Current challenges and future potential of tomato breeding using omics approaches

Application of Exogenous Melatonin Improves Tomato Fruit Quality by Promoting the Accumulation of Primary and Secondary Metabolites

Melatonin plays key roles in improving fruit quality and yield by regulating various aspects of plant growth. However, the effects of how melatonin regulates primary and secondary metabolites during fruit growth and development are poorly understood. In this study, the surfaces of tomato fruit were sprayed with different concentrations of melatonin (0, 50, and 100 µmol·L^-1) on the 20th day after anthesis; we used high-performance liquid chromatography (HPLC) and liquid chromatography/mass spectrometry (LC/MS) to determine the changes in primary and secondary metabolite contents during fruit development and measured the activity of sucrose metabolizing enzymes during fruit development. Our results showed that 100 µmol·L^-1 melatonin significantly promoted the accumulation of soluble sugar in tomato fruit by increasing the activities of sucrose synthase (SS), sucrose phosphate synthase (SPS), and acid convertase (AI). The application of 100 µmol·L^-1 melatonin also increased the contents of ten amino acids in tomato fruit as well as decreased the contents of organic acids. In addition, 100 µmol·L^-1 melatonin application also increased the accumulation of some secondary metabolites, such as six phenolic acids, three flavonoids, and volatile substances (including alcohols, aldehydes, and ketones). In conclusion, melatonin application improves the internal nutritional and flavor quality of tomato fruit by regulating the accumulation of primary and secondary metabolites during tomato fruit ripening. In the future, we need to further understand the molecular mechanism of melatonin in tomato fruit to lay a solid foundation for quality improvement breeding.

miRNAs involved in transcriptome remodeling during pollen development and heat stress response in Solanum lycopersicum

Cellular transitions during development and stress response depend on coordinated transcriptomic and proteomic alterations. Pollen is particular because its development is a complex process that includes meiotic and mitotic divisions which causes a high heat sensitivity of these cells. Development and stress response are accompanied by a reprogramming of the transcriptome, e.g. by post-transcriptional regulation via miRNAs. We identified known and potentially novel miRNAs in the transcriptome of developing and heat-stressed pollen of Solanum lycopersicum (tomato). The prediction of target mRNAs yielded an equal number of predicted target-sites in CDS and 3'UTR regions of target mRNAs. The result enabled the postulation of a possible link between miRNAs and a fine-tuning of transcription factor abundance during pollen development. miRNAs seem to play a role in the pollen heat stress response as well. We identified several heat stress transcription factors and heat shock proteins as putative targets of miRNAs in response to heat stress, thereby placing these miRNAs as important elements of thermotolerance. Moreover, for members of the AP2, SBP and ARF family members we could predict a miRNA-mediated regulation during development via the miR172, mir156 and mir160-family strengthening the current concept of a cross-connection between development and stress response in plants.

The manipulation of gene expression and the biosynthesis of Vitamin C, E and folate in light-and dark-germination of sweet corn seeds

This study investigates the potential interrelationship between gene expression and biosynthesis of vitamin C, E and folate in sweet corn sprouts. Germination of sweet corn kernels was conducted in light and dark environments to determine if this relationship was regulated by photo-illumination. Results indicated that light and dark environments affected the DHAR, TMT and GTPCH expression and that these genes were the predominant genes of vitamin C, E and folate biosynthesis pathways respectively during the germination. Levels of vitamin C and folate increased during the germination of sweet corn seeds while vitamin E had a declining manner. Sweet corn sprouts had higher vitamin C and E levels as well as relevant gene expression levels in light environment while illumination had little influence on the folate contents and the gene expression levels during the germination. These results indicate that there might be a collaborative relationship between vitamin C and folate regulation during sweet corn seed germination, while an inhibitive regulation might exist between vitamin C and E.

SesameFG: an integrated database for the functional genomics of sesame

Sesame (Sesamum indicum L.) has high oil content, a small diploid genome and a short growth period, making it an attractive species for genetic studies on oilseed crops. With the advancement of next-generation sequencing technology, genomics and functional genomics research of sesame has developed quickly in the last few years, and large amounts of data have been generated. However, these results are distributed in many different publications, and there is a lack of integration. To promote functional genomics research of sesame, we collected genetic information combined with comprehensive phenotypic information and integrated them in the web-based database named SesameFG. The current version of SesameFG contains phenotypic information on agronomic traits of 705 sesame accessions, de novo assembled genomes of three sesame varieties, massive numbers of identified SNPs, gene expression profiles of five tissues, gene families, candidate genes for the important agronomic traits and genomic-SSR markers. All phenotypic and genotypic information in SesameFG is available for online queries and can be downloaded freely. SesameFG provides useful search functions and data mining tools, including Genome Browser and local BLAST services. SesameFG is freely accessible at http://ncgr.ac.cn/SesameFG/. SesameFG provides valuable resources and tools for functional genomics research and the molecular breeding of sesame.

Exploiting the Genetic Diversity of Maize Using a Combined Metabolomic, Enzyme Activity Profiling, and Metabolic Modeling Approach to Link Leaf Physiology to Kernel Yield

A combined metabolomic, biochemical, fluxomic, and metabolic modeling approach was developed using 19 genetically distant maize (Zea mays) lines from Europe and America. Considerable differences were detected between the lines when leaf metabolic profiles and activities of the main enzymes involved in primary metabolism were compared. During grain filling, the leaf metabolic composition appeared to be a reliable marker, allowing a classification matching the genetic diversity of the lines. During the same period, there was a significant correlation between the genetic distance of the lines and the activities of enzymes involved in carbon metabolism, notably glycolysis. Although large differences were observed in terms of leaf metabolic fluxes, these variations were not tightly linked to the genome structure of the lines. Both correlation studies and metabolic network analyses allowed the description of a maize ideotype with a high grain yield potential. Such an ideotype is characterized by low accumulation of soluble amino acids and carbohydrates in the leaves and high activity of enzymes involved in the C4 photosynthetic pathway and in the biosynthesis of amino acids derived from glutamate. Chlorogenates appear to be important markers that can be used to select for maize lines that produce larger kernels.

Integrated bioinformatics to decipher the ascorbic acid metabolic network in tomato

Ascorbic acid is involved in a plethora of reactions in both plant and animal metabolism. It plays an essential role neutralizing free radicals and acting as enzyme co-factor in several reaction. Since humans are ascorbate auxotrophs, enhancing the nutritional quality of a widely consumed vegetable like tomato is a desirable goal. Although the main reactions of the ascorbate biosynthesis, recycling and translocation pathways have been characterized, the assignment of tomato genes to each enzymatic step of the entire network has never been reported to date. By integrating bioinformatics approaches, omics resources and transcriptome collections today available for tomato, this study provides an overview on the architecture of the ascorbate pathway. In particular, 237 tomato loci were associated with the different enzymatic steps of the network, establishing the first comprehensive reference collection of candidate genes based on the recently released tomato gene annotation. The co-expression analyses performed by using RNA-Seq data supported the functional investigation of main expression patterns for the candidate genes and highlighted a coordinated spatial-temporal regulation of genes of the different pathways across tissues and developmental stages. Taken together these results provide evidence of a complex interplaying mechanism and highlight the pivotal role of functional related genes. The definition of genes contributing to alternative pathways and their expression profiles corroborates previous hypothesis on mechanisms of accumulation of ascorbate in the later stages of fruit ripening. Results and evidences here provided may facilitate the development of novel strategies for biofortification of tomato fruit with Vitamin C and offer an example framework for similar studies concerning other metabolic pathways and species.

Dissecting a QTL into Candidate Genes Highlighted the Key Role of Pectinesterases in Regulating the Ascorbic Acid Content in Tomato Fruit

Tomato (Solanum lycopersicum) is a crucial component of the human diet because of its high nutritional value and the antioxidant content of its fruit. As a member of the Solanaceae family, it is considered a model species for genomic studies in this family, especially since its genome has been completely sequenced. Among genomic resources available, Solanum pennellii introgression lines represent a valuable tool to mine the genetic diversity present in wild species. One introgression line, IL12-4, was previously selected for high ascorbic acid (AsA) content, and a transcriptomic analysis indicated the involvement of genes controlling pectin degradation in AsA accumulation. In this study the integration of data from different "omics" platforms has been exploited to identify candidate genes that increase AsA belonging to the wild region 12-4. Thirty-two genes potentially involved in pathways controlling AsA levels were analyzed with bioinformatic tools. Two hundred-fifty nonsynonymous polymorphisms were detected in their coding regions, and 11.6% revealed deleterious effects on predicted protein function. To reduce the number of genes that had to be functionally validated, introgression sublines of the region 12-4 were selected using species-specific polymorphic markers between the two Solanum species. Four sublines were obtained and we demonstrated that a subregion of around 1 Mbp includes 12 candidate genes potentially involved in AsA accumulation. Among these, only five exhibited structural deleterious variants, and one of the 12 was differentially expressed between the two Solanum species. We have highlighted the role of three polymorphic pectinesterases and inhibitors of pectinesterases that merit further investigation.

Nitrogen-use efficiency in maize (Zea mays L.): from 'omics' studies to metabolic modelling

In this review, we will present the latest developments in systems biology with particular emphasis on improving nitrogen-use efficiency (NUE) in crops such as maize and demonstrating the application of metabolic models. The review highlights the importance of improving NUE in crops and provides an overview of the transcriptome, proteome, and metabolome datasets available, focusing on a comprehensive understanding of nitrogen regulation. 'Omics' data are hard to interpret in the absence of metabolic flux information within genome-scale models. These models, when integrated with 'omics' data, can serve as a basis for generating predictions that focus and guide further experimental studies. By simulating different nitrogen (N) conditions at a pseudo-steady state, the reactions affecting NUE and additional gene regulations can be determined. Such models thus provide a framework for improving our understanding of the metabolic processes underlying the more efficient use of N-based fertilizers.

A network perspective on nitrogen metabolism from model to crop plants using integrated 'omics' approaches

Nitrogen (N), as an essential element in amino acids, nucleotides, and proteins, is a key factor in plant growth and development. Omics approaches such as metabolomics and transcriptomics have become a promising way to inspect complex network interactions in N metabolism and can be used for monitoring the uptake and regulation, translocation, and remobilization of N. In this review, the authors highlight recent progress in omics approaches, including transcript profiling using microarrays and deep sequencing, and show recent technical developments in metabolite profiling for N studies. Further, network analysis studies including network inference methods with correlations, information-theoretic measures, and a network concept to examine gene expression clusters in relation to N regulatory systems in plants are introduced, and integrating network inference methods and integrated networks using multiple omics data are discussed. Finally, this review summarizes recent omics application examples using metabolite and/or transcript profiling analysis to elucidate the regulation of N metabolism and signalling and the coordination of N and carbon metabolism in model plants (Arabidopsis and rice), crops (tomato, maize, and legumes), and trees (Populus).

Integrated network analysis and effective tools in plant systems biology

One of the ultimate goals in plant systems biology is to elucidate the genotype-phenotype relationship in plant cellular systems. Integrated network analysis that combines omics data with mathematical models has received particular attention. Here we focus on the latest cutting-edge computational advances that facilitate their combination. We highlight (1) network visualization tools, (2) pathway analyses, (3) genome-scale metabolic reconstruction, and (4) the integration of high-throughput experimental data and mathematical models. Multi-omics data that contain the genome, transcriptome, proteome, and metabolome and mathematical models are expected to integrate and expand our knowledge of complex plant metabolisms.