Human-specific lncRNAs contributed critically to human evolution by distinctly regulating gene expression
Bibliographic record
Abstract
Abstract What genomic sequences make conserved genes generate divergent expression in closely related species, which may have critically driven human evolution, has puzzled researchers for decades. Genomic studies have examined species-specific gene birth, gene loss, and changes in promoters and transcription factor binding sites, but species-specific epigenetic regulation remains barely explored. This study identified human-specific long noncoding RNAs (lncRNAs) from GENCODE-annotated human lncRNAs, predicted their DNA binding sites (DBSs) genome-wide, analyzed these DBSs and their counterparts in modern humans (CEU, CHB, and YRI), archaic humans (Altai Neanderthals, Denisovans, and Vindija Neanderthals), and chimpanzees, and analyzed the impact of DBSs on gene expression in modern and archaic humans. The results suggest that human-specific lncRNAs and their DBSs have substantially rewired gene expression human-specifically and that the rewiring has evolved continuously from archaic to modern humans. Rewired gene expression promotes brain development, makes humans adapt to new environments and lifestyles, and causes differences in modern humans. These results uncover a critical dimension of human evolution and underscore the diverse functions of species-specific lncRNAs.
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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 | 0.001 |
| 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 | 0.000 |
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".