Soldier‐biased gene expression in a subterranean termite implies functional specialization of the defensive caste
Bibliographic record
Abstract
In a termite colony, reproduction is typically monopolized by a small number of sexuals that are supported by reproductively altruistic soldiers and workers. We expect caste differentiation to be associated with clear-cut differences in gene expression, and for these differences to reflect caste function and development. Here, we use RNA-Sequencing to compare the gene expression profiles of sexual nymphs and two non-reproductive helper castes (i.e., workers and soldiers) of the Eastern subterranean termite Reticulitermes flavipes. We found that of n = 93 genes that are strictly expressed as a function of caste, a majority (78%) show a soldier-specific pattern. This conspicuous soldier-bias in genome-wide expression suggests that this defensively specialized caste is functionally well-differentiated from both the reproductive and the other non-reproductive caste of this species, despite a shared developmental program with workers. Gene ontology analysis supports the notion of functional specialization by soldiers, as soldier-biased gene sets are enriched for novel biological processes. Whether this pattern reflects ancient or more recent bouts of selection for caste novelty at the gene-regulatory level is not known, but because soldiers are sterile and thus have no direct fitness, any selection for novelty must have been mediated indirectly, through reproducing relatives.
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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".