Genes related to apoptosis predict necrosis of the liver as a phenotype observed in rats exposed to a compendium of hepatotoxicants

<p>Abstract</p> <p>Background</p> <p>Some of the biochemical events that lead to necrosis of the liver are well-known. However, the pathogenesis of necrosis of the liver from exposure to hepatotoxicants is a complex biological response to the injury. We hypothesize that gene expression profiles can serve as a signature to predict the level of necrosis elicited by acute exposure of rats to a variety of hepatotoxicants and postulate that the expression profiles of the predictor genes in the signature can provide insight to some of the biological processes and molecular pathways that may be involved in the manifestation of necrosis of the rat liver.</p> <p>Results</p> <p>Rats were treated individually with one of seven known hepatotoxicants and were analyzed for gene expression by microarray. Liver samples were grouped by the level of necrosis exhibited in the tissue. Analysis of significantly differentially expressed genes between adjacent necrosis levels revealed that inflammation follows programmed cell death in response to the agents. Using a Random Forest classifier with feature selection, 21 informative genes were identified which achieved 90%, 80% and 60% prediction accuracies of necrosis against independent test data derived from the livers of rats exposed to acetaminophen, carbon tetrachloride, and allyl alcohol, respectively. Pathway and gene network analyses of the genes in the signature revealed several gene interactions suggestive of apoptosis as a process possibly involved in the manifestation of necrosis of the liver from exposure to the hepatotoxicants. Cytotoxic effects of TNF-α, as well as transcriptional regulation by JUN and TP53, and apoptosis-related genes possibly lead to necrosis.</p> <p>Conclusion</p> <p>The data analysis, gene selection and prediction approaches permitted grouping of the classes of rat liver samples exhibiting necrosis to improve the accuracy of predicting the level of necrosis as a phenotypic end-point observed from the exposure. The strategy, along with pathway analysis and gene network reconstruction, led to the identification of 1) expression profiles of genes as a signature of necrosis and 2) perturbed regulatory processes that exhibited biological relevance to the manifestation of necrosis from exposure of rat livers to the compendium of hepatotoxicants.</p

The Ataxia telangiectasia Gene Product Is Required for Oxidative Stress-induced G 1 and G 2 Checkpoint Function in Human Fibroblasts

Ataxia telangiectasia (AT) is an autosomal recessive disorder characterized by neuronal degeneration accompanied by ataxia, telangiectasias, acute cancer predisposition, and sensitivity to ionizing radiation (IR). Cells from individuals with AT show unusual sensitivity to IR, severely attenuated cell cycle checkpoint functions, and poor p53 induction in response to IR compared with normal human fibroblasts (NHFs). The gene mutated in AT (ATM) has been cloned, and its product, pATM, has IR-inducible kinase activity. The AT phenotype has been suggested to be a consequence, at least in part, of an inability to respond appropriately to oxidative damage. To test this hypothesis, we examined the ability of NHFs and AT dermal fibroblasts to respond to t-butyl hydroperoxide and IR treatment. AT fibroblasts exhibit, in comparison to NHFs, increased sensitivity to the toxicity of t-butyl hydroperoxide, as measured by colony-forming efficiency assays. Unlike NHFs, AT fibroblasts fail to show G(1) and G(2) phase checkpoint functions or to induce p53 in response to t-butyl hydroperoxide. Treatment of NHFs with t-butyl hydroperoxide activates pATM-associated kinase activity. Our results indicate that pATM is involved in responding to certain aspects of oxidative damage and in signaling this information to downstream effectors of the cell cycle checkpoint functions. Our data further suggest that some of the pathologies seen in AT could arise as a consequence of an inability to respond normally to oxidative damage

Cdc7-Dbf4 and the Human S Checkpoint Response to UVC

The S checkpoint response to ultraviolet radiation (UVC) that inhibits replicon initiation is dependent on the ATR and Chk1 kinases. Downstream effectors of this response, however, are not well characterized. Data reported here eliminated Cdc25A degradation as intrinsic components of the UVC-induced pathway of inhibition of replicon initiation in human cells. A sublethal dose of UVC (1 J/m2), which selectively inhibits replicon initiation by 50%, failed to reduce the amount of Cdc25A protein or decrease Cdk2/cyclin E kinase activity. Cdc25A degradation was observed after irradiation with cytotoxic fluences of UVC, suggesting that severe inhibition of DNA chain elongation and activation of the replication checkpoint might be responsible for the UVC-induced degradation of Cdc25A. Another proposed effector of the S checkpoint is the Cdc7/Dbf4 complex. Dbf4 interacted with Chk1 in vivo and was recognized as a substrate for Chk1-dependent phosphorylation in vitro. Flag-Dbf4 formed complexes with endogenous Cdc7 and this interaction was stable in UVC-irradiated HeLa cells. Over-expression of Flag- or Myc-tagged Dbf4 abrogated the S checkpoint response to UVC. These findings implicate a Dbf4-dependent kinase as a possible target of the ATR- and Chk1-dependent S checkpoint response to UVC

Phenotypic Anchoring of Acetaminophen-Induced Oxidative Stress with Gene Expression Profiles in Rat Liver

Toxicogenomics provides the ability to examine in greater detail the underlying molecular events that precede and accompany toxicity, thus allowing prediction of adverse events at much earlier times compared to classical toxicological endpoints. Acetaminophen (APAP) is a pharmaceutical that has similar metabolic and toxic responses in rodents and humans. Recent gene expression profiling studies with APAP found an oxidative stress signature at a sub-toxic dose that we hypothesized can be phenotypically anchored to conventional biomarkers of oxidative stress. Liver tissue was obtained from experimental animals used to generate microarray data where male rats were given APAP at sub-toxic (150 mg/kg), or overtly toxic (1500 and 2000 mg/kg) doses and sacrificed at 6, 24, or 48 hrs. Oxidative stress in liver was evaluated by a diverse panel of markers that included assessing expression of base excision repair (BER) genes, quantifying oxidative lesions in genomic DNA, and evaluating protein and lipid oxidation. A sub-toxic dose of APAP produced significant accumulation of nitrotyrosine protein adducts, while both sub-toxic and toxic doses caused a significant increase in 8-hydroxy-deoxyguanosine. Only toxic doses of APAP significantly induced expression levels of BER genes. None of the doses examined resulted in a significant increase in the number of abasic sites, or in the amount of lipid peroxidation. The accumulation of nitrotyrosine and 8-hydroxy-deoxyguanosine adducts phenotypically anchors the oxidative stress gene expression signature observed with a sub-toxic dose of APAP, lending support to the validity of gene expression studies as a sensitive and biologically-meaningful endpoint in toxicology

Gene expression response in target organ and whole blood varies as a function of target organ injury phenotype

Histopathology, clinical chemistry, hematology and gene expression data were collected from the rat liver and blood after treatment with eight known hepatotoxins

Predictors and correlates for weight changes in patients co-treated with olanzapine and weight mitigating agents; a post-hoc analysis

<p>Abstract</p> <p>Background</p> <p>This study focuses on exploring the relationship between changes in appetite or eating behaviors and subsequent weight change for adult patients with schizophrenia or bipolar disorder treated with olanzapine and adjunctive potential weight mitigating pharmacotherapy. The aim is not to compare different weight mitigating agents, but to evaluate patients' characteristics and changes in their eating behaviors during treatment. Identification of patient subgroups with different degrees of susceptibility to the effect of weight mitigating agents during olanzapine treatment may aid clinicians in treatment decisions.</p> <p>Methods</p> <p>Data were obtained from 3 randomized, double-blind, placebo-controlled, 16-week clinical trials. Included were 158 patients with schizophrenia or bipolar disorder and a body mass index (BMI) ≥ 25 kg/m²who had received olanzapine treatment in combination with nizatidine (n = 68), sibutramine (n = 42), or amantadine (n = 48). Individual patients were analyzed for categorical weight loss ≥ 2 kg and weight gain ≥ 1 kg. Variables that were evaluated as potential predictors of weight outcomes included baseline patient characteristics, factors of the Eating Inventory, individual items of the Eating Behavior Assessment, and the Visual Analog Scale.</p> <p>Results</p> <p>Predictors/correlates of weight loss ≥ 2 kg included: high baseline BMI, low baseline interest in food, and a decrease from baseline to endpoint in appetite, hunger, or cravings for carbohydrates. Reduced cognitive restraint, increase in hunger, and increased overeating were associated with a higher probability of weight gain ≥ 1 kg.</p> <p>Conclusion</p> <p>The association between weight gain and lack of cognitive restraint in the presence of increased appetite suggests potential benefit of psychoeducational counseling in conjunction with adjunctive pharmacotherapeutic agents in limiting weight gain during antipsychotic drug therapy.</p> <p>Trial Registration</p> <p>This analysis was not a clinical trial and did not involve any medical intervention.</p

Gene set enrichment analysis for non-monotone association and multiple experimental categories

Background Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed. Results For continuous or ordinal phenotypic outcome, we propose to use as the local statistic the coefficient of multiple determination (i.e., the square of multiple correlation coefficient) R2 from fitting natural cubic spline models to the phenotype-expression relationship. Next, we incorporate this association measure into the GSEA/SAFE framework to identify significant gene sets. Unsigned local statistics, signed global statistics and one-sided p-values are used to reflect our inferential interest. Furthermore, we describe a procedure for inference across multiple GSEA/SAFE analyses. We illustrate our approach using gene expression and liver injury data from liver and blood samples from rats treated with eight hepatotoxicants under multiple time and dose combinations. We set out to identify biological pathways/processes associated with liver injury as manifested by increased blood levels of alanine transaminase in common for most of the eight compounds. Potential statistical dependency resulting from the experimental design is addressed in permutation based hypothesis testing. Conclusion The proposed framework captures both linear and non-linear association between gene expression level and a phenotypic endpoint and thus can be viewed as extending the current GSEA/SAFE methodology. The framework for combining results from multiple GSEA/SAFE analyses is flexible to address practical inference interests. Our methods can be applied to microarray data with continuous phenotypes with multi-level design or the meta-analysis of multiple microarray data sets

Gene set enrichment analysis for non-monotone association and multiple experimental categories

Abstract Background Recently, microarray data analyses using functional pathway information, e.g., gene set enrichment analysis (GSEA) and significance analysis of function and expression (SAFE), have gained recognition as a way to identify biological pathways/processes associated with a phenotypic endpoint. In these analyses, a local statistic is used to assess the association between the expression level of a gene and the value of a phenotypic endpoint. Then these gene-specific local statistics are combined to evaluate association for pre-selected sets of genes. Commonly used local statistics include t-statistics for binary phenotypes and correlation coefficients that assume a linear or monotone relationship between a continuous phenotype and gene expression level. Methods applicable to continuous non-monotone relationships are needed. Furthermore, for multiple experimental categories, methods that combine multiple GSEA/SAFE analyses are needed. Results For continuous or ordinal phenotypic outcome, we propose to use as the local statistic the coefficient of multiple determination (i.e., the square of multiple correlation coefficient) R2 from fitting natural cubic spline models to the phenotype-expression relationship. Next, we incorporate this association measure into the GSEA/SAFE framework to identify significant gene sets. Unsigned local statistics, signed global statistics and one-sided p-values are used to reflect our inferential interest. Furthermore, we describe a procedure for inference across multiple GSEA/SAFE analyses. We illustrate our approach using gene expression and liver injury data from liver and blood samples from rats treated with eight hepatotoxicants under multiple time and dose combinations. We set out to identify biological pathways/processes associated with liver injury as manifested by increased blood levels of alanine transaminase in common for most of the eight compounds. Potential statistical dependency resulting from the experimental design is addressed in permutation based hypothesis testing. Conclusion The proposed framework captures both linear and non-linear association between gene expression level and a phenotypic endpoint and thus can be viewed as extending the current GSEA/SAFE methodology. The framework for combining results from multiple GSEA/SAFE analyses is flexible to address practical inference interests. Our methods can be applied to microarray data with continuous phenotypes with multi-level design or the meta-analysis of multiple microarray data sets.</p