DNA mutates, and it's a good thing it does. If it didn't, there could only be one kind of life, not the millions there are today, and species could not adapt to new challenges. This is because mutations in genes—the coding portion of DNA—are the raw material for evolution. However, genes make up a surprisingly small fraction of our DNA. If the genome were a cookbook, its 30,000-odd genetic recipes would be scattered among millions of pages of apparently meaningless nonsense.Mutations affect all DNA, not just the genes, and this provides population geneticists with a veritable toolbox of methods useful, for example, in DNA profiling. Importantly, all these methods rely on the idea of a “molecular clock,” the notion that mutations rain down on noncoding DNA like a fine drizzle, so constantly that genetic similarity is a good measure of evolutionary time. Thus, if orangutans diverged from humans twice as long ago as did chimpanzees, on any given piece of DNA we would find twice as many differences between the orangutan sequence and the human sequence as between humans and chimps. The mutations are marking time.If the molecular clock works, scientists can do wonderful things like estimating how long ago it was that the common ancestor of all humans lived, or when birds evolved from dinosaurs. The clock assumes that mutations occur independently of each other and at a constant rate. By analyzing thousands of noncoding DNA sequences scattered throughout the human genome, Edward Vowles and William Amos have found that the clock is anything but constant. Instead, a mutation in one spot in the genome affects the chance of getting another mutation nearby.Not all noncoding DNA is made up of benign tracts of random letters. Some sequences appear to be more difficult to copy than others, and these trouble spots can give rise to alphabetic stuttering. DNA is made up of four component chemical units, called nucleotides, which are often referred to by their initial letters: A, C, G, and T. Stuttering occurs when the same pairs or triplets of letters occur together, for example ACACAC. Such regions are called microsatellites, and instead of mutating by swapping one letter for another, as most nucleotides do, these sequences evolve mainly by gaining and losing triplets or pairs like “AC.”In this study, Vowles and Amos used the published sequence of the human genome to track down and compare thousands upon thousands of microsatellites. If the molecular clock ran smoothly, they would expect to find no similarity at all between the DNA sequences surrounding any pair of unrelated microsatellites. To their surprise, they found the complete reverse, with entirely unrelated microsatellites showing widespread and obvious similarities in their flanking DNA. This meant that mutations near microsatellites were not random, but favored certain letters in certain positions. Just as a new shipwreck will attract its own special community of marine life, so microsatellites appear gradually to change the surrounding DNA towards a common pattern. The result is convergent evolution, an unusual state of affairs where, as time goes by, DNA sequences become more similar, not less.As yet, the exact mechanisms remain unclear, though it probably has something to do with how comfortably different combinations of letters sit next to each other. In English, “U” always follows “Q” and “B” never follows “V.” Similar rules may apply to DNA, albeit on a much subtler level. For example, if a microsatellite contains alternating As and Cs, the flanking regions also tend to have As at alternate positions, in phase with the As in the microsatellite. It is as if the DNA prefers the pattern in the microsatellite to extend into the flanking DNA, rather than abruptly stopping at the end of the microsatellite.These findings suggest that it may be wise to take the notion of a molecular clock at face value. With a perfect clock, two or three identical mutations would be highly unlikely, but we now know that this may be possible near microsatellites. Vowles and Amos estimate that as much as 30% of the genome may show evidence of convergent evolution, simply because microsatellites are so common. These mutation biases probably exist to a lesser extent in most sequences. Once scientists understand more fully how and where these biases operate, they may be able to estimate more accurately the risk of any given mutation occurring, be it one that causes human disease or makes a virus more virulent. These findings represent yet another windfall from the Human Genome Project, and act as a powerful reminder that unexpected results always lurk around the corner as we delve deeper into the secret world of the genome.


Sequences flanking microsatellites of similar length show remarkable levels of convergent evolution, indicating shared mutational biases. These biases extend 25–50 bases either side of the microsatellite and may therefore affect more than 30% of the entire genome. To explore the extent and absolute strength of these effects, we quantified the observed convergence. We also compared homologous human and chimpanzee loci to look for evidence of changes in mutation rate around microsatellites. Most models of DNA sequence evolution assume that mutations are independent and occur randomly. Allowances may be made for sites mutating at different rates and for general mutation biases such as the faster rate of transitions over transversions. Our analysis suggests that these models may be inadequate, in that proximity to even very short microsatellites may alter the rate and distribution of mutations that occur. The elevated local mutation rate combined with sequence convergence, both of which we find evidence for, also provide a possible resolution for the apparently contradictory inferences of mutation rates in microsatellite flanking sequences.Microsatellites are a major component of the human genome, and their evolution has been much studied. However, the evolution of microsatellite flanking sequences has received less attention, with reports of both high and low mutation rates and of a tendency for microsatellites to cluster. From the human genome we generated a database of many thousands of (AC) An analysis of sequences flanking microsatellites in the human and chimpanzee genomes suggests that mutations do not occur independently and randomly, as is commonly assumed in models of DNA sequence evolution DNA base substitutions do not occur randomly . InsteadMicrosatellites are sequences of repeated 1–6-bp motifs that mutate primarily through the gain and loss of repeat units, in a process thought to depend on DNA replication slippage . PreviouTo our knowledge, no one has yet conducted a systematic study of mutational biases operating around microsatellites. The direct study of naturally occurring mutations in flanking sequences is virtually prohibited by their slow rate of accumulation, and inferences based on comparisons between homologous microsatellite loci rely on small numbers of sequences. However, an indirect approach is possible, based on comparisons among very large numbers of microsatellite flanking sequences from the finished human genome. If microsatellites have little or variable influence on their flanking regions, among-locus similarities will be minimal or absent. Conversely, if microsatellites generate similar local mutation biases, nonhomologous loci should betray evidence of convergent evolution. With the publication of large blocks of sequence from the chimpanzee genome, one can extend this approach to ask questions about rate of divergence between homologous flanking sequences.Here we use a combination of these indirect approaches to show that microsatellites appear to create regions around them in which both the rate and spectrum of mutations are modified.n, and for simplicity considered only ‘isolated’ repeats, defined as those at least 100 bp from the nearest AC repeat as small as two units in length. 47% of AC repeats on human Chromosome 1 match these criteria. From the human genomic sequence, maximum sample size was set at 5,000 randomly selected loci for length classes (AC)2 to (AC)5. For longer microsatellites, of which fewer than 5,000 could be found, all sequences encountered were included. 2+ repeats, was generated from Chromosome 1.We studied the most abundant class of human dinucleotide repeats, (AC)n and (CA)n repeats, (AC)n microsatellites were divided into subclasses, termed ‘cassettes’, according to their immediate 5′ and 3′ flanking bases such that 5′-X(AC)nY-3′ is referred to as cassette X/Y. Hence, a (CA)3 repeat would be classed as cassette C/A around (AC)2. Such a distinction may appear pedantic, but since both DNA replication fidelity and repair efficiency are known to be influenced by base order and local sequence context (n and (CA)n are equivalent. This nomenclature also helps to resolve the problem of defining microsatellite length because (CA)3 equals (AC)2. nY is viewed as comprising two dinucleotides, XA and CY. The probability of observing XA and CY jointly is then calculated from the individual frequencies of XA and CY estimated using 1 Mb of randomly sampled sequence from Chromosome 1 containing no (AC)2+ repeats.To distinguish between (AC) context , it seem2 microsatellites, the cassette frequencies are broadly similar to those expected from the frequencies of the component dinucleotides in unique sequence DNA, though eight cassettes show significant differences (Around (AC)ferences A. Howeve5, since this exhibits the strong patterns while at the same time retaining sufficient sample sizes for analyses to be conducted on the rarer cassette types. Several features are apparent. First, levels of patterning can be remarkably strong, with the probability of observing a given base at a given site varying from less than half of that in unique sequence to more than double, often at adjacent sites. Second, many of the patterns show strong dinucleotide periodicities, presumably reflecting the dinucleotide structure of the microsatellite. Third, there are several examples of clear 5′ to 3′ asymmetry, indicating that mutational patterns on one side of a microsatellite may not be the same as those on the other.We consider flanking sequences to extend 50 bases either side of a microsatellite. To compare flanking sequences, we first divided microsatellites according to (AC) repeat number and cassette type, and then calculated the frequency of each of the four nucleotides at each of the 100 possible positions. Any mutation biases present should be revealed by locally changed base composition, and this appears to be the case. For many microsatellite length–cassette combinations, the flanking sequences exhibit strong deviations from random. The observed patterns can be placed in six broad classes according to the strength of a two-base periodicity and the degree of 5′ to 3′ asymmetry. These patterns are illustrated in 9 but then declines in longer microsatellites. As well as showing variation between cassettes, flanking sequence patterning also changes with microsatellite length. For those cassettes where sample size is sufficient to show a trend, that is, T/A and to some extent C/A and A/A, both amplitude of deviation from random and the breadth of patterning tend to increase with increasing AC repeat number. We do not see dramatic changes such as a reversal in 5′ to 3′ asymmetry or the emergence of new patterns. For cassette T/A, pattern strength increases up to (AC)In view of the strong two-base periodicities seen for some base–cassette combinations, we next examined the distribution of frequencies of all 16 possible dinucleotide motifs. As expected from the single nucleotide patterning, dinucleotides also tend to show periodic patterning  and S2, From 3, then increases towards a peak in strength at (AC)9 before diminishing as AC repeat number increases further. Cassettes A/A and C/A show similar trends and together suggest that, where patterning occurs, it is apparent by (AC)6. When data were plotted without first categorizing them by cassette type, although a number of the dinucleotide patterns in As with the mononucleotide patterning, the periodicity in dinucleotide frequencies changes in amplitude and width 5′ to 3′) as repeat number increases. For most cassettes, the paucity of long microsatellites precludes study of how the patterning changes with repeat number. However, cassette T/A is sufficiently abundant for the progression to be described, and cassettes A/A and C/A, although less common, nonetheless yield meaningful results. ′ to 3′ a5. Classifying loci by cassette type, length, and whether they occurred in LINE/L1, SINE/Alu, or unique sequence DNA yielded in most classes sample sizes too low to be of use. However, where sample sizes were adequate, that is, cassette T/A, dinucleotide AT, patterning in unique sequence loci was indistinguishable from that in LINE and SINE microsatellites 5 to provide strong patterning and large sample sizes. The results of deleting all flanking sequences containing AT microsatellites with n or more repeats, where n = 2, 3, 4, and 5, are given in n allowed, suggesting that the dominant AT patterning results from (AT)n microsatellites developing immediately adjacent to the AC tract.The strongest two-base periodicities we find tend to involve motif AT. Such periodicity might arise in two main ways: either because AT motifs in phase with the microsatellite tend to expand through slippage to form (AT)n repeats. This is an important test given that our strict definition of a microsatellite restricts our analysis to pure AC repeats and allows the possibility that compound repeats, for example, (AT)n(AC)n(AT)n are included. To do this, we plotted the distribution of single AT motifs around (AC)3+ microsatellites in the subset of flanking sequences with no (AT)n microsatellites, where n > 1. Summed over all AC microsatellite lengths, single AT dinucleotides are overrepresented in 5′ sequences at odd numbered positions and at even numbered positions in 3′ sequences (We next examined whether the phase of AT dinucleotides was determined solely by their tendency to form clusters next to (AC)equences . Excesse2 or longer were assigned back to their own class 57% of the time, showing that these sequences consistently differ from those near to AC repeat tracts. Remarkably, this figure falls to almost half (32%) for sequences flanking (AC)2, indicating that, even with just two repeats, similarities to other microsatellite flanking sequences already exist. The same pattern extends to other length microsatellites, with flanking sequences tending to be preferentially assigned back to their own or to an adjacent length class. When a flanking sequence is not assigned back to its own class, it is usually assigned to one of three other classes: the ‘1’ class of random sequences, the ‘9’ class where patterning is strongest, or to the longest class, class 21 (unpublished data).To assess the level of any convergent evolution, we used a simple assignment test to determine how often individual sequences resemble others flanking unrelated microsatellites of similar length see . Figure These analyses reveal a tendency for microsatellite flanking sequences to be similar to each other, but fail to quantify the level of sequence change involved. To do this, we sought to estimate similarity among three classes of sequence: (1) blocks of 50 bp lying immediately adjacent to a microsatellite; (2) blocks of 50 bp chosen randomly to lie between 500 and 600 bases downstream from a microsatellite (the random selection aims to remove possible complications of exact position and phase with the microsatellite); and (3) blocks of 50 bp randomly selected from around the genome. Comparisons within class 3 define the average level chance similarity in the genome, here estimated at 12.77 ± 3.28 (sd) bases out of 50. Comparisons within class 1 estimate how much convergent evolution is apparent at any given repeat number, and reveal a profile that rises to a maximum of 14.31 at a length of seven repeats, followed by a gentle decline with increasing length thereafter A. SimilaThus, in all cases, sequences immediately flanking a microsatellite show greater similarity to each other, to sequences nearby, and to sequences elsewhere in the genome than randomly selected sequences do to each other, a trend that is maximal for microsatellites around 7–10 repeats in length. We believe these similarities are generated primarily by the enhanced simplicity of microsatellite flanking regions and their tendency to gain AT motifs. Such characteristics allow unusually high matches when compared with random blocks of 50 bases that have high simplicity or contain polyA tracts. Over and above this background level of elevated similarity, proximity to any microsatellite appears to increase similarity, implying that microsatellites tend to lie more generally in regions of similar base composition. This might reflect either a tendency for microsatellites to arise preferentially in certain broad sequence contexts, or modification of the local base composition by the microsatellite itself. Moreover, similarity is further enhanced when a flanking sequence is compared with a neighbouring block. This suggests a local context effect such as might arise through the isochore structure of the genome, with neighbouring blocks being located in the same isochore and hence ‘coloured’ by the same nucleotide biases.2 flanking sequences to their own class is significant up to ten bases away from the microsatellite , and only when the window reaches 24 bases from the microsatellite does the assignment level fall to the value of 4.8% expected of random sequences.To define more precisely the regions where convergent evolution is occurring, we repeated the assignment test but instead of using the full 50 bases either side we now used a symmetric pair of moving 25-base windows placed either side of the AC microsatellite . Close tftp://ftp.ncbi.nih.gov/blast/executables) to compare microsatellites identified in completed sections of the chimpanzee genome against homologous loci identified in humans. If homologous loci are identified through comparisons among their immediate flanking sequences, an element of circularity will be introduced, since loci with lower rates of evolution will be identified preferentially. To circumvent this problem, we used a region 300 bases in length and 220 bases downstream of the microsatellite to conduct each Megablast search.So far we have considered only the nature of the mutations that affect flanking sequences, and not their rate. To examine whether mutation rates are affected by the presence of a microsatellite, we used Megablast ; A total of 8218 chimpanzee loci were identified, and they yielded 5537 unique human homologues. Since microsatellite flanking sequences may contain insertions or deletions, we adopted the following approach so as to minimise problems of alignment. The chimpanzee flanking sequence was divided into 20 contiguous blocks of 20 bases, ten on each side of the microsatellite. Each block was then compared against 1,000 bases of human sequence, downstream from the region identified by Megablast, to find the best possible match. To filter sequences with major rearrangement we required both that all 20 blocks match with at least 15/20 bases, and that matched blocks all lie in the same order as their homologues. Differences between pairs of homologous flanking sequences were then quantified, at each of the 20-block positions, as the proportion of perfectly matching blocks and the average number of matches within each block .2–3 there is little apparent variation in either measure of similarity with block position apart from a small tendency for 5′ blocks to show an increasing average percentage match closer to the microsatellite. However, although the trends are by no means strong, around longer microsatellites similarity of blocks near to the microsatellite is reduced, particularly when measured using average percentage matches , and among these, approximately 77% of the blocks were identical. Around (AC) matches .Our analysis of dinucleotide frequencies around microsatellites shows patterns of similarity on a smaller scale than might be revealed with 20-base blocks of sequence. To investigate the number of changes in the immediate flanking bases, we also calculated the proportion of mismatches occurring at a given base for each 5′ and 3′ sequence in the immediate flanking blocks 9, but appears present even around sequences as short as (AC)2 and may extend as many as 50 bases either side. Some patterning is more or less symmetrical, but we also found several examples showing strong 5′ to 3′ asymmetry, implying that the two ends of a microsatellite are by no means equivalent. The net result is that sequences flanking microsatellites of a given length tend to be more similar to each other than to random sequences or to sequences flanking microsatellites of different lengths. Thus, there appears to be convergent evolution. Finally, we compared large numbers of homologous flanking sequences between humans and chimpanzees, and found evidence that mutation rates near microsatellites tend to be somewhat elevated.We have studied very large numbers of (AC)n tracts exhibit remarkable levels of patterning, with any given dinucleotide motif tending to be much more likely to occur at even numbered positions rather than odd, or vice versa. For several reasons, we believe that the patterning arises due to the structural properties of the microsatellite (see below), becoming more pronounced as repeat number increases. These reasons include the following: the consistently central placement of microsatellites within the patterning, the dependence of the strength of patterning on AC repeat number, the similarity between microsatellites in LINE and SINE elements and those elsewhere, the weakness of the patterning around (AC)2, and the strong influence of cassette type on the form of patterning. Unfortunately, it is surprisingly difficult to eliminate the alternative hypothesis, namely that the patterning arises due to some other force and that AC repeats then either form or expand more rapidly when placed centrally within the pattern. This ambiguity is particularly relevant to the question of cassette distribution, where it seems reasonable both that (AC)n tracts might cause biased interconversion between cassettes and that certain cassettes may allow slippage more than others. For example, while the structural properties of AC repeats are known to generate mutational biases in adjacent bases (Sequences surrounding (AC)nt bases  capable nt bases .2 already showing a small but significant bias in the distribution of cassette types and greater similarity to other sequences flanking AC microsatellites than to random sequences. In addition, the moving window assignment test indicates that significant convergence exists even when the ten bases closest to the microsatellite are excluded. Such a wide influence around such a common, short motif is remarkable and suggests that a high proportion of the genome may be affected by these and similar forces. To illustrate, (AC)2 is expected to occur every 250 bases, as is (GT)2. Taking the sphere of influence on each side as ten bases plus half the 25-bp window yields a value of 45. This predicts that over 30% (approximately 45 bases of every 125) of the genome will be affected by (AC)2 on one strand or the other, a figure that will only increase with inclusion of longer arrays and other microsatellite motifs.The relationship between an AC microsatellite and its flanking sequences begins surprisingly early, with (AC)5 and peaking in strength at (AC)9. Although patterning is seen in several different dinucleotide motifs, even in the human genome there are insufficient data to study any but the commonest cassette–motif combinations over a wide range of microsatellite lengths. Focusing on the motif AT, we found evidence that the strongest patterning was due to the development of AT microsatellites abutting AC tracts. However, this is not the only effect. After removal of all (AT)2 or longer microsatellites, there remains a significant tendency for single AT motifs to appear in phase with AC tracts, suggesting that mutation bias as well as slippage is involved.As AC repeat number increases, so does the strength of patterning, becoming pronounced by (AC)2 and (AC)9, it might seem logical that the pattern would become stronger and stronger as repeat number increases further. Instead, (AC)9 appears to be the peak strength, with longer microsatellites showing lower amplitude but a broader spread of patterning. It is interesting that this peak coincides with the length at which microsatellites begin to become polymorphic: a common rule of thumb for marker development in mammals is that primers are designed for loci carrying ten or more repeats (Given the increase in strength of patterning between (AC) repeats . This ma repeats . Again,  repeats .The rich patterning we find presumably arises through local mutation biases. Previous work on mutation biases has tended to reveal either generic effects such as isochors , where sn and (A)n induce local mutation biases  or A to Y (pyrimidine) transversion mutations, and also the increase in pattern strength around longer microsatellites, with interruptions in longer arrays more likely to be internal to the repeat tract and hence be excluded from the analysis. However, we suggest that this model is unlikely for two reasons. First, such a model fails to accommodate the strong asymmetry in patterning that is observed for some dinucleotides and specific cassette bases around the (AC)n repeat tract. Polarity has been noted for minisatellite mutations, with mutational processes differing between the two ends of the repeat tract at tract , but a mat tract .The patterning we describe appears to represent an important component of the forces that shape genome evolution, both in terms of its ubiquity and the absolute strength of its effect. It follows that there are many possible practical and theoretical implications. For example, even very short microsatellites appear able to cause some level of convergent sequence evolution, and hence to confound phylogenetic analyses. Similarly, microsatellites near genes may increase local mutation rates and influence the spectrum of new mutations that arise. To explore the size of these effects we designed experiments both to measure absolute convergence and to ask about evidence for changes in mutation rate.7–10 both for assignment to their own class and for similarity to each other relative to random blocks provide a clear indication that convergent sequence evolution is occurring. Interestingly, any given flanking sequence tends to be more similar to a block 500 bases away than to a similarly placed block near a different microsatellite, suggesting longer range patterning such as might arise through placement within the same isochore isochore . FurtherIn conclusion, previous studies of microsatellite flanking sequences have identified several features, including a tendency to harbour other microsatellites, a locally increased mutation rate, and, conversely, conservation over unexpectedly large tracts of evolutionary time. Our analyses support all these trends and provide a possible resolution for the apparent contradiction between faster evolution but at the same time greater sequence conservation. Although there is evidence that mutation rates near microsatellites are elevated, we also find evidence of convergent evolution. Consequently, the increased rate of change may be to some extent neutralised and perhaps even reversed by the tendency for similar changes to occur in related lineages. Furthermore, the greatest changes appear to occur in flanking sequences around microsatellites that are below the length used as markers, at least in humans. Overall, therefore, we have been able to formalise previous anecdotal evidence and hence to document a remarkably widespread source of directional change and nonrandom evolution that undoubtedly plays an important role in shaping the make-up of our genomes.n dinucleotide repeats was extracted from the human genome  using a custom macro written in Visual Basic. Only microsatellites separated by at least 100 bp from the nearest (AC)2 or longer were included in the dataset. Thus (AC)2AT(AC)10 would not be included in the dataset, whereas ACAT(AC)10 would be included as (AC)10. Flanking sequences are here defined as the 50 bases lying either side of a microsatellite. No attempt was made to translate TG repeats with complementary AC repeats on the opposite strand. Consequently, all our microsatellites are 5′-(AC)n-3′.Our dataset of (AC)n < 200 observations) or a normal approximation (n ≥ 200 observations). Bases used to define cassette type were excluded from all calculations, and expected frequencies were taken as the average frequency across all positions.For each frequency estimate, 95% confidence intervals were derived based on the binomial distribution (5 and declined to 175 for (AC)20 C)20 see .As an index of similarity, we calculated the log likelihood of observing a given sequence based on its position-specific dinucleotide motif composition:ijkf is the frequency of dinucleotide i at position j  in flanking sequences of class k. To avoid bias, when a sequence was compared with its own class, its contribution to the dinucleotide frequencies was first removed. For each sequence in turn, A was calculated for every class and the sequence was then assigned to the class that yielded the highest index value. Under convergent evolution, we expect sequences to tend to be assigned to their own or similar length classes.where Sequences were again divided into length classes 2 to 21, and each sequence contributed four 50-bp blocks of sequence, one from each side immediately adjacent to the microsatellite but excluding the cassette bases (class 1), and one from each side displaced by a randomly selected number 500–600 bases distal (class 2). In addition, we also generated a database of 5,000 non-microsatellite-associated sequences. When making comparisons within a class, nonindependence was avoided by randomising the sequence order and then comparing sequence 1 with sequence 2, 2 with 3, …, (n − 1) with n. Our index of similarity was simply a count of the number of matching bases. A few pairs of sequences (less than 0.1%) gave high similarity scores of over 30/50 matching bases, presumably because these loci have been duplicated or lie in repetitive elements. Such sequences were discarded. As with all other analyses, sequences containing base ambiguities (marked base N) were also discarded.n repeat microsatellites were extracted from the available chimpanzee finished-quality high-throughput genomic sequence (NCBI) as outlined above for humans. A 300-base region 220 bases upstream from each chimpanzee microsatellite was used by Megablast  to identify homologous human loci in the finished genome sequence. Sequences with multiple high-scoring hits were discarded, as they presumably occur because a locus is found in repetitive elements or has been duplicated. Those nonoverlapping hits with at least 280/300 matching bases and an expectation  greater than five times that of any other hit to the same sequence were thus retained, giving a dataset of 5,537 sequences.(AC)Figure S1From little structure of any kind (A) to complicated aperiodic clustering (C). Plots are as described in (1.7 MB TIF).Click here for additional data file.Figure S2Plots are as described in (3.0 MB TIF).Click here for additional data file.Figure S3Figure depicts equivalent patterns of asymmetry in AT dinucleotide frequencies for the commonest cassette type, (T/A), around microsatellites in unique sequence DNA (A), LINE/L1 elements (B), and SINE/Alu elements (C). Plotting conventions are the same as for (1.4 MB TIF).Click here for additional data file.Figure S4Block position is relative to the central microsatellite (not shown).2–3 microsatellites .(A and B) Proportion of exact matches  and average number of matches, excluding exact matches (± standard error), with block position around (AC)4+ microsatellites (n = 356). Average proportion of exact matches and number of matches, calculated separately for 5′ and 3′ blocks around (AC)2–3 microsatellites, are shown by a black line in (A) and (C), and (B) and (D), respectively. Average percentage match rather than average match is plotted in (B) and (D) because overlapping blocks were truncated to exclude overlapping regions from the analysis, with the result that not all blocks contained 20 bases.(C and D) As (A and B) but for (AC)(2.9 MB TIF).Click here for additional data file.Figure S52–3 microsatellite loci (A) and over (AC)4+ microsatellite loci (B). Shown ± standard error. The microsatellite at base position 0 is not shown. Expectation, calculated separately for 5′ and 3′ sequences around (AC)2–3 microsatellites, is shown by a black line in both plots.The proportion of mismatches occurring at a given base in a flanking sequence are averaged over (AC)(3.4 MB TIF).Click here for additional data file.


Out of 130 ovarian cancer patients the DNA index of cells from ovarian carcinoma was studied in 56 cases in which cytospin preparations showed the presence of atypical cells. In 24 patients the population had a diploid DNA index (1.0) and in the others the DNA index ranged from 1.2 to 2.0 (tetraploid). No hypodiploid or hypertetraploid populations were detected. Repeated samples from the same patients did not show any significant differences and primary culture did not alter the DNA index. In contrast, cell cycle phase distribution differed greatly from sample to sample, as also the ratio between DNA diploid and DNA aneuploid populations. Primary culture was successful in 57% of the tumours, with a higher percentage of success in DNA aneuploid tumours. After primary culture the ratio between DNA aneuploid cells and DNA diploid cells increased. In relation to the histological gradings of malignancy, DNA aneuploid cells clustered in the highest grade of malignancy. The mean S-phase for tumours with a DNA index of 1.0 was 3.5 and 14.1% for those with DNA index greater than 1. Ovarian carcinomas show a large difference in DNA index between patients even after primary culture.


By Ronald J. TrentAmsterdam:Elsevier, 2005. 320 pp. ISBN: 0-12-699057-3, $79.95Molecular Medicine: An Introductory Text, like the previous editions, continues to provide a contemporary and succinct overview of DNA in medicine. Chapter topics include a history of molecular medicine, DNA, RNA, genes, and chromosomes; Mendelian genetic traits; complex genetic traits; genomics, proteomics, and bioinformatics; genetic and cellular therapies; reproduction and development; infectious diseases; forensic medicine and science; and ethical, legal, and social issues. The new chapters added to this edition represent the developments in genomics, proteomics, and bioinformatics.The third edition of Molecular Medicine valuable because the author pulls together a comprehensive assessment, highlighting Internet-based resources that greatly assist readers in further exploring the depths and details of the related topics.Many books describe DNA in medicine, from monographs to multiple-author heavy volumes, but the uniqueness of this single-author introductory textbook of molecular medicine is that it simplifies the seemingly complex concepts of DNA in medicine so that readers can find an easy entry into the world of molecular medicine. This certainly helps medical students and biomedical researcher trainees to grasp the fundamentals of the field. This book also contains many clinical examples that make the reading more relevant to practice and raise the level of interest. Senior scientists and health care professionals will also find This introductory textbook presents a well-balanced incorporation of the basic concepts, applicable clinical examples, advances in molecular biology and their impact on medicine, and gaps that will be filled by further developments in DNA biology and medicine. Emphasizing the role of DNA in molecular medicine, the book provides an interesting history of the development in DNA knowledge, covering the early discovery of DNA structure to the completion of human genome project. Detailed relationships among DNA structure, biology, and medicine highlight the role of DNA in medicine, which is further enhanced by an explanation of traditional and complex genetic traits. The incorporation of genomics, proteomics, and bioinformatics adds a new dimension. The author’s presentation of the principles of using DNA information for genetic and cellular therapies and their applications for reproduction and development, infectious diseases, forensic medicine, and basic science provides a more practical view of DNA in medicine. Finally, the discussion of ethical and legal issues, of concern to the public, brings health care professionals and laboratory scientists an awareness of the social implications of molecular medicine.The author’s definition of molecular medicine in this book—used to describe the role that knowledge of DNA is having on medical practice—may mislead readers to assume that molecular medicine has to do only with the manipulation of DNA. Posttranslational modification of proteins is a critical part of molecular medicine. Although the author has covered this aspect within the context of the book, the definition should have considered the independent role of proteins in molecular medicine as a basis for the text.This book can be recommended to medical students, health care providers, and laboratory researchers as an entry-level textbook, introductory reference, and fundamental resource for knowledge of molecular medicine. It would easily open the door for all medical professionals to molecular medicine, its past, present, and future.


Ascaris lumbricoides, Schistosoma japonicum and Trichuris trichiura were 30.9%, 15.7% and 47%, respectively. Hookworm infection prevalence was low (0.7%). A significant association was observed between A. lumbricoides and T. trichiura infection, and also between S. japonicum and T. trichiura infection. Variance components analysis was undertaken to investigate the aggregation of S. japonicum and the soil-transmitted helminths, A. lumbricoides and T. trichiura. While A. lumbricoides was found to aggregate only at a household level, T. trichiura was shown to cluster predominantly in families. Both genetic and household effects were found to be important in determining the risk of infection with S. japonicum. Variance components analysis for A. lumbricoides/T. trichiura co-infections indicated a significant domestic environmental effect, attributable for 32.7% of the co-infection risk. Aggregation of S. japonicum/T. trichiura co-infection was also observed at a household level. The risk of infection with multiple helminth species, although mainly environmentally influenced, was also shown to have significant involvement of genetic and household components. The results of this study indicate that a shared household is a major contributing risk factor for helminth co-infections and emphasises the need for increased standards of sanitation and hygiene to prevent parasite transmission. Further, the results suggest that susceptibility to one helminth infection is not completely independent of another, and that there exist common genetic factors underlying infection with multiple helminth species.Human helminthiases are common in China, especially in rural areas where sanitation conditions are poor. Co- and multiple infections with helminths are strikingly frequent. A cross-sectional parasitological and questionnaire survey was carried out in a population of 3205 individuals belonging to 498 families from five villages in the Poyang Lake region, Jiangxi Province, China, to assess their helminth infection status and to collect information on risk factors for infection. The prevalences for  Indeed,ng China . Althougng China  indicatemic foci . Given tthe norm .A. lumbricoides in Nepal  but were still included as dummy relatives in order to link families further. In-laws, who were not biologically related to anyone else in a family (i.e. had no children), were not included, and thus were not used in the household analysis. When inconsistencies were identified between the questionnaires of relatives (i.e. a wrong PID code of a relative), the information was checked, where possible, or omitted from the final data analysis. The final data comprised a total of 3205 individuals, belonging to a total of 498 families which ranged in size from three to 188 and spanned as many as four generations.2.3S. japonicum and STHs  eggs and 10% of slides were re-read for quality control by a senior microscopist ; minor infection (5–12 EPG); moderate infection (13–60 EPG); high infection (61–132 EPG) and highest infection (133+ EPG).All individuals from the study population were asked to provide two stool samples for parasitological examination using the Kato–Katz thick smear technique . The secoscopist . Positivpulation ; as a re2.4P < 0.001). Variance components analysis is based on a regression model whereby the phenotype is assumed to be normally distributed. If this assumption is violated, the standard errors of the parameters are underestimated and can result in type I errors, thus leading to incorrect conclusions on the model of best fit. As a result, only the binary phenotype (infected versus uninfected) and not EPG was used for S. japonicum. Aggregation of multiple helminth infections was investigated using the number of infections with any parasite as the phenotype. For this purpose a variable was generated, with values ranging from 0 (having no infection) to 3 (harbouring three parasite species), and was treated as a continuous variable with a normal distribution . If the phenotype under investigation is dichotomous, and therefore cannot be normally distributed, a multifactorial threshold model is used that assumes: (i) that there are several factors that are involved in the disorder in question; (ii) that the effect of each individual factor is small but that the effects of each factor are additive; and (iii) once the additive effect of these factors pass a critical threshold, an individual becomes affected. This implies that the underlying liability of affection is normally distributed but once the threshold is reached, one becomes affected and the phenotype is dichotomous.All collected parasitological and questionnaire data were double-entered into an Access database and cross-checked. Investigation of prevalence estimates, parasite distributions and associations was carried out in SPSS 13.0. SOLAR, a Unix-based software program, which was also used to carry out the variance components analysis , the modBriefly, in a variance components analysis, the total residual variance can be expressed as the sum of the additive genetic P < 0.05) identified in the bivariate logistic regression were included in the models as well as administration village (village effects) to account for any potential geographical differences between the study sites. The software implements a maximum likelihood method which provides a log-likelihood estimate for each model. As all models are nested on the household/polygenic model, they can then be compared with one another using a likelihood ratio test (LRT) which calculates a chi square value to be twice the difference between the likelihoods with one degree of freedom. The significance of the household and genetic effects can thus be tested individually (versus the sporadic model) and in the presence of each other (versus household/polygenic model). The model of best fit was selected as the model with most significant effects (<0.05).Here, all significant covariates  who were involved in the project. Study participants identified as stool egg-positive for schistosomiasis were treated with 40 mg/kg of praziquantel, the current dosage recommended by the WHO. Subjects infected with STHs were advised to seek treatment from a local doctor or hospital.33.1A. lumbricoides and T. trichiura ranged from 0% to 38.8% (mean 30.9%), and from 2.4% to 58.1% (mean 47%), respectively, whereas the S. japonicum prevalence ranged from 12.2% to 20.9% (mean 15.7%). Hookworm infection prevalence was low (0.7%), and was not considered in further analysis. The difference in helminth prevalence between the administrative villages was highly significant . Of the total population, 61.7% harboured at least one helminth species and 27.8% were identified as harbouring at least two parasite species. Again, differences between the mean number of helminth species harboured by individuals among villages were highly significant  although the overall distribution of the number of parasite species among individuals was normal.Among the five administrative villages, the observed prevalences for S. japonicum. Individuals with low S. japonicum infection intensity were shown to have a higher mean number of parasite species. When a higher intensity of infection was experienced, the number of other helminth species decreased. The mean number of parasite species increased again in those with highest egg counts. These differences between the egg count groups were not significant .3.2S. japonicum and A. lumbricoides infections. However, there was a significant (P = 0.03) increase in risk of T. trichiura infection in an individual infected with S. japonicum  = 1.03–1.60). Further, there was a strong association between A. lumbricoides infection and T. trichiura infection .There were no associations observed between S. japonicum compared with older age groups. Gender, water contact and the number of past treatments were highly significant risk factors for S. japonicum infection. There was no age or gender association with A. lumbricoides infection; however, there was a significant association with water contact. Age and water contact were significant risk factors for T. trichiura infection. The highest T. trichiura prevalence (63.4%) was observed in the 5–14 years age group.3.3A. lumbricoides indicating that 31.7% of risk of infection was attributable to domestic environment. No clustering of infection was observed at a familial level. T. trichiura infection was best described by the polygenic model; heritability was strong (29.9%) and significant (P < 0.05). Significant heritability was also estimated for S. japonicum infection (23.7%) as well as a strong household component (24.7%). The Kullback–Leibler R2 value estimated for all models was low, indicating that the covariates did not account for much of the variation in the models.Four models were tested for each individual helminth infection: environmental, household, polygenic and general (household and polygenic). All models were compared with each other using the LRT. The favoured models for each infection, with corresponding parameter estimates, are provided in 3.4A. lumbricoides and T. trichiura infection, a variance components analysis was undertaken to determine the factors underlying risk of having both infections concurrently (P < 0.05) and accounted for 32.7% of the risk of being infected with both parasites.Given the strong association observed between urrently . HousehoS. japonicum and T. trichiura infection was also investigated. Again a household model was favoured with a strong effect size of 43.16% .The variance components analysis estimated a heritability of 16.32% and a household effect of 8.8% for multiple helminthic infections. When both the household model and the polygenic model were compared to the general model , both ge4S. japonicum infections, males were more likely to be infected than females  was not shown to be high in any of the models and thus had little impact on the final parameter estimates. The variance components analysis for A. lumbricoides infection indicated household as a major contributing risk factor. In contrast to a previous study by T. trichiura infection although there was no aggregation at the household level. These findings are similar to those of S. japonicum infection as well as a genetic involvement. Heritability remained high at 23.7% and was a significant risk factor; household was found to account for 24.7% of the risk of infection. These results are comparable with those of studies on S. mansoni . This again may be explained, in part, by the commonality in the mode of transmission but, more notably, it could be a reflection of the socio-economic status of a particular household and the sanitation and hygiene conditions prevailing. Although data on household-specific risk factors were not collected (and merit further investigation) the results suggest that while mass chemotherapy is fundamental in the control of geo-helminths, an emphasis on the development of sanitation and clean water facilities, improved health and hygiene awareness, and more targeted treatment of those living in poor conditions is crucial to prevent transmission. Studies where sanitation and health awareness in a population have been improved and followed over time have shown marked decreases in prevalence that had been previously unattainable through chemotherapy alone  and to identify possible gene–gene and gene–environment interactions involved in single and multiple infection events. As such, this genetic analysis may result in the possible identification of new drug targets and more targeted treatment of individuals susceptible to infection with parasitic helminths.


Coxiella burnetii, the etiological agent of Q fever, is based on a semi-synthetic, second-generation tetracycline, doxycycline. Here, we report on the comparison of the proteomes of a C. burnetii reference strain either cultured under control conditions or under tetracycline stress conditions. Using the MS-driven combined fractional diagonal chromatography proteomics technique, out of the 531 proteins identified, 5 and 19 proteins were found significantly up- and down-regulated respectively, under tetracycline stress. Although the predicted cellular functions of these regulated proteins did not point to known tetracycline resistance mechanisms, our data clearly reveal the plasticity of the proteome of C. burnetii to battle tetracycline stress. Finally, we raise several plausible hypotheses that could further lead to more focused experiments on studying tetracycline resistance in C. burnetii and thus reduced treatment failures of Q fever.The recommended antibiotic regimen against  Coxiella burnetii, until recently considered to be an obligatory intracellular bacterium C. burnetii infection can result in outcomes ranging from asymptomatic seroconversion to death, the most typical clinical manifestations of acute Q fever are fever (91%), severe headaches (51%), myalgias (37%), arthralgias (27%) and cough (34%) Treatment of both acute as well as chronic Q fever is based on doxycycline, a semi-synthetic and second-generation tetracycline invented and clinically developed in the early 1960s C. burnetii strains resistant to doxycycline (MIC of 8 mg/mL) have been isolated from patients with Q fever endocarditis C. burnetii was recovered from cardiac valve tissue removed from a patient with Q fever endocarditis despite 4 years of antibiotic therapy with tetracycline Doxycycline in combination with hydroxychloroquine is the main therapy for treating patients with chronic Q fever. However, the optimal duration of therapy for chronic Q fever is unknown and ranges from 18 months to life-long antibiotic administration Tetracyclines are broad-spectrum agents and exhibit antibiotic activity against a wide range of microorganisms. Their favorable antimicrobial properties and the absence of major, adverse side-effects have led to their extensive use in treating infected humans and animals. Tetracyclines inhibit bacterial protein synthesis by preventing the association of aminoacyl-tRNA with the bacterial ribosome Resistance to tetracyclines mainly occurs through five mechanisms: production of ribosomal protection proteins (RPPs), active efflux of tetracycline from the cell, decreased drug permeability, mutation of the antibiotic target and their enzymatic degradation. Of note is that the first two mechanisms currently predominate in clinical settings C. burnetii (CbuG_Q212) either propagated in the presence of doxycycline or propagated without any antibiotic present. The CbuG_Q212 strain was particularly chosen since it is considered as a prototype C. burnetii chronic disease isolate in vivo conditions and might have increased possibilities to develop drug resistance. The COFRADIC proteomics technology  was used here, which is a so-called gel-free proteomics technology based on the principle of diagonal chromatography Coxiella burnetii can implement to resist protein synthesis inhibition by tetracyclines..Here, we compared the proteomes of the reference strain The COFRADIC technology used here enriches for methionine-containing peptides out of protease-digested proteomes with the aim to reduce the sample complexity before the actual LC-MS/MS analysis, and thereby, attempts to increase the overall proteome coverage The Rover tool developed by the Gevaert lab was used to compare the proteomes of both bacterial samples in silico analysis of these 24 proteins.In total, 24 proteins were found significantly regulated upon doxycycline treatment; 5 proteins were up-regulated, whereas 19 proteins were down-regulated as a result of doxycycline treatment. PSORTb v.3.0 was further used to allocate the identified proteins to the cytoplasm, the cytoplasmic membrane, the periplasmic space, the outer membrane or to extracellular sites. 5 proteins were annotated to reside in the cytoplasm, 3 in the cytoplasmic membrane, 1 was in the periplasmic space, but the majority (15) had no known localization. The regulated proteins are involved in energy metabolism (2/24), transport and binding (4/24), protein fate (2/24), amino acid biosynthesis (2/24), biosynthesis of cofactors (1/24), purine and pyrimide (1/24), DNA metabolism (1/24) and detoxification (1/24). Finally, the overrepresentation of proteins with unknown function (10/24) is of particular note .C. burnetii changed upon tetracycline stress, with the goal of providing insights into as of yet unexplored mechanisms of tetracycline resistance, which might eventually point to protein(s) that could be considered for targeting when developing novel drugs. Comparison of the proteome profiles of the two C. burnetii samples indeed pointed to several proteins with regulated expression under tetracycline stress conditions, and these proteins are discussed in the following sections.The aim of the present study was to analyze how the proteome profile of 5 proteins were found to be up-regulated upon doxycycline treatment. The predicted cellular functions of these proteins were protein and peptide secretion and trafficking (Q83CL5), energy metabolism, pentose phosphate pathway (Q83DM4), biosynthesis of pantothenate and coenzyme A (Q83EA2), aspartate biosynthesis (Q83A62) and DNA replication (Q83C00).E. coli, which spans the membrane twice C. burnetii to survive under tetracycline stress conditions. Furthermore, the functional relation of this protein with secreted and membrane proteins suggests a possible role for additional activation of such proteins by SPI and thus a more active mode of excretion of the antibiotic. Type I SPases can be irreversibly inhibited by certain penem compounds that acylate the serine residue in the active site. Since SPase activity was found to be essential for cell viability, SPases are considered to be potential targets for developing novel antibacterial agents C. burnetii SPI as a possible target for inhibition by penem derivatives.Signal peptidase I (SPI) (Q83CL5) belongs to a group of serine proteases Staphylococcus aureus strains has been reported, its increase was not associated with a possible mechanism of bacterial resistance against antibiotics Transaldolase (Q83DM4) functions in the pentose phosphate pathway and is almost ubiquitously found in the three domains of life . It provides cells with important redox cofactors such as NADPH, building blocks for nucleotide and nucleic acid biosynthesis The DNA polymerase III alpha subunit (Q83C00) might also be associated with a molecular mechanism of antibiotic resistance. This protein is considered to be the main mediator of pathogen survival through induction of mutagenicity. It contributes to the creation of mutants that are able to address the host immune system and administered antibiotics http://cmr.jcvi.org/cgi-bin/CMR/CmrHomePage.cgi). Coenzyme A has been implicated in resistance mechanisms against aminoglycoside-based antibiotics (AGs). AGs bind to the 30S subunit of the prokaryotic ribosome and interrupt bacterial protein synthesis, a mode-of-action that is similar to that of tetracycline. Coenzyme A is directly involved in the enzymatic covalent modification of AGs, which causes a strong decrease in the AG's binding affinity to the prokaryotic ribosome C. burnetii's coenzyme A to tetracycline resistance.3-methyl-2-oxobutanoate hydroxymethyltransferase (Q83EA2) participates in the biosynthesis of pantothenate and coenzyme A (see http://cmr.jcvi.org/cgi-bin/CMR/CmrHomePage.cgi). Despite extensive literature search however, no connection between methionine biosynthesis and antibiotic resistance mechanisms was found.Methyltetrahydropteroyltriglutamate–homocysteine methyltransferase (Q83A62) was also over-expressed upon doxycycline treatment. This enzyme is involved in methionine biosynthesis , aspartate biosynthesis (P24703), detoxification (Q83AQ8), threonine catabolic process (Q83F39), purine ribonucleoside salvage (Q83FC4), transport and binding  and unknown function . One example of the latter is ScvA (B5QSC0), a highly basic DNA binding protein specific for the small cell variant (SCV) of C. burnetii infection was compared with an acute one, and found to be only present in the acute strain Down-regulation of the phosphoenolpyruvate-protein phosphotransferase (Q83BF9) is of particular interest. This protein was identified in a previous study in which a persistent model of 2+) is a cofactor in a variety of biological processes and many bacteria transport iron through Feo systems (ferrous iron transport). It has been suggested that bacterial iron metabolism is relevant for the activity of antibiotics and that mutations in proteins important for this metabolism might challenge bacterial antibiotic susceptibility Pseudomonas, whereas its over-expression accelerates antibiotics-induced cell death C. burnetii down-regulate iron transport to avoid cytokine activation.Levels of the ferrous iron transport protein B (Q83AW2) were also found to be down-regulated. Iron  found down-regulated upon doxycycline treatment . Ribosomes are clearly of utmost importance for controlling protein expression. The pY protein has been studied in Escherichia coli without an apparent role in the fidelity of protein synthesis, suggesting a role in tetracycline resistance Of further note is that the vast majority of the proteins with predicted role in translation and protein biosynthesis (69 out of 77 proteins identified) had protein ratio values that place them at the border of statistically significant over-expressed proteins upon doxycycline treatment. Furthermore, stretching the limits for statistical significance a bit, of the additional 158 proteins with ratio values up to 1.1, the predicted cellular function of 52 of them was translation and protein biosynthesis. This observation, coupled to the mode of action of tetracyclines leads us to suspect the existence of a mechanism protective for bacterial ribosomes, a well-known tetracycline resistance mechanism. Tetracycline resistance by expressing ribosomal protection proteins (RPPs) is achieved by weakening the interaction of tetracycline and the ribosome with subsequent antibiotic release ++-driven multidrug efflux pump (Q83DC6) have been identified in our study. In silico analysis of these proteins identified 11 and 12 transmembrane helices, respectively (http://www.cbs.dtu.dk/services/TMHMM-2.0/), and thus, these proteins could be capable of secreting tetracycline.As mentioned before, active efflux of tetracycline from bacteria is the second most predominant tetracycline resistance mechanism in clinical settings. This efflux mainly occurs via integral membrane transporters that belong to the major facilitator transporter superfamily (MFS) C. burnetii strain C. burnetii, when referring to antibiotic resistance, one may not simply limit the potential mechanisms of resistance to those already known from other studies. Instead, we must consider that resistance to an antibiotic is not exclusively the result of a mutated gene, but rather a multifaceted process as indicated by the proteome rearrangements observed in this study.The fact that certain antibiotics may be signaling molecules at low concentrations C. burnetii strain propagated under tetracycline stress conditions with an antibiotic-free cultured strain identified 5 up-regulated and 19 down-regulated proteins upon tetracycline stress. The predicted cellular functions of the differentially regulated proteins include secretion and trafficking, energy metabolism, amino acid biosynthesis, DNA metabolism and protein fate. Several plausible hypotheses have been formed to establish a link between these proteins and tetracycline stress conditions. Our further analysis of the identified proteins indicated the proteome potential of C. burnetii to resist tetracycline. Higher antibiotic concentrations or a proteome analysis of a tetracycline resistant C. burnetii clinical isolate could further indicate proteins that are directly implicated in tetracycline resistance mechanisms, leading to more effective drug treatment and minimization of treatment failures.Quantitative proteome comparison of a C. burnetii Q212 (CbuG_Q212) phase II Q fever reference strain was propagated in confluent African Green Monkey kidney fibroblasts  in 225 cm2 angled neck flasks . Infected cells were cultured in minimum essential medium  supplemented with 4% fetal bovine serum  at 35°C in a 5% CO2 incubator. Infection was monitored by Gimenez staining g for 15 min. The pellet was re-suspended in fresh culture medium, and the cells were disrupted by three freeze-thaw cycles (from −210°C to 37°C). After centrifugation at 1000×g for 15 min, the supernatant with the liberated bacteria from the infected cells was used to infect fresh Vero cell monolayers. The inoculum was removed after 1 h of incubation at 35°C in a 5% CO2 atmosphere, the cells were washed with culture medium in order to eliminate unphagocytosed bacteria, and fresh medium containing 0.2 mg/L of doxycycline (DOX) was added. The monolayer was incubated at 35°C in a 5% CO2 atmosphere. The supernatant was removed every three days, and fresh medium containing 0.2 mg/L of DOX was added. The same procedure was followed with constantly increasing concentrations of the antibiotic until bacteria were collected for inoculation titration test and MIC determination The antibiotics used in this study was doxycycline hydrochloride (DOX) . DOX was directly diluted into freshly prepared culture medium to the appropriate concentration. Culture media containing the antibiotics were sterilized by filtration (pore size: 0.22 µm) and stored at 4°C. DOX was shown, by shell vial assay (data not included), to be nontoxic to Vero cells to concentrations up to 128 mg/L.C. Burnetii, inoculation titration tests and Minimum Inhibitory Concentration determinations were performed as described previously 2, the MIC of DOX was determined by direct immunofluorescence as the concentration of the antibiotic at which there were no intracellular bacteria observed following a six days incubation period. Even though during MIC determination tests bacteria were initially challenged by an initial concentration well beyond the MIC mentioned in the literature for C. burnetii Q212 , negative controls (no antibiotic present) were included at all times. During C. burnetii culture under tetracycline stress conditions (15 weeks), the observed MIC for doxycycline increased from 2 mg/l to 16 mg/l (MIC Q212Dox: 16 mg/l), while bacteria propagated in the absence of antibiotic for the same time period, did not show a MIC change (MIC Q212: 2 mg/l).To determine the effect of tetracycline in the culture medium on the MIC of 2PO4, 33.5 mM K2HPO4, 100 mM KCl, 15.5 mM NaCl, pH 7.0) containing a protease inhibitor cocktail (Sigma-Aldrich) underwent five 5-min freeze-thaw cycles (from −210°C to 37°C) and the protein concentration was determined by the Bradford assay. All manipulations involving viable bacteria were performed in a Biosafety Level III laboratory. It has to be noted that upon completion of the experiments all bacteria cultured under tetracycline stress conditions were killed by incubation in 8% formaldehyde at room temperature for 24 h followed by autoclaving. Prior to disposal, bacteria were used to inoculate Vero cells to check their inactivation.Bacteria propagated with DOX (Q212Dox) and without DOX (Q212) were mass cultured in parallel and under identical conditions for 10 days (with the exception of doxycycline present in Q212Dox) and finally isolated from their host cells using renographin  density gradient ultracentrifugation as described 12C3-propionate whereas the Q212Dox proteome digest with 13C3-propionate. Each peptide thus had a label on its N-terminal alpha-amino group and on its C-terminal lysine epsilon-amino group, evoking a difference of 6 Da between light and heavy peptides. Samples were mixed and analyzed by LC-MS/MS on an Ultimate 3000 HPLC system  in-line connected to a LTQ Orbitrap XL mass spectrometer . Instrument settings for LC-MS/MS analysis and generation of MS/MS peak lists were as described Coxiella burnetii database downloaded from Uniprot on June 17th, 2009 . Lys-C/P was set as the used protease with one missed cleavage allowed, and the mass tolerance on the precursor ion was set to ±10 ppm and on fragment ions to ±0.5 Da. S-carbamidomethylation of cysteine and oxidation of methionine (to its sulfoxide) were set as fixed modifications. In addition, Mascot's C13 setting was set to 1. The light and heavy labels were defined in Mascot Distiller's quantitation method. Peptide quantifications were carried out using the Mascot Distiller Quantitation Toolbox (version 2.2.1). The quantification method details were as follows: constrain search: yes, protein ratio type: average, report detail: yes, minimum peptides: 1, protocol: precursor, allow mass time match: yes, allow elution shift: no, all charge states: yes. Ratios for identified proteins were calculated by comparing the XIC peak areas of all matched light peptides with those of the heavy peptides, and the results were verified by visual inspection of MS spectra with the in-house developed Rover tool http:www.ebi.ac.uk/pride) under the experiment number 18640 .The methionine-COFRADIC procedure was performed on both Q212Dox and Q212 whole lysates as described http://au.expasy.org/). The purpose of this analysis was to calculate the molecular weight (MW) and isoelectric point (pI) of each protein using the software ProtParam Tool (http://au.expasy.org/tools/protparam.html) and Compute pI/Mw Tool (http://au.expasy.org/tools/pi_tool.html).Analysis of the amino acid sequences of all identified proteins was carried out using several Web-based software tools, freely accessible from the ExPASy Proteomics Server of the Swiss Institute of Bioinformatics (SIB) (Table S1C. burnetii samples. All 531 proteins were identified in both samples (i. e. C. burnetii cultured in presence of tetracycline and C. burnetii cultured with no antibiotic present) in different expression levels. Proteins are categorized according to their predicted cellular function; their ratio value, gene locus, molecular weight (MW), isoelectric point and predicted cellular function are indicated.List of proteins identified in both (DOCX)Click here for additional data file.Table S2C. burnetii when cultured in presence of tetracycline (Q212Dox) or in C. burnetii when cultured with no antibiotic (Q212).Proteins that were significantly over-expressed in (DOCX)Click here for additional data file.


A West Nile virus (WNV) human risk map was developed for Suffolk County, New York utilizing a case-control approach to explore the association between the risk of vector-borne WNV and habitat, landscape, virus activity, and socioeconomic variables derived from publically available datasets. Results of logistic regression modeling for the time period between 2000 and 2004 revealed that higher proportion of population with college education, increased habitat fragmentation, and proximity to WNV positive mosquito pools were strongly associated with WNV human risk. Similar to previous investigations from north-central US, this study identified middle class suburban neighborhoods as the areas with the highest WNV human risk. These results contrast with similar studies from the southern and western US, where the highest WNV risk was associated with low income areas. This discrepancy may be due to regional differences in vector ecology, urban environment, or human behavior. Geographic Information Systems (GIS) analytical tools were used to integrate the risk factors in the 2000–2004 logistic regression model generating WNV human risk map. In 2005–2010, 41 out of 46 (89%) of WNV human cases occurred either inside of (30 cases) or in close proximity (11 cases) to the WNV high risk areas predicted by the 2000–2004 model. The novel approach employed by this study may be implemented by other municipal, local, or state public health agencies to improve geographic risk estimates for vector-borne diseases based on a small number of acute human cases. Since its emergence in 1999, West Nile virus (WNV) triggered the largest recorded arbovirus outbreak in North America In the absence of a human vaccine, vector surveillance and control are the most effective tools for arboviral disease prevention on the population level Vector-borne disease modeling has emerged as a methodology. Vectors and pathogen reservoirs are often associated with environmental factors Many spatial analytic studies of WNV risk predictors utilized aggregated data based on administrative divisions such as census tracts or zip codes. The statistical power of analysis associated with this approach may be low due to greatly reduced sample size. It may thus be difficult to detect significant differences in disease risk where municipalities exhibit sporadic or highly clustered WNV human cases. Additionally, aggregated spatial scale characterization can be susceptible to ecological fallacy, lack of precision, and measurement error Our goal was to employ vector biology and knowledge of environmental and socioeconomic risk factors to predict spatial patterns of human West Nile Virus risk in Suffolk County, New York, USA on a local scale. A large number of factors relevant to vector, host, and human ecology were tested and the significant predictors used to generate a logistic model rendered geographically by GIS tools into a county-wide WNV human risk map. Similar approach can be easily adopted by state, county, or municipal public health agencies to investigate factors associated with WNV human transmission to enhance surveillance and control efforts, and to better understand WNV landscape epidemiology.2) with densely populated suburban areas, commercial and light industrial sites, agricultural areas, forested parkland, and numerous fresh and saltwater wetlands. WNV enzootic activity in birds and mosquitoes has been detected every year since the original virus introduction to North America in 1999, with sporadic epidemic outbreaks resulting in human cases.The study was conducted in Suffolk County, NY located on Long Island east of New York City . SuffolkThis study utilized a case-control design with household geographic location as the unit of analysis. Study cases included a) 19 households with acute WNV human cases in 2000–2004, and b) 81 additional households with no acute WNV human cases, but located inside the WNV hotspots delineated by SatScan spatial scan statistic The predictors of risk of WNV human transmission were derived from those reported in the literature  at all three spatial scales were tested for multicollinearity and either combined or removed until resolved.For pairwise comparisons between cases and controls, chi-square test, t-test, 2-way ANOVA, or Mann-Whitney test were used depending on data type and distribution . To minimize collinearity, variables with Pearson's correlation coefficient >0.75 were either combined or excluded from further analysis . Statistp<0.1 were used for the final parsimonious model with the lowest Akaike's Information Criterion (AIC). The statistical power analysis of the final model was performed using the algorithm specifically developed for multiple logistic regressions I statistics in ArcMap PWNV = eY/(eY+1), where e is a base of the natural logarithm. The final model and map were validated by 46 acute WNV cases in 2005–2010.The resulting set of IVs was characterized by Principal Component Analysis (PCA) to uncover grouping among IVs and to understand the data structure . The entInitially, 64 environmental and socioeconomic factors were developed from the geodatabases listed in p<0.05 resulting in 53 independent variables due to multiple spatial scales, out of which 14 redundant IVs were eliminated to reduce multicollinearity  using eigenvalue >1.0 to retain the principal components (PCs). A total of 8 PCs accounted for ∼73% of the total variance and were interpreted as follows . PC1 Ur.p<0.1 and were used for the final model created by removing the least significant IV and introducing a new IV with the goal of increasing the parsimony while preserving the overall model fit. The final reduced model contained 12 IVs classifying correctly 89% of the cases including 16 out of 19 (84%) acute WNV human cases in 2000–2004 , and 85% of controls (p = 0.181), however, the final model (AIC = 147.5) was more parsimonious than the full model (AIC = 168.1). Given the sample size n = 200, statistical significance α = 0.05, power π = 0.8, the baseline probability of the high WNV risk (PWNV = 0.5–1.0) P0 = 0.3, and the multiple correlation coefficient R2 = 0.53 estimated for the model as an average of (1 - tolerance) for all IVs, the final model's effect size was OR = 1.9 which was in line with the original goal.The same 39 IVs were entered in a logistic regression model. The full model classified correctly  93% of the cases including 16 out of 19 (84%) acute WNV human cases in 2000–2004, and 91% of the controls with a U-shape distribution around the cut value of 0.5. Out of 39 original IVs in the full model, 14 IVs were statistically significant at controls . FurtherI statistics 0.092, Z-score = 1.78, p = 0.0744) indicating negligible to weak unexplained clustering on a global scale. Visual map examination showed large residuals  in both cases and controls scattered throughout the western part of the County (data not shown). Slightly higher number of large residuals in and around Cluster 4  of the total land area in Suffolk County.To generate the WNV human risk map, the shapefiles of individual IVs were processed by Neighborhood function  to calculate statistics for each IV at the corresponding spatial extent of 0.5, 1.0, or 2.0 km. The resulting raster files see . Census 2X = 4.0, df = 1, p = 0.047. Out of 65 WNV acute human cases in 2000–2010, 47 (∼72%) occurred in high WNV risk areas, which comprised ∼33% of Suffolk County's land area This distribution of WNV human cases was significantly different from random based on the land area (21 out of 65 expected), Chi-square exact test 2X = 20.1, df = 1, p<0.001. Only 11% of all acute WNV human cases (7 out of 65) occurred at a distance exceeding 1.0 km from high risk areas delineated by the model.The final model and map were validated using 46 acute WNV human cases in 2005–2010 . The disTo be useful for disease surveillance and control program, a geographic human risk model should a) use predictors that are easily available and interpretable, b) be accurate against independent data, and c) generate outputs that can assist control decisions Socioeconomic conditions have emerged as the key determinants of WNV human risk Culex pipiens, was most prevalent in urban areas with significant vegetation cover and plentiful avian hosts The middle class suburban areas appeared to support the appropriate combination of vegetation, open space, and potential vector habitat favoring WNV transmission. Wealthier neighborhoods had more vegetation, more diverse land use, and less habitat fragmentation likely resulting in higher biological diversity potentially protective against the WNV human transmission, e.g. the avian host “dilution effect” Although these findings have clearly demonstrated the interdependence between socioeconomic and natural environments, such relationships may be multifaceted. For example, elderly population and vacant housing were negatively associated with WNV human risk in our study, contrary to the expectation Among predictors of WNV human risk in the model, habitat fragmentation  is an important factor facilitating transmission of many vector-borne diseases worldwide. The anthropogenic habitat fragmentation effects vary from increased erosion and surface water accumulation, reduced species richness, extinction of top predators with increase of prey species, enhanced host-vector interactions, and a shift to anthropophyllic feeding by vectors Habitat fragmentation by roads and WNV enzootic activity parameters (positive birds and distance to a positive mosquito pool) were grouped within Urbanized/WNV PC in our study. While proximity to a WNV positive mosquito pool was a strong human risk factor in the model as expected, WNV positive mosquito pool density was not significant in the final multiple regression model, and, moreover, was not correlated with Urbanized/WNV PC. These results supported inconsistent association of WNV positive mosquito pool density with human risk Culex and container breeding Aedes species The remaining predictors in this study's model described natural or manmade wetlands. WNV human risk association with freshwater wetlands and open water, potential mosquito larval habitat, has been well documented In addition to interpretability, predictive accuracy assessment is crucial for determining the model's utility, yet vector-borne disease model evaluation mostly focused on the past rather than predicted outcomes Apart from those limitations due to incomplete data or imperfect understanding of the epidemiological and biological processes, multiple regression analysis may encounter two important methodological caveats, namely collinearity and spatial autocorrelation. Collinearity is caused by inclusion of many highly correlated environmental and socioeconomic factors in a model leading to instability in the estimation of the partial regression coefficients. This is especially relevant to WNV risk modeling, since the complex epidemiology of the virus transmission cycle typically necessitates entering multiple factors at several spatial scales in the analysis. One plausible approach to deal with this problem is to use principal components analysis to reduce the dimensionality among the predictors Despite limitations and technical caveats, in our study, the WNV risk map developed using 2000–2004 human cases predicted the locations of the 2005–2010 human cases with sufficient operational accuracy. The map serves in conjunction with the entomological data, which did not fully accounted for the patterns of WNV human transmission risk in Suffolk County This study demonstrated the feasibility of state or local level GIS based modeling using limited epidemiological data to create risk maps for outbreak investigations, arbovirus surveillance, and scientific discovery. The risk map may be further improved by incorporating entomological and climatic data ultimately leading to a real-time risk model. However, the increased complexity of such undertaking will likely require much closer cooperation than is currently in place between local public health agencies and academic or research institutions.Table S1Environmental and socioeconomic independent variables in this study. Bivariate analysis compared cases  and controls  at 3 spatial scales. Principal component analysis grouped significant independent variables determined by bivariate analysis and assisted in the interpretation.(DOC)Click here for additional data file.


Despite a cold temperate climate and low human population density, the Northern Great Plains has become a persistent hot spot for human West Nile virus (WNV) disease in North America. Understanding the spatial and temporal patterns of WNV can provide insights into the epidemiological and ecological factors that influence disease emergence and persistence. We analyzed the 1,962 cases of human WNV disease that occurred in South Dakota from 2002–2012 to identify the geographic distribution, seasonal cycles, and interannual variability of disease risk. The geographic and seasonal patterns of WNV have changed since the invasion and initial epidemic in 2002–2003, with cases shifting toward the eastern portion of South Dakota and occurring earlier in the transmission season in more recent years. WNV cases were temporally autocorrelated at lags of up to six weeks and early season cumulative case numbers were correlated with seasonal totals, indicating the possibility of using these data for short-term early detection of outbreaks. Epidemiological data are likely to be most effective for early warning of WNV virus outbreaks if they are integrated with entomological surveillance and environmental monitoring to leverage the strengths and minimize the weaknesses of each information source. Emerging infectious diseases, including those that have appeared for the first time, rapidly increased in incidence, or expanded into new geographic areas, are a significant concern in the fields of human and veterinary medicine as well as wildlife conservation ,2. DiseaWNV is indigenous to Africa, Asia, Europe, and Australia and was first identified in North America in the New York City metropolitan area during the summer of 1999 . In 2002Disease mapping and exploratory spatial analysis of epidemiological data can help to identify the geographic locations of populations at risk and facilitate the development of hypotheses about the epidemiological and ecological processes that drive these patterns ,6. Time-Culex tarsalis, a highly ornithophilic and particularly efficient amplifying vector of WNV that can also serve as a bridge vector to humans [Culex tarsalis abundance was lowest in urban areas and highest in grass-dominated rural habitats [Culex tarsalis abundance in South Dakota exhibited much stronger lagged relationships with temperature than with precipitation [Since its arrival in 2002, WNV has had a significant public health impact in South Dakota and neighboring Great Plains states. From 1999–2008 South Dakota had the highest average incidence of WNV neuroinvasive disease in the United States . During o humans . Previouhabitats , and thehabitats . Seasonapitation ,17, and pitation .The overarching goal of our study was to extend this previous work by characterizing the spatio-temporal epidemiology of WNV disease in South Dakota from 2002–2012. The underlying rationale was the expectation that identifying spatial and temporal patterns of human disease occurrence will provide a clearer understanding of when and where WNV risk is the highest and whether these patterns have changed since the emergence of WNV in South Dakota. We addressed the following specific questions: (1) Where in South Dakota is WNV risk highest and has this geographic pattern changed over time? (2) During what part of the year is WNV risk highest and has this seasonal pattern changed over time? (3) What are the temporal patterns  of WNV anomalies and can they be used to predict future WNV risk? (4) Do WNV epidemics arise simultaneously throughout the state or do cases increase earlier in some geographic locations than in others?The study area encompassed the state of South Dakota, which is located in the northern Great Plains of the United States, a region historically dominated by prairie vegetation. Environmental variability across the state is strongly affected by precipitation, which generally decreases along an east-to-west gradient , referenced temporally by the reported date of disease onset, and referenced spatially by county, ZIP code, and city of residence. Each case was georeferenced using the 2010 map of ZIP code tabulation areas (ZCTAs) from the U.S. Census Bureau. A total of 23 cases  were missing ZIP codes and were assigned to a ZCTA based on the county and city of residence. In addition, 60 cases  had ZIP codes that were not present in the 2010 ZCTA map and were assigned to a 2010 ZCTA by referencing the 2000 ZCTA map or based on the county and city of residence. The 2010 population data for each ZCTA were also obtained from the U.S. Census Bureau.2 and a mean population of 2,115. The mean population density at the ZCTA level was 34 per km2, but individual ZCTAs ranged from 0.05 to 4,628 per km2 reflecting the variety of settlement patterns from dense cities to rural areas.ZIP codes were developed by the U.S. Postal Service to classify street segments, address ranges and delivery points for mail delivery. ZCTAs were created by the U.S. Census Bureau as spatial units to provide approximate mapping of ZIP code boundaries using aggregated Census blocks. However, it is difficult to map ZIP code boundaries precisely, and ZIP codes are frequently split, discontinued, added, or expanded . As a reSpatial patterns of WNV cases were displayed as dot density maps by selecting a random location for each case within its assigned ZCTA. Spatial patterns of WNV risk were mapped using annual incidence rates and were spatially smoothed by incorporating information from surrounding areas to reduce variability caused by small population sizes at the ZCTA level. For this initial assessment of WNV risk, smoothing was carried out using a simple population-weighted mean smoothing approach as suggested by Waller and Gotway . To geneSeasonal patterns of total human WNV cases were summarized based on a standardized 18-week season in which all cases prior to 1 June were included in week 1; the months of June (weeks 2–5), July (weeks 6–9), August (weeks 10–13), and September (weeks 14–17) were each divided into four weeks; and all cases after September 30th were included in week 18. Weekly cumulative totals of human WNV cases were graphed for each year and for the three time periods used in the mapping . Two-sample Kolmogorov-Smirnov tests were used to test for differences in the seasonal distribution of WNV cases among these three time periods. These tests were carried out using all human WNV cases (including both WNV fever and neuroinvasive disease), only the WNV fever cases, and only the WNV neuroinvasive disease cases.The 18 weeks of WNV data for each year were concatenated to generate a continuous time series of WNV cases from 2002–2012. This log-transformed time series was then decomposed into seasonal, trend, and remainder components with the seasonal and trend decomposition using loess (STL) procedure . In thisN = 9). We examined the changes in these correlations across the different weeks to determine the point during the season when WNV cases provided a reliable indicator of the total magnitude of WNV cases throughout the season.WNV cases were also analyzed to determine whether the numbers of cases occurring during the early part of the WNV season (June and July) were predictive of the total number of WNV cases occurring throughout the entire season. For this analysis, we summarized the cumulative total number of WNV cases occurring in each year from week 2 (the first week in June) through week 9 (the last week in July). We computed the Spearman rank correlation between each of these cumulative variables and the total number of cases occurring for 2004–2012 . For each of these years, we used a generalized additive model (GAM) to model the day of year (DOY) of each case as the dependent variable using a smoothed functions of the spatial coordinates (eastings and northings) of the ZCTA centroids as independent variables. Statistically significant relationships between DOY and one or both of the independent variables indicated that different geographic locations had different mean dates of WNV onset. For years where there was significant spatial variability in mean DOY, the models were applied across the entire state to produce a continuous map of the mean DOY of WNV cases for each ZCTA.r = 0.96, p < 0.001).The dot density maps emphasized the strong spatial and temporal heterogeneity of human WNV cases within South Dakota , Table 1r = 0.75, p < 0.001), exhibited a weaker correlation in 2004–2007 , and had the highest correlation in 2008–2012 . As with the maps of total WNV cases, the incidence of neuroinvasive disease shifted from central and western South Dakota in 2002–2003 toward the eastern portion of the state in 2004–2007 and 2008–2012.The smoothed maps of WNV incidence further highlighted changes in the geographic distribution of WNV risk over time. The maps of total WNV incidence showed a shift in the geographic patterns of risk from central and western portions of South Dakota in 2002–2003 to the eastern portion of the state in 2004–2007 and 2008–2012 . The smop < 0.001) and in 2002–2003 compared to 2008–2012 , but the difference was not statistically significant in 2004–2007 compared to 2008–2012 . Similar results were obtained when only the same tests were performed using only the WNV fever cases and only the WNV neuroinvasive disease cases.Seasonal patterns of WNV cases varied from year to year , indicating that these space-time patterns were relatively weak.There was statistically significant variability in the onset date of WNV cases in both the north-south and east-west directions in 2003, and in the east-west direction in 2005 and 2007 . There wThere has been a shift in the spatio-temporal niche of human WNV risk in South Dakota from the initial invasion and first major epidemic of WNV in 2003–2003 to the subsequent years of endemic WNV from 2004-onward. In 2002–2003, human WNV cases were broadly distributed across the state and in particular were higher in the central and western portions. Starting in 2004, cases were more concentrated in the eastern portion of the state, and this eastward shift became even more pronounced during the 2008–2012 period. In addition, there were significantly fewer cases in the early part of the WNV season (before the last week of July) and more cases in the later part of the WNV season (after the third week of August) in 2002–2003 compared to 2004 and later years. These changes suggest that the spatial and temporal patterns observed during the large 2003 epidemic have become less relevant to current ecological and epidemiological processes that drive WNV risk, and that disease maps and other inferences about human WNV risk should be based on epidemiological data from 2004 onward.Culex tarsalis, a particularly efficient WNV vector. Subsequent research showed that interannual variability in temperature and moisture during the spring and early summer was associated with spatial and temporal variability in WNV incidence across the region from 2004–2011 [Climatic variability is one possible explanation for the observed changes. A geographic analysis of the 2003 WNV epidemic in the northern Great Plains highlighted strong clustering of WNV in the Dakotas, Nebraska, and eastern Montana and Wyoming . This cl004–2011 . Thus, a004–2011 ,24. Resu2. It is characterized by a high density of seasonal and permanent wetlands and a landscape mosaic of row crops and grasslands (From 2004 onward, and particularly in 2008–2012, one of the regions of highest WNV risk within South Dakota was concentrated in eastern South Dakota within the James River Valley, which lies between the uplands of the Missouri Couteau to the West and the Prairie Couteau to the East. This broad lowland was created by the James Lobe of the Laurentide ice sheet and occupies approximately 49,000 kmasslands , and is Early detection systems are widely used to identify the onset of mosquito-borne disease outbreaks by tracking human disease cases. Forecasting techniques range from relatively simple methods based on thresholds determined from historical data , to moreTwo major limitations to the use of human surveillance data for forecasting disease outbreaks are data quality and data availability. For human WNV disease in the United States, all cases are assessed based on a consistent definition established by the Centers for Disease Control and are subject to laboratory confirmation. However, an unknown degree of spatial uncertainty is introduced because some patients may contract WNV at a location away their residence. In addition, the delay introduced as a result of the time lags between amplification of the virus in mosquito and bird populations, transmission to humans, onset of symptoms, medical diagnosis, and case confirmation mean that human cases are a lagging indicator of WNV risk. As result, WNV amplification will have already occurred and mosquito numbers and infection rates will likely already be high by the time WNV is detected in the human population. In a state like South Dakota that experiences human WNV cases every year, detection of anomalies in early season case data does have some potential to predict the magnitude of WNV in human populations during the peak transmission season. In particular, our results suggest that human surveillance data may be able to indicate the onset of a significant WNV outbreak with several weeks of lead time. This type of alert would not provide enough time to take action to prevent virus amplification, but it might still allow public health officials to take steps such as expanding adult mosquito control and issuing preventive warnings before the period of peak transmission to humans.One promising approach toward leveraging the strengths and minimizing the limitation of human case surveillance for disease outbreak detection is to use blended approaches that integrate multiple streams of surveillance data. For example, a study of WNV in Colorado combined environmental data, entomological data, and human WNV case data to develop an improved map of disease risk in the Northern Colorado Front Range . ResearcHuman WNV disease exhibits strong patterns of geographic variability, seasonal cycles, and interannual fluctuations in South Dakota. The geographic and seasonal patterns of WNV have changed since the invasion and initial epidemic in 2002–2003, with cases shifting toward the eastern portion of South Dakota and occurring earlier in the transmission season in more recent years. The underlying cause of these shifts is currently unknown, but the results suggest that more recent data on endemic WNV from 2004 onward should be used for risk mapping and assessment of seasonality for public health planning purposes. Temporal analyses indicate that there is a potential for forecasting upcoming large WNV outbreaks with lead times of up to several weeks. Although this time frame is likely insufficient for preventing virus amplification, it could still help reduce transmission to humans by providing time to ramp up emergency mosquito control and disseminate messages to the public to take personal protection measures. Ultimately, information from epidemiological surveillance should be integrated with environmental monitoring, entomological surveillance, and other sources of information to provide more effective predictions of the likelihood of future WNV outbreaks.