The transcriptomic responses of blunt snout bream (Megalobrama amblycephala) to acute hypoxia stress alone, and in combination with bortezomib
Bibliographic record
Abstract
Abstract Background Blunt snout bream (Megalobrama amblycephala) is sensitive to hypoxia. A new blunt snout bream strain, “Pujiang No.2”, was developed to overcome this shortcoming. As a proteasome inhibitor, bortezomib (PS-341) has been shown to affect the adaptation of cells to a hypoxic environment. In the present study, bortezomib was used to explore the hypoxia adaptation mechanism of “Pujiang No.2”. We examined how acute hypoxia alone (hypoxia-treated, HN: 1.0 mg·L− 1), and in combination with bortezomib (hypoxia-bortezomib-treated, HB: Use 1 mg bortezomib for 1 kg fish), impacted the hepatic ultrastructure and transcriptome expression compared to control fish (normoxia-treated, NN). Results Hypoxia tolerance was significantly decreased in the bortezomib-treated group (LOEcrit, loss of equilibrium, 1.11 mg·L− 1 and 1.32 mg·L− 1) compared to the control group (LOEcrit, 0.73 mg·L− 1 and 0.85 mg·L− 1). The HB group had more severe liver injury than the HN group. Specifically, the activities of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in the HB group (52.16 U/gprot, 32 U/gprot) were significantly (p < 0.01) higher than those in the HN group (32.85 U/gprot, 21. 68 U/gprot). In addition, more severe liver damage such as vacuoles, nuclear atrophy, and nuclear lysis were observed in the HB group. RNA-seq was performed on livers from the HN, HB and NN groups. KEGG pathway analysis disclosed that many DEGs (differently expressed genes) were enriched in the HIF-1, FOXO, MAPK, PI3K-Akt and AMPK signaling pathway and their downstream. Conclusion We explored the adaptation mechanism of “Pujiang No.2” to hypoxia stress by using bortezomib, and combined with transcriptome analysis, accurately captured the genes related to hypoxia tolerance advantage.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".