Multi-Omics and Integrated Network Analyses Reveal New Insights into the Systems Relationships between Metabolites, Structural Genes, and Transcriptional Regulators in Developing Grape Berries (Vitis vinifera L.) Exposed to Water Deficit
Bibliographic record
Abstract
Grapes are one of the major fruit crops and they are cultivated in many dry environments. This study comprehensively characterizes the metabolic response of grape berries exposed to water deficit at different developmental stages. Increases of proline, branched-chain amino acids, phenylpropanoids, anthocyanins, and free volatile organic compounds have been previously observed in grape berries exposed to water deficit. Integrating RNA-sequencing analysis of the transcriptome with large-scale analysis of central and specialized metabolites, we reveal that these increases occur via a coordinated regulation of key structural pathway genes. Water deficit-induced up-regulation of flavonoid genes is also coordinated with the down-regulation of many stilbene synthases and a consistent decrease in stilbenoid concentration. Water deficit activated both ABA-dependent and ABA-independent signal transduction pathways by modulating the expression of several transcription factors. Gene-gene and gene-metabolite network analyses showed that water deficit-responsive transcription factors such as bZIPs, AP2/ERFs, MYBs, and NACs are implicated in the regulation of stress-responsive metabolites. Enrichment of known and novel cis-regulatory elements in the promoters of several ripening-specific/water deficit-induced modules further affirms the involvement of a transcription factor cross-talk in the berry response to water deficit. Together, our integrated approaches show that water deficit-regulated gene modules are strongly linked to key fruit-quality metabolites and multiple signal transduction pathways may be critical to achieve a balance between the regulation of the stress-response and the berry ripening program. This study constitutes an invaluable resource for future discoveries and comparative studies, in grapes and other fruits, centered on reproductive tissue metabolism under abiotic stress.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".