This study is the first comprehensive meta-analysis of the gut microbial 16S rRNA sequencing data of participants with depressive symptoms. Eight different cohorts with 16s rRNA sequencing data were systemically screened from published journals and combined for analysis. There was no significant difference in alpha diversity between the depressive-associated microbiota and microbiota of healthy controls. But the overall community of depressive-associated microbiota was significantly different from that of health controls in terms of the Bray Curtis distance. Regarding significantly different taxonomic and functional biomarkers between depressive and control samples, consistent biomarkers across eight cohorts was identified. Thus, we established a classifier with an AUC of 0.95 in training set and 0.64 in testing set from the combined 16s sequencing data of eight cohorts, using a group of microbes that differentiate the gut microbiota between samples of depressive and healthy individuals. An interaction network analysis was conducted to further characterize differences in gut microbiota ecology between depressive and control groups. A gut microbiota cluster with a reduced risk of depressive symptoms was represented by lower abundance of Escherichia-Shigella and a higher abundance of Faecalibacterium, Oscillospiraceae UCG 002, Ruminococcus and Christensenellaceae R.7 group. These findings indicated that the overall ecology of gut microbiota and interaction between microbial communities might be more important for the development of depression, rather than a single taxonomy player. In summary, the findings of the present study emphasize the need for the holistic monitoring of gut microbiota to assist in the diagnosis of depression.
The present study does not show a significant alteration in alpha diversity of the gut microbiota among depressive groups. However, we identified significant changes in beta diversity and the relative abundance of certain taxa after combining results from different cohorts. These results corroborate findings by recent systematic reviews of gut microbiota associated with psychiatric disorders[10, 22]. Moreover, we observed a significant enrichment in the abundance of Firmicutes, particularly the abundance of Dialister, in the pooled result of the adjusted model. Dialister, which is considered to be positively associated with quality of life, was consistently depleted in depressive groups in four studies (Supplementary Fig. 3). Acetate, lactate and propionate, being the metabolic end-products produced by Dialister[23, 24], could increase the ability to resist stress and exert antidepressant-like effects by modulating histone acetylation and deacetylases[25, 26]. At the species level, B. plebeius was reported to be significantly enriched in the control group by our meta-analysis. Glutamate can be synthesized by B. plebeius PB-SLKZP[27, 28], acting as a primary precursor of γ-aminobutyric acid (GABA), which plays a prominent role in stress control by the brain, improving the resilience of individuals towards depression.
The relative abundance of Bacteroidetes was enriched in the depressive group compared to controls. Parabacteroides, Barnesiella and Bacteroides are members of the Bacteroidetes phylum, which are also reported to be enriched in depression (Supplementary Fig. 3). Parabacteroides supplementation was reported to interact with tryptophan (Trp) metabolism pathways in the mouse hippocampus, ameliorating the toxic, depression-related production of kynurenine (Kyn) and the associated metabolites. Despite the proposed beneficial role of Parabacteroides against depression, the enrichment of Parabacteroides in microbiota of depressed individuals has been consistently reported by multiple studies (Supplementary Fig. 3), and can induce depressive-like behaviour in mice. These inconsistencies highlight the importance of standardizing protocols for meta-analysis, as we have done in the current review, and to achieve a higher taxonomic resolution to establish a more precise interaction between microbiota and depression.
At the species level, A. inops, and B. vulgatus were enriched in the depressive group. A. inops is an indole-positive bacterium, which produces indole to decrease serotonin availability. Since it disrupts the gut-brain axis, A. inops may be associated with the onset of depression. B. vulgatus also promotes NF-κB expression and the downstream pro-inflammatory signalling cascade in intestinal epithelial cells to induce depressive-like phenotypes. We found that Sutterella, a Betaproteobacteria belonging to the Proteobacteria, was enriched in depression. In contrast, two other Proteobacteria, Morganella and Klebsiella, which are both Gammaproteobacteria, were not different in our analysis, although they have been postulated to have causal effects on MDD in a large cohort study including 5,959 individuals.
GABA deficiency may contribute to depressive disorders. In our meta-analysis of this pathway, we identified consistent results according to 16S rRNA predicted pathways. L-glutamate is the primary precursor of GABA. In our study, several L-glutamate production pathways (UDP-N-acetyl-D-glucosamine biosynthesis I; UMP biosynthesis I; L-arginine biosynthesis I (via L-ornithine)) were enriched in the control group, whereas the pathway related to L-glutamine degradation, dTDP-N-acetylthomosamine biosynthesis, was enriched in the depressive group. Remarkably, the L-isoleucine biosynthesis I, II, III, IV, pathways and the L-isoleucine biosynthesis I superpathway were clustered in the control group. In keeping with previous studies that observed an inverse association between isoleucine intake and odds of depression and anxiety, our meta-analysis also reveal a reduction of L-isoleucine biosynthesis in depressive samples. Overall, analysis of biochemical pathways identified more markers associated with depression compared with taxonomy, which may indicate convergent functional changes regardless of the inconsistent taxonomic changes in depression.
Even though consistent taxonomic markers can be found across the cohorts, more markers need to be used to establish the classifier to discriminate between depressive and control groups. The classifier, which combined the selected microbiota and metadata, had an AUC of 0.86 in the overall dataset. The AUC was higher than 0.8 in most cohorts after excluding BD patients and mucosa samples. The predicted score also shows a high correlation with measured depression scores, indicating the potential of this classifier in predicting the initial phase of depressive symptoms or mild depressive patients. The different interaction networks in depressive and control groups further suggest that rather than single taxa, the ecology of the overall gut microbiota might be more dominantly involved in the development of depression, especially two of the hub nodes, Oscillospiraceae UCG 002 and Christensenellaceae R7 group, which were also associated with microbial cluster representing low risk of depression. Furthermore, the reduced abundance of an inflammatory taxon, Escherichia-Shigella, and the higher abundance of butyrate-producing bacteria Faecalibacterium, Oscillospiraceae UCG 002, Ruminococcus and Christensenellaceae R.7 were associated with a reduced risk of developing depressive symptoms. The higher abundance of Oscillospiraceae UCG 002, Christensenellaceae R.7 and lower abundance of Parabacteroides in the low risk cluster were consistent with the meta-analysis result. The contradiction and heterogeneity between previous studies may be partially explained by uneven sample sizes between the two clusters during comparison. It might also influence the response and non-response to anti-antidepressants and probiotics. The reduced risk identified in the cluster analysis provides insight into the potential preventive function of gut microbiota in the development of depression.
Our study is the first multi-cohort meta-analysis to aggregate microbiota sequence data from diverse depressive populations and use a uniform analytical method to identify gut microbiota characteristics in depressive participants across cohorts and across different analytical approaches. We controlled common confounding factors such as age, sex and BMI to reduce bias caused by statistical analyses. However, other important confounders such as the use of antidepressants, frequency of alcohol consumption and bowel movement quality were not considered due to lack of data. Another limitation is that the number of cohorts was much less than the systematic screening result. A total of 21 of the 29 studies could not be included in the multi-cohort analysis due to lack of raw sequence data. Hence, further multi-cohort studies with more high-quality sequences will improve the accuracy of microbiota-based classification and provide a deeper understanding of microbiota function in depression.
In conclusion, the standardization of data analysis using raw 16s rRNA sequencing data collected from available literature revealed an absence of significant changes in alpha diversity of gut microbiota between participants with depressive symptoms and healthy controls. However, the current meta-analysis demonstrates significant changes in beta-diversity of gut microbiota between depressive and control groups. This study has also successfully identified consistent changes of certain bacterial taxa associated with depressive symptoms, as well as metabolic pathways that contribute to interactions with host physiology. Universal community and ecological shifts related to depressive symptoms have also been discovered in the depressive group, providing directions for the potential inclusion of gut microbiota analysis in depression scoring. The classifier established in this study had AUC values higher than 0.78 in cohorts, further indicating that the gut microbiota could be used as a practical predictive tool and, possibly, as preventive and treatment option for depression.