Multi-Omics Analysis Reveals That SlERF.D6 Synergistically Regulates SGAs and Fruit Development
Bibliographic record
Abstract
Steroidal glycoalkaloids (SGAs) are cholesterol-derived molecules that contribute to the pathogen defense in tomato but are toxic and considered to be antinutritional compounds to humans. APETALA2/Ethylene Responsive Factor (AP2/ERF) family transcription factors (TFs) play an indispensable role in various biological processes, such as plant growth and development, fruit ripening, biotic and abiotic stresses responses, and SGA biosynthesis. In this study, we identified 176 AP2/ERF genes that were domesticated or improved SlAP2/ERF in the tomato variome (Solanum lycopersicum) within either domestication or improvement sweeps, respectively. According to the RNA-sequencing data, 93 of the ERF genes with high transcriptional level (Transcripts Per Million, TPM > 1) belong to six clusters. Weighted gene co-expression network analysis (WGCNA) and metabolite-based genome-wide association study (mGWAS) analyses revealed that the expression level of the Solyc04g071770 (SlERF.D6) gene in the cluster six gradually increased as the fruit matured. Transient transformation verified that the overexpression of SlERF.D6 significantly promoted fruit ripening and regulated the expression of multiple genes in the SGA synthesis pathway, thereby affecting the SGA content of the fruit. Virus-induced gene silencing (VIGS) showed that the silencing of SlERF.D6 delayed fruit ripening and influenced the content of SGAs. Our data provide new insights into AP2/ERF TFs in tomato, offer a candidate TF for fruit development and steroidal glycoalkaloids, and provide new resources for tomato breeding and improvement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".