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.

Automated summary

Respiratory infections, including COVID-19, have a significant impact on global health and economy. This study investigates the microbiota profiles of healthy individuals and those infected with COVID-19, revealing microbial biomarkers associated with the disease. Machine learning techniques were applied to effectively classify the biomarkers, providing valuable insights into disease progression.