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In Letters in applied microbiology

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. Unraveling 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 datasets 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 are associated with the Clostridium difficile infections in gut. A machine learning random forest classifier applied to the datasets 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.

Aishwarya S, Gunasekaran K


diversity, gut - lung axis, interplay, microbiota, random forest classifier