High-throughput and efficient multilocus genome-wide association study on longitudinal outcomes.

MOTIVATION: With the emerging of high-dimensional genomic data, genetic analysis such as genome-wide association studies (GWAS) have played an important role in identifying disease-related genetic variants and novel treatments. Complex longitudinal phenotypes are commonly collected in medical studies. However, since limited analytical approaches are available for longitudinal traits, these data are often underutilized. In this article, we develop a high-throughput machine learning approach for multilocus GWAS using longitudinal traits by coupling Empirical Bayesian Estimates from mixed-effects modeling with a novel ℓ0-norm algorithm. RESULTS: Extensive simulations demonstrated that the proposed approach not only provided accurate selection of single nucleotide polymorphisms (SNPs) with comparable or higher power but also robust control of false positives. More importantly, this novel approach is highly scalable and could be approximately >1000 times faster than recently published approaches, making genome-wide multilocus analysis of longitudinal traits possible. In addition, our proposed approach can simultaneously analyze millions of SNPs if the computer memory allows, thereby potentially allowing a true multilocus analysis for high-dimensional genomic data. With application to the data from Alzheimer's Disease Neuroimaging Initiative, we confirmed that our approach can identify well-known SNPs associated with AD and were much faster than recently published approaches (≥6000 times). AVAILABILITY AND IMPLEMENTATION: The source code and the testing datasets are available at https://github.com/Myuan2019/EBE_APML0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A forward selection algorithm to identify mutually exclusive alterations in cancer studies.

Mutual exclusivity analyses provide an effective tool to identify driver genes from passenger genes for cancer studies. Various algorithms have been developed for the detection of mutual exclusivity, but controlling false positive and improving accuracy remain challenging. We propose a forward selection algorithm for identification of mutually exclusive gene sets (FSME) in this paper. The method includes an initial search of seed pair of mutually exclusive (ME) genes and subsequently including more genes into the current ME set. Simulations demonstrated that, compared to recently published approaches (i.e., CoMEt, WExT, and MEGSA), FSME could provide higher precision or recall rate to identify ME gene sets, and had superior control of false positive rates. With application to TCGA real data sets for AML, BRCA, and GBM, we confirmed that FSME can be utilized to discover cancer driver genes.

Modeling Therapeutic Antibody-Small Molecule Drug-Drug Interactions Using a Three-Dimensional Perfusable Human Liver Coculture Platform.

Traditional in vitro human liver cell culture models lose key hepatic functions such as metabolic activity during short-term culture. Advanced three-dimensional (3D) liver coculture platforms offer the potential for extended hepatocyte functionality and allow for the study of more complex biologic interactions, which can improve and refine human drug safety evaluations. Here, we use a perfusion flow 3D microreactor platform for the coculture of cryopreserved primary human hepatocytes and Kupffer cells to study the regulation of cytochrome P450 3A4 isoform (CYP3A4) activity by chronic interleukin 6 (IL-6)-mediated inflammation over 2 weeks. Hepatocyte cultures remained stable over 2 weeks, with consistent albumin production and basal IL-6 levels. Direct IL-6 stimulation that mimics an inflammatory state induced a dose-dependent suppression of CYP3A4 activity, an increase in C-reactive protein (CRP) secretion, and a decrease in shed soluble interleukin-6 receptor (IL-6R) levels, indicating expected hepatic IL-6 bioactivity. Tocilizumab, an anti-IL-6R monoclonal antibody used to treat rheumatoid arthritis, has been demonstrated clinically to impact small molecule drug pharmacokinetics by modulating cytochrome P450 enzyme activities, an effect not observed in traditional hepatic cultures. We have now recapitulated the clinical observation in a 3D bioreactor system. Tocilizumab was shown to desuppress CYP3A4 activity while reducing the CRP concentration after 72 hours in the continued presence of IL-6. This change in CYP3A4 activity decreased the half-life and area under the curve up to the last measurable concentration (AUClast) of the small molecule CYP3A4 substrate simvastatin hydroxy acid, measured before and after tocilizumab treatment. We conclude that next-generation in vitro liver culture platforms are well suited for these types of long-term treatment studies and show promise for improved drug safety assessment.

Dose-related differences in the pharmacodynamic and toxicologic response to a novel hyperglycosylated analog of recombinant human erythropoietin in Sprague-Dawley rats with similarly high hematocrit.

We recently reported results that erythropoiesis-stimulating agent (ESA)-related thrombotic toxicities in preclinical species were not solely dependent on a high hematocrit (HCT) but also associated with increased ESA dose level, dose frequency, and dosing duration. In this article, we conclude that sequelae of an increased magnitude of ESA-stimulated erythropoiesis potentially contributed to thrombosis in the highest ESA dose groups. The results were obtained from two investigative studies we conducted in Sprague-Dawley rats administered a low (no thrombotic toxicities) or high (with thrombotic toxicities) dose level of a hyperglycosylated analog of recombinant human erythropoietin (AMG 114), 3 times weekly for up to 9 days or for 1 month. Despite similarly increased HCT at both dose levels, animals in the high-dose group had an increased magnitude of erythropoiesis measured by spleen weights, splenic erythropoiesis, and circulating reticulocytes. Resulting prothrombotic risk factors identified predominantly or uniquely in the high-dose group were higher numbers of immature reticulocytes and nucleated red blood cells in circulation, severe functional iron deficiency, and increased intravascular destruction of iron-deficient reticulocyte/red blood cells. No thrombotic events were detected in rats dosed up to 9 days suggesting a sustained high HCT is a requisite cofactor for development of ESA-related thrombotic toxicities.

Cytokines associated with increased erythropoiesis in Sprague-Dawley rats administered a novel hyperglycosylated analog of recombinant human erythropoietin.

We previously reported an increased incidence of thrombotic toxicities in Sprague-Dawley rats administered the highest dose level of a hyperglycosylated analog of recombinant human erythropoietin (AMG 114) for 1 month as not solely dependent on high hematocrit (HCT). Thereafter, we identified increased erythropoiesis as a prothrombotic risk factor increased in the AMG 114 high-dose group with thrombotic toxicities, compared to a low-dose group with no toxicities but similar HCT. Here, we identified pleiotropic cytokines as prothrombotic factors associated with AMG 114 dose level. Before a high HCT was achieved, rats in the AMG 114 high, but not the low-dose group, had imbalanced hemostasis (increased von Willebrand factor and prothrombin time, decreased antithrombin III) coexistent with cytokines implicated in thrombosis: monocyte chemotactic protein 1 (MCP-1), MCP-3, tissue inhibitor of metalloproteinases 1, macrophage inhibitory protein-2, oncostatin M, T-cell-specific protein, stem cell factor, vascular endothelial growth factor, and interleukin-11. While no unique pathway to erythropoiesis stimulating agent-related thrombosis was identified, cytokines associated with increased erythropoiesis contributed to a prothrombotic intravascular environment in the AMG 114 high-dose group, but not in lower dose groups with a similar high HCT.

Challenges to Using Big Data in Cancer.

Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Case Studies for Overcoming Challenges in Using Big Data in Cancer.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

Efficient algorithms for covariate analysis with dynamic data using nonlinear mixed-effects model.

Nonlinear mixed-effects modeling is one of the most popular tools for analyzing repeated measurement data, particularly for applications in the biomedical fields. Multiple integration and nonlinear optimization are the two major challenges for likelihood-based methods in nonlinear mixed-effects modeling. To solve these problems, approaches based on empirical Bayesian estimates have been proposed by breaking the problem into a nonlinear mixed-effects model with no covariates and a linear regression model without random effect. This approach is time-efficient as it involves no covariates in the nonlinear optimization. However, covariate effects based on empirical Bayesian estimates are underestimated and the bias depends on the extent of shrinkage. Marginal correction method has been proposed to correct the bias caused by shrinkage to some extent. However, the marginal approach appears to be suboptimal when testing covariate effects on multiple model parameters, a situation that is often encountered in real-world data analysis. In addition, the marginal approach cannot correct the inaccuracy in the associated p-values. In this paper, we proposed a simultaneous correction method (nSCEBE), which can handle the situation where covariate analysis is performed on multiple model parameters. Simulation studies and real data analysis showed that nSCEBE is accurate and efficient for both effect-size estimation and p-value calculation compared with the existing methods. Importantly, nSCEBE can be >2000 times faster than the standard mixed-effects models, potentially allowing utilization for high-dimension covariate analysis for longitudinal or repeated measured outcomes.

Application of genomics for identification of systemic toxicity triggers associated with VEGF-R inhibitors.

The key to the discovery of new pharmaceuticals is to develop molecules that interact with the intended target and minimize interaction with unintended molecular targets, therefore minimizing toxicity. This is aided by the use of various in vitro selectivity assays that are used to select agents most potent for the desired target. Typically, molecules from similar chemical series, with similar in vitro potencies, are expected to yield comparable in vivo pharmacological and toxicological profiles, predictive of target effects. However, in this study, we investigated the in vivo effects of two analogue compounds that similarly inhibit several receptor tyrosine kinases such as vascular endothelial growth factor receptor 1 (VEGFR/Flt1), vascular endothelial growth factor 2 (VEGFR2/kinase domain receptor/Flk-1), vascular endothelial growth factor receptor 3 (VEGFR3/Flt4), platelet-derived growth factor receptor (PDGFR), and Kit receptors, which bear similar chemical structures, have comparable potencies, but differ markedly in their rodent toxicity profiles. Global gene expression data were used to generate hypotheses regarding the existence of toxicity triggers that would reflect the perturbation of signaling in multiple organs such as the liver, adrenal glands, and the pancreas in response to compound treatment. We concluded that differences in pharmacokinetic properties of the two analogues, such as volume of distribution, half-life, and organ concentrations, resulted in marked differences in the chemical burden on target organs and may have contributed to the vast differences in toxicity profiles observed with the two otherwise similar molecules. We propose including select toxicokinetic parameters such as V(ss), T(1/2), and T(max) as additional criteria that could be used to rank order compounds from the same pharmacological series to possibly minimize organ toxicity. Assessment of toxicokinetics is not an atypical activity on toxicology studies, even in early screening studies; however, these data may not always be used in decision making for selecting or eliminating one compound over another. Finally, we illustrate that in vivo gene expression profiles can serve as a complementary assessor of this activity and simultaneously help provide an assessment of on or off-target biological activity.

The integration of pharmacophore-based 3D QSAR modeling and virtual screening in safety profiling: A case study to identify antagonistic activities against adenosine receptor, A2A, using 1,897 known drugs.

Safety pharmacology screening against a wide range of unintended vital targets using in vitro assays is crucial to understand off-target interactions with drug candidates. With the increasing demand for in vitro assays, ligand- and structure-based virtual screening approaches have been evaluated for potential utilization in safety profiling. Although ligand based approaches have been actively applied in retrospective analysis or prospectively within well-defined chemical space during the early discovery stage (i.e., HTS screening and lead optimization), virtual screening is rarely implemented in later stage of drug discovery (i.e., safety). Here we present a case study to evaluate ligand-based 3D QSAR models built based on in vitro antagonistic activity data against adenosine receptor 2A (A2A). The resulting models, obtained from 268 chemically diverse compounds, were used to test a set of 1,897 chemically distinct drugs, simulating the real-world challenge of safety screening when presented with novel chemistry and a limited training set. Due to the unique requirements of safety screening versus discovery screening, the limitations of 3D QSAR methods (i.e., chemotypes, dependence on large training set, and prone to false positives) are less critical than early discovery screen. We demonstrated that 3D QSAR modeling can be effectively applied in safety assessment prior to in vitro assays, even with chemotypes that are drastically different from training compounds. It is also worth noting that our model is able to adequately make the mechanistic distinction between agonists and antagonists, which is important to inform subsequent in vivo studies. Overall, we present an in-depth analysis of the appropriate utilization and interpretation of pharmacophore-based 3D QSAR models for safety screening.

Genome-scale metabolic model of the rat liver predicts effects of diet restriction.

Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting in silico model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver's energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected "hub" genes (Pklr, Hadha, Tkt, Pgm1, Tpi1, and Eno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver's response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease.

Assessment of repeated microarray experiments using mixed tissue RNA reference samples.

Genome-scale gene expression technologies are increasingly being applied for biological research as a whole and toxicological screening in particular. In order to monitor data quality and process drift, we adopted the use of two rat-tissue mixtures (brain, liver, kidney, and testis) previously introduced as RNA reference samples. These samples were processed every time a microarray experiment was hybridized, thereby verifying the comparability of the resulting expression data for cross-study comparison. This study presents the analysis of 21 technical replicates of these two mixed-tissue samples using Affymetrix RAE230_2 GeneChip over a period of 12 months. The results show that detection sensitivity, measured by the number of present and absent sequences, is robust, and data correlation, indicated by scatter plots, varies little over time. Receiver operating characteristic (ROC) curves show the sensitivity and specificity of the current measurements are consistent with arrays previously classified as well performing. Overall, this paper shows that the inclusion of standard samples during microarray labeling and hybridization experiments is useful to benchmark the performance of microarray experiments over time and allows discovery of any process drift that, if it occurs, may confound the comparison of these datasets.

Use of a mixed tissue RNA design for performance assessments on multiple microarray formats.

The comparability and reliability of data generated using microarray technology would be enhanced by use of a common set of standards that allow accuracy, reproducibility and dynamic range assessments on multiple formats. We designed and tested a complex biological reagent for performance measurements on three commercial oligonucleotide array formats that differ in probe design and signal measurement methodology. The reagent is a set of two mixtures with different proportions of RNA for each of four rat tissues (brain, liver, kidney and testes). The design provides four known ratio measurements of >200 reference probes, which were chosen for their tissue-selectivity, dynamic range coverage and alignment to the same exemplar transcript sequence across all three platforms. The data generated from testing three biological replicates of the reagent at eight laboratories on three array formats provides a benchmark set for both laboratory and data processing performance assessments. Close agreement with target ratios adjusted for sample complexity was achieved on all platforms and low variance was observed among platforms, replicates and sites. The mixed tissue design produces a reagent with known gene expression changes within a complex sample and can serve as a paradigm for performance standards for microarrays that target other species.

Transcriptional Profiling of Laser Capture Microdissected Subpopulations of the Osteoblast Lineage Provides Insight Into the Early Response to Sclerostin Antibody in Rats.

Sclerostin antibody (Scl-Ab) increases bone formation through a process dependent on the activation of canonical Wnt signaling, although the specific signaling in the osteoblast lineage in vivo is largely unknown. To gain insight into the signaling pathways acutely modulated by Scl-Ab, the transcriptional response of subpopulations of the osteoblast lineage was assessed by TaqMan and microarray analyses of mRNA isolated from laser capture microdissection (LCM)-enriched samples from the vertebrae of ovariectomized rats during the first week after Scl-Ab administration. Briefly, 6-month-old Sprague-Dawley rats were ovariectomized and, after 2 months, received a single dose of vehicle (VEH) or 100 mg/kg Scl-Ab (n = 20/group). Lumbar vertebrae were collected at 6, 24, 72, and 168 hours postdose and cryosectioned for LCM. Osteocytes were captured from bone matrix, and osteoblasts and lining cells were captured from bone surfaces based on fluorochrome labeling. mRNA was isolated, amplified, and profiled by TaqMan and microarray. Expression analysis revealed that Scl-Ab caused strikingly similar transcriptional profiles across all three cell types. Only 13 known canonical Wnt target genes, the majority with known functions in bone, showed a significant change in expression by microarray in response to Scl-Ab, with Wisp1 and Twist1 being the most responsive. Coincident with increased expression of Wnt target genes was the upregulation of numerous extracellular matrix (ECM) genes. The acute and progressive upregulation of ECM genes in lining cells supports their activation into matrix-producing osteoblasts, consistent with modeling-based bone formation. A similar transcriptional profile in osteocytes may indicate that Scl-Ab stimulates perilacunar/pericanalicular matrix deposition. Pathway analyses indicated that Scl-Ab regulated a limited number of genes related to cell cycle arrest and B-cell development. These data describe the acute downstream signaling in response to Scl-Ab in vivo and demonstrate selected canonical Wnt target gene activation associated with increased bone formation in all mature osteoblast subpopulations.

Utilization of human nuclear receptors as an early counter screen for off-target activity: a case study with a compendium of 615 known drugs.

Off-target effects of drugs on nuclear hormone receptors (NHRs) may result in adverse effects in multiple organs/physiological processes. Reliable assessments of the NHR activities for drug candidates are therefore crucial for drug development. However, the highly permissive structures of NHRs for vastly different ligands make it challenging to predict interactions by examining the chemical structures of the ligands. Here, we report a detailed investigation on the agonistic and antagonistic activities of 615 known drugs or drug candidates against a panel of 6 NHRs: androgen, progesterone, estrogen α/ß, and thyroid hormone α/ß receptors. Our study revealed that 4.7 and 12.4% compounds have agonistic and antagonistic activities, respectively, against this panel of NHRs. Nonetheless, potent, unintended NHR hits are relatively rare among the known drugs, indicating that such interactions are perhaps not tolerated during drug development. However, we uncovered examples of compounds that unintentionally agonize or antagonize NHRs. In addition, a number of compounds showed multi-NHR activities, suggesting that the cross-talk between multiple NHRs co-operate to elicit in vivo effects. These data highlight the merits of counter screening drug candidate against NHRs during drug discovery/development.

Oxidative stress and its role in skin disease.

Skin is a major target of oxidative stress due to reactive oxygen species (ROS) that originate in the environment and in the skin itself. ROS are generated during normal metabolism, are an integral part of normal cellular function, and are usually of little harm because of intracellular mechanisms that reduce their damaging effects. Antioxidants attenuate the damaging effects of ROS and can impair and/or reverse many of the events that contribute to epidermal toxicity and disease. However, increased or prolonged free radical action can overwhelm ROS defense mechanisms, contributing to the development of cutaneous diseases and disorders. Although ROS play a role in diseases such as skin cancer, their biological targets and pathogenic mode of action are still not fully understood. In addition, strategies useful in the therapeutic management of ROS action in human skin are still lacking. This review is intended to give investigators an introduction to ROS, antioxidants, two skin disorders influenced by ROS action (skin cancer and psoriasis), and relevant model systems used to study ROS action.

Cross-site comparison of gene expression data reveals high similarity.

Consistency and coherence of gene expression data across multiple sites depends on several factors such as platform (oligo, cDNA, etc.), environmental conditions at each laboratory, and data quality. The Hepatotoxicity Working Group of the International Life Sciences Institute Health and Environmental Sciences Institute consortium on the application of genomics to mechanism-based risk assessment is investigating these factors by comparing high-density gene expression data sets generated on two sets of RNA from methapyrilene (MP) experiments conducted at Abbott Laboratories and Boehringer-Ingelheim Pharmaceuticals, Inc. using a single platform (Affymetrix Rat Genome U34A GeneChip) at seven different sites. This article focuses on the evaluation of data quality and statistical models that facilitate the comparison of such data sets at the probe level. We present methods for exploring and quantitatively assessing differences in the data, with the principal goal being the generation of lists of site-insensitive genes responsive to low and high doses of MP. A combination of numerical and graphical techniques reveals important patterns and partitions of variability in the data, including the magnitude of the site effects. Although the site effects are significantly large in the analysis results, they appear to be primarily additive and therefore can be adjusted in the statistical calculations in a way that does not bias conclusions regarding treatment differences.

Identification of putative gene based markers of renal toxicity.

This study, designed and conducted as part of the International Life Sciences Institute working group on the Application of Genomics and Proteomics, examined the changes in the expression profile of genes associated with the administration of three different nephrotoxicants--cisplatin, gentamicin, and puromycin--to assess the usefulness of microarrays in the understanding of mechanism(s) of nephrotoxicity. Male Sprague-Dawley rats were treated with daily doses of puromycin (5-20 mg/kg/day for 21 days), gentamicin (2-240 mg/kg/day for 7 days), or a single dose of cisplatin (0.1-5 mg/kg). Groups of rats were sacrificed at various times after administration of these compounds for standard clinical chemistry, urine analysis, and histological evaluation of the kidney. RNA was extracted from the kidney for microarray analysis. Principal component analysis and gene expression-based clustering of compound effects confirmed sample separation based on dose, time, and degree of renal toxicity. In addition, analysis of the profile components revealed some novel changes in the expression of genes that appeared to be associated with injury in specific portions of the nephron and reflected the mechanism of action of these various nephrotoxicants. For example, although puromycin is thought to specifically promote injury of the podocytes in the glomerulus, the changes in gene expression after chronic exposure of this compound suggested a pattern similar to the known proximal tubular nephrotoxicants cisplatin and gentamicin; this prediction was confirmed histologically. We conclude that renal gene expression profiling coupled with analysis of classical end points affords promising opportunities to reveal potential new mechanistic markers of renal toxicity.

Coordination of altered DNA repair and damage pathways in arsenite-exposed keratinocytes.

Human exposure to arsenic, a ubiquitous and toxic environmental pollutant, is associated with an increased incidence of skin cancer. However, the mechanism(s) associated with AsIII-mediated toxicity and carcinogenesis at low levels of exposure remains elusive. Aberrations in cell proliferation, oxidative damage, and DNA-repair fidelity have been implicated in sodium arsenite (AsIII)-mediated carcinogenicity and toxicity, but these events have been examined in isolation in the majority of biological models of arsenic exposure. We hypothesized that the simultaneous interaction of these effects may be important in arsenic-mediated neoplasia in the skin. To evaluate this, normal human epidermal keratinocytes (NHEK) were exposed to nontoxic doses (0.005-5 micro M) of AsIII and monitored for several physiological endpoints at the times when cells were harvested for gene expression measurements (1-24 h). Two-fluor cDNA microarray analyses indicated that AsIII treatment decreased the expression of genes associated with DNA repair (e.g., p53 and Damage-specific DNA-binding protein 2) and increased the expression of genes indicative of the cellular response to oxidative stress (e.g., Superoxide dismutase 1, NAD(P)H quinone oxidoreductase, and Serine/threonine kinase 25). AsIII also modulated the expression of certain transcripts associated with increased cell proliferation (e.g., Cyclin G1, Protein kinase C delta), oncogenes, and genes associated with cellular transformation (e.g., Gro-1 and V-yes). These observations correlated with measurements of cell proliferation and mitotic measurements as AsIII treatment resulted in a dose-dependent increase in cellular mitoses at 24 h and an increase in cell proliferation at 48 h of exposure. Data in this manuscript demonstrates that AsIII exposure simultaneously modulates DNA repair, cell proliferation, and redox-related gene expression in nontransformed, normal NHEK. It is anticipated that data in this report will serve as a foundation for furthering our knowledge of AsIII-regulated gene expression in skin and other tissues and contribute to a better understanding of arsenic toxicity and carcinogenesis.

Gene expression analysis reveals chemical-specific profiles.

The application of gene expression profiling technology to examine multiple genes and signaling pathways simultaneously promises a significant advance in understanding toxic mechanisms to ultimately aid in protection of public health. Public and private efforts in the new field of toxicogenomics are focused on populating databases with gene expression profiles of compounds where toxicological and pathological endpoints are well characterized. The validity and utility of a toxicogenomics is dependent on whether gene expression profiles that correspond to different chemicals can be distinguished. The principal hypothesis underlying a toxicogenomic or pharmacogenomic strategy is that chemical-specific patterns of altered gene expression will be revealed using high-density microarray analysis of tissues from exposed organisms. Analyses of these patterns should allow classification of toxicants and provide important mechanistic insights. This report provides a verification of this hypothesis. Patterns of gene expression corresponding to liver tissue derived from chemically exposed rats revealed similarity in gene expression profiles between animals treated with different agents from a common class of compounds, peroxisome proliferators [clofibrate (ethyl-p-chlorophenoxyisobutyrate), Wyeth 14,643 ([4-chloro-6(2,3-xylidino)-2-pyrimidinylthio]acetic acid), and gemfibrozil (5-2[2,5-dimethylphenoxy]2-2-dimethylpentanoic acid)], but a very distinct gene expression profile was produced using a compound from another class, enzyme inducers (phenobarbital).