Transcriptional boosting and buffering during neurodevelopment by shifts in mRNA instability
Bibliographic record
Abstract
The contribution of mRNA half-life is commonly overlooked when examining changes in mRNA abundance during development. mRNA levels of some genes are regulated by transcription rate only, but others may be regulated by mRNA half-life only shifts. Furthermore, transcriptional buffering is predicted when changes in transcription rates have compensating shifts in mRNA half-life resulting in no change to steady-state levels. Likewise, transcriptional boosting should result when changes in transcription rate are accompanied by amplifying half-life shifts. During neurodevelopment there is widespread 3'UTR lengthening that could be shaped by differential shifts in the stability of existing short or long 3'UTR transcript isoforms. We measured transcription rate and mRNA half-life changes during induced human Pluripotent Stem Cell (iPSC)-derived neuronal development using RATE-seq. During transitions to progenitor and neuron stages, transcriptional buffering occurred in up to 50%, and transcriptional boosting in up to 15%, of genes with changed transcription rates. The remaining changes occurred by transcription rate only or mRNA half-life only shifts. Average mRNA half-life decreased two-fold in neurons relative to iPSCs. Short gene isoforms were more destabilized in neurons and thereby increased the average 3'UTR length. Small RNA sequencing captured an increase in microRNA copy number per cell during neurodevelopment. We propose that mRNA destabilization and 3'UTR lengthening are driven in part by an increase in microRNA load in neurons. Our findings identify mRNA stability mechanisms in human neurodevelopment that regulate gene and isoform level abundance and provide a precedent for similar post-transcriptional regulatory events as other tissues develop.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".