Oncogenic Potential of Hepatitis C Virus Proteins

Chronic hepatitis C virus (HCV) infection is a major risk factor for liver disease progression, and may lead to cirrhosis and hepatocellular carcinoma (HCC). The HCV genome contains a single-stranded positive sense RNA with a cytoplasmic lifecycle. HCV proteins interact with many host-cell factors and are involved in a wide range of activities, including cell cycle regulation, transcriptional regulation, cell proliferation, apoptosis, lipid metabolism, and cell growth promotion. Increasing experimental evidences suggest that HCV contributes to HCC by modulating pathways that may promote malignant transformation of hepatocytes. At least four of the 10 HCV gene products, namely core, NS3, NS5A and NS5B play roles in several potentially oncogenic pathways. Induction of both endoplasmic reticulum (ER) stress and oxidative stress by HCV proteins may also contribute to hepatocyte growth promotion. The current review identifies important functions of the viral proteins connecting HCV infections and potential for development of HCC. However, most of the putative transforming potentials of the HCV proteins have been defined in artificial cellular systems, and need to be established relevant to infection and disease models. The new insight into the mechanisms for HCV mediated disease progression may offer novel therapeutic targets for one of the most devastating human malignancies in the world today

MicroRNAs: Role in hepatitis C virus pathogenesis

AbstractHepatitis C virus (HCV) is a global health burden with an estimated 170–200 million peoples chronically infected worldwide. HCV infection remains as an independent risk factor for chronic hepatitis, liver cirrhosis, hepatocellular carcinoma, and a major reason for liver transplantation. Discovery of direct acting antiviral (DAA) drugs have shown promising results with more than 90% success rate in clearing the HCV RNA in patients, although long-term consequences remain to be evaluated. microRNAs (miRNAs) are important players in establishment of HCV infection and target crucial host cellular factors needed for productive HCV replication and augmented cell growth. Altered expression of miRNAs is involved in the pathogenesis associated with HCV infection by controlling signaling pathways such as immune response, proliferation and apoptosis. miRNA is emerging as a means of communication between various cell types inside the liver. There is likely possibility of developing circulating miRNAs as biomarkers of disease progression and can also serve as diagnostic tool with potential of early therapeutic intervention in HCV associated end stage liver disease. This review focuses on recent studies highlighting the contribution of miRNAs in HCV life cycle and their coordinated regulation in HCV mediated liver disease progression

Variation of Gender Biases in Visual Recognition Models Before and After Finetuning

We introduce a framework to measure how biases change before and after fine-tuning a large scale visual recognition model for a downstream task. Deep learning models trained on increasing amounts of data are known to encode societal biases. Many computer vision systems today rely on models typically pretrained on large scale datasets. While bias mitigation techniques have been developed for tuning models for downstream tasks, it is currently unclear what are the effects of biases already encoded in a pretrained model. Our framework incorporates sets of canonical images representing individual and pairs of concepts to highlight changes in biases for an array of off-the-shelf pretrained models across model sizes, dataset sizes, and training objectives. Through our analyses, we find that (1) supervised models trained on datasets such as ImageNet-21k are more likely to retain their pretraining biases regardless of the target dataset compared to self-supervised models. We also find that (2) models finetuned on larger scale datasets are more likely to introduce new biased associations. Our results also suggest that (3) biases can transfer to finetuned models and the finetuning objective and dataset can impact the extent of transferred biases.Comment: 10 pages, 3 Figure

Accessibility and partner number of protein residues, their relationship and a webserver, ContPlot for their display

<p>Abstract</p> <p>Background</p> <p>Depending on chemical features residues have preferred locations – interior or exterior – in protein structures, which also determine how many other residues are found around them. The close packing of residues is the hallmark of protein interior and protein-protein interaction sites.</p> <p>Results</p> <p>The average values of accessible surface area (ASA) and partner number (PN, the number of other residues within a distance of 4.5 Å from any atom of a given residue) of different residues have been determined and a webserver, ContPlot has been designed to display these values (relative to the average values) along the protein sequence. This would be useful to visually identify residues that are densely packed, or those involved in protein-protein interactions. The skewness observed in the distribution of PNs is indicative of the hydrophobic or hydrophilic nature of the residue. The variation of ASA with PN can be analytically expressed in terms of a cubic equation. These equations (one for each residue) can be used to estimate the ASA of a polypeptide chain using the PNs of the individual residues in the structure.</p> <p>Conclusion</p> <p>The atom-based PNs (obtained by counting surrounding atoms) are highly correlated to the residue-based PN, indicating that the latter can adequately capture the atomic details of packing. The average values of ASA and PN associated with each residue should be useful in protein structure prediction or fold-recognition algorithm. ContPlot would provide a handy tool to assess the importance of a residue in the protein structure or interaction site.</p

Forkhead box transcription factor regulation and lipid accumulation by hepatitis C virus

We have previously shown that hepatitis C virus (HCV) infection modulates the expression of forkhead box transcription factors, including FoxO1 and FoxA2, which play key roles in gluconeogenesis and β-oxidation of fatty acid, respectively. The aim of the present study was to determine the role of forkhead box transcription factors in modulating lipid metabolism. HCV infection or core protein expression alone in transfected Huh7.5 cells increased expression of sterol regulatory element binding protein 1c (SREBP-1c) and its downstream target, fatty acid synthase (FASN), which are key proteins involved in lipid synthesis. Knockdown of FoxO1 by small interfering RNA in HCV-infected cells significantly decreased SREBP-1c and FASN expression. Further, HCV infection or core protein expression in Huh7.5 cells significantly decreased the expression of medium-chain acyl coenzyme A dehydrogenase (MCAD) and short-chain acyl coenzyme A dehydrogenase (SCAD), involved in the regulation of β-oxidation of fatty acids. Ectopic expression of FoxA2 in HCV-infected cells rescued the expression of MCAD and SCAD. Oil red O and neutral lipid staining indicated that HCV infection significantly increases lipid accumulation compared to that in the mock-infected control. This was further verified by the increased expression of perilipin-2 and decreased activity of hormone-sensitive lipase (HSL) in HCV-infected hepatocytes, implying increased accumulation of neutral lipids. Knockdown of FoxO1 and ectopic expression of FoxA2 significantly decreased HCV replication. Taken together, these results suggest that HCV modulates forkhead box transcription factors which together increase lipid accumulation and promote viral replication. IMPORTANCE Hepatic steatosis is a frequent complication associated with chronic HCV infection. Its presence is a key prognostic indicator associated with the progression to hepatic fibrosis and hepatocellular carcinoma. Several mechanisms have been proposed to account for the development of steatosis and fatty liver during HCV infection. We observed that HCV infection increases expression of both SREBP-1c and FASN. Further investigation suggested that the expression of SREBP-1c and FASN is controlled by the transcription factor FoxO1 during HCV infection. In addition, HCV infection significantly decreased both MCAD and SCAD expression, which is controlled by FoxA2. HCV infection also increased lipid droplet accumulation, increased perilipin-2 expression, and decreased HSL activity. Thus, knockdown of FoxO1 (decreased lipogenesis) and overexpression of FoxA2 (increased β-oxidation) resulted in a significant disruption of the platform and, hence, a decrease in HCV genome replication. Thus, targeting of FoxO1 and FoxA2 might be useful in developing a therapeutic approach against HCV infection