Gut microbiota alterations are linked to COVID-19 severity in North African and European populations

International audience Although COVID-19 primarily affects the respiratory system, many patients experience gastrointestinal symptoms, suggesting a role for the gut microbiota in disease pathogenesis. To explore this, we performed shotgun metagenomic sequencing on stool samples from 200 COVID-19 patients and 102 healthy controls in Morocco and France. Despite geographic differences in microbiota composition, patients with COVID-19 in both continents exhibited significant gut microbiota alterations, which were more pronounced in severe cases, with similar features compared with controls. Functional pathways, including L-Tryptophan biosynthesis, were disrupted, particularly in patients with severe disease. Machine learning models accurately predicted disease severity based on gut microbial profiles in the Moroccan cohort, though not in the French cohort. These results highlight consistent microbiota changes associated with COVID-19 and support a potential link between gut dysbiosis and disease severity.

The ongoing coronavirus disease 19 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been a major cause of death worldwide in the last three years (World Health Organization). Although COVID-19 is a respiratory illness, about 10-20% of patients suffer from gastrointestinal symptoms, including loss of appetite, diarrhea, nausea, and vomiting 1,2 . These symptoms are often associated with a high level of fecal calprotectin (an indicator of intestinal inflammation) and interleukin 18 (IL-18, a cytokine that mediates intestinal inflammatory reactions) in patients' stools 3,4 . COVID-19 RNA and, more rarely, infectious viruses can be detected in infected patients' fecal samples, suggesting that the gastrointestinal tract is a major target organ of SARS-CoV-2 infection 5,6 . Moreover, the Angiotensin-Converting Enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2), both critical proteins for entry of SARS-CoV-2 into host cells, are expressed at high levels in the gastrointestinal epithelial cells 7 .

Increasing evidence from preclinical models and clinical studies suggests that SARS-CoV-2 infection leads to alterations of the gut microbiota and that gut dysbiosis could be involved in COVID-19 severity 8-15 . Patients experiencing severe COVID-19 have significant alterations in gut microbiota's composition, characterized by a loss of microbial diversity and richness. In several cohorts, SARS-CoV-2 infection was associated with depletion of beneficial taxa, including butyrate producers (e.g., genera from the Ruminococcaceae family) and bacterial species with known immunomodulatory potential (e.g., Faecalibacterium prausnitzii and Eubacterium rectale) 10,16-18 . In contrast, enrichment of potential opportunistic pathobionts was reported in COVID-19 patients, such as Bacteroides nordii, Rothia, Actinomyces, Ruminococcus, and Clostridium hathewayi 10,16-19 . Of interest, a limited number of studies, mostly in Asian populations, pointed out stronger microbiota alterations in patients developing severe COVID-19 15,20,21 . Beyond the taxonomy, recent studies have shown functional alterations linked to COVID-19, including metabolic pathways related to SCFA production and bile acid metabolism 12,22 . However, only European, North American, and Asian populations have been studied in this regard, and no data are available for African patients suffering from COVID-19 infection.

In the current study, we compared the gut microbiota of 200 patients with COVID-19 and 102 healthy subjects from Moroccan and French cohorts. Through shotgun metagenomics and targeted quantitative metabolomics, we showed that COVID-19 infection is associated with alterations in the gut microbiota diversity, composition, and functions in both populations. Interestingly, we found common signals in the patients from the two continents. The strength of the alterations is more marked in severe COVID-19 patients. Finally, we showed that a machine learning-based approach using only bacterial taxa could predict COVID-19 infection severity with high accuracy, but it was not transposable from one population to the other.