Regulation of insulin resistance and type II diabetes by hepatitis C virus infection: A driver function of circulating miRNAs

Abstract

Hepatitis C virus (HCV) infection is a serious worldwide healthcare issue. Its association with various liver diseases including hepatocellular carcinoma (HCC) is well studied. However, the study on the relationship between HCV infection and the development of insulin resistance and diabetes is very limited. Current research has already elucidated some underlying mechanisms, especially on the regulation of metabolism and insulin signalling by viral proteins. More studies have emerged recently on the correlation between HCV infection-derived miRNAs and diabetes and insulin resistance. However, no studies have been carried out to directly address if these miRNAs, especially circulating miRNAs, have causal effects on the development of insulin resistance and diabetes. Here, we proposed a new perspective that circulating miRNAs can perform regulatory functions to modulate gene expression in peripheral tissues leading to insulin resistance and diabetes, rather than just a passive factor associated with these pathological processes. The detailed rationales were elaborated through comprehensive literature review and bioinformatic analyses. miR-122 was identified to be one of the most potential circulating miRNAs to cause insulin resistance. This result along with the idea about the driver function of circulating miRNAs will promote further investigations that eventually lead to the development of novel strategies to treat HCV infection-associated extrahepatic comorbidities.

Keywords: bioinformatic analysis; circulating miRNA; diabetes; hepatitis C virus; insulin Resistance; miR-122; microRNA.

Figure 1

Study approach. A flow chart illustrating the methods and tools used in this study

Figure 2

miR‐122 target profile reveals IR‐relevant ontologies and pathways. miR‐122 predicted targets were analysed for molecular function, biological process and associated pathways with PANTHER Gene Ontology Tool. (A) Biological process ontology includes protein metabolism (56), carbohydrate metabolism (13) and lipid metabolism (10). (B) Molecular function ontology revealed several IR‐relevant biochemical processes including transmembrane transport (27), calcium ion binding (9), GPCR activity (2), peroxidase activity (2) and lipid transporter activity (1). (C) PANTHER pathway analysis identified key signalling pathways including apoptosis signalling (8), EGFR signalling (6), GPCR signalling (11), Ras pathway (5), insulin/IGF pathway‐PKB signalling (4) and PI3 kinase pathway (4). *Indicates a general level 1 ontology in (B) and level 2 ontology in (A), all other listed ontologies are more specific terms at either level 2 in (B) or level 3 in (A). DIANA‐microT‐CDS predictions for miR‐122 are provided in Data S1. PANTHER results with ontologies and corresponding gene lists are provided in Data S2

Figure 3

miR‐122 targets IR‐relevant genes differentially expressed in adipose, skeletal muscle and pancreas. Distribution of miR‐122 predicted targets (511 input genes) differentially expressed in various tissues and compartments was analysed via GeneAnalytics LifeMap Tool, including adipose (22), skeletal muscle (8) and pancreas (27)

Figure 4

miR‐122 structural and thermodynamic characteristics make it an ideal candidate for AGO2‐mediated silencing. miR‐122 sequence was folded with the mFold RNA tool to assess its stem‐loop structure features and thermodynamic stability. (A) pre‐miR‐122 stem‐loop structure and thermodynamic stability. (B) miR‐122 sequence, relevant segments and nucleotide composition. (C) miR‐122 guide strand stem‐loop structure and thermodynamic stability. “hsa‐” stands for homo sapiens

Figure 5

Working model for hepatic and circulating miR‐122 to mediate IR and diabetes associated with Hepatitis C virus (HCV) infection. Key metabolic and cell signalling pathways impacted are illustrated, summarizing the findings from literature review and bioinformatics analyses in this study