Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Abstract

Major depressive disorder (MDD) is a recurrent episodic mood disorder that represents the third leading cause of disability worldwide. In MDD, several factors can simultaneously contribute to its development, which complicates its diagnosis. According to practical guidelines, antidepressants are the first-line treatment for moderate to severe major depressive episodes. Traditional treatment strategies often follow a one-size-fits-all approach, resulting in suboptimal outcomes for many patients who fail to experience a response or recovery and develop the so-called "therapy-resistant depression". The high biological and clinical inter-variability within patients and the lack of robust biomarkers hinder the finding of specific therapeutic targets, contributing to the high treatment failure rates. In this frame, precision medicine, a paradigm that tailors medical interventions to individual characteristics, would help allocate the most adequate and effective treatment for each patient while minimizing its side effects. In particular, multi-omic studies may unveil the intricate interplays between genetic predispositions and exposure to environmental factors through the study of epigenomics, transcriptomics, proteomics, metabolomics, gut microbiomics, and immunomics. The integration of the flow of multi-omic information into molecular pathways may produce better outcomes than the current psychopharmacological approach, which targets singular molecular factors mainly related to the monoamine systems, disregarding the complex network of our organism. The concept of system biomedicine involves the integration and analysis of enormous datasets generated with different technologies, creating a "patient fingerprint", which defines the underlying biological mechanisms of every patient. This review, centered on precision medicine, explores the integration of multi-omic approaches as clinical tools for prediction in MDD at a single-patient level. It investigates how combining the existing technologies used for diagnostic, stratification, prognostic, and treatment-response biomarkers discovery with artificial intelligence can improve the assessment and treatment of MDD.

Keywords: antidepressant; biomarkers; major depressive disorder; precision medicine; system biomedicine.

Figure 1

Multi-omic approach in MDD. The heterogeneity of MDD and its diverse etiology hinders the accurate patient stratification and appropriate treatment choice. The integration of high-throughput data generated by single omic technologies, such as genomics, transcriptomics and proteomics, into a multi-omic system permits the identification of specific individual features able to predict disease susceptibility, prognosis, and treatment response, leading to a more effective personalized care. Created with BioRender.com.

Figure 2

Immunomics approach in MDD. Various immunological techniques provide an overview of the involvement of the immune system in MDD. Immunophenotyping through flow cytometry, MHC tetramers, HLA-binding assays, and ELISpot assays are used to study neuro- and systemic inflammation, immunosuppression, blood-brain barrier (BBB) leakage, and cytokine production in MDD patients. Created with BioRender.com.

Figure 3

Network for resolving heterogeneity in MDD. On a biological level, our bodies consist of numerous interconnected networks that communicate across various scales (organ, cell, gene and metabolite). Artificial intelligence and machine learning-based bioinformatic tools analyze the role of each single network component through the integration of high-throughput biological data, originated from multi-omic techniques. Created with BioRender.com.