Sex-Specific Expression of Non-Coding RNA Fragments in Frontal Cortex, Hippocampus and Cerebellum of Rats
Bibliographic record
Abstract
Non-coding RNA fragments (ncRFs) are processed from various non-coding RNAs (ncRNAs), with the most abundant being those produced from tRNAs. ncRFs were reported in many animal and plant species. Many ncRFs exhibit tissue specificity or/and are affected by stress. There is, however, only a handful of reports that describe differential expression of ncRFs in the brain regions. In this work, we analyzed the abundance of ncRFs processed from four major ncRNAs, including tRNA (tRFs), snoRNA (snoRFs), snRNA (snRFs), and rRNA (rRFs) in the frontal cortex (FC), hippocampus (HIP), and cerebellum (CER) of male and female rats. We found brain-specific and sex-specific differences. Reads mapping to lincRNAs were significantly larger in CER as compared to HIP and CER, while those mapping to snRNAs and tRNA were smaller in HIP than in FC and CER. tRF reads were the most abundant among all ncRF reads, and FC had more reads than HIP and CER. Reads mapping to antisense ncRNAs were significantly larger in females than in males in FC. Additionally, males consistently had more tRF, snRF, and snoRF reads in all brain regions. rRFs were more abundant in males in FC and females in HIP. Several tRFs were significantly underrepresented, including tRF-ValCAC, tRF-ValACC, and tRF-LysCTT in all brain regions. We also found brain- and sex-specific differences in the number of brain function-related mRNA targets. To summarize, we found sex-specific differences in the expression of several ncRNA fragments in various brain regions of healthy rats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".