Ribonuclease activity undermines immune sensing of naked extracellular RNA
Bibliographic record
Abstract
The plasma membrane and the membrane of endosomal vesicles are considered physical barriers preventing extracellular RNA uptake. While naked RNA can be spontaneously internalized by certain cells types, functional delivery of naked RNA into the cytosol has been rarely observed. Here we show that extracellular ribonucleases, mainly derived from cell culture supplements, have so far hindered the study of extracellular RNA functionality. In the presence of active ribonuclease inhibitors (RI), naked bacterial RNA is pro-inflammatory when spiked in the media of dendritic cells and macrophages. In murine cells, this response mainly depends on the action of endosomal Toll-like receptors. However, we also show that naked RNA can perform endosomal escape and engage with cytosolic RNA sensors and ribosomes. For example, naked mRNAs encoding reporter proteins can be spontaneously internalized and translated by a variety of cell types, in an RI-dependent manner. In vivo, RI co-injection enhances the activation induced by naked extracellular RNA on splenic lymphocytes and myeloid-derived leukocytes. Furthermore, naked extracellular RNA is inherently pro-inflammatory in ribonuclease-poor compartments such as the peritoneal cavity. Overall, these results demonstrate that naked RNA is bioactive and does not need encapsulation inside synthetic or biological lipid vesicles for functional uptake, making a case for nonvesicular extracellular RNA-mediated intercellular communication.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".