key: cord-0982122-hn9dzzzw
authors: Klann, Emily; Rich, Shannan; Mai, Volker
title: Gut microbiota and COVID-19: A superfluous diagnostic biomarker or therapeutic target?
date: 2020-08-11
journal: Clin Infect Dis
DOI: 10.1093/cid/ciaa1191
sha: 8272a7af62e3c1d255ee5a490cd908a3c081d105
doc_id: 982122
cord_uid: hn9dzzzw

nan

M a n u s c r i p t 2 Dear Editor:

We read with great interest the article entitled "Alterations of the Gut Microbiota in Patients with or H1N1 Influenza" by Gu et. al. In this study, the authors compared the compositional differences of gut microbiota between patients with Coronavirus Disease 2019 (COVID-19), influenza A (H1N1), and healthy controls. Given that both diseases have similar clinical manifestations, including involvement of the gastrointestinal tract, the authors suggest the gut microbiota may serve as a distinguishing biomarker. They report key differences in the microbiota profiles of these populations, including seven taxa able to discriminate between COVID-19 and H1N1 patients (AUC=0.94). Although the results appear promising, we have doubts concerning the practicality of this approach for diagnosis of COVID-19. We believe there are several factors that could affect the validity and interpretation of the results which warrant clarification.

When concluding that butyrate-producing microbiota, including members of the Ruminococcaceae and Lachnospiraceae families, are depleted in H1N1 and COVID-19 patients compared to healthy controls, the authors did not discuss the possibility that these differences are driven by the presence of diarrheal symptoms. Diarrhea is associated with depletion of Ruminococcaceae and Lachnospiraceae as well as decreased alpha diversity, regardless of origin [1] . Therefore, these findings are likely due to decreased gut transit time associated with diarrheal symptoms, and not causally associated with either disease.

Details regarding the bioinformatic methods used is lacking. To ensure appropriate interpretation of the results, it would be beneficial to clarify the platform used, method of p-value correction, and whether rarefaction or normalization procedures were conducted. Both procedures can influence downstream analyses. Without rarefaction, observed differences may be influenced by variations in sequencing efficacy rather than true biological differences. Further, different normalization methods have been found to lead to different correlation structures and clustering results and are dependent on the nature of the data [2, 3] . 

Meta-analysis of gut microbiome studies identifies disease-specific and shared responses

Normalization methods for microbial abundance data strongly affect correlation estimates

Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible

A c c e p t e d M a n u s c r i p t 5