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25 mei 2022, Contains fulltext : 250401.pdf (Publisher’s version ) (Open Access)

In all fruit and vegetable crops, reduction in quality during postharvest storage leads to substantial losses of primary production with enormous economic consequences. Also in tomato, fruit shelf-life is an important quality trait. In this study a collection of tomato accessions, consisting of 92 S. lycopersicum landraces and old cultivars and several S. pimpinellifolium accessions, was phenotyped for several shelf-life parameters and biochemical characterization was performed during the postharvest shelf-life of fruit from selected accessions. This collection was selected based on available genotypic data and represents the genetic diversity present in the EU–SOL tomato core collection (Roohanitaziani, 2020). The core collection was grown in a greenhouse, and fruit were harvested at the breaker-turning stage and stored in a controlled climate chamber for 42 d at 18 ⁰C. The shelf-life attributes firmness loss, weight loss, as well as color pigments, were measured once a week and evaluated over time. All three shelf-life-related parameters varied markedly among accessions, resulting in fruit with different shelf-life. The most promising accessions of the first screen were re-grown and analyzed to validate the initial results and six accessions with contrasting shelf-life were selected for metabolite analysis. Fruit were harvested at the breaker stage and stored for 35 d at 18 ⁰C. Samples were taken at weekly intervals and analyzed for volatile compounds, primary metabolites and cell wall polysaccharide monomers. During storage long and short shelf-life accessions showed considerable differences in their content of sugars, such as galactose and polyamines, such as putrescine in their pericarp. The content of three cell wall sugars, galactose, arabinose and galacturonic acid, underwent considerable changes during postharvest storage. The short shelf-life accessions contained a higher amount of arabinose and galactose in their cell wall than other accessions which is i

Contains fulltext : 226781.pdf (publisher's version ) (Open Access)

A tomato core collection consisting of 122 gene bank accessions, including landraces, old cultivars, and wild relatives, was explored for variation in several plant growth, yield and fruit quality traits. The resequenced accessions were also genotyped with respect to a number of mutations or variations in key genes known to underlie these traits. The yield-related traits fruit number and fruit weight were much higher in cultivated varieties when compared to wild accessions, while, in wild tomato accessions, Brix was higher than in cultivated varieties. Known mutations in fruit size and shape genes could well explain the fruit size variation, and fruit colour variation could be well explained by known mutations in key genes of the carotenoid and flavonoid pathway. The presence and phenotype of several plant architecture affecting mutations, such as self-pruning (sp), compound inflorescence (s), jointless-2 (j-2), and potato leaf (c) were also confirmed. This study provides valuable phenotypic information on important plant growth- and quality-related traits in this collection. The allelic distribution of known genes that underlie these traits provides insight into the role and importance of these genes in tomato domestication and breeding. This resource can be used to support (precision) breeding strategies for tomato crop improvement.

Contains fulltext : 175484.pdf (publisher's version ) (Open Access)

Contains fulltext : 175479.pdf (publisher's version ) (Open Access)

Tomato production is threatened by climate change. High temperatures lead to a decrease of fruit set which correlates with a decrease of pollen fertility. The low viability of tomato pollen under heat stress was previously shown to be associated with alterations in specific metabolites. In this thesis, we used untargeted metabolomics approaches to broaden the identification of metabolites affected by heat stress. We assessed the suitability of pollen isolation methods for metabolomics analysis and considered the pitfalls for our further analysis. We explored the developmental metabolomes of pollen and anthers of different tomato genotypes under control and high temperature conditions and identified that microsporogenesis is a critical developmental stage for the production of mature and fertile pollen grain under heat stress. Several metabolites were putatively associated with tolerance to high temperature such as specific flavonoids, polyamines and alkaloids. These metabolites can be further used as markers in breeding programs to develop new genotypes tolerant to high temperatures.

Contains fulltext : 144523.pdf (Publisher’s version ) (Open Access)

The transformation of the ovary into a fruit after successful completion of pollination and fertilization has been associated with many changes at transcriptomic level. These changes are part of a dynamic and complex regulatory network that is controlled by phytohormones, with a major role for auxin. One of the auxin-related genes differentially expressed upon fruit set and early fruit development in tomato is Solanum lycopersicum AUXIN RESPONSE FACTOR 9 (SlARF9). Here, the functional analysis of this ARF is described. SlARF9 expression was found to be auxin-responsive and SlARF9 mRNA levels were high in the ovules, placenta, and pericarp of pollinated ovaries, but also in other plant tissues with high cell division activity, such as the axillary meristems and root meristems. Transgenic plants with increased SlARF9 mRNA levels formed fruits that were smaller than wild-type fruits because of reduced cell division activity, whereas transgenic lines in which SlARF9 mRNA levels were reduced showed the opposite phenotype. The expression analysis, together with the phenotype of the transgenic lines, suggests that, in tomato, ARF9 negatively controls cell division during early fruit development.

[EN] The present review aims to synthesize our present knowledge about the mechanisms implied in the biosynthesis of volatile compounds in the ripe tomato fruit, which have a key role in tomato flavour. The difficulties in identifiying not only genes or genomic regions but also individual target compounds for plant breeding are addressed. Ample variability in the levels of almost any volatile compound exists, not only in the populations derived from interspecific crosses but also in heirloom varieties and even in commercial hybrids. Quantitative trait loci (QTLs) for all tomato aroma volatiles have been identified in collections derived from both intraspecific and interspecific crosses with different wild tomato species and they (i) fail to co-localize with structural genes in the volatile biosynthetic pathways and (ii) reveal very little coincidence in the genomic regions characterized, indicating that there is ample opportunity to reinforce the levels of the volatiles of interest. Some of the identified genes may be useful as markers or as biotechnological tools to enhance tomato aroma. Current knowledge about the major volatile biosynthetic pathways in the fruit is summarized. Finally, and based on recent reports, it is stressed that conjugation to other metabolites such as sugars seems to play a key role in the modulation of volatile release, at least in some metabolic pathways.

Background A RIL population between Solanum lycopersicum cv. Moneymaker and S. pimpinellifolium G1.1554 was genotyped with a custom made SNP array. Additionally, a subset of the lines was genotyped by sequencing (GBS). Results A total of 1974 polymorphic SNPs were selected to develop a linkage map of 715 unique genetic loci. We generated plots for visualizing the recombination patterns of the population relating physical and genetic positions along the genome. This linkage map was used to identify two QTLs for TYLCV resistance which contained favourable alleles derived from S. pimpinellifolium. Further GBS was used to saturate regions of interest, and the mapping resolution of the two QTLs was improved. The analysis showed highest significance on Chromosome 11 close to the region of 51.3 Mb (qTy-p11) and another on Chromosome 3 near 46.5 Mb (qTy-p3). Furthermore, we explored the population using untargeted metabolic profiling, and the most significant differences between susceptible and resistant plants were mainly associated with sucrose and flavonoid glycosides. Conclusions The SNP information obtained from an array allowed a first QTL screening of our RIL population. With additional SNP data of a RILs subset, obtained through GBS, we were able to perform an in silico mapping improvement to further confirm regions associated with our trait of interest. With the combination of different¿~¿omics platforms we provide valuable insight into the genetics of S. pimpinellifolium-derived TYLCV resistance.

[EN] Phenylpropanoid volatiles are responsible for the key tomato fruit (Solanum lycopersicum) aroma attribute termed "smoky." Release of these volatiles from their glycosylated precursors, rather than their biosynthesis, is the major determinant of smoky aroma in cultivated tomato. Using a combinatorial omics approach, we identified the NON-SMOKY GLYCOSYLTRANSFERASE1 (NSGT1) gene. Expression of NSGT1 is induced during fruit ripening, and the encoded enzyme converts the cleavable diglycosides of the smoky-related phenylpropanoid volatiles into noncleavable triglycosides, thereby preventing their deglycosylation and release from tomato fruit upon tissue disruption. In an nsgt1/nsgt1 background, further glycosylation of phenylpropanoid volatile diglycosides does not occur, thereby enabling their cleavage and the release of corresponding volatiles. Using reverse genetics approaches, the NSGT1-mediated glycosylation was shown to be the molecular mechanism underlying the major quantitative trait locus for smoky aroma. Sensory trials with transgenic fruits, in which the inactive nsgt1 was complemented with the functional NSGT1, showed a significant and perceivable reduction in smoky aroma. NSGT1 may be used in a precision breeding strategy toward development of tomato fruits with distinct flavor phenotypes.

Contains fulltext : 164745.pdf (publisher's version ) (Closed access)

BACKGROUND: Our previous study on ripe apples from a progeny of a cross between the apple cultivars 'Prima' and 'Fiesta' showed a hotspot of mQTLs for phenolic compounds at the top of LG16, both in peel and in flesh tissues. In order to find the underlying gene(s) of this mQTL hotspot, we investigated the expression profiles of structural and putative transcription factor genes of the phenylpropanoid and flavonoid pathways during different stages of fruit development in progeny genotypes. RESULTS: Only the structural gene leucoanthocyanidin reductase (MdLAR1) showed a significant correlation between transcript abundance and content of metabolites that mapped on the mQTL hotspot. This gene is located on LG16 in the mQTL hotspot. Progeny that had inherited one or two copies of the dominant MdLAR1 alleles (Mm, MM) showed a 4.4- and 11.8-fold higher expression level of MdLAR1 respectively, compared to the progeny that had inherited the recessive alleles (mm). This higher expression was associated with a four-fold increase of procyanidin dimer II as one representative metabolite that mapped in the mQTL hotspot. Although expression level of several structural genes were correlated with expression of other structural genes and with some MYB and bHLH transcription factor genes, only expression of MdLAR1 was correlated with metabolites that mapped at the mQTL hotspot.MdLAR1 is the only candidate gene that can explain the mQTL for procyanidins and flavan-3-ols. However, mQTLs for other phenylpropanoids such as phenolic esters, dihydrochalcones and flavonols, that appear to map at the same locus, have so far not been considered to be dependent on LAR, as their biosynthesis does not involve LAR activity. An explanation for this phenomenon is discussed. CONCLUSIONS: Transcript abundances and genomic positions indicate that the mQTL hotspot for phenolic compounds at the top of LG16 is controlled by the MdLAR1 gene. The dominant allele of the MdLAR1 gene, causing increased content

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust—a software tool for analysis GC–MS and LC–MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC–MS and LC–MS alignment data sets

The search for models which link tomato taste attributes to their metabolic profiling, is a main challenge within the breeding programs that aim to enhance tomato flavor. In this paper, we compared such models calculated by the traditional statistical approach, stepwise regression, with models obtained by the new generation of regression techniques, known as penalized regression or regularization methods. In addition, for penalized regression, different scenarios and various model selection criteria were discussed to conclude that classical crossvalidation, selects models with many superfluous variables whereas model selection criteria such as Bayesian information criterion, seem to be more suitable, when the goal is to find parsimonious models, to explain tomato taste attributes based on metabolic information. An exhaustive comparison of the discussed methodology was done for six sensory traits, showing that the most important covariates were identified by the stepwise regression as well as by some of the penalized regression methods, despite the general disagreement on the size of the regression coefficients between them. In particular, for stepwise regression the coefficients are inflated due to their high variance which is not the case with penalized regression, showing that this new methodology, can be an alternative to obtain more accurate models.

A role for differential glycoconjugation in the emission of phenylpropanoid volatiles from ripening tomato fruit (Solanum lycopersicum) upon fruit tissue disruption has been discovered in this study. Application of a multiinstrumental analytical platform for metabolic profiling of fruits from a diverse collection of tomato cultivars revealed that emission of three discriminatory phenylpropanoid volatiles, namely methyl salicylate, guaiacol, and eugenol, took place upon disruption of fruit tissue through cleavage of the corresponding glycoconjugates, identified putatively as hexose-pentosides. However, in certain genotypes, phenylpropanoid volatile emission was arrested due to the corresponding hexose-pentoside precursors having been converted into glycoconjugate species of a higher complexity: dihexose-pentosides and malonyl-dihexose-pentosides. This glycoside conversion was established to occur in tomato fruit during the later phases of fruit ripening and has consequently led to the inability of red fruits of these genotypes to emit key phenylpropanoid volatiles upon fruit tissue disruption. This principle of volatile emission regulation can pave the way to new strategies for controlling tomato fruit flavor and taste.

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes

Network analysis of correlations between abundances of metabolites across tomato genotypes can help in unraveling the biological basis of organoleptic variation in tomato. We illustrate how to construct and interpret simple correlations networks using metabolic data collected on a diverse set of tomato genotypes. Various types of correlations are calculated and displayed in the form of networks. Interpretations on the basis of network analyses are compared to interpretations following principal components analysis.