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Newly Discovered Alleles of the Tomato Antiflorigen Gene SELF PRUNING Provide a Range of Plant Compactness and Yield.

ABSTRACT: In tomato cultivation, a rare natural mutation in the flowering repressor antiflorigen gene SELF-PRUNING (sp-classic) induces precocious shoot termination and is the foundation in determinate tomato breeding for open field production. Heterozygous single flower truss (sft) mutants in the florigen SFT gene in the background of sp-classic provide a heterosis-like effect by delaying shoot termination, suggesting the subtle suppression of determinacy by genetic modification of the florigen-antiflorigen balance could improve yield. Here, we isolated three new sp alleles from the tomato germplasm that show modified determinate growth compared to sp-classic, including one allele that mimics the effect of sft heterozygosity. Two deletion alleles eliminated functional transcripts and showed similar shoot termination, determinate growth, and yields as sp-classic. In contrast, amino acid substitution allele sp-5732 showed semi-determinate growth with more leaves and sympodial shoots on all shoots. This translated to greater yield compared to the other stronger alleles by up to 42%. Transcriptome profiling of axillary (sympodial) shoot meristems (SYM) from sp-classic and wild type plants revealed six mis-regulated genes related to the floral transition, which were used as biomarkers to show that the maturation of SYMs in the weaker sp-5732 genotype is delayed compared to sp-classic, consistent with delayed shoot termination and semi-determinate growth. Assessing sp allele frequencies from over 500 accessions indicated that one of the strong sp alleles (sp-2798) arose in early breeding cultivars but was not selected. The newly discovered sp alleles are potentially valuable resources to quantitatively manipulate shoot growth and yield in determinate breeding programs, with sp-5732 providing an opportunity to develop semi-determinate field varieties with higher yields.

SUBMITTER: Kang MS

PROVIDER: S-EPMC9266710 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe objective of this study is twofold. First, ascertain the important variables that predict tomato yields from plant height (PH) and vegetation index (VI) maps. The maps were derived from images taken by unmanned aerial vehicles (UAVs). Second, examine the accuracy of predictions of tomato fresh shoot masses (SM), fruit weights (FW), and the number of fruits (FN) from multiple machine learning algorithms using selected variable sets. To realize our objective, ultra-high-resolution RGB and multispectral images were collected by a UAV on ten days in 2020's tomato growing season. From these images, 756 total variables, including first- (e.g., average, standard deviation, skewness, range, and maximum) and second-order (e.g., gray-level co-occurrence matrix features and growth rates of PH and VIs) statistics for each plant, were extracted. Several selection algorithms (i.e., Boruta, DALEX, genetic algorithm, least absolute shrinkage and selection operator, and recursive feature elimination) were used to select the variable sets useful for predicting SM, FW, and FN. Random forests, ridge regressions, and support vector machines were used to predict the yield using the top five selected variable sets.ResultsFirst-order statistics of PH and VIs collected during the early to mid-fruit formation periods, about one month prior to harvest, were important variables for predicting SM. Similar to the case for SM, variables collected approximately one month prior to harvest were important for predicting FW and FN. Furthermore, variables related to PH were unimportant for prediction. Compared with predictions obtained using only first-order statistics, those obtained using the second-order statistics of VIs were more accurate for FW and FN. The prediction accuracy of SM, FW, and FN by models constructed from all variables (rRMSE = 8.8-28.1%) was better than that from first-order statistics (rRMSE = 10.0-50.1%).ConclusionsIn addition to basic statistics (e.g., average and standard deviation), we derived second-order statistics of PH and VIs at the plant level using the ultra-high resolution UAV images. Our findings indicated that our variable selection method reduced the number variables needed for tomato yield prediction, improving the efficiency of phenotypic data collection and assisting with the selection of high-yield lines within breeding programs.

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Newly Discovered Alleles of the Tomato Antiflorigen Gene SELF PRUNING Provide a Range of Plant Compactness and Yield.

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