Pierre R. Bushel

Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models

The determination of a list of differentially expressed genes is a basic objective in many cDNA microarray experiments. We present a statistical approach that allows direct control over the percentage of false positives in such a list and, under certain reasonable assumptions, improves on existing methods with respect to the percentage of false negatives. The method accommodates a wide variety of experimental designs and can simultaneously assess significant differences between multiple types of biological samples. Two interconnected mixed linear models are central to the method and provide a flexible means to properly account for variability both across and within genes. The mixed model also provides a convenient framework for evaluating the statistical power of any particular experimental design and thus enables a researcher to a priori select an appropriate number of replicates. We also suggest some basic graphics for visualizing lists of significant genes. Analyses of published experiments studying human cancer and yeast cells illustrate the results.

The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance

The concordance of RNA-sequencing (RNA-seq) with microarrays for genome-wide analysis of differential gene expression has not been rigorously assessed using a range of chemical treatment conditions. Here we use a comprehensive study design to generate Illumina RNA-seq and Affymetrix microarray data from the same liver samples of rats exposed in triplicate to varying degrees of perturbation by 27 chemicals representing multiple modes of action (MOAs). The cross-platform concordance in terms of differentially expressed genes (DEGs) or enriched pathways is linearly correlated with treatment effect size (R20.8). Furthermore, the concordance is also affected by transcript abundance and biological complexity of the MOA. RNA-seq outperforms microarray (93% versus 75%) in DEG verification as assessed by quantitative PCR, with the gain mainly due to its improved accuracy for low-abundance transcripts. Nonetheless, classifiers to predict MOAs perform similarly when developed using data from either platform. Therefore, the endpoint studied and its biological complexity, transcript abundance and the genomic application are important factors in transcriptomic research and for clinical and regulatory decision making.

Global transcriptome and deletome profiles of yeast exposed to transition metals.

A variety of pathologies are associated with exposure to supraphysiological concentrations of essential metals and to non-essential metals and metalloids. The molecular mechanisms linking metal exposure to human pathologies have not been clearly defined. To address these gaps in our understanding of the molecular biology of transition metals, the genomic effects of exposure to Group IB (copper, silver), IIB (zinc, cadmium, mercury), VIA (chromium), and VB (arsenic) elements on the yeast Saccharomyces cerevisiae were examined. Two comprehensive sets of metal-responsive genomic profiles were generated following exposure to equi-toxic concentrations of metal: one that provides information on the transcriptional changes associated with metal exposure (transcriptome), and a second that provides information on the relationship between the expression of ∼4,700 non-essential genes and sensitivity to metal exposure (deletome). Approximately 22% of the genome was affected by exposure to at least one metal. Principal component and cluster analyses suggest that the chemical properties of the metal are major determinants in defining the expression profile. Furthermore, cells may have developed common or convergent regulatory mechanisms to accommodate metal exposure. The transcriptome and deletome had 22 genes in common, however, comparison between Gene Ontology biological processes for the two gene sets revealed that metal stress adaptation and detoxification categories were commonly enriched. Analysis of the transcriptome and deletome identified several evolutionarily conserved, signal transduction pathways that may be involved in regulating the responses to metal exposure. In this study, we identified genes and cognate signaling pathways that respond to exposure to essential and non-essential metals. In addition, genes that are essential for survival in the presence of these metals were identified. This information will contribute to our understanding of the molecular mechanism by which organisms respond to metal stress, and could lead to an understanding of the connection between environmental stress and signal transduction pathways.

Methapyrilene Toxicity: Anchorage of Pathologic Observations to Gene Expression Alterations

Methapyrilene (MP) exposure of animals can result in an array of adverse pathological responses including hepatotoxicity. This study investigates gene expression and histopathological alterations in response to MP treatment in order to 1) utilize computational approaches to classify samples derived from livers of MP treated rats based on severity of toxicity incurred in the corresponding tissue, 2) to phenotypically anchor gene expression patterns, and 3) to gain insight into mechanism(s) of methapyrilene hepatotoxicity. Large-scale differential gene expression levels associated with the exposure of male Sprague—Dawley rats to the rodent hepatic carcinogen MP for 1, 3, or 7 days after daily dosage with 10 or 100 mg/kg/day were monitored. Hierarchical clustering and principal component analysis were successful in classifying samples in agreement with microscopic observations and revealed low-dose effects that were not observed histopathologically. Data from cDNA microarray analysis corroborated observed histopathological alterations such as hepatocellular necrosis, bile duct hyperplasia, microvesicular vacuolization, and portal inflammation observed in the livers of MP exposed rats and provided insight into the role of specific genes in the studied toxicological processes.

Blood gene expression signatures predict exposure levels

To respond to potential adverse exposures properly, health care providers need accurate indicators of exposure levels. The indicators are particularly important in the case of acetaminophen (APAP) intoxication, the leading cause of liver failure in the U.S. We hypothesized that gene expression patterns derived from blood cells would provide useful indicators of acute exposure levels. To test this hypothesis, we used a blood gene expression data set from rats exposed to APAP to train classifiers in two prediction algorithms and to extract patterns for prediction using a profiling algorithm. Prediction accuracy was tested on a blinded, independent rat blood test data set and ranged from 88.9% to 95.8%. Genomic markers outperformed predictions based on traditional clinical parameters. The expression profiles of the predictor genes from the patterns extracted from the blood exhibited remarkable (97% accuracy) transtissue APAP exposure prediction when liver gene expression data were used as a test set. Analysis of human samples revealed separation of APAP-intoxicated patients from control individuals based on blood expression levels of human orthologs of the rat discriminatory genes. The major biological signal in the discriminating genes was activation of an inflammatory response after exposure to toxic doses of APAP. These results support the hypothesis that gene expression data from peripheral blood cells can provide valuable information about exposure levels, well before liver damage is detected by classical parameters. It also supports the potential use of genomic markers in the blood as surrogates for clinical markers of potential acute liver damage.

CEBS—Chemical Effects in Biological Systems: a public data repository integrating study design and toxicity data with microarray and proteomics data

Abstract CEBS (Chemical Effects in Biological Systems) is an integrated public repository for toxicogenomics data, including the study design and timeline, clinical chemistry and histopathology findings and microarray and proteomics data. CEBS contains data derived from studies of chemicals and of genetic alterations, and is compatible with clinical and environmental studies. CEBS is designed to permit the user to query the data using the study conditions, the subject responses and then, having identified an appropriate set of subjects, to move to the microarray module of CEBS to carry out gene signature and pathway analysis. Scope of CEBS: CEBS currently holds 22 studies of rats, four studies of mice and one study of Caenorhabditis elegans. CEBS can also accommodate data from studies of human subjects. Toxicogenomics studies currently in CEBS comprise over 4000 microarray hybridizations, and 75 2D gel images annotated with protein identification performed by MALDI and MS/MS. CEBS contains raw microarray data collected in accordance with MIAME guidelines and provides tools for data selection, pre-processing and analysis resulting in annotated lists of genes of interest. Additionally, clinical chemistry and histopathology findings from over 1500 animals are included in CEBS. CEBS/BID: The BID (Biomedical Investigation Database) is another component of the CEBS system. BID is a relational database used to load and curate study data prior to export to CEBS, in addition to capturing and displaying novel data types such as PCR data, or additional fields of interest, including those defined by the HESI Toxicogenomics Committee (in preparation). BID has been shared with Health Canada and the US Environmental Protection Agency. CEBS is available at http://cebs.niehs.nih.gov. BID can be accessed via the user interface from https://dir-apps.niehs.nih.gov/arc/. Requests for a copy of BID and for depositing data into CEBS or BID are available at http://www.niehs.nih.gov/cebs-df/.

The Evolution of Bioinformatics in Toxicology: Advancing Toxicogenomics

As one reflects back through the past 50 years of scientific research, a significant accomplishment was the advance into the genomic era. Basic research scientists have uncovered the genetic code and the foundation of the most fundamental building blocks for the molecular activity that supports biological structure and function. Accompanying these structural and functional discoveries is the advance of techniques and technologies to probe molecular events, in time, across environmental and chemical exposures, within individuals, and across species. The field of toxicology has kept pace with advances in molecular study, and the past 50 years recognizes significant growth and explosive understanding of the impact of the compounds and environment to basic cellular and molecular machinery. The advancement of molecular techniques applied in a whole-genomic capacity to the study of toxicant effects, toxicogenomics, is no doubt a significant milestone for toxicological research. Toxicogenomics has also provided an avenue for advancing a joining of multidisciplinary sciences including engineering and informatics in traditional toxicological research. This review will cover the evolution of the field of toxicogenomics in the context of informatics integration its current promise, and limitations.

Development of gut microbiota in infants not exposed to medical interventions.

Eggesbø M, Moen B, Peddada S, Baird D, Rugtveit J, Midtvedt T, Bushel PR, Sekelja M, Rudi K. Development of gut microbiota in infants not exposed to medical interventions. APMIS 2010.

Genomic interrogation of mechanism(s) underlying cellular responses to toxicants

Assessment of the impact of xenobiotic exposure on human health and disease progression is complex. Knowledge of mode(s) of action, including mechanism(s) contributing to toxicity and disease progression, is valuable for evaluating compounds. Toxicogenomics, the subdiscipline which merges genomics with toxicology, holds the promise to contributing significantly toward the goal of elucidating mechanism(s) by studying genome-wide effects of xenobiotics. Global gene expression profiling, revolutionized by microarray technology and a crucial aspect of a toxicogenomic study, allows measuring transcriptional modulation of thousands of genes following exposure to a xenobiotic. We use our results from previous studies on compounds representing two different classes of xenobiotics (barbiturate and peroxisome proliferator) to discuss the application of computational approaches for analyzing microarray data to elucidate mechanism(s) underlying cellular responses to toxicants. In particular, our laboratory demonstrated that chemical-specific patterns of gene expression can be revealed using cDNA microarrays. Transcript profiling provides discrimination between classes of toxicants, as well as, genome-wide insight into mechanism(s) of toxicity and disease progression. Ultimately, the expectation is that novel approaches for predicting xenobiotic toxicity in humans will emerge from such information.

Research Resource: Whole-Genome Estrogen Receptor α Binding in Mouse Uterine Tissue Revealed by ChIP-Seq

To advance understanding of mechanisms leading to biological and transcriptional endpoints related to estrogen action in the mouse uterus, we have mapped ERα and RNA polymerase II (PolII) binding sites using chromatin immunoprecipitation followed by sequencing of enriched chromatin fragments. In the absence of hormone, 5184 ERα-binding sites were apparent in the vehicle-treated ovariectomized uterine chromatin, whereas 17,240 were seen 1 h after estradiol (E₂) treatment, indicating that some sites are occupied by unliganded ERα, and that ERα binding is increased by E₂. Approximately 15% of the uterine ERα-binding sites were adjacent to (<10 kb) annotated transcription start sites, and many sites are found within genes or are found more than 100 kb distal from mapped genes; however, the density (sites per base pair) of ERα-binding sites is significantly greater adjacent to promoters. An increase in quantity of sites but no significant positional differences were seen between vehicle and E₂-treated samples in the overall locations of ERα-binding sites either distal from, adjacent to, or within genes. Analysis of the PolII data revealed the presence of poised promoter-proximal PolII on some highly up-regulated genes. Additionally, corecruitment of PolII and ERα to some distal enhancer regions was observed. A de novo motif analysis of sequences in the ERα-bound chromatin confirmed that estrogen response elements were significantly enriched. Interestingly, in areas of ERα binding without predicted estrogen response element motifs, homeodomain transcription factor-binding motifs were significantly enriched. The integration of the ERα- and PolII-binding sites from our uterine sequencing of enriched chromatin fragments data with transcriptional responses revealed in our uterine microarrays has the potential to greatly enhance our understanding of mechanisms governing estrogen response in uterine and other estrogen target tissues.