A Macro Role for Imprinted Clusters of MicroRNAs in the Brain
Bibliographic record
Abstract
Evidence suggests that ~95% of the human genome may produce noncoding RNAs (ncRNAs). Approximately 30% of parentally imprinted transcripts are ncRNAs that are found abundantly--and in some cases specifically--in the brain; however, the role ncRNA plays during development and day-to-day life is not apparent. Interestingly, ~1,300 transcripts show a consistent parental expression bias in the brain. This is in contrast to most tissues that show only rare imprinted transcripts. Furthermore, there are only two clusters of imprinted small nucleolar RNA (snoRNA) encoding genes in mammals, which are also expressed in the brain. These clusters, Snrpn-Ube3a (Human 15q11-q13/Murine 7qC) and Dlk1-Dio3 (Human 14q32.2/Murine 12qF1) and rodent specific Sfmbt2 (Murine 2qA1), form the focus of this review. These imprinted clusters are localized to imprinted regions that are associated with processes involved in neuronal plasticity and several neurodevelopmental disorders. Several miRNAs from the Dlk1-Dio3 region are also involved in chromatin methylation and remodelling. The final loci of interest is the proximal region of murine chromosome 2 that contains Sfmbt2 and an overlapping antisense transcript that is unique within mice and rats. This suggests that Sfmbt2 may be in the process of becoming imprinted that is being driven by a cluster of intergenic miRNAs. Ultimately, imprinted clusters of ncRNA have the potential to offer novel insight into the understanding of the complex processes of cognition given their role in brain function.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".