Hepatitis B virus (HBV) infection has been causally linked to hepatocellular carcinoma (HCC) in more than 50% cases. MicroRNAs (miRNAs) play cross-cutting mechanistic roles in the complex interplay between viral pathogenesis, host survival, and clinical outcomes. The present study set out to identify etiologically significant human miRNAs associated with HBV infection in liver-related pathologies leading to HCC. In diverse tissue types, we assembled 573 miRNAs differentially expressed in HBV-associated liver pathologies, HBV infection, fibrosis, cirrhosis, acute on chronic liver failure, and HCC. Importantly, 43 human differentially expressed miRNAs (hDEmiRs) were regulated in serum/plasma and liver tissue of patients with HBV-positive conditions. However, only two hDEmiRs, hsa-miR-21-5p and hsa-miR-143-3p, were regulated across all disease conditions. To shortlist the functional miRNAs in HBV-induced HCC pathogenesis, a reverse bioinformatics analysis was performed using eight GEO datasets and the TCGA database containing the list of differentially regulated mRNAs in HCC. A comparative study using these data with the identified targets of hDEmiRs, a set of unidirectionally regulated hDEmiRs with the potential to modulate mRNAs in HCC, were found. Moreover, our study identified five miRNAs; hsa-miR-98-5p, hsa-miR-193b-3p, hsa-miR-142-5p, hsa-miR-522-5p, and hsa-miR-370-3p targeting PIGC , KNTC1 , CSTF2 , SLC41A2 , and RAB17 , respectively, in HCC. These hDEmiRs and their targets could be pivotal in HBV infection and subsequent liver pathologies modulating HCC clinical progression. HBV infection is the largest contributor to HCC, and the present study comprises the first of its kind compendium of hDEmiRs related to HBV-related pathologies.

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa , another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

Camellia sinensis L. , commonly known as the tea plant, produces derivatives such as green tea, which are among the most extensively consumed beverages worldwide. Green tea is rich in polyphenolic compounds, such as epigallocatechin-3-gallate (EGCG) and gallocatechin gallate. These phytochemicals have drawn particular attention as antioxidants, especially in relation to their potential to reduce the risks for prostate cancer (PC) among other common human diseases. However, the molecular evidence base needs to be strengthened before large-scale controlled clinical trials with C. sinensis L. and/or specific phytochemicals are pursued. We investigated cytochrome P45017A1 (CYP17A1), a key enzyme in androgen biosynthesis, as a molecular target for the green tea phytochemicals. In this study, molecular docking, pharmacokinetic and toxicity evaluations, molecular dynamics (MD) simulations, and post-MD simulation analyses were performed to assess the binding potential of green tea phytochemicals with the CYP17A1 enzyme. A library of 92 green tea-derived phytochemicals, along with the reference inhibitor abiraterone, was docked against the CYP17A1 enzyme. MD simulations validated the stability and enhanced binding affinity of the CYP17A1–EGCG complex compared with the abiraterone complex, as further confirmed by post-MD simulation analyses. Collectively, these findings suggest that EGCG inhibits CYP17A1, potentially reducing androgen biosynthesis and thereby highlighting green tea as a promising natural source for PC therapeutics. Further preclinical and translational studies are warranted to substantiate the clinical applicability of green tea phytochemicals.

Klebsiella pneumoniae (K. pneumoniae) is a Gram-negative bacterium that causes severe community- and hospital-acquired infections. Its rising multidrug resistance complicates therapy, highlighting the need for novel drugs with broad-spectrum, multi-target potential. Leveraging the traditional use and therapeutic evidence of Sesbania grandiflora, this study performed structure-based computational screening of its phytochemicals against K. pneumoniae targets. Initially, 93 proteins with high annotation scores and resolved X-ray structures were identified. Six key therapeutic targets, including LpxH, fabG, KPC-2, GlmU, chbG, and ompA, were prioritized for their pathogenic role. Molecular docking revealed that 59 of 73 compounds interacted with all six targets with high affinity, while the remaining 14 compounds interacted with five targets. Network pharmacology indicated KPC-2, fabG, and ompA had the highest connectivity (73 compounds), followed by chbG and LpxH (72), and GlmU (61). ‘3′,6-di-O-feruloylsucrose’ had the strongest affinity for ompA, LpxH, and GlmU, while ‘Acarbose hydrate’ ranked top for chbG, fabG, and KPC-2. Out of 47 drug-like compounds, 9 passed ADMET filters. Sonchuionoside A was selected for molecular dynamics simulations, demonstrating stable binding to all targets. This suggests S. grandiflora phytoconstituents as multi-target regulators against K. pneumoniae and highlights Sonchuionoside A as a promising lead for further validation.