Editorial: The why of RNA granules: Form, function, and regulation
Bibliographic record
Abstract
The Why of RNA Granules: Form, Function, and Regulation RNA granules represent a broad variety of RNA and protein condensates.These granules can be constitutive or stress-induced, nuclear or cytoplasmic, static or dynamic.RNA granules have been functionally associated with a myriad of biological processes and disease states, and the depth of their physiological importance is ever growing.The aim of this Research Topic was to bring together the latest information about the form and function of a diversity of RNA granules.It was our pleasure as guest associate editors to collect and juxtapose these articles, which reveal the breadth of RNA granules and the exciting research frontiers yet to come.A mini-review by Rhine et al. describes the dynamics of RNA granules, particularly stress granules (SGs), in the development of age-related disease.The authors frame RNA granule formation as an "inherently risky maneuver for cells", given the propensity for these granules to transition from functional liquid-like bodies to pathogenic gel-like or solid-like aggregates in the context of aging and neurodegeneration.The article highlights the current state of imaging, sequencing, and biochemical technologies for studying RNA granules.The application of these technologies to understand liquid-to-solid granule transitions will be key to understanding and treating the formation of pathological aggregates that cause neurodegeneration.Also related to SGs, a Perspective article by Cabral et al. collates the existing knowledge on so-called canonical and non-canonical SG subtypes.The authors focus a discussion on an oft-cited but little-studied non-canonical SG subtype, ultraviolet radiation (UV).The authors present some preliminary new evidence that suggests UV SGs may not contain mRNA and may be cell-type specific, in that cell types that experience UV regularly, such as keratinocytes, may be resistant to UV-induced SGs.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".