Peroxisomal proteome mining of sweet pepper (Capsicum annuum L.) fruit ripening through whole isobaric tags for relative and absolute quantitation analysis

fpls-13-893376 May 3, 2022 Time: 19:0 # 1 ORIGINAL RESEARCH published: 09 May 2022 doi: 10.3389/fpls.2022.893376 Edited by: Agepati S. Raghavendra, University of Hyderabad, India Reviewed by: Amr Kataya, University of Calgary, Canada Ronghui Pan, Zhejiang University, China Greg B. Moorhead, University of Calgary, Canada *Correspondence: Francisco J. Corpas javier[email protected] Specialty section: This article was submitted to Plant Physiology, a section of the journal Frontiers in Plant Science Received: 10 March 2022 Accepted: 21 April 2022 Published: 09 May 2022 Citation: González-Gordo S, Palma JM and Corpas FJ (2022) Peroxisomal Proteome Mining of Sweet Pepper (Capsicum annuum L.) Fruit Ripening Through Whole Isobaric Tags for Relative and Absolute Quantitation Analysis. Front. Plant Sci. 13:893376. doi: 10.3389/fpls.2022.893376 Peroxisomal Proteome Mining of Sweet Pepper (Capsicum annuum L.) Fruit Ripening Through Whole Isobaric Tags for Relative and Absolute Quantitation Analysis Salvador González-Gordo, José M. Palma and Francisco J. Corpas* Group of Antioxidants, Free Radicals and Nitric Oxide in Biotechnology, Food and Agriculture, Department of Biochemistry, Cell and Molecular Biology of Plants, Estación Experimental del Zaidín, Spanish National Research Council (CSIC), Granada, Spain Peroxisomes are ubiquitous organelles from eukaryotic cells characterized by an active nitro-oxidative metabolism. They have a relevant metabolic plasticity depending on the organism, tissue, developmental stage, or physiological/stress/environmental conditions. Our knowledge of peroxisomal metabolism from fruits is very limited but its proteome is even less known. Using sweet pepper (Capsicum annuum L.) fruits at two ripening stages (immature green and ripe red), it was analyzed the proteomic peroxisomal composition by quantitative isobaric tags for relative and absolute quantitation (iTRAQ)-based protein profiling. For this aim, it was accomplished a comparative analysis of the pepper fruit whole proteome obtained by iTRAQ versus the identified peroxisomal protein profile from Arabidopsis thaliana. This allowed identifying 57 peroxisomal proteins. Among these proteins, 49 were located in the peroxisomal matrix, 36 proteins had a peroxisomal targeting signal type 1 (PTS1), 8 had a PTS type 2, 5 lacked this type of peptide signal, and 8 proteins were associated with the membrane of this organelle. Furthermore, 34 proteins showed significant differences during the ripening of the fruits, 19 being overexpressed and 15 repressed. Based on previous biochemical studies using purified peroxisomes from pepper fruits, it could be said that some of the identified peroxisomal proteins were corroborated as part of the pepper fruit antioxidant metabolism (catalase, superoxide dismutase, ascorbate peroxidase, monodehydroascorbate reductase, dehydroascorbate reductaseglutathione reductase, 6-phosphogluconate dehydrogenase and NADP-isocitrate dehydrogenase), the β-oxidation pathway (acylcoenzyme A oxidase, 3-hydroxyacyl-CoA dehydrogenase, enoyl-CoA hydratase), while other identified proteins could be considered “new” or “unexpected” in fruit peroxisomes like urate oxidase (UO), sulfite oxidase (SO), 5-methyltetrahydropteroyltriglutamatehomocysteine methyltransferase (METE1), 12-oxophytodienoate reductase 3 (OPR3) or Frontiers in Plant Science | www.frontiersin.org 1May 2022 | Volume 13 | Article 893376 fpls-13-893376 May 3, 2022 Time: 19:0 # 2 González-Gordo et al. Proteomic Analysis of Fruit Peroxisomes 4-coumarate-CoA ligase (4CL), which participate in different metabolic pathways such as purine, sulfur, L-methionine, jasmonic acid (JA) or phenylpropanoid metabolisms. In summary, the present data provide new insights into the complex metabolic machinery of peroxisomes in fruit and open new windows of research into the peroxisomal functions during fruit ripening. Keywords: iTRAQ, phenylpropanoids, pepper fruit, ripening, jasmonic acid, peroxisomes, proteome, sulfite oxidase INTRODUCTION Plant peroxisomal metabolism shares common enzymatic components with peroxisomes from other eukaryotic organisms. However, the metabolic plasticity of plant peroxisomes differs depending on the species, organ (root, leaf, cotyledon, flower, and fruit), stage of development, or environmental conditions (Hu et al., 2012;Reumann and Bartel, 2016;Pan et al., 2020; Huang et al., 2022). This indicates that the protein and metabolic peroxisomal profiles are diverse being a good example of the metabolic changes occurring from peroxisomes present in cotyledons (called glyoxysomes) to leaf-type peroxisomes (GotoYamada et al., 2015). As reflected in their name, peroxisomes are defined as subcellular compartments with a high content of hydrogen peroxide (H2O2) which is generated by different oxidase enzymes and contain catalase as the key and major enzyme to control the potential oxidative damage triggered by the overproduction of these reactive oxygen species (ROS) (Corpas et al., 2019, 2020). Sweet pepper (Capsicum annuum L.) fruits are one of the horticultural crops of the greatest agronomic importance due to their worldwide consumption. The fruits are characterized by having a high content of vitamin C and A (Palma et al., 2015, 2020b). Pepper fruits have an ethylene-independent ripening and therefore it is considered a non-climacteric fruit. During ripening, pepper fruits undergo drastic biochemical and phenotypical changes, being the color shift from green to red the most evident since chloroplasts are transformed into chromoplasts as a consequence of the chlorophyll degradation and the biosynthesis of carotenoids, xanthophylls, and anthocyanins. In previous studies, it has been analyzed the sweet pepper fruit ripening at biochemical and molecular levels including the transcriptome (González-Gordo et al., 2019), as well as the metabolism of NADPH-generating enzymes (Mateos et al., 2009;Muñoz-Vargas et al., 2018, 2020), ROS metabolism (Rodríguez-Ruiz et al., 2017a, 2019;Chu-Puga et al., 2019; González-Gordo et al., 2020, 2022a;Palma et al., 2020a), reactive nitrogen species (Chaki et al., 2015;Rodríguez-Ruiz et al., 2017b), the metabolome (Guevara et al., 2021), and the plastidial and mitochondrial proteomes (Rödiger et al., 2021;González-Gordo et al., 2022b). However, little is known about the proteome from peroxisomes and how it can be modulated during ripening. In a preliminary study, using pepper fruit peroxisomes purified by the combination of differential and sucrose density-gradient centrifugations and then subjected to 2-D electrophoresis and MALDI-TOF/TOF analyses, it was possible to discriminate 39 polypeptides, but only 13 of them were identified with a high protein score (99%) confidence interval (C.I.) (Palma et al., 2018). In the present study, an alternative and powerful approach has been used, the quantitative isobaric tags for relative and absolute quantitation (iTRAQ), and the whole protein profiling in immature (green) and mature (red) fruits was obtained. This has allowed identifying a total of 57 peroxisomal proteins from pepper fruits present in the matrix or membrane-bound. Furthermore, it was also found that some of these peroxisomal proteins were differentially expressed during the ripening process. To our knowledge, this is the first report about the analysis of the peroxisomal protein profile during the ripening of pepper fruits and provides the basement for future studies on the physiological relevance of peroxisomes in fruits. MATERIALS AND METHODS Plant Material Sweet California-type pepper (Capsicum annuum L., cv. Melchor) fruits were obtained from plants grown in plasticcovered greenhouses (Syngenta Seeds, Ltd., El Ejido, Almería, Spain). Fruits at two ripening stages were analyzed: immature green and ripe red. Fruits selected at each stage had similar phenotypic characteristics (size, shape, and color). In all cases, pericarp was prepared from, at least, four fruits (biological replicates) at each stage (one fruit per plant, five plants, each one having green and red fruits). After harvesting, fruits were cut into small cubes (5 mm/edge), frozen under liquid nitrogen, and then stored at –80◦C until use. Fruit Sample Preparation for Whole Proteomic Analysis Fruit samples (green and red, four replicates each) were ground under liquid nitrogen in a IKA A11 Basic mill and then dissolved in a ratio 1:1 (w:v) with 50 mM Tris-HCl buffer, pH 7.5, 0.1 mM EDTA, 0.1% (v/v) Triton X-100, 10% (v/v) glycerol, 5 mM DTT. The extract was filtered through two layers of nylon cloth and centrifuged at 27,000 gfor 15 min. Proteins from the supernatant were precipitated with 70% (v/v) cold acetone at 4◦C for 30 min. The mix was spun at 16,000 gfor 15 min and the pellet was resuspended into Tris-HCl 50 mM, pH 7.5 for 12–14 h at 4◦C. Finally, samples were centrifuged at 39,000 gfor 20 min, and the supernatants were recovered and cleaned by passing through PD-10© columns (General Electric). Eluted fractions from the columns were divided into 200 µg-protein aliquots and were Frontiers in Plant Science | www.frontiersin.org 2May 2022 | Volume 13 | Article 893376 fpls-13-893376 May 3, 2022 Time: 19:0 # 3 González-Gordo et al. Proteomic Analysis of Fruit Peroxisomes lyophilized for isobaric tags for relative and absolute quantitation (iTRAQR ) analysis. Protein Digestion and Tagging With iTRAQ-8-plexR Reagent For digestion, 50 µg of protein from each pepper sample was precipitated using methanol/chloroform. Protein pellets were re-suspended and denatured in 20 µL of 6 M guanidine hydrochloride prepared in 100 mM HEPES, pH 7.5, reduced with 1 µL of 50 mM Tris (2-carboxyethyl)phosphine (TCEP, AB SCIEX), pH 8.0, at 60◦C for 30 min, followed by the addition of 2 µL of 200 mM cysteine-blocking reagent (methyl methanethiosulfonate (MMTS, Pierce) for 10 min at room temperature. Samples were then diluted up to 120 µL to reduce guanidine concentration with 50 mM TEAB (tetraethylammonium tetrahydroborate). Digestions were initiated by adding 2 µg of sequence grade-modified trypsin (Sigma-Aldrich, Madrid, Spain) to each sample in a ratio of 1/25 (w/w), which were then incubated at 37◦C overnight on a shaker. Sample digestions were evaporated to dryness. Each peptide solution was labeled at room temperature for 2 h with a half unit of iTRAQ Reagent Multi-plex kit (AB SCIEX, Foster City, CA, United States) previously reconstituted with 80 µL of 70% ethanol/50 mM TEAB. After labeling, samples were combined and labeling reaction was stopped by evaporation in a Speed Vac. Liquid Chromatography and Mass Spectrometry Analysis A 2 µg aliquot of the iTRAQ-labeled mixture was subjected to nano-LC ESI-MS/MS analysis using a nano liquid chromatography system (Eksigent Technologies nanoLC Ultra 1D plus, AB SCIEX, Foster City, CA, United States) coupled to high-speed Triple TOF 5600 mass spectrometer (SCIEX, Foster City, CA, United States) with a Nanospray III source. The injection volume was 5 µL. The analytical column used was a silica-based reversed-phase Peptide BEH C18 column 75 µm×15 cm, 1.7 µm particle size, and 130 Å pore size (Waters, Ireland). The trap column was an Acclaim PepMap 100 C18 ChromXP (Thermo Scientific, Madrid, Spain), 3 µm-particle diameters, 100 mm ×2 cm, switched on-line with the analytical column. The loading pump delivered a solution of 0.1% (v/v) formic acid in water at 2 µL/min. The nano-pump provided a flow-rate of 250 nL/min and was operated under gradient elution conditions, using 0.1% (v/v) formic acid in water as mobile phase A, and 0.1% (v/v) formic acid in acetonitrile as mobile phase B, under the following schema: isocratic conditions of 96% A: 4% B for 5 min, a linear increase to 40% B for 205 min, then a linear increase to 90% B for 15 additional minutes, isocratic conditions of 90% B for 10 min and return to initial conditions in 2 min. The total gradient length was 250 min. Data acquisition was performed with a TripleTOF 5600 System. Ionization occurred under the following conditions: ion spray voltage floating (ISVF) 2800 V, curtain gas (CUR) 20, interface heater temperature (IHT) 150, ion source gas 1 (GS1) 20, declustering potential (DP) 85 V. All data were acquired using information-dependent acquisition (IDA) mode with Analyst TF 1.7 software (AB SCIEX, United States). For IDA parameters, 0.25 s MS survey scan in the mass range of 350–1250 Da was followed by 25 MS/MS scans of 150 ms in the mass range of 100–1500 (total cycle time: 4 s). Switching FIGURE 1 | Distribution of the GO (Gene Ontology) categories assigned to the identified proteins from pepper fruit. Proteins were classified according to three categories: Biological Processes, Molecular Function and Cellular Components. After GO term enrichment analysis, the different categories with their corresponding p-values were filtered using the REViGO tool to remove redundant GO terms. Frontiers in Plant Science | www.frontiersin.org 3May 2022 | Volume 13 | Article 893376 fpls-13-893376 May 3, 2022 Time: 19:0 # 4 González-Gordo et al. Proteomic Analysis of Fruit Peroxisomes FIGURE 2 | Differentially abundant proteins involved in redox metabolism and (a)biotic stress responses during the ripening sweet pepper fruits. Each colored square represents a protein. Green and red colors indicate downand up-regulation at fruit ripening, respectively, according to log2FC values. FC, fold change. In the above right scale, red means log2FC >0, whereas green means log2FC <0. AsA-GSH cycle, ascorbate-glutathione cycle; CAT, catalase; PR-proteins, pathogenesis-related proteins; SOD, superoxide dismutase. criteria were set to ions greater than mass to charge ratio (m/z) 350 and smaller than m/z 1250 with a charge state of 2–5 and an abundance threshold of more than 90 counts per second (cps). Former target ions were excluded for 20 s. IDA rolling collision energy (CE) parameters script was used for automatically controlling the CE. Database Search and Quantitative Analysis MS/MS spectra were exported to Mascot generic format (MGF) using Peak View v1.2.0.3 and searched using Mascot Server 2.5.1, OMSSA 2.1.9, X!TANDEM 2013.02.01.1, and Myrimatch 2.2.140 against a composite target/decoy database built from the 38,628 Capsicum annuum bell pepper sequences at Uniprot (proteome ID UP000189700)1plus some commonly occurring contaminants. Correctly identified peptides from an initial X! TANDEM search with a mass error tolerance of 35 ppm were used to recalibrate parent ion mass measurements in all spectra using linear models. All search engines were then configured to match potential peptide candidates to recalibrated spectra with a mass error tolerance of 10 ppm and fragment ion tolerance of 0.02 Da, allowing up to two missed tryptic cleavage sites and a maximum isotope error (13C) of 1. Fixed MMTS modification of cysteine and variable oxidation of methionine, pyroglutamic acid from glutamine or glutamic acid at the peptide N-terminus, 1https://www.uniprot.org/proteomes/UP000189700 and modification of lysine, tyrosine, and peptide N-terminus with iTRAQ 8-plex reagents were also considered. Score distribution models were used to compute peptide-spectrum match p-values (Ramos-Fernández et al., 2008), and spectra recovered by a False Discovery Rate (FDR) ≤0.01 (peptide-level) filter were selected for quantitative analysis. Differential regulation was measured using linear models (López-Serra et al., 2014), and statistical significance was measured using q-values (FDR). For quantitative analysis of the sweet pepper fruit whole proteome iTRAQ was used due to its accuracy, discriminating capacity among treatments, and reproducibility. In fact, isobaric tagging quantification by mass spectrometry generally underestimates the magnitude of the differential abundance of proteins (DAP) due to contamination of quantitative signals with co-eluting background peptides, the so-called ratio compression effect, rendering fold change cutoff criteria useless. The strength of this compression effect is highly dependent on the tradeoff between sample complexity and data acquisition effort (peptide fractionation and gradient length), as well as instrument-specific factors. Furthermore, due to the stochastic nature of proteome sampling in quantitative shotgun proteomics approaches, data quality is highly variable across quantified proteins. Relatively low magnitude fold change values may achieve outstanding statistical significance if backed up by a sufficiently high number of peptides with good quality signals, with measured abundance ratios being consistently reproducible across biological replicates. On the other hand, high fold change proteins with very poor Frontiers in Plant Science | www.frontiersin.org 4May 2022 | Volume 13 | Article 893376 fpls-13-893376 May 3, 2022 Time: 19:0 # 5 González-Gordo et al. Proteomic Analysis of Fruit Peroxisomes FIGURE 3 | Gene Ontology enrichment analysis of the identified peroxisomal proteins from sweet pepper (Capsicum annuum, L.) fruit. (A) Biological process. (B) Molecular function. After GO term enrichment analysis, the different categories with their corresponding p-values were filtered using the REViGO tool to remove redundant GO terms. The number of proteins assigned to each functional category is expressed as a percentage (%). quality evidence rarely achieve low q-values. The log2-foldchange (log2FC) value represents the fold change of protein between two situations (A, green fruits; B, red fruits) and it is calculated according to the formula: log2FC = log2(B) – log2(A). All analyses were conducted using software from Proteobotics (Madrid, Spain). Functional Analysis The different sets of obtained proteins were used to carry out functional analyzes by searching for enriched GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) terms, from different tools hosted on different web servers: AgriGO v2.0 (Tian et al., 2017), PlantRegMap (Jin et al., 2017), and KOBAS 3.0 (Xie et al., 2011). Finally, the GO terms that were significantly enriched were analyzed with REViGO (Supek et al., 2011) to eliminate redundant categories. RESULTS Whole Proteome of Pepper Fruit During Ripening The analysis of pepper fruit whole proteome by the iTRAQ approach allowed the identification of a total of 2,284 proteins, of which 2,253 (98.6%) shared at least one orthologous sequence with Arabidopsis thaliana. With this information, an enrichment analysis was carried out in GO terms to determine the functional processes in which these proteins were involved. Among the different orthologs, 1,971 proteins were assigned to GO categories, with 1,331 of them (67.5%) being statistically enriched after analysis. Within the most significant GO terms 827, 340, and 163 proteins were grouped into the categories of biological processes, molecular functions and cellular components, respectively (Figure 1). Regarding the different categories belonging to biological processes, a high percentage of proteins were linked to metabolic and cellular processes. Likewise, it is worth highlighting other functions associated with response to different types of stress, along with other metabolic aspects involved in catabolism such as proteolysis. In this same group, a smaller number of proteins were related to the developmental processes of the reproductive system, highlighting the growth of some organs such as seeds. Among the different protein functions, the binding categories and different catalytic activities such as hydrolases, peptidases, isomerases, lyases, ligases and phosphatases stood out, and 2% of identified proteins were linked to antioxidant activities. Concerning the cell components, the majority of proteins were classified in the category corresponding to the cell, the intracellular space, and other subcellular locations such as the cytoplasm, plastids or mitochondria. Frontiers in Plant Science | www.frontiersin.org 5May 2022 | Volume 13 | Article 893376 fpls-13-893376 May 3, 2022 Time: 19:0 # 6 González-Gordo et al. Proteomic Analysis of Fruit Peroxisomes TABLE 1 | Identified peroxisomal matrix proteins containing PTS which are differentially expressed during sweet pepper fruit ripening. Protein name Abbreviation UniProt ID PTS log2FC Regulation PTS1 Alanine-glyoxylate aminotransferase AGT A0A1U8ESR9 SRI 1.396 UP Probable acyl-CoA dehydrogenase IBR3 A0A2G2ZKA1 AKL 1.376 UP 2-hydroxyacyl-CoA lyase HPCL2 A0A1U8FNI1 HKN(1)1.109 UP Acyl-coenzyme A oxidase 4 ACOX4 A0A1U8ERN1 SRL 0.906 UP Monodehydroascorbate reductase 1 MDAR1 A0A1U8E6R6 SKI 0.972 UP 4-coumarate-CoA ligase 7 4CLL7 A0A2G2YHF4 SKL 0.793 UP Carboxypeptidase CP A0A1U8H246 KNI(1)0.894 UP Glutamate-glyoxylate aminotransferase 2 GGAT2 A0A1U8EMR4 SRM 0.884 UP Enoyl-CoA hydratase 2 ECH2 A0A1U8F2R5 SSL 0.595 UP Glutathione reductase GR2 A0A1U8G435 TNL 0.459 UP Superoxide dismutase [CuZn] SOD A0A1U8EPJ0 SSV 0.655 UP Amine oxidase AOX A0A1U8EUZ0 AKL 0.374 UP Probable acetyl-CoA acetyltransferase THIC2 A0A1U8GL52 SSL 1.302 UP Hydroxypyruvate reductase HPR A0A1U8DY61 SKL – NONE Sulfite oxidase SO A0A2G3ACR2 ANL – NONE 3-hydroxyacyl-CoA dehydrogenase HADH A0A1U8EU39 SRL – NONE Dienoyl-CoA isomerase DCI A0A2G3ACC8 AKL – NONE 4-coumarate-CoA ligase 5 4CLL5 A0A2G2YA92 SKL – NONE Acyl-coenzyme A oxidase 1 ACOX1 A0A2G3AEV5 ARL – NONE Zinc-binding alcohol dehydrogenase domain-containing protein 2 ZBADH A0A1U8DX36 AKL – NONE Insulin-degrading enzyme-like 1 IDE1 A0A1U8GL11 VKL(2)–1.689 DOWN NADP-isocitrate dehydrogenase ICDH A0A2G2Y555 PKI(2)–1.373 DOWN 6-phosphogluconate dehydrogenase 2 6PGDH2 A0A1U8F1E8 SKI –0.017 DOWN Isopentenyl-diphosphate isomerase IDI2 A0A2G2ZKS5 HKL –1.543 DOWN (S)-2-hydroxy-acid oxidase HAO A0A1U8EZL8 PRL –0.638 DOWN NADH:ubiquinone reductase NDB1 A0A1U8EN49 SRI –1.147 DOWN Isopentenyl-diphosphate isomerase IDI1 A0A2G2Y814 HKL –1.206 DOWN Methyltetrahydropteroyltri glutamate-homocysteine S-methyltransferase(3)METE1 A0A1U8F6S1 SAK –0.899 DOWN Urate oxidase UO A0A2G2Y6K0 SNM –1.889 DOWN Acyl-CoA thioesterase 2 ACTES A0A1U8E2R5 PKL –0.319 DOWN 4-coumarate-CoA ligase 4 4CLL4 A0A2G3AFX5 SKL – NONE 12-oxophytodienoate reductase 3 OPR3 A0A1U8H923 SRL – NONE Probable 6-phosphogluconolactonase 4 6PGL4 A0A1U8FYP2 SKL – NONE Uncharacterized protein UP3 A0A2G2Z370 ASL – NONE Catalase 2 CAT2 A0A1U8FMA2 QKL (internal) –1.102 DOWN Catalase 3 CAT3 A0A1U8GAA6 QKL (internal) –1.374 DOWN PTS2 Acyl-coenzyme A oxidase 3 ACOX3 A0A1U8ENR3 RIx5HL 0.786 UP Malate dehydrogenase PMDH A0A1U8FSV3 RIx5HL 1.333 UP 3-ketoacyl-CoA thiolase 2 ACAA1 A0A2G2ZTY4 RQx5HL 2.402 UP Mevalonate kinase MKV A0A1U8GJW7 DVx5QM – NONE Citrate synthase CISY2 A0A1U8FK78 RLx5HL – NONE Long chain acyl-CoA synthetase 6 ACLS A0A2G2Y0B5 RLx5HL – NONE Hydroxyisourate hydrolase(4)HSH A0A2G3ALZ5 RVx5HL – NONE 14 kDa zinc-binding protein(4)ZBP A0A1U8F6W1 RLx5HF – NONE (1)This putative PTS1 is experimentally not yet validated (Lingner et al., 2011). (2)The Uniprot database assigns these proteins to be located in pepper peroxisome. The tripeptides VKL and PKI at the C-terminus have been experimentally verified (Lingner et al., 2011). (3)The Uniprot database assigns the Arabidopsis ortholog protein is protein to be located in peroxisome. (4)The Uniprot database assigns this protein to be located in rice peroxisome. Frontiers in Plant Science | www.frontiersin.org 6May 2022 | Volume 13 | Article 893376 fpls-13-893376 May 3, 2022 Time: 19:0 # 7 González-Gordo et al. Proteomic Analysis of Fruit Peroxisomes TABLE 2 | Peroxisomal matrix protein lacking PTS which are differentially expressed during sweet pepper fruit ripening. Protein name Abbreviation UniProt ID log2FC Regulation Serine/threonineprotein phosphatase 2A STP2A A0A1U8EKG3 1.531 UP Serine/threonineprotein phosphatase STP A0A2G2YDX1 – NONE Probable sarcosine oxidase PIPOX A0A1U8F0Q0 –0.644 DOWN Nucleoside diphosphate kinase NDK1 A0A2G3AM91 –0.456 DOWN Glyoxalase 1 GLX1 A0A2G3A977 –0.915 DOWN TABLE 3 | Peroxisomal membrane-associated proteins (PMPs) which are differentially expressed during sweet pepper fruit ripening. Protein Abbreviation UniProt ID log2FC Regulation Peroxisome biogenesis protein 19 PEX19 A0A2G3AER5 1.084 UP Fission 1 protein FP1 A0A1U8FBV7 0.669 UP ABC transporter D 1 ABCD1 A0A2G2Y840 0.724 UP Peroxin 4 PEX4 A0A1U8EYW8 – NONE Peroxisome biogenesis protein 22 PEX22 A0A1U8F7K6 – NONE L-ASCORBATE PEROXIDASE 5 APX5 A0A2G2Y2S6 – NONE Ras-related protein RABE1C RABE1C A0A1U8GLM9 – NONE Dehydroascorbate reductase 2 DAR2 A0A1U8GZY4 –0.662 DOWN Within the set of identified proteins, it was estimated that around 50% of them modified their abundance due to the ripening process. Among the different quantified proteins, 692 (27%) were found to be significantly more abundant in the green fruits compared to red ones. On the contrary, 497 (19%) showed a higher abundance after the ripening process. To determine the different physiological events in which these proteins were involved, a functional analysis was carried out using MapMan software. Special attention was paid to those proteins related to redox metabolism and to response mechanisms against different types of stress (Figure 2). Thus, remarkable differences were found in the abundance of proteins that are part of complex antioxidant systems such as superoxide dismutases (SODs), catalase (CAT), peroxidases, peroxiredoxins and enzymes associated with the ascorbate-glutathione cycle (AsA-GSH), among others (Figure 2). Regarding the processes of response to abiotic stress, numerous proteins involved in hormonal signaling mechanisms were also found. Within this group, it is worth highlighting the regulation of those proteins involved in the synthesis of ethylene (ET), such as 1-aminocyclopropane-1-carboxylate (ACC) oxidase 4 (ACO4), whose relative abundance was higher in green fruits compared to red ones. Other affected metabolic categories were related to the maintenance of the cell wall structure, secondary metabolism, proteolysis and different signaling processes. Finally, a large group of proteins associated with different types of abiotic stress showed a greater abundance in green fruits compared to red ones. However, the levels of response proteins to cold stress were higher in red fruits. The proteomic data were deposited in the repository PRIDE/ProteomeXchange with reference PXD010457. In silico Analysis of the Peroxisomal Proteome Mined From the Whole Fruit Proteome Once the identifiers (Arabidopsis Genome Initiative –AGILocus Code) were obtained, these proteins were confronted with the most recently updated list of Arabidopsis peroxisomal proteins previously reported (Pan and Hu, 2018). This comparative analysis allowed identifying a total of 57 peroxisomal proteins. With this information, an enrichment analysis was carried out in GO terms to determine the functional processes where these peroxisomal proteins are involved (Figure 3). Among the different metabolic functions, it should be highlighted several catabolic processes such as the fatty acid β-oxidation, photorespiration and lipid homeostasis. Other proteins were involved in the structural organization of the peroxisome and associated with metabolic processes linked to reactive oxygen (ROS) and nitrogen (RNS) species (Figure 3A). Regarding the different molecular functions, the majority of peroxisomal proteins detected were related to catalytic activity and binding categories (Figure 3B). A deeper analysis of the potential peroxisomal proteins from pepper fruits allowed classifying them according to their subcellular location and/or the absence/presence/type of peroxisomal targeting signals (PTSs). Table 1 shows the 44 identified peroxisomal matrix proteins containing canonical PTSs, with 36 proteins having a PTS type 1, and 8 containing a PTS type 2. In the same Table 1, their relative abundance, either upregulated, downregulated, or unaffected during sweet pepper fruit ripening, is also indicated. Table 2 shows the 5 peroxisomal matrix proteins lacking PTS, whereas Table 3 displays the 8 identified peroxisomal membrane proteins (PMPs). Consequently, this experimental approach using the iTRAQbased protein profiling has increased the number of peroxisomal proteins of pepper fruits in comparison to our previous studies where, using pepper fruit peroxisomes purified by the combination of differential and sucrose density-gradient centrifugations and then subjected to 2-D electrophoresis and MALDI-TOF/TOF analyses, allowed to discriminate 39 polypeptides although only 13 out of them were identified with a high protein score (99%) confidence interval (C.I.) (Palma et al., 2018). Modulation of Peroxisomal Proteins During Pepper Fruit Ripening Besides the identification of the peroxisomal proteome, the used experimental approach (iTRAQ) also allowed to compare and quantify the relative abundance of these proteins at the immature (green) and ripe (red) stages. Figure 4 depicts the biological process of each of these peroxisomal proteins and its Frontiers in Plant Science | www.frontiersin.org 7May 2022 | Volume 13 | Article 893376 fpls-13-893376 May 3, 2022 Time: 19:0 # 8 González-Gordo et al. Proteomic Analysis of Fruit Peroxisomes corresponding heat map regulation during ripening from green to red. Thus, among the 35 identified proteins with showed significant differences in their abundance during ripening, 19 proteins were overexpressed, 16 were repressed, and the other 22 peroxisomal proteins were unaffected. Thus, it can be seen in Figure 4 and Tables 1-3 that most of the proteins linked to the β-oxidation process, together with the ABCD1 transporter responsible for the import of fatty acids into the peroxisome, showed a higher abundance in red fruits. This same trend was found in the peroxins PEX4 and PEX19. Regarding the antioxidant systems, different components of the ascorbateglutathione cycle were detected, among which the overexpression of two isoenzymes (MDAR1 and GR2) stand out. In this same group, we found the CAT3 isoenzyme, with higher levels of abundance in green peppers, while a CuZn-SOD isoenzyme displayed the opposite pattern. In addition, the expression of two enzymes that generate reducing power in the form of NADPH (6PGDH and NADP-ICDH) decreased during maturation. DISCUSSION Proteomic studies on fruits are relatively scarce compared to other plant organs such as cotyledons, roots or leaves (Palma et al., 2018). However, the technological advances associated with the proteomic field have led to a real scientific revolution in the analysis of different biological processes, including fruit ripening and senescence (Palma et al., 2011;Wu et al., 2017). In recent years, two-dimensional electrophoresis (2-DE) has been the most widely used protein separation method in proteomic analyses, carrying out extensive research on the expression and accumulation of proteins in different fruits such as apples (Marondedze and Thomas, 2012;Sánchez-Bel et al., 2012;Zheng et al., 2013), tomatoes (Rocco et al., 2006;Faurobert et al., 2007;Choi et al., 2022), grape (Sarry et al., 2004), or peaches (Prinsi et al., 2011;Zhang et al., 2011). However, these studies have certain limitations that are intrinsic to the methodology used, such as its low reproducibility, the difficulty in separating hydrophobic proteins, the low number of identified proteins, as well as the low solubility of certain proteins due to their physical-chemical properties (Görg et al., 2004;Magdeldin et al., 2014). Alternatively, the use of iTRAQR involves several advantages in proteomic analyses, including the conservation of information related to post-translational modifications (PTMs) (Chaube, 2014). During Fruit Ripening, the Whole Proteome Shows a Remarkable Modulation of Proteins Involved in Redox Metabolism and (A)biotic Stress Responses In our experimental sweet pepper fruit model, the proteomic analysis allowed the identification of a total of 2,284 proteins. This value was higher than the proteins found in similar studies in other fruits such as grapes (674; Kambiranda et al., 2014) or 1,384 in peaches (Wu et al., 2017); but also somehow lower than those identified in tomato with 2,336 proteins (Liu et al., 2016) or 5,578 in melon (Chen et al., 2021). Likewise, the functional annotation of protein relative abundance in GO terms was similar to those described in other fruits of agronomic interest such as tomatoes (Liu et al., 2016) or grapes (Kambiranda et al., 2014). It is remarkable that above the half of the identified proteins was modulated during ripening. The identification of numerous proteins involved in redox homeostasis represents new evidence of the importance of oxidative metabolism during the ripening of sweet pepper fruit. In addition, different proteins related to hormonal signaling mechanisms were found. Among them, it is worth highlighting the indoleacetic acid (IAA) amido hydrolase enzymes, responsible for controlling the levels of IAA in plant cells by converting auxin-amino acid conjugates into free IAA molecules (Bartel and Fink, 1995;LeClere et al., 2002). Although it was thought that this was their only function, it has recently been shown that these proteins can regulate the expression of the enzyme 1-aminocyclopropane-1-carboxylic (ACC) synthase (ACS), altering the synthesis of ethylene during peach ripening (Wang et al., 2021). This would imply a new point of interaction between auxin metabolism and ET, whose possible implications in pepper fruit ripening could be interesting to address in future research. On the other hand, the abscisic acid (ABA) seems to play a fundamental role in the ripening of nonclimacteric fruits. In this sense, we found a greater accumulation in red fruits of an enzyme involved in their biosynthesis, specifically an aldehyde oxidase (AO4). Its respective orthologous protein is involved in the senescence delay in Arabidopsis siliques through the oxidation of different aldehydes of a toxic nature, so that in pepper fruits it could have similar functions (Srivastava et al., 2017). Although the ripening of the pepper fruit has been classified as non-climacteric, it was observed a greater abundance in green fruits of the enzyme ACC oxidase (ACO), which is responsible for catalyzing the last step in the synthesis of ET (Houben and Van de Poel, 2019;Pattyn et al., 2021), which would indicate that, regardless of the physiological response of the fruits, this phytohormone could play essential roles in the ripening process (Bapat et al., 2010;Hou et al., 2018). During ripening, a series of changes take place in fruits such as the accumulation of pigments, volatile compounds, organic acids and sugars that, added to different alterations in the firmness and texture of the fruits, constitute evolutionary strategies aimed at attracting animals that could help to seed dispersion. A considerable part of these modifications is dependent on secondary metabolism, which implies that during the ripening process different patterns of the enzymes involved in their metabolic pathways are produced. Therefore, the overaccumulation observed in red pepper fruits of enzymes involved in the synthesis of carotenoids such as phytoene desaturase (PDS), zeta-carotene desaturase (ZDS1), and lycopene β-cyclase (LCY1) (Hornero-Méndez et al., 2000;Berry et al., 2019) is not surprising. On the other hand, certain processes related to the growth and consistency of the fruits are determined by different modifications associated with the cell wall. In fact, several proteins related to the maintenance and syntheses of the different components that make up this structure Frontiers in Plant Science | www.frontiersin.org 8May 2022 | Volume 13 | Article 893376 fpls-13-893376 May 3, 2022 Time: 19:0 # 9 González-Gordo et al. Proteomic Analysis of Fruit Peroxisomes FIGURE 4 | Regulation of peroxisomal proteins during sweet pepper fruit ripening. Each colored square represents a protein. Green and red colors indicate downand up-regulation at fruit ripening, respectively, according to log2FC values. 6PGDH, 6-phosphogluconate dehydrogenase; ABCD1, ABC transporter D 1; ACAA1, 3-ketoacyl-CoA thiolase; ACOX, acyl-CoA oxidase; ACSL, long-chain acyl-CoA synthetase; AGT, alanine:glyoxylate aminotransferase; APX5, L-ascorbate peroxidase 5; CAT, catalase; DCI, dienoyl-CoA isomerase; FP1, fission protein 1; GGAT2, glutamate glyoxylate aminotransferase 2; GR, glutathione reductase 2. HAO, (S)-2-hydroxy-acid oxidase; HPCL2, 2-hydroxyacyl-CoA lyase; HPR, hydroxypyruvate reductase; ICDH, isocitrate dehydrogenase; MDAR1, monodehydroascorbate reductase 1; MKV, mevalonate kinase; PEX, peroxin; PIPOX, probable sarcosine oxidase; PMDH, malate dehydrogenase 1; RABE1C, Ras-related protein RABE1C; DAR2, dehydroascorbate reductase 2; SOD, superoxide dismutase; UO, urate oxidase. FC, fold change. Red means log2FC >0, green means log2FC <0. were found (Brownleader et al., 1999;Paniagua et al., 2014; Forlani et al., 2019). Throughout fruit development, a greater accumulation of proteins linked to different defense processes was also observed, including glucan endo-1,3-β-D-glucosidase (GLB) that could play important roles in protection against pathogens in sweet pepper, as has been proposed for other types of fruit (Prinsi et al., 2011; Kambiranda et al., 2014). Thus, these series of changes that occur during the ripening of the fruits imply an exhaustive control of the mechanisms associated with protein degradation and synthesis, as it has been observed in sweet pepper fruits (Palma et al., 2011). Despite their categorization as response proteins against different types of abiotic stress, many of these proteins participate in cell signaling processes through mechanisms of interaction with different phytohormones (Ku et al., 2018). Thus, the possible implications of these proteins during the ripening of the pepper fruit should be analyzed more exhaustively in future studies. Scrutiny of Peroxisomal Proteome Mining From the Whole Fruit Proteome Peroxisomes remain one of the least studied organelles in plant biology and the information available on their proteome in fruits is virtually very limited (Palma et al., 2018). There are few biochemical characterizations of peroxisomes from some fruits such as pepper (Mateos et al., 2003;Palma et al., 2015, 2018;Rodríguez-Ruiz et al., 2019), olive (López-Huertas and del Río, 2014), and apple (Zarei et al., 2015). And, there are only available few proteome studies from isolated peroxisomes, i.e., from etiolated soybean cotyledons (Arai et al., 2008), leaf peroxisome from spinach leaves (Babujee et al., 2010) and etiolated Arabidopsis seedlings (Quan et al., 2013). At present, the best characterized peroxisomal proteome has been obtained from Arabidopsis thaliana leaves using different technical approaches and under different events (Fukao et al., 2002;Reumann, 2004; Reumann et al., 2004, 2007, 2009;Pan and Hu, 2018;Pan et al., 2018, 2019), including some novel machine learning prediction methods (Lingner et al., 2011;Reumann et al., 2016; Reumann and Chowdhary, 2018). To our knowledge, the most recent peroxisomal proteome in Arabidopsis is constituted of 199 proteins, including 144 targeting signal (PTS)-containing matrix proteins, 45 membrane proteins, and 10 proteins lacking recognizable PTS (Pan et al., 2018). This information from Arabidopsis peroxisomes has been very valuable for our mining study and has allowed identifying in the pepper fruit proteome obtained by iTRAQ a total of 57 peroxisomal proteins located in the matrix and membrane, and the majority harboring either a PTS1 or PTS2. Although some of the assigned putative PTS1 (HKN and KNI) have experimentally not yet been validated (Lingner et al., 2011;Wang et al., 2017), it cannot be discarded the occurrence of a “piggyback import” mechanism which could occur in the PTS1 and PTS2 import pathway where a protein without a PTS can dimerize with a PTS-cargo protein and Frontiers in Plant Science | www.frontiersin.org 9May 2022 | Volume 13 | Article 893376 fpls-13-893376 May 3, 2022 Time: 19:0 # 16 González-Gordo et al. Proteomic Analysis of Fruit Peroxisomes Wang, H., Sun, S., Zhou, Z., Qiu, Z., and Cui, X. (2020). Rapid analysis of anthocyanin and its structural modifications in fresh tomato fruit. Food Chem. 333, 127439. doi: 10.1016/j.foodchem.2020.127439 Wang, J., Wang, Y., Gao, C., Jiang, L., and Guo, D. (2017). PPero, a computational model for plant pts1 type peroxisomal protein prediction. PLoS One 12:e0168912. doi: 10.1371/journal.pone.0168912 Wang, Y., Deng, L., Meng, J., Niu, L., Pan, L., Lu, Z., et al. 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Proteomics 93, 276–294. doi: 10.1016/j.jprot.2013.02. 006 Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publisher’s Note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Copyright © 2022 González-Gordo, Palma and Corpas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). 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