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Russell S Thomas

Publications and source records attributed to Russell S Thomas.

11 recordsLinked to original sources
PubMed2006

A comparison of transcriptomic and metabonomic technologies for identifying biomarkers predictive of two-year rodent cancer bioassays.

Two-year rodent bioassays play a central role in evaluating the carcinogenic potential of both commercial products and environmental contaminants. The bioassays are expensive and time consuming, requiring years to complete and costing $2-4 million. In this study, we compare transcriptomic and metabonomic technologies for discovering biomarkers that can efficiently and economically identify chemical carcinogens without performing a standard two-year rodent bioassay. Animals were exposed subchronically to two chemicals (one genotoxic and one nongenotoxic) that were positive for lung and liver tumors in a standard two-year bioassay, two chemicals that were negative, and two control groups. Microarray analysis performed on liver and lung tissues identified multiple biomarkers in each tissue that could discriminate between carcinogenic and noncarcinogenic treatments. The discriminating biomarkers shared a common expression profile among carcinogenic treatments despite different genotoxicity categories and potential modes of action, suggesting that they reflect underlying cellular changes in the transition toward neoplasia. Statistical classification analysis exhibited 100% accuracy in both tissues when the number of genes was less than 5000. Additional genes reduced the predictive accuracy of the model. Serum samples were analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy, and chemical-specific metabolites were removed from the spectra. The statistical classification analysis of the endogenous serum metabolites showed relatively low predictive accuracy with few metabolites in the model, but the accuracy increased to a maximum of 94% when all metabolites were added. These results suggest that individual endogenous metabolites are relatively poor biomarkers, but the metabolite profile as a whole is altered following carcinogen treatment.

Animals↗
PubMed2006

Genome-wide analysis of human HSF1 signaling reveals a transcriptional program linked to cellular adaptation and survival.

Although HSF1 plays an important role in the cellular response to proteotoxic stressors, little is known about the structure and function of the human HSF1 signaling network under both stressed and unstressed conditions. In this study, we used a combination of chromatin immunoprecipitation microarray analysis and time course gene expression microarray analysis with and without siRNA-mediated inhibition of HSF1 to comprehensively identify genes regulated directly and indirectly by HSF1. The correlation between promoter binding and gene expression was not significant for all genes bound by HSF1, suggesting that HSF1 binding per se is not sufficient for expression. However, the correlation with promoter binding was significant for genes identified as HSF1-regulated following siRNA knockdown. Among promoters bound by HSF1 following heat shock, a gene ontology analysis showed significant enrichment only in categories related to protein folding. In contrast, analysis of the extended HSF1 signaling network following siRNA knockdown showed enrichment in a variety of categories related to protein folding, anti-apoptosis, RNA splicing, ubiquitinylation and others, highlighting a complex transcriptional program regulated directly and indirectly by HSF1.

Adaptation, Physiological↗
PubMed2005

EDGE: a centralized resource for the comparison, analysis, and distribution of toxicogenomic information.

Transcriptional profiling via microarrays holds great promise for toxicant classification and hazard prediction. Unfortunately, the use of different microarray platforms, protocols, and informatics often hinders the meaningful comparison of transcriptional profiling data across laboratories. One solution to this problem is to provide a low-cost and centralized resource that enables researchers to share toxicogenomic data that has been generated on a common platform. In an effort to create such a resource, we developed a standardized set of microarray reagents and reproducible protocols to simplify the analysis of liver gene expression in the mouse model. This resource, referred to as EDGE, was then used to generate a training set of 117 publicly accessible transcriptional profiles that can be accessed at http://edge.oncology.wisc.edu/. The Web-accessible database was also linked to an informatics suite that allows on-line clustering and K-means analyses as well as Boolean and sequence-based searches of the data. We propose that EDGE can serve as a prototype resource for the sharing of toxicogenomics information and be used to develop algorithms for efficient chemical classification and hazard prediction.

Animals↗
PubMed2005

New directions in incidence-dose modeling.

Many cellular responses are quantal; that is, they either take place or they do not. Examples of "either-or" responses include cell replication, differentiation and apoptosis. Surprisingly, induction of suites of genes and coordinated phenotypic changes in cells are also often quantal, where embedded molecular circuitry creates on-off switches. Mechanistic incidence-dose (ID) models need to account for the quantal characteristics of cellular switches that contribute, in turn, to dose thresholds and to the incidence of biological responses in individuals. Interdisciplinary systems biology approaches create mechanistic ID models based on: (i) detailed knowledge of the cellular circuitry controlling signal transduction; (ii) evolving biological modeling tools describing cellular circuits and their perturbations by chemicals and (iii) high throughput, high coverage "omic" screens for examining cell signaling pathways and biological responses. These interdisciplinary approaches should produce novel, quantitative ID models for biological responses and greatly improve the biological basis of safety and risk assessments.

Incidence↗
PubMed2003

Genome-scale functional profiling of the mammalian AP-1 signaling pathway.

Large-scale functional genomics approaches are fundamental to the characterization of mammalian transcriptomes annotated by genome sequencing projects. Although current high-throughput strategies systematically survey either transcriptional or biochemical networks, analogous genome-scale investigations that analyze gene function in mammalian cells have yet to be fully realized. Through transient overexpression analysis, we describe the parallel interrogation of approximately 20,000 sequence annotated genes in cancer-related signaling pathways. For experimental validation of these genome data, we apply an integrative strategy to characterize previously unreported effectors of activator protein-1 (AP-1) mediated growth and mitogenic response pathways. These studies identify the ADP-ribosylation factor GTPase-activating protein Centaurin alpha1 and a Tudor domain-containing hypothetical protein as putative AP-1 regulatory oncogenes. These results provide insight into the composition of the AP-1 signaling machinery and validate this approach as a tractable platform for genome-wide functional analysis.

Animals↗
PubMed2003

Stochastic simulation of hepatic preneoplastic foci development for four chlorobenzene congeners in a medium-term bioassay.

A combination of experimental and simulation approaches was used to analyze clonal growth of glutathione-S-transferase pi (GST-P) enzyme-altered foci during liver carcinogenesis in an initiation-promotion regimen for 1,4-dichlorobenzene (DCB), 1,2,4,5-tetrachlorobenzene (TECB), pentachlorobenzene (PECB), and hexachlorobenzene (HCB). Male Fisher 344 rats, eight weeks of age, were initiated with a single dose (200 mg/kg, ip) of diethylnitrosamine (DEN). Two weeks later, daily dosing of 0.1 mol/kg chlorobenzene was maintained for six weeks. Partial hepatectomy was performed three weeks after initiation. Liver weight, normal hepatocyte division rates, and the number and volume of GST-P positive foci were obtained at 23, 26, 28, 47, and 56 days after initiation. A clonal growth stochastic model separating the initiated cell population into two distinct subtypes (referred to as A and B cells) was successfully used to describe the foci development data for the four chlorobenzenes. The B cells are initiated cells that display a selective growth advantage under conditions that inhibit the growth of initiated A cells or normal hepatocytes. The simulation exercise for the four chlorobenzenes indicates a positive correlation between the estimated net growth rate of B cells during the 2-week regeneration period following partial hepatectomy and final foci volume at the end of the bioassay. This observation is consistent with the sensitivity analysis of model parameters. While TECB, PECB, and HCB all significantly increased foci volume, only HCB increased normal hepatocyte proliferation. Together, these results indicate that examining effects of chemicals on regenerative responses following partial hepatectomy may be a means for understanding the carcinogenicity potential of chlorobenzene compounds.

Administration, Oral↗
PubMed2002

A conditional density error model for the statistical analysis of microarray data.

MOTIVATION: In many microarray experiments, relatively few intra- and inter-array replicate measurements are made due to significant cost limitations and sample availability. Compounding this problem is a lack of robust statistical methods for analyzing gene expression data with limited experimental replicates. As a result, the interpretation of the results of these experiments are difficult with little understanding of the probability of type I and type II errors. RESULTS: The variability in a series of replicate microarray measurements was modelled using a combination of parametric and non-parametric methods. A 3-dimensional surface was created for the conditional distribution of the variability given the mean signal intensity in both the Cy3 and Cy5 channels. The results were used as the basis for developing statistical methods for analyzing gene expression data with limited experimental replicates. AVAILABILITY: The statistical analysis scripts are available upon request.

Computer Simulation↗
PubMed2002

Sequence variation and phylogenetic history of the mouse Ahr gene.

The Ahr locus encodes for the aryl hydrocarbon receptor (AHR), which plays an important toxicological and developmental role. Sequence variation in this gene was studied in 13 different mouse lines that included eight laboratory strains, two Mus musculus subspecies and three additional Mus species. The data presented represent the largest study of sequence variation across multiple mouse lines in a single gene (approximately equal to 15.9 kb/mouse line). Among all mice, the average frequency of all polymorphisms in the intronic regions was 20.3 variants/kb and the average exonic frequency was 14.1 variants/kb. For substitutions alone, the average frequencies in the intronic and exonic regions for all mice were 13.3 and 8.9 substitutions/kb, respectively. Between laboratory strains, the average intronic and exonic frequencies for all polymorphisms dropped to 5.4 and 2.9 variants/kb, respectively. There were 111 non-synonymous polymorphisms that resulted in 42 different amino acid changes, of which only 10 amino acid changes had been previously identified. Based on the nucleotide sequence, the phylogenetic history of the gene showed mice from the Ahr(b2) and Ahr(d) alleles in separate branches while mice from the Ahr(b1) and Ahr(b3) alleles exhibited a more complex history. Evolutionarily, the AHR protein as a whole appears to be under purifying selective pressure (K(a) : K(s) ratio = 0.237). Despite significant functional constraint in the basic helix-loop-helix and PAS domains, ligand binding is not constrained to the high-affinity allele, which supports further the role of the AHR in development and its importance beyond the adaptive response to environmental toxicants.

Amino Acid Sequence↗
PubMed2002

Application of genomics to toxicology research.

Traditional models of toxicity have relied on dissecting chemical action into pharmacokinetic and pharmacodynamic processes. However, the integration of genomic information with toxicology will enhance our basic understanding of these processes and significantly change the way we apply toxicological information to risk assessment and regulatory problems. In this article, we summarize the application of gene expression information and polymorphism discovery to four areas in toxicology: toxicity testing, cross-species extrapolation, understanding mechanism of action, and susceptibility.

Animals↗
PubMedDate not supplied

Dose-response modeling in reproductive toxicology in the systems biology era.

Systems biology approaches for modeling cellular signaling networks affected by chemical exposures should soon produce integrated methodologies capable of predicting dose-response relationships for developmental toxicants and for other toxic responses. This paper outlines an emerging strategy for systems biology approaches in dose-response modeling. Genome-wide functional screens, bioinformatic tools, and network mapping technologies together can provide directed graph representations of the cellular signaling networks. The graphical representations can be converted into mathematical models that permit predicting the shapes of dose-response curves for altered cell signaling by test compounds during development. Systems biology approaches require interdisciplinary teams with expertise in reproduction, cell biology, signal transduction, mathematical/biomedical modeling, and risk assessment. In addition to outlining a systems approach for dose-response research, this paper discusses initial stages of application of this strategy to examine inhibition of steroidogenesis in testes by phthalate esters.

Animals↗