The role of gut microbiota and metabolomic pathways in modulating the efficacy of SSRIs for major depressive disorder

Study information

Needs review

study design

prospective cohort

Citation

PMID PubMed identifier for scientific articles.

39695082

DOI Digital object identifier for electronic documents.

10.1038/s41398-024-03208-z

URI Uniform resource identifier for web resources.

Authors

Jiang Y, Qu Y, Shi L, Ou M, Du Z, Zhou Z, Zhou H, Zhu H

Journal

Translational psychiatry

Year

2024

This study aims to explore the mechanism by which gut microbiota influences the antidepressant effects of serotonin reuptake inhibitors (SSRIs) through metabolic pathways. A total of 126 patients were analyzed for their gut microbiota and metabolomics. Patients received SSRI treatment and were categorized into responder and non-responder groups based on changes in their Hamilton Depression Rating Scale (HAMD-17) scores before and after treatment. The association between gut microbiota composition and the efficacy of SSRIs was investigated through 16S rRNA gene sequencing and metabolomic analysis, and a predictive model was developed. As a result, the study found significant differences in gut microbiota composition between the responder and resistant groups. Specific taxa, such as Ruminococcus, Bifidobacterium, and Faecalibacterium, were more abundant in the responder group. Functional analysis revealed upregulation of acetate degradation and neurotransmitter synthesis pathways in the responder group. The machine learning model indicated that gut microbiota and metabolites are potential biomarkers for predicting SSRIs efficacy. In conclusion, gut microbiota influences the antidepressant effects of SSRIs through metabolic pathways. The diversity and function of gut microbiota can serve as biomarkers for predicting the treatment response, providing new insights for personalized treatment.

Experiment 1

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Curated date: 2025/03/10

Curator: MyleeeA

Revision editor(s): MyleeeA

Subjects

Location of subjects: China

Host species Species from which microbiome was sampled. Contact us to have more species added.: Homo sapiens

Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology: Feces Cow dung,Cow pat,Droppings,Dung,Excrement,Excreta,Faeces,Fecal material,Fecal matter,Fewmet,Frass,Guano,Matières fécales@fr,Merde@fr,Ordure,Partie de la merde@fr,Piece of shit,Porción de mierda@es,Portion of dung,Portion of excrement,Portion of faeces,Portion of fecal material,Portion of fecal matter,Portion of feces,Portion of guano,Portion of scat,Portionem cacas,Scat,Spoor,Spraint,Stool,Teil der fäkalien@de,Feces,feces

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: Treatment outcome measurement Treatment outcome measurement,treatment outcome measurement

Group 0 name Corresponds to the control (unexposed) group for case-control studies: Non-Responders

Group 1 name Corresponds to the case (exposed) group for case-control studies: Responders

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: First-episode Major Depressive Disorder patients who have not taken any other antidepressant medications; able to understand and cooperate with scale assessments and whose Serotonin Reuptake Inhibitors (SSRIs) treatments were effective.

Group 0 sample size Number of subjects in the control (unexposed) group: 61

Group 1 sample size Number of subjects in the case (exposed) group: 65

Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any): 1 month

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: V3-V4

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Illumina

Statistical Analysis

Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).: relative abundances

Statistical test: LEfSe

Significance threshold p-value or FDR threshold used for differential abundance testing (if any): 0.05

MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?: No

LDA Score above Threshold for the linear discriminant analysis (LDA) score for studies using the popular LEfSe tool: 2

Alpha Diversity

Chao1 Abundance-based estimator of species richness: unchanged

Signature 1

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Mark reviewed

Your review change was submitted

Needs review

Curated date: 2025/03/10

Curator: MyleeeA

Revision editor(s): MyleeeA

Source: Figure 1 H/I

Description: Differential analysis of species at the genus level between responders and non-responders.

Abundance in Group 1: increased abundance in Responders

NCBI	Quality Control	Links
Acidaminococcus
Bilophila
Coprobacillus
Dietzia
Enterorhabdus
Gemella
Lysobacter
Mitsuokella
Nesterenkonia
Peptoniphilus
Phascolarctobacterium
Succinivibrio
Synergistes
uncultured Thermoanaerobacterales bacterium
Burkholderia-Caballeronia-ParaburkholderiaBurkholderia-Caballeronia-Paraburkholderia
Rikenellaceae RC9Rikenellaceae RC9
Ruminococcaceae UCG-010Ruminococcaceae UCG-010
Ruminococcus 2Ruminococcus 2