Proteo-metabolomic analysis of fruits reveals molecular insights into variations among Italian Sweet Cherry (Prunus avium L.) accessions

Abstract

Mass spectrometry-based proteomics and metabolomics tackle the complex interactions between proteins and metabolites in fruits. Independently used to discern phenotypic disparities among plant accessions, these analytical approaches complement well-established DNA fingerprinting methods for assessing genetic variability and hereditary distance. To verify the applicability of integrated proteomic and metabolomic procedures in evaluating phenotypic differences between sweet cherry cultivars, and to potentially relate these findings to specific pomological traits, we conducted a comparative analysis of fruits from ten Italian accessions. We identified 3786 proteins, of which 288 exhibited differential representation between ecotypes, including key components influencing fruit quality and allergenic potential. Furthermore, 64 polyphenols were identified, encompassing anthocyanins, hydroxycinnamic acids, flavanols, hydroxybenzoic acids, flavonols, and flavanones subgroups. Multivariate analysis of total quantitative data outlined cultivar differences and phenotypic relationships. Coherent associations between proteomic and metabolomic data underscored their complementary role in characterizing genetic relationships elucidated through DNA fingerprinting techniques. Proteo-metabolomic results verified a certain correlation between the relative abundance of specific polyphenols, enzymes involved in their metabolism, and color characteristics of fruits. These findings highlight the significance of integrating results from diverse omics approaches to reveal molecular drivers of ecotype-specific traits and identify biomarkers for selecting and breeding cultivars in the next future.

Keywords: Prunus avium; biodiversity; fruit; metabolomics; proteomics; sweet cherry.

Figure 1

Hierarchical clustering of the 288 differentially represented proteins (DRPs) identified in the TMT-based proteomic analysis of ten sweet cherry cultivars. The heatmap displays normalized (total abundance normalization) and scaled protein abundance values, with color intensity indicating relatively higher (red) and lower (green) abundance levels. Hierarchical clustering was performed using Pearson distance and the average linkage method. Rows represent DRPs (p < 0.05, log₂FC≥1 and log₂FC≤ -1), and columns correspond to sweet cherry cultivars. Plant cultivars with similar proteomic profiles were effectively grouped based on the similarity of their protein expression patterns. Detailed information on DRPs shown in this figure is provided in Supplementary Table S2 .

Figure 2

Functional classification of the proteins identified and quantified in the sweet cherry cultivars. Histograms represent the relative percentages of KO functional categories within the annotated sets of all identified proteins (blue) and the subset of DRPs (orange).

Figure 3

Hierarchical clustering of the 63 differentially represented proteins (DRPs) classified under the KEGG Orthology category “Biosynthesis of secondary metabolites” (k01110) in sweet cherry cultivars. The heatmap displays normalized (total abundance normalization) and scaled protein abundance values, with color intensity indicating relatively higher (red) and lower (green) abundance levels. Hierarchical clustering was performed using Pearson distance and the average linkage method. Rows represent DRPs (p < 0.05, log₂FC≥1 and log₂FC≤ -1) involved in the biosynthesis of secondary metabolites, and columns correspond to sweet cherry cultivars. Two main protein clusters (A, B) were identified, each subdivided into three sub-clusters (A1-A3, B1-B3). Plant cultivars with similar protein abundance profiles are grouped together. Detailed information on the DRPs shown in this figure is provided in Supplementary Table S3 .

Figure 4

Heat-map reporting the variable abundance of polyphenols in sweet cherry cultivars. Each column corresponds to a cultivar, whereas each row represents a phytochemical upon manual curation of tandem MS spectra. Heat-map encompassed centered and reduced intensities (area counts) moving from red to blue, through white, including Euclidean distance function and Ward linkage method. The dendrograms from the hierarchical cluster analysis of the columns and the rows illustrate the similarity of the cultivars and the distribution of secondary metabolites, respectively.

Figure 5

Pie-chart summarizing the distribution of polyphenol classes in sweet cherry accessions according to publicly available databases. Each compounds identified was included in the following families using as a reference area counts of full MS signals in FTMS mode: anthocyanins, hydroxycinnamic acids, flavanols, hydroxybenzoic acids, flavones and flavanones in line with phenol-explorer database.

Figure 6

Anthocyanin pathway in sweet cherry. The enzymes identified and quantified in this study have been reported in green. Abbreviations are as follows: PAL, phenylalanine ammonialyase; PAL1, phenylalanine ammonialyase; C4H, cinnamate 4-hydroxylase; 4CL, 4-coumarate-CoA ligase; CHS, chalcone synthase; CHI, chalcone isomerase; F3’H, flavonoid 3’-hydroxylase; F3’5’H, flavonoid 3’,5’-hydroxylase; F3H, flavonoid 3-hydroxylase; DFR, dihydroflavonol 4-reductase; FLS, flavonol synthase; ANS, anthocyanidin synthase; UFGT, UDPglucose flavonoid 3-O-glucosyl transferase. Differences among cultivars for each enzyme were analyzed by multiple comparisons through one-way ANOVA (α = 0.05) and the results have been reported in Supplementary Table S6 .