Many Microbe Microarrays Database (M3D) is designed to facilitate the analysis and visualization of expression data in compendia compiled from multiple laboratories. M3D contains over a thousand Affymetrix microarrays for Escherichia coli, Saccharomyces cerevisiae and Shewanella oneidensis. The expression data is uniformly normalized to make the data generated by different laboratories and researchers more comparable. To facilitate computational analyses, M3D provides raw data (CEL file) and normalized data downloads of each compendium. In addition, web-based construction, visualization and download of custom datasets are provided to facilitate efficient interrogation of the compendium for more focused analyses. The experimental condition metadata in M3D is human curated with each chemical and growth attribute stored as a structured and computable set of experimental features with consistent naming conventions and units. All versions of the normalized compendia constructed for each species are maintained and accessible in perpetuity to facilitate the future interpretation and comparison of results published on M3D data. M3D is accessible at http://m3d.bu.edu/.

Summary Understanding the population genetic consequences of declining population size is important for conserving the many species worldwide facing severe decline [1]. Thorough empirical studies on the impacts of population reduction at a genome-wide scale in the wild are scarce because they demand huge field and laboratory investments [1, 2]. Previous studies have demonstrated the importance of gene flow in introducing genetic variation to small populations [3], but few have documented both genetic and fitness consequences of decreased immigration through time in a natural population [4-6]. Here we assess temporal variation in gene flow, inbreeding, and fitness using longitudinal genomic, demographic, and phenotypic data from a long-studied population of federally Threatened Florida Scrub-Jays (Aphelocoma coerulescens; hereafter FSJ). We exhaustively sampled and genotyped the study population over two decades, providing one of the most detailed longitudinal investigations of genetics in a wild animal population to date. Immigrants were less heterozygous than residents but still introduced genetic variation into our study population. Owing to regional population declines, immigration into the study population declined from 1995-2013, resulting in increased levels of inbreeding and reduced fitness via inbreeding depression, even as the population remained demographically stable. Our results show that, contrary to conventional wisdom, small peripheral populations that already have undergone a genetic bottleneck may play a vital role in preserving genetic diversity of larger and seemingly stable populations. These findings underscore the importance of investing in the persistence of small populations and maintaining population connectivity in conservation of fragmented species.

A central goal of population genetics is to understand how genetic drift, natural selection, and gene flow shape allele frequencies through time. However, the actual processes underlying these changes—variation in individual survival, reproductive success, and movement—are often difficult to quantify. Fully understanding these processes requires the population pedigree, the set of relationships among all individuals in the population through time. Here, we use extensive pedigree and genomic information from a long-studied natural population of Florida Scrub-Jays (Aphelocoma coerulescens) to directly characterize the relative roles of different evolutionary processes in shaping patterns of genetic variation through time. We performed gene dropping simulations to estimate individual genetic contributions to the population and model drift on the known pedigree. We found that observed allele frequency changes are generally well predicted by accounting for the different genetic contributions of founders. Our results show that the genetic contribution of recent immigrants is substantial, with some large allele frequency shifts that otherwise may have been attributed to selection actually due to gene flow. We identified a few SNPs under directional short-term selection after appropriately accounting for gene flow. Using models that account for changes in population size, we partitioned the proportion of variance in allele frequency change through time. Observed allele frequency changes are primarily due to variation in survival and reproductive success, with gene flow making a smaller contribution. This study provides one of the most complete descriptions of short-term evolutionary change in allele frequencies in a natural population to date.

Microarray data are available at the Many Microbe Microarrays Database (M3D, http://m3d.bu.edu). Algorithm scripts are available at the Gardner Lab website (http://gardnerlab.bu.edu/SSEMLasso).

The Arabian horse, one of the world’s oldest breeds of any domesticated animal, is characterized by natural beauty, graceful movement, athletic endurance, and, as a result of its development in the arid Middle East, the ability to thrive in a hot, dry environment. Here we studied 378 Arabian horses from 12 countries using equine single nucleotide polymorphism (SNP) arrays and whole-genome re-sequencing to examine hypotheses about genomic diversity, population structure, and the relationship of the Arabian to other horse breeds. We identified a high degree of genetic variation and complex ancestry in Arabian horses from the Middle East region. Also, contrary to popular belief, we could detect no significant genomic contribution of the Arabian breed to the Thoroughbred racehorse, including Y chromosome ancestry. However, we found strong evidence for recent interbreeding of Thoroughbreds with Arabians used for flat-racing competitions. Genetic signatures suggestive of selective sweeps across the Arabian breed contain candidate genes for combating oxidative damage during exercise, and within the “Straight Egyptian” subgroup, for facial morphology. Overall, our data support an origin of the Arabian horse in the Middle East, no evidence for reduced global genetic diversity across the breed, and unique genetic adaptations for both physiology and conformation.

Previous reports have provided evidence that p53 mutation is a strong negative predictor of response to MDM2 inhibitors. However, this correlation is not absolute, as many p53Mutant cell lines have been reported to respond to MDM2 inhibition, while many p53WT cell lines have been shown not to respond. To better understand the nature of these exceptions, we screened a panel of 260 cell lines and noted similar discrepancies. However, upon extensive curation of this panel, these apparent exceptions could be eliminated, revealing a perfect correlation between p53 mutational status and MDM2 inhibitor responsiveness. It has been suggested that the MDM2-amplified subset of p53WT tumors might be particularly sensitive to MDM2 inhibition. To facilitate clinical testing of this hypothesis, we identified a rationally derived copy number cutoff for assignment of functionally relevant MDM2 amplification. Applying this cutoff resulted in a pan-cancer MDM2 amplification rate far lower than previously published.

No abstract

BackgroundTranscriptional regulatory network inference (TRNI) from large compendia of DNA microarrays has become a fundamental approach for discovering transcription factor (TF)-gene interactions at the genome-wide level. In correlation-based TRNI, network edges can in principle be evaluated using standard statistical tests. However, while such tests nominally assume independent microarray experiments, we expect dependency between the experiments in microarray compendia, due to both project-specific factors (e.g., microarray preparation, environmental effects) in the multi-project compendium setting and effective dependency induced by gene-gene correlations. Herein, we characterize the nature of dependency in an Escherichia coli microarray compendium and explore its consequences on the problem of determining which and how many arrays to use in correlation-based TRNI.ResultsWe present evidence of substantial effective dependency among microarrays in this compendium, and characterize that dependency with respect to experimental condition factors. We then introduce a measure neff of the effective number of experiments in a compendium, and find that corresponding to the dependency observed in this particular compendium there is a huge reduction in effective sample size i.e., neff = 14.7 versus n = 376. Furthermore, we found that the neff of select subsets of experiments actually exceeded neff of the full compendium, suggesting that the adage 'less is more' applies here. Consistent with this latter result, we observed improved performance in TRNI using subsets of the data compared to results using the full compendium. We identified experimental condition factors that trend with changes in TRNI performance and neff , including growth phase and media type. Finally, using the set of known E. coli genetic regulatory interactions from RegulonDB, we demonstrated that false discovery rates (FDR) derived from neff -adjusted p-values were well-matched to FDR based on the RegulonDB truth set.ConclusionsThese results support utilization of neff as a potent descriptor of microarray compendia. In addition, they highlight a straightforward correlation-based method for TRNI with demonstrated meaningful statistical testing for significant edges, readily applicable to compendia from any species, even when a truth set is not available. This work facilitates a more refined approach to construction and utilization of mRNA expression compendia in TRNI.