Klee, H. | bioRxiv Science

bioRxiv2024

Genetic architecture of the tomato fruit lipidome; new insights link lipid and volatile compounds

Tomato (Solanum lycopersicum L.) fruit flavor is determined by a combination of multiple volatile compounds, including several derived from lipids and fatty acids. Although fruit flavor has been intensively studied, the linkage between lipid metabolism and flavor remains largely undefined. Here, we performed a genome-wide association study (GWAS) and QTL mapping for the fruit lipid content from 550 tomato accessions and 107 backcross inbred lines (BILs) in two consecutive seasons. Over 130 lipid compounds were identified and mapped, allowing for the identification of over 600 metabolic QTL (mQTL). We further described and validated candidate genes associated with lipid content. Among them is a lipase-like protein (TomLLP) whose function was validated in vivo using overexpression lines in tomato and knockout mutants in Arabidopsis. We also identified functions for three enzymes: a class III lipase (Sl-LIP8), a cyclopropane-fatty-acyl-phospholipid synthase (CFAPS1), and Lipoxygenase C (TomLoxC). By utilizing knockout lines for CFAPS1 and CRISPR-Cas9 loss-of-function lines for Sl-LIP8 and TomLoxC, we demonstrated the functional importance of these enzymes in fruit lipid metabolism. Our study provides a comprehensive analysis of the tomato fruit lipidome and insights into key genes that shaped the natural variation in tomato lipid content and their links to flavor-associated volatile compounds.

molecular biology↗

bioRxiv2020

Metabolomic Selection for Enhanced Fruit Flavor

Although they are staple foods in cuisines globally, commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, many 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 varieties, previously limited by the low-throughput and high cost 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 are assessed. The best performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. The prediction accuracies were high for most attributes in both blueberries and tomatoes. 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.

plant biology↗