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. 2018 Oct 23;97(2):391–403. doi: 10.1111/tpj.14095

A Solanum neorickii introgression population providing a powerful complement to the extensively characterized Solanum pennellii population

Yaacov Micha Brog 1,†, Sonia Osorio 2,3,†, Yoav Yichie 1,†, Saleh Alseekh 3,4,†, Elad Bensal 1, Andriy Kochevenko 3, Dani Zamir 1,✉, Alisdair R Fernie 3,4,✉
PMCID: PMC7379295  PMID: 30230636

Summary

We present a complementary resource for trait fine‐mapping in tomato to those based on the intra‐specific cross between cultivated tomato and the wild tomato species Solanum pennellii, which have been extensively used for quantitative genetics in tomato over the last 20 years. The current population of backcross inbred lines (BILs) is composed of 107 lines derived after three backcrosses of progeny of the wild species Solanum neorickii (LA2133) and cultivated tomato (cultivar TA209) and is freely available to the scientific community. These S. neorickii BILs were genotyped using the 10K SolCAP single nucleotide polymorphism chip, and 3111 polymorphic markers were used to map recombination break points relative to the physical map of Solanum lycopersicum. The BILs harbor on average 4.3 introgressions per line, with a mean introgression length of 34.7 Mbp, allowing partitioning of the genome into 340 bins and thereby facilitating rapid trait mapping. We demonstrate the power of using this resource in comparison with archival data from the S. pennellii resources by carrying out metabolic quantitative trait locus analysis following gas chromatography–mass spectrometry on fruits harvested from the S. neorickii BILs. The metabolic candidate genes phenylalanine ammonia‐lyase and cystathionine gamma‐lyase were then tested and validated in F2 populations and via agroinfiltration‐based overexpression in order to exemplify the fidelity of this method in identifying the genes that drive tomato metabolic phenotypes.

Keywords: tomato, Solanum lycopersicum, Solanum neorickii, backcross inbred lines, high‐resolution mapping, phenylalanine, methionine

Significance statement

Here we present a complementary developed population derived from crossing the wild species Solanum neorickii (LA2133) with cultivated tomato (cultivar TA209) using sequenced backcrosses. The population is composed of 107 lines (backcross inbred lines, BILs) with 3111 polymorphic markers used to map recombination break points relative to the physical map of Solanum lycopersicum. In addition we carried out metabolic quantitative trait locus analysis on fruits harvested from the S. neorickii BILs. The metabolic candidate genes phenylalanine ammonia‐lyase and cystathionine gamma‐lyase were identified and validated.

Introduction

The combination of a burgeoning human population and environmental deterioration means that we are faced with the need to find more effective solutions for feeding the planet (Ludewig and Sonnewald, 2016). To this end genetic improvement of crop plants represents a major approach to enhancing agricultural output. This is challenging, since natural genetic variance is commonly displayed across a quantitative phenotypic range as opposed to qualitative phenotypes that fall into discrete categories (Mackay, 2001; Fernie and Tohge, 2017). By contrast, for traits controlled by a single gene or several genes with large effects, variation in quantitative traits tends to be caused by segregation of multiple genes with individually small effects whose expression is shaped by interactions with other genes and the environment (Ofner et al., 2016).

The theoretical basis for the genetic dissection of complex traits is relatively simple. At a basic level, quantitative trait locus (QTL) mapping simply involves finding an association between a genetic marker and a measurable phenotype (Mauricio, 2001). Since the early research of Sax (1923), considerable effort has been expended to identify the genetic basis of continuous traits using linkage analysis. However, these pioneering works were severely constrained by the dependence on visible morphological markers (Barton and Keightley, 2002). Over the past three decades, the establishment of large collections of molecular and genetic markers which can be used to construct detailed genetic maps of both domesticated and experimental species has overcome these limitations (Doerge, 2002). Indeed such maps paved the way for modern‐day QTL mapping (Mackay, 2001; Mauricio, 2001; Doerge, 2002). Currently two major approaches are employed to genetically dissect complex traits: traditional QTL analyses that usually focus on bi‐parental populations (Salvi and Tuberosa, 2005) and genome‐wide association studies (GWAS; Atwell et al., 2010), which are now more commonly used.

As a general rule QTL experiments are designed with a limited number of sources of genetic variation in order to allow facile dissection of complex phenotypes. This can be accomplished by constructing a bi‐parental population originating from homozygous distantly related inbred lines that exhibit genetic polymorphism which drives phenotypic variation. In practice, several different crossing schemes are used to generate such experimental mapping populations. However, irrespective of the crossing scheme used, in all cases the parents are mated to generate an F1 population. There are several bi‐parental population structures that are widely used for the detection of QTLs. Recombinant inbred lines are created by self‐pollinating each of the F2 progeny for several consecutive generations (single seed descent). In the ‘F2 design’, the mapping population is generated by mating the F1 progeny to each other. By contrast in the ‘backcross design’, the mapping population is generated by crossing the F1 progeny to either, or both, of the parents. Several variations of the above‐mentioned crossing schemes have been designed to fully optimize the shuffling of parental alleles (Mauricio, 2001). For example, backcrossed inbred lines (BILs), near‐isogenic lines (NILs) and introgression lines (ILs) have all been demonstrated to facilitate the incorporation of desired alleles into an agriculturally superior genetic background (Tanksley and Nelson, 1996).

The use of traditional QTL mapping in experimental populations has become a standard procedure in quantitative genetics (Salvi and Tuberosa, 2005), but although thousands of research papers have reported original QTL data only a small proportion of these QTLs have been cloned (Nordborg and Weigel, 2008). Furthermore, those QTLs that have been cloned generally involve loci that exhibit large effects and high heritability (Salvi and Tuberosa, 2005). Following the release of the human genome sequence (Venter et al., 2001), GWAS have been extensively used to identify genes associated with an ever‐growing number of human diseases (Hindorff et al., 2009). In plants, GWAS studies have been successfully used to identify loci that explain a considerable proportion of phenotypic variation (Brachi et al., 2011). Genome‐wide association studies are similar to single marker mapping in that they both involve associating a single marker with a phenotype, albeit with a far greater power of detection due to the massive increase in the number of markers (which facilitates stronger associations). However, GWAS have several fundamental limitations; for example, they generate false positives due to population structure (Brachi et al., 2010) and often fail to detect minor‐frequency alleles or environment–genotype interactions, rendering the results of certain GWAS harder to reproduce (Greene et al., 2009). Statistical methods to control for population structure have been developed to reduce the increasing number of false‐positive associations in GWAS (Yu and Buckler, 2006); however, an alternative to this could be the complementary use of traditional linkage mapping. In the past few years a combination of these two approaches has been used to validate the position of QTLs in several studies in mice (Manenti et al., 2009), Arabidopsis (Brachi et al., 2010) and maize (Wen et al., 2014). These studies were among the first to point out the advantage of using combined approaches that link traditional mapping methods relying on recombination‐based genetic positions with high‐throughput genotyping techniques that enable dense molecular maps anchored to reference genomes (Sim et al., 2012). However, the use of dense molecular maps with thousands of markers with bi‐parental populations is limited by the fact that in order to fully exploit the potential provided by the high number of markers a growing number of individuals are needed to ensure that the requirement for a large number of recombination events is met. This allows higher resolution for mapping of the QTLs – but is non‐trivial given the high cost of the genotyping techniques and the amount of work involved in the generation, maintenance and extensive phenotyping of a large number of individuals.

Tomato (Solanum lycopersicum), belongs to the Solanaceae family which includes vegetable crops such as potato (Solanum tuberosum; Xu et al., 2011), eggplant (Solanum melongena; Hirakawa et al., 2014) and pepper (Capsicum annuum; Qin et al., 2014). The tomato genome is considered to be a reference for solanaceous species because it is one of the smallest diploid genomes within the Solanaceae family and a particularly high conservation of gene order (synteny) was recorded within the genus Solanum (Sato et al., 2012). It is also a model organism for the study of fleshy fruit development and ripening (Klee and Giovannoni, 2011), compound leaf development, floral systems and plant architecture (Kimura et al., 2008), as well as defense response(s)/resistance against abiotic and biotic stresses (Kennedy, 2003; Sun et al., 2011; Koenig et al., 2013). Moreover, due to the availability of robust phenotyping platforms for quantifying traits that determine agricultural output, tomato is an extremely useful system for studying multigenic complex traits (Gur et al., 2004). Furthermore, the broad germplasm comprising inter‐specific mapping populations (Tanksley and McCouch, 1997) and mutant populations (Menda et al., 2004) has proved to be a powerful tool for breaking down complex traits into their Mendelian components (see Rodriguez‐Leal et al., 2017; Soyk et al., 2017) – a challenging prerequisite for isolating complex trait genes.

Over the last 20 years one of the most studied inter‐specific tomato populations has been the Solanum pennellii IL population (Eshed and Zamir, 1995). The population comprises marker‐defined genomic regions taken from the green‐fruited wild species S. pennellii and introduced, through genetic crosses, onto the genetic background of the tomato inbred cv. M82 (Eshed and Zamir, 1995). The isogenic background of IL populations has made them suitable for the mapping of Mendelian phenotypes in tomato, and enabled the positional cloning of genes with major effects on distinct quantitative traits. Besides Brix9‐2‐5, which was one of the first QTLs subjected to positional cloning (Fridman et al., 2000, 2004), there are many examples of the use of the IL population for the dissection of qualitative and quantitative traits. Introgression lines have been publicly available and have been thoroughly phenotyped for hundreds of qualitative and quantitative traits, including repeated measurements of the same traits, thus allowing for the identification of more than 4000 QTLs. In recent years efforts have been made to produce hundreds of sub‐ILs for the IL population (Alseekh et al., 2013). This facilitates better mapping resolution, which alongside the presence of the sequenced parental genomes (Bolger et al., 2014) and the abundance of molecular markers (Sim et al., 2012) has already been useful in narrowing the size of the genomic region harboring QTLs (Gur et al., 2010). The recent release of an S. pennellii BIL population (Ofner et al., 2016) and the uptake of GWAS by tomato geneticists (Sauvage et al., 2014; Ye et al., 2017), have additionally extended the resources available for QTL mapping in tomato. Here we report on yet another genetic resource for tomato, namely a complementary BIL population generated from the cross between the wild tomato species Solanum neorickii and the tomato cv. TA209 (Figure 1). We illustrate the utility of this population by rapidly identifying a metabolite QTL for primary metabolites in the fruit. Two QTLs – those for phenylalanine on chromosome 10 and for methionine on chromosome 8 – were validated by the use of F2 families segregating for the trait in question and by agroinfiltration‐based overexpression studies.

Figure 1.

Figure 1

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Breeding scheme of the backcross inbred lines (BILs).

For the first cross, pollen was collected from Solanum neorickii and located on the stigma of cv. TA209 to produce the F1 plants. Advanced backcrosses with cv. TA209 were performed in order to minimize the S. neorickii genome introgressed in the BILs. The number of plants in each generation is shown in brackets. Successive selfing brought the population close to homozygosity. A final cross of the homozygous BILs with cv. TA209 generated a set of lines in a heterozygous condition (BILs‐hybrids).

Results

Genome analysis of the S. neorickii BILs

Mapping analysis was carried out on the S. neorickii BILs based on 3111 single‐nucleotide polymorphism (SNP) markers that were polymorphic between the S. neorickii and S. lycopersicum parents (Figure S1, Table S1 in the online Supporting Information). The analysis revealed 34 out of 141 lines with no S. neoricki introgressions detected in S. lycopersicum. However, 107 lines exhibited at least a single introgression (Figure 2a), with the average number of introgressions per line being 4.3 and the most introgressions per line being 12 (Figure 2b). The size of each introgression was calculated based on the physical map of the tomato genome (v2.4). The margins of each line were identified by the markers that showed the S. neorickii SNPs and the following marker that showed the TA209 SNP (Table S1). The border of the introgression was arbitrarily calculated to be half‐way between the inclusive and exclusive wild‐species SNP. If the first or the last SNP on a chromosome showed a S. neorickii SNP then the tip of the chromosome was used for the length calculation. Based on these criteria, the mean sum of introgression length in the 107 lines was 37.3 Mbp while the median sum of introgression length per line was 16.3 Mbp.

Figure 2.

Figure 2

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The number and length distribution of the backcross inbred line (BIL) introgressions.

(a) The length distribution of introgressions.

(b) Distribution of the number of introgressions in the BIL population.

Box plots showing the mean and median length for all introgressions are presented.

Metabolite profiling of the S. neorickii BILs

We next subjected the 107 homozygous and 107 heterozygous BILs to GC‐MS‐based metabolite profiling (Schauer et al., 2006). For this purpose we took ripe pericarp material from fruit harvested from each homozygous line and its recurrent parent, S. lycopersicum TA209, in the harvests of 2008 and 2009 and heterozygous lines of the 2009 harvest and snap‐froze it in liquid nitrogen. We were able to determine the relative levels of 35 metabolites including sugars and sugar derivatives and organic and amino acids in every sample (values are presented in the heat maps of Figures 3 and S2 and Tables S2 and S3, respectively). The metabolites exhibited variance of between 0.05‐ and 133‐fold across the population, considerably larger ranges than those previously reported for S. pennellii and S. chimielewskii ILs (Schauer et al., 2006, 2008; Do et al., 2010).

Figure 3.

Figure 3

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Heat map of the metabolite profiles of Solanum neorickii homozygous backcross inbred lines (BILs).

Data are normalized to the average response calculated for the TA209 line. The scale is logarithmic. Values presented are means of three replicates and are shown in false‐color code.

However, given that each of these populations was based on a different cultivar of S. lycopersicum as recurrent parent it is currently impossible to ascertain if this difference is a consequence of the wild species introgressed or the cultivar which it is introgressed into. Irrespective of this, the differences observed here allowed the determination, using analysis of variance (anova), of a total of 71, 132 and 74 putative metabolite QTLs (mQTLs) in the 2008 and 2009 inbred lines and in the 2009 hybrid lines, respectively (Table S4). These corresponded to between 1 and 12 mQTLs per metabolite. However, only 25 of the mQTLs overlapped in at least two experiments (considering the homozygotes and heterozygotes as separate experiments). Interestingly, approximately 80% of these QTLs were similarly localized to previously identified ones (Schauer et al., 2006, 2008; Do et al., 2010), including those for amino acids such as isoleucine and lysine on chromosome 9 (Maloney et al., 2010; Kochevenko and Fernie, 2011) and sugars such as fructose and glucose on chromosome 6 (Pnueli et al., 1998). These QTLs were previously functionally tested as being due to polymorphism in enzymes of branched chain amino acid metabolism and/or cloned as the self‐prunning gene responsible for determinacy. By contrast, approximately 20% of the QTLs reported here are unique to this population. It will, ultimately, be very interesting to compare the number of QTLs that are found in the S. neorickii BILs but not the S. pennellii BILs; however, as yet, very few of the S. pennellii BILs have been subject to metabolite profiling. Indeed, to date, most of these have been associated with a region of chromosome 10 that was recently identified as a hotspot for metabolite canalization (Alseekh et al., 2017) In this region five QTLs were detected in the S. neorickii BILs and while six were detected in S. pennellii BILs. Interestingly, only two of the QTLs were conserved in this region, namely those for phenylalanine and galactinol, while three were unique to the S. neorickii BIL population, confirming our postulate that this material will be useful in its own right for identifying new QTLs. Given that they were strong QTLs and highly conserved in all three harvests (i.e. homozygous lines harvested in 2008 and 2009 and heterozygous lines harvested in 2009) we chose to carry out further validation studies on the mQTL for phenylalanine on chromosome 10 and that for methionine on chromosome 8.

A corresponding QTL to the conserved mQTL for phenylalanine on chromosome 10 (Figure 4a) had previously been documented as localized to IL10‐3 of the S. pennelli IL population (Schauer et al., 2006). In the S. neorickii population, two QTLs for phenylalanine were identified. In the first QTL, mapped to chromosome 4 (Table S4), the wild S. neorickii allele increased the level of phenylalanine. The second QTL mapped to chromosome 10 and the wild allele resulted in a decrease in phenylalanine levels (Figure 4b, Table S4). The LOD (logarithm of odds) drop‐off interval around the corresponding genomic region spans more than 590 kbp and contains 88 annotated gene models. One of the annotated gene models in the corresponding genomic region is Solyc10g086180, which is functionally annotated as the key chorismate pathway enzyme phenylalanine ammonia‐lyase (PAL). In order to gain higher genetic resolution, we took advantage of recently available BIL populations derived from a cross between S. pennellii and the cultivated tomato (cv. M82; Ofner et al., 2016) to examine the levels of phenylalanine in selected BILs covering the QTL region. This experiment allowed us firstly to validate the QTL in a different population, and secondly to narrow down the QTL region to a 250‐kbp region containing 40 annotated gene models, including the previously described PAL gene (Figure 4a). In addition, and in order to validate the effect of the phenylalanine QTL from S. neorickii, seedlings obtained from the self‐pollinated heterozygous BILs neo‐089 and neo‐097 (F2 BILs) with an overlapping introgression in the investigated QTL region of chromosome 10 were tested in the field in 2013. Seedlings were genotyped prior to planting using cleaved amplified polymorphic sequence (CAPS) markers generated from the CT240 sequence which is physically located in the introgression region (Table S7). Tissue was collected from fruit pericarp of mature green and red fruits, mature and young leaves, and stems of the F2 families and the recurrent parent TA209, and subjected to GC‐MS analysis to determine the phenylalanine levels. A significant decrease in the relative content of phenylalanine was found in the red fruit pericarp when the mean values of plants homozygous for the wild‐species allele were compared with those of plants homozygous for the cultivated tomato allele (Figures 4c and S2). No significant difference in the phenylalanine levels was found in others tissues, indicating fruit specificity of this effect (Figure S3).

Figure 4.

Figure 4

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Fine mapping and validation of the phenylalanine quantitative trait loci (QTLs) on chromosome 10.

(a) Fine mapping using the defining backcross inbred lines (BILs) of Solanum neorickii (s. neo) and Solanum pennellii (s. penn) for the phenylalanine QTL on chromosome 10. Green bars represent the wild introgression segments, red bars correspond to the cultivated background cv. M82 and TA209 segments. The schematic representation also shows the interval region (Mb) according to the SNP genomic position and the open reading frame of phenylalanine ammonia‐lyase gene.

(b) Phenylalanine levels in S. neorickii BILs. Values represent the mean (± standard error) of three harvests.

(c) Phenylalanine levels in F2 segregating S. neorickii families and the S. pennellii heterozygous introgression line ILH10‐3. The bar graph plots the relative content of phenylalanine in red fruit pericarp (± standard error). Numbers in parenthesis refer to (1) material homozygous for the cultivated tomato allele, (2) the heterozygote and (3) material homozygous for the wild species allele.

(d) Solyc10g086180 expression levels in fruit pericarp of F2 segregating S. neorickii families and the S. pennellii heterozygous introgression line ILH10‐3.

In a parallel approach, given that this was only 1 of 16 paralogs of PAL (Solyc10g086180) encoded by the tomato genome, we next evaluated, by quantitative (q)RT‐PCR, the expression of the 11 paralogs which displayed expression in tomato fruits (Sato et al., 2012; Bolger et al., 2014). For this purpose we analyzed mature green and red fruit of the recurrent parent and F2 families derived from neo89 and neo97 in addition to the heterozygote IL10‐3 (ILH10‐3). In red fruit, the expression of the gene encoded within this interval, Soly10g086180, was at least 20‐fold higher in all lines than in the recurrent parent, while expression in the F2 families having the genotype of the homozygote from wild species displayed expression levels which were over 60‐fold higher (Figure 4d). However, the levels of the transcript were unaltered in mature green fruits (Figure S3). Evaluation of the levels of phenylalanine, the substrate of this enzyme, revealed that it was dramatically decreased in all lines expressing wild alleles of PAL with respect to the recurrent parent (Figure 4c), confirming the candidature of this gene as a major determinant of phenylalanine levels in red tomato fruit. Intriguingly, analysis of the expression of the other 10 paralogs in mature green and red fruits revealed expression for only four further genes, but none of these genes had an expression level different from that of TA209 (Table 1). Similarly, the levels of phenylalanine were invariant in mature green fruit, stems and young and mature leaves of the various genotypes, suggesting that this effect is specific both to this paralog of PAL and to the red fruit (Figure S4). In addition, in order to provide additional support for the observed phenotypes, we performed genomic sequence analysis of the promoter regions (defined as 1000 bp upstream of the start codon) of the PAL gene and compared the promoter sequences by aligning the sequences from S. lycopersicum cv. M82 and S. pennellii genome sequences (Bolger et al., 2014). Results showed that there is a 21‐bp deletion in the M82 sequence at position −50 bp, while the coding region was very similar between M82 and S. pennellii, albeit there were a few SNPs (Figure S5). The S. neorickii allele showed a similar deletion in the promoter region to that of S. pennellii. Together, these results suggest that the wild alleles of this PAL gene harbored by both S. neorickii and S. pennellii resulted in elevated expression of the gene and thereby enhanced the efficiency of the PAL enzyme in metabolizing phenylalanine. To test this hypothesis we decided to carry out targeted reverse genetics using a transient overexpression strategy. Transient overexpression of genes in tomato fruit by agroinfiltration has been successfully used to investigate gene function (Voinnet et al., 2003). Injection of green fruits with a solution containing Agrobacterium tumefaciens harboring a pBI‐Solyc10g086180 construct did not evoke any phenotypic changes in the fruit. Further analysis of Solyc10g086180 transcripts by qRT‐PCR was performed in the pericarp of independent fruits that were injected with either the pBI‐Solyc10g086180 construct or an empty vector control. These studies revealed a significant (more than 30‐fold) increase in expression in the pericarp of the fruits injected with the pBI‐Solyc10 g086180 construct compared with the control (Figure 5a). To order to conclusively define Solyc10g086180 as being responsible for the changes in phenylalanine levels, we next evaluated the phenylalanine content in the same fruits. As anticipated, a decrease of more than 50% in the levels of phenylalanine was confirmed in those fruits in which Solyc10g086180 was overexpressed (Figure 5b).

Table 1.

Expression of phenylalanine ammonia‐lyase (PAL) in mature green and red fruits of heterozygous and homozygous chromosomal segmental of F2 families from Solanum neorikii (S. neo), as well as the Solanum pennellii (S. penne) IL10‐3 heterozygous lines (ILH 10‐3). The abundance of PAL mRNAs was measured by quantitative RT‐PCR. Values are normalized to mean values of cv. TA209. Values are means ± SD of three to eight replicates depending of the genotype

Red fruit
Gene ID S. neo F2 (1) S. neo F2 (2) S. neo F2 (3) S. penne (ILH 10‐3)
Solyc09g007890 1.01 ± 0.03 0.93 ± 0.08 1.11 ± 0.07 1.22 ± 0.11
Solyc09g007900 1.02 ± 0.05 1.07 ± 0.07 1.17 ± 0.08 1.16 ± 0.10
Solyc09g007920 0.97 ± 0.04 1.08 ± 0.06 1.17 ± 0.06 0.84 ± 0.07
Solyc03g078270 1.00 ± 0.03 1.05 ± 0.06 1.23 ± 0.07 1.26 ± 0.06
Mature green fruit
Gene ID

TA209

(1)

neo89 × TA209

(2)

neo97 self

(3)

 ILH 10‐3
Solyc09g007890 1.04 ± 0.04 1.06 ± 0.06 1.17 ± 0.06 0.93 ± 0.04
Solyc09g007900 1.00 ± 0.04 1.2 ± 0.07 0.95 ± 0.06 0.85 ± 0.08
Solyc09g007920 0.98 ± 0.02 1.15 ± 0.04 0.88 ± 0.04 1.14 ± 0.06
Solyc03g078270 1.03 ± 0.06 1.22 ± 0.07 0.87 ± 0.04 0.92 ± 0.02
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Numbers in parenthesis refer to: (1) material homozygous for the cultivated tomato allele, (2) the heterozygote and (3) material homozygous for the wild species allele.

Figure 5.

Figure 5

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Transient overexpression of phenylalanine ammonia‐lyase (PAL) and cystathionine gamma‐lyase (CL).

(a) Expression levels of PAL (Solyc10g086180 gene) in fruit pericarp of lines transiently overexpressing an empty vector (control) or the 35S‐Solyc10g086180 vector (35S‐PAL). Values presented the are mean of five biological replicates ± SD. Asterisks indicate significant differences from the control as assessed by Student's t‐test (P < 0.05). Data are from red ripe fruit which were inoculated at the breaker stage.

(b) Phenylalanine levels in transient overexpression fruits of 35S‐PAL and the empty vector (control). Values presented are the mean of five biological replicates ± SD. Asterisks indicate significant differences from the control as assessed by Student's t‐test (P < 0.05).

(c) Expression levels of CL (Solyc08g083110 gene) in fruit pericarp of lines transiently overexpressing an empty vector (control) or the 35S‐Solyc08g083110 vector (35S‐CL). Values presented the are mean of five biological replicates ± SD. Asterisks indicate significant differences from the control as assessed by Student's t‐test (P < 0.05). Data are from red ripe fruit which were inoculated at the breaker stage.

(d) Methionine levels in transient overexpression fruits of 35S‐CL and the empty vector (control). Values presented are the mean of five biological replicates ± SD. Asterisks indicate significant differences from the control as assessed by Student's t‐test (P < 0.05). *P ≤ 0.05.

Similarly, a QTL corresponding to the conserved methionine mQTL has previously been documented to localize to IL8‐3‐1 of the S. pennelli IL population (Schauer et al., 2006). The QTL analysis using S. neorickii BILs identified several QTLs on different chromosomes (Table S4). For three of the identified QTLs, those on chromosomes 7, 8 and 11, the wild‐species allele resulted in a quantitative decrease of the trait, while for the QTL on chromosome 4 the wild allele resulted in a quantitative increase in methionine levels. The LOD drop‐off interval around the corresponding genomic region spans more than 537 kbp, containing 78 annotated gene models. One of the annotated genes is cystathionine gamma‐lyase which has a critical role in the methionine to cysteine pathway degrading cystathionine into cysteine and α‐ketobutyrate (Goyer et al., 2007). Next, in order to validate our finding, we measured the levels of methionine and cysteine in F2 segregating families derived from different S. neorickii and S. pennellii populations (Figure 6a–c). The results of these studies revealed a significantly lower methionine and higher cysteine content in F2 families harboring the wild allele, providing support for our hypothesized candidate gene. Furthermore, given that cystathionine gamma‐lyase (Solyc08g083110), the enzyme associated with methionine degradation, is encoded uniquely within the corresponding interval we carried out further experiments for this gene analogous to those described above. For this purpose we analyzed the expression of this gene in mature green and red ripe fruits of F2 families derived from the S. neorickii population alongside the heterozygote of IL8‐3‐1 (ILH 8‐3‐1). The expression of Solyc08g083110 was significantly higher in both mature green fruit (Figure S6) and ripe red fruit (Figure 6d) in all lines bearing a wild‐species allele of this gene.

Figure 6.

Figure 6

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Levels of methionine and cysteine in Solanum neorickii and Solanum pennellii F2 families.

Levels of methionine (a) and cysteine (b) in the red fruit pericarp of S. neorickii F2 segregating families and the S. pennellii heterozygous line (ILH8‐3‐1).

(c) Levels of methionine in red fruit pericarp of S. pennellii F2 segregating families and the S. pennellii heterozygous line (ILH8‐3‐1).

(d) Expression levels of the cystathionine gamma‐lyase gene (Solyc08g083110) in red fruit pericarp of S. neorickii F2 segregating families and the S. pennellii heterozygous line (ILH8‐3‐1).

Bar graphs represent the relative content ± SE. Numbers in parenthesis refer to (1) material homozygous for the cultivated tomato allele, (2) the heterozygote and (3) material homozygous for the wild species allele. *P ≤ 0.05.

Injection of green fruits with a solution containing Agrobacterium tumefaciens harboring a pBI‐Solyc08g083110 construct did not evoke any phenotypic changes in the fruit. Further analysis of Solyc08g083110 transcripts by qRT‐PCR was performed in the pericarp of independent fruits that were injected with either the pBI‐Solyc08g083110 construct or the empty vector. These studies revealed a more than sevenfold increase in expression in the pericarp of fruits injected with the pBI‐Solyc08g083110 construct compared with the control (Figure 5c). In order to conclusively define Solyc08g083110 as being responsible for the changes in methionine levels, we next evaluated the methionine and cysteine content in the same fruits. As anticipated, a mild yet significant decrease in the levels of methionine was confirmed in those fruits in which Solyc08g083110 was overexpressed, but the levels of cysteine were invariant (Figures 5d and S9).

Discussion

The S. neorickii population consists of 107 BILs that were genotyped with 3111 SNPs, covering all 12 tomato chromosomes and assembled according to cultivated tomato variety Heinz 1706 (Sato et al., 2012). The BILs divided the tomato genome into 340 mapping bins with an average of 4.3 introgressions per line, as described in Ofner et al. (2016)As such, the population structure does not offer as high a resolution as that recently described for the S. pennellii BILs for which 446 lines exist, and the population contains an average of 2.7 introgressions per line and 633 mapping bins. In spite of the lower resolution, as we describe in detail below, the S. neorickii BILs, like the S. pennellii BILs before them (Ofner et al., 2016), greatly shorten the time required for fine mapping and provide a more global resolution of wild‐species traits via the analysis of multiple independent lines carrying the introgressed segments. Furthermore, by contrast to the 600‐odd sub‐ILs that represent recombinants of particular segments but often lack high‐density maps of the recombinant events in question (Alseekh et al., 2013), the BILs are mapped to a high resolution. The utility of the S. pennellii BILs in providing higher resolution for the genetics underlying tomato traits is best illustrated by the large number of papers in which this resource was used. To date, this population has been used to identify five candidate genes involved in cuticular wax biosynthesis (Ofner et al., 2016), as well as genes encoding an enzyme in the biosynthetic pathway for insecticidal acyl sugar production (Ning et al., 2015; Fan et al., 2016) and in mapping traits for leaf complexity, leaflet shape, flowering time (Fulop et al., 2016), resistance to dodder (Krause et al., 2018) and metabolite canalization (Alseekh et al., 2017). The S. pennellii BILs were used to identify a homolog to the Arabidopsis gene EID1 that is responsible for slowing down the circadian clock of cultivated tomato during domestication (Müller et al., 2015).

Here we analyzed mQTLs in the S. neorickii BILs. However, it is highly likely that this population will also provide an important resource for a wide range of other traits. The population was notably characterized by fewer conserved mQTLs than we determined previously for the S. pennellii (Schauer et al., 2006, 2008) and Solanum chmielewskii (Do et al., 2010) IL populations. Interestingly, some 80% of the conserved QTLs in the S. neorickii BILs were previously reported in either the S. pennellii or the S. chmielewskii IL populations, or both. Both those QTLs that are conserved on the introduction of diverse wild‐species alleles and those that are unique to the S. neorickii BILs are of interest; the former suggests that our contention that this population can be used as a powerful complementary resource to the S. pennellii introgression lines, particularly given the relatively small size of the population, largely holds true. By contrast, the fact that 20% of QTLs are new in this study suggests that there are some species‐specific differences that are likely to be very interesting to follow in future studies. Among the QTLs that were conserved are phenylalanine on chromosome 10 and methionine on chromosome 8. By contrast, the first reported GWAS study on primary metabolism in tomato did not reveal 44 candidate loci for fruit metabolic traits (Sauvage et al., 2014), and a larger study by the same group (Bauchet et al., 2017) did not identify associations with phenylalanine or methionine levels. This observation thus provides further support for the value of such populations. It will eventually be interesting to compare the QTL discovery efficiency of the S. neorickii and S. pennellii BIL populations; however, as mentioned above, from the small‐scale comparison that is already possibly it is clear that additional QTLs not found in the S. pennellii BIL population can be anticipated.

In this study we validated the quantitative importance of genes involved in the metabolism of both phenylalanine and methionine. In the case of the former a detailed evaluation revealed the following properties of the gene in question. First, alteration in its expression follows its expression pattern in S. lycopersicum, i.e. it is fruit specific, being mainly expressed in red ripe fruit and is more highly expressed in the wild species than the cultivated variety. Available expression data suggest that this specific gene is highly expressed in all wild species but that its expression is negligible in cultivated tomato (Koenig et al., 2013; Bolger et al., 2014). Similarly, expression QTL data (TFGD, http://ted.bti.cornell.edu/) show 80‐fold increased expression of PAL in S. pennellii IL10‐3 which harbors this gene. By comparison we found a 3.5‐fold increase in gene expression in the S. neorickii BILs. Available genome sequence data suggest that this is most likely due to a deletion in the promoter region of the cultivated tomato genome (Figure S5) which is not present in any of the wild‐species genomes sequenced to date (Aflitos et al., 2014). Consistent with these observations, a previous metabolomics survey of the wild species of the S. lycopersicum complex revealed that S. neorickii, S. pennellii and indeed all wild species studied exhibited considerably lower levels of phenylalanine than the cultivated tomato. Given that phenylalanine represents the precursor for a range of phenylpropanoids and volatiles it is not unreasonable to assume that reduction of this activity may have been selected for on domestication, since several of these compounds have a bitter taste. By contrast, certain volatiles have been identified to be highly beneficial to taste (Tieman et al., 2012, 2017; Rambla et al., 2014) and phenylpropanoids often confer both biotic and abiotic stress resistance (Tohge et al., 2016) as well as health benefits within the diet (Martin et al., 2013). Given that all of these are deficient in the cultivated tomato, reintroduction of a wild allele of this gene may prove a highly effective strategy for future crop improvement. In keeping with this theory it is interesting to note that IL10‐3 was additionally characterized by increased levels of several secondary metabolites, including hydroxycinnamates and flavonoids (Wen et al., 2015) which are formed downstream of the reaction catalyzed by PAL.

In the second example we were able to demonstrate that the methionine content of the fruit was quantitatively determined by the expression level of the cystathionine gamma‐lyase gene which encodes an enzyme in the pathway that converts methionine to cysteine. Interestingly, analysis of a segregating F2 population revealed that lines displaying a decreased level of methionine universally exhibited elevated levels of cysteine – as would be expected following the crossover theorem of Crabtree and Newsholme (1987), although admittedly the cysteine levels were not significantly changed in the transient overexpression experiments. Nevertheless, as was observed for the phenylalanine QTL, the level of expression of cystathionine gamma‐lyase, which is not fruit specific although it is predominantly expressed in fruits, was significantly higher in all green and red fruits containing a wild‐type allele while the methionine content was lower and, most convincingly, transient overexpression of this gene led to a decrease in the content of methionine and a decrease in the ratio of methionine to cysteine. The sequence polymorphism between M82 and S. pennellii revealed four insertions or deletions of different sizes, ranging between 6 and 18 bp, in the promoter region for cystathionine gamma‐lyase, while in addition four amino acids were deleted in M82 compared with the S. pennellii allele (Figures S7 and S8). Importantly, an additional amino acid residue is present in all wild species, suggesting a deletion upon cultivation. Similar to the case above for phenylalanine, the levels of methionine were significantly decreased in the fruits of all wild species (0.1 to 0.5 times the levels in cultivated M82; Schauer et al., 2005). Given that this reaction is not as well studied in tomato as that catalyzed by PAL, it will probably be highly informative to proceed to stable transformation to better analyze its in vivo functional role across a range of different tissues and developmental stages.

In addition to the availability of the germplasm described here and access to the genetic maps which describe it, all data reported here have been added to the Phenome Networks warehouse (http://phenome-networks.com/): raw data can be freely download the data analyzed within the database using a range of genetic and statistical software. Studies of a previously mapped QTLs are thus rendered far more facile in that the number of BILs needed to fine‐map the trait can be rapidly reduced. The process of gene identification is also further accelerated, as highlighted in the examples described above for the S. pennellii BILs. It is our firm belief that the S. neorickii population defined here will provide both a powerful complement to the S. pennellii BILs, and due to the new QTLs identified here will also be a potent tool for QTL mapping of the less well studied wild species S. neorickii in its own right.

Experimental Procedures

Plant material

The S. neorickii BILs were constructed from a cross between the green‐fruited, self‐compatible wild accession LA2133 and the processing‐tomato inbred variety cv. TA209 (S. lycopersicum) (Fulton et al., 2000).AUTHOR: Fulton et al., 2000 has not been included in the Reference List, please supply full publication details. F1 hybrids were then backcrossed for three cycles to the processing‐tomato inbred variety cv. TA209 as described by Fulton et al. (2000) followed by 10 cycles of self‐pollination in order to achieve BILs with maximum homozygosity of the wild genomic introgressions. The S. neorickii BILs are composed of a total of 142 lines; however, due to poor germination and problematic genotyping results, only 107 lines were analyzed in this work. In addition, hybrids for all BILs were produced in the background of the recurrent parent cv. TA209 in order to evaluate the wild introgressions in a heterozygous state.

Genotyping

DNA was extracted from each of the S. neorickii BILs. Concentrations of 50–80 ng L−1 from each the S. neorickii BILs and the appropriate controls of LA2133, TA209 and their F1 hybrid were genotyped using an Illumina 10K SNP chip (https://www.illumina.com/). The genotyping was performed as described by Sim et al. (2012) in the Trait Genetics GmbH genotyping service (http://www.traitgenetics.com/). Primers and sequences of both sets of markers can be accessed at the Solanaceae Genome Networks (http://solgenomics.net/) database.

Solanum neorickii genetic map and quantitative trait locus mapping

The genetic map was constructed from 3111 genome‐anchored SNP markers that were found to be polymorphic between the wild species S. neorickii and the recurrent parent cv. TA209. The markers were divided into 340 bins with an average length of 2.07 Mbp per bin and composited from an average of 9.15 SNPs per bin. The calculated map is 537.8 cM long and covers 91.5% of the wild‐species genome. Due to the incomplete genome coverage of the introgressions of the population there are more designated linkage groups than chromosomes, resulting in a total of 20 linkage groups. For example, chromosomes 1 and 3 are each composed of three linkage groups. The longest linkage group represents chromosome 4 (54.7 cM) and the shortest are mapped to chromosomes 1, 2, 3 and 11 (0 cM). These short linkage groups are in fact not true linkage groups, and each of them is composed of SNP markers that co‐segregate with no recombination events between them across the entire BIL population. Therefore, the corresponding genomic regions between and around these SNP markers are designated only on a physical scale (Mbp). The construction of genetic maps enabled the application of the Haley–Knott regression method that takes into consideration flanking markers to provide a better estimation of the presence of the QTL and its position.

Field trails

The field experiments were performed in Akko, Israel. Seedlings were grown for 35 days in the Hishtil nursery in Ashkelon and then transplanted in the field with 50 cm between plants and 2 m between rows (one plant per m2). Whole‐genome surveys for BIL inbreds and hybrids were conducted for 2 years (2008 and 2009). Every year seedlings were transplanted in the field around the first week of April and harvested during the last week of July or the first week of August according to their relative maturation. Three biological triplicates were used for each line in each year.

The whole‐genome survey experiments where all BILs and BIL hybrids were evaluated were planted in a randomized block design

F 2 validation

After a preliminary whole‐genome survey using all BILs, seeds from self‐pollinated heterozygous BILs were collected and used the following year for QTL effect validation in a segregating F2 design. The respective genotypes in the F2 generation were classified by screening with PCR‐based DNA markers for the defined introgressed segment. In metabolite analysis samples from two independent families of the same genotype were considered as replicates.

Metabolite extraction for GC‐MS

For primary metabolites an established GC‐time of flight (ToF)‐MS protocol was used. Plant material was extracted using a method described in Schauer et al. (2006). Metabolite profiling and data analysis were performed as described in Lisec et al. (2006) using a Chroma TOF 1.0 (Leco, https://www.leco.com/) and TagFinder 4.0 software and metabolites are reported following community‐based reporting standards (Table S5; Fernie et al., 2011).

Heat maps

Heat maps were produced using the ‘heat map’ function of the R software. The log2‐transformed metabolite data from three biological replicates were used to create the heat maps.

Gene expression analysis

Total RNA was extracted from fruit pericarp as described by Bugos et al. (1995) with minor changes. First‐strand cDNA synthesis of 500 mg of RNA in a final volume of 20 ml was performed with Moloney murine leukemia virus reverse transcriptase, Point Mutant RNase H Minus (Promega, http://www.promega.com/), according to the supplier's protocol using oligo(dT) T19 primer. Expression of the PAL genes was evaluated by qRT‐PCR using the fluorescent interacting dye SYBR Green in an iCycler detection system (Bio‐Rad, http://www.bio-rad.com/). Expression data were normalized to the reference genes elongation factor1a (Zanor et al., 2009) (GenBank accession no. X14449) and Ubiquitin3 (Osorio et al., 2012) (GenBank accession no. X58253). The primers used for qRT‐PCR analysis are listed in Table S6.

Transient overexpression of tomato fruit by agroinfiltration

The full‐length cDNA of Solyc10g086180 and Solyc08g083110 genes from S. neorickii was cloned into the pBI121 vector (Figure S10). Transient overexpression was performed in S. lycopersicum cv. M82 fruits by the co‐infiltration of an Agrobacterium tumefaciens strain containing PBI‐Solyc10g086180, PBI‐Solyc08g083110 and the pBin61‐p19 construct to overexpress a gene encoding the silencing suppressor p19 protein of Tomato bushy stunt virus under the control of the CaMV 35S promoter (Zanor et al., 2009). For tomato fruit infiltration, flowers were labeled at anthesis and inoculated 3.5 weeks later (mature green stage) by agroinfiltrating 0.7 ml of bacterial suspension (a mix of the A. tumefaciens strain containing PBI‐Solyc10g086180, PBI‐Solyc08g083110 and pBin61‐p19 vectors in a 1:1:1 ratio) through the peduncle into the fruit. Agroinjected fruits were collected at breaker stage. Five fruits from the same position of four independent plants were agroinfiltrated. The agroinfiltration experiments were repeated three times.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Figure S1. Graphical representation of the 107 Solanum neorickii backcross inbred lines.

Figure S2. Heat map of the metabolites profiles of Solanum neorickii homozygous backcross inbred line heterozygous lines.

Figure S3. Phenylalanine levels in mature green fruit, stem, mature and young leaves of F2 families from Solanum neorickii, as well as the Solanum pennellii heterozygous line (ILH10‐3).

Figure S4 . Solyc10g086180 expression in mature green fruit of F2 families from Solanum neorickii, as well as the Solanum pennellii heterozygous line (ILH10‐3).

Figure S5. Sequence analysis of 1000 bp upstream and the open reading frame of cv. M82 and Solanum pennellii for the phenylalanine ammonia‐lyase gene (Solyc10g086180).

Figure S6 . Solyc08g083110 expression in mature green fruit of F2 families from Solanum neorickii, as well as the Solanum pennellii heterozygous line (ILH8‐3‐1‐3).

Figure S7. Sequence analysis of 1000 bp upstream and the open reading frame of cv. M82 and Solanum pennellii for the cystathionine gamma lyase gene (Solyc08g083110).

Figure S8. Protein sequence of the cystathionine gamma lyase (Solyc08g083110) gene of cv. M82 and S. pennellii.

Figure S9. Cysteine levels in transient overexpression of the cystathionine gamma lyase (Solyc08g083110) gene and the control.

Figure S10. Vector map used for cloning and transient overexpression.

Click here for additional data file. (765.5KB, pdf)

Table S1. Genotype data set used for 107 Solanum neorickii backcrossed introgression line populations.

Click here for additional data file. (1.2MB, xlsx)

Table S2. Average fold changes of the metabolite content of the homozygous backcrossed introgression lines compared with the parental control (cv. TA209) from 2008 to 2009.

Click here for additional data file. (38.5KB, xlsx)

Table S3. Fold changes of metabolite content of the heterozygous backcrossed introgression lines compared with the parental control (cv. TA209) from 2009.

Click here for additional data file. (28.4KB, xlsx)

Table S4. Solanum neorickii metabolic quantitative trait loci.

Click here for additional data file. (32.6KB, xlsx)

Table S5. Overview of the metabolite reporting list.

Click here for additional data file. (47KB, xls)

Table S6. Primers used for quantitative RT‐PCR analysis.

Click here for additional data file. (10.9KB, xlsx)

Table S7. Cleaved amplified polymorphic sequence markers used for genotyping the F2 generations from backcrossed introgression lines.

Click here for additional data file. (11.1KB, xlsx)

Acknowledgements

The Zamir lab was supported by the Israel Science Foundation (ISF; no. 653/15) and by the Horizon‐2020 grant G2P‐SOL (677379). We gratefully acknowledge the contribution of Martin Ganal (Trait Genetics, Germany) to the SNP chip data. Work in Fernie's lab was supported in part by grants from the Max‐Planck‐Gesellschaft (to SO, SA and ARF) and by Ministerio de Ciencia e Innovacion and University of Malaga (Spain) through the grant Ramón y Cajal Program (SO, RYC‐ 09170). Also SA and ARF acknowledge funding of the PlantaSYST project by the European Union's Horizon 2020 Research and Innovation Programme (SGA‐CSA no. 664621 and no. 739582 under FPA no. 664620).

Contributor Information

Dani Zamir, Email: dani.zamir@mail.huji.ac.il.

Alisdair R. Fernie, Email: fernie@mpimp-golm.mpg.de.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. Graphical representation of the 107 Solanum neorickii backcross inbred lines.

Figure S2. Heat map of the metabolites profiles of Solanum neorickii homozygous backcross inbred line heterozygous lines.

Figure S3. Phenylalanine levels in mature green fruit, stem, mature and young leaves of F2 families from Solanum neorickii, as well as the Solanum pennellii heterozygous line (ILH10‐3).

Figure S4 . Solyc10g086180 expression in mature green fruit of F2 families from Solanum neorickii, as well as the Solanum pennellii heterozygous line (ILH10‐3).

Figure S5. Sequence analysis of 1000 bp upstream and the open reading frame of cv. M82 and Solanum pennellii for the phenylalanine ammonia‐lyase gene (Solyc10g086180).

Figure S6 . Solyc08g083110 expression in mature green fruit of F2 families from Solanum neorickii, as well as the Solanum pennellii heterozygous line (ILH8‐3‐1‐3).

Figure S7. Sequence analysis of 1000 bp upstream and the open reading frame of cv. M82 and Solanum pennellii for the cystathionine gamma lyase gene (Solyc08g083110).

Figure S8. Protein sequence of the cystathionine gamma lyase (Solyc08g083110) gene of cv. M82 and S. pennellii.

Figure S9. Cysteine levels in transient overexpression of the cystathionine gamma lyase (Solyc08g083110) gene and the control.

Figure S10. Vector map used for cloning and transient overexpression.

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Table S1. Genotype data set used for 107 Solanum neorickii backcrossed introgression line populations.

Click here for additional data file. (1.2MB, xlsx)

Table S2. Average fold changes of the metabolite content of the homozygous backcrossed introgression lines compared with the parental control (cv. TA209) from 2008 to 2009.

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Table S3. Fold changes of metabolite content of the heterozygous backcrossed introgression lines compared with the parental control (cv. TA209) from 2009.

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Table S4. Solanum neorickii metabolic quantitative trait loci.

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Table S5. Overview of the metabolite reporting list.

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Table S6. Primers used for quantitative RT‐PCR analysis.

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Table S7. Cleaved amplified polymorphic sequence markers used for genotyping the F2 generations from backcrossed introgression lines.

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