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.

Suppression of elongation and growth of tomato seedlings by auxin biosynthesis inhibitors and modeling of the growth and environmental response.

To develop a growth inhibitor, the effects of auxin inhibitors were investigated. Application of 30 µM L-α-aminooxy-ß-phenylpropionic acid (AOPP) or (S)-methyl 2-((1,3-dioxoisoindolin-2-yl)oxy)-3-phenylpropanoate (KOK1101), decreased the endogenous IAA levels in tomato seedlings at 8 days after sowing. Then, 10-1200 µM AOPP or KOK1101 were sprayed on the leaves and stem of 2-3 leaf stage tomato plants grown under a range of environmental conditions. We predicted plant growth and environmental response using a model based on the observed suppression of leaf enlargement. Spraying AOPP or KOK1101 decreased stem length and leaf area. Concentration-dependent inhibitions and dose response curves were observed. Although the effects of the inhibitors on dry weight varied according to the environmental conditions, the net assimilation rate was not influenced by the inhibitors. Accordingly, the observed decrease in dry weight caused by the inhibitors may result from decreased leaf area. Validation of the model based on observed data independent of the dataset showed good correlations between the observed and predicted values of dry weight and leaf area index.