Gene networks and transcription factor motifs defining the differentiation of stem cells into hepatocyte-like cells

Background & AimsThe differentiation of stem cells to hepatocyte-like cells (HLC) offers the perspective of unlimited supply of human hepatocytes. However, the degree of differentiation of HLC remains controversial. To obtain an unbiased characterization, we performed a transcriptomic study with HLC derived from human embryonic and induced stem cells (ESC, hiPSC) from three different laboratories.MethodsGenome-wide gene expression profiles of ESC and HLC were compared to freshly isolated and up to 14days cultivated primary human hepatocytes. Gene networks representing successful and failed hepatocyte differentiation, and the transcription factors involved in their regulation were identified.ResultsGene regulatory network analysis demonstrated that HLC represent a mixed cell type with features of liver, intestine, fibroblast and stem cells. The “unwanted” intestinal features were associated with KLF5 and CDX2 transcriptional networks. Cluster analysis identified highly correlated groups of genes associated with mature liver functions (n=1057) and downregulated proliferation associated genes (n=1562) that approach levels of primary hepatocytes. However, three further clusters containing 447, 101, and 505 genes failed to reach levels of hepatocytes. Key TF of two of these clusters include SOX11, FOXQ1, and YBX3. The third unsuccessful cluster, controlled by HNF1, CAR, FXR, and PXR, strongly overlaps with genes repressed in cultivated hepatocytes compared to freshly isolated hepatocytes, suggesting that current in vitro conditions lack stimuli required to maintain gene expression in hepatocytes, which consequently also explains a corresponding deficiency of HLC.ConclusionsThe present gene regulatory network approach identifies key transcription factors which require modulation to improve HLC differentiation

Enhanced activation of human NK cells by drug-exposed hepatocytes

Drug-induced liver injury (DILI) represents one of the major causes why drugs have to be withdrawn from the market. In this study, we describe a new interaction between drug-exposed hepatocytes and natural killer (NK) cells. In a previous genome-wide expression analysis of primary human hepatocytes that had been exposed to clinically relevant concentrations of 148 drugs, we found that several activating ligands for NK cell receptors were regulated by various drugs (e.g., valproic acid, ketoconazole, promethazine, isoniazid). Especially expression of the activating NKG2D ligands (MICA, MICB and ULBPs) and the NKp30 ligand B7-H6 were upregulated in primary human hepatocytes upon exposure to many different drugs. Using the human hepatocyte cell lines Huh7 and HepG2, we confirmed that protein levels of activating NK cell ligands were elevated after drug exposure. Hepatocyte cell lines or primary human hepatocytes co-cultivated with NK cells caused enhanced NK cell activation after pretreatment with drugs at in vivo relevant concentrations compared to solvent controls. Enhanced NK cell activation was evident by increased cytotoxicity against hepatocytes and interferon (IFN)-γ production. NK cell activation could be blocked by specific antibodies against activating NK cell receptors. These data support the hypothesis that NK cells can modulate drug-induced liver injury by direct interaction with hepatocytes resulting in cytotoxicity and IFN-γ production

Toxicogenomics directory of chemically exposed human hepatocytes

A long-term goal of numerous research projects is to identify biomarkers for in vitro systems predicting toxicity in vivo. Often, transcriptomics data are used to identify candidates for further evaluation. However, a systematic directory summarizing key features of chemically influenced genes in human hepatocytes is not yet available. To bridge this gap, we used the Open TG-GATES database with Affymetrix files of cultivated human hepatocytes incubated with chemicals, further sets of gene array data with hepatocytes from human donors generated in this study, and publicly available genome-wide datasets of human liver tissue from patients with non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular cancer (HCC). After a curation procedure, expression data of 143 chemicals were included into a comprehensive biostatistical analysis. The results are summarized in the publicly available toxicotranscriptomics directory ( http://wiki.toxbank.net/toxicogenomics-map/ ) which provides information for all genes whether they are up- or downregulated by chemicals and, if yes, by which compounds. The directory also informs about the following key features of chemically influenced genes: (1) Stereotypical stress response. When chemicals induce strong expression alterations, this usually includes a complex but highly reproducible pattern named 'stereotypical response.' On the other hand, more specific expression responses exist that are induced only by individual compounds or small numbers of compounds. The directory differentiates if the gene is part of the stereotypical stress response or if it represents a more specific reaction. (2) Liver disease-associated genes. Approximately 20 % of the genes influenced by chemicals are up- or downregulated, also in liver disease. Liver disease genes deregulated in cirrhosis, HCC, and NASH that overlap with genes of the aforementioned stereotypical chemical stress response include CYP3A7, normally expressed in fetal liver; the phase II metabolizing enzyme SULT1C2; ALDH8A1, known to generate the ligand of RXR, one of the master regulators of gene expression in the liver; and several genes involved in normal liver functions: CPS1, PCK1, SLC2A2, CYP8B1, CYP4A11, ABCA8, and ADH4. (3) Unstable baseline genes. The process of isolating and the cultivation of hepatocytes was sufficient to induce some stress leading to alterations in the expression of genes, the so-called unstable baseline genes. (4) Biological function. Although more than 2,000 genes are transcriptionally influenced by chemicals, they can be assigned to a relatively small group of biological functions, including energy and lipid metabolism, inflammation and immune response, protein modification, endogenous and xenobiotic metabolism, cytoskeletal organization, stress response, and DNA repair. In conclusion, the introduced toxicotranscriptomics directory offers a basis for a rationale choice of candidate genes for biomarker evaluation studies and represents an easy to use source of background information on chemically influenced genes

Toxicogenomics directory of rat hepatotoxicants in vivo and in cultivated hepatocytes

Transcriptomics is developing into an invaluable tool in toxicology. The aim of this study was, using a transcriptomics approach, to identify genes that respond similar to many different chemicals (including drugs and industrial compounds) in both rat liver in vivo and in cultivated hepatocytes. For this purpose, we analyzed Affymetrix microarray expression data from 162 compounds that were previously tested in a concentration-dependent manner in rat livers in vivo and in rat hepatocytes cultivated in sandwich culture. These data were obtained from the Japanese Toxicogenomics Project (TGP) and North Rhine-Westphalian (NRW) data sets, which represent 138 and 29 compounds, respectively, and have only 5 compounds in common between them. The in vitro gene expression data from the NRW data set were generated in the present study, while TGP is publicly available. For each of the data sets, the overlap between up- or down-regulated genes in vitro and in vivo was identified, and named in vitro-in vivo consensus genes. Interestingly, the in vivo-in vitro consensus genes overlapped to a remarkable extent between both data sets, and were 21-times (upregulated genes) or 12-times (down-regulated genes) enriched compared to random expectation. Finally, the genes in the TGP and NRW overlap were used to identify the upregulated genes with the highest compound coverage, resulting in a seven-gene set of Cyp1a1, Ugt2b1, Cdkn1a, Mdm2, Aldh1a1, Cyp4a3, and Ehhadh. This seven-gene set was then successfully tested with structural analogues of valproic acid that are not present in the TGP and NRW data sets. In conclusion, the seven-gene set identified in the present study responds similarly in vitro and in vivo to a wide range of different chemicals. Despite these promising results with the seven-gene set, transcriptomics with cultivated rat hepatocytes remains a challenge, because in general many genes are up- or downregulated by in vitro culture per se, respond differently to test compounds in vitro and in vivo, and/or show higher variability in the in vitro system compared to the corresponding in vivo data.publishe

A transcriptome-based classifier to identify developmental toxicants by stem cell testing : design, validation and optimization for histone deacetylase inhibitors

Test systems to identify developmental toxicants are urgently needed. A combination of human stem cell technology and transcriptome analysis was to provide a proof of concept that toxicants with a related mode of action can be identified and grouped for read-across. We chose a test system of developmental toxicity, related to the generation of neuroectoderm from pluripotent stem cells (UKN1), and exposed cells for 6 days to the histone deacetylase inhibitors (HDACi) valproic acid, trichostatin A, vorinostat, belinostat, panobinostat and entinostat. To provide insight into their toxic action, we identified HDACi consensus genes, assigned them to superordinate biological processes and mapped them to a human transcription factor network constructed from hundreds of transcriptome data sets. We also tested a heterogeneous group of 'mercurials' (methylmercury, thimerosal, mercury(II)chloride, mercury(II)bromide, 4-chloromercuribenzoic acid, phenylmercuric acid). Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants. A support vector machine (SVM)-based classifier predicted all HDACi correctly. For validation, the classifier was applied to legacy data sets of HDACi, and for each exposure situation, the SVM predictions correlated with the developmental toxicity. Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1 and LHX2) are sufficient to separate HDACi from mercurials. Our data demonstrate how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants. Extension of this concept to mechanisms beyond HDACi would allow prediction of human developmental toxicity hazard of unknown compounds with the UKN1 test system.publishe