Functional genomics in the wild: a case study with paper wasps shows challenges and prospects for RNA interference in ecological systems
Bibliographic record
Abstract
RNA interference (RNAi) is a useful tool to assess gene function by knocking down expression of a target gene and has been used successfully in domestic and laboratory organisms. However, the use of RNAi for functional genomics has not fully extended into ecological model organisms in natural environments. Assessment of gene function in the wild is important because gene function can be environmentally and context dependent. Here, we present a case study using RNAi to assess gene function in wild paper wasps Polistes metricus, to test roles for two candidate genes (NADH dehydrogenase (NADHdh) and retinoid and fatty acid binding protein (RfaBp)) in the development of reproductive castes. Previous studies have shown that these genes are upregulated in larvae that become queens compared to workers, but this pattern was reversed in the laboratory, making field-based studies necessary. We orally administered dsRNA to larvae in field colonies and found evidence of a short-term knockdown followed by a compensatory rebound in expression for RfaBp. We also observed the predicted worker-like decrease in lipid stores in NADHdh dsRNA treated wasps, suggesting a possible role for NADHdh in caste development. We discuss our results in the context of challenges for using RNAi for functional genomics in ecological model organisms in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".