The opportunistic pathogen Klebsiella pneumoniae is a causative agent of several hospital-acquired infections. It has become resistant to a wide range of currently available antibiotics, leading to high mortality rates among patients; this has further led to a demand for novel therapeutic intervention to treat such infections. Using a series of in silico analyses, the present study aims to explore novel drug/vaccine candidates from the hypothetical proteins of K. pneumoniae. A total of 540 proteins were found to be hypothetical in this organism. Analysis of these 540 hypothetical proteins revealed 30 pathogen-specific proteins essential for pathogen survival. A motifs/domain family analysis, similarity search against known proteins, gene ontology, and protein–protein interaction analysis of the shortlisted 30 proteins led to functional assignment for 17 proteins. They were mainly cataloged as enzymes, lipoproteins, stress-induced proteins, transporters, and other proteins (viz., two-component proteins, skeletal proteins and toxins). Among the annotated proteins, 16 proteins, located in the cytoplasm, periplasm, and inner membrane, were considered as potential drug targets, and one extracellular protein was considered as a vaccine candidate. A druggability analysis indicated that the identified 17 drug/vaccine candidates were “novel”. Furthermore, a host–pathogen interaction analysis of these identified target candidates revealed a betaine/carnitine/choline transporters (BCCT) family protein showing interactions with five host proteins. Structure prediction and validation were carried out for this protein, which could aid in structure-based inhibitor design.

Breast cancer is the leading cause for mortality among women worldwide. Dysregulation of oncogenes and tumor suppressor genes is the major reason for the cause of cancer. Understanding these genes will provide clues and insights about their regulatory mechanism and their interplay in cancer. In the present study, an attempt is made to compare the functional characteristics and interactions of oncogenes and tumor suppressor genes to understand their biological role. 431 breast cancer samples from seven publicly available microarray datasets were collected and analysed using GEO2R tool. The identified 416 differentially expressed genes were classified into five gene sets as oncogenes (OG), tumor suppressor genes (TSG), druggable genes, essential genes and other genes. The gene sets were subjected to various analysis such as enrichment analysis (viz., GO, Pathways, Diseases and Drugs), network analysis, calculation of mutation frequencies and Guanine-Cytosine (GC) content. From the results, it was observed that the OG were having high GC content as well as high interactions than TSG. Moreover, the OG are found to have frequent mutations than TSG. The enrichment analysis results suggest that the oncogenes are involved in positive regulation of cellular protein metabolic process, macromolecule biosynthetic process and majorly in cell cycle and focal adhesion pathway in cancer. It was also found that these oncogenes are involved in other diseases such as skin diseases and viral infections. Collagenase, paclitaxel and docetaxel are some of the drugs found to be enriched for oncogenes.

Many members of nontuberculous mycobacteria (NTM) are opportunistic pathogens causing several infections in animals. The incidence of NTM infections and emergence of drug-resistant NTM strains are rising worldwide, emphasizing the need to develop novel anti-NTM drugs. The present study is aimed to identify broad-spectrum drug targets in NTM using a comparative genomics approach. The study identified 537 core proteins in NTM of which 45 were pathogen specific and essential for the survival of pathogens. Furthermore, druggability analysis indicated that 15 were druggable among those 45 proteins. These 15 proteins, which were core proteins, pathogen-specific, essential, and druggable, were considered as potential broad-spectrum candidates. Based on their locations in cytoplasm and membrane, targets were classified as drug and vaccine targets. The identified 15 targets were different enzymes, carrier proteins, transcriptional regulator, two-component system protein, ribosomal, and binding proteins. The identified targets could further be utilized by researchers to design inhibitors for the discovery of antimicrobial agents.

Antimicrobial resistance (AMR) is one of the most pressing global health challenges, often referred to as a ‘silent pandemic.’ Towards this, ARKbase is developed as an integrated, curated, value-added knowledgebase for AMR, with focus on WHO Bacterial Priority Pathogens. ARKbase has three core modules namely—Database, Insight, and Comparative Analysis Module—each offering curated data, including data analysed with custom-built pipelines. The Database Module has curated reference genomes and pan genomes along with antimicrobial susceptibility testing (AST) profiles. The Insight Module has 14 sub-modules and offers deep annotations encompassing antimicrobial resistance genes, virulence factors, structure annotation, host–pathogen interactions, biosynthetic gene clusters, known antibiotics, protein–protein interactions, drug targets, co-targets, drug-target interactions, machine learning models, and curated transcriptomic datasets for antibiotic exposure. The Comparative Analysis module offers a simple interface for comparing antimicrobial resistance genes, virulence factors, and drug targets among different priority pathogens. The data included in ARKbase are quality-checked and curated as per CLSI standards for AST, EUCAST guidelines for genome sequence, and the FAIR data principles. ARKbase is the first comprehensive knowledgebase focusing on WHO priority pathogens and AWaRe classification, enabling a combined evidence approach towards a holistic understanding of AMR. Available at https://datascience.imtech.res.in/anshu/arkbase.

At times of rapid global climatic changes, refining drought tolerance in tomatoes is a demanding concern. Drought-resistant wild tomato relatives offer a valued resource for studying drought response mechanisms and leveraging them for breeding. To understand drought-response mechanisms in Solanum, we performed an integrated gene expression, transcription factor (TF), and network-based analysis in wild (Solanum pimpinellifolium L.) and cultivated (Solanum lycopersicum L.) tomato species with differing drought tolerance. This integrated analysis unveiled distinct gene expression patterns under drought stress in both species. Ethylene-responsive factors (ERFs) were notably the most significantly expressed transcription factors in both varieties. Additionally, species-specific TF expression was observed, such as Ethylene Insensitive3-like (EIL) and MIKC_MADS in the wild type, and Zinc Finger homeodomain (ZF-HD) in the cultivated variety. Differential expression pattern in ROS rummaging and heat shock proteins was observed in the top DEGs of both species. Network analysis highlighted shared genes involved in crucial pathways like jasmonic acid biosynthesis, the protein kinase pathway, and reactive oxygen species foraging in both species. Notably, the wild variety showed altered expression of genes associated with jasmonic acid/ethylene biosynthesis, antioxidant response, heat shock proteins, and cell wall remodeling. These observations offer insights into the differences in drought response in both species and suggest the role of jasmonic acid/ethylene biosynthesis, antioxidant response and ROS scavenging as significant factors contributing to improved drought tolerance in S. pimpinellifolium.