Identification of perturbed pathways rendering susceptibility to tuberculosis in type 2 diabetes mellitus patients using BioNSi simulation of integrated networks of implicated human genes.

In type 2 diabetes mellitus (T2DM) patients, chronic hyperglycemia and inflammation underlie susceptibility to tuberculosis (TB) and result in poor TB control. Here, an integrative pathway-based approach is used to investigate perturbed pathways in T2DM patients that render susceptibility to TB. We obtained 36 genes implicated in type 2 diabetes-associated tuberculosis (T2DMTB) from the literature. Gene expression analysis on T2DM patient data (GSE26168) showed that DEFA1 is differentially expressed at Padj <0.05. The human host TB susceptibility genes TNFRSF10A, MSRA, GPR148, SLC37A3, PXK, PROK2, REV3L, PGM1, HIST3H2A, PLAC4, LETM2, and EMP2 and hsa-miR-146a microRNA were also differentially expressed at Padj <0.05. We included all these genes and added the remaining 28 genes from the T2DMTB set and the remaining differentially expressed genes at Padj <0.05 in STRING and obtained a well-connected network with high confidence score (≥0.7). Further, we extracted the KEGG pathways at FDR <0.05 and retained only the diabetes and TB pathways. The network was simulated with BioNSi using gene expression data. It is evident from BioNSi analysis that the NF-kappa B and Toll-like receptor pathways are commonly perturbed with high ranking in multiple gene expression datasets of type 2 diabetes versus healthy controls. The other pathways, necroptosis pathway and FoxO signalling pathway, appear perturbed with high ranking in different gene expression datasets. These pathways likely underlie susceptibility to TB in T2DM patients.

Knowledgebase of potential multifaceted solutions to antimicrobial resistance.

Antimicrobial resistance (AMR), a top threat to global health, challenges preventive and treatment strategies of infections. AMR strains of microbial pathogens arise through multiple mechanisms. The underlying "antibiotic resistance genes" (ARGs) spread through various species by lateral gene transfer thereby causing global dissemination. Human methods also augment this process through inappropriate use, non-compliance to treatment schedule, and environmental waste. Worldwide significant efforts are being invested to discover novel therapeutic solutions for tackling resistant pathogens. Diverse therapeutic strategies have evolved over recent years. In this work we have developed a comprehensive knowledgebase by collecting alternative antimicrobial therapeutic strategies from literature data. Therapeutic strategies against bacteria, virus, fungus and parasites were extracted from PubMed literature using text mining. We have used a subjective (sentimental) approach for data mining new strategies, resulting in broad coverage of novel entities and subsequently add objective data like entity name (including IUPAC), potency, and safety information. The extracted data was organized in a freely accessible web platform, KOMBAT. The KOMBAT comprises 1104 Chemical compounds, 220 of newly identified antimicrobial peptides, 42 bacteriophages, 242 phytochemicals, 106 nanocomposites, and 94 novel entities for phototherapy. Entities tested and evaluated on AMR pathogens are included. We envision that this database will be useful for developing future therapeutics against AMR pathogens. The database can be accessed through http://kombat.igib.res.in/.

Phytovid19: a compilation of phytochemicals research in coronavirus.

The COVID-19 pandemic has immensely impacted global health causing colossal damage. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has increased the quest to explore phytochemicals as treatment options. We summarize phytochemicals with activity against various coronaviruses including SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). We compiled 705 phytochemical compounds through text mining of 893 PubMed articles. The physicochemical properties including molecular weight, lipophilicity, and the number of hydrogen bond donors and acceptors were determined from the structures of these compounds. A structure-based evaluation of these properties with respect to drug likeness showed that most compounds have a positive score of drug likeness. QSAR analysis showed that 5 descriptors, namely polar surface area, relative polar surface area, number of hydrogen bond donors, solubility, and lipophilicity, are significantly related to IC50. We envisage that these phytochemicals could be further explored for developing new potential therapeutic molecules for COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s11224-022-02035-6.

Computer-Aided Tools and Resources for Fungal Pathogens: An Application of Reverse Vaccinology for Mucormycosis.

Increasing fungal infections in immunocompromised hosts are a growing concern for global public health. Along with treatments, preventive measures are required. The emergence of reverse vaccinology has opened avenues for using genomic and proteomic data from pathogens in the design of vaccines. In this work, we present a comprehensive collection of various computational tools and databases with potential to aid in vaccine development. The ongoing pandemic has directed attention toward the increasing number of mucormycosis infections in COVID-19 patients. As a case study, we developed a computational pipeline for assisting vaccine development for mucormycosis. We obtained 6 proteins from 29,447 sequences from UniProtKB as potential vaccine candidates against mucormycosis, fulfilling multiple criteria. These criteria included potential characteristics, namely adhesin properties, surface or extracellular localization, antigenicity, no similarity to any human proteins, nonallergenicity, stability in vitro, and expression in fungal cells. These six proteins were predicted to have B cell and T cell epitopes, proinflammatory inducing peptides, and orthologs in several mucormycosis-causing species. These data could aid in vaccine development against mucormycosis for at-risk individuals.

In silico prediction of natural compounds as potential multi-target inhibitors of structural proteins of SARS-CoV-2.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a colossal loss to human health and lives and has deeply impacted socio-economic growth. Remarkable efforts have been made by the scientific community in containing the virus by successful development of vaccines and diagnostic kits. Initiatives towards drug repurposing and discovery have also been undertaken. In this study, we compiled the known natural anti-viral compounds using text mining of the literature and examined them against four major structural proteins of SARS-CoV-2, namely, spike (S) protein, nucleocapsid (N) protein, membrane (M) protein and envelope (E) protein. Following computational approaches, we identified fangchinoline and versicolactone C as the compounds to exhibit strong binding to the target proteins and causing structural deformation of three structural proteins (N, S and M). We recommend the inhibitory effects of these compounds from our study should be experimentally validated against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.