Conserved changes in the dynamics of metabolic processes during fruit development and ripening across species

Computational analyses of molecular phenotypes traditionally aim at identifying biochemical components that exhibit differential expression under various scenarios ( e.g. , environmental and internal perturbations) in a single species. High-throughput metabolomics technologies allow quantification of (relative) metabolite levels across developmental stages in different tissues, organs, and species. Novel methods for analyzing the resulting multiple data tables could reveal preserved dynamics of metabolic processes across species. The problem we address in this study is twofold: ( i ) we derive a single data table, referred to as a compromise , which captures information common to the investigated set of multiple tables containing data on different fruit development and ripening stages in three climacteric ( i.e. , peach and two tomato cultivars, Ailsa Craig and M82) and two non-climacteric ( i.e. , strawberry, pepper) fruits; in addition, we demonstrate the power of the method to discern similarities and differences between multiple tables by analyzing publically available metabolomics data from three tomato ripening mutants together with two tomato cultivars, and (ii) identify the conserved dynamics of metabolic processes, reflected in the data profiles of the corresponding metabolites which contribute most to the determined compromise. Our analysis is based on an extension to principle component analysis, called STATIS, in combination with pathway over-enrichment analysis. Based on publically available metabolic profiles for the investigated species, we demonstrate that STATIS can be used to identify the metabolic processes whose behavior is similarly affected during the fruit development and ripening. These findings ultimately provide insights in the pathways which are essential during fruit development and ripening across species.


Introduction
While the field of transcriptomics has experienced revolution due to the advent of nextgeneration sequencing technologies, facilitating the identification of differentially expressed genes, alleles, and spliced transcripts (Alba et al., 2005;Vriezen et al., 2008;Bombarely et al., 2010;Matas et al., 2011;Rohrmann et al., 2011;Gordo et al., 2012;Kong et al., 2013), metabolomics allows the quantification of metabolites which are generally conserved across the kingdoms of life. Existing metabolomics technologies are routinely applied to obtain a (nontargeted) snapshot of biological systems operating under given environmental conditions (Schauer et al., 2006;Hanhineva et al., 2008;Hanhineva et al., 2009;Do et al., 2010;Melendez-Martinez et al., 2010;Moing et al., 2011;Osorio et al., 2011a;Lenucci et al., 2012;Toubiana et al., 2013;Wahyuni et al., 2013), and result in metabolic phenotypes (i.e., multivariate data sets) gathered from the same set of metabolites under various conditions and/or time domains. These metabolic phenotypes can readily be used in comparative analyses of metabolic processes across species to obtain conserved aspects of the cellular physiological status, and, as such, may highlight common temporal aspects of biochemical regulation. However, the comparative analysis requires the development of methods for simultaneous analysis of multiple data sets (or data tables). Such methods can in turn be employed to determine preserved changes in the dynamics of metabolic processes across species. Thus, in this multi-species setting, the metabolomics-driven analysis goes a step further than the classical computational studies aimed at identifying biochemical components, which exhibit differential expression under various scenarios (e.g., environmental and internal perturbations) within a single species.
The goal of our study is to introduce the STATIS approach in the analysis and interpretation of metabolomics data over the same set of metabolites in (not necessarily identical) fruit development and ripening stages of different species. The idea of STATIS is based on the integration of a given set of data tables into an optimum weighted average, called a compromise, which captures what is common to all or a subset of analyzed tables. The compromise is obtained based on principal component analysis (PCA) of a specially constructed matrix. In addition, enrichment analysis based on metabolite compound class and pathway participation can be carried out to facilitate further investigations of the resulting compromise. This approach has already been successfully used in the analysis of transcriptomics time-series data (Klie et al., 2012).
Fruit are generally classified into two physiological groups, climacteric and non-climacteric, according to the presence or absence of ethylene biosynthesis peaks and respiratory bursts during ripening. Ethylene synthesis in climacteric fruits, such as: tomato, peach, apple, and banana, is essential for normal fruit ripening; moreover, blocking either synthesis or perception of this phytohormone prevents ripening (Giovannoni, 2001). Fruits such as strawberry, pepper, and grape have been classified as non-climacteric, based on the low endogenous production of ethylene compared to standard climacteric fruits and due to their inability to accelerate fruit ripening by the external application of ethylene or ethylene-releasing compounds (Perkins-Veazie, 2010). Metabolomics data from different stages in the development and ripening of both climacteric and non-climacteric fruits offer an excellent case for investigating which metabolic processes/functions show preserved dynamics and are regulated similarly in the two physiological groups. The main changes associated with ripening include color (i.e., loss of green color and increase in non-photosynthetic pigments that vary depending on species and cultivar), firmness (i.e., softening by cell wall degrading activities), taste (e.g., increase in sugar and decline in organic acids), and flavour (i.e., production of volatile compounds providing the characteristic aroma) (Carrari and Fernie, 2006;Howard and Wildman, 2007;Fait et al., 2008;Zhang et al., 2011;Wahyuni et al., 2013). These transformations are the result of dynamic processes which involve a complex series of molecular and biochemical changes under genetic regulation and/or in response to environmental perturbations (Giovannoni, 2004;Do et al., 2010;Page et al., 2010;Klee and Giovannoni, 2011;Osorio et al., 2011a;Osorio et al., 2011b;Pan et al., 2012;Tieman et al., 2012;Osorio et al., 2013a).
To better understand fruit development and ripening mechanisms, numerous studies have focused on measuring transcript and metabolite levels in climacteric fruits, such as: tomato (Alba et al., 2005;Carrari et al., 2006;Vriezen et al., 2008;Enfissi et al., 2010;Karlova et al., 2011;et al., 2013), and grape (Deluc et al., 2007;Grimplet et al., 2007). These studies have been complemented by investigation of transcriptomics, proteomics, and metabolomics data in the three dominant ripening mutants of tomato, ripening-inhibitor (rin), nonripening (nor), and never-ripe (Nr), along the developmental and ripening periods. While integration of genomics, transcriptomics and metabolomics data during fruit development and ripening can give important insight into gene-regulatory and metabolic events associated with these processes in a single species (Carrari et al., 2006;Grimplet et al., 2007;Enfissi et al., 2010;Zamboni et al., 2010;Osorio et al., 2011a;Osorio et al., 2011b;Rohrmann et al., 2011;Osorio et al., 2012), identifying the metabolic functions which are similarly regulated across difference species has not yet been attempted.

Contribution of species in STATIS reveals the influence of physiologically distinct fruit groups and developmental stages on the compromise space
Similar to the classical PCA, the analysis based on STATIS allows a description of the contributions of tables, observations, and variables. However, the advantage of STATIS is that it allows simultaneous investigation of multiple tables, which is not readily achievable by PCA.
Here, the tables contain the metabolic profiles from organs (four from the following: two tomato cultivars, peach, and pepper as well as strawberry fruit, which was separated into two organs -achenes, representing the true fruit, and receptacles) from which metabolomics data during fruit development and ripening were available, as detailed in the Materials and Methods.
The variables denote the 16 metabolites measured across species, while fruit development and ripening stages correspond to observations in the data tables.
Analysis of the contributions of the individual data tables to the compromise, , then renders it possible to determine the species which has the strongest influence on the determined principal components and which, furthermore, reflects good overall similarity to the data tables from the remaining species. We note that the employed data tables (i.e., sampling time points of a given species; see Figure 1) differ in the coverage of the fruit development and ripening stages as well as in the number of tables from the considered species. Therefore, we first investigated not only the extent to which each data table contributes to the compromise but also the stability of the derived compromise upon exclusion of a data table (i.e., sampling time point).
The contribution of a given table to the compromise is quantified by the obtained table- Figure 2 (A).
Since in this analysis we consider two data sets from strawberry, from the achene and the receptacle, respectively, we next ask if the obtained compromise is biased with respect to strawberry. To this end, we first determined the contribution of each table to the compromise   obtained by leaving out the data table from strawberry receptacle and then compare it to the compromise obtained from all tables. As shown in Figure 2 (B), the new compromise remained largely unaffected. This analysis prompt us to investigate the stability of the compromise from all leave-one-out scenarios, whereby we determine the compromise with each of the tables omitted and quantify its similarity to the compromise with all tables included by using the Mantel correlation. Interestingly, the removal of each data table results in very similar compromise spaces, supported by the statistically significant Mantel correlation of high magnitude (see Table I). This finding provides further evidence for the hypothesis of preservation of metabolic patterns in fruit development and ripening irrespective of the subgroups of species used in the analysis per se.
This conclusion may appear counter-intuitive given the observed difference between the contributions of the data tables from climacteric and non-climacteric fruits to the compromise.
Since the considered data tables partly differ in the included types of stages, i.e., development and ripening, we next investigate the effect of removal of later ripening stages on the resulting compromise. To this end, we remove the time points corresponding to the later ripening stages (marked with a star, *, in Figure 1) in all six data sets. Strikingly, all four species still have similar contribution to the determined compromise, as seen on Figure 2 (C). This unexpected finding illustrates that metabolic profiles differ between the three climacteric fruits and two nonclimacteric fruits across the entirety of the developmental time course, and not only during the ripening stages (with an increase of ethylene).
To further confirm the robustness of this result, we next considered applying STATIS on data Altogether, this analysis confirms the stability of the derived compromise and points to some inherent differences between climacteric and non-climacteric fruits based on their corresponding metabolic profiles. Therefore, in the following, the analysis will focus on the first scenario, illustrated on ( Figure 2 (A)), including all species and all available time points. In this case, the data tables of highest weight correspond to non-climacteric fruits, i.e., strawberry and pepper, followed by the climacteric fruits, including peach and the two tomato genotypes. The compromise space includes the effects of both climacteric and non-climacteric fruits and thus could reveal coordinated key metabolic adjustments in fruit development and ripening across all investigated species.

Interstructure of the data tables separates climacteric from non-climacteric fruits
The effect of different organs/species on the compromise is further supported by investigating the contribution of the six data tables to the 1 st , 2 nd , and 3 rd principal components (PC1-3, Figure   3) also known as the The observed differences in M82 and Ailsa Craig tomato fruits in the contribution to PC1 and PC2 are likely due to the fact that they are quite distinctive cultivars. Both prominent genotypic and phenotypic differences have been already reported between these cultivars (Shirasawa et al., 2010;Kusano et al., 2011;Matas et al., 2011) which is perhaps not surprising since M82 is a processing or ketchup cultivar, whilst Ailsa Craig has been bred as a salad variety. Further differences between these tomato cultivars are highlighted by investigating their more extensive data tables (with 23 metabolites) and relations to three tomato ripening mutants (see section below).
The STATIS analysis depends on determining the cross-product matrix of coefficients capturing the similarity between the data tables. As illustrated in Figure 4, we observed a clear metabolic distinction between climacteric and non-climacteric fruits. Both data tables of strawberry exhibit greatest similarity with that of pepper as all of them belong to the nonclimacteric fruit group. Within the climacteric fruits, similarities between peach and Ailsa Craig tomato fruits are the strongest. Perhaps surprisingly, although as previously seen by principal component analysis, M82 tomato fruits exhibit stronger similarity on the metabolic level to the non-climacteric species than to the Ailsa Craig tomato fruits (see section on tomato mutants, below).

Investigation of metabolites by inferential STATIS reveals housekeeping metabolic functions during fruit development and ripening
Characterization of variables is a pivotal step of any descriptive statistical method. We note that in our setting, the variables correspond to the 16 metabolites quantified across all developmental stages in all six of the investigated organs. To this end, STATIS allows not only the investigation of the contribution of tables (i.e., species) but also the analysis of the contributions of considered variables to the principal components. In particular, for the purpose of visualization, contributions of individual variables, columns in matrix , are obtained by projection onto the principal components of the compromise, as depicted in Figure 5.

1
To determine which metabolic functions are associated with the adjustments of metabolic content, a compound class/pathway enrichment analysis of ontology terms is typically performed (cf. Material and Methods). However, an important intermediate step is to identify the variables (i.e., metabolites), which contribute significantly to either principal component. Here, bootstrap ratios (cf. Material and Methods) were used to identify significant contributions at a significance level of . The metabolites found to significantly contribute to PC1, PC2, or PC3, illustrated as circles in Figure 5 and detailed in Table II, were next subjected to an enrichment analysis. Table III provides an overview of the pathways found to be over-represented at a significance levels of 0.01 and 0.05. We note that the very small 'universe' (i.e., 16 metabolites) warrants caution when interpreting these values. On inspection of Table III, it becomes apparent that the metabolites whose contribution to PC1 is significant are enriched for the pathway of volatile organic compound biosynthesis as well as that of branched chain amino acid biosynthesis. More precisely, we find that (i) amino acids, such as: isoleucine, phenylalanine, threonine, tyrosine, and valine, (ii) sugars, such as fructose and glucose, as well as (iii) citric acid are strongly contributing to PC1. On the other hand PC2 is characterized by glutamate, malate and sucrose. Finally, we observe that aspartic acid strongly, albeit not significantly, contributes to PC3 (see Supplemental Figure  Other metabolites that also have high bootstrap values for the contributions to PC2 and PC3 that separate the climacteric from non-climacteric fruits include: sucrose, one of the major sugars in fruits, and myo-inositol, which both relate to malate. Malate plays an important role in starch accumulation and in the total soluble sugar levels (Glc, Fru, and Suc) in developing fruits (Centeno et al., 2011;Osorio et al., 2013b). Phosphoenolpyruvate (PEP), a closely linked metabolite to malate, is transformed via the gluconeogenic pathway to sugar phosphates, which can subsequently be converted to starch (Lara et al., 2004). Evidence from radiolabel feedings (Halinska and Frenkel, 1991;Beriashvili and Beriashvilli, 1996;Osorio et al., 2013b) suggests that gluconeogenesis does occur in grape and tomato fruits, particularly during ripening stages, when sugars are accumulating rapidly. Therefore, the combined data reveal that malate metabolism may have an effect on fruit ripening and strongly influences programs of fruit development. Interestingly, the oxidation of myo-inositol provides an alternate starting point to a pathway furnishing uronosyl and pentosyl residues for cell wall biogenesis (Loewus, 2006), which may indicate that the flux through this pathway in climacteric and non-climacteric fruits is differentially regulated during development and ripening. The results of this meta-analysis indicate that, while many metabolites display commonly dynamics across fruit development among the species tested, several divergent mechanisms likely underlie the overall dynamics of metabolism during development.

Analysis of stages suggests the presence of diverse temporal patterns in fruit development and ripening
The analysis of the contributions of observations reflects the influence of the particular stage with respect to the overall metabolic adjustments of the investigated species during fruit development and ripening. Figure 6 shows the contributions of each stage per species on PC1 (xaxis) and PC2 (y-axis). Arrows between two successive stages illustrate the sequential progression of the contributions over time with the compromise space defined by PC1 and PC2.
While all the trajectories show substantial contribution to either PC1 or PC2 -clearly illustrating the ongoing transformations in the content of the levels of the investigated metabolites -of particular interest are the observations regarding the differences between and within climacteric and non-climacteric fruits.
Although the M82 cultivar of tomato and peach are climacteric fruits, they exhibit different behavior with respect to the compromise. The metabolic phenotypes of these species show not only opposite contribution to PC2, but also the progression of contributions to PC1 follows different directions, especially in the later stages, as illustrated in Figure 6. Moreover, peach exhibits similar contribution of its stages of development and ripening as that of pepper with respect to PC2, although its contributions to PC1 are of a different sign in comparison to those of pepper (see Figure 6). In contrast, strawberry (achene and receptacle) as a non-climacteric fruit, demonstrates almost completely stable contribution of the different stages to the compromisea pattern distinct in comparison to the other investigated species.
Finally, we investigated the distances between the consecutive time points on the resulting trajectories. Irrespective of the number of time points used, we observed that non-climacteric fruit undergo less dramatic changes across development in comparison to climacteric fruits ( Figure 7). Moreover, all six species exhibited more severe changes during fruit development (dark grey, Figure 7, corresponding to time points without * in Figure 1) in comparison to the changes during ripening (light grey, Figure 7).

STATIS with data tables from tomato cultivars and ripening mutants
The previous findings from the six data tables were based on 16 metabolites measured across the different organs/species, which may be seen as a drawback in relating the computational findings to similarities of the underlying biochemical pathways. To demonstrate the power and robustness of the method, and to address the questions arising from the detected differences between the two tomato cultivars, we next focused on the analysis of data tables from the two tomato cultivars and three tomato ripening mutants, namely, nor, Nr, and rin. Altogether, 23 metabolites were measured across these cultivars/mutants (Table IV), over eight (for Ailsa Craig, nor, and rin), five (M82), and four (Nr) time points, respectively (see Supplemental Figure 3).
STATIS analysis of the five data tables revealed that the typological values for the nor and rin mutants were the highest and most similar to the obtained compromise (Supplemental Figure  4A). This might be attributed to the fact that they are both in Ailsa Craig background and that  Figure 4), we identified that malate, previously discussed in the context of fruit ripening, while not exhibiting a very pronounced contribution to either PC1 or PC2, exhibited the second highest bootstrapped value for PC1 (Table IV).
Moreover, xylose, a cell wall-related compound, was the metabolite with the highest bootstrap support to PC1. This could be related to a recent finding that this abundant sugar, which is important in the formation of plant cell wall polymers, might be affected by down-regulation of cell-wall-related gene expression in the ripening mutants (Gilbert et al., 2009;Osorio et al., 2011a). GABA was found to have the most pronounced bootstrapped value for PC3, which might be attributed to the pathways separating the Nr mutant from the M82 cultivar. Finally, three amino acids of the aspartate family, namely, serine, threonine, and aspartate, exhibited strong negative contributions to PC1. This pathway has been implicated in the distribution of the carbon from aspartate into the branches for the synthesis of lysine, threonine, methionine, and isoleucine. It has been already reported that aspartate metabolism in A. thaliana is tightly regulated, and, furthermore, threonine, the most sensitive variable in the system, has previously been suggested to have a regulatory role within the network of amino acid metabolism (Curien et al., 2009).

Discussion and Conclusions
The increased availability of high-throughput technologies (e.g., transcriptomics, proteomics, Here we present an attempt to determine similarities in publically available metabolic phenotypes, characterized by the collection of metabolic profiles, of six organs (five fruits and one receptacle) from four species, namely, tomato (M82 and Ailsa Craig varieties as well as three ripening mutants), peach, pepper, and strawberry (achene and receptacle). Our analysis is based on STATIS, an extension of classical principal component analysis, which allows combining and simultaneously investigating multiple data tables. The crux of the approach is the concept of the compromise space, which not only captures the congruence between the investigated tables, but also, as in PCA, facilitates quantification of the contribution of metabolites as well as developmental and ripening stages.
As we illustrate in this study, the findings from STATIS can readily be coupled with enrichment analysis to posit and test hypotheses based on the established biochemical knowledge gathered in pathway databases. Interestingly, although only 16 metabolites were unequivocally measured across the investigated organs from climacteric and non-climacteric species, STATIS highlights the difference between climacteric and non-climacteric fruits in the contributions of their 1 6 temporally changing metabolic phenotypes to the compromise. Moreover, the stability of the compromise upon removal of any of the employed data tables suggests that there is a robust pattern of similarity, which merits further investigation.
The availability of well-characterized mutants in fruit development and ripening allowed us to investigate the metabolic differences during ripening. Here we included the analysis of metabolic profiles from two mutants in the ripening-associated transcription factors, rin, which encoded a SEPALLATA MADS box gene (Vrebalov et al., 2002), nor (GenBank accession no. AY573802; Tigchelaar et al., 1973), and two-component His kinase ethylene receptor, ETR3 (Nr, Wilkinson et al., 1995;Yen et al., 1995). Growing evidence has pointed that nor and rin are necessary for ethylene biosynthesis and both act upstream of Nr (Giovannoni et al., 1995;Osorio et al., 2011a). Moreover, the nor and rin mutants are phenotypically similar in that both fruits fail to produce climacteric ethylene and to complete normal ripening. In this context, STATIS revealed that some of the metabolites important for discriminating climacteric from nonclimacteric fruits such as malate and three amino acids of the aspartate family (i.e., serine, threonine, and aspartate) were also important for distinguishing the two tomato mutants (nor and rin) from the third ripening mutant, Nr, and the M82 variety. Therefore, these finding reinforces the importance of these precursor metabolites in the regulation of the shift in the flux to volatile organic compound biosynthesis concomitant with an increase during climacteric fruit ripening.
Degradation of plant cell wall polymers is another process directly associated to fruit ripening; therefore, the content of some cell-wall-related metabolites, like xylose, increases during this process (Rose andBennett, 1999: Osorio et al., 2011a). Interestingly, we found that xylose has high bootstrap value for the contribution to PC1 that separates the nor and rin mutants from the third studied ripening mutant (Nr) and the M82 variety. This result could be explained by the evidence that the cell wall degradation-related genes are up-regulated during normal tomato ripening but not in equivalent-stage nor and rin fruits (Osorio et al., 2011a).
Similar to the conclusions drawn from PCA, one may conclude that the metabolites (i.e., variables) furthest from the origin in the compromise space are of highest importance. In such a setting, importance implies strong variation. More specifically, this would imply strong variation in the covariance patterns between data tables, since STATIS is based on (normalized) covariances of variables. Nevertheless, it is often the case that these variables in fact do not have the highest bootstrap values: a metabolite with a strong (co)-variance pattern that is conserved across the data tables may obtain a lower bootstrap value than a metabolite whose (co)-variance pattern may be strongly affected by removal of a particular data table although its pattern may not be preserved. Therefore, the distance from the origin in the compromise should be understood only as a heuristic for judging the importance of a variable, suggesting that both forms of evidence should be taken into account when performing the data interpretation.
Our findings regarding the contribution of individual metabolites to the PCs of the compromise space in combination with the PC-based separation of the species point that the climacteric and non-climacteric fruits most significantly differ with respect to metabolism of some sugars and amino acids. Moreover, our analysis indicates that climacteric and non-climacteric fruits exhibit distinct patterns not only in ripening but also in the developmental stages (although more pronounced changes are observed in the ripening stages).
Most of these data sets come from different experimental set-ups followed by measurement of different metabolic profiles over varying fruit developmental and ripening stages obtained, nevertheless, with the same measuring technology. At present, obtaining larger coverage of metabolism for cross-species studies is severely hampered by the aforementioned heterogeneities in the publically available data sets, and would require experiments undertaken with the idea of simultaneous analysis across multiple fruits. Like other covariance-driven analyses, we note that our findings are contingent on the metabolites considered in the analysis.

Metabolic Data
Our study employs recently obtained metabolic phenotypes from two tomato (Solanum Since the subsequent analysis requires simultaneous consideration of the same set of metabolites (i.e., variables), we focused only on the 16 metabolites, which were quantified across all considered species, given in the rows of Table I. The three tomato ripening mutants were not considered in this analysis in order to have a balanced representation of only wild types from the two types of fruit. From these 16 metabolites, 13 were categorized to belong in one of the following three groups: sugars, amino acids, and organic acids, while the remaining three metabolites could not be assigned a compound class based on BRITE hierarchies. In addition, the set of metabolites, which were quantified across the tomato cultivars and mutants comprised 23 metabolites, given in the rows of Table IV. The same compound class categorization was also applied to this richer data set, which was subsequently used to analyze similarity and differences only for the tomato varieties and mutants.

Overview of STATIS
STATIS can be regarded as a generalization of principal component analysis (PCA) that allows simultaneous investigations similarities and differences between multiple data tables over the same set of variables (e.g., metabolites), even when data about these variables have been gathered in different number of experiments. The essence of the approach lies in combining the data tables into a common structure, called compromise, which is then analyzed based on PCA.
Hence, like PCA, STATIS allows for examining projections of the original data sets on the compromise and, thus, for quantifying the concordance between the data sets and the extent to which variables and observations contribute to it. The principal steps of STATIS are schematically represented in Fig. 8, and consist of the following: First, a similarity transformation (i.e., computation of a scalar product matrix) is performed for every data table, where rows correspond to variables (Fig. 8A). Therefore, the resulting square matrix has as many rows as there are variables in the original data matrix. The cosine similarity (i.e., a value between 0 and 1) for each pair of the resulting matrices is then calculated based on the R v coefficient.
The obtained R v coefficients yield a new matrix C (Fig. 8B). This captures the similarity between the data tables, and its eigenvalue decomposition can be employed for comparing the contribution of different data tables and for visual inspection (Fig. 8C). The entries of the scaled first eigenvector of C quantify the overall similarity of one data table to all others, and are used as weights to combine the scalar product matrices into a single one as a weighted sum. This matrix is termed the compromise matrix (Fig. 8D), and is denoted by S in what follows. Finally, the resulting compromise matrix can also be investigated via PCA to obtain factor scores and loadings for the variables (Fig. 8E). The overview of the steps in STATIS indicates that the determined compromise matrix (and, thus, its investigation with PCA) is contingent on the variables and data tables used. For more technical details, the reader is directed to the Supplemental Methods.

Enrichment analysis using KEGG pathway information and BRITE hierarchy compound classes
To infer which metabolic functions or compound classes are associated with the adjustments of metabolic content in the studied organs/species, we rely on enrichment analysis of ontology terms in combination with STATIS. Similar to a typical gene set enrichment analysis (GSEA), for a given a set of metabolites, we aim at assessing whether certain associated pathway memberships or compound class are overrepresented, i.e., over-enriched (Subramanian et al., 2005;Rivals et al., 2007)  compound classes for the 16 metabolites used in the analysis is given in Supplemental Table I. We point out that, given that the metabolic pathways of the considered fruits substantially differ from the used reference species, manual curation was required. This curation affected only four pathways, "Taurine and hypotaurine metabolism", "Tropane, piperidine and pyridine alkaloid biosynthesis", "Indole alkaloid biosynthesis" and "Glucosinolate biosynthesis". Three of them were removed "Taurine and hypotaurine metabolism", "Tropane, piperidine and pyridine alkaloid biosynthesis" and "Indole alkaloid biosynthesis", whereas "Glucosinolate metabolism" was renamed "Volatile organic compound biosynthesis" since it shares common precursors to the specialized Brassica pathway. As a further control, we inspected whether the 12 metabolites not assigned to the "Glucosinolate pathway" belong to the "Volatile organic compound biosynthesis" pathway, and concluded that that none of them belong to this pathway.

Acknowledgments
SO acknowledges the support by Ministerio de Ciencia e Innovación, Spain (Ramón and Cajal contract).

Supplemental data
The following materials are available in the online version of this article: Supplemental Table I. Overview of metabolites, Pathway membership and BRITE hierarchies.     The separation between the climacteric (i.e., tomato and peach) and non-climacteric fruits (i.e., strawberry and pepper) is already captured in this matrix of R v coefficients, although, surprisingly, M82 indicates higher similarity to non-climacteric fruits. Metabolites contributing significantly to any of the two components are denoted by circles, while the remaining metabolites are indicated as squares. The color code corresponds to the compound class of each metabolite, i.e., sugars (magenta), amino acids (blue), organic acids (orange), and others (grey). The compounds are identified by the corresponding numbers given in Table II.  tables normalized for the number of time points available for each data set (cf. Figure 6). Later ripening stages (indicated by * in Figure 1) are colored in lighter grey. Non-climacteric fruit undergo less dramatic changes across development in comparison to climacteric fruits, with M82

Supplemental
again showing pronounced divergence from this pattern.        table weights  table weights  table weights cos 2 cos 2  The heatmap illustrates the R v coefficient for each pair of data tables (shown on the right-most column). A value of 1 for the R v coefficient corresponds to equivalence (dark blue), while a value of 0 indicates complete dissimilarity (white) (see inlay for distribution of values). The separation between the climacteric (i.e., tomato and peach) and non-climacteric fruits (i.e., strawberry and pepper) is already captured in this matrix of R v coefficients, although, surprisingly, M82 indicates higher similarity to non-climacteric fruits. Color Key and Histogram Figure 6. Visualization of the contribution of observations (i.e., developmental and ripening stages) according to STATIS. The corresponding time stages are projected onto PC1 and PC2 for all six data tables. The position of a time point reflects the influence of the particular stage with respect to the overall metabolic adjustments of the investigated species during fruit development and ripening. Arrows between two successive stages illustrate the sequential progression of the contributions over time with the compromise space defined by PC1 and PC2. Although the M82 cultivar of tomato and peach are climacteric fruits, they exhibit different behavior with respect to the compromise, while peach and pepper exhibit similar behavior although they belong to different categories of fruit. Six data tables are considered for this analysis: two tomato varieties, peach, pepper and two strawberry organs; here a data table is comprised of 16 metabolites -variables -and developmental stages -observations. The scalar product captures the covariances of each metabolite pair (A). Based on data table similarity, a new matrix of data table similarity can be derived (B). Successively, a PCA of this compromise yields weights for combination of all 6 data tables (D) that allow to combine the data tables in a common representation -the compromise (E).