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

Lobenhofer, Edward K.; Auman, J. Todd; Blackshear, Pamela E.; Boorman, Gary A.; Bushel, Pierre R.; Cunningham, Michael L.; Fostel, Jennifer; Gerrish, Kevin; Heinloth, Alexandra N.; Irwin, Richard L.; Malarkey, David E.; Merrick, B. Alex; Sieber, Stella O.; Tucker, Charles J.; Ward, Sandra; Wilson, Ralph E.; Hurban, Patrick; Tennant, Raymond W.; Paules, Richard S.

doi:10.1186/gb-2008-9-6-r100

Cited by 45 publications

(50 citation statements)

References 39 publications

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“…A recent study of 8 hepatotoxicants administered to rats demonstrated that transcriptomes present in peripheral blood correlated with direct measures of these genes in the liver (111). In fact these studies suggested the peripheral blood transcriptomic data might be more sensitive to liver injury than traditional liver injury tests such as serum ALT and that unique signatures for individual drugs including APAP hepatotoxicity could be determined (112).…”

Section: Transcriptomics and Metabolomicsmentioning

confidence: 99%

Pathogenesis of Idiosyncratic Drug-Induced Liver Injury and Clinical Perspectives

2014

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Idiosyncratic drug-induced liver injury (DILI) is a rare disease that develops independently of drug dose, or route or duration, of administration. Furthermore, idiosyncratic DILI is not a single disease entity, but rather a spectrum of rare diseases with varying clinical, histologic, and laboratory features. The pathogenesis of DILI is not fully understood. Standardization of the DILI nomenclature and methods to assess causality, along with the information provided by the LiverTox website, will harmonize and accelerate DILI research. Studies of new serum biomarkers such as glutamate dehydrogenase, high-mobility group box-1 protein, and microRNA-122 could provide information for use in diagnosis and prognosis, and provide important insights into mechanisms of DILI pathogenesis. Single nucleotide polymorphisms in the HLA region have been associated idiosyncratic hepatotoxicity attributed to flucloxacillin, ximelagatran, lapatanib, and amoxicillin- clavulanate. However, genome-wide association studies of pooled cases have not associated any genetic factors with idiosyncratic DILI. Whole-genome and whole-exome sequencing analyses are underway to study DILI cases attributed to a single medication. Serum proteomic, transcriptome, and metabolome, along with intestinal microbiome, analyses will increase our understanding of the mechanisms of this disorder. Further improvements to in vitro and in vivo test systems should advance our understanding of the causes, risk factors, and mechanisms of idiosyncratic DILI.

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Section: Transcriptomics and Metabolomicsmentioning

confidence: 99%

Pathogenesis of Idiosyncratic Drug-Induced Liver Injury and Clinical Perspectives

2014

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“…23 We used a comprehensive and robust approach to evaluate whether a predictive classifier could be developed using the signature genes derived from a different microarray platform (that is, transferability of signature genes) and whether the classifier from one platform could yield an accurate prediction for the samples whose expression data were generated from another platform (that is, transferability of classifiers).…”

Section: Introductionmentioning

confidence: 99%

Consistency of predictive signature genes and classifiers generated using different microarray platforms

Fan

Chen

et al. 2010

Pharmacogenomics J

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Microarray-based classifiers and associated signature genes generated from various platforms are abundantly reported in the literature; however, the utility of the classifiers and signature genes in cross-platform prediction applications remains largely uncertain. As part of the MicroArray Quality Control Phase II (MAQC-II) project, we show in this study 80–90% cross-platform prediction consistency using a large toxicogenomics data set by illustrating that: (1) the signature genes of a classifier generated from one platform can be directly applied to another platform to develop a predictive classifier; (2) a classifier developed using data generated from one platform can accurately predict samples that were profiled using a different platform. The results suggest the potential utility of using published signature genes in cross-platform applications and the possible adoption of the published classifiers for a variety of applications. The study reveals an opportunity for possible translation of biomarkers identified using microarrays to clinically validated non-array gene expression assays.

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“…Lobenhofer et al . 2 used gene expression data from rats exposed to a compendium of hepatotoxicants to show that blood gene expression patterns could be used to provide an indication of the severity level of liver injury. Wang et al .…”

Section: Introductionmentioning

confidence: 99%

“…2 gene expression data set that was contributed to the Food and Drug Administration (FDA) led MicroArray Quality Control Phase-II (MAQC-II) effort as a training data set and for internal validation to identify genes and biological processes in the blood that are predictive of liver necrosis (a particular form of DILI). This data set was chosen for analysis for several key reasons: 1) it was the only data set publicly available at the time which contained gene expression measurements from the two tissues of interest, histopathology, clinical chemistry and other ancillary biological data from exposure to a compendium of compounds, 2) the experimental design and data acquisition were performed in a rigorous, standardized fashion (i.e.…”

Section: Introductionmentioning

confidence: 99%

Genomic indicators in the blood predict drug-induced liver injury

Huang

Shi²,

Zhang

et al. 2010

Pharmacogenomics J

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Genomic biomarkers for the detection of drug-induced liver injury (DILI) from blood are urgently needed for monitoring drug safety. We used a unique data set as part of the Food and Drug Administration led MicroArray Quality Control Phase-II (MAQC-II) project consisting of gene expression data from the two tissues (blood and liver) to test cross-tissue predictability of genomic indicators to a form of chemically-induced liver injury. We then use the genomic indicators from the blood as biomarkers for prediction of acetaminophen-induced liver injury and show that the cross tissue predictability of a response to the pharmaceutical agent (accuracy as high as 92.1%) is better than, or at least comparable to, that of non-therapeutic compounds. We provide a database of gene expression for the highly informative predictors which brings biological context to the possible mechanisms involved in DILI. Pathway-based predictors were associated with inflammation, angiogenesis, Toll-like receptor signaling, apoptosis and mitochondrial damage. The results demonstrate for the first time and support the hypothesis that genomic indicators in the blood can serve as potential diagnostic biomarkers predictive of DILI.

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

Cited by 45 publications

References 39 publications

Pathogenesis of Idiosyncratic Drug-Induced Liver Injury and Clinical Perspectives

Pathogenesis of Idiosyncratic Drug-Induced Liver Injury and Clinical Perspectives

Consistency of predictive signature genes and classifiers generated using different microarray platforms

Genomic indicators in the blood predict drug-induced liver injury

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