Identification of long non-coding RNA MSTRG.5748.1 and MSTRG.7894.1 from Megalobrama amblycephala and their potential roles in innate immunity
Bibliographic record
Abstract
Megalobrama amblycephala is one of the most economically important freshwater fish in China, and the bacterial septicemia caused by Aeromonas hydrophila is a serious threat to the breeding industry of M. amblycephala. Unfortunately, the characterization of long noncoding RNA (lncRNA) in response to A. hydrophila infection has not been performed in M. amblycephala. To better understand the biological significance of lncRNA in the immune system, we identified two lncRNA, named MSTRG.5748.1 and MSTRG.7894.1, as playing critical roles in the antibacterial response of M. amblycephala. After separating the nucleus and cytoplasm of the hepatocytes from M. amblycephala, cellular localization of MSTRG.5748.1 and MSTRG.7894.1 was performed to predict their functions. The results showed that MSTRG.5748.1 was mainly expressed in the nucleus, suggesting that its functions are mostly to regulate the expression of downstream genes through epistasis and transcription. MSTRG.7894.1 existed in both the nucleus and cytoplasm, which indicated that it has many regulatory modes. qPCR analysis showed that MSTRG.5748.1 and MSTRG.7894.1 were expressed in the immune-related organs of M. amblycephala, and significantly changed in the liver after A. hydrophila infection. RNA-seq analysis revealed that differentially expressed genes (DEGs) were mainly enriched in antigen processing and presentation via MHC class I, RIG-I-like receptor (RLR) signaling pathway, and IFN-related pathway, and a large number of pathway-related genes were significantly regulated after lncRNA overexpression in muscle cell of M. amblycephala. Overexpression of MSTRG.5748.1 and MSTRG.7894.1 significantly inhibited the expression of STING and IFN, significantly upregulated muscle cell viability, and promoted cell proliferation by targeting STING and IFN.
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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) | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".