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Here, we performed a case-control study with a total of 105 subjects, 43 with major depressive disorder (MDD) and 62 non-depressed controls free from psychiatric comorbidities, to identify gut microbiome signatures associated with MDD and dissect its relation to body mass index (BMI) and lifestyle (diet and exercise). We performed shotgun metagenomics, followed by taxonomic and functional annotations. Using different machine learning methods, we were able to classify subjects into depressed and non-depressed controls with a balanced accuracy of 0.90 and into depressed or non-depressed and normal weight or overweight with a balanced accuracy of 0.78 based solely on taxonomic profiles. We identify novel bacterial taxa associated with depression, including reductions in\n                      <jats:italic toggle=\"yes\">Butyrivibrio hungatei<\/jats:italic>\n                      and\n                      <jats:italic toggle=\"yes\">Anaerocolumna sedimenticola,<\/jats:italic>\n                      and also replicate previously reported associations, such as decreased\n                      <jats:italic toggle=\"yes\">Faecalibacterium prausnitzii<\/jats:italic>\n                      in patients with MDD. Functional annotation of metagenomes shows differences in pathways linked to the synthesis of fundamental nutrients, which have been associated with diet, as well as inflammation. Strikingly, we found an increase in tryptophan degradation and a decrease in queuosine synthesis pathways, both of which are directly related to a decrease in monoaminergic neurotransmitter availability. Additionally, our functional analysis shows that most of the functions that are more abundant in controls than in depressed subjects are encoded by\n                      <jats:italic toggle=\"yes\">F. prausnitzii<\/jats:italic>\n                      . These findings reveal distinct microbial and functional signatures associated with depression, including taxa and pathways linked to neurotransmitter metabolism and independent of other covariates. This suggests that gut microbiome profiling could support diagnosis and the development of gut-directed depression treatments.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>IMPORTANCE<\/jats:title>\n                    <jats:p>\n                      This study identifies gut microbiome signatures that are predictive of major depressive disorder (MDD) and explores their links to body mass index (BMI). We uncover bacterial species and metabolic pathways that are associated with MDD, some of them related to neurotransmitter metabolism and inflammation. Among the differences identified, depletion of\n                      <jats:italic toggle=\"yes\">Faecalibacterium prausnitzii<\/jats:italic>\n                      stands out as an important feature in the MDD microbiome, which suggests the possible use of this species to improve depression symptoms. Importantly, we demonstrate shared microbiome features between MDD and BMI, suggesting common underlying mechanisms. This research not only provides a framework for developing microbiome-based diagnostics but also informs future stratified interventions targeting gut microbial functions to improve mental health outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/msystems.01263-25","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T14:00:33Z","timestamp":1769781633000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Gut microbiome signatures associated with depression and obesity"],"prefix":"10.1128","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0542-2654","authenticated-orcid":false,"given":"Carlos","family":"Mora-Mart\u00ednez","sequence":"first","affiliation":[{"name":"Microbiome Innovation in Nutrition and Health Unit, Institute of Agrochemistry and Food Technology, National Research Council (IATA-CSIC)","place":["Valencia, 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