Periodic Reporting for period 2 - BiomeRiskFactors (Discovering microbiome-based disease risk factors)
Período documentado: 2020-09-01 hasta 2022-02-28
Identifying risk factors for diseases that can be prevented or delayed by early intervention is of major importance. Much effort was directed to this task and, indeed, numerous genetic, lifestyle, anthropometric and clinical risk factors are routinely used for many different diseases, and screening of the population or of high-risk individuals is implemented in several countries for a wide range of conditions. Ongoing efforts are aimed at finding risk factors in rich sources of genomics and omics data, as in the case of early detection of cancer by deep sequencing of circulating cell-free DNA. However, the predictive power of existing risk factors is limited, and most of them appear when physiological derangements have already occurred.
Another rich source of information with the potential to contain pertinent disease risk factor data is the human microbiome – the collective genome of trillions of microbes, including bacteria, viruses, fungi, and parasites that reside in the human gastrointestinal tract. The microbiome contains 100-fold more genes than the human genome, and is considered a bona-fide ‘second genome’ with fundamental roles in multiple aspects of human physiology and health, including obesity, non-alcoholic fatty liver disease, inflammatory diseases, cancer, metabolic diseases, cardiovascular disease, aging, and neurodegenerative disorders. As such, it should capture different aspects of disease than existing risk factors, and their combination can lead to earlier and more robust disease detection. However, very few microbiome-based markers predictive of disease onset and progression were found to date and none are currently used by healthcare systems. Thus, discovery of microbiome-based risk factors is a promising yet mostly unexplored research area.
Another rich source of information with the potential to contain pertinent disease risk factor data is the human microbiome – the collective genome of trillions of microbes, including bacteria, viruses, fungi, and parasites that reside in the human gastrointestinal tract. The microbiome contains 100-fold more genes than the human genome, and is considered a bona-fide ‘second genome’ with fundamental roles in multiple aspects of human physiology and health, including obesity, non-alcoholic fatty liver disease, inflammatory diseases, cancer, metabolic diseases, cardiovascular disease, aging, and neurodegenerative disorders. As such, it should capture different aspects of disease than existing risk factors, and their combination can lead to earlier and more robust disease detection. However, very few microbiome-based markers predictive of disease onset and progression were found to date and none are currently used by healthcare systems. Thus, discovery of microbiome-based risk factors is a promising yet mostly unexplored research area.
In this project, we set out to find novel disease risk factors based on the human microbiome that are more accurate than existing ones in their ability to predict the likelihood of a person to develop a particular condition or disease within 5-10 years. To this end, in this project we already recruited and profiled over 3,000 individuals as well as a few disease cohorts. We combined these with a cohort of >2200 individuals that my group previously assembled. In all of these individuals, we obtained clinical profiles, microbiome data, and banked blood and stool samples. We developed several novel experimental and computational methods for in-depth characterization of microbial gene function, the metabolites produced by the microbiome, and the interactions between the gut microbiota and the host adaptive immune system. These results were summarized already in 23 publications resulting from this grant. Notably, we identified several microbiome risk factors as in the original plan, on several different levels, including microbiome-immune interactions, metabolite levels, and in the context of several diseases.
We devised novel microbiome analysis tools and immune assays to profile the microbiome. The microbiome analysis tools analyze bacteria at the strain levels to reveal associations at the level of bacteria strains with phenotype. The immune assay identifies the entire infectious history of a person and we applied it also to find bacteria that people’s antibodies recognize as well as to identify Covid-19 infections. On the metabolomic front, we found determinants of numerous metabolites using our large-scale cohort, namely we uncovered which factors (genetics, diet, microbiome) determine levels of blood metabolites. We expect to continue and recruit more participants until the end of the project, and to unravel determinants that are predictive of future development of disease.