The intricate interplay between the gut microbiome and the host plays a pivotal role in the development and progression of steatotic liver diseases, which are characterized by significant alterations in the gut microbiome. However, the precise mechanisms by which the microbiome influences disease behavior and progression remain poorly understood. Previous studies have highlighted that metabolic interdependencies between microorganisms, particularly auxotrophies, are widespread in the human microbiome and are disrupted in chronic diseases. Metabolomics, as a critical pathway linking the intestinal microbiome to the host, offers a transformative approach to exploring microbiome-host crosstalk.
This talk will examine how metabolomics decodes the complex interactions between the microbiome and host, identifying key pathways and biomarkers that drive disease progression. By integrating microbiome composition with metabolomic profiles, we aim to provide novel insights into the pathophysiology of steatotic liver disease and its advanced stages, including MASLD and MASH, while exploring potential therapeutic avenues.

Mental health is a field of medicine that is not only deeply influenced by the gut microbiome, but also critically lacking physical markers of disease. For these reasons, using the metabolome as a readout of mental health disorders not only offers a solution for novel diagnostic, prognostic and efficacy markers, but also a rich avenue for novel therapeutic approaches based on quantifiable measures of disease. In this talk, you’ll learn how other scientists have applied metabolomics to the study of mental health disorders, leveraging the deep involvement of the gut microbiome through the gut-brain axis.

The gut microbiome, the comprehensive set of bacterial genes in our
guts, is now recognized as a key driver in the pathophysiology of obesity,
type 2 diabetes and cardiometabolic diseases and their common lowgrade
inflammatory component. However, the chemical signals sent by
the gut microbes to the host remain elusive. Through machine learning
and multivariate analysis of metabolomes and metagenomes, our group
identified clinically relevant and drug-deconfounded microbiome
signatures for the progression along the cardiometabolic disease
spectrum. We will elaborate on a few examples of metabolites, paving
the way for new hypotheses and elucidation of the mechanisms
impacted by the gut microbiome, in precision medicine.