The objective of this study is to clarify the underlying biochemical mechanisms related to the resistance and susceptibility of peach towards Myzus persicae, and to identify the key secondary metabolites of peach responding to the M. persicae infection.

New shoots of resistant (‘96-5-1’ (9651), ZaoYouTao (ZYT)) and susceptible (Zhong You 13 (CN13), Zhong Nong Jin Hui (ZNJH)) peach trees were inoculated with M. persicae for 3 days and used for secondary metabolite extraction and UPLC-MS/MS analysis. Differentially altered metabolites (DAMs) were screened with |log₂ fold change|≥1, P-value≤0.01 as threshold. The VIP value of the OPLS-DA model was used to perform differences between resistant and susceptible peach.

To illustrate the biochemical mechanisms of M. persicae resistance in peach, aphid-resistant/aphid-susceptible peach varieties infested with M. persicae for 3 days. A total of 528 metabolites were identified in the treated samples through widely targeted metabolomics analysis. Using principal component analysis (PCA), hierarchical cluster analysis (HCA) and Venn diagram analysis, it was found that 7 DAMs were identified from the susceptible variety CN13, and 2 of them were significantly decreased, 5 of them were significantly increased. 7 DAMs were identified from the susceptible variety ZNJH, and 3 of them were significantly decreased, 4 of them were significantly increased. 33 DAMs were identified from the resistant variety ‘96-5-1’, and 1 of them was significantly decreased, 32 of them were significantly increased. 55 DAMs were identified from the resistant variety ZYT, and 12 of them were significantly decreased, 43 of them were significantly increased. The majority of the DAMs were identified from the two resistant varieties, and the overall magnitude of change was greater in the resistant varieties than that in the susceptible varieties. Finally, 15 secondary metabolites (6 amino acids and their derivatives, 5 phenolic acids, 3 nucleotides and their derivatives, and 1 organic acid) were considered to be involved in M. persicae resistance of ‘Shou xing tao’.

The significantly up-regulated secondary metabolites obtained in M. persicae-resistant peach varieties were mainly involved in the response to M. persicae feeding. The regulation of these secondary metabolites (amino acids and their derivatives, phenolic acids, nucleotides and their derivatives, organic acid) is the important mechanism of defense reaction to M. persicae.

The aim of this study was to explore the differences between melting flesh and stony hard peaches at the peptide level and precursor protein level during fruit ripening, so as to provide a theoretical basis for mining the key peptides of determining or regulating the ripening process.

The characteristics of peptides and precursor protein functions in CN13 (melting flesh, MF) and CN16 (stony hard, SH) peaches were analyzed by peptidome, the relative contents of precursor proteins and peptides during MF and SH peach fruit ripening were compared, and the precursor proteins of different peptide segments were analyzed by function enrichment.

In this study, the peptides of CN13 and CN16 (S3 and S4III) were extracted for mass spectrometry. A total of 473 precursor proteins were identified, including 2 580 specific peptide sequences. The molecular weight, isoelectric point and cleavage sites of the peptide were summarized. In addition, the high-abundance precursor proteins corresponding to endogenous peptides were explored by COG function annotations and pathway enrichment analysis, and the results showed that the precursor proteins were mainly involved in the processes of general function prediction, post-translational modification, protein turnover, energy production and conversion, carbohydrate transport and metabolism. The enrichment analysis showed that the differential peptide precursor proteins of CN13 were related to biological processes, such as oxidation reduction, oxygen and oxygen and electron transport chain, which were mainly involved in glycolysis/gluconeogenesis, pentose phosphate pathway and RNA transport; the differential peptide precursor proteins in CN16 were related to biological processes, such as response to metal ion, response to inorganic substance, and response to cadmium ion, which were mainly involved in microbial metabolism in diverse environments, spliceosome and RNA transport; the differential peptide precursor proteins at the same stage in CN13 and CN16 at S4III were related to gene expression, translation and cellular macromolecular biological processes, which were mainly involved in RNA degradation, RNA transport and splicing.

There were significant differences in peptides between CN13 and CN16 during fruit ripening. The precursor proteins of differential peptide were involved in starch/sucrose metabolism, glycolysis and ribosome synthesis, and it was suggested that these metabolic pathways were closely related to peach fruit ripening, which provided a theoretical reference for further exploring the key peptides of regulating peach fruit ripening and senescence.

The aim of this study was to perform genotyping and genetic diversity analysis of 79 germplasms taken from National Fruit Germplasm Nanjing Peach Resource Nursery by using SSR fluorescent-labeled capillary electrophoresis technology, and to screen primers with high polymorphism, which could be used for identification of peach varieties and genetic relationship analysis and also be used for the establishment of molecular marker-assisted selection system.

De novo sequencing was performed on the female parent Zhongyou No. 4, and SSR markers were developed in the whole genome with a physical distance of 1 Mb as the unit. 79 peach varieties in Nanjing were used as materials for PCR amplification, and the amplified products were detected by polyacrylamide gel electrophoresis and screened for markers with clear target bands and abundant polymorphisms. The 5' end of the screened markers were fluorescently modified, and the PCR products were sequenced on the ABI3730XL sequencer to realize the rescreening of the markers. Genmapper 4.0 software was used to count the sequencing results, and Data Formater 2.1 software converted the bp data obtained by statistics into the data format required by Power Marker v3.25. The selected primers were analyzed for polymorphism, and the fluorescent SSR primers with polymorphism information content (PIC) greater than 0.45 were selected as the core primers of 79 germplasm materials. Using Nei's as a parameter, the UPDM clustering method in NTSYSpc 2.1 software was used to analyze the genetic diversity among cultivars. The Structure v2.3.4 software based on Bayesian model was used to analyze the population genetic structure of 79 accessions.

Based on 79 germplasms, the SSR markers covering the whole gene were screened by polyacrylamide gel electrophoresis, and 207 pairs of primers with good polymorphism were screened out. The 207 pairs of primers were re-screened by SSR fluorescent-labeled capillary electrophoresis technology, and 26 pairs of core primers were finally screened, of which 5 pairs of core primers could completely distinguish 79 varieties. 26 pairs of SSR core primers amplified a total of 174 polymorphic genotypes in 79 peach germplasms, and each pair of primers were amplified 4-13 genotypes, with an average of 6.69 genotypes amplified per pair of primers. The polymorphism information content PIC of each pair of primers was above 0.45. Based on the locus information amplified by 207 pairs of SSR primers in 79 accessions, the genetic relationship map and population genetic structure map of 79 accessions were constructed. 207 pairs of primers divided 79 accessions into 7 groups based on genetic distance and 2 groups based on population structure. The genetic diversity of 79 cultivars was high, and the clustering results of some cultivars were consistent with the pedigree.

The cluster analysis map and population genetic structure map of 79 materials were constructed, revealing the genetic relationship of 79 materials to a certain extent. The overall results showed that due to the complex genetic background of peach, the cultivars according to a single characteristic or determine the relationship between cultivars could not be classified. The screened 26 pairs of core primers could be used for genome-wide identification of linked traits, identification of peach germplasm resources, identification and protection of new varieties, and construction of molecular marker-assisted breeding systems.