Spatiotemporal transcriptomics and metabolic profiling provide insights into gene regulatory networks during lentil seed development
Bibliographic record
Abstract
Lentil (Lens culinaris Medik.) is a nutritious legume with seeds rich in protein, minerals and an array of diverse specialized metabolites. The formation of a seed requires regulation and tight coordination of developmental programs to form the embryo, endosperm and seed coat compartments, which determines the structure and composition of mature seed and thus its end-use quality. Understanding the molecular and cellular events and metabolic processes of seed development is essential for improving lentil yield and seed nutritional value. However, such information remains largely unknown, especially at the seed compartment level. In this study, we generated high-resolution spatiotemporal gene expression profiles in lentil embryo, seed coat and whole seeds from fertilization through maturation. Apart from anatomic differences between the embryo and seed coat, comparative transcriptomics and weighted gene co-expression network analysis revealed embryo- and seed coat-specific genes and gene modules predominant in specific tissues and stages, which highlights distinct genetic programming. Furthermore, we investigated the dynamic profiles of flavonoid, isoflavone, phytic acid and saponin in seed compartments across seed development. Coupled with transcriptome data, we identified sets of candidate genes involved in the biosynthesis of these metabolites. The global view of the transcriptional and metabolic changes of lentil seed tissues throughout development provides a valuable resource for dissecting the genetic control of secondary metabolism and development of molecular tools for improving seed nutritional quality.
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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".