Genetic and gene expression analysis of flowering time regulation by light quality in lentil
Bibliographic record
Abstract
BACKGROUND AND AIMS: Flowering time is important due to its roles in plant adaptation to different environments and subsequent formation of crop yield. Changes in light quality affect a range of developmental processes including flowering time, but little is known about light quality-induced flowering time control in lentil. This study aims to investigate the genetic basis for differences in flowering response to light quality in lentil. METHODS: We explored variation in flowering time caused by changes in red/far-red-related light quality environments of a lentil interspecific recombinant inbred line (RIL) population developed from a cross between Lens culinaris cv. Lupa and L. orientalis accession BGE 016880. A genetic linkage map was constructed and then used for identifying quantitative trait loci (QTLs) associated with flowering time regulation under different light quality environments. Differential gene expression analysis through transcriptomic study and RT-qPCR were used to identify potential candidate genes. KEY RESULTS: QTL mapping located 13 QTLs controlling flower time under different light quality environments, with phenotypic variance explained ranging from 1.7 to 62.9 %. Transcriptomic profiling and gene expression analysis for both parents of this interspecific RIL population identified flowering-related genes showing environment-specific differential expression (flowering DEGs). One of these, a member of the florigen gene family FTa1 (LcFTa1), was located close to three major QTLs. Furthermore, gene expression results suggested that two other florigen genes (LcFTb1 and LcFTb2), MADS-box transcription factors such as LcAGL6/13d, LcSVPb, LcSOC1b and LcFULb, as well as bHLH transcription factor LcPIF6 and Gibberellin 20 oxidase LcGA20oxC,G may also be involved in the light quality response. CONCLUSIONS: Our results show that a major component of flowering time sensitivity to light quality is tightly linked to LcFTa1 and associated with changes in its expression. This work provides a foundation for crop improvement of lentil with better adaptation to variable light environments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".