Wild tomato species represent a rich gene pool for numerous desirable traits lost during domestication. Here, we exploited an introgression population representing wild desert-adapted species and a domesticated cultivar to establish the genetic basis of gene expression and chemical variation accompanying the transfer of wild-species-associated fruit traits. Transcriptome and metabolome analysis of 580 lines coupled to pathogen sensitivity assays resulted in the identification of genomic loci associated with levels of hundreds of transcripts and metabolites. These associations occurred in hotspots representing coordinated perturbation of metabolic pathways and ripening-related processes. Here, we identify components of the Solanum alkaloid pathway, as well as genes and metabolites involved in pathogen defense and linking fungal resistance with changes in the fruit ripening regulatory network. Our results outline a framework for understanding metabolism and pathogen resistance during tomato fruit ripening and provide insights into key fruit quality traits.
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The transcriptomic data used in the study are publicly available from the Gene Expression Omnibus89 (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE151451. Mass spectrometry data are publicly available from the e!DAL PGP Repository90,91 (https://doi.org/10.5447/ipk/2020/22). Source data are provided with this paper.
Custom code used in the study is available from the GitHub repository (https://github.com/NAMlab/kILBIL).
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This project has received funding from the European Research Council (grant agreement 204575-SAMIT), EU Framework Program FP7/2007–2013 (grant agreement 613692-TriForC) and the Israel Science Foundation (grant number 1805/15). We thank the Adelis Foundation, Leona M. and Harry B. Helmsley Charitable Trust, Jeanne and Joseph Nissim Foundation for Life Sciences, Tom and Sondra Rykoff Family Foundation Research and Raymond Burton Plant Genome Research Fund for supporting A.A.’s laboratory activity. A.K. is thankful for a short-term EMBO fellowship (EMBO-ASTF-146-2014). We are thankful to A.A.’s laboratory members who assisted in harvesting, handling, peeling, freezing and grinding the thousands of fruit tissue samples used in this study. We also thank Y. Iijima and K. Aoki for providing purified acetoxy-hydroxytomatine. A.A. is the incumbent Peter J. Cohn Professorial Chair.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Histogram of annotated gene count per mapping bin. b, Histogram of annotated gene count per mapping bin, for which expressed transcripts have been quantified in transcriptome analysis.
Extended Data Fig. 2 Correlation between the known FUL2 regulatory loci and its downstream target genes.
a, eQTLs of FUL2 target genes identified by ChIP-seq and mutant analysis20. The boxplot compares FUL2 expression in BILs carrying an introgression in bin-223 (n = 5) with its expression in background lines (Sly; n = 64) and across all BIL lines (BILs; n = 511). b, Distribution of Pearson correlation coefficients between RIN, FUL1 and FUL2 expression and their respective activated (red line) and repressed (blue line) direct targets identified in the ChIP-seq analysis and mutant studies. The grey area represents the distribution of Pearson correlation coefficients between RIN, FUL1 and FUL2 and all measured transcripts. Presented boxplots describe the data distribution in terms of: median (box center), first and third quartile (lower and upper box hinge) and the lower and upper adjacent (lower and upper whisker).
Extended Data Fig. 3 Distribution of metabolic QTLs across the genome at the level of chemical classes.
a, Distribution of the number of mQTLs of each chemical class per mapping bin in the breaker stage. b, Distribution of the number of mQTLs of each chemical class per mapping bin in the red stage. The color legend also provides a summary of the mQTL count per metabolite class.
a, Metabolic changes resulting from S. pennellii introgression in bin-424 (n = 19) compared to lines with S. lycopersicum background sequence in bin-424 (n = 492). b, Expression of genes located in the largest mQTL hotspot, bin-424, in lines carrying S. pennellii introgression in bin-424 (n = 19) and those with S. lycopersicum background sequence in bin-424 (n = 492). (c) Gene expression changes for genes located outside bin-424 (i.e. trans-eQTL) in response to S. pennellii introgression in bin-424 (n = 19) compared to lines with S. lycopersicum background sequence in bin-424 (n = 492). Boxplots in panels a-c describe the data distribution in terms of: median (box center), first and third quartile (lower and upper box hinge) and the lower and upper adjacent (lower and upper whisker). Individual LOD (presented next to an upper whisker for all cases with LOD ≥ 5 and absolute fold-change ≥ 2) were calculated using negative binomial distribution model and quasi-likelihood F-test with genotype data as a design matrix and adjusted for multiple comparisons using Benjamini-Hochberg correction, as described in Methods section.
Extended Data Fig. 5 A detailed scheme of the tomato steroidal glycoalkaloid biosynthetic pathway in developing tomato fruit.
The characterized enzymatic steps are noted with the respective protein names. Dashed and solid arrows represent multiple and single enzymatic steps, respectively. The known steroidal glycoalkaloids (SGA) biosynthetic enzymes are labelled in blue. α-tomatine-derived SGAs are marked in red while dehydrotomatine-derived SGAs are marked in black.
The GAME31 gene was silenced in E8:Del/Ros S. lycopersicum by the use of VIGS. a, Relative expression level of GAME31 gene in VIGS-silenced red fruit tissue as compared to control (plants infected with the pTRV2 vector harboring Del/Ros sequences). b, Levels of α-tomatine, dehydrotomatine, acetoxy-hydroxytomatine and esculeoside A in red ripe fruit of GAME31-silenced tomato plants compared to control plants. Values (panel a and b) represent mean ± standard error (n = 7 for silenced and n = 3 for control plants), P values were calculated using two-sided Student’s t-test (only P values < 0.05 are shown). c-f, Hydroxylation of α-tomatine (c) and dehydrotomatine (d) by recombinant S. pennellii GAME31 produced in E. coli cells (marked in red). Recombinant S. pennellii GAME31 enzyme also converts solasodine (e) and α-solamargine (f) to hydroxy-solasodine and hydroxy-solamargine, respectively (marked in red). Extracted ion chromatograms are presented. The control reaction (shown in black) was performed with the respective substrates using extracts from E. coli cells transformed with an empty pET28 vector. For each substrate and hydroxylated product, m/z is shown. Enzyme assay reactions were analyzed by LC-MS. m/z; mass to charge. Green asterisks in panel c and d indicate additional hydroxytomatine or hydroxy-dehydrotomatine isomers formed in respective GAME31 assay reaction.
a, Boxplots representing accumulation of putative metabolites significantly associated with the genomic region of bin-1006. The colored boxes represent metabolite accumulation in lines carrying S. pennellii introgression in the genomic region of bin-1006 (n = 38), as compared to lines with the background S. lysopersicum sequence in bin-1006 (n = 473). A single annotated metabolite, esculeoside A, is highlighted in bold font. b, Expression of genes located in bin-1006, colored accordingly (n = 38 and n = 473, as in panel a). Three glycosyltransferases, including a truncated gene next to GAME5 are highlighted with bold text. Boxplots in both panels describe the data distribution in terms of: median (box center), first and third quartile (lower and upper box hinge) and the lower and upper adjacent (lower and upper whisker).
Extended Data Fig. 8 Identification of pantothenic acid as a potential antifungal metabolite in tomato fruit.
a, Extracted-ion chromatograms (XIC) from LCMS analysis at m/z = 220.11 Da; dashed line at RT = 2.5 min indicates the position of Pantothenic Acid (PA); Y-axis are linked. b, Comparison between fragments of the compound eluted at RT = 2.5 min obtained from chromatograms of BIL-2387 and the PA standard. c, Inhibitory effect of PA (10 µM and 100 µM) on mycelium growth of B. cinerea after 3 days of inoculation on potato dextrose agar plates.
a, VIGS-based silencing of ChlH leads to development of a yellowish bleached phenotype, visible in green tissues (leaf and green fruit). b, Quantitative real time PCR of Mature Green (MG) and Red Ripe (RR) tomato fruit showing ChlH transcript level (n = 3 for both groups). c, Quantitative real time PCR of pTRV:EV and pTRV:ChlH infiltrated MG fruit showing ChlH transcript level (n = 3 for both groups). d, Quantitative real time PCR of pTRV:ChlH and pTRV:ChlH:ACO5 or pTRV:ChlH:ACD2 or pTRV:ChlH:4CL-Like infiltrated RR fruit showing transcript abundance of ACO5, ACD2 and 4CL-Like genes, respectively (n = 4 for each experiment and the respective control). e, pTRV:ChlH:ACO5 or pTRV:ChlH:ACD2 or pTRV:ChlH:4CL-Like fruit show increased susceptibility to B. cinerea (infected area marked in circle). f, Necrotic lesion size in B. cinerea inoculated fruit at 3 days post infection (experiment 1: pTRV:ChlH n = 38, pTRV:ChlH:ACO5 n = 37, pTRV:ChlH:ACD2 n = 64, pTRV:ChlH:4CL-Like n = 57; experiment 2: pTRV:ChlH n = 40, pTRV:ChlH:ACO5 n = 26, pTRV:ChlH:ACD2 n = 40, pTRV:ChlH:4CL-Like n = 40). In panels b, c, d and f the data in bars represents mean ± s.d., and data points denote individual biological replicates. All P values (in panels b, c, d and f) were calculated using two-sided Student’s t-test (only P values < 0.05 are shown).
Supplemental notes and Figs. 1–3
Supplementary Tables 1–9
Mapping of the introgressions used in the study. Introgressions were mapped as described in the Supplementary Information. The table denotes background S. lycopersicum genes as 0 and genes within S. pennellii introgressions as 1. The genome was divided into mapping bins as described in the Methods. Bin 0 contains all chromosome 0 genes.
Results of B. cinerea susceptibility assays. BILs and ILs pathogen susceptibility assays (sheet 1; scoring of BILs and ILs susceptibility to B. cinerea infection as the lesion diameter in mm 5 d after spore application (R = replicate; X = not available), as described in the Methods) and independent validation (sheet 2; pathogen susceptibility assays on selected BILs in an independent experiment).
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Szymański, J., Bocobza, S., Panda, S. et al. Analysis of wild tomato introgression lines elucidates the genetic basis of transcriptome and metabolome variation underlying fruit traits and pathogen response. Nat Genet 52, 1111–1121 (2020). https://doi.org/10.1038/s41588-020-0690-6
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