Chloroplast activity provides <i>in vitro</i> regeneration capability in contrasting cultivars
Bibliographic record
Abstract
Abstract Existence of potent in vitro regeneration system is a prerequisite for efficient genetic transformation and functional genomics of crop plants. We know little about why only some cultivars in crop plants are tissue culture friendly. In this study, tissue culture friendly cultivar Golden Promise (GP) and tissue culture resistant DWRB91(D91) were selected as contrasting cultivars to investigate the molecular basis of regeneration efficiency. Multiomics studies involving transcriptomics, proteomics, metabolomics, and biochemical analysis were performed using GP and D91 callus to unravel the regulatory mechanisms. Transcriptomics analysis revealed 1487 differentially expressed genes (DEGs), in which 795 DEGs were upregulated and 692 DEGs were downregulated in the GP-D91 transcriptome. Genes encoding proteins localized in chloroplast and involved in ROS generation were upregulated in the embryogenic calli of GP. Moreover, proteome analysis by LC-MSMS revealed 3062 protein groups and 16989 peptide groups, out of these 1586 protein groups were differentially expressed proteins (DEPs). Eventually, GC-MS based metabolomics analysis also revealed the higher activity of plastids and alterations in key metabolic processes such as sugar metabolism, fatty acid biosynthesis, and secondary metabolism. Higher accumulation of sugars, amino acids and metabolites corresponding to lignin biosynthesis were observed in GP as compared to D91. Highlights: Multi omics analysis revealed chloroplast play crucial role in providing in vitro regeneration capability in contrasting genotypes
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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 | 0.001 |
| 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".