<scp>MicroRNA bmo‐miR</scp>‐31‐5p inhibits apoptosis and promotes <scp>BmNPV</scp> proliferation by targeting the <scp><i>CYP9e2</i></scp> gene of <scp><i>Bombyx mori</i></scp>
Bibliographic record
Abstract
BACKGROUND: Bombyx mori nuclear polyhedrosis virus (BmNPV), as a typical baculovirus, is the primary pathogen that infects the silkworm B. mori, a lepidopteran species. Owing to the high biological safety of BmNPV in infecting insects, it is commonly utilized as a biological insecticide for pest control. Apoptosis is important in the interaction between the host and pathogenic microorganisms. MicroRNAs (miRNAs) influence immune responses and promote stability of the immune system via apoptosis. Therefore, the study of apoptosis-related miRNA in silkworms during virus infection can not only provide support for standardizing the prevention and control of diseases and insect pests, but also reduce the economic losses to sericulture caused by the misuse of biological pesticides. RESULTS: Through transcriptome sequencing, we identified a miRNA, miR-31-5p, and demonstrated that it can inhibit apoptosis in silkworm cells and promote the proliferation of BmNPV in BmE-SWU1 cells. We identified a target gene of miR-31-5p, B. mori cytochrome P450 9e2 (BmCYP9e2), and demonstrated that it can promote apoptosis in silkworm cells and inhibit the proliferation of BmNPV. Moreover, we constructed transgenic silkworm strains with miR-31-5p knockout and confirmed that they can inhibit the proliferation of BmNPV. CONCLUSION: These data indicate that miR-31-5p may exert functions of inhibiting apoptosis and promoting virus proliferation by regulating BmCYP9e2. The findings demonstrate how miRNAs influence host cell apoptosis and how they are involved in the host immune system response to viruses, providing important insights into the applications of biological insecticides for pest control. © 2024 Society of Chemical Industry.
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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 | 0.001 |
| 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".