Moderate Elevation of Homocysteine Induces Endothelial Dysfunction through Adaptive UPR Activation and Metabolic Rewiring.

Elevation of the intermediate amino acid metabolite Homocysteine (Hcy) causes Hyperhomocysteinemia (HHcy), a metabolic disorder frequently associated with mutations in the methionine-cysteine metabolic cycle as well as with nutritional deficiency and aging. The previous literature suggests that HHcy is a strong risk factor for cardiovascular diseases. Severe HHcy is well-established to correlate with vascular pathologies primarily via endothelial cell death. Though moderate HHcy is more prevalent and associated with an increased risk of cardiovascular abnormalities in later part of life, its precise role in endothelial physiology is largely unknown. In this study, we report that moderate elevation of Hcy causes endothelial dysfunction through impairment of their migration and proliferation. We established that unlike severe elevation of Hcy, moderate HHcy is not associated with suppression of endothelial VEGF/VEGFR transcripts and ROS induction. We further showed that moderate HHcy induces a sub-lethal ER stress that causes defective endothelial migration through abnormal actin cytoskeletal remodeling. We also found that sub-lethal increase in Hcy causes endothelial proliferation defect by suppressing mitochondrial respiration and concomitantly increases glycolysis to compensate the consequential ATP loss and maintain overall energy homeostasis. Finally, analyzing a previously published microarray dataset, we confirmed that these hallmarks of moderate HHcy are conserved in adult endothelial cells as well. Thus, we identified adaptive UPR and metabolic rewiring as two key mechanistic signatures in moderate HHcy-associated endothelial dysfunction. As HHcy is clinically associated with enhanced vascular inflammation and hypercoagulability, identifying these mechanistic pathways may serve as future targets to regulate endothelial function and health.

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/.

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.