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  1. Abstract

    Methyl salicylate imparts a potent flavor and aroma described as medicinal and wintergreen that is undesirable in tomato (Solanum lycopersicum) fruit. Plants control the quantities of methyl salicylate through a variety of biosynthetic pathways, including the methylation of salicylic acid to form methyl salicylate and subsequent glycosylation to prevent methyl salicylate emission. Here, we identified a subclade of tomato methyl esterases, SALICYLIC ACID METHYL ESTERASE1-4, responsible for demethylation of methyl salicylate to form salicylic acid in fruits. This family was identified by proximity to a highly significant methyl salicylate genome-wide association study locus on chromosome 2. Genetic mapping studies in a biparental population confirmed a major methyl salicylate locus on chromosome 2. Fruits from SlMES1 knockout lines emitted significantly (P < 0,05, t test) higher amounts of methyl salicylate than wild-type fruits. Double and triple mutants of SlMES2, SlMES3, and SlMES4 emitted even more methyl salicylate than SlMES1 single knockouts—but not at statistically distinguishable levels—compared to the single mutant. Heterologously expressed SlMES1 and SlMES3 acted on methyl salicylate in vitro, with SlMES1 having a higher affinity for methyl salicylate than SlMES3. The SlMES locus has undergone major rearrangement, as demonstrated by genome structure analysis in the parents of the biparental population.more »Analysis of accessions that produce high or low levels of methyl salicylate showed that SlMES1 and SlMES3 genes expressed the highest in the low methyl salicylate lines. None of the MES genes were appreciably expressed in the high methyl salicylate-producing lines. We concluded that the SlMES gene family encodes tomato methyl esterases that convert methyl salicylate to salicylic acid in ripe tomato fruit. Their ability to decrease methyl salicylate levels by conversion to salicylic acid is an attractive breeding target to lower the level of a negative contributor to flavor.

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  2. 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 variancemore »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.« less
    Free, publicly-accessible full text available February 15, 2023