Nowadays, depressive disorder is spreading rapidly all over the world. Therefore, attention to the studies of the pathogenesis of the disease in order to find novel ways of early diagnosis and treatment is increasing among the scientific and medical communities. Special attention is drawn to a biomarker and therapeutic strategy through the microbiota–gut–brain axis. It is known that the symbiotic interactions between the gut microbes and the host can affect mental health. The review analyzes the mechanisms and ways of action of the gut microbiota on the pathophysiology of depression. The possibility of using knowledge about the taxonomic composition and metabolic profile of the microbiota of patients with depression to select gene compositions (metagenomic signature) as biomarkers of the disease is evaluated. The use of in silico technologies (machine learning) for the diagnosis of depression based on the biomarkers of the gut microbiota is given. Alternative approaches to the treatment of depression are being considered by balancing the microbial composition through dietary modifications and the use of additives, namely probiotics, postbiotics (including vesicles) and prebiotics as psychobiotics, and fecal transplantation. The bacterium Faecalibacterium prausnitzii is under consideration as a promising new-generation probiotic and auxiliary diagnostic biomarker of depression. The analysis conducted in this review may be useful for clinical practice and pharmacology.
【저자키워드】 Biomarkers, Depression, machine learning, gut microbiota, Psychobiotics, new generation probiotic,