Integrative Multi-Omics Pofiling of Major Depressive Disorder Patients: Unveiling Biomarkers of Disease Severity and Therapy Response

Major Depressive Disorder (MDD) is a widespread psychiatric illness that profoundly impacts social and occupational functioning. Despite its prevalence, diagnosis remains clinical, based on DSM-5 criteria, with no validated biomarkers available to guide diagnosis or predict treatment response. Therapeutic decisions often rely on a trial-and-error approach, leading to poor outcomes for many patients. Recent studies highlight the gut-immune-brain axis as a key player in MDD pathophysiology, involving immune dysregulation, gut microbiota imbalance (dysbiosis), and molecular mediators such as metabolites, proteins, and extracellular vesicles (EVs). This thesis stems from the CARIPLO-funded project MIND-ME ("Microvesicles at the INtersection between Dysbiosis and Major dEpressive disorder: an OMIC approach", 2019-3277), a multi-omics observational study of MDD patients during a major depressive episode (MDE) and undergoing treatment change. At baseline, blood, fecal samples, and clinical data were collected, with a one-year follow-up. The project aimed to identify novel biomarkers linked to disease severity and treatment response by integrating immunomics, vesicleomics, and metagenomic/metaproteomic analyses. These data were combined using the BiomiX platform and Multi-Omics Factor Analysis (MOFA). The results indicate that MDD is characterized by systemic alterations at several biological levels, including immune dysregulation, microbial dysbiosis, and EV-mediated intercellular communication. Integrative analysis using BiomiX combined with MOFA enabled a comprehensive interpretation of these complex, multi-omic layers, both validating previously identified disease mechanisms and revealing novel interactions that had remained undetected. In conclusion, our findings provide compelling evidence in support of a personalized medicine model in MDD. Cinicians may soon be able to tailor treatments for MDD based on an individual’s unique molecular and clinical signature