COVID-19 Infection Alters the Microbiome: Elite Athletes and Sedentary Patients Have Similar Bacterial Flora.

Regular exercise can upgrade the efficiency of the immune system and beneficially alter the composition of the gastro-intestinal microbiome. We tested the hypothesis that active athletes have a more diverse microbiome than sedentary subjects, which could provide better protection against COVID-19 during infection. Twenty active competing athletes (CA) (16 male and 4 females of the national first and second leagues), aged 24.15 ± 4.7 years, and 20 sedentary subjects (SED) (15 male and 5 females), aged 27.75 ± 7.5 years, who had been diagnosed as positive for COVID-19 by a PCR test, served as subjects for the study. Fecal samples collected five to eight days after diagnosis and three weeks after a negative COVID-19 PCR test were used for microbiome analysis. Except for two individuals, all subjects reported very mild and/or mild symptoms of COVID-19 and stayed at home under quarantine. Significant differences were not found in the bacterial flora of trained and untrained subjects. On the other hand, during COVID-19 infection, at the phylum level, the relative abundance of Bacteroidetes was elevated during COVID-19 compared to the level measured three weeks after a negative PCR test (p < 0.05) when all subjects were included in the statistical analysis. Since it is known that Bacteroidetes can suppress toll-like receptor 4 and ACE2-dependent signaling, thus enhancing resistance against pro-inflammatory cytokines, it is suggested that Bacteroidetes provide protection against severe COVID-19 infection. There is no difference in the microbiome bacterial flora of trained and untrained subjects during and after a mild level of COVID-19 infection.

COVIDOUTCOME-estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome.

Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. We used an automated machine learning approach where 1594 viral genomes with available clinical follow-up data were used as the training set (797 'severe' and 797 'mild'). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm, was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV) and then adjusted for multiple testing with Bootstrap Bias Corrected CV. We identified 26 protein and Untranslated Region (UTR) mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patient's age as the input and shows high classification efficiency with an area under the curve (AUC) of 0.94 [confidence interval (CI): [0.912, 0.962]] and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform (https://covidoutcome.com/) that is capable to use a viral sequence and the patient's age as the input and provides a percentage estimation of disease severity. We demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes.