Identification and characterization of transcribed enhancers during cerebellar development through enhancer RNA analysis
Bibliographic record
Abstract
BACKGROUND: The development of the brain requires precise coordination of molecular processes across many cell-types. Underpinning these events are gene expression programs which require intricate regulation by non-coding regulatory sequences known as enhancers. In the context of the developing brain, transcribed enhancers (TEs) regulate temporally-specific expression of genes critical for cell identity and differentiation. Transcription of non-coding RNAs at active enhancer sequences, known as enhancer RNAs (eRNAs), is tightly associated with enhancer activity and has been correlated with target gene expression. TEs have been characterized in a multitude of developing tissues, however their regulatory role has yet to be described in the context of embryonic and early postnatal brain development. In this study, eRNA transcription was analyzed to identify TEs active during cerebellar development, as a proxy for the developing brain. Cap Analysis of Gene Expression followed by sequencing (CAGE-seq) was conducted at 12 stages throughout embryonic and early postnatal cerebellar development. RESULTS: Temporal analysis of eRNA transcription identified clusters of TEs that peak in activity during either embryonic or postnatal times, highlighting their importance for temporally specific developmental events. Functional analysis of putative target genes identified molecular mechanisms under TE regulation revealing that TEs regulate genes involved in biological processes specific to neurons. We validate enhancer activity using in situ hybridization of eRNA expression from TEs predicted to regulate Nfib, a gene critical for cerebellar granule cell differentiation. CONCLUSIONS: The results of this analysis provide a valuable dataset for the identification of cerebellar enhancers and provide insight into the molecular mechanisms critical for brain development under TE regulation. This dataset is shared with the community through an online resource ( https://goldowitzlab.shinyapps.io/trans-enh-app/ ).
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".