Integrated Lipidomic and Transcriptomic Analysis Reveals Phospholipid Changes in Somatic Embryos of Picea asperata in Response to Partial Desiccation
Bibliographic record
Abstract
Partial desiccation treatment (PDT) is an effective technology for promoting the germination and conversion of conifer somatic embryos (SEs). PDT, as a drought stress, induces intensive physiological responses in phospholipid metabolism, which are not well understood in the conifer SEs. Here, we integrated lipidomics, transcriptomics and proteomics analyses to reveal the molecular basis of lipid remodeling under PDT in Picea asperata SEs. Among the 82 lipid molecular species determined by mass spectrometry, phosphatidic acid (PA) had a significant effect after PDT and was the most critical lipid in the response to PDT. The transcriptomics results showed that multiple transcripts in the glycerolipid and glycerophospholipid metabolism pathways were differentially expressed, and these included five PLDα1 transcripts that catalyze the conversion of phosphatidylcholine (PC) to PA. Furthermore, the enzyme activity of this phospholipase D (PLD) was significantly enhanced in response to PDT, and PDT also significantly increased the protein level of PLDα1 (MA_10436582g0020). In addition, PA is a key factor in gibberellin, abscisic acid and ethylene signal transduction. One GDI1, one DELLA, three ABI1s, two SnRK2s, one CTR and 12 ERFs showed significantly differential expression between SEs before and after PDT in this study. Our data suggest that the observed increases in the PA contents might result from the activation of PLDα by PDT. PA not only affects the physical and chemical properties of the cell membrane but also participates in plant hormone signal transduction. Our work provides novel insight into the molecular mechanism through which PDT promotes the germination of SEs of coniferous tree species and fills the gap in the understanding of the mechanism of somatic embryo lipid remodeling in response to PDT.
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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) | 0.000 | 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".