Leaf shape is a predictor of fruit quality and cultivar performance in tomato.

Commercial tomato (Solanum lycopersicum) is one of the most widely grown vegetable crops worldwide. Heirloom tomatoes retain extensive genetic diversity and a considerable range of fruit quality and leaf morphological traits. Here the role of leaf morphology was investigated for its impact on fruit quality. Heirloom cultivars were grown in field conditions, and BRIX by yield (BY) and other traits were measured over a 14-wk period. The complex relationships among these morphological and physiological traits were evaluated using partial least-squares path modeling, and a consensus model was developed. Photosynthesis contributed strongly to vegetative biomass and sugar content of fruits but had a negative impact on yield. Conversely leaf shape, specifically rounder leaves, had a strong positive impact on both fruit sugar content and yield. Cultivars such as Stupice and Glacier, with very round leaves, had the highest performance in both fruit sugar and yield. Our model accurately predicted BY for two commercial cultivars using leaf shape data as input. This study revealed the importance of leaf shape to fruit quality in tomato, with rounder leaves having significantly improved fruit quality. This correlation was maintained across a range of diverse genetic backgrounds and shows the importance of leaf morphology in tomato crop improvement.

eQTL Regulating Transcript Levels Associated with Diverse Biological Processes in Tomato.

Variation in gene expression, in addition to sequence polymorphisms, is known to influence developmental, physiological, and metabolic traits in plants. Genetic mapping populations have facilitated identification of expression quantitative trait loci (eQTL), the genetic determinants of variation in gene expression patterns. We used an introgression population developed from the wild desert-adapted Solanum pennellii and domesticated tomato (Solanum lycopersicum) to identify the genetic basis of transcript level variation. We established the effect of each introgression on the transcriptome and identified approximately 7,200 eQTL regulating the steady-state transcript levels of 5,300 genes. Barnes-Hut t-distributed stochastic neighbor embedding clustering identified 42 modules revealing novel associations between transcript level patterns and biological processes. The results showed a complex genetic architecture of global transcript abundance pattern in tomato. Several genetic hot spots regulating a large number of transcript level patterns relating to diverse biological processes such as plant defense and photosynthesis were identified. Important eQTL regulating transcript level patterns were related to leaf number and complexity as well as hypocotyl length. Genes associated with leaf development showed an inverse correlation with photosynthetic gene expression, but eQTL regulating genes associated with leaf development and photosynthesis were dispersed across the genome. This comprehensive eQTL analysis details the influence of these loci on plant phenotypes and will be a valuable community resource for investigations on the genetic effects of eQTL on phenotypic traits in tomato.

Leaf form diversification in an ornamental heirloom tomato results from alterations in two different HOMEOBOX genes.

Domesticated plants display diverse phenotypic traits. However, the influence of breeding effort on this phenotypic diversity remains unknown. Here, we demonstrate that a single nucleotide deletion in the homeobox motif of BIPINNATA, a BEL-LIKE HOMEODOMAIN gene, led to a highly complex leaf phenotype in an heirloom tomato (Solanum lycopersicum), Silvery Fir Tree (SiFT), which is used as a landscaping and ornamental plant. A comparative gene network analysis revealed that repression of SOLANIFOLIA, the ortholog of WUSCHEL RELATED HOMEOBOX 1, caused the narrow leaflet phenotype seen in SiFT. Comparative genomics indicated that the bip mutation in SiFT likely arose de novo and is unique to SiFT and not introgressed from other tomato genomes. These results provide new insights into the natural variation in phenotypic traits introduced into crops during improvement processes after domestication and establish homeobox genes as evolutionary hotspots.

Using gene networks in EvoDevo analyses.

An intricate web of regulatory relationships between DNA, RNA, proteins and metabolites regulates how organisms achieve form and function. Genome sequencing combined with computational methods has allowed us to look at diverse readouts and generate a comprehensive framework for how molecules generate morphological phenotypes. RNAseq has evolved and proved useful for identifying links between transcription factor activity and transcript abundance, and for the generation of transcriptomes in non-model species through de novo assembly. Gene coexpression networks, combined with differential correlation analysis, offer the most versatile gene interaction exploratory tools, using gene expression patterns to determine potential associations and modularity of gene interactions and the differential associations between networks. Networks incorporating different types of biological data can help reduce the complexity of the entirety of biological information into discrete and interpretable pieces of data that can be the starting point for hypothesis construction and testing.