Caste‐specific microRNA expression in termites: insights into soldier differentiation
Bibliographic record
Abstract
Eusocial insects have polyphenic caste systems in which each caste exhibits characteristic morphology and behaviour. In insects, caste systems arose independently in different lineages, such as Isoptera and Hymenoptera. Although partial molecular mechanisms for the development of eusociality in termites have been clarified by the functional analysis of genes and hormones, the contribution of microRNAs (miRNAs) to caste differentiation is unknown. To understand the role of miRNAs in termite caste polyphenism, we performed small RNA sequencing in a subterranean termite (Reticulitermes speratus) and identified the miRNAs that were specifically expressed in the soldier and worker castes. Of the 550 miRNAs annotated in the R. speratus genome, 74 were conserved in insects and 174 were conserved in other termite species. We found that eight miRNAs (mir-1, mir-125, mir-133, mir-2765, mir-87a and three termite-specific miRNAs) are differentially expressed (DE) in soldiers and workers of R. speratus. This differential expression was experimentally verified for five miRNAs by real-time quantitative PCR. Further, four of the eight DE miRNAs in soldier and worker termite castes were also differentially expressed in hymenopteran castes. The finding that Isoptera and Hymenoptera shared several DE miRNAs amongst castes suggests that these miRNAs evolved independently in these phylogenetically distinct lineages.
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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".