The long non-coding RNA Snhg3 is essential for mouse embryonic stem cell self-renewal and pluripotency
Bibliographic record
Abstract
BACKGROUND: Small nucleolar RNA host gene 3 (Snhg3) is a long non-coding RNA (lncRNA) that was shown to participate in the tumorigenesis of certain cancers. However, little is known about its role in embryonic stem cells (ESCs). METHODS: Here, we investigated the role of Snhg3 in mouse ESCs (mESCs) through both loss-of-function (knockdown) and gain-of-function (overexpression) approaches. Alkaline phosphatase staining, secondary colony formation, propidium iodide staining, western blotting, and quantitative reverse transcription polymerase chain reaction (qRT-PCR) were used to access self-renewal capacity, whereas immunofluorescence, qRT-PCR, and embryoid body formation were performed to examine pluripotency. In addition, the effect of Snhg3 on mouse embryonic development was determined based on the morphological changes, blastocyst rate, and altered pluripotency marker (Nanog, Oct4) expression. Moreover, the relationship between Snhg3 and key pluripotency factors was evaluated by chromatin immunoprecipitation qPCR, qRT-PCR, subcellular fractionation, and RNA immunoprecipitation. Finally, RNA pull-down and mass spectrometry were applied to explore the potential interacting proteins of Snhg3 in mESCs. RESULTS: We demonstrated that Snhg3 is essential for self-renewal and pluripotency maintenance in mESCs. In addition, Snhg3 knockdown disrupted mouse early embryo development. Mechanistically, Snhg3 formed a positive feedback network with Nanog and Oct4, and 126 Snhg3-interacting proteins were identified in mESCs. CONCLUSIONS: Snhg3 is essential for mESC self-renewal and pluripotency, as well as mouse early embryo development.
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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".