Fundulus heteroclitus was examined. Only a small subset (31%) of tissue-specific differences was consistent in all three populations, indicating that many tissue-specific differences in gene expression are unique to one population and thus are unlikely to contribute to fundamental differences between tissue types.The expression of a selected suite of 192 metabolic genes in brain, heart and liver in three populations of the teleost fish  Fundulus heteroclitus using a highly replicated experimental design.Variation in gene expression is extensive among tissues, individuals, strains, populations and species. The interactions among these sources of variation are relevant for physiological studies such as disease or toxic stress; for example, it is common for pathologies such as cancer, heart failure and metabolic disease to be associated with changes in tissue-specific gene expression or changes in metabolic gene expression. But how conserved these differences are among outbred individuals and among populations has not been well documented. To address this we examined the expression of a selected suite of 192 metabolic genes in brain, heart and liver in three populations of the teleost fish Half of the genes (48%) were differentially expressed among individuals within a population-tissue group and 76% were differentially expressed among tissues. Differences among tissues reflected well established tissue-specific metabolic requirements, suggesting that these measures of gene expression accurately reflect changes in proteins and their phenotypic effects. Remarkably, only a small subset (31%) of tissue-specific differences was consistent in all three populations.These data indicate that many tissue-specific differences in gene expression are unique to one population and thus are unlikely to contribute to fundamental differences between tissue types. We suggest that those subsets of treatment-specific gene expression patterns that are conserved between taxa are most likely to be functionally related to the physiological state in question. The regulation of gene expression varies extensively among tissues, individuals, strains, populations and species -6 and vaAlthough tissue-specific gene expression patterns are often used as a method to identify functionally relevant genes, how conserved these differences are among outbred individuals and among populations has not been well documented. It is possible that many of these changes represent polymorphism among individuals or populations and are not specifically associated with disease. To address this we used a well established system (tissue-specific gene expression) and genes with well defined function and tissue-specific distributions (metabolic genes).Fundulus heteroclitus. A cDNA microarray was used to measure levels of expression in normal healthy male fish for 192 genes involved in central metabolic pathways. We used this compact array in order to impose a high degree of technical and biological replication . Also, this array was used because metabolic genes are essential, are known to have tissue-specific expression, especially in fish, and are often misused as controls with little characterization of variation in expression among individuals or tissues. Analysis of variance (ANOVA) was used as a statistical test to determine which genes were differentially expressed among tissues and populations. Tissue-specific patterns of gene expression were compared among populations. As expected, we detected extensive variation in gene expression among tissues. Unexpectedly, only a fraction (31%) of tissue-specific differences was conserved between all populations.Given the high variance in gene expression among individuals and populations, our goal was to examine the conservation of tissue-specific gene expression among populations of the same species. Specifically, we assessed the among-population variance of tissue-specific patterns of gene expression (in brain, heart and liver) in the teleost fish p < 0.05) among individuals within populations and tissues  differentially expressed among brains, hearts and livers (ANOVA, p < 0.05). Selecting the α level at which differences between treatments are considered significant is problematic because of the large number of comparisons performed. As such, we present a volcano plot to illustrate the range of expression differences between tissues and associated p-values , the proportion of significant genes is 67% (129 genes), 50% (96) and 39% (75), respectively. Significant differences in expression ranged from less than 1.2-fold to nearly 16-fold , hepatocyte nuclear factor 4-alpha (a transcription factor) was more highly expressed in liver than in other tissues (p < 0.001), and two genes involved in glycerolipid metabolism -lipoprotein lipase and phopholipase XIII A2 - were more highly expressed in liver than other tissues (p < 0.001 for both genes).Many expected tissue-specific patterns emerged. For example, the brain-specific fatty-acid-binding protein was typically more highly expressed in the brain than in other tissues . However, it should be noted that although the split-plot design is powerful for detecting differences between split-plot factors (tissues), it is considered to have low power for detecting differences between blocks (populations) [p < 0.05, or 6.3% of genes at p < 0.01. Each tissue contributed a similar number of genes differentially expressed among populations.A small proportion of genes (six genes, 3%) differed in expression among populations (lations) . As suchSurprisingly, differences among tissues in gene expression were not consistent across all three populations. More than one-third (37%) of the genes differentially expressed between tissues were significant in only one of the three populations Figure . PopulatA relatively small proportion of tissue-specific genes 31%) have consistent expression patterns in all three populations Figure , Table 1% have coVariation among technical replicates was low, and permutation tests indicated that the ANOVA model was robust. Sample coefficients of variation (CVs (standard deviation/mean) × 100), which estimate technical variance due to replicate spots (six spots per hybridization), repeated measures (two hybridizations per dye), and dye (two dyes per sample), were calculated for each gene of each of the 27 samples. CVs less than 5% accounted for 95% of sample/genes, respectively. Of the many comparisons performed (differences among tissues, populations, interaction), permutation tests results agreed with ANOVA results (the same comparisons identified as significant or not significant) for 99.1% of comparisons, suggesting that our ANOVA model was robust.Considerable variation occurs among the 27 samples  used to measure inter-individual and tissue-specific variation in gene expression. We are able to precisely describe the patterns of gene expression for 192 metabolic genes because of the low experimental variation; for 95% of the replicate measures of gene expression the standard deviation is less than 5% of the mean. Notably, gene expression is statistically different for many genes among individuals within a population for a tissue (48%), between tissues (76%), and between populations (3%). For genes with tissue-specific expression, only a fraction (31%) had expression patterns consistent across all three populations. These data do not specifically identify tissue-specific differences that are inconsistent across populations, but rather emphasize that tissue-specific differences detected can vary from one population to another. When measured from a single population, highly significant differences in tissue-specific expression do not necessarily represent genes relevant to general functional or morphological differences between tissues.p-values ranging down to 10-7. Differences in gene expression among individuals are unlikely to be due to common reversible environmental factors that affect physiological performance (acclimation effects) since all individuals used in this study were housed in a common environment and fed the same food for at least two months. However, the differences could be due to irreversible developmental effects or genetic variations that affect gene expression. Regardless of this, if these differences are heritable or due to developmental plasticity, they represent variation one would expect to find among outbred organisms, including humans.Variation in gene expression among healthy male individuals raised under controlled laboratory conditions was high. Nearly half of the metabolic genes (48%) were differentially expressed among individuals within a population for any one tissue Figure , with foFundulus populations assayed here detected 18% of genes differentially expressed among healthy individuals [Fundulus also detected a very high proportion of genes (94%) differentially expressed among individuals [Other studies that have measured inter-individual differences in gene expression have also detected high levels of variation in a variety of taxa. Among crosses of different yeast strains a large number of differences in expression (6% of genes varying more than twofold) were detected between morphotypes . A previividuals . Althougividuals . AlternaConsidering the high inter-individual variation detected, the data presented here underscore the importance of including biological replicates within treatment groups in order to ascribe differences in expression to treatment rather than to inter-individual variation. Statistically, an analysis of variance can be used to examine the effects of technical and biological variation, and these tests have proved powerful for detecting significant differences in gene expression ,4, even et al. [Drosophila, whereas genotype was less of an influence, and the influence of age was weak [Fundulus indicates that most inter-individual variation in metabolism can be accounted for by differences in metabolic gene expression [A variety of factors can contribute to differences in gene expression among individuals. Pritchard et al.  proposedwas weak . Furtherpression .Another important source of biological variation in gene expression is differences in expression among different tissues; 76% of genes were differentially expressed between brain, heart and liver, and expression in the liver was the most distinct compared to heart and brain. In this study, genes printed on our array are primarily enzymes functional in central metabolic pathways such as fatty-acid metabolism, glycolysis and oxidative phosphorylation. Of the oxidative phosphorylation genes differentially expressed between tissues, 92% were more highly expressed in heart or brain than in liver Figure . The prip-values ranging down to 10-7. Lee et al. [Many microarray studies have used expression levels of 'housekeeping' genes as an internal control for comparisons among arrays, individuals and treatments. Housekeeping genes may be defined as those that are involved in routine cellular metabolism and always expressed in all cells. Accordingly, many, if not most, of the genes studied here could be considered housekeeping genes. Nearly half of these genes were expressed at different levels between individuals, with fold differences ranging from 1.2- to 5-fold and e et al.  applied Although genes differentially expressed among tissues reflect their different metabolic requirements, it should be noted that the purpose of the current study was not to comprehensively identify suites of genes responsible for functional differences between tissues. The relatively small number of printed probes was useful for a high degree of technical replication, and obviously represents a small portion of the expressed genes. However, this approach shows that highly significant differences in gene expression among tissues may be apparent but not consistent among closely related taxa. Therefore, highly significant differences in gene expression found only within a single population may not necessarily represent genes relevant to general functional or morphological differences between tissues.Fundulus hearts. Similarly, approximately 1% of genes were differentially expressed in brain tissue among inbred strains of mice [Although the pattern of metabolic gene expression among tissues reflects established patterns of tissue-specific metabolism, there is additional variation due to population. It should be noted that the split-plot statistical design is not as powerful for detecting among-block differences (among populations) as for detecting differences among split-plot factors . We dete of mice . Differe of mice ,6.F. heteroclitus. One needs to be careful about this interpretation, however. Our emphasis was not to specifically identify genes that have significant interaction between tissue and population. Rather, we emphasize that genes detected as tissue specific will vary from one population to another, and most microarray studies measure treatment-specific expression patterns in only one population of test organism. Because inter-individual variation is high, it is probable that inclusion of more replicate individuals in each group would increase the sensitivity of ANOVA, and the number of genes that distinguish tissues consistently in all populations may change.An unexpected finding is that the tissue-specific differences depend on which population was assayed. Differences in gene expression are expected between tissues because of functional divergence and between populations because of neutral genetic divergence. In addition, one might expect that the number of genes significantly different between populations would depend on the tissue. One might also expect tissue-specific differences to be consistent in all taxa. Yet our data indicate that tissue-specific expression patterns are not fixed within a species. The genes for which expression is significantly different between tissues are not all the same in all three populations. Of the 128 genes that have tissue-specific patterns of expression in any population, 37% are tissue-specific in only one of the three populations and 32% are found in only two of the three populations. Overall, it would appear that only 31% of tissue-specific differences in gene expression are consistent among all populations of The consistent tissue-specific differences still support expectations based on the metabolic requirements of each tissue  in recirculating 100-gallon tanks for at least two months before experiments. Fish were sacrificed by cervical dislocation and tissues were excised and stored in RNAlater (Ambion) at -20°C. Fish were collected at Wiscasset, Maine; Stone Harbor, New Jersey, and Sapelo Island, Georgia. Only healthy male fish were used for the following experiments.Teleost fish F. heteroclitus cardiac library encoding essential proteins for cellular metabolism [Microarrays were printed using 192 cDNAs from a tabolism . These ctabolism . These 1F. heteroclitus. Each of these 27 samples was measured four times, twice with Cy3 and twice with Cy5  from three individuals collected from three populations of 5 Figure . In addi5 Figure .g for 20 min. Supernatant was removed and combined with 400 μl isopropanol, stored at -20°C for 30 min, then centrifuged at 4°C at 16,000g for 30 min. The remaining RNA pellet was rinsed twice with 400 μl of 70% ethanol, then further purified using the Qiagen RNeasy Mini kit (Qiagen) following the manufacturer's protocols. Purified RNA was quantified spectrophotometrically, and RNA quality was assessed using the Agilent 2100 Bioanalyzer. RNA was stored in 1/10 volumes 3 M sodium acetate and 2.5 volumes 100% ethanol at -20°C.RNA was extracted from tissue homogenate in a chaotropic buffer using phenol/cholorform/isoamyl alcohol. All reagents were from Sigma unless otherwise noted. Tissues were removed from RNAlater, blotted dry, and homogenized using an electric homogenizer in 400 μl chaotropic buffer (4.5 M guanidinium thiocyanate, 2% N-lauroylsarcosine, 50 mM EDTA pH 8.0, 25 mM Tris-HCl pH 7.5, 0.1 M β-mercaptoethanol, 2% antifoam A). An equal volume of 2 M sodium acetate (pH 4.0) was added to the homogenate, followed by 400 μl acidic phenol (pH 4.4), and 120 μl chloroform/isoamyl alcohol (23:1). The mixture was kept at 4°C for 10 min then centrifuged at 4°C at 16,000RNA for hybridization was prepared by amplification using a modified Eberwine protocol . The Ambet al. [2), and covered with a coverslip. Slides were placed in an airtight chamber humidified with paper soaked in 1 × SSC buffer and incubated 12-18 h at 42°C. Following hybridization, slides were scanned using the Packard Bioscience ScanArray Express microarray scanner (PerkinElmer Life Sciences). Resulting .tiff images were imported into spot grids built in ImaGene (Biodiscovery) for each array, and spot signals were collected as fluorescence intensities for each dye channel.Labeled aRNA aliquots of the two individual samples for each hybridization (18 pmol each of Cy3 and Cy5) were vacuum dried together and resuspended in 12 μl hybridization buffer . Hybridization buffer consisted of 5 × SSPE, 1% SDS, 50% formamide, 1 mg/ml poly(A), 1 mg/ml sheared herring sperm carrier DNA, and 1 mg/ml BSA. Slides were washed in sodium borohydride solution according to Raghavachari et al.  to reduc2 values of lowess-transformed sum-normalized data were used for all subsequent statistical analyses. MIAME-compliant data [Raw data were first sum normalized , which iant data  have beey = grand mean + population + tissue + population-tissue interaction + individual in population + tissue-by-individual within population + dye within individual + hybridization within dye + spot within hybridizationy is the normalized log2 expression and individual in population and tissue-by-individual within population are random effects. To test for differences among multiple means (for example, among population and tissue groups), and to correct for multiple comparisons, the T-method [where T-method  was applkv] α[× SEMSR = Qkv] α[is the studentized range [k = number of groups in the comparison (for example, if comparisons are among tissues then k = 3), v = degrees of freedom of MStissue-by-individual within population, and SE is the standard error among tissue-by-individual samples within populations. The T-method following ANOVA was used to identify genes differentially expressed among tissues in each population. These data were then used to contrast tissue-specific and population-specific expression patterns. Robustness of ANOVA data was tested using a permutation test; means for the 27 biological samples were randomly permuted 1,000 times between population and tissue and test statistics were recalculated for differences among populations, tissues and tissue-by-population interaction. Agreement between ANOVA and permutation test results would indicate the robustness of the ANOVA model. Finally, in order to graphically illustrate expression similarity among tissues, expression distance between samples was calculated as the sum of differences of log2 expression values over all genes, and neighbor-joining trees of global similarity of expression patterns among tissues  were constructed [where the critical value Qed range , k = numstructed  for eachp-values associated with statistical tests for differences in expression between populations, tissues, tissue-by-population interaction, and among individuals within populations. Also listed are mean expression for each sample, and columns comparing differences in expression between tissues within each population. Final columns tabulate whether a tissue difference was detected for each comparison, whether this difference was consistent between populations, and whether significant interaction was detected for that gene.The following additional data are available with the online version of this paper. Additional data file The results from statistical analyses for all genesClick here for additional data file


A previous systematic review reported that topical NSAIDs were effective in relieving pain in chronic conditions like osteoarthritis and tendinitis. More trials, a better understanding of trial quality and bias, and a reclassification of certain drugs necessitate a new review.Studies were identified by searching electronic databases, and writing to manufacturers. We identified randomised, double blind trials comparing topical NSAID with either placebo or another active treatment, in adults with chronic pain. The primary outcome was a reduction in pain of approximately 50% at two weeks, and secondary outcomes were local and systemic adverse events and adverse event-related withdrawals. Relative benefit and number-needed-to-treat (NNT), and relative harm and number-needed-to-harm (NNH) were calculated, and the effects of trial quality, validity and size, outcome reported, and condition treated, were examined by sensitivity analyses.Twelve new trials were added to 13 trials from a previous review. Fourteen double blind placebo-controlled trials had information from almost 1,500 patients. Topical NSAID was significantly better than placebo with relative benefit 1.9 , NNT 4.6 . Results were not affected by trial quality, validity or size, outcome reported, or condition treated. Three trials with 764 patients comparing a topical with an oral NSAID found no difference in efficacy. Local adverse events (6%), systemic adverse events (3%), or the numbers withdrawing due to an adverse event were the same for topical NSAID and placebo.Topical NSAIDs were effective and safe in treating chronic musculoskeletal conditions for two weeks. Larger and longer trials are necessary to fully elucidate the place of topical NSAIDs in clinical practice. A systematic review of topical NSAIDs reported that they were effective for relieving pain in both acute and chronic conditions . Number-There are three reasons why an updated review of topical NSAIDs in chronic pain is needed. First, we have a better appreciation of factors that can introduce bias -4, and wRelevant studies were sought regardless of publication language, type, date or status. Studies included in the previous review were considered for inclusion, and the Cochrane Library, MEDLINE and PreMedline, EMBASE and PubMed, were searched for relevant studies published since the last review, for the years 1996 to April 2003. The search strategy included "application: topical" together with "cream", "gel" etc, together with generic names of NSAIDs, and proprietary preparations of topical treatment in which the principal active ingredient was an NSAID   were extracted. Longer outcomes were also accepted when available.Trial quality was assessed using a validated three-item scale with a maximum quality score of five . IncludeWe defined our own outcome of clinical success, representing approximately a 50% reduction in pain . This wa1) number of patients with a 50% or more reduction in pain2) patient reported global assessment of treatment3) pain on movement4) pain on rest or spontaneous pain5) physician or investigator global assessment of treatmentIn addition, the number of patients showing undefined "improvement" was also accepted. All of these outcomes were grouped together as a "success", and categories 1–4 were used as preferred outcomes in the sensitivity analysis.Secondary outcomes were extracted from included papers that reported them. These were the number of patients (i) reporting one or more local adverse event (itching, stinging, rash), (ii) reporting one or more systemic adverse event (iii) withdrawing from trials due to adverse events.The number of patients randomised into each treatment group (intention to treat) was used in the efficacy analysis. Information was pooled for the number of patients in each trial approximating at least 50% pain relief, or similar measure, for both topical NSAID and control. These were used to calculate NNT with a 95% confidence interval (CI) . RelativOur prior intention was to perform sensitivity analyses on pooled outcomes using the z test  for qualTen out of the 20 UK companies, and two out of the 66 continental European companies replied to our request for studies. Only three companies supplied useful material, either published studies or bibliographies. None provided unpublished material.Searches identified 60 target papers, but 35 were excluded; 23 studies failed to meet the inclusion criteria and 12 had no useable data. Twenty-four of these 60 target papers were included in the previous review. We included 13 of those in this review, and excluded 11; seven were not double blind, two compared a salicylate with placebo or oral analgesics, one did not have daily application, and one had insufficient data . Five studies in osteoarthritis specified use of a standard scale (ACR, Kellgren and Lawrence, ISK) to assess the severity of disease, four specified that the disease was radiologically confirmed, one specified that patients had "well documented mild osteoarthritis", and one made no statement.Quality scores were high, with 16/18 placebo controlled and 9/10 active controlled trials scoring 3 or more points out of a maximum of 5. Validity scores were also high, with 14/18 placebo controlled and 8/10 active controlled trials scoring 9 or more out of a maximum of 16  and 5.Dichotomous information was available to pool from 14 placebo controlled trials for efficacy, from 16 placebo controlled trials for local adverse events, 17 placebo controlled trials for systemic adverse events, and from 11 placebo controlled trials for adverse event related withdrawals.Fourteen trials (1,502 patients) provided data on efficacy. Topical NSAIDs were significantly better than placebo  provided some information on adverse events  provided some information on adverse events Table . In two Patients in these trials all had moderate to severe baseline pain, and for those with osteoarthritis, disease severity was generally mild to moderate. Patients with most severe disease were specifically excluded in several trials because authors regarded topical NSAID to be inappropriate for their treatment.Both the original and this updated review concluded that topical NSAIDs were effective in chronic conditions. However, removing trials of lower quality, and topical agents that are not now regarded as topical NSAIDs, increased (worsened) the NNT from 3.1 (95% CI 2.7 to 3.8) to 4.7 (95% CI 3.8 to 5.9) for the outcome of at least half pain relief at two weeks for all topical NSAIDs compared to placebo. For every four or five patients with chronic pain treated with topical NSAID, one would benefit who would not have done with placebo. Three trials comparing topical with oral NSAID found no difference in efficacy.There are a number of aspects of this review that might question this demonstration of efficacy. The trials spanned several decades and retrospective examination finds fault with them in several respects. Many trials were small, and small size can allow chance effects to influence treatment and placebo event rates . DiffereWe addressed these limitations with pre-planned sensitivity analyses. Using studies with higher quality and validity scores, larger size, or higher rather than lower preference outcomes made no difference. Patients treated for knee osteoarthritis derived the same degree of pain relief as those treated for general musculoskeletal conditions. The evidence was that topical NSAIDs were effective whatever strategy was used for sensitivity analysis, improving the robustness of the overall result.A possible criticism might be that there has been selective publication of trials showing topical NSAIDs to be effective, and suppression of trials where there was no difference between topical NSAID and placebo. Funnel plots do not reliably detect publication bias ,14, so wIt is important to emphasise that both active and placebo treatments were rubbed on, making any effect of rubbing equal in both groups. The mean placebo response in the included trials was 26%, compared with the mean response of 48% with topical NSAID. The response with placebo is consistent with that found in acute and chronic pain with a variety of conditions and endpoints .Local adverse events were reported with equal frequency for topical NSAID and topical placebo in placebo-controlled trials, but more often for topical NSAID than oral NSAID in active controlled trials. There were no differences between topical NSAID and topical placebo, or topical NSAID and oral NSAID, for systemic adverse events, or withdrawals due to adverse events. Studies of short duration will not capture important long-term safety information, and this may be important for ongoing applications of gels, creams or sprays in chronic conditions. There is, however, information that indicates that topical NSAIDs do not cause the gastrointestinal harm found with oral NSAIDs , nor areClearly there is a body of evidence to support the efficacy of topical NSAIDs in chronic painful musculoskeletal conditions. Despite removing smaller studies that were not double blind, and substituting newer, larger trials of higher quality, topical NSAIDs remained effective, though the NNT was higher (worse) than originally estimated . More inWe are able to compare the evidence for different topical analgesics in chronic musculoskeletal pain Table . SystemaLM was involved with planning the study, searching, data extraction, analysis, and preparing a manuscript; RAM with planning, data extraction, analysis and writing the manuscript; JE with searching, data extraction, and writing; SD with data extraction, analysis, and writing; HJM with planning, analysis and writing. All authors read and approved the final manuscript.RAM & HJM have consulted for various pharmaceutical companies. RAM, HJM & JE have received lecture fees from pharmaceutical companies related to analgesics and other healthcare interventions. All authors have received research support from charities, government and industry sources at various times, but no such support was received for this work. No author has any direct stock holding in any pharmaceutical company.The pre-publication history for this paper can be accessed here:Search strategy for RCTs of topical NSAIDs in chronic painClick here for fileExcluded studiesClick here for fileQUOROM flow diagramClick here for fileOutcome details of placebo-controlled trialsClick here for fileOutcome details of active-controlled trialsClick here for file


Coccidioidomycosis was first recognized as a serious disease over 100 years ago, but the disease remains an enigma and often goes undiagnosed, even in endemic areas It has been more than a century since coccidioidomycosis was first recognized as a serious disease, and its etiology and epidemiology have been well documented. But the disease remains an enigma to many, and it often goes undiagnosed, even in endemic areas. As management of this chronic disease remains problematic, new preventive or therapeutic options are needed.Coccidioides immitis and C. posadasii, generically referred to as the “Californian” and “non-Californian” species respectively Coccidioidomycosis is a fungal disease found only in the Western Hemisphere. It is caused by two nearly identical species, Mycelial phase—The growth form in the soil, composed of filamentous hyphae and reproductive spores called arthroconidia.Arthroconidia—Reproductive spores, highly resistant to dessication, which are the infectious particles inhaled by man and animals.Spherules—The parasitic phase of this dimorphic fungus; spherules are round cells of 30–100 µM or more that reproduce the progeny endospores.Endospores—The progeny units of the parasitic phase, derived from spherules.C. posadasii is thought to be the predominant species in Mexico Endemic regions for coccidioidomycosis have long been identified in semiarid areas in Mexico As the soil dries or nutrients become limiting, the fungus reproduces asexually by disarticulating the hyphae into small, environmentally-resistant arthroconidia (reproductive spores) . These aIt is estimated that upwards of 100,000 primary coccidioidal infections occur in humans each year in the endemic areas of the United States In their pioneering epidemiologic studies, Smith and colleagues found that about 60% of exposures to the fungus result in asymptomatic infection In regions where tuberculosis rates are high, the two diseases may occur together. Tuberculosis and coccidioidomycosis share common epidemiological, clinical, radiographic, and even histopathological features, making a correct diagnosis extremely difficult in cases where both diseases coexist. In areas where both diseases are endemic, the pertinent studies for diagnosing both conditions should be performed in every patient with compatible clinical features. The diagnosis of one of them does not exclude the possible existence of the other Historically, patients with the primary respiratory form of the disease were not treated because the vast majority recovered on their own. Instead, such patients were given supportive care and were monitored, often with radiographs, until the disease resolved. In recent years, however, an increasing number of physicians are prescribing azole antifungals in cases of primary disease, both because drugs like fluconazole have a good safety record, and because there is a perception that treatment may prevent progression to more serious forms of the disease. This latter presumption, however, is not supported by controlled trial data.All cases of chronic or disseminated disease call for antifungal therapy, but the choice of drugs, route, and duration of therapy is highly dependent on the form of the disease, the severity and site(s) of infection, and the immune status of the patient. Galgiani and colleagues have published clinical practice guidelines on the choice of drug and duration of therapy for a given form of the disease There are only two classes of antifungal therapy routinely used for treatment of coccidioidomycosis. The first class is the polyenes, with amphotericin B desoxycholate and the newer lipid formulations used for the more serious forms of disease. The second class is the azoles, with ketoconazole, fluconazole, itraconazole, and the newer analogue voriconazole as available options. Voriconazole, in particular, is being used more and more often in life-threatening mycoses, and was found to be better than amphotericin B in the primary therapy of invasive aspergillosis Treatment of the more serious or aggressive forms of the disease is typically of long duration and often results in less than complete resolution of disease; relapse is common In addition to drugs, surgery is sometimes indicated to remove focalized infections, such as pulmonary cavities, or to debride osseous forms of the disease Acquired resistance to coccidioidomycosis strongly correlates with the development of a delayed-type hypersensitivity skin test response to coccidioidal antigens Although all humans are equally susceptible to initial infection, there is evidence of genetic predisposition to dissemination, independent of socioeconomic or environmental factors, particularly among African-Americans and Filipinos In cases of marked immunosuppression, either in advanced AIDS or other forms of depressed cellular immunity, the management of coccidioidomycosis is particularly challenging and requires aggressive treatment www.valleyfever.com), has identified and cloned immunogenic proteins that have proven effective in the prevention of deaths and fungal burdens in mouse models of coccidioidomycosis. This suggests that a vaccine for use in humans could be created As previously mentioned, recovery from disease confers lifelong immunity to reinfection, and is a rationale for the development and implementation of a vaccine for the prevention of symptomatic or serious forms of the disease. The combination of increasing incidence of disease, a growing population in the endemic area, and the lack of a highly effective drug treatment justifies efforts to prevent (rather than treat) this disease. To that end, a university-based consortium, the Valley Fever Vaccine Project (Although the vast majority of infected individuals emerge from coccidioidomycosis without complications, an unlucky minority are faced with a debilitating disease that lacks adequate drug options for rapid and completely effective treatment. In the absence of newer therapeutics, discoveries that lead to immunologic intervention


By integrating phenotypic and transcriptional profiling and mapping the data onto metabolic and regulatory networks, it was shown that arsenic probably channels sulfur into glutathione for detoxification, leads to indirect oxidative stress by depleting glutathione pools, and alters protein turnover via arsenation of sulfhydryl groups on proteins. Saccharomyces cerevisiae was determined by profiling global gene expression and sensitivity phenotypes. These data were then mapped to a metabolic network composed of all known biochemical reactions in yeast, as well as the yeast network of 20,985 protein-protein/protein-DNA interactions.Arsenic is a nonmutagenic carcinogen affecting millions of people. The cellular impact of this metalloid in While the expression data unveiled no significant nodes in the metabolic network, the regulatory network revealed several important nodes as centers of arsenic-induced activity. The highest-scoring proteins included Fhl1, Msn2, Msn4, Yap1, Cad1 (Yap2), Pre1, Hsf1 and Met31. Contrary to the gene-expression analyses, the phenotypic-profiling data mapped to the metabolic network. The two significant metabolic networks unveiled were shikimate, and serine, threonine and glutamate biosynthesis. We also carried out transcriptional profiling of specific deletion strains, confirming that the transcription factors Yap1, Arr1 (Yap8), and Rpn4 strongly mediate the cell's adaptation to arsenic-induced stress but that Cad1 has negligible impact.By integrating phenotypic and transcriptional profiling and mapping the data onto the metabolic and regulatory networks, we have shown that arsenic is likely to channel sulfur into glutathione for detoxification, leads to indirect oxidative stress by depleting glutathione pools, and alters protein turnover via arsenation of sulfhydryl groups on proteins. Furthermore, we show that phenotypically sensitive pathways are upstream of differentially expressed ones, indicating that transcriptional and phenotypic profiling implicate distinct, but related, pathways. Saccharomyces cerevisiae have changed the face of biological study from the investigation of individual genes and proteins to a systems-biology approach involving integration of global gene expression with protein-protein and protein-DNA information [Global technologies in the budding yeast ormation . These dormation -4. In thormation .Arsenic, a ubiquitous environmental pollutant found in drinking water, is a metalloid and human carcinogen affecting the skin and other internal organs . It is aSaccharomyces cerevisiae. Bobrowicz et al. [YCF1 [Bacillus subtilis putative arsenic-resistance protein and encodes a small ) efflux transporter that extrudes arsenite from the cytosol [et al. [et al. [A number of proteins involved in metalloid detoxification have been described in different organisms, including z et al.  found thl. [YCF1 . Arr3 is cytosol ,23. Ycf1 cytosol ,22. Unti [et al.  found th [et al. , on the YAP1.Although Arr1 and Yap1 seem specifically suited for arsenic tolerance, the other seven YAP-family proteins are still worthy of investigation in light of the fact that each one regulates a specific set of genes involved in multidrug resistance with overlaps in downstream targets. One such interesting protein is Cad1 (Yap2). Although Yap1 and Cad1 are nearly identical in their DNA-binding domains, Yap1 controls a set of genes (including Ycf1) involved in detoxifying the effects of reactive oxygen species, whereas Cad1 controls genes that are over-represented for the function of stabilizing proteins in an oxidant environment . HoweverUnderstanding the role of AP-1-like proteins (such as YAP family members) in metalloid tolerance was one of the goals in this study within the realm of the larger objective - using an integrative experimental and computational approach to combine gene expression and phenotypic profiles (multiplexed competitive growth assay) with existing high-throughput molecular interaction networks for yeast. As a consequence we uncovered the pathways that influence the recovery and detoxification of eukaryotic cells after exposure to arsenic. Networks were analyzed to identify particular network regions that showed significant changes in gene expression or systematic phenotype. For each data type, independent searches were performed against two networks: the network of yeast protein-protein and protein-DNA interactions, corresponding to signaling and regulatory effects (the regulatory network); and the network of all known biochemical reactions in yeast (the metabolic network). For the gene-expression analysis, we found several significant regions in the regulatory network, suggesting that Yap1 and Cad1 have an important role. However, no significant regions in the metabolic network were found. In order to test the functional significance of Yap1 and Cad1, we used targeted gene deletions of these and other genes, to test a specific model of transcriptional control of arsenic responses.In contrast to the gene-expression data, the phenotypic profile analysis revealed no significant regions in the regulatory network, but two significant metabolic networks. Furthermore, we found that phenotypically sensitive pathways are upstream of differentially expressed ones, indicating that metabolic pathway associations can be discerned between phenotypic and transcriptional profiling. This is the first study to show a relationship between transcriptional and phenotypic profiles in the response to an environmental stress.S. cerevisiae, we performed a series of dose-response studies. We found that treatment of wild type cells with 100 μM and 1 mM AsIII had a negligible effect on growth, but that these cells still exhibited a pronounced transcriptional response . Microarray analysis of biological replicates (four chips per replicate experiment) of the high-dose treated cells (1 mM AsIII) clustered extremely well together when using Treeview . The lower dose time-course (100 μM AsIII) showed the beginning of gene-expression changes at 30 minutes, with the robust changes occurring at 2 hours, or one cell division . The 2 hour, 100 μM dose clustered together with the 30 minute, 1 mM biological replicates and was in fact so similar to them that an experiment of one set of four chips for the 2 hour lower dose was deemed sufficient. Furthermore, when combining the three datasets (2 hour, 100 μM AsIII and each 30 minute, 1 mM AsIII replicate data) and using a 95% confidence interval  we found 271 genes that were not only statistically significant in at least 75% of the total data (9 out of 12 chips), but also that the direction and level of expression of these genes were similar between the datasets. The lower dose time-course also included a 4 hour treatment, or two cell divisions. This experiment demonstrated the greatest degree of variability, indicating either a cycling effect or the cell's return to homeostasis, which was further exemplified by a decrease in the transcriptional response .Before gene-expression analysis of arsenic responses in Genes were categorized by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Simplified Gene Ontology  Table . In totaet al. [We used the Cytoscape network visualization and modeling environment together with the ActiveModules network search plug-in to carry out a comprehensive search of the regulatory and metabolic networks ,28. The et al.  with 2,2de novo protein synthesis in response to arsenic allows energy to be diverted to the increased expression of genes involved in stress responses and protection of the cell. One such pathway may involve sulfur metabolism, which leads to glutathione synthesis. In fact, included in Figure The highest-scoring regulatory network neighborhood was defined by the transcription factor Fhl1 Figure . Its expAnother high-scoring neighborhood comprises part of the proteasome protein complex Figure . The comMany of the central proteins in the significant neighborhoods uncovered by ActiveModules were transcription factors Figure . AlthougTranscription factors Msn2, Yap1, Msn4, Cad1 and Hsf1 were the central proteins for many of the significant neighborhoods found Figure . Togethe2O2) [OYE3 (encoding a NADPH dehydrogenase)). Moreover, Yap1 regulates GSH1, which encodes γ-glutamylcysteine synthetase (an enzyme involved in the biosynthesis of antioxidant glutathione), TRX2 (the antioxidant thioredoxin), GLR1 (glutathione reductase) and drug-efflux pumps ATR1 and FLR1 [GSH1 and ATR1 are examples of several genes also targeted by Cad1. All of these specified Yap1 targets are induced after arsenic exposure, recapitulating the toxicant's role as a likely oxidant. During the course of this work, Wysocki et al. [When overexpressed, Yap1 confers resistance to several toxic agents, and Yap1 mutants are hypersensitive to oxidants ,40-44. C2O2) ,35. Folland FLR1 -50. It si et al.  also impyap1Δ and cad1Δ deletion strains with 100 μM AsIII for 2 hours . Surprisingly, we did not find that Cad1 was involved in regulation in response to arsenic-mediated stress. The yap1Δ strain was not only sensitive to AsIII by phenotypic profiling  but also defective in the induction of several downstream enzymes with antioxidant properties . These were generated using Rosetta Resolver with a p-value less than 0.001 . Also, Additional data files 8 and 9 contain tables of genes failing to be induced or repressed (or showing such a decrease in expression that they no longer make significantly expressed gene lists) in the yap1Δ and cad1Δ experiments, compared to the parent experiment, after treatment with 100 μM AsIII for 2 hours. These are lists of genes that would be potentially regulated by Yap1 and Cad1 in the presence of arsenic.As Cad1 and Yap1 share many downstream targets, the genes defined by these transcription factors are very similar. To determine which transcription factor is playing the most active role in the high level of differential expression for this group  of YAP1, indicating that Yap1 not only activates Rpn4, but that Rpn4 may in fact activate Yap1 [rpn4Δ strain under arsenic stress conditions, whereas Rpn4 was equally induced in the yap1Δ strain .Rpn4 is required for tolerance to cytotoxic compounds and may regulate multidrug resistance via the proteasome . Moreoveet al.  identifiate Yap1 . In supprpn4Δ after treatment with arsenic was the most dramatically altered, save for arr1Δ  in the ARR1 mutant, but show defective induction in the yap1Δ strain treated in parallel . These results indicate that Yap1 may control Arr2 and Arr3 when yeast is subjected to 100 μM AsIII for 2 hours.Arr1 is structurally related to Yap1 and Cad1 ,24. Howeet al. ,59 show  [et al.  supportss et al.  which foet al. [et al.'s studies looked at genes in the normal chromosome context rather than genes ectopically expressed from a plasmid; in addition, in our study, we treated the yeast with 100 μM AsIII while Wysocki et al. [et al. [ARR2 or ARR3 in either the ARR1- or YAP1-deleted strains. Taken together, these data indicate that both ARR1 and YAP1 are important genes involved in the process of arsenite detoxification in the yeast cell, but because of the different strains and treatment protocols used between these three studies, further experiments are warranted to resolve the differences.Our results and those of Menezes et al. , in contet al. , might bi et al.  started  [et al.  used an arr1Δ and parent strains after arsenic treatment  and suggesting that it may have a role in protein degradation as well.These observations indicate that Arr1 may regulate sulfur-assimilation enzymes that are necessary for arsenic detoxification. This is particularly interesting considering that the ActiveModules algorithm identified the node Met31 Figure , the traOur arsenic-treatment experiments revealed the strong induction of over 20 enzymes in the KEGG sulfur amino acid and glutathione biosynthesis pathways Table . This iset al. [GSH1 induction by cadmium is dependent on the presence of Met4, Met31, Met32 and Cbf1 in the transcriptional complex of MET genes. Met4 and Met32 are also differentially expressed in response to arsenic and interact with Met31, which defines a network neighborhood as shown in Figure 2O2 profiles (400 μM H2O2) from Causton et al. [2O2 gene-expression profiles in regard to oxidative-stress response genes, sulfur and methionine metabolism genes, in response to H2O2, were either repressed or did not change  .To identify genes and pathways that confer sensitivity to arsenic, we identified deletion mutants with increased sensitivity to growth inhibition using a deletion mutant library of nonessential genes (4,650 homozygous diploid strains) . Each stERO1 (7- to 10-fold induced), HCA4 (5- to 9-fold induced), and DCP1 (9- to 22-fold induced). Second, there are redundant pathways mediated by multiple genes, such that deletion of one does not lead to sensitivity. OYE2, OYE3, and a large number of reductases fall into this category. Finally, gene products that do not change significantly, mediate important biological responses and thus when deleted could sensitize the cell to a specific stressor. ARO1, ARO2, THR4 and HOM2 are examples of genes that are not differentially expressed but are very sensitive to arsenic.Only 10% of the top 50 sensitive mutant strains were significantly differentially expressed in the transcript profile. This lack of direct correlation between gene expression and fitness data is consistent with data from our own and other laboratories ,4,65. Atp-aminobenzoic acid (PABA) and folates, which are donors to homocysteine [Like the gene-expression data, the phenotypic data was subjected to searches performed against the regulatory network of yeast protein-protein and protein-DNA interactions as well as the metabolic network of all known biochemical reactions in yeast. Unlike the transcription profile, the phenotypic data analysis revealed no significant regions in the regulatory network, but did map to two statistically significant metabolic networks. The first significant pathway was amino acid synthesis/degradation with the terminal products being L-threonine and L-homoserine, beginning with precursors such as L-arginine, fumarate and oxaloacetate Figure . These pcysteine -69.Combining transcript profiling and phenotypic profiling provides deeper insights into the biology of arsenic responses. Until now there has been a lack of correlation between the differential expression of genes and sensitivity of deletion mutants ,4,65 andThus, we believe our work shows that the deletion of an individual gene can lead to a change in sensitivity to an agent only if the protein product of that gene is important for some process (for example, amino-acid synthesis or a transcription factor required for the increased expression of genes needed to protect against the agent). On the other hand, expression profiling shows the end product of the cell's response to arsenic. Therefore, an agent such as arsenic might cause a transcription factor (Yap1, for example) to increase the expression of as many as 50 genes, 20 of which might help to protect against the agent. However, deletion of any of the 50 would not be expected to have an effect on the response to arsenic. The effect of gene deletion would be on the transcription factor itself (whose expression might not be affected by the agent). Thus, in the case of arsenic exposure, we conclude that phenotypic profiling interrogates genes upstream of the genes that ultimately protect against arsenic toxicity and that the downstream targets that demonstrate differential expression probably share redundant functions and are not vulnerable in the phenotypic profiling Figure .Systems biology represents an important set of methods for understanding stress responses to environmental toxicants, such as arsenic. In this study we have catalogued the centers of activity associated with arsenic exposure in yeast, identifying the key neighborhoods of activity in the regulatory and metabolic networks using the visualization tools and algorithms in Cytoscape. The transcriptional profile mapped to the regulatory network, revealing several important nodes (Fhl1, Msn2, Msn4, Yap1, Cad1, Pre1, Hsf1 and Met31) as centers of arsenic-induced activity. From these results we can conclude that arsenic detoxification in yeast focuses around: nucleotide and RNA synthesis; methionine metabolism and sulfur assimilation; protein degradation; and transcriptional regulation by proteins that form a stress-response network. In summary, protein synthesis in response to arsenic allows energy to be diverted toward the genes channeling sulfur into glutathione, which then leads to indirect oxidative stress by depleting glutathione pools and alters protein turnover. These processes require regulation by transcription factors, the understanding of which we refined by analysis of specific knockout strains. Our experiments, in fact, confirmed that the transcription factors Yap1, Arr1 and Rpn4 strongly mediate the cell's adaptation to arsenic-induced stress but that Cad1 has negligible impact. Finally, contrary to the gene-expression analyses, the phenotypic profiling data mapped to the metabolic network. The two significant metabolic networks unveiled were shikimate and serine, threonine and glutamate biosynthesis. Our goal was to integrate the computational identification of these important pathways found via transcript and phenotypic profiling by regulatory and metabolic network mapping. In doing so, we have shown that genes that confer sensitivity to arsenic are in pathways that are upstream of the genes that are transcriptionally controlled by arsenic and share redundant functions.S. cerevisiae strain BY4741 (MATa, his3Δ, leu2Δ0, met15Δ0, uraΔ0) was used and grown in synthetic complete medium at 30°C. Cells were grown to a density of 1 × 107 cells per ml. Cultures were split into two; NaAsO2  was added to one culture, and both were incubated at 30°C for 0.5, 2 or 4 h. Cells were pelleted and washed in distilled water before RNA extraction. Deletion strains (yap1Δ, cad1Δ, arr1Δ and rpn4Δ) of the same background were obtained from Research Genetics, confirmed and treated the same way, for 2 h and 100 μM NaAsO2.+ RNA purification with the Oligotex oligo (dT) selection step. Total RNA for the specific knockout strains and parent experiment was isolated by enzymatic reaction, following the RNeasy yeast protocol (Qiagen).For the cDNA hybridization experiments, total RNA was isolated using an acid-phenol method. Pellets were resuspended in 4 ml lysis buffer (10 mM Tris-HCL pH 7.5, 10 mM EDTA, 0.5% SDS). Four milliliters of acid (water-saturated, low pH) phenol was added followed by vortexing. The lysing cell solutions were incubated at 65°C for 1 h with occasional vigorous vortexing and then placed on ice for 10 min before centrifuging at 4°C for 10 min. The aqueous layers were re-extracted with phenol (room temperature, no incubation) and extracted once with chloroform. Sodium acetate was then added to 0.3 M with 2 volumes of absolute ethanol, placed at -20°C for 30 min, and then spun. Pellets were washed two or three times with 70% ethanol followed by Qiagen Poly(A)et al. [A cDNA yeast chip, developed in-house at National Institute of Environmental Health Sciences (NIEHS), was used for gene-expression profiling experiments. A complete listing of the ORFs on this chip is available at . cDNA miet al.  except tet al. . Genes tet al. . GeneSpret al.  were useThe knockout experiments were conducted on an Agilent yeast oligo array platform. Samples of 10 μg total RNA were labeled using the Agilent fluorescent direct label kit protocol and hybridizations were performed for 16 h in a rotating hybridization oven using the Agilent 60-mer oligo microarray-processing protocol. Slides were washed as indicated and scanned with an Agilent scanner. Data was gathered using the Agilent feature extraction software, using defaults for all parameters, save the ratio terms. To account for the use of the direct label protocol, error terms were changed to: Cy5 multiplicative error = 0.15; Cy3 multiplicative error = 0.25; Cy5 additive error = 20; Cy3 additive error = 20.p-value was less than 0.001. p-values were calculated using the Rosetta Resolver error model with Agilent error terms. The signature genes were analyzed with GeneSpring. The entire in-house and Agilent-based dataset is available in the Additional data files.GEML files and images were exported from the Agilent feature extraction software and deposited into Rosetta Resolver (version 3.2, build 3.2.2.0.33) (Rosetta Biosoftware). Two arrays for each sample pair, including a fluor reversal, were combined into ratio experiments in Rosetta Resolver. Intensity plots were generated for each ratio experiment and genes were considered 'signature genes' if the Genes have previously been categorized into various ontologies and pathways. If a particular pathway is enriched for genes that are significantly expressed in response to a process, we conclude that the pathway is likely to be involved in this process. In total, 829 genes out of 6,240 had a significant alteration in expression in at least one experimental condition. Along with the size of each functional category, a statistical measure for the significance of the enrichment was calculated by using a hypergeometric test. The level of significance for this test was determined using the Bonferroni correction, where the α value was set at 0.05 and the number of tests conducted for KEGG pathway and Simplified Gene Ontology  were 27 and 11, respectively.et al. [The ActiveModules algorithm was used to identify neighborhoods in the regulatory network corresponding to significant levels of differential expression. In this search, if a protein has many neighbors, it is likely that at random a few will show significant changes in expression and these could be selected as a significant sub-network. Neighborhood scoring is a method we used to correct for this bias. In this scheme, a significant sub-network must contain either all or none of the neighbors of each protein. The significance then represents an aggregate over all neighbors of a protein. This prevents the biased selection of a few top-scoring proteins out of a large neighborhood in the search for significant sub-networks. For an in-depth description of this algorithm see Ideker et al. .2O from the search. Scores for each ORF were generated by mapping the fitness significance value to a Z-score. To assign scores to the individual reactions, Förster's mapping from ORF to reaction was used to generate a list of ORFs for each reaction. The Z-scores of these ORFs were then aggregated into a single score for that reaction using the following equation:In defining the network used in the metabolic analysis, edges corresponding to metabolites linking more than 175 reactions were eliminated. This excludes metabolic cofactors such as ATP, NADH and Het al. [n) ending at each node is determined by combining the scores of the individual node and the highest-scoring path of length (n - 1) ending at a neighbor node using the following formula:We used a dynamic programming algorithm adapted from Kelley et al.  to identSince a node with many neighbors is more likely to belong to a high-scoring path by random chance, the score of the neighboring path is corrected against the extreme-value statistic with the number of observations equal to the number of neighbors.p-value, which represents the null hypothesis that there is no significant correlation between the topology of the metabolic network and the assignment of significance values to nodes in that network.The significances of the top-scoring networks were determined by comparison to a distribution of the top-scoring networks from random data (reaction scores randomized with respect to the nodes of the network). After running the path finding/scoring algorithm, the score of the single highest-scoring path was added to the null distribution. This process was repeated for 10,000 interactions. This null distribution was then used to determine an empirical p-value was less than 0.001 and if the fold-change value was greater than or equal to twofold. Signature genes were then broadcasted on the intensity plot and exported as text files. Lists were imported into GeneSpring. The 'Filter on Fold Change' function was used to compare the parent control vs. parent AsIII experiment with each deletion (AsIII) experiment. The gene list selected for each filter on fold change analysis was a combination of the parent signature gene list and the signature gene list of the AsIII-treated deletion being analyzed at the time. For example, if the comparison was being done between parent (AsIII-treated) and Yap1 (AsIII-treated), the list used in the analysis was the combination of the parent signature genes and the Yap1 signature genes. The filter on fold change function reports genes that were selected from the one condition (parent) that had normalized data values that were greater or less than those in the other condition (deletion under investigation) by a factor of twofold. Each resulting gene list was saved. All the resulting gene lists were combined and an annotated gene list was exported for use in Eisen's Cluster/Treeview package (described earlier). The format of the exported data was the natural log. The gene tree generated for the paper was generated in GeneSpring. Each filter on fold change was saved as an annotated gene list.The intensity plots were generated from each experiment in Rosetta Resolver. A gene was considered a signature gene if the Signature gene lists were generated in Rosetta Resolver from intensity plots as described above. Each signature gene list was saved as a 'Bioset' in Resolver. The parent Bioset was compared to each deletion Bioset using the 'Minus' function. This function finds those members in Bioset group 1 (parent) that do not exist in Bioset group 2 (deletion). Each of the resulting lists was saved as a new Bioset. The new 'minus' Bioset was broadcasted on its corresponding intensity plot and exported as a text file. This was repeated for each experiment with fine-tuning of the data using GeneSpring.600 0.05-0.1, split into tubes and treated with arsenic for 1-2 h at 1 mM, 2 mM and 5 mM concentrations. Similar responses were observed at each concentration, so the results were pooled. These cultures and a mock-treated sample were maintained in logarithmic phase growth by periodic dilution for 16-18 h. UPTAG and DOWNTAG sequences were separately amplified from genomic DNA of the drug and mock-treated samples by PCR using biotin-labeled primers as described previously [Homozygous diploid deletion strains and pooling of the strains were done as described . Aliquoteviously . The ampeviously , and acceviously .S. cerevisiae strain BY4741 (MATa, his3Δ, leu2Δ0, met15Δ0, uraΔ0) grown in synthetic complete medium at 30°C after treatment with arsenic. Treatment with 1 mM, 2 mM and 5 mM AsIII resulted in a negligible effect on growth (after 18 h) and survival (1 h treatment followed by plating and colony formation counting), but still exhibited a pronounced transcriptional response . The control parent vs. parent experiment (100 μM AsIII for 2 h) was compared with the yap1Δ . Additional data files yap1Δ, cad1Δ, rpn4Δ and arr1Δ, respectively, compared to the parent without arsenic treatment. Additional data file The following additional data files are available with the online version of this article and at . Additioe et al. ). The deh after 1 h and suh after 1 h and suth after  h and suS. cerevisiae strain, BY4741The dose-response curve of Click here for additional data fileA self-organized tree of arsenite treated yeast experiments and a table depicting the numbers of significant genesClick here for additional data fileThe primary raw cDNA data from all the experimentsClick here for additional data fileThe primary raw data for all the deletion experimentsClick here for additional data fileThe ranked arsenite sensitivity (phenotypic profiling) dataClick here for additional data fileGenes two-fold or more differentially expressed after arsenite in the Yap1 deletion strain compared to the parentClick here for additional data fileGenes two-fold or more differentially expressed after arsenite in the Cad1 deletion strain compared to the parentClick here for additional data fileGenes failing to be induced or repressed by arsenite in the Yap1 deleted strainClick here for additional data fileGenes failing to be induced or repressed by arsenite in the Cad1 deleted strainClick here for additional data fileUnder arsenite-treated conditions, Yap1 might regulate Arr2 and Arr3, and does not regulate Rpn4Click here for additional data fileGenes two-fold or more differentially expressed after arsenite in the Rpn4 deletion strain compared to the parentClick here for additional data fileGenes failing to be induced or repressed by arsenite in the Rpn4 deleted strainClick here for additional data fileGenes two-fold or more differentially expressed after arsenite in the Arr1 deletion strain compared to the parentClick here for additional data fileGenes failing to be induced or repressed by arsenite in the Arr1 deleted strainClick here for additional data fileSelf-organized clustering of deletion strains with AsIII treatment and parent strain vs. deletion strains without arsenicClick here for additional data fileyap1Δ vs. parent without arsenic treatmentGene list of two-fold differential expression in Click here for additional data filecad1Δ vs. parent without arsenic treatmentGene list of two-fold differential expression in Click here for additional data filerpn4Δ vs. parent without arsenic treatmentGene list of two-fold differential expression in Click here for additional data filearr1Δ vs. parent without arsenic treatmentGene list of two-fold differential expression in Click here for additional data fileA file of all the genes mentioned in the paperClick here for additional data file


Bacillus anthracis strains. Sequence quality was shown to be very high by replication and by comparison to independently generated shotgun sequenceCustom-designed resequencing arrays were used to generate 3.1 Mb of genomic sequence from a panel of 56  Bacillus anthracis strains. Sequence quality was shown to be very high by replication (discrepancy rate of 7.4 × 10-7) and by comparison to independently generated shotgun sequence (discrepancy rate < 2.5 × 10-6). Population genomics studies of microbial pathogens using rapid resequencing technologies such as resequencing arrays are critical for recognizing newly emerging or genetically engineered strains.We used custom-designed resequencing arrays to generate 3.1 Mb of genomic sequence from a panel of 56  Bacillus anthracis, genomic sequences from multiple strains and non-pathogenic close relatives could aid studies that definitively identify B. anthracis in environmental and clinical samples, determine forensic attribution and phylogenetic relationships of strains, and uncover the genetic basis of phenotypic variation in traits such as mammalian virulence. Moreover, first recognizing the presence of a novel pathogen, and then attempting the difficult task of discerning between novel naturally occurring pathogenic organisms (for instance Bacillus cereus G9241 [Population genomics, the study of genome-wide patterns of genetic variation in a large number of organisms, is emerging as a vigorous new field of study -3. RapidB. anthracis Ames chromosome (5.2 Mb, NC_003997) and plasmids pXO1 (181.6 kilobases (kb), NC_001496) and pXO2 (96.2 kb, NC_002146) have been determined [B. cereus ATCC 14579 [B. cereus ATCC 10987 [B. cereus G9241 [B. anthracis Ames strain isolated from a victim of the autumn 2001 bioterror attack in Florida was also sequenced to a high level of coverage using the random shotgun method and compared to the Ames sequence to identify 60 new markers that included single nucleotide polymorphisms (SNPs), inserted or deleted sequences, and tandem repeats [B. anthracis . Whole-orted by -15. HoweB. anthracis and near neighbors of the Bacillus cereus sensu lato group [B. anthracis has employed multiple locus variable number of tandem repeats analysis (MLVA) [Numerous molecular methods for genotyping to group  have beeto group -21, repeto group ,23 and ato group -27. Howeto group , the moss (MLVA) -31. SimiClearly, a method for rapid, inexpensive genome resequencing of bacterial strains would be of great benefit for genotyping, forensics and studies of the genetic basis of strain phenotypic variation. Developing DNA-based biodetection assays depends upon prior knowledge of patterns of genetic variation within and between bacterial species. It would be ideal to enable technologies that could combine the high information content of whole-genome resequencing of strains while also being rapid and inexpensive like MLVA, AFLP and multi-locus sequence typing (MLST). Furthermore, while conventional strain typing methodologies have focused on the utility of common variants, rare variants may prove to be especially informative for forensic applications.High-density oligonucleotide resequencing microarrays are a highly parallel technology that can enable the rapid identification of DNA sequence variants with minimal laboratory effort and infrastructure ,33. PrevB. anthracis isolates. Our study first reaffirms, and then directly demonstrates that the quality of microarray-generated DNA sequence data is directly comparable to that produced by conventional shotgun sequencing. We then estimate the levels of genetic variation in the annotated genomic regions we resequenced, characterize the frequency spectrum of DNA sequence variants we observe, and finally explore patterns of linkage disequilibrium and recombination among those variants. Because of the scalability and minimal effort associated with microarray-based resequencing, our work demonstrates the possibility of a rapid and cost-effective method of genome resequencing that could be applied to both environmental, and ultimately clinical specimens.Here we present the first microarray-based high-throughput resequencing of a large collection of B. anthracis strains from the Biological Defense Research Directorate's strain collection  was resequenced using Affymetrix resequencing arrays (RAs) and base calls determined using the ABACUS software package [B. anthracis genome from a single isolate sample . Long PCR sample preparation and chip processing was conducted for 118 RAs. Analysis of these 118 RAs with the ABACUS software package shows that 115 are successful (97.5%). Experimental failure occurs when less than 60% of the total possible bases fail to achieve quality scores exceeding the ABACUS user-defined threshold. For this study, the total threshold was set at 31 and a strand minimum of -2 [A panel of 56  package . Each RAum of -2 , as deteThe 115 successful RAs call 92.6% of the possible bases . Figure P < 10-22). A similar pattern is detected if we limit our analysis to guanine-rich probes (P < 10-9). This latter result is surprising given that the B. anthracis genomic sequences we examined have a low G+C content (~34%). Nevertheless, these analyses demonstrate that both purine-rich and guanine-rich oligonucleotide probes are significantly more likely to fail to generate quality scores exceeding the experimental threshold.Previous results demonstrated that base-calling failure was concentrated among RA oligonucleotide probes containing multiple purines. Purine-rich probes were observed to have lower hybridization intensities at identical positions across multiple RAs. Guanine-rich probes, in particular, showed the greatest reduction in hybridization intensity . Conseqet al. [et al. [-7 , 90.6%  [et al. . Of the -7 Table . If repereported , providiB. anthracis strain had been sequenced using the random shotgun approach and deposited in GenBank . In a comparison of 398,467 bp of RA- and shotgun-generated sequence, we observed 15 discrepancies occurring at six sites. This corresponds to a discrepancy rate of 3.8 × 10-5. If we make the conservative assumption that all discrepancies lie in the RA-generated sequence, this level of accuracy would correspond to a phred score of at least 44.While RA data is highly replicable, repeated systematic errors would not be detected in a replicate experiment. To obtain an independent estimate of RA sequence accuracy, we compared the sequence data from 30 RAs where the same C_003997 , Vollum,et al., unpublished work) with the RA Kruger B strain base calls agreed with the RA base calls. The latest Vollum shotgun assembly  still disagreed at the one site , but this discrepancy was based on a single shotgun sequencing read with a phred score of 7 at the discrepant base. Clearly, the shotgun coverage lacks sufficient depth at this site to make a reliable base call and it seems far more likely that the fivefold RA base call is correct. Hence, the RA sequence data has less than one discrepancy per 398,467 bases called, or a discrepancy rate of < 2.5 × 10-6 , were found in Kruger B strain sequences. The one remaining site, accounting for five discrepancies (fivefold RA replication at this site), was found in Vollum strain sequences. At all 15 discrepancies, the RA called a base identical to the Ames reference sequence , while tB. anthracis strains. The SNP location, base-call, and position relative to the respective GenBank reference sequences [et al., unpublished work)). Of the remaining 13 SNPs not independently verified by The Institute of Genomic Research (TIGR), 11 were seen only once in our collection of strains and two SNPs were seen three times.We identify 37 SNPs among 56 equences  are contB. anthracis. The number of SNPs identified, the amount of sequence generated and the nucleotide diversity [B. anthracis is very low. This finding is in agreement with previous studies [D. melanogaster [B. anthracis chromosome appears less variable than either the pXO1 or pXO2 plasmids, although this difference is not statistically significant. Third, the patterns of genetic variation by SNP class (see Table P = 0.0011). Intergenic regions are observed to have intermediate levels of genetic variation, whereas replacement sites, those sites that when mutated alter the protein primary structure, are the least variable. Replacement sites are marginally significantly less variable than intergenic sites (P = 0.039) whereas silent sites are not significantly more variable than intergenic sites (P = 0.22).Population genetic inference typically assumes that study samples are selected without prior knowledge of their patterns of genetic variation. For this study, we selected diverse strains from widely distant geographic regions in an attempt to sample the full extent of genetic variation in iversity  of the 5 studies ,28. This studies , roughlynogaster ,39,45-48B. anthracis SNPs we discovered have minor allele frequencies that fall into this class, a statistically significant difference  454) [We used the Tajima's D statistic  to furthSD) 454)  and 1,58B. anthracis (NC_003997, Ames) and B. cereus  the pat species ,51,52.B. anthracis chromosome and plasmids pXO1 and pXO2, possess in total, 636 pairs of sites where two alleles are observed. In principle, the alleles at each pair of sites could form four distinct haplotypes. Plasmid transfer between different B. anthracis strains would affect physically unlinked site pairs resulting in four distinct haplotypes. Homologous recombination or gene conversion between physically linked site pairs is also expected to produce all four haplotypes. The straightforward counting of the number of haplotypes that one detects in a large population sample, such as the one used in this study, is often referred to as the four-gamete test [The 37 SNPs discovered on the ete test .P < 10-3, we observe that 52 site pairs exhibit statistically significant LD. Four of the six site pairs showing significant LD on the B. anthracis main chromosome are over 500 kb apart.Among the 636 site pairs in our sample, we observe 26 pairs of sites with two haplotypes, 610 pairs of sites with three haplotypes, and no pairs of sites with four haplotypes. This striking result implies that the value of D', the standardized measure of linkage disequilibrium (LD) , is equaB. anthracis  in 11 diverse B. anthracis strains finding three polymorphic nucleotides [B. anthracis strains.Because of the low level of genetic variation in thracis  tree . Strain genotyping of common variants provides an incomplete description of genomic patterns of DNA sequence variation, while obtaining most or all of the genomic sequence from multiple strains allows a maximally informative analyses of DNA sequence variation, its function, and ultimately, the evolutionary history of the organisms. The ability to rapidly, accurately and inexpensively resequence entire bacterial genomes should also contribute to an understanding of a variety of important phenotypic traits in athogens -62.-7) and accurate when compared to conventional shotgun sequence (discrepancy rate of < 2.5 × 10-6). Clearly, RA-generated resequencing data from a single experiment is comparable, in terms of data quality, to DNA sequence generated from multiple shotgun reads by a DNA sequencing center. The major technical challenge facing RA-based resequencing is to increase overall call rates while not compromising data quality. Modifications of RA synthesis, experimental protocols and the ABACUS software algorithm could all contribute to improved base-calling rates. While it is possible to increase call rates while sacrificing data quality, there is a need to focus on generating very high-quality data at virtually all sites. If this is absent, the second-best outcome is to call all bases in an environment in which we understand the nature of probable errors. In diverse fields where RAs might be widely used as a first-stage screening tool, such as BW agent identification or human clinical testing, the imperative is to use highly sensitive technologies that minimize the false-negative rate. False-positive findings could be confirmed later in a second-stage screen with an alternative technology such as conventional dideoxy chain termination sequencing.Our study demonstrates that microarray-based resequencing is technologically robust and generates highly replicable and accurate data when compared to alternative sequence technologies Table . In thisB. anthracis that have low levels of genetic variation ([B. anthracis genome on approximately 15 RAs.Microarray-based resequencing identifies and genotypes SNPs in a single experiment. No prior knowledge of the variability of a site is required - only a reference genomic sequence. Microarray design and applications are flexible. It is, however, important to note that the use of RAs in this study is not as a SNP typing technology. Thus, problems in interpreting the inferred phylogenetic relationships between strains that arise from SNP typing schema are avoided . RA-baseriation (,51,55 anB. anthracis. We report three main findings. First, among B. anthracis isolates sampled in the same fashion as in this study we would expect two randomly selected B. anthracis strains to differ, on average, at between 944 (SD 454) and 1,586 SNPs (SD 762). The variance surrounding these expectations is large, and any two isolates may differ from the expectation. Closely related, nonrandomly sampled isolates, such as those sequenced in [B. anthracis genomes, sufficient genetic variation is likely to be found to make very fine-level discrimination of strain collections. Resequencing offers the best chance to identify newly arising, rare, strain-specific variants that will discriminate between very closely related strains, since we expect identical genotypes at the known common genetic variants [Our data provides the first population genetic estimation of the levels and patterns of DNA sequence variation in enced in , will havariants . We alson 944 SD 4 and 1,5Second, the neutral theory of molecular evolution predicts that in a population at equilibrium, a significant proportion of the observed genetic variation will consist of rare genetic variants . We obseB. anthracis strains in the regions that we resequenced. Some of the regions that we resequenced contain genes whose function influences B. anthracis pathogenicity or surrounded the bacterial origin of replication. In other bacterial species, these types of regions are the most likely to exhibit recombination [Finally, we see no evidence for plasmid exchange or recombination altering the patterns of DNA sequence variation among bination . The facB. anthracis strains that we examined are ultimately derived from a single clonal ancestor and that the exchange of plasmids and recombination between strains during the course of their evolution is either very rare or nonexistent. While models of natural selection could also account for the patterns that we see, we think a simple demographic model of recent, rapid clonal expansion is parsimonious and best supported by our data. Hence, our findings suggest that B. anthracis populations consist of multiple closely related clones whose life histories prevent the opportunity for homologous recombination between different strains. We note, however, that while we resequenced 0.5% of the B. anthracis genome, including regions where we expected to detect recombination, further data collection from multiple genomic regions, or the entire genome, would allow a more thorough analysis of this pattern. Sequencing a larger percentage of the genome in a similar-sized or larger sample of isolates would provide greater power to detect rare recombination events. We are undertaking such a project to test the validity of our inference and to better determine if recombination is rare or absent among B. anthracis strains.The simplest interpretation of this observation is that the B. anthracis, a potential biowarfare agent, suggests a novel approach to identifying a newly arising or a genetically engineered strain. A recombination event could arise through rare natural genetic exchange or as a consequence of genetic engineering. Irrespective of the cause, the discovery of a B. anthracis strain possessing evidence of genetic recombination would warrant close examination and probably demand immediate further phenotypic and genomic characterization.The absence of recombination in B. anthracis has expanded recently from a single clone derived from, and nested within a diverse species, B. cereus. Other bacterial pathogens, such as the potential biowarfare agent Yersinia pestis, possess a similar recent pattern of rapid expansion [Y. pestis are quite different from that seen in B. anthracis, for instance in the much more active role of insertion sequences in Yersinia. We speculate that the B. anthracis history of clonal expansion could arise as a consequence of the life history of a highly pathogenic sporulating mammalian pathogen. Exploring the population biology of less virulent members of the B. cereus group could directly test this. These population genomics studies could determine if clonal clusters of B. cereus strains exhibit similar population dynamics and patterns of genetic variation, or whether the picture of B. anthracis emerging from studies such as this is as unusual as the level of pathogenicity of the species itself.Taken together, the findings of a low number of differences between strains, a preponderance of rare variants, and an absence of recombination all point to a scenario where the current world population of xpansion . HoweverB. anthracis strains in the regions that we resequenced The patterns of genetic variation in the B. anthracis regions resequenced are consistent with that expected for a bacterial species that has undergone a rapid, historically recent expansion from a single clone. Detecting plasmid exchange or recombination between B. anthracis genetic variants could act as an indicator of a newly emerging or genetically engineered strain.Microarray-based resequencing can rapidly generate very high quality data, enabling population genomics studies in bacteria. We find no evidence for plasmid exchange or recombination altering the patterns of DNA sequence variation among B. anthracis strains from the Biological Defense Research Directorate collection . Twenty-four of the strains originated from the Louisiana State University collection [We selected a geographically diverse panel of 56 llection . These hllection  and in ollection , which aB. anthracis Ames chromosome (5.2 megabase-pair (Mb), NC_003997) and plasmids pXO1 (181.6 kb, NC_001496) and pXO2 (96.2 kb, NC_002146). The genomic regions that we resequenced included at least one gene of interest , but also included many surrounding loci . The total chip design consisted of 6,191 bp from pXO1, 6,725 bp from pXO2, and 16,584 bp from the Ames chromosome . From these unique sequences, a single 20 × 25 μm RA design capable of resequencing 29,212 bp or 0.5% of the B. anthracis genome was fabricated by Affymetrix . The final sequences submitted for RA design are contained in Additional data file 5.Unique genomic sequences were identified using Miropeats  at the dg. Pellets were resuspended in 720 μl enzymatic lysis buffer (20 mM Tris-Cl pH 8.0, 2 mM EDTA, 1.2% Triton X-100, 20 mg/ml lysozyme) and incubated at 37°C for 1 h. After incubation 100 ml of Proteinase K was added along with 800 ml of Qiagen buffer AL, and incubated at 70°C for an additional 30 min. Then, 800 ml of 100% ethanol was added and this was split onto four of the Qiagen DNAeasy tissue kit. The DNA was then washed and eluted according to the Qiagen protocol. After the DNA was eluted, it was passed through a 0.22 mm filter. Sterility was confirmed by plating 10% DNA preparation directly on SBA plates with a second 10% inoculated into a 5 ml broth culture. The plate and the broth were allowed to incubate for 7 days. Two hundred milliliters of the broth culture was subcultured onto SBA at day 4. If there was no growth on any of these cultures the DNA was considered sterile and removed from the BSL-3 lab for subsequence analyses.Five milliliters of brain heart infusion (BHI) was inoculated and grown 12-16 h at 37°C. One-ml aliquots of cells were centrifuged for 10 min at 5,000-7,500et al. [Genomic DNA was amplified using Long PCR (LPCR) protocols described in Cutler et al. . The priant8 AAAAAGACGAGATGCGTCAACATCCCGTCCCA,ant9 TCAACTAAATCCGCACCTAGGGTTGCTGTAAG,ant10 ATTACTTTGAGTGGTCCCGTCTTTATCCCCCT,ant11 ACATTAGCAGGCAAGGACAGTGGTGTTGGAGA,ant14 ATTCACGCTCTCCCACCCAGATATTCCTACAT,ant15 GTCCTAATATCGGTGAGCAACGCAGGGTAGTT,ant20 GAGAAGAACCCCTACTACACGCATTGATACTG,ant21 TTTAGTAGCGAGGGTACAGGCGCGTTTATACC,ant26 TGGAAGCAGGCTTCGTAAGTGTAGGCGACGTT,ant27 GTTGCATGTTCGCTCCCATAAGTGCGCGGTTA,ant 32 AATGGGTGTATAGGGGTGATCTGTTGTGATGG,ant33 TCCATGTTCGGCCATCTGATTCCGTCACTACT.et al. [Long PCR product concentration was determined by using Pico-Green (Molecular Probes, Inc.) with lambda DNA standards (Invitrogen). The LPCR products were then pooled, DNAse digested, biotin endlabelled and hybridized to individual RAs overnight following established protocols contained in . Subsequet al.  and wereB. anthracis strains, while the remaining 11 RAs were used as additional multiple replicates of these same strains. In total, sequence data was generated from 56 unique B. anthracis strains . In order to obtain the most complete data possible, for those strains with replicate RA sequences, a single composite strain sequence was generated for subsequent population genetic analyses. The current version of ABACUS algorithm is not designed to detect insertion/deletion variation.An ABACUS parameter search was employed to determine those parameters that called the maximal number of bases while minimizing discrepancies . This tot test with unequal variances was used to test for difference in mean sequence composition (purines/guanines) between the always called and never called classes. The DNA sequence files for the 115 RAs and the original RA image files (.DAT files) are available from the authors and will be made available through the NCBI Trace Archive.The effect of oligonucleotide probe composition was determined by choosing for each base, the probe with the most purines or the most guanines. The number of times that a given base was called was tabulated across all 115 successful RAs. The mean purine and guanine composition was determined for the classes that were called in all 115 RAs and uncalled in all 115 RAs. A Student's B. anthracis fasta files. The fasta files were analyzed in total and separately for the main chromosome and plasmids pXO1 and pXO2. The identification of genes was taken from publicly available annotation contained in the relevant GenBank refseq files . The statistical significance of linkage disequilibrium between site pairs was performed by using the Fisher's Exact Test at P < 10-3[All population genetic analyses were calculated using the popgen_fasta2.0.c code (Cutler DJ, unpublished work) on the collection of 56 sets of  P < 10-3.n(Sn/an)]/L, where Sn is the number of observed segregating sites at positions with exactly n alleles sequenced (n is a maximum of 56, fewer with missing data), an = Σn-1 i = 1..1/i, and L is the total length of the sequence examined. Var{θ} is estimated by [Σn (Lnθ/an + (Ln)2bnθ2/(an)2]/L2, where Ln is the number of sites with data from exactly n alleles, and bn = Σn-1 i = 1..1/i2. With missing data π is estimated by [Σi 2piqini/(ni - 1)]/L, where the sum is taken over all sites i, pi and qi are the allele frequencies at site i, and ni is the number of alleles sequenced at site i.To account for missing data, θ is estimated by [Σp-values from the likelihood ratio test.To determine if the estimates of theta between SNP types (silent, replacement, intergenic) are significantly different, we used the number of samples sequenced, the number of segregating sites, and the length of the region to find a maximum-likelihood estimate of theta per site for each SNP type using equations 11 and 12 in Hudson . We compnθLn [1/i + 1/(n - i)] and from this determine the expected percent of sites under the neutral expectation. This is directly compared with the observed percent of SNPs in Figure Comparing the observed site frequency spectrum with that expected under the neutral theory is a powerful approach to detect unusual patterns of genetic diversity. We employed two different approaches for this analysis. First, we calculated the expected number of sites with minor allele frequency i as Σ is their observed frequency, and .where N is the number of SNPs observed for each class, B. anthracis genome length (5,505,178), to determine the expected number of SNPs that we would expect to observe among two B. anthracis strains sampled in same random fashion as isolates in this study were chosen. Using Equations 6-9 in [ and θ estimators. The one standard deviation (SD) that we report is the square root of this variance.As a second method, we employed Tajima's D statistic , estimats 6-9 in , we calcThe 37 variable positions identified in this study were concatenated together to create artificial sequence types. A DNA distance matrix was created using DNADIST, plotted as a UPGMA tree using NEIGHBOR and the tree plotted using DRAWGRAM .B. anthracis strains from the Biological Defense Research Directorate (BDRD) strain collection resequenced in this study. Additional data file B. anthracis SNPs identified in this study. The data include the BDRD SNP ID, the GenBank reference sequence and RA fragment containing the SNP, the SNP position relative to the GenBank reference sequence and the RA sequence, the SNP frequency, and the listing of the base calls in all strains at sites harboring SNPs. Additional data file B. anthracis genes partially or wholly resequenced in this study. The observed number SNPs by SNP type (silent vs replacement) for each gene are provided. Finally, Additional data file The following additional data are available with the online version of this article. Additional data file B. anthracis strains from the Biological Defense Research Directorate (BDRD) strain collection resequenced in this studyClick here for additional data fileThe BDRD-01 RA fragment names, the GenBank reference sequence from which they are derived, the length of the unique genomic sequences submitted to RA design, the length of the unique genomic sequences capable of being queried, and the LPCR primer pairs used to amplify the RA fragmentsClick here for additional data fileB. anthracis SNPs identified in this study. The data include the BDRD SNP ID, the GenBank reference sequence and RA fragment containing the SNP, the SNP position relative to the GenBank reference sequence and the RA sequence, the SNP frequency, and the listing of the base calls in all strains at sites harboring SNPsThe Click here for additional data fileB. anthracis genes partially or wholly resequenced in this studyThe 31 Click here for additional data fileThe genomic sequences submitted to RA design for BDRD-01Click here for additional data file


West Nile virus (WNV) recently became a major public health concern in North America, the Middle East, and Europe. In contrast with the investigations of the North-American isolates, the neurovirulence properties of Middle-Eastern strains of WNV have not been extensively characterized. Israeli WNV strain IS-98-ST1 that has been isolated from a white stork in 1998, was found to be highly neuroinvasive in adult C57BL/6 mice. Strain IS-98-ST1 infects primary neuronal cells from mouse cortex, causing neuronal death. These results demonstrate that Israeli strain IS-98-ST1 provides a suitable viral model for WNV-induced disease associated with recent WNV outbreaks in the Old World. Flaviviridae, genus flavivirus) with a worldwide distribution ranging Africa, Europe, the Middle East, and Asia. WNV was first recognized in the Western Hemisphere in 1999. The emergence of WNV has been associated with a dramatic increase in severity of disease in humans and other species[West Nile virus (WNV) is a single-stranded RNA flavivirus  and the lineage I  that caused the outbreaks of WNV infection in North Africa, Europe, Israel, and in the United States. Nucleotide sequencing revealed that American strains of WNV isolated between 1999 and 2000 are nearly identical to Israeli strains of WNV isolated in 1998 and 2000 ,4. This Aedes pseudoscutellaris AP61[The murine model of WNV-associated encephalitis has been widely used to address the viral pathogenesis. Strainsaris AP61 and its aris AP61. InfectiIn this study, we demonstrated that IS-98-ST1 has a high neuroinvasive potential in adult C57Bl/6 mice, and that the virus is capable to replicate in primary neuronal cultures from mouse brain cortex.Mouse experiments were performed according to the European Convention 2001–486. After anesthesia, six-week-old female C57BL/6 mice (Harlan, France) were inoculated with 1,000 FFU of WNV via different routes : intraperitoneal (i.p.), intradermal (i.d.), intracerebral (i.c.), and intranasal (i.n.). At Days 5 and 7 of infection, three animals per group were euthanasied; brain and spinal cord were rapidly removed, processed for viral titration or sectioned on cryostat . Sections were fixed with 3.7% formaldehyde or acetone for 30 min and processed for indirect immunofluorescence with mouse polyclonal anti-WNV antibodies[5 (i.d. route) to 3.108 FFU/g (i.c. route).When infected i.c., mice died at day 7.3 ± 1 post-infection (p.i.) ; 100% mortality was also reached after i.p., i.n., or i.d. inoculation but with delayed kinetics (day 9.5 ± 0.5, 10.7 ± 0.7 and 10.5 ± 0.5 p.i. respectively). In all cases, WNV-infected mice exhibited characteristic disease progression with hind limb paralysis, cachexia and tremors. By day 7 p.i., WNV was found in brain tissue in all mice, reaching virus titers from 3.10To investigate WNV location within the CNS, cryostat brain sections from three WNV-infected mice were assessed for the presence of viral antigens by immunofluorescence at day 7 p.i. When inoculated i.c, virus was found widespread in most of the brain structures , including cortex Fig. , pyramid7 FFU of WNV per ml was detected in the culture supernatant. Time course studies showed that IS-98-ST1 infection induced cell death through neuronal necrosis within 48 h of infection, and ~90% of cells had detached by 96 h  (Harlan, France). Briefly6 h Fig. . Whateve6 h Fig. . GFAP poAlthough our study was limited in its scope, the results indicate that WNV strain IS-98-ST1 is suitable as viral model for West Nile encephalitis in the Old World. The Israeli strain IS-98-ST1 that caused the epizootic in Israel in 1998, was found to be highly neuroinvasive in mice following peripheral inoculation. Consistent with this observation, we reported that IS-98-ST1 has an i.p. LD50 value as low as 10 FFU. High viin vivo. Because neurons are believed to be main target neural cells of WNV, we developed an ex-vivo model of infection, by culturing primary neural cells from the brain cortex of susceptible mice. More than 90% of the neurons are found to be infected by IS-98-ST1 and infected neurons undergo necrosis. In contrast, astrocytes were mainly resistant to WNV infection. This is consistent with in vivo data showing a massive infection of brain structures such as brain stem, hippocampus and cortex of WNV-infected animals [Our study confirms the neurotropism of WNV and the huge preferential infection of neurons  animals  and huma animals . The hig animals ,19. In cex-vivo studies, M-PF and TM participated in in vivo experiments, VD and J-LG revised critically the article, PD and P-EC have written, drafted the article, and participated to in vivo and ex-vivo experiments.ML carried out The authors declare that they have no competing interests.