Genetic screening of long non-coding RNAs in human embryonic stem cells reveals novel regulators of pluripotency
Bibliographic record
Abstract
The human genome encodes thousands of long non-coding RNAs (lncRNAs), transcripts of over 200 nucleotides that lack protein-coding potential. lncRNAs are emerging as key players in diverse cellular processes, particularly in tissue-specific contexts, yet their functionality remained poorly understood. Here, we performed a CRISPR interference (CRISPRi) screen in human embryonic stem cells (hESCs), identifying over 100 essential and about 150 growth-restricting lncRNAs. We show that growth-modifying lncRNAs display distinctive properties, including unique expression signatures, genomic structure, evolutionary conservation, chromosomal distribution, and potential involvement in teratoma formation. Notably, we uncovered two primate-conserved, uncharacterized, essential lncRNAs that regulate neighboring pluripotency transcription factors: lncOCT4, which positively regulates OCT4 and induces p53-mediated apoptosis upon knockdown, and lncVRTN, which acts as a putative negative regulator of VRTN, affecting cell fate determination. These findings shed light on the contribution of lncRNAs to the human-specific pluripotency network and provide insights into lncRNA-mediated regulation of hESC growth and differentiation.
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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 | 0.000 | 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) | 0.001 | 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".