Collective effects of human genomic variation on microbiome function

Systematic identification of the role of gut microbiota in mental disorders: a TwinsUK cohort study

Mental disorders are complex disorders influenced by multiple genetic, environmental, and biological factors. Specific microbiota imbalances seem to affect mental health status. However, the mechanisms by which microbiota disturbances impact the presence of depression, stress, anxiety, and eating disorders remain poorly understood. Currently, there are no robust biomarkers identified. We proposed a novel pyramid-layer design to accurately identify microbial/metabolomic signatures underlying mental disorders in the TwinsUK registry. Monozygotic and dizygotic twins discordant for mental disorders were screened, in a pairwise manner, for differentially abundant bacterial genera and circulating metabolites. In addition, multivariate analyses were performed, accounting for individual-level confounders. Our pyramid-layer study design allowed us to overcome the limitations of cross-sectional study designs with significant confounder effects and resulted in an association of the abundance of genus Parabacteroides with the diagnosis of mental disorders. Future research should explore the potential role of Parabacteroides as a mediator of mental health status. Our results indicate the potential role of the microbiome as a modifier in mental disorders that might contribute to the development of novel methodologies to assess personal risk and intervention strategies.

A comprehensive overview of microbiome data in the light of machine learning applications: categorization, accessibility, and future directions

Metagenomics, Metabolomics, and Metaproteomics have significantly advanced our knowledge of microbial communities by providing culture-independent insights into their composition and functional potential. However, a critical challenge in this field is the lack of standard and comprehensive metadata associated with raw data, hindering the ability to perform robust data stratifications and consider confounding factors. In this comprehensive review, we categorize publicly available microbiome data into five types: shotgun sequencing, amplicon sequencing, metatranscriptomic, metabolomic, and metaproteomic data. We explore the importance of metadata for data reuse and address the challenges in collecting standardized metadata. We also, assess the limitations in metadata collection of existing public repositories collecting metagenomic data. This review emphasizes the vital role of metadata in interpreting and comparing datasets and highlights the need for standardized metadata protocols to fully leverage metagenomic data's potential. Furthermore, we explore future directions of implementation of Machine Learning (ML) in metadata retrieval, offering promising avenues for a deeper understanding of microbial communities and their ecological roles. Leveraging these tools will enhance our insights into microbial functional capabilities and ecological dynamics in diverse ecosystems. Finally, we emphasize the crucial metadata role in ML models development.

Multi-omic approaches for host-microbiome data integration

The gut microbiome interacts with the host through complex networks that affect physiology and health outcomes. It is becoming clear that these interactions can be measured across many different omics layers, including the genome, transcriptome, epigenome, metabolome, and proteome, among others. Multi-omic studies of the microbiome can provide insight into the mechanisms underlying host-microbe interactions. As more omics layers are considered, increasingly sophisticated statistical methods are required to integrate them. In this review, we provide an overview of approaches currently used to characterize multi-omic interactions between host and microbiome data. While a large number of studies have generated a deeper understanding of host-microbiome interactions, there is still a need for standardization across approaches. Furthermore, microbiome studies would also benefit from the collection and curation of large, publicly available multi-omics datasets.

Contribution of HLA DRB1, PTPN22, and CTLA4, to RA dysbiosis

This narrative review gathers current evidence for a contribution of rheumatoid arthritis (RA) HLA-DRB1, PTPN22 and CTLA4 polymorphisms to the gut dysbiosis observed in RA, especially at its onset (transient excess of Prevotella). The gut microbiome contains elements which are 30% heritable, including genera like Bacteroides and Veillonella, and to a lesser extent Prevotella. The first months/year seems a critical period for the selection of a core of microbiota, that should be considered as a second self by the immune system, and tolerized by regulatory T and B cells. Imperfect tolerization may increase the risk of RA following further repeated silent translocations of various gut microorganisms, including Prevotella copri, from gut to joints (fostered by a concurrent loss in gut mucosa of protective bacteria like Faecalibacterium prausnitzii). Genetics studies confirmed that Prevotella copri was partly heritable, and strong associations were observed between the overall microbial composition of stools and the HLA-DRB1 RA risk allele, either in a US cohort (P=0.00001), or the Twins UK cohort (P=0.033). This finding also stands for persons still free from RA, and was replicated in the Swiss SCREEN-RA cohort. Gene variants of PTPN22 also modify intestinal microbiota composition, compromise granulocyte-mediated antibacterial defence in gut, and reduce the suppressive effect of gut regulatory B cells. CTLA4 variants may similarly contribute to RA dysbiosis, since immunotherapy by CTLA-4 blockade depends on microbiota, and CTLA4 activates T follicular regulatory cells to reduce immune responses to segmented filamentous bacteria. Suggestions for future works are made.