Distinct fingerprints of tRNA-derived small non-coding RNA in animal models of neurodegeneration
Bibliographic record
Abstract
Transfer RNA-derived small RNAs (tsRNAs) - categorized as tRNA-derived fragments (tRFs), tRNA-derived stress-induced RNAs (tiRNAs) and internal tRF (itRF) - are small non-coding RNAs that participate in various cellular processes such as translation inhibition and responses to cellular stress. We here identified tsRNA profiles within susceptible tissues in animal models of amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD) and Parkinson's disease (PD) to pinpoint disease-specific tsRNAs and those shared across neurodegenerative diseases. We performed small RNA sequencing in the SOD1G93A and TDP43A315T mouse models of ALS (spinal cord), the TauP301S model of FTD (hippocampus), and the parkin/POLG model of PD (substantia nigra). Bioinformatic analysis showed higher expression of 5' tiRNAs selectively in the two ALS models, lower expression of 3' tRFs in both the ALS and FTD mouse models, and lower expression of itRF Arg in the PD model. Experimental validation confirmed the expression of tsRNAs. Gene Ontology analysis of targets associated with validated 3' tRFs indicated functions in the regulation of synaptic and neuronal pathways. Our profiling of tsRNAs indicates disease-specific fingerprints in animal models of neurodegeneration, which require validation in human disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".