A primary insight into gut microbiome, MicroRNA and stemness, in a PCOS rat model
Bibliographic record
Abstract
Polycystic ovary syndrome (PCOS) is a common endocrine disorder associated with reproductive and metabolic dysfunctions, including gut microbiome dysbiosis. This study aimed to examine the alterations in stemness in ovarian surface epithelium (OSE), gut microbiome microRNA expression in granulosa cells and plasma in a dihydrotestosterone (DHT)-induced rat model of PCOS. Female rats were administered DHT to induce PCOS, and the expression of stem cell markers in OSE was assessed to evaluate the impact on stemness. Alterations in the gut microbiome composition were assessed using 16S rRNA gene Long-Read sequencing and changes in the microRNA profile of granulosa cells and plasma were analyzed using qPCR. Our results demonstrated alterations in stemness markers and, a significant alteration in gut microbiome composition in DHT-induced rats compared to controls, characterized by shifts in the relative abundance of specific bacterial taxa, particularly Akkermansia muciniphila. Elevated levels of miR-574 and miR-378 were observed in plasma, whereas miR-21 and miR-574 showed increased expression in ovarian granulosa cells. Concurrently, increased expression of stem cell markers was observed in OSE, suggesting an enhancement of stemness in response to PCOS-like conditions. These findings imply a potential link between gut microbiome dysbiosis and increased ovarian stemness in PCOS, suggesting that the gut microbiome may contribute to ovarian dysfunction through modulation of stem cell activity. Understanding this interaction could provide novel insights into therapeutic targets in restoring ovarian function in PCOS patients.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".