Hepatitis E virus (HEV) is a pathogen that is transmitted by the fecal-oral route. Owing to the lack of an efficient laboratory model, the life cycle of the virus is poorly understood. During the course of infection, interactions between the viral and host proteins play essential roles, a clear understanding of which is essential to decode the life cycle of the virus. In this study, we identified the direct host interaction partners of all HEV proteins and generated a PPI network. Our functional analysis of the HEV-human PPI network reveals a role of HEV proteins in modulating multiple host biological processes such as stress and immune responses, the ubiquitin-proteasome system, energy and iron metabolism, and protein translation. Further investigations revealed an essential role of several host factors in HEV replication. Collectively, the results from our study provide a vast resource of PPI data from HEV and its human host and identify the molecular components of the viral translation/replication machinery.

Expression of tumor suppressor p53 is regulated at multiple levels, disruption of which often leads to cancer. We have adopted an approach combining computational systems modeling with experimental validation to elucidate the translation regulatory network that controls p53 expression post DNA damage. The RNA-binding protein HuR activates p53 mRNA translation in response to UVC-induced DNA damage in breast carcinoma cells. p53 and HuR levels show pulsatile change post UV irradiation. The computed model fitted with the observed pulse of p53 and HuR only when hypothetical regulators of synthesis and degradation of HuR were incorporated. miR-125b, a UV-responsive microRNA, was found to represses the translation of HuR mRNA. Furthermore, UV irradiation triggered proteasomal degradation of HuR mediated by an E3-ubiquitin ligase tripartite motif-containing 21 (TRIM21). The integrated action of miR-125b and TRIM21 constitutes an intricate control system that regulates pulsatile expression of HuR and p53 and determines cell viability in response to DNA damage.

Summary: Riboswitches are cis-regulatory non-coding genomic segments that control the expression of downstream genes by undergoing conformational change upon ligand binding. We present a comprehensive database of prokaryotic riboswitches that allows the user to search for riboswitches using multiple criteria, extract information about riboswitch location and gene/operon it regulates. RiboD provides a very useful resource that can be utilized for the better understanding of riboswitch-based gene regulation in bacteria and archaea. Availability and implementation: RiboD can be freely accessed on the web at http://ribod.iiserkol.ac.in/.

PPIMpred is a web server that allows high-throughput screening of small molecules for targeting specific protein–protein interactions, namely Mdm2/P53, Bcl2/Bak and c-Myc/Max. Three different kernels of support vector machine (SVM), namely, linear, polynomial and radial basis function (RBF), and two other machine learning techniques including Naive Bayes and Random Forest were used to train the models. A fivefold cross-validation technique was used to measure the performance of these classifiers. The RBF kernel of SVM outperformed and/or was comparable with all other methods with accuracy values of 83%, 79% and 90% for Mdm2/P53, Bcl2/Bak and c-Myc/Max, respectively. About 80% of the predicted SVM scores of training/testing datasets from Mdm2/P53 and Bcl2/Bak have significant IC50 values and docking scores. The proposed models achieved an accuracy of 66–90% with blind sets. The three mentioned (Mdm2/P53, Bcl2/Bak and c-Myc/Max) proposed models were screened in a large dataset of 265 242 small chemicals from National Cancer Institute open database. To further realize the robustness of this approach, hits with high and random SVM scores were used for molecular docking in AutoDock Vina wherein the molecules with high and random predicted SVM scores yielded moderately significant docking scores ( p -values < 0.1). In addition to the above-mentioned classification scheme, this web server also allows users to get the structural and chemical similarities with known chemical modulators or drug-like molecules based on Tanimoto coefficient similarity search algorithm. PPIMpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/PPIMpred/ .

The incidence of non-alcoholic fatty liver disease (NAFLD), encompassing the more severe non-alcoholic steatohepatitis (NASH), is rising alongside the surges in diabetes and obesity. Increasing evidence indicates that NASH is responsible for a significant share of idiopathic hepatocellular carcinoma (HCC) cases, a fatal cancer with a 5-year survival rate below 22%. Biomarkers can facilitate early screening and monitoring of at-risk NAFLD/NASH patients and assist in identifying potential drug candidates for treatment. This study utilized an ensemble feature selection framework to analyze transcriptomic data, identifying biomarker genes associated with the stage-wise progression of NAFLD-related HCC. Seven machine learning algorithms were assessed for disease stage classification. Twelve feature selection methods including correlation-based techniques, mutual information-based methods, and embedded techniques were utilized to rank the top genes as features, through this approach, multiple feature selection methods were combined to yield more robust features important in this disease progression. Cox regression-based survival analysis was carried out to evaluate the biomarker potentiality of these genes. Furthermore, multiphase drug repurposing strategy and molecular docking were employed to identify potential drug candidates against these biomarkers. Among the seven machine learning models initially evaluated, DISCR resulted as the most accurate disease stage classifier. Ensemble feature selection identified ten top genes, among which eight were recognized as potential biomarkers based on survival analysis. These include genes ABAT, ABCB11, MBTPS1, and ZFP1 mostly involved in alanine and glutamate metabolism, butanoate metabolism, and ER protein processing. Through drug repurposing, 81 candidate drugs were found to be effective against these markers genes, with Diosmin, Esculin, Lapatinib, and Phenelzine as the best candidates screened through molecular docking and MMGBSA. The consensus derived from multiple methods enhances the accuracy of identifying relevant robust biomarkers for NAFLD-associated HCC. The use of these biomarkers in a multiphase drug repurposing strategy highlights potential therapeutic options for early intervention, which is essential to stop disease progression and improve outcomes.

Myc is an essential gene having multiple functions such as in cell growth, differentiation, apoptosis, genomic stability, angiogenesis, and disease biology. A large number of researchers dedicated to Myc biology are generating a substantial amount of data in normal and cancer cells/tissues including Burkitt’s lymphoma and ovarian cancer. MYCbase ( http://bicresources.jcbose.ac.in/ssaha4/mycbase ) is a collection of experimentally supported functional sites in Myc that can influence the biological cellular processes. The functional sites were compiled according to their role which includes mutation, methylation pattern, post-translational modifications, protein-protein interactions (PPIs), and DNA interactions. In addition, biochemical properties of Myc are also compiled, which includes metabolism/pathway, protein abundance, and modulators of protein-protein interactions. The OMICS data related to Myc- like gene expression, proteomics expression using mass-spectrometry and miRNAs targeting Myc were also compiled in MYCbase. The mutation and pathway data from the MYCbase were analyzed to look at the patterns and distributions across different diseases. There were few proteins/genes found common in Myc-protein interactions and Myc-DNA binding, and these can play a significant role in transcriptional feedback loops. In this report, we present a comprehensive integration of relevant information regarding Myc in the form of MYCbase. The data compiled in MYCbase provides a reliable data resource for functional sites at the residue level and biochemical properties of Myc in various cancers.