Importantly, aggregates formed by polypeptides with the Q/N-rich or polyQ domains are not necessarily self-propagating aggregates, i.e., prions. In fact, there are additional structural properties of the polypeptides that provide the self-propagation (see below). Even if a protein with a polyQ domain does not form a prion, its aggregation may depend on certain prions. For example, recent experiments demonstrated that [RNQ + ] prion dramatically stimulated aggregation of fragments of recombinant human huntingtin or ataxin-3 with an expanded polyQ domain cloned in yeast ( Osherovich and Weissman 2001 ; Meriin et al. 2002 ). [RNQ + ] facilitated the nucleation phase of the huntingtin fragment aggregation, suggesting that this prion can be directly involved in seeding of the aggregates. The major question now is whether there are analogous prion-like proteins in mammalian cells that are involved in aggregation of huntingtin or ataxin-3 and subsequent neurodegenerative disease. The first indication that mammalian proteins with Q/N-rich domains can form self-propagating prions came from recent work with a regulator of translation cytoplasmic polyadenylation element-binding protein (CPEB) from Aplysia neurons ( Si et al. 2003 ). The neuronal form of this protein has a Q/N-rich domain similar to the prion domains of yeast prions. The Q/N-rich domain from CPEB (CPEBQ), when fused to green fluorescent protein (GFP), conferred upon it prion-like properties. The CPEBQ–GFP fusion polypeptide existed in yeast cells in one of the three distinct states, i.e., soluble, many small aggregates, or few large aggregates. Mother cells almost always gave rise to daughter cells in which the CPEBQ–GFP polypeptide was in the same state, indicating the ability of these aggregates to be inherited, i.e., to self-propagate. Furthermore, full-length Aplysia CPEB protein, when cloned in yeast, can also exist in two distinct states, soluble and aggregated, which is an inherited feature. Very unexpectedly, unlike other prions, the aggregated state of CPEB was more functionally active than the soluble form ( Si et al. 2003 ). These data strongly suggest that metazoan proteins with Q/N-rich domains are potentially capable of forming prions. The challenge now will be to establish whether CPEB can exist as a self-propagating aggregate in Aplysia or mammalian neurons. Mystery of Propagation What makes protein aggregates in yeast propagate? The key cellular element that is critical for this process is molecular chaperone Hsp104 ( Chernoff et al. 1995 ). This factor is specifically required for maintenance of all known prions within generations and probably is not involved in prion formation (i.e., initial protein aggregation). [PSI + ] yeast cells have about 60 seeds of this prion (although this number differed in different [PSI + ] isolates), and maintenance of about this number of seeds after cell divisions requires functional Hsp104 ( Eaglestone et al. 2000 ). In fact, in the absence of Hsp104, prion aggregates continue to grow without increase in number and are rapidly lost in generations ( Wegrzyn et al. 2001 ). Since this chaperone can directly bind to protein aggregates and promote there disassembly ( Glover and Lindquist 1998 ), it was suggested that the main function of Hsp104 in prion inheritance is to disaggregate large prion aggregates to smaller elements, thus leading to formation of new seeds ( Kushnirov and Ter-Avanesyan 1998 ). Interestingly, although Hsp104 is conserved among bacteria, fungi, and plants, animal cells do not have this chaperone or its close homologs. Therefore, if yeast-type prions with Q/N-rich domains exist in animal cells, there should be alternative factors that disaggregate large prion aggregates into smaller species in order to keep the number of seeds relatively constant and thus maintain the prions. The fact that some proteins with Q/N-rich domains form self-propagating aggregates, while others can aggregate but cannot form prions, suggests that there should be some structural elements either within the Q/N-rich sequence or close to it that confer the ability to propagate. In an article in this issue of PLoS Biology by Osherovich et al. (2004) , the authors examined sequence requirements for prion formation and maintenance of two prion proteins, Sup35 and New1. They noted that both prion proteins contain an oligopeptide repeat QGGYQ in close proximity to Q/N-rich sequences and examined the functional significance of the repeats for aggregation and maintenance of the prions. In New1, in contrast to a deletion of the N-rich domain, deletion of the repeat did not affect aggregation of the protein or formation of the prion, but abrogated inheritance of the prion. With Sup35, the situation was somewhat more complicated, since repeats adjacent to Q/N-rich domain affected both protein aggregation and prion maintenance while more distant repeats affected only the prion inheritance. The authors suggested that the oligopeptide repeats facilitate the division of aggregates, either by serving as binding sites for Hsp104 or by altering the conformation of the polypeptides in aggregates to promote access for Hsp104 ( Figure 2 ). Figure 2 Distinct Domains of Sup35 Are Responsible for Aggregation and Division of Aggregates The likely possibility was that the oligopeptide repeats could be interchangeable between different prions, leading to creation of novel chimeric prions. In fact, the authors constructed an F chimera, a fusion protein having the N-rich domain of New1 and the oligopeptide repeat of Sup35. This fusion polypeptide efficiently formed prion [F + ]. Furthermore, when the oligopeptide repeat sequence was added to a polyQ sequence, this fusion polypeptide also acquired the ability to form self-propagating aggregates. This work, therefore, clarifies the architecture of prions by defining two structural motifs in prion proteins that have distinct functions in aggregation and propagation. Interestingly, not all yeast prions have similar oligopeptide repeat motifs, indicating that distinct structures could confer prion properties to polypeptides that can aggregate. It would be important to identify these structures in order to understand the mechanisms of aggregate propagation. The work of Osherovich et al. (2004) may help to identify proteins from mammalian cells, plants, and bacteria that can potentially form prions. Finding these novel prions could be of very high significance since they may provide insight into a wide range of currently unexplained epigenetic phenomena.

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548685

A conditional-lethal vaccinia virus mutant demonstrates that the I7L gene product is required for virion morphogenesis

A conditional-lethal recombinant virus was constructed in which the expression of the vaccinia virus I7L gene is under the control of the tetracycline operator/repressor system. In the absence of I7L expression, processing of the major VV core proteins is inhibited and electron microscopy reveals defects in virion morphogenesis subsequent to the formation of immature virion particles but prior to core condensation. Plasmid-borne I7L is capable of rescuing the growth of this virus and rescue is optimal when the I7L gene is expressed using the authentic I7L promoter. Taken together, these data suggest that correct temporal expression of the VV I7L cysteine proteinase is required for core protein maturation, virion assembly and production of infectious progeny.

Proteolytic cleavage of precursor proteins is an essential process in the life cycle of many viruses, including vaccinia virus (VV). The cysteine proteinase encoded by the VV I7L gene, was originally identified based on a sequence comparison with the African Swine Fever virus proteinase and an ubiquitin-like proteinase in yeast [ 1 , 2 ]. We have previously shown through trans processing assays that the I7L gene product is capable of cleaving the core protein precursors p4a, p4b, and p25K at conserved AG/X sites and have used reverse genetics to identify active site residues [ 3 , 4 ]. To determine the role that the I7L proteinase plays in the VV replication cycle, we report here the construction and in vivo analysis of a VV mutant in which the expression of the I7L gene can be conditionally regulated. While this work was in progress, Ansarah-Sobrinho and Moss [ 5 ] published a report demonstrating that the I7L proteinase, in an inducible mutant virus regulated by the lac operator and driven off of the T7 promoter, was responsible for cleaving the A17L membrane protein as well as the L4R core protein precursor. In this work, we show that I7L proteinase, in a different inducible mutant virus, this one regulated by the tetracycline (TET) operator/repressor system and driven off of the I7L native promoter, is responsible for cleaving the other core protein precursors (p4a and p4b). We also demonstrate that expression of the I7L gene from its native promoter appears to be important for optimal viral assembly and replication. To investigate the role of the I7L proteinase in the viral life cycle, an inducible mutant virus was constructed in which the expression of the I7L gene could be regulated by the presence or absence of TET using the components of the bacterial tetracycline operon [ 6 , 7 ]. This system has been shown to be successful in the regulation of the vaccinia virus G1L [ 8 , 9 ] and A14L [ 10 ] genes. A plasmid was constructed containing the tetO just upstream of the I7L open reading frame (ORF) in order to regulate expression of I7L proteinase with TET in the presence of a tetracycline repressor (TetR). Also included was the genomic DNA sequence from 250 bp upstream of the I7L ORF, to include the native promoter, and to aid in homologous recombination. This plasmid was used to create the recombinant virus vtetOI7L using the transient dominant selection method [ 11 ]. A commercially available cell line, T-Rex-293 (Invitrogen), expressing the TetR was used to regulate the expression of the I7L gene from the infecting recombinant virus. This conditional-lethal expression system has recently been used to show that the enzymatic activity of the VV G1L metalloproteinase is essential for viral replication [ 9 ]. The conditional-lethal phenotype of the recombinant virus was shown by plaque assay (Fig. 1 ), in which the formation of plaques from vtetOI7L is dependent on the presence of TET, while the wild-type virus is unaffected by either the presence or absence of TET. To determine the optimum TET concentration required for replication of vtetOI7L, TREx-293 cells were infected with vtetOI7L in the presence of varying concentrations of TET, harvested 24 h later, and the titer determined on BSC 40 cells [ 12 ]. A 2-log increase in viral yield was observed with 1 μg/ml TET (data not shown). To confirm that expression of the I7L gene was essential for viral replication, TREx-293 cells were infected with vtetOI7L at a multiplicity of infection (MOI) of 0.1, 0.5, 5, or 10 in the presence or absence of TET, harvested 24 h later, and the titer of the virus infected cell lysates determined on BSC 40 cells. At an MOI of 0.1 or 0.5 there was an average reduction of 99.1% of infectious virus particles (Fig. 2 ). At an MOI of 5 there was an average reduction of 95.7%, and at an MOI of 10 there was an average reduction of 90.3% (Fig. 2 ). This multiplicity-dependent breakthrough of viral replication is likely due to gene copy overwhelming the amount of TetR being expressed by the TREx-293 cell line. Figure 1 Effect of TET on plaque formation. TREx-293 cells were infected with vtetOI7L or wild-type virus in the presence or absence of 1 μg/ml TET and harvested 24 hpi. BSC 40 cells were then infected and stained with crystal violet 48 hpi. Figure 2 Effect of TET on viral replication and rescue of the vtetOI7L mutant. TREx-293 cells were infected with vtetOI7L in the absence (-) or presence of 1 μg/ml TET at an MOI of 0.1, 0.5, 5, or 10. Infected cells were harvested 24 hpi and titrated on BSC 40 cells. To test whether the insertion of the TET operator just upstream of the I7L ORF had an effect on the viral growth kinetics, a one-step growth curve was conducted. TREx-293 cells were infected with wild type virus or vtetOI7L in the presence or absence of TET and infected cell lysates were harvested at the indicated times and the titer determined on BSC 40 cells (Fig. 3A ). In the presence of TET, the recombinant virus grew to the same yield and with the same kinetics as wild type virus while in the absence of TET the production of infectious virus was much lower indicating that the presence of the TET operator did not have an effect on the growth kinetics of the inducible mutant virus. Figure 3 Panel A: One step growth curve. TREx-293 cells were infected with wild-type virus (circle) or vtetOI7L in the presence (square) or absence (triangle) of 1 μg/ml TET. Infected cells were harvested at the indicated times and the titer determined on BSC 40 cells. Panel B: Rescue of replication. TREx-293 cells were infected with vtetOI7L and transfected with either vector alone (pRB21), plasmid with wild-type I7L driven off of a synthetic early/late promoter (pI7L), plasmid with mutant I7L, mutated in the putative active site, driven off of a synthetic early/late promoter (pI7LH241A), or wild-type I7L driven off of its native promoter (pCB26) in the absence of TET. Infected cells were harvested 24 hpi and the titer determined on BSC 40 cells. Transfection of plasmid borne wild-type I7L but not of mutant I7L or vector alone partially rescued the replication of vtetOI7L. To demonstrate that the replication defect of the vtetOI7L mutant virus in the absence of TET was due to the I7L gene we tested whether viral replication could be rescued by the introduction of a plasmid-borne I7L gene. TREx-293 cells in 6-well plates were transfected with 1.8 μg of plasmid DNA (containing either no insert, a wild type I7L gene under the control of the synthetic early-late promoter, a I7L gene with the catalytic His241 mutated to Ala, or the I7L gene under the control of its native promoter) and infected with vtetOI7L at an MOI of 0.2 plaque-forming units per cell in the absence of TET. Cells were harvested 24 hours post infection (hpi) and the titer determined on BSC 40 cells. As an additional control, TREx-293 cells were mock transfected and infected with vtetOI7L in the presence of 1 μg/ml TET to compare growth conditions. A partial rescue of viral replication was observed when cells were transfected with the I7L gene under the control of the synthetic early/late promoter, but not when cells were transfected with plasmid alone or with a mutant I7L gene (Fig. 3B ). This was an approximate 5-fold increase in virus replication compared to the pRB21 or pI7LH241A transfected controls. When the I7L gene was driven off of its own promoter in pCB26 and transfected in, there was a much higher level of rescue (Fig. 3B ), suggesting that the timing and amount of I7L gene expression has important implications for the viral life cycle. We have previously shown through transient expression assays that the I7L proteinase is capable of cleaving the p4b, p4a, and p25k core protein precursors [ 3 , 4 ] which are products of the A3L, A10L, and L4R open reading frames respectively. Here we were interested to see whether the I7L proteinase in the conditional lethal mutant system was also capable of cleaving these proteins in the presence but not the absence of TET. First, to see whether I7L protein was expressed at the same time from the mutant virus as from the wild type virus, TREx-293 cells were infected in the presence of TET and cells harvested at various time points. Proteins in the crude cell extracts were separated by SDS-PAGE and detected by Western blot with anti-I7L antisera. I7L enzyme from both viruses appeared at late times after infection, around 8 hpi and increased as time progressed (data not shown). To determine the effect of TET on I7L protein expression, cells were infected and treated with 0 to 5 μg/ml TET. After 6 h, the infected cells were labeled with 60 μCi/ml 35 S-met and harvested after 24 h. Extracts were immunoprecipitated with I7L antisera and protein detected by autoradiography. With wild type virus, I7L protein was expressed at each TET concentration (data not shown). However, in the mutant virus, expression of I7L enzyme was repressed in the absence of TET and increased with the addition of TET. To determine the effect of TET concentration on p4b core protein precursor processing, cells were infected in the presence of 0 to 5 μg/ml TET, harvested 24 hpi, and the extracts immunoblotted with anti-4b antisera. With wild type virus p4b was processed at each TET concentration as expected, however with the mutant virus, p4b processing was repressed in the absence of TET (data not shown). The slight processing in the absence of TET is likely due to slight leak-through of I7L gene expression in this system. The same results were seen for the processing of p4a, with processing in each of the wild type virus lanes, repressed processing with the mutant in the absence of TET and increased processing in the presence of TET (data not shown). Kane and Shuman [ 13 ] have previously shown that I7L protein is located in the virus core. To verify that the I7L protein from the inducible mutant was localized correctly, purified virions were treated with DTT and NP-40 to separate the envelope fraction from the core fraction and protein from each sample was separated by SDS-PAGE and detected by Western blot with anti-I7L antisera. As expected, the I7L enzyme from the inducible mutant was detected in the core sample, as was the wild type virus (data not shown). The morphogenesis of vtetOI7L under nonpermissive conditions was analyzed via electron microscopy. TREx-293 cells were infected with vtetOI7L at an MOI of 1 in the presence or absence of TET and harvested 24 h later. In the presence of TET, cells contained a variety of both immature and mature forms of the virus (Fig. 4 , panels A-C), which were indistinguishable from cells infected with wild type virus (not shown). However, in the absence of TET, no mature virions were observed in any of the infected cells observed. There appeared to be an accumulation of immature viral particles, some with nucleoids, as well as the appearance of crescent shaped particles (Fig. 4 , panels D-F), similar to those observed by Ansarah-Sobrinho et al [ 5 ]. Also observed were numerous dense virus particles. Virion morphogenesis appears to arrest at a stage prior to core condensation. The observation that there is still some processing of p4b in the absence of TET and yet the morphology of the mutant virus in the absence of TET shows only immature virus particles suggests the hypothesis that there is a requirement for the processing threshold of the core protein precursors to be achieved before morphogenesis can proceed. Figure 4 Electron microscopy of cells infected with vtetOI7L. TREx-293 cells were infected with vtetOI7L at an MOI of 1 in the presence (panels A, B, and C) of 10 μg/ml TET or in the absence (panels D, E, and F) of TET. Cells were harvested at 24 hpi, immediately fixed and prepared for transmission electron microscopy. The bar in panels A, B, D, E, and F represents 400 nm. The bar in panel C represents 200 nm. Taken together, the data we have presented here, as well as analysis of the VV G1L conditional lethal mutant [ 9 ], suggests a morphogenesis model in which these two putative proteases operate sequentially to regulate assembly. According to this model, if we assume that both I7L and G1L are associated with the immature virus along with the accompanying DNA and other viral proteins, then activation of I7L leads to the process of core protein precursor cleavage and the initiation of core condensation. Following this activity, the activation of G1L completes core condensation and allows progression to the formation of intracellular mature virus. If the activity of the I7L proteinase is blocked, viral morphogenesis arrests prior to core condensation. If the activity of G1L proteinase is blocked, viral morphogenesis arrests at a stage subsequent to this but still prior to complete core condensation. To test this model, it will be of interest to isolate biochemically active I7L and G1L enzymes and determine the series of events that lead to their activation. Competing Interests The author(s) declare that there are no competing interests. Authors' contributions CMB conducted all the experiments and wrote the manuscript. DEH conceived the study, coordinated the research efforts and edited the paper. Both authors read and approved the final manuscript.

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529253

Neural stem cells express melatonin receptors and neurotrophic factors: colocalization of the MT1 receptor with neuronal and glial markers

Background In order to optimize the potential benefits of neural stem cell (NSC) transplantation for the treatment of neurodegenerative disorders, it is necessary to understand their biological characteristics. Although neurotrophin transduction strategies are promising, alternative approaches such as the modulation of intrinsic neurotrophin expression by NSCs, could also be beneficial. Therefore, utilizing the C17.2 neural stem cell line, we have examined the expression of selected neurotrophic factors under different in vitro conditions. In view of recent evidence suggesting a role for the pineal hormone melatonin in vertebrate development, it was also of interest to determine whether its G protein-coupled MT 1 and MT 2 receptors are expressed in NSCs. Results RT-PCR analysis revealed robust expression of glial cell-line derived neurotrophic factor (GDNF), brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) in undifferentiated cells maintained for two days in culture. After one week, differentiating cells continued to exhibit high expression of BDNF and NGF, but GDNF expression was lower or absent, depending on the culture conditions utilized. Melatonin MT 1 receptor mRNA was detected in NSCs maintained for two days in culture, but the MT 2 receptor was not seen. An immature MT 1 receptor of about 30 kDa was detected by western blotting in NSCs cultured for two days, whereas a mature receptor of about 40 – 45 kDa was present in cells maintained for longer periods. Immunocytochemical studies demonstrated that the MT 1 receptor is expressed in both neural (β-tubulin III positive) and glial (GFAP positive) progenitor cells. An examination of the effects of melatonin on neurotrophin expression revealed that low physiological concentrations of this hormone caused a significant induction of GDNF mRNA expression in NSCs following treatment for 24 hours. Conclusions The phenotypic characteristics of C17.2 cells suggest that they are a heterogeneous population of NSCs including both neural and glial progenitors, as observed under the cell culture conditions used in this study. These NSCs have an intrinsic ability to express neurotrophic factors, with an apparent suppression of GDNF expression after several days in culture. The detection of melatonin receptors in neural stem/progenitor cells suggests involvement of this pleiotropic hormone in mammalian neurodevelopment. Moreover, the ability of melatonin to induce GDNF expression in C17.2 cells supports a functional role for the MT 1 receptor expressed in these NSCs. In view of the potency of GDNF in promoting the survival of dopaminergic neurons, these novel findings have implications for the utilization of melatonin in neuroprotective strategies, especially in Parkinson's disease.

Background Neural stem cells are multipotent cells which are capable of self-replication and differentiation into neurons, astrocytes or oligodendrocytes in the central nervous system [ 1 ]. Because of their intrinsic plasticity and multipotency, there are great expectations that NSC transplantation will ultimately provide immense benefits in the treatment of neurodegeneration. However, it is essential to fully understand the cellular and molecular mechanisms involved in the differentiation and function of NSCs, in order to fully harness their therapeutic potential. Because of the very limited availability of NSCs in the central nervous system (CNS), neural stem cell lines are very useful for the study and characterization of NSC biology. For example, transplantation studies with the C17.2 neural stem cell line [ 2 ] have revealed that these cells express diverse neurotransmitter phenotypes, depending on the environment prevailing in the CNS area of engraftment [ 3 , 4 ]. Recently, transplanted C17.2 NSCs, genetically modified to express glial cell line-derived neurotrophic factor (GDNF), were found to engraft in the 6-hydroxydopamine-lesioned mouse striatum and to express therapeutic levels of this neurotrophin, with consequent protection of dopaminergic neurons in this model of Parkinson's disease [ 5 ]. Although this and other similar approaches are promising, limitations including the stability and regulation of transduced genes await resolution. Therefore, it was of interest to determine whether C17.2 cells have the intrinsic ability to express neurotrophins or neurotrophic factors, which would make them amenable to modulation by appropriate agents in vitro or in vivo . In addition, we examined whether these NSCs express receptors for the pineal hormone melatonin, which can induce GDNF mRNA and protein expression [ 6 , 7 ] and which has been implicated in the development of vertebrates including humans [ 8 - 10 ]. Initially, different concentrations and types of sera were used for cell culture in order to select optimal conditions for gene expression studies. We now report that C17.2 NSCs exhibit heterogeneous phenotypes and express neurotrophic factors and melatonin MT 1 receptors. Results Effects of culture conditions on neurotrophic factor and cell-specific marker mRNA expression in C17.2 NSCs Following two days in culture, C17.2 cells remain in an undifferentiated state, as indicated by their flat and rounded appearance (Fig. 1A ) and high expression of the stem cell/progenitor cell marker, nestin (Fig. 1C,1E,1G ). These cells also expressed the early neuronal marker, β-tubulin III, but there was little or no expression of the mRNA for the glial marker, glial fibrillary acidic protein (GFAP). After seven days in culture, differentiating C17.2 cells exhibit an elongated shape with an extension of neurite-like processes, as shown in Fig. 1B . However, as observed in undifferentiated cells after two days, there was still strong expression of nestin and β-tubulin III, with little or no detectable GFAP mRNA (Fig. 1D,1F,1H ). An examination of neurotrophin mRNA expression in undifferentiated C17.2 cells, revealed a robust expression of GDNF, BDNF and NGF, regardless of the type or concentration of serum used for culturing (Fig. 1C,1E,1G ). A similar strong expression of BDNF and NGF was observed in differentiating cells after seven days, but GDNF mRNA was relatively lower in cells maintained in 1% fetal bovine serum (FBS) or 10% FBS + 5% horse serum (Fig. 1F,1H ). The mRNA levels of the control gene, GAPDH, did not change under the conditions examined (Fig. 1I ). Detection of MT 1 receptor mRNA and protein in C17.2 NSCs Melatonin MT 1 receptor mRNA was detected by RT-PCR in NSCs maintained for two days, especially in cells cultured in 1% FBS, as shown in Fig. 2A . GAPDH mRNA expression did not change under the conditions examined (Fig. 2B ). C17.2 NSCs maintained for indicated periods in 1% FBS or 10% FBS + 5% horse serum, expressed the MT 1 receptor protein, as revealed by western analysis (Fig. 2C,2D ). Interestingly, when cells were cultured for 2–3 days, the MT 1 protein detected had a molecular weight of about 30 kDa, which is less than the predicted size of the mature receptor. However, when cells were cultured for 10–12 days, a mature MT 1 receptor of about 40–45 kDa was present, as shown in Fig. 2D . The MT 2 receptor transcript was not detected under any of the conditions used in this study. Immunocytochemical detection of the MT 1 receptor and cell-specific markers in C17.2 NSCs MT 1 receptor immunoreactivity was detected within C17.2 cells maintained in 1% FBS for two days, as shown in Figure 3A,3B . Omission of the primary antibody or its preincubation with a blocking peptide (CIDtech Research Inc., Cambridge, ON) abolished MT 1 immunoreactivity (Fig. 3C ), indicating the specificity of MT 1 detection. In keeping with RT-PCR results, nestin (Fig. 3D,3E ) and β-tubulin III (Fig. 3F ), were detected by immunocytochemical analysis. Double- labeling studies indicated that the MT 1 receptor is coexpressed with the stem /progenitor cell marker, nestin (Fig. 4A,4B,4C ), the glial marker, GFAP (Fig. 4D,4E,4F ) and the early neuronal marker, β-tubulin III (Fig. 4G,4H,4I ). Induction of GDNF mRNA expression by melatonin in C17.2 NSCs In order to assess the potential functionality of the MT 1 receptor detected in C17.2 NSCs, the effect of low physiological concentrations of melatonin on GDNF mRNA expression was examined. Cells were grown as described in Methods and treated with melatonin or vehicle (0.001% DMSO) for 24 hours. Following RT-PCR analysis, GDNF mRNA levels were converted to optical density (OD) values and normalized to GAPDH OD levels, as reported previously [ 6 ]. After conversion of GDNF/GAPDH OD ratios to percentage values, one-way ANOVA indicated a significant treatment effect (F 3,7 = 7.03, p < 0.04). A Neuman-Keuls test indicated a significant increase in relative GDNF mRNA expression in cells treated with 0.05, 0.1 and 1 nM melatonin as shown in Figure 5 . Discussion The expression of nestin in undifferentiated C17.2 cells is consistent with the presence of this intermediate filament protein in stem and progenitor cells in the mammalian CNS [ 11 ]. However, as noted above, nestin mRNA was also readily detected in cells exhibiting morphological changes characteristic of differentiation, after one week in culture. Similarly, mRNA for the early neuronal marker, β-tubulin III, was found under all conditions examined, whereas GFAP mRNA was detected only in some cultures. These observations suggest that the C17.2 cells examined in this study are an heterogeneous population of stem and progenitor cells in keeping with evidence that NSCs exhibit morphological and phenotypic heterogeneity [ 12 , 13 ]. The expression of diverse neurotrophins by NSCs is consistent with the role of these factors in the differentiation and development of the CNS. Presumably, the robust mRNA expression observed, particularly in cells maintained in 10% FBS + 5% HS, is driven by the serum-enriched milieu of potential inducers including neurotransmitters, hormones and growth factors, such as basic fibroblast growth factor and epidermal growth factor, which can stimulate C17.2 cell growth in vitro [ 14 ]. In contrast to BDNF and NGF, which exhibited strong mRNA expression under all conditions examined, GDNF expression was weaker or not detectable in differentiating cells after seven days in culture. The suppression of GDNF expression might have been due to the prolonged exposure of NSCs to regulatory factors in the serum, as its decline appears to be inversely correlated with the concentration or enrichment of serum used for cell culture. Thus, moderate, weak or no expression of GDNF was observed in cells cultured for 1 week in 1% CS, 1% FBS or 10% FBS + 5% HS, respectively (see Fig. 1D,1F,1H ). Various biological agents or pathways have been implicated in the regulation of GDNF expression. For example, fibroblast growth factor-2 and proinflammatory cytokines such as interleukin(IL)-1β, IL-6 and tumor necrosis factor-α stimulate GDNF synthesis and secretion [ 15 ]. Activation of protein kinase C by phorbol esters increases GDNF expression [ 15 , 16 ], whereas the adenylate cyclase activator, forskolin, inhibits GDNF production in cultured cells, suggesting an inhibitory role for the cyclic AMP- protein kinase A pathway [ 15 ]. The cAMP pathway and its transcriptional factor cAMP response element binding protein (CREB) have been shown to induce differentiation in neuronal progenitor cells [ 17 , 18 ]. Therefore, it is possible that activation of this pathway was involved in both the initiation of differentiation and the inhibition of GDNF expression observed in C17.2 cells after seven days. While this work was in progress, it was reported that C17.2 neural stem cells constitutively secrete BDNF, GDNF and NGF, but do not label for GFAP or neuronal markers like β-tubulin III [ 19 ]. Our findings are in agreement with these observations with regard to neurotrophin expression. However, in contrast to their findings, β-tubulin III mRNA and immunoreactivity were readily detected in our study. In addition, although GFAP mRNA was weakly expressed or not detectable in some cultures, immunoreactivity for this glial cell marker is present in C17.2 cells, as shown in Figure 4 . These differences may be due to our examination of β-tubulin III and GFAP expression in cells maintained for 2–12 days in culture, whereas their C17.2 cells were examined after 2–3 weeks [ 19 ]. Other factors, such as our use of low serum concentrations, as compared with the enriched culture medium used by Lu et al. [ 19 ], may also be involved. The detection of melatonin MT 1 receptor mRNA in C17.2 cells after 2 days but not after 7 days, presumably involves downregulation of this receptor. There is considerable evidence that many G protein-coupled receptors are downregulated by their agonists [ 20 ]. More importantly, melatonin, which is present in serum, has been found to suppress MT 1 transcription in vitro [ 21 ]. Interestingly, our immunocytochemical studies revealed MT 1 immunoreactivity within C17.2 cell bodies and extensions, as shown in Figure 3A,3B . Although an intracellular localization could result from internalization of receptors [ 20 ], it is also possible that the immunoreactivity detected within these neural stem/progenitor cells is due to the presence of newly synthesized MT 1 receptors. In accordance with this view, the MT 1 protein detected in short-term (2-day) cultures is about 30 kDa, which is less than the approximately 37–45 kDa molecular weight observed in various mammalian tissues [ 22 - 24 ]. Moreover, when cells were cultured for 10–12 days, a MT 1 receptor of about 40–45 kDa was detected, as shown in Fig. 2D . The mammalian MT 1 contains two glycosylation sites in its N-terminal [ 24 ] and it may exist in more than one glycosylated form, as has been reported for other G protein-coupled receptors [ 25 , 26 ]. Thus, the above cytochemical observations suggest that newly synthesized immature MT 1 receptors,which have yet to undergo posttranslational modification and translocation to the plasma membrane, were detected in cells cultured for 2 days in 1% FBS, whereas a mature glycosylated receptor was present in cells grown for longer periods. Although the MT 2 receptor transcript was not detected under any of the conditions used in this study, additional studies are required before the possibility of its expression in these cells can be ruled out. It is possible that MT 2 mRNA may undergo rapid turnover/degradation, while a functional protein may still be present. This is the first evidence that melatonin receptors are expressed in neural stem or progenitor cells and raises the obvious question of whether this hormone plays a role in neuronal development. Although studies in this field are limited, there is increasing evidence that melatonin is involved in the early development of vertebrates. For example, melatonin is produced in chick embryos as early as the 7 th day of embryonic development [ 27 ], and a physiological concentration of this hormone has been shown to significantly enhance mouse embryogenesis in vitro [ 8 ]. Similarly, when sheep blastocysts were treated with melatonin for 24 hr in vitro , there was a significant increase in the percentage of embryonic survival [ 28 ]. Other studies have shown that functional G i protein-coupled melatonin receptors, which mediate inhibition of the adenylate-cyclase-cAMP pathway, are present in the embryonic (day 8) neural retina [ 29 ]. Melatonin receptor transcripts for all the known G i protein-coupled receptor subtypes have been found in 24 hr-old embryos from Japanese quail [ 30 ]. Various studies have detected melatonin receptors in human fetal brain [ 31 , 32 ] and peripheral tissues [ 33 ]. Moreover, recent autoradiographic and in situ hybridization studies indicate that the melatonin MT 1 receptor is expressed in diverse areas of the human fetal brain [ 9 ]. Thus, the presence of MT 1 receptors in NSCs is in keeping with the foregoing, and supports the view that melatonin is involved in neurodevelopment. Colocalization evidence that the MT 1 receptor is present in both neural and glial progenitor cells is consistent with a neurodevelopmental role for melatonin, and suggests that in addition to the presence of the MT 1 in mammalian neurons [ 34 ], it may also be expressed in astrocytes, as observed in similar cells from rat [ 6 ] and chick brain [ 35 ]. The detection of nestin in some cells expressing the MT 1 receptor is consistent with its presence not only in neural progenitor cells but also in GFAP positive glial progenitors [ 36 ]. Preliminary evidence that melatonin induces GDNF mRNA expression in C17.2 NSCs, as we have observed previously in C6 glioma cells [ 6 ], supports the foregoing as this neurotrophic factor plays a critical role in both central and peripheral neurodevelopment [ 37 , 38 ]. GDNF also exerts neuroprotective effects in the CNS, including a potent role in the survival of dopaminergic neurons in the midbrain [ 39 , 40 ]. Therefore, modulation of GDNF expression may be one of the mechanisms underlying physiological neuroprotection by melatonin in the CNS [ 6 ]. Conclusions In summary, the NSCs utilized in this study exhibited an intrinsic ability to express neurotrophins under various cell culture conditions. This ability was not affected by their morphological state, except in the case of GDNF mRNA expression which was lower in cells undergoing differentiation in FBS-supplemented media. Novel evidence that neural stem/progenitor cells express MT 1 receptors adds to the increasing evidence that NSCs can respond to diverse modulators [ 41 ], and suggests an early role for melatonin in CNS development. Moreover, since melatonin induces GDNF expression in NSCs, its potential in vivo modulation of this and/or other neurotrophic factors, via its G protein-coupled receptors in the brain or on transplanted NSCs, could have important implications for optimizing therapeutic strategies in neurodegenerative disorders such as Parkinson's disease. Methods Cell culture The C17.2 cell line was derived by retrovirus-mediated oncogene ( v-myc ) transduction of cells from the external germinal layer of neonatal mouse cerebellum [ 2 ]. C17.2 cells were grown on 10 cm Corning culture dishes (Fisher Scientific Ltd., Nepean, ON, Canada) in DMEM supplemented with 2 mM glutamine and calf serum, fetal bovine serum or horse serum (Invitrogen Canada Inc., Burlington, ON) in the concentrations indicated. Cells were maintained in a humidified 5% CO 2 – 95% air incubator at 37°C and routinely split at approximately 90% confluency [ 3 ]. RT-PCR Total RNA was isolated from C17.2 cells with TRIzol as described by the supplier (Invitrogen Canada Inc., Burlington, ON). After DNase treatment, cDNA was synthesized from 1–2 μg of total RNA using the Omniscript reverse transcriptase kit (Qiagen Inc., Mississauga, ON) and oligo dT primers. PCR was carried out using 1.5 μl (or 3 μl for melatonin MT 1 and MT 2 receptors) of the RT product and the HotStarTaq master mix kit (Qiagen Inc., Mississauga, ON), together with appropriate primers (Table 1 ). Following a hot start at 95°C for 15 min, samples were amplified for 36 cycles (or 38 cycles for MT 1 and MT 2 ) at 94°C for 30 s, 57°C for 30 s and 72°C for 1 min, followed by a final incubation at 72°C for 10 min. Treatment of C17.2 NSCs with melatonin For semi-quantitative examination of the effects of melatonin on GDNF mRNA expression, cells were grown in 10% FBS + 5% horse serum (HS) for 1 week. After subculture, cells were kept in 10% FBS + 5% HS for 2 days followed by another subculture to 1% FBS for 2 or 3 days. The cells were then treated with vehicle (0.001% DMSO) or melatonin (0.05, 0.1, and 1 nM) for 24 hours. Following RNA extraction, RT-PCR was performed as described above, except that an annealing T m of 55°C was used and samples were amplified for 30 cycles. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), was amplified with intron-spanning primers [ 42 ], in order to control for DNA contamination. Immunocytochemistry After fixation for 15 minutes in 4% paraformaldehyde on poly L-ornithine-coated glass cover slips, cells were incubated overnight at 4°C with anti-melatonin MT 1 receptor serum (1:100; CIDtech Research Inc., Cambridge, ON). Cells were washed three times with PBS and then incubated with a fluorescein (FITC)-conjugated donkey anti- rabbit IgG (1:100 dilution; Jackson ImmunoResearch Labs. Inc.,West Grove, PA). In some experiments, the primary antibody was omitted or it was preincubated with the corresponding peptide immunogen (CIDtech Research Inc., Cambridge, ON), before use. In order to examine cell marker expression, mouse monoclonal antibodies against nestin (1: 500), β-tubulin III (1:200) or GFAP (1:400; Chemicon International, Temecula, CA) were used together with a FITC-conjugated donkey anti-mouse IgG (1:100; Jackson ImmunoResearch Labs.Inc.,West Grove, PA). For double- labeling studies of the MT 1 and cell markers, a rhodamine (TRITC)-conjugated donkey anti-mouse IgG (1: 100; Jackson ImmunoResearch Labs. Inc.,West Grove, PA) was used to detect nestin, GFAP and β-tubulin III. Digital images were recorded on a Zeiss confocal microscope. Western analysis C17.2 NSCs were grown as described in Figure 2 , and proteins were extracted in a modified RIPA buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% NP-40, 0.25% sodium deoxycholate) supplemented with PMSF (1 mM), aprotinin (2 μg/ml), leupeptin (2 μg/ml), and sodium orthovanadate (2 mM). Extracted proteins (80 μg per lane) were separated by SDS- polyacrylamide gel electrophoresis and transblotted to nitrocellulose membranes. The blots were blocked with 5% nonfat dry milk in TBS-T buffer (50 mM Tris-HCl, 150 mM NaCl, 0.1% Tween 20; pH 8.5) for 1 hour at room temperature, and then incubated overnight with a 1:100 dilution of rabbit anti-MT 1 antibody (CIDTech Research Inc., Cambridge, ON) at 4°C. After washing, membranes were incubated with a horseradish peroxidase-conjugated second antibody (1:1000; Santa Cruz Biotechnology, Inc., Santa Cruz, CA) for 1 hour. Following washing and exposure to enhanced chemiluminescence (ECL) reagents (Amersham Biosciences, Inc., Baie d'Urfé, Québec) for about 5 minutes, proteins were detected by autoradiography, as described previously [ 43 ]. Buffer reagents and protease inhibitors were obtained from Sigma- Aldrich Canada Ltd. (Oakville, ON). Authors' contributions LPN conceived and planned the study, designed PCR primers, supervised all aspects of the study and wrote the manuscript. KJA cultured cells and performed initial RT-PCR experiments. LMRC treated cultured cells and carried out immunocytochemistry experiments. CVD cultured cells and carried out western blotting. RS treated cultured cells and performed RT-PCR experiments. CRM performed double- labeling of cultured cells. LCD provided the C17.2 cells, and collaborated on immunocytochemical studies. DLK assisted with initial cell culture. All authors read and approved the final manuscript.

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549558

Hospital use of systemic antifungal drugs

Background Sales data indicate a major increase in the prescription of antifungal drugs in the last two decades. Many new agents for systemic use that only recently have become available are likely to be prescribed intensively in acute care hospitals. Sales data do not adequately describe the developments of drug use density. Given the concerns about the potential emergence of antifungal drug resistance, data on drug use density, however, may be valuable and are needed for analyses of the relationship between drug use and antifungal resistance. Methods Hospital pharmacy records for the years 2001 to 2003 were evaluated, and the number of prescribed daily doses (PDD, defined according to locally used doses) per 100 patient days were calculated to compare systemic antifungal drug use density in different medical and surgical service areas between five state university hospitals. Results The 3-year averages in recent antifungal drug use for the five hospitals ranged between 8.6 and 29.3 PDD/100 patient days in the medical services (including subspecialties and intensive care), and between 1.1 and 4.0 PDD/100 patient days in the surgical services, respectively. In all five hospitals, systemic antifungal drug use was higher in the hematology-oncology service areas (mean, 48.4, range, 24 to 101 PDD/100 patient days, data for the year 2003) than in the medical intensive care units (mean, 18.3, range, 10 to 33 PDD/100) or in the surgical intensive care units (mean, 10.7, range, 6 to 18 PDD/100). Fluconazole was the most prescribed antifungal drug in all areas. In 2003, amphotericin B consumption had declined to 3 PDD/100 in the hematology-oncology areas while voriconazole use had increased to 10 PDD/100 in 2003. Conclusion Hematology-oncology services are intense antifungal drug prescribing areas. Fluconazole and other azol antifungal drugs are the most prescribed drugs in all patient care areas while amphotericin B use has considerably decreased. The data may be useful as a benchmark for focused interventions to improve prescribing quality.

Background There has been a major increase in the prescription of antifungal drugs after the introduction of fluconazole into the market in the late 1980s, and again in the late 1990s. The systemic antifungal market has continued to experience growth since 1999, increasing in value from $2.1 billion to $3.3 billion in 2003. The azoles dominate the systemic antifungal market, accounting for 52% of total sales in 2003 [ 1 - 8 ]. The reasons for the increasing antifungal drug use are manifold. Among hospitalized patients, the empiric use of antifungals in both hematology-oncology as well as intensive care patients is now common. Often, treatment is initiated based on preliminary microbiology results, and definite diagnosis of invasive infection versus colonization may be difficult [ 4 , 9 - 11 ]. New antifungal drugs such as itraconazole, caspofungin, and voriconazole have become available and broadened therapeutic options [ 12 ]. In some settings an increasing incidence of invasive fungal infections and the emergence of infections due to rare and atypical organisms has been observed, and this changing epidemiology has contributed to more intense use of antifungal drugs [ 13 ]. In the ambulatory care setting there was a shift from prescribing intravaginal antifungal preparations to fluconazole over-the-counter, raising concern about the possible development of azole drug resistance [ 14 - 16 ]. Although multiple current and projected market and sales data on systemic antifungal drugs are available, few studies have provided estimates of antifungal drug use density especially in hospitals. Alvarez-Lerma and colleagues reported a prescription rate of 14% in intensive care unit patients [ 9 ]. In a survey we conducted in 1994 the prescription prevalence rate in hospitalized patients was 10.2% per patient-week in the medical service and 3.5% per patient-week in the surgical services [ 17 ]. Hospital expenditures were also evaluated in some studies. However, we were unable to find information on recent hospital antifungal drug utilization that uses the daily doses per 100 patient days format which is now common in pharmacoepidemiologic surveys. We therefore collected data from the pharmacies of five university hospitals and here report overall and comparative use density values for defined patient care areas. Methods Pharmacy data on systemic antifungal drug use in the medical and surgical services of five university hospitals located across Germany were obtained for the period 2001 to 2003. The five university hospitals included, here designated A through E, varied in size from ~1,000 to ~1,700 beds, and differed from each other in structure, special services offered, and in the availability of interdepartmental guidelines and an antiinfective therapeutics committee, drug formularies, formulary restrictions, and infectious disease consultation services. We used a consensus definition of (usually) prescribed daily doses (PDD) in adults (Table 1 ) according to local guidelines. This definition differs from the daily doses defined by the WHO/ATC classification which defines lower doses for amphotericin B, fluconazole, and itraconazole (Table 2). Antifungal drug use density was calculated as yearly PDD/100 patient days (i.e. occupied bed days). Separate data were calculated for the medical ICU (MICU), the surgical ICU (SICU), and the hematology-oncology services, respectively. We also calculated yearly means of overall and specific antifungal use densities to assess time trends. Table 1 Definitions of prescribed daily doses (PDD) and WHO/ATC defined daily doses (DDD) for systemic antifungal drugs. PDD DDD amphotericin B deoxycholate* 50 mg 35 mg liposomal amphotericin B 250 mg nd # flucytosin 10 g 10 g ketoconazole 400 mg 400 mg fluconazole 400 mg 200 mg itraconazole 400 mg 200 mg voriconazole 400 mg 400 mg caspofungin 50 mg 50 mg *conventional amphotericin B # not defined Results and discussion The yearly antifungal drug use densities differed between the five hospitals in particular for the medical services. Hospital A showed use density values of consistently >20 PDD/100 patient days while hospital E values were consistently <10 PDD/100 patient days (Figure 1 ). Less variation between the hospitals were observed in the surgical services (Figure 1 ). Here, 3-year averages for the hospitals ranged between 1.1 (hospital A) and 4.0 PDD/100 patient days (hospital B), respectively. Figure 1 Yearly systemic antifungal drug use density in the medical and surgical services of five university hospitals (A through E) for the years 2001–2002–2003. Time trend Overall, the mean antifungal drug use for the five hospitals increased between the years 2001 and 2003 from 12.4 to 15.4 PDD/100 patient days in the medical services (+24%), but only from 2.1 to 2.2 PDD/100 patient days in the surgical services (+5%). Applying the WHO/ATC definition of daily defined doses (DDD; including our daily dose definition for liposomal amphotericin B), corresponding values for the years 2001 and 2003 were calculated to be 22.8 to 26.3 DDD/100 patient days (+15%) in the medical services, and 4 to 4.1 DDD/100 patient days (+4%) in the surgical services, respectively (data not shown). Use of specific antifungal drugs As in other reports [ 5 ], fluconazole was the most frequently prescribed antifungal drug in the medical as well as surgical services of the five hospitals. Its use did not decrease over time. Figure 2 shows the yearly mean use density for fluconazole and other antifungal drugs (except the rarely used 5-flucytosin and ketoconazole) in the medical service. Interestingly, conventional as well as liposomal amphotericin B use decreased over time (Figure 2 ). In the year 2003, the mean use of fluconazole in the medical service was 7.7 PDD/100 patient days (representing 50% of all PDDs), and 1.8 PDD/100 patient days in the surgical service (representing 78% of all PDDs), respectively. Figure 2 Use density for different antifungal drugs in the medical service of five university hospitals. Data are yearly means for 2001, 2002 and 2003. cAmB, conventional amphotericin B; L-AmB, liposomal amphotericin B; Caspo, caspofungin; Fluco, fluconazole; Itra, itraconazole; Vori, voriconazole. Differences between patient care areas As expected, antifungal drug use was much more intense in the hematology-oncology services and intensive care areas (Figure 3 ) than in general internal medicine (mean use, 2.3 PDD/100 patient days, data for the year 2003) and general surgery (mean use, 1.1 PDD/100 patient days, data for the year 2003). Figure 3 shows that there was some variation between the hospitals in the use density values, particularly in hematology-oncology and the SICU area. These differences were not explained by different incidences of invasive fungal infections as perceived by the local physicians, but in none of the hospitals specific surveillance for fungal infections was activated. Large differences were also noted in the fluconazole use, with very high use density values in hospital A hematology-oncology and comparatively low density values in hospital E hematology-oncology areas (53.8 versus 5.8 PDD/100 patient days, data for the year 2003). The high use density values in hospital A hematology-oncology could primarily be explained by the heavy use of relatively high doses of fluconazole (400 mg daily) for prophylactic purposes which was much less common in the other hospitals. Figure 3 Use of fluconazole (grey bars) versus other systemic antifungal drugs (black bars) in the SICU, MICU, and in the hematology-oncology services of five university hospitals (A through E) during the years 2001–2002–2003. Of note, hospital E had a moderately active infectious disease consultant service with an antimicrobial agents management program, and this was previously associated with low antibacterial drug use in the medical service [ 18 , 19 ]. According to the present study, this programme was also perhaps linked to the low antifungal drug use density in the hospital E medical service including hematology-oncology. In hospital C, there was a program in the MICU attempting to decrease the use of fluconazole based solely on positive cultures for yeasts in tracheal or bronchial secretions. This program, which was primarily a focused infectious diseases consultation program was started in 2002, and appeared to be effective in decreasing fluconazole use from 13.5 to 5 PDD/100 patient days without changing the use density of other systemic antifungal drugs (Figure 3 ). The decreasing use of amphotericin B consumption seen in the medical service was to a large part explained by decreasing use of the drug in the hematology-oncology wards. Mean use density values changed between 2001 and 2003 from 5.8 to 2.4 PDD/100 patient days for conventional amphotericin B, and from 1.6 to 0.6 for liposomal amphotericin B, respectively. These changes were associated with increasing values for voriconazole in hematology-oncology. This new drug after its introduction into the market in 2002 increased from zero to a use density of 10.3 PDD/100 patient days in 2003. Interestingly. 80% of all doses of voriconazole in hematology-oncology were by the oral route. Limitations and conclusions Our study was not designed to evaluate appropriateness of antifungal drug therapy. Few studies in the hospital setting have addressed this issue. In two previous studies, it was found that dosages of fluconazole were not always adequate [ 20 , 21 ]. In another study, therapy was considered "unconventional" in 27% of the courses and 41% of the regimens, mainly because either the indication or the duration of treatment did not conform to conventional practice [ 4 ]. Conventional practice, however, can differ considerably as indicated by our results. We think it is unlikely that the observed high use density values in hospital A hematology-oncology (>50 PDD/100 patient days) represents an unusual epidemiologic situation or a major difference in hematology-oncology patient-mix. Rather, the intense use can be explained by liberal antifungal drug use in high doses for prophylaxis and perhaps empiric combination therapy. The present study, thus, provided a useful benchmark suggesting that more detailed analysis of antifungal therapy indication practice is warranted in this particular hospital. In summary, this report describes the range of antifungal drug use in certain patient care areas of large tertiary-care teaching hospitals in Germany. Consistent with other reports, we found that fluconazole has remained the most frequently prescribed drug in this setting. Competing interests The author(s) declare that they have no competing interests. Authors' contributions KdW and WVK analysed and interpreted the data and wrote the article. MSB is data manager, analysed the data and presented them through a searchable database. HK, FD, ES, UR and LM checked the data for consistency and correctness, provided them in electronic format, and helped with interpretation of the data and revision of the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

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549202

Graph-representation of oxidative folding pathways

Background The process of oxidative folding combines the formation of native disulfide bond with conformational folding resulting in the native three-dimensional fold. Oxidative folding pathways can be described in terms of disulfide intermediate species (DIS) which can also be isolated and characterized. Each DIS corresponds to a family of folding states (conformations) that the given DIS can adopt in three dimensions. Results The oxidative folding space can be represented as a network of DIS states interconnected by disulfide interchange reactions that can either create/abolish or rearrange disulfide bridges. We propose a simple 3D representation wherein the states having the same number of disulfide bridges are placed on separate planes. In this representation, the shuffling transitions are within the planes, and the redox edges connect adjacent planes. In a number of experimentally studied cases (bovine pancreatic trypsin inhibitor, insulin-like growth factor and epidermal growth factor), the observed intermediates appear as part of contiguous oxidative folding pathways. Conclusions Such networks can be used to visualize folding pathways in terms of the experimentally observed intermediates. A simple visualization template written for the Tulip package can be obtained from V.A.

Background The process of protein folding whereby a linear polypeptide chain reaches its native structure has been one of the most intensely studied biomolecular problems over the past 50 years (for current reviews see [ 1 - 3 ]). Folding of a protein is usually pictured as a search for the native conformation within the conformational space of all possible conformational states, each characterized by a set of parameters. Even though most of the conformational states are not accessible to experiment, graphic representations of the potential energy surface have played pivotal roles in explaining how the conformational space is gradually restricted during the process folding [ 4 ]. Key concepts such as folding pathways [ 5 ] are also best explained by graphic representations. The particular kind of folding that this article is concerned with is oxidative folding, which is the fusion of native disulfide bond formation with conformational folding [ 6 - 8 ]. This complex process is guided by two types of interactions: first, non-covalent interactions giving rise to secondary and tertiary structure, and second, covalent interactions between cysteine residues, which ultimately transform into native disulfide bridges. The process of disulfide formation is a simple chemical reaction in which two SH groups join to form a disulfide link (Figure 1A ). If the SH groups are on a polypeptide chain, the in vitro reaction can be promoted by an external redox system such as a mixture of oxidized and reduced glutathione, or cysteine and cystine, respectively. In vivo , the oxidative power comes from specific agents such as the molecular chaperones protein disulfide isomerases[ 9 ]. The underlying chemical mechanism is disulfide interchange (Figure 1B ). In this scheme there are two kinds of reactions: i) in a redox reaction a protein disulfide bond is created (or abolished), i.e. the oxidative state of the polypeptide is changed. This is the case when one of the participants of the reaction (say RSH) is not part of the protein. ii) In a shuffling reaction both participants of the reaction are protein-bound, so the oxidative state of the polypeptide does not change. In view of these possibilities it becomes obvious that there are a great many ways in which disulfide bridges can form and rearrange during the folding process. Today it is generally accepted that non-covalent interactions guide the process of folding and formation of disulfide bridges will lock the protein into the right conformation. The advantage of oxidative folding as opposed to general protein folding is that disulfide intermediates can be chemically isolated and studied using such techniques as acid trapping of the intermediates and analysis of the disulfide bridges using a combination of enzymatic cleavage and mass spectrometry. There is a body of literature in describing the pathways of oxidative folding in terms of disulfide intermediates [ 6 - 8 ], and our goal is show how graph theory can be used to visualize them. Graph theory has been applied to many aspects of protein research (for a recent review see [ 10 ]). The applications followed two broad avenues: i) First, protein structure itself can be considered as a graph consisting of various interactions (such as covalent bonds, hydrogen bonds, spatial vicinities, contacts etc.) as edges, the nodes being atoms or residues of the protein. For instance, one of the classical definitions of protein secondary structures is based on main/chain H-bond contacts between residues [ 11 ]. Structural neworks have been used in folding research as well. It was found, among others, that the so-called contact order, i.e. the average sequence distance between residues in atomic contact, seems to be a key determinant of folding speed [ 12 ]. Another line of research concentrates on characteristic networks of inter-atomic contacts that may form stabilization centres in protein structures and can be the reason of the differential stability of various proteins [ 13 , 14 ]. It was found that populated conformations seen in molecular dynamics simulations contain characteristic networks of residues [ 15 , 16 ]. ii) In the network descriptions of the folding space, on the other hand, the folding states are the nodes, and transitions are the edges between them. This approach was fostered by the finding that the robustness and stability of networks may be the result of simple topological properties that are invariant throughout various technical as well as biological systems [ 17 ]. In the following years the network topology of a large number of systems have been described, and it was found that some topology classes, such as those characterized by a scale-free distribution of the number of links at each node, or the so called "small world models" that are characterized by densely connected subnetworks loosely linked between each other, are indeed found in various systems within and without biology (for reviews see [ 18 ]). The various network types were described in terms of simple measures borrowed from graph theory, such as the clustering coefficient, the diameter of the graph etc [ 19 ]. Scala and associates described the folding states of short peptides using Monte Carlo simulation on lattice models [ 20 ]. They found that that the geometric properties of this network are similar to those of small-world networks, i.e. the diameter of the conformation space increases for large networks as the logarithm of the number of conformations, while locally the network appears to have low dimensionality. Shakhnovich and co-workers analysed the folding states of proteins during molecular dynamics simulations. It was found that the folding space is reminiscent of scale-free network, characterized by a majority of less populated states as well as some highly populated states reminiscent of "hubs" seen in other systems [ 21 ]. Our purpose is to describe the folding space of the oxidative folding process using graph representations. This is an intriguing task since, in contrast to "ordinary" protein folding, the number of states defined in terms of disulfide links is not exceedingly high, moreover the actual disulfide intermediates can be isolated and studied. We will approach the problem in two steps as: i) We will use graph theory to describe the disulfide intermediates, and to enumerate the states of the folding space. ii) Then we will represent the folding space as a network (graph) of all possible intermediates. We show with few examples that experimentally observed intermediates mapped onto this network appear as contiguous folding pathways. Results and discussion Graph representation of oxidative folding intermediates The disulfide topology of a protein is unequivocally defined by describing which cysteines are connected to each other. For example, a topology "1–3, 2–4" means that a protein with four cysteines has two disulfide bridges that connect cysteines (1,3) and cysteines (2,4) respectively. Cysteines can be labelled by their sequence position, or – as in the previous example – in a serial order from the N-terminus (Figure 2 ). The number of fully oxidised (disulfide bonded) isomers in a protein chain with n disulfide bonds (2 n cysteines) can be deduced from simple combinatorial considerations as (2 n )!/( n !*2 n ). According to this formula proteins with two disulfide bridges have 3 fully oxidized isomers, 3-disulfide proteins have 15 and 4-disulfide proteins have 105. In other words, the number of intermediates increases very fast as a function of the number of constituent cysteines. For a complete description of the folding process we have to consider both fully oxidized intermediates as well as the ones with free cysteine residues. For this purpose we will use a formal description of the intermediates as undirected graphs, with cysteines as nodes and disulfide bridges as edges (the main chain will not be represented). For the majority of naturally occurring protein structures the resulting graphs will be extremely simple. If the protein has n cysteines, the n × n adjacency matrix of the graph is symmetrical; it will contain a value of 1 if two cysteines form a disulfide bond and zero otherwise. As one cysteine can form only one disufide bridge, each column and each row will have at most one value of 1. Examples are shown in Figure 3 . Description of the oxidative folding space as graphs The transitions between folding intermediates can be conveniently described by comparing the adjacency matrices of the two states. For the enumeration of the transition reactions we introduce a few simple variables. NB is the number of disulfide bonds, calculated as the sum of the elements of the adjacency matrix. The sum of elements in the i-th column plus the i-th row, is 1 if the i-th cysteine is part of a disulfide bridge and zero otherwise. The sum of the differences calculated between the S i measures of two adjacency matrices, shows how many cysteines gain or loose a bond as the molecule passes from one state to the other. Here we are interested only in the two kinds of elementary reactions depicted in Figure 1B . In shuffling reactions , the number of disulfide bridges NB remains the same by definition, and it is easy to show that SD will differ exactly by 2. In redox steps in which one disulfide bridge is established or lost, NB and SD will increase or decrease by one and two, respectively. On the above basis one can easily draw a network of all possible oxidative folding pathways. For a protein of n cysteines, we first generate the graphs (adjacency matrices) of all possible intermediates, i.e. those with 0,1...(i ≤ n/2) disulfide bridges. Then we compare all pairs of intermediates in terms of the above parameters. A shuffling edge will be drawn between two intermediate states if |SD| = 2 and ΔNB = 0; redox edges will be drawn if |SD| = 2 and ΔNB = 1. If the values of |SD| and ΔNB are different from these two combinations, no edge will be drawn. The graph characteristics of a few systems are summarized in Table 1 . The results show that as the number of cysteines increases, the clustering coefficient of the system decreases. While the average path length increases. Both findings are consistent with the intuitive view that the folding space of peptides with many cysteines may be too complex and thus the systems may be unable to fold fast enough. The pathways can also be graphically represented, and in order to simplify the resulting picture, we chose a 3D representation wherein the states (species) are grouped according to the number of disulfide bridges (Figure 4 ). Species with the same number of disulfide bridges are placed on the same plane, so shuffling reactions, which do not change the number of disulfide bridges are represented as edges within the same plane. It is noted that on each of the separate planes we find a regular graph. This is not surprising: exhaustive enumeration of theoretical states, such as Eigen's quasi-species [ 22 ], can produce highly connected regular graphs. On the contrary, reactions in which a disulfide bridge is gained or lost, are represented as edges between two neighbouring planes. The fully reduced state (zero disulfide bridges) is on top, the fully oxidized species, one of which is the native state, is on the bottom. Panel B shows a peptide with an odd number cysteines, such as granulocyte-colony stimulating-factor [ 23 , 24 ] in which the native state contains one free cysteine residue. In this case there are shuffling edges even in the lowest plane in the figure, so the native state (one of the states in the lowest plane) can be subject to shuffling transitions. On the contrary, if the number of cysteines is an even number (i.e. in the majority of known proteins), the fully oxidized DISs can not interconvert into each other in one step. In some cases however an additional cysteine is in fact used to facilitate the process of oxidative folding: the propeptide of BPTI contains an additional free cysteine that seems to significantly speed up the in vitro folding of the molecule [ 25 ]. In vivo , the propeptide is subsequently cleaved, and in this way the structure is locked into the native disulfide configuration. In principle, the oxidative folding pathways can be pictured as routes within the full network, starting at the fully reduced species and ending at the native state. In the literature there are a few well-studied examples in which folding intermediates have been determined. The experimentally observed disulfide intermediates of three examples, bovine pancreatic trypsin inhibitor, insulin-like growth factor and epidermal growth factor, are shown in Table 2 and Figure 5 , respectively. It is noted that experimental methods do not necessarily reveal all possible intermediates; some of the intermediates may be too short-lived or not abundant enough so as to be noticed an isolated. In spite of these limitations, the folding pathways appear as connected subgraphs within the network of all possible intermediates, showing that the experimental techniques actually identified states that can interconvert into one another. Only in EGF do we see an "isolated" intermediate, which suggests that some intermediates of the pathway were not observed experimentally. Conclusions The oxidative folding space of polypeptides can be represented as networks in which the nodes are the disulfide intermediates while the edges are transitions between them. A simple visualization tool written was developed to draw 3D pictures of such networks in which the states having the same number of disulfide bridges are placed on separate planes. These pictures provide a simple method for the visualization of oxidative folding pathways as studied by experimental methods. In the case of bovine pancreatic trypsin inhibitor, insulin-like growth factor and epidermal growth factor, the folding pathways appear as a small network of contiguous routes that connect the fully reduced state to the native state. A further plausible extension of this method would include colouring of the folding states by quantitative properties and look for correlations between the coloured areas of the network and the experimentally determined folding pathways. Even though the topology of the theoretically complete folding space appears to be highly regular, data currently available are insufficient to draw general conclusions on the topology of the experimentally observed folding pathways. Experimentalists find folding intermediates as a series of chromatographic peaks, and usually the disulfide bridges of more abundant species are analysed first. The question whether or not all the relevant intermediates have been analyzed is difficult to answer, and mapping the intermediates onto the graphs presented here may help one to decide whether or not the pathways found are contiguous. Authors' contributions V.A. designed and implemented the algorithms and carried out the calculations. M.C. helped to compile the experimental data and to draft the manuscript. L.K. designed the representation of folding intermediates. The project was coordinated by S.P.

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521182

A New Cell Model for Parkinson's Disease

Clinical descriptions of Parkinson's disease remain remarkably similar to those first described by James Parkinson nearly 200 years ago. Patients with “shaking palsy” experience a progressive loss of muscle control, increased muscle rigidity, inhibited movement, and tremors. These symptoms, it was later discovered, result from the loss of dopamine-producing neurons specifically in an area of the ventral midbrain called the substantia nigra. Midbrain dopamine neurons relay chemical signals that regulate motor control and less quantifiable attributes like mood and motivation, and therefore the loss of these cells is predicted to lead to the symptoms of Parkinson's. Despite the well-characterized cellular basis of Parkinson's disease, the molecular mechanisms responsible for dopamine neurodegeneration remain unknown. There is evidence that both genetic and environmental components are involved. That a person with Parkinson's disease is three to four times more likely than an unaffected individual to have a close family member with “parkinsonian” symptoms suggests a genetic factor; furthermore, several genes have been associated with relatively rare, familial forms of the disease. For example, mutations of the protein alpha-synuclein (α-synuclein), which is found to aggregate in the brains of patients with Parkinson's, lead to a familiar parkinsonism syndrome. Mutations in a second gene called DJ-1 were recently found in two families with an inherited form of Parkinson's. Importantly, mutations in DJ-1 have previously been linked to the pesticide paraquat in unrelated research on cell stress and reactive oxygen species, and have been linked to dopamine neuron toxicity. Reactive oxygen species are molecular byproducts of oxygen metabolism that react with and damage cellular components like proteins and DNA, and there is evidence from postmortem studies that reactive oxygen species may play a role in Parkinson's disease. Part of the challenge of untangling the relative contributions of all these components stems from the difficulty in finding a model that can adequately mimic the loss of dopamine cells. In two papers published in PLoS Biology , Asa Abeliovich and colleagues make the case that a model based on mouse embryonic stem cells offers a promising platform for dissecting the disease mechanism of Parkinson's. Working with these cells, the researchers report that DJ-1-deficient cells—and especially DJ-1-deficient dopamine neurons—display heightened sensitivity to oxidative stress. In a second paper, they link DJ-1 dysfunction to alpha-synuclein aggregation. Oxidative stress has long been associated with neuronal cell death and neurodegenerative diseases like Parkinson's. Proving a causal relationship between oxidative stress and neurodegeneration, however, requires establishing a molecular mechanism. In the first paper, to explore the hypothesis that DJ-1 contributes to the cellular response to oxidative stress, Abeliovich and colleagues created mouse embryonic stem cells lacking functional copies of DJ-1 and exposed them to hydrogen peroxide, a powerful oxidizer. Compared to normal cells, DJ-1 mutants showed signs of greater toxicity and higher levels of cell death. These defects were corrected when the researchers reintroduced the protein in the mutants, confirming DJ-1's responsibility for the defects. DJ-1 protects against oxidative damage, the results show, not by inhibiting the accumulation of the reactive oxygen species associated with hydrogen peroxide, but by mitigating the damage created by them. Abeliovich and colleagues then explored DJ-1's function in dopamine neurons by inducing mutant and control embryonic stem cells to differentiate in cell cultures. Production of dopamine neurons was significantly reduced in the DJ-1-deficient cultures relative to the control cultures. And like DJ-1-deficient embryonic stem cells, DJ-1 dopamine mutants were vulnerable to oxidative stress. “DJ-1 deficiency,” the authors conclude, “leads to reduced dopamine neuron survival and predisposes these cells to endogenous and exogenous insults.” Inhibiting DJ-1 activity in neurons from the embryonic mouse midbrain produced the same results. In the second paper, Abeliovich and colleagues go on to probe the molecular basis of DJ-1's activity. There have been several leads regarding how DJ-1 functions, based on homology to related genes, including a potential role as a molecular protein chaperone; protein chaperones assist in the folding and refolding of damaged proteins, and thus play a central role in the cellular response to oxidative stress. Abeliovich and colleagues found that DJ-1 acts as an unusual molecular chaperone that is specifically induced under oxidative conditions, and acts to prevent the aggregation of cellular proteins. Interestingly, the researchers go on to show that one substrate of DJ-1 activity is alpha-synuclein, thus providing a possible mechanism linking these two molecules implicated in Parkinson's disease. Altogether, these results support a link between toxin-induced oxidative damage and disease, and provide a tractable model for studying the molecular mechanisms of neurodegenerative disease.

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544929

Evidence for Widespread Degradation of Gene Control Regions in Hominid Genomes

Although sequences containing regulatory elements located close to protein-coding genes are often only weakly conserved during evolution, comparisons of rodent genomes have implied that these sequences are subject to some selective constraints. Evolutionary conservation is particularly apparent upstream of coding sequences and in first introns, regions that are enriched for regulatory elements. By comparing the human and chimpanzee genomes, we show here that there is almost no evidence for conservation in these regions in hominids. Furthermore, we show that gene expression is diverging more rapidly in hominids than in murids per unit of neutral sequence divergence. By combining data on polymorphism levels in human noncoding DNA and the corresponding human–chimpanzee divergence, we show that the proportion of adaptive substitutions in these regions in hominids is very low. It therefore seems likely that the lack of conservation and increased rate of gene expression divergence are caused by a reduction in the effectiveness of natural selection against deleterious mutations because of the low effective population sizes of hominids. This has resulted in the accumulation of a large number of deleterious mutations in sequences containing gene control elements and hence a widespread degradation of the genome during the evolution of humans and chimpanzees.

Introduction Functionally important sequences are expected to evolve more slowly than neutrally evolving sequences. This is because long periods of selection for functional efficiency lead to sequences in which most advantageous mutations have already become fixed. The majority of new mutations in a sequence are then deleterious, because they perturb the highly adapted state. Studies of protein-coding DNA evolution have supported this expectation by showing that rates of amino acid substitution are substantially lower than rates of synonymous substitution in the majority of genes (e.g., [ 1 ]). Recently there has been great interest in using sequence conservation to detect functionally important regions of the genome outside of protein-coding sequences. However, detecting conservation has proven difficult because noncoding DNA sequences often appear to be weakly conserved. In mammals, for example, more than 98.5% of the genome is believed to be intron or intergenic DNA [ 2 ], and at least 40% of this is composed of the remnants of transposable element insertions that are presumably decaying in a neutral fashion. Comparisons of the rates of evolutionary divergence between species have revealed evolutionary constraints in certain classes of intergenic DNA, particularly in DNA close to coding sequences [ 3 ], regions in which gene expression control motifs are believed to be prevalent. For example, on the basis of comparisons between rodent genomes, it has been suggested that there are at least as many selectively constrained sites outside, as within, protein-coding sequences [ 4 , 5 ]. Between mouse and rat, comparisons of rates of evolutionary divergence of non-protein-coding DNA imply that about 17% of sites in the 2 kb upstream and downstream from genes and in first introns are selectively constrained sites [ 6 ]. There is also evidence for highly conserved DNA sequences at locations distant from coding sequences [ 6 , 7 , 8 , 9 , 10 , 11 ]. However, the extent to which constraint in noncoding regions varies among species is unclear. In this paper, we investigate sequence conservation in introns and intergenic DNA in interspecific comparisons of mouse and rat (murids) and human and chimpanzee (hominids). To estimate the levels of constraint in segments of non-protein-coding DNA, we compare the observed numbers of substitutions to the number expected from substitution rates at linked sequences assumed to be neutrally evolving. Unexpectedly, we find that selective constraints are essentially absent in hominids in regions upstream of genes and in first introns, in contrast to murids, in which these regions are subject to moderate levels of constraint. Results/Discussion Selective Constraints in Hominids and Murids We investigated the frequency of deleterious mutations and the pattern of sequence conservation in regions containing gene expression control sequences in hominids by compiling a dataset of 1,000 well-annotated, randomly chosen human genes. The genomic sequences upstream and downstream of each coding sequence and samples of their introns were aligned against the draft chimpanzee sequence. We compared the pattern of evolution in these hominid sequences against that in a previously compiled dataset of murid sequences [ 6 ]. Numbers of nucleotides sampled and other summary statistics for the sequences are shown in Table 1 . As others have noted previously, intron sequences evolve slightly faster (10%–25%) than 4-fold degenerate synonymous sites, when hypermutable CpG dinucleotides are excluded. (CpG dinucleotides are more frequent in coding sequences than intergenic DNA.) This could imply the presence of selection on synonymous sites [ 12 , 13 ]. Of the genomic sequences surveyed, intron sequences, other than intron 1, appear to be the fastest-evolving sequences in mammalian genomes and are therefore used as our neutral standard. Table 1 Summary Statistics for Sequences Sampled from Hominid and Murid Genomes Numbers of bases sampled and aligned in different categories from the human and chimpanzee genomes and mouse and rat genomes, levels of sequence divergence ( K ), and proportions of non-CpG-prone sites are shown We calculated levels of selective constraint in blocks of 500 bp, averaged over loci, using the aligned human–chimpanzee and mouse–rat genome sequence datasets. We corrected our constraint estimates for the decline in GC content that appears to be occurring in most mammalian genomes, including hominids and murids [ 14 ], although this correction made little difference, since the GC content of the flanking regions is similar to that of introns. Because different parts of a sequence can differ markedly in their frequency of CpG dinucleotides, and most CpG dinucleotides are hypermutable and are saturated by nucleotide substitutions between mice and rats, we excluded potentially CpG-prone sites by excluding sites preceded by C or followed by G. Simulation results ([ 6 ]; D. J. Gaffney and P. D. Keightley, unpublished data) indicated that failure to exclude CpG-prone sites leads to biased estimates of constraint. Selective constraints in hominid sequences in the regions 6,000 bp upstream and downstream of coding sequences and in the 5′ regions of first introns are clearly far lower than in murid sequences ( Figures 1 and 2 ). In particular, for the first 2,000 bp of the 5′ flanking region and the first 2,000 bp at the 5′ end of intron 1, constraint estimates (± standard error) are 0.0016 (± 0.019) and −0.0029 (± 0.019), respectively, for hominids, and are 0.17 (± 0.016) and 0.16 (± 0.018) for murids ( Table 2 ). The differences in constraint between hominids and murids in these regions are therefore about six times larger than the standard errors of differences between constraint estimates. Constraint is significantly different from zero near the 3′ end of hominid coding sequences, but still more than 50% lower than in murids ( Table 2 ). The standard errors for these estimates are very similar in murids and hominids, indicating that we have similar power to detect constraint in the two datasets. Differences in mean levels of constraint between hominids and murids are somewhat smaller if all nucleotide sites (including CpG-prone sites) are analyzed ( Table 2 ), but constraint in 5′ flanking regions and first introns is still very low in hominids (approximately 0.03) and significantly higher in murids. Such estimates of constraint are likely to be downwardly biased in murids, owing to saturation at CpG dinucleotides, giving an underestimate of the substitution rate in introns. This may be partly offset by the fact that some gene control regions are in unmethylated CpG islands, which would tend to increase estimates of constraint in both hominids and murids. Figure 1 Selective Constraint Plotted against Distance from the Coding Sequence in 5′ Flanking Regions and 3′ Flanking Regions Results for (A) hominids and (B) murids. The 5′ flanking regions are shown left of the origin, and 3′ flanking regions, right. Segments of 500 bases starting from the start or stop codon were analyzed. Bootstrap 95% confidence limits are shown in light grey. Figure 2 Selective Constraint at the 5′ End of First Introns Plotted against Distance from the Intron Start Results for (A) hominids and (B) murids. Segments of 500 bases starting from the 5′ end of intron 1 were analyzed. Bootstrap 95% confidence limits are shown in light grey. Constraint at the 3′ end of first introns is nonsignificant and close to zero in both hominids and murids. Table 2 Constraint in Hominids and Murids Calculated for Datasets Excluding CpG-Prone Sites and for Datasets Including All Sites The first 2,000 bases upstream and downstream from the coding sequence and at the 5′ end of intron 1 were analyzed There are a number of possible explanations for the apparent absence of constraint in hominid 5′ flanking and first intron sequences (and for the lower constraint in the 3′ flanking region), which we examine below. Sequencing Errors, Pseudogenes, and Genome Reorganization Because the mean sequence divergence of hominids is an order of magnitude lower than that of murids, sequencing errors are expected to disproportionally downwardly bias estimates of constraint in hominids. To estimate the extent of sequencing error in our data, we investigated how many conserved intron splice junctions had been mis-sequenced in the chimpanzee—the chimpanzee sequence has been produced and assembled without reference to the human sequence, so this should provide an unbiased estimate of the error rate. We observed 25 differences in 21,048 intronic splice donor/acceptor nucleotides, giving a maximum error rate (ɛ) of 1.19 × 10 −3 . This is expected to reduce constraint by approximately one-tenth since the relationship between the true level of constraint ( C true ) and observed level ( C obs ) is C obs = C true /(1 + ɛ/ k ), and the human–chimp divergence ( k ) is approximately 1%. If the true levels of constraint in the 5′, 3′, and intron 1 sequences closest to hominid genes were 30%, as we find in murids, the observed constraint in hominids would be 27%. Sequencing errors can therefore explain only a small proportion of the difference in constraint between hominids and murids. Further evidence that our data do not have unusually high rates of sequencing errors is that the divergences (see Table 1 ) are very similar to values that have been reported previously [ 12 , 15 , 16 , 17 ]. The results are also unlikely to be explained by polymorphism since levels of diversity in hominids are about 0.1% [ 18 ]. A second possibility that could explain the differences in constraint between hominids and murids is that our dataset of hominid genes contains large numbers of pseudogenes. However, several lines of evidence argue against this. First, the genes are well annotated and contain no stop codons. Second, the intronic splice donor/acceptor nucleotides are highly conserved. Third, the exons of our gene sample show strong conservation: constraint at second positions of codons estimated in the same manner as for noncoding sequences is 0.750 ± 0.013. And finally, a very substantial proportion of genes would have to be pseudogenes to explain our data. For example, to reduce the constraint by one-half would require 50% of the hominid genes to be pseudogenes. A third possibility is that the lower constraints in hominid 5′ flanking and first intron regions could be a consequence of a reorganization of gene regulation such that murids have a concentration of regulatory sequences in 5′ regions and in first introns and hominids have regulatory sequences concentrated in introns outside intron 1. However, two lines of evidence suggest that this is unlikely. First, there is remarkable conservation of syntenic blocks [ 19 ] and per locus intron/exon number [ 4 , 5 ] between murids and human. Second, the vast majority of known mammalian gene expression regulatory regions are situated within 2 kb of promoters [ 4 ]. Adaptive Evolution The lower level of constraint in hominids could be due to higher rates of adaptive substitution in the 5′, 3′, and first intron regions in hominids, masking constraint on other sites. If this is the case then we expect reduced nucleotide diversity in the 5′, 3′, and first intron regions for two reasons. First, the level of diversity in a region is largely determined by the number of effectively neutral mutations, because adaptive substitutions contribute little to polymorphism. This implies that levels of polymorphism will be lower if some sites are subject to constraint. Second, levels of diversity are expected to be lower in regions undergoing adaptive substitution because adaptive substitutions can remove variation by genetic hitchhiking [ 20 ]. To test for reduced levels of variation, we analyzed single nucleotide polymorphism data from the 5′ flanking, 3′ flanking, and intron sequences of 305 human genes compiled from the Environmental Genome Project ( http://www.niehs.nih.gov/envgenom/home.htm ). This dataset was chosen because it represents the most extensive and consistently sampled database of human single nucleotide polymorphisms. The genes were sampled as being “environmentally responsive,” and are therefore not a random sample, but they show average levels of constraint in all regions similar to those of our sample of 1,000 hominid genes. The analysis revealed no reduction in diversity in the 5′, 3′, and intron 1 regions when compared to the levels of diversity in intron sequences outside first introns ( Figure 3 ). In contrast, the level of diversity is significantly lower at nonsynonymous sites. To test the adaptive hypothesis further, we aligned the polymorphism dataset of 305 human genes against the chimpanzee genome sequence, measured divergence, and tested for adaptive evolution by an extension of the McDonald–Kreitman test [ 21 ] under the assumption that introns other than intron 1 are evolving neutrally ( Table 3 ). There was no evidence of adaptive evolution in these tests or in tests in which polymorphisms segregating at less than 10% were excluded—excluding rare polymorphisms has the effect of removing any slightly deleterious mutations that might be segregating in the 5′, 3′, and intron 1 sequences [ 22 ]. Interestingly the estimate of proportion of substitutions driven by adaptive evolution (α) was significantly negative for the 3′ flanking region. This is likely due to the segregation of slightly deleterious mutations, which is consistent with the low but significant level of constraint we observed in this region. Figure 3 Mean Nucleotide Diversity in Human Intergenic DNA in Blocks of 500 Bases Upstream and Downstream of Genes Data shown separately for first introns, introns excluding first introns (“Introns > 1”), and nonsynonymous sites. 95% confidence limits are indicated. Table 3 Tests of Adaptive Evolution for 5′, 3′, and Intron 1 Sequences in Hominids Fixation of Mildly Deleterious Mutations Two lines of evidence suggest that many mutations that affect gene expression may be under only weak purifying selection. First, sequences involved in gene regulation often appear to evolve rapidly [ 23 ]. Second, the rate of divergence in gene expression of primate genes is as fast as that of expressed pseudogenes [ 24 ]. These observations suggest another explanation for the low constraint in hominids: selection on mutations in the 5′ and 3′ flanking and intron 1 sequences that affect gene expression may be ineffective in hominids, since hominids have low effective population sizes ( N e ) . Based on polymorphism data and estimates of nucleotide mutation rates ( u ), estimates of human and chimpanzee N e are typically in the range 10,000–30,000 for nuclear sequences [ 25 , 26 , 27 ], and this is likely to have been the case for much of their evolution, since the ancestral N e for both species is estimated to be approximately 20,000 [ 28 ]. Unfortunately, we have little data from murids with which to estimate effective population sizes. A recent survey of nucleotide diversity in Mus musculus domesticus yielded an estimate of 4 N e u of 0.0054 [ 29 ]. Combining this with as estimate of the nucleotide mutation rate of between 1.67 × 10 −9 and 2.98 × 10 −9 [ 25 ], we estimate N e for the house mouse to be between 450,000 and 810,000. The fate of a deleterious mutation depends on the product N e s ; if N e |s| > 1, then the fixation probability for a deleterious mutation starts to become appreciable. Therefore, deleterious mutations, whose strength of selection falls within the range 1/ N e (murids) < | s| < 1/ N e (hominids), will tend to be removed by natural selection in murids, but can drift to fixation in hominids. Under the assumption that selection coefficients against deleterious mutations are equivalent in all taxa, the levels of selective constraint in noncoding DNA of murids and hominids imply that approximately 83%, 17%, and 0% of mutations in the first 2,000 bp of 5′ flanking DNA and intron 1 have a strength of selection such that | s | < 1/ N e (rodents), 1/ N e (rodents) < | s | < 1/ N e (hominids) and | s | > 1/ N e (hominids), respectively. For the the first 2,000 bp of 3′ flanking DNA we estimate that about 81% of mutations have | s | < 1/ N e (rodents), 12% are in the range 1/ N e (rodents) < | s | < 1/ N e (hominids) and 7% have | s | >1/ N e (hominids). In contrast to noncoding DNA, the fraction of slightly deleterious mutations fixed in hominid coding sequences is quite low. Constraint estimates at second codon positions outside CpG-prone sites in hominids and murids are 0.750 ± 0.016 and 0.900 ± 0.0085, respectively. Taking into account sequencing errors in hominids, the predicted “true” constraint value in hominids is 0.84, and this implies that only about 6% of mutations are in the slightly deleterious class. Gene Expression Divergence The lower level of constraint in hominid 5′, 3′, and intron 1 sequences leads to a testable prediction about the evolution of gene expression. Since the flanking regions of genes contain a high concentration of cis- acting gene control sequences [ 4 ], we expect gene expression to be evolving more rapidly in hominids than in murids, relative to the rate of neutral sequence evolution (i.e., the rate of mutation). To test this prediction, we used the microarray data of Enard et al. [ 30 ], who examined gene expression profiles in brain and liver tissue for humans, chimpanzees, M. domesticus , and M. spretus . From an analysis of 3,801 orthologous genes across all four species, we found that levels of divergence in expression between human and chimp are very similar to levels of divergence in expression between the two mouse species ( Table 4 ). At the same time, the level of nucleotide divergence in introns, K i , between the two hominid species is about 55% that of the mouse species ( Table 4 ). Thus, when measured relative to the level of intron nucleotide divergence, the divergence in gene expression d is almost 1.8-fold higher in hominids than it is in murids. This acceleration is significant both for liver (hominid/murid ratio of d / K i , 1.71; 95% confidence interval, 1.46–2.12) and for brain (hominid/murid ratio of d / K i , 1.79; 95% confidence interval, 1.53–2.21). Table 4 Expression Divergence ( d ) and Intron Nucleotide Divergence outside CpG-Prone Sites ( K i ) for Hominids and Murids Comparisons are human–chimpanzee and M. musculus–M. spretus CI, confidence interval As demonstrated in an analysis across different primate species, substantial increases in expression distances are still observed when going beyond the evolutionary distances examined here [ 30 ]. Thus, our finding of similar expression distances between the two species pairs cannot be due to expression distances reaching saturation. Because the Euclidean expression distances in Table 4 are of comparable magnitude, our conclusions are independent of the exact relationship between expression divergence and sequence divergence; similar results are obtained when using the mean squared gene-wise difference in log-expression (data not shown), as used, for example, by Khaitovich et al. [ 24 ]. A potential source of error in this analysis is the use of microarrays designed for humans on chimpanzee samples, and of microarrays designed for M. musculus on M. spretus samples. Sequence differences between the species will lead to lower hybridization efficiencies in chimpanzee and M. spretus , and will consequently exaggerate expression distances. However, this problem will be more pronounced in the mouse comparisons, since sequence divergence between M. musculus and M. spretus is higher than between human and chimpanzee. Thus, this would bias our results towards higher rates of gene expression evolution in mice, making our test conservative. Conclusion The lack of conservation of regions containing expression control sequences demonstrated above is consistent with the observation that the vast majority of Mendelian genetic disease mutations are located in coding sequences [ 31 ]. Our results have a number of interesting repercussions. The virtual absence of constraint in 5′ flanking regions and in 5′ regions of first introns, in which the majority of mammalian gene expression control sequences are believed to reside [ 4 ], implies that there has been widespread degradation of regions containing gene control sequences in hominids. We estimate that humans and chimpanzees have accumulated approximately 140,000 slightly deleterious mutations each, mutations that would have been eliminated by selection in murids. These mutations have small effects, since it can be inferred that they have selection coefficients less than 1/ N e for hominids, i.e., less than 10 −4 . It should be noted that it is unlikely that the mutation accumulation is due to a recent relaxation of natural selection in humans due to an improvement in our living conditions [ 32 ], since the time of this improvement is short relative the overall length of human evolution. We would not expect the decline in fitness to continue indefinitely, since the absolute strength of selection on new mutations, both advantageous and deleterious, may increase as fitness declines [ 33 ]. Furthermore, this accumulation of deleterious mutations may have been compensated in part by adaptive substitutions in gene expression control regions and elsewhere in the genome. We have also demonstrated that gene expression evolution is significantly accelerated in hominid brain and liver compared to the respective murid tissues. This result has important implications for theories of neutral gene expression evolution [ 24 , 34 ]. First, our results are consistent with the view that most variation in gene expression level found between human alleles is neutral [ 30 , 35 , 36 ]. However, the difference in expression divergence, relative to nucleotide divergence, between hominid and murid genomes implies that the proportion of gene expression changes that are under natural selection varies between different lineages, and that many of the mutations that affect gene expression in murids may in fact be under selection. Consequently, the strict notion of a gene expression clock [ 24 ], like the molecular clock, may only apply within closely related species. This is true irrespective of whether time is measured in units of sequence divergence (See Table 1 ) or in years: the divergence time between human and chimp is approximately 6 million years, while between M. musculus and M. spretus it is approximately 1.8 million years [ 24 ], so the absolute rate of gene expression divergence is much slower in hominids than murids, whilst it is substantially faster compared to the rate of neutral sequence evolution. The importance of effective population size in influencing the organization and complexity of genomes has recently been highlighted [ 37 ]. Our findings support the idea that microevolutionary processes are also strongly influenced by population size, and are evidence for the nearly neutral model of molecular evolution [ 38 ] in mammalian genomes. Materials and Methods Sampling of hominid genomic sequences DNA sequences of 1,000 annotated loci were compiled from the reference sequence (build 33) of the human genome. In a preliminary analysis of a smaller dataset, we determined that such a dataset of 1,000 loci would provide standard errors on constraint estimates of less than 2%. We randomly sampled loci by the criterion that each record contained the description of at least one mRNA. We extracted all exons, up to eight introns, including first, second, last, and second last introns, and up to 6 kb of intergenic DNA 5′ and 3′ from the start and stop codon, respectively. Intergenic DNA was extracted up to the midpoint between the sampled coding sequence and the start or stop codon of the following or preceding locus in the genomic contig. We extracted complete introns if they were less than 30 kb in length, otherwise the first and last 10 kb. For more than 80% of the hominid loci sampled, the 6-kb 5′ region includes all annotated untranslated exons and introns. We used reciprocal best-hits BLAST [ 39 ] to identify sequences orthologous to the human sequences in the reference assembly of the whole genome shotgun assembly of the chimpanzee genome. If a DNA segment exceeded 2 kb in length, this was subdivided into approximately 1-kb segments for analysis via BLAST. Sequences were aligned using MCALIGN [ 40 ] under a model of indel evolution appropriate to hominid intronic DNA. Parts of the chimp genome are of relatively low sequencing coverage, and errors in the assembly are expected. We therefore masked off sequence segments containing more than ten mismatches in a stretch of 100 bp and more than five mismatches in a stretch of 25 bp. Assuming independently distributed substitutions, the first level of nonhomology is expected almost never to appear by chance in our dataset, and the second level is expected to occur approximately four times in the approximately 27 Mb surveyed (see Table 1 ). Sampling of murid sequences Orthologous genes from 300 well-annotated loci were randomly sampled from the whole-genome assemblies of mouse and rat from GenBank. Loci were chosen for which annotation evidence included at least one complete mRNA sequence in both species. Further details of the sampling are given in [ 6 ]. Coding sequences, a sample of up to three introns (including first and last introns), and up to 6 kb of intergenic DNA 5′ and 3′ from the start and stop codon, respectively, were extracted from both species. Sequences were aligned by MCALIGN [ 40 ] using a model of indel evolution appropriate to rodent intronic DNA as described previously [ 6 ]. Calculation of evolutionary constraint We estimated selective constraints ( C ) for each of the above categories of sites by comparing the observed numbers of substitutions ( O ) with numbers expected ( E ) if substitution rates were equal to that of a class of putatively neutral sites. In this analysis, these putatively neutral sites were intronic sites excluding intronic splice control regions (bases 1–6 and 1–16 at the 5′ and 3′ ends, respectively) and first introns, since these show evidence of moderate selective constraint [ 6 ]. If the effect of higher substitution rates within the CpG dinucleotide context is removed, mean substitution rates in both murid and hominid lineages are somewhat higher at introns than at 4-fold degenerate sites ([ 5 ]; see Table 1 ); synonymous sites are believed to be under weak selection in mammals [ 1 ]. The level of evolutionary constraint for a specific category of sites in n loci is [ 41 ]. Constraint was calculated excluding nucleotides preceded by C or followed by G. Such CpG-susceptible sites have a high probability of being part of a hypermutable CpG dinucleotide, which approach saturation between mouse and rat, and have multiple substitutions sufficiently frequently between human and chimp as to induce bias in estimating substitution rates. The mutation process at microsatellite loci differs radically from that for nucleotide substitutions, so these were excluded from the analysis [ 6 ]. A nonequilibrium model of base composition evolution was used to calculate E, as described previously [ 42 ], assuming an equilibrium GC content of 0.4. For coding sequences, constraint was calculated for second positions of codons, where substitutions always lead to an amino acid change, using intronic sequences as the neutral reference. In noncoding DNA, constraint was calculated for blocks, typically of 500 bp, and averaged over loci using equation 1 . Standard errors and confidence intervals were computed by bootstrapping over loci. The number of slightly deleterious mutations fixed in hominids was calculated from the product of 25,000 (genes) × 4,000 bp (of 5′ and intron 1 sequence) × 0.17 (difference in constraint between murids and hominids) × 0.012/2 (human–chimp divergence/2) + 25,000 (genes) × 2,000 bp (of 3′ sequence) × 0.12 (difference in constraint between murids and hominids) × 0.012/2 = 138,000. Test of adaptive evolution Single nucleotide polymorphism data for 335 genes were compiled from the Environmental Genome Project ( http://www.niehs.nih.gov/envgenom/home.htm ). Of these, 305 had more than two intron sequences, and were suitable for further analysis. The sequences were aligned against the chimpanzee genome sequence, as described above, and the number of substitutions estimated by counting the number of differences, with no correction made for multiple substitutions (humans and chimps are sufficiently close as to make corrections for multiple substitutions unnecessary, particularly when CpG dinucleotides are excluded). We only considered sites that were not preceded by C or followed by G to maintain consistency with the other analyses reported here. For each gene we calculated the numbers of 5′ (or 3′ or intron 1) substitutions ( D n ) and polymorphisms ( P n ), along with the equivalent figures for the other introns, which acted as our neutral standard (respectively D i and P i ). To test for adaptive evolution we estimated the proportion of substitutions that were driven by adaptive evolution using the method of Smith and Eyre-Walker [ 21 ]: where L n and L i are the numbers of putatively selected and intron sites, respectively. Note that the number of sites appears in this formula because the number of sites for the polymorphism and divergence data are slightly different, since not all the sequence could be aligned against the chimpanzee genome. Figures for the divergence data are indicated by a prime. To obtain confidence limits for α, we bootstrapped the data by gene. Gene expression data Affymetrix oligonucleotide microarray data for brain cortex and liver tissue samples from three individuals each of human, chimpanzee, M. musculus , and M. spretus were obtained from Enard et al. [ 30 ]. To have comparable data from all species, only the first replicate of each human and chimp was used. Raw hybridization intensities were converted to expression levels using the Affymetrix MAS5 function as implemented in the BioConductor package [ 43 ], and then log 2 -transformed. The primate expression data were restricted to probe sets contained in both the HG-U95Av2 microarray (used for liver) and the HG-U95A microarray (used for cortex). We restricted the expression analysis to orthologous genes as follows. From Ensembl ( www.ensembl.org ), we obtained a mapping of probe sets to Ensembl gene IDs, and a list of human–mouse orthologs. If more than one probe set matched the same Ensembl gene, we averaged expression levels over all probe sets for this gene. We retained only one-to-one orthologs (i.e., genes where human and mouse IDs uniquely match each other), further requiring that each sequence cover at least 70% of the other. Expression matrices for the individual experiments were scaled to the same mean. All mouse experiments (and, separately, all primate experiments) were then normalized relative to each other by means of a quantile normalization [ 44 ]. For each species/tissue combination, these normalized expression values were averaged over the three individuals, resulting in expression vectors for 3,801 genes orthologous between human and mouse. Expression distances between species were calculated as Euclidean distances between expression vectors. Bootstrap analysis (resampling of genes; 1,000 datasets) was used to estimate standard errors (standard deviation of bootstrap distances) and confidence intervals (2.5% and 97.5% quantiles of bootstrap distances). We compared expression differences to intronic nucleotide divergence levels calculated without correction for multiple substitutions, excluding CpG-prone sites as described above. We analyzed the complete 1,000 gene chimp–human intronic dataset described above and a dataset of 39 introns from 24 orthologous loci of M. spretus and M. domesticus , compiled from GenBank. In order to obtain confidence intervals for the ratio of expression divergence d over sequence divergence K i , we used a bootstrap analysis of 1,000 datasets, each combining d values obtained from resampling genes from the expression analyses, and K i values obtained from resampling genes from the divergence analyses.

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544875

Computation of elementary modes: a unifying framework and the new binary approach

Background Metabolic pathway analysis has been recognized as a central approach to the structural analysis of metabolic networks. The concept of elementary (flux) modes provides a rigorous formalism to describe and assess pathways and has proven to be valuable for many applications. However, computing elementary modes is a hard computational task. In recent years we assisted in a multiplication of algorithms dedicated to it. We require a summarizing point of view and a continued improvement of the current methods. Results We show that computing the set of elementary modes is equivalent to computing the set of extreme rays of a convex cone. This standard mathematical representation provides a unified framework that encompasses the most prominent algorithmic methods that compute elementary modes and allows a clear comparison between them. Taking lessons from this benchmark, we here introduce a new method, the binary approach, which computes the elementary modes as binary patterns of participating reactions from which the respective stoichiometric coefficients can be computed in a post-processing step. We implemented the binary approach in FluxAnalyzer 5.1, a software that is free for academics. The binary approach decreases the memory demand up to 96% without loss of speed giving the most efficient method available for computing elementary modes to date. Conclusions The equivalence between elementary modes and extreme ray computations offers opportunities for employing tools from polyhedral computation for metabolic pathway analysis. The new binary approach introduced herein was derived from this general theoretical framework and facilitates the computation of elementary modes in considerably larger networks.

Background The background section presents the importance of computing elementary modes for metabolic system analysis, its computational difficulties and the existence of various known algorithms. A theoretical section brings these algorithms into a unified framework. In a following section we introduce a new approach, called the binary approach. Although relying on concepts introduced in the theoretical section, this section gives enough practical details to be stand-alone for the implementer. Results obtained from example networks and a conclusion section close the article. Definition and benefits of elementary modes We consider a metabolic network with m metabolites and q reactions. Reactions may involve further metabolites that are not considered as proper members of the system of study. The latter metabolites, considered to be buffered, are called external metabolites in opposition to the m metabolites within the boundary of the system, called internal metabolites. The stoichiometry matrix N is an m × q matrix whose element n ij is the signed stoichiometric coefficient of metabolite i in reaction j with the following sign convention: negative for educts, positive for products. Some reactions, called irreversible reactions, are thermodynamically feasible in only one direction under the normal conditions of the system. Therefore, reaction indices are split into two sets: Irrev (the set of irreversible reaction indices) and Rev (the set of reversible reaction indices). A flux vector (flux distribution), denoted v , is a q -vector of the reaction space q , in which each element v i describes the net rate of the i th reaction. Sometimes we are interested only in the relative proportions of fluxes in a flux vector. In this sense, two flux vectors v and v' can be seen to be equivalent, denoted by v ≃ v' , if and only if there is some α > 0 such that v = α · v' . Metabolism involves fast reactions and high turnover of substances compared to events of gene regulation. Therefore, it is often assumed that metabolite concentrations and reaction rates are equilibrated, thus constant, in the timescale of study. The metabolic system is then considered to be in quasi steady state. This assumption implies Nv = 0 . Thermodynamics impose the rate of each irreversible reaction to be nonnegative. Consequently the set of feasible flux vectors is restricted to P = { v ∈ q : Nv = 0 and v i ≥ 0, i ∈ Irrev }     (1) P is a set of q -vectors that obey a finite set of homogeneous linear equalities and inequalities, namely the | Irrev | inequalities defined by v i ≥ 0, i ∈ Irrev and the m equalities defined by Nv = 0 . P is therefore – by definition – a convex polyhedral cone [ 1 ]. Metabolic pathway analysis [ 2 - 5 ] serves to describe the (infinite) set P of feasible states by providing a (finite) set of vectors that allow the generation of any vectors of P and are of fundamental importance for the overall capabilities of the metabolic system. One of this set is the so-called set of elementary (flux) modes (EMs). For a given flux vector v , we note R ( v ) = { i : v i ≠ 0} the set of indices of the reactions participating in v . Hence, R ( v ) can be seen as the underlying pathway of v . By definition, a flux vector e is an elementary mode (EM) if and only if it fulfills the following three conditions [ 6 , 7 ]: In other words, e is an EM if and only if it works at quasi steady state, is thermodynamically feasible and there is no other non-null flux vector (up to a scaling) that both satisfies these constraints and involves a proper subset of its participating reactions. Note that with this convention, reversible modes are here considered as two vectors of opposite directions. The concept of elementary modes (and, with some restrictions, the very similar concept of extreme pathways [ 8 - 10 ]) has proven useful in many ways and has become an important theoretical tool for systems biology as well as for biotechnology and metabolic engineering (see review [ 5 ]). Because the metabolic network structure becomes now available at a genome-scale for an increasing number of microorganisms, this approach is well-suited to today's metabolic studies. Here, we give a short overview on the major applications and variants: (1) Identification of pathways : The set of EMs comprises all admissible routes through the network and thus of "pathways" in the classical sense, i.e. of routes that convert some educts into some products [ 5 ]. (2) Network flexibility : The number of EMs is at least a rough measure of the network's flexibility (redundancy, structural robustness) to perform a certain function [ 11 - 13 ]. (3) Identification of all pathways with optimal yield : Consider the linear optimization problem, where all flux vectors with optimal product yield are to be identified, i.e. where the moles of products generated per mole of educts is maximal. Then, one or several of the EMs reach this optimum and any optimal flux vector is a convex combination of these optimal EMs [ 3 , 14 ]. (4) Importance of reactions : The importance or relevance of a reaction can be assessed by its participation frequency or/and flux values in the EMs. (4a) Inference of viability of mutants : If a reaction is involved in all growth-related EMs its deletion can be predicted to be lethal, since all EMs would disappear [ 11 ]. (4b) A more quantitative measure of the importance of a reaction has been given by "control-effective fluxes" (CEFs, [ 11 ]). The CEFs take also the efficiency of each mode as well as the absolute flux values of the respective reaction in the EMs into account. CEFs have been used to predict transcript ratios [ 11 , 15 ]. (5) Reaction correlations : EMs can be used to analyze structural couplings between reactions, which might give hints for underlying regulatory circuits [ 14 , 16 , 17 ]. An extreme case is an enzyme (or reaction) subset (set of reactions which can operate only together) or a pair of mutually excluding reactions (two reactions never occurring together in any EM [ 10 ]). (6) Detection of thermodynamically infeasible cycles : EMs representing internal cycles (without participation of external material or energy sources) are infeasible by laws of thermodynamics and thus reflect structural inconsistencies [ 18 , 19 ]. (7) The framework of pathway analysis also allows us to combine and to study stoichiometric constraints together with regulatory rules [ 20 ]. (8) Minimal cut sets: EMs allow for a computation of minimal cut sets that represent minimal cuts (deletion sets) in the network repressing certain metabolic functions [ 21 ]. (9) The α-spectrum has been introduced to quantify the involvement of extreme pathways in a particular flux distribution (e.g. from an experiment) [ 22 ]. Since the decomposition of a flux vector into extreme pathways is usually not unique, the α-spectrum specifies a range of possible weights for each extreme pathway. The same could be defined for EMs. Computational limitations and algorithm variants Due to the combinatorial explosion in the number of EMs in large networks [ 23 ], computing EMs is known to be a hard computational task, so far restricting elementary-mode analysis to medium-scale networks. Several algorithms (and derivations thereof) have been developed for computing EMs. The two most prominent ones are the algorithm elaborated by Schuster et al. [ 4 ] and the recently developed null-space approach by Wagner [ 24 ]. The latter considerably accelerated the computation speed and thus shifted the current limitation – at least for a typical PC – from computation time to the memory requirement. Here we show that both the Schuster algorithm as well as that by Wagner can be embedded in a more general algorithmic framework stemming originally from computational geometry. These studies do not only give a summarizing point of view, they also lead to a crucial modification of the existing algorithms, decreasing the required memory for computing and storing EMs drastically. Results A unified framework Elementary modes as extreme rays in networks of irreversible reactions In the particular case of a metabolic system with only irreversible reactions, the set of admissible reactions reads: P = { v ∈ q : Nv = 0 and v ≥ 0 }     (3) Compared with (1) P is in this case a particular, namely a pointed polyhedral cone (an example is depicted in Figure 1 ). This geometry can be intuitively understood, noting that there are certainly 'enough' intersecting half-spaces (given by the inequalities v ≥ 0 ) to have this 'pointed' effect in 0 : P contains no real line (otherwise there coexist x and -x not null in P , a contradiction with the constraint v ≥ 0 ). The figure even suggests that a pointed polyhedral cone can be either defined in an implicit way, by the set of constraints as we did until now, or in an explicit or generative way, by its 'edges', the so-called extreme rays (or generating vectors ) that unambiguously define its boundaries. In the following, we show that elementary modes always correspond to extreme rays of a particular pointed cone as defined in (3) and that their computation therefore matches to the so-called extreme ray enumeration problem, i.e. the problem of enumerating all extreme rays of a pointed polyhedral cone defined by its constraints. An overview on general and current issues on extreme ray enumeration can be found in [ 25 ]. For the sake of consistency, we use this reference as a main source and adopt the same mathematical notations. Figure 1 A pointed polyhedral cone. Dashed lines represent virtual cuts of unbounded areas The pointed polyhedral cone is the central mathematical object throughout this work; therefore we shall introduce more precise definitions and results surrounding it. P is a pointed polyhedral cone of d if and only if P is defined by a full rank h × d matrix A ( rank ( A ) = d ) such that, P = P ( A ) = { x ∈ d : Ax ≥ 0 }     (4) The h rows of the matrix A represent h linear inequalities, whereas the full rank mention imposes the "pointed" effect in 0 . Note that a pointed polyhedral cone is, in general, not restricted to be located completely in the positive orthant as in (3). For example, the cone considered in extreme-pathway analysis may have negative parts (namely for exchange reactions), however, by using a particular configuration it is ensured that the spanned cone is pointed [ 8 ]. Now we must characterize the extreme rays. A vector r is said to be a ray of P ( A ) if r ≠ 0 and for all α > 0, α · r ∈ P ( A ). We identify two rays r and r' if there is some α > 0 such that r = α · r' and we denote r ≃ r' , analogous as introduced above for flux vectors. For any vector x in P ( A ), the zero set or active set Z ( x ) is the set of inequality indices satisfied by x with equality. Noting A i • the i th row of A , Z ( x ) = { i : A i • x = 0}. Zero sets can be used to characterize extreme rays. For simplicity, we adopt in this document the following characteristic ([ 25 ] for example) as a working definition of extreme rays. Definition 1: Extreme ray Let r be a ray of the pointed polyhedral cone P( A ). The following statements are equivalent : ( a ) r is an extreme ray of P( A ) (b) if r' is a ray of P( A ) with Z ( r ) ⊆ Z ( r' ) then r' ≃ r Since A is full rank, 0 is the unique vector that solves all constraints with equality. The extreme rays are those rays of P ( A ) that solve a maximum but not all constraints with equalities. This is expressed in (b) by requiring that no other ray in P ( A ) solves the same constraints plus additional ones with equalities. Note that in (b) Z ( r ) = Z ( r' ) consequently holds. An important property of the extreme rays is that they form a finite set of generating vectors of the pointed cone ([ 25 ] for example): any vector of P ( A ) can be expressed as a non-negative linear combination of extreme rays, and the converse is true: all non-negative combinations of extreme rays lie in P ( A ). Moreover, the set of extreme rays is the unique minimal set of generating vectors of a pointed cone P ( A ) (up to positive scalings). Lemma 1: EMs in networks of irreversible reactions In a metabolic system where all reactions are irreversible, the EMs are exactly the extreme rays of P = { v ∈ q : Nv = 0 and v ≥ 0 }. Proof : P is the solution set of the linear inequalities defined by where I is the q × q identity matrix. Since it contains I , A is full rank and therefore P is a pointed polyhedral cone. All v ∈ P obey Nv = 0 , thus the 2 m first inequalities defined by A hold with equality for all vectors in P and the inclusion condition of Definition 1 can be restricted to the last q inequalities, i.e. the inequalities corresponding to the reactions. Inclusion over the zero set can be equivalently seen as containment over the set of non-zeros in v , i.e. R ( v ). Consequently, e ∈ P is an extreme ray of P if and only if: for all e' ∈ P : R ( e' ) ⊆ R ( e ) ⇒ e' = 0 or e' ≃ e , i.e. if and only if e is elementary. Thus, all three conditions in (2) are fulfilled. The general case In the general case, some reactions of the metabolic system can be reversible. Consequently, A does not contain the identity matrix and P (as given in (1)) is not ensured to be a pointed polyhedral cone anymore [ 7 ]. Because they contain a linear subspace, non-pointed polyhedral cones cannot be represented properly by a unique set of generating vectors composed of extreme rays, albeit a set of generating vectors exists, sometimes also called convex basis [ 7 ]. One way to obtain a pointed polyhedral cone from (1) is to split up each reversible reaction into two opposite irreversible ones. Note that this operation is completely analogous to a transformation step used in linear programming to obtain a linear optimization problem in canonical form: free variables v are also split into two variables v + and v - with v = v + - v - and v + , v - ≥ 0 [ 26 ]. It has been noticed earlier that this virtual split does not change essentially the outcome: the EMs in the reconfigured network are practically equivalent to the EMs from the original network [ 10 ]. Here we prove and precisely characterize this result. We first introduce some notations. We denote the original reaction network by S and the reconfigured network (with all reversible reactions split up) by S' . The reactions of S are indexed from 1 to q . Remember that Irrev denotes the set of irreversible reaction indices and Rev the reversible ones. An irreversible reaction indexed i gives rise to a reaction of S' indexed i . A reversible reaction indexed i gives rise to two opposite reactions of S' indexed by the pairs ( i ,+1) and ( i ,-1) for the forward and the backward respectively. The reconfiguration of a flux vector v ∈ q of S is a flux vector v' ∈ Irrev ∪ Rev × {-1;+1} of S' such that Let N' be the stoichiometry matrix of S' . N' can be written as N' = [ N - N Rev ] where N Rev consists of all columns of N corresponding to reversible reactions. Note that if v is a flux vector of S and v' is its reconfiguration then Nv = N'v' . If possible, i.e. if v' ∈ Irrev ∪ Re v × {-1;+1} is such that for any reversible reaction index i ∈ Rev at least one of the two coefficients v' ( i ,+1) or v' ( i ,-1) equals zero, then we define the reverse operation, called back-configuration that maps v' back to a flux vector v such that: Theorem 1: EMs in original and in reconfigured networks Let S be a metabolic system and S' its reconfiguration by splitting up reversible reactions. Then the set of EMs of S' is the union of a) the set of reconfigured EMs of S b) the set of two-cycles made of a forward and a backward reaction of S' derived from the same reversible reaction of S Proof : see Methods. Thus, the set of EMs of the original network is equivalent (up to the two-cycles) to the set of EMs in the reconfigured network and therefore can be seen as a reduced set of extreme rays of the pointed convex polyhedron as defined by: P = { v' ∈ q + | Rev | : N'v' = 0 and v' ≥ 0 }     (5) Hence, EMs computation can be derived from any extreme ray enumeration algorithm applied to the reconfigured network and followed by vector back-configuration and the elimination of meaningless vectors, namely the two-cycles. Note that exactly the same procedure – splitting reversible reactions into two irreversible ones – was carried out also in the original work of Clarke [ 27 ] on stability analyses in stoichiometric networks. Clarke called the extreme rays of the corresponding cone (5) extreme currents . Thus, extreme currents are identical to the EMs in the reconfigured network and, hence, also (up to the 2-cycles) equivalent to the EMs from the original network All known algorithms for computing EMs are variants of the Double Description Method In the following we present a simple yet efficient algorithm for extreme ray enumeration, the so-called Double Description Method [ 28 ]. We show that it serves as a common framework to the most prominent EM computation methods. To reach this generality, we concentrate on mathematical operations regardless to the actual data-structures used in the implementation. Therefore we manipulate objects such as matrices, vectors or inequalities and leave their implementation into tableaus, arrays and so on to the next section. A generating matrix R of a pointed polyhedral cone P ( A ) is a matrix such that P ( A ) = { x ∈ d : x = Rλ for some λ ≥ 0}. The pair ( A , R ) is called a Double Description pair , or DD pair. As mentioned above, the extreme rays form the unique set of minimal generating vectors of P ( A ) and thus, considered as set of d -vectors, the extreme rays of P ( A ) form the columns of a generating matrix R that is minimal in terms of number of columns. The pair ( A , R ) is then called a minimal DD pair . The strategy of the Double Description Method is to iteratively build a minimal DD pair ( A k , R k ) from a minimal DD pair ( A k - 1 , R k - 1 ), where A k is a submatrix of A made of k rows of A . At each step the columns of R k are the extreme rays of P ( A k ), the convex polyhedron defined by the linear inequalities A k . The incremental step introduces a constraint of A that is not yet satisfied by all computed extreme rays. Some extreme rays are kept, some are discarded and new ones are generated. The generation of new extreme rays relies on the notion of adjacent extreme rays . Here again, for the sake of simplicity, we adopt a characteristic ([ 25 ] for example) as a working definition of adjacent extreme rays. Definition 2: Adjacent extreme rays Let r and r' be distinct rays of the pointed polyhedral cone P( A ). Then the following statements are equivalent : (a) r and r' are adjacent extreme rays (b) if r" is a ray of P( A ) with Z ( r ) ∩ Z ( r' ) ⊆ Z ( r" ) then either r" ≃ r or r" ≃ r' Initialization The initialization of the double description method must be done with a minimal DD pair. One possibility is the following. Since P is pointed, A has full rank and contains a nonsingular submatrix of order d denoted by A d . Hence, ( A d , A d -1 ) is a minimal DD pair which works as initialization and leads directly to step k = d . Note that there is some freedom in choosing a submatrix A d or some alternative starting minimal DD pair. Incremental step Assume ( A k - 1 , R k - 1 ) is a minimal DD pair and consider a k th constraint defined by a not yet extracted row of A , denoted A i • . Let J be the set of column indices of R k - 1 and r j , j ∈ J , its column vectors, i.e. the extreme rays of P ( A k - 1 ), the polyhedral cone of the previous iteration. A i • splits J in three parts (Figure 2 ) whether r j satisfies the constraint with strict inequality ( positive ray), with equality ( zero ray) or does not satisfy it ( negative ray): Figure 2 Double description incremental step . The scene is best visualized with a polytope; consider the cube pictured here as a 3 projection of a 4 polyhedral cone. Extreme rays from the previous iteration are {a,b,c,d,e,f,g,h} whose adjacencies are represented by edges. For the considered constraint, whose null space is the hyperplane depicted by the bold black border lines, b and f are positive rays, a and c are zero rays, d, e, g and h are negative rays. b, f, a and c satisfy the constraint and are kept for the next iteration. {f,e} and {f,g} are the only two pairs of adjacent positive/negative rays and only they give rise to new rays: i and j at the intersection of the hyperplane and the respective edges. The new polytope is then defined by its extreme rays: {a,b,c,f,i,j}. J + = { j ∈ J : A i • r j > 0} J 0 = { j ∈ J : A i • r j = 0}     (6) J - = { j ∈ J : A i • r j < 0} Minimality of R k is ensured in considering all positive rays, all zero rays and new rays obtained as combination of a positive and a negative ray that are adjacent to each other [ 25 ]. For convenience, we denote by Adj the index set of the newly generated rays in which every new ray is expressed by a pair of indices corresponding to the two adjacent rays combined. Hence, R k is defined as the set of column vectors r j , j ∈ J' with The incremental step is repeated until k = h i.e. having treated all rows of the matrix A . The columns of the final matrix R m are the extreme rays of P ( A ). Computing EMs The Double Description Method together with Theorem 1 offers a framework for computing EMs. The only steps to include are a reconfiguration step that splits reversible reactions and builds the matrix A , and a post-processing step that gets rid of futile two-cycles and computes the back-configuration. The dimension of the space is given by the number of reactions in the reconfigured network: q ' = q + | Rev |. This results in the general algorithmic scheme as given in Table 1 (from here, all variables for the reconfigured network are written without prime): Table 1 General double description method for EM computation. N ← reconfigured stoichiometry matrix [ N - N Rev ] A ← [ N T - N T I ] T Reconfiguration A q ← q independent rows of A R ← A q -1 Initialization for each unprocessed row A i • of A do J + ← { j ∈ J : A i • r j > 0} J 0 ← { j ∈ J : A i • r j = 0} J - ← { j ∈ J : A i • r j < 0} R' ← { r j : j ∈ J 0 ∪ J + } For ( j + , j - ) ∈ J + × J - do Processing of constraints in a given order If and adjacent in R then Adjacency test end if end for R ← R' End for Gaussian combination step R ← R \ { futile two-cycles } R ← back-configuration of R Back-configuration As mentioned in the introduction section, the two most efficient algorithms for computing EMs available are the recently introduced null-space approach [ 24 ] and the Schuster algorithm [ 6 ], that we call "canonical basis approach" (implemented, for example, in METATOOL [ 29 ] version 4.3 and FluxAnalyzer version 5.0 [ 30 ]). Both algorithms handle reversible reactions directly. A direct handling of reversible reactions, meaning without network reconfiguration, is feasible in each setting and has been described in the respective original articles. This requires adapted adjacency tests. However, it does not affect the overall strategy. For simplicity, we describe these algorithms with networks of irreversible reactions only (the issue of reversible reactions is discussed below). We are now able to see that the algorithms of Schuster and Wagner differ basically only in the chosen initialization for R . The canonical basis approach (Schuster approach; CBA) The matrix I represents q independent rows extracted from A = [ N T - N T I ] T and can thus be used for A q . The matrix A q -1 = I -1 = I gives the q extreme rays that obey to these q independent constraints and works as initialization of R . The remaining constraints are 2 m linear inequalities defined by Nr ≥ 0 and - Nr ≥ 0 , i.e. m equalities: Nr = 0 . The processing of an equality constraint is done in a single pass by only keeping rays of J 0 instead of J + ∪ J 0 . This is achieved by replacing the line R' ← { r j : j ∈ J + ∪ J 0 } with R' ← { r j : j ∈ J 0 } in the part "Processing of constraints in a given order" in Table 1 . Note that in the original Schuster algorithm the values of Ar j (required for the Gaussian combination step) are explicitly stored throughout the algorithm (in the left-hand side of the tableau [ 4 ]) and adapted after each iteration. The null space approach (Wagner approach; NSA) The idea there is to initialize R by a well-defined kernel (or null-space) matrix K of N with a particular structure (the transposed K T is in (reduced) row-echelon form): which can be computed, for example, by the MATLAB command null ( N ,' r '). One can assume N to be of rank m , the opposite case being discussed below ("On redundancies and network compression"). This implies to be of size m × ( q - m ) and the identity of size q - m . This structure is obtained by allowing a reordering of the rows of K , i.e. of the reaction indices. Without losing generality, one can assume that the reactions corresponding to the block I are indexed from 1 to q - m . Consider the ( q + m ) × q matrix . For all x in P ( A q + m ), there is some vector λ ≥ 0 such that x = Kλ . Reciprocally, for all λ ≥ 0 , the vector x = Kλ lies in P ( A q + m ). Thus ( A q + m , K ) is a DD pair. Since K is a kernel matrix, its columns are independent vectors therefore ( A q + m , K ) is a minimal DD pair. K as defined in (8) works as initial value for R . Hence, the initialization in this setup delivers directly k = q + m solved constraints. The remaining constraints are m linear inequalities defined by r i ≥ 0, i = q - m + 1... q . The Gaussian elimination step simplifies too The right hand-side is practically a positive combination of the two vectors and , because is positive and negative due to the definitions of J + and J - . Adjacency tests Here we give explicitly the adjacency test in the case of reconfigured networks for each setup. Variants handling reversible reactions directly were introduced for CBA and NSA. They lead in general to more complex algorithmic steps for a little (at most 2-fold) memory gain. The test is used when processing the constraint k + 1 to check whether two extreme rays r and r' of the cone P ( A k ) are adjacent. The adjacency test is based on Definition 2(b). Note that for a given extreme ray r of the cone P ( A k ), the considered zero set Z ( r ) is defined over the k constraints A k . CBA : As mentioned above, in a CBA setup, equality constraints are solved within a single iteration. After the l -th iteration step, k = q + 2 l constraints are processed, therefore . The last 2 l constraints are satisfied with equality for all computed rays. We denote by Z u ( v ) the Zero set of a vector v over the u first constraints. Here, with u = q it matches to the set of non-participating reactions in v . The adjacency test is then equivalent to the search of a third extreme ray r" such that Z q ( r ) ∩ Z q ( r' ) ⊆ Z q ( r" ). If such an r" exists, then r and r' are not adjacent. NSA : After the l -th iteration step in an NSA setup, k = q + m + l constraints including p = q - m + l sign constraints are processed. Thus . The last 2 m constraints are satisfied with equality for all computed rays. Therefore, the adjacency test is then equivalent to the search of a third extreme ray r" such that Z p ( r ) ∩ Z p ( r' ) ⊆ Z p ( r" )     (10). Thus, for NSA we only have to check the first p ( q - m ≤ p ≤ q ) elements of the rays, in contrast to all q elements for CBA. This is one reason behind the relative velocity of NSA compared to CBA. On redundancies and network compression It is common practice to reduce the problem of extreme ray enumeration by restricting the input set to the set of irredundant constraints [ 25 ]. Although the general problem of extreme ray enumeration is non-polynomial, the reduction into irredundant constraints is equivalent to linear programming and therefore of polynomial complexity. To our best knowledge, this important pre-processing has never been spelled out explicitly in the context of EM computation. However some network simplification steps have been proposed earlier [ 4 , 30 ] that deeply relate to the notion of redundancy removal. These simplifications include three heuristics that reduce the size of the original stoichiometric matrix N and thus the input size of the problem: the detection of conservation relations, of strictly detailed balanced reactions and of enzyme subsets. Conservation relations of metabolites are captured as linear dependencies between rows of the stoichiometry matrix N (thus, in the left null-space of N ; [ 31 ]). This implies that some of the equality constraints in Nr = 0 are linearly dependent. Satisfying a maximal linearly independent subset of these equations suffices to satisfy all equations. Therefore the problem can be reduced to , where is the reduced stoichiometry matrix. For example, in Figure 3a , metabolites B and C build up one conservation relation and thus one of these metabolites can be removed. Note that conservation relations need not to be considered explicitly in the null-space approach since their removal does not affect the computed null-space matrix. Figure 3 Small example networks illustrating redundancies. For explanations see text. Conservation relations only consider redundancies among the equalities. The general approach handles also inequality constraints. Strictly detailed balanced reactions [ 32 ] and enzyme subsets [ 29 ] are particular cases of such redundancies. Strictly detailed balanced reactions are reactions with null flux at any steady-state. Many of them can be identified as null row vectors of K , the kernel matrix of N , and can be eliminated from the system. A non-trivial example is shown in Figure 3b , where R1 is strictly detailed balanced and would be detected by using the kernel matrix. However, there may be further reactions with a fixed zero-flux in steady state that cannot be identified by K . Some of those can be found by a simple analysis of N . For example, all the uni-directional reactions pointing into an internal sink (or emanating from a source) are certainly not participating in any steady-state flux (Figure 3c ). An enzyme subsets is defined as a group of reactions with relative constant flux ratio at steady state. Many of them can be identified as row vectors of K differing only in a scalar factor α . Reactions R1, R2 and R5 in Figure 3d would represent one enzyme subset. Assume one works on the reconfigured network and reactions R1 and R2 are members of the same enzyme subset. Thus, at steady state, we have for the respective rates r 1 = α · r 2 . If α > 0, the constraints r 1 ≥ 0 and r 2 ≥ 0 are redundant, r 1 ≥ 0 being sufficient. In that case the practice is to lump both reactions into one lowering the number of reactions (and often also of the metabolites). If α < 0, the constraints r 1 ≥ 0 and r 2 ≥ 0 imply r 1 = r 2 = 0, hence, a special case of strictly detailed balanced reactions. In this case we say that the reactions contradict each other. Both reactions are not used and can be eliminated from the system as reactions R1 and R4 in Figure 3e . We identified another kind of redundancies. We call a metabolite M uniquely produced (respectively consumed ) if only one single reaction, say i , can produce (respectively consume) M for several consuming (respectively producing) it (see Figure 3f ). In that case, balancing metabolite M at steady-state implies that r i is always non-zero whenever the other reactions connected to M are active. We can therefore lump each reaction consuming (respectively producing) M with reaction i and remove metabolite M, decreasing the dimension of the problem further (see also the example in Figure 5 which is discussed below). Note that some enzyme subsets and strictly detailed balanced reactions can be seen as special cases of this type of redundancy. Figure 5 Example network . Full structure (a), compressed structure (b) and compressed structure with split reversible reaction R4c (c). Elimination of redundancies and network compression should be done in a pre-processing step leading to a compressed network structure. Thereby, it is important to detect and remove such redundancies iteratively until no further redundancy can be found. A MATLAB function compressSMat which removes all redundancies discussed above in an iterative fashion can be obtained from the corresponding author. After the computation of EMs, lumped reactions can be expanded to their single components. There is a general approach for identifying redundancies in a set of linear constraints that uses linear programming, for example with the software redund distributed together with the software lrs [ 33 ]. This approach does not require any iterative process, but only identify redundant inequalities. Rows of A can be eliminated but no consequent column-wise reduction is done. Therefore, a simple redundancy removal is not as powerful as the accompanying network compressions presented above. The method however has the advantage to be systematic and might lead in the future to further network simplifications not yet identified. The binary approach General idea Using the reconfigured network with only irreversible reactions we have shown that the most important algorithms for EM computation belong to the same general framework. However, the original algorithms from Schuster and Wagner operate directly on the original network without splitting reversible reactions. At a first look, this seems to be more efficient since the dimension (number of reactions) is lower, decreasing seemingly also the memory requirement and the costs for adjacency tests. However, using the reconfigured network S' offers great simplifications. First, as already mentioned in an earlier section, the adjacency tests are easier to handle. The most important advantage, however, is the following. For the CBA in S' it follows that all non-zero elements of a ray r k will be retained if a new ray is obtained by combining r k with another (adjacent) ray because only positive combinations of rays are performed. The same holds for the NSA with respect to the p already processed inequality (irreversibility) constraints. This is of great importance since the adjacency test requires the information on zero/non-zero places in the rays only. We illustrate this idea for NSA because this approach turned out to be more efficient than CBA. We assume that N has full rank m , i.e. there is no conservation relation. In this section, all variables correspond again to the network with split reversible reactions. As described above, for an initialization of R we use a kernel matrix K of N having form (8): Note that we use here the transposed representation of the tableau compared to Wagner's original article [ 24 ]. Since by eq. (9) only positive column combinations are performed during the algorithm, no negative number can show up in the upper part (consisting of q - m rows (reactions)) during the next iterations. The first row to be processed now is p = q - m + 1. Using the general algorithmic scheme provided above all rays with non-negative entries at row p are retained and all negative entries can be combined with positive ones that are adjacent to them to obtain a zero at position p . Assuming that the procession of the p -th row leads to a collection of t rays, we have: The upper part, R 1 , contains the p processed rows which only contain non-negative values. Again, positive combinations of rays performed during the next iterations lead in the upper part to sums of non-negative numbers. Hence, it is easy to keep track of the zeroes in the upper part R 1 by the use of bit masks. After the procession of the p -th inequality constraint the p -th row (i.e. the first row of R 2 ) can be transformed to its binary representation and moved from R 2 to R 1 . Using a binary representation for R 1 has many advantages: (i) For the next row p + 1 to be processed we have to perform the adjacency test for pairs of vectors , . This test only requires the first p elements of these rays (see (10)), hence, exactly the columns of R 1 . Test (10) can then be written as a simple (and fast) bit operation. Two distinct vectors , are adjacent if and only if for all vectors r k distinct from and , it holds: ( taken from R 1 ; r 1... p denotes the first p elements of r ). Of course, the identical terms in the parentheses are computed only once. (ii) Combination step of two adjacent rays (eq. (9)) reduces for the part in R 1 to a simple OR operation, which is already computed for (13). The other (real number) components of the two rays (contained in R 2 ) are combined as usual by eq. (9). (iii) Bit operations as applied in R 1 are not only fast, they are numerically exact in contrast to operations on real numbers. (iv) The binary representation requires much less memory. Taking a typical 64-bit floating-point variable, storing R 1 binary takes only 1.6 % of the memory needed for real numbers. Taking into account that in the worst case (all reactions reversible) the number of reactions in the reconfigured network is twice of that from the original one we still have a reduction in memory requirements of more than 96%. Note that R 2 is empty at the end of the algorithm, hence, all EMs are then stored binary. Bitmap representations of EMs have already been used in earlier implementations for accelerating the adjacency (elementarity) tests. However, binary tableaus had then been stored and updated in parallel to the full (real number) tableau of EMs which is not necessary here. After the whole processing, EMs (extreme rays) are obtained for the reconfigured network S' as binary vectors. Binary patterns of EMs are completely sufficient for many applications of EMs (see discussion). However, a well-known lemma ([ 25 ] for example) ensures that this information is also sufficient to retrieve the real values up to a positive scalar: Lemma 2 In a d-dimensional Euclidean space, let r be a ray of the pointed polyhedral cone P( A ). The following statements are equivalent : (a) r is an extreme ray of P( A ) (b) rank ( A Z ( r ) ) = d - 1 Each obtained binary vector provides the zero set Z q ( e ) and its complement the reaction set R ( e ) of an EM e in the reconfigured network S' . Lemma 2 says that the equation and therefore N R ( e ) e R ( e ) = 0 (14) admit a one-dimensional solution space, i.e. the dimension of the null space of N R ( e ) is 1. N R ( e ) denotes the m × | R ( e )| sub-matrix of N containing all those reactions (columns) of N which are involved in e . Solving the homogeneous linear system (14) gives a vector that can be normalized and properly oriented for example by dividing it by the value on its first participating reaction (see the example below). The reconstruction process reflects the fact that an EM is – up to a scalar – determined by its participating reactions. In a second post-processing step, we transform the (real number) EMs of S' back into their representation in the original network S by using the rules given before Theorem 1. Note that it is also possible to transform first the binary EMs from S' into the binary EMs of S and then to reconstruct the real numbers (by using eq. (14) for the stoichiometric matrix of the non-reconfigured network S ; see pseudo-code). In both cases, if the original network had been compressed during pre-processing, the EMs can finally be expanded to their corresponding modes in the uncompressed network. Pseudo-code of the binary (null-space) approach Using the results of the previous sections we are now able to give a pseudo-code of the binary (null-space) approach (Figure 4 ). The code follows MATLAB style, which provides a convenient and comprehensible notation for operations on vectors and matrices. We use several native MATLAB routines (written in bold). For concision, we also make the use of some other routines (indicated in italic). The code of the latter routines is not given here explicitly but their names and accompanying comments should allow the reader to implement them. For readers not familiar with MATLAB notation we give in the Methods section some basic explanations which should suffice for understanding the pseudo-code. Figure 4 Pseudo-code: Core algorithm for computing elementary modes with the binary approach. Note that the pseudo-code in Figure 4 is not given in its computationally most efficient form. It should just present the basic structure of the algorithm. There are two important issues in the algorithm we still have to discuss. Minimal number of zeros in extreme rays (maximal pathway length) In the null-space approach, the m equality constraints are always solved for each ray during the procession of sign constraints. Since any ray satisfies by Lemma 2 at least a total of q - 1 constraints, this implies that at least q -1- m sign restrictions are solved by equality. Hence each ray contains at least q -1- m zero-places. This fact can be used as a shortcut when checking the adjacency of two rays (see pseudo-code). At the end of the algorithm, it follows that the maximal pathway length | R ( e )| max , that is the maximal number of involved reactions in an EM, reads (cf. [ 7 ]): | R ( e )| max = q - ( q - m - 1) = m + 1     (15) Initialization of R As for the non-reconfigured network, the initialization of R for the reconfigured network can be done with a null space matrix K' of N' having the special structure (8). Several of such kernel matrices may exist. We are interested in such a one that contains as many zeros as possible because the number of zeros in the starting tableau R has great impact on the number of ray combinations to be performed. For this purpose, it can be exploited that very sparse vectors of the null space of N' (not contained in the null space of N ) are known, namely the two-cycles emerging by splitting up reversible reactions. We detail in Method section a technique that incorporates as many two-cycles as possible into K to construct K' . Simple example This section is devoted to illustrate our binary approach for computing elementary modes. Figure 5(a) shows a simple example network consisting of four metabolites (A,B,C,D) and 7 reactions (R1...R7), whereof R5 is reversible. The stoichiometric matrix N of this network reads accordingly: Using our rules for removing redundancies, this network can be compressed as depicted in Figure 5(b) . Metabolite A is uniquely produced, hence, R1 and R2 can be combined to R1c and reactions R1 and R3 are lumped into R2c. R3c and R4c correspond to the original reactions R4 and R5, respectively. Finally, R6 and R7 are enzyme subsets and are combined to R5c. Metabolites A and D can be removed, since they do not occur in any reaction anymore. Thus, the network dimension could be reduced by two metabolites and two reactions. The stoichiometric matrix N C of the compressed system reads: From this compressed network, we can compute a null space matrix having structure (8), here even without permuting rows (reactions): K C would be the starting tableau in the original null-space approach. Applying our binary approach we have now to split the (only) reversible reaction R4c in the compressed network (Figure 5(c) ). This results in the stoichiometric matrix N C ', where R4c b denotes the additionally introduced column of the backward direction of R4c: Now we need to determine a null space matrix K C ' of N C ', if possible in the sparse form as in eq. (M1) (Methods section). K C – as given in (18) – contains only irreversible reactions in the identity sub-matrix. Therefore, without further rearrangements, we can already use it to construct K C ' as described in the Methods section. We introduce an additional row in the identity sub-matrix of K C (corresponding to R4c b ) and an additional column representing the two-cycle from the split reversible reaction R4c: K C ' is now a proper initialization for the R tableau according to (11). The first four rows (in the identity sub-matrix) can be seen as already completed, we therefore denote the starting tableau as R 4 . According to (12) we can divide R 4 into a binary (a non-zero entry is demarked by "×") and unprocessed real number part: We proceed now with the 5-th row (R4c). All columns with non-negative entries in R4c are retained (columns 1 and 4). Columns 2 and 3 have a negative entry at position R4c and are therefore combined with 1 and 4 to obtain a zero at position R4c. In the binary sub-tableau, the combination step is a simple OR operation. Thereby, using the obtained binary patterns, the adjacency test (13) must be performed for each pair of combined columns. Here, all 4 possible pairs are adjacent. Accordingly, after completing row 5, tableau R 5 has 6 columns and reads: Now we have already reached the last iteration step where R5c – the last row in real number format – is processed. Columns 1–5 are retained and column 6 is combined with columns 1,3 and 5. However, the column pairs (1,6) and (3,6) are not pairs of adjacent rays. This can be detected in two alternative ways. The usual way is that both column pairs violate condition (13) because of column 4. The second and quicker way is to observe that the minimal number of zeros in this network is 3 ( q' - m -1 = 6-2-1) and that their respective combinations would give columns with only 2 zeros. These combinations are therefore not included in the tableau. We obtain: Tableau R 6 is the binary representation of the EMs (extreme rays) from the split compressed network. Now, the post-processing begins. First, we remove the spurious 2-cycle (second column in R 6 ) raised by splitting R4c. Then, rows R4c and R4c b are combined by an OR operation and row R4c b is dropped. Note, if a completely reversible elementary mode exists in the non-split network, it would lead to two EMs – one for each direction – in the split network. In such a case, either both are kept or only one, then marked as reversible EM. We have now obtained the 5 EMs of the compressed network as binary vectors: Here, it is easy to reconstruct the real numbers of the EMs from their binary patterns. For illustrating the general case, we reconstruct the first mode e 1 using eq. (14): The dimension of the null space of , hence of the solution space of eq. (25) is 1 (as it is for all EMs). A scalable solution vector is (2,1,1) T , normalizing to the first component yields the unique solution (1,0.5,0.5) T . Thus, the first EM in the compressed network is e 1 = (1,0,0,0.5,0.5) T . Reminding that we lumped the original reactions R1 and R2 into R1c and R6 and R7 into R5c, we can finally reconstruct the original elementary mode from the uncompressed network, that is R 1 + R 2 + 0.5 × R 5 + 0.5 × R 6 + 0.5 × R 7. Results from real networks We implemented the binary null-space approach (binary NSA) in MATLAB (Mathworks Inc.) and incorporated it into the FluxAnalyzer [ 30 , 34 ]. The function includes a pre-processing step where the network is compressed as described. Some sub-routines of the algorithm are performed by compiled C-code (via MATLAB MEX interface), since this proved to accelerate the implementation drastically. In order to check the capabilities of our algorithm we computed the elementary modes in realistic and large metabolic networks. The three networks (S1-S3) considered here are variants from a model of the central metabolism of Escherichia coli investigated originally in [ 11 , 23 ]. For considering networks with different complexities we inserted an increasing number of substrate uptake or/and product excretion (pseudo) reactions, which increase the number of EMs much faster than the insertion of internal reactions. For a (rough) comparison with the original NSA we used the program coverN (developed by Clemens Wagner and co-workers; available upon request from clemens.wagner@pki.unibe.ch , which is also implemented in MATLAB and uses external C-files for some sub-routines. The original as well as the binary CBA algorithm proved to be slower than both methods of NSA (not shown). Table 2 summarizes the computations. As a first result, it can be noted that redundancy removal and network compression during pre-processing results in much smaller networks. Note that the dimensions of the compressed networks of S1 and S2 are even lower than given in [ 23 ] due to the additional removal of uniquely produced/consumed metabolites. A lower number of reactions reduces the dimension of the null-space (hence, the number of iterations) and, in particular, the effort for adjacency tests. Generally, the proportion of the pre-processing on the overall computation time is negligible. Table 2 Computations of elementary modes in a realistic metabolic network (central metabolism of Escherichia coli ). Computations were performed on a typical PC with AMD Athlon XP 3000 + CPU and 1 GB RAM. Abbreviations: Form = formiate, Ac = acetate, Glc = glucose, Succ = succinate, Asp = aspartate, Glyc = glycerol, Eth = ethanol, Lac = lactate, CO2 = carbon dioxide. S1 S2 S3 substrates Glc Glc, Succ , Glyc , Ac Glc, Succ, Glyc, Ac, Asp products Ac, Form, Eth, Lac, CO2 Ac, Form, Eth, Lac, CO2 Ac, Form, Eth, Lac, CO2, Succ #reactions (q) # metabolites (m) 106 (28 reversible) 89 110 (28 reversible) 89 112 (28 reversible) 89 compressed network: # reactions # metabolites 42 (17 reversible) 25 47 (17 reversible) 26 51 (17 reversible) 28 final number of elementary modes 27,100 507,632 2,450,787 binary NSA NSA binary NSA NSA binary NSA NSA computation time 0.16 min (9.63 sec) 0.54 min (32.20 sec) 51.20 min 116.77 min 1546 min (25.78 h) not finished back transformation 0.13 min (7.97 sec) 2.57 min 13 min total computation time 0.29 min (17.60 sec) 0.54 min (32.20 sec) 53.77 min 116.77 min 1559 min (25.98 h) Comparing the required computation times, the binary NSA seems to be slightly faster than the original NSA. This observation should not be considered as a general result, since we cannot exclude that there are different potentials in optimizing the source code of coverN and in FluxAnalyzer , respectively. Besides, different row orders in the starting tableau can generally result in different computation times. However, it seems that the original and the binary NSA are comparable with respect to computation time. The adjacency tests in the binary null-space approach need to consider more elements (due to the split of reversible reactions) but are simpler to perform because preliminary modes from a previous iteration cannot lose their elementary property. Note also that implementing the full algorithm in C (and not only parts of it as in coverN and FluxAnalyzer ) might further accelerate the computation considerably. Using a special null space matrix K' as initialization of R (as explained in the Methods section) contributes considerably to a reduced computational effort. We can estimate this by the total sum over the number of candidates P i occurring in the tableau before iteration i . In S1, for example, . Computing instead an arbitrary null-space matrix K' for N' (e.g. via MATLAB null command) results in a more dense initialization for R and the naive initialization would lead to . The larger numbers of candidates increase the costs for adjacency tests and accordingly the running time drastically. This underlines that the success of the null-space approach (in its original or binary form) depends strongly on the initially chosen null space matrix. Generally, computing the stoichiometric coefficients of the EMs from their binary patterns is in larger networks in low proportion to the overall computation time (S3: ca. 0.8%). Whereas the computational demands seem to be comparable for both null-space approaches, the memory requirements for the binary NSA are much lower, in particular during the last iterations. For this reason, the 2.45 millions of EMs from network S3 could be computed on a typical PC, whereas the original NSA ends in the 26-th iteration step (from a total of 28) due to memory overflow. Discussion Elementary modes are smallest functional sub-networks, which can be interpreted geometrically as extreme rays from a pointed convex cone (corresponding to the network with split reversible reactions). The computation of extreme rays has been intensively studied by the polyhedral computation community and we think that the metabolic community can benefit from it. We shall also mention another abstraction of elementary modes within the framework of matroid theory [ 35 ]. In an oriented vector matroid , the elementary modes correspond to the positive circuits (or positive cycles ), which are minimal dependent sets. In fact, an elementary mode is a minimal linearly dependent set of the column vectors of the stoichiometric matrix (in the reconfigured network with only non-negative coefficients). This has been mentioned only rarely so far [ 36 ]. Matroid theory could be a source for new theoretical investigations on elementary modes and could lead to further improvements in the computation procedure as well as to new applications in the sense of metabolic pathway analysis. Adjacent extreme rays can also be detected by an algebraic characterization that completes Definition 2 [ 25 ]: ( c ) r and r' are extreme rays and the rank of the matrix A Z ( r ) ∩ Z ( r' ) is d-2 In practical cases the characterization of adjacency is mostly computed in its combinatorial form than its algebraic one [ 25 ]. However, improvements could be done by using both characterizations. In fact, the test on EM length done before the actual adjacency test in our MATLAB pseudo-code is a consequence of the algebraic test. A striking feature of the algebraic test is that it only requires access to the two rays tested for adjacency ( r and r' ) and to the fixed size matrix A , in practice to the stoichiometry matrix. In comparison, the combinatorial test implies a loop over all other rays ( r" ). Therefore, the algebraic test could be suited for distributed computing. Some theoretical issues of the combinatorial complexity of EMs were discussed in [ 23 ]. An upper bound B for the number of EMs is (reversible modes are counted only once): Assuming that no conservation relations occur in the stoichiometric matrix, we obtain: Note that q and m should be taken from the non-split, compressed network to obtain the lowest upper bound. In larger, realistic networks, even if compressed, the values for B explode quickly. Fortunately, the actual number of modes in real networks proved to be much smaller than the boundary (cf. B ≈ 2.54 · 10 11 for S1 in Table 2 ), although it grows also exponentially. One reason is that many routes are not admissible due to violation of the sign restrictions. Another reason is the low connectivity of many metabolites leading to sparse stoichiometric matrices. A third reason is related to short pathway length. The upper bound reflects the case where all EMs have maximal pathway length | R ( e )| max which is, by eq. (15), m + 1. However, many EMs, if not all, have a lower length immediately reducing the possible number of modes [ 23 ]. The pathway length distribution of the E. coli modes on glucose (network S1) is shown in Figure 6 . The maximal length of an EM in the uncompressed network is m + 1 = 89 + 1 = 90. Modes that are not involved in biomass synthesis, in particular, are much smaller. In terms of linear algebra this means that there exist vector sets W containing fewer than m + 1 column vectors of N that are linearly dependent. In polyhedral computation this phenomenon is known as degeneracy . Generally, degenerate systems may cause annoying difficulties and must be handled often differently to non-degenerate systems, albeit they reduce here the number of modes. The algorithms related to EM computation may be, in general, especially suited for computing extreme rays in such strongly degenerate systems, whereas other programs may be better suited for only weakly degenerate problems. For example, the software lrs [ 33 ] implements the so-called reverse search enumeration algorithm [ 37 ] that is polynomial for non-degenerate cases. Note that the new binary approach as introduced herein can easily be adapted for computing extreme rays of any pointed cone as given in eq. (4) and may therefore improve the performance of extreme ray computation in many other applications. Figure 6 Pathway length distribution in elementary modes of E. coli. (Substrate: glucose; network S1 in Table 2). Albeit the general framework was formulated long time ago, the explicit introduction of the null-space approach was an important mile-stone in accelerating the computation of EMs. The binary null-space approach as introduced herein increases the efficiency of this approach also with respect to the memory requirements and enables now to compute EMs in networks significant larger as those investigated before. A simple computation gives the number of about 85 millions of EMs in a network of 100 (compressed) reactions that can be stored in 1 GB RAM (cf. compressed and reconfigured S3: q' = 51 + 17 = 68). Of course, only a fraction of this amount can be stored during the algorithm due to other (partially large) temporary variables. Besides, reactions that are not yet processed are still stored as real numbers. The amount M of memory required for storing E modes after the procession of p reactions (stored binary) is (assuming 64-bit real numbers) M = E · ( p + 64 · ( q - p )).     (28) It depends on the evolution of the number of EMs during the algorithm where the maximal memory demand occurs. Generally, much larger networks can now be treated. Conclusions The four main results of this work are: (i) showing the equivalence between extreme rays and elementary modes, (ii) showing that algorithms for computing elementary modes can be seen as variants of the double description method for computing extreme rays in pointed polyhedral cones, (iii) introduction of a general framework and of new methods for redundancy removal and network compression, (iv) introduction of the new binary approach for computing extreme rays and elementary modes. The binary approach computes elementary modes as binary patterns of participating reactions that are sufficient to compute the respective stoichiometric coefficients in a post-processing step. For many applications – following the computation – it is even sufficient to operate on the binary patterns of EMs. Among all applications of EMs presented in the introduction section, only the identification of all pathways with optimal yield, the "control-effective fluxes", and the α -spectrum need the explicit (real number) coefficients, i.e. the reaction rates, in the EMs. Whenever needed, the explicit representation of an EM can be determined (possibly temporarily) from its binary pattern. The binary approach decreases the memory demand up to 96% without loss of speed and without loss of information giving the most efficient method available for computing elementary modes to date. The limiting step in computing elementary modes has thus been shifted back to the computation time. Parallelization – as investigated within the traditional, not-binary, schema in [ 38 ] – might lead to a further acceleration bringing us again a step closer to the complete set of EMs in genome-scale metabolic networks. Methods Proof of Theorem 1 We prove first that each case a) and b) defines EMs of S' . Let e' be a flux vector defined by either case a) or b). Clearly N'e' = 0 and e' ≥ 0 . In case b) e' is not elementary only if the single forward or backward reaction balances all internal metabolites, i.e. if the reaction includes not any internal species. We can safely exclude this pathologic case by considering that N does not contain a null column. Therefore, e' is elementary. In case a), assume e' is not elementary, i.e. there exists a non-null flux vector x' of S' not equivalent to e' such that x' ≥ 0 , N'x' = 0 and R ( x' ) ⊆ R ( e' ). By definition of the reconfiguration, for each i ∈ Rev , at least one among e' ( i ,+1) or e' ( i ,-1) equals zero and this holds consequently also for x' . Thus one can define e and x , the back-configurations of e' and x' . Now, by definition, e is an EM of S and is not equivalent to x , Nx = 0 , x i ≥ 0 i for i ∈ Irrev and R ( x ) ⊆ R ( e ), a contradiction. Hence each case a) and b) defines EMs of S'. We prove now that there is no other case. Assume there exists e' neither defined by a) nor b), such that e' ≥ 0 , N'e' = 0 and e' elementary. For each i ∈ Rev at least one among e' ( i ,+1) and e' ( i ,-1) equals zero (otherwise the two-cycle defined on reaction i would satisfy the constraints and involve only a subset of the reactions of e' ). Thus the back-configuration e of e' can be defined. By definition, e is not an EM of S. There exists x not equivalent to e such that Nx = 0 , x i ≥ 0 i for i ∈ Irrev and R ( x ) ⊆ R ( e ). The reconfiguration x' of x is such that x' is not equivalent to e' , x' ≥ 0 , N'x' = 0 and R ( x' ) ⊆ R ( e' ), a contradiction. Initialization of the R tableau in reconfigured networks As in the case of non-reconfigured networks, we must initialize R in reconfigured networks as a null space matrix K' of N' having the special structure (8), i.e. . Several kernel matrices having this form can exist. Here we are interested in such a one that contains as many zeros as possible because the number of zeros in the starting tableau R has a great impact on the number of ray combinations to be performed. For this purpose, we can exploit the fact that we already know | Rev | many very sparse vectors of the null space of N ', namely the two-cycles emerging by splitting up reversible reactions. Our goal is therefore to incorporate many (if possible all) of these vectors into K to obtain K' . For this purpose, we first compute the kernel matrix K of N . Then, by simple linear combinations of columns (analogous to the well-known computation of a row-echelon form of a matrix) and possibly by permutation of rows in K , we try to obtain , where only irreversible reactions (rows) are contained in the identity matrix I . If this is possible then we can easily include the backward directions of reversible reactions (as rows) and the two-cycles (as columns) into K yielding K' : The first q - m columns in K' correspond to the original columns in K , but contain additionally zeros for the inserted backward reaction of originally reversible reactions. These columns are obviously linearly independent and are contained in the null space of N' . Sub-matrix is a | Rev | × | Rev | identity matrix whose rows correspond to the backward directions of split reactions. Finally, C is a | Rev | × m sub-matrix which complements in such a way that they represent together the two-cycles of the split reactions. (Thus, each column c i in C contains only zeros, except a unity at that row, which corresponds to the forward direction of the split reversible reaction i . See also the example network.) I and yield together the new I' , whereas and C represent together of K' . Thus, K' contains q - m + | Rev | linearly independent (basis) vectors of the null space of N' and is in form (8). To our experience, in most realistic networks, a matrix K' as in (M1) can be found. Using instead an arbitrary K' can lead to a much larger computation effort because much more candidates are computed at an early state (see real network examples). A further simple strategy avoiding that many rays are computed early is to sort the rows in ascending with respect to the number of their non-zero entries. In case it is not possible to arrange only irreversible reactions into the sub-matrix I of K , we can nevertheless find a matrix K' with the same basic structure as in (M1). However, for some originally reversible reactions, the forward (in I ) and backward (in ) direction will then be contained in I' . For each of those, the two-cycle cannot be represented by C and (because the row of the forward direction is contained in I' and not in K' ) and another corresponding column in C has to be constructed. Assume a reversible reaction is contained as j -th row in I . Assume further that the inserted backward direction of this reaction corresponds to the k -th row of . For the k -th column c k of C we can then chose the j -th column of multiplied by -1, i.e. . Together with the k -th column in , this gives a null space vector of N' , which is linearly independent of the others and can therefore serve as basis vector in K' . The vector c k is now probably not that sparse. However, it enables us to retain the 2-cycles at least for those split reactions whose forward direction is not contained in I . A MATLAB function initializeR that provides a proper initialization of R as described above (starting with the stoichiometric matrix N and the indices of the reversible reactions) can be obtained from the corresponding author. Short introduction into MATLAB notation Numeric variables in MATLAB can be scalars, vectors or two-dimensional arrays (i.e. matrices). To be more precise, a scalar in MATLAB is actually a 1 × 1 array and a vector is a 1 × n or n × 1 array. Size and type of a variable are automatically declared (or changed) by assignments to it. The following examples illustrate how to assign or access values of variables: • scalar: a = 1; • b(3) = 5; the value 5 is assigned to the third element of (vector) b . • c(1:3) = [5,8,9]; here, "1:3" expresses "from 1 to 3", thus, 5, 8 and 9 are assigned to the first three elements of vector c . It is also possible to use an array of integers to access the elements of a vector, e.g. a = [2,3,4]; b = [1,3]; c = a(b). Vector c reads then [2,4]. • mat(2,5) = 3; value 3 is assigned to the element in the second row and fifth column of matrix mat . • mat1(3,:) = mat2(5,:); the values of the fifth row of matrix mat1 is copied into the third row of matrix mat2 . Here, the colon operator ":" expresses "all elements of the respective dimension" (here: columns). Of course, it must be ensured that mat1 and mat2 have the same number of columns. • a = mat(7,1:3); the first three elements of the seventh row of matrix mat are assigned to a which is now a 3-element vector. • a= [17,34,39]; a(2)= []; deletes the second element of a and shifts all elements behind one position back, i.e. vector a reads now [17,39]. The pseudo-code given in Figure 4 in the main text uses several basic routines pre-defined in MATLAB (written in bold) : • c = length (a); if a is a vector (as in all cases in the pseudo-code) then length returns the number of elements in a . • c = find (a); if a is a vector (as in all cases in the pseudo-code) then find returns all positions in a which are not zero. Example: find ([23,0,5,9,0]) returns (1, 3, 4). • c = or (a,b) returns the result of the logical OR operation applied element-wise to a and b . a and b can be scalars, vectors or matrices and must have the same size. Example: if a = [1,0,29], b = [1,0,0] then or ( a , b ) returns [1,0,1]. In the pseudo-code, we use this routine exclusively for OR-operations of bit masks (arrays with only "ones" and "zeros"). • c = zeros (m,n) returns a matrix of size m × n filled with zeros. • c = null (a) returns a null-space matrix of matrix a. • c = intersect (a,b) returns the intersection of elements in vectors a and b . • c = all (b) returns "1" if all entries in vector b are not zero and "0" otherwise. List of abbreviations EM(s): Elementary Mode(s) also known as Elementary Flux Mode(s). Authors' contributions Both authors contributed equally to this work, the starting idea of the binary approach coming from a discussion between them. JG mainly established the relationships between extreme ray and elementary modes computation. SK mainly devised and implemented the binary null-space algorithm. Both authors prepared the manuscript jointly.

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549216

Simplicity within complexity: Seasonality and predictability of hospital admissions in the province of Ontario 1988–2001, a population-based analysis

Background Seasonality is a common feature of communicable diseases. Less well understood is whether seasonal patterns occur for non-communicable diseases. The overall effect of seasonal fluctuations on hospital admissions has not been systematically evaluated. Methods This study employed time series methods on a population based retrospective cohort of for the fifty two most common causes of hospital admissions in the province of Ontario from 1988–2001. Seasonal patterns were assessed by spectral analysis and autoregressive methods. Predictive models were fit with regression techniques. Results The results show that 33 of the 52 most common admission diagnoses are moderately or strongly seasonal in occurrence; 96.5% of the predicted values were within the 95% confidence interval, with 37 series having all values within the 95% confidence interval. Conclusion The study shows that hospital admissions have systematic patterns that can be understood and predicted with reasonable accuracy. These findings have implications for understanding disease etiology and health care policy and planning.

Background Health care is a complex human endeavor constituted by the interaction of multiple professions, organizations, industries, technologies and the public. Health itself is also a complex concept, with multiple determinants including genetic, socio-cultural, economic and environmental influences [ 1 ]. At the centre of this complex system is the hospital. Arguably, after a physician visit, the hospital admission represents the key event in the delivery of health care. Do hospital admissions have consistent patterns? While individual diseases are extensively studied, there is a paucity of systematic approaches to the study of health care events. Epidemiology is not regarded as a science with the predictive accuracy and explanatory power of the physical sciences [ 2 ]. Health services research is in its scientific infancy and is directed towards policy and practice, however, recent trends in theoretical epidemiology have focused on more powerful computational approaches [ 3 ]. Using time series analysis, our research program investigates seasonality in the occurrence of health care events. Seasonality is an important aspect of disease manifestation as well as a clue to the etiology of disease. Our initial studies explored seasonality in hospital admissions in discrete disease categories including asthma [ 4 ], falls [ 5 ] and aortic aneurysms [ 6 ]. Subsequently, we hypothesized and confirmed that the hospital admissions in the system considered in totality also demonstrated consistent seasonal effects [ 7 ]. Consistent seasonal behavior suggests the possibility of predictable behavior. To the best of our knowledge, there are no studies systematically evaluating the seasonality and predictability of multiple hospital admissions using health services data. We therefore assessed the seasonality and predictability of the most common causes of hospital admission in the province of Ontario, Canada. Methods We conducted a retrospective, population-based study to assess temporal patterns in hospitalisations for the 52 most common admission discharge diagnoses from April 1, 1988 to December 2001. Approximately 14 million residents of Ontario eligible for universal healthcare coverage during this time were included for analysis. The Canadian Institute for Health Information Discharge Abstract Database was used to obtain information on the most responsible diagnosis. This database records discharges from all Ontario acute care hospitals, documenting a scrambled patient identifier, date of admission and discharge, up to 16 diagnoses as coded by the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), and up to 10 procedures. Researchers using these databases have found that diagnoses and surgical procedures are coded with a high degree of accuracy. There is very little missing information in the Ontario databases; other studies have similarly found that less than 1 percent of the basic information on patients is missing in various provincial databases [ 8 - 10 ]. The 52 most common discharges diagnoses over the 10 years were identified by summing all admissions and calculating in rank order the frequencies of admission. Owing to the influence of obstetric related admissions, we limited obstetric codes to the consideration of singleton births. Categories of closely related health conditions (such as myocardial infarction) were combined. Numerator data consisted of the total number of discharges for each month for each of the most responsible diagnoses. Denominator data was derived from annual census data for each age group for residents of Ontario provided by Statistics Canada. Monthly population estimates were derived through linear interpolation. All transfers from within one acute care hospital to another within this study group were excluded from the analysis. To take into account the population changes over time we analyzed monthly admission rates per 100,000. Analytic method This study employed time series methods to assess the presence of statistically significant seasonality, the strength of the seasonal effect and the predictability of the time series. A time series can be decomposed as the sum or product of trend, seasonality, and random components. Trend is the long term movement of the series which is a systematic component that changes over time and generally does not repeat itself within the time range of the available data. If we eliminate the trend then the time series will consist of seasonal and random components. Assessment of seasonality Analysis of the data involved the use of the following statistical techniques in identical fashion to each series in order to assess statistical significance of seasonal patterns and the consistency and magnitude of seasonal effect. Spectral analyses were conducted to detect statistically significant seasonality. Spectral analysis detects periodicity in time series, by plotting the periodogram or spectral density of the series against the period or frequency [ 11 ]. The data series was de-trended using moving averages prior to conducting spectral analysis. Two tests for the null hypothesis that the series is strictly white noise were conducted. The Fisher Kappa (FK) Test is designed to detect one major sinusoidal component buried in white noise, whereas the Bartlett Kolmogorov Smirnov (BKS) Test accumulates departures from the white noise hypothesis over all frequencies [ 12 ]. Finally, R-squared autoregression coefficients (R 2 Autoreg ) were calculated. Autoregression uses the coefficient of determination of the autoregressive regression model fitted to the data, and can be used for quantifying the strength of the seasonality within a set of serially correlated observations as occurs with time series data [ 13 ]. The R 2 Autoreg is interpreted the same way as the coefficient of determination in classic regression: values from 0 to less than 0.4 represent non-existent to weak seasonality, 0.4 to less than 0.7 moderate to strong seasonality, and 0.7 to 1 strong to perfect seasonality. The magnitude of the R 2 Autoreg shows how well the next value can be predicted when the seasonal component is the only predictor. In other words it shows the contribution of seasonality in the total variation of the data. Thus 1-R 2 Autoreg would be the variance that remains unexplained [ 13 ]. When the autoregression procedure is applied to observed data, it is important to validate the stationarity of the series as the R 2 Autoreg may be underestimated when the seasonal variation is non-stable. To account for this, data transformations were conducted where appropriate, to stabilize the seasonal variations [ 13 ]. All statistical analyses were performed using SAS (v8.2). Predictive modeling Of the 160 monthly observations for each series, the first 148 (April 1988 to December 2000) were used for fitting the model and estimating the parameters. We set aside the last 12 observations (January to December 2001) for assessing the performance of the suggested model and used the rest for fitting the model and estimating the parameters. We applied the first order differencing to eliminate the trend [ 14 ] and then used a very simple regression model to predict 12 new monthly observations for each series. We compared the observed 12 observations with the corresponding predicted values. Then we checked to see which observed value falls outside the 95 percent confidence interval. Suppose n monthly observations x 1 , x 2 , ..., x n are available and we are interested in predicting the next k unobserved data points x x +1 , x x +2 ,..., x x + k using the n observed data points. Here we will assume that the time series is an additive composition of trend, seasonality, and random components. The multiplicative case can be converted to additive by simply taking the log transformation. The time plot of the series did not indicate large changes in the variations of the amplitude of either seasonal or irregular components of the series whereas the level of the trend increased or decreased. Thus an additive model is appropriate. The first component we should deal with is trend. Visual inspection of the time plots of the 52 series indicate different trend patterns ranging from simple linear to more complex nonlinear patterns. We did not attempt to model the trend component parametrically as estimating the pattern of the trend components globally by a closed mathematical function of time may severely misestimate the true trend beyond the range of fitting period. Instead we decided to use the first order differencing to eliminate the trend component. The first order differencing of a time series x t , t = 1,2, ..., n is the series w t , t = 2,3, ..., n where w t = x t - x t -1 [ 14 ]. Visual inspection of the time plots of the differenced series showed elimination of the trend components. For monthly rates of hospitalization data it is reasonable to anticipate seasonal components of order 12 and 6 due to seasonal variation of the weather or administration (e.g. winter, Christmas, and vacation season). This was confirmed in spectral analysis. By modifying the components of the following regression equation we can model the series at different seasonal orders. In the regression model we included for seasonal factors of period 12 and 6. Thus the regression model takes the following form where β i ' s can be estimated through linear regression framework. Having fitted the model, one can substitute t = n + 1, n + 2, ..., n + k to estimate the next k differenced observations with their corresponding confidence intervals. The predicted differenced data points can be converted to raw data points by applying the following simple transformation: x n + j = w n + j + x n + j -1 , j = 1,2, ..., k Confidence intervals can be transferred in a similar manner. For j > 1we can substitute the predicted values for x n + j -1 . Results A total of 6,560,210 million admissions were included in the analysis. Figures 1 and 2 provide examples of the heterogeneity of the time series. There is visual evidence of non-linearity and clear seasonality in the time plot graphs. Figure 1 Time plots (rates per 100,000 population) of highly seasonal hospital admission patterns: Chronic obstructive pulmonary disease and bronchiolitis. Figure 2 Time plots (rates per 100,000 population) of moderately seasonal and non-linear trend in hospital admission patterns: Coronary atherosclerosis and dehydration. Table 1 provides the Fisher Kappa and BKS and R 2 Autoreg test statistics for each diagnosis, rank ordered by R 2 Autoreg, and the number of predictions that fall outside the 95 percent confidence interval. The R 2 Autoreg values range from a high of 0.95 (bronchiolitis) to a low of 0.11 (infantile cataract). Fourteen series showed evidence of strong seasonality (R 2 Autoreg greater than 0.7), nineteen series showed evidence of moderate seasonality (R 2 Autoreg between 0.4 and 0.69) and eleven showed evidence of weak seasonality (R 2 Autoreg less than 0.4). Time series with strong seasonal effects by R 2 Autoreg also showed consistent statistical evidence of seasonality by BKS and Fisher Kappa tests. Those with moderate and weak evidence of seasonality by R 2 Autoreg showed inconsistent statistical evidence of seasonality by BKS and Fisher Kappa tests. Table 1 Statistical summary of seasonality and predictability of the 52 admission time series Health Outcome R 2 Autoreg Fisher Kappa (p-value) 1 BKS (p-value) 1 # outside 95% CI 2 Acute bronchiolitis 0.95 76.56 (<0.01) 0.77 (<0.01) 0 Non-infectious gastroenteritis 0.91 66.28 (<0.01) 0.65 (<0.01) 0 Pneumonia/influenza 0.88 68.64 (<0.01) 0.68 (<0.01) 0 Osteoarthritis 0.86 49.81(<0.01) 0.37 (<0.01) 0 Appendicitis 0.84 52.99 (<0.01) 0.50 (<0.01) 0 Uterine fibroids 0.83 40.05 (<0.01) 0.27 (<0.01) 0 Congestive heart failure 0.82 44.14 (<0.01) 0.42 (<0.01) 0 Previous C-section 0.82 44.52 (<0.01) 0.39 (<0.01) 0 Prostatic hyperplasia 0.80 36.49 (<0.01) 0.31 (<0.01) 0 Singleton birth 0.76 39.20 (<0.01) 0.37 (<0.01) 0 Croup 0.75 47.84 (<0.01) 0.56 (<0.01) 0 Diverticulosis 0.75 29.57 (<0.01) 0.33 (<0.01) 0 Excessive menstruation 0.72 34.02 (<0.01) 0.26 (<0.01) 0 Chronic obstructive pulmonary disease 0.71 50.14 (<0.01) 0.50 (<0.01) 0 Urinary tract infection 0.69 52.24 (<0.01) 0.48 (<0.01) 3 Coronary atherosclerosis 0.69 31.60 (<0.01) 0.21 (<0.01) 0 Kidney stones 0.67 40.21 (<0.01) 0.35 (<0.01) 0 Breast cancer 0.67 39.47 (<0.01) 0.24 (<0.01) 0 MyocardiaI infarction 0.67 32.48 (<0.01) 0.30 (<0.01) 1 Gall bladder 0.66 34.69 (<0.01) 0.27 (<0.01) 0 Prostate cancer 0.62 33.42 (<0.01) 0.26 (<0.01) 3 Senile cataract and cataract unspecified 0.60 26.09 (<0.01) 0.27 (<0.01) 0 Acute pancreatitis 0.60 25.30 (<0.01) 0.18 (<0.05) 0 Threatened premature labour 0.59 26.74 (<0.01) 0.19 (<0.01) 1 Gall bladder w/acute cholecystitis 0.57 20.08 (<0.01) 0.15 (NS) 0 Convulsions 0.54 22.46 (<0.01) 0.21 (<0.01) 0 Trochanteric fracture 0.53 22.62 (<0.01) 0.14 (NS) 0 Chronic tonsillitis 0.51 20.82 (<0.01) 0.24 (<0.01) 0 Recurrent manic depression (depressed phase) 0.51 25.43 (<0.01) 0.20 (<0.01) 0 Premature rupture of membrane 0.50 32.01 (<0.01) 0.25 (<0.01) 1 Displacement of inter-lumbar disc 0.50 26.38 (<0.01) 0.18 (<0.01) 1 Dehydration 0.50 55.40 (<0.01) 0.58 (<0.01) 2 Syncope and collapse 0.48 22.57 (<0.01) 0.18 (<0.05) 5 Uncomplicated diabetes 0.48 22.54 (<0.01) 0.23 (<0.01) 0 Lung cancer 0.46 19.41 (<0.01) 0.12 (NS) 0 Depressive disorder 0.45 12.28 (<0.01) 0.14 (NS) 1 Fractured femur 0.44 12.72 (<0.01) 0.10 (NS) 3 Unilateral inguinal hernia 0.43 16.52 (<0.01) 0.18 (<0.01) 0 Abdominal pain 0.43 19.15 (<0.01) 0.26 (<0.01) 0 Transient cerebral ischemia 0.41 18.42 (<0.01) 0.12 (NS) 2 Acute but ill defined cardiovascular disease 0.40 14.69 (<0.01) 0.19 (NS) 0 Angina 0.40 11.72 (<0.01) 0.14 (NS) 0 Unspecified intestinal obstruction 0.38 10.80 (<0.01) 0.15 (<0.05) 0 Other acute ischaemic heart disease 0.36 17.08 (<0.01) 0.15 (<0.05) 1 Recurrent manic depression (manic phase) 0.35 13.77 (<0.01) 0.10 (NS) 4 Fetal distress 0.34 20.24 (<0.01) 0.26 (<0.01) 0 Spontaneous abortion unspecified 0.33 10.95 (<0.01) 0.12 (NS) 0 Stroke 0.31 10.32 (<0.01) 0.14 (NS) 0 Chest pain (nonspecific) 0.29 11.34 (<0.01) 0.14 (NS) 4 Gastrointestinal bleed 0.26 7.84 (<0.05) 0.14 (NS) 0 Other IHD 0.17 6.24 (NS) 0.12 (NS) 0 Infantile cataract 0.11 4.85 (NS) 0.28 (<0.01) 0 1 NS = not significant (p > 0.05) 2 95% CI = 95% confidence interval In total, 96.5 percent of the predictions fell within the 95 percent confidence interval (602/624). In terms of complete series, the performance of the proposed predictive model is very good. Overall 37 (37/52 = 73 percent) had all 12 observed values falling within 95 percent prediction intervals, 10 series had only 1 observed value outside prediction limits and 4 series had 2 observed values outside 95 percent prediction intervals. For the worst case, only 1 series had 4 out of 12 observed values falling outside the 95% prediction intervals. The standard deviations for the confidence intervals of the predicted values are within 2 admissions per 100,000 for 48 of the 52 series (data not shown). Discussion Hospital admissions in the province of Ontario show remarkable consistency and predictability of occurrence. A heterogeneous group of health conditions are represented in the sample including surgical and medical conditions, acute and chronic diseases, communicable and non-communicable diseases. The performance of the proposed model for predicting the one-year ahead number of hospital admissions in the province of Ontario is excellent for the 52 most frequent hospital admissions series considered in this study. Are these results of significance? We believe so. Most health care planning is based on what could be termed the 'invariance principle' that holds that all events are equally likely to happen and therefore hospitals should be staffed and managed accordingly [ 15 ]. Our study indicates that demand for hospital services varies, can be predicted with a high degree of accuracy and therefore planning and resource allocation could possibly be reorganized to reflect this knowledge. Furthermore, there are significant seasonal fluctuations to at least one third of the series analyzed, indicating that planning could be tailored to predictable demands. Understanding such seasonal patterns also promises to shed light on disease causality as not all highly seasonal conditions can be explained by infectious diseases known to have seasonal occurrence. Our study is limited to the context of Ontario, and is applicable at a population level. Focusing on the most responsible diagnosis may bias the account of seasonal occurrence, although this bias is likely to be non-differential. In this study we focused on total counts for each most responsible diagnosis, which may obscure significant variation in rates between age and gender. The proposed methods enjoy simplicity and stability. The prediction approach does not require model selection or any other sophisticated statistical methods. Selecting an appropriate seasonal model can be a challenging task in time series analysis. For example, the Box Jenkins approach is popular for selecting linear time series models. In this approach sometimes the analyst has to select a model subjectively from among several potentially appropriate models. Our proposed regression model does not require model selection. The first order differencing eliminates trend; sin and cosine terms estimate the seasonal factors. The simple regression model works well for highly seasonal to non-seasonal data. Although the seasonal factors of some of the series are changing over time, the simple first order differencing in conjunction with the regression model forecast the future observations within the 95 percent confidence bounds. The confidence intervals around the predicted values are tight, reflecting the accuracy of the projections. This attenuates concerns expressed about the robustness of predictive models in epidemiology [ 16 ]. Conclusion The results of this study demonstrate a simplicity underlying the complexity of hospital admissions. We believe these results are promising and can lead to more rational planning of hospital resources and open up areas of exploration for understanding the determinants of disease causation, specifically in those conditions with moderate to strong seasonality. Further research is necessary to look at whether more complex models have greater predictive power, and whether the analytic approach is robust at different time and space aggregations. Competing interests The author(s) declare that they have no competing interests. Authors' contributions RU conceived the study and wrote the first draft. RM contributed the statistical analysis. MM, LK and EC made substantial contributions to the design and interpretation of the data. All authors contributed to subsequent drafts, have read and approve of the content of the final submitted manuscript. All authors have access to all data in the study and they hold final responsibility for the decision to submit for publication. Pre-publication history The pre-publication history for this paper can be accessed here:

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514703

Function and evolution of the serotonin-synthetic bas-1 gene and other aromatic amino acid decarboxylase genes in Caenorhabditis

Background Aromatic L-amino acid decarboxylase (AADC) enzymes catalyze the synthesis of biogenic amines, including the neurotransmitters serotonin and dopamine, throughout the animal kingdom. These neurotransmitters typically perform important functions in both the nervous system and other tissues, as illustrated by the debilitating conditions that arise from their deficiency. Studying the regulation and evolution of AADC genes is therefore desirable to further our understanding of how nervous systems function and evolve. Results In the nematode C. elegans , the bas-1 gene is required for both serotonin and dopamine synthesis, and maps genetically near two AADC-homologous sequences. We show by transformation rescue and sequencing of mutant alleles that bas-1 encodes an AADC enzyme. Expression of a reporter construct in transgenics suggests that the bas-1 gene is expressed, as expected, in identified serotonergic and dopaminergic neurons. The bas-1 gene is one of six AADC-like sequences in the C. elegans genome, including a duplicate that is immediately downstream of the bas-1 gene. Some of the six AADC genes are quite similar to known serotonin- and dopamine-synthetic AADC's from other organisms whereas others are divergent, suggesting previously unidentified functions. In comparing the AADC genes of C. elegans with those of the congeneric C. briggsae , we find only four orthologous AADC genes in C. briggsae . Two C. elegans AADC genes – those most similar to bas-1 – are missing from C. briggsae . Phylogenetic analysis indicates that one or both of these bas-1 -like genes were present in the common ancestor of C. elegans and C. briggsae , and were retained in the C. elegans line, but lost in the C. briggsae line. Further analysis of the two bas-1 -like genes in C. elegans suggests that they are unlikely to encode functional enzymes, and may be expressed pseudogenes. Conclusions The bas-1 gene of C. elegans encodes a serotonin- and dopamine-synthetic AADC enzyme. Two C. elegans AADC-homologous genes that are closely related to bas-1 are missing from the congeneric C. briggsae ; one or more these genes was present in the common ancestor of C. elegans and C. briggsae . Despite their persistence in C. elegans , evidence suggests the bas-1 -like genes do not encode functional AADC proteins. The presence of the genes in C. elegans raises questions about how many 'predicted genes' in sequenced genomes are functional, and how duplicate genes are retained or lost during evolution. This is another example of unexpected retention of duplicate genes in eukaryotic genomes.

Background Aromatic L-amino acid decarboxylase (E.C. 4.1.1.28, AADC) catalyzes the second enzymatic step in synthesis of the neurotransmitters dopamine and serotonin, which are found in neurons of all animals (Figure 1 ). Alteration in the normal expression of these transmitters is associated with human neurological disorders such as Parkinson's disease and depression [ 1 , 2 ]. In mammals, AADC is expressed in many tissues beside the nervous system, associated with additional regulatory roles of dopamine and serotonin in a wide range of tissues [ 3 ]. In insects, AADC is further required to produce amines for cuticle synthesis and pigmentation [ 4 ]. Because of its role in the synthesis of both transmitters, by decarboxylation of L-dopa and 5-hydroxytryptophan, AADC is also known as dopa decarboxylase or 5-hydroxytryptophan decarboxylase (reviewed in [ 3 ]). AADC belongs to the α family (subgroup II) of pyridoxal-5'-phosphate (PLP) dependent enzymes. Other subgroup II enzymes include histidine, tyrosine, tryptophan and glutamate decarboxylases [ 5 ]; in animals some of these enzymes mediate synthesis of other biogenic amines (e.g., histamine, tyramine, octopamine) and GABA. In mammals and in Drosophila , a single gene encodes the serotonin- and dopamine-synthetic AADC [ 6 , 7 ], although tissue-specific isoforms of the protein are generated by alternative splicing [ 8 , 9 ]. Different genes encode PLP-dependent decarboxylase enzymes for histamine, octopamine and GABA synthesis. Figure 1 Serotonin and dopamine biosynthetic pathways. Serotonin and dopamine are synthesized from the aromatic amino acids tryptophan and tyrosine, respectively. The first and rate-limiting step in synthesis is carried out by a neurotransmitter-specific aromatic amino acid hydroxylase enzyme, either tryptophan or tyrosine hydroxylase. In C. elegans , these genes are encoded by the tph-1 and cat-2 genes, respectively [19, 25]. The second synthetic step for both neurotransmitters shares the aromatic L-amino acid decarboxylase (AADC) enzyme, which has a relatively broad substrate specificity, and is also known as 5-hydroxytryptophan decarboxylase or dopa decarboxylase. In the nematode Caenorhabditis elegans , serotonin is expressed in at least nine neurons in the hermaphrodite and nineteen in the male; dopamine is found in eight neurons in the hermaphrodite and fourteen in the male [ 10 ]. By examining the behavior of worms in which specific neurons have been ablated and examining mutants lacking serotonin and/or dopamine, we have learned that serotonin is involved in behaviors including egg laying [ 11 - 13 ], pharyngeal pumping [ 14 , 15 ], male mating [ 16 ], and experience-dependent regulation of locomotion [ 17 , 18 ]. Serotonin-deficient mutants also display abnormalities in entry into the diapause-like dauer stage and in fat storage, mediated via an insulin-related signaling pathway [ 19 , 20 ]. Dopamine plays roles in male mating [ 21 ], in regulating locomotion via mechanosensation [ 17 , 22 ], and in foraging behavior [ 23 ]. Identification of genes involved in neurotransmitter synthesis and related aspects of signaling in C. elegans was greatly accelerated by genomic sequencing, which was essentially completed in 1998 [ 23 , 24 ]. For genes identified originally by mutants via a traditional genetic approach, a candidate gene approach often allowed rapid confirmation of a gene's identity; for predicted genes identified from the genomic sequence by homology, a reverse genetic approach has been taken. Many components of the serotonin and dopamine synthesis and transport pathways in C. elegans have now been identified by these traditional and reverse genetic approaches, including tyrosine hydroxylase ( cat-2 ; [ 25 ]), tryptophan hydroxylase ( tph-1 ; [ 19 ]), serotonin reuptake transporter ( mod-5 ; [ 26 ]), dopamine reuptake transporter ( dat-1 ; [ 27 ]) and vesicular monoamine transporter ( cat-1 ; [ 28 ]). Postsynaptic components have also been identified, including various receptors [ 29 - 32 ] and intracellular G protein signaling components [ 33 - 36 ]. Further analysis of gene function, regulation and evolution in C. elegans is being facilitated by genomic sequencing of related nematodes. A whole genome shotgun sequence of Caenorhabditis briggsae was recently completed; the sequence is estimated to be 98% complete [ 37 ]. The divergence of C. briggsae and C. elegans is estimated between 80 – 110 million years ago [ 37 , 38 ], although it should be noted that these estimates lack a fossil record to anchor the dates [ 39 ]. This is considered to be a favorable evolutionary distance to identify conserved non-coding regulatory sequences, although the sequences from only two orthologous genes from related species is often inadequate to identify such sequences unambiguously. Genomic sequencing is planned or underway of three additional congeneric relatives of C. elegans that are more closely related than C. briggsae , which will enhance our ability to analyze the genes of C. elegans . We have used genomic sequences of both C. elegans and C. briggsae to help identify and characterize another component of the serotonin and dopamine signaling systems – the bas-1 gene – and to examine the evolution of this and related genes. The bas-1 [ b iogenic a mine s ynthesis abnormal] mutant is serotonin- and dopamine-deficient, and displays several behavioral abnormalities [ 12 , 16 , 17 ]. Unlike wildtype and other serotonin-deficient mutant worms, bas-1 mutants are unable to convert exogenous 5-hydroxytryptophan (5HTP) into serotonin (5-hydroxytryptamine, 5HT), as assessed by serotonin antiserum staining. Because of this phenotype, we have previously proposed that the bas-1 gene likely encoded the AADC enzyme of C. elegans [ 16 ]. Results Rescue of the bas-1 mutant with an AADC-homologous sequence The bas-1 gene maps to chromosome III, between dpy-17 and unc-32 . When this region was sequenced by the C. elegans Genome Sequencing Consortium, two AADC-homologous predicted genes, designated C05D2.4 and C05D2.3, were found to be located close together on a single cosmid, C05D2 (Fig. 2A ). This suggested that one (or both) of these sequences comprised the gene mutated in bas-1 mutants. To test this hypothesis, we injected bas-1 mutants with the cosmid C05D2 plus rol-6 (dom) plasmid DNA as a co-injection marker. We isolated transgenic Roller progeny (expressing the rol-6 (dom) marker phenotype) of the injected worm and propagated strains that transmitted the marker, then tested these worm strains using serotonin antibody staining. We found that 3 of 3 independent Roller transgenic lines were rescued for serotonin immunoreactivity, confirming that the bas-1 gene was located within this 46 kb of genomic DNA (Fig. 2B ). We then injected plasmid subclones of C05D2, each of which still contained both the predicted C05D2.4 and C05D2.3 genes. A 15.1 kb plasmid subclone (C05D2XN) also rescued bas-1 mutants (n = 4/4), as did smaller subclones of C05D2XN, including an 11.3 kb subclone (pCL3001, n = 11/11) and an 8.8 kb subclone (pCL7001, n = 1/1). These results confirm that at least one of AADC-homologous genes likely corresponds to the bas-1 gene. Figure 2 Molecular genetics and transformation rescue of bas-1. (A) Genetic and physical map of bas-1 region and bas-1 mutant alleles. Locations and extent of mutations for each bas-1 allele are shown to scale with respect to C05D2.4 and C05D2.3 coding sequences, based on known splicing patterns or Genefinder predictions. Exons are indicated by rectangular bars; an alternatively spliced 27 bp exon is also indicated after exon 2 in C05D2.4. Four of five bas-1 mutants affect only C05D2.4; ad446 is a larger deletion removing most coding sequence of both C05D2.4 and C05D2.3. (B) Genomic DNA constructs that rescue or fail to rescue bas-1 mutants. Constructs are shown to scale (top), based on the 15.1 kbp insert of the plasmid clone C05D2XN; construct names are indicated in the box on the left. The cosmid C05D2 is larger, as indicated by the arrows. Clones below are subclones or modifications of C05D2XN. Coding regions for the two predicted AADC genes C05D2.4 and C05D2.3 are indicated by the blue boxes; intergenic regions are shown in yellow. C05D2XN upstream of C05D2.4 contains two other predicted genes, one complete (C05D2.8) and one partial (C05D2.5). There are no predicted genes in the 3 kb downstream of C05D2.3. In the constructs with the least upstream sequence, only a portion of C05D2.8 remains. Constructs mutated to introduce premature stop codons are indicated with a X in the coding sequence, and red downstream of the introduced stop codon. The construct pCL8001 has a GFP gene inserted in a manner that would inactivate the C05D2.3 gene, so is comparable to the pCL7991 construct. [No GFP expression was seen in the C05D2.3::GFP reporter construct lines.] To determine which of the two predicted AADC sequences was needed to rescue bas-1 mutants, we prepared two constructs from C05D2XN, one mutated in C05D2.4, the other in C05D2.3 (Fig. 2B ). In each case, a mutation was created by eliminating a unique restriction site early in the predicted coding region, creating a frameshift resulting in premature stop codons. We found that constructs mutated in C05D2.3 when injected rescued serotonin immunoreactivity in bas-1 mutants (n = 3/3), whereas the construct mutated in C05D2.4 failed to rescue (n = 0/5 rescued). A construct containing a GFP gene inserted into the C05D2.3 coding sequence (and disrupting the gene) also rescued bas-1 mutants (n = 2/2). In Roller transgenic lines lacking rescue, we confirmed the presence of the injected construct by PCR. Therefore, an intact C05D2.4 gene is necessary to rescue bas-1 mutants, whereas the C05D2.3 gene is not. In all rescued transgenic lines, we saw the complete set of known serotonergic neurons, although not necessarily all cells in every animal – mosaicism from somatic loss of extrachromosomal DNA is expected in these transgenics. This result suggests that no critical cell-specific regulatory sequences were missing from even the smallest construct we injected. To confirm further that C05D2.4 is the bas-1 gene, we identified the mutations in four bas-1 mutant alleles; we also examined the phenotypes of deletion mutants in C05D2.4 and C05D2.3 generated by the C. elegans Gene Knockout Consortium (GKC). We found that the bas-1 alleles pa4 , n2948 , and n3008 contained point mutations in C05D2.4 coding sequence resulting in premature stop codons (Fig. 2A ). We found that the original bas-1 allele ( ad446 ) had a 4268 bp deletion from the second exon of C05D2.4 to the final intron of C05D2.3; therefore, ad446 is a knockout of both predicted genes. We examined the phenotypes of GKC-generated deletion mutants in each predicted gene. The C05D2.4 knockout ( tm351 ) removes the entire predicted second exon. We found that both tm351 homozygotes and tm351/ad446 worms were deficient in serotonin immunoreactivity. On the other hand, a knockout of C05D2.3 ( ok703 ) is wildtype for serotonin staining. Therefore, tm351 is a fifth mutant allele of the bas-1 gene, and C05D2.4 corresponds to the gene bas-1 . Transcripts from the bas-1 gene and the predicted BAS-1 protein To continue our characterization of the bas-1 gene, we isolated cDNAs using RT-PCR; we also obtained cDNA clones from the C. elegans EST/Transcriptome project (courtesy of Yuki Kohara) and the ORFeome project [ 40 ]. We found that C05D2.4/ bas-1 cDNAs are trans-spliced to SL1 just 15 nucleotides upstream of the predicted translation start site. The consensus sequence from our clones and others we examined predicts a 514 amino acid, 58 kDa protein product (Fig. 3A ). This is similar in size to other known AADC/dopa decarboxylase proteins such as those of Drosophila (510 aa) and human (480 aa). The predicted protein possesses a conserved lysine PLP binding site at residue 343, and has other amino acids identical to those shown to be essential for rat AADC function [ 5 , 41 , 42 ]. A number of possible phosphorylation sites can be predicted, including three serines and one tyrosine that are conserved in all known AADCs and HisDCs (Fig. 3A ). Figure 3 C. elegans bas-1 cDNA sequences. (A) Consensus cDNA sequence and translation for C05D2.4/ bas-1 , based on the most common splice form. Nucleotide numbering is shown on the left side and amino acid numbering on the right side of the sequence. SL1 spliced leader sequence is overlined in blue in the top line. Intron locations are indicated with blue arrowheads; the phase at all intron locations is 0 (between codons; see also Fig 6). The conserved lysine (K) pyridoxal 5-phosphate binding site at amino acid 343 is boxed in black. Red amino acids in the predicted Bas-1 protein (T286, D292, H309, D311, S336, K343, K357, V378, R393, and W401) are identical to those shown to be essential for rat AADC function [5, 41, 42]. Possible phosphorylation sites that are absolutely conserved in known DDC and HisDC proteins are boxed in green (Y37, S149, S229, S230). The polyadenylation signal in the final line is underlined. Mutations found in bas-1 alleles are indicated with the allele designation and the changed base over the wildtype sequence. The allele tm351 deletion, which removes the entire second exon, is indicated by a red line over the missing sequence. The wildtype cDNA sequence shown is consistent with our RT-PCR clones (primers SL1B, C05D2-B), those we sequenced from the ORFeome project (from predicted translation start to stop), and C. elegans EST project 'YK clones' ends (used to determine the 3' end, including the site of polyadenylation). (B) Alternatively spliced 27 bp exon and surrounding genomic sequence in C. elegans and C. briggsae . The additional exon is found in a fraction of C. elegans bas-1 transcripts, and the sequence is conserved in genomic sequence from C. briggsae as shown. [We did not isolate a cDNA containing this exon among our C. briggsae bas-1 cDNAs, S. DePaul & C. Loer, unpublished results.] Predicted translation of the exon is shown above or below the nucleotide sequence. Consensus splice signals are overlined in blue, and identical nucleotides are indicated by vertical lines between the two nucleotide sequences. We found two splice variants different from the Genefinder-predicted cDNA described above, which was the predominant form. About 20% of clones we sequenced had a 27 bp microexon inserted between the predicted exons 2 and 3 (Fig. 3B ). The 27 bp microexon is found within what is the second intron in the more commmon splice form. This intron is not conserved among other AADCs, and is inserted within a region of the BAS-1 protein that is not conserved among AADC proteins. Modeling of BAS-1 protein structure, based on a recent crystal structure of porcine DDC [ 43 ], indicates that this region is located at the surface of the protein where it would not interfere with the conserved enzymatic function of the protein (data not shown). We observed that this additional exon is conserved in the C. briggsae ortholog of bas-1 in genomic sequence (Fig. 3B ), although we did not isolate any splice variants with this exon among C. briggsae bas-1 cDNAs we sequenced (see also below). We found a single clone that used an alternative splice acceptor 60 bp upstream of the usual splice site for exon 3; this alternate splice introduces a premature stop codon in the coding sequence. This transcript may be a rare, aberrant splice form without functional significance. Expression of a bas-1 ::GFP reporter fusion in transgenic worms We examined the pattern of expression of a GFP reporter construct with ~4500 bp upstream of the predicted bas-1 translation start site and an in-frame fusion with the 2nd exon, injected with rol-6(dom) plasmid into wild-type worms (kindly provided by Ian Hope). Two independent transgenic Roller lines with extrachromosomal arrays had the same pattern of expression. The reporter was reliably expressed in several easily identified cells including the paired serotonergic neurons NSM and HSN and the dopaminergic PDE postdeirid sensory neurons (Fig. 4 ). NSM processes studded with varicosities were apparent in the isthmus of the pharynx labeled with GFP (Fig. 4A,4D ). The egg-laying neuron HSN normally expresses serotonin only in adulthood, and we found the reporter to be expressed in adult hermaphrodites and sometimes late L4 larvae. Often the HSN processes were apparent extending to vulval muscles and anteriorly within the ventral nerve cord (Fig. 4C,4F ). We saw a cell we identified as PDE, which is born during L2, only after this stage. In some worms, we saw a PDE process and dendrite, confirming our identification (Fig. 4F ). Figure 4 Expression pattern of a bas-1 ::GFP reporter fusion in transgenic Roller worms. (Panels A-C are from the same adult hermaphrodite. Ventral is down and anterior to the right.) A. Ventral, slightly oblique view of the head, showing NSMs, CEPDs, ADEL and likely AIMs. B. Same head, higher (more dorsal) focal plane, showing CEPDs and ADER. C. Photomontage showing ventral oblique view of HSNs and their processes in the ventral nerve cord; note also apparent labeling of muscles associated with the vulva. A second worm is immediately adjacent above, obscuring the edge of the worm shown. (Panels D-F: Anterior is to the left.) D. Adult hermaphrodite head, ventral view, chosen to show the characteristic highly varicose processes of the NSM cells within the isthmus of the pharynx. E. Larval head, ventral view with fluorescence and brightfield. This clearly shows the location of the NSM somata in the ventral pharynx, anterior bulb; it also shows the serotonergic ADF neurons not seen in A, B. CEPDs would be seen in a dorsal focal plane in this worm. F. Adult hermaphrodite lateral view of body wall. Ventral is down. Shows HSN and PDE; note PDE process extending ventrally toward the ventral nerve cord and dendrite extending dorsally into postdeirid sensillum. Twisting of the body axis associated with Roller phenotype makes HSN and PDE somata appear at the same lateral level when HSN is actually located sublateral and PDE subdorsal; twisting also takes ventral nerve cord out of plane of focus in the right of the panel. (Panels G – I are from males; anterior is to the right.) G. Late L4 male tail showing ray neurons (RNs) with processes extending into the rays. In some males we saw spicule cell staining likely belonging to spicule socket cells (SpSo). Ventral, slightly oblique view. H. Adult male tail showing RNs and their neurites in rays 7 and 9 on the right side, view ventral, slightly oblique. I. Male-specific ventral nerve cord motoneurons CP5 and CP6, the CP neurons most commonly expressing the transgene. The PDE soma in the lateral body wall is out of the plane of focus. The bas-1 ::GFP reporter was also expressed in other neurons in the head, around the nerve ring. We believe that all of these cells are known serotonergic and dopaminergic neurons. It was somewhat more difficult, however, to be certain about these identifications since we saw few processes, and even when present we could not always unambiguously associate a process with a particular neuronal soma. Nevertheless, the reporter was expressed in probable dorsal and ventral cephalic sensilla neurons CEPD and CEPV; we sometimes observed as many as four processes extending to the tip of the nose (Fig 4A,4B,4E ). We also saw expression in the anterior deirid sensory neurons ADE (Fig. 4A,4B,4E ). Less frequently we saw expression in probable ADF and AIM neurons (Fig 4A,4E ). We saw as many as 12 neurons (6 bilateral pairs) expressing the reporter in the head of young larvae. This includes all the identified serotonergic (NSM, ADF, AIM) and dopaminergic (CEPD, CEPV, ADE) head neurons excepting the unpaired RIH neuron [ 10 ]. In a small number of males examined, we saw expression in male-specific serotonergic and dopaminergic neurons, including up to 6 pairs of ray sensory neurons (RNs) in both adults and late L4 larvae (Fig. 4G,4H ). (There are three pairs of serotonergic, and three pairs of dopaminergic RNs among the 18 RNs.) Expression in CP neurons, male-specific ventral cord motoneurons controlling tail curling during mating, was limited and usually weak in the male worms we examined. Six CP neurons are strongly serotonin-immunoreactive in males [ 16 ]. At most we saw three posterior cells staining, and usually only one or two posterior cells (CP5, CP6) weakly stained, when expression was present at all (Fig. 4I ). We never saw CP staining in L4 animals, and often none even in male worms expressing GFP strongly in the RNs. C05D2.4 ( bas-1 ) and its downstream homolog C05D2.3 Just downstream of the bas-1 /C05D2.4 gene is C05D2.3, the product of an ancient tandem duplication event. The two genes have diverged considerably – being only 59% identical at the amino acid level (Table 1 ). The genomic structures of the two genes have also diverged. The two genes share four introns, but C05D2.4 has one and C05D2.3 has three introns not found in the other (Fig. 7 ). Nevertheless, comparisons with other AADC proteins showed that bas-1 /C05D2.4 is most similar to C05D2.3 and the predicted gene F12A10.3 (Fig. 5 , Table 1 ). The predicted amino acid sequence of C05D2.3 contains one noteworthy gap: it is missing six amino acids from a highly conserved region found in all other PLP-dependent decarboxylases. This sequence, the consensus of which is VDAAYA, contains an aspartate (D) residue that is absolutely essential for function of Rat DDC. Substitution of an alanine or asparagine completely abolishes enzymatic activity, and even the conservative substitution of a glutamate at this site reduces activity to 2% of wildtype [ 5 ]. It is therefore unlikely that a C05D2.3 protein could function enzymatically as a typical AADC. Table 1 Pairwise BLAST comparisons with C. elegans AADCs. C05D2.4 C05D2.3 F12A10.3* K01C8.3 ZK829.2 C09G9.4 Ce GAD C05D2.4 ( bas-1 ) Score %Id / %Sim - - - - - - C05D2.3 1674 59 / 75 - - - - - - F12A10.3* 1756 60 / 77 1793 63 / 78 - - - - - K01C8.3 ( tdc-1 ) 970 37 / 57 844 34 / 54 733 33 / 51 - - - - ZK829.2 583 29 / 47 541 27 / 46 559 27 / 49 1147 44 / 66 - - - C09G9.4 224 22 / 40 150 18 / 38 190 19 / 40 272 22 / 42 275 23 / 44 - - Ce GAD ( unc-25 ) 210 22 / 38 164 20 / 36 216 23 / 37 299 25 / 43 250 24 / 41 129 20 / 40 - Dm DDC 1095 41 / 60 909 36 / 56 984 38 / 58 1390 50 / 69 883 37 / 57 256 22 / 42 328 24 / 40 Hs DDC 1067 41 / 60 912 36 / 55 949 38 / 58 1458 55 / 73 935 39 / 59 233 20 / 42 322 26 / 44 Dm HisDC 996 39 / 57 816 33 / 53 807 32 / 54 1348 52 / 70 910 40 / 59 278 23 / 42 339 26 / 44 Hs HisDC 988 38 / 56 802 33 / 53 876 34 / 56 1290 48 / 69 920 39 / 59 231 21 / 42 306 26 / 42 Dm G30446 971 38 / 57 845 34 / 53 686 30 / 50 1671 64 / 77 1008 40 / 62 254 20 / 44 n.s. Dm AMD 961 38 / 57 799 35 / 53 668 36 / 53 1124 44 / 63 741 33 / 51 201 20 / 40 278 25 / 41 Dm G30445 837 34 / 52 797 35 / 53 814 33 / 53 1407 55 / 71 905 39 / 57 259 22 / 42 n.s. Cr TrpDC 743 30 / 51 674 28 / 50 735 30 / 52 916 37 / 59 269 31 / 51 272 24 / 43 333 25 / 42 Dm GAD 226 22 / 39 207 21 / 38 232 22 / 41 374 27 / 43 226 22 / 41 99 25 / 55 1146 44 / 64 Hs GAD67 215 22 / 36 173 19 / 36 192 21 / 36 324 24 / 44 228 24 / 41 86 16 / 41 1535 56 / 73 Comparisons of bas-1 /C05D2.4 and other pyridoxal-phosphate dependent decarboxylase amino acid sequences were made using "BLAST 2 Sequences" [version 2.2.6, [73]; Settings (largely default): Matrix – BLOSUM62, Open gap penalty – 11, extension gap penalty – 1, low complexity filtering – OFF). As shown in the table above, on the top line, each comparison shows the blast score; below is the percent identity and percent similarity for the 'alignable' sequence. The highest scoring match (excluding among C. elegans AADCs) is indicated in bold. Sequences in the left column are arranged in order of blast score in comparison to C05D2.4 C. elegans AADCs are indicated by their predicted gene designation: C05D2.4, C05D2.3, F12A10.3, K01C8.3, ZK829.2 and C09G9.4. Abbreviations: Ce – C. elegans , Cr – Caranthus roseus (periwinkle plant), Dm – Drosophila melanogaster , Hs – Homo sapiens , DDC – dopa decarboxylase, HisDC – histidine decarboxylase, AMD – Alpha-methyl dopa hypersensitive protein, TrpDC – tryptophan decarboxylase, GAD – glutamate decarboxylase. *An amino acid sequence for F12A10.3 was generated from cDNA sequence by introducing 2 frameshifts to preserve AADC homology in the predicted aa sequence merely for sake of comparison to other AADC's (see text); this sequence is different from predicted sequences in found in Genbank and Wormbase which are based on incorrect cDNA predictions. Figure 5 Alignments of AADC protein sequences with C. elegans BAS-1 predicted protein. Gaps are indicated with a dash (-); at the beginning or end of a sequence, periods indicate additional sequence upstream or downstream that is not shown. Alignments were performed with CLUSTALW. Abbreviations for species and gene names are the same as listed in the legend for Table 1. For genes with multiple splice forms, the most readily aligned sequence was chosen. Red shading indicates amino acids are identical in ≥ 90% of the aligned sequences. Yellow shading indicates similar amino acids found in that position in ≥ 90% of the aligned sequences. Figure 6 Phylogenetic trees of AADC protein and nucleotide sequences. Trees were made from sequences aligned with CLUSTALW. Species and gene names are abbreviated as listed in the legend for Table 1. (A) The single minimum-length tree resulting from a heuristic search using parsimony from alignments of core protein sequences (531 characters) of selected C. elegans , C. briggsae , Human and Drosophila AADCs. C. roseus (periwinkle plant) TrpDC was used as an outgroup. Branch lengths are indicated, with bootstrap values using the same search conditions (1000 replicates) in parentheses. The search used the tree-bisection-reconnection (TBR) branch-swapping algorithm; characters were equally weighted. An identical tree topologically was obtained by a branch-and-bound search. C. elegans F12A10.3 was excluded from this analysis since it lacks a functional protein sequence (see Fig. 7 and text). Trees determined by distance methods were similar, but rearranged some of branches with low bootstrap values in the tree shown. (B) The single minimum-length tree resulting from a heuristic search using parsimony (same settings as above) of nucleotide sequence alignments (1608 characters) from a subset of AADCs above, with the addition of C. elegans F12A10.3. Dm DDC was used as an outgroup. Branch lengths and bootstrap values using the same search conditions (1000 replicates) are shown as in A. An identical tree topologically was obtained by a branch-and-bound search. Because the bas-1 and C05D2.3 genes are so close together – only 369 bp from predicted translation stop to predicted translation start – we considered whether they might be expressed as an operon. In C. elegans and other nematodes, genes that are very close together (and often functionally related) may be expressed from a single promoter initially as a single primary transcript [ 44 ]. Operon transcripts are subsequently processed to yield separate mRNAs. The first gene in an operon is trans-spliced to the leader sequence SL1; downstream genes are typically spliced to a slightly different leader sequence termed SL2. We would expect to find C05D2.3 transcripts trans-spliced to SL2 if it is a downstream gene in an operon with bas-1 . We were unable to isolate either SL1 or SL2-spliced transcripts from C05D2.3 by RT-PCR, although we did isolate a partial cDNA using internal primers. DNA microarray experiments suggest the gene is not expressed above background levels, unlike C05D2.4/ bas-1 (Table 2 ). Furthermore, a global analysis of expression specifically designed to identify operons did not select C05D2.4 and C05D2.3 as likely members of an operon [ 45 ]. Since genes comprising an operon should be expressed at similar levels, these data provide no support for the idea that bas-1 and C05D2.3 constitute an operon. Table 2 C. elegans AADC genes expression and C. briggsae orthologs. C. elegans AADC C.e. cDNAs C.e. Microarray C. briggsae ortholog C.b. cDNAs C05D2.4 ( bas-1 ) +(3) a,b,c,d + FPC2187 (84,978 / - strand) + a C05D2.3 + a,c - none NA C09G9.4 + d - FPC4079 (~28,330 / + strand) - F12A10.3 +(2) b,d - none NA K01C8.3 ( tdc-1 ) +(2) c,d + FPC0011 (663,153 / + strand) + Y37D8A.23 ( unc-25 ) +(3) c,d + FPC4030 (765,572 / - strand) - ZK289.2 + c,d - FPC0143 (1,747,392 / - strand) - C. e. cDNAs: Parentheses indicate number of splice forms found. a cDNAs found via our RT-PCR experiments and b our sequencing of ORFeome project clones. c C. elegans EST project. d Worm ORFeome project. Microarray: Expression levels at all developmental stages as shown by C. elegans microarray experiments found in Wormbase. "+" indicates significant expression at some stage; "-" indicates no expression above background detected at any stage. C. briggsae AADC orthologs: We did TBLASTN searches of the C. briggsae whole genome shotgun assembly (cb25.agp8) on the Sanger Centre C. briggsae blast server using complete predicted amino acid sequences for each C. elegans AADC gene. C. briggsae genes are designated by contig location, first nucleotide of predicted coding sequence, and strand, based on predicted C. elegans sequence. In each case, alignments showed extended regions of 100% or near 100% amino acid identity beginning at the site indicated. (We did not locate the beginning of the C.b. C09G9.4 ortholog; alignments did not identify a matching site for the first 11 C. elegans amino acids). For C05D2.3 and F12A10.3, the best matches in C. briggsae were the C05D2.4 ortholog (FPC2187), followed by the K01C8.3 ortholog (FPC0011); these two genes appear to be absent from C. briggsae . We have isolated an SL1-spliced C. briggsae bas-1 cDNA by RT-PCR (S. DePaul & C. Loer, unpublished); C. briggsae tdc-1 ESTs are in GenBank. NA – not applicable. The bas-1 -AADC and other AADC genes in C. elegans We compared the predicted amino acid sequences of five other C. elegans AADC-like genes revealed by deletion mapping [ 46 ] and by whole genomic sequencing [ 24 ], along with a previously identified C. elegans glutamate decarboxylase (GAD) gene, unc-25 [ 47 ] to related PLP-dependent decarboxylases from other organisms. Some of the C. elegans genes are clearly closely related to other AADCs, whereas others are more divergent (Fig 5 , Table 1 ). All contain the core conserved domain (PFAM 00282) defining this group of PLP-dependent decarboxylases. None of the AADC or GAD predicted proteins in C. elegans appears to have a signal sequence. The protein predicted from K01C8.3 is now believed to encode a tyrosine decarboxylase ( tdc-1 ) used for tyramine and octopamine synthesis, which both appear to be used as neurotransmitters in C. elegans [ 48 , 49 ]. The best match to K01C8.3/ tdc-1 is a predicted Drosophila AADC-homologous protein of unknown function (G30446). Interestingly, K01C8.3/ tdc-1 shows a stronger match to known DDCs than any of the other C. elegans AADCs, including C05D2.4 (Table 1 ), although it is equally similar to known histidine decarboxylases (HisDCs). The strongest match of C05D2.4/ bas-1 (outside of nematodes) is to insect and mammalian DDCs, but again the match is only slightly better than to HisDCs. The predicted genes C05D2.3, F12A10.3 and ZK829.2 also have about the same level of identity and similarity to known AADCs and HisDCs. The ZK829.2 predicted protein, however, is much larger (830 AA) than a typical AADC, having extended N- and C-terminal domains not found in other PLP-dependent DCs. Most of ZK829.2 predicted coding sequence is confirmed by cDNA sequences, suggesting that the predicted protein 'extensions' likely are real. The predicted gene C09G9.4 is the most divergent from known AADC's with only 20 – 24% amino acid identity; it is even more divergent than C.e. GAD/ unc-25 . It also appears to lack the absolutely conserved Lys of PLP-dependent decarboxylases, although it otherwise retains considerable homology with the conserved domain of this family of proteins. There are no similar proteins among other organisms to provide clues about a possible function for this gene; C09G9.4 is a truly novel member of the group II PLP-dependent enzyme family. Proteins with a similar level of divergence with AADC (~20% identity over a few hundred amino acids) include other group II PLP-dependent enzymes such as sphingosine-1-phosphate lyase and cysteine sulfinic acid decarboxylase. C09G9.4, however, has very little or no significant similarity to these other enzymes. The C. elegans GAD/ unc-25 predicted protein has a strong match to identified Drosophila and mammalian GADs (Table 1 ), and is found as a single copy. There are no other GAD-like genes in C. elegans such as cysteine sulfinate decarboxylase, which is the rate-limiting enzyme in taurine synthesis, and the closest non-AADC relative to GAD in the vertebrates [ 50 ]. Comparison of C. elegans and C. briggsae AADC genes We performed BLAST searches of a C. briggsae whole genome shotgun assembly using predicted protein sequences of all six C. elegans AADC genes and the unc-25 /GAD gene. We found five orthologous genes in C. briggsae – four AADC homologs and one GAD homolog (Table 2 ). All of these matches included 100% or near 100% identity over extended regions of aligned predicted amino acid sequences, and were paired with high confidence in phylogenies (Fig. 6 ). Using a core AADC sequence for alignments and tree building, we found that the bas-1 orthologs have evolved more quickly than some of the other AADC's. The C. elegans gene K01C8.3 and its ortholog, for example, are 98% identical in this core region (vs. 91% identity for bas-1 orthologs). Most of the divergence between K01C8.3 and its ortholog is in N- and C-terminal extensions that are not found in other AADC's. The C. elegans C09G9.4 and C. briggsae ortholog are even less similar to one another than are the bas-1 orthologs. Figure 7 Genomic structure of C. elegans AADC genes compared with Human DDC. Rectangular blocks represent coding exons of the genes indicated (relative size of exons is approximate). Red triangles indicate ancient conserved introns found in both Human DDC and at least one of the C. elegans AADC genes; blue triangles indicate introns conserved among C. elegans AADC genes; and open triangles indicate non-conserved splice sites (comparing only among the genes shown). Roman numerals above triangles indicate the phase of the intron. Vertical dashed lines between solid triangles indicate splice sites conserved between at least two genes. Diagonal dashed lines indicate probable conserved sites that are shifted by 2–3 amino acids relative to the other splice site. Alignments of homologous splice sites are based on amino acid multiple alignments of the predicted proteins; insertions and deletions are ignored in the drawing. No alternative splicing is indicated; the most readily "alignable" version of each gene was used in cases with multiple splice variants. Dashed boxes at the ends of genes indicate non-AADC homologous extensions unique to the given gene. The most divergent AADC, C09G9.4, is more difficult to align; assignments of splice sites on either side of exon 9 as conserved are more tentative (indicated by question marks). In a few cases where gaps occur in the protein sequence alignments at intron-exon boundaries, introns marked as homologous only begin or end at an homologous location. The splicing pattern shown is fully supported by cDNA sequences for C05D2.4/ bas-1 , F12A10.3, K01C8.3, ZK829.2 ; the pattern is supported by partial cDNA sequences for C05D2.3 and C09G9.4. The extent of supporting cDNA sequence is shown by the heavy black line beneath the colored blocks. F12A10.3 is a special case in that frameshifts (indicated by 'fs') occur in the cDNA relative to the AADC homologous sequence. The first frameshift occurs at a site where many AADCs are spliced (and where a splice is incorrectly predicted by gene prediction programs), and the second at a splice junction. Our most striking observation is that C. briggsae appears to lack orthologs for the C. elegans predicted genes C05D2.3 and F12A10.3. This suggests that gene duplications giving rise to these two genes, which are most closely related to bas-1 /C05D2.4, occurred either in the C. elegans lineage after its split with the C. briggsae lineage, or that C. briggsae lost both C05D2.3 and F12A10.3 orthologs (or their common ancestor) following the split. Using phylogenetic analysis of aligned amino acid and nucleotide sequences, we found that C05D2.3 and F12A10.3 share a common ancestor and that the gene duplication giving rise to bas-1 and C05D2.3/F12A10.3 likely occurred prior to the C. elegans/C. briggsae divergence. This is also suggested by the pattern of introns in the genes. [We have confirmed the splicing pattern of C. briggsae bas-1 by isolating a cDNA (S. DePaul & C. Loer, unpublished data).] The C. elegans and C. briggsae bas-1 genes have identical genomic structure which differs from that of C05D2.3 and F12A10.3, which are more similar to one another (Fig. 7 ). Therefore C05D2.3 and F12A10.3 (or their common ancestor) were retained in the line leading to C. elegans but lost in the C. briggsae line. The original duplication event giving rise to the tandem copies of C05D2.4 and C05D2.3 on chromosome III probably occurred via an unequal crossing-over or similar event. The duplication creating F12A10.3, which is found on chromosome II, presumably occurred subsequently. We noted no homology of other predicted genes downstream of F12A10.3 and C05D2.3 that might suggest an event duplicating more than the AADC gene. The retention of the genes and their expression in C. elegans suggests that they may have acquired a new function that is under selection, retain a subfunction of the AADC, or instead that they are still in the process of being lost. After sequencing F12A10.3 cDNAs (courtesy of the ORFeome Project), we found that current splicing predictions for the gene were incorrect. We sequenced six F12A10.3 clones and found two slightly different splicing patterns, both different from Genefinder and Intronerator predictions. The two types of clones differed only in whether a final intron was removed or not. We found 4 clones with 9 exons, and 2 clones with 8 exons. The failure of the gene prediction programs in this case is likely to be due to their preference for creating functional transcripts. All F12A10.3 cDNAs instead appear to be non-functional: they have frameshifts relative to AADC-homologous reading frames. The first frameshift occurs in the second exon and quickly leads to a premature stop codon. At best F12A10.3 transcripts would result in a 158 amino acid protein that could not function as an AADC. F12A10.3 therefore appears to be an expressed pseudogene. DNA microarray experiments and representation in cDNA sequencing projects suggest that F12A10.3, like C05D2.3, is likely expressed at a low level (Table 2 ). In order to assess whether the bas-1 -like genes C05D2.3 and F12A10.3 might be under reduced selection pressure, we calculated the ratio of non-synonymous to synonymous substitutions (K A /K S ) comparing the bas-1 orthologs and bas-1 -like genes. We also calculated these values for the other AADC ortholog pairs from C. elegans and C. briggsae (Table 3 ). K A /K S < 1 indicates purifying (negative) selection, K A /K S = 1 indicates no selection (as in true pseudogenes), and K A /K S < 1 indicates Darwinian (positive) selection. K A /K S for 8179 C. elegans and C. briggsae ortholog pairs had a mean value of 0.06, indicating most genes are under purifying selection [ 37 ]. We found that the bas-1 genes are under purifying selection (K A /K S = 0.039), but the bas-1 -like genes appear to be under reduced selective pressure; the average K A /K S for comparisons with bas-1 -like genes was 0.148, more than three times the value of the bas-1 ortholog comparison. The proportion of observed to potential non-synonymous substitutions (pN) among the bas-1 -like gene comparisons was similarly much higher than for the bas-1 orthologs. Table 3 Synonymous vs. non-synonymous codon substitution between C. elegans and C. briggsae AADC orthologs and bas-1 -like paralogs. Ce , Cb AADCs compared Codons pS pN K A /K S bas-1 521 0.68 0.07 0.039 C05D2.3, F12A10.3, bas-1 * 521 0.71 ± 0.03 0.26 ± 0.02 0.148 ± 0.044 ZK289.2 833 0.71 0.09 0.043 C09G9.4 507 0.74 0.12 0.037 tdc-1 full length 626 0.79 0.03 NA tdc-1 core 474 0.82 0.02 NA tdc-1 N, C terminals 152 0.71 0.07 0.035 Nucleotide alignments of C. elegans and C. briggsae genes were analyzed by SNAP software (see Methods). Only the C. elegans member of the ortholog pair is named. pS = proportion of observed/potential synonymous substitutions; pN = proportion of observed/potential nonsynonymous substitutions. NA – not applicable (cannot be calculated when pS > 0.75). *Includes all pairwise comparisons (n = 5) except C.e. vs. C.b. bas-1 . Values are mean ± SD (strict statistical comparison with other values is not intended, as K A /K S values are not distributed normally). Two other AADC ortholog pairs showed strong purifying selection at work, with values like that calculated for the bas-1 orthologs (Table 3 ), but a value could not readily be calculated for the tdc-1 orthologs. In all the AADC ortholog comparisons, the proportion of observed to potential synonymous substitutions (pS) was near mutational saturation (pS > 0.75); K A /K S cannot be calculated when pS > 0.75. Thus, a value could not be calculated either for full-length tdc-1 alignments, or using a tdc-1 core sequence. We were able to calculate a value by aligning the N- and C-terminals sequence of the tdc-1 orthologs (Table 3 ). These regions of the protein are under levels of selection like the other AADCs, whereas the core has a very low rate of non-synonymous substitution, consistent with the high level of amino acid conservation in this region of the protein. Discussion Our experiments demonstrate that the predicted gene C05D2.4, which encodes an aromatic L-amino acid decarboxylase (AADC), corresponds to the genetically-defined bas-1 gene. Serotonin immunoreactivity is restored in bas-1 mutants by DNA containing an intact C05D2.4 gene, but not with DNA mutated in C05D2.4. The adjacent AADC-homologous gene, C05D2.3, is not needed to rescue bas-1 mutants. The bas-1 gene is therefore likely to encode the serotonin- and dopamine-synthetic AADC of C. elegans . Although we did not test for rescue of dopamine expression, it is likely that bas-1 encodes the same AADC required for DA synthesis. Mutants with point mutations in C05D2.4 – bas-1 alleles n2948 and n3008 – have been shown previously to be DA-deficient [ 17 ], and neither of these appears to contain mutations in the C05D2.3 gene. Furthermore, AADC proteins from other animals have been consistently shown to catalyze both 5HTP and L-dopa decarboxylation reactions [ 3 ]. Finally, a bas-1 reporter construct is expressed both in identified serotonergic and dopaminergic cells. The bas-1 gene is expressed in at least two alternatively spliced forms, one of which appears to be less common and contains a small additional 27 nucleotide exon. The short segment of protein encoded by the additional exon, and the surrounding region are not found in other AADC proteins, suggesting a novel function for this region of the AADC protein. In other organisms, the serotonin- and dopamine-synthetic AADC genes have alternative splicing that result in tissue-specific protein isoforms. Currently we have no indication that bas-1 is expressed in any cells other than serotonergic and dopaminergic neurons, and no information about the functional significance of this alternative splicing. AADC has received somewhat less attention with respect to the regulation of serotonin and dopamine synthesis than the specific, rate-limiting synthetic enzymes tryptophan hydroxylase and tyrosine hydroxylase [ 51 ]. This is in part due to the view that AADC activity is not limiting, and that its activity is not regulated. Regulation of AADC activity by protein kinase A-dependent phosphorylation has recently been proposed based on in vitro experiments [ 52 ], although its functional significance has been questioned [ 53 ]. Our examination of the predicted BAS-1 protein revealed several potential phosphorylation sites that are highly conserved, although none fit the consensus sequence for PKA phosphorylation. Any possible regulation of C. elegans AADCs by phosphorylation remains speculation. Possible functions of other AADC homologous genes in C. elegans We compared the protein sequences of other predicted AADCs in C. elegans with those of other organisms in order to guess about their possible functions. This is particularly relevant because all bas-1 mutants retain weak, residual serotonin immunoreactivity ([ 13 ]; C. Loer, unpublished) suggesting that other enzymes may be able to carry out the same reaction. This would not be surprising since animal AADCs tend to have broad specificity [ 3 ]. Based purely on sequence homology, it seems that predicted genes K01C8.3 and ZK829.2 could act as AADCs or as HisDCs. In fact, the predicted gene K01C8.3 is now believed to be a tyrosine decarboxylase and has been named tdc-1 [ 48 ]. If correct, then its best match in Drosophila (G30446), an uncharacterized AADC homolog, is likely to encode the fly's octopamine-synthetic tyrosine decarboxylase. It has long been known that a separate gene encoded this enzymatic activity in flies, since the activity is still detectable in Ddc deletion mutants [ 4 ]. It will be interesting to see whether such tyrosine decarboxylases in animals have more restricted substrate specificity, such as the tyrosine and tryptophan decarboxylases in plants [ 54 ], or are more similar to typical animal AADCs with a broad specificity. Tighter substrate specificity of a tdc-1 protein could be reflected in the much slower rate of amino acid substitution seen in its C. elegans & briggsae orthologs than in the bas-1 orthologs which encode more 'promiscuous' enzymes. Whether C. elegans or other nematodes make the neurotransmitter histamine, and therefore need a HisDC enzyme, is unclear. Although histamine has been reportedly isolated from C. elegans [ 55 ], this observation is unique among nematodes, and has not subsequently been confirmed. There is no particularly good candidate for a HisDC in C. elegans . The ZK829.2 predicted protein may be most closely related to tdc-1 in its core sequence, although its long N- and C-terminal extensions are perhaps suggestive of a new function. Unfortunately, transgenics with reporter fusions of this gene to date have shown no expression, the pattern of which might suggest a function (C. Loer, unpublished; M. Alkema, personal communication). As with tdc-1 , C. elegans ZK829.2 and its C. briggsae ortholog have also evolved more slowly than the bas-1 orthologs. A recent analysis of eukaryotic AADC sequences that includes the C. elegans ZK829.2 and its C. briggsae ortholog as the only nematode representatives clearly demonstrates that AADC genes can evolve at very different rates, and that a constant "molecular clock" cannot be assumed in phylogenetic analyses [ 56 ]. Finally, since the C09G9.4 predicted protein is so highly divergent from the typical AADC, and lacks a critical lysine residue that binds the PLP cofactor, it is unlikely to be an AADC enzyme. It has a similar level of divergence from genuine AADCs as do other group II PLP-dependent enzymes such as cysteine sulfinic acid decarboxylase, to which it has little or no similarity. Whatever the function of a C09G9.4-encoded protein, it appears to represent a new PLP-DC-related protein; sequencing of more genomes may yet reveal additional members. Duplicate gene retention and loss in Caenorhabditis We found that the closest relatives of C05D2.4/ bas-1 in C. elegans , the genes C05D2.3 and F12A10.3, are missing from C. briggsae . Furthermore, phylogenetic analysis indicates the two extra genes did not arise in the C. elegans line, but were present (or their commmon ancestor was present) in the species that gave rise to both the C. elegans and C. briggsae lines. Finally, careful examination of the cDNAs and predicted protein sequences of C05D2.3 and F12A10.3 reveals that neither is likely to be functional as an AADC: the former lacks critical amino acids and the latter can encode only a truncated protein. Both are expressed, based on the presence of cDNAs, but probably at a very low level, which is not above background in microarray experiments. It is possible that the duplicate genes are functionally 'lost' in C. elegans as well. The features of C05D2.3 and F12A10.3 raise a number of interesting questions about the fate of duplicate genes, and the true nature of many 'predicted genes' in C. elegans . Taking a random sampling of predicted genes and generating transgenics with reporter fusion constructs (in order to determine a pattern of expression), Mounsey and colleagues [ 57 ] found that a much higher percentage of recently duplicated genes than conserved or unique genes failed to show expression. Assuming that failure of expression was no more likely among recently duplicated genes for technical reasons, this meant that many more of these are in reality not expressed. The numbers suggested that up to 20% of annotated, predicted genes in C. elegans may be pseudogenes. In fact, careful inspection of recently duplicated genes showed that many were actually pseudogenes, like we found to be the case for F12A10.3. Overall, close inspection of predicted genes revealed at least 4% were pseudogenes. So, why are C05D2.3 and F12A10.3 still present in C. elegans if they lack a function? C. briggsae and C. elegans may have diverged 80 – 110 million years ago [ 37 , 38 ]. Since the bas-1 -like gene or genes were likely present in the common ancestor of C. elegans and C. briggsae , then there seems to have been ample time for loss in the C. elegans line. Under a simple model of gene loss following duplication, only a few million generations would be the mean time to fix a null allele of the gene duplicate [ 58 ]. In Caenorhabditis , a million generations could be completed in 10,000 years or less. This seems to suggest that the downstream duplicate of bas-1 (ancestor of C05D2.3) may have continued to function for a considerable time after the duplication, perhaps by gene conversion which might have continued until sufficient divergence from bas-1 [ 59 ]. Loss of the critical six amino acids occurred after the second duplication giving rise to the ancestor of F12A10.3, since the appropriate sequence is still present there (although frame-shifted). It is also possible that the C05D2.3 gene retains some function. The gene still encodes a respectable protein, albeit one that seems unable to function as an AADC. It has diverged considerably from bas-1 , but has not accumulated stop codons and frameshifts expected for a pseudogene. Walsh [ 60 ] has proposed that fixation of an allele with an advantageous new function, vs. becoming a pseudogene, may be the fate of many duplicate genes even when such mutations are rare, given a population that is sufficiently large. C05D2.3 and F12A10. 3 seem to have been retained longer than expected. Lynch and Force [ 61 ] proposed that the unexpectedly high rate of gene duplicate retention in eukaryotic genomes is due to 'subfunctionalization' – the retention of a portion of the original single gene's function by each of the duplicates, which then complement one another. Although this was suggested to occur primarily by regulatory mutations that partition expression of the genes spatially, other forms of subfunctionalization could also occur. Another possible reason for retaining such genes is the presence of non-coding regulatory functions associated with transcription and splicing of these sub-functional transcripts that affect the transcription of other nearby genes, although a bas-1 ::GFP construct is expressed well without such sequences in cis . Our analysis of synonymous vs. non-synonymous substitutions indicates that the bas-1 -like genes C05D2.3 and F12A10.3 are under relaxed selection relative to bas-1 and other AADCs. It should be noted that precise quantitative comparisons cannot be made with the results presented in the C. briggsae whole genome analysis [ 37 ], since we used a different method of calculating K A /K S ; however our calculations indicate that bas-1 and the other AADC's, like most genes in the Caenorhabditis genomes, are under strong purifying selection. Even if both C05D2.3 and F12A10.3 are now pseudogenes, some significant period of time during which they functioned and were under purifying selection could act to obscure this fact in an analysis of K A /K S . Even if C05D2.3 has acquired a new, adaptive function, such a new function might result from changes in only a few sites in the protein, and so again this could be obscured by a majority of sites under purifying selection. With the sequencing of three related Caenorhabditis species it will be interesting to learn of the fates of bas-1 and the bas-1 -like genes in other lines. Conclusions The bas-1 gene encodes a serotonin- and dopamine-synthetic AADC enzyme in C. elegans . The C. elegans genome possesses five other AADC-homologous genes, two of which are closely related to bas-1 . These bas-1 -like genes are missing, however, from the congeneric C. briggsae , and evidence suggests that, despite their persistence in C. elegans , the genes do not encode functional AADC proteins. Since one or more of the bas-1 -like genes was likely present in the common ancestor of C. elegans and C. briggsae- which may have diverged over 80 million years ago – it is unclear why the bas-1 -like genes have been retained in the C. elegans line. This is another example of unexpected retention of duplicate genes in eukaryotic genomes. Methods Routine culturing of Caenorhabditis elegans was performed as described by Brenner [ 62 ]. Nomenclature used here for C. elegans genetics conforms to the conventions set forth by Horvitz et al . [ 63 ]. Strains used include N2 (wild type); CB1490: him-5(e1490)V ; MT7988: bas-1(ad446)III ; MT7990: bas-1(n2948)III ; MT8002: bas-1(n3008)III ; LC7: bas-1(pa4)III ; LC33: bas-1(tm351)III . The him-5(e1490) strain generates approximately 30% males by increased X chromosome non-disjunction [ 64 ], but is otherwise essentially wild-type. Mutant allele sequencing Mutations were identified by PCR-amplifying small regions from genomic DNA, then sequencing purified PCR product. We isolated genomic DNA (Puregene Kit, Gentra Systems) from four bas-1 mutant strains (alleles ad446, n2948, n3008, pa4 ) and then PCR-amplified small segments of C05D2.4 and C05D2.3 protein-coding sequence. Five pairs of primers were used to survey the C05D2.4 gene (A+B, E+F, G+H, P+Q, R+S; see below for primer sequences) and 4 pairs for C05D2.3 (C+D, K+L, M+N, P+Q). Bands of the predicted size were excised from 2% agarose gels and purified with GeneClean (Bio101/Qbiogene), then sequenced. All mutations were confirmed by sequencing both strands for two independent PCR reactions. All PCR and sequencing primers were designed using the program Primer3 ( ; [ 65 ]). Primer sequences were as follows. Within C05D2.4: C05D2-A: gaggaaactcaaggcgacac; C05D2-B: tgttgatggaaccaagtgga; C05D2-E: cgtccttttctctttgcgac; C05D2-F: tggctccgacttgattctct ; C05D2-G: ttacaattaggccgcaaacc; C05D2-H: ccacctgaactgtggtgatg; C05D2-P: ggactcacatgtttccgattg; C05D2-R: ttagacgttggttgcacgag; C05D2-S: attggcgagcagtcaaagtt ; C05D2-T: tcttatgggattaccagaac; C05D2-U: ctacataaagctggaatggt; C05D2-V: gtttcctaaaaatccacgtg; C05D2-W: atgatcgattgatagctgag. Within C05D2.3: C05D2-C: ctaggtgcctttgccttctg; C05D2-D: caagagacgctcgttgtcag; C05D2-K: gccatctaatcctccaacca; C05D2-L: acattgctcccttttcaacg [note that primer L can also prime within C05D2.4]; C05D2-M: ccatcaactttccaatggct; C05D2-N: tctcgacgcccatatttctc; C05D2-Q: ccaattccagcggagaagta. Microinjection to create transgenics All DNAs for microinjection were purified with Qiagen tip20 columns. Experimental DNAs were co-injected with the dominant marker plasmid pRF4 containing the mutant gene rol-6(su1006) [ 66 ] into N2 (wildtype), CB1490, or bas-1 mutant worms. Progeny of injectees that express the rol-6 dominant plasmid have the easy-to-identify Roller phenotype which results in worms with a helically-twisted body along the anterior-posterior axis. Roller transgenic worms typically carry co-injected DNAs. Transgenic Roller progeny were isolated and propagated; rescue of bas-1 mutants was scored by staining with serotonin antiserum as described previously[ 16 , 67 ]. Mutant rescue with subclones of cosmid C05D2 and plasmid C05D2XN; GFP Reporter Construct The rescuing plasmid C05D2XN contains a 15.8 kbp genomic DNA insert (XhoI to NheI) derived from the cosmid C05D2, and contains both C05D2.4 and C05D2.3 predicted genes. A number of deletions of C05D2XN were made to test for rescue of bas-1 mutants. Clones were analyzed by restriction digests. We also made C05D2XN derivatives in which either C05D2.4 and C05D2.3 was mutated to introduce a premature stop in coding sequence. Clones were sequenced to determine the nature of the introduced mutation. Clone pCL6991 had a 4 bp deletion in the second exon of C05D2.4; pCL7991 had a 2 bp insertion in the first exon of C05D2.3; each causing a frameshift mutation. In the clone pCL7003, derived from the rescuing plasmid pCL3001, almost the entire C05D2.3 coding sequence is deleted. The bas-1 GFP reporter construct (within transgenics) was kindly provided by Ian Hope, and consists of a PCR-generated fragment of C05D2 with 4595 bp upstream of the predicted translation start site and 403 bp protein coding region to make an in-frame protein fusion in the 2nd exon. The construct was created by a multi-site recombination reaction with the C05D2 fragment, PCR-generated GFP, and vector using the Invitrogen Gateway cloning system (I. Hope, personal communication) as continuation of a project to determine expression patterns for C. elegans genes through reporter gene technology [ 57 ]. RT-PCR and cDNA clones We isolated RNA for RT-PCR from mixed stage CB1490 worms. (The CB1490 strain has ~30% males, whereas wildtype N2 has ~0.2% males. Since adult males have 13 more serotonergic neurons and 6 more dopaminergic neurons than hermaphrodites, we reasoned that these worms might express more bas-1 mRNA.) Worms were isolated from six 100 mm NGM plates, washed several times with M9 buffer, and pooled to form a ~100 μl pellet of worms. The pellet was mixed with 175 μl RNA lysis buffer (SV Total RNA Isolation System, Promega), frozen and ground to a fine powder with a pestle and mortar cooled with liquid nitrogen. The powder was recovered in a fresh microfuge tube, mixed with 350 μl SV RNA dilution buffer and centrifuged to remove debris. The cleared lysate was transferred to a new tube and precipitated with 200 μl 95% ethanol and applied to the SV spin column assembly. The remaining RNA purification was performed exactly as described for the SV System "RNA Purification by Centrifugation." Purified RNA was eluted from the spin column with 100 μl nuclease-free water. RNA was converted to cDNA in a 80 μl reaction containing 800 Units of M-MLV Reverse Transcriptase (Life Technologies/BRL), 16 μl 5X RT buffer, 25 mM dNTPs, 80 Units RNAsin (Promega) 2.0 μg random hexamer primers (Life Tech), and 50 μl purified RNA (from above). Bas-1 (C05D2.4) and C05D2.3 cDNAs were amplified by PCR from him-5 cDNA produced as described above, typically using Failsafe PCR mixes (Epicentre Technologies). Conditions for PCR were 94°C (1 min.), 50°C (1 min.), 72°C (3 min.) for 40 cycles, then 72°C (10 min.). Primers used to amplify C05D2.4 cDNAs were SL1-B + C05D2-L and SL1-B + C05D2-B; a partial C05D2.3 cDNA was amplified with primers C05D2-M and -N. Spliced leader primer sequences were as follows: SL1-B: AAAGGATCCTTTAATTACCCAAGTTTGAG; SL2-B: AAAGGATCCTTTTAACCCAGTTACTCAAG. Appropriately-sized bands were isolated with GeneClean or Ultrafree-DA (Millipore), then cloned directly into the pCRII-TOPO vector using the TOPO-TA Cloning kit (Invitrogen). From seven of our RT-PCR derived clones we sequenced, we found two different splice variants different form the Genefinder prediction (see results). We also obtained cDNA clones from the ORFeome project [ 40 ] and the C. elegans EST project (courtesy Yuki Kohara). DNA we received from the ORFeome project is purified from a pool of transformants derived from ligation and transformation of their original RT-PCR, thereby allowing the isolation of internal splice variants from the mix. We transformed with this DNA and isolated several clones. We sequenced two ORFeome clones completely; six more clones were partially sequenced. Interestingly, each of the 8 clones appeared to have at least one mutation (compared to known genomic sequence). Since we found one splice variant containing the 27 bp microexon from among the eight clones that we sequenced, we analyzed 29 additional ORFeome project-derived clones by PCR from single isolated bacterial colonies. Amplification of the region between primers C05D2-T and -L allowed us to distinguish between clones with or without the 27 bp microexon based on product size (464 bp versus 437 bp). Seven of 29 clones were the larger size, so likely contained the 27 bp microexon. None of the ORFeome clones analyzed by sequence or PCR appeared to use the alternative exon 3 splice acceptor noted above. Finally, the two 'YK' cDNA clones from the C. elegans EST project that we examined are from a 'full-length' capped library, and each has an SL1 leader and a poly-A tail. We found that each bas-1 'YK' clone, however, contained a different internal deletion (overall abnormality of these clones is reported at ~5% – J. Theirry-Meig, personal communication). Each of the deletions was adjacent to a short repeated sequence. Sequence Analyses C. elegans genomic and predicted cDNA sequences were retrieved from ACeDB, WormBase, and/or GenBank. Blast searches and Blast2 comparisons were performed using the NCBI Blast server. C. briggsae genomic sequence was searched using TBLASTN of the 7/12/02 shotgun assembly . Assembly and consensus sequence determination of our own cDNA sequences was done using the program SeqMan (DNAStar, Madison, WI). Some information about ORFeome clones was retrieved from WorfDB ( ; [ 68 ]). Phosphorylation predictions were made with NetPhos 2.0 and PhosphoBase . Signal sequence predictions were performed with SignalP v.1.1 ( , [ 69 ]). Multiple sequence alignments were performed primarily with CLUSTALW , with some manual adjustments. Phylogenetic analyses were performed with PAUP* version 4.0b10 (Sinauer Associates, [ 70 ]). Some alignments of cDNAs with genomic DNA to determine intron locations were performed with SIM4 [ 71 ]. Estimates of rates of synonymous and non-synonymous substitutions were made with SNAP (Synonymous/Non-synonymous Analysis Program – using pairwise or multiple sequence nucleotide alignments generated with CLUSTALW and adjusted manually. This program uses the method of Nei and Gojobori [ 72 ]. List of Abbreviations AADC – aromatic amino acid decarboxylase DDC – dopa decarboxylase HisDC – histidine decarboxylase GAD – glutamic acid decarboxylase TrpDC – tryptophan decarboxylase PLP – pyridoxal 5'-phosphate NSM – neurosecretory motoneuron HSN – hermaphrodite-specific neuron PDE – postdeirid sensory neuron ADE – anterior deirid sensory neuron RN – ray sensory neuron CEPD, CEPV – dorsal or ventral cephalic sensory neuron ADF – amphid sensory neuron, dual cilia, designation F AIM – ring interneuron, designation M RIH – ring interneuron (unpaired), designation H CP – posterior daughter of designation C cell, male-specific ventral cord motoneuron Authors' contributions EH prepared deletion and expression constructs, cloned and sequenced bas-1 cDNAs and mutant alleles, participated in anti-serotonin staining and genetics, and drafted the manuscript. CL conceived and directed the study, generated transgenics by microinjection, performed sequence and phylogenetic analyses, and completed the manuscript. Both authors read and approved the final manuscript.

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514887

Submission of Microarray Data to Public Repositories

The Microarray Gene Expression Data Society believe that the time is right for journals to require that microarray data be deposited in public repositories, as a condition for publication

A fundamental principle guiding the publication of scientific results is that the data supporting any scholarly work must be made fully available to the research community, in a form that allows the basic conclusions to be evaluated independently. In the context of molecular biology, this has typically meant that authors of a paper describing a newly sequenced genome, gene, or protein must deposit the primary data in a permanent, public data repository, such as the sequence databases maintained by the DNA Data Bank of Japan (DDBJ), European Bioinformatics Institute (EBI), and National Center for Biotechnology Information (NCBI). Similarly, we, members of the Microarray Gene Expression Data Society (MGED; http://www.mged.org ), believe that all scholarly scientific journals should now require the submission of microarray data to public repositories as part of the process of publication. While some journals have already made this a condition of acceptance, we feel that submission requirements should be applied consistently and that journals should recognize ArrayExpress ( Brazma et. al. 2003 ), Gene Expression Omnibus (GEO) ( Edgar et. al 2002 ), and the Center for Information Biology Gene Expression Database (CIBEX) ( Ikeo et. al. 2003 ) as acceptable public repositories. To this end, the members of MGED propose the following as a new paradigm for the publication of microarray-based studies. (1) Authors should continue to take primary responsibility for ensuring that all data collected and analyzed in their experiments adhere to the “Minimum Information about a Microarray Experiment” (MIAME) guidelines and should continue to use the MIAME checklist ( www.mged.org/Workgroups/MIAME/miame_checklist.html ) as a means of achieving this goal. (2) Scientific journals should require that all primary microarray data are submitted to one of the public repositories—ArrayExpress, GEO, or CIBEX—in a format that complies with the MIAME guidelines. (3) Public databases should work with authors and scientific journals to establish data submission and release protocols to assure compliance with MIAME guidelines. (4) To assist with the review process, the databases should continue to work in collaboration with publishers to provide qualified referees with secure means of accessing prepublication data. Authors should be strongly encouraged to submit data to the databases during review. Naturally, data should be protected from general release prior to either publication or authorization from the data submitters, whichever comes first. At a minimum, journals should require valid accession numbers for microarray data as a requirement for publication, and these accession numbers should be included in the text of the manuscript to allow members of the community to find and access the underlying data. Since its inception in 1999, MGED has been working with the broader scientific community to establish standards for the exchange and annotation of microarray data. In December 2001, we proposed the MIAME guidelines ( Brazma et al. 2001 ) and requested that interested parties provide feedback on its relevance and utility. The feedback from both researchers and scientific journals was overwhelmingly positive, yet almost everyone who responded also asked for help in implementing these guidelines. Subsequently, in the summer of 2002, we submitted an open letter to various journals (e.g., Ball et al. 2002a , 2002b ) urging the community to adopt the MIAME requirements for microarray data publication. We provided a checklist so that authors could ensure that sufficient information to allow their data to be re-analyzed by others would be available. Again, the response from the community was extremely positive, and most of the major scientific journals now require publications describing microarray experiments to comply with the MIAME standards. While the adoption of these standards has greatly improved the accessibility of microarray data, much of it remains on individual authors' websites in a variety of formats; consequently, obtaining and comparing datasets remains a significant challenge. Clearly we need additional requirements for publication that include submission of expression data to public data repositories. Though one might ask why this requirement was not part of the original MIAME recommendation, the answer is quite simple—MIAME was ahead of its time. While NCBI and the EBI had developed nascent microarray data repositories, and work was underway to create a similar database at the DDBJ, submitting data to these databases was a considerable burden for authors. However, since that time, improvements in the data-entry utilities available for GEO ( www.ncbi.nlm.nih.gov/geo ), ArrayExpress ( www.ebi.ac.uk/arrayexpress ), and CIBEX ( cibex.nig.ac.jp ), as well as a growing number of commercial and academic software packages capable of writing MAGE-ML documents ( Spellman et al. 2002 ) that can be directly submitted to these public databases, have lowered the barriers for data submission to the point where we as a community must now reconsider that submission to one of these databases be a requirement. Requiring authors to submit microarray data to a public database will provide a number of distinct advantages to the entire research community. (1) These established repositories have a commitment to continued community service and to providing some level of assurance that published gene expression datasets will continue to be available into the future. (2) Having the data available in these public repositories in a standardized format will not only make them more accessible, but it will allow expression data to be integrated with other relevant data, including the available genome sequences, single nucleotide polymorphism and haplotype mapping information, the literature, and other resources that can aid in further interpretation of expression patterns. Although many authors now provide some or all of this information, the established databases are much more likely to assure that these links are maintained and current. (3) Curation of data submitted to public data repositories will assist authors, reviewers, and publishers in assuring that the data comply with the MIAME requirements, further enhancing their utility. (4) The standardization of microarray data formats will enable the development of additional data analysis and integration tools and makes it easier for scientists to access, query, and share data. (5) Finally, submission prior to publication will make it easier for referees to access the data confidentially, facilitating the review and publication process. In the same way that availability of sequence data had a profound impact on a wide range of disciplines, we believe that requiring that microarray data be deposited in public repositories as a necessity for publication will accelerate the rate of scientific discovery. What this proposal requires is a change in the way in which we approach the publication of microarray-based studies. Both authors and journals have a responsibility to assure that the requisite data are available, and because submitting MIAME-compliant data can take considerable time and effort, this process should be factored into review and publication timelines. However, while this process may be time consuming and painful at first, we believe that the benefits of building an open repository of microarray data will far outweigh any initial disadvantages. As always, it is our sincere hope that these suggestions stimulate discussion within the community and that together we can arrive at a consensus that ensures that microarray data are widely and easily accessible. Finally we would like to urge the DDBJ, EBI, and NCBI to work together towards exchanging all MIAME-compliant microarray data.

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523232

Slime Mold Myosin Thick Filament Assembly Dissected

The movements needed to read this synopsis—turning the page, tracking along the lines, sitting, breathing—all require myosin, a molecular motor in muscle that transforms chemical energy into small but deliberate motions. But beyond these macro-movements, myosin is also required for the micro-movements of individual cells and their organelles and for determining cellular architecture. There are many different myosins, but they all have the same general structure. At one end is a globular head, which is responsible for motor activity. This head binds ATP—the cell's power supply—and actin, an important component of the cytoskeleton of cells. Next comes a helical neck or lever region. Finally, there is a long helical tail, which has different and somewhat poorly understood functions in the different myosins. Myosin II, the classical form of myosin found in essentially all eukaryotic cells, is constructed from two heavy chains (which contain the three regions described above) and two pairs of light chains (which stabilize the neck region). The long helical tail of myosin II is formed by the two heavy chains wrapping around each other and is involved in getting myosin II to the right place in the cell, as well as in assembling it into filaments. Individual myosin II molecules can make tiny molecular motions. ATP cleavage induces a shape change in the globular head, which is transmitted to the lever region of the molecule. Angular rotation of this region moves the myosin along the actin filament. But to achieve the larger movements that are necessary to, for example, split cells apart during cell division, individual myosin II molecules group together to form highly regular bipolar structures called bipolar thick filaments (BTFs). In these, the globular myosin heads are positioned on either side of the filament, and the tail regions are clustered in the middle. This geometry enables myosin II molecules in thick filaments to pull from either side, generating contractile forces. James Spudich's team has been studying the assembly of these thick filaments in the slime mold Dictyostelium discoideum , an organism beloved by developmental and cellular biologists because of its simple development and ease of manipulation. In the present study, the researchers examined the physical properties of various fragments of the myosin tail to find out how the self-assembly and disassembly of the BTFs are regulated. They already knew that the addition of phosphate groups on three specific threonine amino acid residues in this region (through a chemical reaction called phosphorylation) is important for regulating BTF assembly; they knew this from studies showing that mutation of these residues to aspartic acid, which mimics phosphorylated threonine, inhibits BTF formation. Here, the researchers show that a specific tail fragment of the myosin heavy chain containing the three crucial threonine residues assembles into a structure with some, but not all, of the properties of BTFs. However, replacing these threonine residues with aspartic acid prevents any self-assembly of the fragment. Further experiments in which different tail regions were nibbled away and the assembly properties of the remaining fragments were determined suggest that the myosin tail contains a series of elements that correlate with the distribution of charged amino acids along the tail, some of which favor assembly and some of which favor disassembly. But it's not just the tail that is important. For myosin II to form fully fledged BTFs of a defined size, it seems that the addition of some kind of globular head—in these experiments one composed of green fluorescent protein so that it could be examined—is necessary. The overall result is a molecule that is finely poised to self-assemble into BTFs in response to one or two charge changes produced by phosphorylation. Consequently, the myosin contractile system can respond rapidly to environmental changes. Although Dictyostelium myosin II is somewhat different from vertebrate myosin II, the general principle by which myosin assembly and disassembly are regulated seems likely to hold for other myosins and so might throw light onto human disorders that involve myosin defects. But more fundamentally, similar principles may hold for spatial and temporal regulation of the many other macromolecular assemblies that are at the heart of cell and developmental biology.

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387278

Troubled Waters: The Future of Global Fisheries

Scientists debating how to assess global fisheries are now including studies of long-term ecosystem effects and options for recovery efforts. But is it possible to both conserve and farm the sea?

It is becoming increasingly apparent that the vast blue expanse of ocean—the last frontier—is not as inexhaustible as it once seemed. While we have yet to fully explore the reaches of the sea, technology has granted humans the ability to harvest its wealth. We can now fish anywhere, at any depth, for any species. Like the American frontier range's bison and wolf populations brought to the brink of extinction swordfish and sharks are the ocean's most pursued prizes. The disadvantages associated with the depth and dimensions of this open range, however, have long obscured the real consequences of fishing. Indeed, scientists have the formidable challenge of assessing the status of species whose home covers over 75% of the earth. Three recent highly publicized papers—a trifecta detailing troubled waters—call attention to overfishing's contributions to the dramatic declines in global fisheries. Delving into the past, Jeremy Jackson and colleagues (2001) combined local historic records with current estimates to detail the ecological impacts of overfishing, Reg Watson and Daniel Pauly (2001) drew attention to distortions of global catches, and Ransom Myers and Boris Worm (2003) highlighted the depletion of the majority of the largest ocean predators. While some have valid criticisms of the assumptions and aggregation of historic data used to assess the global situation, few disagree with the overriding conclusion that humans have drastically altered not only fish biodiversity, but, increasingly, the ocean itself. Recent reports by the United Nation's Food and Agriculture Organization (FAO) which maintains the world's most complete global fisheries database, appear to validate the conclusions of these studies. The most recent FAO report states that 28% of global stocks are significantly depleted or overexploited, and 47% are either fully exploited or meet the target maximum sustainable yield. Only 24% of global stocks are either under- or moderately exploited. As the sea is increasingly harvested, many ecologists wonder how the ecosystem will continue to function ( Jackson et al. 2001 ). Although economic and social considerations often supercede scientific assessments, science will continuously be called upon to deliver management options that will straddle the needs for conservation and production, even in areas where there is only subsistence fishing (Box 1) . As scientists debate the details of global fisheries assessment, they are also including studies of the long-term ecosystem effects and options for recovery efforts. Like was done on the open range, shall we conserve or farm the sea—or both? Catches, Collapses, and Controversies The FAO began keeping fisheries records in 1950. Unfortunately, an enormous amount of data comes directly from each country's fishing industry, which is often biased as a result of unreported discarding, illegal fishing, and the misreporting of harvests. For example, mid-level Chinese government officials seeking promotions systematically enhanced China's fisheries numbers in recent years—which inflated and skewed international catch rates. The FAO data show that catches, excluding a recent surge in anchoveta and China's suspect numbers, reached a peak of 80 million metric tons in the late 1980s and have since begun to decline. Regional studies validate these trends. “Most of the line fish around the coast of South Africa are depleted to 5%–15% of pristine levels,” says George Branch, a marine biologist from the University of Cape Town (Cape Town, South Africa). Meryl Williams, Director General of WorldFish in Penang, Malaysia, notes that the Asia-specific database called TrawlBase ( www.worldfishcenter.org/trawl/ ) confirms that the region's commercial species have been depleted to 10%–30% of what they were 30–40 years ago. Obtaining accurate information on highly migratory species is challenging, to say the least. It is not hard to imagine that data quality is the biggest disadvantage to any scientific assessment. Of the 50 managed stocks in the northeast Atlantic Ocean—including invertebrates, sport fishes, and major commercial finfish—data are kept on only one-fifth of the species. There are 250 fish species in the region, but only 55 species are of commercial interest and merit inquiry. “We know next to nothing about noncommercially fished species,” notes Jeff Hutchings, a conservation biologist at Dalhousie University (Halifax, Nova Scotia, Canada). And that is where fisheries have adequate access to current monitoring programs. “With the recent expansion of the Taiwanese and Chinese fleets, we don't have the kind of sampling programs needed for those kinds of fisheries,” says Rick Deriso, a fisheries scientist with the Inter-American Tropical Tuna Commission (IATTC) (La Jolla, California, United States). Couple these inadequacies with previously unknown bycatch rates (i.e., the fish caught in addition to the target catch) and illegal catches, and it is easy to see that the task is formidable. The FAO estimates that roughly one-quarter of the marine commercial catch destined for human consumption—some 18–40 million metric tons of fish—is thrown back in the sea, a harvested catch that is never utilized or counted. It is estimated that the illegal, unreported, and unregulated (IUU) fisheries surpass allowed fishing quotas by 300%. IUU fishers operate in areas where fishing is not permitted, use banned technologies or outlawed net types, or underreport catches. “The IUU fishery for Patagonian toothfish expanded rapidly in the mid-1990s, likely on the order of 20–30 vessels,” says Andrew Constable, an ecological modeler at the Australian Antarctic Division (Kingston, Australia), who also works with the Scientific Committee of the Commission for the Conservation of Antarctic Marine Living Resources (Hobart, Australia). “These rates of IUU fishing could reduce stocks to threshold levels in some areas in two to five years,” he adds. Often overlooked is the inescapable fact that even sustainable harvest rates reduce fish populations quickly. “If the goal is a productive fishery, we're automatically talking about up to a 70% decline in population across the board,” says Deriso. The FAO's Chief of Marine Resource Services, Jorge Csirke, states that “from a stock point of view, there is no way to preserve integrity of wild stocks and exploit them at the same time.” Indeed, the United States' National Marine Fisheries Service (NMFS) considers optimal harvest rates to be between 40%–60% of virgin levels. But once fish populations dip below the 10%–20% mark, declines are of serious concern. Atlantic cod in Canadian waters suffered a total population collapse and are now on Canada's endangered species list ( Figure 1 ). From 2 billion breeding individuals in the 1960s, Atlantic cod populations have declined by almost 90%, according to Hutchings. While advisors called attention to declining cod stocks, Constable notes that by the time a significant declining trend has been detected by traditional catch assessments, stocks are likely to be in poor shape, if not already depleted. Figure 1 Cod in a High Arctic Lake in Canada These cod resemble those of past Atlantic catches. Measuring 47–53 inches (120–135 cm) long and weighing between 44 and 57 pounds (20 and 26 kg), it is easy to see that today's 16–20 inches (40–50 cm) commercially caught cod are less than half this size. (Photo, with permission, by David Hardie, Dalhousie University.) Given the task of compiling data on only the economically important species, fisheries biologists developed a single-species management approach in the 1960s, which assumed that fisheries affect each species in isolation. This approach, although now rife with problems, served the community and the politicians well during the decades of abundant resources. “They brought the approach of single-species management to near-perfection,” says Boris Worm, a marine ecologist at the Institute for Marine Science in Kiel, Germany. A growing discontent with the model, in addition to greater awareness of ecological interactions, however, prompted Worm and his Dalhousie University colleague Ransom Myers to question the sustainability of the single-species approach. Attempting a comprehensive assessment, their widely cited recent paper ( Myers and Worm 2003 ) indicated that the global ocean has lost more than 90% of large predatory fishes, such as marlin, sharks, and rays. However, this new approach to assess fish stocks is not without its critics. Fisheries biologists point out that the nuances of management contained in fisheries data—such as altered fisher behavior, the variable “catchability” of individual species, and altered gear use—were discounted in the Myers and Worm (2003) assessment and led to misinterpretations for some species, notably tropical tunas ( Figure 2 ). A number of tuna biologists have expressed concern that these omissions have left the mistaken impression that all tuna species are among the list of declining predators ( Hampton et al 2003 ). Worm acknowledges that his approach can be improved, but says, “The whole point of our paper was to aggregate species to communities to see what the overall ecosystem is doing.” Figure 2 Pole Fishing for Medium-Sized (40–50 lb or 18.1–22.7 kg) Big-Eye Tuna aboard the Live-Bait, Pole-and-Line Vessel Her Grace (Photo, with permission, by Kurt Schaefer and Dan Fuller, IATTC.) Ecosystem Sustainability Despite the controversy, most agree that the large predators, particularly sharks, skates, rays, and marlin, are in the most dire straits. Unlike other lower-trophic order species, the wholesale removal of top predators has enormous effects on the rest of the ecosystem. One consequence is that overall reproduction rates can potentially suffer. Fish size, gender, and age at maturity have a substantial impact on individual species' reproduction rates. Since larger fish are the most susceptible to fishing, the population's age structure can shift as individuals, particularly females, are fished out. For example, a 23-inch (59-cm) female vermilion rockfish can produce 17 times the young of a 14-inch (36-cm) fish. Given uncertainties with population dynamics, the fact that basic biological data are missing makes the job even harder. While knowledge of these components is still quite spotty, tuna inventories, for example, have started collecting gender data on catches. Daniel Pauly, a fisheries biologist at the University of Vancouver (Vancouver, British Columbia, Canada), has shown that increased fishing has caused the industry to “fish down the food web,” or systematically move to lower trophic levels over time as higher ones were depleted ( Pauly et al. 1998 ). The impact to ecosystems is only beginning to be uncovered. “If you fish out an abundant predator, the species that it was eating or competing with will increase,” says Worm. “The problem is that the ecosystem may change in such a way that recovery is inhibited because a species niche space is taken or altered.” Fisheries science has taken steps to increase the quality of data in recent years. “Traditional fishery models assumed that a fishery was a homogenous thing—like bacteria in a bottle—rather than a spatially diverse system,” says Pierre Kleiber, a fisheries biologist with the Pacific Islands Fisheries Science Center of the NMFS (Honolulu, Hawaii, United States). He adds that recent work accounts for spatial diversity. In addition, fisheries are now dealing with the inherent uncertainty of their work and are factoring that into models and decision-making. “Uncertainty didn't used to be dealt with at all in formulating fishery management advice,” confirms Keith Sainsbury, a marine ecologist with the Commonwealth Scientific and Industrial Research Organisation (CSIRO) (Clayton, South Victoria, Australia), adding that its absence gave rise to an awful lot of troubles. “Traditional models tended to assume perfect data with no holes in it,” says Kleiber. “Now we've tried to craft a model to fit the realities of missing data.” As well as incorporating spatial diversity and uncertainty, researchers are beginning to comprehend the ecological damage caused by different types of fishing gear. Indeed, trawling the bottom of the seafloor for groundfish can destroy a half-acre footprint of habitat ( Figure 3 ). Detailed reports document that, depending on the habitat's stability, bottom trawling can not only remove fish from seafloor habitats, but alter bottom relief such that it compromises the ability of other fish to survive ( NRC, 1002 ). In Australia, for example, lingcod rely on undisturbed bottom relief to lay their eggs, while other groundfish species depend on complex seafloor habitats for the majority of their food. Figure 3 The Effect of Trawling the Seafloor for Groundfish (A) The coral community and seabed on an untrawled seamount. (B) The exposed bedrock of a trawled seamount. Both are 1,000–2,000 meters (1094–2188 yards) below the surface. (Photo, with permission, by CSIRO Marine Research.) “Science is getting more realistic, but it is getting more difficult,” says Branch. Ecological models are far more complex than traditional fisheries models, says Csirke, adding that more model variables make it more difficult to apply to fisheries, an industry whose focus is, understandably, not conservation. Despite its incorporation into national fisheries policies, ecosystem-based management remains a loosely defined term. It is not a well-defined concept because it is not possible to optimize every species, says Deriso. An additional concern to scientists is that of biomass resilience in the face of environmental changes. Francisco Chavez, a biologist with the Monterey Bay Aquarium Research Institute (Moss Landing, California, United States), recently demonstrated that over a 25-year period, warmer and cooler Pacific waters tilt the distribution of anchoveta versus sardines, both open-ocean dwellers ( Chavez et al. 2003 ). Indeed, El Niño influenced the crash of the heavily fished Peruvian anchoveta industry in the late 1970s. These examples illustrate how susceptible fisheries are to environmental fluctuations. When the biomass of a population is reduced, it is much more sensitive to environmental change. We do not know how environmental fluctuations like these will affect the natural production of young fish, says Kleiber, expressing the concern that without a better understanding of climate, fisheries scientists end up trying to estimate moving targets. In the end, many scientists have their doubts about the influence of science on decision-making. “My personal view is that it's naïve to think that modifying and improving models will necessarily lead to improved natural resource management,” says Simon Jennings, a fisheries biologist with the United Kingdom's Centre for Environment, Fisheries and Aquaculture Science in Lowestoft. Indeed, the International Council for the Exploration of the Seas (Copenhagen, Denmark) recently recommended a total ban on North Sea and Irish Sea cod stocks, based on single-species assessment. Although the more intensive ecosystem-based models could not have produced a more stringent recommendation, politicians allowed harvests at roughly half of last year's catch. To Conserve or to Farm? While lowering fisheries' effort seems the most logical approach to the recovery of depleted fisheries, social and economic concerns often stymie political action. Yet demand for seafood continues. Therefore, scientists also are investigating both conservation and alternative production options. Given the social, economic, and political problems associated with that, managers have often used closures to help a hard-hit species recover. In many cases, however, the recovery time for exploited species is longer than once thought ( Hutchings 2000 ). “Based on the available information, it is not unusual for fish populations to show no or little recovery even after 15 years,” says Hutchings. “All else being equal, we predict the earlier the age of maturity, the faster the rate of recovery,” he adds. And that depends on environmental conditions as well. “In the case of Antarctic species, some overexploited populations remain at less than 5% pre-exploitation abundance after 30 years,” says Constable. One management strategy to recover species is to create marine protected areas (MPAs), zones that restrict all removal of marine life (Box 2) . A number of marine ecologists are staunch supporters of MPAs for both conservation and fishery's recovery. What looked like sustainability in the past were fisheries out of our reach—naturally protected areas—says Pauly, adding that our increasing ability to harvest fisheries necessitates the creation of MPAs now. In theory, these areas are refugia for fishes to reproduce, spilling over not only healthy adults but also potentially transporting thousands of viable young—seeding surrounding waters. To date, less than 1% of the ocean's area is protected, which hinders the ability to conclusively determine if spillover rates have the predicted impact on fishery's recovery. A review of 89 studies of MPAs by Ben Halpern, a student at the University of California, Santa Barbara (Santa Barbara, California, United States), demonstrated that the average number of fish inside a reserve increases between 60%– and 150% ( Halpern 2003 ). In addition, 59% of the sites had increased diversity. While the numbers inside the reserves look good, the crucial condition of larval spillover has yet to be proven. Most scientists involved in the debate agree that MPAs should be one component in an overall management scheme, but worry that until the crucial element of fishing effort is resolved, MPAs may just displace the vast industrial fleets. In terms of simply producing fish for global food needs, aquaculture (also known as fish farming) is another, increasingly popular, option. In 2001, the European Union produced 17% of total fishery's production via aquaculture. These numbers are projected to steadily increase, but some question whether aquaculture would be sufficient to supply what has been lost by overexploited fisheries. Concentrated in coastal areas, aquaculture has aroused numerous concerns. Indeed, in developed countries, most operations grow carnivorous fish, which necessitates growing fish to feed fish. While the process has become more efficient in recent years, due in part to a growing reliance on vegetarian diets, it still takes about 3 pounds (1.36 kg) of fish to create 2.2 pounds (1 kg) of desirable meat ( Aldhous 2004 ). Yet, the total catch of food fish continues to grow, as do concerns about nutrient runoff and estuary pollution resulting from aquaculture. Increasingly, coastal residents often complain about the aesthetics of such activities, and there is also new research that indicates that farm-raised fish harbor more cancer-causing pollutants than wild species ( Hites et al. 2004 ). To alleviate many of these concerns, open-ocean aquaculture is now being considered. Indeed, the NMFS is set to propose a Code of Conduct for Offshore Aquaculture, which would open up the 200-mile (322-km) United States Exclusive Economic Zone to net pens seaward of coastal state boundaries and authorities. The Sea Grant program in conjunction with interested business, is also currently assessing the carrying capacity of open-water pens as well as their potential environmental impact. Given increased industrial interest and unchanging demand for seafood, many think farming the sea may be around the corner. Undoubtedly, scientific effort will continue to inform both conservationists and industry about fisheries' capacity and potential recovery options. As attitudes towards fisheries continue to change, increased understanding of the ecological underpinnings should help strike a more informed balance between fisheries' conservation and production. “The big mistake is suggesting that you can manage fish stocks,” says Niels Daan, a biologist with the Netherlands Institute for Fisheries Research (IJmuiden, The Netherlands). “In my opinion, we can only manage human activity.” Box 1. Fisheries Management in Developing Countries While industrial-scale fishing is a growing concern to fisheries biologists, the management of subsistence fishing in developing countries is equally complex. Indonesia alone has 1.3 million fishers. Given the lack of alternative economic options for subsistence fishers, it is much more difficult to reduce fishing because it meets immediate food and resource needs. Local scientists, often lacking in resources, have a much more difficult time assessing the effects and offering advice to governmental fisheries regulators, who have limited political influence. Kenyan researcher Tim McClanahan notes that a main problem is a lack of coordination and respect between traditional and national programs of management. Therefore, he focuses on the fishing gear used. By reconciling the impact of certain fishing gear with traditional knowledge, McClanahan has developed a basis for suggested restrictions deemed acceptable to the local community. Box 2. The Establishment of High Profile MPAs While MPAs are heavily touted as one of the best management tools to address both conservation and fisheries management, few have been enacted. In 2001, following a strong mandate by the Australian Minister to the Environment and overwhelming political will, the Great Barrier Reef Marine Park Authority (GBRMPA) in Australia established a network of marine protected, or no-take, areas as an ecosystem-based management approach. In setting up the reserve networks, scientists determined the most effective areas to protect biodiversity with little impact to productivity. “We tried to avoid peak use areas, while protecting at least one-third of each bioregion and minimizing the impact to users of the Great Barrier Reef Park,” says Phil Cadwallader, Director of Fisheries at the GBRMPA. Off the coast of California, the Channel Islands network of marine reserves, established in April 2003, consists of 13 areas designed to protect biodiversity and critical habitat for breeding fish and to maintain biodiversity. The area has suffered serious declines of red snapper, angel sharks, and abalone, once plentiful off the California coast, over the past decade. Scientists designed the network to protect those productive habitats that would help ensure that larval dispersal was maintained between the individual reserves. Totaling 132 nautical square miles (342 nautical square kilometers), 11 of the areas are no-take reserves—allowing no fishing or harvest of any kind.

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517829

Open Access As Public Policy

Governments around the world have turned their attention to the issue of taxpayer access to primary research literature stemming from publicly funded investigations

The global debate over access to primary research literature heated up this summer, fueled by a slew of congressional and parliamentary recommendations, claims of political victory by critics and proponents of open access, and redoubled lobbying efforts on every side of the issue. After months of often dizzying rhetoric from virtually all camps, one concrete development has indisputably emerged from the fray: governments around the world have begun to take an interest in the question of who can and can't read the results of the scientific research they fund. “We are convinced,” concluded a recent report from the Science and Technology Committee of the United Kingdom's House of Commons, “that the amount of public money invested in scientific research and its outputs is sufficient to merit Government involvement in the publishing process” ( House of Commons Science and Technology Committee 2004 ). United States National Institutes of Health (NIH) Director Elias Zerhouni echoed the British assessment, asserting that “the public needs to have access to what they've paid for,” in a July 28 meeting of stakeholders in scientific and medical publishing. “The status quo,” he added, “just can't stand” ( Park 2004 ). While such pronouncements may sow fear in the hearts of some scientists and publishers, concerns that governments are poised to tell researchers where or how to publish seem largely unfounded. Both the UK report and rumblings from the US government suggest that any legislative dictates on access to scientific literature are likely to be structured to minimize potentially deleterious implications for established, subscription-based journals, for-profit and not-for-profit alike. Mandates for open access to articles summarizing the results of publicly funded research would not be mandates for scientists to submit work only to the handful of journals, like PLoS Biology and PLoS Medicine, that currently make their content immediately free online in centralized repositories. A US House of Representatives Committee on Appropriations, for example, recently passed language that would allow many, though not all, publishers six months between the date of publication of NIH-funded research articles and the date of their deposition in a free-to-use archive. (At the time of this writing, the bill is awaiting further discussion in the House and Senate.) In any case, it is a perfectly reasonable premise that governments should attach conditions to grants mandating public access to resulting peer-reviewed, published articles. Making funding for research contingent on the results of the work being disseminated as widely as possible is hardly a revolutionary proposition. All funders expect, of course, that scientists won't simply stash their findings in a desk drawer. Most, like NIH, include in their mission statements clauses about “fostering the communication of medical and health sciences information” ( NIH 2004 ). The US National Library of Medicine, a division of NIH, goes so far as to provide the infrastructure for hosting and storing the full texts of journal articles online, in the form of PubMed Central. Actually requiring that publicly funded works be included in publicly funded electronic archives like PubMed Central, as the US Congress might, would be less a paradigm shift or a radically interventionist mandate than a sensible extension of existing policy for most governments and their funding agencies. Increasingly, it seems, this is the view being adopted by policy makers—that it is the status quo, rather than prospective policy revision, that is anomalous or hard to justify. “We would be very surprised,” the Science and Technology Committee notes, “if Government did not itself feel the need to account for its investment [in research] in the publishing process. We… hope that this report will be a catalyst for change” ( House of Commons Science and Technology Committee 2004 ). As a matter of sheer principle, it strikes many people as odd that “anyone can download medical nonsense from the Web for free, but citizens must pay to see the results of carefully conducted biomedical research that was financed by their taxes,” as Rick Weiss noted on the front page of the Washington Post last year ( Weiss 2003 ). While neither the US nor the UK has yet to legislate a remedy for this prima facie paradoxical state of affairs, both appear ready to address the issue systematically, and—more significantly—with the input of a wide range of affected constituents: scientists, publishers, librarians, patient advocates, text-miners, entrepreneurs, and more. The Science and Technology Committee (2004) report was the product of a seven-month investigation, featuring some 127 submissions of written evidence and four days of oral testimony from the likes of Nature Publishing Group, Reed Elsevier, and indeed, the Public Library of Science. NIH has promised a period of public comment on its plan for implementing the Appropriations Committee's requirement before moving forward, in addition to the information-gathering meeting of publishers in July and subsequent meetings hosted by Dr. Zerhouni. All told, the current spate of government attention to the issue of public access to research results seems methodical, inclusive, and likely to prove productive for scientific communities and the public.

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548691

Construction of a radiation hybrid map of chicken chromosome 2 and alignment to the chicken draft sequence

Background The ChickRH6 whole chicken genome radiation hybrid (RH) panel recently produced has already been used to build radiation hybrid maps for several chromosomes, generating comparative maps with the human and mouse genomes and suggesting improvements to the chicken draft sequence assembly. Here we present the construction of a RH map of chicken chromosome 2. Markers from the genetic map were used for alignment to the existing GGA2 (Gallus gallus chromosome 2) linkage group and EST were used to provide valuable comparative mapping information. Finally, all markers from the RH map were localised on the chicken draft sequence assembly to check for eventual discordances. Results Eighty eight microsatellite markers, 10 genes and 219 EST were selected from the genetic map or on the basis of available comparative mapping information. Out of these 317 markers, 270 gave reliable amplifications on the radiation hybrid panel and 198 were effectively assigned to GGA2. The final RH map is 2794 cR 6000 long and is composed of 86 framework markers distributed in 5 groups. Conservation of synteny was found between GGA2 and eight human chromosomes, with segments of conserved gene order of varying lengths. Conclusion We obtained a radiation hybrid map of chicken chromosome 2. Comparison to the human genome indicated that most of the 8 groups of conserved synteny studied underwent internal rearrangements. The alignment of our RH map to the first draft of the chicken genome sequence assembly revealed a good agreement between both sets of data, indicative of a low error rate.

Background Chicken is a model organism in various fields of biology, such as embryology or immunology. It is also the only bird species for which the genome has been studied in detail and a lot is expected from its use in comparative genome analyses. This will help to detect sequences conserved between species, which should correspond to unknown exons and to regulatory or other functional regions. Such analyses will therefore be essential for the annotation of other genomes, including that of human [ 1 , 2 ]. Chicken is also actually the only major agricultural species for which a draft assembly of the genome sequence is available [ 3 , 4 ]. Thanks to a significantly lower rate of interspersed repetitive elements and to the use of a highly inbred bird for sequencing, this draft is probably more accurate than the first one published for human three years ago [ 5 , 6 ]. Nevertheless, previous comparisons of RH mapping data with the sequence data showed that some sequence segments are in wrong positions of the genome assembly [ 7 ]. Therefore the integration of all available chicken mapping resources will be essential for improving the quality of the assembly, building a more reliable and informative resource. In addition to the genetic and BAC contig maps that have already been used, the RH map will thus provide an independent source of data to assist the chicken genome sequence assembly process towards a finished quality sequence. RH panels are available for several domestic animals: cow [ 8 ], pig [ 9 ], horse [ 10 ], dog [ 11 ] and cat [ 12 ]. The successful production of a RH panel in chicken is quite recent [ 13 ] and therefore only a limited number of chicken chromosomes have been studied to date, namely: GGA4 [ 14 ], GGA5 [ 15 ], GGA7 [ 16 ], GGA14 [ 7 ], and GGA15 [ 17 ]. We present here the radiation hybrid map of chicken chromosome 2, built by using markers chosen from the GGA2 genetic map and a substantial number of additional markers developed from chicken EST data. Markers from the genetic map are essential to anchor the RH map onto GGA2. Markers developed from EST data were chosen on the basis of existing comparative mapping information data, indicating conservation of synteny with HSA1, HSA3, HSA6, HSA7, HSA8, HSA9, HSA10, HSA18 and HSA22. They were used primarily to saturate the map in markers and also to increase the precision of comparative maps. The GGA2 RH map obtained was aligned with the genomic sequence assembly to detect eventual discordances. Results and discussion Development of EST markers Eighty eight microsatellite markers and 10 genes were selected from available data in the literature or the databases. In addition to this, 219 markers were developed from EST data to saturate the map. At the time this study was initiated, comparative mapping data suggested conservation of synteny of GGA2 with 9 human chromosomes and therefore genes were selected from regions of HSA1 (9 markers), HSA3 (28 markers), HSA6 (22 markers), HSA7 (10 markers), HSA8 (84 markers), HSA9 (19 markers), HSA10 (7 markers), HSA18 (35 markers), HSA22 (5 markers). Primers were developed using the Iccare web server [ 18 ] with the constraints specific to RH mapping. One hundred and seventy eight out of 219 markers (81 %) amplified successfully. This high success in primer design using EST is comparable to what was achieved in other studies using the Iccare web server [ 16 , 19 , 20 ]. GGA2 RH map (figure 1 ) Figure 1 Radiation hybrid map of chicken chromosome 2 and comparison the draft sequence assembly . The GGA2 RH map (left) is 2794 cR long and is aligned to the genome sequence assembly (right). The limits between the 5 framework groups of the RH map are indicated by double slashes. Markers present only in the comprehensive map are indicated with their most likely position and the confidence interval, to the left of the map. The coloured framed boxes indicate the results from the BLAST analysis, for the markers that were not found on the chromosome 2 sequence assembly . Altogether, genotyping data were obtained for a total of 270 markers. Two-point analysis with CarthaGene using a LOD threshold of 4 enabled the construction of 5 GGA2 linkage groups containing a total of 198 markers, including all the microsatellite and gene markers from the genetic map. Out of the remaining markers, two ( LOC51059 and ALDH5A1 ) should have been integrated into the GGA2 RH map, given their location within the sequence assembly. One explanation, concerning the lack of these 2 markers, could be that the retention frequency, that is unusually low for these two markers and indicative of PCR problems, did not allow attaining a LOD score value significant for linkage. The other 70 markers map either to other chromosomes or to unknown regions. They correspond to the external boundaries of the regions of conserved synteny with human, from which EST were chosen for marker development. The final RH map of chicken chromosome 2 is 2794 cR 6000 long and comprises 86 markers. A one-to-one comparison with the genetic map shows a good overall agreement, with a few improvements over the genetic map; for example markers ADL114 and MCW310 are mapped with a higher precision than previously on the GGA2 linkage group. At the end of the chromosome (position 2794 cR), the 3 markers ( MCW157 , MCW189 , and MCW0073 / HSF1 ) were in the same order in the linkage group, but its orientation is different from the genetic map. These markers are mapped on the comprehensive RH map (less significant than the framework one), so the orientation given in the genetic map may be the right one. The corresponding comprehensive map comprises a total of 198 markers (figure 1 ). Comparative maps (figure 2 ) Figure 2 Comparison of gene orders between the GGA2 RH map and the homologous human regions. The GGA2 RH map (this study) is compared to the order of homologous genes in human (left) Each colour (plain boxes) corresponds to a human chromosome. Arrows on the right indicate the gaps between the 5 framework maps. As a result of our map construction strategy, based in great part on the development of EST markers, we found a few observations worthy of comment. Based on the information that the microsatellite MCW0189 , identified as being in the LIMK2 gene, mapped to the GGA2 genetic linkage group[ 21 ], 5 chicken EST markers were developed, corresponding to 8 Mb of HSA22q12.2 containing the homologous gene. Recently, Jennen et al indicated that the mapping of LIMK2 was erroneous and that it was located on the GGA15 RH map [ 17 ]. We confirm here this result, as all of our five markers mapped to the GGA15 RH linkage group (data not shown). The MCW0189 marker remains on the GGA2 RH map, at the position corresponding to where the former LIMK2 would have been expected. The result of the BLAST search of MCW0189 in the genome assembly indicated a homology with a sequence fragment of unknown location. No similarity with a LIMK sequence could be found in this fragment. A recently published paper describing the mapping of IL1B on GGA2 suggested a conservation of synteny with HSA2q [ 22 ]. The IL1B primers we tested failed to amplify correctly, but other chicken EST markers located near IL1B , whose position is 113.3 Mb on HSA2q, had been used in another mapping project in our laboratory [ 16 ]. Of these, ACTR , positioned at 111.9 Mb and BIN1 at 125.1 Mb on HSA2, were mapped to GGA7. If IL1B , located between these two markers in human, is indeed on GGA2, the fragment of conserved synteny must be very small. The sequence of IL1B in the chicken genome assembly is in a fraction of unknown location. Previous data suggested that the RYR2 gene, mapped on HSA1, was located on GGA2 [ 23 ]. We have therefore tested 9 chicken EST markers orthologous to HSA1 genes, but only one ( FH ) could be localized on the GGA2 RH map. As this localisation could be questioned, the identity of the fragment was confirmed after sequencing of the PCR product. As already observed for other chicken chromosomes [ 7 , 15 - 17 , 24 - 26 ], the GGA2 regions investigated here, corresponding to regions of human chromosomes 3, 6, 7, 8, 9, 10 and 18 showed a high number of intra-chromosomal rearrangements within the regions of conserved synteny. Alignment of the RH map to the genomic sequence (figure 1 ) A first draft chicken genome assembly was recently deposited into public databases by a team led by R. Wilson and W. Warren, from the Washington University School of Medicine in St. Louis (1st March, 2004, ). The sequence coverage is 6.6X. For GGA2, 147.590.765 bp were sequenced, 166 known genes were identified, and a total of 1432 genes were defined altogether when including results from prediction programs. For each gene or microsatellite marker from our GGA2 RH map, we compared the fragment sequence with the chicken genome sequence assembly by using the BLAST algorithm [ 27 ]. The position for each marker in the sequence is indicated in figure 1 . The agreement between the RH map and the sequence order is almost perfect, with only a few local inversions that can be put on the account of genotyping errors or other artefacts that may remain despite all precautions taken, such as the double genotyping process. In the upper part of the chromosome, several markers absent in the sequence assembly could be localized on the RH map: sequences corresponding to CENTG3 and CSPG5 genes are aligned with "Unknown" sequence (not assigned to known chromosomes) and ABR0336 is found on the GGA27 sequence. The RH two point LOD scores between CENTG3 , CSPG5 , ABR0336 and their neighbours on GGA2 ( MCW082 , ACVR2B or PAXIP1L ) are higher than 6. In addition, the genetic map confirms the localization of ABR0336 on chicken chromosome 2. In the same region of the RH map, MCW071 (a microsatellite in the EN2 gene) belongs to a genomic region with no sequence available (no blast hit); the genetic map confirms the RH position of this marker. These results suggest possible improvements to be made in the sequence assembly for this region of chromosome 2. On the sequence assembly, the TSG gene, at position 116.4 Mb, is close to EYA1 and STAU2 , whereas on the RH map TSG maps at 1660 cR, close to PTPN2 and LEI0147 , located at positions 96.4 and 97.3 Mb respectively. At the same position as TSG on the RH map, the VAPA gene corresponds to a sequence fragment of unknown location. Moreover, the RH map in this region is in agreement with comparative mapping, so we suspect there may be problems in the sequence assembly in the TSG region. Around position 2100 cR, two local inversions between the RH map and the sequence orders are observed. The first concerns PRKDC and MAPRE2 , and the second SDCBP and ADL114 . In both cases, EST markers mapped close to these genes are missing in the GGA2 sequence assembly: CEBPD near PRKDC and ASPH near SDCBP are in the fraction of unassigned sequence. However, in the case of the PRKDC - MAPRE2 region, the sequence assembly is in agreement with the gene order on human chromosome 8. The order of the markers in the RH map is supported in both cases by a difference of LOD greater than 8, when compared to the alternate order. In the lower part of the chromosome, several markers localized on the RH map are absent from the GGA2 sequence assembly: the HEY gene at position 2330 cR is localized on GGA26 in the assembly, whereas the two-point LOD scores with its neighbours on the GGA2 RH map are trust-worthy (11.5 with IMPA1 , 8.8 with PKIA ). The MATN2 and NCALD genes are in the unknown fraction of genomic sequence, whereas the 2-point LOD scores with their neighbours, around 2550 cR, are higher than 6. At the end of the chromosome (position 2690 cR), we observed an inversion between the RH map and the sequence concerning the NOV gene and the microsatellite LEI228 . The order in the RH map is supported by a difference of LOD greater than 4 when compared to the order in the sequence assembly. Altogether, out of 198 markers localized on GGA2 through RH mapping, only one, labelled "no hit" in figure 1 , could not be found by BLAST in the chicken genome sequence; 10 were assigned to existing genomic sequence of unknown location; 2 were assigned to a wrong chromosome and 4 were not localized at the same position when both maps were compared. With less than 10% of markers either missing in the sequence or for which the two maps disagree, these results confirm the high quality of the genome assembly, with only few finishing improvements to be made in the near future. The GGA2 RH map is available on the ChickRH web server also used for RH genotyping data collection. Conclusions We have built a high resolution radiation hybrid map of chicken chromosome 2 using the chickRH6 panel. Our goal was to provide jointly a source of potential polymorphic markers and of candidate genes for QTL mapping on this chromosome. In the course of our work, the first draft chicken genome assembly was released and we aligned it to our GGA2 RH map. Although the sequence assembly is globally in good agreement with our data, a limited number of discrepancies and the mapping of sequence fragments of unknown location or of markers not present in the genomic sequence, show that RH mapping it still useful. Future developments of the chicken RH map will now be based on the genomic sequence, using it for choosing STS markers in selected regions to develop RH framework maps and thereby detect eventual problems in the genomic sequence assembly. This is clearly needed in the regions for which the genetic map is still not complete, such as some microchromosomes, but also for parts of macrochromosomes, as shown in this study. Methods Radiation hybrids The generation of the 6000 rads chicken RH panel has already been described [ 13 ]. This panel, named ChickRH6, consists of a total of 90 hybrids. The mean retention frequency of markers is 21.9%. Gene selection and primers design Seventy-seven microsatellite markers well distributed along chicken chromosome 2 were selected from the published chicken genetic map. In addition, 11 other microsatellite markers and 10 genes were selected from other published data [ 28 ]. Primer information for these markers can be found in the ARKdb farm animal database and the ChickAce database . Primers for 219 EST were designed using the Iccare web server [ 18 ], . All publicly available chicken EST (> 420,000) were collected in a local database and compared to the human genome sequence. EST selection targeted towards GGA2 was based on available comparative mapping data with the human genome. Long introns, whose positions could be predicted in chicken on the basis of their position in the human genes, were avoided and primers were chosen in the most divergent regions of the chicken/human sequence alignments, to avoid cross-amplification of the hamster DNA present in the hybrids. Information on primers for EST markers is given in the additional table. PCR conditions PCR were carried out in 15 μl reactions containing 25 ng hybrid DNA, 2 mM MgCl 2 (Life technologies: Carlsblad, CA, USA), 0.3 U Taq DNA polymerase (Life technologies), 1X buffer (Life technologies), 200 μM of each dNTPs (Life technologies), 0.25 μM of each primer, and 1X loading buffer (350 mM sucrose and 0.2 mM cresol). After a 5 min denaturation step, 30 to 35 cycles (depending on the primers) of 30 sec at 94°C, 30 sec at annealing temperature, 30 sec at 72°C, were performed, followed by a final elongation step of 5 min at 72°C. Chicken DNA was used as positive control; hamster DNA and TE were used as negative controls. PCR amplification results were scored on 2% agarose gels. Each marker was genotyped twice. Map construction The CarthaGene program [ 29 ] was used to build the RH map for chromosome 2 . First, linkage groups were constituted by using a two-point LOD threshold of 4, after which a 1000:1 framework map (a map whose likelihood is at least 1000 fold higher than the next possible highest likelihood using the same markers in alternate orders) for each group was built under a haploid model. Then, the framework maps obtained were aligned on the genetic map and the orientation and distance between each group were estimated using the "printbestmap" option in the Carthagene program. Finally, after this final validation, all distances between markers were re-evaluated under a diploid model. Markers not included in the framework map, but displaying two point Lod scores higher than 4 with framework markers, were mapped relative to the framework map by using the CarthaGene "buildfw" option which calculates the most likely position of a marker and gives a confidence interval. The map was drawn using the MapChart software [ 30 ]. Maps comparison Data on the location of the chicken genes in the sequence assembly was obtained by BLASTN searches using the Ensembl browser. Data on human gene order were obtained from the Iccare web server. Authors' contributions SL, MD, SB, FV and KF carried out the molecular study. SL built the RH map, and drafted the manuscript with FP. MM made the ChickRH6 panel. AV coordinated the project and finalized the manuscript. Supplementary Material Additional File 1 Description of the markers . The GCTXXXX markers were developed using the Iccare web server. The EXTXXXX markers were chosen from published information. Click here for file

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546333

An HIV Protein Plays a Surprising Role in Gene Activation

Retroviruses are expert manipulators when it comes to co-opting their host's cellular resources. A great deal of human complexity stems from the vast repertoire of proteins and mechanisms dedicated to the business of regulating gene expression, and retroviruses like HIV have evolved myriad ways of redirecting that machinery to their own benefit. Humans and other eukaryotes have three types of RNA polymerases, each charged with transcribing different types of genetic elements. RNA polymerase II transcribes protein-coding genes. RNA polymerases join with so-called general transcription factors to form a pre-initiation complex (PIC) on the gene's promoter, where it binds to region rich in thymine (T) and adenine (A) named the TATA box. The first transcription factor to associate with the TATA box is called TFIID, a large protein complex containing a protein that binds the TATA box (aptly named the TATA-box-binding protein, or TBP) and several cofactors called TBP-associated factors (TAFs). PIC assembly sometimes also requires activator proteins, which can enhance transcriptional activity by supporting proper elongation of nascent transcripts. Tat, an activator encoded in the HIV genome, is required for HIV gene activation and viral replication. It affects these processes, the current model holds, by stimulating transcript elongation and increasing RNA polymerase's processing efficiency. In a new study, Tamal Raha, Grace Cheng, and Michael Green work with human cell lines and find evidence that Tat can also stimulate PIC assembly. While most transcription factors bind to DNA, Tat binds to an area at the end of newly emerging viral RNA called the transactivation response element (TAR). Once bound, Tat recruits a cellular complex called P-TEFb (consisting of two subunits) to the HIV promoter, and enhances RNA polymerase's transcribing capacity. Previous studies in yeast had shown that activators appear to stimulate transcription complex assembly, leading the authors to ask whether Tat could play a similar role. To study this question in living human cells, Green and colleagues turned to chromatin immunoprecipitation, a technique that detects proteins bound (directly or indirectly) to DNA. Working with three well-known effectors of transcription—an activator (Gal4-VP16), a transcriptional enhancer, and another viral activator called E1a—the authors show that what's true for yeast also holds for mammals, or at least for the human cell lines investigated here. Each effector was required for PIC assembly, which was in turn required to activate transcription. An unexpected mechanism of HIV-1 Tat action The big surprise came in the next round of experiments, which explored Tat's influence on transcription and PIC assembly on the HIV promoter. As expected, transcription factors were “virtually undetectable” at the core promoter in the absence of Tat. Adding Tat recruited all the usual transcription factors to the promoter and increased transcription. But none of the TAFs that normally associate with TFIID were found. When the authors used the activator Gal4-VP16 to initiate HIV transcription, every one of the 11 TAFs studied appeared. None of them did so in the presence of Tat, suggesting that Tat-mediated HIV transcription doesn't rely on TAFs. Green and colleagues confirmed this hypothesis in experiments showing that Tat-driven transcription proceeded as usual in cells lacking TAFs. And they demonstrated that it is Tat—along with its cofactor P-TEFb, which is normally bound to RNA through Tat—that recruits the TAF-deficient TBP. Altogether, these results show a surprising new role for Tat in stimulating assembly of a transcription complex. What's more, the complex lacks the TAFs typically linked to TBP in mammalian cells. Because their experiments analyzed only transcription complex assembly, the authors are careful to note that Tat may well stimulate assembly in addition to promoting transcription elongation. And it may be this resourcefulness that makes Tat such a potent activator—and HIV so hard to control. (For more on Tat's role in HIV transcription, see “Novel Enzyme Shows Potential as an Anti-HIV Target” [DOI: 10.1371/journal.pbio.0030074 ] and “A New Paradigm in Eukaryotic Biology: HIV Tat and the Control of Transcriptional Elongation” [DOI: 10.1371/journal.pbio.0030076 ].)

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548134

The impact of diabetes mellitus and other chronic medical conditions on health-related Quality of Life: Is the whole greater than the sum of its parts?

Background Diabetes mellitus (DM) is an important public health concern, the impact of which is increased by the high prevalence of co-existing chronic medical conditions among subjects with DM. The aims of this study were therefore to (1) evaluate the impact of DM and co-existing chronic medical conditions on health-related quality of life (HRQoL) (which could be additive, synergistic or subtractive); (2) to determine the extent to which the SF-6D (a single-index preference measure) captures the multidimensional information provided by the SF-36 (a profile measure). Methods Using data from a cross-sectional, population-based survey of Chinese, Malay and Indians in Singapore, we developed 9 separate multiple linear regression models, with each SF-36 scale or SF-6D index score being the dependent variable for one model. The influence of DM and a second chronic medical condition (hypertension (HTN), heart disease (HD), musculoskeletal illnesses (MS)) and their interactions were studied after adjusting for the influence of potential confounding variables. Results Among 5,224 subjects, the prevalence of DM, HTN, HD and MS were 5.9%, 10.7%, 2.4% and 26.6% respectively. DM lowered SF-36 scores by more than 2 points on 3 SF-36 scales and lowered SF-6D scores by 0.03 points. Subjects with DM and HTN, DM and HD or DM and MS experienced further lowering of SF-36 scores exceeding 2 points on at least 6 scales and further lowering of SF-6D scores by 0.05, 0.08 and 0.10 points respectively. Generally, DM and co-existing medical conditions exerted additive effects on HRQoL, with the exception of DM and heart disease, where a subtractive effect was noted. SF-6D index scores generally reflected the patterns of influence of DM and chronic medical conditions on SF-36 scores. Conclusion DM and chronic medical conditions generally reduced HRQoL in this multiethnic general population in an additive, rather than synergistic or subtractive fashion. In this study, the SF-6D was a reasonably good summary measure for the SF-36.

Background Diabetes mellitus, the prevalence of which is reaching epidemic proportions in many parts of the world, is an increasingly important public health concern. In the United States, diabetes is present in 8% of the adult population, and is associated with a two-fold increase in age-adjusted mortality [ 1 , 2 ]. In addition, there is a high prevalence of chronic medical conditions among subjects with diabetes [ 3 - 5 ]. For example, in the United States, the prevalence of cardiovascular diseases, stroke and depression in subjects with diabetes was at least twice as high as in subjects without diabetes [ 6 - 9 ]. Diabetes has detrimental effects on a range of health outcomes including health-related quality of life (HRQoL) [ 10 - 12 ]. For example, in the Medical Outcomes Study, diabetes was found to impair all dimensions of health except mental health and pain [ 13 ]. In a more recent multinational study, diabetes was found to have a notable impact on general health, measured using the Medical Outcomes Short-Form 36 (SF-36) [ 14 ]. The magnitude of impact of diabetes on HRQoL was reported to be equivalent to that of having cardiovascular conditions, cancer and chronic respiratory disease [ 15 ]. Subjects with diabetes and multiple co-existing chronic medical conditions have poorer HRQoL than those without these conditions [ 16 , 17 ]. For example, subjects with diabetes and co-existing cardiovascular diseases reported significantly lower scores on RAND-36 social functioning, vitality and health-change scales [ 16 ]. In another study, subjects with diabetes and co-existing coronary artery disease, peripheral sensory neuropathy and peripheral vascular diseases reported significantly lower scores on several SF-36 scales [ 17 ]. In combination, the influence of multiple chronic medical conditions on HRQoL may exhibit an additive, synergistic or possibly subtractive relationship. Assuming that each chronic medical condition results in the lowering of HRQoL, in an additive relationship, the combined effect of two or more chronic medical conditions on HRQoL approximates the sum of the independent effect of each of these conditions, while in a synergistic relationship, the combined effect is greater than the sum of the independent effect of each of these conditions and in a subtractive relationship the combined effect is smaller than the sum of the independent effect of each of these conditions (Figure 1 ). For example, the SF-36 developers [ 18 ] reported an additive relationship between hypertension and other co-existing chronic medical conditions on HRQoL, while Gaynes et al. [ 19 ] reported synergistic relationships between depression and co-existing arthritis on physical functioning and between depression and co-existing diabetes on role functioning. Several mechanisms could account for these observed synergistic effects. For example, treatment for one medical condition might also adversely affect another pre-existing medical condition, leading to a greater lowering of HRQoL than would occur due to the pre-existing condition alone [ 20 ]. Additionally, a medical condition itself (e.g. depression) might adversely affect patient behavior and thus negatively affect treatment outcomes [ 21 ]. Although there have not been any reports in the literature, it is theoretically possible that subtractive relationships exist. For example, it was reported that patients undergo changes in their conception of poor levels of functioning, personal values (e.g. changes in life priority) and/or meaning of life in response to their chronic medical conditions, a concept known as response shift [ 22 ]. These changes in self-assessment and values may help to cushion the impact of the second medical condition, resulting in a smaller decrement in HRQoL than might otherwise be expected. Figure 1 An overview of the possible relationships among multiple chronic medical conditions on HRQoL. *These relationships (subtractive or synergistic) are discussed in relation to additive relationships. † If net effect is zero, then discussion of subtractive and synergistic relationships do not apply. Two published studies have evaluated the relationship between diabetes and other chronic medical conditions on HRQoL in the general population [ 23 , 24 ]. In the first study [ 23 ], diabetes and stroke were found to increase the risks of disability (measured with the Activity of Daily Living and Instrumental Activity of Daily Living scales) among older Mexican Americans in an additive fashion. In the second study, the impact of co-existing obesity, hypertension or diabetes and heart disease on HRQoL among 17,195 U.S. middle- and older-age adults was evaluated [ 24 ]. A strong synergistic relationship between heart disease and diabetes on the odds of mobility difficulty (1.8 to 4.0 times), activity of daily living limitations (2.2 to 4.0 times) and poor perceived health (2.1 to 6.8 times) was found, after adjusting for gender, ethnicity, insurance status, history of cancer, and lung diseases [ 24 ]. However, there were two important limitations in both studies. First, these studies focused on the physical domains of HRQoL, without assessing the mental and social domains of HRQoL. Second, both studies were conducted among middle-aged and/or elderly adults. Thus, the burden of diabetes and other chronic medical conditions on HRQoL in the rest of the general population with diabetes are not known. Further studies to elucidate the relationship between diabetes and other chronic medical conditions on physical, mental and social domains of HRQoL in the general population are clearly needed. The primary purpose of this study was therefore to evaluate the impact of diabetes and co-existing chronic medical conditions on the physical, mental and social domains of HRQoL in the general population. We also sought to determine if the impact of diabetes and co-existing chronic medical conditions on HRQoL would be additive, synergistic or subtractive. For these purposes, we measured HRQoL using both the SF-36 and the SF-6D. The SF-36 is a comprehensive, generic HRQoL measure that has been extensively validated and allows comparison of HRQoL in subjects with various chronic medical conditions. The SF-6D is a single index utility score derived from the SF-36 which measures health preference, and which is therefore suitable for pharmacoeconomic analyses to assist healthcare resource allocation. We sought to determine the extent to which the SF-6D would reflect the multidimensional information provided by the SF-36. Methods Study design We analyzed data from a cross-sectional, population-based disproportionately stratified sample of ethnic Chinese, Malays and Indians listed in the 1996 electoral register for the Bishan-Toa Payoh district (representative of the general population) in Singapore from April 1998 to January 1999, details of which have been reported previously [ 25 ] and are summarized here. Participants completed either the UK English or Chinese (Hong Kong) SF-36 and the Family Functioning Measure. Eligibility criteria were: age 21 to 65 years on 1 st January 1998 and ability to read a newspaper in English or Chinese; Chinese, Malay or Indian ethnicity as reflected in the National registration identification card, which reflects parental ethnicity; and status as free-living adults in the community. Persons older than 65 years of age were excluded because of low literacy rates in this age group in Singapore [ 26 ]. Fieldworkers visited eligible subjects at their house within 7 days after an introductory letter was sent, invited them to self-administer the SF-36, checked returned questionnaires for completeness and obtained information on ethnicity, socioeconomic status and chronic medical conditions (including diabetes, hypertension, heart disease, lung diseases, musculoskeletal illnesses and mental illnesses) and other potential determinants of HRQoL through a structured interview. Participants were asked to indicate either "yes" or "no" on a list of chronic medical conditions including diabetes, high blood pressure, heart disease, etc. Instruments The Short-Form 36 Health Survey (SF-36) The SF-36 measures perceived health in the areas of physical functioning (PF), role-physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role-emotional (RE), and mental health (MH), with higher scores (range 0–100) reflecting better perceived health. The UK English [ 27 ] and Chinese (Hong Kong) [ 28 ] SF-36 with 4-week recall and metric units of measurement were used in this study (previously validated for use in Singapore) [ 25 ]. The SF-6D The SF-6D is a six-dimensional health classification system assessing physical functioning (PF), role limitations (RL), social functioning (SF), pain (PN), mental health (MH), and vitality (VT), with 4 to 6 levels per dimension [ 29 , 30 ]. A SF-6D "health state" is defined by selecting one level from each dimension. For example, perfect health on the SF-6D would be represented by the health state 111111. A total of 18,000 health states are thus defined. All responders to the original SF-36 questionnaire can be assigned an SF-6D score provided the items used to construct the SF-6D are completed [ 29 , 30 ]. The SF-6D preference-based measure can be regarded as a continuous variable scored on a 0.29 to 1.00 scale, with 0.29 corresponding to the worst health state (i.e. all dimension being at the worst level) and 1.00 corresponding to "full health" (i.e. all dimensions being at full functional level). Family Functioning Measure The Family Functioning Measure is a 3-item Likert scale assessing the quality of interactions among family members [ 31 ], with higher scores (range 0–100) reflecting better family functioning, and has been validated for use in Singapore [ 32 ]. Statistical analyses Data from participants completing English and Chinese SF-36 versions were pooled to increase the power and representativeness of our study. This approach was supported by previous work demonstrating equivalence of these SF-36 [ 33 ] and SF-6D [ 34 ] versions in this same study sample. Data were entered into an Excel spreadsheet (Microsoft Corporation, Redmond, Washington) and analyzed using SPSS (SPSS Inc., Chicago, Illinois) software. SF-6D index scores were calculated using a utility function derived from the United Kingdom general population [ 29 ], which is currently the only published SF-6D scoring algorithm. Participants with missing scale scores were excluded listwise from analysis. To study the influence of diabetes and other chronic medical conditions on HRQoL, we developed 9 multiple linear regression models, with each SF-36 scale and the SF-6D index score being the dependent variable for a separate model. Each model was built in 3 stages. The first stage assessed the effect of diabetes on HRQoL while adjusting for the influence of sociodemographic factors. The second stage assessed the independent effects of diabetes and a second chronic medical condition on HRQoL while adjusting for the influence of sociodemographic factors. The third stage additionally assessed any interactions between diabetes and this second chronic medical condition on HRQoL. Thus, each model studied the effect of diabetes and one other chronic medical condition. The absence of a significant interaction term would suggest that the influence of two chronic medical conditions on HRQoL scores was additive. The presence of a significant interaction term would suggest that a synergistic or subtractive relationship existed, the former if the coefficient for the interaction term was negative, the latter if the coefficient was positive (since changes in HRQoL scores in the presence of chronic medical conditions were expected to be negative). Ethnicity was coded using dummy variables. Educational level was used as proxy for socioeconomic status. In anticipation that if the number of subjects with a given chronic medical condition was less than 50, the numbers of subjects with this medical condition and diabetes would be very small and not amenable to meaningful interpretation, two chronic medical conditions, stroke and cancer, were thus excluded from analysis because of small numbers of subjects reporting these conditions (n<50). Two other chronic medical conditions were subsequently excluded from analysis because of small numbers of subjects with diabetes and these conditions (diabetes and lung diseases: n = 27 and diabetes and mental illnesses: n = 9). Results Characteristics of study subjects Complete data were available from 5,224 of 5,420 subjects in the study, with 196 (3.6%) subjects excluded from analysis (110 because of missing demographic information and 86 because of missing responses to the SF-36). Table 1 shows subjects' characteristics and distribution of SF-36 scales and SF-6D index scores. Approximately 6% of subjects suffered from diabetes and two-fifths of subjects reported at least 1 chronic medical condition. Prevalence of co-existing conditions in subjects with diabetes ranged from 2.9% (mental illnesses) to 37.2% (hypertension). Table 1 Characteristics of subjects and distribution of SF-36 scales and SF-6D index scores. Subject characteristics (N = 5,224) N (%) unless specified otherwise Mean (SD) age in years 40.3 (11.52) Female gender 2,555 (48.9) Ethnicity Chinese 2,558 (49.0) Malay 1,189 (22.8) Indian 1,477 (28.3) Housing type Lower cost public housing 388 (7.4) Public housing 4,647 (89.0) Private housing 188 (3.6) Years of education ≤ 6 1,266 (24.2) 7–10 2,682 (51.3) >10 1,276 (24.4) Marital status Married 3,686 (70.6) Single 1,272 (24.3) Divorced/Separated 136 (2.6) Widow 130 (2.5) Working 4,022 (75.0) Smoking 1,045 (20.0) Presence of acute medical conditions* 3,493 (66.9) Presence of chronic medical conditions Diabetes mellitus 309 (5.9) Hypertension 557 (10.7) Heart disease 125 (2.4) Stroke 32 (0.6) Lung diseases ‡ 278 (5.3) Cancer 35 (0.7) Musculoskeletal illnesses ‡ 1,389 (26.6) Mental illness ‡ 136 (2.6) Prevalence of co-existing chronic medical conditions in people with diabetes mellitus Hypertension 115 (37.2) Heart disease 46 (14.9) Lung diseases ‡ 27 (8.7) Musculoskeletal illnesses ‡ 107 (34.6) Mental illnesses ‡ 9 (2.9) Mean (SD) Family Functioning Measure scores 10.3 (2.75) Mean (SD) SF-36 scores † Physical functioning (PF) 80.3 (23.19) Role-physical (RP) 80.5 (32.94) Bodily pain (BP) 78.3 (21.73) General health (GH) 68.6 (17.21) Vitality (VT) 63.8 (16.98) Social functioning (SF) 80.6 (20.48) Role-emotional (RE) 79.2 (34.98) Mental health (MH) 72.4 (16.70) Mean (SD) SF-6D index scores † 0.77 (0.130) * Acute medical conditions were self-limiting illnesses and injuries in the preceding one month that might influence SF-36 scores and included acute illnesses (e.g. upper respiratory tract infections, gastroenteritis and headaches), injuries or poor sleep. † Mean scores adjusted for the influence of diabetes mellitus, other chronic medical conditions (and their interactions), socioeconomic status and other factors. ‡ Lung diseases included asthma and other lung diseases; musculoskeletal illnesses included rheumatism, back pain and other bone or muscle illness; mental illness included depression, anxiety disorder and schizophrenia. The influence of chronic medical conditions on SF-36 scores The influence of chronic medical conditions on individual SF-36 scales is presented in Table 2 . Before adjusting for selected sociodemographic variables known to influence HRQoL (i.e. age, gender, ethnicity and years of education), subjects with self-reported chronic medical conditions (diabetes, hypertension, heart disease and musculoskeletal illnesses) generally reported lower scores for most SF-36 scales when compared to subjects without these conditions, indicating poorer HRQoL. After adjusting for the influence of these sociodemographic variables, chronic medical conditions continued to influence HRQoL, generally to a lesser degree than before adjustment. The influences of heart disease and musculoskeletal illnesses on SF-36 scores were of a similar magnitude and were larger than the influence of diabetes or hypertension. Table 2 Influence of diabetes mellitus and other chronic medical conditions on SF-36 scales. Unadjusted differences in mean scores PF RP BP GH VT SF RE MH SF-6D No chronic medical condition † (n = 3155) 82.0 (22.61) 81.9 (32.16) 80.4 (21.20) 70.0 (16.62) 64.8 (16.43) 81.5 (20.12) 77.5 (36.00) 73.1 (16.35) 0.79 (0.123) Diabetes mellitus only (n = 309) -7.2 -7.3 -5.8 -3.5 -1.3 -1.8 0.7 0.2 -0.06 Hypertension only (n = 557) -4.3 -4.1 -3.4 -3.3 -0.5 0.5 1.0 0.3 -0.05 Heart disease only (n = 125) -9.5 -10.1 -7.3 -5.2 -3.6 -0.8 -4.7 -0.3 -0.09 Musculoskeletal illnesses only ‡ (n = 1389) -4.7 -3.6 -6.9 -4.4 -3.1 -2.7 -0.8 -2.5 -0.08 Adjusted differences in mean scores due to medical condition § PF RP BP GH VT SF RE MH SF-6D No chronic medical condition † (n = 3155) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00 Diabetes mellitus only (n = 309) -2.9 * -3.7 -1.8 -2.36 * -1.3 -0.1 0.3 -0.2 -0.03 ** Hypertension only (n = 557) -2.6 * -3.1 * -1.5 -1.6 0.1 0.6 -1.0 0.1 -0.03 *** Heart disease only (n = 125) -5.0 * -6.5 * -3.5 -4.0 * -3.9 * 0.9 -5.9 -1.0 -0.06 *** Musculoskeletal illnesses only ‡ (n = 1389) -3.8 *** -3.1 ** -6.4 *** -3.9 *** -2.9 *** -3.1 *** -3.4 ** -2.6 *** -0.08 *** † Mean (SD) scores ‡ Musculoskeletal illnesses included rheumatism, back pain and other bone or muscle illnesses. § Multiple linear regression, adjusted for influence of ethnicity, age, gender and years of education. *p < 0.05, **p < 0.01, ***p < 0.001 Abbreviations: PF = Physical Functioning, RP = Role-Physical, BP = Bodily Pain, GH = General Health, VT = Vitality, SF = Social Functioning, RE = Role -Emotional, MH = Mental Health The influence of chronic medical conditions on SF-6D scores The influence of chronic medical conditions on SF-6D scores is also presented in Table 2 . Subjects with chronic medical conditions similarly reported lower unadjusted mean SF-6D scores. After adjusting for known determinants of HRQoL, lowering of SF-6D scores persisted for all chronic medical conditions, though again to a smaller magnitude. The influences of heart disease and musculoskeletal illnesses on SF-6D scores were of a similar magnitude and were again larger than the influence of diabetes or hypertension. The influence of diabetes mellitus and co-existing chronic medical conditions on SF-36 scores Characteristics of subjects with diabetes, with and without other co-existing chronic medical conditions, are shown in Table 3 . Subjects with diabetes only (i.e. no co-existing chronic medical conditions) were generally younger and more likely to be male. There were more Indians with diabetes and co-existing heart disease or musculoskeletal illnesses and more Chinese with diabetes and co-existing hypertension. The distribution of years of education completed was fairly similar in the various ethnic groups. Table 3 Characteristics of subjects with diabetes, with and without other chronic medical conditions. Co-existing chronic medical conditions among subjects with diabetes Characteristics No co-existing chronic medical conditions (n = 115) Hypertension (n = 115) Heart disease (n = 46) Musculoskeletal illnesses † (n = 107) Mean age (years) 48.1 (10.52) 53.7 (8.46) 55.5 (7.95) 53.2 (8.68) Male (%) 64.3 56.5 71.7 54.2 Ethnicity (%) Chinese 23.5 40.0 21.7 36.4 Malays 27.8 22.6 21.7 17.8 Indians 48.7 37.4 56.5 45.8 Questionnaire language (English) (%) 86.1 73.9 91.3 76.6 Education (%) ≤ 6 years 36.5 44.3 30.4 41.1 7–10 years 50.4 42.6 56.5 48.6 > 10 years 13.0 13.0 13.0 10.3 The impact of co-existing chronic medical conditions on adjusted SF-36 scores of subjects with diabetes is presented in Table 4 . In general, after adjusting for known determinants of HRQoL, the presence of concurrent hypertension, heart disease and musculoskeletal illnesses further reduced SF-36 scores in subjects with diabetes. For example, subjects with diabetes and co-existing hypertension or musculoskeletal illnesses experienced further lowering of physical functioning scores by 2.3 and 3.7 points, respectively. The influence of two chronic medical conditions on SF-36 scores was generally additive, in that statistically significant interaction terms were not present for most comparisons. There were some exceptions in which subtractive effects were observed, mainly clustered within subjects with diabetes and co-existing heart disease (physical functioning, role-physical, social functioning and role-emotional scales) or musculoskeletal illnesses (bodily pain and social functioning scales). Table 4 Multiple linear regression models of the influence of diabetes mellitus and a second chronic medical condition (i.e. hypertension, heart disease or musculoskeletal illnesses) adjusted for ethnicity, age, gender and years of education on SF-36 and SF-6D scores. Co-existing chronic medical conditions among subjects with diabetes No co-existing chronic medical conditions (n = 115) Hypertension (n = 115) Heart disease (n = 46) Musculoskeletal illnesses † (n = 107) Physical Functioning Differences in scale scores due to Diabetes mellitus -2.9* -2.4 -3.5* -2.8* 2nd chronic medical condition‡ na -2.3* -7.9** -3.7*** Interaction termξ na ns 12.46** ns Role-Physical Differences in scale scores due to Diabetes mellitus -3.7 -3.1 -5.2* -3.6 2nd chronic medical condition‡ na -2.8 -12.5** -3.1** Interaction termξ na ns 20.1** ns Bodily Pain Differences in scale scores due to Diabetes mellitus -1.8 -1.6 -1.5 -3.8* 2nd chronic medical condition‡ na -1.3 -3.1 -6.9*** Interaction termξ na ns ns 7.0** General Health Differences in scale scores due to Diabetes mellitus -2.3* -2.0 -1.9 -2.2* 2nd chronic medical condition na -1.3 -3.5* -3.9*** Interaction termξ na ns ns ns Vitality Differences in scale scores due to Diabetes mellitus -1.3 -1.4 -0.9 -1.3 2nd chronic medical condition‡ na 0.3 -3.7* -2.9*** Interaction termξ na ns ns ns Social Functioning Differences in scale scores due to Diabetes mellitus -0.1 -0.2 -1.3 -1.9 2nd chronic medical condition ‡ na 0.6 -2.5 -3.4*** Interaction term ξ na ns 10.2* 5.4* Role-Emotional Differences in scale scores due to Diabetes mellitus 0.3 -2.8 -0.9 0.4 2nd chronic medical condition ‡ na -2.7 -11.8** -3.4** Interaction term ξ na 9.9* 16.8* ns Mental Health Differences in scale scores due to Diabetes mellitus -0.2 -1.7 -0.1 -0.1 2nd chronic medical condition ‡ na -0.5 -0.9 -2.6*** Interaction term ξ na 5.1* ns ns SF-6D Differences in scale scores due to Diabetes mellitus -0.03** -0.02* -0.02* -0.02** 2nd chronic medical condition ‡ na -0.03*** -0.06*** -0.08*** Interaction term ξ na ns ns ns † Musculoskeletal illnesses included rheumatism, back pain and other bone or muscle illnesses. ‡ Second chronic medical condition refers to the chronic medical condition other than diabetes mellitus listed in the relevant column (e.g. hypertension, heart disease or musculoskeletal illnesses). ξ Interaction term for diabetes mellitus and second chronic medical condition. *p < 0.05, **p < 0.01, ***p < 0.001, ns denotes statistically insignificant. The influence of diabetes mellitus and co-existing chronic medical conditions on SF-6D scores The influence of diabetes and co-existing chronic medical conditions on SF-6D scores is presented in Table 4 . Subjects with diabetes and other co-existing chronic medical conditions reported lower unadjusted SF-6D scores than subjects with diabetes only. After adjustment for known determinants of HRQoL, the influence of co-existing chronic medical conditions on SF-6D scores persisted. As before, subjects with diabetes and co-existing heart disease or musculoskeletal illnesses reported the greatest impairment in HRQoL. Diabetes and other co-existing chronic medical conditions, including heart disease, reduced SF-6D scores in an additive fashion. Discussion In this multiethnic, population-based study, we found that subjects with diabetes experienced lowering of HRQoL as compared to subjects without diabetes. The presence of other chronic medical conditions in subjects with diabetes led to further lowering of HRQoL, the effect of which was generally additive. Our findings further underscore the importance of preventing and treating complications of diabetes to prevent further deterioration in HRQoL among subjects with diabetes, and also highlight the need to identify factors that may be modulated to improve HRQoL in these subjects. Our findings are important for several reasons. First, this is the first study showing that the combination of diabetes and a second chronic medical condition may adversely affect the mental domains of HRQoL as measured by the vitality, social functioning and mental health scales of the SF-36 (previous studies having focused on the physical domains of HRQoL) [ 23 , 24 ]. Second, it is reassuring that the effect of diabetes and a second chronic medical condition on HRQoL was in general additive rather than synergistic. Third, given that subjects were drawn from the general population, it is likely that our findings can be readily generalized to the population at large, especially given that this study was conducted in a multiethnic Asian population with one of the highest diabetes prevalence rates in the world [ 35 ]. We found that the effect of diabetes on HRQoL was generally mild, with greater impact on the SF-36 scales measuring physical (physical functioning, role-physical, bodily pain, general health, role-emotional (in this study sample)) relative to mental health components (vitality, social functioning and mental health). This was not surprising, given that our subjects were recruited from the general population and were therefore likely to have less severe illness than subjects in hospital or clinic-based studies. Further, other studies have shown that impact of diabetes on HRQoL is intermediate, relative to other chronic medical conditions [ 14 ]. For example, Egede [ 9 ] reported that the risk of functional disability was lower in subjects with diabetes than subjects with major depression (odds ratio (OR): 2.42 vs 3.00), while Otiniano et al. [ 23 ] found that the risk of disabilities in ADL were lower in subjects with diabetes than those with strokes (OR: 2.80 vs 5.55). We also found that with the exception of subjects with diabetes and heart disease, the presence of co-existing chronic medical conditions in subjects with diabetes generally resulted in further significant lowering of HRQoL. Our results are important because they demonstrate that the impact of these co-existing chronic medical conditions in diabetes is not only in increasing healthcare costs [ 36 , 37 ] and mortality [ 38 ] but also in increasing the physical and psychosocial burden of diabetes. Given that complications of diabetes constitute the majority of chronic medical conditions commonly present in subjects with diabetes, our findings further underscore the importance of preventing and treating complications of diabetes, and also highlight the need to identify factors that may be modulated to improve HRQoL in these subjects. In our study, the relationships between diabetes and other chronic medical conditions on HRQoL were largely additive. This is in contrast with the study by Gaynes et al. [ 19 ], where synergistic effects between depression and diabetes on HRQoL were observed. Instead, we found in general an additive effect with several comparisons showing a subtractive effect on HRQoL (especially in subjects with diabetes and co-existing heart disease). There are several possible explanations for the presence of subtractive effects. One possible explanation is the presence of response shift related to the specific diseases involved. In this study, the subtractive effects were clustered among subjects with diabetes and co-existing heart disease. Diabetes (in particular Type 2 diabetes) is a well-recognized cardiovascular risk factor with subjects with diabetes commonly developing heart disease in the 5 th or 6 th decade of life [ 40 ], often many years after the diagnosis of diabetes [ 41 ] (although heart disease may on occasion be present at or before diagnosis) [ 42 , 43 ]. In contrast, hypertension typically precedes or occurs soon after diagnosis in subjects with Type 2 diabetes, with up to 50% of subjects with diabetes presenting with hypertension at the time of diagnosis [ 44 ]. Hence, adaptation to the underlying diabetes may have led to response shift occurring in subjects with diabetes and co-existing heart disease but not with hypertension. A second plausible explanation for this observation is a healthy-responder effect. This would occur if subjects with diabetes and co-existing heart disease were more likely to have passed away or were too sick to participate in the study. Thus the subset of subjects with diabetes and co-existing heart disease who did participate in this study would reflect the healthier end of the spectrum, leading to the observed effect of higher scores for respondents with the two co-existing chronic medical conditions. A third possible explanation is that some conditions are so similar in their effects on HRQoL that having more than one such condition is not particularly problematic to the individual. For example, in our study, subjects with diabetes alone and subjects with heart disease alone experienced lower scores on the physical functioning scale of the SF-36; if these two conditions exerted a similar effect on HRQoL, then a subtractive effect would be expected, as was indeed observed among subjects with diabetes and co-existing heart disease. A secondary objective of this study was to understand the extent to which the single index SF-6D captured information from the multidimensional SF-36. We found that in general, the impact of co-existing chronic medical conditions on the SF-36 was well-reflected in the SF-6D. However, the subtractive effects of diabetes and co-existing heart disease on SF-36 scores were not reflected in SF-6D scores. This suggests that there is some inevitable loss of information associated with a reduction from a multi-dimensional scale to a unidimensional scale. Hence, more studies are needed to evaluate the adequacy of the SF-6D as a summary measure of the SF-36. We recognize several limitations of this study. First, identification of chronic medical conditions was based on self-report which may not be as accurate as physician diagnoses. However, reliability of self-report has been found to be acceptable for conditions that required medical or laboratory diagnostic procedures, including diabetes [ 45 - 47 ]. Second, although we captured information on subjects with diabetes and co-existing mental illnesses or lung diseases, we had to exclude these because of the small number of subjects. Finally, in this study we did not differentiate between subjects with Type 1 and Type 2 diabetes. However, given that more than 90% of subjects with diabetes are Type 2, this is not likely to affect our findings. Conclusions In conclusion, in this large, multiethnic, population-based study, we found that subjects with diabetes experienced lowering of HRQoL as compared to subjects without diabetes. The co-existence of other chronic medical conditions in subjects with diabetes led to further lowering of HRQoL, the effect of which was generally additive. Finally, we found that the SF-6D is a reasonably good summary measure of the SF-36 although more studies are needed to confirm this observation. List of abbreviations ADL – Activities of Daily Living, DM – Diabetes mellitus, HD – Heart disease, HRQoL – Health-related quality of life, HTN – Hypertension, MS – Musculoskeletal illnesses, OR – Odds ratio, SF-36 – Medical Outcomes Short-Form 36. Authors' contributions JT conceived of the study, and participated in its design and coordination. HL Wee and YB Cheung participated in the design of the study and performed the statistical analysis. SC Li and KY Fong participated in the design of the study and its coordination. All authors read and approved the final manuscript.

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The apolipoprotein E polymorphism and the cholesterol-raising effect of coffee

Background The response of serum cholesterol to diet may be affected by the apolipoprotein E ( APOE ) ε2/ε3/ε4 polymorphism, which also is a significant predictor of variation in the risk of coronary heart disease (CHD) and CHD death. Here, we test the hypothesis that the APOE polymorphism may modulate the cholesterol-raising effect of coffee. Objective We determined the effect of a coffee abstention period and a daily intake of 600 mL coffee on serum cholesterol and triglycerides with respect to the APOE polymorphism. Design 121 healthy, non-smoking men (22%) and women (78%) aged 29–65 years, took part in a study with four intervention periods: 1 and 3) a coffee free period of three weeks, 2 and 4) 600 mL coffee/day for four weeks. Results APOE ε 2 positive individuals had significantly lower total cholesterol concentration at baseline (4.68 mmol/L and 5.28 mmol/L, respectively, p = 0.01), but the cholesterol-raising effect of coffee was not influenced significantly by APOE allele carrier status. Conclusions The APOE ε 2 allele is associated with lower serum cholesterol concentration. However, the APOE polymorphism does not seem to influence the cholesterol-raising effect of coffee.

Introduction Apolipoprotein E (apoE) is a structural component of triglyceride-rich lipoproteins, chylomicrons, very-low-density lipoproteins (VLDL), and high-density-lipoproteins (HDL) [ 1 ]. Variation in the APOE gene sequence results in the 3 common alleles ( ε2, ε3 and ε4 ), which can produce 6 different genotypes (ε2/ε2, ε2/ε3, ε2/ε4, ε3/ε3, ε3/ε4 and ε4/ε4). The ε2, ε3 and ε4 alleles encode three distinct forms of apoE (E2, E3 and E4) and have approximate frequencies of 8%, 77%, and 15%, respectively, in white populations [ 2 ]. ApoE3 seems to be the normal isoform in all known functions, while apoE4 and apoE2 can each be dysfunctional [ 3 , 4 ]. In most populations, individuals with the APOE ε2 allele display lower levels of plasma cholesterol compared with individuals carrying the APOE ε3 allele, whereas individuals with the APOE ε4 allele show higher levels of plasma cholesterol, especially LDL-cholesterol [ 1 , 2 , 5 ]. Subjects with APOE ε3/ε4 and ε4/ε4 genotypes absorb cholesterol effectively and have higher non-fasting serum triglyceride values than ε4 negative individuals [ 6 , 7 ]. The allelic variation in the APOE gene is shown to be a significant predictor of variation in the risk of coronary heart disease (CHD) and CHD death [ 2 - 4 , 8 - 10 ], but the results from an extensive prospective study showed no associations [ 11 ]. Both the MONICA Project [ 12 ] and the Scandinavian Simvastatin Survival Study [ 13 ] suggest an increased risk of CHD for individuals carrying the APOE ε 4 allele. The APOE ε 4 allele is also considered a strong risk factor for Alzheimer's disease [ 14 - 16 ]. The serum cholesterol-raising effect of coffee is due to the diterpenes kahweol and cafestol [ 17 ]. Earlier studies have shown a cholesterol-raising effect mainly of unfiltered coffee, because a major part of the diterpenes is retained by a paper filter [ 18 - 20 ]. A recent trial, however, indicates that filtered coffee has a more pronounced serum cholesterol-raising effect than previously anticipated [ 21 ]. This finding was further corroborated in a randomized intervention study, where we demonstrated a considerable cholesterol-raising effect of filtered coffee [ 22 ]. In the study two coffee abstention periods were associated with a significant decline in serum cholesterol of 0.22 and 0.36 mmol/L, respectively, while 600 mL filtered coffee a day during two different periods increased serum cholesterol by 0.25 and 0.15 mmol/L, respectively. Here, we test the hypothesis that these effects might be modulated by the APOE ε2 / ε3 / e4 polymorphism. Subjects and methods Trial design The study was organised as a prospective, controlled study with four consecutive trial periods. The first and third periods were 3 weeks of total coffee abstention. The second and fourth period consisted of 4 weeks with the subjects consuming 600 mL filter brewed coffee/day. The main outcome or effect variable was total serum cholesterol and the effect was assessed as the difference between the measurements at the beginning and the end of the coffee free periods (coffee abstention) and the difference between measurements at the beginning and at the end of the four weeks of coffee consumption (Figure 1 ). Trial duration of 3–4 weeks has previously been shown to be sufficient to get an effect of coffee on serum cholesterol [ 21 , 23 ]. Figure 1 Study design Subjects and procedure Participants were recruited by advertising in Gothenburg's major newspaper. Inclusion criteria were age-range 30–65 years, free from clinically recognized chronic diseases, such as cardiovascular diseases, cancer, renal disorders, liver disease and diabetes mellitus. The participants were required to be free from anti-epileptic or cholesterol lowering drugs, and had been using coffee on a regular basis for at least five years and were currently non-smokers (at least for the last six months). During the coffee drinking periods the participants were instructed to drink about 600 mL filter brewed coffee/day (4 cups), according to standardised measures. The coffee was provided to guarantee that they were all exposed to the same brand and quality of filter brewed coffee. All participants also got the same kind of standardised coffee filter and measuring spoon. The coffee filters used were of the brand Euro-Shopper, made by Indupa N.V., Zaventem, Belgium. Divergence from the 4 cups was reported. The participants were allowed to drink tea and other caffeine containing beverages during the coffee-free periods. Effect variables Non-fasting blood samples were drawn at inclusion and at three, seven, ten and fourteen weeks after start of the study. Prior to analysis, prepared serum was stored at -70°C. The blood samples were analysed for blood lipids (total cholesterol, HDL cholesterol, triglycerides, lipoprotein(a) (Lp(a)) and urate in serum. Serum cholesterol and triglycerides were determined by an enzymatic procedure on a Hitachi 917 analyzer. HDL cholesterol was determined enzymatically after precipitation of VLDL, LDL and chylomicrones by α-cyklodextrinsulphate and dextransulphate. Determination of Lp(a) was done by the method Tint Elize Lp(a) of Biopool International. Serum urate was analysed by Hitachi 917 autoanalyser. Body Mass Index (BMI; kg/m 2 ) was recorded once during the study. Blood pressure was recorded by manual device and EKG and heart rate were recorded at all five visits. The dietary habits were assessed by dietary frequency questionnaires at the beginning of the study. A follow-up survey with special emphasis on changes in food habits during the four different periods was undertaken. The dietary questionnaire was based upon a Norwegian version, which has been used in a number of previous studies [ 24 ]. Genotype Analysis APOE genotypes were determined by solid-phase minisequencing as previously described by Blennow et al [ 25 ]. Statistical methods All analyses were performed using the SAS © software version 8. Signed rank test was used to test differences in the groups. Wilcoxon rank sum test was used to test differences at baseline and differences between the groups. The mean was used as location measure and measures of variation were described in terms of standard deviation. P-values < 0.05 were considered statistically significant. Results A total of 156 persons responded to the advertisement and of these 124 fulfilled the criteria and were able to take part. Three persons decided to withdraw during the study, leaving a total of 121 participants. One person was not able to take part during the first two periods and five persons were not able to take part in the last two periods, which resulted in 120 participants in the first trial period and 116 in the subsequent trial period. Genotype frequencies The APOE allele frequencies were 6.1% for the ε2 allele, 75.6% for the ε3 allele and 18.2% for the ε4 allele. This distribution agrees well with those reported in other populations in northern Europe [ 2 , 3 ]. Genotype and allele frequencies for the APOE polymorphism are given in Table 1 . Table 1 APOE genotype and allele frequency, n = 121 n % ε2/ε2 2 1.7 ε2/ε3 9 7.4 ε2/ε4 2 1.7 ε3/ε3 69 57.0 ε3/ε4 36 29.8 ε4/ε4 3 2.5 ε2 15 6.2 ε3 183 75.6 ε4 44 18.2 Serum cholesterol concentrations according to genotype and coffee exposure Individual APOE genotypes (six subgroups, Table 1 ) did not influence baseline values or coffee-induced changes in serum cholesterol, serum HDL cholesterol, serum triglycerides or serum Lp(a), possibly due to a small sample size (data not shown). ε4-positive individuals had similar serum cholesterol levels and coffee-induced changes in cholesterol concentration as ε4-negative individuals (data not shown). However, when grouping ε2 -positive individuals it was revealed that these displayed significantly lower cholesterol at baseline (Table 2 ). There was a significant difference in cholesterol decrease between week 0 and 3 for both groups. There was no difference between the two groups regarding the cholesterol decrease in the first coffee abstention period but there was a significant difference in cholesterol decrease in the second coffee abstention period, where ε 2 -negative individuals displayed a larger decrease in cholesterol (Table 2 ). Table 2 Serum cholesterol concentration (mmol/L) at baseline and after two 3-week periods of coffee abstention (week 0 – 3 and week 7 – 10) for APOE ε 2 -positive (n = 13) and APOE ε 2 -negative (n = 108) individuals APOE ε2 -positive APOE ε 2 -negative p n = 13 n = 107/103 a First trial period week 0 4.68 (0.80) 5.28 (0.93) 0.01 b week 3 4.49 (0.71) 5.05 (0.90) diff week 0–3 -0.18 (0.24) -0.23 (0.55) 0.30 c p (diff 0–3) 0.02 d <0.0001 d Second trial period week 7 4.52 (0.71) 5.34 (0.93) week 10 4.34 (0.64) 4.95 (0.89) week 7–10 -0.18 (0.41) -0.39 (0.55) 0.08 c p (diff 7–10) 0.13 <0.0001 d a 107 participants in the first trial period and 103 participants in the second trial period b Significant difference between APOE ε 2 -positive and APOE ε 2 -negative at baseline, Wilcoxon rank sum test c No significant difference between APOE ε 2 -positive and APOE ε 2 -negative in differences between week 0–3 or week 7–10, Wilcoxon rank sum test d Significant difference between week 0–3 for the APOE ε 2 -positive group and between week 0–3 and week 7–10 for the APOE ε 2 -negative group, Signed rank test Coffee consumption resulted in a significant cholesterol increase in the ε 2 -negative group in both trial periods (w 3–7 and w 10–14), but not in the ε 2 -positive group (Table 3 ). There were no differences between the ε 2 -positive and the ε 2 -negative group according to baseline characteristics as sex, age, body mass index (BMI) and coffee consumption prior to the study (Table 4 ). Table 3 Serum cholesterol concentration (mmol/L) after two 4-week periods of coffee consumption (week 3 – 7 and week 10 – 14) for APOE ε 2 -positive (n = 13) and APOE ε 2 -negative (n = 108) individuals APOE ε 2 -positive APOE ε 2 -negative p n = 13 n = 107/103 a First trial period week 3 4.49 (0.71) 5.05 (0.90) week 7 4.52 (0.71) 5.34 (0.93) diff week 3–7 0.03 (0.57) 0.29 (0.57) 0.09 b p (diff 3–7) 0.70 <0.0001 c Second trial period week 10 4.34 (0.64) 4.95 (0.89) week 14 4.54 (0.84) 5.09 (0.85) diff week 10–14 0.20 (0.47) 0.14 (0.59) 0.37 b p (diff 10–14) 0.15 0.009 c a 107 participants in the first trial period and 103 participants in the second trial period b No significant difference between 2-positive and 2-negative in differences between week 3–7 or week 10–14, Wilcoxon rank sum test c Significant difference between week 0–3 and week 7–10 for the APOE ε 2 -negative group, Signed rank test Table 4 Baseline characteristics for APOE ε 2 -positive (n = 13) and APOE ε 2 -negative (n = 108) individuals APOE ε 2 -positive APOE ε 2 -negative p n = 13 n = 108 Sex (% women) 77 79 ns a Age (years) 46.6 48.7 0.44 b BMI (kg/m 2 ) 25.7 25.8 0.87 b Coffee consumption (cups/day) 4.3 3.7 0.12 b a No significant difference between APOE ε 2 -positive and APOE ε 2 -negative at baseline, Chi square test b No significant difference between APOE ε 2 -positive and APOE ε 2 -negative at baseline, Wilcoxon rank sum test Dietary monitoring and compliance The dietary survey did not reveal any important changes during the four intervention periods [ 22 ]. Coffee consumption or non-compliance was reported by six persons during the first coffee abstention period (mean 1.8 cups/period), whereas four persons reported coffee consumption in the second coffee abstention period (mean 0.7 cups/period). Discussion Subjects with different APOE genotypes differ in the absorption efficiency of cholesterol from the intestine, in the synthesis rates of cholesterol and bile acids, and in the production of LDL apoprotein B [ 3 , 26 ]. This suggests that the response of serum cholesterol to diet may be affected by the APOE e2/e3/e4 polymorphism [ 27 , 28 ]. One previous study examined the effect of purified cafestol on serum lipids in relation to the APOE polymorphism [ 26 ] and found that responses of LDL-cholesterol to cafestol were slightly smaller among carriers of the APOE ε4 allele. Here, we investigate for the first time the possible influence of the APOE polymorphism on the cholesterol-raising effect of filtered coffee. APOE ε4-positive individuals did not differ significantly from ε4-negative individuals with regard to baseline cholesterol concentration or coffee-induced changes in cholesterol concentration. However, we confirm that ε2-positive individuals display significantly lower cholesterol levels at baseline than ε2-negative individuals. A tendency was seen that ε2 -positive individuals might be partly protected from the cholesterol increasing effect of coffee consumption. This was, however, only seen in the first trial period and will require further investigations. In conclusion, the APOE ε2 / ε3 / ε4 polymorphism is not a strong modulator of the cholesterol-increasing effect of coffee. Other genes should be discussed and further investigation is needed to see if there is a genetic factor in the cholesterol-raising effect of coffee.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539242.xml

514893

Identification of genes differentially expressed in T cells following stimulation with the chemokines CXCL12 and CXCL10

Background Chemokines are involved in many biological activities ranging from leukocyte differentiation to neuronal morphogenesis. Despite numerous reports describing chemokine function, little is known about the molecular changes induced by cytokines. Methods We have isolated and identified by differential display analysis 182 differentially expressed cDNAs from CXCR3-transfected Jurkat T cells following treatment with CXCL12 or CXCL10. These chemokine-modulated genes were further verified using quantitative RT-PCR and Western blot analysis. Results One hundred and forty-six of the cDNAs were successfully cloned, sequenced, and identified by BLAST. Following removal of redundant and non-informative clones, seventeen mRNAs were found to be differentially expressed post treatment with either chemokine ligand with several representing known genes with established functions. Twenty-one genes were upregulated in these transfected Jurkat cells following both CXCL12 and CXCL10, four genes displayed a discordant response and seven genes were downregulated upon treatment with either chemokine. Identified genes include geminin (GEM), thioredoxin (TXN), DEAD/H box polypeptide 1 (DDX1), growth hormone inducible transmembrane protein (GHITM), and transcription elongation regulator 1 (TCERG1). Subsequent analysis of several of these genes using semi-quantitative PCR and western blot analysis confirmed their differential expression post ligand treatment. Conclusions Together, these results provide insight into chemokine-induced gene activation and identify potentially novel functions for known genes in chemokine biology.

Background CXC and CC chemokines are small soluble proteins expressed and secreted by a number of cell types during the initial host response to injury, allergens, antigens, or invading microorganisms [ 1 ]. These ligands selectively attract leukocytes to inflammatory foci via facilitation of cellular adhesion, transendothelial migration, chemotaxis and cellular activation. Receptors for chemokines are members of the large family of G-protein receptors that signal via heterotrimeric guanine nucleotide-binding proteins of the Gαi-subclass [ 2 ]. Chemokine receptors can be subdivided into specific families based on their specificity for C, CC, CXC, or CX3C chemokine ligands. Three distinct types of receptor binding are currently recognized: (1) chemokine receptors that bind only one chemokine specific ligand; (2) chemokine receptors that bind more than one chemokine often with different binding affinities; and (3) promiscuous chemokine receptors that bind to numerous chemokines [ 2 ]. The chemokine receptor CXCR4 binds to the CXC chemokine, CXCL12 and functions as a co-receptor for HIV-1 [ 3 ]. CXCR4 is broadly expressed by many cells within the body including cells of the immune and the central nervous system [ 4 - 7 ]. This receptor mediates the migration of resting leukocytes and hematopoietic progenitors in response to its specific ligand [ 8 , 9 ]. CXCL12-induced chemotaxis is inhibited by pertussis toxin, enhanced in vitro by IL-3, and selectively inhibited by soluble ephrin-B receptor. [ 10 ]. In addition, proinflammatory stimuli such as lipopolysaccharide, tumor necrosis factor (TNF-α) or interleukin-1 potentiates lymphocyte-and monocyte-, but not neutrophil-mediated CXCL12 responses [ 11 , 12 ]. Furthermore, CXCL12 is an extremely potent in vitro and in vivo chemoattractant for mononuclear cells and lymphocytes [ 13 ]. CXCL12 is expressed in the cells forming Hassall's corpuscles and plays a significant role in the elimination of apoptotic thymocytes in normal and HIV-1-infected thymic tissues [ 14 ]. In addition to the bone marrow, quantitative PCR analysis has detected expression of CXCL12 in the lymph nodes, lung, and liver [ 15 ]. Autocrine and paracrine production of CXCL12 by peripheral blood CD34 + CD38 + cells also appears to trigger their transition from G 0 to G 1 and, in conjunction with thrombopoietin, enhances their survival through signal transduction mediated by the PI3K/AKT proteins [ 16 ]. Together these data support a role for CXCL12 as a critical factor for cellular growth and differentiation, cellular trafficking, myelopoiesis, and organ vascularization [ 17 , 18 ]. In contrast to CXCL12, considerably less is known about the chemokine CXCL10. CXCR3 (GPR9; CD183), the receptor for CXCL10 also binds the CXC chemokines CXCL9 and CXCL11 [ 19 ]. Recent studies of the CNS have suggested that CXCR3 additionally binds CCL21 [ 20 ]. CXCL10 is secreted by a variety of cell types, including monocytes, endothelial cells, fibroblasts, and astrocytes. CXCL10 is also a chemoattractant for human monocytes, natural killer and T cells (preferentially Th1 cells), and appears to modulate adhesion molecule expression and function [ 21 - 23 ]. CXCL10 is expressed in keratinocytes, lymphocytes, monocytes, and endothelial cells during Th1-type inflammatory diseases such as psoriasis and atopic dermatitis, but only at very low basal levels in normal keratinocytes [ 24 , 25 ]. CXCL10 inhibits bone marrow colony formation by CD34 + cells in the presence of stem cell growth factor (SCGF), colony stimulating factor 2 (granulocyte-macrophage) (CSF2; GM-CSF), or a combination of SCGF and erythropoietin (EPO). Moreover, CXCL10 has antitumor activity in vivo and is a potent inhibitor of angiogenesis [ 26 ]. This antitumor activity appears to be mediated by the ability of CXCL10 to recruit lymphocytes, neutrophils, and monocytes into inflammatory infiltrates. Moreover, CXCL10 has also been recently shown to be a Ras target gene and is overexpressed by a number of colorectal cancers [ 27 ]. Overall, CXCL10 is an important chemokine for mediating delayed-type hypersensitivity responses and a potent regulator of colony formation, angiogenesis, adhesion and cell migration. Alterations in gene expression are important determinants of cellular physiology. As a consequence, the identification, cloning and characterization of differentially expressed genes can provide relevant and important insights into a variety of biological processes. To investigate and compare the similar and distinct genes induced by the chemokines, CXCL12 and CXCL10, in normal physiology, we utilized differential display analysis to identify mRNAs in a Jurkat T cell line expressing endogenous CXCR4 and transfected with human CXCR3 gene. We have identified and cloned several differentially expressed genes displaying both elevated and diminished expression in the context of specific chemokine receptor ligation. The possible relevance of such differential responses within normal immune responses and in normal T-cell physiology is discussed. Methods Differential display Total RNA was isolated using the Qiagen RNeasy ® kit (Qiagen Inc., Valencia, CA) and treated with DNase I (GenHunter, Nashville, TN). Two micrograms of the total RNA was derived from subclone of CXCR3-transfected Jurkat T cells (generously donated by Dr. Thomas Hamilton, Lerner Research Institute, Cleveland, OH) cultured for 24 h in the presence and absence of 1 μg/ml of bioactive CXCL12 or CXCL10 (PeproTech). It should be noted that the CXCR3-transfected Jurkat T cells were subcloned from the original cultures. Subclones of the CXCR3-transfected lines were initially generated at the initiation of these studies so that homogenous CXCR3-bearing cells were available. A single Jurkat subclone was selected and examined for coexpression of both CXCR3 and CXCR4 by flow cytometry (Table 1 ). The isolated RNAs were subsequently reverse-transcribed with 400 units of MMLV reverse transcriptase (GenHunter) in three separate reactions each containing 2 uM of a one-base-anchored H-T 11 M (i.e. H-T 11 G, H-T 11 A and H-T 11 C) primer (RNAimage ® , GenHunter) and 20 uM dNTP for 60 min at 37°C. After heat inactivation of the reverse transcriptase at 75°C for 5 min, 2 μl of each reverse transcription reaction was added to 18 μl of a PCR master mix containing 2 uM of an H-T 11 -arbitrary primer, 1 U Taq polymerase (Qiagen), 2 uM dNTP, and a-[ 33 P]dATP. Each primer pair was denatured at 94°C for 30 sec, annealed at 40°C for 2 minutes and extended at 72°C for 30 sec for 40 cycles with a final extension for 10 minutes. [ 33 P]-labeled PCR products were resolved on a 6% denaturing polyacrylamide gel. The autoradiogram was inspected on a light box and differentially expressed bands marked by needle punches. The punched film was carefully oriented on the dried gel and the marked bands excised with a scalpel blade. Glycogen (10 mg/ml), 3 M sodium acetate, and 85% EtOH were added and after overnight storage at -80°C, the DNA was precipitated by centrifugation. Each DNA was subsequently reamplified using the same PCR primer set and conditions except that the dNTP concentration was increased to 20 uM and no isotope was added to the mixture. Reamplified PCR products were resolved on a 1.5% AmpliSize™ (BioRad, Richmond, CA) agarose gel, stained with SYBR ® Gold (Molecular Probes, Eugene, OR) and extracted from the gel using a QIAEX II kit (Qiagen). Successfully amplified bands were cloned using the PCR-TRAP ® cloning vector system (GenHunter) and ligated into GH-competent cells. Following transformation, only clones that contain an insert are capable of growing on LB-Tet agarose plates. The cloned insert was subsequently checked by colony-PCR using primers flanking the PCR-TRAP ® vector and the insert sequenced to identify genes differentially expressed between control and chemokine-treated CXCR3-transfected Jurkat T cells. Table 1 Flow cytometric analysis of transfected Jurkat T cell lines % Positive (MFI) Cell Line CD3 CXCR4 CXCR3 CXCR2 Jurkat-Neo 98 (138) 99 (118) 2 (5) 4 (4) Jurkat-CXCR3 98 (154) 99 (124) 99 (24) 5 (8) Jurkat-Bcl2 96 (144) 96 (124) 5 (6) 7 (5) CXCR3-, Bcl2-and Neo-transfected Jurkat T cells were stained using IgG FITC-labeled antibodies to CD3, CXCR4, CXCR3, and CXCR2 and analyzed for cell surface protein expression via flow cytometric analysis. The data are expressed as % positive (mean fluorescence intensity). It should be noted that the CXCR3-transfected Jurkat cells expressed low levels of CXCR3 on their cells surface suggesting a lower receptor density in comparison to CXCR4. Quantitative analysis of PCR fragments One microgram of DNase I-treated total RNA from control, CXCL12-or CXCL10-treated CXCR3-transfected Jurkat T cells was reverse-transcribed with 200 units of SuperScript II reverse transcriptase (Invitrogen, Carlsbad, CA) in a 20 ul reaction for 50 min at 42°C followed by heat-denaturation at 70°C for 15 minutes. Two microliters of the first strand reaction product were amplified in six duplicate reactions using standard PCR conditions (94°C for 1 minute, 63°C for 1 minute and, 72°C for 1 minute for 34 cycles) with sequence specific primers. Individual tubes were removed after 24, 26, 28, 30, 32, and 34 cycles and the concentration of specific PCR product determined using an Agilent 2100 BioAnalyzer and DNA 1000 LabChip (Agilent Technologies, Palo Alto, CA). Each RT-PCR was performed twice for each RNA preparation. Two housekeeping control genes, GAPDH and ribosomal protein L32 (RPL32), were utilized as controls with specific primers producing a 599 bp product for GAPDH and a 233 bp product for RPL32. Western blot analysis Cells were lysed in modified RIPA cell lysis buffer (50 mM Tris-HCl, pH 7.4, 1% NP-40, 1% sodium deoxycholate, 0.15 M NaCl, and 1 mM EDTA) with 1 mM phenylmethylsulfonylfluoride (PMSF), 1 mM sodium orthovanadate, 5 μg/ml leupeptin, 2 μg/ml aprotonin and one Complete Protease Inhibitor Cocktail tablet (Roche Diagnostics Corporation, Indianapolis, IN) per 50 ml of buffer. Whole cell protein extract was used directly for Western blot analysis. Protein concentration was determined using Bio-Rad protein assay kit (BioRad). Twenty micrograms of total protein from each sample was separated on a 10% Tris-glycine polyacrylamide gel and transferred onto a polyvinylidene difluoride membrane (Invitrogen, San Diego, CA). Membranes were blocked for 1 h at room temperature in PBS containing 5% non-fat dry milk and 0.1% Tween-20. The membranes were then incubated overnight at 4°C in primary antibody (anti-CA150, anti-thioredoxin, anti-flotillin and anti-ferritin H chain, BD Biosciences Transduction Laboratories, Lexington, KY and Santa Cruz Biotechnologies, Inc, Santa Cruz, CA) diluted 1:1000 in PBS containing 5% non-fat dry milk and 0.1% Tween-20. The membranes were washed in PBS with 0.1% Tween-20 then incubated for one hour in secondary antibody (goat anti-mouse-HRP and rabbit anti-goat-HRP; Santa Cruz Biotechnology, Inc., Santa Cruz, CA). The blots were washed and the proteins detected using the ECL Plus Western Blotting Kit (Amersham Biosciences UK Limited, Buckinghamshire, UK) and X-MAT AR Film (Eastman Kodak, Rochester, NY). Cellular migrations and intracellular calcium mobilization Jurkat T cell migration was examined using a fluorescence-based Transwell chemotaxis assays as previously described [ 21 , 61 ]. CXCR3-and neo-transfected Jurkat T cells were labeled with 10 μg/ml Hoechst 33342 (Molecular Probes) in cRPMI for 30 min at 37°C, and then treated with chol-BCD as described above. The cells were then resuspended in RPMI with 1% FBS to a concentration of 1 × 10 7 /ml. RPMI (0.6 ml) containing 1% FBS with or without 100 ng/ml SDF-1α was added to the bottom wells of the 24-well plate. Transwell chambers with 5 μm pore filters (Corning CoStar, Acton, MA) were then placed into the wells. Cells (1–3 × 10 6 in 100 μl) were then added to the chambers. After 2 h, the migrated cells in the bottom wells were transferred to triplicate wells of a 96-well plate in 150 μl volumes. Hoechst fluorescence was measured on a Fluoroskan Ascent FL fluorescence plate reader (Thermo Labsystems, Franklin, MA) at λ ex = 355 nm, and λ em = 460 nm. Results are expressed as migration index calculated by subtracting the fluorescence intensity of media alone and comparing the values to the fluorescence intensity (relative number) of cells migrated into the bottom chamber in media alone, which is normalized to a value of 1. Fluorescence values were within the linear range of a standard dilution curve. CXCR3-transfected Jurkat T cells were loaded with the fluorescent indicator, Fura-2AM (Molecular Probes, Eugene, OR), for 30 min, then washed and resuspended in PBS containing calcium and magnesium at 10 6 cells/ml [ 61 ]. The ratio of free to bound intracellular calcium was determined by spectrofluorometry by monitoring absorption at 340 nM versus 380 nM and emission at 510 nM. The chemokines, CXCL12α and CXCL10 (Peprotech, Rocky Hill, NJ) were utilized in these experiments at 1 μg/ml. Results CXCR3-transfected Jurkat T cells migrate and mobilize intracellular calcium in response to CXCL12 and CXCL10 The cells were found to be functionally responsive to both CXCL12 and CXCL10. As shown in Fig. 1A , a subclone of a CXCR3-transfected Jurkat T cell line, which was found by flow cytometry to coexpress both CXCR3 and CXCR4 (Table 1 ), specifically migrated in response to CXCL12 and CXCL10 in a dose-dependent fashion. Optimal migration for CXCL12 was noted at 0.5–1 μg/ml, while optimal migration for CXCL10 was observed at 1 μg/ml. In contrast, control neomycin phosphotransferase gene-transfected Jurkat T cells only demonstrated responses to CXCL12. Similarly, CXCR3-transfected Jurkat T cells demonstrated a potent calcium mobilization in response to CXCL12 (1 μg/ml) and a modest response to CXCL10 (1 μg/ml), while neo-Jurkat T cells failed to demonstrate any CXCL10 response (Fig. 1B ). The modest migration and calcium mobilization observed in response to CXCL10 compared to CXCL12 in this cell line suggests either distinct signaling through CXCR3 or lower cell surface CXCR3 density. Based on the flow cytometric data (Table 1 ), the mean fluorescence intensity for CXCR4 is approximately 5-fold greater than that for CXCR3 on this transfected cell line. Similar levels of CXCR4 were expressed on all of these cell lines including a neo control or Bcl2-transfected cell Jurkat line. As expected, CXCR2 failed to demonstrate any staining on these cell lines. While there may be differences in CXCR4 and CXCR3 signaling in these cells, differences in receptor density may influence the chemokine-induced gene expression differences described below and thus cannot be ruled out. Figure 1 CXCR3-transfected Jurkat T cells migrate and mobilize calcium in response to CXCL10 and CXCL12. CXCR3-transfected Jurkat T cells or neo-transfected Jurkat T cells (Panel A) were examined within Transwell chemotaxis chamber for their ability to migrate in response to various concentrations of CXCL10 and CXCL12 as described in the Methods . The migration data are expressed as a migration index relative to the number of migrating cells in the absence of chemokine. Panel B shows the mobilization of intracellular calcium within CXCR3-transfected and control Jurkat T cells stimulated with CXCL10 or CXCL12 (1 μg/ml). The data points were collected every 0.48 s and are presented as the relative ratio of fluorescence excited at 340 and 380 nm. Arrows indicate when the chemokine was added to the chambers. The insert within Panel B is a close-up view of the CXCL10 response within CXCR3-transfected Jurkat T cells. We have never observed any calcium mobilization or chemotactic activity by non-transfected or neo-transfected Jurkat T cells in response to CXCL10 (data not shown). Differential display of mRNA expression in CXCL12-and CXCL10-treated T cells In an effort to identify genes, which may be upregulated or downregulated by CXCL12 or CXCL10, we examined Jurkat T cells that that expressed endogenous CXCR4 and that had been transfected with CXCR3 using DDRT-PCR analysis (Figure 2 , Table 1 ). Jurkat T cells were stimulated with either CXCL12 or CXCL10 at a concentration of 1 μg/ml for 24 hr. The dose of 1 μg/ml was selected as this concentration yielded optimal migration for both CXCL12 and CXCL10 in the CXCR3-transfected T cells. Total RNA prepared from normal and chemokine-stimulated Jurkat cells were reverse transcribed into cDNA. The resultant cDNAs were amplified with 45 combinations of the arbitrary and oligo(dT) anchored primers. Seventeen cDNA bands were found to be differentially expressed in CXCR3-transfected Jurkat T cells. Fig. 3 shows the representative differential display results obtained with six separate primer combinations. Two cDNA fragments, designated as C31.3 (ribosomal protein S25) and A6.7 (thioredoxin) were identified to be differentially expressed by chemokine treated but not in untreated Jurkat cells (Fig. 3A and 3B ). The cDNA fragments were excised from the gel, reamplified, subcloned and sequenced. Figure 2 Flow chart of the RT-PCR-based differential display procedure. A detailed description of each step is found in the Methods. Figure 3 Differential display autoradiography. Panel A and B show examples of DDRT-PCR autoradiographs. Chemokine treatments are indicated above and the primer combinations below each cell lane. Panel C shows densitometric measurements of the relative gene expression of individual bands after treatment of the transfected Jurkat T cells with 1 μg/ml of CXCL12 or CXCL10 for 24 hrs. Direct sequencing of DDRT-PCR products To characterize the sequence identity of DDRT-PCR products, the excised and re-amplified bands were cloned into the PCR-TRAP vector and transformed into GH-competent E. coli. Several colonies were selected to be cultured, plasmids were purified, and the inserts wee subsequently screened by colony-PCR using primers flanking the site of the PCR-TRAP vector. The plasmids containing an insert were sequenced. We successfully sequenced a total of 146 cDNAs. Seventeen mRNAs were differentially expressed post treatment with either CXCL12 or CXCL10, each representing known genes with established functions. Five additional RNAs were also identified defining known genes with unknown functions and nine identified hypothetical or predicted genes of unknown function (Table 2 ). Twenty-one genes were upregulated in CXCR3-transfected Jurkat cells following both CXCL12 and CXCL10, four genes displayed a discordant response and seven genes were down regulated by both chemokines (Fig 3 ). Table 2 Differentially expressed genes post chemokine treatment. BAND Bp GENE LOCUSLINK GENEACC NAME C10.5 AD24 64318 NM_022451 AD24 protein A09.4 BM-002 51569 NM_016617 BM-002 hypothetical protein A09.2 DDX1 1653 NM_004939 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 1 C27.3 DDX30 22907 NM_014966 DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 30 G04.3 DFKZp564M113 none AL049282 Homo sapiens, clone MGC:5564, mRNA A10.3 DFKZp566D193 25847 AL050051 DFKZp566D193 protein A25.1 EIF4B 1975 NM_001417 eukaryotic translation initiation factor 4B A28.3 EPB41L2 2037 NM_001431 erythrocyte membrane protein band 4.1-like 2 C10.1 FLJ12876 64767 NM_022754 hypothetical protein FLJ12876 G01.1 101 FLOT1 10211 NM_005803 flotillin 1 G09.2 444 FTH1 2495 NM_002032 ferritin, heavy polypeptide 2 C07.3 329 GHITM 27069 NM_014394 growth hormone inducible transmembrane protein C10.4 IFITM2 10581 NM_006435 interferon induced transmembrane protein 2 (1–8 D) A01.2 INVS 27130 NM_014425 inversin C03.3 KIAA0478 9923 NM_014870 KIAA0478 gene product G04.6 KIAA0648 23244 AB014548 KIAA0648 gene product G01.4 KIAA1600 57700 AB046820 KIAA1600 protein A04.7 401 LOC51053 51053 NM_015895 geminin C35.1 LOC51633 51633 NM_016023 CGI-77 protein G35.1 MAP3K10 4294 NM_002446 mitogen-activated protein kinase kinase kinase 10 A27.14 MGC10744 84314 NM_032354 hypothetical protein MGC10744 C15.3 MGC4809 91860 AF308287 serologically defined breast cancer antigen NY-BR-20 A25.3 NCBP1 4686 NM_002486 nuclear cap binding protein subunit 1,80 kD A27.12 NCBP2 22916 NM_007362 nuclear cap binding protein subunit 2, 20 kD G11.1 RPL7 6129 NM_000971 ribosomal protein L7 G30.2 RPS12 6206 NM_001016 ribosomal protein S12 C31.3 RPS25 6230 NM_001028 ribosomal protein S25 A11.1 TCERG1 10915 NM_006706 transcription elongation regulator 1 (CA150) A06.7 157 TXN 7295 NM_003329 thioredoxin C15.5 VIT1 55519 NM_018693 vitiligo-associated protein VIT-1 G03.6 WBP11 51729 NM_016312 WW domain binding protein 11 Sequence analysis indicated that the 401 bp A4.7 cDNA is highly homologous to the gene LOC51053 that is also known as geminin, a cell cycle regulator. The 157 bp A6.7 cDNA fragment is 94% identical to the 501 bp human thioredoxin (TXN) gene. The sequence of the band A11.1 was homologous to the transcription elongation regulator 1 gene (TCERG1) also known as CA150. The 329 bp C7.3 cDNA fragment is 87% identical to the published human gene encoding the growth hormone inducible transmembrane protein (GHITM). The sequence of the band C10.4 was homologous to the interferon-induced transmembrane protein 2 (IFITM2) gene, a member of the 1–8 gene family whose members are strongly induced by both type I (IFNα, IFNβ) and type II (IFNγ interferons). The 102 bp G1.1 cDNA fragment is 97% identical to the human gene encoding flotillin 1 (FLOT1) that is thought to play a role in vesicular trafficking and signal transduction. Sequence analysis indicated that the 444 bp G9.2 cDNA, as well as several other bands, were highly homologous (>95%) to the gene encoding the iron-storage protein ferritin heavy polypeptide 1 (FTH1). The sequences of the other cDNAs included in Table 2 showed significant homology to published sequences in Genebank. However, it should be noted that in a number of cases, the nucleotide sequence of the cDNA matched named genes about which little is known or matched, in some cases for over 500 bp, the sequence of hypothetical or predicted genes. Several ribosomal proteins including L7, S12 and S25 and ferritin heavy chain (FTH1) were identified several times in our DDRT analysis. In Figure 4 , these changes in gene expression as assessed by DDRT are more clearly displayed post cluster analysis using an arbitrary densitometric scale of 0–4 for all named genes found by DDRT-PCR. Red indicates upregulation and green down-regulation in response to chemokine treatment. Figure 4 Comparison of the relative gene expression by CXCL12-versus CXCL10-treated T cells. Changes in gene expression were displayed using computer programs (Cluster, Tree-View-Eisen) using an arbitrary densitometric scale of 0–4 for all named genes found by DDRT-PCR. Red indicates upregulation, green down-regulation, and black-to-gray means no change. The columns are labeled as C (Vehicle control), S (CXCL12), and I (CXCL10). Identification of differentially expressed RNA transcripts associated with CXCL12 or CXCL10 treatment As a large fraction of DDRT-PCR products have been shown to yield weak or barely detectable signals in the Northern blot analysis, we sought to confirm the identity and differential expression of these bands via RT-PCR analysis. We focused our attention on a subgroup of the seventeen DDRT-PCR products representing known genes with established functions. Gene specific primers were designed to produce unique amplimers between approximately 150 and 500 base pairs in size. RT-PCR analysis was performed on the Agilent BioAnalyzer 2100 System. The advantage of using this system to examine competitive PCR products lies in the accurate absolute and relative quantitation of each amplified product. Small differences in the amount of amplimer product, which cannot be detected using slab gel analysis, are more easily analyzed on this equipment permitting RT-PCR to be used to measure changes in gene expression. RNA from CXCR3 receptor-transfected T cells, both before and post treatment with CXCL12 or CXCL10 was reverse transcribed in bulk and aliquots RT product amplified by PCR using specific sets of primers. Initially, a 599 bp GAPDH amplimer was utilized as a housekeeping gene. However, it was noted that GAPDH significantly up-regulated post treatment of the cells with either CXCL12 or CXCL10. To address this issue, a 233 bp ribosomal protein L32 amplimer was utilized as a housekeeping gene for the subsequent comparison of gene expression levels. In Figures 5 (CXCL12) and 6 (CXCL10), each of the seven genes (FLOT1, GEM, GHITM, FTH1, IFITM2, TXN and TCERG1) examined were found to be either up-regulated or down-regulated as predicted by the differential display autoradiogram bands in Fig. 3 . However, there was little, if any, relationship between the intensity of the DD band and the quantity of PCR product subsequently detected. These quantitative RT-PCR data confirm our DDRT data and support CXCL12-and CXCL10-mediated gene regulation in the Jurkat line. It should be noted that given that we are utilizing CXCR3-transfected Jurkat T cells in these studies, it is quite possible that differences in CXCR3 and CXCR4 receptor density and signal molecule association may influence the genes induced in response to these chemokines. Moreover, as high doses of chemokine were utilized in the stimulation cultures (1 μg/ml), these supraphysiologic concentrations may also differentially influence the observed differences in gene expression. Figure 5 Quantitative RT-PCR measurement and verification of CXCL12-induced gene expression. A detailed description of the procedure employed is found in the Methods . Each transcript was normalized to the expression of RPL32 in the same PCR reaction. Figure 6 Quantitative RT-PCR measurement and verification of CXCL10-induced gene expression. A detailed description of the procedure employed is found in the Methods . Each transcript was normalized to the expression of RPL32 in the same PCR reaction. Additional studies were performed using primary human T cells derived from several different donors to verify the differential CXCL12-and CXCL10-induced gene expression observed in Jurkat T cells. However, despite the clear differences in gene expression in Jurkat T cells after chemokine stimulation for 24 hr in culture, primary resting and anti-CD3/CD28 mAb-activated human T cells demonstrated variable and non-reproducible expression of the geminin, thioredoxin, DDX1, GHITM and TCERG1 gene products (data not shown). This variability may have more to do with the activation state of the primary T cells being utilized and the chemokine receptor expression and density on primary T cells versus the CXCR3-transfected Jurkat T cell subclone. More detailed studies examining CXCL12-induced gene expression in primary human T cells at various stages of activation are the focus of current studies. Moreover, studies using CXCL10 on primary human T cells are quite difficult as the expression of CXCR3 on resting human T cells is quite low to non-existent (>5% on CD3+ T cells with MFI between 5–12) and may be selectively expressed on certain cell subsets. CXCR3 studies in normal human T cells would require an activation of T cells using IL-2 or Th1 polarizing stimuli and thus may not be a valid comparison with the Jurkat T cells utilized in these studies. Protein expression in Jurkat T cells post CXCL12 treatment To further confirm the expression of several of these genes, Western blot analysis was subsequently performed on total cell lysates of CXCL12-or gp120 IIIB-treated Jurkat T cells. The results shown in Fig. 7A demonstrate that, similar to its gene expression, TCERG1/CA150 levels increased in Jurkat T cells cultured with CXCL12 or the HIV glycoprotein, gp120 IIIB, over a 24 hr time period. This expression was found to be CXCR4-dependent as neutralizing antibody to CXCR4 (but not control mouse IgG) inhibited the CA150 increase in response to CXCL12 and gp120 IIIB. This CA150 increase was inhibited by the addition of pertussis toxin, a Gα1 inhibitor. Given that gp120 IIIB binds to both the CD4 and CXCR4 molecules on the surface of human T cells, the similar results between CXCL12 and gp120 IIIB treatment suggests an active signaling role for gp120 IIIB through CXCR4. Figure 7B demonstrate that TCERG1 (CA150) and thioredoxin were both increased post CXCL12 treatment. In addition, although not identified in our DD studies, the signaling protein, interferon regulatory factor-1 (IRF-1), was also examined on our gels and increased significantly within Jurkat T cells post CXCL12 treatment. Similar to its gene expression, the gene product, flotillin-1, was found to be decreased post CXCL12 treatment in several experiments; however, these results were found to be highly donor variable (data not shown). We believe that this variability was most likely due the use of total cell lysates instead of membrane preparations. In addition, despite examining numerous lysates preparations and blots, we were unable to detect ferritin heavy chain expression by Western blot in any of these studies. Figure 7 CA150/TCERG1, thioredoxin and IRF-1 protein expression by Jurkat T cells post CXCL12α treatment. Jurkat cells were treated for 24 hrs with either CXCL12 (A, B) or gp120 IIIB (A) at 1 μg/ml in the presence or absence of control mouse IgG, mouse anti-human CXCR4 mAb, or pertussis toxin (PTx; 200 ng/ml) at 37°C. After incubation, the cell pellets were isolated, counted, washed, and subsequently lysed with the detergent. Protein determinations were then performed. Samples were loaded at 20 μg per lane on a 10% polyacrylamide gel. After electrophoresis, the gels were transferred using a transfer apparatus to an immobilon membrane and stained for CA150/TCERG1 (A, B), thioredoxin (B) or IRF-1 (B) expression via Western blot analysis (shown in panel B for each of these proteins versus control). The results are expressed as fold change versus control expression (post background subtraction). Discussion Studies on the alteration of gene expression following chemokine-receptor ligation and the identification of genes that are differentially expressed can provide relevant and important insight into a variety of biological processes and disease etiologies. To date, numerous approaches, model systems, and techniques have been used to search and identify chemokine-modulated genes [ 28 - 33 ]. In the present study, we used the PCR differential display method to screen genes and compare the changes that occur following CXCL12/CXCR4 and CXCL10/CXCR3 ligation. Both the CXCR3 and CXCR4 chemokine receptors are broadly expressed in many tissues and ligation to their specific chemokines is known to result in downstream signaling through several different pathways such as Ras, and PI3 kinase. PI3 kinase and JAK/STAT signaling pathways activated by CXCL12/CXCR4 ligation play roles in lymphocyte chemotaxis in response to these signals [ 34 - 37 ]. Although less well characterized, the interaction of CXCR3 with its ligands, in this case CXCL10, results in increased chemotaxis and activation of the Ras/ERK cascade as well as stimulation of Src phosphorylation and kinase activity and increased activity of phosphatidylinositol 3-kinase and its downstream pathway Akt [ 38 ]. Despite knowledge of the activation of multiple cytokine-induced signaling pathways, an understanding of the transcriptional mechanisms whereby CXCR3 and CXCR4 ligation regulates and mediates cellular change remains largely unknown. In the present study, we have identified 31 cellular genes that were either up-or down-regulated in CXCR3-transfected Jurkat T cells following treatment with either CXCL12α or CXCL10. Suzuki and colleagues [ 30 ] have recently examined gene expression in Jurkat T cells treated with 380 ng/ml of CXCL12 (a dose that demonstrated optimal Jurkat migration in their hands) for various time periods up to 12 hours in the presence or absence of serum using cDNA microarray gene analysis. The arrays utilized were 2140 cDNA microarray with 1847 unique genes http://nciarray.nci.nih.gov/cgi-bin/gipo . Many of the genes identified in this study are associated with detoxification, DNA repair, apoptosis, migration, T cell receptor signaling and interferon signaling. While many of the chemokine-induced genes observed in our study are not found on the arrays used by Suzuki et al. [ 30 ], we did identify several genes and proteins with similar functional associations as this group, namely interferon-induced transmembrane protein 2 and interferon regulatory factor-1 (interferon-associated genes), thioredoxin and ferritin (detoxification/redox), growth hormone inducible transmembrane protein (apoptosis), flotillin-1 (cell signaling and migration) and geminin (cell signaling/division). It should be noted that we do believe there are differences in the systems utilized by our groups as we failed to confirm using real time RT-PCR and flow cytometry several of the genes identified by Suzuki et al. Differences in the cell lines (bulk line vs. subclone vs. transfected), culture conditions, serum status, dose of chemokine utilized (380 ng/ml vs. 1 μg/ml) and receptor density may account for such disparity. Regardless of these differences, the relationship between the genes identified in our current study and those within the Suzuki study and their role in chemokine biology and function remains to be determined. Although not previously linked to chemokine receptor-ligand signaling, expression of several of these genes such as interferon-induced transmembrane protein 2 (IFITM2) and growth hormone inducible transmembrane protein (GHITM) are recognized to be a part of the interferon signaling system that includes Janus kinases and their downstream target STAT proteins [ 39 , 40 ]. IFITM2 is a member of the large 1–8 gene family whose members are strongly induced by both type I (IFNα, IFNβ) and type II (IFNγ) interferons [ 41 , 42 ]. However, additional information regarding the molecular function of this protein remains unknown. Likewise, information about the molecular function of GHITM is also quite sparse. GHITM displays some similarity to the testis-encoded transcript (TEGT). While the function of TEGT is also unknown, its amino acid sequence predicts a 26.5 kDa integral membrane protein with seven potential transmembrane domains suggesting a possible receptor function [ 43 ]. Interestingly, TEGT is 100% homologous to BAX-inhibitor 1 (BI1) [ 44 , 45 ]. Studies of cells overexpressing BI1 have shown its role as a regulator of cell death pathways controlled by BCL2 and BAX [ 44 ]. GHITM has also been referred to by several other names including DERP2, My021, PTD010 and HSPC282. DERP2 is a novel protein originating in human hair papilla cells that has an effect on regulating the growth of hair. The logic behind relating these seemingly inane associations is further supported by knowledge that hair follicle development requires Sonic hedgehog expression [ 46 ] that is essential for CXCL12 signaling in the CNS [ 47 , 48 ]. The transcriptional cofactor, CA150, whose gene is now designated transcription elongation regulator 1 (TCERG1) is capable of repressing transcription from many viral and cellular promoters whose initiation is dependent upon the presence of a TATA box [ 49 ]. CA150 represses RNA polymerase II (RNAPII) transcription by inhibiting the elongation of transcripts. CA150 is a transcriptional co-activator of HIV-1 Tat and therefore is likely to regulate many cellular genes involved with cell signaling, proliferation and differentiation [ 50 ]. A portion of the CA150 molecule contains six FF domains and this region appears to directly bind to the phosphorylated carboxyl-terminal domain of the largest subunit of RNAPII. WW1 and WW2 functional domains are also found in CA150 near the FF domains and appear to fine-tune the repression of transcription through their association with the ubiquitous splicing-transcription factor SF1. At present, CA150 is believed to bind to the phosphorylated C-terminal repeat domain of RNA polymerase II of the elongating RNAPII with SF1 targeting the nascent transcripts [ 51 ]. CA150 also has been found by DD to be up-regulated in all-trans retinoic acid (ATRA)-induced apoptosis of H9 and SR-786 T cell lymphoma cell lines [ 52 ] and to be significantly increased in striatal and cortical brain tissue from individuals with the neurodegenerative disorder Huntington's disease (HD) [ 53 ]. Interestingly, a small subset of HD patients with early onset of symptoms have a mutation in the region of the HD gene that increases its binding to CA150 leading to the suggestion that CA150 may interfere with the transcription of genes essential for neuronal survival. Despite these findings, the role of CA150 in T cell activation and survival is currently unknown. Geminin (LOC51053) is a 25-kDa protein that inhibits DNA replication and is degraded during the mitotic phase of the cell cycle [ 54 ]. This protein has generated considerable interest due to its critical role in replication licensing [ 55 , 56 ]. In order to successfully replicate, eukaryotic cells must assure that their chromosomes are duplicated only once in each cell cycle. A process called "licensing" assures that chromatin can only undergo another round of replication after it has passed through mitosis. Geminin inhibits DNA replication by accumulating during metaphase, binding to and inactivating CDT1 and then undergoing degradation during the metaphase to anaphase transition [ 54 , 57 ]. It is hypothesized that geminin may have evolved to couple S-phase regulation to growth and development signals [ 55 ]. As geminin is a powerful negative regulator of the cell cycle, it also may function as a tumor suppressor protein. Flotillin-1 (FLOT1), also known as Reggie-2, encodes a caveolae-associated, integral membrane protein [ 58 ]. Caveolae are small indentations on the plasma membrane that are involved in vesicular trafficking and signal transduction. Purified caveolin-rich membranes are enriched for a variety of lipid-modified signaling molecules such as G proteins, Src family kinases, Ras and nitric oxide synthetase [ 59 ] and also are populated with members of several families of integral membrane proteins [ 60 ]. Recently, flotillin 1 and flotillin 2 have been recognized to comprise a second family of integral membrane caveolin proteins. The function of these flotillins has not been determined but their expression levels are independent of the other caveolin family members. In at least some cell types, caveolins and flotillins are capable of forming hetero-oligomeric complexes and are believed to play a role in receptor signaling [ 60 ]. Movement of CCR5 and CXCR4 molecules into lipid rafts is important in the maintenance of receptor conformation and through this mechanism rafts modulate the binding and function of receptors [ 61 , 62 ]. Flotillin 1 and 2 along with stomatin are the major integral protein components of erythrocyte lipid rafts [ 63 ]. Although flotillins are major components of caveolae, these proteins may also be components of lipid rafts in other differentiated cell types such as adipocytes, endothelial cells, fibroblasts and immune cells. Thioredoxin (TXN) is a 12-kDa oxidoreductase enzyme containing a dithiol-disulfide active site. It possesses a variety of biological functions including the ability to modulate the DNA binding activity of the ligand-activated transcription factor aryl hydrocarbon receptor (AHR) as well as the activity of several other transcription factors including general transcription factor IIIC, NF-κB, and AP-1 [ 64 , 65 ]. Reactive oxygen species generated through cellular metabolism can function as cellular second messengers through the regulation of numerous signal transduction pathways. Thioredoxin protects cells against TNF-induced cytotoxicity, general oxidative stress and is able to scavenge free radicals [ 66 ]. As increasing evidence accumulates that oxidative stress plays a crucial role in many age-associated diseases and various neurodegenerative disorders, the importance of regulating and maintaining cellular redox status by intracellular redox-regulating molecules such as thioredoxin becomes important to maintain tissue homeostasis [ 67 - 69 ]. Ferritin is a highly conserved iron-binding protein that in its cytosolic form is composed of 2 subunits, ferritin H and ferritin L, each encoded by a distinct gene. Depending on many factors including tissue type, redox status, and the inflammatory state of a given cell or tissue, the ratio of H to L subunits varies greatly [ 70 ]. Several proinflammatory cytokines including TNFa, IL-1a (but not IL-1β, IFNg and IL-6) has been shown to transcriptionally induce ferritin heavy chain (FTH1) expression [ 71 , 72 ]. TNFa regulation of FTH1 is through its binding to the p50 and p65 subunits of NFκB [ 73 ]. In addition, expression of FTH1 appears to be regulated by insulin, IGF-1 and thyroid hormone [ 74 , 75 ]. Cytokines also regulate the post-transcriptional modification of ferritin possibly through their ability to induce iNOS [ 74 , 75 ]. As noted by Torti & Torti [ 70 ], the pathways that link ferritin gene expression with cell stress and altered growth regulation are just beginning to be explored and based on present knowledge are very complex and multifaceted. Conclusions In the current report, we do not reveal a specific physiological role for the gene expression changes that have been observed by DDRT-PCR post CXCL12 or CXCL10 signaling. As noted above, many of the more fully characterized genes have multiple interactions utilizing a number of distinct signaling pathways (e.g., JAK/STAT, AP-1) that are frequently utilized by other cytokine family members. A more detailed understanding of the various genes differentially induced by chemokines via ligation of their cell surface receptors or during the chemotactic process should provide some insight into the process of cell migration and activation and may identify novel targets for therapeutic intervention. Competing interests None declared Authors' contributions JEN, RJS, LS, DB, VDD, EMS and DDT performed the experiments. JEN and DDT prepared the figures and wrote the paper. DDT also supervised the work and edited the manuscript. All authors have read and approved the final manuscript. Abbreviations differential display, DD; growth hormone inducible transmembrane protein, GHITM; GEM, geminin; transcription elongation regulator 1, TCERG1; thioredoxin, TXN; DEAD/H box polypeptide 1, DDX1

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539281

Can somatostatin control acute bleeding from oesophageal varices in Schistosoma mansoni patients?[ISRCTN63456799]

Background Management of patients with bleeding oesophageal varices comprises of mainly diagnostic endoscopy, sclerotherapy and band ligation. One of the major problems to do any of the above is the active bleeding which makes any intervention difficult. The neuropeptide hormone somatostatin administered exogenously has caused a reduction in portal hypertension and variceal bleeding in patients suffering from liver cirrhosis. We believe that the symptomatic use of somatostatin for variceal bleeding in Schistosoma mansoni infected subjects can reduce bleeding, thereby alleviating the pathology caused by schistosomiasis. Methods/design We herein present a study protocol for establishing this neuropeptide as a potential therapeutic agent in schistosomiasis. Adolescent subjects, age range varying from 12–17 years will be selected, based on several inclusion criteria, most important being infection with Schistosoma mansoni with bleeding from oesophageal varices in the last 24 hours. One group of schistosomiasis patients will be treated with somatostatin and praziquantel, the other with propanolol and praziquantel. Survival graphs will be set up to correlate somatostatin administration with survival time. A two part questionnaire will be set up to control treatment outcomes. The pre-treatment part of the clinical questionnaire will identify inclusion criteria questions, the post-treatment part of the questionnaire will identify treatment outcomes. Discussion We expect that the administration of somatostatin as a bolus followed by a 24 hour long infusion, will stop bleeding immediately, delay rebleeding as compared to the control study group and delay mortality in the somatostatin treated subjects.

Background Complications due to severe schistosomiasis include fibrosis, hepatomegaly, splenomegaly, haematemesis, varices, portal hypertension, ascites formation and death [ 1 ]. Complications resulting from hepatic fibrosis (such as portal hypertension and variceal bleeding) are the principal cause of death in S. mansoni infected patients. In such patients portal hypertension leads to the formation of portal-systemic collaterals, specifically gastro-oesophageal collaterals (varices). Bleeding of these oesophageal varices can occur depending on the severity of fibrosis, and can be fatal. Praziquantel, the most commonly used anti-schistosomal drug, is effective against the worm stages of the parasite, but has no activity against the pathology (fibrosis) caused by the egg stages or the variceal bleeding that can be fatal in its outcome. These observations have stressed the need to combine praziquantel treatment with symptomatic treatment like with somatostatin. Somatostatin is emerging as the ideal vasoactive drug for the control of variceal bleeding, and is as effective as sclerotherapy [ 2 - 4 ]. In a recent clinical trial, cirrhotic patients with acute bleeding of oesophageal varices were treated with infusion followed by bolus injections of somatostatin just before sclerotherapy. Results showed fewer treatment failures, fewer deaths or use of rescue therapy, reduced blood transfusion and less frequent, active bleeding. This drug also prevents recurrence and is free from any major side effects even when administered over prolonged periods of time. We have studied the potential role of somatostatin in modulating Schistosoma caused morbidity. In endemic regions, at any given time, only a fraction of infected patients develop severe hepatic fibrosis. There may be a direct correlation between the development of severe fibrosis and the inability to generate required somatostatin levels. Our ongoing research at the Laboratory of Pathology give evidence to this fact, since somatostatin levels in Senegalese patients with severe morbidity (haematemesis, portal hypertension, variceal bleeding, ascites, fibrosis) are significantly lower than that in patients with no severe morbidity [ 5 - 10 ]. This effect indicates that somatostatin could be administered exogenously for therapeutic purposes in chronic schistosomiasis patients. Complications linked to hepato-intestinal schistosomiasis are increasing in the area of Richard-Toll, Northern Senegal. These cases are being identified at the local health centre. However, considering the high prevalence of schistosomiasis in this region, it is likely that the number of severe cases is much higher. Clear guidelines for the management of such severe complications and criteria for referral and surgery are required in this region. The establishment of an algorithm on how to treat these patients and create the appropriate infrastructure is urgently needed. The priority is how to take care of these patients and more so what is the best way of doing this under local conditions. One of the first steps is to critically evaluate the Niamey-Belo-Horizonte ultrasound methodology in patients with severe periportal fibrosis. Efficient management of bleeding varices in the afflicted patients is imperative. Given the background that somatostatin is an ideal vasoactive drug in the field of liver pathology, it is our opinion that somatostatin will be more efficacious and safe as compared to currently used beta blocker drugs like propanolol, in the control of acute oesophageal variceal bleeding due to Schistosoma mansoni infection. Moreover using this neuropeptide may increase time to failure of drug treatment, decrease incidences of early re-bleeding (day 4, 8) and incidences of death during the follow up period. Decreased frequencies of late rebleeding (days 30, 60, 90) may occur, all indicating the safety of using somatostatin. Praziquantel cover would be given to all study patients. Study design 1. Selection of patients Age and Morbidity criteria – Adolescent subjects, age range varying from 12–17 years will be selected. The inclusion criterion will be schistosomiasis patients with bleeding from oesophageal varices in the last 24 hours. A random selection will be made to form two groups, a study group and a control group. Control of active infection will be done by means of CAA-strips on urine or blood. Subjects will be asked to fill in an informed consent form and the pre-treatment part of a questionnaire. The inclusion criteria will be established fibrosis due to schistosomiasis of clinical history, physical examination and laboratory findings (and an examination compatible with the presence of portal hypertension due to fibrosis). Clinically active upper gastrointestinal bleeding (haematemesis of fresh or semi fresh blood and/or melena and/or haematochezia) with or without haemodynamic instability (systolic blood pressure < 80 mm Hg and heart rate > 120 bpm) will be selected. Subjects must be male or non-pregnant, non-lactating female subjects. Females of childbearing potential will have to utilize contraception for the duration of the study. Written or verbal documented informed consent will be needed from all subjects. Exclusion criteria will include participation by subjects in another investigational study within the last 14 days. Subjects may not undergo treatment with endotherapy, i.e. band ligation, sclerotherapy or other (balloon tamponade). Treatment with somatostatin, vasopressin or their analogues will also be a exclusion criteria. Subjects with end stage liver disease with hepatorenal syndrome, diffuse hepatocellular carcinoma, patent porto-systemic shunts, known diagnosis of non-fibrotic portal hypertension, severe cardiovascular diseases, i.e. acute myocardial infarction and heart failure will be excluded. Concurrent use of metoclopramide is also not advised. Conduct of trial – Active bleeding episode (haematemesis, haematochezia, melena) from a potential variceal source should be confimed by a medical team (the ER physician, the ICU physician, the investigator). Patients may be outpatients or already hospitalised patients. Patients will be randomised to either arm in a sequential manner. Randomisation, and the start of study drug infusion if in adjunctive therapy arm, should be accomplished as soon as possible following identification of a patient qualifying for the study and following the conduct of pre-randomisation study procedures. Sample Size Criteria – Assuming a 80% chance of finding a significant difference <0.05 between the two study cohorts, the following statistics were established: (A) If 99% of the untreated subjects and – 10% of somatostatin treated subjects bled -5 volunteers per group were sufficient; 20% treated subjects bled – 7 volunteers per group were required; 30% treated subjects bled-9 volunteers per group were needed. (B) If 90% of untreated subjects bled, and – 10% of somatostatin treated group bled – 7 volunteers in each cohort were sufficient; 20% of somatostatin treated group bled – 9 volunteers in each group were required; 30% of somatostatin treated group bled – 10 volunteers in each group were needed. (C) If 70% of untreated subjects bled, and: 10% of somatostatin treated group bled – 12 volunteers per group were sufficient; 20% of somatostatin treated group bled – 18 volunteers per group were required; 30% of somatostatin treated group bled – 28 volunteers per group were needed. In cirrhotic patients with bleeding oesophageal varices somatostatin administration controls bleeding in more than 80% of the treated patients. Based on this report, we propose to start this pilot study with 10 subjects/group. 2. Treatment Two groups of 10 schistosomiasis patients each will be identified. Group (1) will be treated with Somatostatin (3.5 μg/kg/hour; single bolus and i.v. infusion for 24 hours) + Praziquantel (40 mg/kg). For somatostatin treatment the i.v. infusion will be started first; 3 mg somatostatin will be dissolved in the 1 ml of saline provided. This solution will be added to the saline transfusion unit and administered to the patient for the next 12 hours. Once finished the second packet of 3 mg somatostatin will be used similarly for a second saline transfusion unit for the remaining 12 hours. The bolus dose of 250 μg will be dissolved in the 1 ml of saline provided and administered over 90 seconds soon after the start of the i.v. infusion. Group (2) will be treated with Propanolol + Praziquantel. 3. Data analysis Survival graphs will be set up to correlate somatostatin administration with survival time. The primary efficacy variable is the number of patients meeting the failure of therapy definition during the infusion period. Failure criteria are defined as death during infusion, persistence of active bleeding (The haemodynamic instability criteria points to the inability to achieve and maintain a systolic blood pressure of 80 mm Hg OR presence of a 20 mmHg drop in systolic blood pressure from the highest post resuscitation value AND achieving a heart rate of 120 bpm OR a 20 bpm increase from highest post resuscitation value OR Inability to achieve and maintain a Hct of – 27% of Hb of – 9 g/dl despite blood transfusion of 2 units or more. The clinical criteria of active bleeding include hematemesis (fresh or semi fresh blood), hematochezia, melena. 4. Control points A two part questionnaire will be set up to control treatment outcomes. The pre-treatment part of the clinical questionnaire will identify inclusion criteria questions: (1) When was the last haematemesis incidence? (2) When did the present incident start? The post-treatment part of the questionnaire will identify treatment outcomes. The following questions will be answered: (1) What was the reaction to somatostatin infusion? (2) When did bleeding stop after somatostatin infusion? (3) GI disturbances: Is there abdominal pain, nausea, diarrhea, after somatostatin infusion? (4) Control of early rebleeding: Is there early rebleeding in the 8 days following somatostatin administration? (5) Control of late rebleeding: When is the next rebleeding incident? Subjects will be followed up on a monthly basis, by home visits. (6) Control of mortality: Is there any difference in mortality time between the two groups. (7) Control of fibrosis: Does ultrasonography detect anti-fibrotic effect of somatostatin after treatment? Discussion It is expected that the administration of somatostatin as a bolus followed by a 24 hour long infusion, will stop bleeding immediately, delay rebleeding as compared to the control study group and delay mortality in the somatostatin treated subjects. Our protocol that is based on a pilot study will help to establish the importance of somatostatin in schistosomiasis. Competing interests The author(s) declare that they have no competing interests. Authors' contributions Both authors participated in the design of the study. Pre-publication history The pre-publication history for this paper can be accessed here:

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509424

The STATs in cell stress-type responses

In the early 1990's, a new cell signaling pathway was described. This new paradigm, now known as the JAK/STAT pathway, has been extensively investigated in immune-type cells in response to interferons and interleukins. However, recent evidence suggests that the JAK/STAT pathway also mediates diverse cellular responses to various forms of biological stress including hypoxia/reperfusion, endotoxin, ultraviolet light, and hyperosmolarity. The current literature describing the JAK/STAT pathway's role in cellular stress responses has been reviewed herein, but it is clear that our knowledge in this area is far from complete.

Review In multicellular organisms, every cell comprising every tissue, organ, or organ system constantly strives to maintain homeostasis in the face of destabilizing influences. Whether it is external stimuli such as toxic chemical exposure or changes in oxygen tension, or natural alterations in pH or osmolarity due to normal cellular metabolism, each cell in the body is equipped with the molecular machinery required to sense these environmental changes and respond to them. Because these stressful stimuli can impinge on normal cellular functioning, discrete cellular sensing mechanisms have evolved to maintain homeostasis. For example, ATP depletion caused by hypoxia activates AMPK which phosphorylates multiple downstream targets to switch the cell from a mainly anabolic to catabolic state [ 1 ]. Similarly, activation of SAPK/JNK by stressors such as ultraviolet (UV) light results in activation of well-defined molecular targets [reviewed in [ 2 ]]. One interesting question is how do cells translate diverse stressful stimuli into activation of specific molecular pathways? Cellular signaling from membrane to nucleus is typically accomplished through ligand/receptor activation of intracellular second messengers. In many cases these second messengers are kinases which phosphorylate substrates leading to a cascade by which successive macromolecules are triggered. Ultimately, a terminal transcription factor is activated which then translocates to the nucleus to activate specific target genes. This type of cellular signaling has been well-characterized following receptor activation by polypeptide ligands, but some forms of cellular stress such as hypoxia cause activation of specific molecular pathways in the apparent absence of ligand to receptor stimulation. Therefore, there must be alternative routes for direct activation of target genes that circumvents the canonical ligand/receptor/second messenger cascade. One such pathway that may transmit signals to the nucleus by this alternative route is the Janus Activated Kinase/Signal Transducer and Activator of Transcription family of transcription factors (JAK/STAT). The JAK/STAT pathway includes seven functionally related, latent transcription factors (STAT) and four non-receptor tyrosine kinases (JAK) [reviewed in [ 3 ]]. In the typical JAK/STAT paradigm, a cytokine binding to its receptor results in activation of receptor-associated JAKs. JAKs then phosphorylate the cytoplasmic receptor chains creating docking sites for recruited STATs. Finally, STATs are phosphorylated on tyrosine by JAKs, dimerize, and then translocate to the nucleus to activate specific target genes. Because the second messenger (STAT) is also the terminal transcription factor, in many ways the JAK/STAT pathway represents a streamlined apparatus for cellular signaling. Thus, the JAK/STAT pathway differs from many signaling cascades in that the usual system of multiple sequential signaling molecules is bypassed. While signaling through the canonical JAK/STAT pathway has been well-characterized in immune-type cells in response to interleukins and interferons, there is emerging evidence that STATs also mediate cellular responses to various forms of cellular stress. These findings, in addition to mounting evidence suggesting that some STATs are phosphorylated on serine by members of the MAPK family, imply that alternative mechanisms for STAT activation exist. Further, these alternative means of activation may lead to different outcomes with regards to STAT signaling. For example, STAT3 was recently shown to be serine phosphorylated by JNK during UV stress which had a predominately inhibitory role on STAT3 transcriptional activity [ 4 ]. Could these alternative routes of STAT activation account for STAT's "yin and yang" type properties whereby depending on the type of cell stressor, either cell death or cell survival pathways are activated? Although interferons themselves can regulate cellular responses to exogenous stressors such as infection through the canonical JAK/STAT pathway, this literature review will focus mainly on those alternative mechanisms of JAK/STAT signaling during cellular stress. STATs and cellular stress Some of the earliest studies implicating STATs in mediating cell stress responses were performed in cells exposed to UV light. In mouse embryonic fibroblasts (MEFs), UV light treatment resulted in phosphorylation of serine 727 in STAT1 via p38 MAPK [ 5 , 6 ]. Further analysis revealed that STAT1 could be phosphorylated directly by p38 MAPK in vitro . Thus, the MAPK and STAT pathways appear to converge during periods of cellular stress. In another study, UV light caused STAT1 tyrosine phosphorylation, nuclear accumulation, and DNA binding in keratinocytes [ 7 ]. Together, these studies raise the possibility that STATs can be activated in a ligand-independent manner during cellular stress, resulting in the activation of STAT-dependent target genes. Cellular stress can also occur in disease states such as diabetes, which is characterized by vascular dysfunction. For example, prolonged elevated glucose can act as a cell stressor through multiple pathways including hyperosmolarity [ 8 ], protein kinase C (PKC) activation [ 9 ], and oxidative damage [ 10 ]. Recent studies have determined that constitutive JAK/STAT phosphorylation was elevated in cultured smooth muscle-like mesangial cells treated with high glucose [ 11 ]. Furthermore, in these same cells, angiotensin stimulation of STATs was prolonged by high glucose treatment [ 12 , 13 ]. The authors of these studies related these findings to TGFβ-induced extracellular matrix accumulation because collagen and fibronectin secretion could be inhibited with STAT anti-sense RNAs. This implies that the JAK/STAT pathway may play a role in basement membrane thickening observed in diabetic nephropathy and retinopathy. These studies were some of the first to suggest that perturbed JAK/STAT signaling could play a role in disease states like diabetes and possibly link glucose-mediated cell stress and the JAK/STAT pathway with diabetic sequelae. Hyperosmotic stress can also activate STATs. For example, in the slime mold Dictyostelium , hyperosmotic stress leads to STAT1 phosphorylation without any known involvement of JAK or MAPK [ 14 ]. But mammalian cells also utilize STATs during hyperosmotic stress. In one report, sorbitol-induced hyperosmolarity was shown to cause JAK1, JAK2, and TYK2 phosphorylation and subsequent activation of STAT1 and STAT3 in various cell types; this led to the formation of STAT1/STAT3 complexes with the m67SIE oligonucleotide from the c-fos promoter [ 15 ]. Interestingly, these authors speculate that the hyperosmotic signal occurred independently of gp130. This suggests an alternative pathway by which JAKs may be activated beyond the canonical JAK/STAT route. In agreement with this study, hyperosmotic shock in COS-7 cells was shown to lead to tyrosine phosphorylation of STAT1 in a MKK6/p38-dependent pathway [ 16 ]. In this case, STAT1 but not JAK1 phosphorylation could be inhibited by genistein (a non-specific tyrosine kinase inhibitor) leading the authors to conclude that a tyrosine kinase distinct from JAK1 (possibly novel) represented the link between hypertonicity and STAT activation. The most well-investigated reports linking cellular stress with the JAK/STAT pathway are studies in cardiomyocytes undergoing hypoxia/reperfusion. Like UV light, hypoxia/reperfusion led to p38 MAPK phosphorylation followed by serine 727 phosphorylation of STAT1 which was associated with activation of pro-apoptotic FAS/FASL and caspase-1 [ 17 ]. It was concluded that Fas and caspase-1 expression were directly STAT-1 dependent because their expression could be inhibited by STAT1 anti-sense RNAs. Thus, STAT1-dependent FAS activation plays a leading role in cardiomyocyte death during hypoxia/reperfusion injury [ 18 ] and as the authors point out, inhibition of this pathway may prove to be cardioprotective following ischemic insult [ 19 ]. Interestingly, while these studies showed that both tyrosine 701 and serine 727 of STAT1 were phosphorylated in response to hypoxia/reperfusion, only phosphorylation on serine was required for FAS expression. Because serine phosphorylation alone is not sufficient for direct DNA binding, these results indicate alternative pathways by which STAT1 may activate target genes during stress. For example, serine phosphorylated STAT1 may associate with other scaffolding proteins as it does in the case of MCM5 [ 20 ], BRCA1 [ 21 ], or HSF [ 22 ] and act instead as a transcriptional co-activator, rather than a direct activator of target genes [ 23 ]. In vivo models of hypoxia/reperfusion also implicate STAT5 as a player in responses to cellular stress. But unlike STAT1, STAT5 is thought to be mainly protective by activating anti-apoptotic signals. For example, Yamaura et al . report that genetic deletion of STAT6 but not the STAT5A causes resistance to myocardial ischemia/reperfusion injury [ 24 ]. This resistance was thought to be related to two distinct STAT5A-mediated pathways: one involving a Src/STAT5/PI-3 kinase/Akt pathway, and the other a direct JAK2/STAT5A pathway. Like STAT5, STAT3 was also shown to be protective against cardiac ischemia/reperfusion injury through a JAK2/STAT3-dependent mechanism involving up-regulation of anti-apoptotic Bcl2 and down-regulation of pro-apoptotic Bax [ 25 , 26 ]. Studies in our laboratory indicate that in microvascular endothelial cells, hypoxia caused an increased tyrosine phosphorylation of JAK2, down-regulation of FAS/FASL, and that AG490 (a JAK2 inhibitor) de-repressed FAS transcription (unpublished observations). This may suggest a possible mechanism whereby activated JAK2 could mediate protection during ischemia in endothelial cells by repressing pro-apoptotic FAS transcription through downstream STAT3 or STAT5. Intriguingly, a complex of STAT3 and c-Jun was recently shown to be a FAS repressor [ 27 , 28 ] but it remains to be determined if STAT5 or other STATs can behave like STAT3 and act as transcriptional repressors of pro-apoptotic genes during cellular stress. STAT5 nuclear translocation and DNA binding to the GAS (γ-activated site) implicates STAT5 in hypoxic stress responses, but the biological significance of this observation is not yet clear [ 29 ]. Although not as well studied as hypoxia/reperfusion or osmotic stress, reactive oxygen species have also been shown to activate the JAK/STAT pathway. Oxidative stress, such as might occur in diabetes and cardiovascular disease, was shown to activate HSP70 in smooth muscle cells in a JAK-dependent manner [ 30 ]. This response is thought to aid in adapting these cells to oxidative damage. Recently it was shown that STAT1 forms a complex with HSF-1 to activate the HSP promoter while STAT3 filled just the opposite role [ 22 , 31 ]. Thus, it appears that STAT1 and STAT3 can perform entirely different functions with regard to cellular stress-type responses. Other studies have determined that peroxide treatment resulted in STAT3 tyrosine phosphorylation and nuclear translocation [ 32 ] and JAK2, STAT1, and STAT3 were activated by oxidized LDL [ 33 ]. Taken together, the studies reviewed herein support a role for the JAK/STAT pathway in various forms of cellular stress and relate perturbed JAK/STAT signaling to potential disease states. However, consistent with some of the current ideas about STAT biology, it is clear that cellular stress seems to activate STATs in ways that can be both detrimental to and supportive of cell survival. For example, STAT1 activation by hypoxia-reperfusion injury activates cell death pathways, while STAT5 activation by the same type of stressor seems to promote cell survival pathways. Thus, STATs may have evolved to fulfil both sides of a "yin and yang" type mechanism where either death or survival pathways can be activated depending on the strength or type of cellular stressor [ 34 ]. This may be especially true in endothelial cells, which seem to be able to resist short-term hypoxic-stress compared to other cell types but die by apoptosis following prolonged exposure to hypoxia [ 35 ]. The most intriguing aspect of many cellular stress-activated pathways is the apparent absence of ligand-to-receptor stimulation. But the cell must somehow "sense" changes in the external milieu and transmit these signals to the nucleus. How does it do this? Two such examples of this type of cellular sensing are activation of a well-described transcription factor known as HIF-1α (hypoxia-inducible factor), and another is the cellular thermostat called HSF (heat shock factor). In the case of HIF, enzymatic modification by an enzyme requiring oxygen as a cofactor is responsible for HIF activation and switching on of target genes when cells are stressed by low oxygen [ 36 ]. For HSF multiple stressors such as ATP depletion, ischemia, and intracellular acidosis lead to HSF phosphorylation, unfolding, and its translocation to the nucleus to activate target genes [ 37 ]. So these powerful signal transducing pathways are somehow activated directly presumably without ligand stimulation. This is probably the most speedy and efficient way of activating downstream target genes to promote cell survival. Conclusions Is it possible that STATs can also act as a cellular rheostat of various stressors? Recent suggestions that STATs can be post-translationally modified in ways other than phosphorylation (e.g. acetylation, methylation, and ubiquitination) make this a possibility [ 38 ]. For example, changes in the intracellular redox environment by low oxygen tension may modify STAT conformation leading to enhanced availability of its active centers [ 15 ]. This change may facilitate interactions with other STAT-modifying proteins such as p38 MAPK. Alternatively, other previously unknown STAT pathways may be activated during cellular stress, altering its transcriptional capacity. These might include STAT association with other second messengers such as PI-3 kinase and Akt but also STAT upstream activation by molecules like Src [ 24 ]. Figure 1 summarizes the potential role of STATs in cellular stress. Figure 1 Role of STATs in cell stress responses. (A) Autocrine IFN may activate JAK/STAT through the canonical pathway. This activation would involve tyrosine phosphorylation of STAT by JAK resulting in STAT dimers which are 20% transcriptionally active. This process is thought to "prime" STATs for serine phosphorylation by an IFN-inducible serine kinase (possibly PKC) [42]. Both tyrosine and serine phosphorylation results in a 100% transcriptionally active STAT1 dimer. (B) Hypoxia-reperfusion injury may directly activate p38 MAPK which phosphorylates STAT1 on SER727. Serine phosphorylated STAT could then participate in protein-protein interactions with other STAT binding proteins and activate the expression of pro-apoptotic genes like FAS. (C) In this case, hypoxia-reperfusion may activate STAT5 resulting in activation of cell survival pathways. STAT5 activation by hypoxia may be mediated by JAK2 and a STAT5/cSrc/PI-3 kinase/Akt pathway. (D) STAT3 may act as a constitutive FAS repressor, but FAS is de-repressed during UV stress which may involve STAT3 inhibition by PI3-kinase/Akt. Finally, while our understanding of a stress-related p38/STAT1 (pSER) pathway seems to be taking shape, very few studies have investigated the role of serine phosphorylation of other STATs and what the upstream kinase (s) may be during cellular stress. Future studies might focus on identifying whether serine phosphorylation is common to other STATs during cellular stress and how this might relate to activation or inactivation of target genes. Other questions to be answered are what is the function of unphosphorylated STAT dimers found in the nucleus of unstimulated cells and how might other STAT post-translational modifications (other than phosphorylation) mediate STAT signaling beyond the canonical JAK/STAT pathways [ 39 - 41 ]. Answers to these questions may help to begin to unravel the complex nature of STAT signaling and how some of the alternative routes of STAT activation are related to cellular stress-activated pathways. Ultimately, modulation of the JAK/STAT pathway in vivo may prove to be of therapeutic value.

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A sugar beet chlorophyll a/b binding protein promoter void of G-box like elements confers strong and leaf specific reporter gene expression in transgenic sugar beet

Background Modification of leaf traits in sugar beet requires a strong leaf specific promoter. With such a promoter, expression in taproots can be avoided which may otherwise take away available energy resources for sugar accumulation. Results Suppression Subtractive Hybridization (SSH) was utilized to generate an enriched and equalized cDNA library for leaf expressed genes from sugar beet. Fourteen cDNA fragments corresponding to thirteen different genes were isolated. Northern blot analysis indicates the desired tissue specificity of these genes. The promoters for two chlorophyll a/b binding protein genes (Bv cab 11 and Bv cab 12) were isolated, linked to reporter genes, and transformed into sugar beet using promoter reporter gene fusions. Transient and transgenic analysis indicate that both promoters direct leaf specific gene expression. A bioinformatic analysis revealed that the Bv cab 11 promoter is void of G-box like regulatory elements with a palindromic ACGT core sequence. The data indicate that the presence of a G-box element is not a prerequisite for leaf specific and light induced gene expression in sugar beet. Conclusions This work shows that SSH can be successfully employed for the identification and subsequent isolation of tissue specific sugar beet promoters. These promoters are shown to drive strong leaf specific gene expression in transgenic sugar beet. The application of these promoters for expressing resistance improving genes against foliar diseases is discussed.

Background Sugar beet ( Beta vulgaris L.) is a biennial plant, a member of the Chenopodiaceae family [ 1 ]. In the first year after germination, a rosette of leaves develops while the taproot swells and accumulates sucrose. In the second year, flower initiation is induced after vernalization in the preceding winter. Beets are harvested at the end of the first year when sugar content of the taproot is high. Transgenic approaches towards modification of specific traits comprise the increase of pathogen resistance, the increase of sugar content and the improvement of sugar storage. These approaches require promoters that direct gene expression in a timely and spatial manner which is determined by the desired expression profile of the transgene. In many cases improvement of transgenic traits in plants were achieved by using specific promoters [ 2 , 3 ]. Furthermore, to accomplish high tissue specific protein production in transgenic plants, often promoters from photosynthetic or storage specific genes are employed [ 4 , 5 ]. For the identification of desired promoters, a subtractive approach to enrich differentially expressed genes or a large scale approach to identify these genes in cDNA libraries may be employed prior to promoter isolation. One possible way to identify nonredundant clones in a cDNA library is the method of oligonucleotide fingerprinting (ofp) which was recently applied to sugar beet [ 6 ]. With this approach different cDNAs can be identified on a large scale basis within a cDNA library. While a large scale ofp approach is a feasible method to identify differentially expressed genes in different cDNA libraries, this method is very cost intensive and hence not applicable for many research groups. A straight forward approach for the isolation of differentially expressed genes was achieved by the "Suppression Subtractive Hybridization" method (SSH) [ 7 ]. SSH is a cDNA- and a PCR-based technique that includes a step for the equalization of the abundance of different cDNA fragments during subtractive hybridization. Combined with suppression PCR, selective amplification of differentially expressed cDNA sequences was achieved without the application of physical separation methods [ 7 ]. This method was recently applied to isolate taproot expressed genes from sugar beet [ 8 ]. Here we have employed SSH for the isolation of leaf expressed sugar beet genes. Among the genes isolated was a cDNA fragment for a light-harvesting chlorophyll a/b binding protein (CAB). It is shown that sugar beet genotypes harbor either one or two cab genes that are both expressed. To investigate the use of the cab promoters for gene expression, the 5' regulatory sequences were isolated and linked to reporter genes. Transient reporter gene assays indicate that both promoters are expressed in sugar beet leaves. In transgenic sugar beet both promoters are expressed in green tissue. Sequence analysis revealed that the cab 11 promoter, in contrast to the cab 12 promoter, is void of G-box like regulatory sites with a palindromic ACGT core sequence. A leaf specific promoter in transgenic sugar beets can be employed for biotechnological applications. Results Identification of leaf expressed genes from sugar beet To isolate cDNA fragments corresponding to leaf expressed genes, poly(A)+RNAs from leaf and taproot were isolated and subjected to cDNA synthesis. Suppression subtractive hybridization was performed as described in Methods. A total of 23 cDNA clones specific for the subtraction for leaf expressed genes were isolated and sequenced (data not shown). Fourteen out of 23 cDNAs were found to be different. Table 1 shows that 10 out of these 14 clones have homology mainly to nuclear encoded genes involved in the calvin cycle and in photosynthesis. Fragments L6 and L11 detect homology to the same gene but do not overlap. Four clones do not have any sequence homology to a known gene. To further confirm that the enriched cDNA fragments correspond to tissue specific genes, Northern blot hybridizations to total RNA isolated from leaves (L), taproots (R), stems (S), and inflorescences (I) were performed with 7 cDNA fragments. Figure 1 shows that all but one of the cDNA fragments hybridize specifically or much stronger to RNA from leaves than to RNA from taproots. Clone L5 hybridizes with similar intensity to RNA from all four tissues. The expression profile of the gene for SSH fragment L2 encoding a chlorophyll a/b binding protein (CAB) was analysed in more detail. RNA from different tissues and from different developmental stages were used in RNA blot experiments. RNA was isolated from different organs of sugar beets from a field 4, 6, 10, 12, 16 and 19 weeks after sowing. This interval covers the lifetime of a sugar beet in central Europe. Figure 2 shows that the gene is expressed only in above ground tissue like petiole, sink and source leaf regardless of the developmental stage. Since no expression is observed in below ground tissue like primary and lateral root the promoter of the cab gene was isolated and analysed with reporter gene fusions. Compared to the other genes, the expression profile of the gene for SSH fragment L2 showed the strongest tissue specificity during the different time points analysed (data not shown). Isolation of two promoters for the light-harvesting chlorophyll a/b binding protein For the isolation of a sugar beet promoter corresponding to the cab gene, a complete cDNA clone was isolated (GenBank Acc. Nr. AJ579711, see Methods). A homology search with the encoded 252 amino acid long protein reveals a 87% identity to the cab 11 and 85% identity to the cab 12 gene from tomato encoding chlorophyll a/b binding protein [ 9 ]. Prior to the isolation of genomic clones, a gene copy number analysis was performed. DNA from sugar beet genotypes 1K0088 and 4B5421 was digested with different restriction enzymes and hybridized to the L2 fragment (Table 1 ). Figure 3 shows that in genomic DNA from genotype 1K0088 three ( Eco RI, Hin dIII) and four ( Pst I) hybridizing fragments are detected, while only one ( Eco RI) or two ( Pst I, Hin dIII) fragments are detected in genotype 4B5421. From this result it is concluded that genotype 4B5421 harbors one and genotype 1K0088 harbors two copies of the cab gene. Genomic clones for the two different genes were isolated (Methods). Sequence comparison between the cDNA and both genomic clones indicate a very high degree of sequence identity within the coding region. The CAB11 and CAB12 amino acid sequence differ only in one position (data not shown). From both genes the promoter regions were subcloned into plasmid vectors and sequenced (Methods). Sequence of 1148 and 3049 base pairs, respectively, containing most of the upstream region were deposited to GenBank (Acc. Nr. AX449166 and AX449167). The 1148 bp promoter fragment is designated cab 11 promoter and the 3049 bp fragment cab 12 promoter. Both fragments harbor 51 base pairs coding region of the CAB protein and 113 ( cab 11) and 70 ( cab 12) base pairs upstream untranslated sequence. Upstream of the untranslated region only about 300 bp are homologous between the two promoters while the rest of the sequence is highly divergent (data not shown). Because the cDNA clone isolated before originates from the genotype 4B5421 and corresponds to the cab 11 gene, it was investigated if the second gene is also transcribed. Towards these ends 5' RACE amplifications were performed with RNA from genotype 1K0088 and sequenced. This analysis revealed that the cab 12 gene is also transcribed (data not shown). Transient expression assays in sugar beet leaves To test whether the isolated promoters confer reporter gene expression in sugar beet leaves, a transient assay was performed. Towards these ends translational fusions of promoter fragments with the luciferase gene from Photinus pyralis in the vector pGEM-luc were constructed (see Methods). Table 2 summarizes the relative gene expression strength obtained with the different promoter reporter gene constructs when transformed into sugar beet leaves. Relative to the CaMV 35S promoter, the strength of all promoter fragments is approximately 20% and no significant drop in gene expression is observed between the largest and smallest promoter fragments. In summary, it can be concluded that the 1097 bp long fragment from the cab 11 gene and the 342 bp fragment from the cab 12 gene harbor all cis -regulatory sequences required for gene expression in sugar beet leaves at least under the conditions of the transient bioassay. The promoter of two chlorophyll a/b binding protein genes confers leaf specific and light inducible gene expression in transgenic sugar beet plants To investigate if the cab 11 and cab 12 promoters from sugar beet drive reporter gene expression in transgenic plants, promoter reporter gene fusions were introduced via Agrobacterium mediated transformation in sugar beet (see Methods). The length of the cab 11 promoter fragment is 1097 bp and the length of the cab 12 promoter fragment 2998 bp. Leaves of transgenic lines transferred to the greenhouse were analysed for reporter gene activity. The cab 11 promoter of 12 independent transformants showed a specific β-glucuronidase (GUS) activity from 9 to 40599 pmol Mu × min -1 × mg -1 , respectively (Fig. 4A ). The cab 12 promoter of 4 transformants showed a specific activity from 223 to 11656 pmol Mu × min -1 × mg -1 , respectively (Fig. 4B ). These results indicate that the 1097 bp cab 11 promoter fragment and the 2998 bp cab 12 promoter fragment are sufficient to confer promoter activity to transgenic sugar beet leaves. Furthermore, the cab 11 promoter seems to be stronger than the cab 12 promoter although more transgenic lines were analysed for cab 11 than for cab 12. In order to analyse if the cab 11 and cab 12 promoters confer tissue specific expression to sugar beet, the roots of three transgenic cab 11 and three cab 12 promoter lines were analysed. According to the strength of the cab 11 and cab 12 promoters in leaves (Fig. 4A and 4B ) transgenic lines were selected which show low, moderate or high GUS activity in leaves. None of the lines showed GUS activity in the roots which was above the background level of nontransgenic control plants (Fig. 4C ). Therefore the promoter activity of the cab 11 and cab 12 regulatory element is restricted to the above ground tissue of sugar beet and absent in roots. This result is consistent with the observations that transcripts of the cab genes are not detectable in the below ground tissue by Northern blot hybridization (Fig. 2 ). To analyse cab 11 and cab 12 gene regulation in response to light, the reporter gene activtity of etiolated and green transgenic sugar beets was compared. In vitro shoots of the transgenic lines C1-121, C1-122, C2-50 and C2-52 were etiolated for 40 days in the dark. The GUS activity of the etiolated leaflets of one half of the plants was determined. Seven days after illumination the GUS activity of the remaining plants was determined after greening of the leaves. In two independent experiments the cab 11 and cab 12 promoter plants showed a 4,3 to 8,3 fold and a 95 to 118 fold induction during greening, respectively (Table 3 ). Although the GUS activity of the etiolated plants was comparable in the two assays, the greening plants showed a much stronger reporter gene activity in the second experiment. Apparently, different time points during the differentiation of etioplasts to chloroplasts were analysed. Time after illumination seems to have a strong influence on the level of promoter induction. Furthermore, gene expression is much stronger in these experiments compared to the analysis in transgenic plants (Table 3 , Fig. 4 ). Finally, these results show that the cab 11 and cab 12 promoter are activated during the light induced plastid development. The cab 11 promoter lacks G-box elements with a palindromic ACGT core sequence Gene expression is mainly regulated by the binding of transcription factors to specific cis -regulatory elements. Because the two sugar beet promoters show a similar expression profile, it was investigated if there are any differences or similarities in the composition of cis -regulatory sequences in both promoters. Towards these ends, a database-assisted approach was employed [ 10 ]. The Patch™ program [ 11 ] was used to inspect both promoter sequences for the occurence of plant transcription factor (TF) binding sites that are annotated to the TRANSFAC ® database [ 11 ]. The results reveal a large number of putative TF binding sites in both promoters (data not shown). Upon closer inspection of the results it was striking that the cab 11 promoter, in contrast to the cab 12 promoter was completely void of putative G-box like binding sites that contain a conserved ACGT core sequence and are recognized by bZIP transcription factors. Using the program Patch™ and entering a lower score boundary of 100% to detect only experimentally verified binding sites, only two putative binding sites for bZIP transcription factors were found in the cab 11 promoter. Figure 5 shows the positions of these two motifs that both lack the ACGT core sequence. The sequence motifs at position -749 and -489 relative to the translation start site were found in the rice glutelin-B1 promoter and are bound by the rice bZIP transcription factor family RISBZ [ 12 ]. Both sites were also found to be bound by the tobacco bZIP transcription factor TGA1a in a pea lectin promoter [ 13 ]. Inspecting the sequence of the cab 12 promoter for the ACGT core sequence of bZIP factor binding sites reveals 12 positions for this motif (data not shown). Using the program Patch™ six experimentally verified binding sites for bZIP factors were detected among these twelve sites that harbor the ACGT core in the cab 12 promoter (Fig. 5 ). The motif at position -2104 is also present in the glutathione-S transferase 6 gene promoter of Arabidopsis where it is bound by the factor OBF4 [ 14 ]. The same site and the sites at position -1608 and -1247 occur in the embryonic abundant protein 1 promoter of rice and are recognized by the factors OSBZ8 and TRAB1 [ 15 - 17 ]. The sites at position -1767 and -1599 were recognized as bZIP binding sites in many other systems. The sequence TGACGT is part of the as-1 element of the CaMV 35S promoter that was shown to be bound by tobacco TGA1a, TGA1b, and TGA2.2 [ 18 , 19 ]. The site at position -659 is also present in the CaMV 35S promoter where it was shown to be bound by the wheat nuclear factor HBP-1 [ 20 ]. The observation that the cab 11 promoter lacks G-box like elements with a conserved ACGT core sequence indicates that such sites are not required for leaf specific gene expression. Discussion The chlorophyll a/b binding proteins CAB11 and CAB12 from sugar beet belong to the light harvesting complex I – 730 (LHCI-730) Subtractive hybridization was used to isolate leaf expressed genes from sugar beet. The goal was the identification of a promoter that drives leaf specific gene expression in transgenic sugar beet plants. Among seven analysed genes a cDNA fragment corresponding to a chlorophyll a/b binding protein gene was shown by RNA gel blot hybridization to be highly specific for green tissue (Fig. 1 and 2 ). Genomic DNA blot hybridizations indicate that the two sugar beet genotypes investigated harbor either one or two copies of the gene designated Bv cab 11 and Bv cab 12 (Fig. 3 ). A complete cDNA for the gene from genotype 4B5421 was isolated and encodes a protein of 252 amino acids that shows the highest homology (87%) to the cab 11 gene from the light harvesting complex I (LHCI) in tomato [ 9 ]. This and homologies to other LHCI proteins indicate that the sugar beet gene belongs to the type IV LHCI complex [ 21 ]. Further support for this classification comes from the observation that the intron positions between cab 11 from tomato and Bv cab 11 from sugar beet are identical (data not shown). LHCI can be subdivided into at least two different chlorophyll-protein complexes, one of which appears to be responsible for the 730 nm fluorescence of PSI (LHCI-730) and the other complex (LHCI-680) fluoresces at lower wavelength [ 21 ]. In barley the LHCI-730 complex was isolated as a heterodimer composed of the type I and type IV polypeptides [ 22 ]. Furthermore, tomato type I and type IV LHCI polypeptides (Lhca1/ cab 6a and Lhca4/ cab 11) expressed in E. coli form a heterodimer in vitro that closely resembles the native LHCI-730 dimer from tomato leaves [ 23 ]. Therefore, the sugar beet CAB11 and CAB12 proteins may be part of the LHCI-730 complex. G-box like elements are not a prerequisite for leaf specific gene expression The promoters for both sugar beet cab genes were isolated and linked to reporter genes. Transient gene expression studies in sugar beet indicated that 1097 bp upstream of the ATG from the Bv cab 11 gene and 342 bp upstream of the ATG from the Bv cab 12 gene are sufficient for leaf specific gene expression in sugar beet (Table 2 ). Promoter reporter gene constructs for Bv cab 11 and Bv cab 12 were stably transformed into sugar beet ( Beta vulgaris , var. VRB). In sugar beet both promoters are expressed in leaves (Fig. 4 ). When the promoter sequences of both cab genes where analysed for putative transcription factor binding sites, a striking difference was observed. The Bv cab 11 promoter lacks G-box like sequences with a palindromic ACGT core. Are G-boxes required for light or leaf specific gene expression? A 268 bp fragment of the wheat cab -1 promoter functions as a light responsive and organ specific enhancer in transgenic tobacco [ 24 ]. Most notably the three regions that interact with nuclear factors and that were able to enhance gene expression of a 90 bp CaMV 35S minimal promoter did not contain a G-box sequence [ 24 ]. The requirement of G-box sequences for light specific gene expression has also been analysed directly [ 25 ]. A trimer of the G-box motif found in the spinach ribulose-1,5-bisphosphate carboxylase small subunit-1 promoter was fused to a 90 bp CaMV 35S minimal promoter. While a mutant of this G-box did not confer gene expression to the minimal promoter in the dark and under different light conditions, the G-box increased reporter gene expression under these conditions [ 25 ]. Reporter gene expression in the dark was comparably higher than under different light conditions. This is similar to the finding that a G-box like sequence in the cab 1R gene of rice is necessary for high level transient expression of a reporter gene in tobacco leaf tissue [ 26 ]. Taken together, this indicates that the presence of G-box sequences may have a quantitative effect but may not be a prerequisite for green tissue specific gene expression in sugar beet. Biotechnological applications of leaf specific promoters in sugar beet The major goal of this work was the isolation of a strong leaf specific sugar beet promoter that can be used for biotechnological applications. Disease control is one of the most important goals for biotechnological approaches towards improving sugar beet performance. There are many leaf spot diseases that are detrimental to the plant. For example, Cercospora leaf spot is one of the most widespread and destructive foliar diseases of sugar beet [ 27 ]. Expressing resistance improving genes in a strong and specific manner against pathogens causing foliar diseases may require a strong leaf specific promoter. With such a promoter, expression in taproots can be avoided which may otherwise take away available energy resources for sugar accumulation. The work here shows that two promoters, Bv cab 11 and Bv cab 12, have been isolated that drive highly leaf specific gene expression in sugar beet (Fig. 4 ). No expression above background levels was detected for both promoters in sugar beet roots (Fig. 4C ). Based on the expression strength in transgenic plants, the Bv cab 11 promoter may be suitable for biotechnological applications because it achieves a reporter gene activity comparable to the strong CaMV 35S promoter. CaMV 35S-mediated GUS activities in transgenic tobacco plants were reported as 113000 U (average of 10 plants, [ 28 ]) and 9000 U (average of 15 plants, [ 29 ]) in which 1 Unit refers to pmol 4-Mu produced min -1 × mg protein -1 [ 30 ]. The highest level of cab 11 derived GUS expression is 40599 pmol Mu x min -1 × mg -1 which is comparable with the expression strength of the strong CaMV 35S promoter in tobacco. Conclusions In summary, this work presents the isolation and expression analysis of two cab promoters from sugar beet. Particularly, the Bv cab 11 promoter may be useful to drive strong and specific gene expression in transgenic host plants. The lack of bZIP binding sites harboring the ACGT core sequence could also be advantageous for transient analysis of bZIP transcription factors when using a Bv cab 11 reporter gene construct as a transformation control. Furthermore these promoters may be useful to express resistance improving genes against foliar diseases such as Cercospora leaf spot. Methods Preparation of RNA and genomic DNA Two different methods for RNA preparation were employed. To isolate RNA for cDNA subtraction, the procedure described below was followed. For some of the Northern blot analyses a method described earlier was employed [ 31 ]. For RNA isolation plant material was homogenized in liquid nitrogen and resuspended in a solution containing 4 M guanidinthiocyanat, 25 mM Tris-HCl, pH8 und 100 mM β-mercaptoethanol. After centrifugation (4°C, 10 min. at 3300 rcf) nucleic acids in the supernatant were precipitated by addition of 0.03 volume sodium acetate (3 M, pH5) and 0.75 volume ethanol (100%) and incubation over night at -20°C. After centrifugation (4°C, 10000 g , 10 min.) the nucleic acid containing pellet was dissolved in 20 ml 100 mM NaCl, 10 mM EDTA pH8, 50 mM Tris-HCl pH8, and 0.2% SDS. Afterwards, a phenol:chloroform (1:1) and a chloroform:isoamylalcohol (24:1) extraction was performed. The pH of the aqueous solution was adjusted to about 5 with acidic acid and nucleic acids were precipitated by addition of 0.6 volume isopropanol and 0.05 volume 4 M NaCl and incubation for 2 hrs at -20°C. After centrifugation (20–30 min., 10000 g at 4°C) the nucleic acids containing pellet was resuspended in 10 ml H 2 O DEPC containing 0.1% SDS. Total RNA was precipitated by addition of 0.25 volume 8 M LiCl and incubation for at least 15 hrs at 4°C with subsequent centrifugation for 20 min at 4°C, 10000 g . Total RNA was resuspended in 400 μl H 2 O DEPC . After ethanol precipitation (addition of 0.1 volume sodium acetate, 3 M pH4.8, and 2.5 volume ethanol) total RNA was resuspended in a volume of 50–100 μl H 2 O DEPC . The isolation of poly(A)+ RNA was carried out with the Oligotex Kit according to the manufacturers protocol (Qiagen; Hilden, Germany). Measurements of RNA yield and electrophoretic separation on formaldehyde gels were done following standard protocols [[ 32 ], modified]. Genomic DNA was isolated from sugar beet genotypes 1K0088 and 4B5421 according to a previous published method [ 33 ]. For recombinant DNA work standard techniques were employed [ 32 ]. Suppression subtractive hybridization The synthesis of cDNA was performed using the CLONTECH PCR-Select™ cDNA Subtraction Kit (Heidelberg, Germany). Each synthesis was carried out with 8 μg poly(A)+ RNA from sugar beet isolated either from leaves or taproots. Subtractive hybridization was done following the user manual (PT1117-1) of the CLONTECH PCR-Select™ cDNA Subtraction Kit. After the second PCR the amplified fragments from the forward and the reverse subtraction were cloned into the PCR cloning vector pCR ® 2.1. For each microgram PCR product approximately 300 recombinant plasmids were obtained. For the cloning of PCR products the Invitrogen T/A Cloning ® Kit was employed (Karlsruhe, Germany). Prior to ligation into pCR ® 2.1 the subtracted PCR cDNA products were subjected to an additional incubation of 1 hour at 72°C with dATP and Taq polymerase (TaKaRa; Gennevilliers, France) to ensure that the majority of the PCR fragments contain "A-overhangs" for an efficient cloning into the T/A cloning vector. DNA sequence analysis The inserts of the plasmids were sequenced with fluorescently labeled M13 reverse and forward (-20) primers using the AutoRead Sequencing Kit (Pharmacia) and the Automated Laser Fluorescent A.L.F.™ DNA Sequencer from Pharmacia LKB (Freiburg, Germany). The DNA sequence analysis of the genomic and full-length cDNA clones was done by the custom sequencing service of MWG Biotech AG (Ebersberg, Germany). Sequences were subjected to data bank analysis using the BLAST algorithms [ 34 ] and analysed with the PILEUP programme of the GCG Wisconsin Analysis Package. For further promoter analysis the TRANSFAC ® database was employed [ 11 ]. DNA sequences were also processed and analysed on a Macintosh computer using DNA Strider 1.3 [ 35 ] and a PC computer using Vector NTI Suite 8.0 (Informax). Southern and Northern blot hybridizations Radioactive probes were generated by the method of random hexamer priming with the Amersham Multiprime DNA Labelling System (Freiburg, Germany). Southern and Northern hybridizations were carried out following standard protocols [ 32 , 36 ]. For genomic Southern blot hybridizations 10 μg of DNA from sugar beet genotypes 1K0088 and 4B5421 was digested with different restriction enzymes. Electrophoretic separation, transfer to Hybond nylon membranes (Amersham Pharmacia Biotech, Freiburg), hybridization to radioactive probes, and exposure of the membrane to X-ray films were done according to standard protocols [ 32 ]. Radioactive probes were generated by labelling 20 ng of DNA with 50 μCi P 32 -dATP (6000 Ci/mMol, Amersham Pharmacia Biotech, Freiburg). Isolation of cDNA and genomic clones A leaf specific, directional cDNA library from sugar beet genotype 4B5421 was synthesized by the custom cDNA library service of GIBCO BRL (Rockville, USA) and cloned into the plasmid vector pCMV Sport 6.0. Screening of the library was done according to standard protocols [ 32 ]. Seven positive cab cDNA clones were identified after screening of 10000 clones using the SSH fragment L2 as a probe (Table 1 ). The longest cDNA is 1062 bp long, harbors a 114 bp non-translated leader, a 756 bp long reading frame, a 177 bp 3' nontranslated leader, and a 15 bp poly A tail (data not shown, GenBank Acc. Nr. AJ579711). A genomic library from sugar beet genotype 1K0088 was generated in the lambda vector EMBL3 SP6/T7 and screened using standard protocols [ 32 ]. Genomic clones for two different cab loci were isolated. The promoter for the gene cab 11 is present on a Cla I fragment that was subcloned into a plasmid vector and completely sequenced. The fragment is 6294 bp long and contains 51 bp from the coding region of the gene. The resulting plasmid was designated pC1a. Additionally, a 6026 bp large Sal I/ Cla I fragment was released from the phage clone and subcloned into a Bluescript plasmid and designated pC1b. The promoter for the gene cab 12 is present on a Pst I fragment that was also subcloned into a plasmid vector and completely sequenced. The fragment is 4002 bp long and the harboring plasmid was designated pC2. From both genomic clones 1148 and 3049 base pairs containing most of the upstream region were deposited in GenBank (Acc. Nr. AX449166 and AX449167). The 1148 bp promoter fragment is designated cab 11 promoter and the 3049 bp fragment cab 12 promoter. Promoter reporter gene constructs For transient gene expression assays, promoter fragments were linked as translational fusions to the luciferase reporter gene from Photinus pyralis in the reporter gene vector pGEM-luc (Promega, Mannheim). To introduce a plant polyA addition signal into pGEM-luc the respective fragment was isolated from pBI101.3 (Clontech, Heidelberg) by Eco RI digestion, followed by a Klenow fill in reaction and by redigestion with Sac I. This released a 260 bp DNA fragment from the nopaline synthase ( nos ) gene containing the polyA addition signal. To directionally clone this fragment into pGEM-luc, this plasmid was first linearised with Sfi I, treated with T4-polymerase to generate blunt ends and subsequently redigested with Sac I. After inserting the nos fragment the resulting plasmid was designated pLuc-nos2. To insert the cab 11 promoter fragment, a Sal I(fill in)- Avi II fragment was cloned into the Apa I linearised and T4-polymerase treated plasmid pLuc-nos2. This plasmid harbors 1145 bp from the cab 11 promoter including the coding sequence for the first 16 amino acids of the cab 11 gene. This plasmid was designated pC1L-1097. In this plasmid the luciferase gene is translationally fused with the first 16 amino acids from the cab 11 gene. A second plasmid was generated which harbors additional upstream sequences. Towards these ends a 6099 bp Kpn I fragment was released from the plasmid pC1b (see above) and the ends treated with T4-polymerase. The fragment was redigested with Not I and the desired fragment was directionally cloned as a Kpn I(blunt end)- Not I fragment upstream of the cab 11 fragment in pC1L-1097. To generate compatible ends pC1L-1097 was digested with Hin dIII treated with T4-polymerase and redigested with NotI. The resulting plasmid was designated pC1L-7126. To clone the promoter for gene cab 12 upstream to the luciferase coding region, the promoter fragment from pC2 was released by Not I/ Eco RI digestion and subsequently subjected to a partial digestion with Avi II. A 3100 bp long Not I/ Avi II fragment was purified and subcloned into pLuc-nos2. The plasmid pLuc-nos2 was digested with Apa I, the ends treated with T4-polymerase and redigested with Not I. After ligation the resulting plasmid was designated pC2L-2998. In this plasmid the luciferase gene is translationally fused with the first 16 amino acids from the cab 12 gene. To generate 5' promoter deletions pC2L-2998 was (1) digested with Kpn I/ Not I, T4-polymerase treated, and religated to yield pC2L-1827, (2) digested with Sma I and religated to yield pC2L-989, and (3) digested with Not I and Sal I (partial), Klenow polymerase treated, and religated to yield pC2L-342. For stable transformation the cab 11 and cab 12 promoters were cloned 5' to the β-glucuronidase gene ( uid A). Towards these ends a 1.17 kb Hin dIII/ Bam HI fragment was released from pC1L-1097 and cloned into the binary vector pBI101.3 (Clontech, Heidelberg). The resulting plasmid pC1G-1097 harbors a translational fusion between the first 16 amino acids of the cab 11 gene and the uid A gene. Similarly, the cab 12 promoter was released as a Pst I fragment from plamid pC2L-2998, treated with T4-polymerase then digested with Bam HI and subcloned into pBI101.3 which was linearised with Sal I, ends filled in with Klenow and redigested with Bam HI. The resulting plasmid was named pC2G-2998. Transient and stable gene expression assays The luciferase expression from plasmids pC1L-1097, pC1L-7126, pC2L-2998, pC2L-1827, pC2L-989, and pC2L-342 were measured in sugar beet leaves after biolistic transformation [ 37 ]. For biolistic transformation the PDS-1000/He Particle Delivery System (BioRad, München, Germany) was used. Microcarrier was gold powder type 200-03 (Heraeus, Hanau, Germany) with a diameter of 1.09–2.04 micrometer. The transformation protocol supplied by the manufacturer of the particle delivery system was followed. Equimolar amounts of plamids pC1L-1097 and pC1L-7126 were used. Similarly, equimolar amounts of plamids pC2L-2998, pC2L-1827, pC2L-989, and pC2L-342 were used. To quantify gene expression the transformation control plasmid p70Sruc harboring the luciferase gene from Renilla reniformis under the control of the doupled CaMV 35S promoter was employed as a second reporter gene [ 38 ]. For each reporter gene construct three (pC1L-series) or four (pC2L-series) bombardments were made, gene expression strength of both luciferases measured and normalised relative to the luciferase expression of p70Sruc (see below). For each bombardment 13 leaf discs of equal diameter were cut out of sugar beet leaves and preincubated for 6 hours in petri dishes on MS-media containing 0.4 M mannitol at 25°C. The particle delivery conditions were 1550 psi, 9 cm distance and 27 Hg low pressure. After bombardment the petri dishes with the leaf discs were incubated for 16 h at 25°C under constant light. The Photinus and Renilla luciferase activity were measured with the dual-luciferase reporter assay system (Promega, Mannheim, Germany) in a Lumat 9501 luminometer (PE Biosystem) according to the protocol of the supplier. For the generation of transgenic plants pC1G-1097 and pC2G-2998 were directly transformed into Agrobacterium tumefaciens strain GV2260 [ 39 ]. Agrobacterium tumefaciens mediated transformation techniques were performed with the binary T-DNA plasmids on sugar beet ( Beta vulgaris , var. VRB) according to [ 40 ]. Selection of the transgenic plants was carried out on kanamycin. β-Glucuronidase (GUS) activity in crude leaf extracts was determined as described by Jefferson et al. [ 41 ] using 4-methylumbelliferone beta-glucuronide as a substrate. The concentration of the product 4-methylumbelliferone (Mu) was determined with a multiwell fluorescence plate reader (Millipore CytoFluor 2350). Protein content was measured by the method of Bradford (BioRad protein assay kit). Enzyme activity was calculated as pmol Mu × min -1 × mg -1 . Authors' contributions DS isolated the cDNA and genomic clones, analysed the transcription during different developmental stages and tissues, generated promoter reporter gene constructs, transformed sugar beet, and performed the quantitative reporter gene assays. DUK performed the suppression subtractive hybridization and analysed the homology and tissue specificity of the cDNA fragments. RH identified cis -regulatory elements. RH and DS conceived of the study, and participated in its design and coordination.

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554092

Opisthobranchia (Mollusca, Gastropoda) – more than just slimy slugs. Shell reduction and its implications on defence and foraging

Background In general shell-less slugs are considered to be slimy animals with a rather dull appearance and a pest to garden plants. But marine slugs usually are beautifully coloured animals belonging to the less-known Opisthobranchia. They are characterized by a large array of interesting biological phenomena, usually related to foraging and/or defence. In this paper our knowledge of shell reduction, correlated with the evolution of different defensive and foraging strategies is reviewed, and new results on histology of different glandular systems are included. Results Based on a phylogeny obtained by morphological and histological data, the parallel reduction of the shell within the different groups is outlined. Major food sources are given and glandular structures are described as possible defensive structures in the external epithelia, and as internal glands. Conclusion According to phylogenetic analyses, the reduction of the shell correlates with the evolution of defensive strategies. Many different kinds of defence structures, like cleptocnides, mantle dermal formations (MDFs), and acid glands, are only present in shell-less slugs. In several cases, it is not clear whether the defensive devices were a prerequisite for the reduction of the shell, or reduction occurred before. Reduction of the shell and acquisition of different defensive structures had an implication on exploration of new food sources and therefore likely enhanced adaptive radiation of several groups.

Background Very often, non-shelled gastropods are considered to be slimy and non-attractive. This connotation usually refers to terrestrial species of the Stylommatophora belonging to the well-known Limacidae or Arionidae and in particular to garden snails, which do have negative effects on our horticulture. However, the Opisthobranchia are beautifully coloured "slimy" gastropods and exclusively occur in marine habitats. Non-scientists only meet these animals while diving for pleasure. One group of opisthobranchs, however, has become very famous throughout natural and even medical sciences: Aplysia californica Cooper, 1863, the sea hare, belonging to the subgroup Anaspidea (Fig. 1E ). It is a classic example for neurobiological investigations, involving behaviour. It was E.R. Kandel, who performed many of his investigations on learning and memory on this animal [ 1 ]. He created the basic understanding of nerve functioning and learning in human beings and was awarded the noble prize of medicine in 2000 for his life time research on these animals. Figure 1 Examples of opisthobranch species. A Micromelo undata (Bruguière, 1792) (Acteonoidea) – Queensland, Australia, B Scaphander lignarius (Linné, 1758) (Cephalaspidea) – Northern Sea, C Chelidonura pallida Risbec, 1951 (Cephalaspidea) – Queensland, Australia, D Elysiella pusilla Bergh, 1872 (Sacoglossa) from the Indo Pacific, feeding on the green alga Halimeda ; due to incorporation of chloroplasts, Elysiella has the same colour as the algae, E Aplysia punctata (Cuvier, 1803) (Anaspidea) – Mediterranean Sea, F Tylodina perversa (Gmelin, 1791) (Tylodinoidea) – Mediterranean Sea, G Pleurobranchaea meckelii Meckel in Leue, 1813 Mediterranean Sea. Only in very few species of Opisthobranchia, the shell is big enough so that the animal can withdraw completely. In most species the shell is reduced in size, internalised or lost completely. Opisthobranchs are much less diverse in species numbers (5000 to 6000) than the terrestrial Stylommatophora (about 30000 species), or the shelled marine gastropods, in former times named "Prosobranchia" (60000 species), but they show many biological features that are unique or rare in the animal kingdom and that are often related to foraging or defensive strategies [ 2 ]. These include incorporation and usage of intact chloroplasts from algal cells for feeding strategies (Sacoglossa, Fig. 1D ) [ 3 ], or storage of intact cnidocysts from cnidarians for defence (Aeolidoidea, Fig. 2E, G, H ) [ 4 ]. Some of them are able to synthesize toxic compounds or to uptake these secondary metabolites from their food in order to use them as repellents (Nudibranchia, Chromodorididae, Fig. 2C ) [ 5 ]. Many of these biological phenomena are hardly understood because investigation of biological data is scarce. Evolution of different strategies is not known, because of the lack of well-supported phylogenetic analyses. Figure 2 Examples of nudibranch species. A Armina neapolitana (Delle Chiaje, 1824) (Cladobranchia) – Mediterranean Sea, B Risbecia tryoni (Garrett, 1873) (Anthobranchia) – Queensland, Australia, C Glossodoris cruenta Rudman, 1986 (Anthobranchia) – Queensland, Australia, D Bornella stellifer (Adams & Reeve in Adams, 1848) (Cladobranchia) – Queensland, Australia, E Flabellina exoptata Gosliner & Willan, 1991 (Cladobranchia) – Queensland, Australia, F Ceratosoma magnifica (Van Hasselt, 1824) (Anthobranchia) – New South Wales, Australia, G Spurilla major (Eliot, 1903) (Cladobranchia) – Queensland, Australia, H Cuthona sibogae Bergh, 1905 (Cladobranchia) – Queensland, Australia Knowledge on the different subgroups of the Opisthobranchia differs according to their availability and spectacular appearance. For example, many more data on feeding strategies and other biological features are available for the beautifully coloured Nudibranchia (Fig. 2 ), than for the tiny and inconspicuous Acochlidacea. The authors have worked on different aspects of the biology of the Opisthobranchia for many years, trying to promote our understanding of this peculiar group. Although the Opisthobranchia is a rather small taxon, this group is ideal for evolutionary studies. Recently, Wägele reviewed potential key characters that might have enhanced radiation within the Opisthobranchia [ 2 ]. She has used a working hypothesis on opisthobranch phylogeny and published data on different strategies to deduce her proposals. She discussed the gizzard in Cephalaspidea, kleptoplasty in Sacoglossa, kleptocnides in Aeolidoidea, symbiosis with unicellular algae in Phyllodesmium Ehrenberg, 1831 and mantle dermal formations in Chromodorididae. Cimino and Ghiselin [ 6 ] and Cimino et al. [ 7 ] discussed the loss of the shell and the acquisition of toxic substances as a driving force in the evolution of Sacoglossa. Glandular structures and acquisition of chemical defence is subject of several reviews [ 8 - 13 ]. In the present review, our knowledge on Opisthobranchia is briefly summarized with emphasis on reduction of the shell and its implications on life history, especially regarding foraging and defence. Additionally new results on several glandular structures are presented. Some glands are described here for the first time. We point to a new aspect in the evolution of defensive devices. Their primary function as excretory or detoxification organs should be taken into consideration. It is beyond the scope of this review to include all new data on morphology, histology and phylogeny, as well as the literature published on the Opisthobranchia. The intention is to draw attention to a fascinating group of animals with a species number of manageable size, and in which similar evolutionary traits occurring in different groups at the same time and their implications can be analysed. Results Figure 3 represents a preliminary phylogenetic tree of the Opisthobranchia, as well as a few members of the Pulmonata and basal Heterobranchia. This tree is based on data obtained by morphological and histological analyses. Characters are listed in table 1 (see additional file 2 ) and the data matrix in table 2 (see additional file 1 ). A complete discussion of characters and obtained trees is in preparation. In the tree presented here, all characters are treated as unweighted and unordered (see methods below). A similar comprehensive tree of the Opisthobranchia, based on 18S and 28S genes is published by Vonnemann et al. [ 14 ]. These authors did not include basal Heterobranchia, Pteropoda and enigmatic forms, e.g., the Rhodopidae (Fig. 4 ). Comparison of these two most recently obtained morphological and genetic trees shows that nearly all major opisthobranch subgroups are monophyletic, but the position of some of these groups differs between the cladograms. These differences mainly concern the position of the Acteonoidea, Tylodinoidea and Acochlidacea (highlighted in both Figs. 3 and 4 by bold bars). Nevertheless evolutionary traits concerning the fate of the shell can be detected within well-defined clades (see Figs. 3 and 4 : shell internalisation is indicated by grey arrows, shell loss by black arrows). Several groups included in the trees will not be considered in this study, because they are not assigned to the Opisthobranchia. These groups are the basal Heterobranchia, Pyramidellidae and Pulmonata. Furthermore, this discussion focuses more closely on the morphology-based tree, because more taxa are included there. Figure 3 Phylogeny of Opisthobranchia. Cladogram based on morphological data. Grey arrows indicate internalisation, black arrows the loss, of the shell. Positions of Acteonoidea, Tylodinoidea and Acochlidacea are marked by bold lines, because they differ from those on the gene-based tree (Fig. 4) Figure 4 Phylogeny of Opisthobranchia. Cladogram based on 18S and 28S gene, after Vonnemann et al. (in press). Positions of Acteonoidea, Tylodinoidea and Acochlidacea are marked by bold lines, because they differ from those on the morphology-based tree (Fig. 3). Grey arrows indicate the internalisation, black arrows the loss, of the shell. Description of monophyletic groups Acteonoidea The families Acteonidae and Hydatinidae form sister-groups, the position of the debatable monophylum is under discussion. All acteonoids have a shell that resembles that of many prosobranchs (Fig. 1A ). Some of the members are able to withdraw completely into the shell and to close the shell with an operculum, e.g. Acteon tornatilis (Linné, 1758). Acteonidae and Hydatinidae are carnivorous and mainly feed on polychaetes. No defensive strategies are known from these animals although histological investigations show a highly glandular area in the mantle cavity and the mantle rim. The mantle rim glands, for example, are very conspicuous. These comprise large epithelial cells that are filled with a non-staining vacuole (Fig. 5A ). The glandular area is highly folded. The cells appear to lie subepithelially due to their size. They alternate with small ciliated cells. The hypobranchial gland in the roof of the mantle cavity is small and consists of violet-staining epithelial cells indicating acid mucopolysaccharides (Fig. 5B ). Figure 5 Glandular structures in opisthobranch species. A Acteon tornatilis (Linné, 1758) (Acteonoidea), mantle rim glands. B Bullina lineata (Gray, 1825) (Acteonoidea), hypobranchial gland. Note the ciliated raphe (arrow). C Clione limacina (Phipps, 1774) (Pteropoda, Gymnosomata), large single cellular glands (arrows). D Haminoea antillarum (d'Orbigny, 1841) (Cephalaspidea), Blochmann glands. E Chelidonura tsurugensis Baba & Abe, 1959 (Cephalaspidea), hypobranchial gland with violet stained glandular cells and above (arrow), single bluish stained glandular cells. F Dolabrifera dolabrifera (Cuvier, 1817) (Anaspidea), gland of Bohadsch, or opaline gland. G Umbraculum umbraculum (Lightfoot, 1786) (Tylodinoidea), dorsal mantle gland. H Tylodina perversa (Gmelin, 1791) (Tylodinoidea), dorsal mantle glands in the free mantle rim; above lies the shell. Pteropoda This group living exclusively in pelagic waters comprises two major clades, the herbivorous Thecosomata and the carnivorous Gymnosomata. Many thecosomates still have a shell whereas the Gymnosomata have lost it. The latter feed on the former. Many morphological adaptations have occurred due to their life in pelagic waters. Defensive mechanisms are hardly known from pteropods. Whereas the Thecosomata do not show specialized defensive glandular structures in the outer epithelium, peculiar structures of rather unknown function can be found in the two species of Gymnosomata investigated here. Both species show single large glandular cells, with one vacuole and a larger nucleus. The contents of the vacuoles only stain in smaller, probably immature, cells. In larger cells they are translucent (Fig. 5C ). Cephalaspidea Members of following families have been included in this analysis: Smaragdinellidae, Haminoeidae, Retusidae, Cylichnidae, Bullidae, Philinidae, Aglajidae, Gastropteridae and Diaphanidae. Monophyly on family level was not recovered for the Diaphanidae and Cylichnidae. Furthermore, the Cephalaspidea (Acteonoidea excluded) is not monophyletic due to the inclusion of the Anaspidea. In our analysis, the gizzard-bearing groups form one clade, representing the Anaspidea (Fig. 1E ) and the Cephalaspidean families Smaragdinellidae, Haminoeidae, Retusidae, Cylichnidae (Fig. 1B ), Bullidae and Philinidae. Runcina Forbes & Hanley, 1853 and a yet undescribed Philinoglossa Hertling, 1932, both with a gizzard, are not part of that monophyletic group. The gizzard is a muscular oesophagus with 3 to 10 large gizzard plates that function like a grinding mill (Fig. 6A ). Feeding strategies are highly diverse within these different groups. Herbivory is known only from cephalaspids with gizzard plates, whereas carnivory is widely spread within all other cephalaspidean groups with or without a gizzard. Prey items are mainly polychaetes, bivalves and in a few cases congeners. Runcina and Philinoglossa feed on diatoms. Figure 6 Anatomical features characteristic for some opisthobranch species. A Haminoea antillarum (d'Orbigny, 1841) (Cephalaspidea), gizzard. B Aeolidia papillosa (Linné, 1761) (Nudibranchia, Dexiarchia, Aeolidoidea), longitudinal section of ceras with cnidosac with cleptocnides and outleading duct. C Aeolidia papillosa , oral tube with subepithelial glandular tissue. D Tomthompsonia antarctica (Thiele, J., 1912) (Pleurobranchoidea), spicules in notal tissue. E Onchidoris bilamellata (Linnaeus, 1767) (Nudibranchia, Anthobranchia), spicules in notal tissue. Whereas many herbivorous slugs still have an external shell, e.g. Bulla Linné, 1758, Haminoea Turton & Kingston, 1830, Retusa Brown, 1827, Cylichna Lovén, 1846, etc., only a few carnivorous cephalaspids have retained a large shell (e.g. Scaphander Montfort, 1810) (Fig. 1B ). Many have an internalized shell (Fig. 1C , Chelidonura Adams, 1850, Gastropteron Meckel in Kosse, 1813) or have lost it all together ( Siphopteron Gosliner, 1989). Cephalaspideans have several glandular structures, although their function is hardly understood. The hypobranchial gland is composed of epithelial cells staining violet (Fig. 5E ). This gland can be very voluminous (e.g. in Haminoea callidegenita Gibson & Chia, 1989) or can be reduced (in many cephalaspids with small and reduced mantle cavity, e.g. Chelidonura tsurugensis Baba & Abe, 1959). In many Cephalaspidea single glandular cells can be observed that stain bluish and open to the outside by a small duct (Fig. 5E , glandular cells above hypobranchial gland). These glands usually are confined to the mantle cavity roof. A special type of single gland is present in very few members of the Cephalaspidea, namely the Blochmann's glands. One characteristic is the duct leading to the outside that is composed of a few small cuboidal cells. The contents of these glands do not stain (Fig. 5D ). Anaspidea This group, which is mainly characterized by two pairs of head tentacles (Fig. 1E ), is closely related to the gizzard bearing cephalaspids. Nearly all species have a reduced shell or no shell at all (none of the latter species are included in the analyses presented here). In general, Anaspidea feed on red, brown and green algae. The group is known for their defensive habits, by using an ink gland when disturbed. The glands are typical Blochmann's glands already described for Cephalaspidea. Another gland is also widespread in Anaspidea and assumed to be an additional defensive gland, the so-called gland of Bohadsch, or opaline gland (Fig. 5F ). It is composed of large cells containing a large nucleus. In general they are considered to be special forms of the Blochmann's gland [ 8 , 15 ]. The glands open at the bottom of the visceral cavity and stain violet. In a few species single glandular cells are arranged around a single opening. Additionally, the single blue-stained subepithelial glandular cells already described in Cephalaspidea (see Fig. 5E ) are present in the dorsal mantle cavity. Tylodinoidea This tiny group is characterized by a rather large foot, and the umbrella-like shell covering the viscera, but not the foot (Fig. 1F ). Data on biology of this small group are scarce. Probably all of them feed on poriferans. The Mediterranean Tylodina perversa (Gmelin, 1791) fosters the secondary metabolites from its exclusive food, the sponge Aplysina aerophoba Schmidt, 1862 [ 16 ]. Becerro et al. [ 17 ] demonstrated that the slug actively selects for sponges with a high concentration of cyanobacteria, whereas sponges without these bacteria (e.g. A. aerophoba from deeper waters, or A. cavernicola ) were neglected. Histological investigation show that the Tylodinoidea have several peculiar glands. In the dorsal mantle tissue of Umbraculum umbraculum (Lightfoot, 1786), many tubules of a highly ramifying gland (Fig. 5G ) lead to one or two main ducts that open to the outside in the anterior mantle rim above the mouth. Tylodina perversa has glandular tissue in the same area as U. umbraculum , but it has several ducts leading to the outside, all lying at the anterior dorsal mantle rim (Fig. 5H ). Sacoglossa This group is monophyletic. In the morphological tree presented here (Fig. 3 ), the non-shelled Elysia species appears as the most basal one, whereas shell-bearing sacoglossans, like Cylindrobulla , are more derived. External morphology of sacoglossan species shows a high diversity. A rather primitive large and coiled shell is present in Cylindrobulla Fischer, 1857 and Ascobulla Marcus Ev., 1972. Others, like Oxynoe Rafinesque, 1814 and Lobiger Krohn, 1847, have tiny shells, whereas the shell is lost in all Elysiidae (Fig. 1D ). A peculiar bivalved shell is present in the family Juliidae. Many sacoglossans feed on green algae (Ulvophycea) by piercing the algal cells with their radular teeth and by sucking the contents into their digestive tract. Defensive glands are not so obvious, but cryptic appearance obtained by an uptake and storage of chloroplasts is evident for many species as can be seen for Elysiella pusilla Bergh, 1872 feeding on the alga Halimeda Lamouroux, 1816 (Fig. 1D ). Investigated Sacoglossa are characterized by many subepithelial glands with violet-stained contents (Fig. 7A ). But the quantity of these cells differs to a great extent among species. Placobranchus ocellatus van Hasselt, 1824, a shell-less slug, is unique in having many globular structures arranged along the edge of the mantle rim (Fig. 7B ). These structures have a diameter of nearly 1 mm and are composed of many cells each with a large vacuole. The contents of the vacuole stain bluish. Figure 7 Glandular structures in opisthobranch species. A Elysia crispata (Moerch, 1863) (Sacoglossa), subepithelial glandular cells in dorsal epithelium. B Plakobranchus ocellatus van Hasselt, 1824 (Sacoglossa), mantle dermal formation in the edge of the parapodia. C Berthellina citrina (Rüppell & Leuckart, 1828) (Pleurobranchoidea), median buccal gland in visceral cavity, producing sulphuric acid. D Berthella edwardsi (Vayssiére, 1896) (Pleurobranchoidea), acid glands lying in the notum tissue. E Thecacera pennigera (Montagu, 1815) (Nudibranchia, Anthobranchia), subepithelial glandular cells in dorsal epithelium. F Marionia blainvillea (Risso, 1818) (Nudibranchia, Dexiarchia, Dendronotoidea), epithelial glandular cells with unusual large vacuoles filled with homogenously stained contents. G Risbecia tryoni (Garrett, 1873) (Nudibranchia, Anthobranchia), mantle dermal formations (MDFs) along the posterior mantle rim. Pleurobranchoidea Shells, when present, are internalised (Fig. 1G ). Pleurobranch species feed on different prey items, but they are all carnivorous, some even feed on congeners. Typical for the group is a huge acid gland lying in the visceral cavity and opening into the oral tube next to the mouth. The gland is composed of huge cells with non-staining vacuoles (Fig. 7C ). Additionally, several species show a highly glandular notum epithelium with huge subepithelial glandular follicles composed of cells with non-staining vacuoles (Fig. 7D ). Several members, like Tomthompsonia Wägele & Hain, 1991, have spicules in their notum (Fig. 6D ). Nudibranchia All members of the monophyletic Nudibranchia have lost the shell completely (Fig. 2A–H ). This taxon, with about 3000 species and a high diversity in shape and in biology, is the largest opisthobranch group and comprises about half of the known opisthobranch species. Two monophyletic clades can be recognized, the Anthobranchia (Fig. 2B, C, F ) and the Dexiarchia (Fig. 2A, D, E, G, H ). The former mainly feed on Porifera, Bryozoa and Tunicata, the latter on Cnidaria, mainly on octocorals. Defensive strategies are very diverse in the Nudibranchia and comprise different techniques. Many species of the Anthobranchia, especially those feeding on sponges, have spicules in the notum (Fig. 6E ). Many species are characterized by a highly glandular epidermis (Fig. 7E ). Nearly all members of the very species-rich family Chromodorididae (Fig. 2B ) have so called mantle dermal formations (MDFs) lying in the mantle tissue (Fig. 7G ). These are globular structures with a diameter of up to 1 mm. MDFs are composed of cells with huge non-staining vacuoles. Many Dexiarchia species are also characterized by a glandular epidermis. Especially members of the Dendronotoidea (here Tritonia Cuvier, 1798 and Dendronotus Alder & Hancock, 1845) are characterized by epithelial glandular cells in which the vacuole is filled with homogenously stained contents (Fig. 7F ). Aeolidoidea have so called cnidosacs at the tip of their notal cerata that represent the apical parts of the digestive glandular tubes running within the cerata. In these cnidosacs, the cnidocysts from their cnidarian prey are stored and can be used against potential enemies (Fig. 6B ). Other groups in the cladogram According to the phylogeny presented in Fig. 3 , several taxa are united in a monophylum. Systematic relationships of some groups have been discussed for a long time (Acochlidiacea, Rhodopidae), others have been considered to belong to the Cephalaspidea (Philinoglossidae, Runcinidae). Their sister-taxon relationship is not solved yet and the presented cladogram is debatable. Nevertheless they have in common some evolutionary traits, e.g. the complete loss of the shell, their small size compared to other opisthobranchs and their food preference for diatoms and detritus. According to histological results, no particular defensive glands could be detected and defensive strategies probably lay in habits. Many of them burrow in sand and/or are cryptic in colour. Discussion In accordance with published phylogenies based on morphology [ 18 - 20 ], or based on genes [ 14 , 21 , 22 ] all major clades presented here are monophyletic (Acteonoidea – but see the study by Mikkelsen [ 23 ], Cephalaspidea with Anaspidea included, Sacoglossa, Tylodinoidea, Pleurobranchoidea, Nudibranchia, Anthobranchia, Cladobranchia). Concerning relationships of major groups, several congruencies with former analyses can be observed: The sister-taxon relationship of the Nudibranchia and Pleurobranchoidea [ 14 , 24 ] is found in nearly all analyses. This group nowadays is called Nudipleura Wägele & Willan, 2000. A further consistent grouping is formed by Cephalaspidea s.str. and Anaspidea. This relationship was already discussed by Mikkelsen [ 20 , 23 ]. All other presented groupings are still under debate. In our morphology-based tree, Elysia represents the most basal taxon within the Sacoglossa. This contradicts other available phylogenetic analyses and has to be considered with care. Jensen presented a thorough phylogenetic analysis on Sacoglossa [ 25 ]. According to her results, taxa with a shell are more basal and shell reduction occurred at least twice within the Sacoglossa. In the discussion below, we follow the results of Jensen and Mikkelsen and consider the shell-bearing sacoglossans as more basal [ 23 , 25 ]. Despite these incongruities, a discussion of shell reduction in the different groups and its implications on life history (habitat, feeding and defensive strategies) can be undertaken, and will serve as a guideline for further investigations. Implications on life style A shell is generally considered to be a protection against predators, such as fish, crabs and other vagile organisms. "If the shell of a whelk (e.g. Buccinum , a "prosobranchiate" caenogastropod, annotation of the authors) is broken away and the soft animal is then offered to a hungry cod, it is eaten readily." (p: 115) [ 26 ]. Reduction, internalisation or loss of the shell within Opisthobranchia implies other defensive strategies. Shell reduction within molluscs is uncommon, and occurs mainly in the highly mobile cephalopods. In gastropods, shell loss is rare in paraphyletic prosobranchs, and known only from few groups of Pulmonata, e.g., the Gymnomorpha and the stylommatophoran groups Arionidae and Limacidae. However, shell reduction occurred many times within the different subgroups of Opisthobranchia. Here, an internalization or complete loss occurs within the Cephalaspidea s.str, Anaspidea, Sacoglossa, Acochlidiacea and Pleurobranchoidea (Fig. 3 ). Whereas complete loss of the shell is not known from any member of the small taxon Tylodinoidea (about 15 species), this character state occurs in the stemline of the Nudibranchia and Gymnosomata. When estimating species numbers with no shell or a rather tiny internal shell and comparing this to the number of species with a larger external shell, the former outnumber the latter by far. Loss of the shell therefore can be assumed to have advantages compared to the presence of a protective but heavy shell. Advantages probably lay in the exploration of new habitats, which are more difficult to reach when being protected by a shell. This can be observed e.g. in a subgroup of the Cladobranchia. The Aeolidoidea are able to graze on fragile hydrozoans (Fig. 8E ). This kind of prey is used by few other invertebrates, e.g., Solenogastres, members of the Pycnogonida and of the Amphipoda [ 27 - 29 ]. Burrowing forms with a shell, e.g., Scaphander or Acteon Montfort, 1810, have an elaborate cephalic shield that partially covers the shell and renders them streamlined. Loss of the shell probably enables slugs to search for food in sandy or muddy habitats more easily. This is the case for members of the Cephalaspidea s.str. and Acochlidiacea. Figure 8 Examples of cryptic nudibranch species. A Discodoris atromaculata Bergh, 1905 (Anthobranchia) from the Mediterranean, attached to the roof of a cave between Parazoanthus , B Jorunna tomentosa (Cuvier, 1804) (Anthobranchia) from the Northern Sea, attached to rocks with corralineacean red algae and mimicking a sponge (Halichondria), C Phyllidia flava Aradas, 1847 (Anthobranchia) from the Mediterranean Sea, feeding on Axinella cf. cannabina and incorporating the dyes. D Phyllodesmium briareum (Bergh, 1896) (arrow, Dexiarchia, Aeolidoidea) from the Indo Pacific, mimicking its food, the soft coral Briareum violacea . E Flabellina affinis (Gmelin, 1791) (Dexiarchia, Aeolidoidea) from the Mediterranean, crawling on its food Eudendrium racemosus (Cnidaria, Hydrozoa). Basal members of the Sacoglossa have retained a shell, but more derived ones have lost it. Shell loss allowed evolution of a phenomenon that is unique in the animal kingdom. Sacoglossa in general feed on algae by piercing the cells with their tooth and sucking out the contents of the cell. The cytoplasm is digested, but in many species (e.g. Elysia timida (Risso, 1818), Placobranchus ocellatus ) the chloroplasts are stored in distinct branches of the digestive gland. Here they are stored for a period of several days to months [ 30 ]. For this phenomenon, the term "cleptoplasty" is used by several authors [ 25 ]. The functioning chloroplasts continue with photosynthesis within the slug and provide nutritional metabolites for the metabolism of the gastropod [ 3 , 13 , 31 , 32 ]. Penetration of light into the slug would be hindered by the possession of a shell. A similar system is observed in members of the Nudibranchia, e.g., in Phyllodesmium jakobsenae Burghardt & Wägele, 2004, or Melibe bucephala Bergh, 1902) [ 33 , 34 ]. Here unicellular algae (zooxanthellae) from the coral food or from the free water column are stored in the digestive system and metabolites of these zooxanthellae are used for the slug's own purposes [ 35 - 37 ]. According to published phylogenies and to our own results (unpublished data of both authors) on Sacoglossa and Nudibranchia, it can be assumed that uptake of chloroplasts or zooxanthellae first enhanced crypsis (Fig. 1D ) [ 25 ]. The short-term storage allows a continuation of the photosynthetic activity of chloroplasts within the slug. Storage over a longer period allowed the reduction of food uptake with the possibilities to search for new and/or less frequent prey organisms [ 2 , 38 ]. The most effective symbiotic relationships are known for the sacoglossan Elysia chlorotica Gould, 1870, which can survive eight months without food [ 3 ], the aeolid Pteraeolidia ianthina (Angas, 1864) and the dendronotoidean Melibe bucephala , both of which survived in our aquaria for 10 months without food (Burghardt & Wägele unpublished data). Implications on defence Loss of a shell as a protective structure led to an array of different defensive structures. Some of these traits can be observed as a combination in one and the same species. Crypsis can be observed in many groups and is very often achieved by incorporation of the same dyes from the food (Fig. 8C , Phyllidia flava Aradas, 1847). Cryptic appearance also is achieved by mimicking the same patterns or even outline of the substrate. Corambe pacifica MacFarland & O'Donoghue, 1929 perfectly mimics the colour patterns of its prey, the bryozoan Membranipora de Blainville, 1830. Phyllodesmium jakobsenae mimics the feathered polyps of the soft coral Xenia Lamarck, 1816, on which it lives [ 33 ], whereas the cerata of P. briareum (Bergh, 1896) are smooth like the tentacles of its prey, the soft coral Briareum Blainville, 1830 (Fig. 8D ). Zebra effects are achieved by patterns with blotches like that in Peltodoris atromaculata Bergh, 1880 (Fig. 8A ) or by stripes. Looking like unpalatable sponges (Fig. 8B , Jorunna tomentosa (Cuvier, 1804)) is very common in spicule-bearing dorids. According to Gosliner [ 39 ], the cryptic species are rather basal taxa, whereas the taxa with aposematic colour patterns are more derived – a hypothesis that has yet to be proven by thorough phylogenetic analyses that include all species of the subgroup in question. A unique defensive strategy within animals is the storage of cnidocysts ("cleptocnides"), which is typical for nearly all members of the cladobranch Aeolidoidea [ 4 , 9 , 40 ]. This group mainly feeds on cnidarians, with priority on Hydrozoa. The mechanisms of the uptake of cnidocysts, so that explosion is not triggered during consumption, are still not understood. It is assumed that the slug exudes a mucus to hinder explosion [ 9 , 26 ]. Investigated aeolids, like Aeolidia papillosa (Linné, 1761), have a highly glandular oral tube (Fig. 6C ) that supports this hypothesis. Another theory implies that there occurs a kind of acclimation process, similar to that discussed between sea anemones and anemone fish [ 41 ]. According to the investigations of Greenwood and Mariscal [ 42 ] only immature cnidocysts are stored in the cnidosac, whereas mature ones are digested. But, histological investigation of many aeolids directly collected from their food have not revealed high numbers of exploded cnidocysts in the stomach (unpublished data of HW). Only Notaeolidia schmekelae Wägele, 1990 from the Antarctic Ocean has been observed to have many exploded cnidocysts in its digestive tract [ 43 ]. Presence of spicules in the notum as a defensive strategy was discussed by several authors [ 10 , 44 ]. Spicules are present in many shell-less Anthobranchia and Acochlidiacea, but also in members of the Pleurobranchoidea, which sometimes have an internalised small shell. Spicules never occur in opisthobranchs with a larger shell. Cattaneo-Vietti et al. investigated the mineral composition of dorid spicules and found calcite (CaCO 3 ) and brucite (Mg(OH) 2 ) [ 45 ]. Smaller spherules are composed only of calcite. Harris described feeding experiments offering various opisthobranchs to specimens of Navanax Pilsbry, 1895 (Cephalaspidea), who is a ferocious predator on opisthobranchs [ 10 ]. This species rejected all spiculose dorids. Another evolutionary trait for defence, and discussed as a prerequisite for shell reduction at least in sacoglossans [ 13 ], is the uptake or de novo synthesis of secondary metabolites that are toxic to possible predators [ 5 , 46 ]. Uptake by feeding on toxic prey (mainly algae, Porifera, Bryozoa, Tunicata and Cnidaria) is the major source of compounds, whereas de novo synthesis is known only from few taxa [ 5 ]. When dietary derived, Avila called these cleptochemicals, following the terms cleptoplasts and cleptocnides for incorporation and use of chloroplasts in Sacoglossa and cnidocysts in Aeolidoidea [ 5 ]. Literature on chemical compounds in opisthobranchs is numerous. Some reviews summarize our knowledge [ 5 , 7 , 46 - 49 ]. Compounds mainly belong to the terpenoids, especially the insoluble sesquiterpenoids and diterpenoids. Little is known about the function of the biological compounds, although their defensive tasks are very often postulated [ 5 - 7 , 46 ]. Few feeding experiments have been performed in the past, demonstrating a toxic effect on crustaceans and/or fish [ 26 , 50 ]. Also the translocation from prey into the slug, and the transformation by changing the chemical structures either by degradation through digestion, or by an active mechanism into a more effective chemical, is hardly understood [ 5 , 7 ]. Location of the compounds is investigated only for few species, by analysing certain parts of the body [ 51 ], or even by isolating larger organs, like the MDFs [ 52 ]. Tracing the compounds within the tissue, or even cells, using immunohistochemical methods has never been done. Therefore, it is not possible to correlate chemical bioactivity with certain histological structures, except for the mantle dermal formations in the species Hypselodoris webbi (Chromodorididae) [ 52 ]. Inorganic compounds, like sulphuric acid are produced in few groups. Their function and location is better known due to the extensive work of Thompson [ 53 - 55 ]. He analysed the production of sulphuric acid in different members of Gastropoda, including members of the Pleurobranchoidea, Cephalaspidea and Dorididae. The exudated acid contains inorganic chloride and sulphate anions, and traces of organic substances. He was able to localize the acid by histochemistry within the large vacuoles in the median buccal gland (Fig. 7C ) and the subepithelial glands of Pleurobranchoidea (Fig. 7D ). There, the acid is held in active form [ 53 ]. Gillete et al. investigated the role of the central nervous system and peripheral nerves for exudation and showed positive feed back [ 56 ]. Broad histological investigations of the Opisthobranchia show that many species are characterized by a large array of glandular structures [ 8 , 11 , 57 - 59 ]. These comprise single glandular cells lying in the outer epithelia, or subepithelially. Glandular follicles composed of several cells usually lie subepidermally and open via a duct to the outside. Larger organs are the MDFs, or the glandular tubules of the median buccal gland in the Pleurobranchoidea. Some of these structures have been known for a long time and their defensive tasks were discussed in more detail by Hoffmann [ 8 ]. Well known are the ink gland (Blochmann's glands) and the opaline gland (Bohadsch gland) in the Anaspidea. Both glands exude substances that have been shown to be toxic to cnidarians [ 1 ]. Probably these substances also caused severe damage of the liver of a 40-year-old man, who ate Aplysia kurodai Baba, 1937 [ 60 ]. By experimental studies it was shown that the repellent substance in the ink gland is a monomethyl ester of phycoerythrobilin and is derived from phycoerythrin from the consumed red algae [ 61 ]. The role of the opaline gland is less known. According to Carté, the prosteroglandine with the highest known biological activity is Dolastatin 10, a natural product extracted from the anaspidean Dolabella auricularia (Lightfoot, 1786) [ 62 ]. This large species of more than 10 cm lives on the intertidal flats in the tropical Indo-Pacific, where it would represent an ideal food for birds and fishes, if not for that highly toxic chemical. This substance is already applied in medical treatments (see ), and seems to be one of the most potent anticancer agents. Information on other glandular structures are rare, and nearly nothing is known about their contents and their functions. At the moment we are not able to trace the different substances in these glands to find out whether there are any constraints concerning structure (and therefore function) and the stored chemicals. Only few hypotheses are formulated concerning acquisition of toxicity and loss of the shell. Faulkner & Ghiselin assumed that chemical defence based on metabolites derived from food preceded the reduction of the shell and that chemical defence has been a driving force behind the evolution of Opisthobranchia [ 13 , 46 , 63 ]. Cimino et al., by analysing the different compounds and their origin, came to the conclusion that evolution within the Sacoglossa started with the uptake and storage of sesquiterpenoids from algae in species still having a shell [ 7 ]. Within the shell-less members of the family Elysiidae, diterpenoids from the algae were stored, whereas in highly evolved forms, like Elysia timida , the slugs switched to a de novo synthesis of polyproprionates. Cimino & Ghiselin also mentioned that handling and utilization of a particular kind of defensive metabolite allowed the switch to food with similar compounds quite easily, and therefore has driven adaptive radiation [ 46 ]. As an example they named the dorids and in particular the family Chromodorididae, which show a large array of usage of biochemicals from different sponges. Again, the bio-synthesis of compounds, as observed in Dendrodoris Ehrenberg, 1831, is considered to be the most derived form of defence within the Anthobranchia. Information on defensive strategies, as listed above, is available now for several groups of the Opisthobranchia. More and more reliable phylogenies are becoming available, which allow the identification of well-supported branches and stemlines. Combining this knowledge, it becomes evident that several defensive systems evolved before the loss of the shell (several glandular structures, e. g., the hypobranchial gland, mantle rim glands, Bohadsch gland). Here we would like to extend the hypothesis of Cimino et al. by addressing the problem of excretion [ 7 ]. It can not be ruled out that certain glandular structures evolved as a kind of excretory system to get rid of ingested toxic substances. Therefore it is not storage in special organs that preceded the use of toxic substances, but the necessity to expel them. By analysing phylogeny, it is evident that many defensive structures evolved after the internalisation or loss of shells (e.g. acid glands in the notum, cleptocnides, MDFs). But we still have to identify the location of the compounds for a better understanding of the evolutionary history concerning the acquisition of toxicity, which certainly was a driving force in the evolution of these fascinating opisthobranchs. New techniques, e.g., the oligonucleotide aptameres, could help to solve this question [ 64 ]. We also have to keep in mind that chemical substances might not only play a role in defence (allomones), but also in reproduction and development (pheromones). Conclusions In this review it is shown that shell loss led to the evolution of a wide array of defensive strategies in Opisthobranchia. Nevertheless, it is not ruled out that some defensive mechanisms have evolved prior to complete loss of shell. This is evident when analysing the Acteonoidea. Members of this taxon have a rather thick and large shell, but also different glandular structures along the mantle fold, as well as in the mantle cavity. One working hypothesis for future research is that defensive glands evolved from simple storage organs while feeding on toxic prey. Evolution of special structures where toxic substances could be stored without further effect on the body and that functioned as a kind of excretory system could have been the prerequisite for employing these structures as defensive devices. To solve this question, thorough phylogenetic analyses are needed. Tracing toxic substances from the food into the different organs or glands by histochemistry or new analytical methods like aptameres will help to understand the tasks of these glands, and their role as excretory or as defensive organs, or as pheromone-producing organs involved in reproduction and development. Additional field and laboratory experiments with potential predators from the natural surroundings are necessary to understand the functioning of chemicals in the slugs. Methods Material collection About 300 different species of Opisthobranchia have been collected and investigated by the authors in the past 20 years. Collecting was performed from the intertidal (e.g. Australia, Helgoland), to the sublitoral zones (e.g. Mediterranean Sea, North Atlantic, tropical waters in the Red Sea, Australia and others) down to depth of 1000 m (Antarctica). Collecting techniques comprised hand collecting in the intertidal and while diving, or using trawls, like the Agassiz trawl in Polar Seas. Specimens were preserved in 4 – 10 % formaldehyde/seawater for histological investigations, or 96% ethanol for molecular investigations. Investigation techniques Investigation of morphology and anatomy was performed by macroscopical and histological techniques. For histological investigations, entire animals (when small in size) or parts of the animals were embedded in hydroxyethylmethacrylate for serial sectioning (2 μm). Sections were stained with toluidine blue and investigated by light-microscopy. Toluidine blue stains acid mucopolysaccharides in various shades of red to violet, whereas neutral mucopolysaccharides are staining in blue colours. Observation of living animals aided the understanding of external characters and life strategies. Data used for the phylogeny comprise 79 taxa and 110 characters based on morphology and histology. Polarity of characters was obtained by outgroup comparison with Littorina littorea . Due to some trivial characters, an all-zero outgroup was chosen. The characters are explained in full detail by Wägele & Klussmann-Kolb (in prep). Analyses were performed by PAUP 4.0 beta 3a (Swofford, 1999) [ 65 ]. Parameters of maximum parsimony analyses were: ACCTRAN, all characters unordered and unweighted; heuristic search options: stepwise addition = random, branch-swapping option = TBR. Authors' contributions HW and AKK together carried out the phylogenetic analysis based on morphological and histological characters published here for the first time. HW drafted the manuscript. AKK helped to draft the manuscript. Both authors read and approved the final manuscript. Supplementary Material Additional File 1 Data matrix of characters – 79 taxa and 110 characters are included. N = non applicable. This implies that the character is not present and can therefore not be coded. ? = character state not known. Genus with an asterisk indicate that information on this genus is extracted from literature. Detailed information will be given elsewhere in Wägele & Klussmann-Kolb (in prep.). Click here for file Additional File 2 Characters – Characters and coding of character states used for phylogenetic analysis presented in Figure 3 . Click here for file

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Convergent Evolution of Chromosomal Sex-Determining Regions in the Animal and Fungal Kingdoms

Sexual identity is governed by sex chromosomes in plants and animals, and by mating type (MAT) loci in fungi. Comparative analysis of the MAT locus from a species cluster of the human fungal pathogen Cryptococcus revealed sequential evolutionary events that fashioned this large, highly unusual region. We hypothesize that MAT evolved via four main steps, beginning with acquisition of genes into two unlinked sex-determining regions, forming independent gene clusters that then fused via chromosomal translocation. A transitional tripolar intermediate state then converted to a bipolar system via gene conversion or recombination between the linked and unlinked sex-determining regions. MAT was subsequently subjected to intra- and interallelic gene conversion and inversions that suppress recombination. These events resemble those that shaped mammalian sex chromosomes, illustrating convergent evolution in sex-determining structures in the animal and fungal kingdoms.

Introduction Elucidating mechanisms by which sex chromosomes evolved from autosomes has been accelerated by the revolution in genomic sciences. In humans, the male-specific approximately 50–60 Mb Y chromosome evolved via chromosomal rearrangement, gene conversion, duplication, and degeneration over approximately 300 million years to give rise to four distinct evolutionary temporal groupings, or strata ( Lahn and Page 1999 ; Skaletsky et al. 2003 ). In contrast, fungi have much less extensive sexually dimorphic chromosomal regions; in the budding yeast Saccharomyces cerevisiae, the a and α mating types are established by the mating type (MAT) locus, which spans only 642 bp or 747 bp, respectively, and encodes only one or two cell type factors ( Figure 1 ) ( Herskowitz 1989 ). Recent studies of the evolution of ascomycete MAT loci have shown that, despite significant changes in both content and structure, small size has remained a common feature ( Tsong et al. 2003 ; Butler et al. 2004 ). In contrast to mammals and other obligate diploid organisms, fungi are viable as both haploids (the equivalent of gametes in other systems) and diploids. This has influenced the evolution of genes in the sex-determining regions in the two systems, as it enables those in obligate diploids to degenerate to nonfunctional alleles in one of two sex-determining chromosomes. Figure 1 Fungal MAT Locus Paradigms Interaction of mating partners during the fungal sexual cycle is directed by bipolar or tetrapolar mating systems. The budding yeast S. cerevisiae is an ascomycete with bipolar mating (graphic at upper left). The maize pathogen U. maydis is a tetrapolar basidiomycete with multiple mating types conferred by two mating type loci (graphic at upper right). One (a) is biallelic and encodes pheromones and pheromone receptors, while the second (b) is multiallelic and encodes homeodomain transcription factors. In contrast, the human pathogen C. neoformans (lower graphic) is a basidiomycete with a bipolar system with only two mating types ( a and α). The C. neoformans MAT locus encodes homeodomain transcription factors, pheromones and pheromone receptors, other elements of the pheromone activated MAPK cascade, and many genes whose role in mating, if any, is at present unknown. Unlike the exclusively bipolar mating in ascomycetes, basidiomycete fungi usually have more complex tetrapolar mating, in which two unlinked genomic regions establish cell identity, and both must differ for sexual reproduction ( Kahmann et al. 1995 ; Kronstad and Staben 1997 ; Casselton and Olesnicky 1998 ). One locus encodes pheromones and pheromone receptors, while the second encodes homeodomain transcription factors ( Figure 1 ). However, like S. cerevisiae, some haploid basidiomycete species, such as the human fungal pathogen Cryptococcus neoformans, exhibit bipolar mating, in which a single locus establishes mating type . Unlike S. cerevisiae and Schizosaccharomyc pombe, which are homothallic fungi that switch mating type via recombination between silent and active MAT cassettes, Cryptococcus is a heterothallic fungus that has never been observed to switch mating type and lacks any silent MAT cassettes. In contrast to the more restricted ascomycete MAT loci, the C. neoformans MAT locus is unusually large (spanning over 100 kb) and contains more than 20 genes ( Figure 1 ) ( Lengeler et al. 2002 ). Previous studies of this region of the genome have shown the MAT region to be recombinationally suppressed, with meiotic segregants from a cross each receiving a single intact, nonrecombined locus of either the MAT a or MATα type ( Hull and Heitman 2002 ; Lengeler et al. 2002 ). The MAT locus orchestrates sexual development involving cell fusion, formation of dikaryotic hyphae, and subsequent nuclear fusion, meiosis, and sporulation to produce the suspected infectious particles. The α allele of the C. neoformans MAT locus has been linked to environmental prevalence, virulence, differentiation capacity, and unusual fecundity in a recent outbreak ( Kwon-Chung et al. 1992 ; Wickes et al. 1996 ; Fraser et al. 2003 ). C. neoformans exists as two subspecies— Cn. var. grubii and Cn. var. neoformans— that diverged approximately 20 million years ago (mya), and the MAT locus has been characterized in both ( Xu et al. 2000 ; Lengeler et al. 2002 ). The C. neoformans MAT locus encodes the pheromones, pheromone receptors, and homeodomain factors that are usually distributed between the two tetrapolar loci in this phylum, as well as additional pheromone response pathway elements and proteins from many other functional categories ( Lengeler et al. 2002 ), including essential genes ( Figure 2 ). As in the multicellular eukaryotes, this sex-determining structure is large; the impending completion of the Cn. var. neoformans genome reveals that the MAT locus occupies 6% of a 1.8-Mb chromosome in a genome of approximately 20 Mb. Analogous to the human Y chromosome, the sex-determining genes are scattered among others seemingly unrelated to sex. Here we show, on the basis of comparative genomic analysis using new sequences isolated from a primary pathogenic sibling species Cryptococcus gattii, that sequential evolutionary events that fashioned this large, highly unusual region of the Cryptococcus genome can be reconstructed. The Cryptococcus MAT locus therefore provides insights into how complex, dimorphic sex-determining regions evolved from simpler loci containing only one or two genes. Figure 2 Genes of the MAT Locus Comparison of a and α alleles of the MAT genes in the three Cryptococcus lineages based on percent nucleotide identity between the coding sequences of the a and α alleles within that species. K s values were calculated from comparison of the a and α alleles in each species. Genes with unusual K s values are shown in red, and genes from regions flanking MAT are shown in grey. Phylogenetic trees are based on maximum likelihood analysis (scale bar = 0.05 substitutions per site), and are labeled with the phylogenetic class they represent. Further details are presented in Figures S1 and S2 . *The Average K s for CAP1 was calculated after excluding the indicated unusual values. Results The closest known relative to C. neoformans is the sibling species C. gattii , a primary human pathogen which diverged approximately 40 mya ( Xu et al. 2000 , Kwon-Chung et al. 2002 ). C. gattii therefore provides a unique vantage point from which to analyze MAT evolution, via comparative genomics, from a species cluster of human fungal pathogens. The a and α alleles of the MAT locus were cloned and sequenced from two representative C. gattii strains (AY10429, AY10430). Figure 3 shows the structures of the six known MAT alleles. Four features are prominent. First, the mating type-specific sequences span more than 100 kb in all six alleles. Second, the MAT-specific sequences are separated from the genome by sharply demarcated borders; the flanking regions share over 99% nucleotide sequence identity and syntenic gene order, whereas the sequences within MAT are divergent ( Figures 4 and S1 ). Third, comparison of the MAT alleles reveals few genes unique to one mating type (encoding factors Sxi1α and Sxi2 a , the only MAT homeodomain proteins) with the locus composed almost entirely of divergent alleles of a common gene set. Fourth, the MAT gene cohort has been dramatically rearranged during evolution ( Figure 4 ). Whole-genome analysis of Cn. var. neoformans at Stanford and The Institute for Genomic Research (TIGR), Cn. var. grubii at Duke and the Broad Institute, and C. gattii at the University of British Columbia and the Broad Institute, reveal that these rearrangements at MAT are highly atypical compared to the non-MAT regions of the genome in all three species (B. J. Loftus, unpublished data; J. W. Kronstad, personal communication). In addition to the original set of shared genes ( Lengeler et al. 2002 ), comparative analysis employing the new C. gattii sequences revealed that an additional five novel genes are present in all six characterized alleles, including one predicted noncoding gene with no apparent open reading frame. All five were confirmed by RT-PCR analysis to be expressed (unpublished data). In total, genic sequences comprise approximately 50% of MAT (see Figure 3 ). Figure 3 The Structure of MAT Is Highly Rearranged, with Divergent Gene Alleles Embedded in Syntenic Genomic Regions The nonrecombining α (blue) and a (yellow) MAT alleles from the divergent but related species are depicted, spanning more than 100–130 kb and including 10 kb of common flank regions on the left and right demarcated by sharp borders with MAT. The original locations of ancient tetrapolar loci proposed to have given rise to MAT are shown in red (ancestral homeodomain locus) and green (ancestral pheromone/receptor locus), with the most ancient genes (encoding homeodomain transcription factors, pheromones and pheromone receptors) bulleted. Genes that show mating type-specific phylogeny are shown in black, and genes with species-specific phylogeny are white. Synteny between the genes with species-specific phylogeny is indicated with grey boxes. Pseudogenes are labeled in blue, and grey bars represent repeated elements in Cn. var. neoformans . Red arrows represent pheromone amplicons. Figure 4 MAT Is Highly Rearranged between Species and Mating Types The genomic region spanning the nonrecombining α (blue) and a (yellow) MAT alleles from the divergent but related species is depicted, with pink and green colored bars representing regions of synteny, and black lines the relative positions of genes whose position is not conserved. Black arrows depict mating type-specific genes. White arrows represent genes with a species-specific phylogeny. Red arrows represent pheromone amplicons. Our model for the evolution of this unusual structure is that two unlinked sex-determining regions of the genome expanded by acquiring genes of related function, and these two novel gene clusters were then captured into a common genomic region by a chromosomal translocation, entrapping still further genes. This resulted in a tripolar transitional intermediate mating system that collapsed via gene conversion to result in the contiguous, linked MAT alleles and a bipolar system. MAT was then subject to inversions that suppressed recombination, punctuated by ongoing rounds of inter- and intra-allelic gene conversion. Below, we summarize the analyses that support this model. Both maximum likelihood and parsimony analyses reveal that MAT is constructed from genes with different phylogenetic histories; comparing the topologies of the phylograms for each protein-coding gene showed four major classes ( Figures 5 A, S2 , and S3 ). The first class, which we call “ancient,” contains genes in which the alleles share a low level of nucleotide identity and cluster into distinct a and α clades. This pattern represents the most ancient genes contained within this recombinationally suppressed region of the genome, and includes those encoding pheromones, pheromone receptors, and elements of the pheromone-sensing mitogen-activated protein kinase (MAPK) pathway. The second and third classes (which we term “intermediate I” and “intermediate II,” respectively) represent intermediate genes that have progressively higher nucleotide identity between the a and α alleles, and less discrete, although still MAT-specific, phylogenetic patterns ( Figures 2 and 5 A). This pattern reflects genes that have been contained within this recombinationally suppressed region for shorter periods of time. Finally, the last group (“recent”) comprises the five most recently acquired genes that exhibit a species-specific, but not MAT-specific, phylogenetic pattern, similar to genes outside MAT (see Figure 2 ). These distinct patterns mirror the relative length of time each gene has spent within the largely nonrecombining MAT locus (ranging from ancient to recent acquisitions), and provide insight into how this large genomic structure was fashioned. Stated differently, the divergence times for the ancient and intermediate classes in C. gattii and C. neoformans are equivalent (represented by STE20, ZNF1, and SPO14 in Figure 5 ), as these genes entered MAT prior to speciation, while the recent class (represented by RPO41 ) began diverging after this time. The tree topologies of STE20, ZNF1, SPO14, and RPO41 were shown to be statistically different ( p < 0.0001) by the Shimodaira-Hasegawa test ( Shimodaira and Hasegawa 1999 ). Figure 5 MAT Genes Have Different Phylogenetic Histories (A) The genes of the MAT locus can be separated into four distinct groupings based on phylogenetic class, synonymous substitution rate, and nucleotide identity. The C. gattii alleles in each phylogram are encircled in red. (B) The LPD1 gene defines an ancient border of MAT. The 5′ end of the coding region is species-specific, while the 3′ region is mating type-specific. The ancient homeodomain locus is shown in red, and the ancient pheromone/pheromone receptor locus in green. Maximum likelihood trees are shown. Scale bar represents 0.05 substitutions per site. Further details are provided in Figures S2 and S3 . All six MAT alleles contain the same set of five genes that exhibit the unusual species-specific phylogenetic pattern, suggesting that each allele has recruited the same cohort of genes by a common mechanism. One hypothesis we initially considered to explain acquisition of a common gene set is that the locus expanded by recruiting flanking genes. The IKS1 gene is an integral component of the MAT locus in two lineages (Cn. var. grubii and C. gattii), but is a flanking gene in the third (Cn. var. neoformans), providing a unique opportunity to test this model (see Figure 4 ). Phylogenetic analysis reveals that the IKS1 gene tree resembles phylograms for an ancient MAT-specific gene, with the exception of the Cn. var. neoformans lineage, in which gene conversion has fixed the α allele in both mating types with concomitant loss of the ancestral a -specific allele (Figures S2 and S3 ). The ETF1, BSP3, and NCM1 genes share a similar evolutionary history. These genes are therefore not recently acquired, and instead provide examples of gene eviction from MAT by interallelic recombination to result in a reduction in MAT size and complexity in the Cn. var. neoformans lineage. An alternative, more parsimonious model to explain why an identical set of five species-specific genes is present in all six MAT alleles is that these genes were acquired once in the progenitor MAT locus. However, their high level of synteny suggests recent acquisition; the GEF1, CID1, and LPD1 genes are clustered in five of the six MAT alleles, the BSP2 and RPO41 genes are adjacent in all six, and synteny of the entire five-gene cluster has been maintained in three alleles (see Figure 3 ). Our model is that the two ancestral unlinked sex-determining regions were juxtaposed by a chromosomal translocation, entrapping this set of recently acquired common genes which were then subjected to more recent gene conversion. An indication of the relative time over which alleles have resided within MAT and diverged can be inferred from the rate of synonymous mutations (K s ), which accumulate over time and are nearly neutral with respect to selection in organisms such as Cryptococcus, in which strong codon bias is absent ( Li 1993 ; Lahn and Page 1999 ; Nakamura et al. 2000 ). If we compare the alleles of genes outside MAT from a and α strains of the same species, their K s values are close to zero, reflecting freely recombining regions of the genome (see Figure 2 ). By contrast, genes embedded in MAT are largely nonrecombining, and therefore have K s values based on their divergence since the time of acquisition to the locus. The higher the K s value, the longer the genes have been diverging due to entrapment in MAT. This analysis reveals four major classes of genes, which correspond to the ancient ( K s > 2.00), intermediate class I (2.00 > K s > 0.70), intermediate class II (0.60 > K s > 0.35), and most recently acquired genes ( K s < 0.17) (see Figure 2 ). These gene classes are analogous to those shown by phylogenetic analysis. The MAT locus genes therefore partition into four primary groupings based on phylogeny, nucleotide identity, and synonymous substitution rates. In the human Y chromosome, analysis of substitution rates reveals four temporal clusters, or strata, representing the sequential acquisition of genes to the male-specific region ( Lahn and Page 1999 ). The genes in the Cryptococcus MAT locus are no longer stratified by age, because the members of the four identified classes have been heavily shuffled during speciation and mating type divergence, and this rearrangement appears ongoing. However, an analysis of relative gene locations in MAT reveals two alleles in which higher levels of synteny are evident: Cn. var. grubii MATα and C. gattii MAT a (see Figure 4 ). The K s values of genes distributed in these alleles were analyzed to reconstruct the genomic architecture of the common ancestral structure. In both cases, genes from each K s -defined class can be clustered via a single inversion, creating identical groupings in both the a and α alleles—and we hypothesize that this represents the genomic architecture of an ancestral MAT locus ( Figure 6 ). Assuming the components of the ancient tetrapolar system are represented by the pheromone and pheromone receptor genes in one group and homeodomain-encoding genes in the other, the ancient pheromone/pheromone receptor cluster is further divisible into two groups based on K s values ( Figures 2 and 6 ). This implies two major expansion events of this component. First, an ancient recruitment of genes including pheromone-sensing cascade components previously implicated in fertility ( K s > 2.00), followed by more recent acquisition of the intermediate class I genes, whose role in mating has not yet been studied (2.00 > K s > 0.70). In constrast, the intermediate class II genes (0.60 > K s > 0.35) were recruited by expansion of the ancient tetrapolar homeodomain-containing locus. In this model, the expanding ancestral loci are separated by the five genes that exhibit species-specific phylogenies ( Figure 6 ). Figure 6 Reconstructing the Ancient MAT Alleles by Inversion-Mediated Rearrangement Plotting the synonymous mutation rate (K s ) of each protein coding gene in MAT reveals that the different classes of genes in the two least rearranged loci ( Cn. var. grubii MATα and C. gattii MAT a ; see Figure 4 ) can be clustered by a single inversion. This may represent an ancient linked tetrapolar system—one cluster contains the pheromone and pheromone receptor genes (green bars), and the other a homeodomain-encoding gene (red bar). Transposon remnants are present at the extrapolated inversion breakpoint regions in Cn. var. grubii , as indicated (Tn). K s cannot be calculated between the SXI1α and SXI2 a genes, because these are unrelated and not alleles, in contrast to other genes in the locus. We hypothesize that the species-specific genes were then incorporated into the MAT locus by inversions that fused and rearranged the ancient tetrapolar structures. This model is supported by two of the species-specific genes, LPD1 and RPO41, which exhibit an unusual hybrid phylogeny; although the majority of their coding region exhibits a species-specific phylogeny, the 3′ regions are mating type-specific. In two of the three lineages (Cn. var. grubii and Cn. var. neoformans) the LPD1 gene exhibits this hybrid phylogeny, but in C. gattii the 5′ region in both mating types resembles the α alleles, and a K s value of zero for this region is indicative of recent gene conversion ( Figures 2 , 5 B, S2 , and S3 ). Analogous to the Amelogenin locus of primates, which spans an ancient pseudoautosomal boundary ( Iwase et al. 2003 ), this phylogeny suggests that the five entrapped genes were integrated into MAT while the boundary genes were in a state of transition from species to mating type-specificity. Furthermore, it supports models in which these genes maintain species-specific phylogeny via gene conversion. Rearrangement of MAT may be driven by recombination between transposable elements. In the Cn. var. grubii α allele, the breakpoints of the postulated inversion lie in intergenic regions that contain remnants of the Tcn760 mariner-type transposon ( Figure 6 ) ( Lengeler et al. 2002 ). The Cn. var. neoformans MAT locus is rich in transposable elements and remnants (17.3% α and 13.2% a ; see Figure 1 ), a feature shared with the sex chromosomes of humans and mice ( Waterston et al. 2002 ). Comparison with the completed Cn. var. neoformans genome sequence revealed that this represents a greater than 5-fold enrichment relative to the rest of the genome, when the transposon-rich presumptive centromeric regions are excluded. In S. cerevisiae, transposons and their remnants may be principal sites at which chromosomes rearrange in response to growth selection ( Dunham et al. 2002 ), and transposons have been implicated as drivers of genome evolution in a number of eukaryotes, including humans ( Kazazian 2004 ). Similarly, repeated elements may have driven stochastic MAT rearrangements to produce its current structure and efface the vestiges of the ancestral evolutionary strata, as successive random small inversions appear to be a major evolutionary mechanism shaping the locus. Although inversions have previously been implicated in transposing gene order during S. cerevisiae evolution ( Seoighe et al. 2000 ), they have occurred at an unprecedented level in MAT while sparing adjacent regions (see Figure 4 ). In the human Y chromosome, gene decay has been competing with ongoing gene acquisition and conservation ( Skaletsky et al. 2003 ). In Cryptococcus, which is viable as either a haploid or diploid, suppression of meiotic recombination in MAT has not led to loss of any genes, with two minor exceptions. First, unique 5′ truncated pseudogenes exist in three of the alleles ( ΨNCP1 in Cn. var. neoformans MAT a , ΨNAD4 in Cn. var. grubii MATα, and ΨVPS26 in C. gattii MATα). Second, the number of pheromone genes varies between alleles. The a alleles contain three unlinked 130-bp MF a pheromone genes embedded in 900–5,000 bp amplicons identical within an allele but not between species. In contrast, the alleles contain three or four MFα pheromone genes embedded in approximately 500-bp conserved repeats, usually flanking the syntenic PRT1/ZNF1 gene pair in inverted orientation. These repeats are likely maintained by intra-allelic gene conversion, ensuring maintenance of these important fertility genes in the absence of meiotic recombination. This is a striking difference to the MAT locus of S. cerevisiae , where gene conversion plays the unrelated and very distinct role of driving mating type switching ( Strathern and Herskowitz 1979 ; Wu and Haber 1996 ; Haber et al. 2004 ). In the C. gattii MAT a allele one MF a gene repeat has expanded into the adjacent IKS1 gene, duplicating the IKS1 3′ region in a second amplicon. In Cn. var. neoformans , gene conversion has duplicated a retrotransposon fragment adjacent to MFα1 into the MFα2 repeat, while the fourth pheromone gene has been replaced by a retroelement, representing the only clear example of gene loss within MAT. We note that our gene disruption studies reveal that the MAT locus contains five essential genes (see Figure 2 ), and their presence likely constrains MAT to only those rearrangements that ensure their retention. Discussion Our studies reveal that the MAT locus of C. neoformans is strikingly divergent from that of the model budding yeast S. cerevisiae, other ascomyctes, and even related basidiomycetes. Whereas the budding yeast MAT locus is quite small, and encodes only one or two key cell fate determinants that are sequence unrelated, the C. neoformans MAT locus spans over 100 kb, contains more than 20 genes, and with the exception of the SXI1α and SXI2 a genes, is otherwise composed of divergent alleles of a common gene set. The similarity between the MAT locus of S. cerevisiae and that of C. neoformans is restricted to the presence of related homeodomain proteins, suggesting that these may represent the most ancient components of the ancestral MAT locus that was shared between the ascomycete and basidiomycete lineages. One of several unusual features that the C. neoformans MAT locus does not share with the more restricted S. cerevisiae counterpart is the presence of several predicted essential genes, which we have confirmed by gene disruption studies (see Figure 2 ). Given the evidence for rampant inversions and translocations in MAT, the presence of these essential genes, which are spaced throughout the locus, may have served as an evolutionary brake to ensure that large regions of the MAT locus were not lost in haploid recombinants produced by the sexual cycle. We note that essential genes are represented both in the expanded pheromone signaling and homeodomain clusters of the ancestral tetrapolar mating system and within the set of five newly acquired, entrapped genes. The presence of these essential genes embedded within each component of the MAT locus may have thereby contributed to the expansion of the locus from the much smaller MAT loci common in ascomycetes and other basidiomycetes. Another marked distinction is that C. neoformans is a heterothallic yeast that has never been observed to undergo mating type switching, whereas S. cerevisiae is a homothallic yeast in which Ho endonuclease-mediated cleavage effects mating type switching by promoting recombination between the active and silent MAT cassettes. We find no evidence for silent mating type cassettes in C. neoformans, consistent with its classification as a heterothallic fungus. Furthermore, recent studies ( Butler et al. 2004 ) have revealed the acquisition of silent mating type cassettes and both Ho-independent and Ho-dependent switching is restricted to ascomyete fungal lineages related to S. cerevisiae and Sc. pombe . We propose a model of MAT evolution that addresses the four distinct evolutionary classes in which the genesis of a bipolar system occurred in the progenitor of the three Cryptococcus lineages described here ( Figure 7 ). Our evidence suggests that the ancient tetrapolar loci expanded to incorporate additional genes; this process began with acquisition of components of the pheromone signaling MAPK cascade (STE20, STE11, and STE12) into the ancestral pheromone/pheromone receptor locus (ancient class). This was followed by a second round of acquisition of genes with an unknown role in mating (intermediate class I). Next, the ancestral homeodomain locus acquired genes that we hypothesize function in the dikaryon or meiosis ( SPO14, RUM1; intermediate class II) based on their known roles in S. cerevisiae and Ustilago maydis ( Honigberg et al. 1992 ; Quadbeck-Seeger et al. 2000 ). Figure 7 A Model for the Evolution of MAT Our evidence indicates that the ancient loci of a canonical tetrapolar system expanded to incorporate additional genes, beginning with two rounds of expansion of the pheromone/receptor locus: first to acquire genes including components of the pheromone-signaling MAPK cascade (ancient), and second to acquire genes whose role in mating is unknown (intermediate I). Next, the ancestral homeodomain locus acquired genes hypothesized to function in the dikaryon or meiosis (intermediate II). The tetrapolar loci in one mating type fused by chromosomal translocation, entrapping the most recently acquired species-specific gene set (recent) and creating a tripolar intermediate. A second locus fusion event then occurred, to link the two regions from the opposite mating type and create the bipolar ancestors of MAT. Subsequent inversion-mediated rearrangements have erased the discrete evolutionary strata. Subsequent chromosomal translocation fused the pheromone signaling and homeodomain cluster in one mating type, entrapping the set of most recently acquired species-specific genes (recent class). This created an intermediate tripolar mating stage in which one mating type had one large contiguous MAT locus and the partner retained the ancestral gene clusters formed from the unlinked tetrapolar loci. During this transitional tripolar intermediate stage, only half of the meiotic progeny would be fertile. The incipient alleles of the opposite mating type were then either gene converted onto the newly-formed bipolar chromosome or, more likely, were linked via dual recombination events ( Figure 7 ), collapsing the tetrapolar system to a bipolar one via a transitional tripolar intermediate. Evolutionary pressure for this event enabling production of a higher proportion of fertile progeny would then have swept the population, fixing the linked bipolar α and a alleles and leading to extinction of the tetrapolar system. More recent inversions facilitated by repetitive sequence elements then produced more homogeneous bipolar structures resulting in suppression of recombination between MAT alleles. The high degree of identity shared by α and a alleles of the recent gene class suggests that the evolution of MAT is still occasionally punctuated by both inter- and intra-allelic recombination. Our model involving collapse of a tetrapolar system to a bipolar one is supported by studies of the basidiomycete U. hordei; this organism is closely related to the tetrapolar fungus U. maydis, but its tetrapolar loci have been linked and recombination suppressed between the two, resulting in the formation of a bipolar mating type system ( Lee et al. 1999 ). Thus, we hypothesize that a chromosomal translocation juxtaposed the two previously unlinked sex-determining regions and produced a bipolar mating type system in which the two distinct regions are linked by a common block of sequence information. Subsequently, inversions occurred that obscured these boundaries and, as a consequence, suppressed recombination between the two regions. What was the evolutionary pressure to suppress recombination between the two linked loci, leading to their rearrangement in not only one but all three lineages? If the ancestral tetrapolar system was not multiallelic, and the four mating types in the population were in equal proportions, then any given individual in the population could mate with only 25% of the other members. However, in a population in which the two loci are linked, any individual can mate with 50% of the other population members. A recombination event in the common spacer region would result in two new mating types in which SXI1α is now linked to the STE3 a pheromone receptor gene, and in which SXI2 a is linked to the STE3α pheromone receptor gene. These recombinants could mate with each other, but would not complete the sexual cycle with the original MAT a and MATα members of the population, because while cell-cell fusion would occur, two copies of only one of the homeodomain-encoding genes would be present, and both are required for completion of the sexual cycle (C. M. Hull, M. J. Boily, and JH, unpublished data). Thus, whereas the parental strains could mate with 50% of the population, the recombinants could not and would have a disadvantage. This selective pressure for fecundity could have driven the inversions that we hypothesize occurred to prevent recombination between the two linked sex-determining regions. While we can reconstruct a likely model for the evolutionary events that drove the formation of the large MAT locus of Cryptococcus, the events that led to the initial formation of the homeodomain- and pheromone/pheromone receptor-based gene clusters involved in sexual processes are less clear. How can recombinationally suppressed sites such as the original tetrapolar loci initiate expansion to create a larger nonhomologous region? One likely hypothesis involves the presence of the large number of transposable elements in the genome. It has been suggested that the spread of a mobile element through a population can be facilitated by increasing the probability of sex in its host ( Hickey 1982 ). Therefore, a transposon insertion adjacent to either the locus that controls initiation of the sexual stage (the pheromone/pheromone receptor locus) or its completion (the homeodomain locus), and that leads to a significant sexual advantage, would be subject to positive selection. These events, as linked to specific alleles of each locus, could therefore be expected to increase the size of the nonhomologous region. Furthermore, local transposition events have been shown to cause small rearrangements, including inversions, which would further expand the clusters ( Daboussi 1997 ). These transposons may then have contributed to the original expansion of the locus, and would provide additional support for the proposed role of mobile elements in the evolution of sex ( Hickey 1982 ). Another striking parallel with the human Y chromosome is the coherence of genes with common functions. Eight of the ten most ancient genes encoded by or recruited to the fungal ancestral pheromone/pheromone receptor locus mediate pheromone production and sensing (see Figure 2 ), and the remaining two (IKS1, MYO2) may play related roles. Finally, Y-specific genes are maintained by intrachromosomal recombination and repair of genes embedded within palindromes in an inverted orientation ( Rozen et al. 2003 ). This mirrors the Cryptococcus pheromone genes in a striking example of convergence to a common genomic configuration that ensures that genes required for fertility are preserved by intrachromosomal recombination in the absence of homologs on the opposite sex chromosome. While gene conversion has played an essential role in maintaining the multiple pheromone genes, it has also decorated the locus with multiple other examples that would provide no apparent fertility advantage. An interesting feature in the evolutionary history of many genes encoded by sex chromosomes in mammals is the degeneration and loss of one functional copy, as has occurred dramatically on the mammalian X and Y chromosomes. This is in stark contrast with the fungal MAT locus, in which functional copies of each allele have been retained. The basis for this difference is that mammals are obligate diploids, and the haploid form occurs only as the gamete stage (sperm and egg) during the mammalian life cycle. By contrast, in fungi such as C. neoformans, the organism is viable as both a haploid and a diploid, and thus gene degeneration or loss of essential genes cannot occur in either sex-determining region, since the organism most commonly occurs as haploid cells in the environment. By comparing the gene composition of the a and α alleles of the Cryptococcus MAT locus, we see that each essential gene has been retained in both alleles, and that with the exception of the MAT-unique SXI1α and SXI2 a genes, each other nonessential gene has also been maintained as a pair of alleles diverged to different degrees based on their date of acquisition to the locus and evolutionary constraints on sequence divergence. The nonessential genes are presumably maintained in a functional form because they serve a role in mating, and their loss would lead to sterile isolates that would be lost from the population, or they function in other roles that provide a survival benefit to the organism. Another difference between the MAT locus alleles and the mammalian sex chromosomes is the size disparity between the X and Y chromosomes compared to a similar size in Cryptococcus for both the a and α MAT alleles, and therefore for their host chromosome. The MAT locus occupies 6% of the 1.8 MB chromosome on which it resides, and thus has not expanded to occupy nearly the entire chromosome, in contrast to the mammalian sex chromosomes. A more similar analogy to the fungal MAT locus is the sex chromosomes of the plant papaya (Carica papaya), in which the sex-determining region occupies only about 10% of the 41-MB primitive Y chromosome ( Liu et al. 2004 ). In that example, the chromosomes are defined as sex chromosomes, and yet the sex-determining region has not yet expanded to capture the entire host chromosome on which it resides. Thus, there are two issues at play in determining the size of sex-determining regions: expansion, and gene degeneration and loss. Comparison of these divergent sex-determining systems in fungi, plants, and animals reveals both shared principles and unique features as these organisms have specialized to their particular environmental niche and survival strategy. In summary, the Cryptococcus MAT locus resembles the structures hypothesized for the ancient human Y chromosome, in which recombination suppression was limited to a small portion of the chromosome around SRY ( Skaletsky et al. 2003 ). Furthermore, this type of structure has been identified in the plant kingdom in studies that defined the sex chromosomes of the papaya ( Liu et al. 2004 ). These parallels reveal that similar mechanisms drive the evolution of sex-determining regions in all three eukaryotic kingdoms, and establish Cryptococcus as a paradigm to elucidate molecular principles governing cell identity and sex chromosome dynamics. Materials and Methods Strains and media The strains used for construction of bacterial artificial chromosome (BAC) libraries and analysis of the mating type alleles were C. gattii serotype B isolates WM276 (α) and E566 ( a ) from the Australian environment. E. coli DH5α was used as the library host strain. LB or FB media supplemented with the appropriate antibiotic was used for E. coli culture ( Sambrook et al. 1989 ). BAC and sequencing libraries Large-insert libraries (insert sizes 100–120 kb) for the candidate C. gattii serotype B strains WM276 and E566 were constructed in pBACwich ( Choi et al. 2000 ), a derivative of pBeloBAC11 ( Cai et al. 1995 ), using HindIII partially digested genomic DNA ( Lengeler et al. 2002 ). Clones from the BAC library were arrayed on nylon membranes ( Sambrook et al. 1989 ) and hybridized to mating type-specific gene probes from Cn. var. neoformans to identify overlapping sequences that span the MAT locus ( Lengeler et al. 2002 ). BAC DNA from two clones for each isolate (3K12 and 3O16 for WM276, and 3E18 and 1C03 for E566) was prepared using the NucleoBond BAC Maxi Kit (Clontech, Palo Alto, California, United States), and random insert libraries were constructed for each using randomly sheared 1.5- to 3-kb DNA fragments (GeneMachine HydroShear; Genomic Solutions, Ann Arbor, Michigan, United States) ( Oefner et al. 1996 ) that were cloned into pUC18 ( Yanisch-Perron et al. 1985 ). 1,100 clones were picked for each BAC random insert sequencing library. Sequencing and assembly Sequencing reactions were performed with an MJ Research (Reno, Nevada, United States) thermal cycler using standard BigDye chemistry (Applied Biosystems, Foster City, California, United States) and analyzed on an Applied Biosystems PE3700 96-capillary sequencer. Sequence reads were assembled using the PHRED/PHRAP/CONSED package ( Ewing and Green 1998 ; Gordon et al. 1998 ). Additional analysis of the data was performed using BLAST ( Altschul et al. 1990 ) and the GCG software suite (Wisconsin Package; Genetics Computer Group [GCG], Madison, Wisconsin, United States). Based on the initial assembly of the end sequences, oligonucleotides were selected to close gaps in the sequence coverage by primer walking. MAT locus annotation Genes were annotated in the C. gattii sequences based on homology to the existing annotation in C. neoformans ; in some cases, this led to revision of the C. neoformans annotation. Additional genes that did not have previously defined homologs (BSP1, BSP2, BSP3, GEF1, and NCM1) were found by comparing the six available MAT allele sequences and by identifying large regions of identity that were unassigned to genes. Based on ClustalW v1.4 ( Thompson et al. 1994 ) alignment and identification of intron consensus sequences, primers were designed and RT-PCR employed to characterize gene structures using total RNA and the Ready-To-Go RT-PCR Bead system (Amersham Biosciences, Piscataway, New Jersey, United States). RT-PCR products were directly sequenced using primer walking. Repeated elements in the Cn. var. neoformans locus were defined using the BLASTn algorithm to search the JEC21 genome generated at TIGR ( www.tigr.org , 10/21/03 release). Phylogenetic analysis Protein-coding DNA sequences in FASTA format were automatically aligned using ClustalW 1.81 ( Thompson et al. 1994 ). Each gene alignment was imported as a Nexus file into MacClade 4.05 ( Maddison and Maddison 1997 ) and manually edited according to the superimposed amino acid sequences. Aligned data sets ranged in length from 130 to 5,600 bp. In each alignment, the start and the stop codon were excluded from the phylogenetic analysis, as were regions that could not be unambiguously aligned. Exhaustive searches under maximum parsimony and maximum likelihood criterion were conducted using PAUP* 4.0b10 ( Swofford 1999 ) on each single gene data set. Model parameter estimates for the maximum likelihood analysis were obtained from Modeltest 3.06 ( Posada and Crandall 1998 ). Statistical support was calculated using 1,000 bootstrap replicates under maximum parsimony and maximum likelihood. K s values for comparison of the a and α alleles of all MAT protein-coding genes in each lineage were calculated using DnaSP 3.51 ( Rozas and Rozas 1999 ). The tree topologies of STE20, ZNF1, SPO14, and RPO41 were compared with each other as representatives of the different strata by applying the Shimodaira-Hasegawa test ( Shimodaira and Hasegawa 1999 ). Comparisons within a group were performed between two genes of the same stratum. Genome-wide analysis of transposon content To determine the relative frequency of transposons in the MAT locus alleles of Cn. var. neoformans compared to the rest of the genome, the locations of 35 previously identified transposable elements ( Lengeler et al. 2002 ; Goodwin and Poulter 2001 ; Goodwin et al. 2003 ) were mapped on the TIGR JEC21 genome assembly. The relative transposon contents of the MAT and non-MAT regions were calculated as a percentage of total sequence occupied by both complete and partial transposons, yielding a 5.17-fold enrichment of transposons in the MATα allele and a 5.30-fold enrichment in MAT a . The locations of each element have been submitted to TIGR for inclusion in the imminent release of the Cn. var. neoformans serotype D genome paper. In this analysis the transposon-rich presumptive centromeric regions were excluded. Supporting Information Figure S1 The Boundaries of the MAT Locus Are Sharply Defined by Loss of Sequence Identity The entire MAT a and MATα alleles plus an additional 10 kb flanking sequence from each species were subjected to a pairwise comparison using a window size of 100% identity over 30 bp. Sequence identity is indicated by dots, which join to form diagonal lines in regions of high sequence identity. Diagonal lines seen in the upper left and lower right corners represent the flanking sequences (greater than 99% identity) and those in the central portion correspond to those genes hypothesised to have entered the MAT locus most recently. The additional region of identity present in Cn. var. neoformans due to the fixation of the α alleles of the NCM1 , BSP3 and IKS1 genes in the MAT a allele is circled in blue. Scale is given in kb. (64 KB EPS). Click here for additional data file. Figure S2 Genes of the Cryptococcus MAT Locus Define Four Discrete Maximum Parsimony Phylogenetic Groupings Phylograms were generated for each protein-coding gene in MAT under the maximum parsimony criterion in an exhaustive search. Numbers next to branches indicate statistical support as calculated in 1,000 bootstrap replicates. Shaded trees indicate genes with an unusual phylogenetic pattern, due to either gene conversion or a hybrid phylogeny pattern. (218 KB EPS). Click here for additional data file. Figure S3 Genes of the Cryptococcus MAT Locus Define Four Discrete Maximum Likelihood Phylogenetic Groupings The phylograms represent the single most likely trees for each protein-coding gene in the MAT locus. Trees were generated under the best-fitting evolutionary model in an exhaustive search. Numbers besides branches indicate statistical support as calculated in 1,000 maximum likelihood bootstrap replicates. Shaded trees indicate genes with an unusual phylogenetic pattern, due to either gene conversion or a hybrid phylogeny pattern. (225 KB EPS). Click here for additional data file. Accession Numbers GenBank ( http://www.ncbi.nlm.nih.gov/ ) accession numbers for the MAT locus alleles discussed in this paper are WM276 (AY10430) and E566 (AY10429). GenBank accession numbers for other genes discussed in this paper are Cn. var. grubii MAT a (AF542528), Cn. var. grubii MATα (AF542529), Cn. var. neoformans MAT a (AF542530), and Cn. var. neoformans MATα (AF542531).

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526376.xml

554086

Kaposi's sarcoma associated herpes virus-encoded viral FLICE inhibitory protein activates transcription from HIV-1 Long Terminal Repeat via the classical NF-κB pathway and functionally cooperates with Tat

Background The nuclear transcription factor NF-κB binds to the HIV-1 long terminal repeat (LTR) and is a key regulator of HIV-1 gene expression in cells latently infected with this virus. In this report, we have analyzed the ability of Kaposi's sarcoma associate herpes virus (KSHV, also known as Human Herpes virus 8)-encoded viral FLIP (Fas-associated death domain-like IL-1 beta-converting enzyme inhibitory protein) K13 to activate the HIV-1 LTR. Results We present evidence that vFLIP K13 activates HIV-1 LTR via the activation of the classical NF-κB pathway involving c-Rel, p65 and p50 subunits. K13-induced HIV-1 LTR transcriptional activation requires the cooperative interaction of all three components of the IKK complex and can be effectively blocked by inhibitors of the classical NF-κB pathway. K13 mutants that lacked the ability to activate the NF-κB pathway also failed to activate the HIV-1 LTR. K13 could effectively activate a HIV-1 LTR reporter construct lacking the Tat binding site but failed to activate a construct lacking the NF-κB binding sites. However, coexpression of HIV-1 Tat with K13 led to synergistic activation of HIV-1 LTR. Finally, K13 differentially activated HIV-1 LTRs derived from different strains of HIV-1, which correlated with their responsiveness to NF-κB pathway. Conclusions Our results suggest that concomitant infection with KSHV/HHV8 may stimulate HIV-1 LTR via vFLIP K13-induced classical NF-κB pathway which cooperates with HIV-1 Tat protein.

Background The human immunodeficiency virus type 1 (HIV-1) establishes latent infection following integration into the host genome [ 1 ]. The expression of integrated HIV-1 provirus in cells latently infected with this virus is controlled at the level of transcription by an interplay between distinct cellular and viral transcription factors which bind to the HIV-1 long terminal repeat (LTR) [ 1 - 4 ]. The HIV-1 LTR is divided into three regions: U3, R and U5, which contain four functional elements: transactivation response element (TAR), a basal or core promoter, a core enhancer, and a modulatory element [ 1 , 4 ]. The viral transactivator Tat is a key activator of HIV-1 LTR via its binding to the TAR region, while the core region contains three binding sites for Sp1 transcription factor and a TATA box [ 1 ]. The enhancer region of HIV-1 LTR contains two highly conserved consecutive copies of κB elements at nucleotides -104 to -81 that are critical for HIV-1 replication in T cells [ 1 ]. Finally, the modulatory region harbors binding sites for numerous transcription factors, such as c-Myb, NF-AT, USF and AP1. Among the various signaling pathways known to activate HIV-1 LTR, the NF-κB pathway is particularly important as it is activated by several cytokines involved in immune and inflammatory response [ 1 ]. However, all pathways that stimulate NF-κB do not reactivate latent HIV and HIV-1 gene expression is also known to be regulated by NF-κB-independent mechanisms, for example via Tat [ 2 , 3 ]. There are five known members of the NF-κB family in mammalian cells including p50/p105 (NF-κB1), p52/p100 (NF-κB2), p65 (RelA), c-Rel, and RelB [ 5 , 6 ]. Although many dimeric forms of NF-κB have been described, the classical NF-κB complex is a heterodimer of the p65/RelA and p50 subunits. The activity of NF-κB is tightly regulated by their association with a family of inhibitory proteins, called IκBs [ 5 - 7 ]. The best characterized Rel-IκB interaction is between IκBα and p65-p50 dimer, which blocks the ability of NF-κB to enter the nucleus. Stimulation by a number of stimuli results in the activation of a multi-subunit IκB kinase (IKK) complex, which contains two catalytic subunits, IKK1/IKKα and IKK2/IKKβ, and a regulatory subunit, NEMO/IKKγ [ 7 ]. The IKK complex leads to the inducible phosphorylation of IκB proteins at two conserved serine residues located within their N-terminal region [ 5 ]. Phosphorylation of IκB proteins lead to their ubiquitination and subsequent proteasome-mediated degradation, thereby releasing NF-κB from their inhibitory influence [ 7 ]. Once released, NF-κB is free to migrate to the nucleus and bind to the promoter of specific genes possessing its cognate binding site. In addition to the above classical NF-κB pathway, an alternative (or noncanonical) pathway of NF-κB activation that involves proteasome-mediated processing of p100/NF-κB2 into p52 subunit, has been described recently [ 8 ]. Unlike the classical NF-κB pathway, which involves IKK2 and NEMO, activation of the alternative NF-κB pathway by TNF family receptors is critically dependent on NIK and IKK1 [ 9 , 10 ]. Kaposi's sarcoma associated herpes virus (KSHV), also known as Human herpes virus 8 (HHV8), is a γ-2 herpes virus which is frequently associated with malignancy among AIDS patients [ 11 - 13 ]. In addition to Kaposi's sarcoma (KS), KSHV genome has been consistently found in primary effusion lymphoma (PEL) or body cavity lymphoma and multicentric Castleman's disease. KSHV genome is known to encode for homologs of several cytokines, chemokines and their receptors [ 11 - 13 ]. However, none of the above proteins is expressed in cells latently-infected with KSHV [ 11 ]. KSHV also encodes for a protein called K13 (or orf71), which is one of the few viral proteins known to be expressed in cells latently infected with KSHV [ 11 , 14 - 16 ]. The K13 protein contains two homologous copies of a Death Effector Domain (DED) that is also present in the prodomains of caspase 8 (also known as FLICE), caspase 10 and cellular FLICE Inhibitory Protein (cFLIP, also known as MRIT) [ 17 ]. Proteins with two DEDs have been discovered in other viruses as well, including MC159L and MC160 from the molluscum contagiosum virus and E8 from the equine herpes virus 2 [ 18 - 20 ]. These virally encoded DED-containing proteins are collectively referred to as vFLIPs (viral FLICE Inhibitory Proteins) [ 18 - 20 ]. We recently demonstrated that KSHV vFLIP K13 possesses the unique ability to activate both the classical and the alternate NF-κB pathways [ 21 - 24 ]. Several recent studies suggest that binding of NF-κB to HIV-1 LTR may not be sufficient and interaction with additional viral and cellular factors may be required to induce its transcriptional activation [ 25 , 26 ]. As such, in this report we have carried out a detailed analysis of the ability of K13 to activate the HIV-1 LTR and analyzed the contribution of the canonical vs alternate NF-κB signaling pathways, various subunits of the IKK complex and the HIV-1 Tat to this process. Results vFLIP K13 activates the HIV-1 LTR We used a luciferase reporter construct to test the effect of vFLIP K13 on HIV-1 LTR transcriptional activation. This reporter construct expresses the firefly luciferase gene downstream of the HIV-1 LTR. As shown in Fig. 1A–C transient transfection of vFLIP K13 in 293T and Cos7 cells led to significant (3 and 5 fold, respectively) activation of the HIV-1 LTR where as expression of the vFLIP E8 from the equine herpes virus 2 failed to do so. As HIV-1 LTR is known to be responsive to proinflammatory cytokines, we also carried out a comparative analysis of the HIV-1 LTR activation by K13, TNF-α and IL-1β in 293T cells. As shown in Fig 1C , while K13-induced approximately 3-fold increase in HIV-1 LTR transcriptional activation, treatment with TNF-α(50 ng/ml) and IL-1β (50 ng/ml) resulted in 5–6 fold increase. A possible explanation for this difference lies in the fact that unlike TNF-α and IL-1β, K13 lacks the ability to induce the transcription factor AP1, which is known to activate HIV-1 LTR. We also tested whether vFLIP K13 possesses the ability to activate the HIV-1 LTR in cells naturally infected with HIV-1. As shown in Fig. 1D , transient transfection of K13 in Jurkat cells (human T cell lymphoma cell line) led to modest (2-fold) activation of HIV-1 LTR transcription activity. K13 mutants defective in NF-κB activation fail to activate HIV-1 LTR We have recently generated point mutants of the vFLIP K13 which differ in their ability to activate the NF-κB pathway [ 27 ]. In order to test the hypothesis that vFLIP K13 activates the HIV-1 LTR via NF-κB pathway, we carried out a comparative analysis of the ability of wild-type and mutant K13 constructs to activate the HIV-1 LTR reporter construct. In a parallel experiment, we also tested the effect of different K13 constructs on an NF-κB luciferase reporter construct to serve as a positive control. The luciferase expression in the latter construct is driven by four copies of a consensus NF-κB binding-site [ 28 ]. Consistent with our published results [ 27 ], the triple mutant 58AAA demonstrated a complete lack of NF-κB reporter activation while the mutant 67AAA retained partial ability to do so (Fig 2A ). Importantly, essentially a similar pattern of reporter activation was obtained when the wild-type and mutant K13 constructs were tested on the HIV-1 LTR reporter construct (Fig 2B ). Collectively, the above results suggested the involvement of the NF-κB pathway in vFLIP K13-induced HIV-1 LTR activation. vFLIP K13 induces binding of specific transcription factors to HIV-1 LTR In order to test the hypothesis that vFLIP K13 activates HIV-1 LTR by inducing the binding of specific transcription factors to the NF-κB binding sites present in the HIV-1 LTR, we used an electrophoretic mobility shift assay (EMSA). As shown in Fig. 3A , nuclear extracts from Jurkat cells expressing vFLIP K13 demonstrated significant DNA-binding activity on radiolabelled oligonucleotides-derived from the NF-κB binding sites present in HIV-1 LTR. In contrast, no HIV-1 LTR DNA-binding activity was observed in nuclear extracts of empty vector-expressing cells (Fig. 3A , compare lanes 1 and 2). The specificity of the complex was demonstrated by its disappearance upon competition with excess cold HIV-1 LTR oligonucleotide duplex and lack of effect upon competition with a non-specific oligonucleotide duplex (Fig. 3A , lanes 3 and 4). Nature and subunit composition of K13-induced transcription factors bound to HIV-1 LTR In addition to the classical NF-κB pathway, an alternative (or non-canonical) pathway of NF-κB activation, which involves proteasome-mediated processing of p100/NF-κB2 into p52 subunit, has been described [ 8 ]. We have recently demonstrated that vFLIP K13 can activate the alternate NF-κB pathway via an IKK1-dependent and NIK- and IKK2-independent process [ 24 ]. In order to determine the contribution of the classical vs alternate NF-κB pathway to vFLIP K13-induced HIV-1 LTR activation, we used a supershift assay to analyze the nature of the protein complexes bound to HIV-1 LTR from nuclear extracts of vFLIP K13-expressing cells. This assay demonstrated that p50 and c-Rel subunits are the major components of the HIV-1 LTR-bound NF-κB complexes induced by vFLIP K13 with modest contribution from the p65 subunit (Fig. 3B ). As the p50, c-Rel and p65 subunits are primarily activated by the classical NF-κB pathway, the above results support the hypothesis that K13 activates the HIV-1 LTR via the classical NF-κB pathway. Role of classical NF-κB activation in K13-induced HIV-1 LTR reporter activity We have previously demonstrated that vFLIP K13 activates the classical NF-κB pathway via phosphorylation of IκBα, which leads to its ubiquitination and subsequent degradation via proteasome [ 22 ]. We used a phosphorylation-resistant mutant of IκBα to test the involvement of the classical NF-κB pathway in vFLIP K13-induced HIV-1 LTR reporter activity. As shown in Fig. 4A , a phosphorylation-resistant mutant of IκBα (IκBαSS32/36AA), in which the two critical N-terminal serine residues have been mutated to alanine, completely blocked vFLIP K13-induced HIV-1 LTR reporter activity. We used siRNA-mediated downregulation of key subunits of the classical and alternate NF-κB pathways to test their involvement in K13-induced HIV-1 LTR activation. As shown in Fig. 4B , we achieved effective silencing of c-Rel and RelA/p65 expression by siRNA-mediated silencing. Consistent with our supershift assay (Fig. 3B ), siRNA-mediated silencing of c-Rel expression led to almost complete suppression of K13-induced HIV-1 LTR activation (Fig. 4C ). Similarly, silencing of p65 expression led to significant suppression of HIV-1 LTR activity, although some residual activity was still evident (Fig. 4C ). Although p100 acts as a precursor of p52, another important function of p100 is to retain the RelB/p50 and RelB/p52 complexes in the cytoplasm. As such, in order to shut-off the alternate NF-κB pathway, we chose to silence the expression of RelB. As shown in Fig. 4B–C , siRNA-mediated downregulation of RelB, had no significant effect on K13-induced HIV-1 LTR activity. We also failed to observe any effect of p100/p52 silencing on HIV-1 LTR activation (data not shown). Taken together, the above results demonstrate a key role of the c-Rel and p65 subunits of the classical NF-κB pathway in K13-induced HIV-1 LTR reporter activation. Role of individual subunits of the IKK complex in K13-induced HIV-1 LTR activation K13 is known to associate with a 700 kDa multi-subunit IKK complex, which consists of two catalytic subunits, IKK1/IKKα and IKK2/IKKβ and a regulatory subunit, NEMO/IKKγ [ 22 ]. We tested the involvement of the individual components of the IKK complex in vFLIP K13-induced HIV-1 LTR reporter activity by using mouse fibroblast (MEF) cells deficient in IKK1, IKK2 and NEMO, respectively. As shown in Fig. 5A , we observed significant HIV-1 LTR reporter activity by the expression of vFLIP K13 in the wild type MEF cells. In contrast, almost no HIV-1 LTR reporter activity was observed in NEMO-deficient cells. However, some residual HIV-1 LTR reporter activity was observed in IKK1- and IKK2-deficient MEF cells. Collectively, the above results suggest that synergistic action of IKK1, IKK2 and NEMO is required for maximal activation of HIV-1 LTR by K13. Next we sought to determine whether pharmacological inhibitors of the NF-κB pathway may be used to block vFLIP K13-induced HIV-1 LTR reporter activation. Lactacystin and MG132 are inhibitors of proteasome and block the NF-κB pathway by preventing the degradation of IκB. On the other hand, arsenic acid is believed to block the NF-κB pathway by inhibiting the IKK complex [ 29 ]. As shown in Fig. 5B , vFLIP K13-induced HIV-1 LTR reporter activation was effectively blocked by MG132, lactacystin and arsenic acid. These results suggest that inhibitors of the NF-κB pathway might have a role in preventing K13-induced HIV-1 LTR reporter activation. Effect of Murr1 on K13-induced HIV-1 LTR activation Murr1 is a gene product that has been previously implicated in copper regulation [ 30 , 31 ]. A recent study demonstrated that Murr1 is highly expressed in CD4+ T cells and serve as a genetic inhibitor factor for HIV-1 replication in the resting lymphocytes [ 32 ]. Murr1 was shown to block HIV-1 LTR activation and HIV-1 replication by inhibiting the proteasomal degradation of IκB and blocking basal and cytokine-stimulated NF-κB activation [ 32 ]. Based on the above study demonstrating the importance of Murr1 as an endogenous regulator of HIV-1 LTR activation, we tested its effect on K13-induced HIV-1 LTR activation. As shown in Figure 5C , co-expression of Murr1 led to significant block in K13-induced HIV-1 LTR reporter activity, thereby suggesting that K13-induced activation of HIV-1 replication in resting lymphoid cells may be regulated by Murr1 and K13 may selectively activate HIV-1 replication in activated cells in which expression of Murr1 is known to be down-regulated [ 32 ]. Synergistic activation of HIV-1 LTR by vFLIP K13 and HIV Tat protein HIV-1 Tat is a viral nuclear protein that plays an essential role in HIV-1 gene expression at the transcriptional level [ 2 , 3 ]. Tat has been shown to associate with p300/CBP and P/CAF histone acetyltransferases (HAT) and efficient activation of the integrated HIV-1 LTR is largely dependent on Tat-dependent rearrangement of the nucleosome positioned at the transcription start site [ 2 ]. HIV-1 LTR is known to bind and respond to HIV Tat protein via a specific Tat-binding site [ 2 ]. We used deletion mutagenesis of the HIV-1 LTR to test whether vFLIP induced transcriptional activation is dependent on this Tat-binding site. As shown in Figures 6A and 6B , a bulge mutant (containing deletion of nucleotides +23/+25) of HIV-1 LTR, which is defective in Tat activation [ 33 ], had no significant effect on vFLIP K13-induced reporter activity. In contrast, vFLIP K13 failed to activate a luciferase report construct containing an HIV-1 LTR in which the NF-κB binding sites had been mutated (Fig. 6C ). The above results confirm that vFLIP activates the HIV-1 LTR via the NF-κB binding sites and can do so independent of the Tat-binding site. Transcriptional activation of genes is usually regulated by multiple transcription factors acting in concert. Thus, while NF-κB has been shown to play a major role in the activation of the HIV-1 LTR, it fails to do so when acting alone [ 25 , 34 - 36 ]. Along the same lines, the transactivating function of Tat protein requires the presence of NF-κB sites in the HIV-1 LTR and Tat protein is known to cooperate with NF-κB to activate the HIV-1 LTR [ 1 , 34 , 36 ]. We hypothesized that a functional interaction between K13-induced NF-κB and Tat may be particularly important in the early stages of HIV-1 infection when the amount of Tat is limited. To test this hypothesis, we began by performing a dose-response analysis of Tat and selected a dose of Tat (20 ng/ml) which led to sub-maximal activation of HIV-1 LTR activation in 293T cells (Fig. 6D ). Next, we analyzed the effect of co-expression of Tat on K13-induced HIV-1 LTR activation. As shown in Figure 6E , while transfection of K13 (250–500 ng/well) led to approximately 2.5–3.5 fold increase in HIV-1 LTR activation, transfection of Tat (20 ng/well) induced 4-fold increase in HIV-1 LTR activity. However, co-expression of K13 with Tat led to a synergistic 12-fold activation of the HIV-1 LTR. These results suggest that K13-induced NF-κB functions synergistically with the Tat protein to activate the HIV-1 LTR. Effect of vFLIP K13 on LTRs-Derived from different strains of HIV There is considerable sequence diversity among the HIV-1 isolates that comprise the current global pandemic and these can be grouped into several distinct subtypes or clades [ 37 ]. In particular, the LTRs of different subtypes show distinct enhancer-promoter configuration and vary in the sequence and number of binding sites for different transcription factor, including NF-κB [ 38 , 39 ]. Although different HIV-1 LTRs are transcriptionally active, they differ in the level of basal reporter activity [ 38 , 39 ]. In addition, different HIV-1 LTRs are known to show differential response to TNF-α treatment, which correlates with the number of NF-κB binding sites [ 38 , 39 ]. Therefore, we sought to determine whether vFLIP K13 will differentially activate luciferase reporter constructs driven by LTRs derived from different HIV strains. Consistent with the published studies [ 38 , 39 ], we observed considerable difference in the basal activities of different HIV-1 LTRs promoters when transfected into 293T cells along with an empty vector (Fig. 7A ). More importantly, coexpression of vFLIP K13 led to differential activation of luciferase reporter constructs containing LTRs from different subtypes of HIV-1 (Fig. 7A ). Thus, subtype C, which possesses three NF-κB binding sites showed the maximum increase in vFLIP-induced HIV-1 LTR reporter activity while subtype E, which possesses only one NF-κB binding site showed the lowest level of basal and vFLIP-induced HIV-1 LTR transcriptional activation (Fig. 7A,B ). These results demonstrate that, similar to situation with TNFα, K13 may differentially activate LTRs derived from different strains of HIV-1, which correlate with their NF-κB binding sites. Discussion Although co-infection with HHV-8 and HIV-1 is known to synergistically increase the incidence of KS, until recently intracellular interaction between HHV8 and HIV-1 has not received adequate attention under the assumption that these viruses infect distinct cell types. Thus, HHV8 is typically believed to infect B lymphocytes, epithelial cells, keratinocytes, KS tumor cells, and endothelial cells [ 40 , 41 ], while the predominant host cells for HIV-1 are CD4 + T lymphocytes, dendritic cells, and mononuclear phagocytes [ 41 , 42 ]. However, as recently pointed out by Huang et al , several lines of evidence suggest that the above assumption may not be completely true and HHV8 and HIV-1 may, in fact, interact in vivo [ 41 ]. First , both HHV8 and HIV-1 can efficiently infect cells of monocyte/macrophage lineage, including dendritic cells [ 43 , 44 ]. Second , Moir et al have shown that induction of CD4 and CXCR4 on B cells by CD40 stimulation leads to an increased susceptibility of these cells to T-trophic HIV infection [ 45 ]. Third , HHV8-infected B cells can be infected by HIV-1 via a cell-cell pathway and such infected B cells can support productive HIV-1 replication [ 46 ]. Finally, the range of HHV8-susceptible cells in vivo is unclear at the present. Therefore, it stands to reason that HHV8 and HIV-1 genomes may co-exist in the same cells in vivo and reciprocally regulate the gene expression of each other. Support for the above hypothesis is provided by a recent study which demonstrated that co-culture of HIV-1-infected CD4+ T cells with HHV8-infected B cell lines resulted in increased HIV-1 replication [ 47 ]. With the goal of elucidating intracellular signaling interactions which could be potentially involved in the induction of HIV-1 replication by HHV-8, we carried out a detailed analysis of the effect of HHV8 vFLIP on HIV-1 LTR activation. Consistent with an earlier report, we observed that HHV8 vFLIP strongly activates HIV-1 LTR in an NF-κB-dependent fashion [ 48 ]. We further demonstrate that vFLIP K13 could activate HIV-1 LTR in both epithelial and human lymphoma cell lines, although the magnitude of stimulatory effect was more pronounced in the epithelial cells. A possible explanation for this difference may lie in the differential expression of proteins that could modulate the effect of K13 on NF-κB and/or HIV-1 LTR activation. As an example, we demonstrate that K13-induced HIV-1 LTR activation can be effectively blocked by Murr1, a recently identified inhibitor of the NF-κB pathway which is highly expressed in T cells [ 32 ]. However, alternative explanation, including difference in the transfection efficiency between different cell lines, could apply as well. We have recently reported that vFLIP K13 can activate both the classical and alternate NF-κB pathways and, as such, we were interested in determining the relative contribution of these pathways to K13-induced HIV-1 LTR activation. Based on the following data, we believe that activation of HIV-1 LTR is mainly through the classical pathway. First , our gel super-shift assay demonstrated that NF-κB complexes formed by vFLIP expression were primarily composed of c-Rel, p50 and p65 subunits. Second , siRNA-mediated downregulation of c-Rel and p65 led to near complete inhibition of K13-induced HIV-1 LTR activation whereas silencing of RelB expression was without significant effect. Third , K13-induced HIV-1 LTR activation was completely inhibited by super-repressor form of IκBα, which primarily blocks the classical NF-κB pathway. Finally , while K13 activates the alternate NF-κB pathway independent of IKK2, it failed to activate the HIV-1 LTR in IKK2-deficient MEFs. Based on some early gene-knockout studies, IKK1 was believed to be not involved in cytokine-induced activation of the classical NF-κB pathway [ 49 - 51 ]. In the present study, we have observed that, in addition to IKK2- and NEMO-deficient MEFs, K13-induced HIV-1 LTR activation was markedly reduced in IKK1-deficient MEFs as well. We believe that the above results with IKK1-deficient cells do not necessarily support the involvement of the alternate NF-κB pathway in K13-induced HIV-1 LTR activation for the following reasons. First , we have recently reported that K13-induced p65/50 DNA binding and NF-κB transcriptional activation is markedly reduced in IKK1-deficient MEFs [ 23 ] Thus, the reduced HIV-1 LTR activation in the IKK1-deficient cells observed in the current study is consistent with requirement for IKK1 in K13-induced classical NF-κB activation. Second , recent studies suggest that IKK1 may be involved in transcriptional activation of classical NF-κB responsive genes through its ability to phosphorylate histones and p65 [ 52 - 54 ]. Thus, taken together, our results demonstrate that K13 activates HIV-1 LTR through the activation of the classical NF-κB pathway, in which IKK1 plays a major role. Thus, selective inhibitors of IKK1 may have a role in blocking K13-induced HIV-1 LTR transcriptional activation. However, it is important to point out that while IKK1 may be uniquely important for K13-induced classical NF-κB activation pathway, maximal activation of this pathway via K13 relies on cooperative interaction between IKK1, IKK2 and NEMO. The transcription of cellular and viral genes is regulated by structural and functional interactions among a number of transcriptional factors that act in concert. This is also known to be the case with HIV-1 LTR. Thus, while NF-κB plays a major role in the transcriptional activation of HIV-1, it requires synergistic interaction with a number of cellular and viral proteins for maximal stimulation of this activity [ 1 ]. Although NF-κB is known to interact with Sp1, Ets and NF-AT to activate HIV-1 LTR, cooperative interaction between NF-κB and Tat has received the most interaction in the literature [ 1 , 55 ]. Tat has been shown to act synergistically with PMA, PHA and Tax-induced NF-κB to activate the HIV-1 LTR [ 1 , 34 , 36 , 55 ]. Consistent with these previous studies, we demonstrate that although K13 can activate the HIV-1 LTR by itself, it functionally cooperates with Tat to synergistically activate transcription from HIV-1 LTR. HIV-1 infection itself is known to induce persistent NF-κB activation, which is probably mediated via Tat and Nef [ 56 , 57 ], and interacts in a positive-feedback manner with Tat to enhance HIV-1 replication. However, in the immediate post-integration period of the HIV-1 life-cycle, Tat is expressed at very low levels which may not be enough to effectively stimulate HIV-1 LTR activation. Therefore, it is conceivable that vFLIP K13 could amplify the activity of Tat via NF-κB activation and thus support enhanced HIV-1 replication during the early stages of HIV-1 infection or in cells which express Tat at suboptimal levels. The human immunodeficiency virus has considerably diversified during its worldwide spread in the current pandemic and can be classified into several distinct subtypes [ 37 ]. Subtype B is predominant in North America and Europe, subtype E in Southeast Asia and subtype C in sub-Saharan Africa, respectively [ 58 ]. Previous studies have demonstrated that LTRs from HIV-1 subtypes B, C and E vary in number and binding sites for NF-κB in their enhancer elements [ 59 ]. Thus, subtype C isolates are known to contain three functional NF-κB binding sites, as compared to two such sites in the enhancer of the more commonly studied subtype B [ 59 ]. On the other hand, in the subtype E, one of the NF-κB-binding sites has been switched to a GABP site, resulting only one functional NF-κB site and gain of a new specificity [ 60 ]. Consistent with the above results, in the present study we demonstrate that vFLIP-induced HIV-1 LTR activation is strongest in subtype C and weakest in subtype E. Thus, the differential response of different HIV-1 LTRs to K13-induced transcriptional activation may be explained on the basis of number of functional NF-κB sites in their enhancer elements. Future studies should address the question whether co-infection with HHV8 has a differential effect on the replication and natural history of different HIV-1 subtypes. Methods Plasmids, cell lines and reagents Plasmids containing pcDNA3-K13-Flag and pcDNA3-E8-Flag, pRSV/LacZ and 293T cells have been described previously [ 21 ]. An expression construct encoding Murr-1 was generated by RT-PCR using cDNA prepared from H460 cells as a template and subsequently cloned in pcDNA3 vector with a C-terminal HA tag. Luciferase reporter constructs containing LTRs derived from different strains of HIV-1 (pBlue3'LTR-Luc-A-F) were obtained from AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH from Drs. Reink Jeeninga and Ben Berkhout. Wild-type and mutant HIV-1 LTR reporter constructs [ 61 ] and expression constructs for HIV Tat were obtained from Dr. Richard Gaynor. The IKKα-/- and IKKβ-/- mouse embryonic fibroblast cells were generated in Dr. Inder Verma's laboratory [ 62 , 63 ] and IKKγ/NEMO-/- cells were generated in Dr. Michael Karin's laboratory [ 64 ]. These cells were kindly provided by Dr. Richard Gaynor and were maintained in DMEM supplemented with 10% FBS. Jurkat cells were cultured in RPMI medium supplemented with 10% FBS and selected in the presence of 1500 μg/ml of G418 (Invitrogen). 293T cells were grown in DMEM with 10% FBS. Arsenic Acid was purchased from Sigma. MG132 and lactacystin were purchased from Calbiochem and Biomol, respectively. Retrovirus constructs containing C-terminal Flag epitope tagged HHV8 vFLIP (K13-Flag) was generated in MSCV neo-based retroviral vector and amphotropic viruses generated and used for infection as described previously [ 22 ]. Electrophoretic mobility shift assay Electrophoretic mobility shift assay was performed essentially as described previously [ 22 ], except an HIV LTR oligonucleotide duplex (sense strand, 5' TGC TAC AAG GGA CTT TCC GCT GGG GAC TTT CCA GG 3') was used instead of κB binding oligonucleotide. Nuclear extracts were prepared from Jurkat cells stably expressing an empty vector or vFLIP K13, which have been described previously [ 23 ]. Antibodies against p50, p65, RelB and c-Rel were purchased from Santa Cruz Biotechnology. An antibody against p52 was purchased from Upstate biotechnology Luciferase reporter assay 293 T cells were transfected in duplicate in a 24-well plate with the various test plasmids along with an HIV LTR/luciferase reporter construction (10 ng/well) and a pRSV/LacZ (β-galactosidase) reporter construct (75 ng/well) using calcium phosphate transfection protocol as described previously [ 21 ]. Cells were lysed 36–48 hours later and extracts were used for the measurement of firefly luciferase and galactosidase activity. Luciferase activity was normalized relative to the galactosidase activity to control for the difference in the transfection efficiency. Cos-7, Jurkat and MEF cells were transiently transfected with empty vector (pCDNA3) or K13 (500 ng/well) along with a HIV/luciferase reporter construct (100 ng/well) and a synthetic Renilla luciferase (phRL-TK; Promega) reporter vector (75 ng/well) by using LIPOFECTAMINE 2000 Reagent (Invitrogen, Carlsbad, CA) according to manufacturer's instruction. Thirty-six hours after transfection, cells lysates used for reporter assays. Luciferase activity was normalized relative to the Renilla luciferase activity to control for the difference in the transfection efficiency. The values shown are averages (Mean ± S.E.) of one representative experiment out of three in which each transfection was performed in duplicate. siRNA Oligonucleotides siRNA oligonucleotides with two thymidine residues (dTdT) at the 3'-end of the sequence were designed to p65 (sense, 5'-GCCCUAUCCCUUUACGUCAdTdT-3'), c-Rel (sense, 5'-CAACCGUGCUCCAAAUACU dTdT-3'), RelB (sense, AGAUCAUCGACGAGUACAUdTdT-3') and control (sense, 5' GCGCGCUUUGUAGGAUUCGdTdT-3'), along with their corresponding antisense oligonucleotides. The RNA oligonucleotides were synthesized at RNA Oligonucleotide Synthesis Core facility, UT Southwestern Medical center. siRNA oligonucleotides (80 nM) were transfected using calcium phosphate as described previously [ 65 ]. Western Blot Western blot analysis was performed essentially as described previously [ 22 ]. Primary antibodies used in these experiments were: p65, c-Rel, Rel-B (rabbit polyclonal, Santa Cruz biotechnology) and actin (mouse monoclonal, Sigma). List of Abbreviations Used DED, death effector domain; EMSA, electrophoretic mobility shift assay; FLICE, Fas-associated death domain-like IL-1 beta-converting enzyme; FLIP, FLICE inhibitory protein; HHV8, Human herpes virus 8; HIV-1, human immunodeficiency virus 1; KS, Kaposi's sarcoma; NEMO, NF-κB essential modulator; NIK, NF-κB-inducing kinase; NF-κB, Nuclear factor kappa B; MEF, murine embryonic fibroblast; PEL, primary effusion lymphoma, TNFR, Tumor necrosis factor receptor; IκB, inhibitor of NF-κB, IKK, IκB kinase; vFLIP, viral FLICE inhibitory protein; LTR, long terminal repeat. Competing Interests The author(s) declare that they have no competing interests. Authors' contributions QS and HM carried out most of the experiments described in this manuscript. PMC conceived of the study and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.

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493283

Endometrial glands as a source of nutrients, growth factors and cytokines during the first trimester of human pregnancy: A morphological and immunohistochemical study

Background The maternal circulation to the human placenta is not fully established until 10–12 weeks of pregnancy. During the first trimester the intervillous space is filled by a clear fluid, in part derived from secretions from the endometrial glands via openings in the basal plate. The aim was to determine the activity of the glands throughout the first trimester, and to identify components of the secretions. Methods Samples of human decidua basalis from 5–14 weeks gestational age were examined by transmission electron microscopy and immunohistochemically. An archival collection of placenta-in-situ samples was also reviewed. Results The thickness of the endometrium beneath the implantation site reduced from approximately 5 mm at 6 weeks to 1 mm at 14 weeks of gestation. The glandular epithelium also transformed from tall columnar cells, packed with secretory organelles, to a low cuboidal layer over this period. The lumens of the glands were always filled with precipitated secretions, and communications with the intervillous space could be traced until at least 10 weeks. The glandular epithelium reacted strongly for leukaemia inhibitory factor, vascular endothelial growth factor, epidermal growth factor, transforming growth factor beta, alpha tocopherol transfer protein, MUC-1 and glycodelin, and weakly for lactoferrin. As gestation advanced uterine natural killer cells became closely approximated to the basal surface of the epithelium. These cells were also immunopositive for epidermal growth factor. Conclusions Morphologically the endometrial glands are best developed and most active during early human pregnancy. The glands gradually regress over the first trimester, but still communicate with the intervillous space until at least 10 weeks. Hence, they could provide an important source of nutrients, growth factors and cytokines for the feto-placental unit. The endometrium may therefore play a greater role in regulating placental growth and differentiation post-implantation than previously appreciated.

Background The realisation that the maternal circulation to the human placenta is extremely limited prior to 10–12 weeks of pregnancy prompted us to investigate other potential sources of fetal nutrition during the first trimester [ 1 - 4 ]. During the evolution of ovoviviparity and viviparity secretions from the uterus became an increasingly important supplement to the yolk contained within the embryo's yolk sac [ 5 ]. In chondrichthyan fishes they represent an important source of nutrients, even in those species that do not possess a placenta [ 6 ]. Amongst eutherian mammals the uptake of secretions derived from the endometrial glands by the trophoblast continues to provide an important pathway for nutritional exchange in the earliest stages of pregnancy, before the placenta is established. These secretions contain a complex array of carbohydrates, proteins and lipids, and have been referred to variously as uterine milk or histiotroph [ 5 ]. They are particularly significant in ruminants and equids where there is a relatively long interval between the arrival of the conceptus within the uterine cavity and the establishment of placentation. In some species, such as the pig and mouse, they represent a parallel pathway for the exchange of large proteins throughout most of pregnancy [ 7 , 8 ]. More recently, it has been appreciated that the secretions may perform wider functions beyond the simple provision of nutrients. Some components, such as glycodelin, have potent immunosuppresive properties [ 9 ], while others, such as leukaemia inhibitory factor (LIF) and MUC-1, play key roles in regulating implantation [ 10 , 11 ]. Histiotroph may therefore modulate materno-fetal interactions and regulate diverse aspects of placental development. Its importance during the preimplantation period has been powerfully demonstrated in the sheep, where suppression of endometrial gland development leads to failure of the conceptus to survive and develop [ 12 ]. Equally, in the horse increased expression of epidermal growth factor (EGF) in the endometrial glands correlates closely both temporally and spatially with cell proliferation in the overlying fetal membranes [ 13 ]. In the human histiotrophic nutrition has always been considered to be of little importance for two principal reasons. Firstly, the invasive form of implantation displayed by the human blastocyst removes it from the uterine lumen, and hence it was believed the uterine secretions, by day 7–10 post-fertilisation. Secondly, the contemporaneous appearance of maternal erythrocytes within the lacunar spaces of the syncytiotrophoblastic mantle has been widely interpreted as evidence of early onset of the maternal circulation, and hence haemotrophic exchange [ 14 , 15 ]. However, there is now a substantial body of evidence from a variety of techniques indicating that an effective maternal circulation is not established until the start of the second trimester [ 1 , 3 , 16 , 17 ]. Indeed, the human placenta cannot be considered haemochorial prior to this time for the intervillous space is filled with a clear fluid only [ 16 ]. Initially it was considered this fluid was derived as a plasma filtrate percolating through the trophoblastic plugs occluding the tips of the spiral arteries. However, we recently demonstrated that the uterine glands deliver secretions into the intervillous space until at least 10 weeks of gestation, suggesting that they may at least contribute to formation of the fluid [ 18 ]. This raises the possibility that they may play greater roles during early pregnancy than previously anticipated, equivalent to those in other species. The aim of this study was therefore to examine the secretory activity of the endometrial glands within the decidua basalis both morphologically and immunohistochemically over the first trimester in order to assess their potential contribution to fetal nutrition and placental development. Methods Samples of decidua basalis were obtained with informed written consent from patients undergoing surgical termination of normal pregnancies at University College Hospital, London. The study had been approved by the University College London Hospitals Committee on the Ethics of Human Research. Samples were obtained under ultrasound guidance, and were available from 30 cases ranging in gestational age from 5 weeks to 14 weeks (median age 9 weeks). Gestational age was estimated from the crown rump length of the fetus. Immediately after removal the tissues were fixed by immersion for 2 hours in either 4% paraformaldehyde in 0.1 M PIPES buffer for immunohistochemistry or 2% glutaraldehyde for electron microscopy, or frozen in OCT medium. Colourimetric immunohistochemistry Paraformaldehyde-fixed tissues were embedded in paraffin wax and sectioned at 5 μm. After blocking of endogenous peroxidases by incubation with 1% H 2 O 2 for 30 min, the sections were incubated with non-immune serum for 20 min. The primary antibodies (Table 1 ) were applied for 3 hrs at room temperature, and binding was detected using Vectastain Elite ABC kits (Vector Laboratories) and SigmaFast DAB (Sigma), according to the manufacturers' instructions. Sections were then lightly counterstained with haematoxylin. When necessary antigen retrieval was performed prior to blocking using 0.01 M sodium citrate buffer at pH6.0 in a pressure cooker for 3 min. Negative controls were performed by omission of the primary antibody. Table 1 Primary antibodies used for immunohistochemistry. Antigen Species Type Dilution Retrieval Supplier Alpha tocopherol transfer protein Rabbit Polyclonal 1:300 Yes Dr D Kaempf-Rotzoll Cathepsin D Rabbit Polyclonal 1:200 No (frozen) Biogenesis CD56 Mouse Monoclonal 1:100 Yes Zymed CD68 Mouse Monoclonal 1:100 Yes Dako Cytokeratin 7 Mouse Monoclonal 1:100 No Dako Epidermal growth factor Rabbit Polyclonal 1:50 No Autogen Bioclear Glycodelin Mouse Monoclonal 1:10 No (frozen) Prof. M Seppälä Human placental lactogen Rabbit Polyclonal 1:200 Yes Dako Lactoferrin Rabbit Polyclonal 1:200 No Dako Leukaemia inhibitory factor Goat Polyclonal 1:200 No Santa Cruz R5Mucin-1 Mouse Monoclonal 1:100 Yes Abcam Transforming growth factor β 3 Rabbit Polyclonal 1:300 Yes Santa Cruz Vascular endothelial growth factor Goat Polyclonal 1:400 No Santa Cruz Fluorescent dual-labelling immunohistochemistry Paraformaldehyde-fixed samples of decidua basalis were embedded in paraffin wax, and sectioned at 5 μm. After rehydration sections were subjected to antigen retrieval by proteinase K (20 μg/ml for 30 min), permeabilised in TBS containing Triton X-100 (0.1%) and Tween 20 (0.1%) (TBS-TT) for 30–60 min and blocked in 5 % goat serum for 30 min at room temperature. A mixture of a rabbit polyclonal and a mouse monoclonal antibody diluted in TBS-TT was applied, and sections were incubated overnight at 4°C. Negative control sections were left at the blocking stage and were not covered with primary antibodies. After three 10-minute washes in TBS-TT, sections were incubated for 1 hr at room temperature with a mixture of fluorescent secondary antibodies, containing goat anti-rabbit Alexa 488 and goat anti-mouse Alexa 568 (both used 1/200; Molecular Probes) in TBS-TT. Sections were washed in TBS-TT as before and then twice in distilled water for 5 min and subsequently mounted in Vectashield mounting medium containing DAPI (Vector, UK). Frozen sections were used for dual labelling with anti-cathepsin D (rabbit) and glycodelin (mouse). Samples were frozen in cryoembedding medium. Sections (10–12 μm) were cut on a Reichert cryomicrotome, air-dried, fixed briefly in cold methanol/acetone (at -20°C) and permeabilised in TBS-TT for 30–60 min. All subsequent immunolabelling steps were carried out as with the paraffin embedded sections. Images were captured using a Leica confocal microscope (LeicaTCS-NT, Leica Instruments GmbH, Germany). Electron microscopy Glutaraldehyde fixed tissues were secondary fixed in 1% osmium tetroxide for 1 hour, and embedded in Araldite epoxy resin. Semi-thin sections (1 μm) were stained with methylene blue, whereas ultra-thin sections (50 nm) were counterstained with uranyl acetate followed by lead citrate and viewed using a Philips CM100 microscope (Eindhoven, The Netherlands). Archival histological material The Boyd Collection housed within the Department of Anatomy, University of Cambridge, contains a number of placenta-in-situ specimens. Only those with no recorded history of pathology were reviewed, and twelve specimens met this criterion. The gestational age (from the last menstrual period) was estimated from the recorded crown-rump length [ 19 ], and ranged from 43 to 130 days. For each specimen the thickness of the endometrium was measured from the junction of the endometrium with the cytotrophoblastic shell perpendicularly to the border of the endometrium with the myometrium at a minimum of 50 randomly selected points spread over at least 5 slides using the VIDS system (Synoptics, Cambridge). Statistical analyses All analyses were performed using Statview (SAS Institute Inc., Cary, USA). Results Endometrial histology In the earliest specimen available, H710 estimated to be of 43 days menstrual age, the conceptus was embedded within the superficial layer of a highly secretory endometrium (Figure 1A ). The uterine glands displayed the sawtooth appearance characteristic of the late secretory phase of the menstrual cycle, and were filled with copius secretions. These were heterogenous in nature, comprising a carbohydrate-rich flocculent material in which were interspersed numerous smooth round droplets resembling lipid (Figure 1B ). The cytotrophoblastic shell was well developed, and formed a smooth interface with the endometrium. As gestational age advanced the thickness of the decidua basalis reduced dramatically from over 5 mm at 6 weeks to approximately 1 mm at 14 weeks (Figure 2 ). Although there was considerable variation between samples a statistically significant negative correlation existed between the two parameters ( r = -0.644, P = 0.0216). As gestation advanced there was also increasing variability in the thickness of the endometrium across the placental bed, reflecting the formation of placental septa. The profiles of the glands became smoother and more regular, but they still contained precipitated secretions (Figure 3 ). Communications with the intervillous space could be traced until at least 10 weeks gestational age. Figure 1 A) In the earliest specimen available, H710, the conceptus (C) can be seen embedded in the superficial endometrium overlying well-developed endometrial glands (G). M, myometrium. (Haematoxylin and eosin) Scale bar = 1.0 cm. B) The secretions within the lumens of the glands are heterogenous, being a mixture of carbohydrate-rich flocculent material (blue) and what appear to be lipid droplets (red). (Alcian blue and Neutral red) Scale bar = 100 μm. Figure 2 Scattergram showing the relationship between endometrial thickness and gestational age. Figure 3 Placenta-in-situ specimen (H1094) of 13.5 weeks gestational age showing the reduction in thickness of the endometrium (E) at this stage of pregnancy. The glands (G) have a more regular outline, but still contain precipitated secretions within their lumens. M, myometrium; IVS, intervillous space. (Haematoxylin and eosin) Scale bar = 1.0 mm. Glandular epithelium In the early specimens the epithelial cells displayed a tall columnar morphology, often with large apical projections extending into the glandular lumen (Figure 4A ). This was confirmed at the ultrastructural level, at which it could be seen that the apical membrane bounding these projections displayed only scanty short microvilli. Tight junctions were present at the base of the projections, linking the cells. Within the cytoplasm there were numerous mitochondria and large quantities of rough endoplasmic reticulum (Figure 4B ). Numerous droplets resembling lipid were observed in the basal portions of the cells, and this was confirmed by staining with Oil RedO (data not shown). The cells were attached to a well-developed basal lamina, beneath which were occasional stromal cell processes and collagen fibres. Figure 4 A) Photomicrograph of a 1 μm resin section of 6 week decidua illustrating the columnar epithelium of the glands, their large apical projections and the heterogeneous nature of the secretions. (Methylene blue) Scale bar = 10 μm. B) At the ultrastructural level it can be seen that the cells possess large quantities of mitochondria and endoplasmic reticulum, and lipid droplets are abundant in the basal region. The cells are attached to a well-formed basal lamina (arrowed). Scale bar = 2 μm. By 10–11 weeks the cells were more cuboidal in nature with fewer apical projections (Figure 5A ), although there was considerable variation between the glandular profiles even within the same sample. The apical cell membrane was frequently covered with long microvilli, and both Golgi apparatus and short strands of rough endoplasmic reticulum were present within the cytoplasm (Figure 5B ). It was notable that other cell types were now present closely approximated to the deep surface of the basal lamina (Figures 5A and 6 ). One population possessed an irregularly shaped nucleus with dense peripheral heterochromatin, and osmiophilic membrane-bound granules were frequently present in the cytoplasm. Morphologically these resembled uterine natural killer (NK) cells, and this was confirmed using fluorescent immunohistochemistry and antibodies against CD56 (Figure 7F ). The other cell type was larger, less osmiophilic and the cytoplasm resembled that of the stromal decidual cells. In order to attempt to identify these cells further immunostaining was performed for human placental lactogen and cytokeratin as markers for extravillous trophoblast, and CD68 as a marker for macrophages. Many invading trophoblasts and macrophages were present in the stroma between the glands, but only the latter were seen in particularly close proximity to the basal lamina (Figures 7G and 7H ). The secretions within the glandular lumens reacted positively for placental lactogen, indicating communication with the intervillous space, as did the macrophages, suggesting phagocytic uptake of the hormone (Figure 7G ). Figure 5 A) Photomicrograph of a 1 μm resin section of 10 week decidua. By now the epithelium is cuboidal in nature, although secretions are still present within the lumens. There appears to be an almost complete layer of additional cells (arrowed) beneath the basal lamina. Scale bar = 10 μm. B) At the ultrastructural level the cells appear more quiescent at this stage of gestation, although Golgi bodies and a few strands of rough endoplasmic reticulum remain. Scale bar = 1 μm. Figure 6 Low power transmission electron micrograph of 10 week decidua demonstrating the heterogenous population of cells accumulated immediately beneath the epithelial basal lamina (arrowheads) at this stage of gestation. The smaller cells (arrowed) with large numbers of granules resemble uterine NK cells, whereas the larger more electron lucent cells (asterisks) resemble decidual cells. Scale bar = 5 μm. Figure 7 Confocal immunofluorescent images of decidua at 8 weeks (C, E, G, H) and 12 weeks (A, B, D, F) gestational age. In A) and B) the glandular epithelium has been immunolabelled for tocopherol transfer protein (green) and NK cells with CD56 (red). NK cells can be seen within the stroma between the glands, but also closely approximated (arrowed) to the basal lamina of the glandular epithelium. In C-F sections were immunolabelled for epidermal growth factor (EGF) (green) and CD56 (red). The epithelium reacts strongly at 8 weeks for EGF (C), but less so at 12 weeks (D). The NK cells lying beneath the glandular epithelium also react strongly for EGF (co-localisation yellow) (E and F). In G) and H) the sections were immunolabelled for human placental lactogen (red), and in G) for CD68 (green) and in H) for cytokeratin (green). Cells positive for both placental lactogen and CD68 (yellow) were considered to be macrophages, and were observed throughout the stroma but also closely approximated to the glandular epithelium (arrowed in G). Cells reacting only for placental lactogen, or for both placental lactogen and cytokeratin, were considered to be invading extravillous trophoblast cells (arrowheads in G and H), and were not found to be closely associated with the epithelium (E). Blue, DAPI; L, gland lumen. Scale bars C, D. = 60 μm and E - H = 30 μm. By 14 weeks the glandular epithelial cells were markedly flattened and only a few short microvilli were present on the apical surface (Figure 8A ). Few organelles were present in the cytoplasm, but instead there were numerous membrane vesicles containing a highly osmiophilic flocculent material resembling lipofuschin (Figure 8B ). Decidual cells made extensive contact with the basal lamina beneath the epithelium, often extending long processes in order to do so (Figure 8A ). Figure 8 Transmission electron micrographs of 15 week decidua illustrating A) the flattened nature of the glandular cells at this stage of gestation, and B) the accumulation of a flocculent osmiophilic material in their cytoplasm. L, gland lumen. Scale bars = 5 μm and 1 μm. Mucin and Cytokine production The glandular epithelium reacted strongly for leukaemia inhibitory factor (LIF), VEGF and MUC-1 at all gestational ages from 5 to 14 weeks (Figure 9 ). In the early specimens the staining was particularly strong in the apical protrusions of the epithelial cells, whereas in the older cases it was more generalised throughout the cells. The secretions within the lumens also reacted positively for LIF and MUC-1. Figure 9 Photomicrographs of immunolabelled decidua at 6 weeks (A, D, G, J, M, P) and 12 weeks (B, E, H, K, N, Q) gestational age. The glandular epithelium reacted positively for LIF (A, B), VEGF (D, E), MUC-1 (G, H), alpha tocopherol transfer protein (J, K), TGFβ 3 (M, N), and weakly for lactoferrin (P, Q). Negative controls; C, F, I, L, O and R. Scale bar = 200 μm. A similar pattern was observed for alpha tocopherol transfer protein (TTP) and transforming growth factor beta (TGFβ 3 ), although many of the decidual cells also reacted positively as gestational age increased (Figure 9 ). In addition, some of the interstitial cells and those just beneath the epithelium reacted intensely for TGFβ 3 . These were presumed to be macrophages and uterine NK cells. The pattern was different for epidermal growth factor (EGF), for although the glandular epithelium initially displayed strong reactivity, the intensity reduced considerably by 9–10 weeks. By contrast, in the older specimens the cells immediately beneath the epithelium reacted strongly. As these cells also reacted positively for CD56 it was assumed they were NK cells (Figures 7C,7D,7E,7F ). Immunoreactivity for lactoferrin was weak even in the earliest specimens, although occasional cells reacted strongly. In the older specimens only faint staining could be identified (Figure 9 ). In all cases the negative controls showed no staining. Fate of the secretions In order to determine whether the glandular secretions taken up by the trophoblast enter the digestive pathway frozen sections of first trimester villi were dual-labelled for glycodelin, a glandular product, and cathepsin D, a marker of the lysosomal pathway. In the syncytiotrophoblast numerous vesicles immunoreactive exclusively for glycodelin were observed within the superficial layer of the syncytioplasm abutting the intervillous space, whereas lysosomes positive only for cathepsin D were observed in the basal region. In the midzone of the syncytioplasm the two labels were co-localised, indicating lysosomal fusion with the glycodelin-containing vesicles (Figure 10 ). Figure 10 Confocal photomicrograph of a frozen section of an 8 week villus A) immunolabelled for glycodelin (green) and cathepsin D (red) and B) under phase contrast. Vesicles labelled solely for glycodelin predominate in the apical region of the syncytiotrophoblast (S), and those for cathepsin D in the basal region. In the mid-zone the two labels co-localise (yellow) indicating that maternal proteins enter the trophoblast digestion pathway. IVS, intervillous space. Discussion It is clear from this study of placenta-in-situ specimens that the uterine glands are still well-developed and highly active at 6 weeks of pregnancy, and that although there is considerable individual variation they gradually regress, both in terms of their length and the height of their epithelium, as the first trimester advances. Some of this variation may reflect differences in the thickness of the endometrium across the placental bed, for it was generally thinnest in the centre and thicker towards the periphery. Sampling at different sites may therefore yield different measurements. Nonetheless, by the start of the second trimester the endometrium beneath the placenta is very thin, the glandular epithelium is cuboidal and secretory organelles are no longer predominant. Indeed, the accumulations of osmiophilic material within the cytoplasm are reminiscent of lipofuschin, a characteristic of involuting or aging cells. These observations are consistent with a gradual shift from essentially histiotrophic nutrition of the human conceptus during the early first trimester to haemotrophic nutrition towards the start of the second trimester [ 3 , 20 ]. We previously reported that at 6 weeks gestational age the glandular epithelial cells closely resemble those during the luteal phase of the cycle, with large accumulations of glycogen within the apical portions of the cell [ 18 , 21 ]. In the normal menstrual cycle these accumulations disperse around days 23–24, but their persistence indicates that the corpus luteum of pregnancy maintains the glands in a highly active state during early gestation. The composition of the secretions from the uterine glands has been extensively investigated during the various phases of the menstrual cycle [ 22 , 23 ], but their contribution post-implantation has largely been ignored. The secretions are rich in carbohydrates, glycoproteins and, as demonstrated here, lipids. They therefore may provide an important source of nutrients for energy and elements for anabolic pathways within the feto-placental unit. The observation that glycodelin, a protein that is not expressed within placental tissues and so must be of maternal origin [ 24 ], enters the lysosomal digestive pathway within the syncytiotrophoblast supports this hypothesis. We have speculated that reliance on histiotroph during the period of organogenesis may protect the fetus from teratogenic damage by reactive oxygen species, for all mammalian embryos studied so far appear to rely heavily on anaerobic pathways during this period of development [ 25 , 26 ]. Once organogenesis is complete the oxygen concentration within the feto-placental unit rises as placental attachment and development occurs or, as in the case of the human, the maternal circulation to the placenta is fully established. Besides acting as a source of nutrients our results also demonstrate that the glands express a wide variety of growth factors and cytokines, and so may play an important role in regulating placental development as in other species. Receptors for EGF have been localised immunohistochemically to the cytotrophoblast cells in the earliest stages of pregnancy, and on the syncytiotrophoblast in later gestation [ 27 , 28 ]. This switch parallels the dual actions of EGF reported, for in the earliest samples of 4–5 weeks EGF stimulated cytotrophoblast proliferation, whereas at 6–12 weeks it stimulated secretion of human chorionic gonadotropin (HCG) and placental lactogen [ 29 ]. Similarly, receptors for LIF have been demonstrated on first trimester villous and extravillous trophoblast populations, and on villous endothelial cells [ 30 ]. Addition of LIF to purified extravillous trophoblast cells had no effect on proliferation or integrin expression, but did inhibit forskolin-induced HCG production by BeWo cells in a dose-dependent fashion [ 30 , 31 ]. Receptors for VEGF have also been identified on the villous and extravillous trophoblast populations, and on villous endothelial cells [ 32 , 33 ], whilst TGFβ 3 can modulate trophoblast differentiation between the proliferative and invasive phenotype [ 34 ]. Histiotroph may therefore potentially play significant roles in regulating trophoblast proliferation and differentiation during early pregnancy, as well as modulating placental vascularization. Another group of proteins expressed by the glandular epithelium is that of transport carriers. TTP is a cytosolic protein first identified in the liver, but which has recently also been reported in the syncytiotrophoblast of the human placenta [ 35 , 36 ]. The high level of expression in the glandular epithelium suggests that histiotroph may be an important route for transfer of antioxidants during early pregnancy, increasing the defences of the feto-placental tissues against oxidative stress associated with onset of the maternal intraplacental circulation [ 3 , 37 ]. Lactoferrin is glycoprotein (molecular weight 82,400) traditionally associated with the transport of iron in breast milk. It was first identified immunohistochemically in the endometrial glandular epithelium and in their secretions, and although immunoreactivity was variable between specimens it was generally strongest during the late secretory phase of the cycle [ 38 ]. Here we identified significant staining only at the earliest gestational ages. The role of lactoferrin in the transport of iron is doubtful given the presence of transferrin receptors on the syncytiotrophoblast. Potentially, it may act as an antioxidant, for by forming stable complexes with free iron ions within the intervillous space it will reduce the possibility of generation of the highly toxic hydroxyl ion through the Fenton reaction [ 39 , 40 ]. It also possesses anti-microbial properties and so may contribute to the immune defences of the endometrium and early placenta [ 41 ]. Endometrial secretions may also modulate maternal immunological responses to the placental tissues. Thus glycodelin, which is released into the intervillous space, is immunosuppressive and functions as a direct T-cell inhibitor [ 9 , 42 ]. As gestation advances NK and stromal decidual cells migrate and come to lie closely approximated to the basal lamina of the glandular epithelium. The presence of NK cells within the glandular epithelium has been reported previously [ 43 ], and similar cells have been observed in an intraepithelial position in other species [ 44 ]. Whether the subepithelial cells we observed play a role in immune surveillance or support the epithelium in some other way is not clear at present, but the fact that they are immunopositive for EGF raises the possibility of paracrine signalling. Because insufficient decidualization could have an impact on implantation and placentation, evaluation of the endometrial morphology by ultrasound has generated a lot of clinical interest. An endometrial thickness of 8 mm or more is considered to be favourable for implantation in humans [ 45 ], although this remains controversial as other authors have not found an association between endometrial thickness and pregnancy achievement [ 46 ]. One reason for this may be the fact that endometrial growth is not an homegeneous process, and that a single measurement of the endometrial thickness may not reflect the entire endometrial development. Within this context evaluation of the total endometrial volume with 3-D ultrasound could be a more accurate way of evaluating endometrial development [ 47 ]. Adequate endometrial thickness seems to be directly linked to uterine vascularization, and women with a good endometrial thickness on ultrasound but a poor intra-endometrial blood flow tend to have a poor reproductive outcome [ 48 ]. Uterine perfusion appears to regulate endometrial receptivity, and a high uterine resistance to blood flow is associated with recurrent miscarriages [ 49 ]. The visualisation of the endometrial circulation with 3-D doppler ultrasound appears to be an efficient parameter in predicting implantation in IVF cycles [ 50 ]. Attempts to correlate functional activity of the glands with pregnancy outcome have also met with mixed success. Thus, whilst reduced concentrations of MUC-1, LIF and glycodelin in uterine flushings have been reported in women suffering recurrent miscarriages [ 51 , 52 ], expression of these markers within the endometrium shows no significant association [ 53 ]. Why the glands should regress while maternal progesterone concentrations remain high is not known, but it would seem reasonable to assume that the decline of histiotrophic nutrition and the onset of haemotrophic exchange are co-ordinated in some way. How this might be achieved in the human is unknown at present. Authors' contributions JH performed the tissue processing and colourimetric immunohistochemistry. TC-D performed the confocal immunofluoresence and dual-labelling. EJ performed the clinical procedures and collection of samples. GJB conceived the study and performed the electron microscopy and morphometric analysis.

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Ultrasound imaging versus morphopathology in cardiovascular diseases. Coronary atherosclerotic plaque

This review article is aimed at comparing the results of histopathological and clinical imaging studies to assess coronary atherosclerotic plaques in humans. In particular, the gap between the two techniques and its effect on the understanding of the pathophysiological basis of coronary artery disease is critically discussed.

Introduction Amongst the clinical approaches ultrasound imaging is one of the more promising technique to understand dysfunction. The need is to compare morphopathological counterpart to have a correct pathophysiological interpretation. In four reviews, morphopathology of the main cardiovascular disorders, in relation to the status of art of clinical imaging will be presented. The aim is to recall the pathological anatomy to stimulate ultrasound experts to further sharpen their technology till "histological" perfection. The present first review concerns the coronary atherosclerosis since the current dogma of "unifying theory" assumes that the acute coronary syndromes, namely unstable angina, myocardial infarct and sudden death, are caused by atherosclerotic plaque rupture or "explosion" with occlusive thrombus formation preceded by intramyocardial emboli. An assumption which implies to discover a clinical imaging able to show when a coronary atherosclerotic plaque becomes vulnerable i.e. prone to rupture. The risk of the latter has been correlated with large lipid core (atheroma in our definition), thin fibrous cap (< 65 μm) covering atheroma. Therefore any imaging should have a 50 μm resolution to identify a fibrous cap prone to rupture, 100 μm or 150 μm thickness being respectively at low or minimal risk. Matrix-digesting enzymes released from inflammatory cells (monocytes, macrophages, T-cells, B-cells, neutrophils, mastcells) may contribute to plaque rupture. An attractive approach since, despite many years of preventive and therapeutical attempts, coronary heart disease (CHD) remains the main cause of death and morbidity in advanced societies and selects people at the top of their work skillness and productivity. The first question is whether ultrasound imaging may or may not discriminate extent and morphology of plaque variables seen within the intima. Coronary atherosclerotic plaque Physiological intimal thickening Morphology of the atherosclerotic plaque has been described in textbook and articles [ 1 - 4 ]. In comparing many contributions, the major difficulty is to discriminate among different morphologic patterns selected in different groups of patients according to unclear definitions and without distinction between the plaque obtained by hypercholesterol diet and that found in the general population. The other need is to consider the evolution of a plaque in each single arterial system since anyone has its own peculiarity with different response to blood flow dynamics. In this sense, only the coronary arteries have a diphasic blood flow in relation to the cycle of myocardial contraction, i.e. filling of extramural vessels without intramural flow in systole because contracted myocardium compresses intramyocardial vessels. The result is an excess of systolic radial, circumferential, longitudinal and drag pressures on the wall of arteries and branches free to expand on the cardiac surface. In turn, this diphasic hemodynamic induces a structural response of the coronary intima which starts as smooth muscle cell proliferation from the tunica media, followed by elastic fiber hyperplasia ending in fibrosis of the whole intima without lumen reduction (Fig 1 ). First described by Wolkoff in 1929, such thickening becomes greater in the second decade in contrast to its absence in other human muscular arteries (e.g. brain arteries) or in animal with a similar coronary artery anatomy and diphasic flow (Fig. 2 ). The latter finding suggests that hemodynamic pressures may have the intimal hyperplastic effect in association with the neurovegetative regulation of arterial wall tone particularly active in humans in relation to heart function. Any clinical imaging of the coronary wall should consider this physiological intimal thickening [ 3 - 5 ] which in normal adult hearts measures about 200 μm, does not show any atherosclerotic variable, may become thicker in hypertrophic hearts with normal coronary artery and is greatly reduced or absent in segments of extramural coronary artery embedded ("mural artery") within the myocardium. The latter abolishes the systolic arterial wall expansion (Fig. 2 ). Figure 1 Coronary physiologic intimal thickening. This changes starts as nodular (already visible at birth at the site of vessel bifurcation) smooth muscle cells (A) and elastic fibrils (B) hyperplasia which in the second decade is diffuse to the whole intimal surface of all extramural arterial vessels. With aging there is a progressive increase of fibrous tissue which substitutes myo-elastic tissue (C, D) with final total, anelastic, fibrosis (E, F). Arteriosclerosis distinct from atherosclerosis Figure 2 Comparison between the intimal thickening of the LAD (A) and the middle cerebral artery (B) of the same 18-year old subject. In the latter artery the intimal thickening is minimal in contrast to that of LAD which is circumferential with a thickness greater than tunica media. C), difference in maximal thickness in microns found in main coronary arteries and branches in respect of the middle cerebral artery. D), absence of intimal thickening in the LAD of dogs, despite and identical morpho-function. This suggests a possible role of the neurogenic control of coronary arteries in humans. On the other hand the absence of intimal thickening in the "mural tract" of coronary arterial vessels (E) emphasizes the role of systolic dynamic stresses on arterial wall free to expande versus those protected by encircling contracted myocardium. The first conclusion is that such intimal thickening is a physiologic structural respons to hemodynamics and not the initial phase of atherosclerosis as claimed by some authors. Coronary atherosclerotic intimal thickening In contrast to the uniformely diffuse physiological intimal thickening, the atherosclerotic one is focal and protrudes within the lumen which is progressively reduced. In order to quantitatively study this progression, we sampled systematically in each heart the first tract of the main left trunk (LCA), left descending (LAD) and circumflex (LCX) branches, right coronary artery (RCA), posterior descending branch (PD) and the middle tract of LAD and marginal and posterior tracts of RCA. These selected tracts correspond to the sites where atherosclerotic changes generally occur. The coronary arterial sampling was performed in 100 fatal cases of acute myocardial infarct without other diseases and not undergone invasive techniques; 208 cases of sudden and unexpected coronary death (SUD) which occurred in apparently normal people, acting their usual life, without resuscitation attempts and autopsy findings limited to coronary atherosclerosis of any degree, myocardial necrosis or scar, with or without cardiac hypertrophy: 50 cases with chronic angina pectoris who died within 25 day after coronary by-pass surgery; and 97 normal subjects who died by accident without pathological findings at autopsy but coronary atherosclerosis. In a total of 3,640 coronary sections the following variables were quantified: 1. Lumen reduction calculated in percent of the normal diameter measured in normal coronary arteries and branches injected by plastic substance under pressure. Measurement often referred to the cross-sectional area within the internal elastic membrane may result in severe stenosis despite a normal lumen since the atherosclerotic plaque may enlarge the cross-section. 2. Shape of residual lumen: concentric if encircled by pathological intima or semilunar when an arch of the wall was normal. 3. Length calculated in number of segments involved by the plaque, all extramural coronary arteries being sistematically cross-sectioned at 3 mm interval. 4. Intimal and tunica media thickness measured in microns. 5. Atherosclerotic changes within the intima: fibrosis alone, basophilia i.e. proteoglycan accumulation, atheroma or lipoprotein/cholesterol material, calcification , vascularization , hemorrhage , adventitial and intimal lymphocytic infiltration . All these variables were expressed in percent of the total intima but vascularization calculated in number of vessels found. Amongst 1,519 sections without lumen reduction with an intimal physiological thickness less of 300 μm we never found subendothelial or internal lipoprotein/cholesterol infiltration or deposit (fatty streaks), monocytes or macrophages or foam cells, platelet aggregates, fibrin-platelet thrombi or inflammatory elements. A similar negative finding was observed in 1,315 coronary sections with a lumen reduction less than 69% and pathological intimal thickness less than 600 μm. In all these sections we were unable to demonstrate an intimal fissuration. We must emphasize that in selecting our material cases of familial hypercholesterolemia were excluded. In general the atherosclerotic intimal variables increased in frequency and extent in parallel with the lumen reduction and pathological intimal thickeness with the exception of proteoglycan accumulation less found in stenoses >90% and intimal thickness >2000 μm. Of 990 sections with calcification, 488 (49%) had mild stenosis, calcification being severe in 162 (33%). This finding indicates that calcification per se does not necessarily means a severe lumen reduction. The less frequent variable was intimal hemorrhage mainly seen in plaques located in a vessel tributary to an acute infarct. When different groups of CHD and normal people were matched, an excess of atheroma, hemorrhage, calcification and adventitial/intimal lymphocytic inflammation was observed in AMI group while a significant defect was present in normal subjects with the same degree of stenosis. In synthesis, two other main findings are worth of mention: 1) proteoglycan accumulation is a relatively late event which occurred in the deep layer of the intima near to the tunica media and below the fibrous cap of a plaque. Lipoprotein/cholesterol plus macrophages (foam cell) and/or calcium salts appeared only in this proteoglycan pool in agreement with their interaction with glycosaminoglycans (Fig. 3 ); 2 ) adventitial inflammation showed a peculiar tropism for the nervous structures related to the media at plaque level only (medial neuritis). An inflammatory process which involved all plaques present in each CHD patient while absent or limited to one plaque in normal controls. Figure 3 Natural history of the coronary atherosclerotic plaque in general population, including most of CHD patients. The starting point is a nodular hyperplasia of smooth muscle cells and elastic tissue with progressive fibrous replacement. No other changes as subendothelial lipo-protein-cholesterol storage, inflammatory process of any type, platelet aggregation and/or fibrin-platelet thrombi are found (A). Proteoglycan accumulation in the deep intima between tunica media and the fibrous cap is the second step (B). In this proteoglycan pool, lipo-protein/cholesterol cleft, in macrophages ("foam cells") and/or Ca ++ salts appear. Vascularization of the plaque and hemorrhage (C) follow. In the stage of proteoglycan accumulation, lympho-plasmacellular infiltrates occur in the adventitia and intima (C) with specific localization, around adventitial nerves closed to the tunica media (medial neuritis) (D, E). This natural history is totally different from that obtained experimentally by hypercholesterol diet in animals free of spontaneous atherosclerosis or in the small group of patients with familial hypercholesterolemia (F), in which transendothelial lipo-protein insudation is the starting point. This study induced the recognition of two types of coronary atherosclerotic plaque: one, which belongs to the general population, (including CHD patients) and starts as nodular intraluminal proliferation of smooth muscle cells followed by elastic tissue hyperplasia and final substitution by fibrous tissue. Subsequently, a deep proteoglycan pool forms and becomes a deposit of lipoprotein/cholesterol and/or calcium salts. The recurrence of these phenomena explains the radial, circumferential, longitudinal progression of the coronary plaque resulting in increasing lumen reduction. This type of myohyperplastic plaque is totally different from the hypercholesterol plaque obtained experimentally by hypercholesterol diet in animals free of atherosclerosis or observed in a small group of patients with familial hypercholesterolemia. In literature too often the hypercholesterol plaque is taken as a model of an atherosclerotic plaque in man [ 1 ]. In timing the sequence of the events is important to stress that the inflammatory lymphocytic-plasmacellular process (autoimmune phenomenon?) starts after the proteoglycan insudation, being a relatively late complication. The recurrent basic changes in myohyperplastic plaque (smooth muscle cell hyperplasia, fibrosis, proteoglycan accumulation with atheroma and/or calcification) explain the various intimal aspects amongst different plaques and different tracts of the same plaque (Fig. 4 ). A synopsis comparing dogma versus our findings is given in Table 1 : Figure 4 Coronary atherosclerosis. Different aspect of a severe, pin-point lesion (arrow). Plaque with prevailing atheroma (A) or fibrosis (B). Plaque with pale, large zone of proteoglycan accumulation (C) or with small atheroma plus hemorrhage and proteoglycans associated with critical stenosis occluded by an acute thrombus (D). Sequence in the same plaque of "rupture" (E) followed by severe hemorrhagic atheroma with minimal, linear lumen (arrow) without occlusion(F). Occlusive thrombosis connected with hemorrhagic atheromasia at the site of a critical stenosis (G). Semilunar stenosis (H) with a normal half wall and minor lumen reduction. The concept of vessel wall remodeling to compensate plaque growth has not any support (very low frequency of this type of lesion versus severe concentric lumen reduction in the natural history of coronary heart disease). Targets of ultrasound diagnosis: the present and the future Different echocardiographic techniques have been employed in the attempt to provide adequate visualization of coronary arteries (Table 2 ). However, intracoronary ultrasound (ICUS) represents the most valuable method to assess plaque morphology. Given to its limited resolution (approximately 0.3 mm), angiography is a fairly imprecise measure of luminal morphology and size. In particular, it is deficient in providing adequate distinction between plaque and lumen irregularities and assessing the extent of atherosclerotic disease [ 6 ]. Both these issues appear to be much well defined by ICUS. Firstly, the tomographic orientation of ultrasound enables a visualization of the full circumference of the vessel wall and, therefore, a more accurate assessment of size [ 7 - 9 ]. In addition, it allow us to overcome the false assumption that the nonstenotic region surrounding a discrete stenosis is normal and, therefore, to obtain an unbiased assessment of the plaque burden at the site of the stenosis. Finally, the penetrating nature of ultrasound provides unique images of the atherosclerotic plaque. Table 2 Echocardiographic approach to coronary arteries. Technique % success Image quality Anatomic information (plaque) Functional information (flow) Transtoracic 20 ± ± ± Transesophageal 80 + + ++ Epicardic 90 ++ ++ - Intracoronary 95 +++ +++ - - = poor; + = sufficient; ++ = good; +++ = very good ICUS image analysis has been extensively used for determining plaque composition [ 10 - 13 ]. However, there are some limitations to this approach: 1) digitizing videotapes is time-consuming and therefore not suitable for real-time analysis; (2) image resolution is reduced to that of videotape, approximately 330 μm; (3) parameters such as gain, including time gain compensation and intensity, can be adjusted by the operator, thereby adding variability to the data set; (4) the dynamic range, pre- and postprocessing of the images depend on the analog-to-digital converters used in the ICUS consoles; (5) finally, due to the small dimension of the transducer, the transmitted acustic energy is low. Thus, some concerns still exist on whether this technique is ready to go for clinical use. In particular, definition of plaque composition seems not enough reproducible to provide an alternative independent standard to quantitative histology [ 14 ]. New technical development based on noninvasive molecular imaging [ 15 , 16 ], such as the use of novel targeted contrast agents able to identify fibrin deposited within plaque microfissures [ 17 ], adhesion or thrombogenic molecules expressed on endothelium of vulnerable plaques [ 18 - 22 ], matrix metalloproteinases in the cores of progressing lesions [ 23 ], or even early angiogenic expansion of the vasa vasorum that supports plaque development [ 24 ], will contribute to fill the gap between information derived from direct, quantitative histology and ultrasound imaging. Moreover, spectral analysis of the radiofrequency signal allows a more detailed analysis of various vessel components than does image analysis of digitized videotape images and can be potentially employed in real-time. This approach is expected to improve tissue characterization [ 25 ]. Comment The working hypothesis is to stabilize the atherosclerotic plaque by increasing the thickness of the fibrous cap or by regression of atheroma burden (1). However, present imaging techniques (coronary cineangiography, angioscopy, contrast magnetic resonance, contrast echocardiography, nuclear scintigraphy, etc.) cannot provide adequate clinical evaluation of plaque vulnerability. In particular, ICUS is unable to provide discrimination between physiological and pathological intimal thickening and to define the shape of plaque, i.e. concentric or semilunar. In fact, amongst 2121 coronary sections at the site of maximal lumen-diameter reduction the stenosis was concentric in 70% of the cases (99% in supplying vessels of an acute myocardial infarct). Furthermore, in 408 CHD patients the maximal stenosis in each single case was less than 69% in 68, 70%, in 67, 80% in 109 and >90% in 164. These data mean that the residual lumen ranged from 900 to less than 50 μm and catheter of 1500 μm in most instances must break the plaque, being the residual lumen too small. Therefore, shape and contour of a plaque can be altered with a misleading higher frequency of semilunar stenosis giving an erroneous support to the questionable concept of vessel wall remodelling following an atherosclerotic plaque formation. According to the previous data the main conclusions are: 1. The natural history of coronary atherosclerotic plaque in the general population, inclusive of CHD patients, is different from plaques obtained by experimental hypercholesterol diet or found in familial hypercholesterolemia. Most data refer to the latter as a valid model for the human plaque. In particular, fatty streaks is not the starting change of the myohyperplastic atherosclerotic plaque; 2. Emphasis is given to a "macrophagic inflammation" as source of proteolytic and/or thrombogenic moleculae causing plaque rupture. However, macrophagic reaction belongs to a repair process to digest necrotic or extraneous material rather than typical elements of an inflammatory process, as lymphocytes, neutrophils. The assumption that on increased number of labelled macrophages may indicate a risk of rupture is questionable in human coronary myohyperplastic plaque. In the present review we have discussed the behaviour and meaning of components of the human coronary atherosclerotic plaque to emphasize the inconsistency of the current myths: 1. Experimental hypercholesterol model and correspondent human conditions do not represent the natural history of atherosclerosis in coronary arteries in the general population. 2. Physiological intimal thickening can not be interpreted as starting point of the atherosclerotic process. 3. Fatty streak does not represent the early atherosclerotic lesion. 4. Calcification is not synonymous of severe stenosis. 5. Hemorrhage is not consequent to endothelial fissuration. 6. Prevention of macrophage "inflammation" as source of substances able to disrupt the fibrous cap allowing rupture and thrombosis as well as identification of the thickness of fibrous cap to diagnose a vulnerable plaque may have little, if any, sense. Rupture and thrombosis may be secondary phenomena and not the cause of an acute coronary syndrome. 7. Degree and number of severe coronary plaques do not predict onset, course, complications and death in CHD. Authors' contributions Prof. Giorgio Baroldi contributed to the conception and organization of this review and to the final comments. Dr. Riccardo Bigi and Dr. Lauro Cortigiani summarized the use of ultrasound techniques in atherosclerotic plaque imaging Table 1 Natural History of Human Coronary Atherosclerotic Plaque Beliefs Facts Transendothelial lipoprotein/cholesterol infiltration Nodular smooth myocell-elastic hyperplasia protruding in lumen Fatty streaks Fibrous substitution Macrophagic "inflammation" Proteoglycan accumulation below fibrous cap between media/intima Necrotic core-atheroma under fibrous cap Interstitial/macrofagic (foam cells) storage of lipoprotein-cholesterol and/or calcium salts in proteoglycan pool Rupture fibrous cap Plaque tridimensional growth by recurrence of previous phenomena Thrombosis-Embolization Late vascularization of atherosclerotic intima Divergency between dogma and heretic view is easily explained by the fact that the former is founded on experimental hypercholesterolemic plaque which more or less may correspond to plaques in the relatively small group of familial hypercholesterolemia in humans. Furthermore, the main arterial vessel examined was the aorta and in human pathology advanced plaque was not studied comparing study in different CHD patterns and pathologic and normal controls.

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Effects of chemokines on proliferation and apoptosis of human mesangial cells

Background Proliferation and apoptosis of mesangial cells (MC) are important mechanisms during nephrogenesis, for the maintenance of glomerular homeostasis as well as in renal disease and glomerular regeneration. Expression of chemokines and chemokine receptors by intrinsic renal cells, e.g. SLC/CCL21 on podocytes and CCR7 on MC is suggested to play a pivotal role during these processes. Therefore the effect of selected chemokines on MC proliferation and apoptosis was studied. Methods Proliferation assays, cell death assays including cell cycle analysis, hoechst stain and measurement of caspase-3 activity were performed. Results A dose-dependent, mesangioproliferative effect of the chemokine SLC/CCL21, which is constitutively expressed on human podocytes was seen via activation of the chemokine receptor CCR7, which is constitutively expressed on MC. In addition, in cultured MC SLC/CCL21 had a protective effect on cell survival in Fas-mediated apoptosis. The CXCR3 ligands IP-10/CXCL10 and Mig/CXCL9 revealed a proproliferative effect but did not influence apoptosis of MC. Both the CCR1 ligand RANTES/CCL5 and the amino-terminally modified RANTES analogue Met-RANTES which blocks CCR1 signalling had no effect on proliferation and apoptosis. Conclusions The different effects of chemokines and their respective receptors on proliferation and apoptosis of MC suggest highly regulated, novel biological functions of chemokine/chemokine receptor pairs in processes involved in renal inflammation, regeneration and glomerular homeostasis.

Background Originally chemokines ( chemo tactic cyto kines ) were described as key mediators for the selective migration of leukocytes into sites of tissue injury [ 1 ]. Later on chemokines and chemokine receptors have also been described as important mediators in noninflammatory processes, including normal cellular trafficking, hematopoesis, angiogenesis, organ development, tissue remodelling, and tumor metastasis [ 2 - 4 ]. To date more than 40 different human chemokines are characterized. The chemokine superfamily is separated into the C, CC, CXC, and CX3C subfamilies (Where X represents any intervening amino acid residue between the first two cysteines in the amino acid sequence) [ 5 , 6 ]. Chemokines mediate their biological activity by ligation and interaction with seven-transmembrane-spanning G protein-coupled receptors (i.e. C, CC, CXC, and CX3C receptors) [ 7 ]. In the kidney expression of chemokines and chemokine receptors are important for the initiation and regulation of inflammatory glomerular diseases [ 8 ]. Expression of the chemokines monocyte chemoattractant protein-1 (MCP-1/CCL2), regulated upon activation, normal T cell expressed and secreted (RANTES/CCL5), interleukin-8 (IL-8), and interferon-γ (IFN-γ)-inducible protein of 10 kD (IP-10/CXCL10) by human mesangial cells (MC) was shown by our group [ 9 ] and others [ 1 , 10 ]. Inducible expression of the chemokine receptor CCR1 by human MC was previously described [ 9 ]. The expression of the chemokine receptor CXCR3 on human MC was published by Romagnani and colleagues [ 11 ]. A high level of expression of this receptor by MC was seen by immunohistochemistry in kidney biopsies from patients with IgA nephropathy, membranoproliferative glomerulonephritis or rapidly progressive glomerulonephritis. Recently our group showed that SLC/CCL21 is constitutively expressed by glomerular podocytes and CCR7 constitutively expressed by MC [ 12 ]. In the kidney the well-regulated relationship among resident cell proliferation and apoptosis is important for the development of the sophisticated glomerular architecture during ontogenesis as well as maintaining normal function of adult human glomeruli. Dysfunction of the balance between glomerular cell proliferation and apoptosis after leukocyte infiltration has been discussed for many inflammatory kidney diseases [ 13 ]. The finding of expression of chemokine receptors and their respective ligands by intrinsic renal cells not only under inflammatory conditions led to the hypothesis of an involvement of these receptors in glomerular homeostasis. Therefore the influence of chemokines on mesangial cell growth was investigated. We describe the different effects of the chemokines SLC/CCL21, IP-10/CXCL10, Mig/CXCL9, RANTES/CCL5 and the amino-terminally modified RANTES/CCL5 analogue Met-RANTES on mesangial cell proliferation and apoptosis, suggesting novel functions of chemokine/chemokine receptor pairs on local immunomodulation, glomerular regeneration and homeostasis. Methods Cell culture conditions for human mesangial cells Immortalized human mesangial cells (MC) were grown as described previously [ 9 ]. This MC line was characterized for antigenic markers typically expressed by MC in vivo and in vitro and showed no dedifferentiation within approximately 100 passages during a 36 months cultivation period. For all experiments cells in passages 51 to 65 were used. Different preparations of primary human MC served as controls and were cultured as previously described [ 14 ]. Proliferation assay To assess proliferation we performed a MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide, Sigma, Germany) assay [ 15 ]. Aliquots of 20 × 10 3 cells in 100 μl medium were cultured in 96-well microtitre plates for 24 hours under standard conditions to yield firmly attached and stably growing cells. Subsequently the medium was changed to 100 μl of medium containing test substances and the cells were incubated from 24 to 72 hours. After discarding the supernatants 50 μl of a 1 mg/ml solution of MTT were added. The cells were incubated for 3 hours at 37°C and then formazan crystals were dissolved by addition of 50 μl isopropanol. Absorbance was measured at 550 nm against 630 nm reference using a DYNATECH (Germany) MR7000 ELISA reader. For each experiment at least 6 wells were analyzed per experimental condition and time point. Cell death assays Apoptosis of MC was induced by Fas/CD 95 ligation as previously described [ 16 ]. Fas/CD 95 surface expression was induced by starvation of the cells in serum-free medium and pre-stimulation with IFN-γ (70 ng/ml) for 48 h. For analysis of chemokine effects, cells were pretreated with SLC/CCL21 250 ng/ml, IP-10/CXCL10 100 ng/ml, Mig/CXCL9 100 ng/ml, RANTES/CCL5 100 ng/ml, and Met-RANTES 100 ng/ml prior to adding the activating anti-human Fas antibody (400 ng/ml, incubation over 18 hours) (Biomol, Germany). To induce expression of the chemokine receptor CCR1 in a different experiment MC were preincubated with a combination of IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 h before starvation of the cells in serum-free medium and stimulation with IFN-γ (70 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 48 h. Apoptosis was studied with several assays: Flow cytometric cell cycle analysis was performed using propidium iodide staining as described previously [ 17 ]. For visualization of chromatin fragmentation, MC were seeded on chamber slides (NUNC, Germany). After treatment with test substances cells were fixed with ethanol and stained with the nuclear dye Hoechst 33258 (Hoechst, Germany) 5 μmol/ml. The percentage of apoptotic cells was determined by immunofluorescence microscopy, counting nuclei with condensed and fragmented chromatin. Three different experiments were performed, at least 300 cells were analyzed per condition. Counting was performed in a blinded manner by two investigators. For measurement of caspase-3 activity [ 18 ] a commercial assay (R&D systems, Germany) was used according to the manufacturer's specifications. After induction of apoptosis as described above, MC were lysed and caspase-3 specific proteolytic activity was quantitated spectrophotometrically. Three experiments were done analyzing duplicates for any experimental condition. Statistical analysis Values are provided as mean ± SEM. Statistical analysis was performed by unpaired t test. Significant differences are indicated for p values < 0.05 (*) or < 0.01 (**), respectively. Results Effects of SLC/CCL21, IP-10/CXCL10, Mig/CXCL9, RANTES/CCL5 and Met-RANTES on proliferation of human mesangial cells SLC/CCL21 induces proliferation of human MC To demonstrate that activation of CCR7 has an influence on the proliferative activity of MC MTT assays were performed as described in Methods. The CCR7 ligand SLC/CCL21 led to a concentration dependent increase of proliferation of human MC in a range from 10 to 250 ng/ml after 24 hours of stimulation (Figure 1A ). In a time course experiment from 24 to 72 hours the maximum increase of proliferation was seen after incubation with SLC/CCL21 for 48 hours (Figure 1B ). Figure 1 Dose- and time- dependent effect of SLC/CCL21 on proliferation of human MC. (A) Incubation of human MC with various concentrations of SLC/CCL21 (10 ng/ml, 50 ng/ml, 100 ng/ml, 250 ng/ml) induces proliferation of MC in a dose-dependent manner. (B) Time-course stimulation of MC with SLC/CCL21 for various time intervals (24 h, 48 h, 72 h). Cell proliferation was analyzed with the MTT assay as described in Methods. Cells growing under standard conditions served as control. Changes in proliferative activity are given as relative values to the respective controls. Each bar represents a mean ± SEM of 7 parallel incubations for each condition. Statistically significant differences to the control are depicted with * = p < 0.05 and ** = p < 0.01, resp. Comparable results were obtained in three series of independent experiments. IP-10/CXCL10 and Mig/CXCL9 induce proliferation of human MC CXCR3 activation had an influence on the proliferative activity of MC. As shown in Figure 2 , stimulation with the CXCR3 ligands IP-10/CXCL10 and Mig/CXCL9 had an increasing effect on the proliferation of human MC in concentrations 10 to 250 ng/ml (Figure 2 ). Incubation period was 24 hours. Figure 2 Dose-dependent effect of IP-10/CXCL10 and Mig/CXCL9 on proliferation of human MCIncubation of human MC for 24 hours with various concentrations of IP-10/CXCL10 and Mig/CXCL9 (10 ng/ml, 100 ng/ml, 250 ng/ml) induces proliferation of MC in a dose-dependent manner. Cell proliferation was analyzed with the MTT assay as described in Methods. Cells growing under standard conditions served as control. Changes in proliferative activity are given as relative values to the respective controls. Each bar represents a mean ± SEM of 5 parallel incubations for each condition. Statistically significant differences to the control are depicted with * = p < 0.05 and ** = p < 0.01, resp. Comparable results were obtained in three series of independent experiments. RANTES/CCL5 and Met-RANTES have no effect on proliferation of human MC Incubation of human MC with various concentrations of the CCR1 ligands RANTES/CCL5 (10 ng/ml, 50 ng/ml, 100 ng/ml, 250 ng/ml) and Met-RANTES (10 ng/ml, 50 ng/ml, 100 ng/ml, 250 ng/ml) had no effect on the proliferation of MC at an incubation period of 24 hours under standard conditions (Figure 3 ). To induce chemokine receptor CCR1 expression cells were pretreated with a combination of IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 hours prior to adding test substances. Stimulation with RANTES/CCL5 or Met-RANTES without pretreatment with cytokines has no effect on proliferation (data not shown). Figure 3 Effect of RANTES/CCL5 and Met-RANTES on proliferation of human MCIncubation of human MC for 24 hours with various concentrations of RANTES/CCL5 (10 ng/ml, 100 ng/ml, 250 ng/ml, 1 μg/ml, 10 μg/ml) and Met-RANTES (10 ng/ml, 100 ng/ml, 250 ng/ml, 1 μg/ml, 10 μg/ml) has no effect on proliferation of MC under standard conditions. To induce expression of chemokine receptor CCR1 MC were prestimulated with IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 hours prior to adding test substances. Cell proliferation was analyzed with the MTT assay as described in Methods. Cells growing under standard conditions served as control. Changes in proliferative activity are given as relative values to the respective controls. Each bar represents a mean ± SEM of 7 parallel incubations for each condition. Comparable results were obtained in three series of independent experiments. Effects of SLC/CCL21, IP-10/CXCL10, Mig/CXCL9, RANTES/CCL5 and Met-RANTES on apoptosis of human mesangial cells SLC/CCL21 has an anti-apoptotic effect on Fas/CD95-induced apoptosis The effect of CCR7 activation for MC survival in Fas/CD95-induced cell death was studied with three different methods. Flow cytometric cell cycle analysis using propidium iodide staining showed 8.3 ± 1.1% apoptotic cells under normal conditions. After serum-starvation and incubation with IFN-γ for 48 hours human MC express Fas/CD95 on surface (data not shown). Under these conditions the population with a sub-G1 DNA content was 20.4 ± 3.6% (Figure 4A ). Serum-starvation and stimulation with IFN-γ showed no difference in the number of apoptotic MC compared to serum-starvation without stimulation with IFN-γ (data not shown). Subsequent stimulation with an activating anti-Fas antibody increased the amount of MC with a sub-G1 DNA content to 50.1 ± 3.6% consistent with a marked increases in apoptosis (Figure 4B ). When MC were prestimulated with SLC/CCL21 prior to induction of cell death the percentage of apoptotic cells was reduced markedly to 31.2 ± 4.4% (Figure 4C ). The results are from four independent experimental series. The data are shown in Table 1 . Staining with Hoechst visualizes cells with fragmented chromatin. Figure 5 shows a fluorescent microscopic analysis of MC. Figure 5A represents cells stimulated with IFN-γ and Figure 5B cells stimulated with IFN-γ and Fas ligation. Figure 5C shows a significantly reduced number of apoptotic cells when MC were prestimulated with SLC/CCL21 prior to induction of cell death. Apoptotic nuclei were analyzed microscopically in three different sets of experiments counting at least 300 cells per condition. After serum starvation and IFN-γ stimulation 9.4 ± 2.5% of the cells were found to be apoptotic. Subsequent Fas ligation induced apoptosis in 37.3 ± 3.2% of human MC. Prestimulation with SLC/CCL21 reduced Fas-induced cell death effectively to 15.0 ± 2.6% (Figure 6A ). Induction of cell death of MC by Fas ligation increased Caspase-3 activity 2.8-fold compared with control conditions. Coincubation with SLC/CCL21 reduced caspase-3 activity significantly (Figure 6B ) Figure 4 Effect of SLC/CCL21 on Fas-induced cell death of human MC in cell cycle analysis by flow cytometry. Percentage of apoptotic cells was analyzed in cell cycle analysis by flow cytometry after staining with propidium iodide. Histograms represent cell counts (y-axis) versus DNA content (x-axis) with the percentage of apoptotic cells containing sub-G1 DNA indicated. Figure 4A shows the cell cycle analysis of MC after serum starvation and stimulation with IFN-γ for 48 hours. Figure 4B: Apoptosis was induced subsequently by Fas ligation. Figure 4C: Effect of preincubation with SLC/CCL21 250 ng/ml on Fas-induced apoptosis of MC. The profiles shown are representative for four independent experiments. Table 1 Effect of SLC/CCL21, IP-10/CXCL10, Mig/CXCL9, RANTES/CCL5 and Met-RANTES on Fas-induced cell death of human mesangial cells. IFN IFN/aFas IFN/aFas/ SLC IFN IFN/aFas IFN/aFas/IP-10 IFN/aFas/Mig IFN IFN/aFas IFN/aFas/RANTES IFN/aFas/Met-RANTES 15.8 47.2 24.7 11.1 36.7 21.1 25.8 18.3 40.9 31.6 35.5 22.4 53.1 33.7 16.3 33.1 25.3 28.2 19.3 34.1 40.7 41.8 23.9 53.4 34.1 20.2 29.9 28.7 28.9 24.8 46.2 39.6 35.5 19.6 46.8 32.4 13.5 25.9 31.1 36.3 16.2 37.9 34.2 42.6 Mean 20.4 50.1 31.2 15.3 31.4 26.6 29.8 19.7 39.8 36.5 38.1 SEM 3.6 3.6 4.4 3.9 4.6 4.3 4.5 3.7 5.1 4.3 4.9 Data of four separate FACS analyses, respectively. Values are means ± SEM. Figure 5 Effect of SLC/CCL21 on Fas-induced cell death of human MC in Hoechst stain on a fluorescent microscopic analysis of MC (A) MC stimulated with IFN-γ. (B) MC stimulated wit IFN-γ and Fas ligation. (C) Significantly reduced number of apoptotic cells when MC were prestimulated with SLC/CCL21 prior to induction of cell death. Figure 6 Effect of SLC/CCL21 on Fas-induced cell death of human MC in cell count and caspase-3 assay. (A) The percentage of apoptotic MC was determined after visualisation of fragmented chromatin with Hoechst dye. Apoptotic nuclei were analyzed microscopically in three different sets of experiments counting at least 300 cells per condition. (B) Caspase-3 activity was quantitated spectrophotometrically in MC lysates. Data are from three independent sets of experiments, each performed in duplicate. Statistically significant differences are depicted: * = p < 0.05 and ** = p < 0.01, resp. IP-10/CXCL10 and Mig/CXCL9 have no effect on Fas/CD95-induced cell death of human MC To investigate the effect of the CXCR3 ligands IP-10/CXCL10 and Mig/CXCL9 on Fas-induced cell death MC were grown without serum and stimulated with IFN-γ. Induction of cell death by an activating anti-Fas-antibody led to significantly increased number of apoptotic cells. Coincubation with IP-10/CXCL10 or Mig/CXCL9 had no effect on Fas-induced apoptosis of MC (Figure 7 ). The data of four separate experiments are shown in Table 1 . Figure 7 Effect of IP-10/CXCL10 and Mig/CXCL9 on Fas-induced cell death of human MC in cell cycle analysis by flow cytometry. Percentage of apoptotic cells was analyzed in cell cycle analysis by flow cytometry after staining with propidium iodide. Histograms represent cell counts (y-axis) versus DNA content (x-axis) with the percentage of apoptotic cells containing sub-G1 DNA indicated. Cell cycle analysis of MC was performed after serum starvation and stimulation with IFN-γ for 48 hours. (A) Cell cycle analysis of MC after serum starvation and stimulation with IFN-γ for 48 hours. (B) Apoptosis was induced subsequently by Fas ligation. (C, D) Preincubation with IP-10/CXCL10 and Mig/CXCL9 has no effect on Fas-induced apoptosis of MC. The profiles shown are representative for four independent experiments. RANTES/CCL5 and Met-RANTES have no effect on Fas/CD95-induced cell death of human MC To induce expression of chemokine receptor CCR1 cells were pretreated with a combination of IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 hours, followed by serum-starvation and incubation with IFN-γ for 48 hours (Figure 8A ). Cell death was induced again by stimulation with an activating anti-Fas antibody (Figure 8B ). Stimulation with RANTES/CCL5 (250 ng/ml) (Figure 8C ) or Met-RANTES (250 ng/ml) (Figure 8D ) prior to induction of cell death was ineffective in maintaining cell survival. The results are from four independent experimental series (See Table 1 ). The percentage of apoptotic cells varies between the experiments due to biological variance. Figure 8 Effect of RANTES/CCL5 and Met-RANTES on Fas-induced cell death of human MC in cell cycle analysis by flow cytometry. Percentage of apoptotic cells was analyzed in cell cycle analysis by flow cytometry after staining with propidium iodide. Histograms represent cell counts (y-axis) versus DNA content (x-axis) with the percentage of apoptotic cells containing sub-G1 DNA indicated. (A) Cell cycle analysis of MC was performed after serum starvation and stimulation with IFN-γ for 48 hours. (B) Apoptosis was induced subsequently by Fas ligation. Prior to serum starvation the expression of chemokine receptor CCR1 was induced by pretreatment with a combination of IFN-γ (20 ng/ml), TNF-α (25 ng/ml) and IL-1β (10 ng/ml) for 24 hours. (C,D) RANTES/CCL5 250 ng/ml and Met-RANTES 250 ng/ml have no effect on Fas-induced cell death of MC. Discussion Proliferation and apoptosis play a pivotal role in a variety of biological processes, such as morphogenesis during the embryonic stage, cell selection during lymphoid development, tissue repair after injury, regression of inflammation, elimination of cells at risk of developing into a tumor and lymphocyte-mediated killing [ 19 ]. A balance of proliferation and apoptosis is essential for the tissue homeostasis [ 20 , 21 ]. Apoptosis as mechanism of controlled cell death is a well-controlled process and progresses through a series of morphological and biochemical phases, including chromatin condensation and activation of proteolytic enzymes [ 22 , 23 ]. A number of mediators involved in the induction of apoptosis have been identified during recent years. Probably the most thoroughly characterized death receptor is the cell membrane receptor Fas (CD 95), a member of the TNF receptor family. Cross-linking of Fas, either via specific antibodies or via its specific ligand, activates a cascade reaction of caspases, which are responsible for induction of membrane alterations, breakdown of cellular constituents and DNA, and finally cell death [ 19 ]. In the kidney beside the damage induced by infiltrating inflammatory cells the relationship among resident cell proliferation and apoptosis in glomeruli determines the outcome in various glomerulonephritides [ 13 ]. Several groups reported that apoptosis plays an important role in the repair process in experimental and human glomerulonephritis [ 24 , 25 ]. Apoptosis has an additional role in the sclerosing process in the glomeruli [ 26 ]. Cell number abnormalities are frequent in renal diseases, and range from the hypercellularity of postinfectious glomerulonephritis to the cell depletion of chronic renal atrophy. Death ligands and receptors, such as TNF and Fas-ligand, pro-apoptotic and anti-apoptotic Bcl-2 family members and caspases have all been shown to participate in apoptosis regulation in the course of renal injury [ 27 ]. Some reports suggest that altered apoptotic signaling and regulatory mechanisms contribute to further progressive renal impairment, tubular atrophy, interstitial fibrosis, and glomerulosclerosis in a model of focal and segmental glomerulosclerosis in rats [ 28 ]. In the glomerulus a balance between endothelial, mesangial and visceral epithelial cells and their extracellular matrix is essential for structural and functional integrity. During glomerular injury function and morphology of these cells are altered. Intrinsic cell proliferation in the glomerulus is regulated by a large number of mediators and growth factors like IL-10 [ 29 ], insulin-like growth factors [ 30 ] and platelet derived growth factor [ 31 ]. Some of these factors also influence apoptosis in the glomerulus [ 32 ]. The basis for the experiments performed in this work was the hypothesis that chemokines and chemokine receptors expressed by intrinsic renal cells may be involved both in the maintenance of glomerular homeostasis in normal adult human kidney and regulation of glomerular cell numbers during disease states. We previously showed constitutive expression of CCR7 protein and its ligand secondary lymphoid tissue chemokine (SLC/CCL21) in human renal tissue. In immunohistochemistry we found a clear staining pattern for SLC/CCL21 on podocytes and CCR7 on MC during nephrogenesis and in adult kidney. Also constitutive mRNA expression has been shown for CCR7 in cultured MC and for SLC/CCL21 in isolated human glomeruli. Furthermore it was demonstrated that mesangial CCR7 is functionally active since for example SCL/CCL21 induced a dose-dependent migration of MC [ 12 ]. We therefore investigated the influence of chemokines on MC growth and found an significant increase in proliferation of MC after stimulation with SLC/CCL21 in a dose-dependent manner. To study the role of SLC/CCL21 in MC apoptosis cell death was induced by activating mesangial Fas/CD95 receptors. SLC/CCL21 was found to prevent MC apoptosis as shown by cell cycle analysis and Hoechst stain. Since caspase-3 assays revealed impaired activity it can be assumed that this molecule is involved in chemokine-influenced intracellular apoptosis pathways in MC. The finding of an anti-apoptotic function of SLC/CCL21 is novel. At present SLC/CCL21 is known to be constitutively produced by high endothelial venules and stromal cells within T cell zones of lymph nodes [ 33 ]. Its corresponding receptor CCR7 is expressed on naive T cell subpopulations and up-regulated by maturing dendritic cells [ 34 ]. Therefore this chemokine/chemokine receptor pair was described as an prototypic model for the homing of immune cells to lymphoid tissue [ 35 , 36 ]. Anti-apoptotic effects of chemokines seem not to be restricted to SLC/CCL21. The CX3CR1-binding chemokine fractalkine which is constitutively expressed on neuronal cells has been demonstrated as survival factor for brain microglia in Fas-induced cell death [ 37 ]. A role of the chemokine receptor CXCR3 in inflammatory glomerular disease has been proposed before by the group of Romagnani et al. since a mesangial expression of CXCR3 in biopsies from patients with mesangioproliferative glomerulonephritis could be demonstrated [ 11 ]. We therefore investigated the effects of the CXCR3 ligands IP-10/CXCL10 and Mig/CXCL9 on MC and also found a concentration dependent increase of proliferation of MC after stimulation with these chemokines. Interestingly, in contrast to the effect observed with SLC/CCL21 both IP-10/CXCL10 and Mig/CXCL9 had no effect on Fas induced apoptosis of MC. The third chemokine receptor of interest was CCR1 since our group has demonstrated functionally active expression of CCR1 on human MC, inducible after stimulation with a combination of the proinflammatory cytokines TNF-α, IL-1β and IFN-γ [ 9 ]. Futhermore upregulation of CCR1 expression is also known in an animal model for immune complex glomerulonephritis [ 38 ]. In contrast to the effects observed with CCR7 and CXCR3 ligands, stimulation of MC with the CCR1 ligand RANTES/CCL5 had no effect on cell proliferation and apoptosis. In this context an article of Topham et al. is of special interest. This group demonstrated that CCR1 may have anti-inflammatory functions since mice negative for CCR1 showed enhanced Th1 immune responses and worsened histology in a model of nephrotoxic serum nephritis [ 39 ]. Our group showed in a model of horse apoferritin (HAF)-induced glomerulonephritis that CC chemokine ligand 5/RANTES chemokine antagonists aggravate glomerulonephrtis despite reduction of glomerular leukocyte infiltration. These findings were associated with an enhancing effect of the CCL5/RANTES analogs on the macrophage activation state in vitro and in vivo . The humoral response and the Th1/Th2 balance in HAF-glomerulonephritis and mesangial cell proliferation in vitro were not affected by the CCL5/RANTES analogs [ 40 ]. Therefore also the effects of the CCR1 blocker Met-RANTES were studied but showed no influence on MC proliferation and apoptosis. Conclusions In summary it is tempting to speculate that the different effects of SLC/CCL21 and IP-10/CXCL10 and Mig/CXCL9 on proliferation and apoptosis of MC represent specialized functions of chemokine receptos on non-immune cells. CCR7 could be a chemokine receptor important for the development of the glomerular architecture during ontogenesis and for maintaining glomerular homeostasis in adult human kidney. CXCR3 may have its main functions important for mesangial expansion in mesangioproliferative disease. In contrast, the chemokine receptor CCR1 and its ligands RANTES/CCL5 and Met-RANTES seems not to have a special impact for the regulation of proliferation and apoptosis on MC. CCR1 may be important for local immunomodulation especially in glomerular inflammation. The chemokines and their receptors we have analyzed seem to be part of a complex system of factors which regulate proliferation and apoptosis in kidney and therefore play a pivotal role in regulation of glomerulogenesis, during glomerular injury and in homeostatic balance in the glomerulum. Studying these locally synthesized chemokines and their interaction with corresponding receptors on non-immune cells deserves further investigation and will reveal novel chemokine/chemokine receptor functions far beyond their orignal functions in guiding inflammatory cells to sites of tissue injury. Competing interests None declared. Authors' contributions All authors were involved in experimental procedures and manuscript preparation. Abbreviations mesangial cell (MC); chemokines monocyte chemoattractant protein-1 (MCP-1); regulated upon activation, normal T cell expressed and secreted (RANTES/CCL5); interferon-γ (IFN-γ)-inducible protein of 10 kD (IP-10/CXCL10); monokine induced by IFN-γ (Mig/CXCL9); (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-tetrazolium bromide (MTT) Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC493268.xml

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Factors influencing quality of life in patients with active tuberculosis

Background With effective treatment strategies, the focus of tuberculosis (TB) management has shifted from the prevention of mortality to the avoidance of morbidity. As such, there should be an increased focus on quality of life (QoL) experienced by individuals being treated for TB. The objective of our study was to identify areas of QoL that are affected by active TB using focus groups and individual interviews. Methods English, Cantonese, and Punjabi-speaking subjects with active TB who were receiving treatment were eligible for recruitment into the study. Gender-based focus group sessions were conducted for the inner city participants but individual interviews were conducted for those who came to the main TB clinic or were hospitalized. Facilitators used open-ended questions and participants were asked to discuss their experiences of being diagnosed with tuberculosis, what impact it had on their lives, issues around adherence to anti-TB medications and information pertaining to their experience with side effects to these medications. All data were audio-recorded, transcribed verbatim, and analyzed using constant comparative analysis. Results 39 patients with active TB participated. The mean age was 46.2 years (SD 18.4) and 62% were male. Most were Canadian-born being either Caucasian or Aboriginal. Four themes emerged from the focus groups and interviews. The first describes issues related to the diagnosis of tuberculosis and sub-themes were identified as 'symptoms', 'health care provision', and 'emotional impact'. The second theme discusses TB medication factors and the sub-themes identified were 'adverse effects', 'ease of administration', and 'adherence'. The third theme describes social support and functioning issues for the individuals with TB. The fourth theme describes health behavior issues for the individuals with TB and the identified sub-themes were "behavior modification" and "TB knowledge." Conclusion Despite the ability to cure TB, there remains a significant impact on QOL. Since much attention is spent on preventative or curative mechanisms, the impact of this condition on QoL is often not considered. Attention to the issues experienced by patients being treated for TB may optimize adherence and treatment success.

Introduction Globally, tuberculosis (TB) is a major public health problem [ 1 ]. In 1997, the World Health Organization (WHO) estimated that 32% of the world's population was infected with Mycobacterium tuberculosis [ 2 ]. Tuberculosis was a major cause of morbidity and mortality in Canada early in the 20 th Century. However, with the introduction of anti-tuberculosis medications in the 1940's and 1950's, the incidence of TB disease declined significantly [ 3 ]. However, after decades of continuous decline in TB rates, it has reached a plateau of 6 per 100,000 population, corresponding to about 2000 cases per year [ 3 , 4 ]. Although this rate of TB disease within Canada in global terms is relatively low, within special high-risk groups, rates exceed those seen in many developing countries. In particular, high rates are seen among Aboriginal persons – both on and off reserve as well as among the foreign born and marginalized inner city populations, especially injection drug users [ 5 - 8 ]. With the development of effective treatment strategies, the focus of TB management has shifted from the prevention of mortality to the avoidance of morbidity. As such, there is increased interest in the quality of life (QoL) experienced by individuals being treated for TB [ 9 ]. There are numerous aspects of active TB that may lead to a reduction in QoL. Treatment of active TB requires prolonged therapy (at least 6 months) with multiple, potentially toxic drugs that can lead to adverse reactions in a significant number of patients [ 10 , 11 ]. Also, among foreign born patients, there is considerable social stigma associated with active TB leaving the individual feeling shunned and isolated from their friends and families [ 12 - 14 ]. Finally, among Aboriginal and marginalized inner city populations, there is a lack of knowledge regarding the disease process and its treatment which may contribute to feelings of helplessness and anxiety [ 15 - 17 ]. Few studies have examined quality of life in patients with active TB [ 18 , 19 ]. While these studies determined specific decrements in the QoL in these patients, none have included the mixture of patients (marginalized and foreign born) treated within Canada. Therefore, the objective of our study was to identify areas of QoL that are affected by active TB infection using focus groups and individual interviews [ 20 , 21 ]. Methods Design and Setting This was a multi-site study involving three TB Centres in Vancouver, British Columbia. Patients were recruited from the TB Clinic at the BC Centre for Disease Control, Willow Chest Pavilion at Vancouver General Hospital and the Downtown TB Clinic. All these clinics are part of the Vancouver Coastal Health Authority. Ethics approval was obtained from the University of British Columbia Behavioural Research Ethics Board and each subject provided signed, informed consent to participate in the study. Subjects Subjects with active TB who were receiving treatment were eligible for recruitment into the study. Subjects who were less than or equal to 16 years of age and who did not speak English, Cantonese or Punjabi were excluded (interpreters for non-English language participants who spoke these languages were available). Procedures The initial contact was be made by the study nurse at the individual clinics. For those individuals residing in the inner-city region of Vancouver, gender-specific focus groups were planned with 6–8 participants who had active TB. Each participant was reimbursed $25 for their time. Focus group discussions are a variation of interviews designed for the purpose of gathering data about a specific topic from a group of individuals. Each focus group was led by an experienced facilitator. For participants who came to the TB Clinic at the BC Centre for Disease Control (and those who were hospitalized), individual interview sessions were conducted assessing similar information as obtained in the focus groups. The reason for using individual interviews rather than the focus group approach was two-fold. Firstly, as these individuals often had other commitments (such as work or family care), assessments needed to be done at the time of their appointments and could not be specially scheduled. Secondly, due to the cultural backgrounds, most of these participants did not wish to participate in a group setting in which details of their disease and their feelings were explored. Each interview was conducted by an experienced interviewer in combination, when necessary, with an interpreter fluent in Cantonese or Punjabi. Specifically, in either the focus group or interview setting, the facilitator began the session with an open-ended, standard question that began all sessions (e.g. "how did you find out you had TB?"). Using open-ended, probing questions, participants were asked to discuss their experiences of being diagnosed with tuberculosis, what impact it had on their lives, issues around adherence to anti-TB medications and information pertaining to their experience with side effects to these medications. Each participant was invited to comment on each question and provide their perspective on the content area. At the end of each session, the facilitator summarized salient points that arose during the discussion and invited further comments and discussion around these points and confirmed agreement. Data collection and analysis For all participants, data obtained were audio-recorded, transcribed verbatim, and analyzed. For participants who spoke Cantonese or Punjabi, field notes were kept by the nurse facilitators who are fluent in those languages. Constant comparative analysis was used as a method to explore and identify patterns and themes that emerged from the data [ 21 ]. Various strategies were used to systematically monitor the validity and reliability of the data. Data were analyzed by two individuals experienced with qualitative data and consensus validation was used to confirm categories and the matching of transcribed quotes with categories derived from the analysis. Categories and transcript matching were then reviewed by the focus group facilitator to further ensure that the categories made sense and represented the data they contain. The categories were then collapsed and analyzed for emergent themes. Results We conducted two focus group sessions, one with seven male participants and another six female participants; the rest of the participants for the study underwent individual interviews, including 4 hospitalized patients. In total, 39 persons with active TB participated in the study. The demographics of the study participants are described in Table 1 . The mean age was 46.2 years (SD 18.4) and 62% were male. Most of the participants were Canadian-born, either white or Aboriginal, while 38% were foreign-born from South-East Asia, South Asia, Latin America and Africa. The majority of participants were interviewed in English (69%) and the rest required either a Cantonese (18%) or Punjabi (13%) translator. For the majority of patients, concurrent illnesses included HIV, Hepatitis B or C. Thirty-six percent of patients drank alcohol or used illicit drugs on a daily basis. The majority of patients was unemployed with an annual income of ≤$15,999 and a mean level of education of 9.2 years of school (SD 3.1). Table 1 Patient Characteristics Participants (N = 39) Mean age, yrs (SD) 46.2 (18.4) Males, N (%) 24 (62) Foreign-born, N (%) 15 (38) Region of origin, N (%) Canadian – Caucasian 10 (26) Canadian – Aboriginal 14 (36) India/Pakistan 5 (13) South East Asia 8 (21) South America 1 (2) Africa 1 (2) Language used during interview, N (%) English 27 (69) Cantonese 7 (18) Punjabi 5 (13) Interview/focus group session conducted, N (%) Outpatient clinic 35 (89) Hospitalized 4 (11) Concurrent illness, N (%) HIV-positive 12 (31) Hepatitis B or C 12 (31) Diabetes mellitus 5 (13) Cardiovascular disease 3 (8) Cancer 3 (8) Epilepsy 1 (2) Alcohol or recreational drug use, N (%) Alcohol 8 (21) Drugs 6 (15) Employment status, N (%) Full-time 4 (11) Part-time 5 (13) Unemployed 20 (50) Retired 10 (26) Income ≤$15,999 35 (89) $16,000 – $39,999 1 (2) $40,000 – $49,999 1 (2) ≥$50,000 1 (2) Years of education, mean (SD) 9.2 (3.1) Marital status, N (%) Single 14 (38) Married 11 (28) Common-law 5 (13) Divorced 8 (21) Analysis identified four main themes comprising medication related issues, diagnosis, social support and knowledge of TB. The following text provides a summary of the content of the themes with illustrative quotes in Table 2 and 3 . Table 2 Selected Illustrative Quotes for Theme 1: Diagnosis Issues for TB Theme 1: Diagnosis Issues Sub-Theme: Symptoms Coughing "I felt tired all the time and had a cough that just wouldn't go away". (Female) "I had a really bad cough for 3 months and then I started coughing up blood. This made me scared so I went to the doctor". (Male) Fatigue/weakness "I just felt tired all the time. I did not have the energy to do anything". (Male) "I had fatigue and a continuous cough for 6 months. I thought I had persistent flu but then after a while the symptoms got so bad that I went to see a doctor". (Male) Fever/nightsweats "I had a fever and chest pain for 1 month; I thought this was pneumonia so I went to see my family doctor". (Male) "I had night sweats for several months and a fever so after a while I went to see my doctor". (Male) Asymptomatic "I did not know I had TB, I was really surprised because I felt really good". (Male) "I had a general examination and that's when I found out I had TB, otherwise I had no symptoms" (Female) Sub-Theme: Health Care Provision Delayed Diagnosis "I had a friend who was sick with TB in the hospital. I asked my GP to get tested but he did not feel I needed to. Anyway, I was negative but I knew something was wrong so I asked for a chest X-ray. He did not agree at the beginning but finally he did and that's when I found that I had TB". (Female) Hospitalization "I came out of a coma from meningitis and that's when they told me I had TB. They threw me in a TB ward at VGH which was worse then a prison. I didn't like the restrictiveness so I took off...the isolation was too much". (Male) "The only thing to do at the hospital was to eat and sleep. There are no programs there and you are confined in one area". (Male) "Everyone wears gloves and masks to come and see you, you feel like a leper". (Male) Sub-Theme: Emotional Impact Calm, Accepting, or Apathetic "I was okay about it. I knew people who had this before and so I knew I would be in the hospital for a while but then after taking medicines I would be fine". (Male) "I felt calm and confident in the medical profession". (Male) Scared, or Afraid "I was scared of dying. My Grandma had it and she was in the sanitorium before she died of it". (Female) Shocked/Surprised, or Devastated "I was shocked. It was such a surprise because I was working full-time as a nurse in India before immigrating here and I was healthy". (Female) "I was devastated because I had another illness. I didn't feel that I deserved it". (Female) Worried/Concerned or Depressed "I was worried about passing it on to other people". (Male) "I was depressed because I had a daughter whom I could not see while in hospital". (Female) Table 3 Selected Illustrative Quotes for Theme 2: Medication Issues for TB Theme 2: Medication Issues Sub-Theme: Adverse Events Gastrointestinal Disturbances "I had lots of vomiting after I started taking the pills and didn't have any appetite". (Male) "I have to eat before I take my pills, if I don't then I feel sick and my stomach hurts". (Male) Itchiness "I felt itchy all over and was told to take benedryl but that made me really sleepy". (Male) "I had lots of itchiness when I first started taking the pills but it is better now and I put lotion on my skin". (Female) "I was so itchy with one of the pills that I could not sleep all night long for days". (Female) Sub-Theme: Ease of Administration Size of Medication "I felt physically sick because of the size of the pills; they are too big". (Female) "The pills are so big, it is hard to swallow them". (Female) "I feel nauseated when I take the pills because they are so large". (Male) "I can't swallow those white pills; I need to crush then otherwise I vomit it back up". (Female) Number of Medications "I thought that many tablets a day ...it is not possible to take on an empty stomach". (Male) " There were too many pills to take at once, especially at the beginning but now it is much better with just six to take in a day". (Female) Sub-Theme: Compliance Clinic-based patients "I was taking other pills so it was easy to take the TB medications too". (Female) "I did not forget to take my pills because I want to get better". (Female) "I understand the importance of taking the tablets so I do not forget; I take them in the mornings, half-hour before my breakfast". (Male) "I place it in my container the night before so that I remember to take it the next day". (Male) Inner-city Patients "I always take my pills since I get them with my methadone everyday". (Female) "The [street] nurse always finds us and gives me the medications". (Male) "If I've been drinking too much then sometimes I don't know what the time is". (Male) "If I'm picking empty cans and bottles on the other side of town, it's hard to get to [street nurse name] to get my pills every day". (Male) "I missed taking some pills because I was drunk or high on drugs". (Female) Theme 1: Diagnosis issues This theme describes issues related to the diagnosis of tuberculosis (Figure 1 ). Sub-themes were identified as 'symptoms', 'health care provision', and 'emotional impact'. Figure 1 Main themes and sub-themes related to tuberculosis as identified through transcribed focus groups Symptoms Thirty-five quotes pertained to symptoms experienced by the participants at the time of diagnosis. Of these, 19 were related to specific symptoms whereas 16 participants expressed the view that they were asymptomatic at the time of being diagnosed. The most common symptoms experienced by the respondents were cough (n = 13), fatigue/weakness (n = 5), fever/night sweats (n = 5) and shortness of breath (n = 4). Most patients sought medical attention due to cough or "pneumonia-like" symptoms and feelings of general malaise. For example, one patient stated "I was coughing up harsh yellow stuff"; while another stated "I started to feel real tired and had a cold that just wouldn't go away". Four patients expressed that they were "unsure" how they acquired TB. For example, a woman stated "I didn't even know I had it. I was surprised 'cause I felt real good". Other illustrative quotes are shown in Table 2 . Health care provision Most comments, related to the provision of health care at or around the time of diagnosis, were related to community health care providers and their initial hospitalization. Many patients expressed frustration with the health care system at their time of diagnosis due to lack of provider knowledge with respect to tuberculosis. Many patients either felt that they had a delayed diagnosis or delayed treatment due to issues related to their health care provider. For example, on male patient stated, "Family physicians should know more about this disease...where to refer patients to. This is an old disease". Another patient said, "Although my GP gave me a diagnosis, he told me to wait for treatment. We were concerned and phoned the British Columbia Lung Association who referred us to the TB clinic". Another stated, "I contracted a flu-type infection with fever. My GP said go home and take Tylenol but my symptoms continued so I went to see the locum who told me to take Advil. Then I started non-stop coughing. I asked my GP to get an X-ray but he flatly refused." Many participants reported negative experiences with their initial hospitalization after being diagnosed with TB. Specifically, they stated feelings of isolation, rejection and boredom (Table 2 ). No participant gave a positive report about the initial hospitalization experience however one 30 year old male participant stated "I had no negative feelings about my hospital stay but it hurt my financial situation...but I knew I had to be there. There are laws against TB." Emotional impact Thirty-five quotes pertained to emotions experienced by the participants at the time of diagnosis. Of these, patients expressed a wide range of emotions from being calm, accepting or apathetic (n = 11), scared or afraid (n = 7), shocked or surprised (n = 6), "devastated" (n = 4), worried or concerned (n = 4), and depressed (n = 3). Representative quotes for these emotions are presented in Table 2 . Of the individuals expressing apathy or calmness related to the diagnosis, many expressed that TB was just another disease to contend with on top of other chronic conditions. For example, one patient stated "Well, it is like HIV. It is in my system. What can you do?" Another person with terminal cancer stated "I had no reaction to the diagnosis. I am more concerned with the spread of my cancer and that I don't have long to live anyhow". Of those expressing concern, there were two distinct reasons cited for this emotion: 1) concern for themselves as they knew relatives or friends who had previously been infected with TB and had experience prolonged hospitalization or death; and 2) concern for others in terms of passing the disease on to family and friends. For example, a male patient said "I was kind of scared because the only person I knew who had TB died of it. Also, I was worried about other people catching it from me". Another woman stated "I was scared. It is like an old disease and I know when you have it, it is not very nice to have it, especially because I have a seven month old baby." The individuals who expressed shock and surprise at the diagnosis attributed these emotions to their lack of symptoms. As such, they had not expected a diagnosis such as TB when they have visited their health care provider despite having other diseases such as HIV (see Table 2 ). Theme 2: Medication issues This theme discusses the most important factors with respect to medications for the treatment of TB. (Figure 1 ). As such, the sub-themes identified were "adverse effects", "ease of administration", and "adherence". Adverse events There were thirty-nine comments related to adverse events experienced by taking the medications. Most of these were related to specific symptoms that were thought to be related to taking specific drug therapies. The most common complaints were related to gastrointestinal disturbances (nausea, vomiting and diarrhea) and itchiness due to isoniazid. Despite having adverse events, patients stated that they continued to take their medications. For example, one female patient said, "There is nothing you can do. You have to just continue". Representative quotes from these participants are included in Table 3 . Ease of administration Most comments related to the dose and dosing schedule pertained to the size (n = 3) and number of tablets/capsules (n = 7). For example, patients felt that the large size of some of the dosage forms (such as ethambutol and rifampin) led to gagging and vomiting. In addition, many patients expressed consternation at the number of pills that they had to take with each dose. For example, one patient said, "When I looked at ten tablets, I thought, on an empty stomach, I cannot". Representative quotes from these participants are included in Table 3 . Compliance Individuals living in the inner city of Vancouver, expressed little concern for compliance-related issues as they either picked up the anti-TB medications with their methadone or the Street-Nurses would find them daily to administer the medications. As an example, one patient stated "It comes with my methadone. When I get that, I get my TB pills". Another stated, "I never worry about it. I know [the Street Nurse's name] will bring it to me". However, despite high compliance in these patients, several identified alcohol (n = 13) or other illicit drug use (n = 8) as being the reason why they had missed doses. Those who came to the TB treatment clinic expressed high compliance due to the perceived gravity of the diagnosis. For example, one woman patient stated, "It is easy to remember because it is at the fore-front of mind. I want to get rid of it". Another person attributed compliance to the law: "I never forget to take my pills because I don't want to go to jail". Representative quotes from these participants are included in Table 3 . Theme 3: Social support and functioning This theme describes social support and functioning issues for the individuals with TB (Figure 1 ). Specifically, the impact on their relationships with family, friends and peers was affected by TB. In addition, social functioning was impacted through the ability to interact with friends and family as well as engaging in social and leisure pursuits. Most participants expressed that their family and friends were aware of their TB diagnosis (n = 18), while 11 stated that only their friends knew and 10 stated that only their immediate family knew. Of those who stated that only their friends were aware of their diagnosis, most of these persons residing in the inner city noted that they did not have family with whom they communicated. Representative quotes from these participants are included in Table 4 . Table 4 Selected Illustrative Quotes for Theme 3 and 4: Social Support and Health Behavior Issues for TB Theme 3: Social Support and Functioning Sub-Theme: Social Support Clinic-based patients "My family knows and they comforted me so I felt much better". (Male) "My wife was calm about it and this gave me support". (Male) "Mom was concerned for me since her grandmother had died of TB". (Female) Inner-city Patients "My friends stayed away when they found out, they thought I was contagious. I tried to tell them but still I did not see them again". (Male) "My friends do no want to hang around me. It's the fear of the unknown...they just know it's airborne and contagious". (Male) "My partner is okay with it because she has TB too". (Male) "I don't have any family except my aunt but she was scared to come and see me because she has two children". (Female) Theme 4: Health Behavior Sub-Theme: Behavior Modification Clinic-based patients "I run more. I was always a runner. The endorphins help". (Female) "Vitamins might interact with my medications so I don't take them" (Male) Inner-city Patients "I eat better... although with my income, this is difficult". (Male) "This diagnosis was a wake-up call to change my lifestyle. I now eat better and sleep lots" (Male) "I have been drinking more booze to help manage the side effects of the medications". (Female) "I drink bottled water and avoid tap water due to my depressed immune system" (Male) Sub-Theme: TB Knowledge Clinic-based patients "It is important to be cured but you can't get it again" (Female) "It is very important to get cured...if you aren't cured, you could die" (Male) "Family doctors should know more about TB. They didn't know what to do with respect to breastfeeding and TB. There really needs to more public education" (Female) "I don't think I have TB. My doctor told me I have it and now I have to take medications but I am not sure that I really have it" (Male). Inner-city Patients "Once you get TB, it's in your system" (Male) "As long as TB can be arrested ...not necessarily cured, that would be OK (Female)" "People should be more active in spreading the word on the street that TB is still out there...there has to be more outreach programs" (Male) However, one individual stated that she was "secretive because other people will feel that I am contagious". Another male participant stated a reluctance to tell his friends because "I do not want go cause mass hysteria". A school-age boy did not tell his friends out of fear of being shunned. In addition, he missed eight weeks of school and had to retake several courses. In one instance, fear of being shunned in a Punjabi speaking participant was instilled by the treating physician ("I was told by Dr. [physician's name] that if my community knew, it would empty out the hall [referring to the religious prayer hall]". Another reported that his family "told me not to take my pills anymore because they make me sick" despite being very supportive and understanding regarding the diagnosis. A Cantonese-speaking male stated that "I will be happier once I am cured. Then, I can go out to restaurants and public places again." There were 39 comments related to the reactions of friends and/or family members to the participants' diagnosis of TB. Of these, most could be categorized as supportive or concerned (n = 21), although others had negative feelings such as fear (n = 7), shock or disbelief (n = 8), and anger (n = 3). One participant stated "my mom was really concerned but my friends did not believe it...they encouraged me to get the right diagnosis". Another stated that her partner had increased his reading on TB and was receiving regular skin tests although her mother and brother would not talk about the TB diagnosis or the clinic visits. Another stated that since his partner was not understanding about modifying his lifestyle, he was forced to end the relationship with her and move out. Representative quotes from these participants are included in Table 4 . Theme 4: Health behavior This theme describes health behavior issues for the individuals with TB (Figure 1 ). Sub-themes were identified as "behavior modification" and "TB knowledge." Behavior modification When asked if they had done anything else beyond medications to help manage their TB, about half of the participants stated that they had done nothing in particular (n = 16). For example, a Cantonese speaking female stated "I do nothing special as TB is very common". However, of the 12 participants who stated that they had changed their health behavior, seven said that they consumed a healthier diet, four stated that they exercised more, three took vitamin supplements specifically to help their TB, and two used less illicit drugs and alcohol. Representative quotes from these participants are included in Table 4 . TB knowledge In response to the question "do you believe that you will be cured of TB?", most participants (n = 33) stated that they believed that they would be eventually cured. However, some individuals believed that their TB would never be cured ("I believe that I can keep it in remission but it can't be cured") while others were not sure if it could be cured. Two participants denied having TB despite being informed by health care providers and taking medication. There were comments regarding the participants' impressions of provider knowledge of which a representative sample have been included in Table 4 . Discussion This qualitative study has revealed that TB has a large impact on affected individuals' QoL through issues related to its diagnosis, treatment, social support and functioning, and health behavior. Specifically, we found that the domains of QoL that were affected by TB included those that are typically affected by most illnesses such as physical functioning and emotional/mental well-being. However, TB patients' social functioning was also affected through isolation, variable social support by family and friends, and the ability to continue with social and leisure activities. Also, the process of getting treatment for TB from the initial hospitalization to the daily medication schedules adversely affected the lives of our participants, although, almost all recognized the need for appropriate treatment. Although other studies [ 22 , 23 ] have explored patients' attitudes and knowledge regarding TB, we identified only one other study [ 24 ] where general health perceptions of patients with TB were investigated. Similar to ours, this study also involved the use of focus groups to elicit areas of QOL affected by TB and many of their results were in general agreement to ours. For example, as with our study, these investigators found that physical functioning, social functioning, and role functioning were all adversely affected by TB. In addition, the participants reported a wide range of psychological reactions including fear, depression and anger. Finally, both studies found a number of comments regarding the difficulties of treatment including those related to the size, number and frequency of dosing of the medications. However, there were some important differences between our two studies. For example, these investigators included only 10 English speaking patients from the Baltimore city area and 13 health care providers whereas ours included non-English participants through the use of interpreters, a much large sample of patients (n = 39), but no health care providers. In addition, we included hospitalized and ambulatory patients from both inner city and public health clinic environments in order to assess the full spectrum of patients afflicted with this disease. Finally, in our study, all patients had active TB and were receiving treatment at the time of the interviews unlike the Baltimore study who recruited patients who were already cured and had completed treatment. We believe that our methodology of interviewing currently afflicted patients might have minimized recall bias although one potential advantage of the Baltimore approach was determining long-lasting influences of TB on patients lives (the investigators received 17 comments in this regard). Also, the use of health providers in the Baltimore study added an interesting perspective with the provision of comments that were, at times, in direct contrast to those stated by patients with respect to the effects of TB on health related quality of life. For example, most physicians underestimated the impact that TB had on the QoL of patients assuming that, because it was curable, its detrimental effects would be minimal. These differences in design between the two studies might have accounted for some different findings. For example, the Baltimore study found that the financial well-being of some of the participants was adversely affected through loss of income and health care expenses whereas participants in our study did not report this issue (although this might be attributable to the different health care environments that exist between the two countries in which the studies were conducted or differences in employment status between the two samples). Also, some of the male participants in the Baltimore study reported sexual dysfunction whereas this concern was not reported during our interviews. Finally, patients in the Baltimore study reported spirituality as an important domain which we did not identify as an important theme, perhaps due to the different ethnic/religious make-up of our sample. One surprising aspect of our results was the negative feelings associated with TB diagnosis and the initial hospitalization. Some participants expressed frustration with their primary care physicians for the lack of a prompt diagnosis or inappropriate management. There was a common perception among many of the participants that health care providers needed more extensive education regarding TB. We have recently commented on the need to consider TB as a diagnosis and in the appropriate setting, consider the initiation of empiric TB treatment [ 25 ]. In addition, participants complained of boredom, frustration and isolation with their initial hospitalization. These modifiable factors should be the focus on future improvements in the diagnosis and treatment of TB. Despite several negative comments regarding the size, dosing schedule and adverse effects of the anti-TB medications, most patients specified that they understood the need for treatment. As such, self-reported compliance was very high and participants reported a variety of different strategies to help manage adverse events. Our inner-city participants expressed gratitude for the street nurses who delivered their medications to them on a regular basis and did not report the intrusiveness and imposition on lifestyle that has been associated with similar programs (such as directly observed therapy or DOT) in other studies [ 24 , 26 ]. Although most comments were related to adverse impacts of TB on QOL, some participants stated that acquiring TB had resulted in positive health behavior modification. Many participants took the development of TB to be a "wake-up" call to change their lifestyle and improve their health behavior by either eliminating or reducing drug and alcohol intake, increasing exercise, or eating better. These findings were also reported by the Baltimore study group suggesting that the positive health behavior impacts of this disease might be widespread throughout those afflicted with TB in North America. Because many of those afflicted with TB in North America engage in other high-risk behaviors such as use of illicit drugs, the overall effects of this health behavior modification might be significant. Future studies should attempt to quantify this impact on the downstream development of other conditions. Although hospitalization for management of TB has negative aspects we have noted that this interlude in subjects with a history of substance abuse allows access to chemical dependency treatment resources while away from their usual chaotic environments. Although some studies in other countries have shown that TB can result in job loss, participants did not report that this had occurred in our sample [ 27 , 28 ]. One possible reason for this observation could be due to low rate of employment in our sample with only 26% being employed full or part-time. Our study had some limitations. We examined a self-selected group of TB patients who may not be representative of the entire population in Canada affected by TB. For example, in British Columbia, foreign-born persons account for close to 70% of all TB cases in the province. Despite this, we feel that we were able to get a representative sample of foreign-born persons (almost 40% of our sample was foreign born) as well as a good cross section of marginalized inner city patients [ 7 , 29 ]. In fact, because we attempted to select individuals from different socioeconomic groups (inner city patients vs. those voluntarily attending a public health clinic) and from different ethnic backgrounds (foreign-born, aboriginal-Canadian and other Canadian), we believe that the responses that we received are likely indicative of the areas of QoL which are affected by TB. Conclusion Our study indicates that despite the ability to cure TB with medical therapy, there still remains a sizeable impact on the lives of afflicted patients. Since much of the current attention on TB is spent on preventative or curative mechanisms such as drug therapy, the impact of this condition on QoL is either underestimated or rarely considered. In order to fully evaluate the outcomes that are achieved through TB prevention and treatment, QoL of these patients must be considered. Further studies need to build upon these observations and instruments need to be developed to better characterize QoL in patients with this disease. This process will not only provide an added parameter to evaluate the effectiveness of a given program, but will also focus care providers to be attentive to the non-medication aspects of TB management. Authors' contributions CAM conceived of the study, participated in the design, analysis and co-wrote the initial version of the manuscript. FM conceived of the study, obtained funding, participated in the interviews and focus groups, participated in the analysis, coordinated research staff, and co-wrote the initial version of the manuscript. VC participated in the interviews and focus groups and participated in the analysis. AP participated in the design of the study, and the interviews and focus groups. JMF participated in the design and analysis of the study. All authors read and approved the final manuscript.

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Evaluation of genome-wide chromatin library of Stat5 binding sites in human breast cancer

Background There is considerable interest in identifying target genes and chromatin binding sites for transcription factors in a genome-wide manner. Such information may become useful in diagnosis and treatment of disease, drug target identification, and for prognostication. In cancer diagnosis, patterns of transcription factor binding to specific regulatory chromatin elements are expected to complement and enhance current diagnostic predictions of tumor behavior based on protein and mRNA analyses. Signal transducer and activator of transcription-5 (Stat5) is a cytokine-activated transcription factor implicated in growth and progression of many malignancies, including hematopoietic, prostate, and breast cancer. We have explored immunoaffinity purification of Stat5-bound chromatin from breast cancer cells to identify Stat5 target sites in an unbiased, genome-wide manner. Results In this report, we evaluate the efficacy of a Stat5-bound chromatin library to identify valid Stat5 chromatin binding sites within the oncogenome of T-47D human breast cancer cells. A general problem with cloning of immunocaptured, transcription factor-bound chromatin fragments is contamination with non-specific chromatin. However, using an optimized strategy, five out of ten randomly selected clones could be experimentally verified to bind Stat5 both in vitro and in vivo as tested by electrophoretic mobility shift assay and chromatin immunoprecipitation, respectively. While there was no binding to fragments lacking a Stat5 consensus binding sequence, presence of a Stat5 binding sequence did not assure binding. Conclusion A chromatin library coupled with experimental validation may productively identify novel in vivo Stat5 chromatin binding sites in cancer, including abnormal regulatory sites in tumor-specific neochromatin.

Background Transcription factors function uniquely at the interface of the genome and the proteome. A significant portion of transcription factors serve not only as executors of gene transcription programs, but also as biochemical sensors of extracellular stimuli. For instance, members of the nuclear receptor family are directly activated by lipophilic extracellular ligands, and transcription factors of the Smad and Stat families are activated by phosphorylation in response to cytokine stimulation of cell surface receptors. Chromatin-bound transcription factors that act both as sensors of extracellular cues and as transcriptional effectors carry exceptional instructive value about the biological state of individual cells. Their high biological information value makes such factors particularly attractive for use as markers to predict disease activity and outcome, as well as predictive markers of disease-responsiveness to drugs. Based on related and broader rationale, the second phase of the human genome project, ENCODE ( ENC yclopedia O f D NA E lements), has been initiated with the ambitious goal of identifying all regulatory elements of the human genome, including chromatin binding sites for individual transcription factors [ 1 ]. In fact, several transcription factors are prognostic biomarkers in cancer, including estrogen and progesterone receptors [ 2 ] and signal transducers and activators of transcription (Stats) [ 3 , 4 ]. However, as a result of tumor-specific alterations in chromatin accessibility and structure, individual transcription factors may regulate distinct gene sets in different tumor specimens. Specifically, genes that are actively regulated vary as a result of chromatin structure, DNA methylation, histone modifications, and the presence of additional cofactors. Tumor-specific patterns of transcription factor binding to target chromatin are expected to enhance diagnostic information beyond what is achieved through protein and mRNA analyses. Such added diagnostic information may directly improve disease prognostication and prediction of tumor responsiveness to therapy. Our laboratory is particularly interested in the role of transcription factor Stat5 in human breast cancer, which is associated with favorable prognosis, especially in early stage malignancy [ 3 ]. Stat5 belongs to the Stat transcription factor family, which represents latent cytoplasmic transcription factors that are activated by phosphorylation of a conserved tyrosine residue in response to extracellular cytokines and hormones, such as prolactin, growth hormone, erythropoietin, and several interleukins. Basal activation of Stat5 has been shown in healthy breast epithelial cells [ 5 ] and in many early stage breast cancers, but is gradually lost during metastatic progression [ 3 ]. Furthermore, active Stat5 correlated with higher histological differentiation and reduced mitotic rate [ 6 ]. Additional evidence suggests that Stat5 may actively inhibit metastatic progression by promoting homotypic adhesion and inhibiting tumor cell scattering [ 7 ]. Based on technical progress with immunocapture of transcription factor-bound chromatin fragments, genome-wide mapping of interaction sites may be achieved either by hybridization of captured DNA to linear microarrays of genomic DNA, or by cloning and sequencing of captured chromatin fragments. Microarray-based hybridization has been successfully used in the small yeast genome [ 8 ], but high cost and technical hurdles remain for human genome-wide DNA arrays at sufficient nucleotide resolution. Early efforts have focused on medium resolution arrays of restricted portions of the genome, such as the small chromosome 22 [ 9 ], arrays of classical promoter regions immediately upstream of transcriptional start sites [ 10 ], and arrays that contain CpG island clones [ 11 ]. In contrast, generation of a genome-wide library of transcription factor-bound chromatin fragments, a chromatin library, represents an inclusive and unbiased approach to the entire human genome [ 12 ]. Chromatin libraries also hold the potential to identify transcription factor binding to abnormal, tumor-specific neochromatin arising from genomic instability. However, progress has been hampered by a high degree of non-specific capture of irrelevant chromatin fragments and lack of methods for effective validation of captured sequences. The purpose of the work described here is to optimize parameters to generate and validate a chromatin library for genome-wide identification of Stat5 target chromatin in human breast cancer. We identify novel Stat5 binding sites from a genome-wide chromatin library and validate the sites by prolactin-inducible Stat5 binding by electrophoretic mobility shift assay (EMSA) and chromatin immunoprecipitation (ChIP). Results and Discussion In contrast to transcription factors that bind to chromatin in a constitutive manner, Stat5 is a latent cytoplasmic transcription factor that is activated by tyrosine phosphorylation and binds tightly to DNA in response to extracellular cytokines, such as prolactin [ 13 ]. We used the well-differentiated, estrogen receptor positive T-47D human breast cancer cell line, which maintains robust prolactin-induced Stat5 activation [ 5 , 14 ], to generate a library of Stat5-bound chromatin fragments. Sonication is necessary to shear genomic DNA into fragments that can be easily manipulated for PCR amplification, cloning, and sequencing. Fragments of approximately 400 base pairs (bp) allow for a complete sequencing read-through and are of sufficient size to localize the fragment within the human genome with a high degree of statistical certainty. Optimal shearing of chromatin from formaldehyde-fixed T-47D cells into approximately 400 bp fragments was established empirically (Figure 1A , lane 5). This target size of chromatin fragments was confirmed by agarose gel electrophoresis of two parallel sets of sonicates of cells treated with or without prolactin for 30 min (Figure 1B ), prior to immunocapture of Stat5-bound fragments. An antibody that recognizes the highly homologous Stat5a and Stat5b isoforms [ 13 ] was used to capture Stat5-bound chromatin as detailed in Methods . Before subcloning of the captured chromatin fragments, the specificity of immunocapture was verified by analysis of binding to known human Stat5 target chromatin. In particular, we took advantage of earlier work that has identified a group of Stat5 regulated genes that have been shown to contain the Stat5 consensus sequence, TTCNNNGAA, in the traditional promoter element [ 13 , 15 ]. Aliquots of the captured chromatin pool were amplified by PCR using oligonucleotide primers flanking Stat5 binding sites within the gene promoters of Cytokine-Inducible SH2 Protein ( CISH ), β-Casein , and α2-Macroglobulin . Due to the average chromatin fragment size of 400 bp, primers were designed to yield shorter PCR products of 200 – 300 bp. Figure 1 Specificity of immunocapture of Stat5-bound chromatin in T-47D human breast cancer cells . A. Optimization of sonication parameters for shearing genomic DNA in T-47D cells . Sonication of formaldehyde-fixed cells for two 30 s pulses yielded ~400 bp chromatin fragments. The number and time of sonication pulses are listed above each lane. B. Prolactin (PRL) pretreatment did not affect chromatin fragmentation . T-47D cells were treated with (+) or without (-) 10 nM PRL for 30 min, fixed, sonicated (2 × 30 s), and a portion of the pre-IP samples were separated by agarose gel electrophoresis. C-E. Validation of quality of immunocaptured, Stat5-bound chromatin pool by ChIP analysis of known Stat5 target genes . Cells were incubated with (+) or without (-) 10 nM PRL for 30 min, and processed as described in Methods . Non-specific IgG was used as a negative IP control and PCR was performed using primers designed to specifically amplify the known Stat5 response element. PCR products shown are representative of at least two separate amplifications of at least two independently generated pools of Stat5-chromatin complexes. (-) PCR ctrl indicates a negative (no template) PCR control, (+) PCR ctrl Pre-IP indicates a positive PCR control performed on the pre-immunoprecipitated (input) DNA, and (+) PCR ctrl Gen indicates a positive PCR control performed on purified T-47D genomic DNA. PRL activated Stat5 specifically associated with the promoter of CISH (C) and β-Casein (D – upper panel), but not α2-Macroglobulin (D – lower panel), unless cells had been pretreated for four days with glucocorticoid (E – 1 μM dexamethasone; Dex). Stat5 was inducibly associated with the promoter of the CISH gene response element in T-47D cells (Figure 1C ). The capture was specific, since binding was only detected in prolactin-stimulated cells, and only when Stat5 antibody and not control IgG was used. PCR amplification of intact genomic DNA is shown as a control to verify the specificity of the PCR reaction, in addition to amplification of the pre-immunoprecipitation chromatin fragment pool (input DNA). Likewise, Stat5 was inducibly and specifically bound to the β-Casein gene promoter in T-47D cells (Figure 1D , upper panel). In contrast, Stat5 did not associate with the Stat5-response element of the α2-Macroglobulin gene (Figure 1D , lower panel), a gene reportedly responsive to Stat5 in liver [ 16 ], uterine stromal cells [ 17 ], and ovarian cells [ 18 , 19 ]. However, pretreatment of T-47D cells with the glucocorticoid hormone analog, dexamethasone, for four days prior to Stat5 activation made the α2-Macroglobulin promoter accessible to Stat5 binding (Figure 1E ). It has been well established that glucocorticoids play a vital role in many cell types and cell processes, including mammary differentiation. In fact, several genes have been shown to be regulated by cooperative Stat5-glucocorticoid receptor interactions [ 20 - 23 ]. In summary, based on Stat5 inducibility and antibody specificity in testing of known Stat5 chromatin interaction sites, we concluded that the conditions for effective immunocapture of Stat5-bound chromatin from T-47D cells were established. The enriched, Stat5-bound chromatin pool was then cloned into a bacterial vector to generate a chromatin library. Because sonication generates random overhangs in double stranded DNA [ 24 ], T4 DNA polymerase was first used to blunt-end DNA fragments. Subsequently, a single 3' adenosine residue was added using Taq polymerase, and the resulting fragments were ligated into the pCR2.1 TA cloning vector. Transformed bacteria were plated on ampicillin- and S-gal-containing selection plates for blue/white screening. PCR amplification of inserts was performed directly on white bacterial colonies with common primers flanking the vector cloning site. A PCR reaction under standard conditions was used to lyse the bacteria and inactivate endogenous nucleases, cycled 36 times, and the products were separated by agarose gel electrophoresis. Initial analysis of 389 white colonies yielded 185 (48%) insert-containing PCR products. Figure 2 shows a display of PCR products from a run of 17 clones, in which three clones did not contain an insert and 14 clones had inserts of a median size of approximately 300 bp. PCR products containing an insert were purified and sequenced directly without plasmid minipreps in a cost-effective and time-saving manner. BLAST analysis was used to localize sequences to the human genome. Of 185 inserts, 31 (17%) sequences could be unambiguously matched to a location within the human genome. Sequences that could not be localized were either repetitive or did not produce a statistically significant homology to the published human genome. Figure 2 Cloned Stat5-bound chromatin fragments from PRL-stimulated T-47D breast cancer cells . Immunoprecipitated chromatin fragments were modified for cloning into a TA cloning vector and were transformed into electrocompetent bacteria. PCR was performed directly on white bacterial colonies using primers flanking the cloning site and was cycled 36 times. Samples were run on an agarose gel and analyzed for the presence of insert. (+) denotes vector control (no insert yields a vector-derived 172 bp product); (-) denotes negative PCR control. To validate the quality of the chromatin library, ten clones were randomly selected and first tested for ability to bind activated Stat5 from nuclear extracts of T-47D cells in vitro by EMSA. Stat5 binds to the consensus sequence TT(N5)AA and to relatively conservative variations, with TTC(N3)GAA considered to be optimal [ 13 , 15 ]. Typically, Stat5-DNA interaction by EMSA is performed on synthetic oligonucleotides of 20–40 bp size [ 25 ]. To effectively determine whether Stat5 interacts directly with large immunocaptured chromatin fragments, we established conditions for rapid validation by EMSA on chromatin fragments up to 400 bp in length by reducing gel polyacrylamide concentration to 3% and using amplification and isotope labeling by PCR directly from the bacterial clones. Stat5 binding in vitro was detected in seven of the ten chromatin fragments, as evidenced by prolactin-inducible DNA-binding complexes that could be supershifted by a specific anti-Stat5 antibody, but not by non-specific IgG (Figure 3A ). Negative data are only presented for one cloned chromatin fragment (CCF #30) of the three non-binding fragments (CCF # 21, # 25, and #30). Figure 3 Validation of cloned Stat5-bound chromatin fragments from PRL-stimulated T-47D breast cancer cells . A. PRL-activated Stat5 binds in vitro to 7 of 10 (8 shown) Cloned Chromatin Fragments (CCF) obtained from a Stat5-chromatin library as shown by EMSA . Nuclear extracts from confluent T-47D cells overexpressing Stat5 and incubated with (+) or without (-) 10 nM human PRL for 30 min were used. Prolactin-induced protein-DNA complexes are indicated by the retarded migration of probe in the (+) PRL lanes for each of the CCFs. Supershift reactions, or band depletions, are indicated by the antibody (Ab) added and signify a specific Stat5-DNA interaction when compared to the supershift control, non-specific, purified IgG. B. Validation of in vivo Stat5 binding to immunocaptured Cloned Chromatin Fragments (CCFs) by ChIP assay . T-47D cells were incubated with (+) or without (-) PRL as described and Stat5-DNA complexes were enriched by chromatin immunoprecipitation. PCR primers specific for each CCF were designed and used for amplification of two independently generated pools of Stat5-chromatin interaction sites and PCR was performed twice on each pool; a representative experiment is shown. CCFs #5, #18, #23, #28, #29, and #30 were consistently positive for in vivo Stat5-DNA binding (upper panel). Positive PCR controls were performed on pre-IP template (lower panel). As a second and independent means to validate the quality of the Stat5 chromatin library, the same randomly selected fragments were analyzed for inducible Stat5 binding in vivo using the ChIP assay. Independent pools of immunocaptured chromatin fragments from T-47D cells were analyzed in which Stat5 was either inactivated by serum deprivation or activated by prolactin. Densitometry of the PCR products was used to verify at least a 2-fold increase in intensity between the (-) prolactin and (+) prolactin samples. In all cases there was no detectable product from the replicate samples that had been immunoprecipitated with a non-specific IgG antibody (data not shown), indicating a specific Stat5-mediated capture of genomic elements. All PCR amplifications were performed at least twice on at least two separate pools of immunoprecipitated genomic elements. Of the seven chromatin fragments that were positive for in vitro Stat5 binding by EMSA, five (CCFs #5, #18, #23, #28, and #29) were also consistently positive for in vivo Stat5 binding by ChIP assay (Figure 3B , upper panel). In addition, CCF #30 was positive by ChIP, but not by EMSA, possibly reflecting indirect binding via other proteins. Conversely, CCFs #11 and #14, which bound Stat5 in vitro by EMSA, were both negative by ChIP. Corresponding PCR products from the pre-IP DNA is shown (Figure 3B , lower panel) and no product was detected in samples immunoprecipitated with non-specific IgG (data not shown). Due to sequence complexity, fragment-specific flanking primers could not be designed for fragments CCF #21 and #25, neither of which bound Stat5 in EMSA. The localization data and binding validation of the ten clones are summarized in Figure 4 . Each of the CCFs that bound Stat5 by EMSA contained at least one broad consensus TT(N5)AA site as expected. Correspondingly, of the three EMSA-negative CCFs, #21 and #25 lacked consensus binding sites, while a single TT(N5)AA site was present in CCF #30. Furthermore, the five CCFs verified to bind both by EMSA and ChIP may be involved in transcriptional control of nearby genes (Figure 4 ), or alternatively, control transcription of small regulatory RNAs [ 26 ]. Figure 4 Genomic localization of cloned Stat5-chromatin interaction sites from T-47D human breast cancer cells . A karyogram of the human genome illustrates the loci of the cloned Stat5 binding sites. Each site is designated by the CCF number and is localized to its specific area of the genome, based on BLAST alignment. CCFs positive by EMSA are labeled "E", and CCFs positive by ChIP are labeled "C". The genomic localization and two nearest genes of each verified CCF is given by the genomic position of the centrally located nucleotide: CCF#5 : 8q12.1:58980112 (LOC389663-similar to hypothetical protein; MGC39325-hypothetical protein); CCF#18 : 3q29:193835263 (FGF12-fibroblast growth factor 12; LOC151963-similar to BcDNA:GH11415 gene product); CCF#23 : 5q33.1:147357743 (LOC391839-similar to elongation factor 1 gamma; SPINK5-serine protease inhibitor, Kazal type, 5); CCF#28 : 6q24.1:142177080 (LOC340149-hypothetical protein; NMBR-neuromedin B receptor); CCF#29 : 6q13:74774694 (CD109 antigen); SLC17A5-solute carrier family 17 (anion/sugar transporter) member 5). Other CCFs include: CCF#11 : 7q31.1:111547940; CCF#14 : 15q11.2:21265800; CCF#21 : 18q21.2:49639649; CCF#25 : Xq28:153691722; CCF#30 : 1p34.1:43842775. Conclusions While the present work was being completed, an independent report also identified Stat5 binding sites using a genome-wide approach in mouse lymphoma cells [ 27 ], although direct binding by EMSA was not verified. We conclude that unbiased, genome-wide strategies can now be used to identify novel Stat5 binding sites by cloning immunocaptured chromatin fragments. At the current efficacy, approximately 20% of cloned Stat5-immunocaptured fragments from T-47D breast cancer cells could be localized within the normal human genome, and Stat5 binding in vitro and in vivo was confirmed in approximately half of those. Further reductions in capture of non-specific chromatin, combined with refinements in cloning procedures [ 28 ], reduced sequencing cost, direct and high-throughput sequencing from bacterial colonies, and direct labeling of PCR products for EMSA testing, will allow streamlining of the procedure for genome-wide identification of Stat5-chromatin interaction sites. Ongoing efforts are also exploring whether some of the clones with only weak homology to the normal human genome represent Stat5-bound neochromatin unique to the cancer cells as a result of genomic instability. Methods Cell Culture and Hormones The human breast cancer cell line T-47D was grown to confluence in RPMI-1640 medium (Biofluids, Rockville, MD) supplemented with 10% fetal bovine serum, 2 mM L-glutamine, and penicillin-streptomycin (50 IU/ml and 50 μg/ml, respectively) at 37°C with 5% CO 2 . Human prolactin was kindly provided by Dr. A.F. Parlow under the sponsorship of the NIDDKs National Hormone & Peptide Program. Dexamethasone (Dex) was purchased from Sigma Chemical Co. (St. Louis, MO). Chromatin Immunoprecipitation After grown to confluence (~2 × 10 7 cells/T175 cm 2 flask), T-47D cells were serum starved for 24 h and then treated with or without 10 nM hPRL for 30 min. For prodifferentiation experiments with glucocorticoid pretreatment, confluent cultures were maintained in serum-free RPMI-1640 with 1 μM Dex dissolved in DMSO or in DMSO alone for 96 hours, then stimulated with or without PRL for 30 min. Proteins were then crosslinked to the chromatin by the addition of formaldehyde (Fisher Scientific, Fairlawn, NJ) to a final concentration of 1% and incubated for 30 min at 37°C. The cells were rinsed, scraped, and pelleted in ice-cold PBS with 1 mM PMSF, 2 μg/ml aprotinin, and 2 μg/ml pepstatin A. Cell pellets were resuspended in 400 μl lysis buffer (1% SDS, 10 mM EDTA, 50 mM Tris-HCl, pH 8.0, 1 mM PMSF, 2 μg/ml aprotinin, and 2 μg/ml pepstatin A) and the lysates were sonicated using a Sonic Dismembrator (Fisher Scientific, Pittsburgh, PA), fitted with a tapered microtip set to an amplitude of 50% and were pulsed twice for 30 s. Debris was pelleted by centrifugation for 10 min at 13,200 RPM at 4°C. The supernatants were then diluted 10-fold in IP buffer (0.1% SDS, 1.1% Triton-X, 1.2 mM EDTA, 16.7 mM Tris-HCl, pH 8.1, 16.7 mM NaCl, 1 mM PMSF, 2 μg/ml aprotinin, and 2 μg/ml pepstatin A) and 1% was set aside for Pre-IP or input sample. The solution was then pre-cleared with beads (50% protein A-sepharose, 1 mg/ml poly dI-dC, 0.1% BSA, in TE, pH 7.4) for 30 min at 4°C. Next, the samples were incubated overnight with Stat5 antibody (N-20, Santa Cruz Biotechnology) or IgG IP-control followed by immunoprecipitation with pre-incubated beads. The samples (beads) were then washed once with each of these buffers in the following order: Wash Buffer 1 (0.1% SDS, 1% Triton-X, 2 mM EDTA, 20 mM Tris-HCl, pH 8.0, 150 mM NaCl), Wash Buffer 2 (0.1% SDS, 1% Triton-X, 2 mM EDTA, 20 mM Tris-HCl, pH 8.0, 500 mM NaCl), Wash Buffer 3 (0.25 M LiCl, 1% NP-40, 1% Na-deoxycholate, 10 mM Tris-HCl, pH 8.0, 1 mM EDTA), then twice with Wash Buffer 4 (TE, pH 8.0). The samples were eluted from the beads in 1% SDS and 0.1 M NaHCO 3 . Next, the cross-links were reversed with 0.2 M NaCl overnight at 65°C followed by protein digestion with proteinase K for 1 hour at 45°C. The DNA was recovered by phenol:chloroform extraction and ethanol precipitation. This pool was then used as PCR template or could be blunted with T4 DNA polymerase (New England Biolabs, Beverly, MA) at 37°C for 5 minutes under standard conditions for uniform cloning. Taq DNA polymerase (New England Biolabs) was then used to add a 3' A overhang for cloning into a TOPO TA cloning kit (Invitrogen, Carlsbad, CA). The ligated vectors were transformed into electrocompetent bacteria, DH-5 α E (Invitrogen), using the manufacturer's recommendations for transformation in a Bio-Rad Gene-Pulser (Hercules, CA), and plated on S-Gal/Ampicillin plates (Sigma Chemical Co., St. Louis, MO). Positive clones by blue-white screening were then analyzed directly by colony PCR in a standard reaction with vector-specific M13 and T7 primers that flank the cloning site and were incubated at 94°C for 4 min and cycled at 94°C for 30 s, 55°C for 45 s, and 72°C for 30 s. Colonies with quantifiable inserts were then purified (QiaQuick PCR purification kit, Qiagen, Valencia, CA), sequenced, and localized using BLAST (NCBI, NIH, Bethesda, MD). Validation using known Stat5 responsive genes The following primers were designed to flank the Stat5 binding site in the promoter of the following genes: α2-Macroglobulin forward 5' TTT AGC CCT CCA GGG ATT CT 3', reverse 5' CAA TCC ATC TGG TCC CAA AC 3'; β-Casein forward 5' GGA GAA ACA GTT TGC CTC ACA 3', reverse 5' CCT AGT GGG GCC TTG AGA TT 3'; CISH forward 5' CTA TTG GCC CTC CCC GAC 3', reverse 5' AGC TGC TGC CTA ATC CTT TG 3'. PCR amplifications shown are representative of at least 2 separate amplifications of at least 2 independently generated immunocaptured pools of Stat5-chromatin complexes. Preparation of cellular extracts for EMSA After reaching confluence, T-47D cells were infected with adenovirus containing wild type Stat5 at a Multiciplity Of Infection (MOI) = 6.67, as described previously [ 29 ]. Parallel samples of T-47D cells were not exposed to adenovirus as a mock infection (standard control). After infection, the cells were cultured in serum-free medium for 24 hours, then stimulated for 30 min with 10 nM hPRL, the culture medium was removed, and the cells were collected as described above. Nuclear lysates were collected as described previously [ 5 , 25 ]. Generation of radiolabeled DNA probes Radiolabeled products were generated by PCR in a 10 μl reaction under standard conditions with the addition of 0.25 μl α 32 P dATP (10 mCi/ml; Amersham-Pharmacia, Piscataway, NJ). Initially the samples were incubated at 94°C for 1 min, then cycled 36 times at 94°C for 30 s, 55°C for 30 s, and 72°C for 30 s. The reaction was then held at 72°C for 5 min following the cycling to allow for product fill-in and addition of a 3' terminal "A". After completion of the cycling, the PCR products were purified using the Qiagen PCR purification kit, according to manufacturer's instructions. The final products were eluted in 50 μl and stored at -20°C until use. DNA-protein binding reaction The DNA-protein binding reactions were performed in a 10 μl mixture containing 3 μl of nuclear extract from the respective sample, and 1 μg of double-stranded poly dI:dC (Boehringer Mannheim, Indianapolis, IN), as previously described [ 25 ]. After 1 h on ice, samples (with 1 ng specific anti-Stat5 antibody (Santa Cruz Biotechnology), or 1 ng non-specific, purified IgG (Sigma), or no antibody) were incubated with 2.0 μl 32 P-labeled PCR probe and incubated for 20 min at room temperature. The samples were then resolved in a 3% non-denaturing polyacrylamide gel, as previously described [ 25 ]. ChIP in vivo validation As an independent validation technique, we designed primers specific to each cloned chromatin fragment (CCF) then performed PCR on a separately-generated enriched pool of immunoprecipitated Stat5-chromatin interaction sites. The following primers were used for PCR: CCF #5 forward 5' TGA CAT CAG TGA GAG TGG AGG 3', reverse 5' TGA GGC TGT AAT GTC ACT CAG AA 3'; CCF #11 forward 5' GGA CAC ATC CAC TAC TGC CA, reverse 5' AAA AGA AGT GCA GTT CAG GAT AA 3'; CCF #14 forward 5' GCC TGT GTG ATA TCA TAT GGA AAG 3', reverse 5' TTT CCA AGA ACT TAT CAG AAT GAC TT 3'; CCF #18 forward 5' TCA GTC TTC CTC TTT CCC CA 3', reverse 5' CGG CTT ACA TTC TGT GTC GC 3'; CCF #23 forward 5' CAT ATC TGT CTA CAA ATA ACA GTT CCC 3', reverse 5' AAT AGA GGG CAG AGT TAA GAA TCA AA 3'; CCF #28 forward 5' AAG CCT GAA AAG AAA AAT CTC A 3', reverse 5' CCT GAC ACA TCC AGA CTT GG 3'; CCF #29 forward 5' CAG ACG TTT GCT GGA AGA TAT G 3', reverse 5' TTG TTC CAT GAA ACC AGG G 3'; CCF #30 forward 5' AAG GCC ATT GAA GTG AGG TG 3', reverse 5' CCA TCA GCC AGT CAT TGA AG 3'. The PCR was performed with standard conditions using template immunoprecipitated from cells treated with or without prolactin to indicate Stat5 inducibility and was executed at least twice on each of 2 separate pools. Authors' contributions MJL carried out the experimental studies, participated in the design of the study, and drafted the manuscript. JX participated in the design of the study and scientific discussion of the results. HR conceived and participated in the design of the study, contributed in the evaluation of the results, and in the preparation of the manuscript. All authors read and approved the final manuscript.

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300883

Multiple Apoptotic Caspase Cascades Are Required in Nonapoptotic Roles for Drosophila Spermatid Individualization

Spermatozoa are generated and mature within a germline syncytium. Differentiation of haploid syncytial spermatids into single motile sperm requires the encapsulation of each spermatid by an independent plasma membrane and the elimination of most sperm cytoplasm, a process known as individualization. Apoptosis is mediated by caspase family proteases. Many apoptotic cell deaths in Drosophila utilize the REAPER/HID/GRIM family proapoptotic proteins. These proteins promote cell death, at least in part, by disrupting interactions between the caspase inhibitor DIAP1 and the apical caspase DRONC, which is continually activated in many viable cells through interactions with ARK, the Drosophila homolog of the mammalian death-activating adaptor APAF-1. This leads to unrestrained activity of DRONC and other DIAP1-inhibitable caspases activated by DRONC. Here we demonstrate that ARK- and HID-dependent activation of DRONC occurs at sites of spermatid individualization and that all three proteins are required for this process. dFADD, the Drosophila homolog of mammalian FADD, an adaptor that mediates recruitment of apical caspases to ligand-bound death receptors, and its target caspase DREDD are also required. A third apoptotic caspase, DRICE, is activated throughout the length of individualizing spermatids in a process that requires the product of the driceless locus, which also participates in individualization. Our results demonstrate that multiple caspases and caspase regulators, likely acting at distinct points in time and space, are required for spermatid individualization, a nonapoptotic process.

Introduction Most, if not all, cells have the potential to carry out the apoptotic cell death program ( Jacobson et al. 1997 ). Key players in this process are caspase family proteases. Apical caspases are activated through interactions with adapter molecules in response to death signals arising from cellular compartments such as the mitochondria and plasma membrane death receptors. These caspases transduce death signals by cleaving and activating effector caspases. Effector caspases then cleave and alter the function of a number of cellular proteins, leading to the morphological and biochemical events associated with apoptosis ( Kumar and Doumanis 2000 ). Proteolysis is an irreversible protein modification. Therefore, caspase activation is normally kept under tight control in viable cells. However, in Drosophila the apoptotic effector caspase DRICE is cleaved and activated throughout the length of elongated spermatids, and testis-specific expression of the baculovirus caspase inhibitor p35 results in male sterility, despite the fact that apoptosis is not an obligate step in spermatogenesis ( Arama et al. 2003 ). These observations demonstrate that caspase activity is important for male fertility, but leave a number of questions unanswered: For what events in spermatid differentiation are caspases required? Which caspases mediate this requirement? How are they activated and where do they act? And how do these cells avoid apoptosis? Spermatid development in Drosophila takes place within a syncytium (cyst), in which 64 haploid spermatid nuclei descended from a diploid primary spermatogonial cell are connected by abundant cytoplasmic bridges (reviewed in Lindsley and Tokuyasu 1980 ). In mammals, similar bridges facilitate the sharing of haploid gene products between spermatids, thereby allowing spermatid development to be directed by the products of both sets of parental chromosomes ( Erickson 1973 ; Braun et al. 1989 ). It is presumed that intercellular bridges play a similar role in Drosophila . Ultimately, these bridges must be eliminated in a process known as individualization in order to form freely swimming sperm. At the end of male meiosis, each cyst contains 64 haploid spermatids, each approximately 2 mm long, encapsulated by two somatic cyst cells. The 64 nuclei are located at the basal end of the testis, near the seminal vesicle, and the flagellar tails extend apically, throughout the length of the testis. Individualization in Drosophila initiates when an actin-based structure known as an investment cone assembles around each spermatid nucleus ( Tokuyasu et al. 1972 ). These assemble into a macroscopic structure known as the individualization complex ( Fabrizio et al. 1998 ), which moves along the length of the cyst toward the sperm tails. The individualization complex is the site at which the cyst membrane is remodeled to enclose each sperm. Cytoplasm and organelles are extruded from between the sperm tails and pushed ahead of the individualization complex, forming a visible bulge known as the cystic bulge. When the cystic bulge reaches the sperm tails, it is detached and becomes known as the waste bag ( Tokuyasu et al. 1972 ). A similar process, involving encapsulation of syncytial spermatids within individual plasma membranes and elimination of excess cytoplasm, also occurs during mammalian spermatogenesis ( de Krester and Kerr 1994 ). The importance of cytoplasm elimination for human fertility is suggested by the fact that many conditions or treatments resulting in infertility disrupt this process ( Russell 1991 ; Keating et al. 1997 ; Akbarsha et al. 2000 ). Cytoplasm elimination during spermatogenesis may also represent a strategy by which male gametes eliminate cytoplasmic parasites, thereby preventing their transmission to the zygote ( Randerson and Hurst 2001 ). Results Caspase Activity Is Required for Spermatid Individualization To determine whether caspase activity is required for spermatid individualization, we examined cysts from flies in which caspase activity in the male germline was inhibited. We generated flies that expressed the broad-specificity Drosophila caspase inhibitor DIAP1 or the baculovirus caspase inhibitor p35 under the control of the male germline-specific β 2 -tubulin promoter (β2tub-DIAP1 and β2tub-p35 flies, respectively) ( Hay 2000 ; Santel et al. 2000 ). Cysts undergoing individualization contain activated versions of the effector caspase DRICE, as visualized with an anti-active DRICE-specific antibody ( Arama et al. 2003 ). Testis from wild-type animals always contained active DRICE-positive cysts with prominent cystic bulges and waste bags ( Figure 1 A). In contrast, while elongated cysts from β2tub-DIAP1 and β2tub-p35 flies remained active DRICE-positive, cystic bulges and waste bags were largely absent and reduced in size when present ( Figure 1 B and 1 C). In addition, the normally coordinated tailward movement of investment cones in active DRICE-positive wild-type cysts ( Figure 1 D) was dramatically disrupted in β2tub-DIAP1 males ( Figure 1 E). Cysts from β2tub-p35 males showed milder defects in investment cone movement ( Figure 1 F). These phenotypes, in conjunction with related observations by Arama et al. (2003 ), suggest, but do not prove, that caspase inhibition results in defects in individualization. To further test this hypothesis, we examined spermatids for individualization defects directly, using transmission electron microscopy (EM). In cysts from wild-type animals in which individualization had occurred, spermatid tails consisted largely of a flagellar axoneme and major and minor mitochondrial derivatives, all of which were tightly surrounded by a unit plasma membrane ( Figure 1 G and 1 J). In contrast, in many cysts from β2tub-DIAP1 and β2tub-p35 flies, spermatids failed to separate from each other and contained excess cytoplasm, often including an enlarged minor mitochondrial derivative ( Figure 1 H and 1 K and Figure 1 I and 1 L, respectively). Phenotypes similar to those seen in cysts from β2tub-DIAP1 and β2tub-p35 flies were also observed in cysts from flies in which levels of the caspase Drosophila caspase-1 (DCP-1) were decreased specifically in the male germline using RNA interference (RNAi) (β2tub-Dcp-1-RNAi flies) ( Figure S1 ). Short prodomain caspases such as DCP-1 and DRICE are activated in response to cleavage by upstream signal-transducing caspases ( Hawkins et al. 2000 ; Hay 2000 ; Meier et al. 2000 ; Shi 2002 ). Together, these observations demonstrate that caspase activity is required for individualization and suggest that DCP-1 (but perhaps not DRICE; see Discussion below) is an important downstream target caspase. Figure 1 Caspase Activity Is Required for Spermatid Individualization (A–C) Testis of different genotypes were visualized with antibodies specific for activated Drice (green). (A) Wild-type testis. Active DRICE is present in multiple elongated cysts. Cystic bulges (cb) and waste bags (wb) are indicated by arrows. (B and C) Testes from β2tub-DIAP1 and β2tub-p35 males, respectively. Active DRICE is present in elongated cysts, but cystic bulges and waste bags are reduced in number and size. (D–F) Phalloidin-stained investment cones from testes of different genotypes (red). Spermatid axonemes in (D)–(F) are highlighted by the AXO49 antibody, which recognizes polyglycylated β2tub ( Bressac et al. 1995 ) (blue). (D) In wild-type testes, investment cones move as a coordinated group. (E and F) Coordinated investment cone movement is disrupted in cysts from β2tub-DIAP1 and β2tub-p35 males, respectively. (G–L) EM sections of elongated cysts of different genotypes. (G) A cyst from a wild-type male that has undergone individualization. The boxed region is shown at higher magnification in (J), along with the locations of the major mitochondrial derivative (mj), minor mitochondrial derivative (mi), and axoneme (ax). A single spermatid unit is outlined with a dashed line. (H and I) In cysts from β2tub-DIAP1 and β2tub-p35 males, respectively, many spermatid units are present in a common cytoplasm that contains organelles, often including an enlarged minor mitochondrial derivative. Boxed regions of β2tub-DIAP1 and β2tub-p35 cysts shown in (H) and (I) are shown at higher magnification in (K) and (L), respectively. Several examples of multiple spermatids present in a common cytoplasm are outlined by the dashed line in (K) and (L). Scale bar for EM micrographs = 1 μm. Ark and Dronc Participate in Spermatid Individualization What are the pathways that lead to caspase activation during individualization? Cell death in many contexts in the fly requires the activity of the Drosophila APAF-1 homolog ARK, which promotes activation of the apical caspase DRONC ( Dorstyn et al. 2002 ; Igaki et al. 2002 ; Muro et al. 2002 ; Zimmermann et al. 2002 ). DRONC, in turn, can cleave and activate the downstream caspases DCP-1 and DRICE ( Hawkins et al. 2000 ; Meier et al. 2000 ; Muro et al. 2002 ). Genetic and biochemical evidence implicates all three of these caspases as apoptosis inducers ( Kumar and Doumanis 2000 ). Animals homozygous for a hypomorphic Ark allele ( Ark CD4 ) showed a high level of male sterility ( Rodriguez et al. 1999 ), despite the fact that cell death is not an obligate step in spermatogenesis ( Fuller 1993 ). This suggested to us that ARK-dependent DRONC activity might be important. To test this hypothesis, we decreased ARK levels specifically in the male germline by expressing double-stranded RNA homologous to Ark under the control of the β2tub promoter (β2tub-Ark-RNAi flies) ( Figure 2 I). To decrease levels of active DRONC, we generated flies that expressed a dominant-negative version of DRONC (Dn-DRONC) under the control of the β2tub promoter (β2tub-Dn-DRONC flies). Similar versions of DRONC are potent suppressors of DRONC-dependent cell death in other contexts ( Hawkins et al. 2000 ; Meier et al. 2000 ). DRICE was still activated in elongated cysts from β2tub-Ark-RNAi and β2tub-Dn-DRONC males ( Figure 2 A and 2 C). However, as with active DRICE-positive cysts from β2tub-DIAP1 and β2tub-p35 flies, cystic bulges and waste bags were largely absent, and coordinated investment cone movement was disrupted ( Figure 2 B and 2 D). Examination of β2tub-ARK-RNAi and β2tub-Dn-DRONC spermatids using EM showed that inhibition of ARK ( Figure 2 E, 2 G, and 2 H) and DRONC ( Figure 2 F) function resulted in individualization failure in many cysts. In addition, many single spermatid units that were surrounded by a unit plasma membrane still contained large fingers of excess cytoplasm ( Figure 2 E and 2 G). (See Discussion below.) Figure 2 ARK and DRONC Are Required for Spermatid Individualization (A and C) Testis from β2tub-Ark-RNAi and β2tub-Dn-DRONC males, respectively. Active DRICE-positive cysts are present, but cystic bulges and waste bags are largely absent. (B and D) Investment cone movements in testis from β2tub-Ark-RNAi and β2tub-Dn-DRONC, respectively, are uncoordinated. (E, G, and H) EM images of an elongated cyst from a β2tub-Ark-RNAi male. Some individualization failures are observed (E, G, and H), two of which are highlighted by the dashed lines in (G) and (H). In addition, many spermatids that have apparently undergone individualization still contain large amounts of excess cytoplasm (E and G). (F) EM image of a cyst from a β2ub-Dn-DRONC male. A large region in which individualization did not occur is outlined. (I) Western blot from wild-type (Wt) and β2tub-Ark-RNAi (DArki) testis probed with anti-ARK and anti-DRICE antibodies. ARK, but not DRICE, levels are greatly reduced in β2tub-Ark-RNAi testis. ARK-Dependent Activation of DRONC Occurs at Sites of Individualization and Requires the Apoptosis Inducer HID To determine where active DRONC is localized and thus where DRONC is likely to be functioning during individualization, we generated an antibody that recognized versions of DRONC that had undergone autoactivation-associated cleavage at glutamate-352 (TQT E ) ( Figure S2 ). In contrast to active DRICE, which appeared uniformly throughout the cyst, just as the individualization complex began its apical movement away from the spermatid nuclei ( Figure 3 A and 3 B), active DRONC showed a dynamic pattern of localization. It was initially observed in a punctate pattern just apical to the juxtanuclear individualization complex (arrowhead in Figure 3 C). The individualization complex moved through this region (arrow in Figure 3 C), and active DRONC then trailed the individualization complex for the remainder of its apical movement through the cyst ( Figure 3 D). As expected, DRONC activation required ARK and was eliminated in testis in which ARK levels were decreased ( Figure 3 E) or in which access of wild-type DRONC to ARK was inhibited by expression of inactive Dn-DRONC ( Figure 3 F). Figure 3 DRONC Activation Occurs in Association with Individualization Complexes and Is ARK-Dependent (A and B) Wild-type testis stained for active DRICE (green), phalloidin-stained filamentous actin (red), and TOTO-3-stained DNA (blue). (A) Active DRICE is present throughout the length of cysts undergoing individualization. (B) Higher magnification of the testis in (A). The arrowhead points to a cyst in which the individualization complex has assembled around the spermatid nuclei, but DRICE activation has not occurred. The arrow points to a neighboring cyst in which the individualization complex has just begun to move away from the spermatid nuclei. Active DRICE is now present throughout the length of this cyst, indicating that DRICE activation within a cyst occurs rapidly and globally. (C) Active DRONC (green) is initially present in a punctate pattern, apical to the individualization complex (red) at the base of the testis (arrowheads). The individualization complex then moves through the region containing active DRONC (arrow). (D) Subsequently, active DRONC is found associated with the trailing edge of the individualization complex as it moves apical within the cyst. A higher magnification view of active DRONC staining in the left-most cyst is shown in the inset. (E and F) Active DRONC is eliminated in cysts from β2tub-Ark-RNAi and β2tub-Dn-DRONC testis, respectively. How is DRONC activation during individualization regulated? DRONC undergoes continuous ARK-dependent activation in many viable cells ( Dorstyn et al. 2002 ; Igaki et al. 2002 ; Muro et al. 2002 ; Rodriguez et al. 2002 ; Zimmermann et al. 2002 ). DIAP1 promotes the survival of these cells by ubiquitylating DRONC ( Wilson et al. 2002 ; Chai et al. 2003 ) and by inhibiting the activity of caspases activated by DRONC ( Hawkins et al. 1999 ; Wang et al. 1999 ). REAPER/HID/GRIM family proteins promote DRONC activity and apoptosis by disrupting DIAP1–caspase interactions, thereby preventing DIAP1-dependent ubiquitylation of DRONC and inhibition of caspases activated by DRONC ( Wang et al. 1999 ; Goyal et al. 2000 ; Lisi et al. 2000 ; Wilson et al. 2002 ; Chai et al. 2003 ). To determine whether the REAPER/HID/GRIM family proteins played a similar role during individualization, we examined available mutants for these genes. Cysts from flies lacking reaper ( XR38 / H99 ) ( Peterson et al. 2002 ) showed normal investment cone movement. In contrast, the coordinated movement of investment cones was disrupted in hid 05014 / H99 cysts ( Figure 4 D, compared with Figure 4 C), indicating a requirement for HID in spermatogenesis. In addition, HID protein was enriched in the cystic bulge region of wild-type cysts ( Figure 4 A), but not those from animals that lacked HID ( hid 05014 / H99 ) ( Figure 4 B). These observations suggested that HID participates in DRONC activation, stabilization, or both and thereby in spermatid individualization. Figure 4 HID, dFADD, and DREDD Participate in Individualization (A) HID protein (green) is concentrated in the region of the cystic bulge, which is marked by the presence of the phalloidin-stained individualization complex (red). (B) HID immunoreactivity is absent in testis from hid 05014 /H99 flies. (C) Active DRONC (green) is associated with the trailing edge of the individualization complex in a wild-type cyst. (D) Active DRONC is absent from the individualization complex in cysts from hid 05014 /H99 males. (E) EM section from hid 05014 /H99 testis. Essentially all spermatids have failed to individualize. (F) Higher magnification view of boxed area in (E). Multiple spermatid units sharing a common cytoplasm are outlined by the dashed line. (G) Representative EM section of cyst from dFadd f02804 / dFadd f02804 testis. Essentially all spermatids have failed to individualize. (H) EM section of cyst from Dredd B118 / Dredd B118 testis in which individualization has failed to occur. In some other cysts from this same male, individualization proceeded apparently normally (data not shown). Several observations support this hypothesis. First, cysts from two different hid allelic combinations, hid A329 / hid A329 (data not shown) and hid 05014 / H99 , showed defects in individualization similar to those observed in β2tub-DIAP1 or β2tub-p35 males, demonstrating a requirement for HID in this process ( Figure 4 E and 4 F). Second, localized active DRONC was eliminated in hid 05014 / H99 (and hid A329 / hid A329 ; data not shown) flies, consistent with the idea that HID promotes individualization, at least in part, by promoting DRONC activity ( Figure 4 D, compared with Figure 4 C). HID, by virtue of its ability to disrupt IAP (inhibitor of apoptosis)–caspase interactions, may also regulate the activation of other caspase cascades during spermatid individualization (see below). Spermatid Individualization Utilizes Multiple Pathways of Caspase Activation Together the above observations demonstrate that components of a canonical apoptosis-inducing pathway involving ARK, DRONC, and HID are required for spermatid individualization. However, it is important to note that the individualization defects observed in testis from β2tub-Ark-RNAi and β2tub-Dn-DRONC males (see Figure 2 ) were less severe then those seen in β2tub-DIAP1, β2tub-p35, or hid 05014 / H99 males (see Figures 1 and 4 ). These differences may reflect incomplete inactivation of ARK and DRONC. Alternatively, they may reflect roles for ARK- and DRONC-independent caspase activities. DREDD is an interesting candidate to mediate such an activity since it is an apical caspase that can promote cell death in some contexts ( Chen et al. 1998 ; Hu and Yang 2000 ). Its activation is stimulated through interactions with dFADD, the Drosophila homolog of mammalian FADD, an adaptor that mediates recruitment of apical caspases to ligand-bound death receptors, thereby promoting caspase activation ( Hu and Yang 2000 ). Elongated cysts from dFadd f02804 / dFadd f02804 and Dredd B118 / Dredd B118 males (both are genetic null mutations) contained active DRICE, but often showed uncoordinated investment cone movement (data not shown). At the EM level, elongated cysts from testis of single dFadd f02804 / dFadd f02804 and Dredd B118 / Dredd B118 males showed a range of phenotypes. About 50% of cysts from Dredd B118 / Dredd B118 males and almost all cysts from dFadd f02804 / dFadd f02804 males (greater than 90%) displayed defects in individualization similar to those of β2tub-DIAP1 and β2tub-p35 flies ( Figure 4 G and Figure 4 H, respectively). In other cysts, individualization occurred apparently normally (data not shown). Together these observations argue that dFADD and DREDD participate in individualization. The fact that loss of dFadd resulted in phenotypes more severe than those due to loss of Dredd suggests that dFADD has functions in individualization independent of promoting DREDD activation. Finally, we noted that DRICE activation was insensitive to inhibition (but perhaps not to complete elimination) of ARK and DRONC; to complete loss of HID, DREDD, or FADD; and to expression of the potent general caspase inhibitors DIAP1, p35, or p49 (see Figures 1–4 ; data not shown). This, together with the observation that DRONC and DRICE were activated in distinct spatial and temporal patterns (see Figure 3 A– 3 D), suggests that DRICE activation occurs through an unknown HID-, ARK-, DRONC-, dFADD-, and DREDD-independent mechanism. It has been proposed that DRICE activation in spermatids is essential for fertility and that DRICE activation is mediated by an isoform of cytochrome c, cytochrome c-d (cyt-c-d), based on the observation that males homozygous for a P-element insertion ( bln 1 ) in the cyt-c-d gene were sterile and lacked active DRICE staining in testis ( Arama et al. 2003 ). However, as illustrated in Figure 5 , the region surrounding the bln 1 insertion contains multiple transcription units. In addition, cysts from bln 1 males showed multiple defects in spermatogenesis prior to individualization, including failure to carry out polyglycylation of axonemal microtubules ( Figure 5 C and 5 E), and aberrant development of the major and mitochondrial derivatives ( Figure 5 F– 5 H). These observations leave it unclear whether cyt-c-d is in any direct sense required for DRICE activation or whether DRICE is required for fertility. We serendipitously identified a line of flies carrying an X chromosome mutation ( driceless ) in which DRICE activation during spermatid individualization was completely eliminated ( Figure 6 A) (see Materials and Methods for details). Testis from these flies contained large cystic bulges in which individualization complexes were present as a coordinated front, as in wild-type ( Figure 6 B). In contrast to bln 1 males, driceless males were fertile and investment cones moved apically. As expected from this phenotype, some cysts from driceless males underwent individualization normally (approximately 50%) ( Figure 6 C). However, in others, individualization failed completely ( Figure 6 D and 6 E). Figure 5 The bln 1 P-Element Insertion, Which Inhibits Cyt-c-d Expression, Results in Pleiotropic Defects in Spermatogenesis (A) Genomic organization of the cyt-c-d region. Upper half of the panel illustrates the structure of the region, as described by Arama et al. (2003 ). The lower half of the panel indicates the relative locations of several other genes in the region, as annotated by the Berkeley Drosophila Genome Project ( http://flybase.bio.indiana.edu/search/ ) as of August 2002. The bln 1 P element is inserted within the cyt-c-d transcription unit. This P element is also inserted within the transcription unit of a second gene, CR31808-RA ( RE70695 ). Both of these genes and the bln 1 P element reside within the intron of a third gene, CG31782 . (B and D) Wild-type and bln 1 testis, respectively, stained with anti-active DRICE antibodies. Active DRICE immunoreactivity is eliminated in bln 1 testis, as described in Arama et al. (2003 ). (C and E) Wild-type and bln 1 testis, respectively, stained with AXO49 antibodies (blue), which recognize polyglycylated β2tub present in axonemal microtubules, and phalloidin (red). Polyglycylation occurs prior to individualization ( Bressac et al. 1995 ). Axonemes of elongated cysts from wild-type flies stain with AXO49 (C), while those from bln 1 males do not (E). (F–I) EMs of cysts of different developmental stages from wild-type (F and G) and bln 1 (H) testis. (F) Wild-type cyst prior to individualization. Note the structures of the major and minor mitochondrial derivatives, in particular the fact that the major mitochondrial derivative is increased in size and is electron dense. (G) Wild-type cyst following individualization. (H) Representative example of the most mature cysts found in bln 1 testis. Note the dramatically increased cell size and the lack of differentiation of the major and mitochondrial derivatives, as compared to wild-type. Figure 6 driceless Males Lack Active Drice Staining and Show Defects in Individualization (A) Testis from driceless male stained with active DRICE. Active DRICE staining is eliminated. (B) Elongated cysts from driceless male. AXO49 staining (blue) outlines the location of three cystic bulges. Individualization complexes (arrows) are marked with phalloidin (red). (C) Example of a cyst from a driceless male in which individualization has proceeded normally. (D) Example of a cyst from a driceless male in which individualization has failed to occur. (E) Boxed area in (D) shown at higher magnification. A region in which individualization has failed is outlined with a dashed line. The above observations indicate that DRICELESS promotes individualization, but leave the role of DRICE (which we have thus far been unable to effectively inactivate with RNAi) unclear. Interestingly, cysts from driceless males also showed reduced levels of localized active DRONC staining (data not shown), raising the possibility that DRICELESS has at least some of its effects on individualization through regulation of DRONC activity. We do not favor a simple linear model in which DRICELESS mediates its effects on individualization only by promoting DRONC-dependent activation of DRICE. This is because removal of HID or inhibition of ARK or DRONC, each of which inhibited individualization, had no significant effect on DRICE activation. An attractive alternative is that DRICELESS-dependent activation of DRICE promotes individualization, at least in part, by indirectly facilitating local activation of DRONC and perhaps other caspases, such as DREDD (see Discussion below), that are themselves activated through distinct pathways. Positive feedback pathways that perform a similar caspase-activating function have been described in a number of apoptotic contexts ( Adams 2003 ). DRICE can cleave DIAP1 near its N-terminus. This promotes DIAP1 degradation through the N-end rule ubiquitylation pathway ( Ditzel et al. 2003 ), providing one possible mechanism by which active DRICE could facilitate the activation of other caspases. Characterization of driceless should provide insight into the functional relationships between these caspases in spermatogenesis. Discussion All together, our observations demonstrate that multiple caspases and caspase regulators, acting at distinct points in space and time, are utilized to promote spermatid individualization. In one pathway, whose mechanism of activation is unknown, active DRICE appears throughout elongated spermatids just as individualization begins. DRICELESS, which promotes individualization, is required for DRICE activation. But whether active DRICE mediates the requirement for DRICELESS is unknown. In a second pathway, HID, concentrated through unknown mechanisms in the cystic bulge, promotes the local ARK-dependent activation of the apical caspase DRONC, presumably at least in part through disruption of complexes between DRONC and DIAP1. As discussed above, active DRICE may facilitate this activation. Components of a second pathway for apical caspase activation, dFADD and DREDD, are also important for individualization. These proteins bind each other ( Hu and Yang 2000 ; Horng and Medzhitov 2001 ), and dFADD expression promotes DREDD activation ( Hu and Yang 2000 ). Adaptors such as mammalian FADD mediate recruitment of apical caspases to ligand-bound death receptors, thereby promoting caspase activation. Interestingly, dFADD and DREDD are absolutely required for the innate immune response to gram-negative bacterial infection ( Hultmark 2003 ). In this pathway, dFADD-dependent activation of DREDD promotes cleavage and activation of the transcription factor RELISH. DREDD activation is mediated by homophilic death domain interactions between dFADD and IMD (an immune deficiency gene) that occur downstream of the peptidoglyclan recognition protein PGRP-LC receptor binding to bacterial cell wall components ( Hultmark 2003 ). Homophilic death domain interactions also mediate binding of dFADD to the adaptor dMyD88, a component of the Toll receptor-dependent immune response to fungal infection ( Horng and Medzhitov 2001 ). It will be interesting to determine whether these or other receptor pathways mediate the requirements for dFADD and DREDD during spermatid individualization. How do caspases contribute to spermatid individualization? Testis from flies mutant for any one of the above pathways ( Ark , Dronc , and Hid ; dFadd and Dredd ; and Driceless ) contained cysts in which individualization failed to occur. Interestingly, however, other cysts in the same testis, or from testis of sibling males, carried out individualization apparently normally. Thus, these flies were fertile, though in some cases at a reduced frequency (β2tub-Ark-RNAi and dFadd f02804 / dFadd f02804 ; hid mutants have defects in external genitalia that prevent mating). These observations suggest that no one of these caspase pathways is absolutely required for individualization. The stochastic nature of the defects observed complete failure of individualization in some mutant cysts and apparently normal individualization in others may reflect a requirement for a threshold level of caspase activity, which can be achieved through multiple pathways, or as a result of positive feedback between pathways, in order for a cyst to initiate individualization. Consistent with these possibilities, double mutants between components of the Ark and Dronc caspase cascade and mutants in the dFadd and Dredd cascade were almost completely sterile ( Dredd B118 / Dredd B118 ; Ark CD4 / Ark CD4 , 8% fertile, n = 12) or completely sterile ( Ark CD4 / Ark CD4 ; dFadd f02804 / dFadd f02804 , n = 12), while single mutants for any of these components showed significant fertility ( Dredd B118 / Dredd B118 , 79% fertile, n = 24; Ark CD4 / Ark CD4 , 70% fertile, n = 20; dFadd f02804 / dFadd f02804 , 71% fertile, n = 14). Caspase activity may also participate more directly in processes that mediate encapsulation or cytoplasm elimination. Several observations suggest a role for caspases in at least the latter process. First, in contrast to the situation in wild-type cysts, active DRICE was not effectively swept up into the stunted cystic bulges formed in the presence of caspase inhibitors such as p35 ( Figure 7 ) or in other contexts in which caspase activity was inhibited (β2tub-DIAP1, β2tub-Dcp-1-RNAi, β2tub-Ark-RNAi, β2tub-Dn-DRONC, hid 05014 / H99 , dFadd f02804 / dFadd f02804 ; data not shown). Second, spermatids in cysts with decreased levels of ARK often contained large fingers of excess cytoplasm despite the fact that in some cysts membrane encapsulation occurred apparently normally (see Figure 2 ). Together these observations are interesting because they also suggest that the processes of investment cone movement and spermatid encapsulation can be separated from that of cytoplasm elimination. Investment cones carry out a daunting task. They move apically within a cyst for more than 2 mm, sieving and sweeping an ever-increasing body of cytoplasmic organelles, components of the nuclear membrane, nucleoplasm, and bulk cytoplasm in front of them. Little is known about how investment cones function other than that movement is actin-based and that a number of actin-binding proteins are located in or around these structures ( Hicks et al. 1999 ; Noguchi and Miller 2003 ). It is tempting to speculate that spermatid caspase activity functions, at least in part, to free organelles from preexisting attachments, thus facilitating their apical transport. In this way, caspase activity would provide a permissive environment for investment cone movement and cytoplasm removal. More active roles in promoting membrane remodeling or investment cone-dependent force generation or movement, based on spatially restricted cleavage of cytoskeletal components or other proteins, can also be imagined. The identification of caspase substrates will be important in understanding how caspases regulate this process. Figure 7 Active DRICE Is Eliminated from the Cytoplasm of Wild-Type Spermatids Following Passage of the Individualization Complex, but Not from Spermatids in Which Caspase Activity Has Been Inhibited (A) Cystic bulge from a wild-type cyst stained with active DRICE (red). The cystic bulge (arrowhead) is moving to the left. Active DRICE staining is absent in areas of the spermatid bundle that the individualization complex has passed through and in which excess cytoplasm has been eliminated (arrow). (B) Cystic bulge from a β2tub-p35 cyst. The cystic bulge (arrowhead) is decreased in size, and active DRICE is present in areas of the spermatid bundle through which the individualization complex has moved (arrows). These observations suggest caspase inhibition results in at least a partial failure to eliminate excess cytoplasm, but that this is not necessarily associated with lack of movement of the individualization complex. What is the relationship of our observations in Drosophila to spermatid differentiation in mammals? During step 18 of murine spermatid differentiation, a lobe of cytoplasm accumulates around the spermatid head. It then separates from the spermatid body and is ultimately phagocytosed by the associated Sertoli cell ( de Krester and Kerr 1994 ). Separation of this mass, known as the residual body, removes a large volume of spermatid cytoplasm. It also brings about the encapsulation of each spermatid within a single plasma membrane, since the cytoplasmic bridges linking spermatids are between the membrane compartments defined by the residual bodies. Finally, it severs the connection between the spermatid and the Sertoli cell that supported and anchored it, thereby freeing the now-individualized spermatozoa to enter the seminiferous tubule. Residual bodies show several features commonly associated with apoptosis: their plasma membrane binds Annexin V, and they are phagocytosed by Sertoli cells ( Blanco-Rodriguez and Martinez-Garcia 1999 ), which also phagocytose apoptotic germ cells (compare Shiratsuchi et al. 1997 and references therein). In addition, residual body cytoplasm is condensed and contains elevated levels of CASPASE-1 ( Blanco-Rodriguez and Martinez-Garcia 1999 ) and the proapoptotic BCL-2 family member BAK ( Krajewski et al. 1996 ). These observations suggest that, as in Drosophila , local activation of apoptotic caspase cascades within late-stage spermatids promotes their individualization and elimination of excess cytoplasm. Mice lacking the proapoptotic proteins APAF-1 or the BLC-2 family member BAX are infertile and have dramatic defects in spermatogenesis ( Knudson et al. 1995 ; Honarpour et al. 2000 ; Russell et al. 2002 ). However, these phenotypes are thought to be an indirect consequence of a failure in an earlier, normally occurring postnatal spermatogonial cell death. A test of the importance of caspase activity in mammalian spermatid differentiation will be most directly achieved by determining the consequences of caspase inhibition specifically in these cells. Finally, how is it that elongated spermatids avoid apoptosis in the presence of activated apoptotic caspases for prolonged periods of time? Perhaps the caspase substrates are different from those targeted during apoptosis. But, if so, then what is the basis for the selective targeting? If the targets are the same as those activated during apoptosis, then how is the caspase cascade kept from promoting an apoptotic cell fate? Tight control over the subcellular site of caspase activation (or stabilization of the active caspase), such as we observed with DRONC, provides one possible solution. Others may also exist. In particular, it is important to recognize that while active caspase-specific antibodies recognize caspases that are in the cleaved and therefore activated conformation, these caspases may be kept inactive through interactions with other proteins or as a result of posttranslational modification. Drosophila is a powerful system in which to isolate male-sterile mutants (compare Castrillon et al. 1993 ; Fuller 1993 ; Fabrizio et al. 1998 ). It is likely that an exploration of the relationship between the genes identified by these mutations and the apoptotic regulators described here will provide insight into these questions. Materials and Methods Fly strains and constructs All crosses and stocks were maintained at 25°C. The following fly stocks were used: w1118, Ark CD4 /Cyo ( Rodriguez et al. 1999 ), H99/TM3 ( White et al. 1994 ), hid 05014 /TM3 ( Grether et al. 1995 ), dFadd f02804 /TM6B ( Naitza et al. 2002 ), Dredd B118 /FM7 ( Leulier et al. 2000 ), GMR-Dronc F118E ( Chai et al. 2003 ), and bln 1 /Cyo ( Castrillon et al. 1993 ). Dronc F118E contains a mutation that prevents interaction between DRONC and DIAP1. Thus, Dronc F118E has enhanced activity in vivo ( Chai et al. 2003 ). The P-element vector pβ2Tub contains sequences from the β2tub locus (85D) sufficient to direct testis germline-specific expression. It was generated by removing an XhoI–EcoRI promoter fragment from pGMR ( Hay et al. 1994 ) and introducing in its place a 340-bp fragment from the β2tub locus ( Santel et al. 2000 ), amplified by PCR with the primers 5′-gcg ctc gag atc ctc tat tgc ttc caa ggc acc and 5′-gcg gaa ttc agt tag ggc ccc ttt ttc aca ccg. Coding region fragments for Dn-DRONC ( Hawkins et al. 2000 ) and DIAP1C422Y (which results in stabilization of DIAP1 by blocking its ability to autoubiquitinate [ Yoo et al. 2002 ]) were introduced into pβ2Tub to produce pTub-Dn-DRONC and pTub-DIAP1, respectively. A vector to express double-stranded RNA for ARK was generated as follows. A 900-bp fragment of Ark genomic DNA corresponding to the first exon and intron was amplified using primers 5′-gcg gaa ttc ccg aag agg cat cgc gag cat ata cg and 5′-cgc aga tct ata agg ggt gag tgc tcc cag cgg ctc. This was introduced into pβ2Tub using EcoRI and BglII. A second fragment corresponding to the first exon, but in reverse orientation, was amplified using primers 5′-gcg gcg gcc gc gct aac gca ggg tcc ttc gga ggc and 5′-cgc agg cct aag agg cat cgc gag cat ata cgc. This was introduced into the intermediate described above using NotI and StuI, generating pTub-Ark-RNAi. A similar strategy was used to generate pTub-Dcp-1-RNAi. A 540-bp fragment of Dcp-1 genomic DNA corresponding to the first exon and intron was amplified using primers 5′-ctg ccg gaa ttc ttc gac ata ccc tcg ctg and 5′-cgc gga aga tct gtt gcg cca gga gaa gta g. A second fragment corresponding to the first exon, but in reverse orientation, was amplified using primers 5′-aag gaa aaa a gcg gcc gc cgg aat ggt cga gta gga gaa g and 5′-cgc gga agg cct ttg aaa acc tgg gat tc. Germline transformants of pTub-Dn-DRONC, pTub-DIAP1, and pTub-Ark-RNAi were created using standard procedures. Testis characterized in this paper carried multiple copies of the relevant β2tub expression transgene. These were β2tub-DIAP1, β2tub-p35, and β2tub-Dn-DRONC (four copies); β2tub-Ark-RNAi (three copies); β2tub-Dcp-1-RNAi (six copies). Isolation of the driceless mutant We stained testis from puc E69 /TM6B males ( Martin-Blanco et al. 1998 ) with active DRICE antibodies. These males lacked active DRICE staining, but fully elongated axonemes were present, as visualized by staining with AXO49 antibody. The mutation was mapped to the X chromosome using standard procedures. Immunocytochemistry Conditions for immunocytochemistry and confocal microscopy were as described in Yoo et al. (2002 ). Palloidin-Alexafluor488 (Molecular Probes Inc., Eugene, Oregon, United States) was used at 1:40 concentration to label filamentous actin; TOTO-3 was used for DNA labeling at 1:10,000 (Molecular Probes Inc.). Antibodies were used at the following concentrations: purified rabbit anti-active DRICE (1:50) ( Yoo et al. 2002 ); purified rabbit anti-DRONC (1:100) (this paper); mouse anti-DIAP1 (1:400) ( Yoo et al. 2002 ); mouse anti-AXO49 (1:5,000) ( Bressac et al. 1995 ), rabbit anti-HID (1:1,000) ( Yoo et al. 2002 ), and purified rabbit anti-active DRONC peptide (1:50) and anti-DCP-1 (1:100) (this paper). Anti-DCP-1 antibodies were produced in rabbits and purified using a C-terminal 6× His-tagged version of the DCP-1 p20 subunit as the immunogen. Anti-DRONC antibodies were raised against the C-terminal fragment of the DRONC large subunit (amino acid residues 336–352; EPVYTAQEEKWPDTQTE), and anti-active DRONC-specific antibodies were raised in rabbits using a synthetic nonapeptide corresponding to residues just N-terminal to the DRONC autoactivation cleavage site E352 (EKWPD TQTE ), both of which were conjugated with keyhole limpet hemocyanin as the immunogen (Covance Research Products Inc., Richmond, California, United States). Active DRONC-specific antibodies were purified by sequential protein affinity purification. Antisera were first applied to a column bound with full-length inactive DRONC ( Dronc C318A ) to eliminate antibodies reactive with uncleaved DRONC. The flowthrough was applied to a DRONC large subunit (residues 1–352) affinity column. Bound proteins were eluted using 100 mM glycine (pH 2.5). These antibodies detect the large fragment of active DRONC (cleaved after E352), but do not recognize full-length DRONC (see Figure S2 ). Anti-DRONC antibodies were purified using full-length inactive DRONC ( Dronc C318A ). Western blot analysis to demonstrate binding specificity was carried out with 100 ng of full-length Dronc C318A and Dronc 1–352 . These were detected using purified anti-DRONC peptide (1:100) or purified anti-active DRONC peptide (1:100) antibodies. Male fertility tests Individual male flies were placed with 4- to 5-d-old virgin females in vials for 3 d at 25°C. They were then transferred to fresh vials with four new females and allowed to mate for another 3 d. Males were scored as sterile if they failed to produce progeny by day 6. Western blotting of adult testis Testes extracts were prepared in 50 μl of cell lysis buffer (20 mM HEPES–KOH [pH 7.6], 150 mM NaCl, 10% glycerol, 1% Triton X-100, 2 mM EDTA, 1× protease inhibitor cocktail [Roche, Basel, Switzerland], and 1 mM DTT) from 30–50 adults of the appropriate genotype. Total protein (70 μg) was used for Western blot analysis using rabbit anti-ARK (1:1,000) (generously provided by Lai Wang and Xiaodong Wang) or purified rabbit anti-DCP-1 (1:100). Filters were stripped using Restore Western blot stripping buffer (Pierce Biotechnology, Rockford, Illinois, United States) and reprobed with rabbit anti-full-length DRICE (1:1,000) ( Dorstyn et al. 2002 ) as a loading control. Electron microscopy Testes were dissected from adult 2- to 4-d-old males raised at 25°C and prepared for EM as described by Tokuyasu et al. (1972 ). Thin sections were observed and photographed using a Philips 201 transmission electron microscope (Royal Philips Electronics, Eindhoven, The Netherlands) at 80 kV accelerating voltage. Elongated cysts in which spermatids should have been undergoing or have undergone individualization were identified by their central position in the testis as well as the stage of differentiation of major and mitochondrial derivatives ( Tokuyasu et al. 1972 ). At least two to three testes of each genotype were examined. Supporting Information Figure S1 Inhibition of Dcp-1 Prevents Spermatid Individualization (A) EM section from β2tub-Dcp-1-RNAi testis. Individualization has failed to occur throughout the cyst. (B) The boxed area in M is shown at a higher magnification. Spermatid units sharing a common cytoplasm are outlined by the dashed line. (C) Western blot from wild-type (Wt) and β2tub-Dcp-1-RNAi (Dcpi) testis probed with anti-DCP-1 and anti-DRICE antibodies. DCP-1, but not DRICE, levels are greatly reduced in β2tub-Dcp-1-RNAi testis. (552 KB JPEG). Click here for additional data file. Figure S2 Antibodies Specific for Active DRONC (A) Third instar eye imaginal disc from GMR-Dronc F118E larvae stained with purified anti-DRONC peptide antiserum (green). All cells posterior to the morphogenetic furrow labeled with this antiserum, as expected based on the pattern of GMR (glass multimer reporter)-dependent gene expression ( Hay et al. 1994 ). Eye discs from wild-type larvae showed only very low, uniform levels of staining (data not shown). The inset shows a Western blot probed with purified anti-DRONC peptide antiserum. The first lane was loaded with full-length DRONC mutated in its active site ( Dronc C318A ). The second lane was loaded with a version of DRONC consisting of only residues 1–352. This protein terminates following glutamate-352, the DRONC autoactivation cleavage site, and is equivalent to the large subunit of cleaved and active Dronc. The anti-DRONC antibodies react well with both proteins. (B) Third instar eye imaginal disc from GMR-Dronc F118E larvae stained with anti-active DRONC antiserum extensively purified to select for antibodies that react only with versions of DRONC that have been cleaved at glutamate-352, as described in the Materials and Methods. Only cells in the most posterior region of the eye disc, which are presumably undergoing apoptosis, react with these purified antibodies. The inset shows a Western blot, similar to that in (A), which was probed with the purified active DRONC-specific antibodies. These antibodies react with the glutamate-352-cleaved version of DRONC, but not with full-length DRONC. (374 KB JPEG). Click here for additional data file. Accession Numbers The National Center for Biotechnology Information (NCBI) ( http://www.ncbi.nlm.nih.gov/ ) accession number for p35 is P08160. The FlyBase ( http://flybase.bio.indiana.edu/search/ ) accession numbers of the sequences discussed in this paper are Ark (FBgn0024252), cyt-c-d (FBgn0000408), Dcp-1 (FBgn0010501), dFadd (FBgn0038928), Diap1 (FBgn0003691), Dredd (FBgn0020381), Drice (FBgn0019972), Dronc (FBgn0026404), Grim (FBgn0015946), Hid (FBgn0003997), and Reaper (FBgn0011706).

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534799

The bag or the spindle: the cell factory at the time of systems' biology

Genome programs changed our view of bacteria as cell factories, by making them amenable to systematic rational improvement. As a first step, isolated genes (including those of the metagenome), or small gene clusters are improved and expressed in a variety of hosts. New techniques derived from functional genomics (transcriptome, proteome and metabolome studies) now allow users to shift from this single-gene approach to a more integrated view of the cell, where it is more and more considered as a factory. One can expect in the near future that bacteria will be entirely reprogrammed, and perhaps even created de novo from bits and pieces, to constitute man-made cell factories. This will require exploration of the landscape made of neighbourhoods of all the genes in the cell. Present work is already paving the way for that futuristic view of bacteria in industry.

Review Genomes in the thousands At the date of October 30 th , 2004 the GOLD ( ) site provided links to 1205 ongoing or completed genome programmes, most of which from prokaryotic organisms. More than 40,000 pages are indexed in the WWW Browser Engine Google for the keyword "cell factory". Early in 1999 the European Union launched a research programme on the cell factory ( ), stating that « The concept of the "bio-product" is as old as the knowledge involved in the making of bread, beer, wine or cheese. However, recent techniques and knowledge in molecular biology and genetics mean that living cells – from bacteria to man – are now becoming real "factories". In vast fermentation vats, engineers can direct and control natural metabolism in order to produce all sorts of substances with a high added value: proteins, amino acids, alcohols, citric acid, solvents and even bio-plastics. This industrial mastery of the mechanisms of life opens up revolutionary perspectives in the development of new kinds of medicines, foodstuffs with specific nutritional properties, and biodegradable biochemical products » [ 1 ]. Taken together these pieces of information show that exploration of the potential of microbes as industrial tools is shifting from its former status of traditional biotechnology assets to new high technology devices, meant to perform highly specific tasks, with the highest possible yields and security, and genomics as the background support. We shall not here review the use of microbes in traditional production (bread, beer, cheese and wine have been invented since the origin of the Neolithic, and perhaps even earlier [ 2 ]) but, rather, see how the coupling between knowledge of bacterial genome sequences and new genomics techniques such as expression profiling and biotechnology processes have interacted recently. The numbers of works in the domain is growing exponentially (it counts certainly in the thousands), and we shall therefore restrict our choice to leads that may be used for further reading. In order to limit the scope of this already extensive study, we shall also restrict this review to cells using the standard genetic code, not considering the extremely interesting attempts to reprogramme the code (for a review see [ 3 ]). There is also interesting work developed in vitro, that allows variations on the nature of the building blocks of macromolecules, which will lead to fascinating new aspects of the microbial (artificial) cell factory. We shall not consider this further here (see for example [ 4 - 6 ]). General versus particulars Individual genes, from genomics to metagenomics Genomes are most often viewed as bags of genes. While this is not our personal view – we see genomes as highly organized set of genes, certainly not randomly distributed along the chromosomes [ 7 ], this is certainly a limited, but sufficient view for a large area of biotechnology applications. When reading papers emphasizing the biotechnological potential of bacteria, it is not uncommon to witness sentences like that one: « The completion of the <name of species> genome sequencing project resulted in the discovery of a number of new genes with potential interest for biotechnological applications ». Indeed, many biotechnological procedures still rest (and will rest) on the isolation of individual genes, or series of genes involved in the biosynthesis of a specific compound (this is illustrated in the complex case of coenzyme B12 biosynthesis in Escherichia coli , for example [ 8 ]). Remarkably, it has become a practical fact that it is now much easier (and often significantly less costly) to sequence a genome in its entirety than to isolate a gene of interest and then submit it to mutagenesis for improvement. There are already so many examples of this situation that they cannot be all given here. Many proteases, amylases, lipases and other enzymes of general biotechnological interest (in particular in agro-food industry) are side-products of genes isolated from a variety of genomes [ 9 - 11 ]. As a vivid and lively illustration of the potential of genome programs in the domain of complex molecules such as antibiotics, genes involved in non-ribosomal protein synthesis are continuously collected from genomes, sometimes in an unforeseen way. This was the case, for example with the genome of the entomopathogen Photorhabus luminescens [ 12 ], that possesses a variety of such highly complex « megasynthases » [ 13 ], when it was assumed that most would be present in Gram positives, Streptomyces species in particular [ 13 - 16 ], and certainly not in enterobacteria. With this simple gene family expanding exponentially in parallel with the genome programs trend, we need a focused resource to keep track of important developments: the NRPS/PKS database provides us with an updated resource that tries to keep trace of these interesting by-products of genomics certainly promised to a bright future in the domain of chemistry of fine chemicals [ 17 ]. The genome concept for identifying new genes of biotechnological interest has now been expanded to that of a « metagenome », formed of communities of organisms (often non-cultivatable) in a given environment [ 18 , 19 ]. This revived the interest for biotechnology of fine chemicals [ 20 ], that was proposed for a long time, but remained of limited use. Gene prospecting has already been used to extract interesting variants of genes coding for interesting enzyme activities [ 21 ]. There are voices, however, that go against that particular trend, emphasising that the variety provided by artificial means will be much larger than that conceivably produced during evolution [ 22 ]. However, although chemistry is extremely efficient, some steps, in particular associated with the chirality of molecules, are costly in terms of the process and its yield. In contrast, chirality is an in-built property of life. We can therefore safely speculate that we shall witness in the near future the use of metagenomics for the revival of biotechnology processes in solving expensive bottlenecks in chemical industrial processes. However interesting, these « single-gene » approaches remain conceptually very limited, they only explore the surface of what could be provided by the knowledge of genomes. Furthermore, they often aim at the preparation of a single enzyme, that is meant to be used in a process that does not make use of the adaptation and maintenance potential of living organisms. In short, the cell is not used for what it is in reality, a factory. This is however dramatically changed with the advent of genomics as we shall now see. Functional genomics Progresses in genome sequencing were followed by attempts to better understand how a cell behaves as a whole. The knowledge of complete genome sequences permitted scientists to set up expression profiling techniques that play an ever increasing role in biotechnology [ 23 ]. Indeed the corresponding knowledge can be used, when the genome of a bacterium used in industry is known, to improve its behaviour, stability, yield in production or security [ 24 ]. Many metabolic engineering strategies now use genome-wide methodologies such as DNA sequencing, transcription profiling and global analysis of metabolites. These techniques allow the identification of genetic differences and provide insight into their cellular effects. Inverse metabolic engineering endeavours to map differences between strains with different degree of a certain desired phenotype and subsequent identification of factors conferring that phenotype. Briefly reviewed, expression profiling can be divided into three major branches that each have a particular outcome, and gives a specific knowledge on the organism. The transcriptome With all genes known from a cell it has been possible to create DNA arrays sampling a subfamily or all genes on a variety of physical supports. These arrays can subsequently been used to monitor the level of expression of each gene in a particular condition. While this transcriptome approach is widely used, its interpretation is still a matter of research [ 25 , 26 ] but is continuously improving [ 26 , 27 ]. Indeed the very fact that an experiment has, embedded in the data, a collective behaviour is until now rarely used as such, while multifactorial analysis techniques would certainly provide new insights [ 28 ]. However transcriptome expression profiling has already had considerable impact in biotechnology. A case in point is improvement of lysine production, despite the fact that this amino acid has previously been manufactured using bacteria for more than 40 years [ 29 ]. The proteome The second level of expression profiling is, of course, that of the direct access to the gene products, the proteins. Two-dimensional gel electrophoresis has been developed for thirty years, with considerable success, but it is still extremely limited by the lack of reproducibility of 2-D gel patterns [ 30 ]. Other methods try to by-pass the 2-D gel step by direct coupling of high-performance mass spectrometry instrumentation with highly efficient chromatographic and electrophoretic separations [ 31 ]. While it is a method of choice for qualitative studies, the latter however are usually difficult to use when one wishes to compare the outcome of several experiments. 2D-gel electrophoresis appears therefore to have still a bright future in the domain. Proteomic studies are complementary to transcriptome analysis [ 10 , 32 - 35 ], because translation efficiency is variable [ 36 , 37 ], and because mRNA stability can also vary [ 38 , 39 ]. They are just beginning to demonstrate their importance in the study of complexes that organize the cell factory [ 40 ]. The metabolome Fermentation processes often aim at producing a given metabolite. The major problem facing industry in this domain is to improve the production yield, often for products that do not have a very high added-value (as opposed to proteins used in medicine, for example), in a background that has already been improved by generations of mutational improvement. Furthermore, many metabolites have to be as pure as possible, trying to prevent contamination by side-products that may be toxic [ 41 ]. It is therefore of importance to be able to analyse the whole metabolite set of cultures growing in a variety of conditions, and to relate it to gene expression [ 42 , 43 ], so that educated guesses may be explored for improvement of the processes of interest [ 44 ]. While there is currently no efficient large-scale way to systematically monitor metabolites in cells (Nuclear Magnetic Resonance, for example, is limited by its poor sensitivity to those metabolites that are at a high concentration in the cell and Mass Spectrometry needs preliminary purification steps to sort out the zoo of molecules generated in a cell) "metabolomics" is one of the most fashionable "omics" at present [ 45 , 46 ]. It has already been used efficiently in the case of focused production, such as synthesis of antibiotics [ 47 ]. There is little doubt that this domain will expand considerably in the near future [ 45 ]. Gene expression and genome organisation The traditional way for biotechnology to improve its processes was to select mutants having interesting properties (in terms of stability, resistance to foreign agents such as viruses, and of course metabolite or biomass production). This required long and tedious procedures where relevant features were usually gradually improving [ 48 ]. However these slow changes had a remarkable, although unobtrusive, consequence. Rather than involving isolated mutations, in many cases a coordinate set of mutations was improving the quality of production. Unfortunately, in the absence of direct access to the genome sequence, it was not possible either to identify or to tell those which were important and those which were dispensable. Furthermore, even when the sequence is known it is far from being straightforward to tell, from the differences observed with the parent strains, what are the important ones. Genomics, with all its "omics" complements, nevertheless completely changed the picture, and it is now possible to optimise production knowingly, using molecular targets that are directly extracted from knowledge of the genome. This has been applied for example in the case of the much studied Corynebacterium glutamicum [ 49 ]. Further progress is certainly possible. It is important to try to understand whether genomes are simply random collection of genes, or whether they show rules, that might be exploited for using cells as factories. Remarkably, at least in bacteria, the organisation of the genome reflects some kind of optimisation of gene expression [ 50 ]. Genes do not work in isolation, and their products, even in bacteria, are likely to be compartmentalised. The study of the landscape of all neighborhoods of a gene (proximity in the chromosome, codon usage bias, phylogeny of its products, electric charge, amino acid composition, participation in complexes, and even a neighborhood benefiting from the expertise of other scientists, such as the co-occurrence of gene names in a same article – "in biblio") provides a systemic view that must be used to optimise the behavior of the cell [ 51 ]. While this has not yet, to our knowledge, be taken into consideration for improvement of production by industrial strains, it is more than likely that this will be performed in the near future (in fact it is likely that optimisation of the global properties of gene or gene islands text has already been used for the industrial production of proteins, but because protection by patenting is difficult, if this has been done the corresponding know-how is likely to be protected by secrecy). Regulation of gene expression is also of major interest. It must be understood however that this feature of life is evolving much more rapidly than catalytic or structural components of the cell. One should therefore be cautious in extrapolating knowledge from an organism to another one. Theoretical studies, associated to validation experiments have now begun to decipher the rules that govern regulation of gene expression, and it is certainly already possible to construct subtle regulation systems [ 52 ], that are much more sophisticated than the ubiquitous on/off systems using positive or negative control of transcription [ 53 - 56 ]. Among the recent discoveries that will play a considerable role in genome-mediated control of gene expression is that of riboswitches [ 57 ]. This mode of control seems to be ubiquitous, but significantly different between Gram negatives and Gram positives (where it appears to be more widely spread). It is still early to have an exact idea of the impact on industrial processes, but the very fact that many coenzymes (vitamins) biosynthetic pathways are controlled using riboswitches warrants further exploration. In the same way quorum-sensing has much to say for the control of gene expression at high cell density [ 58 ]. Until now this general control process – which is still under investigation – has not been explicitly used to control production in cell factories. It seems likely that, once deciphered in its details, it will be a parameter introduced in large-scale productions. The recent serendipitous discovery that borate was involved in the construction of the mediator AI-2 (autoinducer-2) demonstrates however that unexpected features should always be considered as a possibility when a process does not go entirely as planned [ 59 ]. Model cell factories Many bacteria have been used as cell factories. In most cases this was to produce small molecules (in particular antibiotics, vitamins and amino acids). These bacteria were usually the result of continuous improvement using standard mutagenesis/screening techniques of bacteria isolated in the wild. Streptomyces species, for example, account for a large number of antibiotics production. Streptomyces lividans [ 60 ], Corynebacterium glutamicum [ 61 ], Bacillus subtilis [ 24 ], Escherichia coli [ 62 ], Zymomonas mobilis [ 63 ], to give a few names, are used not only as models but also as large-scale production factories. Perhaps the largest scale production fermenters (often 150 m 3 ) are now growing Xanthomonas campestris , used as a supplement not only in food, but also in dentrifrice, housekeeping products and even in painting, to prevent it from making drops [ 64 ]. In the past decades even larger fermenters were used to produce biomass or ethanol, a trend that was abandoned with cheap oil prices, but that will most probably resume its older importance as the price of oil rises sharply. Among those, most bacteria were chosen for their industrial purpose, as a prime intention. As a consequence, until the advent of genome programs, they were only known for their physiological and physico-chemical properties in fermenters, with limited knowledge of their genetic properties. This was such an inconvenience that, very early on, industry explored the usability of the universal model E. coli as a ubiquitous cell factory. This trend was particularly emphasized as soon as genetic engineering techniques were developed, as early as in the early eighties, with the construction of new vectors for expressing foreign proteins at will (e.g. [ 65 ]). Mid-eighties many proteins of medical interest were produced using E. coli as the factory. This is still so today, with only little shift to the use of other bacterial species as factories. This was initially limited to high added-value products, allowing for very expensive purification steps and compliance to very tight regulations. In quite a few cases however E. coli and sometimes other model bacteria, in which appropriate genes were introduced either on plasmids or in the chromosome, was highly efficient in producing low cost metabolites [ 66 ]. Escherichia coli is even used in the production of amino acids, in industrial quantities, a production that was initially reserved to specific mutants of species that had been slowly improved over the years. GRAS organisms, such as AT-rich Gram positives, such as B. subtilis , are much more difficult to use, except for biomass production, or secreted proteins, because heterologous protein expression is difficult there. This has been understood after the genome was deciphered, as a consequence of the poorly versatile control of translation initiation (lack of ribosomal S1 protein in particular [ 67 ]), as well as of the large number of proteases harbored by the organism [ 68 ]. Taken together these observations suggest the rational choice of a new organism that would play the role of a ubiquitous cell factory. This organism should have several properties. It should be non pathogenic, and its envelope should not trigger inflammation reactions in animals (Man included). It should be easily transformable and allow recombination with linear DNA, with as little matches needed for recombination as possible. It should grow fast at temperature compatible with the size of fermenters (metabolic activity heats up the medium), and it should reach high cell density. More specialized views, adapted to specific productions will also be considered at some point, but it will be interesting to witness the choice of new model bacteria in the new era of the cell-factory. Conclusions Bacteria have been used as factories for a long time. A first step to rationalize this approach has been met with the first genetic engineering of E. coli , producing heterologous proteins. As we now sequence a new genome every third day or so, it is clear that we will be able soon to understand the core of bacterial life, and probably be able to choose new models, better suited to the goals of industry. However we must always remember that life is full of surprises, even in the best explored domains: who would have thought that E. coli communicates with its kins using the boron atom? Discovery cannot be planned, and the most surprising observations, that have the most considerable consequences in terms of applications of research, come from studies that are totally academic in nature (who would have thought that the discovery of RNAi would have come from the study of variagation in petunia flowers?). One should not mix up domains: discovery first, and this needs a considerable degree of freedom of choice in the topics explored, and then, naturally, one can think of applications of research. Constructing the best of bacterial cell factory would be such a goal. List of abbreviations GRAS: Generally Recognized As Safe RNAi: RNA interference Authors' conflict of interest The author declares that he has no competing interests.

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509419

P2 receptor mRNA expression profiles in human lymphocytes, monocytes and CD34+ stem and progenitor cells

Background Extracellular nucleotides (ATP, ADP, UTP and UDP) exert a wide range of biological effects in blood cells mediated by multiple ionotropic P2X receptors and G protein-coupled P2Y receptors. Although pharmacological experiments have suggested the presence of several P2 receptor subtypes on monocytes and lymphocytes, some results are contradictory. Few physiological functions have been firmly established to a specific receptor subtype, partly because of a lack of truly selective agonists and antagonists. This stimulated us to investigate the expression of P2X and P2Y receptors in human lymphocytes and monocytes with a newly established quantitative mRNA assay for P2 receptors. In addition, we describe for the first time the expression of P2 receptors in CD34 + stem and progenitor cells implicating a potential role of P2 receptors in hematopoietic lineage and progenitor/stem cell function. Results Using a quantitative mRNA assay, we assessed the hypothesis that there are specific P2 receptor profiles in inflammatory cells. The P2X 4 receptor had the highest expression in lymphocytes and monocytes. Among the P2Y receptors, P2Y 12 and P2Y 2 had highest expression in lymphocytes, while the P2Y 2 and P2Y 13 had highest expression in monocytes. Several P2 receptors were expressed (P2Y 2 , P2Y 1 , P2Y 12 , P2Y 13 , P2Y 11 , P2X 1 , P2X 4 ) in CD34+ stem and progenitor cells. Conclusions The most interesting findings were the high mRNA expression of P2Y 12 receptors in lymphocytes potentially explaining the anti-inflammatory effects of clopidogrel, P2Y 13 receptors in monocytes and a previously unrecognised expression of P2X 4 in lymphocytes and monocytes. In addition, for the first time P2 receptor mRNA expression patterns was studied in CD34 + stem and progenitor cells. Several P2 receptors were expressed (P2Y 2 , P2Y 1 , P2Y 12 , P2Y 13 , P2Y 11 , P2X 1 , P2X 4 ), indicating a role in differentiation and proliferation. Thus, it is possible that specific antibodies to P2 receptors could be used to identify progenitors for monocytes, lymphocytes and megakaryocytes.

Background Extracellular nucleotides (ATP, ADP, UTP and UDP) exert a wide range of biological effects in blood cells mediated by multiple ionotropic P2X receptors and G protein-coupled P2Y receptors [ 1 - 3 ]. So far, the P2Y family is composed of eight cloned and functionally distinct subtypes (P2Y 1 , P2Y 2 , P2Y 4 , P2Y 6 , P2Y 11 , P2Y 12 , P2Y 13 , P2Y 14 ) [ 4 , 5 ]; the P2X family is composed of seven cloned subtypes (P2X 1 -P2X 7 ) [ 6 , 7 ]. We have previously quantified P2 receptor mRNA expression in platelets (representing megakaryocyte expression), and demonstrated a selective expression of the ADP receptors P2Y 12 and P2Y 1 , together with the ATP receptor P2X 1 [ 8 ]. This is consistent with the clinical effect of the P2Y 12 antagonist clopidogrel for the prevention of myocardial infarctions in patients with acute coronary syndromes [ 9 , 10 ]. However, virtually every hematopoietic cell is responsive to nucleotides [ 2 ]. Because effects as different as proliferation, differentiation, chemotaxis and release of cytokines are regulated by nucleotides, they could play a role in the atherosclerotic inflammatory process. Human lymphocytes, monocytes and macrophages constitute an important line of defence upon infection and exposure to inflammatory stimuli [ 11 ]. Circulating blood monocytes become activated, migrate to tissues, and undergo differentiation into macrophages during inflammation [ 12 ]. Monocytes have been shown to express several P2Y receptors and up-regulation of P2X 7 receptor mRNA in monocytes has been observed upon cell differentiation to macrophages [ 13 , 14 ]. Although pharmacological experiments have suggested the presence of several P2 receptor subtypes on monocytes and lymphocytes, some results are contradictory [ 1 , 2 ]. Few physiological functions have been firmly established to a specific receptor subtype, partly because of a lack truly selective agonists and antagonists. This stimulated us to investigate the expression of P2X and P2Y receptors in human lymphocytes and monocytes with a newly established quantitative mRNA assay for P2 receptors [ 8 , 15 ]. In addition, we describe for the first time the mRNA expression of P2 receptors in CD34 + stem and progenitor cells implicating a potential role of P2 receptors in hematopoietic lineage and progenitor/stem cell function. Results and Discussion Our previous studies of P2 receptor mRNA expression in man with real-time PCR has shown a good resemblance with pharmacological and physiological experiments in vascular smooth muscle cells, endothelial cells and platelets [ 8 , 15 ]. It is therefore likely that our present mRNA findings in inflammatory, progenitor and stem cells are physiologically relevant. The lack of selective agonists and antagonists for most of the receptor subtypes combined with the absence of studies focused on several of the more recently cloned receptors makes the findings important. Furthermore, no pharmacological studies have been made on CD34 + stem and progenitor cells. Expression of P2Y receptors in lymphocytes In lymphocytes, all the target genes P2Y 1 , P2Y 2 , P2Y 4 , P2Y 6 , P2Y 11 , P2Y 12 , and P2Y 13 could be detected (n = 6). To illustrate expression of the P2 receptors relative to each other the P2Y 1 receptor was used as calibrator for the others, i. e. the other receptors were expressed as a ratio of the P2Y 1 . Among the P2Y receptor subtypes the P2Y 12 and P2Y 2 had highest expression (Figure 1A ). The lowest expressed P2Y receptor was P2Y 4 . Figure 1 Relative P2 gene expression in lymphocytes. A, Bar graph shows relative P2Y 1 , P2Y 2 , P2Y 4 , P2Y 6 , P2Y 11 , P2Y 12 and P2Y 13 receptor gene expression normalized to GAPDH. B, Bar graph shows relative P2X 1 , P2X 4 and P2X 7 receptor gene expression normalized to GAPDH. P2Y 1 was chosen to be calibrator. Extracellular nucleotides and their P2 receptors are involved in the regulation, proliferation but also apoptosis and cell death in lymphocytes and monocytes [ 3 , 16 ]. Previous studies have shown that ATP, ADP, UTP and UDP stimulate phospholipase C and Ca 2+ release from intracellular stores, that fits well with the highly expressed P2Y 2 receptor, together with the lesser expressed P2Y 1 and P2Y 6 receptors. ATP and ADP, but not UTP, can also increase cAMP [ 17 ]. This is in agreement with the P2Y 11 receptor that had the third highest mRNA expression. The most interesting finding was that P2Y 12 had the highest expression among the P2Y receptors in lymphocytes. It is not likely that this is the result of platelet contamination, because platelets contain very low amounts of RNA. To the best of our knowledge, there are no studies that have examined the effects of P2Y 12 on lymphocytes, even though selective antagonists exist. It is expected to inhibit cAMP generation and may activate lymphocytes. This could explain the antiinflammatory effect of clopidogrel. Clopidogrel is a P2Y 12 antagonist used in the clinic as a platelet aggregation inhibitor that reduces thrombotic cardiovascular events such as myocardial infarctions. However, it has also been shown to reduce CRP, even though aspirin in antiplatelet doses lacks this effect [ 18 ]. This effect may be mediated via P2Y 12 receptors in lymphocytes. Expression of P2X receptors in lymphocytes The most abundant P2X receptor in lymphocytes was the P2X 4 receptor. As showed in Figure 1B , the expression of P2X 4 was 3.2 times higher than P2Y 1 . The expression of P2X 4 was significantly higher than the expression of the other P2X receptors; P2X 1 (P < 0.001) and P2X 7 (P < 0.01). Selective pharmacological tools to discriminate between P2X receptors are scarce. Nevertheless, several studies have suggested the importance of P2X 7 in lymphocyte regulation. However, B lymphocytes stimulated with ATP do not undergo the typical increase in permeability up to 900 Da that is typical for the P2X 7 receptor. On the other hand, P2X 7 mediated effects on Ba 2+ and ethidium influx, phospholipase D activity and shedding of L-selectin have been blocked by the P2X 7 selective antagonist KN-62 in human lymphocytes [ 19 ]. Thus it is a surprising finding that the P2X 4 receptor was the highest expressed subtype in lymphocytes at the mRNA level. Even though we have demonstrated that more than 90% of the preparation consists of lymphocytes (see methods), it is possible that a small contamination of monocytes may have influenced the results, at least regarding P2X 4 receptor mRNA expression, because of its high expression levels in monocytes. P2X 4 receptors have indeed been demonstrated at the protein level in human B lymphocytes by confocal immunohistochemistry, in which P2X 1 , P2X 4 and P2X 7 were detected at the protein level [ 20 ]. However, the P2X 4 receptor staining was the most variable of the P2X receptors with weak to moderate levels of staining in a large proportion of cells in three patients and weak levels in only a minority of the cells from the other three patients examined [ 20 ]. Expression of P2Y receptors in monocytes Again, the P2Y 1 expression was used as calibrator for the others, i. e. the other receptors were expressed as a ratio of the P2Y 1 . Among the P2Y receptors, the P2Y 2 , P2Y 13 and P2Y 11 had highest expression (Figure 2A , n = 6). The presence of P2Y receptor mRNA in monocytes and lymphocytes is in agreement with previous studies using regular RT-PCR [ 21 ]. Figure 2 Relative P2 gene expression in monocytes. A, Bar graph shows relative P2Y 1 , P2Y 2 , P2Y 4 , P2Y 6 , P2Y 11 , P2Y 12 and P2Y 13 receptor gene expression normalized to GAPDH. B, Bar graph shows relative P2X 1 , P2X 4 and P2X 7 receptor gene expression normalized to GAPDH. P2Y 1 was chosen to be calibrator. Extracellular nucleotides stimulate interleukin secretion, iNOS-generation in monocytes, are involved in differentiation, cytotoxicity and killing of pathogens. All monocyte/macrophage cell lines express P2Y receptors coupled to IP 3 generation and intracellular Ca 2+ release, but the individual subtypes have not been investigated in detail in monocytes [ 2 , 3 ]. However, both ATP and UTP are active agonists, which is in agreement with the highest mRNA expression of the ATP/UTP receptor P2Y 2 (Fig 2 ). ATP mediated increase in cAMP has suggested the presence of P2Y 11 , with a suggested role in maturation of human monocyte-dendritic cells [ 22 ]. A relatively high expression of P2Y 11 was confirmed in our experiments. Interestingly, the P2Y 13 had even higher mRNA levels. To our knowledge, no experiments have addressed the presence of this cAMP inhibitory ADP receptor in monocytes. In fact, it has been an unresolved issue in what tissue this receptor is expressed. High levels in the spleen could be in agreement with monocyte expression [ 23 ]. Thus, the presence of P2Y 2 and P2Y 11 receptors are confirmed as expected, with the interesting addition of P2Y 13 receptors. Future experiments addressing the physiological role of P2Y 13 receptors in monocytes are needed. Expression of P2X receptors in monocytes Early studies demonstrated that ATP activates a receptor on macrophages that increase cell permeability eventually leading to cell death [ 2 , 3 ]. P2X 7 receptor transfection confers susceptibility to ATP-dependent permeabilization and ATP-resistant clones lack the P2X 7 receptor, demonstrating that it is present on macrophages and necessary for permabilization. However, it is not known whether P2X 7 is the only constitutive subunit or if it assembles with other subunits. As showed in Figure 2B , P2X 4 was by far the highest expressed P2 receptor in monocytes and the P2X 1 (P < 0.01) and P2X 7 (P < 0.01) had lower levels. Thus, unexpectedly the P2X 7 receptor was not the highest expressed P2X receptor in monocytes. This is in agreement with patch-clamp experiments suggesting that other P2X receptors are involved [ 24 ]. Interrelation of these experiments has suggested the contribution of P2X 4 receptors, which is supported by our findings [ 25 ]. It should be noted that all the three P2X receptors were expressed at very high levels compared to other cell types (30-fold more than the calibrator gene for P2X 4 and 6–7-fold more for P2X 1 and P2X 7 ). A physiological role for all three subtypes can therefore be expected. Expression of P2 receptors in CD34 + stem and progenitor cells CD34 + stem and progenitor cells are receiving an increasing attention because of their extensive self-renewal and multilineage differentiation ability making them attractive for cellular therapy [ 26 ]. Knowledge of their P2 receptor expression could be used for directing differentiation or for further subtype selection of early progenitors types. There are no previous pharmacological or expression studies of P2 receptors on human CD34 + stem and progenitor cells. We found expression of several P2Y receptors, especially P2Y 1 and P2Y 2 (Figure 3A , n = 3). This indicates that both ATP and UTP are agonists for CD34 + stem and progenitor cells and may stimulate IP 3 and intracellular Ca 2+ release. Figure 3 Relative P2 gene expression in CD34 + stem and progenitor cells. A, Bar graph shows relative P2Y 1 , P2Y 2 , P2Y 4 , P2Y 6 , P2Y 11 , P2Y 12 and P2Y 13 receptor gene expression normalized to GAPDH. B, Bar graph shows relative P2X 1 , P2X 4 and P2X 7 receptor gene expression normalized to GAPDH. P2Y 1 was chosen to be calibrator. Among the P2X receptors the P2X 1 receptor had the highest expression followed by P2X 4 (P2X 1 had significantly higher expression than P2X 7 , P < 0.05) (Figure 3B , n = 3), suggesting a potential role of these receptors in regulation of stem and progenitor cells. P2Y 1 , P2Y 2 and P2X 1 receptors have all been shown to stimulate proliferation, but also to be able to mediate apoptosis [ 26 ]. Such roles could be of major importance in the highly proliferative CD34 + stem and progenitor cells. Antagonists or agonists of these receptors could be used to control their differentiation or proliferation. Conclusions The P2X 4 receptor had the highest mRNA expression in lymphocytes and monocytes. Among the P2Y receptors, P2Y 12 and P2Y 2 had highest expression in lymphocytes, while the P2Y 2 and P2Y 13 had highest expression in monocytes. The most interesting findings were the high mRNA expression of P2Y 12 receptors in lymphocytes potentially explaining the anti-inflammatory effects of clopidogrel, P2Y 13 receptors in monocytes and a previously unrecognised expression of P2X 4 in lymphocytes and monocytes. In addition, for the first time P2 receptor mRNA expression patterns have been studied in CD34 + stem and progenitor cells. Several P2 receptors were expressed (P2Y 2 , P2Y 1 , P2Y 12 , P2Y 13 , P2Y 11 , P2X 1 , P2X 4 ), indicating a role in differentiation and proliferation. Thus, it is possible that specific antibodies to P2 receptors could be used to identify progenitors for monocytes, lymphocytes and megakaryocytes. Methods The studies were approved by the local Ethics Committee of the Lund University and were conducted according to the principles of the Declaration of Helsinki. Preparation of monocytes and lymphocytes Peripheral blood was drawn from each of 6 healthy volunteers (after informed consent) into heparin vials. The mononuclear cells were isolated by density gradient centrifugation on Lymphoprep™ (Axis Shield Poc AS, Oslo, Norway) at 605 g for 30 minutes. The lymphocytes and monocytes thus obtained were washed three times in RPMI 1640 medium with L-glutamine (Gibco/BRL, Life Technologies Ltd, Paisleys, Scotland) and 0.1% human serum albumin (Sigma, St Louise, MO, USA), (medium), and centrifuged each time at 605 g for 5 minutes. The fraction of lymphocytes and monocytes obtained according to this procedure was resuspended in medium with 15% normal human serum (NHS) added to a concentration of 4 × 10 6 cells/ml. Flow cytometry (Epics XL-MCL Beckman-Coulter, Florida, USA) analysis on these cells by detection of cell surface CD14 and CD45 showed that approximately 10% of the cells were monocytes. 800 μl of this cell-suspension was plated on a chamber slide 4 well glass slide (Nalge Nunc International, IL, USA) at 37°C in an atmosphere containing 5% CO 2 and 96% humidity for 1 h in order for the monocytes to adhere. Nonadherent cells were removed by washing three times with medium. Flow cytometry analysis of these nonadherent cells showed that at least 90% were lymphocytes, and were therefore used as source of lymphocytes. The cells attached to the glass slides (<90% monocytes as assessed by flow cytometry) were detached by adding first PBS and then 0.5 mM EDTA-PBS for 3 min in room temperature. Preparation of CD34 + stem and progenitor cells Bone marrow samples were obtained from healthy volunteers (n = 3), after informed consent, using guidelines approved by the Ethical Committee, Lund University. Mononuclear cells were isolated by density gradient centrifugation (Ficoll-Paque; Pharmacia, Uppsala, Sweden). CD34 + cells were isolated by 2passages through magnetic columns (MidiMacs;Miltenyi Biotec, Bergish Gladbach, Germany) by using a hapten-conjugated CD34 antibody (Qbend/10) and an antihapten antibody conjugated to magnetic beads (CD34 + isolation kit; Miltenyi Biotec). CD34 expression was analyzed by immunostaining with a FACSCalibur flow cytometer (Becton Dickinson) by using the CellQuest program (Becton Dickinson) and the purity of isolated populations was reproducibly > 95% [ 27 ]. RNA extraction Total cellular RNAs were extracted using TRIzol reagent (Gibco BRL, Life Technology) according to the supplier's instructions, dissolved in diethyl-pyrocarbonate (DEPC) treated water and stored at -70°C until used. Quantitative analysis of P2 receptors by real-time reverse transcription polymerase chain reaction TaqMan Reverse Transcription Reagents Kit was used to transcribe mRNA into cDNA. Real-time PCR were performed by means of a PRISM 7700Sequence Detector as described previously [ 8 , 15 , 28 , 29 ]. Oligonucleotide primers and TaqMan probes were designed using the Primer Express software, based on sequences from the GenBank database [ 8 , 15 ]. Constitutively expressed GAPDH were selected as endogenous control to correct for potential variation in RNA loading or efficiency of the amplification reaction. Previous analysis showed that amplification efficiencies were almost identical for GADPH and the following receptor mRNAs: P2Y 1 , P2Y 2 , P2Y 4 , P2Y 6 , P2Y 11 , P2Y 12 , P2Y 13 , P2X 1 , P2X 4 , and P2X 7 normalized to GAPDH [ 8 , 15 ]. To confirm equal amplification efficiencies, we used the criterion of a regression slope of less than 0.1 for each gene normalized to GAPDH. This confirms that we could use the comparative C T method for the relative quantification of target without running standard curves on the same plate (Perkin-Elmer Applied Biosystems Inc; User Bulletin No. 2, December 1997). The amount of target and endogenous reference was determined from the comparative C T method. The target gene normalized to GAPDH was expressed as ΔC T (C T of target gene minus C T of GAPDH). P2Y 1 was arbitrarily chosen to be the calibrator in the comparative analysis and is expressed as ΔC TP2Y1 (C T of target minus C T of GAPDH for P2Y 1 ). The normalized calibrated value is given by the equation 2 -ΔΔCt , where ΔΔC T is ΔC T -ΔC TP2Y1 . To further verify the specificity of PCR assays, the PCR was performed with non-reverse-transcribed total cellular RNA and samples lacking the DNA template. No significant amplifications were obtained in any of these samples (data not shown). Drugs Unless otherwise stated, all reagents and drugs were purchased from Sigma Chemical Corp, St. Louis, MI, USA. PCR consumables were obtained from Perkin-Elmer Applied Biosystems Inc, Foster City, CA, USA. Statistical methods Data are expressed as mean and standard error of the mean (SEM) unless otherwise stated. n indicates the number of subjects that were tested. Statistical analysis of the normalized C T values (ΔC T ) was performed with a one-way ANOVA, followed by a multiple comparison post test (Tukey's test) using GraphPad InStat version 3.00 (GraphPad Software Inc., USA). Significant differences were considered at P < 0.05 (two-tailed test). Authors' contributions LW designed the study, carried out the RNA isolation and real-time PCR, and wrote the manuscript. SEWJ supervised the isolation of CD34 + stem and progenitor cells, and participated in writing the manuscript. AB supervised the isolation of monocytes and lymphocytes, and participated in writing the manuscript. DE conceived the study, guided throughout the study, and wrote the manuscript. All authors read and approved the final manuscript.

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546332

DNA Recombination and Repair—A New Twist to RecA Function

Molecular motors harness the energy of ATP (or GTP, a related energy currency) and transform it into mechanical force. Well-known examples of motors include myosin and dynein, proteins that use ATP to ferry intracellular cargo along fibers made of actin or tubulin proteins. The ATP-dependent assembly of actin or tubulin fibers itself can work as a motor: for instance, the march of white blood cells toward pathogens is powered by the growth of actin filaments pushing against the cells' membranes. In all cases, coherent motion implies a coordinated and polarized use of energy. Now, Julia Cox, Oleg Tsodikov, and Michael Cox present evidence indicating that filaments of the bacterial RecA protein, long known for their role in homologous recombination and DNA repair, have properties reminiscent of a molecular motor as well. RecA filaments consist of DNA helices lined with RecA protein. RecA filaments invade a region of double-stranded DNA with similar nucleotide sequence, displacing one strand to pair with the other. Strand invasion can lead to a re-assortment—known as recombination—of DNA regions on either side of the shared sequence. It can also initiate the repair of DNA lesions during replication—the process by which a DNA molecule is copied to make two. RecA is also an ATPase, an enzyme capable of hydrolyzing (breaking down) ATP, when bound to DNA. RecA uses ATP to carry out strand exchange over long sequences and impose direction to the exchange, to bypass short sequence heterogeneities, and to stall replication so DNA lesions can be mended. But how RecA molecules within a filament coordinate and organize their activities to carry out these functions has remained obscure. Cox et al. addressed this problem in the test tube, by examining RecA filaments grown from mixing RecA protein with DNA. Previous experiments have shown that filament assembly spreads rapidly in the 5′-to-3′ direction once the first RecA molecule is loaded onto DNA. At the same time, ATP hydrolysis causes the release of RecA from DNA, but the exact rate of RecA dissociation is not known. Experiments suggest, however, that RecA molecules only dissociate from DNA when they are at the fiber's 5′ end, while ATP hydrolysis occurs all along its length. A RecA filament with every sixth molecule in red Under their experimental conditions, the authors found that a RecA molecule hydrolyzed 20 ATPs per minute—or one ATP every three seconds. If ATP hydrolysis occurred randomly in the fiber, one would expect a RecA molecule to dissociate about once every 1.5 seconds, or 40 RecA molecules to dissociate per minute. But this is not what the authors found—instead they estimated the dissociation to be at a rate of 120 RecA molecules per minute. Hence, six RecA molecules dissociate from a fiber in the time—three seconds—it takes for an individual RecA molecule to burn one ATP. This implies a filament organization in which every RecA molecule hydrolyzes ATP in synchrony with the sixth RecA molecule to its left and the sixth RecA molecule to its right. The authors note that there are approximately six RecA molecules per helical turn in a RecA filament. They propose that the RecA molecules hydrolyzing ATP at any given moment are aligned in a “stripe” that runs along the side of the filament. This stripe of ATP hydrolysis moves around the fiber in a repeating pattern of six steps. At the 5′ end of the fiber, ATP hydrolysis leads to RecA release. But in the middle of the fiber, it could work as a rotary motor, with the power to wind or unwind DNA and drive strand invasion through difficult passages of damaged DNA.

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534798

Dietary effect of pomegranate seed oil rich in 9cis, 11trans, 13cis conjugated linolenic acid on lipid metabolism in obese, hyperlipidemic OLETF Rats

Conjugated fatty acid, the general term of positional and geometric isomers of polyunsaturated fatty acids with conjugated double bonds, has attracted considerable attention because of its potentially beneficial biological effects. In the present study, dietary effect of pomegranate seed oil rich in punicic acid (9 cis , 11 trans , 13 cis -conjugated linolenic acid; 9c, 11t, 13c-CLNA) on lipid metabolism was investigated in obese, hyperlipidemic Otsuka Long-Evans Tokushima Fatty (OLETF) rats. After 2 weeks feeding period, OLETF rats revealed obesity and hyperlipidemia compared with their progenitor LETO rats. Feeding of the diet supplemented with 9% safflower oil and 1% pomegranate seed oil (9c, 11t, 13c-CLNA diet) did not affect abdominal white adipose tissue weights and serum lipid levels compared with the diet supplemented with 10% safflower oil (control diet) in OLETF rats. However, the accumulated hepatic triacylglycerol was markedly decreased by 9c, 11t, 13c-CLNA diet in OLETF rats. Activities of hepatic enzymes related to fatty acid synthesis and fatty acid β-oxidation were not altered by 9c, 11t, 13c-CLNA diet. Levels of monounsaturated fatty acid (MUFA), major storage form of fatty acid, in serum triacylglycerol were markedly higher in obese, hyperlipidemic OLETF rats than in lean LETO rats. In addition, 9c, 11t, 13c-CLNA diet significantly decreased MUFA levels in OLETF rats. This is the first study showing that 9c, 11t, 13c-CLNA suppresses delta-9 desaturation in vivo, and we suggest that the alleviation of hepatic triacylglycerol accumulation by 9c, 11t, 13c-CLNA diet was, at least in part, attributable to the suppression of delta-9 desaturation in OLETF rats.

Background Conjugated fatty acid (CFA) is the general term of positional and geometric isomers of polyunsaturated fatty acids with conjugated double bonds. It has been reported that conjugated linoleic acid (CLA), the CFA form of linoleic acid, has favorable physiological effects, such as anti-atherosclerosis, anti-obesity, anti-tumor, and anti-hypertension [ 1 - 9 ]. There are also other types of CFA in some plant seed oils. Punicic acid (9 cis , 11 trans , 13 cis -conjugated linolenic acid; 9c, 11t, 13c-CLNA) is contained about 72% in pomegranate seed oil [ 10 ]. α-Eleostearic acid (9 cis , 11 trans , 13 trans -CLNA) is contained in bitter gourd oil and tung seed oil about 60% and 70%, respectively [ 10 , 11 ]. Catalpa seed oil also contains catalpic acid (9 trans , 11 trans , 13 cis -CLNA) about 31% and pot marigold seed oil contains calendic acid (8 trans , 10 trans , 12 cis -CLNA) about 33% [ 10 ]. There are some studies showing that mixtures of CLNA isomers, prepared by alkaline isomerization of α-linolenic acid or plant seed oil, have some physiological functions including body fat reduction and anti-tumor activity [ 12 , 13 ]. In addition, purified α-eleostearic acid (9c, 11t, 13t-CLNA) and α-eleostearic acid rich bitter gourd seed oil also reveal anti-carcinogenesis in vitro and in vivo [ 10 , 11 , 14 , 15 ]. However, there are few studies evaluated the physiological function of punicic acid (9c, 11t, 13c-CLNA) [ 10 , 16 ]. Previously, we reported the hypolipidemic effect of purified punicic acid in human liver derived HepG2 cells [ 17 ]. In the present study, we investigated the effects of pomegranate seed oil rich in 9c, 11t, 13c-CLNA on lipid metabolism in Otsuka Long-Evans Tokushima fatty (OLETF) rats. OLETF rats develop a syndrome with multiple metabolic and hormonal disorders that shares many features with human obesity [ 18 - 21 ]. OLETF rats have hyperphagia, because they lack receptors for cholecystokinin, and become obese, developing hyperlipidemia, diabetes, and hypertension. To clarify the physiological function of 9c, 11t, 13c-CLNA, we measured hepatic enzyme activities in relation to lipid metabolism and fatty acid composition in plasma of these obese, hyperlipidemic rats. Results and Discussion In comparison with their progenitor Long-Evans Tokushima Otsuka (LETO) rats, OLETF rats had increased body weight gain with enhanced food intake during 2 weeks feeding period (Table 1 ). In OLETF rats, food intake was not different between the groups. There was also no significant difference between groups in the relative liver weights of LETO and OLETF rats. Food efficiency, however, was higher in 9c, 11t, 13c-CLNA group than in other two groups. Chin et al. previously reported that CLA is a growth factor for rats as shown by enhanced weight gain and improved feed efficiency [ 22 ]. Thus, we consider that 9c, 11t, 13c-CLNA may have some growth promotional function. Table 1 Effect of 9c, 11t, 13c-CLNA on body weight, relative liver weight, food intake, and food efficiency LETO OLETF Control 9c, 11t, 13c-CLNA Body weight (g) Initial 223 ± 3 a 266 ± 12 b 265 ± 8 b Final 282 ± 5 a 357 ± 15 b 369 ± 11 b Gain 59.4 ± 2.7 a 91.3 ± 3.6 b 104 ± 6 b Relative liver weight (g/100 g BW) 3.12 ± 0.09 3.40 ± 0.11 3.35 ± 0.06 Food intake (g) 17.8 ± 0.4 a 25.8 ± 1.1 b 26.2 ± 1.1 b Food efficiency (g BW gain/g intake) 25.7 ± 1.0 a 27.3 ± 0.6 b 30.4 ± 1.1 b a,b Different superscript letters show significant difference at P < 0.05. The effect of dietary 9c, 11t, 13c-CLNA on the accumulation of abdominal white adipose tissue (WAT) was investigated (Figure 1 ). After 2 weeks feeding period, OLETF rats developed marked abdominal obesity. Compared with LETO rats, the control diet increased perirenal, epididymal, and omental WAT weights of OLETF rats to 2.6-, 1.5-, and 2.1-fold, respectively. There was no significant effect of 9c, 11t, 13c-CLNA on the accumulation of abdominal WAT in OLETF rats. However, 2 weeks feeding of the diet supplemented with 5% pomegranate seed oil resulted in a significant reduction of omental WAT weight (by 27%) compared with the feeding of control diet in OLETF rats (unpublished data). These results suggested that 2 weeks feeding of 1% pomegranate seed oil diet might not be enough to reveal anti-obese effect of 9c, 11t, 13c-CLNA. Figure 1 Effect of 9c, 11t, 13c-CLNA on abdominal white adipose tissue weight in LETO and OLETF rats. Rats were fed a control or 9c, 11t, 13c-CLNA diet for 2 weeks. Values are expressed as mean ± SE for 6 rats. a,b Different letters show significant differences at P < 0.05. Omen, omental; Peri, perirenal; Epi, epididymal. After the 2 weeks feeding period, OLETF rats revealed hyperlipidemia. Serum triacylglycerol, phospholipids, and cholesterol levels of OLETF rats fed the control diet were significantly higher than those of LETO rats fed the control diet (Figure 2 ). However, feeding of 9c, 11t, 13c-CLNA did not affect to serum lipid levels in OLETF rats. Although the present results showing that dietary 1% pomegranate seed oil rich in 9c, 11t, 13c-CLNA could not alleviate hyperlipidemia in OLETF rats, our previous report indicated that purified 9c, 11t, 13c-CLNA suppressed the secretion of apolipoprotein B100 from human liver derived HepG2 cells [ 17 ]. Further studies are needed to elucidate the effect of purified 9c, 11t, 13c-CLNA on the pathogenesis of hyperlipidemia in OLETF rats. Figure 2 Effect of 9c, 11t, 13c-CLNA on serum lipid levels in LETO and OLETF rats. Rats were fed a control or 9c, 11t, 13c-CLNA diet for 2 weeks. Values are expressed as mean ± SE for 6 rats. a,b Different letters show significant differences at P < 0.05. TG, triacylglycerol; PL, phospholipids; Chol, cholesterol. Next, we investigated the effect of dietary 9c, 11t, 13c-CLNA on the distribution of lipids to the liver. There was no significant difference in relative liver weight between control and 9c, 11t, 13c-CLNA group in OLETF rats. Previous reports indicated that CLA feeding resulted in the development of hepatomegaly and fatty liver in mice [ 23 - 25 ], and a mixture of CLNA also induced hepatic lipid accumulation in rat [ 13 ]. In the present study, the triacylglycerol concentration in OLETF rats was significantly higher than that in LETO rats, and the triacylglycerol accumulation in the liver of OLETF rats was markedly alleviated by the 9c, 11t, 13c-CLNA diet (Figure 3 ). There was no significant difference in liver phospholipids and cholesterol levels among groups in LETO and OLETF rats. These results suggest that 9c, 11t, 13c-CLNA has a preventive effect against the triacylglycerol accumulation in the liver. Figure 3 Effect of 9c, 11t, 13c-CLNA on hepatic lipid levels in LETO and OLETF rats. Rats were fed a control or 9c, 11t, 13c-CLNA diet for 2 weeks. Values are expressed as mean ± SE for 6 rats. a,b Different letters show significant differences at P < 0.05. TG, triacylglycerol; PL, phospholipids; Chol, cholesterol. To further investigate the regulation of hepatic lipid metabolism, we analyzed the effect of dietary 9c, 11t, 13c-CLNA on the activities of enzymes related to fatty acid synthesis and fatty acid β-oxidation. As shown in Figure 4A , the activities of glucose-6-phosphate dehydrogenase (G6PDH) and malic enzyme (ME), key enzymes of NADPH production, and fatty acid synthase (FAS), a key enzyme of fatty acid synthesis, were markedly increased in OLETF rats fed the control diet compared with LETO rats. There was no significant effect of dietary 9c, 11t, 13c-CLNA on these enzyme activities in OLETF rats. The activities of carnitine palmitoyltransferase (CPT), a key enzyme of fatty acid β-oxidation, and peroxisomal β-oxidation were not different between OLETF and LETO rats, and 9c, 11t, 13c-CLNA diet did not affect on these activities in OLETF rats (Figure 4B ). Koba et al. previously reported that a mixture of CLNA isomers, prepared by alkaline isomerization, enhanced hepatic mitochondrial and peroxisomal β-oxidation compared with linoleic acid, α-linolenic acid, and CLA [ 13 ]. Thus, we consider that the effect of 9c, 11t, 13c-CLNA on the fatty acid β-oxidation is weak compared with those of other CLNA isomers. In addition, the alleviation of hepatic triacylglycerol accumulation by 9c, 11t, 13c-CLNA could not be attributed to the regulation of enzyme activities related to the fatty acid synthesis and fatty acid β-oxidation. Figure 4 Effect of 9c, 11t, 13c-CLNA on activities of enzymes related to lipid metabolism, (A) G6PDH, ME, FAS (B) CPT, peroxisomal β-oxidation, in the liver of LETO and OLETF rats. Rats were fed a control or 9c, 11t, 13c-CLNA diet for 2 weeks. Values are expressed as mean ± SE for 6 rats. a,b Different letters show significant differences at P < 0.05. To gain insight into the effect of dietary 9c, 11t, 13c-CLNA on lipid metabolism, we analyzed fatty acid composition in serum triacylglycerol. As shown in Table 2 , saturated fatty acid (SFA) levels were lower and monounsaturated fatty acid (MUFA) levels were higher in OLETF rats fed the control diet than those in LETO rats. Feeding of 9c, 11t, 13c-CLNA significantly reduced MUFA levels in plasma triacylglycerol of OLETF rats. It has been recognized that MUFAs are the major fatty acid form in fat depots [ 26 ]. Alterations in the ratio of SFA to MUFA have been implicated in various disease states including cardiovascular disease, obesity, and diabetes [ 27 - 29 ]. Therefore, the ratio of SFA to MUFA is of physiological importance in normal and disease states. A key enzyme involved in the cellular synthesis of MUFA from SFA is the membrane-bound stearoyl-CoA desaturase (SCD), which inserts a cis-double bond in the delta-9 position of fatty acid substrates. Previous reports indicated that 10t, 12c-CLA, an active isomer of anti-obese effect of CLA, suppresses delta-9 desaturation and SCD activity in vitro and in vivo [ 30 - 32 ]. In the present study, the index of delta-9 desaturation, ratio of oleic acid (18:1) versus stearic acid (18:0), was higher in obese, hyperlipidemic OLETF rats compared with in lean LETO rats, and it was significantly decreased by dietary 9c, 11t, 13c-CLNA in OLETF rats. As far as we know, this is the first study showing that 9c, 11t, 13c-CLNA also suppresses delta-9 desaturation in vivo. We suggest that the alleviation of hepatic triacylglycerol accumulation by dietary 9c, 11t, 13c-CLNA was, at least in part, attributable to the suppression of delta-9 desaturation in OLETF rats. Table 2 Effect of 9c, 11t, 13c-CLNA on fatty acid composition in serum triacylglycerol. LETO OLETF Control 9c, 11t, 13c-CLNA % 14:0 2.28 ± 0.23 a 1.05 ± 0.15 b 1.10 ± 0.13 b 16:0 36.8 ± 0.7 a 28.1 ± 0.7 b 41.2 ± 2.7 a 16:1 0.492 ± 0.066 a 3.83 ± 0.25 b 2.39 ± 0.20 c 18:0 5.58 ± 0.63 a 3.29 ± 0.26 b 4.04 ± 0.32 b 18:1 11.7 ± 0.7 a 24.5 ± 1.4 b 20.4 ± 1.4 c 18:2 35.5 ± 0.7 a 33.2 ± 0.9 a 27.8 ± 1.4 b 20:4 7.74 ± 0.76 a 4.80 ± 0.40 b 3.12 ± 0.44 c Desaturation index Δ9 desaturation 2.18 ± 0.24 a 7.73 ± 0.83 b 5.34 ± 0.75 c Δ6 desaturation 0.219 ± 0.024 a 0.144 ± 0.009 b 0.110 ± 0.011 b a,b,c Different superscript letters show significant difference at P < 0.05. Conclusions Dietary pomegranate seed oil rich in 9c, 11t, 13c-CLNA alleviates hepatic triacylglycerol accumulation in obese, hyperlipidemic OLETF rats. The mechanism of this effect could not be attributed to the regulation of enzyme activity related to fatty acid synthesis and fatty acid β-oxidation. However, suppression of delta-9 desaturation by dietary 9c, 11t, 13c-CLNA may be, at least in part, involved this effect. Materials and Methods Animals and diets All aspects of the experiment were conducted according to the guidelines provided by the ethical committee of experimental animal care at Saga University. Five weeks old male OLETF rats and LETO rats, the progenitor of OLETF rats, were provided by Tokusima Research Institute (Otsuka Pharmaceutical, Tokushima, Japan). Rats were housed individually in metal cages in temperature-controlled room (24°C) under a 12-hour light/dark cycle. After a 1-week adaptation period, OLETF rats were assigned to two groups (six rats each) that were fed with a semisynthetic diet supplemented with 10% safflower oil (the control group) or a semisynthetic diet supplemented with 9% safflower oil and 1% pomegranate seed oil rich in 9cis, 11trans, 13cis-CLNA (the 9c, 11t, 13c-CLNA group). LETO rats were fed the same diet as the OLETF rats in the control group. The pomegranate seed oil rich in 9c, 11t, 13c-CLNA (69.0%) was prepared by Kaneka Co. (Hyogo, Japan). The semisynthetic diet were prepared according to recommendations of the AIN-76 [ 33 ] and contained (in weight %): casein, 20; fat, 10; cornstarch, 15; vitamin mixture (AIN-76™), 1; mineral mixture (AIN-76™), 3.5; DL-methionine, 0.3; choline bitartrate, 0.2; cellulose, 5; and sucrose, 45. The rats received different diets for 2 weeks and were killed by aortic exsanguinations under diethyl ether anesthesia. Liver and abdominal (perirenal, epididymal, and omental) WATs were also excised for analysis. Analysis of lipids Serum was separated by centrifuging the blood. Triacylglycerol, cholesterol, and phospholipids in serum were measured using enzyme assay kits from Wako Pure Chemicals (Tokyo, Japan). Liver lipids were extracted and purified according to the method of Folch et al [ 34 ]. The concentrations of triacylglycerol, cholesterol, and phospholipids were measured according to the methods of Fletcher [ 35 ], Sperry and Webb [ 36 ], and Bartlett [ 37 ]. Measurement of fatty acid composition in plasma was carried out as previously described [ 38 , 39 ]. Preparation of liver subcellular fractions A piece of liver was homogenized in 6 volumes of a 0.25 M sucrose solution that contained 1 mM EDTA in a 10 mM tris Tris-HCL buffer (pH 7.4). Fractions of mitochondria, microsomes, and cytosol were obtained as previously described[ 40 ]. The protein concentration was determined according to the method of Lowry et al [ 41 ], with bovine serum albumin used as the standard. Assays of enzyme activity The enzyme activities of ME (EC 1.1.1.40) [ 42 ], G6PDH (EC1.1.1.49) [ 43 ], FAS (EC 2.3.1.85) [ 44 ] in the liver cytosol fraction, mitochondrial CPT (EC2.3.1.23) [ 45 ] and peroxisomal β-oxidation [ 46 ] were determined as described. Statistical analysis All values are expressed as means ± SE. Data were analyzed by one-way ANOVA, and all differences were inspected by Duncan's new multiple-range test [ 47 ]. Differences were considered to be significant at P <0.05. List of abbreviations CFA, conjugated fatty acid; CLA, conjugated linoleic acid; CLNA, conjugated linolenic acid; OLETF rat, Otsuka Long-Evans Tokushima fatty rat; LETO rat, Long-Evans Tokushima Otsuka rat; WAT, white adipose tissue; G6PDH, glucose-6-phosphate dehydrogenase; ME, malic enzyme; FAS, fatty acid synthase; CPT, carnitine palmitoyltransferase; SFA, saturated fatty acid; MUFA, monounsaturated fatty acid; SCD, stearoyl-CoA desaturase

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549028

Prolastin, a pharmaceutical preparation of purified human α1-antitrypsin, blocks endotoxin-mediated cytokine release

Background α1-antitrypsin (AAT) serves primarily as an inhibitor of the elastin degrading proteases, neutrophil elastase and proteinase 3. There is ample clinical evidence that inherited severe AAT deficiency predisposes to chronic obstructive pulmonary disease. Augmentation therapy for AAT deficiency has been available for many years, but to date no sufficient data exist to demonstrate its efficacy. There is increasing evidence that AAT is able to exert effects other than protease inhibition. We investigated whether Prolastin, a preparation of purified pooled human AAT used for augmentation therapy, exhibits anti-bacterial effects. Methods Human monocytes and neutrophils were isolated from buffy coats or whole peripheral blood by the Ficoll-Hypaque procedure. Cells were stimulated with lipopolysaccharide (LPS) or zymosan, either alone or in combination with Prolastin, native AAT or polymerised AAT for 18 h, and analysed to determine the release of TNFα, IL-1β and IL-8. At 2-week intervals, seven subjects were submitted to a nasal challenge with sterile saline, LPS (25 μg) and LPS-Prolastin combination. The concentration of IL-8 was analysed in nasal lavages performed before, and 2, 6 and 24 h after the challenge. Results In vitro , Prolastin showed a concentration-dependent (0.5 to 16 mg/ml) inhibition of endotoxin-stimulated TNFα and IL-1β release from monocytes and IL-8 release from neutrophils. At 8 and 16 mg/ml the inhibitory effects of Prolastin appeared to be maximal for neutrophil IL-8 release (5.3-fold, p < 0.001 compared to zymosan treated cells) and monocyte TNFα and IL-1β release (10.7- and 7.3-fold, p < 0.001, respectively, compared to LPS treated cells). Furthermore, Prolastin (2.5 mg per nostril) significantly inhibited nasal IL-8 release in response to pure LPS challenge. Conclusion Our data demonstrate for the first time that Prolastin inhibits bacterial endotoxin-induced pro-inflammatory responses in vitro and in vivo , and provide scientific bases to explore new Prolastin-based therapies for individuals with inherited AAT deficiency, but also for other clinical conditions.

Background α1-antitrypsin (AAT) is a glycoprotein, which is the major inhibitor of neutrophil elastase and proteinase 3 [ 1 , 2 ]. AAT is mainly produced in liver cells, but also in extrahepatic cells, such as monocytes, macrophages and pulmonary alveolar cells [ 3 , 4 ]. The average concentration of AAT in plasma in healthy individuals is 1.3 mg/ml, with a half-life of 3 to 5 days. AAT is an acute phase protein, and its circulating levels increase rapidly to concentrations exceeding 2 mg/ml in response to inflammation or infection [ 5 ]. Individuals with plasma AAT values below 0.7 mg/ml are considered to be AAT deficient [ 6 , 7 ]. Over 75 alleles of AAT have been identified to date, of which at least 20 affect either the amount or the function of the AAT molecule in vivo [ 6 - 8 ]. A very common deficiency allele is termed Z, which differs from the normal M in the substitution of Glu 342 to Lys [ 7 , 9 , 10 ]. This single amino acid exchange causes spontaneous polymerization of the AAT, markedly impeding its release into the circulation [ 11 ]. The retained material is associated with hepatic diseases [ 12 ], while diminished circulating levels lead to antiproteinase deficiency and higher susceptibility to elastase mediated tissue injury [ 13 , 14 ]. The alleles of AAT are inherited in an autosomal codominant manner [ 2 ]. Therefore, individuals heterozygous for the Z allele (MZ) have 30–40% whereas individuals homozygous for the Z allele (ZZ) have only 10–15% of normal plasma AAT levels [ 15 - 17 ]. Tobacco smoke and air pollution have long been recognised as risk factors for the development of chronic obstructive pulmonary disease (COPD); the only proven genetic risk factor, however, is the severe Z deficiency of AAT [ 18 , 19 ]. Cigarette smokers with AAT-deficiency develop COPD much earlier in life than smokers with the normal AAT genotype [ 8 , 10 , 11 ]. The pulmonary emphysema that is associated with inherited AAT deficiency is intimately linked with the lack of proteinase inhibitor within the lungs that is available to bind to, and inactivate, neutrophil elastase. On the basis of clinical observations involving patients with inherited AAT deficiency and various experimental studies, the elastase-AAT imbalance hypothesis became widely accepted as the explanation for lung tissue destruction in emphysema [ 20 , 21 ]. There is now increasing evidence that an excessive activity of various proteolytic enzymes in the lung milieu, including members of the serine, cysteine and metalloprotease families, may damage the elastin network of lungs [ 14 ]. Since the severe ZZ and intermediate MZ AAT deficiency accounts for less than 1–2% and 8–18% of emphysema cases, it is believed that the protease-antiprotease hypothesis provides a rational basis for the explanation of the development and progression of emphysema in general [ 22 , 23 ]. Based on the protease-antiprotease hypothesis, augmentation therapy of emphysema with severe AAT deficiency was introduced during the 1980s [ 24 ]. Intravenous administration of a pasteurized pooled human plasma AAT product (Prolastin; Bayer Corporation; Clayton, North Carolina) is used to increase AAT levels in deficient individuals [ 25 ]. The major concept behind augmentation therapy is that a rise in the levels of blood and tissue AAT will protect lungs from continuous destruction by proteases, particularly neutrophil elastase [ 26 ]. For example, anti-elastase capacity in the lung epithelial lining fluid has been found to increase to 60–70% of normal in homozygous Z AAT-deficient individuals subjected to augmentation therapy [ 26 , 27 ]. Whether this biochemical normalization of AAT levels influences the pathogenic processes of lung disease is still under debate. The most recent results, however, suggest that Prolastin therapy may have beneficial effects in reducing the frequency of lung infections and reducing the rate of decline of lung function [ 28 , 29 ]. There is growing evidence that AAT, in addition to its anti-proteinase activity, may have other functional activities. For example, AAT has been demonstrated to stimulate fibroblast proliferation and procollagen synthesis [ 30 ], to up-regulate human B cell differentiation into IgE-and IgG4-secreting cells [ 31 ], to interact with the proteolytic cascade of enzymes involved in apoptosis [ 32 , 33 ] and to express contrasting effects on the post-transcriptional regulation of iron between erythroid and monocytic cells [ 34 ]. AAT is also known to inhibit neutrophil superoxide production [ 35 ], induce macrophage-derived interleukin-1 receptor antagonist release [ 36 ] and reduce bacterial endotoxin and TNFα-induced lethality in vivo [ 37 , 38 ]. We recently demonstrated, in vitro , that both native (inhibitory) and non-inhibitory (polymerised and oxidised) forms of AAT strongly inhibit lipopolysaccharide-induced human monocyte activation [ 39 ]. AAT appears to act not just as an anti-proteinase, but as a molecule with broader anti-inflammatory properties. Data presented in this study provide clear evidence that Prolastin, a preparation used for AAT deficiency augmentation therapy, significantly inhibits bacterial endotoxin-induced pro-inflammatory cell responses in vitro , and suppresses nasal IL-8 release in lipopolysaccharide-challenged individuals, in vivo . Materials and Methods α1-antitrypsin (AAT) preparations α1-antitrypsin (Human) Prolastin ® (Lot 26N3PT2) was a gift from Bayer (Bayer Corporation, Clayton, North Carolina, USA). This vial of Prolastin contained 1059 mg of functionally active AAT, as determined by capacity to inhibit porcine pancreatic elastase. Prolastin was dissolved in sterile water for injections provided by manufacture and stored at +4°C. Purified human AAT was obtained from the Department of Clinical Chemistry, Malmö University Hospital, Sweden. Native AAT was diluted in phosphate buffered saline (PBS), pH 7.4. To ensure the removal of endotoxins, AAT was subjected to Detoxi-Gel AffinityPak columns according to instructions from the manufacturer (Pierce, IL, USA). Purified batches of AAT were then tested for endotoxin contamination with the Limulus amebocyte lysate endochrome kit (Charles River Endosafe, SC, USA). Endotoxin levels were less than 0.2 enzyme units/mg protein in all preparations used. The concentrations of AAT in the endotoxin-purified batches were determined according to the Lowry method [ 40 ]. Polymeric AAT was produced by incubation at 60°C for 10 h. Polymers were confirmed on non-denaturing 7.5% PAGE gels. Monocyte isolation and culture Monocytes were isolated from buffy coats using Ficoll-Paque PLUS (Pharmacia, Sweden). Briefly, buffy coats were diluted 1:2 in PBS with addition of 10 mM EDTA and layered on Ficoll. After centrifugation at 400 g for 35 min, at room temperature, the cells in the interface were collected and washed 3 times in PBS-EDTA. The cell purity and amount were determined in a cell counter Autocounter AC900EO (Swelabs Instruments AB, Sweden). The granulocyte fractions were less than 10%. Cells were seeded into 12-well cell culture plates (Nunc, Denmark) at a concentration of 4 × 10 6 cells/ml in RPMI 1640 medium supplemented with penicillin 100 U/ml; streptomycin 100 μg/ml; non-essential amino acids 1×; sodium pyruvate 2 mM and HEPES 20 mM (Gibco, UK). After 1 h 15 min, non-adherent cells were removed by washing 3 times with PBS supplemented with calcium and magnesium. Fresh medium was added and cells were stimulated with lipopolysaccharide (LPS, 10 ng/ml, J5 Rc mutant; Sigma, Sweden) in the presence or absence of various concentrations of Prolastin (0–16 mg/ml), constant concentration of native or polymerised AAT (0.5 mg/ml) for 18 h at 37°C, 5% CO 2 . Neutrophil isolation and culture Human neutrophils were isolated from the peripheral blood of healthy volunteers using Polymorphprep TM (Axis-Shield PoC AS, Oslo, Norway) as recommended by the manufacture. In brief, 25 ml of anti-coagulated blood was gently layered over the 12.5 ml of Polymorphprep TM and centrifuged at 1600 rpm for 35 min. Neutrophils were harvested as a low band of the sample/medium interface, washed with PBS, and residual erythrocytes were subjected to hypotonic lysis. Purified neutrophils were washed in RPMI-1640- Glutamax-1 medium (Gibco-BRL Life Technologies, Grand Island, NY) supplemented with 0.1% bovine serum albumin (BSA) and resuspended in the same medium. The neutrophil purity was more than 75% as determined on an AutoCounter AC900EO. Cell viability was > 95% according to trypan blue staining. Neutrophils (5 × 10 6 cells/ml) were plated into sterile ependorf tubes. Zymosan was boiled, washed and sonicated. Opsonized zymosan was prepared by incubating zymosan with serum (1:3) in 37°C water bath for 20 min. After, zymosan was centrifuged, washed with PBS and re-suspended at 30 mg/ml. Cells alone or activated with zymosan (0.3 mg/ml) were exposed to various concentrations of Prolastin (0–8 mg/ml), and native or polymerised AAT preparations (0.5 mg/ml) for 18 h at 37°C 5% CO 2 . Cell free supernatants were obtained by centrifugation at 300 g for 10 min, and stored at -80°C until analysis Cytokine/chemokine analysis Cell culture supernatants from monocytes and neutrophils stimulated with LPS or zymosan alone or in combination with Prolastin, native or polymerised AAT were analysed to determine TNFα, IL-1β and IL-8 levels by using DuoSet ELISA sets (R&D Systems, MN, USA; detection levels 15.6, 3.9, and 31.2 pg/ml, respectively). Subjects Seven subjects (four females and three males) of 26–50 (median 38) years of age, non-smokers, non-allergic volunteers participated in the study. All subjects gave written informed consent before participation in the study. None of the subjects has a history of respiratory disease and none took any medication at the study time. Study Design At 2-week intervals each subject was submitted to a nasal challenge with sterile saline, LPS and LPS-Prolastin combination. All experimental sessions were done in the same room. On each provocation day, the nose was inspected and cleaned with 8 ml of isotonic NaCl. Between nasal lavages the subjects stayed in the same building and asked to keep away from known sources of nasal irritants. The night was spent in their own homes. All participants completed a symptom questionnaire. In the first session, the baseline lavage was taken after instillation to each nostril of 8 ml of sterile isotonic NaCl. In the next session, the subjects were challenged with LPS from Escherichia coli serotype 026:B6, Lot 17H4042 (Sigma-Aldrich, USA). The provocation solution was prepared prior to use. LPS was added to 8 ml of sterile 0.9% NaCl to obtain a final concentration of 250 μg/ml, and 100 μl of the provocation solution was sprayed into each nostril, using a needle-less syringe. In the third session, the subjects were first challenged with LPS, as described above, and after 30 min with 2.5 mg of Prolastin into each nostril. Lavage samples were taken with instillation to each nostril of 8 ml of sterile isotonic NaCl after 2, 6 and 24 h followed by assessment of symptoms by a questionnaire. All subject completed a symptom questionnaire with questions about nasal and eye irritation, and throat and airway symptoms. None of the participants reported symptoms of nasal, eye or throat irritations, and no general symptoms such as muscle pain, shivering, were mentioned. Nasal Lavage The procedure for nasal lavage was performed according to a method described by Wihl and co-workers [ 41 ]. Each nasal cavity was lavaged separately with a syringe (60 ml) to which a plastic nasal olive was connected for close nostril fitting. To prevent lavage spilling into the throat, the subject was bent forward at an angle of 60° during the procedure. Equilibrium was maintained between the mucosal lining and the lavage fluid by injecting the saline gently into the nasal cavity and drawing it back five times into the syringe. The lavage was performed in both nostrils and samples were collected into a test tube. The samples were then centrifuged at 1750 rpm, 6°C for 10 min and immediately frozen at -80°C. The protein concentration in the lavage fluids was measured by Lowry method and IL-8 levels were determined by DuoSet ELISA sets (R&D Systems, MN, USA; detection levels 31.2 pg/ml). Statistical Analysis Statistical Package (SPSS for Windows, release 11.5, SPSS Inc., Chicago) was used for the statistical calculations. The differences in the means of cell culture experimental results were analysed for their statistical significance with the one-way ANOVA combined with a multiple-comparisons procedure (Scheffe multiple range test). The equality of means of experimental results in healthy volunteers were analysed for statistical significance with independent two sample t-test and repeated measures of ANOVA using the SPSS MANOVA procedure . Tests showing p < 0.05 were considered to be significant. Results Concentration-dependent effects of Prolastin on LPS-induced cytokine release from human monocytes Various concentrations of Prolastin (0–16 mg/ml) were added to adherent-isolated human monocytes with or without LPS (10 ng/ml). Cells stimulated with LPS alone served as a positive control, while PBS stimulated monocytes served as negative controls. As illustrated in figures 1A and 1B , simultaneous incubation of monocytes with LPS and Prolastin resulted in a reduction in TNFα and IL-1β release compared to the cells stimulated with LPS alone. Inhibition of LPS-induced cytokine release by Prolastin was concentration-dependent and was typically observed over a concentration range of 0.5–16 mg/ml. At 16 mg/ml the inhibitory effects of Prolastin appeared to be maximal for both TNFα (10.7-fold, p < 0.001) and IL-1β (7.3-fold, p < 0.001), compared to LPS alone. Figure 1 A concentration-response inhibition of lipopolysaccharide-stimulated TNFα (A) and IL-1β (B) release by Prolastin in human blood monocytes. Isolated blood monocytes were treated with LPS (10 ng/ml) alone or together with various concentrations of Prolastin (0–16 mg/ml) for 18 h. TNFα and IL-1β levels were measured by ELISA. Data are the means of quadruplicate culture supernatants ± S.E. and are representative of three separate experiments. Inhibitory effects at 0.5 mg/ml of AATs on LPS-mediated IL-1β and TNFα release We recently found that simultaneous incubation of monocytes with LPS and either the inhibitory (native) or non inhibitory (polymeric) form of AAT resulted in a reduction in TNFα and IL-1β release compared to the cells stimulated with LPS alone. At 0.5 mg/ml the effects of native and polymerised AAT appeared to be maximal (41). Therefore, we selected a 0.5 mg/ml concentration of Prolastin, native and polymerised AAT, and compared their effects on LPS-stimulated cytokine release at 18 h. As shown in figures 2A and 2B , LPS triggered a significant release of TNFα and IL-1β (p < 0.001 v medium alone) by monocytes. At 0.5 mg/ml, native and polymerised AAT remarkably inhibited LPS-induced TNFα and IL-1β release (p < 0.001) (Fig. 2 ). The inhibitory effect of Prolastin (0.5 mg/ml) on LPS-stimulated TNFα release was comparable in magnitude to that of native or polymeric AAT, whereas its inhibitory effect on LPS-induced IL-1β release did not reach significance. Figure 2 Comparisons of the effects of native (nAAT), polymeric (pAAT) and Prolastin on lipopolysaccharide – stimulated TNFα (A) and IL-β (B) production by human blood monocytes isolated from four healthy donors. Isolated blood monocytes were treated with LPS (10 ng/ml) alone or together with 0.5 mg/ml nAAT, pAAT or Prolastin for 18 h. TNFα and IL-1β levels were measured by ELISA. Each bar represent the mean ± S.E. *** p < 0.001. Concentration-dependent effects of Prolastin on neutrophil IL-8 release The effects of Prolastin (0–8 mg/ml) on human neutrophil IL-8 production are shown in Figure 3A . Neutrophils stimulated with opsonized zymosan (0.3 mg/ml) released a large amount of IL-8 (p < 0.001), compared to controls. Prolastin inhibited IL-8 release by neutrophils stimulated with opsonized zymosan (Fig 3A ). This inhibition was concentration-dependant, with maximal suppression of IL-8 release (5.3-fold, p < 0.001 compared to zymosan treated cells) at 8 mg/ml. Figure 3 Effects of AATs on neutrophils activated with zymosan. (A) Concentration-dependent effects of Prolastin on IL-8 release from neutrophils activated with opsonised zymosan. Freshly isolated blood neutrophils were treated with zymosan (0.3 mg/ml) alone or together with various concentrations of Prolastin (0–8 mg/ml) for 18 h. IL-8 levels were measured by ELISA. Data are the means of quadruplicate culture supernatants ± S.E. and are representative of three separate experiments. (B) Effects of opsonised zymosan alone or together with native (nAAT), polymeric (pAAT) AAT or Prolastin on IL-8 release from neutrophils. The release of neutrophil IL-8 was measured in cell free supernatants as described in Materials and methods. Neutrophils were treated for 18 h with a constant amount of zymosan (0.3 mg/ml) alone or together with nAAT, pAAT or Prolastin (0.5 mg/ml) for 18 h. IL-8 levels were measured by ELISA. Each bar represents the means ± S.E. of three separate experiments carried out in duplicate repeats. *** p < 0.001 Inhibitory effects at 0.5 mg/ml of native, polymeric AAT and Prolastin on zymosan-mediated IL-8 release Neutrophils were stimulated with zymosan (0.3 mg/ml) or AATs (0.5 mg/ml) either alone or in combination for 18 h and IL-8 protein determined. As illustrated in figure 3B , polymeric and native AAT and Prolastin significantly inhibited the release of IL-8 protein by activated neutrophils. In terms of maximal effect, native AAT >polymerised AAT>Prolastin. It must be noted that native, polymeric AAT and Prolastin alone showed no effect on neutrophils, relative to non-treated buffer controls (data not shown). Inhibition of the LPS-induced increase in nasal IL-8 release by Prolastin To assess the effect of Prolastin on LPS-induced nasal provocation, IL-8 levels in nasal lavages were measured. Nasal instillation 25 μg per nostril of LPS alone or in combination with 2.5 mg/ml of Prolastin was performed in non-smoking and non-allergic volunteers (n = 7, 4 females and 3 males). The IL-8 release in response to LPS challenge increased over time compared to baseline levels (Fig. 4 ). The levels of IL-8 increased already after 2 h of LPS challenge (245.7% ± 87) and remained higher after 24 h (310 ± 77.5) compared to baseline (100% ± 19.2). By contrast, when IL-8 levels were examined in LPS-Prolastin-treated lavage samples, no significant changes in IL-8 release were observed compared to baseline. In the presence of Prolastin, the LPS effect on IL-8 release was inhibited (p < 0.05) (Fig. 4 ). Figure 4 IL-8 analysis in nasal lavage of subjects challenged with LPS alone or LPS+Prolastin combination. Seven healthy volunteers were treated with LPS (25 μg/nostril) or with LPS followed 30 min later with Prolastin (2.5 mg/nostril), nasal lavage was collected at different time points (0, 2, 6 and 24 h) as described in Material and Methods. The concentration of IL-8 (pg/ml) was measured by ELISA. IL-8 values are expressed as a ratio of IL-8 concentration at selected time point and the basal level. Independent two sample t-test shows after 6 and 24 h significantly higher levels of IL-8 in subjects treated with LPS compared to LPS+Prolastin. * p < 0.05 Disscussion There is now, however, ample evidence that serine proteinase inhibitors (serpins), in addition to their well established anti-inflammatory capacity to regulate serine proteinases activity, may possess broader anti-inflammatory properties. Several studies have shown that the biological responses of bacterial lipopolysaccharide (endotoxin) in vivo may be sensitive to serpins. For example, the serpin antithrombin, has been shown to protect animals from LPS-induced septic shock and also to inhibit IL-6 induction by LPS [ 42 , 43 ]. Our recent study provided first in vitro evidence that native (inhibitor) and at least two modified (non-inhibitory i.e. polymeric and oxidised) forms of AAT can block the release of an array of chemokine and cytokines from LPS-stimulated monocytes [ 39 ]. These studies therefore further support a central role of serpins in inflammation, not only as the regulators of proteinase activity, but also as the suppressers of endotoxin induced pro-inflammatory responses. In line with these findings, we demonstrate here that Prolastin, a preparation of human AAT which is used for augmentation therapy, significantly inhibits endotoxin-induced pro-inflammatory effects in vitro and in vivo . Stimulation of human monocytes and neutrophils with bacterial endotoxin results in the release of a range of inflammatory mediators including the pro-inflammatory cytokines ( e.g. IL-6, IL-1β and TNFα) and the chemokines ( e.g. MCP-1 and IL-8) [ 44 - 46 ]. Together, these play a crucial role in the recruitment and activation of leukocytes and the subsequent release of harmful proteases that may further perpetuate the inflammatory process. We found that Prolastin significantly inhibits endotoxin-induced IL-1β and TNFα release by monocytes and IL-8 release by neutrophils in vitro . The Prolastin exhibited these anti-inflammatory properties in a concentration-dependent manner. Its maximal effects were observed with 16 mg/ml in the monocyte model and with 8 mg/ml in the neutrophil model, since doubling these concentrations did not significantly modify the intensity of the effects. Indeed, Prolastin markedly prevented endotoxin-induced cell activation at 0.5–4 mg/ml concentrations, implying that these lower concentrations of Prolastin might also be sufficient to inhibit endotoxin effects. It is worth noting that in order to reduce a potential risk of transmission of infectious agents the Prolastin preparation is heat-treated in solution at 60° ± 0.5 for not less than 10 h. Data from in vitro studies show that heat-treatment results in AAT polymerization and loss of its inhibitory activity [ 47 , 48 ]. Therefore, in our experimental model we compared anti-inflammatory effects of Prolastin with those of native and heat treated (60°C 10 h) AATs. At concentrations used (0.5 mg/ml), no significant difference was found between the effects of Prolastin and native or heat-treated (polymeric) AAT on endotoxin-induced monocyte TNFα and neutrophil IL-8 elevation. The median concentrations of endotoxin-stimulated IL-1β levels also decreased in the presence of Prolastin but failed to reach statistical significance. In general, inhibitory effects on endotoxin-stimulated monocyte IL-1β and neutrophil IL-8 release were better pronounced by native AAT compared to polymeric AAT or Prolastin. Similarly, in our previous study we found that in terms of maximal effect, native AAT >polymerised AAT>oxidized AAT were efficient in inhibiting LPS-stimulated TNFα and IL-1β, and IL-8 release from monocytes [ 39 ]. Further studies will be necessary to better evaluate how temperature, pH or other physicochemical challenges may influence anti-inflammatory effectiveness of AAT preparations. To explore our hypothesis that AAT functions as a potent inhibitor of endotoxin-induced effects, we examined whether Prolastin also inhibits responses to LPS in the nasal airway, in vivo . In particular, we were interested in concentrations of the neutrophil chemoattractant, IL-8. Endotoxin (or LPS) from gram-negative bacteria is a common air contaminant in a number of occupational conditions, especially those in which exposure to animal waste or plant matter occurs [ 44 , 49 - 51 ]. Levels of LPS in such environments may exceed 20 μg/m 3 air and may be associated with respiratory symptoms and nasal inflammation in exposed persons [ 52 ]. For example, nasal inflammation as evaluated by an increased influx of inflammatory cells into the nasal airway and increased IL-8 levels, has been described in persons occupationally exposed to LPS [ 51 ]. Moreover, it has been suggested that constitutive levels of IL-8 might further enhance responses to an inflammatory stimulus, such as LPS [ 53 ]. A number of experimental studies have shown that a nasal instillation of LPS causes the cytokine and chemokine reaction [ 54 , 55 ]. In our pilot study we also showed that instilled defined amounts of endotoxin (25 μg/per nostril) induce time-dependent nasal IL-8 release in normal subjects. Two hours after LPS instillation the IL-8 levels in nasal lavage reached more than twice the basal level and remained higher during all the times studied. However, during the next session, when 30 min after challenge with LPS, Prolastin (2.5 mg/ per nostril) was instilled, no induction of nasal IL-8 release was found compared to the basal levels. Furthermore, the protective ability of Prolastin did not disappeared over study time. We cannot determine from these experiments whether Prolastin is directly suppressing IL-8 release or suppressing another inflammatory response that leads to IL-8 release; nonetheless, our finding suggests that effects of Prolastin directed against endotoxin-stimulated inflammatory responses may be beneficial. Thus, data from both in vitro and in vivo experiments provide novel evidence that the Prolastin preparation is a potent inhibitor of endotoxin effects. The major concept behind augmentation therapy with pooled plasma-derived AAT has been that a rise in the level of AAT in subjects with severe inherited AAT deficiency would protect the lung tissue from continued destruction by proteinases (i.e. primarily leukocyte elastase) [ 7 , 56 , 57 ]. Recent findings provide evidence that augmentation therapy with AAT reduces the incidence of lung infections in patients with AAT-related emphysema [ 28 , 58 ]. Furthermore, Cantin and Woods have reported that aerosolized AAT suppresses bacterial proliferation in a rat model of chronic Pseudomonas aeruginosa lung infection [ 59 ]. Stockley and co-workers demonstrated that a short-term therapy of AAT augmentation not only restores airway concentrations of AAT to normal, but also reduces levels of leukotriene B4, a major mediator of neutrophil recruitment and activation. Interestingly, authors have suggested that the efficacy of AAT augmentation may be most beneficial in individuals with the most inflammation [ 29 , 60 ]. Data presented in this study clearly show that Prolastin inhibits endotoxin-stimulated pro-inflammatory responses, and thus provides new biochemical evidence supporting the efficacy of augmentation therapy. The current findings also suggest that Prolastin may, in fact, be used for broader clinical applications than merely augmentation therapy. Abbreviations AAT, α1-antitrypsin; COPD, chronic obstructive pulmonary disease; LPS, lipopolysaccharide; ZZ, homozygous AAT-deficiency variant; MM, wild type AAT variant; PBS, phosphate buffered saline; EDTA, ethylenediaminetetraacetic acid; HEPES, 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid Authors' contribution Izabela Nita, performed cell culture experiments, made contribution to acquisition of data; Camilla Hollander, made substantial contribution to patient study design, material collection and analysis; Ulla Westin, contributed to study design and data interpretation; Sabina Janciauskiene, contributed to conception and study design, data interpretation and wrote the article

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523233

BMP Signaling Maintains Healthy Joint Cartilage

The alarm clock rings and you jump straight out of bed, rattling downstairs to start the day. Or maybe you creak downstairs, each step a struggle because of stiffness and pain in your knees and other joints. If the second description fits the start of your day, then maybe, like 70 million Americans, you have arthritis, one of the most prevalent chronic health problems in the United States. Arthritis is an umbrella term for more than 100 medical conditions. What all forms of arthritis have in common is that they affect our joints-places where two or more bones meet. In healthy joints, the ends of the bones are covered with cartilage, a tough but smooth tissue that, like the oil in a car engine, reduces friction between the moving parts. In the most common form of arthritis-osteoarthritis-breakdown of this cartilage, which is called articular cartilage, means the bones rub together, causing pain and loss of movement. Risk factors for osteoarthritis include age and family history. If we could understand the molecular mechanisms that create and maintain articular cartilage, it might be possible to discover what goes wrong in our joints as we age and to find better treatments for arthritis. Embryologists have already discovered quite a bit about the earliest stages of joint formation. It is known, for example, that stripes of cells that form between developing bones subsequently develop into the permanent cartilage found in joints. Several members of a family of secreted proteins known as bone morphogenetic proteins (BMPs) are expressed in these stripes of cells, implicating BMP signaling (the transmission of messages produced by BMPs binding to cell-surface receptors) in early joint development. Targeting genes in joints David Kingsley's team has been investigating whether BMPs are also involved in the later development and maintenance of joint cartilage. To do this, the researchers designed a genetic system that inactivates BMP signaling late in mouse embryonic development. They inserted special DNA sequences called loxP sites on either side of Bmpr1a , a gene that encodes one of the BMP receptors. The loxP sites have no effect until an enzyme known as Cre is expressed, and then the DNA between the loxP sites is cut out and discarded. Because Kingsley's team knew that global inactivation of Bmpr1a early in development causes embryonic death, they linked the gene for Cre to DNA sequences that limit its expression to those regions of the embryo where joints eventually develop. The result: a mouse strain in which Bmpr1a receptor function is specifically lost only in tissues destined to become joints. Most of the joints in this mouse strain formed normally. However, the mice rapidly developed severe arthritis after birth. By 7 days old, the expression of proteins normally found in cartilage was reduced, although at this stage the knee, for example, looked normal. By 7 weeks old (adulthood for mice), there were clear structural changes in the knee joints, and the articular cartilage was thinner and showed signs of wearing away. By 9 months old, the knees of the mutant mice largely lacked articular cartilage and the unprotected leg bones seemed to rub directly against each other. All told, the joints in these mutant mice closely resembled those in people with osteoarthritis, suggesting that BMP signaling is necessary for the maintenance of healthy articular cartilage. This raises the possibility that mutations in BMP signaling components may underlie some of the genetic variation in human osteoarthritis risk and suggests that treatments designed to mimic or augment BMP signaling might help to maintain healthy joints. Finally, the genetic system described by Kingsley and coworkers should be useful for future investigations into joint formation and maintenance.

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517828

Key to Cholesterol's Role in Nematode Development

Cholesterol has a bad rap for its association with human heart disease. But actually cholesterol and other sterols are essential for a wide variety of organisms. For most eukaryotes—organisms whose cells have nuclei—sterols reside in the cell membrane and play major structural roles. Sterols keep cell membranes flexible, for example. These chemicals also hinder leakage of ions across the membrane, which is crucial in order for muscles to contract and nerves to conduct signals. For the tiny (eukaryote) nematode worm Caenorhabditis elegans , sterols are a dietary staple. Worms can't make these chemicals from scratch, just as humans can't make vitamin C or the essential amino acids, so they have to harvest these chemicals from their surroundings. If nematodes hit hard times—they can't find enough sterols, say, or are starved or overcrowded—they can delay developing into adults. Instead, they enter a stage called a dauer in which they don't eat and hardly move a muscle. In this state, they can persist several months—many times their normal lifespan—and then revive when conditions improve. Though C. elegans is extensively studied, there's still controversy over the role of cholesterol in this organism. To develop into adults, the nematodes need only small amounts of cholesterol in their diet, suggesting cholesterol does not play a major role in their membranes. Instead, nematodes—like many other eukaryotes—might use cholesterol to make hormones, which are typically active at very low concentrations. Such hormones could play a key role in the worms' development into either adults or dormant dauers. But no one had found any nematode hormones derived from cholesterol—until now. In this issue of PLoS Biology, Teymuras Kurzchalia and colleagues show definitively that cholesterol does not play an essential structural role in C. elegans . Rather, cholesterol is the precursor for a hormone—or set of hormones—key in the worms' development into adulthood and thus key for reproduction. The researchers have partially purified this cholesterol derivative and named it gamravali, from the Georgian word for reproduction, “gamravleba.” When on sterol-free diets, all larvae showed arrested development, becoming dormant dauers. But, surprisingly, the concentration of cholesterol they needed to develop into adults was miniscule, around 20 nanomoles. When given scant amounts of cholesterol, the worms converted some of it to a sterol called lophenol. The researchers found, however, that supplementing a sterol-free diet with lophenol was not enough to sustain development into adulthood. Apparently the worms need cholesterol, which is fed into two distinct pathways: one makes lophenol and another makes the hormone gamravali. The researchers have only partially purified gamravali, so they don't yet know its molecular weight or composition or even whether it is a single molecule. But by working with mutant worms, they have begun to pin down where gamravali acts in the worms' developmental pathway. One mutant C. elegans line, for instance, was unfazed by the cholesterol-free diet. These mutants were missing the daf-12 gene, one of a set of genes crucial in nematode development and aging. On the cholesterol-free, lophenol-supplemented diet, these mutants developed into normal adults. Other mutant lines that each lacked one of several other daf genes, however, developed into dauers when deprived of cholesterol. In this way the researchers found where gamravali acts in the worms' developmental pathway: the hormone gamravali likely comes into play before daf-12, but after the other daf genes. Kurzchalia and colleagues are currently working to further purify gamravali and identify exactly how it gives cholesterol such a crucial role in the worms' lifecycle.

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522879

Large-scale exploration of growth inhibition caused by overexpression of genomic fragments in Saccharomyces cerevisiae

A screen of the Saccharomyces cerevisiae genome for fragments conferring a growth-impairment phenotype identified 714 fragments in about 84,000 clones tested.

Background The complete genome sequences of various eukaryotic model organisms such as Saccharomyces cerevisiae , Caenorhabditis elegans , Drosophila melanogaster , Arabidopsis thaliana and Schizosaccharomyces pombe , have revealed a large number of novel genes of unknown functions. In S. cerevisiae , for example, around 1,800 genes (of the total of around 5,800) encode proteins that so far remain functionally uncharacterized (compilation from Saccharomyces Genome Database (SGD) [ 1 ] April 2004). Since the completion of its DNA sequence [ 2 ], the genome of S. cerevisiae has been extensively studied, serving as a test case for novel and important developments in functional genomics. Such developments include transposon-mediated gene inactivation and tagging [ 3 ], the analysis of gene-expression networks through partial or complete transcriptome studies [ 4 - 6 ], two-hybrid screening [ 7 - 9 ], protein-complex purification [ 10 , 11 ], two-dimensional gel protein identification [ 12 ], proteome qualitative analysis by protein microarrays (see review in [ 13 ]) and protein abundance measurements after in situ gene tagging [ 14 ]. Even intergenic regions have been studied using microarray technology to characterize transcription-factor-binding sites and to map replication origins or recombination hotspots [ 15 , 16 ] (see also [ 17 ] for a review). Following a large cooperative effort between European and American labs, a nearly complete collection of deletion mutants of all yeast protein-coding genes is now available [ 18 - 20 ], which offers the possibility of systematically screening numerous phenotypes, including synthetic lethals [ 21 - 23 ], in search of novel gene functions. As a complement to gene inactivation, phenotypic changes resulting from gene overexpression may also be informative of gene functions. Indeed, in a number of cases, such as genes encoding cytoskeletal proteins or protein kinases and phosphatases, overexpression may lead to a lethal phenotype (see [ 24 ] for a review). The overexpression approach is complementary to the loss-of-function approach, as it leads to dominant phenotypes even in the presence of the wild-type gene, thus allowing the study of genes for which no loss-of-function mutants can be obtained. Overexpression of gene fragments can be equivalent to 'dominant negative mutation' in which the fragment disrupts the activity of the wild-type gene [ 25 ]. Overexpression can also activate specific pathways, leading to deleterious phenotypes: examples include genes involved in the yeast pheromone response pathway, such as STE4 , STE11 and STE12 (see [ 24 , 26 ] and references therein). In other cases, specific effects are not known, but the region responsible for toxicity has been identified. For example, lethality upon overexpression of Rap1p depends on the presence of the DNA-binding domain and an adjacent region [ 27 ]. In general, however, unless the domain structure of the protein is well understood, one cannot predict which segment(s) of it would act as a dominant mutant when overexpressed. Several yeast cDNA libraries have been screened for lethal or impaired growth phenotypes upon overexpression under the control of the GAL1 or GAL10 promoters on centromeric or multicopy plasmids [ 28 - 30 ]. Other libraries of random genomic DNA have also been screened for toxicity upon overexpression from the same promoters [ 24 , 26 ]. Whereas the four earlier studies each identified only a few genes (from 1 to 24 each, making a grand total of 43), Stevenson et al. [ 30 ] identified 185 genes (20 of which were shared with earlier work) that cause impaired growth when overexpressed. In the work reported here, we have screened the yeast genome with the aim of characterizing a list of fragments whose overexpression confers growth impairment. To do this, we constructed a yeast genomic library in a multicopy plasmid vector in which transcription is driven by a chimeric tetO- CYC1 promoter [ 31 ]. Random genomic inserts of a mean size of 700 base-pairs (bp) were overexpressed in yeast as translational fusions using the plasmid-borne initiation codon. Out of around 84,000 clones tested, we have identified the largest collection yet of toxic overexpressed fragments in yeast: 714 showed overexpression-dependent lethality or various degrees of growth impairments, identifying 454 protein-coding genes (91 of which are of unknown functions), and a variety of intergenic or other regions. Results Screening the library of yeast random genomic fragments for toxic phenotypes We have analyzed a total of 84,086 independent yeast transformants, each of which contains a random fragment of the yeast genome placed under the control of a doxycyclin-repressible promoter (Figure 1a,1b ). Effects on growth or survival were monitored by spotting serial dilutions of the transformants in the presence and absence of doxycyclin (uninduced and overexpression conditions respectively, Figure 1c ). Phenotypes were recorded using numerical values from 0 to 3 (Figure 2 ): value 3 was assigned to normal growth (similar to non-toxic control), 2 and 1 were assigned to intermediate growth levels (less abundant and/or smaller-sized colonies), and 0 was assigned to complete or almost complete absence of colonies (comparable to the toxic control on the same plate). We have retained 714 clones (0.85% of total) that show impaired growth in overexpression conditions (Table 1 ). Among these, 112 also show a slight or severe growth reduction (level 2 for 77 cases, or level 1 for 35 cases, respectively) in unexpressed conditions. Proof that the observed growth defects were caused by the presence of the plasmid rather than an accidental mutation in the clone was directly demonstrated by the recovery of the wild-type phenotype after plasmid loss using selection for resistance to 5-fluoroorotic acid (5-FOA) (Figure 2 ). Identification of the genomic inserts conferring toxic phenotypes Inserts of the selected clones were identified by DNA sequencing (Materials and methods). The complete list of inserts is described in Additional file 1 and 2, and results are summarized in Table 1 . A majority of inserts (493, or 69% of total) carry in-frame portions of annotated open reading frames (ORFs), excluding Ty and Y' ORFs. In addition, a significant number of inserts (162 (23%)) correspond to fragments of ORFs cloned either in antiparallel orientation or out-of-frame with respect to the initiator ATG codon or to intergenic regions. The 59 remaining cases (8% of total) correspond to fragments of transposable elements (17 clones) and subtelomeric Y' elements (9 clones), to RNA-coding genes (4 clones), and to non-chromosomal replicons such as the 2 mm plasmid and mitochondrial DNA (mtDNA) (29 clones). If any random fragment of the yeast genome were capable of generating a toxic phenotype, in-frame ORF fusions would represent only around 10-12% of the selected inserts (around 70% of the genome correspond to coding regions, and only one frame out of six corresponds to the natural frame). The fact that the toxic inserts correspond principally to in-frame portions of natural ORFs suggests that the coding part of the genome is the most prone to confer toxicity when overexpressed. Analysis of domains within in-frame ORF fragments The 493 inserts corresponding to in-frame ORF fragments represent 454 distinct annotated ORFs (see Materials and methods), which are randomly distributed throughout the 16 chromosomes of S. cerevisiae (see Additional file 1). In our screening, 32 ORFs were found twice, two ORFs were found three times and one ORF ( YHR056 c in the CUP1 region) was found four times, the cloned fragments being either overlapping (22 ORFs) or non-overlapping (13 ORFs). Mean size of the coding region of inserts is 659 bp. The chosen cloning strategy favors recovery of central-or carboxy-terminal coding parts of the natural yeast genes, whereas the amino-terminal coding regions are rare [ 7 ]. In our work, the cloned insert encompasses the entire gene in only six cases (additional file 3, column 20 to 23). In 154 additional cases, the insert corresponds to the carboxy-terminal portion of the natural protein (the stop codon is present). In 10 cases, the inserts start upstream of the natural ATG initiator codons, lengthening the natural peptides by reading in-frame through the untranslated region. Other cases correspond to the central coding region of natural genes. To find possible common characteristics, we have compared between themselves all the peptides encoded by in-frame ORF fragments. BLASTP analysis was combined with detection of characterized conserved domains, of COG patterns (clusters of predicted orthologous groups of proteins [ 32 ]), and of transmembrane spans (TMS) to identify toxic inserts similar to each other (see Materials and methods). Out of the 493 in-frame ORF fragments, a total of 170 were divided up into 57 distinct groups of similarity, containing from two to 12 inserts, including overlapping fragments of the same ORF (see Additional file 4). It is expected that several ORFs from a same paralogous gene family are found in a same group. Note that in 16 out of 57 groups, the inserts contain transport-specific domains and/or transmembrane spans. As well as comparing inserts to each other, we also analyzed the totality of the conserved domains present in all peptides encoded by the 493 toxic inserts (see Materials and methods). Characterized domains are found, at least partially, in a total of 281 inserts (see additional file 1 and 3). Of a total of 183 distinct domains, 46 are represented more than once. We have compared the frequency of these 46 domains among the toxic inserts versus their frequency among the 5,803 ORF-encoded proteins of the entire genome (Table 2 ). We find that 37 domains are significantly over-represented compared to a random expectation, suggesting that we have screened specific domains. These 37 domains correspond predominantly to various transporter domains (11 cases), such as amino-acid permeases and mitochondrial carrier protein domains. The toxicity of these domains is probably due to the presence of transmembrane spans. Indeed, 132 out of the 493 toxic peptides contain at least two transmembrane spans, including cases where one span is putative (see Materials and methods). Among these, 63 contain three or more predicted spans and 26 have five spans or more. Putative spans were also recognized in 84 other ORF fragments (seven with at least three spans, 15 with two spans, and 62 with one span) (see Additional file 1 and 3). RNA-and DNA-binding domains (nine cases) involved in replication, transcription or translation functions, such as PUF, KH and rrm, are also much more represented than expected (Table 2 ). The PUF domain is also involved in recruitment of proteins into a complex that controls mRNA translation (see [ 33 ] for review). Other important domains for interactions with polypeptides, phospholipids or small molecules (nine cases) are also over-represented. The WD40 motif, a propeller-like platform for stable or reversible binding of proteins in eukaryotes, has been found in inserts of 12 distinct ORFs (see additional data file 1). The 12 ORFs code for proteins having interactions with other proteins in complexes related to RNA processing or transcription [ 10 ], and nine have at least one partner also selected during our screening (see Discussion). Other interacting domains were found, such as dynamin, MRS6, and adaptin_N domains, which have roles in the dynamics of proteins, membranes and cytoskeleton, and PBD, a small domain which binds small GTPases and inhibits transcription activation. The PH domain, which binds phosphoinositides or other ligands and is involved in signal transduction, was found in inserts of three distinct ORFs involved in different functions: metabolism, cell fate, transcription (see Additional data file 3). Finally, other over-represented domains are related to metabolism and other functions (eight cases), of which several may be involved in interactions with other domains. The serine/threonine protein kinase domain (S_TKc) is significantly under-represented in our screen. Among the 10 toxic inserts whose cognate genes code for protein kinases (PK), only four contain this domain (Additional data file 3). In these four cases, the S_TKc domain is either truncated (Additional data file 4), or flanked by a coiled-coil region and/or a low-complexity segment. Two other inserts contain the PBD (and PH) domains, and the four remaining inserts contain no characterized domain to date. As it is known that overexpression of some protein kinases is deleterious for cells (see [ 24 ] and references therein), our results suggest that a domain different from the catalytic domain is responsible for the toxicity of these proteins, and that the fragments selected in our screen have a role in binding ligands such as substrates or regulators of protein kinase activity, or of proteins involved in the signaling cascades. Three other genes coding for protein kinases of the phosphatidylinositol 3-kinase (PI kinase) family are also represented in our screen by four toxic inserts, none of which contained the kinase domain (see Discussion). The remaining 137 domains (out of 183) were found only once each. Many correspond to functional categories described above, such as transport, metabolism, and interactions with nucleotides, other proteins or other ligands. Seven domains associated with ubiquitination functions were also found (see Additional data file 3 and 5). Several of the domains encountered have also been isolated as mammalian genetic suppressor elements (GSEs), which are cDNA fragments that inhibit cell growth (see [ 34 ] and references therein). In addition to the domains described above, we found toxic inserts coding for natural peptides without recognizable domains but containing regions of low complexity (56 cases). A number of these peptides are highly charged, either negatively or positively (see Additional data file 3). Such charged peptides might interact in an artifactual way with other charged domains of proteins or nucleic acids or with small molecules. Interestingly, the prion-like (Q+N)-rich domain was found in eight of the natural peptides having low-complexity regions. Nature of the selected genes We have seen above that 493/714 toxic inserts are in-frame fragments of protein-coding genes. The complete list of the 454 genes corresponding to these toxic inserts is given in Additional dat file 1 and 2. Their sizes range between 282 bp and 14,733 bp. The mean size of this distribution is 2,401 bp (standard deviation (SD) 1,671 bp), to be compared with a mean size of 1,444 bp (SD 1,094 bp) for the entire set of 5,803 ORFs of the yeast genome. The bias towards longer ORFs is expected from our cloning strategy (see above). Note that the 35 ORFs that we found more than once are nearly randomly distributed in various size classes. We examined the distribution of these genes according to different criteria, such as function, subcellular localization, viability and phylogeny (Table 3 ) and compared it to the distribution of the genes of S. cerevisiae . Among the 454 ORFs identified, 91 are unclassified, and function is not yet clear for six others (see Additional data file 3). The remaining ORFs represent a variety of functional classes (Table 3 ). Distribution of the 454 ORFs shows statistically significant deviations for eight out of the 15 functional classes, taking into account biases due to mean size of genes in each class. Globally, there is a deficit of genes involved in protein synthesis and of unclassified genes, and an excess of genes involved in transport facilitation and cellular transport (echoing the fact that we found many inserts containing transporter domains and transmembrane spans), in cell fate, in transcription and, to a lesser extent, in cell cycle/DNA processing and in homeostasis (regulation of/interaction with the environment). As seen above, many toxic inserts contain multiple predicted TMS. Such inserts correspond most often to genes coding for transporters or for non-transporter membrane proteins [ 35 ]. We have selected a total of 96 transporters (see Additional data file 3) of which 18 belong to the class of putative uncharacterized transporters, whose toxic inserts contain several TMS. Fourteen others belong to the class of transporters of unknown classification, including 13 genes of the nuclear-pore complex family, whereas there is a total of 58 genes in this family in the whole genome. On the other hand, 24 genes coding for non-transporter membrane proteins were also selected. Taken together, 120 transporters and non-transporter membrane proteins are represented in our screen, twice as many as expected (61 expected), as 782/5,803 ORFs are known or predicted as coding for such proteins [ 35 ]. The distribution of the proteins encoded by these genes in the cell is strongly biased in favour of the plasma membrane and against the cytoplasm, and, to a lesser extent, in favour of nucleus and cytoskeleton (Table 3 ). Although the majority of inserts originate from non-essential genes, we have found 96 essential genes (21%) among the selected ORFs. This is a significantly higher percentage than in the whole genome, where 939/5,803 genes (16.2%) are essential (Table 3 ). Using the classification from Malpertuy et al. [ 36 ] and additional updating (Génolevures [ 37 ]), we find that the majority of genes yielding toxic fragments in this work are conserved (336/454 (74%)) between S. cerevisiae and other sequenced organisms, whereas 106 (23%) are ascomycete-specific and 10 (2.2%) are orphan genes. This distribution is significantly different from the distribution among the 5,803 genes of S. cerevisiae , where 64% of protein-coding genes are conserved (see Table 3 ). The under-representation of orphan genes in our screen is already apparent in the under-representation of functionally unclassified genes, as a high rate of orphans of the whole genome (79%) are also unclassified (data from Génolevures [ 37 ] and Munich Information Center for Protein Sequences (MIPS) [ 38 ]). Toxicity of entire genes versus ORF fragments To compare the phenotypes conferred by overexpression of the entire gene and of the gene fragment, we have cloned the cognate entire genes of 13 in-frame toxic inserts into the vector pCMha191 (see Materials and methods). One criterion for the choice of the genes was the absence of a mutant phenotype of the corresponding gene disruption at the time this work was started, except for the NOP4 gene whose disruption is lethal. Six of these genes are singletons; three others have a paralog already known as toxic upon overexpression. Six out of the 13 still have no known function to date (Table 4 ). Expression at the protein level of both entire gene and gene fragment was verified by western-blot analysis, using an anti-hemagglutinin (HA) antibody (data not shown). As seen in Table 4 and Figure 3 , we found that overexpression of 10 genes was as toxic or more toxic than overexpression of the gene fragments. One gene, YGR149w , was less toxic in its entire version than in the truncated form, which was weakly toxic. Finally, we found that two genes, YML128c / MSC1 and YDL112w/TRM3 , showed no toxicity when overexpressed, whereas the cloned inserts were strongly toxic. In these two cases, the immunolocalization of overexpressed products was examined, and the cytoplasmic localization of the fragment agreed with the location of the natural gene product (data not shown), indicating that the toxic effect is not the result of mislocalization of the overexpressed fragment. The gene MSC1 had already been screened [ 24 ] as a toxic fragment in overexpression conditions, the region concerned being the same as in our screening. This gene has low similarity to a stress protein of Schizosaccharomyces pombe and has a role in meiotic recombination. The TRM3 gene contains a carboxy-terminal domain responsible for tRNA methyltransferase activity [ 39 ], which is absent from our insert. The protein is a member of a complex probably involved in signaling [ 10 ]. Analysis of other fragments Additional data file 2 analyzes the 221 other toxic inserts which do not correspond to in-frame fragments of annotated ORFs. Sixty-eight inserts correspond to natural ORF fragments cloned in an antiparallel orientation, most of them being entirely included within the ORF sequence (47 cases), the others overlapping the intergenic upstream region of the natural ORF (17 cases) and sometimes the next gene as well (four cases). Their toxicity can result either from the overexpression of an antisense RNA or from the overexpression of a toxic artificial peptide encoded by a fortuitous ORF. Several arguments favor the second hypothesis. First, short ORFs longer than 24 codons (maximum observed 250 codons), and in-frame with the start codon of the cloning vector, are observed in 53 cases (78% of the total). A number of those artificial ORFs are due to the 'mirror' effect produced by codon-biased natural ORFs [ 40 , 41 ]. But the fact that they are observed more than one-third of the time suggests a positive selection for toxic artificial peptides. Second, antiparallel ORF fragments do not correspond to a majority of essential genes, as might be expected from antisense RNA inhibition. Third, we have directly verified, for two inserts recloned in the same vector, that addition of a stop codon that blocks translation of the artificial ORF also suppresses toxicity (see Additional data file 9). Even if this concerns only two cases, we have no direct results indicating the existence of antisense RNA molecules that could block expression of essential genes. Fifty-three additional inserts correspond to natural ORF fragments cloned out-of-frame with respect to the plasmid-borne ATG codon, of which only 12 code for artificial ORFs longer than 24 codons (see Table 1 and Additional data file 2). Intergenic regions are represented by 41 inserts, of which 27 (65% of total) code for short artificial ORFs. In total, short artificial peptides may be encoded by 92 out of the 162 inserts described above. Comparison of the 92 peptides between themselves reveals several low-complexity sequences (see Additional data file 2), mostly encoded by antiparallel ORF fragments whose direct amino-acid sequence is itself of low complexity. Comparison with the proteins of S. cerevisiae and of all available sequenced organisms compiled in our internal database (GPROTEOME3, see Materials and methods) reveals no significant similarity. None of these artificial ORFs corresponds to the 137 new annotated yeast genes of Kumar et al. [ 42 ], to the 62 new genes of Oshiro et al. [ 43 ] or to the 84 genes of Kessler et al. [ 44 ]. Even though we have no evidence for antisense RNA activity, we cannot exclude a toxic effect due to the overexpressed transcript itself. Among the 59 remaining inserts, 17 belong to Ty elements, 10 of which are in-frame ORF fragments corresponding to TyB only (two of them containing the carboxy-terminal part of the rve domain (integrase core)), whereas all antisense fragments (three inserts) correspond to TyA. Y' elements, which are present in 20 copies in the genome, are represented by nine inserts, all coding for highly basic or acidic peptides (of which three are in-frame fragments of natural ORFs) which contain repeats of amino acids or motifs, and confer a strongly toxic effect (see Additional data file 2). Considering that these inserts are toxic, their observed number is not different from that expected from the size and number of Y' in the genome. Four inserts from yeast chromosome XII are fragments of genes coding for 18S or 25S RNA, two inserts being cloned in the sense orientation. The 2 mm plasmid is represented by 17 fragments, 10 of which are in-frame fragments of ORFs coding for REP1 , REP2 and FLP1 . The seven other inserts are out-of-frame or antisense fragments of FLP1 , or fragments of intergenic regions, all (except two) coding for artificial ORFs. Finally, mtDNA is represented by 12 fragments, mostly corresponding to intergenic regions on the minus strand of the chromosome. Artificial peptides highly enriched in the amino acids tyrosine (Y), isoleucine (I), and lysine (K) are encoded by 10 out of the 12 mitochondrial inserts. Discussion The general fitness of living organisms largely depends on a harmonious equilibrium between the various cellular components and on their capacity to maintain homeostasis. The intricate circuitries that regulate gene expression form the basis of these properties, and massive deregulation of single components may result in flagrant phenotypic defects leading to serious growth impairment or even cell death. Our large-scale screening of the yeast genome using random genomic fragments resulted in a collection of several hundreds of inserts showing toxic effects on cell survival or growth when overexpressed. These toxic effects are expected to result from several distinct molecular situations that have been encountered at various frequencies in our experiments. Of the total of 714 toxic inserts studied, a majority (69%) correspond to the overexpression of fragments originating from natural protein-coding genes (454 genes were identified in total). But, interestingly, a large minority (23%) correspond to noncoding DNA fragments. The remaining cases (less than 10% of the total) correspond to fragments of Ty or Y' elements, of the 2 μm plasmid or of mtDNA which, after analysis, can be attributed to one of the two previous categories. Toxic fragments of natural gene products are interesting to consider with respect to the functions of the corresponding genes. But the second category may be even more promising in that it offers us a description of DNA sequences that cannot be overexpressed in a cell without a deleterious effect. The toxicity of coding fragments may result from the imbalance between products of tightly controlled genes, or from the titration of active complexes by the presence of truncated proteins and/or isolated domains. In addition, nonspecific effects might also exist, for example, as a result of an abnormal intracellular localization of an artificially overabundant peptide or protein. We did not attempt to distinguish experimentally between these possibilities for all the coding inserts isolated in this work. Taking into account only specific effects, in the limited number of cases in which the entire gene corresponding to a toxic insert was cloned in the overexpression vector (see Results), we verified that toxicity was due, in most cases, to the disruption of the precise dosage of an essential cellular component (the entire protein is also toxic when overexpressed) and, in some cases, to the titration effect exerted by the incomplete fragment of the natural protein (the entire protein is not toxic when overexpressed). A few examples where the domain responsible for toxicity upon overexpression is known can be found in the literature. In the case of TOR1 and TOR2 genes, toxicity is specific to a central domain of the proteins distinct from their carboxy-terminal protein kinase domain; overexpression of the entire gene has no effect, and can even cure the negative effect of the overexpressed domain [ 45 ]. Alarcon et al. [ 45 ] have proposed that Tor proteins could serve as a scaffold on which to assemble other proteins for appropriate interaction with the kinase domain. Our results agree with this hypothesis, as four out of the five yeast genes belonging to the conserved family of PI kinase-related protein kinases - TOR1 , TOR2 , TEL1 and TRA1 - were selected in this work, all represented by inserts of the central region of these proteins (Figure 4 and Additional data file 3). In mammalian cells, overexpression of such fragments of ATM , a homolog of TEL1 , also has a negative effect [ 46 ]. In other cases in which overexpression of the entire gene is toxic, certain domains responsible for the toxicity have been mapped, for example the Myb DNA-binding domain of RAP1 (see Background), the ZnF C3H1 domain of CTH1 [ 47 ] and the bZIP domain of GCN4 [ 48 ]. All these DNA-binding domains were significantly over-represented in our screen (Table 2 ). Even in the absence of precise mapping of the toxic domain present in our clones, we were able to explore the nature of the domains found in each insert. Our experiment has shown a bias towards domains corresponding to transport functions and to various interactions (Table 2 ). As mentioned in Results, the toxic effect of transport-specific domains may be due to the presence of corresponding TMS. As our results also showed a bias towards a number of interaction domains, we have examined the known interactions of the proteins encoded by the 454 genes found in this screen (see Materials and methods). Genetic interactions were also considered, excluding the coexpression results obtained in microarray experiments. It appears that 88.3% of our genes (401/454, of which 70 are of unknown function) code for proteins which have known genetic or physical interactions, or are members of complexes (see Additional data file 3). Moreover, for 60% of these (242/401), at least one of their known partners is also found in our screen (see Additional data file 6 and 7). Among the 53 genes having no known interactions, 24 correspond to transporter or membrane proteins (see Additional data file 3). The biases we have observed show little overlap with previous screenings of S. cerevisiae , which had previously identified a total 231 genes or gene fragments that were toxic when overexpressed [ 24 , 26 , 28 - 30 , 49 ]. Among the 185 genes of Stevenson et al. [ 30 ], those involved in protein synthesis are represented twice as frequently as in the whole genome, whereas they are twice less frequent in our own experiment. In contrast, genes involved in transport facilitation and interactions with the environment were not over-represented in the Stevenson et al. experiment. Common biases are, however, observed in favor of transcription, cell-cycle and cellular transport genes. Overall, only 33 of our 454 ORFs were previously identified by the previous authors (the total rises to 78 if one considers individual gene studies). Twenty-five other genes from the previous screenings not found here are members of paralogous gene families represented in our work (see Additional data file 3). The limited overlap may result from partial genome coverage. However, by screening 84,086 clones (a coverage of around 4.5 genome equivalents), we must have encountered a total of 4,677 ORFs, each being represented 1.6 times as an ORF fragment (see Materials and methods). We have thus screened for toxicity around 80% of the natural yeast ORFs. But the limited overlap of results may also be explained by the experimental bias introduced by each technique. The previous experiments were mostly based on cDNA cloning, which favors short and highly expressed genes, whereas our genomic library favors large ORFs (mean size 800 ± 557 codons per ORF) and has no expression bias. In addition, the largest previous experiment [ 30 ] was done using centromeric plasmids and a galactose promoter as opposed to our multicopy vectors. Furthermore, our serial dilution drop assay is probably more sensitive to growth alteration than the replica techniques previously used. Finally, previous overexpression experiments relied on changing the nutrient composition of the growth medium (galactose vs glucose) whereas our experimental set-up relied on the presence/absence of a drug in a medium of the same nutritional composition. The finding of a large minority of toxic inserts corresponding to noncoding DNA is puzzling. Indeed, some of the toxic inserts originate from annotated but questionable ORFs, and some originate from antisense or intergenic fragments which can artificially be translated into small ORFs. None of these peptides has recognizable characterized domains, but many of them are charged, mostly positively (see Additional data file 2) and some have amino-acid sequences of low complexity. It could be proposed that all these small ORFs represent a reservoir of potentially new gene sequences in the genome. In addition, 100 of the in-frame toxic inserts had no characterized domains and sometimes no predicted secondary structure. These inserts do not contain conserved domains, COGs or TMS, and are not biased in amino-acid composition (see Additional data file 3). They may correspond to domains that have not yet been described, or to domains whose structure has diverged, but another possibility would be that some protein domains are perhaps not structured in a permanent way before evolving towards a structurally functional domain. Interestingly, a significant proportion of the expressed peptides we selected are specific to ascomycetes, or are even true orphan genes that have no known homolog in any other species than S. cerevisiae . A collection of toxic polypeptides, acting as genetic suppressor elements and interfering with major cellular functions, is of interest not only in antifungal research but also as a means of identifying new domains with major physiological roles. Finally, given the large number of inserts encoding very short ORFs (around 70 amino acids in the groups of antiparallel, intergene and out-of-frame fragments, and 80 in total, see Additional data file 2), we cannot exclude the possibility that some transcripts are toxic through hypothetical mechanisms that may include, for example, nonspecific interactions with other cellular or nuclear complexes or through overloading of some component(s) involved in RNA metabolism. Conclusions In a large-scale phenotypic screening of overexpressed random DNA fragments, we selected around 470 genes (including Ty, Y' and the 2 μm plasmid) whose domains inhibit or impair growth when overexpressed. Many functional categories are represented, transporter proteins being especially over-represented, and genes of unknown function represent one-fifth of our selection. Our approach gave access to genes controlling intracellular and membrane structures, as well as to genes whose deficiency is compensated for by genetic redundancy. Comparable approaches, using efficient phenotyping technology [ 50 ] and appropriate screening procedures, could be used for identification of genes involved in specific functions, such as homeostasis and response to stress. We have carried out an analysis of toxic protein domains, pointing out the importance of binding domains and of protein-protein interactions correlated to regulation of cell growth and cell division. This provides a large body of data for targeting more specific studies on the modular construction of proteins and the role of interaction domains in multicomponent assembly of physiological complexes. Finally, in some cases, the deleterious effects in our system of inserts that encode very short ORFs may suggest that overexpression of some transcripts is also toxic for cell growth. Materials and methods Strains and media Total yeast DNA from strain FY1679 ( Mata/α, ura3-52/ura3-52, trp1-Δ63/+, leu2-Δ1/+, his3-Δ200/+ ) [ 51 ] was a generous gift of A. Harington. Strains FYBL2-5D ( Matα, ura3-Δ851, trp1-Δ63, leu2-Δ1 ) [ 52 ] and FYAT-01 ( Matα, ura3-Δ851, trp1-Δ63, leu2-Δ1, his3-Δ200, ade2-661 ) (A. Thierry, unpublished work) were used for transformations and growth defect screening. All strains are isogenic derivatives of S288C. The yeast genomic library was constructed using Escherichia coli DH10B cells (Electromax DH10B, Gibco-BRL). Yeast cells transformed by pCMha190 recombinants were grown at 30°C on glucose synthetic complete medium lacking uracil (SC - URA) always supplemented with 10 μg/ml doxycycline (Sigma) (uninduced conditions). Phenotypic tests were done on solid medium (12 cm × 12 cm plates) containing 70 ml of SC - URA + 10 μg/ml doxycycline (uninduced conditions) or SC - URA without doxycycline (overexpression conditions). Yeast cells transformed by pCMha191 recombinants were grown at 30°C on SC - tryptophan medium, with or without addition of doxycycline. Plasmid loss was carried out on SC plates containing uracil (50 mg/l) and 0.1% of 5-fluoroorotic acid (5-FOA). Vector construction and cloning Plasmids pCMha184, pCMha189, and pCMha190 were derived from the centromeric (pCM184, pCM189) or episomal (pCM190) overexpression vectors, containing a tetracycline-regulatable promoter system and URA3 (pCM189, pCM190) or TRP1 (pCM184) as selection markers [ 31 ]. In the original vectors, a 33 bp Bam HI- Not I fragment was replaced by a synthetic linker with ends compatible with these sites and introducing an ATG codon followed by an in-frame HA-tag, a Bam HI cloning site, and stop codons in the three frames (Figure 1a ). The episomal pCMha191 vector was derived from pCMha184 ( TRP1 selection marker) by replacement of the centromere and replication origin with a 2 μm plasmid replication origin. This was PCR-amplified from pCMha190 using primers M1 and M2 (see Additional data file 8) using Pfu polymerase (Stratagene), and ligated to the 5,953 bp Eco RI- Bgl II fragment of pCMha184. The overexpression system was checked by cloning two short genes, MCM1 and AUAI (861 and 285 bp respectively), which are toxic when overexpressed under the control of a GAL1 promoter [ 29 ]. Both genes were PCR-amplified from yeast genomic DNA (see primers in Additional data file 8), cloned into vectors pCMha189 and pCMha190, and transformed into yeast strain FYAT-01. Only gene MCM1 , cloned in the high-copy pCMha190 vector, had a clear and constant toxic effect on yeast growth when overexpressed. We thus decided to build the library into pCMha190 and to choose the MCM1 gene as a control for toxic phenotype in overexpression conditions. Thirteen complete genes corresponding to 13 selected toxic inserts (see Results) and the MCM1 control gene were cloned into the Bam HI digested plasmid pCMha191 ( TRP1 marker) . Genes were PCR-amplified from genomic DNA (see primers in Additional data file 8). For each gene, two independent plasmids were transformed into yeast strain FYBL2-5D. In parallel, the same strain was transformed with the plasmids bearing the corresponding toxic inserts. Two toxic inserts, 156C1 and 57B6, which are antiparallel fragments of YGL039w and YAL062w/GDH3 ORFs, were modified by PCR synthesis (see Additional data file 9), then recloned in vivo into pCMha190 using homologous recombination [ 53 ] in yeast strain FYBL2-5D. Constructions were verified by sequencing. In parallel, original plasmids extracted from transformed strain FYAT-01 were retransformed into strain FYBL2-5D. Phenotypes in uninduced and overexpression conditions were observed in seven independent transformants in each case. Construction of a random yeast genomic library into pCMha190 The adaptor-based strategy [ 7 , 54 ] was used to prevent self-ligation of the vector and ligation of multiple inserts. Sonicated total yeast DNA fragments from FY1679 ranging in size from 200 to 1,200 bp were treated with mung-bean nuclease, T4 DNA polymerase and Klenow enzyme following the manufacturers' protocols. Blunt ends of DNA fragments were ligated to the following adaptor: 5'-pATCCCGGACGAAGGCC-3' 3'-GGCCTGCTTCCGG-5'. Excess of unligated adaptors and small adaptor-DNA fragments were eliminated by two consecutive purifications using Chroma spin+TE-400 columns (Clontech). Vector predigested with Bam HI and filled in with dGTP by the Vent (exo-) polymerase (New England Biolabs) was ligated to the purified adaptor-DNA inserts (800 ng = ~0.16 pmol vector, 800 ng = ~1.7 pmol inserts, in a 40 μl final volume per ligation). The ligation result is drawn in Figure 1b . Electroporations of 40 μl of E. coli DH10B cells were performed with 1.8 μl of ligation mix and plated onto 2YT medium (16.1 g/l Bacto tryptone, 10.1 g/l Bacto yeast extract, 5 g/l NaCl, 15 g/l Bacto agar) containing 100 μg/ml ampicillin (four 12 × 12 cm plates per transformation) giving 25,000 to 45,000 clones per transformation. A total of 51 independent transformations were made. This corresponds to 1,888,000 clones. We tested 150 clones for the presence of an insert and observed that more than 85% contained one (average size 700 bp, minimum 220 bp, maximum 1,620 bp). Colonies from each transformation were pooled and distinct Qiagen Tip 500 DNA preparations were made and stored separately for yeast transformation. Final concentration of DNA was 300 to 1,300 ng/μl. The detailed protocol of library construction is available on request. Another library had previously been constructed with the same vector ligated to a distinct DNA-adaptor preparation and was partially used, giving rise to 160,000 primary clones. Characteristics of the transformants were the same as described above. Eight pools of plasmid DNA were prepared from this first library. Yeast transformations We carried out a total of 28 independent transformations of yeast by the LiAc method [ 55 ]: five with the yeast strain FYAT-01 using five distinct plasmid DNA preparations from the first library and 23 with the strain FYBL2-5D using 23 distinct plasmid DNA preparations from the main library. Aliquots of each transformation were spread onto 24 × 24 cm plates (Q-Pix Trays, Genetix) containing SC - URA + doxycycline, to obtain 1,000 to 3,000 yeast transformants per plate. Screening and storage of toxic clones Transformed yeast clones were transferred into fresh liquid SC - URA + doxycycline medium in 96-well microplates by manual picking (30,015 clones) or with the Q-Pix robot (54,071 clones) for overnight growth. Non-toxic and toxic control clones (transformed by empty pCMha190 vector and by vector bearing MCM1 , respectively) were also inoculated into each microplate. Cultures were grown overnight at 30°C and stored at 4°C before dilutions for phenotypic examination. Screening of the toxic phenotypes after overexpression was done in a two-round selection, using the 'drop test', which allowed us to see even slightly impaired growth effects. Ten-fold serial dilutions in water were made from each 96-well culture microplate with a Beckman Biomek 2000 robot, then manually replicated with the 96-pin Beckman replicator onto SC - URA + doxycycline and SC - URA plates in parallel (Figure 1c ). Clones showing impaired growth in overexpression conditions were streaked onto SC - URA + doxycycline medium for colony isolation, then transferred (one subclone per streak) into a new 96-well microplate and grown for 22 h at 30°C. This plate served as a mother plate for four culture microplates which were grown overnight at 30°C (one plate for the second-round screening, another plate for PCR amplification on colonies for sizing and sequencing the inserts and two plates for storage at -80°C). For the second round of screening, cultures were diluted (1/100 to 1/10,000 dilution) and tested on SC - URA + doxycycline and SC - URA plates in parallel. Phenotypes in the presence and absence of doxycycline (uninduced and overexpression conditions respectively) were scored as described in Results and Figure 2 . Between the two rounds of screening, most of the clones conserved a comparable phenotype. For those displaying an important difference, a new subclone was tested again, and the transformant was rejected if the phenotype revealed was inconsistent. The dependence of the phenotype on the presence of the plasmid was demonstrated using two methods: for 150 tested clones, wild-type phenotypes were recovered after plasmid loss using 5-FOA resistance selection; for 35 other clones, plasmids were extracted from transformed strain FYAT-01 and retransformed into strain FYBL2-5D, in which the toxic phenotypes were confirmed. Identification of the toxic inserts at the nucleotide and peptide levels Inserts of the selected clones were PCR-amplified directly from cultures using primers SEQ4 and SEQ8 (Figure 1b ). The length of each insert was determined by gel electrophoresis and the 5' junction was sequenced using primer SEQ1. Identification of each insert in our internal database (see below) was carried out using the DOGEL program [ 56 ], adapted by Nicolas Joly (Institut Pasteur) to our purpose. This program gives the start position of the insert on chromosomes, the corresponding genetic object and the start position in the ORF relative to the natural ATG (see Additional data files 1 and 2). We first verified the sequence at the junction with the adaptor-insert. Correct in-frame ligation between vector and adaptor-1 was observed for 632 clones (88.5% of total). For the remaining 82 clones, base substitutions, and short (one to three nucleotides) deletions within the adaptor-1 were observed (nine and 18 cases respectively). A total of 46 cases of a single G addition at the junction vector-adaptor-1, and 15 partial vector sequence duplicates were found (see Additional data files 1 and 2). As the incorrect ligations introduced no stop codon between the initiation codon of the vector and the first codon of the insert, these clones were conserved for further analysis. In these cases, the start position of the insert relative to the chromosome and to the ORF coordinates was corrected manually. For analysis of in-frame ORF fragments, sequences of peptides encoded by toxic inserts were extracted from the complete sequences of S. cerevisiae proteins, taking the first amino acid corresponding to the junction with the adaptor as the starting point and the end of the insert or the last codon of the ORF as the end point. Fragments of mtDNA, 2 μm plasmid, and DNA coding for Y'-ORFs, Tys, long terminal repeats (LTRs) and RNA were examined manually for their position relative to the coding sequences. Sequences of inserts other than in-frame ORF fragments were systematically translated into amino-acid sequences from the junction with the adaptor up to the first stop codon encountered in the insert. Sequences coding for more than 24 amino acids were internally compared using BLASTP, then compared to the S. cerevisiae annotated ORFs and to the 308,738 sequences of our internal database (see below). Databases Genetic entities corresponding to the toxic inserts were identified by comparison with the DNA sequences of the 16 chromosomes (available in the Comprehensive Yeast Genome Database (CYGD) at MIPS [ 38 ]); with our own interpretation table containing the coordinates of 6,256 coding sequences (CDS or ORFs), which comprises the new genes found by Blandin et al. [ 57 ]; with the 2 μm plasmid DNA sequence [ 58 ]; and with the yeast mitochondrial sequence [ 59 ]. The set of 6,256 ORFs of S. cerevisiae was filtered to eliminate all spurious ORFs or unlikely real genes, as well as Ty, Y' and mitochondrial ORFs, yielding a final list of 5,803 ORFs [ 60 ]. For all comparisons of the set of 454 toxic ORFs with the set of ORFs of the entire genome, we used these 5,803 ORFs. GPROTEOME3 is an updated version of the GPROTEOME sequence library [ 61 ] containing 308,738 predicted protein sequences from 60 organisms (F. Tekaia, personal communication). Analysis of the toxic inserts and of their cognate genes Comparisons among the peptides encoded by in-frame ORF fragments were done using BLASTP [ 62 ]. Alignments corresponding to E-values equal to or lower than 10 -3 were examined individually before validation. Conserved domains or patterns of COGs [ 32 ] were identified using the NCBI Conserved Domain Search service (CD-Search [ 63 , 64 ]). The NCBI Conserved Domain Database (cdd.v1.62) [ 65 ] contained domains derived from Smart [ 66 ] and Pfam [ 67 ] collections, plus contributions from NCBI such as COGs, leading to 11,088 position-specific score matrices (PSSMs). A routine was written for extraction of the CD-Search results obtained for the toxic inserts and the 5,803 proteins of the entire genome. The cut-off E-value was chosen to be equal to or less than 10 -4 for most domains, and 10 -3 for short domains (60 amino acids or fewer). Domains were considered as present even when represented only partially. In describing genes (Table 4 ) or toxic in-frame inserts (see Additional data files 1, 3 and 4), only one domain (giving the best hit) was chosen for a given insert, among several possible hits. In contrast, to compare the frequency of a given domain among all toxic inserts versus its frequency among the 5,803 proteins of S. cerevisiae (Table 2 ), all occurrences were taken into account, giving a total of 843 occurrences among the 493 toxic inserts, and a total of 15,925 occurrences among the 5,803 proteins. Searches for transmembrane spans (TMS) were done using TopPredII [ 68 ] implemented by Deveaud and Schuerer (Institut Pasteur), predicting both certain and putative TMS. The isoelectric points (IEPs) of proteins or peptides were calculated using iep algorithm from the European Molecular Biology Open Software Suite (EMBOSS) [ 69 ]. Descriptions of selected genes and their products were retrieved from the Yeast Proteome Database [ 70 ] (release of March 2002; this database is no longer freely available), and from MIPS [ 38 ]. Functional classes, cellular localizations and a list of essential genes were retrieved from MIPS [ 38 ]; gene classes (conserved/asco-specific/orphan) are from Génolevures [ 37 ]. Paralogous gene families of S. cerevisiae [ 57 ] are accessible at Génolevures [ 37 ] through gene or ORF name. We searched for the participation of the selected ORFs in protein-protein interactions (genetic and physical) and in protein complexes using three different sources: YPD [ 70 ] files for individual proteins; protein complexes defined by Gavin et al. [ 10 ]; data compilations concerning protein-protein interactions and complexes, extracted from SGD [ 1 ], MIPS [ 38 ] and unpublished two-hybrid experiments (M. Fromont-Racine and C. Saveanu, personal communication). How representative is our screening? We consider that our library contains DNA fragments randomly distributed throughout the genome. Out of 84,086 clones tested, 11% (9,530) contain a DNA fragment cloned in-frame with the frame of the natural ORF (~68% of the genome corresponds to coding regions, and only one frame out of six corresponds to the natural frame), the others containing noncoding, out-of-frame or antisense DNA fragments. If we use the simplifying assumption that all genes are equally represented among the 9,530 clones (not taking into account the size diversity of genes), each of the 5,803 ORFs will be represented 1.64 times (9,530/5,803). The probability P x of encountering any gene x times is described by a Poisson distribution: where m , the mean of the distribution, is 1.64. This is used to estimate the fraction of genes not encountered: for x = 0 and probability p = 0.19, the number of non-encountered genes = 1,126. Thus, by screening a total of 84,086 clones, we have encountered a maximum of 4,677 ORFs (5,803 - 1,126). Additional data files The following additional data are available with the online version of this article. Additional data file 1 contains lists and coordinates of the 493 in-frame fragments of annotated ORFs giving toxic phenotypes when overexpressed, and short description of their cognate genes. Additional data file 2 contains a list and description of the 221 DNA toxic inserts other than in-frame ORF fragments. Additional data file 3 gives a description of the peptides encoded by the 493 toxic ORF fragments, and of the cognate proteins. Additional data file 4 gives the content of the 57 groups of peptide inserts sharing similarities. Additional data file 5 gives a list and description of protein domains found only once among the toxic inserts. Additional data file 6 lists the genes selected in this work whose products are members of complexes [ 10 ]. Additional data file 7 lists genes selected in this work whose products are known as interacting with each other. Additional data file 8 contains the sequences of the oligonucleotides used in this work. Additional data file 9 contains a figure showing the phenotypes induced by overexpression of antiparallel ORF fragments before and after introduction of a stop codon upstream of the artificial ORFs. Supplementary Material Additional data file 1 Lists and coordinates of the 493 in-frame fragments of annotated ORFs giving toxic phenotypes when overexpressed, and short description of their cognate genes Click here for additional data file Additional data file 2 A list and description of the 221 DNA toxic inserts other than in-frame ORF fragments Click here for additional data file Additional data file 3 A description of the peptides encoded by the 493 toxic ORF fragments, and of the cognate proteins Click here for additional data file Additional data file 4 The content of the 57 groups of peptide inserts sharing similarities Click here for additional data file Additional data file 5 A list and description of protein domains found only once among the toxic inserts Click here for additional data file Additional data file 6 The genes selected in this work whose products are members of complexes Click here for additional data file Additional data file 7 Genes selected in this work whose products are known as interacting with each other Click here for additional data file Additional data file 8 The sequences of the oligonucleotides used in this work Click here for additional data file Additional data file 9 A figure showing the phenotypes induced by overexpression of antiparallel ORF fragments before and after introduction of a stop codon upstream of the artificial ORFs Click here for additional data file

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526377

Fungus Holds Clues to the Evolution of Sex Chromosomes

It's a basic biological principle that living things share certain fundamental traits. That's why understanding the mechanisms of cell division in single-celled yeast, say, can offer insight into cell division in humans. Now Joseph Heitman and colleagues report that the evolutionary events that spawned sex chromosomes in yeast resemble those that shaped sex chromosomes in animals. Strictly speaking, yeast—the common name for single-celled fungi—don't have sex chromosomes; they have sex-determining regions within chromosomes, called mating type, or MAT, loci. In a comparative genomic analysis of the MAT locus in three species of the human pathogenic fungus Cryptococcus , Heitman and colleagues found that this fungal sex-determining region arose via a series of discrete events that echo those that shaped mammalian sex chromosomes. A primary benefit of sexual reproduction is the genetic diversity gained from reshuffling genetic material during meiosis, which creates gametes. Yeast sex, such as it is, accomplishes the same thing. Of course, sexual identity for a fungus does not take the form of sperm or egg but of mating type a , for example, and mating type alpha. Still, yeast manage a measure of complexity and considerable elegance in the systems they deploy to sexually reproduce. In ascomycetes, like baker's yeast, the MAT locus is small and includes just a few genes. The genes that determine a cell's a or alpha mating status are alleles (variants) of a single MAT locus. Cells with the MAT a allele are mating type a , while cells with the MATalpha allele exhibit mating type alpha. A cell can switch its mating type when genetic exchange, or recombination, between two mating loci occurs. Human pathogenic fungus Cryptococcus In basidiomycetes, like the corn smut Ustilago maydis —a maize pathogen that some consider a culinary delicacy—mating is more complex, and sexual identity is determined by two unlinked genomic regions with distinct classes of genes. Cells must be of different mating types at both loci to allow sexual reproduction. To their surprise, Heitman and colleagues discovered that the mating locus of Cryptococcus neoformans —a basidiomycete fungus that infects humans and is associated with transplant recipients, patients with AIDS, and other immune-compromised patients—exhibits several unique features, common to neither ascomycetes or their basidiomycete relatives. Unlike most basidiomycetes, the C. neoformans locus occupies a single region and is unusually large, spanning more than 100 kilobases and containing over 20 genes, including those typically segregated in separate locations in other basidiomycetes. Like on the human Y chromosome, the sex-determining genes of C. neoformans are interspersed with non-sex-related genes. And unlike ascomycetes, which also have a single active MAT locus and two mating types, no mating type switching occurs as there are no silent mating type cassettes in the genome. Heitman and colleagues sequenced the a and alpha alleles of C. neoformans ' closest relative, C. gattii , and compared these variants to four already characterized variants derived from two C. neoformans subspecies. All six MAT alleles share characteristic features, including a fairly large size, a common gene set, and dramatic genomic migration during evolution (which is unusual compared to other genomic regions in the three strains). Each MAT allele has genes with different evolutionary histories, ranging from ancient to recent, that fall into distinct patterns based on shared nucleotide composition and mating type. The patterns correlate with how long the genes have occupied the MAT locus, suggesting how it evolved. The authors hypothesize that this novel structure was formed by chromosomal rearrangements that linked two unrelated genomic regions into a single region. Recombination between these sex-determining regions was suppressed after other events blurred their boundaries. Specific genes in the once separated loci then attracted mobile elements in the genome to their sites, thus precipitating expansion of the locus. Because the Cryptococcus MAT locus resembles the evolution and structure proposed for the ancient Y chromosome, the authors argue that Cryptococcus can serve as a valuable model to study the molecular dynamics of sex chromosomes.

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539294

Expression of the Tpl2/Cot oncogene in human T-cell neoplasias

Background Tpl2/Cot oncogene has been identified in murine T-cell lymphomas as a target of MoMuLV insertion. Animal and tissue culture studies have shown that Tpl2/Cot is involved in interleukin-2 (IL-2) and tumor necrosis factor-α (TNF-α) production by T-cells contributing to T-cell proliferation. In the present report we examined a series of 12 adult patients with various T-cell malignancies, all with predominant leukemic expression in the periphery, for the expression of Tpl2/Cot oncogene in order to determine a possible involvement of Tpl2/Cot in the pathogenesis of these neoplasms. Results Our results showed that Tpl2/Cot was overexpressed in all four patients with Large Granular Lymphocyte proliferative disorders (LGL-PDs) but in none of the remaining eight patients with other T-cell neoplasias. Interestingly, three of the LGL-PD patients displayed neutropenia, one in association with sarcoidosis. Serum TNF-α levels were increased in all Tpl2/Cot overexpressing patients while serum IL-2 was undetectable in all subjects studied. Genomic DNA analysis revealed no DNA amplification at the Tpl2/Cot locus in any of the samples analyzed. Conclusions We conclude that Tpl2/Cot, a gene extensively studied in animal and tissue culture T-cell models may be also involved in the development of human LGL-PD and may have a role in the pathogenesis of immune manifestations associated with these diseases. This is the first report implicating Tpl2/Cot in human T-cell neoplasias and provides a novel molecular event in the development of LGL-PDs.

Background Cells may transform to a malignant phenotype following accumulation of distinct genetic events that result in altered protein expression pattern, thus facilitating uncontrolled proliferation. Such genetic events target specific oncogenes that act in concert to provide the malignant phenotype. Tpl2/Cot oncogene was initially cloned as a MoMuLV proviral integration locus in murine T-cell lymphoma cells, resulting in its carboxy-terminal truncation[ 1 , 2 ]. Expression of the truncated form of Tpl2 as a transgene in T-cells under the control of the lck promoter in mice results in rapid development of T-cell lymphomas [ 3 ]. Expression of Tpl2 is associated with T-cell activation. Overexpression of the wild type Tpl2 in the Jurkat T-cell leukemia cell line results in NFkB and NFAT activation and subsequent IL-2 and TNF-α expression [ 4 - 7 ]. In the CTLL2 IL-2 dependent cell line Tpl2 promotes cell proliferation by activating E2F-dependent transcription [ 8 ]. Tpl2/Cot is, therefore, tightly associated with T-cell neoplasms and T-cell activation and proliferation. Studies in human tumor specimens have shown that Tpl2/Cot is overexpressed in early stage breast cancer [ 9 ], in EBV-related Hodgkin lymphomas and nasopharyngeal carcinomas [ 10 ] and occasionally in gastric and colon adenocarcinomas [ 11 ]. To our knowledge, no available data exist on human hematologic neoplasias, other than Hodgkin lymphoma. Given the compelling evidence of the importance of Tpl2/Cot in experimental and tissue culture models of T-cell neoplasias, we designed a study to investigate possible involvement of Tpl2/Cot in the pathogenesis of human T-cell neoplasias. Specifically, we studied 12 adults with various T-cell neoplasias to obtain a broad spectrum of T-cell malignancies, all with predominant leukemic expression, and examined whether Tpl2/Cot expression is deregulated in the transformed cells. The expression levels of Tpl2/Cot were quantitated by SybrGreen real-time RT-PCR using three different quantitation approaches (standard curve[ 12 ], absolute fluorescence increase [ 13 ] and the M.W.Pfaffl method [ 14 ]) as well as the conventional semi-quantitative RT-PCR. Results Evaluation of Tpl2/Cot mRNA expression in T-cell neoplasias To determine the levels of expression of Tpl2/Cot mRNA we first established and validated a real time PCR approach. Melting curves showed that there were no by-products in both Tpl2/Cot and GAPDH reactions (Figure 1B ). CVs of mean triplicate Ct (threshold cycle) ranged from 0.1% to 0.92% which account for a low intra-assay variability. A series of five 10 to 20-fold dilutions of a standard cDNA from different cDNA preparations were also run multiple times to determine primer efficiencies. Linear regression analysis of the standard curves [mean Ct plotted against the log(RNA input)] showed high linearity, with regression coefficients greater than 0.997 (Figure 1A ). We used the standard curve slope in the equation Figure 1 Real-Time PCR validation A . Serial dilutions of a standard cDNA duplicates for the construction of standard curves for GAPDH and Tpl2/Cot. The curve slopes shown on the upper right corner of each plot are -3.61 and -3.59 respectively. Black arrows correspond to dilution 1:10; white arrows to 1:20; left-pointed arrow is NTC (no-template control). X-axes represent the Log of the dilution factor, Y-axes the mean Ct of duplicates. B . Dissociation (melting) curve of the PCR products, showing a peak at 81.7°C for both Tpl2/Cot(black arrow) and GAPDH(white arrow), while NTCs have either no peak or a peak at a much lower temperature (thin arrow). (1) E = 10 -1/slope to calculate the mean efficiency of Tpl2/Cot and GAPDH primers. The slopes were almost equal (from -3.59 to -3.62) for both primers which showed that we could use the Pfaffl method [ 14 ] without the need of a standard curve in every set of reactions. Specifically, to determine the ratio (R) of the normalized Tpl-2/Cot expression of sample vs control we used the equation where Etarget and Eref are the Efficiencies of the target (Tpl2) and reference genes(GAPDH) respectively which both were equal to a mean of 1.89 and ÄCt is the difference between the mean Ct of control cDNA(CTR) and patient cDNA (Sample). To confirm our results we also tried the Absolute Fluorescence Increase method using the LinRegPCR software v.7.5 which measures the actual efficiency of each amplification curve by fitting its linear part in a simulation plot of the Log (fluorescence) versus Cycle and calculates the efficiency from the slope of a linear regression model of the simulation curve [ 13 ]. As control we used triplicates of cDNA from 3 healthy individuals, for which we calculated the mean Ct. The control samples were representative among 22 control specimens with similar values. Results were similar to those obtained by the Standard Curve method [ 12 ] (data not shown). A total of 12 adult samples with T- and NK-cell neoplasias were analyzed according to the described method. They all had leukemic expression in the periphery. Morphology was assessed by light microscopy on peripheral blood smears, including measurement of absolute LGL number. Four out of twelve (33%) patients tested markedly overexpressed Tpl2/Cot (p = 0.034), as determined by either conventional RT-PCR or real-time qPCR (Tables 2 and 3 , Figure 2 ). Interestingly, all the Tpl2/Cot overexpressing patients had LGL-PD, three with the phenotype of CD3+ T-LGL leukemia and one with the CD3- pattern of chronic NK-lymphocytosis. Three of these patients displayed neutropenia not attributable to BM infiltration, one in association with sarcoidosis (Table 1 ). The fourth patient with monoclonal T-LGL lymphocytosis had a co-current cutaneous T-cell lymphoma which, during follow-up, demanded systemic chemotherapy. Table 1 Patient characteristics Patient no. Disease Sex Age %T+NKcells/PBMC Disease state Co-existent conditions 1 SS F 88 87 PP 2 CTCL with monoclonal T- LGL lymphocytosis M 63 86 RD 3 T-LGL leykemia F 75 93 RD Neutropenia 4 MF M 91 71 RD 5 MF M 85 71 RD 6 TLL F 35 77 RD 7 T-PLL M 85 67 RD Myositis 8 Chronic NK -lymphocytosis F 67 64 Stable for two years Neutropenia 9 T-ALL M 16 95 LB RD 10 T-LGL leukaemia with reactive NK lymphocytosis F 60 71 Stable for 10 years Sarcoidosis (past), neutropenia 11 PTCL secondary to MF M 52 85 PR 12 Pre-T-ALL M 37 95 LB RD Abbreviations: SS, Sezary syndrome; CTCL, Cutaneous T cell Lymphoma; T-LGL, T-large granular lymphocyte; MF, Mucosis Fungoides; NK, natural killer cell; TLL, T-lymphoblastic lymphoma; T-PLL, T-Prolymphocytic leukemia; T-ALL, T-acute lymphoblastic leukemia; LB, Lymphoblast ;PP, primary progressive; RD, recently diagnosed; PR, partial response. Table 2 Tpl-2/Cot expression in PBMC Patient no. Tpl2-Norm a Fold increase (R) b CV(%)-R c Ctr (n = 3) 0.04 ± 0.019 1.00 ± 0.119 45.3 1 0.02 ± 0.000 0.59 ± 0.009 1.6 2 0.13 ± 0.005 3.88 ± 0.149 3.8 3 0.13 ± 0.005 4.04 ± 0.143 3.5 4 0.02 ± 0.003 0.66 ± 0.092 13.9 5 0.04 ± 0.005 1.22 ± 0.092 11.8 6 0.06 ± 0.003 1.70 ± 0.085 5.0 7 0.06 ± 0.002 1.81 ± 0.076 4.2 8 0.17 ± 0.008 5.21 ± 0.251 4.8 9 0.01 ± 0.000 0.32 ± 0.007 2.2 10 0.16 ± 0.006 5.04 ± 0.194 3.8 11 0.04 ± 0.001 1.09 ± 0.037 3.4 12 0.04 ± 0.004 1.22 ± 0.108 8.9 a. Tpl2/Cot normalized to GAPDH by equation (3) in the text ± SD. b. Tpl2/Cot-Norm (sample) ratio to Tpl2/Cot-Norm (control) by equation (2) in the text ± SD. Samples in which Tpl2/Cot is overexpressed are shown in bold. c. % Coefficient variance of (R). Table 3 Summary of the results Patient no. Immunophenotype Serum TNF-α (pg/ml) Tpl2/Cot (fold increase) 1 CD3+, CD4+, CD7- 1.7 0.6 2 CD3+, CD8+, CD56+, CD57+, TCRαβ+ 1.9 3.9 3 CD3+, CD8+, CD16, 56-, CD57+, TCRαβ+ 1.8 4 4 CD3+, CD4+, CD5+, CD7+ 1.5 0.7 5 CD3+, CD4+, CD7- 1.9 1.2 6 TdT+, CD5+, CD2+ 2.5 1.7 7 CD3+CD4+CD25-TCRγ+ 3.8 1.8 8 CD2+, CD3-, CD16+, CD56-, CD57-, TCR- 6.2 5.2 9 TdT+, CD2+, CD3+, CD7+, CD4-, CD8- 2.6 0.3 10 2 populations: a) T-LGL CD3+, CD8+, CD56+, CD57+, TCR γ+ b) NK CD2+, CD3-, CD16, 56+, 57- 1.9 5 11 CD3+, CD4+, CD7-, CD25-, TCRαβ+ 1.2 1.1 12 TdT+, cCD3+, CD7+, CD4-CD8- 1.5 1.2 Figure 2 Tpl2/Cot mRNA expression in T-cell neoplasms. Representative semi-quantitative RT PCR for Tpl-2 mRNA expression in patients and controls. A . Tpl2/Cot PCR product of 228 bp B . β-Actin PCR product of 214 bp. Samples no 2, 3, 8 and 10 are overexpressed compared to the control and correspond to the LGL-PD patients shown in Table 1. Evaluation of serum TNF-α and IL-2 levels Overexpression of Tpl2/Cot in Jurkat T-cells induces TNF-α expression [ 7 ]. Tpl2 also regulates TNF-α expression in macrophages by activating ERK and thus controlling the posttranscriptional modification of the TNF-α mRNA, which is necessary for its export from the nucleus [ 15 ]. We, therefore, evaluated serum TNF-α levels in all patients studied. In order to have an internal negative control in the study we analyzed 8 samples from normal blood donors. The mean TNF-α concentration in patient sera was 2.37 ± 1.4 pg/ml with a range between 1.2 pg/ml and 6.2 pg/ml, while in the control group it was 0.6 ± 0.2 pg/m. The mean patient TNF-α value was statistically significant higher than the respective of the healthy controls (p = 0.002), as was TNF-α in the LGL-PD group alone compared to the control group (p = 0.006). It is of interest that the patient with chronic NK-lymphocytosis and neutropenia displayed the highest TNF-α value (6.2 pg/ml) that was associated with the highest Tpl2/Cot expression (5.2 fold compared to control) (Table 3 ) suggesting a relationship between Tpl2/Cot overexpression and TNF-α overproduction in this patient. There was no difference in TNF-α levels between the group of LGL-PD patients and the group of patients with the remaining T-cell other neoplasias (p = 0,392). Tpl2/Cot overexpression in Jurkat and EL-4 T-cells induces IL-2 secretion in culture. We, therefore, examined the expression levels of IL-2 in the sera from the 12 patients studied and 8 control samples. No circulating IL-2 was detected in the sera of the patients or the control donors (data not shown). Overexpression of Tpl2/Cot in LGL-PD is not associated with gene amplification Overexpression of Tpl2/Cot in human breast cancer has been associated with amplification of the tpl2 genomic locus [ 9 ]. We, thus, evaluated whether the overexpression of Tpl2/Cot in T-cell malignancies is associated with amplification of the genomic tpl2 locus. For this purpose genomic DNA was isolated from PBMCs of the same patients and part of the genomic tpl2 locus was amplified using multiplex PCR. As reference gene we used the IFN -γ gene. The results showed a similar tpl2 / IFN -γ ratio in all cases (Figure 3 ) indicating that overexpression of Tpl2/Cot in LGL-PDs is not due to gene amplification. Figure 3 The tpl2 / cot genomic locus is not amplified in the T-cell neoplasms analyzed. Multiplex PCR for the quantification of the Tpl-2 / Cot gene load, relative to IFN -γ gene in patient and control DNA: tpl2 / cot PCR product detected at 139 bp; IFN -γ PCR product at 250 bp. No significant difference between samples is evident. Discussion Experimental data based on in vitro and animal model have shown that Tpl2/Cot is an important regulator in the transduction of signals leading to T-cell activation [ 16 ]. Overexpression of this kinase results in increased proliferation of T-cells by activating E2F-dependent transcriptional activity [ 8 ]. A truncated form present in rodents exhibits increased catalytic activity, and when overexprerssed as a T-cell-specific transgene in mice it induces tumors within 3–9 months [ 3 ]. Tpl2/Cot activates the transcription factors NFAT and NFkB in T-cells, which drive the transcription of several cytokine genes such as IL-2 and TNF-α [ 4 , 7 ]. In macrophages, Tpl2/Cot is essential for the activation of ERK by LPS via TLR4 and the export of TNF-α mRNA from the nucleus [ 15 ]. The preceding evidence supports a possible involvement of Tpl2/Cot in human T-cell neoplasias. Investigation of Tpl2/Cot expression in human tumor specimen has shown that it is occasionally overexpressed in colon and gastric adenocarcinomas [ 11 ] and human breast cancer tissues [ 9 ]. Tpl2/Cot overexpression has also been detected in a hepatocellular carcinoma cell line [ 17 ] and in patient tumor tissue from EBV-related Hodgkin lymphomas and nasopharyngeal carcinomas[ 10 ]. In the present study we analyzed peripheral blood from patients with T- and NK-cell lymphoproliferative diseases at the time where the patients were not receiving any treatment and had profound leukemic expression in the periphery. Out of the 12 cases analyzed, Tpl2/Cot was found overexpressed in three T-LGL-leukemias and in one chronic NK-lymphocytosis accounting for all four LGL-PDs studied. These findings suggest that Tpl2/Cot deregulation may be a defining molecular event for the development of this type of neoplasias. Given the role of Tpl2/Cot in T-cell proliferation via activation of E2F transcription [ 8 ], overexpression of this kinase may contribute to neoplastic cell proliferation. Large granular lymphocyte lymphoproliferative diseases (LGL-PD) are relatively rare and not well defined disorders, frequently associated with autoimmune diseases or immune mediated manifestations such as rheumatoid arthritis (pseudo-Felty syndrome [ 18 ]), RF positivity, neutropenia and pure red cell aplasia [ 19 , 20 ]. They present clinical, morphological and immunological distinct features, resulting from chronic proliferation of CD3+ or CD3- granular lymphocytes. In the CD3+ cases the proliferating cells express CD8 and NK-associated surface antigens such as CD16, the LGL-specific CD57 antigen and CD45RA, and display also clonal rearrangement of the TCR alpha-beta or, less often, gamma-delta chains, thus representing cytotoxic effector T-cells[ 21 ]. The T-LGL leukemias are by definition indolent, but there are rare reports indicating evolution to high grade lymphoma [ 22 ]. Limited data on recurrent chromosomal aberrations exist [ 23 , 24 ]. In the rare CD3- cases the cells are TCR-, CD2+, CD16+ and CD56+ representing, therefore, true NK-cell proliferations corresponding to the aggressive NK-cell leukemias or to the – usually benign – chronic NK-lymphocytosis [ 19 , 20 ]. Lack of Tpl2/Cot gene amplification in our LGL-PD patients indicate that overexpression is either due to changes in the regulation of Tpl2/Cot gene activation or mRNA stability. Such changes can be either primary (i.e. mutations that affect the promoter or the mRNA stability) or secondary (i.e. activation of transcription factors that affect the Tpl2/Cot promoter or signaling molecules that affect the stability of its mRNA). There is accumulating evidence suggesting that patients with LGL-PDs display frequently immune manifestations and increased TNFα production by the neoplastic T-cells has been reported to play a role in their pathogenesis[ 20 , 25 ]. Interestingly, three of our LGL-PD patients displayed neutropenia not attributable to BM infiltration while one of these patients displayed also sarcoidosis. Evaluation of circulating TNF-α level in the patients showed that the highest TNF-α value was found in the LGL-PD patients that displayed also the highest Tpl2/Cot expression among the subjects studied. These findings are in agreement with previous reports demonstrating that Tpl2/Cot is involved in TNF-α expression and secretion [ 7 , 15 ] while provide evidence for a causal relationship between the Tpl2/Cot overexpression, the TNF-α overproduction and the pathogenesis of neutropenia in LGL-PD patients [ 25 ]. TNF-α was increased in the sera of patients where Tpl2/Cot expression was not elevated, indicating that in these patients TNF-α may be upregulated via alternative pathways not associated with overexpression of Tpl2/Cot. Conclusions In conclusion, data from the present study suggest that Tpl2/Cot overexpression may have a role in the development of certain types of human T-cell neoplasms thus confirming experimental data on animal and tissue culture models for the role of Tpl2/Cot in T-cell malignancies. Methods Patients Peripheral blood samples from 12 adults aged 16–88 years (median age 64 years) with various T- and NK-cell neoplasias with peripheral blood leukemic expression were collected during a two-year time period. Patients with signs of infection or recently subjected to cytotoxic therapy were excluded from the study. Diagnosis was based on morphological, immunophenotypic and genomic studies and histological findings of bone marrow and/or lymph node biopsies and disease was classified according to the WHO classification [ 26 ]. There were three patients with T-LGL leukemia, one patient with chronic NK lymphocytosis, one patient with Sezary syndrome (SS), two patients with Mucosis Fungoides (MF), one patient with T-Prolymphocytic Leukemia (T-PLL), two patients with T-acute Lymphoblastic Leukemia (T-ALL), one patient with T-lymphoblastic Lymphoma (TLL) and one patient with Peripheral T-Cell Lymphoma (PTCL) secondary to MF. Detailed patient characteristics are presented in Table 1 . Complete blood counts and flow cytometric analysis of peripheral blood lymphocytes were performed at the day of blood collection for the molecular study. In addition, patient sera were obtained by centrifugation of 4 ml of non-anticoagulated blood at 3000 rpm for 10 min and were stored at -80°C for IL-2 and TNF-α measurement. Peripheral blood specimens from 22 healthy volunteers age- and sex-matched with the patients were collected and used as controls. This research project was subjected to and approved by the Ethics Committee of the University Hospital of Heraclion. Peripheral Blood Mononuclear Cell isolation and RNA extraction Peripheral blood mononuclear cells (PBMC) were isolated from 9 ml of fresh EDTA-K 3 anti-coagulated peripheral blood samples by Lymphoprep density centrifugation (Nycomed Pharma AS, Norway). PBMCs were immediately lyzed in suitable volume of Trizol LS reagent (Invitrogen, UK) and mRNA was isolated according to the manufacturers' protocol. An aliquot of 1 μg of total RNA was treated with 1 IU DNase I, Amplification Grade (Invitrogen, UK) to eliminate any traces of genomic DNA. Semi-quantitative RT-PCR First strand cDNA was synthesized by reverse transcription of 1 μg total RNA using the Thermoscript™ RT kit (Invitrogen, UK). 0.8 μl of cDNA were amplified in a 20 μL PCR reaction containing 250 nM of each primer, 200 nM dNTPs, 0.5 IU Taq polymerase and 2 μl of 10X reaction buffer (Platinum Taq DNA Polymerase kit, Invitrogen, UK). Reactions were first optimized for annealing temperature, Mg and primer concentration (data not shown). Primers for Tpl2/Cot detection were derived from exons 3 and 4 of the human Tpl2/Cot gene (Genbank accession no: AL547407), spanning an 8.5 kb intron to prevent co-current genomic DNA amplification and their sequences were: forward 5'-CAG TAA TCA AAA CGA TGA GCG TTC TAA-3', reverse 5'-GAA CAT CGG AAT CTA TTT GGT AAC GTC-3' producing a 228 bp-length amplicon. For normalization of mRNA input differences human beta actin mRNA (Genbank accession no: BC013835) was detected using the following primers: forward 5'-CCG GCC AGC CAG GTC CAG A-3', reverse 5'-CAA GGC CAA CCG CGA GAA GAT G-3', amplifying a 214 bp cDNA fragment. In each reaction two negative controls were included by either omitting reverse transcriptase at the RT step or cDNA template respectively. PCR reactions were performed on a thermal cycler (PTC-200 MJ Reasearch with heated lid) and repeated 3 times with different cDNAs from the same mRNA. Expression of Tpl2/Cot was determined by semi-quantitative, relative RT-PCR. An amplification curve for each gene was acquired by performing the reaction with increasing number of PCR annealing cycles to identify the exponential phase of the reaction. The thermocycling parameters were as follows: initial denaturation at 94°C for 5 min followed by 34 for Tpl2 or 23 for actin cycles at 94°C for 30 sec, 54°C for 30 sec, 72°C for 30 sec and a final extension at 72°C for 7 min. The RT-PCR products were analysed by electrophoresis in a 2.5% agarose gel, stained with 0.2 μg/ml ethidium bromide and visualized in a UVP transiluminator (Gel Doc 1000 Bio Rad). The band intensity was analysed by a densitometric image analysis system (TINA scan v2.07) and the results were expressed as a ratio between Tpl2/Cot and β-actin band intensity. Real-time PCR Primers for real-time PCR were designed with the Primer Express Software v.2 (Applied Biosystems) and selected so that they amplify a region of no more than 150 bp, they have similar GC content, same Tm, no more than 3 G or C's at the 3' end and no secondary structure formation. To exclude primers with more than 3 consecutive complementary bases between them we used Qiagen Oligo Analysis & Plotting Tool (Qiagen, Germany). Primers forTpl2/Cot annealed to exons 6–7 and their sequences were: forward 5'-TCC TAA GGA CCT CCG AGG AAC-3', reverse 5'-CCC AGG CTG TAG ATG TCT GCT-3', amplifying a 93 bp region. GAPDH was used as a reference gene to compensate for mRNA input differences. Primers for GAPDH derived from exons 2–3 (Genbank accession no: BC023632) and their sequences were: forward 5'-GGA AGG TGA AGG TCG GAG TCA-3', reverse 5'-GTC ATT GAT GGC AAC AAT ATC CAC T-3', amplifying a 101 bp region. Primer concentration, cDNA dilution, Mg concentration and annealing temperature were optimized so that a maximum fluorescent signal with no inhibition from RT components and a similar reaction yield from both primer sets could be obtained. For the real-time PCR study cDNA from first strand synthesis treated with RNase H for 20' at 37°C was diluted 1:20 with DNAse-free water and 5 μl were used in a 20-μl reaction mixture consisting of 10.4 μl 2x SybrGreen PCR Master mix, 6 mM final concentration of MgCl 2 and 500 nM of the Tpl2/Cot primers or 100 nM of GAPDH primers. Reactions were carried out using an ABI Prism 7000 sequence detector (Applied Biosystems, Foster City, CA, USA) according to manufacturer's instructions. The thermal profile used consisted of 2 min at 50°C,10 min at 95°C and 40 repeats of denaturation at 95°C for 15 sec and annealing-extension-fluorescence data acquisition at 60°C for 1 min. A post-PCR Melting Curve Analysis was performed by a 20-min slow ramp from 60° to 95°C to confirm that there were no by-products. Samples were run in a 3% agarose gel after the end of reaction to confirm specificity. All samples were run in triplicates and two negative controls with either no reverse transcriptase or no cDNA template were included. Reaction was repeated twice in different days to estimate inter-assay variation. Results were analyzed using the ABI Prism 7000 SDS software (v.1.1, Applied Biosystems) and Excel for further quantitative study. DNA extraction and multiplex PCR To detect possible gene amplification in cases with Tpl2/Cot overexpression we used a multiplex DNA PCR protocol with interferon-γ (IFN-γ) as a reference gene amplified at the same reaction tube. High molecular weight DNA was isolated from PBMC by proteinase K digestion and phenol chloroform extraction with ethanol precipitation and diluted in TE buffer. The primers for Tpl2/Cot were: forward 5'-GCG ACG GAT TGA GGT TTG-3', reverse 5'-GCG TTT CAG GCG TAT GGA-3' amplifying a 139 bp region of intron 1(Genbank accession no.AY309013) and the primers for IFN-γ were: forward 5'-ATG CAG GTC ATT CAG ATG TAG C-3', reverse 5'-TTG GAT GCT CTG GTC ATC TTT A-3' amplifying a 250 bp fragment containing intron and exon sequences between exons 2–3 (Genbank accession no: J00219). 50 ng of DNA were used in a 20 μl reaction containing 10 μl Platinum qPCR Supermix-UDG (Invitrogen, UK), 250 nM Tpl2/Cot primers and 100 nM IFNγ primers. The number of amplification cycles was adjusted so that the reaction terminated at the middle of the exponential phase of both products (data not shown). The thermal profile consisted of a denaturation step at 95° for 10 min, followed by 29 repeats at 95°C for 15 sec, 58°C for 30 sec and 72°C for 30 sec and a final extension at 72°C for 7 min. PCR products were analysed in a 3% agarose gel, visualized and scanned as described earlier and the Cot/IFNγ ratio was determined. Peripheral blood lymphocyte immunophenotype and assessment of T-cell clonality Two-color flow cytometry was used for the analysis of peripheral blood lymphocyte subpopulations. In brief, 100 μL aliquots of EDTA-anticoagulated peripheral blood samples were surface stained with each of the following PE- or FITC-conjugated mouse antihuman monoclonal antibodies: anti-CD2, anti-CD3, anti-CD4, anti-CD8, anti-CD5, anti-CD7, anti-CD16, anti-CD56, anti-CD57, anti-CD19, anti-CD25, anti-CD11b, anti-CD79a, anti-FMC7 and anti-HLA-DR (Beckman Coulter, France). Cells were also stained for intracellular Terminal deoxy-transferase (TdT) (Beckman Coulter) and CD3 using the IntraPrep intracellular staining kit (Beckman Coulter). PE- or FITC-conjugated mouse IgG of appropriate isotype served as negative controls. Following 30 min incubation at room temperature and two washes with phosphate buffer saline (PBS)-1% fetal bovine serum (FBS)-0.05% azide, contaminating red cells were lysed with 0.12% formic acid and samples were fixed in 0.2% parafolmadeyde using the Q-prep reagent system (Coulter, Louton, England). Analysis on 10,000 events was performed in an Epics Elite model flow cytometer (Coulter) in the lymphocyte gate. Clonality assessment of peripheral blood T-cells was performed by analysing quantitatively different variable regions of the T-cell receptor (TCR) β chain (Vβ repertoire) of CD3 + cells by means of flow cytometry using the IOTest Beta Mark kit (Beckman Coulter), according to the manufacturer's instructions [ 27 ]. T-cell clonality assessment was also performed by PCR analysis using primers for the TCR V(D)J junction in PBMC derived DNA, according to standard methods in a reference laboratory. ELISA TNF-α concentration was determined using the High Sensitivity human TNF-α ELISA kit (R&D, USA) or the IL-2 ELISA kit (R&D, USA) according to the manufacturer's instruction. According to the manufacturer, the sensitivity of these assays are 0.12 pg/ml and 7 pg/ml respectively. Statistical analysis The comparison of Tpl2/Cot mRNA expression between patient and control samples was performed by means of the nonparametric Mann-Whitney U test using as variables the mean normalized Tpl-2 expression of each sample and control, described by the equation (3) Tpl-2 norm = (E) -(mean CtTpl2-meanCtGAPDH) where E is the mean common efficiency of the reference and target gene that we calculated (E = 1.89, see section Results)[ 14 ]. The Mann-Whitney U test was also used for the comparison of the TNF a and IL-2 levels between the Tpl-2 overexpressing patients, the controls and the non-overexpressing patients. Authors' contributions AVC collected specimens and patient data and performed the major body of the experimental work as well as the data analysis and the preparation of the manuscript. HAP was involved in flow cytometric analysis, patient selection and manuscript preparation. ANM and GDE were involved in the study design, the interpretation and evaluation of the clinical data and manuscript preparation. CT conceived the project, performed the ELISAs, was responsible for the coordination of the experimental procedures and was involved in the preparation of the manuscript.

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From biomedicine to natural history research: EST resources for ambystomatid salamanders

Background Establishing genomic resources for closely related species will provide comparative insights that are crucial for understanding diversity and variability at multiple levels of biological organization. We developed ESTs for Mexican axolotl ( Ambystoma mexicanum ) and Eastern tiger salamander ( A. tigrinum tigrinum ), species with deep and diverse research histories. Results Approximately 40,000 quality cDNA sequences were isolated for these species from various tissues, including regenerating limb and tail. These sequences and an existing set of 16,030 cDNA sequences for A. mexicanum were processed to yield 35,413 and 20,599 high quality ESTs for A. mexicanum and A. t. tigrinum , respectively. Because the A. t. tigrinum ESTs were obtained primarily from a normalized library, an approximately equal number of contigs were obtained for each species, with 21,091 unique contigs identified overall. The 10,592 contigs that showed significant similarity to sequences from the human RefSeq database reflected a diverse array of molecular functions and biological processes, with many corresponding to genes expressed during spinal cord injury in rat and fin regeneration in zebrafish. To demonstrate the utility of these EST resources, we searched databases to identify probes for regeneration research, characterized intra- and interspecific nucleotide polymorphism, saturated a human – Ambystoma synteny group with marker loci, and extended PCR primer sets designed for A. mexicanum / A. t. tigrinum orthologues to a related tiger salamander species. Conclusions Our study highlights the value of developing resources in traditional model systems where the likelihood of information transfer to multiple, closely related taxa is high, thus simultaneously enabling both laboratory and natural history research.

Background Establishing genomic resources for closely related species will provide comparative insights that are crucial for understanding diversity and variability at multiple levels of biological organization. Expressed sequence tags (EST) are particularly useful genomic resources because they enable multiple lines of research and can be generated for any organism: ESTs allow the identification of molecular probes for developmental studies, provide clones for DNA microchip construction, reveal candidate genes for mutant phenotypes, and facilitate studies of genome structure and evolution. Furthermore, ESTs provide raw material from which strain-specific polymorphisms can be identified for use in population and quantitative genetic analyses. The utility of such resources can be tailored to target novel characteristics of organisms when ESTs are isolated from cell types and tissues that are actively being used by a particular research community, so as to bias the collection of sequences towards genes of special interest. Finally, EST resources produced for model organisms can greatly facilitate comparative and evolutionary studies when their uses are extended to other, closely related taxa. Salamanders (urodele amphibians) are traditional model organisms whose popularity was unsurpassed early in the 20 th century. At their pinnacle, salamanders were the primary model for early vertebrate development. Embryological studies in particular revealed many basic mechanisms of development, including organizer and inducer regions of developing embryos [ 1 ]. Salamanders continue to be important vertebrate model organisms for regeneration because they have by far the greatest capacity to regenerate complex body parts in the adult phase. In contrast to mammals, which are not able to regenerate entire structures or organ systems upon injury or amputation, adult salamanders regenerate their limbs, tail, lens, retina, spinal cord, heart musculature, and jaw [ 2 - 7 ]. In addition, salamanders are the model of choice in a diversity of areas, including vision, embryogenesis, heart development, olfaction, chromosome structure, evolution, ecology, science education, and conservation biology [ 8 - 15 ]. All of these disciplines are in need of genomic resources as fewer than 4100 salamander nucleotide sequences had been deposited in GenBank as of 3/10/04. Here we describe results from an EST project for two ambystomatid salamanders: the Mexican axolotl, Ambystoma mexicanum and the eastern tiger salamander, A. tigrinum tigrinum . These two species are members of the Tiger Salamander Complex [ 16 ], a group of closely related species and subspecies that are widely distributed in North America. Phylogenetic reconstruction suggests that these species probably arose from a common ancestor about 10–15 million years ago [ 16 ]. Ambystoma mexicanum has a long research history of over 100 years and is now principally supplied to the research community by the Axolotl Colony [ 17 ], while A. t. tigrinum is obtained from natural populations in the eastern United States. Although closely related with equally large genomes (32 × 10 9 bp)[ 18 ], these two species and others of the Complex differ dramatically in life history: A. mexicanum is a paedomorphic species that retains many larval features and lives in water throughout it's life cycle while A. t. tigrinum undergoes a metamorphosis that is typical of many amphibians. Like many other traditional model organisms of the last century, interest in these two species declined during the rise of genetic models like the fly, zebrafish, and mouse [ 19 ]. However, "early" model organisms such as salamanders are beginning to re-attract attention as genome resources can rapidly be developed to exploit the unique features that originally identified their utility for research. We make this point below by showing how the development of ESTs for these two species is enabling research in several areas. Furthermore, we emphasize the value of developing resources in model systems where the likelihood of information transfer to multiple, closely related taxa is high, thus simultaneously enabling both laboratory and natural history research programs. Results and Discussion Selection of libraries for EST sequencing Eleven cDNA libraries were constructed using a variety of tissues (Table 1 ). Pilot sequencing of randomly selected clones revealed that the majority of the non-normalized libraries were moderate to highly redundant for relatively few transcripts. For example, hemoglobin-like transcripts represented 15–25% of the sampled clones from cDNA libraries V1, V2, and V6. Accordingly, we chose to focus our sequencing efforts on the non-normalized MATH library as well as the normalized AG library, which had lower levels of redundancy (5.5 and 0.25% globins, respectively). By concentrating our sequencing efforts on these two libraries we obtained transcripts deriving primarily from regenerating larval tissues in A. mexicanum and several non-regenerating larval tissues in A. t. tigrinum . Table 1 Tissues selected to make cDNA libraries. ID Tissue cDNAs sequenced GARD limb blastema 1029 MATH limb blastema 16244 V1 tail blastema 1422 V2 brain 3196 V3 liver 792 V4 spleen 337 V5 heart 38 V6 gill 3039 V7 stage 22 embryo 96 AG liver, gonad, lung, kidney, heart, gill 19871 Further information is found in Methods and Materials. EST sequencing and clustering A total of 46,064 cDNA clones were sequenced, yielding 39,982 high quality sequences for A. mexicanum and A. t. tigrinum (Table 2 ). Of these, 3,745 corresponded to mtDNA and were removed from the dataset; complete mtDNA genome data for these and other ambystomatid species will be reported elsewhere. The remaining nuclear ESTs for each species were clustered and assembled separately. We included in our A. mexicanum assembly an additional 16,030 high quality ESTs that were generated recently for regenerating tail and neurula stage embryos [ 20 ]. Thus, a total of 32,891 and 19,376 ESTs were clustered for A. mexicanum and A. t. tigrinum , respectively. Using PaCE clustering and CAP3 assembly, a similar number of EST clusters and contigs were identified for each species (Table 2 ). Overall contig totals were 11,190 and 9,901 for A. mexicanum and A. t. tigrinum respectively. Thus, although 13,515 more A. mexicanum ESTs were assembled, a roughly equivalent number of contigs were obtained for both species. This indicates that EST development was more efficient for A. t. tigrinum, presumably because ESTs were obtained primarily from the normalized AG library; indeed, there were approximately twice as many ESTs on average per A. mexicanum contig (Table 2 ). Thus, our EST project yielded an approximately equivalent number of contigs for A. mexicanum and A. t. tigrinum , and overall we identified > 21,000 different contigs. Assuming that 20% of the contigs correspond to redundant loci, which has been found generally in large EST projects [ 21 ], we identified transcripts for approximately 17,000 different ambystomatid loci. If ambystomatid salamanders have approximately the same number of loci as other vertebrates (e.g. [ 22 ]), we have isolated roughly half the expected number of genes in the genome. Table 2 EST summary and assembly results. A. mex A. t. tig cDNA clones sequenced 21830 24234 high-quality sequences 19383 20599 mt DNA sequence 2522 1223 seqs submitted to NCBI 16861 19376 sequences assembled 32891 a 19376 PaCE clusters 11381 10226 ESTs in contigs 25457 12676 contigs 3756 3201 singlets 7434 6700 putative transcripts 11190 9901 a Includes 16,030 ESTs from [20]. Identification of vertebrate sequences similar to Ambystoma contigs We searched all contigs against several vertebrate databases to identify sequences that exhibited significant sequence similarity. As our objective was to reliably annotate as many contigs as possible, we first searched against 19,804 sequences in the NCBI human RefSeq database (Figure 1 ), which is actively reviewed and curated by biologists. This search revealed 5619 and 4973 "best hit" matches for the A. mexicanum and A. t. tigrinum EST datasets at a BLASTX threshold of E = 10 -7 . The majority of contigs were supported at more stringent E-value thresholds (Table 3 ). Non-matching contigs were subsequently searched against the Non-Redundant (nr) Protein database and Xenopus tropicalus and X. laevis UNIGENE ESTs (Figure 1 ). These later two searches yielded a few hundred more 'best hit' matches, however a relatively large number of ESTs from both ambystomatid species were not similar to any sequences from the databases above. Presumably, these non-matching sequences were obtained from the non-coding regions of transcripts or they contain protein-coding sequences that are novel to salamander. Although the majority are probably of the former type, we did identify 3,273 sequences from the non-matching set that had open reading frames (ORFs) of at least 200 bp, and 911 of these were greater than 300 bp. Figure 1 Results of BLASTX and TBLASTX searches to identify best BLAST hits for Ambystoma contigs searched against NCBI human RefSeq, nr, and Xenopus Unigene databases . Table 3 Ambystoma contig search of NCBI human RefSeq, nr, and Xenopus Unigene databases. A. mex A. t. tig # BLASTX Best Matches 6283 5545 < E -100 630 870 < E -50 > E -100 2015 1990 < E -20 > E -50 2153 1595 < E -10 > E -20 967 745 < E -7 > E -10 518 345 The distribution of ESTs among contigs can provide perspective on gene expression when clones are randomly sequenced from non-normalized cDNA libraries. In general, frequently sampled transcripts may be expressed at higher levels. We identified the 20 contigs from A. mexicanum and A. t. tigrinum that contained the most assembled ESTs (Table 4 ). The largest A. t. tigrinum contigs contained fewer ESTs than the largest A. mexicanum contigs, probably because fewer overall A. t. tigrinum clones were sequenced, with the majority selected from a normalized library. However, we note that the contig with the most ESTs was identified for A. t. tigrinum: delta globin. In both species, transcripts corresponding to globin genes were sampled more frequently than all other loci. This may reflect the fact that amphibians, unlike mammals, have nucleated red blood cells that are transcriptionally active. In addition to globin transcripts, a few other house-keeping genes were identified in common from both species, however the majority of the contigs were unique to each list. Overall, the strategy of sequencing cDNAs from a diverse collection of tissues (from normalized and non-normalized libraries) yielded different sets of highly redundant contigs. Only 25% and 28% of the A. mexicanum and A. t. tigrinum contigs, respectively, were identified in common (Figure 2 ). We also note that several hundred contigs were identified in common between Xenopus and Ambystoma ; this will help facilitate comparative studies among these amphibian models. Table 4 Top 20 contigs with the most assembled ESTs. Contig ID # ESTs Best Human Match E-value MexCluster_4615_Contig1 415 (NM_000519) delta globin E -39 MexCluster_600_Contig1 354 (NM_182985) ring finger protein 36 isoform a E -110 MexCluster_6279_Contig1 337 (NM_000559) A-gamma globin E -32 MexCluster_10867_Contig1 320 (NM_000558) alpha 1 globin E -38 MexCluster_5357_Contig1 307 (NM_000558) alpha 1 globin E -37 MexCluster_9285_Contig3 285 (NM_001614) actin, gamma 1 propeptide 0 MexCluster_7987_Contig3 252 (NM_001402) eukaryotic translation elongation f1 0 MexCluster_9285_Contig1 240 (NM_001101) beta actin; beta cytoskeletal actin 0 MexCluster_9279_Contig3 218 (NM_000223) keratin 12 E -113 MexCluster_11203_Contig1 181 (NM_002032) ferritin, heavy polypeptide 1 E -70 MexCluster_8737_Contig2 152 (NM_058242) keratin 6C E -131 MexCluster_3193_Contig1 145 (NM_004499) heterogeneous nuclear ribonucleoprotein E -90 MexCluster_8737_Contig7 134 (NM_058242) keratin 6C E -131 MexCluster_5005_Contig3 132 (NM_031263) heterogeneous nuclear ribonucleoprotein E -124 MexCluster_6225_Contig1 125 (NM_001152) solute carrier family 25, member 5 E -151 MexCluster_1066_Contig1 122 [31015660] IMAGE:6953586 E -16 MexCluster_8737_Contig4 114 (NM_058242) keratin 6C; keratin, epidermal type II E -132 MexCluster_8187_Contig2 113 (NM_005507) cofilin 1 (non-muscle) E -65 MexCluster_2761_Contig1 109 (NM_001961) eukaryotic translation elongation factor2 0 MexCluster_9187_Contig1 105 (NM_007355) heat shock 90 kDa protein 1, beta 0 A. t. tigrinum TigCluster_6298_Contig1 654 (NM_000519) delta globin E -38 TigCluster_10099_Contig2 193 (NM_001614) actin, gamma 1 propeptide 0 TigCluster_6470_Contig1 167 (NM_000558) alpha 1 globin E -39 TigCluster_9728_Contig2 142 (NM_000477) albumin precursor E -140 TigCluster_6594_Contig1 117 (NM_001402) eukaryotic translation elongation f1 0 TigCluster_5960_Contig1 91 (NM_001101) beta actin; beta cytoskeletal actin 0 TigCluster_7383_Contig1 77 (NM_001614) actin, gamma 1 propeptide 0 TigCluster_6645_Contig1 76 (NM_001063) transferrin 0 TigCluster_7226_Contig4 74 (NM_006009) tubulin, alpha 3 E -160 TigCluster_7191_Contig1 67 (NM_019016) keratin 24 E -89 TigCluster_10121_Contig1 64 (NM_005141) fibrinogen, beta chain preproprotein 0 TigCluster_6705_Contig1 63 (NM_000558) alpha 1 globin E -39 TigCluster_7854_Contig1 62 (NM_021870) fibrinogen, gamma chain isoform E -121 TigCluster_6139_Contig1 52 (NM_001404) eukaryotic translation elongation f1 0 TigCluster_7226_Contig2 51 (NM_006009) tubulin, alpha 3 0 TigCluster_10231_Contig1 44 (NM_003018) surfactant, pulmonary-associated prot. E -08 TigCluster_6619_Contig1 36 (NM_000041) apolipoprotein E E -38 TigCluster_7232_Contig2 35 (NM_003651) cold shock domain protein A E -46 TigCluster_5768_Contig1 34 (NM_003380) vimentin E -177 TigCluster_9784_Contig3 32 |XP_218445.1| similar to RIKEN cDNA 1810065E05 E -15 Figure 2 Venn diagram of BLAST comparisons among amphibian EST projects. Values provided are numbers of reciprocal best BLAST hits ( E< 10 -20 ) among quality masked A. mexicanum and A. t. tigrinum assemblies and a publicly available X. tropicalis EST assembly Functional annotation For the 10,592 contigs that showed significant similarity to sequences from the human RefSeq database, we obtained Gene Ontology (23) information to describe ESTs in functional terms. Although there are hundreds of possible annotations, we chose a list of descriptors for molecular and biological processes that we believe are of interest for research programs currently utilizing salamanders as model organisms (Table 5 ). In all searches, we counted each match between a contig and a RefSeq sequence as identifying a different ambystomatid gene, even when different contigs matched the same RefSeq reference. In almost all cases, approximately the same number of matches was found per functional descriptor for both species. This was not simply because the same loci were being identified for both species, as only 20% of the total number of searched contigs shared sufficient identity (BLASTN; E< 10 -80 or E< 10 -20 ) to be potential homologues. In this sense, the sequencing effort between these two species was complementary in yielding a more diverse collection of ESTs that were highly similar to human gene sequences. Table 5 Functional annotation of contigs A. mex A. t. tig Molecular Function (0016209) antioxidant (0016209) 25 29 binding (0005488) 3117 2578 chaparone (0003754) 100 85 enzyme regulation (003023) 193 223 motor (0003774) 73 75 signal transduction (0004871) 344 375 structural protein (0005198) 501 411 transcriptional reg. (0030528) 296 221 translational reg. (0045182) 94 59 bone remodeling (0046849) 8 8 circulation (0008015) 23 78 immune response (000695) 182 263 respiratory ex. (0009605) 254 288 respiratory in. (0009719) 72 58 stress (0006950) 263 320 Biological Process (0008150) Cellular (0009987) activation (0001775) 4 6 aging and death (0008219) 158 148 communication (0007154) 701 696 differentiation (0030154) 31 20 extracellular mat. (0043062) 4 4 growth and main. (0008151) 1731 1445 migration (0016477) 8 14 motility (0006928) 163 154 Developmental (0007275) aging (0007568) 32 21 embryonic (0009790) 6 1 growth (0040007) 2 2 morphogenesis (0009653) 350 272 pigment (0048066) 13 26 post embryonic (0009791) 8 13 reproduction (0000003) 42 27 Physiological (0007582) coagulation (0050817) 22 73 death and aging (0016265) 159 148 homeostasis (0042592) 22 27 metabolism (0008152) 3059 2513 secretion (0046903) 9 16 sex differentiation (0007548) 3 2 Numbers in parentheses reference GO numbers [23]. Informatic searches for regeneration probes The value of a salamander model to regeneration research will ultimately rest on the ease in which data and results can be cross-referenced to other vertebrate models. For example, differences in the ability of mammals and salamanders to regenerate spinal cord may reflect differences in the way cells of the ependymal layer respond to injury. As is observed in salamanders, ependymal cells in adult mammals also proliferate and differentiate after spinal cord injury (SCI) [ 24 , 25 ]; immediately after contusion injury in adult rat, ependymal cell numbers increase and proliferation continues for at least 4 days [[ 26 ]; but see [ 27 ]]. Rat ependymal cells share some of the same gene expression and protein properties of embryonic stem cells [ 28 ], however no new neurons have been observed to derive from these cells in vivo after SCI [ 29 ]. Thus, although endogenous neural progenitors of the ependymal layer may have latent regenerative potential in adult mammals, this potential is not realized. Several recently completed microarray analyses of spinal cord injury in rat now make it possible to cross-reference information between amphibians and mammals. For example, we searched the complete list of significantly up and down regulated genes from Carmel et al. [ 30 ] and Song et al. [ 31 ] against all Ambystoma ESTs. Based upon amino acid sequence similarity of translated ESTs (TBLASTX; E <10 -7 ), we identified DNA sequences corresponding to 69 of these 164 SCI rat genes (Table 6 ). It is likely that we have sequence corresponding to other presumptive orthologues from this list as many of our ESTs only contain a portion of the coding sequence or the untranslated regions (UTR), and in many cases our searches identified closely related gene family members. Thus, many of the genes that show interesting expression patterns after SCI in rat can now be examined in salamander. Table 6 Ambystoma contigs that show sequence similarity to rat spinal cord injury genes. Ambystoma Contig ID RAT cDNA clone E-value MexCluster_7440_Contig1 gi|1150557|c-myc, exon 2 E -29 MexCluster_4624_Contig1 gi|1468968| brain acyl-CoA synthtase II E -09 TigCluster_4083_Contig1 E -09 TigSingletonClusters_Salamander_4_G20_ab1 gi|1552375| SKR6 gene, a CB1 cannabinoid recept. E -08 MexSingletonClusters_NT009B_B04 gi|17352488| cyclin ania-6a E -46 TigCluster_3719_Contig1 E -114 TigCluster_8423_Contig1 gi|1778068| binding zyginI E -102 TigCluster_7064_Contig1 gi|1836160| Ca2+/calmodulin-dependent E -20 MexCluster_3225_Contig1 gi|1906612| Rattus norvegicus CXC chemokine E -68 TigSingletonClusters_Salamander_13_F03_ab1 E -38 MexSingletonClusters_BL285B_A06 gi|203042| (Na+, K+)-ATPase-beta-2 subunit E -63 TigCluster_6994_Contig1 E -65 MexSingletonClusters_BL014B_F12 gi|203048| plasma membrane Ca2+ ATPase-isoform 2 E -112 TigSingletonClusters_Salamander_5_F07_ab1 E -92 MexCluster_1251_Contig1 gi|203167| GTP-binding protein (G-alpha-i1) E -110 TigSingletonClusters_Salamander_3_P14_ab1 E -152 TigSingletonClusters_Salamander_22_B01_ab1 gi|203336| catechol-O-methyltransferase E -47 TigSingletonClusters_Salamander_17_N04_ab1 gi|203467| voltage-gated K+ channel protein (RK5) E -08 MexSingletonClusters_v1_p8_c16_triplex5ld_ gi|203583| cytosolic retinol-binding protein (CRBP) E -77 TigCluster_6321_Contig1 E -18 MexCluster_5399_Contig1 gi|204647| heme oxygenase gene E -67 TigCluster_2577_Contig1 E -67 MexCluster_4647_Contig1 gi|204664| heat shock protein 27 (Hsp27) E -83 TigSingletonClusters_Salamander_12_M05_ab1 E -51 MexSingletonClusters_BL285C_F02 gi|205404| metabotropic glutamate receptor 3 E -41 TigSingletonClusters_Salamander_2_B24_ab1 gi|205508| myelin/oligodendrocyte glycoprotein E -26 TigCluster_5740_V2_p10_M20_TriplEx5ld_ gi|205531| metallothionein-2 and metallothionein 1 E -08 TigSingletonClusters_V2_p5_A2_TriplEx5ld_ gi|205537| microtubule-associated protein 1A E -59 MexCluster_1645_Contig1 gi|205633| Na, K-ATPase alpha-2 subunit E -149 TigSingletonClusters_Contig328 0 TigSingletonClusters_Contig45 gi|205683| smallest neurofilament protein (NF-L) E -63 MexSingletonClusters_NT016A_A09 gi|205693| nerve growth factor-induced (NGFI-A) E -95 TigSingletonClusters_I09_Ag2_p9_K24_M13R E -24 MexSingletonClusters_NT007A_E07 gi|205754| neuronal protein (NP25) E -64 TigCluster_7148_Contig1 E -57 MexCluster_9504_Contig1 gi|206161| peripheral-type benzodiazepine receptor E -73 MexSingletonClusters_BL016B_B02 gi|206166| protein kinase C type III E -36 TigCluster_981_Contig1 E -27 MexSingletonClusters_nm_19_k3_t3_ gi|206170| brain type II Ca2+/calmodulin-dependent E -117 MexSingletonClusters_v11_p42_j20_t3_049_ab1 gi|207138| norvegicus syntaxin B 1e -079 MexSingletonClusters_nm_14_h19_t3_ gi|207473| neural receptor protein-tyrosine kinase E -40 TigSingletonClusters_Contig336 E -34 TigSingletonClusters_E10_Ag2_p18_O19_M13 gi|2116627| SNAP-25A E -123 MexCluster_211_Contig1 gi|220713| calcineurin A alpha E -63 TigSingletonClusters_Salamander_7_K14_ab1 E -87 MexSingletonClusters_NT014A_G03 gi|220839| platelet-derived growth factor A chain E -21 TigSingletonClusters_Salamander_9_M15_ab1 E -56 TigSingletonClusters_Salamander_19_M06_ab1 gi|2501807| brain digoxin carrier protein E -55 MexSingletonClusters_Contig100 gi|2746069| MAP-kinase phosphatase (cpg21) E -108 TigSingletonClusters_Salamander_11_A16_ab1 E -70 MexCluster_8345_Contig1 gi|2832312| survival motor neuron (smn) E -40 TigCluster_8032_Contig1 E -49 MexCluster_3580_Contig1 gi|294567| heat shock protein 70 (HSP70) 0 TigCluster_8592_Contig2 E -161 TigSingletonClusters_Salamander_17_N08_ab1 gi|2961528| carboxyl-terminal PDZ E -10 MexSingletonClusters_BL286C_D09 gi|298325| sodium-dependent neurotransmitter tran. E -12 TigSingletonClusters_Contig95 E -22 MexSingletonClusters_Contig461 gi|2996031| brain finger protein (BFP) E -08 TigSingletonClusters_Salamander_11_O19_ab1 E -23 TigSingletonClusters_E16_Ag2_p8_O20_M13R gi|3135196| Ca2+/calmodulin-dependent E -33 MexSingletonClusters_Contig188 gi|3252500| CC chemokine receptor protein E -15 MexCluster_6961_Contig1 gi|3319323| suppressor of cytokine signaling-3 E -08 MexSingletonClusters_nm_14_p15_t3_ gi|349552| P-selectin E -16 TigCluster_218_Contig2 E -99 MexSingletonClusters_Contig506 gi|3707306| Normalized rat embryo, cDNA clone E -14 TigSingletonClusters_I16_Ag2_p5_N7_M13R gi|3711670| Normalized rat muscle, cDNA clone E -35 MexSingletonClusters_V1_p1_a10_Triplex5Ld gi|3727094| Normalized rat ovary, cDNA clone E -15 TigSingletonClusters_v2_p1_D20_triplex5ld E -16 MexSingletonClusters_NT005B_F02 gi|3811504| Normalized rat brain, cDNA clone E -35 TigSingletonClusters_Salamander_22_I04_ab1 E -34 TigSingletonClusters_Ag2_p34_N23_M13R gi|405556| adenylyl cyclase-activated serotonin E -17 TigSingletonClusters_Salamander_1_H02_ab1 gi|4103371| putative potassium channel TWIK E -22 MexCluster_4589_Contig1 gi|4135567| Normalized rat embryo, cDNA clone E -32 TigSingletonClusters_Contig220 E -09 TigCluster_4093_Contig1 gi|4228395| cDNA clone UI-R-A0-bc-h-02-0-UI E -104 MexSingletonClusters_nm_21_2_m7_t3_ gi|425471| nuclear factor kappa B p105 subunit E -22 TigCluster_8535_Contig1 E -11 MexSingletonClusters_v6_p1_j6_triplex5_1ld_ gi|430718| Sprague Dawley inducible nitric oxide E -13 TigSingletonClusters_Salamander_15_D22_ab1 E -41 MexCluster_3498_Contig1 gi|436934| Sprague Dawley protein kinase C rec. 0 TigCluster_6648_Contig1 0 MexSingletonClusters_BL279A_B12 gi|464196| phosphodiesterase I E -49 TigSingletonClusters_Salamander_25_P03_ab1 E -75 MexCluster_8708_Contig1 gi|466438| 40kDa ribosomal protein E -168 TigCluster_5877_Contig1 E -168 MexSingletonClusters_nm_14_a9_t3_ gi|493208| stress activated protein kinase alpha II E -51 TigSingletonClusters_Salamander_11_A13_ab1 gi|517393| tau microtubule-associated protein E -44 TigSingletonClusters_Salamander_12_J14_ab1 gi|55933| c-fos E -26 MexSingletonClusters_nm_21_2_l13_t3_ gi|56822| major synaptic vesicel protein p38 E -39 TigCluster_2065_Contig1 E -50 MexCluster_10965_Contig1 gi|56828| nuclear oncoprotein p53 E -75 TigCluster_5315_Contig1 E -66 MexCluster_4245_Contig1 gi|56909| pJunB gene E -50 TigSingletonClusters_G05_Ag2_p9_G8_M13R E -09 MexSingletonClusters_NT013D_C12 gi|56919| region fragment for protein kinase C E -33 TigSingletonClusters_Salamander_21_H19_ab1 E -24 MexCluster_9585_Contig1 gi|57007| ras-related mRNA rab3 E -61 TigCluster_4885_Contig1 E -63 TigSingletonClusters_Salamander_1_M03_ab1 gi|57238| silencer factor B E -13 MexSingletonClusters_NT008B_D05 gi|57341| transforming growth factor-beta 1 E -13 TigSingletonClusters_Salamander_24_I16_ab1 E -20 MexCluster_9533_Contig1 gi|57479| vimentin 0 TigCluster_5768_Contig1 0 MexSingletonClusters_BL283B_A11 gi|596053| immediate early gene transcription E -12 TigSingletonClusters_Salamander_13_J19_ab1 E -16 MexSingletonClusters_v6_p4_j2_triplex5_1ld_ gi|790632| macrophage inflammatory protein-1alpha E -22 TigCluster_2146_Contig1 gi|951175| limbic system-associated membrane prot. E -11 MexSingletonClusters_v11_p54_o4_t3_ gi|971274| neurodegeneration associated protein 1 E -09 TigSingletonClusters_Salamander_2_J12_ab1 E -11 Similar gene expression programs may underlie regeneration of vertebrate appendages such as fish fins and tetrapod limbs. Regeneration could depend on reiterative expression of genes that function in patterning, morphogenesis, and metabolism during normal development and homeostasis. Or, regeneration could depend in part on novel genes that function exclusively in this process. We investigated these alternatives by searching A. mexicanum limb regeneration ESTs against UNIGENE zebrafish fin regeneration ESTs (Figure 3 ). This search identified 1357 significant BLAST hits (TBLASTX; E <10 -7 ) that corresponded to 1058 unique zebrafish ESTs. We then asked whether any of these potential regeneration homologues were represented uniquely in limb and fin regeneration databases (and not in databases derived from other zebrafish tissues). A search of the 1058 zebrafish ESTs against > 400,000 zebrafish ESTs that were sampled from non-regenerating tissues revealed 43 that were unique to the zebrafish regeneration database (Table 7 ). Conceivably, these 43 ESTs may represent transcripts important to appendage regeneration. For example, our search identified several genes (e.g. hspc128 , pre- B-cell colony enhancing factor 1 , galectin 4 , galectin 8 ) that may be expressed in progenitor cells that proliferate and differentiate during appendage regeneration. Overall, our results suggest that regeneration is achieved largely through the reiterative expression of genes having additional functions in other developmental contexts, however a small number of genes may be expressed uniquely during appendage regeneration. Figure 3 Results of BLASTN and TBLASTX searches to identify best BLAST hits for A. mexicanum regeneration ESTs searched against zebrafish EST databases. A total of 14,961 A. mexicanum limb regeneration ESTs were assembled into 4485 contigs for this search. Table 7 Ambystoma limb regeneration contigs that show sequence similarity to zebrafish fin regeneration ESTs Mex. Contigs Human ID E-value Zfish ID E-value Contig94 gi|10835079| 1e -63 gnl|UG|Dr#S12319632 1e -58 nm_30_a11_t3_ gi|32306539| 1e -58 gnl|UG|Dr#S12312602 1e -35 Contig615 gi|4502693| 1e -70 gnl|UG|Dr#S12313407 1e -34 nm_23_l13_t3_ No Human Hit gnl|UG|Dr#S12320916 1e -31 nm_9_e22_t3_ gi|4758788| 1e -98 gnl|UG|Dr#S12309914 1e -29 nm_8_l17_t3_ gi|21361310| 1e -16 gnl|UG|Dr#S12313396 1e -27 Contig531 gi|13775198| 1e -27 gnl|UG|Dr#S12309680 1e -26 Contig152 gi|5453712| 1e -32 gnl|UG|Dr#S12239884 1e -26 nm_32h_j20_t3_ gi|39777601| 1e -79 gnl|UG|Dr#S12136499 1e -25 Contig1011 gi|39752675| 1e -65 gnl|UG|Dr#S12136499 1e -24 v11_p50_b24_t3_ gi|41208832| 1e -36 gnl|UG|Dr#S12319219 1e -23 Contig589 gi|4506505| 1e -56 gnl|UG|Dr#S12312662 1e -22 Contig785 gi|33695095| 1e -61 gnl|UG|Dr#S12264765 1e -22 Contig157 gi|21361122| 1e -138 gnl|UG|Dr#S12313094 1e -21 v11_p42_j20_t3_049_ab1 gi|47591841| 1e -100 gnl|UG|Dr#S12137806 1e -21 Contig610 gi|10801345| 1e -114 gnl|UG|Dr#S12310326 1e -20 nm_27_o1_t3_ gi|7706429| 1e -72 gnl|UG|Dr#S12310422 1e -19 Contig439 gi|4504799| 1e -25 gnl|UG|Dr#S12309233 1e -19 nm_31_d5_t3_ gi|8923956| 1e -50 gnl|UG|Dr#S12264745 1e -17 v11_p41_h12_t3_026_ab1 No Human Hit gnl|UG|Dr#S12320916 1e -17 Contig129 gi|34932414| 1e -103 gnl|UG|Dr#S12313534 1e -17 nm_14_j21_t3_ gi|4505325| 1e -42 gnl|UG|Dr#S12136571 1e -17 Contig1321 gi|4501857| 1e -80 gnl|UG|Dr#S12309233 1e -17 nm_19_k3_t3_ gi|26051212| 1e -106 gnl|UG|Dr#S12137637 1e -17 Contig488 gi|4557525| 1e -105 gnl|UG|Dr#S12311975 1e -15 nm_35h_k19_t3_ gi|16950607| 1e -43 gnl|UG|Dr#S12196214 1e -15 Contig195 gi|4557231| 1e -99 gnl|UG|Dr#S12309233 1e -14 nm_14_h19_t3_ gi|4503787| 1e -86 gnl|UG|Dr#S12310912 1e -13 v11_p51_d20_t3_ gi|30520322| 1e -19 gnl|UG|Dr#S12321150 1e -13 g3-n14 gi|13654278| 1e -23 gnl|UG|Dr#S12318856 1e -13 nm_29_f2_t3_ gi|4506517| 1e -65 gnl|UG|Dr#S12312662 1e -13 g4-h23 gi|24111250| 1e -33 gnl|UG|Dr#S12312651 1e -13 Math_p2_A2_T3_ No human Hit gnl|UG|Dr#S12078998 1e -13 nm_35h_f4_t3_ gi|41148476| 1e -67 gnl|UG|Dr#S12319663 1e -13 Contig952 gi|21264558| 1e -61 gnl|UG|Dr#S12318843 1e -12 g4-g21 gi|11995474| 1e -65 gnl|UG|Dr#S12192716 1e -12 Contig854 gi|8922789| 1e -117 gnl|UG|Dr#S12313534 1e -11 Contig1105 gi|6912638|| 1e -83 gnl|UG|Dr#S12079967 1e -11 nm_26_f7_t3_ gi|30181238| 1e -83 gnl|UG|Dr#S12319880 1e -11 Contig949 gi|21284385| 1e -68 gnl|UG|Dr#S12290856 1e -11 g3-n3 gi|18490991| 1e -64 gnl|UG|Dr#S12320832 1e -10 v11_p41_m16_t3_007_ab1 gi|4885661| 1e -33 gnl|UG|Dr#S12310912 1e -10 Contig653 gi|4505047| 1e -124 gnl|UG|Dr#S12239868 1e -09 Contig1349 gi|9665259| 1e -46 gnl|UG|Dr#S12320840 1e -09 6h12 gi|31317231| 1e -43 gnl|UG|Dr#S12321311 1e -09 v11_p43h_i14_t3_070_ab1 No Human Hit gnl|UG|Dr#S12320916 1e -09 nm_35h_d11_t3_ gi|7661790| 1e -35 gnl|UG|Dr#S12196146 1e -09 nm_35h_k22_t3_ gi|5031977| 1e -124 gnl|UG|Dr#S12242267 1e -09 v11_p48_g2_t3_087_ab1 gi|11496277| 1e -60 gnl|UG|Dr#S12312396 1e -09 nm_30_e11_t3_ gi|32483357| 1e -56 gnl|UG|Dr#S12309103 1e -08 nm_28_f23_t3_ gi|42544191| 1e -25 gnl|UG|Dr#S12239884 1e -08 nm_12_p16_t3_ gi|21361553| 1e -21 gnl|UG|Dr#S12310912 1e -08 nm_32h_a8_t3_ gi|11386179| 1e -22 gnl|UG|Dr#S12312152 1e -08 Human RefSeq sequence ID's are provided to allow cross-referencing. DNA sequence polymorphisms within and between A. mexicanum and A. t. tigrinum The identification of single nucleotide polymorphisms (SNPs) within and between orthologous sequences of A. mexicanum and A. t. tigrinum is needed to develop DNA markers for genome mapping [ 32 ], quantitative genetic analysis [ 33 ], and population genetics [ 34 ]. We estimated within species polymorphism for both species by calculating the frequency of SNPs among ESTs within the 20 largest contigs (Table 4 ). These analyses considered a total of 30,638 base positions for A. mexicanum and 18,765 base positions for A. t. tigrinum . Two classes of polymorphism were considered in this analysis: those occurring at moderate (identified in 10–30% of the EST sequences) and high frequencies (identified in at least 30% of the EST sequences). Within the A. mexicanum contigs, 0.49% and 0.06% of positions were polymorphic at moderate and high frequency, while higher levels of polymorphism were observed for A. t. tigrinum (1.41% and 0.20%). Higher levels of polymorphism are expected for A. t. tigrinum because they exist in larger, out-bred populations in nature. To identify SNPs between species, we had to first identify presumptive, interspecific orthologues. We did this by performing BLASTN searches between the A. mexicanum and A. t. tigrinum assemblies, and the resulting alignments were filtered to retain only those alignments between sequences that were one another's reciprocal best BLAST hit. As expected, the number of reciprocal 'best hits' varied depending upon the E value threshold, although increasing the E threshold by several orders of magnitude had a disproportionately small effect on the overall total length of BLAST alignments. A threshold of E< 10 -80 yielded 2414 alignments encompassing a total of 1.25 Mbp from each species, whereas a threshold of E< 10 -20 yielded 2820 alignments encompassing a total of 1.32 Mbp. The percent sequence identity of alignments was very high among presumptive orthologues, ranging from 84–100% at the more stringent E threshold of E< 10 -80 . On average, A. mexicanum and A. t. tigrinum transcripts are estimated to be 97% identical at the nucleotide level, including both protein coding and UTR sequence. This estimate for nuclear sequence identity is surprisingly similar to estimates obtained from complete mtDNA reference sequences for these species (96%, unpublished data), and to estimates for partial mtDNA sequence data obtained from multiple natural populations [ 16 ]. These results are consistent with the idea that mitochondrial mutation rates are lower in cold versus warm-blooded vertebrates [ 35 ]. From a resource perspective, the high level of sequence identity observed between these species suggests that informatics will enable rapidly the development of probes between these and other species of the A. tigrinum complex. Extending EST resources to other ambystomatid species Relatively little DNA sequence has been obtained from species that are closely related to commonly used model organisms, and yet, such extensions would greatly facilitate genetic studies of natural phenotypes, population structures, species boundaries, and conservatism and divergence of developmental mechanisms. Like many amphibian species that are threatened by extinction, many of these ambystomatid salamanders are currently in need of population genetic studies to inform conservation and management strategies [e.g. [ 13 ]]. We characterized SNPs from orthologous A. mexicanum and A. t. tigrinum ESTs and extended this information to develop informative molecular markers for a related species, A. ordinarium . Ambystoma ordinarium is a stream dwelling paedomorph endemic to high elevation habitats in central Mexico [ 36 ]. This species is particularly interesting from an ecological and evolutionary standpoint because it harbors a high level of intraspecific mitochondrial variation, and as an independently derived stream paedomorph, is unique among the typically pond-breeding tiger salamanders. As a reference of molecular divergence, Ambystoma ordinarium shares approximately 98 and 97% mtDNA sequence identity with A. mexicanum and A. t. tigrinum respectively [ 16 ]. To identify informative markers for A. ordinarium , A. mexicanum and A. t. tigrinum EST contigs were aligned to identify orthologous genes with species-specific sequence variations (SNPs or Insertion/Deletions = INDELs). Primer pairs corresponding to 123 ESTs (Table 8 ) were screened by PCR using a pool of DNA template made from individuals of 10 A. ordinarium populations. Seventy-nine percent (N = 97) of the primer pairs yielded amplification products that were approximately the same size as corresponding A. mexicanum and A. t. tigrinum fragments, using only a single set of PCR conditions. To estimate the frequency of intraspecific DNA sequence polymorphism among this set of DNA marker loci, 43 loci were sequenced using a single individual sampled randomly from each of the 10 populations, which span the geographic range of A. ordinarium . At least one polymorphic site was observed for 20 of the sequenced loci, with the frequency of polymorphisms dependent upon the size of the DNA fragment amplified. Our results suggest that the vast majority of primer sets designed for A. mexicanum / A. t. tigrinum EST orthologues can be used to amplify the corresponding sequence in a related A. tigrinum complex species, and for small DNA fragments in the range of 150–500 bp, approximately half are expected to have informative polymorphisms. Table 8 EST loci used in a population-level PCR amplification screen in A. ordinarium Locus ID Forward Primer 5' to 3' Reverse Primer 5' to 3' 1F8 AAGAAGGTCGGGATTGTGGGTAA CAGCCTTCCTCTTCATCTTTGTCTTG 1H3 GGCAAATGCTGGTCCCAACACAAA GGACAACACTGCCAAATACCACAT 2C8 GCAAGCACCAGCCACATAAAG GGCCACCATAACCACTCTGCT 3B10 TCAAAACGAATAAGGGAAGAGCGACTG TTGCCCCCATAATAAGCCATCCATC 5E7 ACGCTTCGCTGGGGTTGACAT CGGTAGGATTTCTGGTAGCGAGCAC 5F4 CCGAGATGAGATTTATAGAAGGAC TAGGGGAAGTTAAACATAGATAGAA 6A3 GTTTATGAAGGCGAGAGGGCTATGACCA ATCTTGTTCTCCTCGCCAGTGCTCTTGT 6B1 TGATGCTGGCGAGTACAAACCCCCTTCT TTTACCATTCCTTCCCTTCGGCAGCACA 6B3 ACCACGTGCTGTCTTCCCATCCAT ACGAAGCTCATTGTAGAAGGTGTG 6B4 CCCACGATGAATTGGAATTGGACAT CTGCCTGCCAGACCTACAGACTATCGT 6C4 ATGGCGCCAAAGTGATGAGTA GGGCCAGGCACACGACCACAAT 6D2 ATCAAGGCTGGCATGGTGGTCA GGGGGTCGTTCTTGCTGTCA 6H8 GAAGAAGACAGAAACGCAGGAGAAAAAC CGGGCGGGGGCGGGTCACAGTAAAAC BL005B_A01.5.1 GACAGGTCATGAACTTTTGAAAATAA AAAGTATATGTACCAAATGGGAGAGC BL006A_G07.5.1 GATGTCCTCTCCACTATACAAGTGTG GTTTGACTTGTCACCACTTTATCAAC BL012D_F02.5.1 ACAGCCAGAAATAGAAACTTTGAACT TGAAAGTATGTATTGTTTTCACAGGG BL013C_E01.5.1 AGGATGAAATAATATGCTGTGCTTC ACCGTGATAAACTCCATCCCTT BL014D_B11.5.1 AGCAAAACTCCTCTATGAATCTCG ATTGCACACTAAATAGGTGAATACGA BL279A_G10.5.1 ATGGCAGGATGAAGAAAGACAT ATGCACTTTGGACCCACTGAG Et.fasta.Contig1023.5.1 TGTGGTTATTGGACTACTTCACTCTC AAACGTCCATTTGACACTGTATTTTA Et.fasta.Contig1166.5.1 GAATGAAGAGAAAATGTTTTGAAGGT GCACAGTATTGGCTATGAGCAC Et.fasta.Contig1311.5.1 AGAAAACTGTGTCAAGCTTATTTTCC CAACTTAGTGTTCACATTTCTGAGGT Et.fasta.Contig1335.5.1 CCACTTATGGTAGTTCCCACTTTTAT GCTAAAGAATACCAAGAACCTTTGAC Et.fasta.Contig1381.5.1 GTCACAGGTATAACATTGAAAGGATG TAAATGAATCAAACATTGAAGAGAGC Et.fasta.Contig1459.5.1 ATAACAAGGACATGTTCTGCTGG CTAGCAGAACCCTGTATAGCCTG Et.fasta.Contig1506.5.1 AGGATATCCGCTCAGAAATATGAAG CTGACCACTTGCAAAACTTACTACCT Et.fasta.Contig1578.5.1 CCTAGAACATTACCAAAACAGACTCA AATGAAGAAGTATTGCATGTGAGAAC Et.fasta.Contig1647.5.1 GTACAACGTCAGGCAAAGCTATTCT ATCTCCAACACCGTGGCTAAT Et.fasta.Contig1717.5.1 GAACTTGTTGGCAGGTTTCTCTT CTAGTGATAGGTTGGACATACCAGAG Et.fasta.Contig1796.5.1 TGTGGGTATGTATATGGCTAACTTGT AGATTTTATGTGCTACTGCATTTACG Et.fasta.Contig1908.5.1 CTCATGACTTAATTGCTGTTCTTCG ATAACCATTCTGAGGTTTTGAGTTG Et.fasta.Contig1941.5.1 ATCTCCTGCTTCATCTCTTGATTTAT TAACAGATTTAATAAACGTCCCCTTC Et.fasta.Contig1943.5.1 AGTACGATGAATCTGGTCCTTCAAT CCACAATACTGACATACTCTGGTCTT Et.fasta.Contig325.5.1 GTGAAGTCAGTGAGTAAAGTCCATGT CTAGGATACCAGTGGGAGAGTGTAAT Et.fasta.Contig330.5.1 GTCATCACCTCCACTACTTCACAAG TTTTGGCACTGTAAGATTCTATGAAC Et.fasta.Contig536.5.1 CCTTAGGTAGAACAGACTGAAGCAG GAAACATGAAACTGGACTTGTTTTAG Et.fasta.Contig917.5.1 GGATGCAGATTCTTCCTATTTTACTC CTGGTCACTTTACTTGTTTTCAGTGT Et.fasta.Contig926.5.1 TTCATCACATTCTACTTCACAAATCA CTAGGCAAGCAAGCTTTCTAATAGTT Et.fasta.Contig93.5.1 GAATAAAAGCAACAATTGCAGAGTTA CTCGACTCCTTCTACGATCTCTACTC Et.fasta.Contig990.5.1 GTTTAGGTTAGTATGAAGGATCCCAA TGCCAGTACTCACCAATTAGTAAAAG G1-C12 CCCAAATCCAGGAGTTCAAA TGGGACCTGGGGCTTCATT G1-C13 TTGCCCGAGAAAAGGAAGGACATA CAAGGGTGGGTGAGGGACATC G1-C5 F-CACTGTTGACTTGGGTTATGTTATT CTGCTCCTAGGGTTTGTGAAG G1-C7 CCCGTGTGGCTGGCTTGTGC TCGGCTACTTTGGTGTTTTTCTCCCTCAT G1-C9 TGGTCCGGCAACAGCATCAGA GCTTTTCGGTATTCAACGGCAGAGTG G1-C9 TGGTCCGGCAACAGCATCAGA GCTTTTCGGTATTCAACGGCAGAGTG G1-D5 AGACCCTTGCTGTGTAACTGCT GACTGGGACTGACTTCTATGACG G1-D6 CAGCGTGCCCACCCGATAGAA TCCCAAAAAGTAAAATGTGCAAAGAAAA G1-D7 CAGCGGTGGAAATGACAAACAGG CCAAGACGACGAGGAACGGTATT G1-E12 CAACCATGAGAGGAGGCCAGAGAAC AAAACAGCACTACCTACAAAACCCTATT G1-F1 TTAGTTTGGGTGCAGACAGGA GGTGCTCAACAACAAATCAACT G1-F20 TCCCCAACAACTCCAGCAGAT GGAAACCACCTAGACGAAAAATG G1-I18 CATGTTTGTGGGTGTGGTGAA AAAAGCGGCATCTGGTAAGG G1-I19 ACCCAGACCTGTCCACCTCA GAACAGCTCTCCAATCCACAAG G1-I21 CCAAGCGAAGGAGGCGTGTG CATGTGGCTCTTTGTTTCTGGA G1-I5 TAATCGTGTTTGGTGGCATCCTTGAGTC AGCAGCAGTTCCATTTTCCCACACCA G1-I8 ACCTGCAGTGGGCTAAGACC ATGGAAATAATAAAATAAAATGTT G1-J10 CGTTCGCTTTGCCTGCCACA GGCTCTTCCCCGGTCGTCCAC G1-J17 AGCGCCTTCTACACGGACAC TATGCCCCAATTACTCTTCTGC G1-J2 TACAGTAACTATGCCAAGATGAAATG CAATATGGATAATGGCTGTAGACC G1-J20 ATCCTCCAAGCTCACTACAACA CCAGCCCCTTCCCAAACAG G1-J9 CTGTCATTGCCTGCATCGGGGAGAAG TGTTGAGGGGAAGCAGTTTTG G1-K2 GCTTTCGCCTTTGACACCTC GGCCGGACCATTGCTGAAGAAG G1-L11 AAAGTGACCATCCAGTGCCCAAACCT CCGGCCGAAACTGACGAGATACATTAG G1-L13 TCAGCTGCACTAGGTTTGTC CATTTTGATTTGCTCCATAA G1-L19 GACAACCTTGAATCCTTTATG AGATGTTGGTTGGTGACTTAT G1-L20 TGGGCATAGATGGCAAGGAAAAA CCCCCAGCATCTCGCATACAC G1-L7 GTGCTACAGGAAGGAATGGATG TAGCACAGGAACAGCCGACAATAA G1-M14 CCGCTTGGACATGAGGAGAT TGGCAAAGAAACAGAACACAACTA G1-M19 GAGAAGTAGTGTCCCGGCAGAAAC ATGGGTGAAAACTTAGGTGAAATG G1-N9 GCGGGGCAATACATGACGTTCCACAG GACCCCCATCTCCGTTTCCCATTCC G1-O1 GGGGTAGAGCACAGTCCAGTT TTGCAAGGCCGAAAAGGTG G1-O12 GGAATTCCGGGGCACTACT TCGCGAGGACGGGGAAGAG G1-O24 CGGCCTTCCTGCAGTACAACCATC TCGGCAACGTGAAGACCATA G2-A11 GCCCCTGGAAGCTGTTGTGA GGGGTCCATCCGAGTCC G2-A7 TTACCCCACAGACAAAATCAACACC GGCGGCCCCTCATAGCAC G2-B1 GGGCCTAGTCCTGCTGGTC CAAAGAGTGCGGAGAAATGG G2-B8 CAACATGCGACCACTATAGCCACTTCCT CGCCACCGCCACCACCACA G2-C2 TTTGCAGGAAGAGTCATAACACAG GTCAACAACACCCTTTTCCCTTCCT G2-D1 GCAGGTCGGCAAGAAGCTAAAGAAGGAA AGGGTTGGTTTGAAAGGATGTGCTGGTAA G2-E17 GGAGCACCAAATTCAAGTCAG CGTCCCCGGTCAATCTCCAC G2-E19 CCAGTTTGAGCCCCAGGAG TCGCGGCAGTCAAGAGGTC G2-F17 TATCCTCTTATTGCTGCATTCTCCTCAC AGTACGGCCGTTCACCATCTCTG G2-F2 CACACCACAGACGCATTGAC TCCCCAGCCTGTGTAGAAC G2-G13 GGGAGGGGAGAAGGCTACCA ATACACGGCTTCCATGCTTCTTCTT G2-G15 CCACGGCCCCACATCCAGC TCCCGCAGAATTTCCGTATCCAT G2-G21 TCCAAGAGGGTGTGAGGTGAAC AAAGCCATGCGAAGCGGAAGAC G2-G23 GGTTTGGTACTTCAGCGGATGT CCAAAGCCTGTACTATGCGAAAAG G2-G5 CGGTCCCTACTGTGGTCTATGGTTTTCA GGCTCTGCATATCCTCGGTCACACTTCC G2-G6 CCCATGGCTGCAAGGATTACG CAGGGGTTGTTGGGAGGCAGTGT G2-H18 TTGTCAAATGGGCGAGTTCA TGTTTTGCACCCAGTTTTTG G2-I18 GATCTCCTCAGGTCTCTTTCA GATTATGGGCCGGTGTCTCT G2-I23 TGACTTTCCCAATGTGAGCAGAC CAGAGGTGGTGTTACAGCAGCAGTTT G2-J12 CCTCTTGTCCCAGTGCCAGTG TCCAGGGATCCGAAACAAAG G2-J21 CCGCCTCAGCCTGTTTCTCTACTTTT CTTTGAATTTCTGCTTTTGGTGCTCTGC G2-K12 ACATTAGTCCTGGTTACGAGAGC AAAGGGCAGTCCAGCATTGA G2-K2 CTGCCCAAGAAGACCGAGAGCCACAAG AGCGCCCCCTGCACCAAAATCA G2-L16 CCAAGGGTAGGAGAACAAGACA ATGGCATGCTGGGAAATCA G2-L21 GAATCTAGGTCCAAGCAGTCCCATCT GACCATCACACCACTACCCACACTCA G2-L3 TGAAAGAGGCCAGAAACAAGTAG TTCCCAAGGTCTCCATAACAAT G2-L4 TGGCCAAGAAGATGAAACAGGAAGAGGAG TGGCAAAGGACACGACGCAGAG G2-M14 CGGCCTCCTCGACGCATACG CCAGGCCGGCCCATTGTTC G2-M24 ACGGAGCACGGTCAGATTTCACG CCCGGCTGGCTCTTCTTGCTCTT G2-M3 CGATCCGCATTGAACGAGT TGTGGCAGGAAGGAGAAGG G2-N2 CGTGTTTTCCTCCTATGTCGACTTCTTTG ACGTGCTCTGCCTTTCTTGATCTTGTGTT G3-D7 AGGATTTCTTGGCCGGTGGAGTGG GAAGTTGAGGGCCTGGGTGGGGAAGTA NT001D_E08.5.1 AGAAGTTCCTAGATGAGTTGGAGGAG AATTAATTTCCTAAACCAGGTGACAG NT010B_E09.5.1 GAAGAGGTCCTAAAATATCAAGATGC ATGATAGACTTCGTCCTTGTCATAGA NT014D_E01.5.1 AAAGAAGTCCCGCATCTAACCT ATTAAATATGAGAAGATGTGTGCAGG V2_p1_b8 AGTCACTGTGTTACATTATCACCCAC ATAATTATACACTGCGGTCTGCATCT V2_p1_c5 AGTACCTGTTCGACAAGCACAC TGAGAACATAGACAAGTTAACATACACC V2_p1_d10 GAGATAGAAAGGCTGCATAAAGAAAT TATGTTTCAACAATGTACAGGAAACC V2_p1_d4 CACCAGAACAAGCTGTATTTTTATGT TGGTTTGCATCATATATTAAAGGGTA V2_p1_g7 GACTTCAAGCACATTGGGAAAC ATTGTAAACTTGATAGGCTGGTGAG V2_p2_g6 AGAATTCCCAATAGCACCTGAAAT CACTTGGTAAATACATACACACAGCA V2_p2_h2 CTTTTTGGCCTGGTCTTTTTG AGATTCTTCAGACTCGTCCTTCTTT V2_p3_a5 TTTACACAGAAACCTTGTTTATTTGGC TTTAAGGATGCTTAGAGGCAAAGTATT V2_p3_b1 AGTCACTGTGTTACATTATCACCCAC TATACACTGCGGTCTGCATCTACT V2_p5_b3 AATGGGATGAAGAGCGAGAAT CTGCCCCATTGACATTTACCTA V2_p5_h3 CCTTCAGACGAAAACAGCACTAAG TACAGTGTATGAGAGCCCAATATTTC V2_p6_a4 AGAAATACATCAAATATCGGGTGG AAAAAGGACAATGTTCAGCTCTCT V3_p1_a21 ACCAAGTTCTTGGAAAGTGGTG CTTAGTGTCTCCTGGGTTTGAATAG V3_p1_b13 GTCTTGGTACTCAATGAAGGAGATG TCAATCTGATGAAGAGTTTACATGTCT Comparative gene mapping Salamanders occupy a pivotal phylogenetic position for reconstructing the ancestral tetrapod genome structure and for providing perspective on the extremely derived anuran Xenopus (37) that is currently providing the bulk of amphibian genome information. Here we show the utility of ambystomatid ESTs for identifying chromosomal regions that are conserved between salamanders and other vertebrates. A region of conserved synteny that corresponds to human chromosome ( Hsa ) 17q has been identified in several non-mammalian taxa including reptiles (38) and fishes (39). In a previous study Voss et al. (40) identified a region of conserved synteny between Ambystoma and Hsa 17q that included collagen type 1 alpha 1 ( Col1a1 ), thyroid hormone receptor alpha ( Thra ), homeo box b13 ( Hoxb13) , and distal-less 3 ( Dlx3) (Figure 4 ). To evaluate both the technical feasibility of mapping ESTs and the likelihood that presumptive orthologues map to the same synteny group, we searched our assemblies for presumptive Hsa 17 orthologues and then developed a subset of these loci for genetic linkage mapping. Using a joint assembly of A. mexicanum and A. t. tigrinum contigs, 97 Hsa 17 presumptive orthologues were identified. We chose 15 genes from this list and designed PCR primers to amplify a short DNA fragment containing 1 or more presumptive SNPs that were identified in the joint assembly (Table 9 ). All but two of these genes were mapped, indicating a high probability of mapping success using markers developed from the joint assembly of A. mexicanum and A. t. tigrinum contigs. All 6 ESTs that exhibited 'best hits' to loci within the previously defined human- Ambystoma synteny group did map to this region ( Hspc009 , Sui1 , Krt17 , Krt24 , Flj13855, and Rpl19 ). Our results show that BLAST-based definitions of orthology are informative between salamanders and human. All other presumptive Hsa 17 loci mapped to Ambystoma chromosomal regions outside of the previously defined synteny group. It is interesting to note that two of these loci mapped to the same ambystomatid linkage group ( Cgi-125 , Flj20345 ), but in human the presumptive orthologues are 50 Mb apart and distantly flank the syntenic loci in Figure 4 . Assuming orthology has been assigned correctly for these loci, this suggests a dynamic history for some Hsa 17 orthologues during vertebrate evolution. Figure 4 Comparison of gene order between Ambystoma linkage group 1 and an 11 Mb region of Hsa17 (37.7 Mb to 48.7 Mb) . Lines connect the positions of putatively orthologous genes. Table 9 Presumptive human chromosome 17 loci that were mapped in Ambystoma Marker ID Primers a Diagnosis b LG c Symbol d RefSeq ID e E-value f Pl_6_E/F_6 F-GAAAACCTGCTCAGCATTAGTGT ASA ul PFN1 NP_005013 E -34 R-TCTATTACCATAGCATTAATTGGCAG Pl_5_G/H_5 F-CTATTTCATCTGAGTACCGTTGAATG PE (A) 23 CGI-125 NP_057144 E -56 R-TAATGTAGAACTAAATGGCATCCTTC E-CCATGGTGCAGGAAGAGAGCCTATAT Pl_0.4_A/B_1 F-GTCTCATTATCCGCAAACCTGT SP 1 RPL19 NP_000972 E -67 R-ATTCTCATCCTCCTCATCCACGAC Pl_4_B_7/8 F-CCTAGAACATTACCAAAACAGACTCA RD (Dpn II) 1 KRT10 NP_061889 E -17 R-AATGAAGAAGTATTGCATGTGAGAAC Pl_4_B_9/10 F-GAACTTGTTGGCAGGTTTCTCTT RD (AciI) 1 KRT17 NP_000413 E -146 R-CTAGTGATAGGTTGGACATACCAGAG Pl_10_C/D_4 F-CTCCACTATTTAAAGGACATGCTACA PE (A) 1 SUI1 NP_005792 E -48 R-TTAATATAGCACAACATTGCCTCATT E-TGCTACATTAATGTAATAAACGGCATCATC Pl_6_E/F_11 F-AAGAGAAGTTCCTAGATGAGTTGGAG PE (A) 1 HSPC009 NP_054738 E -26 R-TGAAGAGAGAACTCAAAGTGTCTGAT E-TCATGTTTTGCTCTGCTGTGCAGT Pl_9_A/B_10 F-TGATAGTTTCTGGATTAAGACGAGTG PE (T) 1 FLJ13855 NP_075567 E -15 R-CTTAGAGCCATTGTTACAAGATGTTC E-GTGATCTAGTGGGATCAAACCCTAAAGACC Pl_10_C/D_9 F-AAAGTGCCAAGAAGGAGATTAACTT PE (T) 9 NME1 NP_000260 E -71 R-GAGCTCAGAAAACAAGGCAGTAAC E-AAATGGATCTACGAGTAGACCTTGACCC Pl_9_C/D_9 F-GAGTCTCCTTTAGGATTGACGTATCT PE (T) 23 FLJ20345 NP_060247 E -17 R-GCTATGTGAGCAGAGATAAAAGTCAG E-GTTACAGCATCAGTGGGATGTGGTATGT Pl_8_C/D_9 F-AGGATACCAACCTCTGTGCTATACAT PE (C) 15 H3F3B NP_005315 E -66 R-TAAATGTATTTACAAACCGAAAGCAA E-CGTGGCGAGCGTGCCTAGT Pl_9_C/D_4 F-GTGGTTATTTGTAACATTTCGTTGAC PE (A) 8 SFRS2 NP_003007 E -40 R-AATTACATTTGGGCTTCTCAATTTAC E-TTTTTAAACGCGTAAAAATGTTAACAGA Pl_6_C/D_5 F-CCGTAAATGTTTCTAAATGACAGTTG PE (G) 2 ACTG1 NP_001605 0 R-GGAAAGAAAGTACAATCAAGTCCTTC E-GATTGAAAACTGGAACCGAAAGAAGATAAA a Sequences are 5' amplification primers, 3' amplification primers, or primer extension probes, and are preceded by F-, R-, and E- respectively. b Genotyping methods are abbreviated: allele specific amplification (ASA), size polymorphism (SP), restriction digestion (RD), primer extension (PE). Diagnostic restriction enzymes and diagnostic extension bases are provided in parentheses. c Ambystoma linkage group ID. "ul" designates markers that are unlinked. d Official gene symbols as defined by the Human Genome Organization Gene Nomenclature Committee . e Best BLASTX hit (highest e-value) from the human RefSeq database using the contig from which each marker was designed as a query sequence. f Highest E-value statistic obtained by searching contigs, from which EST markers were designed, against the human RefSeq database. Future directions Ambystomatid salamanders are classic model organisms that continue to inform biological research in a variety of areas. Their future importance in regenerative biology and metamorphosis will almost certainly escalate as genome resources and other molecular and cellular approaches become widely available. Among the genomic resources currently under development (see [ 41 ]) are a comparative genome map, which will allow mapping of candidate genes, QTL, and comparative anchors for cross-referencing the salamander genome to fully sequenced vertebrate models. In closing, we reiterate a second benefit to resource development in Ambystoma . Genome resources in Ambystoma can be extended to multiple, closely related species to explore the molecular basis of natural, phenotypic variation. Such extensions can better inform our understanding of ambystomatid biodiversity in nature and draw attention to the need for conserving such naturalistic systems. Several paedomorphic species, including A. mexicanum , are on the brink of extinction. We can think of no better investment than one that simultaneously enhances research in all areas of biology and draws attention to the conservation needs of model organisms in their natural habitats. Conclusions Approximately 40,000 cDNA sequences were isolated from a variety of tissues to develop expressed sequence tags for two model salamander species ( A. mexicanum and A. t. tigrinum ). An approximately equivalent number of contigs were identified for each species, with 21,091 unique contigs identified overall. The strategy to sequence cDNAs from a diverse collection of tissues from normalized and non-normalized libraries yielded different sets of highly redundant contigs. Only 25% and 28% of the A. mexicanum and A. t. tigrinum contigs, respectively, were identified in common. To demonstrate the utility of these EST resources, we searched databases to identify new probes for regeneration research, characterized intra- and interspecific nucleotide polymorphism, saturated a human/ Ambystoma synteny group with marker loci, and extended PCR primer sets designed for A. mexicanum / A. t. tigrinum orthologues to a related tiger salamander species. Over 100 new probes were identified for regeneration research using informatic approaches. With respect to comparative mapping, 13 of 15 EST markers were mapped successfully, and 6 EST markers were mapped to a previously defined synteny group in Ambystoma . These results indicate a high probability of mapping success using EST markers developed from the joint assembly of A. mexicanum and A. t. tigrinum contigs. Finally, we found that primer sets designed for A. mexicanum / A. t. tigrinum EST orthologues can be used to amplify the corresponding sequence in a related A. tigrinum complex species. Overall, the EST resources reported here will enable a diversity of new research areas using ambystomatid salamanders. Methods cDNA library construction Ten cDNA libraries were constructed for the project using various larval tissues of A. mexicanum and A. t. tigrinum (Table 1 ). Larval A. mexicanum were obtained from adult animals whose ancestry traces back to the Axolotl Colony [ 17 ]. Larval A. t. tigrinum were obtained from Charles Sullivan Corp. The GARD and MATH A. mexicanum limb regeneration libraries were constructed using regenerating forelimb mesenchyme. Total RNAs were collected from anterior and posterior limbs amputated at the mid-stylopod level on 15 cm animals, and from the resulting regenerates at 12 h, 2 days, 5 days and early bud stages. One hundred μg fractions of each were pooled together and polyA-selected to yield 5 μg that was utilized for directional library construction (Lambda Zap, Stratagene). The V1 ( A. mex ), V2 ( A. tig ), V4-5 ( A. tig ), and V6-7 ( A. mex ) libraries were made from an assortment of larval tissues (see Table 1 ) using the SMART cDNA cloning kits (Clontech). Total RNAs were isolated and reverse transcribed to yield cDNAs that were amplified by long distance PCR and subsequently cloned into pTriplEX. The V3 and AG libraries were constructed by commercial companies (BioS&T and Agencourt, respectively). cDNA template preparation and sequencing cDNA inserts were mass excised as phagemids, picked into microtitre plates, grown overnight in LB broth, and then diluted (1/20) to spike PCR reactions: (94°C for 2 min; then 30 cycles at 94°C for 45 sec, 58°C for 45°sec, and 72°C for 7 min). All successful amplifications with inserts larger than ~500 bp were sequenced (ABI Big Dye or Amersham Dye terminator chemistry and 5' universal primer). Sequencing and clean-up reactions was carried out according to manufacturers' protocols. ESTs were deposited into NCBI database under accession numbers BI817205-BI818091 and CN033008-CN045937 and CN045944-CN069430. EST sequence processing and assembly The PHRED base-calling program [ 42 ] was used to generate sequence and quality scores from trace files. PHRED files were then quality clipped and vector/contaminant screened. An in-house program called QUALSCREEN was used to quality clip the ends of sequence traces. Starting at the ends of sequence traces, this program uses a 20 bp sliding window to identify a continuous run of bases that has an average PHRED quality score of 15. Mitochondrial DNA sequences were identified by searching all ESTs against the complete mtDNA genome sequence of A. mexicanum (AJ584639). Finally, all sequences less than 100 bp were removed. The average length of the resulting ESTs was 629 bp. The resulting high quality ESTs were clustered initially using PaCE [ 43 ] on the U.K. HP Superdome computer. Multi-sequence clusters were used as input sequence sets for assembly using CAP3 [ 44 ] with an 85% sequence similarity threshold. Clusters comprising single ESTs were assembled again using CAP3 with an 80% sequence similarity threshold to identify multi-EST contigs that were missed during the initial analysis. This procedure identified 550 additional contigs comprising 1150 ESTs. Functional annotation All contigs and singletons were searched against the human RefSeq database (Oct. 2003 release) using BLASTX. The subset of sequences that yielded no BLAST hit was searched against the non-redundant protein sequence database (Feb. 2004) using BLASTX. The remaining subset of sequences that yielded no BLAST hit was searched against Xenopus laevis and X. tropicalis UNIGENE ESTs (Mar. 2004) using TBLASTX. Zebrafish ESTs were downloaded from UNIGENE ESTs (May 2004). BLAST searches were done with an E-value threshold of E <10-7 unless specified. Sequence comparison of A. mexicanum and A. t. tigrinum assemblies All low quality base calls within contigs were masked using a PHRED base quality threshold of 16. To identify polymorphisms for linkage mapping, contigs from A. mexicanum and A. t. tigrinum assemblies were joined into a single assembly using CAP3 and the following criteria: an assembly threshold of 12 bp to identify initial matches, a minimum 100 bp match length, and 85% sequence identity. To identify putatively orthologous genes from A. mexicanum and A. t. tigrinum assemblies, and generate an estimate of gene sequence divergence, assemblies were compared using BLASTN with a threshold of E <10 -20 . Following BLAST, alignments were filtered to obtain reciprocal best BLAST hits. Extending A. mexicanum / A. t. tigrinum sequence information to A. ordinarium Polymorphic DNA marker loci were identified by locating single nucleotide polymorphisms (SNPs) in the joint A. mexicanum and A. t. tigrinum assembly. Polymerase chain reaction (PCR) primers were designed using Primer 3 [ 45 ] to amplify 100 – 500 bp SNP-containing fragments from 123 different protein-coding loci (Table 8 ). DNA was isolated from salamander tail clips using SDS, RNAse and proteinase K treatment, followed by phenol-chloroform extraction. Fragments were amplified using 150 ng DNA, 75 ng each primer, 1.5 mM MgCl 2 , 0.25 U Taq, and a 3-step profile (94°C for 4 min; 33 cycles of 94°C for 45 s, 60°C for 45 s, 72°C for 30 s; and 72°C for 7 min). DNA fragments were purified and sequenced using ABI Big Dye or Amersham Dye terminator chemistry. Single nucleotide polymorphisms were identified by eye from sequence alignments. Linkage mapping of human chromosome 17 orthologous genes Putative salamander orthologues of genes on human chromosome 17 ( Hsa 17) were identified by comparing the joint A. mexicanum and A. t. tigrinum assembly to sequences from the human RefSeq (NCBI) protein database, using BLASTX at threshold E< 10 -7 . Linkage distance and arrangement among markers was estimated using MapManager QTXb19 software [ 46 ] and the Kosambi mapping function at a threshold of p = 0.001. All markers were mapped using DNA from a previously described meiotic mapping panel [ 40 ]. All PCR primers and primer extension probes were designed using Primer 3 [ 45 ] and Array Designer2 (Premier Biosoft) software. Species-specific polymorphisms were assayed by allele specific amplification, restriction digestion, or primer extension, using the reagent and PCR conditions described above. Primer extension markers were genotyped using the AcycloPrime-FP SNP detection assay (Perkin Elmer). See Table 9 for amplification and extension primer sequences, and information about genotyping methodology. Author's contributions SP and DK: bioinformatics; JW: clone management and sequencing in support of A. mexicanum and A. t. tigrinum ESTs; JS: comparative mapping and polymorphism estimation; DW: extending ESTs to A. ordinarium ; JM, KK, AS, NM: PCR and gel electrophoresis; BH and ET: cDNA library construction and sequencing for spinal cord regeneration ESTs; MR, SB, DG: cDNA library construction and clone management for limb regeneration ESTs; DP and SV conceived of the project and participated in its design and coordination. All authors read and approved the final manuscript.

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A catalog of human cDNA expression clones and its application to structural genomics

A systematic approach for identifying human proteins and protein fragments that can be expressed as soluble proteins in Escherichia coli is described.

Background Structural genomics and structural proteomics involve the systematic structural analysis of large sets of proteins [ 1 , 2 ]. Structural analysis requires protein samples of high quality and reasonable quantity [ 3 ]. Bacterial protein-expression systems, namely Escherichia coli , are well suited for preparing such samples at high throughput. Genetic manipulation of E. coli is easy and large amounts of recombinant protein can be expressed in a short time. However, low success rates have been reported for the expression of eukaryotic proteins in E. coli : only a small proportion of proteins can be successfully expressed, partly owing to the specific requirements of eukaryotic proteins in regard to the cellular environment [ 4 - 6 ]. Alternative expression systems such as yeast, insect cells/baculovirus or mammalian cell lines are being improved and have great potential to express larger sets of proteins in the amounts and purity required for structural analysis [ 7 ]. Cell-free expression systems represent another valuable alternative [ 8 ]. At the moment, these systems still require more experimental effort compared to expression in E. coli cells. Consequently, one possible approach to structural proteomics for eukaryotic proteins is to study those that can be expressed in E. coli first. A human cDNA expression library (hEx1) was constructed for parallel screening of protein function on high-density protein arrays [ 9 , 10 ]. This library was cloned into a vector for expression of His-tag fusion proteins. The E. coli K-12 strain SCS1 was used for cloning the library and subsequent protein-expression experiments. A total of 193,536 clones were arrayed on protein-binding membranes and putative expression clones were detected immunologically, resulting in a smaller library of 37,830 putative expression clones [ 10 ]. This new library contains a large proportion of clones expressing His-tag fusion proteins from their cDNA inserts. Most of these expression products were found to remain in the insoluble fraction after cell lysis, which indicates that they form inclusion bodies. To identify clones that express their cDNA insert as a soluble, native folded protein, we established a high-throughput procedure for expression and purification of His-tag fusion proteins under non-denaturing conditions. This procedure was used to screen 10,825 clones of the hEx1 library for soluble expression products. Results Clones expressing soluble protein The hEx1 cDNA expression library was screened for expression clones on protein macroarrays. Using an anti-His-tag antibody, a subset of 37,830 clones was detected, as described before [ 10 ]. On the basis of a normalization experiment by oligonucleotide fingerprinting [ 11 , 12 ], redundant clones were removed from this set of putative expression clones, and 10,825 clones were selected for further characterization (Figure 1 ). To identify soluble expression products, small-scale protein expression and purification experiments were performed in microplates in 1 ml cultures. Protein expression was routinely performed at 37°C for 7,316 clones. Because lower induction temperatures have been reported to increase the yield of soluble product for certain proteins [ 13 ], we carried out protein expression at 30°C and 37°C for a set of 284 clones. It was found that for some clones more soluble protein was obtained at 30°C, whereas for a smaller set of clones the yield was reduced. On the basis of these results we tested the remaining 3,509 clones at 30°C. Cells were lysed and aliquots were removed twice - before and after pelleting of cellular debris by centrifugation. These aliquots were termed 'whole' and 'soluble' protein extracts, respectively. Small-scale purification by metal chelate affinity chromatography was performed in batches of 96 in microplates, either manually or with the help of a pipetting robot [ 14 ]. Cellular protein extracts and purified protein samples were analyzed by SDS-PAGE (Figure 2 ). It was found that analysis of the purification eluates is more informative than analysis of the cellular protein extracts. Therefore, only the purification eluates were analyzed for most clones. For each sample, the size of the expression product, if any, and the yield of the recombinant product was recorded. The yield was roughly classified as follows: 0, no expression; 1, weak/doubtful expression; 2, moderate expression; and 3, strong expression. Only clones expressing soluble protein with a size of at least 15 kDa were selected. As found previously [ 10 ], the size of the expression product of a random cDNA expression clone is predictive of the reading frame of the cDNA insert. Most expression products with sizes of less than 15 kDa were found to be artificial products of cDNA inserts in the wrong reading frame, while expression products of at least 20 kDa were almost exclusively expressed from clones with cDNA inserted in the correct reading frame. Screening of the 10,825 hEx1 clones identified 1,866 clones (17%) expressing soluble protein of at least 15 kDa; 1,037 (10%) showed moderate or strong expression. Sequence analysis Clones expressing soluble protein with a size of at least 15 kDa were subjected to DNA tag-sequencing, starting from the 5' ends of the cDNA inserts. For 1,588 clones, sequences of at least 200 base-pairs (bp) of good quality were obtained. Of these sequences, 1,509 (95%) could be matched to transcript sequences from the Ensembl database [ 15 ], using the program cross_match [ 16 ]. These transcripts correspond to 1,105 different genes. By matching their sequences to Ensembl, clones were assigned to human proteins and genes and clones containing complete open reading frames (ORFs) were identified. Transcript sequences from the Ensembl database are annotated with start and end positions of ORFs. Annotation of the ORF start position in Ensembl depends on experimental data from other databases and is not determined automatically. Many transcript sequences in the Ensembl database were generated automatically using cDNA sequences and exon-detection algorithms. If such a transcript is novel and does not correspond to known proteins, the ORF start position cannot be determined reliably by the automated annotation process of Ensembl. The annotation will often assign an ORF starting at position 1 to such transcripts; this is the case for 33% of transcript sequences in the Ensembl release 20.34c. To determine which cDNA clones contain complete ORFs (full-ORF clones), the Ensembl database was used, despite the limitation outlined above. Of 1,509 cDNA clones, 538 (36%) were identified as full-ORF clones, as their 5'-tag sequences align to an Ensembl transcript sequence at a position upstream of the ORF start position on that sequence. These clones, representing 375 distinct transcripts, were annotated as containing a complete ORF, as the cDNA for the hEx1 library was constructed by oligo(dT) priming and is therefore assumed to contain the 3' end of their transcript templates. For expression of cDNA inserts as His-tag fusion proteins, the respective cDNA insert has to be cloned in-frame to the vector-encoded start codon and His-tag. The reading frames of the clones' cDNA inserts were determined from the positions of Ensembl transcript and vector sequences aligned to the clones' sequences (see Materials and methods). We determined the reading frame of 1,447 of the 1,509 clones and found that 1,014 (70%) of the sequences were cloned in the correct frame with respect to the vector. Observed expression product sizes compared to prediction The complete clone insert sequences are unknown as only partial 5'-tag sequences were generated. However, if a clone is matched to an Ensembl transcript sequence, it is possible to construct a putative predicted insert sequence by combining the experimentally derived DNA sequence and the Ensembl transcript sequence. Such a strategy can lead to wrong results if a different splice variant is represented by the clone and the Ensembl sequence. By comparison of predicted sequences with experimentally derived, complete sequences we found that in most cases the prediction is correct (data not shown). Predicted insert sequences were generated for 1,133 clones and the corresponding putative sequences of expression products were calculated. For the remaining sequences, the quality of the experimental sequence was not sufficient, or the alignment to the Ensembl transcript suggested that the clone represents a different splice form. The molecular masses derived from the predicted protein sequences were compared to the sizes of proteins expressed in the corresponding clones. Only clones with inserts in the correct reading frame were considered. As shown in Figure 3 , there is a correlation between the experimental and predicted molecular masses. The correlation is better for clones that express with moderate or high yield (correlation coefficient 0.55, Figure 3a ) than for clones with weak/doubtful expression (correlation coefficient 0.33, Figure 3b ). For those clones, where the observed and predicted molecular mass of the expression product match, it can be assumed that the predicted protein sequence is correct to a large extent, and that the clone indeed expresses the expected protein. For clones of interest, this assumption should be verified by sequencing the complete cDNA insert. For other clones, either the sequence was not predicted correctly, because of alternative splicing, for example, or the observed expression product does not correspond the cloned cDNA, because the insert sequence is not expressed completely or because the expression product is degraded within the E. coli cells. Public database The results of our protein expression screening and DNA sequencing of the hEx1 cDNA library are publicly available [ 17 ]. The corresponding clones are distributed by the RZPD German Resource Centre [ 18 ]. A web interface allows for retrieval of sequence and protein expression data (Figure 4 ). Users can download DNA sequence raw data (chromatograms) and view detailed descriptions of protein expression experiments, including images of SDS-PAGE analyses. Furthermore, users can search for genes and proteins by name, symbol or accession number and display lists of all genes corresponding to clones in the database. These lists can be filtered to display only genes corresponding to full-ORF clones or clones with certain expression properties. Selection of clones and protein preparation for structural analysis Clones expressing soluble recombinant protein and containing full-ORF inserts were selected for the structural analysis pipeline of the Protein Structure Factory [ 2 ]. Clone sequences were matched to the transcript sequences in the Ensembl database. The corresponding Ensembl protein sequences were compared to the protein sequences of the PDB database, using BLASTP [ 19 , 20 ]. Target proteins with known structures were excluded. Specifically, only target sequences were selected with 80% or less sequence identity to PDB entries or with no match to PDB over at least 50 amino acids and at least 10% of the sequence length. One hundred and sixty-three hEx1 clones expressing target proteins with sufficient yield and homogeneity remained after applying these criteria. For preparation of proteins without additional residues such as the His-tag, ORFs were subcloned into the vector pQTEV. This vector allows expression of His-tag fusion proteins and subsequent tag removal by specific protease cleavage using tobacco etch virus (TEV) protease. Of the selected cDNAs, 110 were subcloned into pQTEV, of which 48 were selected for large-scale protein production. A total of 17 of the 48 proteins could be expressed and purified in sufficient yield and quality for protein crystallization. The volume of cultures, grown either in shaker flasks or fermenters, varied between 1 and 5 liters. Protein yields varied from 1.5 to 38 mg/liter of culture volume. Following cell lysis, His-tag fusion proteins were captured by metal chelate affinity chromatography. The His-tag was removed proteolytically and proteins were further purified by ion-exchange and size-exclusion chromatography. The proteins were characterized and prepared for crystallization trials using biophysical methods. A summary of a typical preparation for each clone, and the preparation and characterization data is given in Table 1 . The protein preparations were tested to see whether they were free of aggregates. For 10 of the 17 proteins, this was proven by dynamic light scattering (DLS) analysis. To determine the thermal stabilities, denaturation temperatures ( T m ) were measured by differential scanning calorimetry (DSC). With one exception, all proteins that were free of aggregation showed high T m values, of 49-60°C, at pH 7.0 (Table 1 ). So far, the structures of gankyrin (PDB 1QYM), aortic preferentially expressed protein 1 and prolidase (unpublished data) have been solved by the Protein Structure Factory as a result of the approach described here. Discussion The expression of soluble recombinant protein is still a bottleneck for functional and structural genomics projects studying human proteins. We demonstrate here a method for generating and characterizing a large set of expression clones for human proteins from a cDNA library, yielding a pre-selection of clones for large-scale expression. By matching clone sequences to the Ensembl database, it was shown that expression clones with soluble products were found for 1,509 human proteins corresponding to 1,105 distinct genes. To cover a larger set of proteins with our approach, additional libraries from different tissues and developmental stages could be used. It was found that 36% of expression clones are full-ORF clones expressing complete human proteins, while the remaining clones express carboxy-terminal fragments. It should be noted that because the Ensembl database is generated automatically and start codon positions are still unknown for many human transcripts, this number is inaccurate and will probably be higher. Future releases of Ensembl will benefit from the ongoing efforts to generate and annotate human full-length cDNA sequences [ 21 ], and the information on ORF start positions should improve accordingly. Thre are several reasons for the presence of clones expressing carboxy-terminal fragments. A certain proportion of incomplete inserts is a common feature of cDNA libraries constructed by the cloning technique used here. Furthermore, full-ORF clones containing parts of the 5'-untranslated region (UTR) are not detected in our expression screen if the UTR contains stop codons. The fact that smaller proteins or fragments are often expressed better than very large proteins in E. coli could be another reason why many clones expressing carboxy-terminal fragments were obtained. Full-ORF clones are generally required for determination of protein structures. However, carboxy-terminal fragments can be interesting for other applications, such as structural analysis of the domain by NMR spectroscopy. As an example of the application of the characterized clone library, we show the selection of clones for structure analysis. The high-throughput screening for expression clones took about a year, while the work on the 163 selected proteins is still in progress and additional proteins are being purified. From the 17 protein preparations, three new protein structures were solved. In conclusion, a systematic screening approach for E. coli expression clones of human proteins is described here. Using this approach, a public resource of 2,746 clones was created that allows functional genomics projects to select clones and express human proteins of interest. Materials and methods Sequence analysis and database cDNA sequences have been submitted to the dbEST database and are available under the accession numbers CD579165-CD580594. Clone DNA sequences were matched to transcript sequences of the Ensembl database, release 20.34c, using the program cross_match, version 0.990329, of the swat/cross_match/phrap package [ 16 ]. Protein sequences were compared with BlastP [ 19 ], version 2.0a19MP-WashU (Warren R. Gish, unpublished work). A database was created to store the results of the protein expression and purification experiments as well as clone sequence data. The Oracle database management system 8.1.6 was used. A web-based front end including search functionality was developed, using the Java programming language. Determination of reading frames The reading frame of a cDNA insert was determined using the following formula: | c ce,start - ( c cv,end + l - v cv.end ) + o - e ce,end | mod 3, where l is the length of the vector pQE30NST (3,494 bp). In an alignment of a vector and clone sequence, c cv , end and ν cv , end denote the positions of the end of the matched region on the clone and vector sequence, respectively. Likewise, c ce , start and e ce , start denote the start positions of the match of clone and Ensembl sequence. o is the start position of the ORF on the Ensembl transcript sequence. For clones that are in-frame to the vector-encoded start codon and His-tag, the formula returns 0. Predicted clone insert sequences were generated from experimental tag sequences and Ensembl transcript sequences by the Perl program seqjoin. seqjoin uses alignments generated by cross_match to generate combined sequences. It does not generate output for alignments that indicate alternative splicing. The program and documentation are publicly available online [ 22 ]. Subcloning of cDNA fragments into pQTEV ORFs were PCR amplified from hEx1 cDNA clones using gene-specific primers. Primers were automatically designed using a Perl script that is available on request. Primer length was adjusted to obtain a uniform T m of 60-65°C and sense and antisense primers were equipped with Bam HI and Not I sites, respectively. For ORFs containing these sites, alternative enzymes producing compatible overhangs were used ( Bgl II, Eco 31I or Esp 3I). PCR products were cloned into the vector pQTEV (GenBank AY243506). A pipetting robot and microplates were used for PCR setup, restriction digest and DNA purification steps. The resulting plasmid was introduced into E. coli SCS1 cells carrying the pSE111 helper plasmid. pSE111 provides resistance to 15 μg/ml kanamycin and carries the lacIQ repressor and the argU gene for the arginine tRNA that recognizes the rare codons AGG and AGA. The low abundance of this tRNA is especially critical when expressing eukaryotic genes in E. coli [ 23 ]. The resulting clones as well as hEx1 library clones are available from the RZPD German Resource Center for Genome Research GmbH (Table 1 ). Protein expression in 96-well plates Protein expression was performed as described [ 14 ]. The hEx1 library is stored frozen at -80°C in 384-well microtiter plates (Genetix, X7001) in several copies. Plates were thawed at room temperature, and 100 μl cultures (2× YT supplemented with 2% glucose, 100 μg/ml ampicillin and 15 μg/ml kanamycin) in 96-well deep-well plates were inoculated with steel replicators and grown over night at 37°C with rigorous shaking (> 300 rpm). Nine hundred microliters of pre-warmed SB medium supplemented with antibiotics was added, and cultures were grown for 3 h at 37°C, followed by induction of protein expression for 3 h by addition of 1 mM isopropyl-beta-D-thiogalactopyranoside (IPTG) (final concentration). Cells were harvested by centrifugation at 4°C at 2,000 g for 10 min and frozen at -80°C. Protein purification in 96-well format Proteins were purified via metal chelate affinity chromatography in a 96-well format. We used an automated procedure on a pipetting robot [ 14 ] or a corresponding manual method. According to the manual method, cells were thawed and resuspended in 100 μl lysis buffer (50 mM Tris-HCl pH 8.0, 0.3 M NaCl, 0.1 mM EDTA) by vortexing, followed by addition 2 mg/ml lysozyme and 0.5% Brij 58 in 25 μl lysis buffer. Cells were lysed for 30 min on ice and nucleic acids were degraded by addition of 25 μl of 10 mM MgCl 2 , 0.1 U/μl Benzonase gradeII (Merck) in 50 mM Tris-HCl pH 8.0, brief vortexing and incubation at room temperature for 30 min. An aliquot was collected for SDS-PAGE analysis (whole cellular proteins). Cellular debris was pelleted by centrifugation of the plates at 6,200 rpm for 30 min. Aliquots of the supernatants were collected (soluble cellular protein). Supernatants were transferred to a filter plate (Millipore Multiscreen MADVN6550) and were filtered on a vacuum manifold. Filtrates were collected in a second filter plate. Imidazole was added to 10 mM, and 25 μl of 20% (v/v) Ni-NTA agarose (Qiagen) equilibrated in 50 mM Tris-HCl pH 8.0. Plates were shaken at room temperature for 30 min, followed by removal of cell lysates on the vacuum manifold. The agarose beads were washed three times by shaking in 200 μl wash buffer (50 mM Tris-HCl pH 8.0, 0.3 M NaCl, 20 mM imidazole). Upon complete removal of liquid from the plate, proteins were eluted by addition of 25 μl wash buffer containing 250 mM imidazole. Eluates were collected in a 96-well plate by brief centrifugation. Seven microliters of the eluates and 3.5 μl of the whole and soluble cellular extracts were analyzed by SDS-PAGE (15% polyacrylamide) and Coomassie staining. Large-scale protein production and biophysical characterization Proteins were expressed, purified, concentrated and analyzed as described [ 24 ]. Cells were grown in SB media (see above) containing 50 mg/ml ampicillin and 10 mg/ml kanamycin in 5 l baffle shaker flasks in 2 l volumes or in a 5 l fermenter to a cell density of A 600 of 1.5 and protein expression was induced by addition of 1 mM IPTG for 4 h. The optimal expression temperature was determined in small-scale experiments beforehand (28-37°C). Cells were pelleted by centrifugation and resuspended in a threefold volume of 20 mM Tris-HCl pH 7.4, 300 mM NaCl, 10 mM imidazole, 5 mM 2-mercaptoethanol, 1 mM PMSF, a protease inhibitor cocktail tablet (EDTA-free, Roche) and 500 units Benzonase (Merck). Cells were lysed by treatment with lysozyme and sonification, followed by centrifugation (23,000 g , 45 min) and filtration through a 0.22-μm syringe filter. Proteins were applied to a metal chelate chromatography using a Ni-POROS20-column (Applied Biosystems) or a TALON column (Clontech). After washing with 20 mM Tris pH 7.4, 150 mM NaCl, 10 mM imidazole, the protein was eluted with 250 mM imidazole in the same buffer and eluates were supplemented with 2 mM dithiothreitol and 1 mM EDTA. The His-tag was removed by incubation with TEV protease (molar ratio 1:40 protease:substrate) at 4°C overnight. Proteins were diluted fivefold and depending on the theoretical pI of the protein, anion or cation exchange chromatography was performed. Proteins were further purified by gel filtration on a Superose 12 16/50 column (Amersham Biosciences). Protein concentrations were determined from the absorbance at 280 nm using the extinction coefficient calculated from the amino acid sequence [ 25 ]. Absorbance was corrected for stray light according to the light scattering theory (Tyndall effect, I ( s ) ~ λ -4 ) with the assumption that no absorption due to protein chromophores occurs above 320 nm [ 26 ]. Purified protein concentrations were in the range of 0.2-1 mg/ml. DLS measurements were carried out at room temperature, using the Spectroscatter 201 (660 nm laserdiode, 30 mW, scattering angle 90°, PMT detector, 400 nsec to 30 sec correlator, quasi-logarithmic arranged channels, RiNA, Berlin, Germany). The samples were centrifuged (20,800 g , 3 min, 4°C) and measured in a 1.5 × 1.5 mm cuvette (Hellma, Müllheim, Germany) for 20 sec. The instrument software allows us to judge the autocorrelation function and deduce the dispersity, that is, the distribution N(Rh), of particles according to their hydrodynamic radius. Protein samples were judged 'free of aggregation' when a single peak indicated a monomodal distribution. DSC measurements were performed at a rate of 1 K/min using an automated capDSC calorimeter (MicroCal, LLC, Northampton, MA). Proteins were diluted at least 20-fold in a buffer of temperature-independent pH (20 mM Na/K phosphate pH 7.0, 150 mM NaCl). The resulting scans were baseline-corrected and T m values were calculated using the instrument software (MicroCal Origin, vers. 7.0). cDNA sequencing cDNA inserts were PCR-amplified using primers pQE65 (TGAGCGGATA ACAATTTCAC ACAG) and pQE276 (GGCAACCGAG CGTTCTGAAC), annealing temperature 65°C. PCR products were tag-sequenced using primer pQE65. Additional data files Additional data file 1 , available with the online version of this paper, is a tab-delimited text file listing information on hEx1 clones with inserts in the correct reading frame, giving their clone ID, Ensembl transcript ID, experimental and predicted expression product size, expression strength. Supplementary Material Additional data file 1 A a tab-delimited text file listing information on hEx1 clones with inserts in the correct reading frame, giving their clone ID, Ensembl transcript ID, experimental and predicted expression product size, expression strength Click here for additional data file

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Endodontic flare-ups: comparison of incidence between single and multiple visit procedures in patients attending a Nigerian teaching hospital

Background Until recently the most accepted technique of doing root canal treatment stresses multiple visit procedure. Most schools also concentrated upon teaching the multi-visit concept. However, it has now been reported that the procedure of single visit treatment is advocated by at least 70% of schools in all geographical areas. It was therefore the aims of the present study to find the incidence of post-obturation flare-ups following single and multiple visit endodontic treatment procedures, and to establish the relationship between pre-operative and post-obturation pain in patients referred for endodontic therapy in a Nigerian teaching Hospital. Methods Data collected included pulp vitality status, the presence or absence of pre-operative, inter-appointment and post-obturation pain. Pain was recorded as none, slight, or moderate/severe. Flare-ups were defined as either patient's report of pain not controlled with over the counter medication or as increasing swelling. The patients were recalled at three specific post-obturation periods, 1 st , 7 th and 30 th day. The presence or absence of pain, or the appropriate degree of pain was recorded for each recall visits and the interval between visits. The compiled data were analysed using chi-square where applicable. P level ≤ 0.05 was taken as significant. Results Ten endodontic flare-ups (8.1%) were recorded in the multiple visit group compared to 19 (18.3%) flare-ups for the single visit group, P = 0.02. For both single and multiple visit procedures, there were statistically significant correlations between pre-operative and post-obturation pain (P = 0.002 and P = 0.0004 respectively). Teeth with vital pulps reported the lowest frequency of post-obturation pain (48.8%), while those with nonvital pulps were found to have the highest frequency of post-obturation pain (50.3%), P = 0.9. Conclusion The present study reported higher incidences of post-obturation pain and flare-ups following the single visit procedures. However, single visit endodontic therapy has been shown to be a safe and effective alternative to multiple visit treatment, especially in communities where patients default after the first appointment at which pain is relieved.

Background Until recently the most accepted technique of doing endodontic treatment stresses multiple visit procedures. Most schools also concentrated upon teaching the multi-visit concept. However, it has now been reported that the procedure of single visit treatment is advocated by at least 70% of schools in all geographical areas [ 1 ]. Some of the problems of root canal treatment are post-obturation pain, inter-appointment pain and swelling. Although these in most cases do not last long, but could be a source of embarrassment to the dentist and annoying for the patient, more so if the tooth was symptomless before the commencement of treatment. Literature review revealed varied opinions on the incidence and severity of post-obturation pain. Some authors reported slightly more post-obturation pain following single visit than with multiple visit procedures [ 2 , 3 ]. Others found no significant differences in the post-obturation pain experienced by patients following single or multiple visit treatment procedures [ 4 ]. O'Keefe [ 4 ] however proposed a correlation between pretreatment pain and post-obturation discomfort. The rate of endodontic flare-ups was reported to be more following multiple visits than for the single visit [ 5 - 7 ], Imura & Zuolo [ 7 ] also reported a positive correlation between flare-ups and multiple appointment, retreatment cases, peri-radicular pain prior to treatment and presence of radioluscent lesions. They reported no correlation between post-obturation flare-ups and the status of the pulp. However, Sim [ 8 ] reported a significantly higher incidence of flare-ups in necrotic teeth than in vital teeth (p = 0.01). Fox et al. [ 9 ] in their study showed that female patients had more post operative pain than did males. Factors of age, bacteriologic status, tooth position and type of filling material showed no clear effect upon post-operative results. Endodontic treatment in Nigeria is carried out in the department of restorative dentistry of the four dental schools and in few private dental clinics located in major cities, health centres and general hospitals. Previous report revealed that few cases of root canal treatment were undertaken and that root canal treatment was completed in multiple visits, specifically three visits for about half the teeth treated [ 10 ]. Reasons for the reported few cases of root canal treatment included patient's preference for extraction, which is a cheaper option (the cost of root canal treatment is about twice that of extraction). Also because most of the patients had to travel a considerable distance for the treatment, they prefer extraction, which is completed in a single visit (except in complicated cases). However, it has been recently observed that the acceptability of endodontic treatment is on the increase among Nigerian patients, with more people desiring to keep their teeth. Despite the desire, they present for treatment late only after the onset of pain. Also some patient do not come back to complete the treatment after the first appointment at which pain is relieved. Hence more dentists are embracing the single visit procedure particularly in the Teaching Hospitals. It was therefore the aims of the present study to find the incidence of post-obturation flare-ups following single and multiple visit endodontic treatment procedures. Establish the relationship between pre-operative and post-obturation pain. Find the incidence and degree of pain at the 1 st , 7 th , and 30 th post-obturation days, and to compare these results with those reported in previous studies. Methods Consenting patients referred to the department of Restorative Dentistry for root canal therapy within a period of twelve months were randomly assigned for either single visit or multiple visit procedures. For the multiple visit procedures, Patients that defaulted after the first appointment (incomplete treatment) were excluded from the study. For each tooth treated, the clinical factors and conditions existing before, during and after the completion of treatment were recorded. This data included pulp vitality status, the presence or absence of pre-operative pain, post-obturation flare-ups and degree of post-obturation pain. For patients requiring root canal treatment on more than one tooth, the treatment of each tooth was separated by a period of at least four weeks to allow for proper evaluation. The pulp vitality was determined by an electric pulp tester (Parkell pulp vitality tester, Farmingdale, NY 111735) in combination with the presence of pulpal haemorrhage. The patients were recalled at three specific post-obturation periods, the 1 st , 7 th and 30 th day. At each post-obturation recall visit, the patients were interviewed to determine whether or not there were symptoms at the present visit and whether or not there had been symptoms during the interval between the present visit and the previous one. The presence or absence of pain, or the appropriate degree of pain was recorded for each recall visit and the interval between visits. Pain was recorded as none, slight, or moderate/severe. Slight pain was defined as any discomfort no mater how brief in duration that did not require medication and that did not impair masticatory function in any way. Moderate/severe pain was defined as pain requiring medication or other palliative treatment. Impairment of masticatory function (discomfort in chewing) was recorded as moderate/severe pain. Endodontic flare-ups were defined as either patient's report of pain not controlled with over the counter medication and or increasing swelling. The root canals were obturated with multiple gutta-percha cones and a zinc oxide-eugenol based sealer, using the lateral condensation technique. The compiled data were analysed using chi-square where applicable. P level ≤ 0.05 was taken as significant. Results Two hundred and eighty three (283) teeth in 255 patients were treated in all, given a ratio of 1.11 teeth per patient. Of these 56 were excluded from the study due to non-availability of patients at post-obturation recall visit. These exclusions were randomly distributed between treatment groups, with no differential loss to follow-up (25 from the single visit group, 31 from the multiple visit group). The treatment groups were fairly comparable, with similar distribution of tooth types between treatment groups, Table 1 . Two hundred and fourty three (243) were available for check-up on the 1 st post-obturation day, of these 107 were completed in single visit and 136 were completed in multiple visit. Eighty-six (86) had vital pulps and 157 had nonvital pulp canal contents. Two hundred and twenty seven (227) reported for check-up on the 7 th post-obturation day, of these 104 was completed in single visit and 123 completed in multiple visit. Two hundred and twenty two reported for check-up on the 30 th post-obturation day, 102 completed in single visit and 120 in multiple visit. Table 1 Tooth distribution between treatment groups. Single visit Multiple visit Tooth types No (%) No (%) Maxillary incisors 40 (38.5) 49 (39.8) Maxillary canines 3 (2.9) 3 (2.4) Maxillary premolars 20 (19.2) 22 (17.9) Maxillary molars 6 (5.8) 9 (7.3) Mandibular incisors 13 (12.5) 12 (9.8) Mandibular canines 1 (1.0)* 2 (1.6) Mandibular premolars 9 (8.6) 13 (10.6) Mandibular molars 12 (11.5) 13 (10.6) Total 104 (100.0) 123 (100.0) *Rounded up percentage Percentage is based on total number in treatment group. Each interval between visits and subsequent interview were combined and considered as a single post-obturation period. The highest degree of pain reported in either the interval or at the subsequent interview was recorded as the degree of pain for the specific post-obturation period. Ten flare-ups (8.1%), that is patients presenting with pain not controlled by over the counter medication and or increasing swelling, were recorded in the multiple visit group compared to 19 (18.3%) flare-ups for the single visit group. This shows a significant difference (Mantel Haenszel chi-square = 5.18, p = 0.02), Table 2 . Of the 107 teeth whose treatments were completed in single visit 67 had pre-operative pain, out of which 50 (74.6%) reported post-obturation pain. Of the 40 teeth with no pre-operative pain, 8 (20%) had post-obturation pain (x 2 = 9.04, p = 0.002). For the multiple visit procedures, 88 teeth presented with pre-operative pain out of which 55 (62.5%) reported post-obturation pain. 48 teeth had no pre-operative pain out of which 6 (12.5%) had post-obturation pain (x 2 = 12.5, p = 0.0004). These show that for both single and multiple visit procedures, there were statistically significant correlations between pre-operative and post-obturation pain (Table 3 ). Table 2 Incidence of post obturation flare-ups Group Number in study No flare-ups Flare-ups present No. (%) No. (%) Single visit 104 85 (81.7) 19 (18.3) Multiple visit 123 113 (91.9) 10 (8.1) Mantel Haenszel Chi square = 5.18, df, = 1, p = 0.02. Table 3 Relationship between pre-operative pain and pain on 1 st post obturation day. Group No preop. Pain Postob. Pain Preop. Pain Postob. Pain No. (%) No. (%) No. (%) No. (%) Single visit(n = 107) 40 (37.4) 8 (20.0) 67 (62.6) 50 (74.6) x 2 = 9.04, p = 0.002 Multi Visit(n = 136) 48 (35.3) 6 (12.5) 88 (64.7) 55 (62.5) x 2 = 12.5, p = 0.0004 Teeth with vital pulps reported the lowest frequency of pain (48.8%), while those with nonvital pulps were found to have the highest frequency of pain (50.3%), Table 4 . The difference was however, not statistically significant (p = 0.90). Table 4 Incidence of pain on 1 st post obturation day: Vital and nonvital. Group Number in Group None Slight Moderate/severe No (%) No (%) No (%) Vital 86 44 (51.2) 27 (31.4) 15 (17.4) Nonvital 157 80 (51.0) 52 (33.1) 25 (15.9) Incidence of pain x 2 = 0.02, df = 1, p = 0.90. Percentage incidence of pain, vital = 48.8. Percentage incidence of pain, nonvital = 50.3. The percentages of single visit patients who exhibited slight post-obturation pain on the 1 st and 7 th days respectively 35.5% and 16.3% were higher than those in the multiple visit group 30.2% and 9.8%. Chi square test indicated no statistically significant differences (Tables 5 & 6 ). The same trend was recorded for moderate/severe pain on the 1 st day post-obturation review. The percentage of patient with moderate/severe pain on the 7 th day post-obturation was higher for the multiple visits than the single visit group (Table 6 ). No post-obturation pain persisted to the 30 th day. Table 5 Comparison of pain on 1 st post obturation day: single and multiple visit. Group Number in study None Slight Moderate/severe No. (%) No. (%) No. (%) Single visit 107 49 (45.8) 38 (35.4) 20 (18.7) Multiple visit 136 75 (55.1) 41 (30.2) 20 (14.7) Total 243 124 (51.0) 79 (32.5) 40 (16.5) Incidence of pain: x 2 = 1.74, df = 1, p = 0.19, degree of pain: x 2 = 2.14, df = 2, p = 0.34. Table 6 Comparison of pain on 7 th post obturationday: single and multiple visits. Group Number in study None Slight Moderate/severe No. (%) No. (%) No. (%) Single visit 104 87 (83.7) 17 (16.3) 0 (0.0) Multiple visit 123 109 (88.6) 12 (9.8) 2 (1.6) Total 227 196 (86.3) 29 (12.8) 2 (0.9) Incidence of pain: x 2 = 0.79, df = 1, p = 0.37. Teeth with non-vital pulp recorded more post-obturation pain. There was however no significant difference in post-obturation pain between teeth treated (either by the single or multiple visit procedures) whose pulps were non-vital. Discussion Many authorities in the field of endodontics advice against the completion of root canal treatment in single visit in order to prevent post-obturation pain, especially in cases presenting with pre-operative pain [ 11 , 12 ]. In the present study more flare ups occurred in the single visit group (18.3%) than in the multiple visit group (8.1%), showing a disadvantage for single visit treatment at a 95% confidence level, (Table 2 ). This is in contrast with the findings of Eleazer & Eleazer [ 6 ] who reported fewer flare-ups for the single visit group (3.0%) and (8.0%) for the multiple visit group. Other studies also have reported lower incidence figures for endodontic flare-ups [ 7 , 13 ], Walton & Fouad [ 13 ] in the United States of America reported an incidence of 3.17%, while Imura & Zuolo [ 7 ] in Brazil reported a further lower figure of 1.58%. In Nigeria and possibly in most developing nations, patients do not present for treatment before the onset of severe pain. In most cases they would have tried self prescribed analgesics. These may explain the high incidence of flare-ups reported in the present study, since endodontic flare-ups have been reported to be positively correlated with more severe presenting symptoms and in patients on analgesics [ 7 , 13 ]. Previous studies have shown that there is a strong positive correlation between pre-operative and post-obturation pain [ 4 , 14 , 15 ]. The present study supports this correlation, in both the single and multiple visit groups there were statistically significant correlation between pre-operative and post-obturation pain, p = 0.002, p = 0.0004 respectively (Table 3 ). No significant correlation was found between pulp vitality and the reported incidence of post-obturation pain (p = 0.9), Table 4 . This finding is in agreement with those of Roan, Dryden & Grimes [ 16 ], and Fox et al [ 9 ], who reported that whether a tooth pulp was vital or not had little effect on post-obturation pain. It is however in direct conflict with the traditional belief that only vital cases should be considered for single visit endodontics. Although the single visit patients seemed to experience more pain (slight, moderate/severe) than did the multiple visit patients during the first 24 hours, the differences were not statistically significant (Table 5 ). This finding is supported by that of Pekruhn [ 17 ] who also reported no statistically significant difference between the two groups when the total number of pain days was considered, but in contrast with the findings of Roan, Dryden & Grimes [ 16 ] that discloses a significant difference in the incidence of post-obturation pain between single and multiple visit. Despite the high percentages of post-obturation pain reported on the first post-obturation day in both groups (Table 5 ), seven days after obturation, 83.7% and 88.6% of teeth treated by the single and multiple visit respectively were free of symptoms (Table 6 ). Also since no post-obturation pain persisted to the 30 th day in both groups, these present a strong indication that practitioners should not overreact to early post-obturation symptoms by immediately initiating endodontic retreatment procedures or extraction of the involved tooth. Apart from the reported higher incidence of flare-ups in the single visit group, the post-obturation pain experienced by the patients in both groups compares favourably well with each other. Therefore the higher incidence should not be taken as condemnation for the single visit endodontic therapy, it should however stress the fact that a thorough understanding of the basic endodontic principles is important in considering each case on an individual basis before making a decision as to whether or not it can be completed in one visit [ 18 ]. As it is common with all hospital-based studies, the subjects in the present study may not be a true representation of the population. Therefore the ability to generalize the results is weak. However, a careful case selection and adherence to the basic principles of endodontic therapy will reduce the incidence of flare-ups and post-obturation pain and thus enhance a successful outcome. Conclusions The present study reported higher incidences of post-obturation pain and flare-ups following the single visit procedures. However, single visit endodontic therapy has been shown to be a safe and effective alternative to multiple visit treatment, especially in communities where patients default after the first appointment at which pain is relieved. Competing interests The author(s) declare that they have no competing interests. Authors' contributions AOO conceived of the study, participated in its design, performed the statistics, and participated in the final write-up of the manuscript. CIU participated in the design, collected the data, drafted the initial manuscript, and participated in the final write-up of the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

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554087

Asymmetry in host and parasitoid diffuse coevolution: when the red queen has to keep a finger in more than one pie

Background Coevolution between pairs of antagonistic species is generally considered an endless "arms race" between attack and defense traits to counteract the adaptive responses of the other species. Presentation of the hypothesis When more than two species are involved, diffuse coevolution of hosts and parasitoids could be asymmetric because consumers can choose their prey whereas preys do not choose their predator. This asymmetry may lead to differences in the rate of evolution of the antagonistic species in response to selection. The more long-standing the coevolution of a given pair of antagonistic populations, the higher should be the fitness advantage for the consumer. Therefore, the main prediction of the hypothesis is that the consumer trophic level is more likely to win the coevolution race. Testing the hypothesis We propose testing the asymmetry hypothesis by focusing on the tritrophic system plant/aphid/aphid parasitoid. The analysis of the genetic variability in the virulence of several parasitoid populations and in the defenses of several aphid species or several clones of the same aphid species could be compared. Moreover, the analysis of the neutral population genetic structure of the parasitoid as a function of the aphid host, the plant host and geographic isolation may complement the detection of differences between host and parasitoid trophic specialization. Implications of the hypothesis Genetic structures induced by the arms race between antagonistic species may be disturbed by asymmetry in coevolution, producing neither rare genotype advantages nor coevolutionary hotspots. Thus this hypothesis profoundly changes our understanding of coevolution and may have important implications in terms of pest management.

Background Coevolution is the result of reciprocal selective pressures exerted by interacting species. Many studies have been devoted to the hypothesis of an endless "arms race" between antagonistic species, in which each species develops escalating attack and defense traits to counteract the adaptive responses of the other species. In reference to Lewis Carroll's book "Through the Looking Glass", Van Valen [ 1 ] named this model of coevolution "the Red Queen Hypothesis" (RQH) because, even in a constant physical environment, interacting species must evolve continuously to maintain their position. The RQH can be seen as an arms race between "resistance" and "virulence" where, following recent reviews on coevolution (e.g. [ 2 ]), "resistance" is the target's ability to survive attacks by the consumer, and "virulence" is the consumer's ability to defeat the target's defenses. The RQH was initially developed in the context of multiple species interactions, to account for the constant probability of species extinction. However, as modeling the coevolution of many species involves a number of difficulties, later studies based on the RQH have mostly been limited to interactions between pairs of species. From this restricted situation, two main predictions can be made: first, arms races induce an advantage of rare genotypes of which resistance or virulence is more efficient and thus, frequency-dependent fluctuations of resistance and virulence may be predicted. Second, a geographic view of the coevolutionary process suggests a dynamic mosaic structure, with local and temporary "hot spots" of antagonistic species coevolution [ 3 ]. However, for plant-pathogen interactions [ 4 ] and for animal host-parasite interactions [ 5 - 7 ], empirical observations and experimental tests of RQH have not given entirely convincing results. For instance, there is considerable evidence of genetic variation for resistance, but parasite-driven genetic change in resistance has never been observed directly [ 8 ]. Conversely, in certain systems of interacting species, matching genetic diversity, which is expected under the RQH, is lacking [ 9 ]. Another study [ 2 ] has suggested that, for a given host-parasitoid association, the rank order of survival of different host strains exposed to different parasitoid strains remains constant. This lack of frequency dependence has been interpreted as evidence against the RQH. Nevertheless, the RQH continues to lie at the heart of the debate concerning the coevolution of antagonistic species, probably because of the lack of plausible alternative hypotheses. Although metapopulation structures or time lags between the responses of antagonistic species to selective pressures have been evoked as reasons for the lack of clear experimental evidence in favor of the RQH [ 10 ], the additional hypothesis that is considered the most satisfactory relates to the cost of resistance [ 11 ]. Defense is thought to carry costs when it is not needed, i.e. the fitness of resistant individuals in the absence of enemies is reduced compared to susceptible individuals. Following this hypothesis, the arms race is constrained by the resistance cost and thus resistance and virulence of antagonistic species reach fixed levels that can be considered optimal. This hypothesis may result in a low level of genetic polymorphism of resistance and virulence in spatially restricted areas. In contrast, resistance and virulence are expected to be polymorphic on a larger spatial scale if, because of ecological factors, the densities of antagonistic species are spatially heterogeneous. However, although resistance costs have often been demonstrated, expected geographical patterns of resistance and virulence are not observed: antagonistic species densities are not clearly spatially associated with resistance and virulence variability levels [ 2 ]. Thus, although taking account the resistance cost hypothesis is a useful addition to Red Queen arms race models, this theoretical view of coevolutionary process does not provide predictions that fit well with most situations observed in the field [ 10 , 11 ]. Presentation of the hypothesis Adaptive responses to selective pressures exerted by antagonistic organisms are not necessarily the same for the target species and the consumer species. There is a first level of potential evolutionary asymmetry between them because the failure of virulence for a consumer is a delay in fitness acquisition whereas the failure of defense for a prey is the loss of its entire fitness. Such a difference was underlined by Dawkins [ 12 ] noting Aesop's fable of the hare outrunning the fox, because the former runs for life whereas the latter runs for a meal. The opposite asymmetry has been suggested in the case of host-parasitoid relationships [ 2 , 13 ]: all consumers are obliged to overcome the defenses of their target whereas not all targets suffer attacks from consumers. Extending the field of interest to interactions and coevolution between more than two species (i.e. diffuse coevolution) may offer new perspectives. Considering the reciprocal selective pressures exerted by many species leads us to take into account the specificity of virulence and resistance. Until now, few works have been devoted to this subject (but see [ 14 ]). However, when considering diffuse coevolution, there is a second level of potential asymmetry because a consumer may choose its targets, whereas targets cannot be certain which of its enemies will attack it. A recent model described the evolution of resistance of one host subjected to the attacks of two types of parasitoids differing in their virulence and specificity (different genotypes of a species or different species) and the evolution of virulence of one parasitoid attacking two types of hosts differing in their resistance and specificity [ 15 ]. The results suggested that the level of specialization of resistance traits was not affected by the total probability of being attacked. They also suggested that, if the probabilities of encounters fluctuate and differ between trophic levels, generalist traits of resistance and partially specialist traits of virulence are favored. Finally, this model showed that fluctuating host encounter probabilities across or within generations will promote a partial specialization of the parasitoid virulence rather than a total one. The asymmetry hypothesis (AH) may lead to differences in the rate of evolution of the antagonistic species in response to selection. For a specialist consumer capable of target choice, chosen targets constitute a more or less "constant environment". On the other hand, for a generalist defender, the diversified, facultative and fluctuating attacks by a set of enemies constitute a "variable environment". The constancy of the environment may lead to the faster adaptation of specialists (mostly consumers under AH) than of generalists (mostly targets under AH) [ 16 ]. Thus, under the asymmetry hypothesis, the evolution of host defense traits is likely to be slower than the evolution of parasitoid virulence traits. The more long-standing the coevolution of a given pair of antagonistic populations, the higher should be the fitness advantage for the consumer. Therefore, the main prediction of AH is that the consumer trophic level is more likely to win the coevolution race [ 17 ]. This may account for the paradox described by Holt & Hochberg [ 18 ] – the lack of clear published examples of an increase in host resistance after biological control using parasitoids, despite the potentially strong selective pressure associated with parasitism. The asymmetry hypothesis seems to fit well with numerous data concerning geographic structures published in the literature (reviewed in [ 15 ] but see also [ 19 ]). In most of these studies, the interaction trait under consideration is the capability of Drosophila species to encapsulate parasitoid eggs. In this case, it can be also noted that the resistance trait is not specific (artificial eggs are encapsulated as well as parasitoid ones) when virulence traits (reviewed in [ 20 ]) are diversified and specialized (i.e., escaping the host's immune response, just swamping the host with virus-like particles, or mimicking host tissue on the egg surface). Testing the hypothesis We propose a dedicated test of the hypothesis of the specialization of the parasitoid virulence and the absence of specialization in the defense of the aphids, against the RQH. The study will focus on the tritrophic system plant/aphid/aphid parasitoid. Two complementary experimental approaches could be considered: A- The analysis of the genetic variability of virulence and defense of the parasitoid Lysiphlebus testaceipes and the aphid Aphis gossypii . To measure this variability, several populations of the parasitoid collected on different clones of the aphid will be confronted to different clones of this aphid. The different clones of the aphid will be confronted to the different parasitoid populations. This system that considers intra specific genetic variability for each trophic level will be used to evaluate the fitness of the consumers and of the targets in the different parasitoid /aphid combinations. An important point is that virulence will be estimated from the success rate of parasitism (i.e. the production of offspring) whereas defense will be estimated through the measure of the aphid fitness, i.e. the number of offspring produced by the aphid whatever the outcome of the parasitism (success or failure). Two key factors of the host fitness will be considered: 1) the host survival rate in case of parasitism failure (an aphid may die or not, even if the parasitism does not result in parasitoid offspring production); and 2) the number of offspring produced by the host after parasitism: after a parasitoid's sting, the host may produce offspring until mummification in case of parasitoid embryo development or until a variable date in the case of parasitism failure. B- The analysis of the neutral population genetic structure of the parasitoid as a function of the aphid host, the plant host and geographic isolation. The specialization of L. testaceipes on different aphid clones can lead to a neutral genetic differentiation of the parasitoid as a function of the host and the plant because the reproduction of the parasitoid occurs soon after the emergence of the adults from their host. The genetic differentiation between populations of the parasitoid sampled from different aphid clones and from different plant species could be evaluated and compared to a putative effect of isolation by distance. This test may allow local verification or rejection of the predictions of the AH (specialization of the parasitoid and generalism of the host). When performed several times on animals from diverse geographic origins, this should eventually allow rejection of the classical interpretation of RQH and the host spots theory of coevolution [ 3 ]. Implications of the hypothesis The consequences of the asymmetry hypothesis are important. Genetic structures induced by the arms race between antagonistic species may be disturbed by asymmetry in coevolution, producing neither rare genotype advantages nor coevolutionary hotspots [ 17 ]. However some consequences of the AH are compatible with the resistance cost or the Red Queen hypotheses. Under the AH, a low variability in generalist resistance level, as expected under the hypothesis of regulation by the cost of resistance, would be selected for locally by the global pressure exerted by all the species of the upper trophic level. Also, higher levels of local variability in consumer specialist virulence would be selected, as in situations for which the classical RQH holds true. The AH cannot be applied to every situation. In the case of host-parasitoid associations, the high level of genetic variability in resistance observed within some local host populations [ 20 , 21 ] is better explained by RQH if one dominant parasitoid species is present rather than a complex of species. Moreover, as generalism can be seen as a bet-hedging response to unpredictable environmental variations [ 15 ], the advantages of specificity or generalism depend on the level of such fluctuations. One can predict that host defenses will be less generalist when facing more stable enemy communities. Another key point deals with the supposed difference of speed in evolutionary responses between generalist and specialist traits. It may depend on other important biological factors like the discrepancy between population sizes, generation times and the reproductive mode (e.g. a sexual parasitoid versus an asexual aphid) or the genetic make up (e.g. an haplo-diploid parasitoid versus a diploid aphid) of the considered species. The relative speed of evolutionary responses of antagonistic species has thus to be evaluated in each biological model studied. Asymmetry may be suspected in cases involving successive trophic levels other than host-parasitoid associations, such as plant-insect or parasitoid-hyperparasitoid combinations, as soon as individuals of the upper trophic level can choose their target. However, random attacks, such as those due to plant pathogens, may favor more generalist traits of virulence, but the specificity of consumers, and therefore asymmetry, may be restored through indirect choices: pathogens transmitted by vectors may use or manipulate the specificity traits of the vector. Large sets of species interactions may thus lead to asymmetric coevolution. The AH may have consequences in terms of pest management. For instance, it is generally thought that variations in parasitism outcome result from a variability of host resistance due to the selection for higher resistance [ 2 , 22 ]. However, under the AH, virulence of the parasitoid is expected to evolve faster than does the resistance of the host. Therefore, the asymmetry hypothesis implies that variations in parasitism outcome more probably result from variability in parasitoid virulence. Asymmetry could also help to understand some surprising resistance-virulence patterns issuing from biological control. For instance, in the U.S.A., the pea aphid Acyrthosiphon pisum is parasitized by the hymenopteran Aphidius ervi and is specialized on different plant species. Hufbauer & Via [ 23 ] observed that pea aphids that are specialized on alfalfa are successfully parasitized less often than are pea aphids specialized on clover. Hufbauer [ 24 ] also observed that parasitoids collected from alfalfa and clover fields do not differ in their ability to overcome pea aphid resistance. They concluded that alfalfa host population is more resistant to A. ervi than is the clover host population. If the association they studied dealt with two aphid and one parasitoid populations, under the AH, virulence specialization rather than resistance level variations can explain the observations: the parasitoid population is specialized on the clover host population, but keeps a partial virulence on the alfalfa aphid biotype. This suggests that short-term parasitoid specialization may be a key factor in biological control efficiency. For instance, consumers introduced to control a pest could rapidly specialize against non-target hosts. The asymmetry hypothesis thus provides food for thought concerning diffuse coevolution and could be applied to domains beyond host-parasitoid coevolution. Similar thoughts may be applicable to the durability and efficiency of plant resistance or immunological responses to diseases transmitted by vectors. Its theoretical implications and its consequences in terms of population management are potentially important and remain unexplored. List of abbreviations RQH: Red Queen Hypothesis AH: Asymmetry Hypothesis Authors' contributions Both authors have been involved in the elaboration of the hypothesis and the evaluation of its consequences, and in the drafting of the paper.

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