Data availabilitySummary statistics generated by COVID-19 Host Genetics Initiative are available online (https://www.covid19hg.org/results/r6/). The analyses described here use the freeze 6 data. The COVID-19 Host Genetics Initiative continues to regularly release new data freezes. Summary statistics for samples from individuals of non-European ancestry are not currently available owing to the small individual sample sizes of these groups, but the results for 23 loci lead variants are reported in Supplementary Table 3. Individual-level data can be requested directly from the authors of the contributing studies, listed in Supplementary Table 1.

The gut microbiota is composed of bacteria, archaea, phages, and protozoa. It is now well known that their mutual interactions and metabolism influence host organism pathophysiology. Over the years, there has been growing interest in the composition of the gut microbiota and intervention strategies in order to modulate it. Characterizing the gut microbial populations represents the first step to clarifying the impact on the health/illness equilibrium, and then developing potential tools suited for each clinical disorder. In this review, we discuss the current gut microbiota manipulation strategies available and their clinical applications in personalized medicine. Among them, FMT represents the most widely explored therapeutic tools as recent guidelines and standardization protocols, not only for intestinal disorders. On the other hand, the use of prebiotics and probiotics has evidence of encouraging findings on their safety, patient compliance, and inter-individual effectiveness. In recent years, avant-garde approaches have emerged, including engineered bacterial strains, phage therapy, and genome editing (CRISPR-Cas9), which require further investigation through clinical trials.

Fecal microbiota transplantation (FMT) consists of infusion of feces from a donor to a recipient patient in order to restore the resident microbial population. FMT has shown to be a valid clinical option for Clostridioides difficile infections (CDI). However, this approach shows several criticalities, such as the recruiting and screening of voluntary donors. Our aim was to evaluate the therapeutic efficacy of a synthetic bacterial suspension defined “Bacterial Consortium” (BC) infused in the colon of CDI patients. The suspension was composed by 13 microbial species isolated by culturomics protocols from healthy donors’ feces. The efficacy of the treatment was assessed both clinically and by metagenomics typing. Fecal samples of the recipient patients were collected before and after infusion. DNA samples obtained from feces at different time points (preinfusion, 7, 15, 30, and 90 days after infusion) were analyzed by next-generation sequencing. Before infusion, patient 1 showed an intestinal microbiota dominated by the phylum Bacteroidetes. Seven days after the infusion, Bacteroidetes decreased, followed by an implementation of Firmicutes and Verrucomicrobia. Patient 2, before infusion, showed a strong abundance of Proteobacteria and a significant deficiency of Bacteroidetes and Verrucomicrobia. Seven days after infusion, Proteobacteria strongly decreased, while Bacteroidetes and Verrucomicrobia increased. Metagenomics data revealed an “awakening” by microbial species absent or low concentrated at time T0 and present after the infusion. In conclusion, the infusion of selected bacteria would act as a trigger factor for “bacterial repopulation” representing an innovative treatment in patients with Clostridioides difficile infections.

We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as “Respiratory or thoracic disease”, supporting their link with COVID-19 severity outcome.