Meta-analysis of the microbial biomarkers in the gut-lung crosstalk in COVID-19, community-acquired pneumonia and Clostridium difficile infections.

Respiratory infections are the leading causes of mortality and the current pandemic COVID-19 is one such trauma that imposed catastrophic devastation to the health and economy of the world. Unravelling the correlations and interplay of the human microbiota in the gut-lung axis would offer incredible solutions to the underlying mystery of the disease progression. The study compared the microbiota profiles of six samples namely healthy gut, healthy lung, COVID-19 infected gut, COVID-19 infected lungs, Clostridium difficile infected gut and community-acquired pneumonia infected lungs. The metagenome data sets were processed, normalized, classified and the rarefaction curves were plotted. The microbial biomarkers for COVID-19 infections were identified as the abundance of Candida and Escherichia in lungs with Ruminococcus in the gut. Candida and Staphylococcus could play a vital role as putative prognostic biomarkers of community-acquired pneumonia whereas abundance of Faecalibacterium and Clostridium is associated with the C. difficile infections in gut. A machine learning random forest classifier applied to the data sets efficiently classified the biomarkers. The study offers an extensive and incredible understanding of the existence of gut-lung axis during dysbiosis of two anatomically different organs.

From genomes to molecular dynamics - A bottom up approach in extrication of SARS CoV-2 main protease inhibitors.

The recent pandemic Coronavirus disease-19 outbreak had traumatized global countries since its origin in late December 2019. Though the virus originated in China, it has spread rapidly across the world due its firmly established community transmission. To successfully tackle the spread and further infection, there needs a clear multidimensional understanding of the molecular mechanisms. Henceforth, 942 viral genome sequences were analysed to predict the core genomes crucial in virus life cycle. Additionally, 35 small interfering RNA transcripts were predicted that can target specifically the viral core proteins and reduce pathogenesis. The crystal structure of Covid-19 main protease-6LU7 was chosen as an attractive target due to the factors that there were fewer mutations and whose structure had significant identity to the annotated protein sequence of the core genome. Drug repurposing of both recruiting and non recruiting drugs was carried out through molecular docking procedures to recognize bitolterol as a good inhibitor of Covid-19 protease. The study was extended further to screen antiviral phytocompounds through quantitative structure activity relationship and molecular docking to identify davidigenin, from licorice as the best novel lead with good interactions and binding energy. The docking of the best compounds in all three categories was validated with molecular dynamics simulations which implied stable binding of the drug and lead molecule. Though the studies need clinical evaluations, the results are suggestive of curbing the pandemic.

Computational gene expression profiling in the exploration of biomarkers, non-coding functional RNAs and drug perturbagens for COVID-19.

The coronavirus disease, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a global health crisis that is being endured with an increased alarm of transmission each day. Though the pandemic has activated innumerable research attention to decipher an antidote, fundamental understanding of the molecular mechanisms is necessary to halt the disease progression. The study focused on comparison of the COVID-19 infected lung tissue gene expression datasets -GSE155241 and GSE150316 with the GEO2R-limma package. The significant up- and downregulated genes were annotated. Further evaluation of the enriched pathways, transcription factors, kinases, noncoding RNAs and drug perturbations revealed the significant molecular mechanisms of the host response. The results revealed a surge in mitochondrial respiration, cytokines, neurodegenerative mechanisms and deprived oxygen, iron, copper, and glucose transport. Hijack of ubiquitination by SARS-CoV-2, hox gene differentiation, histone modification, and miRNA biogenesis were the notable molecular mechanisms inferred. Long non-coding RNAs such as C058791.1, TTTY15 and TPTEP1 were predicted to be efficient in regulating the disease mechanisms. Drugs-F-1566-0341, Digoxin, Proscillaridin and Linifanib that reverse the gene expression signatures were predicted from drug perturbations analysis. The binding efficiency and interaction of proscillaridin and digoxin as obtained from the molecular docking studies confirmed their therapeutic potential. Two overlapping upregulated genes MDH1, SGCE and one downregulated gene PFKFB3 were appraised as potential biomarkers candidates. The upregulation of PGM5, ISLR and ANK2 as measured from their expressions in normal lungs affirmed their possible prognostic biomarker competence. The study explored significant insights for better diagnosis, and therapeutic options for COVID-19. Communicated by Ramaswamy H. Sarma.

Structure-based drug designing towards the identification of potential anti-viral for COVID-19 by targeting endoribonuclease NSP15.

The world is facing health and economic havoc due to the Corona Virus Disease-2019 (COVID-19) pandemic. Given the number of affected people and the mortality rate, the virus is undoubtedly a serious threat to humanity. By analogy with earlier reports about Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV) - viruses, the novel Coronavirus' replication mechanism is likely well understood. The structure of an endoribonuclease (NSP15) of SARS-CoV-2 was reported recently. This enzyme is expected to play a crucial role in replication. In this work, attempts were made to identify inhibitors of this enzyme. To achieve the goal, high throughput in silico screening and molecular docking procedures were performed. From an Enamine database of a billion compounds, 3978 compounds with potential antiviral activity were selected for screening and induced fit docking that funneled down to eight compounds with good docking score and docking energy. Detailed analysis of non-covalent interactions at the active site and the apparent match of the molecule with the shape of the binding pocket were assessed. All the compounds show significant interactions for tight binding. Since all the compounds are synthetic with favorable drug-like properties, these may be considered for immediate optimization and downstream applications.