Identification of gut microbiome markers for schizophrenia delineates a potential role of <i>Streptococcus</i>
Bibliographic record
Abstract
Abstract Emerging evidence has linked the gut microbiota to schizophrenia. However, the functional changes in the gut microbiota and the biological role of individual bacterial species in schizophrenia have not been explored systematically. Here, we characterized the gut microbiota in schizophrenia using shotgun metagenomic sequencing of feces from a discovery cohort of 90 drug-free patients and 81 controls, as well as a validation cohort of 45 patients taking antipsychotics and 45 controls. We screened 83 schizophrenia-associated bacterial species and constructed a classifier comprising 26 microbial biomarkers that distinguished patients from controls with a 0.896 area under the receiver operating characteristics curve (AUC) in the discovery cohort and 0.765 AUC in the validation cohort. Our analysis of fecal metagenomes revealed that schizophrenia-associated gut–brain modules included short-chain fatty acids synthesis, tryptophan metabolism, and synthesis/degradation of neurotransmitters including glutamate, γ-aminobutyric acid, and nitric oxide. The schizophrenia-enriched gut bacterial species include several oral cavity-resident microbes, such as Streptococcus vestibularis . We transplanted Streptococcus vestibularis into the gut of the mice with antibiotic-induced microbiota depletion to explore its functional role. We observed that this microbe transiently inhabited the mouse gut and this was followed by hyperactivity and deficit in social behaviors, accompanied with altered neurotransmitter levels in peripheral tissues. In conclusion, our study identified 26 schizophrenia-associated bacterial species representing potential microbial targets for future treatment, as well as gut–brain modules, some of which may give rise to new microbial metabolites involved in the development of schizophrenia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".