Genome‐Wide Mining of lncRNAs Reveals Their Potential Regulatory Role in the Evolution of Viviparity
Bibliographic record
Abstract
Reproduction in vertebrates usually involves egg-laying (oviparity) or live-bearing (viviparity). Oviparity is the ancestral trait from which viviparity has independently evolved more than 100 times in squamate reptiles. This transition involves a series of physiological and structural changes, including the degeneration of eggshell and the evolution of a placenta and differences in the temporal and spatial expression patterns of some functional genes that drive the structural transformation. Long non-coding RNAs (lncRNAs) play important roles in the regulation of gene expression, yet it remains unclear whether they participate in gene expression shifts during the transition from oviparity to viviparity, and if so how. Therefore, we employ deep mining to identify novel lncRNAs of a closely related oviparous-viviparous pair of lizards (Phrynocephalus przewalskii and P. vlangalii). We construct cis- and trans-regulatory networks between lncRNAs and target genes using the transcriptomic data of oviduct or uteri tissues across reproductive periods. Results show that lncRNAs that regulate eggshell gland developmental genes in the oviparous lizard are lost or less expressed in the viviparous lizard. A number of lncRNAs involved in the regulation of placental development and embryo attachment in viviparous species have no orthologs in oviparous species, and others show little or no expression. Accordingly, lncRNAs may play important regulatory roles in the physiological and structural changes in the transition from oviparity to viviparity. These results open doors to the further elucidation of genetic regulatory networks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".