Cryo-Treatment Enhances the Embryogenicity of Mature Somatic Embryos via the lncRNA–miRNA–mRNA Network in White Spruce
Bibliographic record
Abstract
In conifers, somatic embryogenesis is uniquely initiated from immature embryos in a narrow time window, which is considerably hindered by the difficulty to induce embryogenic tissue (ET) from other tissues, including mature somatic embryos. In this study, the embryogenic ability of newly induced ET and DNA methylation levels was detected, and whole-transcriptome sequencing analyses were carried out. The results showed that ultra-low temperature treatment significantly enhanced ET induction from mature somatic embryos, with the induction rate from 0.4% to 15.5%, but the underlying mechanisms remain unclear. The newly induced ET showed higher capability in generating mature embryos than the original ET. DNA methylation levels fluctuated during the ET induction process. Here, WGCNA analysis revealed that OPT4, TIP1-1, Chi I, GASA5, GST, LAX3, WRKY7, MYBS3, LRR-RLK, PBL7, and WIN1 genes are involved in stress response and auxin signal transduction. Through co-expression analysis, lncRNAs MSTRG.505746.1, MSTRG.1070680.1, and MSTRG.33602.1 might bind to pre-novel_miR_339 to promote the expression of WRKY7 genes for stress response; LAX3 could be protected by lncRNAs MSTRG.1070680.1 and MSTRG.33602.1 via serving as sponges for novel_miR_495 to initiate auxin signal transduction; lncRNAs MSTRG.505746.1, MSTRG.1070680.1, and MSTRG.33602.1 might serve as sponges for novel_miR_527 to enhance the expression of Chi I for early somatic embryo development. This study provides new insight into the area of stress-enhanced early somatic embryogenesis in conifers, which is also attributable to practical applications.
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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 | 0.000 |
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".