Whole Genome Transcript Profiling of Drug Induced Steatosis in Rats Reveals a Gene Signature Predictive of Outcome

Abstract

Drug induced steatosis (DIS) is characterised by excess triglyceride accumulation in the form of lipid droplets (LD) in liver cells. To explore mechanisms underlying DIS we interrogated the publically available microarray data from the Japanese Toxicogenomics Project (TGP) to study comprehensively whole genome gene expression changes in the liver of treated rats. For this purpose a total of 17 and 12 drugs which are diverse in molecular structure and mode of action were considered based on their ability to cause either steatosis or phospholipidosis, respectively, while 7 drugs served as negative controls. In our efforts we focused on 200 genes which are considered to be mechanistically relevant in the process of lipid droplet biogenesis in hepatocytes as recently published (Sahini and Borlak, 2014). Based on mechanistic considerations we identified 19 genes which displayed dose dependent responses while 10 genes showed time dependency. Importantly, the present study defined 9 genes (ANGPTL4, FABP7, FADS1, FGF21, GOT1, LDLR, GK, STAT3, and PKLR) as signature genes to predict DIS. Moreover, cross tabulation revealed 9 genes to be regulated ≥10 times amongst the various conditions and included genes linked to glucose metabolism, lipid transport and lipogenesis as well as signalling events. Additionally, a comparison between drugs causing phospholipidosis and/or steatosis revealed 26 genes to be regulated in common including 4 signature genes to predict DIS (PKLR, GK, FABP7 and FADS1). Furthermore, a comparison between in vivo single dose (3, 6, 9 and 24 h) and findings from rat hepatocyte studies (2 h, 8 h, 24 h) identified 10 genes which are regulated in common and contained 2 DIS signature genes (FABP7, FGF21). Altogether, our studies provide comprehensive information on mechanistically linked gene expression changes of a range of drugs causing steatosis and phospholipidosis and encourage the screening of DIS signature genes at the preclinical stage.

In vitro to in vivo extrapolation for drug-induced liver injury using a pair ranking method

“…The hepatic related side effects were collected by attributing PTs to their primary System Organ Class (SOC) level of hepatobiliary disorders in Med-DRA. Compounds that caused hepatic steatosis were curated from a literature survey and corrected by domain experts by observation of pathologic images (Liu et al, 2016;Sahini et al, 2014) (Tab. S2 1 ).…”

Genomics of human fatty liver disease reveal mechanistically linked lipid droplet–associated gene regulations in bland steatosis and nonalcoholic steatohepatitis

“…Moreover, intrahepatic CD8 and natural killer T (NKT) cells aggravate the condition of NASH 4,12,13 with high regional concentrations of cytokines further harming liver cells. Additionally, activated Kupffer cells may also assist in the removal of excess lipids from hepatocytes as evidenced by electron microscopy 14 .…”

“…Briefly, hepatocytes were obtained by collagenase perfusion of histologically normal liver tissue and seeded at a density of 0.375x10 6 viable cells on collagen coated DMEM culture system supplemented with 10% fetal calf serum, 1% penicillin-streptomycin and 1% glutamine. The primary human hepatocytes (PHH) cultures were treated with 0.5mM PA/OA or 0.5mM PA/OA+5ng TNFα for 72h 14,28 . The vehicle control (DMSO) was given in equal amounts and the culture medium was changed daily.…”

“…Additionally, we considered NAFLD responsive genes with those regulated in animal models of drug induced steatosis (DIS) 14 The DEGs depicted in panel B were grouped based on 6 metabolic pathways that include lipid transport, lipogenesis, FA-β-oxidation, LD growth, inflammation/signalling and glucose metabolism. Genes that were up/down regulated are shown in red/green, respectively while those coloured in blue were inconsistently regulated among patients.…”

Using a gene expression biomarker to identify DNA damage‐inducing agents in microarray profiles

“…A critical gap in the application of Tier 0 screening is the availability of validated gene expression signatures (or biomarkers) that have been robustly tested across laboratories, human cell culture models, gene expression platforms, and experimental designs. Transcriptional signatures have been developed to predict organ toxicities including liver cancer (Uehara et al, 2011;Doktorova et al, 2013;Eichner et al, 2013;Thomas et al, 2013;Yamada et al, 2013;Melis et al, 2014;Romer et al, 2014), renal tubular injury (Minowa et al, 2012), hepatocellular steatosis (Sahini et al, 2014), and immunotoxicity (Schmeits et al, 2015). Fewer examples exist in which transcriptional signatures have been built and validated for prediction of molecular initiating events (MIE) or downstream key events (KE) in adverse outcome pathways (AOP); examples include biomarkers predicting MIEs for rodent liver cancer and steatosis (Oshida et al, 2015a(Oshida et al, , 2015b(Oshida et al, , 2015c(Oshida et al, , 2016a(Oshida et al, , 2016b and human endocrine disruption (Ryan et al, 2016).…”