Virus Genotype-Dependent Transcriptional Alterations in Lipid Metabolism and Inflammation Pathways in the Hepatitis C Virus-infected Liver

Despite advances in antiviral therapy, molecular drivers of Hepatitis C Virus (HCV)-related liver disease remain poorly characterised. Chronic infection with HCV genotypes (1 and 3) differ in presentation of liver steatosis and virological response to therapies, both to interferon and direct acting antivirals. To understand what drives these clinically important differences, liver expression profiles of patients with HCV Genotype 1 or 3 infection (n = 26 and 33), alcoholic liver disease (n = 8), and no liver disease (n = 10) were analysed using transcriptome-wide microarrays. In progressive liver disease, HCV genotype was the major contributor to altered liver gene expression with 2151 genes differentially expressed >1.5-fold between HCV Genotype 1 and 3. In contrast, only 6 genes were altered between the HCV genotypes in advanced liver disease. Induction of lipogenic, lipolytic, and interferon stimulated gene pathways were enriched in Genotype 1 injury whilst a broad range of immune-associated pathways were associated with Genotype 3 injury. The results are consistent with greater lipid turnover in HCV Genotype 1 patients. Moreover, the lower activity in inflammatory pathways associated with HCV genotype 1 is consistent with relative resistance to interferon-based therapy. This data provides a molecular framework to explain the clinical manifestations of HCV-associated liver disease

Accumulation of Deleterious Passenger Mutations Is Associated with the Progression of Hepatocellular Carcinoma

<div><p>In hepatocellular carcinoma (HCC), somatic genome-wide DNA mutations are numerous, universal and heterogeneous. Some of these somatic mutations are drivers of the malignant process but the vast majority are passenger mutations. These passenger mutations can be deleterious to individual protein function but are tolerated by the cell or are offset by a survival advantage conferred by driver mutations. It is unknown if these somatic deleterious passenger mutations (DPMs) develop in the precancerous state of cirrhosis or if it is confined to HCC. Therefore, we studied four whole-exome sequencing datasets, including patients with non-cirrhotic liver (n = 12), cirrhosis without HCC (n = 6) and paired HCC with surrounding non-HCC liver (n = 74 paired samples), to identify DPMs. After filtering out putative germline mutations, we identified 187±22 DPMs per non-diseased tissue. DPMs number was associated with liver disease progressing to HCC, independent of the number of exonic mutations. Tumours contained significantly more DPMs compared to paired non-tumour tissue (258–293 per HCC exome). Cirrhosis- and HCC-associated DPMs do not occur predominantly in specific genes, chromosomes or biological pathways and the effect on tumour biology is presently unknown. Importantly, for the first time we have shown a significant increase in DPMs with HCC.</p></div

Summary data of publicly-available WES datasets used in this study.

<p>Summary data of publicly-available WES datasets used in this study.</p

DPMs in HCC and surrounding non-tumour tissue.

<p>Variants were classified based on the predicted effect on the amino acid sequence (A). Total benign missense variants (B and D) and DPMs (C and E) in the datasets 1000G and WES 1–4 are shown as a percentage of all somatic exonic mutations. Significantly more DPMs (but not benign missense SNVs) were detected in tumour compared to paired non-tumour tissue (* p<0.05, ** p<0.01, *** p<0.001 and **** p<0.0001, Wilcoxon matched-pairs signed-rank test). Lines link matched non-tumour and tumour tissues samples. NC-non-cirrhosis; C-cirrhosis; NT-non-tumour; T-tumour.</p

Driver mutations in non-tumour tissue.

<p>Patient samples were separated based on the number of mutations in putative driver genes (x-axis, defined as the 20 top recurrently mutated genes in HCC according to COSMIC database, listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162586#pone.0162586.s011" target="_blank">S3 Table</a>) and analysed the number of benign missense SNVs (A and C) and DPMs (B and D). Significant correlation between DPMs and putative driver mutations (p<0.0001, Spearman rank correlation test) was observed in non-tumour tissue of WES 2. No significant correlation was seen in HCC tissues (p>0.05, Spearman rank correlation test).</p

Clinical characteristics of patients analysed in WES 1: non-HCC liver injury samples.

<p>Clinical characteristics of patients analysed in WES 1: non-HCC liver injury samples.</p

Summary statistics for normalised DPMs between datasets.

<p>Summary statistics for normalised DPMs between datasets.</p

Bioinformatics analysis pipeline.

<p>Each resultant data file is indicated by a sloped rectangle and each process represented by a square rectangle. Our pipeline contains 3 stages: alignment and calibration; variant calling and filtering; and variants annotation and filtration of putative germline mutations.</p

Hypothetical model of HCC progression.

<p>HCC progression is presented here as multiple waves of driver sweeps within hepatocyte subclones. The equilibrium between DPM accumulation and negative selection on the hepatocyte subclones are shown in the top row. A schematic model of the liver (with each circle representing a hepatocyte and the colour gradient representing the DPM load within each hepatocyte) is shown in the centre row. The average DPM load for the tissue is depicted in the bottom row.</p

Frequency distribution of DPMs.

<p>A frequency distribution of the genes containing DPMs in 1000G and WES 1 (A), WES 2 (B), WES 3 (C), and WES 4 (D) shows that most are unique to a given patient. Each gene containing a DPM was grouped based on the number of patients in which that gene contained a DPM (x-axis).</p