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. 2018 Jul 26;3(14):e120274.
doi: 10.1172/jci.insight.120274.

Hepatic Expression Profiling Identifies Steatosis-Independent and Steatosis-Driven Advanced Fibrosis Genes

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Free PMC article

Hepatic Expression Profiling Identifies Steatosis-Independent and Steatosis-Driven Advanced Fibrosis Genes

Divya Ramnath et al. JCI Insight. .
Free PMC article

Abstract

Chronic liver disease (CLD) is associated with tissue-destructive fibrosis. Considering that common mechanisms drive fibrosis across etiologies, and that steatosis is an important cofactor for pathology, we performed RNA sequencing on liver biopsies of patients with different fibrosis stages, resulting from infection with hepatitis C virus (HCV) (with or without steatosis) or fatty liver disease. In combination with enhanced liver fibrosis score correlation analysis, we reveal a common set of genes associated with advanced fibrosis, as exemplified by those encoding the transcription factor ETS-homologous factor (EHF) and the extracellular matrix protein versican (VCAN). We identified 17 fibrosis-associated genes as candidate EHF targets and demonstrated that EHF regulates multiple fibrosis-associated genes, including VCAN, in hepatic stellate cells. Serum VCAN levels were also elevated in advanced fibrosis patients. Comparing biopsies from patients with HCV with or without steatosis, we identified a steatosis-enriched gene set associated with advanced fibrosis, validating follistatin-like protein 1 (FSTL1) as an exemplar of this profile. In patients with advanced fibrosis, serum FSTL1 levels were elevated in those with steatosis (versus those without). Liver Fstl1 mRNA levels were also elevated in murine CLD models. We thus reveal a common gene signature for CLD-associated liver fibrosis and potential biomarkers and/or targets for steatosis-associated liver fibrosis.

Keywords: Fibrosis; Hepatitis; Hepatology; Inflammation.

Conflict of interest statement

Conflict of interest: This work was partially funded by CSL Limited.

Figures

Figure 1
Figure 1. An overview of DEGs in early- versus late-stage liver fibrosis.
(A) Principal component analysis of the corrected counts for the filtered genes (9,624 genes), highlighting the components that contribute to maximum variability (n = 69 patients; circles, early stages; triangles, advanced stages). (B) Volcano plot illustrating genes, from the filtered data set, that are significantly differentially expressed between early and advanced stages of liver fibrosis, as identified by RNA sequencing (n = 69 patients; EdgeR-generated FWER value and log2 fold change). (C) A heatmap showing the expression patterns of the 168 DEGs after hierarchical clustering using the complete linkage method (n = 69 patients). (D) KEGG pathway analysis of the DEGs between early- versus late-stage fibrosis (n = 69 patients; 1,595 FDR-corrected list). Circle size correlates with number of genes, and circle color indicates statistical significance.
Figure 2
Figure 2. Gene signatures correlating with patient ELF scores.
Correlation analysis was performed using log2-transformed corrected counts of genes and patient ELF scores (n = 69 patients). Linear regression of the top 12 genes that most strongly correlate with ELF score (r2 value of 0.39 or more), showing gene expression as an independent variable and ELF score as a dependent variable.
Figure 3
Figure 3. EHF is a candidate regulator of multiple genes associated with advanced liver fibrosis.
(A) A Venn diagram showing the overlap between the ELF score–correlated genes and previously identified transcriptional targets of EHF (31). (B) A list of the 10 ELF score–correlating EHF candidate target genes from A. (C) The EHF-binding motif is significantly enriched in 9 ELF score–correlated genes (enrichment score 3.16), as identified by RcisTarget. (D–H) Expression of EHF and IRF6 (control gene) was silenced in LX-2 cells, with two independent siRNAs being used for EHF. After 24 hours, cells were stimulated with 10 ng/ml TGF-β (gray bars) for 24 hours or were left unstimulated (white bars), after which RNA was prepared and qPCR was performed. (D) Basal levels of EHF mRNA as well as TGF-β–regulated levels of mRNAs, relative to HPRT, for (E) VCAN, (F) DHRS2, (G) COL1A1, and (H) DTNA were quantified by qPCR. Data represent mean ± SEM from 3 independent experiments. FDR values were calculated using (D) nonparametric ANOVA (Kruskal-Wallis test) or (E–H) 2-way ANOVA followed by Benjamini-Hochberg multiple-testing corrections. FDR values ≤ 0.05 were considered statistically significant.
Figure 4
Figure 4. Meta-analysis of existing microarray and RNA-sequencing data sets for liver fibrosis.
(A) A Venn diagram comparing the DEGs from this study (gl5, red) to 4 other published gene lists (gl1, gl2, gl3, and gl4). 16 genes (green) were common across all 5 gene lists, while 32 genes (purple) were present in 4 of the 5 gene lists (including this one). 31 genes were unique to this data set (red). (B) The list of 48 DEGs that were common in our data set and at least 3 other published data sets (green, present in all data sets; purple, present in 4 of 5 data sets, including this one; gray, extracellular proteins).
Figure 5
Figure 5. VCAN is upregulated in advanced stages of liver fibrosis.
(A) A scatter plot showing the log2-corrected counts of VCAN for all patients from the RNA-sequencing analysis (n = 69 patients; horizontal bars indicate mean ± SEM; circles, early stages; triangles, advanced stages). (B) A schematic diagram of the different isoforms of VCAN. (CF) qPCR analysis was performed on the mRNA from all patient biopsies used in the RNA-sequencing study. Levels of the different isoforms of VCAN, (C) V0, (D) V1, (E) V2, and (F) V3, were determined, relative to 18S ribosomal RNA, by qPCR (n = 69 patients; horizontal bars indicate mean ± SEM; circles, early stages; triangles, advanced stages). (G) Circulating levels of VCAN were determined by performing ELISA on human patient sera samples (see Supplemental Table 1 for details of the patient cohort; n = 35; detectable levels in 9 patients; horizontal bars indicate mean ± SEM; circles, early stages; triangles, advanced stages). P ≤ 0.05, calculated using the Mann-Whitney test.
Figure 6
Figure 6. Identification of steatosis-associated late-stage fibrosis genes.
A volcano plot illustrating significant differences in genes between early and advanced stages of fibrosis by RNA sequencing in patients with (A) HCV alone (n = 26 patients; EdgeR-generated FWER value and log2 fold change) and (B) HCV with steatosis (n = 29 patients; EdgeR-generated FWER value and log2 fold change). (C) A heatmap showing the average expression patterns of the 36 genes, identified as differentially expressed in the HCV and steatosis patient cohort, in the patients with either HCV alone or HCV and steatosis. (D) A bar graph of 12 steatosis-enriched late-stage fibrosis genes, showing fold change in expression between late- versus early-stage fibrosis in HCV patients (blue bars) and HCV with steatosis patients (red bars).
Figure 7
Figure 7. FSTL1 is elevated in patients with steatosis-associated late-stage fibrosis.
(A) A scatter plot showing the log2-corrected counts of FSTL1 for all patients from the RNA-sequencing analysis (n = 69 patients; horizontal bars indicate mean ± SEM; circles, early stages; triangles, advanced stages). (B) Correlation analysis showing FSTL1 expression as the independent variable and ELF score as the dependent variable in HCV (blue), HCV with steatosis (red), NAFLD (orange), and alcoholic FLD (brown) patients (n = 69 patients). (C) FSTL1 corrected counts were plotted in early (E) versus advanced (A) stages of fibrosis from patients with HCV alone or HCV and steatosis (n = 55 patients; horizontal bars indicate mean ± SEM; circles, early stages; triangles, advanced stages). (D and E) Circulating levels of FSTL1 in sera from (D) patients with chronic HCV or NAFLD (see Supplemental Table 1 for details of the cohort; n = 35 patients; detectable levels in 29 patients; circles, early stages; triangles, advanced stages) and (E) patients with NAFLD (see Supplemental Table 2 for details of the cohort; n = 25 patients; detectable levels in 18 patients; circles, early stages; triangles, advanced stages) were determined by ELISA (data represented as mean ± SEM). (F and G) mRNA levels of Fstl1 were measured, relative to the housekeeping gene Hprt, in (F) 6- and 12-week-old TAA- or sham-treated mice (n = 5 mice per group) and (G) 1- and 3-week-old CDE- or sham-treated mice (n = 4 mice per group) (data represented as mean ± SEM). For A and C, FWER values were calculated using Bonferroni multiple-testing corrections; for D and G, FDR values were calculated using nonparametric ANOVA (Kruskal-Wallis test) followed by Benjamini-Hochberg multiple-testing corrections; and for E and F, P values were calculated using the Mann-Whitney test. P or FDR ≤ 0.05 was considered statistically significant.

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