Blood gene expression signatures predict exposure levels

Biostatistics Branch, Environmental Stress and Cancer Group, Environmental Toxicology Program, Microarray Group, **Cancer Biology Group, National Institute of Environmental Health Sciences, P.O. Box 12233, Research Triangle Park, NC 27709.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 12/2007; 104(46):18211-6. DOI: 10.1073/pnas.0706987104
Source: PubMed


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.

Download full-text


Available from: Richard Paules, Oct 03, 2015
27 Reads
    • "Global gene profiling in extrahepatic sources can also be used to assess DILI, and may be more interesting for clinical purposes. In this context, the extent of liver injury following acetaminophen intoxication can be predicted by monitoring a predefined gene cluster in the blood [155]. Transcriptomic biomarkers are in general more sensitive than classical functional and morphological indicators and allow early detection of hepatotoxicity [7]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Drug-induced liver injury is a ubiquitous issue in clinical settings and pharmaceutical industry. Hepatotoxicity elicited by drugs may be intrinsic or idiosyncratic, both which are driven by different molecular mechanisms. Recently, a unifying mechanistic model of drug-induced liver injury has been introduced. According to this model, drug-induced hepatotoxicity relies on 3 consecutive steps, namely an initial cellular insult that leads to the occurrence of mitochondrial permeability transition, which in turn ultimately burgeons into the onset of cell death. Clinically, drug-induced liver injury can be manifested in a number of acute and chronic conditions, including hepatitis, cholestasis, steatosis and fibrosis. These pathologies can be diagnosed and monitored by addressing well-established physical, clinical chemistry and histopathological biomarkers. In the last few years, several novel read-outs of drug-induced liver injury have been proposed, involving genetic, epigenetic, transcriptomic, proteomic and metabolomic parameters. These new concepts and recent developments in the field of drug-induced liver injury are revised in the current paper.
    Current Medicinal Chemistry 05/2013; 20(24). DOI:10.2174/0929867311320240006 · 3.85 Impact Factor
  • Source
    • "ALT elevation and histopathological changes showed only minor differences among the three toxic dose groups at 24 h, so all of these animals were combined as one group for further analysis. In this study, there was a good correlation between ALT elevation and histological evidence of liver injury (Bushel et al., 2007). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The ability to identify mechanisms underlying drug-induced liver injury (DILI) in man has been hampered by the difficulty in obtaining liver tissue from patients. It has recently been proposed that whole blood toxicogenomics may provide a non-invasive means for mechanistic studies of human DILI. However, it remains unclear to what extent changes in whole blood transcriptome mirror those in liver mechanistically linked to hepatotoxicity. To address this question, we applied the program Extracting Patterns and Identifying co-expressed Genes (EPIG) to publically available toxicogenomic data obtained from rats treated with both toxic and subtoxic doses of acetaminophen (APAP). In a training set of animals, we identified genes (760 at 6 h and 185 at 24 h post dose) with similar patterns of expression in blood and liver during APAP-induced hepatotoxicity. The pathways represented in the coordinately regulated genes largely involved mitochondrial and immune functions. The identified expression signatures were then evaluated in a separate set of animals for discernment of APAP exposure level or APAP-induced hepatotoxicity. At 6 h, the gene sets from liver and blood had equally sufficient classification of APAP exposure levels. At 24 h when toxicity was evident, the gene sets did not perform well in evaluating APAP exposure doses, but provided accurate classification of dose-independent liver injury that was evaluated by serum ALT elevation in the blood. Only 38 genes were common to both the 6 and 24-h gene sets, but these genes had the same capability as the parent gene sets to discern the exposure level and degree of liver injury. Some of the parallel transcript changes reflect pathways that are relevant to APAP hepatotoxicity, including mitochondria and immune functions. However, the extent to which these changes reflect similar mechanisms of action in both tissues remains to be determined.
    Frontiers in Genetics 09/2012; 3:162. DOI:10.3389/fgene.2012.00162
  • Source
    • "Blood-based mRNA biomarkers have been investigated in almost all disease areas with promising results for detecting and monitoring disease and treatment outcomes (Chaussabel et al. 2010; Fang 2007; Han et al. 2008; Hanash et al. 2011; Julia et al. 2009; Marshall et al. 2010; Pankla et al. 2009; Pascual et al. 2010; Quartier et al. 2011; Tattermusch et al. 2012). Efforts were made even in areas not obviously or traditionally connected with blood, such as in neurological diseases (Kurian et al. 2011; Le-Niculescu et al. 2009; Runne et al. 2007; Scherzer et al. 2007) and drug toxicology (Bushel et al. 2007; Fannin et al. 2010; Huang et al. 2010; Lobenhofer et al. 2008). The biomarker discovery process is usually initiated with genome-wide expression profiling to identify significantly modulated gene sets, followed by employing more stringent criteria to down-select a small subset of genes for assay development (Barth and Hare 2006; van't Veer and Bernards 2008). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The pharmaceutical industry is spending increasingly large amounts of money on the discovery and development of novel medicines, but this investment is not adequately paying off in an increased rate of newly approved drugs by the FDA. The post-genomic era has provided a wealth of novel approaches for generating large, high-dimensional genetic and transcriptomic data sets from large cohorts of preclinical species as well as normal and diseased individuals. This systems biology approach to understanding disease-related biology is revolutionizing our understanding of the cellular pathways and gene networks underlying the onset of disease, and the mechanisms of pharmacological treatments that ameliorate disease phenotypes. In this article, we review a number of approaches being used by pharmaceutical and biotechnology companies, e.g., high-throughput DNA genotyping, sequencing, and genome-wide gene expression profiling, to enable drug discovery and development through the identification of new drug targets and biomarkers of disease progression, drug pharmacodynamics, and predictive markers for selecting the patients most likely to respond to therapy.
    Current topics in microbiology and immunology 08/2012; 363. DOI:10.1007/82_2012_252 · 4.10 Impact Factor
Show more