Gene expression data from acetaminophen-induced toxicity in human hepatic in vitro systems and clinical liver samples

Abstract

This data set is composed of transcriptomics analyses of (i) liver samples from patients suffering from acetaminophen-induced acute liver failure (ALF) and (ii) hepatic cell systems exposed to acetaminophen and their respective controls. The in vitro systems include widely employed cell lines i.e. HepaRG and HepG2 cells as well as a novel stem cell-derived model i.e. human skin-precursors-derived hepatocyte-like cells (hSKP-HPC). Data from primary human hepatocytes was also added to the data set “Open TG-GATEs: a large-scale toxicogenomics database” (Igarashi et al., 2015) [1]. Changes in gene expression due to acetaminophen intoxication as well as comparative information between human in vivo and in vitro samples are provided. The microarray data have been deposited in NCBI׳s Gene Expression Omnibus and are accessible through GEO Series accession number GEO: GSE74000. The provided data is used to evaluate the predictive capacity of each hepatic in vitro system and can be directly compared with large-scale publically available toxicogenomics databases. Further interpretation and discussion of these data feature in the corresponding research article “Toxicogenomics-based prediction of acetaminophen-induced liver injury using human hepatic cell systems” (Rodrigues et al., 2016) [2].

Regulation of Cytosolic Sulfotransferases in Models of Human Hepatocyte Development

“…Genes that are primarily expressed in fetal hepatocytes, such as CYP3A7 and pyruvate kinase muscle isozyme, are more highly expressed in undifferentiated HepaRG cells, whereas genes that are predominantly expressed in adult hepatocytes, such as CYP3A4 and CYP2E1, are more highly expressed in differentiated HepaRG cells (Tsuji et al, 2014;Bucher et al, 2017). HepaRG cells are becoming established as a useful model for studying hepatocellular differentiation, xenobiotic metabolism and toxicity, and development of liver diseases (Sharanek et al, 2015;Nunn et al, 2016;Rodrigues et al, 2016;Sayyed et al, 2016;Xia et al, 2016). In this study, we have defined the temporal patterns of SULT expression in HepaRG cells during differentiation.…”

Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation

“…4b ). Gene expression data of healthy liver biopsies 29 and metabolite utilization rates 30 (Supplementary Table 2 ) were integrated with a model extraction algorithm 21 to establish a liver-specific GSMN model and a reference flux distribution before drug administration. After these prerequisite steps, dMOMA was used to combine the PBPK and GSMN models (Fig.…”

“…Stoichiometric modeling was performed within using the COBRA toolbox 58 and the gurobi solver (Gurobi Inc.). Gene expression data of healthy liver biopsies 29 and hepatic metabolite utilization data 30 was used together with the integrated metabolic analysis tool (iMAT) 21 to prune a generic human cell 28 into a context-specific GSMN model of a healthy human liver. The unperturbed liver biochemistry was simulated in a fasted state, where the uptake of gluconeogenic substrates, non-esterified fatty acids, and amino acids, as well as gases and minerals (oxygen, phosphate, etc.…”

“…Differential gene expression data of healthy liver biopsies (GSE74000) 29 were averaged and filtered for genes in the generic human cell. The data was then normalized to the maximal expression value, and translated to reaction-based expression scores.…”

Retrospective Identification of Herpes Simplex 2 Virus-Associated Acute Liver Failure in an Immunocompetent Patient Detected Using Whole Transcriptome Shotgun Sequencing

“…Sequence data can also be mapped against the human genome and used to measure differential gene expression relative to healthy controls [ 22 ]. Microarrays could also be used for this purpose and compared against publically available expression data [ 23 , 24 ]. Establishment of gene expression profiles from patients with ALF of known etiology might reveal useful biomarkers that could facilitate diagnosis and lead to more effective treatment.…”