The influence of glutathione metabolism on alkaline adaptation of Amur ide (Leuciscus waleckii) and potential role of gut microbiota
Bibliographic record
Abstract
Amur ide ( Leuciscus waleckii ), which inhabits Lake Dali, a soda lake in Northeast China with extremely high alkalinity (~ 53.57 mmol/L) and pH value (~ 9.6), is considered to be an ideal model for elucidating alkaline adaption mechanisms. To uncover the molecular mechanisms underlying this adaptation, we conducted a comparative study between the alkaline water ecotype (JY) and freshwater ecotype (DY). Both groups were exposed to a gradient of NaHCO 3 stress levels (0, 10, 30, and 50 mmol/L), and their responses were systematically assessed through integrated multi-omics analyses alongside physiological assays. Our results revealed that under low and moderate alkaline stress (10 and 30 mmol/L), JY group significantly upregulated the gene anpep , facilitating the hydrolysis of cysteinyl-glycine to release l -cysteine, thereby enhancing antioxidant capacity. Under high stress conditions (50 mmol/L), JY further synergistically upregulated gpx to activated the glutathione peroxidase (GPx) pathway to eliminate excess ROS. In contrast, the DY group predominantly relied on upregulating chac1 -mediated γ-glutamyltransferase activity to facilitate glutathione cycling. Notably, while cysteinyl-glycine content significantly increased in the alkaline water ecotype (JY) under moderate and high alkalinity stress (30 and 50 mmol/L), the expression of its upstream gene chac1 was significantly downregulated. This paradox suggests alternative sources or regulatory mechanisms for cysteinyl-glycine accumulation in JY. Microbial tracing analysis revealed a positive correlation between cysteinyl-glycine levels and the gut microbiota genus Stenotrophomonas in JY, whose relative abundance increased progressively with elevated alkalinity. It is speculated that Stenotrophomonas may modulate host glutathione metabolism by regulating cysteinyl-glycine levels, thereby facilitating alkaline adaptation.
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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 | 0.000 | 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 | 0.001 |
| 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 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".