Small Luggage for a Long Journey: Transfer of Vesicle-Enclosed Small RNA in Interspecies Communication
Bibliographic record
Abstract
In the evolutionary arms race, symbionts have evolved means to modulate each other's physiology, oftentimes through the dissemination of biological signals. Beyond small molecules and proteins, recent evidence shows that small RNA molecules are transferred between organisms and transmit functional RNA interference signals across biological species. However, the mechanisms through which specific RNAs involved in cross-species communication are sorted for secretion and protected from degradation in the environment remain largely enigmatic. Over the last decade, extracellular vesicles have emerged as prominent vehicles of biological signals. They can stabilize specific RNA transcripts in biological fluids and selectively deliver them to recipient cells. Here, we review examples of small RNA transfers between plants and bacterial, fungal, and animal symbionts. We also discuss the transmission of RNA interference signals from intestinal cells to populations of the gut microbiota, along with its roles in intestinal homeostasis. We suggest that extracellular vesicles may contribute to inter-species crosstalk mediated by small RNA. We review the mechanisms of RNA sorting to extracellular vesicles and evaluate their relevance in cross-species communication by discussing conservation, stability, stoichiometry, and co-occurrence of vesicles with alternative communication vehicles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".