Global Analysis of Small RNA Dynamics during Seed Development of Picea glauca and Arabidopsis thaliana Populations Reveals Insights on their Evolutionary Trajectories
Bibliographic record
Abstract
While DNA methylation carries genetic signals and is instrumental in the evolution of organismal complexity, small RNAs (sRNAs), ~18-24 ribonucleotide (nt) sequences, are crucial mediators of methylation as well as gene silencing. However, scant study deals with sRNA evolution via featuring their expression dynamics coupled with species of different evolutionary time. Here we report an atlas of sRNAs and microRNAs (miRNAs, single-stranded sRNAs) produced over time at seed-set of two major spermatophytes represented by populations of Picea glauca and Arabidopsis thaliana with different seed-set duration. We applied diverse profiling methods to examine sRNA and miRNA features, including size distribution, sequence conservation and reproduction-specific regulation, as well as to predict their putative targets. The top 27 most abundant miRNAs were highly overlapped between the two species (e.g., miR166, -319 and -396), but in P. glauca, they were less abundant and significantly less correlated with seed-set phases. The most abundant sRNAs in libraries were deeply conserved miRNAs in the plant kingdom for Arabidopsis but long sRNAs (24-nt) for P. glauca. We also found significant difference in normalized expression between populations for population-specific sRNAs but not for lineage-specific ones. Moreover, lineage-specific sRNAs were enriched in the 21-nt size class. This pattern is consistent in both species and alludes to a specific type of sRNAs (e.g., miRNA) being selected for. In addition, we deemed 24 and 9 sRNAs in P. glauca and Arabidopsis, respectively, as sRNA candidates targeting known adaptive genes. Temperature had significant influence on selected gene and miRNA expression at seed development in both species. This study increases our integrated understanding of sRNA evolution and its potential link to genomic architecture (e.g., sRNA derivation from genome and sRNA-mediated genomic events) and organismal complexity (e.g., association between different sRNA expression and their functionality).
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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 | 0.000 |
| 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 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".