Gene-class analysis of expression patterns induced by psychoactive pharmaceutical exposure in fathead minnow (Pimephales promelas) indicates induction of neuronal systems

Psychoactive pharmaceuticals are among the most frequently prescribed drugs, contributing to persistent measurable concentrations in aquatic systems. Typically, it is assumed that such contaminants have no human health implications because they exist in extremely low concentrations. We exposed juvenile fathead minnows (Pimephales promelas) to three pharmaceuticals, fluoxetine, venlafaxine and carbamazepine, individually and in a mixture, and measured their effect on the induction of gene expression in fish brains using microarray analysis. Gene expression changes were accompanied by behavioral changes and validated by qPCR analysis. Gene Set Enrichment Analysis was used to perform gene-class analysis of gene expression, testing for enrichment of gene sets known to be involved in human neuronal development, regulation and growth. We found significant enrichment of gene sets for each of the treatments, with the largest induction of expression by the mixture treatment. These results suggest that the psychoactive pharmaceuticals are able to alter expression of fish genes associated with development, regulation and differentiation of synapses, neurons and neurotransmitters. The results provide a new perspective for the consideration of potential consequence for human health due to environmental exposure to unmetabolized psychoactive pharmaceuticals.     

How to cluster gene expression dynamics in response to environmental signals

Organisms usually cope with change in the environment by altering the dynamic trajectory of gene expression to adjust the complement of active proteins. The identification of particular sets of genes whose expression is adaptive in response to environmental changes helps to understand the mechanistic base of gene-environment interactions essential for organismic development. We describe a computational framework for clustering the dynamics of gene expression in distinct environments through Gaussian mixture fitting to the expression data measured at a set of discrete time points. We outline a number of quantitative testable hypotheses about the patterns of dynamic gene expression in changing environments and gene-environment interactions causing developmental differentiation. The future directions of gene clustering in terms of incorporations of the latest biological discoveries and statistical innovations are discussed. We provide a set of computational tools that are applicable to modeling and analysis of dynamic gene expression data measured in multiple environments.     

The human secretin gene: fine structure in 11p15.5 and sequence variation in patients with autism

Secretin is a peptide hormone involved in digestion that has been studied as a potential therapeutic agent in patients with autism. We characterized the human secretin locus to determine whether mutations in this gene might play a role in a fraction of autism patients. While the secretin gene (SCT) was not found to be mutated in the majority of autistic patients, rare heterozygous sequence variants were identified in three patients. We also investigated length variation in a variable number of tandem repeats (VNTR) immediately upstream of SCT and found no significant differences in length between patients with autism and normal controls. SCT is located on 11p15.5, adjacent to DRD4 and HRAS. This region has been reported to be associated with both autism and attention deficit hyperactivity disorder (ADHD). Although imprinting is a characteristic of some genes in the vicinity, we could find no evidence for methylation of SCT in lymphoblast cells from patients or control individuals.     

Phenotypic plasticity in gene expression contributes to divergence of locally adapted populations of Fundulus heteroclitus

We examine the interaction between phenotypic plasticity and evolutionary adaptation using muscle gene expression levels among populations of the fish Fundulus heteroclitus acclimated to three temperatures. Our analysis reveals shared patterns of phenotypic plasticity due to thermal acclimation as well as non‐neutral patterns of variation among populations adapted to different thermal environments. For the majority of significant differences in gene expression levels, phenotypic plasticity and adaptation operate on different suites of genes. The subset of genes that demonstrate both adaptive differences and phenotypic plasticity, however, exhibit countergradient variation of expression. Thus, expression differences among populations counteract environmental effects, reducing the phenotypic differentiation between populations. Finally, gene‐by‐environment interactions among genes with non‐neutral patterns of expression suggest that the penetrance of adaptive variation depends on the environmental conditions experienced by the individual.     

Clustering formal concepts to discover biologically relevant knowledge from gene expression data

The production of high-throughput gene expression data has generated a crucial need for bioinformatics tools to generate biologically interesting hypotheses. Whereas many tools are available for extracting global patterns, less attention has been focused on local pattern discovery. We propose here an original way to discover knowledge from gene expression data by means of the so-called formal concepts which hold in derived Boolean gene expression datasets. We first encoded the over-expression properties of genes in human cells using human SAGE data. It has given rise to a Boolean matrix from which we extracted the complete collection of formal concepts, i.e., all the largest sets of over-expressed genes associated to a largest set of biological situations in which their over-expression is observed. Complete collections of such patterns tend to be huge. Since their interpretation is a time-consuming task, we propose a new method to rapidly visualize clusters of formal concepts. This designates a reasonable number of Quasi-Synexpression-Groups (QSGs) for further analysis. The interest of our approach is illustrated using human SAGE data and interpreting one of the extracted QSGs. The assessment of its biological relevancy leads to the formulation of both previously proposed and new biological hypotheses.     

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

Background

Availability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles.

Methods

We created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals.

Results

Next-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals.

Conclusions

Gene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect.     

Hepatic proteome sensitivity in rainbow trout after chronically exposed to a human pharmaceutical verapamil

Verapamil (VRP), a cardiovascular pharmaceutical widely distributed and persistent in the aquatic environment, has potential toxicity to fish and other aquatic organisms. However, the molecular mechanisms that lead to these toxic effects are not well known. In the present study, proteomic analysis has been performed to investigate the protein patterns that are differentially expressed in liver of rainbow trout exposed to sublethal concentrations of VRP (0.5, 27.0, and 270 μg/liter) for 42 days. Two-dimensional electrophoresis coupled with MALDI-TOF/TOF mass spectrometry was employed to detect and identify the protein profiles. The analysis revealed that the expression of six hepatic acidic proteins were markedly altered in the treatment groups compared with the control group; three proteins especially were significantly down-regulated in fish exposed to VRP at environmental related concentration (0.5 μg/liter). These results suggested that the VRP induce mechanisms against oxidative stress (glucose-regulated protein 78 and 94 and protein disulfide-isomerase A3) and adaptive changes in ion transference regulation (calreticulin, hyperosmotic glycine-rich protein). Furthermore, for the first time, protein Canopy-1 was found to be significantly down-regulated in fish by chronic exposure to VRP at environmental related levels. Overall, our work supports that fish hepatic proteomics analysis serves as an in vivo model for monitoring the residual pharmaceuticals in aquatic environment and can provide valuable insight into the molecular events in VRP-induced toxicity in fish and other organisms.     

Association and Mutation Analyses of 16p11.2 Autism Candidate Genes

Background

Autism is a complex childhood neurodevelopmental disorder with a strong genetic basis. Microdeletion or duplication of a ∼500–700-kb genomic rearrangement on 16p11.2 that contains 24 genes represents the second most frequent chromosomal disorder associated with autism. The role of common and rare 16p11.2 sequence variants in autism etiology is unknown.

Methodology/Principal Findings

To identify common 16p11.2 variants with a potential role in autism, we performed association studies using existing data generated from three microarray platforms: Affymetrix 5.0 (777 families), Illumina 550 K (943 families), and Affymetrix 500 K (60 families). No common variants were identified that were significantly associated with autism. To look for rare variants, we performed resequencing of coding and promoter regions for eight candidate genes selected based on their known expression patterns and functions. In total, we identified 26 novel variants in autism: 13 exonic (nine non-synonymous, three synonymous, and one untranslated region) and 13 promoter variants. We found a significant association between autism and a coding variant in the seizure-related gene SEZ6L2 (12/1106 autism vs. 3/1161 controls; p = 0.018). Sez6l2 expression in mouse embryos was restricted to the spinal cord and brain. SEZ6L2 expression in human fetal brain was highest in post-mitotic cortical layers, hippocampus, amygdala, and thalamus. Association analysis of SEZ6L2 in an independent sample set failed to replicate our initial findings.

Conclusions/Significance

We have identified sequence variation in at least one candidate gene in 16p11.2 that may represent a novel genetic risk factor for autism. However, further studies are required to substantiate these preliminary findings.     

Association of prostate cancer susceptibility variant (MSMB) rs10993994 with risk of spermatogenic failure

β-Microseminoprotein (MSMB) is one of the most abundant proteins in human seminal plasma. It has been identified that MSMB increased significantly in oligoasthenoteratozoospermic patients compared with fertile controls. We hypothesized that the functional polymorphism (rs10993994) of MSMB gene could be a risk factor for spermatogenic failure. For this study, 338 patients with idiopathic oligozoospermia or azoospermia and 382 fertile controls were recruited from an infertility clinic. Semen analysis was performed by computer-assisted semen analysis system. The functional polymorphism of MSMB gene was genotyped using TaqMan method. Sixty three seminal plasma samples were used to test the expression of MSMB by enzyme-linked immunosorbent assay (ELISA). The TT genotype and T allele were associated with an increased risk of idiopathic infertility with azoospermia (TT genotype: OR, 1.75; 95% CI, 1.03–2.95; T allele: OR, 1.34; 95% CI, 1.03–1.75). However, no differences were found in risk for the TT genotype or T allele among men with oligozoospermia. In addition, idiopathic infertile males have significantly higher MSMB expression levels than fertile controls. We present the first epidemiologic evidence supporting the involvement of common genetic polymorphism in MSMB gene in spermatogenic failure. These results suggest that men carrying the variant have an increased risk of spermatogenic failure associated with male infertility. Further studies are needed to confirm the roles of the polymorphism in idiopathic azoospermia and investigate the biological mechanism of elevated MSMB expression in infertile males.     

Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations

MOTIVATION: With the recent availability of large-scale data sets profiling single nucleotide polymorphisms (SNPs) and quantitative traits data across different human subpopulations, there has been much attention directed towards discovering patterns of genetic variation and their connection to gene regulation and the onset/progression of disease. While previous work has focused primarily on correlating individual SNP markers with gene expression and disease, it has been suggested that using haplotype blocks instead of individual markers can significantly increase statistical power. RESULTS: We present BlockMapper, a probabilistic generative model for genotype data and quantitative traits data, such as gene expression or phenotype measurements. BlockMapper discovers the block structure of genotype data and associates these inferred blocks to patterns of variation in quantitative traits data, whilst accounting for non-genetic factors. Our model achieves high accuracy for predicting Crohn's disease phenotype in Chromosome 5q31 and reveals novel cis-associations between two haplotype blocks in the ENm006 genomic region and GDI1, a gene implicated in X-linked mental retardation. Our results underscore the importance of accounting for the influence of large sets of SNPs on patterns of regulatory/phenotypic variation and represent a step towards an understanding of human genetic variation.