| Literature DB >> 32217639 |
Frank Jühling1,2, Nourdine Hamdane1,2, Emilie Crouchet1,2, Shen Li3, Bryan C Fuchs3, Thomas F Baumert4,2,5,6, Houssein El Saghire1,2, Atish Mukherji1,2, Naoto Fujiwara7, Marine A Oudot1,2, Christine Thumann1,2, Antonio Saviano1,2,5, Armando Andres Roca Suarez1,2, Kaku Goto1,2, Ricard Masia8, Mozhdeh Sojoodi3, Gunisha Arora3, Hiroshi Aikata9, Atsushi Ono7,9, Parissa Tabrizian10, Myron Schwartz10, Stephen J Polyak11,12, Irwin Davidson13, Christian Schmidl14,15, Christoph Bock14,16, Catherine Schuster1,2, Kazuaki Chayama9, Patrick Pessaux1,2,5, Kenneth K Tanabe3, Yujin Hoshida7, Mirjam B Zeisel1,2,17, François Ht Duong1,2.
Abstract
OBJECTIVE: Hepatocellular carcinoma (HCC) is the fastest-growing cause of cancer-related mortality with chronic viral hepatitis and non-alcoholic steatohepatitis (NASH) as major aetiologies. Treatment options for HCC are unsatisfactory and chemopreventive approaches are absent. Chronic hepatitis C (CHC) results in epigenetic alterations driving HCC risk and persisting following cure. Here, we aimed to investigate epigenetic modifications as targets for liver cancer chemoprevention.Entities:
Keywords: chemoprevention; gene expression; hepatitis C; hepatocellular carcinoma; non-alcoholic steatohepatitis
Mesh:
Year: 2020 PMID: 32217639 PMCID: PMC7116473 DOI: 10.1136/gutjnl-2019-318918
Source DB: PubMed Journal: Gut ISSN: 0017-5749 Impact factor: 23.059
Figure 1Patients with non-alcoholic steatohepatitis (NASH) and chronic hepatitis C (CHC) with advanced liver disease share similar epigenetic and transcriptional changes associated with hepatocellular carcinoma (HCC). (A) RNA-Seq (left panel) and ChIP-Seq (right panel) mapping of NASH-induced and CHC-induced transcriptomic and H3K27ac modifications from patient-derived liver biopsies and resections. Left panel: unsupervised clustering of significant 4790 differentially expressed genes in livers from NASH (n=3) and CHC (n=6) compared with control patients (n=3 and 5, respectively). Right panel: differential signals in H3K27ac ChIP-Seq peaks for corresponding genes in livers from NASH (n=7) and CHC (n=6) compared with control patients (n=6). (B) Significant H3K27ac modifications correlate (Spearman’s rank correlation coefficients and p values) with gene expression changes in both patients with NASH (left panel) and CHC (right panel). Prognostic association of gene expression was determined using Cox score for time to overall death in a cohort of patients as described in the ‘Materials and methods’ section. (C) Hallmark pathways significantly enriched for H3K27ac modifications in NASH (n=7) and CHC (n=6) compared with control (n=6) patient samples. (D) Significant H3K27ac changes of the 1693 genes with corresponding transcriptomic changes in patients with NASH and CHC derived from B. (E) Venn diagram showing the overlap of significant epigenetically modified genes (shown in D) with corresponding expression changes in patients with NASH and CHC with advanced liver disease derived from the ChIP-Seq and RNA-Seq experiments shown in B.
Figure 2Risk for hepatocellular carcinoma (HCC) development and prognostic liver signature (PLS) expression in patients with advanced liver disease. (A) The probabilities of future hepatocarcinogenesis and overall survival according to the presence of the epigenetic dysregulation. The dysregulation was significantly associated with future HCC development and mortality in patients with HCV-related early stage cirrhosis. (B) The prevalence of the presence of the epigenetic dysregulation in patients with non-alcoholic steatohepatitis (NASH). The dysregulation was more frequently observed in patients with advanced fibrosis, one of the well-known HCC risks, compared with those with mild fibrosis. (C) The probabilities of future hepatocarcinogenesis and overall survival according to the presence of dysregulation of a gene subset termed the ‘prognostic epigenetic signature’ (PES). (D) The prevalence of the presence of the epigenetic dysregulation in patients with NASH. The PES, including 25 genes, showed better or similar capability to identify patients with higher HCC risk compared with the full signature. The PES was defined as commonly prognostic genes in both HCV and NASH (FDR<0.25). (E) Heatmap of the 186-gene PLS including modulation of the HCC high-risk (top) and low-risk (bottom) genes based on patient-liver transcriptome (left panel) and epigenome (right panel). Expression and H3K27ac changes of the gene members of the PLS in NASH, chronic hepatitis C (CHC) and DAA/HCC-cured compared with control patient livers measured using the RNA-Seq and ChIP-Seq experiments shown in figure 1A. (F) H3K27ac modifications correlate (Spearman’s rank correlation coefficients and p values) with transcriptomic changes of gene members of the PLS in patients with NASH (left panel) or CHC (right panel).
Figure 3Modeling of HCV-induced and non-alcoholic steatohepatitis (NASH)-induced histone modifications associated with liver carcinogenesis in a cell culture model. (A) Schematic representation of the experimental setup. H3K27ac marks were profiled by ChIP-Seq following free fatty acid (FFA) treatment (top panel: day 3) or persistent HCV infection (bottom panel: day 10). (B) H3K27ac data of the 1693 genes with significant transcriptomic changes in patients with NASH and chronic hepatitis C (CHC) derived from figure 1B, and corresponding changes in FFA-treated or HCV-infected cells derived from the ChIP-Seq experiment shown in panels A and B. (C) Significant H3K27ac modifications correlate (Spearman’s rank correlation coefficients and p values) with gene expression changes in FFA-treated or HCV-infected cells. Prognostic association of gene expression was determined as described for figure 1B. (D) Gene Set Enrichment Analysis (GSEA) pathway analysis of genes associated with H3K27ac modifications in FFA-treated or HCV-infected compared with Mock or non-infected cells from the ChIP-Seq experiment shown in panels A and B. (E) Heatmap of the prognostic liver signature (PLS) based on the transcriptome (left panel) and epigenome (right panel) of FFA-treated or HCV-infected cells. (F, G) Effect of ectopic expression of HCV proteins, thapsigargin and 1,2-bis(o-aminophenoxy)ethane-N, N, N′, N′-tetraacetic acid (BAPTA) treatment on the PLS status. Heatmaps show: (top) the classification of the PLS global status as poor (orange) or good (green) prognosis; (bottom) the significance of induction (red) or suppression (blue) of PLS poor-prognosis or good-prognosis genes. FDR, false discovery rate.
Figure 4Effect of small molecules and knockdown targeting chromatin modifiers and readers on gene expression driving hepatocellular carcinoma (HCC) risk in cell culture. (A) Schematic representation of the experimental setup. (B) Mode of action of inhibitors of chromatin modifiers/readers targeting histone acetylation and methylation. TF, transcription factors. (C) Cytotoxicity of the inhibitors in Huh7.5.1dif cells depicted in panel B. Cell viability of non-infected cells (n=3) was measured 72 hours following incubation with inhibitors using MTT assay. Results (mean±SEM) show percentage of viable cells in compound-treated conditions relative to untreated cells. The concentrations used for functional assays shown in panel D are indicated by red asterisks. (D) Effect of compounds on the prognostic liver signature (PLS) gene expression. Heatmaps show: (top) the classification of the PLS global status as poor (orange) or good (green) prognosis; (bottom) the significance of induction (red) or suppression (blue) of PLS poor-prognosis or good-prognosis genes. FDR, false discovery rate. (E) mRNA expression of EGF and NFκB2 in cells treated with the indicated compounds for 72 hours (*p<0.05; **p<0.01; ***p<0.001, unpaired t-test). (F) Inhibitors of chromatin modifiers/readers do not modulate HCV viral load in the experiment shown in panel E. (G) Western blot analysis showing c-Myc protein expression in the cell-based system. One representative gel is shown (see online supplementary figure S4). Right panel graph shows the quantification of western blot analysis intensities in arbitrary units normalised to total protein level (n=3) (stain-free staining). Results show the mean±SEM of integrated blot densities of four independent experiments and are not significantly (‘n.s.’) modulated. (H) Gene silencing of BRD3/4 and HDAC9 reverses the HCC high-risk status of the PLS in Huh7.5.1dif-Cas9 cells. HCV-infected Huh7.5.1dif-Cas9 cells expressing the PLS poor-prognosis status were transduced with lentiviral vectors coding for sgRNA targeting p300, BRD3, BRD4 and HDAC9 genes or non-targeting sgRNA (sgCTRL) (means of two experiments). (I) Proliferation analyses: (left) JQ1-treated or untreated Mock and HCV-infected cells; (right) Mock or HCV-infected Huh7.5.1dif-Cas9 cells transduced with lentiviral vectors coding for sgCTRL or sgRNA targeting BRD3 and BRD4 genes. (J) Analysis of BRD3, BRD4, c-Myc and Actin protein expression levels in Huh7.5.1dif-Cas9 cells KO for BRD3 or BRD4 by western blot analysis. Effective sgRNA for BRD3 and BRD4 genes used in the screening experiment shown in (H) are labelled with a red asterisk.
Figure 5Bromodomain (BRD)4 inhibitor JQ1 reduces liver tumour burden in a mouse model of non-alcoholic steatohepatitis (NASH)-hepatocellular carcinoma (HCC). (A) Schematic representation of the proof-of-concept study using a mouse model of DEN and choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD)-induced hepatocarcinogenesis. (B) Transcriptomic changes of genes with significant H3K27ac modifications from livers from patients with NASH and chronic hepatitis C (CHC) as explained in figure 1D (overlapping genes) and corresponding changes in vehicle or JQ1-treated DEN/CDAHFD mice. (C) JQ1 significantly reduces tumour burden in vivo. While body weights are stable, liver weights as well as the numbers of tumours are significantly (*p<0.05; ***p<0.001; ****p<0.0001, unpaired t-test) reduced in JQ1-treated (n=8) compared with untreated (n=8) mice. Results are expressed as means±SEM. (D) Representative macroscopic photographs of livers (x 1.5 magnified), H&E and Sirius red staining of liver sections from vehicle and JQ1-treated mice. Tumour nodules are indicated by an arrow head and are delimited by dashed lines. (E) JQ1 efficiently reduces liver fibrosis and inflammation. Fibrosis stage was evaluated through quantitative digital analysis of whole-scanned liver sections (collagen proportional area (CPA)) and fibrotic gene expression in JQ1-treated (n=3) compared with JQ1-untreated (n=3) mice. Results are expressed as means±SD. (D) Expression of inflammatory genes Ccl2 and Tnfα are shown as means±SD (*p<0.05; **p<0.01, unpaired t-test). Gene expression was assessed by quantitative reverse transcription (qRT)-PCR. (F) Gene Set Enrichment Analysis (GSEA) pathway analysis of transcriptional changes in JQ1-treated DEN/CDAHFD mouse livers. Normalised enrichment scores (NES) of significantly enriched hallmark pathways derived from RNA-Seq analysis of livers from vehicle (n=3) and JQ1-treated (n=3) mice compared with control/phosphate-buffered saline (PBS) (n=5) mice. JQ1 partially reverses the NASH-mediated and HCV-mediated induction (red labels) or repression (green labels) of pathways identified in patients with NASH and/or CHC. (G) PLS status and expression of HCC high-risk/low-risk genes from the RNA-Seq analysis shown in panel D. Heatmaps show: (top) the classification of the PLS global status as poor (orange) or good (green) prognosis; (bottom) the significance of induction (red) or suppression (blue) of PLS poor-prognosis or good-prognosis genes. FDR, false discovery rate. (H) Western blot analysis showing c-Myc protein expression in control/PBS (n=3), vehicle (n=3) or JQ1-treated (n=3) mouse livers. Bottom panel graph shows the quantification of western blot analysis from six different animals (n.s., non-significant; unpaired t-test). (I) Recruitment (ChIP-qPCR assays) to the promoter-enhancer regions of indicated genes, using liver of control, DEN/CDAHFD and JQ1-treated mice, using IgG and BRD4 antibodies (*p≤0.05; **p≤0.01; Mann-Whitney U test).
Figure 6Proof-of-concept for therapeutic impact of JQ1 in patient-derived ex vivo in liver disease and hepatocellular carcinoma (HCC) models. (A) JQ1 reverts the poor-prognosis prognostic liver signature (PLS) in culture of patient-derived tissue that were surgically resected from patients diagnosed with chronic hepatitis C (CHC) (fibrosis stage=F3) and ex vivo treated with JQ1 0.1 and 1 µM. Heatmaps show: (top) the classification of the PLS status as poor (orange) or good (green) prognosis; (bottom) the significance of induction (red) or suppression (blue) of poor-prognosis or good-prognosis genes. FDR, false discovery rate. (B) JQ1 decreases HCC cell viability in a three-dimensional patient-derived tumour spheroid model. HCC spheroids were generated from HCC tissues with different aetiologies from five different patients and incubated with JQ1 (50 nM) or sorafenib (10 µM) as described in ‘Materials and methods’ section. Cell viability was assessed 6 days after treatment by measuring ATP levels. Each experiment shows mean±SEM in JQ1 and sorafenib-treated spheroids in percentage compared with dimethyl sulfoxide-treated tumour spheroids (n=4 for each condition). (C) Heatmap recapitulating the effect of JQ1 on the different patient-derived tumour spheroids shown in panel B. Colours indicate the percentage of viable cells (black=100%, light green=50%). NAFLD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis.
Figure 7Targeting liver disease-induced epigenetic modifications and hepatocellular carcinoma (HCC) chemoprevention. Model: chronic hepatitis C (CHC) or non-alcoholic steatohepatitis (NASH) induce a transcriptional reprogramming of liver cells through genome-wide H3K27ac changes driving HCC risk and hepatocarcinogenesis. H3K27ac-mediated transcriptional reprogramming constitutes a target for HCC chemoprevention by bromodomain (BRD)4 inhibitors.