Erin M. Conlon

Integrating regulatory motif discovery and genome-wide expression analysis

We propose motif regressor for discovering sequence motifs upstream of genes that undergo expression changes in a given condition. The method combines the advantages of matrix-based motif finding and oligomer motif-expression regression analysis, resulting in high sensitivity and specificity. motif regressor is particularly effective in discovering expression-mediating motifs of medium to long width with multiple degenerate positions. When applied to Saccharomyces cerevisiae, motif regressor identified the ROX1 and YAP1 motifs from Rox1p and Yap1p overexpression experiments, respectively; predicted that Gcn4p may have increased activity in YAP1 deletion mutants; reported a group of motifs (including GCN4, PHO4, MET4, STRE, USR1, RAP1, M3A, and M3B) that may mediate the transcriptional response to amino acid starvation; and found all of the known cell-cycle regulation motifs from 18 expression microarrays over two cell cycles.

The Program of Gene Transcription for a Single Differentiating Cell Type during Sporulation in Bacillus subtilis

Asymmetric division during sporulation by Bacillus subtilis generates a mother cell that undergoes a 5-h program of differentiation. The program is governed by a hierarchical cascade consisting of the transcription factors: σE, σK, GerE, GerR, and SpoIIID. The program consists of the activation and repression of 383 genes. The σE factor turns on 262 genes, including those for GerR and SpoIIID. These DNA-binding proteins downregulate almost half of the genes in the σE regulon. In addition, SpoIIID turns on ten genes, including genes involved in the appearance of σK . Next, σK activates 75 additional genes, including that for GerE. This DNA-binding protein, in turn, represses half of the genes that had been activated by σK while switching on a final set of 36 genes. Evidence is presented that repression and activation contribute to proper morphogenesis. The program of gene expression is driven forward by its hierarchical organization and by the repressive effects of the DNA-binding proteins. The logic of the program is that of a linked series of feed-forward loops, which generate successive pulses of gene transcription. Similar regulatory circuits could be a common feature of other systems of cellular differentiation.

The σE regulon and the identification of additional sporulation genes in Bacillus subtilis

We report the identification and characterization on a genome-wide basis of genes under the control of the developmental transcription factor sigma(E) in Bacillus subtilis. The sigma(E) factor governs gene expression in the larger of the two cellular compartments (the mother cell) created by polar division during the developmental process of sporulation. Using transcriptional profiling and bioinformatics we show that 253 genes (organized in 157 operons) appear to be controlled by sigma(E). Among these, 181 genes (organized in 121 operons) had not been previously described as members of this regulon. Promoters for many of the newly identified genes were located by transcription start site mapping. To assess the role of these genes in sporulation, we created null mutations in 98 of the newly identified genes and operons. Of the resulting mutants, 12 (in prkA, ybaN, yhbH, ykvV, ylbJ, ypjB, yqfC, yqfD, ytrH, ytrI, ytvI and yunB) exhibited defects in spore formation. In addition, subcellular localization studies were carried out using in-frame fusions of several of the genes to the coding sequence for GFP. A majority of the fusion proteins localized either to the membrane surrounding the developing spore or to specific layers of the spore coat, although some fusions showed a uniform distribution in the mother cell cytoplasm. Finally, we used comparative genomics to determine that 46 of the sigma(E)-controlled genes in B.subtilis were present in all of the Gram-positive endospore-forming bacteria whose genome has been sequenced, but absent from the genome of the closely related but not endospore-forming bacterium Listeria monocytogenes, thereby defining a core of conserved sporulation genes of probable common ancestral origin. Our findings set the stage for a comprehensive understanding of the contribution of a cell-specific transcription factor to development and morphogenesis.

Evidence for a Novel Late-Onset Alzheimer Disease Locus on Chromosome 19p13.2

Late-onset familial Alzheimer disease (LOFAD) is a genetically heterogeneous and complex disease for which only one locus, APOE, has been definitively identified. Difficulties in identifying additional loci are likely to stem from inadequate linkage analysis methods. Nonparametric methods suffer from low power because of limited use of the data, and traditional parametric methods suffer from limitations in the complexity of the genetic model that can be feasibly used in analysis. Alternative methods that have recently been developed include Bayesian Markov chain-Monte Carlo methods. These methods allow multipoint linkage analysis under oligogenic trait models in pedigrees of arbitrary size; at the same time, they allow for inclusion of covariates in the analysis. We applied this approach to an analysis of LOFAD on five chromosomes with previous reports of linkage. We identified strong evidence of a second LOFAD gene on chromosome 19p13.2, which is distinct from APOE on 19q. We also obtained weak evidence of linkage to chromosome 10 at the same location as a previous report of linkage but found no evidence for linkage of LOFAD age-at-onset loci to chromosomes 9, 12, or 21.

Rapid Changes in Gene Expression Dynamics in Response to Superoxide Reveal SoxRS-Dependent and Independent Transcriptional Networks

Background SoxR and SoxS constitute an intracellular signal response system that rapidly detects changes in superoxide levels and modulates gene expression in E. coli. A time series microarray design was used to identify co-regulated SoxRS-dependent and independent genes modulated by superoxide minutes after exposure to stress. Methodology/Principal Findings soxS mRNA levels surged to near maximal levels within the first few minutes of exposure to paraquat, a superoxide-producing compound, followed by a rise in mRNA levels of known SoxS-regulated genes. Based on a new method for determining the biological significance of clustering results, a total of 138 genic regions, including several transcription factors and putative sRNAs were identified as being regulated through the SoxRS signaling pathway within 10 minutes of paraquat treatment. A statistically significant two-block SoxS motif was identified through analysis of the SoxS-regulated genes. The SoxRS-independent response included members of the OxyR, CysB, IscR, BirA and Fur regulons. Finally, the relative sensitivity to superoxide was measured in 94 strains carrying deletions in individual, superoxide-regulated genes. Conclusions/Significance By integrating our microarray time series results with other microarray data, E. coli databases and the primary literature, we propose a model of the primary transcriptional response containing 226 protein-coding and sRNA sequences. From the SoxS dependent network the first statistically significant SoxS-related motif was identified.

Bayesian Areal Interpolation, Estimation, and Smoothing: An Inferential Approach for Geographic Information Systems

Geographic information systems (GISs) offer a powerful tool to geographers, foresters, statisticians, public health officials, and other users of spatially referenced regional data sets. However, as useful as they are for data display and trend detection, they typically feature little ability for statistical inference, leaving the user in doubt as to the significance of the various patterns and ‘hot spots’ identified. Unfortunately, classical statistical methods are often ill suited for this complex inferential task, dealing as it does with data which are multivariate, multilevel, misaligned, and often nonrandomly missing. In this paper we describe a Bayesian approach to this inference problem which simultaneously allows interpolation of missing values, estimation of the effect of relevant covariates, and spatial smoothing of underlying causal patterns. Implemented via Markov-chain Monte Carlo (MCMC) computational methods, the approach automatically produces both point and interval estimates which account for all sources of uncertainty in the data. After describing the approach in the context of a simple, idealized example, we illustrate it with a data set on leukemia rates and potential geographic risk factors in Tompkins County, New York, summarizing our results with numerous maps created by using the popular GIS Arc/INFO.

Bayesian meta-analysis models for microarray data: a comparative study

BackgroundWith the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies. Two common approaches for meta-analysis of microarrays include either combining gene expression measures across studies or combining summaries such as p-values, probabilities or ranks. Here, we compare two Bayesian meta-analysis models that are analogous to these methods.ResultsTwo Bayesian meta-analysis models for microarray data have recently been introduced. The first model combines standardized gene expression measures across studies into an overall mean, accounting for inter-study variability, while the second combines probabilities of differential expression without combining expression values. Both models produce the gene-specific posterior probability of differential expression, which is the basis for inference. Since the standardized expression integration model includes inter-study variability, it may improve accuracy of results versus the probability integration model. However, due to the small number of studies typical in microarray meta-analyses, the variability between studies is challenging to estimate. The probability integration model eliminates the need to model variability between studies, and thus its implementation is more straightforward. We found in simulations of two and five studies that combining probabilities outperformed combining standardized gene expression measures for three comparison values: the percent of true discovered genes in meta-analysis versus individual studies; the percent of true genes omitted in meta-analysis versus separate studies, and the number of true discovered genes for fixed levels of Bayesian false discovery. We identified similar results when pooling two independent studies of Bacillus subtilis. We assumed that each study was produced from the same microarray platform with only two conditions: a treatment and control, and that the data sets were pre-scaled.ConclusionThe Bayesian meta-analysis model that combines probabilities across studies does not aggregate gene expression measures, thus an inter-study variability parameter is not included in the model. This results in a simpler modeling approach than aggregating expression measures, which accounts for variability across studies. The probability integration model identified more true discovered genes and fewer true omitted genes than combining expression measures, for our data sets.

Bayesian models for pooling microarray studies with multiple sources of replications

BackgroundBiologists often conduct multiple but different cDNA microarray studies that all target the same biological system or pathway. Within each study, replicate slides within repeated identical experiments are often produced. Pooling information across studies can help more accurately identify true target genes. Here, we introduce a method to integrate multiple independent studies efficiently.ResultsWe introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes. Each study has multiple sources of variation, i.e. replicate slides within repeated identical experiments. Our model produces the gene-specific posterior probability of differential expression, which provides a direct method for ranking genes, and provides Bayesian estimates of false discovery rates (FDR). In simulations combining two and five independent studies, with fixed FDR levels, we observed large increases in the number of discovered genes in pooled versus individual analyses. When the number of output genes is fixed (e.g., top 100), the pooled model found appreciably more truly differentially expressed genes than the individual studies. We were also able to identify more differentially expressed genes from pooling two independent studies in Bacillus subtilis than from each individual data set. Finally, we observed that in our simulation studies our Bayesian FDR estimates tracked the true FDRs very well.ConclusionOur method provides a cohesive framework for combining multiple but not identical microarray studies with several sources of replication, with data produced from the same platform. We assume that each study contains only two conditions: an experimental and a control sample. We demonstrated our models suitability for a small number of studies that have been either pre-scaled or have no outliers.

Oligogenic segregation analysis of hereditary prostate cancer pedigrees: Evidence for multiple loci affecting age at onset

Previous studies have suggested strong evidence for a hereditary component to prostate cancer (PC) susceptibility. Here, we analyze 3,796 individuals in 263 PC families recruited as part of the ongoing Prostate Cancer Genetic Research Study (PROGRESS). We use Markov chain Monte Carlo (MCMC) oligogenic segregation analysis to estimate the number of quantitative trait loci (QTLs) and their contribution to the variance in age at onset of hereditary PC (HPC). We estimate 2 covariate effects: diagnosis of PC before and after prostate‐specific antigen (PSA) test availability, and presence/absence of at least 1 blood relative with primary neuroepithelial brain cancer (BC). We find evidence that 2 to 3 QTLs contribute to the variance in age at onset of HPC. The 2 QTLs with the largest contribution to the total variance are both effectively dominant loci. We find that the covariate for diagnosis before and after PSA test availability is important. Our findings for the number of QTLs contributing to HPC and the variance contribution of these QTLs will be instructive in mapping and identifying these genes.

A Bayesian Model for Pooling Gene Expression Studies That Incorporates Co-Regulation Information

Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes. However, in prokaryotic organisms, genes are arranged in units that are co-regulated (called operons). Here, we introduce a new Bayesian model for pooling gene expression studies that incorporates operon information into the model. Our Bayesian model borrows information from other genes within the same operon to improve estimation of gene expression. The model produces the gene-specific posterior probability of differential expression, which is the basis for inference. We found in simulations and in biological studies that incorporating co-regulation information improves upon the independence model. We assume that each study contains two experimental conditions: a treatment and control. We note that there exist environmental conditions for which genes that are supposed to be transcribed together lose their operon structure, and that our model is best carried out for known operon structures.