Molecular insights into the regulation of expression of snake venom toxin genes in <i>Pseudonaja textilis</i>
Bibliographic record
Abstract
Snake venom toxin genes are specifically expressed in the venom gland when it is empty (e.g. after milking). Tissue‐ and time‐specific expression of toxin genes is regulated at transcriptional level by cis‐ elements and transcription factors. Recently, we have identified that multiple AG‐rich motifs in intron 1 of trocarin D, a gene encoding venom prothrombin activator in Tropidechis carinatus, silence gene expression in both mammalian and unmilked venom gland cells. Several transcription factors were identified to bound AG‐rich motifs and repress gene expression. However, the complete regulatory mechanisms of specific expression of toxin genes are still unknown. To understand this, we have sequenced the transcriptomes of both milked and unmilked venom gland tissues from a common snake P. textilis (which possesses similar AG‐rich motifs). We have compared the expression levels of different genes and performed gene ontology enrichment analysis. We also analyzed the microRNAs for their expression levels and targets of regulation in these tissues. Here I will present our findings, which will contribute to the understanding of roles of transcription factors and microRNAs in the regulation of specific expression of toxin genes. Support or Funding Information Source of research support: Academic Research Grants from NUS
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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".