Conserved expression of transposon-derived non-coding transcripts in primate stem cells
Bibliographic record
Abstract
BACKGROUND: A significant portion of expressed non-coding RNAs in human cells is derived from transposable elements (TEs). Moreover, it has been shown that various long non-coding RNAs (lncRNAs), which come from the human endogenous retrovirus subfamily H (HERVH), are not only expressed but required for pluripotency in human embryonic stem cells (hESCs). RESULTS: To identify additional TE-derived functional non-coding transcripts, we generated RNA-seq data from induced pluripotent stem cells (iPSCs) of four primate species (human, chimpanzee, gorilla, and rhesus) and searched for transcripts whose expression was conserved. We observed that about 30% of TE instances expressed in human iPSCs had orthologous TE instances that were also expressed in chimpanzee and gorilla. Notably, our analysis revealed a number of repeat families with highly conserved expression profiles including HERVH but also MER53, which is known to be the source of a placental-specific family of microRNAs (miRNAs). We also identified a number of repeat families from all classes of TEs, including MLT1-type and Tigger families, that contributed a significant amount of sequence to primate lncRNAs whose expression was conserved. CONCLUSIONS: Together, these results describe TE families and TE-derived lncRNAs whose conserved expression patterns can be used to identify what are likely functional TE-derived non-coding transcripts in primate iPSCs.
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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 | 0.000 | 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) | 0.000 | 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".