Multi-kingdom microbial changes and their associations with the clinical characteristics in schizophrenia patients
Bibliographic record
Abstract
Accumulating evidence has highlighted alterations in the gut microbiome in schizophrenia (SZ); however, the role of multi-kingdom microbiota in SZ remains inadequately understood. In this study, we performed metagenomic sequencing of fecal samples from 36 SZ patients and 55 healthy controls (HC) to profile bacterial, fungal, archaeal, and viral communities, along with functional pathways. We also conducted co-occurrence network analysis to explore the relationships among differential microbial species and metabolic pathways separately. Additionally, we assessed the associations of these differential species and functional pathways with clinical characteristics. Our findings revealed significant differences in species between SZ patients and HC, identifying not only 17 bacterial species, but also 8 fungal, 26 archaeal, and 19 viral species. Functional pathway analysis revealed 21 metabolic pathways significantly altered in SZ patients, including an increase in tryptophan metabolism, while biosynthesis of amino acids was decreased. Network analysis further uncovered more complex inter-kingdom interactions in SZ patients, with specific fungal species appearing exclusively in the SZ network. Importantly, significant associations were observed between microbial species and functional pathways with clinical characteristics, including symptom severity, cognitive function, and clinical biochemical marker. For instance, the abundance of Streptococcus vestibularis was positively correlated with homocysteine levels; the ubiquinone and other terpenoid-quinone biosynthesis was positively correlated with both symptom severity and C-reactive protein. Our findings reveal the intricate microbial dysbiosis present in SZ patients, suggesting multi-kingdom microbial interactions play a crucial role in SZ patients, highlighting promising avenues for potential diagnostic and therapeutic applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".