Innate Immune Defense Through RNA Interference
Bibliographic record
Abstract
RNA interference (RNAi, also known as RNA silencing) has recently emerged as a fundamental and widespread regulator of gene expression. New developments in this field implicate RNAi in the innate immune response to infection in plants and animals. Evidence from plants, tissue culture cells, and Caenorhabditis elegans-based systems previously suggested that RNAi plays a role in the defense against viral infection, but definitive evidence using viruses and whole animals has been lacking. Two recent reports now show that both Drosophila embryos and adult flies mount a substantial innate immune response to insect viruses that requires the RNAi machinery. This innate response is distinct from known bacterial and fungal defense systems provided by the Toll and immune deficiency (Imd) pathways, thus defining a previously unrecognized strategy to fight viral infection. Whether RNAi, aside from its function in counteracting viruses, is also used to fight bacterial infection remained enigmatic. New evidence, however, now shows that in Arabidopsis, the bacterial component, flagellin, induces the expression of a specific microRNA, which in turn leads to the down-regulation of the signaling pathways that are implicated in disease susceptibility. This down-regulation then increases the plant's resistance to infection. Whether RNAi mechanisms also exist for combating bacterial diseases in animals remains an intriguing question for future studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 0.007 |
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".