Differences in xylem development between Dutch and Japanese tomato (Solanum lycopersicum) correlate with cytokinin levels in hypocotyls.

BACKGROUND AND AIMS: Dutch tomato cultivars tend to have a greater yield than Japanese cultivars even if they are grown under the same conditions. Factors contributing to the increased yield of the Dutch cultivars were a greater light use efficiency and greater leaf photosynthetic rate. On the other hand, the relationship between tomato yields and anatomical traits is still unclear. The aim of this study is to identify the anatomical traits related to the difference in yield between Dutch and Japanese cultivars. METHODS: Anatomical properties were compared during different growth stages of Dutch and Japanese tomatoes. Hormone profiles and related gene expression in hypocotyls of Dutch and Japanese cultivars were compared in the hypocotyls of 3- and 4-week-old plants. KEY RESULTS: Dutch cultivars have a more developed secondary xylem than Japanese cultivars, which would allow for greater transport of water, mineral nutrients and phytohormones to the shoots. The areas and ratios of the xylem in the hypocotyls of 3- to 6-week-old plants were larger in the Dutch cultivars. In reciprocal grafts of the Japanese and Dutch cultivars, xylem development at the scion and rootstock depended on the scion cultivar, suggesting that some factors in the scion are responsible for the difference in xylem development. The cytokinin content, especially the level of N6-(Δ 2-isopentenyl) adenine (iP)-type cytokinin, was higher in the Dutch cultivars. This result was supported by the greater expression of Sl-IPT3 (a cytokinin biosynthesis gene) and Sl-RR16/17 (a cytokinin-responsive gene) in the Dutch cultivars. CONCLUSIONS: These results suggest that iP-type cytokinins, which are locally synthesized in the hypocotyl, contribute to xylem development. The greater xylem development in Dutch cultivars might contribute to the high yield of the tomato.

Assessment of Greenhouse Tomato Anthesis Rate Through Metabolomics Using LASSO Regularized Linear Regression Model.

While the high year-round production of tomatoes has been facilitated by solar greenhouse cultivation, these yields readily fluctuate in response to changing environmental conditions. Mathematic modeling has been applied to forecast phenotypes of tomatoes using environmental measurements (e.g., temperature) as indirect parameters. In this study, metabolome data, as direct parameters reflecting plant internal status, were used to construct a predictive model of the anthesis rate of greenhouse tomatoes. Metabolome data were obtained from tomato leaves and used as variables for linear regression with the least absolute shrinkage and selection operator (LASSO) for prediction. The constructed model accurately predicted the anthesis rate, with an R2 value of 0.85. Twenty-nine of the 161 metabolites were selected as candidate markers. The selected metabolites were further validated for their association with anthesis rates using the different metabolome datasets. To assess the importance of the selected metabolites in cultivation, the relationships between the metabolites and cultivation conditions were analyzed via correspondence analysis. Trigonelline, whose content did not exhibit a diurnal rhythm, displayed major contributions to the cultivation, and is thus a potential metabolic marker for predicting the anthesis rate. This study demonstrates that machine learning can be applied to metabolome data to identify metabolites indicative of agricultural traits.