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Abstract Ripening is crucial for the development of fleshy fruits that release their seeds following consumption by frugivores and are important contributors to human health and nutritional security. Many genetic ripening regulators have been identified, especially in the model system tomato, yet more remain to be discovered and integrated into comprehensive regulatory models. Most tomato ripening genes have been studied in pericarp tissue, though recent evidence indicates that locule tissue is a site of early ripening-gene activities. Here we identified and functionally characterized an Ethylene Response Factor gene,SlERF.D6, by investigating tomato transcriptome data throughout plant development, emphasizing genes elevated in the locule during fruit development and ripening.SlERF.D6loss-of-function mutants resulting from CRISPR/Cas9 gene editing delayed ripening initiation and carotenoid accumulation in both pericarp and locule tissues. Transcriptome analysis of lines altered inSlERF.D6expression revealed multiple classes of altered genes including ripening regulators, in addition to carotenoid, cell wall and ethylene pathway genes, suggesting comprehensive ripening control. Distinct regulatory patterns in pericarp versus locule tissues were observed indicating tissue-specific activity of this transcription factor. Analysis of SlERF.D6 interaction with target promoters revealed an AP2/ERF transcription factor(SlDEAR2) as a target of SlERF.D6. Furthermore, we show that a third transcription factor gene,SlTCP12, is a target of SlDEAR2, presenting a tri-component module of ripening control. 

Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of genotypes, previously limited by the low throughput of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation, and the accuracies of 18 different models were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.