Identification of tomato accessions as source of new genes for improving heat tolerance: from controlled experiments to field.

BACKGROUND: Due to global warming, the search for new sources for heat tolerance and the identification of genes involved in this process has become an important challenge as of today. The main objective of the current research was to verify whether the heat tolerance determined in controlled greenhouse experiments could be a good predictor of the agronomic performance in field cultivation under climatic high temperature stress. RESULTS: Tomato accessions were grown in greenhouse under three temperature regimes: control (T1), moderate (T2) and extreme heat stress (T3). Reproductive traits (flower and fruit number and fruit set) were used to define heat tolerance. In a first screening, heat tolerance was evaluated in 219 tomato accessions. A total of 51 accessions were identified as being potentially heat tolerant. Among those, 28 accessions, together with 10 accessions from Italy (7) and Bulgaria (3), selected for their heat tolerance in the field in parallel experiments, were re-evaluated at three temperature treatments. Sixteen tomato accessions showed a significant heat tolerance at T3, including five wild species, two traditional cultivars and four commercial varieties, one accession from Bulgaria and four from Italy. The 15 most promising accessions for heat tolerance were assayed in field trials in Italy and Bulgaria, confirming the good performance of most of them at high temperatures. Finally, a differential gene expression analysis in pre-anthesis (ovary) and post-anthesis (developing fruit) under heat stress among pairs of contrasting genotypes (tolerant and sensitive from traditional and modern groups) showed that the major differential responses were produced in post-anthesis fruit. The response of the sensitive genotypes included the induction of HSP genes, whereas the tolerant genotype response included the induction of genes involved in the regulation of hormones or enzymes such as abscisic acid and transferases. CONCLUSIONS: The high temperature tolerance of fifteen tomato accessions observed in controlled greenhouse experiments were confirmed in agronomic field experiments providing new sources of heat tolerance that could be incorporated into breeding programs. A DEG analysis showed the complex response of tomato to heat and deciphered the different mechanisms activated in sensitive and tolerant tomato accessions under heat stress.

Assessment of genetic variation in Bulgarian tomato (Solanum lycopersicum L.) genotypes, using fluorescent SSR genotyping platform.

Genetic variability in modern crops is limited due to domestication and selection processes. Genetic variation in eight Bulgarian tomato varieties and breeding lines (variety Plovdivska karotina, variety IZK Alya, L21ß, L53ß, L1140, L1116, L975, L984) differing in their morphological and biochemical composition was assessed using a highly efficient and low-cost fluorescent simple sequence repeat (SSR) genotyping platform. Genotyping was conducted with 165 publicly available microsatellite markers developed from different research groups under a number of projects in tomato (SOL Genomics SSRs, Kazusa TGS and TES, SLM, TMS and LEMDDNa) among which only five (3.03%) failed to amplify the expected PCR fragments. Of the remaining markers, 81 (50.62%) were polymorphic in the whole collection of eight genotypes. Among the marker groups used, SLM markers were most polymorphic, followed by TMS and SOL Genomics SSR markers. The total number of amplified alleles was 299, with a mean of 1.869; and the average polymorphic information content (PIC) was 0.196. The genetic diversity within the collection was relatively low (0.2222). Nei's genetic distance varied from 0.0953 to 0.3992. Cluster analysis using the un-weighted pair group method with arithmetic mean (UPGMA) method indicated that the studied tomato genotypes are grouped in four main clusters, which is to some extent consistent with the morpho- and hemo-types of the studied tomatoes. Variety IZK Alya (cherry type) and two of the breeding lines (L1140, L1116) formed three separate and more distant clusters. The fourth cluster includes the other five genotypes. The observed grouping of these genotypes in two sub-clusters reflects their similar morphological and biochemical composition. The genetic distance information from this study might be useful for further implementation of breeding strategies and crosses among these inbred lines.

Establishment and development of the Center of Plant Systems Biology and Biotechnology in Plovdiv, Bulgaria.

The Bulgarian research landscape, presented mainly by the research institutes that are part of the Bulgarian Academy of Sciences and the Agricultural Academy, needs diversification to match the research and innovation potential of the other European Union (EU) countries. This article describes the establishment of the Center of Plant Systems Biology and Biotechnology (CPSBB), a new innovative type of independent research organization that is changing the research landscape in Bulgaria. Supported by the EU Commission, Bulgarian Government, and Plovdiv Municipality, CPSBB has quickly become the leading plant science institute in Bulgaria, creating knowledge in diverse fields such as bioinformatics, biotechnology, genetics and genomics, metabolomics, and systems biology. We outline the organizational structure of CPSBB, the development of its infrastructure, and its scientific productivity. Finally, we compare CPSBB with other similar research establishments in Europe and we conclude that such new types of institutes have a bright future in Bulgaria due to their operational flexibility, productivity, and connections with academia and industry.

Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits.

Selection of high-performance lines with respect to traits of interest is a key step in plant breeding. Genomic prediction allows to determine the genomic estimated breeding values of unseen lines for trait of interest using genetic markers, e.g. single-nucleotide polymorphisms (SNPs), and machine learning approaches, which can therefore shorten breeding cycles, referring to genomic selection (GS). Here, we applied GS approaches in two populations of Solanaceous crops, i.e. tomato and pepper, to predict morphometric and colorimetric traits. The traits were measured by using scoring-based conventional descriptors (CDs) as well as by Tomato Analyzer (TA) tool using the longitudinally and latitudinally cut fruit images. The GS performance was assessed in cross-validations of classification-based and regression-based machine learning models for CD and TA traits, respectively. The results showed the usage of TA traits and tag SNPs provide a powerful combination to predict morphology and color-related traits of Solanaceous fruits. The highest predictability of 0.89 was achieved for fruit width in pepper, with an average predictability of 0.69 over all traits. The multi-trait GS models are of slightly better predictability than single-trait models for some colorimetric traits in pepper. While model validation performs poorly on wild tomato accessions, the usage as many as one accession per wild species in the training set can increase the transferability of models to unseen populations for some traits (e.g. fruit shape for which predictability in unseen scenario increased from zero to 0.6). Overall, GS approaches can assist the selection of high-performance Solanaceous fruits in crop breeding.

Tomato Phenotypic Diversity Determined by Combined Approaches of Conventional and High-Throughput Tomato Analyzer Phenotyping.

Morphological variation in vegetative and fruit traits is a key determinant in unraveling phenotypic diversity. This study was designed to assess phenotypic diversity in tomatoes and examine intra- and intervarietal groups' variability using 28 conventional descriptors (CDs) and 47 Tomato Analyzer (TA) descriptors related to plant and fruit morphometry. Comprehensive phenotyping of 150 accessions representing 21 countries discerned noticeable variability for CD vegetative traits and TA quantified fruit features, such as shape, size, and color. Hierarchical cluster analysis divided the accessions into 10 distinct classes based on fruit shape and size. Multivariate analysis was used to assess divergence in variable traits among populations. Eight principal components with an eigenvalue >1 were identified by factor analysis, which contributed 87.5% variation to the total cumulative variance with the first two components contributing 32.0% and 18.1% variance, respectively. The relationship between vegetative and fruit descriptors was explained by respective CD and TA correlation networks. There was a strong positive correlation between fruit shape and size whereas negative correlations were between fruit shape index, internal eccentricity, and proximal end shape. The combined approach of CD and TA phenotyping allowed us to unravel the phenotypic diversity of vegetative and reproductive trait variation evaluated at pre- and post-harvest stages.