Datasets:

keerthanapacific

/

sample6

like 0

509425

Evaluation of in vivo labelled dendritic cell migration in cancer patients

Background Dendritic Cell (DC) vaccination is a very promising therapeutic strategy in cancer patients. The immunizing ability of DC is critically influenced by their migration activity to lymphatic tissues, where they have the task of priming naïve T-cells. In the present study in vivo DC migration was investigated within the context of a clinical trial of antitumor vaccination. In particular, we compared the migration activity of mature Dendritic Cells (mDC) with that of immature Dendritic Cells (iDC) and also assessed intradermal versus subcutaneous administration. Methods DC were labelled with 99m Tc-HMPAO or 111 In-Oxine, and the presence of labelled DC in regional lymph nodes was evaluated at pre-set times up to a maximum of 72 h after inoculation. Determinations were carried out in 8 patients (7 melanoma and 1 renal cell carcinoma). Results It was verified that intradermal administration resulted in about a threefold higher migration to lymph nodes than subcutaneous administration, while mDC showed, on average, a six-to eightfold higher migration than iDC. The first DC were detected in lymph nodes 20–60 min after inoculation and the maximum concentration was reached after 48–72 h. Conclusions These data obtained in vivo provide preliminary basic information on DC with respect to their antitumor immunization activity. Further research is needed to optimize the therapeutic potential of vaccination with DC.

Background Dendritic Cell (DC) vaccination is one of the most promising tools of immunological therapy for cancer. Administration of DC, generated and loaded with tumor antigens ex vivo , can be used to circumvent tumor immunotolerance [ 1 , 2 ]. A large number of immature DC (iDC) can be produced by culturing peripheral blood monocytes with GM-CSF and IL-4 in vitro. These iDC possess functional characteristics typical of this maturation status, such as phagocytosis, macropinocytosis, receptor-mediated endocytosis and antigen processing [ 3 , 4 ]. After antigen uptake and processing, under inflammatory stimuli, iDC undergo functional changes that result in their maturation (mDC) [ 5 ]. Following the up-regulation of HLA class I and II and costimulatory molecules (CD80, CD86) and other specific markers such as CD83, DC-LAMP and CCR7, mDC migrate to the T-cell zone of lymphoid tissue, where they have an optimal stimulatory capacity [ 6 , 7 ]. The migration of DC to regional lymph nodes therefore represents one of the most important requirements for lymphocyte priming. Migration probably occurs through lymphatic pathways, but it is not known whether it is active or passive. Furthermore, factors such as PGE 2 may considerably increase migration, inducing CCR7 expression on the surface of DC. Penetration may be limited to the peripheral zones of lymphoid tissue when the DC are still immature, or may reach the deeper T-cell zones, where a greater number of naïve T-cells are present, when DC are mature and activated. Surface antigen CCR7, present on the cell membrane of DC, strongly influences migratory capacity through its interaction with transporter molecules, TREM-2, LTC4, LTD4, etc. [ 8 - 10 ]. The mDC that reach lymph nodes prime naïve T-cells for a limited time and then exhaust their active functions. This can be verified by measuring IL-12 production, which rapidly decreases, and by determining the presence of IL-10, previously absent. Special conditions such as the linkage with lymphocyte ligand CD40 may prolong the active phase of mDC [ 11 - 13 ]. Recent studies on cancer patients evaluating the efficacy of in vitro -generated vaccines have shown that mature, but not immature DC, induce an effective antitumor response [ 14 - 18 ]. Animal studies have provided direct evidence that subcutaneously injected DC preferentially migrate to draining lymph nodes to induce a measurable antitumor effect [ 18 , 19 ]. Similarly, the use of radiolabelled DC in humans demonstrates the ability of these cells to migrate to draining lymph nodes. It has also been observed that migration efficiency is linked to their maturation status or administration route (intravenous, subcutaneous, or intradermal) [ 20 - 23 ]. In the course of a vaccination trial using DC pulsed with autologous tumor lysate (ATL) in cancer patients, we evaluated the in vivo migration ability of DC by labelling them with 99m Tc-HMPAO or 111 In-Oxine. In particular, migratory activity was assessed in iDC and mDC in terms of time required for migration to lymph nodes, duration of activity, and number of cells that migrated. Migratory capacity was further evaluated by comparing subcutaneous and intradermal administration. Materials and methods Patients The case series consisted of a subset of the 19 patients enrolled onto a phase I/II vaccination trial for advanced melanoma and renal cell carcinoma in which the first 9 patients were treated with iDC and the remaining 10 received mDC, both pulsed with autologous tumor lysate and keyhole limpet hemocyanin (Biosyn, Fellbach, Germany). In the present study 8 patients were analyzed (7 melanoma, 1 renal carcinoma) for a total of 11 treatments. In vivo migration was assessed using a part of the DC obtained for one of the therapy cycles. Three of the 8 patients were evaluated twice. Two melanoma patients were treated with iDC (one of whom twice), while 4 other patients with melanoma and 1 with renal cell carcinoma (treated twice) received mDC. The remaining melanoma patient was treated with iDC and subsequently with mDC. The clinical trial was approved by the Italian Ministry of Health and by the Ethical Committee of Forlì Health and Social Services (Azienda USL – Forlì, Italy). All patients gave written informed consent. Tumor lysate Tumor samples surgically removed from the patients were immediately placed in PBS. Adjacent non malignant tissue was removed by scalpel and tumor cells were dispersed to create a single-cell suspension. Cells were lysed by incubation in sterile distilled water. Lysis was monitored by light microscope. Larger particles were removed by centrifugation (10 min at 600 g ) and the supernatant was passed through a 0.2-μm filter. Protein contents were determined and aliquots were stored at -80°C until use. Treatment Patients were generally vaccinated intradermally with DC (4–6 inoculations at the base of the thigh, about 10 cm from the groin, in the absence of visible disease). From days 2–6, IL-2 (Chiron, Milan, Italy) was administered subcutaneously at a dose of 3 million IU/die. This procedure was repeated after two weeks and once a month until progression occurred. DC generation DC were prepared from peripheral blood monocytes (PBMC) obtained by leukapheresis without previous mobilization. 5–9 liters of blood were processed in each collection. PBMC were purified on Ficoll-Paque. An aliquot of PBMC was utilized immediately for DC generation and the rest was frozen in bags for use at a later date (4–5 bags/1 collection). PBMC were incubated in tissue culture flasks with CellGro DC Medium (Cell Genix, Freiburg, Germany) at 10 × 10 6 cells/ml for 2 h. The non-adherent cells were discarded and the adherent cells were incubated in CellGro DC Medium containing 1000 IU/ml rhIL-4 (Cell Genix) and 1000 IU/ml rhGM-CSF (Shering Plough, Milan, Italy) for 7 days to generate a DC-enriched cell population. On day 6 DC were pulsed with autologous tumor lysate (100 mg/ml) and with KLH (50 mg/ml) and incubated overnight. On day 7, they were defined as iDC. After eliminating the previous culture medium, pulsed iDC were cultured for a further 2 days with a cocktail of cytokines (TNFα, IL-1β, IL-6, Endogen, Pierce Biotechnology, Rockford, USA; PGE 2 , Cayman Chemical, Ann Arbor, MI, USA). On day 9 they were defined as mDC. iDC or mDc were removed, washed and suspended in sterile saline for therapeutic infusion into the patient. DC labelling and migration evaluation Labelling of DC was performed according to the methods described for leucocyte radiolabelling [ 24 - 26 ]. A part of both iDC and mDC destined for vaccination (about 9.10 6 ) were resuspended in platelet-poor autologous plasma (CFP1) and incubated for 15 min at room temperature with 99m Tc-HMPAO (20 mCi) (Nycomed Amersham plc, Little Chalfont, UK) 111 In-Oxine (1 mCi) (Altana Pharma, Milan, Italy). After two washes to eliminate the unbound isotope, the cells were resuspended in a total volume of 1.5 ml of CFP1. Radiolabelling of the DC and of the culture supernatant was evaluated with a gamma counter, after which DC were inoculated intradermally into the patient near healthy lymph nodes and in the contralateral zone not used for therapeutic vaccination (3 inoculations at 10 cm from inguinal or axillary lymph nodes). The patient then underwent serial acquisitions with gamma-camera positioned at the site of inoculation, with a field of view that included all the lymphatic regions of interest. The first acquisition was performed with a dynamic study of 20 min, followed by 10-min static acquisitions carried out every 30 min for the first 4–6 h and from 18 to 28 h. Other static determinations were made at 36, 48 and 72 h. The maximum duration of observation of DC migratory activity, which depended on the half-life of the radioisotope used, was 72 h for 111 In-Oxine and 36 h for 99m Tc-HMPAO. The identification of lymph node stations involved in the migratory activity was initially visual, after which we carried out a semiquantitative evaluation of the percentage of DC that migrated to lymph nodes from the inoculation site and an assessment of the speed of DC migration, expressed by activity/time curves obtained through the compartmental mathematical model. Evaluation of labelling stability DC obtained from the culture of frozen PBMC were divided into two parts: one was labelled with 99m Tc-HMPAO and the other was labelled with 111 In-Oxine. The labelled cells were then suspended in CellGro DC Medium, divided into 4–5 culture flasks for each labelling molecule and incubated for 0 h, 4 h, 21 h, 24 h ( 99m Tc-HMPAO) and 0 h, 4 h, 21 h, 24 h, 48 h ( 111 In-Oxine). The DC from one flask were removed and centrifuged. The supernatant containing the free molecule, and the pellet containing the labelled cells, were then measured with a gamma counter. Phenotype analysis iDC and mDC phenotypes were determined by single or two-color fluorescence analysis. 3–5·10 5 cells were suspended in 100 μl of buffer (PBS, 2% FCS, 1% sodium azide) and incubated for 30 min at 4°C with 10 μl of appropriate fluorescein isothiocyanate or phycoerythrin-labelled monoclonal antibodies (mAbs). The cells were then washed twice and resuspended in 500 μl of assay buffer. The fluorescence was analyzed by a FACS Vantage flow cytometer (Becton Dickinson, Milan, Italy). mAbs specific for human CD1a, CD14, CD80, CD86, (Becton Dickinson) CD83 (Immunotech, Marseille, France) and CCR7 (BD Pharmingen, Milan, Italy) were used. Cytokine production At each pre-set time the supernatant was collected and stored at -80°C until analysis was carried out using commercially available ELISA Kit (Biosource, Nivelles, Belgium) to measure the production of IL-12 + p40 (bioactive heterodimer of IL-12) and IL-10 by DC. Endocytosis evaluation Single cell-based measurement of endocytosis was carried out as described (27). Dendritic cells were incubated for 30 min at 37°C with 0.5 mg/ml FITC-Dextran (40S DX-FITC Sigma, Milan, Italy). DX-FITC (average MW 42,000) was centrifuged before use to remove aggregates. As negative control, cells were incubated with DX-FITC at 4°C. The cells were washed with cold PBS containing 2% FCS and 2 nM sodium azide to exclude dead cells and were then analyzed on a FACS Vantage flow cytometer (Becton Dickinson) [ 27 ]. Results Patient characteristics All the patients (6 males, 2 females) had advanced disease and all but one had undergone previous treatment. Median age was 49 years (range 46–52 years). Three patients were HLA-A1, 3 were HLA-A3, 1 was HLA-A2 and 1 was HLA-A11 (Table 1 ). Two melanoma patients were treated with iDC, while 2 other patients with melanoma and 1 with renal cell carcinoma received mDC. The remaining 3 melanoma patients were treated with iDC and subsequently with mDC (Table 1 ). The 8 patients received a total of 73 therapeutic vaccination cycles (20 with DC obtained from fresh PBMC and 53 from frozen PBMC) and 11 labelled DC evaluations were carried out. Table 1 Patient characteristics Patients Sex/Age Pathology Site of Metastasis Previous Treatment i/mDC HLA 1 M/47 Mel Liver, mediastinal lymph nodes IFN iDC A 1 A 2 B 8 B 35 Bw 6 Cw 4 Cw 7 2 M/52 Mel Liver BIOCT iDC A 3 A 28 B 35 B 53 Cw 4 3 M/49 Mel Liver, adrenal glands No treatment iDC + mDC A 11 A 31 B 14 B 60 Bw 6 Cw 3 4 M/42 Mel Liver, mediastinal and axillary lymph nodes BIOCT iDC+ mDC A 1 A 9 B 17 Bw 4 Bw 6 Cw 3 Cw 4 5 F/49 Mel Lung, lymph nodes, skin, peritoneum BIOCT iDC+ mDC A 1 A 9 B 7 B 44 Bw 4 Bw 6 Cw 4 Cw 7 6 M/50 Renal ca. Skin, adrenal glands BIOCT mDC A 2 A 3 B 7 B 51 Bw 4 Bw 6 Cw 1 Cw 7 7 F/52 Mel Lung, liver HdIFN + CT mDC A 3 A 29 B 44 Bw 4 8 M/46 Mel Abdominal lymph nodes IFN+BIOCT mDC A 3 A 28 B 21 B 35 Cw 4 IFN, alpha interferon; BIOCT, biochemotherapy; CT, chemotherapy; HdIFN, high-dose adjuvant alpha interferon (ECOG 1684) DC characteristics The characteristics of iDC and mDC used to evaluate migration activity were similar to those of the DC utilized by us for therapeutic vaccination and to results published in the literature. Data on the purity and vitality of DC, the presence of surface markers and DC functional features (endocytosis and production of IL-12 and IL-10) are reported in Table 2 . Table 2 Biological characteristics of dendritic cells used for vaccination iDC median % (range) mDC median % (range) DC surface markers: CD 1a 20 (4–58) 2 (0–8) CD 14 3 (0–7) 2 (0–11) CD 80 3 (1–23) 37 (27–87) CD 86 30 (10–55) 81 (15–94) HLA-DR 45 (17–82) 78 (56–88) CD 83 2 (0–13) 55 (34–73) CCR7 4 (2–5) 86 (48–92) Endocytosis % of positive cells 70 (39–91) 15 (1–42) IL-12 production pg/ml 49 (17–225) > 1350 IL-10 production pg/ml 0 0 % purity * 74 (66–98) 59 (31–100) % vitality ** 75 (68–79) 82 (66–89) * viable DC/viable DC + viable lymphocytes ** viable DC + viable lymphocytes/total cells DC labelling efficiency and stability The in vitro stability of DC labelled with 99m Tc-HMPAO and 111 In-Oxine was evaluated using DC cultured from frozen PBMC. 99m Tc-HMPAO-labelled DC showed a 75% loss of activity 4–24 h after the beginning of in vitro culture. 111 In-Oxine-labelled DC showed a higher labelling stability (50%) that lasted for up to 24 h (Fig. 1 ). This accounts for the differences in lymph node uptake percentages observed in our migration studies. More accurate information on the linkage stability of 111 In-Oxine-labelled DC over time would permit the opportune correction of the uptake percentage and would enable data to be compared with those obtained using indium. Figure 1 A sample of mature dendritic cells cultured in vitro for vaccination was divided into two parts, one labelled with 99m Tc-HMPAO and the other with 111 In-Oxine. The DC were then suspended in DC medium and cultured in vitro for 24 h ( 99m Tc-HMPAO) and 48 h ( 111 In-Oxine). At 0, 4, 21, 24, and 48 h, the activity of the supernatant containing the free molecule and of the pellet containing labelled cells was measured. After 24 h, a 75% and 50% loss of activity was observed for 99m Tc-HMPAO-and 111 In-Oxine-labelled DC, respectively. Administration routes The migration activity of mDC administered simultaneously by intradermal and subcutaneous injection in the arms of two patients (nos. 7 and 8) was evaluated by comparing radioactive uptake in axillary lymph nodes. The intradermal route showed a threefold higher migration than that observed for the subcutaneous route (Table 3 ). Evaluations were made at intervals from 0 to 44 h after inoculation. The sites of inoculation showed an exponential type washout that was virtually identical for both routes of administration (data not shown). The final migration percentage ratio (measured after 44 h) was fairly similar in both patients, but was obviously not statistically significant (Fig. 2A,2B , 3 ). Table 3 Different vaccine administration routes: intradermal vs. subcutaneous lymph node uptake Patients mDC × 10 6 Administration route Isotope Max uptake (%) * R.L. (no. 7) 4 Intradermal 99m Tc-HMPAO 0.95 G.D. (no. 8) 4 Intradermal 99m Tc-HMPAO 1.02 R.L. (no. 7) 4 Subcutaneous 99m Tc-HMPAO 0.30 G.D. (no. 8) 4 Subcutaneous 99m Tc-HMPAO 0.37 UPTAKE RATIO: intradermal/subcutaneous average: 3 *Maximum uptake = maximum lymph node activity/inoculation site activity at 0 h Figure 2 In patients no. 7 (A) and 8 (B), the same number of 99m Tc-HMPAO-labelled DC were administered simultaneously: subcutaneously (sc) in the left axilla and intradermally in the right axilla (id). The acquisition times with gamma camera are reported along the X-axis and the Y-axis shows the counts per 10 min. In both patients intradermal administration presents a greater concentration of labelled cells in lymph nodes than the subcutaneous route. Figure 3 In patient no. 7 the same number of 99m Tc-HMPAO-labelled DC were administered simultaneously: subcutaneously (s.c.) in the left axilla and intradermally (i.d.) in the right axilla. The figure shows acquisition images with gamma camera at 0, 3, 5, and 18 h after inoculation. Greater migration capacity after intradermal administration is clearly visible. (IS, inoculation site: LN, lymph node). iDC/mDC migration iDC and mDC migration was evaluated in all 8 patients (4 iDC and 7 mDC treatments) (Table 4 ). The data presented refer to 4 groups of patients treated with mature or immature cells labelled with 111 In-Oxine or 99m Tc-HMPAO. A simple numerical analysis shows that the maximum uptake ratio between 99m Tc-HMPAO-labelled mDC and iDC varies from 2 to 35, with an average of 8.4. The same ratio for 111 In-Oxine-labelled cells varies from 4 to 7, with an average of 6. 99m Tc-HMPAO labelling is influenced by the very low iDC uptake due to its greater binding instability and to the short half-life of the radioisotope, which does not permit the acquisition of reliable counts beyond 24–36 h. Table 4 Comparison between iDC and mDC lymph node uptake Patients DC i/m × 10 6 Isotope Max uptake (%) * G.C. (no. 1) i 6 99m Tc-HMPAO 0.22 G.C. (no. 1) i 6.9 99m Tc-HMPAO 0.05 G.L. (no. 2) i 9 99m Tc-HMPAO 0.05 P.A.M. (no. 3) i 6 111 In-Oxine 0.42 T.N. (no. 5) m 5.6 99m Tc-HMPAO 1.75 P.I.M. (no. 4) m 12 99m Tc-HMPAO 0.53 PA.M. (no. 3) m 7 111 In-Oxine 3.14 S.G. (no. 6) m 6.4 99m Tc-HMPAO 0.39 S.G. (no. 6) m 6.7 111 In-Oxine 1.88 R.L. (no. 7) m 4 99m Tc-HMPAO 0.95 G.D. (no. 8) m 4 99m Tc-HMPAO 1.02 UPTAKE RATIO: mDC/iDC 111 In-Oxine average: 6 (range 4–7) 99m Tc-HMPAO average: 8.4 (range 2–35) *Maximum uptake = maximum lymph node activity/ inoculation site activity at 0 h A lymph node uptake can be observed in all patients within the first two hours of inoculation, reaching a maximum uptake after 12 h in 7 patients (Fig. 4A,4B , 5 ). In the last 4 experiments in which a more accurate temporal analysis was performed, the uptake percentage continued to increase for the entire temporal range studied (24–30 h with 99m Tc-HMPAO and 48–60 h with 111 In-Oxine). A curve fitting analysis also seemed to indicate a progressive increase in uptake after the first 60 h, but the number of patients evaluated is too low for any definitive conclusions to be drawn. Figure 4 This figure shows the migration activity of mDC (patient no. 5) and iDC (patient no. 2) labelled with 99m Tc-HMPAO (A) and of mDC and iDC (patient no. 3) labelled with 111 In-Oxine (B). The acquisition times with gamma camera are reported along the X-axis and the Y-axis shows the counts per 10 min. mDC migrating to regional lymph node are always higher than iDC. 99m Tc-HMPAO-labelled DC were detected in lymph nodes within 1 h of administration and the maximum concentration was reached within 60 min for iDC and between 18 and 20 h after inoculation for mDC. Figure 5 The figure shows static acquisition images with gamma camera 2, 24, and 48 h, and 2, 24, 48 and 72 h after inoculation with 111 In-Oxine-labelled iDC and mDC, respectively, for patient no. 3. Greater migration activity of mDC is clearly visible. (IS, inoculation site: LN, lymph node). Pure 99m Tc-HMPAO injection After injection of 99m Tc-HMPAO alone in the same inoculation sites used for the study, no labelled hot spots were observed. This would seem to suggest that pure tracers move through lymphatic vessels without accumulating inside lymph nodes. Discussion DC-based immunotherapy has undergone a remarkable transformation in its development from basic research to clinical application [ 10 , 28 ]. However, many issues remain to be clarified to improve functionality and therapeutic effects and to insure a powerful and wide-ranging antitumor response by T-cells. Two stages of fundamental importance in the therapeutic use of DC are the optimization of the maturation stimulus and the induction of an effective migration to regional lymph nodes to guarantee powerful and long-lasting priming of naïve T-cells [ 5 , 29 ]. Migration activity is therefore one of the functional characteristics of DC that warrants further investigation in an attempt to increase their potential [ 30 ]. Recent published data have shown that the choice of maturation stimulus may be crucial for therapeutic success. In particular, it has been seen that PGE 2 is essential for activating DC chemotaxis through the expression of CCR7 on the cell surface [ 31 , 32 ]. This receptor permits migration through a concentration gradient of its ligands CCL19 and CCL21 inside lymphoid organs. The use of PGE 2 may therefore prove to be important for increasing migration activity and DC efficacy. However, it has also been observed that PGE 2 inhibits IL-12 production, resulting in a weaker in vivo activation of T-cells [ 33 , 34 ]. These contradictory research data obviously require validation by in vivo experimentation. In the present study we aimed to clarify some issues concerning DC migration activity in a clinical vaccination trial utilizing radiolabelled ( 99m Tc-HMPAO and 111 In-Oxine) iDC and mDC [ 20 , 35 ]. Technetium has a high labelling intensity and a short half-life, while indium, despite having a lower intensity, has a longer half-life, which enabled us to monitor the migration activity of labelled DC for up to 72 h. The first step was to evaluate whether radioactive tracer would be scintigraphically visualized in lymph nodes. For this purpose technetium alone was injected intradermally and its progression followed. At the same time labelled DC were administered in the contralateral zone. It was seen that the tracer was not entrapped in lymph node stations and this confirmed that the radioactive molecule detected in contralateral regional lymph nodes was undoubtedly the expression of labelled DC that had migrated to that site. One of the simplest analyses carried out was the evaluation of mDC migration activity using two different routes of administration: subcutaneous and intradermal. We chose two patients at random and compared migration activity simultaneously in both arms (right intradermal and left subcutaneous); it was observed that intradermal administration had a threefold higher migration to lymph nodes than the subcutaneous route. Although it remains to clarify the extent to which this migratory capacity is active or passive, it is clear that DC must be administered intradermally to obtain a higher migration. A crucial phase of the study was the comparison between the migratory activity of iDC (used in the first part of the clinical trial) and that of mDC (used in the second half of our ongoing study). The result was once again unequivocal, showing a greater progressive concentration of mDC that was, on average, six-eightfold higher than that of iDC, in accordance with data reported by other authors [ 20 , 23 ] and in contrast to results published by Blocklet [ 17 ], who probably used iDC. The phenotype obtained in our study bears witness to the fact that mDC exhibit a much higher CCR7 surface expression than iDC (86% vs. 4%) (Table 2 ). Furthermore, mDC have an extremely high IL-12 production, which confirms a marked stimulatory activity. Notwithstanding the results obtained from the present study, many issues remain to be clarified. It has yet to be determined whether the increased activity detected in lymph nodes corresponds to an effectively greater migratory capacity or whether it is the result of a more effective adhesion capacity between surface molecules. Both hypotheses could even prove to be correct. We also do not know how long DC remain in lymph nodes. The increase in activity in lymph nodes is high in the first few determinations but tends to diminish or stabilize after around 36 h. It remains to be seen whether this presumed stabilization is the result of a sort of saturation or whether it can be attributed to the attainment of a dynamic equilibrium. The former hypothesis would indicate the need for an optimization of the number of DC to administer, as only a limited number would be functionally active. The latter would highlight the importance of the timing of administration and perhaps also the degree of DC maturation. To further investigate this, we plan to administer 111 In-Oxine-and 99m Tc-HMPAO-labelled DC in succession and in the same site, to follow their migratory course. Finally, we aim to assess the migratory capacity of in vitro transiently stimulated DC (semimature DC). The therapeutic use of this type of DC, which have already begun the process of maturation and may be capable of reaching lymph nodes before their functional exhaustion, could increase the duration of their activation and stimulation. If these semimature DC prove to be equipped with a good migratory capacity, further improvement in the therapeutic use of DC may be possible. Conclusions The migration activity of DC to regional lymph nodes is one of the many critical factors that influence the therapeutic result of antitumor vaccination. In the present study we used radioisotope-labelled DC and demonstrated that a better migration activity is obtained using intradermal than subcutaneous administration and that mDC show, on average, a six-to eightfold higher migration than iDC. Numerous other issues on DC functionality have yet to be clarified before antitumor therapeutic efficacy can be improved. The next important step will be to closely monitor the quantity and quality of responses observed in T-cells, and it is hoped that a consensus will be reached on standardized criteria for the definition and validation of clinical results obtained. Abbreviations DC, dendritic cell; iDC, immature dendritic cell; mDC, mature dendritic cell; ATL, autologous tumor lysate; PBMC, peripheral blood monocytes. Authors's contributions RR and LR participated in the design of the study and were responsible for the clinical side of the study. AR, MP, LF and MS participated in the design of the study and were responsible for the biological part of the study. GM performed the apheresis collections. RG, AM and GF carried out DC labelling and migration evaluation. GG performed the mathematical and statistical analysis. All authors read and approved the final manuscript. Competing interests None declared.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC509425.xml

521183

High Affinity: Making Up for Being Male

Because males and females possess different numbers of the two sex chromosomes (for instance, in mammals, XX in females versus XY in males), the potential “dose” of each gene differs. Without some compensating mechanism, female mammals would express twice the quantity of an X-linked gene as males. The same holds true in the fruitfly Drosophila , in which the female carries two X chromosomes, while the male carries only one. In mammals, dosage compensation is achieved by silencing one of the X's in the female. Drosophila takes the opposite tack, doubling the output from the single male X chromosome. It does so through the creation of “compensasomes,” protein–RNA complexes that bind to the X chromosome and boost gene transcription. One model of compensasome activity has posited a two-step mechanism, in which the complexes form only at 35–40 specific “entry sites” along the X, and then spread out to the surrounding regions. In this issue, Delphine Fagegaltier and Bruce Baker test this model and show that its predictions do not match experimental results. The compensasome complex includes half a dozen proteins collectively known as MSLs (for “male-specific lethal”), along with two pieces of RNA, roX1 and roX2 . Fagegaltier and Baker reasoned that, according to the entry-site model, if a piece of the X not containing one of the entry sites was transposed to an autosome (non-sex chromosome), it should be unable to recruit MSLs and therefore be unable to form compensasomes. To test this prediction, they used autosomes into which various pieces of the X had been transposed. Contrary to prediction, they found that even the smallest pieces could recruit MSLs, whether or not they contained entry sites. Furthermore, the pattern of MSL binding was exactly the same as if the fragment of the X was still on its native chromosome, suggesting that each of the hundreds of sites at which compensasomes are found function autonomously to recruit them. Compensasomes do not spread from the X chromosome onto autosomal material translocated onto the X Another prediction of the entry-site model is that compensasomes should spread out from the entry site, along the chromosome. And here again, the model does not hold up—Fagegaltier and Baker found that even when entry sites from the X chromosome are put close to an autosomal region, compensasomes never spread from the X onto these regions. These results suggest that spreading is not an innate function of the compensasome, and further strengthens the case for autonomous recruitment all along the X. In place of the two-step “entry site plus spreading” model, the authors propose a model based on differential affinity for compensasome components. They suggest that the 35–40 “entry sites” are simply high-affinity sites that recruit MSLs first, based on intrinsic differences that allow them to bind and hold MSLs more strongly than other sites. Once these sites are occupied, additional compensasome components can bind to lower-affinity sites. This mechanism can account for observed compensasome activity without the restriction to a limited number of entry sites and the requirement for spreading. Fagegaltier and Baker note that while compensasome spreading does not normally occur during dosage compensation on the X chromosome, it has nonetheless been documented for some roX transgenes. They propose that the additional binding observed specifically around roX transgenes results from a mass action of compensasomes, as roX transgenes would act as assembly sites for compensasomes, just as ribosomal RNA genes do for ribosomes. Once formed, compensasomes may bind locally to other neighboring sites. While the details of dosage compensation and the dosage compensation complexes now clearly differ between mammals and flies, there are broad similarities, including the widespread modification of chromatin structure and the use of RNA components in the compensation machinery. A deeper understanding of the process in flies may help shed light on the details of compensation in other organisms as well.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC521183.xml

539280

Age-specific prevalence, transmission and phylogeny of TT virus in the Czech Republic

Background TT virus is prevalent worldwide, but its prevalence and genotype distribution in Central and East-Europe has not been determined. The high prevalence of TTV in multiply-transfused patients points to the importance of a parenteral mode of transmission, but since more than half of the general population is infected other possible routes of transmission must be considered. Methods In our study, we investigated the epidemiology, transmission and phylogeny of TTV in the Czech Republic. The following groups were selected: a control group of 196 blood donors, 20 patients with hemophilia, 49 intravenous drug users, 100 sex workers, 50 penitentiary prisoners, 208 healthy children aged 1 to 14 years, 54 cord blood samples, 52 patients with non-A-E hepatitis, 74 patients with hepatitis C, and 51 blood donors with increased ALT levels. Primers specific for the non-coding region were used. The genotype distribution was studied in 70 TTV-positive samples. Results The prevalence rate of TTV among the Czech population was 52.6%. We have shown that TTV is not transmitted prenatally. Children were infected after birth with two peaks: one at the age of two years and the other after the beginning of primary school. Adults have shown a further increase in the TTV prevalence with age. The highest TTV prevalence was found in the group of patients who had received multiple blood transfusions. The TTV prevalence rate in subjects at an increased risk of sexual transmission was not significantly higher than in the general population. Genotypes G2 and G1 were most prevalent among the Czech population, followed by G8 and G3. The subjects positive for markers of HBV and/or HCV infection tested significantly more often TTV DNA positive, which is suggestive of a common route of transmission of these three infections. Conclusions This study on TTV prevalence, mode of transmission and age-specific prevalence is the most extensive study performed in Central and Eastern Europe. It showed insights into the epidemiology of TTV infection, but failed to associate TTV infection with clinical manifestations.

Background TTV, a non-enveloped small circular single-stranded DNA virus, was recently placed in a novel virus family named Circinoviridae [ 1 ]. In spite of being a DNA virus, TTV has an extremely wide range of sequence divergence. At least 40 TTV genotypes from five major phylogenetic groups (G1–G5) have been identified [ 2 , 3 ]. The evolutionary distance between the classified genotypes, as measured by nucleotide substitutions per site, is greater than 30% in the N22 region of ORF1 [ 4 ]. TTV has a worldwide distribution. The prevalence of the most common TTV genotypes G1 and G2 is similar all over the world, while the reports on the distribution of other genotypes are scarce and not conclusive. The rate of TTV DNA detection is influenced by the selection of the primer annealing sites. PCR using primers which target the ORF1 region can detect only TTV genotypes 1–6 of group 1, but PCR primers designed for the NCR can detect nearly all genotypes or genetic groups known so far [ 2 ]. The prevalence of TT virus in the general population has ranged between 1.9 and 98%, with the highest rates detected in the African and South American countries [ 5 , 6 ]. The TTV prevalence and genotype distribution in Central and East Europe have not yet been determined. One Polish study has reported the prevalence of TTV in blood donors to be 10% by ORF1 primers and 78% by NCR primers [ 7 ]. In a small group of blood donors in the Czech Republic, Krekulova et al. detected a TTV prevalence rate of 13.5% by ORF1 primers [ 8 ]. The mechanisms of TTV transmission have not yet been elucidated. Even though numerous studies have suggested that the parenteral transmission via transfusion of contaminated blood and blood products is the most common route of TTV infection [ 9 ], the detection of TTV in many individuals with no history of blood transfusion indicates that other routes of transmission of TTV may exist. This assumption has been further supported by the detection of TTV in saliva [ 10 ], breast milk [ 11 ], semen [ 12 ] and vaginal fluid [ 13 ]. There is evidence that TTV is excreted into feces of infected individuals, suggestive of possible fecal-oral transmission [ 14 ]. Some studies have reported placental transmission of TTV [ 15 - 17 ], whereas others have not detected TTV in cord blood and amniotic fluid [ 18 , 19 ]. Since children of TTV-infected mothers apparently tend to get infected more often and earlier after birth than children of TTV negative mothers, the role of postnatal transmission of TTV is being considered [ 20 , 21 ]. Furthermore, variation in the TTV prevalence in children from 5.1% in Japan [ 22 ] to 54% in the Democratic Republic of Congo [ 23 ] is also suggestive of the possible involvement of other specific environmental factors in the acquisition of TTV infection. TTV was originally isolated from a blood-transfused patients in which increased alanine aminotransferase (ALT) levels were detected [ 24 ]. Therefore, TTV was thought to be the possible etiological factor of non-A-G hepatitis. However, further research has ruled out the notion that a clinically evident liver disease is a consequence of TTV infection. Other studies, which investigated the possible link of TTV to other than hepatic diseases are scarce and so far fail to show any association of TTV infection with clinical manifestation. Nevertheless, the spectrum of diseases studied in association with TTV infection is very narrow and justifies keeping TTV in the category of "orphan" viruses [ 25 ]. To investigate the possible routes of TTV transmission, age-specific prevalence and genotype distribution in the population of the Czech Republic, we analyzed sera of 854 subjects divided into 5 groups based on the type of risk of TTV transmission. The age-specific distribution of TTV and correlation with the anti-HBV and/or anti-HCV status were determined. TTV genotypes were determined in 70 patients selected from the different groups. Methods Population studied The Human Subjects Committee of the Institutional Review Boards approved all experimental protocols, and all subjects enrolled in the study signed an informed consent form. Additional samples were obtained from the collection of the national reference laboratory for viral hepatitis (NRL-VH). Five groups of subjects were analyzed: Group 1 (normal population, control group) was selected from 778 healthy blood donors (mean age 29 years, age range 18–59 years). All donors had normal ALT levels, and were negative for anti-HCV, anti-HIV and HBsAg. Since the TTV DNA prevalence in the first 100 subjects tested was very high we decided to randomly select sera [ 26 ] to include 20 first-time donors and 10 regular donors from each of five age groups (18–20, 21–30, 31–40, 41–50 and 50–60 years). In total, 136 sera of first-time donors and 60 sera of regular blood donors were analyzed. Group 2 (at high risk of parenterally transmitted infection) consisted of 20 hemophiliacs (peripheral blood mononuclear cells (PBMCs) were collected from 10 patients) (mean age 42 years, age range 21–74 years) and 49 IVDUs. Patients with hemophilia were screened for anti-HCV, anti-HIV, anti-HBc and HBsAg, IVDUs were tested for anti-HBc and anti-HCV. Group 3 (at high risk of sexually transmitted infection) included 85 sex workers and 15 promiscuous men (mean age 25 years, age range 18–43 years). All of them were screened for anti-HCV, anti-HBc, HbsAg and HIV. Fifty penitentiary prisoners (mean age 29, age range 16–58 years) were tested for a potential increased risk of parenteral and/or sexual transmission of the virus. They were screened for anti-HCV and HBsAg. Group 4 (at risk of transuterine and mother to child virus transmission) consisted of 54 cord blood samples and 208 sera of children selected by age (we analyzed 28–30 subjects in each of the following 7 age groups: 1, 2, 3, 5, 8, 11 and 14-year-olds). Group 5 (at risk of potential etiological involvement of TTV) included 52 patients with non-A-E hepatitis (mean age 40 years, age range 9–76 years), 51 blood donors with elevated ALT levels (mean age 39 years, age range 25–64 years) and 74 patients with hepatitis C (mean age 27 years, age range 2–56 years). All blood donors with elevated ALT levels and all patients with non-A-E hepatitis tested negative for anti-HCV and HBsAg. All blood donors were also negative for anti-HIV. All patients with hepatitis C were HBsAg negative. DNA purification DNA extraction from sera DNA was extracted from 200 μL of serum using the QIAamp Blood kit (QIAGEN Ltd., Crawley, UK) and dissolved in 100 μL of elution buffer (QIAGEN Ltd., Crawley, UK). Extracted DNA was stored at -20°C. DNA extraction from PBMCs PBMCs were separated by centrifugation from the whole blood on a Ficoll-Paque gradient (SIGMA, St. Louis, MO) according to the manufacturer's protocol. PBMCs were digested with proteinase K (100 μg/ml) (SIGMA, St. Louis, MO) in 1 ml of lysis buffer (50 mM Tris-HCl, pH 8.0; 1 % Tween; 5 mM EDTA, pH 8.0). Thereafter, proteinase K was inactivated for 10 min at 95°C and samples were stored at -20°C. Polymerase chain reaction Five microlitres of total DNA were analyzed in a PCR thermocycler PTC 200 (MJ Research, Inc, Waltham, MA). TTV DNA detection was performed with two sets of primers. A 271 bp long fragment of the ORF1 was amplified in a semi-nested PCR with a modified primer set designed by Okamoto [ 4 ]. By nested PCR a 110 bp fragment was amplified from the NCR [ 2 ]. For the first PCR with ORF1 specific primers, 50 pmol of both modified primers NG059mod (3' CAGACAGAGGMGAAGGMAAYATG 5') and NG063mod (3'CTGGCATYTYWCC MTTTCCAAARTT 5') were used in a 50 μl reaction mixture containing 10 mM Tris-HCl, pH 8.8, 50 mM KCl, 0.8% Nonidet P40, 200 μM each dNTP and 2.5 U Taq polymerase (Fermentas, Hanover, MD). Each of the 35 cycles consisted of 30 s of denaturation at 94°C, 30 s of annealing at 58°C and 45 s of elongation at 72°C. The last cycle was followed by 7 min incubation at 72°C. One microliter of the product from the first PCR was transferred to 50 μl reaction mixture with primers NG061 (3' GGMAAYATGYTRTGGATAGACTGG 5') and NG063mod. The reaction mixture and cycling conditions for the second PCR were the same except that 25 cycles were run. For the PCR with NCR specific primers the same conditions as for ORF1 specific PCR were used. In the first PCR we used 50 pmol of primer NG133 (3'GTAAGTGCACTTCCGAATGGCTGAG 5') and NG147 (3'GCCAGTCCCGAGCCCG AATTGCC 5'), for the second PCR 50 pmol of primers NG134 (3'AGTTTTCCA CGCCCGTCCGCAGC 5') and NG132 (3'AGCCCGAATTGCCCCTTG AC 5'). Each of the 35 cycles of the first PCR and 25 cycles of the second PCR consisted of 30 s of denaturation at 94°C, 30 s of annealing at 58°C and 45 s of elongation at 72°C, with a final extension for 7 min at 72°C. Ten microlitres of the PCR product were separated electrophoretically on a 3% agarose gel (NuSieve 3:1, FMC BioProduct, Rockland, ME). TTV viral load in serum and PBMCs of patients with hemophilia DNA extracted from sera and PBMCs of 4 patients with hemophilia was serially diluted (in 10-fold steps) in distilled water and TTV DNA was determined by PCR with NCR primers. The highest dilution (10 N ) testing positive was used as a relative titer for determining the viral titer per 1 ml of TTV DNA in serum and PBMCs. Sequence analysis A nucleotide DNA sequence of PCR ORF1 products (selected from all groups of study subjects), which revealed a clear band on the agarose gel, was determined. PCR products were excised from a 3% agarose gel and purified with MinElute Gel Extraction kit (QIAGEN Ltd., Crawley, UK) according to the manufacturer's protocol. Both strands of the 271 bp long products generated by PCR with ORF1-specific primers were sequenced directly with the NG061 and NG063mod primers using the ABI Big Dye Sequencing kit (Applied Biosystems, Foster City, CA). The sequencing was performed on an ABI PRISM 310 automated DNA sequencer (Applied Biosystems, Foster City, CA). Phylogenetic analysis DNA sequences (of the following accession numbers: AY429576 – AY429589, AY433961 – AY434008, AY456097 – AY456103, AY484597) were aligned using the CLUSTAL X program [ 27 ] with the corresponding 222 bp long ORF1 region of previously reported sequences (of the following accessions numbers: AB017768 – AB017770, AB017774 – AB017779, AB017886, AB018889, AB018961, AB021796, AB021798, AB021800, AB021803, AB021815, AF060546, AF060547, AF072749, AF077274, AF079541, AF123914, AF123948, AF124009, AF124027) obtained from GenBank at NCBI (NCBI, Bethesda, MA). Phylogenetic trees were constructed using the neighbor-joining and maximum likelihood method in PHYLIP package, version 3.5 [ 28 ]. Serology of hepatitis B and C HBsAg (V2) AxSYM, CORE AxSYM, AUSAB AxSYM, HCV3.0 AxSYM (Abbott, Chicago, IL) tests were used for the detection of HBsAg, anti-HBc, anti-HBs and anti-HCV markers. HBsAg reactive samples were confirmed with the HBsAg Confirmatory AxSYM test. Statistical analysis The statistical analysis was performed using the Fisher exact test. Odds ratios (OR) with 95% confidence intervals (CI) and two-tailed P values were calculated in 2 – 2 tables using the EPI INFO statistical package (version 2002) and GraphPad InStat (version 3.05) (GraphPad Software, San Diego, CA). In all tests, the basic significance level was P = 0.05. Results In total, 854 samples were screened for the presence of TTV DNA using a NCR-PCR. The TTV genotype was determined by sequencing part of the ORF1 region from 70 TTV isolates. Prevalence of TTV DNA in different groups of subjects The prevalence rates of TTV DNA in different groups are shown in Table 1 . The prevalence of TTV DNA in healthy blood donors representing the normal population was 52.6% (103/196). We found no difference in the prevalence of TTV DNA between first-time and regular blood donors (results not shown). The highest prevalence rates were recorded in group 2 (at higher risk of parenteral transmission of infection): 95% (19/20) (OR = 17.16, CI 2.25–130.73, P = 0.0002) for hemophiliacs and 91.8% (45/49) (OR = 10.16, CI 3.52–29.34, P < 0.0001) for IVDUs. In group 3, sex workers and promiscuous men had a prevalence of TTV DNA comparable with that of the normal population, i.e. 62% (62/100), but penitentiary prisoners had a significantly higher prevalence of TTV DNA, i.e. 74% (37/50) (OR = 2.38, CI 1.21–4.69, P = 0.011) than blood donors. We detected no TTV DNA in the cord blood samples, but children had a similar prevalence of TTV DNA as the control group, i.e. 67.8% (141/208). Patients in group 5 had a slightly higher prevalence of TTV DNA than healthy blood donors, the difference being significant only for patients with hepatitis C virus infection, i.e. 89.2% (66/74) (OR = 7.45, CI 3.40–16.3, P < 0.0001). Age-specific prevalence of TTV DNA As indicated in Figure 1 , we observed a dramatic increase in TTV-prevalence during the first two years of life (at the age of 2 years it equaled 85.7%). The prevalence decreased to 43.3% for 8-year-olds and started to rise again to 73.3% in 14-year-olds. Figure 2 shows an age-dependent distribution pattern of TTV DNA prevalence in the three groups of subjects (blood donors, a group at increased risk of sexual infection, and a combined group of patients with non-A-E hepatitis and hepatitis C). TTV prevalence showed a tendency to increase with a significant linear trend for the group at a higher risk of sexual transmission (Chi square for trend= 8.002, p = 0.0047). Even although the control group showed an obvious increase in the TTV DNA prevalence with age, this trend was not significant. TTV presence and viral load in serum and PBMCs As indicated in Table 2 for patients with hemophilia, TTV DNA was more often detected in serum than in PBMCs. TTV DNA was found in sera of all patients (10/10, 100%) and in most (7/10, 70%) PBMCs. Seven patients were positive in serum and PBMCs samples, three subjects had TTV DNA detectable only in serum. The amplification of the internal control beta-globin gene was positive for all PBMCs samples. Viral loads in PBMCs and in corresponding serum samples were compared for four hemophiliacs. All individuals had TTV DNA titers 10 to 100 times higher in their sera than in PBMCs. Heterogeneity of TTV genotypes The results of our sequencing and phylogenetic analysis are summarized in Table 3 and Figure 3 . The topography of the tree using either the neighbour-joining or maximum likelihood method was identical. We analysed 9 samples from group 1, 23 from group 2, 15 from group 3, 10 from group 4 and 13 from group 5. According to Okamoto's classification [ 2 ], 9 isolates were classified into genotype G1a (2 patients with hemophilia, 3 penitentiary prisoners, 1 child, 3 patients with hepatitis C), 18 isolates into genotype G1b (3 blood donors, 3 IVDUs, 1 prostitute, 3 penitentiary prisoners, 4 children, 2 blood donors with increased ALT levels and 2 patients with non-A-E hepatitis), 18 isolates into genotype G2b (4 blood donors, 5 patients with hemophilia, 1 IVDU, 3 sex workers, 2 children, 1 blood donors with an elevated ALT level, 1 patient with hepatitis C and 1 patient with non-A-E hepatitis), 21 isolates into genotype G2c (2 blood donors, 5 patients with hemophilia, 3 IVDUs, 4 sex workers, 1 penitentiary prisoner, 3 children, 2 blood donors with increased ALT levels, 1 patient with hepatitis C), 3 isolates into genotype G8 (3 patients with hemophilia) (Table 3 ) and 1 isolate from a IVDU (I5s) was most closely related to genotype G2. The similarity of this product with a G2a reference genotype sequence (AB017770) was 74.8 %; its similarity with the reference sequence of G2c (AB017768) was 73.9% on the nucleotide level. In the Czech population, the most prevalent genotype was G2c (30.0%), followed by G1b (25.7%), G2b (25.7%), G1a (12.9%) and G8 (4.3%). No association between any of the detected genotypes and a particular population group was revealed. Comparison of TTV genotypes present in sera and PBMCs of the same patient Tree of four patients positive in both serum and PBMCs by the ORF1 PCR system yielded a PCR product adequate for sequencing analysis. Genotypes and sequence homologies are shown in Table 2 . Two paired sequences were of the same genotype, G2b and G2c, respectively, and the sequence similarity between the genotype detected in PBMCs and serum was 96.8% and 100%, respectively. In one patient we detected genotype G8 in PBMCs and genotype G2b in serum (similarity 32.6%). Co-infection markers in association with TTV DNA presence Four groups of subjects were compared for correlation of past HBV and/or HCV infection with TTV DNA prevalence. We compared subjects at a higher risk of sexual transmission, IVDUs, patients with hemophilia and penitentiary prisoners. There was no evidence of present or past HCV or present HBV infection in blood donors. The prevalence of anti-HBc in blood donors in the Czech Republic is very low (1–2%, unpublished data). These data imply that our control group was at a low risk of sexually and parenterally acquired infections. Also all children were anti-HBc and anti-HCV negative. Both the groups of hemophiliacs and IVDUs presented evidence of frequent past or current HBV (50% and 22%) and HCV (82% and 72%) infection. Thirteen percent of sex workers and promiscuous men showed past or current HBV exposure. The study subjects positive for HBV and/or HCV markers had a significantly higher prevalence of TTV DNA regardless of the primer set used (Table 4 ). Discussion The present study on TTV prevalence, mode of transmission and age-specific prevalence is the largest study performed in Central and East Europe. This study showed that TTV infection is quite common among the Czech population. The prevalence rates of TTV in blood donors (52.6%) were similar to those found in other developed countries (for review see [ 29 ]). The most interesting result was the lack of TTV DNA in 54 cord blood samples, suggestive of the absence of transuterine transmission of TTV. Furthermore, we have shown that the prevalence of TTV DNA was age dependent. In children, we observed a dramatic increase of TTV prevalence within the first two years of age. The prevalence of TTV further gradually increased in children aged 8 to 14 years. Similarly, Ohto et al. reported that the TTV prevalence in children at the age of 2 years was comparable with that in mothers, while children younger than 3 months of age were infected only exceptionally [ 19 ]. In the Czech Republic, children start schooling at the age of six or seven years. Based on our data, postnatal infection from mother to child and an increased number of social contacts are likely to be the most important routes of TTV transmission in children. In adults, we have shown the increase of TTV prevalence with age irrespective of study group. Similar results have been reported by others [ 21 , 30 - 33 ]. We observed an increased prevalence of TTV in various groups of hepatitis patients. Since many hepatitis viruses share the same modes of transmission, multiple viral infections may occur in one patient [ 34 ]. Our results showed a significantly higher prevalence of TTV infection in patients with hepatitis C than in healthy individuals, implying that HCV and TTV may share common modes of transmission. In agreement with other studies we detected the highest prevalence of TTV in hemophiliacs and IVDUs, which supports the importance of the parenteral route of transmission of TTV [ 9 , 35 - 38 ]. Nevertheless, the prevalence of 52.6% in blood donors and in healthy children suggests that the higher prevalence in hemophiliacs and IVDUs can also be attributed to a higher TTV viral load. Different TTV concentrations of virus in hemophiliacs and blood donors have been previously reported by Touinssi [ 39 ]. Additionally, Simmonds has shown that in hemophiliacs the prevalence of TTV increased with the amount of clotting factor treatment received and was also dependent on whether the blood concentrates tested had been virally inactivated [ 9 ]. As for TTV sexual transmission, the results of our study suggest that if TTV is sexually transmitted, this mode of transmission is likely to be less important. Even though 13% subjects of the group at risk for sexual transmission of TTV showed past or current HBV exposure, indicative of high promiscuity, the prevalence of TTV was not significantly different from that of the control group (62% and 52.6%). To the best of our knowledge, so far only one study found a significant difference in the TTV prevalence in sex workers [ 40 ], while others did not. In the group of penitentiary prisoners the significantly higher prevalence of TTV seems to be most probably a consequence of intravenous drug abuse rather than sexual promiscuity since the prevalence of HCV was four-times higher than in the group at risk for sexual transmission (20% versus 5%). An increased prevalence of TTV in non-A-E hepatitis patients was observed in the present study in agreement with many previous studies. Additionally, our data showed an increased TTV prevalence in blood donors with increased ALT levels. Several studies have shown a correlation between TTV-titer and elevation of serum ALT levels [ 24 , 30 ] but the experimental infection of chimpanzees with TTV did not show any biochemical or histological evidence of hepatitis [ 1 ]. The heterogeneity of TTV is extreme. Because the NCR region is too conserved for evolutionary analyses, the ORF1 region of the TTV genome is most often used for genotyping. Out of the 70 sequenced isolates, G2 followed by G1 were the most frequent genotypes among the Czech population. The phylogeny analysis showed no evidence of association of particular TTV genotypes with any of the risk groups. Conclusions Our results demonstrated a high prevalence of TTV in the Czech population. Our data show the absence of transuterine transmission of TTV, but postnatal route of transmission from mother to child and infection via frequent social contacts seem to be very important modes of transmision in children. The sexual mode of transmission is most likely to be low effective. No convincing evidence was found to support the involvement of TTV in the pathogenesis of hepatitis. Our data, as well as the results of other studies, show that optimization of the primer set for more standard TTV detection and genotyping is still needed. Improved serological approach to TTV detection could be of value. It is evident that more data are still needed for a better understanding of the natural history of TTV infection. Competing interests The author(s) declare that they have no competing interests. Authors'contributions RT and VN conceived of the study and participated in its design and coordination. MS carried out most of the experimental work and participated on the preparation of the initial draft of the manuscript. Part of the experimental work was done by JK. RT did all the statistical analysis and evaluation of the results and writing of the final version of the manuscript. All authors contributed to the preparation of the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539280.xml

535337

Snoring in primary school children and domestic environment: A Perth school based study

Background The home is the predominant environment for exposure to many environmental irritants such as air pollutants and allergens. Exposure to common indoor irritants including volatile organic compounds, formaldehyde and nitrogen dioxide, may increase the risk of snoring for children. The aim of this study was to investigate domestic environmental factors associated with snoring in children. Methods A school-based respiratory survey was administered during March and April of 2002. Nine hundred and ninety six children from four primary schools within the Perth metropolitan area were recruited for the study. A sub-group of 88 children aged 4–6 years were further selected from this sample for domestic air pollutant assessment. Results The prevalences of infrequent snoring and habitual snoring in primary school children were 24.9% and 15.2% respectively. Passive smoking was found to be a significant risk factor for habitual snoring (odds ratio (OR) = 1.77; 95% confidence interval (CI): 1.20–2.61), while having pets at home appeared to be protective against habitual snoring (OR = 0.58; 95% CI: 0.37–0.92). Domestic pollutant assessments showed that the prevalence of snoring was significantly associated with exposure to nitrogen dioxide during winter. Relative to the low exposure category (<30 μg/m 3 ), the adjusted ORs of snoring by children with medium (30 – 60 μg/m 3 ) and high exposures (> 60 μg/m 3 ) to NO 2 were 2.5 (95% CI: 0.7–8.7) and 4.5 (95% CI: 1.4–14.3) respectively. The corresponding linear dose-response trend was also significant (P = 0.011). Conclusion Snoring is common in primary school children. Domestic environments may play a significant role in the increased prevalence of snoring. Exposure to nitrogen dioxide in domestic environment is associated with snoring in children.

Background Snoring occurs when there is an obstruction to the free flow of air through the airways at the back of the mouth and nose. The prevalence of habitual snoring in children has been reported to vary between 3.2 and 11%. Infrequent snoring is present in 17–27% of all children [ 1 - 3 ]. A study of young Australian children (2–5 years old) found the prevalence of snoring to be 10.5% [ 4 ]. Approximately one third of children who snore regularly have obstructive sleep apnea syndrome (OSAS) [ 5 ]. A few studies have claimed that snoring in children can affect neurocognitive function, behaviour and blood pressure to some extent even in the absence of apnea [ 6 , 7 ]. Thus, concerns about causes of snoring and prevention strategies for children have arisen among both professional medical workers and parents. Numerous risk factors for snoring and OSAS have been reported including enlarged adenoids and/or tonsils, obesity, allergies or other causes of nasal obstruction, and exposure to environmental tobacco smoke (ETS) [ 8 - 10 ]. However, there has been very little research on exposure to environmental irritants, other than ETS, as contributing factors for snoring in children. The home is the predominant environment for exposure to many environmental irritants such as allergens and air pollutants. We hypothesized that high levels of exposure to common indoor irritants including volatile organic compounds, formaldehyde and nitrogen dioxide, could increase the risk of snoring. The aim of this study, therefore, was to investigate domestic environmental factors associated with snoring in children. Methods Study design Nine hundred and ninety-six (996) school children, aged between 4 and 12 years, were recruited from four primary schools within the Perth metropolitan area. Parents/guardians of the children completed a questionnaire related to respiratory health of their children and domestic environments. A sample of 88 children, aged 4–6 years, was then selected randomly to participate in an indoor air quality assessment of their domestic environments. Ethics approval was obtained from the Human Research Ethics Committee of Curtin University of Technology. Respiratory survey The survey instrument adopted was taken from a questionnaire on respiratory health and indoor air quality [ 11 ]. Some questions related to respiratory symptoms and domestic environments have been modified in order to conform to the study objectives. The questionnaire included two parts: the first part covered questions related to children's health and demographic characteristics, the second part consisted of questions about the home environment. Several terms relevant to the study were defined as follows. Children who had asthma were classified as "ever asthma", while those reported having asthma attack or taking any asthma medication within the past 12 months were regarded as "current asthma". Children who had coughed up phlegm on most days over a period of three months were referred to having "chronic productive cough. "habitual snoring" was defined as snoring more than 4 times per week, whereas "infrequent snoring" meant snoring less than 4 times per week. In this context, "snoring" included both "habitual snoring" and "infrequent snoring". The questionnaires were distributed to parents by school teachers and later collected from the classrooms. The survey was conducted between March and April 2002. A consent form was signed by each participating parent or guardian. The response rate was 62.5%. Domestic air pollutant assessment A sample of 88 year one and pre-primary students was randomly selected from participants of the respiratory survey for domestic air pollutant monitoring. Among them, 34 (38.6%) children were snorers (20 habitual and 14 infrequent). Two home visits were subsequently carried out during the winter of 2002 and summer of 2003 to measure indoor volatile organic compounds (VOCs), formaldehyde and nitrogen dioxide levels. VOCs were collected in the living room by active sampling using charcoal sorbent tubes. The air-sampling rate was 1 L/min with sampling undertaken for 10 hours during daytime. The analyses were performed using a Perkin Elmer Autosystem XL gas chromatograph equipped with a flame ionization detector. Eleven common compounds were identified and quantified by comparing the retention times: benzene, chlorobenzene, 1,2-dichlorobenzene, 1,3-dichlorobenzene, 1,4-dichlorobenzene, ethylbenzene, styrene, toluene, m-xylene, o-xylene and p-xylene. Their total amount was expressed as total VOCs (TVOCs). Formaldehyde (HCHO) and nitrogen dioxide (NO 2 ) were collected by a passive sampling method in both the living room and the child's bedroom for 24 hours. Formaldehyde was analyzed using high-performance liquid chromatography [ 12 ]. Nitrogen dioxide was analyzed by a photometric method [ 13 ]. The method utilized a Palmes diffusion tube, containing stainless steel screens coated with trithanolamine (TEA), which was used as an absorbent. The concentrations of NO 2 were measured based on the quantity of the nitrogen dioxide gas transferred through the tube to the absorbent by molecular diffusion during a given exposure period [ 14 , 15 ]. Data entry and statistics analysis Preliminary data screening and cleaning were conducted prior to statistical analysis. Associations between the prevalence of snoring and environmental and geographic factors and respiratory symptoms were examined using Chi-square tests. Multivariate logistic regression analysis was undertaken to estimate the risk of snoring adjusting for possible confounders. Since the distributions of VOCs, HCHO and NO 2 were positively skewed, geometric means (GM) of these variables were calculated after applying a logarithmic transformation. All statistical analyses were performed using the SPSS package Version 10.0. Results Of the 996 participants, 985 children (98.9%) had intact records for snoring. There were 248 children (24.9%; 95% CI: 21.2%–28.6%) reported infrequent snoring and 151 children (15.2%; 95% CI: 12.6%–17.8%) suffered from habitual snoring. Snoring by age and gender Table 1 shows the prevalence of infrequent and habitual snoring by age and gender. Boys had a slightly higher rate of snoring than girls, but the difference was not statistically significant. The rates of habitual snoring decreased significantly with age (P = 0.03). Table 1 Prevalence of infrequent and habitual snoring by age and gender Gender Boys Girls n % n % P Infrequent snoring 130 25.9 118 24.4 >0.05 Habitual snoring 82 16.3 69 14.3 >0.05 Age < 7 years 7 – 9 years > 9 years P n % n % n % Infrequent snoring 77 26.6 70 25.5 100 24.0 >0.05 Habitual snoring 58 20.1 42 15.3 51 12.2 0.03 The prevalences of respiratory symptoms, asthma and other allergic conditions were significantly different among non-snoring, infrequent snoring, and habitual snoring children, with habitual snorers having the highest rates. Results are presented in Table 2 . A significant association (P < 0.001) was evident between snoring and respiratory symptoms, asthma and other allergic conditions. Table 2 Snoring and respiratory symptoms, asthma and other allergic conditions Non-snoring (N = 586) Infrequent snoring (N = 248) Habitual snoring (N = 151) n % n % n % P Phlegm with a cold 145 24.7 97 39.1 66 44.0 <0.001 Phlegm without a cold 38 6.5 31 12.5 25 16.6 <0.001 Chronic productive cough 12 2.1 13 5.2 14 9.3 <0.001 Wheeze during or after exercise 71 12.1 54 21.8 37 24.5 <0.001 Wheeze without exercise 47 8.0 27 10.9 26 17.2 <0.001 Any current wheeze 110 18.8 83 33.5 59 39.3 <0.001 Dry cough at night without a cold 134 22.9 98 39.7 66 43.7 <0.001 Ever asthma 140 23.9 86 34.7 56 37.1 <0.001 Current asthma 83 14.3 62 25.0 36 24.2 <0.001 Allergic rhinitis or hay fever 217 37.6 123 49.6 92 61.7 <0.001 Snoring and household characteristics Table 3 shows the proportion rates of various household characteristics. The snoring and non-snoring groups were similar in terms of "gas cooking", "dampness at home" and "carpet in child's bedroom". However, children suffering from infrequent snoring or habitual snoring were more likely to live in "smoking" households (P = 0.004). Children with pets at home seemed to be less likely to develop habitual snoring (P = 0.02). Table 3 Snoring and household characteristics Non-snoring Infrequent snoring Habitual snoring N n % n % n % P Type of cooking Gas cooking 565 331 57.5 155 63.0 79 53.0 >0.05 Electric cooking 221 142 24.7 43 17.5 36 24.2 >0.05 Gas and electric cooking 185 103 17.9 48 19.5 34 22.8 >0.05 Dampness at home Damp patch 85 49 8.5 25 10.2 11 7.5 >0.05 Condensation 273 153 26.7 74 30.2 46 30.7 >0.05 Mould 167 92 18.1 42 19.2 33 24.1 >0.05 Other characteristics Carpet in child's bedroom 827 491 83.9 208 84.2 128 85.3 >0.05 Smoking household 432 235 41.3 113 46.3 84 56.4 0.004 Pet at home 787 474 82.7 203 83.2 110 73.3 0.022 To further investigate the impact of passive smoking and pet ownership on snoring, logistic regression analysis was undertaken, controlling for confounders age, gender, asthma and other allergic conditions. The results indicated that passive smoking increased the risk of habitual snoring significantly (OR = 1.77; 95% CI: 1.20–2.61), while having pets decreased the risk (OR = 0.58; 95% CI: 0.37–0.92). However, the corresponding effects of passive smoking and pet ownership on infrequent snoring were statistically not significant. The Hosmer-Lemeshow statistic confirmed adequacy of the fitted logistic regression model (P > 0.10). Snoring and indoor pollutant exposure The levels of pollutants exposure were similar between houses of habitual snorers and houses of infrequent snorers. To facilitate analysis, data from the two groups were combined to improve statistical power for comparison with houses of the non-snoring children. Table 4 shows the pollutant measurements. The levels of TVOCs and HCHO were not significantly different between houses of snorers and non-snorers regardless of season. However, the geometric means of NO 2 concentration in the living rooms of snoring children were higher. In particular, the levels of NO 2 in snoring children's bedroom were significantly higher than those in non-snoring children's bedroom during winter. Table 4 Snoring and indoor pollutants Houses of non-snoring children Houses of snoring children n 1 GM 2 Min Max n 1 GM 2 Min Max P TVOCs Living room Summer 47 11 1 254 32 15 2 204 >0.05 Winter 52 15 1 247 34 22 1 575 >0.05 HCHO Living room Summer 48 7 ND 34 32 6 ND 26 >0.05 Winter 51 15 2 92 33 19 ND 92 >0.05 Bedroom Summer 48 9 ND 126 32 8 ND 47 >0.05 Winter 49 16 2 84 33 18 2 98 >0.05 NO 2 Living room Summer 48 37 11 244 32 41 8 511 >0.05 Winter 51 38 9 314 32 48 6 345 >0.05 Bedroom Summer 48 32 6 293 32 31 6 199 >0.05 Winter 50 33 6 267 32 56 8 511 0.015 ND = not detectable 1 Missing data or lost to follow-up present 2 Geometric mean of pollutant concentration (μg/m 3 ) Recognizing that the main source of indoor NO 2 could be a gas heater and/or gas cooker, we compared NO 2 concentration between houses with and without a gas heater in the child's bedroom. The results confirmed that NO 2 levels (GM: 49 μg/m 3 , 95% CI: 37–65 μg/m 3 ) in houses with a gas heater were significantly higher than those (GM: 27 μg/m 3 , 95% CI: 20–38 μg/m 3 ) recorded in houses without a gas heater. Logistic regression analysis was next conducted to assess the dose-response relationship between bedroom exposure to NO 2 during winter and snoring in children. Based on the empirical NO 2 distribution, the monitored households were classified as: 'low' exposure (<30 μg/m 3 ), 'medium' exposure (30 – 60 μg/m 3 ) and 'high' exposure (> 60 μg/m 3 ). After adjusting for age, gender, asthma, passive smoking and pet ownership, domestic NO 2 exposure level was still positively associated with snoring, the ORs being 2.5 (95% CI: 0.7–8.7) for medium exposure and 4.5 (95% CI: 1.4–14.3) for high exposure. There was also evidence of a linear dose-response relationship (P = 0.011 for trend). Discussion Snoring is an important symptom and major risk factor for obstructive sleep apnea [ 5 ]. Several studies in Italy and Thailand reported that the prevalence of habitual snoring varied from 4.9 to 34.5% in primary school children [ 5 , 16 , 17 ], while the prevalence of snoring was 10.5% according to a study of Australian children aged 2–5 years [ 4 ]. The present study found the prevalence of habitual snoring among primary school children in Perth was 15.2%, and 24.9% of the children had infrequent snoring. The total prevalence of snoring was 40.1%. That the participants had high rates of current asthma (18.7%) and allergy (44.0%) (allergic rhinitis or hay fever) may explain the apparently high snoring prevalence taking into account the link between snoring and asthma and allergy. The prevalence of snoring among older children was significantly lower than that of younger children. No significant difference in snoring prevalence between boys and girls was observed, which appeared to be consistent with the literature [ 16 , 18 ]. Strong associations were also found between snoring and respiratory symptoms, asthma and other allergic conditions, as in previous studies [ 10 , 19 , 20 ]. In relation to the domestic environment, passive smoking was identified as a major risk factor for habitual snoring and consistent with other studies [ 10 , 21 ]. An interesting finding was the observed inverse relationship between snoring and pet ownership. There is evidence in the literature suggesting that pet ownership in early life can protect against the development of allergic disease [ 22 ]. Although the protective effect of pet ownership on habitual snoring was significant after controlling for allergic diseases, the mechanism that led to a lower risk of snoring remains to be investigated. Unlike TVOCs and HCHO, it appears that domestic exposure to NO 2 was significantly associated with snoring. It should be remarked that the low exposure threshold was set below the annual value of 40 μg/m 3 recommended by WHO [ 23 ], whereas the high exposure cut off is higher than the guideline value. Our results suggested that high exposure to NO 2 could increase the risk of snoring by 4.5 times. A previous study reported that children aged 5–12 years had a 20% increased risk of respiratory symptoms and disease for each increase of 28.3 μg/m 3 in NO 2 concentrations (2-week average), when the weekly average concentrations were in the range 15–128 μg/m 3 or possibly higher [ 23 ]. Another study in Australia confirmed the link between NO 2 exposure from gas appliances and the prevalence of respiratory symptoms [ 24 ]. Our results also suggested that exposure to NO 2 was related to gas heating during winter. Although the effect of NO 2 exposure on snoring was significant even after adjustment for asthma, atopy and other confounding factors, caution must be taken when interpreting the NO 2 findings and further investigation is required before they can be generalized to the pediatric population at large. A limitation of this cross-sectional study is that only 9% of the study sample was monitored for environmental testing due to budget and other constraints. Nevertheless, this subgroup of children did not differ significantly from the whole sample or other populations of young children in Perth [ 25 , 26 ] with respect to home environment, respiratory symptoms and atopy. Secondly, the causal effects of NO 2 could not be determined because the measurements of exposure and illness were taken at the same time. The assessment of snoring was retrospective in relation to the time of environmental monitoring. Moreover, the significant association between snoring and NO 2 exposure in winter may be attributed to NO 2 emission from gas heaters in conjunction with low ventilation during the winter season. As for potential mechanisms for this association, there is little in the literature that can directly explain how exposure to NO 2 might result in snoring. Although there is evidence to suggest that exposure to NO 2 is associated with development of allergic disease [ 27 ], the observed association between NO 2 exposure and snoring is independent of atopy. Snoring occurs due to upper airway obstruction during sleep. The obstruction commonly occurs at the level of the nasal turbinates as with anterior rhinitis or the nasopharynx due to adenoid hypertrophy. Exposure of airway epithelium in vitro results in the release of inflammatory cytokines and adhesion molecules [ 28 ]. Therefore, it is possible that exposure to NO 2 increases upper airway inflammation, resulting in mucosal oedema and airway obstruction. Alternatively, upregulation of ICAM1 the primary ligand for rhinovirus [ 28 ] could increase the susceptibility to, or severity of upper respiratory tract infection, resulting in upper airway oedema and/or adenoid hypertrophy. Finally, it has been suggested that NO 2 increases lipid membrane fluidity [ 29 ] that in turn can alter receptor-ligand interactions. Thus NO 2 exposure might produce changes in cell-cell and cell-pathogen interactions that could result in altered upper airway physiology. Given the high prevalence of snoring in our population and the knowledge that snoring is a significant risk factor for obstructive sleep apnea, the mechanisms that might underpin the association between NO 2 exposure and snoring require further study. In conclusion, the present study shows that snoring is common among primary school children in Perth, and snoring is associated with other respiratory symptoms. Passive smoking increases the risk of snoring in children but pet ownership may decrease the risk. The level of nitrogen dioxide in domestic environment is positively associated with the prevalence of snoring in children. Authors' contributions GZ, JS, KR, SS Study design, coordination and management GZ, JS, KR Field measurement and laboratory work AHL, GZ Data analysis and interpretation of results GZ, JS, KR, AHL, SS Preparation and revision of the manuscript

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535337.xml

514886

A Developmental Role for Fatty Acids in Eukaryotes

Health food stores have long hawked fish oil capsules as a cure-all for everything from migraines to heart disease. And though such claims are often weak on scientific evidence, fish oil, it turns out, is no snake oil. A recent review of scientific studies concludes that omega-3 fatty acids can indeed protect against heart disease, and the American Heart Association now recommends fish oil capsules for patients with coronary heart disease. Fatty acids come in hundreds of varieties, distinguished primarily by their structure, which in turn determines their physiological role. Unlike proteins or genes—which are polymers made up of amino acids and nucleotides, respectively—fatty acids are a large group of compounds containing long chains of carbon and hydrogen atoms with a carboxylate group (acid) attached at the end. It is this asymmetrical chemical configuration that gives fatty acids their unique properties. Fatty acid diversity comes from variations in the length of the carbon chain and in the number of double bonds between carbons. Fatty acids with one or more double bonds are called unsaturated fatty acids. Fatty acids play an essential role in metabolism, providing the cell with a concentrated source of energy, and form the structural foundation of the cell membrane, where they are most conspicuous and perhaps best understood. Long-chain (unbranched) fatty acids, which run ten to 22 carbons long, are the most common fatty acids in animal cells and the most studied. One much less understood class of fatty acids—the monomethyl branched-chain fatty acids (mmBCFAs)—has been found in organisms from bacteria to humans, but its role remains obscure. In this issue of PLoS Biology , Marina Kniazeva et al. explore the origin and function of mmBCFAs in the worm Caenorhabditis elegans and find that these relatively obscure fatty acids play a crucial role in growth and development. mmBCFAs are abundant in diverse genera of bacteria, which use a supply of branched-chain amino acids and enzymes to assemble the fatty acid chains. mmBCFA biosynthesis has been characterized in bacteria, but not in eukaryotes. (Worms, and humans, are eukaryotes; our cells have nuclei.) Here, Kniazeva et al. identified worm genes that are homologous to the gene that codes for an enzyme called elongase in another eukaryote, yeast. Elongases are enzymes that extend the length of fatty acid chains by two carbons. To see what kind of fatty acid molecules the homologous worm genes were synthesizing, the authors used a technique called RNA interference (RNAi) to “silence” the genes' expression in the worms. Surprisingly, two of the eight inhibited genes had a specific effect on branched-chain fatty acid levels: elo-5 and elo-6 . Inhibiting elo-5 function had deleterious effects on the growth and development of the worms. The progeny of worms treated as embryos with RNAi for elo-5 stopped growing at the first larval stage, while the progeny of worms treated at later stages developed to adulthood but got progressively sicker and showed reproductive problems. These defects were corrected when the researchers fed the mmBCFAs directly to the worms, indicating that these mmBCFAs are essential for normal larval growth and development. Given the widespread distribution of mmBCFAs in organisms as diverse as bacteria and humans, it's perhaps not too surprising that they regulate essential physiological functions during animal development. It's still not clear, however, what all the components of the fatty acid manufacturing machinery are or how an organism monitors production levels. And though it's still an open question as to how these ubiquitous molecules function in mammals, the fact that they have been conserved throughout evolution underscores their importance—and suggests they may play a similar role.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514886.xml

300882

Similarities and Differences in Genome-Wide Expression Data of Six Organisms

Comparing genomic properties of different organisms is of fundamental importance in the study of biological and evolutionary principles. Although differences among organisms are often attributed to differential gene expression, genome-wide comparative analysis thus far has been based primarily on genomic sequence information. We present a comparative study of large datasets of expression profiles from six evolutionarily distant organisms: S. cerevisiae , C. elegans , E. coli , A. thaliana , D. melanogaster , and H. sapiens . We use genomic sequence information to connect these data and compare global and modular properties of the transcription programs. Linking genes whose expression profiles are similar, we find that for all organisms the connectivity distribution follows a power-law, highly connected genes tend to be essential and conserved, and the expression program is highly modular. We reveal the modular structure by decomposing each set of expression data into coexpressed modules. Functionally related sets of genes are frequently coexpressed in multiple organisms. Yet their relative importance to the transcription program and their regulatory relationships vary among organisms. Our results demonstrate the potential of combining sequence and expression data for improving functional gene annotation and expanding our understanding of how gene expression and diversity evolved.

Introduction Microarray experiments are now being used to address a large diversity of biological issues. The large datasets obtained by pooling those experiments together contain a wealth of biological information beyond the insights gained by individual measurements. For example, it was demonstrated that diverse datasets of genome-wide expression profiles can be applied for facilitating functional assignment of uncharacterized ORFs and for identification of cis -regulatory elements ( Eisen et al. 1998 ; Kim et al. 2001 ; Ihmels et al. 2002 ). Comparing the genomic sequences of different organisms presents an alternative prominent approach for gene annotation and identification of regulatory elements ( Chervitz et al. 1998 ; Lynch and Conery 2000 ; Rubin et al. 2000 ; Yanai and DeLisi 2002 ; Frazer et al. 2003 ). Sequenced-based comparative analyses also proved crucial for deciphering evolutionary principles. As evolutionary changes frequently also involve modifications of the gene regulatory program ( Carroll 2000 ; True and Carroll 2002 ; Wray et al. 2003 ), integration of expression data into interspecies comparative analyses could potentially provide new insights into the relation between genomic sequence and organismal form and function. So far, however, such an approach has been mostly applied to small numbers of genes ( Carroll 2000 ; True and Carroll 2002 ; Wray et al. 2003 ) or has been restricted to variations in the genome-wide expression profiles during the development of closely related species ( Rifkin et al. 2003 ). With the accumulation of large-scale expression data for a number of diverse species, the time may be ripe for a macro-evolutionary comparison of gene expression. Expression data differ from sequence data in two main aspects, which make their integration into comparative analysis challenging. First, unlike sequence information, which is direct and accurate, expression profiles provide only indirect and noisy information about the regulatory relationships between genes. Second, while the genomic sequence is essentially complete, expression profiles only cover a subset of all possible cellular conditions and thus provide only partial information about the underlying regulatory program. Moreover, this subset is typically very different for each organism, reflecting distinct physiologies as well as different research foci. One way to circumvent this problem is to restrict the data to a small subset of similar conditions, such as timepoints along the cell cycle ( Alter et al. 2003 ). Such an approach, however, drastically reduces the size of the dataset and limits the scope of comparison. Here, we present a comparative analysis of large sets of expression data from six evolutionarily distant organisms ( Table 1 ). We integrate the expression data with genomic sequence information to address three biological issues. First, we verify that coexpression is often conserved among organisms and propose a method for improving functional gene annotations using this conservation. We provide a Web-based application suitable for this purpose. Second, we compare the regulatory relationships between particular functional groups in the different organisms, giving initial insights into the extent of conservation of the gene regulatory architecture. Interestingly, we find that while functionally related genes are frequently coexpressed in several organisms, their organization and relative contribution to the overall expression program differ. Finally, we compare global topological properties of the transcription networks derived from the expression data, using a graph theoretical approach. This analysis reveals that despite the differences in the regulation of individual gene groups, the expression data of all organisms share large-scale properties. Table 1 Large-Scale Expression Data Used in This Study Publicly available large-scale expression data were obtained from different sources (see Materials and Methods for references). We excluded genes or conditions with more than 90% missing datapoints, resulting in expression matrices of the dimensions shown. The data for Saccharomyces cerevisiae , Caenorhabditis elegans , and Escherichia coli are genome-wide, while those for Arabidopsis thaliana , Drosophila melanogaster , and Homo sapiens contain a large fraction of the respective genomes. The datasets comprise diverse experimental conditions, including environmental changes, time courses, tissues, and mutants. The Drosophila data consist of 75 timepoints during development Results and Discussion Combining Sequence and Expression Data for Improving Functional Gene Annotations With the rapid increase in the number of sequenced genomes, assigning function to novel ORFs has become a major computational challenge. Functional links are often imputed based on sequence similarity with genes of known functions. Despite the large success of this approach, it has several well-recognized limitations. Foremost, an ORF can have several close homologues, some of which may be related to different functions. Furthermore, the sequence of an ORF may have diverged beyond recognition although the gene maintained its function. Gene expression analysis can provide functional links for new ORFs based on their coexpression with known genes. However, in this case, only links between genes of the same organism can be established. Moreover, owing to biological interference and the noise in the expression data, the inferred coexpression could be accidental and may not necessarily reflect similar function. Combining expression and sequence data may help to overcome the abovementioned limitations. Specifically, homologous genes whose function has been preserved are expected to be coregulated with genes related to that function. Conserved coexpression could thus distinguish them from homologues whose function diverged. This can be done, for example, by focusing on a group of functionally related genes in a characterized genome, identifying simultaneously all the respective homologues in a second genome, and then examining which of the homologues are indeed coexpressed ( Figure 1 A). Importantly, restricting the search for coexpressed genes to a limited set of candidates provides an effective mean to overcome the noise in the expression data ( Ihmels et al. 2002 ). Figure 1 Using Expression Data to Identify and Refine Sequence-Based Functional Assignments (A) Starting from a set of coexpressed genes (yellow dots in left box) associated with a particular function in organism A, we first identify the homologues in organism B using BLAST (middle box). Only some of these homologues are coexpressed while others are not (blue dots). The signature algorithm selects this coexpressed subset and adds further genes (light yellow) that were not identified based on sequence, but share similar expression profiles (right box). (B) The 15 coexpressed genes associated with heat shock in yeast (center) have eight homologues in E. coli (left) and 14 in C. elegans (right). Among the ten genes whose expression profiles are the most similar to these homologues (bottom), many are known to be associated with heat-shock response (boldface). (C) For each of the six organisms, the distribution of the Z -scores for the average gene–gene correlation of all the “homologue modules” ( see Materials and Methods ) obtained from the yeast modules is shown (top). Rejecting the homologues that are not coexpressed gives rise to the “purified modules,” whose Z -scores generally are larger (except for the yeast modules, which contain only coexpressed genes from the beginning). Adding further coexpressed genes yields the “refined modules,” which have significantly larger Z -scores (bottom). Conserved coregulation of functionally related genes To explore systematically the utility of this approach, we first examined to what extent coexpression is conserved among different organisms. We performed a statistical analysis comparing the pairwise correlations between genes in one organism to the correlations between their respective homologues. Indeed, a significant fraction of such correlations were similar (see Figure S8 ). The strongest conservation of coexpression was found between pairs of genes associated with particular cellular processes, such as core metabolic functions or central complexes (e.g., ribosome and proteasome) (lists of gene pairs with conserved coexpression are available at http://barkai-serv.weizmann.ac.il/ComparativeAnalysis ). Next, we examined whether coexpression is conserved among groups of genes that are associated with the same cellular function. To this end, we used as a benchmark coexpressed groups of genes (termed transcription modules; see Materials and Methods for a precise definition) that we extracted from the Saccharomyces cerevisiae expression data ( Ihmels et al. 2002 ; J. Ihmels, unpublished data). (The yeast data are the most comprehensive and best annotated, resulting in a large number of transcription modules that can be associated with a specific cellular function.) For each yeast module, we constructed five “homologue modules,” which contain the respective S. cerevisiae homologues in the other organisms, and measured the correlation between the genes of these homologue modules. The average correlation between the genes of the homologue modules was indeed statistically significant (see the top panel of Figure 1 C), indicating that coexpression of functionally linked genes is often conserved among organisms. Coexpression can be used for refining homologue modules Examining the pairwise correlations themselves, however, revealed that usually only a fraction of the genes are correlated with each other (see Figure S9 ). Such lack of correlation probably reflects the inadequacy of defining function solely based on homology. To search for a coexpressed subset within each homologue module, we applied the signature algorithm we proposed recently ( Ihmels et al. 2002 ). The algorithm identifies those homologues that are coexpressed under a subset of the experimental conditions. Furthermore, it reveals additional genes that are not homologous with any of the original genes, but display a similar expression pattern under those conditions ( see Materials and Methods ). Studying the output of the algorithm, we found that the rejected homologues are usually not associated with the original function, while many of the added genes are. For example, from the 15 coexpressed yeast genes involved in heat-shock response, we identified eight homologues in Escherichia coli and 16 in Caenorhabditis elegans . While only some of these homologues are highly coexpressed, they are sufficient to retrieve additional genes known to be involved in heat shock ( Figure 1 B; see Figure S10 for other modules). A statistical analysis using all yeast modules revealed that many homologue modules are significantly coexpressed. The extent of coregulation increases drastically upon removing uncorrelated homologues and adding related genes ( Figure 1 C). We note that in some cases such a “purified module” may contain two or more distinct coexpressed groups. Such substructures are identified by clustering all pairwise gene correlations (see Data S3 ). We conclude that sequence-based functional annotation can be significantly improved through the integration of expression data. We provide an interactive tool for this purpose on our Web site at http://barkai-serv.weizmann.ac.il/ComparativeAnalysis (see also Figure S7 ). We note that while this paper was in review, the possibility of enhancing functional assignment based on the conservation of coexpression was reported independently by Stuart et al. (2003 ). Higher-Order Regulatory Structures Regulatory relations between functional groups vary among organisms The observation that groups of functionally related genes are often coexpressed in multiple organisms prompted us to ask whether also the higher-order regulatory relationships between these groups have been conserved ( see Materials and Methods ). To address this question, we focused on eight representative yeast modules related to cellular core processes. Several of the regulatory relations among the homologues of these modules have been conserved ( Figure 2 A). For example, in all organisms the modules associated with protein synthesis and protein secretion are positively correlated, while the rRNA synthesis and the peroxide modules are anticorrelated. Interestingly, however, most of the relations between modules differ among organisms. In particular, one of the prominent features of the yeast transcription program, namely the strong anticorrelation between heat-shock and protein-synthesis modules ( Ihmels et al. 2002 ), was observed only in the yeast and Drosophila data. In contrast, those two modules displayed a significant positive correlation in the expression data of all other organisms. We note that both types of regulation are consistent with the role of heat-shock proteins as chaperones; it appears that in yeast their primary role is to assist in protein folding during stress conditions (when ribosomal protein genes are repressed), while in the other organisms they may be required to accelerate folding during cell growth. Figure 2 Regulatory Relations between Modules A selection of eight transcription modules whose function is known in yeast was used to generate the corresponding (refined) homologue modules in the other five organisms. Each module is associated with a “condition profile” generated by the signature algorithm based on the expression data. (A) Correlations between these profiles were calculated for all pairs of modules in each organism. Note that for E. coli there is no proteasome and that the mitochondrial ribosomal proteins (MRPs) correspond to ribosomal genes. Modules are represented by circles (legend). Significantly correlated or significantly anticorrelated modules are connected by colored lines indicating their correlation (color bar). Positively correlated modules are placed close to each other, while a large distance reflects anticorrelation. See Figure S11 for a numerical tabulation of all pairwise correlations. (B and C) Correlations between pairs of modules according to the cell-cycle data as a function their correlation in the full data. Each circle corresponds to a pair of S. cerevisiae modules (B) or human modules (C). (D) To check the sensitivity of our results with respect to the size of the dataset, we reevaluated the correlations between the sets of conditions for randomly selected subsets of the data. Shown are the mean and standard deviation of the correlation coefficient between the heat-shock and protein-synthesis modules as a function of the fraction of removed conditions (see Figures S4 and S5 for correlations between other module pairs). In order to test whether the variations in the regulatory relations among functional groups in different organisms are due to the use of unrelated sets of experimental conditions, we restricted both the human and the yeast expression data to the cell cycle experiments. We found that the correlations between modules did not change qualitatively due to this restriction ( Figure 2 B and 2C). We also examined the sensitivity of our results to the number of conditions used ( see Materials and Methods ). Removal of up to 50% of all conditions did not considerably change the gene content of most refined modules (see Data S2 ). Importantly, this analysis also revealed that the correlations between modules are insensitive to the subset of conditions used ( Figure 2 D; see also Figure S2 ). Note, for example, that for the largest datasets (yeast and C. elegans ), the standard deviations of the correlation coefficients do not exceed 0.1, even when removing half of the expression profiles. Taken together, these results indicate that, despite the sparseness of the data, our findings reflect real properties of the expression networks and not the specific subset of experimental conditions used. Global decomposition of the expression data of different organisms To compare the higher-order regulatory structures more systematically, we decomposed the expression data of each organism into a set of transcription modules using the iterative signature algorithm (ISA) we proposed recently ( Bergmann et al. 2003 ; J. Ihmels, unpublished data). A transcription module consists of coexpressed genes and the conditions that induce their coregulation. Importantly, the stringency of coregulation is determined by a threshold parameter, which allows for a modular decomposition at different resolutions. At low resolution, a few relatively large transcription modules are identified. At higher resolution, the data are usually decomposed into a large number of modules, which contain fewer but more tightly regulated genes. We visualize the modular decomposition by a module tree ( Figure 3 A and 3B). Highly similar modules, identified at adjacent thresholds, are connected by lines and define the branches of the tree. In contrast to the common dendrograms used to summarize the results of hierarchical clustering, here distinct branches may share common genes, and when two branches merge, the resulting branch is not necessarily their union. Figure 3 Properties of Transcription Modules (A and B) Module trees summarize the transcription modules identified by the ISA at different resolutions. Branches represent modules (rectangles) that remain fixed points over a range of thresholds. Fixed points that emerge at a higher threshold converge into an existing module when iterated at a lower threshold (thin transversal lines). Modules are colored according to the fraction of homologues they possess in the other organism (see the color bar). Among the yeast modules, those associated with protein synthesis (arrow) have the largest fraction of worm homologues. Searchable trees for all six organisms are available at http://barkai-serv.weizmann.ac.il/ComparativeAnalysis . (C) Histogram for the number of yeast modules with a given fraction of genes possessing a homologue in C. elegans (black bars). The distribution indicates that a significant number of modules have either much less or much more homologues than expected; indicated p -value were computed according to Kolmogorov–Smirnov test against control distribution (gray) generated from random sets of modules preserving their size. (D) Same as in (C) for C. elegans modules considering yeast homologues (see Figure S12 for other organisms). Modular architectures of the transcription programs are distinct Modular architectures, as reflected by the structure of the associated module trees, vary greatly among organisms. Differences were observed in the total number of modules, the threshold ranges over which modules are stable, and the overall hierarchical organizations. For example, in yeast the data were composed into just five transcription modules at low resolution, which remained stable for a wide range of thresholds ( Figure 3 A). As we reported previously ( Bergmann et al. 2003 ), these modules correspond to the central yeast functions (protein synthesis, stress, amino-acid biosynthesis, cell cycle, and mating). At high resolution, a large number of modules with specific cellular functions were identified. The corresponding module tree reveals a clear hierarchy in the transcriptional network, with gradually increasing complexity. In contrast, the C. elegans tree exhibits a sharp transition between a regime dominated by a single branch (from which only few less-stable modules branch off) to a part of the tree that rapidly bifurcates into many branches at higher thresholds ( Figure 3 B). Interestingly, the functional groups that dominate the transcription program of each organism are also distinct. For example, in S. cerevisiae and E. coli , genes coding for ribosomal proteins are associated with a central branch that persists over a wide range of thresholds, reflecting the large number of the experimental perturbations that induce the coregulation of these genes. In contrast, although ribosomal proteins are also coregulated in higher organisms, they are associated with short branches that extend only over a small range of thresholds. This suggests that transcriptional regulation of genes involved in protein synthesis plays a major role in the transcription program of unicellular organisms, but a less dominant role in multicellular organisms. Conserved and organism-specific transcription modules We observed that several functional groups were repeatedly identified as coexpressed in several organisms. This includes modules related to core biological functions such as protein synthesis, rRNA processing, the proteasome, and oxidative phosphorylation. Still, most of the transcription modules were observed in just one organism. In order to distinguish more systematically between generic modules and those that are involved in an organism-specific function, we determined for each module the fraction of genes that possess at least one homologue in a second organism ( see Materials and Methods ). For S. cerevisiae and C. elegans (the two largest datasets), most modules have either significantly less or significantly more homologues than expected ( Figure 3 C and 3D). This indicates that while a number of generic modules have been conserved under evolution, each transcriptome also contains more recently evolved modules that are associated with organism-specific functions. Comparing Global Features of Gene Expression Networks Power-law connectivity distribution We next sought to compare global topological properties of the expression data. To this end, we represented the data by an undirected “expression network,” whose nodes correspond to genes. Two genes are connected by an edge if their expression profiles are sufficiently correlated ( see Materials and Methods ). We use this mapping to explore the global structure of the expression data using tools of graph theory. A well-established indicator of the network topology is the distribution n(k) of the connectivity k (the number of edges of a particular gene). We find that for all organisms, the connectivity is distributed as a power-law, n(k) ∼ k −γ , with similar exponents γ ≈ 1.1–1.8 (see Figure 4 A). The expression networks thus belong to the class of scale-free networks, which comprises many real-world networks ( Albert and Barabasi 2002 ). Power-law distributions have been attributed to dynamically evolving networks (Barabasi and Albert 1999) and to systems that are optimized to provide robust performance in uncertain environments ( Doyle and Carlson 2000 ). In the present context, a power-law connectivity distribution indicates that there is no typical size for sets of coexpressed genes and that there is a significant enrichment of highly connected genes as compared to random networks (see also Guelzim et al. 2002 ; Lee et al. 2002 ; Shen-Orr et al. 2002 ). Figure 4 Global Properties of Transcription Networks (A) The number of genes n(k) with connectivity k is plotted as a function of k ( see Materials and Methods ). For each of the six organisms n(k) is distributed as a power-law, n(k) ∼ k −γ , with similar exponents γ ≈ 1.1–1.8 (see Figure S13). (B) The fraction of lethal genes is shown as a function of k for S. cerevisiae , E. coli , and C. elegans . The control (gray line) is obtained from 10,000 random choices for the lethal genes (preserving their total number). The dashed lines indicate standard deviations. (C) The fraction of genes with at least one yeast homologue is shown as a function of k for all six organisms. Control (gray) as in (B). (D) Z -score quantifying the deviation of the number of connections between genes with connectivities k and k ′ from that expected by randomly rewired networks (see Maslov and Sneppen 2002 ). Note that connections between genes of similar connectivity are enhanced (red regions), while those between highly and weakly connected genes are suppressed (blue). (E) The clustering coefficient C is plotted against k . Each dot corresponds to a single gene and is colored according to the transcription module it is associated with (see also Figure 2 ). Note that genes associated with the same module correspond to a specific band in the k – C plane. Several genes with high connectivity belong to more than one module (green dots superimposed on orange ones). Highly connected genes are often essential and evolutionarily conserved To see whether higher-order features of the connectivity distribution are also conserved, we calculated the likelihood P(k, k′) that two genes of connectivity k and k ′, respectively, are connected with each other ( Maslov and Sneppen 2002 ). In all expression networks, connections between genes with similar connectivity occur much more often than expected, while connections between highly and weakly connected genes are suppressed ( Figure 4 D). The common topology of the expression networks is thus different from the topology of the yeast protein–protein interaction network, although both exhibit a scale-free connectivity distribution ( Maslov and Sneppen 2002 ). We next examined whether highly connected genes are involved in central biological functions. In yeast, most of such genes are associated with protein synthesis, in particular rRNA processing. In the other organisms, the functional role of the highly connected genes is different and less coherent (lists of these “hub” genes are available at http://barkai-serv.weizmann.ac.il/ComparativeAnalysis ). Interestingly, in the three organisms in which large-scale knockout information is available ( see Materials and Methods ), the likelihood of a gene to be essential increases with its connectivity ( Figure 4 B). Similar results were recently reported for the yeast expression and protein–protein interaction networks ( Jeong et al. 2001 ; Farkas et al. 2003 ). We also observed that the highly connected genes are more likely to have homologues in the other organisms ( Figure 4 C). This finding is consistent with the framework of dynamically evolving networks, where nodes that were added at an early stage (and may thus correspond to highly conserved genes) are more likely to develop many connections. Expression networks are highly clustered A further indicator of the network structure is the clustering coefficient C , which quantifies the degree of modularity ( Watts and Strogatz 1998 ). For expression networks, C g measures to what extent the genes connected to a specific gene g are also connected with each other ( see Materials and Methods ). The networks of all organisms exhibit a high modularity with 〈 C g 〉 ≈ ½, several orders of magnitude higher than what would be expected for random networks ( Albert and Barabasi 2002 ). We also examined the relation between the clustering coefficient and the connectivity of each gene. For all six organisms, we observed an approximately triangular region in the k–C plane where genes clustered into several localized elongated regions ( Figure 4 E). Within these “bands,” the clustering coefficient decreases monotonically as a function of the connectivity. Recently, a similar monotonic relation was observed in metabolic networks as well as in several nonbiological networks ( Ravasz et al. 2002 ; Ravasz and Barabasi 2003). For random networks and for simple dynamically evolving networks, it was shown that C is independent of k . However, deterministic models that lead to a hierarchical organization of modularity predict C ∼ k –1 ( Dorogovtsev et al. 2002 ; Ravasz and Barabasi 2003 ). Intriguingly, we found that genes belonging to the same band are often coexpressed and associated with one of the dominating coexpressed units (transcription modules) identified by our modular analysis. The decrease of C as a function of k may reflect overlap between modules. Genes that are associated with only one module have a connectivity reflecting the size of the module and a large clustering coefficient. In contrast, genes that belong to several modules are correlated with a larger number of genes, but many of these genes are not connected with each other, leading to a smaller clustering coefficient. In support of this, we found that highly connected genes with a small clustering coefficient are often associated with several modules ( Figure 4 E). Thus, the band-like structures we observed may reflect the combinatorial regulation of gene expression. Conclusions Comparing genomic properties of different organisms is of fundamental importance in the study of biological and evolutionary principles. Although much of the differences among organisms is attributed to different gene expression, comparative analysis thus far has been based primarily on genomic sequence information. The potential of including functional genomic properties in a comparison analysis was demonstrated in recent studies that compared protein–protein interaction networks of different organisms ( Matthews et al. 2001 ; Kelley et al. 2003 ). In this paper we presented a comparative analysis of large datasets of expression profiles from six evolutionarily distant organisms. We showed that all expression networks share common topological properties, such as a scale-free connectivity distribution and a high degree of modularity. While these common global properties may reflect universal principles underlying the evolution or robustness of these networks, they do not imply similarity in the details of the regulatory programs. Rather, with a few exceptions, the modular components of each transcription program as well as their higher-order organization appear to vary significantly between organisms and are likely to reflect organism-specific requirements. Nevertheless, coexpression of functionally linked genes is often conserved among several organisms. Based on this finding, we proposed an efficient method that uses coexpression analysis for improving sequence-based functional annotation. An interactive implementation of this algorithm is available at http://barkai-serv.weizmann.ac.il/ComparativeAnalysis/ . Our analysis was based on the available expression data, which are still sparse for most organisms. It is likely that the modular decompositions we obtained are partial, so additional modules can be identified as more expression data become available. Nevertheless, by analyzing the sensitivity of our results to the number of conditions, we concluded that the composition of the modules themselves is rather robust. Moreover, the higher-order correlations between modules are only slightly affected by the number of conditions. The absence of a large set of common experimental conditions, however, does limit the scope of the present analysis and reduces the possibility of addressing particular evolutionary issues. It would be interesting, for example, to compare how different organisms respond to a variety of stress conditions, which were found to induce a unified transcription program in S. cerevisiae ( Gasch et al. 2000 ). Similarly, it would be intriguing to examine whether knockouts of homologous genes induce a similar transcriptional response in the different organisms. Comparative studies of gene expression pattern could be largely facilitated by unified datasets, which examine the genome-wide expression profiles of diverse as well as related species, under comparable experimental conditions. Materials and Methods Expression data Preprocessed expression data from E. coli , Arabidopsis thaliana , and Homo sapiens were downloaded from the Stanford Microarray Database ( Sherlock et al. 2001 ) using default parameters and selecting data from all experimenters and categories. For technical reasons (see Data S5 ), we only used the first 720 experimental conditions for the human dataset or all conditions related to the cell cycle. C. elegans expression data were obtained from Kim et al. (2001 ) and Drosophila melanogaster data from Arbeitman et al. (2002 ). The yeast expression data ( Gasch et al. 2000 ; Hughes et al. 2000 ; Causton et al. 2001 ) contain more than 1,000 experiments (see http://barkai-serv.weizmann.ac.il/modules/page/references.html for a complete list of references). We excluded genes or conditions with more than 90% missing datapoints, resulting in expression matrices of the dimensions shown in Table 1 (see Data S4 for comment on missing values in the expression data). Sequence data FASTA files for amino-acid sequences of coding regions were downloaded from the sources detailed in Table 2 . We ran the BLASTP 2.2.2 ( Altschul et al. 1997 ) locally in order to determine the sequence similarity among all coding regions. Gene/ORF identifiers were used to link the sequence data with the expression profiles. Table 2 The Sources of the Sequence Data Used in This Study Knockout data Data for deletion mutants ( S. cerevisiae and E. coli ) and RNAi experiments ( C. elegans ) were obtained from the sources indicated in Table 3 . Note that the fraction of the genome that was tested for viability varies among the three organisms. Table 3 Sources of Knockout Data Used in This Study Module definition A transcription modules consist of a set of coregulated genes (a subset G m of the genome G ) and an associated set of regulating conditions (a subset C m of all conditions C ). The defining property of a transcriptional module is self-consistency, which is achieved as follows. First, we assign scores to both genes and conditions that reflect their degree of association with the module. The gene score is the average expression of each gene over the module conditions, weighted by the condition score: . Analogously, the condition score is the weighted average over the module genes, . Here, and are the log-expression ratio of gene g in condition c normalized over genes and conditions, respectively, such that , and , . Self-consistency denotes the property that the genes of the module are those genes of the genome that receive the highest scores s g , while the module conditions are those conditions in the dataset with the highest scores s c . The ISA identifies transcription modules through iterative refinement of a large number of random gene scores. Module analysis For the analysis of the fraction of homologues (see Figure 3 C and 3 D) as well as the average pairwise correlations (see Figure 1 C), we used most of the transcription modules identified by the ISA. In order to avoid bias from similar modules identified at adjacent thresholds, we considered only modules with less than 70% similarity to any module identified at a lower threshold. Two sequences were considered homologues if they could be aligned along at least 40% of the shorter sequence by the BLAST algorithm and obtained an E-value smaller than 10 –5 . The precise parameter values have only a minor effect on our results (see Data S1 for detailed statistical analysis). We only considered modules with at least five homologues. Module purification and refinement A “homologue module” consists of the genes homologous to a transcription module in another organism. We used the signature algorithm to purify and refine these homologue modules (see Ihmels et al. 2002 for details of the algorithm). A “purified module” is the intersection between the homologue module, used as input for the signature algorithm, and the resulting output. It contains only genes that are coexpressed. A “refined module” is obtained by applying the signature algorithm again, this time using the purified module as input. The output consists both of the coexpressed genes and the conditions inducing their coexpression. This twofold application of the signature algorithm usually provides a more accurate determination of the coexpressed genes related to the original transcription module than a single application. In order to also capture weakly coexpressed modules, we used relatively low thresholds ( t g = t c = 1.5) in the present analysis, but retained only genes whose score is not less than 70% of the most significant gene ( Ihmels et al. 2002 ). Correlations between modules Both a transcription modules and the refined homologue module derived from it are associated with a set of coregulating experimental conditions ( Ihmels et al. 2002 ). The significance of each condition is characterized by a score s c . The sets of scores can be used to compute the regulatory relation between two modules of the same organism. We use C ij = (Σ c s c ( i ) · s c ( j ) )/(Σ c s c ( i ) · s c ( i ) ·Σ c s c ( j ) · s c ( j ) ) ½ as the correlation coefficient between two modules with score sets { s c ( i ) } and { s c ( j ) } , respectively. Note that, unlike for the Pearson correlation, this definition of C ij does not center the scores. Network analysis Each expression network can be described by a symmetric adjacency matrix A ij , whose elements are 1 if the expression of gene i and gene j are sufficiently similar and 0 otherwise. Similarity was measures by the Pearson correlation coefficient between the expression profiles. Owing to the very different sizes of the respective sets of expression data, we demanded that the average connectivity < k > (rather than the minimal correlation) is identical in all expression networks and fixed it to < k > = 0.001. Using the top 0.1% of all possible correlations corresponds to a lower limit on the correlation coefficients between 0.63 for S. cerevisae and 0.85 for D. melanogaster . The results are insensitive to the precise threshold value (see Figure S2 for detailed analysis). The connectivity of gene i is k = Σ j ≠ i A ij . In order to obtain the connectivity distributions n(k) , we used logarithmic binning. The edges of the bins were powers of 2, and we counted the number of genes with k i between two edges and normalized by the bin width. We applied a linear fit to the log values of the bin centers against the normalized counts. We note that the resulting connectivity distributions scale as a power-law for a wide range of thresholds and the exponents only depend weakly on the choice of the threshold. The clustering coefficient of gene i is C i = (Σ k > j ≠ i A ik A kj A ji )/[ k i ( k i −1)/2]. Web site Interactive applications for the refinement of sets of homologous genes and the exploration of our modular decompositions of the expression data are available online. We also present details about the highly connected genes in each organism, the pairs of genes that are significantly correlated in two organisms, and the eight modules related to core processes in yeast (and their homologue modules before and after refinement) on our website at http://barkai-serv.weizmann.ac.il/ComparativeAnalysis . Supporting Information Data S1 Testing the Robustness of Our Analyses with Respect to the Precise Values of Threshold Parameters This note includes Figure S1 and Figure S2 . (38 KB PDF). Click here for additional data file. Data S2 Controls to Verify That Our Results Are Not Impaired by the Sparseness of the Available Expression Data This note includes Figure S3 , Figure S4 , and Figure S5 . (59 KB PDF). Click here for additional data file. Data S3 Testing for Coregulated Subsets within the Homologue Modules This note includes Figure S6 . (11 KB PDF). Click here for additional data file. Data S4 Comment on Missing Values in the Expression Data (3 KB PDF). Click here for additional data file. Data S5 Comment on the Size of the Human Dataset Used in This Work After this work was completed, we succeeded in processing the more than 2,000 human chip experiments deposited at the SMD. Removing genes and conditions with more than 90% missing values resulted in 1,474 expression profiles for 24,795 genes. Our Web tools (“GeneHopping” and “ModuleTree”) allow researchers to use also this updated dataset. (3 KB PDF). Click here for additional data file. Figure S7 The Interactive Web Tool (137 KB PDF). Click here for additional data file. Figure S8 Statistical Analysis Comparing the Pairwise Correlations between Genes in One Organism to the Correlations between Their Respective Homologues (16 KB PDF). Click here for additional data file. Figure S9 Pairwise Correlations of C. elegans Homologues to the Yeast Heat-Shock Module (15 KB PDF). Click here for additional data file. Figure S10 Correlations between the Genes of Eight Representative Yeast Modules and Their Homologue Modules, Purified Modules, and Refined Modules (33 KB PDF). Click here for additional data file. Figure S11 Pairwise Correlations between Eight Transcription Modules of Known Function in Yeast and Their Refined Homologue Modules in the Five Other Organisms (11 KB PDF). Click here for additional data file. Figure S12 Histograms Showing the Number of Modules of One Organism with a Given Fraction of Homologues in Another Organism (29 KB PDF). Click here for additional data file. Figure S13 Connectivity Distributions for the Six Organisms in Separate Plots (19 KB PDF). Click here for additional data file.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC300882.xml

387279

A Window into the Brain Demonstrates the Importance of Astrocytes

Did you ever wish you could peek inside someone's brain and see what was going on in there? In research reported in this issue of PLoS Biology , Hajime Hirase and his colleagues at Rutgers University have done just that by focusing their microscope on the brains of living rats in order to examine how certain cells called astrocytes function in vivo. Astrocyte in the cerebral cortex In the longstanding quest to understand how the brain works, scientists have focused on neurons. Neurons conduct action potentials, electrical signals that transmit information in the nervous system. But the brain also contains several other types of cells called glia. ( Glia is derived from the Latin for “glue”; these cells were thought to “hold it all together.”) One type of glial cell, the astrocyte (named for its starlike shape), is the most populous cell in the brain and forms an intimate association with neurons and their synapses. It was thought that these cells played a supporting role in the brain, ensuring the proper chemical environment for synapses. Recent research, however, has suggested that astrocytes and other glial cells may play a more significant role. When examining astrocytes cultured in the lab, scientists have observed behavior suggesting that astrocytes can communicate with neurons. Though astrocytes cannot propagate electrical signals like neurons do, they can sense the transmission of such signals at the synapse between two neurons. Furthermore, astrocytes are able to propagate a different kind of signal, a chemical signal based on the release of calcium ions. Calcium signaling is a mechanism of chemical signaling that has been observed in many other cell types. The exact properties of neuron–astrocyte communication, however, are not clear because different preparations of these tissues have yielded different results. It has also not been established that this type of communication occurs in the living brain. To explore such questions, Hirase and colleagues have taken the next step by investigating the calcium signaling properties of astrocytes in the brains of living rats. To accomplish this feat, the researchers used a combination of two technologies. They monitored calcium signaling using a fluorescent dye called Fluo-4, which fluoresces in response to calcium ions. Then they used a special type of microscope called a two-photon laser scanning microscope to visualize the dye. Since this type of microscope uses a lower energy laser, it can image the dye in living tissue without causing harm. The researchers applied the dye to the brains of anesthetized rats, washed out the excess dye that had not penetrated into cells, and then imaged the tissue under the microscope. They first confirmed that they indeed were examining astrocytes and noticed that cells displayed a moderate level of baseline calcium signaling activity. They then used a drug called bicuculline to stimulate neurons and observed a significant increase in the calcium signaling activity of the astrocytes. Because bicuculline only affects neurons, this implies that the astrocytes are responding to the activity of the neurons. The researchers also found that neighboring astrocytes often also displayed coordinated calcium signaling activity, suggesting that the communication among astrocytes is facilitated by increased neuronal activity. This research confirms the complexity of astrocyte signaling functions in the living brain and demonstrates that astrocytes play far more than a supporting role in brain function. It also establishes an important experimental system for scientists seeking to understand how these distinct elements of the brain—neurons and astrocytes—work together. Though this research makes it clear that signaling exists both among astrocytes and between neurons and astrocytes, scientists have yet to understand the effect of this signaling. Some possibilities include regulation of synapse formation, modification of synaptic strength, or more complicated roles in information processing resulting from the coordination of neuronal activity. Future research using this and other systems will help reveal these functions.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC387279.xml

526388

A CpG island hypermethylation profile of primary colorectal carcinomas and colon cancer cell lines

Background Tumor cell lines are commonly used as experimental tools in cancer research, but their relevance for the in vivo situation is debated. In a series of 11 microsatellite stable (MSS) and 9 microsatellite unstable (MSI) colon cancer cell lines and primary colon carcinomas (25 MSS and 28 MSI) with known ploidy stem line and APC , KRAS , and TP53 mutation status, we analyzed the promoter methylation of the following genes: hMLH1 , MGMT , p16 INK4a ( CDKN2A α-transcript), p14 ARF ( CDKN2A β-transcript), APC , and E-cadherin (CDH1) . We compared the DNA methylation profiles of the cell lines with those of the primary tumors. Finally, we examined if the epigenetic changes were associated with known genetic markers and/or clinicopathological variables. Results The cell lines and primary tumors generally showed similar overall distribution and frequencies of gene methylation. Among the cell lines, 15%, 50%, 75%, 65%, 20% and 15% showed promoter methylation for hMLH1 , MGMT , p16 INK4a , p14 ARF , APC , and E-cadherin , respectively, whereas 21%, 40%, 32%, 38%, 32%, and 40% of the primary tumors were methylated for the same genes. hMLH1 and p14 ARF were significantly more often methylated in MSI than in MSS primary tumors, whereas the remaining four genes showed similar methylation frequencies in the two groups. Methylation of p14 ARF , which indirectly inactivates TP53, was seen more frequently in tumors with normal TP53 than in mutated samples, but the difference was not statistically significant. Methylation of p14 ARF and p16 INK4a was often present in the same primary tumors, but association to diploidy, MSI, right-sided location and female gender was only significant for p14 ARF . E-cadherin was methylated in 14/34 tumors with altered APC further stimulating WNT signaling. Conclusions The present study shows that colon cancer cell lines are in general relevant in vitro models, comparable with the in vivo situation, as the cell lines display many of the same molecular alterations as do the primary carcinomas. The combined pattern of epigenetic and genetic aberrations in the primary carcinomas reveals associations between them as well as to clinicopathological variables, and may aid in the future molecular assisted classification of clinically distinct stages.

Background During the last decade, epigenetic changes have been reported in many cancers and they are now recognized to be at least as common as genetic changes [ 1 ]. Aberrant methylation of cytosine located within the dinucleotide CpG is by far the best-categorized epigenetic change. The genome of the cancer cell demonstrates global hypomethylation [ 2 , 3 ] as well as regional promoter hypermethylation of several tumor suppressor genes [ 4 ]. Hypermethylation of selected CpG sites within CpG islands in the promoter region of genes is associated with loss of gene expression and is observed in both physiological conditions, such as X chromosome inactivation [ 5 ], and neoplasia [ 6 ]. By inactivating various tumor suppressor genes, this epigenetic modification can affect many important cellular processes, such as the cell cycle ( RB , p15 INK4b , p16 INK4a ), the TP53 pathway ( p14 ARF ), the WNT signaling pathway ( APC , E-cadherin ), DNA repair ( MGMT , hMLH1 , BRCA1 ), apoptosis ( DAPK ), and the metastasizing process ( E-cadherin , TIMP3 ) (reviewed in [ 1 , 7 , 8 ]). Development of colorectal cancer through various morphological stages has been linked to several genetic and epigenetic changes. The majority of carcinomas have several chromosomal aberrations, a phenotype often referred to as chromosomal instability. Approximately 15% of the tumors are near diploid but exhibit microsatellite instability (MSI), seen as genome-wide short nucleotide insertions and deletions [ 9 ]. This phenotype is caused by a defect DNA mismatch repair system [ 9 ]. Subgroups of both types of colorectal carcinomas reveal aberrant methylation of tumor suppressor genes leading to lack of expression [ 10 , 11 ]. Human cancer cell lines are important tools in cancer research. Their commercial availability and unrestrained growth make them well suited for in vitro studies. Although many of the known genetic aberrations in colon cancer cell lines have been comprehensively described [ 12 ], several of these cell lines have not been analyzed for methylation status of pathogenetically important target genes. The frequencies of both methylation and gene mutation differ among various studies of cell lines and primary tumors. The genome characteristics, profiles of gene mutations, and methylation status are rarely reported in the same samples, let alone in large series. In the present report we address these potentially connected pathogenetic mechanisms by presenting methylation profiles of a set of genes in a series of MSI and microsatellite stable (MSS) colon cancer cell lines and primary colorectal carcinomas. The methylation profiles are compared with various known genetic and clinicopathological features of the same series. Results Methylation status of target genes in colon cancer cell lines The colon cancer cell line methylation-specific PCR (MSP) results are summarized in Table 1 and Figure 1a . Among the MSI cell lines 3/9, 5/9, 7/9, 8/9, 2/9, and 2/9 showed promoter hypermethylation of hMLH1 , MGMT , p16 INK4a , p14 ARF , APC , and E-cadherin , respectively, whereas 0/11, 5/11, 8/11, 5/11, 2/11, and 1/11 of the MSS cell lines were hypermethylated for the same genes (Table 2 ). Hence, the cell lines with MSI generally showed higher methylation frequencies than did the MSS cell lines (Figures 1a , 2a ). In most cases, methylation of the target genes was biallelic, but in 10 of the 20 cell lines, monoallelic methylation (detection of both methylated and unmethylated MSP gel bands) was found for one or more of the genes (Table 1 ). The MSS V9P was the only cell line unmethylated for all six genes analyzed. Table 1 Promoter methylation of colon cancer cell lines. MSI, microsatellite instable; MSS, microsatellite stable; U, unmethylated; M, methylated. The references give results in agreement with our own data except when the reference is underlined. Note that reference 15 does not use the category monoallelic methyaltion, but reports the promoters only as methylated or unmethylated. Cell line hMLH1 MGMT p16 INK4a p14 ARF APC E-Cadherin MSI Co115 M 12 M M 12 M U/M U HCT15 U 12,13,14,15 U/M 15 ,16 M 12,14,15 M 14,15,17 U U 15 HCT116 U 12,13, 15 ,18,19,20,21,22 U/M 15 ,20 U/M 12, 15 ,20,21,22,23 U/M 15 , 17 ,21,24 U/M U 15 LoVo U 12,13,14,15,18,22,26 U 15,31 M 12,14,15,22 M 14 ,15,24,25 U U 15 LS174T U 12,13,18, 22 U/M U 12,22 U/M U U RKO M 15,18,19,20,22,26 U 15,20 M 15,20,22,27 M 15,24 U M 15 SW48 M 12,13,14, 15 ,18,20,22,26,28,29 M 15,20,31 M 12,14,15,20,22,27,29 M 14, 15 ,24 U U 15 TC7 U 12 U U 12 U/M U U TC71 U 12 U M 12 U U U/M MSS ALA U 12 U M 12 U M U Colo320 U 12,14,18, 30 M M 12,14,27 U 14 U 30 M EB U 12 M M 12 U U U FRI U 12 U/M U 12 U/M U U HT29 U 12, 13 , 14 ,15,18, 21 ,22, 26 ,30 U 15,31,32,33 M 12,14,15,21,22,27 U 14,15,21, 24 U 30 U 15 IS1 U 12,21 U M 12,21 M 21 U U IS2 U 12 U U/M 12 M U U IS3 U 12 U U 12 M U U LS1034 U 12,13 U/M U/M 12 M U/M U SW480 U 12,14,15,19,21,22,26, 30 U/M 15 M 12,14,15,21,22,27 U 14,15,21,24,25 U 30 U 15 V9P U 12 U U 12 U U U Table 2 Methylation frequencies among MSS and MSI colon cancer cell lines and primary colorectal tumors. Abbreviations; MSS, microsatellite stable; MSI, microsatellite instable; CRC, colorectal cancer; U, unmethylated;M, methylated. Note that the calculated methylation frequencies of the MSS cell lines includes results from three cell lines derived from the same patient. MSS MSI Total Gene Cell lines CRCs Cell lines CRCs Cell lines CRCs hMLH1 0/11 (0%) 0/25 (0%) 3/9 (33%) 11/28 (39%) 3/20 (15%) 11/53 (21%) MGMT 5/11 (45%) 10/25 (40%) 5/9 (56%) 11/28 (39%) 10/20 (50%) 21/53 (40%) p16 INK4a 8/11 (73%) 7/25 (28%) 7/9 (78%) 10/28 (36%) 15/20 (75%) 17/53 (32%) p14 ARF 5/11 (45%) 3/24 (12%) 8/9 (89%) 17/28 (61%) 13/20 (65%) 20/52 (38%) APC 2/11 (18%) 7/25 (28%) 2/9 (22%) 10/28 (36%) 4/20 (20%) 17/53 (32%) E-cadherin 1/11 (9%) 10/24 (42%) 2/9 (22%) 11/28 (39%) 3/20 (15%) 21/52 (40%) Figure 1 Distribution of simultaneously methylated promoters in MSS and MSI colon cancer cell lines and colorectal carcinomas. The two panels illustrate the percentage of MSS and MSI samples displaying methylation of zero to all of the promoters analyzed in the present study in a) cell lines and b) primary colorectal tumors. Abbreviations: MSS , microsattelite stable; MSI , microsattelite instable. Methylation status of target genes in primary colorectal carcinomas. Comparison with colon cancer cell lines Methylation status was assessable in more than 99% of the total number of analyses (53 tumors × 6 genes = 318 analyses). The results of the methylation analyses of 53 primary colorectal carcinomas (25 MSS and 28 MSI) are shown in Table 2 and illustrated in Figures 1b and 2b . All the methylated primary tumors examined showed an unmethylated band in addition to the methylated one, probably due to the presence of normal cells. The methylation frequencies varied from 0% among MSS tumors at the hMLH1 promoter to 61% among the MSI tumors for the p14 ARF gene (Table 2 ). Figure 2 Promoter hypermethylation in colon cancer cell lines and colorectal primary tumors. Methylation was evaluated by methylation-specific PCR (MSP). A visible PCR product in Lanes U indicates the presence of unmethylated alleles whereas a PCR product in Lanes M indicates the presence of methylated alleles. The upper panel (a) illustrates the methylation status of all the loci analyzed in a MSI cell line (RKO) and a MSS cell line (HT29). The lower panel (b) shows the methylation status of representative primary colorectal tumors. Abbreviations: NB , normal blood (positive control for unmethylated samples); MP , methylated placenta (positive control for methylated samples); neg , negative control (containing water as template); U , lane for unmethylated MSP product; M , lane for methylated MSP product. Several of the primary tumor samples displayed widespread CpG island methylation (Figure 1b ). Eighteen of 52 tumors (35%) were methylated in 3 or more of the 6 genes analyzed. Only 5/52 (10%) of the tumor samples did not show hypermethylation in any of the genes analyzed. We saw no statistical difference in the number of methylated target genes in colon cancer cell lines versus colorectal primary tumors (Mean Rank 32 for primary tumors versus 38 for cell lines, P = 0.231, Mann-Whitney test). Methylation profiles compared with genetic characteristics The methylation status of the primary tumors was compared with genetic characteristics of the same tumors (Table 3 ). In general, higher frequencies of gene methylation were found among diploid than among aneuploid tumors, reflecting the MSI status, but the differences reached statistical significance only for p14 ARF ( P < 0.001) and hMLH1 ( P = 0.015). Sixteen of 49 primary tumors harbored TP53 mutations, and all of the tumors with TP53 mutations also harbored unmethylated hMLH1 ( P = 0.009). p14 ARF hypermethylation was less common in tumors with mutated TP53 than in tumors with wild type TP53 , although this was not statistically significant ( P = 0.127). Four tumors displayed a G:C to A:T TP53 mutation and three of them simultaneously harbored a methylated MGMT gene. Four of 11 tumors with G:C to A:T KRAS (KRAS2) mutations were methylated at the MGMT promoter. Overall, the presence of KRAS mutations was not associated with the methylation status of the genes analyzed. Among the 20 tumors with p14 ARF methylation, 10 were also methylated at the adjacent p16 INK4a gene ( P = 0.067). Finally, the APC promoter was methylated in 17/53 (32%) tumors, and 8/17 (47%) tumors displayed both APC mutation and methylation. Table 3 CpG island methylation of selected genes compared with the patients clinicopathological features and tumor genetics. Abbreviations: Gen. Characteristics, Genetic Characteristics; MSI, microsatellite instability; MSS, microsatellite stable; NS, not significant; Clin. and Path. Features, Clinical and Pathological Features. Comparison of different groups were tested with Fisher exact test or Pearsons χ2 test, P values are two sided and are considered statistically significant when P ≤ 0.05. The table is based on primary tumors (53) and not patients (52) *Statistically significant Pearsons χ2 tests with expected count less than 5. hMLH1 MGMT p16 INK4a p14 ARF APC E-cadherin M U M U M U M U M U M U Individuals No 11/53 42/53 21/53 32/53 17/53 36/53 20/52 32/52 17/53 36/53 21/52 31/52 Gen. Characteristics Ploidy Diploid 10 20 10 20 10 20 18 12 11 19 13 17 Aneuploid 1 22 11 12 7 16 2 20 6 17 8 14 P value 0.02 NS NS < 0.001 NS NS MSI-status MSI 11 17 11 17 10 18 17 11 10 18 11 17 MSS 0 25 10 15 7 18 3 21 7 18 10 14 P value < 0.001 NS NS 0.001 NS NS TP53 Wild type 11 22 12 21 11 22 16 16 8 25 13 19 Mutation 0 16 7 9 5 11 4 12 7 9 7 9 P value 0.01 NS NS NS NS NS wt+non G-A mutation 11 33 15 29 14 30 18 25 13 31 17 26 G-A mutation 0 4 3 1 1 3 1 3 1 3 2 2 P value NS NS NS NS NS NS K-Ras Wild type 8 19 13 14 9 18 12 15 7 20 9 18 Mutation 1 14 6 9 3 12 2 12 6 9 6 8 P value NS NS NS 0.08 NS NS wt+non G-A mutation 8 23 15 16 9 22 13 18 8 23 10 21 G-A mutation 1 10 4 7 3 8 1 9 5 6 5 5 P value NS NS NS NS NS NS APC Wild type 7 19 12 14 10 16 9 17 9 17 12 14 Mutation 3 23 8 18 7 19 10 15 8 18 9 16 P value NS NS NS NS NS NS Clin. and Path. Features Sex Male 2 23 9 16 8 17 6 19 10 15 8 17 Female 9 19 12 16 9 19 14 13 7 21 13 14 P value 0.04 NS NS 0.05 NS NS Age (years) <68 2 21 10 13 4 19 7 16 8 15 9 14 ≥68 9 21 11 19 13 17 13 16 9 21 12 17 P value 0.09 NS 0.07 NS NS NS Location Right 10 8 7 11 7 11 12 6 7 11 7 11 Left 1 19 8 12 9 11 5 14 6 14 8 11 Rectum 0 14 6 8 1 13 2 12 4 10 5 9 P value < 0.001* NS 0.05 0.01 NS NS Histologic grade Poorly differentiated 4 8 7 5 6 6 7 4 5 7 4 7 Moderately differentiated 7 30 13 24 11 26 12 25 11 26 14 23 Well differentiated 0 3 1 2 0 3 0 3 1 2 2 1 P value NS NS NS NS NS NS Dukes' classification A 2 2 3 1 1 3 2 2 0 4 1 3 B 5 22 10 17 8 19 9 17 13 14 12 14 C 2 13 4 11 4 11 4 11 3 12 5 10 D 2 5 4 3 4 3 5 2 1 6 3 4 P value NS NS NS NS 0.07 NS Among the tumors with widespread methylation (3 or more methylated genes), 13/18 (72%) tumors demonstrated MSI, whereas 5/24 (21%) were MSS ( P = 0.080). We found no statistically significant associations between tumors with widespread methylation and presence of TP53 , KRAS , or APC mutations. Methylation profiles and clinicopathological features The clinicopathological features and methylation status of the primary tumors are summarized in Table 3 . We saw more methylation among tumors from females than in those from males for both hMLH1 ( P = 0.043) and p14 ARF ( P = 0.050). Tumors from patients younger than the mean age (68 years) had a lower methylation frequency for p16 INK4a than did tumors from older patients, although this was not statistically significant ( P = 0.074). There was a strong association between methylation and right-sided tumor location as 10/11 (91%) tumors methylated in hMLH1 and 12/19 (63%) of the tumors methylated in p14 ARF were located in the right side of the colon ( P < 0.001 and P = 0.005, respectively). There was no statistically significant association between methylation and histological grade. Most of the tumors with APC methylation (13/17, 76%) belonged to the Dukes' B group, but the differences were not statistically significant ( P = 0.068). Tumors with widespread methylation (≥ 3 loci) are associated with right-sided localization; 10/17 (59%), versus 5/17 (29%) left-sided ( P = 0.035). We saw no statistically significant associations between presence of widespread methylation and the remaining clinicopathological variables included in the present study. Discussion Tumor cell lines are commonly used as experimental tools in cancer research, including studies designed to assess epigenetic changes. But whereas the genetic aberrations of colon cancer cell lines have been comprehensively described [ 12 ], the methylation profiles of potential target genes in the same or similar cell lines are often described only sparingly. A literature survey of the 20 colon cancer cell lines and their methylation status analyzed in this study showed that some cell lines and genes had been extensively studied, whereas others were left undescribed (Table 1 ). For half of the cell lines included in the present study, both methylated and unmethylated alleles have been found for one or more of the genes studied. As non-neoplastic cells are not found in cultured cancer cell lines, this can not be caused by the presence of normal cells, and although several biological and technical explanations may exist, allele specific methylation seems the most likely interpretation [ 23 , 34 ]. In contrast, admixture of normal cells, tumor heterogeneity and/or monoallelic methylation may explain the coexistence of unmethylated and methylated bands in primary tumors. It has been debated for some time whether cell lines are more frequently methylated than primary tumors [ 35 ]. Regarding overall CpG island hypermethylation, cancer cell lines have in general demonstrated an increased frequency of hypermethylation compared with primary tumors [ 15 ]. However, only a limited number of the genes analyzed have shown a statistically significant difference in methylation frequency [ 15 ]. Among several cancer types examined, colon cancer cell lines have been shown to resemble the most their respective primary tumor in this respect [ 36 ]. For the cell lines and primary tumors included in this study, the fraction of MSI and MSS samples was about the same and we saw no statistical difference in the overall number of methylated target genes in colon cancer cell lines versus colorectal primary tumors. Seemingly, large methylation percentage differences for individual genes were seen (Table 2 ) but they were statistically significant only for p16 INK4a methylation, independent of MSI stratification. Comparisons of in vitro tumor cells with primary tumors of each subtype (MSS and MSI) have also shown similar frequencies of TP53 , KRAS and APC mutations [ 12 ] and ploidy stem line [ 37 ], which further supports the conclusion that the in vitro system is a suitable experimental tool that closely reflect the in vivo situation. Previously reported variations in promoter hypermethylation frequencies of different tumor suppressor genes in colorectal cancer can be explained by various ratios of MSI versus MSS samples in the series analyzed, different methods for analyzing methylation, the inter-individual variation in scoring of methylated samples, incomplete bisulphite modification, tumor heterogeneity, and the fact that different parts of the gene promoter region in question have been analyzed. In the present study, we used primer sets known to only detect methylation in tumor cells, never in normal tissues from the same patients [ 24 , 31 , 38 - 42 ]. The promoter hypermethylation in these areas has also shown an impressive correlation with lack of protein expression, confirming that these are essential regions for gene expression [ 24 , 31 , 38 - 42 ]. The hMLH1 primers we designed amplify a region of the promoter, in which methylation invariably correlates with the lack of hMLH1 expression [ 18 , 43 , 44 ]. Methylation of this region has only been detected in tumor cells and not in normal mucosa [ 18 , 43 , 44 ]. As expected, the MSI primary tumors showed more methylation overall than did the MSS group. However, this was only significant for the hMLH1 and p14 ARF genes, whereas the four additional genes analyzed revealed similar methylation frequencies in the MSS and MSI groups. Promoter methylation of the hMLH1 gene was, not surprisingly, found only in tumors and cell lines with MSI, not in the MSS samples. The MSS tumors and cell lines per definition contain functional hMLH1 protein, and transcriptional silencing of hMLH1 by hypermethylation is known to be the main cause of MSI in sporadic CRC [ 26 , 28 , 45 ]. Also p14 ARF methylation may have a specific role in MSI tumors, since it seems to be most often inactivated in tumors with wild type TP53 (see below). However, the relatively high methylation frequencies of the remaining analyzed genes, and also their overall similar frequency in MSI and MSS samples, imply that they are important in colorectal carcinogenesis independently of tumor site and MSI status. Inactivation of tumor suppressor genes by promoter hypermethylation has been recognized to be at least as common as gene disruption by mutation in tumorigenesis [ 1 ]. Indeed, most types of primary tumors harbor several genes inactivated in this way and some genes, like p16 INK4a , have been reported to be methylated consistently in most tumor types analyzed [ 46 ]. In colorectal carcinomas, the reported p16 INK4a methylation frequencies vary from 18% [ 47 ] to 50 % [ 48 ] with most of the observations centered around 36–40% [ 11 , 27 , 46 , 49 - 51 ], i.e., slightly higher than our result. Both p16 INK4a and p14 ARF are more commonly methylated in tumors with MSI than in MSS [ 10 , 11 , 51 - 53 ], although we found that the methylation frequency of p14 ARF is higher than that for p16 INK4a in MSI colorectal carcinomas. The DNA repair protein MGMT is able to remove promutagenic alkyl groups from O 6 -guanine by an irreversible transfer to an internal cysteine residue [ 54 ]. Left unrepaired, the alkylated O 6 -guanine has a tendency to base pair with thymine during replication, thereby introducing a G:C to A:T transition mutation in the DNA [ 55 ]. Inactivating promoter hypermethylation of the MGMT gene has previously been reported to be associated with G:C to A:T mutations in the tumor suppressor gene TP53 [ 56 ] and the proto-oncogene KRAS [ 57 , 58 ]. Our data support this assumption for TP53 but seemingly not for KRAS , although no certain conclusions can be drawn from the limited number of samples with G:C to A:T mutations. The p14 ARF protein interacts in vivo with the MDM2 protein, neutralizing MDM2's inhibition of TP53 [ 59 ]. Less hypermethylation of p14 ARF in tumors with mutated TP53 than in tumors with wild type TP53 has been reported previously [ 24 ]. Additionally, several reports have described an inverse relationship between MSI and TP53 mutation in colorectal carcinomas [ 60 - 62 ]. The frequent methylation we report for the p14 ARF gene in MSI tumors with few TP53 mutations is in agreement with a recent study [ 53 ] and supports the existence of this alternative pathway for TP53 inactivation. Inactivation of the APC gene is frequent in colorectal and other gastrointestinal carcinomas, usually by truncating mutations [ 63 , 64 ]. An alternative mechanism to inactivate the gene in colorectal tumors is by promoter methylation, and we report a frequency of APC methylation in the upper range of what has been seen in previous studies [ 51 , 65 , 66 ]. Somatic mutations in APC are common in colorectal cancer [ 67 , 68 ] and, similar to what has been seen by others [ 12 , 22 , 69 ], almost half of the tumors displaying APC mutations in our study were also methylated. We have not looked at allele-specific mutation, but methylation and mutation in the same tumor might reflect one mutated allele and methylation of the other, in accordance with Knudson's two hit hypothesis. This has previously been demonstrated for APC in colorectal cancer samples by Esteller et. al [ 65 ]. APC has a central role in the WNT signaling pathway, which is suggested to play a part in colorectal carcinogenesis by its constitutive activation. Activation of this pathway results in increased transcription levels of genes like MYC and CCND1 (cyclin D1) further stimulating cell proliferation [ 63 ]. Among the 52 successfully analyzed primary tumors in this study, 35 had altered APC caused by methylation (n = 17) and/or gene mutation (n = 26). The E-cadherin gene was also methylated in 14/34 tumors with altered APC , presumably further stimulating WNT signaling [ 63 ]. Interestingly, APC methylation seemed to be more common in Dukes B stage tumors. The present study confirms that methylation of hMLH1 in sporadic carcinomas is associated with proximal tumor location in the large bowel [ 14 , 21 , 45 , 70 ], as above 90% of the primary tumors harboring a methylated hMLH1 promoter were taken from the right side of the colon. An association between sporadic proximal colon carcinomas and methylation has also been reported for p16 INK4a and p14 ARF [ 14 , 21 , 45 ]. Among our 53 primary tumors, we can only confirm this statistically for p14 ARF . However, p16 INK4a demonstrated the same tendency. Both hMLH1 and p14 ARF are strongly associated with MSI and MSI is in turn strongly associated with proximal tumor location [ 71 , 72 ], hence, it is not unexpected that the methylation of both genes is associated with proximal location. When it comes to gene methylation and its association with other clinicopathological features, contradictory results have been reported. Our observation that methylation of p14 ARF does not exclude p16 INK4a methylation, is in accordance with previous studies [ 21 , 24 ]. Correlation of p16 INK4a or p14 ARF methylation with female gender and increased age has been described in some studies [ 14 , 47 ] but not in others [ 11 , 21 , 24 ]. We found such an association between female gender and methylation of p14 ARF and hMLH1 , but not of p16 INK4a . We also found a weak association between p16 INK4a methylation and increasing age. This potential age-specific methylation was not confirmed for any of the other genes studied. The gender-associated methylation of hMLH1 has previously been described [ 73 , 74 ] and might explain the increased prevalence of colorectal tumors of the MSI type in the female patient group [ 74 ]. Like Toyota et. al [ 51 ], we found no statistically significant associations between tumors with widespread methylation and age, gender, or stage of the colorectal cancer. Conclusions The data presented here demonstrate that multiple genes are methylated in colorectal carcinomas. This underlines the important role epigenetic inactivation of tumor suppressor genes plays during the process of tumor development. Epigenetic changes in colon cancer cell lines are overall comparable with those of primary carcinomas of the large bowel, which make the cell lines relevant models for the in vivo situation. The methylation profile of specific genes, in particular hMLH1 and p14 ARF , has strong associations with genetic and clinicopathological features and might be related to biologically distinct subsets of colorectal tumors. Methods Patients and cell lines Fifty-three primary colorectal carcinomas from 52 patients, including 25 MSS tumors and 28 MSI tumors, were submitted to methylation analyses. One of the tumors was from a patient with hereditary non-polyposis colorectal cancer (HNPCC), whereas the rest of the cases were sporadic [ 71 ]. The tumors have known DNA ploidy pattern [ 75 ], MSI status [ 76 ], as well as mutation status for TP53 , KRAS and APC [ 62 , 64 , 77 ]. The genetic and clinicopathological variables are found in Table 3 . Twenty colon cancer cell lines, 11 MSS and 9 MSI, were also included in the study. These cell lines have previously been characterized for MSI status [ 61 , 78 - 80 ], 31 different genetic alterations [ 12 ], and total genome profiles by Kleivi et. al [ 37 ]. The primary tumors included in the present study are from a series of carcinomas evaluated to contain a mean number of 84% tumor cells [ 81 ]. The DNA was extracted by standard phenol -chloroform procedure. Methylation-specific PCR (MSP) Promoter methylation was studied in hMLH1 , MGMT , p16 INK4a , p14 ARF , APC and E-cadherin by MSP, a method that distinguishes unmethylated from methylated alleles of a given gene [ 38 ]. After bisulphite treatment of DNA, which converts unmethylated but not methylated cytosines to uracil, DNA is amplified by PCR using primers specific to methylated and unmethylated sequences. One or two μg DNA from each sample was modified as described [ 82 ]. Previously reported primer sets were used for amplification of the MGMT [ 31 , 82 ], p16 INK4a [ 38 , 82 ], p14 ARF [ 24 ], APC [ 39 , 40 ] and E-cadherin fragments [ 41 ] (island 3). The primers for amplifying unmethylated and methylated hMLH1 fragments were designed in accordance with hMLH1 promoter methylation and gene expression studies [ 18 , 44 ]. All primer sets (see Additional file 1 ) were purchased from Medprobe AS (Oslo, Norway). All the PCRs were carried out in a total volume of 25 μl containing 1 × PCR Buffer (15mM MgCl 2 or no MgCl 2 ; QIAGEN Inc., Valencia, CA), 200 μM dNTP (Amersham Pharmacia Biotech Products Inc., Piscataway, NJ), and 0.625 U HotStarTaq DNA Polymerase (QIAGEN). PCR products were loaded onto 7.5% polyacrylamide gels, stained with ethidium bromide, and visualized by UV illumination. An independent second "methylated reaction" of the MSP was performed for all the samples included in the present study. In cases with diverging results from the two rounds of MSP, we did a third independent MSP round. Human placental DNA (Sigma Chemical Co., St. Louis, MO) treated in vitro with SssI methyltransferase (New England Biolabs Inc., Beverly, MA) was used as a positive control for MSP of methylated alleles, whereas DNA from normal lymphocytes was used as a control for unmethylated alleles. Water was used as a negative PCR control in both reactions. Statistics All 2 × 2 contingency tables were analyzed using Fisher's exact test. Three × 2 tables were analyzed by the Pearson χ 2 test. Two of the statistically significant cross-tables analyzed by the Pearson χ 2 had cells with expected count less than 5, with a minimum count of 2.96 (Table 3 ). The Mann -Whitney test was in addition performed when appropriate. All P values are derived from two tailed statistical tests using the SPSS 11.5 software. Authors' contributions GEL cultured and isolated DNA from all cell lines and carried out the MSP analyses of these and of the patient samples. GEL interpreted the results, performed the statistics and drafted the manuscript. LT participated in the study design, scored the MSP results independently of author 1, and contributed to manuscript preparation. TL was responsible for the update of the APC mutation status in the cohort. GIM and TOR have collected the series of human primary tumors and provided all clinical and pathological information. RH provided all cell lines and information about them. ME contributed with scientific discussions of the results and participated in the writing of the manuscript. RAL conceived the study, was responsible for its design and coordination, and contributed in the evaluation of the results and in preparation of the manuscript. All authors have read and approved of the final manuscript. Supplementary Material Additional File 1 Additional file 1 lists the MSP primers used in the present study. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526388.xml

212706

Why PLoS Became a Publisher

Public Library of Science has grown from a grassroots movement to a nonprofit publisher, in order to catalyze change towards open-access publishing of the scientific literature

Communication among scientists has undergone a revolution in the last decade, with the movement of scientific publication to a digital medium and the emergence of the Internet as the primary means for distributing information. Millions of articles are, in principle, just a mouse click away from our computers. For many of us, PDFs have replaced printed journals as the primary form in which we read about the work of our colleagues. Yet we have barely begun to realize the potential of this technological change. For practicing scientists, it provides myriad opportunities to expand and improve the ways we can use the scientific literature. Equally important, it is now possible to make our treasury of scientific information available to a much wider audience, including millions of students, teachers, physicians, scientists, and other potential readers, who do not have access to a research library that can afford to pay for journal subscriptions. We founded the Public Library of Science three years ago to work toward realizing these opportunities. We began as a grassroots organization of scientists, advocating the establishment and growth of online public libraries of science, such as the National Institutes of Health's PubMed Central, to provide free and unrestricted access to the scientific literature. Today, with the launch of PLoS Biology , we take on a new role as publishers, to demonstrate that high-quality journals can flourish without charging for access. Open Access PLoS Biology , and every PLoS journal to follow, will be an open-access publication–everything we publish will immediately be freely available to anyone, anywhere, to download, print, distribute, read, and use without charge or other restrictions, as long as proper attribution of authorship is maintained. Our open-access journals will retain all of the qualities we value in scientific journals—high standards of quality and integrity, rigorous and fair peer-review, expert editorial oversight, high production standards, a distinctive identity, and independence. Although most readers will be satisfied with the free and unrestricted use of the online edition (including the right to print their own copies), a printed edition of PLoS Biology will be made available, for the cost of printing and distribution, to readers who prefer the convenience and browseability of the traditional paper format. And the full contents of every issue will immediately be placed in the National Library of Medicine's public online archive, PubMed Central, guaranteeing their permanent preservation and free accessibility. Our aim is to catalyze a revolution in scientific publishing by providing a compelling demonstration of the value and feasibility of open-access publication. If we succeed, everyone who has access to a computer and an Internet connection will be a keystroke away from our living treasury of scientific and medical knowledge. This online public library of science will form a valuable resource for science education, lead to more informed healthcare decisions by doctors and patients, level the playing field for scientists in smaller or less wealthy institutions, and ensure that no one will be unable to read an important paper just because his or her institution does not subscribe to a particular journal. Open access will also enable scientists to begin transforming the scientific literature into something far more useful than the electronic equivalent of millions of individual articles in rows of journals on library shelves. The ability to search, in an instant, an entire scientific library for particular terms or concepts, for methods, data, and images—and instantly retrieve the results—is only the beginning. Freeing the information in the scientific literature from the fixed sequence of pages and the arbitrary boundaries drawn by journals or publishers— the electronic vestiges of paper publication—opens up myriad new possibilities for navigating, integrating, “mining,” annotating, and mapping connections in the high-dimensional space of scientific knowledge. Consider how the open availability and freedom to use the complete archive of published DNA sequences in the GenBank, EMBL, and DDBJ databases inspired and enabled scientists to transform a collection of individual sequences into something incomparably richer. With great foresight, it was decided in the early 1980s that published DNA sequences should be deposited in a central repository, in a common format, where they could be freely accessed and used by anyone. Simply giving scientists free and unrestricted access to the raw sequences led them to develop the powerful methods, tools, and resources that have made the whole much greater than the sum of the individual sequences. Just one of the resulting software tools—BLAST—performs 500 trillion sequence comparisons annually! Imagine how impoverished biology and medicine would be today if published DNA sequences were treated like virtually every other kind of research publication—with no comprehensive database searches and no ability to freely download, reorganize, and reanalyze sequences. Now imagine the possibilities if the same creative explosion that was fueled by open access to DNA sequences were to occur for the much larger body of published scientific results. Paying the Bill for Open Access The benefits of open access are incontestable. The questions and concerns that remain focus on finances. As everyone acknowledges, publishing a scientific journal costs money—the more rigorous the peer review, the more efficient and expert the editorial oversight, the more added features and the higher the production standards, the greater the cost to publishers. Most journals today depend on subscriptions and site-licensing fees for most of their revenue. Since these access tolls are incompatible with open access, how will newly formed open-access journals pay their bills, and how will the traditional journals that have served the scientific community for many years survive in an open-access world? Because publishing is an integral part of the research process, a natural alternative to the subscription model is to consider the significant but relatively small costs of open-access publication as one of the fundamental costs of doing research. The institutions that sponsor research intend for the results to be made available to the scientific community and the public. If these research sponsors also paid the essential costs of publication—amounting, by most estimates, to less than 1% of the total spent on sponsored research (statistics found at http://dx.doi.org/10.1371/journal.pbio.0000036.sd001 )—we would retain a robust and competitive publishing industry and gain the benefit of universal open access. The subscription model—in which the publishers own the works they publish and dictate the conditions under which they can be accessed or used—is sometimes presented as the only possible way to pay for scientific publishing. This pay-for-access model was well suited to a world in which the most efficient way to record and transmit scientific information on a large scale was by printing and distributing scientific journals. When each incremental copy represented a significant expense to the publisher, any sustainable business model depended on recovering the cost for each copy—the recipients of the information had to pay for access. But the essential rationale of the pay-for-access model has disappeared, now that electronic publication and Internet distribution have become routine. Instead, this business model is what stands in the way of all the benefits of open access. Asking research sponsors to pay for publication of the research they support may seem to impose new financial burdens on the government agencies, foundations, universities, and companies that sponsor research. But these organizations already pay most of the costs of scientific publishing—a huge fraction of the US$9 billion annual revenue of scientific, medical, and technology journals comes from subscriptions, site licenses, and publication fees ultimately billed to grants or employers. Much of the rest is borne by society in the form of increments to university tuitions; healthcare costs, including drug prices; and state and federal taxes that subsidize healthcare, libraries, and education. Surely the cost of open-access digital publishing cannot, in total, be more than we are already paying under the subscription and licensing model. By simply changing the way we support the scientific publishing enterprise, the scientific community and public would preserve everything we value in scientific publishing and gain all of the benefits of open access. There are reasons to believe that open-access publishing would cost significantly less than the current system. Today, each journal has a monopoly on a resource vital to scientists—the unique collection of articles it has published. Anyone who depends on the information in a specific article has no choice but to pay whatever price the publisher asks (or find a colleague or library that has done so). Because scientists are so dependent on ready access to previously published work, publishers are able to set their prices with little fear of subscription cancellations. Indeed, journal prices have been rising at a rate well in excess of inflation, straining the budgets of universities, hospitals, and research institutions. Open access would eliminate monopolies over essential published results, diminishing profit margins and creating a more efficient market for scientific publishing—a market in which publishers would compete to provide the best value to authors (high quality, selectivity, prestige, a large and appreciative readership) at the best price. Joining Forces In recent months, we have witnessed a remarkable surge of awareness and support for open-access publication, both within the scientific community and in the public at large, exemplified by recent newspaper articles and editorials supporting PLoS and open access; by the recent introduction of the Public Access to Science Act in the United States Congress; by the Bethesda Workshop on Open Access; and by public statements of support from organizations as diverse as the NIH Council of Public Representatives, the Association of Research Libraries, and the Susan G. Komen Breast Cancer Foundation. Achieving universal open access will require action from funding agencies and institutions. The Howard Hughes Medical Institute, the largest private sponsor of biomedical research in the United States, has already taken a leading role in promoting open access. They will provide each of their investigators with supplemental funds to cover the costs of publishing in open-access journals like PLoS Biology. Other major institutional sponsors of biomedical research are actively considering similar policies. Private foundations with a commitment to science and education have contributed generously to this cause. Like any new business, PLoS needed to raise funds to cover our startup costs. A generous grant from the Gordon and Betty Moore Foundation enabled PLoS to launch our nonprofit publishing venture. Other individuals and organizations, notably the Irving A. Hansen Foundation, also provided generous and welcome support. These start-up funds made it possible for us to assemble an outstanding editorial board and staff, who have today accomplished the extraordinary feat of launching a new publisher and a premiere journal from scratch in less than nine months. The opposition of most established journals to open access has left it to new journals like PLoS Biology and BioMed Central's Journal of Biology to lead the way. These new journals face a double challenge. First, we are introducing an unfamiliar model—open-access publication. Second, any new journal, even one with the stringent standards and the extraordinary editorial team of PLoS Biology , must begin without the established “brand name” of the older journals, which, like a designer logo, elevates the perceived status of the articles that bear it. With all that is at stake in the choice of a journal in which to publish—career advancement, grant support, attracting good students and fellows—scientists who believe in the principle of open access and wish to support it are confronted with a difficult dilemma. We applaud the courage and pioneering spirit of the authors who have chosen to send to a fledgling journal the outstanding articles you will read in the premiere issues of PLoS Biology. In the end, it's the contributions of these authors that will make PLoS Biology a success.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC212706.xml

539243

Relationship between three palliative care outcome scales

Background Various scales have been used to assess palliative outcomes. But measurement can still be problematic and core components of measures have not been identified. This study aimed to determine the relationships between, and factorial structure of, three widely used scales among advanced cancer patients. Methods Patients were recruited who received home or hospital palliative care services in the south of England. Hope, quality of life and palliative outcomes were assessed by patients in face to face interviews, using three previously established scales – a generic measure (EQoL), a palliative care specific measure (POS) and a measure of hope (Herth Hope Index). Analysis comprised: exploratory factor analysis of each individual scale, and all scales combined, and confirmatory factor analysis for model building and validation. Results Of 171 patients identified, 140 (81%) consented and completed first interviews; mean age was 71 years, 54% were women, 132 had cancer. In exploratory analysis of individual means, three out of the five factors in the EQoL explained 75% of its variability, four out of the 10 factors in POS explained 63% of its variability, and in the Hope Index, nine out of the 12 items explained 69% of its variability. When exploring the relative factorial structure of all three scales, five factors explained 56% of total combined variability. Confirmatory analysis reduced this to a model with four factors – self-sufficiency, positivity, symptoms and spiritual. Removal of the spiritual factor left a model with an improved goodness of fit and a measure with 11 items. Conclusion We identified three factors which are important outcomes and would be simple to measure in clinical practice and research.

Background Measurement of the effect of illness and its treatment on patients is now an accepted part of clinical trial design [ 1 ]. Such measurement is also proposed as an aid to improve clinical practice and decision making [ 2 , 3 ]. However, as the illness becomes more advanced the value of many well validated quality of life instruments has been challenged [ 4 - 9 ]. There are three main difficulties. First, many quality of life scales focus on the assessment of physical functioning, which deteriorates as illness progresses [ 4 , 8 ]. This can render the measure insensitive to, or mask, other changes. Second, most quality of life scales have been validated among patients in early stage illness, such as cancer or whilst undergoing chemotherapy or curative treatment [ 8 , 9 ]. Sometimes their validation was founded on an assumption that patients in terminal disease had a poorer quality of life than those at diagnosis [ 10 ]. This assumption has been consistently challenged [ 8 ]. Concerns among patients with more advanced illness are often different to earlier stages, as patients reframe their priorities in the face of impending death [ 8 ]. Existential, relationships, information, the provision of care, and use of remaining time become more important [ 9 ]. Third, collecting information from patients at late stages of disease is practically difficult; questionnaires need to be kept short, be easy to use, and be few in number. Even then there are often difficulties of missing data and loss to follow-up [ 8 , 9 ]. In response to these difficulties, different measures have been developed and tested among patients receiving palliative and hospice care in different countries and contexts [ 8 , 11 ]. These include scales that assess, to different degrees, symptoms, existential aspects or spirituality, the impact of therapy, hope, information, social and family concerns [ 8 , 9 , 12 ]. Some are completed directly by patients, some by family members or other proxies, and some by a combination of these. However, there is little information on how different measures compare, particularly in relation to more traditional measures. Clinicians and researchers need such information to determine which core factors should be measured, especially when it is not possible to collect a battery of measures. This study therefore sought to determine the relationships between three such scales and their factorial structures to recommend short, self-contained scales for future use among people with advanced cancer. Methods Design Secondary analysis of a prospective observational study. Patients and setting Patients living in Chichester in the South of England receiving home or hospital palliative care support, from community, hospice or hospital palliative care team staff, were approached to take part in the study. Local research ethics committee approval was obtained. The local hospice was planning to develop a day care unit and patients were recruited during this period. A historical group was recruited before the day care unit was established. Consecutive consenting patients were recruited for both series. Patients were eligible if they were in the care of the hospice home care team, or neighbouring home care teams, that had access to the day care unit. Patients were excluded if they were judged by staff to be too ill for interview, if staff felt it would distress them, or they lived outside the catchment area of the hospice day unit. Two concurrent groups were recruited after day care was established – patients who did (Group AD) and did not (Group AN) choose to receive day care. Data collection Data was collected using trained interviewers. Interviews took place in the patients' preferred location, usually their own home. Interviewers explained the background to the study and used a structured schedule to collect clinical, demographic and use of service data. They then administered three scales. All were short, taking less than 10 minutes on average to complete, and were acceptable to advanced cancer patients. Scales were administered in the order they are listed below. 1. EQoL EQ-5D. This generic questionnaire defines health in five dimensions: mobility, self-care, usual activities, pain or discomfort, and anxiety or depression. Each dimension is divided into three categories – whether the respondent has no problem, a moderate problem, or an extreme problem. A sixth item scores the person's overall health on a visual analogue (0 – 100) scale. The questionnaire has been validated and applied in a wide range of patient groups [ 13 - 16 ]. 2. Palliative care Outcome Scale (POS). This 10 item scale (plus an open question) was specifically developed and validated for palliative care and covers physical symptoms, patient and family or caregiver anxiety/fears and well being. The effect of the items on the daily life of the patient over the past three days is scored on a five-point Likert scale ranging from 'none' (0) to 'overwhelmingly' (4). For example: "over the past 3 days, have you been feeling anxious or worried about your illness or treatment? (0) not at all – (4) overwhelmingly" [ 17 , 18 ]. In the POS the term 'family' describes the caregiver or significant other, such as a partner, spouse or other closest individual. 3. Herth Hope Index (Hope). This 12 item instrument assesses hope in adults in clinical settings, and is designed to assess change. For example: "I have a positive outlook toward life? strongly disagree (1) to strongly agree (4) -". Patients are asked how much they agree with the statement right now [ 19 ]. Full details of the scales are shown in the Appendix 1 (see additional file 1 ). Patients were interviewed immediately after referral to the study. Follow-up interviews occurred but these data are not considered here. Analysis Data were analysed separately for the historical and concurrent (post day care) groups. The relative factorial structure of the three scales was explored in two steps. First, we performed a preliminary exploratory factor analysis (EFA) on each individual scale and on all the items of the three scales combined, using Principal Component Analysis on the historical sample. Second, we performed further exploration and final validation using confirmatory factor analysis (CFA) on the combined historical and concurrent samples. The EQS software [ 20 ] was used to compare several models to the covariance matrix of the 28 variables. Although this was an observational follow-up study, for the purpose of this paper we always used the baseline measures, when complete data for all patients was available. Results 171 patients were identified and asked to take part in the study, 82 in the historical group, and 89 in the concurrent group (40 were AD). Of these, 140 (81%) were successfully approached, agreed to take part in the study, and completed the first interview. As shown in Table 1 , 66 were from the historical group and 74 were from the concurrent group (of whom 28 were AD). Failure to interview was due to: refused 12, felt too unwell 11, died 8. Complete data in all three scales were obtained in 137 patients. As Table 1 shows the AD and AN were similar, and so were subsequently merged to form the concurrent group. The concurrent and historical samples were very similar in terms of characteristics like age, ethnicity, willingness to take part in the study, diagnosis, as well as their relationship to the carer and whether they resided with family, spouse or alone and housing. In spite of the age similarity the proportion of retired people was slightly larger in the concurrent sample. Differences between the two samples were only observed for place of death and for gender. Although not statistically significant, the concurrent sample tended to have more patients dying at home while the historical sample tended to have more patients dying in hospice. The proportion of women was larger in the historical sample (60% vs 40% P = 0.02). The distribution of cancers was similar to those in the general population. Table 1 Patient socio-demographics (completed 1st interview) for historical group and concurrent group who did (AD) and did not (AN) receive day care Demographics Historical Group (n = 66) Group AD (n = 28) Group AN (n = 46) Age in years Mean (SD) 69.2 (12.4) 74.0 (10.1) 70.8 (11.9) Median/range 71.0/34–94 77.0/50–94 72.0/39–90 Gender Women 40 (61%) 12 (43%) 23 (50%) Men 26 (39%) 16 (57%) 23 (50%) Ethnicity White UK 66 (100%) 28 (100%) 46 (100%) Employment Status Working (F/T or P/T) 6 (9%) 2 (7%) 2 (4.5%) Not working (unable) 14 (22%) 2 (7%) 9 (20.5%) Retired 45 (69%) 23 (85%) 33 (75%) Carer Spouse 43 (65%) 20 (71%) 30 (70%) Other carer 12 (18%) 5 (18%) 9 (21%) No carer 11 (17%) 3 (11%) 4 (9%) Carer Contact Lives with spouse 43 (65%) 20 (71%) 30 (70%) Lives with family 2 (3%) 0 2 (5%) Lives alone 21 (32%) 8 (29%) 11(25%) Carer employment Working (F/T or P/T) 18 (29%) 6 (21%) 8 (19%) Not working (unable) 5 (8%) 2 (7%) 4 (9%) Retired 29 (46%) 17 (61%) 27 (63%) No carer 11 (17%) 3 (11%) 4 (9%) Housing Own/private 28 (42%) 11 (39%) 17 (37%) Own/council 7 (11%) 7 (25%) 7 (15%) Own/rented 28 (42%) 9 (32%) 20 (44%) Other (N/home) 3 (5%) 1 (4%) 2 (4%) Primary diagnosis Lung cancer 11 (17%) 4 (14%) 11 (26%) Gastrointestinal 11 (17%) 8 (29%) 9 (21%) Breast 9 (14%) 4 (14%) 4 (10%) GU/Prostate 11 (17%) 6 (21%) 8 (19%) Gynae 7 (11%) 0 3 (7%) Other cancer 10 (15%) 4 (14%) 7 (17%) Non-cancer 6 (9%) 2 (7%) 0 Place of death (n = 46) (n = 11) (n = 22) Home 8 (17%) 4 (36%) 6 (27%) Hospital 7 (15%) 1 (9%) 3 (14%) Hospice 31 (67%) 6 (55%) 13 (59%) Individual Scales Summaries of the distribution of scores on the three instruments assessing hope, quality of life and palliative outcomes for the combined sample as well as results of the exploratory factor analysis, are shown in Table 4 (see additional file 2 ). On principal component analysis (unrotated), three factors in EQoL explained 75% of the total variability brought up by the six items in this scale. The first factor, explaining 40%, comprised general health: Health Status and the three self-sufficiency items. Anxiety-Depression defined the second factor, which explained 20% of the variability, and Pain-Discomfort formed the third factor, which explained 15% of the variability. For POS, the exploratory factor analysis grouped the 10 POS items in four factors explaining 67% of its variability. The first factor, which alone explained 27% of the variability, consisted of the two items measuring positivism (life-worthwhile and feel-good) and in addition, worry-anxiety. The second factor, which alone explained 16% of the variability, was mainly determined by information, followed by time-wasted and practical-matters. The third factor, which explained 12% of the variability, was solely determined by the item family-anxious. The fourth factor, also explaining 12% of the variability, was determined by pain and symptom-control. In the individual exploration we found that the 12 items of Hope grouped into four factors that explained approximately 69% of the variability present in the scale. The first factor was items 1, 8, 10, 11, and 12 representing positivity (39%), the second factor had items 2 and 4, measures of goals (12%), the third was items 3 and 6 (10%) and the fourth was items 7, 9 and 5 (9%). These last two factors represented a measure of support loneliness. Three scales combined The exploratoty factor analysis of EQoL, POS and Hope on the historical sample alone gave rather consistent results for different extraction methods. Table 4 (see additional file 2 ) shows the results of the unrotated principal component analysis. Five factors explained 54% of the total variability present in the three combined scales. The first principal factor, explaining 25% of the total variability of the combined scales, was determined by the three items of positivity contained in POS (share-feelings, feel-good and life worthwhile), together with all the Hope items and the anxiety items in both, POS and EQoL. The EQoL items "General Health" and items of "self-sufficiency", which constituted the most important factor of the EQoL scale, loaded together into the second factor, explaining 10% of the total variability of the combined scale. The third most important principal component, explaining 8% of the variability, comprised a general measure of patient anxiety (measured by both EQoL and POS), and family anxiety (measured by POS). The fourth principal component explained 6% of the variability and was defined by pain (measured by both EQoL and POS). The POS items "information" and "time-wasted" loaded together into the fifth factor, explaining only 6% of the total variability. In addition, the POS item "symptom control" did not load into any of these five factors and appeared to be acting independently. One of the extractions explored was principal axis factoring with a varimax rotation. This provided a better definition of the structure, with items loading more exclusively onto one of the factors. The first factor that we had obtained with the unrotated matrix essentially separated into two. The first axis, explaining 29% of the variability, was defined by the POS item "life worthwhile" loading with those items of Hope that reflected positivity and direction: positive outlook, goals, inner strength, loving, sense of direction, days have potential and life has value. The second axis, explained 11% of the variability and contained the anxiety items of EQoL and POS, the "feel good" and "share feeling" items of POS and the items of Hope that reflected pessimism or anxiety: "alone", "scared of future" and "past memories". The third factor was the EQoL general health and self-sufficiency and explained 7% of the variability. The fourth factor was solely defined by the pain items in EQoL and POS and explained 6% of the variability. The rest of the items played only a minor role. The POS items: practical matters, information and time wasted loading in a minor factor while the POS item "family-anxious" and the Hope items "tunnel" and "faith" disappeared altogether from the rotated matrix. Therefore, the model derived from this data is one in which the following items are omitted: POS2 and POS4 from the POS scale and Hope4 Hope5 from the Hope scale, leaving the rest of the items to define four major latent factors in the following manner: Spiritual, positivity, symptoms and self-sufficiency. Confirmatory Factor Analysis (CFA) Several models were explored and the most relevant are presented in Table 2 with the various measures of fit given by EQS. Model 1 was a three-factor model allowing each scale (EQoL, POS and Hope) to individually determine each factor. The goodness of fit measures suggest that the model does not provide a good fit for the data, although most of the residuals (observed-predicted covariances) were found to be relatively small and their frequency distribution is symmetric and centred around zero [ 21 ]. This model confirmed that the POS and Hope factors were indeed very highly correlated [Estimated correlation = 0.81; 95% CI (0.71–0.91)]. Consequently, a second model was fitted to the data in which only two factors were postulated, the first was the EQoL items as in the previous model and the second factor having as its indicators both the POS variables and the HOPE variables. The fit was very similar to the fit of Model 1 but it appeared that the two factor model needs to be considered as a serious alternative to model 1. In addition, the results of these two models suggest that some of the POS variables (family anxious, information given, time wasted and practical matters) are not needed for defining the second factor. As a result of this, we explored a range of models, allowing for the strong correlation between POS and Hope and giving special attention to those items that were unimportant in either the exploratory or preliminary confirmatory factor analysis. Three of the POS items, which confirmed a latent construct that we called "practical", proved to be superfluous in the overall construct. These items were: information given, time wasted and practical matters. The POS item family-anxious did not particularly disrupt the identifiability of the model but its presence reduced the goodness of the fit. Similarly, four Hope items were discarded – alone, light at the end of the tunnel, faith and scared of future – to give a total of seven items discarded. We arrived to two models, exhibited in Table 2 : Model 3, fitting the four factors listed in Table 3 , and Model 4, fitting only the first three factors, leaving out the spiritual factors construed by the Hope scale. Table 2 includes the goodness of fit statistics for these models. Table 2 Goodness of fit summaries for the four models derived by Confirmatory Factor Analysis (CFA) Model 1 Model 2 Model 3 Model 4 Independence 1076 1076 717 279 Chi-square (378 df) (378 df) (171) (55 df) Average standardised residuals 0.11 0.09 0.09 0.09 Average off-diagonal st. residuals 0.12 0.10 0.10 0.11 Chi-squared fit 534.7 520 213 67.7 (df)s (347 df) (347 df) (150 df) (43) P-value 0.00001 0.00001 0.001 0.01 Free parameters 59 57 40 23 Akaike's information criterion (AIC) -193 -173 -87 -18 Bozodgan's version of AIC (C-AIC) (-1437) (-1424) (-627) (-173) Comparative Fit Index (CFI) 0.73 0.75 0.89 0.90 Normed Fit Index (NFI) 0.50 0.52 0.70 0.76 Non-normed Fit Index (NNFI) 0.71 0.73 0.87 0.86 Model 1 comprised the basic 3 factors: EQoL, POS and HOPE. Model 2 was 2 factors: EQoL, and POS and HOPE combined. Model 3 was 4 factors: items relating to self-sufficiency, positivity, symptoms and spiritual. Model 4 was 3 factors, items relating to self-sufficiency, positivity and symptoms. AIC and CAIC measure degree of fit. The smaller, the better the fit. The larger are NFI, NNI and CFI, the better the fit, with an upper value of 1. Table 3 The factorial structure of the proposed model (MLE Estimators of regression coefficients (Standard Error) Scale Item SYMPTOMS SELF SUFFICIENCY POSITIVITY SPIRITUAL EQoL1 Mobility 0.32 (0.06) EQoL2 Self-care 0.49 (0.08) EQoL3 Usual activities 0.39 (0.08) EQoL4 Pain-Discomfort 0.38 (0.08) EQoL5 Anxty-Depression 0.34 (0.06) EQoL6 Health Status -6.9 (2.34) POS1 Pain Control 0.93 (0.17) POS2 Symptom Control 0.16 (0.11) POS3 Anxious/Worried 0.52 (0.11) POS4 Family anxious 0.26 (0.15) POS5 Information POS6 Share feelings 0.69 (0.15) POS7 Life Worthwhile 0.72 (0.10) POS8 Feel Good 0.97 (0.12) POS9 Time Wasted POS10 Practical matters HOP1 Positive outlook 0.42 (0.07) HOP2 Goals 0.47 (0.08) HOP3 Alone HOP4 Tunnel HOP5 Faith HOP6 Scared of future HOP7 Happy memories 0.27 (0.07) HOP8 Inner strength 0.65 (0.08) HOP9 Loving 0.32 (0.07) HOP10 Sense of direction 0.77 (0.08) HOP11 Days Potential 0.67 (0.07) HOP12 Life has value 0.55 (0.08) Significant coefficients are highlighted. In all the models presented, the matrix was positively definite, the test of independence was significant and the frequency distribution of the standardised residuals was symmetrical around 0. Models 3 and 4, not only omit the superfluous items but also separate the factors on clinical considerations. Both provide a huge improvement over the first two models. Model 3 allowed for a high correlation between the positivity and spiritual factors. More remarkably, the results show that Model 4, which disposes completely of the spiritual factor defined by the remaining Hope items, is an enormous improvement on the other models. The chi-square statistic was greatly reduced and almost reached the threshold indicating that no lack of fit was detected. Discussion An important next step in quality of life measurement is the translation of measurement into clinical practice to improve patient care [ 2 ]. One important barrier among patients with advanced illness is ensuring that relevant items are captured from a sufficiently small range of instruments relevant to this stage of illness. The three measures used in this study all have relevance in advanced illness. The EQoL deals with general aspects of health related quality of life, generating within the scale 243 possible health states. It has been used to provide indexed preferences for health states [ 22 ], and health state valuations in national and cross cultural studies [ 23 ]. Standardised measures, such as the Medical Outcome Study (MOS) short form 12 (SF-12) map to this scale [ 24 ]. Among our patients with advanced illness, primarily cancer, we found variability within the EQoL, although patients tended to score at the poorer end of the scale. Health status and the self-sufficiency items of mobility, self-care and usual activities explained 40% of the variation of this scale in our patient population. We included the self-sufficiency aspect in our model of summary factors, but it is debatable whether items such as mobility, self-care and usual activities are relevant outcomes in palliative care. Functional status and those items within quality of life measures that reflect functional status are highly correlated to survival [ 25 ], thus the scores will inevitably deteriorate towards death. However, to provide consistency with other scales used in general health care and cancer treatment, measurement may be valuable [ 24 ]. A factor which we entitled 'positivity' appeared to be highly important among people with advanced illness. Spirituality/positivity has also been related to communication outcomes [ 26 ]. In the exploratory factor analysis it explained 24% of the total variability of the combined scales. Its importance was maintained in the confirmatory factor analysis. In model 3 positivity could be seen as separate from spirituality, but if a smaller model is required, spirituality can be assessed through positivity, because it is strongly correlated. Items that reflect this domain of positivity are found in a number of measures of palliative care [ 9 , 12 , 18 ]. However, our study is the first to quantify the extent to which this positive domain is relevant in patients with advanced illness. Our data suggests it can be measured in a variety of ways, through questions related to sharing feelings, feeling good, anxiety, as well as questions directly tapping hope. When attempting to develop a reduced scale we identified two models, one with four factors (19 items) and another one with three factors which provided a good fit (11 items). All the Herth Hope items are excluded from the latter model, which captured self-sufficiency, symptoms and positivity. Positivity appeared very close to spirituality, as measured by the Hope index. Further work is needed to determine the relationship of these questions with specific spirituality scales [ 27 - 29 ]. Symptom control was absent in the structures obtained by EFA. We suspected that this was because this item was not a structured question designed for any specific symptom, but elicited in an open way what symptoms had troubled the patient. In the CFA this item loaded with the General Health status factor. Measures which specifically address symptoms are available and have been used in palliative care populations [ 29 , 30 ]. Work with the POS has now developed to separate symptom modules and these are in the process of further testing and validation [ 31 ]. Special attention was given to the three items forming the practical factor in POS. The information item in POS (POS-5) was constant in the concurrent sample and only a few patients in the historical sample recorded non zero for this item. The time wasted item of POS was also essentially constant. It may be that the grading for these items needs to be reviewed to ensure that they give greater sensitivity to change. In our analysis this could have contributed to the poor fit shown when attempting to fit a general POS factor containing these items. It may also be because that none of the three items is an indicator of QOL; they are rather items of the provision of health care. In this study all of the patients were in receipt of a wide range of services, including specialist care teams and their practical needs were likely to have been met. Research among patients in different circumstances has shown deficiencies in these aspects of care [ 32 , 33 ]. The POS item family-anxious was intriguing. It did not disrupt the validity of the model but if kept and loaded in the positivity factor, it reduced the goodness of fit. This item also showed erratic behaviour in the exploratory factor analysis. Family anxiety may be related to a large number of factors, some of which are determined by the circumstances of the patient and some of which are determined by other events. Family needs often increase as patients deteriorate and are difficult to resolve. Further work is required directly targeting the needs of families [ 34 ]. Our study was limited by the comparison of these scales among patients in one setting: we do not know if similar results would be found if patients were not in receipt of specialist palliative care available in the United Kingdom. However, our findings are consistent with other work assessing quality of life measurement in palliative care and in advanced cancer [ 11 ]. Correlation between the scales may also have occurred because individuals were aware of the answers they had given for the different scales. It would be impossible to avoid this process in the completion of the questionnaires. We did not vary the order in which the questionnaires were administered. However, we believed that the questions appeared to be sufficiently different for patients not to be influenced by their prior responses. Future research should analyse this. Our reduced model suggests that clinicians may sensibly target quality of life measurement in advanced cancer towards three main components, positivity, self-sufficiency, and symptoms. This might be achieved by the 12 items in model 4 of our factor analysis. Such a scale would be short and simple to use in both clinical practice and research, improving the measurement of outcomes in this population. Supplementary Material Additional File 1 Appendix 1: Full details of the three palliative care outcome scales Click here for file Additional File 2 Table 4: Summary Statistics and Principal Component Analysis (unrotated) of the three scales (POS-EQoL-Hope) in the historical sample. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539243.xml

554093

L-histidine inhibits production of lysophosphatidic acid by the tumor-associated cytokine, autotaxin

Background Autotaxin (ATX, NPP-2), originally purified as a potent tumor cell motility factor, is now known to be the long-sought plasma lysophospholipase D (LPLD). The integrity of the enzymatic active site, including three crucial histidine moieties, is required for motility stimulation, as well as LPLD and 5'nucleotide phosphodiesterase (PDE) activities. Except for relatively non-specific chelation agents, there are no known inhibitors of the ATX LPLD activity. Results We show that millimolar concentrations of L-histidine inhibit ATX-stimulated but not LPA-stimulated motility in two tumor cell lines, as well as inhibiting enzymatic activities. Inhibition is reversed by 20-fold lower concentrations of zinc salt. L-histidine has no significant effect on the Km of LPLD, but reduces the Vmax by greater than 50%, acting as a non-competitive inhibitor. Several histidine analogs also inhibit the LPLD activity of ATX; however, none has greater potency than L-histidine and all decrease cell viability or adhesion. Conclusion L-histidine inhibition of LPLD is not a simple stoichiometric chelation of metal ions but is more likely a complex interaction with a variety of moieties, including the metal cation, at or near the active site. The inhibitory effect of L-histidine requires all three major functional groups of histidine: the alpha amino group, the alpha carboxyl group, and the metal-binding imidazole side chain. Because of LPA's involvement in pathological processes, regulation of its formation by ATX may give insight into possible novel therapeutic approaches.

Background Lysophosphosphatidic acid (LPA) is both an intracellular and an extracellular signaling molecule that affects biological processes such as cell proliferation, rescue from apoptosis, cell migration, neurite retraction, wound healing, platelet aggregation and vascular remodeling [ 1 ]. As a cytokine affecting such varied and important functions, LPA production is normally tightly regulated. Its dysregulation is implicated in a number of pathophysiological states, including certain cancers and atherogenesis. Intracellularly, LPA is produced by calcium-dependent and calcium-independent phospholipase A2, acting on phosphatidic acid. Most of the extracellular LPA appears to be produced by a two-step process: production of lysophosholipid from phospholipids by the action of phospholipase A1 or A2, followed by conversion to LPA by the plasma enzyme lysophospholipase D (LPLD) [ 2 , 3 ]. Recently, this plasma LPLD has been shown to be identical to autotaxin (ATX, NPP2) [ 4 , 5 ]. ATX was originally purified as a potent tumor cell motogen [ 6 ], an effect that appears to be mediated by LPA [ 7 ] acting through the LPA1 receptor [ 8 ]. Recent studies have revealed that ATX/LPLD not only hydrolyzes lyso-phosphoglycerolipids to form LPA, but also hydrolyzes sphingosylphoshorylcholine (SPC) to produce sphingosine-1-phosphate (S1P) [ 9 ]. S1P can stimulate or inhibit cellular migration, depending upon the context of receptor expression [ 10 ]. Therefore, ATX can produce either agonists or antagonists of cell migration. In addition, ATX has been shown to stimulate tumor aggressiveness and to be over-expressed in certain malignancies [ 11 - 13 ]. The link between ATX and its putative 'mediator' LPA has led us to investigate possible mechanisms of regulating the enzymatic action of ATX in generating LPA. ATX is a member of the nucleotide pyrophosphatase and phosphodiesterase (NPP) family of enzymes. The NPPs are part of the superfamily of alkaline phosphatases, metalloenzymes in which the active site is characterized by histidine residues coordinated around central divalent cations and by a serine, threonine, or cysteine residue, which is utilized to form an intermediate during the reaction [ 14 , 15 ]. Site-directed mutagenesis of human ATX established that a specific residue, T210, [ 7 , 16 ] and 3 histidine residues [ 7 , 9 ], corresponding to similar loci in other members of the alkaline phosphatase superfamily, were essential for the motility and enzymatic activities of ATX. Histidine has been implicated as a requirement for many metalloenzymatic reactions, presumably by virtue of its imidazole moiety. Reversible reagents such as diethylpyrocarbonate, which react with imidazole, inhibit these enzymes [ 17 ]. Such findings led us to investigate whether histidine itself and some of its derivatives could inhibit the activities of ATX, perhaps by destabilizing the putative metallic cation-imidazole complex at the active site of ATX. Since the migratory effects of ATX depend upon its ability to generate LPA or S1P, focusing upon ATX as a target for regulation of tumor cell motility presents an attractive strategy for therapeutic intervention in metastasis. Results Effect of L-Histidine upon Cell Motility ATX, isolated as a motility-stimulating protein, is a member of the NPP family of metalloenzymes. Both its motogenic and its enzymatic activities appear to require the same active site since both are abolished when T210 [ 16 ] or any of three histidines (H316, H360 or H476) are altered by site-directed mutagenesis [ 7 ]. Because of the crucial role that these histidines play in the ATX activities, we tested the effect of adding exogenous L-histidine upon ATX-stimulated cell motility. Fig. 1 shows that L-histidine inhibited ATX-stimulated migration of both A2058 human melanoma cells and SKOV-3 human ovarian carcinoma cells in a concentration-dependent manner. Addition of 10 mM L-histidine to motility assays resulted in a 90 – 95% reduction in stimulated motility, whereas equivalent concentrations of control amino acids, glycine or alanine (data not shown), had no effect. In contrast, adding the same concentration of L-histidine failed to inhibit LPA-stimulated motility. These concentrations of L-histidine did not affect cell viability in either cell line, as measured by Trypan Blue exclusion. Whether the inhibitor was added to both wells of the assay (i.e. to cells and to chemoattractant) or just to the lower well (chemoattractant) did not affect the result. Figure 1 Effect of histidine on ATX-stimulated motility . The modified Boyden chamber motility assays are described in Materials and Methods. L-Histidine inhibits the motility response of A2058 and SKOV-3 cells to ATX but not to LPA (concentrations shown). All data are shown as Mean ± SEM. Means were analyzed utilizing the ANOVA/Tukey's post-test (GraphPad Prism, San Diego, CA): * (p < 0.001) for histidine-treated vs. untreated controls. ATX was pre-incubated with 200 nM LPC with or without addition of various concentrations of L-histidine, then heated to abolish its enzymatic activity [ 9 ] and added to the bottom chamber of motility assays utilizing A2058 cells as responders. As seen in Fig. 2A , addition of L-histidine to the pre-incubation mixture resulted in a 75% inhibition of motility for A2058 cells at concentrations similar to those utilized in Fig. 1 . These data, which were identical in SKOV-3 cells (data not shown), suggest that the L-histidine acts upon the ATX-catalyzed LPLD reaction to produce its inhibitory effect by preventing the formation of LPA from LPC. Previously, we have shown that SPC is an alternate substrate for ATX: pre-incubation of SPC with ATX, followed by heat-inactivation of ATX, resulted in production of the inhibitor of LPA-stimulated A2058 cell motility, S1P [ 9 ]. As seen in Fig 2B , inclusion of 20 mM L-histidine in the ATX + SPC pre-incubation step prevented the formation of an inhibitor of A2058 cell motility. L-histidine therefore appears to inhibit the hydrolysis of both glycerophospholipids and phosphosphingolipids. Figure 2 Histidine appears to act by inhibiting the ATX LPLD activity . The modified Boyden chamber motility assays with A2058 cells are described in Materials and Methods. In these assays, pre-incubations were in DBA media for 3 hr at 37°C. A) L-histidine decreases formation of a chemoattractant by ATX . Shown concentrations of L-histidine were pre-incubated with ATX plus 200 nM LPC, then ATX was heat-inactivated prior to utilizing the resulting mixture as chemoattractant in a motility assay (solid black bars). Data are compared to identical treatments of 100 nM LPA as chemoattractant (white bars). B.) L-histidine decreases formation of a motility inhibitor by ATX . Shown concentrations of L-histidine were pre-incubated with (ATX + 100 nM SPC + 100 nM LPA). ATX was heat inactivated prior to utilizing the mixture as chemoattractant. Controls are shown for comparison: DBA, 100 nM LPA, (100 nM LPA + 100 nM S1P), (100 nM LPA + 100 nM SPC). All data are shown as Mean ± SEM. Means were analyzed utilizing the ANOVA/Tukey's post-test (GraphPad Prism, San Diego, CA): * (p < 0.001) for histidine-treated vs. the appropriate untreated control. Effects of Histidine and Metal ions upon ATX Activities The PDE activity of the NPPs has been shown to be dependent upon the presence of metal ions [ 15 ]. Tokumura and co-workers [ 18 ] demonstrated a similar requirement for a metal cation in the action of LPLD. Both Co ++ and Zn ++ were markedly effective in restoring enzymatic activity to LPLD in EDTA-treated plasma. In fact, addition of Co ++ to EDTA-treated heparinized plasma resulted in a recovery from 37% of the untreated value in the presence of EDTA alone to 154% in the presence of Co ++ and EDTA. Since histidine plays an important structural role in the catalytic process, we determined first that addition of free histidine has an inhibitory effect upon ATX-catalyzed hydrolysis of both nucleotide and lyso-phospholipid substrates. After establishing the concentration requirements for histidine inhibition, we next examined the capacity of different metal cations to reverse this inhibition. Finally, we determined how histidine and metal cations affect the kinetics of the LPLD reaction. As seen in Fig. 3A , histidine inhibited both LPLD (with LPC as substrate) and PDE (with p-Nitrophenyl-TMP or pNp-TMP as substrate) activities of ATX in a dose dependent manner. At a concentration of 20 mM, this inhibition was virtually complete for both reactions. The normalized curves coincided closely with each other and the IC50 values (4.0 ± 1.1 mM and 4.5 ± 1.1 mM for LPLD and PDE activities, respectively) were not significantly different from each other. Figure 3 Histidine inhibition of ATX enzyme activities and sensitivity to zinc . For these reactions, 1 mM LPC served as substrate for LPLD activity and 1 mM pNp-TMP as substrate for 5'-nucleotide PDE activity; approximately 6 pmoles of ATX was added to each reaction. A.) Product was measured in the presence of variable concentrations of L-histidine. Data are shown as Mean ± SEM. The two reactions were not significantly different from each other. B.) Different metal cations (0.5 mM) were compared for their capacity to abrogate L-histidine (10 mM) inhibition of enzymatic reactions. C.) Effect of Zn ++ on LPC- and ATX-stimulated motility. Utilizing A2058 cells as responders and either ATX or 25 nM LPA as chemoattractant, 20 mM histidine or 0.25 mM ZnSO4 or both were added to the bottom wells throughout the motility assay. Data are shown as Mean ± SEM. Means were compared with ANOVA/Tukey's post-test (GraphPad Prism, San Diego, CA). For B), comparisons were between histidine-treated vs. the equivalent untreated reactions with * (p < 0.001). For C), comparisons were as follows: * (p < 0.001) vs. ATX alone, ** (p < 0.001) vs. ATX + His, # (p < 0.01) vs. ATX alone. We tested the effects of a number of divalent cations in both the presence and absence of histidine upon the PDE and LPLD activities of ATX (Fig. 3B ). Neither Mg ++ nor Ca ++ significantly reversed the inhibitory effect of histidine although Ca ++ alone enhanced both reactions by at least 30%. However, Zn ++ and Co ++ each significantly overcame inhibition caused by free histidine. In the case of Zn ++ , the presence of the cation alone increased both reactions by up to 50% above control, and virtually abolished the inhibition caused by histidine, with recoveries of 90 – 100% of the activity measured in the presence of Zn ++ alone. In the case of Co ++ , the cation alone increased the PDE activity by 50% and the LPLD activity by 300%; however, histidine still inhibited PDE by ~70% and LPLD by 50% compared to adding Co ++ alone. Zn ++ , therefore, appears to be the most effective cation in reversing the inhibition by histidine. It should also be noted that none of the cations at the levels tested had any inhibitory effects upon the enzymatic reactions of ATX. Histidine therefore inhibits a process that is required for the hydrolysis of both nucleotides and phospholipids. This may be a rate-limiting cation-dependent step in their common reaction mechanism [ 7 ]. Since Zn ++ reversed the inhibitory effect of histidine upon ATX enzymatic reactions, we tested its effect upon the histidine-induced inhibition of ATX- and LPA-stimulated motility. As shown in Fig. 3C for A2058 cells, 20 mM histidine, 0.25 mM Zn ++ , or both together had no effect upon LPA-induced motility. In contrast, ATX-induced motility was affected in a more complex manner. Histidine (20 mM) alone inhibited ATX-induced motility by ~65%, while 0.25 mM Zn ++ alone increased this same motility by ~30%. When these same concentrations of histidine and zinc were added together to an ATX-stimulated motility assay, the histidine inhibition was largely reversed with greater motility than seen with ATX alone, though not quite up to levels seen with ATX + Zn ++ . However, statistical analysis of these results revealed no statistically significant difference between ATX + Zn ++ + histidine vs. ATX + Zn ++ . In contrast, there was a statistical significance between ATX + Zn ++ + histidine vs. ATX + histidine (p < 0.001). Fig. 4 shows a concentration curve of the ATX-catalyzed LPLD reaction with LPC as substrate, both with and without addition of 10 mM histidine. The two reactions had Km values (0.49 ± 0.05 mM untreated vs. 0.66 ± 0.18 mM with histidine) that were not significantly different from each other; however, Vmax was reduced more than 50% by the addition of histidine. Also shown are the effects of 0.25 mM Zn ++ , alone or in addition to histidine. Zn ++ alone had no significant effect on the Vmax or Km of the reaction. In the presence of histidine, Zn ++ reversed the inhibition, restoring Vmax to untreated levels. It might be noted that the pattern of inhibition seen when histidine is added is typical of a non-competitive inhibitor. Figure 4 Reaction rate vs. substrate concentration curves in the presence of L-histidine or zinc LPLD determinations were carried out in the presence of 10 mM L-histidine or 0.25 mM Zn ++ , as indicated. Curves were analyzed with GraphPad Prism (San Diego, CA) to calculate Km and Vmax, as shown. Effect of Chelating Agents upon the ATX-stimulated Reaction Because the histidine-induced inhibition of ATX activities could be reversed by addition of appropriate metal ions (i.e., Zn ++ ), the possibility arose that the imidazole ring of histidine acts as a weak chelation agent, competing with the enzymatic active site for binding to the metal ion. EDTA has been shown to inhibit the nucleotide phosphodiesterase and pyrophosphatase activities of PC-1/NPP1 [ 19 ]. We, therefore, utilized EDTA, as well as the metal chelating agent 1,10-phenanthroline, in order to examine their effect upon the LPLD and PDE activities of ATX. We also tested the reversibility of their effects with Ca ++ and with Zn ++ . In a series of preliminary experiments (data not shown), we found that, under the conditions of our enzymatic assays, addition of 10 – 20 mM Na 4 -EDTA resulted in profound inhibition of both LPLD and PDE activities with an IC50 of approximately 5 mM. We also found that 5 mM Na 4 -EDTA required very nearly equimolar concentrations of metal cations to reverse its inhibitory effects. As shown in Fig. 5 , addition of equimolar concentration of Zn ++ resulted in essentially complete recovery of activity (range 90 – 110% recovery) for both LPLD and PDE activities, while an equimolar concentration of Ca ++ resulted in ~75% recovery of control activity. Figure 5 Effect of chelating agents on ATX's LPLD activity The LPLD activity of ATX was compared in the presence of 0.5 mM phenanthroline A (phenA), 5 mM EDTA, or 10 mM L-histidine. The effect of adding Zn ++ or Ca ++ was compared for each chelating agent. Results are expressed as Mean ± SEM. Statistical analysis of results was performed utilizing GraphPad Prism (San Diego, CA). Data that is significantly different from untreated control is shown by: * (p < 0.001), ** (p < 0.05); recovery with metal cations is shown as statistically different from appropriate inhibitor-treated samples: # (p < 0.001). Addition of 0.5 mM 1,10-phenanthroline also profoundly inhibited the LPLD activity of ATX (Fig. 5 ). The IC50 for 1,10-phenanthroline in this reaction was approximately 0.25 mM (data not shown). As has been reported previously, a significant recovery from 1,10-phenanthroline-induced inhibition requires less than equimolar concentrations of Zn ++ [ 20 ]. Addition of 0.05 mM Zn ++ resulted in a 70 – 80% recovery compared to control values, while adding up to 5 mM Ca ++ had no significant effect upon phenanthroline-induced inhibition of activity. As can be seen in Fig. 5 , the pattern of recovery from 1,10-phenanthroline-induced inhibition is similar to the pattern seen with histidine-induced inhibition of LPLD. Testing the effect of these chelating agents on motility was problematic because chelation agents have multiple, complex effects upon living cells. For example, Na 4 -EDTA treatment killed our responder cells (A2058) in about 15 – 20 min, and 1,10-phenanthroline inhibited cellular adhesion at concentrations utilized for inhibition of LPLD and PDE activity. Therefore, we were unable to obtain motility data in the presence of these agents. Effects of Histidine Analogs upon the LPLD Reactivity of ATX A number of histidine-derived agents, as well as histamine and imidazole (Fig. 6A ), were tested for their effects upon the LPLD activity of ATX. Since 15 – 20 mM L-histidine gave similar levels of inhibition, all reactions were carried out in the presence of 15 mM inhibitor. Figure 6 Effect of histidine analogs on LPLD activity A.) Chemical structure of histidine analogs compared. B.) Histidine analogs (15 mM) were added to the LPLD assay and analyzed for their effect on LPLD activity with (white bars) or without (black bars) addition of 1.0 mM Zn ++ . Results are shown as Mean ± SEM. Means, for each histidine analog vs. appropriate untreated control, were analyzed utilizing ANOVA/Tukey's post-test (GraphPad Prism, San Diego, CA): * (p < 0.001), ** (p < 0.01). As seen in Fig. 6B , D-histidine and L-histidine are the most effective inhibitors of the ATX LPLD activity, resulting in approximately 75% reduction. They were not statistically significant from each other. Nearly as inhibitory was histidine methyl ester, which has an esterified alpha carboxyl group and which reduced LPLD activity by 65%. Other compounds with substitutions on the alpha carboxyl group, histidinamide and histidinol, were markedly less effective as inhibitors. Histidinol, in which the carboxyl group is reduced to a hydroxy-methyl group, was not statistically different from untreated control reactions. Histidinamide, with amidylation of the alpha carboxyl group, was mildly inhibitory, reducing activity ~20%. In contrast, histamine, which lacks the alpha carboxyl group, altogether, was slightly stimulatory to LPLD activity. Histidine analogs with methyl groups added to the alpha nitrogen were also less active than histidine itself. N, N-dimethyl histidine was not statistically different from untreated control, while N-methyl histidine, resulted in just a 30% reduction in LPLD activity. Surprisingly, at the tested concentrations, the metal-binding agent imidazole had no effect on the LPLD activity of autotaxin. Likewise, amino acids that resemble histidine without its imidazole ring (e.g., glycine and L-alanine) also lack inhibitory activity (data not shown). Among the five histidine analogs found to inhibit ATX LPLD activity, the relative inhibitory activity is: D-histidine = L-histidine > histidine methyl ester >> N-methyl histidine > histidinamide. The effects of all of these agents are reversed by zinc. In contrast, glycine, L-alanine, imidazole, histidinol, and N, N-dimethyl histidine had no significant effect on activity; and histamine appeared to be slightly stimulatory. Most of these histidine analogs, including D-histidine, histidine methyl ester, histidinamide, N, N-dimethyl histidine, and histamine, were toxic to cells. N-methyl histidine resulted in reduced adhesion to our gelatin-coated membranes, precluding an effective motility assay. Only imidazole could easily be tested for its effect on ATX-stimulated motility. At 10–20 mM concentrations, imidazole had no significant effect on ATX-stimulated motility (data not shown). Discussion We have shown that L-histidine, at concentrations that are innocuous to cells, can inhibit the motility-stimulating action of ATX upon two tumor cell lines derived from human melanoma (A2058) and human ovarian carcinoma (SKOV-3). This inhibition is largely abrogated by addition of 0.25 mM Zn ++ to the motility assays. Pre-incubation of ATX and LPC in the presence of L-histidine, followed by heat-killing the ATX, also resulted in much decreased activity, suggesting that L-histidine inhibits ATX-stimulated motility by ablating its LPLD activity. This hypothesis was in fact confirmed both by direct enzymatic studies in which L-histidine inhibited both PDE and LPLD activities and by the failure of ATX, in the presence of L-histidine, to produce an inhibitor of LPA-stimulated motility. Again, addition of Zn ++ abrogated this inhibition of enzyme activity. These data provide further evidence that the LPLD activity of ATX, which results in the production of the mediator LPA, is essential for ATX stimulation of cellular motility. The role of metal cations in the activation of ATX was further investigated by first comparing the capacity of Ca ++ , Mg ++ , Zn ++ and Co ++ to reverse the histidine-induced inhibition of ATX enzymatic activities. Neither Ca ++ nor Mg ++ reversed the inhibitory effect of histidine, whereas Co ++ alone stimulated a 50% increase in PDE and a 300% increase in LPLD activities of ATX. Histidine partially reversed both of these Co ++ -stimulated increases: PDE activity was reduced to below uninhibited levels of product while LPLD activity, though reduced by 50% compared to treatment with Co ++ alone, remained at levels above untreated controls. The mechanism by which cobalt activates ATX remains unexplained; however, Co ++ has long been reported to replace one or more zinc cations in a variety of zinc-bound metalloenzymes [ 21 - 23 ], sometimes with a notable increase in activity [ 24 , 25 ]. In contrast, Zn ++ at concentrations as low as 0.25 mM abrogated the inhibitory effects of 10 mM histidine upon both the PDE and LPLD activities of ATX. This 40:1 ratio of ligand to cation appears to be inconsistent with a simple stoichiometric coordination of the cation with the metal-binding imidazole moiety. This would predict that the maximum number of ligand groups to coordinate with a Zn ++ would be six. Our result is, therefore, difficult to explain as simple chelation in solution between cations and histidine. Perhaps, Zn ++ acts upon ATX in a manner that blocks the entry of histidine into the active site. For example, Zn ++ might coordinate with an electronically available locus in proximity with the active site of ATX or it might induce conformational changes in ATX that lowers the affinity of free histidine for the active site. Another possibility is that Zn ++ replaces another metal ion, resulting in a binding conformation with a lower affinity for L-histidine. Interestingly, 0.25 mM Zn ++ appeared to have a slightly stimulatory effect upon ATX-induced motility, but not LPA-induced motility. This suggests that Zn ++ might act in a way that stabilizes ATX in an active configuration. Many members of the alkaline phosphatase superfamily of metalloenzymes have Zn ++ or Zn ++ plus Mg ++ incorporated into their catalytic site, although a number of other metal requirements have been documented [ 14 , 26 ]. The chelation agents, Na 4 -EDTA and 1,10-phenanthroline both inactivate ATX, confirming that it is a metalloenzyme. Na 4 -EDTA complexes with a variety of cations, including transition metals as well as alkali and alkaline earth metals, and is thought to inactivate metalloenzymes by removing the metal cation from its binding site. Under the conditions of our assays, the IC50 of EDTA was ~5 mM. This inhibition was completely reversed by addition of equimolar Zn ++ and significantly reversed (about 75% recovery) by addition of equimolar Ca ++ . In contrast, 1, 10-phenanthroline has more complicated and diverse mechanisms of action. When utilized with metalloenzymes, it can form mixed complexes with the metal ions as well as other cationic sites on the protein, resulting in inactivation of the enzyme but not necessarily removal of the metal cation [ 27 ]. Like histidine, 1,10-phenanthroline requires less than equimolar concentrations of Zn ++ (approximately 1:10) to reverse its inhibitory effect on ATX. Based on all of our data with metal cations, it appears that Zn ++ , Co ++ and perhaps Ca ++ can function as the metal moiety in at least one of the two metal binding sites [ 15 ] of the active metalloenzyme ATX, although we do not know the predominant metal cations in the native enzyme. In order to determine what portion of the histidine molecule was responsible for its inhibitory activity, we utilized a number of commercially available histidine analogs. These analogs were predominantly substituted on the alpha carboxyl and alpha amino groups of histidine; but they also included histamine, which lacks the alpha carboxyl group altogether, and imidazole, the weakly chelating ring that makes up the major portion of the histidine side chain. Except for D-histidine, none of the other imidazole-containing compounds were as inhibitory as L-histidine. Among the 5 agents found to have significant inhibitory activity, the relative potency is as follows: D-histidine = L-histidine > histidine methyl ester >> N-methyl histidine > histidinamide. Methylation of the alpha amino group of histidine resulted in a stepwise loss of potency, with a single methylation (N-methyl histidine) resulting in about a 40% reduction in activity and a double methylation (N, N-dimethyl histidine) not significantly different from untreated control reactions. Since these two compounds should retain the positive charge properties of the native alpha amino group, this could be a steric effect. Similarly, change in the carboxyl group (particularly amidylation or reduction) also decreased the inhibitory potency, though that of esterified histidine methyl ester was only slightly reduced. Interestingly, imidazole itself, a moiety postulated to coordinate with the cation at the active site of ATX, was not effective at the tested concentrations. Likewise, amino acids, which lacked imidazole on their side chains (e.g. glycine and L-alanine), were also ineffective as inhibitors. Clearly, three functional groups of histidine are required for full inhibitory activity: the alpha amino group, the alpha carboxyl group, and the metal-binding imidazole side chain. Recent work with Zn ++ -binding groups in matrix metalloproteinase inhibitors indicated that slight changes to the Zn ++ -binding groups can result in major changes in efficacy of inhibition [ 28 ], suggesting an approach for developing more potent, and perhaps highly specific, pharmacologic inhibitors. ATX, the major source of plasma LPA, has been implicated in a number of physiological and disease-related processes [ 29 ], including tumor metastasis [ 30 ] and angiogenesis [ 31 ]. A pharmacologic agent that inhibits ATX activity could have major therapeutic implications. Other than the highly non-specific chelation agents [ 18 ], L-histidine is the first reported inhibitor that acts on the LPLD activity of ATX rather than acting on its downstream activation cascade. This inhibition, requiring millimolar concentrations of L-histidine, is reversible by Zn ++ metal cations. Our data suggest that the L-histidine-induced inhibition of the ATX enzymatic activities is predominantly non-competitive, i.e. Vmax is decreased significantly, while Km is not significantly affected. Furthermore, the mechanism of action of L-histidine does not appear to be simple stoichiometric chelation of the metal cations within a metalloenzyme but is more likely a complex interaction with a variety of moieties, including the metal cation, at or near the active site. At concentrations required for an inhibitory effect, most available histidine analogs resulted in loss of cell viability. Free L-histidine, therefore, appears to be unique in its ability to inhibit LPLD, and hence regulate a major source of LPA, in living systems. Since LPA has significant pathological effects, the regulation of its formation is of considerable interest. Potential problems with L-histidine as a therapeutic agent include its lack of potency and its conversion in vivo to histamine. While there are known inhibitors of histidine decarboxylase, such as the polyphenols of green tea [ 32 ], potent, stable, and non-toxic analogs of histidine would appear to be better therapeutic agents. Conclusion L-histidine inhibits the LPLD activity of ATX at millimolar concentrations, reducing its capacity to produce its major mediator, LPA. Methods Reagents LPA (18:1) and S1P were purchased from Biomol Research Laboratories, Inc. (Plymouth Meeting, PA). LPC (18:1), SPC, N-ethyl-N-(2-hydroxy-3-sulfopropyl)-m-toluidine (TOOS), 4-aminoantipyrene (4-AAP), horseradish peroxidase, choline oxidase, p-nitrophenyl-TMP, L-histidine, D-histidine, imidazole, histamine, histidinamide, histidine methyl ester, N,N-dimethyl histidine, histidinol, Na 4 -EDTA, and 1,10-phenanthroline were from Sigma-Aldrich (St Louis, MO). N-methyl histidine was from Bachem Bioscience (King of Prussia, PA). Cell Lines A2058 [ 33 ] and SKOV-3 cells were maintained in Dulbecco's Modified Essential Medium (DMEM) supplemented by 2 mM glutamine, 1X penicillin/streptomycin and 10% (v/v) heat-inactivated fetal bovine serum. SKOV-3 cells (ATCC # HTB-77) were purchased from American Type Culture Collection (Manassas, VA). ATX Preparations Highly purified recombinant human ATX (vATX), cloned from an MDA-MB-435 cell library, was prepared from a Vaccinia viral lysate, as described previously, through the concanavalin A-agarose step [ 34 ]. In vitro Motility Assays Cells were detached from their flasks with a brief exposure to 0.05% trypsin and 0.02% versene and then resuspended at 2 × 10 6 cells/ml in DMEM supplemented with 1 mg/ml bovine serum album (DBA). Ten min before the start of the assay, appropriate concentrations of potential inhibitors were added to the different treatment groups. Chemotaxis was assayed as described previously in detail [ 35 ], utilizing gelatin-coated membranes for A2058 cells and Type IV collagen-coated membranes for SKOV-3 cells. Chemotaxis chambers were incubated 3 hr for A2058 cells and 5 hr for SKOV-3 cells. Migrated cells were fixed and stained, then quantified by cell counting under light microscopy at 200X (SKOV-3 cells) or 400X (A2058 cells). Enzymatic Activity Assays Enzyme activities were determined using a modification of the previously described assays [ 9 ]. Briefly, a 5 ml aliquot of vATX (adjusted to give ≈ 6 pmoles vATX/reaction) was incubated (50 μl reaction volume) with either 1 mM pNp-TMP or, alternatively, with 1 mM LPC, for 45 min at 37°C in DBA. DBA was utilized in order to mimic the conditions of motility assays. For assays with histidine and its analogues, stock solutions were prepared and adjusted to physiological pH with NaHCO 3 or HCl, as appropriate. The inhibitor was added to the reaction mixture; then, reactions were initiated by adding ATX. For determination of 5'-nucleotide PDE activity, reactions were stopped by the addition of 450 μl 0.1 N NaOH and the nitrophenol product was detected by reading the absorbance at 410 nm (A410 × 64 = nmoles). In order to determine LPLD activity, released choline was detected as follows: a 450 μl cocktail containing 50 mM Tris-HCl (pH 8), 5 mM CaCl 2 , 0.3 mM N-ethyl-N-(2-hydroxy-3-sulfopropyl)-m-toluidine (TOOS), 0.5 mM 4-aminoantipyrene (4-AAP), 5.3 U/ml horseradish peroxidase, and 2 U/ml choline oxidase was added to the 50 μl reaction mixture and incubated for 20 min at 37°C. Absorbance was read at 555 nm and converted to nmoles of choline by comparison to a choline standard curve (A555 × 17 = nmoles). List of Abbreviations Used ATX Autotaxin DBA DMEM with 1% bovine serum albumin DMEM Dulbecco's Modified Essential Medium LPA Lysophosphatidic acid LPC Lysophosphatidylcholine LPLD Lysophospholipase D NPP Nucleotide pyrophosphatase and phosphodiesterase PDE Phosphodiesterase pNp-TMP p-Nitrophenyl-TMP S1P Sphingosine-1-phosphate SPC Sphingosylphosphorylcholine Authors' Contributions TC carried out the enzymatic studies, EK performed cellular studies with A2058 cells, MP performed cellular studies with SKOV-3 cells, and RWB carried out background studies and fine-tuned methodology. ES originated the idea of studying the effect of L-histidine on LPLD activities. LAL and MLS analyzed and interpreted data. MLS drafted and coordinated the manuscript between authors. All authors participated in reading and refining the manuscript drafts.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC554093.xml

539055

Treatment Interruptions in Chronic HIV Infection

Adverse side effects, viral resistance, and the high cost of antiretroviral therapies remain obstacles in the way of turning HIV/AIDS into a manageable chronic disease. Structured treatment interruptions (STIs) in individuals who have good viral control on therapy have been proposed as a strategy for overcoming these obstacles. The initial hope that STIs would help patients achieve greater viral control has so far not been supported by data from clinical trials, but interrupting treatment has also been proposed as a strategy to reduce the cost of long-term therapy and drug-associated toxicity. Luis Montaner and colleagues now report results from a randomized trial of 42 participants (75% on their second to fourth regimen, 66% on regimens containing non-nucleoside reverse-transcriptase inhibitors) who received either continuous therapy for 40 weeks or three successive treatment interruptions of two, four, and six weeks, followed by a final open-ended interruption for both groups. The study was designed to be able to detect a difference of four weeks or greater between the two groups for the time to viral rebound during the open-ended interruption—the primary outcome. No difference between the two groups was seen (median time for the group on continuous treatment was four weeks, and for the STI group was five weeks). Secondary outcomes included serious adverse events (disease progression, acute retroviral syndrome, therapy failure, or opportunistic infections at any point in the study), changes in CD4 count on therapy, immune reconstitution changes (CD4 recall responses and CD4 naïve/memory T cell distribution), and detection of viral mutations There were no study-related serious adverse events in either group and no increase of therapy failure in the STI arm. CD4 counts fluctuated between the start and end of each monitored treatment interruption, but levels recovered after resuppression of virus, with retention of recall responses throughout. Viral resistance was detected in both groups (in seven of 21 patients in the continuous treatment group and ten of 21 patients in the STI group), but it was more commonly detected (50% versus 18%) in the STI group during the open-ended final interruption, even though all subjects suppressed virus upon reinitiating the same therapy. Possible risks and benefits of STIs remain controversial, but data from this and other published trials do not support short-term clinical benefits of treatment interruptions. However, because they do not see increased therapy failure and find preservation of immune function in the STI group, the authors conclude that, in light of the possibility of reducing costs and drug-related toxicity, additional trials of STIs are warranted. Particularly important in the debate over the safety of STIs is whether the detection of resistant mutants should be of concern. The authors point out that all participants were able to resuppress the mutant virus when they resumed their previous drug regimens but state that it remains undetermined to what extent resistant mutations are a signal for future therapy failure. Moreover, viral replication and rebound—which eventually occurred in all participants—is seen by some researchers as inherently detrimental, and these experts argue that treatment interruptions are unsafe and their use should be discontinued. What seems clear is that STIs have no place outside controlled clinical trials and that questions regarding long-term safety remain unanswered. At least a dozen additional trials that examine STIs are currently recruiting patients and will help answer these questions.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539055.xml

539041

Preserving Creativity in Medicine

Imagination and creativity are essential traits that medicine, and medical insurers, must again learn to recognize and reward

Galvanized by rising costs, increased calls for greater, accountability, and an Institute of Medicine (Washington, D. C., United States) report suggesting that medical errors may kill nearly 100,000 Americans every year [1] , United States health care experts have tried to boost the quality of patient care by focusing on the speed and precision of service delivery. Several insurance companies have already started to place a surcharge on patients who elect to receive care from “inefficient” providers (a definition that includes most teaching hospitals), hoping to encourage patients to seek more cost-effective service, and to encourage physicians to provide it [2] . The problem is, most of these reform efforts, while critically important, only capture half the picture. Efficiency isn't everything, and unless we learn to cultivate creativity as avidly as we pursue consistency, future generations of patients may find themselves stuck with the same basic treatments they're receiving today. It will be the same medicine, just served quickly. Benefits of Quality Reform From its earliest days, medical training was based on an apprenticeship model, in which junior acolytes learned the art from senior practitioners. Even with the evolution of modern medical schools, which offered future physicians a rigorous common training, once doctors entered the real world they essentially did as they pleased. Consequently, there were pronounced differences in approaches to common problems from one clinician to another. There was also little to guarantee that once doctors had hung out their shingle, they were actually competent (and remained competent) to practice their craft. While most physicians remained committed to the general professional standard—do the best that you can for each individual patient— many well-meaning doctors ultimately were not delivering their patients the best care available. More recently, and largely due to the contagious spread of the so-called “business model,” there has been an increased emphasis on the consistency and quality of care. The clear goal is ensuring that all patients truly receive the very best care available, as defined by rigorous scientific studies. Contemplation can provide new medical insights (Illustration: Rusty Howson, sososo design) This discrepancy between what patients should be receiving and what patients are actually receiving is the major focus of quality reform, and reflects the new recognition that there are truly preferred approaches—pathways—to guide disease management. These pathways are not meant to represent a rigid algorithm reflexively applied to each patient, but are intended as a summary of the best available data, a useful template to guide further medical decisions. The renewed emphasis on quality has also resulted in a newfound appreciation for the role of experience and repetition in patient care. Study after study has shown that the best physician to treat a particular problem is the one who has treated it the most [3] . What Gets Lost: Innovation The great paradox here is that the same reforms that are improving our current care may also be endangering our future health. As medicine has become more standardized and increasingly regulated, it turns out there is much less room for innovation. The spirited pursuit of the unknown—so long a defining quality of medicine—now seems seriously endangered. The new world of rapid throughput and endless documentation provides little time to reflect upon important clinical problems and consider fresh approaches. If anything, thinking about a patient or a question too much is now implicitly discouraged because it slows doctors down; contemplation is bad for productivity. Academic medical centers like our own have played a particularly important role in the history of medical discovery; the hallmark of these institutions is our commitment to thinking and reflecting about the patients we see, patients who are often extremely sick and whose management is exceptionally complex. Unfortunately, many of the measurements now used by insurance companies to assess quality pay little attention—if any at all—to the complexity of a patient's illness, or to the importance of spending time trying to define the underlying malady. Insurance companies' major concern seems to be how fast a patient is “processed,” ideally with as few tests as possible. These measures provide no mechanism for distinguishing between the addled physician who inappropriately orders every test that springs to mind, and the reflective physician who is trying to get to the bottom of a patient's complaint, rather than simply throw a Band-Aid over the symptoms [4] . Situated on the front lines, clinicians have a unique opportunity to provide new medical insights and to identify critical, unanswered questions. Classic examples include Archibald Garrod, a British physician whose desire to understand why a patient produced black urine led to the hypothesis that diseases can result from defective metabolic enzymes, and Fuller Albright, a clinical investigator at Harvard whose thoughtful approach to his patients yielded insights that revolutionized the field of endocrinology. More recently, the astute clinical observations of UCLA immunologist Michael Gottlieb resulted in the original description of the Acquired Immune Deficiency Syndrome (AIDS) in 1981 [5] . Preserving Creativity in Medicine But where are these types of insights going to come from today? It seems difficult to imagine that a medical care environment characterized by staccato-quick patient visits covering an ever-increasing number of compulsory topics will support or encourage such reflection and innovation. Our failure to nourish and sustain inquisitive physicians seems particularly tragic because medicine has traditionally attracted some of our brightest and most imaginative individuals. Even at the height of the dot-com boom, for example, there were still more medical school applicants than there were spaces to train them. But if current trends continue, many of these creative minds will head elsewhere, while those who stay will risk becoming stultified by repetitious routine. Several medical schools and a handful of foundations have recognized this emerging problem, and have initiated programs aimed at sparking curiosity in young doctors (our own school's program is called the PASTEUR initiative—see www.pasteur.hms.harvard.edu ) [6] . But as well-intentioned as these efforts are, simply changing the curriculum isn't likely to fix the underlying problem. Unless ever-savvy medical students perceive that inquisitive thinking is truly valued in clinical medicine, and unless exasperated physicians are inspired to believe that they have the ability to change some aspect of the way medicine is practiced, nothing is going to change. We may lose the best hope we have of defeating the terrible diseases that now plague us. Even as we strive to improve the consistency of care—and striving is clearly a very good idea—we must continue to cultivate novelty and originality, rather than penalize it. Imagination is perhaps the most essential trait that medicine, and medical insurers, must again learn to recognize and reward. Even with the best algorithms and the brightest computers, the future of health care ultimately depends upon the creativity of the hardy men and women still entrusted with its delivery.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539041.xml

549203

Evaluating concentration estimation errors in ELISA microarray experiments

Background Enzyme-linked immunosorbent assay (ELISA) is a standard immunoassay to estimate a protein's concentration in a sample. Deploying ELISA in a microarray format permits simultaneous estimation of the concentrations of numerous proteins in a small sample. These estimates, however, are uncertain due to processing error and biological variability. Evaluating estimation error is critical to interpreting biological significance and improving the ELISA microarray process. Estimation error evaluation must be automated to realize a reliable high-throughput ELISA microarray system. In this paper, we present a statistical method based on propagation of error to evaluate concentration estimation errors in the ELISA microarray process. Although propagation of error is central to this method and the focus of this paper, it is most effective only when comparable data are available. Therefore, we briefly discuss the roles of experimental design, data screening, normalization, and statistical diagnostics when evaluating ELISA microarray concentration estimation errors. Results We use an ELISA microarray investigation of breast cancer biomarkers to illustrate the evaluation of concentration estimation errors. The illustration begins with a description of the design and resulting data, followed by a brief discussion of data screening and normalization. In our illustration, we fit a standard curve to the screened and normalized data, review the modeling diagnostics, and apply propagation of error. We summarize the results with a simple, three-panel diagnostic visualization featuring a scatterplot of the standard data with logistic standard curve and 95% confidence intervals, an annotated histogram of sample measurements, and a plot of the 95% concentration coefficient of variation, or relative error, as a function of concentration. Conclusions This statistical method should be of value in the rapid evaluation and quality control of high-throughput ELISA microarray analyses. Applying propagation of error to a variety of ELISA microarray concentration estimation models is straightforward. Displaying the results in the three-panel layout succinctly summarizes both the standard and sample data while providing an informative critique of applicability of the fitted model, the uncertainty in concentration estimates, and the quality of both the experiment and the ELISA microarray process.

Background Proteomic approaches are resulting in the identification of large numbers of proteins that can potentially be used as disease markers or drug targets. Unfortunately, proteomic approaches currently lack the throughput or quality metrics necessary to evaluate hundreds or thousands of samples that may be required to determine clinical usefulness of a biomarker [ 1 ]. Traditionally, candidate biomarkers have been commonly evaluated using a 96-well enzyme-linked immunosorbent assay (ELISA). However, this approach is not suited for analyzing more than a few proteins when sample volumes are limited, as is commonly the case for early tumor samples. For this reason, we and others are developing ELISA microarray systems to evaluate 20 to 50 proteins using only a few microliters of sample in an efficient and quantitative manner [ 2 , 3 ]. Processing a ELISA microarray experiment produces large volumes of data of wide variety and high complexity. Similar to traditional 96-well ELISA data, ELISA microarray data often are perturbed by processing error [ 4 - 7 ]. Processing errors are introduced by unintended variation in sample preparation, slide or pin arrangement, printing, imaging, and estimation of spot summary statistics. The specific role of concentration error estimates and the general role of statistical diagnostics is to reveal process accuracy and precision. This evaluation then enables an insightful interpretation of the biological significance, an informative critique of the current experiment, and insights to improve the accuracy and precision of future experiments. In a high-throughput ELISA microarray system, there is a need to not only quickly and accurately generate the standard curves and estimate concentrations from the sample data, but also to quickly evaluate the quality of those estimates. The resulting information can be used in both the development stage for optimizing assay conditions and in the production phase for ensuring that the overall analytic process is working well on a day-to-day basis. Statistically evaluating ELISA microarray concentration estimation errors depends upon both the availability of the appropriate set of comparable measurements and the choice of data analysis methods. Sufficient replication within and across arrays is key to making precise estimates of both concentrations and errors [ 8 ]. Hence, evaluating concentration estimation errors in an ELISA microarray experiment begins with the design of the experiment. Evaluation of these estimation errors also depends on the recording of the pedigree, or history, of each result from probe preparation and array printing through sample preparation and spot intensity estimation [ 9 - 12 ]. Screening for anomalous results and normalizing within and across arrays may significantly reduce obscuring variation and improve homogeneity [ 13 - 15 ]. Although the mathematical statistics and algorithms are quite sophisticated, software makes actual estimation and application of the standard curve and the concentration error function straightforward. This is true also for the presentation of modeling results for diagnostic purposes. In this paper, we describe and illustrate a methodology for calculating the concentration estimation error of each assay in an ELISA microarray experiment based on a statistical analysis of the most likely sources of error. We expect the resulting data analysis algorithms to be a key component in a bioinformatics package for evaluating ELISA microarray data. Methods Making concentration estimates and estimating their errors in our ELISA microarray studies involve a sequence of steps beginning with the layout of the ELISA microarray and design of the experiment. Following execution of the analytical components of the experiment, the statistical analysis proceeds with data screening, normalization, and model identification. Estimation and evaluation of the standard curves and error estimation functions come next. Finally, the standard curves and error estimation functions are applied and then evaluated using a modeling diagnostic. Layout of the ELISA microarray and design of the experiment To estimate errors in concentration estimates, it is necessary to carefully lay out the microarray and design the experiment. Our layout features several distally separate replicates of each assay spot on each microarray to evaluate local processing effects. Our design addresses selection and application of treatments – in particular, replicate treatments – to a collection of arrays. This replication facilitates adjustments for the sources of variability that lead to ambiguous concentration estimates [ 16 , 17 ]. In array experiments featuring relatively small numbers of assays, usually 50 or fewer analytes, thoughtful design is critical to normalization, calibration, and estimation of concentrations due to the significant lack of technical replicates found in arrays with thousands of assays. With regard to error estimation, the major consideration in the design of the experiments is replication of treatments across arrays to capture the effects of process error. To illustrate our technique for evaluating estimation errors in an ELISA microarray experiment, we used a subset of data from an ELISA microarray investigation of breast cancer biomarkers. The ELISA microarray experiments were performed as previously described [ 2 , 3 ]. Briefly, capture antibodies were covalently attached to an aminosilanated glass slide surface (Sigma, St. Louis, Missouri, USA) using a Microgrid 2 robot from Genomic Solutions (Ann Arbor, Michigan, USA) equipped with ChipMaker2 split pins from TeleChem (Sunnyvale, California, USA). As demonstrated previously, these spots are typically uniform in shape with a reasonable homogenous distribution of protein across the spot [ 1 - 3 ]. That is, "donut" formation is not normally observed. These spatially confined antibodies bind a specific antigen from a sample overlaying the array. A second, biotinylated antibody that recognizes the same antigen as the first antibody but at a different epitope is then used for detection. Detection of the second antibody is based upon streptavidin (which binds biotin) and an enzymatic signal enhancement method known as tyramide signal amplification (TSA). The resultant fluorescence was detected at 10-micron scan resolution using a ScanArray 3000 from General Scanning (Billerica, Massachusetts, USA). The experiment used 94 arrays printed in pairs on 47 slides. Each array contained 4 (2 × 2) replicate subarrays of 25 (5 × 5) spots. A subarray contained 21 unique assays, 1 positive control and 3 negative control spots. A set of 7 known standard concentrations and a buffer blank was assembled by performing a three-fold dilution series of a single mixture of all the standards. Each standard concentration was applied to duplicate slides. The remaining 39 slides were treated with serum samples from women with or without breast cancer. These sera were encoded to prevent knowledge of the study group during sample processing. The treated microarrays were imaged with a ScanArray microarray scanner (PerkinElmer, Boston, Massachusetts, USA). The spot fluorescence estimates were calculated with custom array-image-analysis software that was developed in-house. Data screening, normalization and model identification Data screening, an exploratory data analysis, serves several purposes – identifying outliers, anomalous values, and experimental design shortcomings; identifying data transforms to improve curve-fitting and application; identifying measurement trends and other processing effects; and suggesting an appropriate functional form for the standard curve [ 6 , 18 - 21 ]. This exploratory analysis combines simple summary statistics and graphical displays. For instance, graphs of control spot intensities versus processing variables such as array print order or pin number may reveal variability due to processing. These processing trends can be made more apparent with locally weighted regression, or loess, a statistical technique to fit a smooth curve through the scatterplot [ 22 , 23 ]. These graphs can be used as the basis for modifying the process or for data normalization. Because our protein arrays feature fewer spots per array than do typical gene expression microarrays, a different approach to normalization, suitable for low spot-frequency arrays, is required. This normalization is critical, given that array-to-array processing error is common and that standard curves are estimated from reference spot intensities calculated from one set of arrays and then applied to sample spot intensities estimated from a separate set of arrays. A scatterplot of intensity estimates of standard spots versus concentration is particularly useful. First, outliers and anomalies may be readily apparent. Second, the spacing between concentration values may be assessed. If standard concentrations follow from a dilution series, then the separation between concentrations decreases significantly with the decrease in concentration. This results in spot intensities measured at higher concentrations having much more leverage on the fit of the model than may be desirable. It should also be apparent whether the variability in spot intensity is increasing with mean spot intensity. Both increasing spacing in the concentrations and heteroskedasticity in the measured intensities affect the model fit and follow-on statistical inferences [ 24 ]. These may be minimized with log e transformations of both concentrations and spot intensities. A scatterplot of raw or transformed standard spot intensity versus concentration also provides an indication of the appropriate model for the data. In particular, data following a sigmoid curve favor the logistic curves while data apparently lacking the horizontal asymptotes of a sigmoid curve favor a linear or power law model. Although several models may be fit and one selected based on a goodness-of-fit statistic (see next section), the scatterplot is a useful visual check on this selection. Several plots provide useful information about the quality of the fitted model. Of special importance are the scatterplot of residuals versus concentration and the scatterplot of residuals versus estimated intensity. In both cases, the variability of the residuals should be centered about zero and constant across concentration or intensity. Model bias is indicated by a systematic drift of residuals to one side of the zero line. Heteroskedasticity is indicated by a systematic change in the variation of the residuals. Both may indicate that a better model is necessary before proceeding to estimation of sample concentrations and estimation of concentration errors. Standard curves and estimation errors An ELISA standard curve expresses protein concentration as a function of spot intensity. One standard curve is required for each assay. In an ELISA microarray experiment, the standard data are collected by fixing a set of concentrations and measuring spot intensities via imagery of the treated arrays. A standard curve is estimated by fitting an appropriate function to the set of (concentration, intensity) measurement pairs [ 25 ]. This equation is then inverted to obtain the standard curve. Common parametric choices for standard curve models are multiparameter logistic functions and power law functions. For an ELISA microarray, a strictly monotone model is consistent with the belief that a monotone change in concentration should result in a monotone change in spot intensity. We estimate standard curves with both logistic and power law parametric models. The four-parameter logistic model [ 26 ], expressing intensity I as a function of concentration C and parameters P 1 , P 2 , P 3 and P 4 , is defined as The two-parameter power law model [ 27 ] expressing intensity I as a function of concentration C and parameters P 1 and P 2 , in log e terms, is log e ( I ) = P 1 + P 2 log e ( C ) + ε We assume the errors, denoted by the term ε , are independent and normally distributed with mean 0 and variance σ 2 . With either of these parametric models, concentration estimation errors may be estimated using propagation of error, also known as the delta method. To choose between competing candidate models, a number of measures exist for evaluating model fit when replicate observations of each assay are available. These include partitioning the mean squared error, or MSE, into components representing pure error and lack of fit [ 28 ], and penalized measures such as Akaike (AIC) and Bayesian (BIC) information criteria [ 29 ]. We also examine the PRESS statistic, a direct measure of the predictive capability of each candidate model [ 30 ]. To calculate the PRESS statistic for each candidate model, suppose we exclude each poin ( x j , y j ) in turn and fit the model to the remaining points. We predict the value at the excluded point x j and calculate the PRESS residual defined by e j , - j = y j - . Then, the PRESS statistic is the sum of the squared PRESS residuals The candidate model with the lowest PRESS score as the best predictive model to estimate concentrations. The basic approach to estimating concentration errors with the propagation of error method has three steps [ 31 ]. First, fit intensity as a function of concentration and estimate the covariance among model parameter estimates. Next, solve the fitted function for concentration as a function of intensity. Finally, propagate error estimates from the fitted model through the inverted model and combine with the error estimate of the sampled spot intensity to estimate the concentration estimation error. Let C ( I | ), with , denote the inverted N parameter model expressing concentration C as a function of intensity I and the parameter estimates Suppose is the NxN parameter covariance matrix estimated by fitting I as a function of C , say I ( C | P ). Now, let C s be the estimated concentration from the sample intensity estimate I S , say C S = C ( I S / P ) and be the corresponding estimated standard error of I S . Then, the propagation of error estimate for the concentration estimate C S is the square root of the product of , the sample covariance matrix augmented with , and the Jacobian matrix J evaluated at I S and the parameter estimates . In this application, the Jacobian is the matrix of partial derivatives of C ( I | P ) with respect to the intensity I and the parameters P . Hence, the concentration estimation error of C ( I | P ) is the square root of the concentration estimation variance V ( C ( I | P )) V ( C ( I | P )) = J ( C ( I | P )) T Σ J ( C ( I | P )) where the Jacobian is J ( C ( I | P )) T = [∂ C /∂ I , ∂ C /∂ P 1 ,..., ∂ C /∂ P N ] and the augmented covariance matrix is Hence, the formula for estimated standard error of C S is For a given intensity estimate I S and standard error , the estimated concentration and approximate 95% confidence interval ( C 95% L , C 95% U ) are C S = C ( I S ) C 95% L = C S - 2 SE [ C S ]     (2) and C 95% U = C S + 2 SE [ C S ]     (3) For example, consider the four parameter logistic model, Eqn. 1. The concentration estimation equation is obtained by solving this equation for C in terms of I and the four parameters The Jacobian matrix is obtained by taking the partial derivatives of the inverted four-parameter logistic function of C (Eqn. 4) with respect to I and the parameters P 1 , P 2 , P 3 and P 4 Diagnostic visualizations A three-panel display combining a histogram of normalized sample spot intensities for a given antigen, its corresponding standard curve, and the graph of the concentration coefficient of variation, or relative error, versus concentration provides pertinent information about the conduct of the current experiment as well as information to improve future experiments. The standard curve panel presents a scatterplot of normalized standard spot intensities versus standard concentrations. The scatterplot is overlain with the estimated standard curve expressing concentration as a function of spot intensity. This panel also includes approximate 95% confidence intervals. These intervals summarize the uncertainty in concentration estimates due to both the uncertainty in estimating the standard curve and the uncertainty in the sample spot intensity estimate. Finally, a highlighted region helps distinguish concentration estimates s with acceptable errors from concentration estimates with possibly less than acceptable errors. The segment of the standard curve corresponding to acceptable concentration errors may be determined using the 95% confidence intervals. The lower and upper endpoints of this segment, ( I L , C L ) and ( I U , C U ), are the two points such that the confidence intervals begin to increase significantly in length. This segment generally corresponds to the linear segment of a standard curve. We identify the intensity I L of the lower pair as the smallest intensity such that 95% UB( I L ) is less than 95% UB( I ) for intensity values I less than I L . Similarly, we identify I U as the largest intensity such that 95% LB( I U ) is greater than 95% LB( I ) for intensity values I greater than I U . We define C L and C U to be C L = C ( I L ) and C U = C ( I U ), respectively. We believe that this is a conservative approach to identifying intensities that generate concentration estimates with acceptable errors. An informative visualization of acceptable concentration estimates may be generated using the points ( I L , C L ) and ( I U , C U ). Consider the union of the two rectangular regions defined by the two sets of vertices [( I L , 0), ( I L , C L ), ( I U , C U ), ( I U , 0)], and [(0, C L ), (0, C U ), ( I U , C U ), ( I U , C LU )]. This union defines an L-shaped region covering the standard curve segment and bound at its extremes by the intensity and concentration segments. From this visualization, one can quickly grasp the dynamic range of acceptable intensities and the potential range of acceptable concentration estimates. In regard to this first panel, two notable aspects of this propagation of error methodology are noteworthy. First, the error bands are computed pointwise and provide reasonable error estimates for a small number of concentrations. As the number of concentration estimates grows, the impact of the multiple testing problem grows [ 32 ]. This a problem in any biomedical testing that features numerous simultaneous tests and has spawned considerable debate and research. The second aspect of note is the divergence of the error bands from the estimated standard curve as the standard curve approaches a horizontal asymptote. We see this apparent deficiency in the method as a plus. This divergence is a clear indicator that concentration estimates in the segment of a standard curve approaching a horizontal asymptote are highly suspect. The second panel in this display shows the concentration coefficient of variation – that is, % CCV = 100 * SE ( C | I )/ C ( I ), or relative error of a concentration estimate – as a function of concentration. This provides an alternative view of the error in concentration estimation over the concentration range covered by the concentration estimation equation. A standard curve modeled with a four-parameter logistic function generally will have a bathtub shape due to the increasing uncertainty in concentration estimates at the two ends of the concentration range where the curve approaches horizontal asymptotes. The third panel in this display features an annotated histogram of sample spot intensity estimates on the intensity axis opposite the scatterplot. In this representation, it is easy to see the extent of overlap between the distribution of sample intensity estimates and the range of intensities that result in concentration s estimates with acceptable errors. Results and discussion To evaluate concentration estimation errors in the example analysis, we attempted to quantify or understand those errors that we can and minimize those errors that we cannot. We began with data screening. The most significant anomaly uncovered during this exploratory analysis of the cancer biomarker data was a decreasing trend in control spot intensity as a function of array print order (Figure 1 ). The trend was quantified using loess, a flexible, nonparametric method to fit a smooth curve through a scatterplot to uncover trends in data [ 22 , 23 ]. This trend suggests that 1) normalizing across arrays would improve precision; 2) in future experiments, assigning study groups to arrays should address array print order; and 3) array printing should be monitored and, if possible, modified to reduce this source of obscuring variation. In this case, we normalized for slide-level processing errors by subtracting from each spot's log e (fluorescent intensity) the difference between the mean of its slide's control spot log e (intensities) and the corresponding loess estimate. Our evaluation addressed the selection and fitting of an acceptable concentration estimation model. To that end, we examined two plots. The first displays the fluorescent intensities of the standards as a function of concentration (Figure 2A ). Two characteristics of the data that significantly affect selecting and fitting the model and then interpreting the results in a statistically meaningful way are apparent. The first is heteroskedasticity, or the increasing variation in fluorescent intensity with increasing concentration. Meaningful statistical inferences about concentration estimation errors depend upon correct modeling assumptions. To apply propagation of error when estimating and then interpreting the approximate 95% confidence intervals, we rely on normal distribution theory and require that the random variability in spot intensities be homogeneous across concentrations [ 28 ]. In this case, a log e transformation of the intensity estimates stabilizes the variability across concentrations (Figure 2B ). The second characteristic is the undue leverage of data at high concentrations due to the increasing separation between standard concentrations with increasing concentration. Although both are expected (the first due to the randomness generally observed when counting photons, and the second due to the use of a concentration dilution series in the design), each must be addressed to achieve the best fit of the standard curve and resulting concentration estimation inferences. A log e transformation of the concentrations standardizes the separation in concentrations (Figure 2B ). With the heteroskedasticity and undue leverage addressed, we estimated a standard curve by selecting one of two models: a four-parameter logistic model (Eqn. 1) and a power curve model (Eqn. ??). We chose the logistic model as the model that fits the data best visually and in terms of the PRESS statistic (Figure 3 ). The logistic curve more closely follows the data points, while the power curve is too high in the lower concentrations and too low in the higher concentrations. We confirmed our choice with a review of the modeling diagnostics. In this case, we examined graphs of the standardized residuals as a function of concentration and the estimated intensities (Figure 4 ). In both graphs, the residuals show no significant systematic trends or deviations from the zero line and vary uniformly. Further, the preponderance of standardized residuals falls between -2 and 2, indicating that a statistical interpretation of the 95% confidence intervals is warranted. Figure 5 presents the three-panel diagnostic visualization for the HER-2 data. HER-2 belongs to the family of epidermal growth factor receptors and has been used as a serum biomarker for the detection of breast cancer. This figure illustrates how data from a large study measuring HER-2 levels in the serum of women with and without breast cancer can be visualized using this statistical approach. A standard curve of HER-2 was generated, and the concentration of HER-2 in 39 samples was determined. To estimate manually the concentration for a sample HER-2 spot intensity, say I, locate I on the vertical axis, then scan across horizontally to the standard curve and 95% confidence intervals (Figure 5A ). Scan down from these points to find the appropriate estimated concentration and lower and upper 95% concentration confidence bounds. In this manner, the estimated concentration and confidence interval can be determined. In the standard curve panel (Figure 5A ), we see that near the asymptotes of the standard curve, the uncertainty grows much more quickly than the curve, causing the concentration confidence bounds to diverge. Although this divergence is due to the approximation (Eqns 2 and 3), it is true that near the asymptotes, the uncertainty of the estimated concentration increases greatly. For this reason we have defined our optimal region of this curve to be the range of spot fluorescence values such that both the upper and lower bounds are monotonically increasing in intensity. The boundaries of this range are indicated by the shaded area and the dashed red lines, which also show the concentration values corresponding to the acceptable fluorescence range. Our optimal concentration range spans approximately two orders of magnitude. The histogram (Figure 5B ) shows the fluorescence values of the sample spots for which this curve may best be used to estimate concentration. The plot shows that many of the sample values lie outside our optimal region. The researcher must then decide if too many of these values lie outside this range and, if so, what can be done to fix this problem. Nevertheless, we were able to compare the HER-2 concentrations and found a 3.5-fold increase in HER-2 protein levels in women with stage Ill/stage IV breast cancer (7 samples) compared to women without breast cancer (12 samples). The concentration coefficient of variation, or the ratio of the concentration estimation error to the corresponding estimated concentration, offers an alternative expression to a confidence interval as a means to evaluate concentration estimation error. A graph of this estimation error as a function of concentration offers a comprehensive summary of the variation in the concentration coefficient of variation over the concentration range (Figure 5C ). Presenting the results in this type of plot allows us to immediately look for several potential problems. First, does the fitted curve seem reasonable, given the data points to which we are fitting? We also can determine whether most of the unknown sample data fall within the acceptable range of the curve. The usable concentration range is made clear and, if it is too limited in range, it is immediately apparent. If problems are identified, several fixes are available, including changing the settings on the imager or using a different concentration range to create the standard curves. Conclusions Evaluation of errors in estimating concentrations is important to establishing confidence in protein concentration estimates. Propagation of error provides a straightforward approach to estimating concentration estimation errors in ELISA microarray experiments. When presented in a simple multi-panel visualization, the propagated errors provide valuable information about individual concentration estimates, the applicability of the estimated standard curve, quality of the experiment, and the conduct of the ELISA microarray processing. The visualization provides a rapid assessment of the quality of the data, particularly in regard to the goodness of fit of the estimated standard curve and its capability to estimate concentrations over the observed range of intensities of biological samples. Authors' contributions DSD framed evaluation of ELISA microarray concentration estimation errors as a statistical problem. KKA suggested the use of propagation of error and outlined the initial derivation. DSD and AMW derived the appropriate propagation of error equations, developed the algorithms, and designed the diagnostic visualizations. AMW encoded the algorithms, analyzed the microarray imagery, and produced the statistical results. SMV designed and printed the arrays and then carried out the ELISA microarray experiments. RCZ conceived the study, designed it with SMV, and then coordinated all our efforts. All authors submitted comments on drafts, then read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC549203.xml

549559

A statistical approach for array CGH data analysis

Background Microarray-CGH experiments are used to detect and map chromosomal imbalances, by hybridizing targets of genomic DNA from a test and a reference sample to sequences immobilized on a slide. These probes are genomic DNA sequences (BACs) that are mapped on the genome. The signal has a spatial coherence that can be handled by specific statistical tools. Segmentation methods seem to be a natural framework for this purpose. A CGH profile can be viewed as a succession of segments that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. We model a CGH profile by a random Gaussian process whose distribution parameters are affected by abrupt changes at unknown coordinates. Two major problems arise : to determine which parameters are affected by the abrupt changes (the mean and the variance, or the mean only), and the selection of the number of segments in the profile. Results We demonstrate that existing methods for estimating the number of segments are not well adapted in the case of array CGH data, and we propose an adaptive criterion that detects previously mapped chromosomal aberrations. The performances of this method are discussed based on simulations and publicly available data sets. Then we discuss the choice of modeling for array CGH data and show that the model with a homogeneous variance is adapted to this context. Conclusions Array CGH data analysis is an emerging field that needs appropriate statistical tools. Process segmentation and model selection provide a theoretical framework that allows precise biological interpretations. Adaptive methods for model selection give promising results concerning the estimation of the number of altered regions on the genome.

Background Chromosomal aberrations often occur in solid tumors: tumor suppressor genes may be inactivated by physical deletion, and oncogenes activated via duplication in the genome. Gene dosage effect has become particularly important in the understanding of human solid tumor genesis and progression, and has also been associated with other diseases such as mental retardation [ 1 , 2 ]. Chromosomal aberrations can be studied using many different techniques, such as Comparative Genomic Hybridization (CGH), Fluorescence in Situ Hybridization (FISH), and Representational Difference Analysis (RDA). Although chromosome CGH has become a standard method for cytogenetic studies, technical limitations restrict its usefulness as a comprehensive screening tool [ 3 ]. Recently, the resolution of Comparative Genomic Hybridizations has been greatly improved using microarray technology [ 4 , 5 ]. The purpose of array-based Comparative Genomic Hybridization (array CGH) is to detect and map chromosomal aberrations, on a genomic scale, in a single experiment. Since chromosomal copy numbers can not be measured directly, two samples of genomic DNA (referred to as the reference and test DNAs) are differentially labelled with fluorescent dyes and competitively hybridized to known mapped sequences (referred to as BACs) that are immobilized on a slide. Subsequently, the ratio of the intensities of the two fluorochromes is computed and a CGH profile is constituted for each chromosome when the log 2 of fluorescence ratios are ranked and plotted according to the physical position of their corresponding BACs on the genome [ 6 ]. Different methods and packages have been proposed for the visualization of array CGH data [ 7 , 8 ]. Each profile can be viewed as a succession of "segments" that represent homogeneous regions in the genome whose BACs share the same relative copy number on average. Array CGH data are normalized with a median set to log 2 (ratio) = 0 for regions of no change, segments with positive means represent duplicated regions in the test sample genome, and segments with negative means represent deleted regions. Even if the underlying biological process is discrete (counting of relative copy numbers of DNA sequences), the signal under study is viewed as being continuous, because the quantification is based on fluorescence measurements, and because the possible values for chromosomal copy numbers in the test sample may vary considerably, especially in the case of clinical tumor samples that present mixtures of tissues of different natures. Two main statistical approches have been considered for the analysis of array CGH data. The first has focused many attentions, and is based on segmentation methods where the purpose is to locate segments of biological interest [ 7 , 9 - 11 ]. A second approach is based on Hidden Markov Models (aCGH R-package [ 12 ]), where the purpose is to cluster individual data points into a finite number of hidden groups. Our approach can be put into the first category. Segmentation methods seem to be a natural framework to handle the spatial coherence of the data on the genome that is specific to array CGH. In this context the signal provided by array CGH data is supposed to be a realization of a Gaussian process whose parameters are affected by an unknown number of abrupt changes at unknown locations on the genome. Two models can be considered, according to the characteristics of the signal that is affected by the changes: it can be either the mean of the signal [ 7 , 10 , 11 ] or the mean and the variance [ 9 ]. Since the choice of modeling is crucial in any interpretation of a segmented CGH profile, we provide guidelines for this choice in the discussion. Two major issues arise in break-points detection studies: the localization of the segments on the genome, and the estimation of the number of segments. The first point has lead to the definition of many algorithms and packages: segmentation algorithms [ 9 , 10 ] and smoothing algorithms [ 11 ] where the break-points are defined with a posterior empirical criterion. These methods are defined by a criterion to optimize and an algorithm of optimization. Different criteria have been proposed: the likelihood criterion [ 9 , 11 ], the least-squares criterion [ 7 ], partial sums [ 10 ], and algorithms of optimization are based on genetic algorithms [ 9 ], dynamic programing [ 7 ], binary segmentation (DNAcopy R-package [ 10 ]) and adaptive weigths smoothing (GLAD R-package [ 11 ]). Since many criteria and algorithms have been proposed, one important question is the resulting statistical properties of the break-point estimators they provide. Note that smoothing techniques do not provide estimators of the break-point coordinates, since the primary goal of the underlying model is to smooth the data, and break-points are not parameters of the model (in this case, they are defined after the optimization of the criterion [ 11 ]). Here we consider the likelihood criterion and we use dynamic programming that provides a global optimum solution, contrary to genetic algorithms [ 9 ], in a reasonable computational time. As for the estimation of the number of segments, the existing articles have not defined any statistical criterion adapted to the case of process segmentation. This problem is theoretically complex, and has lead to ad hoc procedures [ 9 - 11 ]. Since the purpose of array CGH experiments is to discover biological events, the estimation of the number of segments remains central. This problem can be handled in the more general context of model selection. In the discussion we explain why classical criteria based on penalized likelihoods are not valid for break-points detection. Criteria such as the Akaike Information Criterion (AIC) and the Bayes Information Criterion (BIC) lead to an overestimation of the number of segments. For this reason, an arbitrary penalty constant can be chosen in order to select a lower number of segments in the profile [ 9 ]. We propose a new procedure to estimate the number of segments, choosing the penalty constant adaptively to the data. We explain the construction of such penalty, and its performances are compared to other criteria in the Results Section, based on simulation studies and on publicly available data sets. Put together, we propose a methodology that considers a simple modeling, a fast and effective algorithm of optimization and that takes advantages of the statistical properties of the maximum likelihood. Our procedure has been implemented on MATLAB Software and is freely available . Results Comparison of model selection criteria To show the importance of the choice of the model selection criterion on simple data, we use the results of a single experiment performed on fibroblast cell lines (see the Materials Section), with one known chromosomal aberration. Figure 1 shows the resulting segmentations when using the Bayesian Information Criterion, and our criterion. BIC leads to an oversegmented profile that is not interpretable in terms of relative copy numbers. Our procedure estimates the correct number of segments . This example shows the practical consequences of the use of theoretically unappropriated criteria. This point constitutes the main purpose of the discussion (see the Discussion Section). Numerical simulations are performed to study the sensitivity of different criteria to varying amounts of noise. The simulation design is described in the Methods Section. We compare four different criteria: the Bayesian Information Criterion, two previously described criteria [ 9 , 13 ], and the criterion we propose, in their ability to estimate the correct number of segments. Two configurations were tested, for a true number of segments K * = 5. In the first situation, the segments are regularly spaced with a jump of the mean of 1 (Figure 3 ), whereas in the second case, the segments are not regularly spaced and the differences of means vary between d = 2 and d = 0.5 (Figure 4 ). The first result is that BIC overestimates the number of segments, whatever the noise and the configuration (Figure 2 ). On the contrary, previously described criteria [ 9 , 13 ] tend to underestimate the number of segments when the noise increases, whatever the configuration. These results suggest that those two criteria "prefer" to detect no break-point as the noise increases, leading to possible false negative results. The behavior of the criterion we propose is different. It seems to be more robust to the noise, as it will give a number of segments that is close to the true number. In particular, the irregular configuration presents a segment of small size (5 points at t = 80) that could be interesting to detect in the case of array CGH profile (a putative gained region for instance). Since the previously described criteria [ 9 , 13 ] tend to underestimate the number of segments, this particular region would not be detected. On the contrary, the adaptive criterion will be able to detect it, even if the noise is important, since it selects a constant number of segments close to the true number whatever the noise. These simulation examples perfectly illustrate the capacity of an adaptive criterion to find a reasonable number of segments even in configurations where the profile is not very separated. We also compare the performance of our criterion and of the arbitrary criterion [ 9 ] on breast cancer cell lines. Figure 5 shows the resulting segmentations on chromosomes 9 and 10 of the Bt474 cell line (see the Materials Section for further description). As previously mentioned, the arbitrary criterion [ 9 ] selects a lower number of segments compared to the adaptive criterion, and we note that interesting regions are not detected (a putative outlier on chromosome 9 at 1.58 Mb and a putative deleted region on chromosome 10 at 1.76 Mb). Since the aim of array CGH experiments is to discover unknown chromosomal aberrations, the use of an adaptive criterion seems more appropriate in this context since it allows the identification of regions that seem biologically relevent. The second simulation-based result concerns the ability of dynamic programming to locate the break-points at the correct coordinate, given different amounts of noise (Figures 3 and 4 ). In the regular configuration (Figure 3 ), simulation results show that dynamic programming perfectly localizes the break-points when the variability of the noise σ 2 is low regarding the jump d of the mean. If d / σ = 10 the estimated probability to localize the break-points at the correct coordinate is 1, and this probability deacreases with the noise (probability close to 0.65 for d / σ = 2 and 0.25 for d / σ = 1). The effect of additional noise is to widden the zone of estimation, but the estimated break-points remain close to the true break-points. If the true break-point is located at t *, the estimated break-point stays in the interval t * ± 3. In the irregular configuration, additional noise has similar effects on the break-point's positioning, but the probability to correctly estimate a break-point depends on the jump of the mean between two segments. In the irregular case, Figure 4 , at position t = 40 the difference of mean is d = 2, and the probability to locate the break-point at the true coordinate is higher than 0.65 for any additional noise. On the contrary, at position t = 85 where the different of mean equals d = 0.5 the probability to correctly locate the break-point decreases dramatically with the noise (probability 1 for σ = 0.1 and probability 0.25 for σ = 0.5). This means that dynamic programming is sensitive to small segments that present little differences in the mean regarding the noise. Nevertheless, the example on the real data set presented in Figure 5 shows that using an adaptive criterion with dynamic programming allows for the identification of small regions of putative biological interest as mentioned above. Put together, these simulation results show that the adaptive method selects the good number of segments even in the presence of important noise, and that when this number is selected, dynamic programming is able to correctly localize the break-point. In addition to its ability to locate precisely the break-points, it is important to notice that dynamic programming provides a global optimum of the likelihood that is required for any model selection procedure to select the number of segments, compared to genetic algorithms [ 9 ]. Segmentation models in the Gaussian framework The CGH profile is supposed to be a Gaussian signal. In a segmentation framework, two types of changes can be considered: changes in the mean and the variance of the signal, or changes in the mean only. Let us define model where each segment has a specific mean and variance [ 9 ], and model , where the variance is common between segments [ 7 ]. Since both models can be used, it is important to explore their behavior in order to know which model is the best adapted to the special case of array CGH data. We use clinical data obtained from primary dissected tumors of colorectal cancers (see the Materials Section for further details). Figure 6 presents the results of segmentations for three experiments obtained with the two models and when our criterion is used to estimate the number of segments. The main result of this comparison is that the number of segments is higher using model compared to model . This behavior of model could be interpreted as a trend to divide large segments into smaller parts, in order to maintain the variance homogeneous between segments. This leads to a more segmented profile, maybe more precise, but that may be more difficult to interpret in terms of relative copy numbers. Nevertheless, as model allows the exploration of segments with one observation, it will be more efficient for the identification of outliers, as shown in Figure 6 (experiment X411, model , point at 100 Mb). Discussion The definition of an appropriate penalized criterion has been an issue for previous works using segmentation methods for array CGH data analysis [ 8 , 9 , 11 ]. In this section, we explain the specificity of model selection in the case of process segmentation, in order to give further justification to the inefficiency of classical criteria to select the number of segments, as shown in the Results Section. Estimating the number of segments via penalized likelihood When the number of segments is known, the maximization of the log-likelihood gives the best segmentation with K segments (see the Methods Section). In real situations this number is unknown, and one has to choose among many possible segmentations. The maximum of the log-likelihood can be viewed as a quality measurement of the fit to the data of the model with K segments, and will be maximal when each data point is in its own segment. Therefore selecting the number of segments only based on the likelihood criterion would lead to overfitting. Furthermore, the number of parameters to estimate is proportional to the number of segments, and a too large number of segments would lead to a large estimation error. A penalized version of the likelihood is used as a trade-off between a good adjustement and a reasonable number of parameters to estimate. It is noted where pen ( K ) is a penalty function that increases with the number of segments, and β is a constant of penalization. The estimated number of segments is such as : It is crucial to notice that the criterion which is penalized should provide the best partition of K -dimensional, ie for a fixed K the criterion has to be globally maximized to ensure convergence of the break-point estimators to the true break-points [ 14 ]. This optimum is provided by dynamic programming, but not by other algorithms [ 9 , 10 ]. Choice of the penalty function and constant Classical penalized likelihoods use the number of independent continuous parameters to be estimated as a penalty function. Even though those criteria are widely used in the context of model selection, theoretical considerations suggest that they are not appropriate in the context of an exhaustive search for abrupt changes. Let us focus on the penalty function in a first step. Table 1 provides a summary of different penalties. For classical information criteria, such as the Akaike Information Criterion and the Bayes Information Criterion, the penalty function equals to 2 K ( K means and K variances) for a heteroscedastic model with K segments. Penalized criteria have already been used in the context of array CGH data analysis to estimate the number of segments [ 9 ]. In addition to the 2 K parameters, they implicitly consider that the break-points are also continuous parameters, leading to a new penalty function pen ( K ) = 3 K - 1, which considers K - 1 break-points. Nevertheless, the characteristic of break-point detection models lies in the mixture of continuous parameters and discrete parameters that can not be counted as continuous parameters, since the number of possible configurations for K segments is finite and equals (with n the total number of points) [ 13 ]. This leads to the definition of a new penalty function adapted to the special context of the exhaustive search of abrupt changes. This function (table 1 ) is proportional to the number of continuous parameters, but is also proportional to a new term in that takes the complexity of the visited configurations into account. It is written pen ( K ) = 2 K ( c 1 + c 2 ), where c 1 and c 2 are constant coefficients that have to be calibrated using numerical simulations. Since AIC and BIC and the criterion proposed in [ 9 ] do not consider the complexity of the visited models, they select a too high number of segments. The second term of the penalty is the penalty constant β . This term is constant in the case of AIC and BIC ( β = 1, β = , respectively), and contributes to the oversegmentation as mentioned above. This can lead to an empirical choice for the constant, in order to obtain expected results based on a priori knowledge. For this reason, an arbitrary penalty constant can be chosen for the procedure to select a reasonable number of segments ( β = 10/3 in [ 9 ]). Instead of an arbitrary choice for this constant, β can be adaptively chosen to the data [ 13 , 14 ]. Furthermore, when the number of segments is small with respect to the number of data points (which is the case in CGH data analysis), the log-term can be considered as a constant [ 14 ]. The author rather suggests to use the penalty function pen ( K ) = 2 K and to define an automatic procedure to choose the constant of penalization β adaptively. We explain the estimation procedure for the penalty constant in the Methods Section. The power of adaptive methods for model selection lies in the definition of a penalty that is not universal (such as in the case of AIC and BIC). This means that the dimension of the model is estimated adaptively to the data. The efficiency of such method has been shown on simulated data as well as on experimental results (Results Section), and adaptive model selection criteria seem to be very appropriate for array CGH data analysis. Choice of modelling for array CGH data Since the choice of modeling affects the resulting segmentation, it is crucial to provide guidelines for their use. This can be done with the interpretation of the statistical models in terms of their biological meaning. The difference between model and concerns the modeling of the variance: model assumes that the variability of the signal is organized along the chromosome, whereas model specifies that the variance is constant. Since it has been shown that the vast majority of clones all had the same response to copy number changes in the aneuploid cell lines [ 6 ], the use of model would be justified regarding this experimental argument. Outliers seem to be a major concern in microarray CGH data analysis. For instance, if only one BAC is altered whereas its neighbors are not, the conclusion could be either that it is biologically relevant, or that the signal is due to technical artefacts. Replications are crucial in this situation, as well as secondary validations. An other possibility could be that the BAC is misannotated: if the ratio is plotted at the wrong coordinate on the genome, it will appear as an outlier, when it is not. The importance of outlier identification is another argument in favor of model , that can detect changes for one data point, whereas with model outliers would belong to segments with higher variance. It has to be noted that classical models used in segmentation methods assume the independence of the data. This may be a reasonnable assumption for BAC arrays whose genome representation is approximately 1 BAC every 1.4 Mb [ 6 ]. Nevertheless, a new generation of arrays now provides a tiling resolution of the genome [ 15 ]. The overlapping of successive BACs could lead to statistical correlations that will require developments of new segmentation models for correlated processes. Conclusions Microarray CGH currently constitutes the most powerful method to detect gain or loss of genetic material on a genomic scale. To date, applications have been mainly restricted to cancer research, but the emerging potentialities of this technique have also been applied to the study of congenital and acquired diseases. As expression profile experiments require careful statistical analysis before any biological expertise, CGH microarray experiments will require specific statistical tools to handle experimental variability, and to consider the specificity of the the studied biological phenomena. We introduced a statistical method for the analysis of CGH microarray data that models the abrupt changes in the relative copy number ratio between a test DNA and a reference DNA. We discuss the effects of different modelings that can be used in segmentation methods, and suggest the use of a model that considers the homogeneity of the signal variability based on experimental arguments and regarding the specificity of array CGH data. The main theoretical issue of array CGH data analysis lies in the estimation of the number of segments that requires the definition of appropriate penalty function and constant. We define a new procedure that estimates the number of segments adaptively to the data. This method selects the number of segments with high accuracy compared to previously mapped aberrations, and seems to be more efficient compared to others proposed to date. The use of dynamic programming remains central to localizing the break-points, and the simulation results show that when the good number of segments are selected, the algorithm localizes the break-points very close to the truth. Assessing the number of segments in a model is theoretically complex, and requires the definition of a precise model of inference. To that extent, microarray CGH analysis not only requires computational approaches, but also a careful statistical methodology. Methods Materials We briefly present the data we used in this article. The first data we use in the Results Section consist of a single experiment on fibroblast cell lines (Coriell Cell lines) whose chromosomal aberrations have been previously mapped. Those defaults concern partial or whole chromosome aneuploidy. This data have been previously used by other authors [ 10 ]. The second group of data used in the Results section is described in [ 6 ]. A test genome of Bt474 cell lines is compared to a normal reference male genome. The last data set used is described in [ 16 ] and consists of 125 primary colorectal tumors that were surgically dissected and frozen. The arrays used for these analysis are BAC arrays described in [ 6 ]. Models and Likelihoods In this section, we define the models and . Let us consider a CGH profile, and note y t , the log 2 -ratio of the intensities for the t th BAC on the genome. Precisely y t represents the average signal obtained from the replicated spots on the slide. BACs are the basic units in our model, and are ordered according to their physical position. We suppose that the y t are the realizations of independent random variables { Y t } t = 1... n , with Gaussian distributions . We assume that K - 1 changes affect the parameters of the distribution of the Ys , at unknown coordinates ( t 0 , t 1 , t 2 ,..., t K - 1 , t K ) with convention t 0 = 1 and t K = n , and that the parameters of the Ys distributions are constant between two changes: where μ k is the mean of the k th segment. Model specifies that the variance is segment-specific ( ), whereas considers that the variance is common between segments ( σ 2 ). Since BACs are supposed to be independent, the log-likelihood can be decomposed into a sum of "local" likelihoods, calculated on each segments: , with Estimation of the segment's mean and variance Given the number of segments K and the segments' coordinates (t 0 , t 1 , t 2 ,..., t K -1 , t K ), we estimate the mean and the variance for each segment using maximum likelihood : If the variance of the segments is homogeneous, its estimator is given by: Notice that when the segment coordinates are known, the estimation of the mean and variance for each segment is straightforward. Then, the key problem is to estimate K and ( t 0 , t 1 , t 2 ,..., t K - 1 , t K ). We will proceed in two steps: in the first step, we will consider that the number of segments is known, and the problem will be to estimate the t k s, that is, to find the best partition of a set of n individuals into K segments. In the second step, we will estimate the number of segments, using a penalized version of the likelihood. A segmentation algorithm when the number of segments is known When the number of segments K is known, the problem is to find the best partition of {1,..., n } into K segments, according to the likelihood, where n is the size of the sample. An exhaustive search becomes impossible for large K since the number of partitions of a set with n elements into K segments is . To reduce the computational load, we use a dynamic programming approach (programs are coded in MATLAB language and are available upon request). Let be the maximum log-likelihood obtained by the best partition of the data { Y ( i ), Y ( i + 1),..., Y ( j )} into k + 1 segments, with k break-points, and let note . The algorithm is as follows: Dynamic programming takes advantage of the additivity of the log-likelihood described above, considering that a partition of the data into k + 1 segments is a union of a partition into k segments and a set containing 1 segment. This approach presents two main advantages: it provides an exact solution for the global optimum of the likelihood [ 17 ], and reduces the computational load from ( n K ) to ( n 2 ) for a given K (the algorithm only requires the storage of an upper n × n triangular matrix). At the end of the procedure, the quantities are stored and will be used in the next step. Notice that this problem of partitioning is analogous to the search for the shortest path to travel from one point to another, where represents the total length of a ( k + 1)-step-path connecting the point with coordinate 1 to the point with coordinate n . An adaptive method to estimate the penalty constant The purpose of this section is to explain an adaptive method to estimate the number of segments. Further theoretical developments can be found in [ 14 ]. If we consider that the likelihood measures the adjustment of a model with K segments to the data, we aim at selecting the dimension for which ceases to increase significantly. For this purpose, let us define a decreasing sequence ( β ) such as β 0 = ∞ and If we represent the curve ( pen ( K ), ), the sequence of β i represents the slopes between points ( pen ( K i + 1 ), ) and ( pen ( K i ), ), where the subset {( pen ( K i ), ), i ≥ 1}) is the convex hull of the set {( pen ( K ), )}. Since we aim at selecting the dimension for which ceases to increase significantly, we look for breaks in the slope of the curve. We define l i , the variation of the slope, that exactly corresponds to the length of the interval ] β i , β i - 1 ] : l i = β i - 1 - β i . The length of these intervals is directly related to the second derivative of the likelihood. The automatic procedure to estimate the number of segments is then to calculate the second derivative (finite difference) of the likelihood: and we select the highest number of segments K such that the second derivative is lower than a given threshold : Other procedures have been developed to automatically locate the break in the slope of the likelihood. Nevertheless, the criterion we use can be interpreted geometrically and is easy to implement. The choice of the constant s is arbitrary. According to our experience, a threshold s = -0.5 seems appropriate for our purpose. A criticism that can be made to this procedure is its dependency on the threshold which is chosen. Nevertheless, it is important to point out that despite this thresholding the procedure remains adaptive, since the penalty constant is estimated according to the data. Simulation studies We performe numerical simulations to assess the sensitivity of our procedure to the addition of noise. In the first case, we simulate 100 points with K * = 5 segments. In the first case Figure 3 , the segments are regularly spaced and the difference of the means between two segments is d = 1. In the second case (Figure 4 ) the segments are irregularly spaced and the difference of the means varies between d = 2 and d = 0.5. The standard deviation of the Gaussian errors varies from σ = 0.1 to σ = 2. Each configuration is simulated 500 times, and we calculate the average selected number of segments over 500 simulations. In order to assess the performance of the dynamic programming algorithm, we calculate the empirical probability over 500 simulations for a break-point to be located at coordinate t (for t = 1 to 100). Authors' contributions FP developed the statistical models and the programs dedicated to array CGH data analysis, ML developped the adaptive selection of the number of segments. SR, CV and JJD supervised the study.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC549559.xml

544928

The Indirect Benefits of Mating with Attractive Males Outweigh the Direct Costs

The fitness consequences of mate choice are a source of ongoing debate in evolutionary biology. Recent theory predicts that indirect benefits of female choice due to offspring inheriting superior genes are likely to be negated when there are direct costs associated with choice, including any costs of mating with attractive males. To estimate the fitness consequences of mating with males of varying attractiveness, we housed female house crickets, Acheta domesticus , with either attractive or unattractive males and measured a variety of direct and indirect fitness components. These fitness components were combined to give relative estimates of the number of grandchildren produced and the intrinsic rate of increase (relative net fitness). We found that females mated to attractive males incur a substantial survival cost. However, these costs are cancelled out and may be outweighed by the benefits of having offspring with elevated fitness. This benefit is due predominantly, but not exclusively, to the effect of an increase in sons' attractiveness. Our results suggest that the direct costs that females experience when mating with attractive males can be outweighed by indirect benefits. They also reveal the value of estimating the net fitness consequences of a mating strategy by including measures of offspring quality in estimates of fitness.

Introduction Whether mate choice can be maintained by indirect selection when females incur direct costs by being choosy is the subject of ongoing theoretical controversy [ 1 , 2 , 3 , 4 , 5 ]. This is particularly true when the principal or only benefit of mating with attractive males is that they sire attractive sons. Weatherhead and Robertson [ 6 ] suggested 25 y ago that the genetic benefits of mating with an attractive male could outweigh the cost of reduced investment in parental care that such a male makes. This suggestion has been opposed by several important theoretical models [ 5 , 7 , 8 ]. More generally, some recent theoretical work has suggested that because of the weakness of indirect selection relative to direct selection, genetic benefits of choice are likely to have little effect on the evolution of costly mate choice [ 2 , 3 ]. This assertion has been contested by other theoretical work [ 1 ]. In order to understand how mate choice evolves, it is necessary to estimate the overall effect of mate choice on female fitness [ 9 , 10 , 11 , 12 ]. The effect of mating with males of differing attractiveness on total female fitness depends on both positive and negative effects on a variety of fitness components. The signs and strengths of these effects are paramount to distinguishing between the relative importance of various models of mate-choice evolution. Evidence from studies that have measured one or a few fitness components has been invoked to support direct benefits [ 13 , 14 ], “viability genes” [ 15 , 16 , 17 , 18 ], “Fisherian runaway” [ 19 , 20 ], and “sexually antagonistic coevolution” [ 21 , 22 ] models of mate-choice evolution. Similar evidence has also been used in tests for differential allocation of reproductive effort to offspring sired by attractive males [ 14 , 23 , 24 ]. Understanding the relative significance of these processes, however, requires measuring as complete a set of fitness components as possible [ 12 , 19 ] and estimation of the multigenerational effects of mate choice on fitness [ 11 , 25 ] through both sons and daughters [ 12 , 26 ]. To date, only two studies have compared the number of grandchildren produced when females mate with attractive or unattractive males [ 10 , 16 ]. Unfortunately, neither study accounted for the beneficial effects of heritable male attractiveness, an important consideration in most models of mate-choice evolution. How fitness should be estimated is controversial [ 27 , 28 ]. Measuring total fitness is logistically preclusive, but rate-insensitive estimates, such as the number of grandchildren, or rate-sensitive estimates, such as the intrinsic rate of increase, may offer reasonable approximations [ 10 , 11 , 25 ]. The key difference between these two estimates is that rate-sensitive estimates take into account both the timing of reproduction and the developmental time of offspring, whereas rate-insensitive measures do not. To date, most empirical studies have employed rate-insensitive estimates, whereas theoretical models tend to focus on the intrinsic rate of increase [ 28 ]. Here, we measured both direct and indirect fitness components of female house crickets, Acheta domesticus , mated to either attractive or unattractive males for the term of their adult life span. We present a female's total fitness as both a rate-sensitive (the intrinsic rate of increase) and a rate-insensitive estimate of fitness (the total number of grandchildren) in interpreting our findings. Results Our treatment did not affect the number of grandchildren produced via daughters, via sons, or in total ( Table 1 ). Thus there was no difference in the rate-insensitive estimate of fitness for females mated to males of differing attractiveness. Females that mated with attractive males did, however, experience higher relative intrinsic rates of increase ( r est ) than females mated with unattractive males ( Table 2 ). Table 1 The Effects of Mating with Either Attractive or Unattractive Males on a Number of Fitness Components Table 2 The Sensitivity of r est to Variation in Individual and Combined Fitness Components In each reduced model individual females' scores for the component(s) listed were replaced with experiment-wide mean scores. r¯ a and r¯ u are the mean r est for females mated with attractive and with unattractive males, respectively. Test 1 indicates the significance of the r¯ a versus r¯ u comparison within the reduced model (based on 10,000 randomizations). Test 2 assesses the significance of the change in effect size (based on 10,000 jackknifed pseudoestimates) between the reduced model and the full model The overall difference between the treatments on r est was not due to any single fitness component ( Table 2 ). When looking at the fitness components individually, the strongest effects were a survival cost experienced by females mated to attractive males ( Figure 1 ), and an indirect benefit because sons of attractive males were more than twice as likely to mate as those of unattractive males (see Table 1 ). However, neither of these components alone can explain the significant difference in r est between females mated to attractive or to unattractive males (see Table 2 ). Treatment differences in other fitness components, although individually not significant, still influenced our estimates of the overall fitness consequences of mating with attractive males. In particular the combined effect of sons' attractiveness and daughters' fecundity had a significant effect on our model (see Table 2 ). Figure 1 Female Survival in Relation to Experimental Treatment Females housed alone (black line) survived longer than females housed with either type of male (Cox regression Wald 1 = 29.636, p = 0.000). Females mated to unattractive males (blue line) survived longer than females mated to attractive males (red line) (Wald 1 = 10.802, p = 0.001) n = 40, 40, 40. When we combined a female's egg number, egg width, and egg length (from the first week of egg laying) into a single index of reproductive effort, we found that females mated to attractive males exerted greater reproductive effort in the first week of the experiment than those mated to unattractive males (principal component 1: attractive = 0.239 ± 0.116, unattractive = −0.233 ± 0.199, randomisation test p = 0.043). Of the constituent measures of week 1 reproductive effort, only egg width differed significantly between treatments (egg number: attractive = 129.07 ± 15.08, unattractive = 108.17 ± 18.84, p = 0.382; egg width: attractive = 0.618 ± 0.008, unattractive = 0.568 ± 0.014, p = 0.005; egg length: attractive = 2.71 ± 0.017, unattractive = 2.68 ± 0.025, p = 0.373). Discussion To provide an inclusive estimate of the total fitness consequences of mating with an attractive or unattractive male, we quantified both the direct costs to females and the indirect benefits to their offspring. We made two main findings. First, the mating-associated costs borne by females are greater when mating to attractive males throughout their life than when they are mated to unattractive males. Second, these costs are cancelled out (when we use the rate-insensitive measure of the number of grandchildren) and may be outweighed by (when we use the rate-sensitive estimate of the intrinsic rate of increase) the benefits of having offspring with elevated fitness (i.e., indirect benefits). Contrary to some theoretical predictions [ 2 , 3 , 5 ], but see [ 1 , 6 ], our results suggest that it may be possible for female mate choice to evolve via indirect benefits, despite the presence of direct costs. Whether this is the case or not will, however, depend on the magnitude of other costs of choice not measured here, such as the costs associated with being choosy, as well as the accuracy of female choice [ 3 ]. The costs of choice, including the costs of mating with attractive males, are of central importance to theoretic models of mate-choice evolution [ 1 , 2 , 3 , 4 , 5 , 29 ]. In many species females incur survival or fecundity costs due to being courted or harassed by males [ 30 , 31 ], mating [ 32 , 33 ], and allocating resources to egg laying, gestation, and/or parental care [ 34 ]. Female Drosophila melanogaster mated to large (and thus presumably attractive) males incur a greater survival cost than females mated to smaller males [ 21 , 22 ], and this appears to be due to a higher mating rate with large males [ 22 ]. A potential criticism of such studies is that they are based on single traits that are taken to be an indirect measure of a male's attractiveness. By using a direct biological measure that incorporates all traits that contribute to a male's ability to induce a female to mate during short-range courtship, our results provide the first direct evidence that females sustain greater direct costs when mating with males that are more attractive in this context. While we do not know the exact mechanisms driving the survival cost seen in our experiment, our finding that females mated to more attractive males experience lower survival is consistent with sexual conflict between males and females over mating decisions [ 25 , 35 ], and with differential allocation [ 34 ]. Females mated to attractive males exerted greater reproductive effort in the first week of the experiment. This could be the result of male manipulation, for example, increased mating rate [ 32 ], or stimulants in seminal fluids [ 36 , 37 , 38 ] whereby more attractive males manipulate females to invest more in their offspring than is optimal for the females. The possibility of male manipulation is also supported by a study by Murtaugh and Denlinger [ 39 ], which shows that in A. domesticus , males pass substances in their ejaculate that promote higher rates of short-term oviposition. Alternatively, it may be adaptive for females to invest more in the offspring of attractive males [ 34 , 40 ]. Differential allocation is only likely to be adaptive if there is an indirect fitness benefit to allocating greater reproductive effort when mated to attractive males [ 34 ]. The indirect fitness benefits that we report here, particularly the benefit of having more attractive sons, may provide an adaptive basis for differential allocation by females to the offspring of more attractive males. Several studies have reported fitness benefits of mating with attractive males. Females mated to such males have been reported to have offspring that have greater longevity [ 15 , 41 ], faster growth rate [ 16 , 17 , 42 ], increased fecundity of daughters [ 16 , 42 ], and increased attractiveness of sons [ 19 , 20 , 42 , 43 , 44 ]. In our study, the net fitness benefit of mating with attractive males is not due to any single indirect benefit but to a combination of fitness components. This illustrates the importance of measuring net fitness, especially if fitness components act in opposition to each other. A number of studies have proposed the use of an aggregate measure of male attractiveness rather than a single morphological indicator [ 44 , 45 ]. Our use of time to mounting allows us to gain a measure of male attractiveness that is based on all traits that contribute to male mating success (hence ‘attractiveness') during short-range courtship interactions [ 46 ]. It is the use of such a measure that may explain the high correlation between fathers' and sons' attractiveness in this experiment and others based on similar measures [ 12 , 44 ]. The greater attractiveness of sons sired by attractive males may also be explained by differential allocation; studies have shown that maternal effects may enhance the heritability of male traits [ 47 ]. An important role for maternal effects is unlikely in our experiment, however, because no other fitness components of sons or daughters differ significantly between the treatments. Regardless of whether sons' greater attractiveness is due to additive genetic variation for attractiveness per se or to the ability to manipulate females into allocating more resources to the offspring, such a trait will increase a female's net fitness if it increases the reproductive success of her sons sufficiently. Due to the nature of our experimental design we were unable to measure all the costs and benefits associated with choosing and mating with attractive males. First, we did not measure sons' ability to compete with other males for access to females. However, in this population of A. domesticus , fighting ability has been shown to be positively correlated with attractiveness as we have measured it here [ 48 , 49 ]. Thus, if anything we may have underestimated the fitness benefit gained through having attractive sons. Second, we did not measure long-range attraction of males through advertisement calling. Third, our design simplifies the way mating takes place for females paired with attractive or unattractive males. Pairing females with a single male for 7 d at a time may decrease or increase the costs associated with mating with males. For instance, costs may be increased because females are unable to escape male harassment, or they may be decreased because there is no male–male competition. Despite these limitations, we believe that our estimate of the intrinsic rate of increase offers a reasonable approximation of net fitness. The fitness estimate of choice in empirical studies may depend on the importance of reproductive timing in the system in question [ 28 ]. Brommer et al. [ 27 ] compared estimates of lifetime reproductive success and intrinsic rate of increase to real long-term data from two species of bird. They showed that lifetime reproductive success was a better estimate of genetic contribution to future generations. However, their estimates did not include measures of offspring quality, and as they point out, their results may depend on the species life history, and the generality of their conclusions thus remains to be tested. There are several reasons why reproducing early and having short maturation times is likely to be advantageous in crickets. First, extrinsic mortality of crickets in the wild is likely to be high. Second, females become less choosy [ 50 ], lose condition, and produce fewer eggs as they age (M. L. Head, unpublished data). Also, individuals with shorter generation times will contribute their genes to future generations more rapidly [ 51 ]. Our research constitutes one of the first attempts to directly and simultaneously test the combined direct and indirect effects of mating with males that differ in attractiveness. Only by following the effects of mating with attractive or unattractive males through at least two generations, and through both sons and daughters, is it possible to observe the combined direct effects on female lifetime fecundity and the genetic effects on offspring fitness [ 11 , 12 , 25 ]. Although the need to conduct such a study under laboratory conditions may constrain our ability to definitively answer this question, our results suggest that indirect genetic benefits have the potential to outweigh direct costs of mating with attractive males. Moreover, this effect comes about largely, but not exclusively, due to the production of more attractive sons. Materials and Methods Study species We obtained approximately 1,000 final-instar A. domesticus nymphs from a commercial cricket breeder (Pisces Enterprises, Phoenix, Arizona, United States). Nymphs were separated into single-sex culture tubs (4 × 80 l containers per sex) to ensure their virginity, and reared with constant access to food (Friskies Go-Cat senior) and water until eclosion. At eclosion, adults were maintained in single-sex cultures for a further 10 d to ensure sexual maturity. In the cultures from which the insects have been derived, crickets are raised in densities ranging from 23,000–34,000 m −3 and fed grain ad libitum. In these conditions males and females mate multiply. Males fight with other males and court females, and there is a positive relationship between male dominance and attractiveness [ 49 ]. Despite high densities, female cooperation is needed for mating to occur because a female must actively mount the male and align her genitalia with his to mate. Mate choice in both culture and wild populations of A. domesticus is generally sequential. That is, females choose males by either mating or rejecting males one at a time, rather than choosing between males simultaneously. We chose to work on cultured A. domesticus because our laboratory conditions closely resemble the culture conditions under which they have recently evolved. This similarity maximises the evolutionary relevance of our measures. Our experimental design, however, requires that females be kept alone, creating an important environmental difference from the culture conditions to which females have been adapted. Male attractiveness The attractiveness trials throughout our experiment were based on latency to mounting for pairs of crickets. While this protocol does not allow all elements of female choice to be measured, in A. domesticus a female mounting a male is a reliable predictor of mating success (in a previous study 46 out of 50 mountings led to successful transfer of a spermatophore [ 49 ]). Also, females have been shown to produce more eggs for males that they choose quickly (M. L. Head, unpublished data). This indicates that latency to mounting is representative of other aspects of choice in this species. To obtain males that were either attractive or unattractive to females we ran a two-round tournament. In round one, each male was placed in a clear plastic container (7 × 7 × 5 cm) with a single randomly assigned female, at night, under red lighting. When a female mounted a male, but before spermatophore transfer, they were separated. Once half of the females had mounted, all remaining pairs were separated. Round two commenced with a new female assigned at random to each male. The first half of first-round mounted males to be remounted became our “attractive” treatment males. The half of first-round unmounted males that remained unmounted longest in round two became the “unattractive” treatment males. Only males that courted females during the tournament were used. This biological assay of male attractiveness incorporates all traits that make a male attractive during short-range courtship, rather than a single trait correlated with attractiveness (see [ 10 , 11 , 44 ]). Experimental design Forty females were randomly assigned to each of three treatments: attractive, unattractive, and an unmated control. Females were weighed and placed individually in a small plastic container (as above) with food, water, and a petri dish of moist sand for egg laying. Males from the appropriate treatment were randomly assigned to a female. Every 7 d, or whenever a male died, a new male from the same treatment (but from a new tournament) was placed with the female. This allowed us to measure the fitness consequences of the strategy of mating with attractive or unattractive males, rather than the consequences of mating with a given individual male. Food, water, and sand were replaced every 7 d. Fitness measures Female survival was monitored daily, and the number of eggs laid was counted weekly. Hatching success was estimated as the proportion of eggs that hatched within 14 d of the first egg hatching in each collection. Hatchlings were collected every 3 d, and their mean weight was recorded. Each week, 50 hatchlings per female were separated into two boxes (20 × 13 × 13 cm), each containing 25 nymphs. We monitored offspring survival every 7 d and recorded the time to mature and sex and body weight at eclosion. If a female had fewer than 50 hatchlings in a given week these were discarded. For these females, the actual number of hatchlings was multiplied by the overall experimental mean for each subsequent offspring fitness measure, to predict the number of grandchildren produced. This is a conservative approach to missing values because it reduces the difference between the treatments. Offspring generation times were calculated from the time females were first placed with a male until the offspring matured. This takes into account not only the time it takes for the offspring to mature, but also the timing of egg laying. Mature offspring were housed individually, and their survival monitored daily. Ten days after eclosion each son's attractiveness was estimated by placing him with a stock virgin female in a small plastic container for 90 min. Mounted males were separated from females before spermatophore transfer occurred. We used the proportion of a female's sons that were successful in this assay as our measure of sons' average attractiveness (e.g., if 8 of 16 sons were mounted, we assumed that, on average, each son had a 50% chance of mating per encounter with a female). Ten days after eclosion daughters were placed with a stock male for 12 h to allow mating. Afterwards, survival of sons and daughters was again monitored daily, and sand was collected from daughters weekly. Eggs from daughters were counted to estimate lifetime fecundity. Statistical analysis We calculated two estimates of female relative net fitness when mating with either an attractive or unattractive male. A rate-insensitive estimate, the relative number of grandchildren produced by a female ( g est ) , and a rate-sensitive estimate, r est . To estimate the absolute number of grandchildren each female had ( G est ) , we added the number of grandchildren she had through daughters, estimated as to the number she had through sons, G sons , estimated as The attractiveness of a female's sons was estimated as the proportion of her sons that were mounted in attractiveness trials; longevity is the mean adult life span of a female's sons and c is the ratio of the total number of grandchildren through daughters in the experiment to the total number of sons mounted in the attractiveness trials. This correction factor converts an attractiveness score into units of the number of grandchildren. Using this correction factor ensured that mean son and daughter reproductive success across the entire experiment was equal, satisfying the assumption that mean reproductive success for males and females is the same in populations with an equal sex ratio [ 52 ]. G est for each female was then divided by the experimental mean to give the relative g est . We estimated the absolute intrinsic rate of increase for each female as where t is the generation time from parental first mating to offspring maturity in a particular lineage. We converted our rate-sensitive measure into a measure of relative intrinsic rate of increase ( r est ) , by dividing each female's R est by the experiment-wide mean. Due to the non-normal distributions of many fitness components, we tested the significance of treatment differences for each fitness component using two-tailed randomisation tests. In each randomisation test the observed data was randomly assigned to the two treatments 10,000 times. P -values are based on the proportion of randomisations in which the absolute value of the estimated difference was greater than that observed in the original data. To explore the sensitivity of our estimates of r est to variation in each fitness component we used a model-building approach. We removed the variance of each fitness component from our full model, in turn, by assigning every female the overall experimental mean value of that component. We similarly excluded every combination of two fitness components. We then ran a randomization test (as above, 10,000 randomizations) for each reduced model to test whether the treatment effect remained. We also obtained 10,000 jackknifed estimates of the difference between the treatments for each reduced model (by randomly omitting 20% of the sample in each estimate), to test whether the reduced model resulted in a significantly different effect size than the original full model. P -values are based on the proportion of jackknifed estimates in which the absolute value of the difference between the treatments was greater than the absolute difference in the full model. We used principal components analysis to investigate the effects of mating with attractive or unattractive males on week 1 reproductive effort via egg number, egg width, and egg length. All three measures showed a strong positive loading on the first principal component, which explained 66% of the variation in the constituent measures. We then tested for differences in female reproductive effort between the treatments using a randomisation test.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544928.xml

545203

Integration of Retroviruses: A Fine Balance between Efficiency and Danger

Since retroviruses can integrate a copy of their DNA into the host cell DNA, they are good vectors for gene therapy. But such vectors also have oncogenic potential

One of the key characteristics of retroviruses is their ability to integrate a copy of the DNA reverse-transcribed from their viral RNA genome into host cell DNA. This integration is mediated by a preintegration complex (PIC) comprising viral DNA, reverse transcriptase, and integrase, as well as poorly characterized host proteins [ 1 ]. It is this property that makes retroviruses good vectors for the transfer of therapeutic genes, and many such vectors have been made. The Perspectives section is for experts to discuss the clinical practice or public health implications of a published study that is freely available online. Stages in Vector Development Early vectors included those derived from murine oncoretroviruses (RVs) such as murine leukemia virus (MLV). A limitation of these viruses was that their PIC requires that the nuclear membrane is dissolved so that the PIC can come in direct contact with host cell DNA; hence, there is efficient integration only in dividing cells. Later vectors were based on lentiviruses (LVs) such as human immunodeficiency virus and simian immunodeficiency virus (SIV), whose PIC can penetrate into the nucleus so that integration can occur in nondividing cells that are in the G1 phase of the cell cycle. Insertional Mutagenesis MLV-derived vectors have been used with success to achieve sustained correction of two forms of severe combined immunodeficiency (SCID)—SCID-X1 (Gamma-c deficiency) [ 2 ] and adenosine deaminase deficiency [ 3 ]. In both cases, hematopoietic progenitors were infected out of the patient's body with nonreplicative MLV-derived vectors. However, integration carries the risk of insertional mutagenesis. This mutagenesis has been demonstrated in the chicken by using replicative RVs. RV integration close to protooncogenes has been shown to induce their activation, leading to tumorigenesis. Nonreplicative MLV vectors have also been reported to induce insertional mutagenesis in a murine model [ 4 ] and, more worryingly, in two patients from the SCID-X1 trial [ 5 ]. In both instances, it is suggested that cooperation between vector-associated transgene expression (dLNGFR in one case and common Gamma chain in the other) and long terminal repeat–driven enhancement of protooncogene expression ( evl-1 and LMO-2, respectively) was responsible for aberrant clonal proliferation. No such events have been reported yet in the use of LV vectors in experimental settings. Sites of Retroviral Integration It was initially believed that integration of retroviruses occurred randomly, but the advent of technology allowing the assessment of RV or LV integration into host cell genomes has led to a reassessment of this assumption. Using a combination of ligation-mediated polymerase chain reactions and sequencing of amplified integration sites (unique sequences made from the viral long terminal repeat and the host-genome-associated sequence), it is possible to determine all the integration sites that can be found in a given transduced cell population (or its progeny). Exact mapping can be done back to the human (or relevant animal) genome database. However, even this methodology may not detect all of the integration sites present in a given transduced cell population. Key papers have determined the “rules of the game” for RV and LV integration into a variety of cell lines [ 6 , 7 , 8 ]; these rules seem to differ between the virus types. In both cases, however, the integration pattern is not random. RV integration tends to be close to transcription start sites of active genes—close enough to regulatory sequences to potentially exert a long terminal repeat–mediated enhancer effect [ 7 , 8 ]. By contrast, LVs integrate mostly in transcription units, with a preference for actively transcribed genes, but do not target the region downstream of transcription start sites [ 6 , 8 ]. These data indicate that there are virus-specific PIC-associated determinants that cause specific targeting with the host cell genome. However, much remains to be done to identify viral factors and host ligands involved in these interactions. Data from these pioneering papers were obtained by in vitro infection of mature cells or cell lines with the relevant RV or LV [ 6 , 7 , 8 ]. However, it is possible that the pattern of integration might differ in other cell subsets, particularly immature cells such as hematopoietic progenitors. In fact, as shown by Mitchell et al. [ 8 ], infection of mature cells of different tissue types leads to a partially distinct pattern of integration sites related to the set of genes transcribed in these different cell types [ 8 ]. In Vivo Models In a paper published in last month's PLoS Biology, Hematti et al. [ 9 ] took the in vivo analysis further by analyzing the pattern of integration sites in cells derived from simian hematopoietic progenitor cells transduced either with a RV (MLV) or a LV (SIV) vector and transplanted into monkeys. This experimental setting is the closest possible to human trials. The results essentially confirm the nonrandom insertion pattern of both types of vectors as shown by the analysis of cell lines transduced in vitro. The analysis showed the Gaussian distribution of insertions centered on the transcription start site—thus very close to regulatory elements—of RV ( Figure 1 ), and the preference of LV for the transcription units, with a concentration of integrations into some gene-dense regions [ 9 ]. A study such as this is therefore a useful piece of preclinical work that will help the interpretation of the analysis of clinical samples. Figure 1 Distribution of MLV and SIV Integration Sites within a 60-kb Window Centered on Transcription Start Sites The vertical arrow points to 0 kb. Each gray bar corresponds to the percentage of SIV integration sites within a 5-kb interval, and black bars correspond to the percentages of MLV integration sites in a 5-kb interval. The distribution of a set of 65,000 in silico–generated random integration sites is represented by the dashed line. (Source: [ 9 ].) Similar data were also obtained by Laufs et al. in the analysis of a set of RV integration sites into human hematopoietic progenitors xenotransplanted into immunodeficient mice [ 10 ]. It thus appears that the overall distinct pattern of RV and LV integration could be independent of transduced cell types (immature versus differentiated, and tissue type). However, the targeted genes could differ considerably depending on the set of genes expressed in the target cell. Several parameters could potentially influence the transcription profile in a clinical setting, including stage of cell maturity, tissue type, ex vivo transduction culture conditions, patient age [ 5 ], underlying genetic disease, and any modification of the vector. Designing Future Vectors It will thus be essential to build a free, accessible database incorporating all relevant information gathered from both experimental and clinical settings. In this respect, information gathered from the SCID-X1 and adenosine deaminase deficiency trials is awaited with great interest. Combining all available information will be the only way to determine the frequency of insertions that have a potential to induce activation of a protooncogene (a risk primarily associated with the use of RV) or to induce disruption of a regulatory gene (a risk primarily associated with the use of LV). Studies of relevant gene expression activation or suppression should therefore be carried out in parallel. It is only from these multiple analyses, including a careful comparison of the oncogenic potential of vectors in relevant animal models, that a precise assessment of the risk associated with use of retroviruses for gene therapy will come. These data will thus be the basis for objective comparison between technologies and for the design of safer vectors.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545203.xml

549217

HIV-1 Tat interacts with LIS1 protein

Background HIV-1 Tat activates transcription of HIV-1 viral genes by inducing phosphorylation of the C-terminal domain (CTD) of RNA polymerase II (RNAPII). Tat can also disturb cellular metabolism by inhibiting proliferation of antigen-specific T lymphocytes and by inducing cellular apoptosis. Tat-induced apoptosis of T-cells is attributed, in part, to the distortion of microtubules polymerization. LIS1 is a microtubule-associated protein that facilitates microtubule polymerization. Results We identified here LIS1 as a Tat-interacting protein during extensive biochemical fractionation of T-cell extracts. We found several proteins to co-purify with a Tat-associated RNAPII CTD kinase activity including LIS1, CDK7, cyclin H, and MAT1. Tat interacted with LIS1 but not with CDK7, cyclin H or MAT1 in vitro . LIS1 also co-immunoprecipitated with Tat expressed in HeLa cells. Further, LIS1 interacted with Tat in a yeast two-hybrid system. Conclusion Our results indicate that Tat interacts with LIS1 in vitro and in vivo and that this interaction might contribute to the effect of Tat on microtubule formation.

Background HIV-1 Tat protein is the viral transactivator encoded in the HIV-1 genome of infected cells [ 1 - 3 ]. Tat stimulates formation of full-length transcripts from the HIV-1 promoter by promoting efficient transcript elongation (reviewed in [ 4 ]). Tat interacts with the bulge of transactivation response (TAR) RNA, a hairpin-loop structure at the 5'-end of all nascent viral transcripts [ 5 - 7 ]. Tat induces elongation of HIV-1 transcription by recruiting transcriptional co-activators that include Postive Transcription Elongation Factor b (P-TEFb), an RNA polymerase II C-terminal domain kinase [ 8 - 10 ] and histone acetyl transferases [ 11 - 13 ]. Whereas P-TEFb induces HIV-1 transcription from non-integrated HIV-1 template [ 8 - 10 ], histone acetyl transferases allow induction of integrated HIV-1 provirus [ 11 - 13 ]. Tat may also increase initiation of HIV-1 transcription by enhancing phosphorylation of SP1, a transcription factor involved in the basal HIV-1 transcription [ 14 ]. In addition to its function in HIV-1 transcription, Tat may contribute to HIV-1 pathogenesis by regulating signal transduction in endothelial cells [ 15 , 16 ]; functioning as a secreted growth factor for Kaposi sarcoma and endothelial cells [ 17 ]; and inducing apoptosis in T-cells by binding to microtubules and delaying tubulin depolymerization [ 18 , 19 ]. Tat induces apoptosis through BIM, a pro-apoptotic protein of the Bcl-2 family that antagonizes Bcl-2 anti-apoptotic proteins [ 18 ]. The effect of Tat is similar to the effect of Taxol, a drug that stabilizes microtubules and induces apoptosis [ 18 ]. Mutations in the glutamine-rich region of Tat protein (residues 60–72) were found to correlate with rapid progression of HIV disease, and with induction of apoptosis and binding to tubulin [ 20 ]. We previously showed that microtubules polymerization is facilitated by LIS1 protein [ 21 ], a causative factor for Lissencephaly [ 22 ], a severe brain disorder resulted from inefficient neuronal migration during early stages of brain development [ 23 ]. LIS1, a 45 kDa protein, contains seven repeating units called WD (Trp-Asp) repeats [ 24 ] that form antiparallel sheets making up a toroidal propeller structure [ 25 ]. WD repeats containing proteins are confined to eukaryotes and participate in protein-proteins interactions [ 24 ]. In addition to being a microtubule binding protein, LIS1 is also a subunit of platelet-activating factor acetyl hydrolase (PAF-AH) [ 26 ]. LIS1 interacts with dynein motor, NudC and Dynactin, a complex that regulates microtubule dynamics [ 27 , 28 ]. LIS1 in addition associates with Nudel [ 29 ], also a component of the dynein motor complex, and this interaction affects dephosphorylation of microtubules by protein phosphatase 2A (PP2A) [ 30 ]. Thus, LIS1 may function as a scaffold that help to assemble dynein motor and serve to regulate proper microtubule dynamics. In the present paper, we fractionated extracts of Jurkat T-cells using HIV-1 Tat as an affinity bait and RNAPII CTD activity of the Tat-associated proteins as a selection criteria. We identified by mass-spectrometry and immunoblotting components of the partially purified protein fraction and found LIS1, CDK7, cyclin H, and MAT1. We analyzed interaction of Tat with the identified individual proteins and found that Tat interacts with LIS1. We confirmed this finding by co-immunoprecipitating Tat and LIS1 from HeLa cells that were expressing Tat. And we also confirmed binding of Tat to LIS1 in a yeast two-hybrid system. Our results indicate that HIV-1 Tat interacts directly with LIS1, and therefore this interaction might contribute to the effect of Tat on microtubules formation in the cells. Results LIS1, CDK7, cyclin H, and MAT1 co-purify with Tat-associated RNAPII CTD kinase activity We reported previously that HIV-1 Tat associates with two distinct protein kinase complexes purified from mitogenically stimulated human primary T-lymphocytes; one complex containing CDK2 and the other one CDK7 [ 31 ]. The CDK2-containing protein complex was previously purified and characterized by us [ 32 , 33 ] and we showed that CDK2 regulates HIV-1 transcription [ 34 ]. In the present paper, we purify and characterize the CDK7-containing protein elution peak. Whole-cell lysate from Jurkat T cells was prepared and subjected to (NH 4 ) 2 SO 4 fractionation as described previously [ 32 ]. In accord with our previous report [ 32 ], the 40% (NH 4 ) 2 SO 4 cut contained Tat-associated CTD kinase activity (Fig. 1A ). The 40% (NH 4 ) 2 SO 4 cut was subsequently fractionated on DEAE-Sepharose (Fig. 1B ). As we previously reported, separation of the ammonium sulphate cut on DEAE-Sepharose resulted in the appearance of Tat-associated CTD hyperphosphorylating activity (Fig. 1B , fractions 34 to 36). Hyperphosphorylated CTD (CTDo) migrated on SDS-PAGE with a high degree of retardation, because of SDS repelling effect. Immunoblotting of the DEAE-Sepharose fractions 34 to 36 showed the presence of CDK7, CDK9 and a PSTAIRE-motif containing kinase, but not TFIIH (See Additional File 1 ) in accordance with our previous observations [ 32 ]. Further resolution of DEAE fractions 34–36 on SP-Sepharose column showed that part of the Tat-associated CTD kinase activity was retained by the column and we previously identified this activity as containing CDK2 [ 32 ]. The other part of the Tat-associated CTD kinase activity was not retained by the column and was eluted as a flow-through fraction (Fig. 1C , flow through fraction). This fraction was collected and further resolved on Hi-Trap heparin column (Fig. 1D ). Immunoblotting of the Hi-Trap heparin fractions showed that Tat-associated kinase activity co-eluted with CDK7 and also with cyclin H, but not with CDK9 or a PSTAIRE-motif containing kinase (Fig. 2A ). Silver staining of the Hi-Trap heparin fractions showed that fractions 22 and 24 contained three protein bands of 35, 40, and 50 kDa which co-eluted with the Tat-associated CTD kinase activity (Figure 2B ; fractions 22 and 24, protein bands marked by stars). The Hi-Trap heparin fractions 22 to 24 were further analyzed on Sephacryl S-300 gel filtration column to determine whether CDK7, cyclin H and unknown protein bands comigrate as a single macromolecular mass. Following gel filtration on Sephacryl S-300, the Tat-associated CTD kinase activity was found in the fractions corresponding to the eluted proteins with a mass of 350 kDa (Fig. 3A , fraction 16–18). Immunoblotting analysis showed that CDK7, cyclin H (Fig. 3A ) and MAT1 (not shown) co-eluted with the Tat-associated CTD kinase activity. Fractions 16–18 contain 32, 35, 40, 50 and 60 kDa protein bands (Fig. 3B , protein bands marked by stars). To determine composition of unknown protein bands, fractions 22 to 24 were combined, concentrated on Centricon-10 spin column (Amicon), recovered in SDS-loading buffer and resolved on 12% SDS-polyacrylamide gel. Following staining with colloidal Coumassie blue, only two protein bands of 35 and 50 kDa were visualized and subjected to tryptic digestion and nanoelectrospray MS (described in Experimental procedure section). The 35 kDa protein contained peptides vpflPGDSDlDqltr and YPilENPEilr (lower case letters indicate residues observed with less than full confidence) with sequence identity to CDK7 and cyclin H, respectively. The 50 kDa protein contained a peptide VWDYETGDfER with sequence identity to LIS1. Figure 1 Purification of Tat Associated CTD Kinase. A , Ammonium sulfate fraction of T-cell extract. Whole cell extract of Jurkat T cells was fractionated by ammonium sulfate added sequentially to 10%, 20%, 40% and 80% saturation as described in Experimental procedures . Fractions were analyzed for Tat-associated CTD kinase activity as described in the Experimental procedures section. A portion of each fraction was bound to GST-Tat 72 immobilized on glutathione-agarose beads and then incubated with [γ- 32 P] ATP and recombinant GST-CTD. Phosphorylated GST-CTD was resolved on SDS/10%-(w/v)-PAGE. B , DEAE-Sepharose column-chromatographic elution profile. Jurkat T-cell extract 40%-(NH 4 ) 2 SO 4 cut was applied to a DEAE-Sepharose column. Fractions were analyzed for Tat-associated CTD kinase activity as described above. C , SP-Sepharose column-chromatographic elution profile. DEAE-fractions 32 to 36 containing hyperphosphorylating CTD kinase activity were combined and applied to SP-Sepharose column. D , heparin-agarose column-chromatographic elution profile. SP-Sepharose flow-through fraction was collected and further fractionated on Hi Trap heparin column. Fractions 22 to 24 (labelled as purified complex) contained Tat-associated CTD hyperphosphorylating activity. Positions of CTDa and CTDo are shown. The figure is an autoradiogram. Figure 2 Analysis of protein composition of heparin-agarose purified fraction of Tat-associated CTD kinase. A , Heparin-agarose-purified fraction contains CDK7 but not CDK9. Fractions from the heparin-agarose column fractionation shown in Fig. 1 were analyzed by Western blotting with antibodies against CDK7, Cyclin H, PSTAIRE and CDK9. Fractions 22 to 24 which contain Tat-associated CTD hyperphosphorylating activity also contain CDK7 and cyclin H, but not CDK9 or PSTAIRE-like kinase. B , Tat-associated CTD kinase co-purifies with 35, 40 and 50 kDa protein bands. Fractions from the heparin-agarose column fractionation were resolved on 12% SDS PAGE and stained with silver. Protein bands of 35, 40, and 50 kDa that co-purify with the CTD kinase activity are marked by stars. Figure 3 CDK7 and cyclin H co-migrate as a 350 kDa complex. Hi-Trap heparin fractions 22 to 24 were analyzed on Sephacryl S-300 gel filtration column. A , Tat-associated CTD kinase activity co-purify with CDK7 and cyclin H. Fractions from the Sephacryl S-300 column fractionation were analyzed for Tat-associated CTD kinase activity and also by Western blotting with antibodies against CDK7 and Cyclin H. B , Fractions from Sephacryl S-300 column fractionation were resolved by 12% SDS PAGE and stained with silver. HIV-1 Tat interacts with WD domains of LIS1 in vitro Next we analysed which one of the identified proteins in the elution complex might interact with Tat. We expected that CDK7 might bind to Tat as their interaction was previously reported [ 35 ]. We incubated fractions 18 to 24 with GST-fused Tat 1–72, then precipitated GST-Tat with glutathione-agarose beads and analysed associated proteins on SDS-PAGE followed by a silver staining. We found that a 50 kDa protein associated with GST-Tat in fractions 20 and 22 (see Additional file 2 , lanes 3 and 4). We then asked whether LIS1, a candidate for a 50 kDa Tat-interacting protein, binds to Tat. We translated LIS1 and also translated as controls CDK7, cyclin H and MAT1, in reticulocyte lysate (Fig. 4A ) and performed GST pull down assays using GST-fused Tat 1–72 (Fig. 4B ). LIS1 bound to Tat (Fig. 4B , lane 4). In contrast, almost no binding was detected for CDK7, cyclin H or MAT1 (Fig. 4B , lanes 1 to 3). These results contrasted with the previous report in which recombinant Tat interacted with CDK7 immunopurified from reticulocyte lysate [ 35 ]. The main difference of our study was that we used programmed lysates rather than purified proteins. Immunoaffinity analysis showed that reticulocyte lysate contains substantial amount of endogenous LIS1 which is comparable to the amount of LIS1 in the LIS1-programmed lysate (see Additional file 3 , compare lanes 1–3 to lane 4). Thus the excess of LIS1 might compete for the binding to Tat and prevent CDK7 interaction with Tat. To analyze whether WD domains of LIS1 might associate with Tat, we expressed each of WD domain, except domain 2 as well as the N-terminal part of LIS1, which contains a coiled-coiled motif and which is devoid of WD domains. The WD domain 1, 4, 5 or 7 bound to Tat (Fig. 4B , lanes 6 to 11). Also the N-terminal portion of LIS1 bound weakly to Tat (Fig. 4B , lane 5). To analyze specificity of the binding and to determine a domain of Tat that binds LIS1, several Tat mutants were utilized including Tat 1–72, Tat 1–48, and Tat 37–72 and also GST as a control (Fig. 5 ). Full length LIS1 bound with equal efficiency to a full length Tat, Tat 1–48 or Tat 37–72 but not to GST alone (Fig. 5 , lanes 2 to 5). In contrast, isolated WD5 domain of LIS1 bound most efficiently to the full length Tat 1–72 and less efficiently to Tat 1–48 or to Tat 37–72 (Fig. 5 , lanes 6 to 9). The isolated N-terminal domain of LIS1 bound strongly to GST (Fig. 5 , lane13), and thus its weak binding to GST-Tat (Fig. 5 , lane 12) is likely to be mediated by the binding to the GST moiety. Figure 4 LIS1 binds to HIV-1 Tat in vitro . Individual protein components of Tat-associated complex were translated in reticulocyte lysate containing [ 35 S]methionine as described in the Experimental procedures section. A , Input lysates, resolved on 12% SDS-PAGE. Lane 1 - CDK7; Lane 2 -Cyclin H; Lane 3 -MAT1; Lane 4 -LIS1; Lane 5 -the N-terminal domain of LIS1 (LIS NT); Lane 6 - WD7; Lane 7 -WD6; Lane 8 – WD5; Lane 9 – WD4; Lane 10 - WD3; and Lane 11 - WD1. B , programmed reticulocyte lysates from panel A precipitated with GST-Tat 72 immobilized on glutathione-agarose beads, and resolved on 12% SDS-PAGE. Figure 5 WD5 domain of LIS1 interact with HIV-Tat. LIS1, WD5 domain of LIS1 (WD5) and N-terminal portion of LIS1 (LIS1 NT) were translated in reticulocyte lysate containing [ 35 S] methionine as described in the Experimental procedures section. Lysates were precipitated with GST-fused Tat 1–72, Tat 1–48, Tat 37–72 or GST alone, immobilized on glutathione-agarose beads, and resolved on 12% SDS-PAGE. Lanes 1, 6 and 11 – Input; Lanes 2, 7 and 12 – precipitation of LIS1, WD5 or LIS1 NT with Tat 1–72; Lanes 3 and 8 – precipitation of LIS1 and WD5 with Tat 1–48; Lanes 4 and 9 – precipitation of LIS1 and WD5 with Tat 37–72; Lanes 5, 10 and 13 – precipitation of LIS1, WD5 or LIS1 NT with GST alone. The figure is an autoradiogram. Tat co-immunoprecipitates with LIS1 from HeLa cellular extracts To analyze interaction of Tat with LIS1 in cultured cells, co-immunoprecipitation analysis was performed. Tat was expressed in HeLa cells infected with adenovirus vector expressing Flag-tagged Tat [ 36 ]. Tat expression in the extract was verified by immunoblotting analysis with anti-Flag antibodies (Fig. 6A , compare lane 2 to lane 1) and also with anti-Tat antibodies (not shown). LIS1 was expressed equally in control cells without Tat and in the cells expressing Flag-Tat (Fig. 6B , lanes 1 and 2). Tat co-precipitated with LIS1 when LIS1 was immunoprecipitated with LIS1-specific monoclonal antibodies, resolved by 12% Tris-Tricine PAGE and immunoblotted with anti-Flag antibodies (Fig. 6A , lane 3). No Tat was detected in the control immunoprecipitation (Fig. 6A , lane 4). Similar, LIS1 co-precipitated with Tat when Flag-Tat was immunoprecipitated with anti-Tat polyclonal antibodies, resolved by 10% Tris-Tricine PAGE and immunoblotted with anti-LIS1 antibodies (Fig. 6B , lane 3). No LIS1 was detected in the control immunoprecipitation (Fig. 6B , lane 4). These results indicate that Tat associates with LIS1 in cultured cells. Figure 6 Co-immunoprecipitation of HIV-1 Tat with LIS1 from HeLa cells. HeLa whole cell extracts, with and without Flag-Tat, were prepared from uninfected and Adeno-Tat infected cells as described in the Experimental procedures section. A , LIS1 was immunoprecipitated with monoclonal anti-LIS1 antibodies, resolved by 10% Tris-Tricine gel and immunoblotted with anti-Flag antibodies to detect Flag-Tat. B , Flag-Tat was immunoprecipitated with polyclonal anti-Flag antibodies, resolved by 12% Tris-Tricine gel and probed with monoclonal anti-LIS1 antibodies to detect LIS1. Tat binds to LIS1 in yeast two-hybrid system To analyze whether Tat interacts with LIS1 directly and not through another protein, we utilized LexA-based yeast two hybrid system (Clontech, see details in Experimental procedures ). EGY48 yeast cells pretransformed with pSH18–34 reporter plasmid (-Ura selection) were further transformed with different combinations of pJG-LIS1 or pJG4–5 empty vector (-Trp selection) and pLexA Tat or pLexA empty vector (-His selection). Colonies grown on-His/-Trp/-Ura media with glucose were plated on Galactose/Raffinose His /-Trp/-Ura plates, to induce LIS1 and Tat production. The plates also contained 5-Bromo-4-Chloro-3-Indolyl-β-D-galactopyranoside (X-Gal) substrate for β-galactosidase. Tat interacted with LIS1 as it was detected by development of blue color upon conversion of X-gal (Fig. 7D ). In contrast Tat did not interact with the acid activation domain alone (Fig. 7C ). Also no interaction was detected for LexA DNA binding domain and acid activation domain (Fig. 7A ) or LexA DNA binding domain and LIS1 (Fig. 7B ). Figure 7 LIS1 interacts with Tat in yeast two-hybrid assay. EGY48 yeast cells were transformed, as described in Experimental procedures , with pSH18–34 reporter and combinations of pLexA and pJG4–5 empty vectors ( panel A ); pLexA and pJG LIS1 ( panel B ); pLexA Tat and pJG 4–5 ( panel C ); pLexA Tat 86 and pJG LIS1 ( panel D ). Six independent colonies from each transformation were cultured on plates containing Galactose/Raffinose to induce Tat and LIS1 synthesis and X-Gal substrate to detect β-galactosidase. Taken together, these results indicate that LIS1 directly and specifically binds to Tat in vivo . Discussion In this study, we show that HIV-1 Tat protein associates with LIS1 protein. LIS1, a microtubule binding protein [ 21 ] contains WD repeats [ 24 ] that are likely to participate in protein-protein interactions [ 24 ]. LIS1 regulates microtubule dynamics by interacting with dynein motor, NudC and Dynactin [ 27 , 28 ] and also with Nudel [ 29 ]. A yeast homologue of LIS1, NudF associates with NudC to regulate dynein and microtubule dynamics [ 37 , 38 ]. Thus, interaction of Tat with LIS1, a scaffold that assembles dynein motor, may affect microtubule dynamics. We purified several candidate proteins that might interact with Tat, and found CDK7, cyclin H, MAT1 and LIS1. We expected that CDK7 might bind to Tat as previously it was shown to interact directly with Tat [ 35 ]. In contrast, analysis of the binding of individually translated proteins showed that LIS1 and not CDK7 bound to Tat. We hypothesized that WD domain(s) of LIS1 might bind Tat, as these domains form a planar surface. Correspondingly, domains WD1, WD4, WD5 and WD7 were found to bind Tat but not the N-terminal part of LIS1 that contains coil-coil region, and which is devoid of WD domains. We analyzed whether a particular domain of Tat binds LIS1 or WD5 domain of LIS1. Full length Tat 1–72 was most efficient in binding of either LIS1 or WD5 domain of LIS1. It would be interesting to determine whether CDK7 also binds to LIS1, and whether LIS1 promotes activation of the kinase activity of CDK7 by Tat. Although LIS1 is a cytoplasmic protein, it may be required for initial assembly of a protein complex containing CDK7. Our results contrasted with the previous report in which Tat binds to purified CDK7 [ 35 ]. We hypothesize that under our experimental conditions, excess of endogenous LIS1 present in the reticulocyte lysate might compete with interaction of Tat with CDK7. Interestingly, Gaynor an colleagues only detect specific interaction of Tat with TFIIH but not with of CDK7 or CAK alone [ 39 ]. Therefore, it is possible that in a complex protein mixture Tat interacts with CDK7 indirectly through another protein such as LIS1. To explore interaction of Tat and LIS1 in cultured cells, Flag-tagged Tat was expressed in HeLa cells and then immunoprecipitated with anti-Flag-antibodies. LIS1 was found to co-immunoprecipitate with Tat. Correspondingly, when LIS1 was immunoprecipitated with anti-LIS1 monoclonal antibodies, Flag-Tat was found in the immunoprecipitates. These results suggest that Tat associates with LIS1 in cultured cells. To confirm that LIS1 and Tat interact in vivo , we used yeast two-hybrid system, in which Tat was expressed as a bait and LIS1 as a prey. Again, we found that LIS1 and Tat interacted in this system. Taken together, our in vitro and in vivo results demonstrate that HIV-1 Tat binds to LIS1 and that this binding is likely to occur through one of the WD domains of LIS1. Tat contains several functionally important regions, including the N-terminal region I (residues 1–21); cystein-rich region II (residues 22–37); core region III (residues 38–48); basic region IV (residues 49–59); glutamine-rich region V (residues 60–72); and C-terminal region VI [ 20 , 40 ]. Zhou and his colleagues showed that Tat interacts with microtubules through parts of region II (residues 35–37) and region III (residue 38) [ 18 ]. More recently, Loret and his colleagues showed that the glutamine-rich region of Tat may also interact with microtubules and promote apoptosis in T cells [ 20 ]. In a following study which will appear in the same issue of Retrovirology, Loret and his colleagues show that Tat residues 38–72 are sufficient to enhance microtubule polymerization and that the extent of the enhancement correlates with the severity of Tat-induced apoptosis[ 41 ]. Taken together these studies indicate that residues 35–38 of regions II and III and glutamine-rich region of Tat may interact with microtubules. These results correlate well with our finding that full length Tat binds LIS1 better than the isolate domains of Tat. Whether LIS1, a cellular structural protein and also an enzymatic subunit of PAF-AH, plays a role in Tat-induced apoptosis remained to be determined. As Tat-associated proteins include CDK7, Cyclin H, MAT1 and LIS1, it is possible that interaction of Tat with LIS1 might promote binding of CDK7 and ultimately affect viral gene expression through a direct activation of CDK7 or indirectly through activation of a down stream kinase, CDK2, by CDK7. As Tat is shuttling between nucleus and cytoplasm, its interaction with LIS1 and CDK7-containing protein complex might allow a temporary activation/modulation of the CDK7 activity. It is remained to be determined whether such interaction has an effect on Tat-induced transcription of HIV-1 genes. LIS1 may also function as an adaptor that brings HIV-1 Tat to microtubules that may release microtubules-associated BIM-1 protein and induce apoptosis [ 18 ]. A more detailed future study will address the questions of the regulation of HIV-1 transcription and Tat-mediated apoptosis by LIS1. Methods Materials Jurkat T-cells were purchased from National Cell Culture Center (CELLEX BIOSCIENCES, MN). DEAE-Sepharose (FF), SP-Sepharose (FF), Hi Trap heparin columns, [γ- 32 P] ATP (6000 Ci/mmol) and ( 35 S)-labeled Methionine were purchased from Amersham Pharmacia Biotech (Piscataway, NJ). Econo-Pac CHT-II Cartridge (ceramic hydroxyapatite) was from Bio-Rad (Hercules, CA). Glutathion-agarose was from Sigma (Atlanta, GA). GST-CTD was expressed in Escherichia coli and purified as we described [ 32 ]. The Tat expression plasmids GST-Tat (1–72), GST-Tat (1–48), GST-Tat (37–72) were obtained from AIDS Research and Reference Reagents Program (NIH), expressed in Escherichia coli and purified on Glutathione-agarose beads as described [ 31 ]. CDK7, cyclin H and MAT1 expression vectors were kindly provided by Dr. Marcel Doreé (CNRS, Montpellier, France). Coupled transcription/translation system based on rabbit reticulocyte lysate was purchased from Ambion (Austin, TX). Protein (G) and protein (A) agarose were purchased from Sigma (Atlanta, GA). Antibodies Anti-Tat rabbit polyclonal (HIV-1 BH10 Tat antiserum) and monoclonal (NT3 2D1.1) antibodies were received from AIDS Research and Reference Reagents Program (NIH). Anti-Flag antibodies were purchased from Sigma (Atlanta, GA). Polyclonal antibodies to CDK7, and PSTAIRE were purchased from Santa Cruz Biochemical (Santa Cruz, CA). Polyclonal antibody to CDK9 (PITALRE) were purchased from Biodesign Company (Saco, ME). Monoclonal antibodies for LIS1 were as described [ 21 ]. Tat-associated CTD kinase assay Tat-associated kinase activity was assayed as described previously [ 32 ]. Briefly, portions of eluted fractions (about 1/1000 of the total amount) from each chromatography column were incubated with 10 μg of GST-Tat (1–72) immobilized on glutathione-agarose beads for 1 hour at 4°C. The beads were washed with the buffer B containing 20 mM HEPES (pH 7.9), 250 mM NaCl, 1% NP-40, 5 mM EDTA, 0.5 mM DTT, 0.5 mM PMSF and 10 μg/ml aprotinin, followed by washing with the kinase buffer (50 mM HEPES (pH 7.9), 10 mM MgCl 2 , 6 mM EGTA and 2.5 mM dithiothreitol). Tat-associated CTD kinase activity was assayed by incubating the kinase-bound beads with 100 ng GST-CTD in kinase buffer containing 50 μM ATP and 10 μCi of ( 32 p)ATP for 10 min at room temperature. Phosphorylated GST-CTD was resolved on 10% SDS-PAGE and subjected to autoradiography and quantification with PhosphorImager Storm 860 (Molecular Dynamics). Purification of Tat-associated CTD kinase Purification of Tat-associated CTD kinase from Jurkat T-cells was carried as previously described [ 32 ]. Briefly, 100 liters of Jurkat T cell culture at concentration of 5 × 10 5 cells/ml were centrifuged, washed and Dounce-homogenized in Buffer A (50 mM HEPES [pH 7.9], 5 mM EDTA, 0.5 mM DTT, 0.5 mM PMSF, 10 μg/ml aprotinin and 10% glycerol) supplemented with 0.1% NP-40. The whole cell extract was prepared and fractionated by ammonium sulfate precipitation. Ammonium sulfate was added to 10% saturation to extract nuclei. After centrifugation, the supernatant, containing approximately 10 g of protein, was further fractionated with ammonium sulfate added to 20%, 40% and 80% saturation. The 40% ammonium sulfate fraction (about 3.5 g of protein) was found to contain the major part of Tat-associated CTD kinase activity. This fraction was diluted with Buffer A until the conductivity was equivalent to 50 mM KCl and then loaded on a DEAE-Sepharose column (about 500 mg of protein per 50 ml column). The column was eluted with a linear gradient of KCl (0.1 to 1 M) in Buffer A. Fractions were assayed for Tat-associated CTD kinase activity as described above. A peak of Tat-associated CTD kinase activity was collected, diluted with Buffer A until conductivity was equivalent to 50 mM KCl and loaded on a 10 ml SP-Sepharose column which was eluted with linear gradient of KCl (0.1 to 1 M) in Buffer A. A flow-through fraction containing Tat-associated CTD kinase activity was further fractionated on Hi Trap heparin columns (1 ml, three in series). Fractions were collected and analyzed for the Tat-associated CTD-kinase activity as described above, as well as by immunoblotting. Fractions containing Tat-associated CTD kinase activity TTK were resolved on 12% SDS-PAGE (20 × 20 cm) stained with colloidal Coumassie Blue and subjected to protein microsequencing. NanoLC ion trap mass spectrometry and peptide sequencing The procedure for peptide sequencing was performed as described previously. Protein bands visible after colloidal Coomassie blue staining and corresponding to the peak of CTD hyperphosphorylating activity after the heparin-agarose column were subjected to in-gel reduction, carboxyamidomethylation and tryptic digestion (Promega, Madison, WI). Multiple peptide sequences were determined in a single run by microcapillary reverse-phase chromatography directly coupled to a Finnigan LCQ quadrupole ion trap mass spectrometer equipped with a custom nanoelectrospray source. The column was packed in-house with 5 cm of C18 support into a New Objective one-piece 75 um I.D. column terminating in a 15 μm tip. Flow rate was 190 nanoliters/min. The ion trap was programmed to acquire successive sets of three scan modes consisting of full scan MS over alternating ranges of 395–800 m/z or 800–1300 m/z, followed by two data dependent scans on the most abundant ion in those full scans. These data dependent scans allowed the automatic acquisition of a high resolution (zoom) scan to determine charge state and exact mass, and MS/MS spectra for peptide sequence information. MS/MS spectra were acquired with a relative collision energy of 30%, an isolation width of 2.5 Dalton and recurring ions dynamically excluded. Interpretation of the resulting MS/MS spectra of the peptides was facilitated by programs developed in the Harvard Microchemistry Facility and by database correlation with the algorithm SyQuest [ 42 ]. In vitro proteins synthesis Proteins were transcribed/translated as described previously [ 32 ]. Briefly, the CDK7, cyclin H and MAT1, LIS1 and different domains of LIS1 were transcribed/translated in a coupled rabbit reticulocyte system according to manufacturer recommendations (Ambion, Austin, TX). Proteins were resolved on 12% SDS-PAGE. The gel was treated with Amplify solution (Amersham Pharmacia Biotech, Piscataway, NJ), dried and exposed to X-ray film with intensifying screen at -70°C. Co-immunoprecipitation and Western blot HeLa cells were infected with adenovirus vector expressing Flag-tagged Tat protein as we previously described [ 36 ]. HeLa whole cell extracts were prepared as described previously [ 43 ]. Cell extracts were also prepared from non-infected HeLa cells and used as a control. About 100 μg of whole cell extract was supplemented with 5 μg of anti-Flag or anti LIS1 antibodies. Then protein G-agarose beads preblocked with 5% BSA and suspended in TNN buffer (50 mM Tris-HCl (pH 7.5), 0.5% NP-40, 150 mM NaCl) buffer were added and the reaction was incubated in TNN buffer at 4°C for 2 h with rocking. The beads were precipitated and washed once with TNN buffer and once with the kinase buffer (50 mM HEPES-KOH (pH-7.9), 10 mM MgCl 2, 6 mM EGTA, 2.5 mM DTT). The pellet was then resuspended in a 30 μl of 1X SDS loading buffer (4% SDS, 10% glycerol, 5% 2-mecarpthaethanol, 0.002% bromophenol blue) and heated at 90°C for 3 minutes. The proteins were resolved on SDS Tris-Tricine PAGE, 10%, to detect LIS1, or 12%, to detect Tat, and immunoblotted with anti-LIS1 or anti-Flag antibodies. Yeast two-hybrid system The parent yeast cells EGY48 (LexA 2H) genotype ( MATα , ura3 , his3 , tryp1 , LexA op ( x 6) - LEU2 ), auxotrophic for tryptophan (Trp), uracil (Ura), histidine (His), with LEU2 as a reporter gene. Yeast were transformed by electroporation as follow. One colony of the yeast cells was resuspended into 10 ml of appropriate selective media and grown at 30°C overnight. Cells were collected at 3000 rpm for 10 min, washed twice with HEPES/Sorbitol (20 mM HEPES pH 7.9, 1 M Sorbitol), resuspended in 200 μl of HEPES/Sorbitol and supplemented with 1 μg of a plasmid DNA. The mixture was pulsed with 2500 V in 0.4 cm cuvette, then 1 ml of appropriate selective media was added and cells were shaken at 30°C for 2 hours. The cells were collected by centrifugation, resuspended into 250 μl of HEPES/Sorbitol and plated on appropriate selective plates. EGY48 cells were transformed with pSH18–34 vector containing Lac Z reporter under the control of LexA op(x8) and also URA3 and amp r genes as selection markers. The transformed yeast cells (EGY48-lacZ) were selected on Uracyl deficient media. HIV-1 Tat first exon was subcloned into pLexA in frame with the LexA (1–202) , the DNA binding domain to create the bait vector (pLexA-Tat). LIS1 was subcloned into pJG 4–5 ( amp r ) in frame with the acid activation domain to create pJG-LIS1 carrying hemagglutinin (HA) tag (Trp selectable marker). The EGY48-lacZ yeast cells were transformed with pLexA-Tat vector, and selected for growth on uracyl and histidine deficient media. The Tat expressing yeast cell growing on Uracyl, Histidine deficient plates were then transformed with pJG-LIS1. To detect interaction between Tat and LIS1 interaction, yeast cells were plated on galactose/raffinose-containing plates to allow expression of Tat and LIS1, and production of β-galactosidase was visualized with 5-bromo-4-chloro-3-indolyl-β-D-galactoside (X-gal) substrate. Competing interests The author(s) declare that they have no competing interests. Authors' contributions NE carried out studies of LIS1 and Tat interaction in vitro and in vivo and participated in the writing and assembling of the manuscript. TA carried out yeast two-hybrid assays. YV provided technical help. WSL performed protein sequencing. WT participated in the design and discussion of the study. TS and OR created vectors for expression of LIS1 and participated in the design of the study. SN purified LIS1 containing protein complex for protein sequencing, performed general control and coordination of the study. All authors read and approved the manuscript. Supplementary Material Additional File 1 Analysis of protein composition of DEAE-Sepharose purified fraction of Tat-associated CTD kinase. Fractions from the DEAE-Sepharose column fractionation shown in Fig. 1B were analyzed for Tat-associated CTD kinase activity and also by Western blotting with antibodies against CDK7, CDK9, p62 subunit of TFIIH and PSTAIRE. Click here for file Additional File 2 HIV-Tat interacts with a 50 kDa protein from purified Tat-associated CTD kinase. GST-fused Tat 1–72, immobilized on glutathione-agarose beads, was incubated without (lane 1), or with fraction 18 (lane 2), fraction 20 (lane 3), fraction 22 (lane 4), or fraction 24 (lane 5) from the heparin-agarose, shown in Fig. 2B . Precipitated Click here for file Additional File 3 Endogenous LIS1 is present in reticulocyte lysates. Individual protein components of Tat-associated complex were translated in reticulocyte lysate. The lysates were resolved on 12% SDS-Tris-Tricine gel and immunoblotted with anti-LIS1 monoclonal antibodies. Lane 1 - CDK7; Lane 2 -Cyclin H; Lane 3 -MAT1; and Lane 4 -LIS1-programmed lysate. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC549217.xml

529252

Development of the time course for processing conflict: an event-related potentials study with 4 year olds and adults

Background Tasks involving conflict are widely used to study executive attention. In the flanker task, a target stimulus is surrounded by distracting information that can be congruent or incongruent with the correct response. Developmental differences in the time course of brain activations involved in conflict processing were examined for 22 four year old children and 18 adults. Subjects performed a child-friendly flanker task while their brain activity was registered using a high-density electroencephalography system. Results General differences were found in the amplitude and time course of event-related potentials (ERPs) between children and adults that are consistent with their differences in reaction time. In addition, the congruency of flankers affected both the amplitude and latency of some of the ERP components. These effects were delayed and sustained for longer periods of time in the children compared to the adults. Conclusions These differences constitute neural correlates of children's greater difficulty in monitoring and resolving conflict in this and similar tasks.

Background Conflict tasks involve the selection of a sub-dominant object or response in the presence of a competing dominant object or response. One of the most common tasks used in the literature to measure conflict is the flanker task. In this task, a target surrounded by stimuli suggesting either the same (congruent) or the opposite (incongruent) response is presented. Conflict is induced by incongruent flankers which, compared to congruent ones, produce larger reaction times and reduced response accuracy [ 1 ]. Different cognitive operations appear to be involved in processing conflict [ 2 ]. First, conflict has to be detected. This involves not only the recognition of the presence of conflict in the display but also the evaluation of the degree of conflict and the realization that the situation calls for a particularly careful action, therefore we use the term conflict monitoring to describe these operations. Once conflict is detected, it is necessary to determine the appropriate action in a goal-directed manner. Depending on the task, the resolution of conflict might involve different processes (e.g. inhibition, rule-holding, set switching, planning, etc.). Monitoring and resolving conflict is a function of executive attention [ 3 ]. A network of brain areas have been shown to be active in tasks that involve conflict between stimulus dimensions, including the Stroop, flanker and spatial conflict tasks. These three tasks have been shown to activate a common neural network including the anterior cingulate cortex (ACC) and lateral prefrontal areas [ 2 ]. Recent studies have dissociated the brain areas within the executive network that are responsible for monitoring and resolving of conflict [ 4 ]. In a fMRI study, [ 5 ] the ACC was shown to be involved in the detection and monitoring of conflict, while lateral prefrontal areas have been shown to be mainly related to processes required to resolve the conflict [ 6 ]. Conflict processing has also been anatomically dissociated from orienting to relevant information that involves areas of the superior parietal cortex and superior frontal gyrus [ 7 ]. Young children have more difficulty than older children and adults performing tasks that involve conflict. Using conflict tasks adapted to children, we have reported a considerable reduction in the amount of interference produced by distracting information in children from 2 to 3 years of age [ 8 ]. This reduction continues up to 7 years but we have found a striking stability after this age up to adulthood [ 9 ]. We have interpreted these data as indicating a greater difficulty in monitoring and resolving conflict from competing stimulation in young children compared to older children and adults. The greater susceptibility to interference from irrelevant stimulation for young children has been reported using many different tasks: Flanker [ 10 - 12 ], S-R compatibility [ 13 ], Stroop [ 14 ], negative priming [ 15 ], etc. Other tasks have been used to assess the ability to inhibit non-appropriated responses (e.g. Go-NoGo [ 6 ] and Stop-signal [ 16 , 17 ] tasks). In these studies, children also show greater difficulty than adults in controlling prepotent, but incorrect, responses. Depending on the difficulty of the task, developmental differences in the ability to resolve conflict between children and adults can be observed up to middle childhood and early adolescence, suggesting that full maturation of the executive control network does not take place until early adulthood. Some developmental studies have been carried out using neuroimaging aimed at understanding the brain mechanisms that underlie the development of executive functions. For instance, Casey and her colleagues [ 6 ] conducted a fMRI study comparing 7 to 12 year old children and adults in a Go-NoGo task. Despite a very similar pattern of activations in the prefrontal cortex following No-Go trials, the average volume of activation was significantly greater for children than for adults. The same pattern of results was obtained in similar studies [ 18 , 13 ], suggesting that the brain circuitry underlying executive functions is more focal and refined as it becomes more efficient with development. However, due to the limited time resolution of MRI, these studies cannot analyze whether there are additional maturational differences in the time course of activation of these areas. A number of studies have used the high temporal resolution of event related potentials (ERPs) to assay the timing of action-monitoring processes with adults. The N2 is one of the ERP indexes that have been associated with executive attention. The N2 is a pre-response negative deflection in the ERP at around 300 ms post-stimulus, which appears to be larger (more negative) for trials that involve more conflict. The N2 is observed over parietal and frontal leads and has been obtained with both flanker [ 19 , 20 ] and Go-NoGo tasks [ 21 ]. In both situations, the N2 has been associated with the withholding of a prepotent, but inappropriate, response. In a recent ERP study with a flanker task, van Veen and Carter [ 20 ] linked the scalp distribution of activity associated with the N2 to a source of activation originating at the caudal portion of the ACC, supporting a connection between this electrophysiological index and the executive attention network. Only a few ERP studies have been conducted with children using conflict tasks. In one of these studies, a flanker task was used to compare conflict resolution in three groups of children aged 5 to 6, 7 to 9 and 10 to 12, and a group of adults [ 12 ]. As expected, the behavioral results showed a consistent reduction of the interference produced by the flanker with age. In addition, developmental differences were found in two ERP components, the lateralized readiness potential (LRP) and the P3. The LRP seems to be related to response preparation [ 22 ] while P3 is thought to be an index of stimulus evaluation [ 23 ]. Ridderinkhof & van der Molen [ 12 ] found differences between children and adults in the latency of the LRP, but not in the latency of the P3 peak, suggesting that developmental differences in the ability to resist interference are mainly related to response competition and inhibition, but not to stimulus evaluation. Recently, Davis et al. [ 24 ] conducted an ERP study using a Go-NoGo task with a group of 6 year old children and a group of adults. In this study, differences between children and adults in the latency of the P3 peak were also reported. Both the amplitude and the latency of the P3 were greater for NoGo trials compared to Go trials, although this pattern was similar for children and adults. Nevertheless, in contrast with the literature, no differences in the amplitude of the N2 component were observed as a function of type of trial. Finally, a late positive component (LPC) was observed only for children over the frontal leads. The amplitude of this component was reduced for NoGo trials compared to Go trials. This difference started around 550 ms post stimulus and extended over a time window of 600 ms. The modulation of the amplitude of the LPC might result from the greater prefrontal activation observed in children when a response has to be inhibited, as shown by imaging studies that used the same type of task [ 6 ]. This would be consistent with the study by Ridderinkhof and van der Molen, in which developmental differences seem to appear in ERP components related to response selection to a greater extent than those associated with stimulus selection. So far, the literature suggests that monitoring and resolution of conflict involve separate brain areas in adults, and that children activate similar, but somewhat larger areas. Moreover, the N2 component of the ERPs appears to be related to activation coming from the ACC and to be mainly associated with conflict monitoring, whereas later components (e.g. LPC) might result from prefrontal sources of activation and could be related to conflict resolution. The flanker task is an appropriate experimental paradigm for assessing conflict processing. The aim of this study was to use a version of this task with children and adults to assess developmental differences in the time course of the different operations involved in conflict processing. We have recently developed a flanker task appropriated for use with children as young as 4 years [ 9 ]. In this task, a row of five fish appear in the center of the screen and the child's job is to help "feed" the middle fish by pressing the key that corresponds to the direction in which the middle fish is pointing. In the congruent trials, the fish surrounding the middle one point in the same direction as the middle fish, while in the incongruent trials, the flanker fish point in the opposite direction, suggesting an incorrect response (see Figure 1 ). To study the time course of conflict processing, we examined the latency to significant differences between the ERPs for congruent and incongruent trials, and the sustainability over time of these differences. In the adult literature, the amplitude of the N2 component has been shown to be modulated by the congruency of distracting information in flanker tasks and has been related to conflict monitoring, but this component was not assessed in the study by Ridderinkhof and van der Molen. We aim to replicate the effect with adults using our child-friendly flanker task as well as analyzing this ERP component in 4 year olds. While there may be subcomponents of the N2 sensitive to other types of manipulations such as the degree to which the predicted identity of a display is violated [ 25 ], in our study we will focus on the effect of the congruency of flankers that are equally expected to ensure the activity we measure is related to conflict. In addition, differences between children and adults in stimulus selection processes as reflected by the P3 component could be playing a role in selecting a relevant stimulus among distractors, and these were examined. Finally, we explored whether the reduced amplitude of the LPC for NoGo situations reported by Davis et al. [ 24 ] for children, but not adults, is also observed with a flanker task. This outcome will rule out the possibility that the reduction of the LPC is associated with the withholding of a motor response, and will make more plausible the hypothesis of it being related to resolving situations that call for particularly careful actions as those in which conflict is induced by distracting flankers. Results Behavioral results Means of the median RT and percentage of errors are shown in Table 1 for both children and adults. A mixed-designed ANOVA was performed with Group as a between subject factor and Flanker Type as a repeated measure with RT as the dependent measure. The ANOVA revealed significant main effects of Group (F(1,38) = 74.42; p < .001) and Flanker Type (F(1,38) = 6.15; p < .05) as well as a significant Group × Flanker Type interaction (F(1,38) = 4.65; p < .05). In addition, conflict effects were examined in the two groups. Conflict effects refer to the difference between congruent and incongruent conditions, and they can be measured using both RT and accuracy variables. Statistical significance of these effects was tested for each group independently using paired t-tests. The conflict effect was significant for both RT and accuracy for adults (t(17) = -6.2; p < .001 and t(17) = -2.54; p < .05 respectively) as well as for children (t(21) = -2.57; p < .05 and t(21) = -2.51; p < .05 respectively for RT and accuracy effects). Conflict effects were also examined for the subgroup of children (n = 14) with useful ERP data. For this group, the conflict RT was marginally significant (t(13) = -2.004; p = .066) while the conflict accuracy was significant (t(13) = -2.4; p < .05). A similar 2 (Group) × 2 (Flanker Type) ANOVA was conducted using percentage of errors as the dependent measure. Again, the main effects of Group (F(1,38) = 22.33; p < .001) and Flanker Type (F(1,38) = 7.08; p < .05) were significant, whereas the Group × Flanker Type interaction was marginal (F(1,38) = 3.28; p = .078). ERP snalysis Figure 2 shows the ERPs of adults and children at leads located at the midline of frontal and parietal sites. Despite general differences in overall amplitude and latency, the waveforms for the two groups were strikingly similar. We observed N1 and N2 components over frontal leads and a P3 over parietal leads for both children and adults. In addition, children showed a pre-response late positive component (LPC) over frontal channels. We hypothesized that particular ERP components would be sensitive to the presence of conflict in the display. To examine these predicted effects, we computed the latency and amplitude of the peaks of the N1, N2 and P3 components in both groups and of the LPC in the group of children separately for congruent and incongruent conditions in a selection of frontal and parietal leads (see Table 2 ). The selected leads had equivalent locations in the children's 128 and adults' 256 channels arrays and corresponded to particular 10-10 international system positions [ 26 ]. Overall amplitude refers to the maximum negative or positive voltage values (in microvolts, μVolts) within the ERP component. Latency was computed in milliseconds (ms) from the time the target was presented to the time of the maximum positive or negative peak within the ERP component. To calculate both peak amplitudes and peak latencies we selected time-windows in which the waveforms deflections defining each ERP component were included, and computed the latency and amplitude of the peak within those windows using the tool provided by the Net Station 3.0 (EGI software). These time-windows, specified in Table 2 , were different for children and adults. Because of the greater presence of artifacts in the children's ERPs, a significantly larger number of segments were used to compute the averaged ERPs in the adult data (see Method section). To control for possible influence of this difference on the amplitude of the ERPs [ 27 ], the number of segments used to compute the averaged ERPs was included as a covariate of the differences between children and adults in the dependent measures entered in the analysis. Separate 2 (Group) × 2 (Flanker Type) × 3 (Channel) ANCOVAs with the means of peak latency and amplitude as dependent measures were conducted separately for the N1, N2 and P3 components. In addition, we conducted a 2 (Flanker Type) × 3 (Channel) ANOVA with both the peak latency and amplitude of the LPC only for children. In these analysis we used the Huynh-Feldt correction for sphericity as needed. The results of these ANOVAs are summarized in Table 3 . For the peak latency, the main effect of Group was significant in all the ERP components. The main effect of Flanker Type was significant for the N1, P3 and LPC. No significant interactions were found for any of the ERP components for the latency data. For the peak amplitude values, the main effect of Group was significant for the N1 and N2. The main effect of Flanker Type was not significant for any of the components although it was marginally significant for the N1 and for the LPC in the children. The main effect of Channel was marginal for the N2 and highly significant for the P3. Interestingly, there was a significant Group × Flanker interaction for the P3, indicating a significant effect of the type of flankers in the peak amplitude of this component for children (F(1,30) = 6.0; p < .05) but not for adults (F < 1). Although the peak amplitude of ERP components is a widely used measure to look at effects of the variables of interest in the patterns of brain activation, these effects can certainly occur along the entire epoch and not only in the peaks of the components. To examine the effect of congruency in the amplitude of the registered activity in the entire epoch, we computed amplitude differences between congruent and incongruent conditions sample by sample along the ERP segment in all channels for children, and a selection of channels around the Fz, Fcz and Pz positions in the adult data. T-tests were carried out to assess the significance of these differences along the epoch. In Figure 3 , the leads in which the congruent vs incongruent differences in amplitude were found significant are highlighted for both children and adults, as well as the time windows for these differences and the ERP components in which the differences appear. In addition, graphs displaying the ERPs for each flanker condition at Fz, Fcz and Pz positions are shown in Figure 4 for adults and children. The shadowed areas in these figures show the sections of the ERPs in which congruent vs. incongruent differences were significant. Correlations between reaction time and electrophysiological measures In order to explore possible associations between the amplitude and latency dimensions of the ERP components and the particular cognitive processes measured by reaction time (RT), we examined correlations between RT measures and patterns of brain activity at Fz, Fcz and Pz positions and their left and right equivalents. Correlations were computed independently for adults and children. The first set of correlations involved the conflict effect as measured by subtracting the RT for congruent trials from the RT for incongruent trials (conflict score) and the overall RT as behavioral measures, and the overall (across flanker conditions) latency and amplitude of the ERP components as electrophysiological measures. For the adults, the overall RT correlated negatively with the amplitude of the N1 at channel F3 (r = -0.53; p < .05), and positively with the latency of the N2 component at channel Fc4 (r = 0.47; p < .05). For the children, the overall RT correlated negatively with the amplitude of the P3 at channel P4 (r = 0.72; p < .01), and positively with the latency of the N1 at channel F4 (r = 0.60; p < .05) and the N2 at channel Fcz (r = 0.58; p < .05). No significant correlations were established between any of the overall ERP components and the conflict score in either adults nor children. Finally, correlations were calculated between the behavioral measures of overall RT and conflict score, on the one hand, and the effect of flankers on the amplitude and latency of the peaks of the ERP components on the other. For the adults, overall RT correlated positively with the N2 latency effect at Fcz (r = 0.59; p < .01) and the P3 amplitude effect at P4 (r = 0.46; p = .05), whereas the correlation was negative with the N2 latency effect at Fc4 (r = -0.54; p < .05). On the other hand, the conflict score correlated positively with the amplitude effect on the N2 at Fc4 (r = 0.47; p < .05) and Fcz (r = 0.41; p = .09), although the last effect was only marginal. In the children, the overall RT correlated negatively with the N2 latency effect at Fc3 (r = -0.56; p < .05), and marginally with the N1 amplitude effect at Fz and F4 (r = -0.50; p = .07 and r = -0.46; p = .09 respectively). However, the conflict score correlated negatively with the P3 latency effect at channel P4 (r = -0.81; p < .001). Discussion As expected, young children showed increased difficulty compared to adults in both processing the target and dealing with distracting information incongruent with the correct response. The greater difficulty of the task for children was reflected in children's much longer overall RT and conflict scores. The main goal of the current study was to analyze the differences in brain activation between children and adults underpinning their behavioral differences. Our results show differences among children and adults in both the time course of brain activations overall and across flanker conditions. Time course of target processing Significantly larger N1 and N2 amplitudes were found for children than for adults, whereas the P3 showed equivalent amplitudes in the two groups. Children usually show larger event related potentials and often with delayed latency compared to adults [ 12 , 24 ]. These differences in general amplitude and latency relate to a variety of maturational factors as brain size, skull thickening and synaptic density [ 28 ]. It is not clear how the amplitude of the ERPs components relates to the effort to process the target. In both adults and children, the overall RT correlated negatively with the amplitude of some of the waveform components (N1 for adults, P3 for children), consistent with the idea that the amplitudes of the ERPs components are associated with cognitive operations that can facilitate the speed of processing the target. Differences in latency of the ERPs components can be of special interest when it comes to accounting for differences in RT. Accordingly, children showed significant delays in the latency of all components compared to adults. The difference between children and adults was greater in the later components, suggesting that children's delay in target processing is more pronounced in later stages of processing. An objection to this conclusion is that latency and amplitude of the waveforms deflections are not independent, given the fact that greater peak latencies can be expected with more pronounced differences in amplitude. However, two pieces of information in our data point to the fact that differences in amplitude cannot account for all differences in latency. First, the P3 component shows a large difference in latency despite no overall differences in amplitude. Second, the overall RT appears to correlate negatively with the amplitude of some of the components in both children and adults, whereas it correlates positively with latency measures. In addition, overall RT appear to correlate with the overall amplitude and latency of some ERP components, while no significant correlations are established between these and the conflict score, suggesting that the general speed of processing but not the ability to manage conflict might be related to the general form of the ERP. Time course of conflict resolution It should be borne in mind in comparing the adult data with previous studies conducted with other flanker tasks that the child-friendly version of this task used in our study was very easy for adults. This could account for the modest amplitude differences between congruent and incongruent trials in this study compared to what has been found with other versions of the flanker task [ 29 ]. From when children are first able to perform reaction time tasks, the time to respond appears to decline linearly to adulthood as do the conflict scores up to seven years of age [ 9 ]. The continuous nature of the two behavioural reductions suggest that although the flanker task might be easier for older children and adults the same mental processes are involved. As shown in Table 2 , the manipulation of congruence between relevant and distracting information in the display produced some effects on both the amplitude and latency of the ERP elicited by the target (see also Figure 4 ). In consonance with the literature, adults show an effect in the N2 component at frontal and parietal areas around the midline as well as an effect on the P3 observed at the left and mid parietal leads. For the P3, these effects are found on the latency of the peaks as well as the amplitude of particular time windows within the components (see Figure 3 ). For the N2, the effect is mainly observed on the amplitude of the component. In addition, adults show an effect on the amplitude and latency of the N1 that is localized at the frontal midline (Fz). On the other hand, 4 year old children do not show differences in brain activity among the two flanker conditions until approximately 500 ms post target. Therefore, the effect of flankers is not observed at this age in the relatively early N1 component, and only very weakly at the N2 (see Figures 3 & 4 ). However, as in the adults, children show robust frontal and parietal effects. The frontal effect is observed in the LPC, and consists of a less positive amplitude of the component during incongruent relative to congruent trials. The parietal effect in children is observed in a late P3 component that, as for adults, consists of a greater amplitude for incongruent trials than congruent ones. Although the frontal effect appears around 200 ms earlier than the parietal effect, in both cases, the amplitude effect lasts for over 500 ms. The amount of time the amplitude difference is sustained constitutes an important difference between children and adults, and may reflect the time course of brain mechanisms supporting the monitoring and resolution of conflict. As mentioned in the introduction, the N2 effect has been consistently found in different versions of the flanker task, and has been related to action-monitoring processes implemented in the anterior cingulate [ 20 ]. Botvinick, et al [ 5 ] more precisely specified the role of ACC in detecting and signaling conflict. In consonance with these data, our results showed a modest positive correlation between the effect of flankers on the amplitude of the N2 and the conflict score in adults. It is less clear what type of cognitive operation is underlying the P3 effect. In a review of mental chronometry, Coles et al. [ 30 ] distinguished between amplitude and latency effects in their analysis of the conditions that elicit the P3. According to these authors, amplitude differences in the P3 can be elicited by stimuli that differ in their probability (either objective or subjective) of occurrence, but also in the amount of goal-relevant information contained in the stimulus, whereas latency differences might be associated with time differences in stimulus evaluation or categorization. In our task, the two types of trials had equal probability of occurrence. Consequently, the greater P3 amplitude for incongruent trials is more likely to be associated with the need for a more careful evaluation of the stimulus to determine the correct response. If we only look at the amplitude effect on the P3 at the particular time window in which the effect is found significant in the adult data, a positive correlation between the amplitude effect on the P3 and the conflict score is found (r = 0.62; p < .01). This suggests that the greater the effort to select the correct response, the greater the relative P3 amplitude for incongruent trials, and therefore the greater the conflict score. Our data fit quite well within the sequence of cognitive operations suggested by the literature. In the adults, conflict detection, as reflected by the frontal effects, is a few tens of milliseconds delayed for incongruent trials. Immediately after, we observe an effect over parietal leads apparently related to the effort to determine the correct response. This process is approximately 40 ms delayed when incongruent flankers are presented. Nonetheless, the delays in the N2 and P3 components under incongruent conditions may not be completely independent, as suggested by their quite overlapping topographies (see Figure 3 ). In the children, we have also observed frontal and parietal effects occurring prior to the response. However, probably due to their generally slower capacity for processing information, the frontal effect is quite delayed in comparison with adults, and mostly observed on the LPC instead of the N2. Although determining whether the frontal effects observed in these two different ERP components in children and adults are equivalent will require further research, the fact that the effect on the LPC occurs over the frontal leads and prior to the parietal effect supports its involvement in conflict monitoring. Likewise, a remarkable increase in the amplitude and delay of the latency of the P3 peak is observed for the children on incongruent trials. In consonance with the result of the study by Ridderinkhof & van der Molen [ 12 ], both children and adults showed the effect on the peak latency to the same degree. However, in their study, Ridderinkhof & van der Molen did not report amplitude effects. Interestingly, our data reveal a greater effect on the P3 amplitude for the 4 year old children compared to the adults. This suggests that children at this age take longer than adults in evaluating the display. This delay occurs in addition to the delay in response selection revealed by the adults vs. children differences in the LRP shown by Ridderinkhof & van der Molen. At both the frontal and parietal components, the flanker effect is sustained for a longer period of time in the case of children. These differences in patterns of brain activation are likely to underlie the observed behavioral differences in conflict resolution between children and adults. Certainly, the brain processes underlying the detection of conflict and the selection of the appropriate response appear to take longer to be resolved into a correct action in the brains of 4 year old children. The distribution of the flanker effects shown in Figure 3 appears to be another important difference between 4 year old children and adults. In adults, the frontal effects appear to be focalized in the mid line (Fz for N1, and Fcz for N2), while in children we observed the effects mostly at pre-frontal sites and in a broader number of channels, including the mid line (Fz) and leads on the left (F3) and right (F4) sites. In addition, the effect on the P3 appears to be left-lateralized in the adults data but lateralized to the right side in the children. The focalization of the signals in adults as compared to children is consistent with neuroimaging studies conducted on developmental populations in which children appear to activate a broader area of the brain compared to adults when exposed to the same task [ 13 , 18 ]. Conclusions A major new finding of this study is the difference found between 4 years old children and adults in the longer latency and the sustained congruency effect on the ERPs. Consistent with their larger conflict scores in reaction time, these differences shed light on the brain mechanisms underpinning the much greater difficulty for children in monitoring and resolving conflict. Methods Participants Eighteen young adults (12 women, 6 men; mean age: 23 years; SD: 6.45) and twenty-two children (11 girls, 11 boys; mean age: 4 years, 4 months; SD in months: 2.2) participated in the study. All participants were right-handed. The adult participants and the parents of children involved in the study gave written consent prior to the experimental session. Both children and adults were paid for participating in the study. Procedure The stimulus sequence for each trial was controlled using E-Prime (Psychological Software Tools, Pittsburgh, PA). Each trial began with a sound to alert participants about the start of the trial. One second after the sound, a line with five drawn fish was presented in the center of the screen (Figure 1 ). The central fish was the target, and the ones on the sides the flankers. Participants were instructed to press the mouse button that matched the direction toward which the middle fish was pointing while ignoring the flanker fish. Half of the trials were congruent and half incongruent. In the congruent trials, the five fish were pointing in the same direction; in the incongruent trials, the flanker and target fish were pointing in opposite directions. The experiment was presented to the children as a game in which they will be shown a hungry fish surrounded by other fish. The children were told the hungry fish is always the one in the middle and that they will make it happy by feeding it when they press the key corresponding to the direction it is swimming. The target display was presented until a response was made, or up to 1700 ms in the case of adults, or 5000 ms in the case of children. After the response was given, the display did not change for another second, after which feedback was provided. Feedback consisted of a 1500 ms long animation of the middle fish, showing it happy (bubbles coming up from his mouth) for the correct response, or sad (bubbles coming down the eye) for the incorrect or missed trials. The inter-trial interval was 1500 ms for adult participants. For the children, the experimenter initiated each trial once the child was focused on the computer monitor. A fixation cross was continuously displayed in the center of the screen except when targets and feedback were presented. All participants were instructed to be as fast and accurate as possible. Both children and adults completed five blocks of 20 trials each, preceded by 12 practice trials. Children could repeat the practice block as many times as needed until it was clear they understood the instructions. The experimental session was about 35 minutes long for adults and 45-to-60 minutes long for children. EEG recording and data processing EEG was recorded using a 128-channel Geodesic Sensor Net [ 31 ] for children, and a 256-channel net for adult participants. The GSN is a reliable method for acquiring high-density EEG data, and given its fast application, this method is specially convenient for children [ 32 ]. The EEG signal was digitized at 250 Hz. Impedances for each channel were measured prior to recording and kept below 80 kΩ during testing. Recording in every channel was vertex-referenced and the time-constant value was 0.01 Hz for both children and adults. Data were recorded using Net Station 2.0 (EGI Software) and processed using Net Station 3.0. Once acquired, data were filtered using a FIR bandpass filter with 12 Hz low-pass and 1 Hz high-pass cutoffs. Continuous EEG data was segmented into target-locked epochs. The epochs were 1 sec. long for adults (-200 ms to 800 ms around target) and 1.7 sec. long for children (-200 ms to 1500 ms around target). Segmented files were scanned for artifacts with the Artifact Detection NS tool using a threshold of 70 μV (adults) or 100 μV (children) for eye blinks and eye movements. Segments containing eye blinks or movements as well as segments with more than 25 bad channels were rejected. Within each segment, channels with an average amplitude of more than 200 μV or a difference average amplitude of 100 μV were also discarded from further processing. Finally, particular channels were rejected if they contained artifacts of any kind in more than 50% of the segments. Children's data were also visually inspected trial by trial to make sure the parameters of the artifact detection tool were appropriate for each child. As a consequence of the artifact detection procedure, an average of 36% of the ERP segments in the children data and an average of 18.5% of the ERP segments in the adults were rejected. The larger number of rejected segments for the children was due to a higher frequency of blinks, mouth and/or head movements, speaking, and other behaviors that generate artifacts on the EEG signal during the experimental procedure. Thus, we decided to have a criterion of a minimum of 12 clean segments per flanker condition among the correctly responded trials for further processing individual data. All adults participants and a total of 14 children reached this selection criterion. The average number of segments included in the averaged ERPs was 53.2 (SD: 23.1) for the children (26.6 per flanker condition; SD: 11.41 and 11.48 respectively for congruent and incongruent conditions), and 80.3 (SD: 17.3) for adults (40.3, SD: 17.3 for congruent trials; and 40.0, SD: 9.8 for incongruent trials), and this children vs adults difference was significant (t(23.4) = -3.66; p < .001). Artifact-free segments for correct responses were averaged across conditions and subjects and re-referenced against the average of all channels. The 200 ms preceding the target served as baseline. Authors' contributions MRR, MIP & MKR designed the study and participated in the theoretical elaboration of the paper. MRR was responsible for the data collection and data analysis processes. CPD designed and performed part of the statistical analysis on the EEG data.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC529252.xml

548684

Human CD57+ germinal center-T cells are the major helpers for GC-B cells and induce class switch recombination

Background The function of CD57 + CD4 + T cells, constituting a major subset of germinal center T (GC-Th) cells in human lymphoid tissues, has been unclear. There have been contradictory reports regarding the B cell helping function of CD57 + GC-Th cells in production of immunoglobulin (Ig). Furthermore, the cytokine and co-stimulation requirement for their helper activity remains largely unknown. To clarify and gain more insight into their function in helping B cells, we systematically investigated the capacity of human tonsil CD57 + GC-Th cells in inducing B cell Ig synthesis. Results We demonstrated that CD57 + GC-Th cells are highly efficient in helping B cell production of all four subsets of Ig (IgM, IgG, IgA and IgE) compared to other T-helper cells located in germinal centers or interfollicular areas. CD57 + GC-Th cells were particularly more efficient than other T cells in helping GC-B cells but not naïve B cells. CD57 + GC-Th cells induced the expression of activation-induced cytosine deaminase (AID) and class switch recombination in developing B cells. IgG1-3 and IgA1 were the major Ig isotypes induced by CD57 + GC-Th cells. CD40L, but not IL-4, IL-10 and IFN-γ, was critical in CD57 + GC-Th cell-driven B cell production of Ig. However, IL-10, when added exogenously, significantly enhanced the helper activity of CD57 + GC-Th cells, while TGF-β1 completely and IFN-γ partially suppressed the CD57 + GC-Th cell-driven Ig production. Conclusions CD57 + CD4 + T cells in the germinal centers of human lymphoid tissues are the major T helper cell subset for GC-B cells in Ig synthesis. Their helper activity is consistent with their capacity to induce AID and class switch recombination, and can be regulated by CD40L, IL-4, IL-10 and TGF-β.

Background In germinal centers (GC), B cells undergo clonal expansion, somatic hyper-mutation in the variable region of antibody genes [ 1 - 3 ] and class switch recombination (CSR) from IgM to IgG, IgA, and IgE [ 4 - 8 ], processes that are dependent on helper T cells [ 9 - 11 ]. Antibodies to the CD57 epitope (HNK-1) have been used to identify a T cell type in germinal centers in human tonsils, spleen and lymph nodes. These cells are CD4 + T cells [ 12 - 14 ], exhibit a memory phenotype (CD45RO + CD45RA - ) [ 15 ] and are not cytolytic [ 16 ]. CD57 + GC-Th cells proliferate only when they are TCR-activated in the presence of IL-2 [ 17 , 18 ]. CD57 + GC-Th cells express the B-cell zone homing chemokine receptor CXCR5 but not the T cell zone homing chemokine receptor CCR7, a pattern consistent with their specific localization in GC [ 19 ]. Based upon their non-polarized cytokine profile, localization in GC and potential helper activity, it has been proposed that CD57 + GC-Th cells may constitute a novel effector T cell subset distinct from other well known effector T cell subsets such as Th1 and Th2 cells [ 20 ]. Using a gene expression profiling study, we determined that CD57 + GC-Th cells are remotely related to other memory/effector T cells in global gene expression [ 21 ]. The microarray study also revealed that CD57 + GC-Th cells have the unique capacity to produce CXCL13, a follicle chemokine implicated in recruitment of CXCR5 + cells [ 22 , 23 ] and development of follicles/GCs [ 24 ]. Because of their specific localization in germinal centers, the activities of CD57 + GC-Th cells on B cell proliferation and antibody production have been studied by several groups of scientists [ 19 , 25 - 27 ]. The results of these previous studies reveled unique features of CD57 + GC-Th cells, but, when combined, they are inconclusive and widely vary from negative to neutral or positive in assessing the helper activities of CD57 + GC-Th cells. To clarify and gain more insight into their function in helping B cells, we systematically investigated the capacity of human tonsil CD57 + GC-Th cells in inducing B cell Ig synthesis in naïve vs. germinal center B (GC-B) cells in comparison with other T cell subsets in human tonsils. We show that CD57 + GC-Th cells are more efficient than other germinal center or interfollicular T cells in supporting B cell production of Ig. CD57 + GC-Th cells, when compared to other T cells, have better helper activity for GC-B cells than for naïve B cells. CD57 + GC-Th cells induced the expression of activation-induced cytosine deaminase (AID) and CSR in developing B cells. CD40L, but not other major cytokines, is critical for the helper activity of CD57 + GC-Th cells. IL-10 positively and TGF-β1 negatively regulate the helper activity of CD57 + GC-Th cells. Results Distribution and identification of T helper cell subsets in tonsils We examined the distribution of T helper cell subsets in human tonsils based upon the expression of CD4, CD57 and CD69. As reported previously [ 12 , 19 , 28 , 29 ], most CD57 + CD4 + T cells are located in germinal centers surrounded by IgD + naïve B cells (Fig. 1 ). Small numbers of CD57 + T cells were also present in the interfollicular areas (IFA or T cell-rich zone) surrounding GC. Although some are found in IFA, CD69 + CD4 + T cells were also preferentially found in GC (Figure 1C ). In contrast, the T cells in IFA were mostly negative for CD69 expression. Therefore, CD57, CD69 and CD4 are useful markers to identify CD57 + GC-Th cells and other T cell subsets differentially localized in tonsils: CD4 + CD57 + cells (mainly in GC), CD4 + CD57 - CD69 + cells (mainly in GC and a minor proportion in IFA), and CD4 + CD57 - CD69 - cells (mainly in IFA). CD57 + GC-Th cells are highly efficient in supporting Ig production by B cells Based upon the information obtained in Figure 1 , the total tonsil CD4 + T cell population was fractionated into CD57 + GC-Th cells (all of these cells are CD69 + ), total CD57 - T cells, CD57 - CD69 + T cells and CD57 - CD69 - T cells (Figure 2 ). We compared the B cell helping activity of CD57 + GC-Th cells with that of other CD4 + T cell subsets. We co-cultured each of the isolated T cell subsets with syngeneic tonsil CD19 + B cells in the presence of SEB, a superantigen that conjugates MHC class II molecules and TCR (Figure 3 ). B cell receptors were cross-linked by Ab to human Ig μ chain and human Ig (H + L) chain prior to culture to provide BCR activation signals mimicking the antigen signals in vivo. CD57 + GC-Th cells were most efficient in inducing B cell production of IgM, IgG, IgA and IgE among the T cell subsets examined. CD57 - CD69 + T cells, many of which are located in GC in a manner similar to CD57 + GC-Th cells, were able to induce the production of antibodies but at significantly lower levels compared to CD57 + GC-Th cells (Figure 3A ). T cell stimulation, in this study by SEB, was required for efficient induction of the B cell helper activity as it enhanced Ig production up to ~1000 (not shown). GC-B cells are the preferred target cells for the helper activity of CD57 + GC-Th cells Because of their specific localization in germinal centers, the physiological target cells for CD57 + GC-Th cells would be GC-B cells rather than naïve B cells. We compared the helper activities of CD57 + GC-Th cells and CD57 - CD69 +/- CD4 + T cell subsets for B cells. In this study, we fractionated CD19 + B cells into two groups: IgD + CD38 - naïve B and CD38 + IgD +/- GC B cells as shown in Figure 2B . CD57 + GC-Th cells, when co-cultured with GC-B cells, were significantly more efficient than CD57 - CD69 + T cells in inducing the production of all four isotypes of Ig (Figure 3C ). However, when co-cultured with naive B cells, CD57 + GC-Th cells were not significantly different from CD57 - CD69 + T cells in their induction capacity of Ig (Figure 3B ). Again, the helper activities of total CD57 - T cells and CD57 - CD69 - T cells for naïve and GC-B cells were very low. The relative composition of IgM, IgG, IgA and IgE produced in response to CD57 + GC-Th cells in the cultures with GC-B vs. naïve B cells was determined. CD57 + GC-Th cells drove the production of IgM, IgG, IgA and IgE in descending order (Figure 3D ). Class-switched Ig isotypes such as IgG and IgA were more produced in GC-B cell cultures than in naïve B cell cultures. There was no statistically significant difference between the two T cell subsets (CD57 + GC-Th cells and CD4 + CD57 - CD69 + T cells) in the composition of Ig that they induced. CD57 + GC-Th cells induce AID expression and class switch recombination in B cells AID expression in the maturating B cells in GC is necessary for CSR and somatic hypermutation. We examined whether CD57 + GC-Th cells have the capacity to induce AID in B cells. Naïve B cells were co-cultured with CD57 + GC-Th cells, and AID expression was examined (Figure 4A ). CD57 + GC-Th cells induced AID in activated B cells with peaks around days 3–4. CD57 + GC-Th cells were able to induce the expression of productive V H DJ H -C H Ig transcripts. The major subtypes of Ig transcripts in response to CD57 + GC-Th cells were IgG3, IgG2, IgG1 and IgA1 (Figure 4B ). When the peak expression levels of AID and the productive V H DJ H -C H IgG3 transcript (the most readily detected Ig transcript) were compared, AID expression preceded the expression of IgG3 transcript by 1–2 days in culture (Figure 4C ). Ig class switch recombination between tandemly repeated S regions located 5' to each C H gene generates switch circles. We used a nested PCR technique designed to specifically detect switch circles but not genomic Ig sequences. Freshly isolated GC-B, but not naïve B cells, contained switch circles, which were detected as smeared multiple bands on agarose gels as expected. Naïve B cells cultured with CD57 + GC-Th cells generated detectable switch circles in a time-dependent manner (Figure 4D ). We also used a DC-PCR technique [ 30 ] to detect γ3 and α1/2 switch circles (Figure 4E ). Again, GC-Th cells induced switch circles in the naïve B cells cultured with GC-Th cells. CD40L signal is necessary for, while cytokines modulate, the helper activity of CD57 + GC-Th cells Cytokines and CD40L regulate B cell maturation and Ig production. We examined whether CD40L, IL-4, IL-10 and IFN-γ play any roles in the CD57 + GC-Th cell-driven B cell production of Ig. In cultures with naïve B cells, the blockage of CD40L by neutralizing antibody completely suppressed the helper activity of CD57 + GC-Th cells in inducing the B cell production of IgM, IgG1, IgA and IgE (Figure 5A ). In this case, IgG1 was measured instead of total IgG to avoid cross-reaction of the polyclonal capturing antibody for IgG with the neutralizing antibodies to cytokines. Blockage of IL-4 partially but specifically suppressed the production of IgE, but it did not significantly suppressed other isotypes. In contrast, blockage of IFN-γ enhanced the production of IgM, IgG1 and IgA but not IgE. Since CD40L is essential for the helper activity of CD57 + GC-Th cells, we examined CD57 + GC-Th cells and other T cells for the expression of surface CD40L. Freshly isolated CD57 + GC-Th cells expressed CD40L, which became up-regulated upon T cell activation within hours (data not shown), whereas CD4 + CD57 - CD69 - interfollicular T cells did not express CD40L at significant levels. There was no significant difference in the expression of surface CD40L between CD57 + and CD57 - CD69 + cells. In the cultures with GC-B cells, blocking of CD40L again completely suppressed the B cell helping activity of CD57 + GC-Th cells (Figure 5B ). However, IL-4 neutralization did not significantly affect the IgE production induced by CD57 + GC-Th cells, an activity different from that for naïve B cells. For GC-B cells, IFN-γ neutralization significantly increased the production of IgA as it did for naive B cells. The effects of IFN-γ neutralization on other Ig isotypes were smaller. While a slight decrease of IgE production in the cultures of GC-B cells and CD57 + GC-Th cells was observed, neutralization of endogenous IL-10 did not have any statistically significant effect on CD57 + GC-Th cell-driven Ig production in the cultures of either naïve or GC-B cells. Exogenously-added IL-10 enhances while TGF-β1 completely suppresses the B cell helping activity of CD57 + GC-Th cells To further examine the regulatory effect of cytokines, IL-4, IL-10, IFN-γ and TGF-β1 were exogenously added to the cultures of CD57 + GC-Th cells with B cells (Figure 5C and 5D ). In cultures of CD57 + GC-Th cells with naïve B cells, exogenously added IL-4 enhanced the production of some subsets of Ig, but this effect was small and not statistically significant (Figure 5C ). However exogenously added IFN-γ significantly suppressed the production of IgG, IgA and IgE. IL-10, when added exogenously, was highly efficient in enhancing the production of the four subsets of Ig. TGF-β1 completely suppressed the B cell-helping capacity of CD57 + GC-Th cells for naive B cells. In cultures of CD57 + GC-Th cells with GC-B cells, IL-10 was again highly effective in enhancing the helper activity of CD57 + GC-Th cells, while TGF-β1 completely suppressed it (Figure 5D ). IFN-γ partially but significantly suppressed the production of IgM, IgG, IgA and IgE. Exogenous IL-4 added to the cultures had no effect on the CD57 + GC-Th cell-driven Ig production in this condition (Figure 5D ), which is in line with the negligible effect of anti-IL-4 on GC-B cells in Figure 5B . Discussion CD57 + GC-Th cells are unique CD4 + T cells. They express the follicle homing receptor CXCR5 but lack the T cell area localization receptor CCR7 [ 19 ], and reside specifically in germinal centers [ 12 - 14 ]. CD57 + GC-Th cells proliferate only when appropriate signals such as TCR, CD28 and IL-2 are provided [ 17 , 18 ]. GC-Th cells are widely disseminated and diverse in their TCR sequence [ 31 ]. CD57 + GC-Th cells can express CD40L, ICOS and CXCL13 but are non-polarized T cells in their cytokine profile [ 21 ]. It has been controversial and unclear whether CD57 + GC-Th cells are intrinsically more efficient in helping B cells than other T cells or they are simply localized in germinal centers without any significant differences from other T cells in their capacity as helpers. In this report, we systematically investigated the effector function of CD57 + GC-Th cells in regulation of B cell immunoglobulin production and its regulation. When compared for their helper activities in inducing Ig synthesis by total B cells, CD57 + GC-Th cells were most efficient among the T cell subsets in tonsils. CD57 + GC-Th cells were particularly more efficient in their helper activity for GC-B cells vs. naïve B cells. CD57 - CD69 + T cells were equally efficient to CD57 + GC-Th cells in inducing naïve B cell differentiation for Ig production, while they were less effective than CD57 + GC-Th cells in helping GC-B cells. This preference of CD57 + GC-Th cells for GC-B cells is physiologically relevant, since both the helper T cell subset and target B cells are specifically present in germinal centers. Therefore, CD57 + GC-Th cells would constitute an ideal T helper subset that can drive GC-B cell differentiation in germinal centers. The effects of cytokines such as IL-4, IL-10, IFN-γ and CD40L on B cells in humans and mice have been well documented. It is considered that CD40L is a critical factor [ 4 , 11 , 32 - 37 ], and IL-4 and IL-10 are positive factors in regulation of B cell Ig production [ 38 - 44 ]. IFN-γ induces class switch to certain isotypes while it inhibits to others [ 45 , 46 ]. In this study of the helper activity of CD57 + GC-Th cells, the positive role of IL-4 in promoting Ig production was valid only for IgE, but not IgG and IgA in the cultures of naïve B cells with CD57 + GC-Th cells (Figure 5 ). GC-B cells were even more resistant to the neutralization of IL-4 than naïve B cells in CD57 + GC-Th-cell driven Ig production. This smaller than expected effect of IL-4 may be due to the fact that there is not much IL-4 to neutralize in the cultures of GC-Th cells. This also suggests that GC-Th cells may provide helper signals to GC-B cells that are not significantly affected by IL-4. AID [ 47 ] is a molecule essential for somatic hypermutation, CSR and Ig gene conversion [ 48 - 54 ]. We showed in this study that CD57 + GC-Th cells can induce AID expression (Figure 4A ). This capacity is consistent with their ability to induce class switch recombination, which can be detected within a few days in the cultures of naïve B cells with CD57 + GC-Th cells. CD57 + GC-Th cells can induce the expression of productive IgG1-3 and IgA1 transcripts. However, CD57 + GC-Th cells were inefficient in induction of IgE (Figure 3 , 4 and 5 ), which is consistent with their poor production capacity of IL-4 [ 19 ]. CD40L appears to be essential for the helper activity of CD57 + GC-Th cells. CD40L was required for the synthesis of all Ig isotypes in all the conditions tested regardless of whether the target B cells for CD57 + GC-Th cells were naïve or GC-B cells. While neutralization of IL-10 did not have any significant effect on the CD57 + GC-Th cell-driven Ig synthesis, exogenous IL-10 was highly effective in enhancing the Ig synthesis in our study. This could be due to insufficient neutralization of the IL-10 produced by CD57 + GC-Th cells, which are known to produce IL-10 upon TCR activation [ 19 ]. Another possibility is that higher concentration of IL-10 than the level produced by CD57 + GC-Th cells may be necessary to significantly enhance the Ig response. Exogenous IFN-γ negatively regulates the CD57 + GC-Th cell-driven Ig synthesis, suggesting the potential roles of Th1 cells or other IFN-γ producing cells in regulation of the CD57 + GC-Th cells' helper activity. TGF-β1 plays dual roles: it is a switch factor for IgA and a potent immunosuppressive cytokine that inhibits Ig synthesis [ 55 ]. We did not detect any switching effect but were able to detect its suppressive activity for the CD57 + GC-Th cell response. This could be due to the fact that the culture conditions (e.g. the saturating concentration of TGF-β) employed in our study appear to favor the detection of the suppressive function of TGF-β. Taken together, these results imply that Th1, Th2 and regulatory T cells, if present in germinal centers, could positively or negatively control the function of CD57 + GC-Th cells in regulation of humoral immune responses. Indeed, there are regulatory T cells in GCs that express surface TGF-β and can effectively suppress the function of CD57 + GC-Th cells [ 56 ]. Conclusions Our results demonstrated the capacity of CD57 + GC-Th cells in supporting CSR and Ig synthesis in B cells, and revealed the factors that regulate their activity, thereby substantiating the so-far inconclusive function of CD57 + GC-Th cells. The fact that these T cells have preferential and efficient helper activity for GC-B cells and are specifically localized in GCs in large numbers suggests that CD57 + GC-Th cells are probably the major T helper subset responsible for supporting B cell differentiation for Ig production in germinal centers. Methods Cell isolation Mononuclear cells were prepared by density gradient centrifuge on histopaque 1077 (Sigma-Aldrich, St. Louis) from human tonsil pathological specimens obtained from young patients (3–10 yr) undergoing tonsillectomy to relieve obstruction of respiratory passages and improve drainage of the middle ear at Sagamore Surgical Center (Lafayette, IN). The use of human pathological specimens in this study was approved by the institutional review board at Purdue University. CD4 + T cells (purity >97%) were isolated by depleting non-CD4 + T cells using a magnetic bead depletion method (Miltenyi Biotec, Auburn, CA). After staining of the isolated CD4 + T cells with appropriate antibodies, CD57 + GC-Th cells (purity >95%) were isolated by a positive magnetic selection method (Miltenyi Biotec). CD4 + CD57 - CD69 + and CD4 + CD57 - CD69 - T cell subsets (purity >95%) were further isolated from the CD57 - T cell fraction by magnetically selecting CD69 + T cells (Miltenyi Biotec). Total B cells were isolated by rosetting with 2-amino-ethylisothiouronium bromide (AET)-treated sheep red blood cells followed by CD4 + T cell depletion (CD19 + cells > 99.5%). Naïve B cells (CD19 + IgD + cells >99%) were isolated from the total B cell fraction by depleting CD38 + T cells followed by positive magnetic selection of IgD + B cells. CD19 + CD38 + IgD +/- GC-B cells (purity >95%) were isolated from the tonsil CD19 + B cells as described before [ 57 ] using anti-CD44, anti-IgD antibodies and pan-mouse IgG beads (Dynal, Brown Deer, WI). Cell culture All cell cultures were performed in RPMI1640 medium supplemented with 10% FBS, gentamycin, streptomycin, and penicillin. To cross-link the B cell receptors, isolated B cells were incubated for 2 h at 4°C with Sepharose-conjugated rabbit Ab to human Ig μ chain and human Ig (H + L) chain (Irvine Scientific, Santa Ana, CA; mixed 1:1 at 2 μg/ml), and then washed with cold PBS. 10 5 T and 10 5 B cells were co-cultured, unless indicated otherwise, in each well of 48-well plates in the presence of Staphylococcal enterotoxin B (SEB; 1 μg/ml, Sigma-Aldrich, St. Louis, MO). Cells were incubated in 5% CO 2 incubators at 37°C for 3–8 days. Recombinant IL-4, IL-10, and TGF-β1 were purchased from R&D systems (Minneapolis, MN). Recombinant IFN-γ was obtained from BD Pharmingen (San Diego, CA). Purified CD154-blocking antibody (24–31) was obtained from Ancell Corporation (Bayport, MN). IL-4-blocking antibody (MP4-25D2) was purchased from BD Pharmingen. Blocking antibodies for IFN-γ (25718.111) and IL-10 (23738.111), and IgG1 isotype control antibody (11711.11) were purchased from R&D systems. All antibodies and reagents added to culture were azide-free. Cytokines were added at saturating concentrations: IL-4 (40 ng/ml), IL-10 (40 ng/ml), IFN-γ (200 ng/ml) and TGF-β1 (10 ng/ml). Neutralizing antibodies were added at following concentrations: anti-CD40L (20 μg/ml), anti-IL-4 (5 μg/ml), anti-IL-10 (5 μg/ml), anti-IFN-γ (2.5 μg/ml) and isotype antibody (5 μg/ml). Flow cytometry analysis T cells were stained with anti-human CD57 (NK-1; FITC, BD Pharmingen), anti-human CD69 (FN-50; FITC, BD Pharmingen), anti-human CD4 (S3.5; R-PE, Caltag Laboratories, Burlingame, CA), and anti-human CD3 (UCHT1; APC, BioLegend, San Diego, CA). B cells were stained with anti-CD19 (4G7; PerCP, BD Pharmingen), anti-human IgD (IAb-2, FITC, BD Pharmingen), anti-human CD38 (HTT2; R-PE, BD Pharmingen), and anti-human CD3 (UCHT1; APC, BioLegend). Stained cells were analyzed using a 4-color FACSCalibur™ (BD Biosciences). In situ fluorescent immunohistochemistry Frozen sections of tonsils were acetone-fixed and stained using antibodies to CD57 (BD Biosciences – Pharmingen; clone NK-1, labeled with FITC), CD69 (BD Biosciences – Pharmingen; clone FN50, labeled with FITC), IgD (BD Biosciences – Pharmingen; clone IA6-2, labeled with PE) and/or CD4 (Caltag Laboratories; clone S3.5, labeled with APC). Stained sections were analyzed using a confocal microscopy system (Bio-Rad MRC 1024UV and Nikon Diaphot 300 microscope) at Purdue Cytometry Lab. ELISA Culture supernatants were assayed by ELISA as previously described [ 19 ]. The sensitivity of this ELISA system is greater than 5 ng/ml, 300 pg/ml, 30 pg/ml, 600 pg/ml, and 15 pg/ml for IgM, IgG, IgG1, IgA and IgE, respectively. Detection of productive V H DJ H -C H Ig transcripts and reciprocal DNA recombination products Total RNA was extracted from cultured cells with Trizol reagent (Invitrogen, Carlsbad, CA), and was reverse-transcribed into cDNAs with SuperScript™ First-Strand Synthesis System for RT-PCR (Invitrogen) according to the manufacturer's protocol. The primer pairs used in this study were designed by Cerutti et al. [ 37 ]: IgM, FR3 forward (5'-GAC ACG GCT GTG TAT TAC TGT GCG-3') and Cμ reverse (5'-CCG AAT TCA GAC GAG GGG GAA AAG GGT T-3'); IgG1, FR3 forward and Cγ1 reverse (5'-GTT TTG TCA CAA GAT TTG GGC TC-3'); IgG2, FR3 forward and Cγ2 reverse (5'-GTG GGC ACT CGA CAC AAC ATT TGC G-3'); IgG3, FR3 forward and Cγ3 reverse (5'-TTG TGT CAC CAA GTG GGG TTT TGA GC-3'); IgG4, FR3 forward and Cγ4 reverse (5'-ATG GGC ATG GGG GAC CAT TTG GA-3'); IgA1, FR3 forward and Cα1 reverse (5'-GGG TGG CGG TTA GCG GGG TCT TGG-3'); IgA2, FR3 forward and Cα2 reverse (5'-TGT TGG CGG TTA GTG GGG TCT TGC A-3'); IgE, FR3 forward and Cε reverse (5'-CGG AGG TGG CAT TGG AGG-3'); human β-actin, actin forward (5'-ATG TTT GAG ACC TTC AAC AC-3') and actin reverse (5'-CAC GTC ACA CTT CAT GAT GG-3'). PCR reactions were performed on serially diluted cDNA samples using an Eppendorf master cycler (denaturation at 95°C for 15 s, annealing at 55°C for 45 s and extension at 72°C for 30°C; 30–35 cycles). Extrachromosomal switch circles were detected by a nested PCR strategy as previously described by others [ 37 ]. Briefly, genomic/extrachromosomal DNA was isolated from fresh or cultured B cells using a QIAamp DNA Mini Kit (Qiagen, Valencia, CA) and was used as templates for amplification of Sγ1-Sμ, Sγ2-Sμ, Sγ3-Sμ, Sγ4-Sμ and Sα-Sγ. The PCR products were subject to second PCR using internal forward 5' Sγ or 5'Iα1/2i and reverse 3'Sμi or 3'γi primer pairs. This method has been verified for specificity using positive controls [ 37 ]. Additionally, we amplified genomic β-actin gene as a control using 5'-GTA CCA CTG GCA TCG TGA TGG ACT-3' (G-actin-forward-1 primer) and 5'-ATC CAC ACG GAG TAC TTG CGC TCA-3' (G-actin-reverse-1) for the first PCR; and 5'-AGA AGA GCT ACG AGC TGC CTG AC-3' (G-actin-forward-2) and 5'-TGA GGA CCC TGG ATG TGA CAG CT-3' (G-actin-reverse-2) for the second PCR. Additionally, we used a DC-PCR technique [ 30 , 58 ] to demonstrate the presence of switch circles (γ3 and α1/2) in human B cells. Please see the reference [ 30 ] for primer sequences. RT-PCR analysis for AID expression Total RNA was extracted from freshly isolated or cultured cells using Trizol reagent (Invitrogen, Carlsbad, CA), and was reverse-transcribed into cDNAs with SuperScript™ II Reverse Transcriptase. RT-PCR amplification of AID was performed using the two primers: AID-forward (5'-GAT GAA CCG GAG GAA GTT TC-3') and AID-reverse (5'-TCA GCC TTG CGG TCC TCA CAG-3'), which generated a specific 351 bp PCR product after 30 cycles of PCR reaction (30 s at 94°C, 30 s at 60°C, and 60 s at 72°C). β-actin was also amplified as a control. Statistical analysis Student's paired t-test was used. P values smaller than 0.05 were considered significant. List of abbreviations used GC, germinal center; GC-Th cells, germinal center T helper cells; GC-B cells, germinal center B cells; AID, activation-induced cytosine deaminase; CSR, class switch recombination; SEB, staphylococcal enterotoxin B. Authors' contributions CHK conceived, coordinated the study, analyzed the results and wrote the text. JRK, HWL and SGK participated in experiments, data analysis, making figures and proofreading the manuscript. PH provided specimens and helped perform the study. All authors read and approved the manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548684.xml

548690

Characterization of gana-1, a Caenorhabditis elegans gene encoding a single ortholog of vertebrate α-galactosidase and α-N-acetylgalactosaminidase

Background Human α-galactosidase A (α-GAL) and α-N-acetylgalactosaminidase (α-NAGA) are presumed to share a common ancestor. Deficiencies of these enzymes cause two well-characterized human lysosomal storage disorders (LSD) – Fabry (α-GAL deficiency) and Schindler (α-NAGA deficiency) diseases. Caenorhabditis elegans was previously shown to be a relevant model organism for several late endosomal/lysosomal membrane proteins associated with LSDs. The aim of this study was to identify and characterize C. elegans orthologs to both human lysosomal luminal proteins α-GAL and α-NAGA. Results BlastP searches for orthologs of human α-GAL and α-NAGA revealed a single C. elegans gene (R07B7.11) with homology to both human genes (α- ga lactosidase and α- N - a cetylgalactosaminidase) – gana-1 . We cloned and sequenced the complete gana-1 cDNA and elucidated the gene organization. Phylogenetic analyses and homology modeling of GANA-1 based on the 3D structure of chicken α-NAGA, rice α-GAL and human α-GAL suggest a close evolutionary relationship of GANA-1 to both human α-GAL and α-NAGA. Both α-GAL and α-NAGA enzymatic activities were detected in C. elegans mixed culture homogenates. However, α-GAL activity on an artificial substrate was completely inhibited by the α-NAGA inhibitor, N-acetyl-D-galactosamine. A GANA-1 :: GFP fusion protein expressed from a transgene, containing the complete gana-1 coding region and 3 kb of its hypothetical promoter, was not detectable under the standard laboratory conditions. The GFP signal was observed solely in a vesicular compartment of coelomocytes of the animals treated with Concanamycin A (CON A) or NH 4 Cl, agents that increase the pH of the cellular acidic compartment. Immunofluorescence detection of the fusion protein using polyclonal anti-GFP antibody showed a broader and coarsely granular cytoplasmic expression pattern in body wall muscle cells, intestinal cells, and a vesicular compartment of coelomocytes. Inhibition of gana-1 by RNA interference resulted in a decrease of both α-GAL and α-NAGA activities measured in mixed stage culture homogenates but did not cause any obvious phenotype. Conclusions GANA-1 is a single C. elegans ortholog of both human α-GAL and α-NAGA proteins. Phylogenetic, homology modeling, biochemical and GFP expression analyses support the hypothesis that GANA-1 has dual enzymatic activity and is localized in an acidic cellular compartment.

Background Humans have two enzymes with α-galactosidase activity and an acidic pH optimum, α-N-acetylgalactosaminidase (α-NAGA) (previously called α-galactosidase B) and α-galactosidase A (α-GAL). Hereditary deficiency of each of the hydrolases causes a distinct lysosomal storage disorder in humans, Schindler and Fabry diseases, respectively [ 1 , 2 ]. Early studies suggested that both human enzymes were glycoforms with similar substrate specifities. Purified enzymes had similar physical properties, including subunit molecular mass (~46 kDa), homodimeric structure, and amino acid sequences. However, additional studies showed kinetic, structural, and immunologic differences proving that α-GAL and α-NAGA were products of two different genes [ 3 , 4 ]. The two genes differed in the number of exons (7 and 9, respectively) and also in the number, placement, and orientation of Alu repeats. Exons 2 – 7 of the α-NAGA gene showed high similarity to the first six exons of α-GAL gene. Because of the remarkable amino acid identity (49%) and similarity (63%) between the two genes and the similar intron placement, Wang [ 5 ] and co-workers suggested that a duplication event occurred during the evolution of both enzymes. Both enzymes belong to the glycoside hydrolase family 27 clan D [ 6 ]. Glycoside hydrolase family 27 clan D orthologs have been identified in a broad spectrum of prokaryotes and eukaryotes, including plants. Members of the family have a highly similar active site and share the same reaction mechanism. The structures of chicken α-NAGA, human α-GAL and rice α-GAL have been determined by X-ray crystallography [ 7 - 9 ]. Chicken and human enzymes have a homodimeric quarternary structure whereas rice α-GAL acts as a monomer. The monomer units are composed of two distinct domains. Domain I contains the active site and adopts a (β/α) 8 barrel structure, a domain fold observed commonly in glycosidases. Domain II has eight antiparallel β strands, packed into two β sheets in a β sandwich fold containing a Greek key motif [ 8 ]. The physiological importance of both enzymes is evidenced by the severe presentation of α-NAGA and α-GAL deficiencies in humans [ 1 , 2 ]. Our recent study on degradation of blood group A and B glycolipids in Fabry cells indicated a high residual activity in Fabry cells toward natural substrate glycolipid B-6-2 [ 10 ] although α-galactosidase activity was completely absent when measured in vitro by routine procedures using artificial substrates. We proposed that another enzyme, different from α-GAL, contributes in vivo to hydrolysis of α-galactosides. We suggested α-NAGA as the most likely candidate. Human α-NAGA is known to accept α-galactosides albeit with a high K m [ 11 , 12 ]. Its activity must be inhibited when measuring α-GAL in tissues with high α-NAGA activity [ 13 ]. We investigated the phylogenesis of a single C. elegans α- GA L and α- NA GA ortholog ( gana-1 ) to both human genes. We present evidence suggesting that this gene has indeed evolved from the α-GAL/α-NAGA ancestral gene before the duplication event which resulted in separate α-NAGA and α-GAL genes in higher metazoans. We further performed structural analysis of the GANA-1 3D model acquired by homology modeling. We determined the spatial and temporal expression of the gene in transgenic worms using a translational reporter and examined the effect of RNA interference (RNAi) as a first step in the possible use of C. elegans as a model organism for Schindler and Fabry diseases. Results and discussion cDNA amplification and sequencing The complete C. elegans genome [ 14 , 15 ] contains only one open reading frame, designated gana-1 (R07B7.11) that has sequence similar to human genes encoding α-GAL and α-NAGA. Similar results were obtained from searching the available C. briggsae genome sequence [ 15 ]. The gana-1 gene consists of 5 exons and 4 introns and is annotated as an ortholog of human α-NAGA. Several EST clones for this open reading frame (ORF) have been reported and open-reading-frame sequence tag (OST) is present in the Worfdb database [ 16 ]. Available public database data are in agreement with our findings. We verified the gene structure by sequencing the PCR products from genomic DNA and cDNA (Figure 1 ). The analyzed region spanned the entire coding region and the 3' and 5' untranslated regions (UTR). The 5' UTR SL1 element suggests that the gene is either the only gene transcribed from the promoter or is the first gene in an operon including gana-1 and the two predicted downstream genes R07B7.12 and R07B7.13. Although this region has not been reported as an operon, the physical distances between this cluster of three genes are suggestive of an operon [ 17 - 19 ]. No alternative splicing was found using RT/PCR, a feature similar to both human and mouse orthologs. RNA editing was reported in the 3' UTR of human α-GAL, a finding that another group was unable to confirm [ 20 ]. We noted no signs of RNA editing in clones derived from the gana-1 cDNA. Figure 1 gana-1 gene structure. Schematic representation of gana-1 gene structure. The length of genomic DNA from start to stop codons is 1681 bp. The spliced cDNA consists of 1356 bp + 91 bp of 3' UTR. Phylogenetic studies We aligned GANA-1 protein sequence with other melibiase family members (Figure 2 ) and constructed a phylogenetic tree (Figure 3 ). The alignment showed a striking similarity of GANA-1 to all other included sequences. GANA-1 had the highest sequence similarity with Anopheles gambiae GAL (46%), the lowest similarity was observed with Streptomyces avertimilis GAL (22%). The results of our phylogenetic analysis are in accordance with generally accepted evolutionary concepts. The analysis identified four main clades: animal NAGAs, animal GALs, plant/lower organisms GALs and the clade containing sequences of Drosophila melanogaster , Anopheles gambiae and Caenorhabditis elegans . The branch including C. elegans is positioned between higher animal GALs and NAGAs and plant/lower organisms GALs. This position in the tree infers the evolutionary ancestrality of gana-1 gene to both animal GALs and animal NAGAs. However, this conclusion is not in complete agreement with the presence of pairs of genes in Drosophila and Anopheles genomes annotated as α-GALs and α-NAGAs. The presence of these genes in the Caenorhabditis/Drosophila/Anopheles branch (and not in the GAL and NAGA clades) could be due to low divergence of these sequences from a common ancestral gene or to independent gene duplication in the Drosophila/Anopheles ancestral organism. It is also important to note that the phylogenetic analysis by maximum parsimony algorithm placed the Caenorhabditis/Drosophila/Anopheles branch into the neighborhood of the animal NAGAs branch [ 8 ]. In this case the computational algorithm probably favored the lower number of necessary sequence changes (parsimony) between GANA-1 and NAGA clade sequences. Figure 2 Multiple alignment. Multiple alignment of 29 sequences homologous with GANA-1. These sequences represent animal, plant and protozoan kingdoms. The SwissProt/TrEMBL codes are part of the sequence names. Predicted signal peptides are shown in brown letters. In cases where two signal sequence prediction algorithms gave different results the difference is marked by amber color. The residues forming active site pocket of GANA-1 are indicated by arrowheads above the alignment. The catalytic domain I is indicated by green band above the alignment. Figure 3 Cladogram of GANA-1 orthologs. Cladogram of GANA-1 orthologs. The numbers at the branch nodes represent bootstrap values. In our opinion, the phylogenetic analysis provides evidence that the GANA-1 evolved from a common ancestor of α-GAL and α-NAGA enzymes. However, the topology of the tree could also be explained by a loss of the second gene during the evolution of C. elegans . In this case the enzyme found in C. elegans would probably be the ortholog of human α-NAGA and the lost gene would likely be the ortholog of human α-GAL. The likelihood of these two hypotheses depends on functional divergence of duplicated gene products and their dispensability for organism's metabolic pathways [ 21 ]. Homology modeling The best Squared Root of Mean Square Deviations value (RMSD), found between GANA-1 backbone atoms and the chicken α-NAGA template [ 7 ], was 0.52 Å. The structural model of the enzyme molecule has a two-domain structure (Figure 4A ). Domain I, which contains the predicted active site, adopts a (β/α) 8 barrel structure which represents a common motif in many glycoside hydrolases. Less conserved is domain II that has a β domain with β sandwich structure containing a Greek key motif. The active site pocket of GANA-1 is formed by the same twelve amino acids (W31, D76, D77, Y118, C126, K152, D154, C156, S186, A189, Y190 and R211) (Figure 4B ) as in chicken α-NAGA. This finding infers their identical function in catalytic reactions as described for chicken α-NAGA [ 7 ]. D134 carboxyl starts the initial nucleophilic attack and D215 carboxylic oxygen serves as a subsequent donor and acceptor of the proton during the reaction cycle. Figure 4 GANA-1 protein model. A) Ribbon representation of GANA-1 monomer model. A two-domain structure is apparent in the left picture. The N-acetyl-D-galactosamine (inhibitor) is placed into the active site. Dots represent VdW radii of surface atoms. B) Stereo picture of the active site pocket with N-acetyl-D-galactosamine (inhibitor) and amino acid labels. The viewing angles for stereo representation of the protein structure are ±2 degrees from the central axis. Residues forming the "N-acetyl recognition loop" in the chicken α-NAGA [ 8 ] (S172, A175, Y176) have the closest contact with the N-acetyl moiety of the ligand. These residues are completely conserved between human and chicken NAGAs, but in human α-GAL serin 172 is replaced by glutamine and alanine 175 is replaced by leucine. The replacements with bulkier residues apparently discriminate between α-GAL and α-NAGA substrates. While NAGAs can accommodate α-galactose and can have some α-GAL activity, GALs do not have α-NAGA activity and are not inhibited by N-acetylgalactosamine. The corresponding residues of GANA-1 in the NAGA recognition loop are S186 and A189 and are characteristic for NAGAs. According to Garman [ 8 ] the key residue in the dimer interface in human α-GAL is F273. Residues corresponding to this position in other orthologs can serve as predictive markers of the protein quartenary structure. Phenylalanine or tyrosine is present in enzymes that act as homodimers while glycin indicates a monomeric structure [ 8 ]. The equivalent residue to human α-GAL F273 in GANA-1 is lysine at position 257 which is suggestive of homodimeric structure due to its sterical properties. The homology modeling showed that a groove opposing K257 is formed by residues T260, L261, D262, M263, I389, V390 and V391 of the other monomer unit of GANA-1. In the case of chicken α-NAGA these residues are equivalent to S246, Y247, E248, Q249, N375, P376 and S377 (for details see Additional file 1 ). Biochemical studies Standard bacteria/nematode separation protocol previously used by other authors [ 22 , 23 ] while evaluating lysosomal enzyme activites is based on sucrose flotation approach. We avoided standard sucrose flotation of worms because we could not exclude unpredictable artifacts caused by this compound, which is known to induce artificial lysosomal storage in eukaryotic cells and to alter lysosomal gene expression at concentrations significantly lower [ 24 ] than those used in flotation protocols. We found both α-GAL and α-NAGA enzymatic activities in the homogenates from C. elegans N2 strain using 4-methylumbelliferyl (MU) substrates. The α-NAGA activity was dominant over the α-GAL activity. The activity of α-NAGA measured at 37°C was 430 nmol.mg -1 .h -1 with MU-α-N-acetylgalactosaminide compared to the activity of α-GAL with MU-α-galactopyranoside of 43 nmol.mg -1 .h -1 (about 10% of that of α-NAGA). In the assay of α-GAL, the degradation of the MU-α-galactopyranoside was inhibited up to 95% in the presence of N-acetyl-D-galactosamine (D-GalNAc), whereas in the presence of D-galactose (D-Gal) the degradation of the same substrate was inhibited up to 75%. In the assay of α-NAGA, the degradation of the MU-α-N-acetylgalactosaminide was inhibited up to 97% by D-GalNAc and up to 90% by D-Gal. No inhibition of α-NAGA and α-GAL activity by D-glucose was observed. According to published observations in human enzymes, D-GalNAc has no inhibitory effect on α-GAL activity. On the other hand, human α-NAGA activity is inhibited by both, D-GalNAc and D-Gal [ 25 ]. The model of GANA-1 predicts only one active site per monomer of the enzyme. If the enzyme had activity toward both substrates, MU-α-D-galactopyranoside and MU-α-N-acetylgalactosaminide, it is to be expected that D-GalNAc and D-Gal would inhibit both activities. The strong inhibitory effect of D-GalNAc on the α-GAL activity, which is not present in human α-GAL, supports the hypothesis that C. elegans has only one enzyme with both α-GAL and α-NAGA activities. Nevertheless, these experiments were not conducted with the pure enzyme and thus do not provide absolute proof of this hypothesis. RNA interference RNA interference assays directed against the whole coding region of gana-1 , employing combination of microinjection and feeding approaches, did not reveal any abnormal morphological phenotypes. Nevertheless, measurement of α-GAL and α-NAGA activities in four different experiments showed a simultaneous decrease of both enzymatic activities in RNAi-treated worms (Table 1 ) as compared with control animals. In all RNAi experiments, both α-GAL and α-NAGA activities decreased proportionally, usually by tens of percent of activity of appropriate controls. The activity of the control enzyme (β-hexosaminidase) did not differ between the RNAi-treated nematodes and controls (data not shown). This finding supports the specificity of gana-1 RNAi. The differences between individual experiments are not surprising due to the well-known variability in the efficiency of RNAi [ 26 ]. The results of RNAi experiments further support the hypothesis that GANA-1 has both enzymatic activities. Table 1 α-GAL and α-NAGA activities after gana-1 RNAi. The table shows a proportional parallel decrease of both enzymatic activities (α-GAL and α-NAGA) after gana-1 RNAi compared to controls. experiment sample α-GAL α-GAL (% of control) α-NAGA α-NAGA (% of control) α-NAGA/α-GAL (% of control) nmol mg -1 h -1 nmol mg -1 h -1 1 control 1.78 100 53.13 100 gana-1 RNAi 1.19 67 26.63 50 0.75 2 control 13.26 100 221.68 100 gana-1 RNAi 11.1 84 195.13 88 1.05 3 control 3.43 100 61.68 100 gana-1 RNAi 1.02 30 11.75 19 0.63 4 control 9.6 100 212.1 100 gana-1 RNAi 2.9 30 50.69 24 0.80 Both enzymatic activities were lower in RNAi-treated and control worms cultured on the bacterial strain HT115 [ 26 ] compared to a N2 strain cultured on the OP50 strain. RNAi previously provided sufficient level of inhibition of structural lysosomal proteins for development of abnormal phenotypes in the worm [ 27 , 28 ]; however, it is apparently not efficient enough for lysosomal catalytic proteins. Expression of gana-1 To study the expression of gana-1 in C. elegans , we created transgenic worms with a reporter gene containing the entire coding region of gana-1 C-terminally tagged with green fluorescent protein (GFP) under the control of a 3 kb region of the gana-1 hypothetical promoter. The presence of the gana-1::GFP transgene in the worms was confirmed on the level of genomic DNA, cDNA and protein. However, no GFP signal was observed by fluorescence microscopy under the standard laboratory conditions. As Western blotting showed the presence of fusion protein of the expected size (data not shown), we assumed that the absence of the GFP signal was caused by a pH-dependent quenching of GFP fluorescence, which has neutral to alkaline optimum (pH 5.5–12) [ 29 ]. To study the tissue and intracellular distribution of the fusion protein, we resorted to immunofluorescence detection of the transgene product. Immunofluorescence detection of GFP fusion protein showed a specific and coarsely granular cytoplasmic pattern of fusion protein expression. This transgene product was limited to body wall muscle cells (30% of population) (Figure 5A , Additional file 2 ) or found in a broader tissue distribution that included body wall muscle cells, intestinal cells and coelomocytes (3–5% of population) (Figure 5B , Additional file 3 ). This latter staining pattern is consistent with the GFP detection in NH 4 Cl and concanamycin A (CON A) experiments (Figure 6 ) discussed below. The expression of the transgene was observed in about 30% of the population which corresponded to usual expression efficiency of extrachromosomal array transgenes [ 30 , 31 ]. The immunofluorescence staining protocol resulted in a significant decrease of inherent intestinal granular autofluorescence previously assigned to secondary lysosomes [ 32 ]. The decrease of autofluorescence intensity together with its poorly defined emission spectra hampered co-localization study. Figure 5 Immunofluorescence detection of GANA-1::GFP. A) A coarsely granular cytoplasmic distribution of immunopositivity (green) in body-wall muscle cells (arrowheads). Two non-transgenic worms are shown in the background (asterisks) for comparison. Nuclei are counterstained in red. B) Detailed view of two body wall muscle cells with coarsely granular cytoplasmic distribution of immunopositivity (arrowheads) and a coelomocyte (asterisk), both pictures were acquired by 3D rendering of initial confocal Z-stacks. Note: compare with figure 6 . Figure 6 Alkalization of transgenic worms using CON A. Two coelomocytes showing a GFP signal in a membrane bound vesicular compartment (arrowheads) after 24 hour incubation in 50 nM CON A. DIC/fluorescence merged image. To confirm that the absence of the GFP signal was due to the quenching of fluorescence by low pH in the acidic cellular compartment, we used two agents specifically alkalizing acidic cellular compartment [ 33 , 34 ] to enhance the GFP emission. Soaking of gana-1::GFP transgenic animals in NH 4 Cl or CON A resulted in a distinct GFP signal in a vesicular compartment of endocytically active coelomocytes in a small proportion of worms (3–5% of population). The GFP signal intensity was dependent on the time of incubation and the concentration of the alkalizing agent used. The first visible GFP signal was observed after 8 hour incubation in 100 mM NH 4 Cl and within 2 hours of incubation in 50 nM CON A. Lower concentrations of both NH 4 Cl and CON A did not result in visible GFP signal even after prolonged incubation of up to 24 hours. The reappearance of the GFP signal after treatment of the worms with compounds increasing the acidic compartment pH indirectly confirms lysosomal localization of the fusion protein. The GFP signal in coelomocytes had the same coarsely granular pattern as that observed after immunostaining. Limited access of alkalizing agents to the tissues can explain the differences between the results of immunofluorescence and alkalization studies. Conclusions Our findings showed that gana-1 is the only ortholog of human α-NAGA and α-GAL in C. elegans . Based on phylogenetic and homology modeling analyses we speculate that GANA-1 most probably developed from a hypothetical ancestral gene before the duplication event which gave rise to separate α-NAGA and α-GAL genes. We also speculate that gana-1 gene product has both α-NAGA and α-GAL activities as detected in C. elegans homogenates. Importantly, both activities in the worm were inhibited by D-galactose and N-acetyl-D-galactosamine, which is a specific inhibitor of human α-NAGA and does not inhibit α-GAL. The GANA-1::GFP fusion protein had a pattern of distribution that is compatible with lysosomal subcellular localization. The lysosomal localization of the fusion protein was also supported by pH sensitive fluorescence of GFP that was detectable only after alkalization of the acidic cellular compartment. Not suprisingly, RNAi of gana-1 yielded no abnormal morphological phenotypes, most likely because it did not provide sufficient knockdown of enzymatic activities, necessary for development of lysosomal storage as observed in human pathology states. Nevertheless, gana-1 RNAi resulted in a partial decrease of both enzymatic activities supporting the notion that this gene encodes both of them. It is possible that a deletion allele of gana-1 may provide more insight into the function of gana-1 and efforts are underway to isolate such alleles. Deletion alleles of lysosomal hydrolases may serve as valuable models of human lysosomal storage disorders. Methods C. elegans methods, strains and nomeclature The wild type Bristol N2 strain was used for all experiments and was handled under standard laboratory conditions as described previously [ 35 ]. Standard methods were used for DNA microinjection [ 36 ] and dsRNA synthesis and microinjection [ 37 ]. Nomenclature is in agreement with available Genetic Nomenclature for Caenorhabditis elegans [ 15 ] and has been approved prior to manuscript submission. BLAST search Wormbase (2002–2004 versions and freeze versions [ 15 , 38 , 39 ]) databases were repeatedly searched for human α-GAL and α-NAGA orthologs using the BLASTP [ 40 ] program set at default values. Amino acid sequences of human lysosomal α-GAL and α-NAGA (acc. no. NP_000160 and acc. no. NP_000253 [ 41 ]) were used as query sequences. cDNA amplification and sequencing Total RNA was isolated from mixed stages of N2 cultures [ 42 ] and reverse transcribed with an oligodT-T7 (5'-AATACGACTCACTATAG) primer and Superscript reverse transcriptase (Invitrogen). The entire coding region of R07B7.11 was PCR amplified in two overlapping PCR products, with intragenic primers designed according to available Wormbase [ 15 ] and Worfdb [ 16 ] data. SL1 primer (5'GGTTTAATTACCCAAGTTTGAG) and SL2 primer (5'GGTTTTAACCCAGTTACTCAAG) [ 17 ] together with gene specific primer (5'ATCCTGATTAATTTTAATTGC) were used to amplify 942 bp of the 5' part of the cDNA and to evaluate the mode of trans splicing; the 1142 bp fragment of the 3' end of cDNA was amplified with T7 primer and a gene specific primer (5'CTTAAGTTTGGAATTTATGAA). The dominant PCR products were cloned with TOPO TA cloning kit (Invitrogen) into the pCR 2.1 vector. Positive clones were sequenced using the Li-Cor automated fluorescent sequencer and sequences were aligned with R07B7 reference cosmid sequence in the AlignIR software (Li-Cor) to evaluate splicing boundaries and overall gene organization. Multiple alignment and phylogenetic analyses Confirmed or predicted amino acid sequences of melibiase family members [ 43 ] representing plant, unicellular, and animal kingdoms were aligned using ClustalW algorithm [ 44 ] and Blosum62 matrix. The SwissProt/TrEMBL [ 45 ] accesion code and source organism of the sequences are depicted in Figure 2 . The sequence alignment was used for phylogenetic analysis with the software package PHYLIP [ 46 ]. The phylogenetic tree is based on 100 bootstraped input alignments and was constructed by maximum likelihood method with Jones-Taylor-Thornton matrix model [ 47 ]. Sequence identities between species were calculated without signal sequence in EMBOSS by Needleman-Wunsch global alignment algorithm with Blosum62 matrix, gap penalty – 10 and gap extension penalty – 0.5 [ 8 , 48 , 49 ]. Signal peptides were predicted at the SignalP server [ 50 ] both by algorithms using neural networks and Hidden Markov Models. The results were compared to known signal sequences. The differences between signal peptides predicted by the algorithms are depicted in Figure 2 . The 3D model of GANA-1 is based on the X-ray structure of chicken α-NAGA, rice α-GAL and human α-GAL (PDB codes 1ktcA, 1uas and 1r47, respectively) [ 7 - 9 , 51 ]. The model was created using the automated homology modeling server SwissModel with structure refinement and model evaluation in the DeepView program [ 52 ]. The print quality figures (Figure 4 ) and animations ( Additional file 1 ) were ray traced using PovRay software package [ 53 ]. Transgenic GFP expression The entire coding region of the gana-1 gene, including 3 kb of its 5'upstream sequence, was amplified from N2 genomic DNA through a nested PCR reaction using DyNAzyme EXT™ PCR system (Finnzymes) and two pairs of primers: the external pair (5'GTGAGAGTGGGGAGATAGAA and 5'TCAATTTGCTTGAGGTACATA) and the internal primers, with overhangs containing SphI and SalI restriction sites respectively (5'ACATGCATGCAACTTTCACAGGAACACAAC and 5'CGACGTCGACAATTGAACTCTATTGGTTCTCAA). The amplified DNA fragment (4709 bp) was cloned using TOPO-XL cloning kit (Invitrogen) into the pCR-XL-TOPO vector. The SphI and SalI gana-1 restriction fragment was recloned into the GFP reporter vector pPD95.67 (supplied by A. Fire, Stanford University). The in-frame nature of the insert was confirmed by sequencing. The green fluorescent protein (GFP) fusion construct pJH3 (50 ng/μl) and pRF4 plasmid (50 ng/μl) used as the dominant marker were co-injected into the gonads of young adult N2 worms. Transgenic animals were screened for GFP signal. Nikon Eclipse E800 with C1 confocal module and 488 nm and 543 nm lasers and differential interference contrast (DIC) optics was used for specimen examination. EZ-C1 software (Nikon) was used for picture analysis and 3D rendering (Additional Files 2 , 3 ). Alkalization of acidic cell compartment Mixed stage pJH3 and N2 (control) cultures were harvested from NGM OP50 plates and washed with water. Worms were pelleted by centrifugation (max. 1000 RPM, 2 min.) between the washes. Worms were treated with either one of two agents (NH 4 Cl, concanamycin A – CON A) [ 33 , 34 ], that are known to specifically increase pH in the cellular acidic compartment. For the NH 4 Cl method, animals were suspended in 0, 10, 25, 50, 75 and 100 mM aqueous solutions of NH 4 Cl. Small aliquots of worms were examined after 30 min, 2, 4, 6, 8 and 24 hours. For CON A, animals were suspended in 0, 10, 20, 50, 100, 150, 200 nM solutions of CON A in aqueous media. Small aliquots of worms were examined after 1, 3, 6 and 24 hours. Microscopical examination was performed as described above. Immunofluorescence The fixation and immunofluorescence staining procedures were based on the approaches of Nonet et al. [ 54 ]. Mixed stage N2 cultures were harvested from NGM OP50 plates and washed thoroughly in M9 buffer to remove intestinal bacteria. Worms were pelleted by centrifugation (1000 RPM, 2 min.) between the washes. Worms were fixed overnight in 4% paraformaldehyde in 100 mM sodium/potassium phosphate buffer. Afterwards the pellets were washed three times in 1 × PBS, and incubated in 1% Triton X-100, 100 mM Tris (pH 7.0), 1% β-Mercaptoethanol overnight at 37°C to reduce the cuticle. After 5 washes in 1 × PBS, the worms were incubated for 5 hours in 900 U/ml collagenase type IV (Sigma) diluted in Krebs-Ringer solution (119 mM NaCl, 25 mM NaHCO 3 , 11.1 mM glucose, 1.6 mM CaCl 2 ·H 2 O, 4.7 mM KCl, 1.2 mM KH 2 PO 4 , 1.2 mM MgSO 4 ·7H 2 O, pH 7.4). The reduction/digestion step was performed twice. Pellets were washed 3 times with 1 × PBS and stored for further processing in AbA buffer (1 × PBS, 0.1% Triton X-100, 1% BSA, 0.05% NaN 3 ). AbA buffer was used for antibody dilution. Primary antibody (polyclonal rabbit anti-GFP IgG (Abcam)) was diluted 1:500. Secondary antibody (goat anti-rabbit IgG Alexa Fluor 488 labeled (Molecular Probes)) was diluted 1:1000. Both incubations were performed overnight at room temperature, with AbB buffer (1 × PBS, 0.1% Triton X-100, 0.1% BSA, 0.05% NaN 3 ) washes in between. Nuclei were counterstained with SYTOX orange (Molecular Probes) and the microscopic evaluation was performed as described above. Western Blotting Mixed stage pJH3 and N2 cultures were harvested from NGM OP50 plates. Worms were homogenized by sonication and the concentration of protein was measured by the Hartree method [ 55 ]. The proteins (equivalent of 25–50 μg of protein per lane) were separated by SDS-PAGE gradient gel (4% to 20% polyacrylamide) and transferred onto nitrocellulose membrane by semi-dry blotting. The membrane was treated according to a common Western blotting protocol with chemiluminiscence detection (SuperSignal, West Pico) [ 56 ]. Rabbit polyclonal anti-GFP IgG (Abcam, dilution 1:5000) was used as the primary antibody, the secondary antibody was goat anti-rabbit IgG/Px (Pierce, diluted 1:20 000). RNA mediated interference The PCR product containing the entire gana-1 cDNA was cloned into pCRII-TOPO vector (Invitrogen) and L4440 double promoter vector for microinjection and feeding RNAi respectively. In-vitro transcription employing T7 and SP6 RNA polymerases (Promega) was used to generate antisense single stranded RNA molecules, which were annealed to generate double stranded RNA (dsRNA). dsRNA was microinjected into N2 worms which were fed on HT115 [ 26 ] E. coli strain carrying L4440 plasmid with gana-1 insert. The F 1 and early F 2 progeny was screened for morphological phenotypes. N2 worms microinjected with water and fed on HT115 E. coli transformed with L4440 vector without insert were used as a control. 5–7 worms were microinjected both with dsRNA and water in each of 4 separate experiments, single worm progeny reaching 110–150 individuals. Determination of α-GAL and α-NAGA and β-hexosaminidase activities Prior to all activity measurements worms were washed from culture plates and repeatedly (6 times) washed and centrifuged in M9 buffer and finally pelleted by centrifugation. 4-methylumbelliferyl (MU)-α-D-N-acetylgalactosaminide (1 mM), 4-MU-α-D-galactopyranoside and 4-MU-β-D-glucopyranoside in theMcIlvaine buffer (0.1 M citrate/0.2 M phosphate buffer at acidic pH) were used as enzyme substrates. Reaction mixtures (sample and enzyme substrate) were incubated at 37°C and reactions were stopped by 600 μl of 0.2 M glycine/ NaOH buffer (pH 10.6) [ 13 , 57 ]. Fluorescence signal of the 4-methylumbelliferone was measured on the luminiscence spectrofotometer LS 50B (Perkin Elmer) (emission 365 nm and excitation 448 nm). Inhibitors (N-acetyl-D-galactosamine, D-galactose and D-glucose) were used in 0.1 M final concentration. All measurements were performed in doublets. Authors' contributions JH carried out molecular, RNAi, expression and biochemical analyses and wrote the first draft of the manuscript. JS participated in the design of all experiments and participated on the bioinformatic, molecular, RNAi and expression analyses and wrote the final version of the manuscript. RD performed phylogenetic analyses including homology modeling. HP participated on the biochemical analyses. MK and JL participated on the coordination of the project. MH conceived the project and provided fundraising. All authors read and approved the final version of the manuscript. Supplementary Material Additional File 1 Structure of GANA-1 dimer. The color of the backbone represents differences of amino acids between GANA-1 and chicken NAGA. Blue color represents identical residues and orange stands for non-conservative changes. The colors from cyan to green represent different degrees of conservation. The surface of one monomer unit at the interface area is rendered with colors representing electrostatic potential. N-acetyl-D-galactosamine (inhibitor) is placed in the active site pocket of both monomer units (D-GalNAc arrowhead). K257 arrowhead depicts predicted dimerisation residue. Click here for file Additional File 2 Immunofluorescence detection of GANA-1::GFP in muscle cells. 3D volume rendered and animated image corresponding to Figure 5A Click here for file Additional File 3 Immunofluorescence detection of GANA-1::GFP in muscle cells and coelomocytes. 3D volume rendered and animated image corresponding to Figure 5B Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548690.xml

535928

Underweight is independently associated with mortality in post-operative and non-operative patients admitted to the intensive care unit: a retrospective study

Background Low and high body mass index (BMI) have been recently shown to be associated with increased and decreased mortality after ICU admission, respectively. The objective of this study was to determine the impact of BMI on mortality and length of stay in patients admitted to the intensive care unit (ICU). Methods In this retrospective cohort study, the Acute Physiology and Chronic Health Evaluation (APACHE) III database of patients admitted to the ICUs of a tertiary academic medical center, from January 1997 to September 2002, was crossed with a Hospital Rule-based Systems database to obtain the height and weight of the patients on admission to the ICU. The cohort was divided in post-operative and non-operative groups. We created the following five subgroups based on the BMI: <18.5, 18.5 to 24.9, 25 to 29.9, 30.0 to 39.9, ≥ 40.0 Kg/m 2 . A multiple logistic regression analysis was used to determine the independent impact of BMI on hospital mortality. The ICU length of stay ratio was defined as the ratio of the observed to the predicted LOS. P -value < 0.05 was considered significant. The 95% confidence interval (CI) was calculated for the odds ratio (OR). Results BMI was available in 19,669 of the 21,790 patients in the APACHE III database; 11,215 (57%) of the patients were admitted post-operatively. BMI < 18.5 was associated with increased mortality in both post-operative (OR = 2.14, 95% CI, 1.39 to 3.28) and non-operative (OR = 1.51, 95% CI, 1.13 to 2.01) patients. Post-operative patients with a BMI between 30.0 to 39.9 had a lower mortality rate (OR = 0.68, 95% CI, 0.49 to 0.94). Post-operative patients with BMI <18.5 or BMI ≥ 40 had an ICU length of stay ratio significantly higher than patients with BMI between 18.5 to 24.9. The addition of BMI < 18.5 did not improve significantly the accuracy of our prognostic model in predicting hospital mortality. Conclusions Low BMI is associated with higher mortality in both post- and non-operative patients admitted to the ICU. LOS is increased in post-operative patients with low and high BMIs.

Background The body mass index (BMI) is an anthropometric measure of nutritional status that is calculated as the weight in kilograms divided by the square of the height in meters[ 1 ]. The relationship between BMI and mortality has been shown to be J- or U-shaped in large population studies; the highest mortality was observed in persons with low and high BMIs [ 2 - 5 ]. Previous studies have shown that low BMI (but not high) is an independent predictor for mortality in patients admitted to the hospital[ 1 , 6 , 7 ]. Two recent studies have investigated the impact of BMI on ICU outcome. In a large retrospective study, Tremblay et al. found that low BMI (< 20 Kg/m 2 ), but not high, was associated with increased mortality following admission to the ICU; the increased mortality was seen in medical and emergency surgical groups but not in the elective surgical group and the length of stay (LOS) was longer in severely obese and underweight patients[ 8 ]. In the prospective study by Garrouste-Orgeas et al., a low BMI (<18.5) was found to be associated with higher mortality and high BMI (>30 Kg/m 2 ) with lower mortality[ 9 ]. Previous studies did not analyze the data from post-operative and non-operative patients separately when they looked at the impact of BMI on the outcome of critically ill patients. We undertook this study to determine the influence of BMI on mortality in post-operative and non-operative patients admitted to the ICU, in a single tertiary academic medical center. Based on the association of low BMI with increased mortality, a recent publication has highlighted the importance of looking at whether adding BMI would improve the predictive accuracy of the ICU prognostic models[ 9 ]. In the current study, we tested the hypothesis that adding BMI improves the predictive accuracy of the APACHE III prognostic system. Methods In this retrospective, cohort study, we crossed the prospectively collected Acute Physiology and Chronic Health Evaluation (APACHE) III database of adult patients consecutively admitted to the intensive care units of Mayo Medical Center, Rochester, Minnesota, between January 1997 and September 2002, with a Hospital Rule-based Systems database that records the height and weight on admission to the ICU. Patients were admitted to one medical, two surgical and one multi-specialty ICU. Those patients admitted to the neurological, cardiovascular surgery, and coronary care units were not included since they were not part of the APACHE III database. Only first admissions were included in this study. The Mayo Foundation Institutional Review Board approved the study, and a waiver of informed consent was granted. Patients who did not authorize their medical records to be reviewed for research, and those whose weight or height values were missing were excluded. Mayo Medical Center includes two hospitals with a total of approximately 1900 beds. The medical ICU is a 15-bed closed unit in Saint Mary's Hospital. The surgical ICUs consist of a general surgical/trauma 24-bed unit and a 20-bed surgical unit mainly for thoracic and vascular surgery patients, both located at Saint Mary's Hospital. The 12-bed (increased to 17 beds in March 2000) multi-specialty ICU is located in Rochester Methodist Hospital. The patient population in the multi-specialty ICU included liver, kidney, pancreas and bone marrow transplant recipients; and hematology, oncology, general surgery and orthopedic patients. Data were obtained from the APACHE III database using the software provided by Cerner Corporation (Kansas City, MO). Data collected included age, ethnicity, gender, ICU admission source, admission type (postoperative or non-operative), intensity of treatment (low-risk monitor, high-risk monitor, active treatment), ICU admission diagnosis group, ICU length of stay (LOS), APACHE III score, APACHE III-predicted hospital mortality, APACHE III predicted-ICU LOS and hospital discharge status. The admission source was classified as operating room/recovery room (OR/RR), emergency room/direct admission from outpatient clinic (ER/direct), transfer from other floors of the same hospital and transfer from other institutions. All ICU admissions were categorized into three groups based on the intensity of treatment: "active treatment" if a patient received one or more of 33 items of the Therapeutic Intervention Scoring System (TISS) defined as ICU specific therapy on the first ICU day; "high-risk monitor" if a patient who did not receive active treatment on the first ICU day had greater than 10% probability of receiving active treatment during the ICU stay; and "low-risk monitor" if a patient who did not receive active treatment on the first ICU day had less than 10% probability of receiving active treatment during the ICU stay [ 10 - 12 ]. The ICU admission diagnosis groups included cardiovascular, genitourinary, gastrointestinal, hematologic, metabolic/endocrine, musculoskeletal/skin, neurologic, respiratory, transplant and trauma. The APACHE III score and predicted hospital mortality rate for each patient were calculated as described by Knaus and colleagues[ 13 ]. The type of ICU (medical, surgical or multi-specialty) to which each patient was admitted was recorded. The body mass index for each patient (BMI) was calculated as the weight (in kilograms) divided by the square of the height (in meters). All BMIs are presented in Kg/m 2 . Four BMI subgroups were created using the cut-off points of the World Health Organization (WHO): 18.5 to 24.9 (normal range), 25.0 to 29.9 (grade 1 overweight), 30.0 to 39.9 (grade 2 overweight), ≥ 40 (grade 3 overweight)[ 14 ]. A fifth subgroup for BMI < 18.5 (underweight) was added as in the study of Calle et al[ 3 ]. Descriptive data were summarized as mean (standard deviation), median (interquartile range) (IQR) or percentages. The cohort was divided into post-operative and non-operative patients. The primary analysis consisted of a comparison of hospital mortality for each BMI subgroup. A multiple logistic regression model consisting of hospital mortality as a dependent variable, BMI subgroup, APACHE III predicted mortality, admission source, and intensity of treatment as independent variables was created to adjust for potentially confounding variables that could affect hospital mortality. In the post-operative group the admission source was not included in the model since the source in all the patients was either the recovery room or the operating room. This logistic regression model was based on a previous analysis that identified the variables independently associated with hospital mortality[ 15 ]. We performed another logistic regression analysis by entering into the model BMI < 18.5, APACHE III predicted mortality, admission source, and intensity of treatment as independent variables and hospital mortality as dependent variable. We calculated the area under the receiver operating characteristic curve (AUC) to determine the performance of the logistic regression models with and without BMI < 18.5 in discriminating survivors from non-survivors[ 16 ]. Differences in mortality rates were expressed as odds' ratios (OR) for death with 95 % confidence intervals (CI) and corresponding P -values. The ICU LOS ratio was defined as the ratio of the observed to the predicted LOS. LOS ratios less than one indicate stays shorter than predicted[ 17 ]. In comparing differences in ICU LOS ratios among the BMI subgroups, we performed the Kruskal-Wallis test first. If the Kruskal-Wallis test showed statistically significant difference among groups, we subjected the data to further analysis by the Mann-Whitney U test to identify the BMI subgroup that was significantly different from the normal BMI subgroup. StatView 5.0 (SAS Institute, Cary, NC) and MedCalc 7.3 (Mariakerke, Belgium) computer softwares were used for statistical analyses. Patients with missing data were excluded from analysis involving the missing elements. A P -value < 0.05 was considered statistically significant. Results Of the 21,790 patients with first ICU admissions during the study period, 19,669 had height and weight data. Their baseline characteristics are listed in Table 1 . Fifty seven percent of the patients were post-operative. Most of the patients in both post- and non-operative groups were males, whites, and received active treatment during their first ICU day. The most common admission diagnoses were cardiovascular and respiratory for post- and non-operative groups, respectively. The most common BMI subgroup was 25.0–29.9 in post-operative patients and 18.5–24.9 in non-operative patients (Table 2 ). Table 1 Characteristics of 19,669 patients admitted to the intensive care unit Variables Post-Operative Non-Operative N 11,215 8,454 BMI (Kg/m 2 ) (SD) 28.4 (7.9) 27.5 (7.2) Mean age (SD) 64.2 (15.9) 60.9 (19.1) Male sex (%) 59.2 54.1 White ethnicity (%) 96.1 94.6 Median APACHE III (IQR) 39.0 (29.0–51.0) 48 (32.0–67.0) Median predicted mortality % (IQR) 2.5 (1.2–5.7) 7.5 (2.3–22.7) Admission source (%) RR/OR 99.9 0.0 ER/Direct 0.0 49.0 Same hospital transfer 0.1 45.2 Other hospital transfer 0.0 5.8 ICU type (%) Surgical 78.8 26.9 Medical 0.4 53.4 Multi-specialty 20.8 19.7 Treatment intensity (%) Active 51.6 56.0 High-risk monitor 1.4 19.1 Low-risk monitor 46.9 24.9 Admission diagnosis group (%) Cardiovascular 31.4 24.7 Gastrointestinal 24.3 20.1 Respiratory 14.8 28.9 Musculoskeletal 11.3 1.2 Genitourinary 8.1 3.1 Trauma 4.1 6.9 Neurology 1.2 9.8 Metabolic/endocrine 1.3 3.0 Transplant 3.3 0.1 Hematology 0.2 2.2 SD = Standard deviation; IQR = Interquartile range; ICU = Intensive care unit Table 2 The body mass index subgroups of 19,669 patients admitted to the intensive care unit BMI Subgroup Number of patients (%) Post-Operative N = 11,215 Non-Operative N = 8,454 < 18.5 384 (3.4) 428 (5.1) 18.5–24.9 3,461 (30.9) 2,945 (34.8) 25.0–29.9 3,878 (34.6) 2,692 (31.8) 30.0–39.9 2,718 (24.2) 1,947 (23.0) ≥ 40.0 774 (6.9) 442 (5.2) In the overall study population, BMI < 18.5 was independently associated with increased mortality (OR = 1.71, 95% CI, 1.34 to 2.17; P < 0.0001). Among the ICU admission diagnoses, BMI < 18.5 was associated with increased adjusted mortality in cardiovascular, genitourinary and musculoskeletal groups (Table 3 ). BMI of 30 to 39.9 was associated with increased and decreased mortality in the neurology and respiratory groups respectively (Table 3 ). Table 3 The BMI subgroups that are independently associated with hospital mortality in each admission diagnosis group using BMI of 18.5 to 24.9 as reference Admission diagnosis BMI subgroup Odds Ratio (95%CI) P -value Cardiovascular < 18.5 2.84 (1.70–4.74) < 0.001 Genitourinary < 18.5 4.15 (1.21–14.25) 0.0236 Gastrointestinal None Hematology None Metabolism/endocrine None Musculoskeletal < 18.5 3.70 (1.20–11.38) 0.0227 Neurology 30–39.9 2.74 (1.29–5.81) 0.0086 Respiratory 30–39.9 0.72 (0.56–0.94) 0.0138 Transplant None Trauma None For post-operative patients, the crude hospital mortality rate was 3.3 %, and the median (IQR) ICU LOS and ICU LOS ratio were 1.04 (0.82–2.15) and 0.42 (0.25–0.77) days, respectively. After adjusting for confounding variables, postoperative patients with a BMI < 18.5 had a higher hospital mortality, and those with BMI of 30.0–39.9 had a lower hospital mortality rate (Table 4 ). The median observed and predicted ICU LOS and the ICU LOS ratios for each BMI subgroup of the post-operative patients are listed in Table 5 . There were statistically significant differences in the ICU LOS ratio between the various BMI subgroups ( P < 0.0001 by Kruskal-Wallis test). Compared to the normal BMI subgroup, the ICU LOS ratio was higher in the BMI < 18.5 ( P = 0.0411 by Mann-Whitney U test) and BMI ≥ 40 ( P < 0.0001 by Mann-Whitney U test). Table 4 Multivariate logistic regression analysis assessing the association of hospital mortality with APACHE III predicted hospital mortality, intensity of treatment, and BMI subgroup in 11,215 post-operative patients Odds Ratio (95% CI) P -value BMI < 18.5 2.14(1.39–3.28) 0.0005 18.5–24.9 1.00 25.0–29.9 0.86 (0.66–1.13) 0.2752 30.0–39.9 0.68 (0.49–0.94) 0.0186 ≥ 40.0 0.75 (0.45–1.26) 0.2771 Predicted mortality 1.066 (1.059–1.073) <0.0001 Intensity of treatment Low-risk monitor 0.55 (0.42–0.71) <0.0001 High-risk monitor 0.66 (0.30–1.47) 0.3111 Active 1.00 Table 5 Observed and predicted length of ICU stay and ICU length of stay ratio for post-operative patients Median (IQR) ICU Length of Stay BMI Observed Predicted Ratio < 18.5 1.54 (0.85–3.02) 3.80 (2.97–4.73) 0.45 (0.25–0.86) 18.5–24.9 1.03 (0.81–2.08) 3.57 (2.67–4.50) 0.40 (0.24–0.73) 25.0–29.9 1.04 (0.82–2.17) 3.61 (2.68–4.50) 0.41 (0.24–0.77) 30.0–39.9 1.03 (0.81–2.07) 3.55 (2.58–4.49) 0.41 (0.25–0.76) ≥ 40.0 1.57 (0.84–2.54) 3.29 (1.68–4.30) 0.54 (0.30–0.96) LOS = Length of stay; ICU = Intensive care unit; IQR = Interquartile range For non-operative patients, the crude hospital mortality rate was 16.4%, and the median (IQR) ICU LOS and ICU LOS ratio were 1.68 (0.89–3.69) and 0.45 (0.25–0.89) days, respectively. After adjusting for confounding variables, the patients with a BMI < 18.5 had a higher hospital mortality rate (Table 6 ). There were no statistically significant differences in the ICU LOS ratio among the five BMI subgroups as assessed by Kruskal-Wallis test ( P = 0.3414) (Tables 7 ). Table 6 Multiple logistic regression analysis assessing the association of hospital mortality with APACHE III predicted hospital mortality, intensity of treatment, admission source, and BMI subgroup in 8,450 non-operative patients Odds Ratio (95% CI) P -value BMI < 18.5 1.51 (1.13–2.01) 0.0051 18.5–24.9 1.00 25.0–29.9 1.02 (0.87–1.20) 0.8022 30.0–39.9 0.98 (0.82–1.17) 0.7979 ≥ 40.0 0.86 (0.63–1.20) 0.3967 Predicted mortality 1.045 (1.042–1.048) <0.0001 Intensity of treatment Low-risk monitor 0.39 (0.31–0.51) <0.0001 High-risk monitor 0.87 (0.73–1.03) 0.1033 Active 1.00 Admission source Other hospital 0.99 (0.75–1.32) 0.9596 ER/Direct 1.00 Same hospital 1.43 (1.24–1.65) <0.0001 *Four patients were excluded from this analysis because of missing data. CI = Confidence interval; RR = recovery room; OR = operating room; ER = emergency room; Direct = direct admission from the outpatient clinic Table 7 Observed and predicted length of ICU stay and ICU length of stay ratio for non-operative patients Median (IQR) ICU Length of Stay BMI Observed Predicted Ratio < 18.5 1.71 (0.87–3.80) 4.38 (2.82–6.15) 0.45 (0.24–0.87) 18.5–24.9 1.65 (0.87–3.62) 4.01 (2.70–5.82) 0.44 (0.25–0.90) 25.0–29.9 1.63 (0.90–3.58) 4.13 (2.71–5.83) 0.45 (0.25–0.87) 30.0–39.9 1.73 (0.91–3.71) 4.08 (2.83–5.87) 0.46 (0.25–0.91) ≥ 40.0 1.94 (0.92–4.34) 4.39 (3.05–6.25) 0.49 (0.28–0.93) LOS = Length of stay; ICU = Intensive care unit; IQR = Interquartile range In discriminating hospital survivors from non-survivors, the AUC (95% CI) of the model with BMI < 18.5 was 0.859 (0.854 to 0.864) and the AUC of the model without BMI < 18.5 was 0.858 (0.853 to 0.863) ( P = 0.102). Discussion The results of our retrospective study suggest that a BMI <18.5 is independently associated with higher mortality in post- and non-operative patients admitted to the ICU. We found no difference in the ICU LOS among the BMI subgroups in non-operative patients. In post-operative patients, the LOS was longer in patients with a BMI <18.5 or BMI ≥ 40. We also noted that the addition of BMI does not improve significantly the predictive accuracy of the prognostic model. In this study, we wanted to determine the impact of BMI on mortality in post- and non-operative patients separately since patients admitted to the ICUs from hospital wards have higher mortality than patients admitted from the operating room, independent of disease severity[ 17 , 18 ]. In our cohort, both post-operative and non-operative groups had a higher mortality rate when their BMI was < 18.5. Interestingly, a BMI between 30.0–39.9 was associated with lower mortality in post-operative patients. Although previous studies had included both post-operative and non-operative patients, they had not looked at the influence of BMI on mortality in these two groups separately[ 8 , 9 ]. Low BMI has been associated with higher mortality rate in hospitalized patient in both the ICU [ 8 , 9 ] and non-ICU settings[ 1 , 6 , 7 ]. Studies addressing the association of high BMI in patients admitted to the ICU and the hospital have given conflicting results. In a retrospective study of 184 blunt trauma victims, Choban et al found that mortality in patients with a BMI >31 and <27 was 42.1 and 5 %, respectively[ 19 ]. In a more recent study of 117 morbidly obese patients (BMI= 51.3 ± 25.9) compared to 132 other patients (BMI= 27.6 ± 3.1) selected randomly by a computer in the medical ICU, their mortality was 30 and 17 %, respectively[ 20 ]. In contrast, our study was similar to the studies by Tremblay et al.[ 8 ] and Garrouste-Orgeas et al.[ 9 ] in showing that a high BMI was not associated with high mortality in both post- and non-operative patients. We also found that a BMI between 30.0–39.9 (grade 2 overweight) was associated with decreased mortality in post-operative patients. Although obesity has long been considered a potential risk factor for poor outcome from a variety of surgical procedures, the evidence suggests that obesity does not result in an increase in mortality[ 21 , 22 ]. Obesity, however, has been associated with an increase in perioperative complications, particularly wound problems, which could explain our finding of a longer LOS in post-operative patients with BMI ≥ 40. Additionally, in a recent study of patients receiving mechanical ventilation for acute lung injury, a BMI > 30 was not associated with increased mortality [ 23 ]. Our study has several limitations. It has been speculated that a low BMI is simply the consequence of a serious illness before the hospitalization that it is ultimately fatal[ 1 , 6 ]. We have not adjusted for previous weight loss because this information was not available. However, studies that have adjusted for weight loss before the admission found that the independent effect on mortality of low BMI was unchanged[ 1 , 6 ]. Additionally, depending on the balance between fluid intake and output, the weight on admission to the ICU could be different to the patient's real weight, and because of the study design, we cannot ascertain the accuracy of the height and weight measurements. Although the APACHE III data were collected prospectively, our study has a retrospective design. Since our analysis was limited to the variables available in the APACHE III database, other confounding factors that might have influenced outcomes may not have been included. Moreover, because our study reflects the experience of a single tertiary institution with a unique system of health care delivery and without ethnic diversity, the results cannot be extrapolated to other medical centers. Conclusions This study shows that a BMI < 18.5 is independently associated with increased mortality in post- and non-operative patients admitted to the ICU; and in post-operative patients, the LOS was longer in patients with a BMI < 18.5 or BMI ≥ 40. We also found that the inclusion of BMI < 18.5 in a prognostic model does not improve the accuracy of mortality prediction. Competing interests The authors declare that they have no competing interests. Authors' contributions JDF participated in conception, design, acquisition of the data, analysis of the data, and drafting of the manuscript. OG participated in analysis of the data, and critical revision of the manuscript. BA participated in conception, design, analysis of the data, statistical analysis, critical revision of the manuscript, and supervision. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535928.xml

548135

Mutagenicity testing with transgenic mice. Part I: Comparison with the mouse bone marrow micronucleus test

As part of a larger literature study on transgenic animals in mutagenicity testing, test results from the transgenic mutagenicity assays ( lacI model; commercially available as the Big Blue ® mouse, and the lacZ model; commercially available as the Muta™Mouse), were compared with the results on the same substances in the more traditional mouse bone marrow micronucleus test. 39 substances were found which had been tested in the micronucleus assay and in the above transgenic mouse systems. Although, the transgenic animal mutation assay is not directly comparable with the micronucleus test, because different genetic endpoints are examined: chromosome aberration versus gene mutation, the results for the majority of substances were in agreement. Both test systems, the transgenic mouse assay and the mouse bone marrow micronucleus test, have advantages and they complement each other. However, the transgenic animal assay has some distinct advantages over the micronucleus test: it is not restricted to one target organ and detects systemic as well as local mutagenic effects.

Background This and the following presentation are part of a project for the International Programme on Chemical Safety (IPCS) evaluating the possible use of transgenic animal mutagenicity assays in chemical toxicity testing and mechanistic research. It was decided to compare the results obtained from those transgenic mutagenicity test systems where considerable data was available, with other in vivo genotoxicity tests: in the first article (Part 1) with the mouse bone marrow micronucleus test and in the second (Part II) with the mouse spot test. The mouse bone marrow micronucleus test is one of several available in vivo mammalian test system for the detection of chromosomal aberrations [ 1 - 5 ]. A documentation of the test procedure and evaluation of results is given in the OECD guideline 474 [ 6 ]. This test is routinely used with a widespread acceptance in industry and authorities. Mutagenicity assays using transgenic animals have been developed in particular the lacI model [ 7 ] (commercially available as the Big Blue ® mouse), and the lacZ model [ 8 ] (commercially available as the Muta™Mouse). In this article, available data on the results of mouse bone marrow micronucleus test were compared with results from these two transgenic mouse assays for 39 substances. The advantages and disadvantages of the test systems are discussed. Recently, further transgenic rodent mutation assays have been developed, however, the data base is not sufficient for comparison with other test systems. The mouse bone marrow micronucleus test: principles and procedure Micronuclei are chromatin-containing bodies in the cytoplasm arising from acentric chromosome fragments or from whole chromosomes that were not incorporated in the daughter nuclei during the last stages of mitosis. Chromosome fragments are associated with the clastogenic (chromosome breakage) activity of the test substance whereas the presence of a whole chromosome is indicative of an adverse effect of the test substance on the mitotic spindle apparatus (aneugenic effects). The difference in size of the micronucleus is therefore an indicator for clastogenicity (small micronucleus) or aneugenicity (large micronucleus). However, the size of the micronucleus is an inaccurate measure. Micronuclei can be distinguished by further criteria, for example by identification of the presence of a kinetochore or centromeric DNA, indicating aneugenic activity. Overall, an increase in micronuclei is an indirect measure of induced structural or numerical chromosome aberrations [ 1 , 2 ]. In the micronucleus test according to the OECD guideline 474, erythroblasts in the bone marrow of mice (or rats) are used as target cells. When a bone marrow erythroblast develops into a polychromatic erythrocyte, the main nucleus is extruded. Any micronucleus that has been formed may remain in the otherwise anucleated cytoplasm and can easily been detected. An increase in the frequency of micronucleated polychromatic erythrocytes in treated animals is an indication of induced chromosome damage [ 6 ]. In the last three decades, toxicologists have often used the mouse bone marrow micronucleus test because 1) it is part of the regulatory toxicology in the admission procedure for chemicals and drugs and 2) it has advantages in speed, simplicity, and cost effectiveness in comparison to other in vivo systems for testing chromosomal aberrations (e.g. the cytogenetic test). Transgenic mouse models Transgenic mutation test systems contain a foreign gene construct having two essential parts: the transgene containing the target gene that serves as target for mutations, and a shuttle vector for recovering the target gene DNA from the tissue of the transgenic animal. The transgene is constructed using recombinant DNA technologies. LacI transgenic mouse model (Big Blue ® mouse) The proprietary mouse of the Big Blue ® mutagenesis assay system contains about 30–40 copies of a lambda LIZα shuttle phage vector integrated into its genome at a single locus on chromosome 4. The target site for mutagenesis is the lacI gene [ 7 ]. The test compound is administered to the mouse either as a single or repeated dose. After the post treatment period for manifestation of the DNA lesions, the tissue of interest is isolated and the DNA extracted. A proprietary lambda DNA packaging extract automatically excises the lambda vector target and packages it into a lambda phage head and the phage is transfected to bacteria. These bacteria are plated on agar indicator plates containing the chromogenic substance (X-gal). The phage transfected bacteria with mutations in the lacI gene form blue plaques, whereas bacteria with a nonmutated lacI form colourless plaques. The ratio between blue and white plaques is a measure of the mutagenicity. [ 7 , 9 ]. LacZ transgenic mouse model (Muta™Mouse) The lambda-gt10- lacZ shuttle vector for the lacZ mouse model contains the entire lacZ target gene [ 8 ]. The commercially available lacZ mouse model contains about 80 copies of the shuttle vector at chromosome 3. As above, the test compound is administered to the mouse, the genomic DNA is isolated from the tissue of interest, and the lambda genomes are excised by and packed with a bacteriophage packaging extract. The resulting phage particles are then plated on a lacZ - E. coli strain (for transfection) in the presence of the chromogenic substance (X-gal) as indicator. The plaques containing an intact lacZ are β-galactosidase active and are blue, whereas plaques containing mutated lacZ will be white/colourless. In this original model the ratio between colourless and blue plaques is a measure of the mutagenicity. Due to optical difficulties in the evaluation of plaques, this system has been improved using a selection assay in lieu of colour screening whereby only mutant particles form plaques [ 10 ]. The number of plaques under non-selective conditions is a measure for the total number of phage-transfected bacteria (intact or mutant lacZ gene). The ratio between the number of plaques produced under selective conditions versus the number of plaques under non-selective conditions is a measure of the mutagenicity [ 9 , 10 ]. Methods Data presented in this documentation are the results of an extensive literature research. Concerning data on transgenic mouse assays only primary literature was used. Data on the mouse bone marrow micronucleus assay were extracted from reliable reviews on this item or from primary literature. For all other data informations from secondary literature or data compilation bases were used. Results and Discussion Comparison of data from the mouse bone marrow micronucleus test and transgenic mouse test The authors are aware that a comparison of the transgenic mouse assays with the mouse bone marrow micronucleus test is limited by the fact that different genotoxic endpoints are studied in these two systems. In transgenic mouse assays, point mutations and small insertions and deletions are detected whereas in the mouse bone marrow assay, chromosome breakage leading to light microscopically visible micronuclei resulting from chromosome fragment or micronuclei originated from whole chromosomes are investigated. However, both point mutations and micronuclei may be induced by a single agent, so some overlap of results is to be expected. From the literature, 39 substances were identified with data on the mouse bone marrow micronucleus test and the Muta™mouse assay (n = 29) or the Big Blue ® mouse assay (n = 21) or both transgenic mutation assays (n = 11); see Additional file 1 for references. Agreement between the Muta™mouse and the micronucleus test was seen with 18 out of 29 substances, no agreement with 8 substances and in 3 cases the comparison is inconclusive because of questionable results in the micronucleus assay. With the Big Blue ® mouse assay, the results obtained with 14 out of 21 substances agreed with results in the mouse micronucleus test, but 6 showed no agreement and another was inconclusive. Most substances included in this comparison are also carcinogenic in long-term assays on mice. No data on carcinogenicity in mice are available for 4-acetylaminofluorene and negative results were obtained with bromomethane and 2,6-diaminotoluene. Carcinogenic substances with positive results in the bone marrow micronucleus and transgenic gene mutation assays Studies with Muta™mouse The following 17 substances were gene mutagenic in at least one of the examined organs in the Muta™mouse assay, induced micronuclei in mouse bone marrow and showed positive results in carcinogenicity studies on mice: 2-acetylaminofluorene, 4-aminobiphenyl, benzo(a)pyrene, 1,3-butadiene, chlorambucil, cyclophosphamide, 7,12-dimethylbenz(a)anthracene, ethylmethanesulfonate, N-ethyl-N-nitrosourea, methylmethane-sulfonate, N-methyl-N'-nitro-N-nitrosoguanidine, N-methyl-N-nitrosourea, 4-nitroquinoline-1-oxide, N-nitrosodimethylamine, procarbazine, quinoline, and urethane. Further studies on chromosome aberration in vitro and in vivo (see Additional file 1 ) supported the results in the micronucleus test, except data on 4-aminobiphenyl (negative micronucleus test in rats) and quinoline (see below). Of the substances tested for mutagenic activity in bone marrow in Muta™mice, 14 gave positive results, only 1,3-butadiene, methylmethanesulfonate, and quinoline were negative, indicating that other organs are more sensitive. 1,3-Butadiene is clearly clastogenic in other studies on chromosome aberration in mice and induced gene mutation in the bone marrow of Big Blue ® mice (see Additional file 1 ). Quinoline, however, gave inconclusive results in other in vivo studies on clastogenic effects in bone marrow of rats and mice. Studies with Big Blue ® mouse 12 substances showed gene mutagenic effects in at least one of the examined organs in the Big Blue ® mouse assay, chromosome mutagenic effects in the mouse bone marrow micronucleus assay and induced carcinogenic effects in long-term assays on mice,: 2-acetylaminofluorene, aflatoxin B1, benzene, benzo(a)pyrene, 1,3-butadiene, cyclophosphamide, 7,12-dimethylbenz(a)anthracene, ethylene oxide, N-ethyl-N-nitrosourea, N-methyl-N-nitrosourea, N-nitrosodimethylamine, urethane. In the Big Blue ® mouse assay the target organ bone marrow revealed also increased mutation frequencies induced by benzene, 1,3-butadiene, and 7,12-dimethylbenz(a)anthracene. However, negative results in bone marrow were obtained with cyclophosphamide, ethylene oxide, and N-nitrosodimethylamine, indicating that other target organs are more sensitive in the Big Blue ® mouse assay. All of these 12 substances induced also chromosome aberrations in majority of further in vitro and in vivo studies. Substances with carcinogenic effects in mice but no agreement between the transgenic mouse assay and the mouse bone marrow assay Acrylamide The inconclusive result in the bone marrow micronucleus test [ 4 ] is contradictory to the Muta™mouse assay [ 21 - 23 ] which shows mutagenic activity in the target organ bone marrow but also contradictory to other in vivo tests on the endpoint chromosome aberration including a cytogenetic test on mice [ 4 , 19 , 21 ]. Micronuclei were detected in spleen and testis of mice [ 4 ]. Overall, using other experimental design the mouse bone marrow micronucleus assay might give clearly positive results. 2-Amino-3-methylimidazo(4,5-f)quinoline (IQ) IQ is mutagenic in the liver of the Muta™mouse [ 34 ] but negative results were obtained in the mouse bone marrow micronucleus test [ 2 , 35 ]. This negative result is supported by a negative cytogenetic assay on mice. Inconclusive results were obtained in in vitro studies on chromosome aberrations but IQ induced micronuclei in rats (see Additional file 1 ). This discrepancy might be due to the possibility that a) the liver but not the bone marrow of mice is target organ (the liver but not the blood is target organ in carcinogenicity [ 33 ]) or b) IQ is less clastogenic than gene mutagenic in mice. ortho-Anisidine The target organ of carcinogenesis in mice (and humans) is the bladder [ 36 ]. In the Big Blue ® assay on mice mutagenic activity was detected in the bladder but not in the liver [ 37 ]. As expected, the mouse bone marrow micronucleus test [ 4 , 36 ] gave negative results (also in rats; see Additional file 1 ), because the bone marrow is presumably not a target organ of mutagenicity. Asbestos crocidolite Local carcinogenic effects were observed in carcinogenicity studies, the lung is the target organ after inhalation [ 38 - 40 ]. Mutagenic activity was detected in the lung of Big Blue ® mice after inhalation [ 41 ]. As expected, clearly no systemic effects in the bone marrow could be observed in the mouse micronucleus test [ 4 ]. 2,4-Diaminotoluene The main target organ in carcinogenicity is the liver (also in rats) [ 81 ]. Positive results were reported in two Big Blue ® mouse assays examining the liver [ 82 - 84 ]. The mouse micronucleus test gave negative results [ 4 ], however, systemic effects in the bone marrow are not expected from carcinogenicity studies. Results of other studies on chromosome aberration in vivo are inconclusive (see Additional file 1 ). Hydrazine This substance induced no mutagenic effects in lung, liver, or bone marrow of the Muta™mouse which were target organs in mouse carcinogenicity studies [ 119 , 120 ]. The mouse bone marrow micronucleus test gave positive results after repeated application [ 4 , 119 ]. However, single exposure was used in the Muta™mouse assay [ 120 ]. Studies on other in vivo genotoxicity endpoints have shown almost negative results after single exposure but genotoxic activity after repeated application, for example in the mouse bone marrow micronucleus assay [ 4 ]. Positive results might be expected in the Muta™mouse assay using another experimental design since other in vivo as well as in vitro test systems revealed gene mutagenic effects. Methyl methanesulfonate Only weak mutagenic effects in the liver but no effects in bone marrow were observed in the Muta™mouse [ 18 , 124 ] and negative results in the Big Blue ® mouse [ 111 - 113 , 126 ]. In the mouse bone marrow micronucleus test this carcinogenic substance induced chromosome aberration [ 1 , 127 ]; other in vitro and in vivo assays clearly supported the clastogenic activity [ 121 , 122 ]. There is evidence that the chromosome mutagenic activity is detectable at much lower doses than the gene mutagenic activity. Tinwell et al. (1998) [ 18 ] have shown on Muta™mice a weak gene mutagenic effect in the liver but no effect in the bone marrow. The same dose induced in these animals a significant increase in bone marrow micronuclei indicating clear clastogenic activity. Overall, methyl methanesulfonate is more clastogenic than gene mutagenic. Mitomycin C A very similar situation is given with mitomycin C. No mutagenic activity was observed in the Muta™mouse assay in liver and bone marrow after single injection but the same dose induced chromosome aberrations in the bone marrow of the same mice [ 142 ]. The mouse bone marrow micronucleus test [ 1 , 47 ] and all in vivo and in vitro assays on the endpoint chromosome aberration revealed clearly positive results [ 139 - 141 ]. N-Nitrosodiethylamine The main target organ in systemic carcinogenesis is the liver [ 146 - 148 ]. As expected, mutagenic effects were detected in the liver of the Muta™mouse, but none in the bone marrow [ 104 , 106 , 149 , 150 ]. The same mice showed also no micronuclei at these dose levels [ 104 ]. Consequently, the mouse bone marrow micronucleus test gave negative results indicating that this is not a target organ of genotoxicity. However, there is also evidence that this substance shows more gene mutagenic activity than clastogenic activity because other in vivo studies gave no clear indication for chromosome aberration (see Additional file 1 ). N-Nitrosodi-N-propylamine There is evidence that this substance shows more gene than chromosome mutagenic activity. Beside local carcinogenic effects in carcinogenicity studies on mice and rats systemic effects were located in the liver (mouse and rats) and in bone marrow (rat) [ 160 - 163 ]. Several target organs were detected in the Muta™mouse assay including liver and bone marrow [ 164 ]. But no chromosome mutagenic activity was recorded in the mouse bone marrow micronucleus test [ 4 ]. Further in vivo data on this endpoint are not available (see Additional file 1 ). Phenobarbital Liver tumours were detected in carcinogenicity studies on mice and rats [ 165 - 167 ]. In the Muta™mouse assay no mutagenic activity was observed although the liver weight of the treated mice increased indicating systemic effects in this organ [ 149 , 168 ]. Two studies are available on the Big Blue ® mouse, one gave negative [ 16 ] and the other weak positive results [ 169 ]. Taken together the results in transgenic mice are inconclusive which is in accord with the mouse bone marrow nucleus test (inconclusive results). Inconclusive ( in vitro ) or negative results were obtained in other studies on clastogenicity or other endpoints of genotoxicity (see Additional file 1 ). Overall, there is no clear indication of gene or chromosome mutagenic activity in transgenic mouse assays or other genotoxic tests systems. However, genotoxic mechanisms in carcinogenicity cannot be excluded. β-Propiolactone This is an alkylating substance with predominantly local carcinogenic effects[ 175 , 176 ]. In the Muta™mouse assay [ 131 ] local effects in the stomach were observed in gavage studies plus systemic effects in the liver, but no mutagenic activity was seen in bone marrow of the Muta™mouse [ 131 ]. As expected, no increased incidence in micronuclei was detected in the bone marrow of treated mice [ 1 , 4 ] although clastogenic effects were observed in in vitro studies, in insects and plants (see Additional file 1 ) indicating chromosomal aberration after direct contact with this alkylating substance. Trichloroethylene This substance gave clearly positive results in different carcinogenicity studies on mice [ 186 ]. Although the target organs of carcinogenicity (including bone marrow) were investigated in the Muta™mouse assay, no mutagenic activity was noted [ 187 ]. The positive results in the mouse bone marrow micronucleus test are contradictory to other in vivo studies on clastogenicity. However, a further (simple) reason for the negative results in the Muta™mouse assay might be that the MTD was not reached [ 187 ]. Overall, further discussion on the mechanisms of carcinogenicity is necessary. Tris(2,3-dibromopropyl)phosphate This might be a further example for a substance where genotoxic effects are not related to the target organ bone marrow but induce systemic effects in others. The target organ in oral carcinogenicity studies on mice and rats was the kidney, in mice systemic carcinogenic effects were also seen in lung and liver [ 188 - 190 ]. In the Big Blue ® mouse assay, mutagenic activity was detected in the kidney in gavage studies[ 46 , 191 ]. The mouse bone marrow micronucleus assay (i.p. injection)[ 4 ] as well as cytogenetic studies on rats and mice were negative [ 188 - 190 ]. Substance without data on carcinogenicity in mice and differing results in the micronucleus test and transgenic mouse assay 4-Acetylaminofluorene This substance showed mutagenic activity in the Muta™mouse assay [ 18 ] but inconclusive results in the mouse bone marrow micronucleus test [ 2 ]. No data on carcinogenicity are available on 4-acetylaminofluorene. However, data on two in vitro mammalian test systems indicated gene mutagenic activity [ 17 ] supporting results in the transgenic assay. Substances without carcinogenic effects in mice and differing results in the micronucleus test and transgenic mouse assay Bromomethane No increased tumour incidences were observed in mice as well as in re-evaluated studies on rats [ 58 , 59 ]. Negative results were obtained also in the examined organs including liver and bone marrow in the Muta™mouse assay. However, DNA methylation in the liver was observed in the same assay even at lower dose levels [ 60 ] indicating differences in the sensitivity of these two genotoxic endpoints. The mouse micronucleus test revealed chromosome mutagenic activity [ 61 ] although results of other in vivo tests are equivocal (see Additional file 1 ). 2,6-Diaminotoluene No carcinogenic effects were seen in a valid long-term study on mice and rats [ 85 ]. In the Big Blue ® mouse no mutagenic activity was induced [ 82 , 84 ]. However, only the liver was examined and the assays had some limitations (one dose tested, MTD possibly not reached). The mouse micronucleus test revealed chromosome aberrations [ 3 , 47 ] as well as the available in vitro studies but no clastogenic activity was detected in a cytogenetic study on rodents (see Additional file 1 ). Substances with carcinogenic effects in mice but nongenotoxic mechanisms are presumed Chloroform (German MAK Classification 4 = substances with carcinogen effects where the genotoxic effects are absent or only play an insignificant role) Liver tumours were induced in B6C3F1 mice [ 72 ], however no mutagenic effects were detected in the liver of Big Blue ® mice of the same strain in a valid assay [ 74 ]. Accordingly, no increased incidence in micronuclei were observed in the bone marrow of mice [ 1 , 47 ] and no clastogenicity in in vitro studies (see Additional file 1 ). In contrast, rats showed clastogenic activity in the kidney, but only at toxic dose levels [ 73 ]. Di-(2-ethylhexyl)phthalate (German MAK Classification 4) This substance induced liver tumours in mice and rats [ 86 ]. No increased mutation frequency was seen in the liver of the Big Blue ® mouse [ 16 ] (limited validity, MTD presumably not reached) and no chromosome aberration in the mouse micronucleus test [ 4 ]. The majority of in vitro and in vivo tests revealed also negative results with di-(2-ethylhexyl)phthalate (see Additional file 1 ). Tetrachloromethane (German MAK Classification 4) An increased incidence of liver tumours were induced in mice and in rats [ 185 ]. However, no mutagenic activity in the liver was reported in a Muta™mouse assay although the organ weight increased in the same mice indicating some hepatocellular regeneration [ 168 ]. No chromosome aberration was induced in the mouse bone marrow micronucleus test [ 4 ]. Also other studies on this endpoint and the majority studies on other endpoint of genotoxicity revealed negative results (see Additional file 1 ). Discussion on the comparison of both assays Most carcinogens in Additional file 1 are positive in transgenic mouse assays and in the mouse bone marrow micronucleus test although different endpoints are studied. This indicates coincidence in both test systems and/or effects of the test substance on different genotoxic endpoints. However, there are several substances (see above for example ortho-anisidine) whose mutagenic (and carcinogenic) potential could not be demonstrated with the mouse bone marrow micronucleus test but with the transgenic mouse assay. This might be due to the fact that the micronucleus test is restricted to the bone marrow as target organ. In contrast, all organs can be examined in transgenic mouse assay for mutagenic activity without any restrictions. A special subgroup of substances should also be mentioned: substances with predominantly local mutagenic/carcinogenic effects and less systemic direction of effects. For these substances the mouse bone marrow micronucleus test is an unsuitable test system. Beside the restrictions on the target organ there is also given the possibility that a substance induces predominantly gene mutations and not or less chromosome aberrations at the same dose level. The example N -nitrosodi- N -propylamine has shown positive results in different target organs including the bone marrow using the transgenic mouse assay but negative results were shown in the mouse micronucleus test. On the other hand there are also substances for which the transgenic mouse assay is an unsuitable test system. The examples methylmethanesulfonate and mitomycin C have shown that chromosome aberration and not gene mutation is the predominant endpoint at the corresponding dose level in vivo . These genotoxic effects are not easily detectable with the transgenic mouse assay which is restricted to the detection of small deletion and insertions in the DNA. However, the negative Muta™mouse assay on mitomycin C has some limitations in the validity of the test system. With increased dose levels (MTD reached) and/or repeated application also gene mutagenic effects might be detected in the transgenic mouse assay. Interestingly, substances with carcinogenic effects induced by nongenotoxic mechanisms gave mainly correct negative results in both test systems although the protocols for the transgenic mouse assays were not optimised except for chloroform. Predictability of the transgenic animal assays and the mouse bone marrow micronucleus test for carcinogenicity The sensitivity, specificity and predictive values to cancer for the Muta™mouse assay and the Big Blue ® mouse assay combined, and the mouse bone marrow micronucleus test are documented in Table 1 . In the present study data on 38 substances were available concerning carcinogenicity in mice and mutagenic effects in transgenic mice as well as mutagenic effects in the mouse bone marrow micronucleus test ( Additional file 1 ). The two substances with inconclusive results in the mouse bone marrow micronucleus assay (phenobarbital & acrylamide) were not included in the final calculation data and in the comparison of the micronucleus test versus the transgenic mouse assay. Table 1 Characteristics of the Muta™mouse assay and the Big Blue ® mouse assay for predicting mouse carcinogenicity in comparison with the micronucleus test Term# Calculation for the mouse bone marrow micronucleus test Calculation for Muta™mouse and/or Big Blue ® mouse combined * Sensitivity 68% (23/34) 82% (28/34) Specificity 0% (0/2) 100% (2/2) Positive predictability 92% (23/25) 100% (28/28) Negative predictability 0% (0/11) 25% (2/8) Overall accuracy 64% (23/36) 83% (30/36) # Sensitivity = % of carcinogens with a positive result in the specified test system (STS) Specificity = % of noncarcinogens with a negative result in STS Positive predictability = % of positive results in the STS that are carcinogen Negative predictability = % of negative results in the STS that are noncarcinogens Overall accuracy = % of chemicals tested where STS results agree with the carcinogenicity results Carcinogens with genetoxic and nongenotoxic mechanisms were considered but not noncarcinogenic substances; only data on mice were used Weak positive results in transgenic mouse assays judged as positive. *: judged as positive in transgenic assays if positive in one of the two test systems Although the data pool in this document is not sufficient for a comprehensive comparison (low number of examples, especially for specificity and negative predictability; limitations of most transgenic mouse assays with negative results) some differences were apparent between the two test systems. The overall accuracy of the micronucleus test is lower than that of the transgenic mouse assays. This is mainly due to 11 negative results in the micronucleus test system (negative in the micronucleus test but positive in carcinogenicity studies) influencing the terms sensitivity and negative predictability. Three of these negative results in the micronucleus test are obtained with carcinogenic substances [chloroform, di-(2-ethylhexyl)phthalate, and tetrachloromethane] for which carcinogenic effects are considered to be of a nongenotoxic mechanism. However, chloroform, di-(2-ethylhexyl)phthalate and tetrachloromethane gave negative results in transgenic mice, so the comparison of both test system is not essentially affected and the evaluation „nongenotoxic“ supported. For the other 8 substances out of these 11 with false negative results in the micronucleus test these results are explainable (see above): Ortho-anisidine (mutagenic/carcinogenic effects are restricted to the bladder), IQ (bone marrow presumably not target organ of genotoxicity in mice and more gene mutagenic than clastogenic), asbestos (local genotoxic/carcinogenic effects in the lung), 2,4-diaminotoluene (target organ liver, presumably not bone marrow), N-nitrosodiethylamine (target organ liver, more gene mutagenic than clastogenic), N-nitroso-N-propylamine (presumably more gene mutagenic than clastogenic), beta-propiolactone (mainly local effects and less systemic effects [in bone marrow]), tris(2,3-dibromopropyl)phosphate (systemic effects not related to the bone marrow). The term negative predictability is also low in the transgenic mouse assay due to false negative results on six carcinogenic substances; for three of them, hydrazine, mitomycin C, and tetrachloroethylene (detailed treatise in section „direct comparison“, see above) genotoxic mechanisms are presumed. For hydrazine (no repeated application) and tetrachloroethylene (MTD not reached) limitations on the experimentel design might be the reason for the negative results. Mitomycin C is clearly more clastogenic than gene mutagenic, and the transgenic mouse with lacI and lacZ is possibly an unsuitable test system. For 3 out of the 6 substances the carcinogenic effects in mice were attributed to nongenotoxic mechanisms: chloroform, di-(2-ethylhexyl)phthalate and tetrachloromethane (see also above), all gave negative results in transgenic mice. Only two substances with negative results in long-term carcinogenicity studies are available in the data pool: bromomethane and 2,6-diaminotoluene. Both gave correct negative results in the transgenic mouse assay (although of limited validity) but false positive results in the micronucleus test (see term specificity). Generally, the differences between the two test systems might be due to the fact that 1) unequal genotoxic endpoints are investigated (chromosome aberration in the micronucleus test versus gene mutation in the transgenic mouse assay), 2) organotrophy of genotoxic effects (especially bone marrow not target organ) might play an essential role and 3) transgenic rodent assay conditions in the different systems may not be optimal for mutation detection. Advantages and disadvantages of both test systems A comparison of the transgenic mouse assays with the mouse bone marrow micronucleus test is limited by the fact that different genotoxic endpoints are studied in these two systems. In transgenic mouse assays, point mutations and small insertions and deletions are detected whereas in the mouse bone marrow assay, chromosome breakage leading to light microscopically visible micronuclei resulting from chromosome fragment or micronuclei originated from whole chromosomes are investigated. Sensitivity of the test system In comparison to other test systems in genotoxicity testing using endogenous target structures the spontaneous mutant frequency in the transgenic mouse assay is relatively high. This might be related to the fact that bacterial DNA is the target gene (high methylation rate) or the transgene is silent and no transcription related repair occurs like in endogenous genes which are more efficiently repaired [ 9 ]. In the mouse bone marrow micronucleus test the spontaneous rate of micronuclei is low ranging between 1–3 PCEs with micronuclei per 1000 PCEs. However, frequency of chromosome aberrations is not directly comparable with a gene mutantion frequency. Comparing the target organs and cells at risk at the time of exposure, the mouse micronucleus test is restricted to one target organ, the bone marrow, especially to the erythroblasts. This limitation is not given in transgenic mouse assays: target cells are cells in all organs [ 195 ]. Considerations of animal welfare Both test systems are similar in the number of animals used for a valid test. The minimal number of mice needed in the mouse bone marrow assay is 25 per gender (3 dose levels, vehicle control, positive control; 5 mice per group) using a treatment schedule with 2 or more applications at 24 h intervals and sampling 18–24 h following the final treatment. In the limit test (for a test substance of low toxicity) only one dose level of 2000 mg/kg bw is necessary (OECD guideline 474, [ 6 ]). In transgenic mutation assays ca. 20 animals (3 dose groups and 1 concurrent vehicle control group in laboratories which already established this test system) are recommended per species and gender [ 196 , 197 ]. In terms of animal welfare, it is also desired to merge more than one in vivo genotoxicity assay such as transgenic mouse assay and micronucleus assay using the same animals for both assays. Cost effectiveness Due to the simplicity of the mouse bone marrow micronucleus assay and the use of systems for automated analysis, this test is less expensive than the transgenic mouse assay. A comparison both test systems is presented in Table 2 . Table 2 Comparison of the mouse bone micronucleus assay with transgenic mouse models (Muta™mouse and the Big Blue ® assay) Mouse bone marrow micronucleus test [1,2] Transgenic mouse mutation assay [9,195] Type of endpoint Detects light microscopically visible micronuclei resulting from whole chromosomes or chromosome fragments following chromosome breakage Detects 1) gene mutation, 2) small deletions or insertions Regulatory use Widespread acceptance (OECD guideline established since 1983) Not widely used by the industry in toxicological screening; OECD guideline in preparation Background mutation rate Spontaneous incidence of micronuclei is low (ca. 0.3%) and almost uniform High spontaneous rate of mutations comparing with other mutation assays Negative predictivity low negative predictivity for cancer Low negative predictivity for cancer Implementation Simplicity of the test system; easily recognised end-point Higher Complexity of the test system (target cells in mice and expression of mutagenic effects in bacteria; vector system needed) Toxicokinetics and metabolism Restrictions in toxicokinetics: test substance or the toxic metabolites may not reach the bone marrow but other target organs No restrictions after absorption and distribution of the test substance Target tissue Restricted to erythroblasts in the bone marrow No tissue restriction; analysis of mutagenic potency in different organs; measurement of organotrophic effects Dependency of effects on application route Only systemic effects can be detected Systemic as well as local mutagenic effects can be detected Number of animals 5 animals per gender per dose recommended 5 animals per gender per dose recommended Restrictions on the used model Also some recommendations are given in OECD guideline 474, no limitation concerning species, strain, gender, age of animals, exposure duration Limitations: Muta™mouse assay only 1 species & 1 strain; Big Blue ® 2 species (mouse and rat) but 1 (rat) or 2 strains (mouse); no limitations on other parameters Costs Less expensive due to the simplicity of the test system More expensive test system Molecular mechanism Mechanisms of the induction of micronuclei originating from chromosome fragments could not be resolved Detection of the "molecular signature" of a particular mutagenic substance by DNA sequence analysis with standardised methods Parallel examination of different genetic endpoints Combination with other genotoxic endpoints is not recommended but possible if results of the micronucleus test are not influenced and vice versa The transgenic mouse assay can be combined with other in vivo genotoxic endpoints in the same animal: micronuclei, chromosomal aberration, UDS, SCE Type of mutational target In situ end point Target genes are integrated parts of foreign DNA and consequently no "normal" mutational target, no expression UDS: unscheduled DNA synthesis; SCE: sister chromatid exchange. Conclusions In a comparison of the tests available to genetic toxicologists, the results from studies on substances which had been tested in transgenic mutagenicity assays Big Blue ® mouse and the Muta™mouse were compared with those from the more traditional mouse bone marrow micronucleus test. The transgenic animal mutation assay, which is not yet used in toxicological screening, is not directly comparable with the micronucleus test, because different genetic endpoints are examined: chromosome aberration versus gene mutation. However, from the 39 substances, the majority gave the same positive or negative result in both test systems. The substances where differences occurred were discussed in more detail. The advantages and disadvantages of the transgenic Big Blue ® mouse and the Muta™Mouse transgenic model compared to the micronucleus test were discussed and both systems were found to have a place in mutagenicity testing and to supplement each other. The transgenic animal assay has, however, some distinct advantages over the micronucleus test in that it is not restricted to one target organ and detects systemic as well as local mutagenic effects. Authors' contributions UW was the main author. The other authors were involved in the discussions, writing small parts of text and in final preparation of the manuscript. Supplementary Material Additional File 1 Table: Results in the transgenic mouse assay versus mouse bone marrow micronucleus test Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548135.xml

535900

Mineral water intake reduces blood pressure among subjects with low urinary magnesium and calcium levels

Background Several previous epidemiological studies have shown a relation between drinking water quality and death in cardiovascular disease whereas others have not found such a relationship. An intervention study was undertaken to evaluate the effect of water with added magnesium and natural mineral water on blood pressure. Methods A group of 70 subjects with borderline hypertension was recruited and consumed 1) a water low in minerals, 2) magnesium enriched water or 3) natural mineral water, in a random, double blind fashion during four weeks. Results Among persons with an initial low excretion of magnesium or calcium in the urine, the urinary excretion of magnesium was increased in the groups consuming the two waters containing magnesium after 4 weeks. A significant decrease in blood pressure was found in the group consuming mineral water at 2 and 4 weeks. Conclusion The results suggest that minerals taken in water are significant for the body burden and that an intake of mineral water among persons with a low urinary excretion of magnesium or calcium may decrease the blood pressure. Further studies should investigate the extent of mineral deficiency in different populations and the efficiency of different vehicles for supplying minerals, particularly magnesium and calcium.

Background A relation between mortality from ischaemic heart disease (IHD) and drinking water characteristics was first shown in Japan in 1957 [ 1 ]. Since then, several studies have demonstrated the same relationships, one of the last being a study from Finland in 2004 [ 2 ] and reviews have been presented [ 3 , 4 ]. Other studies have, however, not found such relationships or only weak associations between mineral intake and risk for cardiovascular disease [ 5 , 6 ]. This discrepancy may be due to an absence of causality or to variations in the populations studied regarding intake of minerals. In a case-control study, an inverse relation was found between the amount of magnesium in drinking water and death from acute myocardial infarction and for females also between the amount of calcium and death [ 7 ]. Diets rich in vegetables and fruit, which contain high amounts of minerals, had a protective effect on cardiovascular disease [ 8 , 9 ]. This suggests that the mineral balance in individuals depends on different types of intakes which may vary depending on geographical and socio-economical conditions. Regarding individual minerals, several studies have been reported where hypertensive subjects were treated orally with nutritional doses of magnesium [ 10 ]. The results suggested a dose-dependent reduction in blood pressure from the magnesium intervention but it was concluded that the relationship must be confirmed in larger studies, using higher doses of magnesium. A similar meta-analysis reported a very small effect of calcium supplementation [ 11 ]. A meta-analysis of 33 studies on potassium intervention concluded that there might be a beneficial effect on blood pressure [ 12 ]. Many of the studies reviewed were, however, dietary intervention studies, and the intervention thus comprised several minerals and other agents rather than potassium alone. Epidemiological studies on cardiovascular disease suggest that drinking water is an important vehicle for the supply of minerals [ 7 ]. This is supported by data from short-term intervention studies using mineral water, as well as an epidemiological study [ 13 - 15 ]. The present intervention study was undertaken to determine the effect of minerals in water on one of the major risk factors for cardiovascular disease – blood pressure. Subjects with slightly elevated blood pressure consumed water with different levels of minerals. Serum and urinary levels of minerals were measured as a marker of intervention and blood pressure was measured before and after the intervention. Methods Subjects Female and male subjects, aged 45 – 64 years (n = 70) were recruited by advertising in local newspapers. Inclusion criteria were living in an area with low magnesium content in the drinking water, systolic pressure 15 mm above normal values for their age, diastolic pressure above 90 mm Hg, and within 20% of ideal body weight. Exclusion criteria were hypertension target organ damage, chronic diseases (heart, liver, kidney, diabetes mellitus), pregnancy, and taking oral contraceptives or regular intake of mineral supplements. Subjects with a diastolic pressure above 100 mm Hg were advised to consult a physician for treatment. A few persons decided not to seek a physician's advice and choose to participate anyway. The Ethical committee at the Medical faculty, University of Gothenburg, approved the study. Blood pressure Blood pressure was measured using standardized techniques before the intervention, at 2 weeks and at the end at 4 weeks. Two separate recordings were made (diastolic pressure as Korothoff phase 5) after 5 minutes of supine rest. The blood pressure is reported as the average of these recordings. Blood and urine samples Blood samples were taken before and after the intervention to determine the serum concentration of magnesium, calcium, sodium, creatinine and potassium (analysis performed at the accredited laboratory for Clinical Chemistry, Sahlgren's Hospital, Gothenburg). Before and after the intervention period, 24 hours urine samples were collected and the amounts of magnesium, calcium, and creatinine were determined ( idem ). Magnesium and calcium levels in urine were expressed as the creatinine ratio. Intervention The participants were randomly allotted into three groups to which the three waters were supplied in similar bottles marked A, B and C. The composition of the waters (see Table 1 ) was unknown to the persons engaged in the intervention study. The subjects were asked to consume at least one liter of water/day. When preparing coffee and tea, ordinary tap water could be used. There were no difficulties in consuming the allotted quantity and spot checks were made to control for the proper consumption. The intervention lasted 4 weeks. None of the subjects changed their normal dietary habits during the trial. Table 1 Composition of the three waters used in the intervention study. (1) Valvert ® ; (2) Distilled water+MgSO 4 ; (3) Contrex ® . Waters Minerals/mg L A 1 B 2 C 3 Calcium (Ca 2+ ) 67.6 4 486 Magnesium (Mg 2+ ) 2 82.3 84 Sodium (Na + ) 1.9 2.4 9.1 Potassium (K + ) 0.2 0.1 3.2 Sulphate (SO 4 2- ) 18 326 1187 Bicarbonate (HCO 3 - ) 204 12 403 Chloride (Cl - ) 4 0.7 8.6 Fluoride (F - )0 <0.05 0 0.32 Silica (SiO 2 ) 5.7 0 8 Statistical evaluation The three groups were compared using the Student's t-test for paired samples and p < 0.05 was considered statistically significant. For a smaller subgroup of 6 individuals, comparisons were made using the Wilcoxon test for pairs. Results In the analysis of the whole group, no differences in any parameters were found between persons consuming the different types of water. For the subsequent analysis, subjects with serum or urine values in excess of the 75 percentile were excluded on the ground that these represented a group with a sufficient body burden of the minerals and would not be influenced by the intervention. For magnesium in urine, this value was 0.39 mmol/l, and for calcium 0.50 mmol/L. For magnesium, calcium, potassium and sodium in blood the values were 0.9, 2.4, 4.4 and 141 mmol/L, respectively. There was a close relation between the amount of calcium/creatinine and magnesium/creatinine in the urine before the intervention (p = 0.001). Table 2 shows the amounts of calcium and magnesium in urine before and after the intervention with different kinds of waters. It is seen that persons consuming waters B and C had significantly higher amounts of magnesium in the urine after the intervention. No significant effects of the waters on serum levels of magnesium could be detected (data not shown). Table 2 Magnesium/creatinine and calcium/creatinine in urine (mmol/L) among subjects with an initial level less than 0.40 mmol/L magnesium before and after intervention with different waters. Water n Before After p Magnesium A 18 0.25 (0.08) 0.26 (0.07) B 18 0.28 (0.06) 0.34 (0.09) 0.009 C 19 0.30 (0.07) 0.35 (0.09) 0.019 Calcium A 18 0.40 (0.22) 0.35 (0.40) B 18 0.33 (0.12) 0.35 (0.40) C 19 0.34 (0.13) 0.38 (0.16) Table 3 shows the blood pressure before and after intervention. Among persons consuming water C, both the systolic and diastolic blood pressures decreased significantly at 2 and 4 weeks. A similar result was obtained when the group with an initial low level of calcium in the urine was evaluated (data not shown). Table 3 Blood pressure among subjects with an initial magnesium level in urine less than 0.40 mmol/L before and after intervention with different waters. * denotes group with initial systolic values below 170 mm (see text). Water n Before 2 weeks 4 weeks Systolic A 18 151.9 (9.8) 154.2 (15.9) 148.3 (12.4) B 18 148.3 (10.5) 146.8 (13.6) 147.9 (11.5) C 19 156.8 (15.9) 150.1(16.1)p = 0.034 150.4(15.5)p = 0.017 C* 13 148.8 (13.3) 142.5(10.1)p = 0.028 144.3(14.4)p = 0.047 Diastolic A 18 90.1 (4.4) 91.2 (6.1) 89.8 (5.0) B 18 90.4 (4.2) 89.3 (6.0) 90.9 (6.6) C 19 91.7 (6.3) 88.0 (7.6) p = 0.014 89.1 (8.0) p = 0.020 C* 13 91.3 (6.4) 87.3 (6.3) p = 0.004 88.1 (8.6) p = 0.012 In spite of the random allocation to the different waters, it was found that the group consuming water C comprised a larger number of persons with a high initial systolic pressure. In the groups receiving waters A and B, none of the subjects had systolic blood pressures above 170 before the intervention. The subjects drinking water C were divided into those with an initial systolic pressure above and below 170 mm. For the group with the higher pressure (n = 6), there was a decrease in the systolic pressure before and at 4 weeks (p = 0.023) but no difference at 2 weeks or for diastolic pressure. The results from the remainder of the group are also shown in Table 3 . It is seen that significant reductions were observed both for systolic and diastolic blood pressure after 2 and 4 weeks. Discussion The study is of exploratory character, based on a relatively small number of subjects and should be interpreted with care. There is also a lack of some data that retrospectively would have been of interest such as sodium in the urine and the effect of water with only calcium added. We do not think, however, that this has any influence on the major conclusions from the study. The intervention with the two waters with added magnesium influenced the body burden in terms of an increased excretion of magnesium in urine. This is consistent with findings from previous intervention studies [ 16 , 17 ] although the dose of magnesium used here was rather low in comparison to several previous studies [ 10 ]. It could have been of interest to study the effect of different doses of magnesium only, but in view of the conclusions regarding the better effect of total mineral water on blood pressure, this does not have a high priority. The absence of an effect on serum was expected; it has previously been shown that serum magnesium is a poor indicator of the body burden or the intracellular content [ 18 ]. The intervention with water containing high amounts of several minerals decreased the blood pressure significantly in contrast to water with magnesium only where no significant effect was detected. This does not exclude that an effect could have been found with the latter water, had the intervention time been longer. On the other hand, the finding supports the concept that interventions should be performed under conditions similar to the ones present in normal environments, rather than with one specific agent. This could also explain the lack of an effect in previous studies where single minerals have been given as reviewed in the introduction. Conclusion In summary, the results suggest that waterborne minerals constitute a supply for the body burden, that the urinary excretion can be used as a physiologically relevant indicator of the body burden of magnesium and calcium, and that the supplementation of magnesium together with other minerals may reduce blood pressure among persons with a low body burden of magnesium and calcium, either due to an insufficient intake through food or water, or through some metabolic/clinical disturbance. Additional studies are needed to explore this further. Competing interests The study was supported by an unconditional grant to Gothenburg University from the Nestlé Water Institute, Vittel, France. The authors had full freedom for data analysis, manuscript preparation and submittance to a journal. RR has not received any fees or salaries for this work, including article processing charges, nor does he own any shares or has any other financial interest in the company. MA was an employee at the Water Institute at the time of the study. Authors' contribution RR and MA jointly developed the research plan. RR conducted the field study. RR and MA jointly analyzed the data and wrote the manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535900.xml

374246

Depositing a Histone That Protects Active Chromosomal Regions from Silencing

When James Watson and Francis Crick reported the structure of DNA in 1953, the mechanism of inheritance was instantly apparent. The complementary pairing of the DNA bases in the double helix, the pair famously wrote, “immediately suggests a possible copying mechanism for the genetic material.” The structure helped explain one of the central problems of modern biology: how does genetic material get faithfully replicated and then passed on from generation to generation? It was long thought that DNA is the only unit of inheritance. Nucleosome containing H2A.Z Since then, it's become clear that molecules of DNA are packaged into highly organized superstructures that themselves are inherited. These structures play a significant role in the regulation of genes by preventing or facilitating protein–DNA interactions. In the eukaryotic cell (a cell with a nucleus), DNA exists as long threadlike molecules—a typical human cell contains some 6.5 feet (2 meters) of DNA—that associate with a variety of proteins to form a network called chromatin. Genomic DNA wraps around specialized DNA-packing proteins called histones to form nucleosomes, which condense chromatin into chromosomes and thereby influence chromosome behavior. Chromosomes are in turn packaged in increasingly higher levels of organization, with some parts being dispersed and others condensed. The most condensed region is called heterochromatin, or silent chromatin. Gene expression is largely silent in these regions, since the proteins required for transcription can't access DNA to transcribe genes when chromosomes are so tightly packed. Other regions of chromosomes exist in an extended state, called euchromatin. This is the most genetically active state; with genes exposed, transcription can easily occur. As chromatin shifts between these states, it influences gene expression, largely through the interactions of histones and large protein complexes that together assemble, remodel, and modify chromatin. Since proper cell function depends largely on activating the right gene at the right time, mechanisms have evolved that protect active genes from the intrusions of silencing structures like heterochromatin. Both euchromatin and heterochromatin respond to mechanisms that resist encroachments of the opposite state. One mechanism involves replacing “canonical” (that is, archetypal) histones with a histone variant. Previous work on yeast from Hiten Madhani and colleagues had shown that one histone variant, called H2A.Z, is found specifically in euchromatin and prevents silent chromatin from spreading into adjacent euchromatic regions. While researchers have characterized some of the mechanisms that deposit canonical histones onto euchromatin, they knew little about the mechanisms that deposit variant histones. In this issue of PLoS Biology, Jasper Rine, Hiten Madhani, and colleagues identify and characterize the function of a protein complex that helps deposit the variant H2A.Z onto euchromatin in yeast. To investigate which proteins help direct H2A.Z to specific chromosomal locations, the authors isolated H2A.Z, along with whatever proteins were associated with it, from yeast cell extracts. They determined that 15 proteins were true binding partners of H2A.Z and that 13 of them form a complex called SWR1-Com. The largest subunit of this complex, called Swr1p, belongs to a well-known family of adenosine triphosphate (ATP)-dependent chromatin remodeling enzymes (they use the energy of ATP to power remodeling) that provide access to DNA in chromatin. Rine, Madhani, and colleagues show that protein subunits of SWR1-Com associate specifically with the histone variant H2A.Z. By comparing the gene expression profiles of yeast mutants lacking the H2A.Z-encoding gene with mutants lacking the Swr1p-encoding gene, the authors show that H2A.Z depends on the SWR1-Com protein complex to function. Most importantly, they show that SWR1-Com is required in living cells to deposit H2A.Z onto euchromatin. Interestingly, the authors note, SWR1-Com shares subunits with a histone-acetylating enzyme involved in the regulation of transcription (called the NuA4 histone acetyltransferase) and with another chromatin remodeler, which suggests that biochemical modifications of the subunits on histone “tails” may play a role in replacing H2A with H2A.Z. This histone–protein complex, the authors conclude, represents a chromatin remodeling machine with a novel function, revealing a new role for Swr1p-type enzymes and a novel mechanism of genome regulation. By preventing the spread of silent chromatin into transcriptionally active chromosomal regions—the result of the interaction described here—this mechanism allows the cell's gene expression program to operate with precision and on schedule. Since chromosomes can be inherited by daughter cells in this active state, such mechanisms ensure that gene expression programs essential for ongoing fundamental processes like embryogenesis and cellular differentiation proceed without interference.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC374246.xml

523838

Nevirapine and Efavirenz Elicit Different Changes in Lipid Profiles in Antiretroviral- Therapy-Naive Patients Infected with HIV-1

ABSTRACT Background Patients infected with HIV-1 initiating antiretroviral therapy (ART) containing a non-nucleoside reverse transcriptase inhibitor (NNRTI) show presumably fewer atherogenic lipid changes than those initiating most ARTs containing a protease inhibitor. We analysed whether lipid changes differed between the two most commonly used NNRTIs, nevirapine (NVP) and efavirenz (EFV). Methods and Findings Prospective analysis of lipids and lipoproteins was performed in patients enrolled in the NVP and EFV treatment groups of the 2NN study who remained on allocated treatment during 48 wk of follow-up. Patients were allocated to NVP ( n = 417), or EFV ( n = 289) in combination with stavudine and lamivudine. The primary endpoint was percentage change over 48 wk in high-density lipoprotein cholesterol (HDL-c), total cholesterol (TC), TC:HDL-c ratio, non-HDL-c, low-density lipoprotein cholesterol, and triglycerides. The increase of HDL-c was significantly larger for patients receiving NVP (42.5%) than for patients receiving EFV (33.7%; p = 0.036), while the increase in TC was lower (26.9% and 31.1%, respectively; p = 0.073), resulting in a decrease of the TC:HDL-c ratio for patients receiving NVP (−4.1%) and an increase for patients receiving EFV (+5.9%; p < 0.001). The increase of non-HDL-c was smaller for patients receiving NVP (24.7%) than for patients receiving EFV (33.6%; p = 0.007), as were the increases of triglycerides (20.1% and 49.0%, respectively; p < 0.001) and low-density lipoprotein cholesterol (35.0% and 40.0%, respectively; p = 0.378). These differences remained, or even increased, after adjusting for changes in HIV-1 RNA and CD4+ cell levels, indicating an effect of the drugs on lipids over and above that which may be explained by suppression of HIV-1 infection. The increases in HDL-c were of the same order of magnitude as those seen with the use of the investigational HDL-c-increasing drugs. Conclusion NVP-containing ART shows larger increases in HDL-c and decreases in TC:HDL-c ratio than an EFV-containing regimen. Based on these findings, protease-inhibitor-sparing regimens based on non-nucleoside reverse transcriptase inhibitor, particularly those containing NVP, may be expected to result in a reduced risk of coronary heart disease.

Introduction Numerous large epidemiological studies have unambiguously demonstrated a strong inverse relationship between the plasma concentration of high-density lipoprotein cholesterol (HDL-c) and the incidence of coronary heart disease (CHD) [ 1 , 2 ]. Recent attempts to develop therapies aimed at increasing HDL-c as innovative CHD-risk-reducing strategies illustrate the potential of HDL-c as a potent anti-atherogenic mediator [ 3 , 4 , 5 , 6 ]. Combination antiretroviral therapy (ART) for the treatment of HIV-1 infection has been associated with fat redistribution, insulin resistance, and changes in plasma concentrations of lipids and lipoproteins [ 7 , 8 , 9 ]. Each of these phenomena is associated with increased CHD risk in the general population. It is not surprising, therefore, that in the setting of HIV-1 infection, increasing exposure to potent combination ART has been demonstrated to be associated with an incremental risk of CHD in a recent prospective study [ 10 ]. Interestingly, however, the changes in lipids and lipoproteins differ between patients using an ART regimen containing either a protease inhibitor (PI) or a non-nucleoside reverse transcriptase inhibitor (NNRTI). Whereas many of the PI-based regimens are often associated with increased levels of triglycerides (TGs), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-c) [ 8 , 9 , 11 ], NNRTI-based regimens importantly differ from PI-based regimens by being associated with marked increases of HDL-c and lesser increases of LDL-c and TGs [ 12 , 13 ]. Notably, the increases in HDL-c demonstrated with NNRTI-containing ART markedly exceed those that may be induced with any of the currently licensed statins or fibrates [ 14 ]. Although as yet no clinical data have been generated to support this, these differences between ART regimens raise the expectation that NNRTI-based regimens, particularly in view of their effects on HDL-c, may favourably modify the CHD risk compared with many of the PI-containing regimens. With respect to the two currently commonly used NNRTIs, nevirapine (NVP) and efavirenz (EFV), no detailed comparative data have been reported concerning their effect on plasma lipids and HDL-c in particular. We prospectively analysed lipid and lipoprotein changes in a preplanned substudy of the 2NN trial in which ART-naive patients received stavudine (d4T) and lamivudine (3TC) with the randomly assigned addition of NVP, EFV, or both drugs combined. Methods Participants and Treatment Allocation The 2NN trial was an open-label study, the main results of which have been published elsewhere [ 15 ]. Patients enrolled were 16 y of age or older, ART-naive, and had a plasma HIV-1 RNA concentration (pVL) of at least 5,000 copies/ml. Main exclusion criteria were pregnancy or breastfeeding, abnormal laboratory results at screening, the use of immuno-modulating therapy, or anticipated nonadherence. All patients used d4T (40 mg twice daily [bd] or 30 mg bd when less than 60 kg) and 3TC (150 mg bd). In addition, patients were randomly allocated to NVP at 400 mg once daily (od), NVP at 200 mg bd, EFV at 600 mg od, or NVP and EFV at 400 mg od and 800 mg od, respectively. Patients were included from 65 different study sites in 17 countries in Asia, Australia, North America, South America, South Africa, and Europe. The 2NN study had been approved by the ethics committees of all participating institutions, and all patients had given written informed consent. The current analyses were preplanned. Only those patients were included who used all components of their allocated treatment for at least 95% of the time during the 48 wk of follow-up (self-reported). Change of d4T and/or 3TC was allowed for reasons of toxicity. Employing such ‘on treatment’ (OT) analysis, allows the best possible assessment of lipid changes that actually result from differences in regimens. Patients in the NVP-od and NVP-bd groups were combined, given that the virologic efficacy of these treatments was comparable and no differences in risk of virologic failure were observed. Follow-Up and Assessments Plasma samples for prospective determination of lipids and lipoproteins were collected at baseline (before start of treatment) and at weeks 2, 4, 8, 12, 24, 36, and 48. Blood was drawn after a mandatory fast of at least 3 h. The samples were analysed in local laboratories according to predefined protocols. These laboratories were selected by the Virtual Central Laboratory (Zeist, The Netherlands), which selected the laboratories, assured the quality of the analyses and data, and standardised all results. Plasma concentrations of HDL-c, TC, and TGs were assessed by standard enzymatic assays. The concentration of LDL-c was calculated using the Friedewald equation, but only when the concentration of TGs was below 4.5 mmol/l [ 16 ]. Because the calculation of LDL-c depends on the measured TG concentrations and these TG levels might be biased because of the relatively short mandatory fasting period, we also calculated the non-HDL-c levels. These are considered to be much less influenced by TG levels. The pVL was measured at a central laboratory (LabCorp, Research Triangle Park, North Carolina, United States) using Ultra Sensitive Roche Amplicor 1.5 (Roche Diagnostics, Basel, Switzerland) with a lower limit of quantification of 50 copies/ml. Outcome Measurements The primary study outcome was the mean percentage change of HDL-c, TC, TC:HDL-c ratio, non-HDL-c, LDL-c, and TGs between start of allocated treatment and week 48. For each patient at each specific study week we calculated this estimate as concentration at week X minus concentration at baseline divided by concentration at baseline, times 100. Study-week-specific estimates were used for the subsequent analyses. Factors assessed for a possible association with the primary outcome were sex, study region (Asia/Australia, South Africa, South America, Europe/North America), body mass index (BMI) (continuous), increase between start of therapy and week 48 in CD4+ cells (<100, 100–250, or >250 cells/mm 3 ) or decrease in pVL (<2.5, 2.5–3.5, or >3.5 log 10 ), and virologic failure during follow-up. Virologic failure was defined as (1) never having obtained a pVL of less than 50 copies/ml or (2) a rebound to two consecutive pVLs of ≥50 copies/ml. A single pVL of ≥50 copies/ml at week 48 was also considered a virologic failure. Statistical Analyses The analyses included the NVP and EFV treatment groups only. This choice was made since the results of the main 2NN study clearly showed that the simultaneous use of NVP and EFV shouldn't be recommended in clinical practice in view of increased toxicity of this combination in the absence of increased virologic efficacy. The mean percentage changes in lipid concentrations were modelled using a mixed model incorporating repeated measurements. This model handles missing data adequately by estimating the outcome of a specific variable based on the available data given the specified covariate structure. The variables (fixed effects) in the model were tested for significance using the Type III F-statistic. The estimates of a specific level of the fixed effect were modelled using the ‘least squared means' approach. Differences in these estimates between different levels of the variable were tested for significance using the t-statistic. Since the analyses might be biased because of the OT approach or the modelling of data, we performed two sensitivity analyses. The first was an analysis using the same modelling strategy but for an intention-to-treat population including all patients who started their randomised treatment. The second was an analysis using only available data for the OT population, without modelling of data points. Independent risk factors were assessed by multivariable regression analyses. The multivariable analysis included the variable ‘treatment group' and all predefined variables. Interaction between treatment group and a specific variable was assumed at a p -value less than 0.15. A two-sided p -value less than 0.05 was considered statistically significant in the final analyses. The SAS statistical package was used for analyses (version 8.02, SAS Institute, Cary, North Carolina, United States). Results Disposition of Patients Of the 1,216 patients included in the 2NN study, 607 were allocated to the NVP treatment group and 400 to the EFV treatment group. Of these, 42 (6.9%) patients in the NVP group and 25 (6.3%) patients in the EFV group did not start their treatment or were considered a ‘study entry violator’ by a (blinded-to-treatment) independent endpoint committee. These patients were excluded from the analyses. From the remaining patients (565 using NVP and 375 using EFV), only those who remained on their assigned treatment during the follow-up were included in the analyses. This resulted in a final sample size of 417 (68.7%) patients in the NVP group and 289 (72.3%) in the EFV group. All of the included patients had at least one measurement of each lipid parameter and could therefore be used in the statistical models. The baseline characteristics of the subset of patients included in the current analyses are summarised in Table 1 . These baseline characteristics were comparable with those of all patients enrolled in the main 2NN study. Table 1 Baseline Characteristics of Patients Included in the 2NN Lipid Substudy and the 2NN Main Study IQR, interquartile range In the NVP group, 148 of the 565 eligible patients (26.2%) were not included in the OT analyses. Of these 96 (65%) were nonadherent (including patients lost to follow-up while on randomised treatment), 30 (20%) changed their NNRTI to EFV, and 22 (15%) changed their regimen by adding a PI. In the EFV group, 76 of the 375 eligible patients (20.3%) were not included in the OT analyses. Of these 54 (71%) were nonadherent, 17 (22%) changed their NNRTI to NVP, 3 (4%) added a PI, and 2 (3%) added a third nucleoside reverse transcriptase inhibitor. Changes in Lipids and Lipoproteins All changes within the treatment groups in lipid and lipoprotein concentration, as well as in TC:HDL-c ratio were statistically significant. The increase of HDL-c was 8.9% (95% confidence interval [CI], 0.6–17.1) larger in the NVP treatment group (42.5%) than in the EFV treatment group (33.7%). This was statistically significant ( p = 0.036) ( Table 2 ). In contrast, the increase in TC was smaller in the NVP group (26.9%) than in the EFV group (31.1%), but this difference (−4.2%; 95% CI, −8.7 to 0.4) was not statistically significant ( p = 0.073). These changes resulted in a decrease of the TC:HDL-c ratio in the NVP group (−4.1%) compared to an increase in the EFV group (+5.9%; p < 0.001), and a significantly smaller increase of non-HDL-c in the NVP group (difference, −8.9%; 95% CI, −15.4 to −2.5; p = 0.007). Table 2 Lipid Concentrations at Baseline and Week 48 and Mean Percentage Change All percentage changes within a treatment group were statistically significant a Units: mmol/l, median (interquartile range) b Mean percentage change (standard error), modeled by repeated measurements. Mean percentage change was calculated at each specific time point for each individual patient as ((concentration[week X ] – concentration[baseline]) / concentration[baseline]) × 100 The increase of TGs was 28.9% (95% CI, −42.3 to −5.0) smaller in the NVP group (20.1%) than in the EFV group (49.0%; p < 0.001). The difference in LDL-c increase was not statistically significant (35.4% for NVP group; 40.0% for EFV group; p = 0.378). In the first sensitivity analysis (intention-to-treat population), the increases of HDL-c were slightly lower (41.2% for NVP group; 32.4% for EFV group), just as for TC (26.1% for NVP group, 30.4% for EFV group) and non-HDL-c (24.3% for NVP group, 33.1% for EFV group). The TC:HDL-c ratio showed a smaller decrease for patients taking NVP (−2.6%) but a larger increase for patients taking EFV (+7.2%). The increase in TGs was larger for both patients taking NVP (24.3%) and patients taking EFV (49.3%). The LDL-c increase was somewhat smaller for patients taking NVP (33.1%) but larger for patients taking EFV (47.3%). The difference between patients taking NVP and those taking EFV for HDL-c (8.8%; 95% CI, 1.3−16.3) remained statistically significant, just as the difference in the TC:HDL-c ratio (−9.8%; 95% CI, −14.7 to −4.9), non-HDL-c (−8.8%; 95% CI, −14.6 to −3.0), and TGs (−24.9%; 95% CI, −37.2 to –12.6). Additionally, the difference between NVP and EFV treatment groups became statistically significant for TC (−4.2%; 95% CI, −8.5 to 0.0) and LDL-c (−14.2%; 95% CI, −28.4 to 0.0) compared to the original OT analysis. The second sensitivity analysis (using only available data for the OT population) also showed comparable estimates (data not shown). The increase in HDL-c for patients who started their ART when their HDL-c levels were, according to the National Cholesterol Education Program (NCEP) guidelines, low (<1.03 mmol/l), normal (1.03–1.55 mmol/l), or high (>1.55 mmol/l) is reported in Table 3 . In both treatment groups, the majority of patients had a low HDL-c at the start of therapy. These patients showed the largest increase in HDL-c over 48 wk. Even patients with a normal baseline HDL-c level showed statistically significant, marked increases of HDL-c. The effect of baseline HDL-c level on percentage increase was comparable in both treatment groups (interaction, p = 0.409). Table 3 Increase in HDL-c Stratified by Baseline HDL-c (NCEP Categories) a Mean percentage change (standard error), modeled by repeated measurements. Mean percentage change was calculated at each specific time point for each individual patient as ((concentration[week X ] – concentration[baseline]) / concentration[baseline]) × 100 Multivariable Analysis Factors independently associated with changes in the lipid concentrations were analysed by a multivariable regression analysis ( Table 4 ). Table 4 Factors Associated with Percentage Change in Lipid Parameters (Multivariable Analyses) a Units: mmol/l b Percentage increase (standard error) between baseline and week 48, modelled by repeated measurements. Mean percentage change was calculated at each specific time point for each individual patient as ((concentration[week X ] – concentration[baseline]) / concentration[baseline]) × 100 c For percentage increase d A, Asia/Australia; B, South Africa; C, South America; D, Europe/North America Men had a significantly smaller increase of HDL-c, compared to women, but a larger increase of TC. This resulted in an increased TC:HDL-c ratio for men and a decreased ratio for women, while the increase of non-HDL-c was significantly larger in men. The changes in lipid concentrations varied markedly by region. Patients from Asia/Australia and South Africa had the largest decrease in the TC:HDL-c ratio because of an increase of HDL-c that outweighed the increase of TC. Patients from South America, compared to those from other regions, had a significantly smaller HDL-c increase with a comparable TC increase, resulting in an increased TC:HDL-c ratio. Although patients from Europe showed the largest change in HDL-c, the increases in TC and TC:HDL-c ratio were intermediate. A striking finding is the much larger increase of TGs in patients from South America compared to those in patients from other regions, which to a lesser extent was also seen for non-HDL-c. For all lipid concentrations, except TGs, there was a clear pattern of larger increases of lipid levels with larger decreases of pVL over 48 wk. This was also seen when the pVL increase over 48 wk was analysed as a continuous variable. For each log 10 larger decrease in pVL there was a 4.6% increase in TC ( p < 0.001), a 7.8% increase in HDL-c ( p < 0.001), a 10.2% increase in LDL-c ( p < 0.001), and a 3.6% increase in non-HDL-c ( p = 0.002), while the TC:HDL-c ratio declined with 1.6% ( p = 0.051). In this analysis, there was also a clear association between pVL decline and change in TGs (6.3% decline per log 10 ; p = 0.002). In general, a smaller CD4+-cell increase was associated with a smaller increase in lipid concentration, while increases of more than 250 cells/mm 3 did not show markedly different effects compared to increases of 100–250 cells/mm 3 . When analysed as a continuous variable, there was no statistically significant association between CD4+-cell increase and change in any of the lipid parameters. BMI was independently associated with increases in all lipid parameters, except TC. Although the increases per unit increase in BMI were statistically significant, the magnitude of increases was rather low. All these factors exhibited a similar effect in both the NVP and the EFV treatment group (no significant interactions). There were, however two exceptions. For changes in TGs, the effect of sex and pVL decrease differed between the treatment groups (interaction, p = 0.005 and p = 0.075, respectively). Men had a significant increase of TGs in both the NVP group (14.1%) and the EFV group (43.3%); women using NVP had no significant TG increase (6.5%), while those using EFV had (15.9%). The effect of pVL decrease on TG increase was quite different for patients taking NVP versus those taking EFV. In the NVP group, the increase in TG concentration was 17.2% for a pVL decrease less than 2.5 log 10 , 16.6% for a decrease between 2.5 and 3.5 log 10 , and −6.1% (denoting a decrease) for a pVL decrease more than 3.5 log 10 . In the EFV group, these estimates were 27.4%, 37.2%, and 26.0%, respectively. Adjusting for the variables included in the multivariable model, the difference between patients taking NVP and those taking EFV in HDL-c increase (9.8%; 95% CI, 3.4−16.3) and decrease of the TC:HDL-c ratio (−11%; 95% CI, −15.1 to −6.8) remained statistically significant. Also, the difference in non-HDL-c increase (−9.5%; 95% CI, −14.6 to −4.4) and TG increase (−27.2%; 95% CI, −38.0 to −16.4) remained statistically significant. The difference in TC increase (−4.4%; 95% CI, −8.0 to −0.8) became statistically significant. The difference between NVP and EFV groups for the increase in LDL-c remained statistically nonsignificant (−6.1%; 95% CI, −14.7 to 2.6). The adjusted increase of HDL-c was 42.3% and 32.4% for patients taking NVP and EFV, respectively ( p = 0.003). The adjusted change in TC:HDL-c ratio was −4.3% for patients taking NVP and +6.6% for patients taking EFV ( p < 0.001). These values were 26.6% and 31.0% for TC ( p = 0.020), 24.4% and 33.9% for non-HDL-c ( p < 0.001), 17.9% and 45.1% for TG ( p < 0.001), and 35.5% and 41.5% for LDL-c ( p = 0.168). These estimates were very similar in the two sensitivity analyses (data not shown). The proportional changes of the different plasma lipid concentrations over 48 wk are graphically depicted in Figure 1 . Figure 1 Change in Plasma Concentrations of Lipids and Lipoproteins Adjusted for sex, region, pVL decrease, and CD4+-cell increase. Discussion Initiation of an ART regimen containing NVP or EFV is accompanied by a significant increase of HDL-c, with concomitant increases of TC, non-HDL-c, TGs, and LDL-c. The proportional increase of HDL-c was significantly larger in the NVP treatment group compared to the EFV treatment group, while the proportional increase of TC, non-HDL-c, and TGs was significantly smaller. In the NVP group, the TC:HDL-c ratio decreased, compared to an increase in the EFV group. These observations are different from what is seen with most PI-based ART regimens, in which higher concentrations of TC, LDL-c, and TGs are reported but without the concurrent higher levels of HDL-c [ 17 , 18 ]. In contrast to a small randomised study ( n = 67) that did not show significant differences between NVP and EFV [ 19 ], the present study demonstrates a more favourable lipid profile for treatment including NVP than for treatment including EFV in ART-naive patients. HDL-c Increase and NNRTI Increases of HDL-c with the use of NVP or EFV have been described in studies for patients switching from a PI-based regimen to a NNRTI-based regimen [ 20 , 21 ]. Data for ART-naive patients starting therapy with an NNRTI-based regimen are scarce. Van der Valk et al. reported an increase of HDL-c of 0.44 mmol/l for patients initiating treatment with didanosine, d4T, and NVP in the Atlantic trial [ 12 ]. Tashima et al. reported an increase of HDL-c of 0.21 mmol/l in patients treated with EFV and either zidovudine plus 3TC, or indinavir [ 13 ], while Negredo et al. showed an increased HDL-c concentration in therapy-naive patients starting a regimen of didanosine, d4T, and EFV (0.34 mmol/l) [ 22 ]. The present study showed a clear effect of baseline HDL-c on the proportional increase. The largest increases were seen for patients who had an increased CHD risk based on their low HDL-c level (<1.03 mmol/l) according to NCEP guidelines. But also patients with a normal HDL-c level, who are not at an increased CHD risk, showed marked increases in HDL-c. This baseline effect can likewise be distilled from the other studies, where those with the lowest baseline value (0.93 mmol/l; Atlantic study [ 12 ]) showed the largest HCL-c increase, while the smallest increase was seen in the study with the highest baseline value (1.23 mmol/l; Tashima study [ 13 ]). The study by Negredo et al. [ 21 ], which included patients with similar baseline HDL-c levels as in the present study, showed an increase of HDL-c comparable to that in the present study (0.34 and 0.36 mmol/l, respectively). The much more modest HDL-c-increasing effect of statins likewise shows such a correlation with baseline level in patients without HIV-1. One may postulate that the HDL-c increase merely reflects an adequate suppression of HIV-1 infection (‘return towards normal’). In support, a larger decrease in pVL was associated with a larger increase of HDL-c in the present study. However, the magnitude of the HDL-c increase was only slightly different for patients experiencing virologic failure during these 48 wk (29.5%) and those with complete suppression (34.0%; p = 0.161), and the increases of HDL-c remained statistically significant even after adjustment for pVL decrease. Riddler et al. compared changes in lipid concentrations before seroconversion for HIV-1, initiation of ART, and during ART in patients using different ART regimens, which all but one included a PI [ 23 ]. The period between seroconversion and start of ART was characterised by decreases in TC, LDL-c, and HDL-c. Between initiation of ART and the first follow-up visit (mean, 1.3 years), the concentrations of TC and LDL-c increased again to levels that did not differ significantly from before seroconversion. Such a ‘return to normal’ as a result of ART was not seen for HDL-c. The reported increases by Riddler et al. in TC (0.88 mmol/l) and LDL-c (0.41 mmol/l) were of a comparable magnitude to that found for patients taking NVP in this present study (0.97 and 0.55 mmol/l, respectively). However, the HDL-c increase was more than ten times smaller (0.03 mmol/l) in the Riddler et al. study than the 0.36 mmol/l observed in patients taking NVP in the present study, while the mean HDL-c values at which ART was started were comparable in the two studies (1.04 and 1.0 mmol/l, respectively). This indicates that although at least part of the change in TC and LDL-c may reflect a ‘return towards normal’, the magnitude of the HDL-c increase observed in our study must have occurred through additional mechanisms. Since we have no information on the antiretroviral efficacy of the regimens used in the Riddler et al. study, we have to consider that the reported differences between the Riddler et al. study and the present study might be partly due to differences in HIV-1 suppression. However, the type of PI-based regimens used in the Riddler et al. study and the long-term adequate adherence by the patients make large differences in antiretroviral efficacy unlikely. We are currently conducting studies to unravel whether NVP possibly stimulates synthesis of the most important apolipoprotein of HDL-c, apoAI, or alternatively, for instance, decreases the clearance of HDL-c particles. Several studies have convincingly shown that an HDL-c increase is associated with a significant decrease in CHD mortality independent of changes in LDL-c [ 1 , 2 ]. Overall, extrapolation of these studies indicates that a 0.025-mmol/l increase in HDL-c is expected to be associated with a 2%–3% reduction in CHD risk, while an increase of 1.0 mmol/l in LDL-c will increase the CHD risk by 25%. The mean absolute increases in HDL-c and LDL-c were 0.36 and 0.54 mmol/l, respectively, for patients taking NVP, and 0.24 and 0.65, respectively, for patients taking EFV. It can therefore be estimated that, taking the observed effects on both HDL-c and LDL-c into account, the reduction in CHD risk would be 15% for patients taking NVP and 3% for patients taking EFV compared to ART regimens that do not include NNRTIs. Although the differences in absolute concentrations of HDL-c and LDL-c may seem modest when comparing the NVP and EFV treatment groups, the combined effect of these changes on CHD risk seems marked. It should be emphasised that these are theoretical estimates, which do not take into account that increases in TGs would be expected to have an opposite effect on CHD risk. The increase in this last parameter is, however, smaller for patients taking NVP than for patients taking EFV. Furthermore, we do not have information on the presence of conventional risk factors for CHD. The actual effect of the lipid changes associated with particular ART regimens on CHD can only be substantiated by clinical endpoint studies. Changes in TGs, TC, and LDL-c The data indicate that EFV might have a more detrimental effect on TG levels than NVP. That EFV indeed can be associated with an increase in TGs was shown in two studies, in which sporadical hypertriglyceridaemia was reported in patients starting an ART regimen with EFV but without d4T [ 24 , 25 ]. A difference between NVP and EFV treatment with respect to the TG effect is also in line with a study by Negredo et al. [ 20 ]. In this study patients were randomised to either continue their successful PI-based regimen or to change to an NVP-based or an EFV-based regimen. Only patients switching to the NVP regimen showed a significant decrease in TG levels. The proportional increase in TG levels with both NVP and EFV treatment seems large, but the median absolute TG level at week 48 was still low in both treatment groups (1.2 mmol/l and 1.4 mmol/l, respectively). In the NCEP guidelines, a TG concentration below 1.69 mmol/l is still considered normal [ 26 ]. The increase in TG level is therefore probably not clinically meaningful. The differences between patients taking NVP and those taking EFV in changes in TC as well as TGs are unlikely to be explained by the concurrent use of d4T. In both treatment groups, the percentage of patients who used d4T as part of their regimen throughout follow-up was high (96% for the NVP treatment group and 98% for the EFV treatment group). This high rate of d4T use might, however, be responsible for the impression that the increases in TC and TGs seem to continue or even accelerate towards the end of the study period, as opposed to a somewhat declining effect of treatment on HDL-c after 24 wk. A possible explanation for this may be the gradual, progressive worsening of fat redistribution or lipodystrophy that one would expect to occur in this continuously d4T-exposed patient population. Both the incidence and severity of lipodystrophy are particularly increased with d4T-containing ART regimens, and lipodystrophy has been reported to be associated with increased TC and TG levels [ 27 , 28 , 29 , 30 , 31 , 32 ]. It is therefore conceivable that the lipid changes in the second part of the study represent a combined effect of the NNRTI used and a superimposed effect resulting from gradually worsening fat redistribution. Due to the relatively short mandatory fasting period (3 h), the measured TG concentration might be biased, possibly even more so given that HIV-1 infection may be associated with reduced TG clearance following food intake [ 33 ]. As a consequence, the estimates of calculated LDL-c might be biased. TG levels influence changes in non-HDL-c less. The fact that in the present study the increases of non-HDL-c, LDL-c, and TGs are all smaller for patients taking NVP than for patients taking EFV suggests that the LDL-c and TG results are valid despite the potentially short mandatory fasting periods. ART and CHD The relationship between ART and CHD has been the subject of several studies, based on either clinical or validated surrogate endpoints (like arterial intima-media thickness [ 34 ] or endothelial wall function [ 35 ]). Studies examining intima-media thickness in patients with HIV-1 treated with ART, or more specifically with PI-based ART, remain inconclusive as to whether ART use induces accelerated intima-media thickening [ 36 , 37 , 38 , 39 ]. PI use may have a detrimental effect on endothelial function in vivo, or accelerate foam cell formation in vitro [ 40 , 41 ]. In a retrospective clinical endpoint study including almost 37,000 patients, Bozzette et al. reported no relation between ART use and hospital admission for cardiovascular events [ 42 ], a finding confirmed in the ‘Kaiser Permanente’ cohort [ 43 ]. However, the large prospective ‘data collection on adverse events of anti-HIV drugs' (D:A:D) study, specifically designed to identify the extent to which ART may be associated with increased CHD risk, did show that every additional year of ART use was associated with a 26% increased risk of myocardial infarction [ 10 ]. The latter resembles the results from other retrospective and prospective studies [ 44 , 45 , 46 ]. The relatively high prevalence of known CHD risk factors in patients with HIV-1, especially smoking [ 47 , 48 ], complicates interpretation of the relation between ART use and CHD. None of these studies allows definitive conclusions to be made about the potentially different degree of risk associated with particular ART regimens. Limitations and Possible Biases The selection of patients remaining on their allocated treatment for the full 48 wk might have introduced sampling bias with inflated treatment effects. The reported estimates from this OT analysis were very similar to the estimates from the intention-to-treat analysis. This is caused by the fact that only a few patients who were not included in the OT analyses changed their drug regimen by adding a PI that could potentially influence the lipid estimates. The majority remained on their randomised treatment but were insufficiently adherent (or lost to follow-up on their original regimen) to meet the criteria for being eligible for the OT analysis. Patients replacing their assigned NNRTI by the NNRTI from the other treatment group of the study would have little effect on the lipid estimates, since both NNRTIs show changes in lipid concentrations, which go in the same direction. Another possible reason for the similarity between these two analyses could be the relatively late timing of treatment changes (mean of 75 d for patients taking NVP and 95 d for patients taking EFV). The fact that the second sensitivity analysis also showed comparable results indicates that modelling of data did not affect the lipid estimates. A limitation of the present study is the lack of data on conventional CHD risk factors like smoking. The 2NN being a randomised study, it may be expected that these and other confounding variables are equally distributed over the treatment groups. Possible residual confounding, however, cannot be excluded, and the results should therefore be interpreted cautiously. Conclusion While awaiting the results of future studies, the less atherogenic lipid profile of patients taking NVP in comparison to those of patients taking EFV may be among the various factors to consider when selecting the most appropriate initial ART regimen, particularly for those patients with HIV-1 with a significant a priori CHD risk. Including such a consideration seems warranted, since treatment of the ART-induced lipid changes with currently licensed lipid-lowering agents is not without problems. Most of the available statins except pravastatin and fluvastatin, are metabolised through cytochrome isoenzyme CYP3A4, just as the PIs and NNRTIs are, providing concern for potential drug–drug interactions. Furthermore, statin therapy in patients using a PI-based regimen is in general not able to reduce the lipid concentrations to normal levels [ 49 , 50 ]. Studies on the effectiveness of gemfibrozil in patients on a PI-based regimen show conflicting results [ 51 , 52 , 53 ]. Finally, the introduction of yet another type of medication in a patient population that often needs to use not only ART but also a considerable amount of concomitant medication might jeopardise treatment adherence, which is of crucial importance for the sustained success of ART treatment. Use of the novel PI atazanavir may also be considered an attractive option in patients at high risk of CHD, given that it is associated with markedly smaller increases of TC, LDL-c, and TGs compared to previously available PI-based regimens [ 54 , 55 ], but it lacks the concurrently large increase in HDL-c seen with NNRTI-based regimens. The reported increase of HDL-c concentration with NNRTI use is far greater than that seen with conventional lipid-lowering drugs and is of a similar magnitude as the HDL-c increases reported with the most powerful HDL-c-increasing drugs that are currently in clinical development [ 6 , 56 ]. Asztalos et al. reported the effect on HDL-c for five major statins in patients with CHD [ 57 ]. They concluded that the HDL-c increase was between 4% (simvastatin) and 11% (pravastatin and lovastatin). Treatment with fluvastatin or lovastatin proved to be most effective in patients with a low baseline HDL-c level [ 56 , 58 ]. Clinical trials assessing the effects of fibrates reported an HDL-c increase of 6% and 11% for gemfibrozil [ 2 , 59 ], and 18% for bezafibrate [ 60 ]. Unravelling the mechanism or mechanisms by which NVP and EFV raise HDL-c could contribute to the development of novel interventions aimed at increasing HDL-c and thereby ultimately to reducing CHD risk in the population at large. Patient Summary Why Did the Researchers Do the Study? Drugs used to treat HIV (antiretroviral drugs) help patients to live longer, but they can also have some serious side effects. For example, the longer people take them, the higher the risk they'll get heart disease. Why? Part of the reason is that many—though not all—antiretroviral drugs cause changes in cholesterol levels in the bloodstream (an increase in the amount of “bad” cholesterol and a reduction in the amount of “good” cholesterol). Two of the most commonly prescribed antiretroviral drugs are nevirapine and efavirenz. Previous smaller studies showed that treatment with either of the drugs could increase the amount of “good” cholesterol. The researchers now wanted to directly compare these drugs to find out what effect they had on patients' cholesterol levels in a much larger group of patients. What Did the Researchers Do? The scientists studied adults with HIV who had never previously taken antiretroviral drugs. All of the patients then took “triple therapy”—a combination of three antiretroviral drugs. Some of the patients took nevirapine as part of their triple therapy, whereas some took efavirenz. The researchers took blood samples regularly for almost a year and measured patients' cholesterol levels. What Did the Researchers Find? They confirmed that both nevirapine and efavirenz indeed have a beneficial effect on patients' cholesterol levels. They both increase the amount of “good” cholesterol in the bloodstream. The increase was higher with nevirapine than with efavirenz. What Does This Study Mean for Patients? If your treatment includes nevirapine or efavirenz (particularly nevirapine), this can raise your level of “good” cholesterol. The results of the study may be especially important if you are already at risk for heart disease. In other words, if you have risk factors for heart disease—high blood pressure, diabetes, heart disease running in your family, being a smoker—it may be beneficial for your HIV medications to include nevirapine. If you smoke, you can lower your risk of heart disease by quitting. If you have high blood pressure or diabetes, treating these conditions can also lower your risk of heart disease. What Are the Problems with the Study? Although the researchers showed that nevirapine and efavirenz can have a beneficial effect on cholesterol levels, they haven't actually shown that this reduces patients' risk of getting heart disease. The study was funded by the company that produces nevirapine. In theory this could have affected the results (research has shown that company-sponsored studies are more likely to produce results favorable to the company than studies without sponsorship). The study, however, was carried out by a network of independent investigators who state that the company had no influence on the reporting of the results. Where Can I Get More Information? You can get more information on HIV and its treatment from the Terrence Higgins Trust ( www.tht.org.uk ), AIDS.ORG ( www.aids.org ), and The Body ( www.thebody.com ).

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC523838.xml

555762

Do malaria ookinete surface proteins P25 and P28 mediate parasite entry into mosquito midgut epithelial cells?

Background P25 and P28 are related ookinete surface proteins highly conserved throughout the Plasmodium genus that are under consideration as candidates for inclusion in transmission-blocking vaccines. Previous research using transgenic rodent malaria parasites lacking P25 and P28 has demonstrated that these proteins have multiple partially redundant functions during parasite infection of the mosquito vector, including an undefined role in ookinete traversal of the mosquito midgut epithelium, and it has been suggested that, unlike wild-type parasites, Dko P25/P28 parasites migrate across the midgut epithelium via an intercellular, rather than intracellular, route. Presentation of the hypothesis This paper presents an alternative interpretation for the previous observations of Dko P25/P28 parasites, based upon a recently published model of the route of ookinete invasion across the midgut epithelium. This model claims ookinete invasion is intracellular, with entry occurring through the lateral apical plasma membrane of midgut epithelial cells, and is associated with significant invagination of the midgut epithelium localised at the site of parasite penetration. Following this model, it is hypothesized that: (1) a sub-population of Dko P25/P28 ookinetes invaginate, but do not penetrate, the apical surface of the midgut epithelium and thus remain within the midgut lumen; and (2) another sub-population of Dko P25/P28 parasites successfully enters and migrates across the midgut epithelium via an intracellular route similar to wild-type parasites and subsequently develops into oocysts. Testing the hypothesis These hypotheses are tested by showing how they can account for previously published observations and incorporate them into a coherent and consistent explanatory framework. Based upon these hypotheses, several quantitative predictions are made, which can be experimentally tested, about the relationship between the densities of invading Dko P25/P28 ookinetes in different regions of the midgut epithelium and the number of oocyst stage parasites to which these mutant ookinetes give rise. Implications of the hypothesis The recently published model of ookinete invasion implies that Dko P25/P28 parasites are greatly, although not completely, impaired in their ability to enter the midgut epithelium. Therefore, P25 and/or P28 have a novel, previously unrecognized, function in mediating ookinete entry into midgut epithelial cells, suggesting that one mode of action of transmission-blocking antibodies to these ookinete surface proteins is to inhibit this function.

Background P25 and P28 are related major ookinete surface proteins under consideration as candidates for inclusion in transmission-blocking vaccines [ 1 - 4 ]. Consequently, the expression [ 5 - 18 ], localisation [ 8 , 12 , 17 - 24 ] and function [ 21 , 25 - 29 ] of these molecules, together with the effect on parasite development of specific antibodies against them [ 6 , 8 , 21 , 22 , 24 , 30 - 35 ], have been extensively studied in a range of malaria parasite species. P25 and P28 are structurally similar proteins, highly conserved throughout the Plasmodium genus [ 11 , 12 , 31 , 35 - 43 ], which contain four epidermal growth factor-like domains [ 36 ], putatively involved in cell-cell and/or cell-matrix interactions [ 21 , 25 , 26 , 28 , 29 ], that are expressed throughout the early life-cycle stages of the malaria parasite within the mosquito vector – from the macrogamete through to the oocyst stage [ 8 , 12 , 17 - 24 ]. P25 and P28 are located on the parasite surface, from which they are shed during ookinete gliding motility and traversal of the mosquito midgut epithelium [ 19 - 21 , 44 , 45 ]. The conservation of sequence, expression and location suggests that P25 and P28 have functionally equivalent roles in diverse malaria parasite species. Previous research using transgenic Plasmodium berghei rodent malaria parasites lacking P25 and P28 demonstrated that these proteins have multiple and partially redundant functions during parasite infection of the mosquito vector [ 26 , 27 ]. Although Dko P25/P28 P. berghei parasites exhibit greatly reduced levels of oocyst infection compared to wild-type or Sko P25/P28 parasites, ookinetes lacking both P25 and P28 are still able to cross the midgut epithelium and establish oocyst infections [ 27 ]. Wild-type P. berghei ookinetes migrate intracellularly through the midgut epithelium causing significant damage to invaded midgut epithelial cells [ 44 - 48 ], which subsequently exhibit distinct morphological abnormalities [ 44 - 48 ], including loss of microvilli [ 44 , 45 ], protrusion into the midgut lumen [ 44 , 45 , 48 ] and up-regulation of molecules implicated in mosquito immune responses such as NOS [ 44 , 49 ] and SRPN10 [ 45 , 50 ]. Furthermore, P28 is found on the apical surface, and within the cytoplasm, of these abnormal midgut epithelial cells suggesting release/secretion from penetrating parasites during their intracellular migration [ 44 , 45 ]. Dko P25/P28 ookinetes have also been found deep within the midgut epithelium [ 27 , 45 ]. Initially, these parasites were suggested to be retarded in their transit through the midgut epithelium and killed by the epithelial cell defence reactions triggered by wild-type parasites [ 27 ]. Recently, however, Dko P25/P28 parasites were observed apparently deep within the midgut epithelium between morphologically normal midgut epithelial cells [ 45 ]. These midgut epithelial cells did not exhibit the abnormal characteristics typically associated with invasion by wild-type ookinetes, such as protrusion into the midgut lumen and up-regulation of SRPN10 [ 44 , 45 , 48 ]. Consequently, these Dko P25/P28 parasites were proposed to be migrating through the midgut epithelium via a solely intercellular route [ 45 ]. However, a recently published model of ookinete invasion across the mosquito midgut epithelium [ 51 ] suggests an alternative interpretation for the previously published observations of Dko P25/P28 parasites. Presentation of the hypothesis A unified model of the route of ookinete invasion across the mosquito midgut epithelium The route of ookinete migration across the midgut epithelium of the mosquito vector has long been controversial [ 51 ]. The major argument in the literature has been whether ookinete invasion is either solely intercellular between, or intracellular through, midgut epithelial cells [ 51 ]. Recently, an attempt has been made to unify the apparently conflicting literature and integrate it with other recent observations [ 44 , 47 , 52 ] in order to provide a single general model of the route of ookinete invasion across the midgut epithelium applicable to diverse malaria parasite and mosquito vector species [ 51 ]. Subsequent observations of ookinete invasion of the midgut epithelium in vivo support this model [ 48 ]. According to the model, ookinete entry into the midgut epithelium is initially intracellular, occurring through the lateral apical plasma membrane of midgut epithelial cells (Figure 1 ) [ 47 , 51 , 52 ]. Significantly, ookinete entry into midgut epithelial cells is often associated with substantial local invagination of the midgut epithelium [ 52 ], an observation supported by re-interpretation of previously published images (Figure 2 in Ref. [ 19 ] and Figure 5 in Ref. [ 53 ]). Ookinetes pass intracellularly through one or more midgut epithelial cells, causing significant damage similar to that described for wild-type P. berghei ookinetes [ 44 - 48 , 51 , 52 , 54 , 55 ]. Subsequently, ookinetes exit invaded epithelial cells into the basolateral extracellular space between adjacent midgut epithelial cells [ 48 , 52 , 56 ], migrate intercellularly to the basal surface of the midgut epithelium and transform into oocyst stage parasites [ 51 ]. Figure 1 A general model of ookinete entry into the mosquito midgut epithelium. (a) Ookinetes (shown in green) enter the apical surface of the midgut epithelium, through the microvillar brush border (MV), where the lateral membranes (LM) of adjacent epithelial cells (EC) converge [47,51,52]. (b-c) Ookinete movement into the midgut epithelium causes substantial localized invagination of the latter (indicated by small blue arrows) [52,57]. (d) Ookinetes subsequently enter midgut epithelial cells through the lateral apical membrane immediately adjacent to the site where the intercellular junctions (IJ) begin [47,51,52]. (e) The ookinete proceeds intracellularly towards the basal membrane (BM) of the invaded midgut epithelial cell which exhibits morphological abnormalities including protrusion (indicated by large black arrow) into the midgut lumen (LUM) [44–48,52,54,55]. Figure 2 Dko P25/P28 P. berghei ookinete invasion of the midgut epithelium. The unified model of the route of ookinete invasion across the mosquito midgut epithelium (Figure 1) [51] implies that there are two sub-populations of Dko P25/28 parasites: (1) a major sub-population of Dko P25/28 ookinetes (shown in green) unable to penetrate midgut epithelial cells, which remain extracellular within the midgut lumen, embedded against the invaginated apical surface of the midgut epithelium (indicated by small blue arrow); and (2) a minor sub-population of Dko P25/28 ookinetes able to penetrate midgut epithelial cells, causing activation of mosquito immune responses and protrusion of invaded midgut cells, in a manner similar to wild-type parasites. Whether the latter parasites migrate through multiple adjacent midgut epithelial cells (as shown) is uncertain. Significance of the unified model for understanding Dko P25/P28 P. berghei ookinete invasion Following the model of ookinete invasion of the midgut epithelium outlined above (and Figure 1 ), two hypotheses about Dko P25/P28 P. berghei parasite infection of the mosquito vector are proposed. First, some Dko P25/P28 ookinetes invaginate, but are unable to penetrate, the apical surface of the midgut epithelium. Second, other Dko P25/P28 parasites are able to successfully enter and migrate across the midgut epithelium via an intracellular route, in a manner similar to wild-type parasites. Testing the hypothesis Re-interpretation of previously published observations of Dko P25/P25 P. berghei parasites If the unified model of ookinete invasion is correct, the Dko P25/P28 P. berghei ookinetes observed deep within the midgut epithelium between morphologically normal midgut epithelial cells are actually extracellular parasites, outside the midgut epithelium and within the midgut lumen, attempting to enter the lateral apical membrane of midgut epithelial cells. The significant invagination of the midgut epithelium that occurs during parasite entry into midgut epithelial cells creates the appearance that these ookinetes are in intercellular locations within the midgut epithelium. This would be similar to the phenotype recently reported for P. berghei ookinetes in which the maop gene has been knocked out [ 57 ]. Ookinetes lacking MAOP are unable to rupture the apical plasma membrane of midgut epithelial cells [ 57 ]. Consequently, although MAOP-deficient ookinetes invaginate the midgut epithelium, these parasites are unable to enter into midgut epithelial cells and remain extracellular embedded against the apical surface of the midgut epithelium [ 57 ]. The actual extracellular location of Dko P25/P28 ookinetes apparently "within" the midgut epithelium is also suggested by the presence of unmelanized parasites in a refractory line of Anopheles gambiae mosquitoes [ 27 ]. Unmelanized parasites were observed apparently deep within the midgut epithelium exhibiting an abnormal gelatinous appearance suggested to result from exposure to either epithelial cell defence reactions or an early stage of the melanisation reaction [ 27 ]. However, as mentioned above, most Dko P25/P28 parasites do not appear to induce the epithelial cell defence reactions triggered by invading wild-type parasites [ 45 ]. Furthermore, the refractory An. gambiae line melanises wild-type parasites after their passage through midgut epithelial cells into the basolateral extracellular space between adjacent midgut epithelial cells [ 55 , 58 , 59 ]. Consequently, an alternative interpretation is that Dko P25/P28 ookinetes are unmelanized because of their extracellular location against the apical surface of the midgut epithelium, which fails to expose them to either epithelial cell or melanisation immune responses triggered by wild-type parasites. The gelatinous appearance of unmelanized parasites could be explained by prolonged exposure of ookinetes delayed in the process of midgut epithelium entry to the environment of the midgut lumen; for example, prolonged exposure to the mosquito digestive proteases secreted into the midgut lumen. Dko P25/P28 parasites have been shown to be significantly more susceptible to protease digestion in vitro than wild-type parasites [ 27 ]. However, there is also evidence that some Dko P25/P28 ookinetes do enter the midgut epithelium. A minority of Dko P25/P28 ookinetes are found within midgut epithelial cells, which exhibit the re-distribution and up-regulation of SRPN10 associated with invasion by wild-type parasites [ 45 ]. Some Dko P25/P28 parasites are also melanized in the refractory An. gambiae line [ 27 ] implying entry into and passage through midgut epithelial cells to the basal surface of the midgut epithelium. Further, Dko P25/P28 parasites induce transcriptional up-regulation of mosquito immune response genes, defensin and GNBP, associated with midgut invasion by wild-type parasites [ 27 ]. These immune response genes are not induced by transgenic ctrp -disrupted P. berghei parasites that are unable to invade midgut epithelial cells [ 27 , 60 ]. Again, this implies that at least some Dko P25/P28 parasites successfully invade the midgut epithelium and trigger mosquito immune responses. Experimentally testable predictions of our interpretation There are several experimentally testable predictions that follow from the alternative interpretation for the previous observations of Dko P25/P28 P. berghei ookinete invasion of the midgut epithelium outlined above. First, all melanized Dko P25/P28 parasites in the refractory An. gambiae line should be associated with morphologically abnormal midgut epithelial cells – cells through which these parasites have migrated intracellularly – exhibiting protrusion into the midgut lumen, and up-regulation of NOS and SRPN10. In contrast, unmelanized parasites should not be associated with any morphologically abnormal midgut epithelial cells, as these parasites have failed to enter the midgut epithelium and invade midgut epithelial cells. Unmelanized parasites are, however, expected to reside deep "within" the midgut epithelium in apparently intercellular locations between morphologically normal midgut epithelial cells (assuming that ookinetes on the apical surface of the midgut epithelium cannot be melanized). If Dko P25/P28 ookinetes do migrate across the midgut epithelium via a solely intercellular route there is no known reason why these parasites should not also be melanized in the basal region of the midgut epithelium. Consequently, if solely intercellular migration does occur melanized parasites should be found that are not associated with any morphologically abnormal midgut epithelial cells. Second, there should be a quantitative relationship between the density of Dko P25/P28 ookinetes associated with morphologically abnormal midgut epithelial cells and the number of oocysts that subsequently develop on the basal surface of the midgut epithelium. Specifically, the number of oocyst stage parasites should be equal to or less than the number of Dko P25/P28 ookinetes associated with morphologically abnormal midgut epithelial cells, as only ookinetes migrating intracellularly are predicted to become oocysts. The Dko P25/P28 ookinetes located between morphologically normal midgut epithelial cells are not expected to transform into oocysts, as these parasites do not enter, and hence cross, the midgut epithelium. The number of ookinetes apparently migrating via a solely intercellular route greatly exceeds the number of intracellular ookinetes [ 45 ]. Consequently, if Dko P25/P28 ookinetes do migrate across the midgut epithelium via a solely intercellular route, the number of oocysts should greatly exceed the number of ookinetes migrating via an intracellular route (i.e. those associated with morphologically abnormal midgut epithelial cells). Implications of the hypothesis The re-interpretation presented here of previously published work on Dko P25/P28 P. berghei parasites implies that there are two sub-populations of Dko P25/P28 ookinetes, neither of which migrate across the midgut epithelium via a solely intercellular route (Figure 2 ). A major sub-population of Dko P25/28 ookinetes is unable to penetrate into midgut epithelial cells and remains extracellular within the midgut lumen, outside but embedded against the invaginated apical surface of the midgut epithelium. Consequently, these parasites appear to be in intercellular locations deep within the midgut epithelium, between the lateral membranes of adjacent midgut epithelial cells. It is proposed that these parasites fail to elicit mosquito immune responses triggered by intracellularly invading parasites, are not melanized in refractory An. gambiae and do not give rise to oocyst parasite stages. These parasites remain surrounded by morphologically normal midgut epithelial cells, which do not exhibit the morphological abnormalities associated with parasites invading intracellularly [ 45 ]. A second minor sub-population of Dko P25/28 ookinetes is able to penetrate into midgut epithelial cells, in a manner similar to wild-type parasites. These parasites are proposed to elicit mosquito immune responses, including up-regulation of defensin [ 27 ], GNBP [ 27 ], NOS and SRPN10 [ 45 ], undergo melanization in refractory An. gambiae [ 27 ], and form the few oocysts observed in Dko P25/P28 infections [ 27 ]. Accordingly, the latter parasite sub-population should be associated with midgut epithelial cells exhibiting morphological abnormalities associated with intracellular invasion by wild-type parasites [ 45 ]. However, if loss of P25 and/or P28 prevents entry into midgut epithelial cells, intracellular movement between multiple adjacent epithelial cells may also be inhibited in Dko P25/P28 parasites. The reason for the existence of the two distinct sub-populations of Dko P25/P28 P. berghei ookinetes is unknown. One explanation is that loss of P25 and/or P28 impedes, but does not entirely prevent, penetration of the apical plasma membrane of midgut epithelial cells. Consequently, the entry of Dko P25/P28 ookinetes into the midgut epithelium may be protracted, prolonging the period of exposure to the hostile environment of the midgut lumen, which results in the death of most parasites before completion of midgut epithelial cell penetration. This interpretation is consistent with the observation of lysed Dko P25/P28 parasites on the luminal side of the midgut epithelium [ 45 ]. In summary, the unified model of the route of ookinete invasion across the mosquito midgut epithelium suggests a novel, previously unrecognized, function for P25 and/or P28 in mediating ookinete entry into the midgut epithelium. Specifically, the interpretation presented implies that the loss of P25 and/or P28 greatly impairs, but does not entirely abolish, ookinete entry into midgut epithelial cells and probably has relatively little effect on the ability of ookinetes to traverse through the cytoplasm of midgut epithelial cells. A role for P28 in parasite entry into the midgut epithelium is suggested by the deposition of this molecule at the site of ookinete penetration into midgut epithelial cells [ 44 , 45 ]. This interpretation contrasts with the original studies of Dko P25/P28 parasites, which concluded that P25 and P28 do not play a critical role in recognition, attachment or penetration of the luminal surface of the mosquito midgut epithelium [ 26 , 27 ] and suggests that one mode of action of transmission-blocking antibodies to these ookinete surface proteins is to inhibit parasite entry into midgut epithelial cells, as previously hypothesized [ 8 ]. List of Abbreviations CTRP = circumsporozoite and thrombospondin-related anonymous protein-related protein; Dko P25/P28 = double knockout of P25 and P28; GNBP = gram-negative binding protein; MAOP = membrane-attack ookinete protein; NOS = nitric oxide synthase; Sko P25/P28 = single knockout of either P25 or P28; SRPN10 = serine protease inhibitor 10. Authors' contributions LAB wrote the manuscript and prepared the figures. LRC revised the manuscript. Both authors read and approved the final version of the manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555762.xml

423145

Choices: The Science of Bela Julesz

Highlights of Bela Julesz's scientific career in visual neuroscience

Throughout his career, Bela Julesz created new scientific disciplines by remarkable combinations of seemingly disparate approaches. The selection of his major discipline, which would eventually be called visual neuroscience, may have been serendipity or choice. When the unexpected Soviet invasion of Hungary in 1956 spurred his emigration to the United States, Bela Julesz, with his Hungarian doctorate in engineering, joined the numerous mathematical luminaries working at AT&T Bell Laboratories, such as John Tukey, Harry Nyquist, Claude Shannon, and John Kelly. One of the projects underway at the time was the creation of long random-number binary sequences that did not repeat. Bela told the story that he was assigned the problem of testing these number generators; he decided to use the best pattern recognizer that he knew of—the human visual system. The random bits of zeros and ones drawn from the random number sequences were plotted as sequential rows in an image. Any repeats, any correlations across space, would be instantly seen by the human visual system as patterns in the random dots. What caused Bela to choose this unusual approach to looking for patterns, combining computers and vision? His doctoral thesis research in network theory and television signals clearly influenced him, but it was quintessential Bela to give himself a hand up into a new field by building on his base of knowledge, moving in a new and unexpected direction using mathematical and psychological insight. He termed this talent “scientific bilingualism” ( Julesz 1994 ). Bela Julesz, in front of a picture from his and A. Michael Noll's computer art exhibition, “Computer-Generated Pictures,” held at the Howard Wise Gallery, New York City, in 1965. Photograph courtesy of Rutgers University This success in exploiting the visual system, and the intellectual freedom intrinsic to the design of Bell Labs, provided Bela with the opportunity to use these new random dot patterns to explore the visual system. Most of us know well that we can use the small differences in the images in each eye to see depth. Sir Charles Wheatstone showed in 1838 that if two different perspective images were observed through a stereoscope so that each eye observed only one view, a startlingly realistic three-dimensional image occurred. Oliver Wendell Holmes, stereoscope enthusiast, wrote of the experience that “the shutting out of surrounding objects, and the concentration of the whole attention, which is a consequence of this, produce a dreamlike exaltation…in which we seem to leave the body behind us and sail away into one strange scene after another, like disembodied spirits” ( Holmes 1861 ). The basis of this three-dimensional perception was hotly debated between Wheatstone and fellow physicist Sir David Brewster. (Though it may seem odd for physicists to concern themselves with the physiology of optics, this was felt to be a natural extension of the study of the physics of optics.) Brewster opined that perspective was the source of the apprehension of an object's shape. Wheatstone insisted that the images in the each eye had identifiable landmarks that were combined to assign depth to the landmarks. Bela read much of the literature of that time, and he must have seen two greats as wrestling without either finding the overwhelming hold to pin down the other. More than one hundred twenty years after Brewster and Wheatstone, Bela realized that his random dot patterns could be used to probe this question. What Bela did was create a pair of identical random dot patterns. When viewed binocularly through a stereoscope (i.e., fused), they would be seen as a single surface. Then Bela took a central region from the right random dot pattern and displaced it minutely to the right. Now when the two patterns were fused, the central square was not seen double, but after a moment or two, eerily moved into depth, behind the surrounding region. In 1960, Bela's experiment with what eventually became known as Julesz random dot stereograms unambiguously demonstrated that stereoscopic depth could be computed in the absence of any identifiable objects, in the absence of any perspective, in the absence of any cues available to either eye alone. It was a perfect combination of psychological and mathematical insight and technology that solved this puzzle. (It is an interesting aside that Bela sent his first report to the Journal of the Optical Society of America , where it was rejected; the Bell Labs Technical Journal holds the now classic paper [ Julesz 1960 ]. The Journal of the Optical Society of America published Bela's second paper [ Julesz 1963 ].) The stereoscope had existed 125 years. Bela proposed in his book Foundations of Cyclopean Perception (1971) that early in the vision process the two images from the two eyes were combined to form a single view, imbued with inherent depth information. The perceptual “cyclops within us” was proposed to analyze the visual world first, before the motion, color, and contrast systems began their perceptual operations. Bela's book is full of powerful visual experiments that make this point irrefragably; from his psychophysical analysis, binocular vision forces unexpected constraints on the rest of vision, Q.E.D. Foundations of Cyclopean Perception is still considered one of the classics of modern psychophysics and continues to have profound relevance to both those entering the field and established investigators—over thirty years after its publication. At the time of his death, Bela had begun working on a second edition. His success in determining the sequence of visual processing using random dot stereograms led Bela to propose that the anatomical hierarchy of the visual system could be understood in part through visual psychophysics—he termed this approach “psychoanatomy.” His ingenious use of the stereogram established a new approach in the field of vision research and presaged the now common use of carefully controlled computational techniques in brain science. By this time Bela's reputation was established, and in 1983, he received a prestigious MacArthur Fellowship—the “genius award.” He used the funds for travel, including an annual peregrination to the California Institute of Technology, where I first met Bela in 1985. His seminars and lecture courses were enthusiastically received and endorsed by countless students, post-doctoral trainees, and faculty, as evidenced both by his formidable reputation and through the numerous citations of his work. His approach to presenting his research was modest and gently self-deprecating. He always encouraged young scientists; his joy and passion in their science were transmitted both through his warm persona and his suggestions of directions for future study. His insights guided my development of random dot kinematograms (i.e., movies) to examine how motion could be used to construct three-dimensional form ( Siegel and Andersen 1988 ). He collaborated with Derek Fender, David Van Essen, and John Allman at the California Institute of Technology on the combination of the computer, the psychophysical approach, and the physiological experiment. Bela was a fount of ideas, each building on the prior's advance. His later passions were explorations of texture and attention, notably with Jonathan Victor and Dov Sagi. Bela's appealing hypothesis that textons (putative elements of textures) are represented at a cellular level is now questionable ( Julesz et al. 1978 ). Bela was groping for an overarching computational theory for the representation of random geometry, but none was to be had. Nonetheless, the texton elements served useful duty in the demonstration that there were two stages to early vision—an effortless phase preceding attention and a guided identification phase ( Sagi and Julesz 1985 ). Many contemporary laboratories examining vision, studying either perception or the activity of neurons, now incorporate designed, complicated, yet highly controlled stimuli that have evolved (knowingly or not) from Bela's original forays in the 1960s and 1970s. His continuing impact was recognized by his election to the National Academy of Science in 1987. In 1989, Bela retired from Bell Labs (by then he was a department head) and joined the Department of Psychology at Rutgers University to establish the Laboratory of Vision Research. Bela continued investigating mechanisms of form, texture, and stereopsis; his presence led to numerous studies into the implications of his original findings as well as new investigations into computational vision. His collaborations greatly aided the establishment of neuroscience at Rutgers. Bela wrote Dialogues on Perception (1995), a wide-ranging intellectual effort, in which he uses classic dialectics to question both his own successes and those of his chosen field. In the book one reads of two competing intellects, a Bela who believes in his contributions to science and another Bela who is constantly belittling and judging his contributions. Throughout his career Bela Julesz was able to add language after language to his research imperative, becoming a true scientific polyglot. Although his arrival in the United States was propelled by political events beyond his control, his intellectual directions followed a chosen path “less traveled by, and that has made all the difference.” In 1956, an engineer set out from Hungary. By 2003, his unique combination of mathematical precision combined with deep biological insight had carried him to elegant solutions for seemingly intractable problems in visual neuroscience. Bela was always in dialogue, often with others, and often with himself. In the process, he would gently drive each of us, and himself, forward to our final destination of understanding the brain. Bela Julesz died on December 31, 2003, forty-seven years to the day after starting at Bell Laboratories.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC423145.xml

423151

Everything You Always Wanted to Know about Sexes

What defines a sex? Although we tend to think there are only two - males and females - there are many different ways to mix and match the attributes of sexes

From a human perspective, sexes seem a relatively simple thing to get one's head around—there are females, and there are males. But our perspective seems biased and narrow when applied to life as a whole, says evolutionary biologist Laurence Hurst of the University of Bath, United Kingdom.“If you were a single-celled alga sitting in a pond, you wouldn't see the world as splitting into males and females.” In fact, different species have evolved a bewildering number of ways to mix and match the attributes of sexes. Some do not have males and females, but have adaptations that mean each individual performs a specific role during sex. There are other species of which every member is sexually equivalent, but individuals nevertheless divide into groups for the purposes of mating. And in some species, individuals make both eggs and sperm ( Box 1 ). This biological diversity has produced a semantic muddle among biologists—everyone who thinks about the evolution of sexes seems to have a slightly different take on what a sex is. “The literature is highly confusing—we need to clarify our terminology,” comments Rolf Hoekstra, a geneticist at the University of Waageningen in the Netherlands. As things stand, there are three main aspects to the definition of a sex: who you are, who you can mate with, and who your parents are. The third part of this trinity—parental number—shows the least variation in nature. No known organism needs more than one mother and one father. But even this assumption is now starting to break down at the level of biological systems. In a recently discovered hybrid system within the harvester ant genus Pogonomyrmex , queens must mate with two types of males to produce both reproductive individuals and workers ( Figure 3 ). These ants are the first species known which truly has more than two sexes—with colonies effectively having three parents— argues Joel Parker of the University of Lausanne, Switzerland. Figure 3 An Ant with Three Sexes? (A) Two males from the harvester ant genus Pogonomyrmex , one from each genetic strain. In a recently discovered hybrid system, queens must mate with both types of males to produce reproductives and workers. Photo courtesy of Charles Hedgcock, Charles Hedgcock Photography, Tucson, Arizona, United States. (B) Hybrid workers emerging from a nest. Photo courtesy of Veronica Volny, University of California, Berkeley, California, United States. Parker's ideas might reactivate evolutionary biologists' interest in sexes, which has lain somewhat dormant since the 1990s. It could also provide a new route to experiments— something often lacking in the field. Not everyone agrees that it makes sense to define the ants' genetic quirks as new sexes. Each ant is still only a mix the genes from no more than two parents, after all. But Parker believes that our current ideas about mating systems may not be adequate to describe the ingenuity of evolution. “Until you see a three-sex system, you don't know what it'll look like,” he says. Little and Large To address whether these ants have more than two sexes, we first need to look at other candidates for sexes, their numbers in different species, and how these systems evolved. One thing biologists do agree on is that males and females count as different sexes. And they also agree that the main difference between the two is gamete size: males make lots of small gametes—sperm in animals, pollen in plants—and females produce a few big eggs. But researchers also think that before males and females evolved, sex occurred between organisms with equal-sized gametes, a state called isogamy. Evolutionarily speaking, an isogamous species faces two pressures. Individuals can make more smaller gametes, thus increasing their potential number of offspring, or they can make fewer bigger gametes, thus giving their offspring a better start in life by providing them with more resources. Theoretical analyses suggest that this pressure is particularly great if being big carries large benefits, making isogamy unstable. The original identical gametes will evolve towards the opposite ends of the size spectrum. In many species, however, one size of gamete still fits all. The organisms that have hung on to isogamy are found among the less complex branches of life, such as fungi, algae, and protozoa. This might be because large gametes, yielding well-funded zygotes, are likely to be more strongly selected if the resulting offspring needs to grow into a large and complex organism. The benefits of large gametes in simple and unicellular organisms are not so obvious. Some support for this hypothesis comes from the algae belonging to the group Volvocales. The variation in gamete size within each species matches its degree of complexity. For example, the unicellular species Chlamydomonas rheinhardtii is isogamous, while Volvox rouseletti , which lives in balls of up to 50,000 cells, has large and small gametes ( Figure 4 ). Figure 4 Four Different Species of Volvocales Algae (A) Gonium pectorale , (B) Eudorina elegans , (C) Pleodorina californica , and (D) Volvox carteri . These are unicellular organisms that live in colonies and have both large and small gametes. Photo courtesy of Aurora M. Nedelcu, from the Volvocales Information Project ( http:\\www.unbf.ca\vip\index.htm) . The Opposite of Sexes? The question of sexes, and their number, is complex in isogamous species. Such species still typically comprise different groups for mating purposes. They have genes that allow them to mate with everyone except those belonging to the same “mating type” (this is presumably to avoid inbreeding and to produce offspring that are genetically diverse to cope with environmental change or biological enemies). Species with mating types, rather than males and females, aren't limited to two interbreeding groups: the ciliate protozoan Tetrahymena thermophila has seven, and the mushroom Schizophyllum commune has more than 28,000, for example. Some biologists call these mating types sexes; others think that, in the absence of traits other than sexual compatibility or the lack thereof, it makes more sense to view species with many mating types as having no sexes, rather than lots. Yet most isogamous species have only two mating types. This seems perverse—it excludes half the population as potential mates without gaining the benefits of specialization in sexual biology. With William Hamilton, Hurst came up an explanation for this apparent inefficiency. Two-group mating systems, they proposed, evolved as a way for genes in the nucleus to police the DNA in organelles. Cellular structures with their own genomes, such as mitochondria and chloroplasts, can divide more rapidly than the cells that house them. If the inheritance of organelles was biparental, selfish mutations in their DNA could spread rapidly, Hurst and Hamilton showed. A nuclear gene that enforces uniparental inheritance of organelles, along with a label that allows such cells to recognize each other so that their nuclear genes can share the benefits of cytoplasmic policing, should be favored. The mating biology of isogamous species offers considerable support for this idea. The aforementioned C. rheinhardtii , for example, comes in two mating types called plus and minus. When the two fuse, the plastid of the minus cell is detroyed. Most isogamous species that fuse cells have a similar mechanism. Male-killer parasites such as Wolbachia , a parasite of arthropods, show the selection pressure that intracellular passengers can exert (see also the primer by Wernegreen in the March issue of PLoS Biology ). And cellfusion experiments hint that biparental inheritance of organelles does indeed cause problems, says Hurst. “Hybrids are often rubbish, but they can be better if a drug is administered that inhibits the mitochondria of one cell line.” The species that have lots of mating types, such as ciliate protozoa, exchange nuclear DNA, but not cytoplasm, and hence not intracellular organelles. Since individuals are freed from the need to police their organelles or keep out parasites, selection favors the widest assortment of possible mates, and thus the evolution of a large number of mating types so that one's own type—which one can't mate with—is a small subset of the population. It is possible to imagine species with cytoplasmic policing likewise having many mating types, but such a situation would be much more prone to break down and be invaded by selfish agents than one with two clearly defined types, which is what we usually see in nature. Some have argued that cytoplasmic policing might also be a selective force for different-sized gametes. Sperm could be small so that they do not import mitochondria into the egg. More than a decade after he devised it, Hurst's is still the leading hypothesis explaining the number of mating types in a species. But experimental evidence remains frustratingly elusive. “I wouldn't say I was entirely satisfied,” says Hurst. “We've got all these ideas, and they turn out to be quite hard to test—there's no simple thing one can do on a single species.” There are species where the uniparental inheritance of organelles is not so strictly enforced, says Hoekstra, such as yeasts and plants. “It's not easy to see if selection [on organelles] is strong enough,” he says. Three's Company Yet even in a species such as S. commune , with its thousands of mating types, each sexual encounter involves only two cells. Nor are we likely to find a species that defies this pattern. The technical difficulties of combining more than two sets of genetic information into one individual, and of parceling out that information during meiosis, must be vast, says Brian Charlesworth of the University of Edinburgh. “We've reached the point of two cells fusing, and stuck with that; two cells are probably just as good as three,” he says. The ant colonies that Parker suggests have three parents are a hybrid of the species Pogonomyrmex rugosus and P. barbatus . The hybrids have not yet been classed as a new species, but they are well established across the southwestern United States, and there is no evidence of contemporary gene flow between hybrids and their parent species. Each ant has one parent if it is male, because male ants are produced from unfertilized eggs, or two if it is female. But each sex also comes in two genetic strains. If a queen mates with a male of her own strain, her offspring will be queens, and if she mates with a male from the other strain, the sperm will give rise to workers. So, for a colony to function fully it—and the queens it produces, because workers raise queens—must have two fathers and one mother. And if any one group were to disappear, the population as a whole would go extinct—unlike fungal mating types, where it's easy to imagine that the species would carry on if a few disappeared. “If you lose any one, the whole thing collapses,” says Parker. “It's really different from any other system.” So, Parker argues, Pogonomyrmex has four sexes: the males and females of each strain. The idea is particularly potent if one views a social insect colony as a “superorganism,” with the workers equivalent to the cells of a body. It's as if a female mates with one male to produce her offspring's somatic cells, and another to produce its germ cells. The ants form chaotic mating swarms, so most queens have no problem mating multiply and getting sperm from males of both strains, although one would expect that males would strongly favor mating with females of their own strain. It's not known how the system originated. Separating the worker and reproductive castes by genetics—other social insects do this by environment, that is, by rearing workers and reproductives differently—may allow selection to operate more efficiently on each lineage, and the workers may benefit from hybrid vigor: field researchers report them as being highly aggressive. In an echo of Hurst's hypothesis, the system also mixes mitochondrial and nuclear genes differently in queens and workers. Some evolutionary biologists, such as Charlesworth, do not consider Pogonomyrmex ' s mating types sexes, arguing that to define sexes in yet another way only confuses the picture further. “[The ants] are an interesting system, but I wasn't persuaded by Parker's interpretation,” Charlesworth says. “I'm not a fan of the idea that it's useful to use the word ‘sex’ to describe compatibility between mating types—it muddies the waters.” Others are more positive towards Parker's interpretation: “It deserves to be taken seriously,” says evolutionary biologist Eörs Szathmáry of the Collegium Budapest in Hungary. “He's thrown a stone in the water—now we need to see what kinds of ripples it makes. You can't falsify a definition in the way you can a hypothesis; what determines their fate is whether people find them useful or not.” Species in which some individuals give up their reproductive opportunities to form part of a breeding group, such as slime molds, might have a system similar to that of the ants, Parker believes. “There may be hidden mating incompatibilities,” he says. “Now [that] people know to look, we're going to start seeing more of these systems.” Figure 1 Two Individuals of Pseudobiceros bedfordi About to Have a Sperm Battle Species of the flatworm genus Pseudobiceros are hermaphroditic and have two penises that are used to inject sperm into the partner. P. bedfordi is exceptional in that it applies sperm onto the partner's skin rather than injecting it. Photo courtesy of Nico Michiels. Figure 2 Scars of Sex (A) Streaks of sperm (St) received after a mating interaction in the hermaphroditic flatworm, Pseudobiceros bedfordi . (B) Received sperm appears to “burn” holes (H) in the receiver. Some (unknown) component of the ejaculate dissolves the skin tissue. Sc, scar tissue. (C) Exceptional case where an individual received a large amount of sperm somewhere in the middle of the body, resulting in a large hole (asterisk). The the body subsequently tore in two. Individuals like these are occasionally found in the field and can regenerate much of their body. Photo courtesy of Nico Michiels. Box 1. The Best of Both Worlds? One option for dividing up the sexes is “both”—hermaphroditism. This might seem like an ideal solution—everyone becomes a potential partner, and everyone can bear offspring. In practice, however, hermaphroditism is uncommon among multicellular animals. The reasons are similar to those explaining why evolution favors unequal-sized gametes—once sexes have evolved, it's better to commit all one's resources to one role or the other, rather than try and be a jack-of-all-trades. After all, there are many good uses for mating resources other than simply producing eggs or sperm. An animal could defend a territory or provide parental care, for example. Hermaphroditism, however, is useful if one's sexual options are severely limited. In particular, it can be favored when encounters with potential mates are extremely rare. It makes no sense for an animal to invest heavily in the biological equipment of maleness, say, if it will have almost no opportunities to use it: better to hedge your bets. Animals with low or unpredictable population densities and those that are immobile, have poor senses, or lack long-distance signalling are often hermaphroditic. These include sponges, worms—whether flat, nematode, or annelid—and many molluscs (and, of course, plants, the majority of which are hermaphroditic). Most hermaphrodites still need to find at least one mate in their lifetimes: the cost of inbreeding prevents self fertilization from becoming common. Hermaphroditic animals have some weird sexual adaptations. Helix aspersa snails shoot calcareous love darts into one another. And when the marine flatworm Pseudobiceros bedfordi mates, each worm has two penises, which they fence with in a battle to smear one another with sperm without being fertilized themselves in the process ( Figures 1 and 2 ). Such oddities result when the mating opportunities of a hermaphroditic species increase, and specialization starts to become more favorable, says evolutionary biologist Nico Michiels, of the University of Muenster in Germany. In a species with two separate sexes, males and females often have different ideas about whether a mating is a good idea—males tend to be keener, females tend to be choosier. The result can be an evolutionary arms race, with each sex evolving adaptations that help them get their way. By exercising mate choice, each sex has some influence on the types of adaptations that evolve—anything too outlandish is unlikely to be favored. This counterbalance is not present in hermaphrodites, however. Rather than having one half of a species resist a particular mating strategy, the whole species is just as likely to adopt it. “Hermaphrodites run into awkward and bizarre mating conflicts,” says Michiels. Michiels believes that hermaphroditism was the ancestral state for animals, and thinks that we might be able to find the relics of this past in contemporary species with separate sexes. To test these ideas, he is searching for groups containing closely related hermaphroditic and bisexual species. Such taxa are very rare, however.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC423151.xml

514716

The geographic distribution of breast cancer incidence in Massachusetts 1988 to 1997, adjusted for covariates

Background The aims of this study were to determine whether observed geographic variations in breast cancer incidence are random or statistically significant, whether statistically significant excesses are temporary or time-persistent, and whether they can be explained by covariates such as socioeconomic status (SES) or urban/rural status? Results A purely spatial analysis found fourteen geographic areas that deviated significantly from randomness: ten with higher incidence rates than expected, four lower than expected. After covariate adjustment, three of the ten high areas remained statistically significant and one new high area emerged. The space-time analysis identified eleven geographic areas as statistically significant, seven high and four low. After covariate adjustment, four of the seven high areas remained statistically significant and a fifth high area also identified in the purely spatial analysis emerged. Conclusions These analyses identify geographic areas with invasive breast cancer incidence higher or lower than expected, the times of their excess, and whether or not their status is affected when the model is adjusted for risk factors. These surveillance findings can be a sound starting point for the epidemiologist and has the potential of monitoring time trends for cancer control activities.

Background This study is an observational epidemiological investigation of breast cancer incidence in Massachusetts. It examines geographic variations over a ten year period using both purely spatial and space-time models to determine whether observed fluctuations in incidence rates are random or whether fluctuations represent statistically significant deviations from randomness. This study examines whether apparent excesses are stable over time, or are temporary, and also determines whether excesses, high or low, can be accounted for by risk factors such as socioeconomic status (SES) or urban/rural status. This study demonstrates how surveillance data can be analyzed to identify those geographic areas that warrant closer attention, the basis for determining the need for public health action or to aid in assessing the effectiveness of intervention programs [ 1 ]. Massachusetts has been included in studies of inter-region and intra-region variability of breast cancer incidence and mortality in the United States. Several of these studies aggregated breast cancer mortality data to the regional or county level [ 2 - 8 ]. Laden and colleagues studied regional variation using 3603 incident cases among nurses from eleven U.S. states [ 9 ]. They compared California, the Northeast, and the Midwest to the South; no significant excess incidence of breast cancer was observed in the Northeast. However, because regions and counties are large geographic areas, such studies can miss variability at smaller geographic levels, such as tracts within counties. A recent study on the geographic distribution of the proportion of late-stage breast cancer cases diagnosed in Massachusetts females between 1982 and 1986 aggregated cases to town, ZIP Code, and census tract levels [ 10 ]. The town-, ZIP Code-, and census tract-level analyses all identified approximately the same statistically significantly high area in western Massachusetts. The current study examines the incidence of invasive breast cancer with patients diagnosed between 1988 and 1997. Results Principal component analysis The principal components analysis performed on the seven SES variables revealed two components. The two components accounted for about 80% of the variance among the seven economic measures with the loadings of each variable on the two components shown in Table 1 . The first component had high positive loadings from median income, median rent, median house value, and percent with at least a high school diploma. This component explained 49.1% of the variance and will be referred to as wealth. The second component had high positive loadings from the percent unemployed, percent working class, and percent below the poverty level. This second component explains an additional 31.0% of the variance and will be referred to as poverty. Although these components are similar, wealth at one end of a spectrum and poverty at the other, they independently contribute to explain SES. Table 1 SES Index. Rotated Component Matrix from the principal component analysis of socioeconomic status (SES) variables. Component SES Variables Wealth SES Poverty SES Percent Unemployed b 0.099 0.932 Median Income a 0.895 -0.294 Median Rent a 0.923 -0.037 Median House Value a 0.835 -0.264 Percent Working Class b -0.405 0.671 Percent with a High School Diploma a 0.919 -0.081 Percent Below the Poverty Level b -0.269 0.828 a Represented more in the first component. b Represented more in the second component. Component scores for wealth and poverty were calculated for each tract. The scores were divided into quintiles and used in a Poisson regression to determine their capacity to predict breast cancer incidence. SES variables are scaled so that high wealth scores represent the most wealth and high poverty scores represent the most poverty. Poisson regression The initial Poisson regression of the wealth component showed that category 5, the quintile of the most wealth, had a higher associated risk for breast cancer. There was no increasing or decreasing trend when the fifth category was compared to the other four categories, but they were all lower than the fifth, so categories 1 to 4 were collapsed to create a dichotomous wealth variable. A Poisson regression showed that the highest wealth category had an 8.9% increase in incidence over the combined other categories. The analysis also revealed that breast cancer incidence was inversely related to poverty level, as shown in Table 2 . Those census tracts with the highest poverty levels had an incidence rate 36.9% lower than those in the lowest poverty level; those in the second highest poverty level had an incidence rate 32.2% lower than those in the lowest poverty level, with rates of 29.3% and 19.4% lower for those in categories three and two as compared to those in the lowest poverty level. The analysis also showed that incidence is related to urban/rural status with urban tracts having an incidence rate on average 2.7% higher than rural tracts. Table 2 Wealth and poverty SES. Relative changes in breast cancer incidence associated with 2 levels of wealth and five levels of poverty. For wealth, category 2 represents the highest level of wealth. For poverty, category 5 represents the highest level of poverty. For example, the women in the highest poverty level, 5, had an incidence rate 36.9% lower than those in the lowest poverty level, 1. Categories Compared % Change Wealth SES 1 – 2 +8.9 Poverty SES 1 – 5 -36.9 2 – 5 -32.2 3 – 5 -29.3 4 – 5 -19.4 Purely spatial analysis, adjusted for age The purely spatial analysis ignores the time of diagnosis. Figure 1 summarizes the purely spatial age-adjusted analysis of breast cancer incidence from 1988 to 1997. Fourteen areas have been identified as excessive in their variation, ten significantly higher and four significantly lower than expected under the null hypothesis. The ten areas of high breast cancer incidence are numbered and the four areas of low incidence are lettered in order of significance, with "1" and "A" having the lowest p-values. For each area of high or low incidence shown in Figure 1 , the left hand side of Table 3 contains the relative risks (RR), number of observed cases, and p-value for the purely spatial analysis of the age-adjusted cases. Expected case counts can be calculated from Table 3 by dividing the observed count by the RR. The most statistically significant area of excess, High 1, was found west of Boston. It had 3344 observed cases and a relative risk of 1.15, indicating 15% more cases than would have been expected under the null hypothesis. There are two high areas, labeled "4" and "5", east of High 1. To the west of High 1 is High 7, a single tract with five times more cases than were expected. The second most statistically significant area of excess is High 2 located in the southeast, which includes one tract on Cape Cod, with 2.73 times more cases than expected. High 6 in the northeast shows an elevated risk of 17%. Highs 8 and 9, south and north of High 1, show elevated risks of 15% and 18%, respectively. High 3 in the western part of the state has 2.73 times more cases based on 57 observed and 20.9 expected cases. High 10 in the southeast is elevated by 17%. Figure 1 Purely spatial, age-adjusted. Purely spatial analysis results for age-adjusted Massachusetts female invasive breast cancer incidence, 1988–1997. Table 3 Purely spatial analyses. Age-adjusted female invasive breast cancer statistics for the purely spatial analyses, Massachusetts, 1988–1997. a Relative Risk. b Area covers more geographic area than in the age-adjusted analysis. c Area covers more geographic area than in the analysis adjusted for SES. d Area covers less geographic area than in the age-adjusted analysis. e High 11 is a combination of Highs 2 and 10. *High or low area was not significant for this analysis. Age-adjusted only Adjusted for Urban/Rural Status Area Observed RR a p-value Observed RR a p-value High 1 3344 1.15 <0.0001 3344 1.15 <0.0001 2 94 2.73 <0.0001 94 2.40 <0.0001 3 57 2.73 <0.0001 57 2.72 <0.0001 4 126 1.75 0.0002 126 1.74 0.0007 5 276 1.44 0.0003 276 1.43 0.0008 6 1258 1.17 0.0008 1258 1.17 0.0013 7 16 5.05 0.0054 16 5.03 0.0062 8 1354 1.15 0.0060 1354 1.15 0.0097 9 874 1.18 0.0163 874 1.18 0.0190 10 941 1.17 0.0228 997 1.17 0.0120 11 e * * * * * * Low A 1068 0.74 <0.0001 1068 0.74 <0.0001 B 521 0.77 <0.0001 521 0.76 <0.0001 C 2276 0.89 <0.0001 * * * D 588 0.81 0.0012 * * * E * * * 3600 0.91 0.0004 F * * * * * * Adjusted for SES Adjusted for SES & Urban/Rural Status High 1 * * * * * * 2 * * * * * * 3 57 3.20 <0.0001 57 3.21 <0.0001 4 * * * * * * 5 * * * * * * 6 1418 b 1.16 0.0015 1418 b 1.15 0.0021 7 16 5.93 0.0013 16 5.93 0.0006 8 * * * * * * 9 * * * * * * 10 * * * * * * 11 e 1763 1.21 <0.0001 1994 c 1.21 <0.0001 Low A 498 d 0.73 <0.0001 498 d 0.73 <0.0001 B 528 b 0.82 0.0204 528 b 0.82 0.0226 C 2196 0.91 0.0221 * * * D 588 0.83 0.0243 588 0.83 0.255 E * * * * * * F 15 0.36 0.0335 * * * Four areas had significantly fewer cases than expected for the ten year period. Low A had 1068 observed cases and a RR of 0.74, indicating about 26% fewer cases than expected. Low B is west of Cape Cod with 521 observed cases and a RR of 0.77. The other two low areas, "C" and "D," in western Massachusetts had 11% and 19% fewer cases than expected, respectively. Purely spatial analysis, adjusted for multiple covariates The remainder of Table 3 shows how the results of the purely spatial analysis of age-adjusted incidence rates within tracts are changed by the inclusion of urban/rural status and SES in the model. When urban/rural status is added to the model, low areas "C" and "D" in rural parts of the state are no longer statistically significant. An area including part of Low D and southward, labeled "E" in Table 3 but not shown in the figures, becomes statistically significant with 9% fewer cases than expected. All other previous findings from the age-adjusted purely spatial analysis are unaffected by the adjustment for urban/rural status. When the purely spatial model is adjusted for the two SES variables representing wealth and poverty, seven of the high areas are no longer significant. High areas "3," "6" and "7" remain significant. High 6 has expanded to include more tracts and more cases but the RR is about the same. High areas "2" and "10" have merged into a larger area, High 11. Low A covers fewer tracts compared to the age-adjusted analysis, while Low B includes more tracts. A low area, labeled "F" in Table 3 but not shown in the figures, covering Nantucket appears; it includes 15 cases, 64% fewer cases than expected. As compared to the analysis adjusting for SES alone, when the two SES variables and urban/rural status are all included in the purely spatial model, the results show little change, except that High 11 is slightly larger in geographic area and low areas "C" and "F" are no longer significant. Figure 2 maps the results of the purely spatial analysis adjusted for urban/rural status and the two SES variables. While three of the original ten high areas remain statistically significant, the total geographic area covered by the high areas has been reduced considerably. One of the four original low areas is no longer statistically significant and despite the slight expansion of Low B, the entire geographic area represented by the low areas has also diminished. Figure 2 Purely spatial, multiple adjustments. Purely spatial analysis adjusted for socioeconomic status and urban/rural status Massachusetts female invasive breast cancer incidence, 1988–1997. Space-time analysis The results of the space-time age-adjusted analysis are somewhat similar to the purely spatial analysis in that seven of the ten areas of excess incidence from the purely spatial analysis were also statistically significantly high in the space-time analysis, along with all four of the low areas. However, as shown in the left side of Table 4 , only two of the high areas, "4" and "5," and one of the low areas, Low A, remained statistically significant for the ten-year period. The RRs of these areas were not as elevated in the purely spatial analysis because the space-time analysis captures only the most statistically significantly elevated time periods. However, those areas that were statistically significantly high or low during the whole time period in the space-time analysis had the same RRs in the purely spatial analysis, as they should. Table 4 Space-time analyses. Age-adjusted female invasive breast cancer statistics for space-time analyses of Massachusetts, 1988–1997. a Observed count. b Relative Risk. c Area is shifted east compared to the purely spatial analyses. d Area is significant for 1993–1997 and has dramatically increased geographic area. e Area is significant for 1988–1993 only. f Geographic area increased from the age-adjusted analysis. g Geographic area slightly decreased from the age-adjusted analysis. *High or low area was not significant for this analysis. Age-adjusted only Adjusted for Urban/Rural Status Time Frame Obs a RR b p-value Obs a RR b p-value High 1 91–97 3639 1.16 <0.0001 3639 1.16 <0.0001 3 89–96 51 3.05 <0.0001 51 3.04 0.0002 4 88–97 126 1.75 0.0078 126 1.74 0.0127 5 88–97 276 1.44 0.0091 276 1.43 0.0154 6 92–95 492 1.35 0.0004 492 1.35 0.0007 7 88–95 * * * * * * 8 92–97 841 c 1.23 0.0034 841 c 1.23 0.0085 11 93–97 1178 1.28 <0.0001 1178 1.28 <0.0001 Low A 88–97 1068 0.74 <0.0001 1068 0.74 <0.0001 B 88–94 1119 0.81 <0.0001 1119 0.81 <0.0001 C 88–96 1951 0.86 <0.0001 1951 0.87 0.0002 D 88–95 450 0.77 0.0069 450 0.78 0.0246 Adjusted for SES Adjusted for SES & Urban/Rural Status High 1 91–97 * * * * * * 3 89–96 51 3.58 <0.0001 51 3.59 <0.0001 4 88–97 * * * * * * 5 88–97 * * * * * * 6 92–95 492 1.35 0.0008 492 1.34 0.0005 7 88–95 15 6.95 0.0130 15 6.95 0.0102 8 92–97 2991 d 1.12 0.0009 2991 d 1.12 0.0011 11 93–97 1178 1.29 <0.0001 1178 1.31 <0.0001 Low A 88–97 600 e 0.75 <0.0001 600 e 0.74 <0.0001 B 88–94 1219 f 0.86 0.0303 1219 f 0.86 0.0301 C 88–96 1927 g 0.88 0.0134 * * * D 88–95 * * * * * * Figure 3 displays the results of the space-time age-adjusted analysis. Table 4 shows the time frames, the observed number of cases diagnosed in that time period, the RRs and the p-values. The numbers and letters correspond to approximately the same geographic areas as before. However, while all of the identified areas are still statistically significant at p < .05, they are not necessarily listed in order of statistical significance in Table 4 . Figure 3 Space-time analysis, age-adjusted. Space-time analysis results for age-adjusted Massachusetts female invasive breast cancer incidence, 1988–1997. The two high areas, "4" and "5", around Boston that were statistically significant in the purely spatial analysis are also statistically significantly high for the entire study period. High 1 was elevated 16% from 1991 to 1997. In northeast Massachusetts, High 6 is statistically significant from 1992 to 1995 with a RR of 1.35. Another high area, High 3, from the purely spatial analysis is also statistically significant in the space-time analysis and represents a single tract in western MA. It is significant from 1989 to 1996 with 51 cases diagnosed and a RR of 3.05. High 9 from the purely spatial analysis was not significant in the age-adjusted space-time analysis. High 11, a large area in the southeast, includes most of High 2 and High 10 from the purely spatial analysis plus some additional tracts that had not been statistically significant before. Low A in Boston is statistically significant for the entire 10-year period. Low B, in southeastern MA, and Low C, in western MA, remain statistically significantly low from 1988 to 1994, and 1988 to 1996, respectively. Low D, east of Low C, is also statistically significantly low from 1988 to 1995. Space-time analysis, adjusted for multiple covariates Adjusting the space-time analysis for urban/rural status has little effect, except to change the RR and p-values slightly. When the model includes both the wealth and poverty components as covariates, five areas are found to be excessively high, and one of these, High 7, is new to the space-time analyses. This small area had been identified in the purely spatial analysis and remained statistically significant after adjustment for SES. Low D is no longer statistically significant. When adjusting for SES and urban/rural status together, all the high and low areas from the SES and age-analysis, except for Low C, remain significant. These results are displayed in Figure 4 . Figure 4 Space-time analysis, multiple adjustments. Space-time analysis adjusted for socioeconomic status and urban/rural status Massachusetts female invasive breast cancer incidence 1988–1997. Discussion We examined the possibility of a geocoding bias. Of 46,333 total cases, 4440 were randomly assigned to a census tract located within the reported town. These cases could not be assigned directly to a census tract because their records did not have a residential address. Cancer registries like the MCR collect a patient's usual residence at the time of diagnosis. For patients having only postal box addresses, if the MCR staff were unable to obtain a residential address at the time of diagnosis, we assigned these cases to tracts within the town of the mailing addresses, as described in Methods below. This should not be problematic in larger areas of excess or deficit since error would be confined to a town. In the smaller areas identified by the analyses, all had higher than expected case numbers. We examined the smaller areas of excess to determine how many of the observed cases had been randomly placed into a tract. To estimate how the RRs would be affected, the RRs were re-computed from the purely spatial age-adjusted analysis, while excluding these cases. High areas "2" and "3" did not contain any cases assigned to the tract randomly. In High 4, eight of the 126 cases observed were assigned at random; without these cases, the RR would be 1.63 rather than 1.75. High 5 had 31 of the 276 observed cases assigned randomly; without these cases, the RR would be 1.28 rather than 1.44. Thus, even if none of the randomly assigned cases were assigned to these smaller areas, the relative risks would not be altered substantially. However, MCR has studied High 7 and determined that several large apartment complexes were geocoded as being part of the census tract which makes up High 7 instead of placing it in the correct neighboring tract. Since High 7 does not have many females as part of the population, these geocoding errors may have made a large difference and therefore, this tract is not likely to have high breast cancer incidence. This study adjusted for age, SES, and urban/rural status. Other known risk factors could possibly explain the high areas uncovered. The following attributable risk percentages for such factors have been reported in the literature: 10.7% due to high alcohol intake, 15.0% due to low beta-carotene intake, 8.6% due to low vitamin E intake, 11.6% due to low levels of physical activity [ 11 ], 5.0% due to BRCA1 and BRCA2 mutations [ 12 ], 29.5% due to late age at first birth and nulliparity, 9.1% due to family history of breast cancer [ 13 ], and 2.5% due to smoking [ 14 ]. Patient smoking history data were available but were considered unreliable. These other risk factors were not included in this study since the information was not available in the cancer registry. Also, data on town urban/rural status and the SES variables from the 2000 Decennial Census were not available at the time of the analyses, so we were not able to determine if there were any significant changes over time or how these changes might alter the results. For Massachusetts, we would not expect any significant changes. This study used aggregated data to compensate for the lack of individual level data and is therefore exposed to the ecological fallacy. Krieger has shown that this is not a serious problem [ 15 ]. Also, it would be impossible to perform this study of breast cancer incidence at the point level since no denominator data exist for population counts at the individual address level. Conclusions The current study is part of the surveillance process, an observational epidemiologic investigation to provide reliable statistical modeling of the raw surveillance data so that program evaluation or planning can be focused on those variations in incidence rates with a low likelihood of being random. In the current study it is especially interesting to observe whether geographical areas, high or low, are affected by SES adjustment or not. It is also interesting to observe which geographical areas seem to be high or low for the entire study period, and which are high or low on a temporary basis. Recommendations The first step when studying these high and low areas is to look closely at the geocoded data in these areas to ensure that there were not any geocoding-related errors. An example of a geocoding-related error was High 7. However, this problem should only be an issue in areas that only contain a small number of tracts. Roche et al. have made other suggestions as to what can be done when these areas are found to be truly elevated [ 16 ]. Several areas (Highs "1," "4," "5," and "9" and Low C) are no longer significant with the adjustment for SES and urban/rural status. What is it about the high SES in these areas that affects the RR of these areas? It could be that late age at first birth has a substantial contribution since women of high SES delay childbearing to establish their careers. Note that the p-values and RRs are not correlated and both need to be studied. For ease of comparing statistics, refer to Tables 3 and 4 . High areas "3" and "6," as well as Low areas "A" and "B" do not change a great deal with SES adjustment, so these covariates are not having the same effects as they had above. Perhaps a case-control study could be designed on these areas to determine what other risk factors are driving this elevated incidence rate, such as genetic or behavioral factors. High 6 remained unaffected by adjustment for SES and urban/rural status. However, since it was only temporarily elevated for 4 years, perhaps this area should also be monitored to see if its incidence rate significantly elevates. However, High 11 should be investigated for other factors that may be elevating the risk since it remained elevated for 5 years near the end of the study period. The low areas should be assessed since cases might be going undetected if sufficient screening programs are not in place. If screening programs are in place, they may need to be altered and improved. Finally, it is possible for the populations in two different areas to experience the same amounts of cancer; yet if some cancers remain undetected in one area and are diagnosed in the second area, the second area will appear to have a higher incidence. This is why when breast cancer screening is aggressively promoted in an area with worrisome incidence, the cancer rate goes up for a period of time. Furthermore, not all areas have equal access to the latest in diagnostic equipment capable of detecting the most minute cancers or even pre-cancerous cells. A patient's cancer might be diagnosed if she went to a facility with the latest diagnostic equipment, but might not be diagnosed if she had gone to a different place. Incidence may also reflect not just differences in diagnosis rates, but also differences in treatment patterns. If women or physicians in one area tend to opt for more lumpectomies without radiation, the patients still have a chance to develop later cancers in that breast; if women or physicians in another area tend to opt for lumpectomies with radiation or mastectomies for the same stage of disease, they will not be having any subsequent cancers in that breast [ 17 ]. Methods Data Cases are from the Massachusetts Cancer Registry (MCR), and include all invasive breast cancer cases diagnosed in female residents between 1988 and 1997 (n = 46,333), using the standard definition of invasive breast cancer[ 18 ]. Annual reports on MCR data completeness, methods and other issues can be found at the cited website [ 19 ]. Each case record included the town, ZIP Code, and census tract of the patient's usual residence at the time of diagnosis, as well as the age at diagnosis, date of diagnosis, race, and stage of disease at diagnosis. Of the 46,333 patients, 43,529 were white females, 1071 black, 6 American Indian, Aleutian, or Eskimo, 115 Chinese, 17 Japanese, 129 other Asian and Pacific Islander, 119 other non-white, and 1347 of unknown race. Race was not included as a covariate in this study due to the large number of unknown. Age was included as a covariate and was divided into fourteen 5-year categories starting with age 15 years up through age 84, with all ages 85 years and above as the fifteenth age group. Aggregation unit Census tracts were used to aggregate cases because they are more uniform than towns or Zip Codes in population size, provide a more sensitive analysis of densely populated areas, and are more homogeneous in their resident characteristics. However, 12.6% (n = 5832) of the cases diagnosed in 1988–1997 could not be assigned a reliable residential census tract because of inaccuracies or omissions in the address information provided to the MCR. In most of these cases, a mailing address had been provided and, even after extensive research, MCR staff could not assign a reliable residential address for the patient at the time of diagnosis. To assign the unassigned cases to census tracts, we compared town and census tract boundaries for 1196 four-digit census tracts. About 90 of the census tract codes included 2-digit suffixes to designate 2, 3, 4, or 5 distinct tracts. In this study, only the first four digits of such tract codes were used, thus combining some separate tracts into one. For example, census tracts 6002.01 and 6002.02 were combined into the 4-digit tract 6002. 1990 Census block numbering areas in Massachusetts are treated here as if they were census tracts. Nearly all of the 1196 four-digit 1990 tracts could be related to town boundaries, with one or more tracts located entirely within a town, or one or more towns entirely within a census tract. Two census tracts overlapped parts of multiple towns and for these it was possible to estimate the proportion of the total tract population living in each town. Unassigned cases were then assigned as follows: for towns completely contained in one tract, cases were assigned to that tract, accounting for 1392 cases. For a town containing two or more census tracts, cases were randomly assigned to tracts proportionate to the town's female population, accounting for 4440 cases. The allocation of cases should therefore be free from systematic error and any error should be localized to a particular town, while the broader state patterns remain correct. Statistical analyses The spatial scan statistic [ 20 ] was used to perform purely spatial and space-time analyses. Under the null hypothesis, the incidence of breast cancer follows a Poisson distribution and the probability of a case being diagnosed in a particular location is proportional to the covariate-adjusted population in that location. Tract population data are from the 1990[ 21 ] and 2000 Decennial Censuses [ 22 ]. This population data is an excellent match to the MCR data since both the Census and the MCR ask patients for their "usual residence." Therefore the numerator and denominator are created in the same way. Properties that make the spatial scan statistic suitable for geographic surveillance are: 1) it takes into account the uneven geographic distribution of cases and population densities, 2) it does not make assumptions about cluster size or location, 3) it adjusts for multiple testing – a common problem in testing multiple combinations of cluster locations and sizes, 4) it identifies the spatial or space-time locations where the null hypothesis is rejected, and 5) it can detect multiple clusters [ 10 ]. Purely spatial analyses were performed first, ignoring time. The maximum spatial cluster size was first set to include up to 50% of the population for both excesses and deficits and then set at 10%, to test for excesses and deficits separately because testing at the 10% level can identify smaller, more defined areas. However, each area identified in an analysis had a likelihood associated with it that was compared to the 9,999 likelihoods from the initial 50% maximum spatial size test, thus accounting the inference for a multiplicity of statistical tests. Space-time analyses were then performed to determine whether the clusters from the purely spatial analysis were long term or temporary. The maximum temporal cluster size was set at 90% and also included purely spatial clusters with a temporal size of 100% for all space-time analyses. This allows for the entire time period all the way down to the smallest amount of time to be considered as the time window, ten years down to one year. All SaTScan analyses were performed on age-adjusted population counts. To calculate the age-adjusted expected counts, the 1990 and 2000 female population counts were combined into a weighted average of the two based on the years being analyzed, 1988 through 1997. This was done for each age group within each tract. The natural log of this weighted average was entered as the offset variable in a Poisson regression in SAS [ 23 ]. There were a few tracts with a zero population for a certain age group; for these, a population of one was entered so a log could be taken. The Poisson regression included age as a class variable and the number of cases within each tract and age group as the dependent variable to calculate the age-adjusted expected counts. The expected counts were aggregated across age for all SaTScan analyses. This method was verified by entering age as a covariate in a SaTScan analysis with the same results. Although SaTScan accurately estimates age-adjusted incidence rates, when other covariates enter the model, SaTScan computes the interaction of each of the 15 age categories with each covariate. In the current study, that would mean estimating 14 × 1 × 1 × 4 = 56 interaction terms because three risk factors were used as covariates. By first computing the age-adjusted counts in SAS, the number of interactions estimated by SaTScan is reduced to 8. After identifying statistically significant geographic areas and determining whether they were long term or temporary, the next step was to determine whether these areas would change when the model was adjusted for known risk factors. Socioeconomic factors have long been identified as risk factors for breast cancer [ 9 , 24 , 25 ]. Urban/rural status has also been used as a covariate when studying breast cancer [ 4 , 26 - 29 ]. An SES index was created following the method of Yost et al [ 30 ]. Yost and colleagues included the following variables from the U.S. Decennial Census in a principal component analysis (PCA): percent unemployed, percent working class, percent below the Federal poverty level, median income, median rent, median house value, and percent with at least a high school diploma. We used the same variables from the 1990 Census [ 21 ] in a PCA, described in the results section. A PCA factors the matrix of correlations between all pairs of variables into seven uncorrelated components with the hope that a smaller number of these components will account for 70 to 80% of the variation contained in the matrix of correlations among the original seven variables. Instead of using seven highly correlated SES variables as covariates, PCA can reduce the number of covariates to one or two without loss of information. A PCA was also performed on a similar group of variable from the 1990 Census based on what Krieger [ 24 ] used to create a SES index, however it did not account for as much of the variance as the variables used by Yost [ 30 ]. A variable indicating the urban or rural status of each tract was derived from data available on the Massachusetts Institute for Social and Economic Research (MISER) [ 31 ]. An urban area consists of one or more contiguous census blocks with a population density of at least 1,000 persons per square mile, while all other areas are designated as rural [ 32 ]. We assigned all tracts entirely within a town the same classification as that town, regardless of their individual populations. Tracts that included several towns were classified as rural since all towns within such tracts were also classified as rural. The capacity of SES and urban/rural status to predict incident cases within census tracts was tested in Poisson regression models, using the GENMOD procedure in SAS [ 23 ]. SaTScan [ 33 ] created files listing census tracts and the number of the high and low areas they belonged to. These files were brought into Maptitude [ 34 ] to create the figures. Authors' contributions TJS: PI, responsible for design, funding, of project with overall responsibility for implementing the project, including the final paper. LMD: Principal data analysis, responsible for final checks on accuracy of data and all analyses, including their written interpretation. MK: Co-PI, assisted in the overall design of the project and close collaborator on all analyses. MK also assisted a great deal in editorial decisions of drafts and of the final paper. DG: Co-PI, assisted in the design of the project, including funding and implementation. DG also consulted on editorial and technical issues. SG: Director of MCR, she was responsible for the overall integrity of the data, and ensuring that use of the data corresponded to the policies of the Massachusetts Dept of Public Health. She also assisted with editorial help on all drafts. MM is responsible for the geocoding of the data in the MCR. She was a constant help to this project as we tried multiple approaches to our analyses. MM was also very helpful in clarifying limitations of the work and also provided many editorial suggestions. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514716.xml

514892

C-type lectin-like domains in Fugu rubripes

Background Members of the C-type lectin domain (CTLD) superfamily are metazoan proteins functionally important in glycoprotein metabolism, mechanisms of multicellular integration and immunity. Three genome-level studies on human, C. elegans and D. melanogaster reported previously demonstrated almost complete divergence among invertebrate and mammalian families of CTLD-containing proteins (CTLDcps). Results We have performed an analysis of CTLD family composition in Fugu rubripes using the draft genome sequence. The results show that all but two groups of CTLDcps identified in mammals are also found in fish, and that most of the groups have the same members as in mammals. We failed to detect representatives for CTLD groups V (NK cell receptors) and VII (lithostathine), while the DC-SIGN subgroup of group II is overrepresented in Fugu . Several new CTLD-containing genes, highly conserved between Fugu and human, were discovered using the Fugu genome sequence as a reference, including a CSPG family member and an SCP-domain-containing soluble protein. A distinct group of soluble dual-CTLD proteins has been identified, which may be the first reported CTLDcp group shared by invertebrates and vertebrates. We show that CTLDcp-encoding genes are selectively duplicated in Fugu , in a manner that suggests an ancient large-scale duplication event. We have verified 32 gene structures and predicted 63 new ones, and make our annotations available through a distributed annotation system (DAS) server and their sequences as additional files with this paper. Conclusions The vertebrate CTLDcp family was essentially formed early in vertebrate evolution and is completely different from the invertebrate families. Comparison of fish and mammalian genomes revealed three groups of CTLDcps and several new members of the known groups, which are highly conserved between fish and mammals, but were not identified in the study using only mammalian genomes. Despite limitations of the draft sequence, the Fugu rubripes genome is a powerful instrument for gene discovery and vertebrate evolutionary analysis. The composition of the CTLDcp superfamily in fish and mammals suggests that large-scale duplication events played an important role in the evolution of vertebrates.

Background The superfamily of proteins containing the C-type (Ca-dependent) lectin-like domain (CTLD) is a large group of extracellular proteins characterized by evolutionary flexibility and functional versatility [ 1 , 2 ]. Its members have been extensively studied because of their involvement in diverse physiological processes, and their ability to bind selectively a wide variety of ligands. As the superfamily name suggests, carbohydrates (in various contexts) are primary ligands for CTLDs and this binding is Ca-dependent [ 3 ]. However, the fold has been shown to specifically bind proteins [ 4 ], lipids [ 5 ] and inorganic compounds including CaCO 3 and ice [ 6 - 9 ]. In several cases, the domain is multivalent and may bind both protein and sugar [ 10 - 12 ]. Three studies using the whole-genome approach have been published analyzing the distribution of the superfamily in C. elegans [ 13 ], D. melanogaster [ 14 ] and human [ 15 ]. An early study [ 2 ] attempted to generalize findings on vertebrate CTLD-containing proteins (CTLDcps), and to classify them into groups. This classification included 7 groups and, although not sufficient to describe later known CTLDcps even in mammals and other vertebrates, has been widely used by CTLD researchers. The recent work of Drickamer and Fadden [ 15 ] provided an updated classification of human and mouse CTLDcps, based on a comprehensive analysis of CTLDcps encoded by the human genome; this comprises 14 groups. These whole-genome studies and genome annotation projects demonstrated the relative abundance of CTLDcps and importance of the domain. Known fish C-type lectins A number of fish CTLDcp sequences have been reported separately in the literature and public sequence databases. The best-studied and most distinct set are serum antifreeze proteins (AFPs) from several cold-water-living species [ 7 , 16 , 17 ]. These sequences consist mostly of just a CTLD, and were classified as group VII members based on domain architecture. A three-dimensional structure of the sea raven antifreeze protein has been determined experimentally [ 18 ]. Apart from AFPs, several other soluble bony-fish CTLDcps have been described: 5 isoforms of Salmo salar serum lectin (SSL) [ 19 ], three collectins from different Cyprinidae carp family species [ 20 ], skin mucus protein AJL-2 [ 21 ] and two C-type lectins (eCL-1 and eCL-2) from gills of Japanese eel [ 22 ], two lectins from rainbow trout liver [ 23 ], a carp lectin [ 24 ], goldfish lectin OL-1 (GI: 26000685, unpublished), and a liver lectin from Gillichthys mirabilis (long-jawed mudsucker), annotated as "mannose receptor C" [ 25 ]. Known membrane-bound CTLDcps from bony fishes include a polycystic kidney disease protein 1 (PKD1) orthologue from Fugu [ 26 ], a rainbow trout Kupffer cell receptor homologue [ 27 ], and a set of putative killer cell receptors (KLR) identified recently [ 28 ]. Although predicted coding sequences for CTLDcps from winter flounder (GI:28394504, unpublished) and medaka fish [ 29 ] do not contain a recognizable transmembrane (TM) domain, based on CTLD sequence and, in the case of the medaka CTLDcp, domain structure, they should be assigned to group II, as the absence of TM regions may be a result of incomplete prediction. The only known CTLDcp sequence from cartilaginous fishes is a tetranectin homologue from reef shark cartilage [ 30 ]. Fugu genome sequence The Fugu rubripes genome, available since 2002 [ 31 ], is the second vertebrate genome sequenced. It is 8 times smaller than the human genome and is proving to be an effective instrument in analyzing the human genome because of its compactness, low content of repetitive elements and the relatively large evolutionary distance between fish and mammals, which is estimated to be about 430 Myr [ 32 ]. Currently three versions of the Fugu rubripes genome assembly are publicly available. The second version of the assembly (v.2), constructed from 4.1 million sequencing reads (5.4 X sequence coverage), was reported in the original publication announcing the completion of the Fugu rubripes genome sequencing [ 31 ]. The third version (v.3) was released in August 2002, has slightly better coverage (5.7X) and improved scaffold contiguity. Sequence data for all three assembly versions can be downloaded from the Joint Genome Institute web site [ 33 ]. The JGI site and the EnsEMBL web site [ 34 ] are the two main portals to the Fugu rubripes genome annotation. Although EnsEMBL and JGI annotations and genome browsers are different, they share the same gene and transcript structure predictions created by the EnsEMBL pipeline. Several analyses of the draft Fugu genome sequence targeting different protein families have been published recently [ 35 - 39 ], which showed its usefulness for evolutionary and functional studies as well as gene discovery. Here we present an analysis of the presence of the CTLD superfamily in the draft assembly of the Fugu rubripes genome. Results Comparison of assembly versions 2 and 3 At the time this study was started, annotation of the v.3 assembly was not yet published; hence, most of our analysis was done with v.2 of the assembly and later mapped to the v.3 assembly. From our experience, there is no substantial difference between v.2 and v.3 assemblies in the amount of sequence information and its quality, although the v.3 assembly contains longer scaffolds due to more extensive linkage. Despite very high similarity at the sequence level, the v.3 assembly annotation contains no history information that would provide links between contigs, genes, and transcripts in the second and the third versions of the assembly. None of the stable identifiers for genes, transcripts or peptides from v.2 are present in v.3. This information cannot be generated by usual procedures used in EnsEMBL (e.g. ID Mapping Application, which is a part of EnsEMBL Java APIs) and has to be obtained by sequence comparisons. This lack of correspondence creates difficulties for the sequence analyzer and end point reader. To facilitate analysis and allow comparison, references to feature identifiers for both of the assemblies are given in Table 1 . Table 1 CTLD-encoding genes identified in the Fugu rubripes genome. a Name Description v.2 gene ID v.3 gene ID I Hyalectans AGGRECAN AGGRECAN ANUFRUG00000000095 ANUFR2G00000000089 AGGRECAN-F1 Fugu aggrecan paralogue ANUFRUG00000000081 ANUFR2G00000000077 BREVICAN BREVICAN SINFRUG00000078610 SINFRUG00000151617 BREVICAN-F1 Fugu brevican paralogue SINFRUG00000074933 SINFRUG00000128229, SINFRUG00000128230, SINFRUG00000128231 NEUROCAN NEUROCAN SINFRUG00000054833 SINFRUG00000150572, SINFRUG00000150573, SINFRUG00000150574, SINFRUG00000150576 NEUROCAN-F1 Fugu Neurocan paralogue ANUFRUG00000000142 ANUFR2G00000000154 VERSICAN VERSICAN ANUFRUG00000000144 ANUFR2G00000000164 VERSICAN-F1 Fugu versican paralogue (fragment containing EGF, CTLD and CCP domains) ANUFRUG00000000061 ANUFR2G00000000059 VERSICAN-F2 Fugu versican paralogue (fragment containing link and Ig domains) ANUFRUG00000000043 ANUFR2G00000000041 II Dendritic cell receptors, mono-ctld macrophage receptors, ASGR DC-SIGN-F1 Fugu DC-SIGN paralogue ANUFRUG00000000029 ANUFR2G00000000027 DC-SIGN-F2 Fugu DC-SIGN paralogue ANUFRUG00000000067 ANUFR2G00000000063 DC-SIGN-F3 Fugu DC-SIGN paralogue ANUFRUG00000000069 ANUFR2G00000000065 DC-SIGN-F4 Fugu DC-SIGN paralogue ANUFRUG00000000071 ANUFR2G00000000067 DC-SIGN-F5 Fugu DC-SIGN paralogue ANUFRUG00000000073 ANUFR2G00000000069 DC-SIGN-F6 Fugu DC-SIGN paralogue ANUFRUG00000000109 ANUFR2G00000000105 DC-SIGN-F7 Fugu DC-SIGN paralogue ANUFRUG00000000085 ANUFR2G00000000123 DC-SIGN-F8 Fugu DC-SIGN paralogue ANUFRUG00000000087 ANUFR2G00000000081 DC-SIGNR DCSIGN receptor ANUFRUG00000000027 ANUFR2G00000000025 HML2 Similar to human macrophage lectin SINFRUG00000060881 SINFRUG00000120587 SRCL Scavenger receptor with C-type lectin SINFRUG00000071148 SINFRUG00000134389 SRCL-F1 Putative Fugu paralogue of SRCL SINFRUG00000064389 SINFRUG00000152316 XLCMCL eXtra Large Coiled coil region containing Membrane C-type Lectin ANUFRUG00000000053 ANUFR2G00000000051 III Collectins COLEC10 COLEC10 SINFRUG00000077039 SINFRUG00000125405 MGC3279 Uncharacterized collectin family member SINFRUG00000064196 SINFRUG00000147955 IV Selectins SELECTIN-E E-Selectin ANUFRUG00000000001 ANUFR2G00000000001 SELECTIN-L L-SELECTIN ANUFRUG00000000003 ANUFR2G00000000003 SELECTIN-P P-SELECTIN ANUFRUG00000000005 ANUFR2G00000000005 VI Multi-CTLD molecules. Macrophage Mannose Receptor (MMR) family DEC205 DEC205 ANUFRUG00000000011 ANUFR2G00000000011 Endo180 Endo180 SINFRUG00000058766 SINFRUG00000152106 MManR Macrophage mannose receptor SINFRUG00000071196 SINFRUG00000126868, SINFRUG00000134363 MManR-F1 Fugu mannose receptor paralogue (fragment) SINFRUG00000064600 SINFRUG00000152797 MManR-F2 Fugu macrophage mannose receptor paralogue. ANUFRUG00000000039 ANUFR2G00000000035 ANUFR2G00000000037 MManR-F3 Fugu paralogue of MMR-family gene SINFRUG00000066378 SINFRUG00000152288 MManR-F4 Fugu paralogue of MMR-family gene (fragment) SINFRUG00000078047 SINFRUG00000152861 MManR-F5 Fugu MMR-family member (fragment) ANUFRUG00000000091 ANUFR2G00000000085 PLA2R Phosopholipase A2 receptor ANUFRUG00000000009 ANUFR2G00000000009 VIII MT-75, layilin LAYILIN Layilin ANUFRUG00000000089 ANUFR2G00000000083 LAYILIN-F1 Fugu layilin paralogue ANUFRUG00000000075 ANUFR2G00000000071 MT-75 MT-75 SINFRUG00000084745 SINFRUG00000145404 IX Tetranectin family CLECSF1 CLECSF1 SINFRUG00000050048 SINFRUG00000136890 SCGF SCGF ANUFRUG00000000125 ANUFR2G00000000121 TETRANECTIN Tetranectin SINFRUG00000084961 SINFRUG00000144710 TETRANECTIN-F1 Fugu tetranectin paralogue SINFRUG00000083037 SINFRUG00000149544 X PKD PKD1 Polycystic kidney disease protein 1 SINFRUG00000033997 PKD1L2 PKD-1 homologue 2 ANUFRUG00000000121 ANUFR2G00000000117 XI Attractin family ATTRACTIN Attractin SINFRUG00000071911 SINFRUG00000136030 ATTRACTIN-F1 Fugu paralogue of Attractin SINFRUG00000060472 SINFRUG00000147061 KIAA0534 KIAA0534 SINFRUG00000056251 SINFRUG00000121439 XII Eosinophil major basic protein family EMBPL Putative Fugu EMBP-like protein ANUFRUG00000000023 ANUFR2G00000000021 XIII DGCR family DGCR2 DGCR2 SINFRUG00000082125 SINFRUG00000155593 XIV Thrombomodulin family C1qRP C1qRP ANUFRUG00000000049 ANUFR2G00000000047 C1qRP-F1 Putative Fugu C1qRP paralogue (fragment) ANUFRUG00000000013 disappeared CETM Protein containing CTLD, EGF and transmembrane domains ANUFRUG00000000057 ANUFR2G00000000055 ENDOSIALIN ENDOSIALIN ANUFRUG00000000117 ANUFR2G00000000113 THROMBOMOD Thrombomodulin SINFRUG00000077807 SINFRUG00000153798 XV Bimlec BIMLEC Novel C-type lectin from BCG cell wall induced monocyte ANUFRUG00000000007 ANUFR2G00000000007 XVI SEEC SEEC Novel SCP-EGF-EFG-CTLD containing protein. ANUFRUG00000000041 ANUFR2G00000000039 XVII CBCP CBCP Calx-Beta and CTLD containing protein ANUFRUG00000000047 ANUFR2G00000000045 AFP Antifreeze protein AFPL-F1 Antifreeze protein-like ANUFRUG00000000045 ANUFR2G00000000043 AFPL-F2 Antifreeze protein-like ANUFRUG00000000139 disappeared F1 Fugu dual-CTLD molecules FDC-F1 Putative Fugu dual-CTLD protein 1 ANUFRUG00000000025 ANUFR2G00000000023 FDC-F2 Putative Fugu dual-CTLD protein 2 ANUFRUG00000000037 ANUFR2G00000000033 FDC-F3 Putative Fugu dual-CTLD protein 3 ANUFRUG00000000099 ANUFR2G00000000093 FDC-F4 Putative Fugu dual-CTLD protein 4 ANUFRUG00000000103 ANUFR2G00000000097 , ANUFR2G00000000099 FDC-F5 Putative Fugu dual-CTLD protein 5 ANUFRUG00000000107 ANUFR2G00000000103 FDC-F6 Putative Fugu dual-CTLD protein 6 ANUFRUG00000000123 ANUFR2G00000000119 FDC-F7 Putative Fugu dual-CTLD protein 7 ANUFRUG00000000101 ANUFR2G00000000095 FTCP Putative Fugu triple-CTLD protein ANUFRUG00000000015 ANUFR2G00000000013 L Link domain BRAL1 Brain link protein-1 SINFRUG00000078615 SINFRUG00000151615 CD44 CD44 ANUFRUG00000000113 ANUFR2G00000000109 CRTL1 Cartilage linking protein 1 SINFRUG00000078961 SINFRUG00000137046 CRTL1-F1 Putative fugu cartilage linking protein paralogue ANUFRUG00000000059 ANUFR2G00000000057 CRTL1-F2 Putative fugu cartilage linking protein paralogue SINFRUG00000074643 SINFRUG00000142167, SINFRUG00000142169, SINFRUG00000142171 HAPLN3 Hyaluronan and proteoglycan link protein 3 SINFRUG00000052853 SINFRUG00000155413 HAPLN3-F1 Putative Fugu paralogue of HAPLN3 SINFRUG00000079552 SINFRUG00000129575 Lyve-1 Lymphatic vessel endothelial HA receptor-1 ANUFRUG00000000077 ANUFR2G00000000073 STABILIN-1 Stabilin-1 ANUFRUG00000000079 ANUFR2G00000000075 STABILIN-2 Stabilin-2 SINFRUG00000074867 SINFRUG00000146665 TSG-6 TSG-6 SINFRUG00000075173 SINFRUG00000148136 NLSLH NLSLH Novel L-SeLectin Homologue ANUFRUG00000000055 ANUFR2G00000000053 NLSLH-F1 Fugu CTLD containing gene fragment, NLSLH paralogue ANUFRUG00000000097 ANUFR2G00000000091 U Unclassified AGGRECOL Putative Fugu CTLD-containing protein equally similar to aggrecan and placenta collectin. ANUFRUG00000000083 ANUFR2G00000000079 ANZG001 Putative Fugu CTLD-containing protein (fragment) ANUFRUG00000000019 ANUFR2G00000000017 ANZG002 Putative Fugu CTLD-containing protein (fragment) ANUFRUG00000000021 ANUFR2G00000000019 ANZG004 Putative Fugu protein with CTLD and FTP domains ANUFRUG00000000093 ANUFR2G00000000087 ANZG005 Putative Fugu CTLD-containing protein (fragment) ANUFRUG00000000065 disappeared ANZG006 Putative Fugu CTLD-containing protein (fragment) ANUFRUG00000000111 ANUFR2G00000000107 ANZG007 Putative Fugu CTLD-containing protein (fragment) ANUFRUG00000000063 ANUFR2G00000000061 ANZG008 Putative Fugu CTLD-containing protein (fragment) ANUFRUG00000000017 ANUFR2G00000000015 ANZG010 Putative Fugu CTLD-containing protein ANUFRUG00000000051 ANUFR2G00000000049 ANZG011 Putative Fugu CTLD-containing protein ANUFRUG00000000115 ANUFR2G00000000111 CFN3 Protein with CTLD and FN3 domains. ANUFRUG00000000105 ANUFR2G00000000101 DEC205-FUSE Large Fugu protein which looks like a DEC205 fused to another CTLD-containing gene ANUFRUG00000000119 ANUFR2G00000000115 FG75645 Fugu CTLD-containing protein fragment SINFRUG00000075645 SINFRUG00000139863 PTP-GMC1 Protein-tyrosine phosphatase expressed by glomerular mesangial cells ANUFRUG00000000130 ANUFR2G00000000137 a All Fugu CTLDcps identified in this analysis are listed. Columns 3 and 4 contain stable identifiers for gene models in the v.2 and v.3 assembly databases, respectively. Identifiers starting with ANUFRU and ANUFR2 belong to our predictions on the v.2 and v.3 assemblies, respectively, and are underlined. EnsEMBL gene stable identifiers are given if the original predictions were used. Bolded members denote Fugu proteins matched with novel human orthologues. Protein database searches Due to almost complete lack of cDNA or EST sequences for Fugu rubripes , most of the EnsEMBL gene structure predictions are based on homology with known protein sequences from other organisms, mostly mammals. We expected a significant fraction of CTLDcps to be conserved between fish and human, and, therefore, to be predicted correctly by EnsEMBL in the Fugu genome. So our first approach to detecting Fugu CTLDcps was to search a sequence database of predicted Fugu proteins with a hidden Markov model (HMM) for the CTLD. This search returned 69 significant matches. Some of the identified genes had a description assigned to them, apparently derived from the description of the sequence they were found to be homologous to. These descriptions, however, could not be used as a reliable basis for assigning orthology and paralogy relationships. For example, a sequence, which we later identified as an Endo180 orthologue (SINFRUG00000058766 in v.2 assembly annotation) is described as "80 KDA SECRETORY PHOSPHOLIPASE A2 RECEPTOR PRECURSOR PLA2", while another gene, which we designated as an aggrecan orthologue (SINFRUG00000069597 in v.2 annotation) was annotated as "ADRENOLEUKODYSTROPHY PROTEIN (ALDP)". Therefore, we reviewed domain architecture and sequence similarity matches for each of the sequences found to verify phylogenetic relationships. Homology detection The results of Inparanoid [ 40 ] comparison (see Methods) of all human to all Fugu CTLDcps were used to initially cluster the set of Fugu proteins and detect approximate orthology/paralogy links. Inparanoid has an important advantage over phylogenetic tree reconstruction software, as it does not require a multiple alignment of sequences but creates a distance matrix of the local pairwise alignments. This method assigned putative human orthologues to 25 Fugu proteins. Orthology relationships for the other 44 sequences from the set were established by individual analyses. Revision of CTLDcp gene structure predictions While analyzing phylogenetic relationships predicted by Inparanoid, we discovered several systematic and sporadic mistakes in the EnsEMBL gene predictions. The most widespread mistake was a failure to include exons encoding TM domains into gene structure prediction. Consequently, almost all EnsEMBL-predicted Fugu CTLDcps were soluble proteins, whereas very few human CTLDcps are. Simple comparison with the GenScan [ 41 ] features overlapping the CTLD-encoding genes showed that absence of TM domains is a result of coding sequence (CDS) mis-prediction rather than a fundamental difference in Fugu CTLDcps. GenScan predictions, in turn, could not be used as a basis for our analysis because they sometimes contain regions that are absent from human or mouse orthologues, and often merge neighboring genes. Another general problem was observed with proteins that had a previously unknown domain architecture (see below). In such cases individual domains were split into separate gene models. In addition to these systematic problems, there were multiple sporadic ones. For example, our analysis of the Fugu genome shows that, similarly to the human and mouse genomes, the selectin cluster is well conserved and contains all three selectin genes in tandem (SELE, SELL, SELP), located on scaffolds 1045 (32046–41921) and 166 (83937–93826) in the v.2 and v.3 Fugu genome annotations, respectively. However, the EnsEMBL annotation contains a prediction of two overlapping genes (v.2: SINFRUG00000085188 and SINFRUG00000085187; v.3: SINFRUG00000123102 and SINFRUG00000123101), one of which is located in the intron of the other (Figure 1 ). Figure 1 Fugu genome sequence and annotation. A. Fugu selectin gene cluster annotation in the EnsEMBL database (v.2 annotation is shown, v.3 annotation is almost identical to v.2). Gene models predicted by us based on comparison with human selectins are shown in the grey box. As shown, the CTLD is encoded by the 5' exon in fSELP, fSELL and fSELE; the TM segment is encoded by the 3' exon. EnsEMBL predicted transcripts, GenScan predictions and similarity features are shown on the tracks below. Stable IDs for EnsEMBL transcripts are given. The TMHMM track shows ORFs encoding TransMembrane regions predicted by the TMHMM program (see Methods). B. Fragments of group VI genes found on various scaffolds. CTLD numbers indicate sequential number of CTLD in full-length MManR, while numbers for the CTLD in the partial sequences indicate the MManR CTLD sequence they are most closely homologous to. To solve these problems, we had to manually revise the predicted structure for all genes encoding proteins detected by the protein-level searches, and correct them using supporting evidence available in the EnsEMBL database, as well as additional evidence generated by us. The latter included similarity features produced by genome-wide GeneWise and BLAST searches with CTLD profiles and sequences, transmembrane domain predictions, and similarity matches to the complete sequence of supposed human or mouse orthologues. As the final stage of the CTLDcp identification process, we performed a set of DNA-level comparisons to ensure that the CTLD-containing loci that are not covered by EnsEMBL-predicted genes, or for which transcript predictions are wrong and, thus, not detectable by protein database searches, were not omitted from the analysis. This "quality control" step led to identification of an additional set of 25 well conserved CTLDcps, which had both new and known domain architectures, as well as additional individual CTLDs, which were merged with neighboring CTLDcp loci if appropriate. Groups of Fugu CTLDcps After all these searches, we had identified a set of 94 Fugu rubripes loci encoding CTLDcps (Table 1 ), which in total contain 173 individual CTLDs, including PTR/Link-type CTLDs [ 42 ]. Fugu CTLDcps were named according to their human orthologues, established on the basis of domain composition and sequence similarities. Where more than one homologue was present in Fugu , a name was produced by adding a suffix of the form "-FXX", where XX is a sequential number of the paralogue, to the name of the closest human homologue. Predicted CTLDcps that do not have homologues among the known CTLDcps have identifiers of the form ANZ000. A few of these novel genes were orthologous to loci in other vertebrate genomes supported by expression data, but otherwise are un-characterized, and were assigned descriptive names (CBCP, Bimlec, SEEC, CETM, NLSLH). We have clustered Fugu CTLDcps using the classification scheme for human CTLDcps based on domain composition; this comprises 14 groups [ 15 ]. Link/PTR-domain-containing CTLDcps, apart from hyalectans, were placed into a separate group. Among the Fugu CTLDcps that did not have mammalian homologues we detected a distinct group of soluble dual-CTLD sequences, which we have called F1 (Figure 2 ). The remainder of the Fugu -specific CTLDcps were assigned to the U (Unclassified) group. Gene structure prediction for members of the U group is the lowest in quality, due to lack of supporting evidence apart from similarity to CTLD sequence profiles and GenScan predictions. Figure 2 CTLDcps with novel domain architectures. Fugu CTLD-containing proteins, which do not fit into the existing CTLDcp classification are shown. Domain abbreviations are explained in the text. Roman numbers near names indicate suggested new group names for the new Fugu sequences, which also have new predicted human homologues. C-terminal CTLDs of DEC205-FUSE that are not present in the v.3 assembly are shown in light pink. All but two groups of human CTLDcps have detectable representatives in the Fugu genome (Figure 3 , Table 1 ). We did not detect any orthologues for groups V (NK cell receptors) and VII (lithostathine/Reg family). The member repertoire for most of the other groups is very well conserved between Fugu and human. However, groups II and III, which include some of the best-studied mammalian CTLDcps, have a significantly different member composition in Fugu . In summary: Figure 3 Phylogenetic relationships between fish and human CTLDs. A phylogenetic tree built on a ClustalW alignment of a 95% non-redundant collection of predicted Fugu CTLDs and known human and fish CTLDs. Link domains and group VI CTLDs were excluded from the alignment. The tree was built by the neighbor-joining method with 100 bootstrap trials using the ClustalW program. PhyloDraw was used to draw the radial cladogram shown. Branches containing CTLDs from CTLDcps belonging to the same group are shaded; group numbers are marked. Lower case prefixes in the identifiers indicate taxonomic origin: h – Homo sapiens , f – Fugu rubripes , zbrfs – Danio rerio (zebrafish), g – Gillichthys mirabilis , gldhs – Carassius auratus (goldfish), carp – Cyprinus carpio (common carp), rsmlt – Osmerus mordax (rainbow smelt), slmn – Salmo salar (Atlantic salmon), wfldr – Pseudopleuronectes americanus (winter flounder), ahrng – Clupea harengus (Atlantic herring), servn – Hemitripterus americanus (sea raven), jpeel – Anguilla japonica (Japanese eel), medak – Oryzias latipes (Japanese medaka), c – Paralabidochromis chilotes (cichlid fish). Group I All four members of the lectican group that are present in human have orthologues in the Fugu genome. Each of the Fugu hyalectan genes is duplicated. One of the Fugu versican copies is split between two scaffolds in the v.2 assembly. Group II We found only one representative of the asialoglycoprotein receptor (ASGR) family in Fugu (HML2), while in human this family has 3 members encoded by a gene cluster on Ch 17 (ASGR1, ASGR2, HML2). The Fugu sequence was identified as an HML2 orthologue by phylogenetic analysis based on the alignment of CTLD sequences. Another clearly identifiable member of group II is the orthologue of scavenger cell receptor C-type lectin (SRCL), which is duplicated in Fugu and is 50% identical to the human SRCL. The rest of the group II Fugu CTLDcps (DC-SIGN-F1 – DC-SIGN-F8, XLCMCL) do not have clearly identifiable orthologues among known human CTLDcps, although phylogenetic analyses based on CTLD sequence alignment indicate that they are homologous to members of the group II subgroup containing DC-SIGN, Mincle and Dectin-2, which also appear as top hits in BLAST searches. However, this subset of group II Fugu sequences co-clusters in phylogenetic trees and is not similar enough to any tetrapod sequence to establish orthology. Four of the sequences (DC-SIGN-F2, DC-SIGN-F3, DC-SIGN-F4, DC-SIGN-F5) are located in a cluster on scaffold 75 in the v.3 assembly. Two members of the subgroup (DC-SIGN-F1 and DC-SIGN-F6) have unstable placements in phylogenetic trees, and may appear on a branch containing human/mouse group V sequences, if the latter are included in the alignment. This association is, however, unstable and may be due to mistakes in CDS prediction or phylogeny reconstruction. Alternatively, it is possible that these sequences are homologous to the common predecessor of group V and group II CTLDcps. Group III Although Fugu has two collectins, there are no orthologues for mannose binding proteins (MBPs) or pulmonary surfactant proteins (PSP), which are the best studied members of the group in human. Both of the Fugu collectins (COLEC10, MGC3279) are well conserved compared with their human orthologues and co-cluster with them in phylogenetic trees. No functional information is available for the novel collectin MGC3279, which was discovered in a large-scale cDNA sequencing project and maps to chromosome 2p25.3 in the v.31 NCBI assembly of the human genome, but the exceptionally high level of conservation between human and fish (~76% identity) strongly suggests that it is functional and important in both organisms. COLEC10 (collectin liver 1, CL-L1) was originally reported as limited to birds and mammals [ 43 ] based on the Zoo-blot analysis. Group IV As already mentioned, all three selectin genes found in other vertebrates are present in Fugu and have the same genome arrangement. Group VI We identified Fugu orthologues for all four human group VI members: macrophage mannose receptor (MManR), DEC-205 (CD205), phospholipase A2 receptor (PLA2R) and Endo180. In addition, there are 5 sequences (MManR-F1 – MManR-F5) showing high similarity to members of the group, four of which do not contain the minimal number of CTLDs (8) present in the known group VI sequences (Figure 1 ). The fragments belong to at least 3 group VI CTLDcps. Although the most parsimonious explanation of the presence of these fragments would be that each of the genes encoding an eight-CTLD molecule (MManR, Endo180 and PLA2R) was copied in a chromosome or genome duplication event, phylogenetic analysis indicates that all five sequences are paralogues of the MManR gene, which, thus, appears to have been duplicated several times. There is one more potential group VI member in Fugu . A GenScan-predicted DEC-205-FUSE gene, which was assigned to the U group, encodes a large protein (~2000 residues) with multiple CTLDs clustered in two groups: 5 at the N terminus and 10 (7 in v.3 assembly) at the C terminus, with an LCCL domain [named after its presence in Limulus factor C, cochlear protein Coch-5b2, and late gestation lung protein Lgl1; [ 44 ]] and a coagulation factor 5/8 C-terminal domain (discoidin domain, FA58C) lying in the middle separating the two groups of CTLDs (see Figure 2 ). EnsEMBL predictions in the DEC-205-FUSE locus in both versions of the assembly contain a large (4 kb) intron in the region encoding LCCL, FA58C and 8 CTLDs at the center of the molecule. LCCL has been observed in a combination with a CTLD in an invertebrate protein [ 45 ], while FA58C has been found only in combination with LCCL, but not with a CTLD [ 46 ]. Although there is no supporting cDNA or EST evidence for our predicted gene structure, the small intron sizes (e.g. LCCL is separated by 135 bp from the downstream CTLD) and well-conserved CTLDs, suggest that the prediction may be correct if the corresponding region was correctly assembled. There is no orthologue for DEC-205-FUSE in the human genome. Groups VIII and IX We have identified Fugu orthologues for all known human members of groups VIII and IX. One member in each of these groups is duplicated in Fugu (Layilin and Tetranectin). Group X In addition to the PDK1 orthologue, which was identified previously [ 26 ], there is at least one more putative group X member, orthologous to a recently identified human and mouse PKD1 homologue PKD1L2 [ 47 ]. It is interesting to note that the GenScan-predicted Fugu PKD1L2 sequence is very similar to the sequences of human and mouse PKD1L2 cDNAs, even though the latter were deposited in GenBank at the beginning of June 2003 – after GenScan prediction. This example indicates that ab initio GenScan predictions on the Fugu genome can be very accurate. Group XII We found a single sequence resembling mammalian eosinophil major basic proteins (EMBPs) in Fugu (EMBPL). Although the similarity between the mammalian and the fish sequences is very low (~30% identity), several observations suggest that the Fugu EMBP-like sequence is an orthologue of one of the two mammalian genes. First, the overall domain architecture of the fish protein is similar to that of the EMBPs. Although the fish CTLD has a neutral pI (7.1), it is preceded by a 30-residue peptide with a predicted pI of 3.62, analogous to the longer acidic neck of the mammalian EMBPs. In the existing classification [ 15 ], the presence of the acidic neck is used as the defining feature of group XII distinguishing it from the other group of single-CTLD soluble proteins (VII). Second, in the phylogenetic trees EMBPL usually appears on the same branch as EMBPs (e.g. Figure 3 ), albeit with low bootstrap support. Third, the exon-intron structure of the CTLD region is identical in fish and mammalian genes. Finally, the fish sequence has the same rare substitution in the fourth position of the WIGL motif as the EMBP sequences (discussed in more detail below). Group XIV The thrombomodulin family is fully represented in Fugu , with one gene duplicated (C1qRP). In addition, a novel member of the family conserved between Fugu and mammals was identified, which we named CETM (for CTLD, EGF, TransMembrane domain) (see Figure 2 ). Multiple full-length cDNA and EST sequences from different tissues found in nucleotide databases indicate that mammalian CETM is ubiquitously expressed. The sequence of the CETM CTLD contains a putative carbohydrate-binding motif (EPN), which is normally associated with mannose specificity. Antifreeze-protein-like sequences We identified two putative CTLDcp-encoding loci with similarity to antifreeze proteins: AFPL-F1 and AFPL-F2 (antifreeze-protein-like), almost identical to each other and positioned in tandem on scaffold 1930 in the v.2 assembly. In v.3 of the assembly, the AFPL-encoding region was rearranged and one of the AFPL loci disappeared. The intron-exon structure of the CTLD-encoding region is identical to the structure of the sea raven antifreeze protein gene [ 48 ] with three intron insertions (upstream of C1, downstream of the WIGL motif, and between C2 and C3 [ 49 ]), and very similar to the structure of the Salmo salar serum lectins [ 19 ], where only the first two splice sites are present. The Fugu AFPL gene expression is confirmed by an EST sequence BU806418, which covers the whole predicted CDS. Link domain containing CTLDcps All link domain-containing proteins identified in mammals are represented in Fugu and often are highly conserved between fish and human (e.g. TSG-6, 72%; Stabilin-1, 45% identity); we will consider them as a single group despite their different domain architectures. Predicted members of the CD44 family (CD44 and lymphatic vessel endothelium-specific hyaluronan receptor (Lyve-1)), however, are much more divergent from their human homologues, and it is not clear whether the two loci found in Fugu are orthologues of the two human genes or paralogues which arose by duplication of an ancestral gene. In a recently published comprehensive study of another family of the Link group, the hyaluronan and proteoglycan binding link proteins (HAPLN), four homologues were identified in vertebrates (mouse, human and partially zebrafish) each linked to one of the four lecticans [ 50 ]. As all lecticans (i.e. group I) are duplicated in Fugu , we were expecting to also find duplicate copies of all HAPLN members. However, orthologues of only three HAPLNs were found (CRTL1, BRAL1, HAPLN3), two of which are linked to hyalectans in the same way as in mammalian genomes (CRTL1 with Versican, BRAL1 with Brevican). The state of the assemblies does not allow to determine conclusively whether HAPLN3 is linked to Aggrecan or not. Only two of the Fugu lectican gene duplications are accompanied by corresponding HAPLN genes: Aggrecan-F1 is linked to HAPLN3-F1 and the CRTL1 paralogue is present downstream to Versican-F1 in two tandem copies (CRTL1-F1 and CRTL1-F2). In neither version of the assembly could the HAPLN4 homologue be identified downstream to Neurocan or Neurocan-F1. Sequence conservation levels within the HAPLN proteins compared with their human orthologues is quite high (e.g. 76% identity for CRTL1). Fugu dual-CTLD CTLDcps The members of this group are soluble proteins with two or three CTLDs, which we initially characterized as fragments of putative macrophage mannose receptor paralogues. However, phylogenetic analysis showed that these proteins constitute a separate group, with no mammalian orthologues detectable in sequenced genome and protein databases. The domain structure prediction is confirmed by three zebrafish cDNAs (CAE17649, CAE17650, CAE17651), which have the same domain organization, although conservation between zebrafish and Fugu sequences is only moderate (~30%). Another homologue with the same domain structure and similarity to the F1 group members, which was returned as the top-scoring hit by BLAST searches in the nrdb, is the SCARF2 protein from a planarian Girardia tigrina [ 51 ]. A hypothetical dual-CTLD protein from Drosophila (NP_609962), which presumably corresponds to the single member of group B in the Drosophila CTLDcp classification of Dodd and Drickamer [ 14 ], was also detected as a F1 homologue by BLAST. Novel CTLDcps conserved between Fugu and mammals Discovering novel superfamily members in existing database sequences is one of the most important and exciting outcomes of a systematic computer-based study such as this. We predicted putative Fugu orthologues for several uncharacterized mammalian CTLDcps (Bimlec, MGC3279, KIAA0534, CETM, SEEC, CBCP, NLSLH) that are well conserved between Fugu and mammals. Most of the predictions were supported by mammalian cDNA sequences from public databases, but for two of them (NLSLH and CBCP) no full-length cDNA from any organism was found in DBs. The high level of genomic sequence conservation over evolutionary time from fish to human, as in the case of NLSLH, and the presence of partial cDNA and EST sequences from rodents and human, as in the case of CBCP, were strongly suggestive that the predictions are correct. The novel CTLDcps that could be attributed to one of the 14 known groups have been discussed in the preceding sections for the corresponding groups; those that do not fit into the existing classification are described below. A large (~2100 aa) proteoglycan (CBCP), containing a set of chondroitin sulphate proteoglycan (CSPG) repeats [ 52 ], which are homologous to the NG2 ectodomain [ 53 ], a calcium-binding Calx-β domain [ 54 ] and a CTLD, is a novel member of a protein family which had not been reported previously to have members containing CTLDs; examples of this family also include the human MCSP/CSPG4 [ 55 ] and mouse FRAS1 [ 56 ] genes. The prediction was supported by three overlapping but incomplete cDNA sequences from human and mouse, high levels of conservation between human and Fugu (~50% identity), and the compact structure of the predicted Fugu gene. CBCP has been placed in a new CTLD group, XVII; its domain structure is shown in Figure 2 . We have cloned a full-length cDNA of mouse CBCP confirming the domain structure predicted in this study (A.N. Zelensky, in preparation). The CTLD of CBCP lacks Ca-binding residues, and its long loop region is short, resembling that of the group V CTLDs. Another protein with a novel domain organization, whose prediction is strongly supported by available cDNAs, is SEEC ( S CP, E GF, E GF, C TLD-containing protein) (see Figure 2 ), which is well conserved between human and Fugu . Although not described in a publication, a full-length human SEEC cDNA (AK074773) was sequenced in the NEDO high-throughput sequencing project. The predicted Fugu SEEC is 63% identical to the human sequence. The sperm-coating glycoprotein (SCP) domain, which is present in a broad set of organisms from yeast and plants to mammals, but whose function is unknown [ 57 ], is rarely observed in combination with other domains in proteins; in only one other known protein (from sea urchin) is it found together with an EGF domain [ 58 ], and SEEC is the first example of a CTLD-SCP combination. The potential Ca/carbohydrate-binding motif (QPD) characteristic of galactose specificity is present in the CTLD. SEEC has been placed in a new CTLD group XVI. A predicted protein named "novel L-selectin homologue" (NLSLH) because its CTLD is most similar to selectin CTLDs is duplicated in Fugu (NLSLH and NLSLH-F1) but only moderately conserved (32% identity) between Fugu and human. The putative human orthologue is located on Ch1q25.1 about 18 Mb further from the centromere than the selectin cluster and is supported only by EST sequences (AA912157, AA889574), but not cDNAs. No conserved domains except for the CTLD could be detected in the human and Fugu NLSLH loci so, if the predictions are correct, NLSLH is a soluble single-CTLD-containing protein. Carbohydrate-binding motifs are not present in the NLSLH and NLSLH-F1 CTLDs. Finally, a type I transmembrane protein Bimlec, whose prediction is supported by a full-length human cDNA, was placed in a new group XV. Dating the CTLDcp duplications We found 12 groups of unlinked Fugu -specific CTLDcp paralogues (Table 1 ), and attempted to determine the duplication dates using two approaches: (1) based on the estimation of the number of synonymous nucleotide substitutions (Ks) in the coding sequences and (2) based on the molecular clock hypothesis. For all but two pairs of duplicated genes, Ks values estimated with four different methods (see Methods) were between 1.5–2.5, which indicates a complete saturation of the synonymous sites (Figure 4(A) ). Ks values so high cannot provide an accurate estimation of the duplication age, but we can conclude with confidence that the CTLDcp gene duplications are at least 150 Myr old, which is the time required for complete saturation of silent sites assuming a mutation rate of 2.5 substitutions/silent site/billion years in fish [ 59 ]. If, however, Ks values presented in Figure 4(A) and the silent mutation rate are close to correct, the corresponding duplication timeframe is predicted to be 300–500 Myr. Figure 4 CTLDcp duplication dates. A. Average number of synonymous substitutions per synonymous site (Ks) for CTLDcp paralogue pairs based on full-sequence (triangle) and CTLD-only (diamond) alignments, measured with four different methods (see Methods). Error bars show one standard deviation in the CTLD-only measurements. All possible pairwise alignments between the MManR fragments and between the three CRTL1 paralogues were analyzed. Only homologous regions were used for MManR fragment alignments. B. A linearized phylogenetic tree built by the neighbor-joining method from Poisson-corrected distances between ClustalW-aligned sequences of CTLDs 3–5 from Fugu , mouse and human MManRs. Sequence of the human PLA2R region containing CTLDs 3–5 was used as an outgroup. T hm – time of separation between human and mouse [96 Myr; 60], T fish – time of separation between ray-finned and lobe-finned fishes [430 Myr; 32]. Time of duplication (T dupl ) was calculated using average between molecular clock calibrated with T hm and with T fish . In order to date the duplications based on molecular clock measurements, we aligned duplicated Fugu CTLD sequences with their vertebrate orthologues present in GenBank, and built linearized phylogenetic trees based on the alignments. As human and mouse sequences were invariably available, the divergence time between these two species [96 Myr; 60] was used to calibrate the clock, together with the divergence time between Actinopterygii and Sarcopterygii [430 Myr; 32]. Symmetrical tree topology ((H, M) (F, F1)), expected for a Actinopterygian-specific duplication, was revealed by at least one phylogeny reconstruction method we used for the following six homologue groups (data not shown): brevican, neurocan, MManR, SRCL, tetranectin and HAPLN3, with duplications dated 369, 284, 397, 377, 360 and 312 Myr, respectively. A typical tree with symmetrical topology is shown for MManR in Figure 4(B) . The other six alignments (aggrecan, versican, layilin, attractin, C1qRP, CRTL1) produced trees with topologies suggesting a duplication predating the split between Actinopterygian and Sarcopterygian. The portion of symmetrical topologies (50%) in the CTLD set is similar to the ratio reported by Taylor and coworkers in fish: 15 of 27 (55 %) [ 61 ], and 25 of 53 (45%) [ 62 ] for bigger and more heterogeneous gene collections. Discussion Draft assembly limitations A systematic study based on draft-quality whole-genome data for an organism like Fugu rubripes has some limitations, as the genomic sequence is incomplete, fragmented and sometimes misassembled, and the expressed sequence information is scarce. On the other hand, many of the genomes that are currently being sequenced will be released and remain for sometime in the same state as the Fugu genome data are now. Indeed, more than a year after the initial release [ 31 ] very few improvements to the Fugu genomic data [v.3 assembly and EST sequencing project; [ 63 ]] have been published. Therefore, it is essential to extract useful biological information from draft-quality whole-genome sequences. Our study is such an attempt. We have mentioned four limitations of the draft-state assembly – incompleteness, fragmentation, misassembly and lack of expression information. While the last might appear the biggest problem, we found that ab initio predictions combined with manual curation and interspecies comparison have proven to be very accurate (e.g. see PKD1L example), thanks to the compactness of the Fugu genome, smaller ratio between intron and intergenic region sizes compared with mammalian genes, wealth of data for comparative analysis etc. We do not expect that sequencing the remaining 5% of the Fugu genome, which is mostly heterochromatic regions, will lead to discovery of many new CTLDcps. From the comparison of the Fugu CTLDcp repertoire discovered by us and found in other fish species independently, the only surprising omission in our results is a MBP orthologue. MBP sequences have been found in several other fish species. Their absence in Fugu may represent a bona fide gene loss. As to the fragmentation, only a few of the CTLDcps are split between scaffolds, namely versican and some MManR paralogues (Figure 1 ). All of the fragmented genes are big, and in most cases the fragments can be combined easily to reveal the full sequence. Finally, misassembly signs were observed in several CTLDcp loci while comparing two versions of the assembly. These showed as presence of repeated regions in the v.2 assembly, which disappeared in the v.3 assembly. Two groups identified in higher vertebrates are not detectable in Fugu We could not detect CTLDcp representatives for groups V (NK cell receptors) and VII (lithostathine) in the Fugu genome. CTLDs in the members of these groups have lost their carbohydrate-binding activities, and perform functions that have, apparently, evolved after evolutionary separation of tetrapods from fish, or which are mediated by other proteins in fish. For example, group VII members are secreted into the digestive tract – a system that is very flexible evolutionally. Group V is probably one the youngest and most rapidly evolving sets of CTLDcps; its component members vary significantly even between rodents and human, a phenomenon connected to the co-evolution with the acquired immune system proteins that group V CTLDcps interact with. Our conclusion on the absence of group V CTLDcps in the Fugu genome is at odds with the conclusions of two studies describing group V CTLDcp evolution in chordates. A recent paper describes possible CD94 homologues (cichlid killer cell lectin receptor, cKLR) in bony fishes Paralabidochromis chilotes and Oreochromis niloticus , which are encoded by a large multi-gene family with at least 10 members [ 28 ]. Another recent work described sequencing of a CD94 homologue in a tunicate [ 64 ]. The decision by Sato et al. [ 28 ] to assign putative fish killer cell receptors to group V rather than to group II was based on several considerations, including gene structure, absence of canonical Ca 2+ /carbohydrate-binding residues, and phylogenetic analysis based on the CTLD alignment. The latter consideration is mentioned as the most important one. However, as the authors themselves note, bootstrap values for placing cKLR on the group V branch, are "low to moderate". Indeed, we found that in phylogenetic trees built using different methods (maximum parsimony, distance estimation method with PAM matrix followed by neighbor-joining tree reconstruction, maximum likelihood) from the ClustalW alignments of cKLR sequences with group V and group II CTLD sequences from Fugu , mouse and human, cKLR placement is unstable. As shown in Figure 5 , on a tree built by the neighbor-joining method we found cKLR on the branch containing the Fugu -specific subset of group II CTLDcps (DC-SIGN-F1 – DC-SIGN-F8), most of which do contain residues required for Ca 2+ /carbohydrate binding. On a tree built by the maximum parsimony method, we found cKLR on a separate branch equally related to group II and group V sequences (not shown). Also, a BLAST search with the complete cKLR sequence (GI 31789959) in the non-redundant NCBI protein database returns members of the ASGR subgroup of group II as top matches. Therefore, we judge that sufficient support for assignment of cKLR to group V is lacking and the question of the presence of the NK-cell receptor family in fishes is still open. Figure 5 Relationships between fish, mouse and human group V and II CTLDs. Non-redundant set of CTLD sequences from known human and mouse CTLDcps classified as groups II and V, Fugu CTLDcps classified as group II, and putative killer cell receptor from Paralabidochromis chilotes (cKLR) were aligned with ClustalW. A consensus phylogenetic tree was built from 100 bootstrap trials using the protdist (with PAM distance matrix) and neighbor programs from the PHYLIP package. Black triangle shows position of cKLR. Bootstrap values higher than 40 are indicated. As to the putative CD94 homologue from tunicates, it is indeed more similar to CD94 than to any other CTLDcp. However, the low level of sequence homology and the lack of evidence for existence of group V CTLDcps in more advanced taxa does not allow a confident statement that the sequence from tunicates is a CD94 orthologue, rather than a result of convergent evolution. Expansion of the innate immunity CTLDcp groups in Fugu Unlike pairwise unlinked duplications (see below), tandem duplications and other gene family expansions are limited to two groups, namely the DC-SIGN subgroup of group II and MManR. In mammals, members of these subgroups play an important role in innate immune responses. In particular, DC-SIGN is actively studied due to its ability to bind and internalize a broad range of bacterial and viral pathogens, including HIV-1 and Mycobacterium tuberculosis (reviewed in [ 65 ]), while MManR is also implicated in binding and phagocytosis of a wide range of microorganisms [ 66 ]. Expansion of these groups, most notably the DC-SIGN subgroup, in Fugu may reflect a larger role for innate immunity in host defense in lower vertebrates. Interestingly, multi-copy clusters comprising at least 10 genes encoding close cKLR homologues were identified in another cichlid fish species Oreochromis niloticus [ 28 ], which suggests another parallel between the expanded DC-SIGN subgroup in puffer fish and cKLRs of cichlids. There are no extra members, however, in the Fugu collectin group – another CTLD group directly involved in innate immunity in mammals. Moreover, the mannose binding protein (MBP), which is the best-studied mammalian collectin involved in lectin complement activation pathway, was not detected by us. The absence of MBP orthologues in Fugu is rather puzzling, as MBP sequences have been found in several other fish species ( Danio rerio , Cyprinus carpio and Carassus auratus ; [ 20 ]), and are well conserved within the Cyprinidae carp family. The collectin family is also present and expanded in the Urochordate Ciona intestinalis with nine collectin genes identified in the draft genome sequence [ 67 ], although it is not clear whether one of these nine genes is an MBP orthologue. Given the role of MBPs in complement activation in mammals, and their presence and level of conservation in the carp family, it is possible that the Fugu MBP orthologue does exist but is not covered by the draft genome sequence. Complement-activating C-type lectins from lower organisms have been identified but not completely sequenced [ 68 ]; they have multiple CTLDs as in CPL-III from the protochordate Clavelina picta [ 69 ] or lack the collagen domain and show more similarity to other CTLDcps such as the glucose-binding lectin (GBL) from another tunicate, Halocynthia roretzi [ 70 ]. Fugu dual CTLD molecules – a missing link between vertebrate and invertebrate CTLDs? Previous whole-genome studies of the CTLD superfamily in two invertebrates [ 13 , 14 ] failed to identify any groups of CTLDcps common to both invertebrates and vertebrates. A group of predicted dual CTLD-containing proteins in Fugu (F1) may be the first vertebrate group that has detectable homologues in invertebrates. Alternatively, it is possible that none of the Fugu F1 group members are in fact orthologous to the invertebrate sequences, as sequence similarities are only moderate (~30% ID) and the domain architecture is simple and could have evolved independently in different lineages. However, several observations suggest that at least F1 members from Fugu and zebrafish and SCARF proteins from Girardia tigrina evolved from the same predecessor. First, similarity levels between fish sequences and between fish and planarian sequences are about the same. It is unlikely that the fish sequences are unrelated, which implies that F1 members are evolving quickly, and only major structural features of these molecules are under selective pressure [ 49 ]. Second, the CTLDs of planarian and, in all cases at least one CTLD of the fish dual-CTLDcps, contain residues characteristic of Ca/carbohydrate binding. In vertebrates, ability to bind carbohydrates is associated with the oldest CTLDcps groups, and is considered to be an ancestral feature of the CTLD. Indeed, both vertebrate CTLDcp groups that we failed to find in Fugu (V and VII) have lost sugar-binding properties. This is also the case for the antifreeze proteins from fish and snake venom CTLDcps, which have only been found in the corresponding clades. Third, similar domain organization (two CTLDs, no transmembrane domain) is also observed in two other known groups of invertebrate CTLDcps: immulectins from various insect species [ 71 , 72 ] and nine proteins from C. elegans , classified as group D1 by Drickamer and Dodd [ 13 ]. Despite identical domain structure, none of these proteins shows statistically significant homology to the fish F1 group members or their putative homologues from planarian or Drosophila . Altogether, this indicates that domain structure alone cannot establish an evolutionary link between the fish and invertebrate sequences. Hence, the suggestive link between the F1 group fish members and the planarian and Drosophila proteins is even more interesting. CTLDcp classification update The existing classification of CTLDcps is generally accepted and popularly used in studies of the superfamily and recently has been updated [ 15 ]. The classification divides CTLDcps into monophyletic groups of proteins with identical overall domain architecture based on a combination of structural and phylogenetic information. Although two previous large-scale studies [ 13 , 14 ] showed it to be inapplicable for description of invertebrate CTLDcps, our analysis of the puffer fish genome indicates that it is sufficient to describe the superfamily in all vertebrates, with only minor modifications and some extensions. Our newly discovered CTLDcps, with a few exceptions, do not fit into the existing classification because of their unique domain architecture. We propose several new groups to accommodate the novel CTLDcps which have been found in both higher and lower vertebrates and are supported by cDNA sequences: • XV – Bimlec (type I transmembrane protein), which in phylogenetic trees is not placed on the same branch as group VIII sequences, has a distinct exon-intron structure of the CTLD region and a neck not similar to the neck region of the group VIII sequences; • XVI – SEEC, based on unique domain architecture; • XVII – CBCP, based on unique domain architecture; Additional groups may be required for the sequences not supported by sufficient expression data (NLSLH) and other sequences from the "unclassified" group whose presence in higher vertebrates is not clear. Also, clade-specific groups, such as fish antifreeze proteins (AFP), dual-CTLD sequences (group F1) predicted by us and so far identified only in fish, or snake venom CTLDcps which lack orthologues in other vertebrates, are required. It has been suggested previously [ 19 , 48 ] that AFPs belong to group VII based on their domain architecture and exon-intron structure. However, our phylogenetic analysis of an alignment of CTLD sequences from all known human and mouse CTLDcps and 26 different fish CTLD-containing protein sequences identified by searching the NCBI protein database with BLAST, indicates that they constitute a phylogenetically distinct group including all known soluble fish CTLD-containing proteins, except Cyprinidae collectins. As to the exon-intron structure, introns in the group XII (EMBP) CTLDs are at exactly the same positions as in group VII and AFP-like CTLDs, which suggests that all three groups are closely related but does not allow classification of the fish AFP-like sequences to either of the mammalian groups. Interestingly, just like most of the AFPs, mammalian EMBPs contain an atypical WIGL motif with a glycine in the fourth position, a substitution not observed in any other mammalian CTLD we analyzed. Taken together, these observations indicate that in a broader evolutionary perspective the differences between some of the groups including CTLDcps with a very similar domain architecture (VII, XII and AFP; II and V) become less distinct, which makes classification of the "intermediate" or "ancestral" sequences, equally related to more than one group, problematic. Selective duplication of the Fugu CTLDcp-encoding genes and the whole-genome duplication hypothesis The hypothesis that whole-genome duplications were one of the main driving forces in vertebrate evolution, providing genetic material for increased diversity and progressive development [ 73 ], and that there were two rounds of whole-genome duplication in vertebrate phylogeny (the 2R hypothesis) [ 73 , 74 ], is actively debated [ 75 , 76 ]. A more recent whole-genome duplication is suggested for the Actinopterygian branch [ 61 ]. Ray-finned fish are the most diverse group of vertebrates, and based on the initial observation that each of the four human HOX gene clusters has two homologues in zebrafish [ 77 ] it was suggested that they have undergone an additional round of a whole-genome duplication after the divergence from Sarcopterygian about 430 Myr ago [ 61 ]. Analysis of the genome duplication in fish can give a picture of a duplicated genome after 300–400 Myr of evolution and fill the gap between the now generally accepted recent tetraploidizations in plants [ 78 ] and yeast [ 79 ] and the alleged more ancient duplication(s) of the ancestral vertebrate genome. Although many fish genes are indeed duplicated [ 61 , 77 , 80 - 82 ], it is not clear whether the copies were created by a complete genome duplication (autopolyploidy), merge of different genomes (allopolyploidy), regional duplication, or simply a series of tandem duplications. Attempts to show that ancient tetraploidization (has not) occurred usually involve: (i) searching for an excess of paralogue groups where the number of members is double the number of alleged duplications (i.e. 2 in case of Actinopterygian duplication, and 4 in case of vertebrate duplication, the "one to four rule") [ 74 , 76 ]; (ii) showing that a statistically significant number of duplications took place at approximately the same time by molecular clock estimation or synonymous substitution counting [ 83 , 84 ]; (iii) using phylogenetic methods to assess the relation between duplication and speciation events [ 61 ]; and (iv) showing that duplicated genes are arranged in paralogous blocks on chromosomes (paralogons) [ 62 , 85 , 86 ]. We used these approaches to analyze the nature of the observed CTLDcp duplications in Fugu . Our results clearly show that tandem gene copying is a mechanism of CTLD family evolution and led to generation of three gene clusters: DC-SIGN-F2 – DC-SIGN-F5 (4 genes), CRTL1-F1 and CRTL1-F2, and AFPL-F1 and AFPL-F2. Members of the two latter clusters are nearly identical and may be an assembly artifact. Twelve other duplicated genes are not linked in the current assembly and have sequences much more diverged than tandem duplicates. Of the 12 genes only MManR, which has 3 paralogues, is present in more than two copies. We consider this is important evidence in favor of a whole-genome duplication, as sporadic duplications cannot explain such a strong bias towards two-member paralogue groups. Unfortunately, the results of duplication time estimations are less conclusive as they give only a broad timeframe for the possible duplication events of about 300–400 Myr. As in the case of some other fish gene families reported previously [ 61 , 62 , 87 , 88 ], molecular phylogeny reconstruction performed by us often indicates that duplications occurred before the divergence between fish and tetrapods. However, this could be an artifact of the method caused by different selection pressures on duplicates. Unfortunately, there is practically no overlap between vertebrate and invertebrate CTLD families, so we could not use invertebrate sequences to refine phylogenetic analysis. To conclude: phylogenetic relationships between CTLD paralogues and estimated duplication time distribution indicate that there was a burst in duplication activity in the Fugu genome 300–400 Myr ago. While we cannot determine definitively the nature of the duplications (tandem, regional or whole-genome), a pronounced bias in the number of two-member paralogue groups strongly suggests that there was a single large-scale or whole-genome duplication event in fish. Another interesting observation is that CTLDcp genes were either duplicated, or retained after a large-scale duplication, in a pronounced selective manner. One group (I) is duplicated completely, while in other groups only partial duplications are found. Interestingly, group I (lecticans), which in tetrapods contain four large (>2000 amino acids) proteins, very similar to each other in sequence and domain structure, is a good candidate for demonstrating the 2 R hypothesis. If the four lecticans arose as a result of the alleged two rounds of the whole-genome duplication early in vertebrate history, the fact that the family was also completely duplicated in fish and retained after the duplication appears very non-random and implies some functional explanation. In the human genome, all four genes encoding lecticans are located on different chromosomes (1, 5, 15 and 19), but it is not clear whether they are linked in Fugu . Another group that conforms to the 2 R hypothesis is group VI, which in tetrapods has four members with almost identical domain structure in mammals (Pla2R, MManR, DEC-205 and Endo180). Though in the Fugu genome we identified 7 group VI sequences, some of which are fragmented (Figure 1 ), phylogenetic analysis shows that only one member of the family (MManR) was quadruplicated, while others are present in a single copy. Both molecular clock and Ks-based methods date the MManR duplications at approximately the same time as other CTLDcp gene duplications. Phylogenetic trees, built on alignment of the overlapping portions (Figure 1 ) of the complete sequences and three largest fragments (fMManR-F1, fMManR-F2, fMManR-F3) have symmetrical structure, with fMManR-F1, fMManR-F2 and fMManR-F3 forming a separate branch (Figure 4(B) ). A whole-genome duplication, generating fMManR and fMManR-F1, followed by tandem duplications of fMManR-F1, producing fMManR-F2 and fMManR-F3, can explain this topology. Conclusions We have performed an analysis of the CTLD superfamily composition in Fugu rubripes . Although the sequence assembly is in the draft state and lacks physical mapping information and native cDNA sequences that could be used to make and verify gene predictions, the quality of the data is good enough despite these limitations to answer many important questions. Our study demonstrates that all but two groups of CTLDcps present in mammals are also found in fish, that most of the groups have the same composition as in mammals, and that the missing groups are the evolutionarily most dynamic ones involved in physiological processes that may be specific to higher vertebrates. We also identified at least one distinct fish-specific CTLD group, which could be the first known vertebrate CTLD group also found in invertebrates. The compactness of the Fugu genome makes it an extremely convenient reference sequence for identification of new genes based on supporting similarity features, and we were able to identify and predict the structure of several new CTLD-containing genes highly conserved between Fugu and human. The new sequences are supported by cDNA and EST sequences from databases and have previously unknown domain architectures. We are now characterizing some of these sequences experimentally. We also show that CTLDcp-encoding genes are selectively duplicated in Fugu , in a manner that suggests an ancient large-scale duplication event in fish. Methods Corrected gene predictions are made available as a distributed annotation system (DAS) [ 89 ] resource [ 90 ], which can be viewed in the EnsEMBL genome browser. The data source names for predictions based on assemblies v.2 and v.3 are fugu_ctld_1 and fugu_ctld_2, respectively. Transcript sequences (in FASTA format) for the CTLDcp-encoding genes created or modified by us (stable IDs starting with ANU) and their translations are also provided in the additional file 1 and additional file 2 , respectively. Searches and gene annotations were done on version 2 of the Fugu rubripes genome assembly [ 31 ] downloaded from the EnsEMBL web site [ 91 , 92 ]. When the third version of the assembly was released, we mapped gene annotations onto it. Mapping was done on the basis of SSAHA [ 93 ] matches in the v.3 assembly for exons predicted on the v.2 assembly. The v.2 assembly is currently accessible at the Singapore IMCB site [ 94 ] and on our server [ 95 ], which is pre-configured to display the DAS track with our annotations and contains a reference table with hyperlinks for all of the Fugu CTLDcp genes discussed. Version 3 of the assembly can be found on the main EnsEMBL web site [ 34 ]. The EnsEMBL genome browser can be easily configured to display our gene models as a DAS track. We used a multi-step approach to find genes encoding CTLDs. First, a hidden Markov model (HMM) profile of the CTLD was used to scan a FASTA database of EnsEMBL-predicted genes with the hmmsearch program from the HMMER package [ 96 ]. To detect orthologues and paralogues, the set of Fugu sequences found was compared with the 95% non-redundant set of sequences of human CTLDcps that could be found in the Entrez proteins database, using the Inparanoid program [ 40 ]. All of the 25 orthology links detected by Inparanoid were checked manually. Because of systematic and sporadic errors in EnsEMBL gene predictions, we had to manually revise the structure of each of the 69 genes encoding proteins detected by the HMM-based search. This was done using the Apollo genome annotation software [ 97 ] connected to a local installation of the EnsEMBL database. To facilitate annotation, several additional feature tracks were added to the EnsEMBL database: a) Similarity features detected by GeneWise [ 98 ] search of Fugu scaffold sequences with a CTLD HMM built in a global alignment mode. This was done to detect well conserved CTLDs while avoiding many false positives. b) Same as a), but with an HMM built in the local alignment mode; this was done to detect highly conserved fragmented CTLDs; c) Similarity features detected by a TBLASTN search of Fugu scaffold sequences using all known human CTLD sequences; this was done to detect CTLDs that are less conserved; d) ORFs encoding putative transmembrane (TM) domains. To create this track a database of all possible ORFs longer than 45 bp was produced and translated into protein sequence using the EMBOSS programs. This was then scanned with the TMHMM program [ 99 ] to detect ORFs that encode putative TM domains. To verify whether there are CTLDs that were not covered by EnsEMBL gene predictions, we searched for all significant CTLD similarity features detected by GeneWise which do not overlap with any of the genes analyzed in the first stage. This step led to detection of 25 new CTLD-coding genes, including most of the ones that have previously uncharacterized domain organization. At the next stage we analyzed the loci with different CTLD similarity features detected by genewisedb search with a local alignment HMM. Finally, the features identified by BLAST and not overlapping with already detected genes were analyzed. This set of features mostly contained only partial CTLD matches. We translated both the new and already predicted gene CDSs into protein sequences and performed another Inparanoid comparison. Phylogenetic relationships were analyzed with the programs from the Phylip package [ 100 ]. ClustalW [ 101 ] guiding trees were used for quick phylogeny estimation and in cases where a proper multiple alignment could not be made. BioPerl [ 102 ] and EnsEMBL Perl modules were used to automate all stages of the analysis. Domain architectures were analyzed with the SMART web service [ 103 ]. To estimate the proportion of substitutions in synonymous sites, we aligned translated sequences of the duplicated CTLDcp-encoding genes with ClustalW, using either whole sequence or sequence for the CTLD-encoding region only, and built nucleotide sequence alignments based on the protein alignments. Ks estimations were performed with four methods: Lynch and Connery [ 104 ] and Li [ 105 ], both implemented in the ntdiffs package [ 104 ]; and Nei and Gojobori [ 106 ] and Yang and Nielsen [ 107 ], both implemented in the yn00 program from the PAML package. Duplication dating using the calibrated molecular clock approach was performed as in [ 83 ]. Alignments of CTLD-containing regions of Fugu paralogues and their mammalian orthologues were made with ClustalW. The MEGA2 program [ 108 ] was used to build linearized trees from Poisson-corrected distances, p-distances and Gamma-corrected distances by the neighbor-joining method with 1000 bootstrap samplings. The global clock was calibrated using divergence times 96 Myr and 430 Myr for human-mouse and fish-mammal splits, respectively [ 32 , 60 , 83 ]. List of abbreviations AFP, antifreeze protein; AFPL, AFP-like; CBCP, C alx- β and C TLD-containing P rotein; CDS, coding sequence; CETM, C TLD, E GF, T rans M embrane domain CTLD; C-type-lectin-like domain; CTLDcp; CTLD-containing protein; DAS, distributed annotation system; EMBP, eosinophil major basic protein; EST, expressed sequence tag; HMM, hidden Markov model; MBP, mannose-binding protein; Myr, million years; NLSLH, N ovel L - S eLectin H omologue; PKD1, polycystic kidney disease protein 1; SEEC, S CP, E GF, E GF, C TLD; TM, transmembrane. Authors' contributions ANZ carried out the bioinformatics studies and participated in the interpretation of its results. JEG conceived the study and participated in the interpretation of its results. Both authors participated in writing the manuscript and approved its final form. Supplementary Material Additional File 1 Transcript sequences for re-annotated Fugu CTLD genes. The file contains cDNA sequences (in FastA format) for all CTLDcp-encoding genes that were re-annotated by us (sequence identifiers starting with ANU). Click here for file Additional File 2 Protein sequences for re-annotated Fugu CTLD genes. The file contains protein product sequences (in FastA format) for all CTLDcp-encoding genes that were re-annotated by us (sequence identifiers starting with ANU). Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514892.xml

529468

Community effectiveness of chloroquine and traditional remedies in the treatment of young children with falciparum malaria in rural Burkina Faso

Background There is little information on the effectiveness of modern compared to traditional malaria treatment from the rural areas of Africa. Methods Follow-up of 402 episodes of clinical malaria among pre-school children in Nouna Health District, northwestern Burkina Faso. The exposure of interest was the type of treatment (chloroquine versus traditional); the outcome was clinical response to treatment. Results Out of the 402 observed malaria episodes, 87% were treated with chloroquine and 13% with traditional remedies. Overall, community effectiveness was 67% with chloroquine and 54% with traditional treatment. Chloroquine effectiveness was associated with age and ethnicity. An additional interview survey demonstrated wide variations in the dosages of chloroquine given to young children in this community. Conclusions The effectiveness of chloroquine, when used within the community, was significantly lower in this study than its known efficacy in the study area. This concerns, in particular, the very young children. These findings demonstrate the need for better education of parents about correct dosage of first-line malaria drugs, and for particular attention in the treatment of very young children.

Introduction It has been estimated that at least one million annual malaria deaths occur among young children in rural sub-Saharan Africa (SSA). Most of these deaths are in areas with little access to health services [ 1 - 4 ]. Under such circumstances, home treatment with chloroquine, antipyretics and traditional remedies is the most frequent response of caretakers to fever episodes in children [ 5 ]. There is also increasing evidence that improved home management of malaria in young children of SSA can be effective [ 6 , 7 ]. The situation is now complicated by the increasing resistance of Plasmodium falciparum to chloroquine in most SSA countries [ 8 ]. Moreover, compared to the efficacy under trial conditions, the community effectiveness (the drug efficacy under real life conditions) of malaria treatment is significantly lower [ 9 ]. Chloroquine has been used in Burkina Faso for decades and reported clinical failure rates in children with uncomplicated malaria were around 5% in the early 1990s [ 10 ]. However, data from a more recent study on the efficacy of chloroquine in a representative sample of villages in the Nouna Health District in north-western Burkina Faso demonstrated a clinical failure rate of 10% in young children [ 11 ]. Nevertheless, chloroquine has remained the official first-line treatment for uncomplicated malaria in Burkina Faso until today. Traditional treatment for malaria is very common, particularly in the rural areas of SSA, but only few data exist regarding the efficacy of such treatments [ 5 , 12 ]. In rural north-western Burkina Faso, treatment of uncomplicated malaria usually comprises a combination of modern and traditional methods. Chloroquine and antipyretics, often combined with traditional treatments, are the most common community-based treatment regimens for young children during fever episodes [ 4 ]. Traditional treatments usually comprise oral and/or skin applications of extracts from eucalyptus plants, acacia, citronella, papaya, guava and the neem tree [ 13 ]. This paper reports on the community effectiveness of chloroquine compared to traditional remedies in the treatment of uncomplicated falciparum malaria in young children of rural Burkina Faso. Methods Study area The study was conducted in the rural part of the research zone of the Centre de Recherche en Santé de Nouna (CRSN), in Nouna Health District, north-western Burkina Faso. The CRSN study zone comprises 41 villages and Nouna town, with a total population of 55,000. The Nouna area is a dry orchard savanna, populated by subsistance farmers of different ethnic groups. There is a short rainy season which usually lasts from June until October. Malaria is holoendemic but highly seasonal in the study area [ 14 ]. Formal health services in the CRSN study zone consist of four village-based health centres and the district hospital in Nouna town [ 4 ]. Study design This is an observational study at the community level. Data for this study were extracted from the database of a trial on the effects of zinc supplementation on malaria morbidity conducted in the Nouna area in 1999 [ 14 ]. During this trial, 709 children aged 6–31 months at enrollment in June 1999 were recruited from 18 villages of Nouna Health District. Study children were followed up until December 1999. Field methods for data collection have already been described [ 14 ]. In brief, the children in the study were regularly visited by village-based field staff for temperature measurement and malaria slide preparation from finger-prick blood in case of fever over the main malaria transmission period (June to December). If fever persisted or recurred, another blood slide was prepared at least one week after the start of the febrile episode. If children were found to have fever or other obvious illness, their caretakers were advised by the field staff to use chloroquine for febrile episodes or to seek diagnosis and treatment at the next governmental health centre. Children found ill during four cross-sectional surveys were treated appropriately by the study physician or referred to Nouna hospital, where they were treated free of charge. All relevant signs and symptoms manifested by the children were recorded on a standard questionnaire during regular visits of field staff. Moreover, a comprehensive questionnaire was filled in for each disease episode which included information on specific signs and symptoms as well as place, time and type of treatment received. No specific information was recorded on the dosage of chloroquine and on the type of traditional remedies given to the study group. For this study, only children with a diagnosis of falciparum malaria, defined as an axillary temperature of ≥37.5°C together with a density of ≥5,000 P. falciparum parasites per μl of blood were considered. Inclusion criteria were falciparum malaria treated with chloroquine (with or without antipyretics) but without traditional remedies (group CQ), or with traditional remedies only (group TRAD). Exclusion criteria were a history of febrile illness during the previous four weeks, malaria treatment during the previous four weeks, any treatment with another antimalarial drug during follow up, and missing temperature measurements during the two-weeks follow up period. Multiple episodes of malaria in individual children were considered to be independent with regard to treatment choice [ 4 ]. To evaluate treatment outcome, a modified definition of the WHO protocol for assessment of therapeutic efficacy of antimalarial drugs in areas with intense transmission was used [ 15 ]. Treatment failure (TF) was defined as development of severe malaria on day 1–14 or fever recurrence (axillary temperature ≥37.5°C) on day 3–14. Adequate clinical response (AR) was defined as the absence of fever on day 3–14, without meeting any of the criteria of TF. Chloroquine dosage survey To retrospectively estimate the chloroquine dosages usually given to febrile young children in the study area, 55 individual interviews with randomly selected mothers of young children from three villages in the study area were conducted during a malaria survey at the end of the 2001 rainy season. The villages were purposely selected to represent the rural study population in its socio-cultural, demographic and geographical diversity. Questions concerned the kind of treatment (western and/or traditional) and the dosages of western drugs used during treatment of the last febrile episode of survey children, the current availability of chloroquine in the household and the origin of chloroquine drugs. The weight of children was measured with a Salter hanging spring scale. Laboratory procedures Blood films were kept in closed slide boxes until they were transported to Nouna (two to three times per week). They were Giemsa-stained at the Nouna hospital laboratory and transported afterwards to the Centre National de Recherche et de Formation sur le Paludisme (CNRFP) in Ouagadougou for reading. All films were examined by two experienced laboratory technicians using a ×100 oil immersion lens and ×10 eyepieces. In case of significant discrepancy between the results of the two technicians, blood slides were read by a third investigator. Blood films were analysed for the species-specific parasite density per μl by counting against 500 white blood cells and multiplying by sixteen (assuming 8,000 white blood cells per μl of blood). Slides were declared negative if no parasites were seen in 400 fields on the thick film. Statistical analysis Data were entered at the data management department of the CRSN into a data bank (Microsoft Access, version 97). All questionnaires were checked by supervisors before computer entry. Parasitological data were entered into EpiInfo (version 6.0) at the CNRFP, and the data were transferred to the CRSN. All data were checked for range and consistency before data entry. The outcome of malaria episodes (AR vs. TF) was assessed dependent on the treatment given by the caretakers. Episodes in which the child had received at least one dose of chloroquine were grouped in the treatment group CQ, and episodes in which caretakers had given only traditional treatment in the treatment group TRAD. The hypothesis was tested that the samples in the CQ and the TRAD treatment groups were from populations with the same distribution of baseline variables using the Wilcoxon ranksum test for continuous variables and the chi-squared test for categorical variables. Community effectiveness of treatment with chloroquine was assessed both by intention to treat (i.e. adherence to advice by the field workers to give chloroquine) and considering only those cases who actually complied. A stratified analysis by age bands was started, assuming that age would be an important determinant of treatment outcome. Logistic regression modelling was then used to control for differences in the distribution of baseline characteristics such as age, sex and parasite density in the two treatment groups. Stata 7.0 statistical software was used for all calculations. Ethical aspects Approval for the RCT from which the data used were drawn was granted by the Ethical Committee of the Heidelberg University Medical School and the Ministry of Health in Burkina Faso. Results Follow-up study A total of 330 children (175 boys and 155 girls), who contributed 402 episodes of falciparum malaria, were followed up. Most of the episodes (65.7%) occurred between August and October (Table 1 ). Table 1 Distribution of febrile episodes by treatment type over the main malaria transmission period in young children of rural Burkina Faso Month 7/99 8/99 9/99 10/99 11/99 total Group CQ 25 126 108 75 16 350 Group TRAD 1 15 15 19 2 52 Total 26 141 123 94 18 402 In 350 (87.1%) of the episodes, the child had received at least one dose of chloroquine (treatment group CQ); in 52 episodes (12.9%), the caretakers had given only traditional treatment (treatment group TRAD). Table 2 shows the distribution of baseline characteristics in the two treatment groups. The median parasite density was significantly lower in the TRAD group (p = 0.001), but diarrhoea was observed in a larger proportion of episodes in this group than in the CQ group (p = 0.04). In the Bwaba ethnic group, 33.3% of malaria episodes had been treated with traditional treatment, compared to 8.7% in the other ethnic groups (p < 0.0001). Table 2 Baseline characteristics of malaria episodes in study children in rural Burkina Faso Group CQ (n = 350) Group TRAD (n = 52) p-value * Male/female 183/167 30/22 0.47 Median age, range (months) 20 (8 – 35) 19 (9 – 33) 0.36 Median parasite density/μl, D0 Range 26 220 5000 – 1 140 000 16 352 5000 – 86 656 0.001 Median temperature D0 (°C) 38.2 38.2 0.72 Diarrhoea D0 (%) 64/350 (18.3) 16/52 (30.8) 0.04 Vomiting D0 (%) 39/350 (11.1) 3/52 (5.8) 0.24 Ethnicity <0.0001 Marka 180 16 Mossi 72 8 Bwaba 46 23 Peulh 44 5 Others 8 0 * Chi-squared test for categorical variables, ranksum test for continuous variables Group CQ: received at least one dose of chloroquine Group TRAD: received only traditional treatment D0: Day of onset of malaria episode The caretakers had been advised to give chloroquine to all children participating in this study. Compliance with this advice, stratified by age group, is shown in Table 3 . Compliance was independent of sex (data not shown), except in the age group 15–21 months, in which 98.2% of episodes in girls, but only 86.8% of episodes in boys, were treated with chloroquine (p = 0.02). Community effectiveness as a result of following the advice to give chloroquine, measured by the proportion of episodes in which the child had an AR was 64.9% (95% CI: 60.0–69.6%). This proportion did not increase appreciably when we restricted the analysis to episodes in which the child had actually received chloroquine (Table 3 ). In the TRAD group, children had an AR in 53.9% of the episodes. Our study did not have sufficient power to show a difference between the TRAD and the CQ groups as small as the one observed (-11%) at the 5% significance level. In the CQ group, outcome was strongly and positively associated with age, as demonstrated by the chi-squared test for linear trend (p = 0.014). A comparable result was not shown in the TRAD group, possibly because of small case numbers. Table 3 Compliance with treatment and outcome of treatment, stratified by age group Compliance Outcome of treatment Age group CQ given n (%) AR, total n (%) AR, CQ group n (%) AR, TRAD group n (%) 8 – 14 months 87 (81.3) 58 (54.2) 49 (56.3) 9 (45.0) 15 – 21 months 121 (91.7) 89 (67.4) 83 (68.6) 6 (54.6) 22 – 28 months 86 (87.8) 63 (64.3) 57 (66.3) 6 (50.0) 29 – 35 months 56 (86.2) 51 (78.5) 44 (78.6) 7 (77.7) Total 350 (87.1) 261 (64.9) 233 (66.6) 28 (53.9) CI a - 60.0 – 69.6% 61.4 – 71.5% 39.5 – 67.8% p-value 0.13 b 0.004 c 0.014 c 0.17 c a CI: Binomial exact 95% confidence interval b p-value: Chi-squared test for heterogeneity (within each column) c p-value trend: Chi-squared test for linear trend (within each column) CQ group: Received at least one dose of chloroquine AR: Adeaquate clinical response The crude odds ratio for TF in the CQ group vs. the TRAD group was 0.59 with a 95% confidence interval that included unity (Model 1 in Table 4 ). Logistic regression modelling was used to assess the degree to which the observed advantage of chloroquine treatment over traditional treatment was due to a different distribution of baseline characteristics in the two treatment groups. Inclusion of the variables age group (with four seven-month age bands) and sex (Model 2 in Table 4 ), as well as parasite density (log-transformed and grouped in three bands) and diarrhoea on day 0 (Model 3 in Table 4 ) did not change the odds ratio much; if there was any advantage of chloroquine over traditional treatment it was slightly attenuated. However, removing all episodes among children of the Bwaba ethnic group from the analysis decreased the odds ratio, and the corresponding confidence interval no longer included unity (Model 4 in Table 4 ). This means that in the other ethnic groups, chloroquine treatment carried only 0.43 (95% CI: 0.19–0.91) times the odds of treatment failure of traditional treatment and thus conveyed a significant advantage. Table 4 Regression modelling of treatment failure, CQ vs. TRAD group Model Variables Odds Ratio (95% CI) p-value 1 crude 0.59 (0.33 – 1.06) 0.08 2 age group, sex 0.61 (0.34 – 1.11) 0.10 3 age group, sex, parasite density, diarrhoea 0.64 (0.35 – 1.17) 0.15 4 age group, sex, parasite density, diarrhoea excluding Bwaba ethnicity 0.43 (0.19 – 0.97) 0.04 CQ group: received at least one dose of chloroquine TRA group: received only traditional treatment Table 2 already showed evidence of an interaction between treatment pattern and ethnic group. Therefore, a subgroup analysis was done in which additional differences between the Bwaba and the other ethnic groups were found. First, among the other ethnic groups, children who received chloroquine had a significantly higher log-transformed mean parasite density than those who received traditional treatment (p = 0.002). The difference in parasite density was smaller and not significant among the Bwaba (p = 0.26). Secondly, treatment outcome was not significantly associated with age group among the Bwaba, while it was so among the other ethnic groups. In Model 4, an increase in age by one age band (equivalent to seven months) was associated with an almost 30% reduction in the odds of treatment failure (p = 0.004) after control for treatment type, sex, parasite density and diarrhoea on day 0 (also see Table 3 ). With a number of important factors determining treatment type and/or outcome varying by ethnic group, the stratified analyses were warranted. Chloroquine dosage survey A total of 55 mothers participated in the survey with 28 girls and 27 boys aged 3–27 months (median: 14 months). During the most recent fever episodes of respective children, home treatment with chloroquine was reported in 32/55 (58%) (8/32 in combination with paracetamol and 1/32 in combination with traditional treatment), treatment at the local health centre in 15/55 (27%), traditional treatment in 6/55 (11%), and only paracetamol treatment in 2/55 (4%). The mean weight of chloroquine-treated children was 8.5 kg (range 3.2 – 12.0 kg). The mean (median) total dose of chloroquine used was 34 mg/kg and 25 mg/kg respectively (range 5–110 mg/kg) (figure 1). Only 9/55 (13%) mothers reported current availability of chloroquine tablets in their household (median 3 tablets, range 1–10). Most chloroquine drugs were purchased from local shops and drug sellers. Discussion The main finding of this study is a rather low community effectiveness of chloroquine in the treatment of malaria episodes among young children in rural Burkina Faso. Overall, a failure rate of 35% was observed which declined only marginally to 33% when malaria episodes in which caretakers did not comply with the advice to give chloroquine were excluded. This is considerably higher than the 10% treatment failure rate observed in a study on chloroquine efficacy in young children of the area [ 11 ]. These findings support the hypothesis that treatment efficacy declines drastically under real life conditions in the community and demonstrate that efficacy trials need to be complemented with observational studies to assess the actual health benefits conferred by an intervention [ 9 , 16 ]. The effectiveness of treatment with chloroquine increased significantly and almost linearly with age, even after controlling for parasite density. The most likely explanation is a rapid development of immunity within the age range of our study children, as has been observed in other studies [ 1 , 17 , 18 ]. This implies that particular care is needed in the treatment of the youngest children in whom immunity will be weakest. Given the fact that the children who participated in the study on chloroquine efficacy in the Nouna area were in the most susceptible age group for developing clinical malaria (mean age 10 months, range 6–15), the loss of efficacy under community conditions observed in this study becomes even more pronounced [ 11 ]. Only a small number of episodes were treated solely with traditional treatment, possibly a consequence of the view of the local population that western malaria treatment is more effective than traditional treatment [ 13 ]. This may not apply to all strata of the population, as there was an interaction between type of treatment and ethnicity. Moreover, while children receiving traditional treatment had a higher risk of treatment failure than children treated with chloroquine after control for age, sex and parasite density, this association did not reach statistical significance until episodes of children belonging to the Bwaba ethnic group were removed from the analysis. Interestingly, crude failure rates were not higher among cases from the Bwaba group compared to those belonging to other ethnic groups. Also these results support the findings from another study in Burkina Faso which has also shown a lower effectiveness of traditional compared to modern malaria treatment, traditional treatments are not identical in different populations [ 17 ]. Thus, it may well be that the traditional treatment used in the Bwaba ethnic group is more effective against malaria compared to other traditional treatments in the area. Finally, the observed significant association of traditional treatment with diarrhoea on day 0 may well be a chance finding, given the lack of a difference between treatment groups in all other clinical and parasitological parameters. This study has two limitations. First, information on the dosage of chloroquine and on the nature of traditional remedies given to children in the study was lacking. However, data from the interview survey provide a good estimate of chloroquine dosages used. Although these data demonstrate a wide variation of chloroquine dosages, it is reassuring that the reported median dosage complies with the national recommendations of a total dose of 25 mg/kg chloroquine for uncomplicated malaria. So why is there such a difference between efficacy and effectiveness? The reason may well be a combination of partly chloroquine underdosing, poor quality of drugs purchased from local shops and drug sellers and high frequency of vomiting in febrile young children [ 19 - 23 ]. Moreover, the possibility of a significant reporting bias during interview surveys also has to be taken into consideration. Secondly, a purely clinical definition of treatment failure was used. Intercurrent febrile illnesses may thus have been misclassified as persisting malaria, thereby overestimating failure rates. However, this is likely to have only a small effect, since the fraction of febrile episodes attributable to malaria (using a highly specific definition) in this cohort of children was found to be above 50 percent [ 24 ]. In conclusion, this study provides clear evidence for a rather low and age-dependant community effectiveness of chloroquine in the treatment of P. falciparum malaria in an area of rural Burkina Faso where the efficacy of chloroquine is still sufficiently high in use as first-line treatment. The observed discrepancy between efficacy and community effectiveness implies that many children received insufficient doses of chloroquine. Our findings thus point to the importance of (1) better education of parents on correct dosage of first-line malaria drugs, as well as on danger signs requiring a visit at a health centre, (2) quality-control of all malaria drugs used in the community, and (3) particular care in the treatment of immunologically naïve very young children. Finally, more studies on the types and effects of traditional treatments used in different population groups are needed.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC529468.xml

529454

Screening for known mutations in EIF2B genes in a large panel of patients with premature ovarian failure

Background Premature Ovarian Failure (POF), defined as the development of hypergonadotropic amenorrhea before the age of 40 years, occurs in about 1% of all women. Other than karyotype abnormalities, very few genes are known to be associated with this ovarian dysfunction. Recently, in seven patients who presented with POF and white matter abnormalities on MRI (ovarioleukodystrophy) eight mutationswere found in EIF2B2 , 4 and 5 . Methods To further test the involvement of known mutations of EIF2B genes in POF, we screened 93 patients with POF who did not have identified leukodystrophy or neurological symptoms. We evaluated these eight mutations and two additional mutations that had been found in patients with milder forms of eIF2B-related disorders. We used restriction enzymes and direct sequencing. Results None of the known mutations in EIF2B genes, either homozygous or heterozygous, were identified in our 93 patients with pure 46,XX POF. The upper 95 % confidence limit of the proportion 0/93 is 3.2%. Conclusions We conclude that eIF2B mutations, already described in cases of POF associated with white matter abnormalities, are an uncommon cause of pure spontaneous premature ovarian failure.

Background Premature Ovarian Failure (POF) can present as a primary or secondary amenorrhea and is associated with elevated gonadotropins before 40 years of age. POF affects 1% of all women and occurs in 0.1% before the age of 30 years [ 1 ]. POF has been associated with karyotype abnormalities, including various X chromosome aberrations such as Turner syndrome, which causes depletion of ovarian follicles during development [ 2 ]. While conditions such as autoimmune diseases are also associated with POF, the cause is unknown in about 90 % of cases. However, since many affected women have a family history of the condition, predisposition to POF may be inherited [ 3 ]. To date, mutations associated with POF have been identified in a small number of genes [ 4 ], including those encoding the inhibin alpha [ 5 ], the FSH receptor [ 6 ], the LH/choriogonadotrophin receptor [ 7 ], and the forkhead transcription factor 2 [ 8 ]. No more than 10% of women with ovarian failure have mutations in these different genes [ 8 ]. Recently three of the five EIF2B genes ( EIF2B2 , 4 and 5 ) were reportedly involved in seven patients who presented with POF and white matter abnormalities on MRI (ovarioleukodystrophy) [ 9 ]. These genes encode the five subunits of the eucaryotic initiation factor 2B (eIF2B alpha to epsilon), which is involved in the first step of protein synthesis. eIF2B-related disorders include a large group of phenotypes with a recognizable MRI pattern but different clinical severities. The clinical spectrum can range from a rapid course leading to death in severe congenital forms to asymptomatic MRI findings in adult patients [ 10 , 11 ]. Ovarioleukodystrophy might present in a phase without neurological symptoms and an apparently isolated form of POF [ 9 ]. Therefore, we screened a series of 93 patients with apparently pure, karyotypically normal POF for mutations in EIF2B genes. Methods Selection of patients with premature ovarian failure In the current study, we evaluated the presence of EIF2B mutations in 93 unrelated and well-characterized women with POF. An institutional review board approved the study and all participants gave a written informed consent. Referring physicians made the diagnosis of premature ovarian failure based on the following criteria: development of at least 4 months of amenorrhea before age 40 associated with two serum FSH levels in the menopausal range. Women with premature ovarian failure as a result of surgery, radiation, chemotherapy, or known karyotype abnormalities were not included in the study. There were 6 Asians, 12 Blacks, 4 Hispanics and 71 Caucasians. The median age at the onset of menstrual irregularity was 24.5 years (range 13 to 39). Eighteen women had a family history of POF. All women underwent a history and physical examination and laboratory screening to confirm the diagnosis of POF and all had a normal karyotype. None of the women had evidence of a neurological disorder. EIF2B mutations screening Genomic DNA was extracted from peripheral blood using standard procedures. The exons of the genes EIF2B2 , 4 and 5 which contain mutations found in POF patients or in milder forms of eIF2B-related disorders were amplified by the polymerase chain reaction (PCR) as previously described (Table 1 )[ 11 ]. Table 1 Sequences of PCR primers used and their PCR conditions. Nucleotide change tested (gene) Primers sequences (5'-3') PCR conditions C512T ( EIF2B2 ), C547T ( EIF2B2 ) F: GCAAAACCGTTCTTAC R: CCTACCCATCTCTCGTTTAT PCR preparation : 1.5 mM MgCl2, 0.225 mM dNTP, 0.8 μM primers, 100 ng primers, 1 unit AmpliTaq Gold™(Applied Biosystems), 1X Taq buffer. 607–612del/insTG ( EIF2B2 ), A638G ( EIF2B2 ) F: GGAAATTATGTGCTGGATATG R: ACTTTATTCTCTCACCGTGGAT P243L ( EIF2B4 ) F: ATGCTCAAGCTCCCTTTCAA R: CTTCACAACTTACAAAGCCT R374C ( EIF2B4 ) F: ATTCAAGCACCTGGCATGAT R: CGCTGCACTCCATCCTTATC PCR reaction : 95°C 12 min, 35 cycles (94°C 30 s, 55°C 30 s, 72°C 45 s), 72°C 10 min, 4°C. T1393C ( EIF2B4 ), T1465C ( EIF2B4 ) F: TGTCCTGTAAGTAGGGGACCTT R: AAGGGGTTGTGAAGTCTGGA G338A ( EIF2B5 ), C583T ( EIF2B5 ) F: GAGAAGGACTGTGAGTGCTGA R: GCCTTCTAAGGGGACAATAAC F: forward primer, R: reverse primer Nine mutations were tested by restriction enzymes directly on PCR products (Table 2 ): 500 ng of PCR products were incubated with 1 unit of specific restriction enzyme from Biolabs ® Inc. for 90 minutes, according to the supplier's instructions. Restriction fragments were analyzed by standard acrylamide gel electrophoresis. Table 2 EIF2B2 , 4 and 5 mutations tested with restriction enzymes. Mutation tested: nucleotides changes (amino acid changes) Mutated gene Restriction enzyme used PCR product size (base pairs) Restriction profile (number of restriction fragments: their size in base pairs) No mut* Het mut* Hom mut* C512T (S171F) EIF2B2 Hpy188III 251 4 fragments: 51, 29, 16 and 155 bp. 5 fragments: 51, 29, 16, 155 and 171 bp. 3 fragments: 51, 29 and 171 bp. 607–612del/insTG (M203fs) EIF2B2 HphI 313 2 fragments: 310 and 3 bp. 4 fragments: 310, 142, 164 and 3 bp. 3 fragments: 142, 164 and 3 bp. C547T (R183stop) EIF2B2 EcoNI 253 2 fragments: 120 and 133 bp. 4 fragments: 120, 133, 14 and 119 bp. 3 fragments: 120, 14 and 119 bp. A638G (E213G) EIF2B2 BsmAI 313 2 fragments: 153 and 160 bp. 3 fragments: 313, 160 and 153 bp. 1 fragment: 313 bp P243L (C728T) EIF2B4 AciI 612 3 fragments: 353, 223 and 36 bp. 4 fragments: 576, 353, 223 and 36 bp. 2 fragments: 576 and 36 bp. R374C (C1120T) EIF2B4 HpyCH4IV 640 2 fragments: 447 and 193 bp. 3 fragments: 640, 447 and 193 bp. 1 fragment: 640 bp. T1393C (C465R) EIF2B4 BsrDI 694 3 fragments: 368, 32 and 294 bp. 4 fragments: 368, 32, 294 and 326 bp. 2 fragments: 368 and 326 bp. T1465C (Y489H) EIF2B4 NlaIII 707 3 fragments: 109, 310 and 288 bp. 5 fragments: 109, 310, 288, 57 and 231 bp. 4 fragments: 109, 310, 57 and 231 bp. G338A (R113H) EIF2B5 Fnu4HI 800 3 fragments: 115, 633 and 52 bp. 4 fragments: 115, 633, 52 and 748 bp. 2 fragments: 748 and 52 bp. * No mut: no mutation; Het mut: heterozygous mutation; Hom mut: homozygous mutation. The C583T (R195C) mutation in the EIF2B5 gene was tested by direct sequencing of exon 4 as previously described [ 11 ]. Results None of the eight mutations already described in ovarioleukodystrophy were detected in our 93 patients with pure 46,XX POF, neither in a homozygous nor in a heterozygous state. In addition, the mutations C728T and C1120T ( EIF2B4 ) described in milder forms of eIF2B-related disorders were not found in this series of 93 patients with POF. The upper 95 % confidence limit of the proportion 0/93 is 3.2%. Discussion eIF2B-related disorders include a large group of phenotypes with different clinical severities. Individuals can be classified into three clinical groups according to their age at disease onset: <2 years (group 1), 2 to 5 years (group 2) and > 5 years (group 3) [ 11 ]. Group 3 corresponds to individuals with the milder form of the disease, including the six families (seven patients) already described presenting with ovarioleukodystrophy [ 9 ]. In these six eIF2B-mutated families, neurological symptoms with abnormalities of the cerebral white matter on MRI were associated with primary or secondary amenorrhea due to POF [ 9 ]. A correlation was observed between the age of onset of the neurological deterioration and the severity of the ovarian failure, suggesting a common pathophysiological pathway [ 9 ]. The mutated eIF2B may be responsible for both increased apoptosis of ovarian follicles leading to POF, and a defect in glial cell development causing abnormal formation of white matter structures. In ovarioleukodystrophy, a phase of amenorrhea without neurological symptoms can be observed, suggesting that an apparently isolated case of POF might be due to EIF2B mutations. In the present study, we tested for EIF2B mutations a series of 93 patients with pure, karyotypically normal POF without identified signs of cerebral dysfunction. In eIF2B-related disorders, a correlation exists between genotype and disease onset [ 11 ]. The mutations G338A ( EIF2B5 gene) and A638G ( EIF2B2 gene) are found in 71% of families with late onset forms of eIF2B-related disorders (group 3) [ 11 ]. In ovarioleukodystrophy, 4/6 families have a G338A or A638G mutation in a heterozygote or a homozygote state. Thus, to further evaluate involvement of eIF2B mutations in apparently isolated cases of POF, we restricted our screening to the 10 mutations associated with the late onset form (group 3) of eIF2B-related disorders. In the present series of 93 patients with pure, karyotypically normal POF, no mutations were detected, suggesting a low frequency of EIF2B mutations in women with POF who have no apparent neurological signs. Conclusions For patients presenting with POF without neurological signs or MRI abnormalities, the routine screening of the EIF2B mutations is not clinically indicated. Competing interests The author(s) declare that they have no competing interests. Authors'contributions AF and FGB carried out the molecular genetics studies, including enzyme restrictions (AF) and sequencing (AF and FGB). AF drafted and conceived of the study. RS participated in the coordination of the study. VHV, VKB, LMN recruited and evaluated the patients, collected DNA samples, participated in the design and coordination of the study, and helped in drafting the manuscript. OBT conceived of the study, and participated in its design and coordination. All authors participated in the writing of the manuscript and have read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC529454.xml

524170

Diagnostic polymorphisms in the mitochondrial cytochrome b gene allow discrimination between cattle, sheep, goat, roe buck and deer by PCR-RFLP

Background As an alternative to direct DNA sequencing of PCR products, random PCR-RFLP is an efficient technique to discriminate between species. The PCR-RFLP-method is an inexpensive tool in forensic science, even if the template is degraded or contains only traces of DNA from various species. Results Interspecies-specific DNA sequence polymorphisms in the mitochondrial cytochrome b gene were analyzed using PCR-RFLP technology to determine the source (i.e., species) of blood traces obtained from a leaf. Conclusions The method presented can be used for the discrimination of cattle ( Bos taurus ), sheep ( Ovis aries ), goat ( Capra hircus ), roe buck ( Capreolus capreolus ) and red deer ( Cervus elaphus ).

Background Determination of the species from which traces of source material, such as blood stains on a leaf, originate can sometimes be a difficult task in forensic DNA analysis. For instance, insurance claims that involve car accidents with animals require authentication. Species identification is also essential in food quality control-procedures or for the detection and identification of animal material in food samples. Numerous analytical methods that rely on protein analysis have been developed for species identification, such as electrophoresis techniques [ 1 , 2 ], immunoassays [ 3 ] and liquid chromatography [ 4 ]. However, proteins are heat labile and lose their biological activity. Furthermore, their presence and characteristics depend on special cell types. Thus, for species identification, DNA analysis would be preferred over protein analysis. The first genetic approach for determination of species identity was the dot-blot technique [ 5 ]. At present, polymerase chain reaction (PCR) is the technique of choice for species identification [ 6 ]. Some PCR approaches are RAPD-PCR (random amplified polymorphic DNA fingerprints) [ 7 ] and others are focused on RFLP analysis [ 8 ]. In this work we present a more sensitive method to detect DNA from degraded samples. Usually, the required specimen (hair, a part of skin or a piece of meat) contains degraded DNA and the PCR products must be cloned before sequencing [ 9 ]. Existing techniques consist of laborious and costly DNA sequencing procedures. We therefore used a less time-consuming PCR method to amplify a short mitochondrial (mt) DNA fragment. The PCR-RFLP allowed discrimination between different species even in cases in which the source material contained only degraded DNA. The mitochondrial cytochrome b gene has been used in phylogenetic as well as in forensic investigations [ 10 - 12 ] and has been shown in a variety of studies to be a very useful DNA-region for species determination [ 13 - 18 ] However, before applicable for routine analysis, species-specific diagnostic polymorphisms or mutations must be determined. If these mutations affect restriction enzyme sites, a simple PCR-RFLP can be used as a "sceening tool" for detection. One area in which the PCR-RFLP screening tool would be useful is in training hunting hounds. Before hounds are accepted and approved for hunting, they have to be evaluated by different tests. These tests include an examination of the dog's ability for winding and trailing. To examine these abilities normally a track is prepared consisting of minute amounts of blood from game animals, e. g., wild boar, spotted on a few leaves that are distributed in the forest or field. A trained hound should be capable of winding and trailing without any problems. However, in a case that came to our attention, hounds were unable to wind and it was assumed by the owners that the examiner had used blood from domestic animals. Hence, the question as to the source of the track (i.e., blood from domestic or game animals) was open. To solve this problem we established genetic test (PCR-RFLP) that focused on the use of diagnostic polymorphisms in the mitochondrial cytochrome b gene: Our method can be used for the discrimination between the following mammal species: cattle ( Bos taurus ), sheep ( Ovis aries ), goat ( Capra hircus ), roe buck ( Capreolus capreolus ) and red deer ( Cervus elaphus ). Results and discussion For the analysis, DNA was prepared from blood remains on different leaves. The DNA was amplified as described in Methods. Figure 1 shows RFLP results of five different species ( Bos taurus , Ovis aries , Capra hircus , Capreolus capreolus and Cervus elaphus ) and the pattern of bands from the blood sample of one of the dry leaves. As controls, DNA-samples from known species were used. Specific RFLP patterns that distinguish between 5 species are analyzed here. To estimate the exact size of fragments, DNA sequence information was used. C. hircus shows a single fragment of 182 bp, whereas with C. capreolus a fragment of 162 bp was obtained. The RFLP pattern of C. elaphus consisted of 2 fragments of 108 bp and 54 bp respectively. The blood sample on the leaf shows four fragments: 114 bp and 68 bp-fragments, which are characteristic for B. taurus, and a 105 bp and 75 bp fragment which can be assigned to O. aries. This result indicates that the blood sample on the leaf represents a mixture from two species ( B. taurus and O. aries) . The results were obtained from two independent DNA extractions and confirmed several times by independent PCRs, as well as by DNA sequencing. Amplifications were repeated and species identification was verified by diagnostic Dloop sequencing [ 19 ]. The Dloop DNA sequence of the PCR amplified fragment of the blood track was aligned with the mtDNA sequence of B. taurus and three nucleotide differences were observed. Figure 1 Restriction cleavage patterns of DNA mixtures with varying amounts of target DNA. In addition to species determination using PCR-RFLP analysis, we tested for different DNA ratios of mixed samples, by which an assignment to a species is still possible. A valid assignment of a mixture of B. taurus and O. aries is still possible for a ratio of 59:1(Fig. 1 ). The data clearly illustrate that it is possible to identify the species from unknown material using PCR-RFLP, provided that comparison to a known species is performed on the same gel. Furthermore, the PCR-RFLP enables the observation of frequent contaminants such as cattle DNA in routine diagnostic labs. While direct sequencing of coamplified endogenous DNA would lead to multiple sequences, the PCR-RFLP is able to separate two signals. Sometimes, possible contaminants (such as human DNA) can lead to false results, but with the designed primer pair this problem was circumvented. For this reason we designed maximally discriminatory primers and the mismatches in each primer are sufficient to exclude human DNA under stringent PCR conditions. The primers were tested with different amounts of added DNA. This method can be used for analysis of mixed samples, since up to three species in different proportions can be determined. The RFLP test using other tissues, e.g., muscle, hair with roots, bones, and saliva, yielded reproducible results. Faeces have not been tested so far. However, when minimizing target DNA, the bands tend to fade away on the agarose gel. The presence of a specific PCR-RFLP for the species analysed, a fragment length of less than 200 bp and the exclusion of contaminating sequences improved methods for existing species determination. The principle of RFLP is often used in food analysis [ 20 , 21 ]. However, the new PCR-RFLP method is capable of analysing degraded DNA, especially in forensic cases. Furthermore, the PCR-RFLP utilizes only a small fraction of apomorphic sites. The speed and the efficiency of current nucleotide technology, such as automatic sequencing, will permit the identification of additional taxa. However, the establishment of a PCR-RFLP test would likely need further extensive experimentation. Conclusions In summary, we were able to show that a simple PCR-RFLP is efficient in differentiating between B. taurus , O. aries , C. hircus , C. capreolus and C. elaphus . However, after further development, the tested mitochondrial nucleotide sequences may allow the forensic identification of other animal species. Methods DNA extraction DNA extraction (Normal samples) DNA was extracted from blood and tissue samples using QIAamp ® Tissue Kit (QIAGEN GmbH, Hilden, Germany) according to the manufacturers' handbook. Isolated DNA was diluted in 50 μL HPLC-H 2 O and used for further analyses. DNA extraction (Trace material e.g. bones) Bone samples were roughly ground with a pestle and mortar, then finely powdered in a Retsch mill. Bone powder (0.3 g) was incubated in 1.5 mL of 0.5 M EDTA (pH 8.3) for 20 h while rotating. The suspension was centrifuged for 4 min at 4000 rpm. The supernatant was transferred to a fresh tube or to an automated nucleic acids extraction system (Nucleic Acid Extractor 341A, Applied Biosystems) and 1.6 mL sterile distilled water (Ampuwa, Fresenius) was added. As the extraction procedure was automated the volumes of reagents dispensed may have varied between runs. Five hundred microliters of Proteinase K was added and the mixture incubated for 1 h at 58°C with shaking. Three milliliters of phenol/chloroform/isoamyl alcohol (25:24:1, pH 7.5–8.0) were added and the mixture was further incubated at room temperature for 6 min while shaking. The phases were allowed to separate by incubating at room temperature for 8 min without shaking and the organic phase and interphase, if present, were discarded. Chloroform (4.5 mL, 100%) was added to the aqueous phase and the mixture incubated for 6 min at room temperature while shaking. The phases were again allowed to separate by incubating at room temperature for 8 min without shaking and the organic phase and interphase, if present, were discarded. Ninety microliters of sodium acetate (pH 4.5) and 3.2 mL of 100% isopropanol were added followed by incubation for 2 min with shaking. Five microliters of Glasmilk (Dianova) were added and the suspension was shaken for another 10 min. To obtain a pellet, the solution was filtered through Precipitette filters (Applied Biosystems) or centrifuged for 3 min at 5000 rpm. The pellet was washed with 80% ethanol and eluted into 50 μL sterile distilled water (Ampuwa, Fresenius). Five to ten microliters of extract were used for PCR amplification or the extract was stored at -20°C. Glasmilk was not removed prior to amplification. PCR-RFLP For RFLP analysis, a 195 bp long PCR fragment was amplified from mitochondrial cytochrome b region. The following primer pair was used for amplification, CB7u (5'-GCGTACGCAATCTTACGATCAA-3') and CB7l (5'-CTGGCCTCCAATTCATGTGAG-3'). The PCR was carried out in a total volume of 50 μL consisting of 10 ng DNA, 60 mM KCl; 12 mM Tris-HCl; 2.5 mM MgCl 2 ; 150 μM dNTPs; 0,18 μM of each Primer and 2 U AmpliTaq Gold (PE Applied Biosystems). Cycling conditions included a denaturation step at 95°C for 3 minutes, followed by 32 cycles of 95°C for 1 minute, primer annealing at 54°C for 1 minute and elongation at 72°C for 1 minute in a thermocycler (Hybaid). The 195 bp fragment was digested with TSP509 (New England Biolabs) for two hours at 65°C. The resulting fragments were separated by gelelectrophoresis in a 2.5 % agarose gel. MtDNA Dloop PCR sequencing reaction A single fragment of the mitochondrial DNA Dloop region was amplified using primers Dloopu (5'-AAATGTAAAACGACGACGGCCAGTAATCCCAATAACTCAACAC-3') and Dloopll (5'-AAACAGGAAACAGCTATGACCACTCATCTAGGCATTTTC-3'). Amplifications were performed in a final volume of 20 μL in 10 × PCR buffer (15 mM MgCl 2 , pH 8.3) and Q-solution, 100 μM for each dNTP, with 1 M Taq Polymerase and 10 pmol of each primer. Four microlitres of the DNA-extract were added to the PCR mix. The amplification was carried out with initial denaturation at 95°C for 10 min, followed by 35 cycles of one denaturation step at 94°C for 40 sec, primer annealing at 52°C for 40 sec and primer extension at 72°C for 45 sec in a Hybaid thermocycler. PCR-products were purified using the QIAEX II Gel Extraction Kit (QIAGEN GmbH, Hilden, Germany) according to the manufacturers' instructions. Sequencing was performed using ABI-Prism™ Dye Kit V3 (Applied Biosystems) in a 10 μL volume containing 2 μL purified PCR-product and 5 pmol of primer. Sequencing reactions underwent 27 cycles of 30 sec at 94°C, 30 sec at 50°C and 3 min at 60°C in a Techne thermocycler. The dye terminators were removed by sephadex-G45 column purification (Millipore). Sequencing reactions were electrophoresed for 2 h on an ABI Prism ® 3100 genetic analyzer (Applied Biosystems) according to the manufacturers' instructions. Sample selection In order to test the specifity of the technique the following numbers of specimens were tested: 7 unrelated samples from cattle: Holstein Frisian, Charolais, Limousin, Angus 7 unrelated samples from sheep 7 unrelated samples from goat 7 unrelated samples from deer collected from Lower Saxony and North Hesse 7 unrelated samples from roe deer collected from Lower Saxony Regarding closely related species, we analyzed mouflon DNA ( Ovis aries musimon ) and observed approximately 100% sequence identity compared with Ovis aries . Furthermore, we investigated different cattle breeds and we could not find any sequence differences within the tested cytochrome b DNA-fragment. Authors' contributions IP performed the DNA extractions, PCR-RFLP analysis and mtDNA Dloop DNA sequencing. JB developed and provided the PCR-RFLP protocol for species identification. BB was responsible for funding, supervision of the research project, manuscript writing and editing as well as scientific correspondence. Figure 2 Restriction profiles of the 195 bp cytochrome b PCR fragments showing interspecies-specific polymorphism between B. taurus , O. aries , C. hircus , C. capreolus and C. elaphus . The unknown blood sample shows the fragment length of B. taurus and O. aries . Nr. 1 and Nr.11 controls (Bos taurus , undigested PCR product), Nr. 2: C. hircus ., Nr. 3: O. aries , Nr. 4: B. taurus , Nr. 5, 6 and 7 blood sample on a leaf, Nr. 8: DNA mixture B. taurus and O. aries , Nr. 9: C. elaphus , Nr. 10: C. capreolus .

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524170.xml

544860

Dietary isoflavones alter regulatory behaviors, metabolic hormones and neuroendocrine function in Long-Evans male rats

Background Phytoestrogens derived from soy foods (or isoflavones) have received prevalent usage due to their 'health benefits' of decreasing: a) age-related diseases, b) hormone-dependent cancers and c) postmenopausal symptoms. However, little is known about the influence of dietary phytoestrogens on regulatory behaviors, such as food and water intake, metabolic hormones and neuroendocrine parameters. This study examined important hormonal and metabolic health issues by testing the hypotheses that dietary soy-derived isoflavones influence: 1) body weight and adipose deposition, 2) food and water intake, 3) metabolic hormones (i.e., leptin, insulin, T3 and glucose levels), 4) brain neuropeptide Y (NPY) levels, 5) heat production [in brown adipose tissue (BAT) quantifying uncoupling protein (UCP-1) mRNA levels] and 6) core body temperature. Methods This was accomplished by conducting longitudinal studies where male Long-Evans rats were exposed (from conception to time of testing or tissue collection) to a diet rich in isoflavones (at 600 micrograms/gram of diet or 600 ppm) vs. a diet low in isoflavones (at approximately 10–15 micrograms/gram of diet or 10–15 ppm). Body, white adipose tissue and food intake were measured in grams and water intake in milliliters. The hormones (leptin, insulin, T3, glucose and NPY) were quantified by radioimmunoassays (RIA). BAT UCP-1 mRNA levels were quantified by PCR and polyacrylamide gel electrophoresis while core body temperatures were recorded by radio telemetry. The data were tested by analysis of variance (ANOVA) (or where appropriate by repeated measures). Results Body and adipose tissue weights were decreased in Phyto-600 vs. Phyto-free fed rats. Food and water intake was greater in Phyto-600 animals, that displayed higher hypothalamic (NPY) concentrations, but lower plasma leptin and insulin levels, vs. Phyto-free fed males. Higher thyroid levels (and a tendency for higher glucose levels) and increased uncoupling protein (UCP-1) mRNA levels in brown adipose tissue (BAT) were seen in Phyto-600 fed males. However, decreased core body temperature was recorded in these same animals compared to Phyto-free fed animals. Conclusions This study demonstrates that consumption of a soy-based (isoflavone-rich) diet, significantly alters several parameters involved in maintaining body homeostatic balance, energy expenditure, feeding behavior, hormonal, metabolic and neuroendocrine function in male rats.

Background Some phytochemicals are considered to be endocrine disrupters that mimic or modulate the physiological effects of steroid hormones, especially that of estrogens [ 1 , 2 ]. Of all estrogenic endocrine disrupters examined thus far, phytoestrogens have been extensively studied [ 1 - 6 ]. Phytoestrogens represent hundreds of molecules possessing non-steroidal, diphenolic structures found in many plants (e.g. fruits, vegetables, legumes, whole-grain and especially soy food products) that have similar chemical and structural properties to those of estrogens [ 1 - 4 ]. There are three main classifications of phytoestrogens: 1) isoflavones (derived principally from soybeans), 2) lignans (found in flaxseed in large quantities) and 3) coumestans (derived from sprouting plants like alfalfa) [ 2 - 6 ]. Of these three main classifications, human consumption of isoflavones has the largest impact due to its availability and variety in food products containing soy. Furthermore, the phytoestrogens principally derived from soy foods have received prevalent usage due to their 'health benefits' of decreasing: a) age-related diseases (cardiovascular & osteoporosis), b) hormone-dependent cancers (e.g. breast & prostate) and c) postmenopausal symptoms [ 2 - 6 ]. However, little is known about the influence of dietary (soy-derived) phytoestrogens on neuroendocrine, hormone and metabolic parameters. In spite of this fact, the Food and Drug Administration (FDA) in the United States in October of 1999 authorized the use of-on food labels- the health claim that: soy protein can reduce the risk of coronary heart disease by lowering blood cholesterol levels (when included in a diet low in saturated fat and cholesterol) [ 5 ]. The purpose of this study was to examine, in a comprehensive manner, important hormonal and metabolic health issues by testing the hypotheses that dietary soy-derived phytoestrogens influence: 1) body weight and adipose deposition, 2) food and water intake, 3) metabolic hormones (i.e., leptin, insulin, T3 and glucose levels), 4) brain neuropeptide Y (NPY) levels, 5) heat production [in brown adipose tissue (BAT) quantifying uncoupling protein (UCP-1) mRNA levels] and 6) core body temperature. This was accomplished by conducting longitudinal studies where male Long-Evans rats were exposed (from conception to time of testing or tissue collection) to a diet rich in phytoestrogens vs. a diet low in phytoestrogens. Methods Animals Long-Evans male and female rats [10 per sex at 50 days old] were purchased from Charles River Laboratories (Wilmington, MA, USA) for breeding. These animals were caged individually and housed in the Brigham Young University Bio-Ag vivarium and maintained on an 11-hour dark 13-hour light schedule (lights on 0600–1900). The animals and methods of this study were approved by the institute of animal care and use committee (IACUC) at Brigham Young University (BYU). Treatment-Diets Upon arrival all animals were allowed ad libitum access to either a commercially available diet with high phytoestrogen levels (Harlan Teklad Rodent Diet 8604, Madison, WI, USA) containing 600 micrograms of phytoestrogens/gram of diet [or specifically this diet is high in isoflavones, 600 parts per million or ppm]; referred to hereafter as the Phyto-600 diet, or a custom phytoestrogen-free diet; referred to hereafter as the Phyto-free diet, obtained from Ziegler Bros. (Gardner, PA, USA) and water [ 7 ]. In the Phyto-free diet, the phytoestrogen concentrations were below the detectable limits of HPLC analysis [ 7 ]. The content and nutrient composition of these diets is described in detail elsewhere [ 7 ]. The diets were balanced and matched for equivalent percentage content of protein, carbohydrate, fat, amino acids, vitamins and minerals, etc. [ 7 ]. Circulating phytoestrogen serum levels from rats maintained on these diets (lifelong) have been reported previously by our laboratory using GC/MS analysis [ 7 ]. The animals were time mated within their respective diets so that the offspring of these pairings would be exposed solely to either the Phyto-600 or Phyto-free diet. Parameters were measured and/or the male rats were sacrificed and blood and tissues collected mainly at 33, 55 or 75 days of age; other ages were tested where indicated. Serum was prepared and stored at -20°C until assayed for metabolic hormones. For this study serum isoflavone levels are shown in Figure 1 from animals at 75 days of age. Male rats were only examined in this study since the influence of the estrous cycle on several of the measured parameters is unknown. Figure 1 Serum Isoflavone Levels in Phytoestrogen-rich (Phyto-600) vs. Phytoesterogen-low (Phyto-Free) Fed Male Rats at 75-Days of Age. For each phytoestrogen measured the Phyto-600 animals displayed significantly higher (** p < 0.01) isoflavone levels compared to Phyto-free fed rats. ODMA = O-desmethylangolensin. Equol levels in Phyto-600 animals accounted for approximately 78% of the total phytoestrogen levels. Weight measurements Body weights and food intake were measured on a Mettler 1200 balance [in grams (g) ± 1 g; St. Louis, MO, USA], white and brown adipose tissue and prostate weights were measured on a Sartorious balance [in milligrams (mg) ± 1 mg; Brinkman Inst. Co., Westbury, NY, USA]. Water intake was measured in drinking tubes [in milliliters (ml) ± 1 ml]. White adipose tissue (WAT) was dissected inferior to the kidneys and superior to the testes in the abdominoplevic cavity (representing a majority of intra-abdominal WAT) and then weighed in grams ± 0.01 g. Brown adipose tissue was dissected from between the scapular blades (inter-scapular region) and weighed in milligrams (mg) ± 1 mg. Metabolic Hormones Serum leptin and insulin levels were determined by kits purchased from Linco Res. Inc. (St. Charles, MO, USA) [from arterial blood samples of 33 and 55 day-old male animals and venous blood samples collected from 75 day-old rats. This was due to exhausting the arterial supplies from the available blood samples for other assays and thus venous blood was assayed at 75 days of age]. Serum thyroid (T3) levels were assayed by a kit purchased from Diagnostic Systems Labs. Inc. (Webster, TX, USA) and glucose levels were detected by a kit (#510) purchased from Sigma Chem. Co. (St. Louis, MO, USA). Hypothalamic NPY Levels Subsequent to blood collection (above), after the animals were sacrificed, brains were removed rapidly, frozen on dry ice and then stored at -80°C until microdissection. Coronal slices 300 μm thick were sectioned on a microtome cryostat. The paraventricular nucleus, arcuate nucleus and median eminence regions of the hypothalamus were microdissected by punch technique and homogenized in 100 μl of 0.1 M HCl. Tissue protein was determined by the Lowry method [ 8 ] and NPY was measured using a solid-phase radioimmunoassay in Protein G-coated 96-well plates, as described previously [ 9 ]. The NPY antiserum was used at a final concentration of 1:16,000. The sensitivity of the assay is 0.2 pg, with an intra-assay coefficient of variation of 8 %. All samples were run in duplicate in the same assay to avoid inter-assay variation. Body temperature Body temperature was monitored by radio telemetry by implanting a very small electronic chip [under the skin above the left thoracic cavity near the heart] that measured and transmitted core body temperature (± 0.1°C) to a notebook-sensor monitor (BioMedic Data Systems Inc., Seaford, DE, USA) within 2 seconds and repeated measurements were made throughout the day and/or the duration of the experiments. Body Heat Production Uncoupling protein (UCP-1) mRNA levels were determined in brown adipose tissue (BAT) collected from the interscapular region of each male rat. The BATs were homogenized in Trizol reagent (Invitrogen, Carlsbad, CA, USA) and total RNA was extracted. Two micrograms (2 μg) of total RNA were reverse transcribed (RT) for 60 min at 42°C using Superscript II (Invitrogen, Carlsbad, CA, USA) (200 U). Each 20 μl reaction contained 0.1 M DTT (2 μl), 10 mM dNTP mix (1 μl), 10X PCR buffer (2 μl), random decamers (0.4 μl), and RNaseOUT (Invitrogen) (40 U). A duplex PCR reaction was then performed on each RT product, with 18S rRNA serving as the internal control. Each 50 μl reaction contained the RT product (2 μl), UCP-1 primers (2 μl), 18S primers [2 μl of 3:7 ratio of 18S primer to18S competimer (Ambion, Austin, TX, USA)], 10X PCR buffer (5 μl), 10 mM dNTP mix (0.625 μl), 32 P-dCTP (0.1–0.15 μl), and Jumpstart Taq polymerase (Sigma Chem. Co., St. Louis, MO, USA) (1 U). Each tube was then subjected to the following protocol: 95°C for 5 min, 20 cycles of 94°C for 30 sec, 60°C for 30 sec, 72°C for 45 sec, followed by 72°C for a final 10 min. interval. With this profile, the UCP-1 and 18S fragments were amplified within the linear range (20 cycles for UCP-1). The primers for UCP-1 were GTGAAGGTCAGAATGCAAGC (sense) and AGGGCCCCCTTCATGAGGTC (antisense), the resultant fragment was 197 bp. The sequence of the UCP-1 fragment was verified by the DNA Sequencing Center at BYU. The PCR products were then subjected to non-denaturing polyacrylamide gel electrophoresis and the gels were exposed to autoradiographic film. Optical density (O.D.) of each band was determined using the NIH imaging system (Version 1.61). For each sample the O.D. ratio UCP:18S was determined. Each RT-PCR protocol was repeated and O.D. ratio values averaged over at least two runs. Statistical Analysis All data are presented as the mean ± SEM with p < 0.05 deemed significant. The data were tested by analysis of variance (ANOVA) (or where appropriate by repeated measures), followed by pairwise comparisons (via Neuman-Keuls analysis) to detect significant differences between the diet treatment groups (p < 0.05). Results Body Weight, White Adipose Tissue Weight and Food/Water Intake When food and water intake was measured in young adult animals, surprisingly the Phyto-600 fed males displayed slight but significantly higher food (Figure 2A ) and water (Figure 2B ) consumption compared to Phyto-free fed males [for food intake: Phyto-600 = 24.3 vs. Phyto-free = 21.7 grams/day (p < 0.05) and for water intake: Phyto-600 = 37.7 vs. Phyto-free = 31.2 ml/day (p < 0.05). Figure 2 Effects of Dietary Phytoestrogens on Food and Water Intake in 75 Day-Old Male Long-Evans Rats. Males fed a phytoestrogen-rich (600) diet displayed significantly greater (* p < 0.05) food (A) and water intake (B) compared to males fed a phytoestrogen-free (Free) diet. The average food and water intake represents the volumes consumed over 3 consecutive days. The effects of dietary phytoestrogens on body weights in pre-, early adult and young adult age male rats are shown in Figure 3 . At every age examined (i.e., 33, 55 and 75 days old), males exposed to the Phyto-free diet displayed significantly higher body weights (around 10–15%) compared to animals fed the Phyto-600 diet. Figure 3 Effects of Dietary Phytoestrogens on Body Weight in Male Long-Evans Rats fed either a phytoestrogen-rich (600) or a phytoestrogen-free (Free) diet. At 33, 55 and 75 days-old Free-fed male body weights (* p < 0.05) were significantly greater compared to 600-fed male values. White adipose tissue (WAT) weights were not measured in 33 day-old animals, since relatively little fat deposition was observed in the abdominopelvic cavity (especially around the reproductive structures) at this age. However, at 55 and 75-days of age, males fed the Phyto-free diet displayed significantly higher white adipose tissue weights (approximately 50% greater) compared to Phyto-600 values (Figure 4 ). Figure 4 Effects of Dietary Phytoestrogens on White Adipose Tissue in Male Long-Evans Rats fed either a phytoestrogen-rich (600) or a phytoestrogen-free (Free) diet. At 55 and 75 days post-birth white adipose tissue weight was significantly greater in Free-fed males (** p < 0.01) compared to 600-fed male values. Circulating Leptin, Insulin, Glucose and Brain NPY Levels In 33, 55 and 75 day-old male rats, circulating leptin and insulin levels were within the normal ranges (as described by the vendor's assay kit values), however, at each age males fed the Phyto-free diet displayed significantly higher leptin (Figure 5A ) and insulin (Figure 5B ) levels compared to Phyto-600 values. Notably, the leptin levels significantly increased with age that corresponded with significantly higher white adipose tissue deposition seen in these animals. Figure 5 Plasma Leptin (A) and Insulin (B) Levels from 33, 55 or 75 day-old Male Long-Evans Rats fed either a phytoestrogen-rich (Phyto-600) or a phytoestrogen-free (Phyto-Free) diet. At 33, 55 and 75 days of age, males fed the phytoestrogen-free (Free) diets displayed significantly higher leptin and insulin levels (* p < 0.05, ** p < 0.01) compared to males fed the Phyto-600 (600) diet. From the animals collected on 33, 55 and 75 days of age not enough serum was left after other assays were performed to quantify glucose levels on these days. However, circulating glucose levels were assayed in non-fasting 65, 80 or 110 day-old animals, Phyto-600 fed males displayed slightly higher values (that were not significantly different) compared to Phyto-free fed males [age 65 days old- Phyto-600 = 113.5 (± 4.4) vs. Phyto free = 93.5 (± 9.0) mg/dl, n = 8 per group; age 80 days old- Phyto-600 = 137.2 (± 3.8) vs. Phyto-free = 122.5 (± 3.9) mg/dl, n = 10 per group; age 110 days old- Phyto-600 = 123.5 (± 5.0) vs. Phyto-free = 113.8 (± 3.6) mg/dl, n = 5 per group, (mean ± SEM), data not shown graphically]. Since leptin plays an important role in regulating brain NPY levels that in turn influences food/water intake, NPY levels were determined in three hypothalamic regions [i.e., the periventricular nucleus (PVN), median eminence (ME) and the arcuate nucleus (ARC)] in 75 day-old males exposed to the diet treatments. In the PVN and ARC (but not the ME) NPY levels were significantly higher (by approximately 40 %) in Phyto-600 fed males vs. the Phyto-free male values (Figure 6 ). Figure 6 Dietary Phytoestrogens Influence on Brain NPY Levels in 75 day-old Male Long-Evans Rats. In the paraventricular (PVN) and arcuate (ARC) nucleus, males fed the Phyto-600 (600) diet displayed significantly greater NPY levels (* p < 0.05) compared to males fed the Phyto-Free (Free) diet. In the median eminence (ME) no significant differences were observed between male rats fed 600 vs. the Free diet. Circulating Thyroid (T3), UCP-1 mRNA Levels and Core Body Temperature In non-fasting young adult rats at 65 and 110 days of age, circulating thyroid (T3) levels were determined from venous blood samples. Phyto-600 fed males displayed significantly higher T3 levels compared to Phyto-free fed values [age 65 days old- Phyto-600 = 2.4 ± 0.2 vs. Phyto-free = 1.5 ± 0.4 pg/ml (± SEM), n = 8 per diet treatment; age 110 days old- Phyto-600 = 1.9 ± 0.4 vs. Phyto-free = 0.8 ± 0.3 pg/ml, n = 5 per group, (mean ± SEM) data not shown graphically]. The effects of dietary phytoestrogens on uncoupling protein-1 (UCP-1) mRNA levels in brown adipose tissue (BAT) from 75 day-old animals is shown in Figure 7 . Phyto-600 fed males displayed significantly higher (≈ 2-fold) UCP-1 mRNA levels in BAT compared to Phyto-free values. Notably, the BAT weights of Phyto-600 animals were significantly less (by approximately 1/3) to that of Phyto-600 males (Phyto-600 = 293.6 ± 22.1 vs. Phyto-free = 437.5 ± 25.9 mg (mean ± SEM), n = 8 per group. Figure 7 Dietary Phytoestrogens Influence on Uncoupling Protein-1 (UCP-1) mRNA Levels in Brown Adipose Tissue (BAT) from males fed either a phytoestrogen-rich (Phyto-600) or a phytoestrogen-free (Phyto-free) diet. For each sample, the optical density (O.D.) signal ratio of BAT UCP-1 mRNA abundance to ribosomal 18S RNA intensity was determined. The results represent the mean O.D. values and S.E.M. of 8 independent samples by diet treatment of at least two runs of the RT-PCR protocol. Males fed the Phyto-600 diet expressed significantly higher BAT UCP-1 mRNA levels (* p < 0.05) compared to Phyto-free values. Finally, when core body temperatures were recorded during a 24-hour interval, Phyto-free fed males displayed, in general, slight but significantly higher values compared to Phyto-600 animals during the dark phase of the light/dark cycle when rodents are most active (Figure 8 ). However, during one time point during the dark cycle (3 am) and one time point during the light phase of the cycle (3 pm) Phyto-600 males displayed slight but significantly higher core body temperatures vs. Phyto-free values. Figure 8 Dietary Phytoestrogens Influence on Core Body Temperature in 75 day-old rats. In general, during the dark phase of the light cycle, Phyto-free fed males displayed significantly higher core body temperatures (* p < 0.05) compared to Phyto-600 fed values. However, near the end of the dark (at 3 am) and light (at 3 pm) phase of the dark/light cycle Phyto-600 fed males displayed significantly higher core body temperatures (▲ p < 0.05) compared to Phyto-free values. Discussion Estrogen is known to play a dual role in regulating body weight, food intake and adipose tissue deposition. On the one hand, estrogens decrease food intake, increase locomotor activity and hence decrease body weight [ 10 , 11 ]. However, adipose tissue deposition increases with puberty and early pregnancy in women, suggesting that estrogens influence body fat accumulation [ 12 ]. Additionally, in aging, estrogens promote adipose deposition and insulin resistance [ 13 ]. Conversely, results from aromatase, FSH and ER-knockout studies indicate that estrogens regulate adiposity where the complete lack of estrogens or blocking estrogen hormone action increases adipose tissue deposition [ 14 - 18 ], whereas, estrogen replacement in these models decreases adiposity. Notably, in the present study, male rats fed the Phyto-600 diet displayed significantly decreased adipose tissue and body weights compared to Phyto-free fed animals. While there is not extensive data on phytoestrogens and metabolism, other investigators have reported that genistein, increases lipolysis and decreases lipogenesis in rodent adipocytes [ 19 ] by a tyrosine kinase independent mechanism and these estrogen mimics inhibit glucose uptake by altering membrane-associated glucose transporters [ 20 , 21 ]. Thus, our data suggests that dietary soy phytoestrogens significantly decrease: 1) body and adipose tissue weights and 2) circulating leptin and insulin levels (that correspond with adipose deposition) compared to Phyto-free fed animals, implying that the hormonal action of phytoestrogens is beneficial to body fat regulation. Recent studies imply that insulin helps to regulate leptin expression in humans [ 22 ] and estrogens appear to enhance the action of insulin [ 23 , 24 ]. This may account for the decreased incidence of obesity in Asian countries where isoflavone consumption is high compared to Western countries. Decreased adipose tissue deposition by decreasing lipogenesis and increasing lipolysis may help to prevent insulin resistance (by reducing body fat) and the estrogenic actions of dietary phytoestrogens may augment the efficiency of insulin. It was previously observed in our laboratory that dietary phytoestrogens significantly alter food and water intake [ 7 , 25 , 26 ]. The differential effects of the Phyto-free vs. Phyto-600 diets observed in the present studies on hypothalamic NPY levels, circulating insulin and leptin concentrations and food intake are consistent with the well established interrelationships among these parameters. Thus, relative to animals maintained on the Phyto-free diet, food intake was significantly increased in animals fed the Phyto-600 diet. Phyto-600-fed rats also exhibited higher concentrations of NPY in the arcuate and paraventricular nuclei of the hypothalamus. It is well established that NPY neurons whose perikarya reside in arcuate nucleus and project to PVN comprise an extremely important orexigenic neural pathway [ 27 ]. It therefore appears likely that at least one factor contributing to the higher food intake in Phyto-600-fed rats is the increased levels of NPY in this system. The present studies also suggest a mechanism that may underlie the diet-induced effects on NPY (i.e., plasma insulin and leptin concentrations were significantly reduced in the Phyto-600 fed rats, relative to the Phyto-free animals). A number of previous studies have demonstrated a reciprocal relationship between circulating insulin and leptin titers and NPY concentrations in PVN. Thus, experimentally-induced reductions in either insulin [ 28 ] or leptin [ 29 ] are associated with increased pre-proNPY messenger RNA expression in arcuate nucleus and increased NPY levels in PVN, and moreover, it has been proposed that reductions in insulin and leptin that occur physiologically, e.g., with food deprivation, provide an important signal to the NPY system to initiate feeding [ 27 , 29 ]. Hence, taken together, the present findings suggest that by reducing secretion of insulin and/or leptin, chronic consumption of the Phyto-600 diet results in up-regulation of the orexigenic NPY circuit in the hypothalamus, which in turn stimulates food intake (and water consumption, since rodents and humans display prandial characteristics). While it is clear that thyroid hormone levels are influenced by estrogens where increases are seen in T3 and T4, presumably by increasing in the production of thyroid binding globulin in the liver [ 30 ], the published data examining thyroid function and hormone levels are problematic at best in the soy research field due to the history of soy food formulations, parameters examined and iodine deficiencies [ 6 , 31 , 32 ]. In agreement with more recent studies, our results demonstrate that circulating T3 levels increase with soy consumption [ 33 ], and "personal communication- Dr. David Baer-USDA". Furthermore, there appears to be a link between increased thyroid levels with soy consumption and cardiovascular protection in lowering serum cholesterol levels [ 6 ] and thyroid hormones along with estrogens protecting against osteoporosis [ 34 ]. However, in animals consuming the Phyto-600 diet (that displayed higher T3 levels) we observed a lower core body temperature compared to Phyto-free fed rats. In subsequent (unpublished) studies, we have consistently recorded slight (approximately 0.5°C) but significantly lower core body temperatures in Phyto-600 vs. Phyto-free fed rats during pregnancy. This suggests that the overall effect on body temperature via these estrogen mimics in the soy-rich diet may act primarily by increasing cutaneous vasodilation, thus decreasing core body temperature. Animal studies have shown that estrogens can act centrally (in the preoptic/anterior hypothalamus) or peripherally to regulate body temperature [ 35 , 36 ]. Support for this view is seen in humans where changes in skin blood flow via cutaneous vasodilation during the menstrual cycle and in hormone replacement therapy studies correspond with estrogen levels [ 36 , 37 ]. Also, one report showed that soy-derived phytoestrogens have a similar effect to our findings where ovariectomized rats fed a soy diet displayed an approximate 0.8°C decrease in skin temperature, whereas, estradiol treatment decreased temperature values by 1.4°C [ 38 ]. Finally, in association with temperature regulation, several studies have reported that soy consumption may be an effective therapy for relief of hot flushes in women [ 39 ]. Finally, the various metabolic parameters examined in a global fashion seem to suggest that declines in insulin and leptin levels are the dominant systemic regulators in regard to body weight, since overall the Phyto-600 animals weigh less compared to Phyto-free fed animals. However, the present findings also suggest that body temperature is reduced in Phyto-600 fed animals vs. Phyto-free fed animals and previous behavioral studies suggest that Phyto-600 animals exhibit more locomotor active vs. Phyto-free fed animals [ 7 , 50 ] (see summary Figure 9 ). Figure 9 Summary of Dietary Phytoestrogen (Isoflavone) Influences on Metabolic and Hormonal Parameters. The left-hand column: Phyto-600 arrows are relative to the effects seen in Phyto-free fed animals-right-hand column [in other words, relative to one another]. * = general increase in body temperature compared to Phyto-600 values Uncoupling proteins (UCP-1 through UCP-5) are expressed in various tissues from many different species (mammals, birds, fish, insects and plants) that play important (but controversial) role(s) in the regulation of energy expenditure, or thermogenesis [ 40 , 41 ]. Uncoupling protein-1 is expressed mainly in BAT. When the influence of dietary phytoestrogens on UCP-1 mRNA levels in BAT was examined, Phyto-600 fed male rats, expressed significantly higher levels of the uncoupling protein (approximately 2-fold) compared to Phyto-free values (but BAT weights were significantly less in the Phyto-600 vs. Phyto-free fed males). To date, we are unaware of any studies that have investigated this aspect of soy consumption on thermogenesis. The decrease in BAT mass in Phyto-600 animals but increased expression of UCP-1 may represent a compensation mechanism for energy expenditure, and there are several neural inputs and hormonal factors that influence UCP-1 in BAT that make it difficult to differentiate the regulatory aspects of UCP-1 expression. For example, sympathetic denervation of inter-scapular BAT markedly reduced UCP-1 mRNA levels and estrogen, T3 and adrenergic agents [norepinephrine (NE)] stimulate UCP-1 expression in BAT [ 42 , 43 ]. In fact, it has been reported that T3 synergizes with NE to increase UCP-1 in BAT and stabilizes its mRNA transcripts [ 44 ]. These factors overlap with the changes seen in Phyto-600 fed vs. Phyto-free fed rats, in the present study, where T3 levels were increased and, presumably, along with the estrogenic influence of circulating isoflavones resulted in stimulating UCP-1 expression in BAT. Previously, we have not observed any significant alterations in circulating estradiol (or LH) levels in Phyto-600 vs. Phyto-free fed intact males [ 7 ]. Conversely, it has been reported that increases in hypothalamic NPY decrease UCP-1 [and reduces sympathetic outflow to BAT, but increases adipose tissue lipoprotein lipase activity] [ 30 ]. Also, plasma leptin levels are thought to stimulate UCP-1 in BAT [ 45 , 46 ], results opposite, in general, to that obtained in the present study. Based upon the obtained data sets, it is difficult to identify a common stimulatory or inhibitory pattern for the expression of UCP-1 in BAT of soy fed animals and especially define a functional role for the physiological properties associated with these UCPs in thermoregulation. Therefore, it is reasonable to speculate that multiple factors act collectively to regulate UCPs in BAT that in turn contribute to adaptive changes in body temperature. Conclusions This study demonstrates that consumption of a widely used commercially available soy-based rodent diet, (i.e., the Phyto-600 diet rich in isoflavones), alters several hormonal, metabolic and neuroendocrine parameters involved in maintaining body homeostatic balance, energy expenditure and feeding behavior in male rats. Further research is warranted in examining the important aspects of the neuroendocrine and metabolic influences of dietary phytoestrogens via the consumption of soy in humans and laboratory animals. This is especially true when diet is usually not considered as an influencing factor in the experimental design [ 47 - 50 ]. Abbreviations neuropeptide Y (NPY), white adipose tissue (WAT), brown adipose tissue (BAT), uncoupling proteins (UCP), phytoestrogen-rich diet (Phyto-600), phytoestrogen free diet (Phyto-free), periventricular nucleus (PVN), median eminence (ME), arcuate nucleus (ARC), norepinephrine (NE), thyroid (T3, T4), National Institutes of Health (NIH), reverse transcriptase-polymerase chain reaction (RT-PCR), Food and Drug Administration (FDA), optical density (OD), luteinizing hormone (LH), follicle stimulating hormone (FSH), Brigham Young University (BYU), estrogen receptor (ER), high performance liquid chromatography (HPLC) Competing interests The author(s) declare that they have no competing interests. Author's contributions JPP, TDL, LB and GR contributed equally to this paper in various aspects of this study. KDRS carried out the isoflavone quantifications, WRC conducted the NPY analyses and EDL conceived of the study, designed, coordinated and drafted the manuscript along with the other authors.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544860.xml

544874

Association between mortality from suicide in England and antidepressant prescribing: an ecological study

Background Antidepressant prescribing has been increasing in England. Studies in other countries suggest that while this may be associated with reduced suicide rates, it may also be associated with increased fatal poisoning from antidepressant drugs. We therefore conducted an ecological study to assess the association between prescription rates for antidepressants and suicide or fatal antidepressant-related poisoning in England. Methods The Office for National Statistics provided information on the number of suicides, antidepressant-related poisoning deaths and populations for England between 1993 and 2002. The Department of Health supplied data on prescriptions for all antidepressants dispensed in England. Associations between prescriptions and deaths were assessed using Spearman's rank correlation coefficient. Results There were 46,747 suicides, 3,987 deaths involving tricyclic antidepressants and 430 involving selective serotonin re-uptake inhibitors and other antidepressants. Increased antidepressant prescribing was statistically associated with a fall in suicide rates (Spearman's r s = -0.73, p = 0.02) and fatal poisoning involving tricyclic antidepressants (r s = -0.64, p = 0.05). In contrast, increased prescribing of selective serotonin re-uptake inhibitors and other antidepressants was statistically associated with an increase in fatal poisoning involving these drugs (r s = 0.99, p < 0.001). Conclusion Increased prescribing of antidepressants may indicate improved diagnosis and treatment of depression in primary care. Our analysis suggests that this was accompanied by lower suicide rates. A decrease in poisoning deaths involving tricyclic antidepressants may suggest a change in preference for using serotonin reuptake inhibitors and other antidepressant drugs for high-risk patients. This may also partially explain the increase in deaths involving these drugs. Due to the ecological nature of the design, we cannot say conclusively whether reduced suicide rates are a direct consequence of increased antidepressant prescribing rates. To confirm these associations, individual level data on prescribing and suicide is needed.

Background Around 5000 people take their own lives in England every year [ 1 ]. The Government's White Paper Saving Lives: Our Healthier Nation sets a target to reduce suicide deaths by 20% by 2010 [ 2 ]. One of the most common risk factors amongst suicide victims is a major depressive episode (present in about 60–90% of victims) [ 3 ]. Treatment of depressed patients can reduce suicide risk by half [ 3 ]. Hence, increased diagnosis and treatment of depression in primary care is a key element in reducing suicide risk. Studies in Sweden [ 4 ], Denmark [ 5 ], Finland [ 5 ], Norway [ 5 ] and Australia [ 6 ] suggest that increased used of antidepressants drugs is related with lower suicide rates. However, other studies have also reported a positive association between increased prescribing of antidepressants and higher death rates from antidepressant overdose in Finland [ 7 ], Norway [ 8 ], Australia [ 9 ] and England [ 10 ]. In contrast, studies in Italy [ 11 ], Austria [ 12 ] and Ireland [ 13 ] have not observed increased suicide or fatal antidepressant poisoning following increases in antidepressant prescribing. In England, prescriptions for antidepressants have been increasing [ 14 , 15 ]. We conducted an ecological study to assess trends in prescription rates for antidepressants in England and rates for suicide and fatal antidepressant-related poisoning. Methods Suicide Suicide deaths were defined as deaths where the coroner has given a verdict of suicide or where an open verdict was reached in a death from injury or poisoning. This is because it is thought that most open verdicts are cases where the harm was self-inflicted but there was insufficient evidence to prove that the deceased deliberately intended to kill themselves [ 16 ]. Open verdicts account for about 30% of male and 40% of female suicide deaths [ 16 ]. Suicide deaths were identified using the International Classification of Diseases codes shown in Table 1 . Table 1 ICD-9 and ICD-10 classification for suicides Description ICD-9 ICD-10 All suicides E950–E959, E980–E989 excluding E988.8 with verdict pending X60–X84, Y10–Y34 excluding Y33.9 with verdict pending Non-drug poisoning suicide As above excluding E950.0–E950.5 and E980.0–E980.5 As above excluding X60–X64 and Y10–Y14 Antidepressant-related deaths The Office for National Statistics has stored drug-poisoning mortality data for England and Wales from 1993 onwards in a dedicated database [ 17 ]. The database contains data on cause of death, individual characteristics (age and sex) as well as textual information from the death certificate. This textual information has been examined to identify and code the substances involved in the death. All drugs mentioned are also coded to British National Formulary categories where appropriate. Drug poisoning deaths were defined using the International Classification of Diseases codes shown in Table 2 . Antidepressant-related deaths were defined as any drug poisoning death where an antidepressant drug was mentioned on the death certificate, with or without mentions of alcohol or other drugs. Antidepressant drugs were further classified according to their BNF categories (Table 3 ). Table 2 ICD-9 and ICD-10 classification for drug poisoning Description ICD-9 ICD-10 Mental and behavioural disorders due to drug use (excluding alcohol and tobacco) 292, 304 305.2-9 F11–F16, F18–F19 Accidental poisoning by drugs, medicaments and biological substances E850–E858 X40–X44 Intentional self-poisoning by drugs, medicaments and biological substances E950.0–E950.5 X60–X64 Poisoning by drugs, medicaments and biological substances, undetermined intent E980.0–E980.5 Y10–Y14 Assault by drugs, medicaments and biological substances E962.0 X85 Table 3 British National Formulary Categories for Antidepressant Drugs BNF Category Description 4.3.1 Tricyclics and related antidepressants 4.3.2 Monoamine oxidase inhibitors 4.3.3 Selective serotonin re-uptake inhibitors 4.3.4 Other antidepressants Prescriptions for antidepressant drugs The Department of Health supplied data on prescriptions for all antidepressants dispensed in England between 1993 and 2002. Prescription information is derived from the Prescription Cost Analysis (PCA) system, which collects data from all prescriptions dispensed in the community [ 18 ]. This includes community pharmacists, dispensing doctors and prescriptions submitted by prescribing doctors for items personally administered (i.e. given by the doctor during a consultation). PCA data also includes prescriptions written in Wales, Scotland, Northern Ireland and the Isle of Man but dispensed in England. Drugs dispensed in hospital or private prescriptions are not included. Analysis Directly age-standardised rates for suicide and antidepressant-related deaths were calculated using the European Standard Population. Populations for England between 1993 and 2002 were interim revised estimates published by ONS in September 2003. Prescription rates were presented as number of prescriptions per 100 population. Statistical associations between prescription rates and directly age-standardised rates for antidepressant-related poisoning deaths and suicides were assessed using Spearman's rank correlation coefficient. Results In England between 1993 and 2002, there were 46,747 suicides. Suicide was twice as common in men as in women. There were 3,987 deaths involving tricyclic antidepressants (TCAs, BNF 4.3.1,) and 430 involving selective serotonin re-uptake inhibitors and other antidepressants (SSRIs & others, BNF 4.3.3 & 4.3.4). The number of antidepressant-related deaths was similar for men and women. Changes in age-specific rates were similar across all age groups for suicide and antidepressant-related deaths. Between 1993 and 2002, age-standardised mortality rates for suicide decreased from 98.2 to 84.3 per million population (Table 4 ). Rates for TCA poisoning also decreased from 8.6 to 5.3 per million while mortality rates for SSRIs & others increased from 0.2 to 1.8 per million. During the same period, prescriptions per 100 population for all antidepressants increased almost two and a half times from 22.4 to 53.2 per 100 population (Table 4 ). While there was a modest increase for TCAs from 17.5 to 19.9 per 100 population, prescriptions for SSRIs and others increased more than seven-fold from 4.6 to 33.1 per 100 population. Table 4 Age-standardised mortality rates for suicide and antidepressant-related poisoning deaths and prescriptions per 100 population in England Year Age-standardised mortality rates per million population Prescriptions per 100 population Suicide TCAs SSRIs All TCAs SSRIs 1993 98.2 8.6 0.2 22.4 17.5 4.6 1994 94.8 8.7 0.1 24.5 17.9 6.2 1995 95.6 8.7 0.4 27.4 18.3 8.7 1996 90.6 9.7 0.4 30.9 18.8 11.8 1997 92.8 9.4 0.6 34.7 19.3 15.1 1998 95.7 8.8 0.7 37.9 19.7 17.9 1999 95.1 8.0 1.1 41.2 19.7 21.2 2000 90.2 6.9 1.3 44.9 19.7 25.0 2001 84.9 5.8 1.8 49.3 19.8 29.3 2002 84.3 5.3 1.8 53.2 19.9 33.1 Note: Directly standardised to the European Standard Population Spearman's rank correlation indicate that rates for all suicides were inversely related to prescribing rates for all antidepressants combined, r s = -0.73, p = 0.016 (Table 5 ). Mortality rates for non-drug poisoning suicides showed a strong statistical association with prescription rates for all antidepressants, r s = -0.89, p = 0.005. Antidepressant-related poisoning mortality rates were also inversely statistically associated with prescription rates for all antidepressants combined, but the association was much weaker, r s = -0.45, p = 0.187. As most antidepressant-related poisoning deaths were due to TCAs, the relationship between TCA deaths and prescribing were similar to all antidepressants combined. In contrast, SSRI and other-related deaths increased and prescriptions for SSRIs & others both increased during the study period r s = 0.99, p < 0.001. Table 5 Spearman's rank correlation coefficients and p-values for directly age-standardised suicide and antidepressant poisoning rates and prescription rates, England 1993 to 2002 Cause of death Prescriptions r s p All suicides All antidepressants -0.73 0.016 Non-drug poisoning suicide All antidepressants -0.89 0.005 Antidepressant-related poisoning deaths All antidepressants -0.45 0.187 Tricyclic antidepressant -related poisoning deaths Tricyclic antidepressants -0.64 0.05 Selective serotonin re-uptake inhibitor and other antidepressant-related poisoning deaths Selective serotonin re-uptake inhibitors and other antidepressants 0.99 <0.001 Antidepressant only poisoning deaths* All antidepressants -0.53 0.12 Tricylclic antidepressant only poisoning deaths* Tricyclic antidepressants -0.71 0.022 Selective serotonin re-uptake inhibitor and other antidepressant only poisoning deaths* Selective serotonin re-uptake inhibitors and other antidepressants 0.94 <0.001 * Where no other drugs were mentioned on the death certificate A previous analysis found that about a third of antidepressant-related deaths also have other drugs or substances mentioned on the death certificate (26% of deaths involving TCAs, 72% involving SSRIs and others) [ 19 ]. Hence, death certification data may over-estimate deaths attributable to antidepressant overdose. When we excluded deaths involving other substances from our analysis, antidepressant mortality rates were more closely related with antidepressant prescribing (TCAs r s = -0.71, p = 0.022, SSRIs & others r s = 0.94, p < 0.001). Discussion Increased prescribing of antidepressants was statistically associated with reduced suicide mortality rates. TCA-related poisoning deaths also decreased during the study period although the evidence for a statistical association with prescribing rates was weaker. Increased prescribing of SSRIs and other antidepressants was statistically associated with increased mortality rates involving SSRI & other-related poisoning, although the rates were much smaller than for TCA-related poisoning or suicide. Because our study uses population level data, we cannot conclude that these associations are necessarily causal. Limitations Although the ONS drug poisoning database is the most complete record of drug poisoning statistics available, about 10% of these deaths have no specific information about drug(s) taken [ 1 ]. Inconsistency in the investigation and recording of drug-poisoning deaths may mean that not all antidepressant-related deaths are identified [ 17 ]. Prescribing Cost Analysis data do not record why the drug was prescribed and antidepressants are increasingly used for conditions other than depression [ 20 ]. This may lead to an over-estimation of their use in the treatment of depression. Furthermore, prescribing data does not provide information on the age or sex of the patient. Both of these variables are likely to be associated with prescribing and suicide or poisoning, and may have introduced confounding into our analysis. Further bias may have been introduced due to prescriber and patient factors. Doctors may perceive SSRIs and other antidepressants to have clinical advantages over TCAs due to their lower toxicity, possibly making them more popular for treating newly diagnosed (an uncontrolled) depression [ 21 ], treating individuals who are at a greater risk of overdose [ 22 ] or individuals who have not previously responded to treatment. Interpretation Assessing the impact of increased antidepressant prescribing is difficult. This is because antidepressants are both a treatment and method for suicidal behaviour. Suicide is also closely linked to availability of methods [ 23 ], generating concern that increased prescribing of antidepressants may increase fatal poisoning rates. There has also been concern that some antidepressants, particularly the SSRIs, may actually precipitate suicide behaviour [ 24 ]. In a recent article, Jick et al suggested that both older and newer antidepressants were associated with 3–4 fold increased risk of suicidal behaviour in the first month after starting treatment [ 25 ]. Hence, determining the balance of risk and benefit of treatment with antidepressants is not straightforward. Using a theoretical model based on paediatric trails of antidepressants drugs and increased prevalence of suicidal thoughts and self-harm, Gunnell and Ashby suggest that compared to 1991, antidepressants may have contributed to an excess of 388 suicides (95% credibility interval -202 to 704) in 2002 [ 26 ]. However, our study indicates that at a population level, increased antidepressant prescribing in England was statistically associated with lower rates of suicide and antidepressant-related poisoning. One explanation for our findings is that SSRIs and other antidepressants are being used in preference to TCAs to treat depression. The perceived side effects of TCAs means they are often prescribed or taken at sub-therapeutic doses [ 21 , 27 ], leading to poor management of depression and increased risk of suicide [ 28 , 29 ]. Furthermore, the lower toxicity profiles of the newer antidepressants may also lead to preferential use of SSRIs for high-risk patients, which may in turn explain the increase in deaths associated with SSRIs and other antidepressants. Some support for this is provided by a previous analysis of antidepressant-related deaths during the same study period, which showed a decrease in antidepressant-related deaths per million prescriptions for all TCAs [ 19 ]. Nevertheless, the clinical benefits of antidepressant drugs for the management of depression and prevention of suicide are still unclear. A recent Cochrane review found little difference between antidepressants and active placebos on improvement of mood [ 30 ]. (Active placebos contain a drug, which is not thought to have an effect on the disorder being treated, but which mimics the effect of taking an active substance). The possibility that antidepressants have limited effectiveness on treatment of depression suggests that we should be cautious when drawing conclusions about the relationship between antidepressant prescribing and suicide. Increased antidepressant prescribing may be a marker of improved diagnosis and treatment of depression in primary care with greater use of non-pharmacological and psychosocial interventions [ 6 ]. Alternatively, suicide rates may reflect other trends during the study period: unemployment, which is associated with suicide [ 31 ], fell from about 10% to below 5% during the study period [ 32 ]. In contrast, consumption of alcohol, mentioned in 28% of antidepressant poisoning deaths, remained relatively stable [33]. Hence, the ecological nature of our data means that we cannot say conclusively whether reduced suicide rates is a direct consequence of increased antidepressant prescribing rates. Conclusions The public health impact of increased prescribing of antidepressants remains unclear, including whether this leads to lower suicide rates, and whether this could be a marker for improved diagnosis and treatment of depression in primary care. Our analysis of data for England between 1993 and 2002 suggest that increased prescribing of antidepressants was associated with lower suicide rates and probably lower rates of poisoning involving TCAs. This may have been due to a change in preference for SSRIs and other antidepressants for high-risk patients but also to other secular trends in areas like unemployment. The increase in prescribing rates does however probably explain the increase in fatal poisonings involving SSRIs and other antidepressants, which are generally considered to be less toxic than older agents. Further analysis of individual level data, possibly from longitudinal general practice patient data as well as from primary care based clinical trials, are needed to provide better evidence of the role of antidepressants in reducing suicide. Competing interests The author(s) declare that they have no competing interests. Authors' contributions OM designed the study, conducted the analysis and drafted the manuscript. CG participated in conducting the analysis and drafting the manuscript. AM participated designing the study and drafting the manuscript. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544874.xml

555951

Mortality registration and surveillance in China: History, current situation and challenges

Background Mortality statistics are key inputs for evidence based health policy at national level. Little is known of the empirical basis for mortality statistics in China, which accounts for roughly one-fifth of the world's population. An adequate description of the evolution of mortality registration in China and its current situation is important to evaluate the usability of the statistics derived from it for international epidemiology and health policy. Current situation The Chinese vital registration system currently covers 41 urban and 85 rural centres, accounting for roughly 8 % of the national population. Quality of registration is better in urban than in rural areas, and eastern than in western regions, resulting in significant biases in the overall statistics. The Ministry of Health introduced the Disease Surveillance Point System in 1980, to generate cause specific mortality statistics from a nationally representative sample of sites. Currently, the sample consists of 145 urban and rural sites, covering populations from 30,000 – 70,000, and a total of about 1 % of the national population. Causes of death are derived through a mix of medical certification and 'verbal autopsy' procedures, applied according to standard guidelines in all sites. Periodic evaluations for completeness of registration are conducted, with subsequent corrections for under reporting of deaths. Conclusion Results from the DSP have been used to inform health policy at national, regional and global levels. There remains a need to critically validate the information on causes of death, and a detailed validation exercise on these aspects is currently underway. In general, such sample based mortality registration systems hold much promise as models for rapidly improving knowledge about levels and causes of mortality in other low-income populations.

Introduction Data on the causes, levels and patterns of mortality are critical to support the development of evidence-based health policy. Cause of death statistics represent the longest historical series of data on the health of populations, in some cases extending back well over 150 years [ 1 ]. Yet, complete vital registration systems, which have traditionally generated these data, are often difficult and expensive to establish and maintain in developing countries. China is no exception. With 1.3 billion people, complete registration and medical certification of deaths is logistically and financially unattainable at present. However, mortality registration systems have been established in China that provide useful data on the health status of all Chinese, and how it is changing. In this paper, we review the history of vital registration in China, and describe the establishment and operation of the Chinese Disease Surveillance Points (DSP) system. We focus on vital registration because we consider it as the 'gold standard' for mortality statistics, since it provides population level data on causes of death on an annual basis. There are other sources of mortality data such as the census series and annual surveys of population change [ 2 ], large scale retrospective household surveys conducted in the periods 1973–75 and 1986–88 [ 3 ], and the National Maternal and Child Health Surveillance System [ 4 ]. However, censuses and annual population change surveys do not provide routine information on causes of death, retrospective surveys could be affected by recall bias, and the child mortality surveillance system does not inform about causes of death or adult mortality. As a result, owing to these shortcomings, statistics on causes of death from the DSP have been the principal data source for estimating burden of disease in China [ 5 ]. Nonetheless, despite the current utility of the DSP system for generating evidence for health policy, there are several challenges yet to be overcome, as discussed in this paper. The primary aim of this paper is to describe the operation, strengths and weaknesses of mortality registration systems in China. Little is known outside of China about the characteristics of such systems and their potential application to other low-income countries. History and Function of Vital registration in China Prior to 1950 vital registration hardly functioned in China, and even then only yielded reports on causes of death for the cities of Beijing and Nanjing[ 6 ]. The reported crude death rates ranged from about 18 to 21 per 1000, and the only causes of death reported were tuberculosis, measles, acute infectious disease, 'infant disease', respiratory disease, heart disease, urinary disease, digestive disease, stroke, and ill-defined causes. Cancer was not listed. In 1957, vital registration was expanded to several other large cities, including Shanghai, Tianjin, Harbin, and Wuhan. Thereafter, the vital registration system was extended to include more cities and counties. In 1976, a nationwide mortality survey was undertaken yielding information on the causes of about 20 million deaths [ 7 ]. Data on symptoms experienced by the deceased were collected through retrospective enquiry of all households in China, and based on this information, the cause of death was assigned by a team of physicians. Although this survey was primarily targeted to collect information on cancer mortality, the survey vastly increased the level of appreciation in China about the utility of reliable cause of death data for health planning, and gave impetus to the expansion of vital registration to its current extent. Subsequently, in 1987, the Ministry of Health established a vital registration system to record the fact and cause of death. At present, the vital registration system covers 41 cities (15 large cities and 21 middle/small cities) and 85 counties, among which 25 are 'suburban' counties adjacent to large cities, such as Beijing, Tianjin, and Shanghai. The other counties are located in 12 provinces, mostly in the eastern and central areas of China. Only a few are located in the western regions. The total population covered by this vital registration system in 2000 was about 110 million, half in cities and half living in rural counties. Half of the population covered lives in the eastern region, 40% in the central area, and 10% in the western regions [ 8 ]. In brief, the system functions as follows. When a person dies, family members report the death to the registration office of the nearest township hospital in order to get a death certificate, then to the police station to deregister permanent residence and obtain ratification for the burial procedure. Staff in the vital registration office fill in the death certificate based on information from family members, and available medical records or documents. One copy of the death certificate is kept in the registration office, and another is sent to the county Center for Disease Control (CDC), where the cause of death is coded. At the time of inception of the coding system in 1987, a specially designed Chinese Classification of Diseases List consisting of over 500 specific diseases or injuries was used, which could be translated into codes from the Ninth Revision of the ICD [ 9 ]. From 1990 onwards, coding was directly based on ICD – 9. The county CDC then uses a prescribed summary tabulation format to submit monthly reports of deaths by age, sex and cause to the Center of Health Information and Statistics, a Department within the Ministry of Health. This is known as the Ministry of Health – Vital Registration (MOH-VR) system, the annual compilations of which are submitted to the World Health Organization and published by WHO. Since 1996, several cities have started producing local tabulations for internal use. Quality control measures, including training of staff and development of guidelines and regulations for death registration vary between different areas. Registration in cities is better than in counties, and is better in eastern than western areas. An annual review of data is conducted, and areas that reported implausibly low death rates are excluded from statistical tabulations produced by the MOH-VR system. Assessment of Chinese Vital Registration Data A good test to assess the quality of the vital registration data is to examine trends in cause-specific death rates [ 10 ]. For example, the reported death rate from cancer fluctuated improbably in rural areas between 1975 and 1989, then remained relatively stable during the 1990 s (Figure 1 ). This fluctuation belies an expectation of steady change in cancer mortality over time. Factors causing the fluctuations in cancer mortality trends reported in the vital registration data might include an increase in population coverage with poor quality of data from new reporting areas, or a change in proportions of people accessing health facilities. Another possible reason is that data cleaning was arbitrary, without explicit standards for exclusion of data from specific sites. Figure 1 Trends in reported cancer mortality in urban and rural areas of China, 1973–2000 An additional limitation of the data system is that birth registration was not included. This undoubtedly contributed to the implausibly low death rates reported for infants in both the MOH-VR data and the DSP system (Table 1 ), since there was no mechanism for linking infant deaths to births. In comparison, the Census and the National Maternal and Child Health Surveillance system reported much higher infant death rates for the same points in time (see Table 1 ). Hence, although routine death registration systems provide important information on causes of death, they need to be strengthened to provide a more complete picture on the levels and causes of mortality. Table 1 Unadjusted death rates during infancy (per 1000 population) reported from various data sources in China, 1991 and 2000. Data source Total population Urban Rural 1991 2000 1991 2000 1991 2000 Census 32.9 32.0 - - - - NMS - - 16.5 8.0 25.4 15.2 DSP 21.4 13.5 8.2 7.7 24.6 14.5 CMSS 50.2 32.2 - 11.9 - 36.4 Source: [4] More importantly, the coverage of the MOH-VR system is biased towards the more urban and better-off populations of eastern China. Death rates from infectious disease are lower in the MOH-VR system than those reported by the more representative DSP system, which includes populations in poor rural areas (Figure 2 ). Similarly, the rates from non-communicable diseases are higher in the MOH-VR system than the DSP system. These observations suggest that the data from the vital registration system are not a true reflection of the mortality profile in China. This concern led to the establishment of the Disease Surveillance Points system as described below Figure 2 Comparison of age standardized mortality rates* due to broad cause groups, from MOH -VR and DSP systems in China . * Standardized onto WHO World Population [19] National Diseases Surveillance Points System (DSP) In order to improve the usability of data from the vital registration system, the Peking Union Medical University/Chinese Academy of Medical Sciences put forward a proposal in 1978 to develop a sample based Disease Surveillance Point (DSP) system. The system was designed primarily to collect data on births, causes of death, and the incidence of infectious diseases. A pilot study was carried out in East Town and Tongxian counties of Beijing in 1978. The Ministry of Health then instructed departments of health at province and county levels to recognize disease surveillance as an important public health task, and set up disease surveillance points under the technical guidance of the Chinese Academy of Preventive Medicine. By 1989, there were 71 DSPs scattered throughout 29 provinces in the country, with a standard working procedure for data collection, management, analysis and dissemination. However, the system was not representative of the national population [ 11 ]. In 1990, the Chinese Academy of Preventive Medicine established a nationally representative population sample of 145 points based on random sampling. This revision of the DSP was an activity under the 'Health I plus' project, supported by a loan from the World Bank. Revised DSP Sampling Plan Based on the principle that the characteristics of the population under surveillance should be similar to that of the general population in different geographic areas, a multi-stage cluster probability sampling was designed with stratification at three levels. The first level of stratification was according to 7 geographic regions (Northeast, North, East, South, Southwest, Northwest and Central areas) and 3 municipalities (Beijing, Tianjin and Shanghai) in China. The second level was based on the urban and rural location of primary sampling units. Within rural areas, a third level of stratification was based on a classification of rural sites into four socio economic strata, based on the 1982 Census returns about average levels of variables such as literacy rates, GDP per capita, and dependency ratios. Also, urban areas were re-classified according to population size into big cities, with over 1 million population, middle sized cities with 0.5 -1 million population and small cities with 0.2–0.5 million population. The primary cluster unit in urban areas was the city, and in rural areas, the county. Probability proportionate to population size sampling (PPS) was used to select a city or county, using 1982 Census data. In the second stage cluster, in selected cities or counties, the unit of sampling was a 'neighborhood' (Jiedao) within cities, or 'townships' (Xiang) in rural areas. Both the 'Jiedao' and the 'Xiang' represent a community with a primary government, with a population ranging from 30 000 – 100 000. PPS sampling was again used for selection of units at the second stage, such that the probability of selection was according to population size of the neighborhood or township[ 12 ] The resultant new DSP system consists of 145 points, which are scattered over the 31 provinces or autonomous regions or municipalities of China (Figure 3 ). Figure 3 Distribution of sample points in DSP system, China, 2000 A population of about 10 million resides in the areas covered by the system (a little under 1% of the Chinese population). Based on national data on public health indicators used for stratification, the selected DSP sites are representative of the national population[ 12 ], and the socioeconomic characteristics of these sites derived from the 2000 Census data are shown in Table 2 . As expected, a general gradient can be observed in socioeconomic status across the different rural strata, ranging from 1 (best off) to 4 (worst off). Table 2 Socio economic characteristics of sites representing different strata in the DSP (Rural 1 best off; Rural 4 worst off) Socioeconomic characteristic Urban Rural 1 Rural 2 Rural 3 Rural 4 Average GDP* (Million RMB per site) 5098 5108 2602 2054 552 Average literacy rate (%) 91.6 79.5 80.6 78.5 60.5 Average dependency ratio(%) 32.6 44.2 48.4 50.1 57.8 Average Infant mortality rate (per 1000 live births) 9.9 15.8 26.5 42.6 67.8 Source: Chinese Academy of Medical Sciences, 2004, based on data from the 2000 Census * GDP derived from 1982 Census data on county specific gross agricultural and industrial products. The GDP for each strata was calculated as an average of GDPs for it constituent counties. Mortality registration in the DSP Since 1990, the system has covered natality, mortality, and the incidence of 35 notifiable diseases. In this section, we describe the process of mortality registration within the DSP system, and comment on aspects regarding quality control of data, particularly with respect to completeness of reporting, and the use of the data for public policy. In each DSP site, there is at least one township hospital, and the 'Disease Prevention Unit' in these hospitals is responsible for vital registration. The detailed working procedure for mortality registration is described in the guidelines for surveillance in the DSP[ 9 ]. In urban areas, almost half of all deaths occur in health facilities, and there are standard protocols for death registration that are closely adhered to. For deaths occurring at home, the attending physician issues a medical certificate of cause of death, in compliance with the registration protocol. Here, we describe briefly the procedure for death registration in rural sites of the DSP. In rural areas, about 80% of adult deaths occur at home, with few occurring at the township hospital, or other tertiary hospitals in the vicinity. Even for those deaths that occur at home, there is often clinical evidence available from recent consultations with medical staff at township or other hospitals. The procedure for collection and compilation of cause of death data is as follows: • For deaths occurring at home, a village health worker reports the event to the Prevention Unit at the township hospital. A staff member from the Unit visits the household, and completes a death certificate based on a description of symptoms from family members, and available documents from recent contact with health services. • For deaths occurring in the township hospital, the DSP staff collect the death certificate from the hospital, completed by the physician who attended the death. • For deaths occurring in other hospitals, relatives of the deceased submit physician-certified death certificates to the Prevention Unit at the township hospital. • In the event of a childhood death, or deaths in women of maternal age, the Maternal and Child Health Unit at the township hospital undertakes the investigation of the cause of death, and screens death certificates for such deaths from other hospitals for accuracy. • Data cleaning and compilation is done at the county or provincial level, and following computerization, an electronic data-file is transferred to the Chinese Academy of Preventive Medicine. • ICD coding of the underlying cause of death and subsequent tabulation and publication of results is done at the central level in Beijing. Annual reports of deaths by causes, age and sex have been published in Chinese by the Chinese Academy of Medical Science since 1990, and a public access website for these data is currently under development. There are instances where the above procedures are not strictly adhered.to. In situations where there is a delay in the household investigation by the Prevention Unit staff, family members or neighbours visit the unit to deregister the residential status of the deceased, and obtain the necessary documentation for corpse disposal and other legal purposes. Such instances can promote improper assignment of cause of death in individual cases, since the respondents in these cases may not be familiar with the disease and related conditions experienced by the deceased. Data quality control and improvement Within the DSP system, there are two methods employed for controlling data quality. The first is an internal procedural check system, which evaluates timeliness of death registration, completeness of entries in the registration form, and the accuracy of data entry. Errors detected from these checks are corrected through re- enquiry, and enhance the usability of the datasets. At a second level, the datasets are evaluated using statistical measures. The completeness and accuracy of population enumeration in the DSP has been evaluated using the standard United Nations Age Sex Accuracy Index [ 13 ], and the results of the evaluation in 1999 are shown in Table 3 . The index for almost all regions is around 20, suggestive of accurate age-sex data in the DSP population enumeration (see footnote to Table 3 ). Table 3 UN Age Sex accuracy Index* for DSP population, by region, 1999 Region UN Index North China 17.7 Northeast China 15.2 East China 21.9 Central China 19.7 South China 22.9 Northwest China 20.5 South West China 23.0 Source: Chinese Academy of Medical Sciences, 2004 The United Nations Index measures the quality of population data as follows: < 20 = accurate; 20 to 40 = inaccurate; > 40 = highly inaccurate [13] While, there is no mechanism for evaluating the completeness of death registration in the MOH-VR system, the DSP evaluates completeness of both birth and death registration. This is done through independent resurveys, and statistical techniques based on "capture – mark – recapture" methods are used to estimate the completeness of registration [ 14 ]. These surveys are conducted once every three years, on a sample of 5000 households in each province. Results from three such surveys conducted in 1992, 1995 and 1998 are presented in Table 4 , for infant deaths and deaths at all ages separately[ 15 , 16 ]. These data suggest that the coverage of infant deaths remains problematic, and as might be expected, is lower than the coverage of adult deaths. Somewhat surprisingly, the extent of undercount was similar in both urban and rural areas, and has shown no improvement in successive surveys. . Although the overall completeness in 1998 was 86 %, there has been no such survey since then, and there is an urgent need to assess coverage in recent years, to ascertain current levels of completeness. Table 4 Estimated under registration of deaths (%) in the DSP system during the 1990 s Region 1992 1995 1998 Infant deaths All ages Infant deaths All ages Infant deaths All ages Urban - 10.9 25.8 15.1 20.5 13.2 Rural 25.4 13.1 35.6 13.0 21.9 14.9 National 16.0 12.8 34.7 13.5 20.7 14.1 Source: Calculated from 1992, 1995 and 1998 completeness surveys carried out by the Chinese Academy of Preventive Medicine. Discussion In this paper, we have described in detail for the first time in English, the Disease Surveillance Points System that operates in China, and provides critical information on the health of one-fifth of the world's population, from a sample of less than 1 % of the Chinese population. This is a remarkable achievement, and perhaps the most cost-effective system of data collection to inform health policies and programs ever devised. Yet, the performance, even the existence of the system is not widely appreciated outside of China, despite its obvious implications for rapidly improving knowledge about causes of death in several other low income populations. Undoubtedly, complete vital registration of deaths with full medical certification is the most appropriate means to monitor the health of populations, but to establish, and particularly to maintain such a system will be outside the realms of possibility for most developing countries for decades to come. Meanwhile, novel, affordable and sustainable approaches to data collection on mortality that is representative of populations are required. The Chinese DSP system described in this paper has many advantages. Almost all countries conduct censuses at least every ten years, and hence the socio-demographic information on which to select a representative sample of surveillance sites is available. Adequate information can be obtained from relatively small samples (≈1% in China and India), and substantial progress has been made with the development of 'verbal autopsy' instruments and procedures to have sufficient confidence in the utility of cause of death data that they produce, at least for broad causes of death. While this may not be sufficient for specific disease or injury control programmes, field experience in Tanzania suggests that the data are useful for determining the need for priority health programs [ 17 ]. The data from the DSP have been used to monitor the emergence of tobacco-caused mortality in China[ 18 ], and to assess the global and regional burden of disease [ 5 ]. Certainly, from a national perspective, much insight has been gained from these data into the levels and patterns of mortality in China over the past decade or so. However, any system that is not based on complete registration and medical certification is of questionable validity for two reasons. Firstly, any undercount of deaths is likely to bias the overall cause of death patterns, with communicable diseases more likely to be missed in poorer segments of the population. Hence, complete registration or at least an assessment of completeness is absolutely necessary in a system like the DSP. Secondly, 'verbal autopsies' are a blunt instrument and can never be expected to capture the full medical history of the deceased. Data generated by systems such as the DSP in China require periodic validation to calibrate the degree of uncertainty in cause of death statistics and to suggest appropriate adjustment factors for specific causes of death. The authors are currently undertaking such a validation study based on a sample of 2900 deaths in six cities and 3500 deaths in nineteen rural counties in China. In the urban sites of the study, two arms of the project are being implemented. Firstly, medical records of the sampled deaths are being reviewed to develop a reference 'gold standard' diagnosis of the underlying cause of death, using the international form of medical certificate of cause of death. For the same deaths, the diagnosis from the routine registration system is compared and validated against this reference diagnosis, to assess the validity of the routine system. Secondly, for each of these deaths, a verbal autopsy interview was conducted to derive a cause of death, and this will be compared with the reference diagnosis to establish the validity and operational characteristics of the verbal autopsy procedure to be used in the DSP system. In rural areas, the same standard procedures for verbal autopsy are being introduced, and diagnoses from these standard procedures will be compared with the diagnoses from the routine registration system to measure the reliability of cause of death ascertainment in rural China. It is envisaged that the results from these studies, as well a proposed under-reporting survey in 2005, will enable correction of datasets from mortality registration systems in China to improve knowledge of cause specific mortality at the population level. The research will also provide the evidence base to strengthen mortality registration in China by identifying structural weaknesses and areas for development, which will minimize undercount and misclassification of deaths in the future. In addition, this evaluation research will build capacity that will result in long term improvements in data quality from the DSP, given the recent changes in the funding, management and coordination of activities within the system. In particular, opportunities to build on existing networks, such as the family planning services system, will need to be more effectively exploited in future to accelerate the implementation of vital registration nationwide.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555951.xml

514702

MPSS profiling of human embryonic stem cells

Background Pooled human embryonic stem cells (hESC) cell lines were profiled to obtain a comprehensive list of genes common to undifferentiated human embryonic stem cells. Results Pooled hESC lines were profiled to obtain a comprehensive list of genes common to human ES cells. Massively parallel signature sequencing (MPSS) of approximately three million signature tags (signatures) identified close to eleven thousand unique transcripts, of which approximately 25% were uncharacterised or novel genes. Expression of previously identified ES cell markers was confirmed and multiple genes not known to be expressed by ES cells were identified by comparing with public SAGE databases, EST libraries and parallel analysis by microarray and RT-PCR. Chromosomal mapping of expressed genes failed to identify major hotspots and confirmed expression of genes that map to the X and Y chromosome. Comparison with published data sets confirmed the validity of the analysis and the depth and power of MPSS. Conclusions Overall, our analysis provides a molecular signature of genes expressed by undifferentiated ES cells that can be used to monitor the state of ES cells isolated by different laboratories using independent methods and maintained under differing culture conditions

Background Multiple large-scale analytical techniques to assess gene expression in defined cell populations have been developed. These include microarray analysis, EST enumeration, SAGE and MPSS. Each of these techniques offers unique advantages and disadvantages. Technique selection largely depends on the expertise of the investigator, the cost, the availability of the techniques, the amount of RNA/DNA that is available, and the existence of the genome databases. The human genome dataset is the best annotated one available [ 1 , 2 ]- making large scale gene expression analysis of human tissues and cells uniquely fruitful for investigators due to the increased ability to identify full length transcripts with predicted gene function instead of EST's. Human ES cells have been isolated relatively recently and ES cell genes are underrepresented in current databases. More importantly, recent evidence has suggested that mouse ES and human ES cells differ significantly in their fundamental biology [ 3 , 4 ] and one cannot readily extrapolate from one species to another. However, comparing results between species may provide unique insights. Given the wealth of SAGE and microarray data available from rodent ES cells examining human ES cells with similar techniques as has been done recently by several investigators [ 3 - 11 ] should be very useful in furthering our understanding of this special stem cell population. Until recently however, it has been difficult to obtain RNA from a homogenous population of undifferentiated hESC for such an analysis as cells could not be grown without feeders and few unambiguous ES cell markers had been described. However, we and others have now described markers that will clearly assess the state of ES cells using a combination of immunocytochemistry and RT-PCR [ 3 , 12 , 13 ] In addition, techniques of harvesting ES cells away from feeder layers have been developed and verified (our unpublished results) and methods of growing ES cells without feeders have been described [ 14 ]. These techniques, have allowed us (and others) to obtain large amounts of validated RNA/cDNA samples for comparison by microarray [ 3 - 11 ], SAGE [ 8 ] or EST enumeration [ 9 ]. We selected MPSS for this analysis as it offers some unique advantages over other methods including SAGE [ 15 , 16 ]. MPSS offers sufficient depth of coverage when over one million transcripts are sequenced [ 16 ] and is efficient, as the numbers of sequences obtained are an order of magnitude larger than with shotgun sequencing or SAGE. It is relatively rapid with a turnaround of a six to ten weeks, and if done with human tissues, more than 80% of transcripts can be mapped to the human genome with current tools. Further, independent analysis has suggested that expression at greater than 3 tpm (transcripts per million) is predictive of detectable, reliable expression, equivalent to roughly one transcript per cell – a sensitivity that is unparalleled when compared to other large-scale analysis techniques [ 16 ]. Finally, MPSS libraries can be translated into SAGE libraries and compared to existing SAGE library sets using freely available tools such as digital differential display, allowing ready comparisons to existing SAGE/MPSS libraries of mouse ES cells. It is important to note that we found 14 base pair SAGE tags are generally not as specific as 17 base MPSS signatures and that SAGE sampling depth is usually insufficient. Newer technologies such as extended sequencing to 20 base pairs in MPSS, 24 base pairs in SAGE or cheaper bead alternatives such as those described by Illumina may offer additional depth of coverage and a cheaper price but these at present remain limited in availability. We have utilized MPSS using a pooled sample of three human ES cell lines grown in feeder-free culture conditions over multiple passages [ 17 , 18 ] to assess the overall state of undifferentiated ES cells. Our rationale for using pooled sample rather than individual samples was based on the fact that no standardized medium and culture conditions have been established for growing and propagating ES cell lines. Variation observed by sampling single lines may be due to culture conditions rather than intrinsic differences. We reasoned therefore that a need existed to establish a reference baseline using pooled samples to enhance the similarities and provide evidence for candidate genes that should be examined for differences such as expression of HLA genes, Y chromosome and X chromosome genes, imprinted genes and genes regulating the methylation state. Our results show that MPSS provides a greater depth of coverage than EST scan or microarray and provides a comprehensive expression profile for this stem cell type. The data set generated allows us and others to identify multiple genes that were not previously known to be expressed in this population, including novel gene as well as obtain a global overview of pathways that are active during the process of self-renewal. Results MPSSS analysis of pooled samples A pooled sample of undifferentiated human ES cell lines H1, H7, and H9 grown in feeder-cell free conditions [ 19 ] was used for the preparation of mRNA as previously described [ 20 ]. Growth without feeders avoids complication from feeder contamination, which even with good harvesting techniques [ 14 , 21 ] ranges between 1–3% (unpublished data) and is sufficient to be detected by MPSS (Dr. B. Lim-Harvard University personal communication). Under these conditions, 80–95% of the cells express SSEA-4, 91–94% express TRA-1-60, and 88–93% express TRA-1-81, previously described markers for undifferentiated hESC [ 19 ]. Microarray analysis of 2802 genes suggests that these cells are remarkably similar in their gene expression profiles, with only 5 genes being more than 2-fold different between the three cell lines [ 17 , 18 ] (and data not shown). The undifferentiated state of the cells was also assessed by RT-PCR of known markers of undifferentiated hESC on mRNA of the pooled hESC sample (Figure 1 ). In addition absence of early markers of differentiation was assessed. No expression of GATA, Sox-1, nestin, Pdx-1 or markers of trophoectoerm were detected in samples used (Supplementary table 3a, see also 3) Figure 1 RT-PCR analysis (a), cumulative tpm (b) and tpm of known ES cell markers (c) is shown. Note that MPSS identifies most known markers of huES cells and expression is at high tpm levels. * – signature maps to >100 location in the genome (class 0); ** – artifactual (class 5) signature Pooled mRNA of the three hESC lines was subjected to MPSS analysis at Lynx Therapeutics (Hayward, CA), generating 22,136 distinct and significant signature sequences from a total of 2,786,765 sequences (see Methods and additional file 1 ). Each signature was ranked, as outlined in Methods (Table 1 ), based on its position and orientation within the transcript, and the presence of a polyadenylation signal and polyA in the transcript sequence. 16,675 signatures (75%) mapped to UniGene transcripts; 40 signatures (0.2%) mapped to mitochondrial transcripts; 3,818 signatures (17%) matched genomic sequences but did not map to a UniGene cluster; 927 (4%) signatures matched sequences present at more than 100 genome locations (class 0, representing transcripts containing repetitive elements in their 3' UTR). 676 (3%) signatures did not match to genome or UniGene sequences. Some UniGene clusters contain multiple signatures. These signatures likely represent either transcripts of alternative termination sites, or artefacts of MPSS library construction. Signature classification helps to distinguish artifactual signatures from signatures representing expressed transcripts. For example, signatures of class 1 to 3 are 3'most signatures in mRNA or EST sequences with poly (A) signal and/or polyA tail and most likely represent transcripts with multiple polyadenylation sites. Artifactual signatures constituted 1–3% of the tpm count of the "real" signature, although occasionally close counts were observed (data not shown; see supplementary data tables, additional files 2 , 3 ). To simplify the MPSS data analysis and pair-wise comparison of ES cell data from this study to other datasets, multiple signatures mapping to the same Unigene ID (Hs build 169) were combined into one tpm count as the sum of tpm for signatures of class 1, 2, 3, 22, 23 if any found. These are 3'most signatures close to polyA signal and/or polyA tail, most probably representing true transcripts with alternative termination. If no signatures of above classes were found, then sum of class 4 (3'most, no polyA features) was used. If none the above, the sum of class 5 signatures was used for the tpm calculation per unigene cluster. Resulting table containing data for 8679 unigene clusters, 11 mitochondrial genes, and including 1991 signatures that did not map to unigene but uniquely matched genomic sequences (potential novel transcripts), is presented in supplementary table ( additional file 4 ) and available for download from Lynx [ 27 ]. Table 1 Classification of the MPSS cDNA signatures. The signature classification used for annotation is shown * The Class 0 signatures are the signatures that hit genome more than 100 times, which is treated as a "repeat sequence". ** The polyA tail is defined as a stretch of A's (at least 13 out of 15 bases) that is no more than 50 bases away from the end of the source sequence. The polyA signal is either AATAAA or ATTAAA that has at least one base within the last 50 base before the end of the source sequence or the polyA tail. *** All the virtual signatures extracted from the genomic sequences are classified as class 1000 signatures. Virtual Signature Class MRNA Orientation Poly-Adenelation Features ** Position 0* Either – Repeat Warning Not applicable Not applicable 1 Forward Strand Poly-A Signal, Poly-A Tail 3' most 2 Poly-A Signal 3' most 3 Poly-A Tail 3' most 4 None 3' most 5 None Not 3' most 6 Internal Poly-A Not 3' most 11 Reverse Strand Poly-A Signal, Poly-A Tail 5' most 12 Poly-A Signal 5' most 13 Poly-A Tail 5' most 14 None 5' most 15 None Not 5' most 16 Internal Poly-A Not 5' most 22 Unknown Poly-A Signal Last before signal 23 Poly-A Tail Last before tail 24 None Last in sequence 25 None Not last 26 Internal Poly-A Not 3' most 1000*** Unknown – Derived from Genomic Sequence Not applicable Not applicable The frequency distribution of the signatures shows that the 200 most abundant signatures represent 99% of the total number of signature counts obtained from the hESC (Figure 1 ). Most of top 200 genes (unigene clusters, additional file 5 ) represent ribosomal genes and genes involved in protein and nucleic acid synthesis and are consistent with results obtained by EST scan and other analyses (data not shown, and [ 5 , 8 , 9 ]). We note that several ribosomal genes were identified as being overexpressed by microarray, SAGE and EST scan as well (see additional files 16 , 17 , 18 ). Comparison of the pattern of gene expression with other cell types showed a very similar expression profile with housekeeping genes being the predominant population of sequences in all cell types examined (data not shown). Only three known ES cell specific genes were present in the top 200 genes ( additional file 5 and Figure 1 ). These included SOX-2, DNMT3β, and Oct-4. As in other cells cell type specific genes, transcription factors and cytokines were present at much lower abundance (<50 tpm on average). These low tpm level genes were often not detected by other methods (discussed below). The expression level of cell surface receptors for fibronectin are high (ITGB1 – 578 tpm) and their presence was confirmed by immunocytochemistry and RT-PCR, suggesting that feeder-free clones may grow well on this substrate (data not shown, see also Figure 2 and [ 14 , 21 ]). The major signaling pathways represented in the top 200 most abundant genes are the FGF signaling pathway, with FGFR1 being most abundant (673 tpm, Figure 2 ), and the ras activated pathway, with two members of the ras family (NRAS-related and ran) being present in the top 200. This is consistent with data that E-Ras is critical for rodent ES cell self-renewal [ 22 ]. No transcripts for HRASP (Homologue of ERAS pseudogene) were detected however (Figure 2 ), suggesting that these other ras family members may subserve this critical role of self-renewal [ 9 ]. The absence of E-Ras was confirmed by RT-PCR (data not shown), as was the presence of FGFR1 (Figure 2 , [ 22 ], and data not shown). Figure 3 RT-PCR for E-ras/RASP, FGFR1 and novel genes identified as enriched in undifferentiated ES cells is shown in Panel A and B. Localization of E-cadherin and β-catenin in undifferentiated ES cell is shown in Panel C. All of the genes identified by MPSS and tested were present in undifferentiated ES cells and most were significantly downregulated as cells differentiated. Note the high expression at the cell surface and low or undetectable levels of β-catenin in the nucleus. Major pathways present at detectable levels by MPSS To gain a broad overview of the properties of hESC, we mapped the genes found in the hESC cells to the human genome to get an overview of the chromosomal distribution of genes expressed in hESC (Figure 3 and additional files 6 , 7 , 8 , 9 , 10 , 11 ). Overall, MPSS detected gene expression in most of the previously identified zones of transcriptional activity within chromosomes. Two chromosomal regions contained more genes expressed in hESC – than expected, and several regions where fewer genes were expressed, compared to the total number of genes located within a particular chromosomal region. No bias to chromosome 17, 12 or X was seen either in overall gene expression or in a particular cytoband. The failure to detect a bias was confirmed by mapping EST scan data [ 8 ] as well. The overall distribution patterns were similar and did not show any bias at this level of resolution. Interestingly, gene expression from both X and Y chromosomes was observed. Unlike rodent ES lines both male and female ES lines have been obtained with roughly equal frequency [ 20 ] suggesting that when individual cell lines are examined differences between levels of expression between male and female will be present and detectable. Figure 2 Cytoband mapping of ES cell expressed genes and regions of relatively high and low transcription relative to the refseq database is shown. More detailed mapping information is presented in supplementary tables. Likewise, MPSS detected expression of several MHC Class I and II genes, suggesting that MPSS can identify differences between ES cell samples when HLA gene expression is used to type cells [ 17 , 18 ]. We also note that both H19 and Igf2 were expressed at detectable levels. H19 and Igf2 are located adjacent to each other on chromosome 11p15.5 and are reciprocally regulated by imprinting, H19 being paternally imprinted, and IGF2 being maternally imprinted [ 23 , 24 ]. It is therefore likely that their ratio of expression is likely to differ between cell populations and may represent a simple assessment of the imprinting status of cells. We classified genes expressed into ECM related, homeobox containing, zinc finger proteins, novel genes as well as genes which could assigned to major signaling pathways such as wnt, BMP/TGFβ, LIF, receptors, etc. This data is provided in excel files in the supplementary information provided ( additional files 12 , 13 ). Overall certain general themes emerged when genes were classified into such a fashion. We find that: A) hESC express markers characteristic of ES cells in general and few markers characteristic of differentiated cells confirming the initial purity of ES cells used for this analysis and the fidelity of the analysis B) Ribosomal protein transcripts, and mitochondrial genes are highly expressed in ES cells (relative to other transcripts) and constitute more than 50% of the total transcripts analyzed (Figure 1 , additional files 5 , 16 , 17 , 18 ). And this is similar to other samples analyzed [ 3 - 11 ], (Lynx Inc. data not shown) C) Positive regulators of the cell cycle, TERT and antisenescence related genes and DNA repair pathway regulators are expressed at high levels while proapototic genes, Rb and p53 pathways regulators are expressed at low levels (see table 2 for an example of TERT related gene expression, see supplementary tables ( additional files 12 , 13 ) for cell cycle, apoptosis and other pathways) D) The number of novel genes or genes of unknown function is high (2600/11,000) and constitutes approximately 25% of the unique signatures (see additional file 13 for a listing of genes of unknown function, their chromosomal mapping, and UniGene identity). Comparison with other samples suggest that the number of novel genes or genes of unknown function seen are higher in ES cells (25% versus 20%). E) Components of most major signaling pathways are present but so are negative regulators (including zinc finger proteins), suggesting that inhibition plays an important role in maintaining cells in an undifferentiated state (see additional file 13 ). Table 2 Senesence and Aging related genes A subset of genes related to senescence and aging that may regulate the lack of senescense in ES cells is shown. Note that the telomerase, morf's, nortalins and sirtuins are all expressed in ES cells. *The TERT gene has a signature uniquely mapping to an intron (cryptic exon?), which was present in all runs of the ES cell analysis and was not found in other human samples (not shown). HuES_TPM Gene Bank Hs169 Gene chr 802 BC029378 Hs.442707 TERF1 8q13 56 AF289599 Hs.274428 TERF2IP 16q23.1 38 AI742882 Hs.409194 TNKS 8p23.1 15 AF002999 Hs.63335 TERF2 16q22.1 10 AW271065 Hs.9645 TNKS1BP1 11q12.1 9 BC005030 Hs.7797 TINF2 14q11.2 7 AF264912 Hs.280776 TNKS2 10q23.3 10* NM_003219 Hs.439911 TERT 5p15.33 321 NM_004134 Hs.184233 HSPA9B 5q31.1 94 AF070664 Hs.374503 MORF4L1 15q24 80 BC017305 Hs.528641 SIRT7 17q25 42 AF100620 Hs.411358 MORF4L2 Xq22 27 NM_012238 Hs.31176 SIRT1 10q22.1 10 BM803485 Hs.511950 SIRT3 11p15.5 16 AL579291 Hs.282331 SIRT5 6p23 Examination of signaling pathways suggest that wnt, TGFβ and FGF signaling pathways are likely important in regulating the ES cell state while LIF/gp130 signaling is not as important. These conclusions are based on examining the expression of the positive and negative regulators of a particular pathway by MPSS, and EST scan. When critical components are low or absent we have tentatively assumed that the pathway is unlikely to be active. An example of the Igf/PTEN pathway is shown to illustrate the logic (Table 3 ) and other pathways along with verification with EST scan are summarized in the supplementary tables ( additional files 12 , 13 ). Note the high levels of soluble frizzled receptors and the expression of E-cadherin (negatively regulating β-catenin translocation). The expression of cadherin and β-catenin was confirmed by immunocytochemistry (Figure 2 ). The relatively fidelity of the conclusion was confirmed by examining the expression of E-cadherin by immunocytochemistry and localizing β-catenin expression. Table 3 IGF-/PTEN/Akt and Ras/Raf/MAP pathway A subset of genes related to Igf/PTEN pathway that are expressed in undifferentiated ES cells is shown. Note that the overall pattern of expression suggest that this pathway is active in undifferentiated ES cells. Tpm ES Tpm EB Unigene ID Locus ID Description 14 32 Hs.239176 3480 IGF-1 receptor N.D. N.D. Hs.390242 3667 IRS-1 0 0 Hs.253309 5728 PTEN N.D. N.D. Hs.32942 5294 PI3K 11 8 Hs.433611 5163 PDK1 0 15 Hs.92261 5164 PDK2 N.D. N.D. Hs.6196 3611 ILK 75 157 Hs.368861 207 AKT1 15 82 Hs.170133 2308 FKHR (FoxO1A) 78 54 Hs.14845 2309 FKHRL1 (FoxO3A) 15 88 Hs.282359 2932 GSK3beta 39 14 Hs.238990 1027 p27 280 240 Hs.371468 595 Cyclin D1 0 594 Hs.370771 1026 p21 1 10 Hs.329502 842 Caspase 9 0 39 Hs.76366 572 Bad 98 193 Hs.260523 4893 N-Ras 2 0 Hs.37003 3265 H-Ras 35 128 Hs.257266 5894 Raf1 N.D. N.D. Hs.132311 5604 MEK1 128 218 Hs.366546 5606 MEK2 37 75 Hs.324473 5594 ERK (p42 MAPK) We compared the signature sequences detected in the hESC to an MPSS database of 36 human tissues and cell lines to look for genes that are unique to, or highly overexpressed in hESC. A list of several hundred was generated when a cutoff of 30 tpm or higher (ten fold above detection level) that were elevated in ES cells when compared to neural stem cells examined in a similar manner was used. This list is provided in supplementary materials ( additional file 14 ). A list of 13 highly enriched genes of unknown function is shown in Table 4 , and the tpm values for the corresponding signatures in each of 36 tissues or cell lines is provided in the supporting information ( additional file 15 ). The expression in ES cells, of these 13 genes was confirmed by designing PCR primers to different regions and examining gene expression (Figure 2 ). Several of these genes are highly expressed in hESC and absent in most other tissues tested (Table 4 , additional file 15, and data not shown), are downregulated as ES cells differentiate (Figure 2 ), and are good novel, candidate markers for undifferentiated hESC. Table 4 Novel genes enriched in hESC as assessed by MPSS A short list of genes of unknown function that are highly enriched in three ES cell lines comparing to 36 different tissues and cells are shown. A complete list of unknown genes expressed in pooled hESC cells is presented in supplementary tables. * NS-neural stem cells, TH-thymus, HY-hypothalamus, PG-pituitary gland, TE-testis ** this gene (Hs.507833 in the unigene Hs.169) is transcribed in antisense to HDCMA18P (Hs.278635) SIGNATURE HuES,TPM Chr GB:description Other 36, TPM* GATCTCCAGTAGACTTA 1646 4 CD250365:Homo sapiens transcribed sequence ** NS-10 GATCTGTTAACAAAGGA 967 16 BC008934:claudin 6 ND GATCTAGAAGTTGCAAC 489 1 NM_019079:hypothetical protein FLJ10884 ND GATCTTTTTTTTTGCCC 455 3 NM_018189:hypothetical protein FLJ10713 TH-47, HY-3, PG-3 GATCCCCATCCAAAAGA 366 7 AI636928:Homo sapiens transcribed sequences MCF7-2 GATCCACCTAGGACCTC 244 X CD174249:Homo sapiens transcribed sequence ND GATCCGCCTCCTTGGCC 240 4 AK092578:Sapiens cDNA FLJ35259 fis ND GATCCTAGCCAAGCCCC 169 3 BF223023:Homo sapiens transcribed sequences ND GATCTGGCCCGCCACCA 150 16 NM_032805:hypothetical protein FLJ14549 (ZNF206) ND GATCGTTGTGGTGGACT 146 3 XM_067369:similar to Heterochronic gene LIN-41 ND GATCCACCACATGGCGA 92 11 CD176172:Homo sapiens transcribed sequence ND GATCCAACAATTCTACT 78 U CD173198:Homo sapiens transcribed sequences TE-33 GATCTTCTAAACCCATC 75 12 BU608353:Homo sapiens transcribed sequence ND Comparing with other data sets Recently we and others have begun examining hESC with EST scan [ 10 ] and microarray analysis to develop a characteristic profile of this unique population [ 3 - 10 ]. We used this data to compare the sensitivity of MPSS with EST scan and microarray analysis. We have previously reported a set of 90 genes reported common to 6 different hESC lines [ 10 ]. Of these, eighty-five were detected by MPSS showing a high degree of concordance (>90%). Of the five genes missing from the MPSS hESC data set, four of the genes had valid MPSS signatures (Table 5 ) and were readily detected in other human samples (data not shown). One gene (SNRPF) lacked a DpnII (GATC) site making it non-detectable by MPSS. GDF3 was detected at non-significant level in the hESC, though was detected by MPSS at higher level (10–30 tpm) in other ES cells tested (Dr. B. Lim-Harvard University personal communication, and additional file 17 ). Sperger et al., also used microarray to examine gene expression in undifferentiated cell lines [ 11 ]. They compared expression in undifferentiated cells with expression in EC carcinoma lines and with microarray data from several other cell lines. They have identified 895 genes (GenBank accession numbers) which reduce to 718 number of unigene identities when mapped to the unigene build Hs161. We have compared this data with the MPSS data and see that MPSS identified the large majority of these genes as well ( additional file 16 ). Similar results were obtained when data was compared with that reported by Sato et al., and Abetya et al., [ 6 , 7 ] and a similar concordance in gene expression was observed (data not shown). Thus, MPSS provides an independent verification of the microarray results and in addition identifies other genes that may not be present on the arrays or detectable by current microarray techniques. Table 5 MPSS tpm of genes reported as enriched by microarray in hESC Table 5 Tpm of genes identified as overexpressed microarray analysis of six pooled human ES cell lines. Note that most of them have high tpm values and are detected by MPSS. * – PSIP2 and PSIP1 have 3' alternate termination and distinguished by MPSS (but not by microarray); ** – PODXL: TPM for signature of class 5; 3' most signature has double palindrome and underrepresented. *** – higher expression of GDF3 was detected in other ES cells (suppl.table for BG02 and not shown). **** – expression detected in other human samples (not shown). GB_accession Gene Symbol HuES_TPM X85372 SNRPF No GATC NM_002295 LAMR1 6135 D23660 RPL4 5269 NM_001002 RPLP0 4656 NM_002520 NPM1 3207 X69391 RPL6 3745 M31520 RPS24 3183 AF070600 OK/SW-cl.56 2702 X57958 RPL7 1923 NM_024674 LIN-28/ 1692 NM_145899 HMGIY 1618 NM_018407 LAPTM4B 1326 M94314 RPL24 1279 X62534 HMGB2 989 D13748 EIF4A1 1070 NM_006086 TUBB4 809 J04164 IFITM1 788 X69804 SSB 874 M93651 SET 1323 D00760 PSMA2 673 AL162079 SLC16A1 991 AF225425 SEMA6A 742 U28386 KPNA2 542 X74929 KRT8 543 NM_002300 LDHB 527 M97856 NASP 536 AF311912 SFRP2 457 AF020038 IDH1 450 D83174 SERPINH1 477 S74445 CRABP1 437 NM_000165 GJA1 392 AB040903 TD-60 524 AF063020 PSIP2* 389 U76713 HNRPAB 166 NM_000224 KRT18 302 NM_021144 PSIP1* 389 M94856 FABP5 257 NM_016304 Ribo 60S L30 247 AK094423 HNPRA1 like 214 AF055270 HSSG1 (SFRS7) 201 M77140 GAL 199 AF257659 CALU 100 AF098158 C20orf1 338 U41387 DDX21 179 AD001528 SMS 175 NM_006548 IMP-2 177 AJ223953 PTTG1 154 X54326 EPRS 210 D13627 CCT8 167 NM_012247 SEPHS1 306 D00762 PSMA3 123 AF005418 CYP26A1 121 M25753 CCNB1 168 NM_000884 IMPDH2 174 X16396 MTHFD2 113 NM_005159 ACTC 98 U31814 HDAC2 112 J04031 MTHFD1 104 NM_006341 MAD2L2 95 J03746 MGST1 88 NM_020997 LEFTB 62 M74091 CCNC 86 AK001962 BRIX 66 M36981 NME2 93 AL133611 Novel 63 X05360 CDC2 62 AB040930 LRRN1 46 AF071592 KIF4A 71 AF015254 STK12 41 X14253 TDGF1 37 AB023420 HSPA4 42 M19309 TNNT1 54 BC004200 PPAT 34 NM_024090 ELOVL6 23 NM_014366 NS 30 U97519 PODXL 26** AF048722 PITX2 25 NM_024498 ZNF117 32 NM_001878 CRABP2 24 X59244 ZNF43 13 BC001068 C20orf129 17 NM_024865 Nanog 15 NM_024900 Jade-1 11 AB046793 KIAA1573 11 Z26317 DSG2 18 NM_020634 GDF3 1*** AF070651 ZNF257 0**** NM_016448 RAMP 0**** U88573 NBR2 0**** AB044157 GSH1 0**** Comparison with an EST scan analysis of 37,081 EST sequenced from a similar pooled sample of hESC [ 9 , 10 ] also showed a high degree of concordance. The EST scan analysis detected 8,801 distinct UniGene clusters in hESC versus 9,996 distinct UniGene clusters expressed at 4 tpm or higher in the MPSS dataset. Of the 8,801 UniGene clusters identified by the EST scan, 1,139 are singletons, i.e. identified by only one EST out of the 37,081 total EST's. 5,286 UniGene clusters have 5 or more ESTs as evidence, and only 118 UniGene clusters have more than 100 EST's as evidence. In contrast, all 9,996 UniGene clusters identified by MPSS were detected at 4 or more tpm and identified in multiple sequencing runs. More than 8,000 have at least 10 tpm, and over 1,000 have more than 100 tpm. Thus, although the EST's are longer in length and thus easier to assign to a particular gene, MPSS appears more sensitive than EST scan. MPSS for example identified almost twice as many genes as EST scan consistent with the difference in the depth of analysis (No of sequences MPSS/EST). Richards et al [ 8 ] have used SAGE analysis to two ES cell lines. Their analysis revealed expression of approximately four thousand genes which was significantly fewer than that identified by MPSS consistent with the fewer number of gene tags sequenced. Comparison of the data sets however showed good concordance particularly for genes expressed at higher tpm levels. The entire comparison is presented in supplementary table ( additional file 18 ) and is available for download from Lynx [ 27 ]. Overall MPSS could identify genes that other methods identified with an average concordance rate of 70%. The depth of analysis with MPSS at 2.4 million signatures however was significantly greater. MPSS in general identified many more genes than microarray or EST scan or SAGE (see above). The most direct comparison is with EST scan or SAGE, which do not rely on comparative gene expression to establish significance of gene expression. Overall our comparison suggests that MPSS results provide a complementary global overview of the transcriptome of the ES cell. The data supplement and extend the microarray, SAGE and EST scan data sets and provide an independent verification of the same. MPSS in addition identifies additional genes expressed particularly at lower tpm, that are either not present on microarrays or not detected with a lower resolution analysis. Discussion Our results provide a global overview of the gene expression pattern of undifferentiated human ES cells and allow comparisons with other data sets. These results suggest the hESC are an actively dividing population of cells that exhibit high metabolic activity. Our analysis detected expression of approximately 10,600 unique transcripts, a figure that about a third of the total number of mapped genes. Unlike other cell types, however, a much larger fraction of unknown or novel genes was present. This high ratio likely represents the paucity of information available in existing libraries on this relatively newly characterized cell population rather than the possibility that ES cells use radically different pathways for self-renewal, survival, proliferation and differentiation. Our results confirm the reported differences between rodent and human ES cells. We confirm the absence of expression of ERAS, Ehox and the orthologs PEPP1 and 2. The apparent lack of LIF requirement of hESC is reflected by the absence or low tpm levels for genes of the LIF pathway and high tpm for suppressors of LIF mediated signalling (see supporting information). The high level of expression of genes in the FGF pathway likely reflects the requirement of hESC for bFGF. The high level of FGFR1 expression suggests that FGFR1 is an important signal transducer and that FGF's other than FGF4 are important in hESC self-renewal. The high tpm of the fibronectin receptor also suggest that fibronectin or vitronectin are likely useful substitutes for matrigel and that activation of ras mediated signalling is likely critical, as has been described in the rodent ES cell analysis [ 20 ]. Comparing data from the MPSS analysis with microarray, SAGE and EST scan analyses suggest that MPSS is a powerful alternative to these techniques. MPSS identified virtually all of the genes highlighted as genes common between six different human ES cell lines surveyed by microarray. We noted that most genes detected by microarray were expressed at high tpm indicating that MPSS is more sensitive than microarray analysis. MPSS however appeared to be able to identify genes detected by microarray. Analysing an additional 400 markers detected by MPSS using focused microarray or RT-PCR confirmed their expression [ 3 ], (data not shown). Likewise, MPSS analysis showed good concordance with the EST scan data at a fraction of the price. In contrast to the EST scan, tpm levels determined by MPSS are highly correlated to the mRNA levels present in the cells, even at low tpm values [ 25 ], and (Lynx unpublished results). Due to the low sampling number of most EST scans, this is not true for relatively low number of EST's found for a particular gene, and can be used only as a rough estimate of gene expression. Unlike other in depth analyses, the absence of markers in MPSS runs is also a powerful control provided that the marker possesses a GATC site. The chromosomal distribution of the genes expressed in hESC did not reveal any bias for a particular chromosome or chromosomal region. While a couple of "hotspots" and several "cold spots" were identified, in no case was any region comprised of all transcribed or all silent genes. Another important conclusion from our analysis is that selection of input RNA is critical. In our case we tested samples repeatedly to assess their purity and made considerable efforts to establish subclones that did not require feeder cells that could be potentially contribute transcripts to the analysis. Given the range of tpm of biologically relevant molecules (5 to 32,000 in this experiment) we predict that even a 5% contamination can confound results or detailed comparisons across different laboratories. We note also that gene transcription from both the X and Y chromosome is observed indicating that at least subtle differences will exist between male and female lines even in the undifferentiated state. Sex-based gene expression, along with MHC gene expression and ratio of expression of imprinted genes could serve to distinguish between different ES cell populations. The present results further suggest that analysing embryoid bodies that differentiate stochastically or analysing tissue samples (with variable proportions of cells) by MPSS will prove more difficult and that results will be variable. We suggest that variability can be reduced by pooling samples, normalizing by careful testing for known markers of differentiation, by semi quantitative PCR, or by focused microarray analysis. While MPSS is cost-effective and sensitive, it is by no means perfect. MPSS is limited by the requirement that DpnII sites (GATC) be present in a gene and be present in a unique locus such that the signature obtained is unique. For example, SNRF expression could not be assessed directly, as no GATC site is present. The signatures for ZFP42 are ambiguous and map to multiple transcripts. Although MPSS can distinguish between alternate transcript termination sites, MPSS cannot distinguish between alternative splicing events and possible incomplete digestion during the sample preparation process. Signature lengths are relatively short and it is possible to have to select between multiple genome hits (reviewed in [ 16 ]. Sequencing is performed four bases at a time and transcripts that contain palindromic sequences (in particular double palindromes) are often undetected because of self-hybridization of single DNA strands on the bead. A survey of the genome suggests that this is a rare event (approximately 3% of all virtual signatures in human MGC database have double palindromes). The NODAL gene is an example for such an event, where the class 1 signature was lost and NODAL expression is detected only by a signature resulting from incomplete digestion during library construction (see results). The success of MPSS analyses also depends to a large extent on the quality of genomic information available and, in our opinion, currently is best utilized to analyse human cells. Furthermore, MPSS itself may not be the best method for routine, lower throughput analyses, given price per sample, sample processing time and the large amount of data generated, which requires considerable analysis. However, the database, once developed, is extremely valuable provided it is freely available to make comparisons and to select subsets of genes for further analysis. MPSS information can be effectively utilized by establishing a common database of markers expressed at a defined stage in the differentiation of cells. Additional data sets from sampling of cells at well-controlled stages of differentiation that can be readily accessed and compared to existing datasets will provide the most information while still being cost effective. The genome database is an example of such sharing that has proven to be an invaluable resource for our experiments. Such a strategy requires cooperative pooling of information and free sharing such that individual results can be readily compared against validated datasets. Our future experiments will be directed and developing additional data sets of ES cell differentiation, which can be shared in a manner similar to the present set. Conclusions Our results provide a comprehensive data set that can be effectively utilized to analyse expression patterns of known and unknown genes. Comparison with other data sets provides independent confirmation of results and shows a high level of concordance. The caveats to all such large-scale comparisons are discussed and the importance of pooling data and comparing across multiple data sets is demonstrated. Methods Cell culture The human ES cell lines H1, H7, and H9 were maintained under feeder-free conditions in MEF-conditioned medium supplemented with bFGF as described previously [ 19 , 26 ]. MPSS MPSS was performed using RNA from three pooled ES cell lines (H1, H7, and H9) that had been maintained in feeder free culture conditions and evaluated for the presence of ES cell markers and absence of markers of differentiation. The mRNA was converted to cDNA and digested with DpnII. The last DpnII site and the downstream 16 bases were cloned into Megaclone vectors and their sequences determined according to the MPSS protocol [ 15 , 16 , 25 ]. A total of 2.786.765 sequences were read from four different runs and 48,388 unique signatures were identified. The abundance for each signature was converted to transcripts per million (tpm) for the purpose of comparison between samples. Signatures at an abundance of less than 4 tpm or those that were not detected in at least two runs were removed and a total of 22,136 sequences were analyzed further. All data is available for download from Lynx [ 27 ] MPSS signature classification and annotation To generate a complete, annotated human signature database, we extracted all the possible signatures (" virtual signatures ") from the human genome sequence, the human Unigene sequences, and human mitochondrion. Each virtual signature was ranked, as outlined in the table 1a , based on its position and orientation in the original sequence. Unigene, genomic, and mitochondrial hits were combined and grouped by signature. The annotation was then assigned to the signature in following order of preference: repeat warnings (signature hits more than 100 genome locations); mitochondrial hits; Unigene hits; genome hits (if no transcript match found). If a signature matched only one Unigene cluster, the MPSS signature class is the lowest class of the member sequences of the cluster. If a signature hits multiple Unigene clusters, the best cluster hit is selected based on the lowest MPSS signature class or the largest number of member sequences. The resulting signature database was used to annotate the data from the experiments Initially the signatures were annotated using genome version hg15 (April 2003, Golden Path, UCSC,) and Unigene build #161 ( additional file 2 ). Recently we re-annotated all signatures using genome version hg16 (July 2003, Golden Path, UCSC) and Unigene build #169 ( additional file 3 ). Both annotations are available for download in supplemental tables [ 27 ]. Microarray Analysis was performed as described in Bhattacharya et al., [ 9 ] using six different samples. These included two lines from Bresagen (01 and 02), the pooled sample from Geron comprising feeder free subclones of (H1, H7, H9), H1, grown in our laboratory on feeders and H9 and I6 from Dr. Itskovitz-Eldor grown following their published protocols. EST-enumeration EST frequency counts of genes expressed in human ES cells were done as described ([ 8 ]). Statistical significance was determined using the Fisher Exact Test [ 28 ]. Chromosomal mapping of MPSS signatures and UniGene clusters to the human genome MPSS signatures with a hit to a UniGene cluster were mapped to the Giemsa staining cytobands of the hg15 release of the human genome (April 10, 2003 freeze, [ 29 ]). By this method, 7731 MPSS signatures were mapped to the cytobands of the human genome. Similar mapping was done for all UniGene clusters for which the chromosomal mapping is known. In order to achieve a gene-based rather than a transcript (i.e. splice variant) based distribution of genes splice variants the UniGene clusters were filtered using LocusLink data [ 30 ], since LocusLink captures all characterized splice variants of a particular gene. 23,828 UniGene clusters were identified by this method and mapped to the cytobands of the human genome. To discover differences in the number of genes mapped to each cytoband, the number of genes mapped to each cytoband was compared to the total number of genes analyzed, for both the MPSS signatures as well as for the UniGene clusters. The Fisher test [ 28 ] was used to determine the statistical significance, using a p-value = 0.05 as cutoff. Gene detection by RT-PCR Total RNA was isolated from cell pellets using RNAeasy Qiagen mini protocol and kit. cDNA was synthesized using 100 ng of total RNA in a 20-μl reaction. Superscript II (Gibco-BRL), a modified Maloney murine leukemia virus RT, and Oligo (dT)12–18 primers were used according to the manufacturer's instructions (Gibco-BRL). The list of primers used for RT-PCR and annealing conditions are described previously [ 3 ]). Authors' contributions RB, IK and MR were primarily responsible for the data analysis and writing and editing the manuscript. ST generated the ES cell samples and verified their quality, TM performed the RT-PCR and JC performed the immunocytochemistry. RP generated the microarray data and TV and JL provided support and supervision for RB, IK, ST. Supplementary Material Additional File 1 The document describing details of MPSS analysis of the HuES cells performed at Lynx, and algorithm for initial MPSS signature annotation and classification. Click here for file Additional File 2 The file contains MPSS data for 22,136 significant and reliable signatures, annotated with genome version hg15 (April 2003, Golden Path, UCSC) and the human Unigene build Hs.161. Click here for file Additional File 3 The file contains MPSS data for 22,136 significant and reliable signatures, annotated with genome version hg16 (July 2003, Golden Path, UCSC) and the human Unigene build Hs.169. Click here for file Additional File 4 table containing data for 8679 unigene clusters, 11 mitochondrial genes, and including 1991 signatures that did not map to unigene but uniquely matched genomic sequences (potential novel transcripts). Click here for file Additional File 5 Top 200 rows from the table HuES17_onetpmHs169hg16.xls. Click here for file Additional File 16 List of tpm of genes identified by Sperger et al [11] present in the MPSS dataset. Click here for file Additional File 17 List of tpm of genes identified by Richards et al [8] present in the MPSS dataset. Click here for file Additional File 18 List of tpm of genes identified by MPSS in the BG02 dataset. Click here for file Additional File 6 This file lists the genes located on the X and Y chromosome for which a MPSS signature sequence was found in the HuES cells. The X and Y chromosome genes are listed in separate worksheets. Chromosome: X or Y chromosome Cytoband: Giemsa staining cytoband Signature: MPSS signature sequence Tpm: mean abundance for a signature derived from all MPSS runs for the sample, in transcripts per million Seq_id: Repeat if hit genome more than 100 locations; UniGene cluster ID if hit one or more UniGene clusters; Chromosome number if hit one genome location; MultiGenome if hit genome multiple times; Description: description of annotation (see Appendix B for more details). Click here for file Additional File 7 List of all UniGene clusters (release 161) located on the X and Y chromosome. The X and Y chromosome genes are listed in separate worksheets. Chromosome: X or Y chromosome Cytoband: Giemsa staining cytoband Locusid: Locus Link identifier UniGene cluster: UniGene cluster ID Description: description derived from UniGene Click here for file Additional File 8 Graphical representation of hotspots and coldspots for all human chromosomes. Cytobands with significantly higher number of genes in HuES cells are marked with '+' and cytobands with lower number of genes in HuES cells are marked with '-'. The number of '+' or '-' signs is proportional to the difference, with one '+' or '-' representing the fold difference. For example '++' represents a 2-fold higher number of genes expressed in HuES cells compared to the expected number based on the number of genes known to be located in the same cytoband. Click here for file Additional File 9 List of all chromosal regions that appeared statistically distinct. Click here for file Additional File 10 List of genes expressed in HuES cells and located in the hotspots and coldspots. Chromosome: X or Y chromosome Cytoband: Giemsa staining cytoband Signature: MPSS signature sequence Tpm: mean abundance for a signature derived from all MPSS runs for the sample, in transcripts per million Seq_id: Repeat if hit genome more than 100 locations; UniGene cluster ID if hit one or more UniGene clusters; Chromosome number if hit one genome location; MultiGenome if hit genome multiple times; Description: description of annotation. Click here for file Additional File 11 List of all UniGene clusters located in the hotspot and coldspot regions. Chromosome: X or Y chromosome Cytoband: Giemsa staining cytoband UniGene ID: UniGene cluster ID Locusid: Locus Link identifier Description: description derived from UniGene; Click here for file Additional File 12 This file contains the mapping information of MPSS signatures to the EST scan genes for major signaling pathways. Each pathway is presented in a separate worksheet Columns: Locusid: Locus Link identifier Pathway: Pathway the gene has been assigned to. Signature: MPSS signature sequence Tpm: mean abundance for a signature derived from all MPSS runs for the sample, in transcripts per million Stdev: standard deviation of the mean abundance from multiple MPSS runs Hit genome: HitGenome – Numbers of genomic locations a signature maps to HitUniGene: Numbers of UniGene Clusters a signature maps to; Seq_id: UniGene cluster ID ('Hs.' Has been omitted). NULL if there is no MPPS signature but an EST from the EST scan. Class: signature class (see Appendix B for more details); Title: description, derived from UniGene Click here for file Additional File 13 This file contains additional lists of potential homeodomain proteins, genes of unknown function, zinc finger proteins in separate worksheets. Tpm: mean abundance for a signature derived from all MPSS runs for the sample, in transcripts per million Stdev: standard deviation of the mean abundance from multiple MPSS runs Hit genome: HitGenome – Numbers of genomic locations a signature maps to HitUniGene: Numbers of UniGene Clusters a signature maps to; Seq_id: UniGene cluster ID. Class: signature class (see Appendix B for more details); Title: description, derived from UniGene. Click here for file Additional File 14 List of tpm of potential 400 ES enriched genes in other cell lines examined. Click here for file Additional File 15 List of all tpm of potential 13 ES novel genes in all tissues and cell lines examined. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514702.xml

526767

GeneLink: a database to facilitate genetic studies of complex traits

Background In contrast to gene-mapping studies of simple Mendelian disorders, genetic analyses of complex traits are far more challenging, and high quality data management systems are often critical to the success of these projects. To minimize the difficulties inherent in complex trait studies, we have developed GeneLink, a Web-accessible, password-protected Sybase database. Results GeneLink is a powerful tool for complex trait mapping, enabling genotypic data to be easily merged with pedigree and extensive phenotypic data. Specifically designed to facilitate large-scale (multi-center) genetic linkage or association studies, GeneLink securely and efficiently handles large amounts of data and provides additional features to facilitate data analysis by existing software packages and quality control. These include the ability to download chromosome-specific data files containing marker data in map order in various formats appropriate for downstream analyses (e.g., GAS and LINKAGE). Furthermore, an unlimited number of phenotypes (either qualitative or quantitative) can be stored and analyzed. Finally, GeneLink generates several quality assurance reports, including genotyping success rates of specified DNA samples or success and heterozygosity rates for specified markers. Conclusions GeneLink has already proven an invaluable tool for complex trait mapping studies and is discussed primarily in the context of our large, multi-center study of hereditary prostate cancer (HPC). GeneLink is freely available at .

Background In the past decade, hundreds of genes involved in the etiology of simple Mendelian disorders such as cystic fibrosis and Huntington's disease have been identified [ 1 - 3 ]. The genetic localization of these disorders, primarily through positional cloning approaches, has been highly successful because of the relatively simple model underlying disease pathogenesis. In the majority of these cases, a single mutated disease gene is both necessary and sufficient to cause the observed trait. In contrast, susceptibility to complex traits is heterogeneous, involving both multiple genetic and environmental risk factors, acting either independently or together. Efforts to identify susceptibility genes involved in complex traits such as cancer, diabetes, hypertension, or Alzheimer's disease are complicated by genetic heterogeneity, incompplete penetrance, phenocopies, and the later age of onset of disease (thus unavailable DNA samples for parents of affected individuals). Each of these factors results in a significant reduction in power for any given study. Therefore, gene-mapping studies of complex traits require high-throughput genotyping performed on large collections of DNA samples using hundreds to thousands of polymorphic markers. The significant amounts of data generated during these genome surveys pose numerous data management challenges. In order to address these challenges, which are inherent in any large, collaborative genotyping study, we have developed a robust, easy-to-use database system named GeneLink. GeneLink was initially developed to facilitate our studies of genetic susceptibility to prostate cancer, whose aims are to identify novel high- and moderate- penetrance genes involved in hereditary prostate cancer risk. These studies are multi-center collaborative efforts involving researchers from the United States, Finland, and Sweden [ 4 - 7 ]. The project included 496 families containing 5,247 individuals; DNA on 2,374 of these individuals was available for genotyping. We genotyped over 400 microsatellite markers for these individuals generating close to one million genotypes. This number is large but not atypical in gene mapping studies of complex traits. Given the considerable number of genotypes requiring analysis, it was obvious that we needed to develop a database management system that could handle such large quantities of data, as well as address data management issues unique to complex trait genetic analysis. Implementation GeneLink is a platform-independent, Web-accessible Sybase database that can manage complete genotypic, phenotypic and pedigree data for genetic linkage or association studies. Figure 1 shows the comprehensive GeneLink user's menu available following login. Access to the GeneLink database can be limited using two independent mechanisms. First, users can be granted one or more activity or privilege levels. The admin (administrator) privilege provides a user the ability to view data as well as manage access to the data. Users without admin privileges can be assigned the following privileges by the administrator: export , import , modify and view , with obvious permissions. Project- or user-specific reports summarizing assigned privileges can be generated. Second, as GeneLink provides the ability to associate a "group" (of users) with a collection site-specific set of families, collaborators can be provided the ability to view and/or manipulate all or only a subset of data. Figure 1 Main menu GeneLink's main menu available following login. GeneLink is a platform-independent, Web-accessible database. Access to GeneLink is password-protected. Here the user (lgilland) has been given import , export , view , and admin , permissions. Currently, GeneLink's database uses Sybase SQL server ASE 12.5.1, which runs on a Sun V880 computer running Unix. GeneLink's Web scripts to access the database require Perl version 5.6.1 or greater. The necessary CPAN Perl modules required by GeneLink are DBI, DBD::Sybase, CGI, and Carp. These modules are usually included in standard Perl 5 releases. A Web server such as Apache 1.3.29 is also required to run the GeneLink Web scripts. GeneLink can operate on a Sun Enterprise 6500 or similar machine configured to operate as a Web server. Database structure Figure 2 shows the relational design of the GeneLink database in detail. Data within GeneLink is stored in 11 primary tables: Families, Pedigrees, Trait Score, Traits Translations, Markers, Primers, Maps, Genotypes, Allele Translations, Liability Classes and DNA. Each table can be populated either by importing multiple records from a delimited text file or by inserting a single record at a time through a Web interface. The Families table stores pertinent clinical information regarding each family as a whole; each record reflects a single family included in the linkage or association study. For example, in our HPC study, we used the Families table to store information regarding evidence of male-to-male transmission of disease as well as the occurrence of other cancer types in the family. The Pedigrees table stores one record per individual within a family and contains the biological relationships ( FatherID , MotherID ) for each individual. Also included in the Pedigrees table is age, sex, whether the individual has been or will be genotyped ( Age , Sex , DNA ), two ways to store qualitative phenotype information ( StatusBroad , StatusNarrow ), and individual liability class information. In our HPC study, we defined prostate cancer affection status in two ways; specifically, we used the StatusBroad field to classify individuals as affected, unaffected or unknown, while the StatusNarrow definition was used for an affecteds-only analysis in which individuals were coded either as affected or unknown. More extensive trait or covariate information can also be stored in the Trait Score table. This table stores an unlimited number of qualitative or quantitative phenotypes as trait 1, trait 2, trait 3, and so forth. Definitions of the traits are stored in the Traits Translation table. Figure 2 Relational design Relational design of GeneLink's 11 primary tables. Primary keys are indicated in red and foreign keys in blue. GeneLink enables pedigree information stored in the Families and Pedigrees tables to be easily merged with genotypic data stored in the Genotypes table. In the Pedigrees, Liability Classes, Trait Score , and Trait Translation tables GeneLink also manages extensive phenotypic data. The Markers and Primer tables store information regarding polymorphisms being genotyped and the Maps table stores genetic or physical map information, which determines the order in which data is exported. GeneLink's Primer and DNA tables provide labs with an easily implemented inventory system. The Markers table stores information regarding all markers typed in a given project, including the panel in which the marker was run. A panel is defined as a group of microsatellite or SNP markers which can be electrophoresed simultaneously by taking advantage of different fluorescent dye labels and varying amplicon sizes. Also stored in the Markers table is the allele size range ( ASR ) of the marker, the fluorescent dye used to label the forward primer, and the marker-specific genotype for a CEPH control individual (e.g., CEPH 1347-02 was used in our prostate cancer study). The Primers table provides additional information for each specific marker. This information includes UniSTS ID, GenBank accession number, forward primer sequence, reverse primer sequence, and primer purchasing and inventory information. The Primers table also contains comprehensive genetic map information ( deCODE , Généthon , and Marshfield positions ) as well as physical location ( Build , physical start position , and physical stop position ). The Maps table stores the genetic map location of a marker in the genome, as well as the relative order of markers along a chromosome and the distance between adjacent markers. The markers typed thus far in our HPC study are di-, tri-, or tetra- nucleotide repeats; however GeneLink is capable of handling any combination of microsatellite and single nucleotide polymorphisms (SNP) data. The Genotypes table is where final genotype data are stored. Each record represents an individual/marker combination and alleles are stored as size in base pairs. The Genotypes table also holds information regarding the laboratory run ( Grid , Lane ). When exporting data in LINKAGE or RelCheck format, GeneLink systematically recodes genotype labels (1, 2, 3,...) to provide properly-formatted data for analysis by these programs. The Allele Translations table provides a key or legend linking the newly "translated" genotype to the original size in base-pairs score. Future exports use identical "allele translation codes." New alleles identified after the first export will be added to the end of a marker's allele translation ensuring consistent recoding of alleles across exports (Figure 3 ). The Liability Class table stores information regarding specific liability classes, which can be incorporated in each LINKAGE export for analysis. Liability classes can currently be defined using any combination of age, sex, and affection status (based on StatusBroad , or StatusNarrow from the pedigrees table). For example, in our HPC study we used five different liability classes (Figure 4 ). Specifically, we were able to assign older, unaffected men (more likely not to be gene carriers) to a different liability class than younger, unaffected men who may be gene carriers but who have not yet presented with the disease. Finally, the DNA table stores information regarding all available DNA samples in the laboratory. This includes specifics regarding date received, concentration, quantity, and storage location. Figure 3 Allele translation The Allele Translations table provides a key linking the "translated" allele ( Translation ) to the original size in base-pairs score ( Allele ). All LINKAGE or RelCheck exports will use identical "allele translation codes." In this example, 15 alleles have been identified for marker D1S206. New alleles identified after the first export (in this example alleles 10 to 15) will be added to the end of a marker's allele translation ensuring consistent recoding of alleles across families. Figure 4 Liability classes Within GeneLink liability classes can be defined using any combination of age, sex, and affection status. Each of GeneLink's 11 tables has measures built-in for quality control purposes. First, all changes (import, modification or delete) to GeneLink records are stamped with the date, time, and USER ID of the individual doing the editing. Changes to the families, pedigrees, or genotypes tables can be easily reviewed in a Histories table. Second, contents of every field are verified on import, and users are warned of any failures, such as invalid format or duplicate records. Within the Pedigrees table specifically, checks were designed to confirm the presence of all individuals designated as parents within the families, as well as confirm that all fathers are male and all mothers are female. In addition, when genotypes are imported into the Genotypes table, GeneLink confirms that each individual included in the import is designated as having DNA in the Pedigrees table. Next, GeneLink checks that each allele falls within the marker's designated allele size range (Markers table) and that the genotype for the control individual (e.g., 1347-02) matches what is expected (Markers table). Exporting genotypes We designed GeneLink to facilitate the merging of our genotype data with pedigree and phenotype information (from either the Pedigrees table or the Trait Score table). This facilitates exporting data in formats commonly used in downstream analyses (e.g., GAS or LINKAGE format). There are three ways data can be exported. First, any user with administrator ( admin ) privileges can export data by Panel in LINKAGE or GAS formats. We use this option when checking our data for Mendelian inconsistencies because most laboratory errors could be detected if multiple markers within a panel showed up as inconsistent. Next, other users with export privileges can export data by chromosome. All data are exported by chromosome in the map order specified in the maps table. Chromosomes are only available for exporting once data for all markers on that chromosome have been imported, though we did design GeneLink to accommodate the possibility that all markers on a chromosome may not have been typed for all sites. The export genotype data screen (Figure 5 ), from the Export by chromosome menu link, prompts users to specify which chromosome to export, which trait(s) to export, how to define liability classes if necessary, what file format is desired and which families to include in the export. Only the family collection sites to which a user's group has access will appear in this screen. The final way data can be exported by users with export privileges is directly from the Families table. Using the Export by chromosome option described above means that all families from the selected site(s) will be exported. Alternatively, exporting directly from the Families table makes it possible to export only a subset of families from a collection site or a subset of families from across sites. Regardless of the exporting mechanism employed, GeneLink generates an Export Report, which summarizes all the pertinent information regarding the export, including families, chromosome (exported markers and distance between them), phenotypes included, liability class definition, and file format (Figure 6 ). Each export is given a name, which includes the project ID, chromosome exported, user ID, and a random number. This naming convention was designed to facilitate file management. Figure 5 Export by chromosome The Export Genotype Data screen prompts users to specify 1) which chromosome to export, 2) which trait(s) to export ( Status Field ), 3) how to define liability classes, 4) what file format is desired and 5) which families to include in the export. Figure 6 Export report GeneLink's Export Report provides a summary of all pertinent information relating to an export. This includes the file name (which incorporates the project ID, chromosome exported, user ID, and a random number), date the file was created and by whom. The Export Report also records which chromosome, phenotypes ( status field ) and families, were exported and in what file format. Finally, the Export Report also provides the distance between markers being exported. Results and discussion When faced with the challenge of studying 496 hereditary prostate cancer families and a total of 5,247 individuals, we sought a publicly available database management system capable of handling the unique challenges that accompany a large-scale, multi-center genetic linkage study of a complex trait. Although data management systems have been developed [ 8 - 12 ], none could securely and efficiently handle a very large amount of data, as well as provide additional features to facilitate quality control and analysis of data generated. Therefore, we developed GeneLink, a database with unique features, to address these needs. We designed GeneLink to use a Sybase database backend to take advantage of Sybase's ability to process large amounts of data. Currently, GeneLink is the only publicly available freeware database capable of efficiently storing millions of genotypes. The need for efficient data management will grow in importance as researchers explore genome-wide SNP association studies that may generate close to one billion genotypes (500 cases, 500 controls and 500,000 to 1,000,000 SNPs) [ 13 ]. We are currently updating GeneLink so it can run using either Sybase or Oracle. Furthermore, GeneLink was designed to avoid database-specific code and therefore should be portable to other open access DB engines, such as PostgreSQL, without too much difficulty. To collect the necessary number of DNA samples needed to provide sufficient power to detect linkage or association, collaborative efforts are almost always required. The Web-based interface of GeneLink facilitates multi-center collaborations, as data can easily be accessed via the Internet. GeneLink's Web-based interface also makes it platform-independent, a feature that was essential given the number of researchers who would be accessing it using various hardware-browser combinations. Other publicly-available databases described in the literature do not have this advantage. In this paper, we have presented GeneLink in the context of a collaborative effort in which multiple sites will need access to data generated in a single laboratory. However, GeneLink would also be valuable in the context of a meta-analysis of data generated in more than one laboratory. Making data access easier for our collaborators translated into the need for a sophisticated security system. Specifically, in our study of hereditary prostate cancer, researchers are permitted access to only their own set of data. This is important because, in some cases, a site's internal review board (IRB) protocol may not allow for raw data to be shared with other analysts. GeneLink provides several other advantages for investigators performing linkage or linkage disequilibrium studies of complex traits. For example, the process by which genotypes can be imported into GeneLink was designed to be flexible enough to handle data from laboratories like our own which employ duplicated samples and double-scoring methods for quality control purposes. Using duplicated samples and double scoring aids us in keeping our genotyping error rate low (< 1%). In our HPC study, we included 92 duplicated samples (~ 4% of total samples) in order to evaluate our genotyping error rate. The entire import process is outlined in Figures 7A and 7B . After each of the initial steps (the import (step 1), duplicates check (step 2), and check for differences (step 3)), GeneLink produces a summary report (Figures 8A,8B , and 8C ). The "Import Report" summarizes the details of the import, including the date, user ID, number of records imported, and file name of the uploaded flat file containing the genotype data (Figure 8A ). Examples of the duplicates and differences reports are shown in Figures 8B and 8C . Figure 7 A, and B. Import process Outline of Import process illustrates GeneLink's ability to be used in laboratories that include duplicated samples and double scoring for quality control purposes. Import process includes within table duplicate check and across table differences check. Using the Single table import allows the differences step to be skipped. Figure 8 A, B, and C. Example of import (A), duplicates (B), and differences (C) reports Import report stores all information regarding genotypes imported into GeneLink. This information includes number of records imported (how many unique individuals, how many markers) as well as the name of the file in which records are stored. The duplicates process checks for duplicate genotypes within a table. A duplicate is defined as records with the same FamInd ID and marker. All duplicate records are reported. If duplicate records have the same two alleles then one record is deleted. If the two duplicate records do not have matching alleles than the user is prompted to select which record to delete. The differences process looks for differences in genotypes compared across tables (independently scored by two researchers). All differences are reported and the user is prompted to resolve each appropriately. The user is given the option to save either record or if either score isn't acceptable then new genotype can be indicated. Another challenge of complex trait linkage or association studies is formatting data appropriately for analysis by existing software packages. Chromosome-specific LINKAGE, GAS, and RelCheck format files can easily be exported by GeneLink. By design GeneLink's exporting capabilities also provide several additional advantages. First, GeneLink is capable of exporting multiple traits at the same time, thus facilitating analyses in which covariate information will be included. Second, by taking advantage of GeneLink's ability to generate liability classes defined by age, sex, and affection status, researchers can maximize power in the investigation of complex traits, which often exhibit reduced penetrance and phenocopies. Third, GeneLink's Allele Translation table allows comparison of alleles across families or across analyses, as each allele for each polymorphic marker will only be recoded once. This is particularly important as linkage disequilibrium or association studies become more common. Fourth, GeneLink's ability to export only a subset of families is critical, as genetic heterogeneity is a significant factor contributing to the difficulty of mapping genes involved in many complex traits. Multiple genes (RNASEL, ELAC2, and MSR1, among others; [ 14 - 16 ]) have been implicated in hereditary prostate cancer susceptibility, suggesting that genetic heterogeneity is likely to be a complicating factor in the gene mapping of HPC risk alleles regardless of the analysis method. Finally, GeneLink maintains a list of previously exported files, which eliminates redundant generation of data files by collaborators and functions as an archive of data files used for analyses. Additional quality control measures were included in GeneLink's design. First, all changes to the database are recorded. As genetic studies of complex traits can be spread over many years, it is important to keep a detailed log of any changes made to the data. For example, an individual's affection status may change during the course of a study; therefore it is critical to track when this information was updated in the database. Second, in order to monitor data quality, GeneLink was also designed to perform several built-in checks, as described above. Given that genetic studies of complex traits will generate millions (or even billions) of genotypes, it is essential to have appropriate mechanisms in place to ensure data integrity. In our study of hereditary prostate cancer, these checks immediately discovered a typographical error, which, if left undiscovered, would have resulted in data from an affected individual never being exported or analyzed. Finally, GeneLink generates detailed reports storing pertinent information regarding all imports and exports (Figures 6 and 8A ), the status of projects (Figure 9 ), statistical information about markers (success rates and heterozygosity; Figure 10A ), and DNA samples tested (Figure 10B ). These reports are helpful in maintaining data quality. For example, in our HPC study with over 2,500 DNA samples, it would have been very easy to miss that any single individual was failing for greater than 95% of markers if we were not using GeneLink. We were able to request new DNA samples for such individuals, as well as flag the stored data as potentially problematic. Figure 9 Status report GeneLink's status reports allow collaborators to easily tract the project's progress by site. Reports show markers by chromosome (in map order) and the status of each marker for each site. By site, markers can be Not started , In Lab , Genotypes Imported , Single Table Imported , Waiting for Comparison , Compared and Ready , Ready to Finalize and Ready to Export . Data can be exported only after all markers on a given chromosome for a given site are Ready to Export . In this example we are looking at markers D21S1256, D21S1914, D21S1909, D21S1252, D21S2055, D21S266, and D21S1446 on chromosome 21 for sites JHU, AAHPC, AAHPC2, Sweden, Finland and Michigan. Here data is ready to export for family sites AAHPC2 and Sweden. Figure 10 A, and B. Marker (A) and individual (B) summaries GeneLink's Marker Summary provides success rates and heterozygosity for individual markers typed in the study. The Marker Summary also provides information regarding when the genotype records for this marker were imported ( Import Dates ). Marker quality can also be evaluated using the Flags column. Genotypes can be flagged with a T to temporarily blank scores from analyses. This is used for un-resolvable Mendelian inconsistencies. The R flag can be used for replaced DNA samples until the new DNA sample is evaluated. Neither T nor R flagged genotypes are exported. Individual summaries also provide global quality assurance information such as success or flag rates. GeneLink was designed primarily in the context of family-based studies of complex traits. It is capable of handling both linkage and association data, and can be used for both whole genome scans and/or candidate-gene studies. Further development of GeneLink will focus on extending its capabilities in regard to the case-based design. We recognize that both the family-based and case-based study designs have unique advantages, so we see it as critical to make GeneLink flexible enough to accommodate a case-based design. Currently, there is no limitation in storing case-based data however changes to GeneLink's exporting mechanisms should be made. Finally, in the same way that GeneLink is capable of storing "exported" data input files, future work will center on the storage of analysis results. Again, this would be helpful for multi-center collaborative studies, which will continue to be critical to successful efforts to identify genes important in complex trait etiology. Conclusions In summary, GeneLink was designed specifically to ease the data management burden of mapping complex traits. It provides many functions that make it a uniquely powerful tool for use in genetic linkage or association studies. GeneLink simplifies merging genotypic data with pedigree, phenotype, and genetic or physical map information. Specifically, GeneLink's design makes it ideal for large-scale, multi-center studies, which will become more and more common in efforts to dissect the genetic factors contributing to complex trait etiology. Availability and requirements Project name: GeneLink Project home page: Operating system(s): Platform independent Programming language: Perl Other requirements: Sybase SQL server ASE 12.5.1, Perl version 5.6.1 or greater, CPAN Perl modules DBI, DBD::Sybase, CGI, and Carp, Web server such as Apache 1.3.29 License: Sybase SQL server ASE 12.5.1 Any restrictions to use by non-academics: none Author's contributions EG, JT, JEBW and AB participated in database's design. AM, LU, KT and TW did all of the programming. EG, DG, PD, APK, MJ, DFL, and GI performed extensive testing of the database. EG, PD, TW, JEBW and AB drafted the manuscript. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526767.xml

524164

Consumers as tutors – legitimate teachers?

Background The aim of this study was to research the feasibility of training mental health consumers as tutors for 4 th year medical students in psychiatry. Methods A partnership between a consumer network and an academic unit in Psychological Medicine was formed to jointly develop a training package for consumer tutors and a curriculum in interviewing skills for medical students. Student attitudes to mental health consumers were measured pre and post the program. All tutorial evaluation data was analysed using univariate statistics. Both tutors and students evaluated the teaching program using a 4 point rating scale. The mean scores for teaching and content for both students and tutors were compared using an independent samples t-test. Results Consumer tutors were successfully trained and accredited as tutors and able to sustain delivery of tutorials over a 4 year period. The study found that whilst the medical students started with positive attitudes towards consumers prior to the program, there was a general trend towards improved attitude across all measures. Other outcomes for tutors and students (both positive and negative) are described. Conclusions Consumer tutors along with professional tutors have a place in the education of medical students, are an untapped resource and deliver largely positive outcomes for students and themselves. Further possible developments are described.

Background Interview skills, while important in all areas of medicine, are a prime focus of educational endeavour in the teaching of psychiatry. The interview is the cornerstone of psychiatric investigation and the scene for the establishment of rapport and therapeutic engagement. The importance of effective skills training in interviewing for medical students was highlighted in the core curriculum in psychiatry published by The World Psychiatric Association and the World Federation for Medical Education [ 1 ]. An additional perspective on this issue has come from consumers who report a distinct difference between effective and ineffective interviewing styles, highlighting the hindering effect of bored, impersonal interviewers who make judgemental assumptions about an individual's behaviour [ 2 ]. Moving beyond the traditional teaching of interview skills by psychiatrists in the "see one, do one, teach one" mode, we report the development of an innovative approach to the teaching of psychiatric interview skills. This approach began four years ago as a partnership between mental health consumers and academic psychiatry to examine the ongoing feasibility of training mental health consumers as tutors for 4 th year medical students in psychiatry. The idea is based on the belief that consumers have a legitimate experience to share and a rich skill base on which to draw. The purpose of this ongoing project is to contribute to the ultimate development of a workforce of medical practitioners with clinical assessment skills that are better tailored to the needs of mental health consumers. More immediate potential benefits are the promotion of an engaging curriculum in psychiatry for students, offering direct meaningful contact with mental health consumers. Literature review Consumers and patients are not new to medical education, offering a unique perspective on their experiences with the health system. There are 3 levels of consumer participation in education. Consumers may be the subject of teaching tutorials (a demonstration), a visitor to a tutorial (sharing their experiences with few guidelines) or a paid trained tutor delivering an agreed curriculum (a 'consumer tutor'). Consumer tutors are distinct from 'professional tutors' (those with a career obligation to teach) a distinction that does not imply a lack of professionalism regarding the consumer tutor. The issue of language is not trivial: the local user support network was committed to the term 'consumer' rather than other common labels like client or patient. The language used was chosen for the perception of action and autonomy and to reflect respect for the role and to make it clear to students and staff that these people were a committed part of the teaching workforce, not visitors only present to tell of their (often negative) experiences. This work drew on the Partners in Arthritis project [ 3 ] that demonstrated that arthritis patients are at least equal to Consultant Rheumatologists in the teaching of examination techniques for arthritis. Related work included the use of families with experience of paediatric illness in the growth of communication skills for medical students [Reynolds 2003: personal communication] and the use of clinical teaching assistants in pap smear training [Vivienne O'Brien 2003: Personal communication]. Consumer and carer involvement in mental health education has been documented: nurse education by a consumer academic [ 4 ] with largely positive outcomes, user and carer involvement in curriculum development and delivery in interprofessional postgraduate mental health education [ 5 ], a randomised trial of brief mental health staff training by consumers with positive post-training attitudes from those taught by consumers [ 6 ] and a reminder that there are few published examples of the translation of policy rhetoric regarding consumer and carer involvement in education into teaching reality [ 7 ]. In the local area, consumers are involved in Mental Illness Education ACT (MIEACT), a national non-profit outreach education project to high school students and community groups, which aims to reduce stigma and improve mental health literacy of young people [ 8 ]. In all these projects consumers were trained, assisted in ongoing curriculum review and contributed their lived experience in the domain of interest. Despite this fertile environment for the establishment of this project and the growing consumer and carer action in psychiatry in general, there was no literature identified using trained consumers in teaching psychiatry to medical students. In summary this project set out to train and support mental health consumers as consumer tutors for the delivery of a jointly developed curriculum for 4 th year medical students in effective approaches to interviewing. There was commitment to evaluate the effectiveness of the teaching in terms of (i) changing student attitudes towards consumers, (ii) preparing students for a common examination in psychiatry, (iii) monitoring consumer tutor involvement and feedback. The Setting The project was set in a medical school (University of Sydney) undergoing transition to graduate entry, with adult self-directed and problem based learning models providing an opportunity to students keen to explore new ways of learning. This environment of educational change added to student and staff enthusiasm to trial new ideas. There was recognition that carers and consumers required the same respect and courtesy as professional tutors, and inequities such as only being paid travel expenses were unsatisfactory [ 9 ]. Consumer consultant positions were established to improve the relevance and user friendliness of psychiatry services using trained, paid and supervised staff [ 10 ]. Methods Phase 1: Curriculum development The Academic Unit of Psychological Medicine approached consumers from the local mental health consumer network seeking expressions of interest to join a steering committee to oversee the project. Consumers were involved at all stages including planning, development, implementation and evaluation. The steering committee met weekly to determine an approach to consumer tutor recruitment and training, and to author the student curriculum for delivery. Consumer tutor development Consumer tutors were recruited, trained in small group methods and assessed. The assessment task was a nine-item quiz. Typical questions included "List three strengths of working in small groups" and "List three strategies to get a discussion going amongst students". Those who wished to teach were then allocated in pairs to a consistent student group of six to eight students for weekly tutorials over seven weeks. Consumer tutors conducted the planned curriculum, provided feedback on each tutorial and contributed to curriculum review. Evaluation of the content of the curriculum, the quality of the teaching and handouts was measured using a 4-point Likert scale (4 = excellent, 3 = good, 2 = fair, 1 = poor) followed by an open comment item. After each tutorial consumer tutors were debriefed by academic staff and arrangements for the following tutorials confirmed. An expedient payment method was established. Over time, consumer tutors and academic staff periodically reviewed tutor and student feedback and decided on tutorial modification. Partnership establishment The steering committee comprised six consumers, three with previous teaching experience at secondary or tertiary level. Professional approaches were adopted for recruitment, training, assessment, graduation and payment. Academic staff drafted a structure for consumer tutor training completed by the committee. The committee determined the priorities for the student curriculum and each oversaw the writing of a tutorial in conjunction with academic staff. Consumer input was central and meaningful rather than at the level of editing academic staff work. The committee were paid regular sitting rates for committee attendance. The steering committee met during the first trial to review progress, an overseeing role replaced later by the consumer tutors themselves. Consumer tutor recruitment and training Expressions of interest from potential consumer tutors were sought through the consumer network against selection criteria. These included the essential criteria of being a current consumer of mental health services and having an interest in the development of medical student interviewing skills. Desirable criteria included previous experience in teaching. Applications were reviewed by the steering committee and all applications for training were accepted. Payment for training and tutoring was set at the current university casual tutor rates appropriate to qualifications. Recruitment resulted in a cross section of consumers including several 'marginalised consumers' who are usually under represented in such activities Three training cycles have occurred with 20 consumers (12 women, 8 men) commencing training and 18 consumers graduating from training. Fifteen consumer tutors have taught over the four years, whilst three decided after graduation that they did not wish to teach. Drop out occurred because of illness, disinterest or realising that tutoring was more difficult than first imagined. The tutor training program comprised six weekly, one and a half hour tutorials delivered by academic staff encouraging discussion, controversy and practice in a supportive environment. See Table 1 for a summary of the consumer tutor training program. A graduation ceremony was conducted and the Dean of the clinical school awarded university badged completion certificates, although the university did not formally accredit the course. Trainee tutor evaluations revealed they valued the training experience, reporting a sense of initial nervousness later replaced by a sense of assurance of their own abilities. Table 1 Content of 'consumers tutor' training program Session Topic Content 1 Orientation to 4 th year medical students What have they learnt so far? What other teaching and experiences do they receive during the psychiatry term? What are the characteristics of medical students? 2 Working in small groups Why are groups effective learning settings? How do groups work? How do you manage dominant and quiet group members? 3 Giving effective feedback Strategies to improve giving and receiving feedback with hands on practice 4 Review of the student curriculum Practice delivery of the planned student material through small group role-play 5 Review of the student curriculum Practice delivery of the planned student material through small group role-play 6 Trouble shooting, problem solving and tutor assessment Common fears amongst the trainee tutors? What will go wrong? How to deal with inability to attend? Completing the written assessment Phase 2: Curriculum delivery Student participation Students in one of four teaching centres of the University of Sydney participated in the consumer tutor-led tutorials. Students were oriented by academic staff and completed a pre-participation measure. This measured student attitudes to mental health consumers adapted from work on the 'hated patient' [ 11 ]. Five statements were rated on a five point Likert scale (strongly agree to strongly disagree). Typical items included "I value learning from consumers" and "I would like mental health consumers as part of my practice." The final item was an open-ended question about concerns in interviewing. Students were then introduced to their consumer tutors and participated in an 'ice breaker' session involving an interactive board game designed to sensitise medical students to the experience of being a mental health consumer. This was followed by six tutorials which both consumer tutors and medical students evaluated using the same measure. Students repeated the attitude measure at the end of the program and completed assessment tasks as per the university requirements. Students also participated in a seminar series (including didactic teaching on interview content) with professional tutors similar to that delivered in the other teaching centres. All tutorial evaluation data was analysed using univariate statistics. The pre and post attitude measure was compared using term group means (as completion was anonymous) using an independent samples t-test. The mean scores for teaching and content for both students and tutors were compared using an independent samples t-test. Results Delivery of the student curriculum The student curriculum was developed as six one-hour tutorials (see Table 2 for a summary). Table 2 Content of Consumer tutor-led tutorials for medical students. Session Title of session Content "Ice breaker" Lemon Looning Board Game An interactive game designed to simulate the experience of a mental health consumer. 1 'Person centred interviewing' Discussion about medical student concerns re interviewing, dealing with fears and previous experience of psychiatric contact. Role play- Practicing sensitive interviewing styles 2 'Dealing with sensitive issues' Further developing effective interviewing styles for the purpose of exploring social and family circumstances, talking about lifestyle including sexuality, drug use, relationships and parenting. 3 "Reality Check" Dealing with fixed beliefs and delusions. Using the therapeutic relationship to enhance understanding of patients affected by delusions and hallucinations. 4 "Art Express" Developing skills in talking about self harm. Activity: using an art therapy exercise to more effectively respond to people who are depressed. 5 "Bringing it all together" Practice interviews with volunteer in-patients and using peer discussion for feedback. Aims: practicing sensitive interview skills for history taking. 6 "Dealing with the unexpected" Practice interviews with volunteer in-patients and using peer discussion for feedback. Aims: dealing with time constraints, dealing with challenging or unexpected behaviour, effectively closing an interview The tutorials were delivered by pairs of consumer-tutors to small groups of six to eight medical students. The tutorials were graded in terms of level of difficulty beginning with general discussion of sensitive interviewing styles, role playing with tutors, followed by live interviews with volunteer inpatients from the psychiatric unit. Each tutorial included discussion of the pertinent issues, practice, review by the practicing student and feedback from peers and consumer tutors. The consumer tutors independently facilitated and participated in the tutorial without the involvement of academic staff who was available to assist if needed. They were rarely required (usually to resolve room double bookings). The curriculum and written materials underwent several revisions in response to feedback aimed at improving interactivity and clarity. Consumer tutors used the ground rules set out in training for dealing with absences. Reserve tutors were introduced at the start of the term so they were familiar to students if required. Tutor pairs were able to support each other and compensate for ebbs and flows in performance. The consumer tutors debriefed following each tutorial with academic staff. These meetings were an important opportunity for tutors to give positive and constructive feedback to each other as well as addressing ways to improve their delivery of the tutorials. They discussed problems and obstacles and brainstormed effective solutions. The larger consumer tutor group contributed to successfully resolving most conflict. Discussions were frank. Formal mediation was used to resolve a conflict between two tutors, in dispute over matters beyond teaching. Mediation was successful in terms of allowing ongoing involvement in teaching for both people. Academic staff informally debriefed the students during other tutorials. While a few students complained about the whole experience of being taught by a consumer, most students were positive. Indeed many reported they used the consumer tutors as a sounding board for other interview-related experiences during the week, seeking advice about alternative styles and approaches. Tutor maintenance Like all tutors, the consumer tutors required support, stimulation and refreshment. This happened in the tutorial debriefs outlined and in occasional workshops to review feedback, revise curriculum and refresh skills. Consumer tutors were encouraged to present reports of their experiences at appropriate meetings and received a national award for consumer research. Consumer tutors' motivation particularly increased when clinical mental health staff asked them about their teaching experiences and recognised that the individual had made many gains since the last episode of acute care. Consumer and professional tutors were commonly concerned about intervening illness and the impact on teaching. Some consumer tutors became acutely unwell during the term and required care. As a result they developed an agreement to postpone their involvement in the tutoring whilst they received necessary treatment. Consumer tutors and students were understanding of this and students had a rare experience of the longitudinal patterns of an illness and the person. Student attitudes Out of a total cohort of 104 medical students, students completed the pre (n = 72) and post (n = 68) attitudes questionnaire using a 5 point scale (5 = strongly agree, 4 = agree, 3 = uncertain, 2 = disagree, 1 = strongly agree). A comparison of mean scores on 3 items reflecting student attitudes towards consumers was conducted using an independent samples t-test. Results showed that prior to the program the medical students began with positive attitudes towards learning from consumers (n = 57, x = 3.89, s.d. = .865) and working with mental health clients (n = 72, x = 3.68, s.d. = 747). Whilst there was a general trend towards further improvement in their attitudes, their mean scores pre and post the program were not significantly different. However, the medical students did show a significant improvement in their belief that "clients in psychiatric units give reliable histories" (n = 72, x = 3.07, s.d. = .657, p < .005) (see Table 3 ). This general improvement in attitudes to learning from and working with consumers was reflected in the open comments (see sample of comments in Table 4 ). Table 3 Comparison of mean scores of student attitudes pre and post the program. Statement Pre and Post N= Mean Std. Deviation Sig. (2-tailed t-test) "I value learning from mental health clients" Pre 72 3.89 .865 .731 Post 68 3.94 .929 "Clients in psychiatric units give reliable histories" Pre 72 3.07 .657 .005 Post 66 3.42 .805 "As a doctor I would like mental health clients as part of my continuity of care practice" Pre 72 3.68 .747 .084 Post 68 3.91 .824 Total Pre 72 10.64 1.550 .063 Post 68 11.18 1.836 Table 4 Sample of medical students' open comments pre and post program Pre training Post training "For our purposes, consumers lack the ability to instruct us with relevant information." "The consumers give us insight into what it is like to be on the other side of the mental health system. This is invaluable in helping us to be better doctors and increase our empathy." In addition, students' greatest concern regarding interviewing mental health consumers changed before and after the teaching. Before, students were preoccupied with violence in the interview. At the conclusion of the program, students remained concerned about violence and unpredictable reactions. However, they reported increased concern with their ability to build rapport, engage and understand the client. The program has since been modified to address their concerns about violence early on in the training. Tutorial evaluations Tutor and medical student evaluations using a 4-point Likert scale (4=excellent, 3=good, 2=fair, 1=poor) were returned on 452 occasions. Analysis of the mean scores of students on the quality of the 'teaching' and 'content' of the program revealed favourable ratings ('teaching' n = 450, x = 2.81, sd.76; 'content': n = 451, x = 2.82, sd= .689). Whereas, tutors tended to rate the program even higher ('teaching': n = 372, x = 3.08, sd = .510; 'content': n = 369, x = 3.14, sd=.496). A comparison of the mean scores of students and tutors using an independent samples t-test showed that this difference was statistically significant (p < 0.001), see Table 5 . Open comments about the program varied as shown in Table 6 . Table 5 Analysis of mean scores on the quality of the 'consumers as tutors' program. Tutor/student N= Mean Std. Deviation Sig. (2-tailed t-test) Content Student 451 2.82 .689 .000 Tutor 369 3.14 .496 Teaching Student 450 2.81 .760 .000 Tutor 372 3.08 .510 Table 6 Open comments about the quality of teaching: I am impressed with the astute feedback which is encouraging and critical"- Medical student. "It was helpful. The tutors explained that even if the interview was going nowhere to keep persisting gently as the patient is just sizing you up"- Medical Student. "The consumers are a valuable source of encouragement and feedback"- Medical student. "The students interviewed a patient and showed warmth, good questions and rapport. He did not press when the patient did not want to divulge"- Consumer- tutor. Assessment All medical students who participated in the consumers as tutors program passed the university wide assessment of an observed psychiatric interview rated against defined criteria. Discussion This project established the feasibility of training and supporting mental health consumers as tutors for delivery of a jointly developed curriculum for 4 th year medical students in effective approaches to interviewing. Training and delivery has continued requiring modest maintenance, perhaps in keeping with sustaining professional tutors. Consumer tutors have shown themselves to be reliable, professional in approach and amenable to feedback. Benefits for students (as measured in their open evaluations) included the extended experience of working with a consumer of health services, the development of a clearer perspective regarding consumer views and an opportunity to see people with mental illness in recovery. Students were at least as well prepared as their peers for a structured assessment in interviewing (from the combined effect of traditional and novel teaching). Students largely reported positive experiences, found the curriculum and delivery acceptable and saw tutor experience and knowledge as legitimate and valuable. Ideally it would have been useful to follow up medical students over a longer term to assess their psychiatric interviewing skill, however, this was not practically possible within this study. The attempt to measure attitudes deserves discussion. Attitudes are recognised as an important component of curriculum development yet remain the personal business of each of us. It would be reasonable to see education as a means of working past one's own attitudes rather than seeking to refine or replace student attitudes. Guidelines for working with consumers in health care assume that "for consumer participation to be effective, all participants in the process need to respect the different skills and expertise of the other participants" [ 12 ]. In this study, student attitudes to consumers had a tendency to improve across all dimensions measured. On average the medical students began the program with largely positive attitudes to working with and learning from consumers which may explain the lack of statistically significant difference in their attitudes pre and post the program. In addition, a finding of lack of significance using a pre and post test design with a small sample of subjects is not usual. The one attitude measured that did improve throughout the program and reached statistical significance was towards mental health clients in psychiatric wards. This finding is understandable in light of the fact that the training took place within a psychiatric unit and the program incorporated practice at live interviews with clients from the unit. In addition, the study found a change in the primary focus of medical student concerns regarding interviewing which moved from issues focused on the consumer (such as violence or unpredictability) to those focused on improving their skills in interviewing and seeing this as a worthwhile activity. In terms of their satisfaction with the training program, based on their open comments, the few students who objected at least had the challenge of working in an educational model they did not admire. This was thought provoking and engaging even if the response produced was negative. Benefits for consumer tutors (as measured by their open evaluations) included enhanced self esteem and financial reward for work done. Consumer tutor curriculum development was novel such as utilising an art therapy vehicle to experience a non-pharmaceutical therapeutic device. Most consumer tutors have continued to teach, with appropriate breaks, and have mentored new tutors. Some have used this experience to step further into paid employment and to rehabilitate previous work skills. Consumer tutors have remained resilient and episodes of relapse appear to be multifactorial in origin (with teaching perhaps one of the factors). This robustness was also found in a study of psychological impact on consumers working in a peer support role in an acute care setting [ 13 ]. Consumer presentations have centred on the powerful personal effects of participation in learning new skills and gaining confidence. The largely positive ratings of tutors about the program was not as positive as the medical students, highlighting the need to evaluate both groups to adequately measure the effectiveness of the program. Benefits for the health system included the placement of consumers in a 'professional' light. Consumer tutors shared the staff tearoom, were paid as other casual tutors and were seen as well contributors rather than being in the sick role. Professional tutors were aware of the consumer tutor teaching and perhaps viewed it as 'politically correct' rather than educationally effective. Dissemination of findings via service and conference presentations has helped address this common view. Tutoring medical students is a skilful and potentially stressful role, and is not suitable for all mental health consumers. Following the training program some trained tutors realised that teaching was not their interest or strength (a proportion of whom did not teach at all). This was anticipated and should be factored into training plans. Some consumer tutors realised their tolerance level was insufficient to manage student junior skill level and found it hard to resist retelling their 'war stories' of difficult clinical encounters. This was a common theme in debriefing and required active refocusing on the curriculum of effective and ineffective interview techniques. The occasional protesting student required gentle persuasion to see that ongoing participation was a way of exploring contact with consumers. Like most education programs this approach did not run itself. Tutors required sustenance; feedback needed action and materials needed review. New tutors needed to be trained to add to the growing pool of available people. Despite these issues, our experience was that this was manageable and in keeping with maintenance of quality teaching by professional tutors. We believe this model is another valuable option in a range of consumer involvement programs and could be replicated in health, emergency services and support agency education. Discussions have occurred with carers about possible involvement. At this stage it was decided to invite carers to speak about specific topics in the mainstream program as consumers see their expertise as fundamentally different to that of a carer. Despite the potential for use with other groups, our attempt to use this experience in refreshing interview skills in general practitioners was unsuccessful. Notwithstanding the diligent work by all parties, local general practitioners held fast to the view that consumer tutors would lack the emotional robustness to survive teaching. They were welcome to come and talk of their experience but were not seen as competent to deliver a curriculum. This may well have been shorthand for more complex issues of concerns about confidentiality, power and autonomy. This example reminds us, however, that health education is in change and that new strategies are required to engage today's students in experiences that will produce clinicians skilled to support effective consumer participation in healthcare. Conclusions We have detailed a feasibility study which demonstrates a new level of consumer participation in the design, implementation and evaluation of a medical student training program. The effort has been sustained over four years with appropriate maintenance. Largely positive outcomes were seen for students, consumer tutors and the health care system. These included raising the profile of consumers as 'legitimate teachers' in medical education and contributing to an improvement in the attitude of medical students towards mental health consumers. Together the joint partners in the program were able to manage obstacles, such as, pessimistic attitudes towards the involvement of consumers and difficulties adhering to the curriculum. Adopting a continuous review of the feedback from both medical students and consumer tutors has helped to further refine our ability to deliver the curriculum and better support the participants. Lastly, our experience has been that consumer tutors are an untapped resource offering a richness of experience and a professional approach to teaching that deserves closer examination in other health settings. Competing interests Pfizer funded the initial pilot in the first year of the study. Authors' contributions CO conceived of the study, participated in the design of both the consumer tutor curriculum and medical student curriculum and performed the data analysis. CO and RR participated in the training of consumers, monitored the implementation and evaluation phases and coordinated the program. Both authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524164.xml

543464

How Statins May Protect against Alzheimer Disease

Epidemiological studies suggest that statins, a class of cholesterol-lowering drugs, may lower the risk for Alzheimer disease. The mechanism for this effect is unclear. Alzheimer disease is characterized by accumulation of amyloid deposits in the brain. These deposits are composed of amyloid-beta (Aβ) peptide, a protein fragment that is cleaved off from the amyloid precursor protein APP. APP can be cleaved in two different ways. Amyloidogenic (“amyloid generating”) cleavage by an enzyme called beta-secretase yields “sticky” Aβ peptides that aggregate to form deposits, whereas non-amyloidogenic cleavage by alpha-secretases generates soluble peptides that do not form deposits. Studies in animal models and cell culture suggest that statins might modulate APP processing and shift the balance toward “healthy” (non-amyloidogenic) cleavage. APP secretases (α, β, and γ) In their quest to understand how statins affect APP processing, Sam Gandy and colleagues focused on a molecule called ROCK1, a kinase enzyme that had recently been implicated in APP processing. The theoretical link between statins and ROCK1 goes as follows: statins inhibit the isoprenoid pathway, isoprenoids are regulators of the enzyme Rho, and Rho in turn activates ROCK1. And while such potential connections could be drawn for any number of molecules, Gandy and colleagues went on to test whether statins exert their effect on APP cleavage by interfering with the isoprenoid/Rho/ROCK1 pathway. Working in mouse neuroblastoma cells, they confirmed that two different statins increased healthy cleavage of APP. When they directly interfered with the isoprenoid/Rho/ROCK1 pathway by adding a drug that inhibits Rho activation, they saw effects similar to those of the statins (i.e., an increase in healthy cleavage). The same effects were seen when they transfected the cells with a dominant negative form of ROCK1 (which inactivates the normal ROCK1 molecules in the cell); this outcome shows that the pathway can influence APP cleavage. Most conclusively, when they added a version of ROCK1 that was constitutively (always) active, they reduced basal levels and abolished statin-stimulated levels of healthy cleavage. Taken together, these results suggest that statins influence APP processing, at least in part, by modulating the isoprenoid pathway and inactivating the ROCK1 kinase. Future studies are necessary to determine whether this mechanism is actually responsible for the apparent clinical benefits of statins. Another question worth exploring further is whether ROCK1 might be a suitable target for therapeutic interventions that aim to decrease harmful, and promote healthy, cleavage of APP.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC543464.xml

544848

Accuracy of body composition measurements by dual energy x-ray absorptiometry in underweight patients with chronic intestinal disease and in lean subjects

Background To assess the accuracy of Dual-energy X-ray absorptiometry (DXA) in underweight patients with chronic gastrointestinal disease, we investigated the ability of DXA to detect variations in body composition induced by infusion of parenteral nutrition (PN). Furthermore, the influence of a low body weight per se on the accuracy of DXA was studied by placing packets of lard on lean healthy subjects. Methods The hydration study included 11 patients with short bowel syndrome on long-term home parenteral nutrition (9 women and 2 men), and (mean ± SD) 49.5 ± 17.1 yr., 19.3 ± 3.1 kg/m 2 . The lard study, where packets of lard were placed either over the thighs or the trunk region, was performed in 8 healthy lean male volunteers, 26.4 ± 7.4 yr., and 21.0 + 0.9 kg/m 2 . Body composition, including measures of the total mass (TM), soft tissue mass (STM), lean tissue mass (LTM), fat mass (FM), and total body mineral content (TBBMC), was assessed by DXA. The fat fraction of the lard packets (3.49 kg), measured in triplicate by chemical fat extraction, was 52.2%. Results Hydration study; The increase in scale weight (BW) of approximately 0.90 kg due to infusion of PN correlated significantly to the increase in TM (R-square = 0.72, SEE 0.36 kg, p < 0.01), and the increase in STM (R-square = 0.69, SEE 0.38 kg, p < 0.01), however not with the increase LTM (R-square = 0.30, SEE 1.06 kg, p = 0.08). Mean changes in TM (0.88 kg), STM (0.88 kg), and LTM (0.81 kg) were not significantly different from changes in BW (p > 0.05). Lard study; Regardless of position, measurements of FM and LTM of the added lard were not significantly different from expected values. However, the composition of the lard packets into FM and LTM was more accurately detected when the packets were placed over the thighs than over the trunk region. The accuracy of DXA in individual subjects, expressed as the SD of the difference between expected and measured values, was 1.03 kg and 1.06 kg for the detection of changes in LTM and FM, respectively, and 0.18 kg for the detection of changes in STM and TM. Conclusions On a group level, DXA provided sufficient accuracy to detect small changes in body composition in underweight patients with chronic gastrointestinal disease. However, the accuracy errors were higher than reported in normal weight subjects. The accuracy was not influenced by a low body weight per se.

Background Malnutrition is commonly observed in patients with chronic gastrointestinal disease and nutritional support is therefore an integral part of the management of these patients. Accurate and precise methods that are sensitive enough to track small changes in body composition are important for assessing the effect of nutritional intervention. At present, there are available a variety of well-established techniques such as hydrodensitometry, isotope dilution, air-displacement plethysmography [ 1 ], and potassium spectroscopy, methods which are based on the 2-compartment model separating the body into fat tissue mass (FM) and fat-free tissue mass (FFM). However, these methods rely on assumptions that are often not met in malnourished patients, e.g. a fixed hydration and density of the FFM, which may negatively influence the accuracy of measurements. Therefore, such methods may not be sufficiently accurate to detect small changes (<1.5 kg) in FM or FFM, changes that would be considered clinically significant when making decisions about treatment of underweight patients with chronic gastrointestinal disease. Dual-energy X-ray absorptiometry (DXA) is a precise, accurate, non-invasive, safe, and convenient technique, founded on a three compartment model separating the body into total body mineral mass (TBBMC), FM, and lean tissue mass (LTM), the latter being the remaining bone-free fat-free tissue mass [ 2 , 3 ]. In theory, DXA has the distinct advantage over most other body composition methods of not requiring any assumptions about the chemical constancy of the LTM, although it does assume constant attenuation of the lean and fat tissues. Thus, DXA may be an appealing option for body composition analysis in underweight patients with chronic gastrointestinal disease. However, the accuracy of DXA measurements in this particular group has only been sparsely documented [ 4 - 7 ]. Several factors may hypothetically affect the accuracy of DXA measurements in these patients, such as a low body weight per se, and an abnormal hydration status. In addition, it is unknown whether the constants and standard mathematical algorithms used for soft tissue determination are appropriate in this subgroup of patients. The purpose of this study was twofold. Primarily, in underweight patients with short bowel syndrome dependent on long term home parenteral nutrition we evaluated whether DXA could accurately detect small changes in body composition induced by intravenous infusion of parenteral nutrition (PN). Secondly, to explore the influence of a low body weight per se on the accuracy of DXA, we investigated the ability of DXA to detect changes in body composition by placing packets of lard on the trunk and thighs of lean healthy volunteers. Methods Two separate experiments were undertaken. In the hydration study, changes in body composition were induced by intravenous infusion of PN in underweight patients with short bowel syndrome. In the lard study, changes were induced by placing packets of lard on lean healthy volunteers. Anthropometry All participants were weighed (after voiding) on a calibrated digital scale accurate within 0.1 kg (subjects wearing light clothes). The heights were measured after a maximal inhalation to the nearest 0.1 cm by using a wall-mounted stadiometer. The averages of two measurements for both height and weight were used as the criterion measurement. The body mass index was calculated as weight divided by height squared (kg/m 2 ). Dual energy X-ray absorptiometry Measurements of body composition were performed with the Norland XR-36 DXA densitometer (Norland Corporation, Fort Atkinson, WIS, U.S.A.) with the subject supine. The host software was rev. 2.5.2. and the scanner software rev. 2.0.0. The theory and methodology for body composition by DXA has previously been described [ 2 ]. Briefly, while the patient lightly dressed lay on a scan table for about 20 min., transverse scans approximately 1 cm apart were performed from top to heel. The instrument uses X-rays of two distinct energy levels that are attenuated by fat, bone and lean mass to different extent. By computerization of data inputs from approximately 11000 pixels DXA estimates body composition based on a three-compartment model, measuring TBBMC, FM, and LTM. The total mass (TM) by DXA is the sum of all three compartments, whereas the soft tissue mass (STM) by DXA includes only the LTM and FM. The fat free tissue mass (FFM) includes both the TBBMC and LTM. Among others, Hendel et al . [ 8 ] have reported precision errors of body composition of the Norland XR-36 densitometer. They were 2.2% for TBBMC, 2.7% for FFM, and 2.6% for FM%. In our hands the between-measurements CV%'s of TBBMC, LTM and FM, were 1.5%, 1.6%, and 3.9%, respectively [ 5 ]. Hydration study This study comprised 11 patients (9 women and 2 men) with short bowel syndrome treated with daily supplements of PN. The participants were selected for low body weight (BMI < 22 kg/m 2 ). The diagnoses were: Crohn's disease (n = 6), ischaemic infarction (n = 2) and other (n = 3). Patients were on average (mean ± SD) 49.5 ± 17.1 yr., 1.58 ± 0.07 m, 48.5 ± 9.5 kg, and 19.3 ± 3.1 kg/m 2 . The PN was in all patients provided as a 3 L 'all in one' plastic bag containing a fixed composition of protein, glucose, and electrolytes, and four patients had additional supplements of 1–2 L of saline. The infusion was given continuously over a period of 8–10 hours during the night. Before starting the infusion all patients were weighed on a scale and scanned as described below. Immediately after completing the infusion the patients were reweighed and rescanned. Due to the large volume of intravenous fluid provided with the parenteral nutrition, patients were allowed to void during the study. The theoretical soft-tissue attenuation (R ST ) of an 'all in one' 3 L bag of PN was calculated using the equation R ST = Σ (-f i × (μ mi ) L ) / Σ (-f i × (μ mi ) H ), where (f i ) is the mass fraction, (μ mi ) is the mass attenuation coefficient of the i'th component at high (H) and low (L) photon energy levels [ 9 ]. The theoretical R ST -value for PN was calculated to 1.365, which is very close to that of normal saline (1.377). Given the calculated R ST -value PN should theoretically be scanned by DXA as consisting of approximately 2% FM and 98% LTM. Such values were confirmed in vivo by scanning one subject three times before and after placing 2 bags (6.96 kg) of PN on the subject's legs. By DXA, the equivalent 6.99 kg increase in TM (TM) resulted from a 7.31 kg gain of LTM (104.5%) and a 0.32 kg loss of FM (- 4.6%), whereas the TBBMC changed only 6 g (0.1%). Lard study For this experiment two packets of porcine lard (with a small amount of muscle-tissue attached) were constructed and enclosed in plastic wrap. The lard packet dimensions were 19.8 cm × 38.8 cm × 2.6 cm, and weighed, using a beam scale, 3.49 kg. The total fat fraction of the lard, measured in triplicate by chemical fat extraction according to the method of Folch et al. [ 10 ], was 52.2% (CV% = 6.4%). The participants for this study were selected for low body weight (BMI < 22 kg/m 2 ). Eight healthy lean male volunteers, who were on average (mean ± SD) 26.4 ± 7.4 years of age, agreed to participate. Anthropometric measures of the participants were taken immediately before the study and averaged 1.81 ± 0.07 m in height, 69.0 ± 7.7 kg in weight, and 21.0 ± 0.9 kg/m 2 in BMI. Without reposition, four consecutive total body DXA scans were performed on each participant. Two scans without added lard served as baseline measurements (the average values were used as the criterion measurements), and two scans were performed with the lard packets placed alternately on the abdomen centred over the lumbar vertebrae, and on the thighs at midpoint of the femur. The placements of the lard over the thighs and trunk were chosen to represent regions where the ability of DXA to correctly measure soft tissue composition is known to be good and poor, respectively. Ethics The Ethics Committee for Medical Research in Copenhagen, Denmark, approved the study protocol and the study was conducted in accordance with the Declaration of Helsinki of 1975, as revised in 1983. Written and oral informed consent was obtained from all patients prior to inclusion. Statistics All results are expressed as means ± standard deviation (SD) unless otherwise indicated. A paired Students t-test was used to compare paired variables. Association between variables was established by Pearson's correlation coefficients and linear regression. The CV%'s for the measurements of TBBMC or FM were calculated from the within-subjects SD's divided by their respective grand means. All statistical tests were two-tailed, and a p value of less than 0.05 was considered statistically significant. The SPSS statistical program version 10.0 (SPSS Inc., Chicago, USA) was used for all analyses. Results Hydration study The descriptive statistics for body weight (BW) and body composition variables by DXA before and after intravenous infusion of PN are given in Table 1 . An average increase of 0.90 ± 0.45 kg of BW was achieved by infusion of PN. This corresponded to an increase in TM of 0.88 ± 0.63 kg, STM of 0.88 ± 0.64 kg, and LTM of 0.81 ± 1.20 kg which were significantly higher than the baseline values. No significant differences were found between baseline and post infusion estimates of FM and TBBMC. The average within subject CV%'s for TBBMC and FM were 2.0% and 3.5%, respectively (CV% for FM was expressed as the geometric mean, due to violation of the normality assumption). The correlations between changes in BW and changes in mass and composition by DXA are summarised in Table 2 . The increase in BW correlated significantly to the increase in TM (R-square = 0.72, SEE 0.36 kg, p < 0.01), and the increase in STM (R-square = 0.69, SEE 0.38 kg, p < 0.01), however not with the increase LTM (R-square = 0.30, SEE 1.06 kg, p = 0.08). For all three regression lines (diff. BW vs. diff. TM, STM, and LTM) the intercepts were not significantly different from zero and the regression slopes were not significantly different from 1.00. Fig. 1 displays the limits of agreement plots of the comparison of change in BW and changes in TM, STM and LTM by DXA. The accuracy of DXA in the individual subject, expressed as the 95% confidence intervals for the difference between changes in BW and changes in DXA variables, was ± 2.06 kg for the detection of changes in LTM, and ± 0.71 kg for the detection of changes in STM and TM. Table 1 Changes in body weight and body composition variables by DXA after infusion of parenteral nutrition in 11 patients on permanent home parenteral nutrition. Baseline Post infusion Change Range Body weight (kg) 48.39 ± 9.42 49.43 ± 9.20* 0.90 ± 0.45 0.30 ; 1.70 Total mass (kg) 47.91 ± 9.46 48.78 ± 9.06* 0.87 ± 0.65 -0.05 ; 1.72 Soft-tissue mass (kg) 45.88 ± 9.15 46.75 ± 8.75* 0.88 ± 0.65 -0.14 ; 1.74 Lean-tissue mass (kg) 32.51 ± 5.92 33.04 ± 5.33* 0.53 ± 1.36 -1.53 ; 3.54 Fat mass (kg) 13.37 ± 7.46 13.72 ± 7.82 0.35 ± 1.00 -1.93 ; 1.88 Total body bone mineral mass (kg) 2.03 ± 0.38 2.03 ± 0.39 0.00 ± 0.07 -0.10 ; 0.11 Values are mean ± SD. *Significantly different from baseline values (Students paired t-test) Table 2 Intercorrelation of change in body weight and change in total mass, soft tissue mass, and lean tissue mass by DXA after infusion of parenteral supplements in 11 patients on permanent home parenteral nutrition. Body weight Total mass Soft-tissue mass Total mass 0.846 * - - Soft-tissue mass 0.832 * 0.994 * - Lean-tissue mass 0.550 0.497 0.471 * Correlations are significant (p < 0.01) Figure 1 The figure displays the limits of agreement plots of the comparison of change in BW and changes in TM, STM and LTM by DXA following intravenous infusion of parenteral nutrition in 11 patients with short bowel syndrome. Lard study The placement of 3.49 kg of exogenous lard (with a composition of 52.2% FM and 47.8% LTM) over central and peripheral regions of the body of eight lean healthy volunteers had no effect on the estimate of TBBMC (p > 0.05, CV% = 1.4%). The descriptive statistics for the corresponding mean changes in TM, STM, LTM, and FM are given in Table 3 . Except from a minor overestimation (0.23 kg) of TM and STM when the lard packets were placed over the trunk region, the measured changes in DXA variables were not significantly different from the expected values (p > 0.05, for all comparisons). However, DXA appeared slightly more accurate in detecting both the mass and composition of the added lard when the packets were placed over the thighs Table 3 . Thus, whereas the composition into FM and LTM of the added lard placed over the thighs corresponded closely to the actual values, the FM was slightly underestimated and LTM correspondingly overestimated when the lard was placed over the trunk region. Fig. 2 shows the individual differences between the expected value of LTM, FM, TM and STM and the measured changes in the respective DXA variables. The accuracy of DXA in the individual subjects, expressed as the SD of the difference between expected and measured values, was 1.03 kg and 1.06 kg for the detection of changes in LTM and FM, respectively, and 0.18 kg for the detection of changes in STM and TM. Table 3 The actual weight and chemical composition of lard packets placed on the thighs and abdomen of 8 healthy lean male volunteers and the composition measured by DXA. Thighs Trunk Actual Mean ± SD 95% CI Percentage Mass Detected Mean ± SD Mean ± SD 95% CI Percentage Mass Detected Mean ± SD Total mass (kg) 3.49 3.48 ± 0.17 3.34 ; 3.62 99.7 ± 4.9 3.72 ± 0.18 3.57 ; 3.87 106.6 ± 5.1 Soft-tissue mass (kg) 3.49 3.48 ± 0.18 3.33 ; 3.63 99.6 ± 5.2 3.66 ± 0.19 3.50 ; 3.82 104.9 ± 5.5 Fat mass (kg) 1.82 1.92 ± 1.13 0.97 ; 2.86 105.2 ± 62.3 1.42 ± 0.92 0.65 ; 2.19 77.9 ± 50.8 Lean-tissue mass (kg) 1.67 1.56 ± 1.16 0.59 ; 2.53 93.5 ± 69.5 2.24 ± 0.96 1.44 ; 3.04 134.3 ± 57.3 Figure 2 The figure shows the differences between the actual weight and composition of added lard and the measured values by DXA, in eight lean healthy volunteers. In each subject DXA measurements were performed with lard placement both on the trunk and on the thighs. Each symbol indicates measurements in one subject. Error bars are the 95% confidence intervals of the difference. Discussion Compared to several other body composition techniques DXA has a very high precision [ 8 , 11 ], which on paper should make DXA able to detect small changes in body composition. The precision errors, generally reported as the coefficient of variation (CV%) of repeated measurements, are about 2–3% for TBBMC, FM, and FFM in healthy subjects, and values of a quite similar proportion have been documented in underweight patients with chronic intestinal disease [ 5 ]. In addition to a high precision, DXA has proven an accurate method for body composition analysis in normal weight healthy subjects [ 12 - 17 ]. However, the accuracy in underweight patients with chronic gastrointestinal disease or in very lean subjects has only been sparsely elucidated [ 4 , 5 , 18 ], but may theoretically be lower due to factors inherent in the DXA methodology. DXA operates on a scanning principle separating the body into approximately 11 000 pixels of each 6.5 × 13.0 square mm, of which about 6000 pixels only contain soft tissue and about 5000 pixels contain both soft tissue and bone. Determination of total body composition of bone, fat, and lean tissue masses is based on computerised analysis of the soft tissue composition of each pixel separately. One important limitation of the DXA methodology is that direct estimation of soft tissue composition is possible only in pixels with no bone present. Evaluation of soft tissue composition in pixels with bone mineral as well as soft tissue is performed by extrapolating calculated values for soft tissue composition in adjacent bone-free pixels to the pixels with bone. In underweight patients with chronic gastrointestinal disease or other lean subjects, the number of none-bone containing pixels available is obviously reduced compared to normal weight subjects, and in theory, this may lower the accuracy of DXA. Additionally, in malnourished underweight patients, deviations in the state of hydration frequently occur, which might influence the accuracy of soft tissue determination by DXA. Thus, Pietrobelli et al. [ 19 ] demonstrated that fluctuations in the hydration affected soft tissue attenuation and gave rise to systematic and predictable errors in the determination of LTM and FM, although these errors were quite small with changes in hydration within normal physiological limits. Thus, simulated experiments showed that errors in the detection of FM% is <1% with hydration changes of 1–5% [ 19 ]. We studied underweight patients with gut failure due to short bowel syndrome, who were dependent on long-term home parenteral nutrition. The changes in TM, STM, and LTM of 0.90 kg induced by infusion of PN were accurately detected on a group level. Furthermore, measurements of TBBMC and FM, body constituents that should not be affected by changes in hydration, remained unchanged during the experiment, and the respective CV%'s were very close to normal precision errors for repeated measurements [ 5 , 8 , 11 ]. Our data agree with results of comparable experiments were changes in the hydration status of healthy volunteers were induced by intravenous saline infusion [ 15 , 16 ]. However, the accuracy errors (SEE's of the regression lines) of DXA in the individual patient were about 35% higher for the detection of TM and STM, and nearly 100% higher for the detection of LTM in our study compared to results in healthy subjects reported by Going et al. [ 16 ]. Yet, in the present study BW increased by only 0.90 kg compared to an increase of 1.21 kg in the study by Going et al. [ 16 ], a difference that may have affected the results. To explore the influence of a low body weight per se on the accuracy of DXA, we investigated otherwise healthy lean young men (BMI = 21 kg/m 2 ) with packets of lard placed over the trunk and thigh region. In agreement with previous studies in normal weight healthy volunteers [ 3 , 12 - 14 , 17 ] we found that the TM and STM of packets of lard were very accurately assessed by DXA regardless of position. In addition, the measured composition of the lard packets into FM and LTM was not significantly different from expected values with the packets overlying either the thigh or the trunk region. The accuracy error of DXA in lean subjects, expressed as the SD of the difference between expected and measured values, was about 1.04 kg for the detection of changes in LTM and FM, and 0.18 kg for the detection of changes in STM and TM. These values agreed closely with the reported accuracy errors for the Norland XR-36 scanner in normal weight healthy volunteers [ 17 ]. This indicates that a low body weight (BMI = 21 kg/m 2 ) per se does not affect the accuracy of DXA. DXA appeared somewhat more accurate in detecting both the mass and composition of the added lard when the packets were placed over the thighs. Thus, the FM was slightly underestimated and the LTM correspondingly overestimated when the lard was placed over the trunk. Limitations in the ability of DXA to accurately assess the composition of soft tissue in the trunk region have been reported earlier. Thus, in common with our results Snead et al. [ 12 ] and Milliken et al. [ 13 ] reported that DXA underestimated FM of added lard placed on the trunk of healthy volunteers. This may be related to the fact that the trunk region contains a high degree of pixels with bone present because of the complex bone geometry in the trunk, which leaves relatively fewer bone-free pixels for the calculation of soft tissue composition in this region. Therefore, measurement of fat and lean may be less accurate in the trunk region compared to the extremities, which have more simple bone geometry and a relatively higher number of bone-free pixels. We evaluated the performance of the Norland XR-36 (software version 2.5.2) densitometer to measure small changes in soft tissue composition in underweight patients with chronic gastrointestinal disease. DXA accurately detected changes in TM, STM, and LTM induced by infusion of PN on a group level, however the accuracy errors were up to 100% higher in this group compared to normal weight healthy subjects. Also, DXA performed well in detecting the composition of added lard placed on lean healthy subjects with accuracy errors similar to those reported in normal weight subjects, supporting that a low body weight per se does not affect the accuracy of DXA. Conclusions We conclude that DXA is an accurate method for body composition analysis in underweight patients with chronic gastrointestinal disease. The individual accuracy errors however were higher than in normal weight subjects and this should be taken into account when evaluating the changes in body composition in the individual patient. Authors' contributions KH and MS were responsible for conception and design of the study. KH, PHH and MS were responsible for data interpretation, and manuscript preparation. None of the authors have personal or financial interests in any organization sponsoring the research.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC544848.xml

549607

How Tumor Cells Acquire Resistance to Kinase Inhibitors

Acquired resistance to chemotherapy is a major obstacle to successful cancer treatment. Understanding the mechanisms by which tumors become resistant to a particular agent is key to identifying new drugs or combination regimens. Kinases are signaling molecules that control many aspects of cell behavior, including cell proliferation, i.e., whether and how fast cells divide. Abnormally active kinases promoting tumor growth are found in many cancers and are a focus of rational cancer drug design. One target for kinase inhibitors is the epidermal growth factor receptor (EGFR). Two EGFR inhibitors, gefitinib and erlotinib, showed therapeutic benefits in a subset of patients with non-small cell lung cancer. Recent work has helped us understand why some patients respond and some don't: responsive tumors usually harbor activating mutations in the EGFR gene, which somehow make the tumors sensitive to treatment. Nearly all patients whose tumors initially respond to EGFR inhibitors, however, eventually become resistant to the drugs and progress despite continued therapy. William Pao and colleagues examined tumors from six patients with non-small cell lung cancer who initially responded to gefitinib or erlotinib but subsequently relapsed. Tumors from all six patients carried activating mutations in the EGFR gene. In addition, in three out of the six cases, the resistant tumor cells carried an identical second mutation in the EGFR gene. Whereas the activating mutation was present in tumor cells before treatment with erlotinib or gefitinib, the second mutation was not found in pre-treatment biopsies from these patients, nor in over 150 lung cancer samples from patients who had not been treated with either drug. Additional cell culture studies supported the notion that the secondary mutation causes resistance to gefitinib or erlotinib. It is clear, though, that this is only one mechanism of resistance, because in the three other cases resistance occurred in the absence of the second mutation. What caused the resistance in those tumors is not known. All kinases share some common features, and a resistance mutation very similar to the one identified here has also been found in other kinase genes from tumors with acquired resistance to imatinib, another kinase inhibitor. As Gary Gilliland and colleagues point out in an accompanying Perspective (DOI: 10.1371/journal.pmed.0020075 ), the initial identification three years ago of resistance mutations against imatinib led to the rapid development of alternative kinase inhibitors that work even against tumors with the resistance mutation. Similarly, the results by Pao and colleagues should help researchers develop second generation drugs for lung cancer. Re-biopsy of progressing lung lesion

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC549607.xml

212699

Developmental Origin and Evolution of Bacteriocytes in the Aphid–Buchnera Symbiosis

Symbiotic relationships between bacteria and insect hosts are common. Although the bacterial endosymbionts have been subjected to intense investigation, little is known of the host cells in which they reside, the bacteriocytes. We have studied the development and evolution of aphid bacteriocytes, the host cells that contain the endosymbiotic bacteria Buchnera aphidicola . We show that bacteriocytes of Acyrthosiphon pisum express several gene products (or their paralogues): Distal-less, Ultrabithorax/Abdominal-A, and Engrailed. Using these markers, we find that a subpopulation of the bacteriocytes is specified prior to the transmission of maternal bacteria to the embryo. In addition, we discovered that a second population of cells is recruited to the bacteriocyte fate later in development. We experimentally demonstrate that bacteriocyte induction and proliferation occur independently of B. aphidicola . Major features of bacteriocyte development, including the two-step recruitment of bacteriocytes, have been conserved in aphids for 80–150 million years. Furthermore, we have investigated two cases of evolutionary loss of bacterial symbionts: in one case, where novel extracellular, eukaryotic symbionts replaced the bacteria, the bacteriocyte is maintained; in another case, where symbionts are absent, the bacteriocytes are initiated but not maintained. The bacteriocyte represents an evolutionarily novel cell fate, which is developmentally determined independently of the bacteria. Three of five transcription factors we examined show novel expression patterns in bacteriocytes, suggesting that bacteriocytes may have evolved to express many additional transcription factors. The evolutionary transition to a symbiosis in which bacteria and an aphid cell form a functional unit, similar to the origin of plastids, has apparently involved extensive molecular adaptations on the part of the host cell.

Introduction Endosymbiosis is common in insects, with more than 10% of insect species relying upon intracellular bacteria for their development and survival ( Baumann et al. 2000 ). Full genome sequencing of the endosymbiotic bacteria, Buchnera aphidicola , of several species of aphids has revealed extensive gene loss ( Shigenobu et al. 2000 ; Tamas et al. 2002 ; van Ham et al. 2003 ), but has failed to reveal the genetic basis for the interaction between the bacteria and host cells. The key adaptations that allow incorporation of the bacteria into host cells may therefore be encoded by the host genome. The symbiotic bacteria of aphids, B. aphidicola , live within large polyploid cells, called bacteriocytes, that are grouped into organ-like structures, called bacteriomes, located adjacent to the ovarioles. During most of the aphid lifecycle, embryos develop parthenogenetically from unfertilized diploid oocytes, and multiple embryos develop serially within a single ovariole ( Dixon 1985 ) ( Figure 1 A). Maternal bacteria are transferred directly to the developing blastoderm-stage embryos through an opening in the posterior of the embryo ( Buchner 1965 ; Miura et al. 2003 ) ( Figure 1 B). Several researchers have described this transovarial transfer of bacteria (e.g., Uichanco 1924 ; Klevenhusen 1927 ; Toth 1933 , 1938 ; Lampel 1958 ; Buchner 1965 ), but the details of bacteriocyte development have remained unclear. Figure 1 Expression of Three Transcription Factors during Early Bacteriocyte Development (A) Drawings of some stages of pea aphid embryonic development, approximately to scale. Embryos develop viviparously within a follicular epithelium of the ovariole (data not shown). For a complete description, see Miura et al. (2003 ). Bacteria are transferred at stage 7. Embryos are labeled with bacteria (b), head (h), thoracic (t), and abdominal (a) regions. The three thoracic segments (t1, t2, t2) and germ cells (gc) are indicated in the stage 14 embryo. (B) A drawing of a stage 7 embryo illustrates transovarial transfer of the bacteria (red arrowhead) to the embryo and the presumptive bacteriocyte nuclei (arrow). (C) Confocal micrograph of a stage 6 embryo stained with anti-Dll antibody (red, indicated by arrow). Anti-Dll labels syncytial nuclei (presumptive bacteriocyte nuclei) in the posterior of the embryo. (D) Confocal micrograph of stage 7 embryo stained with anti-Dll and FP6.87 antibodies. Soon after the bacteria begin to invade the embryo, we observe staining with the FP6.87 antibody localized to the nucleoli (blue), which recognizes both Ubx and Abd-A in diverse arthropods, in the same nuclei that are already expressing Dll (red). The region outlined with a broken white box is enlarged in (D′) to show the bacteria, and only the green channel is shown in monochrome. The red arrow indicates one bacterium. (E and F) In these two panels of the same focal plane from the same stage 9 embryo, Ubx/Abd-A staining (blue) is observed throughout the entire nucleus of all nuclei that also express Dll (red). (G) Confocal micrograph of a stage 8 embryo stained with anti-En (yellow). As the transfer of bacteria (arrowhead) is being completed, the bacteriocyte nuclei begin to express En (yellow, indicated with arrow). In (C)–(G), confocal micrographs show only one focal plane of the embryo, so not all bacteriocyte nuclei in each embryo can be seen. In all figures, F-actin is stained with phalloidin (green). Embryos in all figures, except Figure 2 , are oriented with anterior of the entire embryo (towards the germarium) to the left. We have identified bacteriocyte-specific markers that allow us to track the proliferation of bacteriocytes throughout the development of the pea aphid Acyrthosiphon pisum (Harris) (Hemiptera: Aphididae). Using these markers, we aimed to determine the developmental origin of bacteriocytes and to what extent bacteria are required for the formation of the bacteriocytes. We also tested whether the observed patterns of bacteriocyte development are evolutionarily conserved among distantly related aphid species. We show that three transcription factors are expressed in a specific temporal order during early bacteriocyte development of the pea aphid. The final population of bacteriocytes originates from two distinct populations of nuclei recruited at different times of development. Furthermore, we experimentally demonstrate that the specification and proliferation of bacteriocytes occur independently of B. aphidicola . In distant relatives of the pea aphid, we found that the two-step determination of bacteriocytes is conserved. We also investigated two cases involving the loss of B. aphidicola . In the first case, in which the bacterial symbionts have been replaced with extracellular, eukaryotic symbionts, bacteriocyte development appears to proceed normally. In a second case, in which males do not inherit B. aphidicola , the bacteriocytes have been lost. Results Three Transcription Factors Are Expressed in a Specific Temporal Order during Early Bacteriocyte Development We tested five cross-reacting antibodies (see Materials and Methods ) for their expression patterns in aphids. In every case, we observed antibody staining in the expected population of cells in the developing embryo (also see Miura et al. 2003 ). In addition, we found that three of the antibodies stained nuclei that form bacteriocytes of A. pisum . We infer that these antibodies are recognizing the homologues, or possibly paralogues, of their respective target proteins. The three proteins are expressed in a specific temporal order. We first observe expression of the Distal-less (Dll) protein (FlyBase ID: FBgn0000157) ( Panganiban et al. 1994 ) in syncytial nuclei at the posterior of the blastoderm embryo just prior to the invasion of bacteria into the embryo ( Figure 1 C). As the bacteria enter the embryo, these nuclei associate with the bacteria and start to express a second protein, Ultrabithorax (Ubx) (FBgn0003944) or Abdominal-A (Abd-A) (FBgn0000014) or both, detected by the FP6.87 antibody ( Kelsh et al. 1994 ) ( Figure 1 D–1F). The bacteria can be easily observed as spheres 2–4 μm in diameter ( Buchner 1965 ) that stain with phalloidin ( Figure 1 D′). As the transfer of bacteria to the embryo is being completed, expression of the Engrailed (En) protein (FBgn0000577) ( Patel et al. 1989 ) is detected ( Figure 1 G). Two Populations of Cells Are Recruited to the Bacteriocyte Fate at Different Times in Development The early embryo contains approximately eight bacteriocyte nuclei that express Dll ( Figure 1 C), whereas the adult aphid contains 60–90 uninucleate polyploid bacteriocytes ( Baumann et al. 2000 ) that also express Dll (data not shown). We found that the increase in bacteriocyte number occurs through two mechanisms. First, we infer that the original bacteriocyte nuclei divide, apparently in a syncytium and perhaps synchronously, through two rounds of division because we observe that the number of Dll-expressing nuclei increases from approximately eight to 16 by stage 12 and then to approximately 32 by stage 13 (data not shown). By stage 14, these original bacteriocytes have formed cell membranes and become polyploid ( Figure 2 A). At stage 13, a second population of approximately 40–60 cells located near the posterior end of the dorsal germband begins to express Dll ( Figure 2 B) . The nuclei of these cells are visibly smaller than those of the original bacteriocytes ( Figure 2 A–2E). Based on observations of multiple fixed specimens, we infer that these cells then migrate across the germband ( Figure 2 E) and intercalate between the original bacteriocytes ( Figure 2 C and 2D). The bacteria are presumably then subdivided among all of the Dll-expressing nuclei and the final bacteriocytes are formed. Figure 2 The Second Wave of Bacteriocyte Determination In (A)–(D), the embryos, which are normally folded in upon themselves in a pretzel shape within the ovariole ( Miura et al. 2003 ), have been dissected flat, stained with anti-Dll antibody (red) and phalloidin (green), and examined with a confocal microscope. (A) Dll expression (red) in a stage 14 embryo is detected in the labrum (La) and all developing limbs on the ventral surface except the mandibular segment (Mn). (Other abbreviations: An, antenna; Mx, maxilla; Lb, labium; T1, T2, T3, first, second, and third thoracic leg, respectively.) The dorsal surface of the abdomen of the same embryo is shown illustrating Dll expression in the original bacteriocytes (white arrow) and in a more posterior population of nuclei or cells (blue arrow). Germ cells (gc) are labeled. (B) Dll expression is first observed in the new bacteriocyte nuclei at stage 13. (C) By stage 15, many of the new bacteriocytes have migrated to and begun intercalating between the original bacteriocytes. (D) By stage 16, all of the new bacteriocytes have intercalated between the original bacteriocytes. (E) The migration of the new bacteriocytes is seen in a confocal section of an undissected stage 14 embryo. Embryos in (A)–(D) are oriented with the anterior of the germband towards the left. Bacteriocytes Are Specified and Maintained When the Bacteria Have Been Experimentally Removed The observations described in the first section suggest that the initial specification of the bacteriocyte may occur independently of B. aphidicola . We tested this idea by eliminating B. aphidicola from pea aphids by feeding aphids on an artificial diet containing antibiotics. We found that the embryos within these aposymbiotic aphids specify the bacteriocyte cell fate, as revealed by Dll expression, and maintain the bacteriocyte cell fate in the absence of bacteria ( Figure 3 ). In addition, we have observed that the number of bacteriocytes in aposymbiotic embryos increases precisely as described for symbiotic embryos, including the second wave of bacteriocytes ( Figure 3 F; data not shown). Figure 3 Elimination of B. aphidicola by Treatment with Antibiotics Has No Effect on the Determination and Maintenance of the Bacteriocyte Cell Fate in A. pisum (A–C) Confocal micrographs of control embryos stained with anti-Dll antibody (red) show expression of Dll, as described in Figure 1 . Enlarged views of the bacteria within the broken white boxes in each embryo are shown in (A′)–(C′). (D–F) Embryos within aposymbiotic aphids at comparable stages as the controls in (A)–(C) express Dll in bacteriocyte nuclei. No bacteria are observed within these embryos, as seen in the enlarged views of (D′)–(F′). The Two-Step Determination of Bacteriocytes Is Evolutionarily Conserved The two-step determination of bacteriocytes described in the previous sections appears to be a conserved feature of the aphids. Using the anti-Dll antibody, we examined development of the bacteriocytes in two species of aphids that diverged from A. pisum (subfamily Aphidinae) approximately 80–150 million years ago ( von Dohlen and Moran 2000 ): Pemphigus spyrothecae (Eriosomatinae) and Tuberaphis styraci (Hormaphidinae) (discussed below). In both cases, Dll is expressed in a small number of bacteriocyte nuclei of the blastoderm-stage embryo and additional Dll-expressing cells are recruited later. In P. spyrothecae, one or two nuclei are originally determined as bacteriocytes, as suggested by Lampel (1958 ) ( Figure 4 A). These nuclei become highly polyploid prior to bacterial invasion and do not divide ( Figure 4 B and 4C). A second population of bacteriocytes is determined at approximately stage 14 ( Figure 4 D). These surround the original bacteriocyte ( Figure 4 E) and appear to divide the bacteria into independent bacteriocytes. Figure 4 Expression of Dll in Bacteriocytes and the Pattern of Bacteriocyte Development Are Conserved in Parthenogenetic Females of P. spyrothecae Confocal micrographs of P. spyrothecae parthenogenetic embryos stained with anti-Dll antibody (red). (A) Dll is first detected in stage 6 embryos in one or two nuclei posterior to the cellular blastoderm (arrow). (B) By stage 8, the bacteria have been transferred to and entirely fill the embryo (red arrowhead). The Dll-expressing nuclei (arrow) have become highly polyploid. (C and D) At stage 12, only the original bacteriocyte nuclei are observed expressing Dll (white arrow), but by stage 14 (D) additional nuclei (blue arrow) closely apposed to the dorsal germband express Dll. (E) By stage 15, these new nuclei surround the original bacteriocyte, and at later stages the bacteria are divided into individual cells. Bacteriocytes Develop in Aphids in Which the Bacteria Have Been Replaced with Extracellular Eukaryotic Symbionts B. aphidicola has been lost in the lineage leading to T. styraci and has been replaced by a yeast-like symbiont ( Buchner 1965 ; Fukatsu and Ishikawa 1992a ; Fukatsu et al. 1994 ). These symbionts live in the hemolymph and occasionally invade cells of the fat body ( Buchner 1965 ). Previous studies have therefore claimed that these species lack bacteriocytes ( Buchner 1965 ; Fukatsu and Ishikawa 1992a ). We found that these aphids contain one or two nuclei in the posterior of the blastoderm embryo that express Dll ( Figure 5 A). These nuclei divide once or twice and then become polyploid. At approximately stage 14, we observed a second population of Dll-expressing cells that migrate to the original Dll-expressing cells ( Figure 5 B). Therefore, T. styraci appears to retain the bacteriocyte cell fate although these cells do not apparently house the novel symbionts. Figure 5 Bacteriocytes Are Retained in One Species That Has Evolutionarily Lost Bacteria, but Not in Males of Another Species That Do Not Inherit Bacteria (A and B) Confocal micrographs of embryos of T. styraci stained with anti-Dll antibody (red). In T. styraci , in which B. aphidicola has been evolutionarily lost ( Fukatsu and Ishikawa 1992a ), embryos still contain nuclei that express Dll in the correct time and place to be bacteriocyte nuclei. (A) Dll expression is first detected in posterior nuclei at blastoderm at approximately stage 6 (arrow). (B) By stage 14, the original nuclei have divided once or twice and become polyploid (original bacteriocytes), and new cells begin to express Dll (new bacteriocytes; blue arrow) and migrate towards the original bacteriocytes. (C–F) Confocal micrographs of embryos of P. spyrothecae stained with anti-Dll antibody (red). (C) Stage 16 male embryos of P. spyrothecae do not contain B. aphidicola, and no Dll-expressing cells are observed in the expected location for bacteriocytes. We believe that the cells in this location are sperm (marked with an asterisk). Sexual female embryos within the same ovary do contain Dll-expressing original and new bacteriocyte nuclei (white and blue arrows, respectively). (D and E) Transient expression of Dll in putative bacteriocytes is observed in stage 7 male embryos (arrow in male embryo of [D]), but this expression does not persist into stage 10 male embryos (E), where no Dll-expressing nuclei are observed. By contrast, stage 6 female embryos (D) contain polyploid Dll-expressing nuclei (arrow in female embryo of [D]). The sex of each embryo could be determined because males develop synchronously and earlier than females ( Lampel 1958 , 1968). (F) In stage 14 male embryos, we observe transient Dll expression in nuclei (blue arrow) adjacent to the germ cells (gc) in the correct location to be the second wave of bacteriocyte nuclei. This Dll expression does not persist (see stage 16 male in [C]), and the fate of the cells is unknown. The Bacteriocyte Fate Has Been Lost in Male Eriosomatine Aphids That Do Not Harbor B. aphidicola Males of some species in the subfamily Eriosomatinae do not harbor B. aphidicola ( Toth 1933 ; Buchner 1965 ; Fukatsu and Ishikawa 1992b ). As these males lack mouthparts and do not feed, B. aphidicola are not required for growth. In addition, inheritance of B. aphidicola is strictly maternal, so males do not require symbionts for passage to their offspring. We did not detect any putative bacteriocyte cells that persist in male embryos of P. spyrothecae , although we observed them in female sexual embryos ( Figure 5 C and 5D). In stage 7 male embryos, we detected weak Dll expression in a few nuclei ( Figure 5 D), although this expression does not persist ( Figure 5 E). In addition, in stage 14 males we detected weak expression in cells that are in the correct location to be the second population of bacteriocytes ( Figure 5 F), but this expression also does not persist (see the stage 16 male in Figure 5 C). Discussion The aphid bacteriocyte expresses three transcription factors: Dll, En, and Ubx or Abd-A. These transcription factors play important roles during later stages of development in insects. For example, Dll is required for limb development, En is required for segmentation, and Ubx and Abd-A are the products of Hox genes, required for patterning thoracic and abdominal body regions ( Kuner et al. 1985 ; Hidalgo 1996 ; Weatherbee et al. 1999 ; Panganiban and Rubenstein 2002 ). We know of no other cases in other insects in which any of these three transcription factors are expressed at such early stages of development as we have observed in the bacteriocytes (approximately cellular blastoderm). We cannot exclude the possibility that bacteriocytes evolved from a cell type that expressed this combination of transcription factors, but there are no obvious candidate cell types, such as fat cells or vitellophages, in other insects that fulfill this criterion. We do not yet know whether these genes are involved in the determination of bacteriocytes. However, bacteriocytes may require a novel combination of transcription factors to regulate the symbiont population and to mediate transovarial transmission. We have demonstrated that two cell populations express Dll in spatially and temporally distinct patterns before incorporating bacteria. Our observation of the initial putative bacteriocytes in the blastoderm embryo is consistent with observations of earlier researchers, who suggested—based on morphological observations—that the nuclei located at the posterior of the embryo constitute the future bacteriocyte nuclei ( Lampel 1958 ; Buchner 1965 ). In addition, we have found that the second population of presumptive bacteriocytes appears to migrate across the germband to the original bacteriocytes, where they take up bacteria. This is an unusual process that has not to our knowledge been described previously. In contrast, earlier studies indicated that bacteriocyte proliferation occurs solely by cell division or by budding of small nuclei from an existing polyploid bacteriocyte nucleus (e.g., Lampel 1958 ). We have not yet performed experiments that would allow us to positively identify the embryonic origin of this second population of cells. Based on their position—posterior to the germ cells and dorsal—these cells may be the descendants of the nuclei of the central syncytium (syncytial nuclei in the center of the blastoderm embryo) (see Miura et al. 2003 ). Our results suggest that B. aphidicola is required for neither bacteriocyte induction nor for the origin and migration of the second population of bacteriocytes. While bacteria do not seem to be required for the developmental maintenance of this cell type, the bacteria may provide signals to the cells that are involved in mediating the symbiosis at the physiological level. Nonetheless, the absence of an effect of the bacteria on bacteriocyte development contrasts with other symbioses where the bacteria induce specific developmental changes in host tissues ( McFall-Ngai and Ruby 1991 ). We investigated two cases in which B. aphidicola have been lost during the evolution of aphids. Given our observations that bacteria are not required for the developmental maintenance of bacteriocytes, it is possible that the bacteriocyte cell type might be lost if it had no other function. This does not appear to be the case. In the lineage including T. styraci , B. aphidicola was lost and a eukaryotic “yeast-like” symbiont has been gained ( Buchner 1965 ; Fukatsu and Ishikawa 1992a ; Fukatsu et al. 1994 ). Buchner (1965 ) suggested that the bacteriocytes of Cerataphis freycinetiae , another species in the same lineage, are originally specified, become polyploid and then degenerate. We found Dll-expressing putative bacteriocyte nuclei to be specified and maintained over extensive periods of embryonic development in T. styraci . Buchner documented considerable variation in the details of symbiotic transmission and bacteriocyte development, and it is possible that bacteriocyte development proceeds along different paths in these two species. We also examined the development of bacteriocytes in males of P. spyrothecae . The males do not have bacteria and we have observed, consistent with observations of earlier researchers ( Lampel 1958 ; Buchner 1965 ), that bacteriocytes are not maintained in this morph. We found that bacteriocytes initially express Dll, but this expression is not maintained, which is consistent with Lampel's and Buchner's observations that the original bacteriocytes appear to be present but are not maintained. In addition, we found that the second wave of bacteriocytes is also initiated, as shown by brief, weak Dll expression. It is not clear whether these cells are subsequently respecified or are eliminated. B. aphidicola are derived from free-living bacteria ( Baumann et al. 2000 ), and both the bacteriocyte and the symbiont must have evolved mechanisms for integrating the bacteria into the workings of the cell. The aphid– Buchnera symbiosis represents a particularly intimate form of symbiosis. In some symbioses, the bacteria reside both intra- and intercellularly and actively invade the host cell ( Dale et al. 2001 ). In contrast, B. aphidicola always exist either within host cells, within a membrane-bound maternal package, or with host nuclei in a syncytium. This advanced stage of symbiosis is similar to the presumptive early stages of plastid evolution. Materials and Methods Aphid rearing and collecting. Colonies of A. pisum were reared on broad bean ( Vicia faba ) or alfalfa ( Medicago sativa ) ( Miura et al. 2003 ). P. spyrothecae were collected from galls on Populus nigra var . italica in Cambridge and London, United Kingdom. T. styraci were collected from galls on Styrax obassia in Gunma Prefecture, Japan. Asexual aphid embryos of various developmental stages were dissected and fixed as described previously ( Miura et al. 2003 ). Antibody staining. A limited number of antibodies recognize the homologues of their target proteins across insects. We tested five of these antibodies in aphids and found that three stained the bacteriocyte nuclei: rabbit anti-Dll ( Panganiban et al. 1994 ), mouse anti-En (4D9) ( Patel et al. 1989 ), and mouse anti-Ubx /Abd-A (FP.6.87) ( Kelsh et al. 1994 ), kindly provided by G. Panganiban, N. Patel, and R. White, respectively. Two antibodies, rabbit anti-Vasa (FBgn0000606 ) (a gift of C.-C. Chang [ Chang et al. 2002 ]) and mouse anti-Even-skipped (Eve) (2B8) (FBgn0003970) ( Patel et al. 1994 ) did not stain bacteriocytes, but, as expected, anti-Vasa stained the germ cells ( Chang et al. 2002 ) and anti-Eve stained cells in the nervous system ( Patel et al. 1989 ). Secondary antibodies conjugated with fluorescent moieties (Jackson ImmunoResearch, West Grove, Pennsylvania, United States) were tested for cross-reactivity to aphid cells by staining embryos with secondary antibodies alone. No cross-reactivity was detected. We further tested whether an additional mouse antibody (mouse anti-digoxigenin; Jackson ImmunoResearch) cross-reacted with bacteriocyte nuclei, and it did not stain any parts of the aphid embryo. In addition, the anti-Dll, anti-En, and anti-Ubx/Abd-A all stained the expected cells ( Patel et al. 1989 ; Kelsh et al. 1994 ; Panganiban et al. 1994 ; Miura et al. 2003 ) in other regions of the embryos, indicating that the antibodies were working as expected. Cell outlines were visualized by staining for F-actin with fluorescein-conjugated phalloidin. Embryos were stained using standard protocols ( Miura et al. 2003 ) and visualized on Leica SP and Zeiss confocal microscopes. Antibiotic treatment. In the pea aphid, Buchnera can be eliminated by treating animals with antibiotics ( Wilkinson 1998 ). First- or second-instar aphids were fed on an artificial diet containing 50 μg/ml of the antibiotic rifampicin for 72 h (e.g., Caillaud and Rahbé 1999 ). Aphids were then transferred to leaves of Medicago arborea in Petri-dish cultures ( Miura et al. 2003 ). Control aphids were treated identically, except that the antibiotic was omitted from the artificial diet. Embryos that were less than 4 d old ( Miura et al. 2003 ) were dissected from aposymbiotic aphids within 2–4 d after the end of the antibiotic treatment and stained with anti-Dll and FP6.87 antibodies and fluorescein-conjugated phalloidin. The absence of bacteria in aposymbiotic aphids was confirmed by observation with a confocal microscope (see Figure 3 ). Supporting Information Accession Numbers The FlyBase accession numbers discussed in this paper are FBgn0000014, FBgn0000157, FBgn0000577, FBgn0000606, FBgn0003944, and FBgn0003970.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC212699.xml

555945

Mice have a transcribed L-threonine aldolase/GLY1 gene, but the human GLY1 gene is a non-processed pseudogene

Background There are three pathways of L-threonine catabolism. The enzyme L-threonine aldolase (TA) has been shown to catalyse the conversion of L-threonine to yield glycine and acetaldehyde in bacteria, fungi and plants. Low levels of TA enzymatic activity have been found in vertebrates. It has been suggested that any detectable activity is due to serine hydroxymethyltransferase and that mammals lack a genuine threonine aldolase. Results The 7-exon murine L-threonine aldolase gene (GLY1) is located on chromosome 11, spanning 5.6 kb. The cDNA encodes a 400-residue protein. The protein has 81% similarity with the bacterium Thermotoga maritima TA. Almost all known functional residues are conserved between the two proteins including Lys242 that forms a Schiff-base with the cofactor, pyridoxal-5'-phosphate. The human TA gene is located at 17q25. It contains two single nucleotide deletions, in exons 4 and 7, which cause frame-shifts and a premature in-frame stop codon towards the carboxy-terminal. Expression of human TA mRNA was undetectable by RT-PCR. In mice, TA mRNA was found at low levels in a range of adult tissues, being highest in prostate, heart and liver. In contrast, serine/threonine dehydratase, another enzyme that catabolises L-threonine, is expressed very highly only in the liver. Serine dehydratase-like 1, also was most abundant in the liver. In whole mouse embryos TA mRNA expression was low prior to E-15 increasing more than four-fold by E-17. Conclusion Mice, the western-clawed frog and the zebrafish have transcribed threonine aldolase/GLY1 genes, but the human homolog is a non-transcribed pseudogene. Serine dehydratase-like 1 is a putative L-threonine catabolising enzyme.

Background Elucidating the factors involved in threonine homoeostasis is important for the development of nutritional strategies in human clinical diets for treating patients suffering from wasting diseases. In farmed animals the regulation of livestock feed is required to ensure optimal growth and to reduce nitrogen excretion which poses environmental disposal problems. Threonine is required for protein synthesis and the removal of excess threonine by oxidation is needed to prevent its accumulation both intracellularly and in the circulation. The rate of catabolism of many amino acids, including threonine, increases when dietary protein exceeds the body's requirements. Gluconeogenesis occurs mainly in the liver where it helps maintain blood glucose homeostasis in mammals. During starvation amino acid catabolism increases to support gluconeogenesis. Glucocorticoids and glucagon hormones are known to up regulate and insulin down regulate the gene expression of many amino acid-catabolising enzymes [ 1 ]. There are three L-threonine (L-alpha-amino-beta-hydroxybutyric acid) degradation pathways in living organisms; via L-threonine aldolase (L-TA)(EC 4.1.2.5)(gene abbreviation GLY1), via L-serine/threonine dehydratase (SDH)(EC 4.2.1.16)(gene abbreviation SDS)(in bacteria also called L-threonine deaminase) and via L-threonine 3-dehydrogenase (EC 1.1.1.103)(TDH) [ 2 - 5 ]. L-threonine is broken down by; L-TA to yield glycine and acetaldehyde, by SDH to yield NH 4 + and 2-ketobutyrate and TDH to yield 2-amino-3-ketobutyrate. The subsequent reaction between 2-amino-3-ketobutyrate and coenzyme A to form glycine and acetyl-CoA is catalysed by 2-amino-3-ketobutyrate coenzyme A ligase (KBL)(EC 2.3.1.29), also called glycine acetyltransferase (gene abbreviation GCAT). Together with the cofactor, pyridoxal-5'-phosphate (PLP), SDH uses threonine and serine as substrates to generate glycine which is used in gluconeogenesis. Serine dehydratase-like 1 gene (SDH1) is a second SDH gene found in vertebrates, but has yet to be characterised. I suggest that it is also a putative L-threonine catabolising enzyme. Vitamin B 6 -dependant enzymes can be grouped according to their fold type. L-TA belongs to fold type I. L-TA enzymes are unrelated to D-TA enzymes which possess type III folds [ 6 ]. In vertebrates, the TA enzyme has not been purified by protein fractionation, only assayed in homogenised tissue fractions and isolated hepatocytes. In vertebrates most L-threonine degradation occurs via the enzymatic activities of serine/threonine dehydratase and threonine dehydrogenase. However, the presence of threonine aldolase enzymatic activity has been demonstrate in rat liver extracts [ 7 - 14 ]. Threonine aldolase contributes 1–3% of total threonine degradation under a variety of nutritional states in both rat and quail [ 4 , 15 ]. L-TAs from a number of species of bacteria and fungi have been isolated and characterized (reviewed in [ 16 ]). In the yeast, Saccharomyces cerevisiae , the glycine synthase-1 gene, GLY1 was identified as threonine aldolase [ 17 , 18 ]. Previously, gene ablation studies had shown that the GLY1 pathway is a major source of glycine [ 19 ]. But it only plays a minor role in Candida albicans [ 20 ]. In a number of bacteria species such as Escherichia coli , Aeromonas jandaei , Pseudomonas and Thermatoga maritima the GLY1 gene has been cloned and their enzymatic activity characterised [ 21 - 24 ]. In thale cress, Arabidopsis thaliana , there are two threonine aldolase genes ( THA1 and THA2 ). THA1 has been shown to play a role in seed nutritional quality [ 25 ]. Putative GLY1 genes have been also identified in nematodes and flies [ 21 ]. Recently, the X-ray crystal structures of L-threonine aldolase from the bacteria Thermotoga maritima have been determined as the apo-enzyme, bound to L- allo -threonine and to glycine [ 21 ]. These GLY1/threonine aldolases are distinct from the serine hydroxymethyltransferases (EC 2.1.2.1)(SHMT). However, some SHMT also possess some threonine aldolase enzymatic activity. SHMT from E. coli and the yogurt bacterium, Streptococcus thermophilus , have TA activity [ 26 , 27 ]. SHMT isolated from rabbit liver has been shown to possess weak TA activity [ 28 ]. Consequently, it has been thought that the minor threonine aldolase activity in liver extracts was due solely to SHMT, and that mammals lack a true threonine aldolase, but this has been questioned [ 29 ]. Here I report that TA genes are present in vertebrates. Results Analysis of murine L-threonine aldolase cdnas I conducted a search of the GenBank database for a putative mouse L-threonine aldolase gene using the sequence of the E. coli TA protein [ 22 ]. PCR primers were designed to the 5' and 3' ends of EST sequences that matched the genomic DNA sequence of the putative L-threonine aldolase gene. These primers were used to amplify the cDNA from murine liver RNA by RT-PCR. The amplicons were electrophoresised on an agarose gel. Two bands of similar intensity were obtained. Both bands were excised from the gel, cloned and sequenced. The upper band encoded an 1855 bp murine L-threonine aldolase cDNA sequence. It has a 127 bp 5'UTR containing an in-frame stop codon, an ORF which encodes a 400 residue protein and has an ATTAAA polyadenylation signal at 1822–1827 (GenBank accession No. AY219871)(Fig. 1 ). The predicted protein has a 43,496 Da molecular mass and an isoelectric point 6.73. The lower band encoded a second cDNA clone that was identical to the first clone except that it skipped exon 3. On translation, this results in a frame shift in the ORF that would encode a severely truncated protein of 124 residues that would not be expected to have any enzymatic activity (GenBank accession No. AY219872). Both cDNA sequences matched the mouse genomic DNA sequence. The mouse L-threonine aldolase/Gly1 gene is located on chromosome 11 band E2 (clone RP23-268N22, EMBL accession No. AL591433, Sanger Institute, UK) towards the telomere, between the baculoviral IAP repeat-containing 5 (Birc5) and suppressor of cytokine signalling 3 (Socs3) genes. The L-threonine aldolase gene spans 5.6 kb, consisting of 7 exons (Fig. 2 ). All splice donor/acceptor sites have consensus GT/AG dinucleotides. There is a 507 bp CpG island (66% GC) encompassing exon 1. Such CpG islands are generally associated with active housekeeping genes [ 30 ]. The predicted start of transcription, CCAT, on the genomic DNA is just 2 bp upstream of the cDNA sequence suggesting that the clone is almost full-length. Figure 1 The cDNA sequence and translation of murine L-threonine aldolase. A potential polyadenylation signals (aataaa at 1822–1827) is shown in bold and underlined with the polyadenylation sites indicated by a. An * indicates the tga stop codon. The underlined nucleotide pairs indicate the positions of the exon/exon boundaries. The in-frame stop codon in the 5'UTR is indicated, tag , (coloured red). Figure 2 Chromosomal localisation and the gene structure of murine L-threonine aldolase gene. (A) The gene is located on chromosome 11, band E2 (accession No. AL591433, the Sanger Institute, UK). (B) The 7-exon gene spans 5.6 kb. There is a CpG island spanning the 5' untranslated exon. The ORF is indicated by closed boxes. The sizes, in bp, of the exons and introns are indicated. Predicted secondary structure of the murine threonine aldolase protein A comparison of the predicted secondary structure of the murine TA protein with the known secondary structure of T. maritima [ 21 ] is shown (Fig. 3 ). The proteins have 44% identity and 81% similarity and are similar throughout their length. Overall there is good correspondence between the position of the predicted α-helices and β-sheets in the murine protein with those determined from the crystal structure of T. maritima . However, the mouse protein has an additional putative amino-terminal mitochondrial import leader peptide. Given long evolutionary distance between mouse and bacteria this high degree of homology strongly suggests that this murine protein is also a threonine aldolase. Most functional residues are conserved between the two proteins. By homology with the T. maritima protein, Lys242 is expected to form a Schiff-base with the cofactor, pyridoxal-5'-phosphate (PLP), with Asp211 and Arg214 expected to interact with PLP. Those residues that contact the ligands L- allo -threonine and glycine, Ser45, His123, Tyr127, Arg214 and Arg372, are conserved. T. maritima His125 from the second subunit is predicted to bind the hydroxyl group of L-threonine. This residue is homologous to murine Tyr168, a conservative substitution since both residues are polar and aromatic. In other TA proteins from diverse phyla this residue is mainly histidine, but in rice it is a tyrosine also. At the catalytic dimer interface electrostatic interactions occur among the side chains of Arg44-Glu71, Thr47-Asp66 and Arg274-Ser241. These residues are conserved. Residues involved in ion coordination are also conserved with Ala246, Thr49 and Ser241 contacting Ca 2+ with Arg112 contacting a chloride ion. In the Arabidopsis THA1 enzyme a Gly114 to Arg mutation, located between two beta-sheets, results in loss of enzymatic activity [ 25 ]. This residue, Gly149 in mouse, is conserved in all four vertebrate TA enzymes. Figure 3 Comparison of the predicted secondary structure of the murine threonine aldolase protein with that of the crystal structure of threonine aldolase from the bacteria Thermotoga maritima . The labels are: mouse predicted secondary structure, MmTA_PSSM; mouse protein sequence, MmTA_seq; T. maritima protein sequence, TmTA_seq and T. maritima secondary structure 1JG8_ss; alpha-helix, H, highlighted in light blue; beta-sheet, E, highlighted in yellow; c = turn, coil or loop. Identical residues in both proteins are illustrated with a "+" indicating positive equivalence and a "-" a negative equivalence. The PLP-binding lysyl residues are indicated with a pink asterisk and those residues that interact with PLP are indicated with a black asterisk. Those residues that contact the substrates, L-threonine and L- allo -threonine, and the product, glycine, are indicated with a green hash. Residues involved in electrostatic interactions in the catalytic dimer interface are indicated with an ampersand. Residues making contact with calcium ions are indicated with a plus sign and those contacting a chloride ion with a negative sign. Sequence homology to other vertebrate threonine aldolase proteins Database searches revealed the presence of other L-threonine aldolase genes in other vertebrates (Fig. 4 ). There is a single seven exon gene in the Japanese puffer fish genome ( Takifugu rubripes )(accession No. BK005561). The exon/exon boundaries on the proteins are highly conserved between mouse and Japanese puffer fish with only one being displaced slightly. The Japanese puffer fish gene encodes a 421-residue protein that has 46% identity and 74% similarity to the murine protein. Similar L-threonine aldolase cDNAs for the western-clawed frog and the zebrafish were identified (accession Nos. BK005562 and AAH72718 respectively). The proteins are of similar lengths with the functional residues identified in T. maritima being well conserved. Homology extends throughout their lengths, apart from the amino-terminal regions. Despite their low sequence identity in the amino-terminal region all four proteins contain putative mitochondrial import leader peptides, being positively charged. They possess also a predicted cleavage site that would be utilised during their import into mitochondria. After cleavage of the mitochondrial import sequence the mature murine TA enzyme would have a mass 39,778 Da and pI 6.11. Recently, two other vertebrate GLY1 genes have been sequenced. In the dog there is a complete gene (GenBank accession number NW_140385) that would encode a 391-residue protein with 82% identity and 94% similarity to murine TA. Three overlapping unassigned genomic DNA sequences from the freshwater puffer fish, Tetraodon nigroviridis , would encode a gene encoding a 431-residue protein with 44% identity and 73% similarity to murine TA. Additionally, homologous coding ESTs from mammals (rat, pig and cow), birds (chicken), amphibians (African clawed frog) and fish (little skate, rainbow trout, Atlantic salmon, channel catfish and Japanese medaka) were identified, indicating that L-threonine aldolase expression in vertebrates is widespread. Figure 4 Comparison of vertebrate L-threonine aldolase protein sequences. The L-threonine aldolase sequences are: mouse, Mus musculus , MmTA; pufferfish, Takifugu rubripes , TrTA; (BK005561); western clawed frog, Xenopus tropicalis , XtTA (BK005562) and zebrafish, Danio rerio , DrTA (AAH72718). Predicted cleavage sites during import into mitochondria are indicated by a backwards slash (\). Where known, the locations of the exon/exon boundaries are shown on the translated protein as underlined residues. Stop codons are indicated by a hash. Conserved residues are indicated by a (*), strongly similar residues by a (:) and weakly similar residues by a (.). Residues are colour coded: basic, DE, red; acidic, KR, pink; polar, CGHNQSTY, green and hydrophobic, AFILMPVW, red. Human GLY1/threonine aldolase gene is a pseudogene A database search identified 17q25 as the location for the human GLY1 gene. All exon/intron boundaries found in the murine threonine aldolase gene are conserved in man. The mouse to human conserved synteny map shows that both GLY1 genes have the synaptogyrin 2 (SYNGR2), baculoviral IAP repeat-containing 5 (BIRC5) and soluble thymidine kinase 1 (TK1) genes as near neighbours. Starting with human liver RNA, I was neither able to amplify any threonine aldolase transcripts using a variety of 5' and 3' RACE methods nor to detect any transcripts in a wide range of tissue and cell line cDNAs by RT-PCR. A search of the human EST database identified five potential EST transcripts scattered throughout the threonine aldolase gene, but they lack supporting evidence that they are truly transcribed sequences, being unspliced singletons. There is a potential polyadenylation signal site in the human threonine aldolase gene with good homology to the murine site. However, corresponding 3'UTR ESTs and SAGE tags are conspicuously absent from the databases leading to the conclusion that the GLY1 threonine aldolase gene is not transcribed in man. If the human GLY1 threonine aldolase gene were transcribed there are two single nucleotide deletions that would cause frame-shifts. They are the equivalent of murine nucleotide 55 in exon 4 (Fig. 5A ) and the equivalent of murine nucleotide 198 in exon 7 (Fig. 5B ). Both these deletions are found in genomic DNA clones from two individuals showing that these deletions are not sequencing errors (accession Nos. AC032035 and AC010532, MIT Center for Genome Research, USA and DOE Joint Genome Institute, USA, respectively). The presence of the frame-shift in exon 4 would create a truncated ORF of 144 residues that does not include the PLP-binding lysine residue, consequently the protein would not be functional (Fig. 5C ). Also there is a premature in-frame stop codon towards the carboxy-terminal. Even if the frame-shifts in the human GLY1 gene were not present then the translated human TA protein would not function due to the mutation of four important residues. These four residues have remained conserved during evolution since the last common ancestor of the bacteria, T. maritima , and vertebrates. One residue that would be expected to interact with the PLP ligand, murine Arg214, would be mutated to Gln in man. Murine residue Arg372 that would be expected to interact with threonine is mutated to Ala. The side chains of two residues that form electrostatic interactions at the catalytic dimer interface are also mutated, murine Thr47 to Lys, and murine Arg274 to His. If the frame-shifts were not present, the mouse and human proteins would have 66% identity and 85% similarity. Likewise, the chimpanzee threonine aldolase gene is a pseudogene possessing the same frame-shifts as the human gene. Additionally, it has lost the splice donor site in exon 1 and, by comparison with the mouse gene, has a 64 bp deletion in exon 7 (Fig. 5C ). Figure 5 Comparison of the mouse threonine aldolase cDNA and ORF with the human and chimpanzee genes. (A) There is a cytidine deletion in exon 4 of the human threonine aldolase gene resulting in a frame-shift. (B) There is a guanosine deletion in exon 7 of the human threonine aldolase gene resulting in a frame-shift. (C) Comparison of the mouse protein with a translation of human and chimpanzee genes shows that the presence of the frame-shift in exon 4 creates a truncated ORF of 144 residues that does not include the PLP-binding lysine residue (pink K); consequently the protein would be non-functional. All exon/exon boundaries are conserved and shown on the translated protein as black underlined residues except that of chimpanzee exon 1 which is shown as a red underlined residue. Stop codons are indicated by red hashes. ORF residues generated by frame-shifts are shown in lower case. Conserved residues are indicated by a (*), strongly similar residues by a (:) and weakly similar residues by a (.). Abbreviations: mouse, Mus musculus , Mm; Homo sapiens , Hs; Pt, Pan troglodytes ; exon 4, Ex4; exon 7, Ex7; translations in frame 1, F1; frame 2, F2 and frame 3, F3. Homology of serine/threonine dehydratase and serine dehydratase like-1 proteins in vertebrates The sequences of murine SDH and SDH-1 cloned cDNAs matched those of reference sequences (accession numbers NM_145565 and NM_133902 respectively). In mammals, these two genes are adjacent, being arranged in a 5' to 5' orientation. Database searches identified both the SDH and SDH1 genes in man, rat, freshwater puffer fish and the Western and African clawed frogs. But in the chicken only the SDH1 gene is present since SDH is absent from the draft genome and all expressed sequences. A comparison of vertebrate SDH and SDH1 proteins with the crystal structure of rat SDH [ 31 ] suggests that SDH1 is also a serine/threonine dehydratase because residues with important functions are conserved (Fig. 6 ). By homology, Lys48 of murine SDH1 is the PLP binding residue forming a Schiff base and the amino acid sequence around Lys48 SxKIRG is well-conserved in other SDHs from vertebrates, plants, yeasts and bacteria [ 32 - 35 ]. Two other conserved amino acid sequences, S(A/G)GNA and GGGG(L/M) and Cys309 (murine SDH1 numbering) form hydrogen bonds with PLP. In SDH1 a potassium ion near the active site would be expected to be coordinated by six oxygen atoms, five of which are from conserved residues; Gly174, Glu200, Ala204, Ser206, Leu229, but Ala231 replaces Val225 of rat SDH. Figure 6 Comparison of vertebrate SDH and SDH1 proteins. The species are: house mouse, Mus musculus , Mm; Norway rat, Rattus norvegicus , Rn; human, Homo sapiens , Hs; chicken, Gallus gallus , Gg; western clawed frog, Xenopus tropicalis , Xt; African clawed frog, Xenopus laevis , Xl; freshwater puffer fish and Tetraodon nigroviridis , Tn. By comparison with the crystal structure of rat SDH, important conserved residues found in SDH enzymes are conserved also in SDH1 and are shown above the sequence alignment. The amino acid sequence SFKIRG (blue), around the PLP binding Lys41 (pink), is conserved in SDH and SDH1. Two other conserved amino acid sequences, SAGNA (brown) and GGGGL (purple), form hydrogen bonds with PLP, as does Cys303 (green). A potassium ion near the active site is coordinated by six oxygen atoms from Gly168, Ala198, Leu223, Val225, Glu194, and Ser200 (orange). Conserved residues are indicated by a (*), strongly similar residues by a (:) and weakly similar residues by a (.). Residues are colour coded: basic, DE, red; acidic, KR, pink; polar, CGHNQSTY, green and hydrophobic, AFILMPVW, red. Exon/exon boundaries determined from genomic DNA are indicated on the proteins by black underlining. Unknown sequences are indicated by xx. Expression of threonine aldolase, serine/threonine dehydratase and serine dehydratase like-1 mRNA in mouse tissues To identify those tissues which are likely to contribute to TA activity in the mouse, the expression of TA mRNA in adult tissues was examined by RT-real time PCR normalised to the expression of the housekeeping genes, β-actin and glyceraldehyde-3-phosphate dehydrogenase (G3PDH). Low levels of TA mRNA were detected in all tissues examined. They varied 20-fold between tissues, being highest in prostate, heart and liver (Fig. 7A ). In contrast, the mRNA levels of SDH, another enzyme that catabolises L-threonine, has a very specific tissue distribution. It is expressed highly in the liver at a level similar to the two housekeeping genes. It is over 300 fold more abundant in liver than heart, the second highest expressing tissue (Fig. 7B ). Low levels of SDH1 mRNA were found also in all tissues. Like SDH, SDH1 was most abundant in the liver with moderate levels being found in testis, heart, kidney and spleen (Fig. 7C ). Figure 7 Expression of TA, SDH and SDH1 mRNA in mouse tissues by real time PCR. The expression levels in each tissue were normalised to that of the housekeeping genes beta-actin and G3PDH. (A) For TA the expression levels in all tissues were standardised to that of prostate, which was taken as 100; (B) and for SDH and SDH1 the expression levels were standardised to that of liver, which was taken as 100. Expression of threonine catabolic enzymes in mouse embryos The mRNA expression of threonine catabolic enzymes was examined by real time PCR in cDNAs derived from whole mouse embryos from days 7, 11, 15 and 17 (Fig. 8 ). Overall, TA, TDH and SDH expression were low prior to E-15, but increased more then four-fold by E-17. KBL expression was low at E-7, but increased earlier than the other enzymes. SDH1 did not change substantially with increasing embryonic age. Figure 8 Expression of TA, SDH, SDH1, TDH and KBL mRNA in whole mouse embryos by real time PCR. Their expression levels in each embryonic stage were normalised to that of the housekeeping genes beta-actin and G3PDH. Each gene was standardised to its expression level at embryo day 17, which was taken as 100. The gene abbreviations are: threonine aldolase, TA; serine/threonine dehydratase, SDH; serine dehydratase like-1, SDH1; threonine dehydrogenase, TDH and 2-amino-3-ketobutyrate coenzyme A ligase, KBL. Discussion In vertebrates, L-threonine is one of the indispensable amino acids. It is obtained from protein in the diet, typically being the second or third limiting amino acid in herbivorous diets. Some of it is utilized in synthesising new protein, but the rest is converted to other amino acids by oxidative catabolism by three different enzymes that are found in most organisms; TDH, TA and SDH. Both the TDH and TA pathways produce glycine. However, the TDH pathway occurs in two steps, requiring KBL as the second step. Using protein homology searches of the mouse genome with the bacterial enzymes has allowed me to identify and clone TDH and KBL cDNAs [ 36 , 37 ]. GLY1/TA genes have been identified previously in bacteria, fungi and plants [ 16 , 21 , 25 ]. Here I describe the first TA cDNA found in vertebrates. The murine TA cDNA encodes a 400-residue protein that is highly similar to that from T. maritima with an expect value of 2e -73 , being clearly distinct from glycine dehydrogenase, the second most closely related protein, with an expect value of 0.002. This remarkable conservation, over billions of years of evolution since the last common ancestor, shows the general importance of these metabolic pathways. However, the presence of some abnormal TA mRNA splicing in mouse, the low levels of mRNA found in mouse tissues, together with the low levels of TA enzymatic activity found in rat liver [ 4 , 15 ] plus the loss of a functional TA gene in humans suggests that TA has reduced importance in mammals. The L-TA enzymes can act on the stereoisomers, L-threonine and L- allo -threonine. These can be divided into three types based on the stereospecificity towards the β-carbon of threonine. Low-specificity L-TA can use both L-threonine and L- allo -threonine as substrates. L-TA only acts on L-threonine and L- allo -TA is specific to L- allo -threonine [ 16 ]. Murine L-TA is likely to be a low-specificity L-TA with a preferences for the allo isomer in a manner similar to the T. maritima enzyme, because Tyr127 (Tyr87 in T. maritima ) in the TA active site is conserved, a residue which appears to be involved in discriminating L-threonine from L- allo -threonine [ 21 ]. All the vertebrate TA proteins have putative amino-terminal mitochondrial import sequences, suggesting that the mitochondrion is its intracellular localisation. In contrast, fractionation studies in the yeast, S. cerevisiae , revealed a cytosolic localisation for TA [ 38 ]. Additionally, the yeast TA protein does not possess an amino-terminal mitochondrial import sequence. In vertebrates, threonine catabolism is mostly confined to the liver when the mass of the organ is taken into consideration. Expression of SDH, TDH and KBL mRNA are highest in liver [ 36 , 37 , 39 ]. However, low levels of murine TA expression were found in a wide range of tissues suggesting a role in housekeeping metabolism in all tissues. Generally, during embryogenesis, expression of threonine catabolic enzymes increased with maturation of the developing liver. Humans have lost two of the three enzymes of threonine catabolism with both GLY1 and TDH [ 40 ] genes being defective, both pathways produce glycine from threonine. In man, the loss of a functional GLY1 gene appears to be a more ancient event than the loss of TDH because GLY1 genes in both man and chimpanzees have a number of frame-shifts and mutations of functional amino acid residues, whereas the mutated exon 6 splice-acceptor site in human TDH is intact in chimpanzees (data not shown). This suggests that GLY1 has been lost prior to, and TDH after, the divergence of man and chimpanzees, about 6–8 million years ago. Consequently, humans may not be as metabolically well equipped as other species to cope with diets high in threonine/protein. Perhaps a reduction in the rate of threonine catabolism in man's ancestors would have conferred a selective advantage on those individuals with these defective genes under conditions of protein starvation. Although humans have lost both the glycinergic pathways of threonine catabolism their gut microbial flora will have both TA and TDH enzymes, therefore, gut microbial flora may make significant contributions to human threonine catabolism. With the loss of TA and TDH genes in humans this leaves serine/threonine dehydratase as our only major threonine catabolic enzyme. However, vertebrates also have a second SDH gene, called SDH-like-1 which, by homology, is likely to function also as a serine/threonine dehydratase since all the residues that bind the PLP co-factor are conserved between the two proteins. Only the SDH1 gene is present in the chicken, therefore the serine dehydratase activity found in chick livers [ 41 ] must be due to SDH1. The SHMT enzymes are members of the α-class of pyridoxal phosphate enzymes, catalyzing the reversible interconversion of serine and glycine. Mammals have two SHMT genes. One encodes a cytosolic and the other a mitochondrial enzyme. Purified SHMT enzyme from rat liver possesses some threonine aldolase activity [ 28 ] and both SHMT genes may also contribute to threonine catabolism in vertebrates. With the identification of murine TA and SDH-1 mRNA the way is open to study their enzymatic activity in vitro and relative contribution to threonine catabolism under different physiological states in vivo . Changes in TA and SDH-1 mRNA expression in response to diet have yet to be examined, but rats fed a high protein diet or fasted showed an increase in TA enzymatic activity [ 42 ]. In contrast, quails and rats fasted or on threonine enriched diets did not show any statistically significant changes in TA enzymatic activity [ 15 ]. Conclusion I have shown that GLY1/TA genes are present in vertebrates. TA genes and enzymatic activities have been previously isolated from bacteria, fungi and plants. These enzymes are distinct from the serine hydroxymethyltransferases. The mouse GLY1 gene is located on chromosome 11, band E2 and the 1855 bp cDNA from this gene encodes a 400-residue threonine aldolase. The presence of a positively-charged amino-terminal import leader peptide sequence in mammalian, amphibian and fish TA proteins, that are not present in bacterial proteins, suggests that the vertebrate TA enzymes are mitochondrial. Man and chimpanzees have lost a functional GLY1 gene. Vertebrates also have a second SDH gene, SDH1, that by homology to the crystal structure of SDH may function as a threonine dehydratase and contribute to threonine catabolism. Methods Molecular cloning of murine L-threonine aldolase Total RNA was extracted from mouse liver using guanidine thiocyanate and treated with DNase-I to remove any contaminating genomic DNA (SV total RNA isolation system, Promega, UK). Total RNA was reversed transcribed with AMV RNase H- reverse transcriptase (ThermoScript, Life Technologies, UK) at 50°C using an oligo-dT primer. The cDNA was amplified by touchdown PCR using the Advantage cDNA polymerise mix (Clontech, UK) on a Perkin-Elmer 2400 thermocycler. Amplification conditions for the first 10 cycles were 94°C for 5 sec, 72°C less 0.4°C per cycle for 3 min and for the next 20 cycles 94°C for 5 sec, 68°C for 10 sec, 72°C for 3 min per cycle using primers (100 nM) derived from the sequence of the mouse genomic DNA from clone RP23-268N22 (accession number AL591433 forward 5'-ATAGTGCCCCGGGCTTGC-3' and, first reverse 5'-TTTTTTTTTTTTTTTGTGCCTTCAGTATTT-3' and (Amersham-Pharmacia Biotech, UK). PCR amplicons were electrophoresised in a low-melting point agarose gel stained with ethidium bromide. They were excised from the gel. The agarose digested with agarase (Promega, U.K.). These PCR amplicons were cloned into pCR-II-TOPO, a T-A vector (Invitrogen, The Netherlands) and sequenced in both directions using the big dye terminator cycle sequencing ready reaction kit with AmpliTaq DNA polymerase FS on an ABI 373XL Stretch Sequencer (both from PE Applied Biosystems, UK). SDH and SDH-1 cDNAs were cloned in a similar manner and were used as positive controls in RT-PCR assays. Gene expression in mouse tissues by real time PCR Quantitative PCR was carried out on a GeneAmp 5700 Sequence Detection System (AB Applied Biosystems) and a Rotor Gene 3000 utilising a CAS-1200 robotic precision liquid handling system (Corbett Research, Australia) using a SYBR Green I double-stranded DNA binding dye assay (Applied Biosystems, UK). For the determination of TA, SDH, SDH1, TDH and KBL mRNA expression in adult mouse tissues and whole mouse embryos, cDNAs were generated from polyA + selected RNA by reverse transcriptase using an oligo-dT primer (BD Clontech, UK). Approximately 8.0 ng of cDNA were used for each PCR. Tissue master mixes were divided into gene specific mixes and primers were added to a final concentration of 200 μM. The primers were: TA, CCCAGAGATTCGCTAAAG GACTC (exon 6/7) and CACGGCCAGTCTGAGCCAC (exon 7), which produced a 171 bp amplicon; SDH, TTTTACGAACACCCCATTTTCTC (exon 7) and AGAATCTTCTCATCGT CCACGAA (exon 8/7), which produced an 89 bp amplicon; SDH1, CCTGCCAGACATCACCAG TGT (exon 6/7) and GCGCTCATCGT CCAGGAA (exon 8/7), which produced a 154 bp amplicon; G3PDH, TCCCACTCTTCCACCTTCGA and GTCCACCACCCTGTTGCTGTA, which produced a 111 bp amplicon. Primers for beta-actin, TDH and KBL have been described previously [ 36 ]. Amplification conditions were; a 10 min hot start to activate the polymerase followed by up to 50 cycles of 95°C for 15 sec and 60°C for 1 min. The number of cycles required for the fluorescence to become significantly higher than background fluorescence (termed cycle threshold [C t ]) was used as a measure of abundance. A comparative C t method was used to determine gene expression. Expression levels in each tissue cDNA sample were normalised to the average expression levels of the housekeeping genes beta-actin and G3PDH (ΔC t ). Ratios of gene of interest mRNA/housekeeping mRNA from each tissue were standardised to that of the highest expressing tissue for that gene which was taken as 100% (ΔΔC t ). Formula E -ΔΔCt was used to calculate relative expression levels where E is the efficiency of the PCR per cycle. Amplification specificity was confirmed by melting curve analysis and agarose gel electrophoresis. Bioinformatics The predicted start site of transcription of murine TA mRNA was determined using the program Eponine [ 43 ]. The predicted secondary structure of the protein was determined using the Psi-Pred program [ 44 ] aligned with that of the crystal structure of TA from the bacteria T. maritima [ 21 ] using 3D-PSSM [ 45 ]. Mitochondrial locations were predicted for the TA proteins using MITOPRED [ 46 ]. Cleavage-sites in the mitochondrial targeting peptides were identified using PSORT [ 47 ]. Authors' contributions A.J.E. initiated and carried out the molecular genetic studies, drafted the manuscript and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555945.xml

514528

Colorectal cancers with microsatellite instability display mRNA expression signatures characteristic of increased immunogenicity

Background Colorectal cancers displaying high-degree microsatellite instability (MSI-H) have an improved prognosis compared to microsatellite stable (MSS) cancers. The observation of pronounced lymphocytic infiltrates suggests that MSI-H cancers are inherently more immunogenic. We aimed to compare the gene expression profiles of MSI-H and MSS cancers to provide evidence for an activated immune response in the former. Results We analysed tissue from 133 colorectal cancer patients with full consent and Local Ethics Committee approval. Genomic DNA was analysed for microsatellite instability in BAT-26. High-quality RNA was used for microarray analysis on the Affymetrix ® HG-U133A chip. Data was analysed on GeneSpring software version 6.0. Confirmatory real-time RT-PCR was performed on 28 MSI-H and 26 MSS cancers. A comparison of 29 MSI-H and 104 MSS cancers identified 2070 genes that were differentially expressed between the two groups [P < 0.005]. Significantly, many key immunomodulatory genes were up-regulated in MSI-H cancers. These included antigen chaperone molecules (HSP-70, HSP-110, Calreticulin, gp96), pro-inflammatory cytokines (Interleukin (IL)-18, IL-15, IL-8, IL-24, IL-7) and cytotoxic mediators (Granulysin, Granzyme A). Quantitative RT-PCR confirmed up-regulation of HSP-70 [P = 0.016], HSP-110 [P = 0.002], IL-18 [P = 0.004], IL-8 [0.002] and Granulysin [P < 0.0001]. Conclusions The upregulation of a large number of genes implicated in immune response supports the theory that MSI-H cancers are immunogenic. The novel observation of Heat Shock Protein up-regulation in MSI-H cancer is highly significant in light of the recognised roles of these proteins in innate and antigen-specific immunogenicity. Increased mRNA levels of pro-inflammatory cytokines and cytotoxic mediators also indicate an activated anti-tumour immune response.

Background Colorectal cancer remains a leading cause of cancer death in the Western world despite recent advances in surgery, radiotherapy and chemotherapy [ 1 ]. Immunotherapy has attracted attention as a novel treatment modality that may exploit the host immune response against tumour cells. However, definitive evidence that colorectal cancer cells can stimulate a specific immune response has been elusive. Approximately 15–20% of sporadic colorectal cancers and nearly all large bowel malignancies in the Hereditary Non-Polyposis Colorectal Cancer (HNPCC) syndrome are characterised by widespread microsatellite instability [ 2 , 3 ]. Microsatellites are very short repetitive nucleotide sequences, distributed throughout the human genome, that are prone to insertion and deletion mutations during DNA replication. These mutations are normally corrected by the inherent proofreading capacity of DNA polymerase and a group of genes involved in mismatch repair (MMR). Defective mismatch repair allows the accumulation of errors in microsatellites and this is termed microsatellite instability (MSI). In HNPCC a germline mutation in a mismatch repair gene is inherited and a subsequent "second hit" leads to failure of MMR, resulting in MSI. In sporadic cancers epigenetic silencing by hypermethylation of the MMR genes has been implicated. Despite these differences in their molecular genesis the two groups share common clinicopathological features [ 4 ]. Several studies have confirmed that patients with tumours displaying a high degree of microsatellite instability (MSI-H) appear to possess a survival advantage over those with cancers that are microsatellite stable (MSS) [ 5 - 7 ]. This improvement in outcome appears to be an inherent feature of the unstable phenotype. It has been shown that MSI-H cancers generate abnormal peptides that can be used to excite cytotoxic T cell responses in in vitro experiments [ 8 , 9 ]. These peptides may act as Tumour specific antigens (TSA's) in vivo and hence, excite a host immune response. In keeping with this observation MSI-H cancer is characterised by the presence of a significant infiltrate of lymphocytes, a feature that has been previously associated with better patient prognosis [ 10 ]. Lymphocytes that infiltrate tumour epithelium (intra-epithelial lymphocytes, IEL's) are specifically associated with improved survival and may be involved in an immune response [ 11 ]. Immunohistochemical analyses have shown that the IEL's infiltrating MSI-H colorectal cancers are predominantly cytotoxic, activated and release mediators of target cell death [ 12 ]. Follow-up analyses confirm improved survival in patients with these tumours [ 13 ]. Increased apoptosis has also been demonstrated in MSI-H cancers but the link between increased lymphocyte infiltrate and apoptotic cell death has not yet been proven. Some argue that these infiltrates are secondary phenomena with no biological relevance [ 14 ] and it has been suggested that intra-epithelial lymphocyte populations in MSI-H colorectal cancers simply represent proliferation of resident lamina propria lymphocytes with no immunological activation or role. The development of high-density data analysis techniques such as microarray technology allows rapid gene expression profiling of tissue-derived RNA to give an mRNA expression signature for the tissue under study. The gene expression signature of a tumour microenvironment reflects the interactions between tumour, stroma and host response therein. We aimed to compare these signatures between groups of MSI-H and MSS colorectal cancers to identify genes that are differentially expressed between the two phenotypes. Specifically, we focus on genes involved in anti-tumour immune responses whose activity may be modified in colorectal cancers, in order to clarify the nature of any immune response in MSI-H colorectal cancer. Results We analysed 133 colorectal tumours of which 29 (22%) tumours were identified as MSI-H (Table 1 ). This is at the upper end of accepted figures but reflects the frequency of MSI-H in a subset of our tumour bank that yielded high-quality RNA. The overall prevalence of MSI-H colorectal cancer in our tumour bank is lower (16%) and consistent with other large series. As expected the MSI-H group showed a statistically significant association with the right side of the colon (P < 0.0001, χ 2 test). During histological assessment each tumour was graded for lymphocytic infiltration on standard Haematoxylin and Eosin stained sections by a consultant pathologist (RF). Tumours with minimal or mild infiltration were scored 1, those with moderate infiltration scored 2 and those with pronounced lymphocytic infiltrates were scored 3. As expected the MSI-H cancer group had higher proportions of tumours with moderate and pronounced infiltration but this difference did not reach statistical significance (P= 0.287, χ 2 test for trend). Table 1 Summary of patient demographics for microarray and RT-PCR analyses. Microsatellite Stable (MSS) Microsatellite Unstable (MSI-H) Microarray analysis Patients 104 27 Mean age (SD) (yrs) 69.6 (12.3) 65.5 (15.6) Male: Female (%) 62:42 (60:40) 13:14 (48:52) Cancers (n) 104 29 Right: Left (%) 28:76 (27:73) 19:10 (66:34) Dukes' Stage A (%) 14 (13.5) 3 (10.3) B (%) 48 (46.1) 15 (51.7) C (%) 39 (37.5) 10 (34.5) D (%) 3 (2.9) 1 (3.4) Lymphocyte score 1(%) 78(75) 19(65.5) 2(%) 17(16.3) 6(20.7) 3(%) 9(8.7) 4(13.8) RT-PCR analysis Patients 26 26 Mean age (SD) (yrs) 71.4 (13.6) 66.4 (15.1) Male: Female (%) 14:12 (54:46) 12:14 (46:54) Cancers (n) 26 28 Right: Left (%) 17:9 (65:35) 19:9 (68:32) Dukes' Stage A (%) 2 (7.7) 3 (10.7) B (%) 15 (57.7) 15 (53.6) C (%) 8 (30.8) 10 (35.7) D (%) 1 (3.8) 0 For RT-PCR analysis groups were matched for age, tumour side and Dukes' Stage. Two MSI-H patients had two tumours each. An initial comparison of the gene expression profiles of MSI-H versus MSS tumours, using a parametric unpaired t-test (with Welch's correction for unequal variances) and the Benjamini and Hochberg False Discovery Rate (multiple correction method), identified 2070 genes that were differentially expressed at a significance of p < 0.005 ( Additional Table 1 ). This represents 9.3% of those included on the chip and statistically less than 0.5% of these genes would be selected by chance. 1293 genes (62.5%) had significantly increased signal intensity in MSI-H cancers and 777 genes (37.5%) had reduced signal intensity. The clear differences between the two groups can be demonstrated in a cluster map (Figure 1 ). This was generated using 542 of the most significant genes in our list, selected by performing a group comparison at p < 0.05 and the more stringent Bonferoni multiple testing correction ( Additional Table 1 ). The expression signatures of the MSI-H group on the right of the cluster map display marked homogeneity, in contrast to the heterogeneous MSS cancers. Four tumour profiles came from patients who satisfied family history criteria for HNPCC (Amsterdam criteria). These profiles were clustered amongst the other MSI-H cancers but did not form a distinct group. Analysis after exclusion of these HNPCC tumour profiles yielded very similar gene lists ( Additional Table 2 ). The small number of HNPCC MSI-H profiles (<5) precluded meaningful comparison of expression profiles with the sporadic group. Figure 1 Two-way hierarchical clustering by the 542 most significantly differentially expressed genes between MSS and MSI-H colorectal cancers. Samples arranged along the x-axis and genes along the y-axis. Each square represents the expression level of a given gene in an individual sample. Red represents increased expression and blue represents decreased expression relative to the normalised expression of the gene across all samples. Samples with similar gene expression profiles are clustered together. 1328 of our 2070 genes (64.2%) had a recognised function. We noted differences in several cancer-related genes that were consistent with our existing knowledge of the genetic profiles of MSI-H colorectal cancers (Table 2 ). Notably, the mismatch repair gene hMLH1 had reduced signal intensity in our MSI-H group, as did the TGFβ RII and IGFIIR genes. The mismatch repair gene PMS2 was also underexpressed in our MSI-H cancers (P = 0.003, Fold change 1.4). The mRNA of TP53 gene was more abundant in MSI-H tumours when compared to MSS tumours. Similarly, the β catenin gene also had a high signal in our MSI-H tumours. Significantly, several transcripts related to the heat shock protein family (HSP 70, 110 and 90) were up-regulated in MSI-H tumours (Table 2 ), as were several other genes that may be involved in a putative antigen-directed immune response. We focussed on genes relevant to immunological responses but many other interesting differences are evident in our list but are not discussed in this paper. Table 2 A highly selective list of gene specific probes that are differentially expressed (P < 0.005) between MSI-H and MSS colorectal cancers with a fold-change of at least 1.5. Genes Up-regulated in MSI-H Colorectal Cancer Gene name P value Fold change Catenin (cadherin assoc. protein) beta 1 6.5 × 10 -12 2.9 Interleukin 8* 1.7 × 10 -4 2.8 Granulysin* 1.3 × 10 -7 2.8 Caspase 2* 6.0 × 10 -8 2.4 Interleukin 24 0.004 2.3 Heat shock protein (HSP 110 family)* 2.4 × 10 -5 2.2 TP53 (Li Fraumeni)* 2.5 × 10 -4 2.2 Toll-like receptor 2 (TLR-2) 1.9 × 10 -4 1.9 Heat shock protein 70* 3.5 × 10 -6 1.8 Granzyme A 0.001 1.7 Interleukin 1β 0.004 1.7 Survivin 0.001 1.6 Calreticulin 2.9 × 10 -5 1.6 Human Natural killer Cell enhancing factor 0.001 1.6 CD68 antigen 4.8 × 10 -4 1.6 ICAM 1 (CD54) 0.003 1.6 Interleukin 18 (interferon-gamma inducing factor)* 0.003 1.5 Interleukin 7 4.6 × 10 -4 1.5 Interleukin 15* 0.002 1.5 Genes down-regulated in MSI-H colorectal cancer Gene name P value Fold change Insulin-like growth factor 2 (somatomedin A) 1.9 × 10 -5 4.3 TGFβ RII 9.3 × 10 -5 3.0 hMLH 1* 6.7 × 10 -5 2.4 P values denote results of Welch's t test with Benjamini and Hochberg False Discovery Rate. * denotes genes selected for RT-PCR analysis. To validate our microarray results we used real time RT-PCR to confirm the findings on nine genes of immunological interest. The RT-PCR results confirmed the significant differences between the two groups (Table 1 ) in seven out of the nine genes selected. The mismatch repair gene hMLH1 was significantly down-regulated in MSI-H (Figure 2a ), whilst transcription of TP53 was significantly higher in the unstable group (Figure 2b ). Similarly the heat shock protein (HSP) 70 (Figure 2c ) and HSP-110 (Figure 2d ), the Interleukins (IL) 18 (Figure 2e ) and IL-8 (Figure 2f ), and the protease Granulysin (Figure 2g ), were all significantly up-regulated in MSI-H when compared to the MSS group. Two analyses, IL-15 (p = 0.17) and Caspase 2 (p = 0.16), had reduced sample numbers in each group and did not reach statistical significance. However, both showed trends of up-regulation in MSI-H cancers consistent with the microarray analysis. Figure 2 Boxplots showing RT-PCR data analysis of seven genes of interest:A hMLH1, B TP53, C HSP-70, D HSP-110, E IL-18, F IL-8, and G Granulysin. Data analysed using non-parametric Mann Whitney test (P values as shown). Discussion Our study examines the differences in overall gene expression profiles in the tumour micro-environments of MSI-H and MSS colorectal cancers. The observation that a large number of pro-inflammatory genes are upregulated in MSI-H colorectal cancer is a strong indicator that an immune response is indeed activated in these tumours. These results support the notion that the lymphocytic infiltrates in these cancers represent immune activation rather than simple proliferation of resident lymphocytes. The exact nature of the immune response remains unclear but our novel observation that heat shock proteins are upregulated in MSI-H colorectal cancer may be highly significant. Microarray data analysis demonstrated that several members of the HSP family are up-regulated in MSI-H cancers (Table 2 ) and RT-PCR analyses confirmed increased levels of both HSP-110 and HSP-70 mRNA in our MSI-H cancers. Heat shock proteins have roles in both innate and adoptive immunity and have excited much interest as natural adjuvants for immunotherapy [ 15 , 16 ]. Some members of the HSP family act directly to excite an innate immune response that might be more marked in MSI-H colorectal cancer. Such a response might be mediated by Natural Killer cells that characteristically release Granulysin to induce tumour cell death, a recognised feature of MSI-H colorectal cancer, which in turn releases intra-cellular TSA's. Alternatively, the concept of "effete malignancy", in which accumulation of mismatch errors overwhelms the tumour cells' viability, [ 17 ] may explain increased tumour cell death and release of TSA's. In fact, these two possible explanations of increased apoptosis in MSI-H colorectal cancers may actually be complementary rather than mutually exclusive. However, the release of TSA's into the tumour micro-environment is the pivotal step in the generation of an antigen-specific immune response. Heat shock proteins act as chaperone proteins in the processing and presentation of antigenic peptides [ 15 , 16 ]. They promote antigen uptake and induce expression of antigen presenting and co-stimulatory molecules on dendritic cells. By example, HSP-70 has been shown to recruit dendritic cells (and T cells) and enhances their ability to uptake antigen [ 18 ]. This is a crucial step in the cross-priming of dendritic cells necessary for an antigen-directed immune response [ 19 ]. The CD68 antigen, expressed by immature dendritic cells and macrophages that are ready to take up antigen, is included in our list of genes up-regulated in MSI-H colorectal cancer, as is the TLR-2 gene, one of a family of receptors to which HSP's bind to activate dendritic cells. Heat Shock Proteins have also been shown to induce cytokine profiles that promote antigen-specific responses. This role may underscore our observation that several immunogenic interleukins are up-regulated in MSI-H cancers. An important function of these cytokines is to promote the presentation of antigen by dendritic cells (and macrophages) to effector T cells. This represents another crucial step in the development of an antigen-specific immune response, prior to the interaction of primed cytotoxic (CD8+) and helper (CD4+) T cells with the tumour cells. This interaction subsequently results in lytic tumour cell death. Specifically, such cytokines also modulate which arm of the T helper system is activated: Th1 activity favours immune activity whilst Th2 pathways favour tolerance. In this context, our data reveals significant up-regulation of IL-18 and other pro-inflammatory cytokines in MSI-H cancers that promote Th1 activity. Our microarray data are validated by the existing knowledge of key gene expression in MSI-H colorectal cancers. The presence of hMLH1 mRNA was reduced in MSI-H cancers, as shown by both microarray and RT-PCR analyses. This observation reflects the fact that hMLH1 is frequently silenced, due to promoter region hypermethylation, in sporadic cancers, which formed the majority of our MSI-H group. Other genes known to be affected by the MSI pathway such as TGFβ RII, IGFIIR, TP53, APC, β catenin and Bcl-2 were also differentially expressed between our two groups [ 20 , 21 ]. The inclusion of HNPCC cancers within our MSI-H group appears not to affect the differentially expressed genes we identify and this is likely to reflect their small number. Accrual of further HNPCC gene expression profiles should allow us to sub-analyse the MSI-H group in the future. However, the immunological focus of this paper appears unaffected by any differences in the biology of these cancers. As expected a large number of pro-apoptotic genes were upregulated in our MSI-H group as these cancers are characterised by increased apoptosis (Table 3 ). The increased levels of Bcl-2 related transcripts are consistent with previous findings despite the anti-apoptotic functions of this gene [ 22 , 23 ]. Clearly, the interaction between pro-and anti-apoptotic agents in MSI-H cancers is complex and needs further elucidation. Table 3 Additional genes related to apoptosis and the Major Histocompatibility Complex shown to be up-regulated in MSI-H colorectal cancer in comparison to MSS cancers. Apoptosis related genes up-regulated in MSI-H colorectal cancers (P < 0.005, Benjamini and Hochberg False Discovery Rate) Gene name Fold change TNF-induced protein 1.9 Tumour necrosis factor receptor superfamily 10d 1.9 Tumour necrosis factor receptor superfamily 9 1.7 Tumour necrosis factor receptor superfamily 10b 1.7 Tumour necrosis factor receptor superfamily 6 1.6 Death-associated protein kinase 1 1.5 DNA fragmentation factor, beta polypeptide (caspase-activated DNase) 1.4 Bcl-2-related protein A1 2.2 Bcl-2/adenovirus E1B 19 kDa interacting protein 3-like 1.8 Bcl-2 antagonist/killer 1.4 Bcl-2 associated athanogene 3 1.4 Bcl-2/adenovirus E1B 19 kDa interacting protein 1 1.2 Baculoviral IAP repeat-containing 3 1.6 Baculoviral IAP repeat-containing 2 1.2 Chemokine (C-X-C motif) receptor 4 1.8 CD27-binding (Siva) protein 1.5 Thioredoxin-like, 32 kDa 1.4 Mitogen-activated protein kinase kinase kinase 5 1.4 Microtubule associated protein tau 1.4 Macrophage erythroblast attacher 1.3 Testis enhanced gene transcript (BAX inhibitor 1) 1.2 Major histocompatibility complex-related genes up-regulated in MSI-H colorectal cancer (P < 0.05, Benjamini and Hochberg False Discovery Rate) Gene name Fold change Major histocompatibility complex, class II, DR alpha 2.0 Major histocompatibility complex, class II, DQ beta 1 1.8 Major histocompatibility complex, class II, DP alpha 1 1.8 H. sapiens HLA-DMA gene 1.7 Major histocompatibility complex, class II, DR beta 1 1.6 Major histocompatibility complex, class II, DR beta 5 1.5 HLA-B associated transcript 1 1.2 Major histocompatibility complex, class I, C 1.2 It is of interest that reduction of the stringency of our statistical comparison to P < 0.05 (Benjamini and Hochberg False Discovery Rate) generates a list of 4788 differentially expressed genes ( Additional Table 1 ). These include several more genes with key immunomodulatory functions. Transcripts specific to co-stimulatory molecules (CD80 and CD86), HSP60, MHC peptides, pro-inflammatory cytokine receptors, Perforin 1 and Caspase 9 were amongst those that were up-regulated in MSI-H colorectal cancers. These subsidiary data provide further evidence that these cancers excite an antigen-specific immune response. Closer study of HLA molecules that were up-regulated in MSI-H cancers reveals that the majority are Class II-related (Table 3 ). This finding is consistent with previous studies [ 24 , 25 ] and supports the notion that immunogenicity of these cancers relies on antigen presentation by Antigen Presenting Cells (cross-priming) rather than directly by the tumour. The HLA Class I molecule β 2 -microglobulin has been shown to be a target for mutation in the MSI-H pathway and this renders HLA Class I machinery ineffective in these tumours [ 26 ]. This gene does not, however, appear in our list of differentially expressed genes. This study compares gene expression profiles in 133 primary human cancers and confirms the previous finding on a smaller sample set that microarray profiling can differentiate cancers according to microsatellite stability status [ 27 ]. A recent report on differential gene expression from microarray profiling of smaller numbers of MSI-H (n = 8) and MSS (n = 14) colorectal cancer tissue samples yielded findings similar to ours in genes such as TP53, IGF2, RAN, MORF4L1, ZFP36L2 and CCNF [ 28 ]. Their observation that EIF3S2 is downregulated in MSI-H was confirmed in our study, as was the downregulation of TGFβ RII. However, they did not observe differences in MMR genes, such as hMLH1 and PMS2, or indeed the immunomodulatory genes that we report. The likely explanation is that the smaller sample numbers used in their analysis as well as the smaller size of their spotted cDNA array (8000 genes) limits the sensitivity of their microarray comparisons. Previously two groups have reported the results of cDNA microarray comparisons of MSI-H and MSS cancer cell lines but both were restricted to very small numbers [ 29 , 30 ]. Interestingly, of the 122 differentially expressed genes identified by these two studies 33 transcripts (27%) were also included in our list of 4788 genes (P < 0.05, Benjamini and Hochberg False Discovery Rate). However, some genes noted to be down-regulated in MSI-H cancer cell lines were up-regulated in the MSI-H cancers in our analysis, and vice versa. These disparities can be attributed to the fact that cancer cells cultured in vitro behave differently to cells from primary tumours. Indeed Bertucci et al report that colorectal cancer lines show overexpression of genes involved in cellular proliferation and underexpression of several gene clusters, including a cluster associated with immunomodulatory genes, when compared to colorectal cancer tissue samples using microarrays [ 28 ]. These biological differences and the small numbers used in the cell line experiments render any concordance between our data and cell line analyses altogether encouraging. We acknowledge that mRNA profiles cannot be presumed to reflect functional significance at a protein level. However, the upregulation of such a large number of genes, known to be involved in innate and antigen-specific immune responses, in MSI-H colorectal cancer indicates a genuine difference in host-tumour interactions. These findings are entirely novel and add considerable weight to the argument that MSI-H cancers excite an immune response. Clearly, additional work on protein expression specific to the genes we have identified will help to elucidate the exact nature of immune mechanisms in these cancers. In this study microdissection was deliberately eschewed as our focus was the interaction between tumour, stroma and inflammatory cells and the expression profiles therefore reflect contributions from each of these groups. Microdissection would have excluded the contribution of certain cell populations which may have important roles in the modulation of host-tumour interactions. We have previously extensively analysed the composition of tumour biopsies obtained by sampling exophytic areas of resected specimens. We consistently found that a random sample taken from a fresh frozen segment of tumour tissue contained at least 80% tumour cells (Unpublished data). We know from experience that necrotic tumour does not yield high quality RNA and our study included only those samples that yielded high quality RNA and thus, the sampling technique we used inherently excludes necrotic tumour. It is therefore unlikely that our results are attributable to the presence of tumour necrosis. Furthermore, a DNA microarray analysis of the gene expression profiles of naïve versus activated tumour-specific lymphocytes did not show differences of gene expression in heat shock proteins or the interleukins noted in our study [31]. This suggests that our observations do not simply reflect the more pronounced lymphocyte infiltrates of MSI-H colorectal cancer although we accept that this remains a possibility. Conclusions Certainly, our results provide new evidence to support the notion that MSI-H colorectal cancers are immunogenic and new insights into the pathways that may be involved. Further focussed study of these cancers may clarify the immunology of colorectal cancer and, specifically, may provide useful targets for directing immunotherapeutic strategies. Methods Patient population and microsatellite analysis The Local Ethics Committee approved this study and all patients gave informed consent prior to surgery. Tissue was available from a bank of 223 colorectal cancers resected at The Royal London Hospital between December 1997 and March 2003. Tissue samples of tumour and normal mucosa were taken within 20 minutes of resection and snap frozen in liquid nitrogen. Normal mucosa and tumour DNA was extracted and used in PCR reactions to amplify the mononucleotide BAT-26 marker, as described elsewhere [32]. Products were separated and visualised on micro-fabricated chips to identify tumours displaying bandshifts characteristic of a high degree of microsatellite instability [33]. Microarray profiling and analysis Total RNA was prepared from samples using an RNeasy ® kit (QIAGEN, Hilden, Germany) and quality was assessed on the Agilent Bioanalyser 2100. Only 129 samples, from our tumour bank, that yielded high quality mRNA with minimal degradation and clear 18S/28S ribosomal peaks, were included in the analysis. Preparation of in vitro transcription (IVT) products, oligonucleotide array hybridization and scanning were performed according to Affymetrix ® (Santa Clara, California) protocols. In brief, 5 μg of total RNA from each colon tumour and T7-linked oligo-dT primers were used for first-strand cDNA synthesis. IVT reactions were performed in batches to generate biotinylated cRNA targets, which were chemically fragmented at 95°C for 35 minutes. Fragmented biotinylated cRNA (10 μg) was hybridized at 45°C for 16 hours to Affymetrix ® high density oligonucleotide array human HG-U133A chip, which contains 22,283 probe sets representing more than 14,500 well-substantiated human genes. The arrays were washed and stained with streptavidin-phycoerythrin (SAPE, final concentration of 10 μg/ml). Signal amplification was performed using a biotinylated anti-streptavidin antibody. The array was scanned according to the manufacturer's instructions (Affymetrix Genechip ® Technical Manual, 2001). Scanned images were inspected for the presence of obvious defects (artefacts or scratches) on the array. Defective chips were excluded and the sample was re-analysed. To minimize discrepancies due to variables such as sample preparation, hybridization conditions, staining, or array lot, the raw expression data was scaled using Affymetrix ® Microarray Suite 5.0 software. The trimmed mean signal of all probe sets on the HG-U133A chip was adjusted to a user-specified target signal value (1500) for each array for global scaling. No specific exclusion criteria were applied. Comparative analysis between expression profiles for MSI-H and MSS samples was carried out on GeneSpring™ software version 6.0 (Silicongenetics, Redwood, California). The "Cross gene error model for deviation from 1.0" was active. Gene expression data was normalised in two ways: "per chip normalisation" and "per gene normalisation". For "per chip normalisation" all expression data on a chip is normalised to the 50 th percentile of all values on that chip. For "per gene normalisation" the data for a given gene is normalised to the median expression level of that gene across all samples. The data sets are then assigned to the two groups MSI-H and MSS, and the expression profiles of the two groups were compared using unpaired t-tests (with Welch's correction for unequal variances) and multiple testing corrections to identify genes that were differentially expressed between them. RT-PCR Analysis Nine genes were further analysed using quantitative real-time RT-PCR. To compare similar groups each MSI-H tumour was matched to an MSS tumour from patients of similar age, the same side of the colon and, where possible, same Dukes' Stage. Sample RNA was extracted, quantified and quality-controlled as for microarray analysis. High-quality RNA was available on 28 tumours of the 29 tumours with MSI-H. These were matched to a group of 26 MSS cancers (Table 1 ). Three analyses were performed on smaller groups (Table 4 ) because high-quality RNA was no longer available for analysis. Gene-specific primers and probe sets for each gene (Table 4 ) were obtained from Assays-on-Demand Gene Expression Products (Applied Biosystems, Warrington, UK). RT-PCR reactions were carried out in a one-tube system for seven genes but reactions for two genes were sub-optimal in this set-up and so a two-tube system was used instead (Table 4 ). Sample RNA was processed in duplicate with serial dilutions of Human Universal Reference RNA (Stratagene, LaJolla, California), in triplicate, and No Template Controls on the same 96-well plate. Standard curves were constructed from the Universal RNA wells with arbitrary units of 1 Unit equivalent to 1 picogram of Universal RNA. Duplicate wells that differed by more that one Ct count were repeated or were excluded from the analysis. The results were compared using the non-parametric Mann-Whitney test and significance was taken at P < 0.05. Table 4 Summary of RT-PCR details and the numbers of samples in each analysis. Analysis of hMLH1, IL-15 and Caspase2 restricted to smaller numbers due to limited availability of high-quality RNA. Gene name GENBANK NUMBER Chemistry (Applied Biosystems, Warrington, UK) Sample Numbers ( n ) MSS MSI-H hMLH1 NM_000249 High-Capacity cDNA Archive Kit + TaqMan Universal PCR Master Mix (with AmpErase UNG) 12 16 TP53 NM_000546 TaqMan ® EZ-RT-PCR Kit 26 27 HSP-70 kD 1B NM_005346 TaqMan ® EZ-RT-PCR Kit 26 28 HSP-110 NM_014278 TaqMan ® EZ-RT-PCR Kit 26 28 IL-18 NM_001562 TaqMan ® EZ-RT-PCR Kit 24 28 IL-8 NM_000584 TaqMan ® EZ-RT-PCR Kit 26 28 IL-15 NM_000585 High-Capacity cDNA Archive Kit + TaqMan Universal PCR Master Mix (with AmpErase UNG) 12 16 Granulysin NM_006433 TaqMan ® EZ-RT-PCR Kit 26 27 Caspase 2 NM_001224 TaqMan ® EZ-RT-PCR Kit 21 18 List of abbreviations MSI = Microsatellite instability MSI-H = High degree microsatellite instability MSS = Microsatellite stable (including low degree instability) Authors' contributions AB carried out RT-PCR experiments and analysis, microarray data analysis and drafted the manuscript. SA participated in microarray experiments and carried out microarray data analysis. RH, FH, XH and PS coordinated and performed microarray experiments. RF performed histopathological analysis of cancers. SB and SD conceived the study, supervised the project and drafted the manuscript. All authors read and approved the final manuscript. Supplementary Material Additional File 1 2070 genes differentially expressed between MSI-H and MSS colorectal cancers (P < 0.005, Benjamini and Hochberg False Discovery Rate). 4788 genes differentially expressed between MSI-H and MSS colorectal cancers (P < 0.05, Benjamini and Hochberg False Discovery Rate). 542 genes differentially expressed between MSI-H and MSS colorectal cancers (P < 0.05, Bonferoni Mutilple Testing Correction). Click here for file Additional File 2 2184 genes differentially expressed between MSI-H (HNPCC profiles excluded) and MSS colorectal cancers (P < 0.005, Benjamini and Hochberg False Discovery Rate). 1436 genes upregulated in sporadic MSI-H cancers (with fold change). 748 genes upregulated in MSS cancers (with fold change). 4874 genes differentially expressed between MSI-H and MSS colorectal cancers (P < 0.05, Benjamini and Hochberg False Discovery Rate). Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC514528.xml

529440

Gel shift analysis of the empA promoter region in Vibrio anguillarum

Background The induction of metalloprotease encoded by empA in Vibrio anguillarum occurs at high cell density in salmon intestinal mucus. Previously we have shown that there are significant differences in empA expression in two strains of V. anguillarum , M93Sm and NB10. It is hypothesized that differences in empA regulation are due to differences in binding of regulatory elements. Results Two strains of V. anguillarum , M93Sm and NB10, were examined and compared for the presence of DNA regulatory proteins that bind to and control the empA promoter region. Gel mobility shift assays, using a digoxigenin (DIG)-labeled oligomer containing a lux box-like element and the promoter for empA , were done to demonstrate the presence of a DNA-binding protein. Protein extracts from NB10 cells incubated in Luria Bertani broth + 2% NaCl (LB20), nine salts solution + 200 μg/ml mucus (NSSM), 3M (marine minimal medium), or NSS resulted in a gel mobility shift. No gel mobility shift was seen when protein extracts from either LB20- or NSSM-grown M93Sm cells were mixed with the DIG-labeled empA oligomer. The azocasein assay detected protease activity in all incubation conditions for NB10 culture supernatants. In contrast, protease activity was detected in M93Sm culture supernatants only when incubated in NSSM. Since the luxR homologue in V. anguillarum , vanT , has been cloned, sequenced, and shown to be required for protease activity, we wanted to determine if vanT mutants of NB10 exhibit the same gel shift observed in the wild-type. Site-directed mutagenesis was used to create vanT mutants in V. anguillarum M93Sm and NB10 to test whether VanT is involved with the gel mobility shift. Both vanT mutants, M02 and NB02, did not produce protease activity in any conditions. However, protein extracts from NB02 incubated in each condition still exhibited a gel shift when mixed with the DIG-labeled empA oligomer. Conclusions The data demonstrate that protein extracts of V. anguillarum NB10 cells contain a protein that binds to a 50 bp oligomer containing the empA promoter- lux box-like region. NB10 cells express empA during stationary phase in all growth conditions. The DNA binding protein is not present in M93Sm extracts. M93Sm cells express protease activity only when incubated at high cell density in fish gastrointestinal mucus. The gel shift observed with NB10 cells is not due to VanT binding. The data also suggest that the DNA binding protein is responsible for the less restrictive expression of empA in NB10 compared to M93Sm.

Background Vibrio anguillarum is the causative agent of vibriosis, one of the major bacterial diseases affecting fish, bivalves, and crustaceans [ 1 - 3 ]. Vibriosis has been a major problem for the aquaculture industry around the world. Large economic losses due to this fish pathogen are sustained by the fish farming industry. Annual losses of cultured fish species in Japan alone exceed $30 million [ 1 ]. Vibriosis in fish is observed as a hemorrhagic septicemia. Infected fish display skin discoloration and erythema around the base of the fins, vent, and mouth. Necrotic lesions form in the abdominal muscle. Mortalities within affected fish farm stocks range from 30–100% [ 1 , 4 , 5 ]. Extracellular metalloproteases are important virulence factors for many pathogenic bacteria including Vibrio cholerae [ 6 , 7 ] and Vibrio vulnificus [ 8 - 10 ]. The EmpA metalloprotease genes of V. anguillarum strains NB10 and M93Sm have been cloned and sequenced by Milton et al [ 11 ] and Denkin and Nelson [ 12 ], respectively. The EmpA metalloprotease of V. anguillarum shares significant sequence homology with other known proteases. Although the role of the EmpA metalloprotease is not completely understood, we showed previously that V. anguillarum empA mutants are either avirulent or attenuated for virulence in Atlantic salmon depending on the route of inoculation [ 12 ]. It can be hypothesized that EmpA causes tissue damage during pathogenesis. Recently, the Vibrio harveyi luxR (LuxR Vh ) homologue in V. anguillarum , vanT , was cloned, sequenced, and shown to be required for protease activity [ 13 ]. LuxR Vh dependent regulation of luminescence in V. harveyi occurs at the promoter of the lux operon [ 14 , 15 ]. While the amino acid sequence of LuxR Vh in V. harveyi is not similar to the LuxR Vf protein in V. fischeri which also regulates luminescence [ 16 ], it is thought that LuxR Vh binds to the lux box and activates the expression of the luciferase genes. Since LuxR Vh functions as a transcriptional activator in V. harveyi and VanT is required for protease expression in V. anguillarum , we wanted to determine whether any proteins bind to the regulatory regions (e.g. the lux box-like element and promoter) immediately upstream of empA . We have previously shown that V. anguillarum cells grow rapidly in Atlantic salmon intestinal mucus [ 17 , 18 ] and that empA is strongly induced when cells are incubated at high density in mucus [ 17 ]. Further, we have demonstrated that empA transcription is RpoS-dependent [ 12 ]. Our findings also suggest that quorum sensing and undescribed autoinducer(s) help to regulate the expression of empA in V. anguillarum [ 12 ]. Additionally, Milton et al [ 19 ] previously identified a lux box-like element about 70 bp upstream of the translational start of empA , which we subsequently showed to span the transcriptional start site [ 12 ]. In this study, we examine, compare, and contrast the expression of metalloprotease encoded by empA in two strains of V. anguillarum (M93Sm and NB10). Differences in empA expression were investigated with regards to various growth conditions, the presence or absence of DNA-binding proteins, and putative regulatory proteins. Results Gel shift analysis of the empA promoter The possibility of DNA-binding proteins interacting with regulatory sequences of empA , were examined using a digoxigenin (DIG) labeled oligomer containing the lux box-like element and empA promoter region. The location of double stranded oligomer relative to the promoter and coding sequences is shown in Fig. 1 . Protein extracts from V. anguillarum M93Sm and NB10 cells incubated in LB20 and NSSM were mixed with the DIG-labeled oligomer (Fig. 2 ). NB10 and M93Sm cells were incubated in LB20 and NSSM for 3 h and protein extracts from cells at 0 and 3 h were prepared. DNA binding was observed in protein extracts from NB10 incubated in LB20 and NSSM (Fig. 2A and 2B ) at both 0 and 3 h. A 3.7-fold increase (from T = 0 h) in the amount of DNA binding was observed by 3 h in extracts from NSSM-grown NB10 cells (Fig. 2B , NB10 lane 2). An additional 1.4-fold increase in DNA binding was observed when the amount of protein was increased from 5 μg to 7.5 μg (Fig. 2B , NB10 lane 3). Although only a minor change in the amount of DNA binding (1.4-fold increase) was detectable by 3 h in NB10 extract from LB20-grown cells, a 4.7-fold decrease (from T = 0 h) in free DIG-labeled oligomer was observed (Fig. 2A , NB10 lane 2). In addition, a 8.9-fold decrease in free DIG-labeled oligomer was detected when 7.5 μg protein was added (Fig. 2A , NB10 lane 3). Specificity of protein binding to the DIG-labeled oligomer was tested by competitive inhibition of binding by the addition of excess amounts of an identical unlabeled oligomer. When excess unlabeled lux box- empA promoter oligomer was added to protein extracts from LB20- and NSSM-grown NB10 cells binding to the DIG-labeled oligomer was abolished. Further, the addition of unlabeled oct2A (a non-competitive oligomer) to protein extracts did not affect the gel mobility shift of the DIG-labeled oligomer caused by protein extracts from NB10 cells incubated either in LB20 or NSSM. In contrast to protein extracts from NB10 cells grown in LB20 or NSSM causing a gel shift, protein extracts from M93Sm cells incubated in either condition did not show any binding to the DIG-labeled oligomer (Fig. 2A and 2B ). Even when 7.5 μg of protein was used, a band shift was not observed. In a separate experiment, protein extracts from NB10 cells incubated in 3M or starved in NSS also caused a similar gel mobility shift for the DIG-labeled oligomer (Fig. 3 ). Additionally, even when 5 μg of protein extract from exponentially growing NB10 cells in LB20 was added to the DIG-labeled oligomer, a band shift was observed. Further, in this gel shift experiment, M93Sm protein extract was added as a negative control to the labeled DIG-labeled oligomer and no shift was observed. Protease activity in LB20, NSSM, 3M, and NSS Expression of empA in NB10 occurs in both LB20 and NSSM, while M93Sm exhibits empA expression only in NSSM [ 12 ]. Since a gel mobility shift was observed in NB10 protein extracts in all conditions of growth and during starvation, these cells were tested for protease activity in 3M and NSS in addition to LB20 and NSSM (Fig. 4 ). Protease activity was detected in NB10 cells after 1 and 3 h of incubation in NSSM or LB20, respectively (Fig. 4A ). Maximum protease activity was observed at 4 h for both conditions of incubation. In addition, protease activity was observed in NB10 cells incubated in 3M or starved in NSS after 1 h and continued to increase during the next hour of the 4 h experiment. Throughout the incubation, NB10 cells in 3M produced 3–4 fold greater amounts of protease activity than cells in NSS. The amount of protease measured in NSS at 4 h was only 14 and 17% of the amount produced in LB20 and NSSM, respectively. In contrast, protease activity was observed in M93Sm cells only when incubated in NSSM (Fig. 4B ). M93Sm cells did not produce protease activity in any of the other conditions examined. Effects of vanT mutations on protease activity and gel mobility shift Since vanT of V. anguillarum NB10 was cloned, sequenced and shown to be required for protease activity [ 13 ], vanT mutants of both M93Sm and NB10 were created by site directed mutagenesis and tested for protease activity in LB20 and NSSM. When the mutants (NB02 and M02) were incubated in NSSM for 3 h, a small amount of protease activity was detected (5 and 13% of the wild-type levels, respectively) (Fig. 5 ). No protease activity was observed in M93Sm, M02, or NB02 incubated in LB20. The vanT mutant strains (M02 and NB02) and their parental wild-types (M93Sm and NB10, respectively) were also examined for vanT transcription by RT-PCR (Fig. 6 ). Neither vanT mutant produced vanT transcripts. Interestingly, both wild-type strains produced vanT mRNA in both LB20 and NSSM. To determine if VanT was binding to the lux box- empA promoter, protein extracts from NB02 were tested in the gel shift assay (Fig. 7 ). Protein extracts from NB02 cells incubated in NSSM and LB20 exhibited binding to the DIG-labeled oligomer similar to that observed with protein extracts from wild-type NB10 cells (compare to Fig. 2 ). These data show that VanT was not responsible for the gel mobility shift of the lux box – empA promoter oligomer. Discussion It has been demonstrated that the transcriptional activator, LuxR Vf , in Vibrio fischeri , activates expression of the lux operon via interaction with the lux box [ 15 , 16 , 20 , 21 ]. Although there is incomplete understanding of LuxR Vf interaction with the lux box, good evidence is available that shows a specific lux box sequence required for efficient LuxR Vf binding [ 21 ]. LuxR Vh from V. harveyi and LuxR Vf from V. fischeri are not the same transcriptional activator proteins [ 22 ]. These proteins differ in their activation of the lux operon. In V. fischeri , LuxR Vf interacts with an acylhomoserine lactone autoinducer and then binds to the lux box to activate transcription of the lux genes [ 23 ]. In V. harveyi , the regulation of luminescence occurs via a phosphorelay signal transduction system, which involves LuxR Vh binding to the lux box independent of an autoinducer [ 24 ]. Additionally, a central response regulator, LuxO, has been shown to regulate the expression of luxR [ 24 ] in V. harveyi . Recently, the LuxR Vh homologue of V. fischeri was identified as LitR [ 25 ]. However, it is not known if V. fischeri uses a phosphorelay cascade to control LitR interaction with the lux operon. In this study, two strains of V. anguillarum (NB10 and M93Sm) were examined for a empA promoter region binding protein. In all conditions, incubation with NB10 protein extracts resulted in a gel mobility shift to the DIG-labeled oligomer. In contrast, M93Sm extracts did not show any protein binding to the labeled DNA in any conditions tested. This was interesting because NB10 protease production occurs in all conditions, while M93Sm protease activity is detected only when the cells are incubated in mucus. Recently, VanT, the LuxR Vh homolog in V. anguillarum , was identified by Milton et al [ 13 ] and shown to positively regulate EmpA metalloprotease and biofilm production. We created vanT mutants in both M93Sm and NB10 and tested them for vanT expression, protease activity, and DNA binding. These two mutants, M02 and NB02, did not produce detectable vanT mRNA. However, both exhibited low levels of protease activity in mucus - about 5% and 13% of wild type activity in mucus, respectively. For NB02, no protease activity in LB20 was observed. These results demonstrate that vanT is required for protease activity in a non-mucus containing medium ( V. anguillarum NB10 in LB20), confirming the result reported by Milton et al [ 13 ]. Our results also show that in NSSM, vanT mutants still exhibit some protease activity, but VanT is required for full protease activity in both M02 and NB02 cells. We tested a vanT mutant of V. anguillarum for the possibility of VanT binding to and activation of empA expression. Since the V. anguillarum NB10 strain expresses empA during stationary phase in all conditions tested and protein extracts from these conditions show DNA binding to the empA promoter region, it was hypothesized that VanT binds to this region. The vanT mutant, NB02, was analyzed for gel shift using protein extracts from cells incubated in LB20 and NSSM. Protein extracts from NB02 cells incubated in each condition still caused a band shift. This result suggests that VanT, the LuxR Vh homolog in V. anguillarum , either does not bind to the empA promoter region containing the lux box-like element or perhaps VanT binds to a second protein that binds to this DNA sequence. The former possibility is more likely since the gel shift observed in NB02 is identical to that observed in NB10 extracts. Further, since the M93Sm vanT mutant, M02 still exhibits about 13% of the wild type protease activity in NSSM and does not exhibit protein binding in the gel shift analysis, it is probable that VanT does not bind to the empA promoter- lux box region. However, Croxatto et al. [ 26 ] demonstrated recently that purified VanT protein binds to the V. anguillarum NB10 promoter regions for empA , vanOU , and vanT . Examination of their data shows that VanT binds more strongly to the promoters for vanOU and vanT (binding observed with 0.3 μg purified VanT per 2 ng DIG-labeled DNA) than to the empA promoter (binding observed with 2.5 μg VanT per 2 ng DIG-labeled DNA). It should be noted that in our study only 5 μg of total protein extract was used in the gel shift analysis. That binding to the empA promoter is still observed in vanT mutants strongly suggests that an unidentified protein binds to the promoter- lux box region. Previously, we have shown that empA in V. anguillarum M93Sm is positively regulated by at least two components of salmon gastrointestinal mucus [ 17 ]. In this study we show that NB10 cells exhibit protease activity under all nutritional conditions including starvation in NSS when at high density, whereas M93Sm only induces protease activity in mucus. RpoS is required for expression of empA in both M93Sm and NB10 [ 12 ]. This was demonstrated by the observation that rpoS mutants of M93Sm (M03) and NB10 (NB03) exhibit no protease activity when cells are incubated in either LB20 or mucus during stationary phase. Extracellular factors present in conditioned media showed both positive and negative effects on empA expression [ 12 ]. LB20 conditioned supernatants from M93Sm cause a more rapid and increased production of protease activity (in NB10 and M93Sm cells). However, conditioned LB20 from the luxS mutant, M01added to wild-type cells results in a more rapid induction of protease activity, which suggests that AI-2 may act as a negative regulator of empA . In this study we confirmed that VanT is required for empA expression in NB10 when incubated in LB20; however, when NB10 or M93Sm cells are incubated in NSSM, a small percentage of protease activity remains in the vanT mutants. Our data support the observation by Croxatto et al [ 13 ] that VanT positively regulates empA expression. Further, the role of VanT as a transcriptional activator of empA via the lux box-like element has not been established. Our data show that VanT does not bind to the empA promoter lux box-like region, but is required for maximum protease activity. In addition, we show that in NB10 an unknown protein binds to the empA promoter- lux box region that may increase protease activity in all conditions. In contrast, M93Sm protein binding to this region is not observed and protease activity is detected only when cells are incubated in mucus. In conclusion, the expression of the empA virulence gene in V. anguillarum is regulated transcriptionally by the alternative σ-factor RpoS (σ S ) and the transcriptional activator VanT (a LuxR Vh homologue). Additionally, an unknown DNA binding protein binds specifically to the empA promoter lux box-like region in NB10 cells, but not in M93Sm cells. We hypothesize that this protein permits empA expression in the absence of the inducing factors in fish gastrointestinal mucus. A better understanding of how and why these two wild-type strains of V. anguillarum exhibit different levels of empA expression will further reveal how virulence genes are regulated in this fish pathogen. Conclusions Differences in empA expression between the two strains of V. anguillarum , M93Sm and NB10, correspond with the production of an unknown regulatory protein that binds to a 50 bp oligomer identical to the promoter- lux box region of empA . For NB10, protein binding to the oligomer correlated with the expression of empA in all incubation conditions. However, M93Sm, which expresses empA only in mucus, did not exhibit protein binding in this condition. Additionally, a null mutation in vanT ( luxR homologue) results in severely decreased or no empA expression, but does not abolish protein binding (in NB10) to the oligomer. Our data suggest that while empA expression in both V. anguillarum M93Sm and NB10 is dependent upon RpoS and VanT, the two strains differ in production of an unknown protein that binds to the promoter- lux box-like region of empA . We also hypothesize that the presence of this DNA binding protein permits NB10 cells to express empA in the absence of fish gastrointestinal mucus. Methods Bacterial strains, plasmids, and growth conditions All bacterial strains and plasmids used in this report are listed in Table 1 . V. anguillarum strains were routinely grown in Luria-Bertani broth + 2% NaCl (LB20) [ 18 , 27 ] supplemented with the appropriate antibiotic, on a rotary shaker at 27°C. Escherichia coli SM10 was grown in LB10 (LB + 1% NaCl) on a rotary shaker at 37°C. Experimental media included: LB20, 3M (marine minimal media) [ 18 , 28 ], nine salt solution (NSS, a carbon-, nitrogen-, and phosphorus-free salt solution) and NSS plus 200 μg mucus protein/ml (NSSM) [ 18 ]. Gastrointestinal mucus was harvested from Atlantic salmon as previously described by Garcia et al. [ 18 ]. Overnight cultures of V. anguillarum were grown in LB20, centrifuged (9,000 × g , 10 min), and pelleted cells washed twice with NSS [ 18 ]. Washed cells were resuspended to appropriate cell densities in experimental media. Specific conditions are described in the text for each experiment. Cell densities were determined by serial dilution and plating on LB20 agar plates or by measuring optical density at 600 nm (OD 600 ). Antibiotics were used at the following concentrations: streptomycin (Sm), 200 μg/ml and chloramphenicol (Cm), 5 μg/ml. Bacterial matings Plasmids were introduced into V. anguillarum M93Sm and NB10 from E. coli SM10 by conjugation using the procedure described by Milton et al. [ 11 , 29 , 30 ]. Briefly, overnight cultures of V. anguillarum M93Sm or NB10 and E. coli SM10 containing the pNQ705-1 vanT clone, pNQVanT, were prepared and mixed using ratios of 1:1 or 3:1 (recipient: donor) in NSS plus 10 mM MgSO 4 . The cell suspension was vacuum filtered onto a 0.22 μm nylon membrane, which was placed on an LB15 (LB + 1.5% NaCl) agar plate and allowed to incubate overnight at 27°C. Following incubation, the cells were removed from the filter by vigorous mixing in NSS plus 10 mM MgSO 4 . The cell suspension (100 μl) was plated on LB20 Sm 200 Cm 5 (for M93Sm mutants) or on TCBS Cm 5 (for NB10 mutants) and allowed to incubate at 27°C until V. anguillarum colonies were observed (usually 16–24 h). Site-directed mutagenesis of vanT Site-directed mutagenesis was used to create gene interruptions within the structural gene of vanT . Primers (Table 2 ) were generated based on the vanT sequence for V. anguillarum NB10 (accession # AF457643). A 320 bp region from vanT was PCR amplified using Qiagen Taq DNA polymerase and cloned into the suicide vector, pNQ705, using SacI and XbaI restriction endonucleases to yield pNQVanT. The presence of the 320 bp empA -derived insert was confirmed by both PCR amplification and restriction analysis using SacI and XbaI . The mobilizable suicide vector, pNQVanT, was transferred into V. anguillarum by conjugation with E. coli SM10. E. coli SM10 contains the λ pir protein that is required for replication of pNQ705. Chloramphenicol resistant colonies were selected and screened for insertion within vanT . PCR and Southern blot analysis were used to confirm the incorporation of pNQVanT. For PCR analysis, a primer described previously by Milton et al. [ 11 ] complementary to the pNQ705 vector was utilized (Table 2 ). The forward primer, SD vanT -Forward (Table 2 ) is complementary to a region upstream of the insertion. PCR products were analyzed by electrophoresis through a 1.0 % agarose gel in Tris-acetate EDTA (TAE) [ 31 ] buffer containing 0.2 μg/ml ethidium bromide. The gene was interrupted within the 320 bp region of vanT rendering the mutants resistant to chloramphenicol at 5 μg/ml. The resulting V. anguillarum vanT mutants were designated M02 (derived from M93Sm) and NB02 (derived from NB10) (Table 1 ). Gel mobility shift assay Protein extracts were prepared from 2 ml of cells using the Sigma CelLytic™ B bacterial cell lysis/extraction reagent containing 0.2 mg/ml lysozyme, 1 mM DTT, 2 mM EDTA, and 1 mM phenylmethylsulfonyl fluoride (PMSF). Briefly, the cells were pelleted from the experimental conditions by centrifugation at 9,000 × g for 10 min, 4°C. In addition, cells were lysed by multiple freeze-thaw cycles in crushed dry ice or in liquid nitrogen and then heated at 37°C. Insoluble cell debris was centrifuged at 12,000 × g for 10 min, 4°C. Protein concentration was determined using the Bradford assay (Bio-Rad). The digoxigenin (DIG) gel shift kit for 3'-end labeling of oligonucleotides (Roche Applied Science, Indianapolis, IN) was used for protein-DNA binding assays. The 50 bp oligomers (Table 2 ) used contain the empA promoter region and putative lux box. These oligomers were synthesized and purified by HPLC (Integrated DNA Technologies, Coralville, IA). The oligomers were annealed, labeled, and used in the gel shift reactions according to the manufacturer's instructions (Roche). An 8% native polyacrylamide gel in 0.25 × TBE (Tris-borate-EDTA buffer) was prepared and used for electrophoresis (in 0.5 × TBE) of the gel shift reactions. Blotting was performed using a Biorad electro-blotting system (model Trans blot) according to the manufacturer's instructions. Chemiluminescence detection of DIG-labeled DNA-protein complexes on the nylon membranes was detected using Hyperfilm ECL (Amersham Pharmacia). Gel mobility shift fluorograms were examined quantitatively using a densitometer (Molecular Dynamics, Personal Densitometer, SI). All gel mobility shift experiments were repeated three times and the data presented are representative examples of each experiment. Detection and quantification of protease activity Culture supernatants were assayed for proteolytic activity using our previously described modification of the method by Windle and Kelleher [ 17 , 32 ]. Briefly, culture supernatant was incubated with azocasein (5 mg/ml) dissolved in Tris-HCl (50 mM, pH 8.0) containing 0.04% NaN 3 . Culture supernatant was prepared by centrifuging 1 ml of cells at 12,000 × g (10 min, 20°C). Supernatant was removed and filtered through a 0.22 μm pore size cellulose-acetate filter. Filtered supernatant (100 μl) was incubated at 30°C with 100 μl of azocasein solution. Incubation time of 30 min was sufficient for assays of supernatants from cell suspensions of ≥ 5 × 10 8 cells/ml. Reactions were terminated by addition of trichloroacetic acid (TCA) (10% wt/vol) to a final concentration of 6.7% (wt/vol). The mixture was allowed to stand for 1 to 2 min and centrifuged (12,000 × g , 4 min) to remove unreacted azocasein, and supernatant containing azopeptides was suspended in 700 μl of 525 mM NaOH [ 32 ]. Absorbance of the azopeptide supernatant was measured at 442 nm using a Pharmacia Ultrospec 4000 spectrophotometer. A blank control was prepared by boiling V. anguillarum M93Sm supernatant (100°C, 10 min). TCA (final concentration 10%) was added to the blank control supernatant immediately after the addition of azocasein. The mucus used was also boiled (10 min) to destroy any inherent protease activity. Protease activity units were calculated using the following equation: 1 protease activity unit = [1000 (OD 442 )/CFU] × (1 × 10 9 )] [ 17 ]. RNA isolation and RT-PCR conditions Total RNA was isolated from V. anguillarum cells as previously described [ 12 ] using the RNeasy purification kit (QIAGEN). Reverse transcriptase (RT) reactions were performed using the OneStep RT-PCR system from QIAGEN. Prior to the RT reaction, RNA samples (2 μg) were treated with RQ1 (RNase-free) DNase (1 U/μl) according to the manufacturer's specifications (Promega, Madison, WI.). Primers used for RT-PCR reactions were vanT -F2 and vanT -R2 (Table 2 ). PCR amplification without the addition of RT was performed on an equal amount of RNA to demonstrate the absence of DNA. Amplification products were analyzed by agarose gel electrophoresis and sizes were determined using Kodak digital imaging software (Kodak 1D image analysis software, version 3.0.2; Eastman Kodak Co., Rochester, N. Y.). DNA sequencing DNA sequencing was performed by the University of Rhode Island Genomics and Sequencing Center (Kingston, RI). Sequencing was performed on a Beckman-Coulter CEQ 8000. The Dye Terminator Cycle Sequencing (DTCS) quick start kit was used for the sequence reactions that were prepared according to the manufacturer's specifications and run in a thermal cycling program. DNA samples were mixed with the appropriate primer (Table 2 ) and then submitted for sequencing. Authors' contributions S.M.D. carried out the experimental part of the study and drafted the manuscript. D.R.N. conceived of the study, participated in its design and coordination, and edited the manuscript. P.S. participated in the experiment design and assisted with gel shift analysis. All authors have read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC529440.xml

555560

BrdU-positive cells in the neonatal mouse hippocampus following hypoxic-ischemic brain injury

Background Mechanisms that affect recovery from fetal and neonatal hypoxic-ischemic (H-I) brain injury have not been fully elucidated. The incidence of intrapartum asphyxia is approximately 2.5%, but the occurrence of adverse clinical outcome is much lower. One of the factors which may account for this relatively good outcome is the process of neurogenesis, which has been described in adult animals. We used a neonatal mouse model to assess new cells in the hippocampus after H-I injury. Results Neonatal mice underwent permanent unilateral carotid ligation on the seventh postnatal day followed by exposure to 8% hypoxia for 75 minutes. The presence of new cells was determined by bromodeoxyuridine (BrdU) incorporation into cells with sacrifice of the animals at intervals. Brain sections were stained for BrdU in combination with neuronal, glial, endothelial and microglial stains. We found a significant increase in BrdU-positive cells in the neonatal mouse hippocampus in the injured area compared to the non-injured area, most prominent in the dentate gyrus (DG) (154.5 ± 59.6 v. 92.9 ± 32.7 at 3 days after injury; 68.9 ± 23.4 v. 52.4 ± 17.1 at 35 days after injury, p < 0.0011). Among the cells which showed differentiation, those which were stained as either microglial or endothelial cells showed a peak increase at three days after the injury in the DG, injured versus non-injured side (30.5 ± 17.8 v. 2.7 ± 2.6, p < 0.0002). As in the adult animal, neurogenesis was significantly increased in the DG with injury (15.0 ± 4.6 v. 5.2 ± 1.6 at 35 days after injury, p < 0.0002), and this increase was subsequent to the appearance of the other dividing cells. Numbers of new oligodendrocytes were significantly higher in the DG on the non-injured side (7.0 ± 24.2 v. 0.1 ± 0.3, p < 0.0002), suggesting that oligodendrocyte synthesis was reduced in the injured hippocampus. Conclusion These findings demonstrate that the neonatal animal responds to brain injury with neurogenesis, much like the adult animal. In addition, H-I insult leads to more neurogenesis than hypoxia alone. This process may play a role in the recovery of the neonatal animal from H-I insult, and if so, enhancement of the process may improve recovery.

Background The incidence of intrapartum asphyxia was recently determined to be 2.5% in a large population of singleton pregnancies [ 1 ]. Even so, the occurrence of adverse neurological outcome following delivery is actually much lower. There are undoubtedly a number of reasons why greater numbers of infants do not suffer lasting neurological impairment, but one of the reasons may be the formation of new cells in the damaged tissue. Recent studies have demonstrated the presence of multipotent neural stem cells in the subventricular zone and the subgranular zone of the hippocampus [ 2 ]. Hippocampal stem cells from the adult brain have been demonstrated to proliferate and differentiate into neurons, astrocytes, and oligodendrocytes [ 3 ], as a response to multiple factors, including hypoxic-ischemic (H-I) injury, or trauma [ 4 , 5 ]. New neuronal growth has been demonstrated in several studies of adult H-I models, including in humans [ 6 - 10 ], but this finding has received less attention in neonatal animals. The pluripotent stem cells found in the neonatal brain have not been characterized as completely as those in the adult brain. As this process may be relevant to the recovery of the neonatal brain from H-I injury, we sought to determine whether new cells arose in the neonatal brain following this type of injury. Bromodeoxyuridine (BrdU) is a thymidine analog incorporated into the DNA of dividing cells, rendering them detectable by immunohistochemical means [ 11 ]. We employed a neonatal mouse H-I brain injury model with BrdU injection to determine what types of BrdU-positive cells are present in the hippocampus after an H-I event and to determine if this response if different than that due to hypoxia alone. Results Figure 2 shows the area of damage produced in the affected hippocampus. The majority of the BrdU positive/NeuN positive cells were found in the granule cell layer on the experimental side. The side of the hippocampus contralateral to ligation, although also exposed to hypoxia, did not demonstrate the same degree of injury, remaining histologically normal in appearance. The finding that common carotid artery occlusion or hypoxia alone does not cause apparent brain damage has been previously reported [ 12 , 13 ]. Thus, we compared the hypoxia alone side of the brain to the hypoxic-ischemic side of the brain in the statistical analyses. Regional differences The DG on the hypoxic-ischemic side of the brain exhibited significantly higher numbers of BrdU-positive cells at P10, P17, and P42 than either CA1 or CA3. BrdU/RCA-positive cells were significantly higher at P10 in the DG. The morphology and location of the BrdU/RCA-positive cells suggested that they were endothelial or microglial cells. BrdU/NeuN-positive cells were higher at P17 and P42 in the DG (Table 1 ). Hypoxia alone versus hypoxic-ischemic side differences Table 2 shows the trends in the DG at the three sacrifice times for hypoxia alone and hypoxic-ischemic sides of the brain. BrdU-positive cells without co-labeling and BrdU/RCA-positive cells occur first after the H-I injury (significant increase at P10), followed by BrdU/NeuN-positive cells (significant increase at P42). Colabeling of BrdU and NeuN was confirmed by confocal microscopy (Figure 2 ). For BrdU/CNPase-positive cells the only significant difference was seen in the DG at 10 days, where the hypoxia alone side was significantly higher (7.0 ± 24.2 v. 0.1 ± 0.3, p < 0.0002). No significant differences were seen in the BrdU/GFAP-positive cells when the hypoxic-ischemic side was compared with the hypoxia alone side. Both GFAP positive and CNPase positive cells without BrdU staining were seen in appropriate areas. Discussion The hippocampus has been the focus of much research involving H-I injury, both for its susceptibility to H-I injury and its regenerative capacity due to the adult neural stem cells found there. The bulk of this work has been compiled using adult models of H-I injury. In the neonatal mouse, the degree of damage to the hippocampus from this type of injury has been found to be variable, based on multiple factors, including the strain of mouse used [ 16 ]. In addition, the timing of the insult has been demonstrated to affect the severity of damage. The mouse hippocampus is remarkably resistant to H-I injury at 2–3 post-natal days but becomes progressively vulnerable, and by age 13 post-natal days hippocampal damage exceeds that of cortex [ 16 ]. Scheepens et al [ 17 ] found an increase in proliferation of hippocampal cells following global asphyxia in the newborn rat, much as we found in the model we used, but these investigators did not assess maturation of the proliferating cells into neurons and other cell types. In one study neonatal hypoxia alone triggered neurogenesis in a neonatal rat model [ 18 ]. The changes they noted were mainly in the CA1 region, as opposed to the DG, where we found the primary changes. Because the hippocampus is vulnerable to H-I damage and has the potential for neurogenesis, yet has not been extensively examined in a neonatal model, the hippocampus was the focus for this study. We can summarize the regenerative process as follows: the initial phase is characterized by comparatively large numbers of incompletely differentiated cells, primarily in the DG (10 days of age or 3 days after injury), followed rapidly by the appearance of BrdU/RCA-positive cells, again mainly in the DG, and then by BrdU/NeuN-positive cells (putative neurons) developing only in the DG (at 42 days of age). There were minimal numbers of BrdU/GFAP-positive cells observed at all points. Ischemia has been shown to increase neurogenesis in several adult animal models of brain ischemia. Following global cerebral ischemia in adult gerbils, neurogenesis has been shown to increase in the hippocampal SGZ, while newly born neurons were demonstrated in the CA1 pyramidal cell layer [ 8 ]. In another study of adult gerbils, after 10 minutes of bilateral carotid occlusion, newborn cells with a neuronal phenotype were first seen 26 days after injury [ 5 ]. In the adult rat, ischemia has been shown to increase the incorporation of BrdU-positive cells co-expressing neuronal markers in both the DG and the SVZ [ 7 ]. Furthermore, this effect was more prominent on the side of ischemic insult [ 7 ]. Similar studies in the adult rat demonstrate that up to 60 percent of the new cells found after this type of ischemic brain damage demonstrate neuronal characteristics. In adult mice, after transient forebrain ischemia, BrdU-positive cells have been demonstrated to significantly increase in the DG [ 4 ]. In view of the fact that we observed the BrdU/NeuN-positive cells to increase only in the DG, it seems probable that the regenerative process in neonates is similar to that in adult animals. The data strongly suggested that the development of oligodendrocytes (BrdU/CNPase-positive cells) was impaired in the DG on the hypoxic-ischemic side of the brain, with significantly fewer of these cells observed in the hypoxic-ischemic side as compared to the hypoxia alone side at 10 days. This finding is in contradiction to findings reported by Zaidi et al [ 19 ], who demonstrated that neonatal H-I injury in rats led to increased numbers of oligodendrocytes ipsilateral to H-I injury. There are several possible explanations for this discrepancy. In our study, oligodendrocytes were found to be decreased on the side of the brain ipsilateral to carotid ligation at P10. In the Zaidi study, they were found to be increased at P35. It is possible that oligodendrocytes proliferate at the site of damage after P10. It is also possible that the oligodendrocytes are at the site of damage at P10, but are not mature enough to stain with CNPase. Lastly, the Zaidi group evaluated the SVZ, while we reported dual staining in the hippocampus. There could be regional differences between the two. White matter damage in neonates is thought to be in part due to vulnerability of the immature oligodendrocyte to H-I damage [ 20 ]. In several neonatal animal models, oligodendrocytes have been demonstrated to be susceptible to H-I injury [ 21 - 24 ]. This effect seems to be dependent on both the degree of H-I damage [ 24 ] and the timing of the injury, with late oligodendrocyte progenitors being more susceptible than early oligodendrocyte precursors or more mature oligodendrocytes [ 25 ]. Our results were consistent with these reports. This window of vulnerability of the oligodendrocyte coincides with the human population at high risk for the development of PVL. This vulnerability of the immature oligodendrocytes might explain the paucity of new, mature oligodendrocytes in our model on the hypoxic-ischemic side [ 26 ]. We found a significant, early increase in BrdU-positive cells expressing RCA, an endothelial cell marker and microglial marker, on the side of the brain affected by hypoxic-ischemic injury. The mechanisms of angiogenesis in the brain after H-I injury are not well understood, and there is also little known about angiogenesis in neonatal animal models. Vascular endothelial growth factor (VEGF) is endothelial cell specific and has been implicated in hypoxia-mediated angiogenesis [ 27 ]. VEGF has also been demonstrated in microglial cells after cerebral infarct in adult rats [ 27 ]. In the rat brain, angiogenesis is not completed until about post-natal day 20 [ 28 ]. Therefore, it remains a possibility that the increase in cells co-labeling for BrdU and RCA after H-I injury in our study partly represents the normal brain maturation process. However, the increase of these cells on the hypoxic-ischemic side of the brain would imply that the injury led to this effect. Further studies will be needed to more accurately identify if there are changes in angiogenesis following H-I injury. The majority of these BrdU positive/RCA positive cells appeared morphologically to be activated microglia, which are thought to play a role in the damage to immature oligodendrocytes [ 20 ]. The present study did not demonstrate an increase in BrdU-positive cells co-labeling for an astrocyte marker, GFAP, after H-I injury. Astrocytes have been demonstrated to express apoptotic enzymes within hours after H-I injury in neonatal rats [ 29 ]. In neonatal piglets, GFAP-positive cell bodies were reduced by roughly 50% at 48 hours after H-I injury, but subsets of astrocytes were subsequently shown to proliferate later after the insult [ 30 ]. In adult rats, the number of astrocytes was shown to remain unchanged after traumatic brain injury in one study [ 31 ], while in another similar study, there was loss of GFAP reactivity in the ipsilateral CA3 region of the hippocampus 30 minutes after injury with progressive astrocyte loss over the next 24 hours [ 32 ]. As evidenced by the variability reported in the astrocytic response to H-I brain injury, the response of this cell in this environment needs to be better characterized. Because astrocytes help to maintain the extracellular milieu needed for neuronal formation and function, it is possible that their early loss after H-I injury may contribute to subsequent neuronal degeneration. Future studies in our neonatal mouse model could be geared towards better understanding the early response of astrocytes. Postponing evaluation until three days post-injury in this model likely resulted in underestimation of the astrocytes' role in this injury repair process. Chronic hypoxia has been demonstrated to induce astrocytes into more immature phenotypes, which do not express normal levels of GFAP [ 33 ]. The ability of acute hypoxia to cause this effect is not well understood. Therefore, it remains a possibility that evaluating for immature astrocytes in this model would have resulted in the visualization of more astrocytic cells. Although the BrdU-positive cells in our study were co-labeled with stains identifying four separate types of cells, there were many BrdU-positive cells that were not identified by these four stains. The co-labeling studies we performed accounted for only about 20% of all the BrdU-positive cells. The BrdU labeling protocol we used consisted of BrdU injections over the course of the experiment, rather than a one time injection at the time of H-I injury, which resulted in the staining of more cells than would be found following a single injection. In this manner, we were able to evaluate how each cell type varied its number and location at different intervals after the injury. This provided information about the cellular response to hypoxic-ischemic brain injury in both the acute and long-term settings. The role and identity of the non-co-labeled cells remains unclear. It is probable that these were immature cell types, not yet expressing the markers for which we evaluated. The validity of cells staining for BrdU representing new or dividing cells versus apoptotic or necrotic cells is also an issue to be considered. As a result, TUNEL staining was performed on several hippocampal sections. At P10, three days after H-I injury, TUNEL staining does reveal apoptotic and necrotic nuclei. However, by P42, this finding is virtually non-existent, suggesting that any BrdU-positive cells are present due to cell proliferation or growth. (See Figures 4 and 5 .) Previous studies have indicated that neurons subjected to H-I injury do, indeed, incorporate BrdU, but that these injured neurons which undergo apoptosis have disappeared by 28 days [ 34 ]. A possible criticism of the present study is that we did not count the cells with stereological methods. As a result, our findings might be due to increased cell density due to surrounding tissue loss as opposed to an increase in the absolute number of new cells present. However, the cell counts were conducted in non-overlapping sections within a very short segment of the anterior hippocampus, in which the sections' thickness remained constant. No gross differences were observable in brain size in either the coronal or sagittal planes. Furthermore, hippocampal volume on both the hypoxia alone and hypoxic-ischemic sides of the brain did not vary noticeably histologically. Lastly, confocal imaging revealed cell density on each side of the brain to be roughly equivalent, although exact volumetric measurements can not be made without stereologic techniques. Conclusion Neuronal and other cell regeneration was significantly increased in the DG on the side of the brain exposed to H-I injury, as opposed to hypoxic injury alone. This study addresses the neonatal mouse brain's ability to repair H-I damage and can serve as a platform to determine if interventions can be made to augment these inherent repair mechanisms. The rate of neuronal regeneration found in our study, while showing a significant increase in the DG, was not robust. Perhaps the stimulus was insufficient to result in a great degree of neurogenesis. The degree of brain injury was not stratified in this study. Neurogenesis might prove to be proportional to the degree of injury. It is also possible that this regeneration could increase further with time, but this is unlikely because new cell production was decreasing by the final time point after the injury in our study. Stem cell transplantation or manipulations of factors affecting intrinsic stem cell activity could be applied to this system in the future. Methods Animal procedures This study was performed in accordance with the guidelines provided by the Laboratory Animal Studies Committee of the Veterans Administration Hospital in Augusta, GA. Under 2% isoflurane anesthesia C57 BL/6 mouse pups underwent permanent ligation of the left common carotid artery at post-natal day of life seven (P7). The pups were then placed with the dams for two hours prior to placement in an 8% oxygen chamber partially immersed in a water bath at 37° for 75 minutes. This procedure was described previously in the rat by Levine [ 12 ] and Rice et al [ 13 ], and then adapted to the mouse by Sheldon et al [ 14 ]. Within the literature, there is variation of the neonatal mouse H-I brain injury protocol. Our seventy-five minute hypoxia time was determined by exposing animals to varying intervals of hypoxia. In our laboratory, seventy-five minutes of hypoxia time resulted in reliable, reproducible brain injury, with minimal animal death. Any animal that died during surgery, while in the hypoxia chamber, or prior to sacrifice time was excluded. The time at which the animal would be sacrificed was determined prior to surgery. Forty-eight hours after the H-I injury, the pups were injected intraperitoneally with BrdU, at a dose of 100 mg/kg. This dose was administered twice weekly until the pups were sacrificed at 3, 10, or 35 days after the injury (see Figure 1 ) with 70 mg/kg of ketamine and 15 mg/kg of xylazine, and tissue fixation done by transcardiac perfusion with 0.9% saline, followed by 4% paraformaldehyde in PBS. BrdU injection was continued twice weekly after injury until the time of sacrifice in order to elucidate if the types of new cells found in the hippocampus varied at different time intervals after the injury. This protocol allowed evaluation of an accumulation of cells, reflecting both the acute and long-term cellular response to hypoxic and hypoxic-ischemic injury. Brains were removed and postfixed in 4% paraformaldehyde for 4 hours, cut into 3–5 mm sections, and postfixed in 4% paraformaldehyde for an additional 20 hours. Tissue was then dehydrated in an ethanol series, cleared in xylene, and infiltrated and embedded in PolyFin Embedding and Infiltration Wax (TBS Biomedical Sciences, Durham, NC). Paraffin embedded tissue was sectioned to 5 μ thickness and mounted on Fisher Superfrost Plus slides (Fisher Scientific). Immunohistochemistry and cell counting Paraffin was removed from slides using xylene, followed by rehydration in an alcohol dilution series. Slides were soaked in 0.1% Triton-X 100 in PBS for ten minutes to increase permeability of fixed tissue, followed by rinsing in 1X PBS. Antigen retrieval was performed using a microwave method. Slides were incubated for twenty minutes after slow boiling for ten minutes and then rinsed in PBS and blocked using 2% normal calf serum for 20 minutes. Sheep anti-BrdU antibody (Biodesign #M20105S), diluted 1:200 in PBS, was selected for the visualization of new cells. Co-labeling runs used mouse anti-NeuN (Chemicon #MAB377) 1:2000 for the visualization of neurons, rabbit anti-GFAP (DAKO # Z0334), diluted 1:300 in PBS for the visualization of astrocytes, biotinylated RCA (Vector #B-1085) diluted 1:300, for the visualization of endothelial cells and microglia cells, and CNPase (Sigma #C-5922), diluted 1:200, for the visualization of oligodendrocytes. All primary antibodies except NeuN were allowed to incubate at room temperature for one hour. Primary NeuN antibody was allowed to incubate for thirty minutes at room temperature, and washed three times in a 1X PBS solution. Biotinylated anti-mouse antibody (Vector Elite ABC Kit #PK-6102), diluted 1:200 in 1.5% normal horse serum, was applied to each section and incubated for thirty minutes at room temperature. Slides were washed in a PBS series before applying Vector Elite ABC reagent and incubation for thirty minutes at room temperature. Sections were washed again in PBS and TSA amplification reagent (NEN #SAT700) with biotinylated tyramide 1:200 in amplification buffer was applied. Sections were incubated for eight minutes at room temperature. Preliminary staining demonstrated that only NeuN antibody required amplification. All other antibodies were used in an indirect staining method, with a secondary, fluorescent antibody. Cy3 anti-Sheep (Jackson # 713-165-147), diluted 1:400, was used to visualize BrdU in all cases. FITC anti-rabbit (Jackson # 711-095-152), diluted 1:100, was used to visualize GFAP. Strepavidin FITC (Jackson #016-010-084), diluted 1:100, was used to visualize biotinylated RCA and NeuN. FITC anti-mouse (Jackson #715-095-151), diluted 1:100, was used to visualize CNPase. To reduce background, slides were rinsed in 4 changes of PBS for 5 minutes each. Secondary antibodies were applied and incubated for 1 hour. Slides were rinsed in PBS, and immersed in Hoechst Stain for 8 minutes. Another rinse series in PBS was applied before coverslipping, using Vectashield Mounting medium. (Vector #H-1000) Cells were counted in 4–6 animals at each time interval. For each animal, the cells were counted from the injury area in the anterior hippocampus (equivalent adult mouse interaural coordinates 2.10 to 1.98 mm) [ 15 ] in CA1, CA3, and DG. (In Figure 2 , the circles show the specific fields, CA1, CA3, and the DG, counted under one 40× magnification field in each section of brain. Corresponding areas of hippocampus were estimated in each section of brain when choosing the 40X field.) At least three brain sections from each animal for each stain type were counted. Therefore, each brain had three CA1, three CA3, and three DG 40X fields stained and counted with BrdU, one of the four co-labeling cell markers, and Hoescht stain. In each case, the same observer performed the cell counting in all brain sections. The counter could not be blinded due to the loss of normal architecture on the side of the brain consistent with H-I injury. A separate observer did count numerous sections in order to ensure consistency and accuracy. Each 40X field counted by separate individuals was found to have consistent data. Counting was completed by counting the total number of cells labeled for BrdU only and for BrdU co-labeled with one of the four the cell markers found in one field under 40× magnification in each of the three regions in the hippocampus, on both the hypoxia alone and hypoxic-ischemic side of the brain. Due to the large size of the hippocampus and the H-I injury, analysis was confined to one level of the brain cut through the middle of the injury. Any BrdU-only cells with abnormal morphology and any co-labeled cells were confirmed by corresponding Hoescht staining. Confocal imaging To confirm dual labeling, confocal imaging was performed in selected cells with a Zeiss 510 laser scanning confocal microscope with the use of Zeiss software. The objective used was Zeiss63x C-Apochromat 1.2 NA. To excite the FITC fluorochrome (green), a 488-nm laser line generated by an argon laser was used, and for the Cy3 fluorochrome (red), a 543-nm laser line from a HeNe laser was used. Filter sets used were a bandpass 500- to 600- filter ("green" channel) and a long-pass 585- to 650-nm filter ("red" channel). We identified cells that were labeled for BrdU and the neuronal marker NeuN. These cells were then examined by confocal microscopy, and 1-μm step Z series were obtained. (See Figure 3 .) TUNEL staining Staining for the presence of apoptotic cells was completed using the ApopTag Peroxidase Kit (Chemicon #S7100), according to manufacturer recommendations. Therefore, after rehydration, the tissue was treated with Proteinase K (20 μg/mL) and incubated for 15 minutes. Endogenous peroxidases were quenched with hydrogen peroxide. Sections were then treated with equilibration buffer and TdT enzyme treatment. Next, the sections were stained with anti-dioxigenin peroxidase conjugate for 30 minutes, and the slides were developed with stable DAB. Statistical analyses Nested, repeated measures analysis of variance was used to examine differences in number of cells between times of sacrifice (10, 17, or 42 days), side of brain (hypoxia alone or hypoxic-ischemic injury), region of the hippocampus (CA1, CA3, DG) and antibody type (CNPase, GFAP, NeuN, and RCA). A second analysis was performed using only BrdU-positive cells. Post hoc analyses were performed using the adjusted least square means and applying a Bonferroni correction to the overall alpha level. Statistical significance was assessed using an alpha level of 0.05. All analyses were performed using SAS 8.2. Abbreviations hypoxic-ischemic – H-I, bromodeoxyuridine – BrdU, subventricular zone – SVZ, subgranular zone – SGZ, dentate gyrus – DG, glial fibrillary acidic protein – GFAP, 2'3'-cyclic nucleotide 3'-phosphodiesterase -CNPase, Ricinus communis agglutinin I –s RCA, neuronal nuclear marker – NeuN

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555560.xml

555548

A simplified high-throughput method for pyrethroid knock-down resistance (kdr) detection in Anopheles gambiae

Background A single base pair mutation in the sodium channel confers knock-down resistance to pyrethroids in many insect species. Its occurrence in Anopheles mosquitoes may have important implications for malaria vector control especially considering the current trend for large scale pyrethroid-treated bednet programmes. Screening Anopheles gambiae populations for the kdr mutation has become one of the mainstays of programmes that monitor the development of insecticide resistance. The screening is commonly performed using a multiplex Polymerase Chain Reaction (PCR) which, since it is reliant on a single nucleotide polymorphism, can be unreliable. Here we present a reliable and potentially high throughput method for screening An. gambiae for the kdr mutation. Methods A Hot Ligation Oligonucleotide Assay (HOLA) was developed to detect both the East and West African kdr alleles in the homozygous and heterozygous states, and was optimized for use in low-tech developing world laboratories. Results from the HOLA were compared to results from the multiplex PCR for field and laboratory mosquito specimens to provide verification of the robustness and sensitivity of the technique. Results and Discussion The HOLA assay, developed for detection of the kdr mutation, gives a bright blue colouration for a positive result whilst negative reactions remain colourless. The results are apparent within a few minutes of adding the final substrate and can be scored by eye. Heterozygotes are scored when a sample gives a positive reaction to the susceptible probe and the kdr probe. The technique uses only basic laboratory equipment and skills and can be carried out by anyone familiar with the Enzyme-linked immunosorbent assay (ELISA) technique. A comparison to the multiplex PCR method showed that the HOLA assay was more reliable, and scoring of the plates was less ambiguous. Conclusion The method is capable of detecting both the East and West African kdr alleles in the homozygous and heterozygous states from fresh or dried material using several DNA extraction methods. It is more reliable than the traditional PCR method and may be more sensitive for the detection of heterozygotes. It is inexpensive, simple and relatively safe making it suitable for use in resource-poor countries.

Background The successful trials of pyrethroid insecticide-treated nets for malaria control in various endemic settings has led to the Roll Back Malaria initiative adopting the approach as one of the cornerstones of its malaria control programmes [ 1 - 3 ]. However, the increasing prevalence of insecticide resistance in Anopheles gambiae , the major vector of malaria in sub-Saharan Africa, threatens to compromise the successful use of insecticide-treated materials [ 4 ]. Resistance to pyrethroid insecticides was first seen in An. gambiae sensu stricto in West Africa [ 5 ] and has subsequently been detected in East Africa [ 6 ]. Whilst much of the observed resistance is thought to have been selected for by the use of pesticides in agriculture [ 7 ], there is already some evidence in East Africa that the introduction of treated bednets has selected for reduced susceptibility to permethrin [ 6 ]. One allele commonly associated with resistance to permethrin is the knock-down resistance or kdr allele. This allele encodes a modified voltage-gated sodium channel that has reduced sensitivity to DDT and pyrethroids. Molecular studies identified a single point mutation in the kdr allele that causes an amino acid substitution in domain II of the protein [ 8 ]. Two different mutations have been found in An. gambiae ; the first causes a leucine to phenylalanine amino acid change and has been found in several West African countries [ 8 - 11 ], whilst the second found mainly in East African populations causes a leucine to serine substitution at the same amino acid position [ 6 , 12 ]. The importance of these mutations to the control of Anopheles mosquitoes is not yet fully understood. However, monitoring its frequency, as a rapid indicator of the development of resistance, should be an integral component of insecticide resistance management programmes. The most commonly used method for identifying the kdr mutations involves a multiplexed PCR technique. Single Nucleotide Polymorphism (SNP) detection is problematic with simple PCR approaches, requiring the use of highly toxic reagents [ 13 ] or prohibitively expensive equipment. Many of these approaches are difficult to transfer to field laboratories where the ability to monitor gene frequencies is most acutely needed. The technique detailed here, adapted from one originally designed by W.C. Black IV requires only a thermal cycler and provides an easily interpretable, colorimetric genotyping system. No toxic reagents are involved. While this system has been specifically designed to assay kdr resistance allele frequencies in An. gambiae , it is broadly applicable where target-site insensitivity is an important mechanism of resistance to insecticides and to chemotherapeutics. Methods Mosquito strains and bioassays Specimens were obtained from laboratory colonies of RSP (a homozygous line for the East African kdr mutation), Kisumu (a susceptible line from Kenya, established in 1953), and Odumasi (a partially resistant line, not yet fixed for the West African kdr mutation). Adult females were stored at -20°C before extraction. Field caught specimens were collected using resting catches from Asembo in western Kenya in May 2004, and by pyrethrum spray collections in Odumasi, Ghana in June 2003. Samples were dried over silica gel for later analysis. PCR All PCR reactions were performed in ABI GeneAmp ® PCR system 2700 or MJ Research PTC-200 DNA Engine thermal cyclers. Primers Agd1 and Agd2 [ 8 ] were used to amplify a 293 bp fragment from domain II of the voltage-sensitive sodium channel protein sequence (EMBL #Y13592). PCR was carried out with the DNA of 1/80 th or 1/160 th of a single mosquito in a 25 μl volume with a final concentration of 1x Buffer, 2.0 mM MgCl 2 , 0.2 mM dNTP's (Sigma dNTP-100), 0.3 μM each primer (Qiagen), Taq DNA polymerase 0.034 U/μl (Qiagen 201203). Reaction conditions were 94°C for 4 min, 25 cycles of 94°C for 25 sec, 56°C for 20 sec, 72°C for 8 sec; and a final extension step of 72°C for 10 min (modified from [ 12 ]). Artificial heterozygote controls were created using DNA from two homozygous samples. DNA from a single mosquito was extracted using the Livak method, [ 14 ] or the Ballinger Crabtree method [ 15 ] and resuspended in 100 μl or 200 μl of ddH 2 0. Species identification was carried out on all specimens using a PCR method [ 16 ]and specimens were characterized for kdr status using PCR methods [ 8 , 12 ]. PCR products were visualized under UV light on 1.5% agarose, 0.5x TBE gels stained with ethidium bromide. Hot Ligation 3 μl of PCR product from the above reaction was used in a hot ligation with Detector and Reporter oligonucleotides (MWG Biotech) (Table 1 ). Aliquots were made for each oligo pair containing 1 μM detector and 1 μM reporter in ddH 2 0. A 20 μl reaction volume containing 1x Buffer, 50 nM detector and reporter mix and 0.05 U/μl Ampligase ® (Cambio A32250) was set up for each oligo pair. Four reactions were set up for each PCR sample to test for the East and West resistant alleles and the susceptible allele (two different oligo pairs must be used to test for the susceptible allele in these assays, as the potential oligo binding site differs by one base pair). The reaction conditions were 95°C for 5 min, 25 cycles of 94°C for 1 min, 58°C for West African kdr detection or 60°C for East African kdr detection for 2 min; with a final hold at 4°C. Ligated products were kept at 4°C in the dark and used as soon as possible for SNP analysis. Table 1 Oligonucleotide sequences used in the Hot Ligation Description Oligo Name bp Position a Oligo sequence 5' – 3' Modifications Suspt. East kdr detector Kdr104L-DTe 311-15 i ATTTGCATTACTTACGACTA 5' Biotin Resist. East kdr detector Kdr104S-DTe 311-15 i ATTTGCATTACTTACGACTG 5' Biotin East kdr reporter Kdr104-RTe 291–310 AATTTCCTATCACTACAGTG 5' Phosphorylation 3' Fluorescein Suspt. West kdr detector Kdr104L-DTw 312-16 i AATTTGCATTACTTACGACT 5' Biotin Resist. West kdr detector Kdr104F-DTw 312-16 i AATTTGCATTACTTACGACA 5' Biotin West kdr reporter Kdr104-RTw 292–311 AAATTTCCTATCACTACAGT 5' Phosphorylation 3' Fluorescein a Using sequence from Martinez-Torres et al ., as reference; i intron 2 position. SNP Detection 96-well plates (VWR 402 200 402) were prepared using 100 μl of 5 μg/ml streptavidin (Sigma S4762) per well. The plate was left to dry overnight and then washed 4 times in 250 μl of 1 x PBS with 0.1% v/v Tween 20. Buffer was removed by tapping the plate upside down and 200 μl of blocking solution (1x PBS, 0.1%v/v Tween 20, 2%w/v BSA) added for 1 hour. Four more washes of 250 μl with PBS were carried out before plates were covered with a plastic seal and stored at 4°C for up to one week. 20 μl of TNE (10 mM Tris-HCl pH7.5, 1 mM EDTA pH 8.0, 0.2 M NaCl) was added to the hot ligation reaction and then all 40 μl was placed in a well of the streptavidin plate and allowed to incubate at room temperature for 30 min in the dark. The ligation reaction was carefully removed with a multichannel pipette and the plate washed twice in 250 μl of freshly prepared wash buffer 1 (10 mM NaOH, 0.05%v/v Tween 20) and then twice in 250 μl of wash buffer 2 (0.1 M Tris-HCl pH7.5, 0.15 M NaCl, 0.05%v/v Tween 20). 40 μl of 75 mU/ml HSP-conjugated antifluorescein Ab (Roche 1 426 346) solution in 1% w/v BSA solution was placed in each well and incubated at room temperature for 30 min. The plate was then washed three times in 250 μl of wash buffer 2. All buffer traces were removed by tapping the plate upside down on a paper towel and 100 μl of room-temperature TMB solution (Roche BM Blue Pod Substrate 1 484 281) added. At least 5 min were allowed for the colour to develop before plates were scored. Plates were read at 680 nm in a Molecular Devices Versa Max plate reader to provide a quantitative method of scoring which could be compared to the visual method of scoring to check reliability. Results and Discussion A schematic of the HOLA approach is given in Figure 1 and a photograph of the HOLA 96-well plate is shown in Figure 2 . Susceptible individuals score positively for both the East and West African susceptibility tests although a somewhat weaker reaction may be seen in East African susceptible individuals for the West Susceptibility test. Resistant individuals show a positive colour change only for their specific kdr allele. Heterozygotes are easily distinguishable. The protocol presented here for kdr detection is reliable and gives unambiguous results (Table 1 ). Visual and colorimetric scoring results were always comparable (data not shown). A double-blind trial was carried out on 12 wild-caught specimens of An. gambiae from East Africa compared to the commonly used PCR multiplex approach. The genotype was unambiguously determined by the HOLA technique, whereas the PCR results were more difficult to interpret and often required a repeat reaction (Table 2 ). There was one discrepancy between the two approaches which was not resolved after repeated analyses (Specimen Kenya 3, Table 2 ). It is believed that the HOLA method gave the correct result since three HOLA repetitions were carried out on the sample which all scored the specimen as heterozygous. Contamination may be excluded as a cause of this discrepancy as HOLA reactions were performed before and after the PCR tests. Furthermore the kdr allele is rare in the Kenyan population [ 17 ] and so would be much more likely to occur more frequently in a heterozygous rather than homozygous state. Figure 1 Schematic of Hot Oligonucleotide Ligation Assay for West African Allele Figure 2 Photograph of HOLA plate, including DNA extraction method and expected results. Abbreviations: SS, homozygous susceptible. RR, homozygous resistant. RS, heterozygous. a Livak [14] extraction method b Ballinger-Crabtree [15] extraction method c Artificially created heterozygote Table 2 Double blind trial of HOLA approach versus conventional PCR Specimen HOLA PCR 1 e PCR 2 e NK5 a SS SS SS NK6 SS X SS NK7 RR RR RR NK8 SS SS SS Kenya 1 b SS X SS Kenya 2 SS X SS Kenya 3 RS X RR Thyolo 7 c SS SS SS Thyolo 33 SS SS SS Thyolo 34 SS X SS Thyolo 64 SS X SS Thyolo 75 SS X SS RSP d RR RR RR a Specimens labelled NK collected by Pie Muller, Ben Oloo, and Nadine Randle from Asembo, Kenya on 05/2004, DNA extracted by Ballinger-Crabtree method [15] on 09/2004. b Specimens labelled Kenya collected by Pie Muller, Ben Oloo, and Nadine Randle from Asembo Bay, Kenya on 05/2004 DNA extracted by Livak method [14] on 08/2004. c Specimens labelled Thyolo collected by Philimon Tambala and Bill Hawley from Thyolo, Malawi on 01/1995, DNA extracted by Ballinger-Crabtree method [15] on 09/1997. d Specimen from RSP colony. Abbreviations: SS, homozygous susceptible. RR, homozygous resistant. RS, heterozygous. e Conditions for the PCR reactions were identical. The HOLA method allows for over 40 samples to be screened on a single microtitre plate. As shown in Figure 2 , the method works for a variety of DNA extraction techniques, on fresh and stored material. Although costs per reaction are slightly higher than for the traditional multiplex PCR, the greater reliability ensures that repeat reactions are unlikely to be required, reducing costs in the long term. In addition, since this technique dispenses with the need for gel electrophoresis apparatus there is a lower initial equipment outlay, greater comparative safety and greater ease of this technique, making the method ideal for field laboratories. Conclusion The HOLA method allows fresh and stored An. gambiae mosquitoes to be characterized for the East and West African kdr mutations. Homozygotes and heterozygotes can be easily distinguished using low cost equipment and a simple methodology which makes this technique suitable for use in resource-poor countries. In our hands the method is more reliable than the current multiplex PCR approach, less ambiguous and may be more sensitive for the detection of heterozygotes. List of Abbreviations used DNA – Deoxyribonucleic acid. ELISA – Enzyme-linked immunosorbent assay HOLA – Heated oligonucleotide ligation assay. Kdr – Knock down resistance. PCR – Polymerase chain reaction. SNP – Single nucleotide polymorphism. Authors' contributions AL developed the HOLA method for the kdr mutation and drafted the manuscript. HR conceived of the study and participated in its design. NPR carried out the multiplex PCR. PJM and EDW helped draft the manuscript. WCB developed the HOLA technique. MJD participated in the design of the study and substantially helped draft the manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555548.xml

548519

The absence of reactive oxygen species production protects mice against bleomycin-induced pulmonary fibrosis

Background Reactive oxygen species and tissue remodeling regulators, such as metalloproteinases (MMPs) and their inhibitors (TIMPs), are thought to be involved in the development of pulmonary fibrosis. We investigated these factors in the fibrotic response to bleomycin of p47 phox -/- (KO) mice, deficient for ROS production through the NADPH-oxidase pathway. Methods Mice are administered by intranasal instillation of 0.1 mg bleomycin. Either 24 h or 14 days after, mice were anesthetized and underwent either bronchoalveolar lavage (BAL) or lung removal. Results BAL cells from bleomycin treated WT mice showed enhanced ROS production after PMA stimulation, whereas no change was observed with BAL cells from p47 phox -/- mice. At day 1, the bleomycin-induced acute inflammatory response (increased neutrophil count and MMP-9 activity in the BAL fluid) was strikingly greater in KO than wild-type (WT) mice, while IL-6 levels increased significantly more in the latter. Hydroxyproline assays in the lung tissue 14 days after bleomycin administration revealed the absence of collagen deposition in the lungs of the KO mice, which had significantly lower hydroxyproline levels than the WT mice. The MMP-9/TIMP-1 ratio did not change at day 1 after bleomycin administration in WT mice, but increased significantly in the KO mice. By day 14, the ratio fell significantly from baseline in both strains, but more in the WT than KO strains. Conclusions These results suggest that NADPH-oxidase-derived ROS are essential to the development of pulmonary fibrosis. The absence of collagen deposition in KO mice seems to be associated with an elevated MMP-9/TIMP-1 ratio in the lungs. This finding highlights the importance of metalloproteinases and protease/anti-protease imbalances in pulmonary fibrosis.

Background Pulmonary fibrosis is a severe chronic disease with various causes and poor prognosis. Its main histological features include lesions of the alveolar septa, fibroblast and myofibroblast proliferation in lung parenchyma, abnormal reepithelialization, and excessive extracellular matrix macromolecule deposition [ 1 - 3 ]. Lung fibrosis is associated with chronic inflammation and is characterized by the recruitment of macrophages, neutrophils, and lymphocytes in the airways [ 4 ]. During lung inflammation, activated phagocytes release large amounts of reactive oxygen species (ROS), which may be involved in tissue injury and in impeding tissue repair, both of which lead to pulmonary fibrosis [ 4 - 6 ]. Recent studies show that antioxidant compounds such as N-acetylcysteine and bilirubin protect rats against the tissue damage and pulmonary fibrosis induced by bleomycin, an antineoplastic antibiotic commonly used in such experimental models [ 7 , 8 ]. Because these compounds can attenuate the oxidant burden in tissue, they may prevent the lung damage caused by ROS and subsequent fibrosis. Metalloproteinases (MMPs) and their specific inhibitors, the tissue inhibitors of MMPs (TIMPs), are the hallmark of this fibrogenic microenvironment. MMPs are key enzymes that regulate tissue remodeling through turnover of the extracellular matrix in both normal and pathological conditions (for review see [ 9 ]). They play a crucial role in the fibrogenic process, as demonstrated recently through the marked reduction of bleomycin-induced pulmonary fibrosis in mice by batimastat, a selective MMP inhibitor [ 10 ]. Gelatinase A (MMP-2) and gelatinase B (MMP-9) are two MMPs that appear to be involved in pulmonary fibrosis, but their specific roles in the process remain unclear [ 9 ]. While MMP-9 is released primarily by inflammatory cells, MMP-2 is synthesized by structural cells including fibroblasts and endothelial and epithelial cells. Both may be associated with chronic impairment of tissue remodeling and abnormal collagen deposition [ 9 ]. Strong evidence indicates that various MMP/TIMP imbalances are crucial elements in the fibrogenic process. Several authors suggest that a "nondegrading microenvironment" induces fibrogenicity, that is, more specifically, that various events cause TIMP-1 levels to rise in lung tissue, which in turn lowers MMP/TIMP ratios [ 2 , 11 , 12 ]. Bleomycin-induced pulmonary fibrosis, for example, causes the expression of significant levels of TIMP-1 [ 13 , 14 ]. Further study is needed to illuminate the pathway that leads from lung injury, associated with ROS and acute inflammation, to initiation of the fibrogenic process, which involves remodeling mediators such as MMPs and TIMPs. The aim of the present study was to investigate the involvement of the ROS released by inflammatory cells during the development of pulmonary fibrosis and to consider the consequence on MMP/TIMP balances. We therefore examined the fibrogenic response to bleomycin administration in mice deficient for the p47 phox subunit of NADPH-oxidase [ 15 ] and analyzed the variations in the MMP/TIMP balance during this process. Materials and methods Materials This study used the following materials, from the manufacturers mentioned: bleomycin sulfate from Bellon Laboratories (Montrouge, France); gelatin, Triton X-100, Coomassie Brilliant Blue, EDTA, Tween 20 solution, hydroxyproline, and trypan blue from Sigma (St Louis, MO, USA); May-Grünwald and Giemsa stains from RAL (Paris, France); sodium pentobarbital from Sanofi Santé Animale (Libourne, France); etomidate (Hypnomidate ® , 2 mg/mL) from Janssen-Cilag (Issy-les-Moulineaux, France); acrylamide, sodium dodecyl sulfate (SDS), Tris, and BSA from Eurobio (Les Ulis, France); ELISA kits for IL-6, TIMP-1, and pro-MMP-9 detection from R&D Systems (Minneapolis, MN, USA); formaldehyde from Merck (Darmstadt, Germany); isopentane from Prolabo (Fontenay-sous-Bois, France); a low-range weight marker for SDS-PAGE from Biorad (Munich, Germany); and an ABEL ® chemiluminescence kit for measurement of in vitro ROS release from Knight Scientific Limited (Plymouth, UK). Bleomycin administration Ten week-old p47 phox +/+ "wild-type" (WT) and p47 phox -/- "knockout" (KO) mice (origin: LHD/NIAID/NIH, Bethesda, MD, USA) with C57BL/6J backgrounds [ 15 ] were housed under controlled and ethical conditions that complied with the Interdisciplinary Principles and Guidelines for the Use of Animals in Research, Marketing and Education, New York Academy of Sciences' Ad Hoc Committee on Animal Research. Pulmonary fibrosis was induced by intranasal (i.n.) instillation (under etomidate anesthesia, 15 mg i.p.) of 0.1 mg bleomycin sulfate in saline solution (50 μL/mouse). Control mice received saline vehicle only. Either 24 h or 14 days after i.n. administration, mice were quickly anesthetized by an i.p. injection of sodium pentobarbital (60 mg/kg) and underwent either bronchoalveolar lavage (BAL) or lung removal. These samples were stored at -80°C until either hydroxyproline measurement or homogenization for zymography. Bronchoalveolar lavage and preparation of tissue homogenates Mice were anesthetized with an i.p. administration (20 mL/kg) of sodium pentobarbital 0.6%. The BAL protocol called for washing the airways 10 times with 0.5 mL of 0.9% NaCl solution at 37°C with a 1 mL syringe. The BAL fluid was centrifuged (600 g for 10 min, 4°C), and the supernatant of the first two fractions (1 mL) divided into aliquots and frozen at -80°C until analysis. The cell pellets were then pooled with the last fractions. Total cells were counted with a Coulter Z2 ® (particle counter and size distribution analyzer, Beckman Coulter). Red blood cells were eliminated by adding 3 mL of distilled water for 30 seconds and then 1 mL of KCl 0.6 M onto the pellets. After centrifugation (600 g for 10 min, 4°C), supernatant was eliminated and the cells were suspended in 1 mL of PBS. They were then cytospun at 700 rpm for 10 minutes (Cytospin 3 ® , Thermo Shandon, Ltd, Astmoor, United Kingdom) and stained with the May-Grünwald Giemsa method. Differential cell counts of 200 cells used standard morphological criteria. After in vitro stimulation with phorbol 12-myristate 13-acetate (PMA), 0.8 μM, ROS production was assayed with a chemiluminescence technique that used the ABEL ® detection kit. At day 14, following BAL processing, lungs were removed and homogenized with an adapted grinder (Fast-Prep FP 120 cell disrupter, QBiogene Inc., Illkirch, France). Lung tissue homogenates were then stored at -80°C until analysis. Zymographic analysis of MMPs Since MMPs can degrade gelatin, zymographic techniques were used to detect MMPs in both BAL (day 1 and day 14) and lung homogenates (day 14). In nonreducing conditions and in the presence of SDS, as previously described [ 10 ], aliquots of BAL fluid or lung homogenate underwent electrophoresis onto a 6% acrylamide stacking gel/10% acrylamide separating gel containing 1 mg/mL gelatin. After electrophoresis, gels were washed twice with 2.5% Triton X-100, rinsed with water, and incubated overnight at 37°C in 50 mM Tris, 5 mM CaCl 2 , 2 μM ZnCl2, pH = 8. The gels were stained with Coomassie Brilliant Blue in a solution of 25% ethanol-10% acetic acid in water and rinsed in an identical solution. Gelatinase activity appeared as clear bands against blue background. We used recombinant protein molecular weight markers (20 kDa-112 kDa) to estimate the molecular weights of the gelatinolytic bands. Relative enzyme amounts were quantified by measuring the intensity of the bands with a densitometric analyzer (Bio-Profile, Vilber-Lourmat, Marne la Vallée, France). Results were expressed as a percentage of the band of migration of one control BAL sample loaded onto each gel. This sample was used as an internal standard to allow further comparisons between gels. Determination of IL-6, TIMP-1 and pro-MMP9 levels in BAL The amounts of total IL-6, TIMP-1, and pro-MMP-9 were determined with ELISA methods, performed according to the manufacturer's recommendations. Assay sensitivity was 31 pg/mL for TIMP-1, 15 pg/mL for IL-6, and 8 pg/mL for pro-MMP-9. Hydroxyproline measurement Lung tissue was lyophilized, weighed, ground to a fine powder with a mortar and pestle, homogenized in PBS pH = 7.4, and divided into aliquots. After hydrolysis for 45 min in NaOH 2 N, the hydroxyproline content was assayed in duplicate aliquots, as previously described [ 16 ]. Expression of the results and statistical analysis Results were expressed as means ± SEM. The differences between the groups for treatment and strain effects were analyzed with a nonparametric Mann-Whitney U test. Correlations between the BAL analysis data and the MMP levels and activity were assessed with the nonparametric Spearman correlation test. For each analysis, P values less than 0.05 were considered to be statistically significant. Results Reactive oxygen species production and cell recruitment in BAL fluids At both day 1 and day 14, bleomycin treatment elicited enhanced ROS production by BAL cells from WT mice stimulated in vitro with PMA (Figure 1 ). In contrast, neither the cells removed from control mice (WT, not treated with bleomycin) nor those from KO mice (regardless of bleomycin treatment) could produce ROS in vitro after PMA stimulation. No chemiluminescence was detected when BAL cells were not stimulated by PMA in vitro . Figure 1 Production of reactive oxygen species (ROS) by bronchoalveolar lavage (BAL) cells, 1 day (A) and 14 days (B) after intranasal administration of bleomycin (0.1 mg, BLM) or saline (Control) to mice with the p47 phox subunit of NADPH-oxidase deleted (p47 phox -/- knockout mice (KO): solid bars), compared with "Wild Type" p47 phox +/+ mice (WT: blank bars). Cells were stimulated in vitro with PMA (0.8 μM) and ROS production was evaluated by chemiluminescence. Data were collected at the time of maximum light emission. Results are expressed as relative light units (mean ± SEM). ***: p < 0.001 comparison with control mice exposed to saline solution alone. ###: p < 0.001 for KO mice compared with WT mice. n = 5–9. At day 1, ROS release by BAL cells from bleomycin-treated WT mice was accompanied by a significant neutrophil influx, but the total BAL cell count did not rise (Table 1 ). This bleomycin-induced neutrophil influx was quite noticeable in BAL from KO mice, significantly greater than in WT mice. Table 1 Total and differential cell counts of BAL fluid from p47 phox +/+ WT and p47 phox -/- KO mice, at day 1 and day 14 after intranasal administration of bleomycin (BLM, 0.1 mg/mouse) or saline vehicle (Control, NaCl 0.9%). Results are presented as the mean (.10 3 cells) ± SEM. n: number of mice. WT: wild-type; KO: knockout; a: P < 0.05, b: P < 0.01, c: P < 0.001 compared with control mice exposed to saline solution only. *: P < 0.05, **: P < 0.01, ***: P < 0.001 for KO mice compared with WT mice. Treatment Strain N Total Cells Macrophages Neutrophils Eosinophils Lymphocytes Control [WT] 10 540 ± 99 536 ± 97 4 ± 2 0 0 [KO] 10 535 ± 105 393 ± 69 129 ± 37 *** 1 ± 1 11 ± 5 ** BLM, day 1 [WT] 8 490 ± 77 317 ± 50 164 ± 28 c 0 0 [KO] 9 2208 ± 733 b, e 281 ± 102 1922 ± 634 c, ** 0 2 ± 2 BLM, day 14 [WT] 8 1108 ± 80 b 991 ± 77 a 76 ± 15 c 20 ± 6 b 24 ± 13 b [KO] 9 793 ± 118 634 ± 97 a, * 62 ± 12 18 ± 16 80 ± 33 a Fourteen days after bleomycin administration, the total cell count in the WT mouse BAL increased significantly. Specifically, the alveolar macrophage count rose markedly, as did the neutrophil, eosinophil, and lymphocyte counts, although to a lesser extent. The total cell count in the BAL fluid of the KO mice did not increase significantly, but the number of macrophages and lymphocytes did. The WT mice had significantly more alveolar macrophages than the KO mice (Table 1 ). Lung hydroxyproline measurement Lung hydroxyproline concentration, which reflects collagen deposition in lungs, was measured 14 days after bleomycin administration to quantify pulmonary fibrosis (Table 2 ). Hydroxyproline levels did not increase in the KO mice. Moreover, although the hydroxyproline level was similar in both strains of mice at baseline, it was significantly higher in WT than in KO mice at day 14. Table 2 Hydroxyproline content (mg/g of dry tissue) in lung homogenate from p47 phox +/+ WT and p47 phox -/- KO mice, at day 14 after intranasal administration of bleomycin (BLM, 0.1 mg/mouse) or saline vehicle (Control, NaCl 0.9%). Results are presented as mean ± SEM. WT: wild-type; KO: knockout; ** : P < 0.01 for WT mice compared with KO mice. n = 3–6. Strain Control BLM p47 phox +/+ [WT] 1.20 ± 0.26 1.74 ± 0.10** p47 phox -/- [KO] 1.29 ± 0.24 1.04 ± 0.10 IL-6 levels in BAL fluids IL-6 levels in the BAL fluids of WT and KO mice rose one day after bleomycin administration (figure 2 ) and were significantly higher in WT than KO mice. Fourteen days after bleomycin administration, IL-6 levels were not significantly different from baseline. Figure 2 Level of IL-6 (pg/mL) in BAL fluids, 1 day after intranasal administration of bleomycin (BLM, 0.1 mg/mouse) or saline vehicle (Control), to p47 phox +/+ WT mice (blank bars) and p47 phox -/- KO mice (solid bars). Results are presented as the mean ± SEM. ** : p < 0.01 compared with control mice exposed to saline solution alone. #: p < 0.05 for p47 phox -/- KO mice compared with p47 phox +/+ WT mice. n = 4–9. MMP activity in BAL fluids and lung homogenates Zymography identified the following gelatinolytic bands as MMP activity: pro-MMP-9 (105 kDa), MMP-9 (86 kDa), pro-MMP-2 (70 kDa), and MMP-2 (64 kDa). At day one, pro-MMP-9 activity was significantly higher in the BAL of bleomycin-treated KO mice than in that of their bleomycin-treated WT counterparts, which in turn was significantly higher than in the control mouse BAL (figure 3 and figure 4A ). At day 14, no pro-MMP-9 activity was observed in any of the mice. The active form of MMP-9 (86 kDa) was detected only in KO mouse BAL fluid at day 1 (figure 3 ). Pro-MMP-9 was significantly correlated with the neutrophil count in the BAL fluids of both WT (P = 0.001) and KO (P = 7 × 10 -6 ) mice. Figure 3 Representative gelatin zymogram of BAL supernatant fluids, 1 and 14 days after intranasal administration of bleomycin (BLM, 0.1 mg/mouse) or saline vehicle (Control) to p47 phox +/+ WT mice (A) and p47 phox -/- KO mice (B). The following gelatinolytic bands were identified as MMP activity: pro-MMP-9 (105 kDa), MMP-9 (86 kDa), pro-MMP-2 (70 kDa), and MMP-2 (64 kDa). M: molecular weight marker. Figure 4 Quantification by densitometry of 105-kDa pro-MMP-9 (A), and 64-kDa MMP-2 (B) gelatinase activity on zymograms of BAL fluid, performed 1 day or 14 days after intranasal administration of bleomycin (BLM, 0.1 mg/mouse) or saline vehicle (Control), to p47 phox +/+ WT mice (blank bars) and p47 phox -/- KO mice (solid bars). Results are represented as the mean of relative intensity ± SEM. * : p < 0.05, ** : p < 0.01, *** : p < 0.001 compared with control mice exposed to saline solution alone. ###: p < 0.001 for p47 phox -/- KO mice compared with p47 phox +/+ WT mice. n = 8–10. At one day and 14 days after bleomycin administration, MMP-2 activity was observed in both its latent (70 kDa) and active (64 kDa) forms (figure 3 ). Although densitometry analysis could detect only the 64-kD form (figure 4B ) at day one, bleomycin elicited a significant increase in the 64-kDa MMP-2 activity in both strains, compared with the control; and this activity was substantially stronger in the BAL of KO than WT mice. At day 14, MMP-2 activity remained higher in both strains of bleomycin-treated mice (compared with controls) and did not differ significantly between them (figure 4B ). MMP activity was also evaluated at day 14 in lung homogenates (figure 5 ). Homogenate from both WT and KO mice showed similar levels of pro-MMP-9 activity levels, unexpected lower than in the homogenate from the control mice. In contrast, MMP-2 activity increased in the lungs of WT mice only (figure 5 ). Figure 5 Quantification by densitometry of Pro-MMP-9 (105 kDa, A) and MMP-2 (64 kDa, B) gelatinase activity on zymograms of lung homogenates, performed 14 days after intranasal administration of bleomycin (BLM, 0.1 mg/mouse) or saline vehicle (Control) to p47 phox -/- KO mice (solid bars) and p47 phox +/+ WT mice (blank bars). Results are represented as the mean of relative intensity ± SEM. * : p < 0.05, ** : p < 0.01, compared with control mice exposed to saline solution alone. ##: p < 0.01 for p47 phox -/- [KO] mice compared with p47 phox +/+ WT mice. n = 3–5. Pro-MMP-9/TIMP-1 ratio in BAL fluids We also evaluated pro-MMP-9 as well as TIMP-1 with ELISA (table 3 ). The results for pro-MMP-9 confirm those obtained with zymography. In WT mice, TIMP-1 in BAL fluid was markedly higher at day one and day 14 after bleomycin administration than at baseline. In contrast, the TIMP-1 level in the BAL fluid of KO mice was not significantly modified by bleomycin administration at either day 1 or day 14 (table 3 ). Table 3 Levels of pro-MMP-9 and TIMP-1, and pro-MMP-9 / TIMP-1 ratio in BAL supernatant fluids, recovered from p47 phox +/+ [WT] and p47 phox -/- [KO] mice, 1 day or 14 days after intranasal administration of bleomycin (BLM, 0.1 mg/mouse) or saline vehicle (Control, NaCl 0.9%). For Pro-MMP-9 and TIMP-1, results are represented as the mean (pg/ml) ± SEM. N : number of mice. a : P < 0.05, b : P < 0.01, in comparison to control mice exposed to saline solution only. * : P < 0.05, ** : P < 0.01, for [KO] mice in comparison to [WT] mice. Treatment Strain N Pro-MMP-9 TIMP-1 Ratio Pro-MMP-9 / TIMP-1 Control [WT] 5 46.3 ± 14.9 38.7 ± 13.5 2.7 ± 1.3 [KO] 5 1314.7 ± 798.2 ** 325.2 ± 173.6 10.4 ± 4.7 BLM, day 1 [WT] 7 2645.9 ± 1 011.6 b 781.8 ± 158.9 b 3.8 ± 1.3 [KO] 8 23646.8 ± 2 359.7 b, ** 1 213.8 ± 396.4 60.8 ± 23.7 a, ** BLM, day 14 [WT] 7 37.7 ± 9.1 2693.3 ± 378.3 b 0.02 ± 0.00 b [KO] 6 137.9 ± 15.5 b 567.9 ± 163.5** 0.2 ± 0.1 b, ** Table 3 presents the calculation of the pro-MMP-9/TIMP-1 ratio. At day one after bleomycin administration, this ratio remained stable in the BAL of WT mice, both treated and untreated, but rose significantly in that of the KO mice (table 3 ). At day 14, the ratio was significantly lower in both strains of treated mice than in the control mice, and levels in the WT mice were significantly lower than those in the KO mice. Discussion This study shows that mice deficient in the p47 phox subunit of the NADPH oxidase complex do not develop pulmonary fibrosis after intranasal administration of bleomycin. It also suggests that an imbalance of the molar MMP-9/TIMP-1 ratio may influence the fibrogenic process in this model. Several studies previously reported that antioxidant treatment attenuates the bleomycin-induced oxidative burden and subsequent pulmonary fibrosis [ 7 , 8 , 17 ]. Moreover, the absence of extracellular superoxide dismutase exacerbates conditions that lead to inflammation and pulmonary fibrosis [ 18 ]. Although these studies suggest that ROS contribute to lung damage and fibrosis, they do not clearly indicate the mechanisms of the antioxidant effect. That is, antioxidant compounds may attenuate oxidative damage caused directly by bleomycin [ 19 ], or they may limit the impact of ROS produced by phagocytes such as macrophages and neutrophils [ 5 ] and thus interfere with the inflammatory process. To clarify the role of ROS produced by phagocytes in the development of pulmonary fibrosis, we induced pulmonary fibrosis by i.n. bleomycin administration to p47 phox -/- KO mice. Unlike antioxidant compounds, which nonspecifically target all ROS sources in the tissue, knocking out the p47 phox subunit of the NADPH oxidase complex shuts down only the main pathway of phagocytic ROS production. In this study, in vitro PMA stimulation of BAL cells from KO mice produced no detectable ROS, while BAL cells from WT mice did produce ROS, as previous studies of circulating neutrophils from these mice have shown [ 15 ]. Hydroxyproline content did not increase in the lungs of these KO mice. This finding reveals the absence of fibrosis and thus provides strong evidence that phagocytic ROS production is an important component of the fibrogenic environment. Although no ROS production was detected in KO mice, their BAL gave evidence of an acute inflammatory response, as did that from WT mice: bleomycin administration elicited acute inflammation, characterized by an influx of neutrophils and associated with increased pro-MMP-9 activity. Moreover, the response of the KO mice to the bleomycin resembled the abnormal "exuberant" inflammation in vivo previously described in such KO mice [ 15 , 20 , 21 ]. It is, however, possible that this neutrophil influx and the large amounts of MMP-9 it releases have a protective effect against the development of pulmonary fibrosis. This exaggerated inflammatory response may be caused by defective down-regulation of the inflammatory process in these KO mice, perhaps due to the absence of ROS and the failure to degrade chemotactic signals [ 22 ]. This acute inflammation was accompanied by significantly elevated IL-6 levels in the BAL fluids of both strains of mice on the day after bleomycin administration, levels significantly higher in WT than KO mice. Given that IL-6 is secreted primarily by mononuclear cells, specifically macrophages, the difference between strains suggests that these cells are activated more weakly in the KO than in the WT mice. Moreover, in SP-D -/- alveolar macrophages, the NADPH oxidase inhibitor apocynin inactivates NF-kappa B, the transcription factor that regulates numerous proinflammatory responses, including IL-6 release [ 23 ]. Similarly, lipopolysaccharide-induced NF-kappa B activation is impaired in nuclear protein extracts of lung tissue from p47 phox -/- KO mice [ 24 ]. This would explain why IL-6 response seems to be redox-sensitive in our experiment. MMP induction, assessed by gelatinase release, has been reported in various cases of pulmonary fibrosis in human and experimental models [ 13 , 25 , 26 ]. We therefore analyzed the gelatinolytic activities of MMPs, in both BAL fluid and lung homogenate. MMP-9 and MMP-2 activities in the BAL fluid of the KO mice reveal an intense response to bleomycin. MMP-9 levels were highly correlated with neutrophil infiltration, while MMP-2 is known to be produced mainly by epithelial [ 27 ] and mesenchymal cells, such as fibroblasts, which are involved in collagen production and deposition [ 9 , 25 ]. This suggests that the exaggerated response observed in the BAL fluid of KO mice involves a wide spectrum of cell types. Surprisingly, at day 14, gelatinase profiles were different in the lung homogenates than in BAL fluids. Although MMP-2 activity was equivalent in BAL of both strains, MMP-2 activity increased substantially in the lung homogenate of the WT mice. One possible explanation is that the increased release of MMP-2 in the inner lung parenchyma may result from downstream events caused by phagocyte activation and ROS production during inflammation. This is consistent with another study, mentioned above, in which apocynin, an inhibitor of NADPH oxidase, also inhibited the release of MMP-9 and MMP-2 in SP-D -/- alveolar macrophages [ 23 ]. Moreover, Pardo et al. [ 28 ] report increased levels of gelatinases (MMP-2 and MMP-9) in isolated type II alveolar cells from hyperoxic rats; these increases are associated with alterations in the balance between MMPs and TIMPs and finally lead to diffuse alveolitis and its progression to pulmonary fibrosis. It is thus difficult to reach a definitive conclusion at this time about the exact function of MMPs. They play a role in promoting tissue remodeling and counterbalancing excessive matrix deposition, but may also facilitate tissue damage and disruption. Their involvement in bleomycin-induced pulmonary fibrosis has been demonstrated with the inhibition of collagen deposition by bastimastat, a nonselective MMP-inhibitor [ 10 ]. Complete understanding of the dynamic process of remodeling nonetheless requires consideration of TIMPs, which are natural MMP inhibitors. Increased TIMP-1 expression has been observed in lung extracts and in BAL fluids after bleomycin administration and after the transfer of the active TGF-beta gene to "fibrosis-prone" C57BL/6 mice [ 11 , 14 ]. In humans, increased levels of TIMP protein and RNA are observed in lungs of patients with idiopathic pulmonary fibrosis, and TIMP expression there exceeds that of MMP [ 12 ]. A reduced molar MMP/TIMP ratio seems to be a hallmark of pulmonary fibrosis, distinguishing it from other reversible interstitial lung diseases [ 26 ] and from chronic obstructive pulmonary disease (COPD) [ 29 ]. This ratio might be considered to be a "snapshot" of the dynamic matrix remodeling in lung tissue. Interestingly, in our study, the pro-MMP-9/TIMP-1 ratio was significantly higher for KO than WT mice, at both day one and day 14. At day 1 this was due to the lower MMP-9 level and higher TIMP-1 level in the BAL from WT mice, and at day 14, only to the latter. The correlation of these levels with differences in hydroxyproline levels in the lungs of bleomycin-treated mice strongly suggests that a reduced molar pro-MMP-9/TIMP-1 ratio in BAL fluid is associated with collagen deposition, beginning as early as the inflammatory events at day 1 after bleomycin administration. The usefulness of the pro-MMP-9/TIMP-1 ratio as a marker of fibrosis nonetheless requires discussion. Although a molar ratio appears to play a protective role against fibrotic changes, MMP-9 is considered primarily to be an inflammatory mediator released by leukocytes during acute inflammatory events to facilitate their progression across the basement membrane [ 30 ]. Moreover, MMP-9 depletion in KO mice does not substantially alter the extent of either pulmonary fibrosis or lung inflammation after bleomycin administration [ 31 ]. TIMPs may also counterbalance the activity of MMP-2 or other proteinases, such as collagenases. Ruiz et al. [ 32 ] recently observed that MMP-8 and MMP-13 RNA levels decreased and TIMP-1 RNA increased in the paraquat- and hyperoxia-induced pulmonary fibrosis rat model. Matrilysin (MMP-7), which can degrade various substrates, seems to have a crucial role in pulmonary fibrosis [ 33 ]. Finally, the exuberant neutrophil influx observed at day one in p47 phox -/- KO mice could provide great amount of other kind of protease, such as serine proteases. Indeed, neutrophil elastase was shown to have an impact on the severity of bleomycin-induced pulmonary fibrosis [ 34 , 35 ] Conclusion In summary, this study demonstrates that the inability of phagocytes from p47 phox -/- KO mice to produce large quantities of ROS via the NADPH oxidase pathway inhibits the development of bleomycin-induced pulmonary fibrosis. This inhibition is associated with changes in IL-6 production and in the molar MMP-9/TIMP-1 ratio, both probably key factors in airway remodeling and fibrosis. These rapidity of these differences after bleomycin administration suggests that early inflammatory events and remodeling events may establish a favorable environment for further chronic fibrogenic processes. Abbreviations BAL: Bronchoalveolar lavage IL: interleukin KO: knock out MMP: matrix metalloproteinase TIMP: tissue inhibitor of metalloproteinase ROS: reactive oxygen species WT: wild type Authors' contributions BM, SN, OL, IG:and EB have made substantial contributions to acquisition and analysis of data BM, SN, EB and VL have made substantial contributions to conception and design BM, EB and VL have been involved in drafting the article JMP and CPB have been involved in revising it critically for important intellectual content

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548519.xml

519030

Dichotomous factor analysis of symptoms reported by UK and US veterans of the 1991 Gulf War

Background Factor analysis is one of the most used statistical techniques to analyze the inter-relationships among symptoms reported by Gulf War veterans. The objective of this study was to apply factor analyses to binary symptom data from the UK study of Gulf War illness and the US Air Force study of Gulf War veterans, and to compare the symptom domains derived from the distinct samples. Methods UK veterans of the 1991 Gulf War (n = 3,454), individuals deployed to Bosnia on U.N. peacekeeping operations (n = 1,979) and Gulf War-era servicemen (n = 2,577) who were not deployed to the Gulf were surveyed in 1997–1998, and US 1991 Gulf War veterans from four Air Force units (n = 1,163) were surveyed in 1995 to collect health characteristics including symptoms. Each sample was randomly split in half for exploratory and confirmatory dichotomous factor analyses with promax oblique rotation. Results Four correlated factors were identified in each of the samples. Three factors (Respiratory, Mood-Cognition, Peripheral Nervous) overlapped considerably across the UK cohorts. The Gastrointestinal/Urogenital factor in the UK Gulf cohort was noticeably different from the Gastrointestinal factor identified from the Bosnia and Era cohorts. Symptoms from Gulf War UK and U.S cohorts yielded similar Gastrointestinal, Respiratory and Mood-Cognition factors, despite differences in symptom inventories between the two surveys. A Musculoskeletal factor was only elicited from the US Gulf sample. Conclusion Findings of this report are consistent with those from other factor analysis studies that identified similar symptom dimensions between Gulf and non-Gulf War veterans, except that the Gastrointestinal factor in Gulf veterans included other symptom types. Correlations among factors raise the question as to whether there is a general illness, even if not unique to Gulf veterans, representing the common pathway underlying the identified factors. Hierarchical factor analysis models may be useful to address this issue.

Introduction Reports that veterans of the 1991 Gulf War were suffering from unexplained signs and symptoms started to appear as early as one year after the conflict [ 1 ]. Veterans complained of several symptoms including myalgia, arthralgia and debilitating fatigue, but no cause could be found. Over a decade later, it remains uncertain whether or not Gulf War illness is a specific response to a specific exposure/hazard, or alternatively a non-specific response to what may be a variety of hazards/stressors and circumstances [ 2 ]. Factor analysis may assist this debate by determining whether or not there is a specific structure to the symptoms endorsed by Gulf War veterans that differentiates them from symptoms shown by non-Gulf veterans. Several studies have applied factor analysis (or principal components analysis) to examine and compare the inter-relationships among symptoms reported by veterans [ 3 - 13 ]. In general, factor structures have been found to be similar in veterans deployed and not deployed to the Gulf War [ 3 , 9 - 11 ]. Despite different symptom inventories, differences in analytical procedures, and personal choices for factor labeling, studies of Gulf War illness report between three and seven factors that represent combinations of the following domains: (a) mood, cognition, fatigue, psychological; (b) respiratory condition; (c) neurological condition; (d) musculoskeletal pain; (e) peripheral nervous system; (f) gastrointestinal disorder; and (g) mixed somatic complaints. When symptoms are measured on a continuous scale normally distributed, linear factor analysis (e.g. common factor analysis [ 14 ]) can be applied. However, when symptoms are measured on a nominal (Yes/No) or binary scale (0/1), linear factor analysis may yield biased estimates of the factor structure [ 15 , 16 ]. A dichotomous factor analysis model [ 15 ] would be more appropriate. The objective of this report was to apply dichotomous factor analyses to binary symptom data from two studies, i.e., the UK study of Gulf War illness [ 11 , 17 - 19 ] and the US Air Force study of Gulf War veterans [ 12 ], and to assess whether observed symptom patterns represented similar syndromes across nations. Methods Sources of data The data used in this report came from two sources: the UK Study of Gulf War illness [ 11 , 17 - 19 ] that included three cohorts: individuals deployed to the Persian Gulf in 1991 or to Bosnia on U.N. peacekeeping operation, and Gulf War-era servicemen who were not deployed to the Gulf, and a study of US Air Force Gulf War veterans [ 12 ]. Briefly, the UK study was a postal survey conducted between August 1997 and November 1998 that asked veterans of the Royal Navy, Army, Royal Air Force about socio-demographic, military and health characteristics [ 17 - 19 ]. In Gulf veterans, previous work has shown that ill health was associated with rank and socio-demographic factors but it was similar across all three services [ 19 ]. Also, linear factor analysis and cluster analysis indicated that symptom patterns were similar across the three cohorts [ 11 , 17 ]. In the US study, four Air Force units (2 in Pennsylvania and 2 in Florida) were surveyed between January and March 1995. Questionnaires were distributed to volunteers and queried about deployment to the Persian Gulf, health status, demographic and military characteristics and symptoms [ 12 ]. A case definition of multi-symptom illness was derived using linear factor analysis [ 12 ]. Symptoms In the UK study, we considered the analysis of 50 symptoms that occurred in the preceding month (" During the past month , have you suffered from any of the following symptoms?") [ 11 , 17 , 18 ] (Table 1 ). In the US study, we considered 35 symptoms that were reported as current health problems [ 12 ] (Table 2 ). The analyses only included subjects with complete symptom data (i.e., only a small proportion of veterans had missing symptoms: 2.2% in the UK Gulf cohort, 3.5% in the UK Bosnia cohort, and 1.4% in the UK Era cohort, 0% in the US study). Table 1 Prevalence (%) of symptoms present in the past month across UK Study: Gulf, Bosnia and Era Cohorts During the past month have you suffered from: Gulf* N = 3,454 Bosnia N = 1,979 Era N = 2,577 Feeling unrefreshed after sleep 56.1 32.5 31.5 Irritability/outburst of anger 54.7 32.2† 25.5 Headaches 54.2 36.5 36.8 Fatigue 51.1 26.9 28.2 Sleeping difficulties 47.8 30.5 28.3 Forgetfulness 44.7 19.4† 16.8 Loss of concentration 39.5 16.6 15.0 Joint stiffness 39.3 20.9 22.8 Flatulence or burping 34.0 15.7† 21.0 Pain without swelling or redness in several joints 31.7 13.7 14.2 Feeling distant or cut off from others 27.9 14.4† 10.3 Avoiding doing things/situations 26.5 12.4† 10.1 Feeling jumpy/easily startled 24.7 12.9† 9.4 Chest pain 24.5 12.5 11.6 Tingling in fingers and arms 24.3 8.4† 10.9 Night sweats that soak the bed sheets 23.7 12.0† 9.5 Itchy or painful eyes 22.9 10.5 11.9 Sore throat 22.3 15.1 13.6 Distressing dreams 21.6 13.1† 9.0 Numbness or tingling in fingers or toes 21.4 8.1† 10.9 Ringing in the ears 20.5 10.8 12.5 Wheezing 20.4 10.1 9.7 Diarrhea 20.2 11.1 11.9 Unable to breathe deeply enough 20.0 9.8† 7.8 Unintended weight gain greater than 10 lbs 18.7 10.8† 8.5 Dry mouth 17.4 9.1† 6.6 Loss of interest in sex 17.3 7.1 6.7 Dizziness 17.0 7.0 7.7 Tingling in legs and arms 16.8 5.4† 6.8 Rapid heartbeat 16.4 7.5 7.6 Feeling short of breath at rest 15.3 6.5 5.5 Increased sensitivity to noise 15.0 6.3 5.6 Increased sensitivity to light 14.7 6.0 5.8 Stomach cramp 14.6 7.8 7.5 Passing urine more often 14.3 4.9† 6.3 Persistent cough 13.9 7.8† 5.8 Loss or decrease in appetite 13.3 8.5† 5.2 Intolerance to alcohol 11.9 5.0 4.0 Shaking 11.6 5.3† 3.7 Constipation 10.9 5.9 5.2 Faster breathing than normal 10.4 4.4 3.3 Feeling disoriented 10.3 3.2 3.5 Feeling feverish 8.7 3.5 3.0 Nausea 8.7 3.7 3.7 Lump in throat 8.0 3.8 3.0 Unintended weight loss greater than 10 lbs. 5.5 3.9† 2.6 Double vision 5.4 2.5 2.1 Pain on passing urine 5.2 2.3 1.9 Burning sensation in sex organs 5.0 1.3 1.6 Vomiting 4.7 3.2 2.8 In general would you say your health is (mean, standard deviation) 1=Excellent, 2=Very Good, 3=Good, 4=Fair, 5=Poor 2.8 (1.1)‡ 2.3 (1.0) 2.3 (1.0) * Gulf War veterans significantly different (p < 0.05) from Era and Bosnia veterans with respect to all symptoms † Bosnia veterans were significantly different (p < 0.05) from Era veterans. ‡ Gulf War veterans significantly different (p < 0.05) from Bosnia and Era veterans Table 2 Prevalence (%) of symptoms reported as current health problems in the US Study of Gulf War veterans Current health problem Deployed to the Gulf (N = 1,163) Sinus congestion 51.8 Headache 50.0 Fatigue 42.9 Joint pain 35.5 Difficulty remembering or concentrating 34.4 Joint stiffness 30.4 Difficulty or problems to sleep 27.6 Gas, bloating, cramps or abdominal pain 26.7 Trouble finding words 26.1 Irritability or moodiness 25.5 Skin rashes or sores 23.0 Numbness or tingling in fingers or toes 21.0 Muscle pains 19.9 Hay fever or other allergies 19.0 Depression 18.0 Diarrhea (3 or more loose bowel movements in 24 hours) 17.6 Sore throat 17.4 Cough 17.1 Anxiety 17.0 Unintended weight gain greater than 10 lbs 16.9 Shortness of breath 16.4 Chest pain 15.0 Decreased sexual interest 14.3 Dizziness 13.9 Night sweats that soak your bed sheets 13.3 Fatigue lasting 24 hours after exertion 12.6 Sores inside your nose 10.8 Swollen lymph glands in your neck, armpit, groin 10.1 Inability to tolerate milk 7.1 Episodes of disorientation 6.6 Nausea or vomiting 6.3 Wheezing 5.9 Sensitivity to chemicals 5.2 Fever 4.9 Unintended weight loss greater than 10 lbs 2.7 In general would you say your health is (mean, standard deviation) 1=Excellent, 2=Very Good, 3=Good, 4=Fair, 5=Poor 2.3 (0.9) Statistical analyses We used chi-squared tests to compare symptom reporting among the UK Gulf, Bosnia and non-deployed cohorts, and between the UK and US Gulf War veterans. For factor analyses, data from each UK group and US study were randomly split into 2 halves (exploratory and confirmatory samples). Exploratory dichotomous factor analyses [ 20 ] were performed to determine the number of factors that explained the correlations among symptoms. Confirmatory dichotomous factor analyses [ 20 ] were conducted to test the reproducibility of the factor structure identified in the exploratory phase. We used a robust weighted least squares estimator to calculate factor loadings for the dichotomous model [ 20 ]. The promax oblique rotation was used to estimate factor correlations. For exploratory analyses, the scree plot was used to estimate the number of factors and utilized eigenvalues from the tetrachoric correlation matrix. The number of factors was considered sufficient to explain symptom correlations if the root mean square error of approximation (RMSEA) was ≤ 0.06 [ 20 , 21 ]. Since in general, factor loadings are considered meaningful when they exceed 0.30 or 0.40 [ 14 ], we determined the stability of the factor structures by repeating the exploratory factor analyses in the exploratory sample after eliminating symptoms with factor loadings of <0.40. The confirmatory dichotomous model [ 20 ] specified the number of factors and the leading symptom in each factor (i.e., one with highest loading in its factor, and fixed zero loadings in the remaining factors) to test the exploratory structure in the confirmatory sample. We also set the factor variances to 1 so that the model would be identifiable. No other parameters were fixed. The confirmatory model was deemed to fit the data well if any of the following goodness-of-fit indices was satisfied: RMSEA of ≤ 0.06, Tucker-Lewis Index (TLI) of ≥ 0.95, Comparative Fit Index (CFI) of ≥ 0.95, or standardized root mean square residual (SRMR) of ≤ 0.08 [ 21 , 22 ]. Finally, we fitted the confirmatory model to data from all subjects from the exploratory and confirmatory samples. We used M-plus version 2.14 [ 20 ] to fit the dichotomous factor models and SAS version 8.1 (SAS Inc., Cary, NC) to perform all other analyses. Results Description of the samples The UK Study Of the 3,454 veterans of the Gulf War, 93.3% were men, 75% were married or living with a partner, 92.5% had regular military status when they were deployed to the Gulf. Their average age was 34.4 years (standard deviation = 6.8). The Bosnia cohort had 1,979 veterans with average age of 29.3 years (standard deviation = 6.7) including 89.4% men, 57.5% married or living with a partner, and 91.1% with regular military status when deployed to Bosnia. The Era cohort included 2,577 veterans (92.7% men, 75.3% married or living with a partner, average age of 35.3 years (standard deviation = 7.2) and 48.8% regular military status). The US Air Force Study The US Gulf sample included 1,163 veterans who were 94.0% men, 74.9% married or living with a partner, with an average age of 37.9 years (standard deviation = 8.4). Symptom distribution The most common symptoms across all UK groups were feeling unrefreshed after sleep, irritability, headaches, fatigue, sleeping difficulties, forgetfulness, loss of concentration, joint stiffness and flatulence/burping (Table 1 ). The prevalence of all symptoms reported by Gulf War veterans was significantly higher than that reported by Bosnia or Era veterans. On average, veterans of all groups reported their general health was at least good. However, scores for Gulf War veterans were significantly lower than those for Bosnia or Era veterans (t-test with Bonferroni adjustment, p-value <0.05). Table 2 displays the symptom distribution among US veterans of the Gulf War and Table 3 shows the equivalence between symptoms assessed in the UK and US studies. Sixteen symptoms in the UK study were not assessed in the US study, and 11 symptoms in the US study were not assessed in the UK study. Among the 24 symptoms that were equivalent in both studies, 18 were significantly more prevalent among UK Gulf veterans than their US counterparts (chi-square p-value <0.05). Table 3 Equivalence between symptoms in UK and US Studies* UK Study US Study Irritability/outburst of anger Irritability or moodiness Headaches Headaches Fatigue Fatigue Sleeping difficulties Difficulty or problems to sleep Forgetfulness OR Loss of concentration Difficulty remembering or concentrating Joint stiffness Joint stiffness Flatulence or burping OR Stomach cramp Gas, bloating, cramps or abdominal pain Pain without swelling or redness in several joints Joint pain Chest pain Chest pain Night sweats that soak the bed sheets Night sweats that soak your bed sheets Sore throat Sore throat Numbness or tingling in fingers or toes Numbness or tingling in fingers or toes Wheezing Wheezing Diarrhea Diarrhea (3 or more loose bowel movements in 24 hours) Unintended weight gain greater than 10 lbs Unintended weight gain greater than 10 lbs Loss of interest in sex Decreased sexual interest Dizziness Dizziness Feeling short of breath at rest Shortness of breath Persistent cough Cough Feeling disoriented Episodes of disorientation Feeling feverish Fever Nausea OR Vomiting Nausea or Vomiting Unintended weight loss greater than 10 lbs. Unintended weight loss greater than 10 lbs. Diagnostic Criteria for Anxiety Disorder† Feeling distant or cut off from others OR Feeling jumpy/easily startled OR Unable to breathe deeply enough OR Tingling in legs and arms OR Rapid heartbeat OR Shaking OR Faster breathing than normal OR Lump in throat Anxiety 16 could not find equivalent: Avoiding doing things/situations, Tingling in fingers and arms, Feeling unrefreshed after sleep, Itchy or painful eyes, Distressing dreams, Ringing in the ears, Dry mouth, Increased sensitivity to noise, Increased sensitivity to light, Passing urine more often, Loss or decrease in appetite, Intolerance to alcohol, Constipation, Double vision, Pain on passing urine, Burning sensation in sex organs 11 could not find equivalent: Trouble finding words, Depression, Muscle pains, Hay fever or other allergies, Fatigue lasting 24 hrs after exertion, Swollen lymph glands in your neck, armpit, groin, Inability to tolerate milk, Sensitivity to chemicals, Sinus congestion, Skin rashes or sores, Sores inside your nose †All equivalent symptoms were significantly more prevalent among UK Gulf veterans than among UK Gulf veterans, except cough and joint pain that were less prevalent, and diarrhea, numbness or tingling in fingers or toes, shortness of breath, and weight gain that were not different in the 2 samples. † Diagnostic and statistical manual of mental disorders: DSM-IV–4th ed. Washington, DC: American Psychiatric Association; 1994 Dichotomous Factor Analyses UK Gulf Cohort The exploratory sample consisted of 1,783 persons. The scree plot suggested 1 major factor, but it was not clear how many additional factors should be investigated (data available from authors). We removed the first eigenvalue from the plot, to better determine how much each additional factor contributed to the variance, and decided to examine 2 to 5-factor solutions. Although all solutions indicated a good fit between data and model (RMSEA ≤ 0.06), the 2-, and 3- factor models yielded non-interpretable factors, and the 5-factor solution could not be confirmed. Thus, we tested the 4-factor exploratory solution in the confirmatory sample that included 1,671 subjects. We specified the number of factors to be 4, the factor variances to be 1 and the leading symptoms of the first (pain on passing urine), second (loss of concentration), third (unable to breathe deeply enough), and fourth (tingling in fingers and arms) factors. Table 4 summarizes the final confirmatory 4-factor solution using data from all 3,454 subjects and 35 symptoms. This model fits the data well (CFI = 0.95, TLI = 0.98, RMSEA = 0.04, SRMR = 0.05). The order of the factors is not important, because in this model the variances were standardized to 1. The factors were labeled Gastrointestinal/Urogenital, Respiratory, Mood-Cognition, and Peripheral Nervous. The Respiratory, Mood-Cognition and Peripheral Nervous factors represent the same domains as the three factors in the study by Ismail et al [ 11 ]. This solution also yielded a very high correlation among the factors (range = 0.52–0.62). We could not estimate the variance explained by each factor, because the model standardized all variances to 1. Since factors correlate in an oblique solution, it is quite complex to calculate the proportion of variance explained by each factor. However, for descriptive purposes, we used the varimax orthogonal solution of the exploratory factor model to have an idea of the importance of each factor. Using all 3,454 subjects, 35 symptoms, and the 4-factor exploratory model we estimated that the proportion of variance explained by each factor was 16.3% (Gastrointestinal/Urogenital), 10.1% (Respiratory), 22.4% (Mood-Cognition), and 8% (Peripheral Nervous). Thus the Mood-Cognition Factor contributes most of the variance in the data, followed by the Gastrointestinal/Urogenital Factor. Table 4 Factor loadings for final 4-factor confirmatory model for symptoms reported in the UK Study-Gulf Cohort (N = 3,454), UK Study-Bosnia Cohort (N = 1,979), and UK Study-Era Cohort (N = 2,577) During the past month have you suffered from: Gastrointestinal/Urogenital Gastrointestinal Gulf Bosnia Era Nausea .78 .56 - Vomiting .77 - - Diarrhea .76 .76 .76 Stomach cramp .74 .73 .71 Constipation .59 .78 .57 Flatulence or burping .55 .63 .58 Sore throat .53 .49 - Feeling feverish .52 .51 - Dry mouth .48 .49 - Pain on passing urine .53 - - Burning sensation in sex organs .48 - - Headaches .48 - - Loss or decrease in appetite .47 - - Unintended weight loss greater than 10 lbs. .43 - - Respiratory Gulf Bosnia Era Unable to breathe deeply enough .89 .87 .89 Wheezing .77 .75 .91 Feeling short of breath at rest .73 .89 .81 Faster breathing than normal .66 .63 .66 Persistent cough .50 - .48 Chest pain - .61 - Rapid heartbeat - .51 - Mood-Cognition Gulf Bosnia Era Loss of concentration .93 .70 .79 Forgetfulness .87 .66 .71 Feeling distant or cut off from others .77 .80 .86 Avoiding doing things/situations .74 .67 .75 Irritability/outburst of anger .64 .71 .74 Feeling unrefreshed after sleep .62 .68 - Feeling jumpy/easily startled .60 .73 .79 Sleeping difficulties .57 .66 .56 Feeling disoriented .57 .64 .70 Fatigue .57 .53 - Increased sensitivity to noise .56 .58 .63 Distressing dreams .52 .66 .78 Loss of interest in sex .51 .54 .65 Intolerance to alcohol - .50 .46 Shaking - .45 .55 Night sweats - - .52 Peripheral Nervous Gulf Bosnia Era Tingling in fingers and arms .97 .99 .91 Numbness or tingling in fingers or toes .84 .89 .90 Tingling in legs and arms .77 .80 .89 - Blank entries in the table indicate that symptom had a factor loading < .40 during the exploratory phase and was not considered in the confirmatory model UK Bosnia Cohort The Bosnia Cohort exploratory sample included 1,008 subjects and the confirmatory included 971. A 4-factor solution with 32 symptoms was confirmed (goodness-of-fit measures: CFI = 0.961; TLI = 0.986; RMSEA = 0.029; SRMR = 0.045; factor correlations range = 0.44–0.63). Results for all 1,979 subjects are displayed in Table 4 . The proportion of variance explained by each factor, based on the orthogonal varimax solution was 13.4% (Respiratory), 24.7% (Mood-Cognition), 9.4% (Peripheral Nervous), and 14.6% (Gastrointestinal). Although the constructs identified from symptoms reported by Bosnia veterans were similar to those confirmed in the Gulf War veteran sample, we were unable to confirm the Gulf War confirmatory 4-factor solution in the Bosnia sample. Some symptoms from the Gulf cohort Gastrointestinal/Urogenital factor loaded in several Bosnia cohort factors, yielding a structure that was difficult to interpret. UK Era Cohort There were 1,325 observations in the exploratory and 1,252 in the confirmatory samples. A confirmatory 4-factor model with 26 symptoms and all 2,577 subjects is displayed in Table 4 (goodness-of-fit measures: CFI = 0.99, TLI = 1.00, RMSEA = 0.02, SRMR = 0.04; factor correlations range = 0.41–0.58). Fatigue and unrefreshing sleep could not be confirmed in any factor. The proportion of variance explained by each factor based on the orthogonal varimax exploratory solution was 13.3% (Respiratory), 28.8% (Mood-Cognition), 10.3% (Peripheral Nervous), and 9.9% (Gastrointestinal). Comparing the factor structures of the UK Gulf Cohort with Bosnia and Era Cohorts The overlap of symptom composition across samples was remarkable for the Respiratory, Peripheral Nervous and Mood-Cognition factors (Table 4 ). The factor mostly defined by gastrointestinal symptoms comprised the major difference among the cohorts. US Air Force Gulf War Study The exploratory sample consisted of 590 subjects and the confirmatory included 573. A 4-factor solution with 26 symptoms was confirmed and results for all 1,163 subjects are displayed in Table 5 . The proportion of variance explained by each factor based on the orthogonal varimax exploratory solution was 19.1% (Gastrointestinal/Respiratory), 7.7% (Allergies), 20.7% (Mood-Cognition), and 10.8% (Musculoskeletal). Table 5 Factor loadings for final confirmatory 4-factor model for symptoms reported in the US Study of Gulf War veterans (N = 1,163) Current health problem Factor 1 Factor 2 Factor 3 Factor 4 Gastrointestinal/ Respiratory Allergies Mood-Cognition Musculoskeletal Nausea or vomiting .80 .00 .00 .00 Diarrhea (3 or more loose bowel movements in 24 hours) .73 .06 -.04 .02 Gas, bloating, cramps or abdominal pain .71 -.01 .06 .02 Skin rashes or sores .56 -.10 -.05 .20 Sore throat .54 .36 -.17 .14 Fever .53 .18 .06 .17 Swollen lymph nodes in your neck, armpit, groin .52 .23 -.14 .08 Night sweats that soak your bed sheets .52 -.07 .10 .16 Wheezing .52 . 22 -.10 .18 Cough .47 .36 -.16 .13 Chest pain .44 .16 .13 .12 Sinus congestion .00 .90 .00 .00 Hay fever or other allergies -.04 .60 .05 -.09 Difficulty remembering or concentrating .00 .00 .87 .00 Depression -.07 .17 .77 -.08 Trouble finding words . 04 .05 .75 -.11 Irritability or moodiness -.11 .28 .74 .00 Anxiety -.05 .29 .73 -.12 Episodes of disorientation .25 -.01 .66 -.03 Fatigue lasting 24 hours after exertion .15 .05 .48 .28 Fatigue .15 .13 .48 .24 Decreased sexual interest .15 .00 .45 .07 Dizziness .36 .04 .40 .01 Joint pain .00 .00 .00 .96 Joint stiffness .06 .01 .08 .80 Muscle pain .07 .03 .15 .65 Goodness-of-fit measures: CFI = .970; TLI = .988; RMSEA = .029; SRMR = .043 Inter-factor correlations: Factor1, Factor2 = .474; Factor1, Factor3 = .670; Factor1, Factor4 = .525; Factor2, Factor3 = .388; Factor2, Factor4 = .488; Factor3, Factor4 = .511 Comparing the factor structures of the UK Gulf Cohort with US Gulf Cohort We could not directly compare the factor structures because the symptom inventories were so different. For example, 3 peripheral nervous symptoms were asked from the UK veterans (Tingling in fingers and arms, Numbness or tingling in fingers or toes, Tingling in legs and arms) while the US study included only 1 (numbness or tingling in fingers or toes). Thus, a separate factor could not be derived from the US study. Nevertheless, both UK and US data yielded similar constructs, namely a mixed gastrointestinal factor, a mood-cognition factor, and a respiratory-related factor. The main difference was that the musculoskeletal construct could not be confirmed in the UK Gulf cohort, and it represented a separate factor in the US sample (Tables 4 and 5 ). Discussion The objective of this report was to identify and compare syndromes among 4 samples collected from UK [ 11 , 17 - 19 ] and US [ 11 ] studies of Gulf War illness by using factor analysis. We used dichotomous factor analysis models because symptoms were measured on a nominal scale (Yes/No), either during the past month (UK study) or currently (US study). UK data included Gulf War servicemen, individuals deployed to Bosnia on a U.N. peacekeeping operation, and active duty military that had not been deployed. US data included only Gulf War veterans. We identified and confirmed at most 4 correlated factors in each of the samples. Three of the four constructs (Respiratory, Mood-Cognition, Peripheral Nervous) overlapped considerably across the UK cohorts. These factors were identical to those derived in a linear factor analysis of these data [ 11 ]. However, the current study identified one factor including gastrointestinal and urogenital symptoms in the UK Gulf cohort that was noticeably different from the gastrointestinal factor identified from the Bosnia and Era cohorts. One possible explanation is that Gulf War veterans were more stressed than Bosnia or Era veterans, and this fact maybe associated with multi-system symptom reporting. More needs to be investigated in this area. In addition, despite differences in study designs, methods of data collection, military populations and symptom inventories between the UK and US studies of Gulf War veterans, Gastrointestinal, Respiratory and Mood-cognition factors were identified in both UK and US studies. Of note, although joint pain and joint stiffness were measured in both UK and US samples of Gulf War veterans, a Musculoskeletal factor was only elicited as a separate factor from the US data. In general, findings of this report were consistent with those from other studies that used factor analysis of symptoms to compare symptom patterns between Gulf and non-Gulf War veterans [ 3 , 9 - 11 ]. However, in one comparative study, the factor structure derived from symptoms reported by Gulf War veterans included a neurological impairment factor that was absent among non-Gulf War subjects [ 6 ]. Based on our experience in analyzing symptom data, we suggest that, to achieve more precise comparability across studies, a standardized symptom questionnaire be developed and used on future studies of war-related illnesses. For example, symptoms can be measured on an interval, rather than binary or nominal scale, accounting for frequency and intensity as in the Psychosomatic Symptom Checklist [ 23 ]. In this report, we encountered difficulties confirming the dichotomous factor structures, or reproducing a factor structure in another sample, because many symptoms were rare, which created numerical problems. On the other hand, failure to reproduce factor structures across samples may also be due to different symptom distributions in the samples being considered, as was the case of the UK Gulf and Bosnia cohorts. We also acknowledge the difficulties in analyzing self-report symptom data. However, since the UK and US studies were independent of the military and confidential, we do not believe there was a reason form service personnel to exaggerate symptoms in order to gain compensation or eligibility for veterans. Finally, it must be noted that, in each of the UK or US cohorts, factors were moderately or highly correlated. Correlated factors are complex to interpret because it is difficult to separate their independent effects [ 24 ]. This finding raises the question as to whether there is higher-order dimension, or general illness, representing the common pathway underlying all four factors. Hierarchical factor analysis models [ 24 - 26 ] may be useful in addressing this issue. In conclusion, considerable progress has been made in defining medically unexplained illness associated with deployment to the 1991 Gulf War. Our results from independent studies conducted in the UK and US confirmed occurrence of an illness comprised of 4 correlated groups of symptoms (factors) in deployed military personnel from both countries. Similar illness occurred in troops who did not participate in the Gulf War (albeit at lower rates and with different specific characteristics), so we believe that this pattern of symptoms is not unique to Gulf War service nor does it represent a unique illness or "Gulf War syndrome." In fact, similar illnesses to those affecting Gulf War veterans have been noted among veterans of US Civil War [ 27 ] and British Boer War [ 28 ]. Similar illnesses can also be expected to occur in association with current deployments in Afghanistan and Iraq. A better understanding of predisposing, precipitating, and perpetuating factors must be obtained to provide appropriate care for veterans and to devise prevention strategies. A central question remains: how to resolve whether such illnesses reflect a common pathophysiologic process. Competing Interests None declared. Authors' Contributions RN conceived of this analysis was responsible for its execution and had primary responsibility for the manuscript; KI was instrumental in the conception and design of the UK veterans' study and had primary responsibility for its analysis; collaborated in analysis and interpretation of the present data and writing the manuscript; SW was Principal Investigator for the UK veterans' study, Collaborated in the concept of the present study and collaborated in analysis, interpretation and the manuscript; CU collaborated in the UK veteran's study and collaborated in analysis and interpretation of data and drafting the manuscript for this study; LH collaborated in the UK veteran's study and collaborated in analysis and interpretation of data and drafting the manuscript for this study; WCR was Principal Investigator of the US Gulf War study, conceived the idea for the present study, served as Principal Investigator for the present study and collaborated in all aspects of data interpretation and writing the manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC519030.xml

526773

Reduction of arsenic content in a complex galena concentrate by Acidithiobacillus ferrooxidans

Background Bioleaching is a process that has been used in the past in mineral pretreatment of refractory sulfides, mainly in the gold, copper and uranium benefit. This technology has been proved to be cheaper, more efficient and environmentally friendly than roasting and high pressure moisture heating processes. So far the most studied microorganism in bioleaching is Acidithiobacillus ferrooxidans . There are a few studies about the benefit of metals of low value through bioleaching. From all of these, there are almost no studies dealing with complex minerals containing arsenopyrite (FeAsS). Reduction and/or elimination of arsenic in these ores increase their value and allows the exploitation of a vast variety of minerals that today are being underexploited. Results Arsenopyrite was totally oxidized. The sum of arsenic remaining in solution and removed by sampling represents from 22 to 33% in weight (yield) of the original content in the mineral. The rest of the biooxidized arsenic form amorphous compounds that precipitate. Galena (PbS) was totally oxidized too, anglesite (PbSO 4 ) formed is virtually insoluble and remains in the solids. The influence of seven factors in a batch process was studied. The maximum rate of arsenic dissolution in the concentrate was found using the following levels of factors: small surface area of particle exposure, low pulp density, injecting air and adding 9 K medium to the system. It was also found that ferric chloride and carbon dioxide decreased the arsenic dissolution rate. Bioleaching kinetic data of arsenic solubilization were used to estimate the dilution rate for a continuous culture. Calculated dilution rates were relatively small (0.088–0.103 day -1 ). Conclusion Proper conditions of solubilization of arsenic during bioleaching are key features to improve the percentage (22 to 33% in weight) of arsenic removal. Further studies are needed to determine other factors that influence specifically the solubilization of arsenic in the bioleaching system such as: pH, dissolved oxygen concentration, redox potentials, nature of concentrate and temperature among others. At. ferrooxidans was able to completely oxidize the minerals present during the arsenic bioleaching. Other elements present originally in the concentrate such as Zn, Sb, and Cu were also solubilized. The process of bioleaching is expected to be influenced by mechanisms that still need to be established due to the diversity of the minerals involved and by the presence of traces of metals in the concentrate. The increase in pulp density generates a decrease in the dissolved arsenic concentration. This decrease is greater in runs where air was not injected to the system. The maximum rate of arsenic dissolution in the concentrate was found using; small surface area of particle exposure, low pulp density, injecting air and adding 9 K medium to the system. The effect of addition of ferric chloride during the arsenic bioleaching resulted in a decrease of the solubilized arsenic in the system. The presence of CO 2 is associated to the decrease in arsenic dissolution.

Background The term bioleaching refers to the bacterial conversion of an insoluble metal (usually a metal sulfide, e.g., CuS, NiS, ZnS) into a soluble form (usually the metal sulfate e.g., CuSO 4 , NiSO 4 , ZnSO 4 ). When this happens, the metal is extracted into water [ 1 , 2 ]. The first bacterium discovered that was able to oxidize minerals was Acidithiobacillus ferrooxidans ( At. ferrooxidans , previously Thiobacillus ferrooxidans ), a gram-negative, acidophilic, chemolithoautotrophic, non-spore forming rod. Nutritionally, typical At. ferrooxidans isolates are considered obligate autotrophs. At. ferrooxidans is able to use either ferrous iron or a wide variety of reduced inorganic sulfur species as an electron donor compounds. It is also able to grow using ferric iron as an electron acceptor, provided that an electron donor, such as reduced inorganic sulfur compound is present. Energy is derived from the oxidation of reduced iron and sulfur compounds, including ferrous ion, sulfide, elemental sulfur and thiosulfate, with final oxidation products being ferric ion and sulfate [ 3 , 4 ]. Other microorganisms considered important in commercial mineral biooxidation processes are: Acidithiobacillus thiooxidans , Acidithiobacillus caldus , Leptospirillum ferrooxidans , and Acidiphilium acidophilum [ 3 ]. Bioleaching has emerged as a simpler, safer, and less expensive process than other alternatives for most limestone, granitic, or other host rocks that have secondary replacement of pyritic minerals containing metal values. In recent years, biooxidation has shown itself to require less capital, reduced operating cost, and less skilled operating and maintenance personnel than the traditional pressure oxidation or roasting techniques [ 5 ]. This technology has been used for treating specific mineral ores, mainly copper and gold bearing ores [ 6 - 8 ]. Moreover, bacterial leaching in acid medium has been successfully applied in: uranium metallurgy [ 9 ]; silver, gold and lead recovery [ 10 ]; zinc [ 11 ]; and new processes have been developed for cobalt recovery [ 12 , 13 ]. A complex sulfide ore is an association of galena (PbS), sphaelerite (ZnS) and chalcopyrite (CuFeS 2 ), disseminated in a pyritic matrix. Besides of lead, zinc and copper as valuable metals, such deposits may contain significant quantities of silver, gold, arsenic, antimony, bismuth and mercury. Numerous economically important deposits of these ores exist in the world [ 14 ]. Complex ores are often characterized by particularly fine intergrowth of the mineral values. Due to these specific mineralogical characteristics, it is necessary to finely grind and concentrate the ore prior to the solubilization of the valuable metals. To obtain separate concentrates by selective flotation involves high unit-cost, poor quality of the concentrates and relatively low overall recoveries [ 15 ]. Arsenic is a major impurity present in numerous sulfide deposits. The presence of arsenic in mineral concentrates drastically diminishes their value, and results in two types of problems. On one hand, arsenic produces metallurgical problems, making difficult the metal extraction and the recovery of a final product of high purity. On the other hand, arsenic is regarded as a highly toxic contaminant resulting in environmental problems due to its atmospheric release and possible water contamination associated to the processing of arsenic bearing ores and concentrates [ 16 ]. Furthermore, this element is generally toxic for microorganisms and its dissolution could inhibit bacterial activity in the bioleaching process. It has been shown that high concentrations of arsenic in solution inhibit bacterial growth, with As(III) reported to inhibit bacteria to a greater degree than As(V) [ 17 ]. The processing of complex arsenic bearing ores and concentrates requires a good understanding of the mechanisms involved previous to the design and development of an adapted sequence of process units. In particular, a complete chemical and mineralogical characterization of the ore is essential from both biological and metallurgical points of view in order to determine the possible inhibition problems and the requirement for effluent treatment processes [ 12 ]. The interest in the study of bioleaching of arsenic-containing minerals, started two decades ago with a publication dealing with the degradation of arsenopyrite (FeAsS) through At. ferrooxidans in gold-arsenic concentrates. This study established the economic feasibility of this technology and its potential for alleviating some environment related problems [ 18 ]. Later, other researchers [ 19 ] studied the physical changes occurring in the mineral-bacteria interphase in pure crystals of arsenopyrite. They found that bacterial oxidation is characterized by three stages that coincide with the phases of the general bacterial growth and that the amorphous arsenates produced are deposited over the crystal surface, thus interrupting the bioleaching process. There have been other studies aimed to determine the influence of several factors over the arsenic bioleaching in gold minerals and concentrates. These studies have been mainly focused to maximize gold extraction and considered bioleaching as a pretreatment in the cyanidation process, consequently leaving the importance of arsenic dissolution and extraction in a second term. Ubaldini et al [ 20 ] studied the arsenopyrite bioleaching over a refractory mineral using a mixture of At. ferrooxidans and At. thiooxidans . They achieved an increase in gold extraction from 55.3% to 96.8% using the following conditions; pulp density 20%, pH 2, stirring conditions 200 rpm, temperature 30°C, time 7 days. Another research group used a domestic strain of At. ferrooxidans and a gold refractory mineral to study the influence of a magnetic field, surfactant addition and the presence of Ag + , Bi 3+ , Co 2+ y Hg 2+ ions over the bioleaching process [ 21 ]. They found a reduction to 120 hours to leachate 60% of arsenic using magnetized water and the addition of tween-80 surfactant and Ag + ion [ 21 ]. As indicated above, previous studies are focused in using bioleaching as a technique to eliminate arsenic in high-value minerals such as gold concentrates. However, there is no reported work on arsenic elimination or reduction in minerals of low value such as lead. This work is aimed to reduce the arsenic content in complex concentrates of galena and to generate preliminary data to allow us to conduct further studies to understand the complex behavior of the bioleaching process. This preliminary study deals with the influence of main factors in batch bioleaching over the arsenic solubilization from a complex lead concentrate. The main factors influencing the biooxidative treatment were tested using a two level fractional factorial plan of experiments (2 7–4 ), and they were: surface area, pulp density, carbon dioxide bubbling, air bubbling, 9 K medium addition, FeCl 3 addition, and two different At. ferrooxidans strains. Results and discussion Chemical and mineralogical characteristics of the concentrate The chemical composition of the concentrate, determined by atomic absorption spectrometry (AAS) and AAS Hydride System for arsenic was: 3.85% As, 14.75% Fe, 23.53% S, 49.76% Pb, 2.48% Zn, 0.51% Sb, 0.29% Cu, 0.28% Bi. The remaining 4.56% being other elements such as: Cd, Ca, Ag, K, Mn, Na, Ni, Ba, Mo, Sn, Si, O. Table 1 shows major phases in the concentrate determined by X-ray diffraction (XRD). Photographs of prepared mounts (Figures 1 , 2 and 3 ) show several mineral species and some mineral associations found in the concentrate. Studies dealing with bioleaching through At. ferrooxidans have provided great amount of basic knowledge about this process and have been useful in the understanding of the physicochemical and microbiological aspects of this phenomenon [ 3 , 22 ]. Biooxidative dissolution of arsenopyrite [ 19 , 23 - 29 ] and the parameters for adapting the bacteria to arsenic have also been studied [ 17 , 30 ]. However, a major disadvantage of these studies is the fact that they were performed using chemicals reagents or pure crystals of pyrite and/or arsenopyrite, synthetic or manually sorted with the aid of a microscope and therefore, they do not represent the conditions and complexity involved in the bioleaching treatment of concentrates. There are only a few of studies dealing with arsenic-bearing ores and concentrates of natural sources [ 12 , 14 , 31 , 32 ]. It is desirable that the trend of today's studies of bioleaching of natural ores be aimed to understand the factors influencing these phenomena if this technology is expected to reach industrial importance. However, it is clear that the complexity of some sulfide minerals are due to the association among species and to the coexistence of inclusion forms of many of these sulfide materials. These two issues (complexity and inclusions) are important factors that may inhibit the process and produce non-successful intrinsic behavior of the bioleaching mineral systems [ 33 ]. The concentrated used in this study was highly complex as can be seen in Figures 1 , 2 and 3 . Also in these Figures the relative composition of the mineral species and their associations described in Table 1 are shown. The complex array of compositions showed in these Figures means that the process of bioleaching is expected to be influenced by mechanisms that still need to be established by the diversity of the mineral species involved and by the presence of traces of metals in the concentrate. Mineral species oxidized XRD analysis of the residual solid from bioleaching (Figure 4 ) shows only the presence of crystalline phase anglesite (PbSO 4 ). Chemical analysis performed through AAS resulted in the presence of residual arsenic. This indicates that all crystalline species present in the concentrate were completely oxidized through the bioleaching process. This means that all arsenic present in arsenopyrite was either solubilized (22 to 33%) or precipitated in amorphous compounds (67 to 78%). Also, galena (PbS) was completely oxidized to anglesite. Only a very small portion of anglesite remained in solution (25 mg/L, see Figure 5 ) while, the main phase of this appeared as a solid precipitate. Other elements present originally in the concentrate such as Zn, Sb, and Cu were also solubilized. This fact opens a window of a great potential for separating lead from other elements that often are present along in the mineral concentrates [ 2 ]. Formation of precipitates during biooxidation Formation of precipitates of arsenic and iron has been commonly observed with the use of At. ferrooxidans [ 34 ]. Several other compounds have also been observed in solid state during the bioleaching of arsenopyrite, among these are; ferric arsenate, elemental sulfur, amorphous ferric arsenate FeAsO 4 · x H 2 O, jarosite KFe 3 (SO 4 ) 2 (OH) 6 and scorodite FeAsO 4 · 2H 2 O [ 19 , 25 - 27 ]. It has been reported that the elemental sulfur and ferric arsenate are accumulated in the surface of the grains forming a coating barrier that avoids the contact between the mineral and the Fe +3 ions in the indirect oxidation, inhibiting the leaching of the mineral species [ 19 , 27 ]. The formation of precipitates of the jarosite-type are prevented by a pH control to levels lower than 1 [ 33 ], while the formation of S 0 can be avoided by the use of bacterial consortia in which sulfur-oxidant bacteria such as At. caldus [ 28 ] or At. thiooxidans are included [ 3 ]. In this study the precipitation of amorphous arsenic compounds was important during the bioleaching process. In samples taken at the end of the experiment there was a difference in the arsenic content in the digested mineral residue, which was treated by a hydrochloric acid digestion, to the non-digested residue (see Table 2 ). This last, suggests the presence of two types of arsenic compounds in the residue, probably amorphous ferric arsenates and jarosite-type precipitates due to the acidic conditions used in the experiment (pH ≥ 2). However, other studies are needed using this kind of concentrates in order to determine the type and amount of precipitates formed and the kinetics involved in this process. Significant factors in arsenic dissolution Arsenic bioleaching results for the eight experimental runs (Table 3 ) are shown in Figure 6 . In this plot a great deal of scattered data among experimental runs is observed, this means that still a great deal of variability exists among the effect of the factors considered in this study. Table 4 shows the results of the least-squares-multiple-regression model fitting to arsenic dissolution data using time and factors as explanatory variables. With the exception of At. ferrooxidans strains, all of the other factors resulted to be highly significant in its influence to the arsenic dissolution. The resulting model was the following (Minitab 13.0): Arsenic = 52.0 - 66.3 Pulp Density - 32.6 Surface Area - 21.1 Ferric chloride - 12.5 Carbon dioxide + 29.3 Air + 29.3 9 K Medium + 3.00 Strain + 3.94 Days.     (1) With a determination coefficient of R 2 = 0.834 The negative sign in some of the variables of the model indicates that in order to maximize bioleaching of arsenic, these factors must be kept in low levels. These were denoted with a zero value in the model. Thus, the levels of factors that should be considered in the model are the following: pulp density at 10%, low surface area, no FeCl 3 and CO 2 addition, air and 9 K Medium addition and any strain present. Best run One of the main disadvantages for the commercial use of bioleaching is the slow nature of this process, which is due mainly to the relative slow growth rate of the bacteria [ 5 , 35 ], to the long period of time needed for the bacteria to adapt to the mineral environment and particularly to mineral complexes [ 31 ] and to inhibition problems due to the products of the bioleaching [ 36 ]. In Figure 6 , Run 1 presents the highest arsenic concentration in the leachate (approximately 200 mg/L). The combination of the previously established levels of factors for this run are shown in Table 3 and coincides with the required combination of levels to maximize the arsenic dissolution as stated in the model of the above Equation (1). The graphical pattern of the arsenic bioleaching for Run 1 (Figure 7 ) shows some features that are potentially adequate for the use of this combination in reduction of arsenic content in future studies. Some of these features are: the lack of an adaptation period (lag-time), a very pronounced slope and that the stability of the system is reached in a relatively short time (four days) compared to the other experimental runs. The data for this run (Run 1) were fitted through a third order polynomial linear regression model and statistically analyzed over a 95% confidence interval (see Figure 7 ). The model of arsenic present in solution in Run 1 is the following (Minitab 13.0): Arsenic = 100.16 + 11.487 Days - 0.4705 Days 2 + 0.006 Days 3 (2) With a determination coefficient of R 2 = 0.6585 Besides of all these convenient features in Run 1, the percentage of bioleached arsenic is still low. Proper conditions of solubilization of arsenic during bioleaching are key feature to improve the percentage (yield) of arsenic removal. Therefore, further studies are needed in order to determine the other factors (not considered in this work) that influence specifically the solubilization of arsenic in the bioleaching system such as: pH, dissolved oxygen concentration, redox potentials, nature of concentrate and temperature among others. General evolution of arsenic in the leachate When time (days) was used as the only independent variable, data collected in all runs were adjusted to a third order polynomial regression model and the analysis is shown in Figure 8 . The resulted general equation of this model is the following (Minitab 13.0): Arsenic = 23.888 - 1.0597 Days + 0.5161 Days 2 - 0.0131 Days 3 (3) The determination coefficient of this model was R 2 = 0.3237, which represents a value relatively low due to the absence of the other significant factors (pulp density, CO 2 , 9 K medium, particle size, air injection and ferric chloride added). It is worth to note that this model resembles the well known general three-phase bacterial growth model. Effect of pulp density The pulp density effect is shown in Figure 9 . Here a greater arsenic dissolution is achieved with a low level of 10% of solids. This result is in agreement with results reported by other researchers working with complex concentrates of copper and zinc [ 14 ], complex sulfides of copper, lead and zinc [ 37 ] and copper concentrates [ 38 , 39 ]. In all of these studies greater levels of leaching were achieved with low pulp densities. The reduction in the bioleaching rate can be due to the fact that at higher concentrations of solids causes an increase in the friction between particles, and probably avoiding the adhesion between the particle and bacteria [ 40 ]. This friction may consequently cause some mechanical damage to the cell [ 41 ]. In the case of arsenic, it is possible that a high pulp density may cause a greater interaction between the dissolved arsenic and other products of biooxidation. Thus, increasing the rate of formation of precipitates and consequently decreasing the concentration of arsenic in solution. In our study it was found a strong interaction between the pulp density and the air injection with respect to the arsenic removal. The ANOVA analysis of this interaction is shown in Table 5 , and the plot in Figure 10 . The increase in pulp density generates a decrease in the dissolved arsenic concentration in all runs. However, this decrease is greater in runs where air was not injected to the system. Effect of surface area The effect of surface area (particle size) can be observed in Figure 11 . Here a greater arsenic dissolution is presented when the particle surface area is small, in other words, where bigger particles are present in the system. One would expect that a greater exposed surface area to the microbial attack would reflect an increase of the arsenic dissolution. However, this unexpected result is in agreement with other studies reported in the literature [ 42 , 43 ]. Explanations for this phenomenon are in the sense that it is possible for the bacteria to preferentially attack some sites formed during the solids grinding process [ 42 ]. Other study of the leaching of chalcopyrite reported that the rate of leaching increased with the use of larger particles sizes [ 42 ]. They suggested that this was due to a greater efficiency of bacteria attachment to the particles. Other explanations are focused on disregarding the associated physical factors such as surface area and stressing the specific and non specific interactions. Among the specific are the ionic and hydrogen bonds, and chemical and protein interactions. Non-specific are hydrophobic interactions such as surface free energy and electrostatic [ 44 ]. A possible explanation for the behavior observed in this study is based in the great complexity of the mineral concentrate that may enhance the specific interactions. These interactions are affected by the particles of smaller sizes where the greater surface area leaves each particle exposed to a greater amount of different mineral species. These species increase the complexity of the medium thus affecting directly the solubilization of arsenic during the bioleaching. The complexity of the medium due to small size particles can be further increased when a high pulp density is used as pointed out in the previous section. These two issues, complexity and pulp density, combined in the conditions above mentioned may produce a negative effect over the arsenic dissolution. Effect of ferric chloride In the present study the effect of addition of ferric chloride during the arsenic bioleaching resulted in a decrease of the solubilized arsenic in the system as observed in Figure 12 . Information on this effect is limited in the literature with only some studies reporting the influence of the ion chloride on the sulfur and iron oxidation during bioleaching of some chemical species using At. ferrooxidans and /or At. thiooxidans [ 45 - 47 ]. Effect of carbon dioxide It is well known that autotrophic organisms such as At. ferrooxidans require CO 2 for its growth. This is due to the fact that these bacteria are able to use CO 2 as its only source of carbon for their biosynthetic reactions. CO 2 fixation is usually achieved through the so-called Calvin-Benson-Bassham cycle [ 48 ]. In Figure 13 it can be seen that the arsenic dissolution is reduced when CO 2 is bubbled into the system. This behavior is in agreement with studies reported in literature that established that the consumption of carbon by the bacteria decreases at high CO 2 concentrations implying the formation of intracellular bicarbonates [ 49 ]. Other authors coincide in that the amount of CO 2 present in the air is enough to withstand the bacterial growth [ 14 , 25 ]. In our study, it was found a strong interaction between the two levels of CO 2 and air into the system. The ANOVA analysis of this interaction is also shown in Table 6 and Figure 14 . However, in our study the presence of CO 2 according to the results shown in Figure 13 is associated to the decrease in arsenic dissolution. At this time, it is difficult to establish the influence of the presence and concentration of CO 2 on the dissolution of arsenic. Therefore, further studies oriented to establish the relationship between the dissolved oxygen and the concentration of CO 2 in the system are needed in order to elucidate this effect. Effect of air Acidithiobacillus ferrooxidans is an aerobic microorganism that uses oxygen as a final electron acceptor during the oxidation process. However, in absence of oxygen it is still able to grow in the presence of inorganic reduced sulfur compounds by using the ferric ion as an alternative electron acceptor [ 50 ]. In these conditions, however, its growth is slower [ 48 ]. In the present study, it is not surprising that the arsenic dissolution was increased with the aeration of the system as can be observed in Figure 15 . As it was shown previously, the availability of oxygen in the medium decreases when the pulp density increases and also when CO 2 is admitted into the system. Again a more definite study targeted to measure dissolved oxygen in the system may bring light into the effect of air during the arsenic bioleaching. Effect of 9 K medium The 9 K medium is the source of nitrogen and phosphorous [ 51 ] needed for bacterial growth. These two elements are not available in the sulfide minerals. Therefore, they need to be provided during the bioleaching process. Figure 16 shows the effect of the addition of 9 K medium. As expected, the arsenic bioleaching is increased when 9 K medium is used. Effect of strain In Figure 17 differences of two At. ferrooxidans strains used in this study are shown. Strain T1 presents the common bacterial model of growth in three stages, while strain T18 presents a straight model that requires less adaptation time. This can be due to the fact that T18 was previously grown in systematically increased arsenic concentrations as discussed in the material and methods section of this paper. Continuous culture dilution rate calculus Data generated from the batch arsenic bioleaching were used as a first approximation for the calculation of the dilution rate D for a future design of a continuous bioleaching system. The procedure used consisted in to derive the third order polynomial equations (2) and (3) with respect to time and plot each equation versus the arsenic concentration in the system. Then to draw a straight line from the origin to the maximum point in each curve (Figure 18 ). The slope of this line was then defined as dilution rate in agreement with the equation of product balance in a continuous culture vessel in steady state [ 52 , 53 ] as shown in equation (4): where F = Rate of medium flow through the vessel (volume/time) V = Volume of the vessel (volume) D = F/V Dilution rate (time -1 ) P n-1 = Inner product concentration P n = Outer product concentration dP n / dt = Total variation of the product concentration in the vessel ( dP n / dt ) production = Product concentration variation due to the production in the vessel Rearranging Eq. (4): In steady state, dP n / dt = 0 and assuming that P n-1 = 0 in a single vessel, From which Equation (7) represents a straight line through the origin having a slope of D (dilution rate) and corresponds to the straight line plotted in Figure 18 . Using this method, the calculated dilution rate for the general model of equation (3) is 0.088 days -1 , while for run 1 in equation (2) is of 0.103 days -1 (not shown in the plot). However, these dilution rates are still relatively small compared to the dilution rates used in industrial scale processes (0.05–0.6 h -1 ) [ 53 ]. Conclusions Since the bioleaching rate and the levels of leached arsenic are limited (22–33% of the originally present in the concentrate), proper conditions of solubilization of arsenic during bioleaching is the key feature to improve the percentage (yield) of arsenic removal. Therefore, further studies are needed in order to determine the other factors (not considered in this work) that influence specifically the solubilization of arsenic in the bioleached system such as: pH, dissolved oxygen concentration, redox potentials, nature of concentrate and temperature among others. The performance of the bacteria used in this study ( At. ferrooxidans ) was able to completely oxidize the minerals present during the arsenic bioleaching. Since, galena (PbS) was completely oxidized to anglesite (PbSO 4 ) with only a very small portion of anglesite remaining in solution, while the main phase of this appeared as a solid precipitate. Other elements present originally in the concentrate such as Zn, Sb, and Cu were also solubilized. The complex array of compositions contained in the concentrate, employed in this study, means that the process of bioleaching is expected to be influenced by mechanisms that still need to be established due to the diversity of the mineral species involved and by the presence of traces of metals in such concentrate. The precipitation of amorphous arsenic compounds was important during the bioleaching process. Results suggest the presence of two types of arsenic compounds contained in the residue, probably amorphous ferric arsenates and jarosite-type precipitates due to the acidic conditions used in the experiment (pH ≥ 2). The increase in pulp density generates a decrease in the dissolved arsenic concentration. However, this decrease is greater in runs where air was not injected to the system. The maximum rate of arsenic dissolution in the concentrate was found using small surface area of particle exposure, low pulp density, injecting air and adding 9 K medium to the system. The effect of addition of ferric chloride during the arsenic bioleaching resulted in a decrease of the solubilized arsenic in the system. The presence of CO 2 according to the results is associated to the decrease in arsenic dissolution. Further studies oriented to establish the relationship between the dissolved oxygen and the concentration of CO 2 in the system are needed in order to elucidate this effect. Arsenic dissolution was increased with the aeration of the system. The availability of oxygen in the medium decreases when the pulp density increases and also when CO 2 is admitted into the system. A study using dissolved oxygen measurements is needed in the future to determine the effect of air during the arsenic bioleaching. Methods Chemical and mineralogical analysis The flotation concentrate was obtained from La Soledad mine (Parral, Chihuahua, México). Chemical analysis was carried out by atomic absorption spectrometry through AAS (GBC Avante Σ), arsenic was determined by AAS Hydride System. The major phases in the concentrate were determined by X-ray diffraction (Siemens D5000). Mineral samples were mounted in polyester resin blocks using approximately 0.2 g per mount and surface was polished. Mounts were examined using a microscope (Olympus AX70) and photographs were taken at various sites of each sample. Design of experiments One of the most useful types of multifactor experiment is the 2 k factorial series. In this series, there are k factors each at two levels. Hence there are a total of 2 k treatments in the full factorial set. This series is particularly useful in the exploratory stages of an investigation because it permits the examination of a fairly large number of factors and their interactions in a trial of reasonable size [ 54 ]. In almost all experiments the investigator would like to reduce the number of observations required for a complete factorial. If certain assumptions can be met the use of fractional factorials is a most efficient technique to reduce the number of observations and still obtain the desired information. The usual fractional factorial is still orthogonal, which means that certain effects are estimated independently of one other [ 55 ]. A major use of fractional factorial designs is in screening experiments. These are experiments in which many factors are considered with the purpose to identifying only the important variables that affect the response and their interactions. The factors that are identified as important are then investigated more thoroughly in subsequent experiments. A fractional factorial of the 2 k design containing 2 k-p runs is called a 1/2 p fraction of the 2 k or, more simply, a 2 k-p fractional factorial design. It is possible to construct this type of designs for investigating up to k = N-1 factors in only N runs. If k = N-1 the fractional factorial design is said to be saturated. Of particular importance is a very useful saturated fractional factorial design for studying seven factors in eight runs; that is, the 2 7–4 design. This design is a one-sixteenth fraction of the 2 7 [ 56 ]. This was the design used in this work to test the factors influencing the biooxidative treatment. The objective was to maximize the arsenic solubilization. The factors, levels and runs are presented in Table 3 . Factors and levels in experimental runs Pulp density The bioleaching experiments were carried out using two solid concentrations. The low level for pulp density was settled as 10 % w/v; the high level as 20% w/v. Surface area The lead concentrate was washed with distilled water; the mineral suspension was wet sieved using a 75 μ m sieve in order to obtain two fractions. Both of them were dried and analyzed for specific surface area by a laser scattering particle sizer (Malvern Master Sizer 2000). The specific surface area for fractions >75 μ m and <75 μ m were 0.42 and 1.65 m 2 /g and were established as low and high levels respectively. Ferric chloride Addition of ferric chloride at concentration of 150 mg per liter of liquid medium was settled as high level, no ferric chloride addition was low level. Carbon dioxide Low level: no carbon dioxide bubbling; high level: carbon dioxide flow from a compressed cylinder, injected into the mixture at a rate of 0.2 vol/vol min -1 . Air No air bubbling was considered as low level; no sterile air bubbles injected into the liquid-concentrate mixture at a rate of 0.3 vol/vol min - 1 was high level. 9 K Medium Pure distilled water as culture medium was established as low level; the use of 9 K medium [ 51 ], which contained (per liter of distilled water) 3.0 g of (NH 4 ) 2 SO 4 , 0.5 g of MgSO 4 ·7H 2 O, 0.1 g of KCl, 0.5 g of K 2 PO 4 , 0.01 g of Ca(NO 3 ) 2 was settled as high level. The pH was adjusted to 5.7 with sulfuric acid. Strains A native Acidithiobacillus ferrooxidans wild type strain termed T1 [ 57 ], isolated from a domestic mining site acid drainage, was used as a low level; and an arsenic-resistant strain, called T18, derived from T1 by serial transfers to flasks containing increasing arsenic amounts, was used as high level. T18 is able to grow at arsenic concentration as high as 1800 mg l -1 [ 57 ]. The strains were cultured in a rotary shaker incubator (30°C, 175 rpm), in a medium containing 44.22 g FeSO 4 , 3.0 g (NH 4 ) 2 SO 4 , 0.5 g KH 2 PO 4 , 0.5 g MgSO 4· 7H 2 O, 0.1 g KCl, 0.01 g Ca(NO 3 ) 2 per liter, adjusted to pH 2.0 with sulfuric acid. After bacterial growth for ten days, cultures were filtered and the clear liquid was used as inoculum (20% v/v). Conditions of cultivation and sampling The eight biooxidation runs (Table 3 ) were conducted in 1000 ml Pyrex culture flasks containing 500 ml of mixture, placed in a rotary shaker incubator (30°C, 175 rpm). The pH was maintained to 2.0 with sulfuric acid. The experiment was monitored each 48 h. After a short period for sedimentation of solid particles, each flask was sampled extracting 2 ml of clear leachate in which total arsenic, lead and iron concentrations were determined. The liquid extracted was compensated by the addition of distilled water or 9 K medium. Little hoses were submerged in liquid mixture to inject compressed air, carbon dioxide, or both. The experiment was carried out for 28 days; this is twice the time needed to reach the stability of arsenic concentration in a previous laboratory test. At the end of the experiment the resulting pulp in each run was filtered and the bioleached mineral was washed with distilled water and dried in a stove at 40°C. Two grams of this material were taken and were submitted to digestion using 10 ml of HCl 0.6 N during 3 hours at room temperature. The digested mineral was filtered, washed with distilled water and dried at 40°C. In both cases the arsenic content was determined through chemical analysis. Data analysis Table 3 shows the saturated experimental design used to carry out the experiment, the eight trials provide a total of seven degrees of freedom for the entire experiment, allocated to seven columns of two levels, each column having one factor assigned (Pulp density, Surface area, Ferric chloride, Carbon dioxide, Air, 9 K medium, Strain). All columns provide four tests under the low level of the factor and four tests under the high level of the factor. This is one of the features that provides the orthogonality among all the columns (factors) [ 58 ]. Orthogonality permits the comparison between low and high levels for each factor in their ability to dissolve arsenic. Comparison was performed by fitting a multiple regression model to arsenic dissolution data, to make the model function, all seven factors were treated as dummy variables [ 59 ] taking the low level as 0 and high level as 1 (Table 7 ). Time in days was included in the model as the only true quantitative factor. Model fitted is: Arsenic = β 0 + β 1 Pulp Density + β 2 Surface Area + β 3 Ferric chloride + β 4 Carbon dioxide + β 5 Air + β 6 9 K Medium + β 7 Strain + β 8 Days .     (8) Where Arsenic Expected value of arsenic concentration in mg l -1 in leachate β 0 , β 1 ,... β 8 Regression coefficients As the experiment was monitored each 48 hours, and arsenic concentration was determined in leachate, arsenic dissolution data gathered during the bioleaching experiment were analyzed as time series (time as independent variable) by multiple regression to fit the third order polynomial model: Arsenic = β 0 + β 1 t + β 2 t 2 + β 3 t 3 (9) Where Arsenic Expected value of arsenic concentration in mg l -1 in leachate t Time in days of bioleaching β 0 , β 1 , β 2 , β 3 Regression coefficients Software Analytical procedures and graphing was performed using MS Excel or Minitab 13.0 Authors' contributions MM designed and performed the experimental runs and prepared the manuscript. ME performed the statistical analysis. BP provided the bacterial strains and helped in the culture production. AL performed the technical revision of the manuscript. EO got the funds and provided the tutoring during the development of this work. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526773.xml

551538

Fast and systematic genome-wide discovery of conserved regulatory elements using a non-alignment based approach

The authors describe a powerful approach for discovering globally conserved regulatory elements between two genomes that does not require alignments. Its application to pairs of yeasts, worm, flies and mammals, yields a large number of known and novel putative regulatory elements, many of which show surprising conservation across large phylogenetic distances.

Background One of the major challenges facing biology is to reconstruct the entire network of protein-DNA interactions within living cells. A large fraction of protein-DNA interactions corresponds to transcriptional regulators binding DNA in the neighborhood of protein-coding and RNA genes. By interacting with RNA polymerase or recruiting chromatin-modifying machinery, transcriptional regulators increase or decrease the transcription rate of these genes. Transcriptional regulators bind specific DNA sequences upstream, within or downstream of the genes they regulate, and a large number of experimental and computational studies are aimed at locating these sites and understanding their functions (for example [ 1 , 2 ]). The increasing availability of whole-genome sequences provides unprecedented opportunities for identifying binding sites and studying their evolution. The strong conservation of functional elements (binding sites, protein-coding genes, noncoding RNAs, and so on) across even distantly related species should make it possible to predict these functional elements and prioritize them for experimental validation. The few large-scale comparative genomics approaches for finding transcriptional regulatory elements have so far relied mostly on detecting locally conserved motifs within global alignments of orthologous upstream sequences [ 3 , 4 ]. Although very powerful and straightforward, these approaches cannot be used when upstream regions are very divergent or have undergone genomic rearrangements. For example, aligning the mouse and puffer fish orthologous upstream regions would be very difficult, because of the great reduction that the puffer fish intergenic regions have undergone [ 5 ]. Also, global alignments cannot be used when the positions of regulatory elements within functionally conserved promoter regions have been scrambled, for example through genomic rearrangements. Also, global alignment-based approaches often generate an overwhelming number of predictions because of the basal conservation between the genomes under study. To reduce the number of predictions, multiple global alignments of upstream sequences from several related species have been used, yielding many new candidate binding sites [ 3 , 4 ]. However, multiple (more than two) closely related genome sequences are not always available; moreover, by focusing only on regulatory elements that are conserved between several genomes, these approaches might miss elements that are conserved in more local areas of the phylogenetic tree. Here we describe a simple and efficient comparative approach for finding short noncoding DNA sequences that are globally conserved between two genomes, independently of their specific location within their respective promoter regions. Our method, which we call FastCompare, is based on a principle that we have termed 'network-level conservation' [ 6 ], according to which the wiring of transcriptional regulatory networks should be largely conserved between two closely related genomes. Our previous attempts at using network-level conservation relied on Gibbs sampling to find candidate regulatory elements [ 7 ]. However, Gibbs sampling and related algorithms are not fully appropriate in this context, because of the low density of actual binding sites in pairs of orthologous upstream regions. Moreover, these algorithms are non-deterministic, relatively slow, and rely on sequence sampling, which makes them likely to miss many regulatory elements. While our previous approach was successful at predicting a large fraction of functional regulatory elements in the relatively small yeast genome, analyzing larger and more complex metazoan genomes requires faster and more exhaustive algorithms. Here, we use a faster, simpler and more comprehensive approach for detecting conserved and probably functional regulatory elements using the network-level conservation principle. FastCompare allows comprehensive exploration of the conserved - but not aligned - motifs between two genomes, while retaining a linear time complexity. We apply our approach to a large number of species, including yeasts, worms, flies and mammals, and describe some of the most conserved known and unknown regulatory elements within these genomes. We also show how this approach may help reconstruct part of the transcriptional network and reveal some of its associated constraints. Finally, we show that a large number of predicted motifs are conserved within and across different phylogenetic groups. Results In the following sections, pairs of closely related species are termed phylogenetic groups. We applied FastCompare to the four following phylogenetic groups: yeasts ( Saccharomyces cerevisiae and S. bayanus ), worms ( Caenorhabditis elegans and C. briggsae ), flies ( Drosophila melanogaster and D. pseudoobscura ) and mammals ( Homo sapiens and Mus musculus ). For each phylogenetic group, we describe some of the most interesting, known and novel, predicted regulatory elements. For each of these regulatory elements, we perform independent validation using gene expression data, chromatin immunoprecipitation (IP) data, known motifs and data from several biological databases (Gene Ontology (GO)/MIPS, TRANSFAC), and show that the most globally conserved predicted regulatory elements are strongly supported by these independent sources. Yeasts The average nucleotide identity between S. cerevisiae and S. bayanus upstream regions is approximately 62% [ 4 ] (similar to the identity between human and mouse upstream regions) and divergence times are estimated between 5 and 20 million years [ 4 ]. The number of ortholog pairs between S. cerevisiae and S. bayanus is 4,358 (see Materials and methods). We chose to analyze 1 kb-long upstream regions, because most of the known transcription factor binding sites in S. cerevisiae are located within this range [ 8 ]. Using FastCompare, we calculated a conservation score for all possible 7-, 8- and 9-mers on the corresponding 8.6 megabase-pairs (Mbp) of sequences and sorted each list separately according to conservation score (see Figure 1 ; the raw sorted lists are available on our website [ 9 ]). On a typical desktop PC, this analysis took approximately 5 minutes (for example, the entire set (8,170) of 7-mers was processed in 35 seconds). Distribution of conservation scores As described in Materials and methods, conservation scores are calculated for all k -mers (with fixed k ), and are relative measures of network-level conservation for these k -mers (the higher the conservation score, the more conserved the corresponding k -mer). We first describe the distribution of conservation scores for all 7-mers. As shown in Figure 2 , the distribution of conservation scores has a very long tail and many 7-mers on the tail correspond to well known regulatory elements in S. cerevisiae (see below for a detailed description of these sites). To verify that such high conservation scores could not be obtained by chance, we generated randomized sequences as described in Materials and methods and re-ran FastCompare on these sequences. The corresponding distribution of conservation scores is shown on Figure 2 and clearly shows that the high conservation scores corresponding to known regulatory elements are extremely unlikely to arise by chance. Validation using independent biological data We used various independent sources of biological data to demonstrate that k -mers with the highest conservation scores are likely to be functional. For a given k -mer, we define the 'conserved set' as the set of ORFs corresponding to the overlap between the two sets of orthologous ORFs containing at least one exact match to the k -mer in their upstream regions (see Materials and methods). We found that conserved sets defined for the highest-scoring 7-mers are significantly enriched with genes whose upstream regions contain occurrences of known motifs in yeast (Figure 3a ), significantly enriched with genes whose upstream regions were shown to be bound by known transcription factors in vivo (Figure 3b ), and significantly enriched in at least one MIPS functional category (Figure 3c ). We also show that the number of 7-mers found upstream of over- or underexpressed genes in at least one microarray condition increases with the conservation score (Figure 3d ) and that the number of 7-mers matching at least one TRANSFAC consensus also increases with the conservation score (Figure 3e ). Altogether, these data provide strong and independent evidence that our method identifies functional yeast regulatory elements by giving them a high conservation score. Closer examination of Figure 3a-d shows that the 400 highest-scoring 7-mers are most strongly supported by independent data. Therefore we retain them for further analysis and, when possible, replace them by 8-mers and 9-mers with higher conservation scores and also add the high-scoring 8-mers and 9-mers without high-scoring substrings, as described in Materials and methods. This processing yields 398 k -mers ( k = 7, 8 and 9). Then, for each of these 398 k -mers, we determine the optimal window within the initial 1 kb which maximizes the conservation score (see Materials and methods); we then re-evaluate the functionality of each of the 398 k -mers with the independent biological information described above, using the new conserved sets. The full information for the 398 k -mers is available at [ 9 ]. Known regulatory elements Using known transcription factor binding site motifs, genome-wide in vivo binding data, functional annotation and literature searches, we found at least 27 different known transcription factor binding sites among the 398 highest scoring k -mers. These regulatory elements, along with their support from independent biological data, are shown in Table 1 . Some of the best-known binding sites are represented several times within the 398 top scoring k -mers, in the form of slightly distinct or overlapping sequences (see [ 9 ]). Note also that we use very stringent criteria for identifying known binding sites among our predictions. When we matched our predictions to the known motifs published in [ 4 ] (regular expressions), we predicted 42 out of 53 known motifs (Kellis et al . [ 4 ] predict exactly the same number of motifs, and essentially the same motifs, but using multiple alignments of four yeast genomes). Among the 27 different known regulatory elements returned by FastCompare, several (Swi4, Mbp1, Sum1/Ndt80, Fkh1/2) are involved in regulating the yeast cell cycle. The other known sites are also involved in fundamental biological processes in yeast: amino-acid metabolism (Cbf1, Gcn4), meiosis (Ume6), rRNA transcription (PAC and RRPE), proteolytic degradation (Rpn4), stress response (Msn2/Msn4) and general activation/repression (Rap1, Reb1). As described in Materials and methods, our approach also handles gapped motifs. Thus, the binding sites for Abf1, a chromatin reorganizing transcription factor (CGTNNNNNNTGA), and Mcm1, a factor involved in cell-cycle regulation and pheromone response (CCCNNNNNGGA), were also identified as very high-scoring patterns and strongly supported by independent information (known motifs and chromatin immunoprecipitation). When we used the same independent biological data to evaluate the 400 highest-scoring 7-mers obtained on randomized data, we found only three known binding sites (RRPE, FKH1 and BAS1). Several known binding sites are not found among the 398 top-scoring k -mers, perhaps because their transcriptional network has undergone extensive rewiring since the speciation of the two yeasts, or because the corresponding transcription factors regulate few genes. In some cases, the presence of several known sites (clearly identified in terms of independent data) among the full set of 7-mers argues in favor of the rewiring hypothesis. For example, the binding site for the Rcs1 transcription factor, TGCACCC, only appears at the 1,883rd position within the list of ranked 7-mers. Despite its lack of conservation, this site is strongly backed by independent biological information: it is identified as a known motif, it is found in 33 microarray conditions, and its conserved set is significantly enriched in genes annotated with homeostasis of metal ions ( p < 10 -5 ), which is the known function for Rcs1 [ 10 ]. Similarly, the known binding sites for the Ace2/Swi5 and Hsf1 transcription factors were clearly identified (in terms of independent data) within the complete list of 7-mers, but not among the 398 highest scoring k -mers. Positional constraints It is now known that functional regulatory elements can be positionally constrained, relative to other regulatory elements or to the start of transcription [ 7 , 11 , 12 ]. To assess whether some of the predicted regulatory elements are positionally constrained in yeast, we calculated the median distance to ATG for the conserved sets of each of the 398 k -mers and independently built the distribution of median distances to ATG for all 7-mers as described in Materials and methods (the distribution is shown in Figure 4 ) and found d 0.025 = 350 and d 0.975 = 680. In other words, a median distance to ATG of less than 350 or higher than 680 should each arise by chance with only a 2.5% probability. Among the 398 most conserved k -mers, more than a fifth (86) have their median distance below 350 ( p < 10 -52 ), while only seven have a median distance greater than 680. A closer examination reveals that a few known sites are particularly constrained. For example, the binding sites for Reb1, PAC, TATA, Swi4, Rpn4, RRPE and Mbp1 are found to be situated relatively close to the start of translation, with a median distance to ATG between 150 and 300 bp. Some of these constraints were also found to be good predictors of gene expression in a recent study [ 11 ] (for RPN4, PAC and RRPE, for example). In contrast, binding sites for Met4, Ume6, Hap4, Rap1, Ino4 and Ste12 are found to be situated at a greater median distance, between 400 and 500 bp from ATG. Novel predicted regulatory elements We found many novel motifs among our highest-scoring predictions. For example, we found two strongly conserved motifs, AGGGTAA (rank 17) and TGTAAATA (rank 31), which are situated relatively close to ATG (with a median distance to ATG of 349 and 378.5 bp, respectively) and more often in upstream regions than in coding regions (with ratios of 1.95 and 1.83, respectively). Interestingly, TGTAAATA also has a statistically significant 5' to 3' orientation bias (binomial p -value < 10 -7 ). However, neither of the two putative sites is supported by independent biological data. Additional expression data may help define their biological role. Other sites, such as CAGCCGC or GCGCCGC are found upstream of over- or underexpressed genes in many microarray conditions (15 and 6, respectively). While these two sites are similar to the canonical Ume6-binding site, the latter was not found in any microarray conditions (as none of the microarray experiments we used is related to meiosis, the biological process which Ume6 is known to be involved in), suggesting that the two sites are bound by other factors. Comparing closer and more distant yeast species We repeated the same analysis on distinct pairs of yeast species other than S. cerevisiae / S. bayanus . We first compared S. cerevisiae and S. paradoxus (a much closer relative of S. cerevisiae ) and found 15 of the 27 known motifs we obtained when comparing S. cerevisiae and S. bayanus (results are available at [ 9 ]). We also compared S. cerevisiae with S. castellii , which is a more distant relative within the Saccharomyces phylogenetic group. S. castelli is interesting in that its upstream regions cannot be globally aligned with those of S. cerevisiae , because of extensive sequence divergence [ 3 ]. We also found 15 of the 27 known motifs found in the S. cerevisiae / S. bayanus comparison (results at [ 9 ]), although they were different from the S. cerevisiae/S. paradoxus conserved motifs. Interesting similarities and differences in conservation were revealed when comparing the known motifs discovered in each comparison. For example, the PAC, RRPE and Mbp1 motifs were found within the highest-scoring k -mers in all three comparisons, hinting at the conserved role of the corresponding proteins. However, the Reb1-binding site, which was found to be highly conserved between S. cerevisiae and S. bayanus (rank 1), is much less conserved between S. cerevisiae and S. castelli (rank 230). This argues for extensive rewiring in the Reb1 transcriptional network in the lineage that led to S. castelli . Motif interactions To discover interactions between regulatory elements, we searched for co-conservation of pairs of high-scoring predicted regulatory elements, as described in Materials and methods. Not surprisingly, the most conserved interaction is between RRPE (AAAAATTTT) and PAC (CTCATCGC), with a median distance D = 22 bp [ 11 , 13 ]. We also find that the Cbf1-binding site (CACGTGA) is strongly co-conserved with the Met4-binding site (CTGTGGC), and that these two sites are separated by a short distance ( D = 44.5) in S. cerevisiae . Indeed, it has been shown that the binding of Cbf1 in the vicinity of a very similar sequence (AAACTGTG) enhances the DNA-binding affinity of a Met4-Met28-Met31 complex for this sequence [ 14 ], and that the median distance between the above Cbf1 and Met4 sites is small [ 15 ]. Many of the predicted interactions have not yet been experimentally studied. For example, we found that the highest scoring Reb1 motif (CGGGTAA) is significantly co-conserved with both the highest scoring RRPE motif (AAAAATTTT) and the highest scoring PAC motif (CTCATCGC), with a short median distance between the two sites in both cases ( D = 38 and D = 63.5, respectively). The Reb1/RRPE interaction was also discovered independently as a good predictor of expression [ 11 ]. We also found that Reb1 interacts with the Cbf1 motif (CACGTGA), also at a short median distance ( D = 30). An interesting interaction between RRPE and an unknown motif, TGAAGAA, displays a conserved set strongly enriched in translation (p < 10 -11 ), while RRPE alone is more strongly enriched in rRNA transcription (p < 10 -14 ). The full sorted list of interactions is available at [ 9 ]. Worms In contrast to yeast, relatively little is known about cis -regulatory sequences in C. elegans . There is a dramatically greater complexity of transcriptional regulation in multicellular organisms. Indeed, transcription factors in multicellular organisms regulate cohorts of genes in different tissues and at different times during development [ 16 ]. C. elegans promoter regions often contain many domains of activation/repression and, as a result, are much larger than those in yeast. We applied FastCompare to the genomes of C. elegans and C. briggsae , two worms that diverged about 50-120 million years ago [ 17 ]. The number of orthologous open reading frames (ORFs) between these two species is 13,046 and here we have only considered 2,000 bp upstream regions. It takes approximately 11 minutes for FastCompare to process the corresponding 50 Mbp of sequences and calculate a conservation score for all 7-, 8- and 9-mers on a typical desktop PC. Validations The distribution of conservation scores for all 7-mers shows that high conservation scores are unlikely to be obtained by chance (Figure 5a ). As shown in Figure 5a , many known regulatory elements fall on the tail of the distribution. We then used functional categories, over- or underexpression, and TRANSFAC motifs to assess the ability of FastCompare to predict functional regulatory elements. Figure 5b-d shows that support for the highest-scoring k -mers by functional enrichment, expression and TRANSFAC strongly increases with conservation score. We have only retained the 400 highest-scoring 7-mers, which are particularly well supported by independent biological information as shown in Figure 5b,c . Starting from these 400 highest-scoring 7-mers, we obtain 437 k -mers ( k = 7, 8 or 9) using the procedure described in Materials and methods. Known regulatory elements As shown in Table 2 , at least 15 distinct known binding sites in C. elegans and other metazoan organisms were identified among the 437 predicted regulatory elements. One of the most conserved is TGATAAG, the binding site for the GATA factors, a family of regulators controlling intestinal development (see [ 18 ] for review). Another motif returned by FastCompare, GTGTTTGC, corresponds to the binding site for the forkhead-related activator-4 (Freac-4) [ 19 ]. Note that this motif is also compatible with the PHA-4-binding site (published consensus: T[AG]TT[GT][AG][CT] [ 20 ]), present in the upstream regions of pharyngeal genes [ 20 ] (PHA-4 is also a member of the forkhead family of transcription factors). FastCompare also returned TGTCATCA, the known binding site for the SKN-1 transcription factor (published consensus [AT][AT]T[AG]TCAT). In C. elegans , SKN-1 is known to initiate mesendodermal development by inducing expression of the GATA factors MED-1 and MED-2 (required for mesendodermal differentiation in the EMS lineage) [ 21 ]. The GAGA-factor binding site (AGAGAGA) was also found as a highly conserved pattern. GAGA repeats in upstream regions have been shown to be functional in C. elegans in at least two separate studies [ 22 , 23 ]. At least one GAGA-binding protein has been identified in D. melanogaster , and is assumed to create nucleosome-free regions of DNA, thus allowing additional transcription factors to bind those regions [ 24 ]. However, the ortholog of this protein has not yet been identified in C. elegans [ 24 ]. We also found CAGCTGG, a site known to be bound by the myogenic basic helix-loop-helix (bHLH) family of transcription factors (in worms, flies and mammals) and AP-4 transcription factors (in mammals) [ 25 , 26 ] (published consensus CAGCTG [ 27 - 29 ]). The homolog of human AP-4 was found to be ubiquitously expressed in D. melanogaster and a C. elegans homolog has also been identified [ 25 ]. FastCompare returned GTAAACA, the known binding site for the DAF-16 transcription factor (published consensus GTAAACA [ 30 , 31 ]). DAF-16, a FOXO-family transcription factor, was shown to influence the rate of aging of C. elegans in response to insulin/insulin-like growth factor-1 signaling [ 31 , 32 ]. Searching for gapped motifs found few strongly conserved sites. However, when searching for 8-mers with a 5-bp gap, we found that TGGCNNNNNGCCA, the known binding site for nuclear factor I (NFI) [ 33 ], had a score comparable to those of the highest-scoring k -mers. Several of the C. elegans sites returned by FastCompare and shown in Table 2 are known to be functional transcription factor binding sites in other species. For example, TGACTCAT, identical to the AP-1-binding site [ 34 ], is known to be bound in yeast (by Gcn4), Drosophila [ 35 ], mouse and human (see [ 36 ] for a review). FastCompare also returns the CACGTGG motif, which is the binding site for the Myc/Max complex, a family of bHLH transcription factors [ 37 ]. Among the top-scoring motifs in Table 2 , we also find AAGGTCA, the hormone response element (HRE), bound by several transcription factors in human, mouse, fruit fly and silkworm (published consensus [CT]CAAGG[CT]C[AG] [ 38 , 39 ]); TGACGTC, the cAMP response element (published consensus TGACGTCA [ 40 ]); CCCGCCC, the binding site for the mammalian Sp1 transcription factor (known consensus CCCCGCCCC); ATCAATCA, the known binding site for the human proto-oncogene Pbx-1 [ 41 ]. A similar site, ATCAATTA, has been shown to be bound in vitro by the Drosophila homolog of Pbx-1, the extradenticle (exd) protein [ 42 ]. Moreover, CEH-20C was identified as the C. elegans homolog of both Pbx-1 and exd. Other known sites discovered by FastCompare include CAGGTGA, similar to the known binding site for the Snail protein, a transcription factor involved in dorso-ventral pattern formation in Drosophila (published consensus [AG][AT][AG]ACAGGTG[CT]AC [ 43 ]), and TTCGCGC, the known binding site for the E2F proteins, a family of transcription factors involved in regulating the cell cycle in Drosophila and mammals (published consensus TTTCGCGC [ 44 ]). An E2F homolog has been identified in C. elegans and recently shown to be involved in cell-cycle regulation [ 45 , 46 ]. Position and orientation biases As in yeast, several of the known binding sites in C. elegans appear to be constrained in terms of position. Using the distribution of median distances for all 7-mers (see Materials and methods), we found d 0.025 = 690 and d 0.975 = 1,135. Among the 437 highest-scoring k -mers, we found that 75 are located below the lower threshold, a proportion that is much higher than the expected 2.5% ( p < 10 -38 ). The binding sites for forkhead-related activator-4 (Freac-4), Sp1, E2F and AP-1 are particularly constrained (see Figure 6 ). We found only 21 k -mers to be located further away from the distant d 0.975 threshold. Interestingly, the most conserved k -mer among these 21, CCACCAGGA (rank 96), is found in the upstream regions of over- or underexpressed genes in 57 microarray conditions. Note that for a few predicted elements (for example, CAGGTGA, rank 111), the median distance falls outside of the optimal window; this is due to the fact that, for these elements, the median distance does not correspond to the peak of the distribution of distances to ATG. Hence, for these elements, the optimal window provides a better descriptor of the positional bias than the median distance. Additional analysis reveals that several of the known binding sites discovered in this study are constrained in term of orientation. For example, the binding site for the GATA-factor(s) (as shown in Table 2 ) is significantly more often found in the 3' to 5' orientation, relative to downstream genes. Probably the most interesting finding is that the GAGA repeats appear to be strongly oriented 3' to 5' relative to their downstream genes. Indeed, 2,375 out of 3,557 (67%) of the AGAGAGA sites are oriented 3' to 5', a proportion that is much larger than the expected 50% (p < 10 -90 ). This bias is confirmed by the fact that TCTCTCT alone (not taking into account its reverse complement) has a much higher conservation score (129.2) than AGAGAGA (34.3). We also found that several related motifs display a similar, albeit weaker, orientation bias, for example, GAAGAAG ( p < 10 -16 ), GGAGGAG ( p < 10 -10 ). It is interesting that all the GAGA repeats found to be necessary for correct expression of the ceh-24 and unc-54 genes are in fact TCTC repeats [ 22 , 23 ]. The conserved sets for TCTCTCT or AGAGAGA were not found to be enriched in any GO category. Note that this orientation bias is not due to genes with the repeats in their upstream regions being predominantly located on one strand, as these genes are approximately identically distributed on each strand (1,065/1,122, p = 0.89). Interestingly, conserved GAGA repeats in D. melanogaster were also found to be constrained in terms of orientation, but at a much lower significance (p < 10 -4 , see below). Although it is possible that the TCTC repeats are bound at the 5' untranslated region (UTR) mRNA level, the positional distribution of the conserved AGAGAGA sites does not indicate a strong positional bias with respect to ATG (D ATG = 893). Novel predicted regulatory elements FastCompare also returned many novel motifs; some of the most interesting ones are shown in Table 3 . The top-scoring motif, CTGCGTCT, belongs to this category. A larger version of that motif, TCTGCGTCTCT, was found in a recent study to be necessary for the expression of several ethanol-response genes [ 47 ]. However, the very high conservation of this site suggests a broader role. It is interesting to note that this site was not significantly found upstream of under- or overexpressed genes in any microarray conditions (including the data from [ 47 ]). Interestingly, the most conserved k -mer found in yeast, the binding site for the Reb1 protein, had the same property. Moreover, this site displays a relatively strong orientation bias 5' to 3' ( p < 10 -10 ). Several of the other novel predicted regulatory elements in Table 3 have interesting properties. For example, the fourth most-conserved k -mer, CGACACTCC, is one of the closest motifs to ATG, with a median distance of 234 bp, and its conserved set is strongly enriched in genes involved in positive regulation of growth (a biological process defined in GO as the increase in size or mass of all or part of the worm) (p < 10 -7 ). Another predicted regulatory element, CGAGACC (rank 20), is found upstream of downregulated genes in 23 microarray conditions. Interestingly, it is found upstream of downregulated genes in a study measuring gene-expression changes at several time points during worm aging [ 48 ], in two distinct strains ( fer-15 and spe-9;fer-15 ) and at similar time points (6, 9 and 10 days for fer-15 , 9 and 11 for spe-9;fer-15 ). In addition, the functional enrichment of its conserved set points at a potential role in embryonic development ( p < 10 -7 ). Another strongly conserved and novel motif, CTCCGCCC (rank 14), was independently found upstream of almost all transcribed worm microRNA genes in a recent study [ 49 ]. Motif interactions We found many interactions between the most conserved k -mers found at the previous stage. For example, the most conserved k -mer, TCTGCGTCT, is very often co-conserved with AGAGAGA. The high-scoring interaction between the DRE-like motif, AATCGAT and the putative E2F-binding site, TTTTCGC, also appears interesting. Indeed, the conserved sets for both k -mers are separately enriched significantly with genes involved in embryonic development, according to GO ( p < 10 -8 and p < 10 -7 , respectively). However, the conserved set of genes having both elements in their upstream regions is even more enriched in this GO category ( p < 10 -9 ). TTTTCGC also seems to interact with the novel site CGACACTCC, and the corresponding conserved set is enriched with genes involved in modification-dependent protein catabolism ( p < 10 -5 ). The full list of motif interactions is available at [ 9 ]. Flies We applied FastCompare to the genomes of D. melanogaster and D. pseudoobscura , two species of Drosophila that diverged about 46 million years ago [ 50 ]. The number of orthologous ORFs between these two species is 11,306 and here we only consider 2,000-bp upstream regions. Using 5,000 bp instead produced similar results, but also produced additional putative binding sites (results are available at [ 9 ]). It takes approximately 10 minutes for FastCompare to process the corresponding 45 Mbp of sequences and calculate a conservation score for all 7-mers, 8-mers and 9-mers on a typical desktop PC. Validations The distribution of conservation scores shown in Figure 7a , for actual and randomized data, shows once again that the high conservation scores obtained with the real sequences are very unlikely to be achieved by chance. Also, as shown in Figure 7a , many known regulatory elements fall on the tail of the distribution. As for the yeast and worm genomes, we used functional annotations (GO), expression data and known TRANSFAC sites to evaluate the FastCompare predictions. Unfortunately, expression data is often available for only a subset of genes and its analysis led to very few validations. However, Figure 7b,c clearly shows that functional enrichment of the conserved sets and TRANSFAC matches strongly correlate with conservation score. As with yeasts and worms, we focused on the 400 highest-scoring 7-mers, which are particularly well supported by the functional enrichment analysis (see Figure 7b ). The simple processing described in Materials and methods yielded 469 k -mers ( k = 7, 8 or 9), which we further analyze below. Known regulatory elements As shown in Table 4a , we found at least 16 distinct known regulatory elements among the 469 highest-scoring k -mers. The most conserved element, AACAGCTG, is similar to the site known to be bound by AP-4 (mammals) and MyoD (worms, flies and mammals). One of the most interesting predictions is TATCGATA (rank 12); this palindromic motif, known as the DNA replication-related element (DRE), has been experimentally proved to be necessary for proper expression of several cell proliferation-related genes in D. melanogaster [ 51 ] and, more recently, the genes encoding the TATA-binding protein (TBP) [ 52 ] and catalase [ 53 ] in the same organism. Interestingly, it is both the motif with the closest median distance to ATG (D ATG = 168), and the most over-represented k -mer (among the 469 highest scoring ones) within D. melanogaster upstream regions compared to exons, with a ratio of 5.39. Several of the other predicted sites are known to be bound by Drosophila transcription factors involved in development. For example, FastCompare predicts TTTATGGC (rank 14) and TAATTGA (rank 24), the binding sites for two homeodomain transcription factors. The first site matches the TRANSFAC consensus binding site for Abd-B ([CG]NTTTATGGC), while the second site is the known consensus binding site for the Antennapedia (Antp) class of homeodomain proteins [ 54 ] (TAATTGA matches the TRANSFAC consensus binding site for Ubx, a member of the Antp class). FastCompare also predicts ATTTATGC, a site matching the TRANSFAC consensus binding site for the chicken CdxA protein ([AC]TTTAT[AG]), the homolog of the Caudal protein in D. melanogaster . Also, FastCompare predicts CAGGTGC, the binding site for the Snail repressor/activator protein, a transcription factor required for proper mesodermal development [ 43 ]. FastCompare also predicts ATTTGCATA (rank 3) as one of the most conserved putative regulatory elements between the two flies. This site is the binding site for the POU-domain family of transcription factors, and it is probably bound by one or several of the three POU-domain transcription factors in Drosophila : DFR, PDM-1 and PDM-2. These three proteins are involved in different stages of Drosophila development: DFR is expressed in midline glia and in tracheal cells [ 55 ], whereas the redundant PDM-1 and PDM-2 are essential for proper neuronal development [ 56 ]. Many of the known motifs found when comparing the two Drosophila genomes were also found when analyzing the worm genomes. For example, GAGA repeats are found to be strongly conserved, slightly oriented 3' to 5' ( p < 10 -4 ), and very significantly found upstream of genes involved in morphogenesis ( p < 10 -23 ). GTAAACA (rank 147), the DAF16-binding site in C. elegans , is also one of the most conserved sites between the two Drosophila genomes. This site is probably bound by dFOXO, the unique homolog of the C. elegans DAF16 protein in D. melanogaster [ 57 ]. As for both previous phylogenetic groups (yeasts and worms), the median distances to ATG for the conserved elements show that some of the predicted regulatory elements are severely constrained in terms of position. Among the most constrained k -mers are the DRE site (TATCGATA, D ATG = 168) and the known AP-4/MyoD binding site (AACAGCTG, D ATG = 373). However, both the optimal windows and the median distances in Table 4a show that, compared to previously studied organisms, a smaller number of conserved regulatory element are constrained. Using the distribution of median distances for all 7-mers, we find that the d 0.025 = 798 and d 0.975 = 1,126. Among the 469 highest scoring k -mers, 45 fall below 798 ( p < 10 -13 ) and 36 above 1,126 ( p < 10 -8 ), once again suggesting weaker positional constraints than in yeasts and worms, at least when considering the first 2,000 bp of 5' upstream sequences. Novel predicted regulatory elements FastCompare predicts many putative regulatory elements in Drosophila that to the best of our knowledge are unknown (Table 4b ). One of these novel sites, CAGGTAG (rank 143), was found upstream of several genes that are activated before widespread activation of zygotic transcription (which begins during the 14th nuclear cycle), in several Drosophila species [ 58 ]; it was also found to be necessary for the early expression of several of these genes ( Sxl and sisterlessB ) in a subsequent study (J.R. ten Bosch, J.A. Benavides and T.W. Cline, personal communication). It is interesting to see that this particular site is significantly conserved upstream of genes involved in cell fate commitment ( p < 10 -8 ). Some of these sites, such as the palindromic TTAATTA (rank 31), are found much more often in upstream regions than in exons (with an over-representation ratio of 3.07). Others, such as ACACACAC, are found to be significantly enriched upstream of genes in known functional categories (embryonic development, p < 10 -9 ). The same site appears to be strongly oriented 5' to 3' ( p < 10 -12 ). Others, such as GTGTGACC or AAATGGCG, appear to be located closer to ATG than most other sites (D ATG = 296 and 592, respectively). Motif interactions We found many potential interactions between the most conserved sites discovered by FastCompare. For example, the POU-domain-binding site ATTTGCATA was found to be strongly co-conserved with TAATTGA, the Antp-binding site, and with many other potential homeodomain sites, such as AATAAAT and TAATTAA. The CACA repeats were also found to be co-conserved with several different sites, and in some cases, the set of genes having both sites simultaneously conserved in their upstream regions (conserved sets) was found to be enriched in certain functional categories, for example, ACACACAC and GAGAGAG, regulation of transcription ( p < 10 -12 ); ACACACAC and TAATTGC (an Antp variant site), embryonic development ( p < 10 -5 ). The full list of interactions is available at [ 9 ]. Mammals The much larger noncoding regions of mammalian genomes present significant challenges for computational motif discovery. Also, many repeat elements (for example, Alu ) have colonized mammalian genomes and are likely to be conserved between closely related genomes. The distance between enhancers and the transcriptional start of the genes they regulate can be extremely large, reaching tens of kilobases. Finally, gene predictions and gene boundaries are still largely unverified experimentally for a large number of genes. We applied FastCompare to the genomes of H. sapiens and M. musculus ,, which diverged about 75 million years ago [ 59 ]. The number of orthologous ORFs between these two species is 15,983 and again, we have only considered 2,000-bp upstream regions. As in flies, using 5,000-bp instead produced similar results. It takes approximately 15 minutes for FastCompare to process the corresponding 60 Mbp of sequences and calculate a conservation score for all 7-mers, 8-mers and 9-mers on a typical desktop PC. Validations Unlike the other genomes considered so far, the output of FastCompare from the mammalian genomes is dominated by GC-rich sequences, probably corresponding to CpG islands (GC-rich regions known to be associated with the promoters of many genes). However, analysis of the FastCompare output yielded the same validations as for other species. Indeed, the distribution of conservation scores obtained on actual and randomized sequences shows that high conservation scores are very unlikely to be obtained by chance (Figure 8a ). As with other species, many known regulatory elements are on the tail of the distribution (Figure 8a ). Also, as shown in Figure 8b-d , more k -mers are found upstream of over or underexpressed genes, more k -mers have their conserved set enriched with GO functional categories, and more k -mers match TRANSFAC consensus sites as the conservation score increases. We found that masking Alu repeats did not influence the output of FastCompare (data not shown). To overcome the overabundance of GC-rich sequences in the FastCompare output, we use longer k -mers as starting points, namely 8-mers instead of 7-mers. We started with the 600 highest-scoring 8-mers, and replaced each of these 8-mers by one of its substrings (7-mer) or one of its superstrings (9-mer), when their conservation score is higher. We then removed duplicates in the list and added the high-scoring 9-mers that have no substrings within the list. This procedure yielded 284 k -mers ( k = 7, 8, 9). Subsequent validation was limited to this small set of high-scoring predictions. Known regulatory elements As shown in Table 5a , we found 17 distinct known regulatory elements among the 284 highest-scoring k -mers. Among these are the well characterized sites for the Sp1, C/EBP, CREB and Myc/Max proteins or families of proteins. These four sites reside very close to ATG (their median distance to ATG is between 100 and 250 bp), suggesting that the four proteins (or families of proteins) may be involved in intimate interactions with the transcriptional complex. Sp1 is an ubiquitous transcription factor, involved in the basal expression of a large number of genes in mammals (see [ 60 ] for review). The CCAAT/enhancer binding protein (C/EBP) has been implicated in the regulation of cell-specific gene expression mainly in hepatocytes, adipocytes and hematopoietic cells (see [ 61 ] for review). Both Sp1 and C/EBP are constitutive transcription factors whose presence is necessary for significant induction of a large number of genes [ 62 ]. The CRE-binding protein (CREB or CBP) is a transcription factor that binds cyclic AMP (cAMP) response elements (CREs) in the promoters of specific genes, and functions as a co-activator for a large number of other transcription factors (see [ 63 ] for review). The Myc/Max heterodimer binds the CACGTG sequence, and also acts as a transcriptional activator (see [ 64 ] for review). Interestingly, we found that some of the most conserved interactions between k -mers (see Materials and methods) involve Sp1-binding sites (CCCGCCC or CCGCCCC) with other known sites such as CACGTGAC (Myc/Max), TGACGTCA (CREB), CGCAGGCGC (unknown), GCCAATC (CCAAT-box) and ACTTCCG (Ets), and that the median distances between these sites are relatively small (138, 164, 200.5, 234 and 234, respectively). Among the other predicted regulatory elements returned by FastCompare are CCGCCTC, a site known as the insulin response element [ 65 ]; CGGAAGTGA, a site known to be bound by the GA-binding protein in human [ 66 ]; CGCATGCG, a site known as the palindromic octamer sequence, which was found at 132 bp (relative to ATG) upstream of the inosine-5'-monophosphate dehydrogenase type II gene in human, and shown to be functional in resting and activated T cells using site-directed mutagenesis, in vivo footprinting and electrophoretic mobility shift assay (EMSA) [ 67 ]; TTTCGCGC, the E2F-binding site; TAATCCCAG, a site known to be bound in D. melanogaster by the anterior morphogen Bicoid, and also recently shown to be bound in human by Goosecoid-like (GSCL) [ 68 ]. Interestingly, this site has a relatively strong orientation bias 3' to 5' ( p < 10 -14 ). It is also the site with the strongest over-representation in upstream regions compared to exons that we observed, with a ratio of 7.06. FastCompare also predicts ATTTGCAT, the binding site for the POU-domain Oct-1 and Oct-2 proteins, known to bind the promoter and intronic enhancer of immunoglobulin genes [ 69 ]; it also returns GGAAGTCCC, a site that was shown to bind NFκB [ 70 , 71 ], a transcription factor involved in a variety of pathways (including inflammation, response to infection and oxidative stress, and apoptosis). The distribution of distances to ATG for all 7-mers (Figure 9 ) shows an interesting bimodal shape, indicating that a large number of short sequences are constrained to reside around 500 bp to ATG. We calculated d 0.025 = 342 and d 0.975 = 1,185 and found that 83 k -mers among the 284 highest-scoring ones have a shorter median distance than 342 ( p < 10 -63 ) and only 11 have a larger median distance than 1,185. Indeed, a majority of the known sites identified by FastCompare are preferentially located near the 5' start of genes, with some elements being very close to ATG (for example, the CREB site, whose median distance to ATG is 107, whereas the optimal window is [0;1,000]). Nonetheless, a few known motifs do not seem to show any positional constraints. For example, the Bicoid-like site TAATCCCAG has a median distance to ATG of 1,258. Novel predicted regulatory elements FastCompare identifies many putative regulatory elements which to the best of our knowledge are novel (Table 5b ). Some of these predicted regulatory elements are found upstream of over- or underexpressed genes in many microarray conditions. One example is CCCCAGC, which is significantly found upstream of overexpressed genes in 21 conditions (out of 30) of the human cell-cycle experiment [ 72 ]. Other conserved elements are found much more often in upstream regions than in exons, for example, CCCCTCCC or TCTCGCGA, with ratios of 5.12 and 4.45, respectively. Others appear to be positionally constrained, for example, the palindromic CTGCGCA with an optimal window [0;300] and a median distance to ATG of 199, or constrained in terms of orientation, for example, GTGAGCCAC, which is significantly oriented 5' to 3' ( p < 10 -6 ). Inter-groups comparisons To gain a better understanding of the network-level conservation of regulatory elements between the different phylogenetic groups, we compared the results we obtained by applying FastCompare to yeasts, worms, flies and mammals in the previous sections. We calculated the overlap (and its significance) of the 400 highest-scoring 7-mers and 8-mers found for each phylogenetic group. As shown in Table 6a,b , the number of shared predicted sites correlates with phylogenetic distance (the number of high-scoring putative motifs that two phylogenetic groups have in common decreases as the phylogenetic distance between the groups increases). All of the overlaps were found to be statistically significant, except for the yeast-human comparison. For both 7-mers and 8-mers, the best overlap is the one obtained between the two invertebrate phylogenetic groups: worms and flies. Indeed, simple observation of the identified known regulatory elements in Tables 2 and 4a reveals that these two organisms have a large number of predicted binding sites in common. However, when we looked at the overlap between conserved sets for identical high-scoring k -mers in different phylogenetic groups (after determination of reciprocal best BLAST hits between the considered species), we found little overlap. The only significant overlap we found (after Bonferroni correction) was between the GATA sites (GATAAGA) in worm and fly ( p = 2.5 × 10 -4 ). As a control, we performed the same analysis within the yeast phylogenetic group, using the S. cerevisiae / S. bayanus and S. paradoxus / S. mikatae 400 most conserved 7-mers. One hundred and ninety-five sites were found in both groups of 7-mers, and for all of them, the overlaps between the conserved sets obtained separately in the S. cerevisiae / S. bayanus and S. paradoxus / S. mikatae analyses were highly significant, with hypergeometric p -values < 10 -40 . Therefore, our results strongly suggest that, while transcription factors have largely retained their ability to recognize specific DNA sites, their targets have largely changed through appearance or disappearance of those binding sites in promoters. This hypothesis is supported by recent analysis of the fission yeast cell cycle using microarrays, which showed that the role and the binding sites for several of the main transcription factors involved in regulating the yeast cell cycle (Swi4/Mbp1, Fkh1/Fkh2, Swi5/Ace2) are conserved between budding and fission yeasts (which diverged about 1 billion years ago), but the sets of genes that they regulate overlap much less than expected (only about 50 orthologous genes are cell-cycle-regulated in both species) [ 73 ]. It is particularly interesting to consider the seven 8-mers that are top predictions for all three multicellular phylogenetic groups (note that many more 7-mers are conserved between these groups). These sites include the CRE (TGACGTCA, GACGTCAC and ATGACGTC), the POU-domain binding site (ATTTGCAT), and the HRE (CAAGGTCA). A fourth site is also shared (GCCACGCC, CCACGCCC), which to the best of our knowledge is a novel motif. Its strong over-representation in upstream regions compared to coding regions, and its closeness to ATG (median D ATG = 230 for GCCACGCC) make it a promising candidate for experimental testing. Interestingly, the location constraints on these conserved sites can vary across phylogenetic groups. For example, the CRE appears weakly constrained in worms and flies in terms of distance to ATG (D ATG = 708 and 825, respectively), but is very close to ATG in mammalian genomes (D ATG = 107). However, the distances to ATG of the POU-domain-binding sites (862, 882 and 729, respectively) indicate that their positional constraints are shared among the phylogenetic groups. The same holds for the HRE binding site (845, 1,015.5 and 895, respectively). Discussion and conclusions We have presented a powerful approach for discovering transcriptional regulatory elements that are globally conserved between pairs of genomes. Our approach requires only two unaligned genomes, thus allowing the use of genomes of arbitrary divergence and those with extensive rearrangements of noncoding regions. Moreover, our motif-finding strategy does not use any parameters other than a conservation score threshold, used to separate presumptive functional from nonfunctional motifs. We have shown that such thresholds can be roughly estimated using independent biological data, when available. Our approach is also computationally efficient: whole eukaryotic genomes can be processed in minutes on a typical computer. In turn, this efficiency allows FastCompare to explore exhaustive pattern lists. Our results show that FastCompare can recover most of the known functional binding sites in S. cerevisiae when its upstream regions are compared to those of a related species, S. bayanus . We comprehensively explored the globally conserved motif content between worms, flies and mammalian genomes, discovering large sets of known and novel motifs. The use of external information (expression data, functional categories, TRANSFAC, chromatin IP and known motifs) clearly shows that our method is able to detect conserved and functional motifs in all the phylogenetic groups that we studied. In all analyses, we have shown that some of the discovered known or novel motifs were severely constrained, either in terms of position relative to the start of translation or in orientation. We also observed that some of the known or novel motifs are co-conserved within upstream regions, potentially revealing interactions between the (often unknown) transcription factors that bind them. We have created a set of web tools to superimpose the most globally conserved k -mers discovered by FastCompare to user-supplied sequences or multiple alignments. An example is shown in Figure 10a , in which the upstream regions of the STE2 gene (encoding the alpha-factor pheromone receptor) from four different yeast species were aligned using ClustalW, and the most globally conserved k -mers are highlighted. All experimentally determined sites for STE2 were also predicted to be globally conserved by FastCompare. Moreover, several other sites also appear to be conserved, both at the global level (predicted by FastCompare) and the local level (shown by the multiple alignment). In Figure 10b , the same analysis was performed on only two orthologous upstream regions instead of four. Many more sites appear to be locally conserved than when using four species, but the globally conserved sites found by FastCompare allow the efficient selection of experimentally verified and putative binding sites. These tools should be particularly useful in designing stepwise promoter deletions and site-directed mutagenesis experiments for understanding the regulatory code of specific genes. While powerful, our approach has potential limitations. Our current approach allows matches to a given k -mer to be on different strands within pairs of orthlogous upstream regions. This flexibility substantially increases the number of k -mers that are supported by independent biological data (that is, true positives), at least for yeasts and worms (data not shown). However, it is difficult to evaluate whether this flexibility introduces more true positives than false positives. Also, transcription factors often bind several slightly distinct sites with different affinities, and it is widely acknowledged that binding-site degeneracy is better captured by using position-weight matrices (PWM) instead of k -mers or consensus patterns [ 74 ]. To evaluate whether weight matrices would display better conservation scores, we calculated a conservation score for weight matrices corresponding to 20 well characterized yeast binding sites, and compared them to the conservation scores obtained for the best k -mers that unambiguously correspond to the same binding sites. Conservation scores for weight matrices were calculated as described for k -mers in Materials and methods, except that we used the weight-matrix score thresholds that maximize the significance of the overlap between the two sets of ORFs containing matches to the weight matrices in each species. This involves progressively lowering the score threshold by small increments, and for each threshold, calculating the overlap and its hypergeometric p -value. We then choose the score threshold corresponding to the most significant p -value, and use the negative natural logarithm of this p -value as the conservation score. As shown in Table 7 , only in 11 cases out of 20 did weight matrices have a higher conservation score than the corresponding k -mers. These results suggest that k -mers provide results that are almost as good as those obtained using weight matrices, when utilizing the network-level conservation criterion. One reason why, in many cases, k -mers have a higher conservation score than weight matrices may have to do with the more narrow selection of k -mers for binding sites with similar or identical affinities. In fact, we recently showed that PWM scores, widely seen as proxies for binding affinity, are statistically conserved in a comparison between S. cerevisiae and S. bayanus [ 6 ]. In the context of the present study, the different k -mers representing each transcription factor binding site may be defining affinity classes that are more strongly conserved than a looser definition of a binding site represented by a weight matrix. Recent work in bacteria has established the importance of binding affinity, especially with respect to coordinating the temporal order of events [ 75 ]. However, Table 7 shows that the conservation score for weight matrices describing very degenerate binding sites, such as RAP1, is significantly higher than the conservation score obtained for the best corresponding k -mer. This suggest that our k -mer based approach is limited in its ability to discover highly degenerate binding sites. As shown by our inter-group analysis, many regulatory elements have remained functional across evolution, but few have remained upstream of the same genes. The network-level conservation principle thus appears less applicable to species that diverged very long ago. For example, when we compared the Drosophila and mosquito genomes (which diverged approximately 400 million years ago), we only found a handful of k -mers (interestingly including GATA-factor and Myc/Max binding sites) to have conservation scores above those obtained from randomized data. There are also several directions in which our approach could be extended. From a methodological standpoint, the approach could be extended to take into account local over-representation of identical or nearly identical copies of the same binding sites, a well known feature in the promoter regions of higher eukaryotic species [ 16 ]. To discover highly degenerate regulatory elements, k -mers could be used to seed weight matrices whose individual weights could be optimized for network-level conservation, using stochastic optimization procedures (for example, simulated annealing; Mike Beer, personal communication). Introns and downstream noncoding regions could also be explored using our approach, as these regions are known to harbor functional regulatory elements in metazoan genomes. While our approach can deal with genomes presenting arbitrary levels of divergence and rearrangements, it would be interesting to investigate how global alignments or suboptimal and non-overlapping local alignments [ 76 ] could be used to filter out regions of non-conservation. This approach would be particularly interesting when analyzing very long upstream regions, in order to increase the signal-to-noise ratio. Finally, mRNA 3' UTRs could be compared in order to find specific downstream regulatory elements involved in post-transcriptional mRNA regulation (for example, mRNA localization, decay or translational repression). Materials and methods Outline of approach First we determined orthology relationships between ORFs on the basis of reciprocal best BLAST hits (Figure 1a ) and extracted the corresponding upstream regions from the genome sequences. Then, we considered every possible short DNA sequence of length k ( k -mer, with k between 7 and 9) as a candidate regulatory element. For each k -mer, we found the set of ORFs whose upstream regions contain at least one exact match to the k -mer, anywhere in the upstream region, in the first genome. We did the same for the second genome, obtaining another set of ORFs. Then, we calculated the overlap between the two sets and assessed its statistical significance (Figure 1b ). The statistical significance of the overlap provides a measure of conservation with which we score and rank every possible k -mer (Figure 1c ). Note that our approach is very different from the classical k -tuple DNA sequence-analysis methods [ 77 , 78 ], which are not based on comparative genomics and are local methods; that is, they only deal with single promoters or small sets of functionally related promoters (while our approach provides a genome-level measure of conservation for candidate regulatory elements). Sequence sources and orthology determination Sequence data were downloaded from the Saccharomyces Genome Database (SGD) for all yeast species considered in this paper; worm ( C. elegans and C. briggsae ), Drosophila ( D. melanogaster ), human ( H. sapiens ) and mouse ( M. musculus ) sequence data were downloaded from Ensembl [ 79 ]. The D. pseudoobscura genome sequences (contigs) were downloaded from [ 80 ]. The upstream regions used in this study are immediately adjacent to the ATG codons of their downstream genes, and are 1-kb long (yeasts) or 2-kb long (worms, flies and mammals). Note that transcription-factor-binding sites generally reside in the region situated upstream of the transcription start site. Unfortunately, not all genes have well annotated transcription start sites. This problem should not, however, strongly influence the output of FastCompare, as distances between start of transcription and start of translation should be at most on the order of a few hundred base-pairs (except in certain cases, for example when 5' UTRs are interrupted by long introns). However, as gene structures become better annotated (mainly as a result of massive cDNA sequencing projects) and promoter regions become more accurately delimited, we expect that the ability of FastCompare to discover regulatory elements will be significantly improved. Orthology information provided by Ensembl or by Kellis et al . [ 4 ] was used throughout this study, when available. Ensembl provides strong homology relationships between genes from different species, but does not provide reciprocal best matches. Therefore, we determine reciprocal best matches using the provided sequence identity between homologous genes. When orthology information is not available in Ensembl (for example, between D. melanogaster and D. pseudoobscura , or between distant species such as S. cerevisiae and C. elegans ), we determine orthologs using the reciprocal best BLAST hits approach. Motif-finding algorithm and simple clustering Given a value of k , we first generated the set of all possible k -mers and removed half of them on the basis of reverse complementarity. We also removed k -mers with very low complexity and which are over-abundant in the intergenic regions of the genomes we analyzed (that is, those that contain k - 1 or more As or Ts), as these sequences are unlikely to be regulatory elements. Every remaining k -mer (that is, 8,170 for k = 7) is then considered as a candidate regulatory element. For each k -mer, we found the set of ORFs in the first species that have at least one exact occurrence of the k -mer in their upstream regions. We then found the set of ORFs in the second species that have at least one occurrence of the same k -mer in their upstream region. Importantly, the matches can be anywhere in the upstream regions: they do not have to be at the same positions in two orthologous upstream regions (as with multiple alignment) and can be on any strand. Since both functional and non-functional elements are expected to be conserved between two closely related species, the two sets are expected to overlap. However, under the network-level conservation principle, the extent of the overlap - and therefore its statistical significance - will be even greater for k -mers that represent functional transcription factor binding sites. The significance of the overlap can be measured using the hypergeometric distribution. The probability of two sets of size s 1 and s 2 , drawn from a set of N elements, to have i or more elements in common is given by : In this way, all k -mers can be ranked by their hypergeometric p -values. It is important to note that due to basal conservation (that is, conservation arising from common ancestry), the hypergeometric p -values will generally be very small for most k -mers. Therefore, we only use these p -values as relative measures of network-level conservation and focus on k -mers with the greatest conservation. For simplicity, we define the 'conservation score' to be the negative logarithm (base e ) of the hypergeometric p -value obtained for a given k -mer. Therefore, the more extensive the overlap between the two sets, the higher the conservation score. Also, for the same k -mer, we call 'conserved set' the set of ORFs corresponding to the overlap between the two sets of orthologous ORFs containing at least one exact match to the k -mer in their upstream regions. Conserved sets are used throughout this study to get insights into the function of the most conserved k -mers, using functional annotation [ 81 , 82 ], chromatin IP [ 1 ], known motifs, and to evaluate whether these k -mers are constrained in terms of position or orientation. The current FastCompare implementation handles k -mers with a user-specified gap (termed gapped k -mers), which is a straightforward extension of the approach described above. The conservation score returned by FastCompare is independent of the size of the patterns (that is, the value of k ); therefore k -mers with different sizes, and gapped k -mers (for example, CGTNNNNNNTGA) can be compared. We use the following strategy when applying FastCompare to pairs of genomes. First, we calculate conservation scores for all 7-mers, 8-mers and 9-mers. We then retain only the m highest-scoring 7-mers, with m chosen according to independent biological data (alternatively, m could be chosen according to the estimated number of transcription factors in the species being considered). We then replace each of the retained 7-mers by an 8-mer (if there is one) with higher conservation score for which the considered 7-mer is a substring. We also include within the final list the 8-mers which do not have any substrings within the m 7-mers. We then repeat the same process for the retained 8-mers, replacing each of them by its higher scoring 9-mer superstring if there is one, and add the 9-mers that do not have any substring within the 8-mers. This strategy thus allows the optimal length for candidate regulatory elements to be determined. FastCompare is implemented in the C language and uses efficient data structures (hash tables and prefix trees [ 83 ]). For a given value of k , the worst-case time complexity is O ( kn + 4 k ( p + k )), where n is the total amount of upstream sequences and p is the total number of orthologous pairs. Note that the first term is generally much larger than the second one; therefore the complexity of our approach can be seen as linear in the combined sizes of the genomes to be compared (when k is restricted to 7, 8 and 9). The calculation of hypergeometric p -values involves factorials of large integers, so we use specialized C routines, as described in [ 84 ]. FastCompare runtimes provided in the Results section are obtained using a standard desktop PC (2.0 GHz CPU, 1 GB RAM). Discovering positional constraints for conserved regulatory elements As described in Results, we applied FastCompare to 1 kb (yeast) or 2 kb upstream regions (worms, flies and mammals). While these lengths are reasonable, they are somewhat arbitrary, and it is known that some regulatory elements are constrained to be within specific distances (often shorter than 1 kb) from the start of transcription, reflecting mechanistic constraints for transcription factor-transcription factor or transcription factor-RNA polymerase interactions [ 11 ]. Moreover, some regulatory elements have orientation biases (see [ 11 , 12 ] for examples). To discover such constraints, we analyzed the most conserved k -mers found at the previous stage in the following ways. First, for each high-scoring k -mer, we calculated the median distance to ATG (as the start of transcription is generally not known) for the set of all (non-overlapping) occurrences of this k -mer within the upstream regions of its conserved set (see previous section for a definition of the conserved set of a given k -mer). To statistically assess whether the median distance to ATG for a given k -mer is unusually small or large, we built the distribution P ( d ) of median distances to ATG, for the entire set of 8,170 7-mers. We first created a histogram by binning the median distances to ATG for all 7-mers into 20-bp bins, and then smoothed the histogram (using a normal kernel and a bandwidth of 50 as implemented in the ksmooth function of the R statistical software package). Then, using numerical integration, we sought the distance thresholds d 0.025 and d 0.975 such that P ( d < d 0.025 ) = 0.025 and P ( d < d 0.975 ) = 0.975. We then considered the median distance to ATG for a given k -mer as unusually short or long when it is less than d 0.025 or greater than d 0.975 , respectively. For each k -mer, we also sought the sequence window which maximizes the conservation score by progressively shortening all upstream regions (all having equal lengths) by 100 bp increments from the 5' end. Then, we did the same from the 3' end using the optimal 5' end found in the previous step. Evaluating every possible window whose length is a multiple of 100 bp almost always yields identical results. We then calculated the conserved sets for these windows, and output the orientation (strand) for each k -mer occurrence within its conserved set (palindromes were counted on both strand). Finally, using the results of the previous step, for each k -mer, we used the binomial distribution to assess whether the proportion of occurrences of this k -mer (within its conserved sets) on one strand is significantly smaller (or larger) than 0.5. Binomial p -values less than 0.05 (after Bonferroni correction) are considered significant. Motif interactions It is now known that the regulatory code governing the expression of genes is combinatorial [ 11 , 85 , 86 ]. The network-level conservation principle can be trivially extended to discover interactions (that is, co-conservation) between two k -mers. To focus on heterotypic interactions, we only examined k -mers that differ by more than l nucleotides, after optimal ungapped alignment. We tested several values of l and found that l = 4 was most appropriate when using 7-, 8- and 9-mers. Then, we proceeded as described above, except that instead of seeking two sets of ORFs (one for each species) whose upstream regions contain a single k -mer, we sought the two sets of ORFs that contain the two k -mers simultaneously. Once these two sets were available, we evaluated the extent of their overlap as described above, and rank interaction pairs according to their conservation score. Validations We used randomized data to show that high conservation scores (obtained as described above) are unlikely to be obtained by chance, and independent biological information to assess the ability of FastCompare to predict functional regulatory elements by giving them a high conservation score. We also estimated the over-representation of predicted regulatory elements in upstream regions compared to coding regions. Validation using randomized data Our goal was to generate new pairs of upstream regions that are conserved at the same level of divergence as the actual sequence data. We align each pair of orthologous sequences using the Needleman-Wunsch algorithm [ 87 ], and calculate substitution frequencies between all pairs of nucleotides (A → A, A → T, and so on). Then, we reconstructed new pairs of orthologous sequences by mutating one of the sequences in each initial pair using the estimated frequencies. Generating the sequences to be mutated using locally estimated first-order Markov models yielded the same results. Validation using independent biological information The proportions of 7-mers supported by each type of independent data, as presented in Figures 3 , 5 , 7 and 8 , is calculated as follows. In these figures, support for a given 7-mer is considered as binary, and depends on whether the 7-mer meets the particular validation criterion or not (or whether it is found upstream of over- or underexpressed genes, in at least one microarray condition, see below). 7-mers are first sorted by conservation score, and the proportion of supported 7-mers were calculated using a sliding window of 100 7-mers. For each window and each type of independent biological data, we simply calculated the number of 7-mers for which support is available and divided this number by 100. Functional annotations Yeast ( S. cerevisiae ), worm ( C. elegans ), fly ( D. melanogaster ) and human ( H. sapiens ) functional categories and corresponding ORF annotations were downloaded from the MIPS [ 88 ] and GO [ 89 ] websites. The statistical significance of the functional enrichments within sets of ORFs was evaluated using the hypergeometric distribution, as discussed above. Hypergeometric p -values for functional enrichment were not corrected for multiple testing, but only p -values smaller than 10 -4 are reported, providing a slightly less stringent thresholds than Bonferroni corrections. Known motifs Weight matrices corresponding to known yeast motifs were obtained from Gibbs sampling-based motif finding on chromatin IP data [ 1 ], functional categories and clusters of co-expressed genes [ 85 ]. Only high-confidence binding sites (that is, sites confirmed by several sources including the literature) were included in our list of known motifs. We label a given k -mer as a known motif if it meets the following two criteria. The first is significant overlap ( p < 10 -4 ) between the conserved set for the given k -mer and the set of ORFs whose upstream regions contain at least one match to the known motif (the sets of ORFs were defined using ScanACE with the weight matrix for the known motif, and with the standard average minus two standard deviations threshold [ 7 ]). The second criterion is strong sequence similarity between the considered k -mer and the known motif weight matrix. To evaluate this similarity, we turn the considered k -mer into a weight matrix of 0s and 1s, and use CompareACE [ 7 ] to calculate the Pearson correlation between the weights of this matrix and the weights of the known motif weight matrix; correlation coefficients > 0.65 are considered significant. Finally, for a given k -mer, we report the known motif for which the above hypergeometric p-value is the smallest. In vivo binding data (chromatin IP) Genome-wide binding locations were previously evaluated for 106 transcription factors in S. cerevisiae [ 1 ]. For each transcription factor, we retain the set of ORFs with p -value < 0.001 (see [ 1 ] for details of the error model). To evaluate a given k -mer with respect to chromatin IP, we evaluate the statistical significance of the overlap between the conserved set of the considered k -mer and the set of ORFs defined as described above corresponding to each transcription factor. We report the most significant chromatin IP enrichment, with hypergeometric p -value < 10 -4 . TRANSFAC The 309 weight matrices and corresponding consensus patterns for known transcription factor binding sites were downloaded from [ 90 , 91 ]. k -mers were then simply matched to the consensus patterns. We eliminated consensus patterns that match too many k -mers, by matching each of them to all (8,170) 7-mers and removing consensus patterns that matched more than 50 7-mers. Microarray expression data Expression data for all species considered were downloaded from diverse sources [ 92 , 93 ]. Overall, we downloaded 765 microarray conditions for S. cerevisiae , 555 conditions for C. elegans , 156 conditions for D. melanogaster , and 1,384 conditions for H. sapiens . We use these expression data in the following way. We evaluated the over-representation of each k -mer in the upstream regions of genes that are themselves over- or underexpressed in certain microarray conditions. Over- or underexpressed genes are operationally defined as having a log ratio of intensity above average plus two standard deviations, or below average minus two standard deviations, respectively (averages and standard deviations are calculated for each condition; using fold changes instead of standard deviations produced roughly the same results). To evaluate the over-representation of a given k -mer in a given microarray condition, we defined as O 1 the set of overexpressed genes in this condition, and as O 2 the set of ORFs whose upstream regions contain at least one occurrence of the considered k -mer, genome-wide. Then, we evaluated the significance of the overlap between O 1 and O 2 using the hypergeometric distribution, as described above. Overlaps whose hypergeometric p -value is smaller than 0.05 (after Bonferroni correction) were considered significant. We proceeded separately with the set of underexpressed genes in the same way. The total number of microarray conditions (overexpressed plus underexpressed) for which a k -mer was found to be significantly over-represented is reported. Note that we do not use the conserved set for the considered k -mer here, as we do not want to restrict our analysis to orthologous genes. Indeed, except for yeast, microarrays often contain only a fraction of all genes within the considered organism. In these cases, the overlap between conserved sets and over- or underexpressed genes can be very small, reducing statistical power. Using all genes, therefore, increases our power to detect significant associations, while retaining a uniform approach for all species considered. Over-representation in upstream regions compared to coding regions As shown in [ 94 ] for the yeast RAP1 transcription factor, some transcription factors bind intergenic regions much more frequently than they bind coding regions. Consequently, it is expected that sequences corresponding to regulatory elements are more often present in intergenic regions than in coding regions. To evaluate this bias, we calculate the ratio of the number of genes that have the k -mer in their upstream regions over the number of genes that have the k -mer in their coding regions (using only exons), and we correct this ratio using the average length of the upstream and coding regions. Availability The FastCompare implementation, all the sequences, and results are available on our website [ 9 ].

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC551538.xml

543470

An information integration theory of consciousness

Background Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition? Presentation of the hypothesis This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Φ>0 that is not part of a subset of higher Φ. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex. Testing the hypothesis The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness. Implications of the hypothesis The theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.

Background Consciousness is everything we experience. Think of it as what abandons us every night when we fall into dreamless sleep and returns the next morning when we wake up [ 1 ]. Without consciousness, as far as we are concerned, there would be neither an external world nor our own selves: there would be nothing at all. To understand consciousness, two main problems need to be addressed. [ 2 , 3 ]. The first problem is to understand the conditions that determine to what extent a system has consciousness. For example, why is it that certain parts of the brain are important for conscious experience, whereas others, equally rich in neurons and connections, are not? And why are we conscious during wakefulness or dreaming sleep, but much less so during dreamless sleep, even if the brain remains highly active? The second problem is to understand the conditions that determine what kind of consciousness a system has. For example, what determines the specific and seemingly irreducible quality of the different modalities (e.g. vision, audition, pain), submodalities (e.g. visual color and motion), and dimensions (e.g. blue and red) that characterize our conscious experience? Why do colors look the way they do, and different from the way music sounds, or pain feels? Solving the first problem means that we would know to what extent a physical system can generate consciousness – the quantity or level of consciousness. Solving the second problem means that we would know what kind of consciousness it generates – the quality or content of consciousness. Presentation of the hypothesis The first problem: What determines to what extent a system has conscious experience? We all know that our own consciousness waxes when we awaken and wanes when we fall asleep. We may also know first-hand that we can "lose consciousness" after receiving a blow on the head, or after taking certain drugs, such as general anesthetics. Thus, everyday experience indicates that consciousness has a physical substrate, and that that physical substrate must be working in the proper way for us to be fully conscious. It also prompts us to ask, more generally, what may be the conditions that determine to what extent consciousness is present. For example, are newborn babies conscious, and to what extent? Are animals conscious? If so, are some animals more conscious than others? And can they feel pain? Can a conscious artifact be constructed with non-neural ingredients? Is a person with akinetic mutism – awake with eyes open, but mute, immobile, and nearly unresponsive – conscious or not? And how much consciousness is there during sleepwalking or psychomotor seizures? It would seem that, to address these questions and obtain a genuine understanding of consciousness, empirical studies must be complemented by a theoretical analysis. Consciousness as information integration The theory presented here claims that consciousness has to do with the capacity to integrate information. This claim may not seem self-evident, perhaps because, being endowed with consciousness for most of our existence, we take it for granted. To gain some perspective, it is useful to resort to some thought experiments that illustrate key properties of subjective experience: its informativeness, its unity, and its spatio-temporal scale. Information Consider the following thought experiment. You are facing a blank screen that is alternately on and off, and you have been instructed to say "light" when the screen turns on and "dark" when it turns off. A photodiode – a very simple light-sensitive device – has also been placed in front of the screen, and is set up to beep when the screen emits light and to stay silent when the screen does not. The first problem of consciousness boils down to this. When you differentiate between the screen being on or off, you have the conscious experience of "seeing" light or dark. The photodiode can also differentiate between the screen being on or off, but presumably it does not consciously "see" light and dark. What is the key difference between you and the photodiode that makes you "see" light consciously? (see Appendix, i) According to the theory, the key difference between you and the photodiode has to do with how much information is generated when that differentiation is made. Information is classically defined as reduction of uncertainty among a number of alternatives outcomes when one of them occurs [ 4 ]. It can be measured by the entropy function, which is the weighted sum of the logarithm of the probability (p) of alternatives outcomes (i): H = - Σp i log 2 p i . Thus, tossing a fair coin and obtaining heads corresponds to 1 bit of information, because there are just two alternatives; throwing a fair die yields log 2 (6) ≈ 2.59 bits of information, because there are six equally likely alternatives (H decreases if some of the outcomes are more likely than others, as would be the case with a loaded die). When the blank screen turns on, the photodiode enters one of its two possible alternative states and beeps. As with the coin, this corresponds to 1 bit of information. However, when you see the blank screen turn on, the state you enter, unlike the photodiode, is one out of an extraordinarily large number of possible states. That is, the photodiode's repertoire is minimally differentiated, while yours is immensely so. It is not difficult to see this. For example, imagine that, instead of turning homogeneously on, the screen were to display at random every frame from every movie that was or could ever be produced. Without any effort, each of these frames would cause you to enter a different state and "see" a different image. This means that when you enter the particular state ("seeing light") you rule out not just "dark", but an extraordinarily large number of alternative possibilities. Whether you think or not of the bewildering number of alternatives (and you typically don't), this corresponds to an extraordinary amount of information (see Appendix, ii). This point is so simple that its importance has been overlooked. Integration While the ability to differentiate among a very large number of states is a major difference between you and the lowly photodiode, by itself it is not enough to account for the presence of conscious experience. To see why, consider an idealized one megapixel digital camera, whose sensor chip is essentially a collection of one million photodiodes. Even if each photodiode in the sensor chip were just binary, the camera as such could differentiate among 2 1,000,000 states, an immense number, corresponding to 1,000,000 bits of information. Indeed, the camera would easily enter a different state for every frame from every movie that was or could ever be produced. Yet nobody would believe that the camera is conscious. What is the key difference between you and the camera? According to the theory, the key difference between you and the camera has to do with information integration . From the perspective of an external observer, the camera chip can certainly enter a very large number of different states, as could easily be demonstrated by presenting it with all possible input signals. However, the sensor chip can be considered just as well as a collection of one million photodiodes with a repertoire of two states each, rather than as a single integrated system with a repertoire of 2 1,000,000 states. This is because, due to the absence of interactions among the photodiodes within the sensory chip, the state of each element is causally independent of that of the other elements, and no information can be integrated among them. Indeed, if the sensor chip were literally cut down into its individual photodiodes, the performance of the camera would not change at all. By contrast, the repertoire of states available to you cannot be subdivided into the repertoire of states available to independent components. This is because, due to the multitude of causal interactions among the elements of your brain, the state of each element is causally dependent on that of other elements, which is why information can be integrated among them. Indeed, unlike disconnecting the photodiodes in a camera sensor, disconnecting the elements of your brain that underlie consciousness has disastrous effects. The integration of information in conscious experience is evident phenomenologically: when you consciously "see" a certain image, that image is experienced as an integrated whole and cannot be subdivided into component images that are experienced independently. For example, no matter how hard you try, for example, you cannot experience colors independent of shapes, or the left half of the visual field of view independently of the right half. And indeed, the only way to do so is to physically split the brain in two to prevent information integration between the two hemispheres. But then, such split-brain operations yield two separate subjects of conscious experience, each of them having a smaller repertoire of available states and more limited performance [ 5 ]. Spatio-temporal characteristics Finally, it is important to appreciate that conscious experience unfolds at a characteristic spatio-temporal scale. For instance, it flows in time at a characteristic speed and cannot be much faster or much slower. No matter how hard you try, you cannot speed up experience to follow a move accelerated a hundred times, not can you slow it down if the movie has decelerated. Studies of how a percept is progressively specified and stabilized – a process called microgenesis – indicate that it takes up to 100–200 milliseconds to develop a fully formed sensory experience, and that the surfacing of a conscious thought may take even longer [ 6 ]. In fact, the emergence of a visual percept is somewhat similar to the developing of a photographic print: first there is just the awareness that something has changed, then that it is something visual rather than, say, auditory, later some elementary features become apparent, such as motion, localization, and rough size, then colors and shapes emerge, followed by the formation of a full object and its recognition – a sequence that clearly goes from less to more differentiated [ 6 ]. Other evidence indicates that a single conscious moment does not extend beyond 2–3 seconds [ 7 ]. While it is arguable whether conscious experience unfolds more akin to a series of discrete snapshots or to a continuous flow, its time scale is certainly comprised between these lower and upper limits. Thus, a phenomenological analysis indicates that consciousness has to do with the ability to integrate a large amount of information, and that such integration occurs at a characteristic spatio-temporal scale. Measuring the capacity to integrate information: The Φ of a complex If consciousness corresponds to the capacity to integrate information, then a physical system should be able to generate consciousness to the extent that it has a large repertoire of available states (information), yet it cannot be decomposed into a collection of causally independent subsystems (integration). How can one identify such an integrated system, and how can one measure its repertoire of available states [ 2 , 8 ]? As was mentioned above, to measure the repertoire of states that are available to a system, one can use the entropy function, but this way of measuring information is completely insensitive to whether the information is integrated. Thus, measuring entropy would not allow us to distinguish between one million photodiodes with a repertoire of two states each, and a single integrated system with a repertoire of 2 1,000,000 states. To measure information integration, it is essential to know whether a set of elements constitute a causally integrated system, or they can be broken down into a number of independent or quasi-independent subsets among which no information can be integrated. To see how one can achieve this goal, consider an extremely simplified system constituted of a set of elements. To make matters slightly more concrete, assume that we are dealing with a neural system. Each element could represent, for instance, a group of locally interconnected neurons that share inputs and outputs, such as a cortical minicolumn. Assume further that each element can go through discrete activity states, corresponding to different firing levels, each of which lasts for a few hundred milliseconds. Finally, for the present purposes, let us imagine that the system is disconnected from external inputs, just as the brain is virtually disconnected from the environment when it is dreaming. Effective information Consider now a subset S of elements taken from such a system, and the diagram of causal interactions among them (Fig. 1a ). We want to measure the information generated when S enters a particular state out of its repertoire, but only to the extent that such information can be integrated, i.e. each state results from causal interactions within the system. How can one do so? One way is to divide S into two complementary parts A and B, and evaluate the responses of B that can be caused by all possible inputs originating from A. In neural terms, we try out all possible combinations of firing patterns as outputs from A, and establish how differentiated is the repertoire of firing patterns they produce in B. In information-theoretical terms, we give maximum entropy to the outputs from A (A Hmax ), i.e. we substitute its elements with independent noise sources, and we determine the entropy of the responses of B that can be induced by inputs from A. Specifically, we define the effective information between A and B as EI(A→B) = MI(A Hmax ;B). Here MI(A;B) = H(A) + H(B) - H(AB) stands for mutual information, a measure of the entropy or information shared between a source (A) and a target (B). Note that since A is substituted by independent noise sources, there are no causal effects of B on A; therefore the entropy shared by B and A is necessarily due to causal effects of A on B. Moreover, EI(A→B) measures all possible effects of A on B, not just those that are observed if the system were left to itself. Also, EI(A→B) and EI(B→A) in general are not symmetric. Finally, note that the value of EI(A→B) is bounded by A Hmax and B Hmax , whichever is less. In summary, to measure EI(B→A), one needs to apply maximum entropy to the outputs from B, and determine the entropy of the responses of B that are induced by inputs from A. It should be apparent from the definition that EI(A→B) will be high if the connections between A and B are strong and specialized, such that different outputs from A will induce different firing patterns in B. On the other hand, EI(A→B) will be low or zero if the connections between A and B are such that different outputs from A produce scarce effects, or if the effect is always the same. For a given bipartition of a subset, then, the sum of the effective information for both directions is indicated as EI(A B) = EI(A→B) + EI(B→A). Thus, EI(A B) measures the repertoire of possible causal effects of A on B and of B on A. Information integration Based on the notion of effective information for a bipartition, we can assess how much information can be integrated within a system of elements. To this end, we note that a subset S of elements cannot integrate any information (as a subset) if there is a way to partition S in two parts A and B such that EI(A B) = 0 (Fig. 1b , vertical bipartition). In such a case, in fact, we would clearly be dealing with at least two causally independent subsets, rather than with a single, integrated subset. This is exactly what would happen with the photodiodes making up the sensor of a digital camera: perturbing the state of some of the photodiodes would make no difference to the state of the others. Similarly, a subset can integrate little information if there is a way to partition it in two parts A and B such that EI(A B) is low: the effective information across that bipartition is the limiting factor on the subset's information integration capacity. Therefore in order to measure the information integration capacity of a subset S, we should search for the bipartition(s) of S for which EI(A B) reaches a minimum (the informational "weakest link")." Since EI(A B) is necessarily bounded by the maximum entropy available to A or B, min{EI(A B)}, to be comparable over bipartitions, should be normalized by H max (A B) = min{H max (A); H max (B)}, the maximum information capacity for each bipartition. The minimum information bipartition MIB A B of subset S – its 'weakest link' – is its bipartition for which the normalized effective information reaches a minimum, corresponding to min{EI(A B)/H max (A B)}. The information integration for subset S, or Φ(S), is simply the (non-normalized) value of EI(A B) for the minimum information bipartition: Φ(S) = EI( MIB A B). The symbol Φ is meant to indicate that the information (the vertical bar "I") is integrated within a single entity (the circle "O", see Appendix, iii). Complexes We are now in a position to establish which subsets are actually capable of integrating information, and how much of it (Fig. 1c ). To do so, we consider every possible subset S of m elements out of the n elements of a system, starting with subsets of two elements (m = 2) and ending with a subset corresponding to the entire system (m = n). For each of them, we measure the value of Φ, and rank them from highest to lowest. Finally, we discard all those subsets that are included in larger subsets having higher Φ (since they are merely parts of a larger whole). What we are left with are complexes – individual entities that can integrate information. Specifically, a complex is a subset S having Φ>0 that is not included within a larger subset having higher Φ. For a complex, and only for a complex, it is appropriate to say that, when it enters a particular state out if its repertoire, it generates and amount of integrated information corresponding to its Φ value. Of the complexes that make up a given system, the one with the maximum value of Φ(S) is called the main complex (the maximum is taken over all combinations of m>1 out of n elements of the system). Some properties of complexes worth pointing out are, for instance, that a complex can be causally connected to elements that are not part of it (the input and output elements of a complex are called ports-in and ports-out , respectively). Also, the same element can belong to more than one complex, and complexes can overlap. In summary, a system can be analyzed to identify its complexes – those subsets of elements that can integrate information, and each complex will have an associated value of Φ – the amount of information it can integrate (see Appendix, iv). To the extent that consciousness corresponds to the capacity to integrate information, complexes are the "subjects" of experience, being the locus where information can be integrated. Since information can only be integrated within a complex and not outside its boundaries, consciousness as information integration is necessarily subjective, private, and related to a single point of view or perspective [ 1 , 9 ]. It follows that elements that are part of a complex contribute to its conscious experience, while elements that are not part of it do not, even though they may be connected to it and exchange information with it through ports-in and ports-out. Information integration over space and time The Φ value of a complex is dependent on both spatial and temporal scales that determine what counts as a state of the underlying system. In general, there will be a "grain size", in both space and time, at which Φ reaches a maximum. In the brain, for example, synchronous firing of heavily interconnected groups of neurons sharing inputs and outputs, such as cortical minicolumns, may produce significant effects in the rest of the brain, while asynchronous firing of various combinations of individual neurons may be less effective. Thus, Φ values may be higher when considering as elements cortical minicolumns rather than individual neurons, even if their number is lower. On the other hand, Φ values would be extremely low with elements the size of brain areas. Time wise, Φ values in the brain are likely to show a maximum between tens and hundreds of milliseconds. It is clear, for example, that if one were to stimulate one half of the brain by inducing many different firing patterns, and examine what effects this produces on the other half, no stimulation pattern would produce any effect whatsoever after just a tenth of a millisecond, and Φ would be equal to zero. After say 100 milliseconds, however, there is enough time for differential effects to be manifested, and Φ would grow. On the other hand, given the duration of conduction delays and of postsynaptic currents, much longer intervals are not going to increase Φ values. Indeed, a neural system will soon settle down into states that become progressively more independent of the stimulation. Thus, the search for complexes of maximum Φ should occur over subsets at critical spatial and temporal scales. To recapitulate, the theory claims that consciousness corresponds to the capacity to integrate information. This capacity, corresponding to the quantity of consciousness, is given by the Φ value of a complex. Φ is the amount of effective information that can be exchanged across the minimum information bipartition of a complex. A complex is a subset of elements with Φ>0 and with no inclusive subset of higher Φ. The spatial and temporal scales defining the elements of a complex and the time course of their interactions are those that jointly maximize Φ. The second problem: What determines the kind of consciousness a system has? Even if we were reasonably sure that a system is conscious, it is not immediately obvious what kind of consciousness it would have. As was mentioned early on, our own consciousness comes in specific and seemingly irreducible qualities, exemplified by different modalities (e.g. vision, audition, pain), submodalities (e.g. visual color and motion), and dimensions (e.g. blue and red). What determines that colors look the way they do, and different from the way music sounds, or pain feels? And why can we not even imagine what a "sixth" sense would feel like? Or consider the conscious experience of others. Does a gifted musician experience the sound of an orchestra the same way you do, or is his experience richer? And what about bats [ 10 ]? Assuming that they are conscious, how do they experience the world they sense through echolocation? Is their experience of the world vision-like, audition-like, or completely alien to us? Unless we accept that the kind of consciousness a system has is arbitrary, there must be some necessary and sufficient conditions that determine exactly what kind of experiences it can have. This is the second problem of consciousness. While it may not be obvious how best to address this problem, we do know that, just as the quantity of our consciousness depends on the proper functioning of a physical substrate – the brain, so does the quality of consciousness. Consider for example the acquisition of new discriminatory abilities, such as becoming expert at wine tasting. Careful studies have shown that we do not learn to distinguish among a large number of different wines merely by attaching the appropriate labels to different sensations that we had had all along. Rather, it seems that we actually enlarge and refine the set of sensations triggered by tasting wines. Similar observations have been made by people who, for professional reasons, learn to discriminate among perfumes, colors, sounds, tactile sensations, and so on. Or consider perceptual learning during development. While infants experience more than just a "buzzing confusion", there is no doubt that perceptual abilities undergo considerable refinement – just consider what your favorite red wine must have tasted like when all you had experienced was milk and water. These examples indicate that the quality and repertoire of our conscious experience can change as a result of learning. What matters here is that such perceptual learning depends upon specific changes in the physical substrate of our consciousness – notably a refinement and rearranging of connections patterns among neurons in appropriate parts of the thalamocortical system (e.g [ 11 ]). Further evidence for a strict association between the quality of conscious experience and brain organization comes from countless neurological studies. Thus, we know that damage to certain parts of the cerebral cortex forever eliminates our ability to perceive visual motion, while leaving the rest of our consciousness seemingly intact. By contrast, damage to other parts selectively eliminates our ability to perceive colors. [ 12 ]. There is obviously something about the organization of those cortical areas that makes them contribute different qualities – visual motion and color – to conscious experience. In this regard, it is especially important that the same cortical lesion that eliminates the ability to perceive color or motion also eliminates the ability to remember, imagine, and dream in color or motion. By contrast, lesions of the retina, while making us blind, do not prevent us from remembering, imagining, and dreaming in color (unless they are congenital). Thus, it is something having to do with the organization of certain cortical areas – and not with their inputs from the sensory periphery – that determines the quality of conscious experiences we can have. What is this something? Characterizing the quality of consciousness as a space of informational relationships: The effective information matrix According to the theory, just as the quantity of consciousness associated with a complex is determined by the amount of information that can be integrated among its elements, the quality of its consciousness is determined by the informational relationships that causally link its elements [ 13 ]. That is, the way information can be integrated within a complex determines not only how much consciousness is has, but also what kind of consciousness. More precisely, the theory claims that the elements of a complex constitute the dimensions of an abstract relational space, the qualia space . The values of effective information among the elements of a complex, by defining the relationships among these dimensions, specify the structure of this space (in a simplified, Cartesian analogue, each element is a Cartesian axis, and the effective information values between elements define the angles between the axes, see Appendix, v). This relational space is sufficient to specify the quality of conscious experience. Thus, the reason why certain cortical areas contribute to conscious experience of color and other parts to that of visual motion has to do with differences in the informational relationships both within each area and between each area and the rest of the main complex. By contrast, the informational relationships that exist outside the main complex – including those involving sensory afferents – do not contribute either to the quantity or to the quality of consciousness. To exemplify, consider two very simple linear systems of four elements each (Fig. 2 ). Fig. 2a shows the diagram of causal interactions for the two systems. The system on the left is organized as a divergent digraph: element number 1 sends connections of equal strength to the other three elements. The analysis of complexes shows that this system forms a single complex having a Φ value of 10 bits. The system on the right is organized as a chain: element number 1 is connected to 2, which is connected to 3, which is connected to 4. This system also constitutes a single complex having a Φ value of 10 bits. Fig. 2b shows the effective information matrix for both complexes. This contains the values of EI between each subset of elements and every other subset, corresponding to all informational relationships among the elements (the first row shows the values in one direction, the second row in the reciprocal direction). The elements themselves define the dimensions of the qualia space of each complex, in this case four. The effective information matrix defines the relational structure of the space. This can be thought of as a kind of topology, in that the entries in the matrix can be considered to represent how close such dimensions are to each other (see Appendix, vi). It is apparent that, despite the identical value of Φ and the same number of dimensions, the informational relationships that define the space are different for the two complexes. For example, the divergent complex has many more zero entries, while the chain complex has one entry (subset {1 3} to subset {2 4}) that is twice as strong as all other non-zero entries. These two examples are purely meant to illustrate how the space of informational relationships within a complex can be captured by the effective information matrix, and how that space can differ for two complexes having similar amounts of Φ and the same number of dimensions. Of course, for a complex having high values of Φ, such as the one underlying our own consciousness, qualia space would be extraordinarily large and intricately structured. Nevertheless, it is a central claim of the theory that the structure of phenomenological relationships should reflect directly that of informational relationships. For example, the conscious experiences of blue and red appear irreducible (red is not simply less of blue). They may therefore correspond to different dimensions of qualia space (different elements of the complex). We also know that, as different as blue and red may be subjectively, they are much closer to each other than they are, say, to the blaring of a trumpet. EI values between the neuronal groups underlying the respective dimensions should behave accordingly, being higher between visual elements than between visual and auditory elements. As to the specific quality of different modalities and submodalities, the theory predicts that they are due to differences in the set of informational relationships within the respective cortical areas and between each area and the rest of the main complex. For example, areas that are organized topographically and areas that are organized according to a "winner takes all" arrangement should contribute different kinds of experiences. Another prediction is that changes in the quality and repertoire of sensations as a result of perceptual learning would also correspond to a refinement of the informational relationships within and between the appropriate cortical areas belonging to the main complex. By contrast, the theory predicts that informational relationships outside a complex – including those among sensory afferents – should not contribute directly to the quality of conscious experience of that complex. Of course, sensory afferents, sensory organs, and ultimately the nature and statistics of external stimuli, play an essential role in shaping the informational relationships among the elements of the main complex – but such role is an indirect and historical one – played out through evolution, development, and learning [ 14 ] (see Appendix, vii). Specifying each conscious experience: The state of the interaction variables According to the theory, once the quantity and quality of conscious experience that a complex can have are specified, the particular conscious state or experience that the complex will have at any given time is specified by the activity state of its elements at that time (in a Cartesian analogue, if each element of the complex corresponds to an axis of qualia space, and effective information values between elements define the angles between the axes specifying the structure of the space, then the activity state of each element provides a coordinate along its axis, and each conscious state is defined by the set of all its coordinates). The relevant activity variables are those that mediate the informational relationships among the elements, that is, those that mediate effective information. For example, if the elements are local groups of neurons, then the relevant variables are their firing patterns over tens to hundreds of milliseconds. The state of a complex at different times can be represented schematically by a state diagram as in Fig. 2c (for the divergent complex on the left and the chain complex on the right). Each column in the state diagram shows the activity values of all elements of a complex (here between 0 and 1). Different conscious states correspond to different patterns of activity distributed over all the elements of a complex, with no contribution from elements outside the complex. Each conscious state can thus be thought of as a different point in the multidimensional qualia space defined by the effective information matrix of a complex (see Appendix, viii). Therefore, a succession or flow of conscious states over time can be thought of as a trajectory of points in qualia space. The state diagram also illustrates some states that have particular significance (second to fifth column). These are the states with just one active element, and all other elements silent (or active at some baseline level). It is not clear whether such highly selective states can be achieved within a large neural complex of high Φ, such as that one that is postulated to underlie human consciousness. To the extent that this is possible, such highly selective states would represent the closest approximation to experiencing that element's specific contribution to consciousness – its quality or "quale". However, because of the differences in the qualia space between the two complexes, the same state over the four elements would correspond to different experiences (and mean different things) for the two complexes. It should also be emphasized that, in every case, it is the activity state of all elements of the complex that defines a given conscious state, and both active and inactive elements count. To recapitulate, the theory claims that the quality of consciousness associated with a complex is determined by its effective information matrix. The effective information matrix specifies all informational relationships among the elements of a complex. The values of the variables mediating informational interactions among the elements of a complex specify the particular conscious experience at any given time. Testing the hypothesis Consciousness, information integration, and the brain Based on a phenomenological analysis, we have argued that consciousness corresponds to the capacity to integrate information. We have then considered how such capacity can be measured, and we have developed a theoretical framework for consciousness as information integration. We will now consider several neuroanatomical or neurophysiological factors that are known to influence consciousness. After briefly discussing the empirical evidence, we will use simplified computer models to illustrate how these neuroanatomical and neurophysiological factors influence information integration. As we shall see, the information integration theory not only fits empirical observations reasonably well, but offers a principled explanation for them. Consciousness is generated by a distributed thalamocortical network that is at once specialized and integrated Ancient Greek philosophers disputed whether the seat of consciousness was in the lungs, in the heart, or in the brain. The brain's pre-eminence is now undisputed, and scientists are trying to establish which specific parts of the brain are important. For example, it is well established that the spinal cord is not essential for our conscious experience, as paraplegic individuals with high spinal transactions are fully conscious. Conversely, a well-functioning thalamocortical system is essential for consciousness [ 15 ]. Opinions differ, however, about the contribution of certain cortical areas [ 1 , 16 - 21 ]. Studies of comatose or vegetative patients indicate that a global loss of consciousness is usually caused by lesions that impair multiple sectors of the thalamocortical system, or at least their ability to work together as a system. [ 22 - 24 ]. By contrast, selective lesions of individual thalamocortical areas impair different submodalities of conscious experience, such as the perception of color or of faces [ 25 ]. Electrophysiological and imaging studies also indicate that neural activity that correlates with conscious experience is widely distributed over the cortex (e.g [ 20 , 26 - 29 ]). It would seem, therefore, that the neural substrate of consciousness is a distributed thalamocortical network, and that there is no single cortical area where it all comes together (see Appendix, ix). The fact that consciousness as we know it is generated by the thalamocortical system fits well with the information integration theory, since what we know about its organization appears ideally suited to the integration of information. On the information side, the thalamocortical system comprises a large number of elements that are functionally specialized, becoming activated in different circumstances. [ 12 , 30 ]. Thus, the cerebral cortex is subdivided into systems dealing with different functions, such as vision, audition, motor control, planning, and many others. Each system in turn is subdivided into specialized areas, for example different visual areas are activated by shape, color, and motion. Within an area, different groups of neurons are further specialized, e.g. by responding to different directions of motion. On the integration side, the specialized elements of the thalamocortical system are linked by an extended network of intra- and inter-areal connections that permit rapid and effective interactions within and between areas [ 31 - 35 ]. In this way, thalamocortical neuronal groups are kept ready to respond, at multiple spatial and temporal scales, to activity changes in nearby and distant thalamocortical areas. As suggested by the regular finding of neurons showing multimodal responses that change depending on the context [ 36 , 37 ], the capacity of the thalamocortical system to integrate information is probably greatly enhanced by nonlinear switching mechanisms, such as gain modulation or synchronization, that can modify mappings between brain areas dynamically [ 34 , 38 - 40 ]. In summary, the thalamocortical system is organized in a way that appears to emphasize at once both functional specialization and functional integration. As shown by computer simulations, systems of neural elements whose connectivity jointly satisfies the requirements for functional specialization and for functional integration are well suited to integrating information. Fig. 3a shows a representative connection matrix obtained by optimizing for Φ starting from random connection weights. A graph-theoretical analysis indicates that connection matrices yielding the highest values of information integration (Φ = 74 bits) share two key characteristics [ 8 ]. First, connection patterns are different for different elements, ensuring functional specialization. Second, all elements can be reached from all other elements of the network, ensuring functional integration. Thus, simulated systems having maximum Φ appear to require both functional specialization and functional integration. In fact, if functional specialization is lost by replacing the heterogeneous connectivity with a homogeneous one, or if functional integration is lost by rearranging the connections to form small modules, the value of Φ decreases considerably (Fig 3b,3c ). Further simulations show that it is possible to construct a large complex of high Φ by joining smaller complexes through reciprocal connections [ 8 ]. In the thalamocortical system, reciprocal connections linking topographically organized areas may be especially effective with respect to information integration. In summary, the coexistence of functional specialization and functional integration, epitomized by the thalamocortical system [ 30 ], is associated with high values of Φ. Other brain regions with comparable numbers of neurons, such as the cerebellum, do not contribute to conscious experience Consider now the cerebellum. This brain region contains more neurons than the cerebral cortex, has huge numbers of synapses, and receives mapped inputs from the environment and controls several outputs. However, in striking contrast to the thalamocortical system, lesions or ablations indicate that the direct contribution of the cerebellum to conscious experience is minimal. Why is this the case? According to the theory, the reason lies with the organization of cerebellar connections, which is radically different from that of the thalamocortical system and is not well suited to information integration. Specifically, the organization of the connections is such that individual patches of cerebellar cortex tend to be activated independently of one another, with little interaction possible between distant patches [ 41 , 42 ]. This suggests that cerebellar connections may not be organized so as to generate a large complex of high Φ, but rather to give rise to many small complexes each with a low value of Φ. Such an organization seems to be highly suited for both the learning and the rapid, effortless execution of informationally insulated subroutines. This concept is illustrated in Fig. 4a , which shows a strongly modular network, consisting of three modules of eight strongly interconnected elements each. This network yields Φ = 20 bits for each of its three modules, which form the system's three complexes. This example indicates that, irrespective of how many elements and connections are present in a neural structure, if that structure is organized in a strongly modular manner with little interactions among modules, complex size and Φ values are necessarily low. According to the information integration theory, this is the reason why these systems, although computationally very sophisticated, contribute little to consciousness. It is also the reason why there is no conscious experience associated with hypothalamic and brainstem circuits that regulate important physiological variables, such as blood pressure. Subcortical centers can control consciousness by modulating the readiness of the thalamocortical system without contributing directly to it It has been known for a long time that lesions in the reticular formation of the brainstem can produce unconsciousness and coma. Conversely, stimulating the reticular formation can arouse a comatose animal and activate the thalamocortical system, making it ready to respond to stimuli [ 43 ]. Groups of neurons within the reticular formation are characterized by diffuse projections to many areas of the brain. Many such groups release neuromodulators such as acetylcholine, histamine, noradrenaline, serotonin, dopamine, and glutamate (acting on metabotropic receptors) and can have extremely widespread effects on both neural excitability and plasticity [ 44 ]. However, it would seem that the reticular formation, while necessary for the normal functioning of the thalamocortical system and therefore for the occurrence of conscious experience, may not contribute much in terms of specific dimensions of consciousness – it may work mostly like an external on-switch or as a transient booster of thalamocortical firing. Such a role can be explained readily in terms of information integration. As shown in Fig. 4b , neural elements that have widespread and effective connections to a main complex of high Φ may nevertheless remain informationally excluded from it. Instead, they are part of a larger complex having a much lower value of Φ. Neural activity in sensory afferents to the thalamocortical system can determine what we experience without contributing directly to it What we see usually depends on the activity patterns that occur in the retina and that are relayed to the brain. However, many observations suggest that retinal activity does not contribute directly to conscious experience. Retinal cells surely can tell light from dark and convey that information to visual cortex, but their rapidly shifting firing patterns do not correspond well with what we perceive. For example, during blinks and eye movements retinal activity changes dramatically, but visual perception does not. The retina has a blind spot at the exit of the optic nerve where there are no photoreceptors, and it has low spatial resolution and no color sensitivity at the periphery of the visual field, but we are not aware of any of this. More importantly, lesioning the retina does not prevent conscious visual experiences. For example, a person who becomes retinally blind as an adult continues to have vivid visual images and dreams. Conversely, stimulating the retina during sleep by keeping the eyes open and presenting various visual inputs does not yield any visual experience and does not affect visual dreams. Why is it that retinal activity usually determines what we see through its action on thalamocortical circuits, but does not contribute directly to conscious experience? As shown in Fig. 4c , adding or removing multiple, segregated incoming pathways does not change the composition of the main complex, and causes little change in its Φ. While the incoming pathways do participate in a larger complex together with the elements of the main complex, the Φ value of this larger complex is very low, being limited by the effective information between each afferent pathway and its port in at the main complex. Thus, input pathways providing powerful inputs to a complex add nothing to the information it integrates if their effects are entirely accounted for by ports-in. Neural activity in motor efferents from the thalamocortical system, while producing varied behavioral outputs, does not contribute directly to conscious experience In neurological practice, as well as in everyday life, we tend to associate consciousness with the presence of a diverse behavioral repertoire. For example, if we ask a lot of different questions and for each of them we obtain an appropriate answer, we generally infer that a person is conscious. Such a criterion is not unreasonable in terms of information integration, given that a wide behavioral repertoire is usually indicative of a large repertoire of internal states that are available to an integrated system. However, it appears that neural activity in motor pathways, which is necessary to bring about such diverse behavioral responses, does not in itself contribute to consciousness. For example, patients with the locked-in syndrome, who are completely paralyzed except for the ability to gaze upwards, are fully conscious. Similarly, while we are completely paralyzed during dreams, consciousness is not impaired by the absence of behavior. Even lesions of central motor areas do not impair consciousness. Why is it that neurons in motor pathways, which can produce a large repertoire of different outputs and thereby relay a large amount of information about different conscious states, do not contribute directly to consciousness? As shown in Fig. 4d , adding or removing multiple, segregated outgoing pathways to a main complex does not change the composition of the main complex, and does not change its Φ value. Like incoming pathways, outgoing pathways do participate in a larger complex together with the elements of the main complex, but the Φ value of this larger complex is very low, being limited by the effective information between each port-out of the main complex and its effector targets. Neural processes in cortico-subcortico-cortical loops, while important in the production and sequencing of action, thought, and language, do not contribute directly to conscious experience Another set of neural structures that may not contribute directly to conscious experience are subcortical structures such as the basal ganglia. The basal ganglia are large nuclei that contain many circuits arranged in parallel, some implicated in motor and oculomotor control, others, such as the dorsolateral prefrontal circuit, in cognitive functions, and others, such as the lateral orbitofrontal and anterior cingulate circuits, in social behavior, motivation, and emotion [ 45 ]. Each basal ganglia circuit originates in layer V of the cortex, and through a last step in the thalamus, returns to the cortex, not far from where the circuit started [ 46 ]. Similarly arranged cortico-ponto-cerebello-thalamo-cortical loops also exist. Why is it that these complicated neural structures, which are tightly connected to the thalamocortical system at both ends, do not seem to provide much direct contribution to conscious experience? (see Appendix, x) As shown in Fig. 4e , the addition of many parallel cycles also generally does not change the composition of the main complex, although Φ values can be altered (see Appendix, xi). Instead, the elements of the main complex and of the connected cycles form a joint complex that can only integrate the limited amount of information exchanged within each cycle. Thus, subcortical cycles or loops implement specialized subroutines that are capable of influencing the states of the main thalamocortical complex without joining it. Such informationally insulated cortico-subcortical loops could constitute the neural substrates for many unconscious processes that can affect and be affected by conscious experience [ 3 , 47 ]. It is likely that new informationally insulated loops can be created through learning and repetition. For example, when first performing a new task, we are conscious of every detail of it, we make mistakes, are slow, and must make an effort. When we have learned the task well, we perform it better, faster, and with less effort, but we are also less aware of it. As suggested by imaging results, a large number of neocortical regions are involved when we first perform a task. With practice, activation is reduced or shifts to different circuits [ 48 ]. According to the theory, during the early trials, performing the task involves many regions of the main complex, while later certain aspects of the task are delegated to neural circuits, including subcortical ones, that are informationally insulated. Many neural processes within the thalamocortical system may also influence conscious experience without contributing directly to it Even within the thalamocortical system proper, a substantial proportion of neural activity does not appear to contribute directly to conscious experience. For example, what we see and hear requires elaborate computational processes dealing with figure-ground segregation, depth perception, object recognition, and language parsing, many of which take place in the thalamocortical system. Yet we are not aware of all this diligent buzzing: we just see objects, separated from the background and laid out in space, and know what they are, or hear words, nicely separated from each other, and know what they mean. As an example, take binocular rivalry, where the two eyes view two different images, but we perceive consciously just one image at a time, alternating in sequence. Recordings in monkeys have shown that the activity of visual neurons in certain cortical areas, such as the inferotemporal cortex, follows faithfully what the subject perceives consciously. However, in other areas, such as primary visual cortex, there are many neurons that respond to the stimulus presented to the eye, whether or not the subject is perceiving it [ 49 ]. Neuromagnetic studies in humans have shown that neural activity correlated with a stimulus that is not being consciously perceived can be recorded in many cortical areas, including the front of the brain. [ 26 ]. Why does the firing of many cortical neurons carrying out the computational processes that enable object recognition (or language parsing) not correspond to anything conscious? The situation is similar on the executive side of consciousness. When we plan to do or say something, we are vaguely conscious of what we intend, and presumably these intentions are reflected in specific firing patterns of certain neuronal groups. Our vague intentions are then translated almost miraculously into the right words, and strung together to form a syntactically correct sentence that conveys what we meant to say. And yet again, we are not at all conscious of the complicated processing that is needed to carry out our intentions, much of which takes place in the cortex. What determines whether the firing of neurons within the thalamocortical system contributes directly to consciousness or not? According to the information integration theory, the same considerations that apply to input and output circuits and to cortico-subcortico-cortical loops also apply to circuits and loops contained entirely within the thalamocortical system. Thus, the theory predicts that activity within certain cortical circuits does not contribute to consciousness because such circuits implement informationally insulated loops that remain outside of the main thalamocortical complex. At this stage, however, it is hard to say precisely which cortical circuits may be informationally insulated. Are primary sensory cortices organized like massive afferent pathways to a main complex "higher up" in the cortical hierarchy? Is much of prefrontal cortex organized like a massive efferent pathway? Do certain cortical areas, such as those belonging to the dorsal visual stream, remain partly segregated from the main complex? Do interactions within a cortico-thalamic minicolumn qualify as intrinsic mini-loops that support the main complex without being part of it? Unfortunately, answering these questions and properly testing the predictions of the theory requires a much better understanding of cortical neuroanatomy than is presently available [ 50 , 51 ]. Consciousness can be split if the thalamocortical system is split Studies of split-brain patients, whose corpus callosum was sectioned for therapeutic reasons, show that each hemisphere has its own, private conscious experience. The dominant, linguistically competent hemisphere does not seem to suffer a major impairment of consciousness after the operation. The non-dominant hemisphere, although it loses some important abilities and its residual capacities are harder to assess, also appears to be conscious. [ 5 ]. Some information, e.g. emotional arousal, seems to be shared across the hemispheres, probably thanks to subcortical common inputs. Viewing consciousness as information integration suggests straightforward explanations for these puzzling observations. Consider the simplified model in Fig. 5a . A main complex having high Φ includes two sets of elements ("hemispheres") having similar internal architecture that are joined by "callosal" connections (top panel). When the callosal connections are cut (bottom panel), the single main complex splits and is replaced by two smaller complexes corresponding to the two hemispheres. There is also a complex, of much lower Φ, which includes both hemispheres and a "subcortical" element that provide them with common input. Thus, there is a sense in which the two hemispheres still form an integrated entity, but the information they share is minimal (see Appendix, xii). Some parts of the thalamocortical system may contribute to conscious experience at one time and not at another Until now, we have considered structural aspects of the organization of the nervous system that, according to the information integration theory, explain why certain parts of the brain contribute directly to consciousness and others do not, or much less so. In addition to neuroanatomical factors, neurophysiological factors are also important in determining to what extent a given neural structure can integrate information. For example, anatomical connections between brain regions may or may not be functional, depending on both pathological or physiological factors. Functional disconnections between certain parts of the brain and others are thought to play a role in psychiatric conversion and dissociative disorders, may occur during dreaming, and may be implicated in conditions such as hypnosis. Thus, functional disconnections, just like anatomical disconnections, may lead to a restriction of the neural substrate of consciousness. It is also likely that certain attentional phenomena may correspond to changes in the neural substrate of consciousness. For example, when one is absorbed in thought, or focused exclusively on a given sensory modality, such as vision, the neural substrate of consciousness may not be the same as when we are diffusely monitoring the environment. Phenomena such as the attentional blink, where a fixed sensory input may at times make it to consciousness and at times not, may also be due to changes in functional connectivity: access to the main thalamocortical complex may be enabled or not based on dynamics intrinsic to the complex [ 52 ]. Phenomena such as binocular rivalry may also be related, at least in part, to dynamic changes in the composition of the main thalamocortical complex caused by transient changes in functional connectivity [ 53 ]. At present, however, it is still not easy to determine whether a particular group of neurons is excluded from the main complex because of hard-wired anatomical constraints, or is transiently disconnected due to functional changes. Figure 5b (top panel) shows a simple model obtained by taking three subsets of elements of (relatively) high Φ and connecting them through reciprocal connections. Specifically, the first subset, which stands for supramodal areas of the brain, is reciprocally connected to the second and third subsets, which stand for visual and auditory areas, respectively. In this idealized example, the visual and auditory subsets are not connected directly among themselves. As one can see, the three subsets thus connected form a single main complex having a Φ value of 61 bits. In the bottom panel, the auditory subset has been disconnected, in a functional sense, by mimicking a profound deactivation of its elements. The result is that the main complex shrinks and the auditory subset ends up outside the main complex. Note, however, that in this particular case the value of Φ changes very little (57 bits), indicating that it might be possible for the borders of the main complex to change dynamically while the amount of consciousness is not substantially altered. What would change, of course, would be the configuration of the space of informational relationships. These simulations suggest that attentional mechanisms may work both by changing neuronal firing rates, and therefore saliency within qualia space, as well as by modifying neuronal readiness to fire, and therefore the boundaries of the main complex and of qualia space itself. This is why the set of elements underlying consciousness is not static, but can be considered to form a " dynamic complex " or " dynamic core " [ 1 , 9 ]. Depending on certain neurophysiological parameters, the same thalamocortical network can generate much or little conscious experience Another example of the importance of neurophysiological parameters is provided by sleep – the most familiar of the alterations of consciousness, and yet one of the most striking. Upon awakening from dreamless sleep, we have the peculiar impression that for a while we were not there at all nor, as far as we are concerned, was the rest of the world. This everyday observation tells us vividly that consciousness can come and go, grow and shrink. Indeed, if we did not sleep, it might be hard to imagine that consciousness is not a given, but depends somehow on the way our brain is functioning. The loss of consciousness between falling asleep and waking up is relative, rather than absolute. [ 54 ]. Thus, careful studies of mental activity reported immediately after awakening have shown that some degree of consciousness is maintained during much of sleep. Many awakenings, especially from rapid eye movement (REM) sleep, yield dream reports, and dreams can be at times as vivid and intensely conscious as waking experiences. Dream-like consciousness also occurs during various phases of slow wave sleep, especially at sleep onset and during the last part of the night. Nevertheless, a certain proportion of awakenings do not yield any dream report, suggesting a marked reduction of consciousness. Such "empty" awakenings typically occur during the deepest stages of slow wave sleep (stages 3 and 4), especially during the first half of the night. Which neurophysiological parameters are responsible for the remarkable changes in the quantity and quality of conscious experience that occur during sleep? We know for certain that the brain does not simply shut off during sleep. During REM sleep, for example, neural activity is as high, if not higher, than during wakefulness, and EEG recordings show low-voltage fast-activity. This EEG pattern is known as "activated" because cortical neurons, being steadily depolarized and close to their firing threshold, are ready to respond to incoming inputs. Given these similarities, it is perhaps not surprising that consciousness should be present during both states. Changes in the quality of consciousness, however, do occur, and they correspond closely to relative changes in the activation of different brain areas. [ 54 ]. During slow wave sleep, average firing rates of cortical neurons are also similar to those observed during quiet wakefulness. However, due to changes in the level of certain neuromodulators, virtually all cortical neurons engage in slow oscillations at around 1 Hz, which are reflected in slow waves in the EEG [ 55 ]. Slow oscillations consist of a depolarized phase, during which the membrane potential of cortical neurons is close to firing threshold and spontaneous firing rates are similar to quiet wakefulness, and of a hyperpolarized phase, during which neurons become silent and are further away from firing threshold. From the perspective of information integration, a reduction in the readiness to respond to stimuli during the hyperpolarization phase of the slow oscillation would imply a reduction of consciousness. It would be as if we were watching very short fragments of a movie interspersed with repeated unconscious "blanks" in which we cannot see, think, or remember anything, and therefore have little to report. A similar kind of unreadiness to respond, associated with profound hyperpolarization, is found in deep anesthesia, another condition where consciousness is impaired. Studies using transcranial magnetic stimulation in conjunction with high-density EEG are currently testing how response readiness changes during the sleep waking cycle. From the perspective of information integration, a reduction of consciousness during certain phases of sleep would occur even if the brain remained capable of responding to perturbations, provided its response were to lack differentiation. This prediction is borne out by detailed computer models of a portion of the visual thalamocortical system (Hill and Tononi, in preparation). According to these simulations, in the waking mode different perturbations of the thalamocortical network yield specific responses. In the sleep mode, instead, the network becomes bistable: specific effects of different perturbations are quickly washed out and their propagation impeded: the whole network transitions into the depolarized or into the hyperpolarized phase of the slow oscillation – a stereotypic response that is observed irrespective of the particular perturbation (see Appendix, xiii). And of course, this bistability is also evident in the spontaneous behavior of the network: during each slow oscillation, cortical neurons are either all firing or all silent, with little freedom in between. In summary, these simulations indicate that, even if the anatomical connectivity of a complex stays the same, a change in key parameters governing the readiness of neurons to respond and the differentiation of their responses may alter radically the Φ value of the complex, with corresponding consequences on consciousness. Conscious experience and time requirements Consciousness not only requires a neural substrate with appropriate anatomical structure and appropriate physiological parameters: it also needs time. As was mentioned earlier, studies of how a percept is progressively specified and stabilized indicate that it takes up to 100–200 milliseconds to develop a fully formed sensory experience, and that the surfacing of a conscious thought may take even longer. Experiments in which the somatosensory areas of the cerebral cortex were stimulated directly indicate that low intensity stimuli must be sustained for up to 500 milliseconds to produce a conscious sensation [ 56 ]. Multi-unit recordings in the primary visual cortex of monkeys show that, after a stimulus is presented, the firing rate of many neurons increases irrespective of whether the animal reports seeing a figure or not. After 80–100 milliseconds, however, their discharge accurately predicts the conscious detection of the figure. Thus, the firing of the same cortical neurons may correlate with consciousness at certain times, but not at other times [ 57 ]. What determines when the firing of the same cortical neurons contributes to conscious experience and when it does not? And why may it take up to hundreds of milliseconds before a conscious experience is generated? The theory predicts that the time requirements for the generation of conscious experience in the brain emerge directly from the time requirements for the build-up of effective interactions among the elements of the main complex. As was mentioned above, if one were to perturb half of the elements of the main complex for less than a millisecond, no perturbations would produce any effect on the other half within this time window, and Φ would be equal to zero. After say 100 milliseconds, however, there is enough time for differential effects to be manifested, and Φ should grow. This prediction is confirmed by results obtained using large-scale computer simulations of the thalamocortical system, where the time course of causal interactions and functional integration can be studied in detail [ 38 , 58 , 59 ], Hill and Tononi, unpublished results). For example, in a model including nine functionally segregated visual areas, the time it takes for functionally specialized neurons located in several different areas to interact constructively and produce a specific, correlated firing pattern is at least 80 milliseconds [ 38 ]. These correlated firing patterns last for several hundred milliseconds. After one or more seconds, however, the network settles into spontaneous activity states that are largely independent of previous perturbations. Thus, the characteristic time scale for maximally differentiated responses in thalamocortical networks appears to be comprised between a few tens of milliseconds and a few seconds at the most. In summary, the time scale of neurophysiological interactions needed to integrate information among distant cortical regions appears to be consistent with that required by psychophysical observations (microgenesis), by stimulation experiments, and by recording experiments. Summary: seeing blue The previous examples show that the information integration theory is consistent with several empirical observations concerning the neural substrate of consciousness. Moreover, they show that the theory can provide a principled account of why consciousness is associated with certain parts of the brain rather than with others, and with certain global modes of functioning more than with others. To recapitulate the main tenets of the theory, it may be useful to reconsider the initial thought experiment. Imagine again that you are comfortably facing a blank screen that is alternately on and off. When the screen turns on, you see a homogenous blue field, indeed for the sake of the argument we assume that you are having a "pure" perception of blue, unencumbered by extraneous percepts or thoughts (perhaps as can be achieved in certain meditative states). As you have been instructed, you signal your perception of blue by pushing a button. Now consider an extremely simplified scenario of the neural events that might accompany your seeing blue. When the screen turns on, a volley of activity propagates through the visual afferent pathways, involving successive stages such as retinal short wavelength cones, blue-yellow opponents cells, color constant cells, and so on. Eventually, this volley of activity in the visual afferent pathways leads to the firing of some neuronal groups in color-selective areas of the temporal lobe that, on empirical grounds, are our best bet for the neural correlate of blue: i) their activity correlates well with your perception of blue whether you see, imagine, or dream blue, in a way that is as stable and as susceptible to illusions as your perception of blue; ii) their microstimulation leads to the perception of blue; and iii) their selective lesion makes you unable to perceive blue. Let us assume, then, that these neuronal groups quickly increase their firing, and within a few tens of milliseconds they reach and then maintain increased levels of firing (see Appendix, xiv). We also assume that, at the same time, neuronal groups in neighboring cortical areas go on firing at a baseline level, largely unaffected by the blue light. These include neuronal groups in other visual areas that are selective for shape or movement; neuronal groups in auditory area that are selective for tones; and many others. On the other hand, the volley of activity originating in the retina does not exhaust itself by generating sustained activity in the color areas of the temporal lobe. Part of the volley proceeds at great speed and activates efferent motor pathways, which cause you to push the signaling button. Another part activates cortico-subcortico-cortical loops in your prefrontal cortex and basal ganglia, which almost make you speak the word "blue" aloud. In the meantime, many other parts of the brain are buzzing along, unaffected by what is going on in the visual system: cerebellar circuits are actively stabilizing your posture and gaze, and hypothalamic-brainstem circuits are actively stabilizing your blood pressure. What components in this simplified neural scenario are essential for your conscious experience of blue, and why? The information integration theory makes several claims that lead to associated predictions. A first claim is that the neural substrate of consciousness as we know it is a complex of high Φ that is capable of integrating a large amount of information – the main complex. Therefore, whether a group of neurons contributes directly to consciousness is a function of its belonging to the main complex or not. In this example, the theory would predict that blue-selective neurons in some high-level color area should be inside the main complex; on the other hand, blue-sensitive neurons in afferent visual pathways, neurons in efferent pathways mediating the button-pressing response, neurons in cortico-subcortico-cortical and intracortical loops mediating subvocalization of the word "blue", neurons in the cerebellum controlling posture and neurons in hypothalamic-brainstem circuits controlling blood pressure should be outside . This even though these neurons may be equally active when you see blue, and even though some of them may be connected to elements of the main complex. In principle, joint microstimulation and recording experiments, and to some extent an analysis of patterns of synchronization, could determine participation in the main complex and test this prediction. The theory also predicts that blue-selective neurons in the main complex contribute to the conscious experience of blue only if their activation is sufficiently strong or sustained that they can make a difference, in informational terms, to the rest of the complex. Additional predictions are that, if a group of neurons that is normally part of the main complex becomes informationally disconnected from it, as could occur through attentional effects or in certain phases of sleep, the same group of neurons, firing in exactly the same way, would not contribute to consciousness. Moreover, according to the theory, the other groups of neurons within the main complex are essential to our conscious experience of blue even if, as in this example, they are not activated. This is not difficult to see. Imagine that, starting from an intact main complex, we were to remove one element after another, except for the active, blue-selective one. If an inactive element contributing to "seeing red" were removed, blue would not be experienced as blue anymore, but as some less differentiated color, perhaps not unlike those experienced by certain dichromats. If further elements of the main complex were removed, including those contributing to shapes, to sounds, to thoughts and so forth, one would soon drop to such a low level of consciousness that "seeing blue" would become meaningless: the "feeling" (and meaning) of the quale "blue" would have been eroded down to nothing. Indeed, while the remaining neural circuits may still be able to discriminate blue from other colors, they would do so very much as a photodiode does (see Appendix, xv). A second claim of the theory is that the quality of consciousness is determined by the informational relationships within the main complex. Therefore, how a group of neurons contributes to consciousness is a function of its informational relationships inside the complex and not outside of it. In this example, blue-selective neurons within the main complex have become blue-selective no doubt thanks to the inputs received from the appropriate afferent pathways, and ultimately because of some aspects of the statistics of the environment and the resulting plastic changes throughout the brain. However, the theory predicts that their present firing contributes the quale "blue" exclusively because of their informational relationships within the main complex. If connections outside the main complex were to be manipulated, including the afferent color pathways, the experience elicited by activating the blue-selective neurons within the complex would stay the same. Conversely, if the relationships inside the main complex were to change, as could be done by changing the pattern of connections within the color-selective area and with the rest of the complex, so would the conscious experience of blue. That is, activating the same neurons would produce a different conscious experience. Implications of the hypothesis To conclude, it is worth mentioning some of the implications that derive from the information integration theory of consciousness. At the most general level, the theory has ontological implications. It takes its start from phenomenology and, by making a critical use of thought experiments, it argues that subjective experience is one and the same thing as a system's capacity to integrate information. In this view, experience, that is, information integration, is a fundamental quantity, just as mass, charge or energy are. It follows that any physical system has subjective experience to the extent that it is capable of integrating information, irrespective of what it is made of. Thus, an intriguing implication of the theory is that it should be possible to construct conscious artifacts by endowing them with a complex of high Φ. Moreover, it should be possible to design the quality of their conscious experience by appropriately structuring their effective information matrix. It also follows that consciousness is not an all-or-none property, but it is graded: to varying degrees, it should exist in most natural (and artificial) systems. Because the conditions needed to build complexes of high Φ are apparently not easy to achieve, however, correspondingly high levels of experience are probably available to only a few kinds of systems, primarily complex brains containing the right type of architecture for maximizing functional specialization and integration. A related implication is that consciousness should also exist, to varying degrees, at multiple spatial and temporal scales. However, it is likely that, in most systems, there are privileged spatial and temporal scales at which information integration reaches a maximum. Consciousness is characterized here as a disposition or potentiality – in this case as the potential differentiation of a system's responses to all possible perturbations, yet it is undeniably actual . Consider another thought experiment: you could be in a coma for days, awaken to consciousness for just one second, and revert to a coma. As long as your thalamocortical system can function well for that one second, you will be conscious. That is, a system does not have to explore its repertoire of states to be conscious, or to know how conscious it is supposed to be: what counts is only that the repertoire is potentially available. While this may sound strange, fundamental quantities associated with physical systems can also be characterized as dispositions or potentialities, yet have actual effects. For example, mass can be characterized as a potentiality – say the resistance that a body would offer to acceleration by a force – yet it exerts undeniable effects, such as attracting other masses. This too has intriguing implications. For example, because in this view consciousness corresponds to the potential of an integrated system to enter a large number of states by way of causal interactions within it, experience is present as long as such potential is present, whether or not the system's elements are activated. Thus, the theory predicts that a brain where no neurons were activated, but were kept ready to respond in a differentiated manner to different perturbations, would be conscious (perhaps that nothing was going on). Also, because consciousness is a property of a system, not of a state, the state the system is in only determines which particular experience becomes actual at any given time, and not whether experience is present. Thus, a brain where each neuron were microstimulated to fire as an exact replica of your brain, but where synaptic interactions had been blocked, would be unconscious. The theory predicts that consciousness depends exclusively on the ability of a system to integrate information, whether or not it has a strong sense of self, language, emotion, a body, or is immersed in an environment, contrary to some common intuitions. This prediction is consistent with the preservation of consciousness during REM sleep, when both input and output signals from and to the body are markedly reduced. Transient inactivation of brain areas mediating the sense of self, language, and emotion could assess this prediction in a more cogent manner. Nevertheless, the theory recognizes that these same factors are important historically because they favor the development of neural circuits forming a main complex of high Φ. For example, the ability of a system to integrate information grows as that system incorporates statistical regularities from its environment and learns [ 14 ]. In this sense, the emergence of consciousness in biological systems is predicated on a long evolutionary history, on individual development, and on experience-dependent change in neural connectivity. Indeed, the theory also suggests that consciousness provides an adaptive advantage and may have evolved precisely because it is identical with the ability to integrate a lot of information in a short period of time. If such information is about the environment, the implication is that, the more an animal is conscious, the larger the number of variables it can take into account jointly to guide its behavior. Another implication of the theory is that the presence and extent of consciousness can be determined, in principle, also in cases in which we have no verbal report, such as infants or animals, or in neurological conditions such as coma and vegetative states, minimally conscious states, akinetic mutism, psychomotor seizures, and sleepwalking. In practice, of course, measuring Φ accurately in such systems will not be easy, but approximations and informed guesses are certainly conceivable. At present, the validity of this theoretical framework and the plausibility of its implications rest on its ability to account, in a coherent manner, for some basic phenomenological observations and for some elementary but puzzling facts about the relationship between consciousness and the brain. Experimental developments, especially of ways to stimulate and record concurrently the activity of broad regions of the brain, should permit stringent tests of some of the theory's predictions. Equally important will be the development of realistic, large-scale models of the anatomical organization of the brain. These models should allow a more rigorous measurement of how the capacity to integrate information relates to different brain structures and certain neurophysiological parameters [ 38 , 50 , 59 ]. Finally, the theoretical framework presented here aims primarily at understanding the necessary and sufficient conditions that determine the quantity and quality of consciousness at the most general level. Further theoretical developments will be required to address several issues that are central to the study of consciousness in a biological and psychological context, such as the relationship of consciousness to memory and language, higher order aspects of consciousness [ 60 , 61 ], and its relationship to the self) [ 62 ]. Undoubtedly, a full understanding of how the brain generates human consciousness remains a formidable task. However, if experimental investigations can be complemented by a principled theoretical approach, it may not lay beyond the reach of science. Appendix i. The problem can also be posed in neural terms. When we see light, certain neurons in the retina turn on, as do other neurons higher up in the brain. Based on what we know, the activity of neurons in the retina is not directly associated with conscious experience of light and dark – they behave just like biological photodiodes that signal to higher centers. Somewhere in those higher centers, however, there seem to be some neurons whose activity is indeed tightly correlated with the conscious experience of light and dark. What is special about these higher neurons? ii. Note that this information has nothing to do with how complicated the scene is, or how many different objects it appears to contain, but only with the number of alternative outcomes. iii. This quantity is also called MIB complexity , for minimum information bipartition complexity. Note that, in most cases, the bipartitions for which the normalized value of EI will be at a minimum, everything else being equal, will be bipartitions that cut the system in two halves, i.e. midpartitions [ 2 ]. iv. Complexes can also be defined using mutual information instead of effective information, by exploiting the endogenous sources of variance that may exist in an isolated system [ 8 ]. A related measure could be constructed using the formalism of ε-machines [ 63 ]. Φ would then be related to the H μ of the minimal ε-machine capable of reproducing the causal structure of a process, i.e. of the ε-machine that cannot be decomposed into a collection of lower H μ ε-machines. v. An elementary description of the qualia space is given by the author in [ 9 ], chapter 13. vi. While the entries in the matrix contain all the relevant informational relationships defining this space, they do not reveal necessarily how the space is organized in an economical and explicit manner. This may be done by employing procedures akin to multidimensional scaling although, since the matrix is asymmetrical and involves high-order terms (among subsets of elements), this may not be easy. Satisfactorily mapping the phenomenological differences between modalities, submodalities and dimensions onto the structure of qualia space will require that we thoroughly characterize and understand the latter. vii. Of course, sensory afferents usually play a role in determining which particular conscious experience we have at any given time (they better do so, if experience is to have an adaptive relationship to the environment). Nevertheless, particular experiences can be triggered even when we are disconnected from the environment, as in dreams. viii. Note also that a "pure" sensation of blue defines a point in this N-dimensional qualia space as much as the experience of a busy city street, full of different objects, of sounds, smells, associations, and reflections defines another point. ix. However, certain areas such as the posterior cingulate cortex and precuneus, some lateral parietal areas, and associated paramedian thalamic nuclei, may constitute strategic crossroads for coordinating the interactions among different sensory maps and frames of reference concerning the body and the environment. Bilateral lesions to such areas may lead to a virtual breakdown of information integration in the thalamocortical system [ 22 , 24 ]. A global, persistent disruption of consciousness can also be produced by focal lesions of paramedian mesodiencephalic structures, which include the intralaminar thalamic nuclei. Most likely, such focal lesions are catastrophic because the strategic location and connectivity of paramedian structures ensure that distributed cortico-thalamic loops can work together as a system. x. Statements about the lack of direct contributions to consciousness of basal ganglia loops need to be qualified due to the difficulty of evaluating the precise effects of their selective inactivation, as well as to the unreliability of introspective assessments about the richness of one's experience, especially after brain lesions. Similar considerations apply to brain structures not discussed here, such as the claustrum, the amygdala, and the basal forebrain. xi. A similar kind of analysis could be applied to other neurological disconnection syndromes. xii. An explanation in terms of reduced degrees of freedom may also apply to loss of consciousness in absence and other seizures, during which neural activity is extremely high and near-synchronous over many cortical regions (Tononi, unpublished results). xiii. While we do not yet have such a tight case for the neural correlate of blue, we are close to it with motion sensitive cells in area MT and in somatosensory cortex, at least in monkeys [ 64 ]. xv. In this sense, a particular conscious experience, its meaning, and the underlying informational relationships within a complex end up being one and the same thing. Such internalistic, relationally defined meanings generally relate to and ultimately derive from entities in the world. To the extent that the brain has a long evolutionary history and is shaped by experience, it is clear that internally specified meanings (and conscious states) bear an adaptive relationship to what is out there.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC543470.xml

515178

The effect of hypertensive disorders in pregnancy on small for gestational age and stillbirth: a population based study

Background Hypertensive disorders in pregnancy are leading causes of maternal, fetal and neonatal morbidity and mortality worldwide. However, studies attempting to quantify the effect of hypertension on adverse perinatal outcomes have been mostly conducted in tertiary centres. This population-based study explored the frequency of hypertensive disorders in pregnancy and the associated increase in small for gestational age (SGA) and stillbirth. Methods We used information on all pregnant women and births, in the Canadian province of Nova Scotia, between 1988 and 2000. Pregnancies were excluded if delivery occurred < 20 weeks, if birthweight was < 500 grams, if there was a high-order multiple pregnancy (greater than twin gestation), or a major fetal anomaly. Results The study population included 135,466 pregnancies. Of these, 7.7% had mild pregnancy-induced hypertension (PIH), 1.3% had severe PIH, 0.2% had HELLP (hemolysis, elevated liver enzymes, low platelets), 0.02% had eclampsia, 0.6% had chronic hypertension, and 0.4% had chronic hypertension with superimposed PIH. Women with any hypertension in pregnancy were 1.6 (95% CI 1.5–1.6) times more likely to have a live birth with SGA and 1.4 (95% CI 1.1–1.8) times more likely to have a stillbirth as compared with normotensive women. Adjusted analyses showed that women with gestational hypertension without proteinuria (mild PIH) and with proteinuria (severe PIH, HELLP, or eclampsia) were more likely to have infants with SGA (RR 1.5, 95% CI 1.4–1.6 and RR 3.2, 95% CI 2.8–3.6, respectively). Women with pre-existing hypertension were also more likely to give birth to an infant with SGA (RR 2.5, 95% CI 2.2–3.0) or to have a stillbirth (RR 3.2, 95% CI 1.9–5.4). Conclusions This large, population-based study confirms and quantifies the magnitude of the excess risk of small for gestational age and stillbirth among births to women with hypertensive disease in pregnancy.

Background Hypertensive disorders in pregnancy complicate approximately 10–16% of pregnancies and are leading causes of maternal, fetal and neonatal morbidity and mortality worldwide [ 1 - 3 ]. Definitions, classifications, assessment and management of hypertensive disorders vary considerably in the literature and from country to country [ 4 ]: thus, it is difficult to compare results from different studies. Past studies have attempted to quantify the effect of hypertension on adverse perinatal outcomes. To date, the majority of study designs have included retrospective and prospective cohort studies [ 5 - 9 , 12 - 20 ], as well as randomized-controlled trials that assessed the impact of antihypertensive medication on maternal and perinatal outcomes[ 10 , 11 ]. For the most part, these studies have been concentrated in tertiary referral centres, and suggest that hypertension in pregnancy leads to an increased risk of small for gestational age (SGA) and preterm birth. We, therefore, carried out a population-based study to quantify the frequency of hypertensive disorders in pregnancy and also the excess risk of SGA and stillbirth that is associated with this pregnancy complication. We investigated the way in which SGA and stillbirth were modified by other factors that also have a serious influence on SGA and stillbirth, for example, whether a twin gestation modifies the effect of hypertension on SGA, or whether smoking modifies the effect of hypertension on stillbirth. Methods Population The study population included all births to residents of the province of Nova Scotia, Canada between 1988 and 2000. Information on these births was obtained from the Nova Scotia Atlee Perinatal Database. The Nova Scotia Atlee Perinatal Database includes several hundred variables containing maternal and newborn information, such as demographic variables, procedures, interventions, maternal and newborn diagnoses and morbidity and mortality information for every pregnancy and birth occurring in Nova Scotia hospitals and to Nova Scotia residents since 1988. Pregnancies were excluded if delivery occurred < 20 weeks, if birthweight was < 500 g, if there was a higher order pregnancy (greater than twin gestation), or a known major fetal anomaly. Information in the database on the type of hypertensive disorder is directly coded from medical charts. The diagnosis of hypertensive disorders in pregnancy was made if it occurred in the antepartum or postpartum period. Mild pregnancy induced hypertension (PIH) included physician-diagnosed mild pregnancy induced hypertension if in the chart, transient hypertension or a diastolic blood pressure exceeding 90 mmHg on two or more occasions in a 24-hour period. Severe pregnancy induced hypertension included physician-diagnosed severe pregnancy induced hypertension, diastolic blood pressure ≥ 110 mm Hg on at least two occasions within a 6-hour period, if magnesium sulfate was administered for seizure prophylaxis, if there was ≥ 2+ proteinuria, low platelets (<100,000), and/or elevated liver enzymes (ALT > 35 u/L, AST > 30 u/L and/or LDH > 670 u/L). HELLP syndrome (hemolysis, elevated liver enzymes, low platelets) included physician-diagnosed HELLP. Eclampsia included physician-diagnosed eclampsia or one or more convulsions not attributable to other cerebral conditions such as epilepsy or cerebral hemorrhage in a patient with hypertension. Chronic hypertensive disease included a history of hypertensive disease when not pregnant, prior to current pregnancy or prior to 20 weeks of the current pregnancy. Pregnancy induced hypertension superimposed on chronic hypertension included physician-diagnosed pregnancy induced hypertension superimposed on chronic hypertension or if there was hemolysis, elevated liver enzymes or low platelets. We first examined the maternal characteristics of study subjects with hypertension in pregnancy. For this analysis, hypertensive disorders in pregnancy were defined according to Nova Scotia Atlee Perinatal Database definitions: mild PIH, severe PIH, HELLP, eclampsia, chronic hypertension and chronic hypertension with superimposed PIH. Maternal characteristics which were considered included age, marital status, parity, prepregnancy weight, pregnancy weight gain, administration of antenatal steroids, smoking, drug abuse, the presence of anemia (Hgb < 10 gm%), gestational diabetes (two abnormal values on a glucose tolerance test in pregnancy or if insulin was administered for the first time in pregnancy), pre-existing diabetes, and twin gestation. For the multivariate analyses, these database definitions were grouped to more closely approximate commonly used definitions such as those proposed by the Canadian Hypertensive Society and other international organizations [ 4 ]. These groups were defined as gestational hypertension without proteinuria (including the database entity mild pregnancy induced hypertension), gestational hypertension with proteinuria (including the database entities severe pregnancy induced hypertension, HELLP, and eclampsia) and pre-existing hypertension (including the database entities chronic hypertension and chronic hypertension with superimposed pregnancy induced hypertension). Only live births were considered in the analysis of small for gestational age, while all births were considered for the stillbirth analyses. Small for gestational age was defined as birthweight for gestational age that was less than the sex-specific 10 th percentile cut-off of a recently published Canadian fetal growth reference [ 21 ]. Stillbirth was defined as fetal death before birth, with gestational age ≥ 20 weeks and birthweight ≥ 500 grams. Ethical approval for the study was obtained from the Research Ethics Boards at Dalhousie University in Halifax, Nova Scotia, the Reproductive Care Program of Nova Scotia and the IWK Health Centre in Halifax, Nova Scotia. Statistical analysis Exact binomial 95% confidence intervals were calculated for rates of hypertensive disorders in pregnancy. Descriptive analyses were carried out on maternal data to ascertain the association between maternal characteristics and hypertensive disorders in pregnancy. Categorical data between hypertensive and normotensive pregnancies were compared using chi-square and Fisher's exact tests, where appropriate. Logistic regression analyses were carried out to determine the adjusted odds ratios (OR) and 95% confidence intervals (CI) expressing the relationship between any hypertensive disorder in pregnancy and groups of hypertension (i.e., gestational hypertension without proteinuria, gestational hypertension with proteinuria, pre-existing hypertension) and the two dichotomous primary outcomes (SGA and stillbirth). Backward stepwise elimination of variables was carried out to identify all significant determinants of the outcome. Modification of the effect of hypertensive disorders on SGA or stillbirth was investigated, based on clinical understanding. Variables considered to be in the causal pathway between the determinant and the outcome (e.g., SGA in the analysis of stillbirth) were not adjusted for in the model [ 22 - 24 ]. When the outcome rate was low we assumed the odds ratio was equal to the relative risk, but not in situations when the outcome rate was high (>10 percent). The significance level selected was P < .05 and all tests were two-tailed. Statistical analyses were performed using the SAS programming package for Windows (Version 8.0). Results Frequency of hypertensive disease The study population included 135,466 pregnancies. Of these, 7.7% (95% CI 7.6,7.9) had mild PIH, 1.3% (95% CI 1.3,1.4) had severe PIH, 0.2% (95% CI 0.1,0.2) had HELLP, 0.02% (95% CI 0.02,0.03) had eclampsia, 0.6% (95% CI 0.5,0.6) had chronic hypertension and 0.4% (95% CI 0.3,0.4) had chronic hypertension with superimposed PIH. The overall rate of hypertensive disease in pregnancy was 10.1% (95% CI 10.0,10.3). Maternal characteristics Table 1 summarizes the characteristics of women with hypertensive disorders in pregnancy from 1988–2000. Women with severe PIH were less likely to be married (63.5%), while women with chronic hypertension (with or without superimposed PIH) were more likely to be married (83.2% and 77.3%, respectively) compared with normotensive women (69.6%). Women with hypertensive disorders were more likely to be nulliparous (range 42.2% to 78.2%) as compared with normotensive women (41.9%). Women with hypertensive disorders in pregnancy had higher pre-pregnancy weight (range 66.8 kg to 82.0 kg) compared with normotensive women (64.4 kg). While women with PIH (mild or severe) had on average a greater weight gain in pregnancy (16.2 kg and 16.5 kg, respectively), women with chronic hypertension (with or without superimposed PIH) had a lower weight gain (12.2 kg and 13.2 kg, respectively) compared with normotensive women (14.4 kg). A smaller proportion of women with hypertensive disorders in pregnancy smoked (range 13.0% to 22.7%) as compared with normotensive women (30.7%). Women with hypertensive disorders in pregnancy were more likely to have gestational diabetes (range 4.6% to 9.1%) and pre-existing diabetes (range 0.8% to 2.5%) compared with normotensive women (2.3% and 0.3%, respectively). Twin pregnancies were more likely to be complicated by hypertensive disorders (range 2.3% to 6.4%) as compared with normotensive pregnancies (1.0%). Table 1 Characteristics of women with and without hypertensive disorders in pregnancy, Nova Scotia, 1988–2000. Variable Normotensive n = 121,760 Mild PIH n = 10,460 Severe PIH n = 1,770 HELLP n = 202 Eclampsia n = 32 Chronic HTN n = 767 Chronic HTN +PIH n = 475 Mean age in years (SD) 27.8 (5.3) 27.5 (5.4)* 26.9 (5.7)* 28.9 (5.5)* 25.6 (6.8)* 31.1 (5.0)* 30.5 (5.1)* Married (%) 84,739 (69.6) 7,530 (72.0) 1,124 (63.5)* 145 (71.8) 18 (56.3) 638 (83.2)* 367 (77.3)* Nulliparous (%) 51,057 (41.9) 6,760 (64.6)* 1,328 (75.0)* 158 (78.2)* 20 (62.5)* 324 (42.2)* 259 (54.5)* Mean prepregnancy weight in kg (SD) 64.4 (13.8) 70.6 (16.7)* 67.6 (15.6)* 66.8 (16.0)* 68.2 (16.9) 82.0 (20.9)* 81.3 (20.9)* Mean weight gain in kg (SD) 14.4 (6.0) 16.2 (6.9)* 16.5 (6.9)* 14.8 (7.0) 16.2 (6.2) 12.2 (6.7)* 13.2 (7.3)* Smokes any cigarettes (%) 35,846 (30.7) 2,157 (21.5)* 376 (22.7)* 31 (16.0)* 6 (21.4) 150 (20.4)* 59 (13.0)* Anemia (%) 3,441 (2.8) 242 (2.3)* 78 (4.3)* 15 (7.4)* 0 (0) 31 (4.1) 20 (4.2) Gestational Diabetes (%) 2,749 (2.3) 485 (4.6)* 84 (4.8)* 3 (1.5) 2 (6.3) 70 (9.1)* 34 (7.2)* Preexisting Diabetes (%) 331 (0.3) 85 (0.8)* 29 (1.6)* 4 (2.0)* 0 (0) 19 (2.5)* 9 (1.9)* Anti-hypertensive medication (%) 0 (0) 107 (1.0) 97 (5.5) 23 (11.4) 5 (15.6) 146 (19.0) 122 (25.7) Twins (%) 1,231 (1.0) 244 (2.3)* 60 (3.4)* 13 (6.4)* 0 (0) 7 (0.9) 13 (2.7)* PIH denotes pregnancy induced hypertension, HELLP denotes hemolysis, elevated liver enzymes, low platelets syndrome, HTN denotes hypertension. * Denotes hypertension categories significantly different from the normotensive category. Small for gestational age Table 2 summarizes the crude relationships between hypertension in pregnancy and SGA. There was an increased risk of SGA among infants born to women with any hypertensive disorder (RR 1.6, 95% CI 1.5,1.6) compared with infants born to women with normotensive pregnancies. There was an increased risk of SGA among infants born to women with mild PIH (RR 1.3, 95% CI 1.3,1.4), severe PIH (RR 2.5, 95% CI 2.3,2.8), HELLP (RR 3.8, 95% CI 3.2,4.5), eclampsia (RR 3.5, 95% CI 2.2,5.7), chronic hypertension (RR 1.4, 95% CI 1.1,1.6) and chronic hypertension with PIH (RR 2.2, 95% CI 1.8,2.6) compared with infants born to women with normotensive pregnancies. Table 2 Comparison of small for gestational age (SGA, <10 th percentile) rates among live births to hypertensive vs. normotensive women, Nova Scotia, 1988–2000. Total No. SGA Relative Risk 95% CI P value No. % Normotensive women 122,394 12,032 9.8 1.0 - - All hypertensive women 13,940 2,131 15.3 1.6 1.5,1.6 <0.001 Mild PIH 10,639 1,384 13.0 1.3 1.3,1.4 <0.001 Severe PIH 1,814 453 25.0 2.5 2.3,2.8 <0.001 HELLP 212 79 37.3 3.8 3.2,4.5 <0.001 Eclampsia 32 11 34.4 3.5 2.2,5.7 <0.001 Chronic Hypertension 766 102 13.3 1.4 1.1,1.6 0.002 Chronic hypertension and PIH 477 102 21.4 2.2 1.8,2.6 <0.001 PIH denotes pregnancy induced hypertension, HELLP denotes hemolysis, elevated liver enzymes, low platelets syndrome, HTN denotes hypertension, CI denotes Confidence interval. After controlling for potential confounders, women with any hypertensive disorder were 1.8 (95% CI 1.7,1.9, P < .001) times more likely to have a live birth with SGA as compared with normotensive women. Women with gestational hypertension without proteinuria were 1.5 (95% CI 1.4,1.6, P < .001) times more likely to have a live birth with SGA as compared with normotensive women. Similarly, women with gestational hypertension with proteinuria were 3.3 (95% CI 3.0,3.9, P < .001) times more likely and women with pre-existing hypertension were 2.5 (95% CI 2.1,2.9, P < .001) times more likely to have a live birth with SGA as compared with normotensive women. Stillbirth Table 3 summarizes the crude relationships between hypertension in pregnancy and stillbirth. There was an increased risk of stillbirth among women with any hypertensive disorder (RR 1.4, 95% CI 1.1,1.8) and among women with pregnancies complicated by chronic hypertension (RR 2.4, 95% CI 1.2,5.1) or chronic hypertension with superimposed PIH (RR 4.4, 95% CI 2.2,8.8), compared with normotensive pregnancies. Table 3 Comparison of stillbirth rates among all births to hypertensive vs. normotensive women, Nova Scotia, 1988–2000. Total No. Stillbirths Relative Risk 95% CI P value No. % Normotensive women 122,855 461 0.4 1.0 - - All hypertensive women 14,013 73 0.52 1.4 1.1,1.8 0.01 Mild PIH 10,683 44 0.4 1.1 0.8,1.5 0.55 Severe PIH 1,826 12 0.7 1.8 1.0,3.1 0.50 HELLP 214 2 0.9 2.5 0.6,9.9 0.19 Eclampsia 32 0 0.0 - 0.0,41.0 1.00 Chronic Hypertension 773 7 0.9 2.4 1.2,5.1 0.03 Chronic hypertension and PIH 485 8 1.7 4.4 2.2,8.8 <0.001 PIH denotes pregnancy induced hypertension, HELLP denotes hemolysis, elevated liver enzymes, low platelets syndrome, HTN denotes hypertension, CI denotes Confidence interval. After controlling for potential confounders, women with any hypertensive disorder were 1.4 (95% CI 1.1,1.8, P = .02) times more likely to have a stillbirth as compared with normotensive women (Table 4 ). Women with pre-existing hypertension were 3.2 (95% CI 1.9,5.4, P < .001) times more likely to have a stillbirth as compared with normotensive women. Table 4 Effect of hypertensive disorders in pregnancy on small for gestational age (< 10 th percentile) and stillbirth, Nova Scotia, 1988–2000. Crude Adjusted Odds ratio 95% CI P value Odds ratio 95% CI P value Small for gestational age* Normotensive women 1.0 - - 1.0 - - Hypertensive women (any type) 1.6 1.5,1.6 <0.001 1.8 1.7,1.9 <0.001 Gestational hypertension without proteinuria 1.3 1.3,1.4 <0.001 1.5 1.4,1.6 <0.001 Gestational hypertension withproteinuria 2.7 2.5,2.9 <0.001 3.3 3.0,3.9 <0.001 Pre-existing hypertension 1.7 1.5,1.9 <0.001 2.5 2.1,2.9 <0.001 Stillbirth** Normotensive women 1.0 - - 1.0 - - Hypertensive women (any type) 1.4 1.1,1.8 0.01 1.4 1.1,1.8 0.02 Gestational hypertension without proteinuria 1.1 0.8,1.5 0.55 1.1 0.8,1.5 0.60 Gestational hypertension with proteinuria 1.8 1.0,3.1 0.03 1.6 0.9,2.9 0.08 Pre-existing hypertension 3.2 1.9,5.3 <0.001 3.2 1.9,5.4 <0.001 CI denotes Confidence interval. * Adjusted for smoking, maternal age, gestational diabetes, pre-existing diabetes, maternal anemia, nulliparity, marital status, drug abuse, prepregnancy weight, weight gain, antenatal steroids, twins and infant sex. ** Adjusted for smoking, maternal autoantibodies, maternal age, pre-existing diabetes, maternal anemia,prepregnancy weight and twins. Twin pregnancy modified the effect of gestational hypertension with or without proteinuria on small for gestational age (Table 5 ). A woman with a singleton pregnancy who had gestational hypertension without proteinuria had a 1.5 fold increase in risk of SGA compared with a woman with a normotensive singleton pregnancy. A woman with a twin pregnancy had a 4.7 fold increase in risk of SGA, but the woman with gestational hypertension without proteinuria and a twin pregnancy had a less than expected increase in risk of SGA (1.5 × 4.7 × 0.7 = 4.9 as opposed to 1.5 × 4.7 = 7.1) i.e., the combined effect of a twin pregnancy and gestational hypertension without proteinuria was less than what would be expected under a multiplicative (logistic) model. Similarly, a woman with gestational hypertension with proteinuria and a twin pregnancy had a less than expected increase in risk of SGA (5.2 compared to 17.4). A woman with pre-existing hypertension who smoked had a less than expected increase in risk of SGA (4.4 compared to 7.3). A woman with gestational hypertension without proteinuria and who smoked had a greater than expected increase in risk of stillbirth (2.8 compared to 1.1). Table 5 Effect of hypertensive disorders in pregnancy on small for gestational age (< 10 th percentile) and stillbirth, with modeling of effect-modification by twin pregnancy and maternal smoking, Nova Scotia, 1988–2000. Adjusted Odds ratio 95% CI P value Small for gestational age Normotensive women 1.0 - - Gestational hypertension without proteinuria 1.5 1.4,1.6 <0.001 Gestational hypertension with proteinuria 3.7 3.3,4.1 <0.001 Pre-existing hypertension 2.5 2.1,3.0 <0.001 Twins 4.7 4.3,5.2 <0.001 Gestational hypertension without proteinuria × twins 0.7 0.6,0.9 0.01 Gestational hypertension with proteinuria × twins 0.3 0.2,0.4 <0.001 Pre-existing hypertension × twins 0.7 0.3,1.4 0.29 Smoking 2.9 2.8,3.0 <0.001 Gestational hypertension without proteinuria × smoking 1.0 0.9,1.2 0.96 Gestational hypertension with proteinuria × smoking 0.9 0.7,1.1 0.28 Pre-existing hypertension × smoking 0.6 0.4,0.8 0.002 Stillbirth Normotensive women 1.0 - - Gestational hypertension without proteinuria 0.8 0.5,1.2 0.24 Gestational hypertension with proteinuria 1.4 0.6,2.9 0.42 Pre-existing hypertension 3.5 2.0,6.2 <0.001 Smoking 1.4 1.2,1.7 <0.001 Gestational hypertension without proteinuria × smoking 2.5 1.3,4.7 0.006 Gestational hypertension with proteinuria × smoking 1.8 0.4,5.3 0.33 Pre-existing hypertension × smoking 0.6 0.1,2.5 0.43 CI denotes Confidence interval.** Adjusted for smoking, maternal age, gestational diabetes, pre-existing diabetes,maternal anemia, nulliparity, marital status, drug abuse, prepregnancy weight,weight gain, antenatal steroids, twins and infant sex. ** Adjusted for smoking, maternal autoantibodies, maternal age, pre-existing diabetes, maternal anemia, prepregnancy weight and twins. Discussion This large population-based cohort study examined the magnitude of the risks of small for gestational age and stillbirth in women with hypertension in pregnancy. Hypertensive disorders in pregnancy had a significant effect on rates of SGA after adjusting for potential confounders. Pre-existing hypertension had a significant effect on stillbirth rates after adjusting for potential confounders. Modification of the effect of hypertension on SGA and stillbirth was observed among women who also had a twin pregnancy or were also smokers. The rate of hypertensive disorders in pregnancy in this population (10.1%, 95% CI 10.0,10.3) was similar to that reported in the literature (10–16%). The rate of mild PIH in this population was 7.7%, compared with 6–7% in the literature. The rate of severe PIH (1.3%), HELLP (0.2%) and eclampsia (0.02%) were lower than expected (5–6%). The rate of pre-existing hypertension (0.6%) and pre-existing hypertension with superimposed PIH (0.4%) were lower than expected (3–5% and 0.8–1.3%, respectively) [ 25 , 26 ]. These differences in rates from what is expected from the published literature may be explained by the fact that most previous studies were conducted in high-risk populations in referral hospitals. Differences between our study findings and those from other population-based studies [ 12 - 20 ] may be due to potential differences in population characteristics, and clinical practice factors including quality of diagnostic information. Our study also identified maternal characteristics previously known to be associated with hypertensive disorders in pregnancy, including nulliparity [ 25 ], older age [ 27 ], diabetes [ 28 ], twin pregnancy [ 29 ] and smoking [ 30 ]. Small for gestational age is a more appropriate measure of fetal growth than birthweight alone. Our study evaluated birthweight for gestational age and gender among live births to normotensive and hypertensive pregnancies using a recent population-based Canadian reference [ 21 ]. Women with any hypertensive disorder, gestational hypertension with or without proteinuria, and pre-existing hypertension were at a significantly higher risk of having a SGA infant (relative risk 1.8, 1.5, 3.3, and 2.5, respectively). The risk of SGA was higher among hypertensive women with a twin pregnancy. Similar patterns in risk for SGA have been seen in other populations [ 13 , 14 , 16 ]. Women with any hypertensive disorder and pre-existing hypertension were at significantly higher risk of stillbirth compared with women having normotensive pregnancies (RR 1.4 and 3.2, respectively). Similarities in risk for fetal mortality between women with gestational hypertension with or without proteinuria and women who were normotensive in pregnancy have been reported elsewhere [ 31 ]. Chance and confounding are unlikely explanations for the results of our study, because of the large study size, and adjustment for potential confounders using logistic regression. This study was not able to correct for the degree of blood pressure control, which may have an effect on fetal growth. While retrospective studies in general are limited by the reliability of data, information in the Nova Scotia Atlee Perinatal Database is of high quality. Routine data checks and edits are made at the time of data collection, and validation [ 32 ] and reabstraction studies attest to the quality of the data in this large clinical database. Our study was limited by the definitions for hypertensive disorders in pregnancy used by the Nova Scotia Atlee Perinatal Database, and while these definitions are not exactly the same as commonly used definitions, they approximate definitions proposed by the Canadian Hypertension Society and other organizations [ 4 ]. The number of comparisons carried out requires that P values associated with the results be interpreted with caution. Conclusions This population-based cohort study demonstrates that women with hypertensive disease in pregnancy are at significantly higher risk of having pregnancies complicated by small for gestational age (<10 th percentile) and stillbirth in comparison with women with normotensive pregnancies. Twin gestation and smoking were important modifiers of the effect of hypertension on SGA and stillbirth. This study allowed the quantification of risks of adverse outcomes in women with pregnancies complicated by hypertension, confirming associations in the published literature and allowing appropriate counseling and monitoring in the management of these women, as well as providing baseline risks which may be used in future intervention studies. Competing interests None declared. Authors' contributions VMA proposed the study, carried out the preliminary analyses and wrote the paper. All authors discussed the analyses, contributed to the intellectual content of the paper and approved the final version. VMA and KEM provided the maternal-fetal medicine perspective, KSJ provided the general medical and epidemiologic input, LAM provided the general and obstetrical medicine perspective, and AO provided the neonatal-perinatal perspective and epidemiologic input. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC515178.xml

509237

Accuracy of cDNA microarray methods to detect small gene expression changes induced by neuregulin on breast epithelial cells

Background cDNA microarrays are a powerful means to screen for biologically relevant gene expression changes, but are often limited by their ability to detect small changes accurately due to "noise" from random and systematic errors. While experimental designs and statistical analysis methods have been proposed to reduce these errors, few studies have tested their accuracy and ability to identify small, but biologically important, changes. Here, we have compared two cDNA microarray experimental design methods with northern blot confirmation to reveal changes in gene expression that could contribute to the early antiproliferative effects of neuregulin on MCF10AT human breast epithelial cells. Results We performed parallel experiments on identical samples using a dye-swap design with ANOVA and an experimental design that excludes systematic biases by "correcting" experimental/control hybridization ratios with control/control hybridizations on a spot-by-spot basis. We refer to this approach as the "control correction method" (CCM). Using replicate arrays, we identified a decrease in proliferation genes and an increase in differentiation genes. Using an arbitrary cut-off of 1.7-fold and p values <0.05, we identified a total of 32 differentially expressed genes, 9 with the dye-swap method, 18 with the CCM, and 5 genes with both methods. 23 of these 32 genes were subsequently verified by northern blotting. Most of these were <2-fold changes. While the dye-swap method (using either ANOVA or Bayesian analysis) detected a smaller number of genes (14–16) compared to the CCM (46), it was more accurate (89–92% vs. 75%). Compared to the northern blot results, for most genes, the microarray results underestimated the fold change, implicating the importance of detecting these small changes. Conclusions We validated two experimental design paradigms for cDNA microarray experiments capable of detecting small (<2-fold) changes in gene expression with excellent fidelity that revealed potentially important genes associated with the anti-proliferative effects of neuregulin on MCF10AT breast epithelial cells.

Background Spotted cDNA microarrays are used in high-throughput experiments that interrogate the relative expression of thousands of genes simultaneously for many biological processes with wide applications in biological and medical research. Typically in a two-dye spotted cDNA microarray experiment, two mRNA samples are transcribed into cDNAs, labeled with two different fluorescent dyes, commonly Cy3 and Cy5, and hybridized on the same slide. The relative gene expression level is then measured as a ratio of the intensities of the fluorescent dyes. However, the signal intensity of the dye, which indirectly represents the gene expression level, can be affected by many other sources of error such as dye efficiency, sample preparation, and the variability of the biological samples [ 1 , 2 ]. An important question is how to identify differentially expressed genes, some of which change only minimally (<2-fold), given many known and potentially unknown sources of variance in the microarray experiment. In order to reduce false positive rates, many published experiments use a cut-off of 2- to 3-fold [ 3 - 5 ]. This limits the ability of the microarray experiment to detect small, but biologically important changes. In fact, recent reports have shown that microarrays can significantly underestimate gene expression changes and therefore a high cut-off will miss important changes [ 6 ]. Although more sophisticated statistical methods have been proposed for single slide analysis [ 7 - 13 ], it is becoming clear that in order to reduce random variance, replication becomes more and more important in microarray experimental design by greatly increasing the power of the experiment to measure small gene expression changes [ 2 , 13 - 17 ]. As a relatively new technique, many new theories have been developed for data analysis and experimental design, but few of these theories have been rigorously tested against a well-established standard method such as the Northern blot. In this paper we compared two experimental design and analysis methods performed on quadruplicate arrays that include a dye-swap design [ 18 , 19 ] and a modified reference design method that uses a control-control hybridization to correct for systematic experimental errors, that we refer to as the "control correction method" (CCM). We demonstrate that both experimental designs accurately identified small (<2-fold) gene expression changes after a 24-hour treatment of MCF10AT breast epithelial cells with the growth and differentiation factor neuregulin. These changes correlate well with the anti-proliferative effects of neuregulin resulting in a relative decrease in proliferative genes and increase in anti-proliferative genes that will be important for future investigations. Results The results presented in this paper demonstrate two, complementary cDNA microarray methods capable of reliably revealing small changes in gene expression in transformed human breast epithelial MCF10AT cells after treatment with neuregulin. Since, as shown in Fig. 1 , treatment of these cells with neuregulin significantly slows their growth rate, identifying early gene expression changes in this process will be important in understanding how neuregulin regulates cell growth in both normal and malignant breast epithelium, and will also provide both biological markers and potential targets in breast cancer. Large quantities of highly purified total RNA were isolated from MCF10AT cells treated with or without neuregulin for 24 hours and used both for microarray experiments and northern blot confirmation studies. Experimental designs to address systematic errors As with most experimental methods, replicate measurements can reduce random errors. Equally important are systematic errors. Systematic errors result from a constant tendency to over- and under-estimate true values and cannot be eliminated by replicate analysis, since they are often highly reproducible. An example of such a systematic error is a gene-specific dye effect, also called "dye–gene" interaction [ 18 ], and is shown in Fig. 2A . For a given gene spotted in duplicate (arrows), the red signal labeling the treated sample (T) is much brighter than the green signal for the control sample (C). This was highly reproducible for both spots on the same array and between multiple arrays. One way to determine whether the apparent up-regulation of this gene is true, is to use the same control sample labeled with both red and green dyes and perform a control/control (C/C) hybridization. Fig. 2A shows that the same intense red signal is seen in the C/C hybridization as was seen in the treated/control (T/C), demonstrating that this signal is a systematic error producing a false positive gene expression change. Given the unavoidable presence of these systematic errors, methods to correct these errors are needed. One way to correct for systematic errors in microarray experiments is to take advantage of C/C hybridizations to correct the T/C hybridizations. This requires a modified reference design, which we refer to as a "control correction" design. This is different from a common reference design used previously [ 19 , 20 ]. Here, each spot of the T/C hybridization is "corrected" by the same spot from the C/C hybridization for systematic errors. A second method that will also correct for systematic errors is a "dye-swap" design [ 16 , 17 , 19 ]. The dye-swap design uses an ANOVA to calculate gene expression changes from replicate cDNA microarrays probed with T/C hybridizations performed where the dye color is swapped. Included in the ANOVA are factors to correct for systematic errors such as dye and dye-gene interactions. The "control correction" and the dye-swap designs are compared in Fig. 2B . Each of these experimental designs was performed on quadruplicate arrays. Each of these two designs required its own analysis method. While we used an analysis method that utilizes individual t-tests for each spot for the CCM, we compared both ANOVA and Bayesian analysis methods for the dye-swap design. Control correction method experimental design and results A flow chart for the control correction method is shown in Fig. 2C . All microarrays used in this study were from the same lot of 3333 gene spotted cDNA slides (similar to the commercially available NEN MicroMax 2400 slides with 933 additional genes (Alphagene Inc., Woburn, MA) where each gene was spotted in duplicate, and hybridized using an optimized, two-step hybridization protocol with either Cy3 or Cy5-labeled dendrimer complexes (Genisphere, Hatfield, PA). A key advantage of the Genisphere Dendrimer system is the need for only 3 μg of total RNA per array without the need for a potentially non-linear amplification step to boost the signal. After scanning and spot-wise local background correction (Imagene Software, Biodiscovery, CA), a log Cy5/Cy3 ratio versus log signal intensity MA plot was prepared and shown in Fig 3A [ 20 ]. Without any correction, the ratio vs. intensity plot shows a banana shape as ratios trend downward in the low intensity range. This suggests an intensity-dependent dye effect. In order to correct this and to normalize data sets between different slides, an intensity-dependent normalization procedure was performed that fits the data to a lowess curve as a function of signal intensity [ 21 ]. After normalization, the log ratios became more evenly distributed around zero (Fig. 3B ). However, despite this relatively even distribution, histograms of normalized log ratios for T/C and C/C display long tails to the left as shown in the histograms in Fig. 4A and the quantile-qauntile plots in Fig. 4B . Since there should be no treatment effects on the C/C slides, a symmetric, normal distribution would have been expected. The skewed appearances of the normalized distributions indicate additional, uncorrected systematic errors in both T/C and C/C hybridizations. "Correction" of each spot by subtracting the log (C/C) ratios from the log (T/C) ratios produces an approximately normal distribution of the log (T/C) ratios (shown on the bottom of Figs. 4A and 4B ). In addition to the systematic errors that occur on a spot-by-spot basis shown in Fig. 2A , systematic errors were found as a function of slide location, particularly at the edge of the arrays. These errors were also corrected by this method (data not shown). Yang and Dudoit proposed a within slide normalization for this type of spatial effect [ 21 ], however, one concern for within slide normalization is that if the number of genes is small in each spatial group, the assumption that there will be an equal proportion of up- and down-regulated genes may be untrue. As a final step, a t-test was performed to compare the normalized log ratios of T/C and C/C for each gene. This yields p values for each control-corrected fold change calculated as log (T/C)-log (C/C). In Fig. 5 , the average and standard deviation of gene expression ratios for the log (T/C) and log (C/C) are plotted for the genes using 1.7-fold and p < 0.05 cut-offs. This clearly demonstrates the importance of correcting each log (T/C) value with the corresponding log (C/C) control value. For example, while some log (T/C) ratios are close to zero, by using the log (C/C) as baseline, true gene expression changes above or below this were identified that would otherwise have been missed. The 1.7-fold cutoff was chosen to be within the detection range of northern blot analyses, which we felt would be the most sensitive method to confirm these small changes. A volcano plot, shown in Fig. 6A , summaries 46 differentially-regulated genes that met these criteria for the CCM. Comparison of the control correction to the dye-swap design Many have proposed that a dye-swap experimental design combined with an ANOVA will correct for systematic errors [ 17 - 19 ]. To verify this and compare the dye-swap design to the control correction design, a dye-swap experiment was performed on quadruplicate arrays using the same RNA samples and the two interconnect ANOVA model of Wolfinger et al [ 22 , 23 ]. Using this experimental design with the same cut-off values, 14 differentially expressed genes were identified and are presented as a volcano plot alongside that of the CCM (Fig. 6B ). Table 1 lists those genes that met our selection criteria, together with their fold-change, p values, and functional classifications. Only 5 genes were found in common for both methods. The genes have been broadly grouped into proliferation, differentiation, and unclassified genes in order to observe trends in the neuregulin-induced gene expression changes that could be important in regulating cell growth. A general trend showing a down-regulation of proliferation genes and up-regulation of differentiation genes was observed. This includes several oncogenes, cell cycle control and cell proliferation genes that were all down-regulated; and tumor suppressor genes, growth inhibition and differentiation genes were up-regulated. This pattern is consistent with the anti-proliferative/differentiation effects of neuregulin on MCF10AT human breast epithelial cells. Verification of microarray accuracy by northern blot analysis To confirm these gene expression changes and to determine the accuracy of each experimental method, we selected 23 genes for verification by northern blot. We chose all 5 genes detected by both methods, 6 up-regulated and 5 down-regulated genes from the control correction design, and 7 genes from the dye-swap experiment. The selection of genes was not random, as we selected a balanced complement of genes of variable intensity that were both up- and down-regulated. The probes used for northern blots were generated by PCR from clones used to spot the arrays. Each blot contained triplicate control and treated samples and was re-probed multiple times. Fig. 7 summarizes the northern blot results for these 23 genes. The band intensities were quantified, normalized to total ribosomal RNA for each gel, and averaged to produce a fold change that was compared directly to the fold change from the microarrays. In general, differential gene expression was confirmed by the northern blots for both array design methods. For the dye-swap method only 1 of 12 genes was a false positive, while 4 out of 16 genes were false positives in the control correction method. Down-regulated genes were verified more reliably in the control correction method (10/10) than up-regulated genes (2/6). All differentially expressed genes common to both methods were confirmed.. Since the ANOVA method we used can sometimes underestimate the variance, we re-analyzed our dye-swap data with a Bayesian method using a regularized t-test as implemented in Cyber-T [ 24 ]. This analysis revealed 16 differentially expressed genes using the same cut-offs, 10 of which were in common with the ANOVA method (Table 1 ). A greater number of genes were identified using the regularized t-test, and the corresponding p values for these genes were lower. Based on the previous northern blot data, 8/9 (89%) of these were confirmed. Discussion Gene expression changes in MCF10AT cells suggest a rapid anti-proliferative effect of neuregulin MCF10AT cells are a human breast epithelial cell line stably transfected with a mutant ras oncogene. These cells are pre-malignant, but can progress to invasive carcinoma [ 25 , 26 ]. Given that neuregulin can differentially affect the growth properties of different cell lines, we used the MCF10AT cell line as model system to identify genes that may be down-stream from neuregulin activation and could thus be studied further for their roles in breast cancer cells that respond differentially to neuregulin. Combining two cDNA microarray experimental design methods, we have identified genes differentially expressed by neuregulin treatment that correlated with a significant decrease in their growth rate. The pattern of expression clearly shows an anti-proliferative effect of neuregulin on the MCF10AT cells with a reduction in genes associated with proliferation such as heat shock proteins, oncogenes, cell cycle control genes, genes involved in fatty acid and sugar synthesis, transcription and translation together with an increase in differentiation genes including tumor suppressor genes, DNA damage repair genes, growth inhibition genes and differentiation genes. We further showed that these effects are biologically consistent with the rapid, anti-proliferative effects of neuregulin on cell number. Additional experiments have shown that these genes are important biological markers for the degree of malignancy in other breast epithelial cell lines that have differential proliferation responses to neuregulin (Li Q, Ahmed S, and Loeb JA, unpublished results). Both experimental designs demonstrate a high confirmation rate for small changes in gene expression One of the important tasks in microarray technology is to design experiments and develop statistical tools to obtain data efficiently and accurately to answer fundamental questions in biology. In many experiments, this requires the ability to detect small changes in gene expression with high fidelity. In this study we compared two common experimental design paradigms for cDNA microarrays and determined their accuracy by northern blot. Both methods identified small expression changes with considerable accuracy. In the control correction design, we used control hybridizations to correct for systematic errors on a spot-by-spot basis. The method is based on an assumption that systematic errors from slides made from the same lot and processed identically do not vary significantly. To minimize the possible variance of systematic errors in T/C slides and C/C slides we maintained strict experimental conditions, such as same-day sample preparation and same-day hybridization. We also used the same control samples for both the T/C and C/C hybridizations instead of using an arbitrary control sample that might be quite different in mRNA composition [ 19 ]. This results in similar spot intensities for each gene both in the treatment and the control and will minimize any differences that could be caused by the different mRNA compositions from different samples. This spot-by-spot control correction can eliminate systematic errors that cannot be corrected with slide-wise normalization. Similarly, in the dye-swap design, two different dyes are used to label the same sample, which enables the correction of dye-gene interactions in the ANOVA model. A summary of the results from this study are shown in the Venn diagrams in Fig. 8 . Using the 1.7-fold and p < 0.05 cut-offs, the overall verification rate was 75% for the CCM and 92% for the dye-swap method using ANOVA. Among the 18 confirmed expression changes, all were below 3-fold and only six were above 2-fold. Many of the expression changes below 2-fold on the microarrays underestimated the fold-change measured by northern blotting. The accuracy was not dependent on microarray spot intensity as genes with both low and high signal intensities had similar verification rates (data not shown). The confirmation rates for both methods are comparable to methods reported by Mutch (87.5%) [ 27 ] and Tusher (92%) [ 28 ]. Of particular importance in this study is our high confirmation rates for genes differentially expressed by 2-fold or less. The t-test used for the CCM and ANOVA for the dye-swap method depend on assumptions of Gaussian distributions that may or may not be present in a microarray experiment with a small number of replicates. Some efforts have been made to develop Bayesian frameworks that incorporate prior distributions in order to estimate the noise [ 24 , 29 , 30 ]. We therefore re-analyzed our dye-swap data using a "regularized" t-test [ 24 ]. Using this, we identified 16 genes that met our cut-off criteria, 10 of which were in common with the ANOVA analysis. Of those genes that we measured by northern blot analysis, 8/9 or 89% were verified. In summary, the regularized t-test revealed more genes than the ANOVA method with generally lower p values. If we eliminate the 1.7-fold cut-off, but maintain the p value <0.05, the CCM identified 493 genes, the ANOVA identified 499 genes, and the regularized t-test identified 729 differentially expressed genes (Fig. 8B ). Among these, 399 were in common between the regularized t-test and ANOVA, 248 in common between the CCM and the regularized t-test, and 188 in common between the CCM and the ANOVA. These results demonstrate that if the false-positive rate remains the same, the regularized t-test is more sensitive than the traditional ANOVA and has extensive overlap, while the CCM has the least overlap between the other methods, but identifies different genes with slightly less specificity. In our analysis, we selected genes based on their p values obtained from replicates of individual spots and did not adjust these p-values for multiple comparisons. This may be a major cause for the higher false positive rates for both of our experimental designs. For the CCM, if we apply Bonferroni correction, while we can eliminate all false positives, we would also miss a majority of the differentially expressed genes verified by Northern blotting. Therefore, if accuracy is the main purpose of a study, multiple comparison corrections should be used, while if sensitivity is the main purpose, then it should not be used with the understanding that the accuracy will be lower. Comparison of a dye-swap versus a control correction method experimental design For our experimental design, the dye-swap method had a higher confirmation rate than the control correction method. This is, in part, due to the smaller variance that results from an effective doubling of the number of treated samples in the dye-swap method compared to the control correction method. Despite the higher degree of accuracy, the dye-swap design identified fewer genes and only detected down-regulated genes, whereas the control correction identified 3-times the number of genes that were both up- and down-regulated. However the control correction method was less specific for up-regulated genes. These differences may not solely reflect methodological differences, but likely result from experimental variability produced by performing the experiments independently on different days. Nonetheless, the results presented here suggest that both methods have clear merit in their abilities to show true gene expression changes, particularly for expression changes of 2-fold or less, and for genes with low signal intensities and/or low abundance. The final decision as to which method is preferred depends on the experimental design. For example, the amount of sample and number of replicates required are important considerations both in terms of how difficult the RNA is to obtain and the number of samples that need to be compared. This also translates into the cost to perform the experiment. For instance, the dye-swap method generates a larger sample size for the same number of slides, thus producing greater significance when comparing gene expression between two samples. However this method requires a minimum of two slides and two different labeling reactions per sample. If the amount of sample is limited or population level replication is more desirable than individual sample replication, the control correction is more efficient since individual replicates for reverse dye labeling are not required and each sample can be run with only one slide. For example, to compare 6 treatment samples with a single control sample would require a minimum of 12 microarrays using the dye-swap method, whereas the minimum number of 8 arrays is possible using the control correction method; 6 for treatment samples and 2 for controls. Another common experimental design used for time course or dose response studies is the reference design. In fact, the control correction method described here is essentially a modified reference design method where the zero time or dose point is the control-control comparison. As discussed above, using a very similar control sample to correct the series will give less false positives and negatives and a more accurate absolute value of the observed change than a dissimilar, pooled reference sample. Under-estimation of fold changes by cDNA microarrrays Although our cDNA microarray results were accurate, the measured changes generally underestimated the actual changes measured by northern blots. Yuen et al. [ 6 ] similarly found that both oligonucleotide arrays (GeneChips by Affymetrix) and cDNA arrays underestimate fold changes compared to quantitative RT-PCR. The cause for this underestimation is not clear, however, it may be due to the limited dynamic range of dye signal or non-specific binding of the dye. Nonetheless, the limitations in accuracy and fold change estimation are far outweighed by the ability of microarrays to identify biologically important gene expression changes. Conclusions This study demonstrated that dye-swap and control correction experimental design paradigms for cDNA microarray experiments are capable of detecting small, biologically important changes in gene expression with excellent fidelity while revealing important down-stream anti-proliferative effects of neuregulin on breast epithelial cells for future studies. Methods cDNA microarrays Human cDNA glass microarrays, called the Alphamax Genechip, were obtained from Alphagene Inc. (Woburn, MA) containing 3333 cDNAs spotted in duplicate. The cDNAs used are identical to commercially available Micromax 2400 slides from Perkin Elmer Life Sciences (Boston, MA), most of which were derived from a human fetal brain cDNA library, with an additional 933 genes (gene list available upon request). MCF10AT cell culture – MCF10AT cells were from Dr. Robert Pauley at the Karmanos Cancer Institute (Detroit, MI). The cells were cultured in DMEM/F12 media (Invitrogen) supplemented with 5% horse serum (Invitrogen), 10 mM HEPES buffer (Invitrogen), 10 μg/ml insulin (Sigma), 20 ng/ml EGF (Upstate Biotechnology), 100 ng/ml cholera enterotoxin (CalBiochem) and 0.5 μg/ml hydrocortisone (Sigma) at 37°C in 5% CO 2 incubator. Neuregulin treatment and RNA extraction – A recombinant human NRG β1 polypeptide (amino acids 14–246) was generously provided by AMGEN (Thousand Oaks, CA). After 3 days of culture, MCF10AT cells were treated with human recombinant neuregulin β1 form for 24 hours. MCF10AT cells grown under similar conditions without neuregulin treatment were used as a control. The cells were then harvested and total RNA was extracted using Ultraspec (Biotecx laboratories). The total RNA was cleaned up by Rneasy kit (Qiagen) and quantified using a fluorescent dye binding assay, Ribogreen (Molecular Probes). RNA purity was assessed by agarose gel electrophoresis. Proliferation assays were performed by counting quadruplicate cultures plated at 5000 cells/well using a hemocytometer. Microarray hybridization – cDNA microarrays were used in a 2-step hybridization protocol that was optimized for the Genisphere dendrimer labeling method. Total RNA was reverse transcribed into cDNA containing a unique 5' primer tag, using the Genisphere 3DNA expression array detection kit. In brief, for each reaction, 3 μg of total RNA was reverse transcribed using 0.2 μM oligo-dT-Genisphere capture primer, 0.5 mM dNTP, 200 U Superscript II (Invitrogen) in 1X first strand Superscript II buffer at 42°C for 2 h. The RNA from the DNA/RNA hybrids was denatured with 0.5 M NaOH / 50 mM EDTA at 65°C for 10 min. The reaction was neutralized using 1 M Tris-HCl ph 7.5. The contents of the tube containing the NRG-treated and control cDNA were then mixed together and 3 μl linear acrylamide (Ambion) and 250 μl of 3 M ammonium acetate were added to them. cDNA was precipitated by adding 100% Ethanol and incubating at -20°C for 30 min. The cDNA was collected in a pellet by centrifugation at 13000 rpm for 15 min in a microcentrifuge. and resuspended in Alternate (formamide-containing) Hybridization buffer (Genisphere) at 65°C for 10 min and modified LNA blocker (Genisphere) with denatured Cot 1 DNA. The entire mixture was added to the pre-hybridized array (Alphamax) for hybridization at 55°C for at 36 hr. A clear increase in signal was obtained with a 36 h hybridization compared to 16 h. After hybridization, the arrays were washed with 2X SSC and 0.2% SDS at 60°C for 15 min, followed by a wash with 2X SSC and another with 0.2X SSC at room temperature. For fluorescence detection, a second hybridization with the dendrimer was optimal. 2.5 μl each of the Cy3 and Cy5 dendrimer in Hybridization Buffer (Vial 6, Genisphere kit) were mixed with denatured Cot1 DNA and differential expander and the mixture was added to the pre-hybridized slides for hybridization at 60°C for 2 hrs. The slides were washed again as described above. Microarray data analysis method Analysis of CCM experiment Arrays were scanned with a GenePix 4000 A scanner (Axon Instruments, Inc., Union City, CA). Images were quantified using ImaGene Software (Biodiscovery, Inc. Marina del Rey, CA) that uses a local background subtracted from the signal. Signals not consistently detectable (background corrected signal lower than 2 times of background standard deviation) were eliminated. We fitted loess curve to the log transformed data using the "loess" function in SAS software (SAS Institute Inc., NC) for intensity dependent normalization followed by a t-test to compare T/C with C/C ratio, gene by gene. The t-test was performed on the normalized log ratio with Welch correction for unequal variance. The control corrected fold change was calculated as: log (fold) = log(T/C)-log(C/C) Analysis of dye-swap experiment For the dye-swap method we performed the same background correction and data filtering for absent genes and log transformations. We then used a two interconnect ANOVA model [ 22 , 23 ] and Mixed Model Analysis of Microarray Data (MANMADA) to identify differentially expressed genes. First we use a normalization model for log-transformed intensity measurements: y ij = μ + A i + D j + AD ij + ε ij Where μ is the sample mean, A i is the effect of i th array, D j is the effect of dye cy3 or cy5, AD ij is array dye interaction and ε ij is random error. The residue from normalization model is then used in following gene model to find treatment effects on each gene: r ijkg = A ig + D jg + T kg Where r ijkg is the residual of each gene from the normalization model, T kg is the treatment effect (control or treated), and A ig and D jg are the array and dye effects, respectively. The expression change for each gene is thus: log (fold) = T treated - T control Northern blots 5 μg total RNA isolated from MCF10AT cells was run on a 1.3% Agarose/2.2M Formaldehyde gel as described previously [ 31 ]. Probes were prepared by PCR from the same clones used to spot the slides provided by Alphagene Inc except for AJ224442, X86779 and U62739, where clones BC011696, BI754516 and BG763631, with of over 99% identity, were used as substitutes. Probes were generated by random priming using PrimiT II kit (Stratagene) radiolabeled probes. The auto-radiographs within the linear range of the film were scanned with a flatbed scanner with transparency adapter and quantified using MetaMorph (Universal Imaging) analysis software as described previously [ 32 ]. For time course measurements, the amount of signal normalized for loading with either 18S RNA or GAPDH were plotted together after first setting 100% to the intensity of the control measurement at 48 hours and setting the lowest intensity value to 0%. Authors' contributions BY analyzed microarray data. SR carried out microarray experiment. QL conducted MCF10AT cell culture and mRNA extraction. SA carried out northern blots experiments. RK provide input on microarray experiments. SD contributed ideas to data analysis. JAL conceived and design the experiment.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC509237.xml

519024

Liver sinusoidal endothelial cell modulation upon resection and shear stress in vitro

Background Shear stress forces acting on liver sinusoidal endothelial cells following resection have been noted as a possible trigger in the early stages of hepatic regeneration. Thus, the morphology and gene expression of endothelial cells following partial hepatectomy or shear stress in vitro was studied. Results Following partial hepatectomy blood flow-to-liver mass ratio reached maximal values 24 hrs post resection. Concomitantly, large fenestrae (gaps) were noted. Exposure of liver sinusoidal endothelial cells, in vitro , to physiological laminar shear stress forces was associated with translocation of vascular endothelial cell growth factor receptor-2 (VEGFR-2) and neuropilin-1 from perinuclear and faint cytoplasmic distribution to plasma membrane and cytoskeletal localization. Under these conditions, VEGFR-2 co-stains with VE-cadherin. Unlike VEGFR-2, the nuclear localization of VEGFR-1 was not affected by shear stress. Quantification of the above receptors showed a significant increase in VEGFR-1, VEGFR-2 and neuropilin-1 mRNA following shear stress. Conclusion Our data suggest a possible relation between elevated blood flow associated with partial hepatectomy and the early events occurring thereby.

Background Following partial hepatectomy (PHx) the remaining liver is transfused by normal blood volume, thereby exposing liver sinusoidal endothelial cells (LECs) to excess hemodynamic forces. These forces have been noted as an early event leading to liver restoration in rats [ 1 - 3 ]; however, the idea that quality of the blood rather than quantity has been the accepted dogma [ 4 , 5 ]. Based on time-scale events, shear stress inflicted on liver cells precedes the expression of factors some of which are expressed within minutes. Studies conducted in recent years indicate that shear stress induced NO leads to the expression of genes participating in liver regeneration including c-fos [ 6 - 8 ]. There is evidence demonstrating that increase of c-fos in PHx or portal branch ligation models is inhibited by N-nitro-L-arginine methyl ester, which blocks NO synthase [ 8 ]. The present study was undertaken to examine the molecular and ultrastructural effects of hemodynamic forces on LECs. We have chosen to focus on vascular endothelial cell growth factor (VEGF) receptors (VEGFRs), as these are present on endothelial cells and have been demonstrated not only to have a role in liver regeneration, but also to be affected by shear stress conditions. Following PHx [ 9 ], VEGF is expressed in periportal regions demonstrating lobular heterogeneity [ 10 , 11 ]. VEGFR-1 and VEGFR-2, as well as Tie 1, Tie 2 and platelet-derived growth factor, are all shown to increase in endothelial cells following PHx [ 12 ]. We have demonstrated the stimulatory effects of both VEGF-165 and VEGF-121 on liver cell proliferation following PHx [ 13 , 14 ]. In a recent study [ 15 ], it was shown that shear stress causes the induction and translocation of VEGFR-2 to the nucleus in bovine aortic endothelial cells. In addition, it promotes the formation of a complex comprising VEGFR-2, VE-cadherin and β-catenin. It is postulated that the complex acts as a shear stress receptor, mediating signals into the cells. Here, we describe the relationship between elevated blood flow to the liver following PHx and the morphology alterations associated with lining endothelial cells. We also provide evidence demonstrating that shear stress imposed on LECs in vitro is accompanied by a significant increases in VEGFR-1, VEGFR-2 and neuropilin-1 mRNA levels. Furthermore, following shear stress both receptors alternate from perinuclear and faint cytoplamic orientation to adhere to cytoskeletal components and cell membrane. These changes coincide with the behavior of the adherence junction proteins VE-cadherin and β-catenin. Results Portal blood flow following liver hepatectomy Seventy percent of PHx is associated with cell proliferation and a gradual increase in liver mass (data not shown). Nine days post-hepatectomy close to 80% of the original liver weight was restored. PCNA labeling index peaked at 48 hrs thereby returning to preoperative values. Concomitant with liver resection an immediate increase in blood flow to the remnant liver was evident, reaching a maximum of 2.5 fold at 24 hrs (Fig. 1 ). Elevated values remained for as long as 72 hrs. Ten days following partial hepatectomy blood flow returned to normal. Values recorded earlier than 20 minutes are subject to technical difficulties; therefore, they are not presented. Figure 1 Portal blood flow in normal and 70% partially hepatectomized rats. At the designated time following partial hepatectomy rats were anesthetized and placed on a temperature controlled table. Following tracheotomy and saline infusion an ultrasound sensor was fixed to the portal vein. Blood flow was monitored by ultrasonic flowmetry. Results represent an average of 5 rats + 2 × SD. LEC Ultrastructure following partial hepatectomy The effects of partial hepatectomy and the associated shear stress developing as a result of excessive blood flow to the remnant liver were evaluated with the aid of scanning electron microscopy. Special emphasis was given to the influence of these forces on the surface of liver sinusoids and intactness of the endothelial lining. Under normal conditions, liver lobule sinusoids show an intact endothelial lining, consisting of LECs with flattened processes perforated by small fenestrae. These fenestrae measure 0.15–0.2 μm in diameter and are arranged in groups, sieve plates (Fig. 2a ). As early as ten minutes post hepatectomy, endothelial changes were already noted in the form of fused fenestrae (gaps), ranging in size between 0.3 μm and 2 μm (Fig. 2b ). These gaps were more prominent in periportal than pericentral areas (Table 1 ). Increasing values were noted in subsequent times, 24 (Fig. 2c ), 72 (Fig. 2d ) and 168 hrs (Fig. 2e ) post-surgery in both areas. Ten days after hepatectomy, the morphology of the endothelial lining (Fig. 2f ) and number of gaps returned to preoperative conditions (Table 1 ). Figure 2 Scanning electron micrographs of liver periportal sinusoids. (a) control, (b-f) following partial hepatectomy; control liver (a) demonstrates an intact fenestrated wall (arrow) and undisrupted bordering parenchymal cells (Pc). Inset depicts fenestrae (arrowhead). (b) Numerous gaps (arrow) are observed as early as ten minutes after PHx. Inset shows a detailed image of gaps (arrow) and fenestrae (arrowhead). (c) 24 hrs after PHx, gaps (arrow) are still present. Note the protruding microvilli from the underlying parenchymal cell surface (arrowhead). Small structures (*), probably platelets, could be noticed adhering to endothelial wall. 72 hrs (d) and 168 hrs (e) after PHx, depicting features similar to those seen in (c). (f) Ten days after PHx an intact endothelial lining (arrow) and fenestrae (arrowhead) could be observed. Scale bars: 2 μm; Insets: 0.5 μm. Transmission electron microscopy was used to study in great detail the above alterations. (Fig. 3 ). Control tissue showed an intact relationship between LECs and neighboring liver parenchymal cells (Fig. 3a ). The sinusoid was patent and empty; the wall of the sinusoid was composed of a thin layer of fenestrated endothelium covering the space of Disse, filled by microvilli extending from the parenchymal cell surface. These parenchymal cells contained glycogen, a few lipid vesicles, and numerous organelles in their cytoplasm (Fig. 3a ). Ten minutes after hepatectomy, many blood platelets adhered to the endothelial lining. In addition, the endothelial lining became disrupted as represented by the occurrence of gaps and microvilli, which were facing directly toward the sinusoidal lumen (Fig. 3b ). These morphological alterations were still present 24, 72 and 168 hrs after PHx. Lipid accumulation in the form of droplets could be observed in the cytoplasm of parenchymal cells 10 hrs (data not shown) after partial hepatectomy, persisting until day 3 (Fig. 3d ). To avoid any possible effect caused by the procedure and anesthetic reagents used, a sham operation was conducted (time 0). Figure 3 Transmission electron micrographs of liver periportal areas. (a), control, (b-d), after partial hepatectomy; (a) illustrates an intact histological relationship between liver sinusoidal endothelium (Ec) and neighboring liver parenchymal cells (Pc). Note the patent lumen (L). Inset depicts the intact cytoplasmic processes of endothelial cells bearing fenestrae (arrow). (b) Ten minutes after PHx, the surface area of the sinusoidal lumen (L) decreases and severe damage of the endothelial lining in the form of gaps is noted (arrow). Blood platelets (arrowhead) adhere to the damaged sinusoidal lumen. Inset shows a detailed image of blood platelets. (c) 24 hrs after PHx. Fat droplets (arrow) are evident in the cytoplasm of parenchymal cells. Gaps are still present (arrowhead). (d) 72 hrs after PHx reveals endothelial damage (arrowhead) and large fat droplets (arrow) within the parenchymal cells (compare with Figure 3c for the difference). Scale bars: 2 μm; Insets: 0.5 μm. Effect of laminar shear stress on the expression and distribution of VEGF receptors in liver endothelial cells Purified LECs grown in culture retained their characteristic sieve plates (data not shown). Following 4 hrs of incubation at 37°C and extensive washing, the cells demonstrated nuclear localization of NFκb suggesting an active state. To avoid activation, purified LECs were grown in feeding medium containing 0.25% FCS for 4 hrs, extensively washed and left for 12 hrs before further used. Under these conditions, more than 94% of the cells exhibited cytoplasmic NFκb which was re-localized to the nucleus following shear stress (data not shown). LECs displayed perinuclear and cytoplasmic localization of VEGFR-2 and neuropilin 1 (Fig. 4 ). Following exposure to shear stress conditions (10 dynes/cm 2 /15 minutes), a strong cytoplasmic presence was evident, with a clear tendency to adhere to cytoskeletal components. VEGFR-1 displayed nuclear localization, which was unchanged when shear stress was applied. Figure 4 Immunofluoresence of LECs before and after shear stress. LECs were reacted with anti VEGFR-1, VEGFR-2 and neuropilin-1 before and after exposure to laminar shear forces (10 dynes/cm 2 /15 minutes). Cy2 conjugated labeled second antibodies were used to visualize the binding of the appropriate antibody. Scale bars: 2 μm. Owing to the tendency of VE-cadherin and β-catenin to react with cytoskeletal proteins under hemodynamic forces, both were followed in LECs under static conditions and shear stress. Co-staining analysis of both suggests the formation of a complex demonstrating a strong tendency to the membrane (data not shown). Co-staining of VE-cadherin and VEGFR-2 (Fig. 5 ) exhibits similar profile, pointing to the existence of a possible complex, composed of the two proteins. Figure 5 Immunofluoresence of LECs before and after shear stress. LECs were reacted with anti VE-cadherin and VEGFR-2 alone and in conjunction before and after exposure to laminar shear forces (10 dynes/cm 2 /5 minutes). Cy2 and rhodamine (TRITC) conjugated labeled second antibodies were used to visualize the binding of the appropriate antibody. Scale bars: 2 μm. Real time RT-PCR was used to quantify the amount of mRNA of all receptors before and after shear stress (Fig. 6 ). The results shown represent pooled RNA isolated from six animals. It is evident that VEGFR-1, VEGFR-2 and neuropilin-1 levels increase following shear stress conditions. Figure 6 Real time PCR of VEGFR-1, VEGFR-2 and neuropilin-1 before and after exposure of LECs to laminar shear stress. Pooled RNA from six different experiments was isolated from LECs subjected to laminar shear stress forces (10 dynes/cm 2 /15 minutes) and used to measure mRNA levels of the respective receptors. Discussion Liver regeneration is associated with an increased expression of a diverse number of genes including immediate early genes, delayed genes, cell cycle and DNA replication and mitosis genes [ 4 , 5 ]. Some of these genes increase within moments after PHx; others increase hours post-surgery. Regardless of the timeframe, the most obvious change occurring immediately after PHx is an elevated in hemodynamic forces imposed on liver cells. Those changes are the result of an increase in the ratio of blood flow to liver weight. We documented a 2.5 fold increase in portal blood flow following 70% PHx. These changes occur immediately and persist for a number of days. Endothelial cells lining liver sinusoids are likely to be the first to sense changes in shear stress. Those cells are unique as they have no typical basal lamina. Moreover, the cells are fenestrated allowing free passage of chylomicrons, lipoproteins, hormones, growth factors and proteases [ 16 ]. The size and density of these fenestrae are affected by physical factors, such as portal pressure and shear stress, as well as soluble factors [ 17 - 20 ]. Exploring the effects of shear stress on LECs in vivo is, at the moment, beyond our reach. Therefore, the present study examines the effects of increased blood flow following PHx on the morphology of LECs. We also follow the gene expression and protein distribution in LECs exposed to controlled shear stress in vitro . These forces mimic to the best of our ability physiological conditions. Following 70% PHx an immediate ultrastructural change was noted in the form of fused fenestrae and gaps. Their number increased significantly in both periportal and pericentral areas (Fig. 2 ); yet, expressed differently in both zones (Table 1 ). This observation is not surprising in light of other zonation gradients reported for many liver functions [ 16 , 21 - 23 ], like fenestration pattern, differential expression of receptors, hepatocyte metabolism, and ECM-distribution in the space of Disse. Different high-resolution microscopic methods have shown that gaps may originate from the fusion of several fenestrae [ 24 , 25 ]. In fact, gaps along the endothelial lining have been noted when different sample preparation methods were applied [ 16 , 24 , 26 ] or be induced by hepatotoxins [ 27 ] and high-perfusion pressure [ 28 ]. In accordance with our observation, Wack et al. [ 29 ] reported a gradient behavior in porosity between periportal and pericentral areas following 70% PHx, surprisingly though the gradient described by those authors persists only at 5 minutes and 24 hrs post-surgery. In this study, diameter determinations on gaps were omitted making full comparison difficult. In our experiments, we could not detect statistical variations in the size of gaps between the two zonal areas (our unpublished data). This could be explained by the fact that the size of gaps varied between 0.3 μm and 2 μm and mean values with large standard errors were obtained, excluding therefore valuable statistical analysis. Table 1 Number of gaps along the sinusoidal endothelial lining following partial hepatectomy Time Periportal (zone 1) n gaps / 10 μm 2 Pericentral (zone 3) n gaps / 10 μm 2 Control 0.10 (0.14) 0.06 (0.12) 10 min 1.57 (0.74)* 0.33 (0.13) § 24 hrs 1.47 (0.66)* 0.47 (0.32) § 72 hrs 2.18 (0.91)* 0.84 (0.65) § 168 hrs 2.28 (0.88)* 0.79 (0.55) § 240 hrs 0.09 (0.05) 0.07 (0.05) Morphometric analysis evaluating the number of gaps per area along the sinusoidal endothelial lining, studied by SEM. Results are expressed as mean (standard deviation) and significance was determined with the Mann Whitney two-sided U-test. The symbols * and § denote significant differences between control and respective time points (p ≤ 0.05). Significant differences (p ≤ 0.05) between the number of gaps in the periportal and pericentral zones were also noted at all time points following partial hepatectomy except day 10 and control. For every group, n = 3. In our experiments (Fig. 1 ), maximal values of blood flow per mg of liver were determined at 24 hrs thereby returning to baseline levels. The inconsistency between the number of gaps and the ratio of blood flow per mg of liver tissue, at later time, points may either reflect the time lapse required for liver tissue to recover or that portal pressure is not the only factor influencing lining endothelial cells. Consistent with the increased permeability in zone 1 and zone 2 following PHx, accumulation of lipid droplets was evident 10 hrs post surgery, persisting until day three. At the completion of liver regeneration, lipid content returns to normal values [ 18 ]. Increased lipid uptake seems to correlate with the change in barrier competence presented by sinusoidal endothelial cells; however, the role it has in the regenerating liver is still to be elucidated. Given the increase in blood flow to the liver immediately after PHx, it is likely that the "damage" caused to LECs is the result of excessive shear stress to which the cells are exposed. Interestingly, injections of large volume at a short time, hydrodynamic injections [ 30 ] inflict periportal and pericentral damage in the form of large (fused) fenestra (our data to be published). Shear stress conditions can artificially be applied using the cone and plate apparatus [ 31 ]. We have chosen to limit our observation to VEGF receptors as those were shown to be expressed on endothelial cells and their level changed during liver regeneration. Owing to the fact that neuropilin-1 acts as VEGF co-receptor, we have looked at neuropilin-1 expression following shear stress as well. LECs exhibited nuclear staining of VEGFR-1. This localization was not affected by shear stress conditions. VEGFR-2 and neuropilin-1 present a similar pattern of perinuclear and faint cytoplasmic presence. Following shear stress conditions the two receptors seemed to adhere to membrane and cytoskeletal components. Neuropilin-1 is an isoform specific receptor for VEGF-165 [ 32 ], VEGF-E [ 33 ], PLGF152 [ 34 ] and VEGF-B [ 35 ]. Recent studies have demonstrated a complex dependent signaling involving VEGF-165, neuropilin-1 and VEGFR-2 [ 36 ]. Such a complex was shown to exist on the surface of endothelial cells or between tumor cells and endothelial cells. Activation of VEGFR-2 has been shown to be involved in the formation of complexes with various cytoplasmic proteins including adherence junction proteins [ 37 , 38 ]. Furthermore, nuclear translocation of VEGFR-2 along with caveolin-1 and eNOS was reported to occur following VEGF treatment [ 39 ]. Consistent with data recently presented [ 15 ], VEGFR-2 co-stains with VE-cadherin following exposure to shear stress. Our preliminary data point to the possibility of a large complex consisting of VEGFR-2, neuropilin-1 and the adherence junction proteins VE-cadherin and β-catenin; nonetheless, additional experiments need to be done before any conclusion can be reached. Coinciding with the intense staining of the above following exposure to shear stress are the increased mRNA levels of all three as detected by real time PCR. Hemodynamic forces play a major role in restructuring blood vessels by modulating endothelial structure and functions such as increased permeability to macromolecules or damage to endothelial cells [ 40 ]. Therefore, a key question in liver regeneration is how these forces imposed during the early steps following resection are translated into gene expression, DNA synthesis and cell proliferation. Shear forces dependent signaling is presumably based on cytoskeletal components, which act as a mechano-transducer. Indeed, tyrosine phosphorylation of the endothelial cell adhesion molecule PECAM-1 is observed in response to flow [ 40 ]. Conclusions In summary, the present study documents an increase in blood flow to remnant liver following PHx. This change is associated with an elevated number of endothelial cell gaps in both periportal and pericentral areas. Shear stress in vitro induces in endothelial cells membrane translocation of VEGFR-2 and neuropilin-1. It is conceivable that under shear stress conditions a complex consisting of VEGFR-2/neuropilin-1 and adhesion molecules forms. Such a complex may well be formed following the elevated blood flow associated with partial hepatectomy, playing a role in the early signals leading to liver regeneration. Methods Animals and surgical procedures Male Sprague-Dawley rats weighing 300–325 g were used. PHx was performed on 5 animals under light anesthesia by removing the right lateral and median lobes[ 41 ]. At different time intervals animals were exsanguinated, the liver removed and tissue samples were prepared for immunostaining and RNA extraction. Animals undergoing PHx and analyzed by electron microscopy were anesthetized first by Ketamine and Xylasine followed by intubation with isoflurane 1.5%. Animals received humane care according to the criteria outlined in the "Guide for the care and use of laboratory animals" NIH publication. Monitoring liver regeneration Liver regeneration was monitored using liver mass and PCNA. Liver mass was calculated by weighing the removed lobes following surgery and the regenerating liver at the indicated time point. For PCNA immunostaining, specimens were fixed in paraformaldehyde, embedded in paraffin and sliced. Sections were incubated with anti-PCNA followed by biotin conjugated secondary antibody. The binding of anti-PCNA was monitored using avidin-peroxidase and amino ethyl carbazol as a substrate (Zymed, San Francisco, CA). Blood flow Five rats were anesthetized and placed on a temperature-controlled table. Following tracheotomy and saline infusion an ultrasound sensor was fixed to the portal vein. Portal blood flow was monitored by ultrasound flowmetry and automatically recorded (Ultrasonic System Inc. model T206, Ithaca, N.Y). Preparation of liver tissue for electron microscopy Tissue samples were prepared according to standard protocols [ 16 ]. Briefly, samples were cut into 1 mm 3 blocks in 1.5% glutaraldehyde, in 0.12 M sodium cacodylate buffer. Following fixation, blocks were submerged in 1% osmium tetroxide, dehydrated in ethanol and embedded in Epon. Semithin (1 μm) sections were cut and stained with 1% toluidine blue solution. For detailed EM-study, 50–80 nm ultrathin sections were stained first with uranyl acetate and then with lead citrate. For SEM, dehydrated blocks were dried with hexamethyldisilazane and subsequently broken in liquid nitrogen, mounted on stubs and sputter coated with a thin layer of 20 nm gold [ 24 ]. Morphometric analysis was performed on randomly acquired digitized SEM images at magnifications ×5,000 or ×20,000, as previously described [ 42 ]. The UTHSCSA Image Tool 2.0 software was used to determine the number of liver sinusoidal endothelial gaps. Gaps, an empty area, a hole with a maximum diameter of ≤ 0.3 μm and ≤ 2 μm, were discriminated from fenestrae based on morphology and size [ 16 , 24 , 27 ]. For each experimental variable, 10 images in the periportal and pericentral zones (regions up to 100 μm in diameter) were randomly selected and captured at both magnifications. Three animals were tested at each time point. All experiments were repeated three times and data were expressed as mean (plus standard deviation of the mean). Isolation of liver endothelial cells (LECs) LECs were isolated using a modification of the procedure described by Braet et al. [ 42 ] and Smedsrod and Pertoft [ 43 ]. Briefly, the liver was washed and perfused through the portal vein with balanced salt solution and 0.05% collagenase A. Following excision and mincing, the cells were filtered and centrifuged. Enriched liver sinusoidal cells were then layered on a two-step percoll gradient (25/50%) and centrifuged for 20 minutes at 900 g. The intermediate, 25/50% zone is enriched with LECs and Kupffer cells. Following selective adherence of Kupffer cells, LECs were spread on collagen coated plastic slides for 4 hrs and extensively washed. Based on EM such cultures are estimated to be 95% pure. In vitro shear stress LECs grown on plastic collagen-coated slides were subjected to shear stress forces produced between a stationary base plate and a rotating cone [ 31 ]. High-level shear stress forces of 10 dynes/cm 2 were enforced for 5 or 15 minutes at which time the cells were washed and either used for immunofluorescence or RNA extraction. Immunofluorescence Cells were fixed in 2% paraformaldehyde followed by 1% triton paraformaldehyde solution. The slides were then immersed in blocking solution and stained with either anti VEGFR-1, VEGFR-2, neuropilin-1, VE-cadherin, β-catenin or NFκb. Cy2 or rhodamine (TRITC) conjugated secondary antibodies were used. RNA extraction RNA was extracted from LECs by the RNAeasy kit (Qiagen, Chatsworth, CA) according to manufacturer's protocol and treated with DNase. Real time RT-PCR RNA samples were reversed transcribed and amplified using the QuantiTect SYBR Green RT-PCR kit (Qiagen) and appropriate primers at concentrations of 90 nM to 125 nM. The one-step RT-PCR was carried out at a Rotor-Gene 2000 real time cycler (Corbett Research, Australia). The thermal cycling conditions included 95°C for 15' followed by 45 cycles of amplification at 94°C 20", 60°C 15–30", 72°C 15". Samples were monitored after elongation by SYBR Green dye binding to the amplified double stranded DNA at 72°C–78°C. All samples were amplified in duplicates and each experiment was repeated twice. Quantitation was carried out using a standard curve. The Rotor-Gene analysis software was used for the calculation of the amount of each RNA sample. Statistical analysis Significance was determined with the Mann Whitney two-sided U-test. Differences were considered significant when when p ≤ 0.05. Authors' contributions FB and GS conceived the design and coordination of the study and drafted the manuscript and assessed LEC ultrastructure, MS carried out cell isolation, shear stress experiments and immunofluorescence. MP carried out Real time PCR and participated in animal procedures and drafting the paper. SB carried out portal blood flow evaluation. NK participated in animal procedures, NR participated in design and coordination. All authors read and approved the final manuscript. Acknowledgments Israel Science Foundation 537/01, Chief Scientist's Office of the Israel Ministry of Health 5002, Mars-Pittsburgh Foundation for Medical Research 182-012, Rappaport Family Institute Fund. This research was partially supported by the "Fund for Scientific Research-Flanders" (grant N° 1.5.001.04N (F.B.)). F.B. is a postdoctoral researcher of the "Fund for Scientific Research-Flanders".

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC519024.xml

543458

Comparing functional annotation analyses with Catmap

Background Ranked gene lists from microarray experiments are usually analysed by assigning significance to predefined gene categories, e.g ., based on functional annotations. Tools performing such analyses are often restricted to a category score based on a cutoff in the ranked list and a significance calculation based on random gene permutations as null hypothesis. Results We analysed three publicly available data sets, in each of which samples were divided in two classes and genes ranked according to their correlation to class labels. We developed a program, Catmap (available for download at ), to compare different scores and null hypotheses in gene category analysis, using Gene Ontology annotations for category definition. When a cutoff-based score was used, results depended strongly on the choice of cutoff, introducing an arbitrariness in the analysis. Comparing results using random gene permutations and random sample permutations, respectively, we found that the assigned significance of a category depended strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p -values for large categories with many coexpressed genes. Conclusions In gene category analyses of ranked gene lists, a cutoff independent score is preferable. The choice of null hypothesis is very important; random gene permutations does not work well as an approximation to sample label permutations.

Background In genome-wide microarray experiments, it is possible to analyse the relevance of many different categories of genes, obtained from prior knowledge in the form of database annotations or from other experiments. These gene annotation analyses can unravel new information about pathways and cellular functions responsible for different phenotypes. Computational tools aiding in this process have recently been developed [ 1 - 8 ], most notably for annotations based on the Gene Ontology (GO) [ 9 ]. Generally, category relevance is calculated as the p -value of a score, thus being dependent on both the choice of score and the choice of null hypothesis. In microarray analyses such as clustering, which provide defined subsets of genes with no internal ranking, it is natural to base the score on the number of category genes in the relevant subset. However, ranking of genes appear in many techniques for microarray analysis, such as correlation of gene expression to target profiles [ 10 ] and scoring of genes by their ability to discriminate between experimental conditions [ 11 - 13 ]. A separation of relevant and irrelevant genes can easily be constructed from ranked gene lists by introducing a cutoff, but the choice of cutoff becomes somewhat arbitrary and information in the list is lost. Tools addressing this problem, by using rank-based scores that are independent of a rank cutoff, have adopted the Kolmogorov-Smirnov score [ 14 - 17 ], and a minimized cutoff-based p -value [ 7 , 8 ], which optimizes the cutoff for each category. The Wilcoxon rank sum [ 18 ], investigated here, serves the same purpose. To calculate a p -value for the assigned score, a set of gene lists, ranked according to a chosen null hypothesis, are needed. The simplest choice of null hypothesis is just random gene permutations, and for some rank-based scores, the p -value can then be calculated analytically, without explicitly performing the permutations. However, the random gene permutations null hypothesis assumes independence of gene expression over biological samples, and the p -value is thus a combination of the p -value of how important the category is and the p -value for the genes of the category being coexpressed. When category genes behave similarly over a wide range of experimental conditions, the coexpression does not indicate relevance of the category for the question under study. In many analyses, a more appropriate null hypothesis is therefore sample label permutations, in which a set of ranked gene lists are generated based on the gene expression correlations to randomly permuted target values of the samples. This approach accounts for correlations between category genes and gives p -values that are bounded from below by the number of possible permutations of the samples in the data set. The latter is particularly important in data sets with few samples. Despite this, publicly available tools for gene annotation analysis are restricted to gene permutations [ 1 - 8 ]. We present a program, Catmap, for gene category analysis based on ranked gene lists. The program uses either the number of genes above a cutoff or the Wilcoxon rank sum as score, and the significance of the score can be calculated from a user supplied set of ranked lists, thus allowing for sample label permutations. Furthermore, the program calculates corrections for multiple category testing, using permutation results to assess an effective number of independent categories, which enables Catmap to estimate very small multiple category p -values, that would otherwise have been computationally infeasible. The input to the program is two files and some arguments. The first file contains the biologically relevant ranked list of genes and, if needed, additional ranked gene lists drawn from the null hypothesis. The second file contains the categories and their corresponding genes. The input arguments can either be specified on the command line or in a settings file, and are as follows: 1) a choice between the cutoff score the Wilcoxon rank sum score; 2) a choice of null hypothesis, which can be either the above mentioned user-supplied ranked lists or random gene permutations; 3) the number of permutations used in multiple category testing. If zero, no multiple category testing is performed. The output of Catmap is two files. The main output file contains all the categories, one on each line ordered according to their significance. The line of a category contains the p -value, the multiple comparison p -value, the false discovery rate, the ROC area (which is a normalized way to represent the Wilcoxon rank sum), the number of genes in the category, and the 25th, 50th, and 75th percentiles of the ranks. The other output file, the companion file, contains all the categories, with all the genes and their ranks listed below. Each line contains a gene and its rank. The program can be downloaded at [ 19 ], where file format specification and example files are accessible as well. Results and discussion Comparing cutoff independent and cutoff-based score functions We analysed the breast cancer data set of van 't Veer et al . [ 13 ] with a cutoff-based score function, using different cutoffs. Table 1 presents results for 15 categories with low p -values from cutoff independent scoring, showing that the p -value depends strongly on the choice of cutoff. This is further illustrated by the very different cutoffs at which the minimized cutoff-based p -value was obtained. A table with all categories is provided as a supplement [see Additional file 2 ]. Compared to the variations between the cutoff-based alternatives, the results shown in Table 1 are in reasonable agreement for two cutoff independent p -values, using the Wilcoxon rank sum and the minimized cutoff-based p -value, respectively. The p -value based on the Wilcoxon rank sum was most often larger than the minimal cutoff-based p -value. Since the latter is biased by a minimization process, it must be interpreted as a score, rather than a p -value, thus requiring additional analyses to find statistical significance [ 7 , 8 ]. Comparing null hypotheses Using the Wilcoxon rank sum, we compared the results of different null hypotheses. Three publicly available data sets were examined [ 11 , 13 , 20 ]. As can be seen in Figure 1 , p -values based on gene permutations tend to be lower than those based on sample label permutations. For categories with small p -values, there are remarkable differences, in particular for large categories with more than 20 genes. Since the gene permutation null hypothesis assumes independent genes, we expect a GO category whose genes are uncorrelated to have roughly the same p -value under the two different null hypotheses, whereas a significant category whose genes are highly correlated will get a lower p -value using the gene permutation null hypothesis. To illustrate this coexpression effect, we picked two large categories, "carboxylic acid metabolism" and "M phase", which are encircled in Figure 1 . In the data set of van 't Veer et al . [ 13 ], "carboxylic acid metabolism" has similar p -values for the two null hypotheses, while "M phase" has a p -value of 10 -7 using gene permutations but the much higher p -value of 3 · 10 -2 using sample label permutations. As seen in Figure 2 , the most highly ranked genes of "M phase" are indeed more coexpressed than the most highly ranked genes of "carboxylic acid metabolism". In Table 2 , the ranks of categories for the different null hypotheses are compared. There are distinct differences, with only a small overlap among top ten categories. One can clearly see the tendency for the gene permutation null hypothesis to find categories with very many genes, as discussed above. A table with all categories is provided in the supplement [see Additional file 3 ]. Table 2 also shows category ranks obtained with two alternative cutoff independent score functions: the Kolmogorov-Smirnov score as used in GSEA [ 17 ] and the minimal cutoff-based p -value used in FuncAssociate [ 7 ] and iGA [ 8 ]. These two alternatives do not calculate individual p -values for categories, but ranks categories based on the chosen score. Nevertheless, they give results similar to those obtained with the Wilcoxon rank sum and gene permutation. This is expected, since the minimized p -value is calculated with gene permutations, and the score adopted in GSEA [ 17 ] ranks categories similarly to what a Kolmogorov-Smirnov p -value, based on gene permutations, would do. It should be noted that GSEA, FuncAssociate, and iGA calculate multiple hypotheses corrected p -values, but these do not change the ranking of categories. There is a possible difference (which does not reveal itself in Table 2 ) between the Kolmogorov-Smirnov score and minimized p -value score on one hand, and the Wilcoxon rank sum on the other, in the treatment of categories for which only a subset of genes have expressions correlating significantly with the question under study. The important genes being in the top of the ranked list will give the category a good score with all three score functions, provided the remaining, seemingly insignificant, genes are distributed in the ranked list as expected by random. However, if these less important genes lie higher in the list than expected by random (though not high enough to affect the Kolmogorov-Smirnov or min- p scores), the category will be considered more important by the Wilcoxon rank sum. Reversely, if the less important category genes prevail in the bottom of the list, the Wilcoxon rank sum score function will deem the category as unimportant, while the other two scores will give the category a high significance, based on the top ranked genes alone. Whether seemingly insignificant genes being ranked better or poorer than explainable by random expectations should be observed or ignored is of course a matter of taste, and a possibility is to use several score functions, that may complement each other. The differences are, however, much smaller than those related to choice of null hypothesis, as revealed in Table 2 . Multiple category testing The more categories that are being tested, the more likely it is that at least one category gets a very small p -value by chance. To better evaluate the statistical significance of the best scoring categories, we used Catmap to calculate false discovery rates and family-wise error rates by permutation tests. This also gave us an effective number of independent categories, N eff , as described in Methods. The GO contains many small categories which would be reasonable to ignore in a study aiming at biological conclusions, and they were included in Figure 1 mainly to highlight the differences between the null hypotheses. When performing multiple category testing, we restricted the study to large categories, containing more than 20 genes. We tested the 3 sub-ontologies (biological process, molecular function, and cellular component) both separately and together. As expected from the discussion above, several categories with coexpressed genes got small p multiple and small false discovery rates with random gene permutations. In contrast, when using sample label permutations, the smallest p multiple was obtained in the data set of van 't Veer et al . [ 13 ] for the biological process category "organic acid metabolism", which contained 83 genes and had p = 3 · 10 -4 and p multiple = 0.02. Interestingly, organic acid metabolism is known in the literature to be relevant for breast cancer [ 21 , 22 ]. For this data set and the biological process categories, there was a 38% false discovery rate among the top 15 categories. For all 3 sub-ontologies, the effective number of categories, N eff , was around half of the full number of categories, N . In the data set of van 't Veer et al . [ 13 ] the numbers were N eff = 83 versus N = 166 for biological process, N eff = 69 versus N = 119 for molecular function, and N eff = 22 versus N = 42 for cellular component. For all categories together the real number of large categories was N = 327 whereas N eff = 152. Using random gene permutations for the same data set and categories, we got N eff = 170. The fact that N eff for the two null hypotheses are so close is a general phenomena that we see in all our examples (data not shown). Furthermore, for all data sets and ontologies studied, N eff was approximately half of the total number of categories. If this is a general feature for GO categories, the simple Bonferroni correction would not be totally unreasonable for small p -values. Figure 3 shows that the fit with an effective number of categories was good; in the range where permutations results were available it did not deviate more than a factor of two. The example in Figure 3 was obtained with 100.000 sample label permutations, and minimal p -values were found for 1000 random gene lists. It should be noted that whenever several ranked lists are examined as part of a project, this additional source of multiple hypotheses testing should also be corrected for. An example of such a correction, for cutoff-based score functions, is presented by Corà et al . [ 23 ]. Conclusions We developed a computer program for calculating the significance of gene categories in a ranked list of genes. Corrections for multiple category testing can be performed by the program. To investigate the properties of different scores and null hypotheses, we analyzed three publicly available data sets [ 11 , 13 , 20 ]. Commonly [ 1 - 6 ], a subset of relevant genes is defined from a ranked gene list by introducing a cutoff in the list. Our results show that the obtained p -values of biologically relevant categories depend strongly on the choice of cutoff. The cutoff independent Wilcoxon rank sum score overcomes the problem, representing an alternative to the Kolmogorov-Smirnov score [ 14 - 17 ] and the minimized cutoff-based p -value [ 7 , 8 ]. The ranking of categories for the three cutoff independent scores are very similar. Though sample label permutations in many situations represent a better null hypothesis than gene permutations, available gene annotation analysis tools are restricted to the latter. Our implementation allows for both null hypotheses, and we find that both the p -values and the ranking of categories depend strongly on the choice of null hypothesis. Compared to sample label permutations, gene permutations gave much smaller p -values for large categories with many coexpressed genes. Methods Algorithm The implemented algorithm treats the categories sequentially and independently. As score function for category relevance, the program uses either the Wilcoxon rank sum or the number of genes above a given cutoff in the ranked list. The latter is implemented for method comparison and for the case of a defined subset of relevant genes, without internal ranking. For the case of the Wilcoxon rank sum, the user can supply a set of ranked lists distributed according to an appropriate null hypothesis, or request random permutations of genes as the null hypothesis. In the latter case, the significance of the score is calculated analytically by the program, using either an exact calculation by an iterative method, a Gaussian approximation, or a continuous volume approximation. The program chooses method based on a balance between accuracy and computation time. Details are presented in supplementary information [see Additional file 1 ]. For the case of the cutoff-based score function, the p -value of category relevance is determined with Fisher's exact test [ 24 ], corresponding to randomly permuted genes as null hypothesis. When N independent categories are tested simultaneously, family-wise error rate simply means calculating the probability, p multiple ( q ) = 1 - (1 - q ) N ,     (1) that at least one category has a p -value below any given number q by chance. For correlated categories, we make the assumption that the same functional form describes p multiple ( q ), with N replaced by an effective number of independent categories N eff . We find N eff by generating a number, K , of ordered lists under the null hypothesis and calculating the p -values of all categories. We fit N eff using the maximum likelihood estimation where p k is the minimal p -value for the k 'th ordered list. The false discovery rate for the j highest ranked categories is found by counting the number of p -values from K permuted lists lower than the p -value of the j :th category and divide this number with K · j . For the case of sample label permutations, when a user supplied set of ranked gene lists are used to represent the null hypothesis, the first K lists are used to find N eff and false discovery rates, and the remaining lists are used to calculate p -values for each of the K lists. Implementation The algorithm is implemented in the Perl program Catmap.pl and is released under the GNU General Public License (GPL). Catmap.pl, together with user instructions, is available for download at [ 19 ]. Public data sets Using Catmap, we analysed three publicly available data sets with gene annotations from the Gene Ontology. The data set of van 't Veer et al . [ 13 ] consists of 97 patients with primary sporadic breast cancer, of which 46 had metastases within five years following treatment. Quality filtering was performed as described in [ 13 ], and rendered about 5,000 genes which were ranked according to their absolute Pearson correlation to metastasis class. A Gene Ontology analysis of the data set has previously been performed with the 231 top genes as the subset of important genes and random gene permutations [ 25 ]. The data set of Golub et al . [ 11 ] consists of bone marrow samples from leukemia patients, 27 with AML and 11 with ALL. The published data contains expression levels for 5000 genes, which after removal of genes with no variance across samples rendered 4812 genes which were ranked according to their absolute Pearson correlation to leukemia type. The data set of Alon et al . [ 20 ] consists of 40 tumour and 22 normal colon tissue samples. The 2000 genes in the published data set were ranked according to their absolute Pearson correlation to tissue type. Gene ontology associations All genes were first mapped to corresponding UniGene clusters [ 26 ]. For the data set of Golub et al . [ 11 ] this mapping was given from chip annotation files provided by Affymetrix, whereas for the other data sets [ 13 , 20 ], the mapping was done via GenBank accession numbers. GO annotations for UniGene clusters were extracted with ACID [ 27 ], and completed by back propagating all lower level associations on the GO graph. Authors' contributions TB and MK implemented the algorithms in Catmap. All authors participated in the design of the study, prepared, read, and approved the final manuscript. Supplementary Material Additional File 2 supplement to Table 1. Click here for file Additional File 3 supplement to Table 2. Click here for file Additional File 1 p -values for the Wilcoxon rank sum score. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC543458.xml

545997

Explaining inconsistencies between data on condom use and condom sales

Background Several HIV prevention programs use data on condom sales and survey-based data on condom prevalence to monitor progress. However, such indicators are not always consistent. This paper aims to explain these inconsistencies and to assess whether the number of sex acts and the number of condoms used can be estimated from survey data. This would be useful for program managers, as it would enable estimation of the number of condoms needed for different target groups. Methods We use data from six Demographic and Health Surveys to estimate the total annual number of sex acts and number of condoms used. Estimates of the number of sex acts are based on self-reported coital frequency, the proportion reporting intercourse the previous day, and survival methods. Estimates of the number of condoms used are based on self-reported frequency of use, the proportion reporting condom use the previous day and in last intercourse. The estimated number of condoms used is then compared with reported data on condom sales and distribution. Results Analysis of data on the annual number of condoms sold and distributed to the trade reveals very erratic patterns, which reflect stock-ups at various levels in the distribution chain. Consequently, condom sales data are a very poor indicator of the level of condom use. Estimates of both the number of sexual acts and the number of condoms used vary enormously based on the estimation method used. For several surveys, the highest estimate of the annual number of condoms used is tenfold that of the lowest estimate. Conclusions Condom sales to the trade are a poor indicator of levels of condom use, and are therefore insufficient to monitor HIV prevention programs. While survey data on condom prevalence allow more detailed monitoring, converting such data to an estimated number of sex acts and condoms used is not straightforward. The estimation methods yield widely different results, and it is impossible to determine which method is most accurate. Until the reliability of these various estimation methods can be established, estimating the annual number of condoms used from survey data will not be feasible. Collecting survey data on the number of sex acts and the number of condoms used in a fixed time period may enable the calculation of more reliable estimates of the number of sex acts and condoms used.

Background Programs that promote condom use for HIV prevention typically monitor their progress through survey-based indicators, such as the percentage of the population who ever used a condom or the percentage who used a condom in their last sex act with a casual or regular partner [ 1 , 2 ]. Such information is routinely collected in national surveys, such as the Demographic and Health Surveys (DHS) and the CDC Reproductive Health Surveys [ 3 , 4 ]. In addition, HIV prevention programs often monitor the number of condoms sold and/or the number distributed free of charge. The purpose of this study is to explain inconsistencies between information on reported levels of condom use and data on the number of condoms sold and distributed. Understanding the apparent inconsistencies between sales and survey data will help clarify to what extent the concerns about condom wastage, misreporting, and other related problems are founded. It will also provide guidance for improving the monitoring of condom sales and distribution, and for improving survey questionnaires. To achieve these objectives, we use survey data from six Demographic and Health Surveys to estimate the total annual number of sex acts in a country, and the total number of condoms used in those sex acts, and compare the totals with reported data on condom sales and distribution. At least in some instances, survey information on condom use and condom sales records appear to be inconsistent [ 5 , 6 ]. For example, in some countries we observe steady increases in reported condom sales while survey indicators suggest that there has been no significant increase in the percentage of condom use in last sex across survey rounds. In Zimbabwe, sales of socially marketed Protector Plus condoms increased from 1.9 million in 1997, to 4.8 million in 1998, to 8.9 million in 1999. Data on public sector condom distribution, which we discuss later in this paper, suggest that public sector sales also increased substantially. Yet, nationally representative surveys indicate that condom use in last sex stayed constant between 1996 and 1999 at roughly 34% for males and 17% for females [ 6 , 7 ]. Similarly, in Tanzania, sales of socially marketed Salama condoms increased steadily between 1995 and 2000, as did condom distribution by the Ministry of Health. However, survey data indicate that condom use at last intercourse remained roughly constant between 1996 and 1999, for both men and women [ 5 ]. These discrepancies suggest that either the data on reported levels of condom use or the data on condom sales and distribution are inaccurate, or possibly that both are inaccurate. Inaccuracies in the number of condoms sold or distributed are likely because sales figures typically represent sales to the trade (i.e., sales to wholesalers and distributors) rather than sales to consumers. Consequently, the recorded sales numbers will include condoms that are being stocked at various levels of the distribution chain. In addition, some of the condoms that are sold and/or distributed may be wasted or smuggled to other countries. In addition to these potential problems with condom sales data, there are concerns that reported condom use in surveys may be inaccurate. For example, there are concerns that respondents may overreport condom use because they do not want to admit to the interviewer that they are engaging in risky sexual behavior. There are also concerns that condom use may be underreported because condoms are frequently used with sex workers, which stigmatizes condom use. Women may also underreport condom use because it is a male method. Some questionnaires try to overcome this by asking "The last time you had intercourse, was a condom used?" rather than "The last time you had intercourse, did you use a condom?" [ 3 ]. Methods Sources of data This study uses two types of data: data on condom sales and distribution, and survey data on self-reported condom use. We restrict our analysis to data from four countries in sub-Saharan Africa (Kenya, Tanzania, Nigeria, and Zimbabwe), largely because these countries have strong condom social marketing programs and therefore relatively good data on condom sales and distribution. In addition, Tanzania and Zimbabwe are two of the countries where discrepancies between condom distribution and condom use have been noted. Data on sales of socially marketed condoms were obtained from DKT International's Social Marketing Statistics [ 8 - 14 ], while data on donor-supplied public sector condoms were obtained from UNFPA and USAID [ 15 , 16 ]. Data on commercial condom sales are not readily available, but for recent years very rough estimates were obtained from Population Services International's MIS database [ 17 ]. As commercial sales tend to be negligible in the countries under consideration, the lack of accurate data on commercial sales is unlikely to have a significant effect on our findings. The survey data used in this study include the following Demographic and Health Surveys (DHS): Kenya (1998), Nigeria (1999), Tanzania (1996, 1999), and Zimbabwe (1994, 1999). Each of the six surveys comprises a representative sample of females aged 15–49 and of males 15–54 (note that the upper age limit varies for men, see Table 1 ). For more detailed information on the sampling methods and the data collection, we refer the reader to the DHS reports for these surveys [ 18 - 23 ]. Table 1 Data available in selected DHS surveys on frequency of intercourse and probability of condom use Country Year Sex Age range Time since last intercourse Frequency of intercourse Condom use during last intercourse Frequency of condom use Kenya 1998 Men 15–54 Women 15–49 Nigeria 1999 Men 15–64 Women 15–49 1 Tanzania 1996 Men 15–54 Women 15–49 1999 Men 15–59 Women 15–49 Zimbabwe 1994 Men 15–54 Women 15–49 1999 Men 15–54 Women 15–49 Note: 1 The age range for women in the 1999 NDHS is 10 to 49. To enhance comparability, we restricted our analysis to women aged 15 to 49. Determining the total annual number of condoms used in a population requires information on the frequency of intercourse. Unfortunately, recent sexual behavior surveys typically do not allow the quantification of the number of sex acts [ 24 ]. While some of the DHS surveys from the late 1980s and early 1990s did ask respondents about the frequency of intercourse in a fixed time interval (e.g., frequency of intercourse in the past month), such a question has not been included in recent surveys [ 25 ]. For example, the standard questionnaire for DHS surveys implemented since 1997 does not include a question on the frequency of intercourse. In the surveys included in our study, the 1994 Zimbabwe survey was the only one that included a question on the self-reported frequency of intercourse (see Table 1 ). However, the DHS surveys do ask respondents about the time since they last had intercourse [ 3 , 26 ]. Hence, our analysis estimates the total annual number of sex acts on the basis of reported data on time since last intercourse [ 5 , 27 , 28 ]. Depending on the survey, it may or may not be possible to differentiate the frequency of intercourse by partner type. Differentiation by partner type may be important, as it is believed that men who admit having a nonmarital partner are unlikely to underreport the frequency of intercourse [ 24 ]. All DHS surveys asked whether respondents used a condom in their last sex act. We use this information to estimate the probability of condom use, and, subsequently, to estimate the total annual number of condoms used in the country. General estimation procedure In theory, estimating the total number of condoms used in a population is straightforward. The estimated mean number of condoms used per sexually active person ( C ) equals the product of the frequency of intercourse, or the number of sex acts ( F ), and of the probability of condom use ( p ): C = F × p (1) The total number of condoms used ( C T ) then can be calculated by multiplying C with the proportion of individuals who are sexually active ( s ) in the population at risk and with size of the population at risk ( N ): C T = N × s × C (2) Since the frequency of intercourse and the probability of condom use are known to vary by age and marital status [ 25 , 27 , 29 - 32 ], it is advisable to estimate these coefficients separately for various subpopulations and subsequently to calculate a weighted average for the entire population. In this paper, we stratified our estimates by the respondents' age and marital status. The formula to calculate the mean annual number of condoms used per sexually active respondent is: where w a is the weight for age group a , m a and (1 - m a ) are the proportion of married and unmarried respondents in age category a , and s am and s au are the proportion of sexually actives where the subscripts am and au refer to the rates for married and unmarried respondents in age category a , respectively. We used five-year age categories, and based the age weights on the age distribution within the household file of the DHS, as no other reliable data on the age structure of the population in these countries were available (preliminary analyses with one-year age groups produced similar results). Marital status and the marital status weights were derived from the individual respondent files of the DHS. Following the DHS definition, we define marriage as formal marriage or living together. Information on current sexual activity, defined as having had sex at least once in the past year, was also obtained from the individual respondent files of the DHS. Data on the countries' population size were obtained from the 2003 World Bank World Development Indicators and are summarized in Appendix [see Additional File 1 ]. Although the above procedure is simple, data on the two main components, F and p are not readily available and need to be estimated. The following sections describe the procedures for estimating them. Methods for estimating frequency of intercourse This section describes methods to estimate frequency of intercourse. Three types of estimation methods are presented: 1) estimation based on the reported frequency of intercourse during a four-week period, 2) methods based on the proportion of respondents reporting intercourse the day before the interview, and 3) survival analyses based on the time since last intercourse. All methods follow a similar strategy: 1) Estimate the mean likelihood or frequency of intercourse for a specific time unit (e.g., for a day, one week, or four weeks) for each of the subpopulations, and 2) estimate the mean frequency of intercourse per year for the entire population by calculating a weighted average of the subpopulation results. The general formula is: where F i stands for the annual frequency of intercourse estimated by method i , f iam and f iau for the estimated mean likelihood or frequency of intercourse per time unit using method i for married and unmarried persons in age category a , respectively, and n i the number of time units for this method in a year. Some surveys asked married respondents separate questions about the time since last intercourse with the respondents' spouse and with the respondents' other partners. Such questions were included in the 1998 Kenya and 1996 Tanzania DHS surveys. For these surveys, the formula becomes: where the b subscript in F ib indicates that for married respondents marital and extramarital sex were included separately. Method F 1 When self-reported data on the frequency of intercourse during the past four weeks are available, such as in the 1994 Zimbabwe DHS survey, the annual number of sex acts can be estimated by extrapolation. Assuming the past four weeks are representative of the respondents' behavior, the mean annual number of sex acts can be estimated by multiplying this four-week frequency with 13 ( n 1 = 13). However, because few recent surveys contain this type of information, it is generally necessary to use other estimation methods. Method F 2 The frequency of intercourse can be estimated on the basis of the proportion of respondents reporting intercourse the day before the interview [ 5 ]. Assume each of a group of individuals has 104 sex acts per calendar year (i.e., two sex acts per week). Assuming one sex act per day that intercourse occurs, the probability of intercourse on any given day during the calendar year would equal 104/365, or 0.285. Hence, it is expected that, on average, 28.5% of the population will have intercourse on any given day. In other words, the proportion of the population reporting intercourse on any given day equals the daily probability of intercourse. Therefore, the annual number of sex acts can be estimated by multiplying the proportion of respondents who had intercourse the day before the interview by 365. The advantage of this method is that it is simple to calculate, and that use of data that refer to the day before the interview minimizes recall problems. The disadvantage is that the method does not take into account that some people may have more than one sex act in a day (i.e., only one of those sex acts will be counted), so that the frequency of intercourse may be slightly underestimated. In turn, the impact of this more frequent intercourse on condom use may be somewhat greater than results would indicate, as the uncounted numbers may represent commercial sex workers with a relatively high condom use. Another problem with this method is that for some surveys the percentage of respondents reporting last having intercourse the day before the survey does not appear to be reliable. For example, in the 1998 Kenya survey the percentage of respondents reporting last having sex one day before the survey was smaller than the percentage last having sex two days before the survey (4.1% vs. 8.9%). Similarly, in the 1999 Nigeria survey 0.7% reported last having intercourse one day before the survey, compared to 10.0% who reported having sex two days before the survey. In the other surveys, the percentage reporting last having sex the day before the survey is slightly higher than the percentage last having sex two days before the survey. While it is unclear why so few respondents in the Kenya and Nigeria surveys reported last having intercourse the day before the survey, the implication is that the F 2 estimates for these surveys appear to be unrealistically low. Method F 3 A third alternative is to estimate frequency of intercourse based on data on the duration since last intercourse, which is collected in all DHS surveys [ 27 , 28 ]. This group of techniques is based on the fact that mean duration between two successive acts of intercourse provides an estimate of the frequency of intercourse. The major difficulty with this approach is that the duration between two successive sex acts is a closed interval, while the available data – duration since last intercourse – is an open interval. Slaymaker and Zaba [ 28 ] deal with this inconsistency by using survival analyses with an exponential decay function. The survival analysis estimates the daily probability of intercourse. The estimated annual number of sex acts is obtained by multiplying the average daily probability of intercourse by 365. One of the main weaknesses of this approach is the assumption that daily probability of intercourse is constant and can be estimated with an exponential decay function. Since data on the actual distribution of the intervals between two successive sex acts are not available in DHS surveys, one cannot determine whether the exponential decay function provides a good fit for the data. Using a function that does not match the data well would introduce a very large error in the estimated annual number sex acts (and consequently in the estimated number of condoms used), rendering the results meaningless. Methods for estimating the probability of condom use As most DHS surveys only contain data on whether a condom was used in the respondent's last intercourse, we must assume that condom use at last sex is typical for the likelihood of condom use for a given subpopulation. Three different estimations for the likelihood of condom use are explored in this paper, two of which are based on data on condom use at last intercourse and one of which is based on the self-reported frequency of condom use. Method p 1 For surveys that collected information on the frequency of condom use, this information can also be used to estimate the probability of condom use. Unfortunately, none of the DHS surveys asked direct questions about both the number of sex acts and the number of condoms used (for an example of a survey that collects such data, see [ 33 ]. However, some DHS surveys did ask respondents how frequently they used condoms. For example, the 1994 Zimbabwe DHS first established how often respondents had sex with their spouse and other partners in the past four weeks. Next, respondents were asked, "Was a condom used on any of these occasions?" Respondents who answered that a condom was used were asked, "Was it each time or sometimes?" Hence the frequency of condom use was coded as "Yes, each time," "Yes, sometimes," or "Never." To obtain an estimate for the probability of condom use for each of these categories, we cross-tabulated this reported frequency of condom use against condom use in last intercourse. The results showed that 93% of men claiming to always use condoms reported using a condom in last intercourse. Similarly, 44% of those claiming to sometimes use condoms and 2% of those claiming to never use condoms reported that they had used a condom in last intercourse. Thus, we recoded the three categories for frequency of condom use among men as 0.93, 0.44, and 0.02. For women, the values were 0.94, 0.47, and 0.01, respectively. The probability of condom use was then calculated as the mean value for each of the sub-samples. Method p 2 The first estimate of the probability of condom use simply equals the proportion of a sub-sample (by age and marital status) who reported using a condom at last intercourse. This estimate was also used by Collumbien et al. [ 24 ]. Information on condom use in last intercourse is available in all DHS surveys. For surveys that collected data on condom use at last intercourse by partner type, such as the 1998 Kenya and 1996 Tanzania DHS surveys, taking this information into account can refine the estimate of the probability of condom use. Method p 3 An alternative measure of the probability of condom use equals the proportion of respondents who reported using a condom at last sex among those who had sex the previous day. This indicator has the advantage that it is less likely to be subject to recall errors. It also avoids the problem that condom use at last intercourse may be dependent on the time since last intercourse. However, this measure has the disadvantage that it tends to be less reliable because it is based on information from a much smaller number of observations (those reporting intercourse the day before the interview). Estimating the annual number of condoms used We estimate the annual number of condoms used by multiplying the annual number of sex acts with the probability of condom use for each of the strata by age and marital status, as described in Equation 3. Because we have three different methods to estimate the annual number of sex acts and three methods to estimate the probability of condom use, up to nine estimates of the annual number of condoms used are provided, depending on the available data. Moreover, separate estimates were calculated using data from the female and male DHS surveys, as there are known gender differences in the reported frequency of intercourse and levels of condom use [ 30 , 32 , 34 ]. Results Reported condom sales and distribution Figure 1 shows trends in the annual number of condoms sold or distributed in Kenya, Nigeria, Tanzania, and Zimbabwe. Although these statistics represent the number of condoms sold or distributed to the trade (i.e., to distributors, wholesalers, and retailers), it is often assumed that they will mimic sales to consumers, because the trade is unlikely to re-stock unless there is sufficient consumer demand. Figure 1 Annual number of condoms sold and distributed, by country Figure 1 reveals very erratic patterns in the number of condoms sold or distributed in each of the four countries. The most dramatic pattern is observed for Nigeria. The total number of condoms distributed in Nigeria increased from 13 million in 1989 to 42 million in 1990, but then declined to 14 million in 1992. Between 1992 and 1994, condom distribution increased rapidly to 83 million, and by 1995, Nigerian condom sales jumped to 227 million. However, the very next year the number of condoms distributed dropped back to 103 million and continued to decline to 68 million in 1998. In 1999, condom sales rapidly increased to 108 million. The trend in the number of condoms distributed in Kenya is equally erratic. In Kenya, the total number of condoms distributed increased rapidly from 17 million in 1989 to 39 million in 1992, to 97 million in 1995. However, from 1996 onward, the number of condoms distributed dropped dramatically, to reach only 12 million in 1998. By 1999, condom distribution jumped to 79 million. The number of condoms distributed in Tanzania and Zimbabwe is considerably lower, but also shows very large year-to-year fluctuations. It is clear that these drastic fluctuations in the number of condoms sold or distributed do not reflect real differences in the level of condom use, as this would require major changes in behavior (and behavior is known to change very slowly). Since statistics on the number of condoms sold or distributed reflect sales to the trade, not consumers, it is highly likely that the observed fluctuations in the number of condoms distributed simply reflect fluctuations in condom inventory due to a stock-up of condoms at one or more levels of the distribution system, the addition of new condom outlets, and so on. For example, data from condom distribution surveys in Kenya indicate that the percentage of retail outlets that were selling socially marketed Trust condoms increased from 25% in 1998 to 32% in 1999. Similarly, the percentage of retail outlets selling public sector condoms increased from 2% to 6%. The percentage of retail outlets selling other brands stayed constant at 3% [ 35 , 36 ]. Assuming that outlets sell only one type of condoms, the percentage of retail outlets selling any type of condom increased from 30% to 41%, which implies that that the total number of retail outlets that sell condoms may have increased by as much as 37% (= 41/30 * 100) in just one year. Such an increase in the number of retail outlets that carry condoms would require a substantial increase in the number of condoms sold to the trade in order to fill the pipeline (i.e., to supply national and regional distributors, wholesalers, and retailers). In addition, our estimates of the number of public sector condoms are not the actual number of public sector condoms distributed to the population, but rather the total number of condoms provided to each country by international donors. It is possible that many of these condoms are still stocked at Ministry of Health warehouses and similar distribution hubs, or at local health clinics. The actual number of public sector condoms that reach the hands of consumers is unknown. Therefore, the data that are available on the number of condoms that have been sold or distributed seem to provide an estimate of the total of number of condoms that were in circulation during the course of the year, rather than the number provided to consumers. In other words, the current data on the number of condoms sold or distributed provide a very poor estimate of the actual number of condoms used. For example, as shown in Figure 1 , condom distribution in Nigeria peaked at 227 million in 1995. However, condom distribution subsequently dropped to a level far below that of the period preceding the peak. This drop-off in sales to the trade between 1995 and 1997 suggests that some of the 227 million condoms sold to the trade in 1995 were not sold to consumers until 1996 or 1997, if not later. Hence, changes in condom sales do not necessarily indicate any changes in condom use. Measuring changes in the level of condom use requires either collecting data on retail sales, which is not feasible in most developing countries, or using sample surveys to measure the level of condom use. Estimated annual number of sex acts Table 2 summarizes the results of different estimates for the mean annual frequency of intercourse for both male and female samples in the six DHS surveys used. We first discuss the results from the 1994 Zimbabwe DHS survey, for which all three methods for estimating the per capita annual number of sex acts could be calculated. Hence, these data are ideal for comparing the estimate based on self-reported data, F 1 , with the two estimates based on the duration since last intercourse ( F 2 and F 3 ). Next, we discuss the results for the other surveys, for which only methods F 2 and F 3 could be estimated. Table 2 Estimated annual number of sex acts (mean number per sexually experienced respondent) Country Year Sex Marital Status N of Cases Proportion Currently Sexually Active Estimation Method Self-Reported Coital Frequency (F 1) Proportion Having Sex Previous Day (F 2) Survival Analysis, Constant Hazard (F 3) Kenya 1998 Men Unmarried 1,644 66.1% -.- 4.8 6.2 Married 1,763 98.2% -.- 22.4 16.3 All 3,407 82.7% -.- 15.8 12.4 Women Unmarried 3,034 40.3% -.- 0.9 2.6 Married 4,847 93.5% -.- 16.5 9.2 All 7,881 73.0% -.- 13.2 7.8 Nigeria 1999 Men Unmarried 1,072 42.9% -.- 0.8 5.6 Married 1,608 92.0% -.- 3.4 7.2 All 2,680 72.4% -.- 2.7 6.8 Women Unmarried 4,002 34.5% -.- 2.2 3.5 Married 5,808 82.0% -.- 6.2 4.6 All 9,810 67.8% -.- 5.6 4.5 Tanzania 1996 Men Unmarried 985 43.0% -.- 13.8 8.7 Married 1,268 92.0% -.- 51.8 7.9 All 2,256 70.6% -.- 41.6 8.1 Women Unmarried 2,715 31.2% -.- 14.2 5.3 Married 5,404 86.2% -.- 49.7 5.5 All 8,120 67.8% -.- 44.2 5.4 1999 Men Unmarried 1,544 57.6% -.- 7.0 5.0 Married 1,998 98.1% -.- 48.9 15.6 All 3,542 80.5% -.- 35.9 12.3 Women Unmarried 1,421 47.0% -.- 7.7 3.6 Married 2,608 96.7% -.- 48.5 10.2 All 4,029 79.2% -.- 39.9 8.9 Zimbabwe 1994 Men Unmarried 1,126 53.3% 20.9 8.4 4.2 Married 1,015 99.3% 81.9 60.9 17.0 All 2,141 75.1% 59.4 41.6 12.3 Women Unmarried 2,349 36.5% 9.3 9.3 2.7 Married 3,777 94.9% 82.2 70.3 9.7 All 6,128 72.5% 68.1 58.5 8.3 1999 Men Unmarried 1,406 48.1% -.- 7.9 3.6 Married 1,203 99.4% -.- 57.9 23.3 All 2,609 71.8% -.- 40.4 16.4 Women Unmarried 2,354 38.9% -.- 2.4 2.4 Married 3,553 99.0% -.- 43.7 13.8 All 5,907 75.0% -.- 35.1 11.4 The results from the 1994 Zimbabwe survey show that the three estimation methods yield very different estimates of the annual number of sex acts. Estimates based on the self-reported number of sex acts in the past four weeks ( F 1 ) give the highest estimates. Using this method, it is estimated that in 1994, sexually active unmarried males in Zimbabwe had 21 sex acts per year, while sexually active married men had 82 sex acts per year. For females, the number of sex acts is estimated at 9 per year for unmarried females and 82 for married females. This latter finding is fairly consistent with Brown (2002), who estimated the coital frequency for sexually active married women at 7.9 acts per month, which translates into 95 acts per year. The second estimation method ( F 2 ), which is based on the proportion of respondents who reported having intercourse the day before the interview, results in an estimate of 8 sex acts per year for unmarried males, 61 for married males, 9 for unmarried females, and 59 for married females. Thus, this estimate consistently yields a lower estimate of the number of sex acts than the estimate based on the self-reported frequency of intercourse. This difference appears to be especially large for unmarried males. The third estimation method ( F 3 ), which is based on a survival analysis using the assumption of a constant hazard, yields substantially lower estimates of the per capita annual number of sex acts. For unmarried males, the annual number of sex acts is estimated at only 4, while for married males it is estimated at 17. For females, the corresponding numbers are 3 and 10 per year, respectively. These estimates do not appear to be realistic. For all other surveys examined here, we can also compare the estimates based on the proportion reporting intercourse the day before the survey ( F 2 ) and those based on the survival analysis with the assumption of a constant hazard ( F 3 ). The results confirm that this latter method consistently yields very low estimates of the number of sex acts. For example, among sexually active married males, the estimate of the annual number of sex acts ranges from 7.2 coital acts per year in the 1999 Nigeria survey to 23.3 in the 1999 Zimbabwe survey. For sexually active married females, the range is from 4.6 to 13.8, again in those same surveys. In other words, the results from the survival analysis using the assumption of a constant hazard suggest that in several countries, even married couples have intercourse less than once per month. Method F2 tends to yield higher estimates of the annual number of sex acts, but for both the 1998 Kenya and 1999 Nigeria surveys these estimates are also unrealistically low. In these latter cases, the low estimates are due to the fact that the number of respondents reporting last having intercourse the day before the survey is considerably lower than the number reporting last having intercourse two days ago. The results based on the survival analyses appear unrealistic and are inconsistent with the published literature on the frequency of intercourse. For example, a study on coitus in sub-Saharan Africa estimates that the monthly coital frequency among sexually active married women ranges from 3.0 in Ghana to 8.1 for Rwanda [ 37 ], which corresponds with an annual frequency of 36 and 97 acts, respectively. Similarly, another study estimates the monthly coital frequency among married women at 6.1 act for Burundi, 3.0 for Kenya, and 5.7 for Uganda. Only Ghana has a substantially lower frequency of intercourse, at an average of 1.2 per coital acts per month [ 25 ]. The same study estimates that monthly coital frequency in Latin America ranges from 3.2 in Mexico to 8.0 in Brazil. A study on sexual activity among young women in Africa estimates the average number of sex acts in the past four weeks among women aged 15–24 in Kenya at 1.9 for the never married, and at 4.0 for the married. The corresponding data for Ghana are 0.7 and 1.0, respectively [ 29 ]. Hence, there is reason to believe that the results from the survival analysis are unreliable. (It is noteworthy that the results for Nigeria are substantially lower than those for the other countries, for both F 2 and F 3 , largely because a substantially lower percentage of respondents reported having intercourse the day before they survey. Since the percentage reporting intercourse on other days is more in line with the results from the surveys in other countries, we suspect that this inconsistency is the result of a coding error.) It is important to note that the results of the survival analyses are greatly affected by the type of decay function selected. Preliminary analysis using a Weibull decay function yielded estimates of the annual number of sex acts that are roughly one and a half to two times as high as estimates based on the exponential decay function proposed by Slaymaker and Zaba [ 28 ]. Unfortunately, determining which decay function to use requires information on the distribution of the length of the interval between two successive coital acts, and such information is not available in the DHS surveys. Probability of condom use The estimates of the probability of condom use are shown in Table 3 . As before, the three estimates of the probability of condom use could be calculated only for the 1994 Zimbabwe survey. Moreover, since the self-reported frequency of condom use was coded as "each time," "sometimes," or "never," we estimated the frequency on the basis of the proportion of each of these categories who reported using a condom in last intercourse. Thus, the estimates for p 1 and p 2 are nearly identical (although some differences exist when differentiating by marital status). Table 3 Estimated probability of condom use per sex act Country Year Sex Marital Status N of Cases Estimation Method Self-Reported Frequency of Use ( p 1) Proportion Using at Last Intercourse ( p 2) Proportion Using Day Before Interview ( p 3) Kenya 1998 Men Unmarried 1,644 -.- 40.8% 40.3% Married 1,763 -.- 9.1% 4.9% All 3,407 -.- 21.1% 18.3% Women Unmarried 3,034 -.- 17.2% 0.0% Married 4,847 -.- 5.2% 3.0% All 7,881 -.- 7.7% 2.4% Nigeria 1999 Men Unmarried 1,072 -.- 39.2% 0.0% Married 1,608 -.- 6.1% 9.2% All 2,680 -.- 14.6% 6.9% Women Unmarried 4,002 -.- 22.1% 7.9% Married 5,808 -.- 2.9% 5.4% All 9,810 -.- 5.8% 5.8% Tanzania 1996 Men Unmarried 985 -.- 34.5% 15.9% Married 1,268 -.- 5.5% 2.3% All 2,256 -.- 13.3% 6.0% Women Unmarried 2,715 -.- 16.1% 6.2% Married 5,404 -.- 2.0% 1.0% All 8,120 -.- 4.2% 1.8% 1999 Men Unmarried 1,544 -.- 33.1% 23.4% Married 1,998 -.- 7.9% 3.2% All 3,542 -.- 15.7% 9.5% Women Unmarried 1,421 -.- 20.6% 7.5% Married 2,608 -.- 3.8% 3.4% All 4,029 -.- 7.3% 4.3% Zimbabwe 1994 Men Unmarried 1,126 46.0% 53.6% 35.7% Married 1,015 13.9% 12.1% 6.8% All 2,141 25.8% 27.5% 17.5% Women Unmarried 2,349 31.8% 30.7% 19.1% Married 3,777 5.6% 5.9% 5.0% All 6,128 10.7% 10.7% 7.7% 1999 Men Unmarried 1,406 -.- 65.6% 63.6% Married 1,203 -.- 8.5% 5.1% All 2,609 -.- 28.5% 25.5% Women Unmarried 2,354 -.- 32.6% 19.7% Married 3,553 -.- 4.4% 1.9% All 5,907 -.- 10.3% 5.6% When we compare the different methods to estimate the likelihood of condom use we notice that in the overwhelming number of cases the estimates based on the proportion reporting condom use at last intercourse of those who reported sex on the day before the interview ( p 3 ) are lower than those based on the data from the last sex act ( p 2 ). For example, in the 1999 Tanzania survey, the proportion who used a condom in last intercourse is 15.7% for males and 7.3% for females. By contrast, of those who had sex the day before the interview, the proportion who used a condom is only 9.5% and 4.3%, respectively. In part, these low estimates of p 3 appear to stem from the fact that only a small number of survey respondents reported having intercourse the day before the interview. Consequently, there are some age groups where none of the respondents reported using a condom (not shown), which substantially lowers the estimate of the overall probability of condom use. The results shown in Table 3 also indicate that the likelihood of having used condoms is substantially higher among unmarried than among married respondents. This finding is consistent with the literature [ 7 , 28 , 30 , 32 ] and thus confirms that our stratification by marital status was necessary, as the two groups also substantially differ in frequency of intercourse. As other authors also have noted, women tend to report a much lower likelihood of condom use than men [ 21 , 31 , 32 ]. For example, Table 3 shows that in the 1999 Zimbabwe survey 29% of men but only 10% of women reported using a condom in last intercourse. Similarly, in the 1998 Kenya survey, 21% of men but only 8% of women reported using a condom in last intercourse. These differences persist when differentiating by marital status. It is noteworthy that some gender discrepancies in the probability of condom use would be expected because African men may have sexual partners who are substantially younger. If the age difference between partners explained the gender differential in the probability of condom use, then we would expect that the probability of condom use for males aged 30–34 should be closer to that of women aged 25–29 or 20–24. Several data sets show that these probabilities are indeed closer, but the differences remain very large [ 21 , 31 ]. As most condoms are used in heterosexual sex acts, this discrepancy constitutes a serious problem when estimating overall condom use, because there is no way of verifying which of the two estimates provides the best estimate of the true probability of condom use. Estimated annual number of condoms used Table 4 shows the estimates of the total annual number of condoms used based on different combinations of estimates for the frequency of intercourse and the probability of condom use. To facilitate interpretation, the bottom panel of the table also provides the highest and lowest estimates. For comparison, we also added data on the reported number of condom sales in the survey year, and in the year prior to the survey. Table 4 Estimated annual number of condoms used Estimation Method Kenya 1998 Nigeria 1999 Tanzania 1996 Tanzania 1999 Zimbabwe 1994 Zimbabwe 1999 Frequency of Intercourse Probability of Condom Use Males F 1 Self-Reported p 1 Self-Reported -.- -.- -.- -.- 18,047,620 -.- p 2 Last Intercourse -.- -.- -.- -.- 19,451,694 -.- p 3 Previous Day -.- -.- -.- -.- 11,408,033 -.- F 2 Previous Day p 1 Self-Reported -.- -.- -.- -.- 12,209,655 -.- p 2 Last Intercourse 10,650,977 5,522,394 14,919,839 19,053,896 11,515,528 10,850,758 p 3 Previous Day 7,734,312 6,779,088 6,231,789 9,805,457 6,275,443 7,660,061 F 3 Survival Analysis p 1 Self-Reported -.- -.- -.- -.- 4,136,103 -.- p 2 Last Intercourse 10,121,645 18,858,423 4,891,365 7,493,313 3,999,271 4,468,660 p 3 Previous Day 7,221,404 10,010,100 2,439,635 3,754,680 2,324,967 3,262,927 Females F 1 Self-Reported p 1 Self-Reported -.- -.- -.- -.- 7,980,256 -.- p 2 Last Intercourse -.- -.- -.- -.- 8,406,142 -.- p 3 Previous Day -.- -.- -.- -.- 7,088,876 -.- F 2 Previous Day p 1 Self-Reported -.- -.- -.- -.- 6,913,439 -.- p 2 Last Intercourse 3,375,708 4,632,093 5,529,321 10,744,128 7,253,275 3,700,789 p 3 Previous Day 2,091,845 7,622,258 2,759,809 8,422,675 6,115,040 1,591,401 F 3 Survival Analysis p 1 Self-Reported -.- -.- -.- -.- 1,111,439 -.- p 2 Last Intercourse 2,200,502 4,503,194 993,705 2,756,648 1,137,474 1,395,517 p 3 Previous Day 986,769 5,253,132 444,480 1,994,578 914,083 647,804 Highest Estimate 10,650,977 18,858,423 14,919,839 19,053,896 19,451,694 10,850,758 Lowest Estimate 986,769 4,503,194 444,480 1,994,578 914,083 647,804 Sales, Survey Year 11,797,536 108,444,464 41,629,132 45,024,836 38,316,656 71,432,882 Sales, Previous Year 13,516,931 67,629,732 51,030,840 53,409,352 63,778,992 35,751,329 The results presented in Table 4 indicate that the methodologies yield radically different estimates of the total number of condoms used. This was anticipated, considering that our estimates of the frequency of intercourse and the probability of condom use also varied by estimation method. There are also very large differences between the estimates based on data from the female surveys and those from the male surveys. The bottom panel of Table 4 shows that the range of the estimates is very wide for all surveys. For example, in Kenya the high estimate of the total annual number of condoms used in 1998 is 10.7 million, while the low estimate is only 1.0 million. Similarly, for the 1999 Tanzania survey the highest estimate is 19.1 million while the lowest estimate is only 2.0 million. It is unknown which of the estimates is most accurate. However, as we previously noted, the p 3 estimate (which is based on condom use among those who reported having intercourse the day before the survey) appears unreliable due to the small number of cases. In addition, the survival analyses yielded unrealistically low estimates of the frequency of intercourse ( F 3 ) that appeared inconsistent with the literature. Therefore, estimates that are based on these two factors are unlikely to be reliable. Table 4 confirms that estimates based on F 3 and p 3 usually yield the lowest estimates of the total number of condoms used. When self-reported data are not available, estimates based on F 2 and p 2 are likely to be the most reliable. Data from the 1994 Zimbabwe survey confirm that the estimates based on the self-reported frequency of intercourse ( p 1 ) and the percentage who used a condom in last intercourse ( p 2 ) yield fairly similar results. This was anticipated, given that self-reported frequency of intercourse was coded as a categorical variable and subsequently quantified on the basis of the percentage who reported using a condom in last intercourse. Table 4 shows that estimates based on F 1 and F 2 are also fairly close. Nevertheless, all survey-based estimates of the annual number of condoms used are substantially lower than the reported number of condoms sold for almost every country. The only exception is Kenya, where the high estimate of the total number of condoms used based on the 1998 Kenya DHS is fairly close to the number distributed (10.7 million vs. 11.8 million). For the other surveys, the reported number of condoms sold or distributed tends to be 2.5 to 3.0 times higher than even the highest survey-based estimate of the number of condoms used. Comparison with sales data from the previous year does not resolve these differences. Conclusions The purpose of this paper was to estimate the annual number of sex acts and condoms used based on survey data, and to compare the latter with data on the annual number of condoms sold and distributed. The ability to estimate the number of sex acts from survey data would be a valuable tool for program managers, as it would enable them to estimate the number of condoms needed. Since the available data on condom sales and distribution measure the number of condoms supplied to the trade rather than to the consumer, survey estimates of the total number of condoms used could also help clarify to what extent data on the number of condoms supplied to the trade reflects actual consumer sales. Analysis of the annual reported number of condoms sold and distributed reveals very erratic patterns. The large year-to-year differences in the total number of condoms distributed clearly do not reflect differences in the number of condoms sold to consumers, nor in the level of condom use, as this would imply major changes in behavior. The latter is unlikely to have occurred, since behavior is known to change very slowly. In other words, the large fluctuations in the number of condoms provided to the trade are likely to reflect fluctuations in condom inventory at various levels in the distribution chain. Because of this, the current data on the number of condoms sold and distributed say very little, if anything, about the number of condoms sold to consumers or about actual levels of condom use. To estimate the annual number of condoms used from survey data, survey questionnaires would ideally ask respondents how often they had sex during a given reference period and how often they used a condom during that period. Considering that using very long reference periods (e.g., a year) is likely to cause recall errors, a shorter reference period is preferable. Of the DHS studies used in this paper, only one (Zimbabwe DHS-III, 1994) asked respondents about the frequency of intercourse during the four weeks preceding the survey. For the other surveys, the frequency of intercourse had to be estimated indirectly on the basis of the duration since last intercourse. Although older data on frequency of intercourse are available for some countries, such data may not provide reliable estimates of current behavior, as the HIV/AIDS crisis and other factors may have influenced sexual behavior. If future surveys are to estimate the annual number of condoms used, then questions enquiring about the total number of sex acts and the total number of sex acts in a fixed time period should be added. For example, recent surveys in Zambia asked about the number of sex acts and the number of condoms used in the past week, which can easily be extrapolated to a one-year period [ 33 ]. Asking about the timing of the last two sex acts, rather than only the very last sex act, would also be recommended. This would provide data on the duration between two successive sex acts, which will improve estimation of the total number of sex acts using survival methodologies. Knowing the distribution of the time interval between successive sex acts would also enable researchers to identify a decay function that best fits the data, which will substantially increase the accuracy of the estimates. The results of our survey analyses, which are based on DHS data currently available, show that the estimates of both the number of sexual acts and the number of condoms used vary enormously based on the estimation method used. For several surveys, the highest estimate of the annual number of condoms used is tenfold that of the lowest estimate. While some estimation methods can be disregarded because they yield results that are clearly not plausible, it is impossible to determine which of the remaining methods yield the most accurate results. Until the reliability of these various estimation methods can be established, estimating the annual number of condoms used from survey data will not be feasible. To be able to verify the reliability of the estimates of the number of condoms used, it is necessary to have accurate data on the number of condoms sold and distributed to consumers. In developing countries, such is not feasible, in part due to the lack of standardized record-keeping, and because many condoms are distributed through informal retailers, such as street venders and hawkers, who are unlikely to keep records. For the purpose of testing the feasibility of the estimation methods, it may therefore be more productive to use data from developed countries where retail-level condom sales data are available (assuming such data are not proprietary). Alternatively, it may be possible to test the reliability of the estimates in developing countries, by obtaining the relevant sales data on a smaller scale (e.g., for one district only). However, sales data have the drawback that they do not provide information about the characteristics of the consumers. Consequently, sales data are unable to provide detailed information about program impact. Competing interests The author(s) declare that they have no competing interests. Authors' contributions DM conceived of the study and drafted the manuscript. RVR developed the study design and carried out the statistical analysis. Both 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 This file contains the background data for the calculations Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545997.xml

545940

Gene expression profiling of melanoma cells – searching the haystack

Cancer is being increasingly recognized as a very heterogeneous disease, both within an individual tumor and within a tumor type and among tumor types. This heterogeneity is manifested both at the genetic and phenotypic level and determines the progression of disease and response to therapy. It is possible to see the heterogeneity in examples of differential disease progression and response to therapy of the same tumor type, as morphology does not always reveal underlying biology. The diagnosis of tumors by histopathological and morphological criteria cannot fully account for the variability seen in prognosis and therapy outcome. Here we review some recent concepts that have emerged from the genetic analysis of metastatic melanoma.

Commentary Cancer is being increasingly recognized as a very heterogeneous disease, both within an individual tumor and within a tumor type and among tumor types. This heterogeneity is manifested both at the genetic and phenotypic level and determines the progression of disease and response to therapy. It is possible to see the heterogeneity in examples of differential disease progression and response to therapy of the same tumor type, as morphology does not always reveal underlying biology. The diagnosis of tumors by histopathological and morphological criteria cannot fully account for the variability seen in prognosis and therapy outcome. A classic example of this heterogeneity is diffuse large B cell lymphoma (DLBCL) morphologically defined as one tumor type but only 40% of patients respond to treatment suggesting there are at least two distinct tumor groups. Alizadeh et. al. performed microarrays on DLCBL to assess gene expression profiles and identified several genetically distinct groups that correlated with differential survival rates [[ 1 ], reviewed in [ 2 ]]. Genome wide screening technology such as microarray offer the potential to diagnose, prognose and develop new therapeutic strategies for cancers based on grouping by genetic signature. Whereas previously, it was possible only to study one or a few genes at a time, microarray technology allows the simultaneous assessment of the expression of thousands of genes within a cell population at a single time. By looking at the full spectrum of the genetic contribution within a tumor, microarray technology has furthered our understanding of the complexity in terms of tumor subclassification. The advantage of a global gene expression analysis is that it assesses many genes within a sample at a given timepoint and allows comparison to a myriad of other samples. This has resulted in the improved classification of tumors, identification of potential new biomarkers, and detection of possible therapeutic targets as in DLBCL. In addition, the same gene expression data can be reanalyzed according to a user defined phenotype or without bias while looking for patterns in the data that correlate with a phenotype such as progression, prognosis, or treatment outcome. As data analysis and data mining become more sophisticated, the information acquired will provide scientists and clinicians with a significant improvement in correlating patient data with tumor diagnosis and enabling us to better select patient groups who will respond (or not respond) to therapy. Microarray technology has become the best hope in developing a global and accurate assessment of the tumor type and all its complexity. However, the road to achieve this goal will be long and hard because we have to learn to ask the right questions, select the appropriate patients, collect their material and then verify the initial results. Clinical pathological analysis cannot predict clinical outcome or metastatic potential of melanoma, a very heterogeneous cancer with an unpredictable progression rate. In a previous issue, Wang et. al. performed gene expression analysis on RNA from a number of human solid tumor lesions, including melanoma [ 3 ]. In their comparison they showed that it is possible to identify tumor specific gene expression profiles which can rapidly aid in tumor identification and classification. In addition, the study identified commonly expressed genes between melanoma and Renal Cell carcinoma, both known to be responsive clinically to IL-2 treatment, allowing for comparison of immunologically related genes to identify common response pathways. Gene expression profiles of melanoma lesions can also be used for prognosis by stratifying patients based on risk and thus identifying subtypes. For example, an early study by Clark et. al. assessed the gene expression differences of metastatic versus non-metastatic melanoma cell lines, identifying a metastatic profile which was linked to the small GTPase RhoC [ 4 ]. Bittner et. al. performed a more comprehensive array analysis of 31 cutaneous melanomas and identified a major cluster of melanoma samples [ 5 ]. Further, the authors were able to verify the validity of the cluster by correlating the melanomas within the cluster to reduced motility, invasive ability, and vasculogenic mimicry potential in vitro . This showed that lesions can be stratified into subtypes by gene expression analysis. Further, microarray gene expression data can be used to define responders and non-responders to known anti-cancer treatments prospectively or retrospectively. By combining clinical data with microarray data, it will be possible to predict patient response based on gene expression profile or biomarkers, which may allow for better, more targeted therapies to be selected. This new information will lead to improved treatments and prolonged survival for cancer patients. DNA microarray technology may help us understand the complex pathogenesis of melanoma and will allow us to determine the role of the different genetic profiles in determining different disease outcomes. From this we will be able to identify new biomarkers, leading to the development of more pathologically relevant models. To achieve better prediction for optimal treatment strategies, microarray studies as presented here are only the beginning of a long road, in which we need to drastically par down the markers to be tested. We need to verify and validate biomarker candidates in ways that go beyond the capacity of individual laboratories. Instead we need to establish consortia of scientists from bioinformatics and computational biology, who team up with oncologists, pathologists, and immunobiologists. Any selected biomarker requires validation in independent multi-center analyses. Once the appropriate tools and infrastructures are on hand, we can select better new treatment modalities and may realize that previously unsuccessful regimens would have shown more success, if we would have know how to select most appropriate patients. We have to start now to develop the groundwork for such multidisciplinary, multi-institutional work that will challenge us in the years to come.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545940.xml

545968

An electronic application for rapidly calculating Charlson comorbidity score

Background Uncertainty regarding comorbid illness, and ability to tolerate aggressive therapy has led to minimal enrollment of elderly cancer patients into clinical trials and often substandard treatment. Increasingly, comorbid illness scales have proven useful in identifying subgroups of elderly patients who are more likely to tolerate and benefit from aggressive therapy. Unfortunately, the use of such scales has yet to be widely integrated into either clinical practice or clinical trials research. Methods This article reviews evidence for the validity of the Charlson Comorbidity Index (CCI) in oncology and provides a Microsoft Excel (MS Excel) Macro for the rapid and accurate calculation of CCI score. The interaction of comorbidity and malignant disease and the validation of the Charlson Index in oncology are discussed. Results The CCI score is based on one year mortality data from internal medicine patients admitted to an inpatient setting and is the most widely used comorbidity index in oncology. An MS Excel Macro file was constructed for calculating the CCI score using Microsoft Visual Basic. The Macro is provided for download and dissemination. The CCI has been widely used and validated throughout the oncology literature and has demonstrated utility for most major cancers. The MS Excel CCI Macro provides a rapid method for calculating CCI score with or without age adjustments. The calculator removes difficulty in score calculation as a limitation for integration of the CCI into clinical research. The simple nature of the MS Excel CCI Macro and the CCI itself makes it ideal for integration into emerging electronic medical records systems. Conclusions The increasing elderly population and concurrent increase in oncologic disease has made understanding the interaction between age and comorbid illness on life expectancy increasingly important. The MS Excel CCI Macro provides a means of increasing the use of the CCI scale in clinical research with the ultimate goal of improving determination of optimal treatments for elderly cancer patients.

Background Comorbid illness plays an essential, but poorly defined, role in the diagnosis and management of malignant disease. Increasingly, the importance of measuring comorbidity in consistent and quantifiable ways is being recognized. This movement has stemmed in part from a growing consensus that comorbidity confounds the results of clinical trials and limits the generalization of results to older and sicker patients [ 1 , 2 ]. For various reasons, however, the widespread integration of comorbidity into clinical research has yet to be realized. It is our contention that limited accessibility and cumbersome scoring techniques are in part responsible for the limited use of comorbidity indices. We believe that easily accessible tools for calculating comorbidity can increase their use in clinical research. While multiple comorbidity indices are available, each with unique advantages and disadvantages, no single index has emerged as clearly superior to the others. In fact, we have noted that a distinct trade-off between prognostic utility and ease of use exists. We believe that a scoring system that maximizes ease of use while maintaining prognostic validity represents the optimal balance required for use in clinical research. In addition, a scoring system that can be easily integrated into an electronic medical record will further promote the widespread use of comorbidity data. We have, therefore, chosen to explore the use of the Charlson Comorbidity Index (CCI) as the prototypical comorbidity index in our department. The primary aim of this article, therefore, is to provide an electronic Charlson Comorbidity Index Scoring program and explain its development and use. In an effort to provide the reader with the context in which the electronic application was developed and should be used, the interaction of comorbidity and malignant disease and the validation of the Charlson Index in oncology are discussed. For more detailed reviews of the comorbidity indices outlined in this article, the authors refer readers to three systematic reviews of comorbid illness scoring systems by Extermann and de Groot [ 2 - 4 ]. Comorbidity and cancer In the 1960's, Feinstein initially reported the prognostic importance of patient-related characteristics, such as symptomatology and concurrent illness, in his analyses of differences between actual survival outcomes and those predicted by TNM-based staging among lung cancer patients [ 5 ]. In recent years, the direct influence of comorbid illness on treatment decision-making and survival outcomes has been documented for a variety of malignancies including bladder, lung, head and neck, colorectal, breast, and prostate cancers [ 6 - 11 ]. Hall, et al, for example, evaluated the effect of comorbidity on survival among head and neck cancer patients, concluding that 16% of mortality at 3 years and 18% at 5 years was attributable to comorbid illness alone, with non-cancer causes of death exceeding cancer-related causes of death after 7.5 years [ 6 ]. Satariano & Ragland made comparable observations in their study of breast cancer patients. In their analysis, comorbidity increased directly with age (p < 0.001) and a significant association between comorbidity and the type of treatment received (p < 0.0001) was observed. After controlling for age, cancer stage, and type of treatment, increasing comorbidity remained significantly predictive of increased all-cause-mortality (1 condition, p = 0.04; and for 2 or 3 conditions, p < 0.001) [ 7 ]. Comorbidity has also demonstrated marked predictive power for survival and treatment allocation among prostate cancer patients. A Netherlands cancer registry study, for example, identified comorbidity as the single most important prognostic factor for 3-year survival, with hazard ratios of 2.0 (95% CI = 1.0–4.3) for a single comorbid illness and 7.2 (95% CI = 3.1–16.6) for 2 or more comorbid conditions, and trends toward fewer radical prostatectomies among men with higher degrees of comorbidity [ 8 ]. Total comorbidity counts have been found to be strongly predictive of survival among colon cancer patients as well. In addition to identifying increasing comorbidity with age (p < 0.0001), Yancik, et al, found raw counts of comorbid conditions to be strongly predictive of survival when used in a model containing age group, disease stage, and gender (p = 0.0007), with risk ratios of 1.11 (95% CI 1.10–1.90) and 1.84 (95% CI 1.39–2.46) for total comorbid illness counts of 5–6 and 7–14 respectively [ 9 ]. Additional works by De Marco with colon cancer patients [ 10 ], Firat with lung cancer patients [ 11 ], and Piccirillo with head and neck cancer patients [ 12 ] provide unquestionable support for the importance of comorbidity on survival and treatment-related complications among oncology patients. Although the preceding examples are not intended to provide a comprehensive review of the influence of comorbidity on survival and treatment-related complications in oncology, they provide a clear demonstration of the effect. In addition, the themes of increasing comorbidity with age and the influence of comorbidity on outcomes and treatment decision-making are illustrated. With these interactions in mind, the investigation of comorbidity has become an area of increasing interest in our department. In particular, we have begun focusing on the use of comorbidity indices and their application in clinical research. For a variety of reasons, which will be explained in forthcoming sections of this work, we have focused on the Charlson Comorbidity Index as the prototypical index on which to base this research. The Charlson Comorbidity Index The Charlson Index was developed in 1987 based on 1-year mortality data from internal medicine patients admitted to a single New York Hospital and was initially validated within a cohort of breast cancer patients. The index encompasses 19 medical conditions weighted 1–6 with total scores ranging from 0–37. In the development phase of the index, mortality for each disease was converted to a relative risk of death within 12 months. A weight was then assigned to each condition based on the relative risk (RR); for example, RR <1.2 = weight 0, RR ≥ 1.2<1.5 = weight 1, RR ≥ 1.5<2.5 = weight 2, RR ≥ 2.5<3.5 = weight 3, and for 2 conditions (metastatic solid tumor and AIDS) = weight 6. From the weighted conditions, a sum score can be tallied to yield the total comorbidity score. The CCI can be further adapted to account for increasing age. In the validation phase of the CCI, age was also found to be an independent risk factor for death from a comorbid condition. As a result, relative risk was calculated to increase by 2.4 for each additional decade of life. In the same cohort, the relative risk of death for each 1-point increase in CCI score was 2.3. To account for the effects of increasing age, one point can be added to the CCI score for each decade of life over the age of 50 [ 13 ]. Reviews of the CCI suggests it has good reliability, excellent correlation with mortality and progression-free survival outcomes, and is easily modifiable, particularly to account for the effect of age. The CCI's basic limitations include preservation of data only for the 19 conditions listed in the index, the exclusion of non-malignant hematologic disease, such as anemia, and reduced predictive ability for outcomes < 6-months. The CCI is praised for its ease of use, short rating time, extractability from other indices, and widespread use [ 2 , 3 ]. Validation of the Charlson Index Statistical criteria for the assessment of the validity of a test include content validity, criterion validity, construct validity, and reliability [ 4 ]. Although a detailed discussion of statistical tests of validity is beyond the scope of this review, the assessments provide a basis from which to begin an analysis of the validity of the Charlson Index. Statistical criteria of validity, as applied to comorbidity indices, are ultimately dependent upon the comparison of comorbidity indices to each other, as well as subjective assessments of certain criteria, such as content validity and cutoff points for correlation coefficients. The criteria are, therefore, in and of themselves, problematic. Despite these limitations, their application to the common comorbidity indices has been studied extensively. In a review of validity among comorbidity indices, de Groot, et al, systemically identified articles referring to comorbidity between 1966 and 2000. They compared the Charlson Index with the Cumulative Illness Rating Scale (CIRS), Kaplan-Feinstein Index (KFI), and Index of Coexistent Disease (ICED) and identified correlation coefficients of > 0.40, "good" test-retest reliability and "moderate to good" inter-rater reliability for the CCI [ 4 ]. In addition, the Charlson Index correlated significantly with mortality, disability, readmission, and length of stay outcomes, suggesting good predictive validity leading de Groot, et al, to conclude that the Charlson Index, as well as the ICED, KFI, and CIRS, is a valid and reliable method for assessing comorbidity in clinical research [ 4 ]. A similar review by Extermann suggests the Charlson Index possesses excellent validity and reliability for use in clinical research in oncology. Extermann also reported exceptional predictive validity, correlating the CCI with outcomes involving mortality risk from weeks to years, postoperative complications, length of hospital stay, discharge to nursing home, and progression-free survival among cancer patients. Additionally, inter-rater reliability, by various measures, was reported at 0.74 among a cohort of older general oncology patients and 0.945 within a group of elderly breast cancer patients. Test-retest reliability was also good, ranging from 0.92 among surgical patients and 0.86 among the previously mentioned group of elderly oncology patients. Although Extermann urges some caution based on the tendency of the CCI to result in comorbidity scores that are sometimes lower than those observed with other indices, she concludes that the CCI is easy to use and "highly suitable for vast cohort studies but may under-detect significant problems resulting in non-lethal endpoints" [ 2 ]. The Charlson Index has demonstrated excellent predictive validity for a variety of clinical outcomes as well as numerous malignancies. As discussed previously, the CCI was developed using a prospective analysis of 1-year mortality rates among internal medicine patients and then validated within a population of 588 breast cancer patients. In the validation phase of Charlson's original study, increasing CCI scores were significantly correlated with increased 10-year mortality within a breast cancer cohort (χ 2 = 163, p < 0.0001), with CCI scores of 0, 1, 2, and 3 predicting 10-year survival rates of 93%, 73%, 52%, and 45%, respectively. In the original manuscript, Charlson, et al, cautioned that their index should be considered preliminary and that it required validation in larger populations [ 13 ]. Since the original work by Charlson, et al, the CCI has exhibited substantial prognostic power for both survival and treatment related complications in numerous retrospective studies. Singh, et al, for example, retrospectively analyzed CCI validity within a cohort of head and neck cancer patients. Their analysis revealed reduced median tumor specific survival (12.3 vs. 38.7 months, p = 0.007), and increased risk of cancer death (RR = 2.35) for patients with advanced (≥ 2) CCI scores. The CCI compared similarly to the KFI with respect to frequency of advanced comorbidity (30% for CCI and 32% for KFI) and prognostic power (Spearman correlation coefficient, p <0.001, r = 0.73). However, the CCI was more applicable to the study population than the KFI, with the KFI successfully applied to only 80% of the study population compared with 100% application of the CCI [ 14 ]. Fowler, et al, also examined the validity of the Charlson index in a cohort of men with prostate cancer treated with EBRT or RP. After adjusting for age, a direct relationship between actuarial survival and CCI score (p = 0.00001) was found for all patients. Among individuals with CCI scores of 0, 5 and 10-year survival rates were 86% and 66% compared with 40% and 9% for patients with CCI scores of 3 to 5. Relative mortality risk, based on CCI scores of 0, 1, 2, and 3–5, increased from 1 to 1.7, 2.6, and 5.7, respectively [ 15 ]. Additional studies among prostate cancer patients have compared the CCI, KFI, and ICED. Albertsen, et al, for example, found each of the three comorbidity indices had similar power to predict survival (p < 0.001 for each), with the addition of any of the three indices to Gleason score improving predictive power for survival over Gleason score alone [ 16 ]. We also recently reviewed the importance of comorbidity and prognostic utility of the CCI among prostate cancer patients and found that the CCI consistently correlates with reduced survival as well as treatment allocation [ 17 ]. The Charlson Index has also been validated as a prognostic indicator for survival in lung cancer cohorts. Firat, et al, recently explored the prognostic importance of comorbidity among patients undergoing surgical resection or definitive EBRT for clinical NSCLC. Within the combined group, both CIRS-G scores ≥ 4 (p < 0.001) and Charlson score ≥ 2 (p = 0.004) emerged as significant prognostic indicators of reduced overall survival. Examination of the surgical and EBRT groups separately also demonstrated higher CIRS-G and Charlson scores within the EBRT group as compared with the surgical group [ 18 ]. The effect of comorbidity on complication rates among lung cancer patients has also been investigated. Brim, et al, for example, identified gender, CCI score 3–4, COPD, and prior tumor within the last 5 years as predictors for major complications (re-thoracotomy, empyema, pleural effusion, bronchopleural fistula, ventilatory support >72 hours, ventricular arrhythmia, pulmonary embolism, cardiac failure, or myocardial infarction). Charlson scores of 3–4 maintained statistical significance after multivariate regression (OR 9.8, 95% CI 2.1–45.9) [ 19 ]. CCI scores have also demonstrated prognostic value, both in terms of postoperative complications and survival among colon cancer patients. Rieker, et al, found raw CCI scores reached 0–2, 3–4, and ≥ 5 in 66%, 25%, and 8% of patients, respectively. With respect to survival, CCI score >2 emerged as a poor prognostic indicator for overall survival for all stages (p < 0.001, OR 2.91, 95% CI = 2.00–4.94). Subgroup analysis of stage III and IV patients revealed reduced cancer-specific survival among patients with CCI score >2 (log rank p <0.005). CCI scores > 2 were also correlated with receipt of blood transfusion (p < 0.021, OR 1.56, 95% CI = 1.07–2.28), postoperative complications (p < 0.001, OR 2.18, 95% CI = 1.50–3.16), and ICU stay > 2 days (p < 0.001, OR 3.28, 95% CI = 1.91–5.64) [ 20 ]. Taken together, this series of papers represents a diverse and relatively large experience with the Charlson Index. In each report, CCI scores consistently correlate with disease specific survival, overall survival, or treatment-related complications, confirming its predictive validity. Implementation The CCI Calculator provided with this manuscript is based on the original index proposed by Charlson, et al, and is available in the section: supplementary material/table 1/appendix 1 [see additional file : CCICalc.xls]. The calculator was developed using Microsoft Excel/Visual Basic software and can be downloaded from this journal. Simplicity and ease of use were the main design objectives. Presented as a simple Microsoft Excel tool, it can be easily extended or integrated with other systems that can import Microsoft Excel data, or imported as a flat file. The Calculator functions well with both MS Windows and Macintosh operating systems running any Microsoft Excel version with Macro capabilities and is free to all users of Biomed Central Cancer. There are no restrictions concerning the use of the calculator software. A running CCI score can be calculated by selecting the conditions and age groups within the file. The calculator can be used with or without age modification as proposed by Charlson, et al [ 13 ]. It is important to note that the upper limit scores for this calculator are 37 for "age unadjusted" and 43 for "age adjusted." Charlson scores >8–10 have not received extensive evaluation in the comorbidity literature. We intend the calculator to be widely distributed so that use of the CCI can become a routine aspect of clinical research in oncology. To use the calculator, the user must select "enable macros" when prompted to do so as the file opens. To calculate a CCI score, any of the applicable conditions can be selected. All selected conditions will then be displayed in a lighter shade within the table. Corrections can be made by deselecting conditions, which then removes their weighted value from the score. The CCI score can then be totaled, or an age-modified score can be determined by selecting any one of the applicable "Age by Decade" groups. Scores totaled without age modification will appear in the "Age Unadjusted CCI Score" total and no value will appear in the "Age Adjusted Score" total. Scores totaled by selecting an age group without selecting a comorbidity will result in no value for either total and the user will be prompted to "Reset & Select Condition." To reset the program, the "Reset CCI Calculator" button can be selected. The calculator can be further modified as needed by changing entries in the "Data Sheet" area of the workbook which is hidden in the read-only version of the calculator, but can be unhidden by selecting "Format," then "Sheet," followed by "Unhide" from the Excel menu. The "Data Sheet" can then be selected and will be viewable. To modify the original Macro, users can contact the authors and the password will be provided on a case-by-case basis. Results and discussion The extensive validation of the CCI as a powerful predictor of clinical outcome combined with its simplicity and widespread use in oncology have led to the adoption of the Charlson Index as the prototypical comorbidity index in our department. In addition to validity, our criteria for the use of a comorbidity index focus on simplicity in design, consistency in scoring, and ease of use. It is our contention that many of the commonly used comorbidity indices, such as the ICED, CIRS, and KFI have failed to achieve widespread use because they remain complicated, cumbersome to use, and poorly accessible for use in clinical research. Given the adaptability of the CCI for the inclusion of additional variables, such as age, the CCI also demonstrates marked potential for modification into cancer specific comorbidity indices. We have, therefore, developed a Charlson Comorbidity Calculator based on a Microscoft Excel File to improve the collection of comorbidity data in our department. Comorbid illness has demonstrated increasing importance as a prognostic factor for survival and treatment-related outcomes in oncology. It confounds the results of clinical trials because the lack of a standardized measurement has resulted in the failure to adjust for comorbidity in statistical analysis of outcomes data [ 1 , 2 ]. It also limits the applicability of clinical research to large segments of the oncology population because protocol designs tend to exclude older and sicker patients [ 21 , 22 ]. Recent reviews consistently identify the CCI, ICED, CRIS and KFI as validated and acceptable measurements of comorbidity and recommend their use in clinical research. Although the ICED, CIRS, and KFI obtain superior prognostic power in some series, the CCI consistently demonstrates statistical validity, particularly in terms of prognostic validity, and remains the most structurally simple, easy to use and well-defined of the comorbidity indices. The ICED and CIRS, for example, both require coding manuals and training courses to be used effectively. The KFI has required extensive modification for use in oncology because it was originally designed to assess comorbidity in diabetic patients. Recent modifications of the KFI for use in oncology, such as those applied by Piccirillo in a head and neck cancer specific modification of the KFI (available in electronic calculator format at ) also require training courses for effective use [ 12 ]. By contrast, the Charlson Index is intuitive, requiring users to select a condition from a defined list, rather than searching for disease value or specific information about disease severity. In our department, the cumbersome requirements for use of the ICED, KFI, and CIRS would reduce compliance with collection of comorbidity data. Furthermore, the increased training requirements and intricacies of these indices may increase variability between scores, as it is unlikely that a single staff member would be responsible for the collection of all data. It is, therefore, our belief that the Charlson Index represents the optimal balance between ease of use and prognostic ability and has, therefore, become the method of choice for the collection of comorbidity data in our department. Accordingly, we developed the CCI calculator to improve compliance with the collection of comorbidity data and as a quality assurance tool to ensure that such data is collected correctly and uniformly. The use of comorbidity data in clinical research is at an important crossroads, with necessity of its use becoming imperative as electronic capabilities for its assessment become more feasible. As the US population gets older, the use of comorbidity data in clinical trials will only increase in relevance. Current estimates indicate that the elderly will comprise 20% of the population by the year 2030 [ 23 ]. Studies of older oncology patients also suggest that the elderly shoulder the majority of cancer burden, with risk rates 11 times greater than those of younger patients, with over 50% of all cancer-related mortality [ 24 ]. The rise of comorbidity with increasing age is a theme common to most retrospective studies of comorbidity. In this light, determining the effect of comorbidity on cancer-related survival and treatment-related complications has become increasingly important. Furthermore, evidence to suggest that comorbidity and performance status represent independent prognostic factors is accumulating. Extermann, et al, for example, examined the relationship between comorbidity and performance status. Both Charlson and CIRS-G were found to have little or no correlation with ECOG performance status, activities of daily living (ADL), or instrumental activities of daily living (IADL). More recently, Repetto, et al, found that among 269 elderly cancer patients with a reported ECOG performance score of <2, 13% had 2 or more comorbidities, 9.3% had ADL limitations, and 37.7% had IADL limitations. Although a statistical correlation between ECOG performance status, number of comorbidities, and comprehensive geriatric assessment was identified in univariate analysis, only comorbidity, ADL limitation and IADL limitation maintained statistical significance in multivariate analysis. Firat, et al also found CIRS-G and Karnofsy performace status to be independent predictors of outcome in their analysis of prognostic factors in 112 patients enrolled on 4 RTOG trials of stage III lung cancer [ 11 ]. Without widespread integration of comorbidity data into clinical research, an increasing number of elderly patients, and their physicians, will be left with treatment recommendations and outcomes data that lack relevance for their age and level of comorbidity. Concurrently, electronic medical records (EMR) and data collection systems are becoming increasingly common and easy to use, with EMR use among European countries approaching 60% to 90% [ 27 ]. The EMR ultimately promises increased physician efficiency and improved clinical outcomes for patients. Contemporary EMR systems have improved outcomes by reducing errors with the use of electronic prescribing systems and improving preventative care with automated reminder systems [ 28 , 29 ]. The MS Excel CCI Calculator provided with this manuscript, for example, could easily be integrated into an EMR for aid in data collection. Such integration would eventually provide an enormous data pool on which to base future research on the prognostic importance of CCI. To our knowledge, this is the first electronic data collection system offered for the Charlson Comorbidity Index. The simplicity of the index itself, coupled with the simplicity of MS Excel and the Visual Basic programming language, have resulted in a robust electronic CCI calculator that functions well across both Windows and Macintosh platforms. The latest version of the calculator, which is provided with this manuscript, has performed without error consistently on the first (WH), second (RR) and third (SN) authors' Windows-based PCs. The major limitations of the CCI calculator lie in the limitations known to comorbidity indices and to the index itself. These include lack of understanding as to the relative importance of various individual conditions on mortality, treatment-related complications and quality of life. Furthermore, failure to include some conditions with particular relevance to cancer patients, such as non-malignant hematopoietic disorders and thromboembolic disorders, as well as uncertainty as to whether a few specific diseases or the overall disease burden is more important for prognosis, remain important considerations limiting use of the CCI [ 2 , 3 ]. Additionally, the CCI has a tendency to underscore comorbidity because it is limited to 19 conditions and because it excludes the primary malignant condition. For example, in a patient with localized prostate cancer, history of COPD and myocardial infarction, the CCI score calculated by a urologist would exclude prostate cancer from the calculation resulting in a score of 2. The same patient might receive a score of 3 by a cardiologist because myocardial infarction, as opposed to prostate cancer, was excluded from the calculation. Another limitation of the CCI lays in the frequent use of grouped CCI scores, or CCI grades, rather than the use of scores as continuous variables. Within an elderly cohort in whom comorbidity is likely to be high, the CCI will have reduced utility if it lacks the ability to distinguish between a score of 2, representing mild to moderate comorbidity, and a score of 8, representing severe comorbidity. With this limitation in mind, we recommend the use of CCI score as a continuous variable. Despite its limitations, the general oncology literature supports the use of CCI as a prognostic variable in clinical research. It should be emphasized that the CCI is not meant to replace clinical experience and its use in clinical decision-making should be considered investigational. With additional research, CCI methodological limitations can be addressed and the index modified to improve upon its utility. In an effort to improve our understanding of the CCI and identify areas of the index in need of improvement, we are currently investigating the effect of score thresholds on treatment decision-making among prostate cancer experts. We believe that dissemination of the MS Excel CCI Macro will lead to increased use of the CCI for clinical research purposes as well as modification of the CCI to increase its validity and clinical utility. Ultimately, we hope that the comorbidity indices, such as the CCI, will see widespread use in clinical research and eventual integration into EMRs as a result of these efforts. Conclusions The Charlson Comorbidity Index has demonstrated excellent predictive validity in numerous cancer-related outcome studies. It has met the criteria for statistical validity as outlined by several authors. In our opinion, the CCI represents the optimal balance between ease of use and prognostic ability. Its simplicity in design also makes its adaptation to include additional variables extremely feasible. We have, therefore, adopted the CCI as an acceptable comorbidity measurement tool in our department and created a Microsoft Excel Macro to facilitate its correct and uniform use in clinical research. Availability and requirements • Project name : Charlson Comorbidity Calculator • Project home page : None • Operating system(s) : Windows or Macintosh OS • Programming language : Visual Basic • Other requirements : Microsoft Excel (macro enabled) • License : None • Any restrictions to use by non-academics : None List of abbreviations • CCI : Charlson Comorbidity Index • ICED : Index of Co-Existent Disease • KFI : Kaplan-Feinstein Index • CIRS : Cumulative Illness Rating Scale • RR : Relative Risk • EMR : Electronic Medical Record • CDSS : Computer-Based Decision Support Services Competing interests The author(s) declare that they have no competing interests. Authors' contributions WH, SN, AJ, and SV carried out the literature review, assembly and editing of the manuscript. RR created the CCI calculator. 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 A Microsoft Excel (CCICalc.xls) is included with this manuscript and can be found in supplementary material/table 1/appedix 1. A detailed description of the creation of the file and instructions for its use are included in the implementation section of this manuscript. To Calculate a Charlson Comorbidity Index score using the calculator double click on the CCI-Calc.xls icon or open the file from MS Excel. You must select "enable macros" when prompted to do so by the MS Excel macro warning pop-up window. A CCI score can then be calculated by selecting the conditions and age groups within the file. Selected conditions will appear in the table as a lighter shade than deselected conditions. As comorbidities are selected a running total of the score will be calculated. Scores totaled without age modification will appear in the "Age Unadjusted CCI Score" total and no value will appear in the "Age Adjusted Score" total. A selected condition can be deselected by clicking on once on the button for that condition. A score may be calculated without selecting an age category, however Scores totaled by selecting an age group without selecting a comorbidity will result in no value for either total and the user will be prompted to "Reset & Select Condition." Once finished with a calculation, the calculator can be reset by selecting the green "Reset CCI Calculator" button. The file is presented in a password protected format so that no changes can be made to the categories and weighting as proposed in the original Charlson Comorbidity Index. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545968.xml

527874

Perivascular epithelioid cell tumor (PEComa) of the uterine cervix associated with intraabdominal "PEComatosis": A clinicopathological study with comparative genomic hybridization analysis

Background The World Health Organization recently recognized a family of neoplasms showing at least partial morphological or immunohistochemical evidence of a putative perivascular epithelioid cell (PEC) differentiation. These tumors include angiomyolipoma (AML), clear cell "sugar" tumors of the lung (CCST), lymphangioleiomyomatosis (LAM), clear cell myomelanocytic tumors of the falciform ligament and distinctive clear cell tumors at various other anatomic sites. Case presentation & methods A 41-year old gravida-1 para-1 with tuberous sclerosis presented with an incidentally identified 2.2 cm mass. The morphology and immunohistochemical profile was consistent with PEComa. Distinct aggregates of HMB-45 epithelioid cells were present in an occasionally distinctive perivascular distribution in the myometrium, small bowel lamina propria and ovarian hila. These distinctive aggregates, for which we propose the designation "PEComatosis" based on their intraabdominal distribution, did not display cytological atypia, mitotic activity or necrosis. CGH and DNA ploidy analysis showed a balanced chromosomal profile and diploid nuclei, respectively. There was no recurrence or metastases at 35 months' follow-up. Fifty-one previously reported cases of non-AML, LAM and CCST PEComas [perivascular epithelioid cell tumors- not otherwise specified (PEComa-NOS)] are reviewed. Conclusions The lesions may be a reflection of tumor multicentricity, in which each may be a potential nidus for the development of future more well-developed tumors. Alternatively, they may be a manifestation of a poorly understood "field effect", in which there is an increased propensity to develop tumors of this type throughout the abdomen. Finally, and least likely in our opinion, they may represent tumor spread from its primary site.

Background Perivascular epithelioid cell tumors (PEComa) have been the subject of abundant discussion in the medical literature over the past decade [ 1 - 47 ]. Morphologic and immunophenotypic similarities between some constituent cells of renal angiomyolipomas (AML) and those of a case of clear cell "sugar" tumor of the lung (CCST) were initially noted in 1991 [ 33 ]. One year later, Bonetti et al [ 4 ], formally proposed the concept of "perivascular epithelioid cell" (PEC), a then provisional term meant to describe the epithelioid cells that characterize, at least in part, the aforementioned lesions. Characteristics of PEC (which does not have a normal anatomic homologue) include co-expression for melanocytic and muscle markers, epithelioid to spindle cellular shapes with ample clear to eosinophilic cytoplasm, and at least in some cases, arrangement around blood vessels [ 2 ]. Ultrastructurally, structures interpreted as melanosomes and premelanosomes have been demonstrated in some tumors composed of PECs [ 14 , 18 , 31 , 38 ], but not in others [ 12 , 19 , 20 , 28 , 41 ]; an additional case showed macroscopic pigmentation [ 1 ]. In 1994, based on the morphologic and immunophenotypic distinctiveness of PECs, in addition to the fact that similar cells had been described in some other tumors, Bonetti et al proposed the concept of a family of lesions sharing this cellular phenotype, including CCST, AML, and lymphangioleiomyomatosis (LAM) [ 5 ]. The term "PEComa" was introduced by Zamboni et al [ 42 ] in 1996 as synonym for this family of tumors. Over the past decade, PEC and tumors composed of them have engendered significant discussions and controversies with respect to their very existence as a clinico-pathological entity, their histogenesis, pathogenesis, and nomenclature [ 2 - 6 , 16 , 17 , 25 , 26 , 32 , 33 , 35 , 39 , 40 ]. Nonetheless, in 2002 and 2003, two monographs published under the auspices of the World Health Organization (WHO) recognized a family of neoplasms with perivascular epithelioid cell differentiation and accepted the designation "PEComa" [ 13 , 21 ]. In the WHO soft tissue volume, PEComas are defined as "mesenchymal tumors composed of histologically and immunohistochemically distinctive perivascular epithelioid cells" [ 13 ]. Members of the PEComa family that were recognized include AML, CCST, lymphangioleiomyomatosis (LAM), clear cell myomelanocytic tumor (CCMMT) of the falciform ligament/ ligamentum teres and a heterogeneous group of other "unusual clear cell tumors" at various anatomic sites [ 13 ]. The latter group includes tumors that have been reported under varying designations, such as abdominopelvic sarcoma of perivascular epithelioid cells [ 6 ], primary extrapulmonary sugar tumor (PEST) [ 38 ], clear cell myomelanocytic tumors of the skin [ 7 ] and thigh [ 15 ], and simply PEComa of various anatomic sites [ 1 , 9 , 12 , 19 , 24 , 27 , 28 , 31 , 40 , 41 , 45 , 46 ]; these, in addition to CCMMT of the falciform ligament [ 14 ] will henceforth be referred to as PEComa n ot o therwise s pecified (PEComa NOS). This descriptive designation, as used in this report, excludes the well-established entities LAM, CCST of the lungs and all variants of AML. Most of the reported cases of PEComa NOS have been tumors located in the uterine corpus (21/51; 41%); however, consequent to the publication of the WHO monographs, there has been a recent noticeable increase in the number of reported cases of PEComa NOS, with almost 70% of all cases reported between 2001 and 2004 [ 1 , 6 - 8 , 10 - 12 , 15 , 19 , 20 , 22 , 24 , 27 , 28 , 31 , 38 , 40 , 41 , 43 - 46 ](additional file 1 ). Concurrently, there has been an increase in the diversity of anatomic sites from which they reportedly arose, such that, it now appears that these tumors may potentially arise from any anatomic location. The morphologic and immunophenotypic spectrum as well as exclusion/inclusion criteria of PEComa NOS are not well-defined, and pathologic parameters with prognostic value have yet to be elucidated. In addition, there is a striking scarcity of information on the molecular and cytogenetic data of these lesions, probably due to their rarity. In this report, we present an example of a PEComa NOS of the uterine cervix, with the following objectives: 1) to document the uterine cervix as another potential site for a PEComa NOS, 2) based on the presence of tumorlets outside of their primary site, to analyze morphologic criteria predictive of clinical behavior of PEComa NOS, 3) to present CGH and ploidy analysis of PEComa NOS, and 4) to present another example of a PEComa NOS occurring in a tuberous sclerosis patient, an association which has been documented only twice previously [ 6 , 40 ]). Case presentation In October 2001, a left pelvic adnexal mass was palpated during a routine physical examination of a 41-year-old gravida-1 para-1 Caucasian female. The patient's past medical history was significant for tuberous sclerosis (diagnosed previously based on seizures, radiographic evidence suggestive of lymphangioleiomyomatosis, cutaneous hypopigmented macules and bilateral cystic renal disease that culminated in end-stage renal disease in 1989). A transvaginal ultrasound showed a heterogeneous complex adnexal mass whose size was estimated at 7.0 × 4.8 cm. Also noted was a well-defined lesion (<1.0 cm) in the uterine cervix (figure 1 ); the clinical impression of the latter lesion was a leiomyoma. CA-125 (13 U/ml) and CA19-9 (21 U/ml) levels were within normal limits. The decision was made to resect the adnexal mass and in November 2001, the patient underwent a total abdominal hysterectomy with bilateral salpingo-oophorectomy. The surgical procedure was complicated by severe intra abdominal adhesions (secondary to long-term peritoneal dialysis), the lyses of which resulted in two inadvertent nicks to small bowel segments that necessitated the excision of those segments. The patient did not receive any adjuvant or neoadjuvant therapy, and she remains alive with no evidence of recurrent or metastatic disease after 35 months of close surveillance. Figure 1 Ultrasonographic appearance of the cervical mass showing it to be deceptively circumscribed Sample preparation Standard representative sections, including the entirety of the uterine cervix were processed routinely for microscopic examination. Sections were fixed in 10% neutral buffered formalin, embedded in paraffin and stained with hematoxylin and eosin. Selected sections were stained for Periodic Acid Schiff (PAS) with and without diastase pre-digestion. The immunohistochemical profile of the tumor was evaluated on 4μ thick, formalin-fixed, deparaffinized sections using a DAKO Autostainer (Carpinteria, CA, USA) based on the avidin-biotin-peroxidase complex. Specifications for the various immunohistochemical stains that were utilized are listed in table 1 . The extent and intensity of the immunoreactivity for each antibody was scored semi-quantitatively on a 1+(+) to 4+(++++) scale representing increasing staining extent and intensity. Labeling index for Ki-67 was calculated by assessing at least 4000 cells and determining the percentage showing unequivocal nuclear staining. In our literature review, the significance of the differences between the group means of two continuous variables (patient age and sizes of lesions) was determined using the student's t-test (Excel ® , Microsoft Inc, Redmond, WA). DNA ploidy analysis was performed on isolated tumor nuclei according to standard procedures. Comparative genomic hybridization [ 48 ] was performed on tumor tissue samples from the cervical mass as previously described [ 49 ]. Table 1 Immunohistochemistry: antibody specifications and results Antibody Clone Dilution Antigen retrieval method Vendor Results* Spindle cells Epithelioid cells HMB-45 HMB-45 1:200 None DakoCytomation Carpinteria, CA, USA +++ +++ Vimentin V9 1:5 None Ventana, Tucson AZ, USA +++ + Desmin D33 1:250 Trypsin Ventana +++ + Progesterone receptor (PR) PGR 636 1:2 Steam¶ DakoCytomation ++ ++ Melan-A A103 1:40 Steam DakoCytomation +++ +++ Estrogen receptor (ER) 1D5 1:2 Steam DakoCytomation - - Caldesmon H-CD 1:100 Steam DakoCytomation + - Smooth muscle actin (SMA) 1A4 Neat None Sigma, St Louis, MO, USA +++ ++ Keratin AE1/AE3 1:1200 Trypsin Chemicon Int, Temecula, CA - - S100 Polyclonal 1:2 Pronase DakoCytomation + - Ki-67 (LI) ¶ MIB-1 1:300 Steam DakoCytomation +(<1%) +(<1%) CD68 PG-M1 1:4 Steam DakoCytomation - - CD117 Polyclonal 1:200 Steam DakoCytomation - - EMA E29 1:1000 None DakoCytomation - - p53 D07 1:3200 Steam DakoCytomation - - ¶ Heat-induced epitope retrieval, 20 minutes in 10 mM citrate buffer in steam chamber, 20 minutes cooling down period. ¶ LI: Labeling Index *Semi-quantitative scoring of combined extent and intensity of staining (+ to ++++) Pathological findings Gross and microscopic assessment of the left adnexal mass showed it to be a 7 cm hemorrhagic cyst devoid of any specific lining and involving the ovarian parenchyma. For the uterine cervical mass, a distinct lesion was not grossly appreciated. The ectocervical and endocervical surfaces and the endometrial cavity were described as unremarkable. Microscopically, the cervical mass was unencapsulated but possessed a deceptively circumscribed appearance at scanning magnification, attributable to the architectural homogeneity of its "core" (Figure 2 ). However, the peripheral regions of the tumor showed a significant degree of infiltration. The tumor's maximal dimension was estimated at 2.2 cm, extending from just below the ectocervical basement membrane (Figure 3 ) and extending proximally to the lower uterine segment and attaining 2 cm in depth (the peripheral limits of the tumor were at least 1 cm from the parametrial margins). The aforementioned central "core" (1 cm) was probably responsible for its radiographic appearance and consisted of fascicles of spindle cells with a smooth muscle appearance (Figure 4a ). The spindle cells displayed bland nuclei with regularly distributed chromatin and only rarely conspicuous nucleoli. Towards the periphery, the spindle cells displayed increasingly PAS+, diastase sensitive cytoplasm (Figure 4b ), although occasional cells displayed dense eosinophilia. At its most peripheral regions, the tumor was composed predominantly of solid sheets of large epithelioid cells with bland nuclear features, abundant clear cytoplasm, and well-defined cytoplasmic membranes. Although predominantly solid in the epithelioid regions, a pseudo-alveolar pattern was also evident (Figure 4c ). At the most proximal regions near the lower uterine segment, the tumor appeared to be "invading" in single cells in a hyalinized stroma. The nuclei of the epithelioid cells showed a mild to moderate degree of nuclear atypia, manifested mostly as nucleomegaly and irregularity of nuclear membranes in the absence of hyperchromasia. Rare cells displayed bizarrely enlarged nuclei and multinucleation with a "smudged" chromatin pattern consistent with degenerative atypia (Figure 5 ). Also identified in these regions were CD68+ foamy histiocytes mostly in single cells but occasionally in aggregates especially around the endocervical glands. No tumor necrosis was identified and mitotic figures were extremely sparse (<1/50 HPF). Small arching sinusoidal vessels were prominent throughout the tumor, but no large malformed vascular profiles were present. Pigment-laden cells and adipocytes were not present in the cervical mass. Small bowel segments measuring 21 cm in total length were also processed. Grossly, irregular areas of transmural thickening were noted. Microscopically, aggregates of epithelioid cells with more eosinophillic cytoplasm and vacuolated cytoplasm were noted in the lamina propria in two out of twelve sections (Figure 6 ). In the both ovaries, similar aggregates of cells were present in a distinctive perivascular location in the hilar regions. These aggregates were either subendothelial (predominantly), adventitially attached to affected vessels, or present as free aggregates in the perihilar fat (Figure 7 ). Each measured less than 1 mm in maximum dimension. No intraluminal tumor cells were seen. CGH and DNA ploidy analysis of the cervical mass showed a balanced chromosomal profile (figure 8 ) and diploid nuclei, respectively. Immunohistochemically, both the epithelioid cells and spindle cells stained diffusely with HMB-45 (Figure 9 ) and Melan-A in a cytoplasmic pattern at all sites (cervix, ovary, bowel). Scattered spindle cells showed unequivocal immunoreactivity for S100 while the epithelioid cells were negative. Both components showed at least focal immunoreactivity for muscle markers: smooth muscle actin and desmin with the spindle cells predominating both in the quantity of cell stained and the intensity of staining where positive. The complete immunohistochemical profile of the tumor is shown in table 1 . Figure 2 Photomicrograph of panoramic view of the cervical mass showing a central circular "core" Figure 3 At the tumor's advancing edge, it merges almost imperceptibly with the sub-ectocervical stroma Figure 4 The cervical mass displayed a morphologic spectrum from purely spindle, smooth-muscle-like areas (figure 4a) to transitional areas composed of spindle cells with more clear cytoplasm (figure 4b) to overtly epithelioid areas with abundant, clear cytoplasm, which occasionally displayed a pseudo-alveolar appearance (figure 4c) Figure 5 Occasional cells showed degenerative multinucleation with a "smudged" chromatin nuclear pattern consistent with degenerative atypia Figure 6 Aggregates of spindle to polygonal cells with eosinophillic to clear cytoplasm was present in the lamina propria of the excised small bowel segments. These aggregates were HMB-45-positive. Figure 7 In the ovarian hila, similar tumor aggregates were associated with vascular structures in a subendothelial (figure 7A), intramedial (Figure 7B), or para-adventitial (Figure figrure 7C ) pattern, but were also present freely in the hilar fat (Figure 7D). Figure 8 Comparative genomic hybridization showing a balanced chromosomal profile Figure 9 All components of the tumor were HMB-45-positive Result of literature review Of the 51 cases of PEComa NOS that have been documented in the literature [ 1 , 6 - 8 , 10 - 12 , 14 , 15 , 19 , 20 , 22 - 25 , 27 , 28 , 31 , 32 , 34 , 37 , 38 , 40 - 46 ], 90% (46/51) developed in females and 41% (21/51) were described in the uterine corpus. Follow-up information was unavailable (n = 7) or too recent at the time of the report (n = 4) in 22% of cases [ 7 , 10 , 14 , 15 , 34 , 40 , 43 , 46 ]. One patient whose tumor was primary in the left atrium died postoperatively of "cardiac failure thought to be due to nontumorous coronary artery thromoboemboli" [ 37 ]. Two cases were excluded from the following analysis (44, 45) based on insufficient morphologic information (44) or inconsistence with our analytic paradigm (45). Of the remaining 37 cases (additional file 2 ), the associated tumors behaved in a benign fashion in 25 cases (68%). These tumors were confined to their respective primary sites and showed no evidence of recurrence or metastases with follow-up periods that ranged from 6 weeks to 22 years, and will hereafter be referred to as the "benign cases" [ 1 , 6 , 8 , 14 , 20 , 23 , 25 , 27 , 32 , 37 , 38 , 40 , 42 , 44 ]. However in twelve cases, the tumors recurred after apparently complete surgical resections, were either metastatic at presentation or metastasized after long periods. Four of these cases were ultimately fatal [ 6 , 19 , 22 , 28 ], and all 12 will hereafter be referred to as the "non-benign cases" [ 6 , 11 , 12 , 14 , 19 , 22 , 24 , 28 , 31 , 41 ]. Clinical parameters were not particularly discriminatory between the two groups: the average patient age of the benign cases (37 years) was not significantly different from that of the non-benign cases (43 years) [p = 0.56] and there was a diversity of clinical presentations related primarily to the sites of origin. The non-benign cases (mean diameter of 11 cases: 7.5 cm) tended to be larger than the benign cases (mean diameter of 24 cases: 4.54 cm); the statistical significance of the difference (p = 0.022) is maintained even when the outlier effect of the 1 case with a 20 cm diameter tumor reported by Folpe et al [ 14 ] in the non-benign group is removed. Morphologic features in the benign and non-benign cases are compared below. Discussion We have presented herein the first documented case of a PEComa NOS of the uterine cervix. In addition to the primary tumor, aggregates of HMB-45+ clear cells were present at several other intraabdominal sites, including the small bowel lamina propria, ovarian hila and myometrium. Based on this pattern of distribution of tumor cells, we propose the designation "PEComatosis" to describe such aggregates. The morphogenetic basis for the intraabdominal lesions remains unclear. The similarity in morphologic features and immunophenotype between the cervical and extracervical lesions suggests that either a) both lesions arise from the same site or progenitor, or b) they both represent tissue responses of different degrees to the same stimulus. Several possibilities were considered, all of which are necessarily speculative. The lesions may be a reflection of tumor multicentricity, in which each may be a potential nidus for the development of future more well-developed tumors. Alternatively, they may be a manifestation of a poorly understood "field effect", in which there is an increased propensity to develop tumors of this type throughout the abdomen. Finally, and least likely in our opinion, they represented the tumor spread from its primary site. The diffuse occurrence of apparently heterologous tissue is a well-known phenomenon in the peritoneal cavity. These include leiomyomatosis peritonealis disseminata (LPD), gliomatosis peritonei and the recently described diffuse cartilaginous metaplasia of the peritoneum [ 50 , 52 ]. In LPD, for example, diffusely distributed peritoneal nodules of benign smooth muscle may proliferate or regress based on the hormonal milieu of pregnancy [ 52 ]. In this instance, circulating hormone levels are thought to represent the main stimuli underlying the proliferation of the peritoneal lesions. Even though a uterine smooth muscle tumor is associated with most cases, they are not thought to be the origin of LPD [ 52 ]. In contrast, gliomatosis peritonei is believed to represent an overgrowth of glial implants from ovarian teratomas [ 52 ]. In either instance, the presence of diffuse intraperitoneal lesions associated with these benign lesions (an ovarian teratoma and a uterine leiomyoma) is deemed insufficient to assign them a malignancy status [ 52 ]. These examples are directly relevant to our case, in which a malignant potential has to be assigned to a pathologically benign tumor that is showing similar appearing cells remote from its primary site. In the current case, the distinctive tropism of tumor cells for vascular structures without true intraluminal foci, argues against a hematogenous spread of tumor and argues for a de novo proliferation of PEC at those sites. However, whether these are foci that would have undergone involution or continued proliferating into tumor masses remains unclear. Do these lesions represent a manifestation of a poorly understood "field effect", in which there is an increased propensity to develop tumors of this type throughout abdomen? The most obvious underlying condition in this particular patient is tuberous sclerosis. A potentially comparable condition may exist in the lungs. In a few patients with and without tuberous sclerosis, a distinctive diffuse pulmonary interstitial proliferation has been described [ 53 - 56 ]. These proliferations are composed of clear HMB-45+ cells and are inconstantly associated with LAM [ 50 - 53 ]. Whether a similar mechanism is operational here is not clear. Although we do not believe the extracervical lesions represent metastases, it should be noted that contrary to the conventional paradigm, molecular evidence is accumulating regarding the "metastasis of benign tumor cells" in patients with tuberous sclerosis, although this is currently limited to the renal angiomyolipoma to pulmonary lymphangioleiomyomatosis model [ 57 ]. The issues raised by this case highlight the absence of well-established morphologic criteria predicting aggressiveness or malignancy in PEComas. Based on an analysis of 31 of the 51 reported cases detailed in Additional file 1 , the following information is stated in the WHO monograph regarding the aforementioned criteria [ 13 ]: "it appears that PEComas displaying any combination of infiltrative growth, marked hypercellularity, nuclear enlargement and hyperchromasia, high mitotic activity, atypical mitotic figures, and coagulative necrosis should be regarded as malignant". However, while the presence of all the mentioned features would probably assign malignancy to any tumor, it is unclear what significance there is of the presence or absence of individual features or small combinations thereof. Since the relevance of any set of pathologic criteria is ultimately dependent on their correlation to clinical behavior based on published experience, we analyzed in greater detail the clinicopathologic features of those 12 cases in which aggressive behavior was already manifest and compared them to those of the 25 cases with benign outcomes. It is well recognized by the authors that this separation is artificial, the definitional threshold for 'aggressiveness" is low, and that for example the recurrence of a tumor is by no means necessarily indicative of its malignancy. Nonetheless, this separation allows comparative analysis of groups of cases whose clinical behaviors have been shown to be different. With regard to morphologic appearance, some features appeared to distinguish the two groups. Nuclear atypia (nucleomegaly, multinucleation, pleomorphism etc) was more likely to be present in the non-benign group, with this feature described in 8 of the 10 cases in which a comment was rendered. However, some degree of nuclear atypia was also described in 9 of the 25 cases in the benign group; the atypia in all of the latter cases were described as "minimal" or mild to moderate. Two cases that, in our opinion, showed the highest degree of nuclear atypia (in addition to high mitotic activity and necrosis) were unfortunately reported without follow-up information [ 10 , 34 ]. Mitotic activity was uniformly low in the benign group, with no mitotic figures identified in 13/25 (52%) cases and rare (<1/20HPF) mitotic figures found in the remainder with information. However, for the purposes of answering the more clinically relevant question, i.e. segregation of the non-benign group, mitotic activity was not useful. Only 3 of the 10 non-benign cases (in which mitotic activity information was given) showed significant mitotic activity (≥ 6/10HPF). In the remaining 7 cases, mitotic figures were described as "rare" (n = 3), "low" (n = 2) and <1/20hpF (n = 2). Necrosis was a common feature of cases in the non-benign group, being present in 7 of 11 cases (64%), with an 8 th case described as showing "gelatinous-appearing material" macroscopically [ 11 ]. In contrast, of the benign group, necrosis was present in only 4 of the 18 (22%) cases in which such information was stated. Additionally, the necrosis in one of those 4 cases was described as "infarct-type" (non-coagulative) [ 40 ]. However, one of the benign cases was described as showing "scattered foci of coagulative necrosis and hemorrhage" [ 8 ]. As can be anticipated, lymphovascular invasion (LVI) by tumor was more characteristic of tumors in the non-benign group as compared to their benign counterparts: LVI was present in 4 out of 8 (50%) cases in the non-benign group and in only one case in the benign group. The latter is Case 3 of the "abdominopelvic sarcomas" described by Bonetti et al [ 6 ]; this tumor was a 2.5 cm well-circumscribed pelvic nodule, the patient showed no evidence of tumor recurrence or metastases at 6 months follow-up. For the purposes of this analysis, we placed this case in the benign group, definitionally based on the benign follow-up. We also analyzed the degree and types of tumor infiltration as another potential discriminator between the benign and non-benign groups. In what remains the largest series of PEComa NOS reported to date, Vang and Kempson [ 40 ] divided 8 uterine PEComas into 2 groups (A and B) based on, in part, the degree of tumor infiltration. Of the group A, 75% of tumors showed a tongue-like myometrial infiltration reminiscent of low-grade endometrial stromal sarcoma while this type of infiltration was only focal in 75% of their group B tumors; all cases were limited to the uterus. Due to the absence of follow-up in 75% of their group A cases, the prognostic significance of this classification is unclear. Two of the four "hyalinized uterine mesenchymal neoplasms with HMB-45-positive epithelioid cells" reported by Michal and Zamecnik [ 25 ] showed a similar "tongue-like" infiltration and had a benign follow-up. All of the 7 cases of CCMMT reported by Folpe et al [ 14 ] "appeared circumscribed but displayed an infiltrative pattern microscopically at the periphery", a pattern remarkably similar to our case. One of their 6 cases with follow-up showed evidence of pulmonary metastases at 3 months, while follow-up was unremarkable in others. This patient reportedly died of other causes. Analysis of the usefulness of infiltration was hampered somewhat by the absence of a comment on this subject in some of the non-benign cases; however, it is unlikely to be a criterion of significant use in isolation. Only in 3 of 10 malignant cases infiltration was prominent. In 4 of the 7 remaining cases in the non-benign group, infiltration was not specifically noted; the latter includes a well-circumscribed and encapsulated 9 cm mass involving the terminal ileum and cecum [ 6 ] which metastasized to the liver and the patient died in 28 months. The 7 th and 8 th cases showed only local infiltration [ 11 , 14 ]. Another case reported by Ruco et al [ 34 ], which we excluded from our analysis of the non-benign cases due to an absence of outcome information, consisted of a partially necrotic 5 cm mass showing high mitotic activity (11 m/10hpF) and was described as "poorly circumscribed". One of the cases described by Fukunaga [ 8 ], which we have placed in the benign group showed focal infiltrative growth. For the rest of the benign group, significant infiltration was not described in any case. Overall, the experience with PEComas NOS is currently too limited to make a definitive assessment of prognostic features. In addition, the above analysis presumes a biologic homogeneity to tumors arising from various anatomic sites (table 2 ). Nonetheless, from our analysis of the reported cases with clinical outcomes as end-points, necrosis and large tumor size (both of which were more characteristic of tumors in the non-benign group) appears to have the greatest discriminatory value. However, it is likely that when more cases are described, combinations of features will provide the greatest prognostic information. In the present case, as previously noted, there was no necrosis, mitotic activity or significant pleomorphism, but there were tumor aggregates in the ovaries (perivascular) myometrium, and small bowel. The fact that close surveillance of our patient for 29 months has revealed no evidence of tumor recurrence or metastases is suggestive of tumor benignancy, although even that statement is tempered by the cases of metastases developing after long periods, up to 7 years in one case ([ 11 ], Additional file 1 ). Table 2 Summary of pathologic features in reported cases of PEComa NOS Pathologic feature* Non-Benign cases (n = 12) Benign cases (n = 25) Cytologic atypia 8/10 (80%) 9/25 (36%) Necrosis 7/11 (64%) 4/18 (22%) Lymphovascular invasion 4/8 (50%) 1/11 (9.1%) Size (average diameters) 7.5 cm (n = 11) 4.54 cm (n = 24) Significant mitotic activity (6 mitoses per 10 high power fields or greater 3/10 (30%) 0/24 (0%) * For each histological feature, the denominator represents the number of cases in which the information assessed was available in the published reports. See text for caveats, exceptions and more detailed analysis An important differential consideration for PEComas arising in the uterus is epithelioid smooth muscle tumors. Vang and Kempson [ 40 ] expressed the opinion that PEComas and epithelioid smooth muscle tumors "exist on a morphologic spectrum" and recommended that HMB-45 staining be performed on all epithelioid tumors of the uterus. The contrary view was expressed by Silva et al [ 58 ] who demonstrated immunoreactivity for HMB-45 in 4 (80%) out of 5 "unequivocal uterine leiomyosarcomas" with epithelioid features. They concluded that HMB-45 immunoreactivity is insufficient to designate these tumors as PEComas and separate them from epithelioid smooth muscle tumors. Zamecnik and Michal [ 59 ] found immunoreactivity for HMB-45 in four distinctively hyalinized epithelioid mesenchymal tumors of the uterus, but all four cases were negative for the other three melanogenesis markers tested (Melan-A, tyrosinase and micropthalmia transcription factor). The authors concluded that their cases were closer linked to epithelioid smooth muscle tumors than PEComas. In the report by Ruco et al [ 34 ], twelve uterine leiomyomas, two of which were epithelioid, were negative for HMB-45. In contrast, at least focal HMB-45 positivity was demonstrated in 43 of 79 (54%) typical (non-epithelioid) smooth muscle tumors of the uterus in 2 combined series [ 60 , 61 ]. These somewhat contradictory findings illustrate that at this time, the relationship between epithelioid smooth muscle tumors and PEComas (outside of their shared co-expression of muscular markers) is unclear. However, since both tumors are rare, we agree with Vang and Kempson [ 40 ] that HMB-45 immunostaining should be performed on all epithelioid uterine tumors, not only to better delineate the features of both epithelioid smooth muscle tumors and PEComas, but due to the possibility of an association between the latter and the tuberous sclerosis complex (TSC). Although the association between some members of the PEComa family (AML and LAM) and the tuberous sclerosis complex is well-known, this case represents only the third case of a PEComa NOS reported to occur in a patient with stigmata of this complex. Both of the previous cases were primary in the uterus [ 6 , 40 ]. Even this seemingly low rate of association (6%; 3/50) is almost certainly higher than that associated with most tumors, and may thus warrant an investigation for features of TSC in patients in whom these tumors are diagnosed. The validity of segregating a tumor group based almost entirely on the clear appearance of constituent cells and immunoreactivity for melanogenesis markers may be proven if recurrent molecular or cytogenetic abnormalities are identified in this group. However, remarkably sparse information exists on the cytogenetic or molecular pathogenesis of PEComa NOS. Using conventional cytogenetics, Folpe et al [ 14 ] identified loss of X chromosome and a t(3;10) in 1 of 5 metaphases examined from a case of CCMMT. RT-PCR analysis of one perivascular epithelioid cell tumor has failed to show the EWS/ATF-1 fusion transcript from the t(12;22) characteristic of clear cell sarcoma of soft parts – another differential consideration [ 41 ]. No other cytogenetic analyses of PEComa NOS have been reported to our knowledge. p53 does not appear to be involved in the pathogenesis of these tumors, as neither p53 mutations as determined by single-stranded conformational pleomorphism analysis, nor protein overexpression as determined by immunohistochemistry have been identified [ 33 ]. Our case represents the first PEComa NOS that has been studied by CGH. Although this needs to be confirmed with more cases, the absence of chromosomal gain or loss detectable by this method, in additional to a diploid DNA content of our case, suggests that karyotypical changes may not be features of PEComa NOS. In summary, we have documented herein the first case of a PEComa NOS of the uterine cervix occurring in a tuberous sclerosis patient. With the description of additional cases, more insight into their behavior and predictive morphologic parameters may be achieved. Competing Interests The authors declare that they have no competing interests. Authors' contributions OF wrote the original version of the manuscript. PH and VP diagnosed the case and supervised the entire project. DH and MRM collected clinical and pathologic data and participated in manuscript preparation. DH also contributed statistical analysis. LM, YY, PH and MBQ performed and/or analyzed and interpreted the CGH. All authors have read and approved the final manuscript. Additional files Additional file 1: PEComa additional file 1. doc : All reported cases of PEComa NOS Additional file 2: PEComa additional file 2. doc: Morphologic analysis of the 37 cases of PEComa NOS with adequate follow-up information, classified by outcome Supplementary Material Additional file 1 Click here for file Additional file 2 Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC527874.xml

434149

DNA Display II. Genetic Manipulation of Combinatorial Chemistry Libraries for Small-Molecule Evolution

Biological in vitro selection techniques, such as RNA aptamer methods and mRNA display, have proven to be powerful approaches for engineering molecules with novel functions. These techniques are based on iterative amplification of biopolymer libraries, interposed by selection for a desired functional property. Rare, promising compounds are enriched over multiple generations of a constantly replicating molecular population, and subsequently identified. The restriction of such methods to DNA, RNA, and polypeptides precludes their use for small-molecule discovery. To overcome this limitation, we have directed the synthesis of combinatorial chemistry libraries with DNA “genes,” making possible iterative amplification of a nonbiological molecular species. By differential hybridization during the course of a traditional split-and-pool combinatorial synthesis, the DNA sequence of each gene is read out and translated into a unique small-molecule structure. This “chemical translation” provides practical access to synthetic compound populations 1 million-fold more complex than state-of-the-art combinatorial libraries. We carried out an in vitro selection experiment (iterated chemical translation, selection, and amplification) on a library of 10 6 nonnatural peptides. The library converged over three generations to a high-affinity protein ligand. The ability to genetically encode diverse classes of synthetic transformations enables the in vitro selection and potential evolution of an essentially limitless collection of compound families, opening new avenues to drug discovery, catalyst design, and the development of a materials science “biology.”

Introduction Creation of molecular function represents a fundamental challenge. Nature accomplishes the task through evolution, iterating cycles of selection, amplification, and diversification. Multiple generations of selective pressure and reproduction transform a diverse population into one consisting only of molecules fit to survive. Life on this planet thus emerged from a limited chemical palette, comprising proteins, nucleic acids, sugars, lipids, and metabolites. Over the last two decades, technologies that recapitulate this process in the test tube have been developed, and have produced an amazing collection of biopolymers with unprecedented recognition and catalytic properties (reviewed in Roberts and Ja 1999 ). At present, however, these in vitro selection techniques cannot be applied to compounds of nonbiological origin and have therefore not affected most areas of molecular discovery. The question arises: what would become possible if in vitro selection were applied to chemical populations of arbitrary composition? High-throughput screening of combinatorial chemistry (HTS-CC) libraries represents a first approximation to small-molecule evolution, in that the process roughly mimics the diversification and selection components of evolution. However, amplification and iteration have no functional equivalents in HTS-CC techniques, placing practical limits on library complexity. Amplification and iteration are critical for identifying vanishingly small amounts of material from a complex population. Moreover, these processes make possible the application of bulk selections rather than serial screens to assay libraries, vastly increasing throughput. Accordingly, typical HTS-CC libraries rarely exceed 10 6 unique members ( Dolle 2003 ), whereas the biopolymer libraries used for in vitro selection experiments generally comprise 10 9 –10 13 unique members ( Roberts and Ja 1999 ). A state-of-the-art high-throughput screening facility, capable of performing 300,000 tests per day, would require 9 millennia to screen a typical in vitro selection library ( Morais 2003 ). If the success of molecular discovery correlates with library complexity, then in vitro selection of combinatorial chemistry libraries for functional molecules will be far more powerful than screening. In order to apply in vitro selection to combinatorial chemistry libraries, each compound must be associated with a gene that specifies its structure. DNA has been utilized previously to record the synthetic history of individual beads in a split-and-pool combinatorial synthesis, but the DNA tags could not direct subsequent resynthesis of the corresponding compound ( Brenner and Lerner 1992 ). More recently, hybridization-induced proximity strategies for DNA-templated organic synthesis have been described, but their use has not yet been reported for the synthesis of complex libraries ( Gartner and Liu 2001 and references therein). In this manuscript we present and demonstrate a general method for the in vitro selection and evolution of combinatorial chemistry libraries ( Harbury and Halpin 2000 ). Results Strategy In vitro selection requires iterated rounds of three steps: conversion of genes to gene products, selection of gene products, and gene amplification ( Figure 1 ). The last two steps, selection and amplification, are similar between all forms of in vitro selection. However, conversion of genes to gene products poses a unique problem for the in vitro selection of small molecules. Whereas enzymes convert genetic material into the natural biopolymers, no machinery exists to directly translate genes into small molecules. Figure 1 The In Vitro Selection Cycle Experiments are initiated with a nucleic acid library (colored DNA). The sequence of each gene directs the synthesis of a corresponding gene product (colored ball) that is physically linked to its encoding nucleic acid. The gene products are subjected to selection, for example, through binding to an immobilized macromolecule (cyan widget at bottom). The nucleic acid encoding selected gene products is amplified and used as input for a subsequent cycle. In general, small-molecule libraries are synthesized by the split-and-pool method which is illustrated in Figure 2 ( Furka et al. 1991 ; Thompson and Ellman 1996 ). A mixture of supports (the inert material on which small molecules are built, typically polystyrene beads) is randomly split into subpools. A distinct chemical building block is then coupled to the supports in each subpool, after which the supports are pooled together and mixed. Splitting, coupling, and pooling are repeated until the library synthesis is complete. The series of subpools into which a support partitions determines what chemical building blocks are added to the support. Thus, the trajectory that a support takes through a split-and-pool synthesis is essentially a molecular recipe. If a support could predetermine its own trajectory, it would encode the synthesis of the small molecule ultimately attached to it. Predetermining support trajectories can be accomplished by using a DNA library as the support material, and by directing the splits through hybridization. The DNA sequence of each support then governs its subpool path, and acts as a genetic blueprint for a small molecule. Figure 2 Split-and-Pool Synthesis of a Combinatorial Chemistry Library A mixture of solid supports (balls with rotated “L” at top) is randomly split into subpools. A distinct chemical building block (red, green, or blue ball) is coupled to the supports in each subpool. The supports are repooled and mixed. This process of splitting, chemistry, and pooling is iterated until the library synthesis is complete. The small molecules ultimately synthesized are combinations of the different building blocks (colored circles, squares, and diamonds). As highlighted by the black bead, the path taken by a support through the split-and-pool synthesis (right, middle, left) determines the small molecule synthesized on it (blue ball, green square, red diamond). The number of reactions performed is the sum of the number of subpools in each split (3 + 3 + 3 = 9). The number of unique small molecules generated is the product of the number of subpools in each split (3 × 3 × 3 = 27). The construct we chose for our DNA support library is shown in Figure 3 A. The single-stranded DNA (ssDNA) includes a unique reactive site at its 5′ end, upon which a small molecule is synthesized. The DNA sequence contains 20-base “codons” flanked by 20-base noncoding regions. Within the DNA support library, sequence degeneracy exists at the coding positions. The set of codons in each DNA support specifies a small-molecule synthesis by directing the splitting of the ssDNA into appropriate subpools. The noncoding regions enable genetic recombination of support sequences by PCR ( Halpin and Harbury 2004 ). Figure 3 Chemical Translation (A) Schematic showing the structure of the DNA support library. Small molecules are synthesized at the 5′ end of 340-base ssDNA genes. The ssDNA consists of 20-base noncoding regions (black lines labeled Z 1 –Z 7 ) and 20-base coding positions (colored bars labeled [a–j] 1–6 ). All library members contain the same seven DNA sequences at the seven noncoding regions. At each of the six coding positions, ten mutually exclusive DNA codons, (a–j) n , are present, for a total of 60 different sequences. Each coding region specifies the addition of a single subunit to a growing small molecule. A unique reactive site (in this case a primary amine) for small-molecule synthesis is attached to the 5′ end of the ssDNA through a polyethylene glycol linker (squiggly line). Resin beads coated with an oligonucleotide complementary to one codon (anticodon beads, gray ball at right) capture by hybridization ssDNAs containing the corresponding codon. (B) Chemical translation is a split-and-pool synthesis, with splitting directed by DNA hybridization. A ssDNA library is hybridized to a set of anticodon columns (gray balls) corresponding to the set of codons present at a single coding position. The ssDNA genes partition into subpools based on sequence identity. Distinct chemical subunits (colored balls) are coupled to the DNA in each subpool. Finally, the DNA is repooled, completing the encoded addition of one subunit to the growing small molecule. The process of hybridization splitting, chemistry, and pooling is repeated for all subsequent coding regions. (C) Schematic product of chemical translation. The sequence of the small-molecule subunits (colored balls) corresponds to the sequence of codons (colored bars) in the ssDNA gene. Our scheme for DNA-directed split and pool synthesis is shown in Figure 3 B. The library is first split by hybridization to a set of anticodon columns complementary to the different 20-base sequences present at the first coding position. Distinct chemical building blocks are coupled to each subpool, and the library is repooled. The process is repeated, but the splitting is directed by a subsequent coding position. Each coding position comprises a set of codons that differ in sequence from the codons at all other coding positions. Consequently, splitting is always directed by hybridization at one intended coding region, and not by codons elsewhere. Small molecules are synthesized directly on their encoding DNAs, maintaining the physical linkage between gene and gene product ( Figure 3 C). Direct conversion of genes into small-molecule gene products, combined with selection and amplification steps, enables the in vitro selection of small-molecule libraries. Reduction to Practice We first developed a Sepharose-based resin derivatized with anticodon oligonucleotides complementary to codon sequences ( Halpin and Harbury 2004 ). We tested the resin by hybridizing a library consisting of seven ssDNA sequences to a corresponding set of seven different anticodon columns ( Figure 4 ). There was little crosshybridization, which ensures that DNA genes will be accurately translated. Analysis of splitting efficiencies by a scintillation counting assay of radiolabeled ssDNA showed that 90% or more of the ssDNA inputs were recovered from the correct hybridization columns for all tested sequences ( Halpin and Harbury 2004 ). The resin is also robust. We have not observed any loss in efficiency with over 30 cycles of hybridization and elution. Figure 4 Sequence-Directed Splitting Seven serially truncated ssDNAs differing in sequence at one coding position (illustrated at left of gel, number of bases indicated) were hybridized to seven anticodon columns (cylinders at top of gel). The load (lane 1), flow through (lane 2), and column elutes (lanes 3–9) were analyzed by denaturing polyacrylamide gel electrophoresis. We next addressed chemical synthesis on unprotected DNA. Use of a solid phase in small-molecule synthesis allows for the application of excess reagents, to drive reactions to completion, and simplifies product purification ( Merrifield 1963 ). To realize these advantages, we carried out synthetic steps while DNA was noncovalently bound to diethylaminoethyl (DEAE) Sepharose resin ( Halpin et al. 2004 ). DEAE Sepharose was chosen for solid-phase synthesis because it adsorbs DNA reversibly and in a sequence-independent manner and because it behaves well in organic solvents. Incubation of immobilized DNA with the appropriate reagents results in addition of a building block, completing one step in the synthesis of a small molecule. Following the chemical step, DNA is eluted from the solid phase and manipulated in solution. As an initial chemistry, we chose 9-Fluorenylmethoxycarbonyl [Fmoc]–based peptide synthesis. Figure 5 shows the results of solid-phase peptide synthesis on DNA using Fmoc-protected succinimidyl esters ( Anderson et al. 1963 ; Carpino and Han 1970 ; Halpin et al. 2004 ). Synthesis of the [Leu]enkephalin pentapeptide on an aminated 20-base oligonucleotide ( Figure 5 B) yielded a highly pure [Leu]enkephalin-DNA conjugate. A nonaminated oligonucleotide internal control was not altered by the chemistry, ruling out nonspecific chemical modification of DNA. Over 90% of the recovered nucleic acid was the intended [Leu]enkephalin-DNA conjugate (the overall recovered yield was 60%). The results correspond to a 98% efficiency for each amino acid coupling step. Figure 5 Peptide Synthesis on DNA (A) Structure of the [Leu]enkephalin–DNA conjugate. (B) High performance liquid chromatography chromatogram of the [Leu]enkephalin peptide synthesized using succinimidyl ester chemistry on a 20-base oligonucleotide modified with a 5′ primary amine (20mer). A 10-base oligonucleotide without the 5′ primary amine (10mer) was included in the reactions as a control for nonspecific DNA modification. The red and blue traces are the DNA before and after chemistry, respectively. The mass of the major product peak (42-min retention time) matches the expected mass of the [Leu]enkephalin–DNA conjugate. (C) Electromobility shift assay of peptides synthesized on 340-base ssDNA. Conjugates were eletrophoresed on a native agarose gel in the absence (lanes 1, 3, 5, and 7) or presence (lanes 2, 4, 6, 8, and 9) of the [Leu]enkephalin-binding antibody 3-E7. [Leu]enkephalin (L) or a scrambled sequence (S) was synthesized on a 5′ amino-modified 20-base oligonucleotide, which was subsequently used as a primer for PCR (lanes 1–4), or directly on 5′ amino-modified 340-base ssDNA, which was subsequently converted to dsDNA (lanes 5–9). Addition of free [Leu]enkephalin peptide (lane 9) competes away binding. Synthesis of [Leu]enkephalin on a 340-base ssDNA support, capable of encoding an eight-step synthesis, was analyzed using an electromobility shift assay and the enkephalin-specific 3-E7 antibody ( Hwang et al. 1999 ). Figure 5 C shows that 3-E7 shifts the majority of the [Leu]enkephalin-DNA (approximately 85% when standardized to a positive control), showing that the biological activity of the peptide is maintained while attached to DNA. The 3-E7 antibody does not shift a scrambled-DNA peptide conjugate containing the same amino acids as [Leu]enkephalin but in a different order. Finally, free [Leu]enkephalin peptide eliminates the shifting of [Leu]enkephalin-DNA by 3-E7, demonstrating the specificity of the shift. Our chemical translation strategy requires repeated hybridization-directed splitting and coupling of chemical building blocks to DNA. Two different solid phases were utilized for these tasks. To efficiently transfer DNA from anticodon columns to DEAE Sepharose columns, we cyclically pumped 50% dimethylformamide (DMF) over the columns connected in series ( Figure 6 ). Conversely, to transfer DNA from DEAE Sepharose columns back to anticodon columns, we used a high salt buffer in a closed system. In both cases, a large effective buffer volume flows over each column, which allows the DNA transfer processes to approach thermodynamic equilibrium. These column-to-column transfers remove intermediate storage tubes and require little solvent, minimizing loss of DNA. Figure 6 Reduction to Practice Chemical translation requires iteration of a chemistry step and two column-transfer steps. ssDNA is transferred from anticodon columns to DEAE Sepharose columns by cyclically pumping 50% DMF through a pair of columns (one hybridization, one DEAE) attached in series for 1 h at 45 °C. Chemistry is performed on ssDNA bound to each DEAE column. ssDNA is transferred from DEAE columns to anticodon columns by cyclically pumping a 1.5-M NaCl buffer through all DEAE columns and all anticodon columns associated with the next coding position for 1 h at 70 °C and 1 h at 46 °C. Efficiencies for each step are indicated in red. In Vitro Selection of a Chemically Synthesized Library To test and validate our general strategy, we applied in vitro selection to a primarily nonnatural peptide library, with the goal of identifying a high-affinity ligand for the monoclonal antibody 3-E7 ( Meo et al. 1983 ). Isolation of 3-E7 ligands is a well-defined in vitro selection problem characterized previously ( Cwirla et al. 1990 ; Barrett et al. 1992 ). We designed our library to contain at least one known 3-E7 ligand, [Leu]enkephalin. The [Leu]enkephalin peptide binds to 3-E7 with an affinity of 7.1 nM, and its size (five residues) was well-suited for our experiments. An initial DNA support library consisting of ten distinct sequences (“all a,” “all b,” etc.) was diversified 10 5 -fold by PCR recombination to generate a support library with a complexity of one million, as verified by DNA sequencing ( Halpin and Harbury 2004 ). This library was chemically translated into acylated pentapeptides using Fmoc-protected succinimidyl esters. The peptide library included ten different monomers at each position ( Figure 7 A). The first five positions comprised one of ten amino acids (β-alanine, D-alanine, D-leucine, D-tyrosine, 4-nitro-phenylalanine, glycine, leucine, norleucine, phenylalanine, or tyrosine). The N-terminus was left unmodified or was acylated with one of nine acids (acetic, benzoic, butyric, caproic, glutaric, isobutyric, succinic, trimethylacetic, or valeric). After library synthesis and conversion of the ssDNA into duplex form, the library was subjected to selection using the 3-E7 antibody. The selected DNA was PCR amplified and used as input for the subsequent round of synthesis and selection. Figure 7 In Vitro Selection of a Nonnatural Peptide Library (A) Library building blocks. Proteinogenic building blocks are shown in green. (B) Approximately 70 DNA genes from each round of selection were sequenced, and the results are summarized as a histogram plot. The x-axis indicates the number of amino acid residue matches to [Leu]enkephalin encoded by a library sequence. The y-axis indicates the library generation (0, starting material; 1, after round one selection; 2, after round two selection). The z-axis indicates the number of sequences encoding a particular number of matches (x-axis) in a particular round (y-axis). (C) The top row reports the round two library consensus sequence, which matches [Leu]enkephalin. The second row reports the percentage of round two library clones that encode the [Leu]enkephalin amino acid at each residue position. The third row reports the identity and frequency of the most commonly occurring non-[Leu]enkephalin subunit at each position. In order to monitor library convergence, DNA from the starting material (round 0) and from after one (round 1) or two (round 2) selection generations was subcloned, and approximately 70 different isolates from each round were sequenced ( Figure 7 B). In round 0, none of the sequences encoded more than three residues in common with [Leu]enkephalin. Two sequences from round 1 encoded five [Leu]enkephalin residues, and one sequence encoded four residues. Of the round 2 sequences, twenty encoded full-length [Leu]enkephalin, thirty-four encoded single mutants, and eleven encoded double mutants. Only three round 2 sequences encoded less than four [Leu]enkephalin residues. The round 2 consensus peptide sequence matched [Leu]enkephalin ( Figure 7 C). Previous work has shown that the N-terminal residues (Tyr-Gly-Gly-Phe) are responsible for most of [Leu]enkephalin's affinity for the 3-E7 antibody ( Meo et al. 1983 ; Cwirla et al. 1990 ). We observed high sequence conservation at these residues, recapitulating the earlier results. To assess generality, we carried out a second [Leu]enkephalin in vitro selection experiment using a peptide library of the same size but constructed with a completely different “genetic code.” Every codon in the alternate library coded for an amino acid different from the one it coded for in the first library. The [Leu]enkephalin codon series in the first library was b 1 -j 2 -b 3 -c 4 -h 5 -i 6 , whereas in the alternate library it was d 1 -b 2 -g 3 -g 4 -i 5 -f 6 . Two rounds of selection enriched the alternate [Leu]enkephalin DNA gene 10 5 -fold (data not shown). The data suggest that little, if any, DNA sequence encoding bias exists in our system. Further, they illustrate the reproducibility of the technology. Together, the results demonstrate conclusively that the DNA display strategy can be used for the in vitro selection of synthetic chemical libraries. Discussion Previous efforts to expand the scope of in vitro selection have utilized nonnatural bases or amino acids incorporated into DNA, RNA, and peptide libraries using polymerases and the ribosome ( Bittker et al. 2002 ; Li et al. 2002 ; Forster et al. 2003 ). Such efforts are limited by the extent to which enzymes will tolerate novel monomers. In addition, enzymes can only produce polymers chemically and topologically similar to their natural products, which are not well-suited for all applications. An alternative strategy for expanding the chemical diversity of gene products exploits DNA-templated synthesis, where hybridization-induced proximity promotes covalent bond formation ( Gartner and Liu 2001 ). One great advantage of proximity-based DNA-directed synthesis is its ability to accommodate multiple reactions in “one pot.” However, there are several significant disadvantages. Each building block must be attached to an oligonucleotide, which is both expensive and labor intensive. All chemistry must proceed under conditions compatible with DNA hybridization, ruling out many organic solvents, high pH, and high temperature. Finally, there may be a limitation to the number of steps that can be encoded by the proximity approach. While an impressive array of chemical reactions has been accomplished by this method ( Gartner et al. 2002 ), its use for in vitro selection has not been reported. DNA display is a general method for the in vitro selection of synthetic combinatorial chemistry libraries. The system is modular, so that chemistry and selection protocols can be easily changed. It can take advantage of existing combinatorial chemistry technology as well as chemical transformations previously carried out in the presence of unprotected DNA ( Gartner et al. 2002 ; Summerer and Marx 2002 ). Solid-phase, solution-phase, enzymatic, and proximity effect reaction formats are all suitable. We have developed an extensive set of tools to adapt new chemistries for in vitro selection ( Halpin et al. 2004 ). In addition to diverse chemistries, many different library architectures are also possible. The library reported here was synthesized in six encoded steps with ten distinct building blocks per step. However, essentially any combinatorial scheme can be accommodated. The 20-base codon sequences used here were taken from a larger set (>10,000) of 20-base sequences experimentally verified to exhibit orthogonal hybridization properties ( Giaever et al. 2002 ). As a first approximation, the highest possible fold enrichment per round of selection can be determined by considering its relationship to translation fidelity and the signal-to-noise ratio of the selection. Fold enrichment (E) is defined as the geometric increase in the fraction of target molecules in a library that results from a single round of synthesis and selection. Fidelity (F) is defined as the fraction of genes recovered from a completed library synthesis that have been correctly translated. The signal-to-noise characteristic of a selection (S/N) is defined as the ratio of the fraction of target molecules selected to the fraction of nontarget molecules selected. In most cases, the fold enrichment reduces to the simple expression at the right of Equation 1 where f 0 denotes target gene fraction in the selection input and p denotes the probability of a nontarget gene being mistranslated to the target gene product. Biological systems have such high fidelity that F can be considered to equal one. However, the fidelity of chemical translation processes is the product of hybridization specificity and chemistry efficiency raised to the power of the number of steps. It is important to consider these parameters when adapting new chemistries and selections to the DNA display format. Equation 1 can help determine the minimum number of rounds required for library convergence, and thus the feasibility of a proposed in vitro selection experiment. For example, a library synthesized with a fidelity of 0.01 and subjected to a selection with a S/N of 1000 would give a 10-fold enrichment per round at best. If the library included 10 12 unique members, at least 12 rounds would be required to achieve convergence. In addition to influencing convergence rates, fidelity also limits achievable library complexity. The maximum effective library complexity corresponds to the product of Avagadro's number, the moles of library, and the fidelity. Based on our observed 90% hybridization efficiency and 95% chemistry efficiency, extension of the library reported here to 13 synthetic steps would produce 10 12 distinct small molecules per 30 pmol of DNA starting material, a quantity easily manipulated in a microcentrifuge tube. Diversification between rounds of selection by recombination makes possible in vitro evolution of libraries with complexities exceeding the physical library size. Thus, a “best” molecule can be pinpointed without exhaustive testing of all potential species. Starting with a working population of compounds that sparsely sample a chemical space, molecules containing parts of an optimal molecular solution often have a selective advantage relative to siblings, and become enriched. Subsequent recombination processes splice together fragments from the numerous partially optimal molecules to form a globally optimal molecule. Thus, the best structure is found, even if the odds were negligible that it existed in the initial working population. The same principle accounts for the striking success of gene shuffling in protein engineering ( Kurtzman et al. 2001 ) and of the genetic algorithm optimization procedure in computer science ( Forrest 1993 ). Recombination of a DNA display library by DNA shuffling ( Stemmer 1994 ), which was used here to diversify the initial DNA library ( Halpin and Harbury 2004 ), would enable the in vitro evolution of synthetic libraries with complexities exceeding 10 13 . Prospectus DNA display enables the use of genetic tools such as complementation analysis and backcrossing to analyze small-molecule populations. The approach can be used to study molecular evolution without potential biases resulting from experiments restricted to RNA, DNA, and peptide polymers. A general scientific problem that will be directly addressed is the relationship between small-molecule library complexity and the quality of molecules discovered. With biopolymers, more complex libraries yield higher-affinity ligands ( Takahashi et al. 2003 ). However, many have argued that increasing small-molecule library complexity will not produce higher quality “hits” ( Breinbauer et al. 2002 ). This judgment is based on the paucity of viable drug candidates that have emerged from even the most complex combinatorial chemistry libraries. Analysis of “hits” from increasingly diverse small-molecule populations (as much as 10 6 -fold more complex than current synthetic libraries) will test the validity of this belief. Drug discovery would represent one important application for a small-molecule in vitro selection technology. While the cost of drug discovery has increased continuously over the last decade (from less than $15 billion for research and development in 1996 to more than $25 billion in 2002), the number of new molecular entities approved by the FDA has steadily dropped, from 56 in 1996 to 17 in 2002 ( Hall 2003 ). A fast, inexpensive, and generally accessible procedure for the in vitro selection of druggable small-molecule libraries would accelerate the early stages of drug development. The nonnatural peptide chemistry in this work was developed as a proof of principle, but may nevertheless have practical applications in medicine. For example, the nonribosomal peptide drugs vancomycin and cyclosporin are a widely used antibiotic and immunosuppressant, respectively ( Walsh 2002 ). Annual joint sales of the nonnatural gonadotropin-releasing-hormone peptide analogues gosarelin and leuprolide exceed $2 billion ( Klabunde and Hessler 2002 ). DNA display offers an immediate approach for the in vitro selection of general polyamide libraries that include such compounds ( Halpin et al. 2004 ). Future extensions of DNA display include the development of massively parallel array-based splitting strategies for the in vitro selection of low-molecular-weight small-molecule libraries (for example a library built in three synthetic steps with 10,000 building blocks per step). Massively parallel syntheses will produce compounds that conform better to Lipinski's “rule of five” ( Lipinski et al. 1997 ) and presumably will thus be more druggable. Beyond drug discovery, DNA display can be applied to the engineering of chemical switches, the discovery of transition metal catalysts for aqueous and nonbiological environments, and the identification of enzyme-specific ligands for activity-based profiling. Because the system is inexpensive, is easily implemented by a single individual, and requires only common laboratory equipment, in vitro selection and eventual evolution of large synthetic chemical populations should become a broadly accessible tool. Materials and Methods Materials. The 3-E7 antibody was purchased from Gramsch Laboratories (Schwabhausen, Germany). PANSORBIN cells were purchased from Calbiochem (San Diego, California, United States). BSA was purchased from New England Biolabs (Beverly, Massachusetts, United States). Yeast tRNA was purchased from Ambion (#7119, Austin, Texas, United States). The [Leu]enkephalin peptide and all oligonucleotides were purchased from the Stanford PAN Facility (Stanford, California, United States). Chemistry. Solid-phase peptide synthesis was carried out as previously described ( Halpin et al. 2004 ). 5′ amino-modified ssDNA (#10-1912-90, #10-1905-90, #10-1918-90, Glen Research, Sterling, Virginia, United States) was noncovalently bound to DEAE Sepharose Fast Flow resin (# 17-0709-01, Pharmacia-LKB Technology, Uppsala, Sweden) packed into TWIST column housings (Glen Research #20-0030-00). DNA was loaded onto the columns in 10 mM acetic acid, 0.005% Triton X-100 buffer. To accomplish amino acid additions, columns were washed with 3 ml of DMF and subsequently incubated with 62.5 mg/ml Fmoc succinimidyl esters in 300 μl of coupling solvent (22.5% water, 2.5% DIEA, and 75% DMF) for 5 min. Excess reagent was washed away with 3 ml DMF, and the coupling procedure was repeated. The Fmoc-protecting group was then removed by two 1-ml treatments with 20% piperdine in DMF, one for 3 min and one for 17 min ( Carpino and Han 1970 ). Finally, the columns were washed with 3 ml of DMF followed by 3 ml of DEAE Bind Buffer (10 mM acetic acid, 0.005% Triton X-100). Anhydride couplings followed the same procedure except that a 3-ml water wash was added after DNA loading to remove remaining acetic acid. Columns were incubated with 10 mM of each anhydride (100 mM for trimethylacetic anhydride) in 500 μl of DMF for 30 min. 20-base oligonucleotide–peptide conjugates were eluted off DEAE columns with 2 ml of DEAE Elute Buffer (1.5 M NaCl, 50 mM Tris pH 8.0, and 0.005% Triton X-100). 340-base ssDNA-peptide conjugates were eluted with 2 ml of Basic Elute Buffer (1.5 M NaCl, 10 mM NaOH, and 0.005% Triton X-100) heated to 80 °C. For synthesis of libraries, a 2-ml PBS wash was added at the end of each amino acid coupling step to remove remaining anionic reagents. Following the last coupling step in the library synthesis, free oligonucleotides were separated from 340-base DNA supports by washing with 2 ml of DEAE Elute Buffer. Electromobility shift assay. The electromobility shift assay was performed as previously described ( Hwang et al. 1999 ). No plasmid DNA was added to the samples. Antibody 3-E7 (0.5 μg) was added to the “antibody plus” samples. Samples were run on a 2% NuSieve (#50081, FMC Bioproducts, Rockland, Maryland, United States) agarose gel for 1 h at 100 V in TBE. 840 μM peptide was used to compete away binding to the peptide–DNA conjugate. Selection. ssDNA was converted to double-stranded DNA (dsDNA) by one-cycle PCR with a single end primer. The 50-μl PCR reaction contained 20 μM primer, 200 μM of each dNTP, 5 mM MgCl 2 , 1X Promega Taq reaction buffer, and 5 U of Taq DNA polymerase (#M1661, Promega, Madison, Wisconsin, United States). The PCR program was 94 °C for 2.5 min, 58 °C for 1 min, and 72 °C for 15 min. The dsDNA–peptide conjugates were incubated with PANSORBIN cells in 50 μl of Selection Buffer (TBS, 0.1% BSA, and 0.1 μg/μl yeast tRNA) at 4 °C for 1 h to preclear conjugates that nonspecifically bind to the cells. Then, preclear beads were pelleted by centrifugation and removed. Antibody 3-E7 (0.5 μg) was added to the supernatant and allowed to incubate for 1 h at 4 °C. The solution was then mixed with fresh PANSORBIN cells for 1 h at 4 °C. The cells were pelleted and washed at 25 °C three times with 500 μl of Wash Buffer (TBS, 0.1% BSA, 0.1 μg/μl tRNA, and 350 mM NaCl), followed by a single wash with 500 μl of Selection Buffer. The dsDNA–peptide conjugates were eluted by incubation of the cells with 50 μl of 200 μM [Leu]enkephalin in Selection Buffer for 1 h at 25 °C. Selected genes were amplified from 10 μl of elute supernatant with 25-cycle PCR reactions. General. High performance liquid chromatography analysis of DNA–peptide conjugates, synthesis of anticodon columns, hybridization and transfer of DNA, library assembly, ssDNA generation, and library isolation were performed as previously described ( Halpin and Harbury 2004 ; Halpin et al. 2004 ).

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC434149.xml

517948

Optimisation of the two-dimensional gel electrophoresis protocol using the Taguchi approach

Background Quantitative proteomic analyses have traditionally used two-dimensional gel electrophoresis (2DE) for separation and characterisation of complex protein mixtures. Among the difficulties associated with this approach is the solubilisation of protein mixtures for isoelectric focusing (IEF). To find the optimal formulation of the multi-component IEF rehydration buffer (RB) we applied the Taguchi method, a widely used approach for the robust optimisation of complex industrial processes, to determine optimal concentrations for the detergents, carrier ampholytes and reducing agents in RB for 2DE using commercially supplied immobilised pH gradient (IPG) gel strips. Results Our optimisation resulted in increased protein solubility, improved resolution and reproducibility of 2D gels, using a wide variety of samples. With the updated protocol we routinely detected approximately 4-fold more polypeptides on samples containing complex protein mixtures resolved on small format 2D gels. In addition the pI and size ranges over which proteins could be resolved was substantially improved. Moreover, with improved sample loading and resolution, analysis of individual spots by immunoblotting and mass spectrometry revealed previously uncharacterised posttranscriptional modifications in a variety of chromatin proteins. Conclusions While the optimised RB (oRB) is specific to the gels and analysis approach we use, our use of the Taguchi method should be generally applicable to a broad range of electrophoresis and analysis systems.

Background During cell cycle progression different functional protein complexes associate with and dissociate from chromosomal DNA [ 1 , 2 ]. We have taken a proteomic strategy to identify and then characterize proteins that are bound to chromatin at defined stages of the cell cycle in cell free extracts derived from Xenopus eggs. The combination of 2D gel electrophoresis (2DE) and Mass Spectroscopy (MS) are powerful tools for this analysis. 2DE is capable of resolving thousands of proteins in a single separation procedure [ 3 ]. Development of immobilised pH gradients (IPG) coupled with pre-cast gradient polyacrylamide gels and introduction of new sensitive fluorescent stains have considerably simplified and greatly improved the capacity, sensitivity and reproducibility of 2D gels. These recent technological advances do not however eliminate a number of difficulties associated with the separation of proteins by 2DE. One major problem is the solubilisation of protein mixtures during isoelectric focusing (IEF), (reviewed in [ 4 ]). In addition, reduction and alkylation of protein samples for 2DE has not yet been fully optimised [ 5 , 6 ]. As a consequence, conventional approaches for protein solubilisation and modification do not reliably provide the best samples for electrophoresis. Good solubilisation of protein samples is critical for high performance 2D electrophoresis and there is a wide range of protein solubilisation cocktails reported in the literature. However we have not found any systematic studies reporting optimal concentrations of critical ingredients, possibly because conventional approaches to optimisations are very time consuming: varying all the possible components in turn and in combination is quite laborious. There are, however, methods for reducing the complexity of multi-parametric matrices. The Taguchi method, has been widely used for several decades in the development of industrial processes and recently found its way to the area of life sciences [ 7 - 10 ]. The conventional optimisation experiments require independent testing of each variable in turn. For example, testing the effect and interaction of four different reaction components, each at three separate concentration levels, would require experiment with 81 (i.e. 3 4 ) separate reactions. Using Taguchi approach the same task can be accomplished in the experiment with just nine reactions. To find the optimal and most robust conditions for 2DE, we applied a modified Taguchi method [ 9 ] for the formulation of the rehydration buffer (RB) used to solubilise and run protein mixtures during IEF. We also optimised the sample reduction and alkylation procedure traditionally performed after IEF step. The resulting protocol, substantially improved the solubility and resolution of protein mixtures derived from a variety of sources on 2DE. Results Choosing components for optimisation of RB Rehydration buffers for IEF generally consist of chaotropes (urea, thiourea), detergent(s), reducing agent(s) and carrier ampholytes (reviewed in [ 11 ]). The standard formulation of RB (sRB) contains 8 M urea [ 12 ]. However the combination of chaotropes, 7 M urea and 2 M thiourea, was reported to produce better 2D images with an immobilised pH gradient (IPG) compared to 8 M urea alone [ 13 ] and this mix was chosen as the basis for all subsequent rehydration solutions. During IEF, proteins must be maintained in a fully reduced state. Three reducing agents: DTT, TBP and TCEP were tested in identical conditions with the same protein sample using an RB containing 7 M urea, 2 M thiourea, 4% CHAPS, 0.5% ampholytes and either 20 mM DTT, 2 mM TBP or 10 mM TCEP. 50 μg pellets of Xenopus egg proteins were solubilised in each RB and separated by 2DE (Figure 1 ). Both TBP and TCEP reduced focusing in our gel system. The best focusing was achieved in the RB containing DTT, so this compound was selected for all successive optimization experiments. Figure 1 The effect of different reducing agents on protein resolution in 2D gels. 50 μg aliquots of total Xenopus egg extract were dissolved in RB containing 7 M urea, 2 M thiourea, 4% CHAPS, 0.5% ampholytes and different reducing agents. 2DE separation was conducted as described in Materials and Methods. (A) 20 mM DTT, (B) 2 mM TBP, (C) 10 mM TCEP. Detergents in RBs help to prevent protein interaction and aggregation, and their properties are critical for protein solubilisation. The increasing number of proteins detected in 2D gels was reported when different zwitterionic detergents were added to solubilisation cocktails [ 14 - 16 ]. We therefore decided to optimise the combination of two widely used detergents, CHAPS and ASB14 [ 17 , 18 ]. The addition of carrier ampholytes enhances solubility of individual proteins as they approach their isoelectric points. They also produce an approximately uniform conductivity across a pH gradient without affecting its shape. For these reasons, we also optimised the concentration of carrier ampholytes within the solubilisation buffer. The concentration ranges of components used in one representative optimisation experiment are presented in Table 1 . To accommodate increased carrier ampholyte concentrations, we increased the length of focusing time during electrophoresis. We found that a one step fast ramping gradient between 250–5500 V after the initial phase of 30 min at 250 V worked well for 7 cm IPG strips of different pH ranges (pH 3–10, pH 4–7, pH 6–11). The duration of the IEF step was dependent on both sample conductivity and protein loading, producing good results if performed for a total of more than 33000 volts-hours. The actual voltage was limited since the current was restricted to 50 μA/strip. This total value of volt-hours was enough to complete focusing in samples with the highest carrier ampholyte concentrations. Table 1 Set up of the Taguchi optimisation experiment presented in Figure 2 with total number of spots detected in each gel. Buffer* Ampholytes(%) CHAPS(%) ASB14(%) DTT(mM) Spot number** 1 0.5 0.5 0.4 20 361 2 0.5 1.0 0.8 40 339 3 0.5 2.0 1.6 80 339 4 1.0 0.5 0.8 80 296 5 1.0 1.0 1.6 20 351 6 1.0 2.0 0.4 40 355 7 2.0 0.5 1.6 40 319 8 2.0 1.0 0.4 80 327 9 2.0 2.0 0.8 20 299 * – all buffers contained 7 M urea, 2 M thiourea, 10 mM Tris ** – number of spots detected, using Phoretix 2D Pro imaging software After IEF, the focused gel is prepared for SDS-PAGE, usually by incubating consecutively in two equilibration buffers containing DTT or iodoacetamide (IAA) respectively. IAA serves to alkylate reduced cysteine residues and prevent their modification during and after SDS-PAGE. There is evidence that this protocol is not very efficient as the SDS in equilibration solution interferes with IAA alkylation. Variable alkylation can cause a substantial number of artefactual spots on 2D gels [ 6 , 19 ]. One of the ways to overcome the problem is to alkylate protein mixtures in solubilisation buffer before the IEF. However, the presence of thiourea in the RB prevents effective protein alkylation by IAA [ 5 ]. Acrylamide is an alternative alkylating agent, and is used widely for protein modification in MS studies. To alkylate cysteines we adjusted acrylamide concentration in RB to 60 mM after solubilisation of protein pellets for 2 hours in the presence of the indicated amount of DTT. The acrylamide treatment was also repeated after IEF as part of the equilibration procedure (see Materials and Methods). Taguchi optimisation of RB Aliquots of total Xenopus egg extract containing 50 μg of protein were dissolved in rehydration buffers formulated according to the L9 orthogonal Taguchi array shown in Table 1 and then separated by 2DE. The resulting 2D gels exhibited different degrees of focusing and spot presentation especially in the high molecular weight region of the gels (Figure 2 ). The number of spots detected by commercial image analysing software in each individual gel was used to calculate the Taguchi's SNR values for each level of a given component. The signal-to-noise ratio ( SNR ) function is a statistical measure of performance and takes into account both the mean and variability. In its simplest form, the SNR is the ratio of the mean (signal) to the standard deviation (noise). While there are many different possible SNR s their simple interpretation is always the same: the larger the SNR the better. We used the Taguchi SNR function that is most applicable to the situation where the highest yield of optimised process is desirable (see Materials and Methods). Figure 2 2D gel images from a representative optimisation experiment. Nine rehydration buffers were tested according to Table 1. Each buffer was used to solubilise 50 μg whole Xenopus egg extract and each sample was run on identical gels under identical conditions. (1–9) are images of high molecular weight regions of 9 gels with arrows pointing to some spots whose intensity and focusing pattern changed considerably between different RB compositions. Total numbers of detected spots in individual gels are presented in Table 1. Figure 3 demonstrates the SNR graphs from a representative optimization experiment. For two variables in this experiment, CHAPS and DTT, the SNR functions had a maximum within the analysed range and highest SNR s were achieved at 1.32% CHAPS and at 34 mM DTT (Figure 3A,3B ). By contrast the highest SNRs were achieved with the lowest concentration of ampholytes and ASB14 (Figure 3C,3D ). Figure 3 Effects of reaction components on SNR functions in a representative experiment. The Taguchi calculations were carried out using total number of spots detected by imaging software in 2D gels (Table 1). The optimal concentration of each component corresponded to the highest value of the SNR function (a–d). To analyse reproducibility of our approach we repeated experiments three times and determined optimal concentrations of RB components as (1.20 ± 0.18)% CHAPS, (43 ± 12) mM DTT, 0.25% ampholytes and 0.4% ASB14 (the last two are the lowest concentrations used in our optimisation experiments). The SNR response for ASB14 suggests there may be two concentrations that increase the number of detected spots. While we chose detected spot number as a general reporter of RB performance, other factors such as spot circularity, streaking, spot intensity, etc are also critical. We noted that concentrations of ASB14 slightly higher than 2.0% induced significant streaking and spot shape changes leading to a reduced number of detectable spots (see Additional file 1 ). This suggested that concentrations of ASBB14 near 2.0% could not perform robustly, i.e. small changes would have deleterious effect on 2DE. Figure 3C and 3D suggest that low concentrations of ampholytes and ASB14 may improve detected spot number. To determine if we achieved optimal concentrations for these components, we assayed whether further reducing the amount of these components might increase detected spot number and further improve 2DE performance. Using concentrations of 0.1% – 0.4% ASB14 and 0.05% – 0.25% ampholytes we found no significant changes in detected spot number (see Additional file 1 ). Thus, the chosen concentrations of ASB14 and ampholytes gave a robust performance and we therefore considered them to be optimised for our 2DE system. To confirm that the correct choice was made for the optimal ASB14 concentration we compared 2D gels of total Xenopus eggs proteins separated with sRB (8 M urea, 4% CHAPS, 0.5% ampholytes, 20 mM DTT) and with oRB (7 M urea, 2 M thiourea, 1.2% CHAPS, 0.4% ASB14, 0.25% ampholytes) (Figure 4A,4B ). We observed at least a 50% increase in detected spot number in oRB (659 spots) compared to the standard buffer composition (425 spots). Figure 4 2DE of two different sets of proteins in sRB (A, C) and oRB (B, D). 50 μg of total Xenopus egg extract (A, B) or 25 μg of protein eluted from mitotic chromosomes assembled for 30 min in mitotic Xenopus egg extract (C, D) were dissolved in sRB (8 M urea, 4% CHAPS, 0.5% ampholytes, 20 mM DTT) and oRB (7 M urea, 2 M thiourea, 1.2% CHAPS, 0.4% ASB14, 0.25% ampholytes, 43 mM DTT, 30 mM Tris) and separated as described in Materials and Methods. Spots detected: (A) 425, (B) 659, (C) 112, (D) 350. To extend our analysis we have evaluated the performance of oRB with a variety of different samples. Chromatin associated proteins are often enriched in lysine and arginine residues that mediate interactions with DNA. When we analysed a preparation of mitotic chromosome proteins [ 20 ] using sRB, we noted a distinct lack of basic and high molecular weight polypeptides detected on the gel (Figure 4C ). 2DE of chromosome associated proteins with oRB revealed a large number of basic proteins, as well as many high molecular weight polypeptides, including known high molecular weight chromosome components like DNA topoisomerase II and condensin [ 21 ] (Figure 4D ). Similar performance was noted with preparations of human nucleolar proteins, mouse and shrimp mitochondria (data will be presented elsewhere). We therefore concluded that our optimised 2DE methodology could be successfully applied to a wide variety of samples. Analysis of post-translational modifications by 2DE As a test of the use of an oRB, we characterised a series of well-known chromatin proteins whose function is regulated during the cell cycle. Our improved RB and revised alkylation procedure eliminated ambiguities in immunoblots of high molecular weigh proteins and revealed specific modifications that have not been described previously. XCAP-E (M.W. 140 kDa), a component of condensin, a protein complex involved in the assembly of mitotic chromosomes [ 21 ], was analysed in mitotic chromatin eluates. With sRB, 2D gels stained with SYPRO-Ruby or immunoblotted with an anti-XCAP-E antibody produced only a smear, suggestive of poor solubilisation or focusing (Figure 5A,5B ). The same sample was resolved into 7 distinct spots by oRB and acrylamide alkylation (Figure 5C,5D ). Figure 5 Effect of different rehydration buffers on quality of western blotting analysis of 2D gels. 25 μg of mitotic chromosomes eluates were separated on pH6-11 IPG strips using sRB (A, B) or oRB (C, D) as in Figure 4. 2D gels were stained with SYPRO Ruby (A, C) or immunoblotted using anti-XCAP-E antibody (B, D). The protein spots seen on the top right of (A) were not reproducible and were perhaps due to poor alkylation. The spot pattern observed in (C) was reproducible. Phosphorylation is one of the post-transcriptional modifications that can change the pI of proteins. Previous analysis has not detected significant phosphorylation in XCAP-E and treatment of these samples with phosphatase produced no change in the 2DE pattern (data not shown). We are currently exploring the nature of this apparently specific modification. Regardless, an optimised 2DE methodology can reveal multiple forms of proteins, even relatively large ones that are traditionally poorly resolved. The 6 mini chromosome maintenance proteins MCM2-7, which are a central component of the replication licensing system, are bound to Xenopus chromatin from the end of mitosis and are released during S-phase [ 22 , 23 ]. The six proteins (M.W. ~90–105 kDa) were identified by 2DE from replicating chromatin eluates using immunoblotting analysis and MALDI-TOF (data not shown). Figure 6 shows 2DE images of MCM2, 3,4,6 and 7 eluted from replicating Xenopus chromatin at the beginning of S-phase in a control experiment (Figure 6A ) and in extract treated with geminin, which prevents loading of MCM2-7 onto chromatin (Figure 6B ) [ 24 - 26 ]. The treatment of chromatin eluates with λ-phosphatase changed the distribution of spots corresponding to MCM2, 3 and 4 whereas MCM6 and MCM7 spots remained mostly unchanged (Figure 6C ). With the improved resolution of 2DE provided by oRB and sample treatment, the analysis of post-translational modifications of relatively large proteins is now possible. Figure 6 Cell cycle dependent association of replication licensing complex with chromatin. Proteins eluted from chromatin assembled for 30 min in Xenopus egg extracts were separated on pH 3–10 2D gels and stained with SYPRO Ruby. In control assembly reaction MCM2,3,4,6 and 7, were detectable in eluates as multiple spot chains (A). In the presence of geminin, association of MCMs with chromatin was prevented (B). The treatment of control chromatin eluates with λ-phosphatase changes the MCMs presentation in the 2D gel (C). Discussion In this study we successfully applied a modified Taguchi strategy [ 9 ] to optimise conditions for 2DE. An optimal formulation of the rehydration buffer used to dissolve proteins and run IPG strips in the first dimension was determined using the Taguchi experimental design based on an orthogonal L9 array (Table 1 ). We observed that the resolution of proteins over 100 kDa depended heavily on the composition of the RB and were able to define a recipe that provides significantly improved 2DE performance for many different samples. Our data demonstrated that the combination of different detergents in the rehydration solution improved the solubility and resolution of proteins on 2D gels, though their optimal concentrations in the mix may be very different from the ones published for mono-detergent solutions. In addition, we found that the concentration of ampholytes has the biggest effect on SNR variation and that the minimal values of 0.25% produced the highest SNR . However, different regions of the 2D gel responded differently, with proteins <30 kDa becoming less well resolved at low ampholyte concentrations. While further reduction of ampholyte concentration in RB may be beneficial for the resolution of high molecular weight proteins, this should be performed only when resolution of low molecular weight proteins is not necessary. Our results therefore suggest a method of significantly improving 2DE performance, although we cannot specify a single, universally applicable electrophoresis protocol. To verify the applicability of our oRB for different protein samples we applied standard and optimal conditions for the separation of protein fractions which were eluted from chromosomes reconstituted in Xenopus egg extracts [ 27 , 28 ], whole Xenopus egg extract, human nucleoli and mouse and shrimp mitochondria. The amount of protein generated in these experiments is limited and complete solubilisation of the samples is an important issue for 2DE and MS analysis. Use of our oRB has improved the resolution and detection of 2DE and increased the amount of protein we can provide for MS. To date, we have not observed any dependence of the composition of RB on the source of protein. Within our lab, where we use one type of gel and one gel analysis system, we now have a standard composition for RB that appears to work with all samples. Our optimised composition of RB is determined by scoring the number of spots observed employing specific commercial IPG gel strips, pre-cast SDS-PAGE gels, and commercially available 2DE analysis software. It is likely that the optimal composition of RB depends on each of these parameters. For instance, a different 2DE spot finding algorithm might find a slightly different set of spots, especially in the extreme regions of the gel, and thus change the optimal composition of RB determined by the Taguchi method. Moreover, spot number does not reflect other spot characteristics, such as intensity, circularity, etc. The use of a single parameter for the performance evaluation is a basic tenet of the Taguchi approach and the choice of it defines the result of an optimisation experiment. We therefore used a single performance criterion as the prime determinant for oRB, and incorporated other characteristics to refine our choice of oRB constituents. Therefore our most important findings are: (1) combinations of zwitterionic detergents, appropriately optimised, can provide improved solubilisation of proteins for 2DE; (2) the concentration of carrier ampholytes must be optimised; (3) the use of a technique like the Taguchi method can rapidly and relatively easily determine an optimal combination of RB components for 2DE. Whereas systematic evaluation of all possible combinations in a multi-component system is prohibitively costing and time consuming, the Taguchi method provides a systematic assessment of a systems behaviour that is reasonably straightforward to perform. Our results also extend the power of 2DE in the analysis of post-translational modifications. For subsequent MS identification of proteins and characterisation of post-translational modifications, difficulties with differential solubility and focussing must be resolved. The oRB addressed these issues and substantially improved the resolution of protein isoforms, including those larger than 100 kDa. Conclusions We have successfully applied the Taguchi method to optimise the complex composition of a rehydration buffer used in 2DE. The strategy greatly reduced the number of experiments required compared to classical designs, reduced cost, and has produced a substantially improved 2DE RB. Materials and methods Chemicals and equipment Immobiline DryStrip 7 cm gels and carrier ampholytes of different pH ranges were purchased from Amersham Pharmacia Biotech UK Ltd. The Protean IEF cell from BioRad (Bio-Rad Laboratory Ltd., Hemel Hempsted, UK) was used to run the isoelectric focusing separation. For the second dimension, the focused IPG strips were loaded on precast Novex ZOOM gels (Invitrogen, Ltd, Paisley, UK) and run with NuPage MOPS SDS buffer as instructed by the manufacturer. The zwitterionic detergent ASB14 was obtained from Calbiochem (Merck Biosciences, Ltd., Nottingham UK). Urea of AristaR grade and other general chemicals of AnalaR grade were purchased from BDH (Merck House, Poole, Dorset, UK). SYPRO Ruby (Molecular Probes, Leiden, The Netherlands) was used to stain 1D and 2D gels according to manufacture recommendations. The images of stained gels were acquired by a Fujiimager LAS1000 using the Dark Reader trans-illuminator (Clare Chemical Research, Inc., Dolores, USA). Spot detection, matching, quantification and analysis were carried out using Phoretix 2D Pro software (Nonlinear Dynamics Ltd, Newcastle, UK). The antibody against XCAP-E (R5-5) was kindly supplied by T. Hirano [ 21 , 29 ]. Sample preparations Xenopus egg extract preparation, chromatin reconstitution, and elution of chromosomal proteins was carried out as previously described [ 27 , 28 , 20 ]. For IEF, methanol/chloroform precipitated samples containing 50 μg protein (or as otherwise stated) were solubilised either in the standard rehydration buffer (sRB: 8 M urea/4% CHAPS/0.5% ampholytes/20 mM DTT) or RBs prepared according to the Taguchi orthogonal array (Table 1 ). The composition of optimised rehydration solution was as follows: 7 M urea/2 M thiourea/1.2% CHAPS/0.4%ASB14/0.25% ampholytes/43 mM DTT/30 mM Tris base). Solubilisation was carried out on a vibra-shaker for 2 hours at room temperature. To alkylate proteins before IEF, freshly prepared 9 M acrylamide in water was added to each sample to a final concentration of 60 mM and incubation continued for another 1.5 hours at room temperature. At the end of the incubation, samples were spun for 10 min at 16000 × g in a micro-centrifuge and transferred to rehydration chambers. The dry IPG strips were allowed to re-swell in RBs over night before IEF separation. For samples that were prepared in sRB solubilisation was continued for 2 hours without alkylation before starting the IPG re-swelling. Electrophoresis and spot detection Isoelectric focusing was performed using a two phase protocol: (1) 250 V for 30 min and (2) 250 – 5500 V fast ramping voltage gradient to accumulate 33000 total volt-hours. The focused IPG strips were subjected to additional reduction and alkylation treatment before the second dimension SDS-PAGE. The strips were equilibrated for 20 min in 25 mM DTT dissolved in 6 M Urea/2% SDS/30% glycerol/50 mM Tris HCl pH8.8 and then alkylated by incubating with 360 mM acrylamide for 20 min in the same buffer. Equilibrated IPG strips were applied to precast Novex 4–12% Zoom gels and run at room temperature for 1 hour at 200 V. SYPRO Ruby stained gels were imaged and the total number of spots in individual gels was determined by Phoretix 2D Pro imaging software. The resulting data were visually inspected to remove background artefacts. The number of spots identified in each individual gel was used in the Taguchi calculations. Taguchi experimental design In the Taguchi method, variables under optimisation are arranged into orthogonal array (Table 1 , L9 orthogonal array for the representative experiment). With 2DE, each column would correspond to individual buffer components, and each row would represent individual IEF rehydration buffer. Each component is taken at three defined concentration (A, B and C), covering the range where its effect can be determined. The number of spots detectable in 2D gels prepared with individual RB compositions (the yield of trials) is used to evaluate the effect of the components. This is done by calculating Taguchi's signal to noise ratios ( SNR ) for each component. The goal with 2DE is to maximise the numbers of detectable spots. For this aim G. Taguchi designed the following SNR function: where SNR is signal to noise ratio, n is a number of trials with given concentration and Y i is the yield in correspondent trials [ 9 , 30 ]. To calculate the SNR for 1% CHAPS, for example, we used total spot numbers in gels 2, 5 and 8 ( n = 3), where CHAPS was present at 1% level (Table 1 ). A second order polynomial fit was employed to calculate the concentration corresponding to the maximum of SNR if it was present on a graph. Mass spectrometry The spots of interest were excised from 2D gels and were subjected to MALDI-TOF or LC/MS-MS analysis in the Post-Genomics and Molecular interactions Centre of University of Dundee. List of abbreviations RB – rehydration buffer, sRB – standard rehydration buffer, oRB – optimised rehydration buffer, SNR – signal to noise ratio, TBP – tributylphosphine, TCEP – tris(2-carboxyethyl)phosphine hydrochloride, DTT – dithiothreitol, IAA – iodoacetamide, IPG – immobilised pH gradient, MCM – mini-chromosome maintenance proteins, PTM – post-translational modifications. Competing interests None declared. Authors' contributions GAK designed and carried out optimisation experiments, 2DE analysis of Xenopus licensing complex and human nucleolar proteins and drafted the manuscript. IMP carried out 2DE analysis of Xenopus condensin complex and mitochondrial extracts from mouse and shrimp cells. JJB participated in the design of the study and coordination. JRS conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript. Supplementary Material Additional File 1 2DE response to variations of ASB14 and ampholytes concentrations. 50 μg aliquots of total Xenopus egg extract were dissolved in RBs containing 7 M urea, 2 M thiourea, 1.2% CHAPS, 43 DTT and variable amount of ASB14 and ampholytes as indicated on each picture. Spots detected: (A) 682, (B) 227, (C) 662, (D) 640. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC517948.xml

545954

Splenectomy for solitary splenic metastasis of ovarian cancer

Background Splenic metastases occur in rare cases with a few case reports of patients in the literature. Generally, splenic metastases mean late dissemination of a disease. Solitary splenic metastases from solid tumors are extremely unusual. Case presentation We report a case of a patient with ovarian mucinous cystadenocarcinoma who underwent splenectomy for isolated parenchymal metastasis. Conclusion Ovarian epithelial tumors comprised most of isolated splenic metastases from gynecologic tumor. When isolated splenic recurrence is suspected on image studies and serum tumor markers, intraabdominal gross findings should be examined to exclude peritoneal carcinomatosis. If only spleen was under suspicion of recurrence of ovarian cancer, splenectomy may play a therapeutic role.

Background Splenic metastases from solid tumors occur in late stage of a disease, so those can hardly be an indication for a surgery. Cancers of the ovaries, lung, breast, stomach, skin and colon are known to metastasize to the spleen [ 1 ]. Even though recent reports suggest increasing incidence of splenic metastasis from gynecologic tumors [ 2 - 8 ], the number of the cases that isolated splenic metastasis is fewer than 25 in the literature worldwide. Among them, solitary parenchymal metastasis would comprise the small portion. Fewer than 15 cases of splenic metastasis occur from the ovaries as a primary site, pathology revealed cystadenocarcinoma. We present a case of mucinous cystadenocarcinoma that recurred in the splenic parenchyma. Case presentation A 29-year-old woman was admitted our hospital on August 1999, had been diagnosed as mucinous tumor of borderline malignancy a year ago. She was followed up at a local clinic. On the follow-up study, the patient's CEA level was raised to 43.32 U/L and CT scan showed splenomegaly with cystic lesion. Her past medical history was not significant. She already underwent two surgeries. The first surgery, right salpingo-oophorectomy, was performed at the age of 22 after being diagnosed as dermoid cyst. She was healthy thereafter. Seven years later, left ovarian mass was found on the routine check. At the second surgery, left ovarian mass excision, mucinous tumor of borderline malignancy was diagnosed. During follow-up after the second surgery, she was referred to our hospital on the suspicion of carcinomatosis peritonei. On the preoperative evaluation (Figure 1 ), splenic lesion, which had been existed for 2 years, was merely noticed as simple cystic lesion unrelated to the ovarian mass. To exclude peritoneal carcinomatosis, open laparotomy was perfomed. On opening the abdomen, no abnormal gross findings were found except the splenic lesion, which reported as probable metastatic adenocarcinoma on frozen sections. After splenectomy was carried out, peritoneal washings and multiple biopsies on the omentum, peritoneum, mesentery, and left ovary were performed to rule out possible microscopic peritoneal dissemination. Suspecting the transabdominal metastasis, 100 mg of cisplatin was infused into the peritoneal cavity at the end of the operation; however no intraperitoneal recurrence was confirmed after tissue diagnosis. Final pathologic examination (Figure 2 ) showed metastatic mucinous cystadenocarcinoma. Peritoneal washings and multiple biopsies were all negative. The patient was recovered from the surgery without the evidence of sepsis of severe thrombocytosis. The patient received 5 courses of Taxol and Carboplatin as postoperative chemotherapy. Two years after the surgery, 3 × 3-sized mass on the left ovary, which assumed to be recurrence, was detected. She has been followed up at outpatient department receiving symptomatic treatment and chemotherapy. Discussion The frequency of splenic metastasis has been reported 2.3 to 7.1 per cent from autopsy series of cancer patients [ 9 - 11 ]. Splenic metastases from the ovaries, uterus, uterine cervix, lung, breast, stomach, skin and colon have been reported, and ovarian cancer comprises the three fourth. Until now, in the literature, splenic capsular metastasis has been reported to occur in the case of far advanced stage of a disease or in the case of more than one organ was already involved in terminal stage and especially in the case of peritoneal metastasis. When one or more organ in the thoracic cavity and abdominal cavity was involved, the rate of splenic metastasis was 43% [ 12 ]. But isolated splenic metastasis is rare; only fewer than 25 cases were reported. Some reported hematogenous metastasis to the parenchyma of the spleen [ 13 ]. This mean the spleen is the organ of the privilege [ 11 , 14 , 15 ]. It could be explained by hypothesis of the role of the splenic capsule as physical barrier; the lack of afferent lymphatics in the splenic parenchyma, the acute angle of the origin and the tortuosity of the splenic artery, the rhythmic contractile properties of the spleen, and the immune competence and possible antineoplastic nature of the splenic tissue itself [ 13 ]. In this case, solitary splenic parenchymal metastasis from ovarian epithelial tumor was made hematogenously without intraabdominal dissemination. Among cases of the isolated splenic metastasis, the ovarian cancers make up the most. After Minazawa et al [ 16 ] firstly performed splenectomy in the splenic metastasis of ovarian cancer patient, splenectomy, as a therapeutic modality of splenic metastasis, was supported by similar articles and studies [ 17 , 18 ]. Recently, splenectomy was often included in the cytoreductive surgery of the ovarian cancer [ 2 , 3 , 19 ]. CT scanning and measurement of serum tumor markers, especially CA 125, are helpful for detecting the recurrence and the infrequent splenic metastasis. We proposed that splenectomy be a proper therapeutic modality for an isolated splenic metastasis, especially parenchymal metastasis, from an ovarian cancer. When isolated splenic recurrence is suspected on the CT scanning and serum tumor markers, intraabdominal gross findings should be examined meticulously. If only spleen was under suspicion of recurrence, splenectomy would be a proper therapeutic procedure. Competing interests The author(s) declare that they have no competing interests. Authors' contributions YS carried out the literature search and prepared the manuscript. JC Kim, the corresponding author, was the main operator in charge of the case. CK was involved in the operation and the patient active management. Three authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545954.xml

535531

Knowledge of malaria influences the use of insecticide treated nets but not intermittent presumptive treatment by pregnant women in Tanzania

Background To reduce the intolerable burden of malaria in pregnancy, the Ministry of Health in Tanzania has recently adopted a policy of intermittent presumptive treatment for pregnant women using sulphadoxine-pyrimethamine (IPTp-SP). In addition, there is strong national commitment to increase distribution of insecticide treated nets (ITNs) among pregnant women. This study explores the determinants of uptake for both ITNs and IPTp-SP by pregnant women and the role that individual knowledge and socio-economic status has to play for each. Methods 293 women were recruited post-partum at Kibaha District Hospital on the East African coast. The haemoglobin level of each woman was measured and a questionnaire administered. Results Use of both interventions was associated with a reduced risk of severe anaemia (Hb<8 g/dL) compared to women who had used neither intervention (OR 0.31, 95% CI 0.14–0.67). In a logistic regression model it was found that attendance at MCH health education sessions was the only factor that predicted IPTp-SP use (OR 1.8, 95% CI 1.1–2.9) while high knowledge of malaria predicted use of ITNs (OR 2.3, 95% CI 1.1–4.9). Conclusion Individual knowledge of malaria was an important factor for ITN uptake, but not for IPTp-SP use, which was reliant on delivery of information by MCH systems. When both these interventions were used, severe anaemia postpartum was reduced by 69% compared to use of neither, thus providing evidence of effectiveness of these interventions when used in combination.

Background Plasmodium falciparum malaria in pregnancy poses a substantial risk to pregnant women and their offspring: it has been estimated that malaria in pregnancy is the primary cause of up to 10,000 maternal anaemia-related deaths in sub-Saharan Africa annually [ 1 ]. Further, malaria in pregnancy increases the risk of an infant being born with low birth weight (LBW) and is responsible for up to 35% of preventable LBW in malaria-endemic areas[ 2 ]. Over the last decade a body of evidence has accumulated which supports the use of both ITNs and IPTp-SP to reduce the adverse effects of malaria during pregnancy [ 1 , 3 , 4 ] and both these interventions are currently recommended by WHO [ 5 ]. In Tanzania it is now national policy to offer IPT-p with sulfadoxine-pyrimethamine (SP) for every pregnant woman attending Maternal and Child Health services (MCH). There is strong commitment in Tanzania to achieve the targets agreed on by African countries at the Abuja Conference of 60% coverage of ITNs for pregnant women by 2005 using social marketing strategies and a national voucher scheme [ 6 ] to improve access for pregnant women and their children. However, there are currently no reliable data in Tanzania on current levels of use of these interventions. Evidence suggests that malaria treatment choices are affected by knowledge of the problem [ 7 , 8 ]. The success in implementing preventive interventions amongst pregnant women in Tanzania is thus likely to be determined in part by awareness of malaria and the strategies available to prevent it. This study set out to explore the determinants of uptake for both ITNs and IPTp-SP by pregnant women, with particular regard to knowledge of malaria and socio-economic status, and to estimate the impact that reported use of either of these interventions had on the prevalence of severe anaemia in the post-partum period. Methods Setting This research was carried out at Kibaha District Hospital in the Coastal Region of Tanzania, an area with moderate to high malaria transmission. The area is predominantly rural, supported by a mixture of subsistence and cash crop farming, with a peri-urban belt along the main Tanzania/Zambia highway which crosses the district. Study participants All post-natal women who had delivered their baby in the hospital during April/May 2003, but had not been admitted to hospital during the pregnancy, were eligible for inclusion in the study. Study tools From studies elsewhere [ 9 ] it was estimated that a samples size of 293 women would give sufficient power to show a difference in uptake of malaria interventions by knowledge of malaria. On each working day of the study the first 13 women to come out of the labour ward and who gave informed consent to participate were registered and interviewed. Haemoglobin level was determined at the time of interview using a portable β-haemoglobin photometer (HemoCue © , HemoCue AB, Ängelholm, Sweden). Women with Hb<11 g/dL were referred within the hospital system. Definitions Knowledge of malaria score (KoM) Participants were assigned a 'knowledge of malaria score' (KoM) according to their responses to a series of seven closed questions (Table 1 ). Table 1 Components of the knowledge score Malaria in pregnancy statement: Agreement n/293 % Risk of infection Increases in pregnancy 270 92 Consequences Low birth weight 81 28 Pregnancy Loss 112 38 Maternal anaemia 150 51 Best Interventions* ITN 266 91 SP 115 39 Transmission Mosquitoes alone 102 35 *Women asked to state up to two malaria interventions ITN users Women who reported that during this pregnancy they had normally (i.e. >75% of the time) slept under a bednet which had been impregnated within the previous six months. IPTp-SP users Use of intermittent presumptive treatment of malaria in pregnancy with sulfadoxine-pyrimethamine. Severe anaemia Severe anaemia post-partum was defined as Hb<8 g/dL. Data analysis Data were entered twice in Epi-Info version 6.04 and analysed in Stata version 7 (Stata Corporation, Texas USA). Evidence of an association was sought between the variables listed in Table 1 and the study outcomes (reported use of interventions or severe anaemia defined as Hb<8 g/dl). Variables found to have an association with each outcome (by χ 2 P-value <0.10 or Mantel-Haenszel estimate of the rate ratio, P-value <0.10) were further analysed using multiple logistic regression. Significance in the multi-variate models was defined by a likelihood-ratio test (LRT) P-value <0.05. Results A completed questionnaire and measurement of haemoglobin level were available for 293 post-partum women whose characteristics are shown in Table 1 . The group was predominantly made up of married women in the 20–29 years age-group, with primary level education only. Both urban and rural residents were represented. No women refused to participate. Levels of knowledge The KoM assessment consisted of four parts: knowledge of risk, consequences of risk, transmission and prevention, with seven questions in total (Table 1 ). Each question contributed one point to the overall KoM score (i.e. range of possible scores of 0–7) and overall the mean score was 3.7 (median 4). The KoM score was stratified into three groups: score 0–2 representing low (29% of respondents), score 3–4 representing median (38%) and score 5–7 representing high levels of knowledge (33% of respondents). The lowest knowledge scores related to the impact of maternal malaria on the health of the foetus; only 28% (81/293) recognized low birth weight and 38% (112/293) pregnancy loss as a potential consequence of maternal malaria. There was also some confusion over the mode of transmission of malaria: 95% (279/293) agreed that mosquitoes could transmit malaria but only 35% thought that mosquitoes alone were responsible (excepting blood transfusion). Having high KoM was strongly associated with education level (χ 2 for linear trend 50.03, p < 0.0001). Because of colinearity between education level and high KoM, education was excluded from the logistic regression model used to identify determinants of high KoM. However, models for primary and secondary education respectively showed the same socio-economic effects as the data presented in Table 3 . In the logistic regression model, only ages above teenage (LRT 9.8, p = 0.007), ownership of a radio (LRT 6.0, p = 0.01), ownership of a bicycle (LRT 4.8, p = 0.02) and citing the MCH rather than the community as the most important source of health information (LRT 7.5, p = 0.02) were significantly associated with a high KoM compared to low/median KoM. Table 3 Unadjusted and adjusted OR's for characteristics of women with high knowledge of malaria 1 . N Unadjusted OR Confidence Interval Adjusted OR Confidence Interval LRT P Age 15–19 49 1.0 - 1.0 - 20–29 185 3.3 1.4–7.8 3.2 1.2–8.4 30+ 59 4.4 1.7–11.4 4.6 1.6–13.3 9.8 <0.01 Radio No 29 1.0 - 1.0 - Yes 264 7.7 1.7–33.1 5.1 1.1–24.0 6.0 0.01 Bicycle No 69 1.0 - 1.0 - Yes 224 2.3 1.2–4.5 2.1 1.0–4.3 4.8 0.02 Source of health information Community 36 1.0 - 1.0 - MCH 83 5.8 11.8–18.0 4.3 1.3–14.0 Media 174 4.1 1.3–12.1 2.6 0.8–8.1 7.5 0.02 1 All women with high malaria knowledge had formal schooling Use of IPT-p or ITN 48% (141/293) of women were ITN users, 57% (166/293) had received IPTp-SP once and 12% (34/293) IPTp-SP twice. A multi-variate analysis of factors influencing the uptake of these two interventions is shown in Table 4 . After adjustment, increasing age (LRT 10.3, P = < 0.01), owning a radio (LRT 4.0, P = 0.04) and having a high KoM, compared to a low/median KoM score (LRT 7.3, P = 0.02), were the only factors that independently predicted use of an ITN in pregnancy (table 4 ). By contrast, only a history of having received health education during the pregnancy significantly predicted uptake of any dose of IPTp-SP (LRT 5.6 p = 0.01). Table 4 Factors associated with uptake of insecticide treated nets (ITNs) during pregnancy and at least one dose of sulphadoxine-pyrimethamine as part of intermittent presumptive treatment (IPTp-SP) ITN users N Unadjusted OR 95% Confidence Interval Adjusted OR 95% Confidence Interval LRT P Health Education No 160 Yes 133 Age 15–19 49 1.0 - 1.0 - 20–29 185 3.2 1.6–6.1 2.5 1.3–5.0 30+ 59 4.2 1.8–10.3 3.8 1.5–9.4 10.3 <0.01 Knowledge Low 86 1.0 - 1.0 - Medium 110 1.3 0.7–2.3 1.0 0.5–1.8 High 97 3.6 1.7–7.1 2.3 1.1–4.9 7.3 0.02 Radio No 29 1.0 - 1.0 - Yes 264 2.9 1.3–6.3 2.3 1.0–5.5 4.0 0.04 Used IPTp-SP Unadjusted OR 95% Confidence Interval Adjusted OR 95% Confidence Interval LRT p Health Education No 160 1.0 - 1.0 - Yes 133 1.8 1.1–2.9 1.8 1.1–2.8 5.6 0.01 Age 15–19 49 1.0 - 1.0 - 20–29 185 0.8 0.4–1.6 0.8 0.4–1.6 30+ 59 0.9 0.4–2.0 0.7 0.3–1.7 0.4 0.8 Knowledge Low 86 1.0 - 1.0 - Medium 110 1.7 1.0–3.0 1.7 1.0–3.1 High 97 1.6 0.9–2.9 1.6 0.8–2.9 3.8 0.14 Radio No 29 Yes 264 41% (120/293) of women had used both interventions. Women with a median KoM were twice as likely (OR 2.1 (95% CI 1.1–4.0) and women with a high KoM three times more likely (OR 3.2 (95% CI 1.7–6.0) to have used both IPT-p and an ITN than women with low KoM scores. Timing of first MCH attendance 26% (76/293) of women had first attended the MCH during the first trimester, 65% (192/293) during the second trimester and 9% (25/293) during the third trimester of pregnancy. Predictably the median number of visits to MCH by women was correlated with the trimester of first visit: first trimester – median of 7.5 visits overall (mean 6.9 (s.d.2.2), second trimester – median of 5 visits overall (mean 5.4 (s.d.1.8) & third trimester – median of 2 visits overall (mean 2.4 (s.d.0.8). Women attending MCH for the first time during their first trimester were more than twice as likely to have attended health education sessions as women attending for the first time in their third trimester (χ 2 test for trend 5.1, p = 0.02). Risk factors for severe anaemia Overall, 27% (80/293) of study participants had Hb<8 g/dL immediately post-partum. In the regression model, three factors were found to independently predict the risk of having severe anaemia in the post-partum period (Table 5 ). Firstly, with women using either an ITN or IPTp-SP alone, there was a reduced risk of severe anaemia, but it was not statistically significant (OR 0.77 (CI 0.36–1.65 and OR 0.70 (CI 0.34–1.41 respectively). However, the use of an ITN in conjunction with IPTp-SP was associated with a significant reduction in risk of being severely anaemic post partum compared to women not using any intervention (OR 0.31 (CI 0.14–0.67) LRT 10.1 p = 0.01). Secondly, having had any level of education (as compared to none) and, thirdly, attendance at the MCH clinic during the first rather than third trimester of pregnancy were both associated with a reduced risk of severe anaemia (OR 0.41, 95% CI 0.17–0.98) LRT 4.5 p = 0.03 and OR 0.31, 95%CI 0.11–0.83, LRT 5.3 p = 0.05 respectively). Table 5 Risk factors for severe anaemia post partum. % with severe anaemia Unadjusted OR 95% confidence interval Adjusted OR 95% confidence interval LRT P Formal education No 29 1.00 - 1.00 - Yes 16 0.45 0.19–1.07 0.41 0.17–0.98 4.5 0.03 Time of first ANC use 3 rd trimester 48 1.00 - 1.00 - 2 nd trimester 26 0.39 0.16–0.91 0.41 0.17–1.0 1 st trimester 22 0.31 0.12–0.80 0.31 0.11–0.83 5.3 0.05 Use of malaria intervention None 37 1.00 - 1.00 - ITN only 32 0.82 0.39–1.71 0.77 0.36–1.65 IPTp-SP only 27 0.64 0.32–1.28 0.70 0.34–1.41 ITN + IPTp-SP 16 0.34 0.16–0.72 0.31 0.14–0.67 10.1 0.01 Discussion The key finding was that while knowledge, wealth and age were all found to be independently predictive of ITN use, only participation in health education was associated with use of IPTp-SP. That predictors of uptake for each of the two interventions were different was an interesting and unexpected finding. The data indicate that, while ITN use is driven by both access (i.e. wealth) as well as knowledge, use of IPTp is determined by its being offered in an MCH clinic. This is a positive finding that suggests the need to prioritise strategies for maximising early attendance to boost IPTp-SP uptake. This finding is in contrast to research from Kenya which showed that uptake of IPTp-SP increased with higher levels of formal education [ 9 ]. The National Malaria Control Programme of Tanzania does not currently have any data on IPTp-SP uptake nationally, although it is planned for integration into routine surveillance next year. It is likely that distribution systems for this, as other clinic based interventions, will need more attention. It is possible that the new MCH clinic based voucher system for ITN will have an added benefit by increasing early attendance and therefore uptake of IPTp-SP. These findings on knowledge and awareness suggest that the increased risk posed to pregnant women by malaria was almost universally recognized, but that knowledge of the health impact of that risk – especially to the health of the foetus – was very low. Over 90% of women thought that ITNs were a good intervention against malaria in pregnancy, but less than half thought the same about IPTp-SP. Knowledge of malaria in pregnancy was strongly associated with use of a combination of both ITN and IPTp. Although this was a small observational study where it was not possible to control for all likely confounding variables, there is evidence that the use of a combination of ITN use and IPT-p provides additive protection against severe anaemia, and that this effect is not confined to trial conditions. Use of an ITN or IPT-p alone was associated with a 23% and 30% reduction in the risk of severe anaemia post partum respectively, similar to estimates from other controlled trials [ 10 , 11 ] The data suggest that MCH services are effective when used optimally. Women who accessed MCH services in their first trimester of pregnancy had a significantly lower risk of severe anaemia post-partum compared to women who first presented at MCH during their third trimester. Women attending MCH earlier were more likely to attend health education sessions, and women who attended health education sessions more likely to use IPTp-SP than women who did not attend. Women with high KoM were most likely to cite the MCH as their most important source of health information. It must be noted that by recruiting only women who had uncomplicated pregnancies and who delivered in hospital this study had a selection bias towards the healthier and possibly wealthy members of the community. However, women from a considerable range of socio-economic situations are represented (no education vs. further education; mobile phone owners vs. not owning a radio) which probably reflects the rural/peri-urban catchment area of Kibaha and other newly urbanised areas. The data has shown that many of the factors indicative of relative wealth in a poor community were associated with increasing levels of knowledge – and that knowledge was positively associated with multiple intervention uptake and improved health outcome. The importance of access to resources has been illustrated previously for both preventative interventions and treatment [ 12 ]. At the time of this study, IPTp-SP was offered free of charge to pregnant women via the antenatal clinics in Tanzania. There is social marketing of bednets (price approx $5) at the national level but not targeted at specific high risk groups. The newly initiated ITN voucher scheme for pregnant women is part of the Tanzanian commitment to improve access to ITNs for pregnant women as a whole. It is hoped that via mass health education and substantial price subsidy, some of these socio-economic inequities in access will also be addressed. Conclusions The findings highlight the importance of women's knowledge of malaria in pregnancy and of antenatal attendance for the uptake of preventative interventions. Now that effective malaria interventions are available and there is political will to implement them, to maximise the potential for health impact, it is essential to empower the intended recipients of interventions by providing the knowledge which can influence their health decisions. Authors' contributions RN carried out the fieldwork and performed preliminary analysis. CD & HR contributed to study design and logistics. TM performed statistical analysis and supervised the study. All authors read, edited and approved the final manuscript. Table 2 Socio-economic breakdown of population. n/293 % Age 15–19 49 17 20–29 185 63 30+ 59 20 Gravidity Primigravidae 124 42 Mutigravidae 169 58 Residence Urban 124 42 Rural 169 58 Education None 44 15 Primary 217 74 Secondary + 32 11 Marital status Married 207 71 Unmarried 86 29 Travel time to MCH < 1 hour 252 86 1–2 hours 20 7 >2 hours 21 7 Household ownership Radio 264 90 Bicycle 224 76 TV 16 5 M/phone 13 4 Bednet 245 83 Religion Christian 104 35 Moslem 189 65 Main source of health information Community 36 12 Health workers 83 28 Media 174 59 Used IPTp-SP Once 166 57 Twice 34 12 Used bednet Yes 207 71 Used ITN Yes 141 48 Health education MCH Yes 133 45 Knowledge score Low 86 29 Medium 110 38 High 97 33

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535531.xml

524372

ArrayD: A general purpose software for Microarray design

Background Microarray is a high-throughput technology to study expression of thousands of genes in parallel. A critical aspect of microarray production is the design aimed at space optimization while maximizing the number of gene probes and their replicates to be spotted. Results We have developed a software called 'ArrayD' that offers various alternative design solutions for an array given a set of user requirements. The user feeds the following inputs: type of source plates to be used, number of gene probes to be printed, number of replicates and number of pins to be used for printing. The solutions are stored in a text file. The choice of a design solution to be used will be governed by the spotting chemistry to be used and the accuracy of the robot. Conclusions ArrayD is a software for standard cartesian robots. The software aids users in preparing a judicious and elegant design. ArrayD is universally applicable and is available at .

Background Microarray is a popularly used high-throughput technology to investigate gene expression of thousands of genes simultaneously at the level of mRNA. Ever since the development of this technology [ 1 - 3 ], transcriptional profiling at the genomic level has been employed to address numerous issues in biology and in medicine [ 4 - 8 ]. It is likely that microarrays will continue to be used to explore various biological phenomena. The basic underlying principle involves spotting DNA fragments either derived from polymerase chain reaction or preparation of plasmids or oligonucleotides at high density (~10,000–25,000 spots on a glass slide of 25 mm × 75 mm) representing the probes of the genes under study. The surface on which the DNA fragments or oligonucleotides are spotted is usually glass slides coated with poly-L-lysine or amino alkyl silane that serve to improve adherence of DNA to the surface. Uniform spotting at high density requires robotic operation and a variety of robots are now available for spotting [ 9 ]. The robots employed for the preparation of microarrays are of the cartesian type with movement in x-y-z direction. A critical aspect of microarray production is the design considering space optimization to produce high-density arrays for a given set of samples and replicates. The softwares generally supplied with robotic spotters translate user input parameters into a set of instructions in robot language for printing arrays. These softwares do not offer design capabilities in which spotting parameters and grid configurations can be chosen for a given set of samples and replicates. Presently various solutions have to be derived manually in most academic laboratories. We have developed a user-friendly software 'ArrayD' that can be used by experts and novice alike to fill this gap to simplify and aid in rapid design. ArrayD offers a variety of design solutions given a set of requirements: Number of gene probes, number of replicates, and the source plate (384 well or 96 well). Because the algorithm implemented in ArrayD is inherently simple and uses fundamental principles of robot operation, the design solutions offered by ArrayD are universally applicable to any system. The choice of a design solution would be governed by the spotting chemistry and the humidity used in addition to elegant appearance. The hallmark of ArrayD is its overall simplicity and the variety of alternative designs it offers for users to decide on choosing the appropriate spotting parameters. The multiple design solutions offered by ArrayD provides a wide range of arrays from compact to loosely spaced spots as well as convenient grid patterning, which can be user selected for printing. Implementation ArrayD program is developed in C and can be compiled and operated on UNIX V5.1, IRIX 5.1 and Red Hat Linux 7.0 (or higher) operating systems. A companion computer program ArraySolution was developed in Perl (Practical Extraction Report Language) version 5.6.1 and can be implemented on any UNIX or Linux operating system. Inputs to be defined for ArrayD (a) Type of source plate to be used The input parameter toggles between either 96 well or 384 well plate. (b) Number of gene probes to be printed The number of gene probes including positive and negative controls and blanks. (c) Number of replicates The number of replicates of each gene probe. Although the most common pattern chosen is duplicates, users can choose any number of replicates. Replicates are assumed to be printed in the Y axis. (d) Number of pins to be used for printing This parameter relates to time taken for printing the slides and the number of spots arrayed per slide. The number of pins in X-axis and Y-axis need to be specified. The type of pins used is assumed to be stealth pins, which are widely used. It is not necessary to specify pin type for ArrayD. Instead, this aspect is considered in the printing software according to the pin type used for implementing a particular design. Results and discussion A general microarray design layout is displayed in Figure 1 . ArrayD accepts standard slide dimensions (25 mm × 75 mm), conceptualizes the spotting area to be 50 mm × 22 mm to provide space for barcode labeling and for appropriate placement of coverslip over the print area. The reference direction of the robot for picking probes from source plate is left → right followed by top → down; the printing direction is top → down followed by left → right. Replicates are considered to be spotted in Y-axis (Figure 1 ). After the user has entered the parameters, the software generates a text file called 'solution.txt' that carries possible alternative array design parameters for the given input. The algorithm implemented in ArrayD is displayed in Figure 2 . The program first validates the input given by the user for appropriate number of pins in each direction and the plate type to be used. For a valid input, ArrayD calculates maximum possible number of super grids in X (or Y) direction based on the coverslip dimensions, pin number in X (or Y) direction and pin-to-pin distance (Figure 2 ). The coverslip dimensions have been set in the program as 50 mm × 22 mm for the longest size coverslip that can be effectively used during hybridization. The pin-to-pin distance is fixed at 4500 microns in the print head for 384 well plates and at 9000 microns for 96 well plates. ArrayD uses a predefined inter-spot distance database. Design solutions of ArrayD encompass various inter-spot distances that would be compatible with different spotting chemistries and conditions of humidity. We have used inter-spot distances of 170 μm, 180 μm, 190 μm, 200 μm, 220 μm, 250 μm and 300 μm. This database can be expanded to incorporate even lower inter-spot distances for use with other spotting chemistries by simple modification. We chose 170 μm as least distance based on several trial experiments in our laboratory using 50% DMSO as spotting solution and SMP3 pin type. In our experience, a minimum inter-spot distance of 200 microns works best with 50% DMSO at 40% – 50% humidity at 25°C. The options for the inter-spot distance currently offered by the software can work successfully for SMP2, SMP2B, SMP2XB, SMP2.5, SMP2.5B, SMP2.5XB, SMP3, SMP3B, SMP3XB, SMP4, SMP4B, SMP4XB, SMP5, SMP5B and SMP5XB stealth pins (See Table 1 ). For each possible super grid configuration, the number of grids in each direction is optimized based on the number of gene probes (samples) input by the user as shown in figure 2 . Design solutions offered by the program Alternative array designs for a given set of input parameters are ranked on the basis of 'Distance area ratio' that describes the area covered by the array for each design. The array design spanning least area is ranked highest. This strategy allows for applying the labeled target sparingly. Subsequently, an easy report in tabular form can be generated by feeding the output data file from ArrayD into the companion Perl program 'ArraySolution.pl', which classifies array solutions into 'Square', 'Rectangle (Horizontal bar)', or 90° rotated 'Rectangle (Vertical column)' based on the geometry of a given design solution. If the number of grids are equal in both the direction we have a 'Square' design. In all other cases we obtain a 'Rectangle' design, which can be either of two types: the long side of the array is parallel to the length (Horizontal) or the width (Vertical) of the slide. The output of ArraySolution is a tab-delimited text file called 'filename.solution' where filename corresponds to the input name of the file carrying design solutions. The tabular report consist of Number of super grids in X – direction, Number of super grids in Y – direction, Number of spots per grid in X – direction, Number of spots per grid in Y – direction, Distance between two spots (in microns), Distance Area ratio and geometry of design (Square or Rectangle). This can aid users to decide on a particular design solution based on space optimization and elegant appearance. An example of a sample run is provided in Figure 3 . The number of gene probes (including controls and blanks) to be spotted using a 2 by 2 pin configuration in X-Y axis is fed as 2304 (Figure 3 ). The gene probes have to be spotted in duplicates so the total number of spots on the slide would be 4608. The program provides 67 different array designs for various inter-spot distances, number of grids and number of spots in each grid. Two examples of different solutions are presented in Figure 4 . In this example, the first solution is ranked highest with inter-spot distance of 170 microns and a 24 × 24 grid pattern with 4 grids in Y axis and 2 grids in X axis. An alternative solution provides a design with a higher inter-spot distance of 200 microns and 18 × 16 grid pattern with 4 grids each in X axis and Y axis. The first solution can be used in conditions when humidity is low and the spotting solution does not absorb moisture and spread after printing. The second solution is more appropriate for printing samples in 50% DMSO. The classification of all design solutions based on the geometries obtained from ArraySolution is displayed in Table 2 [see Additional file 1 ]. Conclusion We have developed a simple and rapid software ArrayD that offers various design solutions of designing microarrays for a specific set of user-defined requirements. Availability and requirements The source code and the executable file for ArrayD and ArraySolution programs are freely available and can be downloaded from our website [ 11 ]. The source code can be compiled and executed on Unix v 5.1, or IRIX v 5.1 or Red Hat Linux v 7.0 (or higher). The executable files can be downloaded for Windows platform (Windows 98/NT/XP/2000). Further information can be requested by sending e-mail to ramu@igib.res.in or ramucbt@yahoo.com . Authors' contributions AS explained the operational details to software expert, did the experimentation, maintenance and testing of the software. GPS did the basic software writing and implementation of 'ArrayD'. VKS prepared the code for the companion program 'ArraySolution' to classify the solutions on the basis of design geometry. SR is the Group Leader generating demand, sourcing and linking people, explaining the concepts, testing, critical examination, presentation of data, providing salary through grants. Supplementary Material Additional File 1 Report generated by ArraySolution. A total of 67 different design solutions were classified into three categories of Square, Rectangle (Horizontal) and Rectangle (Vertical) as mentioned in text. The detailed design parameters including number of supergrids in X and Y direction, number of spots per grid in X and Y direction, distance between the spots, distance area ratio and geometry of design are provided for preparing elegant microarrays. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524372.xml

539337

Ancient DNA Tells Story of Giant Eagle Evolution

The recent discovery of a Hobbit-like hominid on the Indonesian island of Flores was startling in some respects—its rather modern existence, for one—but it represents a classic case of Darwinian evolution. For reasons that are not entirely clear, when animals make their way to isolated islands, they tend to evolve relatively quickly toward an outsized or pint-sized version of their mainland counterpart. Following this evolutionary script, the Flores woman, presumably a downsized version of Homo erectus , appears to have shared her island home with dwarf elephants and giant rats. Perhaps the most famous example of an island giant—and, sadly, of species extinction—is the dodo, once found on the Indian Ocean island of Mauritius. When the dodo's ancestor (thought to be a migratory pigeon) settled on this island with abundant food, no competition from terrestrial mammals, and no predators, it could survive without flying, and thus was freed from the energetic and size constraints of flight. New Zealand also had avian giants, now extinct, including the flightless moa, an ostrich-like bird, and Haast's eagle ( Harpagornis moorei ), which had a wingspan up to 3 meters. Though Haast's eagle could fly—and presumably used its wings to launch brutal attacks on the hapless moa—its body mass (10–14 kilograms) pushed the limits for self-propelled flight. As extreme evolutionary examples, these island birds can offer insights into the forces and events shaping evolutionary change. In a new study, Michael Bunce et al. compared ancient mitochondrial DNA extracted from Haast's eagle bones with DNA sequences of 16 living eagle species to better characterize the evolutionary history of the extinct giant raptor. Their results suggest the extinct raptor underwent a rapid evolutionary transformation that belies its kinship to some of the world's smallest eagle species. Giant Haast's eagle attacking New Zealand moa (Art: John Megahan) The authors characterized the rates of sequence evolution within mitochondrial DNA to establish the evolutionary relationships between the different eagle species. Their analysis places Haast's eagle in the same evolutionary lineage as a group of small eagle species in the genus Hieraaetus . Surprisingly, the genetic distance separating the giant eagle and its more diminutive Hieraaetus cousins from their last common ancestor is relatively small. Without the fossils to directly determine divergence times, Bunce et al. relied on molecular dating techniques that use the rate of sequence evolution in the genes studied to establish the relative evolutionary ages of the eagles. Proposing a divergence date of roughly 0.7–1.8 million years ago, the authors acknowledge that while this is the “best available approximation of the ‘true’ date,” additional molecular data could help refine the estimate. Whatever the date of divergence, the extinct giant eagle is clearly an anomaly among the eagles studied here. The increase in body size—by at least an order of magnitude in less than 2 million years—is particularly remarkable, Bunce et al. argue, since it occurred in a species still capable of flight. The absence of mammalian competitors facilitated the evolution of much larger eagles and owls on Cuba and may have likewise precipitated the rapid morphological shift seen here. Haast's eagle, the authors write, “represents an extreme example of how freedom from competition on island ecosystems can rapidly influence morphological adaptation and speciation.” Given its similarity to the smaller Hieraaetus species, the authors recommend reclassifying the New Zealand giant as Hieraaetus moorei . This study shows how quickly morphological changes can occur in vertebrate lineages within island ecosystems. Could it be that anthropologists might some day uncover evidence of a giant version of the Flores woman?

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539337.xml

548294

Adoptive transfer of dendritic cells modulates immunogenesis and tolerogenesis in a neonatal model of murine cutaneous leishmaniasis

We evaluated the adoptive transfer of DCs on Leishmania (L.) mexicana -infected neonatal BALB/c mice. DCs were isolated and purified from the spleens of the following donor groups: a) Adult BALB/c mice infected during adulthood with L. (L) mexicana ; b) Adult BALB/c mice infected during neonatal life; c) Healthy neonatal BALB/c mice; d) Healthy adult BALB/c mice. A neonatal model of infection, generated after inoculation with 5 × 10 5 promastigotes of L. (L) mexicana , was used as the infection control group. Sixteen hours after intraperitoneal transfer of DCs (1 × 10 3 , 1 × 10 5 , or 1 × 10 6 cells/ml), neonatal recipient BALB/c mice were infected. The adoptive transfer of DCs diminished disease progression in neonatal mice. This reduction depends on the quantity and provenance of transferred DCs, since the effect was more evident with high numbers of DCs from adult mice infected during adulthood and healthy neonatal mice. Protection was significantly reduced in animals receiving DCs from healthy adult mice but it was absent in mice receiving DCs from adult mice infected during neonatal life. These results suggest that genetic susceptibility to Leishmania infection can be modified during neonatal life, and that the period of life when antigens are encountered is crucial in influencing the capacity of DCs to induce resistance or tolerance.

Background Medawar et al. [ 1 ] showed almost half a century ago that rodents injected at birth with splenocytes from genetically different donors could accept transplants from that donor as an adult. These milestone experiments guided the notion that the introduction of antigens during neonatal life leads to tolerance and that the immune system functions by making a distinction between self and nonself. For some years, Matzinger et al. have persevered on the hypothesis that tolerance is not an intrinsic property of the newborn immune system [ 2 , 3 ]. For example, many studies have shown that neonatal exposure to antigen may prime T cells and induce both Th1 and Th2 cells [ 4 - 7 ]. Moreover, Adkins et al. have demonstrated that although neonates develop compartmentally distinct primary responses to antigen immunization (mixed Th1/Th2 in lymph nodes and Th2 in spleen), after rechallenge the elicited secondary response is always of the Th2 type [ 7 , 8 ]. They have also proved that even in the lymph nodes, the Th2 function persists for a prolonged period after a single immunization, and that animals initially immunized as neonates are impaired in their capacity to develop the expected Th1 memory effector function observed in adults [ 9 ]. The biased immunogenic neonatal immunity may be attributable to factors associated with antigen presentation such as type of antigen-presenting cell, accompanying adjuvant and the nature, concentration and in vivo availability of the antigen [ 5 , 10 - 13 ]. Resting T cells need two signals to be activated; signal 1 from TCR binding to MHC/peptide and signal 2 (co-stimulation) from a professional phagocyte, such as a dendritic cell or a macrophage. Tolerance is associated to a lack of co-stimulation that usually occurs when antigen is encounter by a non-professional phagocyte, or by professional phagocytes in a non-APC tissue (lymphoid tissue, skin, etc)[ 14 ]. In this study, we have evaluated the effect of adoptive transfer of DCs from adult and neonatal mice infected with L. (L.) mexicana , and from healthy adult and neonatal mice. As in the L. major mouse model, we have shown that infection with L. (L.) mexicana strain MHOM/BZ/82/BEL21, generates a Th1 response associated to protective immunity in C57BL/6 mice, and a Th2 response related to non-healing disease in BALB/6 mice [ 15 ]. Leishmaniasis is an excellent model to study the extremes of host/parasite relationships, particularly the diversity of the immune response associated to the genetic background of the host. In addition, mice can reproduce the distinct clinical forms observed in humans [ 16 , 17 ]. These models have been particularly important to show that skin-derived DCs including Langerhans cells play an important role in cutaneous leishmaniasis, where they can transport Leishmania antigens to the lymph nodes and induce specific immune responses [ 18 - 24 ]. Moll et al. have also shown that Langerhans cells may act as reservoirs sustaining parasite-specific stimulation of T memory cells, thus protecting animals from reinfection [ 25 ]. Results and Discussion Establishment of a L. (L.) mexicana infection model in neonatal BALB/c mice The progress of L. (L.) mexicana infection in neonatal BALB/c mice, after the inoculation with 5 × 10 4 , 1 × 10 5 , 2 × 10 5 or 5 × 10 5 promastigotes was determined by measuring the footpad thickness during 6 weeks. All 4 experimental groups developed lesions. Mice that received 1 × 10 5 , 2 × 10 5 and 5 × 10 5 promastigotes respectively, showed a significant increase (p ≤ 0.05) on footpad thickness starting from the second week, reaching a maximal value on the sixth week of evaluation (Fig. 1A ). This increase in footpad thickness was much greater (p ≤ 0.05) in the group inoculated with 5 × 10 5 promastigotes, with lesions appearing from the first week (Fig. 1A ). Moreover, this experimental group presented a similar evolution to that observed in L. (L.) mexicana -infected adult BALB/c mice inoculated with 1 × 10 6 promastigotes (Fig. 1B ). The statistical analysis using a Wilcoxon matched-pairs signed-ranks test of the percentage increase from the starting footpad thickness in both neonatal and adult BALB/c mice infected with 5 × 10 5 and 1 × 10 6 promastigotes, respectively, showed a significant (p ≤ 0.05) two-tailed value and a very significant Spearman correlation (r = 1.000, p = 0.0014). The starting footpad thickness in neonatal and adult BALB/c mice was 1.67 mm and 1.85 mm, respectively. Figure 1 Progression of L. (L.) mexicana infection in neonatal BALB/c mice. A. Footpad thickness of adult mice infected with 1 × 10 6 promastigotes (■), non-infected mice (○), neonatal mice infected with 5 × 10 4 promastigotes (△), neonatal mice infected with 1 × 10 5 promastigotes (▲), neonatal mice infected with 2 × 10 5 promastigotes (□) and neonatal mice infected with 5 × 10 5 promastigotes (●). B. Percentage increase from the starting footpad thickness in both neonatal (□) and (■) adult BALB/c mice infected with 5 × 10 5 and 1 × 10 6 promastigotes, respectively. We used 5 × 10 5 promastigotes as the optimal concentration for L. (L.) mexicana infection in all the subsequent experiments including the infection control group. This neonatal murine model of L. (L.) mexicana infection used half the numbers of promastigotes previously described to infect adult BALB/c mice [ 16 ]. A significant Spearman correlation attested that our neonatal model was comparable to the adult model of infection. Although infected neonatal mice have a statistically similar clinical outcome that infected adult mice, we ignore whether these mice have similar level of infection and therefore similar concentrations of antigens carried over by the transferred DCs, however, looking at the present results one can speculate that DCs from mice infected during neonatal life induced tolerance probably due to a high parasite burden, and not a lack of adjuvancity since DCs from healthy neonatal mice were able to partially protect against Leishmania infection. Other studies have shown a similar pattern of Th2-biased immune response in other models of neonatal infection [ 7 , 11 ]. We also observed that even after the inoculation of considerable numbers of parasites, neonatal mice differed significantly from adult mice in their percentage increment from the starting footpad thickness, suggesting a functional impairment of the primary immune response. This may be explained, first by the fact that in BALB/c mice carry a point mutation in the Nramp1 (natural-resistance-associated macrophage protein) gene that allows the mRNA degradation of macrophage activation genes, increasing susceptibility to Leishmania infection [ 26 ]. Susceptibility associated with the dominant expression of the costimulatory molecule CD86 (B7-2) and the subsequent generation of the Th2-mediated response [ 27 - 31 ]. Second, the proof that murine naïve neonatal T cells, unlike adult T cells, express a Th2 phenotype and are highly deficient in Th1 functions [ 32 , 33 ]. Morphological and immunophenotypic characterization of murine splenic dendritic cells DCs obtained by our purification method showed characteristic dendritic cell morphology, and a 97% purity as determined by CD11c immunostaining and flow cytometry. A minor fraction of about 3.5 % expressed CD3 and NK1.1 (Fig. 2 ). The expression of CD11c, MHC-II and CD86 molecules was detected by immunocytochemistry, thus demonstrating that these cells showed characteristics of functionally mature DCs. Figure 2 Frequency distributions of purified dendritic cells labelled CD11c-FITC showing 97.17% purity (right), and FITC-isotype control (IgG1) (left). The information shown is from a single cell isolation procedure, representative of various separate experiments. Splenic DCs were isolated for our adoptive transfer experiments since they are mobile antigen-presenting cells that migrate to peripheral lymph organs where they stimulate naive T cells, thus initiating primary T cell responses [ 34 - 36 ]. Further, splenic DCs have been isolated by standardized procedures based on the high expression of CD11c and the lack of CD205 [ 37 ]. Progression of the infection in neonatal recipient BALB/c mice after adoptive transfer of dendritic cells from the distinct experimental groups The adoptive transfer of 1 × 10 3 , 1 × 10 5 or 1 × 10 6 DCs from adult BALB/c mice infected during adulthood with L. (L) mexicana on neonatal recipient mice modified the course of infection, showing a delayed lesion growth from the second week onward (Fig. 3 ) as compared with the infection control group. This reduction in footpad thickness was dependent of DC numbers, since at the highest concentration of 1 × 10 6 , lesions were smaller than those observed with 1 × 10 3 and 1 × 10 5 DCs from the fifth week onward (Fig. 3 ). At the seventh week of infection, lesion size decreased by 40% after the adoptive transfer of 1 × 10 6 DCs, whereas in animals inoculated with 1 × 10 5 and 1 × 10 3 DCs the decrease was of 33% and 22%, respectively. In contrast, the adoptive transfer of 1 × 10 5 or 1 × 10 6 DCs from adult BALB/c mice infected during neonatal life with L. (L) mexicana fail to modify the course of infection of neonatal recipient BALB/c mice as compared with infection control animals (Fig. 4 ). However, those mice receiving 1 × 10 6 DCs showed a significant reduction in lesion growth (p ≤ 0.05) on weeks 2, 3 and 4. This effect disappeared from the fifth week onwards (Fig. 4 ). Moreover, the adoptive transfer of 1 × 10 5 or 1 × 10 6 DCs from healthy adult BALB/c mice modified the course of infection in neonatal recipient mice, showing a delayed and significant decrease (p ≤ 0.05) in lesion growth from the second week of infection (Fig. 5 ). This reduction in footpad thickness was dependent on DC numbers, since at 1 × 10 6 lesions were significantly (p ≤ 0.05) smaller than those observed in mice transferred with 1 × 10 5 DCs, which also initiated their lesions on the third week (Fig. 5 ). At the seventh week of infection, lesion size decreased by 30% after the adoptive transfer of 1 × 10 6 DCs and by 10% in mice receiving 1 × 10 5 DCs. similarly, the adoptive transfer of 1 × 10 3 or 1 × 10 5 DCs from healthy neonatal BALB/c mice modified the course of infection of neonatal recipient mice, showing a delayed and significant decrease (p ≤ 0.05) in lesion growth from the second week of infection. This reduction in footpad thickness was very similar in both tested concentrations (Fig. 6 ). At the seventh week of infection, lesion size decreased by 35% in both groups. Figure 3 Progression of infection in neonatal recipient BALB/c mice after adoptive transfer of DCs from adult BALB/c mice infected during adulthood with L. (L) mexicana . Footpad thickness of neonatal mice infected with 5 × 10 5 promastigotes (■); neonatal mice transferred with 1 × 10 6 (○;), 1 × 10 5 (△) and 1 × 10 3 (□) DCs and subsequently infected with 5 × 10 5 promastigotes. Figure 4 Progression of the infection in neonatal recipient BALB/c mice after adoptive transfer of DCs from adult BALB/c mice infected during neonatal life with L. (L) mexicana . Footpad thickness of neonatal mice infected with 5 × 10 5 promastigotes (■); neonatal mice transferred with 1 × 10 6 (○) and 1 × 10 5 (△) DCs and subsequently infected with 5 × 10 5 promastigotes. Figure 5 Progression of the infection in neonatal recipient BALB/c mice after adoptive transfer of DCs from healthy adult BALB/c mice. Footpad thickness of neonatal mice infected with 5 × 10 5 promastigotes (■); neonatal mice transferred with 1 × 10 5 (○) and 1 × 10 6 (△) DCs and subsequently infected with 5 × 10 5 promastigotes. Figure 6 Progression of the infection in neonatal recipient BALB/c mice after adoptive transfer of DCs from healthy neonatal BALB/c mice. Footpad thickness of neonatal mice infected with 5 × 10 5 promastigotes (■); neonatal mice transferred with 1 × 10 5 (△) and 1 × 10 3 (◇) DCs and subsequently infected with 5 × 10 5 promastigotes. Disease progression was substantially decreased after transferring cells from adult BALB/c mice infected during adulthood with L. (L) mexicana , healthy adult BALB/c mice and healthy neonatal BALB/c mice. The reduction in these 3 groups was statistically significant (p ≤ 0.05) as compared with the infection control group. This reduction in footpad thickness was absent or considerably diminished in mice receiving DCs from adult BALB/c mice infected during neonatal life with L. (L) mexicana (Fig. 7 ). Figure 7 Progression of the infection in neonatal recipient BALB/c mice after the adoptive transfer of DCs from the different experimental groups. Footpad thickness of neonatal mice infected with 5 × 10 5 promastigotes (■); neonatal mice transferred with 1 × 10 6 DCs from adult BALB/c mice infected during adulthood (◇), adult BALB/c mice infected during neonatal life (○), healthy adult BALB/c mice (●) and 1 × 10 5 CDs from healthy neonatal BALB/c mice (△) and subsequently infected with 5 × 10 5 promastigotes. Our results showed that the preceding intraperitoneal adoptive transfer of DCs diminished the progression of L. (L.) mexicana infection in neonatal BALB/c recipient mice. These results contrast with those of Moll and Berberich [ 38 ] showing that only intravenous administration of antigen-pulsed Langerhans cells, but not intradermal or intraperitoneal inoculation, induced resistance against Leishmania infection. In this study, the observed protection depends on the quantity and provenance of the transferred DCs, since the effect was more evident with high cellular numbers of DCs from adult BALB/c mice infected during adulthood and healthy neonatal mice, where lesions were about 40% smaller than in the infection control group. DCs from these two groups have the intrinsic capacity to induce protective or resistant immune responses very early in life. That neonatal DCs appear to be more protective, on a per cell basis, than adults DCs is a very striking result since only Dadaglio et al.[ 39 ] have shown that neonatal DCs are as effective as adult DCs in expressing MHC and costimulatory molecules; taking-up, processing and presenting antigens to T cells inducing CTL responses in vivo. Others have shown that neonatal DCs are not fully functional [ 40 , 41 ]. Also, animals receiving DCs from healthy adult mice showed a slightly but significantly reduced protection from that observed with DCs from adult mice infected during adulthood and healthy neonatal mice. Various studies have shown that epidermal DCs in aged skin are reduced significantly compared with young skin in mice and humans [ 42 - 48 ]. This cellular reduction may be the consequence of a decreased production in the bone marrow of DC progenitors or alternatively, these stem cells may be less responsive to cytokine and chemokine signals required for their homing to the skin [ 49 - 51 ]. Our results favor the latter hypothesis, since the same numbers of transferred DCs from healthy neonatal or adult mice induced a somewhat different disease outcome. More notable was the observed absence of a protective effect in mice receiving DCs from adult BALB/c mice infected with L. (L) mexicana during neonatal life. This result confirmed recent studies by Adkins et al. showing that animals initially immunized as neonates are unable to develop the expected Th1 memory effector function observed in adults [ 9 ]. These investigators proposed that in neonates, the spleen is the primary site of tolerance induction to self-antigens whereas the lymph nodes are the sites of immune responsiveness to foreign antigens. The initial and transitory protection observed at the greatest concentration of DCs from adult mice infected during neonatal period, suggests impairment in their accessory functions specifically in those associated with signal 2 and signal 3. Signal 2 comprises co-stimulatory factors essential for the clonal expansion of T cells and signal 3 involves in situ properties of DCs such as tissue interaction and migration where cytokines, chemokines and extracellular matrix components are crucial [ 36 ]. Conclusions Our results show that tolerizing DCs from animals initially immunized as neonates play a key role in the attenuation of Th1 responses. The present results may have a considerable epidemiological impact on leishmaniasis, where infection at early stages of life may impose a tolerogenic state that favors the development of visceral or diffuse cutaneous leishmaniasis, both characterized by Th2-type responses. In this study, we have shown that intraperitoneal adoptive transfer of splenic DCs is able to surpass the genetic bias of the mice, allowing the development of an immune response that modifies the progression of L. (L.) mexicana infection. Methods Animals Adult (6 weeks) and neonatal (about 24 hour newborn) female BALB/c mice (Taconic, Germantown, NY, U.S.A.) were raised in the Animal House of the Instituto de Biomedicina, under appropriate conditions of temperature, water and feeding. Specific Antibodies The following rat monoclonal antibodies were used to isolate and characterize dendritic cells: CD19 (B cells, clone IBL-2), MOMA-2 (Macrophages/Monocytes), CD45R (B and NK cells; clone RA3-6B2), CD3 (T cells; clone KT3), CD11c (dendritic cells and other leukocytes, clone N418), CD19 (clone 6D5) conjugated to phycoeritrine (PE), NK1.1 (clone PK136) conjugated to PE, Macrophages-Monocytes (MOMA-2) conjugated to fluorescein isothiocyanate (FITC), CD3 (clone KT3) conjugated to FITC. All were purchased from Serotec Ltd. (Oxford, United Kingdom) except CD205 (dendritic cells, clone NLDC-145, DEC205) donated by Georg Kraal, Vrije Universiteit, Amsterdam, The Netherlands; I-A d (MHC-II, clone AMS-32.1) and CD86 (B7.2, clone GL1) purchased from BD Pharmigen (San Diego, USA). Parasite culture and isolation of L. (L.) mexicana promastigotes Amastigotes of Leishmania (Leishmania) mexicana (MHOM/BZ/82/BEL21) were extracted from footpad nodules of hamsters infected a month earlier with 1 × 10 6 amastigotes. The nodules were aseptically dissected out and washed in phosphate-buffered saline (PBS, pH 7.4) with 100 U/ml penicillin and 100 μg/ml streptomycin, and finely cut and ground in a Petri dish containing cold PBS. The suspension was filtered through a sterile sieve to remove large debris. These parasites were cultured on blood agar base (Sigma-Aldrich, St. Louis, U.S.A.) at room temperature for 7 days (the stationary growth phase) to obtain infective promastigotes. For an enriched population of parasites, free of erythrocytes and cellular debris, 100 μl of that sample were cultured in 2 ml Schneider's insect cell culture medium (Sigma-Aldrich, St. Louis, U.S.A.) for one week at room temperature. Promastigotes were isolated after 3 washes with sterile PBS and centrifugation at 1000 g at 4°C for 15 min. Pellets were resuspended in 1 ml of sterile PBS. Viable parasites were counted by trypan blue exclusion. Parasite concentration was adjusted to 5 × 10 4 , 1 × 10 5 , 2 × 10 5 and 5 × 10 5 per μl to be used in the different experimental groups. Experimental infection with promastigotes of L. (L.) mexicana A similar pattern of L. (L.) mexicana infection to that established in adult mice [ 52 ] was determined in neonatal BALB/c mice. Neonatal BALB/c mice (n = 12) were inoculated subcutaneouslly into the left hind footpad with 5 × 10 4 , 1 × 10 5 , 2 × 10 5 , or 5 × 10 5 promastigotes suspended in 10 μl sterile PBS, applied with a tuberculin syringe (29-gauge needle) connected to a stepper repetitive pipette (Tridak, Danbury, U.S.A.). For comparison, adult BALB/c mice were infected the standardized optimal parasite load of 1 × 10 6 promastigotes of L. (L.) mexicana [ 52 ]. The course of infection was evaluated weekly for 6 weeks, measuring the experimental left footpad using a dial gauge caliper (Mituyoto N° 7300, U.S.A.). Isolation and purification of dendritic cells DCs from adult and neonatal BALB/c mice were isolated from the spleen. Under sterile conditions, spleens were minced on a metallic mesh with RPMI-1640 (Life Technologies, Rockville, U.S.A.) supplemented with 10% of decomplemented fetal bovine serum (FBS), 2 mM L-glutamine, 10 mM HEPES, 1 mM sodium pyruvate, 50 μM 2-mercaptoethanol and 100 U/ml penicillin (complete RPMI-10). The cell suspension was filtered on a nylon sieve and transferred to 15 ml centrifuge tubes (Corning Life Sciences, Acton, U.S.A.) and spun at 250 g at 4°C for 10 min. Viable cells were counted by trypan blue exclusion. Cell concentration was adjusted to 1 × 10 7 cells/ml in complete RPMI-10 and 8 ml plated in tissue culture flasks. The flaks were incubated for 2 hr at 37°C in a 5% CO 2 incubator (NuAire, Inc., Plymouth, U.S.A.), allowing DCs to adhere. Non adherent cells were carefully removed and placed in sterile 50 ml centrifuge tubes and spun at 250 g, 4°C for 10 min. Adherent cells were covered with 10 ml complete RPMI-10 and incubated as before for 16–18 hours, allowing DCs to detach. After gently washing the surface of the flasks with a plugged Pasteur pipette and complete RPMI-10, pools of the eluted cells were placed in sterile 15 ml centrifuge tubes and spun at 250 g, 4°C for 10 min. For each tube, the cell pellet was resuspended in 6 ml complete RPMI-10. This volume was carefully layered over a 3 ml NycoPrep™ density gradient (Nycomed Pharma AS, Torshov, Norway) and centrifuged at 600 g, 20°C for 20 min. Mononuclear cells were removed from the interface ring using a Pasteur pipette, transferred to a sterile 15 ml centrifuge tube and spun down in 10 ml complete RPMI-10 at 400 g, 20°C for 15 min three times. The final pellet was resuspended in 1 ml of cold (4°C) Hanks balanced salt solution supplemented with 10% decomplemented FBS and 2 mM HEPES. Cells were quantified and viability assessed by trypan blue exclusion. The final purification stage consisted of an immunomagnetic negative selection of DCs. The cell suspension obtained above was incubated under continuous agitation at 4°C for 1 hour, with primary rat anti-mouse monoclonal antibodies recognizing B and T lymphocytes, NK cells and monocytes/macrophages (1.5 μg/ml antibody per 1 × 10 6 cells). After incubation, cells were washed three times in Hanks centrifuging at 250 g, 4°C for 10 min. The pellet was resuspended in 1 ml cold Hanks in a sterile 15 ml centrifuge tube and incubated under continuous agitation at 4°C for 1 hour with a secondary sheep anti-rat IgG polyclonal antibody coupled to magnetic microspheres (Dynabeads ® M-450, Dynal Biotech Inc., Lake Success, U.S.A.) at a 7:1 sphere/target ratio. Non-dendritic magnetic-coated cells were removed by positive selection in three sequential depletions using a magnetic gadget (Dynal MPC ® Dynal Biotech Inc., Lake Success, U.S.A.) at 4°C for 6 min. Characterization of dendritic cells DC purity was determined by flow cytometry and immunocytochemistry. For flow cytometry, 1 × 10 5 cells were suspended in PBS (1% FBS) and incubated with 10 μl primary monoclonal antibodies directly coupled to PE or FITC recognizing T and B lymphocytes, NK cells and monocytes/macrophages. DCs were characterized by an indirect method using primary monoclonal antibodies to CD11c and a secondary antibody, hamster anti-rat IgG1conjugated to FITC (clone MARG1-2, Serotec Ltd., Oxford, United Kingdom). The incubations were carried out in the dark at 4°C for 45 min, followed by 3 washes and centrifugation at 250 g, 4°C for 10 min. The cell pellet was resuspended in 500 μl PBS and the percentage of labeled cells determined in a flow cytometer (FACScan, Becton Dickinson, Franklin Lakes, U.S.A.). The control consisted of an antibody of irrelevant specificity conjugated to FITC. For immunocytochemistry, 1 × 10 5 cells were suspended in PBS (1% FBS) and spun down at 50 g in a Cytospin (Shandon Inc., Pittsburg, U.S.A.). Sample slides were hydrated in PBS, fixed in fresh acetone for 5 min. and sequentially incubated for 90 min with primary rat monoclonal antibodies to CD11c and CD205, biotinylated goat anti-rat IgG (50 μg/ml) (Vector Laboratories, Burlingame, U.S.A.) for 45 min., and Vectastain ® Elite ABC kit (Vector Laboratories, Burlingame, U.S.A.) at 1:100, 30 min. Five-minute washes with PBS were done between incubations. The reactions were developed for 3 minutes in Vector ® NovaRed™ substrate. The slides were then washed and counterstained with methyl green. Omissions of the primary antibody and incubation with an antibody of irrelevant specificity at the same protein concentration were used as controls. Adoptive transfer of dendritic cells DCs were isolated from the spleens of the following donor groups: a) Adult BALB/c mice infected during adulthood with L. (L.). mexicana (n = 4); b) Adult BALB/c mice infected during neonatal life with L. (L.). mexicana (n = 4); c) Healthy neonatal BALB/c mice (n = 4); d) Healthy adult BALB/c mice (n = 4). The infection control group consisted of neonatal BALB/c mice infected with 5 × 10 5 promastigotes of L. (L) mexicana . DCs from the 4 experimental groups were adjusted to 1 × 10 3 , 1 × 10 5 , or 1 × 10 6 cells/ml in sterile PBS for intraperitoneal transfer to neonatal recipient BALB/c mice. Cells, at the mentioned concentrations, were injected in 20 μl volumes using a tuberculin syringe (29-gauge needle) connected to a stepper repetitive pipette (Tridak, Danbury, U.S.A.). After sixteen hours of adoptive transfer, neonatal recipient BALB/c mice were infected with 5 × 10 5 promastigotes of L. (L) mexicana . Isolation of DCs and adoptive transfer experiments were done in duplicates. Statistical analysis The results were expressed as mean ± standard error of the mean (SEM). Each experimental group consisted of 4–5 individuals. Comparisons between groups were made with Student t test and Welch t test for unpaired samples. Any value of p ≤ 0.05 was considered significant. All tests were performed using GraphPad InStat 3.02 (GraphPad Software, San Diego California USA, ). List of abbreviations APCs: antigen-presenting cells DCs: dendritic cells TCR: T-cell receptor Competing interests The author(s) declare that they have no competing interests. Authors' contributions LVP carried out most of the experimental work and drafted the manuscript. JC developed the experimental design, carried out part of the experimental work and drafted the manuscript. NLD participated in the in experimental design and evaluated the progression of infection in the mice. FJT conceived the study, participated in the experimental design and coordinated the work. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548294.xml

526203

Activation of Erk and JNK MAPK pathways by acute swim stress in rat brain regions

Background The mitogen-activated protein kinases (MAPKs) have been shown to participate in a wide array of cellular functions. A role for some MAPKs (e.g., extracellular signal-regulated kinase, Erk1/2) has been documented in response to certain physiological stimuli, such as ischemia, visceral pain and electroconvulsive shock. We recently demonstrated that restraint stress activates the Erk MAPK pathway, but not c-Jun-N-terminal kinase/stress-activated protein kinase (JNK/SAPK) or p38MAPK, in several rat brain regions. In the present study, we investigated the effects of a different stressor, acute forced swim stress, on the phosphorylation (P) state of these MAPKs in the hippocampus, neocortex, prefrontal cortex, amygdala and striatum. In addition, effects on the phosphorylation state of the upstream activators of the MAPKs, their respective MAPK kinases (MAPKKs; P-MEK1/2, P-MKK4 and P-MKK3/6), were determined. Finally, because the Erk pathway can activate c-AMP response element (CRE) binding (CREB) protein, and swim stress has recently been reported to enhance CREB phosphorylation, changes in P-CREB were also examined. Results A single 15 min session of forced swimming increased P-Erk2 levels 2–3-fold in the neocortex, prefrontal cortex and striatum, but not in the hippocampus or amygdala. P-JNK levels (P-JNK1 and/or P-JNK2/3) were increased in all brain regions about 2–5-fold, whereas P-p38MAPK levels remained essentially unchanged. Surprisingly, levels of the phosphorylated MAPKKs, P-MEK1/2 and P-MKK4 (activators of the Erk and JNK pathways, respectively) were increased in all five brain regions, and much more dramatically (P-MEK1/2, 4.5 to > 100-fold; P-MKK4, 12 to ~300-fold). Consistent with the lack of forced swim on phosphorylation of p38MAPK, there appeared to be no change in levels of its activator, P-MKK3/6. P-CREB was increased in all but cortical (prefrontal, neocortex) areas. Conclusions Swim stress specifically and markedly enhanced the phosphorylation of the MAPKKs P-MEK1/2 and P-MKK4 in all brain regions tested without apparent alteration in the phosphorylation of P-MKK3/6. Curiously, phosphorylation of their cognate substrates (Erk and JNK) was increased to a much more modest extent, and in some brain regions was not altered. Similarly, there was a region-specific discrepancy between Erk and CREB phosphorylation. Possible explanations for these findings and comparison with the effects of restraint stress will be discussed.

Background Increasing evidence implicates stress as an important factor in the vulnerability to depressive and other mental illnesses [ 1 - 3 ]. Consequently, understanding the cellular and biochemical mechanisms that transduce stress signals may inform our insight into the factors that lead to depression, as well as provide potential new avenues for therapeutic intervention. Although current therapies for anxiety and depression usually focus on modulating serotonergic and/or noradrenergic activity in the brain [ 4 ], recent data suggest that neurotrophins such as BDNF may ameliorate depression [ 5 ]. Neurotrophins activate MAPK signaling, and increased signaling via at least one of the MAPKs, Erk, has been shown to regulate plasticity in the brain by modulating gene expression. Thus, activation of the Erk pathway via both a cAMP/PKA-dependent mechanism leading to activation of CREB, and a non-cyclase-linked pathway, leading to activation of CREB and/or the serum response element (SRE) binding protein Elk-1, have been shown to occur in the hippocampus during acquisition of learning, memory consolidation and long-term potentiation (LTP) [ 6 - 8 ]. It is therefore not unreasonable to speculate that these signal transduction pathways may also be involved in the neuronal plasticity that is presumed to underlie aberrant brain function [ 9 ]. The term MAPK is used to denote a family of signal transduction mediators, extensively distributed throughout the central nervous system [ 10 , 11 ], that regulate a diverse array of cellular functions [ 12 - 15 ]. The most common MAPKs are the extracellular signal-regulated kinases Erk1 and 2 (also known as p44 and p42MAPK, respectively), which primarily regulate cellular growth and differentiation, and the p38MAPK and c-Jun-N-terminal kinase/stress-activated protein kinases (JNK/SAPK), which mainly function as mediators of cellular stresses such as inflammation and apoptosis. Physiologically stressful stimuli, including seizure induction [ 16 ], ischemic insult [ 17 ], visceral pain [ 18 ] and electroconvulsive shock (ECS) [ 19 , 20 ] have also been shown to rapidly activate MAPKs in various brain regions. Subjecting rats or mice to acute stressors such as restraint or forced swim results in activation of c-fos gene and/or Fos protein expression in many brain regions) [ 21 - 24 ]. CRE and SRE are DNA sequences found in the promoter regions of many immediate early genes (IEGs) such as c-fos and zif286 . P-CREB and P-Elk-1, by binding to these elements, enhance the transcription of the IEGs and regulate gene expression [ 25 , 26 ]. Recently it was shown that swim stress increased P-CREB expression in several brain regions [ 27 ]. Thus, given that, on one hand, stress activated CREB and increased c-fos expression, and on the other, that certain stressful stimuli (see above) activated Erk, it was reasonable to predict that acute physiological stressors would activate the Erk (and perhaps other) MAPK pathway(s). The brain regions most likely to show such an effect would be those implicated in the response to stress, including the hippocampus [ 28 , 29 ], prefrontal [ 30 - 32 ] and other cortical areas [ 21 , 33 ], and the amygdala [ 21 , 34 - 36 ]. In a previous study we tested this hypothesis and found indeed that acute restraint stress increased P-Erk concentrations in several stress-relevant brain areas; however, neither JNK nor p38MAPK phosphorylation were altered by restraint stress [ 37 ]. The present study followed up that investigation by examining the effects of a different common stressor, forced swim stress, extended the number of brain areas examined, and also evaluated changes in the phosphorylation state of the respective upstream MAPKK activators MEK1/2, MKK4 (also known as SEK1 and JNKK) and MKK3/6. While these studies were in progress, others reported that swim stress activated the MKK4/JNK pathway in mice [ 38 ]. Results Swim stress elicits large increases in phosphorylation of MEK1/2 in all brain regions An acute 15 min session of forced swimming produced obvious large increases in the phosphorylation of the MAPKK MEK1/2 in the hippocampus, neocortex, prefrontal cortex, amygdala and striatum (Fig. 1 , top). Surprisingly, phosphorylation of the substrate(s) of activated MEK1/2, Erk1/2, was increased to a much smaller extent, and in some brain regions (hippocampus, amygdala) not at all (Fig. 1 , bottom). Swim stress significantly increased P-MEK1/2 levels from 4.5-fold (hippocampus) to more than 100-fold (striatum) in the various brain areas (Fig. 2 ). P-Erk2 levels, on the other hand, were modestly elevated only in neocortex (2.3-fold), prefrontal cortex (3.3-fold) and striatum (2.3-fold) (Fig. 2 ). Because P-Erk1 signals were generally much weaker, and in short exposure blots nearly undetectable, only P-Erk2 bands were quantitated. However, qualitative changes in P-Erk1 concentrations paralleled those in P-Erk2 (Fig. 1 , bottom: neocortex, amygdala, striatum); this conclusion was borne out in over-exposed blots, which, however, were difficult to quantitate for P-Erk1 because of overlap with the large adjacent P-Erk2 signal. Swim stress increases phosphorylation of MKK4 in all brain regions Analogous to its effects on P-MEK1/2, but much more dramatically, forced swimming enhanced the levels of P-MKK4, the immediate upstream activator of JNK, in all brain areas tested (Fig. 3 , top). Increases ranged from 12.6-fold (hippocampus) to ~300-fold (striatum) (Fig. 4 ). It should be noted that all increases greater than about 10–15-fold are approximate, since the relationship between optical density and the film exposure-response is no longer linear beyond this range, as determined previously [ 37 ]. Thus, increases greater than 10–15-fold are likely underestimates of the actual changes. Nevertheless, it seemed reasonable to utilize the calculated numerical estimates for statistical purposes. In contrast, and again analogous to the findings in the MEK/Erk pathway, much smaller changes were observed in P-JNK1 (p46) and P-JNK2/3 (p54) levels (Fig. 3 , bottom). Both P-JNK1 and P-JNK2/3 were significantly elevated in all brain areas, with the single exception of a non-significant increase in P-JNK1 in the amygdala (Fig. 4 ). Significant swim stress-induced increases for these kinases ranged from 1.4–5.4-fold across brain regions (Fig. 4 ). Effects of swim stress on activation of the p38MAPK pathway and phosphorylation of CREB In contrast to its effects on the Erk and JNK pathways, swim stress exhibited no effect on the phosphorylation of p38MAPK in any brain region, with the exception of a small but significant increase (55%) in the hippocampus (Figs. 5 and 6 ). As shown above, the major impact of swim stress was to activate the corresponding upstream MAPKKs of Erk and JNK, i.e., to increase P-MEK1/2 and P-MKK4, respectively. Since the major activators of p38MAPK are the closely related, dual specificity kinases phosphorylated MKK3 and MKK6 [ 39 ], we attempted to determine if swim stress increased the phosphorylation of this upstream kinase. Fig. 7 shows that the P-MKK3/6 antibody (CST # 9231) detected at least two swim stress-induced bands in the hippocampus, at ~37 and 48 kD. However, the expected molecular weight is ~40 kD; indeed, cell extracts of UV-treated NIH/3T3 and anisomycin-treated C6 glioma cells both exhibited induced bands at ~40 kD (Fig. 7 ). It seems likely, therefore, that basal levels of brain P-MKK3/6 are very low ( cf. basal levels of P-MKK4, Fig. 3 ), and, unlike P-MEK1/2 and P-MKK4, are not induced by swim stress. Interestingly, use of a different P-MKK3/6 antibody (sc-7994-R, Santa Cruz) yielded very similar results, specifically, swim stress induced a large increase in an ~48 kD band (not shown). Other brain regions examined with the P-MKK3/6 antibody yielded the same pattern; no band at ~40 kD was evident (not shown). We considered the possibility that the swim-induced protein at ~48 kD might be P-MKK7, since that is the approximate molecular weight of this kinase. Moreover, MKK7, like MKK4, phosphorylates JNK [ 40 ]. Since the phosphopeptides used to raise the P-MKK4, P-MKK7 and P-MKK3/6 antibodies share a substantial degree of homology in their amino acid sequences, it was possible that the P-MKK3/6 antibody was detecting a swim-induced increase in P-MKK7. However, at a concentration of 1 ng/ml, neither the P-MKK4 nor the P-MKK7 phosphopeptides affected the protein band pattern observed using the P-MKK3/6 antibody, whereas the P-MKK3/6 phosphopeptide completely abolished all signals (data not shown). This appears to rule out the possibility that the 48 kD band is P-MKK7. The identities of the swim stress-induced bands observed with the P-MKK3/6 antibodies remain unknown. In partial agreement with a previous report [ 27 ], swim stress induced significant increases in CREB phosphorylation in the hippocampus and amygdala (as well as in the striatum in the present study); however, we were unable to replicate reported P-CREB increases in neocortex and prefrontal cortex (Figs. 5 and 6 ). The reasons for this discrepancy are not apparent; however, while the same rat strain (Wistar) and swim protocol (15 min, followed by immediate sacrifice) were used, other differences (e.g., P-CREB antibody source, halothane vs. CO 2 anesthesia, etc.) may account for the difference in results. Note, moreover, that the regional pattern of changes in P-CREB (Fig. 6 ) and P-Erk (Fig. 2 ) differed markedly. Whether this is due to a simple difference in time course or, more substantively, in activation mechanism, awaits further investigation. Time course of swim stress-induced MAPK kinase phosphorylation: onset and offset The effect of varying the duration of swim on the phosphorylation of MEK1/2 and MKK4 was examined in neocortex and hippocampus. The effect on both kinases gradually reached a maximum at 15 min (longer times were not assessed), except for the increase in P-MEK1/2 in neocortex, which peaked at 5 min (Fig. 8 ). The levels of P-MEK1/2 and P-MKK4 in both neocortex and hippocampus were maximal immediately after termination of swim stress (Fig. 9 , top), declined gradually, and all except P-MKK4 in neocortex returned to basal levels by 60 min. The levels of the corresponding phosphorylated substrates of the MAPK kinases, P-Erk2 and P-JNK (combined P-JNK1 and P-JNK2/3) followed a very similar pattern of decline post-swim stress (Fig. 9 , bottom), and all except neocortical P-JNK were at control levels by 60 min. Note that hippocampal P-Erk2 was unchanged at all times after swim stress, consistent with the results shown above in Figs. 1 and 2 . Discussion The results presented establish unambiguously that forced swim stress elicits a rapid and profound but relatively transient increase in the phosphorylation (and consequently the presumed activation) of the MEK-Erk and MKK4-JNK signaling pathways in, at the least, the five brain regions examined. These effects displayed specificity in that the MKK3/6-p38MAPK pathway was essentially unchanged. (Although in the experiment shown in Figs. 5 and 6 , there was a very modest increase in P-p38MAPK that was restricted to the hippocampus, there was no significant change in this phosphoprotein in an independent replication of this experiment; data not shown). Our results may be compared to a very recent study on the effects of forced swim in mice, reported by Liu et al. [ 38 ] while the present study was nearing completion. Both studies are in agreement that swim stress elicits a very large increase in P-MKK4 levels throughout the brain, and that P-JNK levels are correspondingly elevated in the same brain regions. In addition, both studies agree that P-p38MAPK levels are unaffected. However, Liu et al. [ 38 ] did not determine the effects of swim stress on P-MEK1/2 levels (which we found were also markedly elevated: Figs. 1 and 2 ). Moreover, whereas Liu et al. [ 38 ] did not find any significant change in P-Erk levels, our studies indicated region-specific alterations in this kinase: increases in cortical areas (prefrontal and neocortical) and striatum, but not in the hippocampus or amygdala (Figs. 1 and 2 ). The reasons for the dissimilar results on P-Erk are not apparent, but it should be noted that the studies utilized different species (mice vs. rats), which may have contributed to the observed differences. Also, it is possible but unlikely that stress duration (30 min in the study of Liu et al. and 15 min here) accounts for the difference in results. One of the most interesting aspects of the present results was the apparent discrepancy between the magnitude of the changes in the MAPKKs (P-MEK1/2 and P-MKK4) and the corresponding MAPKs (P-Erk and P-JNK). It is obvious from Figs. 2 and 4 that whereas swim stress elevated P-MEK1/2 and P-MKK4 levels dramatically, the effects on their substrates, P-Erk2 and P-JNK, respectively, were much more modest. (This difference appears to be much smaller in mice; [ 38 ]). One possible explanation is a temporal difference in the phosphorylation of the upstream MAPKKs and their downstream MAPK substrates. A gradual increase in P-Erk2 and P-JNK phosphorylation, subsequent to activation of MEK1/2 and MKK4, may have been obscured by the fact that animals were sacrificed immediately after termination of the swim. However, this is clearly not the case, as the time-course of the swim-induced increases in phosphorylation of the MAPKKs and MAPKs were very similar in both the hippocampus and neocortex (Fig. 9 ). Another explanation centers on potential differences in phosphorylation stoichiometry, i.e., levels of basal phosphorylation among the various phosphoproteins may vary greatly. Thus, one may posit that kinases (e.g., MEK1/2, MKK4) whose basal state of phosphorylation is very low may be subject to large increases, whereas those whose basal state is comparatively larger (e.g., Erk, JNK) demonstrate much smaller increases. For example, activation of tyrosine hydroxylase, the rate-limiting enzyme for catecholamine biosynthesis, is related to phosphorylation of the protein on at least three different serine sites [ 41 ]. The basal phosphorylation stoichiometry of these sites varies not only among the sites but also between brain regions [ 42 ]. Interestingly, haloperidol-induced phosphorylation of the sites, as visualized on immunoblots using phosphosite-specific antibodies, appears to be negatively related to the basal phosphorylation state [ 42 ]. To our knowledge, the basal phosphorylation stoichiometry of the various rat brain kinases examined in the present study are not known. However, a recent technique has been described [ 43 ] that may lend itself to estimating basal and swim stress-induced phosphorylation stoichiometry in various brain regions. Nevertheless, differences in phosphorylation stoichiometry cannot explain why, in some brain regions (i.e., hippocampus and amygdala, Figs. 1 and 2 ), there were no discernable increases in P-Erk after swim stress, despite large increases in P-MEK1/2. Activated MAPKKs, unlike their upstream activators or downstream targets, are known to exhibit narrow substrate specificity [ 13 ]. Indeed, Erk1/2 activity is believed to be exclusively regulated by MEK1/2 [ 15 ]. Thus, a large increase in MEK1/2 phosphorylation/activation would be expected to translate into substantial phosphorylation/activation of Erk. However, the activity of P-MEK1/2 is regulated by a negative feedback mechanism involving activation of protein phosphatase(s) that rapidly dephosphorylate P-Erk1/2 [ 44 ]. A large number of potential phosphatases have been implicated in the regulation of Erk phosphorylation [ 44 ]; consequently, identifying a specific phosphatase that may be responsible for such an effect was beyond the scope of the present study. Recently, a detailed kinetic analysis of eleven different protein phosphatases implicated three specific phosphatases as the most likely mediators of Erk2 dephosphorylation, including mitogen-activated protein kinase phosphatase 3 (MKP-3) and protein phosphatase 2A (PP2A) [ 45 ]. It might be instructive therefore in a future study to assess the activity of these protein phosphatases in tissue lysates. A final alternative hypothesis is that swim stress activates a mechanism that serves to uncouple MEK1/2 phosphorylation (fully or in part) from activation of Erk. That such uncoupling is possible has been reported in mammalian cells undergoing mitosis [ 46 ], but the mechanism deduced in that instance is highly unlikely to apply in the present case; nevertheless, the possibility is worthy of consideration. In a previous communication we reported that 30 min of restraint stress elicited modest (1.6–2.5-fold) increases in P-Erk levels in hippocampus and prefrontal and cingulate cortex [ 37 ]. As for swim stress, P-p38MAPK was not altered. In contrast to the effects of swim stress, P-JNK levels were not altered by restraint. That study did not assess changes in any of the MAPKKs, limiting further comparisons. However, in a preliminary experiment we found that restraint stress increased P-MKK4 levels in both the hippocampus and striatum, but the effects were more modest than after swim stress (data not shown). Interestingly, Liu et al. [ 38 ] reported that restraint, like swim, elevated both P-MKK4 and P-JNK levels in mice, but the effects in the former stress model were smaller than in the latter. However, P-Erk and P-p38MAPK levels were unchanged in both restrained and swim stressed mice. It thus appears that both the rodent species and type of stress may be important determinants of the specific regional changes occurring in the various kinases. Additional studies will be required to sort out the influence of a variety of factors on the phosphorylation pattern of the MAPK signaling pathways in the brain regions of different animal species. In particular, restraint and swim differences in ambient temperature and physical activity may impact the results. Finally, because these two forms of stress yield different patterns of effects on MAPKs in brain, we can ask which MAPKs (and which brain regions) are the more relevant to stress effects, i.e., which stress model is more appropriate. Answering this question will require correlating the behavioral and MAPK sequelae of different stress treatments (see below). The nature of the extracellular signaling molecule(s) that are recruited upon subjecting animals to stress are unknown. The pathways leading from stimulation of a particular receptor on the cell surface to activation of MAPK signaling cascades are diverse and complex [ 13 , 14 , 47 - 50 ]. For example, Erk1 and Erk2 can be activated by a variety of extracellular signaling molecules, including growth factors, hormones and neurotransmitters [ 48 - 52 ]. Calcium influx in particular has gained currency as potentially of prime importance [ 53 ]. As mentioned earlier, stress is well known to activate c-fos gene expression) [ 21 - 24 ], and such activation in neurons can occur, at least in part, because of an increase in Erk activity via NMDA glutamate receptor-stimulated calcium influx [ 54 - 56 ]. Stress also activates CREB phosphorylation [ 27 ], which may be mediated at least in part by NMDA receptor activation [ 57 ]. Thus, it is attractive to speculate that NMDA receptor activation (at least in some brain regions) may be the initial trigger leading to activation of the Erk pathway (calcium influx may also lead to activation of the JNK pathway via other signaling mechanisms; see [ 53 ]). NMDA receptors themselves are subject to regulation by phosphorylation on their NR1, NR2A and NR2B subunits [ 58 - 60 ]. Interestingly, dopamine D1 receptor-mediated CREB phosphorylation appears to involve NMDA NR1 subunit phosphorylation [ 61 ]. In preliminary experiments we have found that swim stress increased P-NR1 levels on immunoblots of the hippocampus, neocortex and striatum (data not shown), suggesting that stress-induced activation of the MEK-Erk pathway may involve NMDA receptor-mediated calcium influx. Further studies will be directed toward explicating the differential effects of swim stress on the degree of phosphorylation of the MAPKKs vs. the MAPKs, assessing NMDA receptor involvement, and determining whether (and which) MAPKK pathways participate in some behavioral sequelae of repeated swim stress treatment (e.g., immobility and analgesia). Conclusions Acute swim stress initiated a rapid increase in phosphorylation of the MAPKKs MEK1/2 and MKK4 in hippocampus, neocortex, prefrontal cortex, amygdala and striatum. Concomitantly, their corresponding substrates Erk and JNK were also phosphorylated, but not always in register with the changes in the MAPKKs. Moreover, the magnitude of the increase in phosphorylation of MEK1/2 and MKK4 was much greater than for their cognate substrates. While it is clear that stressors such as forced swim and restraint activate these signaling pathways, much work will be required to define the mechanism(s) responsible for these effects, and to relate potential alterations in the activity of these molecules to stress-related influences on the development of anxiety and depressive disorders. Methods Animals Male Wistar rats (200–300 g; Taconic Farms, Germantown, NY) were used throughout. Rats were housed 2/cage and maintained on a 12 h light/dark cycle with food and water ad libitum; they were acclimated to handling and transportation for 2–3 weeks before being used in experiments. All experimental procedures were carried out in accordance with the NIH Guide for the Care and Use of Laboratory Animals , and were approved by the NYU School of Medicine Institutional Animal Care and Use Committee. Materials Precast 10% polyacrylamide gels were obtained from Cambrex BioScience, Rockland, ME. Complete ® protease inhibitor cocktail was from Roche Molecular Biochemicals, Indianapolis, IN and colored molecular weight markers were purchased from Bio-Rad, Hercules, CA. Biotinylated protein ladder marker and anti-biotin were from Cell Signaling Technology (CST), Beverly, MA. West Pico Super Signal enhanced chemiluminescence (ECL) reagent was obtained from Pierce Biotechnology, Rockford, IL, and Re-Blot Plus stripping buffer was from Chemicon International, Temecula, CA. All primary antibodies were rabbit polyclonal except as noted. Because suppliers often offer more than a single antibody for the same antigen, catalog numbers are specified in parentheses. P-Erk (monoclonal, sc-7383), pan Erk (sc-93) and pan MKK4/SEK1 (sc-964) were all from Santa Cruz Biotechnologies, Santa Cruz, CA. The following antibodies were obtained from CST: P-MEK1/2 (9121), pan MEK1/2 (9122), P-MKK4/SEK1 (9151), P-JNK (monoclonal, 9255), pan JNK (9252), P-MKK3/6 (9231), pan CREB (9192) and pan p38MAPK (9212). P-CREB (06-519) was from Upstate Biotechnology, Inc. (Waltham, MA), while P-p38 was either from CST (9212) or Chemicon (AB3828). Control cell extracts for P-MKK3/6 were obtained from CST: +/- UV-treated NIH/3T3 cell lysates (9233) and +/- anisomycin-treated C6 glioma cell lysates (9213). Stress procedure Groups of rats (n = 4–6) were subjected to forced swim stress in a round glass tank (24 cm W × 44 cm H) filled to a depth of 30 cm with water (25 ± 1°C). In most experiments rats were forced to swim for 15 min and sacrificed by decapitation immediately after narcotization with carbon dioxide for 20 sec. In some experiments the duration of swim stress (2–15 min) or the post-swim interval (0–60 min) was varied as described in the legends to Figures. Control rats were not treated and were sacrificed in parallel as above. Tissue dissection Brains were rapidly removed, briefly chilled in saline and placed in a stainless steel brain matrix (Activational Systems, Warren, MI). Sections of 1–2 mm in thickness were cut, frozen quickly on dry ice, and brain areas of interest (prefrontal cortex, neocortex, amygdala and striatum) were micropunched using the atlas of Paxinos and Watson [ 62 ] as a guide. Hippocampus was dissected freehand from the remaining portion of the brain (approximately posterior to bregma -3.80). All samples were stored at -80°C until processed. Tissue processing Tissues were mechanically disrupted, in glass homogenizers using a Teflon pestle, in 10–20 volumes of buffer (50 mM Tris-HCl, pH 7.4 containing 300 mM NaCl, 1% Nonidet P-40, 10% glycerol, 1 mM EDTA, 1 mM Na 3 VO 4 , 1 mM NaF, 0.5 μM okadaic acid and Complete ® protease inhibitor cocktail). After centrifugation at 30,000 g for 20 min, lysates were mixed with 5X sodium dodecyl sulfate (SDS) sample buffer, boiled for 5 min and stored at -80°C. Protein content of lysates was determined using the bicinchoninic acid assay kit (Pierce). Protein separation and immunoblotting Proteins (10–40 μg/lane) were separated by SDS-PAGE on precast polyacrylamide gels. Gels were also loaded with colored molecular weight markers to assess electrophoretic transfer, and biotinylated protein ladder marker to estimate molecular weights of bands of interest. Following electrophoretic transfer to nitrocellulose, blots were incubated in blocking buffer (5% nonfat dry milk in Tris-buffered saline containing 0.05% Tween-20 (TBST)) for 1 hr at room temperature (RT), washed 3 × 10 min in TBST and incubated with primary phospho-specific antibodies overnight at 4°C (monoclonal antibodies in 5% milk/TBST, polyclonal antibodies in 5% BSA/TBST). Blots were washed 3× in TBST, incubated with the appropriate secondary antibody plus anti-biotin for 1 hr at RT, washed again, treated with ECL reagent and exposed to film. Blots were then stripped by incubation for 15 min at RT with Re-Blot Plus, re-blocked, washed and incubated for 1 hr at RT with the corresponding pan antibody which recognizes total antigen protein (phosphorylated and nonphosphorylated). This was followed by incubation for 1 hr at RT with the appropriate secondary antibody plus anti-biotin. Antigens were again visualized by treatment with ECL reagent and exposure to film. Immunoblot analysis A computerized image analysis system (MCID-M4, Imaging Research, St. Catherines, Ontario, Canada) was utilized to analyze immunoblots. Image analysis was carried out as described previously [ 63 ]. For each blot, relative phosphoprotein levels were calculated from the ratio of absorbance of the phosphoprotein/pan protein to correct for small differences in protein loading. Differences between experimental and control conditions were obtained by comparison to the normalized control ratio (arbitrarily set at 100). Statistical analysis Control and swim-stressed groups were compared using Student's t test (SigmaStat, ver. 2.03, SPPS, Inc., Chicago, IL). Authors' contributions C-PS conducted the majority of the immunoblotting studies. YT performed some of the experiments and quantitated many of the films using image analysis. CS assisted in the design of some of the experiments, carried out the MKK3/6 experiments, and assisted in the writing of the manuscript. EM designed the study, supervised and facilitated the experiments, analyzed the data and wrote the manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526203.xml

524370

The missed lessons of Sir Austin Bradford Hill

Austin Bradford Hill's landmark 1965 paper contains several important lessons for the current conduct of epidemiology. Unfortunately, it is almost exclusively cited as the source of the "Bradford-Hill criteria" for inferring causation when association is observed, despite Hill's explicit statement that cause-effect decisions cannot be based on a set of rules. Overlooked are Hill's important lessons about how to make decisions based on epidemiologic evidence. He advised epidemiologists to avoid over-emphasizing statistical significance testing, given the observation that systematic error is often greater than random error. His compelling and intuitive examples point out the need to consider costs and benefits when making decisions about health-promoting interventions. These lessons, which offer ways to dramatically increase the contribution of health science to decision making, are as needed today as they were when Hill presented them.

Introduction One of the most cited papers in health research is Austin Bradford Hill's "The Environment and Disease: Association or Causation?" [ 1 ], Hill's 1965 Presidential Address to the Section of Occupational Medicine of the Royal Society of Medicine, where he presented what are now commonly called the "Bradford-Hill criteria." This paper ironically gains its fame for providing a checklist for inferring causation, something Hill did not claim to be creating. Meanwhile, largely ignored are its great insights and potential contributions to critical methodological and policy issues. Hill outlined a systematic approach for using scientific judgment to infer causation from statistical associations observed in epidemiologic data, listing nine issues to be considered when judging whether an observed association is a causal relationship. Despite widely distributed and clearly elaborated advice to the contrary [ 2 ], Hill's nine considerations are still frequently taught to students of epidemiology and referred to in the literature as "causal criteria." Typically presented as a checklist approach to assessing causation (though without a method for deciding whether to assign a particular checkmark, let alone how to make a final assessment), Hill's list is commonly taught in epidemiology courses and is probably invoked more often than any other method for assessing causation. At a time when the discussion of the nature of causation and methods for identifying causal effects are reaching new levels of sophistication in epidemiology [ 3 - 5 ], this is particularly unfortunate. Hill never used the term "criteria" and he explicitly stated that he did not believe any hard-and-fast rules of evidence could be laid down, emphasizing that his nine "viewpoints" [ 1 ](p. 299) were neither necessary nor sufficient for causation. His suggestions about how to intuitively assess causation are almost completely lost when his address is distilled into a checklist (See endnote 1). Causal criteria are an intriguing subject for the history of science, including the question of why Hill's list seems more popular than others [ 7 - 10 ] and whether causal conclusions that explicitly appealed to criteria are more likely to be borne out by subsequent evidence. (To our knowledge, there has been no such validation study of causal criteria.) But it is not the main purpose of this analysis to join the extensive discussion of the history and merits of causal criteria. We will say only that Hill's list seems to have been a useful contribution to a young science that surely needed systematic thinking, but it long since should have been relegated to part of the historical foundation, as an early rough cut. Yet it is still being recited by many as something like natural law. Appealing in our teaching and epistemology to the untested "criteria" of a great luminary from the past is reminiscent of the "scientific" methods of the Dark Ages. Hill's own caveats suggest a similar opinion (though such a claim requires some caution, given that Hill repeated his list in his medical statistics textbooks until the time of his death, adding neither an evolution in his perspective nor arguments to support the validity or usefulness of the list [ 11 - 14 ]). This brief analysis of Hill's "criteria" and what has been made of them can add little new to that topic (though we will argue that Hill deserves more credit than he is usually given by critics of "criteria" for the nuances and examples he presented). Our purpose is to call attention to the seldom-cited last page and a half of the article, which presents lessons that remain overlooked today. Analysis Hill eloquently warned about overemphasis on statistical significance testing, writing "the glitter of the t table diverts attention from the inadequacies of the fare" [ 1 ](p. 299). The mistake of drawing conclusions from inadequate samples had been replaced with the mistake of treating statistical significance as necessary and sufficient for action. An intellectual generation passed after 1965 with almost no improvement [ 15 ], and little has changed in another generation after that. Researchers still frequently present results as if statistical significance and p-values are useful decision criteria, and decision makers are left with inadequate information. One implication of Hill's advice is well understood. Emphasis on the p-value (let alone dichotomous statements of significance) has been soundly denounced for decades [ 16 , 17 ]. Estimation of effect sizes, presented as point estimates with confidence intervals, is the preferred method in current textbooks [ 18 ] and these are generally reported, though in practice confidence intervals tend to be interpreted as mere tests of statistical significance by ignoring their range except to note whether or not they include the null value (see endnote 2). A further inadequacy of the fare is less well appreciated, stemming not from the question of p-values versus confidence intervals, but from systematic errors. No statistical test of random sampling error informs us about the possible impacts of measurement error, confounding, and selection bias. Methods for quantifying such errors (and perhaps more importantly, arguments for why we need to do so) have been developed in epidemiology, particularly over the last five years [ 19 - 25 ]. Hill hinted at this more than three decades before the recent spate of attention when he noted that one of his own studies [ 26 ], like many studies, had great potential for selection bias (though he does not use this term). In effect, he asks "why would I bother to do an exaggeratedly precise statistical test when I know that the other sources of error are likely so large?" Rather than emphasize low p-values, he concluded that simple cell counts made both random error and plausible systematic error unlikely to account for the observed association. While his solution was inadequate – indeed, it might even be called hubris (see endnote 3) – he did issue a clear warning about mistaking statistical precision for validity. Despite the influence of Hill's article, the fact that it contained this point is forgotten (and the point, while obviously true, remains widely ignored). Even as modern epidemiologic analysts become less dazzled by the t-table, replacing significance testing with confidence intervals and introducing quantification of systematic errors, there is still a tendency to completely overlook Hill's other important insight. Hill sought to address the question how to decide whether to take action once causal inferences are made. In his last few paragraphs, he offers an important commentary on the policy recommendations that flow from decisions regarding cause and effect in epidemiology. Since "our object is usually to take action" [ 1 ](p. 300), policy considerations are central to the importance of the science. While epidemiology has its roots in specific policy questions ("can we do something to prevent cholera outbreaks?"), epidemiologists have ambivalent attitudes towards the policy decisions associated with their research [ 31 ]. In grant applications and introductions to research reports, it is typical for epidemiologists to justify expensive research based on immediate practical benefits. But in presenting the results, they often deny, implicitly or explicitly, the need to assess the policy contributions [ 32 ], defending the value of science for its own sake (sometimes even as they issue press releases calling for policy responses). Even when policy implications are presented explicitly, they are seldom carefully analyzed. Analyzing the implications of a health research finding for decision making is often not terribly difficult, but making recommendations without such analysis can lead to absurd suggestions [ 33 ]. One epidemiology journal famously goes so far as to instruct authors to avoid the common practice of tacking on policy recommendations at the end of research reports. The argument is that policy analysis is too complicated and too serious to be an afterthought by researchers whose expertise lies elsewhere [ 34 , 35 ]. Judging from Hill's comments, he might have preferred more careful policy analysis be included in epidemiologic research reports, rather than none at all, though it is not clear he could solve the challenge of fitting it into the standard 3000-word, single-result health research paper. The present journal offers a solution by publishing policy analyses that are based on health research results, and allowing the articles to be whatever length they need be [ 36 ]. Hill, who was educated in economics, argued that in order to take policy action, we ought to pay attention to the absolute costs and benefits of potential actions. It would clearly be reading too much into the text to suggest that he had a prescient vision of modern probability-weighted cost and benefit based policy analyses and decision theory (those fields were in their early stages at the time of his writing and he never used any of those terms). But, in another memorable phrase, he did make the case for having "differential standards before we convict" [ 1 ](p. 300), based on costs and benefits. Moving another step beyond statistical significance testing, we need to consider more than the degree of certainty that there is some health hazard, and act based on the expected gains and losses, with or without statistical certainty. Hill points this out (in an example sufficiently ill-chosen that it may have contributed to his important message being ignored): "On relatively slight evidence we might decide to restrict the use of a drug for early-morning sickness in pregnant women. If we are wrong in deducing causation from association no great harm will be done. The good lady and the pharmaceutical industry will doubtless survive [ 1 ](p. 300)." Setting aside the impolitic dismissal of women's preferences and the unsupported assertion that there is no great harm at stake (as well as the irony of the popularity, withdrawl, and rehabilitation of the morning sickness drug, Bendectin) the underlying point might be his most important lesson: Policy actions that appear to create a net benefit (on average, considering all costs and benefits) should be taken, even without statistical "proof" of an association, while actions that entail great costs should only be taken with sufficient certainty of substantial benefit. Hill goes on to strengthen his argument: "On fair evidence we might take action on what appears to be an occupational hazard, e.g. we might change from a probably carcinogenic oil to a noncarcinogenic oil in a limited environment and without too much injustice if we are wrong. But we should need very strong evidence before we made people burn a fuel in their homes that they do not like or stop smoking the cigarettes and eating the fats and sugar that they do like [ 1 ](p.300)." Hill clearly stated that the science and data analysis should not be influenced by what is at stake. But health researchers should recognize that the stakes matter, and incorporate a consideration of them into their work. The alternative to carrying out the policy analysis is to leave the weighing of costs and benefits to an unreliable post-science political process. The observation that the costs and benefits matter, despite being rather obvious, is frequently – indeed, typically – overlooked in public health discussions. The popular decongestant phenylpropanolamine was banned on weak evidence without regard to the high cost to consumers [ 37 ]; dietary recommendations are made without considering absolute benefits, let alone the cost to people of avoiding their favorite foods; and health and safety regulations are tremendously uneven in their cost effectiveness, to cite just three examples. The "policy recommendations" paragraph found in many health research papers sometimes quantifies medical costs, but typically ignores lifestyle, psychological, or productivity costs. It is even rare to find quantification of the absolute aggregate benefit that would result from a policy or behavioral change. Making a good decision does not depend on having studies with confidence intervals that exclude the null. A best decision can be based on whatever information we have now, and indeed a decision will be made – after all, the decision to maintain the status quo is still a decision [ 20 , 38 ]. Hill offered his clearest condemnation of over-emphasizing statistical significance testing, not when he discussed p-values, but when he concluded by saying: "All scientific work is incomplete – whether it be observational or experimental. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us a freedom to ignore the knowledge we already have, or to postpone the action that it appears to demand at a given time [ 1 ](p. 300)." The pursuit of the low p-value (or confidence interval that excludes the null) leaves our society postponing apparently useful policy choices while we do more research to try to show what we already believe to be true. It also creates the incentive to use dubious methods (e.g., unstated multiple hypothesis testing, choosing models or transforming data to maximize the effect size [ 39 ]) in order to squeeze out significant results. Those same methods can be used by those who would prefer to make real causal relationships disappear below the p = .05 horizon. Making the best of the knowledge we have would reduce such temptations. If epidemiologists help empower policy makers to ban an easily-replaced chemical when we believe there is, say, a 50-50 chance that it is a health hazard (based on an honest assessment of all uncertainty), then the payoff for fiddling with the data to show the certainty is a bit higher or a bit lower would be eliminated. This would release us from the trap of letting ignorance trump knowledge. Regulators often fail to act because we have not yet statistically "proven" an association between an exposure and a disease, even when there is enough evidence to strongly suspect a causal relationship. There is a growing movement to escape this mistake by making a similar mistake in the other direction: adopting precautionary principles, which typically call for restrictions until we have "proven" lack of causal association – a decision based on ignorance that merely reverses the default. If we can escape from the false dichotomy of "proven vs. not proven," facilitated by the nonexistant bright line implied by statistical hypothesis testing and by the notion that causality can be definitively inferred from a list of criteria, then we can make decisions based on what we do know rather than what we don't. Conclusions The uncritical repetition of Hill's "causal criteria" is probably counterproductive in promoting sophisticated understanding of causal inference. But a different list of considerations that can be found in his address is worthy of repeating: • Statistical significance should not be mistaken for evidence of a substantial association. • Association does not prove causation (other evidence must be considered). • Precision should not be mistaken for validity (non-random errors exist). • Evidence (or belief) that there is a causal relationship is not sufficient to suggest action should be taken. • Uncertainty about whether there is a causal relationship (or even an association) is not sufficient to suggest action should not be taken. These points may seem obvious when stated so bluntly, but causal inference and health policy decision making would benefit tremendously if they were considered more carefully and more often. The last point may be the most important unlearned lesson in health decision making. In fairness to those who do not appreciate these points even today, it overinterprets Hill's short paper to claim that he clearly laid out these considerations, or that he was calling for modern decision analysis and uncertainty quantification. But the fundamental concepts were clearly there (and the overinterpretation is not as great as that required to derive a checklist of criteria for determining causation). Several generations of advancement in epidemiology and policy analysis provide much deeper exposition of his points. But Hill still offers timeless insightful analysis about how to interpret our observations. Strangely, these forgotten lessons, which are only slowly and grudgingly being appreciated in modern epidemiology, are hidden in plain sight, in what is possibly the best known paper in the field. Endnotes 1. Interestingly, there are more extreme cases of a scholar's name being immortalized for something contrary to his beliefs. The "Coase Theorem" in economics, from one of the most cited article in the economics and legal literatures [ 6 ] (often identified as the most cited article in one of those fields or in their intersection), is usually invoked to make worldly claims that certain beneficial transactions will occur (which, among other things, reduce the need for regulation). But much of Coase's work (including that paper) focuses on how the circumstances required for those transactions to take place are absent in the real world. 2. Reporting confidence intervals provides more information about the estimated association of an exposure and outcome. For example, a large measured effect with a wide confidence interval and a small measured effect with a narrow confidence interval may have the same p-value, but the confidence intervals suggests that a large association is likely in the former case, but not the latter. This has implications for both scientific conclusions and decision making. However, the reporting of confidence intervals addresses only this limitation, not others described subsequently. 3. In effect, Hill claimed that the association was so strong that neither the random nor the systematic error could explain it. In doing so, he failed to heed his own observation that systematic errors might explain an association no matter how low the p-value, and invoked the strength of the statistical association to rule out the possibility it was caused by systematic error. More important, Hill made the mistake of overestimating his ability to intuitively assess complicated quantitative relationships. In Hill's defense, his remark predated the research, primarily from the 1970s and 1980s, that demonstrated that both lay people and experts have poor quantitative intuition (most of the key papers from that literature can be found in a few collected volumes [ 27 - 30 ]). Current researchers who argue that their intuition obviates the need for modern methods for quantifying uncertainty have no such excuse.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524370.xml

524366

Tracking of physical activity, fitness, body composition and diet from adolescence to young adulthood: The Young Hearts Project, Northern Ireland

Background The assumption that lifestyles formed early in life track into adulthood has been used to justify the targeting of health promotion programmes towards children and adolescents. The aim of the current study was to use data from the Northern Ireland Young Hearts Project to ascertain the extent of tracking, between adolescence and young adulthood, of physical activity, aerobic fitness, selected anthropometric variables, and diet. Methods Males ( n 245) and females ( n 231) were assessed at age 15 y, and again in young adulthood [mean (SD) age 22 (1.6) y]. At both timepoints, height, weight and skinfold thicknesses were measured, and physical activity and diet were assessed by questionnaire and diet history method respectively. At 15y, fitness was assessed using the 20 metre shuttle run, while at young adulthood, the PWC170 cycle ergometer test was used. For each measurement made at 15y, subjects were ranked into 'low' (L1; lowest 25%), 'medium' (M1; middle 50%) or 'high' (H1; highest 25%) categories. At young adulthood, similar categories (L2, M2, H2) were created. The extent of tracking of each variable over time was calculated using 3 × 3 matrices constructed using these two sets of categories, and summarised using kappa (κ) statistics. Results Tracking of diet and fitness was poor (κ ≤ 0.20) in both sexes, indicating substantial drift of subjects between the low, medium and high categories over time. The tracking of physical activity in males was fair (κ 0.202), but was poor in females (κ 0.021). In contrast, anthropometric variables such as weight, body mass index and sum of skinfolds tracked more strongly in females (κ 0.540, κ 0.307, κ 0.357 respectively) than in males (κ 0.337, κ 0.199, κ 0.216 respectively). Conclusions The poor tracking of fitness and diet in both sexes, and physical activity in females, suggests that these aspects of adolescent lifestyle are unlikely to be predictive of behaviours in young adulthood. In contrast, the fair to moderate tracking of anthropometric variables, particularly in females, suggests that attempts to reduce the ever increasing incidence of overweight and obesity in adults, should probably begin in earlier life.

Background Numerous epidemiological studies in adults have identified environmental and physiological risk factors that are associated with increased risk for cardiovascular disease (CVD). Among the many that have been identified [ 1 ], the major modifiable risk factors include physical inactivity, poor cardiorespiratory fitness, excess adiposity or obesity, and inappropriate dietary habits. Although the clinically relevant effects of CVD are often not manifest until middle age or later, it is now generally well accepted that the disease is likely to have its antecedents in childhood [ 2 - 4 ]. In addition, children and adolescents have been shown to exhibit many of the potentially modifiable CVD risk factors that have been identified in adults. For example, of the 1015 adolescents aged 12y and 15y who participated in the first phase of the Northern Ireland Young Hearts Project, 18–34% were considered to have excess body fat, 24–29% had low physical activity levels, and 26–34% had poor cardiorespiratory fitness. Furthermore, mean total fat intakes were higher than desirable [ 5 ]. Similarly, in West Virginia, of the 5,887 school children who participated in the school-based Coronary Artery Risk Detection in Appalachian Communities (CARDIAC) Project, almost 43 percent were considered to be overweight, over a quarter were obese, and the high rate of obesity was positively associated with the prevalence of other CVD risk factors [ 6 ]. The conclusions drawn from these studies and others [ 7 - 9 ] have been largely unanimous: minimising the risk of morbidity or premature mortality associated with CVD in adulthood, should begin in childhood or adolescence. However, if health promotion interventions in younger life are to have any hope of success, it must be assumed that physiological and behavioural risk factors exhibited in early life track into adulthood. Tracking has been defined as the maintenance of relative position in rank of behaviour over time, such that subjects who rank highly for unfavourable risk profiles at a young age are likely to maintain their ranks through into adulthood [ 10 , 11 ]. The aim of the current study was to use data from the Northern Ireland Young Hearts Project to ascertain the extent of tracking, between adolescence and young adulthood, of selected modifiable risk factors for CVD. Specifically, we investigated the tracking of physical activity, aerobic fitness, selected anthropometric variables, and diet. Methods Subjects The Young Hearts Project (YH) is an ongoing longitudinal study evaluating the prevalence of CVD risk factors in young people living in Northern Ireland. Sampling procedures and methods used in the first two, school-based screening phases of the YH study have been described fully elsewhere [ 5 ]. Briefly, the initial screening (YH1), conducted in 1989/1990, surveyed 1015 adolescents (12-year old boys, n 251;12-year old girls, n 258; 15-year old boys, n 252; and 15-year old girls, n 254) randomly selected from post-primary schools. At that time, the resulting cohort represented a 2% sample of each of the two age populations in Northern Ireland. In 1992/93, a follow up study (YH2) was undertaken, in which subjects from the original 12 year old cohort were reassessed using procedures identical to those used in YH1. The response rate in the follow-up study was 90%. Between October 1997 and October 1999, all YH1 subjects were invited to participate in the third, hospital-based screening phase (YH3), and a 48.2% response rate was achieved. Reasons for the low response rate in YH3 have been described in full elsewhere [ 12 ]. Briefly, non-attenders reported that they were 'too busy', 'living outside Northern Ireland', 'busy with new job', 'couldn't be bothered' or 'didn't feel that the study was relevant to them'. As described by [ 12 ] and [ 13 ], attempts were made to determine the representativeness of the YH3 cohort by comparing the baseline YH1 data for those who participated in YH3, with the data obtained for those who declined to participate. YH3 participants tended to be from families with higher socio-economic status, and had lower BMI at baseline (YH1) than non-participants. Furthermore, males who declined to attend for screening at YH3, were fatter and reported a greater saturated fat intake at YH1 than YH3 male participants. The analyses reported in the current paper are restricted to males ( n 245) and females ( n 231) for whom there were complete data sets at age 15y (either from YH1 or YH2) and at young adulthood [mean (SD) age 22.0 (1.6)y]. Ethical approval for each phase of the study was obtained from the Medical Research Ethical Committee of The Queen's University of Belfast, and written informed consent was obtained from all subjects prior to participation. Anthropometry Each subject's height, weight and skinfold thicknesses were measured at all study timepoints. Standing height was measured to the nearest millimetre using a Harpenden portable stadiometer (Holtain, UK), and body weight was measured to the nearest 0.1 kg using an electronic balance (Seca, Germany; 200 kg × 0.1 kg). For both measurements, subjects wore light indoor clothing and no shoes. Body mass index (BMI) was then calculated as weight (kg)/ [height (m)] 2 . Skinfold thicknesses were measured to the nearest millimetre using Harpenden callipers at four sites (biceps, triceps, subscapular, suprailiac). Two measurements were taken at each site and the average was recorded. The sum of the four skinfolds thicknesses was then calculated for each subject. Dietary intake At all study timepoints, dietary data were obtained using the diet history method [ 14 ]. This consisted of a detailed, open-ended one-to-one interview, the purpose being to ascertain the habitual weekly food intake of each subject. The diet history method was used for two reasons. Firstly, in subjects aged 15y, the diet history has been shown to provide more valid estimates of energy intake at the group level than weighed records [ 15 ]. Secondly, given that a complete diet history can be obtained from a subject in approximately one hour, it was the most feasible and cost-effective method for obtaining detailed dietary information from the YH1 and YH2 school-based cohorts. The method was used again in YH3 in order to maintain continuity. Reported energy and macronutrient intakes were calculated using computerised databases based on UK food composition tables as previously described [ 16 , 12 ] Physical activity At age 15y, habitual physical activity was assessed by self-report questionnaire, and scored according to the method of [ 17 ]. This method assessed the extent of daily participation in activities that were based around a typical school day. Each activity was assigned a score from 1–100, based on its frequency, intensity and duration. As the school-based questionnaire was not relevant to the young adult subjects, a modification [ 18 ] of the Baecke questionnaire was used in YH3 to quantify habitual work activity, sports activity and non-sports leisure activity. For each of the three activity components, scores based on a five-point Likert scale were calculated and summed, giving total possible scores ranging from 3–15. Aerobic fitness Aerobic fitness at age 15y was assessed by the 20 metre shuttle test (20MST). In order to estimate maximal aerobic capacity, or VO 2 max (ml/kg/min), the number of laps completed by each subject in this maximal endurance test was entered into a sex-specific regression equation, based on data obtained in the Northern Ireland Fitness Survey [ 17 ]. As it was not feasible to conduct the 20MST at young adulthood (due to a lack of space in the hospital setting), VO 2 max was assessed using the Physical Work Capacity at a heart rate of 170 beats per minute (PWC170) cycle ergometer test [ 19 ]. PWC170 was calculated as the workload corresponding to a heart rate of 170 bpm, and expressed per kg body weight. The volume of oxygen consumed and the heart rate were monitored throughout the test (Quinton Metabolic Cart, Quinton, USA). For each subject, a straight line was fitted to three pairs of data (heart rate in bpm, VO 2 in ml/kg/min), and this was used to estimate VO 2 max at the age-adjusted maximum heart rate [ 12 ]. Statistical analyses All data were analysed using SPSS version 11.0.1 (SPSS Inc, Chicago, USA). Means and standard deviations were used to summarise the data for physical characteristics, aerobic fitness, physical activity scores and energy and macronutrient intakes of males and females at age 15y and young adulthood. Tracking of each of these variables over time was assessed by determining the extent to which subjects who were placed into low, medium and high categories at age 15y, maintained their ranking in young adulthood. Owing to the fact that different techniques were used to measure physical activity and aerobic fitness at each timepoint, a method based on ranks, rather than actual measurements, was employed for assessing the tracking of these variables. Tracking of the other variables was also assessed using the rank based method because of its relative simplicity, and its ability to show the numbers of subjects making the transition between low, medium and high categories [ 20 ]. For example, in order to study the tracking of physical activity in females from age 15y to young adulthood, the group of 225 girls aged 15y was divided into three classes by physical activity score: lowest 25% (L1); middle 50% (M1); highest 25% (H1). Rather than using pre-determined fixed values, each class was defined by the first and third empirical quartiles. In young adulthood, the female group was divided into three similar classes; L2, M2 and H2. Using these two sets of classifications, a 3 × 3 tracking matrix was constructed; the entry in a specific cell being the number of subjects belonging to the corresponding classes at age 15y and at young adulthood (see Figure 1 for examples). This approach provides a broad picture of the relative changes in a particular variable over time, such that a matrix with relatively small off-diagonal elements provides evidence of 'good' tracking. For the purposes of this study, the degree of tracking was summarised by a weighted kappa (κ) value, and interpreted according to [ 21 ] as follows: κ ≤ 0.20, poor tracking; κ 0.21–0.40, fair; κ 0.41–0.60, moderate; κ 0.61–0.8, good; κ 0.81–1.0, very good. This procedure was undertaken separately for males and females to assess the tracking, between age 15y and young adulthood, of energy and macronutrient intakes, height, weight, BMI, skinfold thicknesses, aerobic fitness and physical activity scores. Figure 1 Examples of 3 × 3 tracking matrices constructed for the calculation of κ values for (a) height in females, and (b) physical activity scores in females.(a) Height in females (κ 0.813) (b) Physical activity score in females (κ 0.021) L1 and L2 represent lowest 25% of the cohort at baseline and follow-up respectively; M1 and M2 represent middle 50% of the cohort at baseline and follow-up respectively; H1 and H2 represent the highest 25% of the cohort at baseline and follow-up respectively. The entry in a specific cell indicates the number of subjects belonging to the corresponding classes at baseline and at follow-up. Results The physical characteristics, aerobic fitness and physical activity levels of the Northern Ireland Young Hearts cohort at age 15y and at follow-up (young adulthood) are summarised in Table 1 . At young adulthood, weight, height, BMI and skinfold thicknesses were significantly greater than at age 15y in males and females. In both sexes, VO 2 max assessed at young adulthood was significantly lower than at age 15y. At age 15y, males and females were 4.8% and 8.4% heavier than the British age-specific reference population, while at young adulthood, they were 7.9% and 9.9% heavier respectively. Details of the reference populations are described in Annex 1 of the ' Dietary Reference Values for Food Energy and Nutrients for the United Kingdom ' [ 22 ]. Table 1 Physical characteristics, fitness and physical activity levels at age 15y, and at young adulthood (mean age 22.0y). Males Females Baseline Follow-up Baseline Follow-up n Mean SD n Mean SD n Mean SD n Mean SD Weight (kg) 245 59.2 8.9 245 75.5*** 11.5 231 56.9 9.1 231 64.3*** 11.7 Height (m) 245 1.70 0.07 245 1.78*** 0.07 231 1.62 0.06 231 1.64*** 0.06 Body mass index (kgm -2 ) 245 20.4 2.4 245 23.8*** 3.1 231 21.7 3.2 231 23.8*** 4.1 Biceps skinfold (mm) 245 4.73 2.23 244 5.54*** 3.42 231 8.00 2.92 230 9.57*** 5.27 Triceps skinfold (mm) 245 9.21 4.59 244 10.28** 5.41 231 15.97 4.49 230 17.84*** 5.94 Subscapular skinfold (mm) 245 7.75 3.75 244 12.94*** 5.22 231 11.57 4.77 230 15.14*** 6.17 Suprailiac skinfold (mm) 245 10.30 6.58 244 15.87*** 7.43 231 14.08 5.73 230 16.01*** 6.98 Sum of skinfolds (mm) 245 32.00 16.07 244 44.63*** 18.76 231 49.61 15.62 230 58.56*** 20.76 VO 2 max a 241 52.07 5.96 225 38.93*** 8.70 228 41.05 5.48 212 26.90*** 5.43 Physical activity score b 242 28.27 14.44 243 7.95 1.38 227 17.71 12.59 229 7.40 1.20 a VO 2 max at age 15y was derived from the number of 20 metre shuttle run laps completed by each subject. At follow-up, VO 2 max was extrapolated from the results of a cycle ergometer test (Physical Work Capacity at a heart rate of 170 bpm; PWC170). b Physical activity scores at age 15y were calculated according to the method of Riddoch et al (1991). At follow-up, a modification (Pereira et al , 1997) of the Baecke questionnaire was used. At age 15y, the maximum possible activity score was 100, while at follow-up, scores could range from 3–15. For each sex, differences between variables measured at age 15y and young adulthood were assessed using paired t-tests. *** P < 0.001 ** P < 0.01. The energy and macronutrient intakes reported by the Young Hearts cohort at age 15y, and at follow-up, are presented in Table 2 . At young adulthood, the males reported significantly lower intakes of energy (MJ/d; P 0.04), total fat (g/d and % energy; both P < 0.001) and total carbohydrate (g/d and % energy; both P < 0.001) than at age 15y. In contrast, intakes of protein (g/d and % energy; both P < 0.001) reported by males at young adulthood were significantly greater than at age 15y. Similar patterns were observed for females, with the exception that % energy derived from total carbohydrate did not change significantly between age 15y and young adulthood. Table 2 Energy and macronutrient in takes a reported at age 15y, and at young adulthood (mean age 22.0y). Males ( n 245) Females ( n 231) Baseline Follow-up Baseline Follow-up Mean SD Mean SD Mean SD Mean SD Energy (MJ/d) 13.5 3.2 13.0* 3.5 9.4 2.6 8.3*** 2.4 Protein (g/d) 95.0 25.6 101.2** 27.9 63.9 18.6 68.5** 21.4 % energy from protein 12.0 1.9 12.6*** 2.3 11.6 2.1 13.4*** 2.9 Total fat (g/d) 137.1 38.5 113.2*** 38.3 96.8 30.3 73.3*** 25.0 % energy from fat 37.4 4.3 32.1*** 5.5 37.7 4.3 32.5*** 6.0 Carbohydrate (g/d) 411.6 100.3 368.6*** 110.5 289.6 82.2 253.6*** 87.1 % energy from carbohydrate 48.8 4.6 45.7*** 7.2 49.2 4.9 49.1 NS 6.5 a At both timepoints, energy and macronutrient intakes were assessed using the diet history method. For each sex, differences between variables measured at age 15y and young adulthood were assessed using paired t-tests. *** P < 0.001 ** P < 0.01 NS not significant Table 3 summarises the extent of tracking of physical characteristics, fitness and physical activity levels between age 15y and young adulthood, in males and females. In males, tracking of height was moderate, while in females, it was very good. Figure 1(a) , which illustrates the 3 × 3 matrix constructed for the tracking of height in females between age 15y and young adulthood, demonstrates that there was very little drift of subjects between low, medium and high categories over time; hence the relatively high κ value. In both sexes, tracking of BMI was moderate. In males, the tracking of weight, four skinfold thicknesses (biceps, triceps, subscapular, suprailiac) and sum of skinfolds was fair (κ 0.21–0.40) between age 15y and young adulthood. The magnitude of the κ values obtained for biceps and subscapular skinfold thicknesses in females were also greater than in males, while triceps and suprailiac skinfold thicknesses tracked to a similar extent in both sexes. In males, the tracking of aerobic fitness (VO 2 max) was poor, but was greater than the κ value obtained for the females (κ 0.150 vs κ 0.076). A similar pattern was observed for physical activity scores (κ 0.202 vs κ 0.021). Table 3 Tracking of physical characteristics, fitness and physical activity levels between age 15y and young adulthood (mean age 22.0y) Males Females n κ P n κ P Weight (kg) 245 0.337 <0.0001 231 0.540 <0.0001 Height (m) 245 0.444 <0.0001 231 0.813 <0.0001 Body mass index (kgm -2 ) 245 0.422 <0.0001 231 0.452 <0.0001 Biceps skinfold (mm) 244 0.224 <0.0001 230 0.403 <0.0001 Triceps skinfold (mm) 244 0.292 <0.0001 230 0.287 <0.0001 Subscapular skinfold (mm) 244 0.274 <0.0001 230 0.371 <0.0001 Suprailiac skinfold (mm) 244 0.223 <0.0001 230 0.202 <0.0001 Sum of skinfolds (mm) 244 0.216 <0.0001 230 0.357 <0.0001 VO 2 max a 222 0.150 <0.0001 209 0.076 0.128 Physical activity score b 240 0.202 <0.0001 225 0.021 0.669 a VO 2 max at age 15y was derived from the number of 20 metre shuttle run laps completed by each subject. At follow-up, VO 2 max was extrapolated from the results of a cycle ergometer test (Physical Work Capacity at a heart rate of 170 bpm; PWC170). b Physical activity scores at age 15y were calculated according to the method of Riddoch et al (1991). At follow-up, a modification (Pereira et al , 1997) of the Baecke questionnaire was used. κ indicates extent of tracking, and can be interpreted as follows: κ < 0.20, poor tracking; κ 0.21–0.40, fair; κ 0.41–0.60, moderate; κ 0.61–0.8, good; κ 0.81–1.0, very good (Altman, 1991). The extent of tracking of energy and macronutrient intakes reported at age 15y and at young adulthood is presented in Table 4 . In males, the κ values for energy, protein, total fat and total carbohydrate were poor, ranging from 0.019 (% energy from protein) to 0.169 (energy). Similarly, κ values observed in the females ranged from 0.051 (% energy from fat) to 0.202 (protein). Table 4 Tracking of energy and macronutrient intakes a between age 15y and young adulthood (mean age 22.0y). Males Females n κ P n κ P Energy (MJ/d) 245 0.169 <0.0001 231 0.154 0.001 Protein (g/d) 245 0.169 <0.0001 231 0.202 <0.0001 % energy from protein 245 0.019 0.683 231 0.098 0.039 Total fat (g/d) 245 0.117 0.011 231 0.152 0.001 % energy from fat 245 0.143 0.002 231 0.051 0.282 Carbohydrate (g/d) 245 0.114 0.013 231 0.120 0.011 % energy from carbohydrate 245 0.117 0.011 231 0.063 0.182 a At both timepoints, energy and macronutrient intakes were assessed using the diet history method. κ indicates extent of tracking, and can be interpreted as follows: κ < 0.20, poor tracking; κ 0.21–0.40, fair; κ 0.41–0.60, moderate; κ 0.61–0.8, good; κ 0.81–1.0, very good (Altman, 1991). Discussion The current paper describes the extent of tracking for a range of behavioural and biological risk factors for CVD, between adolescence (15y) and young adulthood (22y) in 245 males and 231 females from Northern Ireland. In relation to nutrient intakes, the poor tracking between 15 and 22 years revealed in this study reflects previous findings in this cohort between 12 and 15 years [ 20 ]. This suggests that individual dietary patterns exhibited at 15 years are unlikely to be predictive of dietary intakes at young adulthood. Intuitively, this lack of tracking is to be expected, as the transition from adolescence to adulthood is characterised by considerable physical, cognitive and psychosocial change. To date, however, there has been little evidence for tracking or otherwise of diet in this age group. Reasonably good dietary tracking has been reported for younger pre-school children [ 23 ], but this is not surprising given the high degree of control over diet exerted by parents in this age group. Reasonably high tracking coefficients for diet in the Amsterdam Growth and Health Longitudinal Survey were reported by Kemper et al. [ 24 ], but these were between the ages of 13 and 32 years. Although it is difficult to draw direct comparisons between studies due to differences in methodologies, one possibility for the poor tracking reported in the present study might be a particularly high degree of mis-reporting of intake in 15 year-old adolescents. This has been noted previously in relation to this cohort, particularly in relation to 'under-reporting' in 15 year-old females [ 20 ]. It is also possible that the low κ values obtained for the dietary intakes in the present study indicate that adolescence is, indeed, associated with rapidly changing and erratic patterns of nutrient intake. Adolescents take increasing control of what, when and where they eat and typically consume a greater proportion of their total intake outside the home. Concerns about changing body shape and adiposity may also prompt sudden changes in eating behaviour. While adolescence is widely regarded to be a time of transition, it could also be argued that young adulthood is an equally important time of change, especially with regard to dietary habits. This is a time when people in this age group are likely to move out of home, go to university, start a family or to encounter other environmental or psychosocial factors that influence food intakes. Thus it is possible that the poor maintenance of ranks that we have observed in this study has arisen simply because we attempted to assess tracking between two very unstable periods of time in the life-cycle. It is also possible that at least part of the explanation for poor dietary tracking in a cohort of this nature lies in the unsuitability of the diet history method for this purpose. Although the diet history method has shown good validity at the group level in adolescents, it is prone to significant problems of precision at the individual level [ 15 ]. Moreover, as it assesses perception and memory of usual diet and is susceptible to socially desirable responding [ 25 ], it is possible that changes in memory and motivation over time may contribute to poor tracking. Finally, it is entirely feasible that diet simply does not track well between two time points several years apart. Certainly, the data presented in this study suggest that individual dietary patterns reported at 15 years are unlikely to be predictive of energy and nutrient intakes reported at 22 years. It is clear, therefore, that individual subjects cannot be targeted for long-term dietary intervention based solely on data obtained at 15 years of age. Both physical fitness and physical activity are now accepted as independent risk factors for several chronic diseases. The identification of low levels of fitness and/or activity at an early stage in the lifecourse might, therefore, enable early remedial strategies, provided that a degree of tracking for these risk factors is demonstrable. The results of the present study fail to provide such evidence, with poor tracking being demonstrated for fitness in both sexes, and poor tracking in females and only moderate levels of tracking in males for activity. By and large, these results are in keeping with the handful of other studies which have examined tracking of fitness and activity in this age group [ 26 , 27 ]. While the mechanisms responsible for this lack of stability between adolescence and early adulthood remain obscure, it is tempting to speculate that similar influences are responsible for the poor tracking of both diet and physical activity/fitness. For example, while much activity during adolescence is organised and school-based, by the time the individual reaches early adulthood, activity is likely to be more a matter of choice. In this respect, it is interesting to note that studies of the tracking of physical activity/fitness in both younger [ 28 ] and older [ 29 , 30 ] age groups than that of the present study, show generally higher levels of tracking between time points. In contrast to diet and physical activity/fitness, anthropometric variables relating to body weight and adiposity showed stronger and consistent tracking, particularly in females. Good tracking of BMI from adolescence to young adulthood has been noted in previous studies [ 31 , 24 , 33 ], with stronger tracking for females also highlighted. The results of the present study thus confirm the potential utility of identifying adolescents at the age of 15 years who are at risk of persistent obesity, and targeting such adolescents with appropriate long-term lifestyle advice. Our results for diet, physical activity and fitness, however imply greater instability from adolescence to young adulthood, and consequently the need for shorter-term, ameliorative strategies based on regular monitoring of these behaviours and attributes. Competing Interests The authors declare that they have no competing interests. Authors' contributions CB conceived of the study, participated in its design and co-ordination and co-drafted the manuscript. PR supervised the collection of dietary data and co-drafted the manuscript. AG and GC carried out the statistical analysis. LM participated in the design and co-ordination of the project. JS conceived of the study and participated in its design and co-ordination. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524366.xml

545942

Association of SNP3 polymorphism in the apolipoprotein A-V gene with plasma triglyceride level in Tunisian type 2 diabetes

Background Apolipoprotein A-V (Apo A-V) gene has recently been identified as a new apolipoprotein involved in triglyceride metabolism. A single nucleotide polymorphism (SNP3) located in the gene promoter (-1131) was associated with triglyceride variation in healthy subjects. In type 2 diabetes the triglyceride level increased compared to healthy subjects. Hypertriglyceridemia is a risk factor for coronary artery disease. We aimed to examine the interaction between SNP3 and lipid profile and coronary artery disease (CAD) in Tunisian type 2 diabetic patients. Results The genotype frequencies of T/T, T/C and C/C were 0.74, 0.23 and 0.03 respectively in non diabetic subjects, 0.71, 0.25 and 0.04 respectively in type 2 diabetic patients. Triglyceride level was higher in heterozygous genotype (-1131 T/C) of apo A-V (p = 0.024). Heterozygous genotype is more frequent in high triglyceride group (40.9%) than in low triglyceride group (18.8%) ; p = 0.011. Despite the relation between CAD and hypertriglyceridemia the SNP 3 was not associated with CAD. Conclusion In type 2 diabetic patients SNP3 is associated with triglyceride level, however there was no association between SNP3 and coronary artery disease.

Background Dyslipidemia in type 2 diabetes are most frequently characterized by elevation of total serum triglycerides, of very low density lipoprotein-triglyceride (VLDL-TG) and low level of high density lipoprotein-cholesterol (HDL-C) [ 1 ]. Hypertriglyceridemia is an independent risk factor for coronary artery disease (CAD) in type 2 diabetes [ 2 ]. Triglyceridemia is modulated by environmental and genetic factors. A new identified gene associated with triglyceride level was the gene encoding for apo A-V located at the chromosome 11 (11q23), in the vicinity of apoA-I/C-III/A-IV cluster [ 3 ]. Studies on transgenic mice overexpressing human apo A-V showed a decreased level of triglyceride, whereas knock-out mice showed an increased level of triglyceride [ 3 ]. These results prove the regulator effect of apo A-V on triglyceride metabolism. Moreover apo A-V regulates levels of circulating triglyceride and cholesterol [ 4 ]. Four neighboring single nucleotide polymorphisms (SNP1 to SNP4) within apo A-V were identified by Pennachio et al [ 3 ]. The first three of the SNPs (SNPs1–3) were in significant linkage disequilibrium suggesting the existence of a common haplotype in apo A-V gene. The minor allele of each SNP was associated with high triglyceride level. In other study, the SNP3 (T/C polymorphism) was also associated with HDL-C concentration [ 5 ]. This suggests that genetic variability of the apo A-V gene is likely to also have an impact on the lipid profile of type 2 diabetic patients, but reports on the subjects are few [ 6 ]. We have addressed the issue to examine the interaction between SNP3 and lipid profile and coronary artery disease (CAD) in type 2 diabetic patients compared to controls in Tunisian population. Results Description of the participating groups Type 2 diabetic patients have a BMI values more important than non diabetic subjects. Whereas the WHR in lower in controls. Males and smokers are more frequent in patients with CAD (Table 1 ). To compare lipid parameters, we take into consideration the lipid lowering drugs use, then patients who are taking lipid lowering drugs were excluded (Table 2 ). Plasma total cholesterol and total triglyceride concentrations were significantly higher in type 2 diabetic patients than in the non diabetic patients. Subjects with CAD had lower concentration of HDL-C and higher concentration of triglyceride as compared to those without CAD (Table 2 ). Table 1 Clinical characteristics of the four studied groups Diabetes- CAD- Diabetes+ CAD- Diabetes+ CAD+ Diabetes- CAD+ Diabetes vs non Diabetes CAD vs non CAD Number 99 78 74 57 Sex (% of men) 50.5 49.4 75.4 84.2 ns < 0.001 Age (years) 52.9 ± 8.7 51.5 ± 7.3 55.7 ± 6.8 59.0 ± 6.8 ns < 0.001 BMI (Kg/m 2 ) 27.6 ± 4.0 28.8 ± 4.6 28.1 ± 3.8 26.6 ± 4.8 0.029 ns Smokers (%) 42 53 86 80 0.003 < 0.001 SBP (mmHg) 12.5 ± 1.3 13.2 ± 1.4 13.0 ± 1.8 12.4 ± 2.1 0.001 ns DBP (mmHg) 7.5 ± 0.7 7.8 ± 0.9 7.6 ± 1.0 7.1 ± 1.1 0.006 0.018 WHR 0.82 ± 0.09 0.92 ± 0.07 0.95 ± 0.07 0.92 ± 0.06 ns 0.016 Glucose (mmol/l) 5.2 ± 0.4 10.4 ± 3.4 11.7 ± 3.5 6.8 ± 3.6 < 0.001 < 0.001 HbA1c (%) 4.5 ± 0.9 10.4 ± 3.4 9.4 ± 2.4 11.3 ± 4.1 0.009 < 0.001 Diabetes duration (years) 6.8 ± 5.2 7.9 ± 6.1 Lipid lowering drug use (%) 11.1 0 16.2 70.2 SBP : systolic blood pressure DBP : diastolic blood pressure Table 2 lipid profiles of the four studied groups Diabetes- CAD- Diabetes+ CAD- Diabetes+ CAD+ Diabetes- CAD+ Diabetes vs non Diabetes CAD vs non CAD Number 88 78 62 17 Cholesterol (mmol/l) 4.70 ± 1.22 5.16 ± 1.14 5.13 ± 1.12 4.06 ± 1.22 < 0.001 ns HDL-C (mmol/l) 0.94 ± 0.24 0.96 ± 0.38 0.79 ± 0.22 0.72 ± 0.21 ns < 0.001 Total TG (mmol/l) 1.39 ± 0.75 2.11 ± 1.48 2.27 ± 1.77 2.81 ± 2.53 < 0.001 < 0.001 Heterozygous genotype had the high triglyceride level According to SNP3 of the apo A-V gene, variation of lipid parameters in diabetic or non diabetic patients are shown in Table 3 . Non diabetic subjects having the heterozygous genotype (T/C) showed an increased triglyceride level and decreased HDL-C concentration. However these variations were not significant. In type 2 diabetic patients, triglyceride level increased significantly in C/T genotype in association with non significant decrease in HDL-C concentration. The genotype frequencies of T/T, T/C and C/C were 0.74, 0.23 and 0.03 respectively in non diabetic subjects, 0.71, 0.25 and 0.04 respectively in type 2 diabetic patients. The SNP3 was shown to be in Hardy-Weinberg equilibrium. It is clear that there was no difference in genotype distribution between diabetic and non diabetic subjects. The type 2 diabetic population was classified further into those with high and low triglyceride concentration (cut point 2.2 mmol/l which was more than 90 percentile level in the healthy population). The SNP3 frequencies for T/T, T/C and C/C genotypes in the low triglyceride groups were 77.1 %, 18.8 % and 4.2 % respectively, and those in the high triglyceride group were 59.1 %, 40.9 % and 0 % respectively. The difference in genotype frequencies between low and high triglyceride groups were significant (p = 0.011) (Table 4 ). Table 3 Clinical Characteristics and lipid profile according to SNP3 of apo A-V in type 2 diabetic and in non diabetic subjects Type 2 Diabetes Non Diabetes Characteristics Genotypes TT TC TT TC Number 100 36 76 25 Sex (% men) 61 50 55.8 60 Smokers (%) 67.3 81.3 44.4 46.2 Age (years) 52.8 ± 7.6 54.8 ± 6.2 53.3 ± 8.8 51.5 ± 7.5 BMI (Kg/m 2 ) 28.5 ± 4.3 28.7 ± 3.9 27.3 ± 3.0 28.1 ± 5.0 WHR 0.94 ± 0.06 0.93 ± 0.08 0.93 ± 0.08 0.9 ± 0.08 Cholesterol (mmol/l) 5.17 ± 1.15 5.17 ± 1.07 4.61 ± 1.32 4.53 ± 0.99 HDL-C (mmol/l) 0.90 ± 0.32 0.83 ± 0.34 0.92 ± 0.25 0.83 ± 0.26 Total TG (mmol/l) 2.05 ± 1.61 2.62 ± 1.59* 1.6 ± 1.42 1.75 ± 1.07 * significant difference between TT and TC genotypes (p = 0.016). Table 4 Distribution of different apo A-V genotypes in type 2 diabetes between low and high triglyceride groups Low triglyceride group ≤ 2.2 mmol/l High triglyceride group > 2.2 mmol/l Polymorphism Number % Number % T/T 74 77.1 26 59.1 T/C 18 18.8 18 40.9 C/C 4 4.2 nd nd Total 96 100 44 100 nd : not detected X2 = 8.962, p = 0.011, degree of freedom = 2. No association between SNP3 and CAD To investigate the relation of SNP3 polymorphism with coronary artery disease we studied the association in all subjects (those who are taking lipid lowering drugs are included). There was no association between coronary artery disease and SNP3 either in non diabetic subjects or in type 2 diabetic patients (Table 5 ). Table 5 Association between SNP3 of apo A-V and risk for coronary artery disease in diabetic and non diabetic subjects Genotypes CAD- CAD + Diabetes- T/T 74 (74.7) 41 (71.9) P = 0.514 OR = 1.29 (0.6–2.77) T/C 21 (21.1) 15 (26.3) C/C 4 (4.0) 1 (1.8) Diabetes+ T/T 56 (71.8) 52 (70.3) P = 0.717 OR = 0.87 (0.41–1.83) T/C 21 (26.9) 17 (23.0) C/C 1 (1.3) 5 (6.7) OR was calculated using T/T and T/C genotypes only. Discussion Elevated serum lipid levels are an important risk factor for atherosclerosis. Both environmental and genetic factors contribute to variability in serum lipid levels [ 5 ]. In this study, we choose the apo A-V gene as a genetic factor that predispose to elevated triglyceride level. The dynamic interfacial properties of apo A-V are consistent with the hypothesis that apo A-V impedes triglyceride particle assembly [ 7 ]. Thus, the effect of apo A-V on triglyceride level can be attributed to the intracellularly function of apo A-V to modulate hepatic VLDL synthesis and/or secretion. Also apo A-V can lower plasma triglyceride by activating the lipoprotein lipase (LPL)[ 8 ]. In our study we showed that SNP3 was significantly associated with hypertriglyceridemia especially in type 2 diabetic patients. This finding confirmed the idea that the effect of this SNP on triglyceride metabolism was not influenced by ethnic background [ 9 ]. However, it has been reported that the major and minor allele frequencies differed between populations such as : 0.06, 0.09 and 0.34 for the C allele in UK, Caucasian and Japanese respectively [ 10 , 3 , 9 ]. In our population, the minor allele frequency (0.13) is almost the same as in Caucasian [ 3 ] but lower than in Japanese population [ 9 ]. However, the triglyceride level is higher in Tunisian than in Japanese population. This paradoxical observation confirms that triglyceride level is influenced by environmental factors and other genetic factors (apo CIII...). In type 2 diabetes the triglyceride level is increased, which is due to multiple factors related to insulin and carbohydrate metabolism, LPL activity, CETP activity... The absence of an unusual allele frequency of SNP3 in type 2 diabetic patients compared to non diabetic subjects, in spite of triglyceride level variation, shows that there is no association between apo A-V SNP3 polymorphism and the presence or absence of diabetes. Diabetic homozygous for the major allele are more frequent in low triglyceride group, showing that SNP3 is associated with triglyceride variation in type 2 diabetic patients. Contrary to Esteve et al., who have reported no significant difference in triglyceride concentration with the apo A-V polymorphism in type 2 diabetic patients [ 6 ], our results showed, then, that SNP3 is associated with triglyceride level. The relationship between SNP3 and coronary artery disease is under discussion. The LOCAT study showed that there is no association between SNP3 and progression of coronary heart disease [ 11 ]. Our results find no association between SNP3 of apo A-V and development of coronary artery disease either in diabetic patients or in non diabetic subjects. This suggests that the high triglyceride level in T/C genotype alone was not a good discriminator of coronary heart disease. In contrast ; Szalai et al. showed an association between SNP3 and an increased risk for severe coronary artery disease [ 12 ]. Identifying genetic and environmental factors that influence plasma lipid levels represents a key step towards developing strategies for preventing and treating CAD. Usually, in the case of type 2 diabetes, patients are taking lipid lowering drugs in order to ameliorate their lipid profile, namely lower triglyceride level and increase HDL-C concentration. Fibrates represent a commonly used therapy for lowering plasma triglyceride, its mechanism of action involves the activation of the nuclear receptor peroxisome proliferator activated receptor alpha (PPARα). The apo A-V is a highly responsive PPAR target gene [ 13 ]. While SNP3 is located in promoter region, it should be interesting to study the interaction between this polymorphism and lipid lowering drugs response in different population. Conclusion In summary, the apo A-V SNP3 is associated with triglyceride level in Tunisian type 2 diabetic patients. However, this SNP is unlikely to be associated with the presence of diabetes. Although SNP3 is associated with hypertrigyceridemia, there was no relationship between this polymorphism and coronary artery disease. Further investigations were needed to determine the effect of SNP3 on lipid lowering drug response. Methods Subjects Three hundred and eight subjects, aged 45–70 years, participated in this study. They belong to four groups. The first group contained 74 type 2 diabetic patients with CAD, among whom 12 are taking lipid lowering drugs. The second group contained 78 type 2 diabetic patients without CAD, all of them did not take lipid lowering drugs. The third group contained 57 patients with CAD and without diabetes, 70% of the patients are taking lipid lowering drugs. The last group contained 99 controls without diabetes nor CAD, 11 subjects are taking lipid lowering drugs. The clinical and biological characteristics of each group are summarized in Table 1 . All participants were recruited in the departments of internal medicine and cardiology in Monastir university hospital. Written or verbal informed consent were obtained from all patients and controls before the study. All written informed consent were not possible because the majority of eligible subjects in our study were illiterate. The diagnosis of diabetes was based on a previous history of diabetes according to American Diabetes Association criteria [ 14 ]. The patients with CAD were defined as clinical history of stable angina pectoris, previous acute coronary syndromes with or without ST segment elevation. This CAD was confirmed by coronary angiography. Exclusion criteria were taking insulin, having a renal or liver failure or thyroid disease, alcohol consumption before 3 days or less, Body Mass Index (BMI) more than 35 Kg/m 2 and glycosylated hemoglobin (Hb A1c) more than 12%. Post menopausal women had no hormone replacement therapy. BMI was calculated using the formula : weight (Kg)/height 2 (m 2 ). Obesity was defined as BMI>30 kg/m 2 . Waist-to-hip ratio (WHR) was calculated from measurements of the waist circumference taken at the mid point between umbilicus and xiphoid and hip circumference, at the widest point around the hips, respectively. Blood samples were drawn after subjects had fasted overnight (12 hours) into tubes containing EDTA. Plasma was immediately separated by centrifugation. Laboratory Analysis After DNA extraction, the single nucleotide polymorphism SNP3 within the apo A-V gene (-1311 T/C) was determined by PCR-RFLP analysis using MseI restriction endonuclease as described previously [ 3 ]. Plasma glucose, glycosylated haemoglobin (HbA1c), lipids and lipoproteins were determined as described by Smaoui et al. [ 15 ]. Statistical Analyses Data management and statistical analysis were performed using SPSS 10.0 software. Results are summarized as mean ± SD. Since triglyceride levels were not normally distributed, logarithmic transformation of triglyceride concentration was performed before the statistical analysis. Student's test was used to compare continuous variables and Chi square (χ 2 ) test was used to examine distribution of categorical variables. A value of p < 0.05 was considered significant. List of abbreviations Apo : apolipoprotein BMI : Body Mass Index CAD : Coronary Artery Disease HDL : High Density Lipoproteins HDL-C : HDL cholesterol PCR-RFLP : Polymerase Chain Reaction-Restriction Fragment Length Polymorphism SNP : Single Nucleotide Polymorphism TG : Triglyceride VLDL : Very Low Density Lipoproteins WHR : Waist-to-Hip Ratio Authors' contributions R.Ch and N.A: carried out the molecular studies, participated in the design of the study and drafted the manuscript ; M.S : carried out the biochemical essay ; S.H and Sy.M : interested in the clinical aspect ; M.H : conceived of the study, and participated in its design and coordination and helped to draft the manuscript and revised it critically for important intellectual content and have given final approval of the version to be published; MS.M : revised the article critically for important intellectual content and have given final approval of the version to be published.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545942.xml

545956

Short description of an alternative simplified method for screening recombinant clones within the "AdEasy-System" by Duplex-PCR

Background Recombinant adenoviral vectors are highly efficient for in vitro and in vivo gene delivery. They can easily be produced in large numbers, transduce a wide variety of cell types and generate high levels of transgene expression. The AdEasy system is a widely used system for generating recombinant adenoviral vectors, which are created with a minimum of enzymatic manipulations and by employing homologous recombination in E. coli. In this paper we describe an alternative simplified method for screening recombinant DNA within the AdEasy system. This Duplex-PCR-method is independent of the transgene or insert and can be used for the complete AdEasy system. It is characterized by a simple standard protocol and the results can be obtained within a few hours. The PCR is run with two different primer sets. The primers KanaFor and KanaRev hybridizise with the Kanamycin resistence gene and AdFor and AdRev detect the adenoviral backbone. In case of recombinant clones, two diagnostic fragments with a size of 384 bp and 768 bp are generated. Results The practicability of this method was verified with three different transgenes: Cytosin Deaminase (AdCD), p53 (Adp53) and Granulocyte Macrophage Colony Stimulating Factor (AdGM-CSF). Recombinant clones are indicated by two diagnostic fragments and are then suitable for further processing. Conclusion In summary, the presented protocol allows fast detection of recombinants with an easy technique by minimizing the amount of necessary steps for generating a recombinant adenovirus. This method is time sparing and cost-effective.

Background Recombinant adenoviral vectors are highly efficient for in vitro and in vivo gene delivery. They can easily be produced in large numbers, transduce a wide variety of cell types and generate high levels of transgene expression. The AdEasy system is a widely used, simplified system for generating recombinant adenoviral vectors, which are created with a minimum of enzymatic manipulations and by employing homologous recombination in E. coli [ 1 ]. The system consists of two adenoviral backbone vectors (pAdEasy-1 with deleted E1/E3 and pAdEasy-2 with deleted E1/E3/E4) and four different shuttle vectors (pShuttle, pShuttle-CMV, pAdTrack, pAdTrack-CMV), into which the desired transgenes are inserted. The polylinkers are surrounded by adenoviral sequences that allow homologous recombination with adenoviral backbone plasmids in E. coli. The shuttle vectors differ by partly carrying a cytomegalievirus (CMV) promoter and GFP as a tracer all of which contain a kanamycin resistance gene. Therefore, the various components can easily be combined depending on the desired purpose. In this paper we describe a simplified and easy method for screening recombinant DNA within the AdEasy system. This Duplex-PCR-method is independent of the transgene or insert and can be used for the complete AdEasy-System. It is characterized by a simple standard protocol and the results can be obtained within a few hours. The PCR is run with two different primer sets. The primers KanaFor and KanaRev hybridizise with the Kanamycin resistence gene and AdFor and AdRev detect the adenoviral backbone. In case of recombinant clones, two diagnostic fragments with a size of 384 bp and 768 bp are generated. Methods The presented Duplex-PCR is performed as follows: After Co-transformation of the Pme I-digested shuttle vector with the adenoviral backbone plasmid to E. coli (BJ 5183) and plating on agar (selection on kanamycin), half of the overnight grown colonies are picked and used directly as template for the colony-PCR. The other half of the colony is used for inoculation with LB-kanamycin and then incubation at 37°C. Only the positive, recombinant clones which have been detected by PCR are grown overnight, a minipreparation of DNA is then performed the next morning. The PCR is run using two different primer sets. The primers KanaFor (5' CAA GAT GGA TTG CAC GCA GG 3') and KanaRev (5'AAG GCG ATA GAA GGC GAT GC 3') hybridize to the Kanamycin resistence gene and AdFor (5'GGC TGC TCT GCT CGG AAG AC 3') and AdRev (5'GGC ATA CGC GCT ACC CGT AG 3') detect the adenoviral backbone. The optimised concentrations of the components for the Duplex-PCR were as follows: 2, 5 mM MgCl 2 , 100 mM dNTPs and 0,2 Units Taq polymerase. The bacteria are denatured at 95°C for 10 minutes. The PCR-products are amplified by 40 cycles of annealing at 58°C (30 sec), extension at 72°C (30 sec) and denaturation at 94°C (30 sec). In our experience, this procedure produced the best results without generating false positive clones (Figure 1 ). PCR products are analyzed by agarose gel electrophoresis, half of the reaction volume (25 μl) is size fractionated with 80 V for 1 h in 1% agarose in the presence of ethidium bromide and the resulting bands visualized with ultraviolet illumination. The DNA obtained by small scale alkali lysis from the recombinants is then extracted twice with a phenol-chloroform protocol, precipitated and carefully resuspended in 20 μl RNAse-free water. The construct is linearized with PacI and directly transfected into 911 cells, which are monitored for cytopathic effects, i.e. production of recombinant adenoviruses. The cytopathic effect is usually seen within 5 to 10 days. The expression of the transgene is confirmed by Western blot analysis. The practicability of our procedure was verified with three different transgenes: Cytosin Deaminase (AdCD), p53 (Adp53) and Granulocyte Macrophage Colony Stimulating Factor (AdGM-CSF). Results and discussion The conventional way of screening for recombinants after Co-transformation of the linearized shuttle vector with the adenoviral backbone vector in E. coli is by plating on LB/kanamycin, growing the bacteria overnight, then picking the colonies and growing them again for 10–15 hours. Minipreps are then performed and the size is evaluated on agarose gels. A restriction digest with three different restriction enzymes is then done and finally again another agarose gel is run. This is a relatively time-consuming and laborious procedure, which takes about 2 days. In contrast, the presented alternative protocol allows fast detection of recombinants with a simplified technique by minimizing the amount of necessary steps for generating a recombinant adenovirus. The method is time sparing and cost-effective. In our experience, the above described protocol showed no problems with false negative clones. After optimisation of the PCR protocol, we were able to run the conventional screening method (e.g. by restriction digest) for recombinant clones at the same time as the presented simplified PCR. We found no differences in regard to the final results for the two methods, but it has to be kept in mind that only a limited number of recombinant adenoviruses were actually generated with the new technique. Furthermore, we exclusively then continued our work with the AdCD-virus. Therefore it cannot be ruled out that under other conditions the presented technique may produce other results than the conventional technique. The positive clones were processed and finally transfected into 911 cells. After harvesting recombinant adenovirus, we infected cells with AdCD and checked the expression of the Cytosin deaminase protein by Western blot. The functionality of the gene was proven by FACS analysis. We confirmed expression of the protein as well as its functionality. Conclusions The presented protocol allows fast detection of recombinant clones within the AdEasy system with an easy, cost-effective technique. Therefore, this procedure is a potential alternative for screening recombinants within the AdEasy system. Authors' contributions DA designed the study and was responsible for manuscript preparation, MK, IB optimised the PCR protocol, PK was responsible for manuscript preparation, MB contributed to manuscript preparation and JW was responsible for study design and manuscript preparation. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545956.xml

526217

Reduced proviral loads during primo-infection of sheep by Bovine Leukemia virus attenuated mutants

Background The early stages consecutive to infection of sheep (e.g. primo-infection) by Bovine leukemia virus mutants are largely unknown. In order to better understand the mechanisms associated with this period, we aimed at analyzing simultaneously three parameters: B-lymphocytosis, cell proliferation and viral replication. Results Sheep were experimentally infected either with a wild type BLV provirus or with selected mutants among which: a virus harboring an optimalized LTR promoter with consensus cyclic AMP-responsive elements, two deletants of the R3 or the G4 accessory genes and a fusion-deficient transmembrane recombinant. Seroconversion, as revealed by the onset of an anti-viral antibody response, was detected at 3 to 11 weeks after inoculation. At seroconversion, all sheep exhibited a marked increase in the numbers of circulating B lymphocytes expressing the CD5 and CD11b cluster of differentiation markers and, interestingly, this phenomenon occurred independently of the type of virus. The net increase of the absolute number of B cells was at least partially due to accelerated proliferation as revealed, after intravenous injection of bromodeoxyuridine, by the higher proportion of circulating BrdU+ B lymphocytes. BLV proviral DNA was detected by polymerase chain reaction in the leucocytes of all sheep, as expected. However, at seroconversion, the proviral loads were lower in sheep infected by the attenuated proviruses despite similar levels of B cell lymphocytosis. Conclusions We conclude that the proviral loads are not directly linked to the extent of B cell proliferation observed during primo-infection of BLV-infected sheep. We propose a model of opportunistic replication of the virus supported by a general activation process of B lymphocytes.

Background Bovine leukemia virus (BLV) is an oncogenic retrovirus closely related to the primate T-cell leukemia viruses [ 1 ]. These viruses are exogenous to their host species [ 2 , 3 ], have similar genomic organizations [ 4 ], integrate into dispersed sites within the host genome [ 5 , 6 ] and appear transcriptionally silent in vivo (reviewed by [ 7 ]). However, BLV is unique in the HTLV family of retroviruses because it infects and dysregulates B lymphocytes instead of T cells. The natural host for BLV is cattle but the virus can also be experimentally transmitted to sheep [ 8 ]. The pathogeneses in these species are globally similar despite higher frequencies of leukemogenesis in sheep (up to 100%) and shorter latency periods (1–4 years versus 4–10 in cattle) [ 1 ]. Following BLV infection, the hosts, either cattle or sheep, develop a persistent antibody response to viral proteins and virions can be isolated from ex vivo cultured leucocytes [ 9 ]. Detection of antibodies in the infected animals correlates with a transient B cell lymphocytosis [ 10 - 13 ]. In most cases, BLV infection remains clinically silent, a stage referred to as the asymptomatic or aleukemic stage of the disease [ 7 ]. Only 30% of BLV-infected cattle develop persistent lymphocytosis (PL), a polyclonal expansion of B cells coexpressing high levels of surface IgM, myeloid (CD11b) or T-specific (CD5) markers [ 14 - 16 ] and less than 5% will die from a fatal leukemia, lymphoma or lymphosarcoma [ 17 ]. A major advantage of the BLV system is the possibility to study viral genetic determinants in relation with infectivity and pathogenicity in vivo. A strategy, which we previously described, is based on the use of a cloned BLV provirus whose sequence can be mutagenized in vitro. Well characterized mutants can subsequently be injected into sheep and compared to the wild type virus (WT) [ 18 ]. This experimental protocol permitted the correlation of viral determinants with defined phenotypes in vivo. In particular, we showed that: (i) the R3 and G4 accessory genes are required for efficient viral spread in vivo, although their deletion or mutation does not hamper infectivity (mutants CRX3 and IG4 described by [ 1 , 19 , 20 ]). (ii) restoring a CRE consensus (cyclic-AMP response element; TGACGTCA) in the triplicate motif of the imperfectly conserved Tax-responsive sequence (TxRE: AGACGTCA, TGACGGCA, TGACCTCA) increases LTR promoter activity, as expected, but restricts the proviral loads in vivo, suggesting that repression of expression is required for immune escape (CRE mutant; [ 21 ]). (iii) formation of multinucleated syncytia by envelope dependent cell fusion in vitro is paradoxically not required for infectivity or efficient viral spread in vivo (A60V recombinant; [ 22 ]). Importantly, only the A60V envelope mutant behaves as wild type in terms of infectivity and pathogenesis, in contrast to the others (CRX3, IG4 and CRE) therefore referred to as attenuated. The two categories of viruses, either wild type (WT and A60V) or attenuated (CRX3, IG4 and CRE) thus permit to characterize and compare the processes occurring during primo-infection. Results The extent of transient lymphocytosis is similar in sheep infected by wild type or mutant proviruses BLV recombinants with optimized consensus CRE sequences (CRE clone) and mutants deleted in the R3 and G4 genes (CRX3 and IG4 proviruses) are impaired in their ability to propagate efficiently within their host [ 1 , 19 , 21 ]. The early stages occurring soon after infection by these mutants and, more particularly, the extent of the transient B lymphocytosis are however unknown. Therefore, sheep were infected with well-characterized molecular clones of BLV proviruses. As control, the hematological parameters were first determined in uninfected animals, as illustrated on figure 1A (sheep 4533 BLV-). To this end, PBMCs (Peripheral Blood Mononuclear Cells) were isolated and analyzed by flow cytometry for the presence of IgM, CD4 and CD8 markers. Despite some variations in the absolute numbers of leucocytes, the B (squares), CD4 + (triangles) and CD8 + (crosses) T cell populations remained remarkably constant over extended periods of time, as expected (Figure 1A and data not shown for sheep 4534). In contrast, a marked increase of the absolute numbers of leucocytes occurred between 2 and 3 weeks post-inoculation of the wild type BLV provirus (from 6,331 10 3 /ml to 10,262 10 3 /ml, respectively, at days 21 and 28 in sheep 4536 BLV+ WT, Figure 1A ; and data not illustrated for animal n° 4535). Figure 1 Transient B cell lymphocytosis at seroconversion of sheep infected by BLV-mutants. A. Sheep n° 4536 was injected with 100 μg of proviral DNA (wild type strain 344 cloned into plasmid pBLV344) whereas animal n° 4533 was used as a negative control. At different times post-injection, leucocytes were counted using a Coulter Counter ZN and the total numbers of lymphocytes were estimated after examination under a microscope. In parallel, Peripheral Blood Mononuclear Cells (PBMCs) were isolated by Percoll gradient centrifugation and the proportions of B, CD4 + T and CD8 + T-cells were determined by flow cytometry. The vertical line represents the detection by immunodiffusion of antibodies directed against the virus (day 28: sheep n° 4536). B. Graphic representation of absolute B cell numbers at viral inoculation (Day 0, yellow bars) and at seroconversion (grey bars) of sheep infected by the BLV wild type virus (WT) or mutants (IG4, CRX3, CRE and GP30). The sheep coordinates are indicated. C. Fold increase of the B cell population in the infected sheep. The bars correspond to the mean difference (± standard deviation) between the absolute numbers of B cells at the day of virus inoculation and those determined at seroconversion (means of three values). Phenotypic analysis of the sheep n° 4536 PBMCs revealed that leucocytosis is essentially due to a marked increase in the B cell numbers (from 2,294 10 3 /ml of blood to 4,858 10 3 /ml, respectively, at days 21 and day 28; see squares), while the absolute counts of CD4 + (triangles) and CD8 + (crosses) T cells remained relatively constant (as did the γδ T cell population, data not shown). Furthermore, maximal B cell accumulation corresponded to the day of seroconversion characterized by the onset of an anti-BLV humoral response (the vertical line at day 28 on Figure 1A ). A similar experiment was performed in parallel with 4 selected BLV mutants (IG4, CRX3, CRE and GP30), the latter behaving as wild type in terms of pathogenesis and infectivity in vivo. Interestingly, B cell lymphocytosis occurred in all sheep independently of the type of provirus (Figure 1B , compare absolute numbers of B cells at day of seroconversion). A rate of induction was calculated by dividing the absolute numbers of the B cell population around seroconversion with those measured at the day of proviral injection (day 0). This induction rate (represented in fold increase on Figure 1C ) was independent of the type of provirus, no significant difference being observed between the wild type and the mutants. Expression of the CD5 and CD11b cluster of differentiation markers has been associated with BLV-infection, although B lymphocytes negative for these receptors are also less efficient targets for the virus [ 23 , 24 ]. Therefore, we aimed to determine if lymphocytosis detected at the seroconversion period is preferentially due to an accumulation of B+CD5+ and/or B+CD11b+ cells. For this purpose, PBMCs were dually labeled with an anti-IgM antiserum together with anti-CD5 or anti-CD11b monoclonal antibodies and analyzed by two-color flow cytometry (illustrated on Figure 2A for BLV wild type infected animal n° 4535: x axis = CD5 or CD11b labeling; y axis = B labeling). At day 0, two third of the B lymphocytes expressed CD5 (3,119 in a total of 1,585 + 3,119 B cells) whereas, at seroconversion, most of them became positive for this marker (6,906 cells versus 1,273). In parallel, the number of B+CD11b+ cells also drastically increased (3,547 cells at day 0 versus 7,526 at the seroconversion day). Of note, although most B lymphocytes should be CD5 and CD11b double positive cells, this assumption could not be formally demonstrated because of cross reactions between two IgG1 isotypes. However, the dot plots at right on the figure 2A clearly show that the majority of B cells should express both markers. Figure 2 The lymphocytosis is due to an accumulation of B lymphocytes expressing CD5 and /or CD11b. A. PBMCs were isolated from BLV wild type infected sheep (n° 4535) and labeled with anti-sIgM 1H4 monoclonal in combination with CD5 or CD11b antibodies. Ten thousand cells analyzed by flow cytometry are represented as dot plots (Y axis = B cells; X axis = CD5 or CD11b expressing cells). Illustrated data correspond to the dot plots performed at day 0 (provirus injection) and at the seroconversion day. The total numbers of B cells are indicated in the upper quadrants. B. Histogram representation of the absolute numbers of B cells expressing (right panels) or not (left panels) CD5 or CD11b in sheep infected with wild type viruses (n° 4535 and 4536), or mutants (pBLVIG4 in n° 4537 / 4538, pBLVCRX3 in n° 4539 / 4541, pBLVCRE3X in n° 4542 / 4543, pBLVA60V in n° 4544 / 4545). Sheep n° 4533 and 4534 were used as uninfected controls. * represents the absolute number of cells at day 24 of the experiment. In terms of absolute cell counts (i.e. normalized to the cell numbers per ml of blood), it appeared that B+CD5+ or B+CD11b+ lymphocytes accumulated at the seroconversion day in wild type infected sheep 4535 and 4536 (compare yellow and shaded bars on the right panels of Figure 2B ). This accumulation did not happen in the CD5- or CD11b-negative B cell populations (left panels) or in negative controls (uninfected sheep n° 4533 and 4534), although a relative increase was observed in some infected animals (particularly 4542 and 4543, Figure 2B , B+CD5-). Importantly, the net increase of sIgM+CD5+ or sIgM+CD11b+ populations also occurred in animals infected by the different mutants independently of the type of provirus (Figure 2B ). Together, these data demonstrate that primo-infection in sheep experimentally infected either with a wild type or mutant BLV proviruses is associated with a transient lymphocytosis, extending previous reports [ 11 , 12 ]. At seroconversion, all sheep exhibited a marked increase in the numbers of circulating B lymphocytes expressing for most of them the CD5 and CD11b cluster of differentiation markers and, interestingly, this phenomenon occurred independently of the type of mutant. Increase of in vivo B cell proliferation during seroconversion In terms of cell dynamics, lymphocytosis is the consequence of a homeostatic deregulation provoked by an imbalance in the rates of proliferation and/or death. Alternatively or concomitantly, the net increase in B lymphocyte numbers might be the result of a cell mobilization between the lymphatic system and the peripheral blood. We have previously established a protocol allowing to quantify these parameters in BLV-infected sheep [ 25 ]. This experimental approach is based on intravenous injection of 5-bromo-2'-deoxyuridine (BrdU) which permits, after its incorporation into DNA, the identification by flow cytometry of cells that have undergone proliferation. In order to label proliferating B lymphocytes during primo-infection, BrdU was injected weekly over a two months period and blood was collected at three days after each injection, a delay required for maximal detection of labeled cells (see methods) [ 25 ]. Figure 3A illustrates an example of IgM+BrdU+ dual flow cytometry analysis performed at days 0, 24 and 31 after proviral injection into sheep n° 4536. It appeared that the numbers of B+BrdU+ cells in this sheep increased at day 24 (cell counts of 99 at day 0 versus 385 at day 24 amongst 10,000 events) and remained high at day 31 (324 counted events). In contrast, in sheep n° 4533 used as a negative control, no variation was observed in terms of absolute numbers (72, 66 and 75 at days 0, 24 and 31) as well as in proportion of the sIgM-positive cell population (Figure 3B , yellow bars). In BLV-infected sheep n° 4536, however, this relative proportion of BrdU-labeled B lymphocytes peaked at day 24, indicating that these cells underwent proliferation (black bars on Figure 3B ). Interestingly, the burst of B+BrdU+ cells just preceded the onset of seroconversion (vertical line at day 28 on Figure 3C ) and correlated with the mid phase of total B lymphocytes counts in the blood. The most straightforward interpretation is that the net increase of the B cell population is at least partially due to proliferation. Figure 3 The lymphocytosis correlates with increased B cell proliferation at seroconversion. Once per week over a 2 months period, 500 mg of bromodeoxyuridine (BrdU) were injected intravenously into BLV-infected (n° 4536) and control (n° 4533) sheep. An aliquot of blood (1 ml) was collected at three days post-BrdU injection. After lysis of the red blood cells, B lymphocytes were labeled with an anti-sIgM antibody. Next, cells were stained with anti-BrdU FITC in the presence of DNase and analyzed by two-color flow cytometry. A. Dot Plot graphs of B lymphocytes (Y axis) labeled in combination with BrdU (X axis) in uninfected control animal (n° 4533) and in a BLV wild type infected sheep (n° 4536) performed at days 0, 24 and 31. Day 31 corresponds to the first data obtained just after seroconversion of sheep n° 4536. Ten thousand events were acquired by flow cytometry and PBMCs were selected by the FSC/SSC gating method. The total numbers of B cells are indicated in the upper quadrants. B. Histogram of the proportions of BrdU+ labeled B cells (in % of the total B lymphocyte population). C. Graphic representation of the proportions of B+BrdU+ cells within the total B lymphocyte population (at days 3 and 7 post-injection) and the corresponding absolute numbers of B lymphocytes observed during the experiment. The BrdU was injected at days 14, 21, 28, 35, 42 and 52. The vertical line represents the detection of the seroconversion day. Since BLV mutants also induce a transient lymphocytosis, a kinetics of BrdU incorporation was performed in 4 additional sheep infected with proviruses IG4, CRX3, CRE and GP30 (respectively animals n° 4538, 4541, 4543 and 4544; see figure 4 ). In all sheep, the high absolute numbers of B lymphocytes (squares) observed at seroconversion (vertical line) was preceded by a net increase in the BrdU positive population (lozenges). Figure 4 In vivo B lymphocyte proliferation in sheep infected with BLV mutant proviruses. Graphic representation of the proportions of B+ BrdU+ cells within the total B lymphocyte population (at days 3 and 7 post-injection) and the corresponding absolute numbers of B lymphocytes in sheep (n° 4538, 4541, 4543 and 4544) infected, respectively, with BLV mutants (IG4, CRX3, CRE and GP30). The BrdU was injected at days 14, 21, 28, 35, 42 and 52. The vertical line corresponds to the seroconversion day as determined by an immunodiffusion test. Together these data demonstrate that transient lymphocytosis occurring in BLV infected-sheep is at least partially due to an increase in cell proliferation as assessed by BrdU incorporation and, surprisingly, this phenomenon occurred independently of the type of virus. Lymphocytosis and B cell proliferation are independent of the proviral loads With the aim to quantify the proviral loads, viral DNA levels within the circulating blood of all sheep were determined by semiquantitative PCR. The genomic DNAs were extracted from blood samples collected at seroconversion and the sequences corresponding to the viral tax gene were amplified by PCR. The number of PCR cycles was adapted in order to compare the relative amounts of proviral sequences in the blood samples. The amplicons were then analyzed by Southern Blotting using a BLV tax probe. Amplification of the gapdh gene was used as internal control for chromosomal DNA integrity (Figure 5A ). As controls for PCR contaminations, no signal was generated in DNA samples from uninfected sheep at day 0 (n° 4533 and 4534). In contrast, PCR amplification of the tax gene using the genomic DNA from WT infected sheep (sheep n° 4535 an 4536) yielded a 1 kb fragment as expected. Under similar conditions, a weaker signal was generated by amplification of genomic DNA isolated from animals infected by mutant proviruses (Figure 5A ). Phosphorimager quantification of these hybridization signals revealed that mutants IG4, CRX3 and CRE replicated at lower proviral loads compared to the wild type or the non-attenuated GP30 viruses (Figure 5B ). Using real-time PCR performed as described by [ 26 ], the proviral loads were estimated to be 270 and 245 copies per 1,000 cells in sheep infected, respectively, with the wild type virus and the GP30 recombinant whereas the levels yielded by the other mutants were significantly reduced (27, 40 and 24 copies / 1,000 cells for IG4, CRX3 and CRE, respectively) (Figure 5B ). Figure 5 Reduced proviral loads at seroconversion in sheep infected with attenuated mutants. A. At seroconversion day, DNA was extracted from blood isolated from wild type or mutant-infected sheep, as indicated. Proviral sequences were amplified by 25 cycles of PCR using tax specific primers. The amplification products were resolved on a 1% agarose gel and analyzed by Southern blotting with a BLV tax probe. Blood samples from uninfected sheep n° 4533 and 4534 were used as controls for PCR contamination. A PCR amplification of the gapdh gene was used as an internal control for DNA integrity. B. Mean proviral loads at seroconversion. For each category of mutant-infected sheep, the mean values of the proviral loads (in arbitrary units ± standard deviations as determined after Phosphorimaging scanning) were statistically compared using the Student t test to the mean proviral load of the wild type group (WT). The data result from three independent experiments using DNAs extracted around the seroconversion period (NS: p > 0.05 non-significance; * 0.01 < p < 0.05; *** p < 0.001, Student t test). Using real-time PCR, the proviral loads were estimated to be 270 and 245 copies per 1,000 cells in sheep infected, respectively, with the wild type virus and the GP30 recombinant whereas the levels yielded by the other mutants were significantly reduced (27, 40 and 24 copies / 1,000 cells for IG4, CRX3 and CRE, respectively). Together these data show that, around the seroconversion period, the proviral loads are reduced in sheep infected by attenuated mutants. Conclusions We have studied here the interplay between the efficiency of viral spread, the cellular proliferation within the host and the extent of cell accumulation during the period consecutive to the infection of sheep by bovine leukemia virus. We compared two categories of viruses based on their ability to infect and expand within their host: those behaving as wild type (WT and GP30) or so-called attenuated mutants (IG4, CRX3 and CRE). Experimental infection of sheep with these two types of viruses led to a surprising observation, namely, a similar extent of transient lymphocytosis independently of the proviral loads (see Figure 6 ). In other words, the total B lymphocyte accumulation within the peripheral blood is not modulated by the amount of viral copies. Another contribution of this report is the demonstration that transient lymphocytosis arising just prior to seroconversion is at least partially due to an increase in B cell proliferation (Figure 6 ). In fact, since lymphocytes rest within the peripheral blood in the G0/G1 phase of the cell cycle (unpublished data), proliferation occurs in other sites i.e. the bone marrow, spleen, lymph nodes and Peyer's Patches. Therefore, the accumulation of BrdU+ B lymphocytes might also be the consequence of an increased outflow from these sources estimated, in non-infected sheep, at 30 × 10 6 cells per gram of lymph node in one hour [ 27 ]. Conversely, impaired recirculation to the lymphatic system would also create an imbalance in the B lymphocyte counts (1 g of lymph node receives 1.2 × 10 8 cells per hour [ 27 ]). Answering to this question would require canulation of lymph nodes allowing the precise quantification of the cellular flows. Figure 6 Schematized summary of proliferation rates, proviral loads and lymphocytosis associated with primo-infection. Sheep, which were experimentally infected with BLV wild type or mutant proviruses (virus inoculation), exhibited a similar extent of transient accumulation of B-lymphocytes (—). This transient lymphocytosis arising just before seroconversion is at least partially due to an increase in B cell proliferation (peak of B cell proliferation •••••). In contrast, the proviral loads greatly differ among the two categories of sheep, e.g. infected with wild type (- - -) or attenuated viruses (— ••). With the aim to correlate lymphocytosis with a defined B cell sub-population, we have demonstrated that two surface molecules, CD5 and CD11b, whose expression has been previously associated with late stages of BLV infection [ 13 - 15 , 23 , 24 ] are also important markers at the seroconversion period. In human and mice, the B lymphocytes expressing the CD5 and CD11b proteins are referred to as B-1a cells. B lymphocytes that are CD5- and CD11b+ are called B-1b and have a similar function than the B-1a subset [ 28 ]. Compared to the conventional B-2 cells (e.g. CD5- CD11b-), the B-1 population exhibits different developmental schemes, phenotypes, antibody repertoires, localization and behaviors. In humans, elevated numbers of B-1 cells have been reported in patients with Sjorgen's syndrome, rheumatoid arthritis, chronic lymphocytic leukemia and AIDS [ 29 - 32 ]. In mice, increased numbers of B-1 cells have been observed in a number of naturally occurring and genetically manipulated strains that develop autoimmune manifestations [ 33 ]. B-1 cells are believed to be the major source of natural IgM, a polyreactive and weakly autoreactive antibody, which is produced in the absence of exogenous antigenic stimulation [ 34 ]. Consistent with this model, IgM specificities within the B-1 repertoire include phosphorylcholine, phosphatidylcholine, thymocytes, lipopolysaccharide and influenza virus [ 33 ]. Interestingly, in sheep, the B-1 population also expands in response to infection with other pathogens like Trypanosoma evansi and Pasteurella haemolytica [ 35 , 36 ]. In this context, we speculate about an opportunistic mechanism of BLV replication supported by a general activation of B-1a lymphocyte proliferation, which could thus be a primary B-cell humoral response. Successive divisions of B-1a cells would thereby expand the number of potential targets for the virus. During this period, the ability of the virus to colonize new cells would be crucial, a fact that is reflected by the differential proviral loads between the wild type and attenuated viruses. Differences in the infectious potential of the attenuated viruses are the consequence of mutations in accessory genes (R3 and G4) or in the LTR promoter (CRE) [ 19 , 21 ]. Although the role of R3 is still unknown, its homologue expressed by HTLV-1 (p12) interacts with the IL-2 receptor as well as with calcineurin and is crucial during the initial steps of infection [ 37 - 39 ]. BLV G4 and HTLV-1 p13 bind to the same cellular protein, farnesyl pyrophosphate synthetase [ 40 ] but modulate differentially cell transformation in vitro [ 20 , 41 ]. The defect in BLV propagation associated with the CRE mutant relates to its inability to repress basal expression [ 21 ]. Optimization of the imperfect CRE enhancer sequences present in the LTR creates a more efficient promoter, as expected, but restricts the proviral loads indicating that transcriptional silencing is required for viral persistence and spread. The reduced capacity of the CRE, G4 and R3 mutants to propagate are thus caused by different genetic defects and data presented in this report further extent our previous observations [ 1 , 19 , 21 ]. More surprisingly is the replication efficiency exhibited by the GP30 envelope mutant. Unable to induce membrane fusion, at least as measured by classical syncytia formation tests, the GP30 virus propagates at wild type levels [ 22 ]. Besides a possible experimental caveat based on the lack of sensitivity of the syncytium assay in vitro, our previous (and most straightforward) hypothesis postulated that viral spread mainly occurred via clonal expansion of the infected lymphocytes with few or non-limiting cell-to-cell transmissions. Figure 5 demonstrates that the proviral loads of the GP30 mutant are at wild type levels even during the early steps of infection, a period thought to be associated with active infection of novel cells. Preliminary inverse PCR amplification data indicate that the number of target cells carrying integrated GP30 or wild type proviruses are not significantly different (F. Mortreux, ongoing work). We thus have to reconsider our interpretation and propose that the syncytium assay does not reflect the infectious potential in vivo. In terms of its biological properties in sheep, the GP30 recombinant should thus be considered as, or close to, wild type at least during primo-infection. In conclusion, we have characterized here the initial steps consecutive to BLV infection of sheep. We show that this period is characterized by a transient accumulation of CD5+ / CD11b+ B lymphocytes resulting, at least in part, from increased proliferation. Furthermore, the extent of B cell lymphocytosis is not directly linked to the proviral loads reached by the wild type and mutant viruses. On basis of a comparative leukemia approach, these results could be informative for the related human T-lymphotropic viruses. Methods Experimental animals Twelve sheep of one year old were kept under controlled conditions at the Veterinary and Agrochemical Research Centre (Machelen, Belgium). Two animals (n° 4533 and 4534) were used as uninfected controls whereas sheep n° 4535 and 4536 were experimentally infected with a BLV wild type cloned provirus (strain 344) [ 18 ]. Briefly, 100 μg of plasmid DNA were mixed with 200 μl of N-[1-(2,3 dioleoloxyl)propyl]-N,N,N-trimethylammonium methylsulfate (DOTAP; Roche Diagnostics ) in 1 ml of HBS (20 mM HEPES-150 mM NaCl, [pH 7.4]) and injected intradermally into the back of each sheep. Plasmids containing the mutant proviruses pBLVIG4 (harboring a stop codon in the G4 open reading frame; [ 19 ], pBLVCRX3 (deleted in R3; [ 1 ]), pBLVCRE3X (in which the CRE imperfect sequences were mutated to TGACGTCA; [ 21 ]) and pBLVA60V (alanine codon 60 of the GP30 transmembrane gene being mutated into valine; [ 22 ]) were injected, respectively, in sheep n° 4537 / 4538, n° 4539 / 4541, n° 4542 / 4543 and n° 4544 / 4545. Twice a week, the total leukocyte counts were determined by using a Coulter counter ZN, and the number of lymphocytes was estimated after examination under the microscope after staining with May-Grunwald Giemsa. In parallel, the sera from each sheep were analyzed for BLV seropositivity using immunodiffusion and enzyme-linked immunosorbent assay (ELISA) techniques [ 42 ]. Immunophenotyping of sheep Peripheral blood mononuclear cells (PBMCs) were isolated by Percoll gradient centrifugation and their viability was estimated by trypan blue dye exclusion [ 43 ]). PBMCs were labeled with monoclonal antibodies (Mabs) directed against surface immunoglobulin M (anti-sIgMs, clone 1H4, mouse IgG1; Pig45A2, mouse IgG2b), CD4 (ST4, mouse IgG1), CD5 (CC17, mouse IgG1), CD8 (CC58, mouse IgG1) and CD11b (CC125, mouse IgG1) provided by C. Howard (Institute for Animal Health, Compton, United Kingdom) and by I. Schwartz-Cornil (INRA, Jouy-en-Josas, France) or obtained from VMRD Inc . Cells were then labeled with a rat anti-mouse IgG1 phycoerythrin (PE)-antibody ( Becton Dickinson Immunocytometry Systems ) or with a goat anti-mouse IgG2b fluorescein isothiocyanate (FITC)-conjugate ( Caltag Laboratories ). Finally, PBMCs were analyzed by flow cytometry on a Becton Dickinson FACScan flow cytometer. Ten thousand events were collected for each sample and data were analyzed with the Cellquest software ( Becton Dickinson Immunocytometry Systems ). Analysis of 5-bromo-2'-deoxyuridine in vivo Each week during two months, sheep were injected intravenously with 500 mg of 5-bromo-2'-deoxyuridine ( Sigma Aldrich ) resuspended in physiologic serum (NaCl 0.9%). To evaluate BrdU-incorporation, blood was collected at three and seven days after each BrdU injection. The red blood cells were lysed with 1× FACS Lysing Solution ( Becton Dickinson Immunocytometry Systems ), the leucocytes were washed twice with PBS containing 0.5% Bovine Serum Albumin (BSA) ( Sigma Aldrich ) and incubated in the presence of biotinylated 1H4 monoclonal antibody for 30 min at 4°C. Next, the cells were labeled with streptavidin-phycoerythrin ( Becton Dickinson Immunocytometry Systems ) and incubated with 1× FACS Permeabilizing Solution ( Becton Dickinson Immunocytometry Systems ). Finally, leucocytes were stained with anti-BrdU FITC antibody in the presence of DNase ( Becton Dickinson Immunocytometry Systems ) and analyzed by flow cytometry. Semiquantitative PCR analysis DNA isolations were performed directly on blood using the Wizard ® Genomic DNA Purification Kit ( Promega ). An aliquot of 300 μl of blood were mixed with 900 μl of Cell Lysis Solution and incubated for 10 minutes at room temperature. After two washes with the same buffer, the cells were resuspended in 300 μl of Nuclei Lysis Solution and incubated for one hour at 37°C. Then, the samples were digested during 15 minutes at 37°C in the presence of 1.5 μl of RNase Solution . Proteins were precipitated by adding 100 μl of Protein Precipitation Solution to the nuclear lysates. After centrifugation at 13,000 g, the supernatant was mixed with an equal volume of isopropanol, centrifuged and ethanol precipitated. Five hundred nanograms of the purified DNAs were amplified in the presence of 200 μM of deoxynucleotides, 2.5 U of Taq DNA polymerase, and 200 ng of primers. The primers used (PCRTA 5'-CTCTTCGGGATCCATTACCTGA-3' and PCRTC 5'-CCTGCATGATCTTTCATACAAAT-3') encompass the region from position 7999 to 6990 of the BLV tax gene [ 44 ]. In parallel, the primers G3PDHA (5'-CATGTGGGCCATGAGGTCCACCAC-3') and G3PDHS (5'-GACCCCTTCATTGACCTCAACTACA-3') were used to amplify the gapdh gene. The samples were denatured for 5 min at 94°C, and amplified by 25 cycles of PCR (30 s at 94°C, 30 s at 57°C, and 1 min at 72°C). After a final elongation step of 10 min at 72°C, 20 μl of the amplification products were resolved on a 1% agarose gel, transferred to a Hybond N+ membrane ( Amersham Pharmacia Biosciences ), and hybridized either with a BLV tax (a 1-kb ClaI insert from plasmid pGEM7zfLOR1) or with a gapdh probe labeled with α- 32 P dCTP. Quantification of 32 P signal was performed using a PhosphorImager ( Personal Molecular Imager FX System , Biorad ). Real-time PCR was performed using 6FAM-labeled MGB probes specific for the BLV pol gene and the 18S ribosomal DNA sequences essentially as described in reference [ 26 ]. List of Abbreviations BLV: Bovine Leukemia Virus; BrdU: 5-bromo-2'-deoxyuridine; CRE: Cyclic-AMP Response Element; PBMCs: Peripheral Blood Mononuclear Cells; WT: Wild Type. Competing Interests The author(s) declare that they have no competing interests. Authors' Contributions CD carried out the most experimental work and drafted the manuscript. MS and FM performed the sample collections and the determination of the proviral loads. PK was responsible for the sheep studies. RK participated to experimental design and interpretation of data. LW conceived the study, its design and coordination. All authors read and approved the final manuscript

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526217.xml

555589

A comparative study of reaction times between type II diabetics and non-diabetics

Background Aging has been shown to slow reflexes and increase reaction time to varied stimuli. However, the effect of Type II diabetes on these same reaction times has not been reported. Diabetes affects peripheral nerves in the somatosensory and auditory system, slows psychomotor responses, and has cognitive effects on those individuals without proper metabolic control, all of which may affect reaction times. The additional slowing of reaction times may affect every-day tasks such as balance, increasing the probability of a slip or fall. Methods Reaction times to a plantar touch, a pure tone auditory stimulus, and rightward whole-body lateral movement of 4 mm at 100 mm/s 2 on a platform upon which a subject stood, were measured in 37 adults over 50 yrs old. Thirteen (mean age = 60.6 ± 6.5 years) had a clinical diagnosis of type II diabetes and 24 (mean age = 59.4 ± 8.0 years) did not. Group averages were compared to averages obtained from nine healthy younger adult group (mean age = 22.7 ± 1.2 years). Results Average reaction times for plantar touch were significantly longer in diabetic adults than the other two groups, while auditory reaction times were not significantly different among groups. Whole body reaction times were significantly different among all three groups with diabetic adults having the longest reaction times, followed by age-matched adults, and then younger adults. Conclusion Whole body reaction time has been shown to be a sensitive indicator of differences between young adults, healthy mature adults, and mature diabetic adults. Additionally, the increased reaction time seen in this modality for subjects with diabetes may be one cause of increased slips and falls in this group.

Background Aging slows reflexes and increases the time to react to a number of external stimuli of different modalities [ 1 - 4 ]. What has escaped extensive examination has been the effect of Type II diabetes on these same reaction times and the comparison of modalities across the various sensory inputs. Only two studies have tested older individuals with diabetes. These have demonstrated increased reaction times to visual and auditory stimuli [ 5 , 6 ]. Mohan, et al. [ 5 ] found a 30 ms difference in auditory reaction times between those with diabetes (approximately 210 ms) and a control group (180 ms). Dobrzanski, et al. [ 6 ] found a doubling of visual reaction time in diabetics (473 ms) versus that measured in healthy individuals (216 ms). In addition to the measured effects in these two studies, diabetes has also been shown to affect peripheral nerves in the somatosensory [ 7 ] and auditory system [ 8 ], slows psychomotor responses [ 9 ], and has cognitive effects on those individuals without proper metabolic control [ 10 - 13 ], all of which may affect reaction times. One of the largest implications that an increased reaction time may have is in the area of slips and falls. Falls are incurred by one third of the elderly population and are a common source of morbidity and mortality[ 14 ]. Evidence that older subjects have an increased incidence of slips and falls when compared to healthy young adults have been attributed to increase in sway as seen by center-of-pressure or -of-gravity (COP, COG), or head and hip variability [ 15 , 16 ]. Although no age related changes have been found in the rms distance of the anterior-posterior (AP) COP, changes in both mean velocity and range of AP and medial-lateral (ML) COP have been seen, with stronger changes in the former [ 17 - 20 ]. Diabetics have been shown to have a higher incidence of postural instability [ 21 - 26 ], and reduced peripheral sensations thus leading to an even higher incidence of falls resulting from slips than their healthy elder counterparts. These changes in balance metrics due to both normal aging and diabetes have been well measured, but never accurately explained. It is our contention that the postural instability may be due to slower input of information to the central nervous system, which does not allow the nervous system to react to stimuli as quickly, producing a higher incidence of slips and falls. The aim of this study was to measure and compare reaction times to plantar touch, auditory tone, and whole body lateral movement in subjects over 50 years old with and without diabetes, as well as a group of healthy younger adults under 25 years of age. Subjects with diabetes were expected to have reaction times longer than those of the age-matched controls, while the aged controls were in turn expected to have reaction times greater than those seen in the younger adult group. The implications of the changes in reaction time will be discussed with respect to the central and peripheral nervous system. Methods Subjects Subjects included 37 mature adults over 50 yrs old. Thirteen had a clinical diagnosis of type II diabetes made by their primary physician (group PN, mean = 60.6 ± 6.5 yrs, 7 Female/ 6 Male) and 23 did not (group NI, mean = 59.4 ± 8.0 yrs, 11 Female / 12 Make). The majority of the subjects were recruited from within the Veterans Administration (VA) population at the Overton Brooks VA Medical Center. Reaction times from these groups were compared to a younger adult group (age <25, N = 9, mean = 22.9 yrs, 4 Female/ 5 Male) that were recruited through advertising at Louisiana Tech University, and tested at the VA Medical Center. The recruiting, screening, testing and informed consent procedures were reviewed and approved by the local VA Institutional Review Board. Screening Subjects recruited for this study were relatively healthy individuals with no current or past history of severe heart, circulation, or breathing problems; chronic lower back pain or spasms; deformities of the spine, bones or joints (including advanced arthritis); cerebral stroke, spinal cord injuries or other damage to the nervous system; non-healing skin ulcers; advanced diabetes; current drug or alcohol dependence; or repeated falls. Individuals taking any prescription medicine to prevent dizziness were also excluded. Diabetic individuals targeted for this study were those with very early and mild Type II Diabetes. The subject's primary care physician undertook the diagnosis of diabetes. Targeted recruits had all been diagnosed within the last 10 years. All subjects with diabetes were using either diet or oral medication to manage blood sugar levels. Visual, vestibular, muscoskeletal, and cognitive screening was also done to ensure that no undiagnosed problem existed that would prevent subjects from completing the study. Plantar sensory tactile threshold were measured on each sole for all subjects using graded Semmes-Weinstein Monofilaments, which, upon bending, exerted a known force that depends on the filament diameter. Tactile force perception thresholds on the glabrous skin of the feet were determined for the right and left feet using these monofilaments according to standard clinical testing protocol. Stimuli were presented randomly three times at a given location, and, if two of the three presentations were detected, a threshold force was considered determined. Although force is a ratio metric (a measure in which an absolute zero is present and meaningful fractions or ratios can be constructed), the measurement of force by this method is still an ordinal (or rank ordered) type of data. Therefore, non-parametric statistics were used to compare tactile thresholds among test locations on the foot, and between right and left feet, as well as to compare among groups. A certified audiologist at the Overton Brooks VA Medical Center carried out air conduction auditory threshold testing on all mature subjects (but none of the younger adults due to their health). Both mature adult groups underwent testing at 1, 2, 4, and 8 kHz in both ears. Average threshold level was recorded in decibels. Using a One Way ANOVA on Ranks, the threshold in each ear was compared to determine any differences in threshold. In addition to this screening, all of the mature subjects underwent clinical surface nerve conduction studies of the lower extremity which were performed at the Neurology Service of the Overton Brooks VA Medical Center by a technician under the supervision of a neurologist. Motor (peroneal and tibial nerve) and sensory nerves (sural nerve) were tested bilaterally. F- and M- latency tests that test the entire lower motor loop (sensory nerve -> vertebrae -> motor nerve) were initially performed to ascertain any problems in the Sherrington's final common pathway [ 27 ]. However, the first two subjects expressed severe discomfort in undergoing that part of study. Hence the F- and M- latency tests were optional to subsequent subjects. These tests found peripheral neuropathies in all diabetics and none of the remaining mature subjects, who were thus classified as neurologically intact. Reaction Time Protocol Reaction time was defined as the time between a stimulus onset and a signaled response of the subject. Three different stimuli were presented – touch, tone and platform movement. A manually held, miniature single axis force sensor (Sensotec, Inc) with a 2 mm diameter tip was used by the authors to apply a tactile stimulus to the plantar surface of the big toe of each foot. Since the unloaded sensor force could vary over time or with a change in the position of the sensor, the single axis force sensor was calibrated to a zero state prior to each reaction time test series. A force change of more than 0.01 N was determined to be the trigger for an event. Instructions given to the subjects were to "press the button as soon as you feel the sensory touch the bottom of your foot." Subjects signaled detection of the stimulus via hand held bell button press. The latency between the onset of the rise in applied force measured by the force sensor and the resultant bell-press signal was taken to be the reaction time. Reaction times were measured five times and all trials were averaged. For auditory latencies, a bell tone was presented bilaterally via earphones and a subject signaled by pressing the force sensor with the thumb when he heard the signal. A change of approximately 10 times the sensor's resolution (0.01 N) or greater was determined to be the trigger for the detection of the event. Again, reaction time was averaged over all 5 trials. Finally, a reaction time to a rightward lateral platform movement of 4 mm at 100 mm/s 2 was measured, while a subject stood barefoot on the platform with feet in normal stance. The SLIP-FALLS platform [ 28 ] was used to induce these movements because it produced smooth, precisely controlled, low vibration translations. Subjects were blindfolded and instructions were presented over a white noise background to the subjects though headphones. Subjects signaled detection of the movement though the use of a hand held push-button remote. Reaction times were averaged over ten trials. Results All data sets analyzed failed a normaility test, prompting the authors to use non-parametric tests. In cases where two groups were compared, a Mann-Whitney Rank Sum Test was used. In cases where three or more groups were compared, a Kurskal-Wallis One Way ANOVA was used. The non-normality of the data also precluded the use of two-way ANOVAs. For all tests, the level of significance used was p < 0.05. Thresholds Tactile Thresholds from Semmes- Weinstein Monofilament Tests Table 1 gives the average force necessary for detection of each group tested at each location on the foot sole. None of the diabetics in this study had significant plantar sensory loss. No significant differences were found in thresholds between right and left legs for the metatarsal and toe in any group. Data from the right and left legs were then pooled. A non-paramedic (Kruskal-Wallis) one way ANOVA was used to determine difference in tactile threshold among groups. For both plantar locations, young adults had significantly lower thresholds (median = 0.07 N) than the other groups. The diabetic (median = 3.610 N for metatarsal and median = 3.220 N for toe) and healthy adult groups (median = 3.610 N for both plantar locations) did not differ significantly. Table 1 Plantar, Auditory, Nerve Conduction and Reaction Time Metrics. Average reaction times to a plantar touch, a pure tone auditory stimulus, and rightward lateral movement of 4 mm at 100 mm/s 2 in diabetic (Peripheral Neuropathy), non-diabetic (Neurologically Intact), and young adults. Metrics given in either average ± standard deviation format or median [25% quartile, 75% quartile] format. All metrics for Semmes-Weinstein Monofilament Threshold, Air Conduction Threshold, and Nerve Conduction Studies have been averaged over both the right and left sides of the body. NCV = Nerve Conduction Velocity Group Peripheral Neuropathy (n = 13) Neurologically Intact (n = 24) Young Adults (n = 9) Age (yrs) 60.6 ± 6.5 59.4 ± 8.0 22.7 ± 1.2 Tactile Semmes-Weinstein Monofilament Thresholds (N) Base Metatarsal 3.610 [2.473, 4.000] 3.610 [2.440, 3.610] 0.07 [0.0200, 0.160] † Big Toe 3.220 [2.220, 4.080] 3.610 [2.440, 3.840] 0.07 [0.0200, 0.160] † Air Conduction Thresholds (dB) 1 K Hz 20.0 [15.0, 22.5] 15.0 [10.0, 20.0] † 2 K Hz 20.0 [15.0, 30.0] 15.0 [10.0, 22.5] ‡‡ 4 K Hz 30.0 [17.5, 55.0] 25.0 [20.0, 35.0] ‡ 8 K Hz 35.0 [22.5, 67.5] ‡ 35.0 [20.0, 57.5] ‡ Peak Accel Thresholds (mm/s 2 ) to Platform Lateral Moves 1 mm 122.775 ± 68.964‡ 108.417 ± 59.050 ‡ 60.778 ± 51.832‡† 2 mm 77.407 ± 55.982 ‡† 42.664 ± 37.754 ‡† 10.386 ± 3.091† 4 mm 37.275 ± 30.341 18.572 ± 19.143 13.458 ± 8.343 8 mm 20.297 ± 18.680 14.077 ± 8.122 14.766 ± 8.012 16 mm 18.613 ± 9.790 11.258 ± 7.723 13.590 ± 9.083 Nerve conduction studies Sensory NCV (m/s) – Sural 41.0 [37.0, 45.0] † 45.0 [42.0, 47.0] † Motor NCV(m/s) Peroneal 42.0 [39.0, 46.0] † 48.0 [46.0, 49.0] † Motor NCV(m/s) – Tibial 40.5 [36.0, 45.0] † 45.0 [42.25, 48.75] † M-wave Latency (ms) – Peroneal 4.7 [4.15, 5.45] 4.6 [4.075, 5.3] M-wave Latency (ms) – Tibial 5.35 [4.4, 7.0] * 4.8 [4.2, 5.6] * F-wave Latency (ms) – Peroneal 52.7 [47.625, 60.950] † 49.7 [46.6, 52.325] † F-wave Latency (ms) – Tibial 58.5 [52.6, 64.7] † 52.5 [50.450, 55.275]† Reaction Times (ms) Touch (Big Toe) 353.1 ± 113.6 331.5 ± 140.5 216.0 ± 64.3 Tone (1 kHz) 282.6 ± 65.2 276.9 ± 105.5 218.6 ± 64.3 4 mm Lateral Platform Movement @ 100 mm/s 2 777.8 ± 243.0‡† 623.9 ± 191.4‡† 431.5 ± 59.1‡† †Indicates significant group difference ‡Indicates significant threshold difference *Indicates a trend to significant threshold difference (0.05 < p < 0.07) Audiology Thresholds Because no significant difference (p = 0.481) was found between the right and left ears for all groups, the results of the air conduction testing of both ears were pooled and compared between groups. Averages for each group at each frequency (1, 2, 4, and 8 kHz) can be seen in Table 1 . Significant differences were found both between groups and among frequencies. Diabetics had significantly higher thresholds at 8 kHz (median = 35.0 dB) and the healthy adult group had significantly higher thresholds at 4 and 8 kHz (median = 25.0 dB and 35.0 dB respectively). Additionally, there was no significant difference between the diabetics and non-diabetics at 4 and 8 kHz, but there was a significant difference at 1 kHz, and trend toward significance at 2 kHz (p = 0.055). Thresholds to Rightward Lateral Platform Movements Determining the minimum acceleration threshold required to detect motion requires special psychophysical test procedures. These procedures, results, and conclusions arising from such testing are complex enough to require treatment in entirely separate papers [ 29 , 30 ]. In summary, peak acceleration values required at threshold are a function of the displacement traveled and the group studied. These values are listed in Table 1 . These results show that the lateral perturbation test used in this study (4 mm at 100 mm/s 2 ) is well above the detection threshold of any of the three groups at 4 mm (~ 40.0 mm/s 2 for diabetics, and ~ 14.0 mm/s 2 for healthy mature and younger adults). Hence we have termed this stimulus a Superthreshold stimulus. Lower Limb Nerve Conduction Testing Data was again pooled for both legs because all nerve conduction studies showed no differences between the two legs (values for each group can be seen in Table 1 ). Significantly slower conduction velocities (p < 0.05) were found for the sural, tibial, and peroneal nerves of the diabetic group. No significant differences was seen in the M latency of the peroneal nerve (p = 0.492) between groups, but a trend towards significance was seen in the tibial nerve (p = 0.07). The F latencies of both the peroneal and tibial nerves of the diabetic group were significantly higher than the healthy adults. Although there was a significant slowing found within the diabetic group, the deficit present was not judged as severe by a trained neurologist. According to standards set forth by the VA Medical Center, normal motor nerve conduction studies have velocities greater than 44.0 m/s for the peroneal nerve and greater than 41.0 m/s for the tibial nerve. The median conduction velocities for adults with diabetes are 42.0 m/s and 40.5 m/s for the peroneal and tibial nerve respectively. These values are not within the normal range for nerve conduction studies (NCS), yet they do not represent severe slowing. This indicates that those in this study do not have advanced motor deficits, and that the extent of the peripheral neuropathy is significant, yet not severe and debilitating. Also, as expected, sensory nerve conduction velocities were slower than motor nerve conduction velocities, which validate the data. Reaction Time Measurement Reaction times to platform movement (4 mm at 100 mm/s 2 ), plantar touch, and a bell tone were measured in all subjects and can be seen in Table 1 and Figure 1 (See Attached File). Measurements for reaction times were taken as the time between the beginning of the stimulus and the button press indicating subjects detected the stimuli. Averages were taken from all trials that were detected. Figure 1 Reaction Times to Plantar Touch, Auditory Tone, and Whole Body Lateral Perturbation by Group. Average reaction times of each modality for each group are shown. YA = Young Adult; HMA = Healthy Mature Adult; DMA = Diabetic Mature Adult. Note reaction times for platform movement are significantly longer for each group and also increase from young healthy adults to healthy mature adults and diabetic mature adults. For platform movements at 4 mm at 100 mm/s 2 , reaction times of all groups are significantly different (p < 0.05) from each other, with reaction times in the adults with diabetes being longest (mean = 777.8 ms), followed by aged matched adults (mean = 623.9 ms). Young adults had the shortest reaction times (mean = 431.0 ms) to movements. For the touch modality, reaction times for adults with diabetes are significantly (p < 0.05) longer than both other groups (mean = 353.1 ms). However, reaction times to foot sole touch between young (mean = 216.0 ms) and healthy mature adults (mean = 331.5 ms) were not significantly different. For the tone modality, no significant differences in reaction times were found between groups (diabetic mean = 282.6 ms; healthy adult mean = 276.9 ms; younger adult mean = 218.0 ms). For all groups, movement reaction times were significantly longer than the other two modalities (plantar touch and auditory tone), which did not differ significantly. Discussion A reaction time measurement includes the latency in the sensory neural code traversing peripheral and central pathways; perceptive, cognitive and volitional processing; a motor signal again traversing both central and peripheral neuronal structures; and finally, the latency in end effector (e.g., muscle) activation. Unless there is a greatly lessened sensitivity or loss of the sensory receptors for a given modality, a stimulus well above perceptual threshold, will, by its very nature of being superthreshold, produce a strong neuronal signal in the peripheral nerve subserving the location stimulated. And, conversely once the volitional decision is made to signal that an event has been detected, the motor output emanating from the spinal cord to the finger that will press the signaling button should likewise be robust. Since reaction time measurements have a central component, any decline, including those seen in normal aging, could indicate the presence of a peripheral and/or central neuropathy. Thus, it becomes difficult to tease out peripheral versus central effects when reaction times are slowed. But if the effect of a peripheral neuropathy is known through knowledge of the change in sensory and motor nerve conduction velocities brought about by the neuropathy, the extent to which the neuropathy will slow the reaction time can be estimated. A lateral platform translational perturbation invokes many senses. These include, but are not limited to pressure and rapidly adapting tactile sensors in the feet and toes, proprioceptive sensors from the ankle and hips (assuming that lateral moves do not affect the knees), kinesthetic sensors in the muscles (muscle force from Golgi tendon organs, reflex activation form spindles, and an ill-defined sense of "perceived exertion", vestibular activation, and certainly visual stimuli (unless occluded via blindfold as we have done in this study), Because of the involvement of all of these "senses," attributing the cause(s) of a slowed platform perturbation reaction time to peripheral and central neuronal changes is difficult. But, if peripheral conduction velocities are known, as are reaction times to more "purer" sensory inputs such as foot sole touch or an auditory tone, then this latter knowledge can be factored into platform reaction time calculations to help tease out peripheral and central effects. At this point, it is instructive to review the results reported in a preceding section of this paper and in Table 1 , and then to call out the various findings to build a specific hypothesis. The key results follow: 1) Subjects with controlled type II diabetes all had mild, but measurable peripheral neuropathies in at least one nerve in the lower limb, while those without diabetes of age >50 had no measurable evidence of neuropathy; 2) Subjects with diabetes had increased reaction times to all three test modalities. Touch and Tone reaction times were slightly, but not significantly, higher, while platform reaction time was significantly higher. 3) Older adults, whether diabetic or not, had longer reaction times to platform moves and to foot sole touch (all locations) than did younger adults, and reaction times to the bell tone did differ between groups, even though those with diabetes had higher auditory air conduction thresholds at every frequency (except 8 kHz) tested than their non-diabetic counterparts. 4) Reaction times to platform movement are 200 to 300% longer in all groups when compared to reaction times to touch and tone; Implications Individuals with diabetes often have neurological side effects that affect the peripheral nervous system. However, the increase in whole body movement reaction time seen in adults with diabetes in this study can not solely be related to peripheral nervous system changes due to diabetes. Even when motor nerve conductions slow from 50.0 m/s to 40.0 m/s (as seen in nerve conduction testing here), signal transmission time for a 1 m long nerve increases only 5 ms, which does not account for a 200 ms increase in movement reaction time. An additional slowing has to be occurring in the processing of the signals by the central nervous system. Deficits in the central nervous system (CNS) of those with diabetes may also be seen in cognitive deficits. Dey, et al. found no correlation between the duration of diabetes and cognitive function in those with non-insulin-dependent diabetes less than 18 years old [ 31 ]. They hypothesized that in order to see the decline in cognitive function and other central nervous system effects seen by other researchers [ 10 - 13 ], a longer duration of disease state must be present. However, in our study, diabetics, all with less than 10 years disease duration had a significantly higher reaction time to movement, which could be interpreted to indicate that not only are central effects present, but they manifest themselves early in the disease. These increases in movement reaction times among the mature adults with diabetes may also have an effect on posture and gait. The longer reaction times of a slipping diabetic subject will thus increase the probability of a fall. Diabetics have been shown to have a higher incidence of postural instability [ 21 - 26 ], longer reaction times, and reduced peripheral sensations thus leading to a higher incidence of falls resulting from slips. Reaction times to plantar surface touch indicate the extent of peripheral neuropathy in the population of diabetics. The fact that the mature adults with diabetes had increased reaction times to plantar touch is another indication that peripheral neuropathy was present in these subjects. However, we can see that the peripheral neuropathy of these adults with diabetes was not severe through the measurements of the Semmes-Weinstein monofilaments and sensory nerve conduction velocities. This increase may also play a role in the reaction time increase seen in the platform movement. If the subjects were unable to sense the movement for an additional 100 ms, then the 200 ms increase seen in adults with diabetes could be attributed to this sensory reaction time deficit, plus an increase in signal transmission through the nerves of approximately 5 ms, and an unknown cognitive slowing. Auditory reaction times measured here for diabetics and age-matched controls do roughly concur with the one reaction time study that includes diabetics [ 5 ]. Although no significant differences in auditory reaction times were seen between mature adults with and without diabetes and their young adult counterparts, a sensorineural hearing loss was seen in the mature adults with diabetes at the mid- and high-frequencies. Controversy over the relationship between diabetes mellitus and sensorineural hearing loss has had a long history. Some authors have concluded that no correlation exists [ 32 - 35 ], yet others find significant correlations between diabetes and loss in the low [ 36 ], mid [ 36 , 37 ], and high [ 37 ] frequency ranges. This loss has been attributed to changes in the peripheral portion of the auditory pathway, because no change in signal conduction along the central auditory pathway in patients with diabetes has been seen [ 5 ]. Mean hearing thresholds tested here were consistent with those published in Tay, et al. [ 36 ] for healthy elder subjects. However, subjects with diabetes in this study had slightly higher thresholds than those published in Tay et al, but the higher thresholds were more consistent with those published by Celik, et al. [ 37 ]. No auditory evoked potentials were measured, therefore the source of the dysfunction (be it the central or peripheral auditory pathway) cannot be determined. Conclusion From this study we can conclude that diabetes does affect reaction times, although the type and severity of the slowing may be related to the difficulty of the task and the prevalence of central and peripheral nerve deficits seen as side effects of diabetes. Auditory reaction times, the simplest of the tasks here with the shortest path between peripheral and central nervous system, did not show any differences in reaction times. When using a test that has a significantly longer path in the peripheral nervous system, such as the reaction time to plantar touch, slightly longer reaction times are seen in the adults with diabetes. When a more complicated task including detecting movement, signal transmission and interpretation, and response was required from the body, as in the platform movement reaction time test, a significant difference in reaction times were seen among all groups. This test takes more fully into account the peripheral nervous system signaling as well as the central nervous system processing and thus is a better overall test to determine deficits in healthy aging and aging individuals with diabetes. We have presented here, in addition to normal auditory and touch reaction times, lateral whole body reaction time, which has been shown to be the most sensitive indicator of differences between healthy young, healthy mature adults, and mature adults with mild diabetes among the modalities tested here. In other studies, we have found that adults with diabetes have substantially higher thresholds than healthy adults to detecting whole body motion [ 29 ]. This, in addition to the increased whole body reaction times, indicate that mild diabetes has profound effects on ability to detect and react to motion, which leads to insights on their ability to detect and prevent slips and falls. With the data presented here, it is impossible to determine the relative contribution of peripheral and central neural processes to the slowing seen on the whole body reaction time test. To determine this exact relation, authors are currently working on measures of cognitive processing that may provide more insight. Authors' contributions SJR aided in the experimental design, carried out the data acquisition, data analysis and data interpretation, and drafted the manuscript. CJR aided in the experimental design, built the set-up, and aided in the final manuscript. JS completed the statistical data analysis and prepared all the tables and figures.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555589.xml

534613

Factors involved in the inflammatory events of cervical ripening in humans

Background Cervical ripening is an inflammatory reaction. The glucocorticoid receptor (GR) mediates glucocorticoid anti-inflammatory reactions, whereas nuclear factor (NF)kappaB is a key pro-inflammatory transcription factor. Prostaglandins as well as platelet activating factor (PAF) are inflammatory mediators. Inducible nitric oxide synthase (iNOS) regulates the level of nitric oxide (NO) in response to various inflammatory stimuli. We hypothesize that a changed biological response to glucocorticoids could be a mechanism regulating the inflammatory events resulting in cervical ripening. Methods We monitored GR and NFkappaB, prostaglandin synthases cyclooxygenase (COX)-1 and -2, iNOS, as well as the PAF-receptor (PAF-R) in the uterine cervix from term pregnant women (with unripe cervices) before the onset of labor (TP), immediately after parturition (PP), as compared to non-pregnant (NP), using immunohistochemistry and RT-PCR. Results The GR protein was detected by immunohistochemistry in the nuclei of stroma and squamous epithelium (SQ). Stromal GR staining was increased in TP as compared to the NP group and decreased again after parturition. GR staining in SQ was decreased after parturition as compared to term. NFkappaB was present in SQ and glandular epithelium (GE), stroma and vascular endothelium. Increased nuclear NFkappaB staining was observed postpartum as compared to term pregnancy in stroma and GE. Stromal immunostaining for COX-1 as well as COX-2 was increased in the TP and PP groups as compared to the NP, and GE displayed an intensely increased COX-2 immunostaining at term and postpartum. Stromal PAF-R immunostaining was highest at term, while it was greatly increased in GE postpartum. No difference in the immunostaining for iNOS was found between the groups. RT-PCR showed a predominance of GRalpha to GRbeta mRNA in cervical tissue. The COX-2 mRNA level was increased in the PP group as compared to the TP group. Conclusions There is a decrease in GR levels in human cervix at parturition. Concomitantly there is an increase of factors such as NFkappaB, PAF-R, COX-1 and COX-2, suggesting that they may participate in the sequence of events leading to the final cervical ripening.

Background The human uterine cervix undergoes biochemical changes resulting in softening, effacement and dilatation during pregnancy and labor. This remodeling, or ripening, is a prerequisite for parturition [ 1 ]. It is characterized by inflammatory events, such as extravasation of neutrophils and macrophages [ 2 , 3 ] and an increased cervical level of pro-inflammatory cytokines such as interleukin (IL)-8 [ 3 , 4 ]. Progesterone, essential for the maintenance of pregnancy, and glucocorticoids have anti-inflammatory properties [ 5 ]. Placental production of progesterone and adrenal synthesis of glucocorticoids [ 6 ] increase markedly during human pregnancy. The antiprogestin (RU486), successful for labor induction at term in humans [ 7 ], also has anti-glucocorticoid properties. Progesterone and cortisol regulate the human placental corticotropin-releasing hormone (CRH) gene [ 8 ]. Placental CRH, synthesized in abundance in the human placental syncytiotrophoblasts and trophoblasts [ 9 ], has been proposed to be a key regulator for human parturition trough interactions with adrenal steroids and estrogen [ 10 ]. Among the group of structurally related, ligand-inducible nuclear steroid receptor transcription factor proteins, GR was the first that was cloned and sequenced [ 11 ]. GR and the progesterone receptor (PR) share structural similarities, and they interact with the same hormone responsive elements [ 12 ]. The GRα and GRβ isoforms are derived from the same gene, and GRα is the major form found in human cells and tissues [ 13 ]. NFκB is a key pro-inflammatory regulator. GR and NFκB are both inducible transcription factors with diametrically opposed functions in inflammatory responses. A mutual negative direct and indirect cross talk between GR and NFκB has been well described in previous studies [ 5 , 14 ]. Prostaglandin E 2 (PGE 2 ) is widely implicated for cervical ripening in clinical practice [ 15 ]. Among the COX enzymes, regulating prostaglandin synthesis, the COX-1 form is constitutively expressed, whereas COX-2 is inducible and particularly involved in inflammatory events [ 16 ]. The COX enzymes are down regulated by cortisol in human decidua, myometrium and cervix [ 17 ]. Platelet-activating factor (PAF) is a lipid pro-inflammatory mediator, involved in several reproductive processes, i.e. parturition [ 18 ]. PAF is synthesized by some leukocytes, blood platelets and vascular endothelial cell [ 19 ]. The PAF receptor (PAF-R) is a G-coupled membrane receptor with an estrogen responsive element within its promoter region, enabling regulation by estrogens [ 20 ]. The activation of PAF-R is associated with cytoskeletal remodeling and expression of pro-inflammatory modulators, such as COX-2, IL-6 and IL-8 [ 21 ]. Thus, PAF-R and COX enzymes have been widely demonstrated as factors involved in the events promoting and proceeding parturition, yet their cell origin in the human uterine cervix remains to be clarified. Nitric oxide (NO) is synthesized intracellularly from the amino acid L-arginine through the activity of specific synthase enzymes (NOS) [ 22 ]. The inducible form, iNOS, present in e.g. macrophages, regulates the level of NO in response to various inflammatory stimuli, including proinflammatory cytokines and lipopolysaccharides. NO stimulates PGE 2 release from human cervical tissue explants [ 23 ], and is a powerful regulator of COX-2 thereby increasing local PGE 2 concentrations in inflammatory tissues [ 24 , 25 ]. NO donors do induce cervical ripening in human pregnancy in the first trimester [ 26 , 27 ], at term [ 28 ] and in non-pregnant women [ 29 ]. Besides, treatment with the NO donor isosorbide-5-mononitrate stimulates production of e.g. COX-2 and PGE 2 in human cervix [ 27 ]. The action of NO on cervical ripening appears to be accomplished by effects on connective tissue and smooth muscle cells in a similar way as previously been shown for prostaglandins [ 27 ]. Our hypothesis is that glucocorticoids exert a direct receptor mediated effect in the human cervix uteri, and that a changed biological response to glucocorticoids could be a mechanism behind the events resulting in cervical ripening at parturition. Since NFκB has opposed functions in inflammatory responses as compared to GR, we presume that NFκB could also be a regulator of the inflammatory events leading to cervical ripening. These inflammatory events could be mediated via factors such as the PAF-R, iNOS and/or COX enzymes. Methods Study patients All women gave their informed consent and the Local Ethics Committee of the Karolinska Hospital approved the study. All were healthy, had uncomplicated pregnancies and were without medication prior to parturition. The non-pregnant (NP) women were hysterectomised due to benign disorders not involving the cervix. The women in the term pregnant (TP) group all had unripe cervices with a Bishop score <5 points and none of them were in labor. Biopsies were obtained during elective caesarean sections before onset of labor. The biopsies from the post partal (PP) women were taken after a normal vaginal delivery. For the immunohistochemistry study of GR and NFκB, cervical biopsies were obtained from one para and eleven primipara TP women (n = 12) with a mean age (range) of 33 (28–38) years, and a mean gestational age of 38 (37 to 40) weeks. The women of the PP group (n = 14) were all primipara and had a mean age of 31 (22–37) years, and a gestational age of 40 (39 to 42) weeks. The NP control group (n = 8) had a mean age of 43 (32–49) years, and a mean parity of II (I-III). The cervical samples available for the immunohistochemistry studies of COX-1, COX-2 and PAF-R were TP (n = 8), PP (n = 10) and NP group (n = 6). Biopsies for RNA preparations were not available for the RT-PCR study from all subjects, NP (n = 5), TP (n = 6) and PP (n = 5). The women in the NP group were significantly older than those of the other two groups. Since hysterectomies in young women are uncommon most biopsies in the NP group are from women in the middle of their 40s, but they were all menstruating regularly and did not receive any medication. Tissue collection Cervical biopsies were obtained transvaginally (for the TP and PP groups) from the anterior cervical lip at the 12 o'clock position, from 10–20 mm depth. The tissue samples from the hysterectomies were obtained directly after the uterus was removed during operation. The same physician (YSV) collected all the samples. The biopsies were immersion-fixed in 4% phosphate buffered formaldehyde at 4°C overnight, stored at 4°C in 70% ethanol and thereafter embedded in paraffin. From the biopsies that were large enough, a small piece was cut off prior to fixation, and frozen in -70°C until RNA preparation. RNA preparation and reverse transcription Total RNA from frozen cervical tissue samples was purified with the SV Total RNA isolation system (Promega, Madison, WI) according to a procedure recommended by manufacturer. One microgram of total RNA from each sample was reverse transcribed at 42°C for 45 min in a final volume of 40 μl with a reaction mixture containing 50 mmol/l Tris-HCl (pH 8.3), 75 mmol/l KCl, 3 mmol/l MgCl 2 , 7.5 mmol/l dithiothretiol, 0.5 mmol dNTPs, 1 μg random hexameters, and 400 U of Moloney murine leukemia virus reverse transcriptase (Gibco-BRL, Paisley, UK). RT-PCR Oligonucleotide primers for the GRα gene were as follows [ 30 ]: 5'-CCT AAG GAC GGT CTG AAG AGC-3' and 5'-GCC AAG TCT TGG CCC TCT AT-3', corresponding to nucleotides 2158-2178 and 2616-2635 of the human GRα cDNA (GenBank accession No X03225). Oligonucleotide primers for the GRβ gene were as follows [ 30 ]: 5'-CCT AAG GAC GGT CTG AAG AGC-3' and 5'-CCA CGT ATC CTA AAA GGG CAC-3', corresponding to nucleotides 2158-2178 and 2503-2523 of the human GRβ cDNA (GenBank accession No X03348). Oligonucleotide primers for the COX-1 gene were as follows: 5'-TGC CCA GCT CCT GGC CCG CCG CTT-3' and 5'-GTG CAT CAA CAC AGG CGC CTC TTC-3', corresponding to nucleotides 568-591 and 871-847 of the human COX-1 cDNA [ 31 ]. Oligonucleotide primers for the COX-2 gene were as follows: 5'-TTC AAA TGA GAT TGT GGG AAA ATT GCT-3' and 5'-AGA TCA TCT CTG CCT GAG TAT CTT-3', corresponding to nucleotides 574-601 and 878-854 of the human COX-2 cDNA [ 32 ]. The predicted size of the PCR products was 477 bp for GRα, 366 bp for GRβ, 304 bp for COX-1 and 305 bp for COX-2. For PCR, the cDNAs corresponding to 50 ng RNA were added to 10 μl of HotStarTaq ® master mix (Quiagen GmbH, Hilden, Germany) containing 2.5 μM of each oligonucleotide primer in a final volume of 20 μl. The reaction mixture was overlaid with mineral oil. After an initial incubation for 15 min at 95°C, the samples were subjected to 33 (GRα, COX-1 and COX-2) and 40 (GRβ) cycles of 30 s at 94°C, 40 s at 60°C, and 60 s at 72°C with a final extension step at 72°C for 10 min in the DNA Thermal Cycler 480 (Perkin-Elmer, Norwalk, CT). The amount of PCR product for GRα increased linearly up to 36 cycles and for COX-1 and COX-2 it increased linearly up to 38 cycles (data not shown). Quantitative measurement of GRβ mRNA would require larger amounts of cervical RNA than were available, since GRβ showed very low expression with a visible band only after 40 cycles. To standardize the quantification method, an endogenous 18S rRNA was used as an internal standard. The 18S rRNA primers and Competimers™ (modified at their 3' ends to block extension by DNA polymerase) were obtained from Ambion (Quantum RNA 18S Internal Standards; Ambion Austin, TX). The standard, and the GR and COX mRNAs were amplified in parallel and under the same conditions. A mixture of 18S primers and Competimers™ (1:9) was used to modulate amplification efficiency of 18S rRNA to the same linear range as GRα, COX-1 and COX-2 when amplified under the same conditions. The predicted size of the PCR product for 18S was 489 bp. The PCR products were run on 2% agarose gel and stained with ethidium bromide. Bands were captured and analyzed using ChemiDoc Gel Documentation System (Bio-Rad Laboratories, Hercules, CA). The levels of GR and COX PCR products were normalized against the 18S product. The RT-PCRs were repeated twice. Immunohistochemistry Paraffin sections (5 μm) were used and a standard immunohistochemical technique (avidin-biotin-peroxidase) was carried out as described before [ 33 ] to visualize GR, NFκB, COX-1, COX-2, PAF-R and iNOS. After the tissues were dewaxed and rehydrated, an antigen retrieval procedure was performed. The sections were pre-treated by heating in a microwave oven at 700 W in 0.01 M sodium citrate buffer (pH 6.0). Endogenous peroxidase activity was blocked by incubation with 3% hydrogen peroxide. All tissue sections were exposed to a non-immune block with normal goat serum. A polyclonal rabbit anti-human antibody was used for the detection of GR (ABR PAI-511A, Affinity Bioreagents, Inc., Golden, CO, USA). This antibody recognizes both GRα and GRβ. The tissue sections were incubated over night in +4°C with the primary antibody diluted 1:1000. A polyclonal rabbit anti-human antibody (DB033 Delta Biolabs, Cambell, CA, USA) was used for the NFκB immunostaining. The tissue sections were incubated with the primary antibody diluted 1:500 over night at +4°C. The primary antibodies were replaced with non-immune rabbit IgG in negative controls. Polyclonal goat anti-human (sc-1752 and sc-1745, Santa Cruz) antibodies were used (diluted 1:100) for the COX-1 and COX-2 respectively. The incubation with primary antibody was 1 hr at 37°C for both COX-1 and COX-2. A polyclonal goat anti-human (sc-8741, Santa Cruz) antibody was used for the PAF-R immunostaining. The tissue sections were incubated with the primary antibody diluted 1:100 over night at +4°C. Replacing the primary antibody with non-immune goat IgG was used as negative control. A monoclonal mouse anti-iNOS antibody was used for detection of iNOS (N32020, Transduction Laboratories, Lexington, KY, USA). It recognizes the C-terminal domain of iNOS. The antibody was diluted 1:50 and incubated for 70 minutes in room temperature. Replacing the primary antibody with non-immune mouse IgG was used as negative control. Image analysis A microscope and CCD video camera connected to a computer with an image analysis program (Leica Imaging System Ltd., Cambridge, UK) was used to assess quantitative values from GR and NFκB immunohistochemistry. The quantification of nuclear immunostaining was performed on the digitized images of systematic randomly selected fields of stroma and squamous epithelium. Ten fields were analyzed separately in each section of tissue, using the color-discrimination software. Positive staining is presented as a ratio of the area of positively stained nuclei (brown) to the total area of cell nuclei (brown and blue). Manual scoring Two observers blinded to the identity of the slides, performed all the assessments. The staining was evaluated semi-quantitatively using a grading system. The staining intensity was graded on a scale of (0) no staining, (1) very faint, (2) faint, (3) moderate and (4) strong staining. Statistics Statistical calculations for the data from the relative quantification of RT-PCR products, the immunohistochemistry results by image analysis and manual scoring were performed by ANOVA on Ranks (Kruskal-Wallis' test) and significances were evaluated by Dunn's test. Values with different letter designations are significantly different (p < 0.05). Results GR By immunohistochemistry the GR protein was localized to the nuclei of cervical stroma (S), squamous epithelium (SQ), glandular epithelium (GE) and vascular endothelium (Figure 1a,1b,1c,1d,1e,1f,1g,1h,1i ) in samples from the NP (left column), TP (middle column) and PP (right column) groups. By image analysis the GR levels were determined in SQ and stroma (Figure 2 ). The stromal cells include vascular epithelium and the leukocytes within the stroma. Blood cells within vessels (V) are excluded from the image analysis. Strong immunostaining was present in SQ, particularly in the basal and parabasal cell layers (Figure 1a,1b,1c ). There was a significant decrease in immunostaining of the PP group as compared to the TP group, both in stroma and SQ (Figure 2 ). The stromal GR immunostaining was increased in the TP group as compared to the NP and PP groups (Figure 2 ). It was noted in all groups that GE (Figure 1g and 1i ), vascular endothelium (Figure 1d,1e and 1f ) and some intravascular and perivascular leukocytes (as identified by their morphology, black arrowheads, Figure 1d and 1f ) stained positive for GR, while some leukocytes were negative (white arrowheads, Figure 1d and 1f ). Figure 1 Immunostaining of GR in the non-pregnant (NP) (left column, a , d , g ), term pregnant (TP) (middle column, b , e , h ) and postpartum (PP) (right column, c , f , i ) groups. GR protein (brown staining) is present in the nuclei of cervical stroma (S), squamous epithelium (SQ), vessels (V) and glandular epithelium (GE) in all three groups. Some leukocytes, identified by their morphology, were positive for GR (black arrowheads) while others were negative (white arrowheads) ( d and f ). A negative control where the primary antibody was replaced by rabbit IgG is shown in h . Figure 2 GR levels, as assessed by image analysis of immunohistochemistry results, in cervical squamous epithelium (SQ) and stroma in samples from TP (n = 12) and PP (n = 14) as compared to NP (n = 8) women. Box and whisker plots represent the median value with 50% of all data falling within the box. The "whiskers" extend to the 5 th and 95 th percentiles. Boxes with different letter designations are significantly different, p < 0.05. NFκB Positive NFκB immunostaining in cervix uteri was present in stroma, GE, SQ and vascular endothelium in NP (left column), TP (middle column) and PP (right column) groups (Figure 3a,3b,3c,3d,3e,3f,3g,3h,3i,3j,3k,3l ). Strong positive staining for NFκB was also seen in neuronal ganglions (G, Figure 3h ) and in smooth muscle cells/activated fibroblasts, both around blood vessels (Figure 3j ) and within the stroma (Figure 3l ). In GE the nuclear and cytoplasmatic NFκB immunostaining was increased in the PP group as compared to the NP group (Figure 4 ). No changes in NFκB staining were observed in SQ (Figure 4 ). The stroma displayed an increased nuclear immunostaining, but unchanged cytoplasmatic staining, in the PP group as compared to the other groups (Figure 5 ). Vascular endothelium showed an increased nuclear but unchanged cytoplasmatic staining, in the PP group as compared to the NP group (Figure 5 ). In all groups some leukocytes (black arrowhead, Figure 3l ), stained positive for NFκB. As for image analysis of GR, the manual scoring of NFκB in stromal cells could include leukocytes within the stroma, but not blood cells within vessels. Figure 3 Immunostaining of NFκB protein in stroma (S), glandular epithelium (GE), squamous epithelium (SQ) and vessels (V) in cervical biopsies from the NP (left column, a , d , g , j ), TP (middle column, b , e , h ) and PP (right column, c , f , i , l ) groups. Positive nuclear and cytoplasmic immunostaining is also observed in neuronal ganglions (G) ( h ). Some leukocytes, identified by their morphology, display positive NFκB staining ( l , black arrowhead). A negative control where the primary antibody is replaced by rabbit IgG is shown in k . Figure 4 NFκB protein levels, as assessed by manual scoring of immunohistochemistry results, in nuclei and cytoplasma of glandular epithelium (GE) and squamous epithelium (SQ) in cervical samples from TP (n = 10) and PP (n = 10) as compared to NP (n = 8) women. Box and whisker plots represent the median value with 50% of all data falling within the box. The "whiskers" extend to the 5 th and 95 th percentiles. Boxes with different letter designations are significantly different, p < 0.05. Figure 5 NFκB protein levels, as assessed by manual scoring, in nuclei and cytoplasma of stroma and vascular endothelium in cervical samples from TP (n = 10) and PP (n = 10) as compared to NP (n = 8) women. Box and whisker plots represent the median value with 50% of all data falling within the box. The "whiskers" extend to the 5 th and 95 th percentiles. Boxes with different letter designations are significantly different, p < 0.05. COX-1 Immunostaining for COX-1 (Figure 6a,6b,6c ) was found in platelets (not shown), some leukocytes (not shown), vessel endothelium (Figure 6a,6c ), stroma (Figure 6a,6b,6c ), neuronal ganglion (not shown), SQ (Figure 6c ) and GE (not shown). Manual scoring was performed and there was a significant increase of COX-1 staining in the stroma of the TP and PP groups as compared to the NP group (Figure 7 , top). No differences were found in staining of the SQ, GE and endothelium between the three groups (data not shown). Figure 6 Immunostaining of COX-1 ( a-c ), COX-2 ( d-i ), PAF-R ( j-n ) and iNOS ( p , r ), in cervices from women in the NP (left column), TP (middle column) and PP (right column) group. A representative negative control for COX-1, COX-2 and PAF-R immunohistochemistry (primary antibody replaced by goat IgG, same secondary antibody) is shown in o , the negative control for iNOS is shown in q (primary antibody replaced by mouse IgG). Abbreviations: S = stroma, GE = glandular epithelium, SQ = squamous epithelium, V = vessel and G = ganglion cells. Figure 7 COX-1 (top) and COX-2 (middle, bottom) protein levels, as assessed by manual scoring of stroma (top, middle) and glandular epithelium (bottom) in cervical samples from the TP (n = 8), PP (n = 9) and NP (n = 6) groups. Box and whisker plots represent the median value with 50% of all data falling within the box. The "whiskers" extend to the 5 th and 95 th percentiles. Boxes with different letter designations are significantly different, p < 0.05. COX-2 Immunostaining of COX-2 (Figure 6d,6e,6f,6g,6h,6i ) was found in the stroma, GE and smooth muscle cells/activated fibroblasts, both around vessels and arranged as bundles within the stroma. The intensity of COX-2 staining was overall less than that of COX-1, except in GE where the immunostaining was almost maximal in all samples of the TP and PP groups (Figure 6h,6i ). There was an increased immunostaining in the stroma of the PP group and in GE from the TP and PP groups, as compared to the NP group (Figure 7 middle and bottom, respectively). PAF-R Immunostaining of PAF-R was found in stroma, neuronal ganglion (G) and GE (Figure 6j,6k,6l,6m,6n ). The immunostaining in the stroma was higher in the TP group (Figure 6k ) compared with NP (Figure 6j ) and PP groups (Figure 6l ) (Figure 8 top). The immunostaining in GE was increased in the PP group (Figure 6l ) as compared to the TP group (Figure 6k ) (Figure 8 bottom). A negative control representative for the goat-derived antibodies, where goat IgG replaced the primary antibody, is shown in Figure 6o . Figure 8 PAF-R protein levels, as assessed by manual scoring, in stroma (top) and glandular epithelium (bottom) in cervical samples from the TP (n = 11), PP (n = 13) and NP (n = 9) groups. Box and whisker plots represent the median value with 50% of all data falling within the box. The "whiskers" extend to the 5 th and 95 th percentiles. Boxes with different letter designations are significantly different, p < 0.05. iNOS Faint positive staining was found in SQ, stroma and GE (Figure 6p,6r ). There were no differences in immunostaining of iNOS found between the groups (data not shown). A negative control where the primary antibody was replaced by mouse IgG is shown in Figure 6q . GRα mRNA The GRα mRNA level was determined by RT-PCR (Figure 9 ). GRα mRNA levels were not significantly different between the three study groups, but there was a tendency of an increased level in the TP group as compared to the other two groups (Figure 9a ). When the relative GRα mRNA level in the NP group was defined to 100%, the TP group showed 120% and the PP group 90% of that level. Figure 9 Images of representative RT-PCR gels for a : GRα, b : GRβ and c : COX-1 (upper band) and COX-2 (middle band) in the human cervix in the non-pregnant (NP; n = 5), term pregnant (TP; n = 6) and postpartum (PP; n = 5) groups. The gels are stained with ethidium bromide. a : GRαmRNA(upper band) and 18S mRNA (lower band). b : 18S mRNA (upper band) and GRβmRNA(lower band). c : The 18S mRNA (bottom band) and the COX-1 (upper band) and COX-2 (middle band) PCR products. GRβ mRNA Very low levels of GRβ mRNA were present in 4 out of 6 samples from the TP group, and in 2 out of 5 samples in the NP and PP groups (Figure 9b ). COX-1 mRNA There was no difference in the COX-1 mRNA level between the groups, as assessed by RT-PCR (Figure 9c ) (Figure 10 , top). Figure 10 COX-1 (top) and COX-2 (bottom) mRNA levels in the human cervix from the NP (n = 5), the TP (n = 6) and the PP (n = 5) groups as determined by RT-PCR. Intensities of the PCR product bands were normalized against the internal 18S standard. Box and whisker plots represent the median value with 50% of all data falling within the box. The whiskers extend to the 5th and 95th percentiles. Group medians with different letter superscripts are significantly different (P < 0.05). COX-2 mRNA The COX-2 mRNA level was the highest in the PP group (Figure 9c ) (Figure 10 , bottom), but there was a great variation between the individual PP patients (Figure 9c ), probably due to the different amount of glands present in the biopsies. Discussion This study is, to our knowledge, the first reporting GR expression in human cervix uteri during pregnancy and at parturition. In a recent study on human endometrium [ 34 ], GR protein was observed in stromal cells and within leukocytes invading the stroma. Pujols et al. [ 13 ] reported that the expression of GRα mRNA is 400-fold higher than GRβ mRNA expression in human tissues. The GRα protein was found in all cells and specimens in their study, while GRβ was not detected in any specimen. In our study, GRα mRNA was much more abundant than GRβ mRNA. Therefore, we conclude that GRα is the major receptor type observed in our study, although GRβ could contribute to the GR protein level in the TP group. Glucocorticoids exert their anti-inflammatory effects primarily by inhibiting the expression of cytokines e.g. IL-8, as demonstrated in human fibroblasts [ 35 ], tumour necrosis factor (TNF)-α [ 36 ], colony-stimulating factor (CSF) and macrophage stimulating factor (M-CSF) [ 5 ]. The majority of these pro-inflammatory genes have no glucocorticoid responsive elements (GRE) in their promoter regions that could explain the effect of glucocorticoids, but many of them contain sites for the transcription factors activating protein (AP)-1 and NFκB [ 37 ]. In the review by McKay and Cidlowski [[ 5 ], and references therein] it is obvious though, after comparing the genes transcriptionally regulated by NFκB (e.g. IL-8, iNOS, COX-2) and genes repressed by GR (e.g. IL-8, iNOS, COX-2, GR, CRH, fibronectin, and metalloproteinases (MMPs)), that NFκB and GR have diametrically opposed functions. The oppositely regulated IL-8, iNOS and COX-2 have all proven to be important for cervical ripening and labor induction [ 4 , 17 , 38 ]. Maternal plasma levels of cortisol increase until term [ 6 ], but their diurnal variation is maintained [ 39 ] although more long-term variations have been suggested [ 40 ]. At parturition, maternal plasma levels of placental CRH [ 10 , 41 ], pituitary adrenocorticotropic hormone (ACTH) and adrenal cortisol [ 6 ] increase exponentially. Our data shows that the GR protein was present in cervical stroma, SQ and vascular endothelium in samples from non-pregnant, term pregnant and postpartum women. The decrease in GR levels in stroma and SQ at parturition, as compared to term, could be interpreted in terms of a GR mediated glucocorticoid anti-inflammatory activity during pregnancy that is ended at parturition. The NFκB protein was present in cervices from non-pregnant, term pregnant and postpartum women. This protein, ubiquitously expressed in a variety of cell types, is found in the cytoplasm in its inactive form, but translocates into the nucleus upon activation [ 14 ]. Therefore, the increase in nuclear NFκB levels in cervical stroma, GE and vascular endothelium in the PP group suggests an activation of NFκB at parturition. Thus, our present results of decreased GR and increased NFκB levels at parturition are in agreement with the reports of their opposed effects on the activity of several inflammatory genes [[ 5 ], and references therein] and the idea of cervical ripening being an inflammatory reaction [ 42 ]. Polymorphonuclear leukocytes and macrophages migrate from blood vessels and accumulate in the cervix uteri before parturition [ 2 ]. GR and NFκB were identified by immunohistochemistry in morphologically recognized leukocytes in the samples from all groups in this study. GR-positive leukocytes have also been observed in the endometrium of non-pregnant women and the decidua of early pregnant women [ 43 ]. Cortisol is a potent inhibitor of COX-2 in myometrium, decidua and cervix [[ 17 ], and references therein]. We found the constitutive COX-1 protein to be present at higher levels in the stroma at term and postpartum, whereas COX-1 mRNA was unchanged. Inducible COX-2 mRNA was increased at parturition as compared to term, and the COX-2 protein was increased in stroma postpartum and in GE at term and postpartum as compared to the NP group. Our observations suggest, that prostaglandin synthesis occur especially in the GE, where a highly intensive COX-2 immunostaining was observed at term and postpartum. This is in agreement with a recent report on COX-2 mRNA in the pregnant baboon cervix [ 44 ]. This would also explain the large variation in COX-2 mRNA levels found in the present study. The RNA is prepared from homogenates of cervical biopsies, and the amount of glands present in the biopsies varies from none to plenty. Our results indicate that prostaglandin synthesis could be regulated predominantly via COX-2 in cervical GE at parturition. The stromal increase in COX enzymes could be due to the influx of macrophages and leukocytes before parturition [ 2 ], since leukocytes are an abundant source of PGE 2 in the human body [ 45 ]. We could not exclude that COX-1 and COX-2 are present in the stromal cells, thereby adding another cervical source of prostaglandin synthesis, but it seems likely that the enzymes are mainly expressed in the invading leukocytes. Further, our data together with previous findings, suggest suppression not only by progesterone [ 46 ], but also by cortisol [[ 17 ], and references therein] of prostaglandin synthesis in the human uterine cervix during pregnancy and a release of this suppression at parturition. Since the COX-2 promoter contains NFκB binding sites [ 47 ], the activation of COX-2 could be regulated via NFκB. Nitric oxide stimulates PGE 2 release from human cervical tissue explants [ 23 ]. Nitric oxide donors induce cervical ripening in term pregnancy in humans [ 28 ]. Treatment with the NO donor isosorbid-5-mononitrate stimulates the synthesis of COX-2 and PGE 2 in human uterine cervix [ 27 ]. The immunostaining of iNOS in the present study did not differ between the groups, and thus did not vary due to pregnancy or parturition. This is not in agreement with previous studies on human cervix [ 38 , 48 ]. Tschugguel et al. found increased iNOS immunostaining with an antibody from Transduction laboratories (no number stated), but not on the mRNA level using RT-PCR, indicating a post-transcriptional regulation of iNOS [ 38 ]. Ledingham et al. [ 48 ] found increased cervical immunostaining and stronger bands on Western blot in term pregnancy as compared to the non-pregnant state, using an antibody from Transduction laboratories (39120, clone 54). We also used a monoclonal iNOS antibody from Transduction laboratories, but a different clone (32020, clone 6), which could explain the different results. We found some staining in GE, while Ledingham et al. state no staining of the glands [ 48 ]. Platelet activating factor (PAF), like prostaglandins derived from the arachidonic acid precursor, is a multifactorial pro-inflammatory mediator, which has been implicated in parturition. Local application of PAF in rats induced cervical ripening [ 49 ], whereas a PAF-R antagonist prolonged parturition [ 50 ]. PAF-R has been identified in human cervical fibroblasts in vitro [ 51 ]. We show, for the first time, presence of the PAF-R protein in the human uterine cervix in vivo. Stromal PAF-R immunostaining was most pronounced at term, and decreased after parturition. PAF-R immunostaining was, like for COX-2, further increased in GE postpartum. PAF increases the expression of pro-inflammatory cytokines e.g. IL-8, and this effect can be abolished using a PAF-R antagonist (WEB2170) [ 18 , 51 ]. Furthermore, the COX-2 promoter contains NFκB binding sites [ 47 ], and the PAF stimulated COX-2 induction is NFκB dependent [ 51 ], indicating that the PAF-R could activate NFκB and thereby induce COX-2. PAF also increases expression of MMP-1 [ 51 ], which has been shown to effectuate collagen degradation and cervical ripening [ 1 , 52 ]. If the process of cervical ripening is disturbed, either resulting in a preterm delivery or to a prolonged delivery time, possibly ended by a cesarean section, it will lead to increased risks for both the mother and the child. Preterm delivery is the leading factor causing neonatal mortality and morbidity [ 54 ]. An increased knowledge of the factors regulating the cervical ripening process will give tools for developing pharmaceuticals that can regulate cervical ripening. Conclusions We have demonstrated that the human uterine cervix is a potential target organ for glucocorticoids during pregnancy. The higher GR protein levels in cervical stroma and SQ before parturition may reflect a GR mediated anti-inflammatory effect of cortisol during pregnancy, with a subsequent decline of this activity at parturition. The concomitant increase in nuclear NFκB levels in the cervix suggests activation of this transcription factor at parturition. NFκB activity promotes pro-inflammatory events and could be responsible, at least in part, for the observed increase in COX-1, COX-2 and PAF-R levels.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC534613.xml

535533

Methodology for evaluating Insite: Canada's first medically supervised safer injection facility for injection drug users

Many Canadian cities are experiencing ongoing infectious disease and overdose epidemics among injection drug users (IDUs). In particular, Human Immunodeficiency Virus (HIV) and hepatitis C Virus (HCV) have become endemic in many settings and bacterial and viral infections, such as endocarditis and cellulitis, have become extremely common among this population. In an effort to reduce these public health concerns and the public order problems associated with public injection drug use, in September 2003, Vancouver, Canada opened a pilot medically supervised safer injecting facility (SIF), where IDUs can inject pre-obtained illicit drugs under the supervision of medical staff. The SIF was granted a legal exemption to operate on the condition that its impacts be rigorously evaluated. In order to ensure that the evaluation is appropriately open to scrutiny among the public health community, the present article was prepared to outline the methodology for evaluating the SIF and report on some preliminary observations. The evaluation is primarily structured around a prospective cohort of SIF users, that will examine risk behavior, blood-borne infection transmission, overdose, and health service use. These analyses will be augmented with process data from within the SIF, as well as survey's of local residents and qualitative interviews with users, staff, and key stakeholders, and standardised evaluations of public order changes. Preliminary observations suggest that the site has been successful in attracting IDUs into its programs and in turn helped to reduce public drug use. However, each of the indicators described above is the subject of a rigorous scientific evaluation that is attempting to quantify the overall impacts of the site and identify both benefits and potentially harmful consequences and it will take several years before the SIF's impacts can be appropriately examined.

Introduction Many Canadian cities are currently experiencing Human Immunodeficiency Virus (HIV) and hepatitis C virus (HCV) epidemics as a result of illicit injection drug use [ 1 , 2 ]. Other costly infectious diseases that can be easily acquired from non-hygenic injection practices, such as endocarditis and cellulitis, are also common [ 3 ]. The health of injection drug users (IDUs) is further compromised by avoidance and erratic use of primary care services, costly emergency room visits, and acute care hospitalizations [ 3 - 6 ]. Public drug use also occurs in many inner city neighborhoods, and public drug use and the unsafe disposal of syringes is a major community concern [ 7 , 8 ]. In over two dozen European cities and more recently in Sydney, Australia, safer injection facilities (SIFs), where injection drug users can inject pre-obtained illicit drugs, have been implemented in an effort to reduce the community and public health impacts of illicit drug use [ 9 ]. SIF typically have several primary objectives including: the reduction of public drug use, fatal and non-fatal overdose, and infectious disease risk; improving contact between a highly marginalized 'at-risk' population and the healthcare system; and enhancing recruitment into medical care and addiction treatment [ 9 - 11 ]. Within SIFs, IDUs are provided with clean injecting equipment, medical attention in the event of overdose, as well as access to or referral to primary healthcare and other services including addiction treatment. While it must be stressed that limited quantitative data are presently available, various reports have credited SIFs with a number of public health and community benefits including: improving the health and social functioning of their clients [ 11 ], while reducing overdose deaths [ 12 ], risk behaviors known to transmit infectious diseases [ 13 ], improperly discarded syringes [ 14 ], and public drug use [ 15 ]. In addition, improved access to medical care and drug treatment has been attributed to SIF attendance [ 10 , 16 ]. A limitation of these earlier analyses is that, in a number of settings, there has not been a commitment on the part of health agencies to fund comprehensive evaluations, and in many instances there have not existed prospective cohorts to inform examinations of SIF's impacts [ 17 ]. On September 22, 2003 Vancouver, Canada opened North America's first government sanctioned SIF pilot study [ 18 ]. Federal government approval for the three-year pilot study was granted on the condition that the health and social impacts of the SIF be the subject of a rigorous scientific evaluation. More recently, several Canadian cities have begun to consider their own SIF evaluations, including Montreal and Victoria [ 19 , 20 ]. Since several years were devoted to the development of the Vancouver SIF evaluation methodology, and since the investigators wished to be as open with methodology as possible [ 21 ], the present article was prepared to describe the framework of the evaluation and to report on preliminary observations. The publication of these observations may also be useful for other Canadian considering initiating SIF trials [ 19 , 20 ]. Client Anonymity Prior to the opening of the SIF, a major concern with the evaluation related to willingness of the target community to use the injection facility [ 18 ]. In order to attract the target population without raising fears about confidentiality, and to make the service as low threshold as possible, all clients of the SIF can remain anonymous. Since fears regarding reduced willingness to use SIF, if client registration was required, were observed in feasibility studies conducted prior to Insite's opening [ 18 ], the SIF operated as a completely low threshold service in the first 6 months of operation and maximizing access to the SIF was the top priority. During this time only paper records were maintained. After 6 months of operation, and after trust was developed between the SIF operators and the target community, service use was tracked at an individual level using a database that tracks all client service use and outcomes within Insite. The phasing in of a digital tracking system was successful, although service uptake was so substantial and immediate after the site opened, it is not known if this was necessary. A further challenge was the ethical dilemma posed by providing a health service that must also be rigorously evaluated [ 22 ]. Specifically, it was apparent to the investigators that it would be unethical to limit use of the SIF to those who agreed to participate in research. Instead, equipoise was reached by allowing participation in surveys and other aspects of the research to be optional to SIF users. Aims of Insite In brief, the aims of Insite are to reduce public injection drug use and the unsafe disposal of syringes in public spaces, the reduction of overdoses and infectious disease risk, and improve access to healthcare services among IDUs. The methodology for evaluating these aims is described below and involves both a prospective cohort design and additional data sources including evaluation of community impacts. Evaluation Methodology Data Sources The framework for the Vancouver SIF evaluation was designed prior to the SIF's opening and involved a number of methodological approaches. In light of the lack of existing quantitative efficacy data [ 17 ], the existence of ethical concerns [ 22 ], and an awareness that a non-randomized studies may be vulnerable to substantial selection biases [ 23 ], the Vancouver SIF evaluation is primarily structured around a prospective cohort design that involves the longitudinal measurement of a number of outcomes including blood-borne infection and overdose incidence, risk behavior, drug use practices, such as public drug use, and health services use. The Vancouver SIF evaluation is somewhat unique because of the availability of a number of pre-existing data sources. These data sources include the community health and safety evaluation (CHASE) cohort, which is a community recruited virtual cohort of Downtown Eastside residents that prospectively and retrospectively examines health service use in the community by linking to administrative health record databases. In addition, the Vancouver Injection Drug Users Study (VIDUS) is an ongoing prospective cohort study of injection drug users that involves semi-annual serology of HIV and HCV as well as a semi-annual questionnaire [ 24 ]. VIDUS and CHASE allow for the description of IDUs in the community who are using Insite and a comparison between those that are and are not using the service. In addition, in order to augment these data sources and to allow for close examination of the characteristics of Insite clients over time, a prospective cohort of Insite users has also been established. The Scientific Evaluation of Supervised Injecting (SEOSI) cohort is based on a representative sample of Insite users. The sample is derived through random recruitment of Insite users who are offered an informed consent to enroll into the study. Random recruitment involves attending the SIF at times of the day that are randomly selected using a random number generation program in SPSS, and inviting all users who use the SIF at this time to enroll in the study. As with VIDUS, participants provide a blood sample and conduct an interviewer-administered questionnaire. The SEOSI questionnaire deals with items that are particularly relevant to Insite, such as risk behaviours, public drug use, satisfaction with Insite, and access to medical care and addiction treatment services. All SEOSI participants provide informed consent to link to the Insite database so that SIF use can be tracked, as well as informed consent to access administrative health record databases in the community. As of September 1, 2004 over 900 Insite users have been enrolled into SEOSI and comparisons of socio-demographic variables (age, gender, etc) has shown that the SEOSI cohort is statistically similar to the overall cohort of insight users (all p > 0.05). Client Satisfaction Measures of client satisfaction are compiled as part of the SEOSI questionnaire. Through ratings of service quality in terms of the 5 SERVQUAL dimensions: Tangibles (e.g., the appearance of the physical facilities); Reliability (e.g., the ability of staff to perform the service dependably); Responsiveness (e.g., the willingness of staff to help clients and provide prompt service); Assurance (e.g., security, credibility and courtesy); and Empathy (e.g., ease of access, approachability and effort taken to understand clients' requirements). Similarly, reasons for avoiding the service are measured among IDUs in VIDUS who have not used Insite. Additional Data Sources These above prospective cohort data will be augmented by a number of other data sources including: process indicators, measures of community satisfaction and perceived impact, standardized measures of public order, and qualitative and quantitative measures of the health of the target population. The collection of each of these data sources is described below. Process Measures In order to track service use in the database at an individual level, while allowing for participant anonymity, each client must select a unique client 'handle' or nickname. The SIF database has a search function that allows for rapid searches based on demographic information, such as birth date, if an individual forgets their handle. Similar anonymous tracking of individual clients is commonly used at needle exchanges and other services for illicit injection drug users [ 25 ]. A primary purpose of the evaluation is to measure process indicators related to service uptake within the SIF, and this is enabled through the Insite database. The database tracks what drugs participants are consuming (heroin, cocaine, etc) and what services, such as nursing care and counseling services, are accessed by each client. For instance, in the month of May 2004, over 1300 unique visits were logged into the database. Community and Staff Satisfaction Community satisfaction and the perceived impact of the SIF on business persons are measured through a community survey that is performed in person among street recruited residents and at street-level businesses. The survey is similar to surveys being used in the Sydney SIF trial, and examines perceived changes in the neighborhood after the SIF's opening. In addition, staff satisfaction with the operation of the facility is measured through focus groups and qualitative interviews with staff persons. These interviews focus on how service delivery can be improved and on what measures can be taken to ensure staff safety and satisfaction. Public Order Standardized measures of public order were undertaken to examine the impact of the SIF on several indicators of public injection drug use. In brief, the survey protocol involves measuring specified public order indicators within an a priori defined geographical area in the neighborhood and at a priori defined times of the week. Data collection times are spread evenly throughout the week and involved walking through the study zone in the same pattern. Measures of discarded syringes, injection-related litter, and public injection drug use are all measured prospectively. An evaluation of these indicators has recently been described in detail [ 26 ]. Preliminary observations Following the opening of the SIF in September 2003, there was widespread support among the target population with a steady increase in uptake during the first few weeks. The site reached virtual capacity within two months and currently an approximate average of 500 injections take place each day in the site. The busiest times of the day are mid-afternoon and early evening at which times demand often exceeds capacity and waiting times to get into the 12 seat injection room can result in participants obtaining syringes and injecting elsewhere. Whether the wait times are disproportionately affecting specific populations is presently being investigated. Utilization also fluctuates daily, peaking on the days leading up to, and following welfare day. Exit surveys of IDU clients have been widely supportive of the service and high levels of satisfaction with the service among Insite staff have been reported. Contrary to the suggestion that cocaine users would be unwilling to use the SIF [ 9 ], approximately half of all injections include cocaine. Despite the chaotic behaviours often associated with injection drug use, overall staff safety has been high and the instances of verbal or physical abuse by clients are managed efficiently as per the service's protocols. In outstanding circumstances, Vancouver Police Department has been called to remove disruptive clients, and support and assistance from the police in this regard has been very positive. Overall the staff remains very committed to the activities at Insite and staff satisfaction has been high. Overdoses, from a range of illicit drugs, are commonly observed in the SIF. The severity of these overdoses range from lowered respiration rate to severe emergency situations that have required the administration of naloxone and ambulance responses. Given the high levels of illness (for instance HIV and hepatitis C co-infection) and drug using behaviours (unknown substances of unknown purity) of the target population, it is not inconceivable that a fatality could occur in the SIF despite staff supervision and emergency response. There have been no instances where used syringe borrowing has been seen within Insite. These behaviours are common among street based injectors and it is well recognized that these activities promote the spread of blood-borne infections. It is also noteworthy that alcohol swabs to clean the injection site, and clean water and cookers are all provided to optimize hygenic injection procedures. Research of street-based IDU in Vancouver has shown that alcohol swabs are rarely used, and that non-hygenic water sources, such as puddle water, are commonly used. It is also noteworthy that within the SIF, safer hygenic injection practices are taught by the nursing staff to IDUs who have never been shown how to inject safely. In addition to supervising injections, teaching safer injecting practices, and responding to overdoses, there has been substantial health intervention within Insite. In particular, referrals to medical care at St Paul's Hospital are common as well as referrals to community health centres. Early intervention for primary medical care concerns, such as abscesses, is commonly provided by the Insite nursing team, and coverage with public health interventions, such as flu shots, has been provided to Insite users. In addition, addictions counseling occurs on site and there have been many referrals to detoxification programs and methadone maintenance therapy. Summary Overall, Insite has attracted the target population and preliminary evidence suggests that the experiences within Insite as well as the community impact have been consistent with the experience of over two dozen European settings where SIF exist, and more recently Sydney, Australia. The examination of early changes in public order has been completed and there is strong evidence of improvement in several indicators including public drug use [ 26 ]. However, each of the indicators described above is the subject of a rigorous scientific evaluation that is attempting to quantify the overall impacts of the site and identify both benefits and potentially harmful consequences over a multi-year period. This evaluation is primarily structured around a prospective cohort design that will involve the longitudinal measurement of health and community indicators over the next several years. As such, it will be some time before the overall impact of Insite on a number of outcomes, such as blood-borne infections and IDUs behavior, can be adequately quantified.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC535533.xml

548282

Time-lapse analysis of potential cellular responsiveness to Johrei, a Japanese healing technique

Background Johrei is an alternative healing practice which involves the channeling of a purported universal healing energy to influence the health of another person. Despite little evidence to support the efficacy of such practices the use of such treatments is on the rise. Methods We assessed cultured human cancer cells for potential responsiveness to Johrei treatment from a short distance. Johrei treatment was delivered by practitioners who participated in teams of two, alternating every half hour for a total of four hours of treatment. The practitioners followed a defined set of mental procedures to minimize variability in mental states between experiments. An environmental chamber maintained optimal growth conditions for cells throughout the experiments. Computerized time-lapse microscopy allowed documentation of cancer cell proliferation and cell death before, during and after Johrei treatments. Results Comparing eight control experiments with eight Johrei intervention experiments, we found no evidence of a reproducible cellular response to Johrei treatment. Conclusion Cell death and proliferation rates of cultured human cancer cells do not appear responsive to Johrei treatment from a short distance.

Background Relatively little documentation supports a scientific basis for alternative healing therapies involving the manipulation of a purported healing energy associated with the body. Despite this paucity of evidence, such energy healing modalities are becoming increasingly popular. Recent surveys indicate the majority of the United States public has used alternative medical therapies, and that energy healing therapies are among the fastest growing complementary and alternative medicine treatments [ 1 , 2 ]. Johrei is one such energy healing practice with origins in Japan. Johrei was founded by Mokichi Okada in the 1920's and is now practiced worldwide with significant numbers of adherents in Asia, Europe and the Americas. Practitioners of Johrei believe it possible to improve the health of others by directing a universal healing energy toward them. The Johrei philosophy, as it relates to healing energy, emphasizes what can be described as "spiritual purification." Other aspects of the Johrei philosophy include fostering an appreciation of beauty, and a form of organic farming. Two scientific studies evaluating Johrei healing practices have found provocative results. In one, Johrei was shown to have a beneficial affect on the mood of practitioners [ 3 ]. Results from a second study suggested that those practicing Johrei may display an immune profile consistent with stress reduction [ 4 ]. Clinical studies evaluating potential efficacy of Johrei are ongoing [ 5 , 6 ]. An in vitro study revealed an apparent influence of Johrei treatment on the germination rate of irradiated seeds but the study did not follow standard scientific methods [ 7 ]. To investigate whether Johrei can have a direct effect on human cells, we exposed cultured cancer cells to Johrei treatment from a short distance. Johrei treatments were maintained continuously for four hours by teams of Johrei practitioners. Experiments were conducted using strict blinding procedures, exceeding standards commonly used to evaluate conventional therapeutics. Methods Overall study design For each session Johrei practitioners were asked to direct healing intention toward cell cultures in a temperature, CO 2 , and humidity controlled time-lapse microscope incubator chamber. Johrei practitioners participated in teams of two, alternating every half hour such that a total of 4 hours of Johrei treatment was delivered. A total of eight control and eight Johrei intervention experiments were accomplished. Johrei treatments were initiated after 4 hours of baseline data had been collected and the observation period extended for a total of 22 hours. We quantified tumor cell death and proliferation throughout the observation period. We also quantified the rate of cellular emigration (i.e. how many tracked cells left the microscopic field per hour) in order to accurately assess cell population dynamics (i.e. cell death and proliferation rates). Blinding procedures Experiments were conducted with blinding applied to each of the scientists based on previously reported methods [ 8 ]. The experimental protocol was divided among scientists such that those responsible for preparation of the cell cultures, data acquisition, and data analysis were blind to each other's activities and results until data analysis was complete. Cell culture As the target of Johrei healing intentionality in these studies we used a human cell line derived from a brain tumor biopsy specimen from a patient with glioblastoma multiforme (SF188 GBM). This cell culture model is used widely and can show responsiveness to conventional therapeutic agents including ionizing radiation and chemotherapeutics. SF188 GBM cells were grown in RPMI media supplemented with 10% fetal bovine serum, 100 U/ml penicillin, 100 μg/ml streptomycin sulfate, and 0.25 μg/ml amphotericin. A fresh aliquot of cryogenically preserved cells was thawed at the start of each experiment to ensure uniformity in the genetic profile of the target cells throughout the experiments. Cells were plated at a density of 5,000 cells per well in six-well culture plates and allowed to grow uninterrupted for 20 hours in a humidified incubator maintained at 37°C and 5% CO 2 before beginning each time-lapse experiment. Time-lapse microscopy For each experiment cell cultures were transferred from the incubator to a time-lapse microscope in our laboratory equipped with a heated stage, CO 2 chamber, and a plexi-glass environmental chamber (Axiovert 200; Zeiss, Gottingen, Germany). Cell cultures were maintained at routine incubation settings (37°C, 5% CO 2 ) and optimum humidity. Temperature and CO 2 concentration were independently maintained using digital controlling units (Zeiss, Gottingen, Germany). Two sets of phase contrast images (100 X magnification) from each well were taken in 300 second intervals using a Cohu 2600 Series compact monochrome interline transfer CCD camera. Images were acquired during a four-hour baseline period and then continuously for another 18 hours. An Openlab software automation (Improvision, Lexington, MA) drove the camera and stage movements, and compiled the acquired phase images. Images were subsequently processed as Quicktime movies using Openlab. Johrei intervention Johrei practitioners were selected based on experience and willingness to participate by the Center for the Science of Life, a Johrei organizational body in the United States. Five Johrei practitioners with a minimum of 17 years experience participated in the experiments in teams of two. Johrei treatment was administered by the practitioner teams for a total of four hours, beginning after the four-hour baseline period. This exceptionally long treatment period (4 hours) was chosen as the highest "dose" that was practical within the experimental model. Each practitioner treated the cells for a total of 2 hours per experiment, switching every half hour with the team member. Treatment began with one of the two practitioners being seated in front of the time-lapse microscope and raising one hand toward the cellular target. One hand remained raised toward the cell cultures for the duration of any given Johrei treatment. Johrei treatments were delivered from a distance of 6 – 9 cm, from outside the plexiglass environmental chamber. In collaboration with The Center for the Science of Life, we developed a set of standard mental procedures for practitioners to follow to minimize variability in the mental states among practitioners. These mental cues were used by all practitioners in all experiment. The mental cues can be summarized briefly as: 1) establishing a connection to the divine, 2) consciously relaxing body and mind, 3) visualizing healing energy penetrating the cellular target, 4) taking enjoyment in participating in the experiment, 5) maintaining a feeling of gratitude. Time-lapse microscopy data analysis Every cell in the initial microscopic field was identified and numbered. For each experiment, we counted the initial number of cells in each of 12 microscopic fields. All numbered cells and their progeny were then tracked for the duration of their onscreen viability using compiled Quicktime movies. Each cell's life events were identified using a modified version of a previously described cell pedigree system [ 9 ]. Cells identified as dead at the start of the video or that entered the microscopic field after the initial frame were not included in this analysis. Cataloged data was entered into a Microsoft Excel spreadsheet (using blinding codes) for further analysis. Cell deaths and divisions occurring per half hour were recorded. Statistical analysis Statistical analysis was based on a model which categorizes a cell as engaged in any one of four activities at any time: 1) division, resulting in an additional cell (division), 2) death, resulting in the loss of a cell (death), 3) movement out of the microscopic field (emigration), or 4) the cell can remain unchanged [ 10 ]. Under this model three transition probabilities plus the total number of cells at a previous time determine the number of cells at a future time. The expected number of cells at time t, N(t), is given by the equation N(t) = N(t-1) exp(λ(t) - μ(t) - ν(t))], where λ(t), μ(t) and ν(t) are the transition probabilities for division, death and emigration, respectively, at time t. We estimated the transition probabilities in one-hour time blocks. For example, the estimate for λ(t) is λ(t) = [ln(N(t) + div(t)) - ln(N(t-1))], where div(t) is the number of divisions during (t-1, t). A similar equation was used for estimating death and emigration transition probabilities at each hour. Statistical analyses focused on two types of comparisons: comparisons within the Johrei treatment experiments, where division and death rates (transition probabilities) pre-treatment were compared with those during and post-treatment; and comparisons between Johrei and control experiments at similar times. Data from each experiment was pooled at 30 minute time intervals. Each of the cellular events (division, death or emigration) occurs infrequently to each cell, so it is necessary to pool results from many cells in order to have nonzero data. Additionally, in instances where few events were recorded due to relatively few cells being observed it was necessary to pool over several time intervals in order to have enough data to perform statistical tests. Results Overall we documented the behavior of 336 cells in the eight control experiments and 351 cells in the eight Johrei intervention experiments. Initial numbers of cells per microscopic field ranged from 5 to 10; the average cell count per field was 7.0 for controls and 7.3 for Johrei. The numbers of cells per experiment ranged from 40 to 48 with averages of 42.0 and 43.9 for controls and Johrei. Observation over 22 hours showed 312 divisions, 15 deaths and 17 emigrations in the control experiments and 316 divisions, 21 deaths and 7 emigrations in the Johrei experiments. Differences in number of divisions or deaths is not significant (p = 0.19 for divisions and p = 0.37 for deaths based on chi-square test comparing frequencies). While the difference in emigrations is statistically significant (p = 0.03, chi-square test comparing frequencies), this was largely affected by a difference present in the 0–4 hour baseline period making it unlikely that the observed effect is due to Johrei treatment. Experimental treatments (control or Johrei) were confined to a four-hour period starting after collection of four hours of baseline data. During the four-hour treatment period the numbers of divisions were 84 (control), 59 (Johrei); deaths were 4 (control), 5 (Johrei); and emigrations were 0 (control) and 2 (Johrei). Taking into account the numbers of cells at the start of the treatment period, only the difference in divisions is statistically significant (p = 0.018 based on comparing the division rates during the treatment period). This result is based on pooling observations from all cells over the four-hour period and the p-value is based on an assumption that counts observed follow a Poisson distribution. It is possible that counts do not follow a Poisson distribution and that their variability is greater than that expected for Poisson counts. To examine this we calculated division rates separately for each of the eight control experiments and eight Johrei experiments and then compared these rates using the nonparametric Mann-Whitney rank sum test. This analysis confirmed the result based on the total counts (p = 0.016 for Mann-Whitney test). We also tried fitting parametric models to the data in an effort to gain degrees of freedom for performing statistical tests. For example, we tried fitting low order polynomials to division rates over time, but the fits were unsatisfactory, as judged by assuming counts were Poisson distributed with rates predicted from the polynomial fit. The fits were particularly deficient in following the rapid initial rise in division rates over the first four hours of baseline data (Figure 1 ). We obtained similar results attempting to fit a sum of exponentials model (Figure 2 ). Figure 1 Rates of cell division are shown as plotted against time in hours. The plot depicts a decrease in divisions in Johrei treated cells (pink line) during the 4.0–8.0 treatment period which is statistically offset by a similar dip in the control period. Control and pooled data are depicted by green and blue lines respectively. The solid black line depicts the fit of a 4 th degree polynomial to half-hourly cell division rates for all experiments. Figure 2 The fit of the sum of two exponentials to pooled division rate data for all experiments is depicted. The maroon line is pooled data and the blue line is fit. The division rates for control and Johrei experiments plotted in Figure 1 suggest another method of comparison. The experimental conditions should be identical during the 0–4 hour baseline period, yet we see wide variation in division rates for the two conditions. We can measure the variance between control and Johrei experiments at each half-hour time point and use the pooled value, over the first eight time points, as a baseline standard. We computed the pooled variances for the 4–8 hour time period (treatment) and compared the two using an F-test for equality of variances. The pooled variance for the 0–4 hours period was 5.8 × 10 -5 compared to 6.8 × 10 -5 for the 4–8 hours period. The ratio of these is 1.17 which is not statistically significant (p = 0.41 based on F distribution with numerator and denominator df = 8). This suggests that the discrepancies between the rates during the 4–8 hour period are statistically no larger than those observed during the 0–4 hour period when there were no treatment differences. This comparison requires no assumption of Poisson variability of counts and takes advantage of a priori knowledge that treatment conditions were identical in the first four hours of the experiments. The only assumption is that the variances arise from normally distributed data, so this requirement was tested. A Mann-Whitney comparison of the ranks of the absolute differences in control and Johrei rate during 0–4 hours vs. 4–8 hours give a p-value of 0.92. Thus, there appears to be no evidence that differences in rates of division during 4–8 hours for control and Johrei were any different from those during 0–4 ours. A plot of these absolute differences is shown in Figure 3 . The original data are provided as supplementary material [see Additional file 1 ]. Figure 3 Absolute differences in half-hourly rates of cell division for all experiments are depicted. The plot suggests that differences between control and Johrei experiments were largest during the first 8 hours and that the differences during Johrei treatment (4–8 hrs) were not statistically larger than those during the previous baseline time period, when the treatment conditions were identical. Discussion Our initial examination of the data suggested that Johrei may have affected the rate of division of cultured human brain tumor cells. This analysis, however, was based on the assumption that the baseline period in both the control and Johrei treated cultures were statistically identical with respect to cell divisions and cell deaths. Subsequent analysis revealed that a significant difference existed in the baseline period between control and Johrei treated samples, with Johrei cultures exhibiting fewer divisions. Taking this into account it appears that there was no observable effect of Johrei on these cell behaviors. The failure to observe evidence of a reproducible cellular response to Johrei treatment is consistent with prior studies in our laboratory evaluating another popular energy medicine modality, external Qigong. Like Johrei, practitioners of external Qigong generally claim the ability to emit or direct healing energy to treat patients. Qigong practices originate from China and are based on the manipulation of a purported healing energy called "Qi." Our prior study investigated the ability of experienced Qigong practitioners to enhance the growth of normal human brain cells in culture as measured by a colony-forming efficiency assay. Following a rigorously designed protocol with randomization, blinding and controls for variability, we did not observe reproducible effects of external Qigong treatment on the growth of these cells [ 11 ]. Such "negative" data do not negate the possible therapeutic effects of such practices. Cultured cells offer an incomplete system that may not be sensitive to treatments of this kind. More complete models (e.g., a clinical research model) may be more useful to evaluate Johrei treatment for a variety of reasons. Conclusion Cell death and proliferation rates of cultured human cancer cells do not appear responsive to Johrei treatment from a short distance. Competing interests The Center for the Science of Life, a Johrei organizational body in the United States, has provided funding for this work. Authors' contributions RT and GY conceived of the study and participated in its design, coordination and implementation. DM performed statistical analysis. 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 Johrei Experimental Data. The supplementary file includes the raw data for all experiments, including number of events for cell death, division, and emigration by half-hourly intervals and cumulative totals. Click here for file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548282.xml

526215

Association of mitral annulus calcification, aortic valve calcification with carotid intima media thickness

Background Mitral annular calcification (MAC) and aortic annular calcification (AVC) may represent a manifestation of generalized atherosclerosis in the elederly. Alterations in vascular structure, as indexed by the intima media thickness (IMT), are also recognized as independent predictors of adverse cardiovascular outcomes. Aim To examine the relationship between the degree of calcification at mitral and/or aortic valve annulus and large artery structure (thickness). Methods We evaluated 102 consecutive patients who underwent transthoracic echocardiography and carotid artery echoDoppler for various indications; variables measured were: systemic blood pressure (BP), pulse pressure (PP=SBP-DBP), body mass index (BMI), fasting glucose, total, HDL, LDL chlolesterol, triglycerides, cIMT. The patients were divided according to a grading of valvular/annular lesions independent scores based on acoustic densitometry: 1 = annular/valvular sclerosis/calcification absence; 2 = annular/valvular sclerosis; 3 = annular calcification; 4 = annular-valvular calcification; 5 = valvular calcification with no recognition of the leaflets. Results Patient score was the highest observed for either valvular/annulus. Mean cIMT increased linearly with increasing valvular calcification score, ranging from 3.9 ± 0.48 mm in controls to 12.9 ± 1.8 mm in those subjects scored 5 (p < 0.0001). In the first to fourth quartile of cIMT values the respective maximal percentual of score were: score 1: 76.1%, score 2: 70.1%, score 4: 54.3% and score 5: 69.5% (p > 0.0001). Conclusion MAC and AVC score can identify subgroups of patients with different cIMT values which indicate different incidence and prevalence of systemic artery diseases. This data may confirm MAC-AVC as a useful important diagnostic parameter of systemic atherosclerotic disease.

Background Mitral annular calcification (MAC) and aortic annular calcification (AVC) are observed in populations that develop significant atherosclerosis and more frequently in the elderly [ 1 ]. Previous pathological studies have suggested they represent a degenerative process that progresses with advancing age [ 2 - 4 ]. Consistently with this hypothesis, several ultrasound cardiovascular studies demonstrated a significant association between MAC and coronary artery disease, aortic atheroma and peripheral arterial atherosclerotic disease [ 5 - 8 ]. Currently, there are no accurate and standardized methods to quantify the degree of MAC an AVC; many studies have been performed with the aim to detect categorical scoring systems derived from echocardiographic annular-valvular morphology [ 9 - 12 ]. Alterations in vascular structure such as increased arterial wall thickness, as indexed by the intima media thickness (IMT), are also increasingly recognized as significant independent predictors of adverse cardiovascular outcomes [ 13 - 17 ]. We therefore undertook a cross-sectional study to examine the relationship between the degree of calcification at mitral and/or aortic valve annulus and large artery structure (thickness). Subjects and Methods We evaluated 128 consecutive patients who underwent transthoracic echocardiography and carotid artery echoDoppler for various indications. Patients with significant common carotid artery stenosis, rheumatic valvular disease, cardiomyopathy, prosthetic valves, ischemic heart disease and carotid artery surgery were excluded. Thus, 102 subjects were enrolled for the present study. All participating patients gave informed consent; the study protocol was approved by the institutional ethics committee. Variables Measured Blood pressure Blood pressure determinations were performed with subjects in the supine position, and following a ten minute quiet resting period. Blood pressure was measured in the nondominant arm with a mercury sphygmomanometer using an appropriately sized cuff. The blood pressure values used in this study are the average of the second and third measurements. Values for systolic blood pressure (SBP) and diastolic blood pressure (DBP) were defined by Korotkoff phase I and V, respectively. Hypertension was defined as either systolic or diastolic elevation of blood pressure (>140/90 mmHg) or ongoing antihypertensive pharmacological therapy. Pulse pressure was computed as PP = (SBP-DBP); mean BP was computed as MBP = DBP +(PP/3). Anthropometry and smoking status Height and weight were determined for all participants. Body mass index (BMI) was determined as body weight (kg) / height (m) 2 . Smoking status was ascertained by a questionnaire that classified each subject as a non smoker, former, or current smoker. For the purpose of the present study, current smoker status was used. Plasma lipids and fasting blood glucose Blood samples were drawn from the antecubital vein between 7 and 8 AM after an overnight fast. Subjects were not allowed to smoke, engage in significant physical activity or take medications prior to the collection of the sample. The concentrations of plasma triglycerides and total cholesterol were determined by an enzymatic method [ 18 , 19 ]. HDL-cholesterol levels were obtained by selective precipitation with dextran-MgCl 2 [ 20 ]. Serum LDL-cholesterol concentrations were estimated by the Friedewald's formula [ 21 ]. Fasting plasma glucose concentration was measured. Diabetes was defined by a fasting glucose >120 mg/dl, ore use of insulin ore ipoglicemic medication. Echocardiographic measurements All the echocardiographic examinations were performed using the PHILIPS ® SONOS 5500 with a S3 probe. All patients had an adequate 2D echocardiogram. Evaluation of mitroaortic sclerosis/calcification was made, off line, with the acoustic quantification-densitometry package (PHILIPS ® Medical System) wich restitute values based on echogray scale (0 db = black, 64 db = white, fig. 1 ). From parasternal short/long axis and apical 4 – 5 chamber scans, 3 or more subsequent ECG triggered cardiac cycles were acquired (gain setting: 50, compression: 55, mechanical index: 1.4); focus and region of analysis (ROI) were positioned at the level of mitral/aortic annular/valvular sclerosis/calcification; the dimensions of ROI were 11 × 11 mm. The mitral/aortic lesions were graded by five qualitative independent scores based on 2D morphology and on acoustic densitometry values: 1= annular/valvular sclerosis/calcification absence; 10–25 dB (fig. 2 ); 2 = annular/valvular sclerosis; 26–35 dB (fig 3 ); 3 = annular calcification; 36–40 dB (fig. 4 ); 4 = annular-valvular calcification; 41–45 dB (fig. 5 ); 5 = valvular calcification with no recognition of the leaflets; > 46 dB (fig. 6 ). The resulting patient score was the highest observed for either valvular annulus. All the images were stored in digital format for off-line analysis and independently evaluated by three blinded operators which resulted always concordant in assigning the patient's scores. Figure 1 Panel A Acoustic quantification-densitometry (AD): echogray scale, black = 0 dB; Panel B echogray scale, white = 64 dB. Figure 2 Score 1. Panel A: AD of mitral annulus (apical scan); Panel B: AD of aortic valve (parasternal scan). Figure 3 Score 2. Panel A: AD of mitral valve (apical scan); Panel B: AD of aortic valve (parasternal scan: short axis). Figure 4 Score 3. Panel A: AD of aortic valve (apical scan); Panel B: AD of mitral valve (apical scan). Figure 5 Score 4. AD of aortic valve (apical scan). Figure 6 Score 5. Panel A: AD of aortic valve (parasternal scan: short axis); Panel B: AD of mitral valve (apical scan). Carotid Ultrasonography High-resolution B-mode carotid ultrasonography was performed with a linear-array 5- to 10-MHz transducer. The subject lay in the supine position in a dark, quiet room. The right common carotid artery (CCA) was examined with the head tilted slightly upward in the midline position. The transducer was manipulated so that the near and far walls of the CCA were parallel to the transducer footprint and the lumen diameter was maximized in the longitudinal plane. A region 1.5 cm proximal to the carotid bifurcation was identified, and the carotid intima media thickness (cIMT) of the far wall was evaluated as the distance between the luminal-intimal interface and the medial-adventitial interface. cIMT was measured on the frozen frame of a suitable longitudinal image with the image magnified to achieve a higher resolution of detail. The cIMT measurement was obtained from 5 contiguous sites at 1-mm intervals, and the average of the 5 measurements was used for analyses. All the measurements were performed by a single sonographer. Statistical analysis All analyses were performed using the SPSS 8.0 package. Data are presented as mean ± SD unless otherwise specified. Comparison of groups based on different calcification score was made by ANOVA, followed by Bonferroni's test for all two-way comparisons, or by chi-square analysis – as appropriate. Geometric mean values of vascular end points, adjusting for traditional cardiovascular risk factors, were calculated across categorized features by means of General Linear Model. Results Of the 102 patients evaluated, 24 were scored 1, 19 were score 2, 20 were score 3, 18 were score 4 and 21 were score 5. There were no statistically significant intergroup differences in age, sex distribution, total cholesterol, HDL and LDL cholesterol, smoking habits, diabetes mellitus, and positive family history of coronary artery disease (tab.1). Similarly, clinical indications for ultrasound examinations were not significantly different in the 5 score groups (tab. 2 ). Table 2 Clinical Indications Score 1 Score 2 Score 3 Score 4 Score 5 All pts Carotid murmur % 38.2 37.1 39.1 39.9 39.3 38.7 Cardiac murmur % 34.8 34.3 33.8 34.1 35 34.4 Stroke % 15.3 15.6 14.8 15.1 15.4 15.2 Cardiac surgery % 11.7 13 13.3 10.9 10.3 11.8 Systolic blood pressure showed a non statistical increase from group 1 to 5 while pulse pressure values raised significantly (tab. 1 ; p < 0.04). Table 1 Baseline characteristics Score 1 Score 2 Score 3 Score 4 Score 5 All pts ANOVA p N° of pts 24 19 20 18 21 102 Age yrs 68.4 ± 11.6 65.9 ± 9.8 68.4 ± 4.7 69.3 ± 4.8 65.3 ± 6.8 67.4 ± 7.5 .25 BMI Kg/m 2 27.8 ± 5.1 29.7 ± 5.9 29.5 ± 7.8 28.4 ± 4.8 29.6 ± 4.9 29 ± 5.6 .76 Male Sex % 33 30 38 37 48 37 .31 Current Smoker % 33 21 17 32 35 27 .21 CAD Family history % 36 36 23 48 43 37 .42 Diabetes % 10 11 15 19 17 14 .18 SBP mmHg 142.5 ± 12.9 144.6 ± 11.4 140.5 ± 17.5 142.5 ± 16.3 149.1 ± 18.0 143.8 ± 15.2 .07 DBP mmHg 83.8 ± 8.2 84.6 ± 9.7 83.1 ± 10 82.8 ± 9.8 83.1 ± 9.3 83.4 ± 9.4 .29 PP mmHg 58.7 ± 10.4 60 ± 13.8 57.4 ± 11.5 59.7 ± 13.9 66 ± 17.8 60.3 ± 13.5 .03 Total Chol mg/dl 214.6 ± 42.4 204.9 ± 32.7 216.3 ± 40.6 218.3 ± 36.1 209.9 ± 36.4 212.4 ± 37.6 .62 HDL Chol mg/dl 48.4 ± 9.4 48.2 ± 10.7 52.2 ± 13.5 48.7 ± 11.3 46.7 ± 12.0 48.8 ± 11.3 .47 LDL Chol mg/dl 136.4 ± 33.7 126.9 ± 32.6 139.0 ± 37.8 140.1 ± 38.2 130.3 ± 35.9 134.5 ± 35.6 .52 TGC mg/dl 148.6 ± 56.8 149.3 ± 85.5 125.3 ± 49.1 147.3 ± 35.8 164.8 ± 49.7 147.6 ± 55.3 .19 Fasting blood Glucose mg/dl 98.9 ± 29.7 117.1 ± 45.4 113.1 ± 40.6 103.1 ± 21.9 98.0 ± 20.5 106 ± 31.6 .11 Fibrinogen mg/dl 302.1 ± 59.4 302.1 ± 56.4 325.5 ± 103.8 305.7 ± 57.4 323.0 ± 88.9 311.8 ± 73.1 .61 Pts: patients, BMI: body mass index, CAD: coronary artery disease, SBB: systolic blood pressure, DBP: diastolic blood pressure, PP: pulse pressure, Total Chol: total cholesterol, TGC: triglycerides. Vascular characteristic of the five score groups are shown in Fig 7 ; mean cIMT increased linearly with increasing valvular calcification score, ranging from 3.9 ± 0.48 mm in controls to 12.9 ± 1.8 mm in those subjects with score 5 (p < 0.0001). Figure 7 Positive association between cIMT and score groups; *p < 0.0001 ANCOVA analysis confirmed that the association of valvular calcification score with cIMT was independent of age, sex, BMI, HDL and LDL cholesterol, smoker and diabetes (table 3 ). In the first to fourth quartile of cIMT values the respective maximal percentual of score were: score 1: 76.1%, score 2: 70.1%, score 4: 54.3% and score 5: 69.5% (p > 0.0001) (fig. 8 ); multivariate analysis showed a significant influence of systolic blood pressure from first to fourth quartile and of HDL cholesterol. Table 3 ANCOVA analysis Age Sex BMI C.S HP D HDLC LDLC p (cIMT) .609 .23 .479 .948 .699 .471 .625 .387 Age: years, BMI: body mass index Kg/m 2 , C.S: current smoker, HP: hypertension, D: diabetes, HDLC: HDL cholesterol mg/dl, LDLC: LDL cholesterol mg/dl. Figure 8 Distribution of quartiles of cIMT across scores of valvular calcification; *chi-square = p < .001 Discussion The present study is the first to show a strong and significant association between the presence of MAC-AVC and cIMT values. Patients with severe MAC-AVC (scored 5) had higher values of cIMT. Previous pathologic studies demonstrated that foam cells which represent early atherosclerotic lesions can be found in subjects already during adolescence on the endothelium of the epicardial coronary arteries, the ventricular surface of the posterior mitral leaflet and the aortic aspects of each aortic leaflet [ 1 , 22 ]. Experimentally-induced systemic vascular atherosclerosis is also associated with the deposition of fatty plaques on the aortic surface of aortic valve cups and the ventricular surface of the posterior mitral leaflet [ 22 ]. These findings suggest that coronary atherosclerosis, MAC and AVC have a similar aetiology and pathophisiology, particularly in the elderly: as the fatty plaques grow, their nutritional needs fail to be fulfilled and they degenerate into calcific deposits[ 1 ]. Many recent studies showed a clear association between mitral annulus calcification and the presence of aortic atheromas, atheroma thickness and carotid artery disease [ 4 , 5 ] these studies also found that MAC patients have higher prevalence of carotid artery stenosis [ 6 ], coronary artery stenosis [ 13 ] and peripheral artery stenosis [ 8 ], supporting the theory that MAC is a form of polisegmental atherosclerosis. Adler [ 23 ] in a recent prospective trans oesophageal echocardiographic study showed a significant association between the presence and severity of MAC and aortic atheroma, suggesting MAC as an important marker of aortic atherosclerosis; the author concluded that this association may explain in part the high prevalence of systemic emboli and stroke in patients with MAC. Even the presence and extent of AVC has been demonstrated, in many recent studies, to be directly correlated with atherosclerotic risk factors and atherosclerotic disease suggesting how AVC could represent a marker of polisegmental atherosclerosis [ 2 , 3 , 7 , 24 - 26 ]. In our study we investigated the possible association between AVC, MAC and cIMT in elderly patients; we found a clear and strong significant linear correlation between cIMT and AVC/MAC values; there were no significant influence of the considered variables on this correlation. In addition, we found a strong association of incremental values of score with first to fourth quartile of cIMT Another exclusive characteristic of our study is the creation of a semi quantitative way of AVC-MAC evaluation; in fact, scoring the evolution of AVC and MAC, we were able to correlate this parameter with the continuous variable cIMT. Furthermore, a recent study [ 26 ] showed a strong association of aortic valve sclerosis and systemic endothelial dysfunction evaluated by ultrasonography of the brachial artery; since it is well established and demonstrated that IMT is an early marker of endothelial-organ damage and an initial precursor of systemic atherosclerotic disease [ 1 - 8 ] our results are consistent with those obtained by Poggianti and her collegues. Our data indicate that AVC-MAC could be considered a form of polisegmental atherosclerosis; the semi quantitative evaluation of AVC-MAC is strongly associated cIMT; this semi quantitative way of grading mitral-aortic valvular-annular sclerosis and calcification was also able to identify the quartile of cIMT. These results indicate the score evaluations as an important echocardiographic tool for atherosclerotic disease evaluation. Limitations of the study This study evaluated only elderly patients, therefore the eventual correlation of MAC-AVC and cIMT can be only supposed in younger patients; future studies are needed to demonstrate this hypothesis. We viewed cIMT as a marker for sub clinical atherosclerosis. Although the significance of carotid thickening, particularly in the distal common carotid artery, continues to be debated, its association with prevalent and future cardiovascular events has been supported by a number of studies [ 15 - 17 , 24 , 25 ]. Nonetheless, it should be recognized that atherosclerosis may progress in other vascular districts at different rates. Further work is need to test if these results may apply to atherosclerosis in other major vascular territories (peripheric big arteries, thoracic and abdominal aorta). Additionally, whether our findings are sufficient to indicate a widespread use of carotid ultrasound in those with presence of MAC-AVC requires further studies. Conclusions MAC-AVC presence and their scoring can be detected by transthoracic echocardiography, a simple noninvasive imaging method. Using MAC and AVC values we can identify subgroups of patients with different cIMT values, a well-established precursor of systemic atherosclerosis, which indicate different incidence and prevalence of carotid, coronary and aortic artery diseases. Therefore, mitral or aortic valve calcification should not be regarded as a natural correlate of aging, rather as markers of generalized atherosclerosis. List of abbreviations MAC: Mitral annular calcification AVC aortic annular calcification IMT intima media thickness cIM carotid intima media thickness CCA right common carotid artery

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526215.xml

548296

The Pharmaceutical Benefits Scheme 2003–2004

The Pharmaceutical Benefits Scheme (PBS) grew by 8% in 2003–04; a slower rate than the 12.0% pa average growth over the last decade. Nevertheless, the sustainability of the Scheme remained an ongoing concern given an aging population and the continued introduction of useful (but increasingly expensive) new medicines. There was also concern that the Australia-United States Free Trade Agreement could place further pressure on the Scheme. In 2003, as in 2002, the government proposed a 27% increase in PBS patient co-payments and safety-net thresholds in order to transfer more of the cost of the PBS from the government to consumers. While this measure was initially blocked by the Senate, the forthcoming election resulted in the Labor Party eventually supporting this policy. Recommendations of the Pharmaceutical Benefits Advisory Committee to list, not list or defer a decision to list a medicine on the PBS were made publicly available for the first time and the full cost of PBS medicines appeared on medicine labels if the price was greater than the co-payment. Pharmaceutical reform in Victorian public hospitals designed to minimise PBS cost-shifting was evaluated and extended to other States and Territories. Programs promoting the quality use of medicines were further developed coordinated by the National Prescribing Service, Australian Divisions of General Practice and the Pharmacy Guild of Australia. The extensive uptake of computerised prescribing software by GPs produced benefits but also problems. The latter included pharmaceutical promotion occurring at the time of prescribing, failure to incorporate key sources of objective therapeutic information in the software and gross variation in the ability of various programs to detect important drug-drug interactions. These issues remain to be tackled.

Review This paper reviews the growth of the Pharmaceutical Benefits Scheme (PBS) during 2002–03; concerns about the sustainability of the Scheme, the government's response, a potential new threat that emerged and issues that remain to be tackled. The growth and sustainability of the PBS From March 2003 to March 2004, a total of $5.8 billion was spent on prescription medicines subsidised under the PBS. Of this, $4.89 billion (84%) was paid by the Commonwealth, the remaining $0.91 billion through patient co-payments [ 1 ]. In comparison, in 2002–03, the Commonwealth spent $7.24 billion on public hospital services [ 2 ] and $8.17 billion on medical and diagnostic services (through Medicare benefits) [ 3 ]. Although the PBS is the smallest of these components of Commonwealth expenditure, it has the highest average annual growth rate over the last decade (around 12% pa), compared to 6% pa for public hospital services, and 5% pa for medical services. At these rates, by 2011 the Commonwealth would be spending more on subsidised pharmaceuticals than it would spend on either public hospital or medical services, and by 2022, more on pharmaceuticals than both public hospital and medical services together. Such projections make the sustainability of the PBS a major concern, especially given an aging population and the continued introduction of useful (but increasingly expensive) new medicines [ 4 ]. While the growth rate of the PBS has slowed over the last two years (10% during March 2002–03 and 8% from March 2003–04) the past history of PBS expenditure shows considerable fluctuations over the years. These fluctuations are caused by expensive but valuable new drugs coming onto the Scheme, more cost-effective generic drugs replacing older drugs whose patent has expired and administrative changes, such as increased patient co-payments, transiently reducing usage. The government's response In 2003, as in 2002, the government proposed a 27% increase in PBS patient co-payments and safety-net thresholds in order to transfer more of the cost of the PBS from the government to consumers. Once again this measure was rejected by Labor and other opposition parties in the Senate because of concern that such increases would impact on equitable access to necessary medicines [ 5 ]. Regardless, the government continued to argue that without increased patient contributions (and patient restraint) the PBS would become unsustainable. By mid 2004, the Labor party was faced with an impending Federal election and had serious trouble costing its tax and spending promises. As a consequence, Labor abandoned their previous principled stand in the Senate of blocking the government's proposed increase in PBS copayments and safety-net thresholds [ 6 ] arguing that they needed the additional $1.1 billion to spend on election promises [ 7 ]. They also stated that, if returned to government, a substantial proportion of the $1.1 billion might be achieved through administrative reforms to the PBS and savings achieved by the use of cheaper generic drugs as expensive drugs moving off patent. Not surprisingly, consumer and public health groups were appalled with this Labor "back-flip" while the Greens and Democrats said the decision was a disgrace [ 8 ]. Labor said the decision was difficult but necessary. Several other measures were introduced by the government in order to improve the community's understanding of PBS processes and costs. From June 2003, all recommendations of the Pharmaceutical Benefits Advisory Committee (PBAC) to list, not list or defer a decision to list a medicine on the PBS were made publicly available on the PBS website [ 9 ]. Unfortunately, only summary information was provided; commercial-in-confidence concerns of pharmaceutical manufacturers precluded making more detailed information available, such as cost-effectiveness data, on which PBAC based its decision. From 1 August 2003 the full cost of PBS medicines appeared on medicine labels if the price was greater than the co-payment. The full cost included what the consumer has paid and the amount that is paid through the PBS. The aim was to help people understand what medicines really cost and how the PBS helps make medicines affordable for all. In addition, the government commissioned a $24 million advertising campaign that emphasised that patient responsibility was, "the prescription for a healthy PBS". Critics noted that by neglecting to inform the public that pharmaceutical marketing and inappropriate prescribing habits of doctors also produced pressures on the PBS, the campaign missed an opportunity to initiate a more balanced and constructive debate about the viability of the PBS [ 10 ]. During the year under review, pharmaceutical reforms designed to stop PBS cost-shifting in Victorian public hospitals were evaluated [ 11 ]. The reforms were a joint initiative of Victorian Department of Human Services (DHS) and the Australian Government Department of Health and Ageing (DoHA). Since the early 1990s there had been increasing cost pressures on State and Territory funded public hospitals. Their response included restricting drug supplies to discharged patients, often to only two or three days of treatment. Patients then needed to see their GP to obtain a PBS prescription to cover their needs. The effect was to "cost shift" pharmaceutical supplies from the State and Territories to the Australian Government. The reforms trialed in Victoria allowed public hospital doctors to write PBS prescriptions for both outpatients and discharged inpatients. They also allowed PBS access to a group of cancer chemotherapy drugs for use by day-admitted patients and outpatients. The qualitative evaluation undertaken was generally positive although it noted the reforms had increased the administrative work of both doctors and pharmacists. There was also concern that PBS rules (designed for general practice) were not always appropriate for specialised public hospitals. The Society of Hospital Pharmacists of Australia supported the need to modify PBS procedures to take into account public hospital expertise but also noted the need for more integration of medicines funding [ 12 , 13 ]. Subsequently, the Victorian reforms are being implemented in other States and Territories. Programs promoting the quality use of medicines (QUM) were further developed throughout 2002–03. Specific programs were coordinated by the National Prescribing Service (NPS), Australian Divisions of General Practice and the Pharmacy Guild of Australia. The NPS was set up in 1999 with government funding but with an independent Board of Directors in order to provide unbiased educative activities to assist health practitioners (and more recently consumers) to use medicines wisely. Evaluation of NPS activities has consistently shown that spending money on targeted QUM interventions can save considerably more money on the PBS by reducing inappropriate prescribing. It was estimated that NPS activities during the period 1 July 2000 to 30 June 2002 generated PBS savings in the range of $55.6 million to $83.9 million through the following prescribing intervention programs: antibiotics in primary care; peptic ulcer management; management of dyspepsia; COX-2 selective NSAIDs; managing hypertension; and managing dyslipidaemia [ 14 ]. In 2003, the NPS received an additional allocation of government money to provide educational material about drugs newly listed on the PBS (the RADAR project). The latter was in response to considerable evidence that intensive pharmaceutical promotion at the time of PBS listing was associated with drugs being prescribed for broader indications than those indicated in the PBS listing (causing so-called PBS "leakage" or "blow-outs") [ 15 ]. However, the 2003 NPS educational budget of $12.5 million needs to be compared with the estimated $1.0 billion promotional budget of the Australian pharmaceutical industry [ 16 ]. The Enhanced Divisional Quality Use of Medicines (EDQUM) program was a 1999–2000 Federal Budget initiative, originally announced as the Incentives for Quality Prescribing (IQP) program. The program offered Divisions of General Practice (Divisions) a proportion of monies saved if Divisional QUM activities improved prescribing and lowered PBS costs. The Divisions were allowed to use any savings made for a range of primary health care activities. The program evolved significantly due to feedback from the medical profession. It was implemented on a pilot basis in thirteen Divisions on 1 July 2002. Under the program, Divisions were encouraged to invest their own resources in a range of drug utilisation data collection and/or education related activities. Activities were implemented in close consultation with the NPS and focused on one or more of the following target drug groups: antibiotics, peptic ulcer drugs and cardiovascular drugs. An evaluation of the pilot EDQUM project was undertaken in early 2004 [ 17 ]. Barriers to implementation included perceptions that the program was primarily focused on reducing pharmaceutical costs to government; limited capacity of existing prescribing software systems to extract drug utilisation data; and the need for Divisions to take a commercial risk in developing their EDQUM initiatives (due to the absence of up-front funding) which limited their capacity to systematically implement a comprehensive range of strategies. Program achievements included the development of a wide range of shared resource material [ 18 ] and the creation (by some Divisions in association with software vendors) of data extraction tools. The latter have allowed a small number of practices to gain access to comprehensive information from which further initiatives to improve quality or change practices can evolve. The evaluation report recommended that standards should be established for prescribing software so that comparable data could be extracted from different systems to facilitate comparison of individual prescribing practice with evidence based guidelines. It also noted the difficulties of attributing any cost-savings in the PBS to Divisional activities. Following this report, the Government supported a four year extension to the EDQUM program in the 2003–2004 Budget. The Third Community Pharmacy Agreement between the Government and the Pharmacy Guild of Australia (1 July 2000 to 30 June 2005) also provided a range of QUM activities over the year in question including medication reviews of problem patients (conducted at the request of GPs), quality care pharmacy programs and the provision of consumer medicine information [ 19 ]. A potential new threat that emerged In 2003–2004 the PBS became caught up in negotiations concerning the Australia-United States Free Trade Agreement (AUSFTA). This saga has been extensively reported elsewhere [ 20 , 21 ]. The government remained adamant that the AUSFTA provisions concerning the PBS were benign and would also increase the transparency of PBAC decision-making. Others were concerned that the AUSFTA contained major concessions to the US pharmaceutical industry that undermined the egalitarian principles and operation of the PBS and had the potential to increase the costs of medicinal drugs to Australian consumers. Time will tell who is right. Issues still to be tackled The EDQUM project highlighted the needs for software standards in order to extract comparable drug utilisation data from different prescribing systems. The need for prescribing software standards has also been raised in connection with three other issues of relevance to the PBS: pharmaceutical promotion, independent therapeutic information and drug-drug interaction checking. The uptake of computers by Australian general practitioners (GPs) was stimulated by the Australian government in 1999. A one-off grant of around $10,000 was offered to those practices that purchased a computer, acquired internet connectivity (an E-mail address) and promised to use computer prescribing software to write the majority of their prescriptions. This increased the numbers of GPs writing prescriptions with the aid of a computer from around 50% in 1999 to more than 90% in 2004 [ 22 ]. Legible, printed prescriptions have been one of a number of positive outcomes of this initiative. However, new problems emerged. One software vendor (Health Communication Network Ltd.) became the dominant market leader because its business model relied on pharmaceutical promotion to heavily subsidise the cost of GPs purchasing and updating its prescribing software (Medical Director™). This business model facilitated software uptake but also resulted in advertisements for the latest and most expensive drugs appearing on the computer screen at the time of prescribing (and elsewhere). GPs using this software package were shown to prescribe more antibiotics per patient than those who wrote 'scripts manually. It was suggested that this may have been due to default settings in the software automatically writing in the maximum number of repeat prescriptions allowed.[ 23 ] Another default option in this software was the automatic production of a, "Do not substitute generic drugs" message on the prescription. The latter was eventually changed by the government amending regulation 19(5) of the National Health (Pharmaceutical Benefits) Regulations 1960 [ 24 ]. However, the issue of pharmaceutical promotion in prescribing software has yet to be tackled. Pharmaceutical promotion has never been allowed on government supplied 'script pads. It is hard to understand why it was allowed on the computerised equivalent. Pharmaceutical promotion distorts the information flow to physicians by selectively promoting the benefits of the latest and most expensive drugs. It provides minimal information about drug side-effects, contra-indications and opportunity costs. Cost-effective generic drugs are rarely promoted and non-drug solutions usually not at all. Pharmaceutical promotion has clearly been shown to influence physician's prescribing [ 25 ] and has resulted in cost-blow outs on the PBS due to "leakage" of prescribing away from cost-effective indications approved by PBAC [ 26 ]. In addition, pharmaceutical promotion in prescribing software, occurring at the time of physician decision making, is likely to be much more influential than promotion in medical journals, gimmicks and give-ways. As a consequence, several medical and consumer organisations have advocated further amendment of the National Health (Pharmaceutical Benefits) Regulations 1960, Part V, Regulation 19, to prohibit prescribing software from displaying pharmaceutical advertisements. Government intervention is also required to ensure that key national resources of objective therapeutic information, such as the Australian Medicines Handbook and Therapeutic Guidelines, are incorporated in prescribing software. The provision of objective therapeutic information is an important strategy of the QUM component of Australian National Medicinal Drug Policy [ 27 ]. Ironically, while both the Australian Medicines Handbook and Therapeutic Guidelines have been converted into electronic formats they are not yet included in computerised prescribing software. The problems have included arguments between software vendors and guideline producers over who should pay for the integration and a lack of defined standards for electronic information representation and interfacing. More recently, the NPS RADAR project has shown the way forward. RADAR provides independent information to health professionals about medicines that have a new or a changed listing on the PBS. RADAR drug monographs have recently been incorporated in four leading GP prescribing packages using an open standard interface. This project has moved ahead because the Australian government provided financial support to both the NPS and software vendors to enable the RADAR integration to take place. Following a workshop on electronic decision support, HL7 Australia has presented a work plan to the Australian Health Information Council Electronic Decision Support Steering Committee that would build on the RADAR project by incorporating the Australian Medicines Handbook and Therapeutic Guidelines into clinical software in a standard manner [ 28 ]. However, this plan has yet to proceed because of a current review of E-Health policy and reorganisation of its governance [ 29 ]. The third area of prescribing software requiring government intervention is standards for drug-drug interaction checking. The NPS tested four popular GP software packages by entering a common set of elderly patients on multiple medications [ 30 ]. This revealed very different behaviour by different software packages; some missed serious drug-drug interactions, others produced numerous trivial and clinical unimportant alerts. GPs noted that the latter behaviour caused them to turn off all alerts [ 31 ]. There is an urgent need for standards concerning acceptable drug-drug interaction detection &/or external assessment of prescribing software, another item on the HL7 Australia work plan. Conclusions The PBS remained in the media and policy spotlight during 2003–04. While the growth rate of the PBS has slowed during the year under review the sustainability of the Scheme remains an ongoing concern. One strategy adopted by the government was to transfer more of the cost of medicines to consumers through higher PBS co-payments and increased safety-net thresholds. However, such measures can result in higher costs elsewhere if poorer patients forgo necessary medicines and end up being hospitalised with uncontrolled disease. Cost-shifting (and patient inconvenience) was reduced by allowing State and Territory public hospitals limited access to the PBS but these reforms also showed the need for changes in the PBS to make it more suitable for hospital practice and the desirability of further integrating health funding systems. Educational strategies focusing on the quality use of PBS medicines were successfully pursued but would benefit from increased funding. In addition, there was an exploratory attempt to focus the attention of Divisions on PBS costs by rewarding them with a moiety of any money saved by their members through more cost-effective prescribing. However, the difficulties experienced by the EDQUM project in extracting useful drug utilisation data from computerised prescribing systems highlighted the need for prescribing software standards as did other problems with such software. Information communication technology and information management (ICT/IM) has the potential to allow individual health practitioners, Divisions and governments to compare what is being done with what is recommended best-practice, highlight major discrepancies, and provide targeted education and appropriate incentives to reduce the gap. However, as the events of 2003–04 show, this potential is unlikely to be realised if the development of clinical computer systems is left solely to market forces. Competing interests Dr. Harvey is a past Board member of Therapeutic Guidelines Limited, Co-Chair of HL7 Australia's Decision Support Technical Committee, a Councillor of the Australia Consumer's Association and a member of the Australian Labor Party.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548296.xml

549188

Teaching appropriate interactions with pharmaceutical company representatives: The impact of an innovative workshop on student attitudes

Background Pharmaceutical company representatives (PCRs) influence the prescribing habits and professional behaviour of physicians. However, the skills for interacting with PCRs are not taught in the traditional medical school curriculum. We examined whether an innovative, mandatory workshop for third year medical students had immediate effects on knowledge and attitudes regarding interactions with PCRs. Methods Surveys issued before and after the workshop intervention solicited opinions (five point Likert scales) from third year students (n = 75) about the degree of bias in PCR information, the influence of PCRs on prescribing habits, the acceptability of specific gifts, and the educational value of PCR information for both practicing physicians and students. Two faculty members and one PCR led the workshop, which highlighted typical physician-PCR interactions, the use of samples and gifts, the validity and legal boundaries of PCR information, and associated ethical issues. Role plays with the PCR demonstrated appropriate and inappropriate strategies for interacting with PCRs. Results The majority of third year students (56%, 42/75) had experienced more than three personal conversations with a PCR about a drug product since starting medical school. Five percent (4/75) claimed no previous personal experience with PCRs. Most students (57.3%, 43/75) were not aware of available guidelines regarding PCR interactions. Twenty-eight percent of students (21/75) thought that none of the named activities/gifts (lunch access, free stethoscope, textbooks, educational CD-ROMS, sporting events) should be restricted, while 24.0% (8/75) thought that students should be restricted only from sporting events. The perceived educational value of PCR information to both practicing physicians and students increased after the workshop intervention from 17.7% to 43.2% (chi square, p = .0001), and 22.1% to 40.5% (p = .0007), respectively. Student perceptions of the degree of bias of PCR information decreased from 84.1% to 72.9% (p = .065), but the perceived degree of influence on prescribing increased (44.2% to 62.1% (p = .02)). Conclusions Students have exposure to PCRs early in their medical training. A single workshop intervention may influence student attitudes toward interactions with PCRs. Students were more likely to acknowledge the educational value of PCR interactions and their impact on prescribing after the workshop intervention.

Background Pharmaceutical company representatives (PCRs) influence the prescribing habits and professional behavior of physicians [ 1 ]. Despite the availability of guidelines regarding appropriate interactions with PCRs for practicing physicians [ 2 - 4 ], the skills for interacting with PCRs have not been included as part of the traditional medical school curriculum. Physicians in training may be particularly susceptible to marketing strategies from PCRs. Restricting interactions between physician in training and PCRs is one approach to eliminating adverse effects of contacts with PCRs [ 5 ]. However, physicians in training will likely deal with such marketing influences once in practice. The provision of training or guided experiences in dealing with PCRs seems a more reasonable educational strategy for producing a physician who will be aware of the potential conflict of interest from the profit motive inherent in the pharmaceutical and other health related industries. To our knowledge, there is only one published study of an educational intervention targeting third year medical students on the subject of appropriate interactions with PCRs [ 6 ]. Furthermore, the best means of developing the skills and attitudes for interacting appropriately with PCRs is not well defined. We sought to examine whether a single workshop intervention had immediate effects on the attitudes of third year medical students regarding interactions with PCRs. The goal of the workshop was to increase the student's knowledge and awareness of ethical issues surrounding the PCR encounter, and to improve the students' interactions with representatives by fostering discussion on the profit motive in pharmaceutical marketing, PCR marketing techniques, and appropriate interactions with the PCR, issues necessary for critical thinking about the potential conflict of interest. Unique to our intervention was the participation of a PCR who role played a typical PCR encounter and who offered a perspective on marketing from the perspective of industry. Our findings have implications for institutions considering strategies for controlling PCR interactions and for medical educators seeking to develop curricula for marketing in medicine. Methods During the ambulatory internal medicine clerkship of the third year medical school curriculum, students were required to attend a ninety minute workshop entitled "Appropriate Encounters with Pharmaceutical Representatives". These workshops took place three times during the calendar year 2001 for three different student groups. To the best of our knowledge, there were no other organized learning experiences in the medical school curriculum about interactions with pharmaceutical representatives, either before or during the study period. We did not seek approval by the institutional review board for ethical research practice at our institution, because at that time the study was conducted, approval of student education projects was considered unnecessary. Two faculty members interested in the subject (JW, CO), and a regional manager of pharmaceutical representatives from a major pharmaceutical company facilitated the ninety minute workshop. The workshop began by soliciting student opinions regarding the characteristics of typical interactions with PCRs. After a list of characteristics was compiled, each characteristic was discussed in more detail and compared with previous personal experiences with PCRs. Salient points of the subsequent discussion included the usefulness of patient assistance programs, the use of samples and gifts in PCR marketing strategies, the validity and legal boundaries of information provided by the PCR, and the ethical and legal aspects of physician-industry relations. The final segment of the workshop involved two student volunteers who role played a typical PCR encounter in the office. After discussion of the first role play, a second role play between one faculty member (CO) and the PCR demonstrated desirable characteristics of the PCR encounter. A pre-intervention survey handed out and collected prior to the beginning of the workshop solicited information about the number of previous personal experiences with PCRs, and whether the student was previously aware of guidelines (medical school, federal government, or professional society) for appropriate interactions with PCRs. Using five point Likert scales, the survey solicited student attitudes about the educational value of PCR information for practicing physicians and for medical students, the degree of bias in PCR information, and the degree of influence of PCRs on prescribing habits. One additional question solicited the acceptability to students of specific gifts (lunch access, free stethoscope, textbooks, educational CD-ROMS, sporting events) from PCRs. A post-intervention survey with the same attitude questions was administered and collected as students left the workshop. The available data comes from three groups – the third, or last student group of academic year 2000–1 and the first two student groups of academic year 2001–2. Students were characterized by gender, age, and number of previous personal contacts with PCRs (None, 1–3, 4–6, >6). For the purposes of understanding the Likert scale responses for student attitudes, we collapsed Likert scores into three categories – scale responses of 1 or 2 to signify disagreement, a scale response of 3 to signify neutral, and a response of 4 or 5 to signify agreement with the attitude question. We compared student attitudes toward the educational value of PCR detailing for medical students with the perceived value for practicing physicians using the Pearson chi square test. The association between previous personal PCR experience and attitudes about the educational value of PCR detailing was explored using analysis of variance. We also compared attitudes before and after the workshop intervention using the Pearson chi square test and a dichotomous variable a response of 4 or 5 (versus 3 or lower) on the Likert scale. Results Student characteristics A total of 75 students attended one of the three mandatory workshops on "Appropriate Encounters with PCRs". One student did not complete a post-intervention survey. The mean age of students was 26.4 (± 2.4), and males (56.1%, n = 41) outnumbered females. Fifty-six percent of students (42/75) had experienced more than three personal conversations about a pharmaceutical product with a PCR since starting medical school. Five percent (4/75) claimed no previous personal experience with PCRs. There was no association between the number of PCR contacts, and either gender, age or time of the academic year. Student attitudes toward value of PCR interaction The pre-intervention survey showed that PCR detailing was of educational value to 22.1% (18/75) of students with no perceived difference in educational value to medical students versus that to practicing physicians (chi square, p = .40). While students agreed that the degree of bias from PCR information was substantial (86.7%, 65/75), only 44.0% (33/75) of students felt that pharmaceutical representatives were influential with regard to physicians' prescribing habits. No relationship between number of previous personal PCR contacts and educational value to medical students was demonstrated (ANOVA p = .08) Awareness of guidelines and attitudes toward gifts Forty-three percent (32\75) of students reported awareness of available guidelines regarding PCR interactions. Fifty percent (16/32) of those students reported familiarity with medical school guidelines, 21.8% (7/32) with federal government guidelines, and 40.6% (13/32) with professional society guidelines. When asked which drug company sponsored activities/gifts targeting medical students should be restricted, 28.0% of students (21\75) thought that none of the named activities/gifts (lunch access, free stethoscope, textbooks, educational CD-ROMS, sporting events) should be restricted. Twenty-four percent (8/75) of students thought that only sporting events should be restricted. Effect of workshop intervention on student attitudes Figure 1 shows that the perceived educational value to both practicing and student physicians increased after the workshop intervention from 17.7% to 43.2% (chi square, p = .0001), and 22.1% to 40.5% (p = .0007), respectively. Fifty-eight percent of students (43/74) and 37.8% (28/74), respectively, changed their Likert scale response to the questions on educational value by at least one point after the workshop intervention (Table 1 ). Student perceptions of the degree of bias of PCR information decreased from 84.1% to 72.9% (p = .065), but the perceived degree of influence on prescribing increased (44.2% to 62.1% (p = .02)). The response to the question on the degree of bias in PCR detailing changed by at least one Likert scale point for 17 students (17/74, 23.0%) (Table 1 ). The response to the question on the influence of PCR detailing on prescribing practices changed by at least one Likert scale point for 34 students (34/75, 46.0%). Figure 1 Student attitudes toward PCR detailing before and after workshop intervention . Student perception of the educational value of PCR interactions increased after the intervention at the same time that the perception that PCRs influenced prescribing increased. Student perception of the degree of bias decreased slightly after the workshop, but this decrease was not statistically significant. Table 1 Student attitudes toward PCR detailing before and after workshop intervention In your opinion, what is the educational value to practicing physicians offered by detailing from pharmaceutical representatives? (1 = No value at all, 5 = Extremely valuable) Before-Not Valuable (<3) Before-Neutral (3) Before-Valuable (>3) After-Not valuable (<3) 8 1 0 9 After-Neutral (3) 10 20 3 33 After-Valuable(>3) 2 18 12 32 20 39 15 In your opinion, what is the educational value to medical students offered by detailing from pharmaceutical representatives? (1 = No value at all, 5 = Extremely valuable) Before-Not Valuable (<3) Before-Neutral (3) Before-Valuable (>3) After-Not valuable (<3) 13 0 0 13 After-Neutral (3) 8 19 4 31 After-Valuable(>3) 6 10 14 30 27 29 18 What is you perception of the degree of bias in the information provided by pharmaceutical representatives detailing to practicing physicians? (1 = Not at all biased, 5 = Totally biased) Before-Not Biased (<3) Before-Neutral (3) Before-Biased (>3) After-Not biased (<3) 0 1 1 2 After-Neutral (3) 0 6 12 18 After-Biased(>3) 0 3 51 54 0 10 64 How influential are pharmaceutical representatives with regard to physicians' prescribing habits? (1 = Not at all influential, 5 = Very influential) Before-Not Influential (<3) Before-Neutral (3) Before-Influential (>3) After-Not influential (<3) 2 2 1 5 After-Neutral (3) 7 11 5 23 After- Influential (>3) 2 17 27 36 11 30 33 Discussion Professional relationships with PCRs begin early in a physician's career. Because physicians typically underestimate the influence of pharmaceutical marketing on prescribing practices, countering this naivete early is warranted. Because students do not yet have prescribing privileges, the effect of PCR detailing on the prescribing decision is less relevant than how PCR contact shapes professional values [ 1 ]. Several studies have examined perceptions of the potential influence of PCRs on resident and practicing physicians [ 7 - 13 ]. However, fewer studies have examined the views of medical students [ 6 , 14 , 15 ]. Our data show that students have early exposure to PCRs, perhaps earlier than previously suspected. Only five percent of third year students at this institution had not yet experienced PCR detailing, and many had experienced greater than six such encounters. Although resident physicians understandably have more PCR contacts, on average at least three PCR encounters per month [ 11 , 16 ], to our knowledge, there are no studies of comparable data for medical students. In this study few students were aware of existing guidelines for PCR interactions. Furthermore, students perceived information from PCRs to have educational value, and a value equivalent to that for practicing physicians. That students felt no more or less susceptible to marketing influence than practicing physicians is a marked contrast to the opinions of many educators that physicians in training need special protection from marketing influences [ 1 , 2 , 17 ]. Several interventions have been proposed for educating physicians-in-training about pharmaceutical marketing practices. Shaughnessy et al described a single brief seminar of marketing concepts followed by regular structured evaluation of PCR sales presentations throughout the following year [ 18 ]. Despite this well organized effort, meaningful educational outcomes were meager for the twelve residents evaluated. Hopper et al showed that a single elective forty minute lecture/discussion on ethical and marketing issues in pharmaceutical promotion was successful in improving attitudes and knowledge among residents and faculty [ 19 ]. They presented six vignettes to illustrate marketing techniques related to gifts, guidelines, and the yield of marketing for pharmaceutical companies. Vinson et al showed that a fifty minute lecture for first and second year medical students could have immediate effects on knowledge as measured by repeat anonymous survey six weeks later [ 15 ]. Palmisano et al described a ninety minute lecture and role-play with simulated PCRs to teach analysis of advertising copy and sales techniques, although no data on educational outcomes were offered [ 20 ]. Most similar to our intervention was this study by Wilkes and Hoffman who used pharmacists who were trained to portray PCRs during a one hour seminar targeting third year medical students [ 6 ]. Designed to promote critical thinking about appropriate physician-PCR interactions, the single workshop was successful in increasing the amount of uncertainty students felt about the accuracy and ethics of standard drug "detailing". Similar to these interventions, our workshop intervention targeting third year medical students was a one time intervention and brief in duration. Our intervention differed in that the workshop (1) took place during the clinical clerkship year, (2) made use of practicing PCRs and physicians, and (3) encouraged a distanced but amicable relationship with the PCR. In contrast to many educators who oppose PCR contact for trainees, we encouraged respect for the individual PCR and the success of the pharmaceutical company's business model. We were concerned enough about the growing influence of pharmaceutical marketing on trainees to stage this workshop, but we were careful to remember our goals of encouraging critical thinking about the topic rather than simply condemning the PCR contact. As an example, we emphasized the legal limits regarding what the PCR could and could not say to the physician. This may explain, in part, why attitudes toward PCR information improved after the intervention, at the same time that perceived degree of influence on prescribing increased. Teaching students the "rules of the game" in PCR encounters may explain why students thought that PCR information had more educational value after the workshop. The limitations of this study should be recognized. First, our survey took place at a single academic medical center affiliated with a private hospital. Student attitudes might be different at a state supported institution or at institutions where there are restrictions on the activities of PCRs. Second, students may have answered the post-intervention survey in a socially desirable manner. Respect for the PCR and the business model was presented as a balanced perspective toward marketing in medicine. Third, how immediate changes in student perceptions will ultimately translate into durable attitude changes and prescribing practices was not a goal of this study. We are not naïve enough to be certain that a one time intervention on any subject matter related to ethical issues will change student attitudes in a way that is durable. However, our goal as educators should be to make the students think critically, and demonstrating self-reported attitude changes is a necessary first step toward more durable change. Some academic institutions have chosen to ban PCRs from the academic learning environment. McCormick et al showed that restricting access to PCRs during residency training was associated with less informational dependence on the PCR and a decreased frequency of PCR contact after training [ 5 ]. Underlying such a restrictive policy is the idea that trainees are not able and/or educable to resist the marketing tactics of PCRs. In contrast, our approach is based on the opinion that learning the skills for interacting appropriately with PCRs should not be delayed until a physician has entered practice, and that banning PCRs may simply extend the period of naivete for physicians in training [ 14 ]. Not only does such an omission in the curriculum miss the opportunity to teach physicians about professional relationships surrounding the business model. It also ignores the cost containment needs of the academic medical center and prospective employers. The challenge for medical educators is how to incorporate this increasingly important knowledge domain into training programs. What aspects of marketing strategy need to be taught and how? The growing emphasis on social justice and professionalism should encourage the appropriate distance from and respect for marketing pressures in medicine and add support for this curricular element in medical education [ 21 ]. Our model suggests the possibility of a partnership between the pharmaceutical industry and educators in better preparing phsyicians in training for marketing in medicine. While pharmaceutical companies are the current target of criticism of commercialism in medicine, other services (durable medical equipment, herbal/nutritional supplements, new medical technologies) are all accompanied by marketing pressures that physicians will have to factor into clinical decision making. Conclusions Medical students have exposure to PCRs early in their medical education, at least in this setting. Students perceived information from PCRs to have moderate educational value, and a value equivalent to that for practicing physicians. A brief workshop intervention can have a measurable immediate effect on student attitudes. List of abbreviations PCR – pharmaceutical company representative Competing interests The author(s) declare that they have no competing interests. Authors' contributions JW conceived of the study, participated in its design and coordination, and drafted the manuscript. CO participated in design and coordination of the study, conducted the intervention, participated in data interpretation, and manuscript revision. Both authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC549188.xml

539335

Flux Analysis Uncovers Key Role of Functional Redundancy in Formaldehyde Metabolism

Genome-scale analysis of predicted metabolic pathways has revealed the common occurrence of apparent redundancy for specific functional units, or metabolic modules. In many cases, mutation analysis does not resolve function, and instead, direct experimental analysis of metabolic flux under changing conditions is necessary. In order to use genome sequences to build models of cellular function, it is important to define function for such apparently redundant systems. Here we describe direct flux measurements to determine the role of redundancy in three modules involved in formaldehyde assimilation and dissimilation in a bacterium growing on methanol. A combination of deuterium and 14 C labeling was used to measure the flux through each of the branches of metabolism for growth on methanol during transitions into and out of methylotrophy. The cells were found to differentially partition formaldehyde among the three modules depending on the flux of methanol into the cell. A dynamic mathematical model demonstrated that the kinetic constants of the enzymes involved are sufficient to account for this phenomenon. We demonstrate the role of redundancy in formaldehyde metabolism and have uncovered a new paradigm for coping with toxic, high-flux metabolic intermediates: a dynamic, interconnected metabolic loop.

Introduction The availability of large numbers of genome sequences has facilitated metabolic reconstruction based on predicted gene function, in essence, a prediction of the metabolic blueprint of a cell. Such metabolic reconstructions [ 1 , 2 , 3 ] can be grouped in functional segments, or metabolic modules [ 4 , 5 ], and the compilation of metabolic modules can be used to predict interactions between the different elements of the metabolic network in a cell. However, a major difficulty with this approach is the common occurrence of apparently redundant functional modules. It is often not possible to assign roles to these metabolic segments, which have been referred to as the “gray areas of the genome” [ 6 ]. Expression profiling, either of transcripts or proteins, holds the promise to gain more insight into the function of redundant metabolic modules, but the presence of a transcript or protein does not necessarily correlate with module function, due to posttranslational effects on metabolic flux. In order to determine the true function of such metabolic modules, it is necessary to measure the flux of metabolites through each functional module during relevant physiological changes. One system that has proved amenable to a modular approach to metabolism is the ability to grow on one-carbon (C 1 ) compounds, or methylotrophy [ 7 ]. The availability of a gapped genome sequence for a model methylotrophic bacterium, Methylobacterium extorquens AM1, has accelerated the definition of methylotrophy modules, and a reasonably complete metabolic reconstruction is available for this bacterium [ 7 ]. However, these analyses coupled to genetic and physiological studies [ 8 , 9 , 10 , 11 , 12 , 13 ] have raised a series of fundamental questions that can only be answered through direct flux measurements. As in other such aerobic methylotrophic bacteria, M. extorquens AM1 oxidizes C 1 substrates to formaldehyde and is essentially growing on formaldehyde for both carbon and energy metabolism [ 14 ] ( Figure 1 ). It is not yet understood how the toxic central metabolite formaldehyde is efficiently and dynamically partitioned between assimilatory and dissimilatory metabolism, without toxic buildup. Therefore, this system represents both a key problem of methylotrophy and a paradigm for how toxic metabolites are managed in high-flux conditions. Genomic predictions and mutant analyses have identified three functional modules that direct formaldehyde into two outputs: assimilatory or dissimilatory metabolism ( Figure 1 ). The first module consists of the apparently nonenzymatic condensation reaction between formaldehyde and tetrahydrofolate (H 4 F) [ 9 , 15 ] to generate methylene-H 4 F directly, which is the C 1 donor for assimilation via the serine cycle. The second module is initiated by an enzyme-catalyzed reaction [ 9 ] of formaldehyde with a folate compound found in methanogenic Archaea, tetrahydromethanopterin (H 4 MPT). The resulting methylene-H 4 MPT is subsequently oxidized through a series of reactions to formate [ 8 , 16 , 17 ], which can ultimately be dissimilated to CO 2 via the activity of multiple formate dehydrogenases [ 18 ]. Finally, a third module involves interconversion of methylene-H 4 F and formate via a familiar set of H 4 F-dependent reactions found in most organisms [ 11 , 19 , 20 ]. Mutant analysis has shown that both the H 4 MPT and H 4 F modules are required for growth on C 1 compounds [ 8 , 9 , 10 , 11 , 12 , 13 , 19 ]. Figure 1 Formaldehyde Metabolism of M. extorquens AM1 Three modules work to provide two cellular outputs: formaldehyde assimilation and dissimilation. The direct condensation of formaldehyde with H 4 F is shown in green. A second proposed route for generating methylene-tetrahydrofolate (methylene-H 4 F), the consecutive action of the H 4 MPT and H 4 F modules is shown in blue. Fae, formaldehyde activating enzyme; Fch, methenyl H 4 F cyclohydrolase; FDH, formate dehydrogenase; Fhc, formyltransferase/hydrolase complex; FtfL, formyl H 4 F ligase; H 4 MPT, tetrahydromethanopterin; Mch, methenyl H 4 MPT cyclohydrolase; MDH, methanol dehydrogenase; MtdA, methylene H 4 F/H 4 MPT dehydrogenase; MtdB, methylene H 4 MPT dehydrogenase. Spontaneous and reversible reactions are indicated. Two distinct models exist to explain the necessity of both the H 4 MPT and H 4 F modules in methylotrophy, predicting opposite directions for the net flux through the H 4 F module. It was suggested over 20 y ago that the H 4 F module functions in formaldehyde oxidation [ 21 ]. This predicts that the H 4 MPT and H 4 F modules are parallel, redundant formaldehyde oxidation systems. Recent genetic and biochemical evidence [ 11 , 12 , 13 ], however, suggest that the H 4 F module is not functionally redundant to the H 4 MPT module for formaldehyde oxidation. An alternative hypothesis suggests that the H 4 F module functions in the reductive direction, generating methylene-H 4 F from formate [ 11 , 16 , 17 ]. This model suggests a single dissimilatory module (H 4 MPT module) and two, redundant assimilatory modules: the H 4 F module and the direct condensation of methylene-H 4 F from formaldehyde ( Figure 1 , green arrows). This model predicts two routes for generating the key assimilatory intermediate methylene H 4 F from formaldehyde: one we will term “direct,” involving the direct condensation step, and one we will term “long,” involving the consecutive action of the H 4 MPT and H 4 F modules. Although the direct route ( Figure 1 , green arrows) requires flux through a nonenzymatic reaction, assimilation via the proposed long route ( Figure 1 , blue arrows) involving the action of the H 4 MPT and H 4 F modules is energetically costly due to a net expenditure of one ATP per C 1 unit. If this hypothesis is correct, the H 4 MPT module would play a role in both dissimilatory and assimilatory metabolism, in much the same way that the tricarboxylic acid cycle plays a dual role in growth on multicarbon compounds. Clearly, this is an example in which metabolic reconstruction is not sufficient to predict the roles of the central metabolic modules involved in carbon partitioning. In addition, it provides a test case for how cells cope with a high-flux toxic metabolic intermediate. In order to address this problem, we have used a combination of stable isotope- and radioisotope-labeling approaches, which has allowed the complete determination of flux through every branch of methylotrophy. The results provide a dynamic picture of the response of M. extorquens AM1 during transitions in and out of methylotrophy. Furthermore, a kinetic model of the key formaldehyde utilization systems was developed that successfully predicted key system dynamics. Our data resolve the specific roles for three interconnected metabolic modules that have two cellular outputs, assimilation and dissimilation. Furthermore, we have revealed a new paradigm for handling high-flux toxic intermediates: a dynamic metabolic loop that demonstrates graded response to changing metabolic needs. Results Detection of Serine-Derived Mass Fragments Using Gas Chromatography–Mass Spectrometry A CD 3 OD label tracing strategy ( Figure 2 ) was devised to directly determine what fraction of the methylene-H 4 F that entered the serine cycle was formed from the direct condensation of formaldehyde and H 4 F (direct route), versus the fraction formed through the alternative potential route involving oxidation of formaldehyde to formate by the H 4 MPT module, followed by assimilation through the H 4 F module (long route). The serine that is produced from methanol contains the carbon atom, and both hydrogens, from the methylene group of the methylene-H 4 F donor. Serine produced from CD 3 OD via the direct route contains two D, while that produced via the long route contains one D and in both cases these are relatively nonexchangeable C-D bonds. Therefore, at short labeling times (<1 min) the ratio of serine isotopomers with one or two D is an assay of the ratio of flux through the two routes. Figure 2 GC–MS Method to Assay Ratio of Long Versus Direct Routes (A) Simplified model of formaldehyde metabolism highlighting the deuterium (in red) label-tracing strategy. Oxidation of deuterated methanol (CD 3 OD) leads to the production of formaldehyde with two deuteriums (CD 2 O). Direct condensation with H 4 F (green arrows) and conversion to serine via the serine cycle ( Figure 1 ) generates serine with two deuteriums. Alternatively, methylene-H 4 F may be produced through the long route (blue arrows; Figure 1 ), generating serine containing only one of the original deuteriums. Extraction and derivatizion of small molecules for analysis by GC–MS provides the ratio of (+1)/(+2) serine isotopomers, thereby assaying the proportion of methylene-H 4 F generated via the long route through formate or from the direct route from formaldehyde. (B) Detection of serine by GC–MS. The small peak in total ion abundance detected by the MS denoted by the arrow represents serine. (C) Analysis of the mass fragments present in this peak revealed the presence of ions with M/z values of 156 and 228, which are diagnostic for ECF–TFAA derivatized serine. In order for this label tracing method to be successful, the ratio of serine isotopomers containing one or two deuteriums from CD 3 OD must be determined. Initially, cultures were labeled with standard methanol (CH 3 OH), added to boiling ethanol after labeling, and the derivatized H 2 O-soluble small molecules were prepared and analyzed via gas chromatography–mass spectrometry (GC–MS). Consistent with a derivatized serine standard and previous work [ 22 , 23 ], a peak was observed at approximately 8.6 min that contained two major ions with M/z of 156 and 228 ( Figure 2 B and 2 C). The proportion of (+1) and (+2) M/z ions detected were within 1.1% ± 1.7% and −0.7% ± 0.5% of the predicted distribution (Isoform 1.02, National Institute of Standards and Technology) of naturally occurring heavy isotopomers for these fragments, indicating the feasibility of this GC–MS method for detecting serine isotopomers. Deuterium Labeling Demonstrates Assimilation of C 1 Units through Both Direct and Long Routes Initially, the incorporation of deuteriums from CD 3 OD into serine was investigated with succinate-grown cell suspensions of wild-type M. extorquens AM1. Analysis of the derivatized H 2 O-soluble small molecule preparation from wild-type samples indicated a substantial increase in the proportion of fragments present as (+1) and (+2) isotopomers (>35% of total serine isotopomers). CD 3 OD labeling with a glyA mutant strain (CM239K.1), which lacks the initial serine-cycle enzyme, serine hydroxymethyltransferase, and was therefore completely unable to assimilate carbon from formaldehyde, produced no increase in (+1) or (+2) isotopomers (data not shown). Additionally, mutants defective for the proposed long route for methylene-H 4 F formation were tested for deuterium labeling. These included the ftfL (encodes formate-H 4 F ligase) mutant CM216K.1 [ 11 ], blocked for the H 4 F module, and the dmrA (encodes dihydromethanopterin reductase) mutant CM212K.1 [ 24 ], which has been shown to lack H 4 MPT [ 25 , 26 ]. Consistent with their proposed roles, the proportion of (+1) fragments dropped 8-fold for these mutants, compared to a modest 2-fold decrease in (+2) fragments. These data indicate that both the H 4 F and H 4 MPT modules affect labeling of serine and are required to generate the large increase in (+1) isotopomers seen with wild-type. These data also indicate that potential exchange reactions that could eliminate the deuteriums do not contribute measurably to the presence of (+1) ions. Collectively, these data indicate that the (+1) and (+2) serine mass fragments can serve as an accurate proxy for methylene-H 4 F generated through the long or direct routes. One caveat to this statement is that a portion of the NADPH involved in generating methylene H 4 MPT could be derived from the oxidation of methylene H 4 MPT to methenyl H 4 MPT and, therefore, could have become deuterium labeled. Based on the stoichiometry of the reactions and the known activity ratio of NADPH- versus NADH-producing enzymes for the methylene-H 4 MPT dehydrogenase reaction, we calculated that we at most overestimate the contribution of the direct pathway by 25% during growth on methanol, and by significantly smaller values at times with lower formaldehyde production. This prediction assumes an infinitely small intracellular concentration of NADPH, so depending on the actual pool of NADPH present, the error will be less. Therefore, our results are presented as maximum ratio changes. When labeled with CD 3 OD, the succinate-grown wild-type cultures utilized to verify the GC–MS method produced a ratio of (+1) versus (+2) serine mass fragments of 8.0 ± 0.6. Thus, when succinate-grown cells are first exposed to methanol, the majority of methylene-H 4 F assimilated via the serine cycle is generated via the proposed long route. In contrast, CD 3 OD labeling of mid-exponential-phase methanol-grown cells indicated that the direct route dominated by up to 15-fold (measured ratio of [+1]/[+2] of 0.065 ± 0.006). Therefore, although both methylene-H 4 F production routes operated under both physiological conditions, a significant shift in the ratio of the two routes occurred, up to 100-fold. Relative Contributions of the Long and Direct Routes of Methylene-H 4 F Formation during Transitions to and from Methylotrophic Growth In order to understand the dynamics of the contribution of the long and direct routes for directing C 1 units into assimilatory metabolism during transitions to and from methylotrophic growth, metabolic shift experiments were performed. One hour after samples were removed from succinate- and methanol-grown cultures for the labeling experiments described above, the remaining portions of the two cultures were harvested, washed, and resuspended into medium containing the other substrate (methanol or succinate, respectively). At four intervals during the transition to each of the new growth substrates ( Figure 3 ) samples were harvested and analyzed via CD 3 OD labeling to determine the ratio of flux capacity through the two methylene-H 4 F formation routes. The ratio of the contribution of the long route for methylene-H 4 F formation to the direct route varied in a continuous fashion during the transition from succinate to methanol, or from methanol to succinate ( Figure 3 A). The cultures were followed for 7 or 10 h after the shift—sufficient time to observe the majority of the transition. Figure 3 Change in Ratio of Flux through Long Versus Direct Methylene-H 4 F Formation Routes during Growth Transitions (A) Experimental data as determined by GC–MS analysis of serine isotopomers. The bars for each transition represent a time series from cells harvested 1 h prior to the transition, and four time points following the transition (succinate to methanol: 1, 5, 7.5, and 10 h; methanol to succinate: 1, 3, 5, and 7 h). (B) Predictions based on kinetic model simulations. The bars indicate the succinate to methanol transition (same time points as for the experimental data) and the methanol steady-state prediction. Dynamics of C 1 Fluxes during Transitions between Succinate and Methanol by 14 C Labeling The relative ratio of the routes provides only one of the parameters needed to understand the metabolic dynamics during this transition; the quantitative flux is also necessary. These values were obtained with 14 C-labeling experiments. Concurrent with the CD 3 OD-labeling experiments described above, a portion of each sample was used to determine the rates of methanol oxidation, assimilation of C 1 units, and CO 2 production via 14 C-CH 3 OH labeling [ 11 ]. Methanol oxidation was found to be 10-fold higher in methanol-grown cultures, and the percentage of carbon from methanol assimilated into biomass was 3-fold higher as compared to succinate-grown cultures ( Table 1 ). The other values incorporated into the flux calculations are the stoichiometry of the serine cycle, in which two C 1 units from methylene-H 4 F and one CO 2 are incorporated for every C 3 compound assimilated, and the proportion of external, unlabeled CO 2 incorporated by the serine cycle [ 27 ]. The ten C 1 fluxes (each branch arbitrarily labeled “A” through “J”) calculated using the concurrent CD 3 OD and 14 C-methanol labeling methods are reported in Table 1 and shown in Figures 4 and 5 . Figure 4 C 1 Fluxes during Transition from Succinate to Methanol The fluxes determined are represented schematically (A). The other panels present flux for each branch, labeled A through J. The five bars for each flux represent a time series from cells harvested 1 h prior to the transition from succinate to methanol, and 1, 5, 7.5, and 10 h after the switch. Dissimilatory (B), methylene-H 4 F formation (C), and assimilatory (D) fluxes are presented separately with different scales for clarity. Flux F represents maximum fluxes. Figure 5 C 1 Fluxes during Transition from Methanol to Succinate The fluxes determined are represented schematically (A). The other panels present flux for each branch, labeled A through J. The five bars for each flux represent a time series from cells harvested 1 h prior to the transition from methanol to succinate, and 1, 3, 5, and 7 h after the switch. Dissimilatory (B), methylene-H 4 F formation (C), and assimilatory (D) fluxes are presented separately with different scales for clarity. Flux F represents maximum fluxes. Table 1 Calculated C 1 Fluxes during Transitions between Succinate and Methanol at the Time (h) Relative to the Transition All values are reported in nmol, min −1 , mL −1 , and OD 600 −1 a First number represents flux for succinate to methanol; second number represents flux for methanol to succinate A comparison of the values for succinate- versus methanol-grown cells shows that upon initial exposure of succinate-grown cells to methanol ( Figure 4 and Table 1 ), the measurements suggest that most (at least 99%) of the formaldehyde was handled by the H 4 MPT module (flux B), and only a small amount flowed through the direct route (flux F). Of formate made from the H 4 MPT module (flux B), most (up to 88%) was converted to CO 2 via formate oxidation (flux C), and a smaller amount (at least 12%) flowed through the H 4 F module and into assimilation (flux E), representing at least 90% of the assimilatory carbon. In contrast, for methanol-grown cells ( Figure 5 and Table 1 ), less (only about 70%) of the formaldehyde generated from methanol flowed through the H 4 MPT module (flux B), with up to 30% handled by the direct route (flux F). Only a small portion of the assimilatory carbon (suggested to be about 6%) flowed through the H 4 F module (flux E), which represented about 3% of the formate generated via the H 4 MPT module. The remainder of the formate was oxidized to CO 2 (flux C). These data indicate that, although the relative contribution of the long route to methylene-H 4 F formation decreased during the transition to growth on methanol (see Figure 3 ), the flux through the long route (flux E) increased significantly (see Figure 4 ). Flux through this route peaked 5 h after the transition to methanol, when it reached a value at least 8-fold higher than succinate-grown cells, and dropped somewhat afterward. The flux through the direct route (flux F) also increased to a maximum of up to 20% of the total formaldehyde flux at the final time point during the transition (see Figure 4 ). The fluxes for the transition from methanol to succinate represent the capacity for flux, as no methanol was present after the growth transitions. These changes, however, roughly mirrored the transition from succinate to methanol, but were not an exact reversal (see Figure 5 ). As noted for the deuterium-labeling experiments, the time periods followed in these experiments were sufficient to observe the majority of the transition. Dynamic Mathematical Model of Formaldehyde Partitioning In order to assess whether the known kinetic constraints of the three modules of formaldehyde metabolism were sufficient to account for the experimentally determined flux dynamics, a mathematical model was generated. The model simulated partitioning of C 1 units through the three formaldehyde modules during growth of cells in methanol, and for the transition of succinate-grown cells to methanol. The model consisted of eight ordinary differential equations, based on known kinetic mechanisms, to describe the dynamics of the H 4 F and H 4 MPT modules and the direct condensation reaction. Most binding constants, rate constants, and cofactor concentrations were obtained from the literature ( Table 2 ). For the six cases in which literature values are not known, these were estimated as described in Materials and Methods . Additionally, a dynamic simulation of the succinate to methanol transition was performed. The methanol uptake rate was set to the experimentally measured value at each time point (flux A, Table 1 ) and interpolated linearly between time points to create a smooth gradient. Starting with the values obtained for succinate or methanol growth, the parameters were increased throughout the shift at a rate corresponding to the increase in methanol uptake. Table 2 Equilibrium Constants and Forward Rate Constants (V max ) for Each Reaction in the Model Simulation Equilibrium constants are all dimensionless, except for reaction 9, which has units of mM. Units for kinetic constants are mM/sec unless otherwise noted a The literature value for this constant is 0.71 mM/s. A small adjustment was required to fit the data b Constants of twice the literature values were assumed, due to the presence of multiple formate dehydrogenases H 4 F, tetrahydrofolate; H 4 MPT, tetrahydromethanopterin; me-H 4 F, methylene-H 4 F; me-H 4 MPT, methylene-H 4 MPT; MFR, methanofuran; mn-H 4 F, methenyl-H 4 F; mn-H 4 MPT, methenyl-H 4 MPT; Irrev., irreversible; NA, not applicable Two key results are apparent from the comparison of the model's predictions (see Figure 3 B) to the measured flux ratio of the two methylene-H 4 F production routes (see Figure 3 A). First, the model did not constrain the direction of flux through the H 4 F module. Therefore the prediction that the H 4 F module functions in assimilation both during steady-state methanol growth and upon the first exposure of succinate-grown cells to methanol indicates that the kinetic parameters of the module components are sufficient to account for this phenomenon. Second, the correspondence between the predicted and experimentally determined dynamics of the switch in methylene-H 4 F production routes confirms that the dynamics of the system are also largely attributable to the systems' kinetic constraints. That the kinetics did not exactly mimic the measured values is presumably partly due to differences between the actual induction of enzyme activities versus the model's simplifying assumption that all values change in a manner directly proportional to changes in methanol uptake. However, the model does not suggest a significant effect of methylene H 4 MPT-derived NADPD in the deuterium-labeling studies. The H 4 F Module Could Not Be Eliminated during Growth on C 1 Compounds The combination of CD 3 OD and 14 C-methanol label-tracing studies clearly demonstrate that the long route contributes methylene-H 4 F to the serine cycle and that the flux through the H 4 F module portion of the long route (flux E) increases significantly during the transition to growth on methanol. These results confirm the hypothesis of net reductive flux through this module [ 11 , 16 , 17 ]. However, this route contributes only 6% of the total methylene-H 4 F generated during growth on methanol. Therefore, it seemed possible that the H 4 F module might be required during transitions in and out of methylotrophy, but might not be required for continuous growth on methanol. Given the available genetic techniques, two strategies were employed in an attempt to obtain mutants in one of the key H 4 F module genes, formate-H 4 F ligase, during growth on C 1 compounds. First, attempts were made to obtain null mutants via allelic exchange with cultures maintained on methanol or methylamine, but these efforts were unsuccessful. Second, cultures of the ΔftfL::kan mutant CM216K.1 [ 11 ] bearing the complementing plasmid pCM218 [ 11 ] were grown in medium containing methanol or methylamine without tetracycline for plasmid maintenance. No plasmid-free isolates were obtained for CM216K.1 with pCM218 during growth on methanol. However, they were obtained for wild-type with pCM218 on methanol, or CM216K.1 with pCM218 grown on succinate. Therefore, it appears that the H 4 F module plays an essential role in methylotrophy even after cells have already begun to grow on C 1 compounds. Discussion In the formaldehyde metabolism of M. extorquens AM1, three interconnected metabolic modules are present, involved in two roles: converting formaldehyde to the key assimilatory intermediate methylene H 4 F and net oxidation of formaldehyde to CO 2 . Understanding paradigms for differential roles of redundant modules is central to enabling broadscale metabolic reconstruction from genome sequences. In addition, methylotrophy represents an intriguing example of a metabolic mode in which growth depends on high flux of a toxic metabolite, with subsequent partitioning of that metabolite. Other such modes are known that produce toxic aldehydes, for instance, growth on ethanolamine [ 28 ] and other alcohols [ 29 ]. Numerous other toxic intermediates are known in bacteria, such as the production of hydroxylamine by ammonia-oxidizing bacteria [ 30 ] and mono-oxygenase-dependent production of epoxyalkanes during growth on aliphatic alkanes [ 31 ]. In addition, the liver can be exposed to toxic metabolites, for instance, the production of formate from acute methanol poisoning [ 32 ]. However, the metabolic mechanisms that allow the balancing of flux and toxicity in such situations are not well understood. Understanding paradigms for such metabolic responses is important for assessing and possibly ameliorating toxicity problems in a variety of systems, including bioremediation of toxic compounds, chemical production in bioprocesses, and detoxification in tissues and organs. Through a combination of 14 C and deuterium label-tracing strategies, we have defined flux through each metabolic module in methylotrophic metabolism in M. extorquens AM1 during transitions into and out of methylotrophy, in which the flux of formaldehyde into the system changed by a factor of 10. These methods had the dual advantages of possessing sufficient sensitivity to detect flux under all conditions tested, and being free from the requirement of steady-state growth conditions, which allowed the dynamics of growth transitions to be examined. Furthermore, this approach complements a recently developed 13 C-labeling method that measures flux through the multicarbon branches of central metabolism [ 27 ], but is inherently silent to the C 1 fluxes measured here. The approach described here allowed us to test and confirm the hypothesis that the role of the H 4 F module during growth on C 1 compounds is to supply methylene-H 4 F from formate [ 11 , 16 , 17 ], although the fraction of total flux passing through this route is always small. Given the small percentage of total flux into assimilation via the H 4 F module during growth on methanol, why is this module required under this condition? The results presented here suggest that this requirement is not alleviated even when cells begin to actively grow on methanol. It is possible that this module generates an inducing signal for the serine cycle and, therefore, is necessary to maintain assimilatory flux during growth on methanol. This hypothesis is consistent with the genetic circuit, as two of the genes encoding key enzymes of the H 4 F module (mtdA and fch) are in an operon with serine-cycle genes and are under the control of a single regulatory protein, QscR [ 33 ]. Our results demonstrate a dramatic shift in flux through the primary methylotrophic modules during these transitions. It has long been known that all enzymes of methylotrophy increase 3–6 fold in activity after induction with methanol [ 14 , 16 ], predicting a sizable increase in total flux into the system. However, the flux measurements reported here show that a dynamic repartitioning occurs also. When M. extorquens AM1 encounters methanol, the methanol oxidation system is at low but significant activity [ 34 ]. Under these conditions, the flux of formaldehyde into the system is relatively low ( Figure 6 , left panel), and most of the formaldehyde is oxidized to CO 2 via the H 4 MPT module and formate dehydrogenase, generating NAD(P)H. Only a trace amount is assimilated, almost all of that through the long route involving formate and H 4 F intermediates. As the flux of formaldehyde into the system increases, a greater percentage begins to flow through the direct route into assimilatory metabolism. A smooth transition occurs during the induction of the capacity in the system until approximately one-third of the total formaldehyde flows through this route, and assimilatory and dissimilatory metabolism are balanced for rapid growth on methanol ( Figure 6 , right panel). The metabolic elegance of this interconnected, dynamic metabolic loop creates an effective formaldehyde flux buffer for transitions, in which the cell has time to respond to the presence of a methylotrophic substrate, deriving benefit (energy) without risking buildup of a toxic intermediate. As the activity of the serine cycle begins to increase, more formaldehyde can be safely shunted to assimilatory metabolism via the direct, ATP-independent route, thereby ensuring the transition to growth on the C 1 substrate without build up of formaldehyde. Figure 6 An Interconnected Metabolic Loop for Handling the Toxic Intermediate Formaldehyde A dynamic transition occurs from low to high formaldehyde flux, shifting the ratio of the direct versus long routes, and in the relative proportion of carbon oxidized to CO 2 versus assimilated, creating a buffer system to accommodate large changes in formaldehyde flux. What controls the rate of the nonenzymatic condensation of formaldehyde with H 4 F to form methylene-H 4 F, which was up to 150-fold greater during methanol growth than on succinate? The rate of this spontaneous reaction will be determined by the relative concentrations of reactants and products, with an equilibrium constant for this condensation of 3.2 × 10 −4 [ 15 ]. Although this equilibrium constant favors the production of methylene-H 4 F, flux will only occur if either the concentrations of the reactants (formaldehyde and/or H 4 F) rise above the equilibrium concentration, or utilization of methylene-H 4 F is sufficient to keep the pool of this metabolite below the equilibrium concentration. At this time, it is not technically feasible to measure the intracellular concentrations of free formaldehyde or methylene-H 4 F. However, the most likely explanation for high flux through the nonenzymatic condensation of formaldehyde and H 4 F would be draw-off of the product (methylene-H 4 F) by the serine cycle. In order to test whether the known kinetic parameters explain the relative utilization of the two methylene-H 4 F production routes, a kinetic model was constructed and utilized to simulate formaldehyde partitioning during transitions to and from methylotrophic growth. The ability of the model to recapitulate the observed switch in route utilization (see Figure 3 B) indicates that the architecture of the dynamic loop and the kinetic parameters of the responsible enzymes can predict operation of the H 4 F module in the assimilatory direction and are sufficient to account for partitioning of C 1 units into assimilatory metabolism without accumulation of formaldehyde. In summary, the dual-labeling approach described here for direct flux measurement during metabolic transitions has not only elucidated a key role for redundancy in the three metabolic modules responsible for formaldehyde assimilation and dissimilation, but has also revealed a new paradigm for accommodating high-flux toxic intermediates. It is likely that similar interconnected loop systems operate for other metabolites, toxic or not, and this example can now be used as a framework for predicting functions of other apparently redundant modules that may be involved in the handling of toxic metabolites. Materials and Methods Bacterial strains Wild-type M. extorquens AM1 [ 35 ] and mutant strains were cultured at 30 °C in a minimal salts medium [ 36 ] containing 125 mM methanol or 15 mM succinate. A serine hydroxymethyltransferase mutant strain, CM239K.1 (ΔglyA::kan) was generated using the allelic exchange technique described previously [ 37 ]. CD 3 OD labeling and GC–MS CD 3 OD (99.8%; Cambridge Isotope Laboratories, Andover, Massachusetts, United States) to a final concentration of 1 mM was added to washed cultures that had been resuspended to an OD 600 = 1 in order to label cell metabolites with deuterium for analysis by GC–MS. After shaking for 20 s at room temperature the 2-ml suspension was added to three volumes of boiling 100% ethanol for instant lysis. Following centrifugation, the soluble fraction was dried, resuspended in distilled H 2 O, and centrifuged again to remove H 2 O-insoluble components. The resulting H 2 O-soluble small molecule fraction was then derivatized with ethyl chloroformate and trifluoroacetic acid as previously described [ 22 , 23 ]. All labeling experiments were performed three times. GC–MS methods and data analysis GC–MS experiments were performed using an Agilent 6890 gas chromatograph/Agilent 5973 quadrupole mass selective detector (electron impact ionization) operated at 70 eV equipped with an Agilent 7683 autosampler/injector (Hewlett-Packard, Palo Alto, California, United States). The MS was operated in selected ion monitoring mode to detect M/z = 156/157/158/228/229/230 from 7 min to the end of the method. The GC oven temperature started at an initial temperature of 60 °C, ramping at 20 °C min −1 to 130 °C, 4 °C min −1 to 155 °C, and then 120 °C min −1 to a final temperature of 300 °C that was held for 5 min. Flow through the column was held constant at 1 ml min −1 . The injection volume was 1 μl and the machine was run in splitless mode. The temperature of the inlet was 230 °C, the interface temperature was 270 °C, and the quadrupole temperature was 150 °C. The column utilized was an HP-5MS (Hewlett-Packard). GC–MS data were analyzed using Agilent Enhanced ChemStation G1701CA (Hewlett-Packard). The two mass clusters for serine, M/z = 156/157/158, and 228/229/230, represent fragments of ECF–TFAA derivatized serine (C 10 H 14 O 6 NF 3 ) that have lost one or both of the carboxyl ethyl esters. The data were corrected for the natural abundance of heavy isotopes in the derivatized serine fragments, using proportions calculated with Isoform 1.02 (MS Search Program for Windows, National Institute of Standards and Technology, Gaithersburg, Maryland, United States). For each sample, the ratio of Δ + 1)/Δ + 2) was calculated for both mass clusters and averaged. The mean and standard error for these data were then calculated for the three replicates of each experiment. Assimilation and CO 2 production rates The rate of 14 C-CO 2 production and assimilation of labeled carbon from 14 C-methanol was determined concurrently with the CD 3 OD labeling described above using a modification of a previously described method [ 11 ]. A portion of the labeled cell suspensions was filtered (0.2 μM PVDF, Millipore, Billerica, Massachusetts, United States) to determine net assimilation. All measured and calculated fluxes were determined using the data from each of the three replicate experiments and then utilized to determine the mean and standard error for each flux. Additional values incorporated into flux calculations It has been determined previously that 63.3% of the total CO 2 incorporated originates directly from CO 2 produced from the oxidation of methanol [ 27 ]. This value cannot be determined under the nonsteady state conditions used in the experiments described here, so this value was incorporated directly into our calculations. The sensitivity of the calculated fluxes to a 2-fold increase or decrease in the determined ratio of 1.73:1.00 internal:external CO 2 incorporated into the serine cycle was examined. Besides the direct effect on relative fluxes of internal and external CO 2 into the serine cycle, the calculated incorporation of C 1 units from methylene-H 4 F would vary no more than 7%, which would be balanced by a change in the dissimilatory flux through the H 4 MPT module and formate dehydrogenase of less than 6%. Therefore, deviations in the ratio of methanol-derived and external CO 2 incorporation from the reported work [ 27 ] would not significantly alter the calculated fluxes. Dynamic model The dynamic model of the formaldehyde oxidation and assimilation modules consisted of eight ordinary differential equations, each describing the accumulation of a metabolite involved in the H 4 F and H 4 MPT modules. These equations were derived in a straightforward manner from the kinetic expressions given below. The production of formaldehyde from methanol was set to the measured rate of methanol uptake for each experiment. All enzymatic reactions were treated with either uni- or bimolecular reversible Michaelis–Menten kinetics, with the equilibrium constants taken from the literature [ 38 ]. In cases where K eq > 200, the reverse reaction was ignored for simplicity. Finally, since the dynamics of serine and glycine were not included in this model, serine hydroxymethyltransferase was modeled as an irreversible unimolecular Michaelis–Menten reaction, with the effects of all metabolites other than methylene-H 4 F accounted for in an effective V max . The total internal concentrations of H 4 F and H 4 MPT derivatives were set equal to 0.15 and 0.4 mM, respectively [ 38 ]. Concentrations of other energy and redox cofactors (ATP, NADH, etc.) were assumed equal to those present in Escherichia coli [ 39 ]. The parameters used in the simulation are listed in Table 2 . All K m s could be obtained from the literature (see Table 2 ), except for that of reaction 5. This K m was set arbitrarily to 50 μM, which results in the reaction proceeding at half-maximal rate. Many of the values for V max could be directly calculated from specific activities found in the literature, for both growth on methanol and succinate. To allow for experimental error in the measured rate constants, and to account for the fact that kinetics measured in vitro do not necessarily correlate exactly with what occurs inside the cell, these values were allowed to vary within 50% during the fitting procedure described below. For the remaining parameters, a numerical error minimization technique was used to find the set of parameters yielding model predictions with the best fit to the experimental flux distributions, when integrated to steady state. This was first done for methanol growth, then repeated for succinate growth. The rate constant for spontaneous formaldehyde condensation (k 6 ) was forced to be the same on succinate as on methanol, since this is a fundamental chemical property that is not affected by gene induction. All reverse rate constants were calculated directly from the forward constants, binding constants, and Keq. The spontaneous condensation of formaldehyde with H 4 MPT was assumed to be negligible under physiological conditions compared to the formaldehyde activating enzyme reaction [ 9 ]. All simulations were performed in MATLAB 6.5 (MathWorks, Natick, Massachusetts, United States) using the ODE solving function “ode15s.” The error minimization was also done in MATLAB, using an evolutionary algorithm written previously [ 27 ]. Abbreviations as in Table 2 . Supporting Information Accession Numbers The GenBank ( http://www.ncbi.nlm.nih.gov/Genbank ) accession numbers for genes discussed in this paper are dmrA (AY093431), ftfL (AY279316), and glyA (L33463).

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC539335.xml

549605

Designing an Equitable Strategy for Allocating Antiretroviral Treatments

A study by Wilson and Blower in the February issue of PLoS Medicine addressed the issue of ensuring equity in distributing AIDS medications. Reis and Capron discuss the study's implications

Background Of the roughly 40 million people living with HIV [ 1 ], an estimated 6 million in developing countries urgently need life-saving antiretroviral therapy (ART) [ 2 ]. Yet when the 3 by 5 Initiative was launched by the World Health Organization (WHO) and the Joint United Nations Programme on HIV/AIDS (UNAIDS) in December 2003—an initiative that aims to treat 3 million people with HIV in developing countries with ART by the end of 2005—most people with HIV in these countries did not even know their HIV status, and less than 8% were receiving ART. Moreover, even the success of the initiative would still mean that fewer than half of the people who could benefit from such treatment would be receiving it. Given this gap between what can be done and what needs to be done, the people who set policies and administer programs to provide ART in high-burden countries are faced with difficult questions of distributive justice. Decisions regarding the pricing of ARTs and other care for patients with HIV/AIDS, the distribution of treatment centers, and potential measures to overcome barriers for vulnerable populations will determine who will get access to treatment and who will die. In order to deal with these crucial issues, decision-makers need guidance on how to design policies on equitable access to ART that respect human rights norms and ethical standards. Calculating Equitable Access: A New Study A new study by Wilson and Blower, published in the February issue of PLoS Medicine [ 3 ], addresses an important dimension of equity in AIDS treatment, namely, the accessibility of health facilities to persons in need. Most published measures of spatial accessibility to health care can be classified into four categories based on the measure of accessibility they use: provider-to-population ratios, distance to nearest provider, average distance to a set of providers, and gravitational (which shows the potential interaction between any population point and all service points within a reasonable distance) [ 4 ]. Wilson and Blower developed a mathematical model of the last type that could inform policy-makers' decisions regarding the optimal distribution of treatment sites to ensure equal access by all individuals infected with HIV. Applying this tool to the South-African province of KwaZulu–Natal, Wilson and Blower were able to confirm mathematically the intuitive assumption that using a maximum number of centers, at the least possible distance from most affected populations, would lead to the greatest fairness in the geographical distribution of ART. Strengths and Weaknesses of the Study While the authors suggest that their method could be adapted to take other objectives into account, here they have taken an exclusively egalitarian approach to equity. Although this notion of equity is broadly accepted, other important approaches could have been taken into consideration. Simple equality in access can actually produce inequities (because a fair approach would differentiate among groups in the population according to their different needs); further, under some theories, those who are least advantaged generally should receive a disproportionate share of newly distributed benefits (the maximin principle) [ 5 ]. In geographic terms, this goal could be reached by setting up treatment sites preferentially in neglected rural areas or urban slums. Conversely, utilitarian ethics would favor locating treatment sites so as to maximize overall benefits to the population, such as by concentrating treatment in already existing sites that could scale up treatment volume at the lowest cost per patient. In determining equitable spatial accessibility for the application of their model to KwaZulu–Natal, the authors used a rather rough estimation of HIV prevalence (13% in urban areas and 9% in rural areas). As prevalence greatly varies between specific communities, future studies would certainly benefit from using more disaggregated data where available (see, for example, [ 6 ]). Similarly, as the authors recognize, the geographic accessibility of treatment not only is a function of distance, but may be strongly influenced by other factors, such as available transportation options. The concept of “catchment regions” is a valuable one, though still a factor of great uncertainty. Further research is needed to examine the ability and willingness of patients with HIV to travel, taking into account factors such as disease stage, travel times and transportation prices, and socioeconomic factors. Place matters, but spatial accessibility is only one factor to be overcome in ensuring equitable access to health services. Studies show that even when services are available at a near distance, factors such as temporal accessibility, disease perception, stigmatization, and outright discrimination heavily influence “effective demand” [ 7 ]. Moreover, several studies have shown that the price of ARTs may be one of the greatest barriers to access and adherence [ 8 ], as even small fees at point of service can prove prohibitive for many people. The Future Wilson and Blower have developed a mathematical model to determine the fair geographical distribution of ART treatment sites and have applied it to the specific setting of KwaZulu–Natal. Despite some methodological and data limitations, such studies can inform policy-makers' decisions regarding the location of HIV services. Since distance to a treatment center is strongly determinant of patients' ability to access care, WHO is developing a service availability mapping tool to monitor relative equity between districts and identify major gaps in service availability, for example, availability of ART and prevention of mother-to-child transmission programs. Not only is further research needed to refine the spatial accessibility model presented by the authors but careful attention must be paid to other factors that affect access to HIV services and to the underlying assumptions as to what would constitute fair distribution. In a recent guidance document, WHO and UNAIDS recommended that ART programs include special measures to ensure access of vulnerable and marginalized populations and women to ART [ 9 ]. The decision-making processes regarding who will get treatment and who won't must be closely monitored for transparency and inclusiveness. Evaluators should also be able to determine the extent to which the scaling-up of HIV/AIDS programs are reaching the target populations and producing equitable results (see Figure 1 ). To ensure that this process is robust and evenhanded, the guidance document recommends that national AIDS commissions and programs appoint ethics advisory bodies. These ethics committees are to make sure that issues of equity receive attention alongside technical considerations, such as the manner in which ART programs are integrated into the general health system and the identification and training of health personnel, whose absence is often the greatest barrier to adequate HIV care [ 10 ]. Figure 1 Steps to Equitable Access—The Policy Development Cycle at a Glance IDU, intravenous drug user; NGO, non-governmental organization; PLWHA, people living with HIV/AIDS. (Source: [ 9 ])

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC549605.xml

545798

Integrating phenotypic and expression profiles to map arsenic-response networks

By integrating phenotypic and transcriptional profiling and mapping the data onto metabolic and regulatory networks, it was shown that arsenic probably channels sulfur into glutathione for detoxification, leads to indirect oxidative stress by depleting glutathione pools, and alters protein turnover via arsenation of sulfhydryl groups on proteins.

Background Global technologies in the budding yeast Saccharomyces cerevisiae have changed the face of biological study from the investigation of individual genes and proteins to a systems-biology approach involving integration of global gene expression with protein-protein and protein-DNA information [ 1 ]. These data, when combined with phenotypic profiling of the deletion mutant library of nonessential genes, allow an unparalleled assessment of the responses of yeast to environmental stressors [ 2 - 4 ]. In this study, we used these two genomic approaches to study the response of yeast to arsenic, a toxicant present worldwide, affecting millions of people [ 5 ]. Arsenic, a ubiquitous environmental pollutant found in drinking water, is a metalloid and human carcinogen affecting the skin and other internal organs [ 6 ]. It is also implicated in vascular disorders, neuropathy, diabetes and as a teratogen [ 7 ]. Furthermore, arsenic compounds are also used in the treatment of acute promyelocytic leukemia [ 8 - 10 ]. Consequently, the potential for future secondary tumors resulting from such therapy necessitates an understanding of the mechanisms of arsenic-mediated toxicity and carcinogenicity. However, even though a number of arsenic-related genes and processes related to defective DNA repair, increased cell proliferation and oxidative stress have been described, the exact mechanisms of arsenic-related disease remain elusive [ 11 - 19 ]. This is, in part, due to the lack of an acceptable animal model that faithfully recapitulates human disease [ 15 ]. A number of proteins involved in metalloid detoxification have been described in different organisms, including Saccharomyces cerevisiae . Bobrowicz et al . [ 20 ] found that Arr1 (also known as Yap8 and which is a member of the YAP family that shares a conserved bZIP DNA-binding domain) confers resistance to arsenic by directly or indirectly regulating the expression of the plasma membrane pump Arr3 (also known as Acr3), another mechanism for arsenite detoxification of yeast in addition to the transporter gene, YCF1 [ 21 ]. Arr3 is 37% identical to a Bacillus subtilis putative arsenic-resistance protein and encodes a small (46 kilodalton (kDa)) efflux transporter that extrudes arsenite from the cytosol [ 22 , 23 ]. Ycf1, on the other hand, is an ATP-binding cassette protein that mediates uptake of glutathione-conjugates of AsIII into the vacuole [ 21 , 22 ]. Until recently, very little was known about arsenic-specific transcriptional regulation of detoxification genes. Wysocki et al . [ 24 ] found that Yap1 and Arr1 (called Yap8 in their paper) are not only required for arsenic resistance, but that Arr1 enhances the expression of Arr2 and Arr3 while Yap1 stimulates an antioxidant response to the metalloid. Menezes et al . [ 25 ], on the other hand, found that arsenite-induced expression of Arr2 and Arr3, as well as Ycf1, is likely to be regulated by both Arr1 (called Yap 8 in their paper) and Yap1. Although Arr1 and Yap1 seem specifically suited for arsenic tolerance, the other seven YAP-family proteins are still worthy of investigation in light of the fact that each one regulates a specific set of genes involved in multidrug resistance with overlaps in downstream targets. One such interesting protein is Cad1 (Yap2). Although Yap1 and Cad1 are nearly identical in their DNA-binding domains, Yap1 controls a set of genes (including Ycf1) involved in detoxifying the effects of reactive oxygen species, whereas Cad1 controls genes that are over-represented for the function of stabilizing proteins in an oxidant environment [ 26 ]. However, Cad1 also has a role in cadmium resistance. As arsenic has metal properties, it is conceivable that Cad1 might play a greater part in arsenic tolerance and perhaps more so than the oxidative-stress response gene, YAP1 . Understanding the role of AP-1-like proteins (such as YAP family members) in metalloid tolerance was one of the goals in this study within the realm of the larger objective - using an integrative experimental and computational approach to combine gene expression and phenotypic profiles (multiplexed competitive growth assay) with existing high-throughput molecular interaction networks for yeast. As a consequence we uncovered the pathways that influence the recovery and detoxification of eukaryotic cells after exposure to arsenic. Networks were analyzed to identify particular network regions that showed significant changes in gene expression or systematic phenotype. For each data type, independent searches were performed against two networks: the network of yeast protein-protein and protein-DNA interactions, corresponding to signaling and regulatory effects (the regulatory network); and the network of all known biochemical reactions in yeast (the metabolic network). For the gene-expression analysis, we found several significant regions in the regulatory network, suggesting that Yap1 and Cad1 have an important role. However, no significant regions in the metabolic network were found. In order to test the functional significance of Yap1 and Cad1, we used targeted gene deletions of these and other genes, to test a specific model of transcriptional control of arsenic responses. In contrast to the gene-expression data, the phenotypic profile analysis revealed no significant regions in the regulatory network, but two significant metabolic networks. Furthermore, we found that phenotypically sensitive pathways are upstream of differentially expressed ones, indicating that metabolic pathway associations can be discerned between phenotypic and transcriptional profiling. This is the first study to show a relationship between transcriptional and phenotypic profiles in the response to an environmental stress. Results and discussion Transcript profiling reveals that arsenic affects glutathione, methionine, sulfur, selenoamino-acid metabolism, cell communication and heat-shock response Before gene-expression analysis of arsenic responses in S. cerevisiae , we performed a series of dose-response studies. We found that treatment of wild type cells with 100 μM and 1 mM AsIII had a negligible effect on growth, but that these cells still exhibited a pronounced transcriptional response (see Additional data files 1 and 2). Microarray analysis of biological replicates (four chips per replicate experiment) of the high-dose treated cells (1 mM AsIII) clustered extremely well together when using Treeview (see Materials and methods, and Additional data file 2). The lower dose time-course (100 μM AsIII) showed the beginning of gene-expression changes at 30 minutes, with the robust changes occurring at 2 hours, or one cell division (see Additional data file 2). The 2 hour, 100 μM dose clustered together with the 30 minute, 1 mM biological replicates and was in fact so similar to them that an experiment of one set of four chips for the 2 hour lower dose was deemed sufficient. Furthermore, when combining the three datasets (2 hour, 100 μM AsIII and each 30 minute, 1 mM AsIII replicate data) and using a 95% confidence interval (see Materials and methods) we found 271 genes that were not only statistically significant in at least 75% of the total data (9 out of 12 chips), but also that the direction and level of expression of these genes were similar between the datasets. The lower dose time-course also included a 4 hour treatment, or two cell divisions. This experiment demonstrated the greatest degree of variability, indicating either a cycling effect or the cell's return to homeostasis, which was further exemplified by a decrease in the transcriptional response (see Additional data file 2). Genes were categorized by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Simplified Gene Ontology (biological process, cellular component and molecular function) (Table 1 ). In total, 829 genes out of 6,240 had significantly altered expression (see Materials and methods) in at least one experimental condition. The categories significantly enriched for differentially expressed genes in the KEGG pathways were glutathione, methionine, sulfur and selenoamino-acid metabolism, and in the Simplified Gene Ontology (biological process), cell communication and heat-shock response (Table 1 ). Network mapping of transcript profiling data finds a stress-response network involving transcriptional activation and protein degradation We used the Cytoscape network visualization and modeling environment together with the ActiveModules network search plug-in to carry out a comprehensive search of the regulatory and metabolic networks [ 27 , 28 ]. The former consists of the complete yeast-interaction network of 20,985 interactions, in which 5,453 proteins are connected into circuits of protein-protein or protein-DNA interactions [ 29 , 30 ]. For each protein in this network, we defined a network neighborhood containing the protein and all its directly interacting partners. In the metabolic network, based on a reconstruction by Forster et al . [ 31 ] with 2,210 metabolic reactions and 584 metabolites, nodes represent individual reactions and edges represent metabolites. A shared metabolite links two reactions. We searched for sequences of related reactions governed by sensitive proteins (enzymes) in the phenotypic profiling data. To aid visualization, these sequences of reactions were combined to create metabolic pathways. We then identified the neighborhoods associated with significant changes in expression using the ActiveModules plug-in. This process resulted in the identification of seven significant neighborhoods in the regulatory network, centered on nodes Fhl1, Pre1, Yap1, Cad1, Hsf1, Msn2 and Msn4 (Figure 1 ). Together these neighborhoods narrow the significant data to 20% of the genes with the most significant changes in expression across one or more arsenic conditions (see Materials and methods and Additional data file 2). We did not find the emergence of any significant neighborhoods in the metabolic network. The highest-scoring regulatory network neighborhood was defined by the transcription factor Fhl1 (Figure 1a ). Its expression did not change significantly, but it was the highest-scoring node as judged by the significant expression changes observed for its surrounding neighborhood. Fhl1 controls a group of proteins important for nucleotide and RNA synthesis, as well as the synthesis and assembly of ribosomal proteins [ 32 ] which, from our data, are downregulated by arsenic exposure. Downregulation of ribosomal proteins in response to environmental stress has been reported previously [ 33 , 34 ], but to our knowledge this is the first association of Fhl1 as a key control element in this process. It seems likely that the repression of de novo protein synthesis in response to arsenic allows energy to be diverted to the increased expression of genes involved in stress responses and protection of the cell. One such pathway may involve sulfur metabolism, which leads to glutathione synthesis. In fact, included in Figure 1 is Met31 (Figure 1e ), a transcriptional regulator of methionine metabolism, which interacts with Met4, an important activator of the sulfur-assimilation pathway that is probably involved in the glutathione-requiring detoxification process. While the differential expression of this neighborhood was not strictly significant according to ActiveModules (see Materials and methods), it has high biological relevance in light of the statistically significant alteration in expression categorized using KEGG pathways (Table 1 ). Another high-scoring neighborhood comprises part of the proteasome protein complex (Figure 1b ). The components of the proteasome are likely to be upregulated to meet the increased demand for protein degradation brought about by the binding of AsIII to the sulfhydryl groups on proteins and/or glutathione that subsequently interfere with numerous enzyme systems such as cellular respiration [ 7 , 15 ]. In this paper, we will propose that this occurs through indirect oxidative stress as a result of the depletion of glutathione. The role of transcription factors Yap1 and Cad1 and the metalloid stress response Many of the central proteins in the significant neighborhoods uncovered by ActiveModules were transcription factors (Figure 1a,c-f ). Although some of these proteins were not differentially expressed themselves, they were still high-scoring nodes because of the highly significant expression of their targets. This is also important to keep in mind as we discuss later which genes might be sensitive to arsenic, but not necessarily differentially expressed, and why many genes that are differentially expressed do not display sensitive phenotypes when deleted. Transcription factors Msn2, Yap1, Msn4, Cad1 and Hsf1 were the central proteins for many of the significant neighborhoods found (Figure 1c,d,f ). Together with several genes previously implicated in oxidative-stress responses, these neighborhoods compose a stress-response network [ 24 , 26 , 35 - 39 ]. Of particular interest are Yap1 and Cad1, because of the high number of shared downstream targets (Figure 1c,f ). When overexpressed, Yap1 confers resistance to several toxic agents, and Yap1 mutants are hypersensitive to oxidants [ 33 , 40 - 44 ]. Conversely, Cad1 responds strongly to cadmium, but not to hydrogen peroxide (H 2 O 2 ) [ 26 , 35 ]. Following arsenic exposure, Yap1 is induced at least fourfold, with many of its downstream targets showing high levels of induction (see Additional data file 3). Several of its targets are among the most highly upregulated genes (as high as 178-fold for OYE3 (encoding a NADPH dehydrogenase)). Moreover, Yap1 regulates GSH1 , which encodes γ-glutamylcysteine synthetase (an enzyme involved in the biosynthesis of antioxidant glutathione), TRX2 (the antioxidant thioredoxin), GLR1 (glutathione reductase) and drug-efflux pumps ATR1 and FLR1 [ 35 , 45 - 50 ]. It should be noted that GSH1 and ATR1 are examples of several genes also targeted by Cad1. All of these specified Yap1 targets are induced after arsenic exposure, recapitulating the toxicant's role as a likely oxidant. During the course of this work, Wysocki et al . [ 24 ] also implicated Yap1 in arsenic tolerance. As Cad1 and Yap1 share many downstream targets, the genes defined by these transcription factors are very similar. To determine which transcription factor is playing the most active role in the high level of differential expression for this group (see Figure 1c,f ), we tested the roles of both activators by treatment of yap1Δ and cad1Δ deletion strains with 100 μM AsIII for 2 hours (Additional data file 4). Surprisingly, we did not find that Cad1 was involved in regulation in response to arsenic-mediated stress. The yap1Δ strain was not only sensitive to AsIII by phenotypic profiling (Additional data file 5) but also defective in the induction of several downstream enzymes with antioxidant properties (Figure 2a,b ). Conversely, the cad1Δ strain displayed an almost identical profile to wild type, eliminating it as a strong factor in the arsenic response (Figure 2a,b ). A list of arsenic-mediated genes with at least a twofold difference in expression compared to wild type for yap1Δ and cad1Δ is provided (Additional data files 6 and 7). These were generated using Rosetta Resolver with a p -value less than 0.001 (see Materials and methods for more detail). Also, Additional data files 8 and 9 contain tables of genes failing to be induced or repressed (or showing such a decrease in expression that they no longer make significantly expressed gene lists) in the yap1Δ and cad1Δ experiments, compared to the parent experiment, after treatment with 100 μM AsIII for 2 hours. These are lists of genes that would be potentially regulated by Yap1 and Cad1 in the presence of arsenic. The proteasome responds to arsenic, and Rpn4 mediates a transcriptional role Treatment of yeast with as little as 100 μM AsIII for 2 hours resulted in the induction of at least 14 ubiquitin-related and proteasome gene products (Figure 1b and Figure 3 ). The eukaryotic proteasome consists of a 20S protease core and a 19S regulator complex, which includes six AAA-ATPases known as regulatory particle triple-A proteins (RPT1-6p) [ 51 , 52 ]. Proteins are targeted for degradation by the proteasome via the covalent attachment of ubiquitin to a lysine side chain on the target protein (Figure 3 ). Conjugating enzymes then function together with ubiquitin-ligase enzymes to adhere to the target protein, and are tailored to carry out specific protein degradation in DNA repair, growth control, cell-cycle regulation, receptor function and stress response, to name a few [ 53 , 54 ]. The apparent importance of Yap1 in response to possible oxidative damage by arsenic indicated a potential role for Rpn4 (induced eightfold, Figure 3 ). This is a 19S proteasome cap subunit, which also acts as a transcriptional activator of the ubiquitin-proteasome pathway and a variety of base-excision and nucleotide-excision DNA repair genes [ 34 , 55 , 56 ]. Rpn4 is required for tolerance to cytotoxic compounds and may regulate multidrug resistance via the proteasome [ 57 ]. Moreover, Owsianik et al . [ 57 ] identified an YRE (Yap-response element) site present in the RPN4 promoter. This YRE was found to be functional and important for the transactivation of RPN4 by Yap1 in response to oxidative compounds, such as H 2 O 2 . However, we also located the Rpn4-binding sequence, TTTTGCCACC, 47 bases distant from the open reading frame (ORF) of YAP1 , indicating that Yap1 not only activates Rpn4, but that Rpn4 may in fact activate Yap1 [ 58 ]. In support of this hypothesis we found that relative to wild type, the level of Yap1 induction was lower in the rpn4Δ strain under arsenic stress conditions, whereas Rpn4 was equally induced in the yap1Δ strain (Additional data file 10). With respect to wild type, the profile of rpn4Δ after treatment with arsenic was the most dramatically altered, save for arr1Δ (Figure 2 and Additional data files 11 and 12). These data suggest that arsenic modification of sulfhydryl groups on proteins leads to protein inactivation and therefore degradation via the 26S proteasome. Another scenario is that the proteasome, and/or its proteases, is sensitive to arsenic-related events, leading to dysfunctional protein turnover and an increased requirement for 26S proteasome subunits. A similar idea was proposed for the direct methylating agent, methylmethane sulfonate [ 34 ]. ARR1 transcriptional responses Arr1 is structurally related to Yap1 and Cad1 [ 20 , 24 ]. However, little is known about how Arr1 may be involved in oxidative stress and/or multidrug resistance. Furthermore, Arr1 is not well represented by the interactions present in the yeast regulatory network. However, studies by Bobrowicz et al . [ 20 , 59 ] show that the transcriptional activation of Arr3 requires the presence of the Arr1 gene product. Moreover, a report by Bouganim et al . [ 60 ] supports our finding that Yap1 also is important for arsenic resistance. They show that overproduction of Yap1 blocks the ability of Arr1 to fully activate Arr3 expression at high doses of arsenite, suggesting that Yap1 can compete for binding to the promoter of the Arr1 target gene, ARR3 . While this paper was being written, Tamas and co-workers [ 24 ] showed that Arr1 transcriptionally controls Arr2 and Arr3 expression from a plasmid containing their promoters fused to the lacZ gene and measuring β-galactosidase activities. This was done by growing the cells for 20 hours with a low dose of metalloid and spiking the concentration to 1 mM AsIII for the last 2 hours of incubation. These experiments showed that ARR1 deletion resulted in complete loss of Arr3- lacZ induction, whereas YAP1 deletion did not significantly affect induction. Similar results were obtained for the Arr2- lacZ induction assay and the authors concluded that Yap1 has a role in metalloid-dependent activation of oxidative stress response genes, whereas the main function of Arr1 seems linked to the control of Arr2 and Arr3. Interestingly, this study was shortly followed by another from Menezes et al . [ 25 ] which found contrasting results when looking at mRNA and Northern-blot analysis. In this study, the induction of Arr2 and Arr3, after treatment with 2 mM AsIII for up to 90 minutes, did not occur in either the ARR1 -deleted strain or the YAP1 -deleted strain. These authors conclude that the requirement for both YAP1 and ARR1 is vital to yeast in the function of regulating and inducing genes important for arsenic detoxification. Finally, transcription profiling experiments presented here show that the arsenic transport proteins Arr2 and Arr3 are still expressed (2.9-fold induction for Arr2 and 1.8-fold for Arr3, respectively) in the ARR1 mutant, but show defective induction in the yap1Δ strain treated in parallel (Additional data files 4 and 10). These results indicate that Yap1 may control Arr2 and Arr3 when yeast is subjected to 100 μM AsIII for 2 hours. Our results and those of Menezes et al . [ 25 ], in contrast to the results of Tamas and colleagues [ 24 ], might be explained by the following. Our and Menezes et al. 's studies looked at genes in the normal chromosome context rather than genes ectopically expressed from a plasmid; in addition, in our study, we treated the yeast with 100 μM AsIII while Wysocki et al . [ 24 ] started with a low dose, but spiked the concentration to 1 mM AsIII in the last 2 hours of incubation. However, Menezes et al . [ 25 ] used an even higher dose (2 mM AsIII for a time-course ending at 90 minutes) and obtained more similar results to ours, with the exception that their Northern-blot analysis, which can sometimes miss relatively small changes, indicated an apparent lack of induction of ARR2 or ARR3 in either the ARR1 - or YAP1 -deleted strains. Taken together, these data indicate that both ARR1 and YAP1 are important genes involved in the process of arsenite detoxification in the yeast cell, but because of the different strains and treatment protocols used between these three studies, further experiments are warranted to resolve the differences. Other interesting results from our transcription profiling of the arr1Δ and parent strains after arsenic treatment (Figure 2a,d and Additional data files 13 and 14), included large differences in expression as a whole and in particular the inability of arr1Δ to induce serine biosynthesis-related genes such as SER3 , and sulfur and methionine amino-acid metabolism genes including SAM4 . Conversely, arr1Δ failed to repress SAM3 , as well as CIT2 , a glutamate biosynthesis gene, when compared to the parent profile. These observations indicate that Arr1 may regulate sulfur-assimilation enzymes that are necessary for arsenic detoxification. This is particularly interesting considering that the ActiveModules algorithm identified the node Met31 (Figure 1e ), the transcriptional regulator of methionine metabolism which interacts with Met4, an important activator of the sulfur-assimilation pathway that is likely to be involved in the glutathione-requiring detoxification process. Sulfur metabolism was also a functional category in the Simplified Gene Ontology found to be significantly enriched by the hypergeometric statistical test (see Materials and methods) (Table 1 ). Furthermore, phenotypic profiling results discussed later show the importance of serine and glutamate metabolism in the sensitivity response to arsenic. Lastly, it is important to note that arr1Δ also displays loss of expression of a number of ubiquitin-proteasome-related gene products, sharing similar expression patterns with rpn4Δ (Additional data files 13 and 14) and suggesting that it may have a role in protein degradation as well. Arsenic treatment stimulates cysteine and glutathione biosynthesis and leads to indirect oxidative stress Our arsenic-treatment experiments revealed the strong induction of over 20 enzymes in the KEGG sulfur amino acid and glutathione biosynthesis pathways (Table 1 ). This is consistent with the hypothesis that glutathione acts as a first line of defense against arsenic by sequestering and forming complexes with the toxic metalloid [ 21 ]. Dormer et al . [ 61 ] showed that GSH1 induction by cadmium is dependent on the presence of Met4, Met31, Met32 and Cbf1 in the transcriptional complex of MET genes. Met4 and Met32 are also differentially expressed in response to arsenic and interact with Met31, which defines a network neighborhood as shown in Figure 1e . The biological impact of the sulfur-related stress response was further exemplified by comparisons of our arsenic profiles to H 2 O 2 profiles (400 μM H 2 O 2 ) from Causton et al . [ 62 ] (Table 2 ). Although we found many expected similarities between arsenic and H 2 O 2 gene-expression profiles in regard to oxidative-stress response genes, sulfur and methionine metabolism genes, in response to H 2 O 2 , were either repressed or did not change (Table 2 ). Furthermore, a study by Fauchon et al . [ 63 ] showed that yeast cells treated for 1 hour with 1 mM of the metal Cd 2+ , responded by converting most of the sulfur assimilated by the cells into glutathione, thus reducing the availability of sulfur for protein synthesis. Our arsenic profile showed a similar response to the sulfur-assimilation profile seen with Cd 2+ (Table 2 ). As a consequence, arsenic may be conferring indirect rather than direct oxidative stress mediated by the depletion of glutathione, thus inhibiting the breakdown of increasing amounts of H 2 O 2 by glutathione peroxidase ( GPX2 , up 13-fold) (Figure 4 ) [ 21 , 64 ]. Phenotypic profiling defines arsenic-sensitive strains and maps to the metabolic network To identify genes and pathways that confer sensitivity to arsenic, we identified deletion mutants with increased sensitivity to growth inhibition using a deletion mutant library of nonessential genes (4,650 homozygous diploid strains) [ 65 , 66 ]. Each strain contains two unique 20-bp sequences (UPTAG and DOWNTAG) enabling their growth to be analyzed en masse and the fitness contribution of each gene to be quantitatively assayed by hybridization to high-density oligonucleotide arrays. The top 50 sensitive deletion strains included: THR4 , SER1 , SER2 , CPA2 , CPA1 , HOM2 , HOM3 , HOM6 , ARG1 , YAP1 , CDC26 , ARR3 , CIN2 , ARO1 , ARO2 and ARO7 . A listing of the rank order for all sensitivities is available (Additional data file 5). Only 10% of the top 50 sensitive mutant strains were significantly differentially expressed in the transcript profile. This lack of direct correlation between gene expression and fitness data is consistent with data from our own and other laboratories [ 2 , 4 , 65 ]. At least three factors may contribute to this discrepancy. First, some highly expressed genes when deleted are nonviable (around 1,000 genes) and are therefore unable to be scored for fitness. Some examples of highly expressed, yet nonviable, genes under arsenic stress are ERO1 (7- to 10-fold induced), HCA4 (5- to 9-fold induced), and DCP1 (9- to 22-fold induced). Second, there are redundant pathways mediated by multiple genes, such that deletion of one does not lead to sensitivity. OYE2 , OYE3 , and a large number of reductases fall into this category. Finally, gene products that do not change significantly, mediate important biological responses and thus when deleted could sensitize the cell to a specific stressor. ARO1 , ARO2 , THR4 and HOM2 are examples of genes that are not differentially expressed but are very sensitive to arsenic. Like the gene-expression data, the phenotypic data was subjected to searches performed against the regulatory network of yeast protein-protein and protein-DNA interactions as well as the metabolic network of all known biochemical reactions in yeast. Unlike the transcription profile, the phenotypic data analysis revealed no significant regions in the regulatory network, but did map to two statistically significant metabolic networks. The first significant pathway was amino acid synthesis/degradation with the terminal products being L-threonine and L-homoserine, beginning with precursors such as L-arginine, fumarate and oxaloacetate (Figure 5a ). These products function in serine, threonine and glutamate metabolism. The second network indicated the importance of the shikimate pathway, which is essential for the production of aromatic compounds in plants, bacteria and fungi (Figure 5b ). The shikimate pathway operates in the cytosol of yeast and utilizes phosphoenol pyruvate and erythrose 4-phosphate to produce chorismate through seven catalytic steps. It is a pathway with multiple branches, with chorismate representing the main branch point, and various branches giving rise to many end products. Interestingly, chorismate is also used for the production of ubiquinone, p -aminobenzoic acid (PABA) and folates, which are donors to homocysteine [ 67 - 69 ]. Relationship between gene-expression and phenotypic profiles Combining transcript profiling and phenotypic profiling provides deeper insights into the biology of arsenic responses. Until now there has been a lack of correlation between the differential expression of genes and sensitivity of deletion mutants [ 2 , 4 , 65 ] and this was the case in the present study. However, by mapping each dataset to the regulatory and metabolic networks, we have uncovered the likely reason for this lack of congruence. Our data show that many of the most sensitive genes (Additional data file 5; top 50 ranks) are involved in serine and threonine metabolism, glutamate, aspartate and arginine metabolism, or shikimate metabolism, which are pathways upstream of the differentially expressed sulfur, methionine and homocysteine metabolic pathways, respectively. These downstream pathways are important for the conversion to glutathione, necessary for the cell's defense from arsenic (Figures 4 , 5a , 6 and Table 1 ). This overlap of sensitive upstream pathways and differentially expressed downstream pathways provides the link between transcriptional and phenotypic profiling data (Figures 4 and 6 ). Thus, we believe our work shows that the deletion of an individual gene can lead to a change in sensitivity to an agent only if the protein product of that gene is important for some process (for example, amino-acid synthesis or a transcription factor required for the increased expression of genes needed to protect against the agent). On the other hand, expression profiling shows the end product of the cell's response to arsenic. Therefore, an agent such as arsenic might cause a transcription factor (Yap1, for example) to increase the expression of as many as 50 genes, 20 of which might help to protect against the agent. However, deletion of any of the 50 would not be expected to have an effect on the response to arsenic. The effect of gene deletion would be on the transcription factor itself (whose expression might not be affected by the agent). Thus, in the case of arsenic exposure, we conclude that phenotypic profiling interrogates genes upstream of the genes that ultimately protect against arsenic toxicity and that the downstream targets that demonstrate differential expression probably share redundant functions and are not vulnerable in the phenotypic profiling (Figure 6 ). Conclusions Systems biology represents an important set of methods for understanding stress responses to environmental toxicants, such as arsenic. In this study we have catalogued the centers of activity associated with arsenic exposure in yeast, identifying the key neighborhoods of activity in the regulatory and metabolic networks using the visualization tools and algorithms in Cytoscape. The transcriptional profile mapped to the regulatory network, revealing several important nodes (Fhl1, Msn2, Msn4, Yap1, Cad1, Pre1, Hsf1 and Met31) as centers of arsenic-induced activity. From these results we can conclude that arsenic detoxification in yeast focuses around: nucleotide and RNA synthesis; methionine metabolism and sulfur assimilation; protein degradation; and transcriptional regulation by proteins that form a stress-response network. In summary, protein synthesis in response to arsenic allows energy to be diverted toward the genes channeling sulfur into glutathione, which then leads to indirect oxidative stress by depleting glutathione pools and alters protein turnover. These processes require regulation by transcription factors, the understanding of which we refined by analysis of specific knockout strains. Our experiments, in fact, confirmed that the transcription factors Yap1, Arr1 and Rpn4 strongly mediate the cell's adaptation to arsenic-induced stress but that Cad1 has negligible impact. Finally, contrary to the gene-expression analyses, the phenotypic profiling data mapped to the metabolic network. The two significant metabolic networks unveiled were shikimate and serine, threonine and glutamate biosynthesis. Our goal was to integrate the computational identification of these important pathways found via transcript and phenotypic profiling by regulatory and metabolic network mapping. In doing so, we have shown that genes that confer sensitivity to arsenic are in pathways that are upstream of the genes that are transcriptionally controlled by arsenic and share redundant functions. Materials and methods Strains, media and growth conditions S. cerevisiae strain BY4741 ( MAT a , his3Δ , leu2Δ0 , met15Δ0 , uraΔ0 ) was used and grown in synthetic complete medium at 30°C. Cells were grown to a density of 1 × 10 7 cells per ml. Cultures were split into two; NaAsO 2 (100 μM and 1 mM in two biological repeats) was added to one culture, and both were incubated at 30°C for 0.5, 2 or 4 h. Cells were pelleted and washed in distilled water before RNA extraction. Deletion strains ( yap1Δ , cad1Δ , arr1Δ and rpn4Δ ) of the same background were obtained from Research Genetics, confirmed and treated the same way, for 2 h and 100 μM NaAsO 2 . RNA extraction For the cDNA hybridization experiments, total RNA was isolated using an acid-phenol method. Pellets were resuspended in 4 ml lysis buffer (10 mM Tris-HCL pH 7.5, 10 mM EDTA, 0.5% SDS). Four milliliters of acid (water-saturated, low pH) phenol was added followed by vortexing. The lysing cell solutions were incubated at 65°C for 1 h with occasional vigorous vortexing and then placed on ice for 10 min before centrifuging at 4°C for 10 min. The aqueous layers were re-extracted with phenol (room temperature, no incubation) and extracted once with chloroform. Sodium acetate was then added to 0.3 M with 2 volumes of absolute ethanol, placed at -20°C for 30 min, and then spun. Pellets were washed two or three times with 70% ethanol followed by Qiagen Poly(A) + RNA purification with the Oligotex oligo (dT) selection step. Total RNA for the specific knockout strains and parent experiment was isolated by enzymatic reaction, following the RNeasy yeast protocol (Qiagen). Microarray hybridizations and analyses A cDNA yeast chip, developed in-house at National Institute of Environmental Health Sciences (NIEHS), was used for gene-expression profiling experiments. A complete listing of the ORFs on this chip is available at [ 70 ]. cDNA microarray chips were prepared as previously described [ 71 , 72 ]. The cDNA was spotted as described [ 73 ]. Each poly(A) RNA sample (2 μg) was labeled with Cy3- or Cy5-conjugated dUTP (Amersham) by a reverse transcription reaction using the reverse transcriptase SuperScript (Invitrogen), and the primer oligo(dT) (Amersham). The hybridizations and analysis were performed as described Hewitt et al . [ 74 ] except that genes having normalized ratio intensity values outside of a 95% confidence interval were considered significantly differentially expressed. Lists of differentially expressed genes were deposited into the NIEHS MAPS database [ 75 ]. Genes that were differentially expressed in at least three of the four replicate experiments were compiled and subsequently clustered using the Cluster/Treeview software [ 76 ]. GeneSpring (Silicon Genetics) and Cytoscape [ 28 ] were used to further analyze and visualize the data. The knockout experiments were conducted on an Agilent yeast oligo array platform. Samples of 10 μg total RNA were labeled using the Agilent fluorescent direct label kit protocol and hybridizations were performed for 16 h in a rotating hybridization oven using the Agilent 60-mer oligo microarray-processing protocol. Slides were washed as indicated and scanned with an Agilent scanner. Data was gathered using the Agilent feature extraction software, using defaults for all parameters, save the ratio terms. To account for the use of the direct label protocol, error terms were changed to: Cy5 multiplicative error = 0.15; Cy3 multiplicative error = 0.25; Cy5 additive error = 20; Cy3 additive error = 20. GEML files and images were exported from the Agilent feature extraction software and deposited into Rosetta Resolver (version 3.2, build 3.2.2.0.33) (Rosetta Biosoftware). Two arrays for each sample pair, including a fluor reversal, were combined into ratio experiments in Rosetta Resolver. Intensity plots were generated for each ratio experiment and genes were considered 'signature genes' if the p -value was less than 0.001. p -values were calculated using the Rosetta Resolver error model with Agilent error terms. The signature genes were analyzed with GeneSpring. The entire in-house and Agilent-based dataset is available in the Additional data files. Ontology enrichment Genes have previously been categorized into various ontologies and pathways. If a particular pathway is enriched for genes that are significantly expressed in response to a process, we conclude that the pathway is likely to be involved in this process. In total, 829 genes out of 6,240 had a significant alteration in expression in at least one experimental condition. Along with the size of each functional category, a statistical measure for the significance of the enrichment was calculated by using a hypergeometric test. The level of significance for this test was determined using the Bonferroni correction, where the α value was set at 0.05 and the number of tests conducted for KEGG pathway and Simplified Gene Ontology (biological process) were 27 and 11, respectively. Network searches The ActiveModules algorithm was used to identify neighborhoods in the regulatory network corresponding to significant levels of differential expression. In this search, if a protein has many neighbors, it is likely that at random a few will show significant changes in expression and these could be selected as a significant sub-network. Neighborhood scoring is a method we used to correct for this bias. In this scheme, a significant sub-network must contain either all or none of the neighbors of each protein. The significance then represents an aggregate over all neighbors of a protein. This prevents the biased selection of a few top-scoring proteins out of a large neighborhood in the search for significant sub-networks. For an in-depth description of this algorithm see Ideker et al . [ 1 ]. In defining the network used in the metabolic analysis, edges corresponding to metabolites linking more than 175 reactions were eliminated. This excludes metabolic cofactors such as ATP, NADH and H 2 O from the search. Scores for each ORF were generated by mapping the fitness significance value to a Z-score. To assign scores to the individual reactions, Förster's mapping from ORF to reaction was used to generate a list of ORFs for each reaction. The Z-scores of these ORFs were then aggregated into a single score for that reaction using the following equation: We used a dynamic programming algorithm adapted from Kelley et al . [ 77 ] to identify high-scoring paths in this network. Briefly, the highest-scoring path of length ( n ) ending at each node is determined by combining the scores of the individual node and the highest-scoring path of length ( n - 1) ending at a neighbor node using the following formula: Since a node with many neighbors is more likely to belong to a high-scoring path by random chance, the score of the neighboring path is corrected against the extreme-value statistic with the number of observations equal to the number of neighbors. The significances of the top-scoring networks were determined by comparison to a distribution of the top-scoring networks from random data (reaction scores randomized with respect to the nodes of the network). After running the path finding/scoring algorithm, the score of the single highest-scoring path was added to the null distribution. This process was repeated for 10,000 interactions. This null distribution was then used to determine an empirical p -value, which represents the null hypothesis that there is no significant correlation between the topology of the metabolic network and the assignment of significance values to nodes in that network. Specific deletion experiment filter on fold-change comparisons The intensity plots were generated from each experiment in Rosetta Resolver. A gene was considered a signature gene if the p -value was less than 0.001 and if the fold-change value was greater than or equal to twofold. Signature genes were then broadcasted on the intensity plot and exported as text files. Lists were imported into GeneSpring. The 'Filter on Fold Change' function was used to compare the parent control vs. parent AsIII experiment with each deletion (AsIII) experiment. The gene list selected for each filter on fold change analysis was a combination of the parent signature gene list and the signature gene list of the AsIII-treated deletion being analyzed at the time. For example, if the comparison was being done between parent (AsIII-treated) and Yap1 (AsIII-treated), the list used in the analysis was the combination of the parent signature genes and the Yap1 signature genes. The filter on fold change function reports genes that were selected from the one condition (parent) that had normalized data values that were greater or less than those in the other condition (deletion under investigation) by a factor of twofold. Each resulting gene list was saved. All the resulting gene lists were combined and an annotated gene list was exported for use in Eisen's Cluster/Treeview package (described earlier). The format of the exported data was the natural log. The gene tree generated for the paper was generated in GeneSpring. Each filter on fold change was saved as an annotated gene list. Generation of specific deletion experiment 'minus' lists Signature gene lists were generated in Rosetta Resolver from intensity plots as described above. Each signature gene list was saved as a 'Bioset' in Resolver. The parent Bioset was compared to each deletion Bioset using the 'Minus' function. This function finds those members in Bioset group 1 (parent) that do not exist in Bioset group 2 (deletion). Each of the resulting lists was saved as a new Bioset. The new 'minus' Bioset was broadcasted on its corresponding intensity plot and exported as a text file. This was repeated for each experiment with fine-tuning of the data using GeneSpring. Phenotypic profiling Homozygous diploid deletion strains and pooling of the strains were done as described [ 66 ]. Aliquots were grown until logarithmic phase, diluted to OD 600 0.05-0.1, split into tubes and treated with arsenic for 1-2 h at 1 mM, 2 mM and 5 mM concentrations. Similar responses were observed at each concentration, so the results were pooled. These cultures and a mock-treated sample were maintained in logarithmic phase growth by periodic dilution for 16-18 h. UPTAG and DOWNTAG sequences were separately amplified from genomic DNA of the drug and mock-treated samples by PCR using biotin-labeled primers as described previously [ 66 ]. The amplification products were combined and hybridized to Tags3 arrays (Affymetrix). Procedures for PCR amplification, hybridization and scanning were done as described [ 66 ], and according to the manufacturer's recommendation when applicable. The images were quantified by using the Affymetrix Microarray Suite software. UPTAG and DOWNTAG values were separately normalized, ratioed (treated sample signal/control) and filtered for intensities above background [ 78 ]. Additional data files The following additional data files are available with the online version of this article and at [ 79 ]. Additional data file 1 shows the dose-response curve of S. cerevisiae strain BY4741 ( MAT a , his3Δ , leu2Δ0 , met15Δ0 , uraΔ0 ) grown in synthetic complete medium at 30°C after treatment with arsenic. Treatment with 1 mM, 2 mM and 5 mM AsIII resulted in a negligible effect on growth (after 18 h) and survival (1 h treatment followed by plating and colony formation counting), but still exhibited a pronounced transcriptional response (see Additional data file 2 ). Additional data file 2 contains a figure showing all genes found to be significant by MAPS analysis (see Materials and methods) which were compiled across the four arrays, averaged and subsequently clustered with Cluster/Treeview software (Eisen et al. [ 76 ]). The dendogram highlighted in pink depicts the zoomed in region shown to the right of the entire tree. Genes in red are induced and genes in green are repressed. A table depicts the numbers of genes changing in each experiment at both the 95% and 99% confidence intervals (see Materials and methods). Additional data file 3 contains the primary raw cDNA data from all the experiments. Additional data file 4 contains the primary raw data for all the deletion strain experiments. Additional data file 5 contains the sensitivity (phenotypic profiling) data ranked on the basis of four experiments, 1 mM (2x), 2 mM and 5 mM AsIII, and assigned a new uniform distribution of p -values. Every gene in this table has a percentile rank. In the case that there was slow growth in the wild type, then a default value of 0.5 was assigned. The rankings on this table were used for the metabolic networking. Additional data files 6 and 7 contain data produced by applying the 'Filter on Fold Change' function in GeneSpring after importing the significant gene lists generated using Rosetta Resolver with a p -value less than 0.001 (see Materials and methods for more detail). The control parent vs. parent experiment (100 μM AsIII for 2 h) was compared with the yap1Δ (Additional data file 6 ) and cad1Δ (Additional data file 7 ) profiling experiments treated in parallel (for details see Materials and methods). Additional data files 8 and 9 contain tables of genes ('Minus' lists) that failed to be induced or repressed (or showed such a decrease in expression that they no longer make significantly expressed gene lists), compared to the parent experiment, in the yap1Δ (Additional data file 8 ) and cad1Δ (Additional data file 9 ) experiments after treatment with 100 μM AsIII for 2 h. Additional data file 10 contains a figure showing that Yap1 is likely to regulate Arr2 and Arr3 after 2 h 100 μM AsIII but that it does not regulate Rpn4 under arsenic-induced stress. The self-organized heat map labeling and conditions in this figure are the same as for Figure 2 . (a) The Yap1 knockout strain fails completely to induce Arr2 (0.834 average fold-change) whereas the Arr1 knock-out induces Arr2 (2.90 average fold-change). (b) The Arr1 knockout induction is more elevated compared to the Yap1 knock-out (1.8 and 1.1 average fold-change, respectively). (c) Yap1 is induced 2.7 fold in the Rpn4 knock-out. (d) The wild type parent strain shows an averaged induction of 4.7 fold. (e) Rpn4 is induced 3.7 fold in the Yap1 knock-out compared to 4.1 fold induction in the wild type parent strain. In the presence of arsenic, Yap1 does not appear to regulate Rpn4. Additional data file 11 , as explained for Additional data files 6 and 7 , compares the control parent vs. parent experiment (100 μM AsIII for 2 h) to the rpn4Δ profiling experiment treated in parallel. Additional data 12 contains a table of genes ('Minus' list) that fail to be induced or repressed, compared to the parent experiment, in the rpn4Δ experiment after treatment with 100 μM AsIII for 2 h. Additional data file 13 , as explained for Additional data files 6 and 7 , is from comparing the control parent vs. parent experiment (100 μM AsIII for 2 h) to the arr1Δ profiling experiment treated in parallel. Additional data file 14 contains a table of genes ('Minus' list) that fail to be induced or repressed, compared to the parent experiment, in the arr1Δ experiment after treatment with 100 μM AsIII for 2 h. Additional data file 15 contains the self-organized clustering of specific deletion and parent strain experiments ( yap1Δ vs. yap1Δ 2 h 100 μM AsIII, cad1Δ vs. cad1Δ 2 h 100 μM AsIII, rpn4Δ vs. rpn4Δ 2 h 100 μM AsIII, arr1Δ vs. arr1Δ 2 h 100 μM AsIII, parent vs. parent with 2 h 100 μM AsIII, as well as the parent strain vs. each deletion strain without arsenic). Additional data files 16 , 17 , 18 and 19 contain the gene lists of differential expression in knockout strains yap1Δ , cad1Δ , rpn4Δ and arr1Δ , respectively, compared to the parent without arsenic treatment. Additional data file 20 contains every gene mentioned in this paper and the corresponding gene product descriptions. The primary microarray data will be submitted to the Gene Expression Omnibus (GEO) database at [ 80 ]. Supplementary Material Additional data file 1 The dose-response curve of S. cerevisiae strain, BY4741 Click here for additional data file Additional data file 2 A self-organized tree of arsenite treated yeast experiments and a table depicting the numbers of significant genes Click here for additional data file Additional data file 3 The primary raw cDNA data from all the experiments Click here for additional data file Additional data file 4 The primary raw data for all the deletion experiments Click here for additional data file Additional data file 5 The ranked arsenite sensitivity (phenotypic profiling) data Click here for additional data file Additional data file 6 Genes two-fold or more differentially expressed after arsenite in the Yap1 deletion strain compared to the parent Click here for additional data file Additional data file 7 Genes two-fold or more differentially expressed after arsenite in the Cad1 deletion strain compared to the parent Click here for additional data file Additional data file 8 Genes failing to be induced or repressed by arsenite in the Yap1 deleted strain Click here for additional data file Additional data file 9 Genes failing to be induced or repressed by arsenite in the Cad1 deleted strain Click here for additional data file Additional data file 10 Under arsenite-treated conditions, Yap1 might regulate Arr2 and Arr3, and does not regulate Rpn4 Click here for additional data file Additional data file 11 Genes two-fold or more differentially expressed after arsenite in the Rpn4 deletion strain compared to the parent Click here for additional data file Additional data file 12 Genes failing to be induced or repressed by arsenite in the Rpn4 deleted strain Click here for additional data file Additional data file 13 Genes two-fold or more differentially expressed after arsenite in the Arr1 deletion strain compared to the parent Click here for additional data file Additional data file 14 Genes failing to be induced or repressed by arsenite in the Arr1 deleted strain Click here for additional data file Additional data file 15 Self-organized clustering of deletion strains with AsIII treatment and parent strain vs. deletion strains without arsenic Click here for additional data file Additional data file 16 Gene list of two-fold differential expression in yap1Δ vs. parent without arsenic treatment Click here for additional data file Additional data file 17 Gene list of two-fold differential expression in cad1Δ vs. parent without arsenic treatment Click here for additional data file Additional data file 18 Gene list of two-fold differential expression in rpn4Δ vs. parent without arsenic treatment Click here for additional data file Additional data file 19 Gene list of two-fold differential expression in arr1Δ vs. parent without arsenic treatment Click here for additional data file Additional data file 20 A file of all the genes mentioned in the paper Click here for additional data file

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545798.xml

524358

Morphine for elective endotracheal intubation in neonates: a randomized trial [ISRCTN43546373]

Background Elective endotracheal intubations are still commonly performed without premedication in many institutions. The hypothesis tested in this study was that morphine given prior to elective intubations in neonates would decrease fluctuations in vital signs, shorten the duration of intubation and reduce the number of attempts. Methods From December 1999 to September 2000, infants of all gestations admitted to a level III neonatal intensive care unit and requiring an elective endotracheal intubation were randomly assigned to receive morphine 0.2 mg/kg IV or placebo 5 minutes before intubation. Duration of severe hypoxemia (HR< 90/min and Sp0 2 <85%), duration of procedure, duration of hypoxemia (Sp0 2 <85%), number of attempts and change in mean blood pressure were compared between groups. Results 34 infants (median 989 g and 28 weeks gestation) were included. The duration of severe hypoxemia was similar between groups. Duration of procedure, duration of hypoxemia, number of attempts and increases in mean blood pressure were also similar between groups. 94% of infants experienced bradycardia during the procedure. Conclusion We failed to demonstrate the effectiveness of morphine in reducing the physiological instability or time needed to perform elective intubations. Alternatives, perhaps with more rapid onset of action, should be considered.

Background Endotracheal intubation is a painful and stressful procedure, which is associated with acute increases in blood pressure and intracranial pressure, bradycardia and hypoxemia [ 1 ]. These physiologic changes are potentially of sufficient magnitude to produce the reperfusion injury and venous congestion associated with intraventricular hemorrhage (IVH) and periventricular leukomalacia (PVL) [ 2 , 3 ]. It has been clearly demonstrated that newborn infants feel pain. More so, premature infants likely have an increased sensitivity to pain [ 4 ], which can lead to chronic pain or neurobehavioral and developmental sequelae [ 5 , 6 ]. Most premature infants and many term infants admitted to neonatal intensive care units (NICU) will require one or more endotracheal intubations during their stay. In 1994, 84% of Canadian NICUs, including ours, rarely or never used premedication for elective intubations [ 7 ]. In 2000, the majority of units used premedication 50–75% of the time in infants greater than 30 weeks gestation, but only rarely in those 30 weeks gestation or less [ 8 ]. Perceived lack of evidence of benefits and fear of side effects were reasons. A literature review revealed six randomized controlled trials [ 9 - 14 ], comparing various combinations of premedications, which have enrolled one hundred and thirty newborn infants. Bradycardia can be ameliorated by atropine [ 9 , 10 ] or glycopyrrolate [ 11 ]. Increases in intracranial pressure can be dampened by muscle relaxants [ 9 - 12 ]. Analgesics, which seem warranted, have been minimally studied alone [ 13 ], but seem to limit the increase in blood pressure when combined with muscle relaxants [ 11 ]. A recent metaanalysis concludes that overall, premedication appears beneficial, either in stabilizing vital signs or decreasing the duration of the procedure, but data is limited about which medications are best to achieve optimal conditions [ 15 ]. We reviewed our policy, which did not include premedication for elective endotracheal intubations, in light of the current evidence. As morphine has been used for years in neonates with apparent safety and efficacy for pain and as staff in our unit were comfortable with this medication, we aimed to evaluate the efficacy of morphine, in achieving better intubation conditions and success while maintaining vital signs stability. Methods Study population Infants of all gestations, admitted to McMaster University Medical Center level III NICU and considered likely to need an elective oral or nasotracheal intubation during their hospital stay, were candidates for inclusion in this study. Families were approached for consent as soon as possible after birth when an elective intubation during their hospital stay seemed likely: if their infant(s) was less than 30 weeks gestation, already ventilated (as endotracheal tubes are frequently changed after 10 days if clinical deterioration from a respiratory standpoint), was on NCPAP for respiratory distress or was needing an elective surgery. Others were approached when an elective intubation was needed. At the time of this study, our unit was a 33-bed level 3 NICU, caring for both inborn and outborn patients, and the referral center for 25000 annual deliveries, with 900–1000 admissions per year. Infants were excluded if they met any of the following conditions: 1) absence of an intravenous access, 2) upper airway anomaly potentially leading to a difficult intubation, 3) cyanotic heart disease, 4) upper gastrointestinal obstruction (which would require a rapid sequence intubation) or 5) concurrent opioid administration. Study intervention Infants requiring an elective intubation were randomly assigned to receive either morphine 0.2 mg/kg IV or placebo (0.9% NaCl), given over 1 minute, followed 5 minutes later by the intubation. This larger dose of morphine was chosen for the perceived acuity of pain produced by an intubation; a larger dose may be more effective to decrease the struggling by infants during the procedure, which is caused by pain. Infants were randomized according to a computer-generated random number table with random block sizes. Morphine and placebo were supplied in identical unidose vials, labeled PIN Rx, which were prepared by one pharmacist according to the randomization sequence and placed in sealed, consecutively numbered envelopes, which were opened just before intubation. Thus, randomization occurred just prior to intubation. Three to four minutes after receiving the study medication, infants were preoxygenated with 100% 0 2 and hand-ventilated with a self-inflatable bag for 1–2 minutes prior to intubation. Infants having their endotracheal tube replaced were ventilated through their existing tube until it was removed. Vital signs (HR, BP, Sp0 2 ) were captured to a laptop computer from the infant's monitor (PC Express, Spacelabs Inc., Redmond WA) every 5 seconds (except blood pressure which was obtained with a self inflating cuff every minute) using Procom Plus Communication Software, from the time the study medication was given (which was considered the baseline) to 5 minutes after the infant's vital signs returned to pre-procedure values. One of three investigators, not involved in the procedure collected the following data manually: duration of the procedure (defined as the time between insertion of the laryngoscope in the mouth to confirmation of endotracheal tube placement by auscultation) and the number of intubation attempts (defined as number of times the laryngoscope was inserted in the mouth). If there was more than one attempt, the clock continued between attempts and was stopped only when tube placement was confirmed by auscultation. In our NICU, the preferred method of intubation is via the nasotracheal route if mechanical ventilation is expected for more than a few hours. All team members performed the intubations: staff neonatologists a , neonatal fellows b , pediatric residents c , clinical nurse specialists d , clinical nurse specialist students e and transport nurses f . After 2 unsuccessful attempts by a junior team member (c,d,e,f), a more experienced intubator (a,b) was called. Institutional ethics committee approval and informed consent from the parents were obtained for this study. Outcome measures The study aimed to test the hypothesis that morphine 0.2 mg/kg would decrease fluctuations in vital signs, shorten the duration of the procedure and reduce the number of attempts. The primary outcome was the duration of severe hypoxemia, defined as Sp0 2 < 85% with a HR< 90/min. This was felt to be the most undesirable side effect of endotracheal intubation as cerebral blood flow in neonates is highly dependent upon heart rate. Secondary outcomes included: (1) duration of the procedure, (2) duration of hypoxemia (Sp0 2 < 85%), (3) number of attempts, (4) maximum change in blood pressure from baseline, (5) occurrence of bradycardia (HR<90/min). Sample size The study group's impression was that a majority of infants experience some degree of severe hypoxemia during an elective intubation, which was clinically undesirable. It was estimated to be 30 seconds, based on experience. In order to detect a one standard deviation difference in duration of severe hypoxemia between the 2 groups (α = 0.05, 2-sided, β = 0.2), 17 patients per group were required. Statistical analysis Because the distribution of the main outcome was skewed and groups were small, continuous variables were compared using the Mann-Whitney U test. Dichotomous variables were compared using Fisher's exact test or Chi-square test. A p value < 0.05 (2-sided) was considered significant for the primary outcome; p < 0.01 was considered significant for secondary outcomes to account for multiple analyses in a small sample. Level of experience of the intubator, birth weight and gestational age were separately explored as potential confounders of the primary outcome using ANOVA or linear regression. Results Patients were recruited from December 1999 to September 2000. Patient flow in the study is depicted in figure 1 . Two hundred and fifteen infants were identified as potential candidates for the study. Ninety-nine of them never required an elective intubation but 35 did, they were missed, as parents or investigators were not available at the time. Eighty-one families were approached for consent. Consent was obtained for 64 infants of whom 34 were enrolled and randomized. Thirty were not randomized: 13 never required an intubation and 17 elective intubations were missed, mainly because they happened at night, when investigators were not on site. Missed patients had similar gestation, birth weight and reason for intubation as those enrolled. All patients randomized received the intervention and data from all randomized patients were analyzed. Physiological stability was maintained in all infants, between the time the study drug was given, to the time the endotracheal intubation was performed. Figure 1 Flow of patients at each stage of the study Baseline characteristics are presented in Table 1 . Both groups had similar birth weight, gestation, and baseline vital signs. Importantly, the number of primary intubations/failed extubation and changes of endotracheal tube (which is usually considered easier) were similar between groups. All intubations were nasotracheal. Results of the primary and secondary outcomes analysis are presented in Table 2 . Only 8/17 infants in the treatment group and 7/17 in the control group experienced some degree of severe hypoxemia. The median duration of severe hypoxemia was similar between groups. An outlier (severe hypoxemia lasting for 300 seconds) was identified in the treatment group. Considering the small number of patients, this outlier was taken out and the data reanalyzed, but this did not change the results significantly. The level of experience of the intubator, birth weight and gestation were entered separately in a regression model, but none was a significant contributor to the variance of the results. Table 1 Baseline characteristics of included patients *Values expressed as medians (interquartile range) Morphine group n = 17 Control group n = 17 Birth weight, grams* 1065 (731.5, 2043) 904 (689, 1535.5) Gestation, weeks* 28 (26, 33) 27 (26, 30) Gender male/female, n 11/6 9/8 Age at randomization, days* 3 (0.61, 16) 8 (0.63, 13) Primary intubation/failed extubation, n 7 7 Change of endotracheal tube, n 10 10 Baseline HR, bpm* 152 (142.5, 157.5) 161 (151.5, 166.5) Baseline Sp0 2, %* 94 (92.5, 95) 94 (92.5, 98) Baseline BP, mm Hg* 38.5 (35.25, 44.75) 37.5 (33.25, 44.5) Baseline fi0 2, %* 40 (27, 45) 32 (25, 41.25) Experienced intubator, n 9 7 Junior intubator, n 8 10 Table 2 Primary and secondary outcomes results *Values expressed as medians and interquartile range Morphine group Control group p value Duration of severe hypoxemia, seconds* 10 (0, 62.5) 5 (0, 45) 0.45 Duration of hypoxemia, seconds* 235 (82.5, 340) 90 (20, 187.5) 0.04 Duration of procedure, seconds* 271 (57.5, 418.5) 94 (62, 215.5) 0.27 Maximum increase in mean BP from baseline, mm Hg* 18 (9, 24.25) 20 (11.75, 28) 0.65 Number of attempts, n* 2 (1, 3.5) 1 (1, 2.5) 0.34 Intubation achieved at first attempt, n 7 9 0.49 Intubation needing rescue intubator, n 7 4 0.27 Bradycardia during procedure, n 16 12 0.175 All patients in the treatment group and 14/17 in the control group experienced hypoxemia (Sp0 2 < 85%) during intubation. The median duration of hypoxemia was 235 sec in the treatment group and 90 sec in the control group (p = 0.04). Because of our small sample and the likelihood of finding a significant result by chance alone when assessing multiple outcomes, it was decided a priori that a p value of 0.01 would be considered significant for secondary outcomes. Nevertheless, this represents an interesting but somewhat worrisome trend. No difference was found in the maximal increase in blood pressure. Ninety-four percent of patients experienced bradycardia (HR<90/min) during the procedure with no difference between groups. The median duration of the procedure was 271 sec in the treatment group and 94 sec in the control, which was not statistically significant. Roughly half of the infants required more than one attempt to achieve successful intubation and the clock was not stopped between attempts. Number of attempts in the treatment group (median 2), was similar to controls (median 1); total number of attempts was 38 in the premedicated infants versus 31 in the controls. Success rate at first attempt or need to call a more senior intubator after 2 failed attempts did not differ between groups. Because of the higher than usual dose of morphine that was used, we monitored the need to increase ventilator support over the next 24 h, in infants having their tubes changed, but found no difference between groups. Discussion Newborn infants, especially premature ones have adverse physiological responses to routine care procedures [ 2 , 4 ]. Endotracheal intubation is a stressful procedure, associated with physiologic instability [ 9 - 14 ]. Our data also show this instability, with 94% of infants experiencing bradycardia and the mean blood pressure increasing by as much as 46%. Our hypothesis was that a moderate dose of morphine would facilitate intubation and stabilize vital signs better than placebo. Our data does not support this hypothesis. No significant difference was identified between the treatment and the control group in any prespecified outcome. The choice of severe hypoxemia as the primary outcome, although clinically very important, significantly limited the number of observations and increased the possibility of a type 2 error, as few infants met the criteria defining this outcome. The onset of action of morphine is about 5 minutes in infants, but the peak action occurs only at 15 to 30 minutes [ 16 ], perhaps too long for a procedure such as an intubation, as it does not lead to sufficient relaxation to permit adequate airway visualization. Although there was no formal assessment of the level of sedation of our infants done, bedside nurses reported not being able to discriminate between groups 5 minutes after injection of the study drug. The only trend we identified was the duration of hypoxemia, which appeared longer in the treatment group. Most desaturations were in the mid 70's to low 80's range, but this is still a worrisome finding. We were unable to identify if birth weight, gestation or experience level of the intubator were significant contributors. Our sample size likely did not permit to identify such a contributor. Although morphine may not be potent enough to significantly relax infants to permit quicker and easier intubations, it may lead to decreased functional residual capacity in partially sedated infants, which could account for prolonged desaturations. The hypoxemia could have been compounded by the use of self-inflating bag and masks, which cannot provide a positive end-expiratory pressure (PEEP). Also, the larger dose of morphine used in this study could have contributed to this potential problem, by further decreasing the FRC in partially sedated infants. This trend is in keeping with the finding that, although not statistically significant, median duration of the procedure was 3 times as long in the treatment group as in the controls. Ninety four percent of infants experienced bradycardia, mostly vagal, during their intubation. As cerebral blood flow in infants is greatly dependent on heart rate, our data adds to the current knowledge that including atropine in the premedication appears warranted. Although there may be concern that atropine could mask hypoxia-induced bradycardia, the now universal use of oxygen saturation monitors should ensure that hypoxemia is identified. Previous trials have used various combinations of drugs for premedication and overall, they suggest that premedication is effective and safe. Kelly [ 9 ] and Barrington [ 10 ], using atropine and a muscle relaxant, demonstrated a reduction in vagal bradycardia and a dampening in the rise in intracranial pressure. The use of a muscle relaxant without an analgesic would now be considered unacceptable practice. Friesen [ 12 ] compared atropine alone to atropine and a non-standardized anesthetic and pancuronium in stable term infants preoperatively. The treatment group had less increase in intracranial pressure. This study included only stable infants. Pokela [ 11 ] compared 10 infants randomly allocated to glycopyrrolate and pethidine to 10 infants who received glycopyrrolate, alfentanil and suxamethonium. The addition of a muscle relaxant decreased both the duration of hypoxemia and the duration of the procedure by half. Only experienced physicians performed the intubations, which limits generalizability. The durations of procedure and hypoxemia in our morphine group were similar to their pethidine group. Buthada [ 13 ] compared thiopenthal in anesthetic doses to placebo in infants over 2 kg. Intubations were shorter and heart rate and blood pressure were more stable in the treatment group. Oei [ 14 ] compared morphine, atropine and suxamethonium to awake intubations in 20 infants. Interestingly, even when residents with little or no neonatal experience performed the intubation, the duration of the procedure was significantly shorter and the number of attempts halved in the premedicated group. Barrington [ 17 ] used atropine, fentanyl and succinylcholine in 269 consecutive intubations and reported no major complication; no data was available on duration of procedure or vital signs stability. Few very small infants were enrolled in these trials and most used a combination of premedication, which makes comparison with our trial difficult. Our study has several limitations. First, our sample size is relatively small, which precludes us from eliminating a type 2 error. We began this project with the assumption that severe hypoxemia would occur for about 30 seconds, which was not the case. An observational study would have been useful before making this assumption. Although limited in size, results of this trial should be useful for future investigators and clinicians in their choice of premedication. Second, due to limited resources (unfunded trial), as this study was planned as a pilot, to assess feasibility and adequacy of equipment to obtain data, we decided not to stratify for gestational age. This could have been very useful in refining the findings, as more immature infants may respond differently to premedication in general and have less strength to struggle during an unpremedicated or not sufficiently premedicated painful procedure. Third, as we wanted to mimic our actual NICU practices, in view of modifying such practices, we did not restrict the study intubations to very experienced operators. As a result, there was substantial variability in the level of experience between individuals. Given our small number of patients, this might have impacted on the outcomes. Fourth, several eligible infants were not enrolled. This was due to unavailability of either trial investigators or parents, as many intubations occurred at night. The infants enrolled and those not enrolled had similar birth weights, gestation and reason for intubation, which is reassuring, but does not eliminate the potential for enrolment bias. Conclusions Infants are entitled to effective pain management strategies [ 18 ]. It seems only humane to premedicate infants when possible, for known stressful and painful procedures, as we would for older children and adults. Overall, our findings suggest that morphine probably is not the analgesic of choice or insufficient on its own for elective endotracheal intubations. A more rapid onset analgesic like remifentanil, along with atropine should be evaluated and the role of muscle relaxants needs to be better defined. Infants should be stratified either by gestational age or birth weight, to capture differences in their response to premedication and to intubation. Objective measures of pain like the Premature Infant Pain Profile (PIPP) score [ 19 ] and/or endocrine indicators of stress should be included in outcomes, which should remain focused primarily on the short-term comfort of the infants but also their safety. Long-term physiologic and clinical outcomes should be incorporated into the trial design. Consideration should be given to including various levels of experience of intubators to increase generalizability and applicability of the findings to units where residents and other allied health professionals are trained to intubated infants. Abbreviations HR heart rate, BP blood pressure, Sp0 2 oxygen saturation, ETT endotracheal tube, IVH intraventricular hemorrhage, PVL periventricular leukomalacia, NICU Neonatal Intensive Care Unit, PEEP positive end-expiratory pressure Competing interests The authors declare that they have no competing interests. Authors' contributions BL led the study design and manuscript preparation and contributed to data collection. MM contributed to the study design and manuscript preparation. JD contributed to the study design, manuscript preparation and data collection. AK contributed to data gathering and manuscript editing. SG prepared the study medication and contributed her pharmaceutical expertise to the choice of medication dose for the study. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524358.xml

548280

Genetics and geometry of canalization and developmental stability in Drosophila subobscura

Background Many properties of organisms show great robustness against genetic and environmental perturbations. The terms canalization and developmental stability were originally proposed to describe the ability of an organism to resist perturbations and to produce a predictable target phenotype regardless of random developmental noise. However, the extent to which canalization and developmental stability are controlled by the same set of genes and share underlying regulatory mechanisms is largely unresolved. Results We have analyzed the effects of clinal genetic variation (inversion polymorphism) on wing asymmetry by applying the methods of geometric morphometrics in the context of quantitative genetics using isochromosomal lines of Drosophila subobscura . For the analysis of overall size, developmental stability was positively correlated with levels of heterozygosity and development at the optimal temperature. For analyses of shape, the overall comparisons by matrix correlations indicate that inter- and intraindividual variation levels were poorly correlated, a result also supported when comparing the vectors describing patterns of variation of landmark position. The lack of similarity was basically due to the discrepancy between the genetic and environmental components of the interindividual variation. Finally, the analyses have also underscored the presence of genetic variation for directional asymmetry. Conclusions The results strongly support the hypothesis that environmental canalization and developmental stability share underlying regulatory mechanisms, but environmental and genetic canalization are not functionally the same. A likely explanation for this lack of association is that natural wing shape variation in Drosophila populations is loosely related to individual fitness.

Background Phenotypic robustness refers to the invariance of the specified target phenotype given the genetic makeup and environmental conditions. Whereas the presence of naturally occurring phenotypic variation is at the core of evolutionary biology, developmental geneticists have traditionally considered it as a nuisance. Instead, they have relied on the study of single or multiple mutant combinations to reveal the generation of phenotypic patterns (e.g. [ 1 ]). A resurgence of interest in the issue of phenotypic robustness has emerged in recent years, partly due to experimental results showing that many knock-out mutations have little effect on phenotype ([ 2 ]; although Papp's et al. [ 3 ] metabolic network analysis found that the majority of genes that looked dispensable turn out to be such only under laboratory conditions), and that developmental systems show a high degree of stability with respect to perturbations [ 4 , 5 ]. Three major processes are involved in the control of phenotypic variability (the potential or propensity to vary, in the terminology of Wagner and Altenberg [ 6 ]): canalization, developmental stability (DS), and plasticity [ 7 ]. As first defined by Waddington [ 8 ] the term canalization could be understood as a morphogenetic constrain [ 9 ], where development appears to be buffered so that slight abnormalities of genotype or slight perturbations in the environment do not lead to the production of abnormal phenotypes. However, evolutionary geneticists define canalization as the tendency of traits to evolve a reduction in variability [ 4 , 10 ]. DS can be defined as the ability of organisms to buffer against the random noise that arises spontaneously as a consequence of stochastic variation in the cellular processes that are involved in the development of morphological structures [ 11 ]. Therefore, canalization and DS are subcategories of developmental buffering: the first can be appraised by estimating interindividual variance whereas the most commonly used estimate of DS in bilaterally symmetrical organisms is fluctuating asymmetry (FA); i.e. the intraindividual variation due to random differences between left and right sides. The question of whether or not canalization and DS are different buffering mechanisms has been a constant source of debate. Two recent reviews implicitly [ 4 ] or explicitly [ 10 ] assume that DS is a special case of canalization, a viewpoint also embraced by several authors (e.g. [ 12 - 14 ]). Thus, by using geometric morphometrics Klingenberg and McIntyre [ 13 ] found that the vectors describing inter- and intraindividual variation of landmark position for fly vein traits were highly concordant. On the other hand, Debat et al. [ 15 ] came to the opposite conclusion applying the same methods to cranial landmarks in the house mouse – although Klingenberg's et al. [ 16 ] work with mouse mandibles found patterns of intra- and interindividual variation that were only partly consistent –. At first glance, the different results may suggest that the mechanisms that affect canalization and DS are related in some developmental contexts but not in others. The problem is, however, that according to the causes of phenotypic variation a distinction between genetic and environmental canalization is necessary [ 17 , 18 ]. Selection for environmental canalization may produce genetic canalization as a by-product [ 4 , 10 ], but this may not always be the case. The better way to address these contentious issues is to rely on quantitative genetic analyses devised to partition phenotypic variation into genetic and environmental components [ 19 ]. Environmental variation can be further partitioned into general ( ) and special (micro) environmental effects ( ): the first refer to influential factors (e.g. temperature) that are shared by groups of individuals, whereas the latter are residual deviations from the phenotype that would be specified on the basis of genotype and general environmental effects. Such deviations are unique to individuals and are largely unpredictable. The variance associated with special environmental effects can be estimated when experiments are performed on completely inbred lines (i.e., there is no genetic variance). In bilaterally symmetrical organisms it is also feasible to estimate the two sources that contribute to those special environmental effects: among-individual ( ) and within-individual variance ( ). If the only real cause of asymmetry is variation due to stochasticity in development, then FA can be taken as an estimated of . Therefore, FA is only one source of the phenotypic variation within environments (excluding environmentally induced asymmetry), contrarily to the arguments in Nijhout and Davidovitz [ 20 ]. The other source is . The third process involved in the control of phenotypic variability is plasticity, which can be defined as the ability of an individual to express one phenotype under one set of environmental circumstances and another phenotype under another set. The expressed phenotypes can be discontinuous thus eliciting discrete morphs (i.e., polyphenism), or there can be a continuous range of potential phenotypes (i.e., reaction norm). The reaction norm is thus a property of the genome: genetic canalization and phenotypic plasticity are not mutually exclusive and can combine to form canalized reaction norms [ 7 , 17 ]. Plasticity is thus an alternative to genetic change allowing populations to adapt to changing environmental conditions. To summarize, phenotypic plasticity increases the variance among groups of individuals that produce different phenotypes in different environments, canalization decreases the within-group interindividual variance around the target phenotype by reducing the sensitivity to genetic and environmental conditions, and DS buffers against random perturbations in development (i.e., decreases FA). Because the left and right body sides share the same genome (barring unusual somatic mutation or somatic recombination) and in most organisms also very nearly the same environment, FA provides an intrinsic control for genetic and environmental effects and the important question is to what extent these two sources of variation share underlying regulatory mechanisms. Within the framework of recently developed geometrically based methods for the statistical analysis of size and shape variation (collectively referred to as geometric morphometrics [ 21 , 22 ]), the wing vein network of Drosophila is regarded as an excellent model system to investigate those problems [ 23 , 24 ]. Wing development in Drosophila is well understood [ 25 ], and the vein pattern is highly conserved across species (e.g. [ 26 ]). When flies are reared at low temperatures it is well known that the final wing size increases because of an increase in adult cell size [ 27 ]. This plastic response is parallel to what has been commonly observed in laboratory experiments on thermal evolution, where adaptation to lower temperature resulted in increased wing size (a proxy for body size) entirely as a consequence of cell size divergence [ 28 ]. However, there is circumstantial evidence suggesting that developmental and evolutionary temperature-related cell size divergence have contrasting effects on wing shape. Thus, Birdsall et al. [ 29 ] concluded that wing shape in Drosophila melanogaster is quite resistant to developmental temperature. Conversely, in D. subobscura there are changes in wing proportions along a latitudinal size cline mediated by cell area [ 30 , 31 ]. These populations exhibit, in addition, prominent latitudinal clines for chromosomal inversion polymorphisms, and there is compelling evidence showing that the inversion clines underlie the latitudinal changes in wing proportions [ 32 , 33 ]. Here we report on the effects of clinal genetic variation (inversion polymorphism) on wing form (size and shape) and bilateral asymmetry using isochromosomal lines of D. subobscura . We consider the consequences of inbreeding and temperature on the two components of developmental homeostasis (canalization and DS), and the relationship between them. The remainder of the paper is planned as follows. First, we provide a short account of the inversion polymorphism in D. subobscura and the experimental settings. Then, based on the well balanced data set rendered by the experimental design we used the standard least-squares (ANOVA) method to decompose sources of variation for wing size and shape into causal components at the core of further analyses. Furthermore, because the underlying assumption to use FA as a measure of DS is that left – right-side variation has not heritable basis, the genetic and environmental components of bilateral asymmetry were partitioned. As a result, our approach is unusual in studies of DS in providing estimates of the two components of special environmental effects (co-) variance under different genetic backgrounds and general environmental settings. We also present some evidence for the presence of genetic variation in directional asymmetry (DA) but not in FA. Next, we test whether or not the vectors describing variation of landmark position for fly vein traits are concordant, and finally we discuss the main findings in relation with the evolution of buffering mechanisms and the putative adaptive value of natural wing shape variation in D subobscura . Experimental settings D. subobscura is a particularly inversion-rich species, with up to 38 natural chromosomal arrangements already reported for the largest chromosome O (homologous to arm 3R in D. melanogaster [ 34 ]) for which a balancer stock is available. In colonizing populations of the New World only six gene arrangements are segregating for that chromosome: O st , O 3+4 , O 3+4+2 , O 3+4+7 , O 3+4+8 and O 5 (arrangement O 7 is also present at very low frequency but it is probably the result of a recombination event in the O st /O 3+4+7 heterokaryotype [ 35 ]). In native Palearctic populations arrangements O 3+4+2 and O 3+4+8 are restricted to the Mediterranean region (the likely area from which the original American colonists derived [ 36 ]) and are not involved in latitudinal clines [ 35 ]. On the other hand, arrangement O st shows a world-wide positive correlation with latitude, while arrangements O 3+4 and O 3+4+7 show a contrasting pattern [ 35 ]. Therefore, six independent isochromosomal lines for each of these three chromosome arrangements (i.e., , ..., ; j = st, 3+4, 3+4+7) were used in the present experiments. The experimental flies were obtained from 54 crosses, which will be referred to as inbred (isogenic; i.e., ) with 18 crosses in total, or outbred (including both structural homo- and heterokaryotypes) with 36 (18 + 18) crosses in total. The six lines with a given gene arrangement were crossed to produce the three different outbred homokaryotypes (i.e., ). The three kinds of heterokaryotypic flies were similarly obtained but using lines with different gene arrangements (i.e., ). Since all isochromosomal lines were homogeneous for the same genetic background (except for the male sex chromosome), maternal effects were not considered to be critically important. Anyhow, experimental flies were randomly derived form reciprocal crosses for all outbred combinations. Two developmental temperatures were used in the experiment: optimal (18°C) and warm (23°C). Results and discussion Variation and asymmetry in size a) Basic statistics Signed left-right ( ) differences of centroid size did not significantly departure from normality in any case ( D max ranging from 0.032 for inbred females at 18°C to 0.073 for inbred males at 23°C; P > 0.05). In addition, none of the regressions of centroid size FA on average wing size was statistically significant (ranging from β = -0.045 (95% C.I.: -0.091, 0.001) for inbred females at 18°C to β = 0.030 (-0.005, 0.064) for inbred females at 23°C), thus suggesting independence between size and size FA. b) Causal components of variation For each sex two-way mixed ANOVAs were separately performed for inbred and outbred crosses at each experimental temperature (Tables 1 , 2 , 3 , 4 ). Size variation (CS: centroid size) among individuals comprised the largest part (> 90%) of the variation. The fraction of the total phenotypic variance in wing size associated to genetic differences among karyotypes and/or lines (i.e., ) ranged from 0.235 (inbred males at 18°C) to 0.602 (inbred females at 23°C). (Bear in mind that there is nothing in the ANOVA method of estimation that will prevent a negative variance estimate [ 37 ].) Table 1 Asymmetry of overall wing size for females raised at 18°C Drosophila subobscura flies raised from inbred (isogenic) and outbred crosses reared at 18°C. Centroid size (CS, estimated in a normalized form [22]) is the dependent variable (values in pixels 2 : 1 mm = 144 pixels). The ANOVAs assess measurement error, directional asymmetry (Sides effect), fluctuating asymmetry (Individuals × Sides interaction effect), and genetic components of the trait ( ) and DA of the trait ( (DA)). (CS) and (DA CS ) provide here unbiased estimates of the among-fly (i.e. ) and within-fly ( or FA) special environmental effects. (⊂ means 'nested in'.) Inbred Outbred Source of variation Variance component d.f. Mean Square Estimated variance d.f. Mean Square Estimated variance Individuals (I) 107 39.747*** 9.6175 215 45.773*** 11.2830 Karyotypes (K) (CS) 2 114.593 n.s. 0.1389 5 214.204 n.s. 0.7014 Cross ⊂ K (CS) 15 94.589*** 2.7352 30 113.199*** 3.4726 Among flies (CS) 90 28.944*** 6.9166 180 29.857*** 7.3040 Sides (S) 1 15.982*** 1 18.549*** I × S (CS) 107 1.278*** 0.5467 215 0.641*** 0.2225 Karyotypes (K) (DA CS ) 2 0.067 n.s. -0.0520 5 0.457 n.s. -0.0123 Cross ⊂ K (DA CS ) 15 1.938 ¶ 0.1239 30 0.899 ¶ 0.0492 Within flies (DA CS ) 90 1.194*** 0.5051 180 0.603*** 0.2036 Measurement error (CS) 216 0.184 0.1841 432 0.196 0.1962 Average CS for left ( L ) and right ( R ) wings: inbred females = 0.9918 mm, = 0.9891 ; outbred females = 1.0022, = 1.0002. n.s. P > 0.10; ¶ 0.10 > P > 0.05; *** P < 0.001. Table 2 Asymmetry of overall wing size for males raised at 18°C Same as in Table 1. Inbred Outbred Source of variation Variance component d.f. Mean Square Estimated variance d.f. Mean Square Estimated variance Individuals (I) 107 43.303*** 10.4842 215 38.600*** 9.4200 Karyotypes (K) (CS) 2 232.359 ¶ 1.1331 5 115.718 n.s. 0.1908 Cross ⊂ K (CS) 15 69.186* 1.4332 30 88.246*** 2.5026 Among flies (CS) 90 34.788*** 8.3554 180 28.184*** 6.8159 Sides (S) 1 1.140 n.s. 1 22.492*** I × S (CS) 107 1.366*** 0.5045 215 0.920*** 0.3297 Karyotypes (K) (DA CS ) 2 0.385 n.s. -0.0176 5 1.156 n.s. 0.0035 Cross ⊂ K (DA CS ) 15 1.017 n.s. -0.0715 30 1.031 n.s. 0.0226 Within flies (DA CS ) 90 1.446*** 0.5444 180 0.895*** 0.3172 Measurement error (CS) 216 0.357 0.3574 432 0.261 0.2609 Average CS for left ( L ) and right ( R ) wings: inbred males = 0.8942, = 0.8935; outbred males = 0.9003, = 0.8980. n.s. P > 0.10; ¶ 0.10 > P > 0.05; * P < 0.05; *** P < 0.001. Table 3 Asymmetry of overall wing size for females raised at 23°C Same as in Table 1 for Drosophila subobscura flies reared at 23°C Inbred Outbred Source of variation Variance component d.f. Mean Square Estimated variance d.f. Mean Square Estimated variance Individuals (I) 107 64.857*** 15.8796 215 49.100*** 11.9347 Karyotypes (K) (CS) 2 27.808 n.s. -1.8893 5 457.293*** 2.6178 Cross ⊂ K (CS) 15 299.873*** 11.3901 30 80.332*** 1.9907 Among flies (CS) 90 26.511*** 6.2931 180 32.556*** 7.7987 Sides (S) 1 33.413*** 1 16.825*** I × S (CS) 107 1.339*** 0.5446 215 1.361*** 0.5916 Karyotypes (K) (DA CS ) 2 0.681 n.s. -0.0125 5 4.333* 0.0781 Cross ⊂ K (DA CS ) 15 1.132 n.s. -0.0427 30 1.520 n.s. 0.0447 Within flies (DA CS ) 90 1.388*** 0.5692 180 1.252*** 0.5371 Measurement error (CS) 216 0.250 0.2496 432 0.178 0.1778 Average CS for left ( L ) and right ( R ) wings: inbred females = 0.8999 mm, = 0.8960; outbred females = 0.9203, = 0.9184. n.s. P > 0.10; * P < 0.05; *** P < 0.001. Table 4 Asymmetry of overall wing size for males raised at 23°C Same as in Table 1 for Drosophila subobscura flies reared at 23°C Inbred Outbred Source of variation Variance component d.f Mean Square Estimated variance d.f. Mean Square Estimated variance Individuals (I) 107 44.690*** 10.9045 215 28.772*** 6.8138 Karyotypes (K) (CS) 2 41.926 n.s. -0.6021 5 112.284 n.s. 0.3480 Cross ⊂ K (CS) 15 128.628*** 4.0778 30 62.165*** 1.7199 Among flies (CS) 90 30.762*** 7.4224 180 20.887*** 4.8425 Sides (S) 1 36.691*** 1 13.586** I × S (CS) 107 1.072*** 0.3553 215 1.517*** 0.6465 Karyotypes (K) (CS) 2 2.596 n.s. 0.0366 5 0.862 n.s. -0.0110 Cross ⊂ K (DA CS ) 15 1.277 n.s. 0.0454 30 1.259 n.s. -0.0532 Within flies (DA CS ) 90 1.004*** 0.3213 180 1.578*** 0.6771 Measurement error (CS) 216 0.361 0.3615 432 0.224 0.2240 Average CS for left ( L ) and right ( R ) wings: inbred males = 0.8112, = 0.8072 ; outbred males = 0.8277, = 0.8260. n.s. P > 0.10; ** P < 0.01; *** P < 0.001. No significant size differences were generally detected among karyotypes for average CS, although O 3+4 flies were always the biggest within inbred lines (Fig. 1 ). On the other hand, in outbred crosses heterokaryotypes were bigger than homokaryotypes (females: 18°C F (1,195) = 9.78, P = 0.002; 23°C F (1,195) = 9.19, P = 0.003; males: 18°C F (1,195) = 1.84, P = 0.176; 23°C F (1,195) = 4.23, P = 0.041), but interactions of dominance effects were observed in all samples with discernible heterosis in O st /O 3+4 lines when compared to their homokaryotypic counterparts. Figure 1 Inbreeding and temperature effects on size Homokaryotipic averages for centroid size and centroid size FA (index FA1 in [39]) in inbred (black symbols) and outbred (open symbols) crosses. Small symbols give the average values for each of the three different homokaryotypes to appreciate the dispersion from the corresponding grand average (large symbols connected by lines). Squares give the values at 23°C and circles at 18°C. In concert with some independent preliminary results using a set of O st isochromosomal lines [ 38 ] a quite remarkable finding here was that left wings were consistently bigger than the right ones, thus causing a generally highly significant DA (i.e., "sides" effect in Tables 1 , 2 , 3 , 4 ) of overall wing size even though DA was fairly subtle (see bottom statistics in Tables 1 , 2 , 3 , 4 ). Each Drosophila wing vein has dorsal and ventral components that come together after the apposition of the dorsal and ventral surfaces, but each vein protrudes only in one wing surface ("corrugation") [ 25 ]. When wings were mounted no attempt was made to standardize the surface position: in females 394 (60.8%) left and 387 (59.7%) right wings were mounted on the slides with the dorsal side up ( χ 2 = 0.16, 1 df, P = 0.691); in males the corresponding figures were 383 (59.1%) and 401 (61.9%), respectively ( χ 2 = 1.05, 1 df, P = 0.306). Potential biasing effects when measuring wings; namely, dorsal or ventral Bitmap images or possible differences between left and right wings when Bitmap images are captured from the top or bottom of the microscope slide, were checked from a subset of 75 females and 75 males. An additional set of two images for each wing were taken in the same session from the top and bottom of the slide and digitized once. The centroid size differences between the averages of both measurements was apparently random with respect to digitizing procedure and always lower than 0.07%, whereas left wings were 0.26% bigger than the right ones in females and 0.34% in males. We are, therefore, quite confident that the fairly subtle DA for wing CS is not an experimental artifact but a real phenomenon. In addition to DA, there was subtle but significant FA in all crosses (i.e., "individuals × sides" interaction effect in Tables 1 , 2 , 3 , 4 ) together with a small amount of genetic variation for DA in some of them. This last finding could hardly be attributable to a type I error because similar results had been previously obtained [[ 38 ]; see below]. Conversely, two-level nested ANOVAs to test for genetic components of overall size FA (using index FA1 in Palmer [ 39 ]) failed to show any statistically significant effects whatsoever (variance components ranging from -0.0047 to 0.0071 for karyotypes, and from -0.0343 to 0.0406 for crosses within karyotypes; values in pixels 2 ). c) Consanguinity and temperature effects Inbreeding and environmental effects were simultaneously analyzed by contrasting isogenic vs . outbred homokaryotypic flies reared at both experimental temperatures (Fig. 1 ). Flies were obviously bigger when raised at the lowest temperature, and three-way factorial ANOVAs performed separately for each sex using CS (as log e (pixels), but results were qualitatively identical without a log-transformation) as the dependent variable, with karyotype, temperature and inbreeding as fixed effects, and crosses nested within karyotypes, clearly indicated inbreeding depression together with temperature by inbreeding interaction (i.e., inbreeding was most noticeable at the sub-optimal temperature of 23°C), but no karyotype by temperature interaction was detected. These results confirm that wing size is not a purely additive trait in D. subobscura , in agreement with the previous observation that heterokaryotypes were bigger than homokaryotypes in outbred crosses (see also [ 40 ]). Both inbreeding and (sub-optimal) temperature effects were also apparent in females when overall size FA (index FA1) was used as the dependent variable in three-way factorial ANOVAs, with no differences among karyotypes. On the other hand, no statistically significant effects were detected for males, basically because inbred crosses performed approximately equal at both temperatures (Fig. 1 ). However, overall asymmetry augmented in inbred crosses because DA largely increased (mainly in males) at the highest temperature ("temperature × inbreeding" interaction: F (1,856) = 9.46, P = 0.002). It is worth mentioning here that in outbred crosses overall size FA was about the same for homokaryotypes and heterokaryotypes: the only significant effect was again an increase in FA at the sub-optimal temperature (more than two-fold; c.f. (DA CS ) values in Tables 1 , 2 , 3 , 4 ). Finally, inbreeding appears to have affected among-fly variation only in males as suggested by the consistently lower (CS) estimates in outbred crosses within rearing temperature. In conclusion, overall size DS was positively correlated with levels of heterozygosity (i.e., inbred vs. outbred homokaryotypes) and development at the optimal temperature of 18°C. However, no positive association was found between DS and chromosomal heterozygosity in outbred crosses. Variation and asymmetry in shape a) Sources of variation Two-way MANOVA analyses to quantify inter- and intra-individual variation in wing shape are shown in Tables 5 , 6 , 7 , 8 . For the present study of 13 landmarks, with 2 coordinates each, the shape dimension is 22. Sums of squares and cross-products (SSCP) matrices are therefore not full-ranked, and we retained 22 PC (principal components [ 41 ]) scores for outbred crosses and only 15 PC scores – which accounted for more than 98% of the total shape variance – for inbred crosses to be capable of testing for genetic components. The degrees of freedom in Tables 5 , 6 , 7 , 8 (columns "df 1") are simply the corresponding degrees of freedom in the ANOVAs for centroid size (Tables 1 , 2 , 3 , 4 ) times the number of PC scores retained in each sample. Likewise, the overall covariation in wing shape ("individuals" effect) was decomposed into causal components (karyotypes, crosses in karyotypes, and among flies); and the overall covariation in wing shape FA ("individuals × sides" interaction effect) was decomposed into causal components attributable to wing shape DA (karyotypes, crosses in karyotypes, and within flies). Table 5 Asymmetry of overall wing shape for females raised at 18°C Flies raised from inbred (isogenic) and outbred crosses of Drosophila subobscura reared at 18°C. For the inbred crosses 15 PC scores were retained for analyses (proportion of total shape variance accounted is given in parenthesis). For the outbred crosses 22 PC scores were retained. (⊂ means 'nested in'.) Inbred (98.6%) Outbred Source of variation Wilks' lambda df 1 df 2 P Wilks' lambda df 1 df 2 P Individuals (I) 1.13 × 10 -11 1605 1464 <0.001 5.14 × 10 -15 4730 4442 <0.001 Karyotypes (K) 0.002 30 2 0.505 7.44 × 10 -5 110 48 <0.001 Cross ⊂ K 1.51 × 10 -4 225 843 <0.001 3.68 × 10 -4 660 2932 <0.001 Among flies 2.23 × 10 -8 1350 1457 <0.001 7.55 × 10 -12 3960 4430 <0.001 Sides (S) 0.597 15 93 <0.001 0.563 22 194 <0.001 I × S 1.58 × 10 -9 1605 3083 <0.001 6.46 × 10 -11 4730 9192 <0.001 Karyotypes (K) 0.003 30 2 0.546 0.002 110 48 0.301 Cross ⊂ K 0.074 225 843 0.362 0.018 660 2932 0.023 Within flies 9.69 × 10 -9 1350 3070 <0.001 7.91 × 10 -10 3960 9169 <0.001 Table 6 Asymmetry of overall wing shape for males raised at 18°C Same as in Table 5. Inbred (98.5%) Outbred Source of variation Wilks' lambda df 1 df 2 P Wilks' lambda df 1 df 2 P Individuals (I) 7.18 × 10 -12 1605 1464 <0.001 3.51 × 10 -14 4730 4442 <0.001 Karyotypes (K) 0.004 30 2 0.633 2.49 × 10 -4 110 48 0.006 Cross ⊂ K 1.61 × 10 -4 225 843 <0.001 2.98 × 10 -4 660 2932 <0.001 Among flies 1.47 × 10 -8 1350 1457 <0.001 3.62 × 10 -11 3960 4430 <0.001 Sides (S) 0.658 15 93 <0.001 0.569 22 194 <0.001 I × S 5.58 × 10 -8 1605 3083 <0.001 1.28 × 10 -10 4730 9192 <0.001 Karyotypes (K) 0.004 30 2 0.605 0.003 110 48 0.449 Cross ⊂ K 0.068 225 843 0.236 0.019 660 2932 0.036 Within flies 3.05 × 10 -7 1350 3070 <0.001 1.73 × 10 -9 3960 9169 <0.001 Table 7 Asymmetry of overall wing shape for females raised at 23°C Same as in Table 5 for Drosophila subobscura flies reared at 23°C. Inbred (98.3%) Outbred Source of variation Wilks' lambda df 1 df 2 P Wilks' lambda df 1 df 2 P Individuals (I) 1.07 × 10 -12 1605 1464 <0.001 1.08 × 10 -13 4730 4442 <0.001 Karyotypes (K) 2.18 × 10 -4 30 2 0.200 0.001 110 48 0.146 Cross ⊂ K 3.31 × 10 -4 225 843 <0.001 1.81 × 10 -4 660 2932 <0.001 Among flies 2.32 × 10 -9 1350 1457 <0.001 1.54 × 10 -10 3960 4430 <0.001 Sides (S) 0.450 15 93 <0.001 0.585 22 194 <0.001 I × S 2.57 × 10 -9 1605 3083 <0.001 3.21 × 10 -13 4730 9192 <0.001 Karyotypes (K) 0.007 30 2 0.725 0.006 110 48 0.842 Cross ⊂ K 0.055 225 843 0.062 0.034 660 2932 0.889 Within flies 1.95 × 10 -8 1350 3070 <0.001 3.54 × 10 -12 3960 9169 <0.001 Table 8 Asymmetry of overall wing shape for males raised at 23°C Same as in Table 5 for Drosophila subobscura flies reared at 23°C. Inbred (98.3%) Outbred Source of variation Wilks' lambda df 1 df 2 P Wilks' lambda df 1 df 2 P Individuals (I) 5.39 × 10 -12 1605 1464 <0.001 6.41 × 10 -14 4730 4442 <0.001 Karyotypes (K) 8.81 × 10 -4 30 2 0.364 1.18 × 10 -4 110 48 <0.001 Cross ⊂ K 2.75 × 10 -4 225 843 <0.001 1.96 × 10 -4 660 2932 <0.001 Among flies 8.92 × 10 -9 1350 1457 <0.001 7.94 × 10 -11 3960 4430 <0.001 Sides (S) 0.642 15 93 <0.001 0.540 22 194 <0.001 I × S 8.58 × 10 -9 1605 3083 <0.001 9.84 × 10 -12 4730 9192 <0.001 Karyotypes (K) 5.13 × 10 -5 30 2 0.102 6.40 × 10 -4 110 48 0.052 Cross ⊂ K 0.060 225 843 0.111 0.024 660 2932 0.250 Within flies 5.79 × 10 -8 1350 3070 <0.001 1.26 × 10 -10 3960 9169 <0.001 Similarly to what had been found for CS, differences between left and right wings were also highly significant ("sides" effect), thus indicating that DA was present for overall wing shape. This finding is contrary to our previous claim from a subset of O st isochromosomal lines, where DA for some landmarks (e.g. those defining the position of the anterior crossvein) but not for overall wing shape was detected [ 38 ]. After plotting the Procrustes grand mean shapes of both wings it also became apparent here that the location of the anterior crossvein was indeed slightly more distal in the right wings. Furthermore, the individuals × sides interaction effects were highly significant in all cases and, hence, wing shape FA greatly exceeded measurement error. b) Causal components of variation As has been forcefully stressed [ 42 ] shape is an inherently multidimensional concept and cannot be easily reduced to a scalar index without severe loss of information. Therefore, for a quantitative genetic analysis of shape data a multivariate approach is required [ 43 ]. For overall wing shape, genetic differences among karyotypes were mostly detected for outbred crosses (Tables 5 , 6 , 7 , 8 ), and we have estimated the covariance matrices P = K + C + E as a simple multivariate extension of the two-level nested ANOVAs, where P is the phenotypic covariance matrix and K , C , and E are, respectively, the covariance matrices for karyotypes, crosses within karyotypes, and the residuals. Fig. 2 shows the amount of variation associated with the different dimensions in shape space. Much of the variation was concentrated in the first few PCs, but the K matrices showed the clearest trend to quickly decrease after the first PC. Permutation tests indicated that matrix correlations (MCs) between K and C matrices were generally higher at 18°C (females MC = 0.258, P = 0.1908 ; males MC = 0.305, P = 0.1963) than at 23°C (females MC = 0.157, P = 0.3356 ; males MC = 0.250, P = 0.2665), but none of the MCs was statistically significant. On the other hand, VCV matrices were correlated across rearing temperatures (females: MC K = 0.716, P = 0.0163; MC C = 0.818, P = 0.0001; males: MC K = 0.706, P = 0.0160 ; MC C = 0.587, P = 0.0399 ; this last correlation was no longer significant after the Bonferroni procedure [ 44 ]). A close inspection to Fig. 2 reveals an increase in the genetic components of overall wing shape at 23°C, which agrees with our preliminary findings [ 38 ]. Thus, the ratio between the total variance of genetic ( G = K + C ) covariance matrix onto the total variance of the phenotypic covariance matrix was lower at 18°C in both sexes (females: 0.1312 vs . 0.5450; males: 0.2365 vs . 0.2522). A caveat: these ratios cannot be interpreted as estimates of shape heritability [ 43 ]. Figure 2 Eigenvalues of causal covariance matrices for wing shape First 15 eigenvalues of the phenotypic (black bars), karyotype (hatched) and crosses (open) covariance matrices from outbred crosses. MANOVA results in Tables 5 , 6 , 7 , 8 also point to the presence of genetic variation for overall shape DA, mainly at 18°C (i.e., the "crosses in karyotypes" component from the decomposition of the I × S interaction effect). As far as we are aware, these are the first experiments that found detectable genetic variation in DA for wing traits. The uncovering of DA (i.e., "side" effect) for fly wings is quite general when quantitative analyses of form are carried out using the powerful methods of geometric morphometrics to reveal even small morphological variation that otherwise would remain hidden with less effective techniques [ 13 , 45 ]. This has raised concerns against the conventional wisdom that left and right are not distinguished in Drosophila development [ 46 ] because it provides compelling evidence that DA in fly wings may signal the presence of genetic variation in a phylogenetic conserved left-right developmental axis (i.e., an imaginary plane between the two lateral sides of the body), as discussed by Klingenberg et al. [ 45 ]. Actually, modern treatises in developmental biology (e.g. [ 9 ]) distinguish the left-right axis besides the customary anterior-posterior and dorsal-ventral axes, and several asymmetrically expressed genes (e.g. sonic hedgehog ) have recently been discovered. In Drosophila , Ligoxygakis et al. [ 47 ] were the first (and to our knowledge the only ones) who showed a developmental mechanism for the developmental asymmetry. It seems, therefore, that the detection of genetic variation for DA in this genus appears to be basically a methodological problem, including statistical power and the environmental conditions where the experiments are performed. The mechanisms that constitute the genetic basis of morphological asymmetry in Drosophila obviously require further study. c) Genetic components of wing shape FA Following [ 13 ] a multivariate equivalent of FA1 (i.e., the "unsigned" left-right differences) was defined by changing the signs of all coordinate differences (from left-right to right-left) whenever the inner product (also referred to as the dot product) of a left-right difference vector with the vector of mean left-right difference was negative. For the univariate case (CS) this procedure would render here the absolute ( ) differences, but notice that for the multivariate case it is not equivalent to calculate the absolute ( ) differences of all Procrustes coordinates. MANOVA analyses of these "unsigned" shape asymmetries in outbred crosses did not detect any genetic variation for shape FA at 18°C or 23°C (Tables 9 , 10 ). However, the approach used to define the multivariate equivalent of FA1 might be influenced by the arbitrary choice of the plane (i.e., the mean left-right differences) to subdivide the shape space into "positive" and "negative" halves (Christian P Klingenberg, pers. comm. 2004). A modified Procrustes shape distance for non-isotropic variation (i.e., landmarks usually differ in their amounts of variation) has been recently developed by Klingenberg and Monteiro [ 48 ], and can be used here as a scalar measure of the amount of shape asymmetry because FA is random in origin (i.e., only the magnitude and not the direction may usually be the interesting component of FA shape variation). When this scalar was used in our data set the same conclusion was obtained; namely, there was no detectable genetic variation for wing shape FA in any case (results not shown). Table 9 MANOVAs for female wing shape fluctuating asymmetry A multivariate equivalent of FA1 (i.e., the "unsigned" left-right differences) was defined as explained in the text. Flies raised from outbred crosses of Drosophila subobscura (⊂ means 'nested in'). 18°C 23°C Source of variation Wilks' lambda df 1 df 2 P Wilks' lambda df 1 df 2 P Karyotypes (K) 0.008 110 48 0.908 0.007 110 48 0.856 Cross ⊂ K 0.022 660 2932 0.169 0.029 660 2932 0.604 Table 10 MANOVAs for male wing shape fluctuating asymmetry Same as in Table 9. 18°C 23°C Source of variation Wilks' lambda df 1 df 2 P Wilks' lambda df 1 df 2 P Karyotypes (K) 0.004 110 48 0.627 0.009 110 48 0.938 Cross ⊂ K 0.024 660 2932 0.243 0.042 660 2932 0.988 d) Consanguinity and temperature effects on wing shape To investigate allometric and nonallometric temperature effects on overall wing shape we performed a multivariate analyses of covariance (MANCOVA) of the Procrustes coordinates (after averaging both sides and the two replicated measurements per side) considering temperature and inbreeding (i.e., isogenic vs . outbred homokaryotypic flies) as the categorical predictors and CS (as log e (pixels)) as the covariate. Temperature effects were only significant in males, but inbreeding and temperature × inbreeding interaction effects were highly significant in both sexes (results not shown), which suggests a strong effect of the categorical predictors on the nonallometric component of shape. Size effects were also found to be significant (females: Wilks' λ = 0.881, F (22,405) = 2.496, P < 0.001; males: Wilks' λ = 0.915, F (22,405) = 1.715, P = 0.024), but the allometric effect on shape remained relatively consistent at both temperatures in females (size × temperature interaction: Wilks' λ = 0.930, F (22,405) = 1.395, P = 0.111) but not in males (Wilks' λ = 0.853, F (22,405) = 3.165, P <0.001). The association between size and temperature (Fig. 1 ), measured by the variance inflaction factor ( VIF < 5; [ 49 ]), was found to be lower than the suggested guideline for serious collinearity (i.e. VIF ≥ 10), which indicates that the effects of temperature and size on wing shape could be effectively separated. The conclusion is, therefore, that Drosophila wing shape does not seem to be as resistant to environmental temperature as previously claimed from the analysis of 12 highly inbred D. melanogaster lines [ 29 ]. Inbreeding effects (isogenic vs . outbred homokaryotypic flies) on wing shape FA were tested from the ratio between the traces of the corresponding "individual × side" VCV matrices. Notice that the traces of these interaction matrices are equal to the respective mean squares of the Procrustes ANOVA as implemented by Klingenberg and McIntyre [ 13 ], and are simply the sum of (index FA4 in [ 39 ]) for each x and y coordinates of the corresponding aligned configurations divided by the shape dimension. We performed 10,000 randomization runs for each test. Inbreeding effects were detected at 18°C but only in females (18°C: female F = 1.694, P = 0.0003 ; male F = 0.963, P = 0.6037 ; 23°C: female F = 0.834, P = 0.9231; male F = 0.984, P = 0.5541). Patterns of wing shape variation a) Fluctuating asymmetry Principal component analyses were only implemented for the outbred crosses since they are more representative of the natural situation. The percentages of total shape variation, together with the features of variation associated with the dominant PCs, are graphically plotted in Figs. 3 , 4 , 5 , 6 . For the individual variation several PCs accounted for relatively large amounts of variability. On the contrary, for FA and measurement error PC1 explained almost all total variance (>80%). For all levels in the analysis (i.e. individuals, FA and measurement error) the dominant PCs were connected to the relatively large variability of landmarks 3, 6, 7 and, to a lesser extent, landmark 2. However, the disproportionate amount of variation associated with these landmarks did not spread to all sources of causal variation because their coefficients were relatively small for the PC1 of karyotype variation (which explained ~60% of the total variance; see below). Furthermore, for the individual variation the first two PCs were also linked to the shift of the anterior (landmarks 11 and 12) and posterior (landmarks 7 and 13) crossveins along the adjoining longitudinal veins. Figure 3 Vectors of the landmarks displacements First two axes of wing shape variation for each effect in the two-way mixed MANOVA (individuals, individuals × sides interaction, and measurement error) for females from outbred crosses reared at 18°C. Also plotted are the percentages of total wing shape variation explained by the principal components for the corresponding covariance matrices. Figure 4 Vectors of the landmarks displacements Same as Fig. 3 for males from outbred crosses reared at 18°C. Figure 5 Vectors of the landmarks displacements Same as Fig. 3 for females from outbred crosses reared at 23°C. Figure 6 Vectors of the landmarks displacements Same as Fig. 3 for males from outbred crosses reared at 23°C. Permutation tests indicated that VCV matrices were mostly correlated for FA and measurement error effects within samples (MCs > 0.95, P < 0.01; Table 11 ). The individual VCV matrix was significantly correlated with the FA and measurement error matrices only for females at 18°C. Between temperatures the VCV matrices were highly correlated for FA and measurement error (results not shown), but loosely correlated for the individual variation (females MC = 0.668, P = 0.0355 ; males MC = 0.494, P = 0.1066 ; statistical significance vanishes after the Bonferroni procedure). Table 11 Correlations between VCV matrices of landmarks displacements within groups Results of the permutation tests used for the analyses within sexes and temperatures. Group Effects Correlation P (permutation) P (Bonferroni) Females 18°C Individual / FA 0.7699 0.0001 ** Karyotype / FA -0.1691 0.7583 n.s. Cross / FA 0.5773 0.0871 n.s. Between-fly / FA 0.7517 0.0001 ** Individual / error 0.7550 0.0001 ** FA / error 0.9953 0.0001 ** Males 18°C Individual / FA -0.3998 0.8694 n.s. Karyotype / FA 0.0067 0.4393 n.s. Cross / FA -0.0706 0.6202 n.s. Between-fly / FA 0.2060 0.3296 n.s. Individual / error -0.4280 0.9436 n.s. FA / error 0.9964 0.0001 ** Females 23°C Individual / FA 0.1233 0.2881 n.s. Karyotype / FA -0.0151 0.4771 n.s. Cross / FA 0.6516 0.0264 n.s. Between-fly / FA 0.5764 0.0744 n.s. Individual / error 0.1093 0.3141 n.s. FA / error 0.9959 0.0001 ** Males 23°C Individual / FA 0.5278 0.0523 n.s. Karyotype / FA -0.1469 0.7545 n.s. Cross / FA 0.2752 0.1817 n.s. Between-fly / FA 0.4033 0.1241 n.s. Individual / error 0.5165 0.0519 n.s. FA / error 0.9922 0.0001 ** n.s. = P > 0.05; ** = P < 0.01. The angles between the PC1s for FA and measurement error were very much alike (ranging from angle α = 2.1° to α = 3.4° ; recall that the 0.1% quantile of the resulting distribution between pairs of random vectors in 22-dimensional space was 50.3°), which reflects the similarity due to landmarks 3, 6 and 7. However, the first three PCs for interindividual variation were generally distinct to those of FA: the only clear correspondences were between the PC1s for females at 18°C ( α = 21.5°), and the PC2 of interindividual variation with the PC1 of FA for males at 18°C ( α = 11.8°). (The correspondences were qualitatively the same for interindividual variation and measurement error; results not shown.) Overall, these results seem to suggest that canalization and DS do not generally share the same underlying regulatory mechanisms (but see below). A potentially important problem with the foregoing approach to compare the patterns of intra- and interindividual variation is to rely on the interaction VCV matrix as the source of variation due to FA. As has been previously argued the uncovering of DA is almost ubiquitous for shape data when using the methods of geometry morphometrics, and there was evidence here for statistically significant genetic variation of overall shape DA at 18°C (Tables 5 , 6 ). Therefore, the VCV matrix from the "individuals × sides" interaction effect gives a biased estimate of developmental stability and cannot be taken as the covariance matrix for FA. In other words, this VCV matrix also includes all causal components due to genetic variation for DA, and the corresponding unbiased VCV matrix for FA is that for the within-fly component of the interaction effect (i.e., after removing the genetic variation for DA [ 50 , 51 ]). In any case, all results were qualitatively similar and, hence, the conclusion that canalization and DS seem to be different mechanisms remains unchanged. However, it is difficult to appraise how this potential problem could have affected the previously published conclusions when comparing interindividual variation and "FA" in fly wings and mouse skulls (see Background section). Between rearing temperatures the congruence of PC1 eigenvectors was also very high for FA (females α = 4.0°; males α = 3.5°) and measurement error (females α = 3.1°; males α = 4.1°). For the interindividual variation the correlations between PC1s were significant only in males (females α = 74.3°; males α = 19.3°); however, the PC1 vector describing the joint interindividual variation of landmark position in females at 18°C matched the PC2 of the interindividual covariance matrix at 23°C ( α = 49.6°; recall that the direction of PCs is arbitrary and all the movements in Figs. 3 , 4 , 5 , 6 can be simultaneously reversed by 180°) and vice versa (i.e., PC1 at 23°C vs PC2 at 18°C: α = 26.4°). b) Causal components Besides the interindividual variation in the two-way MANOVAs (which comprises genetic plus environmental covariances due to special environmental effects) it is important here to asses the patterns of joint displacements of landmarks for each of the causal components of wing shape variation (Figs. 7 , 8 , 9 , 10 ). For karyotype variation PC1 accounted for ~60% of the total variance and was linked to a great extent with equivalent movements of those landmarks defining the location of the crossveins, which shifted in the same direction. Landmarks 4 and 5 tended to move away each other, stretching the wing margin between longitudinal veins III and IV. Landmark 9 budged in the opposite direction to crossveins shifts, thus shaping the relationship between L1 to the total length of longitudinal vein IV (i.e. shape index L1 WL ; Fig. 12 ). Figure 7 Vectors of the landmarks displacements First two axes of wing shape variation in the two-level nested MANOVA (karyotypes, crosses nested in karyotypes, and within crosses) for each causal component effect pertaining to the inter-individual variation in females from outbred crosses reared at 18°C. Also plotted are the percentages of total wing shape variation explained by the principal components for the corresponding covariance matrices. Figure 8 Vectors of the landmarks displacements Same as Fig. 7 for males from outbred crosses reared at 18°C. Figure 9 Vectors of the landmarks displacements Same as Fig. 7 for females from outbred crosses reared at 23°C. Figure 10 Vectors of the landmarks displacements Same as Fig. 7 for males from outbred crosses reared at 23°C. Figure 12 Left wing of Drosophila subobscura The image shows the thirteen landmarks (1 – 13) used in this work. I – VI longitudinal veins; cv-a and cv-p anterior and posterior crossveins; Co costal or marginal veins; L1 and L2 lengths of the proximal (Euclidian distance between landmarks 9 and 13) and distal (Euclidian distance between landmarks 13 and 5) segments of longitudinal vein IV, respectively. Wing shape index has been previously used to study shape clines in this species [30]. A relative shortening of the basal length of longitudinal vein IV relative to the total wing length with an increasing dose of standard gene arrangements in all five major chromosomes of D. subobscura had been previously identified in an outbred stock [ 32 , 33 ]. A similar pattern regarding O st dose is also clear here when considering the six karyotypes (Fig. 11 ), but rearing temperature quantitatively modified the shape index (L1/WL was lower at the highest temperature). However, there was no statistically significant karyotype × temperature interaction. The wing shape index appears to be a purely additive trait since heterokaryotypes were always intermediate to their corresponding homokaryotypes (Fig. 11 ). Actually, none of 12 within- group (i.e., sex and temperature) possible contrasts comparing all three heterokaryotypes with the average of the corresponding homokaryotypes was statistically significant (the mean square for "crosses" was used as the error term; see legend in Fig. 11 ). Figure 11 Wing shape index Averages of the relative length (with 95% confidence intervals) of the basal portion of longitudinal vein IV (L1) to the total wing length (WL = L1 + L2) versus karyotype for outbred crosses at the two rearing temperatures. A two-way factorial ANOVA using the shape index as , with karyotype and temperature as fixed effects, and crosses nested within karyotypes, detected statistically significant differences for the main effects (karyotype: female F 5,30 = 12.625, P < 0.001; male F 5,30 = 9.785, P < 0.001. Temperature: female F 1,390 = 30.219, P < 0.001; male F 1,390 = 61.835, P < 0.001) but no karyotype × temperature interaction (females: F 5,390 = 1.570, P = 0.168; males: F 5,390 = 1.111, P = 0.354). PC2 for karyotypes was also connected to the variability of landmarks 3, 6 and 7. For the crosses component, several PCs explained relatively large amounts of variation, and shifts of crossveins now seem to be independent of each other at 18°C but not at 23°C. Finally, for the within-fly variation several PCs accounted for relatively large amounts of variability. PC1s were again connected to the variability of landmarks 3, 6 and 7; and PC2s to shifts in the anterior crossvein. The large amount of variation of the anterior and posterior crossveins for karyotypes and crosses can be interpreted in terms of developmental processes. The crossveins are determined after the longitudinal veins, and mutations that eliminate crossveins (e.g. crossveinless ) do not affect the longitudinal veins; however, some mutants that affect the longitudinal veins also influence the crossveins (e.g. the vn group in [ 1 ]). Intra- and interespecific studies in several Drosophila species have found displacements of one or both crossveins along their longitudinal veins, and such shifts also occur in a number of mutants (see [ 23 ]). However, these shifts do not occur in isolation an also include other landmarks as well (e.g., landmarks 9 and 5 on L4; landmarks 1 and 2 on L1; Figs. 7 , 8 , 9 , 10 ). The matrix permutation tests (Table 11 ) indicated that the VCV matrices of karyotypes and crosses were never significantly correlated with the VCV matrices of FA and measurement error. The high correlation between the VCV matrices of the interindividual and FA effects for females at 18°C was basically due to the (micro-) environmental component. Also notice that all correlations between the VCV matrices of karyotype and FA effects were close to zero or even negative, which clearly suggests that this genetic component of canalization is unrelated to DS. In addition, the PC1s of karyotypes and FA were nearly at right angles (18°C: females α = 85.8°, males α = 77.3°; 23°C: females α = 75.6°, males α = 78.5°). The only matches were between PC2 of karyotypes and PC1 of FA for females at 18°C ( α = 13.0°) and males at 23°C ( α = 31.2°). The PC1s of crosses and FA were also poorly correlated; the only exception being females a 23°C ( α = 43.3°). These results clearly support the hypothesis that genetic canalization and DS are not functionally the same mechanism. On the other hand, all observed angles involving PC1s between "replicated genotypes" (i.e. the between-fly component) and FA were relatively small and highly significant (18°C: females α = 20.1°, males α = 15.9° ; 23°C: females α = 22.7°, males α = 36.7°). (Results were qualitatively the same for all observed angles involving PC1s of the between-fly and measurement error covariance matrices; results not shown.) Together with the overall comparisons of the covariance matrices (Table 11 ), these results indicate that (micro-) environmental canalization and DS share underlying regulatory mechanisms but are not identical. There was not a complete congruence as PC1 of FA accounted for most part of the variation, while PC1 of between-fly variation usually explained less than 50% of the total variance (Figs. 7 , 8 , 9 , 10 ). To conclude, the theoretical lower limit for (micro-) environmental canalization (i.e., the environmental variance among genetically identical individuals) would be FA because the two sides share the same genome (barring unusual somatic mutation or somatic recombination) and nearly the same environment, so differences between sides are likely to be small. Under stabilizing selection this lower limit is obviously associated with higher fitness. However, this "canalization limit" would hardly ever be observed because of unavoidable additional sources of environmental variance (e.g. variation between vials, the position of the pupae in a vial, etc.). A similar logic than the one used in this work has been applied to distinguish between intrinsic and extrinsic stochastic variation in gene expression: intrinsic noise can be separated by contrasting the levels of gene expression in a construct with two identically regulated but fluorescently distinguishable gpf genes in the Escherichia coli chromosome, whereas extrinsic noise is inferred by the correlated variation between the two copies in the same environment [ 52 , 53 ]. Conclusions This study applied the methods of geometric morphometrics in the context of quantitative genetics of wing form variation using isochromosomal lines of D. subobscura . The main findings can be summarized as follows: (i) for the analysis of overall size, DS was positively correlated with levels of heterozygosity (i.e., inbred vs . outbred homokaryotypes) and development at the optimal temperature; however, no positive association was found between DS and chromosomal heterozygosity in outbred crosses; (ii) there was detectable genetic variation (mainly for overall shape) for the directional component of morphological asymmetry (i.e., DA) but not for FA, which likely reflects variation due to stochasticity in development; (iii) for analyses of shape, the patterns of covariation for FA and measurement error were highly concordant in all samples, which also provides strong reasons to conclude that FA is generated by random perturbations of developmental processes (obviously, this does not imply that DS is independent of the genetic background: wing shape FA was found to be higher in inbred females at 18°C when compared to their outbred homokaryotypic counterparts); (iv) the inter- and intraindividual variation patterns were generally poorly correlated, which supports the hypothesis that canalization and DS are distinct mechanisms; however, (v) the patterns of variation due to the (micro-) environmental component of canalization (i.e., the among-fly special environmental effects covariances) were quite similar to those observed for FA; (vi) the lack of a significant within-group correlation between the VCV matrices associated with the interindividual genetic components of canalization and FA, as well as the low similarity between the corresponding vectors describing variation of landmark position, strongly suggest that genetic and environmental canalization are not similar mechanisms. In addition, (vii) a discrepancy between sexes was observed in some situations; e.g. overall size FA increased with inbreeding and (sub-optimal) temperature effects mainly in females, and the allometric effect on wing shape at both experimental temperatures was similar in females but not in males. It is also interesting to note here that wing size (measured as WL; Fig. 12 ) clines for D. subobscura developed in North America after ~20 years since colonization, but males were clearly lagging behind females [ 54 ]. What is not obvious, however, is why there is a difference between the sexes. It has been suggested that a relationship between canalization and DS could only reflect a common underlying association between character and fitness [ 55 , 56 ]. Those traits under strong stabilizing selection may not be genetically canalized and the major source of selective pressure for canalization can result from the benefits gained by buffering the effects of environmental perturbations [ 4 , 10 ]. The strongest evidence in favor of this hypothesis comes from the well-known genotype-phenotype mapping of RNA folding. Conservation of RNA secondary structure is under strong selection, and low structural plasticity is achieved through increasing the thermodynamic independence of any one structural component from the remaining structure [ 57 ]. Likewise, the flux summation theorem developed in the field of metabolic control analysis implies, if true, that phenotypic robustness is an inevitable outcome of the underlying metabolism and not a result of evolution (see [ 58 ]). However, it is still an open question whether or not natural wing shape changes in Drosophila are adaptive. There are no consistent patterns between latitude and wing shape (e.g. [ 30 ]), contrarily to what happens for size-related traits where world-wide latitudinal clines are found with genetically larger individuals derived from higher latitudes (e.g. [ 30 , 59 ]). Many genes with small additive effects on features of wing shape are dispersed along the Drosophila genome (e.g. [ 60 , 61 ]), and we have shown here that the wing shape index L1/WL appears to be a purely additive trait since heterokaryotypes were always intermediate to their homokaryotypic counterparts. The wing shape cline in North America colonizing populations of D. subobscura [ 30 ] can be largely accounted for parallel latitudinal clines in chromosomal gene arrangements [ 32 , 33 ], and the small shifts of (e.g.) the anterior and posterior crossveins in relation to karyotype variation (Figs. 7 , 8 , 9 , 10 ; notice that the plotted joint variation in landmark positions is an exaggeration of the actual variation in the data set) are difficult to link with any adaptive response to a better flight capacity. Actually, we lack even hypothetical functional explanations for subtle shape variation: Gilchrist et al. [ 54 ] speculated that wing shape variation in D. subobscura may simply represent drift around an optimum. Our present results (points (v) and (vi) above) give some credence to that conjecture. Genetic canalization on wing shape does not seem to arise as a by-product of environmental canalization and, therefore, canalization is not a single mechanism to buffer any source of variation as has been suggested [ 10 ]. According to Graham et al. [ 62 ] the classical linear theory of DS can successfully account for both normally distributed error distributions and leptokurtic distributions caused by the admixture of individuals having different levels of DS, but cannot account for transitions between FA and DA. We have previously suggested, however, that a transition from "ideal" FA (i.e., a normal distribution of left – right-side scores whose mean is zero) to a distribution showing DA could be made entirely compatible with what it is already known from classical quantitative genetics [ 38 ]. Shifts between asymmetry types (FA, DA and antisymmetry) have been reported to happen along a species distribution range [ 63 ], but unless the genetic component can be partitioned out the variation in left-right differences cannot be assumed to describe DS. From the results of outbred crosses reared at 18°C (Table 5 , 6 ) it is possible to test here for the congruence between patterns of morphological variation with respect to the variation attributable to FA (i.e. the within-fly environmental component of the interaction term) and that attributable to genetic variation for DA (the within-fly genetic component due to crosses in karyotypes of the interaction term). The corresponding VCV matrices were highly correlated for females (MC = 0.914, P (permutation) = 0.0001) but not for males (MC = 0.064, P (permutation) = 0.485). The angles between the PC1s also reflect this discrepancy between samples (females α = 8.6° ; males α = 45.9°). When considered together, these results clearly suggest that FA and genetic variation for DA may or may not be functionally linked. Methods Extraction of O chromosomes and fly handling A large number of D. subobscura isochromosomal lines for the O chromosome in an otherwise homogeneous genetic background were derived from an outbred stock collected at Puerto Montt (Chile; 41° 28' S) in November 1999 as previously indicated [ 38 ]. Briefly, wild-type males were individually crossed to three or four virgin females from the highly homogeneous ch-cu marker strain, which is homozygous for the morphological recessive markers on the O chromosome cherry eyes ( ch ) and curled wings ( cu ) and fixed for the gene arrangement O 3+4 . A single / O + cu + ch male from the offspring was backcrossed to ch-cu females, and its arrangement on the wild-type chromosome was identified after four generations of backcrosses. Followed by at least another backcross to ch-cu females, a single male from each line carrying the wild chromosome was crossed to two virgin females from the Va/Ba balanced marker stock. This strain was derived from the ch-cu stock and carries the dominant lethal genes Varicose ( Va ) and Bare ( Ba ) on the O chromosome. The isochromosomal lines were established from the final crosses ♀♀ O Va ch + cu / × ♂♂ O Va ch + cu / . All lines used here had a quasi-normal viability according to the recorded proportions of wild-type flies raised in the final crosses to obtain the isochromosomal lines [ 38 ]. The lines were kept at 18°C (12:12 light/dark cycle) in 130-mL bottles with low adult density to standardize the rearing conditions before egg collections. As previously indicated the experimental flies were obtained from 54 crosses. Reciprocal crosses were made for all outbred combinations by mating one-week virgin females and males. After three days the males were discarded and equal numbers of females from each reciprocal cross were placed together in a plastic chamber with a spoon containing non-nutritive agar with a generous smear of live yeast for egg collections. To standardize the experimental conditions, eggs from the inbred (isogenic) crosses were also obtained in a similar way; namely, after mating the flies in bottles and transferring the females to plastic chambers. Eggs were placed in six 2 × 8 cm vials with 6 mL of food (26 eggs/vial); three vials were kept at 18°C (optimal temperature) and the other three at 23°C (sub-optimal temperature). Within each experimental temperature the vials were randomly placed on the same incubator shelf. As a result, the total experiment consisted of 324 vials (162 vials at each experimental temperature), and all eggs were sampled on the same day. Emerging flies (not less than 2 or 3 days old) were stored in Eppendorf tubes with a 3:1 mixture of alcohol and glycerol at 4°C before wing measurements. All fly handling was done at room temperature using CO 2 anesthesia on flies not less than 6 h after eclosion. Wing measurements Two randomly sampled females and males emerged from each vial were used for morphometric analyses. Both wings were removed from each fly and fixed in DPX under coverslips on microscope slides. Bitmap images were captured with a video camera (Sony CCD-Iris, Tokyo, Japan) connected to a PC computer with MGI VideoWave software and mounted on a compound microscope (Zeiss Axioskop, Jena, Germany), using a 2.5 × objective. Calibration of the optical system was checked at each session. The images were stored on a Dell Workstation PWS350. To quantify and minimize measurement error all wings were digitized two times at different sessions as follows: images of both the left and right wings were captured during a given session and after an entire round on all individuals the same process was repeated again. A similar procedure was also used to record the x and y coordinates of 13 morphological landmarks (i.e., labeled geometric points located at the intersections of wing veins or at sites where veins reach the wing margin; Fig. 12 ) by using the Scion Image for Windows software [ 64 ]. Therefore, the process we used guaranteed that the observer was blind with respect to the results from previous measurements. Analysis of wing size and shape Geometric morphometrics precisely separates morphological variation (i.e., variation in form) into size and shape components [ 21 , 22 ]. Size is a one-dimensional trait and the measure most widely used in geometric morphometrics is centroid size (CS), computed here in a normalized form as the square root of the sum of squared Euclidian distances between each landmark to the centroid (center of gravity) of all landmarks divided by the square root of the number of landmarks. Individual size is therefore represented by four scalars, one for each side and session. The shape of an original configuration of landmarks is the geometrical information that is invariant to uniform scaling (variation in size), translation (differences in position), and rotation (differences in orientation). In contrast to size, shape is an inherently multidimensional space and we used Procrustes superimposition to characterize shape variation. This method allows comparing configurations of landmarks by optimally superimposing (according to a least-squares criterion) homologous landmarks in two or more specimens to achieve an overall best fit [ 65 ].Because the data set included both left and right wings (i.e., we are dealing with "matching symmetry" [ 66 , 67 ]) our analyses also removed differences due to reflection by changing the sign of the x coordinate of every landmark for configurations from the right side. The reflection, scaling, and superposition steps were performed for all wings within each cross and temperature simultaneously, which allows contrasting wing shapes between different lines or crosses. The final iteration to minimize the sum of the squared distances between the landmarks of all wings in the sample was done without additional scaling and, consequently, we performed a partial Procrustes fit according to Dryden and Mardia [ 22 ]. Given the small amounts of shape variation in this analysis rescaling the coordinates of each configuration by the scaling option 1/cos( ρ ) [ 65 ] would have negligible effects on the results. The landmark coordinates after Procrustes superimposition are amenable to standard multivariate analyses. However, it is important to remember that the removal of size, position (in two dimensions), and orientation reduces the dimensional space to 2 p – 4, where p is the number of landmarks [ 22 ]. Thus, for the present study of 13 landmarks, with 2 coordinates each, the shape dimension is 22. Sums of squares and cross-products (SSCP) matrices are therefore not full-ranked, and the degrees of freedom need to be adjusted. There are three alternative ways of avoiding these difficulties [ 22 , 67 ]: (i) to omit, after Procrustes superimposition of the complete configurations, the coordinates of any two landmarks; (ii) to retain 22 PC scores from the covariance matrix of the data set; (iii) to slightly modify the multivariate statistics (see below) by using the Moore-Penrose generalized inverse of the SSCP matrices so they can tolerate singular matrices, and compute the product of nonzero eigenvalues instead of the determinant of SSCP matrices. We have used here the second scheme. Experimental design and asymmetry analysis Quantitative genetic studies of directional and fluctuating asymmetry obviously require measures from individuals that can be grouped into families or independent lines. Our final data set was a fully balanced design, comprising 54 crosses × 3 vials per cross × 2 females per vial × 2 males per vial × 2 sides per fly × 2 measurements per wing × 2 temperatures = 5,184 wing landmark configurations in total. Within each sex and temperature, least-squares (ANOVA) estimates of variance components (i.e. CS) can be easily obtained from the linear model: Y ijkl = μ + κ i + l j ( i ) + ν k ( ji ) + ε l ( kji ) , where μ is the overall grand mean, κ i is the effect of the i th karyotype, l j ( i ) is the random effect of the j th cross within karyotype i , ν k ( ji ) is the random effect of the k th vial within cross j and karyotype i , and ε l ( kji ) is the residual error associated with the trait (i.e. the individual means computed from both sides and the two replicated measurements per side) of the l th individual within vial k , cross j , and karyotype i . Since there was no genetic variation within crosses, the residual error provides an estimate of the total special environmental effects variance (i.e. ). Variation among the three replicated vials was generally negligible (results not shown) and, therefore, we have conveniently reduced the previous model to a two-level nested ANOVA after grouping flies across vials. To first partition the total phenotypic variation into interindividual, intraindividual and measurement error components, we used the conventional mixed model, two-way ANOVA (or its MANOVA generalization; see below) for the study of left-right asymmetries [ 39 ]. In this ANOVA the main random effect of individuals stands for phenotypic variation in the trait (i.e. CS), the main fixed effect of body side is for directional asymmetry (DA) and tests whether or no the signed differences between the left and right wings [designated as ( )] have a mean of zero, the interaction term is a measure of fluctuating asymmetry (the variation in left-right differences among individuals) provided that there is no genetic variation for DA [ 51 ], and the error term gives an estimate of the measurement error. The two-level nested ANOVA can be straightforwardly subsumed within the two-way ANOVA. We now digress slightly to point out some inconsistencies in the literature on what is the appropriate error term to test for the "interindividual" effect in the mixed model, two-way ANOVA (either the individual × side interaction effect or the measurement error [ 13 , 15 , 68 , 69 ]). Interindividual variation, even if of no general interest in most studies of asymmetry, comprises here genetic components ("karyotype" plus "crosses within karyotypes") and special environmental effects variance ( ; there is no genetic variance within crosses). An estimated of the among-fly special environmental effects variance (i.e. ) is therefore obtained by subtracting the individual × side interaction effect (which includes plus measurement error) as the appropriate error term. However, when genetic variation for DA is present the unbiased within-fly special environmental effects variance (i.e. FA) is estimated after partitioning the individual × side interaction effect into its causal components [ 51 ]. As pointed out by Klingenberg et al. [ 67 ] it is fairly straightforward to extent the preceding ANOVA approach to a full two-factor MANOVA to analyze wing shape asymmetry since all effects are computed from averages or contrasts in the same shape space. Recall that the traces of the corresponding SSCP matrices are just the sum of squares in the Procrustes ANOVA as implemented by Klingenberg and McIntyre [ 13 ], but this ANOVA is based on an isotropic model (i.e., it assumes that there is an equal amount of non-directional variation at each landmark [ 70 ]) that is not generally correct for any real data. Covariance (VCV) matrices for each effect in the MANOVA were calculated as a simple multivariate extension of the two-way ANOVA. Thus, the SSCP matrices were divided by the appropriate degrees of freedom, and effects were separated according to the expected mean squares in the ANOVA by subtracting the interaction covariance (VCV) matrix from the interindividual VCV matrix, and the error VCV matrix from the interaction one. Therefore, for (e.g.) outbred crosses the interindividual covariance components were calculated as and the covariance components of FA as , were SSCP I is the interindividual SSCP matrix, SSCP IS is the interaction SSCP matrix, and SSCP ME is the measurement error SSCP matrix. The SSCP I matrix was further partitioned into among-karyotype SSCP K matrix, among-cross within karyotype SSCP C⊂K matrix, and the residual SSCP e matrix corresponding to the special environmental effects. As a result, genetic effects for overall wing shape were separated from special environmental effects according to the expected means squares in the two-level nested ANOVA. Therefore, for (e.g.) outbred crosses the karyotype covariance components were calculated as (remind that the entries in the SSCP I matrix are equal to those computed from individual means times twice the number of independent measurements per wing), the cross covariance components as , and the among-fly special environmental effects covariance components as . Similarly, genetic effects for DA can be investigated after partitioning the SSCP IS matrix into their causal components [ 51 ]. Morphological patterns of variation Within each sex and temperature, principal component analyses [ 41 ] of the VCV matrices were performed for each source of variation with the purpose of describing the landmark displacements corresponding to each emerging principal component (PC), and also to test for the congruence of these displacements between effects. This technique extracts new shape variables (PCs) which successively account for the maximal amount of shape variation and contain information on how the variables relate to each other. The PCs form an orthonormal set of vectors (i.e., the inner product for i ≠ j, for i = j; superscript 'denotes transposition) in an n-dimensional space. Correlations between corresponding VCV matrices were computed from the upper triangular part (diagonal entries were included) since covariance matrices are symmetrical, and statistical significance was assessed using permutation tests designed to maintain the associations between pairs of x - and y -coordinates (i.e., by permuting pairs of rows and columns [ 13 , 15 ]); otherwise the null hypothesis would imply the complete absence of all geometric structure. The permutation procedure was carried out 10,000 times. Correlative patterns of whole shape variation are difficult to interpret: a significant correlation would suggest a real congruence, but a weak congruence does not imply a significant correlation. A second test examined the congruence of the landmark displacements corresponding to each emergent PC for the different effects within groups. Because the PCs correspond to directions in the multivariate shape space, correlations can be obtained by angular comparisons of component vectors. Statistical significance of these correlations was then assessed by comparing those observed values to a null distribution of absolute angles between 100,000 pairs of 22-dimensional random vectors [ 71 ]. The 0.1% and 0.001% quantiles of the resulting distribution were 50.3° and 41.6°, respectively. Antisymmetry and allometric effects The occurrence of antisymmetry (AS: a bimodal distribution of signed ( ) [ 39 ]) for centroid size was investigated within each sample using the Lilliefors (Kolmogorov-Smirnov) test for the composite hypothesis of normality [ 69 ]. The independence between size and size FA within each sample was assessed by a linear regression of unsigned ( ) against mean centroid size . Scatter plots of left-right differences for each landmark after Procrustes superimposition were visually checked to see whether or not there was evidence for clustering of these vectors that would have argue for the occurrence of AS [ 13 , 15 ]. No indication of AS was detected. Finally, to test for size effects on shape asymmetry within each sample we used multivariate regression of vectors of both signed and "unsigned" shape asymmetries onto mean centroid size [ 13 ]. Shape asymmetries were not related to size ( P -values > 0.10) and, therefore, no size corrections were necessary. Computer software for statistical analysis The computer programs used for statistical data analyses were MATLAB (V.6. [ 72 ]) together with the collection of tools supplied by the Statistics Toolbox (V.3. [ 73 ]). Some helpful functions in morphometrics from the MATLAB toolboxes Res5 and Res6 developed by R. E. Strauss [ 74 ] were also used. Results (e.g., derivation of SSCP matrices) were checked with the statistical software packages STATISTICA V.6 [ 75 ] and SPSS V.11 [ 76 ]. Authors' contributions MS conceived the study, carried out extraction of O chromosomes, experimental crosses, egg collections, statistical analyses, and drafted the final manuscript. PFI carried out extraction of O chromosomes, experimental crosses, egg collections, wing measurements, and preliminary statistical analyses and drafts of results. WC read all salivary gland squashes for gene arrangement identification and mounted the wings on microscope slides. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548280.xml

524364

The evolution of drug-activated nuclear receptors: one ancestral gene diverged into two xenosensor genes in mammals

Background Drugs and other xenobiotics alter gene expression of cytochromes P450 (CYP) by activating the pregnane X receptor (PXR) and constitutive androstane receptor (CAR) in mammals. In non-mammalian species, only one xenosensor gene has been found. Using chicken as a model organism, the aim of our study was to elucidate whether non-mammalian species only have one or two xenosensors like mammals. Results To explore the evolutionary aspect of this divergence, we tried to identify additional xenobiotic sensing nuclear receptors in chicken using various experimental approaches. However, none of those revealed novel candidates. Ablation of chicken xenobiotic receptor (CXR) function by RNAi or dominant-negative alleles drastically reduced drug-induction in a chicken hepatoma cell line. Subsequently, we functionally and structurally characterized CXR and compared our results to PXR and CAR. Despite the high similarity in their amino acid sequence, PXR and CAR have very distinct modes of activation. Some aspects of CXR function, e.g. direct ligand activation and high promiscuity are very reminiscent of PXR. On the other hand, cellular localization studies revealed common characteristics of CXR and CAR in terms of cytoplasmic-nuclear distribution. Finally, CXR has unique properties regarding its regulation in comparison to PXR and CAR. Conclusion Our finding thus strongly suggest that CXR constitutes an ancestral gene which has evolved into PXR and CAR in mammals. Future studies should elucidate the reason for this divergence in mammalian versus non-mammalian species.

Background A gene superfamily of heme-proteins, the cytochromes P450 (CYP), encodes the main enzymatic system for metabolism of structurally diverse lipophilic substrates [ 1 ]. A subset of these CYPs can be activated or inhibited in the liver by a variety of xenobiotic and endobiotic compounds. Transcriptional activation of these CYPs is part of an adaptive response to exposure to drugs and other xenobiotics and has major clinical and toxicological implications. The enzymatic capacities of the affected CYPs are changed, leading to an altered metabolic profile in the liver [ 2 ]. The barbiturate phenobarbital (PB) is prototypical for a class of compounds that induce or repress hepatic CYPs and many other genes [ 3 ]. PB-responsive enhancer units (PBRU) have been identified in the 5'-flanking regions of several of these CYPs and transcription factors binding to those units could be isolated (reviewed in [ 4 - 7 ]). In mammals, the pregnane X receptor (PXR, official nomenclature NR1I2) and the constitutive androstane receptor (CAR, NR1I3), both belonging to the gene superfamily of nuclear receptors, have been identified to be involved in hepatic drug-induction [ 8 - 12 ]. Strikingly, in contrast to the two xenobiotic-sensing nuclear receptors in mammals, only one xenosensor has been found in non-mammalian species, e.g. chicken [ 13 ], fish (fugu Fugu rubripes [ 14 ] and zebrafish Danio rerio [ 15 ]) or the nematode Caenorhabditis elegans [ 16 ]. The amino acid sequence of the full-length chicken xenobiotic receptor (CXR, NR1I3) is about equally related to those of mammalian PXRs and CARs [ 17 ]. Moreover, chicken CXR and mammalian PXR and CAR as well as drug-inducible CYP enhancer elements from these species could be freely interchanged in transactivation and electrophoretic mobility shift assays suggesting evolutionary conservation of the fundamental hepatic drug-induction mechanisms from birds to man [ 18 ]. In this report, we studied the evolutionary aspects of these findings. Despite using various methods and techniques, we were unable to isolate further genes that encode chicken xenobiotic-sensing nuclear receptors confirming the hypothesis that non-mammalian genomes only have one xenosensor gene. Since PXR and CAR exhibit different typical features concerning their activation, localization and regulation [ 6 , 19 ], we examined the properties of CXR to see whether on the functional and structural level, the chicken xenosensor shares common aspects with one or both of the mammalian receptors. Our findings give important insights the evolution of hepatic detoxification systems that protect different species from toxic compounds in their particular diet and environment. Results and Discussion Orthologs of PXR and CAR have been isolated from man, monkey, pig, dog, rabbit, mouse and rat [ 15 ]. In non-mammalian species, only one xenosensor gene is found and sequence-wise, the corresponding receptors from chicken, zebrafish, fugu fish and C. elegans are about equally related to the mammalian PXRs and CARs (Fig. 1A ). Of the 18 nuclear receptors in the fruitfly Drosophila melanogaster genome, DHR96 shares considerable similarity to the xenosensors but the functions of this receptor have not been elucidated yet. Although the African clawed frog Xenopus laevis has two nuclear receptors, benzoate X receptor α and β (BXRα/β, NR1I2), that are related to the xenobiotic-sensing nuclear receptors, the BXRs are pharmacologically distinct from PXR and CAR and do not respond to xenobiotics [ 15 , 20 ]. No drug-sensing nuclear receptors have thus been isolated in amphibians so far. Figure 1A shows the phylogeny of the xenobiotic-sensing nuclear receptors from different species. The completion of the rat genome allowed a global analysis of the nuclear receptors from three mammalian species, man, mouse and rat. In the nuclear receptor subfamily NR1I which includes the 1,25-dihydroxyvitamin D 3 receptor (VDR, NR1I1) in addition to PXR and CAR, intron-exon junctions are highly conserved [ 21 ]. Human and rodent CARs and PXRs have the same number of introns. Moreover, apart from one intron which is found in the variable region 5' of the DNA-binding domain, all other seven introns are located in the same position on the corresponding genes, even in the ligand-binding domains that in the case of CAR and PXR are unusually divergent for nuclear receptor orthologs [ 22 ]. Using a chicken genomic library, we isolated the gene encoding CXR and analyzed its structure. Again, the number of introns in the CXR coding sequence was the same as those in the mammalian xenosensors and the intron-exon junctions occur at the same locations (Figure 1B ). The apparent conservation of gene structures between the single chicken xenosensors and the two mammalian orthologs suggest a close relationship between these receptors and supports the hypothesis that CXR constitutes an ancestral gene in chicken from which two receptors diverged in mammals. Figure 1 Phylogeny of xenobiotic-sensing nuclear receptors from different species. A , A non-rooted phylogenetic tree depicts the relationship between mammalian CARs and PXRs and non-mammalian intermediate receptors. The scale bar represents 0.1 amino acid substitutions per site. B , The sites of intron-exon junctions in the coding regions of CXR, PXR and CAR are highly conserved as depicted in an alignment of the amino acid sequences of these receptors. To further test this hypothesis, we used different experimental approaches in order to isolate additional chicken xenobiotic-sensing nuclear receptors. Neither high- and low-stringency screening of a chicken liver cDNA library using CXR, CAR and PXR fragments as probes nor PCR-based strategies with degenerate primers designed on CAR and PXR alignments or degenerate primers based on generic nuclear receptor DNA-binding domains [ 23 ] resulted in the identification of novel chicken xenobiotic-sensing receptors (data not shown). The sequences of the previously unknown chicken orthologs for estrogen-related receptor γ (ERRγ, NR3B3) and a partial fragment of ear2 (NR2F6) that were found in these screens have been deposited (Genbank accession numbers AY702438 and AY702439, respectively). If CXR in fact is the only chicken xenobiotic-sensing nuclear receptor, ablation of CXR expression or function is predicted to drastically reduce drug-induction of CYPs and other target genes. To reduce CXR expression, we designed RNAi oligonucleotides targeting CXR and stably expressed those in the chicken hepatoma cell line leghorn male hepatoma (LMH). LMH cells express endogenous CXR and retain induction of genes by PB-type inducer compounds and other drugs [ 18 ]. As shown in Figure 2A , endogenous mRNA levels of CXR were reduced about 60% by the RNAi. LMH cells expressing either control vector or CXR RNAi were subsequently transfected with drug-responsive enhancer elements from CYP2H1 [ 17 ], CYP3A37 [ 24 ], CYP2C45 [ 25 ] and δ-aminolevulinate synthase (ALAS-1) [ 26 ] and treated with vehicle or 400 μM PB for 16 hours. ALAS-1 is the first and rate-limiting enzyme in heme biosynthesis and its transcription is regulated by a variety of factors and stimuli, including PB-type inducers and other drugs [ 26 , 27 ]. In the case of ALAS-1, the 2-fold PB-induction was completely abolished by the CXR RNAi (Figure 2E ). In contrast, PB-activation of the CYP2H1, CYP3A37 and CYP2C45 PBRUs was only partially reduced by 50 to 60% (Figure 2B,2C,2D ). In these cases, reduction of CXR levels by 60% might not be enough. Alternatively, these findings could also be explained by the presence of additional drug-sensing signalling mechanism independent of CXR. Figure 2 Reduced drug-induction of drug-responsive enhancer elements from CYP2H1, CYP3A37, CYP2C45 and ALAS1 in LMH cells stably expressing CXR RNAi. A , mRNA levels of endogenous CXR in LMH cells expressing pSUPER expression vector or CXR RNAi. CXR levels were measured by real-time PCR in LMH cells that stably express control vector or CXR RNAi. B–E , Phenobarbital-induction of drug-responsive enhancer elements from CYP2H1 (B), CYP3A37 (C), CYP2C45 (D) and ALAS1 (E) in LMH cells expressing pSUPER or CXR RNAi. LMH cells were transfected with the reporter gene plasmids and subsequently treated with vehicle or 400 μM PB for 16 hours before reporter gene levels were determined. Thus, we used an alternative method that aimed at reducing CXR activity by designing dominant-negative CXR alleles. These CXR mutants were then tested in reporter gene assays on drug-responding enhancer elements. In our case, we generated three different CXR alleles (Figure 3A ): first, we deleted the N-terminus since in some nuclear receptors, this part harbours a ligand-independent activation domain AF-1 [ 28 , 29 ]. Second, site-directed mutagenesis of the cysteine residues in the zinc-fingers of the DNA-binding domain results in a CXR mutant that is expected to lack DNA-binding but to retain its ability to bind activators and to heterodimerize with its partner retinoid X receptor (RXR, NR2B1/2/3). Third, helix 12 in the ligand-binding domain was deleted which harbours a ligand-dependent activation domain AF-2. Nuclear receptors that act as dominant-negative alleles due to the absence of a functional AF-2 domain have been observed in some diseases (e.g. see refs. [ 30 , 31 ]). These findings were subsequently used to generate various dominant-negative nuclear receptor mutants for cellular assays [ 32 ]. Figure 3 Drug-induction of the 264-bp PBRU is abolished by a dominant-negative CXR allele. A , CXR was subcloned into the pHook-2 expression plasmid (Hook-2) either full-length CXR in positive orientation (CXR+), negative orientation (CXR-), lacking its N-terminal amino acids 1–29 (ΔN-term), full-length CXR with four of its cysteine residues (cysteine 31, 34, 83 and 86) in the DNA-binding domain mutated (DBD) or lacking its C-terminal amino acids 383–391 containing the activation function AF-2 (ΔAF2). B , Electrophoretic mobility shift assays with mock in vitro transcribed/translated reticulocyte lysate (lane 1), expression plasmid pHook-2 (lane 2) and either expression plasmids for the different CXR alleles alone (lanes 3–7) or together with a pSG5-expression plasmid for chicken RXRγ (lanes 8–12). The arrow indicates the specific shift of CXR/RXR complexes with the radiolabeled CYP2H1 264-bp PBRU. C , pHook-2 expression plasmids without insert or containing the various CXR alleles were co-transfected with the CYP2H1 264-bp PBRU in the pBLCAT5 reporter vector as well as a lacZ-expression vector for normalization of transfection efficiencies into non-drug responsive CV-1 cells. After transfection, the cells were treated with either vehicle or 400 μM PB for 24 hours before cells were lysed and analysed for reporter gene expression and β-galactosidase expression. Values are the average of the relative CAT expression normalized for β-galactosidase levels of three independent experiments and error bars represent the standard deviation. D , pHook-2 expression plasmids without insert or containing the various CXR alleles were co-transfected with the CYP2H1 264-bp PBRU in the pBLCAT5 reporter vector into drug-responsive LMH cells expressing endogenous CXR. After transfection, the cells were treated with either vehicle or 400 μM PB for 24 hours before cells were lysed and analysed for reporter gene expression and β-galactosidase expression. Values are the average of the relative CAT expression normalized for β-galactosidase levels of three independent experiments and error bars represent the standard deviation. First, the three CXR mutants were tested for their ability to bind to and activate a 264-bp PBRU isolated from the 5'-flanking region of chicken CYP2H1 [ 17 , 33 ]. As shown in electrophoretic mobility shift assays (Figure 3B ), CXR can heterodimerize with RXR and bind to the 264-bp PBRU as wild-type, full-length receptor and when the N-terminal region from amino acid 1–29 (called ΔN-term) or the C-terminal region from amino acid 383–391 (referred to as ΔAF-2) are deleted, respectively (Figure 3B , lanes 8, 10 and 12). As expected, site-directed mutagenesis of four cysteine within the DNA-binding domain into alanine residues (denominated DBD) that participate in forming the zinc-finger domains abolishes protein-DNA interaction (lane 11). These results show that removal of the N-terminus or the C-terminus of CXR does not influence its binding to DNA. Subsequently, the CXR mutants were tested in CV-1 transactivation assays for functionality. The CV-1 monkey kidney cells constitute an excellent tool to study nuclear receptor function in a cellular system which does not express endogenous xenosensors, is not drug-inducible and thus has a very low background in these assays. Neither CXR lacking its C-terminal activation domain AF-2 (ΔAF-2) nor CXR with the mutated DNA-binding domain (DBD) are able to transactivate the CYP2H1 264-bp PBRU in CV-1 cell assays (Figure 3C ). In contrast, removal of the N-terminus of CXR (ΔN-term) has no effect on its transactivation potential suggesting that no activation function AF-1 is present in these 29 amino acids. Finally, the test whether any of these CXR mutant alleles acts in a dominant-negative fashion is performed in the LMH cells which do express endogenous CXR and which are drug-inducible [ 18 ]. When co-transfected with the 264-bp PBRU, the CXR allele lacking a functional AF-2 domain (ΔAF-2) drastically decreases PB-induction of the PBRU (Figure 3D ). In contrast, the DNA-binding domain (DBD) and the N-terminal truncated (ΔN-term) mutants have no effect. Similar results were obtained with PBRUs from other drug-responsive genes (data not shown). Together, the RNAi experiments and the findings using the dominant-negative CXR mutants show that functionally, CXR is the major drug-sensing nuclear receptor in chicken. A significant difference between PXR and CAR in mammals is their mode of activation and their cellular localization [ 19 ]. PXR is strongly activated by a huge number of compounds. In contrast, CAR exhibits less promiscuity but high constitutive activity in most cellular assays [ 34 ]. However, CAR activity can be modulated by inverse agonists, agonists and different protein phosphorylation events [ 35 ]. In terms of activation, CXR is also highly promiscuous and normally has a low basal activity, thus pharmacologically more resembling a PXR-type than a CAR-type receptor [ 13 , 15 ]. Regulation of CAR activity can in part be explained by its unusual cellular localization. Both PXR and CAR undergo cytoplasmic-nuclear shuttling upon activation [ 36 - 40 ]. However, in contrast to PXR, CAR translocates after activation by PB, other xenobiotics or bilirubin for which no direct binding to the ligand-binding pocket was found. Although some progress in identifying CAR-interaction partners have been made recently [ 41 , 42 ], the mechanisms controlling the cytosolic-nuclear translocation are not clear. Interestingly, CAR translocation is independent of the C-terminal AF-2 function but instead requires the xenochemical response signal (XRS) LXXLXXL located between leucine 313 and leucine 319 in the human CAR sequence [ 37 ]. In contrast, cytoplasmic-nuclear translocation of VDR, the glucocorticoid receptor (GR, NR3C1) and the progesterone receptor (PR, NR3C3) is dependent on AF-2 suggesting a different mechanism for CAR shuttling [ 37 , 43 ]. A putative XRS ( L LL L TE L ) is also found in the CXR sequence between leucine 356 and leucine 362. Thus, to assess the relatedness of CXR to PXR and CAR in terms of cellular localization, we engineered different CXR-green fluorescent protein (GFP) fusion proteins. These were subsequently tested for functionality in CV-1 cell transactivation assays using the 264-bp PBRU as drug-sensitive enhancer. CXR with N-terminal, but not C-terminal GFP is activated by 400 μM PB and 10 μM clotrimazole after 16 hours of incubation (Figure 4A ). Site-directed leucine to glycine mutagenesis in the CXR XRS reduces its ability to confer drug activation in these assays (Figure 4A ). Figure 4 Cellular localization of CXR in transiently transfected LMH cells. A , Full-length CXR, CXR with GFP attached at its C-terminus or at its N-terminus or N-terminal GFP-CXR fusion protein mutated in its xenochemical response signal (XRS) at positions 356, 359 and 362 were expressed in CV-1 cells together with a reporter plasmid containing the CYP2H1 264-bp PBRU. After transfection, the cells were treated with either vehicle, 400 μM PB or 10 μM clotrimazole before cells were lysed and analysed for reporter gene expression. Values are the average of three independent experiments and error bars represent standard deviations. B–F , LMH cells were transfected with either pEGFP-vector alone (B), expression vector for N-terminal GFP-CXR fusion protein treated with vehicle (C), 400 μM PB (D) or 0.1 μM okadaic acid (E) for 16 hours or GFP-CXR fusion protein with the xenochemical response signal mutation as described in Fig. 4A (F). Cells were stained with 300 nM DAPI in PBS and analysed for DAPI and GFP-specific light emissions at 461 nm and 507 nm using excitation wavelengths of 358 nm and 488 nm, respectively. Size bars stand for 20 μm. Subsequently, N-terminal GFP-CXR was transiently expressed in LMH cells, the cells were counterstained with DAPI to stain the nuclei and GFP-CXR localization was compared to that of GFP-expression vector without insert. GFP was found to be evenly distributed throughout the cell (Figure 4B ). As depicted in Figure 4C , GFP-CXR in vehicle-treated LMH cells is exclusively in the nucleus. Treatment of transiently transfected LMH cells with 400 μM PB for 16 hours leads to an increase of GFP-staining in the cytosol (Figure 4D ). Similar observations have been made for a variety of nuclear receptors where activation stimulates their export from the nucleus and subsequent degradation in the cytosol [ 44 , 45 ]. Accordingly, PB-treatment of LMH cells results in decreased CXR protein levels in total cell lysates (Figure 5 , lanes 1 and 2) and even more dramatic in nuclear extracts (Figure 5 , lanes 3 and 4) suggesting that activated CXR protein is more rapidly exported from the nucleus and degraded of this receptor. Most nuclear receptors that are exported and degraded upon activation share a conserved KXFF K / R R motif between the two zinc-fingers in the DNA-binding domain that can serve as binding site for calreticulin which is involved in the nuclear export [ 45 ]. PXR, CAR and CXR also contain a KGFFRR-motif but whether calreticulin plays a role in nuclear export of these receptors remains to be investigated. The protein phosphatase inhibitor okadaic acid inhibits PB-induction of mammalian and chicken PBRUs [ 13 , 18 , 46 , 47 ]. In transiently transfected LMH cells, 100 nM okadaic acid prevents nuclear localization of CXR after 16 hours (Figure 4E ). Moreover, protein levels of the GFP-CXR fusion protein were reduced. Okadaic acid treatment prevents the drug-induced cytosolic-nuclear translocation of CAR [ 36 ]. Our findings regarding CXR are therefore very reminiscent of those results. Furthermore, site-directed mutagenesis of the XRS reduces the nuclear localization of CXR (Figure 4F ) but not as completely as XRS mutations of CAR in mouse primary hepatocyte cultures [ 37 ]. The nuclear-cytoplasmic redistribution of this CXR mutant correlates with the decrease in its ability to activate the 264-bp PBRU in transactivation assays (Figure 4A ). Thus, although CXR is normally found in the nucleus like PXR, it shares some features with CAR concerning its localization after treatment with okadaic acid or when its XRS is mutated. Figure 5 Nuclear CXR protein levels decrease after PB-treatment. LMH cells were treated with vehicle or 400 μM PB for 16 hours before cells were lysed and CXR protein levels in the total lysate (lanes 1 and 2) and in nuclear extracts (lanes 3 and 4) determined by Western blot. As controls, CV-1 cells were transfected with control vector or CXR expression vector (lanes 5 and 6). MW, molecular weight in kDa. In primary human hepatocytes, glucocorticoids have a dual effect on the expression of the drug-inducible CYP3A4 that is regulated by both PXR and CAR [ 48 ]. At low concentrations, these compounds activate GR which subsequently induces transcript levels of PXR and CAR [ 49 , 50 ] whereas higher concentrations of glucocorticoids directly activate PXR [ 9 , 51 ]. We thus wanted to test whether the chicken CXR is regulated in the same way as the mammalian xenobiotic-sensing receptors. Treatment of LMH with 50 μM dexamethasone (Dex) for 16 hours did not alter CXR expression (Figure 6 ). Moreover, dexamethasone does not activate CXR directly, at least at this concentration [ 13 ]. In contrast, dexamethasone increases transcription of the chicken peroxisome-proliferator activated receptor α (PPARα, NR1C1), the chicken liver X receptor (LXR, NR1H3) and the chicken farnesoid X receptor (FXR, NR1H4). These receptors play important roles in maintaining hepatic bile acid, cholesterol and lipid homeostasis, respectively [ 52 ]. PXR, CAR and CXR have been found to be activated by bile acids and thus are involved in the regulation of the intrahepatic levels of lipid soluble compounds by stimulating metabolism and subsequent excretion of these compounds [ 12 , 53 , 54 ]. Therefore, activation of one of these receptors leads to changes in intrahepatic lipid levels which then potentially affects transcription of the other receptors. However, the regulatory network of these receptors is still under investigation. Figure 6 Transcriptional regulation of chicken CXR, FXR, LXR and PPARα in LMH cells by glucocorticoids. LMH cells were treated for 16 hours with vehicle or 50 μM dexamethasone before cells were lysed and RNA was analysed by Northern blotting with probes for CXR, chicken PPARα, chicken LXR, chicken FXR or chicken glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Molecular modelling studies confirm the close relationship between chicken and fish xenosenors to mammalian PXR. X-ray structures of human PXR revealed several peculiarities of the PXR ligand-binding domain which are not found in other nuclear receptors [ 55 , 56 ]. First, PXR has an expanded β-sheet with two more strands. Moreover, helix 6 is completely and helix 7 partially unwinded which leaves a solvent-accessible hole in the ligand-binding pocket that is capped by an extension in helix 1–3. Although an extended β-sheet is not obvious in chicken and fish xenosensors, both receptors have long helix 1–3 inserts which could potentially induce partial unwinding of helix 6 and 7. Thus, molecular modelling of aligned amino acid sequences suggest enlarged ligand binding pockets for both fish and chicken xenobiotic-sensing receptors which could explain their high promiscuity [ 15 ]. In striking contrast, CARs not only lack an extended β-sheet but also have a much shorter helix 1–3 resulting in a more rigid and less promiscuous ligand binding pocket [ 15 , 57 , 58 ]. Therefore, the relatively high degree of promiscuity of CAR could at least partially be due to the ability of different compounds to trigger cytoplasmic-nuclear translocation of this receptor independent of direct binding [ 35 ]. The loop connecting helix 11 and 12 is much shorter in the CAR sequence than most other nuclear receptors [ 15 , 57 ]. This short loop might reduce the ability of helix 12 and AF-2 to reach an inactive conformation and thus could explain the constitutive activity of CAR [ 57 ]. CAR also has a shorter helix 12 than most other nuclear receptors [ 57 ]. Interestingly, helix 12 of CXR is very conserved to that of mammalian CARs in terms of amino acid composition and of length whereas the length of the zebrafish xenosensor helix 12 is intermediate between CARs and PXRs. Conclusions In summary, our results confirm that in contrast to mammals which have two xenobiotic-sensing receptor PXR and CAR, the genome of other species encodes for only one xenosensor. This hypothesis is supported by analysis in the fugu fish genome (data not shown), unsuccessful attempts to isolate further xenosenors in chicken and functional assays showing that ablation of CXR function drastically reduces drug-inducibility in a chicken hepatoma cell line. Our findings presented here and those of other laboratories imply that PXR and CAR origin from one ancestral gene which diverged into two genes in mammals. This ancestral gene, in chicken coding for CXR, is a promiscuous, PXR-like receptor. Thus, CXR and related receptors from fish are activated by a variety of different compounds [ 13 , 15 ]. Interestingly, in a comprehensive study of different classes of ligands on xenosensors from man, monkey, pig, dog, mouse, chicken and fish, CXR was one of the most promiscuous receptors in regard to the compounds tested [ 15 ]. Therefore, the ancestral xenosensors in non-mammalian species might have a broader substrate spectrum than their mammalian counterparts where the task for detoxification is split between two receptors [ 59 ]. On the other hand, CXR also shares some features with CAR that are not found in PXR: its short helix 12, the xenochemical response signal and in part its cellular localization after okadaic acid treatment. Finally, in contrast to both PXR and CAR, CXR is not regulated by glucocorticoid treatment in the chicken LMH cells suggesting that this regulation was acquired only after birds and mammals diverged from a common ancestor. Evolution of drug-metabolizing CYPs and xenobiotic-sensing nuclear receptors is influenced by diet and exposure to other environmental chemicals. Accordingly, drug-induction is very species specific. This is reflected in the unusually divergent ligand-binding domains of PXRs and CARs orthologs [ 22 ]. When comparing PXRs and CARs from human, mouse and rat, nonsynonymous nucleotide substitution rates are considerably higher in comparison to any other nuclear receptor [ 21 ] and reflect the different evolutionary adaptations of these species to their specific environment. It is thus extremely puzzling why in non-mammalian species, one xenosensor is sufficient whereas two xenobiotic-sensing nuclear receptors have evolved in mammals. Furthermore, it is unclear why in addition to the ligand-activated PXR, mammalian genomes encode CAR, a nuclear receptor that is unorthodox in many ways. On one hand, CAR and PXR might just share the workload in hepatic detoxification of xenobiotics. On the other hand, evidence accumulated in recent years that both PXR and CAR have functions that go beyond detoxification. As example, PXR and CAR form an intricate network with other nuclear receptors and transcription factors to regulate hepatic cholesterol and bile acid homeostasis [ 60 ]. It is thus conceivable that these receptors have so-far unidentified functions in mammals which require two receptors and that are thus absent in non-mammalian species. Therefore, further insights into the evolution of drug-sensing nuclear receptors are extremely important in order to gain novel insights into the role of these factors in the physiology and pathophysiology of the liver. Methods LMH and CV-1 cell culture, transfection and reporter gene assays Culture and transfection of LMH cells with FUGENE 6 Transfection Reagent (Roche Molecular Biochemicals, Rotkreuz, Switzerland) were performed as published [ 17 , 33 ]. Before transfections, LMH cells were kept in serum-free medium for 24 hours. CV-1 cell transactivation assays have been described in detail [ 17 , 33 ]. Sixteen or twenty-four hours after drug-treatment, cells were harvested and assays for CAT expression using a CAT ELISA Kit (Roche Molecular Biochemicals, Rotkreuz, Switzerland). CAT concentrations were normalized against β-galactosidase activities to compensate for different transfection efficiencies. Isolation of the CXR gene Chicken BAC filters (UK Human Genome Mapping Project Resource Center, UK) were hybridised with a probe encoding for CXR. Positive clones were purchased, digested with different restriction enzymes and Southern blots obtained using the same probe. Bands hybridising with the CXR probe were isolated, subcloned and CXR genomic information obtained by PCR using primers designed after the CXR mRNA sequence. Site-directed mutagenesis Mutagenesis was carried out using overlapping primers as described [ 17 ]. Mutated fragments were excised, cloned into new vectors and verified by sequencing. Electrophoretic mobility shift assays Electrophoretic mobility shift assays have been described in detail [ 33 ]. Proteins were expressed using the TNT in vitro transcription/translation kit (Promega, Wallisellen, Switzerland) before being subjected to non-denaturing SDS-polyacrylamide gel electrophoresis with [ 32 P]-radiolabeled CYP2H1 264-bp PBRU. Targeting of CXR in LMH cells by RNAi Expression of CXR in LMH cells was repressed by RNAi as described [ 61 ]. In brief, a 19 bp fragment ranging from position 857 to 875 in the open reading frame of CXR was chosen for targeting. A double-stranded oligonucleotide containing this sequence and compatible ends for cloning into pSUPER was obtained by annealing single stranded oligonucleotides for the sense (GATCCCC GGATGGGGCTCTGGCCGGC TTCAAGAGA GCCGGCCAGAGCCCCATCC TTTTTGGAAA) and the anti-sense strand (AGCTTTTCCAAAAA GGATGGGGCTCTGGCCGGC TCTCTTGAA GCCGGCCAGAGCCCCATCC GGG) and subsequent ligation into pSUPER cut with BglII and HindIII (underlined letters refer to CXR-specific targeting sequence). After verification of the ligation product the pSUPER-CXR-RNAi expression cassette was cut out using BamHI and XhoI and subcloned into BglII/XhoI-digested pcDNA3 (Invitrogen, Carlsbad, USA). The ScaI-linearised construct was transfected into LMH cells using FUGENE 6 (Roche Molecular Biochemicals, Rotkreuz, Switzerland). Stable transfectants were selected by addition of 175 μg/ml G418 (PAA Laboratories, Pasching, Austria) to the cell culture medium. A control cell line was selected in parallel which was stably transfected with pcDNA3 carrying the empty pSUPER expression cassette. Reporter gene assays in LMH cells using the CXR-RNAi clones were performed using reporter constructs for CYP2H1, CYP3A37, CYP2C45 and ALAS-1 described previously [ 17 , 24 - 26 ]. Cellular localization studies LMH cells were cultivated on glass cover slips and subsequently transfected with pEGFP-C1 or pEGFP-N1 expression plasmids (Clontech, Allschwil, Switzerland) before cells were either treated with vehicle, 400 μM PB or 0.1 μM okadaic acid for 16 hours. Cells were washed with PBS, fixed in 3% formaldehyde for 30 minutes, washed again with PBS, stained with 300 nM DAPI and subsequently mounted on glass slides. Digital images were captured using a Leica DC 300F camera (Leica, Nidau, Switzerland) mounted on a Leitz DMRB microscope with the Leica IM50 Image Manager program version 1.20. Figures were assembled with Adobe Photoshop version 5.0. CXR antibodies, nuclear extracts and Western blots CXR ligand-binding domain was expressed in bacteria, purified and injected into rabbits for antibody production according to standard procedures. Anti-CXR-ligand-binding domain antibody from rabbit serum was subsequently used in Western blots. LMH cells were grown under standard conditions and treated with vehicle or 400 μM PB overnight. Cells were subsequently washed with PBS and protein extracts prepared using RIPA buffer. As control, CV-1 cells were transfected with empty pSG5 expression vector or vector expressing CXR and subsequently lysed with RIPA buffer. Nuclear extracts were prepared as published [ 62 ]. Northern hybridisation LMH cells were treated with the indicated compounds for 16 hours before total RNA was isolated using the TRIZOL Reagent (Life Technologies, Basel, Switzerland). Twenty μg of total RNA were subjected to electrophoresis and analysed in Northern hybridisations as described [ 17 , 33 ]. List of Abbreviations CYP, cytochrome P450; PB, phenobarbital; PXR, pregnane X receptor; CAR, constitutive androstane receptor; CXR, chicken xenobiotic receptor; PBRU, phenobarbital-responsive enhancer unit; ALAS-1, δ-aminolevulinate synthase; AF-1/2, activation function-1/2; LXR, liver X receptor; PB, phenobarbital; XRS, xenochemical response signal; GFP, green fluorescent protein; PPAR, peroxisome-proliferator activated receptor; FXR, farnesoid X receptor. Competing interests The authors declare that they have no competing interests. Authors' contributions CH carried out the cellular localization assays, cloned the various CXR mutants, performed the reporter gene and the electrophoretic-mobility shift assays as well as the transcriptional regulation studies. SB did the various screens for further chicken xenobiotic-sensing nuclear receptors. AR performed the RNAi experiments. RL and MO isolated the CXR antibody and carried out the protein stabilization and localization assays. MRK helped with the RNAi experiments. MP and CG helped with the CV-1 cell transactivation assays. UAM conceived of the study, and participated in its design and coordination. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC524364.xml

545995

Concepts of patients with alopecia areata about their disease

Background Alopecia areata (AA) is a common and chronic skin disease with an unknown etiology. It may significantly affect the patient quality of life. This study was designed to evaluate the illness perception in patients with AA. Methods A questionnaire consisting of 25 questions about causes, timeline, consequences and control of disease were given to 80 patients with AA attending a skin clinic in Tehran, Iran. The impact of age, gender, duration of disease, education, extent of disease and family history of AA were also assessed. Results Eighty patients (38 male and 42 female) with a mean age of 27.5 years (SD = 9.3) and disease duration of 7.8 years (SD = 7.7) completed the questionnaire. 76.9% of the patients believed that the role of stress was the cause of disease. 17.1 % believed genetic background to be the main cause, this found to be more frequent in patients with positive family history of AA. More than half of patients believed that their illness had major consequences on their lives and 40% of patients believed that their illness would be likely to be permanent rather than temporary, more in patients with longer duration of disease. Only 57.5% of patients considered their treatments to be effective. Conclusion AA may considerably affect various aspects of patients' lives. The patient knowledge about the causes and course of this disease is limited.

Background Alopecia areata (AA) is manifested as a sudden loss of hairs without any inflammation or scarring. The hair loss might be seen in a circumscribed area or the whole scalp (alopecia totalis or AT) or whole body (alopecia universalis or AU) [ 1 ]. It is a common disease and at any given time, 0.2% of the population has AA and 1.7% of the population will experience an episode of AA during their lifetime [ 2 , 3 ]. The etiology of AA is not known exactly. However factors such as genetic predisposition, autoimmunity, and stress have been suggested [ 4 , 5 ]. The course of disease is not predictable and it is often associated with periods of hair loss and regrowth. The clinical severity of a patient's AA may not be a good indicator of subsequent downturn in quality of life or psychological well-being. The onset of a chronic condition brings with it a range of difficulties that may show considerable variation in their nature and severity as perceived by the patient. In order to make sense of and respond to the difficulties that chronic illness may present, patients construct their own common-sense cognitive model of their condition. Such models are based upon information received from a range of sources including their physician, family, friends, and existing social and cultural notions about health and illness. The resulting system of beliefs can of course be flawed or inaccurate; however there is evidence that it is those beliefs that drive attempts to cope with a condition and issues of compliance with treatment. Patient-held beliefs have important implications for the clinical management of their disease. Studies on patients with psoriasis and acne vulgaris have shown that knowledge of patients about their condition, the course of their disease and current treatments is not appropriate [ 6 , 7 ]. In this study, we examined the system of beliefs held by AA patients and the factors that might influence such beliefs. Methods The Illness Perception Questionnaire (IPQ) [ 8 ] with a few modifications was given to 80 patients with AA older than 12 years, attending a private skin clinic in Tehran, Iran in 1999. The study was approved by Institutional Review Board of the Center for Research and Training in Skin Diseases and Leprosy. The IPQ was created to provide a theoretically derived measurement instrument suitable for use with any patient population. It has been used in patients with cardiac disease [ 9 ], chronic fatigue syndrome [ 10 ], diabetes, chronic pain, rheumatoid arthritis [ 8 ], and psoriasis [ 6 ]. As AA is an asymptomatic disease, we did not use the subscale of "symptoms" in our study. Thus the questionnaire that we used consisted of four subscales: Cause subscale (10 items) measures personal ideas about the cause of AA. Time line (3 items) deals with perceptions about how long the disease will last. Consequences (6 items) are concerned with expected effects and outcomes of the illness. Cure/Control (6 items) details beliefs about recovery from or control of the condition. There were four possible answers for each item in the IPQ to be chosen by patients: I strongly agree, I agree, I do not know, I disagree. Furthermore, some demographic information such as age, sex, family history of AA, and duration and extent of disease (alopecia areata or AT/AU), and level of education were obtained from the patients to evaluate their influence on patients' beliefs. Statistical analysis was conducted by means of SPSS statistical software, version 11.0. Because the data were not normally distributed, nonparametric statistics were used. Correlations were processed by Spearman's rank correlation, and differences between means were computed by means of the Mann-Whitney U test. For simplicity of analysis and increasing the power of study, the answers of "I strongly agree" and "I agree" were grouped together and compared with the answers of "I don't know" and "I disagree" which were grouped together as "I do not agree". A p value of less than 0.05 was considered as significant. Results A total of 80 patients with AA (38 male, 42 female), with a mean age of 27.5 years (SD 9.3, ranged from 13 to 56 years) were recruited to the study. The mean duration of illness was 7.8 years (SD 7.7, ranged from 1 month to 30 years). In 75% of patients, AA was patchy and it was totalis or universalis in 25%. Fifteen percent of patients had a positive family history of AA in their first degree relatives. Physicians were the main source of patients' information about their disease in 66.2% of them. Beliefs about cause Table. 1 shows the percentage of patients "agreeing" with each cause item. A total of 76.9% of patients believed that stress was a major factor in onset of their illness and older patients were more likely to believe in this (p < 0.05). Patients who had a belief that their disease was a result of genetic factor were more likely to have a family history of AA and longer duration of the disease (P < 0.05). Younger patients and those with extensive disease (AT/AU) believed that their illness was because of chance or fate (P < 0.05). Table 1 Beliefs about causes of alopecia areata (n = 80) Causes Agree Factors influencing beliefs Stress 76.9% Older patients (p = 0.012) My state of mind 59.2% None My own behavior 47.3% None Other people 34.2% None Chance or fate 31.1% Younger patients(p = 0.021), extensive disease (AT/AU)(p = 0.030) Diet 25.7 % None Pollution 24.3% None Germ or virus 21.9% None Genetic 17.1% Family history of AA(p = 0.006), longer duration(p = 0.017) Poor medical care 11.8% None Beliefs about consequences Majority of the patients (58.2%) believed that their illness had a major consequence on their lives, 53.8% of patients also felt that AA had strongly affected their self-esteem, and 50.6% considered AA as a serious condition. These believes were stronger in younger patients, and in patients who had the disease for a long time (p < 0.05). Table 2 shows the percentage of patients "agreeing" with each consequence item. Table 2 Beliefs about consequences of having alopecia areata (n = 80) Beliefs Agree Factors influencing beliefs My disease has had a major consequence on my life. 58.2% Younger patients(p = 0.012), longer duration(p = 0.014) My disease has strongly affected the way I see myself as a person. 53.8% Younger age at onset(p = 0.003) My disease has strongly affected the way others see me. 51.3% Younger age at onset(p = 0.022), longer duration(p = 0.012) My disease is a serious condition. 50.6% Younger age at onset(p = 0.003), younger patients(p = 0.013) My disease has become easier to live with. 50.6% None My disease has serious economic and financial consequences. 27.8% Younger age at onset(p = 0.015), longer duration(p = 0.010) Beliefs about recurrence or chronicity Half of the patients believed whether their disease cleared, it would always come back and forty percent of patients believed that their illness would be likely to be permanent rather than temporary. They were more likely to have a longer duration of disease (p < 0.05). The minority of patients (25.0%) believed that their illness would last a short time. Beliefs about cure and control More than 60% of patients believed that their behavior could determine improvement or worsening of their illness (table 3 ). This belief was present in female patients more than male patients (p < 0.05). 30.4% of patients believed there was very little that could be done to improve their illness. They were more likely to have longer duration of disease (P < 0.05). Thirty-eight percent of the patients believed that recovery from disease is largely dependent on chance or fate. This belief was stronger in female patients, those with younger age at onset, and patients with extensive disease (p < 0.05). Table 3 Beliefs about cure and control (n = 80) Beliefs Agree Factors influencing beliefs What I do can determine whether my disease gets better or worse. 63.3% Female patients(p = 0.010) My treatment will be effective in curing my disease. 57.5% None My disease will improve in time. 53.2% Older patients(p = 0.006), older age at onset(p = 0.029) There is a lot that I can do to control my disease. 52.5% None Recovery from my disease is largely dependent on chance or fate. 38.0% Female patients(p = 0.014), younger age at onset(p = 0.036), extensive disease (AT/AU)(p = 0.021) There is very little that can be done to improve my disease. 30.4% Longer duration(p = 0.004) Discussion Alopecia areata is a chronic disease which may influence individual or social aspects of patients' lives. The results of our study confirmed this fact as the majority of patients believed that their illness had strongly affected their lives. It also influenced their self-esteem. The results of studies in other chronic diseases with periods of remission and exacerbation have had different results in this respect. For example in a study on acne patients, the disease had affected patients' self-image in nearly all of them, but it had no impact on interpersonal relationships, work, or school activities in majority of patients [ 7 ]. On the other hand, in a study on patients with psoriasis using the IPQ questionnaire, 68% of patients who suffered from psoriasis, agreed that psoriasis had a major consequence on their lives, and 53.4% agreed that psoriasis had strongly affected the way they saw themselves as a person [ 6 ]. The present study also investigated cognitive appraisals held by patients about their illness and showed that such beliefs were not associated in any significant manner with the extent of their condition. Fortune et al also did not find an association between the clinical severity of psoriasis and beliefs held by patients about their condition [ 6 ]. Thus the assumption that the objective severity of a condition will be associated in a linear fashion with patient's subjective experience in terms of beliefs, coping, or distress is unlikely to be correct. On the other hand, young patients and those with longer duration of disease were more likely to be affected by their disease. This implies that the chronicity of the disease has more influence on patient's life than the extent of it. The results of our study also showed that the beliefs about the consequences of having AA were not influenced by the gender of the patients. Thus men are as vulnerable as women in suffering from the consequences of AA. Patients with AA, including 77% of patients in this study, often attribute the onset of their disease to a specific stressful life event. In a study on 178 patients, Van der Steen et al. showed that emotional stress is not an important factor in the initiation of AA [ 11 ]. Brajac et al. did not find a significant role of stress in the onset of AA but stressful life events had an important role in triggering of some episodes of disease [ 12 ]. On the other hand, Gupta et al. found that AA patients who were depressed, were more likely to mention stress as the cause of their disease [ 13 ]. In recent studies, psychologic and psychopathologic factors have been analyzed as modulators of neuroendocrinologic, vascular, and immunologic variables; this is far from the initial concept of stress being the causal agent in the illness. In fact, stress may cause its effect by making alterations in immune responses related to neuropeptides, such as the migration of the macrophages, vasodilator or vasoconstrictor responses, phagocytosis, lymphocytic cellular immunity, and expression of some factors of leukocytic adhesion to the microvascular endothelium [ 14 ]. In addition, the adaptation to the illness is regarded as an important factor with regard to prognosis. However the exact cause of AA is not known and such events are very common, making it difficult for the investigator to prove that they are in fact involved in causing or precipitating the disease. In this study, one-third of the patients believed in chance or fate as the cause of AA, and this belief was stronger in younger patients and those with extensive disease (AT/AU). This study also showed that as AA lasts, the patients feel more hopeless about time line and treatment modalities of their disease. The majority of patients had no hope to get rid of their disease. Almost half of patients expected their disease to relapse after it disappeared. Such perpetual stresses are hardly endurable. The psychiatrists' intervention may alleviate patients' stress and improve their quality of life. This study was performed in a private dermatology clinic. The possibility of socioeconomic homogeneity among recruited patients may be biasing the results. So it should be considered that these results may be different in patients with AA in different socioeconomic and cultural backgrounds. Conclusion There is a need for accessible, accurate, community-based education on the natural history of AA, the effectiveness and expected duration of treatment. The inadequacy of information provided by current sources is evident in ongoing misconceptions on causality and the perceptions of respondents. Incorporating information on this disease may facilitate patient into therapeutic selection, enhance understanding of treatment options and improve patient compliance. Competing interests The author(s) declare that they have no competing interests. Authors' contributions AF participated in the design and conduct of the study and preparation of the manuscript. MRF participated in the conduct of the study and statistical analysis. BG participated in the conduct of the study and statistical analysis. YD participated in the design of the study and preparation of the manuscript. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC545995.xml

527876

Neuronal oxidative damage and dendritic degeneration following activation of CD14-dependent innate immune response in vivo

The cause-and-effect relationship between innate immune activation and neurodegeneration has been difficult to prove in complex animal models and patients. Here we review findings from a model of direct innate immune activation via CD14 stimulation using intracerebroventricular injection of lipopolysaccharide. These data show that CD14-dependent innate immune activation in cerebrum leads to the closely linked outcomes of neuronal membrane oxidative damage and dendritic degeneration. Both forms of neuronal damage could be blocked by ibuprofen and alpha-tocopherol, but not naproxen or gamma-tocopherol, at pharmacologically relevant concentrations. This model provides a convenient method to determine effective agents and their appropriate dose ranges for protecting neurons from CD14-activated innate immunity-mediated damage, and can guide drug development for diseases, such as Alzheimer disease, that are thought to derive in part from CD14-activated innate immune response.

Introduction Activated innate immunity is associated with several degenerative and destructive brain diseases including Alzheimer disease (AD), HIV-associated dementia (HAD), ischemia, head trauma, stroke, cerebral palsy, and axonal degeneration in multiple sclerosis [ 1 ]. In this complex response, some aspects are proposed to be neurotrophic, others neurotoxic, and each potentially a consequence rather than a contributor to neurodegeneration. Indeed, a severe limitation to understanding the precise role of innate immunity in these diseases and their corresponding animal models is that innate immunity is activated simultaneously with multiple other stressors and responses to injury, thereby greatly confounding any clear conclusion about cause-and-effect relationships. For these reasons we have adopted a simple but highly specific model of isolated innate immune activation: intracerebroventricular (ICV) injection of low dose lipopolysaccharide (LPS). LPS specifically activates innate immunity in peripheral organs through a well-described Toll-like receptor (TLR)-dependent signaling pathway [ 2 , 3 ]. There are 9 known human plasma membrane-spanning TLRs expressed in many cell types throughout the body that have been discovered in the context of innate immune response to micro-organisms. TLR-mediated innate immune response can be considered in three phases: initial signal transduction cascade, secondary signaling cascades, and effectors. The initial signaling cascade starts with ligand activating one of the 9 plasma membrane TLRs. All of these receptors require the adaptor protein MyD88 for immediate response to LPS and initiate a bifurcated signal transduction cascade that culminates in altered gene transcription, primarily via NF-κB activation but also through c-Fos/c-Jun-dependent pathways. Some of the activated gene transcripts encode directly for receptor ligands while others are enzymes that catalyze the formation of receptor ligands that in turn activate secondary autocrine and paracrine signaling cascades. These signaling events culminate in the generation of effector molecules including bacteriocidal molecules, primarily free radicals generated by NADPH oxidase and myeloperoxidase (MPO), as well as cytokines and chemokines that can attract an adaptive immune response. Although originally identified as part of the response to exogenous antigens from micro-organisms, a broader pathophysiologic role for TLR-dependent signaling in response to endogenous ligands in now clear. Indeed, from this perspective, the effectors at the culmination of these signaling pathways are more appropriately viewed as cytocidal rather than specifically bacteriocidal. The precise agents responsible for cytocidal activity are not clearly established but likely include free radicals generated principally by NADPH oxidase, MPO, and inducible nitric oxide synthase (iNOS) in combination with cytokines and chemokines. TLR-4 is the receptor for LPS in peripheral organs [ 2 , 3 ]. However, another protein, CD14 is critical to LPS activation of TLR-4. Membrane-anchored CD14 is now thought to act a co-receptor for LPS but not to initiate intracellular signaling cascades. It is important to note that CD14 serves a similar function with TLR-2, although the activating agents here are bacterial products other than LPS [ 4 ]. Within minutes to hours of exposure to LPS, there is increased gene transcription and subsequent translation of cytokines and chemokines, prominently including tumor necrosis factor, interleukin-1, and interferons, as well as several enzymes; important among these are iNOS and cyclooxygenase 2 (COX-2) that catalyze the formation of NO and prostaglandin (PG) H 2 , respectively [ 4 ]. While NO is a potent cell signaling molecule, PGH 2 has relatively low receptor binding affinity but is rapidly and efficiently converted to multiple PGs or thromboxane A2, each of which are potent activators of a large family of G protein-coupled receptors [ 5 ]. The combination of these initial and secondary signaling cascades produces a robust innate immune response. This same response can occur in response to endogenous ligands that also activate the CD14/TLR-4 pathway [ 2 , 3 ]. Indeed, several endogenous CD14/TLR ligands have received increasing attention for their potential roles in human diseases [ 6 ], and polymorphisms in TLR-4 are associated with risk for atherosclerosis and asthma, as well as other human diseases [ 7 ]. With respect to AD, amyloid beta (A ) fibrils have been shown to activate the microglial innate immune response through CD14-dependent mechanisms [ 8 ]. Relevant to a broader range of neurodegenerative diseases, novel peptides and neoantigens exposed by apoptotic cells [ 9 ] also activate CD14-dependent innate immune response in macrophages. While none of these data point to CD14 or innate immune response as etiological in neurodegenerative disorders, these findings from in vitro and cell culture experiments raise the possibility that CD14-dependent signaling may be a common process shared in the pathogenesis of neurodegenerative diseases, especially AD. Here we present our results from studies that have identified the molecular and pharmacologic determinants of ICV LPS-initiated cerebral neuronal damage in vivo . It is important to stress that several laboratories have shown that glia, predominantly microglia, are activated by LPS but that neurons do not respond to LPS because they lack the appropriate receptors [ 10 , 11 ]. We measured two main endpoints; one biochemical and one structural. Since free radicals are a primary mechanism of cytocidal activity from innate immune response, we used a stable isotope dilution method with gas chromatography and negative ion chemical ionization mass spectrometry to quantify compounds formed by free radical attack on the neuronal membrane-enriched fatty acid, docosohexaenoic acid (DHA); we have termed these molecules F 4 -neuroprostanes (F 4 -NeuroPs) [ 12 ]. In addition to this biochemical marker of neuronal oxidative damage, we directly quantified neuron number as well as dendrite length and spine density in pyramidal neurons of hippocampal sector CA1 using the Golgi impregnation technique followed by quantitative morphometry with Neurolucida (MicroBrightField, VT) [ 13 ]. Lack of adaptive immune response, fever, or structural damage to brain following ICV LPS Despite the expectation that LPS would produce a febrile response with widespread damage to brain and an acute encephalitis, we observe that ICV LPS does not yield any of these outcomes (Figure 1 ) [ 14 ]. Indeed, others who injected similar amounts of LPS directly into brain parenchyma also do not observe behavioral changes, tissue damage, or acute inflammatory infiltrate in young wild type (wt) mice [ 14 - 18 ]. We pursued this further by stereological counting of hippocampal CA1 pyramidal neurons 24 and 72 hr following ICV LPS and observed no change in neuron number from untreated controls [ 14 ]. These data show that, at least over 3 days following ICV LPS, there is no gross structural damage to brain, no detectable adaptive immune response, and no loss of pyramidal neurons from hippocampal sector CA1. Figure 1 NeuN immunohistochemistry of mouse hippocampus. Photomicrograph (× 40) of NeuN immunoreactivity in mouse hippocampus and adjacent structures 24 hr after ipsilateral ICV LPS injection. Note normal density and distribution of neurons without a cellular infiltrate. Neuronal oxidative damage Numerous methods exist to determine free radical-mediated damage to cells. While most of these function well in vitro , important limitations arise in living systems where extensive, highly active enzymatic pathways have evolved to metabolize many of the commonly measured products, such as 4-hydroxynonenal [ 19 ]. One method that has been highly replicated as a robust quantitative means of measuring free radical damage in vivo is measuring F 2 -isoprostanes (F 2 -IsoPs) [ 20 ], products generated from free radical damage to arachidonic acid (AA), that are not extensively metabolized in situ (Figure 2 ). Since AA is present throughout brain and in different cells in brain at roughly equal concentrations, measurement of cerebral F 2 -IsoPs, like all other measures of oxidative damage, reflects damage to brain tissue but not necessarily to neurons. For these reasons, we developed an assay to measure the analogous products generated from DHA, F 4 -NeuroPs [ 12 ]. Since DHA is highly concentrated in neuronal membranes, F 4 -NeuroPs offer a unique window into free radical damage to neuronal membranes in vivo [ 21 ]. Figure 2 Diagram showing the formation of F 2 -IsoPs and F 4 -NeuroPs. We first determined the time course of F 4 -NeuroP accumulation in cerebrum of wt mice exposed to ICV LPS and observed a delayed, transient elevation that peaks at approximately 24 hr after exposure and then returns to baseline by 72 hr post exposure [ 14 ]. It is important to note that while detectable neuronal oxidative damage is delayed several hours following ICV LPS, others have shown that altered gene transcription and increased cytokine secretion occur rapidly and peak within a few hours of LPS exposure. As with oxidation of lipoproteins, it is likely that this delay in neuronal oxidative damage is related, at least in part, to the time required to deplete anti-oxidant defenses. Thus, despite the lack of tissue damage, adaptive immune cell infiltrate, or detectable neuron loss, there is significant, reversible free radical damage to neuronal membranes following ICV LPS. We next used a series of mice, all on the C57Bl/6 genetic background, lacking specific genes to establish the determinants of neuronal oxidative damage in this model. Our results showed that genetic ablation of one co-receptor (CD14), the required adaptor (MyD88), or one arm of the initial signal cascade (the p50 subunit of NF-κB) each completely blocks an LPS-induced increase in cerebral F 4 -NeuroPs (Table 1 ). Further investigation of mice lacking iNOS, an element of secondary signaling pathways, also completely blocks ICV LPS-induced neuronal oxidative damage. Finally, mice lacking prostaglandin E 2 receptor subtype 2 (EP2), one of four prostaglandin E 2 (PGE 2 ) receptors expressed in brain and one of the two PGE 2 receptors expressed by microglia, have no neuronal oxidative damage in response to ICV LPS [ 16 ]. There are some important points to consider when interpreting these data. First, not only glia but neurons also will be exposed to LPS in this model. However, we and others have repeatedly shown that primary neurons enriched in cell culture do not respond to LPS [ 10 , 11 , 22 - 24 ]; indeed, neurons do not express CD14 and TLR-4 in vivo [ 25 , 26 ]. Second, genetic ablation was not specific to cell type. While this limits interpretation of data from some mice, such as p50 -/- and EP2-/- mice because these proteins are expressed by both neurons and glia [ 27 - 32 ], it does not influence interpretation of data from CD14 -/- mice because CD14 expression in vivo is restricted to microglia among parencymal cells in brain [ 25 , 26 ]. Thus, these data strongly imply that LPS-activated microglial-mediated paracrine oxidative damage to neurons in vivo is dependent on CD14, MyD88, p50 of NF-κB, iNOS, and EP2. Table 1 Neuronal oxidative damage and dendritic degeneration in various knockout mice. Effects of ICV LPS treatment determined at 24 hr in mice homozygous deficient (knockout) for different genes or wildtype (wt) mice all on the C57Bl/6 genetic background (*P < 0.001 by Bonferroni-corrected repeated pair comparisons with ICV saline-exposed mice). Knockout Function Endpoints* F 4 -NeuroPs Dendrite Length Spine Density None (wt) N/A 352 + 53* 32 + 4* 37 + 6* CD14 Receptor 87 + 14 101 + 8 92 + 11 TLR-2 Receptor ---- 37 + 5* 51 + 8* MyD88 Adaptor 98 + 10 96 + 9 102 + 7 p50 Initial Signal Cascade 108 + 11 105 + 7 106 + 10 iNOS Secondary Signaling 92 + 12 103 + 8 97 + 6 EP2 Secondary Signaling 89 + 9 102 + 12 109 + 5 *% ICV saline-exposed; n > 5 in each group Dendritic degeneration These data left us with an apparent conflict. We have clearly demonstrated neuronal oxidative damage to mouse cerebrum following ICV LPS that is of a magnitude comparable to diseased regions of AD brain [ 33 ]. However, there is no apparent structural damage to brain in our study or in others' following ICV or intraparenchymal LPS. We viewed this as a serious potential challenge to the significance of oxidative damage in neurodegeneration. There are differences, of course, between the acute stress of ICV LPS stress and the presumably chronic stress of AD; nevertheless, these data force at least consideration of the question: could oxidative damage to neurons occur in vivo to the extent that is observed in AD brain without any neurodegeneration? To address this question, we decided to examine directly the dendritic compartment of neurons, which is largely transparent to the standard histological techniques used so far to investigate ICV LPS-induced damage. Using Golgi impregnation and Neurolucida-assisted morphometry of hippocampal CA1 pyramidal neurons [ 13 ], we first determined the time course of dendritic structural changes following ICV LPS in wt mice. Our results show a time course similar to neuronal oxidative damage with maximal reduction in both dendrite length and dendritic spine density at approximately 24 hr post LPS and, remarkably, a return to near baseline levels by 72 hr [ 14 ] (Figure 3 ). Figure 3 Dendritic degeneration of CA1 pyramidal neurons in mouse hippocampus. Neurolucida renderings of CA1 pyramidal neurons stained by Golgi method; blue is soma and first order dendrites, red is second order dendrites, green is third order dendrites, yellow is fourth order dendrites, brown is fifth order dendrites, and pink is sixth order dendrites. A. Typical pyramidal neuron 24 hr after ipsilateral ICV Saline injection. B and C. Pyramidal neurons following ipsilateral ICV LPS injection showing moderate (B) to severe (C) dendrite shortening and spine loss. We next pursued the molecular determinants of ICV LPS-induced dendritic degeneration using the same genetically altered mice that we used above (Table 1 ). We observed perfect concordance between these results in that lack of a gene that protected cerebrum from neuronal oxidative damage also protected hippocampal CA1 pyramidal neurons from dendritic degeneration and vice versa [ 14 ]. Importantly, we had the opportunity to add TLR-2 knockout mice to our analysis. TLR-2, like TLR-4, is one of the plasma membrane TLRs that may be activated by LPS and that also uses CD14 as a co-receptor. Our results show that lack of TLR-2 does not protect hippocampal CA1 pyramidal neurons from ICV LPS-induced neurodegeneration, while lack of CD14 completely protects the dendritic tree of these neurons. Further, it is interesting to note that in mice receiving ICV saline, pyramidal neuron dendrite length (Figure 4 ), but not spine density, is significantly greater in CD14-/- mice than in wt or MyB88-/- mice, suggesting that even in the absence of specific stimuli like ICV LPS, lack of CD14 perhaps has a net neuroprotective or neurotrophic effect. Figure 4 Dendritic arbor in CA1 pyramidal neurons of hippocampus from knockout mice. Adult (6 to 8 week old) wt C57Bl/6, CD14-/-, or MyD88-/- mice received ICV saline 24 hr prior to sacrifice. Tissue sections of hippocampus and surrounding structures were processed for Golgi stain and then evaluated by Neurolucida. Data are dendrite length for CA1 hippocampal pyramidal neurons (n > 15 neurons for each group). One-way ANOVA had P < 0.0001 with Bonferroni-corrected repeated pair comparisons having *P < 0.001 for wt vs. CD14-/- and CD14-/- vs. MyD88-/-. Pharmacologic interventions Considerable controversy surrounds the effective in vivo neuroprotective doses of nonsteroidal anti-inflammatory drugs and anti-oxidants that are being evaluated as potenital protectants from AD. Indeed, a major criticism leveled against nonsteroidal anti-inflammatory drugs (NSAIDs) is that the concentrations that appear to be neuroprotective in epidemiologic studies are lower than those that classically considered anti-inflammatory doses. Moreover, there is some data suggesting that some NSAIDs, such as ibuprofen and naproxen, that may differ in their effectiveness as AD protectants despite being equivalent anti-inflammatory agents in peripheral assays of inflammation suggesting alternative mechanisms of action in AD [ 34 ]. Therefore, we determined the dose-response relationship for ibuprofen and naproxen in our ICV LPS model utilizing a two-week pre-treatment with each NSAID in drinking water (with concentration expressed as μg/ml drinking water) followed by ICV LPS injection [ 14 ]. Neither NSAID alone alters basal levels of cerebral F 4 -NeuroPs. For ibuprofen, the EC 50 for suppressing ICV LPS-induced F 4 -NeuroPs is between 0.1 and 0.5 μg/ml and the maximal effect is reached by 1.4 μg/ml, considerably lower than the classic anti-inflammatory dose. In contrast, naproxen is without effect up to 1.4 μg/ml and thus an EC 50 cannot be calculated from these data. As with F 4 -NeuroPs, ibuprofen completely protects both dendrite length and spine density (Figure 5 ) from the degenerative consequences of ICV LPS; in contrast, naproxen is not significantly protective even at the highest dose. These results are intriguing because some have suggested that ibuprofen may be more effective than naproxen in lowering the risk for AD [ 34 ]. The basis for the differing results with these NSAIDs in our experiments are not entirely clear but may derive from pharmacokinetic differences or pharmacodynamic differences in actions other than COX inhibition. Figure 5 Pharmacologic suppression of dendritic degeneration in CA1 pyramidal neurons of mouse hippocampus. Adult (6 to 8 week old) wt C57Bl/6 mice received ICV saline or ICV LPS 24 hr prior to sacrifice. Tissue sections of hippocampus and surrounding structures were processed for Golgi stain and then evaluated by Neurolucida. Data are dendritic spine density for CA1 hippocampal pyramidal neurons (n > 6 neurons for each group). Two-way ANOVA had P < 0.001 for ICV saline vs. ICV LPS, effect of drugs, and interaction. Post hoc one-way ANOVA showed that no effect of drugs in ICV saline exposed mice. Ibuprofen and α-tocopherol completely protected spine density from ICV LPS exposure (P < 0.01 compared to vehicle treated mice) while naproxen and γ-tocopherol did not significantly protect (P > 0.05). Next, we extended our studies to tocopherols, natural antioxidant products with a number of proposed actions [ 35 ] including both anti-oxidant and anti-inflammatory activities [ 36 ]. As with NSAIDs, α-tocopherol (AT) or γ-tocopherol (GT) alone does not alter basal F 4 -NeuroP levels or dendritie arbor (not shown). AT partially suppresses ICV LPS-induced F 4 -NeuroPs at 10 mg/kg and completely suppresses F 4 -NeuroP formation and both reduction in dendrite length and reduction in spine density at 100 mg/kg (Figure 5 ). GT, an isomer of AT that has one-tenth its anti-oxidant activity in vitro and lacks a specific transporter in vivo, does not, as expected, protect from neuronal oxidative damage or dendritic degeneration at the same dose. Conclusions Our data show that CD14-dependent activation of cerebral innate immunity leads to an acute, transient increase in oxidative damage to neuronal membranes that coincides with reversible dendritic degeneration. Although we did not directly test TLR-4 deficient mice in our studies, given what is know about LPS receptor activation and the fact that TLR-2-/- mice were not protected from neuronal damage caused by ICV LPS, these data argue strongly for CD14/TLR-4-dependent neuronal damage in our model. Moreover, using a wide array of genetically altered mice, we observed complete concordance between dendritic degeneration and neuronal membrane oxidative damage. In combination, these data suggest that these two events are mechanistically related, perhaps with neuronal membrane oxidative damage being a proximate contributor to dendritic degeneration in the context of innate immune activation. One obvious, commonly voiced criticism of the model described here is that it produces an acute stress that does not correspond to chronic neurodegenerative diseases. However, it has yet to be shown whether the stress to individual neurons in these protracted diseases truly is chronic or instead the integration of innumerable microscopic acute stresses over many years. Finally, to the extent that CD14-dependent innate immunity activation contributes to neurodegenerative diseases, such as AD and HAD, the model described here provides a convenient means to screen experimental therapeutics and rapidly optimize dosing and timing parameters before moving to more complex animal models or clinical trials. List of abbreviations used AA: arachidonic acid; AD: Alzheimer disease; AT: α-tocopherol; Aβ: amyloid beta; COX-2: cyclooxygenase 2; DHA: docosohexaenoic acid; EP2: prostaglandin E 2 receptor subtype 2; F 2 -IsoPs: F 2 -isoprostanes; F 4 -NeuroPs: F 4 -neuroprostanes; GT: γ-tocopherol; HAD: HIV-associated dementia; ICV: intracerbroventricular; iNOS: inducible nitric oxide synthase; LPS: lipopolysaccharide; MPO: myeloperoxidase; NSAIDs: nonsteroidal anti-inflammatory drugs; PG: prostaglandin; PGE 2 : prostaglandin E 2 ; TLR: Toll-like receptor; wt: wild type. Competing Interests The authors declare that they have no competing interests.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC527876.xml

509235

A computer simulation analysis of the accuracy of partial genome sequencing and restriction fragment analysis in estimating genetic relationships: an application to papillomavirus DNA sequences

Background Determination of genetic relatedness among microorganisms provides information necessary for making inferences regarding phylogeny. However, there is little information available on how well the genetic relationships inferred from different genotyping methods agree with true genetic relationships. In this report, two genotyping methods – restriction fragment analysis (RFA) and partial genome DNA sequencing – were each compared to complete DNA sequencing as the definitive standard for classification. Results Using the Genbank database, 16 different types or subtypes of papillomavirus were selected as study samples, because numerous complete genome sequences were available. RFA was achieved by computer-simulated digestion. The genetic similarity of samples, based on RFA, was determined from the proportion of fragments that matched in size. DNA sequences of four specific genes (E1, E6, E7, and L1), representing partial genome sequencing, were also selected for comparison to complete genome sequencing. Laboratory error was not taken into account. Evaluation of the correlation between genetic similarity matrices (Mantel's r) and comparisons of the structure of the derived dendrograms (partition metric) indicated that partial genome sequencing (for single genes) had higher agreement with complete genome sequencing, achieving a maximum Mantel's r = 0.97 and a minimum partition metric = 10. RFA had lower agreement, with a maximum Mantel's r = 0.60 and a minimum partition metric = 18. Conclusions This simulation indicated that for smaller genomes, such as papillomavirus, partial genome sequencing is superior to restriction fragment analysis in representing genetic relatedness among isolates. The generalizability of these results to larger genomes, as well as the impact of laboratory error, remains to be demonstrated.

Background Precise estimation of genetic relatedness between isolates of a microorganism is important for determination of phylogenetic relationships, which has important applications in studies of disease transmission [ 1 , 2 ]. The definitive standard for assessing genetic relatedness among organisms is the complete genome sequence of nucleotide bases [ 3 ]. However, nucleotide sequencing is expensive and time-consuming, thus, generally it is impractical for use in most investigations, particularly when a large number of samples is analyzed. Currently, one genotyping technique used frequently as an alternative to complete genome sequencing is restriction fragment analysis (RFA), in which restriction endonuclease enzymes cleave the genome at specific sites, producing DNA fragments that are then separated by size using electrophoresis [ 4 ]. The percentage of fragments matching in size has been commonly used as an index to represent the genetic similarity between samples [ 5 , 6 ]. The accuracy of RFA in determining the true genetic relationships can be influenced by several factors, including the number of restriction enzymes used, the specific enzymes selected for DNA digestion, and laboratory conditions [ 7 - 9 ]. Another common alternative to complete genome sequencing is partial genome sequencing, i.e., the nucleotide sequencing of a particular gene or segment of the genome [ 8 , 10 ]. The gene or genome segment is often targeted by polymerase chain reaction (PCR). Selection of an appropriate gene or region for analysis is critical for accurately representing phylogenetic relationships [ 11 , 12 ]. In a comparison of RFA and partial genome sequencing with respect to their similarity in interpreting a disease outbreak caused by pseudorbabies virus in a swine producing region in Illinois, USA, both genotyping methods generated similar conclusions about patterns of spread of the virus [ 13 ]. However, the accuracy of each genotyping method in representing the complete genome was not evaluated. Restriction fragment analysis detects genetic variation by surveying specific endonuclease restriction sites over the entire genome; in contrast, partial genome sequencing detects genetic variation by comparing nucleotide bases from a specific region of the genome. Each method detects a different dimension of genetic variation, and each can detect only a proportion of the genetic variation present in the entire genome. Therefore, it is important to determine which method, using partial information, provides a more accurate estimation of genetic relatedness. The primary purpose of this study was to compare both restriction fragment analysis and partial genome sequencing to complete genome sequencing, with regard to their agreement in estimating genetic relationships and in reconstructing phylogenies under the ideal conditions of absence of laboratory error. Computer simulation of the genotyping analysis was conducted, using completely sequenced papillomavirus isolates obtained from Genbank. Results Table 1 provides descriptive statistics on fragment size distributions for RFA (using the MaeI enzyme as an example) showing that a moderate number of fragments (mean > 20) were produced by simulated digestion. Fragment sizes were large (median ≈ 280 bps for example enzyme), with only 4 samples having one fragment each ≤ 20 bps. Table 2 shows that with an increase in the number of restriction enzymes, the correlation between the RFA and the complete genome sequencing genetic distance matrices increased slightly and the partition metric measuring dendrogram topological dissimilarity decreased slightly. The highest agreement with complete genome sequencing obtained for RFA was for a 4-enzyme combination, which achieved a maximum Mantel's r = 0.60 and minimum partition metric = 18. Table 3 shows that the similarity with complete genome sequencing in estimating genetic relatedness was much higher for partial genome sequencing, particularly for the E1 and L1 genes, which had the relatively longer sequences (averaging 24.2% and 19.6% of genome, respectively), although all genes selected had Mantel's r ≥ 0.88. The minimum value of the partition metric was 10, and the maximum value was 14, compared to a minimum of 18 for RFA. Phylogenetic trees are presented for complete genome sequencing (Fig. 1 ), RFA (the 4-enzyme condition with the highest agreement with complete genome sequencing) (Fig. 2 ), and sequencing of the E1 gene (the longest gene) (Fig. 3 ). Tree stability, as indicated by bootstrap values, was higher for complete genome sequencing (Fig. 1 : all bootstrap values > 0.90) than for partial genome sequencing of the E1 gene (highest Mantel's r). However, the E1 gene tree structure for the most closely related samples was stable and nearly identical to complete genome sequencing. In contrast to the RFA example given (Fig. 2 ), which did not clearly differentiate the papillomavirus samples into subgroups, partial genome sequencing of the E1 gene identified 2 subgroups with the same composition (and BPV2 as an outlier) as did complete genome sequencing. Discussion Sequencing entire genomes is impractical in most investigations of genetic relationships. The computer simulation conducted here determined that compared to restriction fragment analysis, partial genome sequencing had higher agreement with complete genome sequencing in estimating genetic relatedness and greater similarity in the topology of the dendrograms of phylogenetic relationships derived from these estimates. These results using papallomavirus sequences with a genome length averaging less than 8 kb, indicate that for microorganisms with small genomes, partial genome sequencing targeting genes comprising approximately 20–25% of the total genome length can provide a very good estimate of genetic relatedness. The topological structure of phylogenetic trees was also stable for partial genome sequencing, particularly for the most closely related samples. The degree to which these results generalize to larger genomes is unknown, in part because microorganisms with large genomes are rarely, if ever, sequenced in their entirety. There are also other considerations in selecting partial genome sequencing as a genotyping method, such as presence of the gene in all isolates, and sufficient variability to differentiate isolates [ 12 ]. In addition, whether genetic variation is random or due to natural selection needs to be taken into account [ 14 ], because in the latter case genetic dissimilarity may not reflect time since divergence, thus making it more difficult to infer evolutionary relationships, which are important for making inferences about pathogen transmission. These limitations should be considered as well for restriction fragment analysis. One might expect that increasing genome size would diminish the advantage of partial genome sequencing compared to restriction fragment analysis. As total genome size increases, the number of restriction sites cut by restriction enzymes is expected to increase, providing more fragments and more genetic information for estimating genetic relatedness at no increased cost. This also needs to be taken into account in the selection of a genotyping method. However, it has been argued that if a gene is selectively neutral (i.e., variations are not subject to natural selection), it is only the length of the gene sequenced, not the ratio of sequenced gene length to genome size, that is important for determining the degree of divergence from a common ancestor [ 14 ]. To the extent that these conditions are satisfied, the results of this study indicate that specific gene sequencing is likely to provide a better estimate of genetic relationships than restriction fragment analysis of the complete genome under a wider variety of genome sizes. The general conditions under which partial genome sequencing is more accurate than restriction fragment analysis in representing true genetic relatedness have not been addressed in the analysis conducted here. However, another study from our laboratory [ 15 ], using simulated genomes of various size with different nucleotide substitution rates, and varying degrees of genetic diversity among samples, found that only under conditions of both short partial genome sequence length and low rates of nucleotide substitution did RFA provide a more accurate topological reconstruction of phylogenetic relationships than did partial genome sequencing; the degree of genetic diversity among samples did not affect the advantage partial genome sequencing had in accurately depicting phylogenetic relationships. Thus, whether one is investigating the genetic relatedness among samples collected from a single disease outbreak or a diverse collection of samples from different times and geographic regions, under most conditions partial genome sequencing will represent genetic relationships more accurately than does RFA. Genotyping using partial genome sequencing and phylogenetic reconstruction (using the neighbor-joining algorithm) have become standard for several virus species, including not only papillomavirus [ 16 , 17 ], but also human immunodeficiency virus [ 18 ], classical swine fever virus [ 19 ], porcine reproductive and respiratory syndrome virus [ 20 ], and foot-and-mouth disease virus [ 21 ]. The simulated genotyping conducted here assumed no error of measurement. The sources of error in restriction fragment analysis are well known [ 22 - 24 ]. Fragments of similar size in the same lane of a gel may be indistinguishable, thus appearing to form one fragment. Fragments of small size may be undetectable. The relationship between migration distances and fragment size may be affected by variation in gel density both between and within gels. There are also differences in measurement error between laboratories [ 25 , 26 ]. These deficiencies are accounted for by use of marker DNA fragments of known nucleotide base pair length to assist in estimating cleaved DNA fragment sizes; however, acknowledgement of remaining error of measurement of the size of detectable fragments is inherent in the application of a tolerance range for considering fragments of similar but different sizes as a "match" [ 27 ]. Laboratory error is also inherent in partial genome sequencing [ 28 ]. With the commonly used polymerase chain reaction (PCR) methodology for detection and amplification of genes for sequencing, there can be error in primer development because primer sites may not be specific to the gene sequences or too specific to demarcate all occurrences of the gene. Heterogeneity of amplified DNA, due to replication error, recombination, low primer specificity, or impurity of the template can result in a failure to produce consistent sequencing results. In the comparison of the degree of similarity of DNA sequences between samples, alignment of sequences with unequal sequence lengths due to deletion or duplication, or the management of inverted sequences presents additional challenges for estimating genetic similarity and phylogenetic affinity [ 14 ]. The relative magnitude of sources of error in RFA versus partial genome sequencing is unknown and, thus, the conclusions presented here are those based upon the assumption of the absence or minimization of laboratory error. In practical terms, laboratory error and cost need to be taken into account in the selection of a genotyping method. However, when the impact of these factors is minimized, the computer simulation analysis conducted here indicates that partial genome sequence becomes the preferred alternative for representing genetic relationships. Conclusions For small genomes, partial genome sequencing of target genes comprising 20–25% of the total genome provides a more accurate estimate of genetic relatedness and more accurate representation of evolutionary and transmission histories than does restriction fragment analysis and thus is indicated to be the preferred genotyping method for phylogenetic reconstruction under these conditions. The degree to which these results are generalizable to larger genomes and conditions of laboratory error remains to be determined. Methods Sample DNA sequences The source of information on nucleotide sequences was the Genbank database [ 29 ]. The organism selected for analysis was papallomavirus, for which a moderately large number of isolates with complete genome sequences was available. Human, bovine, canine, and chimpanzee papillomaviruses were considered. Among human papillomavirus (HPV) with complete genome sequencing available, 12 samples were selected at random: HPV 4, 6a, 6b, 20, 24, 49, 63, 13, 29, 32, 54, and 26. For bovine papillomavirus (BPV), complete genome sequences were available for BPV1, BPV2, and BPV4. Because the E1 gene of BPV1 (of interest for partial genome sequencing) could not be located, only BPV2 and BPV4 were chosen and included in the study. One type of canine oral papillomavirus (caninePV) and one type of common chimpanzee papillomavirus (chimPV) were available in the database, and these were chosen. Thus, a total of 16 types or subtypes of papillomaviruses that have been completely sequenced and stored in Genbank were used (Table 1 ). The complete DNA sequences of the 16 papillomavirus samples were aligned using ClustalW software [ 30 ]. The genetic distances among these sequences were then calculated using the Kimura correction [ 31 , 32 ]. Computer simulated restriction fragment analysis Restriction endonuclease enzymes Commonly used restriction endonuclease enzymes were selected [ 33 ], based on the following criteria: (1) Only enzymes with 4-base pair recognition sites were selected, in order to produce a sufficient number of fragments for analysis. (2) Among enzymes having the same recognition site, only one was selected. (3) For simplicity, enzymes with multiple recognition sites were excluded. Using these criteria, 15 restriction enzymes were included (AccII, AciI, AluI, BsuRI, CviRI, HapII, HhaI, MaeI, MaeI, MboI, MseI, NlaIII, RsaI, TaqI, TspEI). Digestion Simulated digestion of each papillomavirus DNA sample by each restriction enzyme was conducted using the DIGEST program [ 34 ]. The resulting restriction fragments for each sample were sorted by size (number of nucleotide base pairs). Calculation of genetic distances Based on the distribution of restriction fragment sizes, the genetic similarity between any two papillomavirus samples was calculated for each restriction enzyme using the Dice coefficient [ 5 , 6 ]: S xy = 2n xy /(n x +n y ), where n xy is the number of fragments matching in size for samples x and y, and n x and n y are the number of fragments in samples x and y, respectively. Then, D xy = 1-S xy was calculated as a distance measure. Pairwise distances between samples were computed for each individual enzyme. Also, pairwise distances were obtained for up to 4 enzymes, by using for each condition (2, 3, and 4 enzymes) the fragment size distributions for 30 randomly selected combinations of enzymes, and calculating the composite distance [ 35 ]. Partial genome sequence analysis The E1, E6, E7, and L1 genes, which have been of interest in studies of papillomavirus, were used for estimating genetic relatedness. The ClustalW program [ 30 ] was used for sequence alignment, and the genetic distances (with the Kimura correction) were calculated for each gene. Agreement between genotyping methods Correlation between distance matrices The matrix of genetic distances based on complete DNA sequences was considered the definitive standard. The genetic distance matrices based on RFA and partial genome sequencing were compared to complete genome sequencing by calculating Mantel's coefficient of correlation between matrices (Mantel's r) [ 36 ]. Comparison of phylogenetic trees The genetic distance matrices for RFA, partial genome sequencing, and complete genome sequencing were used to construct phylogenetic trees, using the Neighboring-joining algorithm [ 37 ], as implemented by MEGA software [ 38 ]. Trees were rooted at the midpoint between the most distantly related samples [ 39 ]. Bootstrap values indicating stability of tree topology were added to trees based on partial and complete genome sequencing [ 14 ]. The trees based on RFA and specific gene sequences were compared to the tree for complete genome sequencing, by using the COMPONENT software [ 40 ] to calculate the partition metric, which measures the difference in tree topology [ 41 , 42 ]. A lower value of partition metric indicates greater topological similarity. List of abbreviations bps: base pairs BPV: bovine papillomavirus caninePV: canine papillomavirus chimpPV: chimpanzee papillomavirus HPV: human papillomavirus kb: kilobase Mantel's r: Mantel's coefficient of correlation between matrices PCR: polymerase chain reaction RFA: restriction fragment analysis Authors's contributions BQ designed the investigation, collected the data, conducted the data analysis, and wrote the manuscript. RW identified the problem to be investigated, provided statistical guidance, assisted in interpretation of results, and edited the final drafts of the manuscript. Both authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC509235.xml

519026

Impairments, activity limitations and participation restrictions: Prevalence and associations among persons living with HIV/AIDS in British Columbia

Background To measure the prevalence of and associations among impairments, activity limitations and participation restrictions in persons living with HIV in British Columbia to inform support and care programs, policy and research. Methods A cross-sectional population-based sample of persons living with HIV in British Columbia was obtained through an anonymous survey sent to members of the British Columbia Persons With AIDS Society. The survey addressed the experience of physical and mental impairments, and the experience and level of activity limitations and participation restrictions. Associations were measured in three ways: 1) impact of types of impairment on social restriction; 2) impact of specific limitations on social restriction; and 3) independent association of overall impairments and limitations on restriction levels. Logistic regression was used to measure associations with social restriction, while ordinal logistic regression was used to measure associations with a three-category measure of restriction level. Results The survey was returned by 762 (50.5%) of the BCPWA participants. Over ninety percent of the population experienced one or more impairments, with one-third reporting over ten. Prevalence of activity limitations and participation restrictions was 80.4% and 93.2%, respectively. The presence of social restrictions was most closely associated with mental function impairments (OR: 7.0 for impairment vs. no impairment; 95% CI: 4.7 – 10.4). All limitations were associated with social restriction. Among those with ≤ 200 CD4 cells/mm3, odds of being at a higher restriction level were lower among those on antiretrovirals (OR: 0.3 for antiretrovirals vs. no antiretrovirals; 95% CI: 0.1–0.9), while odds of higher restriction were increased with higher limitation (OR: 3.6 for limitation score of 1–5 vs. no limitation, 95%CI: 0.9–14.2; OR: 24.7 for limitation score > 5 vs. no limitation, 95%CI: 4.9–125.0). Among those with > 200 CD4 cells/mm3, the odds of higher restriction were increased with higher limitation (OR: 2.7 for limitation score of 1–5 vs. no limitation, 95%CI: 1.4–5.1; OR: 8.6 for limitation score > 5 vs. no limitation, 95%CI: 3.9–18.8), as well as by additional number of impairments (OR:1.2 for every additional impairment; 95% CI:1.1–1.3). Conclusions This population-based sample of people living with HIV has been experiencing extremely high rates of impairments, activity limitations and participation restrictions. Furthermore, the complex inter-relationships identified amongst the levels reveal lessons for programming, policy and research in terms of the factors that contribute most to a higher quality of life.

Background For most people who are able to access and tolerate highly active antiretroviral therapy (HAART), HIV/AIDS has become a chronic condition characterized by cycles of illness and wellness. People live longer lives, but with physical, psychological and social challenges that affect quality of life [ 1 - 3 ]. Evidence of this phenomenon may be found in qualitative studies describing the ways in which improved health has also brought about different and unforeseen social, psychological and physical challenges for many people who had previously been facing end-stage disease. For instance, Brashers et al (1999) identified four categories of "uncertainties" resulting from the experience of "revival" brought about by HAART, including (a) renegotiating feelings of hope and future orientation in the face of questionable durability of immune restoration; (b) fear about social roles and identities, in the transition from a person who is dying to a person living with a chronic illness; (c) concerns with interpersonal relations, including the potential of stigmatizing reactions from employers and co-workers; and, (d) reconsidering the quality of their lives, captured in this quote from one participant, 'The good news is you're going to live, the bad news is you're not going to enjoy the rest of your life' [ 1 ]. Sowell et al. (1998) used in-depth interviews to explore the psychological changes and care delivery issues experienced by HIV-positive men who were facing end-stage disease but had experienced dramatic physical improvements [ 4 ]. Key findings included themes around protease inhibitors as a reprieve from death, shifting perspectives on roles and relationships, and a renewed need for advocacy related to care, treatment and support. Others have examined particular aspects of living with HIV in the post-HAART era, such as challenges related to income and employment [ 5 ]. Along with qualitative literature, the HIV communities themselves have responded with a wave of community-based studies, publications and programming to address challenges related to living with the ups and downs of life on combination therapies [ 6 - 9 ]. Quantitative studies exploring the life-and health-related consequences of living with HIV are limited. An exception is the HIV Cost and Services Utilization Survey in the United States, which described physical and social role restrictions in a nationally representative sample [ 10 ]; however, no similar work exists in Canada. The American study was undertaken during the early years of HAART, and so the majority of participants were not yet on protease inhibitors. As such, there is a gap in the literature in terms of studies that systematically quantify the prevalence of life-and health-related challenges associated with living with HIV since the advent of HAART. The International Classification of Functioning, Disability and Health (WHO, 2001) offers a useful framework for studying disablement and health-related consequences of disease based on the following three concepts: impairments, activity limitations and participation restrictions [ 11 ]. Impairments are understood to be problems with physiological functioning or anatomical (e.g., organs, limbs) structure of the body. Activity limitations are defined as difficulties in executing a task or action. Finally, participation restrictions are problems relating to involvement in life situations. This classification system and its precursor, the International Classification of Impairments, Disabilities and Handicaps (WHO, 1980), have been used to frame a plethora of studies on a diverse array of diseases and conditions [ 12 - 15 ]. Furthermore, this framework has been used to conceptualize HIV [ 16 ], and informs the policy, research and advocacy work of organizations such as the Canadian Working Group on HIV and Rehabilitation [ 17 ]. This article addresses this gap in the literature by reporting on the results of a quantitative investigation into the prevalence of and associations among impairments, activity limitations and participation restrictions experienced by people living with HIV in British Columbia. Methods Data sources Individuals living with HIV were involved in all stages of this project, from identification of the research question to data collection and analysis. A lead partner was the British Columbia Persons With AIDS Society (BCPWA), an organization of more than 3,600 HIV positive individuals living in British Columbia, which was created to provide support, information and advocacy for its members. From May to September of 2002, the BCPWA in conjunction with the British Columbia Centre for Excellence in HIV/AIDS conducted a survey of HIV positive individuals living in British Columbia. The anonymous self-administered questionnaire was mailed to the 1508 HIV positive individuals registered with the BCPWA who had consented to receive mailings. Definition of disability A section of the survey on diagnosed conditions asked participants to indicate if a doctor had ever in their lifetime diagnosed them with any conditions from a list of thirteen, including depression, schizophrenia, bipolar disorder and post-traumatic stress disorder, as well as a space to indicate any diagnoses that was not present in the list. Participants identified their experiences during the past month using check-lists of impairments, activity limitations and participation restrictions that included space to identify unlisted items. Participants were asked: "Within the last month have you experienced any of the following..." after which they were able to check off symptoms from a list of twenty-two, including a space for unlisted items. The list of impairments was categorized into mental, internal system, sensory and neuromusculoskeletal groups based on the International Classification of Functioning, Disability and Health [ 12 ]. Mental impairments included reduced libido, poor concentration, poor appetite, chronic fatigue, decreased endurance, decreased memory, impaired cognition and aphasia. Internal impairments included diarrhea, gastric reflux, shortness of breath, constipation, wasting, weakness, vomiting and incontinence. Sensory impairments included headaches, altered sensations, nausea, mouth pain and decreased vision. Neuromusculoskeletal impairments included altered muscle tone, stiff joints, seizures, hemiparesis and paraparesis. This section was followed by a question which asked participants how much HIV-related pain they had experienced in the past month, with categorical options including none, a little bit, mild or infrequent, moderate, severe or persistent and don't know. Participants were also asked to pinpoint the location(s) of their HIV related pain. Activity limitations were addressed by asking the participants " [h]ow well can you manage these typical daily activities?" with an indication to circle the response which best describes their experience in the past month. A fifteen-item list including ability to walk one block, eat, shower, and dress followed. For each item, participants indicated whether they were (a) completely able, (b) somewhat limited or (c) unable to perform the activity. Overall prevalence of activity limitations was calculated by including anyone indicating (b) or (c) for any one of the fifteen items. In the same way, participants were asked " [h]as your health limited your usual [role/participation]" in any of a number of categorical activities and functions. Participants were indicated to choose the response that came closest to the way they had been feeling during the past month. A ten-item list was used to assess levels of restriction in social, student, and cultural roles. Participants indicated whether they were (a) not limited, (b) somewhat limited or (c) very limited with respect to their ability to function in these roles. Overall prevalence of participation restrictions was calculated by including anyone indicating (b) or (c) for any one of the ten items. Statistical analysis Rates of impairments, activity limitations and participation restrictions among the participants were compared across three categories of CD4 cell counts (≤ 200 cells/mm3, 201 to 500 cells/mm3 and > 500 cells/mm3) using a chi-squared test for categorical variables and the Kruskal-Wallis test for continuous variables. Bonferroni corrections for multiple comparisons were done for each item and those which remained significant are indicated in bold. To test the hypothesis that social role restrictions would be more strongly associated with mental function impairments and personal care and mobility limitations, a series of logistic regression models were tested with each category of impairment and limitation. A dichotomous outcome was used, collapsing "somewhat" and "very much" social role restriction into any social restriction. Likewise, specific activity limitations were dichotomized into "no limitations" vs. "some effort" required or "unable" to accomplish the activity. Associations of social restriction with impairment categories and specific activity limitations were examined univariately and in adjusted models accounting for age, sex, income, depression, pain, risk category (men who have sex with men, injecting drug users, heterosexual contact, combination) and number of symptoms for activity limitation models. A scoring system was then used to develop categories of activity limitation and participation restriction. If a participant indicated an activity limitation item at the highest level ("unable" to accomplish) or a participatory role restriction at the highest level ("very much" restricted), two points were received, while participants indicating an activity limitation item at moderate level (requiring "effort" to accomplish) or a participation restriction item at a moderate level ("somewhat" restricted), one point was received. Overall scores for participation restriction and activity limitation were therefore dependent on both the severity and total number of challenges in activities or participatory roles. The participation restriction score, with an overall maximum of 20, was then categorized into three levels: 0 to 5 points, 6 to 10 points and > 10 points, based on the population distribution of the score. Likewise, the activity limitation score, with an overall maximum of 28, was also categorized based on distribution as follows: 0, 1 to 5 points, and > 5 points. The higher the score, the greater the disablement. An overall model examined the associations of increasing participation restriction level with number of impairments and activity limitation scores, testing the hypothesis that impairments may account for some of the associations seen between activity limitations and participation restrictions, but that both of the former would have independent associations with the latter. Ordinal logistic regression was implemented, using the three-level participation restriction outcome and testing number of symptoms, categorical limited activity score, pain and mental diagnoses as explanatory variables. All models were stratified on CD4 levels, with separate models built for individuals with counts below 200 cells/mm3, and adjusted for age, gender, employment, years since diagnosis and risk category. Results Population characteristics Of the 762 people living with HIV who completed the survey, 614 provided information about their CD4 levels and were included in this analysis. The population answering the BCPWA survey was comprised mainly of white (88.7%), sexual-minority males (76.6%) between the ages of 30 to 49 (63.9%). The 148 respondents who were not included in the analysis because they did not provide CD4 information were in a lower income bracket (42.5% vs 19.9%; p-value < 0.001), were more likely to be current IDUs (11.3% vs 4.3%; p-value < 0.001) and more likely to be First Nations/Inuit/Metis (17.6% vs. 6.5%; p-value < 0.001). A comparison of all BCPWA members who received the survey and the subset who responded found a similar distribution of age and a similar proportion identifying as Aboriginal (7.1% vs. 8.7%). The proportion of females was higher among the total BCPWA population than among the subset of respondents (13.5% vs.10.2%; p = 0.001). Prevalence of impairments, activity limitations and participation restrictions Table 1 describes levels of diagnoses, impairments, activity limitations and participation restrictions among participants. Mental health diagnoses were reported by 62.9% (N = 479) of the participants. The most prevalent diagnosis was depression with an overall prevalence of 58.1%. Among those listing one or more diagnoses, 92.5% experienced depression as one of their diagnoses. While the overall number of participants with depression appeared lower among those with CD4 ≤ 200 cells/ml, the percent of those listing depression out of those with any diagnosis remained close to 92.5% across all strata. Table 1 Prevalence of diagnosed conditions, impairments and pain, activity limitations and participation restrictions experienced by BCPWA participants by CD4 cell counts CD4 < 200 CD4 201 to 500 CD4 > 500 p-value Diagnosed conditions Depression 64 (52.0) 183 (59.2) 110 (61.5) 0.238 General Anxiety 11 (8.9) 34 (11.0) 14 (7.8) 0.488 Post traumatic Stress 6 (4.9) 18 (5.8) 13 (7.3) 0.677 Panic Disorder 8 (6.5) 35 (11.4) 12 (6.7) 0.124 Median number of impairments (IQR) 9 (5, 13) 7 (2.5, 12) 7 (3, 12) 0.006 % With any impairment 120 (97.6) 285 (92.5) 161 (89.9) 0.041 Pain None 25 (20.7) 82 (29.4) 48 (28.4) 0.079 Little/mild 35 (28.9) 89 (31.9) 62 (36.7) Moderate/severe 61 (50.4) 108 (38.7) 59 (34.9) Median number of activity limitations (IQR) 3 (1, 7) 3 (1, 7) 2 (1, 5) 0.015 % With any Activity Limitation 108 (87.8) 236 (77.4) 137 (76.5) 0.031 Median number of Participation Restrictions (IQR) 7 (4, 9) 7 (3, 9) 7 (3, 9) 0.251 % With any Participation Restrictions 121 (98.4) 278 (91.5) 161 (89.9) 0.017 Bold print indicates comparison that remained significant at the p = 0.016 level after Bonferroni correction for multiple comparisons. The presence of multiple impairments among the participants was also high, with a median of 7 (3,12) impairments and approximately one third of the participants experiencing more than ten impairments. At least one impairment was reported by 91.5% (N = 697). There was a significant difference in the distribution of impairments across CD4 categories, which remained after Bonferroni correction (CD4 ≤ 200 cells/ml vs CD4 > 500 cells/ml, p-value= 0.002; CD4 ≤ 200 cells/ml vs CD4 between 200 and 500 cells/ml, p-value = 0.017). Mental impairment was reported by 78.2% (N = 596), sensory impairment by 71.9% (N = 548), neuromuscular impairment by 49.5% (N = 377), and internal impairment by 81.0% (N = 617) of the participants. Pain was reported by over half of the participants, and by over three quarters of the participants with CD4 ≤ 200 cells/ml. Approximately one-third reported little or mild pain and 37.1% reported moderate or severe pain. For participants with lower CD4 counts, more people reported moderate and severe pain (50.4% vs. 38.7% vs. 34.9%; p-value 0.08), although comparisons of each CD4 category to the others showed no significant differences. Activity limitations were reported by 80.6% (N = 607) of the participants. The median number of activity limitations reported by an individual was 3 (1, 7). Six hundred and ninety-nine individuals (93.2%) reported some level of participation restriction. The median number of participatory roles in which individuals felt somewhat or highly restricted was 7 (3, 9). Although distributions of activity limitations and participation restrictions were significantly different, adjustment for multiple comparisons across the CD4 categories resulted in no significant difference in prevalence. Figures 1 , 2 , 3 summarize the prevalence of impairments, activity limitations and participation restrictions, respectively. The most prevalent impairments experienced by participants included diarrhea (57.1%), reduced libido (55.8%), general weakness (48.2%), poor concentration (47.0%), headaches (46.9%) and chronic fatigue (46.6%). Vigorous and moderate activities, sexual activities and household chores were the most frequently reported limitations. The level of participation restrictions was high for all CD4 categories, with sexual roles, student/employee roles and financial roles being the most prevalent. Figure 1 Prevalence of specific impairments for participants with CD4 counts ≤ 200 cells/mm3 (speckled bars), 201 to 500 cells/mm3 (downward diagonally-striped bars) and > 500 cells/mm3 (horizontally-striped bars). Significant p-value from chi-square test across CD4 categories. Figure 2 Prevalence of specific activity limitations for participants with CD4 counts ≤ 200 cells/mm3 (speckled bars), 201 to 500 cells/mm3 (downward diagonally-striped bars) and > 500 cells/mm3 (horizontally-striped bars). Significant p-value from chi-square test across CD4 categories. Figure 3 Prevalence of specific participation restrictions for participants with CD4 counts ≤ 200 cells/mm3 (speckled bars), 201 to 500 cells/mm3 (downward diagonally-striped bars) and > 500 cells/mm3 (horizontally-striped bars). Significant p-value from chi-square test across CD4 categories. Univariate associations of impairments and activity limitations on social role restrictions Table 2 describes the univariate odds ratios for presence of social role restriction (yes vs. no) based on impairment categories and type of activity limitation. All impairments and activity limitations were significantly associated with social role restriction. Social role restriction was most strongly associated with limitations in using the toilet, (OR: 18.5 for toilet difficulties vs. no toilet difficulties; 95%CI: 4.5 – 76.3), followed by banking, (OR: 11.3 for banking difficulties vs. no banking difficulties; 95%CI: 5.4 – 23.5). Social role restriction had the weakest association with getting out of bed, (OR: 3.6 for difficulties getting out of bed vs. no difficulties; 95%CI: 2.3 – 5.6). With respect to impairment categories, social role restriction was most strongly associated with mental impairments (OR 7.0 for mental impairments vs. no mental impairments; 95% CI 4.7–10.4) although the other three impairment categories had odds ratios higher than four. Table 2 Univariate and adjusted odds ratios for social role restriction given each activity limitation and prevalence of these limitations in this population Activity Prevalence (%) Odds Ratio (95% CI) Adjusted Odds Ratio (95% CI)* ≤ 200 cells/ml > 200 cells/ml Getting dressed 8.9 (54) 4.03 (2.03 – 8.00) 7.90** (0.45 – 137) 1.60 (0.47 – 5.44) Using the toilet 6.3 (38) 18.47 (4.47 – 76.3) 9.14** (0.52 – 159) 37.7** (2.29 – 620) Showering 10.2 (62) 6.62 (3.15 – 13.91) 3.38 (0.41 – 28) 2.30 (0.57 – 9.18) Walking one block 13.2 (80) 5.33 (2.86 – 9.91) 3.33 (0.54 – 20) 3.40 (0.72 – 16) Banking 16.4 (99) 11.27 (5.42 – 23.45) 3.78 (0.33 – 42) 3.30 (1.09 – 10) Getting out of bed 20.8 (125) 3.63 (2.34 – 5.63) 1.15 (0.31 – 4.14) 1.89 (0.79 – 4.54) Driving 21.5 (121) 3.51 (2.25 – 5.47) 1.59 (0.45 – 5.49) 1.93 (0.87 – 4.31) Eating 20.1 (122) 4.66 (2.89 – 7.53) 0.87 (0.26 – 2.94) 3.17 (1.07 – 9.37) Public Transportation 25.2 (148) 6.75 (4.19 – 10.86) 4.36 (0.91 – 21) 3.29 (1.32 – 8.20) Laundry 28.1 (171) 7.53 (4.73 – 11.98) 8.41 (1.32 – 54) 3.26 (1.41 – 7.52) Groceries 32.6 (198) 8.43 (5.38 – 13.21) 3.97 (1.21 – 13) 2.97 (1.37 – 6.43) Household chores 39.6 (241) 6.89 (4.72 – 10.06) 5.12 (1.62 – 16.2) 3.11 (1.59 – 6.10) Moderate activity 42.4 (258) 5.87 (4.11 – 8.37) 2.10 (0.76 – 5.77) 3.10 (1.62 – 5.93) Sexual activity 46.6 (283) 5.33 (3.81 – 7.47) 2.56 (1.00 – 6.57) 2.06 (1.16 – 3.68) Vigorous activity 71.9 (437) 5.09 (3.61 – 7.19) 2.69 (0.97 – 7.48) 2.60 (1.37 – 4.96) Impairment Category Mental functioning 78.7 (481) 7.02 (4.73 – 10.4) 18.71 (2.31 – 151) 4.32 (2.20 – 8.51) Neuro-musculoskeletal functioning 49.3 (301) 4.12 (2.98 – 5.69) 1.77 (0.67 – 4.68) 1.76 (1.03 – 3.00) Sensory functioning 72.3 (442) 4.12 (2.94 – 5.78) 0.85 (0.27 – 2.65) 2.17 (1.20 – 3.93) Internal functioning 81.4 (500) 4.15 (2.82 – 6.12) 2.48 (0.69 – 8.91) 1.84 (0.96 – 3.51) *Adjusted for age, gender, income, number of impairments (for activity limitation models), pain, risk category and doctor-diagnosed depression ** Small sample size; analysis stratified on CD4 but not adjusted due to zero cells. Adjusted odds ratios stratified by CD4 counts remained significant for getting groceries, doing laundry, household chores, and mental functioning, regardless of CD4 levels, although the estimates were higher for participants with counts under 200 cells/mm3. For those with CD4 counts above 200 cells/mm3, difficulties with eating, public transportation, moderate or vigorous activities, sexual activities, neuromuscular functioning and sensory functioning also remained significantly associated with social restrictions. Adjusted odds ratios for using the toilet and getting dressed were unable to be estimated as these were co-linear with the outcome. Stratified, unadjusted estimates of 9-fold and 37-fold increases in social restriction were seen with limitations in toileting. Stratification by CD4 levels indicated a general effect modification across activity limitations and impairment categories, with greater, although more unstable, associations with social restrictions being found among participants with < 200 cells/mm3. Multivariate associations of impairments and activity limitations with participation restriction levels Table 3 describes the ordinal logistic regression model examining associations with a three-category measure of participation restriction level, stratified by CD4 cell counts. Among those with CD4 counts under 200 cells/mm3, being in a higher category of participation restriction was strongly associated with having activity limitation scores above ten, and was marginally inversely associated with being on antiretrovirals. Increasing number of impairments did not show any significant association. Table 3 Ordinal logistic regression estimating the probability of being in a higher category of the three level participation restriction score based on levels of impairment, limited activity scores and pain. CD4 ≤ 200 OR* 95% CI Limited Activity score None 1 1–5 3.58 0.91 – 14.2 > 5 24.7 4.85 – 125 Number of impairments 1.01 0.94 – 1.12 Antiretroviral use 0.28 0.08 – 0.93 CD4 > 200 OR* 95% CI Limited Activity score None 1 1–5 2.67 1.40 – 5.12 > 5 8.56 3.90 – 18.8 Number of impairments 1.19 1.12 – 1.25 Pain None 1 Some/mild 1.31 0.71 – 2.44 Mod/severe 1.78 0.85 – 3.75 Antiretroviral use 1.39 0.83 – 2.35 *adjusted for age, gender, employment, education, years since diagnosis and risk category Among participants with CD4 counts above 200 cells/mm3, being in a higher category of participation restriction was associated with increasing levels of limited activity [(OR: 2.7 for limited activity scores of 4–10 vs. scores < 4; 95%CI: 1.4–5.1) and (OR: 8.6 for limited activity scores > 10 vs. scores < 4; 95%CI: 3.9–18.8)]. A higher participation restriction category was also significantly associated with increasing number of impairments, with a 19% increase in the odds with additional impairment. Increased participation restriction level was only marginally significantly associated with moderate or severe pain; however, point estimates for the pain categories suggested a dose response relationship, as did the inclusion of pain as a continuous variable (p-value 0.066). Discussion This study has demonstrated that a population-based sample of people living with HIV in British Columbia have been experiencing strikingly high levels of depression, body impairments, activity limitations and participation restrictions. The latter two categories were higher among this population than a national survey of HIV positive persons in the United States[ 10 ]. However, the American study was conducted prior to HAART availability, underscoring the importance of examining quality of life issues faced in the post-HAART era. In a study examining similar concepts of activity limitation among cancer patients, the percent experiencing any difficulties ranged from 18.0 to 70.0%, depending on the type of cancer, but was only 30.0% overall [ 18 ]. Another study of cancer survivors found a similar prevalence to that seen in the present study (80.0%) when including all ambulatory difficulties, not just activities of daily living [ 19 ]. The elevated levels of limitation among the BCPWA population were also emphasized in a comparison with the general population and with those identifying as suffering from a chronic illness, where the least difference showed a five-fold increase [ 20 ]. The level of depression among this population was extremely high. Nearly 60.0% of the participants reported ever having been diagnosed with depression by a doctor. Levels of depression among HIV positive persons reported in the literature range from 5.0% to 40.0%, although among HIV positive women, 60.0% prevalence has been reported [ 21 , 22 ]. Depression is generally found to be higher, regardless of HIV status, among women and men who have sex with men [ 21 ]. Studies conducted among MSM have found prevalence of major depression to range from 23.0 to 37.0%, while Aboriginal populations in general, and Aboriginal MSM in particular, have been shown to have higher depression scores [ 23 - 25 ]. Likewise, depression among IDU populations has been seen to be as high as 47.0% [ 24 ]. Some study scales may capture current depression but may miss the experience of people with recurrent episodes who feel well at the time of testing. The high level of depression recorded in this study may be the result of a large percentage of men who have sex with men in the sample as well as the survey's ability to capture more cumulative measures of depression. The high prevalence may be due in part to the self-report of the diagnosis as well, which may result in recall bias and increased reporting of non-diagnosed depression. Regardless, this common experience of depression demands consideration by researchers, policy-makers and care providers concerned with the quality of life of people living with HIV. The prevalence of impairments was also high, with diarrhea at the top of the list, followed by problems with fatigue and endurance. Furthermore, challenges with daily activities and social roles were extremely common, at greater than 80.0 and 90.0%, respectively. The high proportion of individuals experiencing impairments, activity limitations and participation restrictions sheds light on the spectrum of challenges related to living with HIV. Even among those with relatively high CD4 counts, the impact of HIV on disability and health is not trivial. Of note, the differences experienced between people according to categories of CD4 levels were less and less apparent going from impairments (problems at the level of organ or body part) to activity limitations to participation restrictions (problems with social roles). This draws attention to the variety of influences affecting a person's ability to perform daily tasks and participate in regular societal roles above and beyond his/her clinical measures of disease status. All types of activity limitation were associated with the experiencing of social role restrictions. After accounting for impairments, depression and pain levels, there remained significant associations between household upkeep, including laundry and groceries, and social role participation. Although it was hypothesized that personal care issues would have stronger associations, this was not the case. This may be because the severity of personal care limitations (dressing, eating, showering) is such that among those experiencing these limitations, the presence of pain or numerous impairments overshadows any independent association between the limitation and social restriction. Household chores, getting groceries and doing laundry as well as moderate and vigorous activities had significant associations with social role restrictions and had the highest prevalence in this population. Therefore, interventions that target these types of limitations may provide the most benefit at a population level. Whether or not these interventions would have any impact on an individual's feelings of participatory restriction remains to be seen; however, coordinating these types of simple interventions might offer contact with people in need of social support. Mental impairments were the most prevalent of the four impairment categories and were found to have a significant association with participation restrictions. These results mirror a study describing disability among a national sample of people living with HIV in the United States which reported a correlation between general fatigue and increased limitations in both physical and role functions [ 10 ]. Other reports have also found relationships among neuropsychological performance, depression, stress levels and perceived disability [ 26 ]. It is suggested that increased social support networks can result in improved mental health, which may indicate that the association between the presence of mental impairment and the ability to interact in social and community roles is not unidirectional. The adjusted models (Table 3 ) indicate that both impairments and activity limitations remain associated with participation restrictions independent of one another for people with high CD4 counts. The use of antiretrovirals among those with low CD4 counts is associated with lower participation restriction levels. Since this cannot be accounted for through a lessening of impairments or limitations among those on antiretrovirals, it is more likely a reflection of the type of support and interaction with the health care system among those who are able to access antiretrovirals. Limitations of the study Limitations of the study include the somewhat homogeneous nature of the participants, which affects the generalizability of these findings to other populations. The participants were mainly white, sexual-minority males with moderate yearly incomes and stable housing. The under-representation of people who are homeless, injection drug users, female and Aboriginals becomes apparent when comparing the low proportions seen amongst the BCPWA membership to the higher proportions seen in incident cases reported by the British Columbia Centre for Disease Control [ 27 ]. The survey was sent to BCPWA members consenting to receive mail. Individuals who did not consent were more likely to reside in the Greater Vancouver region, suggesting a greater geographical representation from outside of this urban area. Non-consenting BCPWA members were also more likely to be female (15.8% vs 11.9%) and more likely to be First Nations, Inuit or Metis (27.1% vs 8.4%). Furthermore, because the survey was anonymous and self-reported, there are issues with missing data and incomplete records. For example, almost 20.0% of the sample, again representing a high proportion of women and First Nations, were excluded because of missing CD4 information. While the exclusion of this population may have affected the power and generalizability of the study, one may argue that challenges reported in this study may be an underestimation of the restrictions in this population due to compounding social inequity issues. Lastly, there are limitations in the nature of self-reported diagnoses. Participants may have trouble recalling the presence or absence of impairments, limitations or restrictions over the past month. Although there was no direct incentive, participants may be biased towards increased reporting of problems as they may feel that this would be beneficial for program funding and support. Despite these limitations, this survey represents a large provincial sample and is one of few attempts to collect information from a population-based sample on this scale. Furthermore, this is one of the first studies to systematically quantify levels of disablement among persons living with HIV. Conclusions This study revealed a strikingly high prevalence of impairments, activity limitations and participation restrictions among a population-based sample of people living with HIV in British Columbia. The complicated interplay among these categories requires further study, but it is clear that interventions designed to help overcome activity limitations and social support programs are required, especially those addressing mental impairments and depression. While impairments and limitations are not always reversible, innovative programs that help people living with HIV address these challenges may help to decrease the subsequent high rates of participatory restrictions experienced. Antiretroviral treatments have enabled the prolongation of the lives of people who are HIV-infected; now we need to give due attention to optimizing the quality of these extended lives. Authors' Contributions MR and KC carried out the statistical analyses; SN and AS participated in the design of the study and the development of the study instrument; PB participated in the conceptualization of the study and the interpretation of the results; RH participated in the conceptualization and design of the study. All authors read and approved the final manuscript.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC519026.xml

548269

Functional promoter upstream p53 regulatory sequence of IGFBP3 that is silenced by tumor specific methylation

Background Insulin-like growth factor binding protein (IGFBP)-3 functions as a carrier of insulin-like growth factors (IGFs) in circulation and a mediator of the growth suppression signal in cells. There are two reported p53 regulatory regions in the IGFBP3 gene; one upstream of the promoter and one intronic. We previously reported a hot spot of promoter hypermethylation of IGFBP-3 in human hepatocellular carcinomas and derivative cell lines. As the hot spot locates at the putative upstream p53 consensus sequences, these p53 consensus sequences are really functional is a question to be answered. Methods In this study, we examined the p53 consensus sequences upstream of the IGFBP-3 promoter for the p53 induced expression of IGFBP-3. Deletion, mutagenesis, and methylation constructs of IGFBP-3 promoter were assessed in the human hepatoblastoma cell line HepG2 for promoter activity. Results Deletions and mutations of these sequences completely abolished the expression of IGFBP-3 in the presence of p53 overexpression. In vitro methylation of these p53 consensus sequences also suppressed IGFBP-3 expression. In contrast, the expression of IGFBP-3 was not affected in the absence of p53 overexpression. Further, we observed by electrophoresis mobility shift assay that p53 binding to the promoter region was diminished when methylated. Conclusion From these observations, we conclude that four out of eleven p53 consensus sequences upstream of the IGFBP-3 promoter are essential for the p53 induced expression of IGFBP-3, and hypermethylation of these sequences selectively suppresses p53 induced IGFBP-3 expression in HepG2 cells.

Background Insulin-like growth factor binding protein (IGFBP)-3 is a multifunctional protein ferrying insulin-like growth factors (IGFs) in circulation and mediating growth suppression signals in cells. Serum IGFBP-3 protein (< 5000 ng/ml) complexes with IGFs and an acid labile subunit (ALS), to extend the half lives and modulate the bio-availability of IGFs [ 1 ]. While a precise mechanism of action is not clear, the growth suppressive activity of IGFBP-3 depends on its nuclear translocation [ 2 ]. Other growth suppressors such as p53, retinoic acids, transforming growth factor (TGF)-β, and tumor necrosis factor (TNF)-α induce IGFBP-3 as a mediator of growth suppression [ 3 - 6 ]. The growth suppression by IGFBP-3 is independent from the modulation of IGFs action [ 7 - 9 ]. The functional importance of the IGFBP-3 in the growth suppression is noteworthy. IGFBP-3 is produced in most tissues, but the main site of production is liver. It is produced by non-parenchymal cells (endothelial and Kupffer cells) while parenchymal cells (hepatocytes) do not produce it under normal condition [ 10 ]. We postulate that IGFBP-3 is a gene induced by growth suppression signals such as p53 in hepatocytes. While the growth suppression imported by IGFBP-3 suggests the potential for tumor suppression, polymorphisms, but no significant mutations were observed in a survey of several tumors [ 11 ]. As gene silencing may occur without mutations, we recently investigated IGFBP-3 promoter hypermethylation in human hepatocellular carcinoma [ 12 ]. These promoter hypermethylations were subsequently reported in other tumors systems [ 13 , 14 ]. Promoter analysis of IGFBP-3 indicated that the NaB-RE sequence is essential for the sodium butyrate (NaB) induced IGFBP-3 expression [ 15 ], but the importance of the eleven upstream p53 binding sites reported by Bourdon et al . were not confirmed until now [ 16 ]. The methylation hot spot we identified exactly matched the putative p53 binding sites that Bourdon et al . indicated. Thus, we postulated that these sites are important for the expression of IGFBP-3 induced by p53. Moreover, we hypothesized that the suppression of apoptosis mediated by IGFBP-3 due to the promoter hypermethylation will be a possible pathway of hepatocarcinogenesis. To explore this possibility, the functions of the promoter upstream binding sites of p53 were examined precisely in this study. Methods Cell culture HepG2 cells were obtained from Japanese Cancer Research Resources Bank (Tokyo, Japan), and maintained in D-MEM supplemented with 10 % FCS (Life Technologies, Tokyo, Japan), antibiotic-antimycotics at 37°C in a humidified atmosphere of 95 % air and 5 % CO 2 . Plasmids pGL2-IGFBP-3, kindly provided by Dr. Youngman Oh (Oregon Health Sciences University, Portland, OR), carries a 1.9 kb IGFBP-3 promoter (-1805/+69) in pGL2-Basic (Promega Corp., Madison, WI). A series of deletion mutant constructs, pGL2-270, pGL2-240, pGL2-210, pGL2-180, pGL2-150, pGL2-120, pGL2-90, pGL2-60, pGL2-30, and pGL2-1 containing the indicated fragments upstream of the transcription start site and 60 bp of fragments downstream of the transcription start site, were generated by PCR amplification of the promoter fragment and subsequent subcloning of the Mlu I- Bgl II fragment to pGL2-Basic (Table 1 , Fig. 2 ). The transcription start site of the IGFBP-3 promoter, +1, is based on the sequence determined by Cubbage et al . [ 17 ]. The plasmid containing site-directed mutations in putative p53 binding sites was generated by replacing the wild type Mlu I- Xho I fragment of pGL2-210 with a mutant fragment synthesized artificially (pGL2-210B, Table 1 , Fig. 3 ). A fusion gene with site-directed methylation was constructed by methylating the Mlu I- Xho I fragment (-210/-174) of pGL2-210 in vitro with Sss I methylase (New England Biolabs, Inc., Beverly, MA), and reconstituting the fragment and unmethylated vector (Fig. 4 ). pCMV-p53 and pCMV-p53mt135 were obtained from BD-Biosciences (East Meadow Circle, Palo Alto, CA). Transient transfection HepG2 cells were transiently transfected using the FuGENE 6 transfection reagent according to the manufacturer's instructions (Roche Molecular Biochemicals, Indianapolis IN). Cells were seeded at a density of 5 × 10 4 cells/well in 24-well plates. After 24 hours, cells were transfected with 0.25 μg/well of reporter plasmid DNA in serum-containing medium. Forty-eight hours post transfection, cells were washed twice with PBS and collected for luciferase assays. Transfections were performed in quadruplicate and experiments were performed at least two times. Luciferase assay Luciferase activities of cell lysates were measured according to the manufacturer's instructions (Promega Corp. Madison, WI) using a liquid scintillation counter (Aloka, LSC-700, Tokyo, Japan). Luciferase activities were normalized for total protein determined using the Bradford Assay (Bio-Rad Laboratories, In., Hercules, CA). EMSA (electrophoresis mobility shift assay) 278 bp of the Mlu I- Bgl II fragment of pGL-210 were labelled using [α- 32 P]dCTP by end filling with Klenow fragment, and used for EMSA. Oligonucleotide DNAs (BP3WPSF: 5'-GGCTGCAGCG GGCGTGCGCA CGAGGAGCAG GTGCCCGGGC GAGTCTCGAG CTGCACGCCC CCGAGCTCGG-3', BP3WPSR: 5'-CCGAGCTCGG GGGCGTGCAG CTCGAGACTC GCCCGGGCAC CTGCTCCTCG TGCGCACGCC CGCTGCAGCC-3'), comprising the promoter sequence of -210/-149, were custom-made (Sigma-genosys Japan, Ishikari, Japan), annealed one hour at room temperature, and used as cold competitor in the assay. EMSA was performed in a 20 μl reaction containing 10 mM Tris (pH 7.5), 2.5% Glycerol, 50 mM KCl, 0.1 mM EDTA, 1 mM DTT, and in the presence of 50 ng/μl of double-stranded poly [d(I-C)], 5000 cpm (2 ng) of 32 P-labeled probe DNA, and 2.5 μg of H 2 O 2 treated MCF7 nuclear extract (Active motif LLC, Palomar, CA). The reaction mixture was incubated at 14°C for 20 min. 20 μl of each reaction mixture was then loaded onto a native 4 % polyacrylamide gel containing 0.5 × Tris-Glycine buffer (25 mM Tris, 190 mM Glycine, 1 mM EDTA pH 8.3), and electrophoresed at 14°C, 100 V for 1 hr. For the supershift assay, a p53 antibody (Ab-2, Oncogene Research Products, San Diego, CA) was used. Results Deletion analysis We identified a hot spot of promoter hypermethylation in human hepatocellular carcinoma and its cell lines in the promoter upstream region of IGFBP-3 (Fig. 1 ). As methylation sites were identified to the cluster of putative p53 binding sites, we examined the role of these sites in IGFBP-3 expression. First, we constructed promoter deletion mutants of IGFBP-3 and examined the expression of the reporter gene in the absence (Fig. 2A ) and presence (Fig. 2B ) of p53 expression by the co-transfection of a p53 expression plasmid (pCMV-p53). As the transfection efficiency was not fully controlled, and p53 overexpression may to cause massive changes in cellular conditions, we did not compare results between the presence and absence of p53. Even though, we can obtain clear results about the effect of p53 to IGFBP-3 expression. The pattern of expression of deletion mutants was clearly different in the absence and presence of p53. In the absence of p53 overexpression, we identified the NaB-RE sequence as an essential site for expression, and deletion of p53 binding site enhanced the gene expression (Fig. 2A ). These observations were similar to those of Walker et al . [ 15 ], but differed as HepG2 cell does not require NaB or Tricostatin A (TSA) for its activation. In contrast, in the presence of p53 over-expression, we identified that four p53 binding sites between -210 to -150 (relative transcription start site as +1) were essential for IGFBP-3 expression (Fig. 2B ). When the deletion constructs were co-transfected with pCMV-p53mt135, the expression of the IGFBP-3 promoter was suppressed to a background level (almost same as blank constructs) in all constructs (data not shown). This indicates that IGFBP-3 expression we observe is tightly regulated by p53. Site-directed mutagenesis To confirm the importance of p53 binding sites between -210 to -150, we abrogated one of p53 binding sites by site-directed mutagenesis (C to T at -179 and G to C at -176, Fig. 3A ). In the absence of p53 overexpression, there exist little differences in expression between the wild type and mutant construct (74 % relative to the wild type) (Fig. 3B ). However, in the presence of p53 overexpression, IGFBP-3 expression was strongly decreased in the mutant construct (3.4 %) relative to wild type (Fig. 3B ). For reasons already mentioned, we did not compare the result between in presence and absence of p53. Site-directed methylation Next, we constructed in vitro methylated promoter constructs to evaluate the effect of methylation (Fig. 4A ). For methylation, the Mlu I- Xho I fragment of pGL2-210 was methylated with Sss I methylase and reconstituted with unmethylated reporter vector fragment. As we used linear constructs for the transfection, the expression of luciferase was strongly suppressed compared to circular plasmids (0.7 %). But similar patterns compared to site directed mutation were observed. Although the expression of IGFBP-3 was slightly enhanced in the absence of p53 overexpression in the methylated construct, it was decreased in the presence of p53 overexpression in the methylated construct (Fig. 4B ). In this experiment, at a glance, we observed induction of IGFBP-3 expression by p53, but the transfection efficiency of linear plasmids is low, while the relative levels of p53 and the availability of putative negative regulators are extremely different from other experiments. Thus, we cannot conclude in this case, whether or not there is induction by p53. EMSA EMSA was performed to determine whether p53 protein binding was disturbed by methylation. In this assay, the wild type and methylated promoter fragments (278 bp, -210/+60) were incubated with H 2 O 2 -treated MCF7 nuclear extract that express high level of p53. In the wild type promoter probe, we observed supershifted complex (Fig. 5 , lane 4, indicated by arrows) when p53 antibody (Ab-2) is added to the reaction mixture. A 100-fold molar excess of a cold 70 bp sequence containing a p53 binding site (-210/-149), competed the probe (Fig. 5 , lane 5). In methylated probe, we observed no supershifted complex (Fig. 5 , lane 9). As we used probes that contain a Sp1/GC box and TATA box, we observed the shift and supershift bands in the methylated probe as well as unmethylated probe. These bands were postulated to be due to p53 binding to the nuclear factor such as p300. These observations indicate that p53 binding to the IGFBP3 promoter sequence is blocked or at least attenuated by hypermethylation. Discussion We have indicated that the p53 binding sites upstream of IGFBP-3 promoter are essential for its induction by p53, and that the induction can be suppressed by promoter hypermethylation in the human hepatoblastoma cell line HepG2. A working model of the p53 action in IGFBP-3 promoter upstream binding sites is summarized in Fig. 6 . In this model, in normal cells, IGFBP-3 is induced by factors such as growth hormone (GH) and IGFs, and steady levels of expression are observed in some cells. That the deletion of p53 binding sites enhances the expression of IGFBP-3, suggests existence of negative regulators (Fig. 6A ). In apoptotic cells, p53 tetramers at high levels of expression bind to upstream binding sites. These tetramers recruit the p300 complex to its binding site, thereby a p53-dependent high level of expression (Fig. 6B ). In tumor cells, deletions, mutations, methylations of p53 binding sites, or mutations of p53 such as p53mt135, disturb the binding of p53 tetramers to their binding sites, and this prevents the binding of the p300 complex to IGFBP-3 promoter. As a result, the expression of IGFBP-3 is suppressed in tumor cells (Fig. 6C ). In this model, promoter hypermethylation has the same effect as promoter mutation in determining IGFBP-3 expression. Our observations of promoter hypermethylation in human hepatocellular carcinomas and derivative cell lines (12), and the observations in this report strongly support the notion that IGFBP3 is a true tumor suppressor gene. IGFBP3 is a gene that is silenced by biallelic hypermethylation or hypermethylation and loss of heterogeneity (LOH) in human hepatocellular carcinoma. As reported recently [ 14 ], we have also observed the reduced expression of IGFBP-3 in several tumors such as, breast (9/41), uterus (11/42), ovary (6/16), kidney (6/20), and prostate (1/4) using the cancer profiling array (BD bioscience, data not shown). We therefore postulate that the tumor suppressor role of IGFBP-3 will not be limited to HCCs. In addition, there are also many reports of IGFBP-3 overexpression in tumors from breast [ 18 ], prostate [ 19 ], kidneys [ 20 ], and lung squamous cells [ 21 ], so on. We thus anticipate the existence of additional defects, such as papilloma virus infections that inactivate IGFBP-3 [ 22 ], TGF-β / Rb signalling abnormalities that often coincide with IGFBP-3 overexpression [ 23 - 25 ], or as yet unknown defects in IGFBP-3 receptor function leading to IGFBP-3, for these overexpression in tumors. IGFBP-3 is a ubiquitous, multifunctional protein, whose importance as a carrier of IGFs is evident. The absence of gross loss-of-function mutations of IGFBP-3 observed to date likely underscores its functional importance. We hypothesize that IGFBP-3 is a gene whose basal level of expression is essential for cell survival, but upon induction by p53, high levels of expression of IGFBP-3 induces apoptosis. Alternatively, IGFBP-3 may be a gene that is essential for cell survival when induced by growth hormones or IGFs, but functions as an apoptotic mediator when induced by p53. We observed slight base changes within p53 binding sites strongly influenced the induction of IGFBP-3 by p53. As SNPs that change the expression level of IGFBP-3 were within p53 binding sites [ 26 ], and it was reported that the IGFBP-3 is differentially activated by p53 mutants [ 27 , 28 ], we postulate that the expression and functions of IGFBP-3 is controlled in some way by p53 binding sites in the promoter of IGFBP-3. This may include the intronic p53 binding sites as well as the upstream sites explored here. IGFBP-3 may, therefore, have an important function in tumor development through p53 control. We identified the MyoD (-195/-186) and WT1 (-164/-156) binding sites as well as p53 binding sites at the hypermethylation hot spot in HCC by promoter analysis using TRANSFAC (v 4.0). As MyoD is a transcription factor that can induce apoptosis, and WT1 is a tumor suppressor gene, the existence of a binding site for these putative regulator genes in the hot spot of the promoter of IGFBP-3 suggest the possibility that these genes also use IGFBP-3 as a mediator of their actions. Conclusions We conclude that four out of eleven p53 consensus sequences upstream of the IGFBP-3 promoter are essential for the p53 induced expression of IGFBP-3, and hypermethylation of these sequences selectively suppresses p53 induced IGFBP-3 expression in HepG2 cells. As IGFBP-3 functions downstream of many growth suppressors and its growth suppression effects are drastic, and as it is a small-sized secreted protein, the use of IGFBP-3 in tumor therapy will be a promising option. Abbreviations ALS; acid labile subunit, D-MEM; Dulbecco's modification of Eagle's medium DTT; dithiothreitol, EDTA; ethylenediaminetetra-acetic acid, EMSA; electrophoresis mobility shift assay, FCS; fetal calf serum, HCC; hepatocellular carcinoma, IGF; Insulin-like growth factor, IGFBP-3; Insulin-like growth factor binding protein-3, LOH; loss of heterogeneity, NaB; sodium butyrate, NaB-RE (sodium butyrate-responsive region), PBS; phosphate buffered saline, PCR; polymerase chain reaction, TGF-β; transforming growth factor-β, TNF-α; tumor necrosis factor-α, SEM; standard error of mean, SNP; single nucleotide polymorphism. Competing interests The author(s) declare that they have no competing interests. Authors' contributions TH carried out these studies and manuscript preparation, KN and YI participated plasmid construction and reporter assay, TS and HS participated the EMSA, EY helped the array study, TO participated in the design of the study and performed the statistical analysis. NK 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. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC548269.xml

543466

Elevated serum procollagen type III peptide in splanchnic and peripheral circulation of patients with inflammatory bowel disease submitted to surgery

Background In the hypothesis that the increased collagen metabolism in the intestinal wall of patients affected by inflammatory bowel disease (IBD) is reflected in the systemic circulation, we aimed the study to evaluate serum level of procollagen III peptide (PIIIP) in peripheral and splanchnic circulation by a commercial radioimmunoassay of patients with different histories of disease. Methods Twenty-seven patients, 17 with Crohn and 10 with ulcerative colitis submitted to surgery were studied. Blood samples were obtained before surgery from a peripheral vein and during surgery from the mesenteric vein draining the affected intestinal segment. Fifteen healthy age and sex matched subjects were studied to determine normal range for peripheral PIIIP. Results In IBD patients peripheral PIIIP level was significantly higher if compared with controls (5.0 ± 1.9 vs 2.7 ± 0.7 μg/l; p = 0.0001); splanchnic PIIIP level was 5.5 ± 2.6 μg/l showing a positive gradient between splanchnic and peripheral concentrations of PIIIP. No significant differences between groups nor correlations with patients' age and duration of disease were found. Conclusions We provide evidence that the increased local collagen metabolism in active IBD is reflected also in the systemic circulation irrespective of the history of the disease, suggesting that PIIIP should be considered more appropiately as a marker of the activity phases of IBD.

Background Crohn's disease (CD) and Ulcerative Colitis (UC) are chronic inflammatory bowel diseases (IBD) of unknown origin of adolescent and young adulthood [ 1 ] where genetic polimorphisms [ 2 , 3 ], abnormal inflammation pathways activation [ 4 ], and environmental influences [ 5 ] seem to concur at different levels in the pathogenesis and the progression of IBD. These pathologic conditions are characterized by focal or diffuse inflammation of the alimentary tract, mucosal damage and epithelial destruction. IBD may be associated with an inability of the intestinal mucosa to protect itself from luminal challenges and inappropriate repair following intestinal injury [ 6 - 10 ]. CD differs from UC by the transmural granulomatous inflammation generally leading to fibrosis, strictures and fistulas [ 11 ]. Current opinions suggest that an increased synthesis of collagen type I, III, and V may play an important role in the pathophysiological mechanism leading to intestinal fibrosis [ 12 - 15 ]. An increased synthesis of collagen, namely an increased of procollagen type III, is well documented in fibrotic processes involving other organs such as liver, pancreas, and lung [ 16 - 18 ]. However, not all authors are in agreement regarding the increased serum levels of the aminoterminal propeptide (PIIIP) of collagen in peripheral and splanchnic circulation of patients with active IBD [ 13 , 14 ]. Below we present the results on the serum level of PIIIP in splanchnic and peripheral circulation in patients with active IBD submitted to surgery. Methods Twenty-seven patients affected by active IBD, 17 with CD (age 40.2 ± 13.1, yrs from diagnosis 9.2 ± 5.5), and 10 with UC (age 50.3 ± 15.6, yrs from diagnosis 9.8 ± 7.4) submitted to surgery, were enrolled in the study in a double blind fashion. The protocol was approved by local Ethical Committee and informed consent was obtained from all participants to the study. Three patients had CD in small bowel only, two in large bowel only, 12 had ileocolonic disease. Disease activity was assessed according to the Crohn's Disease Activity Index (CDAI) [ 19 ] and the Truelove-Witts index (TWI) [ 20 ] for CD and UC, respectively. According to CDAI, 2 patients were subclassified as having a moderate form of disease while 15 patients were subclassified as having a severe form of disease. According to TWI, 3 patients were classified as having moderate form of disease, 4 a mild form, and 3 a severe form of disease. Patients affected by CD were operated on for recurrent obstruction, whereas patients with UC were submitted to surgery because of refractory to medical therapy. The clinical diagnosis was confirmed by histology (Fig. 1 ); all cases under study fulfilled the histological criteria as follows: Figure 1 Histological images obtained from two IBD patients enrolled in the study affected by CD (panel a) and UC (panel b) with 12.0 and 10.3 μg/l splanchnic levels of PIIIP, respectively. Panel a , CD: in the transmural section is clearly evident an ulceration (o) in the mucosa and submucosa with diffuse inflammatory infiltrations, pseudo-follicle nodules (arrow), and fibrosis of the intestinal wall. Panel b, UC: the inflammatory infiltration is more evident in the mucosa and submucosa with criptic abscesses (asterisks). A serpiginous linear ulcer is evident (arrow). - for CD: deep ulcers, marked proliferation of small lymphoid nodules involving all layers of intestinal wall sometime with sarcoid-type granulomas and serosal inflammation; - for UC: mucosal erosions and superficial ulcerations usually limited to the upper submucosa with cryptic abscesses and glandular destruction. The clinical profile of the studied patients is reported (Table 1 ). Two patients did not receive any medication, whereas other patients received two or three drugs for the treatment of IBD. Table 2 shows the treatment protocol for all the studied patients. A control group of 15 healthy age and gender matched subjects was also studied to determine normal range for peripheral PIIIP. Table 1 Clinical aspects of the studied patients Disease N° of patients Age Sex Years from diagnosis Activity index Crohn CDAI 1 34 F 12 Severe 2 35 M 8 Moderate 3 38 M 15 Severe 4 32 F 8 Severe 5 43 F 14 Severe 6 35 M 8 Severe 7 58 M 5 Severe 8 61 F 7 Severe 9 33 M 8 Severe 10 35 F 19 Severe 11 21 F 4 Severe 12 72 F 1 Severe 13 36 M 15 Moderate 14 42 M 7 Severe 15 25 F 6 Severe 16 51 F 18 Severe 17 34 M 1 Severe Ulcerative colitis TWI 1 68 F 15 Severe 2 70 F 1 Moderate 3 21 F 3 Mild 4 54 F 20 Moderate 5 52 M 20 Mild 6 38 M 4 Moderate 7 64 M 6 Mild 8 38 F 16 Severe 9 41 F 3 Severe 10 57 M 10 Mild CDAI: Crohn's Disease Activity Index; TWI: truelove-Witts index. Table 2 Frequency distribution for therapy Therapy N° of patients Crohn Ulcerative colitis No 2 1 1 Aminosalicydic acid 9 8 1 Cortisone 2 2 0 Aminosalicydic acid + Cortisone 14 6 8 Total 27 17 10 Collagen metabolism (PIIIP) Different kinds of collagen have been identified in humans. All of them derive from longer precursor molecules (procollagens). They are synthesized intracellularly and secreted in extracellular space where they are cleaved by aminoproteases [ 21 - 23 ]. Among the different kinds of precursors, type III is one of the most abundant interstitial procollagens. Since its aminoterminal propeptide, PIIIP, is formed in equimolar proportions to collagen, serum measurements of this fragment can provide an index of collagen synthesis [ 23 ]. The blood samples (two, 5-ml each) for PIIIP measurements were taken from the median cubital vein (p-PIIIP) before surgery after an overnight fast, during surgery from a mesenteric vein (s-PIIIP) draining the intestinal segment chosen for resection by the surgeon. Serum levels of PIIIP were assessed by commercial radioimmunoassay (Orion Diagnostics, Finland). The intra-assay and inter-assay variation were respectively 4% and 4.3%, mean 2.6 μg/l. Normal ranges of peripheral PIIIP concentrations assessed in the control group were 2.7 ± 0.7 μg/l. Statistical analysis Data were analyzed using a computer statistical software (SPSS-Rel 10; SPSS Inc., Chicago, Ill). All the quantitative variables were tested for Gaussian distribution with the Kolmogorov-Smirnov test. All that followed this distribution were presented as mean ± standard deviation. Differences at baseline in collagen parameters between IBD patients and controls were tested for significance using the analysis of variance with the Bonferroni correction. The relation between collagen parameters and the estimated duration of the disease and indices of disease were tested with regression analysis. In all cases, a p value less than 0.05 was considered significant. Results Peripheral PIIIP assay At baseline, before surgery, serum p-PIIIP in IBD patients were significantly higher if compared with healthy controls (5.0 ± 1.9 vs 2.7 ± 0.7 μg/l, respectively; p = 0.0001) (fig 2a ). No significant differences were found when comparing CD and UC subgroups (5.0 ± 1.6 vs 4.9 ± 2.4 μg/l, respectively; p = ns) (fig 2b ). Figure 2 Panel a: Differences in baseline p-PIIIP values in Controls and IBD patients. Panel b: No significant differences in p-PIIIP values between CD and UC subgroups. Panel c: Differences between splancnic and periferic values of PIIIP, without significant differences in CD and UC sbgroups. p-PIIIP: periferic (median cubital vein) PIIIP; s-PIIIP: splancnic (mesenteric vein) PIIIP; Δ-PIIIP: differences between s- and p-PIIIP in IBD patients; IBD: inflammatory bowel diseases; CD: Crohn's Disease; UC: ulcerative colitis; Splanchnic PIIIP assay During surgery, serum s-PIIIP in IBD patients was 5.5 ± 2.6 μg/l. No significant differences were found when comparing CD and UC subgroups (5.4 ± 2.3 vs 5.7 ± 3.1 μg/l, respectively; p = ns). A positive gradient was found in IBD patients between splanchnic and peripheral serum concentrations of PIIIP (0.7 ± 1.9 μg/l). This gradient was confirmed when separately considering each disease, without significant differences between the two subgroups (CD 0.3 ± 1.3 vs UC 1.3 ± 2.6 μg/l; p = ns) (fig 2c ). Other variables and PIIIP levels No significant correlation was found between peripheral and splanchnic levels of PIIIP and the age of the patients and the estimated duration of the disease. Regarding the activity indices, the number of patients belonging to each class was not enough to perform a statistical analysis. Notwithstanding, for the TWI in UC patients a significant difference in PIIIP levels was found between mild and severe form of the disease (Table 3 ). Finally, no significant differences were found in PIIIP levels between patients treated with glucocorticoids compared with patients not receiving this treatment. Table 3 Baseline p-PIIIP levels in UC patients TWI Activity Index Mean ± SD Mild 7.05 ± 2.25 Moderate 3.9 ± 1.11 Severe 3.13 ± 1.57* * p = 0.02 vs mild Discussion CD and UC are chronic pathologies characterized by an early onset followed by sporadic episodes of acute symptoms during lifetime, debilitating the affected patients to perform their daily functions [ 24 ]. Until now controversial theories exist about the synthesis and degradation of PIIIP, its level on systemic circulation, and its deposition far for main target organ [ 12 , 13 , 15 , 25 ]. In the present study we have found that intestinal collagen metabolism in IBD patients was increased and that it is reflected in local and systemic circulation. Differently from some experiences [ 12 , 13 ], we have found that serum PIIIP levels in IBD patients was significantly higher if compared with healthy subjects. No significant differences were found in peripheral and splanchnic circulation between patients affected by UC and CD. We also found a positive gradient between serum s-PIIIP and p-PIIIP levels in IBD patients. This gradient was confirmed when considering serum s-PIIIP and p-PIIIP in UC and CD separately, even if the differences between the two subgroups were not statistically significant. In our experience no significant differences were found when considering the age of the patients, the duration of the disease, and the activity indices. This fact implies that serum PIIIP should not be considered a long-term marker of the disease, probably reflecting the short-term fluctuation in the activity phases of the remodeling processes. When comparing the mild with the severe form of the disease, a significant difference in PIIIP levels was found only in patients affected by UC. This data will probably be confirmed when the number of patients enrolled in each disease-related activity categories is extended as presently in our series the majority of the patients were classified as severe. The effect of glucocorticoids on collagen synthesis, collagenase, and collagen degradation has not yet fully been clarified [ 25 ]. In our study the cortisone therapy did not have influence on the PIIIP levels, but the number of patients was too small and it was not possible to speculate on this regard. Conclusions In conclusion we provide evidence that collagen metabolism in IBD is reflected in the systemic and local circulation, without any differences between UC and CD, irrespective of the age of the patients and the duration of the disease. Therefore, this marker may give further information on the activity phases rather than on the entire history of the disease. Further data on the possible use of PIIIP as useful marker of choice for surgical option are attended from the follow-up at 6 and 12 months, which is still on-going [ 26 ]. List of abbreviations Inflammatory bowel diseases = IBD Procollagen III propeptide = PIIIP Peripheral Procollagen III propeptide = p-PIIIP Splanchnic Procollagen III propeptide = s-PIIIP Crohn's disease = CD Ulcerative Colitis = UC Crohn's Disease Activity Index = CDAI Truelove-Witts index = TWI Competing interests The author(s) declare that they have no competing interests. Authors' contributions UC conception and design, interpretation of data, drafting the article MDS conception and design, interpretation of data, drafting the article ECA performed surgical operations, critical revision of the article, final approval of the version BO patients' enrollement, blood samples collection RP statistical analysis, interpretation of data, drafting the article AP echocardiographic studies GB radioimmunoassays EO radioimmunoassays SF histological examinations FM interpretation of data, critical revision of the article, final approval of the version MMC conception and design, interpretation of data, drafting the article All Authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC543466.xml

543472

Incremental genetic K-means algorithm and its application in gene expression data analysis

Background In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis. With the help of clustering algorithms such as K-means, hierarchical clustering, SOM, etc, genes are partitioned into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. Results In this paper, we propose a new clustering algorithm, Incremental Genetic K-means Algorithm (IGKA) . IGKA is an extension to our previously proposed clustering algorithm, the Fast Genetic K-means Algorithm ( FGKA ). IGKA outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value Total Within-Cluster Variation (TWCV) and to cluster centroids incrementally whenever the mutation probability is small. IGKA inherits the salient feature of FGKA of always converging to the global optimum. C program is freely available at Conclusions Our experiments indicate that, while the IGKA algorithm has a convergence pattern similar to FGKA, it has a better time performance when the mutation probability decreases to some point. Finally, we used IGKA to cluster a yeast dataset and found that it increased the enrichment of genes of similar function within the cluster.

Background In recent years, clustering algorithms have been effectively applied in molecular biology for gene expression data analysis (see [ 1 ] for an excellent survey). With the advancement in Microarray technology, it is now possible to observe the expression levels of thousands of genes simultaneously when the cells experience specific conditions or undergo specific processes. Clustering algorithms are used to partition genes into groups based on the similarity between their expression profiles. In this way, functionally related genes are identified. As the amount of laboratory data in molecular biology grows exponentially each year due to advanced technologies such as Microarray, new efficient and effective methods for clustering must be developed to process this growing amount of biological data. Among the various clustering algorithms, K-means [ 2 ] is one of the most popular methods used in gene expression data analysis due to its high computational performance. However, it is well known that K-means might converge to a local optimum, and its result is subject to the initialization process, which randomly generates the initial clustering. In other words, different runs of K-means on the same input data might produce different solutions. A number of researchers have proposed genetic algorithms [ 3 - 6 ] for clustering. The basic idea is to simulate the evolution process of nature and evolve solutions from one generation to the next. In contrast to K-means, which might converge to a local optimum, these genetic algorithms are insensitive to the initialization process and always converge to the global optimum eventually. However, these algorithms are usually computationally expensive which impedes the wide application of them in practice such as in gene expression data analysis. Recently, Krishna and Murty proposed a new clustering method called Genetic K-means Algorithm (GKA) [ 7 ], which hybridizes a genetic algorithm with the K-means algorithm. This hybrid approach combines the robust nature of the genetic algorithm with the high performance of the K-means algorithm. As a result, GKA will always converge to the global optimum faster than other genetic algorithms. In [ 8 ], we proposed a faster version of GKA, FGKA that features several improvements over GKA including an efficient evaluation of the objective value TWCV (Total Within-Cluster Variation), avoiding illegal string elimination overhead, and a simplification of the mutation operator. These improvements result that FGKA runs 20 times faster than GKA [ 9 ]. In this paper, we propose an extension to FGKA, Incremental Genetic K-means Algorithm (IGKA) that inherits all the advantages of FGKA including the convergence to the global optimum, and outperforms FGKA when the mutation probability is small. The main idea of IGKA is to calculate the objective value TWCV and to cluster centroids incrementally. We then propose a Hybrid Genetic K-means Algorithm (HGKA) that combines the benefits of FGKA and IGKA. We show that clustering of microarray data by IGKA method has more tendencies to group the genes with the same functional category into a given cluster. Results Our experiments were conducted on a Dell PowerEdge 400SC PC machine with 2.24G Hz CPU and 512 M RAM. Three algorithms, FGKA, IGKA and HGKA algorithm were implemented in C language. GKA has convergence pattern similar to FGKA and IGKA, but its time performance is worse than FGKA, see [ 9 ] for more details. In the following, we compare the time performance of FGKA and IGKA along different mutation probabilities, and then we compare the convergence property of four algorithms, IGKA, FGKA, K-means and SOM (Self Organizing Map). At the end, we check how we can combine IGKA and FGKA algorithm together to obtain a better performance. Data sets The two data sets used to conduct our experiments are serum data, fig2data , introduced in [ 11 ]and yeast data, chodata , introduced in [ 2 ]. The fig2data data set contains expression data for 517 genes. Each gene has 19 expression data ranges from 15 minutes to 24 hours. In other words, the number of features D is 19. According to [ 11 ], 517 genes can be divided into 10 groups. The chodata is a yeast dataset, composed of expression data for 2907 genes and the expression data for each gene ranges 0 minutes to 160 minutes, which means that the number of features D is 15. According to the description in [ 2 ], the genes can be divided into 30 groups. Since the IGKA is a stochastic algorithm, for each experiment in this study, we obtain the results by averaging 10 independent run of the program. The mutation probability, the generation number, the population number all affect the performance and convergence of FGKA and IGKA. The detailed discussion of the parameters setting can be found in [ 8 ]. In this paper, we simply adopt the result in [ 8 ], the population number is set to 50, and the generation number is set to 100. These parameter setting are safe enough to guarantee the algorithm converge to the optima. Comparison of IGKA with FGKA on time performance As indicated in the implementation section, the mutation probability has great impact on IGKA algorithm. We check the performance impact on IGKA in this section, and the convergence in the next section. Figure 2 shows the time performance results for these two algorithms. We can see that when the mutation probability increases, the running time increases accordingly for both algorithms. However, when the mutation probability is smaller than some threshold (0.005 for fig2data , and 0.0005 for chodata ), IGKA has a better performance. Figure 2 also indicates the thresholds vary from one dataset to another. In order to achieve better performance of IGKA in large data set, mutation probability may need to be set to smaller than that in small data set. For example, in larger data set chodata , we should set the mutation probability to 0.0005 to have IGKA outperform FGKA. On the other hand, in order to have IGKA outperform than FGKA, we only need to set the mutation probability to 0.005 in the small data set fig2data . In general, the threshold value depends on the number of patterns and the number of features in the data set. It is easy to understand that the performance gained in IGKA is mainly dependent on how many patterns change their cluster memberships. So, in a large data set, even small number of mutation probability may cause many patterns change their cluster memberships. Comparison of IGKA with FGKA, K-means and SOM on convergence Figures 3(A) and 3(B) show the convergence of IGKA versus FGKA across different mutation probabilities based on fig2data and chodata , respectively. These two algorithms have similar convergence results. When the mutation probability changes in these two data sets, it has little impact on these two algorithms during the range that is given in Figure 3 , except for the case when the mutation probability is too large. It gives an opportunity to choose IGKA with better performance without losing the convergence benefit. We also make an interesting comparison of IGKA with FGKA, K-means and SOM on TWCV convergence. We treat each algorithm as a black box. Two data sets, the fig2data and chodata , are fed into the algorithms, and the clustering results are exported as a text file. We then use an in-house program to calculate the TWCVs for each result. The experiments on K-means and SOM algorithm are conducted on an open source software [ 12 ]. As we can see in Table 2 , the IGKA and FGKA have almost similar convergence result, and much better than the convergence of K-means algorithm. The TWCV convergence of SOM is much worse than the others although these four algorithms all use Euclidian distance as their measurement. The reason why we do not include another popular clustering algorithm, hierarchical clustering algorithm is because it is hard to define the boundary among the nested clusters, which means we cannot simply define the number of cluster before running the program. Combination of IGKA with FGKA Figure 4 compares three algorithms, IGKA, FGKA and HGKA, based on the running times for 100 iterations. The mutation probability is set to 0.0001 for all three algorithms. It is clearly that the running time for each iteration of FGKA is much stable than others. On the other hand, the running time for IGKA is much higher than FGKA at the beginning because there are a large number of patterns change their cluster belonging during the K-means operator which cause the IGKA spend a lot of computation time. However, the running time for each iteration of IGKA decrease very sharply at late iterations. The HGKA combines the advantage of two algorithms. The turning point when HGKA uses IGKA instead of FGKA as work horse is highly data dependent. In this particular case, we check the computation time every 15 iterations. The result shows that the performance can be really improved by using HGKA when the mutation probability is small. Discussion The clustering results of chodata using our IGKA algorithm were evaluated according to the scheme of gene classification of MIPS Yeast Genome Database [ 13 ]. We found that genes of similar function were grouped into the same cluster. Table 3 shows 8 main clusters including 16 functional categories of genes. The results are comparable to the data of [ 2 ]. The absolute number of ORFs with functional categories in some cluster may not be always higher than Tavazoie's result, but we found that the percentage of the ORF number within functional category of each cluster in the total ORF number of each cluster is usually higher than Tavazoie's result in most cases. For example, they found that there are 40 genes in the functional category of nuclear organization distributed in their cluster 2, in which there are 186 ORFs, so their percentage is 21.5%. But we found there are 50 genes of the same functional category distributed in our cluster 16, in which there are only 133 ORFs, and our percentage is 37.6% that is significantly higher than 21.5%. Most interestingly, we found a remarkable enrichment of ORFs for the functional category of organization of mitochondria. They are mainly located in two clusters: cluster 3 and cluster 18. Cluster 3 has 156 ORFs in total, and 111 ORFs belong to the category, resulting in a very high percentage, 71.2%. Cluster 18, has 184 ORFs in total, in which there are 105 ORFs belonging to the category and the percentage is 57.1%. The percentage of ORFs within the same function category is only 18.8% in the previous paper. It looks that our IGKA method is more likely to increase the degree of enrichment of the genes within functional categories, and to make more biological sense. We also found a new function category: lipid and fatty isoprenoid metabolism distributed in cluster 25, which was not listed in Tavazoie's paper. Conclusions In this paper, we propose a new clustering algorithm called Incremental Genetic K-means Algorithm (IGKA) . IGKA is an extension of FGKA, which in turn was inspired by the Genetic K-means Algorithm (GKA) proposed by Krishna and Murty. The IGKA inherits the advantages of FGKA, and it outperforms FGKA when the mutation probability is small. Since both FGKA and IGKA might outperform each other, a hybrid approach that combines the benefits of them is very desirable. Our experimental results showed that not only the performance of our algorithm is improved but also the clustering result with gene expression data has some interesting biological discovery. Methods The problem of clustering gene expression data consists of N genes and their corresponding N patterns. Each pattern is a vector of D dimensions recording the expression levels of the genes under each of the D monitored conditions or at each of the D time points. The goal of IGKA algorithm is to partition the N patterns into user-defined K groups, such that this partition minimizes the Total Within-Cluster Variation ( TWCV , also called square-error in the literature), which is defined as follows. Let be the N patterns, and X nd denotes the dth feature of pattern X n ( n = 1,... N ). Each partition is represented by a string, a sequence of numbers a 1 .... a N ,, where a n is the number of the cluster that pattern belongs to in this partition. Let G k denote the kth cluster and Z k denote the number of patterns in G k . The centroid c k = ( c k 1 , c k 2 ,..., c kD ) of cluster G k is defined as , ( d = 1,2,... D ) where SF kd is the sum of the d th features of all the patterns in G k . and we use to denote the vector of sum of all patterns in cluster G k . IGKA maintains a population (set) of Z coded solutions, where Z is a parameter specified by the user. Each solution, also called a chromosome , is coded by a string a 1 ... a N of length N , where each a n , which is called an allele , corresponds to a gene expression data pattern and takes a value from {1, 2, ..., K} representing the cluster number to which the corresponding pattern belongs. For example, a 1 a 2 a 3 a 4 a 5 = "33212" encodes a partition of 5 patterns in which, patterns and belong to cluster 3, patterns and belong to cluster 2, and pattern belongs to cluster 1. Definition (Legal strings, Illegal strings) Given a partition S z = a 1 .... a N , let e ( S z ) be the number of non-empty clusters in S z divided by K , e ( S z ) is called legality ratio . We say string S z is legal if e( S z ) = 1, and illegal otherwise. Hence, an illegal string represents a partition in which some clusters are empty. For example, given K = 3, the string a 1 a 2 a 3 a 4 a 5 = "23232" is illegal because cluster 1 is empty. Figure 1 gives the flowchart of IGKA. It starts with the initialization phase, which generates the initial population P 0 . The population in the next generation P i + 1 is obtained by applying genetic operators on the current population P i . The evolution takes place until a terminating condition is reached. The following genetic operators are used in IGKA: the selection, the mutation and the K-means operator. Selection operator We use the so-called proportional selection for the selection operator in which, the population of the next generation is determined by Z independent random experiments. Each experiment randomly selects a solution from the current population (S 1 , S 2 , ..., S z ) according to the probability distribution ( p 1 , p 2 , ..., p K ) defined by ( z = 1,... Z ), where F ( S z ) denotes the fitness value of solution S z with respect to the current population and will be defined in the next paragraph. Various fitness functions have been defined in the literature [ 10 ] in which the fitness value of each solution in the current population reflects its merit to survive in the next generation. In our context, the objective is to minimize the Total Within-Cluster Variation ( TWCV ). Therefore, solutions with smaller TWCV s should have higher probabilities for survival and should be assigned with greater fitness values. In addition, illegal strings are less desirable and should have lower probabilities for survival, and thus should be assigned with lower fitness values. We define fitness value of solution S z , F ( S z ) as where TWCV max is the maxim TWCV that has been encountered till the present generation, F min is the smallest fitness value of the legal strings in the current population if they exist, otherwise F min is defined as 1. The definition of fitness function in GKA [ 7 ] paper inspired our definition, but we incorporate the idea of permitting illegal strings by defining the fitness values for them. The intuition behind this fitness function is that, each solution will have a probability to survive by being assigned with a positive fitness value, but a solution with a smaller TWCV has a greater fitness value and hence has a higher probability to survive. Illegal solutions are allowed to survive too but with lower fitness values than all legal solutions in the current population. Illegal strings that have more empty clusters are assigned with smaller fitness values and hence have lower probabilities for survival. The reason we still allow illegal solution survive with low probability is that we believe the illegal solution may mutate to a good solution and the cost of maintain the illegal solution is very low. We assume that the TWCV for each solution S z (denoted by S z . TWCV ) and the maximum TWCV (denoted by TWCV max ), have already been calculated before the selection operator is applied. Mutation operator Given a solution (chromosome) that is encoded by a 1 .... a N , the mutation operator mutates each allele a n ( n = 1, ..., N ) to a new value a n ( a n might be equal to a n ) with probability MP respectively and independently, where 0 < MP < 1 is a parameter called the mutation probability that is specified by the user. The mutation operator is very important to help reach better solutions. From the perspective of the evolutional theory, offsprings produced by mutations might be superior to their parents. More importantly, the mutation operator performs the function of shaking the algorithm out of a local optimum, and moving it towards the global optimum. Recall that in solution a 1 .... a N , each allele a n corresponds to a pattern and its value indicates the number of the cluster to which belongs. During mutation, we replace allele a n by a n ' for n = 1,..., N simultaneously, where a n is a number randomly selected from (1,....,K) with the probability distribution ( p 1 , p 2 , ..., p K ) defined by: where is the Euclidean distance between pattern and the centroid c k of the k th cluster, and . If the k th cluster is empty, then is defined as 0. The bias 0.5 is introduced to avoid divide-by-zero error in the case that all patterns are equal and are assigned to the same cluster in the given solution. Our definition of the mutation operator is similar to the one defined in the GKA paper [ 7 ]. However, we account for illegal strings, which are not allowed in the GKA algorithm. The above mutation operator is defined such that (1) might be reassigned randomly to each cluster with a positive probability; (2) the probability of changing allele value a n to a cluster number k is greater if is closer to the centroid of the k th cluster G k ; and (3) empty clusters are viewed as the closest clusters to . The first property ensures that an arbitrary solution, including the global optimum, might be generated by the mutation from the current solution with a positive probability; the second property encourages that each is moving towards a closer cluster with a higher probability; the third property promotes the probability of converting an illegal solution to a legal one. These properties are essential to guarantee that IGKA will eventually converge to the global optimum fast. K-means operator In order to speed up the convergence process, one step of the classical K-means algorithm, which we call K-means operator (KMO) is introduced. Given a solution that is encoded by a 1 .... a N , we replace a n by a n ' for n = 1,..., N simultaneously, where a n ' is the number of the cluster whose centroid is closest to in Euclidean distance. More formally, To accommodate illegal strings, we define = +∞ if the k th cluster is empty. This definition is different from mutation operator, in which we defined = 0 if the k th cluster is empty. The motivation for this new definition here is that we want to avoid reassigning all patterns to empty clusters. Therefore, illegal string will remain illegal after the application of KMO. In the following, we first present FGKA algorithm that is proposed in [ 9 ]. We then describe the motivation for IGKA based on the idea of incremental calculation of TWCV and centroids. Finally, we present a hybrid approach that combines the benefits of FGKA and IGKA. Fast Genetic K-Means Algorithm (FGKA) FGKA shares the same flowchart of IGKA given in Figure 1 . It starts with the initialization of population P 0 with Z solutions. For each generation P i , we apply the three operators, selection, mutation and K-means operator sequentially which generate population , , and P i + 1 respectively. This process is repeated for G iterations, each of which corresponds to one generation of solutions. The best solution so far is observed and recorded in S o before the selection operator. S o is returned as the output solution when FGKA terminates. Incremental Genetic K-Means Algorithm (IGKA) Although FGKA outperforms GKA significantly, it suffers from a potential disadvantage. If the mutation probability is small, then the number of allele changes will be small, and the cost of calculating centroids and TWCV from scratch can be much more expensive than calculating them in an incremental fashion. As a simple example, if a pattern is reassigned from cluster k to cluster k' , then only the centroids and WCV s of these two clusters need to be recalculated. Furthermore, the centroids of these two clusters can be calculated incrementally since the memberships of other patterns have not changed; The TWCV can be calculated incrementally as well since the WCV s of other clusters have not changed. In the following, we describe how we can calculate TWCV and cluster centroids incrementally. In order to obtain the new centroid , we maintain the difference values of Z k Δ , for old solution and new solution when allele changes. With these two values, incremental update of Z k and can be achieved as Z k = Z k + Z k Δ , and . Then the new centroids for new solution can be achieved by . Similarly, in order to obtain the new TWCV , we can maintain a difference value TWCV Δ that denotes the difference between old TWCV and new TWCV for one solution. It is obvious that TWCV Δ is attributed from the difference of new WCV k and old WCV k for cluster k. However, WCV k has to be calculated from scratch since is changed. In this way, TWCV can be updated incrementally as well. Since the calculation of TWCV dominates all iterations, our incremental update of TWCV will have a better performance when mutation probability is small (which implies a small number of alleles changes). However, if the mutation probability is large, too many alleles change their cluster membership, the maintenance of Z k Δ and becomes expensive and IGKA becomes inferior to FGKA in performance, as confirmed in the experimental study. Hybrid Genetic K-Means Algorithm (HGKA) The above discussion presents a dilemma – both FGKA and IGKA are likely to outperform each other: when the mutation probability is smaller than some threshold, IGKA outperforms FGKA; otherwise, FGKA outperforms IGKA. The key idea of HGKA is to combine the benefits of FGKA and IGKA. However, it is very difficult to derive the threshold value, which is dataset dependant. In addition, the running times of all iterations will vary as solutions converge to the optimum. We propose the following solution: we periodically run one iteration of FGKA followed by one iteration of IGKA while monitoring their running times, and then run the winning algorithm for the following iterations until we reach another competition point. It has been proved in [ 8 ] that FGKA will eventually converge to the global optimum. By using the same flowchart and operators, IGKA and HGKA will also converge to the global optimum. We summarize the comparison of various clustering algorithms in Table 1 . Availability and requirements IGKA algorithm is available at . The source code and database scheme are freely distributed to academic users upon request to the authors. List of abbreviations WCV: Within-Cluster Variation; TWCV: Total Within-Cluster Variation; IGKA: Incremental Genetic K-means Algorithm; FGKA: Fast Genetic K-means Algorithm; HGKA: Hybrid Genetic K-means Algorithm; ORF: Open Reading Frame. Authors' contributions YL carried out the study and drafted the manuscript. SL and FF designed the algorithms. YD designed the whole project, participated in analyzing gene functional data and wrote part of manuscript. SJB corrected English and helped to interpret the data analysis results.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC543472.xml

519032

Retroviruses 2004: Review of the 2004 Cold Spring Harbor Retroviruses conference

For the past several decades, retrovirologists from around the world have gathered in late May at the Cold Spring Harbor Laboratories in New York to present their studies in formal talks and posters, and to discuss their ongoing research informally at the bar or on the beach. As organizers of the 2004 Cold Spring Harbor Retroviruses Conference, we have been asked by the editors of Retrovirology to prepare a review of the meeting for publication on-line. Our goal in this review is not to provide a detailed description of data presented at the meeting but rather to highlight some of the significant developments reported this year. The review is structured in a manner that parallels the organization of the meeting; beginning with the entry phase of the replication cycle, proceeding with post-entry events, assembly and release, integration, reverse transcription, pathogenesis/host factors, RNA-related events (transcription, processing, export, and packaging) and finishing with antivirals. While the most striking developments this year involved post-entry events and assembly/release, significant progress was made towards elucidating a number of aspects of the retroviral replication cycle.

Entry Although no "new" retrovirus receptors were reported at the meeting, several talks centered on recently discovered receptors. N. Manel, from the groups that identified GLUT-1 as an entry receptor for HTLV-1 (N. Taylor, J.-L. Battini, and M. Sitbon) [ 1 ], proposed that part of the pathogenic effects of this virus may be due to its perturbation of glucose metabolism. HTLV-1-infected tissue culture cells display decreased glucose uptake as a result of envelope (Env) glycoprotein-GLUT-1 interaction. The authors speculated that if this disruption of glucose metabolism also occurs in vivo , it might provide insights into the neuronal damage that occurs in some HTLV-1-infected patients. They also suggested that Env-mediated impairment of GLUT-1 function might contribute to the emergence of preleukemic T cells with new selective advantages [ 2 ]. The co-receptor for feline immunodeficiency virus (FIV), perhaps the best non-primate model for HIV-1, has also recently been identified. Work presented by J. Elder showed that FIV preferentially infects certain subsets of T cells through interaction with CXCR4 and a 43 kDa protein. This 43 kDa protein turns out to be CD134, recently demonstrated to be a receptor for FIV [ 3 ]. CD134 was first described as a member of the tumor necrosis/nerve growth factor receptor family expressed on activated T cells. By analogy with the role of CD4 in HIV infection, CD134 may target FIV infection to a particular subset of T cells. Extending the CD4/HIV parallel, CD134 may be the molecule that initially engages FIV, followed by CXCR4 binding and virus/cell fusion. Elder suggested that CD134 should be referred to as the FIV attachment receptor and CXCR4 as the entry receptor. The use of alternative chemokine co-receptors by primary HIV-1 isolates was discussed by S. Neil from R. Weiss's group. They proposed that some primary dual-tropic HIV-1 isolates, especially those isolated from early seroconverters, might infect primary astrocytes, endothelial cells and macrophages through the use of D6, a promiscuous chemokine receptor highly expressed on these cell types. They hypothesized that D6 usage to infect endothelial cells could influence colonization of endothelial compartments and promote placental transmission. Two groups discussed the risk of pig endogenous retrovirus (PERV) infection of human cells as a potential problem for xenotransplantation. I. Harrison from the Stoye lab showed that recombination between different endogenous PERVs (A and C), which normally have very low titers on human cells, could lead to the production of virus with the capacity to efficiently infect human cells. This tropism change mapped in large part to the Env glycoprotein, but the Gag-Pol region was also implicated. D. Lavillette from the Kabat lab presented evidence that wild-type PERVs, or mutants lacking fully infectious envelopes due to alterations in a conserved PHQ motif (in SU) required for γ-retrovirus infection, could bind and enter human cells when added together with the envelope from gibbon-ape leukemia virus, which does infect human cells. The ability of functional Env glycoproteins from infectious viruses to trans-complement infection by viruses with mutant envelopes or with restricted tropism on particular target cells has been reported for several retroviruses [ 4 , 5 ]. In the case of PERVs, such complementation has the potential of overcoming host-range and interference barriers and could pose a hazard for xenotransplantation. For a number of years, investigators have attempted to engineer peptide ligands for cell surface receptors into retroviral Env glycoproteins with the goal of designing retroviral gene therapy vectors that would efficiently target specific cell types. These attempts have been hampered by low transduction efficiencies. L. Albritton presented work from her lab demonstrating that incorporation of a peptide sequence into the SU receptor binding site (RBS) may overcome this problem. Her lab constructed a chimera in which the RBS of the Moloney murine leukemia virus (MLV) Env was replaced with the peptide ligand somatostatin (Sst). Such chimeras efficiently infected human cells expressing the Sst receptor, but could no longer infect mouse cells expressing the natural receptor, ATCR1. The similarity in length of the peptide with the sequence it replaced, as well as the structural similarity between ATCR1 and the Sst receptor, may have contributed to the success of this approach, since substitution of the RBS with human stromal-derived factor-1 alpha (SDF-1α), which also uses a structurally similar receptor (CXCR4), has also been successful [ 6 ]. In contrast, attempts to make peptide ligand substitutions in this RBS that are either dissimilar in length or use structurally unrelated receptors have met with limited success (i.e. see [ 7 ]. Following binding between retroviral Env glycoproteins and their receptors, conformational changes must take place in both the SU and TM that expose the fusion peptide and enable membrane fusion to occur. H. Garoff presented work extending his recently published report [ 8 ] showing that for γ-retroviruses this conformational change involves SU-TM disulfide bond isomerization. The isomerization, which is inhibited by Ca 2+ , results in breakage of the SU-TM disulfide bond, the generation of an intra-SU bond, and subsequent exposure of the TM fusion peptide. Garoff speculated that the ability of γ-retroviruses such as MLV to fuse at the cell surface, in contrast to α-retroviruses like ALV that lack isomerization activity and which apparently undergo pH-dependent fusion in a subcellular compartment, is the result of this isomerization. Several studies have implicated amino acid changes in the cytoplasmic domain of the MLV TM on SU conformation, with consequent effects on viral infectivity and antibody recognition [ 9 , 10 ]. Work presented by R. Montelaro demonstrated that mutations in the cytoplasmic tail of the HIV-1 TM (gp41) can also result in escape from neutralization. Importantly, at least one of these antibody-escape mutants still retained wild-type infectivity, indicating that sequence changes in the TM intracytoplasmic domain should be considered in the characterization of antigenic variants of HIV-1 that escape neutralization. Dendritic cells express a molecule known as DC-SIGN that binds HIV-1 particles and facilitates their transmission to susceptible target cells [ 11 ]. The mechanism by which this molecule promotes the transfer of infectious virions to target cells has been an active area of investigation for the past several years. Work from V. KewalRamani's lab (Wu et al.) showed that DC-SIGN is able to transfer HIV-1 infectivity in trans when expressed on some cell types but not when expressed on others. The ability of DC-SIGN to promote virus transfer appears to correlate with the localization of the particles on the DC-SIGN-expressing cell; in cells unable to mediate transfer, virions are internalized, whereas cells able to mediate efficient transfer retain virions at the cell surface. Wu and colleagues also observed that binding and transmission of HIV-1 by immature dendritic cells is Env- and DC-SIGN-dependent, whereas virus binding by mature dendritic cells does not require either Env on the particle or DC-SIGN on the dendritic cell. The molecule(s) expressed on dendritic cells that potentiate DC-SIGN-independent virus transmission await discovery. Post-entry events The last two years have seen major advances in our understanding of the mechanisms by which cells from human and non-human primates restrict retroviral infection. In 2002, Sheehy et al. [ 12 ] reported that the HIV-1 accessory protein Vif interacts with the cellular cytidine deaminase APOBEC3G. In the absence of Vif expression, APOBEC3G is incorporated into HIV-1 virions and, during reverse transcription in the target cell, converts cytosines to uracils. This conversion results in the degradation of newly synthesized DNA through the action of host glycosidases and repair enzymes, and leads to G-to-A hypermutation in the newly synthesized viral DNA. Vif counters the antiviral activity of APOBEC3G by blocking its incorporation into virions in the producer cell. Interestingly, Vif displays species specificity in its ability to inactivate APOBEC3G; for example, HIV-1 Vif blocks the antiviral activity of human but not African green monkey APOBEC3G, and SIVagm Vif inactivates African green monkey but not human APOBEC3G. The labs of N. Landau and V. Pathak both reported that the determinant of this species specificity maps to amino acid 128 of APOBEC3G. Data in the Landau lab presentation (Schrofelbauer et al.) suggested that the inability of HIV-1 Vif to block the activity of African green monkey APOBEC3G was due to a lack of binding between these two proteins, whereas the Pathak lab (Xu et al.) reported that mutation of residue 128 of human APOBEC3G did not prevent binding of the mutant protein to Vif. Both labs recently published their findings [ 13 , 14 ]. Since the incorporation of APOBEC3G into virus particles is required for its antiviral activity, several groups have focused on how this cellular protein is incorporated. The mechanism of APOBEC3G incorporation is of particular interest since it appears not to be specific for HIV-1 (e.g., human APOBEC3G is incorporated into MLV particles) and because inhibitors that disrupt Vif's ability to block APOBEC3G incorporation would presumably display antiviral properties. L. Kleiman's lab (Cen et al.) reported that APOBEC3G interacts directly with Gag in a manner dependent on the Gag nucleocapsid (NC) domain. The labs of W. Popik and X.F. Yu also observed that NC plays an important role in the APOBEC3G/Gag interaction. H. Xu from V. Pathak's group reported that mutation of NC reduced but did not abrogate APOBEC3G incorporation into virions. Since NC is a major determinant of RNA packaging into virions, these data, together with the above-mentioned finding that APOBEC3G is incorporated into MLV particles, can be rationalized by a model that proposes an important role for RNA (either viral or cellular) in APOBEC3G incorporation. Such a model would also be consistent with the apparent inability of HIV-1 to evade APOBEC3G incorporation simply by mutation of a specific protein-protein binding site. Interestingly, a study from D. Ho's lab (Simon et al.) found that isolates of HIV-1 that are unable to block APOBEC3G activity are relatively common in infected patients. The authors suggested that sporadic Vif inactivation might be a factor in promoting viral evolution in vivo since Vif-defective variants would exhibit a higher mutation rate. In recent years, several groups have reported that HIV-1 is unable to efficiently infect cells from certain species of Old World monkey (for review see [ 15 ]). The block is imposed early post-entry, prior to reverse transcription, and is mediated by an inhibitory factor expressed in Old World monkey cells. The viral determinant of this restriction maps to the capsid (CA) domain of Gag, making this restriction somewhat reminiscent of the Fv1 block described by Lilly and others decades ago [ 16 ]. J. Sodroski's group recently published that the rhesus macaque version of the cytoplasmic body component TRIM5α [ 17 ] potently blocks HIV-1 infection. Presentations from the labs of J. Sodroski (Stremlau et al.) and P. Bieniasz (Hatziioannou et al.) reported that Ref1 and TRIM5α are species-specific variants of TRIM5α; this finding has now been published [ 18 ]. This factor is quite distinct from Fv1, which bears sequence homology to MLV CA [ 19 ]. A. Lassaux (from the Battini and Sitbon labs) reported that Fv1 and Ref1 recognize different amino acid combinations within the same 100 amino acid determinant of the MLV CA. Hatziioannou et al. also described their results on characterizing the ability of human- and monkey-derived TRIM5α's to block the entry of a panel of retroviruses. D. Sayah from J. Luban's lab provided a preview of the now-published paper [ 20 ] reporting the intriguing finding that in owl monkey cells, the post-entry block to HIV-1 infection is conferred by a TRIM5α-cyclophilin A fusion protein. In another presentation focused on post-entry blocks to retroviral infection, V. KewalRamani's lab (Martin et al.) reported that overexpression of a truncated form of an RNA binding protein that is a component of the poly A machinery blocks an early stage of the HIV-1 infection process without disrupting MLV infectivity. J. Young (Narayan and Young) reported the development of a cell-free uncoating assay that will likely prove useful in defining the role of cellular factors in stimulating or blocking early post-entry steps in the replication cycle. Avian sarcoma/leukosis virus (ASLV) particles can be trapped in endosomes when cells expressing a GPI-linked version of the ASLV receptor are infected in the presence of NH 4 Cl. The virus-containing endosomes can then be isolated; upon removal of the NH 4 Cl in vitro , fusion and reverse transcription take place in a manner dependent upon ATP hydrolysis and the presence of cellular factors. This cell-free uncoating assay can be adapted to other viruses (e.g., HIV-1) by pseudotyping with the ASLV Env glycoprotein. The authors reported that monkey cell restriction factors, and cyclosporin A (which blocks cyclophilin A incorporation into HIV-1 particles), inhibit reverse transcription in this system, suggesting that it faithfully recapitulates certain key aspects of uncoating in infected cells. Some of the data in this presentation were recently published [ 21 ]. Assembly and Release Major developments have taken place in the past several years in our thinking about the location in the host cell at which retrovirus assembly takes place, and the mechanism by which Gag proteins target the subcellular site of assembly. Until recently, it was felt that viruses that follow the type C assembly pathway [including the lentiviruses (e.g., HIV-1), δ-retroviruses (e.g., HTLV-1), γ-retroviruses (e.g. MLV), etc.] assemble at the plasma membrane. However, it has long been recognized that in some cases (for example, HIV-1 in macrophages) assembly and budding take place at an intracellular compartment. Several groups have recently demonstrated that this compartment is the late endosome or multivesicular body (MVB), and it now appears that while the plasma membrane likely represents the predominant site of assembly for these viruses, Gag can also target and assemble in an endosomal compartment in a variety of cell types. M. Resh's lab (Perlman and Resh) used the tetracyteine/biarsenical live-cell labeling system reported by Gaietta et al. [ 22 ] to follow Gag trafficking relatively early (1–4 hrs.) post-synthesis. Their results suggest that Gag may first localize to secretory lysosomes and then subsequently "ride" these vesicles to the plasma membrane. M. Thali's lab recently reported that a significant fraction of HIV-1 Gag colocalizes with late endosomal markers both at intracellular membranes and at the plasma membrane [ 23 ]. Together with D. Ott's lab (Nydegger et al.), they have also started using the tetracysteine/biarsenical technology to analyze Gag localization. Like the Resh lab, they observe some association of Gag with late endosomal/MVB membrane, but a significant fraction of newly synthesized Gag appears to associate directly with discrete plasma membrane microdomains. P. Spearman's lab (Dong et al.) reported interesting findings implicating the AP-3 clathrin adaptor protein complex (which among other things regulates CD63 trafficking) in HIV-1 Gag targeting. They observed that the δ subunit of AP-3 interacts with the MA domain of Gag and that either overexpression of an N-terminal δ subunit fragment or siRNA knockdown of AP-3 δ subunit expression inhibits HIV-1 release. H. Wang from L. Mansky's group observed that HTLV-1 assembly, like that of HIV-1, can take place in the MVB. Interestingly, while in HeLa cells HTLV-1 assembly appears to occur primarily at the plasma membrane, mutational disruption of the Pro-Thr-Ala-Pro (PTAP) late domain results in accumulation of virus particles in the MVB, consistent with published results (e.g., [ 24 ]). T. Hope's lab (Gomez and Hope) confirmed the finding [ 25 ] that HIV-1 mutants lacking the p6 late domain still assemble in MVBs in macrophages, indicating that interactions between p6 and cellular host factors (e.g., Tsg101) are not required for MVB localization. Reports from the labs of D. Muriaux (Grigorov et al.) and F.-L. Cosset (Sandrin et al.) suggested that interactions between MLV Gag and Env may occur in an intracellular compartment and that this interaction might influence both Gag trafficking and Env incorporation into virions. Some of this work has now been published [ 26 ]. A study from the Freed lab (Ono and Freed) examined the possibility that the phosphoinositide phosphatidylinositol-(4,5)-bisphosphate (PI(4,5)P 2 ) plays a role in Gag targeting. This lipid is of interest since it is involved in the trafficking of a variety of cellular proteins that, like many retroviral Gag proteins, contain a basic membrane binding domain. Furthermore, work from A. Rein's lab [ 27 ] has shown that, in vitro , HIV-1 Gag binds molecules structurally related to phosphoinositides. A. Ono used enzymatic approaches to perturb cellular PI(4,5)P 2 levels in virus-expressing cells and observed that Gag targeting and virus assembly were shifted from the plasma membrane to intracellular compartments. These results indicate that cellular PI(4,5)P 2 plays a role in directing Gag to the plasma membrane. Work from J. Lingappa's lab previously suggested the involvement of the cellular RNase L inhibitor HP68 in HIV-1 assembly [ 28 ]. This work was extended at the meeting by Dooher et al., who showed that HP68, Gag, and genomic RNA colocalize in virus-producing cells. The cellular protein nucleolin was also found to be a component of the Gag/HP68 complex. Novel findings relating to the regulation of foamy virus (FV) release were reported by the Lindemann lab. FVs are unusual among retroviruses in that Env glycoprotein expression plays a critical role in particle release. The leader peptide of FV Env, which is generated by a cleavage event during Env trafficking to the plasma membrane and is incorporated into particles, seems to play an important role in the budding process. Lindemann et al. reported the identification of ubiquitylated forms of the leader peptide; suppression of these ubiquitylated forms markedly stimulated subviral particle release. These results suggest that ubiquitylation of the FV Env leader peptide modulates the ratio of particle vs. subviral particle budding. Progress continues to be made in visualizing retrovirus assembly and release in real time in living cells. In addition to use of the tetracyteine/biarsenical live-cell labeling system described above, B. Muller from H.-G. Krausslich's lab discussed the development of an infectious HIV-1 derivative containing a GFP insert near the C-terminus of MA. This derivative produces particles with wild-type morphology; however, release kinetics are impaired. While the Gag-GFP virus is poorly infectious, infectivity can be restored upon coexpression with wild-type HIV-1. This Gag-GFP HIV-1 derivative should be useful for both assembly/release and post-entry studies. In a presentation from the labs of V. Vogt and W. Webb, D. Larson described the use of correlated fluorescence microscopy and scanning electron microscopy to visualize Rous sarcoma virus (RSV) budding in real time using a GagGFP derivative. Major advances have been made in the past three years in elucidating the host cell machinery required for the release of retrovirus particles from infected cells [ 29 , 30 ]. It is now well accepted that retroviruses use their late domains to commandeer machinery that normally plays a central role in promoting the budding of vesicles into the MVB. This machinery includes the so-called "class E Vps" factors originally identified in yeast as being crucial for MVB biogenesis [ 31 ]. Many of these class E Vps proteins are found in three multisubunit complexes known as ESCRT-I, -II, and -III. Retroviral late domains come in three flavors: Pro-Thr/Ser-Ala-Pro [P(T/S)AP], Pro-Pro-x-Tyr (PPxY), and Tyr-Pro-Asp-Leu (YPDL). HIV-1 release is controlled predominantly by a P(T/S)AP-type late domain; many retroviruses, including MLV and RSV, contain PPxY late domains; and equine infectious anemia virus (EIAV) harbors a YPDL late domain. Several retroviruses [e.g., Mason-Pfizer monkey virus (M-PMV) and HTLV-1] encode both P(T/S)AP and PPxY late domains. In addition to its P(T/S)AP motif, HIV-1 contains a secondary YPDL-related sequence whose role in HIV-1 release remains to be defined. It is now well established that the P(T/S)AP motif interacts with the ESCRT-I component Tsg101. Recent data from several labs (those of H. Gottlinger, W. Sundquist, and P. Bieniasz) have strongly suggested that AIP1 (also known as ALIX) is the host factor with which YPDL interacts. There is less certainty about the identity of the biologically relevant PPxY-interacting protein. PPxY motifs in cellular proteins often interact with WW domains, and several PPxY-containing retroviral Gag proteins have been reported to bind the ubiquitin ligase Nedd4 (or related proteins), which contains a series of centrally located WW domains. P. Bieniasz's lab (Martin-Serrano et al.) reported at the meeting that MLV Gag binds the Nedd4-related proteins WWP1 (which has also been shown to associate with HTLV-1 Gag [ 32 ]), WWP2 and ITCHY. The extent to which binding to these proteins was reduced by mutations in the PPPY motif correlated with the severity of the budding defect induced by the mutations. The authors also observed that WWP1 localizes to aberrant endosomes induced by expression of a dominant-negative Vps4 in a manner dependent on the ubiquitin ligase (or HECT) domain, suggesting that the HECT domain may link WWP1 (and consequently Gag) to the class E Vps machinery. J. Leis's lab previously reported that overexpression of the WW domain-containing region from a chicken Nedd4-like protein inhibited RSV particle production. At this year's meeting, this work was extended (Vana et al.) by showing that in cells overexpressing this WW domain-containing fragment RSV virions accumulated in intracellular inclusion bodies. Interestingly, V. Vogt's lab (Johnson et al.) found that overexpression of the C-terminal domain of Tsg101 formed aggresome-like structures that trapped HIV-1 Gag. In yeast, ESCRT-I contains three protein components: Vps23 (the yeast homolog of Tsg101), Vps28 and Vps37. In mammalian cells, only the Vps23 and Vps28 homologs had been identified. At this year's meeting, M. Stuchell from W. Sundquist's lab reported the cloning of human Vps37. As in yeast, human Vps37 binds Tsg101 and is present along with Tsg101 and Vps28 in a high molecular weight ESCRT-I complex. Much of this work has recently been published [ 33 ], as have similar results from H. Stenmark's lab [ 34 ]. Further illustrating the importance of ESCRT-I in HIV-1 budding, fusion of Vps37 to the C-terminus of Gag reverses the release defect imposed by PTAP deletion [ 33 ]. Y. Yardin's lab (Amit et al.) described the identification of an E3 ubiquitin ligase (termed Tal, for Tsg101-associated ligase) that binds the N-terminal UEV domain of Tsg101 and apparently regulates its activity. This work has also recently been published [ 35 ]. M. Palmarini's lab (Mura et al.) described a novel type of retroviral interference that operates at the level of virus assembly and release. The sheep genome harbors a number of copies of endogenous retroviruses closely related to the pathogenic exogenous β-retrovirus Jaagsiekte sheep retrovirus (JSRV). One of these endogenous retroviruses (enJS56A1) displays a defect in assembly/release. EM analysis indicates that enJS56A1 particles form large perinuclear aggregates; these appear to trap JSRV particles intracellularly when enJS56A1 and JSRV are coexpressed. The authors speculate that enJS56A1 may have helped protect sheep from infection with related, exogenous retroviruses during evolution. Much of this study has recently been published [ 36 ]. It has been known for many years that several retroviruses express two forms of Gag: a conventional version and a larger, glycosylated form referred to as glycoGag. GlycoGag is synthesized using an alternative, upstream initiation codon, and unlike its smaller counterpart it is transported to the plasma membrane through the secretory pathway. H. Fan's lab (Low et al.) reported at this year's meeting that a packaging cell line that expresses Gag without glycoGag produces tube-shaped particles at the cell surface. This apparent assembly defect was corrected by coexpression of glycoGag. The expression of glycoGag increased both virus yield and infectivity. These results suggest that glycoGag may function, in a manner that remains to be elucidated, in regulating proper virus assembly and release. Integration Retroviral preintegration complexes (PICs) and the integrase (IN) enzyme itself, interact with a variety of host factors during nuclear import of the PIC and integration of the newly synthesized viral DNA into the host cell chromosome. One such host factor, lens epithelium-derived growth factor (LEDGF/p75), was recently reported to bind HIV-1 IN [ 37 , 38 ]. Several presentations at this year's meeting provided varied and conflicting results regarding the role of LEDGF/p75 in the integration process. S. Emiliani reported data from a collaboration between the labs of R. Benarous and Z. Debyser showing that knockdown of LEDGF/p75 expression using siRNA in HeLa P4 cells inhibited HIV-1 replication without affecting the import of IN to the nucleus. The Q168L mutation in HIV-1 IN abolished both virus replication and the interaction between IN and LEDGF/p75. Busschots from the Debyser lab showed that the interaction of LEDGF/p75 with IN increased the affinity of IN for DNA. Based on these data, it was postulated that LEDGF/p75 may play a role in tethering IN to chromosomal DNA. E. Poeschla's lab (Llano et al., also see [ 39 ]) observed that endogenous LEDGF/p75 co-immunoprecipitated with both HIV-1 and FIV IN. Stable knock-down of LEDGF/p75 expression in 293T cells induced a redistribution of both HIV-1 and FIV IN from the nucleus to the cytoplasm but apparently did not affect nuclear import of HIV-1 or FIV PICs since lentiviral vector infectivity was not reduced under these conditions. Furthermore, stable knock-down of LEDGF/p75 expression in the Jurkat T-cell line did not affect HIV-1 replication. However, LEDGF/p75 was found to be a component of lentiviral PICs. The authors concluded that LEDGF/p75 is not required for lentiviral integration but advanced the hypothesis that it might play a role in target site selection. Vandekerckhove and colleagues from the Debyser lab reported that stable knock-down of LEDGF/p75 expression in HeLa P4 or MOLT cells delayed HIV-1 replication but did not diminish infectivity mediated by a VSV-G pseudotyped lentiviral vector. The authors controlled for non-specific siRNA effects by using double mismatched siRNA. Results from A. Engleman and coworkers (Vandegraaff et al.) raised further questions concerning the role of LEDGF/p75 in HIV-1 integration. They observed that while two LEDGF/p75 siRNAs reduced LEDGF/p75 protein levels, only the one originally described by the Debyser lab impaired HIV-1 infectivity. This infectivity defect could not be reversed by partially restoring LEDGF/p75 expression. Based on these results, the authors cautioned that the effect of LEDGF/p75 siRNA on HIV-1 infectivity may be due to non-specific effects not directly related to LEDGF/p75. Clearly, additional studies need to be performed to clarify the role of LEDGF/p75 in lentiviral integration. A number of presentations focused on the target site specificity of retroviral (or retrotransposon) integration. Different retroviruses and retroelements display strong preferences in selecting their target sites in the genomes of their host cells (for review see [ 40 ]). For example, the Ty1 and Ty3 retrotransposons integrate predominantly upstream of Pol III-transcribed genes; the Tf1 retrotransposon selects sequences upstream of Pol II-transcribed genes; HIV-1 tends to integrate in actively transcribed regions; and MLV prefers to integrate in the promoters of active genes. At this year's meeting, A. Narezkina from R. Katz's and A. Skalka's lab, and R. Mitchell from F. Bushman's group, both described the results of genome-wide analyses of ASLV integration sites. They observed that, unlike MLV, this avian retrovirus does not prefer to integrate at transcription start sites, and unlike HIV-1 does not display a strong preference for highly active genes. Both groups did observe a relatively modest tendency for integration within genes. H. Levin's group (Kelly et al.) extended their previous work on Tf1 integration. Using a target plasmid containing a single gene, they observed that nearly all integration events took place in the gene's promoter region. Interestingly, the integration sites displayed a periodic pattern in which integrated copies of Tf1 were separated by around 30 nucleotides. This targeting appeared to be dependent on promoter activity. Kelly and coworkers speculated that the chromodomain in the Tf1 IN binds histones and regulates integration into specific targets in a promoter-positioned nucleosome. Holman and Coffin reported on the analysis of base preferences immediately surrounding integrated HIV-1 proviruses. Using a large amount of data derived from previously reported integration sites, the authors observed strong base preferences within seven residues at either end of integrated proviruses. The presentation emphasized that HIV-1 integration shows target preferences on both a macro scale (as noted above) and on a microscale, involving the residues immediately adjacent to the integration site. M. Katzman's lab (Konsavage et al.) reported an interesting RSV IN mutant that displayed enhanced 3'-end processing activity but impaired DNA joining. The authors speculated that RSV IN has evolved suboptimal processing activity to allow it to catalyze DNA joining. R. Craigie's lab previously reported that a protein termed "barrier-to-autointegration factor" (BAF) is a component of the MLV preintegration complex and that this factor enhances intermolecular integration reactions and blocks intramolecular integration (autointegration) [ 41 ]. At this year's meeting, Suzuki and Craigie extended this work by showing that BAF interacts with an inner nuclear membrane protein, lamina-associated polypeptide 2α (LAP2α). LAP2α is a component of the MLV preintegration complex and knockdown of its expression inhibits MLV replication. Reverse Transcription A number of studies over the years have demonstrated that retroviral NC proteins possess nucleic acid chaperone activity that plays an important role in various aspects of reverse transcription (for review, see [ 42 ]). Several presentations focused on this activity of NC. Work from J. Levin's lab (Guo et al.) demonstrated that in an in vitro assay that models minus-strand transfer, NC alone is able to catalyze the removal of small 5' terminal genomic RNA fragments, which remain annealed to a minus-strand strong-stop DNA. Strand transfer product increased with increasing NC, and in the presence of NC, strand transfer product was generated even when reverse transcription was catalyzed by an RNase H-deficient RT. The NC zinc fingers appeared to be critical for this activity. These results suggest an important role for NC nucleic acid chaperone activity in removing terminal RNA fragments annealed to minus-strand strong-stop DNA following primary cleavage by RNase H. By examining reverse transcription in infected cells, R. Gorelick's lab (Thomas et al.) observed that mutations in the first zinc finger of NC strongly interfered with the progression of reverse transcription and impaired virus infectivity. The authors speculated that reduced binding of NC to the viral DNA allows cellular enzymes (nucleases and ligases) to modify the viral DNA ends thereby interfering with integration. Reverse transcription is initiated by a host cell-derived tRNA bound to the primer binding site (PBS) near the 5' end of the viral genome. Retroviruses are selective in their utilization of host tRNAs; HIV-1, for example, specifically primes reverse transcription with a tRNA lys3 . The selectivity for particular tRNAs results from interactions between the tRNA and the PBS and it has been proposed by B. Berkhout and colleagues that a second motif in the viral RNA, termed the primer-activation signal (PAS), also forms specific contacts with the tRNA. Berkhout's lab (Abbink et al.) reported at this year's meeting that, by mutagenesis of the PBS and PAS, HIV-1 primer utilization could be shifted from tRNA lys3 to tRNA lys1,2 . Such mutants replicated poorly but could adapt during long-term passaging. In one case, adaptation evidently occurred through optimization of the putative PAS. Interestingly, a single amino acid change in the RNase H domain of RT also arose during adaptation, suggesting a possible role for RT in selective primer utilization. While PCR techniques allow the progression of viral DNA synthesis to be monitored post-infection, it has not been possible to follow reverse transcription in a strand-specific fashion in infected cells. D. Thomas from V. Pathak's lab reported the development of a strand-specific amplification assay that uses so-called "padlock" probes – long single-stranded oligonucleotides that hybridize with their target sequence simultaneously at their 5' and 3' ends. Following hybridization, the ends of the padlock probes are ligated to form circles that are amplified and detected by real-time PCR. This assay, which allows specific steps in reverse transcription to be measured quantitatively in a strand-specific manner, should be very useful for addressing a number of questions regarding the kinetics of reverse transcription and the efficiency of this process under a variety of conditions. F. Maldarelli described the results of studies conducted with S. Palmer, J. Coffin and colleagues aimed at characterizing the evolution of HIV-1 populations in vivo . The authors monitored genetic variation in cohorts of drug-naïve and drug-resistant patients by analyzing individual pro-pol sequences. In both drug-naïve and drug-resistant patients, they observed little change in virus population structure over several years, implying that the replicating population is relatively large. Interestingly, recombination rates appeared to be very high regardless of levels of viremia, suggesting the presence of a substantial number of multiply infected cells even at low viral loads. The results emphasize the importance of recombination in generating viral diversity in vivo . Pathogenesis The keynote speaker at this year's meeting was Neal Copeland, who presented important findings from his and Nancy Jenkins' lab regarding the use of high through-put analysis to map retroviral integration sites in tumors induced by MLVs. This rapidly developing approach is being used by a number of groups to elucidate cancer gene pathways [ 43 - 46 ], and to define retroviral integration site preferences. The Jenkins and Copeland labs have used infection of various hematopoietic stem cells followed by transplantation into lethally irradiated mice to identify not only novel proto-oncogenes, but also cooperating cancer genes, tumor suppressors, and genes involved in stem cell transformation and immortalization. While this technique is currently applicable only to hematopoietic lineage cells, current research in the Jenkins and Copeland labs is testing whether integration by transposable elements in other cell types can be used to identify similar genes and pathways in solid tumors. In contrast to the majority of leukemia-inducing retroviruses in non-human species, most of which cause cancer by insertional mutagenesis, HTLV-I encodes accessory proteins, such as Tax, with known transforming activity. There were several talks at the meeting describing new accessory proteins that may also play a role in cell regulation. Two talks focused on the protein p12 I , which is targeted to the endoplasmic reticulum/Golgi of infected cells and decreases MHC class I trafficking to the cell surface. R. Fukomoto from G. Franchini's lab presented data that p12 I decreases activation of the transcription factor NFATI in T cells by binding to LAT and inhibiting T-cell receptor signaling. In contrast, M. Lairmore presented data that p12 I expression in Jurkat cells results in ~20-fold activation of NFAT-dependent gene expression in a calcium-dependent manner (Kim and Lairmore). These authors also demonstrated that p12 I acts in the endoplasmic reticulum to activate calcium-mediated T-cell activation during the early stages of infection, apparently through an interaction with calcineurin. These studies suggest a prominent role for p12 I in common T cell activation pathways critical to the establishment of a persistent infection. Another protein, p13 II , which was described by V. Ciminale (Silic-Benussi et al.), is localized to the inner mitochondrial membrane and induces changes in Ca 2+ and K + permeability. In culture, p13 II reverses the morphological transformation of rat embryo fibroblasts expressing c-Myc and Ha-Ras and decreases their ability to form tumors in nude mice. There was speculation that HTLV-I encodes p13 II to counteract the growth-inducing properties of the other viral accessory proteins (such as Tax) that are required for establishment of infection, thereby allowing the virus to persist in infected individuals. In a theme that echoed in several of the simple retrovirus talks, V. Armbruester from the Mueller-Lantzsch laboratory reported on a novel protein generated by alternative splicing of the envelope gene of the HERV-K endogenous retrovirus. The transcript for this protein, np9, is highly expressed in mammary carcinomas and germ cells, and the gene product binds to the LNX protein, which is a ligand of Numb and targets it for proteasomal degradation. Since LNX/Numb/Notch is a known transformation pathway in tumors, Armbruester speculated that np9 may play a role in tumorigenesis by sequestering LNX, thereby stabilizing Numb. RNA transcription, processing, export and packaging This session surveyed new findings on retroviral splicing, nuclear retention and export, translation and packaging. J. Madsen and M. Stoltzfus evaluated the role of an exonic splicing silencer (ESS) on HIV-1 replication in cultured T cells. An HIV-1 mutant with ESS substitutions displays a replication-defective phenotype that correlates with increased viral RNA splicing. This mutant was subjected to long-term passage and the viruses that emerged contained second-site reversions in splice sites flanking the exon containing the mutated ESS. Stoltzfus speculated that strains that do not contain the ESS maintain balanced expression of their viral genome by a novel, unknown mechanism. A. Lever's lab (Poole et al.) described confocal microscopy and fluorescence resonance energy transfer (FRET) analysis of the interaction between HIV-1 Gag and its cognate genomic RNA. Using biotinylated probes to the full-length, unspliced RNA, he described an initial perinuclear co-localization between Gag and genomic RNA that subsequently shifted to the plasma membrane. Mutation of the Ψ packaging signal partially disrupted the perinuclear and plasma membrane colocalization. J. Dudley's lab (J. Mertz et al.) described a new MMTV gene that encodes a viral RNA transport protein generated by alternative splicing of the env gene. The gene was designated rem , (for RNA export protein of MMTV RNA). Its product stimulated nucleocytoplasmic transport of the unspliced MMTV transcript, in a manner similar that of the Rec protein of HERV-K [ 47 , 48 ]. This finding indicates that MMTV encodes at least three accessory genes (encoding dUTPase, superantigen, and Rem) in addition to the standard retroviral genes ( gag , pol , and env ). The Dudley group suggested that since MMTV exhibits a complex genetic structure it should be reclassified as a complex retrovirus. K. Boris-Lawrie (Roberts et al.) presented genetic and biochemical data showing that the R-U5 region of SNV RNA adopts a stem-loop structure that stimulates cap-dependent translation. RNA affinity and proteomic analysis showed that RNA helicase A (RHA) bound to wild-type RNA but not to mutants containing substitutions in this structure. RHA interaction with SNV R-U5 stimulated translation of unspliced HIV-1 reporter mRNA. Boris-Lawrie speculated that this could occur by rearrangement of intramolecular RNA interactions that disrupt the packaging signal, thus facilitating mRNA translation. In vivo , only a fraction of HTLV-infected cells actively expresses viral RNA, leading to speculation that the virus negatively regulates gene expression. P. Green (Younis et al.) described a new role for HTLV-1 and -II accessory proteins p30 and p28, respectively, in negatively regulating virus production during chronic infection. Tax/Rex expression was inhibited upon ectopic expression of p28 by a mechanism that involved nuclear retention of the mRNA. Analysis of RNAs bearing a luciferase reporter gene showed that the 3' splice junction was sufficient to confer this nuclear retention. From this work and the studies from the Franchini and Lairmore labs described above, it is clear that HTLV-1 has evolved to regulate its expression in infected cells, thereby evading immune recognition and promoting viral persistence. Antivirals As in previous years, there was a strong emphasis on the development of antiviral agents that interfere with reverse transcription. However, noteworthy progress was also reported in efforts to target a number of additional steps in the replication cycle. S. Sarafianos reported data from E. Arnold's lab (Himmel et al.) on the structure of HIV-1 RT in a complex with an RNase H inhibitor. Interestingly, the binding site of the compound is quite distant (>40 Angstroms) from the RNase H active site and partially overlaps the NNRTI binding pocket. These results raise the intriguing possibility that compounds could be designed that simultaneously act as RNase H inhibitors and as NNRTIs. M. Miller's lab (Shaw-Reid et al.) performed in vitro assays to examine the effect of RT polymerase inhibitors on RNase H activity. They observed that NNRTIs actually increased RNase H activity; structural studies suggested that this enhancement was due to greater accessibility of the DNA/RNA duplex by RNase H. Although a diketo acid RNase H inhibitor displayed decreased potency in the presence of an NNRTI, the diketo acid and NNRTI synergistically inhibited reverse transcription overall. C. Wild, E. Freed, and colleagues, and independently C. Aiken's lab, previously reported that a dimethyl succinyl betulinic acid derivative (referred to as PA-457 or DSB) potently blocks HIV-1 infectivity by specifically disrupting the cleavage of the CA precursor (composed of CA and spacer peptide SP1) to mature CA [ 49 , 50 ]. The block to CA-SP1 processing prevents proper core condensation in virions released from PA-457-treated cells. Interestingly, HIV-2 and SIV are insensitive to PA-457. This work was extended by the labs of C. Wild and E. Freed (F. Li et al.) to demonstrate that the determinant of PA-457 activity maps to the N-terminus of SP1. In addition, (Adamson et al.) the passaging of HIV-1 at sub-optimal concentrations of PA-457 led to the appearance of PA-457-resistant variants that contain mutations in the C-terminus of CA or the N-terminus of SP1. A. Lever's lab (Brown et al.) described their efforts to inhibit HIV-1 replication using oligonucleotides that target the viral genome. The authors observed that oligos targeting the packaging signal (specifically stem-loops 2 and 3) disrupt Gag binding and reduced virus infectivity. T. Murakami and coworkers reported the development of an orally bioavailable compound that binds the HIV-1 chemokine coreceptor CXCR4. In culture, the compound potently inhibits infection by HIV-1 isolates that use CXCR4 as a coreceptor, but, as expected, do not block infection by strains that exclusively use CCR5 as a coreceptor. The compound suppressed HIV-1 infection in the hu-PBL-SCID mouse model. The results of this study suggest that this CXCR4 antagonist could potentially be an effective drug in infected humans. It has been suggested that most virus originating in the central nervous system (CNS) derives from long-lived cells (e.g., macrophages) that would continue to produce virus for a significant period of time after the initiation of antiretroviral therapy. According to this model, CNS-derived virus should decay more slowly following the onset of therapy relative to virus derived from the blood. In the last presentation of the conference, data were presented from R. Swanstrom's lab (Harrington et al.) obtained from a study of HIV-1 population dynamics in cerebrospinal fluid (CSF) immediately following the initiation of antiretroviral therapy. Virus isolates in the CSF apparently derive from both the CNS and the blood plasma. Interestingly, using heteroduplex tracking assays, the authors observed that within the first several days following the initiation of therapy CNS-derived isolates in the CSF decline with similar kinetics to isolates shared with the blood, suggesting that virus from both compartments is produced by cells with a short life span.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC519032.xml

526771

Tracheal adenoid cystic carcinoma masquerading asthma: A case report

Background Tracheal tumors are often misdiagnosed as asthma and are treated with inhaled steroids and bronchodilators without resolution. Case Presentation Here, a patient with tracheal adenoid cystic carcinoma who had been previously diagnosed with difficult asthma was reported. The possibility of the presence of localized airway obstruction was raised when the flow-volume curve suggesting fixed airway obstruction, was obtained. Conclusion The presenting case report emphasizes the fact that not all wheezes are asthma. It is critical to bear in mind that if a patient does not respond to appropriate anti-asthma therapy, localized obstructions should be ruled out before establishing the diagnosis of asthma.

Background Tracheal tumors are uncommon and often overlooked until they reach to an advanced stage. The presenting symptoms are typically prolonged cough and wheezing that can be misdiagnosed as asthma [ 1 ]. Therefore, making precise diagnosis of tracheal tumor may be extremely challenging. Here, a patient with tracheal adenoid cystic carcinoma who had been previously diagnosed with difficult asthma was reported. Case presentation A 29 year-old man was referred to our hospital with a 2-year history of paroxysmal attacks of dyspnea, dry cough and wheezing. He had smoked 2 packs/day cigarettes for 3 years and has been ex-smoker for 5 years. He experienced frequent sudden-onset coughing episodes followed by the development of dyspnea and wheezing a year ago. He was previously diagnosed with difficult asthma and treated with high dose inhaled corticosteroids (1600 μg budesonide) and bronchodilators. Since he was unresponsive to the therapy, he has applied to several institutions for multiple times to seek medical attention. On admission, no stridor, wheezing and cyanosis were present and the general appearance was good. Vital signs were as follows: temperature 37°C, respiratory rate 20/min, pulse 96 beats/min, blood pressure 140/70 mmHg. The chest examination was unremarkable. The results of the routine laboratory analysis, including complete blood cell count, chemistry, arterial blood gas, urinalysis and chest x-ray were within normal ranges. On spirometric examination, flow-volume curve displayed suggestive fixed airway obstruction. Forced vital capacity (FVC) was 122 % of predicted, forced expiratory volume in one second (FEV1) was 31 % of predicted and FEV1/FVC was 21 % (Figure 1 ). In order to exclude the possibility of upper airway obstruction, a work-up of computerized tomography (CT) of the chest and fiberoptic bronchoscopy (FOB) was obtained. The CT scan illustrated a solid, polipoid intratracheal mass originating from the right side of the trachea at 4 centimeter proximal of the carina (Figure 2a ). FOB revealed a smooth, round mass of 2 cm in diameter originating from the right lateral side of the trachea. The lesion was occupying approximately 50 % of the lumen (Figure 3 ). It localized at 4 th centimeter distal to larynx. Histopathological diagnosis was adenoid cystic carcinoma of the trachea. Figure 1 Flow-volume curve displays suggestive fixed airway obstruction Figure 2 a) Chest CT scan displays a polypoid mass occupying 50 % of the lumen. b) Control CT scan displays resolution of the tumor Figure 3 Bronchoscopic examination reveals polypoid mass originating from trachea with a 50 % obstruction of the lumen. The patient underwent resection surgery. At the operational site, there were severe adhesions between the mediastinal surface and the trachea. Therefore, a conservative surgery was performed. The tumor was seen on the right anterolateral wall of the trachea being expanded submucosally from the carina to the proximal end of the trachea. The patient underwent adjuvant radiation therapy after the operation. CT scan of the neck revealed resolution of the tumor (Figure 2b ). Now, 3 months after the operation, the patient has remained well. Conclusions Primary tracheal tumors are rare with the incidence of less than 0.1 % [ 2 , 3 ]. The majority of tracheal tumors in adults are malignant and the most common ones are squamous cell carcinoma and adenoid cystic carcinoma (cylindroma). Tumors of the larynx and lungs are respectively, 75 and 180 times more common than malignant tracheal tumors [ 3 , 4 ]. Benign tracheal tumors such as lipoma, hamartoma and neurilemmoma are much more rare than malignant tracheal tumors [ 2 , 5 , 6 ]. Clinical manifestations of tracheal tumors are developed as a consequence of tumor bulk and location. Patients with tracheal tumor often have exertional shortness of breath, prolonged cough or a new onset of wheezing, which is frequently misdiagnosed as asthma. Patients are usually initially managed accordingly. However, tumor may occlude 75 % of the lumen before leading symptoms. In the literature, most of the reports highlight that there is always a remarkable delay of establishing accurate diagnosis as a result of misdiagnosis of asthma [ 1 , 5 , 7 ]. Pearson et al have reported a 2-month to 2-year delay in diagnosis in their series from Toronto General Hospital [ 8 ]. Therefore, adult-onset asthma that increases the severity despite the adequate therapy should alert one to the possibility of a central obstructing lesion [ 1 ]. In such patients, a flow-volume curve may provide extremely valuable data and may lead the clinician toward accurate diagnosis. Another diagnostic challenge of tracheal tumor is the fact that it can occasionally be visualized by plain chest X-ray. CT scan of the chest or magnetic resonance imaging may yield more valuable data on the site and length of the tracheal lesion [ 1 ]. The cornerstone diagnostic modality is bronchoscopy [ 1 ]. In the presenting report, the patient had been mistakenly diagnosed with difficult asthma because of the presence of uncontrolled asthmatic symptoms and poor lung functions despite the use of high doses corticosteroids. Nevertheless, the flow-volume curve typically displayed localized fixed obstruction of central airways. This led us to have a work-up of CT scan and FOB to rule out upper airway obstructions. Adenoid cystic carcinomas are smooth, firm, and well-circumscribed lesions. These tumors grow extremely slowly. Patients have been known to survive for 10 to 15 years with multiple lung metastases. Spread tends to occur submucosally [ 1 ]. The optimal therapeutic approach is surgical resection and reconstruction. The surgeon should be aware of the fact that the apparent gross margin of the tumor is usually still involved with the tumor cells so that the resection should be done at least 1 cm beyond the gross tumor margin. Postoperative irradiation was recommended by most of the authors [ 1 , 9 ]. Long-term postoperative follow-up is important to discover recurrences. In this case, postoperative irradiation on curative doses has been applied for a month. Control bronchoscopic examination revealed near-complete remission. The presenting case report emphasizes the fact that not all wheezes are asthma. It is critical to bear in mind that if a patient does not respond to appropriate anti-asthma therapy, localized airway obstructions should be rule out before establishing the diagnosis of asthma. Competing interest The author(s) declare that they have no competing interests. Authors' contributions HT has seen the patient and made the diagnosis of tracheal obstruction, participated in the design of the manuscript. NK has seen the patient in the clinical, followed the patient and drafted the manuscript. SD and CK have performed the surgery and followed the patient. All authors read and approved the final manuscript. Pre-publication history The pre-publication history for this paper can be accessed here:

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC526771.xml

555576

Duration of streptozotocin-induced diabetes differentially affects p38-mitogen-activated protein kinase (MAPK) phosphorylation in renal and vascular dysfunction

Background In the present study we tested the hypothesis that progression of streptozotocin (STZ)-induced diabetes (14-days to 28-days) would produce renal and vascular dysfunction that correlate with altered p38- mitogen-activated protein kinase (p38-MAPK) phosphorylation in kidneys and thoracic aorta. Methods Male Sprague Dawley rats (350–400 g) were randomized into three groups: sham (N = 6), 14-days diabetic (N = 6) and 28-days diabetic rats (N = 6). Diabetes was induced using a single tail vein injection of STZ (60 mg/kg, I.V.) on the first day. Rats were monitored for 28 days and food, water intake and plasma glucose levels were noted. At both 14-days and 28-days post diabetes blood samples were collected and kidney cortex, medulla and aorta were harvested from each rat. Results The diabetic rats lost body weight at both 14-days (-10%) and 28-days (-13%) more significantly as compared to sham (+10%) group. Glucose levels were significantly elevated in the diabetic rats at both 14-days and 28-days post-STZ administration. Renal dysfunction as evidenced by renal hypertrophy, increased plasma creatinine concentration and reduced renal blood flow was observed in 14-days and 28-days diabetes. Vascular dysfunction as evidenced by decreased carotid blood flow was observed in 14-days and 28-days diabetes. We observed an up-regulation of inducible nitric oxide synthase (iNOS), prepro endothelin-1 (preproET-1) and phosphorylated p38-MAPK in thoracic aorta and kidney cortex but not in kidney medulla in 28-days diabetes group. Conclusion The study provides evidence that diabetes produces vascular and renal dysfunction with a profound effect on signaling mechanisms at later stage of diabetes.

Introduction Diabetes is a complex and multifarious group of disorders characterized by hyperglycemia that has reached epidemic proportions in the present century. Infection is a leading cause of morbidity and mortality among the diabetic population [ 1 ]. Diabetes is associated with vascular and renal dysfunction characterized by hypertension, dyslipidemia, microalbuminuria, macroalbuminuria and glomerular mesangial expansion [ 2 , 3 ]. Mitogen-activated protein kinases (MAPKs) are implicated in the etiology of diabetes [ 4 , 5 ]. MAPKs are serine-threonine protein kinases involved in cell survival, proliferation and apoptosis [ 6 ]. Three MAPK subfamilies have been well characterized: extracellular signal regulated kinase 1 and 2 (ERK1/2), c-jun N-terminal kinases (JNK) and p38-MAPK [ 7 ]. ERK1/2 is involved in the growth response of cell while p38-MAPK and JNK are associated with cellular response to stress [ 7 ], inflammation [ 8 ] and vasoactive mediators such as endothelin-1 (ET-1) [ 9 ]. Also p38-MAPK activation stimulates inducible nitric oxide synthase (iNOS) expression in serum-deprived RAW 264.7 cells [ 10 ]. These observations suggest that signaling mechanisms can regulate various vasoactive molecules and vice-versa. However it is still not known if progression of diabetes produces a time dependent activation of p38-MAPK in vascular blood vessels and kidneys. We speculate a differential regulation of p38-MAPK and ERK1/2 in thoracic aorta and kidneys depending upon the duration and severity of diabetes. We have earlier demonstrated that diabetes during coronary artery bypass grafting, and chronic peritoneal sepsis produced an imbalance in the myocardial and systemic ET-1 and nitric oxide (NO) profiles [ 11 , 12 ]. However, the profile of ET-1 during progression of diabetes is unclear. Diabetes, both type 1 and type 2 , is associated with decreased NO bioavailability [ 13 , 14 ]. Conflicting reports (i.e. increased, unchanged and decreased) exist regarding the state of inducible nitric oxide synthase (iNOS) and endothelial NOS (eNOS) during diabetes [ 15 , 16 ]. We have earlier reported that elevation of ET-1 and NO mechanisms, either systemically or locally in the myocardium, correlated well with the development of myocardial dysfunction during sepsis [ 11 ] and make the heart susceptible to myocardial injury [ 17 ]. We anticipate that the duration of hyperglycemia would differentially modulate systemic and renal ET-1 and NO mechanisms. We hypothesize that progression of diabetes [mild (14-days) to moderate (28-days)] would produce renal and vascular dysfunction that correlate with altered p38-MAPK phosphorylation in kidneys and thoracic aorta. Therefore, the specific aims of the study are to characterize the progression of STZ-induced renal and vascular dysfunction at 14- and 28-days; and to examine if the progression of STZ-induced diabetes would alter the biosynthesis and activation of ET-1, NO and the phosphorylation of p38-MAPK and ERK1/2 in thoracic aorta, kidney cortex and kidney medulla. Materials and methods Male Sprague-Dawley rats (Harlan, Indianapolis, IN) weighing 350–400 g were used in the study. The rats were acclimatized to the laboratory conditions for at least 7 days following their arrival. All animal experiments were conducted in compliance with humane animal care standards outlined in the NIH Guide for the Care and Use of Experimental Animals , and were approved by the Institutional Animal Care and Use Committee of North Dakota State University. Experimental Protocol All animals were age-matched at the onset of the study. The rats were randomly divided into three groups: control, 14-days and 28-days diabetic rats (n = 6 for each group). Induction of diabetes Diabetes was produced by a single tail vein injection of streptozotocin (STZ; 60 mg/kg, I.V.) [ 15 ]. Diabetes was confirmed by blood glucose estimation (Glucometer Elite ® , Bayer Corporation, IN) 2 days after STZ-injection. Diabetes was confirmed if blood glucose > 200 mg/dL. The animals were not given insulin supplementation. Food intake and water intake were examined everyday after STZ-injection. The non-diabetic control rats received an injection of 1 mL/kg saline. 14-days and 28-days post-STZ administration recordings of systemic hemodynamics, carotid blood flow, and renal arterial blood were done. Arterial blood was collected in plastic tubes containing EDTA (1 mg/mL) and heparin (5 units/mL) to determine the plasma concentration of ET-1, NO by-products (NOx) and creatinine. The animals were euthanitized by pentobarbital (100 mg/kg, I.P.), the thoracic aorta and kidney were harvested. The concentration of ET-1, NOx, creatinine and protein expression of preproET-1, iNOS, eNOS, phosphorylated p38-MAPK (pp38-MAPK), total p38-MAPK, phosphorylated ERK, total ERK were determined in the thoracic aorta, kidney cortex and kidney medulla obtained from each animal. Surgical protocol Separate group of animals (N = 6) were used for hemodynamic study. On the day of experiment, the rats were anesthetized with an intraperitoneal injection of pentobarbital sodium (Nembutal ® , Abbott; 50 mg/kg). Common carotid arterial blood flow measurement (acute) Under pentobarbital (50 mg/kg I.P.) anesthesia, rats were placed in dorsal recumbency and through a midline cervical incision, the left common carotid artery was identified and carefully separated from adhering connective material. The carotid artery was cleared from the vagal nerve and a Transonic ® flow probe (0.3 mm 1RB, Transonic Systems Inc., Ithaca, NY) was placed carefully around the artery. The probe was manually positioned so that the artery was centered within its window and then probe was held in position. Through a sterile 10 CC syringe loaded with sterile K-Y brand lubricating jelly (Johnson & Johnson, Arlington, TX), the acoustical window within the flow probe was filled while avoiding any air bubble. Renal arterial blood flow measurement (acute) Under pentobarbital anesthesia, rats were place in dorsal recumbency, and through a midline abdominal skin incision the left renal artery was carefully separated from renal vein and a 0.5 VB Transonic ® flow probe was placed around it. The probe was manually positioned so that the artery is centered within the window and the probe was held in position. The acoustical window within the flow probe was filled with K-Y brand lubricating jelly (Johnson & Johnson, Arlington, TX). Renal and carotid artery blood flow was measured using Transonic ® flow meter T206 attached to MP100 system of Biopac Systems Inc., CA via analog to digital conversion. The MP100 system was calibrated with minimum and maximum flow capacity of the individual probe connected to the flow meter. The sampling of data was carried out at 1000 Hz and recorded to a dedicated computer using Acq Knowledge™ software. Biochemical estimations Determination of the concentration of plasma creatinine The concentration of creatinine was determined in plasma using creatinine liquid reagents (end-point, colorimetric, DIAZYME). The blood samples immediately after collection were spun down at 5000 rpm for 10 minutes. The plasma was then decanted and stored at -20°C until the time of creatinine determination using manufacturer's instruction. Plasma concentration of creatinine (mg.dL -1 .Kg -1 ) normalized to individual rat body weight was calculated. Determination of the plasma concentration of ET-1 The concentration of ET-1 was determined in plasma. The blood samples were collected in plastic tubes containing EDTA (1 mg/mL) before euthanasia. The sample was centrifuged at 3,000 × g for 15 min at 4°C and plasma was separated and assayed for ET-1. Plasma was acidified adding an equal volume of 20% acetic acid. ET-1 like material was extracted from plasma [ 11 ] using C-18 SEP-Columns (Peninsula labs, CA). The recovery of ET from plasma was approximately 87%. Immunoassay (IA) was performed using EIA kit for ET-1 (R and D systems, Minneapolis, MN). The assay was performed in microtiter plates coated with a rat antibody to human ET-1. Diluted anti-ET-1 HRP conjugate (100 μL) (ET-1 conjugated to horseradish peroxidase) was added in each well. Standards (0.25 pg – 65 pg ET-1), parameter control (24.5 ET-1 pg/ml) or sample extract (100 μL, each) were added. The plates were covered with plate sealers and incubated for 1 hr at room temperature. The contents of each well were aspirated and washed using wash buffer provided with the kit. After the last wash, contents of each well were decanted and tetramethylbenzidine (100 μL) was added. After 30 min, stop solution (100 μL) containing 1 N HCl was added. Within 30 min of addition of the stop solution, the optical density of each well was measured using a micro plate reader at 450 (OD 450) and 620 (OD 620) nm separately. A standard curve was created and the concentration of ET-1 of each sample calculated and expressed as pg/ml of plasma. Determination of the concentration of nitric oxide byproducts (NOx) The concentration of endogenous NO X (nitrate + nitrite) was determined in plasma, thoracic aorta, kidney cortex and medulla. Blood (500 μL) was collected and 40 μL of heparin was added to each sample to prevent clotting. The blood sample was then decanted and stored at -20°C until the time of nitric oxide byproducts, (NO 2 + NO 3 ) NOx, determination. To determine the NOx level of thoracic aorta, kidney cortex and kidney medulla, those tissue samples were harvested from each experimental rat and immediately homogenized with cold phosphate buffered saline (PBS) on ice, which inhibited the activity of NOS ex vivo . The homogenate was centrifuged (3000 × g, 5 min) and the supernatant was collected. The supernatant obtained from tissues and plasma was passed through a 1.2 μm multiscreen filter plate. Plasma NOx and tissue NOx concentration was determined by using Greiss reaction [ 11 , 15 ]. 6 μL of plasma was mixed with 44 μl distilled H 2 O, 20 μl 0.31 M phosphate buffer (pH 7.5) and 10 μL each of 0.86 mM NADPH (Sigma), 0.11 mM flavenidinine dinucleotide and 1.0 U/mL of nitrate reductase. NO 3 was converted to NO 2 by nitrate reductase (Boehriger Mannheim). Unknown tissue samples were run in duplicate. The samples were allowed to incubate for 1 hr at room temperature in the dark. Two hundred microliters of Greiss reagent [1:1 mixture of 1% sulfanilamide in 5% H 3 PO 3 and 0.1% N-(1-naphthyl) ethyl-enediamine] were added to each well and the plates were incubated for an additional 10 min at room temperature. Absorbance was measured at 540 nm using a plate reader and converted to NOx concentration using a nitrate standard curve and expressed as μM in plasma and μmoles/g protein in tissue. Protein in the supernatant obtained from each sample was determined using standard Lowry method. Immunoblot Analysis PreproET-1, iNOS, eNOS, total and phosphorylated p38-MAPK and ERK protein expressions of thoracic aorta, kidney cortex and kidney medulla were determined using standard SDS-PAGE and immunoblot technique. Briefly, thoracic aorta, kidney cortex and kidney medulla tissues were homogenized in lysis buffer and centrifuged as described by Pollack et al. [ 16 ]. The supernatants, at a final protein content of 25 μl, were loaded to the gels using a 2:1 laemmli sample buffer (62.5 mM Tris-HCl, pH 6.8, 25% glycerol, 2% SDS, 0.01% bromophenol blue and 710 mM β-mercaptoethanol). The prepared samples were electrophoresed on 10% denaturing sodium dodecyl sulfate (SDS) polyacrylamide gels. The proteins were transferred electrophoretically onto polyvinylidene difluoride (PVDF) membrane (Gelman Sciences, Pierce, Rockford, IL). Non-specific binding sites on the membrane were blocked overnight at 4°C with 5% nonfat dry milk in Tris-buffered saline containing Tween 20 (TBST, 20 mM Tris-HCl, 150 mM NaCl, 0.2% Tween 20, pH 7.4). The membranes were then probed with the primary antibody (Santa Cruz Biotechnology, Santa Cruz, CA) for 1 hr at room temperature. The primary antibodies are highly specific against the proteins studies and had no cross-reactivity with related members. After five washings in TBST, the membranes were incubated with the secondary antibody (Sigma, St. Louis, MO) for 1 hr at room temperature. Finally membranes were washed three times with TBST. The specific proteins were detected by enhanced chemiluminescence (ECL) reagent (Amersham Pharmacia Biotech). The blots were analyzed using Un-Scan-It™ software to estimate the density of the blots in pixels. Uniform loading was assessed by β-actin (Sigma) protein expression. While analyzing the data for immunoblot analysis, the beta-actin blots for each gel were analyzed first to confirm equal protein loading. Only after confirming that there was equal protein loading in the wells, as evidenced by no significant difference in pixel values of beta-actin blots, the bands for individual proteins were analyzed. Statistical Analyses All the data were expressed as mean ± SEM. One-way ANOVA was performed to analyze the hemodynamic and biochemical data using SPSS software. Following a significant F value, a post hoc Students Newman Keuls test was performed for inter- and intra-group comparisons. Statistical significance was realized at p ≤ 0.05 to approve the null hypothesis for individual parameters. Results General characteristics of the animals All control rats were freely moving in their individual cages through out the study. Although diabetic rats appeared to be lethargic and displayed restricted movements, there was no sign of infection or motor disorder in any of the rats studied. The food intake and water intake of 14-days and 28-days diabetes groups were significantly increased as compared to control group (Fig 1 ). The average body weight change, blood glucose and kidney weight/ 100 g body weight in all groups are summarized in Table 1 . The body weight change (%) was significantly decreased in 14-days and 28-days diabetes groups as compared to their age-matched control group. All STZ-treated rats were diabetic with mean blood glucose around 480 mg/dL. Blood glucose was significantly elevated in 14-days diabetes and 28-days diabetes groups as compared to their age-matched control group. Kidney weight normalized to body weight was significantly greater in diabetic (14-days and 28-days) rats than in control rats. Plasma creatinine concentration was increased 3-fold in 14-days diabetes group and 2-fold in 28-days diabetes group as compared to control group (Table 1 ). Diabetes (14- and 28-days) produced no significant change in mean arterial pressure but caused a significant decrease in heart rate (Table 1 ). Effect of STZ-induced diabetes on aortic and renal blood flow Control groups exhibited aortic and renal blood flow 10.8 ± 0.3 mL/min and 5.4 ± 0.1 mL/min, respectively. Induction of diabetes produced a significant decrease in renal (Fig 2A ) and carotid blood flow (Fig 2B ) at both 14-days and 28-days as compared to control group. Effect of STZ-induced diabetes on the concentration of ET-1 in plasma and expression of preproET-1 in thoracic aorta, kidney cortex and medulla To determine the effect of duration of diabetes (14-days to 28-days) on aortic and renal ET-1 biosynthesis we determined the concentration of ET-1 in plasma and expression of ET-1 precursor, preproET-1 in thoracic aorta, kidney cortex and kidney medulla. A significant elevation in plasma ET-1 concentration was observed in 28-days diabetes as compared to 14-days diabetes and control groups (Fig 3A ). We did not find any significant change in the plasma concentration of ET-1 in 14-days diabetes group as compared to control group. The expression of preproET-1 was significantly increased in thoracic aorta in 14-days and 28-days diabetes groups as compared to control group (Fig 3B ). We also found a significant increase in the expression of preproET-1 in kidney cortex in 14-days diabetes groups as compared to control group. A significant increase in the protein expression of preproET-1 of kidney cortex was observed in 28-days diabetes group as compared to 14-days diabetes and control groups (Fig 3C ). Induction of diabetes did not produce any change in the expression of preproET-1 in kidney medulla at 14-days and 28-days diabetes groups as compared to control group (Fig 3D ). Effect of STZ-induced diabetes on the concentration of NOx and expression of eNOS and iNOS A significant increase in the concentration of NOx in plasma of 28-days diabetes group as compared to the control and 14-days diabetes groups was observed (Fig 4A ). The NOx level was also elevated in thoracic aorta of 28-days diabetes group as compared to the control and 14-days diabetes groups (Fig 4B ). In kidney cortex, we also found that NOx level was significantly increased in 14-days and 28-days diabetes groups as compared to control group (Fig 4C ). But we did not find any significant change in kidney medulla of 14-days and 28-days diabetes groups (Fig 4D ). A significant up-regulation of eNOS and iNOS in thoracic aorta in 14-days and 28-days diabetes groups was obtained as compared to control group (Fig 5A1–A3 ). Also in kidney cortex in 28-days diabetes, eNOS and iNOS were significantly increased as compared to 14-days diabetes and control groups. Protein expression of iNOS was also significantly increased in kidney cortex in 14-days diabetes group as compared to control group (Fig 5B1–B3 ). In kidney medulla we did not find any significant change in eNOS and iNOS in 14-days and 28-days diabetes groups as compared to control group (Fig 5C1–C3 ). Effect of STZ-induced diabetes on the expression of total and phosphorylated p38-MAPK and ERK1/2 in thoracic aorta, kidney cortex and medulla Total p38-MAPK and phosphorylated p38-MAPK protein expressions in thoracic aorta were significantly elevated in 14-days and 28-days diabetes groups as compared to control group (Fig 6A1–A3 ). Expression of phosphorylated p38-MAPK was significantly increased in kidney cortex obtained from 28-days diabetes group as compared to 14-days diabetes and control groups. Total p38-MAPK was not altered in kidney cortex in 14-days and 28-days diabetes groups as compared to control group (Fig 6B1–B3 ). In kidney medulla of 14-days and 28-days diabetes groups we did not observe any significant change in total and phosphorylated p38-MAPK (Fig 6C1–C3 ). A significant decrease in the expression of phosphorylated ERK 1/2 of thoracic aorta in 28-days diabetes group but not in 14-days diabetes group was observed as compared to control group. We found that total ERK 2 but not total ERK 1 was significantly increased in thoracic aorta in 14-days diabetes groups as compared to control group. There was no change in the expression of total ERK 1 in 14-days and 28-days diabetes groups and ERK 2 in 28-days diabetes group as compared to control group (Fig 7A1–A3 ). In kidney cortex phosphorylated ERK 1/2 protein expression was significantly elevated in 28-days diabetes group as compared to 14-days diabetes and control groups. In kidney cortex, phosphorylated ERK 1/2 was significantly elevated in 14-days diabetes group as compared to control group. Total ERK 1/2 was not altered in kidney cortex in 14-days and 28-days diabetes groups (Fig 7B1–B3 ). In addition, in kidney medulla we did not observe any significant change in total and phosphorylated ERK 1/2 in 14-days and 28-days diabetes group as compared to control group (Fig 7C1–C3 ). Discussion STZ has long been used as a drug of choice to induce type 2 diabetes in various animal models. This well-established model is characterized by insulin deficiency associated with insulin resistance [ 18 ]. It was reported that a single intravenous injection of STZ (55 mg/kg) could cause increased plasma glucose levels, decrease in body weight and 17% mortality in rats [ 18 ]. In the present study, too, we have observed a mortality of 20% in 14-days diabetes group and 26% in 28-days diabetes group. STZ-treated rats, post 48-h, were confirmed to be hyperglycemic, lost body weight 10% and 13% in 14-days and 28-days diabetes groups respectively. Kavalali et al . [ 19 ] found that food and water intake amount was higher in diabetic groups than the control group. In our study too, we observed that diabetic rats had an increase in the food intake and water intake following 14-days and 28-days of STZ-administration. These observations suggested that single intravenous injections of STZ (60 mg/kg) produced a reproducible and consistent model of diabetes in our laboratory conditions. Renal hypertrophy can be detected as early as one day after the onset of diabetes and seen regularly post 60-hr of single STZ injection [ 20 ]. It has been reported that diabetes-induced renal hypertrophy produces increased dimensions of renal cells along with increased kidney weight [ 21 ]. In the present study, we observed an elevated kidney weight corrected by body weight in diabetic (14-days and 28-days) rats suggesting that STZ-induced diabetes produced renal hypertrophy. We also observed that both 14- and 28-days diabetic rats exhibited reduced renal blood flow along with 3-2 fold increase in plasma creatinine concentration. Umerani and Goyal [ 22 ] demonstrated an increase in serum creatinine as an indicator of deteriorated renal function in diabetic rats. Similar to our results, Itoh and coworkers [ 23 ] also demonstrated that serum creatinine levels in control group and diabetes group varied from 0.8 to 1.4 mg/dl respectively. Thus the results obtained in our study provide evidence for diabetes-induced renal dysfunction in the rat. ET-1, a potent vasoconstrictor peptide, has been implicated in diabetes and cardiovascular disorders. Elevated, unchanged and attenuated plasma ET-1 levels have been reported during diabetes [ 24 - 27 ]. Although the reason for such a variation in findings appears difficult to fathom, the discrepancies in data may be attributed to differences in species of animals, duration of hyperglycemia, dose of STZ administered etc. In the present study, we observed decreased aortic and renal blood flow in both 14-days and 28-days diabetics rats. However elevation of ET-1 and NOx in plasma was seen only in 28-days diabetic rats group but not in 14 days-diabetes group. Taken together, these observations suggest that STZ-induced hyperglycemia produced alterations in the systemic and regional blood flow, which could be due to altered systemic levels of ET-1 and NO. Makino et al . [ 27 ] demonstrated upregulated preproET-1 mRNA in the aorta from STZ-induced diabetic rats. They suggested that increased release of ET-1 from the aorta contributes to enhanced plasma level of ET-1 seen in diabetic rats. Also ET-1 concentration has been shown to increase in kidneys [ 28 ] following diabetes. Since ET-1 exists not only in the vascular endothelial cells but also in the mesangial cells or tubular cells, this increase in ET-1 could be attributed to increased biosynthesis of ET-1 in the renal cells [ 28 - 30 ]. In the present study, we also observed an elevated expression of preproET-1 proteins in aorta and kidney cortex but not in kidney medulla at 14-days and 28-days following diabetes induction. These findings suggest that during early diabetes an upregulation of ET-1 biosynthesis in vascular (thoracic aorta) and locally at the organ level (kidney cortex) could be responsible for vascular and renal dysfunction seen during diabetes. Although the mechanisms of ET-1 elevation during diabetes are relatively unknown, several research groups speculated that this increase could be due to an abnormal production by the affected endothelium [ 31 ] or lack of suppression of ET-1 release secondary to attenuated endothelium-derived relaxing factor production [ 32 ]. Hirata et al . demonstrated that ET-1 via binding to ET B receptors produces activation of NOS [ 33 ] and generates NO. In the present study, elevated concentration of NOx in plasma, thoracic aorta and kidney cortex but not in medulla was observed in 28-days diabetes group. Stockklauser-Farber et al . [ 7 ] demonstrated an increased myocardial NOS (iNOS and eNOS) activity that reached maximal values after 4 wk and 6 wk diabetes. We speculated that along with elevated NO production, activation of NOS isoforms may play a prominent role in the pathophysiology of nephropathy at different phases of STZ-induced diabetes [ 34 , 35 ]. Increased expression of eNOS in afferent arterioles and glomeruli was found by Sugimoto et al. [ 36 ]. In a previous study, these authors demonstrated an enhanced renal expression of iNOS 5 days post diabetes that was sustained for 20 days, while eNOS and nNOS were not altered [ 37 ]. We also observed that the progression of diabetes from 14-days to 28-days upregulated iNOS and eNOS in thoracic aorta and kidney cortex while decreasing aortic and renal blood flow that correlated well with systemic and local increase in NOx levels in thoracic aorta. This suggests that NO stimulation in thoracic aorta and kidney cortex occurs with increased duration of hyperglycemia in STZ-induced diabetic rats. Since in the present study both ET-1 and NOS activation exhibit a similar course in 28-days diabetic rats, we speculate that NOS stimulation could be due to activation of ET B receptors via elevated ET-1 mechanisms. However, further studies will be required to strengthen this speculation. Signaling mechanisms in diabetes Hyperglycemia has been shown to phosphorylate ERK1/2 in rat glomerular and mesangial cells [ 38 ] and p38-MAPK in vascular smooth muscle cells and aorta in derived from diabetic rats [ 39 ]. Pearson et al . [ 40 ] and Tian et al . [ 41 ] demonstrated that ET-1 stimulation of mesangial cell (MC) proliferation involves several pathways, among which MAPK figures prominently. In the present study, we observed that 14-days diabetes up-regulated phosphorylated p38-MAPK but not ERK1/2 in thoracic aorta. We also observed down-regulation of phosphorylated ERK 1/2 in thoracic aorta post 28-days diabetes. In kidney cortex although p38-MAPK was not altered post 14-days diabetic group, ERK1/2 is elevated. This suggests that ERK1/2 phosphorylation predominate during 14-days diabetes in kidney cortex but not in thoracic aorta, while both p38-MAPK and ERK1/2 remain unaffected in kidney medulla. Both in vivo and in vitro results suggest that ERK and p38MAPK may be involved in high-glucose-induced cellular hypertrophy [ 42 ]. In the present study, we observed that ERK1/2 precedes p38-MAPK phosphorylation depending upon the progression of diabetes from 14-days to 28-days. We observed a profound increase in plasma NOx levels and iNOS expression with a corresponding increase in p38-MAPK activation in kidney cortex and thoracic aorta during 28-days diabetes. We propose that p38-MAPK activation could be an important signaling mechanism that causes iNOS activation as was reported by Liu et al . [ 14 ]. We speculate that activated p38-MAPK and iNOS mechanisms outweigh ERK1/2 mechanisms in kidney cortex and thoracic aorta during moderate diabetes. A similar finding, though not directly related to the present study, was shown by Purves et al . [ 43 ] where they demonstrated using cultured sensory neurons that co-treatment with high glucose and oxidative stress results in an additive effect on p38-MAPK phosphorylation without affecting ERK1/2 activation. The findings of the present study suggested that phosphorylation of p38-MAPK and not ERK1/2 was associated with iNOS activation and renal and vascular dysfunction following 28-days of STZ-induced hyperglycemia. The results obtained in the present study characterize the STZ (60 mg/kg, I.V.)-induced diabetic rat model in our laboratory. The marked characteristics of diabetic rat model include weight loss, increase food and water intake, and bradycardia. STZ-induced diabetes, both at 14-days and 28-days, produced renal dysfunction and vascular dysfunction that correlated well with levels of ET-1 and NOx and expression of preproET-1 and NOS proteins in kidney cortex and thoracic aorta. The data obtained in the present study demonstrate that progression of diabetes from 14-days to 28-days caused a factorial increase in p38-MAPK phosphorylation along with NOS upregulation in kidney cortex and thoracic aorta. The study provides evidence that diabetes produces vascular and renal dysfunction with a profound effect on signaling mechanisms at later stage of diabetes. However, more studies will be required to further delineate the inherent link or interaction between p38-MAPK upregulation, ET-1 and NO mechanisms and development of renal and vascular dysfunction during diabetes.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555576.xml

555562

Activity of glucose oxidase functionalized onto magnetic nanoparticles

Background Magnetic nanoparticles have been significantly used for coupling with biomolecules, due to their unique properties. Methods Magnetic nanoparticles were synthesized by thermal co-precipitation of ferric and ferrous chloride using two different base solutions. Glucose oxidase was bound to the particles by direct attachment via carbodiimide activation or by thiophene acetylation of magnetic nanoparticles. Transmission electron microscopy was used to characterize the size and structure of the particles while the binding of glucose oxidase to the particles was confirmed using Fourier transform infrared spectroscopy. Results The direct binding of glucose oxidase via carbodiimide activity was found to be more effective, resulting in bound enzyme efficiencies between 94–100% while thiophene acetylation was 66–72% efficient. Kinetic and stability studies showed that the enzyme activity was more preserved upon binding onto the nanoparticles when subjected to thermal and various pH conditions. The overall activity of glucose oxidase was improved when bound to magnetic nanoparticles Conclusion Binding of enzyme onto magnetic nanoparticles via carbodiimide activation is a very efficient method for developing bioconjugates for biological applications

Background The immobilization of biomolecules onto insoluble supports is an important tool for the fabrication of a diverse range of functional materials or devices [ 1 ]. Enzyme immobilization for example, is a desired biological procedure because of the possible application of immobilized enzymes in continuous operations, product purification, and catalyst recycling [ 2 ]. Furthermore, immobilization provides many advantages such as enhanced stability, easy separation from reaction mixture, possible modulation of the catalytic properties, and easier prevention of microbial growth [ 3 ]. In the last decade, nanosize materials have been widely used as support for this purpose. Among these materials, magnetic nanoparticles are very popular when used in conjunction with biological materials including proteins, peptides, enzymes [ 4 - 9 ] antibodies and nucleic acids [ 8 ], because of their unique properties [ 4 - 9 ]. The ability to tract magnetically labeled entities or target organelles using magnetic force offer the opportunity to conduct biological operations with increased specificity. Magnetite (Fe 3 O 4 ) are biocompatible superparamagnetic materials that have low toxicity and strong magnetic properties [ 5 ]. They have been widely used for in vivo examination including magnetic resonance imaging, contrast enhancement, tissue specific release of therapeutic agents, hyperthermia [ 10 , 11 ], magnetic field assisted radionucleide therapy [ 11 ], as well as in vitro binding of proteins and enzymes [ 4 - 8 ]. Magnetite nanoparticles have been used as support material for binding of enzymes including yeast alcohol dehydrogenase [ 4 ] and lipase [ 5 ] directly via carbodiimide activation. This method brought about considerable promise because of its simplicity and efficiency. Recently, γ-Fe 2 O 3 magnetic nanoparticles were used for binding Candida rugosa lipase after acetylation of thiophene functionalized nanoparticles, or through nitroso-derivative formed on the surface of the particles by reacting nitroso tetrafluoroborate in methylene chloride. Both methods and more effectively lipase immobilized on acetylated nanoparticles exhibited long term stability. Glucose oxidase (GOX, β-D-glucose oxygen 1-oxidoreductase, EC 1.1.3.4) is a homodimer flavoprotein containing two active sites per molecule [ 12 , 13 ]. It catalyses the oxidation of β-D-glucose to gluconic acid, concomitant with the reduction of oxygen to hydrogen peroxide. Glucose oxidase has been used to test various types of enzyme immobilization, and is the most commonly studied in the construction of biosensors for glucose assay development [ 12 , 14 , 15 ]. A more recent study [ 16 ] examined the activity of cholesterol oxidase activity using carbodiimide activation. Here, we report the stability and enzymatic activity of glucose oxidase immobilized onto Fe 3 O 4 magnetic nanoparticles using two binding methods, the direct binding via carbodiimide activation of amino functionalized particles and binding to thiophene-functionalized acetylated nanoparticles. A comparison of the stability and activity of glucose oxidase immobilized to magnetite using different protocols will lay the foundation for magnetic immunoassays. The size and structure of the nanoparticles were characterized using Transmission electron microscopy (TEM) and Fourier Transform Infrared (FTIR) spectroscopy, respectively. The stability, activity, and kinetic behavior of bound glucose oxidase were also examined. Materials and methods Glucose oxidase (specific activity 200 units/mg protein) from Aspergillus niger was purchased from VWR international (Pittsburgh, USA). Carbodiimide-HCl (1-ethyl-3-(3-dimethyl-aminopropyl), ammonium hydroxide, sodium hydroxide, acetic anhydride, glucose, bovine serum albumin (BSA), iron (II) chloride tetrahydrate 97 % and iron (III) chloride hexahydrate 99%, were obtained from Sigma-Aldrich St Louis (USA). 11-bromoundecanoic acid was obtained from TCI America Portland, USA. The Biorad Protein Assay Reagent Concentrate was purchased from Biorad Laboratories (Hercules, CA). Thiophene-2-thiolate was obtained from Alfa Aesar MA, USA. Iodine was obtained from Mallinckrodt Kentucky, USA, and acetonitrile was obtained from EMD Chemicals (New Jersey, USA). Sodium phosphate monohydrate and potassium phosphate dihydrate were acquired from EM Science (New Jersey, USA). Sodium carbonate was obtained from Orion Research Inc. (Beverly, USA). Magnetic nanoparticles (Fe 3 O 4 ) were prepared by chemical co-precipitation of Fe 2+ and Fe 3+ ions in a solution of ammonium hydroxide (magnetic nanoparticles I or Fe 3 O 4 I), or sodium hydroxide (magnetic nanoparticles II or Fe 3 O 4 II) followed by a treatment under hydrothermal conditions [ 4 , 5 ]. Iron (II) chloride and iron (III) chloride (1:2) were dissolved in nanopure water at the concentration of 0.25 M iron ions and chemically precipitated at room temperature (25°C) by adding NH 4 OH solution (30%) or NaOH 3 M at a pH 10. The precipitates were heated at 80°C for 35 min under continuous mixing and washed 4 times in water and several times in ethanol. During washing, the magnetic nanoparticles were separated from the supernatant using a magnetic separator of strength greater than 20 megaoersted (MOe). The particles were finally dried in a vacuum oven at 70°C. The dried particles exhibited a strong magnetic attraction. Magnetic nanoparticles I (50 mg) produced, using a solution of ammonium hydroxide were added to 1 mL of phosphate buffer (0.05 M. pH 7.4). After adding 1 mL of carbodiimide solution (0.02 g/mL) in phosphate buffer (0.05 M. pH 7.4), the mixture was sonicated for 15 min. Following the carbodiimide activation, 2 mL of glucose oxidase (1000 units /mL) was added and the reaction mixture was sonicated for 30 min at 4°C in a sonication bath. The magnetic nanoparticles were separated from the mixture using a magnetic separator. The precipitates containing Fe 3 O 4 nanoparticles I and Fe 3 O 4 bound glucose oxidase (GOX-Fe 3 O 4 I) were washed with phosphate buffer pH 7.4 and 0.1 M Tris, pH 8.0, and then used for activity and stability measurements. NaCl was added to enhance the separation of the magnetic nanoparticles [ 4 ]. A second functionalization protocol using a modification of the strategy adopted to immobilize Candida rugosa lipase on the γ-Fe 2 O 3 [ 12 ] was implemented (Magnetic nanoparticles II). Briefly, 1.5 g Fe 3 O 4 was added to 5 g of 11-bromoundecanoic acid dissolved in 15 mL of ethanol. 11-bromoundecanoic acid was covalently linked to the nanoparticles surfaces by heating the mixture with microwave irradiation for 10 min. Functionalization of the particle was achieved through nucleophilic substitution with the 2-thiophene thiolate. In practice, 2-thiophene thiolate (7 mL) was added to the mixture containing the particles and heated in microwave for 5 minutes. The mixture was washed with ethanol and transferred in a round bottom flask. Acetic anhydride (4 mL) and 34.6 mL of iodine (0.01N) were successively added to the particles and agitated. The mixture was heated for 1 h under reflux condition [ 17 ]. The particles were washed several times with water, once with 10% sodium carbonate solution and finally with ethanol. The acetylated particles were reacted directly with the enzyme covalently linked to the particles via C = N bond [ 11 ]. For the attachment of glucose to nanoparticles, 2 mL of the GOX solution (1000 units/mL) was added to 50 mg of functionalized magnetic nanoparticles in a test tube and sonicated at 15°C for 3 h. The supernatant containing unbound enzymes was separated from the magnetic nanoparticles using the magnetic separator, and the enzymes bound to magnetic nanoparticles were then used for activity determination. A schematic of the procedures used for both attachments are presented in Figure 1 . The amount of protein in the supernatant was determined by a colorimetric method at 595 nm with the Biorad Protein Assay Reagent Concentrate using bovine serum albumin (BSA) as the protein standard. The amount of bound enzymes was calculated from: Figure 1 Description of GOX attachment procedures. (I) procedure employed for GOX-Fe 3 O 4 I attachment, (II) thiophene functionalization and the acetylation of particles for GOX-Fe 3 O 4 II attachment. A = ( C i - C s )* V (1) Where A is the amount of bound enzyme, C i and C s are the concentration of the enzyme initially added for attachment and in the supernatant, respectively (mg/mL) and V is the volume of the reaction medium (mL). The size of Fe 3 O 4 magnetic nanoparticles, GOX-Fe 3 O 4 I and GOX-Fe 3 O 4 II were characterized by transmission electron microscopy (TEM, JEM 1200 EXII, JEOL) and structure by FTIR spectroscopy (Biorad FTS 6000, Cambridge, MA). The samples for TEM analysis were prepared as follows: a drop of magnetic nanoparticles was dispersed in nanopure water. The resulting solution was sonicated for 4 min to obtain better particle dispersion. A drop of the dispersed solution was then deposited onto a copper grid and dried overnight at room temperature. The binding of GOX onto the magnetic nanoparticles was investigated using FTIR spectroscopy. Samples for FTIR analysis were prepared in phosphate buffer pH 7.4. The activity of bound GOX was determined by measuring the initial rate of formation of hydrogen peroxide at a given temperature following the formation of a red quinoneimine dye. The principle of enzymatic determination of the activity of glucose oxidase is described as follows: Glucose is oxidized by glucose oxidase to gluconate and hydrogen peroxide. Phenol + 4-AAP, in the presence of peroxidase (POX), produces a quinoneimine dye that is measured at 500 nm using a Beckman Du Spectrometer to provide an absorbance that is proportional to the concentration of glucose in the sample. The reaction is described as follows: The activity of glucose oxidase was measured as follows. An assay mixture was prepared by mixing 500 U of horseradish peroxidase, 0.015 mmol of 4-aminoantipyrine (4-AAP), 0.025 mmol of phenol and 5 mmol of glucose in 50 mL of phosphate buffer solution (0.05 M. pH 7.4) to result in a glucose concentration of 0.1 M. To start the enzymatic reaction, 2 mL of the assay solution was added to 15 mL centrifuge test tubes containing GOX-Fe 3 O 4 and mixed by vortex. A solution of free GOX of the same molar concentration was used to evaluate the activity of the free enzyme for comparison. The solution was incubated at various temperatures (37–80°C) at specific intervals of time (30 min) and the supernatant was separated from GOX-Fe 3 O 4 using a magnetic separator. 10 μL aliquots of the supernatant were then taken for determining the concentration of hydrogen peroxide following the procedure by Trinder [ 18 ]. The activity of the enzyme can be calculated using the following equation: Where ABS sample denotes the absorbance of the sample, ABS Std is the absorbance of the standard solution, and C the concentration of glucose in the sample. The effect of temperature on the free GOX, GOX-Fe 3 O 4 I and GOX-Fe 3 O 4 II was estimated by determining the concentration of glucose in the sample at various temperatures. A solution of the assay mixture was added to the various centrifuge test tubes containing bound or free enzymes. The samples were stored in a water bath at specific temperatures (37, 50, 60, 70 and 80°C) and the absorbance was monitored at fixed time intervals to determine the glucose content. The effect of pH on GOX was monitored by measuring the initial rate of glucose oxidation by glucose oxidase in different phosphate and carbonate buffer solutions of pH (5–10) at 25°C. The thermal stability of free GOX, GOX-Fe 3 O 4 I, and GOX-Fe 3 O 4 II were determined by measuring the residual activity of the enzyme at 25°C, after being exposed to different temperatures (37–80°C) in phosphate buffer (0.05 M, pH 7.4) for 30 min. Aliquots of the reacting solutions were taken at periodic intervals (every 30 min for 6 h) and assayed for enzymatic activity as described above. The first-order inactivation rate constant, k was calculated from the equation: ln A = ln A 0 - kt (5) where A 0 is the initial activity, A is the activity after time t (min) and k is the reaction constant. The storage stability was examined by measuring the change in the concentration of glucose at room temperature at different time intervals (4 days). Test tubes with samples of GOX-Fe 3 O 4 I, GOX-Fe 3 O 4 II, or free GOX solutions were stored at 25°C in phosphate buffer (0.05 M. pH 7.4) for 33 days. Thereafter, 3 mL of the assay solution was added, and the residual activity of GOX was assayed. The kinetic parameters of free GOX, GOX-Fe 3 O 4 I and GOX-Fe 3 O 4 II, K m and V max were determined by measuring the initial rates of glucose oxidation (0.2–1 mM) by glucose oxidase (0.25 mg/mL) in phosphate buffer (pH 7.4) at 25°C. Results and discussion Synthesis of magnetic nanoparticles with equivalent amounts of ferric and ferrous chlorides resulted in 66% of magnetic nanoparticles I and 73% of magnetic nanoparticles II. These yields suggested that the synthesis using NaOH is more advantageous for large scale production of magnetic nanoparticles. "Bare" Fe 3 O 4 I and Fe 3 O 4 II, and their GOX bound counterparts shown in the TEM micrographs in Figures ( 2A, B, C and 2D ), respectively reveal that the particles are fine and spherical. The sizes of the particles of each sample were evaluated from 2 different TEM images. The diameter was in the range between 9 and 13 nm. The coefficient of variation between different measurements was less than 7%. There was no significant change in the size of the 'bare" particles and GOX bound particles. However, signs of agglomeration of the particles were visible in the samples. The agglomerates were not considered in the examination of the size distribution of the magnetic particles because of the assumption that agglomerated particles do not describe the original size of the particles. Figure 3 shows the distribution of the particles sizes. Fe 3 O 4 nanoparticles 14.5 mg/mL corresponding to GOX/Fe 3 O 4 weight ratios of 0.2 was used for the binding process. The amount of unbound enzyme was determined by assaying the protein content in the supernatant. The amount of enzymes bound to the magnetic nanoparticles in each binding procedure is given in table 1 . Average binding efficiencies of 38.4 units/mg particles for GOX-Fe 3 O 4 I and 27.6 units/mg particles for GOX-Fe 3 O 4 II were noted. The overall percentage of binding efficiency was between 94 and 100% for GOX-Fe 3 O 4 I and 66 to 72% for GOX-Fe 3 O 4 II. These results show that the binding was successful in both cases, particularly with Fe 3 O 4 I which uses the carbodiimide activation. The percentage of binding of GOX to Fe 3 O 4 II was significant but still below the level obtained through carbodiimide activation with the Fe 3 O 4 I. However, despite the difference in the level of GOX bound, the two procedures adopted were successful. Figure 2 Transmission electron micrographs of Fe 3 O 4 magnetic nanoparticles I (A), Fe 3 O 4 magnetic nanoparticles II (B), GOX-Fe 3 O 4 I (C), and GOX-Fe 3 O 4 II (D). Figure 3 Distribution of particles on the electron micrographs of Fe 3 O 4 magnetic nanoparticles I and GOX-Fe 3 O 4 I (A), and Fe 3 O 4 magnetic nanoparticles II and GOX-Fe 3 O 4 II (B). Table 1 Summary of binding efficiencies of enzyme-functionalized systems (n = 9). Bound enzymes (units/mg nanoparticles) GOX-Fe 3 O 4 I 38.4 ± 0.8 GOX-Fe 3 O 4 II 27.6 ± 0.6 The FTIR spectra of magnetic Fe 3 O 4 I, Fe 3 O 4 II, GOX-Fe 3 O 4 I and GOX-Fe 3 O 4 II are shown in Figure 4(A, B, C, D) , respectively. The characteristic bands of proteins at 1541 and 1645 cm -1 assigned to amide I and amide II, respectively were visible in the spectra of GOX-Fe 3 O 4 I and GOX-Fe 3 O 4 II. These peaks are associated with two sharp peaks in the region 1420-1300 cm -1 typical of carboxylate groups, from the enzyme. A weak peak at 1900 cm -1 appeared in the spectra of GOX-Fe 3 O 4 I and GOX-Fe 3 O 4 II and could be assigned to the C-O bonds in the enzyme molecule. These peaks were absent in the spectra of Fe 3 O 4 I and Fe 3 O 4 II. In the 1100-1000 cm -1 region in all spectra appeared a characteristic adsorption spectra typical to phosphate ion that can be assigned to the phosphate buffer used in the samples preparation. The occurrence of negative peaks in the spectra of Fe 3 O 4 I, and GOX-Fe 3 O 4 II is possibly due to the reduced amount of phosphate in comparison to the amount of phosphate used to background subtraction. The characteristic peaks of proteins found in the spectra of GOX-Fe 3 O 4 I and GOX-Fe 3 O 4 II were not visible in the spectra of Fe 3 O 4 I and Fe 3 O 4 II, indicating enzyme attachment onto the particles. The amino groups on the surface of the particles resulted from the use of concentrated ammonia solution during the co-precipitation of Fe 2+ and Fe 3+ as demonstrated by [ 5 ] and [ 19 ]. Figure 4 FTIR spectra of Fe 3 O 4 magnetic nanoparticles I (A) and Fe 3 O 4 magnetic nanoparticles II (B), GOX-Fe 3 O 4 I (C), and GOX-Fe 3 O 4 II (D). The most important aspect of this study is related to the retention of biocatalytical activity of GOX after binding to magnetic nanoparticles. The amount of bound GOX was estimated using the UV-vis spectrophotometer and the catalytic activity of free and bound GOX was compared. Kinetic parameters ( V m and K m ) were estimated from the double reciprocal plots of the initial rates of glucose oxidation by GOX. The double reciprocal plots are presented in Figure 5 . The Michaelis-Menten constants V max and K m for GOX are shown in Table 2 . V max of free GOX, GOX- Fe 3 O 4 I and GOX-Fe 3 O 4 II were 0.731, 0.803, and 0766 μmol/min mL and, the corresponding K m values were, 0.383, 0.208, and 0.237 mM, respectively. The V max value of GOX immobilized on magnetic nanoparticles (I and II) was higher than that of the free enzyme. The highest V max was obtained with GOX-nanoparticles I. Since a low K m indicates a high degree of affinity of the enzyme to substrate [ 5 ], calculations showed that the affinity of the enzyme to the substrate increased in the order of free GOX, GOX-Fe 3 O 4 II and GOD-Fe 3 O 4 I, respectively. The high affinity of the enzyme to the substrate may be explained by a favorable change in the structural organization of the enzyme due to the immobilization procedure [ 20 ]. Consequently, the active sites of the enzymes could be more readily available for enzymatic interactions. Figure 5 Double reciprocal plots of the initial rates of free GOX (■), GOX-Fe 3 O 4 I (▲) and GOX-Fe 3 O 4 II (●) at pH 7.4, from experimental data. Table 2 Kinetic parameters of free GOX, GOX-Fe 3 O 4 I, and GOX-Fe 3 O 4 II determined from the double reciprocal plots. V max (μmol/min mL) K m (mM) Free GOD 0.731 0.383 GOD-Fe 3 O 4 I 0.803 0.208 GOD-Fe 3 O 4 II 0.766 0.237 The effect of pH on the activities of the free and bound GOX was investigated in the pH range of 5–10 at 25°C (Figure 6 ). Each data point was the average of two measurements. The coefficient of variation between measurements was between 2 and 5%. In the pH range between 6 and 7.4, the enzyme activity increased in all the systems. However, the enzyme activity was higher in GOX-Fe 3 O 4 I than in GOX-Fe 3 O 4 II and free GOX. The activity reached 100% at pH 7.4, and decreased above this pH value to 43% for GOX-Fe 3 O 4 I, and to 26% for GOX-Fe 3 O 4 II and 9% for the free GOX at pH 10. The free GOX experienced a more severe loss in activity, while GOX-Fe 3 O 4 I retained greater activity as the pH increased. In this system, the binding process occurred directly upon activation of the particle surface using carbodiimide, while the binding of GOX-Fe 3 O 4 II involved a C = N bond formed on the acetylated thiophene. It can be argued that the direct binding between the protein and the amino bond in the former case exhibited a greater resistance to a medium with higher alkalinity. This medium appeared even more constraining to the free enzyme and placed the enzyme in an electrostatic state that might affect the activity. Figure 6 Effect of pH on the activities of free GOX (■), GOX-Fe 3 O 4 I (▲) and GOX-Fe 3 O 4 II (●). The effect of temperature on the activity of free GOX was examined by measuring its relative activity when stored at various temperatures. Figure 7a and 7b shows the effect of temperature on GOX-Fe 3 O 4 I and GOX-Fe 3 O 4 II at various temperatures. Each data point represents the average of duplicate measurements (coefficient of variation was less than 6 %). It can be observed that at 37°C, the enzyme retained its activity for about 80 minutes before showing a slight decrease. At 50, 60, 70 and 80°C the activity decreased as the temperature increased in both systems. In GOX- Fe 3 O 4 , the remaining activity was 23% at 50°C and 15% at 60°C after 270 min. For this time period and duration, the remaining activities were 9% and 0%, respectively for GOX-Fe 3 O 4 II. A similar trend was observed at 70 and 80°C with a more drastic loss of activity from the GOX-Fe 3 O 4 II protocol. Loss of activity occurred more rapidly in GOX-Fe 3 O 4 II indicating that direct binding through carbodiimide activation provides a greater thermal stability to GOX. The effect of temperature on the activities of free, GOX-Fe 3 O 4 I, and GOX-Fe 3 O 4 II at pH 7.4 are presented in the Arrhenius plots (Figure 8 ). The activation energies were calculated as 1.4, 0.9, and 1.1 kJ/mol for free-GOX, GOX-Fe 3 O 4 I, and GOX-Fe 3 O 4 II, respectively. These results show that the unbound enzyme has the highest activation energy, while GOX-Fe 3 O 4 I had the lowest. The low activation energy of GOX associated with binding to magnetic nanoparticles suggests that the energy requirement on the surfaces of the nanoparticles for enzymatic activity is reduced. Table 3 shows the inactivation rates constants ( k ) at 50, 60 70, and 80°C. The rate constants increased with increasing temperature in the order GOX-Fe 3 O 4 I, GOX-Fe 3 O 4 II and free GOX. Here again, binding GOX to magnetic nanoparticles minimized structural denaturation due to heat treatment. Covalent binding was expected to provide the enzyme with the protection against structural denaturation due to the unfavorable solvent-protein interactions, and thus result in activation effect [ 21 ], a possible reason for a better activity of the bound enzyme compared with the free enzyme after heat treatment. GOX-Fe 3 O 4 II had a lower stability at higher temperatures compared to GOX-Fe 3 O 4 I. The reason for this difference could reside in the stability of the binding, since the binding methods are so far the major difference between these systems. Indeed with GOX-Fe 3 O 4 I, the binding of the enzyme occurred through the amino groups on the surfaces of the particles and the carboxylic groups of proteins in the enzymes [ 5 ] which is a natural way for protein binding, while in the case of GOX-Fe 3 O 4 II the N atom to which the enzyme is attached shared a double bond with the carbon atom which is less stable than the amide bond. Figure 7 Effect of temperature on the activity of GOX-Fe 3 O 4 I (A) and GOX-Fe 3 O 4 II (B) at pH 7.4. The samples were stored at 37, 50, 60, 70 and 80°C for 30 min and the activities were measured at 25°C. Figure 8 Arrhenius plots of the initial oxidation rates of glucose by free GOX (■), GOX-Fe 3 O 4 I (▲) and GOX-Fe 3 O 4 II (●) for samples at 37, 50, 60, 70, and 80°C. Table 3 Inactivation rate constants ( k ) of the free-GOX, GOX-Fe 3 O 4 I, and GOX-Fe 3 O 4 II at various temperatures. Temperature (°C) Free-GOX k (min -1 ) GOX-Fe 3 O 4 I k (min -1 ) GOX-Fe 3 O 4 II k (min -1 ) 50 3.42. 10 -2 7.10.10 -4 2.87.10 -3 60 9.36.10 -2 1.49.10 -3 3.50.10 -2 70 2.81.10 -1 6.24.10 -2 1.92.10 -2 80 9.3210 -1 4.52.10 -2 5.46.10 -1 Loss of storage stability is a major concern in enzyme preservation. The storage stability of the enzyme was examined for 33 days. Figure 9 shows the storage stabilities of free GOX, GOX-Fe 3 O 4 I, and GOX-Fe 3 O 4 II at 25°C at pH 7.4. Each data point was the average of duplicate measurements (coefficient of variation of the measurements was between 1 and 5%). The activity decreased with time in all the systems. Total loss of activity was observed after 20 days for the free GOX and 28 days for GOX-Fe 3 O 4 II while GOX-Fe 3 O 4 I retained 26% activity after 33 days of storage under identical conditions. The stability of the enzyme was found to improve upon binding to the magnetic nanoparticles but the most significant improvement in stability was observed for the GOX-Fe 3 O 4 I nanoparticle complex. The fixation on the surface of the magnetic nanoparticles has been a tangible argument supporting the prevention of auto-digestion of the enzyme and lysis, and the subsequent conservation of its activity [ 4 ]. This argument supports our results and justifies the long term stability of GOX-nanoparticles I and GOX-nanoparticles II over the free GOX. The efficiency of binding of GOX via carbodiimide activation over the binding by thiophene acetylation may be attributed to the potential of carbodiimide to activate the carboxylic acid side chains partially buried at the surface or in active sites of the enzyme, as well as the amino groups on the nanoparticles, favoring the formation and the stability of the amide bond [ 22 ]. This may explain why the amount of bound enzymes is higher in the binding via carbodiimide than with the thiophene acetylation. Furthermore, carbodiimide might cause cross-linking of the enzyme providing a better stability to its quaternary structure [ 23 ] and a subsequent improvement in stability. Figure 9 Storage stability of free GOX (■), GOX-Fe 3 O 4 I (▲), and GOX-Fe 3 O 4 II (●) measured at a pH of 7.4 at 25°C. Conclusion Magnetic nanoparticles were synthesized by thermal co-precipitation of ferric and ferrous chlorides using two different base solutions. GOX was bound to the particles by direct attachment via carbodiimide activation and chemically via covalent attachment onto thiophene acetylated magnetic nanoparticles. Confirmation of the binding was demonstrated by FTIR spectroscopy and the sizes of the particles were characterized by TEM. The direct binding of GOX via carbodiimide activation was more effective and resulted in binding efficiency in the range between 94–100% while the binding efficiency was only between 66–72% for the GOX-Fe 3 O 4 II complex. Kinetic and stability studies showed that the enzyme activity was more preserved upon binding onto the nanoparticles when the complex was subjected to thermal and pH variations. This study shows that binding onto magnetic nanoparticles can allow the enzyme to acquire the conformational and structural arrangement for a better activity and stability, and suggests that binding of enzyme onto magnetic nanoparticles via carbodiimide activation was efficient for creating bioconjugates for a variety of applications in health and food safety. Authors' contributions Drs. Gilles Kouassi and Joseph Irudayaraj were the primary authors. They were responsible for the concept and experimental plan of the article. Dr Gregory MacCarty was the secondary author and contributed to the overall effort.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC555562.xml

529324

Retroviral Gene Vectors Show Clear Target Preferences

Despite some high-profile setbacks in gene therapy over the past few years, scientists have not lost hope that targeted gene transfer will one day treat a wide range of acquired and congenital diseases. After two young gene therapy patients developed a leukemia-like disorder last year—apparently because the viral vector used to carry the corrective gene activated a cancer-causing gene—researchers redoubled their efforts to develop safer, more effective retroviral gene delivery methods. Such efforts depend on understanding how and where retroviral vectors integrate into the genome. In a new study, Cynthia Dunbar and colleagues describe a nonhuman primate model that mimics gene therapy protocols in humans, and report the integration biases of two classes of retroviral vectors being developed for clinical trials. The retroviruses, the authors show, display clear preferences that not only suggest different genomic integration mechanisms but also have different implications for safety. Retroviral gene therapy exploits a retrovirus's skill at entering a cell, infiltrating its genome, and hijacking its molecular machinery for its own reproductive advantage. In gene therapy, therapeutic genes largely take the place of viral genes, and so the “infected” cell churns out beneficial gene products, not viruses. Before a retrovirus can integrate into a host cell, however, it must copy its genome—which is encoded in RNA—into DNA, so the cell's copying machinery will recognize it. After generating this “pre-integration complex,” the virus must access the cell's chromosomal DNA, which lies behind a nuclear barrier. Different retroviruses accomplish this task in different ways. Lentiviruses—which include AIDS and SIV (simian immunodeficiency virus)—can infect nondividing cells simply by slipping through nuclear pores. Oncoretroviruses—such as murine leukemia virus, or MLV, the vector type used for the vast majority of previous clinical trials, including the trial complicated by leukemia in two patients—must wait until the nuclear membrane dissolves during cell division. Once integrated into the host genome, the provirus—and its therapeutic gene—will persist through each new cell division—a trait that underlies its usefulness as a vector as well as its risk. Retroviruses that insert near proto-oncogenes can activate these genes and set the cell on the path to tumorigenesis. Until recently, researchers assumed this risk was extremely low because retroviral integration was thought to be random—an assumption recently undercut by a number of studies that mapped retroviral integration in different cell lines. Dunbar and colleagues take these studies a step further by mapping the integration patterns of MLV and SIV vectors in hematopoietic stem cells (HSCs) of rhesus monkeys. HSCs are the cells typically used to carry these vectors for therapeutic applications involving any of the cell types, such as red blood cells, produced by the bone marrow. The monkeys had received infusions of HSCs carrying either the SIV or MLV vectors between six months and six years earlier. Two types of white blood cells (granulocytes and mononuclear cells) were harvested from the monkeys and evaluated for proviral insertion sites. Of nearly 1,000 integration sites identified, 760 could be mapped to unique corresponding sites in the human genome (432 MLV and 328 SIV). While both MLV and SIV vectors tended to integrate within genes, MLV showed a strong preference for the starting end of genes, most likely to result in gene activation. In contrast, SIV showed a preference for genomic regions of high gene density, but not for specific sites within a gene. Surprisingly, MLV targeted one gene—known previously to be involved in spontaneous leukemias and in murine retroviral oncogenesis—seven times, a “highly nonrandom” result suggesting that such insertions may occur far more often than previously thought. About 40 genes, including seven known oncogenes, were targeted more than once by one or both vectors. Such differences, Dunbar and colleagues note, “likely reflect the vectors' distinct mechanisms for accessing DNA and integrating,” which could in turn affect their risk of causing insertional mutagenesis. Even though the vectors tend to integrate nonrandomly and can target oncogenes, however, none of the monkeys showed signs of ill effects such as leukemia. But before any widespread applications of retroviral gene therapy can proceed, Dunbar and colleagues argue, potential risks of proviral insertion must be assessed in the specific cell types associated with different gene therapies. And with a model for long-term, genome-wide retroviral integration analysis that mimics human gene therapy protocols, the authors have made an important contribution toward that end.

/Users/keerthanasridhar/biomedlm/data/PMC000xxxxxx/PMC529324.xml