Integrating transcriptomics and metabolomics revealed pathogenic mechanism of Chinese soft-shell turtle (Trionyx sinensis) infected with Trionyx sinensis hemorrhagic syndrome virus (TSHSV)
Bibliographic record
Abstract
Trionyx sinensis Hemorrhagic Syndrome Virus(TSHSV)seriously hinders the aquaculture of Chinese soft-shell turtle (Trionyx sinensis) due to its high mortality. However, the pathogenic mechanisms of TSHSV in T. sinensis are still unclear. In present study, transcriptomic and metabolomic analyses were performed on turtle livers following TSHSV infection. 734 up-regulated and 770 down-regulated differentially expressed genes (DEGs) were identified in different TSHSV challenge groups. These DEGs were categorized into 12 pathways related to virus infection and host immunity. Moreover, 27, 2679, and 4341 differentially expressed metabolites (DEMs) were identified in the D1, D3, and D5 groups, respectively. These DEMs were mapped into the pathways of energy metabolism, amino acid metabolism and fatty acid metabolism. Association analysis revealed TSHSV induced inflammatory responses, hepatocyte apoptosis, and ultimately led to liver tissue damage. Taurine supplementation promoted the survival rate of turtle after TSHSV infection and reduced the inflammatory response of liver by regulating the production of interferons, antioxidases, and the pro-inflammatory cytokine TNF-α. Collectively, our results provide comprehensive profiles of the transcriptome and metabolome in Chinese soft-shell turtle liver after TSHSV invasion, shedding light on the underlying pathogenic mechanism. The method of taurine supplementation might be a promising therapeutic strategy for protecting turtles from TSHSV.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".