To characterize the genetic determinants of resistance to antituberculosis drugs, we performed a genome-wide association study (GWAS) of 6,465 Mycobacterium tuberculosis clinical isolates from more than 30 countries. A GWAS approach within a mixed-regression framework was followed by a phylogenetics-based test for independent mutations. In addition to mutations in established and recently described resistance-associated genes, novel mutations were discovered for resistance to cycloserine, ethionamide and para-aminosalicylic acid. The capacity to detect mutations associated with resistance to ethionamide, pyrazinamide, capreomycin, cycloserine and para-aminosalicylic acid was enhanced by inclusion of insertions and deletions. Odds ratios for mutations within candidate genes were found to reflect levels of resistance. New epistatic relationships between candidate drug-resistance-associated genes were identified. Findings also suggest the involvement of efflux pumps (drrA and Rv2688c) in the emergence of resistance. This study will inform the design of new diagnostic tests and expedite the investigation of resistance and compensatory epistatic mechanisms.

According to WHO report, globally about 10 million active tuberculosis cases, resulting in about 1.6 million deaths, further aggravated by drug-resistant tuberculosis and/or comorbidities with HIV and diabetes are present. Incomplete therapeutic regimen, meager dosing, and the capability of the latent and/or active state tubercular bacilli to abide and do survive against contemporary first-line and second line antitubercular drugs escalate the prevalence of drug-resistant tuberculosis. As a better understanding of tuberculosis, microanatomy has discovered an extended range of new promising antitubercular targets and diagnostic biomarkers. However, there are still no new approved antitubercular drugs of routine therapy for several decades, except for bedaquiline, delamanid, and pretomanid approved tentatively. Despite this, innovative methods are also urgently needed to find potential new antitubercular drug candidates, which potentially decimate both latent state and active state mycobacterium tuberculosis. To explore and identify the most potential antitubercular drug candidate among various reported compounds, we focused to highlight the promising lead derivatives of isoniazid, coumarin, griselimycin, and the antimicrobial peptides. The aim of the present review is to fascinate significant lead compounds in the development of potential clinical drug candidates that might be more precise and effective against drug-resistant tuberculosis, the world research looking for a long time.

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The human-to-human transmitted respiratory illness in COVID-19 affected by the pathogenic Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2), which appeared in the last of December 2019 in Wuhan, China, and rapidly spread in many countries. Thereon, based on the urgent need for therapeutic molecules, we conducted in silico based docking and simulation molecular interaction studies on repurposing drugs, targeting SARS-CoV-2 spike protein. Further, the best binding energy of doxorubicin interacting with virus spike protein (PDB: 6VYB) was observed to be -6.38 kcal/mol and it was followed by exemestane and gatifloxacin. The molecular simulation dynamics analysis of doxorubicin, Reference Mean Square Deviation (RMSD), Root Mean Square fluctuation (RMSF), Radius of Gyration (Rg), and formation of hydrogen bonds plot interpretation suggested, a significant deviation and fluctuation of Doxorubicin-Spike RBD complex during the whole simulation period. The Rg analysis has stated that the Doxorubicin-Spike RBD complex was stable during 15000-35000ps MDS. The results have suggested that doxorubicin could inhibit the virus spike protein and prevent the access of the SARS-CoV-2 to the host cell. Thus, in-vitro/in-vivo research on these drugs could be advantageous to evaluate significant molecules that control the COVID-19 disease.

This work elucidates the idea of finding probable critical genes linked to breast adenocarcinoma. In this study, the GEO database gene expression profile data set (GSE70951) was retrieved to look for genes that were expressed variably across breast adenocarcinoma samples and healthy tissue samples. The genes were confirmed to be part of the PPI network for breast cancer pathogenesis and prognosis. In Cytoscape, the CytoHubba module was used to discover the hub genes. For correlation analysis, the predictive biomarker of these hub genes, as well as GEPIA, was used. A total of 155 (85 upregulated genes and 70 downregulated genes) were identified. By integrating the PPI and CytoHubba data, the major key/hub genes were selected from the results. The KM plotter is employed to find the prognosis of those major pivot genes, and the outcome shows worse prognosis in breast adenocarcinoma patients. Further experimental validation will show the predicted expression levels of those hub genes. The overall result of our study gives the consequences for the identification of a critical gene to ease the molecular targeting therapy for breast adenocarcinoma. It could be used as a prognostic biomarker and could lead to therapy options for breast adenocarcinoma.