Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

doi:10.21203/rs.3.rs-20851/v3

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Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

https://doi.org/10.21203/rs.3.rs-20851/v3

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published 06 Jan, 2021

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Background Fruit ripening in Prunus persica melting varieties involves several physiological changes that have a direct impact on the fruit organoleptic quality and storage potential. By studying the proteomic differences between the mesocarp of mature and ripe fruit, it would be possible to highlight critical molecular processes involved in the fruit ripening.

Results To accomplish this goal, the proteome from mature and ripe fruit was assessed from the variety O’Henry through shotgun proteomics using 1D-gel (PAGE-SDS) as fractionation method followed by LC/MS-MS analysis. Data from the 131,435 spectra could be matched to 2,740 proteins, using the peach genome reference v1. After data pre-treatment, 1,663 proteins could be used for comparison with datasets assessed using transcriptomic approaches and for quantitative protein accumulation analysis. Close to 26% of the genes that code for the proteins assessed displayed higher expression at ripe fruit compared to other fruit developmental stages, based on published transcriptomic data. Differential accumulation analysis between mature and ripe fruit revealed that 15% of the proteins identified were modulated by the ripening process, with glycogen and isocitrate metabolism, and protein localization overrepresented in mature fruit, as well as cell wall modification in ripe fruit. Potential biomarkers for the ripening process, due to their differential accumulation and gene expression pattern, included a pectin methylesterase inhibitor, a gibbellerin 2-beta-dioxygenase, an omega-6 fatty acid desaturase, a homeobox-leucine zipper protein and an ACC oxidase. Transcription factors enriched in NAC and Myb protein domains would target preferentially the genes encoding proteins more abundant in mature and ripe fruit, respectively.

Conclusions Shotgun proteomics is an unbiased approach to get deeper into the proteome allowing to detect differences in protein abundance between samples. This technique provided a resolution so that individual gene products could be identified. Many proteins likely involved in cell wall and sugar metabolism, aroma and color, change their abundance during the transition from mature to ripe fruit.

Epigenetics & Genomics

Fruit ripening

proteome

Rosaceae

Prunus persica (L) Batsch is one of the most economically important fruit crops in the Rosaceae family, with a broad climate distribution, relatively high yield and around 1,000 cultivars produced worldwide [1, 2]. P. persica has also become a very important plant model given its compact, small (227.3 Mb) and publicly accessible genome [3], the availability of homozygous doubled haploids, and its taxonomic proximity to other important fruit species such as apricot (P. armeniaca), plum (P. salicina), almond (P. dulcis) and apple (Malus domestica) [4].

Fruit ripening is a complex process that involves changes at multiple biochemical and physiological levels which impacts gene expression [5], proteins and metabolites abundance [2,6]. It is the last step of the broader process of fruit development, where the fruit increases in volume and, in some species, the endocarp undergoes a hardening process, enclosing the seed in a secondary lignin-rich cell wall. During ethylene-dependent ripening, fruits transit from a photosynthetically active organ into an organ where the photosynthetic machinery is dismantled, carotenoids, sugars, organic acids and volatile compounds are accumulated, and the cell wall is loosed [7]. Overall, this conversion makes the fruit attractive for consumption as a rich source of fibers, vitamins and antioxidants, as well as its flavor, color and aroma.

Peach fruit ripening has been characterized at the molecular level in processes such as volatile and aroma production [8-10], ethylene and other hormone biosynthesis and signaling [11-13], cell wall dismantling [14], pigments biosynthesis [15,16], and organic acids and sugars metabolism [2, 17-19]. P. persica transcription factors (TFs) involved in anthocyanin induction [16, 20], and ethylene biosynthesis [12, 13, 20] have also been characterized. Transcriptomic studies in P. persica have been used to improve the understanding of the molecular processes that underlie fruit chilling injury [21, 22, 23], and have not focused on fruit ripening.

Given the above, knowledge is still lacking about how peach fruit ripening is orchestrated at the molecular level. Proteome is a highly dynamic model for understanding the biological processes in an organ. Two-dimensional (2D) gels followed by mass spectrometry (MS) analysis have been the most frequent approach to evaluate changes in the proteome of fruits undergoing ripening. However, this approach is limited by the low numbers of proteins of interest identified, co-migration of proteins within the same spot, the absence of hydrophobic proteins, the expertise required to generate high quality 2D-gels and the extended time required to perform the images assessment and statistical analysis [24, 25]. We propose that a 1D-gel followed by LC/MS-MS analysis proteomics approach based on a robust experimental and statistical framework can provide information regarding pathways and biological processes that are crucial for peach fruit ripening. Through this technique we could expand the number of proteins identified during the transition from mature to ripen fruit. The variety selected was O’Henry, since it is a proteome and transcriptome characterized melting peach variety [26, 27], which is the parental to several new-varieties.

Mature and ripe mesocarp peach fruit proteomes differ greatly between amongst themselves and with other fruit developmental stages

The transition from mature into ripe fruit entails major changes in the fruit mesocarp firmness, titratable acidity, total soluble solids and respiration rate; and more subtle changes in ethylene biosynthesis [28]. Significant change in the respiration rate was detected in ripe fruits (83.4 mL CO₂ kg^-1 h^-1) compared to mature fruit (15.3 mLCO₂ kg^-1 h^-1) [28]. Ethylene production in ripe fruits (2.9 mL C₂H₄ kg^-1 h^-1) was the double of that measured at harvest (1.7 mL C₂H₄ kg^-1 h^-1), suggesting a climacteric stage. This stage is similar to the stage identified as S4II by Pan et al. [29] when ethylene autocatalytic production is increasing. After 6 days of shelf life at 20^oC, the ripe peaches showed a consistent reduction in firmness from around 60N to 11N. Total soluble solids were 11% at harvest and did not change after ripening [28]. To get a deeper insight into the proteins that are involved in this transition, a 1D SDS-PAGE gel followed by MS analysis was performed (Figure 1). Data was then analyzed using MASCOT and Scaffold to focus on those proteins identified at a high confidence and to retrieve abundance data from these proteins in a format that is robust for downstream quantitative statistical analysis (Figure 1, Supplementary Figure 1). At first, a total of 131,435 spectra could be matched to 2,740 proteins, using the peach genome reference v1. After data cleaning from proteins spuriously identified or with an inadequate number of replicates, a total of 1,663 proteins were thus included in this study, representing a 6.2% of the P. persica primary transcripts proteome (Supplementary Table 1).

In order to check if the proteome represented by these 1,663 proteins had some bias in terms of its physicochemical composition, protein properties such as length, molecular weight, charge, protein stability, and hydrophobicity profiles were compared to the P. persica primary transcripts proteome (26,873 proteins) and to a similar sized 977 proteins set, extracted from juicy and mealy fruit mesocarp from the Spring Lady variety [30]. In terms of data location, results indicated that for most of the parameters selected, the three populations have similar mean values (Supplementary Table 2, Supplementary Figure 2), with the most substantial differences being related to charge and hydrophobicity, using the Guy scale [31]. In terms of data dispersion, the P. persica primary transcripts proteome displayed a higher dispersion for 5 of the 6 parameters assessed, when compared to the datasets from the current proteome and the proteome from Spring Lady (Supplementary Table 2, Supplementary Figure 2).

The functional analysis of the 1,663 proteins identified in this work, performed using Gene Ontology (GO) analysis, indicated that this protein set was enriched in processes related to carboxylic acid metabolism and intracellular transport, and to a lesser extent to protein folding (Figure 2A).

We next asked if the genes encoding the proteins present in the mesocarp of mature and ripe fruits were more expressed at this stage, or if they were expressed at the same levels in any developmental stage of the fruit development. In order to answer this question, a contrast between the transcriptional expression was performed using 18,074 P. persica genes (herein “full-dataset”, variety Fantasia, GEO dataset GSE71561) at 125 days after full bloom (DAFB) against its expression at 41, 54, 69, 83, 111 DAFB. Close to 32% of the “full-dataset” did not display any expression level difference when contrasting its expression at 125 DAFB, corresponding to mature-ripe fruit stage against any other stage (first lane, dark bar, Figure 2B). This proportion was 5.9%, when considering the protein-coding genes assessed in this study (1,136 genes that matched our dataset, herein “proteomics-dataset”, first lane, grey bar, Figure 2B). When assessing the number of genes that displayed its highest expression levels at 125 DAFB (second lane, Figure 2B), 14.6% of the “full-dataset” displayed this behavior, compared to 25.8% of the “proteomics-dataset.” Finally, when assessing the number of genes that displayed its lowest expression levels at 125 DAFB (third lane, Figure 2B), 14.1% of the “full-dataset” displayed this behavior, compared to 17.6% of the “proteomics-dataset”. This result indicates that an important proportion of the protein-coding genes characterized in the mesocarp of the mature-ripe fruit reached their highest expression levels at the mature-ripe stage of the peach fruit developmental curve.

Similar to the previous analysis, we evaluated if the protein-coding genes expressed at the fruit mesocarp during the fruit ripening were mainly expressed at this tissue or if they had an even expression across leaves, immature and ripe fruit. Therefore, a transcriptomic dataset derived from the variety Babygold, consisting of 20,149 genes, whose expression was characterized by means of RNA-seq at leaves, 2 cm immature and ripe fruit mesocarp (Figure 2C) [32], was assessed. Gene expression at leaves was characterized by 35.4% of the genes being more expressed at this tissue, compared to 16.8% in the current “proteomics-dataset.” Gene expression in immature fruit displayed 38.3% of the genes being more expressed at this tissue, whereas in the “proteomics-dataset” this number was of 29.6%. Finally, at ripe fruit, 26.2% of the genes displayed a higher expression in this tissue, whereas in the “proteomics-dataset” this number doubled to 53.7%. This result indicates that half of the protein-coding genes characterized in the mesocarp of the mature-ripe fruit reached their highest expression at this stage.

Proteomic differences between mature and ripe fruits points to the mature fruit as the main stage in which sugar metabolism is modulated in the fruit mesocarp

Principal component analysis (PCA) was used as a diagnostic plot and to identify the main variables that explain the proteomic differences between the samples assessed. PCA was performed using the 1,663 proteins identified in all mature (O1) and ripe (O2) fruit samples assessed in this study. The principal component 1 (PC1) segregates mature from ripe fruit, explaining 33.8% of the variance associated to the samples, a high value considering that the samples used were biological replicates harvested from field grown trees (Figure 3A). In fact, the second component could explain 23.2% of the variability and was likely associated with differences among fruits.

Quantitative changes between mature and ripe fruit proteome were assessed using a t-test after centering, normalizing and scaling the data to achieve a close to normal distribution (Methods section, Supplementary Figure 1). Mature and ripe fruit displayed 52 and 22 proteins with an exclusive presence (referred as “Only pres.”) in each stage respectively, and 88 and 86 proteins with a significative change in abundance during the mature to ripe transition, respectively. Overall, 248 (14.9%) of the proteins identified in this study displayed a differential abundance between mature and ripe fruit (Table 1). A functional enrichment analysis of the proteins with a differential abundance was performed using Gene Ontology (GO). Proteins more abundant in mature fruit were associated to glycogen and isocitrate metabolism, and protein localization (Figure 3B). No enrichment in any of the three GO sub-ontologies was found for proteins more abundant in ripe fruit. In terms of the chromosome distribution of the gene coding for these differentially accumulated proteins, the set differentially accumulated in mature fruit was enriched in chromosome 3; whereas the set differentially accumulated in ripe fruit was enriched in chromosome 6 (Figure 3C). No enrichment was found after assessing all 1,663 proteins identified in this study.

Among the most relevant pathways associated to proteins with differential accumulation in mature fruit, there were several that could have a direct impact in the fruit organoleptic quality: Sucrose and sorbitol conversion into fructose 6-phosphate (PWY-3801), raffinose and stachyose (PWY-5337), phenylacetaldehyde (PWY-5751), farnesyl diphosphate (PWY-5123) and glutamine (PWY-6549) biosynthesis (Figure 4A, panels I to V). In ripe fruit, we selected the ethylene (ETHYL-PWY) and linoleoyl-CoA (PWY-6001) biosynthesis pathways (Figure 4B, panels I and II). Proteins related to cell wall disassembly were also differentially accumulated in ripe fruit (Figure 4B, panel III). Transport reactions, such as sorbitol transport, would also be upregulated in ripe fruit (Prupe.8G101200, Table 1).

Almost all steps of the gluconeogenesis pathway (GLUCONEO-PWY) were represented by proteins characterized in this study (Figure 4C), indicating that the pathway was active in both stages. There were very few differences in the accumulation of the proteins between mature and ripe fruit. However, subtle differences were found including the step that interconverts dihydroxyacetone phosphate (DHAP) into D-glyceraldehyde 3-phosphate (EC 5.3.1.1) and the step that converts oxaloacetate into phosphoenolpyruvate (EC 4.1.1.49), with higher accumulation levels in mature and ripe fruit, respectively.

In terms of regulatory pathways, the signaling cascade that leads to the ethylene biosynthesis inhibition through the action of brassinosteroids over kinases such as the serine/threonine-protein kinase BRASSINOSTEROID-SIGNALING KINASE (BSK, Prupe.6G065600) and the receptor-like protein kinase FERONIA (Prupe.6G295900) was well represented in our mature fruit dataset (Figure 4D).

Potential biomarkers of peach fruit ripening include cell wall modifying proteins and proteins involved in plant hormone biosynthesis

In order to determine which protein-coding genes could be used as biomarkers to differentiate between mature and ripe fruit stages, gene expression data across six developmental stages in fruit seeds and mesocarp, was plotted as heatmaps (Figure 5, Supplementary Figure 3A). An optimal biomarker would be a gene whose expression is low at any stage and tissue, but that achieves maximum expression in the ripe fruit mesocarp tissue. When considering protein-coding genes from mature fruit (Figure 5A), only two proteins came close to displaying this pattern, a raffinose synthase (RAFS, Prupe.6G032400) and an endoglucanase (ENDOGL, Prupe.5G118000, Figure 5C). When considering protein-coding genes from ripe fruit (Figure 5B), this pattern was mainly displayed by seven protein-coding genes, six of which were clustered. These six clustered genes included three cell wall modifying proteins (polygalacturonase, Prupe.4G262200; pectin methylesterase inhibitor, Prupe.1G114500; and protein trichome birefringence-like 41, Prupe.4G157600), the main peach fruit 1-aminocyclopropane-1-carboxylate oxidase (Prupe.3G209900), an omega-6 fatty acid desaturase (Prupe.7G076500), and a thaumatin (Prupe.3G144100). The seventh protein was a gibberellin 2-beta-dioxygenase (Prupe.1G111900). Interestingly, several of these proteins were consistently found to be more abundant in mesocarp of O’Henry ripe fruit using 2D-gel electrophoresis (Supplementary Table 1, Supplementary Figure 4) [27]. It is also interesting to note that, based on gene expression clusters, the “developmental stage” would be more relevant in terms of coordinating gene expression than “tissue specificity” (Supplementary Figure 3A).

Protein-coding genes involved in fruit ripening are potential targets for transcriptional factors enriched in NAC and SANT/Myb domains

The analysis of transcription factors (TFs) that had over-represented targets in the set of genes that coded for differentially accumulated proteins in mature and ripe fruit indicated that two different families of TFs would regulate genes expressed at mature and ripe fruit. Thus, 94, 37 and 45 TFs were found to possess over-represented targets among the 1,663 protein-coding genes assessed in this study, among those differentially accumulated in mature fruit and among those differentially accumulated in ripe fruit, respectively (Figure 6A). The TF that targeted protein-coding genes for both mature and ripe fruit, a homeobox-leucine zipper protein ATHB-40 (Prupe.7G149700), displayed an expression profile that correlates with the ACO1 gene, i.e. a high expression peak at the stage of fruit ripening (Figure 6B). An analysis of the TF protein domains indicated that these three sets of TFs have different domain composition (Figure 6C), with genes coding for proteins more accumulated in mature fruit displaying mainly an enrichment as targets of NAC domain TFs whereas genes from proteins more accumulated in ripe fruit being enriched as targets of MYB domain TFs.

Proteomic approaches based on 1D electrophoresis followed by MS (Figure 1) have been successfully used to better understand fruit ripening in Rosaceae family species such as apricot [33] and strawberry [34]. When compared to proteins extracted from ripening P. armeniaca using 1D SDS-PAGE gel followed by MS, 76% of the 245 proteins identified in mature/ripe apricot fruit were also identified in mature/ripe peach fruit [33]. Moreover when compared to other three P. persica species gel-based proteomic studies which published all the proteins characterized (Supplementary Table 3), on average an 85% of the characterized proteins matched the ones found in our study. Adding the fact that the physicochemical properties of P. persica primary transcripts' proteome were similar to those of the proteome analyzed in the present study (Supplementary Table 2, Supplementary Figure 2) and that low abundant proteins, such as TFs, could be detected in our dataset, the current analysis seems to truly reflect the fruit proteome, but with a bias in terms of primarily representing proteins with average physicochemical properties (Supplementary Figure 2). Comparison with iTRAQ proteome datasets would be highly informative, however only differentially accumulated proteins are reported in these publications with emphasis on the effect of postharvest treatments such as cold storage or disease incidence in ripening process [35-39] (Supplementary Table 3).

In order to take full advantage of the proteins characterized in systems such as ripe fruit, it is important to follow a robust pipeline of MS analysis and protein quantification, to increase the confidence in the identifications performed and to support differential accumulation analysis. MS analysis was performed using a stringent cutoff for both peptide and protein match. Next, protein quantification was performed using the average total ion chromatograms (Average TIC) [40] followed by pre-treatment data to get a data distribution as close to normal as possible (Supplementary Figure 1), data scaling and centering, and data cleaning, to keep only those proteins that had a proper number of replicates to perform a statistical analysis of differential accumulation. All these steps explain the drop from 2,740 detected proteins to 1,663 assessed proteins. It is also worth to remember that label-free quantification, such as the one used in this work, is a good choice for proteomic analysis that seeks to characterize as many unbiased proteins as possible. However, variations are higher than in techniques that rely on labeling, since in label-free quantification samples are individually prepared and comparison occurs later, during data analysis [41]. In addition, variability among fruits in the field can be very high, even if the fruits were manually selected based on size, color and physicochemical parameters. Therefore, in many cases a protein may vary widely between different conditions, and its abundance variation due to biological and technical issues might hurdle the ability to identify this protein as differentially accumulated.

The fruit mesocarp proteome identified in this work (1,663 proteins) was functionally related to carboxylic acid metabolism, intracellular transport and protein folding (Figure 2A). Transport proteins were mainly related to carbohydrates, ions, and vesicle-mediated transport (Supplementary Table 1), with very few transporters differentially accumulated. Protein folding, in turn, was mainly related to chloroplast chaperonins [42]. Both sets point to a fruit mesocarp with active cellular compartment transport and protein folding.

Mature and ripe fruit stages are the last in the fruit developmental curve, making it possible to compare protein-coding genes from our dataset with datasets that characterized ripening-related genes in P. persica (GEO dataset GSE71561) [32]. Protein-coding genes expressed in mature/ripe fruit seemed to be time and tissue-specific, given that close to 25% peak their expression levels in ripe fruit when compared to other developmental stages, and 52% peaked their expression in ripe fruit mesocarp, compared to other tissues (Figure 2B and 2C). Fruit-specific genes that displayed the strongest differentiation between peach and almond (P. dulcis) were also the most highly expressed, pointing to a functional specialization of protein-coding genes that are highly expressed in fruit [43]. Our results point to the same behavior in functionally specialized fruit proteins, i.e., proteins with high accumulation in fruit could be particularly involved in its metabolism.

Samples and protein abundance differences between mature and ripe fruit mesocarp were striking, with a PCA first component splitting both samples and 14.9% of the assessed differentially accumulated proteins (Figure 3A, Table 1). This reinforces the notion that mature and ripe fruits are different not only in terms of metabolic profiles, but also at the proteome level. Processes triggered in the mature fruit would be directed to glycogen and isocitrate metabolism, and protein localization (Figure 3B), whereas a manual inspection indicated that protein-driven cell wall modification was much more represented in ripe fruit than in mature fruit (nine versus two proteins, Table 1, Figure 4B). Seven of these cell wall proteins have been experimentally shown to be ethylene responsive: polygalacturonase (PG) Prupe.2G300900 [44], PpPG21 (Prupe.4G261900) [44], PpPG22 (Prupe.4G262200) [14, 44, 45], beta-galactosidase (Prupe.3G050200) [14], pectin methylesterase inhibitor (Prupe.1G114500) [46], expansin 3 (Prupe.6G075100) [45], and endo-1,4-beta-glucanase PpEG1 (Prupe.5G131300) [47], in according to the increase in ethylene production at ripe stage [28] highlighting its relevance as a ripening promoting hormone in melting peach fruit varieties. In addition, protein-coding genes from PpEG1, PpPG21, Prupe.2G300900 and Prupe.3G050200 (PpBGAL2) belonged to the same gene cluster with higher expression in ripe fruit compared to mature fruit, in the white flesh fast-melting peach “Hu Jing Mi Lu” (HJ) [48], pointing to a conserved mechanism of cell wall dismantling in peach fruits with a different genetic background. PpBGAL2 transcript was also shown to be highly correlated with the fruit softening in the melting variety XH8, with expressions 3–4 times higher during later storage compared to early storage [49].

Peach fruit size is controlled throughout its development mainly by cell divisions and enlargement of mesocarp cells [50]. These processes display a temporal control during the fruit development, with two stages of faster growth alternating with two stages of slower increases in fruit diameter, represented by a double-sigmoid growth pattern [50, 51]. Fruit size increase during the ripening process ceases, however how this size control is accomplished is not clear. One of the mechanisms that are involved in increasing the fruit cell size is the genome size amplification by endoreduplication [52], where endopolyploid cells arise from variations of the canonical cell cycle that replicate the genome without cell division. Recently a GUANYLATE-BINDING PROTEIN1 (SlGBP1)was characterized as a possible inhibitor of cell division in tomato. This protein would maintain endopolyploid cells in a non-proliferative state [53]. The best P. persica homologue of SlGBP1 (Prupe.4G053400, 70.6% identity) was found differentially accumulated in ripe fruit in this study, indicating that endopolyploid cells division would be inhibited during the fruit ripening, providing a molecular mechanism to explain the lack of fruit size increment in this developmental stage in peach.

Key processes involved in the organoleptic changes that occur in the transition from mature to ripe fruit

The ripening process involves changes in the fruit mesocarp flavor, color, aroma, and texture that have a direct impact on the fruit organoleptic quality. By studying the proteomic changes that are triggered during ripening, it would be possible to underscore some of the critical molecular processes involved in this transition. During this research, proteins involved in hormone, soluble sugars, organic acids, lipids, specialized metabolism and vesicle mediated trafficking and protein transport were correlated with this transition, and their possible role in fruit ripening is discussed below.

Hormone metabolism

There is multiple evidence that the plant hormone ethylene is involved in the regulation of the ripening process in climacteric fleshy fruit. Upon binding to its receptors, ethylene’s signal is propagated to several downstream components which in turn target promoters of many ethylene-inducible genes, directly involved in the dramatic changes that occur during the transition from mature to ripe fruit [54]. The ACC synthase (ACS) and ACC oxidase (ACO) enzymes are responsible for turning S-Adenosyl methionine (SAM) into ethylene. In peach, the abundance of the ACO isoform ACO1 (Prupe.3G209900) transcript and protein correlates very closely with fruit ripening [27, 55, 56]. In this work, ACO1 also displayed a similar pattern, being more abundant in ripe fruit than in mature fruit (Table 1, Figure 3D), validating ethylene production measured on the mature and ripe fruit stages at the molecular level [28].

The possible signaling cascades that modulate ethylene biosynthesis in climacteric fruit are poorly understood. Recently, in apple (Malus×domestica) and tomato (Solanum lycopersicum), both climacteric fruits, Feronia-like receptor kinases (FERLs) were shown to act as negative regulators of fruit ripening by inhibiting ethylene production [57]. In the current study, a P. persica FERL (Prupe.6G295900), with over 75% of identity with the apple MdFERL1 and the tomato SlFERL1, was characterized as more abundant in mature than in ripe fruit (Table 1). In addition, upstream key regulators of FERL, such as BRASSINOSTEROID-SIGNALING KINASE (BSK) and BRI1 SUPPRESSOR (BSU) [58], were also more abundant in mature than in ripe fruit. This pattern suggests that this signaling pathway could regulate ethylene biosynthesis during peach fruit ripening, as its downregulation in the transition from mature to ripe fruit is correlated with the increase in ethylene biosynthesis in ripe fruit.

Sugar metabolism

The sugar alcohol sorbitol is the main metabolite used to mobilize photosynthesis-derived carbohydrates from leaves to fruits in Rosaceae [59], being highly correlated with fruit taste and aroma [60], and of great interest for fruit breeders given its nutritional and sweetener qualities [61]. At a molecular level, sorbitol modulation could impact fruit quality by affecting sugar-acid balance and starch accumulation [62]. How this effect is generated is not clear, but it is postulated that sorbitol is catabolized in the cytosol, being the main driver of structural compounds biosynthesis and respiration in the peach fruit [19]. In mature fruit, sorbitol could be used to generate fructose, which in turn could be metabolized into fructose 6-phosphate (F6P), by the enzyme fructokinase (Prupe.1G196700), whose abundance is high at this stage (Figure 4B). F6P can be used to synthesize sucrose, the predominant soluble sugar in mature fruit, or enter the glycolytic pathway [60]. A decrease in the accumulation of fructokinase in ripe fruits indicated an enhanced conversion of sucrose to fructose via suppression of sucrose synthesis and fructose phosphorylation. Similar results were observed in other peach cultivar [37], where a decrease in different isoforms of both hexokinase (ppa004610m/Prupe.1G444000) and fructokinase (ppa007069m/Prupe.3G160500) accumulation were observed in fruits ripening and senescence at room temperatures. In ripe fruit, the putative sorbitol transporter (SOT) (Prupe.8G101200) accumulated more than in mature fruit (Table 1). This protein is a close homologue to candidate SOTs in pear (PbSOT19/21) [63], apple (SOT6) [64] and sour cherry (P. cerasus, PcSOT1) [65]. SOTs are represented by an expanded gene family in apple and peach [66], which is made up of 16 genes in P. persica. The ones that display the highest expression at the ripening stage were Prupe.8G101200 (PpePOL5) [67] and Prupe.3G071100 (Supplementary Figure 3B). Thus, the PpePOL5 transporter could be partially responsible for an increase in sorbitol content in ripe fruit of the O’Henry variety, compared to mature fruit [68].

An intriguing question still under debate is about the role of stored malate in fruit metabolism during the ripening process, as malate synthesis and not dissimilation is detected throughout ripening [17]. Malate levels are mainly regulated by its synthesis due to cytosolic carboxylation of phosphoenolpyruvate (PEP), its degradation due to decarboxylation also in the cytosol or by the conversion of tri- and dicarboxylates in the mitochondria, glyoxysome or cytosol [69]. Malate can be used as a sugar precursor through gluconeogenesis, to help regulate nitrogen metabolism [17], as well as valves to balance metabolic fluxes given their function in indirect transport of reducing equivalents [70]. According to the proteomic profiles assessed in this study, malate would be catabolized by the gluconeogenesis process to different metabolites in mature and ripe fruit (Figure 4). In mature fruit, the higher abundance of the protein triosephosphate isomerase would point to a preferred catalysis of malate to glyceraldehyde 3-phosphate, a triose phosphate that can be oxidized into pyruvate, providing the mature fruit with ATP and NADH. In ripe fruit, the higher abundance of the enzyme phosphoenolpyruvate carboxykinase would help to keep the appropriate physicochemical conditions for the use of glutamate as nitrogen source for the biosynthesis of amino acids required at this stage [17]. Our results also indicated a decrease in other organic acids such as citrate. The enzyme pyruvate dehydrogenase kinase (PDK) inactivates the pyruvate dehydrogenase (PDH). PDK (Prupe.1G105800) was detected only in ripe fruits, thus we could argue that in ripe fruits PDH is inhibited, which could decrease the synthesis of acetyl-CoA an consequently the Krebs cycle organic acids. Our results are in agreement with very recent data by Zheng et al. [71] showing that in low acid peach cultivars such as O’Henry, PDK expression is increased in comparison to high acid peach cultivars, which would impact the citrate synthesis pathway and its accumulation.

Among the soluble sugars present in the peach fruit, sucrose, fructose and glucose do not change much during the transition from mature to ripe fruit; in contrast to xylose, fucose and raffinose, according to an assessment of 15 P. persica varieties [2]. In the current analysis, two key enzymes involved in raffinose biosynthesis (raffinose and stachyose synthases, Table 1, Figure 4A) were more accumulated during the mature stage, indicating that in the O’Henry variety this metabolite accumulation would occur at the mature stage. Raffinose is an important metabolite given its antioxidant properties and role in stress tolerance [2].

Starch biosynthesis and accumulation is believed to happen at the early stages of fruit development, followed by its consumption until almost undetectable levels at maturity [60]. However, enzymes involved on starch biosynthesis, such as 1,4-alpha-glucan-branching enzyme (SBE, Prupe.1G354000) and glucose-1-phosphate adenylyltransferase small subunit (APS1/AGP, Prupe.3G192600) were still detected in mature fruit, with a drop in their levels in ripe fruit (Figure 3C). This same APS1/AGP was detected by iTraq proteome and was showed to decrease in fruits ripening at low temperatures [37]. SBE was also detected by others in the mesocarp of ripe peach fruits [72], indicating that starch biosynthesis could still be active at later stages of fruit development. Another interesting fact is the increased accumulation of the atypical cysteine/histidine-rich thioredoxin 4 P. persica homologous (ACHT4, Prupe.7G001600, 64% identical) in ripe fruit. ACHT4 has been characterized in A. thaliana as a molecular switch of APS1, being able to quench APS1 activity [73]. By combining decreased accumulation of SBE and APS1 with an increase of ACHT4, the ripe fruit metabolism could downregulate starch content at this final developmental stage.

Fruit aroma and lipid metabolism

Among the many dozens of volatiles peach fruits can produce, the γ- and δ-decalactones, C6 aldehydes and alcohols, terpenoids and volatile esters are the ones that are mainly related to fruit aroma [8, 74, 75]. Volatile esters are the product of alcohol acyltransferase-mediated biosynthesis, and degradation through carboxylesterase (CXE) activity [75]. In tomato, low ester levels, which positively correlate with fruit liking, were associated to the enzyme SlCXE1 [76]. In peach, the carboxylesterase PpCXE1 (Prupe.8G121900) was shown to be able to degrade volatile acetate esters [75]. In the present study, two CXE proteins were found to be more abundant in mature fruit than in ripe fruit, Prupe.1G439300 and Prupe.8G121500. The genes coding for these proteins were top-7 and top-8 in expression level in ripe fruit, among 19 putative CXEs in P. persica [75]. Prupe.1G439300 is the best homologue to the tomato SlCXE1 [75] and its protein-coding gene displayed a peak of expression in ripe fruit (GEO dataset GSE71561), being a good candidate for further characterization.

Fruits can synthesize lactones that attract feeders for seed dispersal, mostly the γ-lactones decano-4-lactone and/or dodecano-4-lactone [77]. These compounds, which primarily derive from oleic acids or derivatives, are metabolized by a series of enzymes, including epoxide hydrolases, to generate lactones such as undecano-4-lactone [77]. They can be found both in fruit skin as well as mesocarp [74]. According to our data, lactone metabolism would also be differentially regulated in mature and ripe fruit, due to preferential accumulation of epoxide hydrolase EPH2 (Prupe.7G162300) [78] in mature fruit. On the other hand, the SRK2C kinase (Prupe.6G192200), which has been correlated to lactones using QTL analysis [79], is exclusively found in ripe fruit (Table 1). The target of SRK2 is unknown, and given its high expression (GEO dataset GSE71561), differential accumulation during the ripening process (Table 1), and correlation with lactone biosynthesis, it becomes an interesting candidate for further characterization.

Fruit ripening has been characterized as a senescing process, with cell membrane deterioration being the hallmark. Phospholipase D (PLD) is among the most relevant proteins involved in cell membrane deterioration in ripening fruits, acting upon phospholipids to generate phosphatidic acid (PA) and a free head group [80]. PLD action is also able to trigger a myriad of cellular processes, and therefore its activity is tightly regulated [81]. PLDs have been grouped into five classes (α, β, γ, δ and ζ) according to several parameters such as domain structure and biochemical properties. Similar to most plant PLDs, PLDδ requires Ca²⁺ and is stimulated by phosphatidylinositol 4,5-bisphosphate (PIP2) [81]. However, Arabidopsis PLDδ displays a distinctive property, which is to be activated by oleic acid [81]. A peach PLDδ (Prupe.7G17540) was found to be more abundant in mature fruit than in ripe fruit (Table 1).

Fatty acid desaturase 1 (FAD1, Prupe.7G076500), a ω-6 Oleate desaturase [8], displayed increased abundance in ripe fruit (Figure 4B, panel II), a pattern similar to the one displayed by its transcript in the P. persica Yulu variety [74], in the GSE71561 dataset, and by its closest orthologue in Fragaria vesca [79]. FAD1 gene expression was also sensitive to the ethylene signal transduction pathway inhibitor 1-methylcyclopropene (1-MCP), a compound known to negatively affect peach fruit aroma [82]. This increase could imply that FAD1 could be involved in linoleic and linolenic acids biosynthesis in peach ripe fruit [8], or could be involved in γ-decalactone biosynthesis, as proposed for the F. vesca FaFAD1 [79].

Solute transport

Aquaporins are transmembrane proteins that enable water and small neutral solute translocation across cellular membranes. Among the different types of aquaporins, those located in the vacuolar membranes are called tonoplast intrinsic proteins (TIPs). TIPs are classified into five groups (TIP1-5), and are able to transport hydrogen peroxide and glycerol, in addition to water [83]. The P. persica aquaporin TIP1-1 (Prupe.7G125900) was found to be more abundant in ripe than in mature fruit mesocarp (Table 1). A close homologous (93% identity) protein-coding gene from sweet cherry (P. avium) was among the three most expressed aquaporins in the fruit, being mainly expressed in mesocarp throughout fruit development [84]. The P. persica PpTIP1-1 transcriptional pattern was very similar (GEO dataset GSE71561), with the gene being expressed at a regular level throughout mesocarp development. This indicates that both Prunus TIP1-1 aquaporins conserved their tissue localization and expression patterns. Moreover, it indicates that these aquaporins could be key to transport water across the vacuole in the Prunus fruit mesocarp [84].

Vesicle mediated trafficking and protein transport

Protein trafficking, mediated by vesicular transport, was also shown to be a very important process during the fruit ripening transition (Figures 2 and 3). Vesicle transport can be divided in three broad consecutive steps: vesicle budding from a donor membrane, trafficking and fusion with a specific acceptor membrane. Prior to the fusion stage, a set of tethering factors allows the vesicle to be in close proximity to the target membrane, thus helping vesicle trafficking organization. Recently, the Transport Protein Particle (TRAPP) complex, a multisubunit tethering complex, was experimentally characterized in Arabidopsis [85], including all 13 mammalian TRAPP subunits as well as an additional plant-specific component, the TRAPP-Interacting Plant Protein (TRIPP). TRIPP plays important roles in trafficking-dependent processes, including the formation of new cell walls and reproductive development [85]. Among the 14 A. thaliana TRAPP subunits, we detected 7 homologues (Supplementary Table 1). Besides TRS20, TRIPP and TRS120, which have been shown to interact, were found to be more abundant in mature fruit (Table 1). Both TRIPP and TRS120 are part of TRAPPII, one of a variety of TRAPP modular forms. TRAPPII mutants displayed altered levels of methyl-esterified pectins at cell plates, pointing to a role of this complex in the transport of pectins [86]. Changes in pectin metabolism are one of the hallmarks of the softening and textural changes in melting flesh peach triggered by fruit ripening [87]. Thus, our results help to link cell wall metabolism with vesicle mediated transport of proteins and polysaccharides through the downregulation of TRIPP and TRS120 between mature and ripe fruit.

TRAPPII have also been pointed as a guanine exchange factors (GEFs) for RabA GTPases [88, 89]. RabA GTPases can regulate membrane fusion events and other cell organelle processes once they are recruited to their specific membrane compartment and activated by their cognate RabGEF. Among the proteins downregulated during the mature to ripe fruit transition we found a homologous of RABA5b, a RabA GTPase. Interestingly, the P. persica TRIPP, TRS120 and RABA5b displayed a dramatic and linear increase in transcript abundance from 81 to 125 DAP, pointing to a specific role in fruit development for these 3 proteins, and TRIPP-TRS120 TRAPPII as potential GEFs for RABA5b.

Another key component of the vesicle trafficking machinery are the adaptor protein complexes, which participate in the selection of the vesicles specific transported cargo. Using a combination of organelle density gradients with proteome analysis, Pertl-Obermeyer et al. [90] indicated that the adaptor protein complex 4 (AP4) participates in delivering cell wall proteins. AP4 would also be involved in sorting transmembrane proteins to the vacuole [91], being a key component of the post Golgi trafficking. Alterations in the cell wall proteome content as well as active vacuolar sorting have been associated with fruit ripening [14, 92]. We detected two AP4 subunits down-regulated during the transition from mature to ripe fruit (Table 1). Together, our results indicates that an active vesicle mediated-traffic is operating at mature peach fruit, and that during the transition to ripe fruit this process shifts into a less active state, possibly associated with the fruit senescence.

Transcription factors assessment

Analysis of possible transcription factors (TFs) that could regulate protein-coding genes expressed mostly or specifically during fruit ripening is of high interest given its application in driving the expression of genes of interest in this organ. Their identification could help to uncover the transcription regulation that underlies the extensive changes triggered by fruit ripening. TFs associated to ripening in climacteric fruit have been identified in apricot [93, 94], melon [95], banana [96, 97], tomato [98, 99], papaya [100, 101, 102], among others. By 2015, around 1,533 TFs were identified in P. persica [20], however just a few have been characterized and even less have been associated to the fruit ripening process. MADS-box PrupeSEP1 (Prupe.3G249400) [103], TCP PpTCP.A2 (Prupe.1G272500) [49], homeobox PpHB.G7 (Prupe.1G416800) [104], AP2/ERF PpERF2, PpERF3 and PpERF.E2 (Prupe.5G090800, Prupe.7G194400 and Prupe.3G032300) [54, 105], ARFs PpARF5 (Prupe.1G368300) [106] and EIN3-like PpEIL1 to 5 (Prupe.6G018200, Prupe.2G058400, Prupe.2G058500, Prupe.6G181600, Prupe.2G070300) [107] are among those TFs already characterized and associated with peach fruit ripening. NAC domain TFs were enriched among the TFs that targeted the genes whose respective proteins were more abundant in mature compared to ripe fruit. Several NAC TFs have been reported to be involved in regulating tomato [108], melon [95] and papaya [100] fruit ripening, indicating that NAC TFs role in fruit ripening is evolutionarily conserved [109]. In peach, both ACS and ACO genes, involved in ethylene biosynthesis, display NAC transcription factor binding motifs, and several NAC TFs and key fruit ripening genes display a ripening-specific expression pattern [32]. Protein-coding genes whose products were more abundant in ripe compared to mature fruit also displayed an enrichment as NAC TFs targets, however they were mainly enriched as MYB TFs targets. MYB ripening-related TFs have been characterized in Lycium ruthenicum [110], in tomato [111], in papaya [101] and in plum (P. salicina) [112], indicating that MYBs might also be key in modulating the peach fruits pigmentation, flavour, and texture changes triggered by the fruit ripening [113].

Just one TF would have as targets the protein-coding genes for both mature and ripe fruit when compared to all 1,663 proteins identified in the present work. This protein was a HB40-homolog protein (Prupe.7G149700), which displayed a strong expression peak at the stage of fruit ripening (Figure 6B). In tomato, the MADS box TF RIPENING INHIBITOR (RIN) has been placed as an early ripening regulator. The best tomato homologous of Prupe.7G149700, the gene Solyc02g085630 (64% identity), was among the RIN targets identified by chromatin immunoprecipitation coupled with DNA microarray analysis (ChIP-chip) [114], indicating that these HB40-homolog proteins could be effectors of RIN.

Future Perspectives

Plant Proteomics analysis has benefited, in the last years, from the increasing development of mass spectrometry (MS), the publication of hundreds of plant genomes (430 to date) [115] and generation of customized bioinformatics tools and pipelines. 1D-gel electrophoresis followed by LC/MS-MS analysis (Figure 1), has the great advantage of being straightforward to execute and analyze, as well as being among the most robust pipelines for proteomic analysis [116]. Besides gel based, gel free [117], antibody chips [13] and MS-imaging [118] are also available for analyzing plant sample proteome contents. However, the most promising approaches are related to how validated and integrate proteomics data with other omics approaches in order to get a more systemic vision of the processes under evaluation [119]. In this work we integrated proteomics data generated by us and others, as well as different sets of transcriptomic data publicly available to unveil information regarding critical molecular processes involved in the peach fruit ripening.

P. persica belongs to the Rosaceae family, whose species have developed a wide array of fruit types, including drupe, pome, drupetum, achene, and achenetum, making it one of the most informative plant family to perform comparative developmental and evolutionary studies [120]. P. persica displays a drupe-type of fruit, where a fleshy fruit encloses a lignified endocarp surrounding a seed. The presence of this lignified endocarp imposes a challenge to the proper fruit development and ripening, since the phenylpropanoids allocation must be carefully controlled for the endocarp lignification during development or to the biosynthesis of flavour/aroma compounds in the ripe fruit mesocarp [121]. This characteristic is particular to this kind of fruit, making the extrapolation of what is known in the so far best fleshy fruit model tomato ripening, limited. In addition, as mentioned previously, the tomato ripening regulatory circuit is mainly based on the action of MADS TFs, whereas in peach NAC-type TFs would have a predominant role in controlling the ripening regulatory circuit [32]. Assessing the molecular mechanisms, using integrated omics approaches, of plants with combinations of the slow ripening (SR) allele, which can render plants with unique phenotypes [122], as well as the results of different pre-harvest treatments [67, 123] that affects the fruit quality will help to uncover new key players that are probably particular to drupe-type fruits.

We have identified 1,663 proteins with high confidence using 1D SDS-PAGE fractionation associated to MS-MS detection. Above a quarter of the genes that code for these proteins were preferentially expressed at mature-ripe fruit in terms of developmental stage and tissue, indicating that these proteins were mainly involved in the fruit ripening. The differentially accumulated proteins in mature and ripe fruit identified in this study showed a high correlation with previous transcriptome studies. Differentially accumulated proteins were mainly related to the metabolism of hormones such as ethylene and brassinosteroids, sugar metabolism, cell wall rearrangement, fruit aroma and lipid metabolism.

Mature fruit displayed more changes than ripe fruit, with several changes associated to well characterized pathways, in contrast to ripe fruit, where many changes could not be mapped to coordinated biochemical processes. This indicates that the metabolism of mature fruit is more regulated than the one from ripe fruit, which agrees with the idea that ripening fruit undergoes a coordinated senescence process.

The identification of proteins with a marked differential accumulation during the mesocarp fruit ripening process will help to set experimental designs that could allow a more detailed analysis of the ripening process itself. In the near future, the use of precise and reliable near infrared spectroscopy based non-destructive tools [124] together with the molecular profiling of these proteins could make possible to perform a detailed analysis of the ripening stages SI to SIII, since by now no phenotypical, anatomical, biochemical or morphological parameter can help to discriminate these stages among fruits that display, on the tree, a great variation in terms of maturity stage, hampering this assessment.

Plant material

Regular size fruit were harvested from 8-year-old ‘O’Henry’ trees grown on Nemaguard rootstocks in a commercial orchard located in the Aconcagua Valley, Chile (34^o17’ W, 70^o54’ S) [28]. As harvesting index we used the change in fruit ground color, measured using fruit company’s color table. Two postharvest stages were selected for proteome analysis: mature fruit (O1), that consisted on firm fruit taken immediately after harvest (firmness of around 60 N) and in the stage required for packing; and ripe fruit (O2), that consisted on fruit ripen at 20^oC (firmness of around 11 N) [28]. Physiological parameters such as firmness, color, total soluble solids, titratable acidity, respiration rate and ethylene production were measured by Campos-Vargas et al. [28]. Three fruits from each postharvest stage were used for protein extraction and considered biological replicates (Figure 1A).

Protein extraction and SDS-PAGE

One milligram of mesocarp tissue from each biological sample was pulverized in liquid nitrogen and transferred to a room temperature tube with 5 mL of protein extraction buffer [125]. This solution was mixed with 5.5 mL of Tris-saturated phenol pH 8.0 and shaken vigorously for 5 min at room temperature, followed by centrifugation at 8,500 × g for 14 min at 4°C to achieve phase separation. The phenolic phase was recovered, re-extracted with an equal volume of protein extraction buffer and precipitated for two hours at -20°C by the addition of five volumes of 0.1 M ammonium acetate in methanol at -20°C [26]. The precipitated material was collected by centrifugation at 8,500 × g for 12 min and protein pellets were washed three times with cold ammonium acetate in methanol and once with 80% acetone at -20°C. The pellet was dried at room temperature and then solubilized in 300 μL of resuspension buffer which contained 5 M urea, 2 M thiourea, 2% CHAPS (w/v), 2% SB3-10 (w/v), 0.5% ampholites pH 5–7 (v/v) and 0.25% ampholites pH 3–10 (v/v) [126]. Protein yield was determined by Bradford protein assay [127]. All samples were stored at -80°C prior to electrophoresis.

SDS-PAGE was performed on 12% polyacrylamide gels casted on a BIO-RAD Mini-PROTEAN Tetra cell with 1 mm spacer plates. A hundred and fifty micrograms of protein samples were mixed with 10 μL Coomassie blue 5X loading buffer and run at 80V until the dye front reached the bottom of the gel. Band visualization was achieved by staining the gels with colloidal Coomassie G250. Only one sample was run per gel. Each lane loaded with proteins was excised and divided into ten slices of 4 mm x 4mm x 1mm (Figure 1B).

Experimental LC/MS/MS

Gel slices were digested in-gel according to Shevchenko et al. [128] with modifications. Gel bands were dehydrated using 100% acetonitrile and incubated with 10 mM dithiothreitol in 100 mM ammonium bicarbonate (pH 8) at 56ºC for 45 min, dehydrated again and incubated in the dark with 50 mM iodoacetamide in 100 mM ammonium bicarbonate for 20 min. Gel bands were then washed with ammonium bicarbonate and dehydrated again. Sequencing grade modified trypsin was prepared to a final concentration of 0.01mg mL^-1 in 50 mM ammonium bicarbonate and 50 mL of this solution was added to each gel band. Bands were then incubated at 37ºC overnight. Peptides were extracted from the gel by water bath sonication in a solution of 60%ACN/1%TCA and vacuum dried to approximately 2 mL. Peptides were then re-suspended in 2% acetonitrile/0.1% trifluoroacetic acid to 20 mL. From this, 10 mL were automatically injected by a Waters nanoAcquity Sample Manager and loaded for 5 min onto a Waters Symmetry C18 peptide trap (5mm, 180mm x 20mm) at 4mL/min in 5%ACN/0.1% formic acid. The bound peptides were then loaded onto a Waters BEH C18 nanoAcquity column (1.7mm, 100mm x 100mm) and eluted for 35 min with a gradient of 5%B to 30%B in 24 min, ramped up to 90%B at 25 min and held for 1 min, then dropped back to 5%B at 26.1 min using a Waters nanoAcquity UPLC (Buffer A = 99.9% water/0.1% formic acid; Buffer B = 99.9% acetonitrile/0.1% formic acid) with a constant flow rate of 0.8mL min^-1.

Eluted peptides were sprayed into a ThermoFisher LTQ Linear Ion trap mass spectrometer outfitted with a MICHROM Bioresources ADVANCE nano-spray source. The top five ions in each survey scan were then subjected to data-dependent zoom scans followed by low energy collision induced dissociation (CID) and the resulting MS/MS spectra were converted to peak lists in BioWorks Browser v3.2 (www.thermo.com) using the default LTQ instrument parameters. Peak lists were searched against a custom database containing protein sequences from Prunus persica whole genome assembly, v1.0 and common laboratory contaminants (downloaded from NCBI, www.ncbi.nlm.nih.gov) using the Mascot searching algorithm, v2.3 (www.matrixscience.com). Mascot parameters for all databases were as follows: allow up to two missed tryptic sites; fixed modification of carbamidomethyl cysteine; variable modification of oxidation of methionine; peptide tolerance of +/- 200ppm; MS/MS tolerance of 0.6 Da; peptide charge state limited to +2/+3 (Figure 1C). Translation from v1.0 genome annotation to version 2.1 was achieved by using data provided by Phytozome v12 [129] and by manual analysis of the peptides characterized.

Data Processing

The Mascot output was analyzed using Scaffold v4.8.2 (www.proteomesoftware.com) to statistically support protein identifications. Assignments were validated in Scaffold according to the following criteria: Protein Threshold - 1.0% FDR; Minimum number of peptides - 1; Peptide threshold - 0.1% FDR. To be able to accurately determine changes in protein levels, average total ion chromatograms (Average TIC) was retrieved from Scaffold for each protein assessed in each of the six samples under evaluation [40]. Average TIC for each protein was estimated using only those peptides identified with high confidence (Figure 1D). Supporting material and protein datasets has been deposited to MassIVE platform under accession number MSV000086519 or ftp://massive.ucsd.edu/MSV000086519/.

Data pre-treatment

To circumvent the issue of missing data, average values were calculated for those proteins that had only two biological replicates per biological condition (mature or ripe) and were used as third replicate. All the following analyses were thus performed with proteins which had at least two data points in O1 and O2, being the other proteins discarded. Protein data was scaled by using pareto scaling [130], normalized using Quantile Normalized procedure and centered at zero by subtracting the mean value, using InfernoRDN (version 1.1.5970.31895) [131]. This allowed the data to be transformed to achieve a close to normal distribution, required for many statistical analyses of variance (Supplementary Figure 1). Q-Q and Box-plot graphs were also performed using InfernoRDN.

Assessment of the physicochemical characteristics of proteins

Proteins derived from the primary transcript of each of the 26,973 protein-coding genes (Ppersica_298_v2.1.protein_primaryTranscriptOnly.fa) were retrieved from Phytozome v12 [129] and were assessed using the Peptides R Package [132]. The following parameters were evaluated for each protein: length, molecular weight (MW, in Daltons), charge (pH = 7, pKscale = "Lehninger"), protein stability (instability index), and hydrophobicity (KyteDoolittle and Guy scale). Mean values and Gini’s mean difference, a measure of variability that is robust even for non-normal data distributions, were computed using the Hmisc R Package.

Evaluation of protein differential accumulation

To detect differences in protein accumulation in mature and ripe fruits, a two-sample t-statistics with equal variance was performed for each protein/gene under analysis using the Bioconductor ‘multtest’ package followed by the “ABH” (Adaptive Benjamini-Hochberg) correction procedure, with a cutoff value of 0.1.

Gene ontology and pathways analysis

Gene ontology analysis was performed using Phytomine (https://phytozome.jgi.doe.gov/phytomine/begin.do). The Benjamini-Hochberg test, with a “max p-value” set to 0.05, was used for Multiple Testing Corrections. Redundant GO terms were removed with the webtool REVIGO [133], using the following parameters: 1. Allowed similarity: small (0.5); 2. GO categories associated to: p-values; 3. GO term sizes database: Arabidopsis thaliana; 4. Semantic similarity measure to use: SimRel. Next, CirGO [134] was used to plot a concise version of the GO analysis.

Pathway diagrams were built by first running the program DeepEC [135] to annotate the enzymes included in the 26,873 protein-coding genes from the P. persica genome version 2.1. Next, this annotation was used to generate the input file for the program Pathway Tools [136], which was used to infer the pathways and reactions from P. persica (Supplementary File 1).

Principal Component Analysis (PCA)

PCA analysis was run on normalized and centered data using the Explore/PCA tool from InfernoRDN [130].

Transcriptional analysis of the Gene Expression Omnibus dataset GSE71561

Data from 13 peach samples, which included six samples from fruit seeds (days 41, 54, 69, 83, 111 and 125 after full bloom – DAFB), the same six samples from fruit mesocarp and one sample from flowers from the mid-season Fantasia variety, was downloaded from the GSE71561 dataset. Thus, samples covered until the commercial ripening stage.

The microarray data was log2 transformed following the protocol available from the online tool GEO2R [137]. This data, representing 29,045 genes, was used to generate heatmaps using the “superheat” R package [138]. The main set parameters were: clustering.method = "hierarchical", dist.method = "maximum", linkage.method = "complete". In order to avoid dealing with probes that target the same gene, all genes were ranked according to their average abundance, and then those genes with repeated measures and lower average abundances were removed. Subsets of this data, such as expression patterns from selected protein-coding genes were also plotted as heatmaps using the same parameters.

In addition, data from mesocarp tissue was used to contrast the expression of genes in the 125 DAFB stage against the remaining five mesocarp samples (M_125 vs M_41, M_125 vs M_54, M_125 vs M_69, M_125 vs M_83, M_125 vs M_111). This task was performed using the Limma R package [139], following the protocol available from the online tool GEO2R [137] and using the “decideTests” function. To improve the statistical confidence of the analysis, the probes that targeted the same gene had to display the same “decideTests” results in order to be considered for further analysis. This approach reduced the original dataset size from 29,045 genes to 18,074 genes (62.2%). These 18,074 genes included information from 1,136 protein-coding genes (68.3%) present in our proteomics dataset.

Retrieval and analysis of Transcription Factors regulatory elements

Transcription factors (TFs) that possessed over-represented targets in the protein set differentially accumulated in mature and ripe fruits and among all the proteins assessed in this study, were retrieved from the Plant Transcription Factor Database (PlantTFDB) [140], using the “Regulation Prediction” tool from the Plant Transcriptional Regulatory Map. Next, Phytomine [141] was used to retrieve protein domains enriched for these TFs. The Benjamini-Hochberg test, with a “max p-value” set to 0.05, was used for Multiple Testing Corrections.

Gene family number analysis

P. persica gene family numbers were retrieved from PLAZA 4.0 [142], using the respective P. persica genome v2.1 ID as identifier.

ACHT4 - atypical cysteine/histidine-rich thioredoxin 4ACO - ACC oxidase

ACS - ACC synthase

AP4 - adaptor protein complex 4

APS1 - Glucose-1-phosphate adenylyltransferase small subunit

BSK - BRASSINOSTEROID-SIGNALING KINASE

CXE - carboxylesterase

DAFB - days after full bloom

F6P - fructose 6-phosphate

FAD1 - Fatty acid desaturase 1

FERLs - Feronia-like receptor kinases

GO - Gene Ontology

MS - Mass spectrometry

PA - phosphatidic acid

PCA - Principal Component Analysis

PEP - phosphoenolpyruvate

PG - polygalacturonase

PLD - phospholipase D

RAFS - raffinose synthase

SAM - S-Adenosyl methionine

SBE - 1,4-alpha-glucan-branching enzyme

SOT - sorbitol transporter

TFs - transcription factors

TIC - total ion chromatograms

Affiliations

Escuela de Biotecnología, Facultad de Ciencias, Universidad Mayor, Camino La Pirámide 5750, Huechuraba, Chile.

Ricardo Nilo-Poyanco

Centro de Biotecnología Vegetal, Facultad Ciencias Biológicas, Universidad Andrés Bello, República 330, Santiago, Chile.

Gianfranco Benedetto & Ariel Orellana

Center for Genome Regulation, Blanco Encalada 2085, Santiago, Chile.

Ariel Orellana

Centro de Genómica y Bioinformática, Facultad de Ciencias, Universidad Mayor, Camino La Pirámide 5750, Huechuraba, Chile.

Escuela de Agronomía, Facultad de Ciencias, Universidad Mayor, Camino La Pirámide 5750, Huechuraba, Chile.

Andrea Miyasaka Almeida

Université Claude Bernard Lyon 1, 69622 Villeurbanne, France.

Inria Grenoble Rhône-Alpes, 38334 Montbonnot, France.

Carol Moraga

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and material

Supporting material and protein datasets has been deposited to Mass Spectrometry Interactive Virtual Environment (MassIVE) under accession number MSV000086519 or ftp://massive.ucsd.edu/MSV000086519/.

Data generated during analysis are included in the manuscript as supplementary files.

Competing interests

The authors declare that they have no competing interests.

Funding

The work was supported by Fondo Nacional de Desarrollo Científico y Tecnológico (Fondecyt, Chile) - Fondecyt Iniciación 11150107 to R.N-P. and Fondecyt Regular 1130197 to A.M.A. Fundings were used for conception and design of the study, proteome and bioinformatic analysis as well as for salary to R.N-P. and G.B. Open Access funding was enabled by Fondecyt Regular 1201010 to A.M.A.

Authors’ Contributions

R.N-P. Protein extraction and display, bioinformatics and statistical analysis; C.M., G.B. collaborated on data analysis; A.O., A.M.A. experimental design, provided research opportunity; R.N-P., A.M.A. wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Doug Whitten from Research Technology Support Facility, Proteomics Core Facility at Michigan State University for advising in proteomics analysis. Dr Reinaldo Campos-Vargas for providing peach fruit mesocarp samples.

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Due to technical limitations, table 1 is only available as a download in the supplemental files section.

Table120200810.pdf
SupplementaryTable120200305.xlsx
SupplementaryTable2.xlsx
SupTable3Summaryproteomicsdatasetsmatchs.xlsx
SuppFig1.tif
Supplementary Figure 1. Data pre-treatment. In order to have an insight about the data distribution, Q-Q plots were generated using as input the raw (A), versus imputed-scaled-centered protein abundance data (C). As a result from pre-treatment data, the Q-Q plot curve fitted much closer to the normal expectation curve (straight red diagonal line) than with the raw data. Boxplots of raw (B) vs treated data (D) further highlighted how the data became centered and scaled after been pre-treated.
SuppFig2s.tif
Supplementary Figure 2. Proteome bias assessment. Protein parameters were compared among P. persica primary transcripts’ proteome (left panels), current proteome (middle panels) and a mesocarp-derived proteome extracted from juicy and mealy fruits from the Spring Lady variety (right panels) [30]. Panels I to III contrast proteomes in terms of length and molecular weights (MW). Panels IV to VI contrast proteomes in terms of charge and protein stability based on its amino acids (instalindex). Panels VII to IX contrast proteomes in terms of hydrophobicity, using two scales: KyteDoolittle and Guy. Each dot in each graph represents the intersection of the values one protein has for the two parameters under evaluation. The distributions of values for each of these two parameters are shown above and at the right side of each panel as density plots.
SupplFig3.tif
Supplementary Figure 3. Hierarchical clustering of all genes transcriptionally characterized in the GSE71561 dataset and a subset related to sorbitol biosynthesis. Transcriptional information (average of three replicates of log2 normalized fluorescence intensity values) from 29,045 genes from the peach fruit genome 1.0, assessed in 13 conditions, was displayed using hierarchical clustering and the following conditions: clustering.method = "hierarchical", dist.method = "maximum", linkage.method = "complete“ (panel I). Using the same stages, data from genes encoding a putative sorbitol transporter family is also depicted (panel II). S_45 to S_125, seed samples at 45, 54, 69, 83, 111 and 125 days after full bloom (DAFB); M_45 to M_125, mesocarp samples at 45, 54, 69, 83, 111 and 125 DAFB; F - flower samples.
SupplFig4.tif
Supplementary Figure 4. Comparison of the O’Henry fruit mesocarp proteome characterized by 2D gel vs 1D gel analysis. (a) Mesocarp proteins from mature and ripe O’Henry fruits assessed by 2D-gels had 164 spots that could be quantified [27]. Among these 164 spots, 43 were identified by mass spectrometry analysis and, therefore, were contrasted with the current proteome under analysis. (b) Among these 43 proteins, 16 had accumulation profiles similar to the ones assessed in the current work (“match”), whereas 27 had different patterns (“did not match”).

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Shotgun proteomics of peach fruit reveals major metabolic pathways associated to ripening

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