LARGE-SCALE BIOLOGY ARTICLE

Canalization of Tomato Fruit MetabolismOPEN

Saleh Alseekh,a Hao Tong,a Federico Scossa,a,b Yariv Brotman,a,c Florian Vigroux,a Takayuki Tohge,a Itai Ofner,d

Dani Zamir,d Zoran Nikoloski,a,1 and Alisdair R. Ferniea,1

aMax Planck Institute of Molecular Plant Physiology, 14476 Potsdam-Golm, GermanybConsiglio per la Ricerca in Agricoltura e l’analisi dell’Economia Agraria, 00134 Rome, Italyc Department of Life Sciences, Ben Gurion University of the Negev, 653 Beersheva, Israeld Faculty of Agriculture, The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture at the Hebrew University ofJerusalem, Rehovot 76100, Israel

ORCID IDs: 0000-0003-2067-5235 (S.A.); 0000-0002-6233-1679 (F.S.); 0000-0003-2671-6763 (Z.N.)

To explore the genetic robustness (canalization) of metabolism, we examined the levels of fruit metabolites in multiple harvests ofa tomato introgression line (IL) population. The IL partitions the whole genome of the wild species Solanum pennellii in thebackground of the cultivated tomato (Solanum lycopersicum). We identified several metabolite quantitative trait loci that reducevariability for both primary and secondary metabolites, which we named canalization metabolite quantitative trait loci (cmQTL). Wevalidated nine cmQTL using an independent population of backcross inbred lines, derived from the same parents, which allowsincreased resolution in mapping the QTL previously identified in the ILs. These cmQTL showed little overlap with QTL for themetabolite levels themselves. Moreover, the intervals they mapped to harbored fewmetabolism-associated genes, suggesting thatthe canalization of metabolism is largely controlled by regulatory genes.

INTRODUCTION

The concept of canalization was originally formulated by Conrad H.Waddington (1905–1975) todefinetheabilityofcertainphenotypestoremain relatively constant in spite of environmental and geneticperturbations (Waddington, 1940, 1942). Waddington’s view ofcanalization encompassed developmental stability, i.e., the stabilityof developmental pathways, andwas basedon the observation that,in multicellular organisms, the formation of mature cells and organswas, to a large extent, almost invariable regardless of minor dis-turbances during the process. Today, the best known example ofsuch stability of developmental pathways is the vulva fate patterningin the nematodeCaenorhabditis elegans. The developmental fate ofthevulvaprecursor cells isquasi-invariant, leading to the formationofthe complete organ, despite null heterozygousmutations in the geneencoding epidermal growth factor (Félix and Barkoulas, 2012)

Since Waddington’s original conception, the phenomenon of can-alization has been observed beyond narrowly defined developmentaltraits to encompass a broad range of phenotypes from both uni- andmulticellular organisms, showing that stability of various moleculartraitsmightwellbeundergeneticcontrol (Alonetal.,1999;Lietal.,2009;Lehner, 2010).

Plant breeders have recognized the relevance of canalization inidentifyingthebestvarietiesadaptedtospecificenvironments:Although

these studies usually adopted terms such as stability or uniformity,whatwasactuallyobservedwascanalizationof cropyields (FinlayandWilkinson,1963;BeckerandLeon,1988).Morerecently, theanalysisofstability has been extended beyond the evaluation of yields to quality-related and physiological traits (Dia et al., 2016; Kumagai et al., 2016).It is nowknown that theextent towhichaphenotype iscanalized is

under genetic control, although few studies have investigated themolecular basis of canalization in plants (Hall et al., 2007; Jimenez-Gomez et al., 2011; Lee et al., 2014). Under specific conditions, traitcanalization may be beneficial to the organism, leading to the in-crease of the allele frequencies contributing to the appearance of thecanalized phenotype; on the other hand, theremight be caseswherethe plasticity of a specific trait could confer an adaptive advantage,especially in environments subjected to unpredictable fluctuations(Lachowiec et al., 2016).Although canalization and plasticity may at first appear to be

opposing concepts, they are actually aspects of the same, broaderphenomenon, namely, the effects that external (environment) andinternal (mutations) factors have on overall phenotypic variation.Whether a trait shows a canalized or plastic response ultimatelydependson its underlying genetic architecture: For example, geneticredundancy of close paralogs (gene duplication) has been shown tocontribute to functional compensation (Hanada et al., 2011); on theother hand, control over differential gene expression of distant pa-ralogs is a typical feature enabling plasticity of the trait response(Zhang et al., 2017).In this report, we use the term canalization, irrespective of its

potential adaptive value, to refer to the property of those phenotypictraits showing no environmental effect when individuals of a specificgenotype G are exposed to a set of different environments.In plants, the genetics of microenvironmental canalization was

studied inArabidopsis thaliana bymeasuring developmental stability

1 Address correspondence to fernie@mpimp-golm.mpg.de or nikoloski@mpimp-golm.mpg.de.The author responsible for distribution of materials integral to the findingspresented in this article in accordance with the policy described in theInstructions for Authors (www.plantcell.org) is: Alisdair R. Fernie (fernie@mpimp-golm.mpg.de).OPENArticles can be viewed without a subscription.www.plantcell.org/cgi/doi/10.1105/tpc.17.00367

The Plant Cell, Vol. 29: 2753–2765, November 2017, www.plantcell.org ã 2017 ASPB.

in genetically identical replicates of two recombinant inbred linepopulationsandapopulationofaccessionsofwidegeographicorigingrown under different photoperiods (Hall et al., 2007). This studyallowed the mapping of quantitative trait loci (QTL) associated withdevelopmental stability and revealed that ERECTA likely contributesto microenvironmental canalization in rosette leaf number. It addi-tionally provided evidence of genotypic selection both for increasedand decreased canalization. Recently, another study in Boecherastricta (a close relative of Arabidopsis) included mapping of, amongothers, a QTL associated with genetic canalization of flowering time(Lee et al., 2014). Similarly, treatment of Arabidopsis ecotypes andrecombinant inbred lines with pharmacological inhibitors of Hsp90suggests that this protein acts as a capacitor of phenotypic variationin plants as well as in the fruit fly (Rutherford and Lindquist, 1998;Queitsch et al., 2002).

Beyond analyses of developmental traits, canalization in plants hasreceived little attention; we thus turned our interest to the analysis ofcanalizationofmetabolic traits, given that in thepastdecadeextensivemetabolite profiling studies have been performed aimed at un-derstanding the genetic basis of metabolite accumulation in tomato(Solanum lycopersicum; Mathieu et al., 2009; Smeda et al., 2016;Quadrana, et al., 2014; Schauer et al., 2008; Alseekh et al., 2015). Inthese previous studies, we investigated metabolic QTL (based onphenotypicmeans), theirheritability,andmodeof inheritance fora totalof 219 metabolites of primary and secondary metabolism, includingsugars, organic and amino acids, vitamins, glycoalkaloids, phenyl-propanoids, hydroxycinnamates, and acyl sugars.

Here, we report the assembly of a comprehensive data set of botharchival (Schauer et al., 2008; Alseekh et al., 2015) and gatheredmetabolite data to allow an assessment of the canalization of me-tabolism in tomato.Thecollectionofnoveldata fromthe2003growthtrial provided information from a third harvest that allowed us, on theone hand, to perform a comprehensive analysis of the variation ofprimary and secondary metabolite abundances across three sea-sons, and on the other gave us enough data sets to perform ananalysis of metabolic canalization at the whole genome level.

Quantificationoforganismalcanalization isparticularlychallengingsince other traits may be variable or plastic to maintain stability ofcertain phenotypes. Studies of physiological phenotypes in plantshave focused on their phenotypicmeans rather than on their stabilityor plasticity. However, a couple of recent exceptions have evaluatedstability andplasticity both at the transcriptional andmetabolic levels(Dal Santo et al., 2013; Joseph et al., 2015). In these studies, thecoefficientofvariationwasusedtoassessthestabilityof transcriptionin grapevine (Vitis vinifera; Dal Santo et al., 2013) and of metabolitecontent in Arabidopsis (Joseph et al., 2015). While these pioneeringstudieshave identifiedandcharacterized featuresofcanalizationandplasticity (Fernie andTohge, 2013), alternative statistical approachesfor estimating canalization, especially when applied to mappingpopulations (Dworkin, 2005a),mayprovide abetter understandingofthe molecular mechanisms underlying trait phenotypic robustness.

For this purpose, we analyzed canalization in the framework ofANOVA following the reaction norm of the variance (RxNV) definitionof canalization (Dworkin, 2005b). In contrast to the more commonreaction norm of the mean approach, this approach is based onmeasuring thevariance (rather than themean)of a trait acrossasetofenvironments. In this way, we evaluated both the robustness of thephenotypicmeans and the stability of the trait variances across three

independentenvironments.The resultsobtainedarediscussed in thecontextbothofourcurrentunderstandingofmetabolic regulationandof biological robustness in general.

RESULTS

Analysis of the Variation of Secondary Metabolites in anIntrogression Line Population

Weassembledacompletedatasetof theabundanceandvarianceofsecondary metabolites across three harvests. We included herenewly collectedmetabolic profilingdata resulting froma third harvestthatperformed in2003 (Figure1; seeSupplemental Figure1 for a fullyannotatedversion;SupplementalDataSet1).Figure1providesaheatmap of the average metabolite fold changes across the three yearssuch that large average increases are depicted in deep red, largeaverage decreases are denoted in deep blue, andminor fold changevariation with respect to M82 in a white hue. Hierarchical clusteringhas been applied on both rows and columns to group side-by-sidemetabolites and introgression line (IL) populations showing similarpatterns of variation.The relative difference in the content of any given metabolite in

three harvest years was similar to that previously reported (Alseekhet al., 2015), ranging between 0 (absent) and 94-fold increase incomparison to the levels observed in the parental cultivar M82.Next, we identifiedmetabolite quantitative trait loci (mQTL), based

on phenotypic means, using mixed effect two-way ANOVA, com-paring every IL with the common control (M82). We considered onlythose combinations of metabolites and ILs for which we had at leastthree replicates per harvest (to avoid the risk that the models areoverparameterized). In total, this resulted inexamining9397statisticalmodels out of 11,020 possible for the case of secondarymetabolitesand1946outof6916possible for thecaseofprimarymetabolites.Forsecondary metabolites, we identified 99 mQTL due to the signifi-canceof thegenotypeeffectand647mQTLdueto thesignificanceofthe genotype3 environment interaction (P value < 0.05, Bonferronicorrection for multiple hypotheses testing; Supplemental Data Sets2 and 3). When broken down into compound classes, this corre-sponded to 153 mQTL for hydroxycinnamates, 161 mQTL for fla-vonols, 103 mQTL for glycoalkaloids, 3 mQTL for acyl-sugars,74mQTL for N-containing compounds, 18mQTL for phenolics, and135mQTL for unclassified compounds. These numbers correspondtosharingof57and89mQTLwith thosereportedpreviously (Alseekhet al., 2015) for genotype and genotype 3 environment interaction,respectively.For primarymetabolites,we identified113mQTLon thebasis of the

significance of the genotype effect and 129 on the basis of the sig-nificance of the genotype 3 environment interaction (P value < 0.05,Bonferroni correction for multiple hypotheses testing; SupplementalData Sets 4 and 5).We also determined the extent to which the detected mQTL are

affectedbyexcludingeachoneof theharvests,giventhatoneof them(2004) followed an atypically hot and dry summer. For secondarymetabolites, we identified 211, 177, and 114 mQTL due to the ge-notypeeffect for thepairsof harvests2003and2004, 2001and2003,and 2001 and 2004, respectively (Supplemental Data Set 6). Forsecondarymetabolites,we identified248, 29, and517mQTL due to

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Figure1. Hierarchical ClusteringHeatMapof theSecondaryMetabolite Profiles of Three IndependentStudiesof thePericarpMetaboliteContent of the ILsCompared with the Parental Control S. lycopersicum (cv M82).

Data represent measurements of fruit material harvested in field trials performed in 2001, 2003, and 2004 and are presented as the log2 average of foldchanges across the three seasons compared withM82. Red and blue regions indicate that themetabolite content is increased or decreased, respectively,after introgression of S. pennellii segments. A fully annotated heat map is provided in Supplemental Figure 1, and the complete data set is presented inSupplemental Data Set 1.

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the genotype3 environment interaction for the pairs of harvests2003 and 2004, 2001 and 2003, and 2001 and 2004, respectively(SupplementalDataSet7). Forprimarymetabolites,we identified121, 135, and 93mQTLdue to the genotype effect for the pairs ofharvests 2003 and 2004, 2001 and 2003, and 2001 and 2004,respectively (Supplemental Data Set 8). We also found 95, 60,and 33mQTL due to the genotype3 environment interaction forthe pairs of harvests 2003 and 2004, 2001 and 2003, and2001 and 2004, respectively (Supplemental Data Set 9). The lackof robustness in some of the identified mQTL, particularly thosedue to thegenotype3 environment interaction, canbeexplainedby the data coming from one atypical harvest in comparison totheother two. Thedependenceof theQTLon the season/harvesthas been observed for other tomato traits (Capel et al., 2017b;Rambla et al., 2017). This underscores the need to confirm QTLacross different environments and populations (Collard andMackill, 2008; Capel et al., 2017a).

Analysis of QTL for Metabolic Canalization

Having assembled complete data sets for the abundance andvariance of primary and secondary metabolites across threeharvests, we next sought to identify metabolic canalization QTL(cmQTL) using the reaction normof the variance (RxNV) approachto canalization (Dworkin, 2005a). We first assessed the signifi-cance of the cmQTL based on nominal P values (“permissiveapproach”), with the objective of obtaining a general overview ofthe type and distribution of cmQTL; we then applied Bonferronicorrection for multiple hypotheses testing to retain a reduced setof significant cmQTL (“conservative approach”). The rationale for

analyzing the results using also a permissive threshold is that wehave found the Bonferroni correction to be too strict in removingmanyof themQTL,whichwereactually biologically confirmedandeven cloned (Fridman et al., 2004; Alseekh et al., 2015). Followingour permissive approach, we identified 216 cmQTL for secondarymetabolites (Supplemental Figure 2 and Supplemental Data Set10) and93cmQTL forprimarymetabolites (Figure2;SupplementalData Set 11 and Supplemental Table 1). The cmQTL were dis-tributed over 10 and 12 of the chromosomes for primary andsecondary metabolites, respectively, with a preponderance ofcmQTL located on chromosome 10 (Figure 2 delineates the lo-cation of these cmQTL, while Supplemental Figure 3 providesrepresentative interaction plots of trait variability of cmQTL onIL10-1; the detailed data sets are provided in Supplemental DataSets 10 and 11).When assessing the significance of the ANOVA terms for each

individual metabolite (at a significance level of a = 0.05 afterBonferroni correction for multiple hypotheses testing), we foundthat the G3E interaction was significant for two primary metab-olites:maltitol andphenylalanine (Figure3;SupplementalDataSet12). For the secondary metabolites, the G3E interaction wassignificant after the conservative Bonferroni correction for sevenmetabolites (i.e., F009, F045, F074, F080, F401, F413, and F620;Figure 3; Supplemental Data Set 13).The significance of the genotype effect demonstrates that there is

a difference between the genotypes over all considered environ-mentswith respect to the trait (i.e., thevariationof themetabolite levelper Levene’s transformation). This effect points further at the re-spectivebinharboringQTLforvariation.Thestatistical significanceoftheG3E interactionwasdetected in the following introgression lines:

Figure 2. Putative cmQTL.

Chromosome mapping of putative canalized primary metabolite QTL based on the genetic map of the S. pennellii introgression lines (http://www.sgn.cornell.edu). The figure shows 84 major cmQTL primary metabolites using the reaction norm of the trait (RxNV) approach. cmQTL highlighted in blue aresignificantata levelofa=0.05afterBonferroni correction formultiplehypotheses testing.Formoredetailedanalysis, seeSupplementalDataSets11and12.

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Figure 3. Variability of Canalized Metabolites.

InteractionplotsofcanalizedmetabolitesafterLevene’s transformationandBonferronicorrection formultiplehypotheses testing.Data represent the relativeabundanceofmetabolitesfromM82andILsafterLevene’stransformationfromthreeindependentharvestseasons(2001,2003,and2004).Dataarerepresentedasmean6SD(n>6).

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IL-10-1 for maltitol and phenylalanine, 10-2-2 for phenylalanine,IL-12-2 for F009, F045, F401, and F413, IL-4-4 for F074, IL-7-4 forF080, and IL-12-1 for F620. Visualization via interaction line graphsillustrates that M82 differs from the particular ILs with respect tochange of variability in different years (Figure 3).

Similarly to the analysis ofmQTL,wealsodetermined the extentto which the detected cmQTL were affected by excluding eachone of the harvests. We identified 188, 24, and 119 cmQTL forsecondary metabolites and 36, 35, and 33 cmQTL for primarymetabolites before Bonferroni correction for the pairs of harvests

Figure 4. Validation of Putative cmQTL Using the BIL Population.

(A) Schematic representation of chromosome 10 and BILs that have the S. pennellii genomic segments (represented as blue bars).(B)Box plots of relative abundance of metabolites after Levene’s transformation for the validated cmQTL on IL10-1. The left panel represents the analyzedoverlappingBILgenotypes.The rightpanel shows relativemetabolite content in the ILandM82genotypes fromthreeenvironments.Dataare representedasbox plots and display the full range of variation from minimum to maximum (n > 6).(C)Schematic representation of the genomic interval harboring the cmQTL for Phe on IL10-1 (corrected P value = 4.01E-6, nominal P value = 5.8E-10). Thecorresponding region in S. pennellii is characterized by multiple presence-absence variants.E,environment followedby theharvest year; 01, fromyear2001;03, fromyear2003;04, fromyear2004;BILs-M-E1,backcross inbred lines thathave theM82genomic segments in this region and grew in environment (harvest) 1; BILs_P E1, backcross inbred lines that have S. pennellii segments in this region andgrew in environment (harvest) 1.

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2003 and 2004, 2001 and 2003, and 2001 and 2004, respectively.For secondary and primary metabolites, we found 6, 1, and1 cmQTLand0, 0, and 1 cmQTLafter Bonferroni correction for therespectivepairsof harvests.Given that, in theexplorativeanalysis,we detected cmQTL under field conditions in all pairs of con-ditions, we concluded that the revealed cmQTL over the threeharvests captured a robust signal for QTL underlying metaboliccanalization.

Validation of Identified cmQTL with the Solanum pennelliiBIL Population

We next sought to validate these putative canalization QTL byanalyzing a subset of the independently derived S. pennellii

backcrossed introgression lines (BILs) (Ofner et al., 2016), whichprovide increased resolution of the regions of interest. For thispurpose,we focusedon theputative canalizedprimarymetaboliteQTL on chromosome 10, specifically the hot spot in IL10-1, andwere able to confirm 9 (out of 25) cmQTLmapped to chromosome10, using the BIL population. The validated canalized primarymetabolite QTLwere phenylalanine on both arms of chromosome10 and Fru-6-P, Glc-6-P, and maltose on the upper arm ofchromosome 10 (Figures 4, 5, 6 and 7). It is important to note thatthe higher resolution afforded by the BIL population allowed us todistinguish the QTL for Fru-6-P, Glc-6-P, and maltose from thoseforphenylalanine,malate,myo-inositol, anderythritol. Intriguingly,there is almost nooverlap between thecmQTL identifiedhere (andvalidated by the BILs) and those for the metabolite levels

Figure 5. Validation of Putative cmQTL for Glc-6-P, Fru-6-P, and Maltose Using the BIL Population.

Box plots of relative abundance of Glc-6-P, Fru-6-P, and maltose after Levene’s transformation (left panel) and validation using BILs (right panel). On they axis, the left panel represents the relative abundance of metabolites after Levene’s transformation; data from three independent harvest seasons (2001,2003, and 2004). The right panel represents the analyzed overlapping BIL genotypes from three independent environments. Data are represented as boxplots anddisplay the full range of variation fromminimum tomaximum (n>6). E, environment followedby the harvest year; 01, from year 2001; 03, from year2003; 04, fromyear2004;BILs-M-E1, backcross inbred lines that have theM82genomicsegments in this regionandgrew inenvironment (harvest) 1;BILs_PE1, backcross inbred lines that have S. pennellii segments in this region and grew in environment (harvest) 1.

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themselves (Schauer et al., 2008; Alseekh et al., 2015), with onlyone of them being mapped to the same region, namely, phenyl-alanine on chromosome 10 (IL10-3).

However, while perusal of the genes in the vicinity of thesecmQTL revealed three glucosyltransferases in the subregion ofIL10-2 and phenylalanine ammonia lyase on IL10-3, there werevery few metabolism-associated genes in any of the other inter-vals; with the exception of those mentioned above, none of thecmQTLcontainedgenes thatweredirectly involved in synthesis ordegradation of the metabolite in question (Supplemental Data 2).The high-resolution nature of the BILs, along with the fact that thegenomes of both parental lines have been sequenced (Bolgeret al., 2014), rendered it possible to obtain relatively short genelists for the nine regions that we used for validation via the in-dependent population (Supplemental Data Set 14). Interestingly,several of thegenescontainedwithin the cross-validated intervalsare associated with regulatory proteins, including those encodingchaperones, receptor-like kinases, and transcription factors (seeSupplemental Files 1 and 2 for a detailed discussion) and as suchare similar to previously described genes associated with cana-lization of developmental phenotypes in both plants (Queitschet al., 2002; Hall et al., 2007; Jimenez-Gomez et al., 2011) andother biological systems (Rutherford and Lindquist, 1998;Specchia et al., 2010).

To further investigate thegeneticbasis of canalization,we focusedon thecmQTL forphenylalaninedetected in IL10-1,which representsa significant cmQTL under both the permissive and conservativeapproaches (uncorrected P value = 5.8E-10, Bonferroni-corrected Pvalue = 4.01E-6). The genomic interval for this cmQTL was furtherreduced with a subset of overlapping BILs to a 0.5-Mb segmentcontaining 32 genes (Figure 4C,map of cmQTL for phenylalanine onIL10-1). A genomic survey of the corresponding region inS. pennellii

revealed long stretches of nonorthologous genes and a general lackof colinearity between the two species. The main genomic poly-morphisms observed in S. pennellii are the absence of the 10 FADbinding domain containing genes and a higher number of retro-transposon-related genes (e.g., those encoding gag/pol, reversetranscriptase, and Ribonuclease H).

DISCUSSION

Here, we report a genome-wide analysis for canalization ofmetabolic traits in tomato fruits. Toour knowledge, this representsone of the first experimental approaches attempted in plants onthe subject; as such, it extends our previous mQTL mappingefforts in IL populations, which were based solely on phenotypicdata. Aprevious studyhas examined thecanalization ofmetabolicfluxes in Escherichia coli (Ho and Zhang, 2016): On the basis ofcomputed fluxes in a genome-scale model, the authors identifiedsix capacitor reactions they believed to be important in conferringrobustness of primary metabolism. However, it is important tonote that these data were not validated experimentally.Our findings indicate that in tomato fruit, the levels of only some

metabolites are canalizedand that the tomato genesencoding theproposed capacitor reactions for E. coli are not among the can-didate genes we report here. This is not surprising, given thenotable difference between multi- and unicellular organismsbased on the structure of the underlying metabolic networks(Rosenfeld and Alon, 2003; Milo et al., 2004). Intriguingly, severalputative canalizedprimarymetaboliteQTLmap to thesame, albeitlarge, region of chromosome 10. This finding suggests that traitvariation of these metabolites may be buffered by a commonregulatory locuswithin this region. In the caseof thephenylalaninecmQTL in IL10-1, however, the genetic basis of trait canalization

Figure 6. Validation of Putative cmQTL for Phenylalanine in IL10-3 Using the BIL Population.

Box plots of relative abundance of phenylalanine in IL10-3 after Levene’s transformation (left panel) and the validation usingBILs (right panel). On the y axis,the left panel represents the relative abundance of metabolites after Levene’s transformation. Data from three independent harvest seasons (2001, 2003,and 2004) are shown. The right panel represents the analyzed overlapping BIL genotypes from three independent environments. Data are represented asbox plots and display the full range of variation fromminimum tomaximum (n> 6). E, environment followed by the harvest year; 01, from year 2001; 03, fromyear 2003; 04, from year 2004; BILs-M-E1, backcross inbred lines that have theM82 genomic segments in this region and grew in environment (harvest) 1;BILs_P E1, backcross inbred lines that have S. pennellii segments in this region and grew in environment (harvest) 1.

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seems more complex: The comparison of the genomic sequencebetween the two species highlights a series of presence-absencevariants,withtheS.pennellii regionalsodiffering,withrespecttoM82,for thepresenceof several additional retrotransposon-relatedgenes.In thiscmQTL, thegeneticdifferencebetweenS. lycopersicumandS.pennellii does not appear to reside in a single candidate gene, butinstead extends over 0.5 Mb and includes long stretches of non-colinearity between the two species. However, while most of theputative canalizedmetaboliteQTL represent relatively large genomicregions, oneof thesecorresponding to the15secondarymetabolitesmapped to the same region of chromosome 10, consisted of only58 genes (Supplemental File 1).

Importantly, however, several of theputativeprimarymetaboliteQTL and two of the BIL-validated primary metabolite QTL that wereported share a common metabolite. An evaluation of the met-abolic roles of these metabolites and of the validated canalizedmetabolite QTL for which only a single locus was observedsuggest that the pathways of starch degradation (reflected bymaltose, isomaltose, Fru-6-P, and Glc-6-P content), which playa key role in ripening (Carrari et al., 2006), alongside the metab-olism of phenylpropanoids, volatile organic compounds, and theamino acids asparagine (an important component of foliar nitrateassimilation) and phenylalanine (a precursor of lignin) (Dal Cinet al., 2011), are maintained at constant levels by highly robustgenetic mechanisms within the tomato pericarp. Indeed, the

phenylalanine QTLwas evenmaintained following the applicationof the highly stringent Bonferroni correction. Since the pathway ofstarch degradation (which yields sugars) and those related tophenylalanine-derived volatiles contribute to fruit nutritionalquality and flavor (Zhang et al., 2015; Tieman et al., 2017), it ispossible that these pathways are related to the production ofenticing fruits that aid in seed dispersal.In conclusion, by analyzing two independent populations de-

rived from crossing cultivated tomato with one of its wild speciesrelatives,wehavedemonstrated thatgenetic canalizationextendsbeyond developmental phenotypes and additionally encom-passes metabolite levels as traits. We have shown that, at least insome instances, some of these loci for metabolite canalizationcould bemapped in different genomic locations from thosemQTLfor phenotypic means. This finding opens the possibility ofbreeding for stability of metabolic traits independently of theamount of metabolites present. Our analysis of genomic seg-ments harboring the cmQTL also indicated that canalization islikely achieved through the action of regulatory genes, rather thanbeing embedded in the structural genes of metabolic pathways.As with other QTL studies of metabolic traits, our results may beconfined to the tissue and developmental stages from which thematerial was obtained. Therefore, establishing the extent towhichthe same QTL can be identified in other settings provides anexciting new research perspective. These findings thus not only

Figure 7. Validation of Putative cmQTL Using the BIL Population.

Box plots of relative abundance of erythritol (A), malate (B), glycerate (C), andmyo-inositol (D) after Levene’s transformation (left panel) and their validationusing BILs (right panel). The y axis in the left panel represents the relative abundance of metabolites after Levene’s transformation. Data from threeindependent harvest seasons (2001, 2003, and 2004) are shown. The right panel represents the analyzed overlapping BIL genotypes from three independentenvironments. Data are represented as box plots anddisplay the full range of variation fromminimumtomaximum (n>6). E, environment followedby the harvestyear;01, fromyear2001;03, fromyear2003;04, fromyear2004;BILs-M-E1,backcross inbred lines thathavetheM82genomicsegments in this regionandgrewinenvironment (harvest) 1; BILs_P E1, backcross inbred lines that have S. pennellii segments in this region and grew in environment (harvest) 1.

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openupanovel fundamental researchavenue,giventhat they identifygenomic regions that suppress variability in several traits, but alsohave important implications for commercial applications, particularlywith regard to the renewed interest inbreedinghighquality crops thatare both tasty and nutritious.

METHODS

Growth Conditions

The metabolite data set presented is based on field-grown introgressionlines (and in 2004 their respective heterozygous counterparts; Semel et al.,2006) over three harvests (2001, 2003, and 2004). The field trials wereconducted in the Western Galilee Experimental Station in Akko, Israel.Plantsweregrown inacompletely randomizeddesignwithoneplantperm2.Seedlings were grown in greenhouses for 35 to 40 d and then transferred tothe field. Twelve seedlings of each homozygous IL and heterozygous ILH(IL*M82) were transplanted as well as 70 seedlings of M82. Eight ILs werenot included in the analysis of 2004 because of poor germination (ILH2-4,IL3-1, ILH3-4, ILH6-2, ILH6-2-2, ILH6-4, ILH7-2, and ILH9-3-2). Fruit washarvested when 80 to 100% of the tomatoes were red (Eshed and Zamir,1995). The field was irrigated with 320m3 of water per 1000m2 of field areathroughout the season.

Three to five red ripe fruits were collected from each biological replicate(single plant) and three to six independent plantswere used for each IL. Forthe cultivated variety M82, we had at least 80 independent biologicalreplicates in each harvest season. Fruit pericarp materials were frozen inliquid nitrogen and stored at280°C until further analysis. From the frozentomato powder, an aliquot of fresh weight was weighed and extracted forboth primary and secondarymetabolites as described previously (Schaueret al., 2006; Tohge and Fernie, 2010).

Morphological and reproductive traits have previously been describedfor the 2001 and 2003 harvests (Schauer et al., 2006, 2008) and for the2004 harvest (Semel et al., 2006) and variance in primary metabolite traitshave also been recorded for three harvests (Alseekh et al., 2015).

Secondary Metabolite Profiling

Secondary metabolites were profiled using the Waters Acquity UPLCsystem coupled to an Exactive Orbitrap mass detector according to thepreviouslypublishedprotocol (Giavaliscoetal., 2009).UPLCwasequippedwith a HSS T3 C18 reversed phase column (100 3 2.1-mm i.d. 1.8-mmparticle size; Waters) which was operated at a temperature of 40°C. Themobile phases consistedof 0.1% formic acid inwater (Solvent A) and0.1%formic acid in acetonitrile (Solvent B). The flow rateof themobile phasewas400 mL/min, and 2 mL sample was loaded per injection. The UPLC wasconnected to an Exactive Orbitrap (Thermo Fisher Scientific) via a heatedelectro spray source (ThermoFisher Scientific). The spectrawere recordedusing full scanmode in negative ion detection, covering amass range fromm/z 100 to 1500. The resolution was set to 25,000 and the maximum scantime was set to 250 ms. The sheath gas was set to a value of 60, while theauxiliary gas was set to 35. The transfer capillary temperature was set to150°C, while the heater temperature was adjusted to 300°C. The sprayvoltagewas fixed at 3 kV, with a capillary voltage and a skimmer voltage of25and15V, respectively.MSspectrawere recorded frommin0 to19of theUPLC gradient. Molecular masses, retention time, and associated peakintensities were extracted from the raw files using RefinerMS software(version 5.3; GeneData), Metalign (Lommen, 2012), and Xcalibur software(Thermo Fisher Scientific). Metabolite identification and annotation wereperformed using standard compounds, literature, and tomato metab-olomics databases (Moco et al., 2006; Iijima et al., 2008; Tohge and Fernie,2009, 2010; Rohrmann et al., 2011). Data are reported in a mannercompliant with the standards suggested by Fernie et al. (2011).

Heat Maps

Heat maps were created by MeV software (http://mev.tm4.org), using thelog2 of the metabolite fold changes with respect to the recurrent parentM82. Each cell in the heat map represents the log2 average fold changevalue from the independent seasons.Hierarchical clusteringwasapplied torows and columns using Pearson’s correlation coefficient.

mQTL Mapping in the IL Population

To identify mQTL, a two-way mixed effect ANOVA (see subsection below)withgenotype (IL)wasusedasa fixedeffect andenvironment andgenotype3environment interactions were treated as random effects. mQTL weredetected if the genotype effect or the genotype3 environment effect wasstatistically significant at a level of 0.05. To provide conservative results, allP values were Bonferroni corrected for multiple hypotheses testing.Correlation analysis was also performed across the entire population bymeans of the Pearson correlation coefficient to determine possibletechnical artifacts. A gas chromatography-mass spectrometry analysiswas also performed on fruit materials harvested in three independent fieldtrials from selected Solanum pennellii BILs (Ofner et al. 2016).

Statistical Analysis of Canalization

Starting from theentiremetabolite datasets for thewild-typeM82andeachintrogression line in three seasons, canalization was analyzed usingANOVA following the reaction norm of the trait (RxNT) definition of can-alization (Dworkin, 2005a). According to this definition, canalization is theopposite of phenotypic plasticity (Nijhout and Davidowitz, 2003): Thus,a genotype would be considered canalized with respect to a set of envi-ronments if there is no environmental effect on the trait. By this definition,the more canalized a genotype is, the less its trait should vary acrossenvironments. For themetabolite traitMweconsider its variance, resultingin the reaction norm of the variance (RxNV) approach.

In this approach, canalization is inferred by decanalizing the system viamultiple environments to induce a change in the trait away from its normalmanifestation. In our case, the RxNT applied to metabolite M can becaptured by a two-way ANOVA model:

mijk ¼ mþGi þ Ej þ ðG3EÞij þ «ijk , where mijk is the kth replicatedmeasurement of metabolite M’s trait in genotype i under environment(season) j, m is the grand mean for metaboliteM’s trait, Gi corresponds tothe effect of genotype i (with i∈fM82; ILg), Ej corresponds to the seasonj (with j∈f2001; 2003; 2004g), ðG3EÞij is the interactionbetweengenotypei and season j, and «ijk is a normally distributed error term. We treated Ej

and ðG3EÞij as random effects, while Gi was considered a fixed effect.Themodel was implemented in the R programming environment using thepackage lme4.Thesignificanceof thefixedeffectswasdeterminedwith theANOVA function from the car package. The significance of the randomeffect ðG3EÞij wasdeterminedbycomparing thefitsof themodelswith andwithout inclusion of the interaction (Pinheiro andBates, 2000). InRxNV, themodel is based on the Levene’s transformation of mijk given by���logmijk2logqij

���,whereqij is themedianof the replicates ingenotype iunder

environment j.The following is a code snippet which allows replication of the findings:

fit <- lmerðobs;genþð1jenvÞ þ ð1jgen : envÞ;data ¼ datÞresults <- AnovaðfitÞfit:nointeraction <- lmerðobs;genþ ð1jenvÞ;data ¼ datÞ

results.compare <- anova(fit, fit.nointeraction) where dat contains theANOVA table (see Supplemental File 2). The same lines with the nominallevels of the metabolites (i.e., without Levene’s transformation) were usedto determine the mQTL.

A significantG3EM term then indicates genetic variation for plasticity ofmetabolite M’s trait.

2762 The Plant Cell

Since the two-way ANOVA model is tested for each metabolite andintrogression line, Bonferroni adjustment of P values was performed formultiple hypotheses testing (i.e., the obtained P values were multiplied byml, where m denotes the number of metabolites and l represents thenumber of introgression lines).

Selection of Candidate Genes in the cmQTL

Todetect functional candidate genes in the identifiedcmQTL, the full list of thegenes contained in each introgression/cmQTL was retrieved from the SolGenomics network (https://solgenomics.net/, genomeversion: tomatoSl2.50,ITAG2.4). For each introgression, the gene listswere then filtered to retain onlythose genes which, on the basis of previous studies, have shown to possessa role in the genetics of trait canalization (in plants or other organisms). Thesefunctional candidate genes were subjected to a combination of sequenceanalyses and annotation tools (see Supplemental File 1).

Supplemental Data

Supplemental Figure 1. Fully annotated heat map of secondary metaboliteprofiles of three independent studies of the pericarp metabolite content of theILs compared with M82.

Supplemental Figure 2. Putative canalized secondary metabolitequantitative trait loci.

Supplemental Figure 3. Interaction plots showing the variability ofputative canalized primary metabolites in the hotspot IL10-1.

Supplemental Table 1. Summary of number of mQTL and cmQTLidentified in this study.

Supplemental Data Set 1. Average fold changes of secondary metab-olites in the S. pennellii introgression lines over the 3-year observation period.

Supplemental Data Set 2. Significant mQTL for secondary metabo-lites due to the genotype interaction effect at a level of a = 0.05 afterBonferroni correction for multiple hypotheses testing.

Supplemental Data Set 3. Significant mQTL for secondary metabo-lites due to genotype by environment interaction effect at a level of a =0.05 after Bonferroni correction for multiple hypotheses testing.

Supplemental Data Set 4. Significant mQTL for primary metabolitesdue to genotype effect at a level of a = 0.05 after Bonferroni correctionfor multiple hypotheses testing.

Supplemental Data Set 5. Significant mQTL for primary metabolitesdue to genotype by environment interaction effect at a level of a = 0.05after Bonferroni correction for multiple hypotheses testing.

Supplemental Data Set 6. Significant mQTL for secondary metabo-lites due to genotype interaction effect at a level of a = 0.05 without theconservative Bonferroni correction for multiple hypotheses testing.

Supplemental Data Set 7. Significant mQTL for secondary metabo-lites due to genotype by environment interaction effect at a level of a =0.05 without the conservative Bonferroni correction for multiplehypotheses testing.

Supplemental Data Set 8. Significant mQTL for primary metabolitesdue to genotype effect at a level of a = 0.05 without the conservativeBonferroni correction for multiple hypotheses testing.

Supplemental Data Set 9. Significant mQTL for primary metabolitesdue to genotype by environment interaction effect at a level of a = 0.05without the conservative Bonferroni correction for multiple hypotheses testing.

Supplemental Data Set 10. Significant cmQTL for secondary metab-olites at a level of a = 0.05 without the conservative Bonferronicorrection for multiple hypotheses testing.

Supplemental Data Set 11. Significant cmQTL for primary metabo-lites at a level of a = 0.05 without the conservative Bonferronicorrection for multiple hypotheses testing.

Supplemental Data Set 12. Significant cmQTL for primary metabo-lites at a level of a = 0.05 after Bonferroni correction for multiplehypotheses testing.

Supplemental Data Set 13. Significant cmQTL for secondary metab-olites at a level of a = 0.05 after Bonferroni correction for multiplehypotheses testing.

Supplemental Data Set 14. List of candidate genes of each validatedcanalized mQTL using the backcross inbred lines.

Supplemental File 1. Detailed dissection of genes within validatedcmQTL.

Supplemental File 2. R data R script.codes file.

ACKNOWLEDGMENTS

Work in the Zamir and Fernie laboratories is funded by the DeutscheForschungsgemeinschaft in the framework of Deutsche Israeli ProjectFE 552/12-1. The Zamir lab was also supported by the Israel ScienceFoundation (653/15).

AUTHOR CONTRIBUTIONS

S.A., D.Z., Z.N., and A.R.F. conceived the work. I.O. and D.Z. providedgenetic materials and managed their growth and harvest, while S.A., F.S.,Y.B., F.V., and T.T. conducted the analysis. H.T. and Z.N. designed anddeveloped thebioinformatic analyses.S.A., Z.N., andA.R.F. interpreted thedata and wrote the article.

Received May 10, 2017; revised October 10, 2017; accepted October 31,2017; published November 1, 2017.

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DOI 10.1105/tpc.17.00367; originally published online November 1, 2017; 2017;29;2753-2765Plant Cell

Ofner, Dani Zamir, Zoran Nikoloski and Alisdair R. FernieSaleh Alseekh, Hao Tong, Federico Scossa, Yariv Brotman, Florian Vigroux, Takayuki Tohge, Itai

Canalization of Tomato Fruit Metabolism

 This information is current as of March 22, 2020

 

Supplemental Data /content/suppl/2018/02/01/tpc.17.00367.DC2.html /content/suppl/2017/11/01/tpc.17.00367.DC1.html

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