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Article

Integrating Proteomics and Metabolomics Approaches to Elucidate the Mechanism of Responses to Combined Stress in the Bell Pepper (Capsicum annuum)

by
Brandon Estefano Morales-Merida
1,
Jesús Christian Grimaldi-Olivas
1,
Abraham Cruz-Mendívil
2,
Claudia Villicaña
3,
José Benigno Valdez-Torres
1,
J. Basilio Heredia
1,
Rubén Gerardo León-Chan
4,
Luis Alberto Lightbourn-Rojas
4,
Juan L. Monribot-Villanueva
5,
José A. Guerrero-Analco
5,
Eliel Ruiz-May
5 and
Josefina León-Félix
1,*
1
Laboratorio de Biología Molecular y Genómica Funcional, Centro de Investigación en Alimentación y Desarrollo, A.C., Carretera a Eldorado Km 5.5, Campo el Diez, Culiacán 80110, Sinaloa, Mexico
2
CONAHCYT—Instituto Politécnico Nacional, Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional Unidad Sinaloa, Guasave 81101, Sinaloa, Mexico
3
CONAHCYT—Laboratorio de Biología Molecular y Genómica Funcional, Centro de Investigación en Alimentación y Desarrollo, A.C., Carretera a Eldorado Km 5.5, Campo el Diez, Culiacán 80110, Sinaloa, Mexico
4
Laboratorio de Genética, Instituto de Investigación Lightbourn, A.C., Carretera las Pampas Km 2.5, Jiménez 33980, Chihuahua, Mexico
5
Red de Estudios Moleculares Avanzados, Instituto de Ecología, A.C., Carretera Antigua a Coatepec 351, Congregación el Haya, Xalapa 91073, Veracruz, Mexico
*
Author to whom correspondence should be addressed.
Plants 2024, 13(13), 1861; https://doi.org/10.3390/plants13131861
Submission received: 6 June 2024 / Revised: 21 June 2024 / Accepted: 3 July 2024 / Published: 5 July 2024
(This article belongs to the Special Issue Omics Approaches to Predict Physiological Traits of Plants under Abiotic Stress)
Browse Figures
Review Reports Versions Notes

Abstract

Bell pepper plants are sensitive to environmental changes and are significantly affected by abiotic factors such as UV-B radiation and cold, which reduce their yield and production. Various approaches, including omics data integration, have been employed to understand the mechanisms by which this crop copes with abiotic stress. This study aimed to find metabolic changes in bell pepper stems caused by UV-B radiation and cold by integrating omic data. Proteome and metabolome profiles were generated using liquid chromatography coupled with mass spectrometry, and data integration was performed in the plant metabolic pathway database. The combined stress of UV-B and cold induced the accumulation of proteins related to photosynthesis, mitochondrial electron transport, and a response to a stimulus. Further, the production of flavonoids and their glycosides, as well as affecting carbon metabolism, tetrapyrrole, and scopolamine pathways, were identified. We have made the first metabolic regulatory network map showing how bell pepper stems respond to cold and UV-B stress. We did this by looking at changes in proteins and metabolites that help with respiration, photosynthesis, and the buildup of photoprotective and antioxidant compounds.

Graphical Abstract

1. Introduction

Climate change has caused an increase in the severity and incidence of abiotic stress situations such as heat waves, drought, flooding, salinity, UV-B radiation, and cold. Plant productivity is negatively affected by a changing global climate [1]. When plants are under abiotic stress, many factors are regulated at the physiological, biochemical, and molecular levels [2]. Different molecular levels of regulatory networks control how plants respond to stress. These levels include sensing, signal transduction, transcription, transcript processing, translation, post-translational protein modifications, and changes in metabolite profiles [3,4]. These alterations can be analyzed through systems biology approaches, including genomics, transcriptomics, proteomics, and metabolomics [5,6]. Proteomics studies plant proteomes and protein functions, focusing on their roles as structural and regulatory proteins in several processes, such as growth, development, and coping against different environmental stresses in plants [7,8]. At the same time, metabolomics is a powerful tool for identifying and examining plant metabolites, and this approach can provide novel insights into how plants respond to abiotic stress [9,10]. The data produced by these systems can be enormous, sometimes without apparent interconnections. This has led to the proposal of integrating omic data to identify novel gene targets with differential expression or involvement in key metabolic pathways [11,12].
Bell pepper (Capsicum annuum L.) is an important plant in the Solanaceae family due to its economic, medicinal, nutraceutical, and food value [13,14]. Moreover, C. annuum is affected by biotic and abiotic stresses [15,16]. Recent research has reported using integrative omic approaches in C. annuum fruit development [17,18,19,20] and responses to abiotic stress such as cold, salinity, heat, and high-intensity light [21,22,23,24,25,26]. In addition, both cold and UV-B radiation can have an impact on plant morphology. This combined stress leads to a decrease in the germination rate of seeds, as well as reductions in plant height, leaf area, root diameter, root length, and the number of root tips in cotton seedlings [27]. Arabidopsis also experiences similar effects, such as reduced biomass, rosette diameter, and leaf area [28].
Regarding physiological and biochemical changes, it has been reported that combined UV-B and cold stress decreases the content of chlorophyll a and b and increases the content of chlorogenic acid, total flavonoids, and carotenoids in bell pepper leaves [29]. Furthermore, UV-B and cold stress significantly increase the accumulation of phenolic compounds like kaempferol, hydroxycinnamic acids, and quercetin derivatives in Arabidopsis, as well as apigenin 7-O-glucoside (A7G) and luteolin 7-O-glucoside (L7G) in bell pepper, which serve as antioxidants and absorb UV-B radiation [29,30,31].
Researchers have also found changes in gene expression in the stem of C. annuum, particularly in some genes (ANS, DFR, F3H, F3′5’H, and MYB) involved in flavonoid biosynthesis, such as flavonols and anthocyanins [32]. Also, when UV-B and cold stress were combined in Arabidopsis, cold-responsive genes were induced, such as CBF1, CBF2, CBF3, COR15A, COR15B, and COR78, as well as flavonoid biosynthesis genes like MYB111, CHI, and FLS [31]. In a previous study, we discovered the down-regulation of several genes involved in cell wall growth and pathogen defense. Conversely, the up-regulated genes were associated with protecting the chloroplast, transporting compounds, and synthesizing hormones and flavonoids [33].
Research on plant responses to stress combinations is essential, as it is impossible to accurately predict these responses by only examining how plants react to individual stressors [34,35,36,37]. In such a way, stress-specific proteome and metabolome integration can identify unique mechanisms that respond to combined stress from UV-B radiation and cold. However, at present, there is not sufficient information about changes in the proteome and metabolome profiles of bell peppers under combined UV-B radiation and cold stress.
Therefore, the purpose of this study was to find metabolic changes in bell pepper stems caused by UV-B radiation and cold by integrating omic data.

2. Results

2.1. Differentially Expressed Proteins (DEPs) Identification

To investigate the functions of proteins in C. annuum under combined cold stress and UV-B, we collected stress-treated and control stem proteins and used an LC MS/MS technique to create proteome profiles.
Control and UV-B+cold samples exhibited similar protein profiles on an SDS-PAGE (Figure S1). Principal component analysis (PCA) was used to assess the relationship between biological replicates, allowing us to affirm that the data are reliable. These data indicate that 70.6% of variation corresponds to a regulation of protein abundance due to combined stress (Figure 1A). In the global proteome, a total of 1134 proteins were discovered. Based on log2, folds were considered of more than 0.50 (over-accumulated) and less than −0.50 (down-accumulated) at a p-value < 0.05. A total of 200 DEPs were identified (Table S1), of which 129 were over-accumulated and 71 were down-accumulated (Figure 1B).
Furthermore, GO enrichment analysis and the clustering of redundant terms were conducted to assess the functions of DEPs (Table S2). In the combined UV-B+cold samples, we found over-accumulation of proteins related to the control of specific biological processes such as carbon fixation, developmental process, generation of precursor metabolites and energy, glyoxysome organization, multicellular organismal process, regulation of DNA methylation, response to stimulus, seedling development, and tricarboxylic acid transmembrane transport (Figure 1C). This investigation also showed a negative regulation of the biosynthetic process and heterochromatin organization (Figure 1D).

2.2. Untargeted Metabolomic Analyses

In both negative and positive ionization modes, untargeted metabolomic analyses were carried out on stems from the control and the UV-B+cold groups. The PCA accounted for 95.8% of the total variation, and it shows that the metabolomic profiles of the control and the UV-B+cold plants did not overlap, meaning they have different phytochemical signatures (Figure 2A). We found only 37 over-accumulated (Log2 FC: ≥2) metabolites (Table S3). The normalized abundance of metabolites in each sample was plotted in a heatmap, where two main blocks were observed. The first block includes the control plants, and the second consists of the UV-B+Cold stress (Figure S2).
The findings of the pathway analysis are depicted in Figure 2B. We identified 29 pathways, with only 7 showing significant enrichment (Table S4). The main pathways were flavone and flavonol biosynthesis (quercitrin [LFC: 3.94], quercetin 3-O-rhamnoside 7-O-glucoside [LFC: 2.97], luteolin [LFC: 2.94], and kaempferol 3-O-rhamnoside-7-O-glucoside [LFC: 2.94]), flavonoid biosynthesis (eriodictyol [LFC: 3.04], luteolin [LFC: 2.94], chlorogenic acid [LFC: 2.92], dihydrokaempferol [LFC: 2.85], kaempferol [LFC: 2.74], and delphinidin [LFC: 2.67]), tropane, piperidine and pyridine alkaloid biosynthesis (L-phenylalanine [LFC: 3.10], tropine [LFC: 2.88], and tropinone [LFC: 2.75]), phenylpropanoid biosynthesis (L-phenylalanine [LFC: 3.10], p-hydroxyphenyl lignin [LFC: 3.07], chlorogenic acid [LFC: 2.92], and 5-hydroxyconiferaldehyde [LFC: 2.91]), galactose metabolism (sucrose [LFC: 5.08], D- glucose [LFC: 2.91], and raffinose [LFC: 2.59]), phenylalanine metabolism (L-phenylalanine [LFC: 3.10] and phenylacetic acid [LFC: 3.03]), and tyrosine metabolism (4-hydroxyphenylacetaldehyde [LFC: 3.23] and 4-hydroxyphenylpyruvic acid [LFC: 2.89]).

2.3. Targeted Metabolomic Analyses

According to the untargeted metabolomic analyses, we performed specific assays that examined the phenolic content to verify the previously mentioned results. In addition to the amino acid phenylalanine, 15 compounds from various chemical sub-categories were discovered and measured in steam samples (Table 1). In the control samples, the concentrations of compounds such as phenylalanine, 4-hydroxybenzoic acid, vanillic acid, 4-coumaric acid, 3-coumaric acid, ferulic acid, salicylic acid, vanillin, luteolin-7-O-glucoside, rutin, and penta-O-galloyl-B-D-glucose were higher than those in the UV-B+cold samples. The control and UV-B+cold samples had about the same amount of protocatechuic acid and quercetin-3-glucoside. Finally, we found higher amounts of the phenolics chlorogenic acid, luteolin, and quercitrin in the UV-B+cold samples.

2.4. Data Integration

The analysis of data integration using proteomics and untargeted and targeted metabolomics showed alterations in the accumulation of proteins and metabolites associated with flavonoid biosynthesis, the superpathway of glycolysis and the TCA cycle, porphyrin metabolism, and scopolamine biosynthesis (Figure 3).
In the flavonoid biosynthesis pathway, flavonoid aglycones and their glycosylated derivatives such as eriodictyol, dihydrokaempferol, kaempferol, kaempferol 3-O-α-L-rhamnoside, kaempferol 3-O-rhamnoside-7-O-glucoside, luteolin, apiin (apigenin 7-O-[β-D-apiosyl-(1→2)-β-D-glucoside]), quercitrin (quercetin 3-O-rhamnoside), and quercetin 3-O-rhamnoside 7-O-glucoside displayed positive accumulation (stress/control). In contrast, luteolin-7-O-glucoside, quercetin-3-glucoside, and rutin were down-accumulated (stress/control).
Regarding the superpathway of glycolysis and TCA cycle, enzymes like fructose-bisphosphate aldolase (FbaA; PHT93527 and PHT77434), glyceraldehyde-3-phosphate dehydrogenase (GAPDH; PHT76144, PHT66665, and PHT89152), succinyl-CoA synthetase (SCS; PHT89296), and malate dehydrogenase (MDH; PHT95662) showed changes in accumulation. Stress-treated samples showed a significant increase in malate and glutamic acid levels.
Heme b biosynthesis is the best-known pathway precursor for tetrapyrroles (chlorophyll a and b). We detected over-accumulation of precursor molecules, including glutamic acid, protoporphyrin IX, and biliverdin IXα, as well as enzymes such as glutamate-1-semialdehyde aminotransferase (PHT81091) and coproporphyrinogen III oxidase (PHT70068).
Finally, scopolamine biosynthesis was observed, where tropinone, tropine, and one enzyme, tropinone reductase (PHT71246), were over-accumulated.

3. Discussion

The current work is the first attempt to integrate proteomic and metabolomic approaches to enhance our comprehension of the mechanisms involved in bell pepper plants’ responses when exposed to combined UV-B and cold stress.

3.1. Combined UV-B and Cold Stress Induces Changes in Flavonoid Biosynthesis

Integrative analysis revealed changes in the accumulation of metabolites related to flavonoid biosynthesis. It is well known that UV-B rays damage DNA, proteins, lipids, and chloroplast membranes, as well as various elements of photosystem II [38,39]. Due to their varying UV absorption capabilities, flavonoids can serve as a crucial defense mechanism for plants, acting as a protective barrier when exposed to UV radiation [40]. Similarly, flavonoids, including flavonols, flavanols, flavones, and anthocyanins, play a crucial role in determining resistance to freezing and adaptation to cold. The absence or reduction in these compounds reduces antioxidant and reactive oxygen species scavenger activity, causing harm to the system responsible for freezing resistance [41,42]. Previous research has identified an improved content of flavonoids in Arabidopsis when exposed to a combination of UV radiation and cold stress [31]. In bell peppers, there has been an increase in gene expression of the ANS, CHI, CHS, DFR, F3H, F3′5′H, FLS, and MYB genes, as well as an increased accumulation of L7G and A7G when plants were exposed to low temperatures and UV-B radiation [29,32,33]. Notably, our research reveals that five of the nine metabolites accumulated in flavonoid biosynthesis are flavonoid glycosides. According to earlier studies, treating the plants with cold and UV-B stress increased the amount of flavonoid glycosides [43,44,45,46,47,48]. These alterations of flavonoids enhance their structural stability, solubility at the molecular level, and capacity to be transported into vacuoles and chloroplasts during storage [49,50]. Interestingly, this study also identified glucosides (glucose derivatives) in four of the five flavonoid glycosides, suggesting a connection to the accumulation of sugars such as D-glucose, raffinose, and sucrose, as observed here. These observations highlight the important role that flavonoids have as photoprotectors and antioxidants in the response to combined stress, with an emphasis on flavonoid glycosides.

3.2. Effects of Combined UV-B and Cold Stress on Respiration and Photosynthesis Metabolism

In our data, glycolysis and TCA cycle changes were found. Cold stress in C. annum altered proteins related to glycolysis and the TCA cycle, such as GAPDH, FbaA, and MDH, at 2 h and 12 h after exposure, and after 1 h recovery after 72 h of exposure [51]. This investigation also identified these proteins. GAPDHs are important for many cellular functions in plants, such as energy generation, DNA repair, gene expression control, sugar and amino acid levels maintenance, and the transmission of signals related to abscisic acid (ABA) [52,53,54]. Additionally, the expression of GAPDHs in the shoots and roots of Arabidopsis and Triticum aestivum was induced by drought, salt, osmotic stress, heat, and cold stress [55,56]. On the other hand, FbaA is an enzyme crucial for plant carbon metabolism, including glycolysis, gluconeogenesis, and photosynthesis [57]. A study in potatoes demonstrated the importance of FbaA. Transgenic potato plants with reduced levels of the FbaA gene demonstrate reductions in photosynthesis, carbon metabolism, and growth [58]. Also, cold, heat, drought, as well as blue and red light modify the gene expression of the FBA gene family in Solanaceae, such as Nicotiana tabacum, Solanum tuberosum, and Solanum lycopersicum [59,60,61,62]. Meanwhile, MDH and its catalytic product malate (also identified in this study) play a role in Arabidopsis respiration, CO2 release in the photorespiratory pathway, fatty acid β-oxidation, redox homeostasis, P uptake, and fixing N [63,64,65,66,67]. In S. tuberosum and S. lycopersicum, MDH gene family were shown to participate in plant responses to several abiotic stressors, such as cold, heat, drought, and salt [68,69]. Additionally, the proteomic data revealed that stress samples over-accumulated two cytochrome c oxidase enzymes (PHT79443 and PHT66082), ATP synthase subunit α (PHT94639), and ATP synthase subunit F (PHT89781), which are involved in mitochondrial electron transport from cytochrome c to oxygen. These data suggest that respiratory metabolism may play a role in response to combined UV-B and cold stress.
Heme b biosynthesis is part of tetrapyrroles, essential for nitrate and sulfate assimilation, the detoxification of reactive oxygen species, respiration, photosynthesis, and light signaling [70]. Previous studies have reported that UV-B radiation and cold affect photosynthesis individually. UV-B radiation causes direct and indirect harm to photosystem II and photosystem I, decreasing chlorophyll levels [71,72,73]. Meanwhile, cold generates oxidative damage in chloroplasts and the photoinhibition of PSII, affecting electron transport and chlorophyll content [74,75,76]. Furthermore, analysis of the supercluster GO data revealed the positive regulation of photosynthesis-related proteins (inside of the generation of precursor metabolites and energy), including PSAA (PHT68443), PSBC (PHT78035), MPH2 (PHT73773), ELIP (PHT85861), and CYP38 (PHT85861) (Figure 1C). We detected the over-accumulation of glutamic acid (Glu), protoporphyrin IX (Proto-IX), and biliverdin IXα. Glu is the first precursor to tetrapyrroles, and its levels increase under cold and salinity stress in A. thaliana [77,78]. Proto-IX is the final shared precursor for chlorophyll and heme synthesis, and for metabolites related to respiration and photosynthesis [77]. Some investigations have demonstrated the importance of Proto-IX in plant development. Herbicides such as Pyraflufen ethyl and Saflufenacil inhibit Proto IX accumulation, causing plant death [79,80,81]. Further, researchers have looked into how different abiotic factors, like iron deficiency, salinity, drought, heat, and cold, affect the production of porphyrins like Proto-IX, Mg-Proto-IX and its methyl ester, and protochlorophyllide [82,83,84,85]. Regarding biliverdin IXα, it is known as a precursor to synthesizing phytochromes, sensory photoreceptors that modulate plant growth [86]. These findings suggest that metabolites, intermediates of chlorophyll and heme synthesis, and proteins involved in photosynthesis and their associated complex regulatory networks, contribute to the response to combined stress.

3.3. Combined UV-B and Cold Stress Induces Changes in Tropine Biosynthesis

In this study, tropinone, tropine, and tropinone reductase (TRI; PHT71246) contents showed an increase in combined UV-B and cold stress samples. Tropinone and tropine are tropane alkaloids that exhibit effects in humans as antidepressants, spasmolytics, local anesthetics, improve blood flow in the microcirculation, and reduce asthmatic symptoms [87]. Researchers have discovered that Solanaceae plants use tropane alkaloids to defend against insects, predators, and pathogens [88]. Recent reports indicate that C. annuum possesses the enzymes necessary for producing tropinone and tropine [89]. This demonstrates that UV-B radiation and cold are causing the accumulation of these compounds. However, the role of these compounds in plants under abiotic stress is still unknown.

4. Materials and Methods

4.1. The Plant Materials

Bell pepper seeds, cultivar cannon (Zeraim Gedera-Syngenta, Gedera, Israel), were sprouted and maintained as previously mentioned [29]. Comprehensive information may be found in Appendix A.1.
Seedlings were placed in a plant growth chamber (GC-300TLH, JEIO TECH; Seoul, Republic of Korea) 28 days after sowing for three days (from day 28 to 30). The conditions were as follows: temperature 25/20 °C (day/night), relative humidity 65%, and photoperiod of 12 h (from 6:00 to 18:00 h) of photosynthetically active radiation (PAR) (972 μmol m−2 s−1). After three days, the control plants were kept under the previously described conditions. For the UV-B+cold stress treatment, the conditions were as follows: temperature 15/10 °C (adjusted on day 30 at 18:00), photoperiod of PAR for 6 h (from 06:00 to 10:00 and from 16:00 to 18:00 h), and UV-B radiation (72 kJ m2) for 6 h (from 10:00 to 16:00 h) for 2 days (days 31 and 32). The UV-B radiation was applied in accordance with the methodology outlined by León-Chan et al. [29]. Control and UV-B+cold samples were collected at 11:00 on day 32 (Figure S3). The experiment was repeated thrice, and 20 stems were sampled for each repetition, flash-frozen in liquid nitrogen, and stored at −80 °C.

4.2. Proteomic Analysis

According to Monribot-Villanueva et al.’s instructions, the proteomic analysis included protein extraction, digestions, peptide fractionation, TMT labeling, SPS-MS3, and data processing [90,91]. The proteomic analysis specifications are in Appendix A.2.
Three biological replicates of stem powder (750 mg) were used for protein extraction with the phenol–acetone method. In accordance with the manufacturer’s directions, Tandem Mass Tag (TMT) 6-plex reagents were used in the following order: 126, 127N, and 127C for control samples, and 130N, 130C, and 131 for UV-B+cold samples. We used the C. annuum UniProt Reference proteome (UP000222542) as the database for protein identification. A protein was considered over-accumulated and down-accumulated if its p-value < 0.05 and relative Log2-fold change >0.50 and <−0.50 (Stress/Control), respectively. The ggplot2 (v3.5.1) package created all graphs [92].

4.3. Metabolomic Analyses

Methanolic extracts were subjected to untargeted and targeted metabolomic analyses, following the methodology described by Monribot-Villanueva et al. [90,91]. Comprehensive information may be found in Appendix A.3.
Untargeted metabolomic analyses were performed using an ultra-high-resolution chromatograph coupled to a high-resolution mass spectrometer (UPLC-HRMS-QTOF; Class I-Synapt G2-Si, Waters™, Milford, MA, USA). We employed both positive and negative ionization modes during the analyses. The metabolomic data were initially analyzed using MarkerLynx (v4.1, Waters™, Milford, MA, USA) and MassLynx (v4.1, Waters™, Milford, MA, USA) software. The MetaboAnalyst bioinformatic platform (https://www.metaboanalyst.ca, accessed on 11 October 2023) was utilized to conduct analyses on the untargeted produced data, employing its many modules [93]. The Statistical Analysis module was used to conduct fold change (stress/control) and FDR studies to identify either over-accumulated or down-accumulated features. Over-accumulated features were defined as having Log2 FC values ≥ 2, while down-accumulated features were described as having Log2 FC values ≤ −2 and FDR values ≤ 0.01 for both. The m/z signals were provisionally identified by utilizing the Functional Analysis module. The metabolites that exhibited excessive accumulation and reduced accumulation in both ionization modes were subsequently connected and subjected to analysis using the Pathway Analysis module.
The targeted metabolomic quantification of phenolic chemicals was conducted on a UPLC-QqQ mass spectrometer (Agilent Technologies 1290/6460; Santa Clara, CA, USA), as previously described by Juárez-Trujillo et al. and Monribot-Villanueva et al. [90,94]. Calibration curves were generated for each chemical (60 compounds) in the 0.25 to 17 µM range for quantification purposes. The coefficient of determination (r2) for quadratic regressions was 0.99. Agilent Technologies’ MassHunter software (vB.06.00) processed the data. Statistical analyses were conducted using the Rstudio program (v2023.09.1+494) to identify significant differences (p-value < 0.05) among the samples. Phenylalanine, protocatechuic acid, chlorogenic acid, 3-coumaric acid, salicylic acid, luteolin, luteolin-7-O-glucoside, quercetin-3-glucoside, quercitrin, rutin, and penta-O-galloyl-B-D-glucose exhibited normal distributions, so a t-test was conducted. The compounds 4-hydroxybenzoic acid, vanillic acid, 4-coumaric acid, and vanillin exhibited a non-normal distribution, requiring the Wilcoxon rank sum test. The ggplot2 (v3.5.1) package was used to create all graphs [92].

4.4. Integration Data

The metabolic pathways were constructed with Uniprot annotation (https://www.uniprot.org/, accessed on 14 January 2024) and plant metabolic pathway databases (https://plantcyc.org/, accessed on 21 January 2024) using C. annuum as a reference. The log2-fold change values of proteins and metabolites that showed differences were entered into cellular overview/omics viewer programs to represent the metabolic pathways visually [95]. This analysis was based on a pathway perturbation score (PPS) value of 50. The purpose of the PPS is to measure the amount of activation of a certain route at a specific moment in time. The Pathway Collage tool was used to graph the metabolic pathways activated by combined UV-B and cold stress.

5. Conclusions

This study presents the first approach to identifying the key mechanisms involved in the pepper response to combined stress using proteomic and metabolomic data integration. This analysis suggests that flavonoids and their glycosides participate in absorbing UV-B light and acting as antioxidants. Carbon metabolism showed important changes in proteins and metabolites related to energy generation through cellular respiration. Porphyrin metabolism has proven to be important because it contributes to chlorophyll and heme synthesis, two metabolites involved in photosynthesis. The biosynthesis of scopolamine indicates that bell pepper plants can use combined UV-B and cold stress as an inducer to produce bioactive compounds like tropine and tropinone.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13131861/s1, Figure S1: The protein band pattern in an SDS-PAGE stained with SYPRO-Ruby; Figure S2: The heatmap analysis shows the variation in metabolite abundance in the stems of bell pepper plants in the control (gray) and UV-B+Cold (black) samples; Figure S3: The scheme illustrates the experiment’s design to produce bell pepper seedlings in both control and UV-B+cold conditions; Table S1: Differentially expressed proteins (DEPs) identified; Table S2: Categories of GO grouped into supercluster with REVIGO platform; Table S3: Tentative chemical identification of differentially accumulating compounds; Table S4: Metabolic pathways of differentially accumulated metabolites.

Author Contributions

Conceptualization, C.V., R.G.L.-C., L.A.L.-R. and J.L.-F.; data curation, B.E.M.-M., J.C.G.-O., A.C.-M., C.V. and J.B.V.-T.; formal analysis, A.C.-M., C.V., J.B.V.-T. and J.L.-F.; funding acquisition, J.L.-F.; investigation, B.E.M.-M. and J.L.-F.; methodology, B.E.M.-M., J.C.G.-O., A.C.-M., R.G.L.-C., J.L.M.-V., J.A.G.-A., E.R.-M. and J.L.-F.; project administration, J.L.-F.; resources, L.A.L.-R., J.A.G.-A., E.R.-M. and J.L.-F.; software, B.E.M.-M., J.L.M.-V., J.A.G.-A. and E.R.-M.; supervision, A.C.-M., C.V., J.B.V.-T., J.B.H., R.G.L.-C. and J.L.-F.; validation, B.E.M.-M. and J.B.H.; visualization, B.E.M.-M.; writing—original draft preparation, B.E.M.-M.; writing—review and editing, B.E.M.-M., A.C.-M., C.V., J.B.V.-T., J.B.H., R.G.L.-C., L.A.L.-R., J.L.M.-V., J.A.G.-A., E.R.-M. and J.L.-F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by FOSEC SEP-INVESTIGACIÓN BÁSICA, grant number CB 2017–2018 A1-S-8466; Cátedras CONACYT, grant number 784.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Acknowledgments

The authors thank Q.F.B. Jesús Héctor Carrillo Yáñez, M.C. José Miguel Elizalde Contreras, and Esaú Bojórquez Velázquez for critical technical assistance.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Details of the methodology used to generate pepper seedlings, proteomics, and metabolomics are provided.

Appendix A.1. The Plant Materials

Bell pepper seeds, Cannon cv (Zeraim Gedera-Syngenta, Gedera; Israel), were put in a 5 mm depth of germination mix (Lambert Peat Moss, Inc.; Rivière-Ouelle, QC, Canada) and covered with exfoliated vermiculite (Termolita; Santa Catarina, NL, México) in pots of 40 cm3. The seeds were irrigated periodically with 40 °C water until germination. Bell pepper plants grew in a greenhouse at 26 °C; fifteen days after sowing (DAS), the plants were provided with 3 mL·L−1 water solutions containing Ultra NPK, Ultra P, Ultra Ca, and HBK for nourishment (Nubiotek, Bioteksa®; Cd. Jimenez, CHI, Mexico) (Table A1). Seedlings were placed in a plant growth chamber (GC-300TLH, JEIO TECH; Seoul, Republic of Korea) 28 days after sowing for 3 days (from day 28 to 30). The conditions were as follows: temperature 25/20 °C (day/night), relative humidity 65%, and photoperiod of 12 h of photosynthetically active radiation (PAR) (972 μmol m−2 s−1) from 6:00 to 18:00 h. After three days, the control plants were kept under the previously described conditions. For the UV-B+cold stress treatment, the conditions were as follows: temperature 15/10 °C (adjusted on day 30 at 18:00), photoperiod of PAR for 6 h (from 06:00 to 10:00 and from 16:00 to 18:00 h) and UV-B radiation (72 kJ m2) for 6 h (from 10:00 to 16:00 h) for 2 days (days 31 and 32). The UV-B radiation was applied using three Phillips TL 100W/01 lamps (Philips; Eindhoven, Germany) with a narrow waveband between 305 and 315 nm and peaking at 311 nm; the distance between plant leaves and UV-B lamps was 50 cm, and the UV-B irradiance was measured by a UV A/B light meter (SPER SCIENTIFIC, model 850009; Scottsdale, AZ, USA). Control and UV-B+cold samples were collected at 11:00 on day 32 (Figure S3). The experiment was repeated three times and 20 stems were sampled for each repetition, which were flash-frozen in liquid nitrogen and stored at −80 °C.
Table A1. Nutrition applied to bell pepper plants during the experiment.
Table A1. Nutrition applied to bell pepper plants during the experiment.
DAS *Product AppliedComposition
15Ultra PP (33.53%), K (2.65%)
18Ultra NPKN (10%), P (10%), K (10%)
21Ultra NPKN (10%), P (10%), K (10%)
24HBKN (10%), P (5%), K (10%)
27Ultra CaN (17%), Ca (13%), K (3.5%)
30Ultra SK (30%), S (30%)
* Days after sowing.

Appendix A.2. Proteomic Analysis

Appendix A.2.1. Protein Extraction

Our overall procedure used the phenol-based extraction approach, which was based on Monribot et al. [29,90]. The stem powder (750 mg) was suspended in 2 mL of absolute phenol, 2 mL of ice-cold extraction buffer [(0.1 M Tris-HCl, pH 8.4, 0.15 M NaCl, 30% sucrose, 1% SDS)], 1:3 w/w of polyvinylpolypyrrolidone (PVPP), and 20 µL of β-met. The samples were mixed for 20 min and then centrifuged at 3000× g for 30 min at 4 °C. The upper phase was collected and precipitated overnight with 15 mL of cold acetone at −20 °C. After that, it was centrifuged at 3000× g for 30 min at 4 °C and acetone was removed. Then, 80% acetone was added again to wash protein pellets. After, they were washed twice with absolute methanol. Following this, the pellets were dried under a fume hood and dissolved in 200 µL dissolution buffer (0.1 M TEAB and 1% SDS). A BCA protein test kit was used to measure the protein concentration (Pierce, Thermo Scientific; Sunnyvale, CA, USA). In accordance with the manufacturer’s directions (Bio-Rad; Hercules, CA, USA), denaturing electrophoresis (SDS-PAGE) was carried out as well as staining with Sypro Ruby solution. Precision Protein Broad Range Standards were used to calculate the molecular masses of the bands (Bio-Rad; Hercules, CA, USA).

Appendix A.2.2. Protein Digestion, Prefractionation, Desalting, and Labeling

Two hundred and fifty µg of pure protein extracts from each sample were reduced for 45 min at 60 °C with 27.5 µL at 0.1 M of Tris (2-carboxyethyl) phosphine (TCEP) after they were alkylated with 30 µL of iodoacetamide at 0.3 M for 1 h at room temperature in darkness. Then, 32.5 µL of DTT (0.3 M) was used to quench the process for 10 min. Five volumes of cold acetone were used to precipitate the proteins overnight. The samples underwent centrifugation at 10,000× g for 15 min at 4 °C, followed by vacuum drying. Dried particles were suspended in 100 µL of 50 mM triethylammonium bicarbonate (TEAB) with 0.1% SDS. Trypsin (Trypsin Gold, Mass Spectrometry Grade, Promega; Madison, WI, USA) was used to digest proteins at a 1:30 w/w trypsin–protein ratio overnight at 37 °C. Next, for 4 h at 37 °C, trypsin was again added in a 1:60 w/w trypsin–protein ratio. In accordance with the manufacturer’s directions, Tandem Mass Tag (TMT) 6-plex reagents (Thermo Fisher Scientific; San Jose, CA, USA) were used in the following order: 126, 127N, and 127C for control samples, and 130N, 130C, and 131 for UV-B+cold samples. High-pH reversed-phase liquid chromatography spin columns (RPLC, Pierce, Thermo Scientific; Sunnyvale, CA, USA) were used to fractionate the materials. The C18 cartridges from Thermo Scientific were used to desalt all fractions, and a CentriVap vacuum concentrator (Labconco; Kansas City, MO, USA) was used to dry them.

Appendix A.2.3. Nano LC-MS/MS

The labeled peptides were reconstituted with 0.1% formic acid in LC-MS grade water (solvent A), and 5 µL of this solution was injected into a nano LC platform (UltiMate 3000 RSLC system, Dionex; Sunnyvale, CA, USA) by way of a nanoviper C18 trap column (3 µm, 75 µm × 2 cm, Dionex). This solution was then separated on an EASY spray C-18 RSLC column (2 µm, 75 µm, 25 cm) at a flow rate of 300 nL/min and two solvents (solvent A: 0.1% formic acid in water and solvent B: 0.1% formic acid in 90% acetonitrile). The gradient was as follows: a 10 min gradient using Solvent A and Solvent B, 10 min of Solvent A, 7–20% of Solvent B for over 25 min, 20% of Solvent B for 15 min, 20–25% of Solvent B for over 15 min, 25–95% of Solvent B for over 20 min, and 8 min of Solvent A. The Orbitrap Fusion Tribid mass spectrometer (Thermo Fisher Scientific; San Jose, CA, USA), which has an “EASY Spray” nano ion source, was connected to the nanoLC platform (Thermo Fisher Scientific; San Jose, CA, USA). The source temperature was set to 280 C, and the mass spectrometer was run in positive ion mode with a nanospray voltage of 3.5 kV. Caffeine, Met-Arg-Phe-Ala (MRFA), and Ultramark 1621 (Thermo Fisher Scientific; San Jose, CA, USA) were used as external calibrants.

Appendix A.2.4. Synchronous Precursor Selection (SPS)-MS3

The Orbitrap analyzer ran complete MS scans at 120,000 (FWHM), with a scan range of 350–1500 m/z, AGC of 2.0 × 105, maximum injection time of 50 ms, intensity threshold of 5.0 × 103, dynamic exclusion of 1 to 70 s, and a mass tolerance of 10 ppm. The following parameters for fragmentation were included: a precursor selection mass range of 400–1200 m/z, precursor ion exclusion width low of 18 m/z and high of 5 m/z, CID with 35% of collision energy and an activation Q of 0.25, an AGC of 1.0 × 104 in a maximum injection time of 50 ms, TMT loss, and detection was conducted in an ion trap. MS3 spectra were collected using 10 isolation notches for synchronous precursor selection (SPS). MS3 precursors were fragmented by HCD at 65% collision energy and analyzed with the Orbitrap at 60,000 resolution power, a scan range of 120–500 m/z, a 2 m/z isolation window, 1.0 × 105 AGC, and a maximum injection time of 120 ms in one microscan.

Appendix A.2.5. Data Analyses and Interpretation

The spectra from decision tree-driven MS/MS and (SPS)-MS3 were processed in Proteome Discoverer (v2.4) (PD, Thermo Fisher Scientific; San Jose, CA, USA) with the search engines Mascot (v2.4.1, Matrix Science), AMANDA [96], and SEQUEST HT [97]. The searches were conducted against translated unigene databases created using C. annuum UniProt Reference proteome (UP000222542). The following parameters were used in the search: full-tryptic protease specificity, allowing two missed cleavages. In addition, static modifications covered carbamidomethylation of cysteine (+57.021 Da) and TMT 6-plex N-terminal/lysine residues (+229.163 Da). Dynamic modifications included methionine oxidation (+15.995 Da) and the deamidation of asparagine/glutamine (+0.984 Da). For the SPS-MS3 method, identification was performed in the linear ion trap at a lower resolution; tolerances of ±10 ppm and ±0.6 Da were applied. Using the Percolator algorithm, the resultant peptide hits were filtered for a maximum FDR of 1% [98]. The reporter ion quantification for TMTs was obtained with a PD software template at the MS3 level applying mass tolerances of ±10 ppm in the case of the most confident centroid and a precursor co-isolation filter of 75%. A protein was considered and up-regulated and down-regulated if its p-value was < 0.05 and if relative log2-fold change were > 0.50 and <−0.50, respectively. The ggplot2 (v3.5.1) package was used to create all of the graphs [92].

Appendix A.2.6. Annotation Methods

We performed Gene Ontology (GO) enrichment analysis in the protein sets using Singular Enrichment Analysis in the AgriGO 2.0 database (http://systemsbiology.cau.edu.cn/agriGOv2/index.php, accessed on 14 September 2023). Clustering of biological processes based on GO was conducted using the Revigo platform (http://revigo.irb.hr/, accessed on 28 September 2023), and Voronoi treemaps were plotted on the WeightedTreemaps (v0.1.2) package in R Studio (v2023.09.1+494). The analysis included absolute log10 FDR values. With FDR < 0.05, the GO terms were declared highly enriched.

Appendix A.3. Metabolomic Analyses

Appendix A.3.1. Metabolites Extraction

The samples were kept at a temperature of -80 °C until further examination. They were pulverized using a mortar and pestle. The samples (control and UV-B+cold) were dried at 40 °C for 24 h using an Excalibur Food Dehydrator Parallax Hyperware (Sacramento, CA, USA). Subsequently, each sample, weighing 300 mg, was combined with 100 mg of diatomaceous earth and transferred into 10 mL extraction cells. The complete crude extracts were acquired using an accelerated solvent extraction system (Dionex ASE 350, Thermo Scientific; Sunnyvale, CA, USA). Methanol was employed as the solvent for all extractions. The extraction process was conducted at a temperature of 60 °C, and a single extraction cycle lasting 5 min was employed. The volume of rinse solution utilized was 30% of the total volume of solvent employed, whereas the carrier gas employed was nitrogen. The aliquots were subjected to filtration using polytetrafluoroethylene (PTFE) membranes with a pore size of 0.2 μm, and subsequently transferred into sample vials with a capacity of 1.5 mL.

Appendix A.3.2. Untargeted Metabolomic Analyses

The untargeted metabolomic analyses were conducted on methanolic extracts of stem samples from both the control and UV-B+cold groups. The analyses were performed using an ultra-high-resolution chromatograph coupled to a high-resolution mass spectrometer (UPLC-HRMS-QTOF, Class I-Synapt G2-Si, Waters; Milford, MA, USA). Both positive and negative ionization modes were employed during the analyses. The chromatographic separation was performed using a Waters Acquity BEH column (1.7 μm, 2.1 × 50 mm) in reversed-phase mode. The column oven temperature was set at 40 °C, while the sample temperature was maintained at 15 °C. The mobile phases utilized in the experiment consisted of water (A) and acetonitrile (B), both of which were supplemented with 0.1% formic acid. It is important to note that all solvents employed in this study were of MS grade and were obtained from SIGMA. The capillary, sampling cone, and source offset voltages were 3000, 40, and 80 V, respectively. The source and desolvation temperatures were 100 and 20 °C, respectively. The desolvation gas flow rate was 600 L/h and the nebulizer pressure was 6.5 Bar (650,000 Pa). Leucine–enkephaline was used as lock mass. The mass spectrometer was set with a mass range of 50–1200 Da using an MSe method. The collision energy of Function 1 was 6 V while for Function 2 we used a ramp from 10 to 30 V. The scan time was 0.5 s. Metabolomics data were first processed with MarkerLynx (v4.1, Waters™, Milford, MA, USA) and MassLynx (v4.1, Waters™, Milford, MA, USA). The MetaboAnalyst (https://www.metaboanalyst.ca, accessed on 11 October 2023) bioinformatics platform was utilized to conduct analyses on the untargeted produced data, employing its many modules [93]. The Statistical Analysis module was utilized to conduct fold change (UV-B+cold/control) and FDR studies in order to identify features that were either over-accumulated or down-accumulated. Over-accumulated features were defined as having LFC values ≥ 2, while down-accumulated features were defined as having LFC values ≤ −2 and FDR values ≤ 0.01 for both. The m/z signals were provisionally identified through the utilization of the Functional Analysis module. The metabolites that exhibited excessive accumulation and reduced accumulation in both ionization modes were subsequently connected and subjected to analysis, utilizing the Pathway Analysis module.

Appendix A.3.3. Targeted Metabolomic Analyses

The quantification of phenolic chemicals was conducted on the methanolic extracts of stem samples from both the control and stress groups. This was achieved using a UPLC-QqQ mass spectrometer (Agilent Technologies 1290/6460; Santa Clara, CA, USA). The chromatography procedure was conducted using a reversed-phase Zorbax SB-C18 column (Agilent) with dimensions of 1.8 μm and 2.1 × 50 mm. The column oven temperature was set at 40 °C. The mobile phases utilized in the experiment consisted of water (A) and acetonitrile (B), both of which were supplemented with 0.1% formic acid. All solvents employed in this study were of MS grade and were obtained from SIGMA. For quantification, calibration curves were constructed from 0.25 to 17 µM for each compound (60 compounds). The r2 values for quadratic regressions were 0.99. Data were processed with the MassHunter software (vB.06.00). Statistical analyses were conducted using the Rstudio program (v2023.09.1+494) to identify significant differences (p-value < 0.05) among the samples. Phenylalanine, protocatechuic acid, chlorogenic acid, 3-coumaric acid, salicylic acid, luteolin, luteolin-7-O-glucoside, quercetin-3-glucoside, quercitrin, rutin, and penta-O-galloyl-B-D-glucose exhibited normal distributions, so t-tests were conducted. The compounds 4-hydroxybenzoic acid, vanillic acid, 4-coumaric acid, and vanillin exhibited a non-normal distribution, therefore requiring the use of the Wilcoxon rank sum test.

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Plants 13 01861 g001
Figure 1. Proteomic study of bell pepper stems exposed to both UV-B and cold stress. Principal component analysis of the control (gray) and UV-B+cold (black) biological replicates (A). Volcano graph showing differentially accumulated proteins (B). Green dots indicate over-accumulated proteins, while blue dots indicate down-accumulated ones. In the X axis, the logarithmic fold change is represented, and in the Y axis, the p-value is represented in scale as −Log10. Voronoi plot of the Gene Ontology (GO) analysis of the differentially expressed proteins, where categories of GO were grouped into superclusters. Over-accumulated proteins (C). Down-accumulated proteins (D) based on the absolute log10 p-values.
Figure 1. Proteomic study of bell pepper stems exposed to both UV-B and cold stress. Principal component analysis of the control (gray) and UV-B+cold (black) biological replicates (A). Volcano graph showing differentially accumulated proteins (B). Green dots indicate over-accumulated proteins, while blue dots indicate down-accumulated ones. In the X axis, the logarithmic fold change is represented, and in the Y axis, the p-value is represented in scale as −Log10. Voronoi plot of the Gene Ontology (GO) analysis of the differentially expressed proteins, where categories of GO were grouped into superclusters. Over-accumulated proteins (C). Down-accumulated proteins (D) based on the absolute log10 p-values.
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Plants 13 01861 g002
Figure 2. Untargeted metabolomics analysis of bell pepper stems exposed to both UV-B and cold stress. The principal component analysis was conducted on the control (gray) and UV-B+cold (black) biological replicates (A). The pathway analysis module of MetaboAnalyst identified metabolic pathways in response to UV-B+cold (B). Each circle represents a metabolic pathway: the circle’s color indicates its p-value; the circle’s size represents the pathway impact.
Figure 2. Untargeted metabolomics analysis of bell pepper stems exposed to both UV-B and cold stress. The principal component analysis was conducted on the control (gray) and UV-B+cold (black) biological replicates (A). The pathway analysis module of MetaboAnalyst identified metabolic pathways in response to UV-B+cold (B). Each circle represents a metabolic pathway: the circle’s color indicates its p-value; the circle’s size represents the pathway impact.
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Plants 13 01861 g003
Figure 3. Plant metabolic pathways are based on data from flavonoid biosynthesis, carbon metabolism, heme biosynthesis, and scopolamine biosynthesis (https://plantcyc.org/, accessed on 21 January 2024). The C. annuum database was used. The color scale is based on the log2-fold change value (stress/control) for the relative content of metabolites and enzymes. The thick black lines indicate the purification and functional characterization of the enzymes. In comparison, thin lines indicate that enzymes have not been functionally characterized.
Figure 3. Plant metabolic pathways are based on data from flavonoid biosynthesis, carbon metabolism, heme biosynthesis, and scopolamine biosynthesis (https://plantcyc.org/, accessed on 21 January 2024). The C. annuum database was used. The color scale is based on the log2-fold change value (stress/control) for the relative content of metabolites and enzymes. The thick black lines indicate the purification and functional characterization of the enzymes. In comparison, thin lines indicate that enzymes have not been functionally characterized.
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Table 1. Phenolic compound profiling was performed using metabolomic analyses in stem of bell pepper under combined UV-B and cold stress.
Table 1. Phenolic compound profiling was performed using metabolomic analyses in stem of bell pepper under combined UV-B and cold stress.
CompoundSamples
ControlUV-B+Cold
Phenylalanine 1101.75 ± 2.39 a91.78 ± 1.30 b
Protocatechuic acid 10.31 ± 0.01 * a0.29 ± 0.03* a
4-hydroxybenzoic acid 21 ± 0.05 a0.69 ± 0.00 a
Vanillic acid 21.04 ± 0.01 a0.55 ± 0.03 a
Chlorogenic acid 122.89 ± 0.20 a57.17 ± 0.03 b
4-Coumaric acid 20.26 ± 0.01 * a0.11 ± 0.01* a
3-Coumaric acid 13.61 ± 0.03 a3.04 ± 0.06 b
Ferulic acid 30.05 ± 0.01 * ---
Salicylic acid 12.31 ± 0.14 a0.30 ± 0.06* b
Vanillin 21.44 ± 0.06 a0.92 ± 0.02 a
Luteolin 10.66 ± 0.01 * a0.72 ± 0.04* a
Luteolin-7-O-glucoside 182.86 ± 2.17 a63.99 ± 3.38 b
Quercetin-3-glucoside 14.97 ± 0.12 a4.93 ± 0.05 a
Quercitrin 13.97 ± 0.05 a4.34 ± 0.05 b
Rutin 19.17 ± 0.33 a6.31 ± 0.19 b
Penta-O-galloyl-B-D-glucose 11.47 ± 0.42 * a0.32 ± 0.05* b
The unit for concentration is μg/g dry matter. Values show mean ± standard deviation (n = 3). * Data that fall below the quantification threshold. --- Data beneath the detection threshold. 1: t-test analyses. 2: Wilcoxon rank sum test analyses. 3: Non-statistical analyses. Significant disparities between UV-B+cold and control samples for each compound are indicated using different letters.
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Morales-Merida, B.E.; Grimaldi-Olivas, J.C.; Cruz-Mendívil, A.; Villicaña, C.; Valdez-Torres, J.B.; Heredia, J.B.; León-Chan, R.G.; Lightbourn-Rojas, L.A.; Monribot-Villanueva, J.L.; Guerrero-Analco, J.A.; et al. Integrating Proteomics and Metabolomics Approaches to Elucidate the Mechanism of Responses to Combined Stress in the Bell Pepper (Capsicum annuum). Plants 2024, 13, 1861. https://doi.org/10.3390/plants13131861

AMA Style

Morales-Merida BE, Grimaldi-Olivas JC, Cruz-Mendívil A, Villicaña C, Valdez-Torres JB, Heredia JB, León-Chan RG, Lightbourn-Rojas LA, Monribot-Villanueva JL, Guerrero-Analco JA, et al. Integrating Proteomics and Metabolomics Approaches to Elucidate the Mechanism of Responses to Combined Stress in the Bell Pepper (Capsicum annuum). Plants. 2024; 13(13):1861. https://doi.org/10.3390/plants13131861

Chicago/Turabian Style

Morales-Merida, Brandon Estefano, Jesús Christian Grimaldi-Olivas, Abraham Cruz-Mendívil, Claudia Villicaña, José Benigno Valdez-Torres, J. Basilio Heredia, Rubén Gerardo León-Chan, Luis Alberto Lightbourn-Rojas, Juan L. Monribot-Villanueva, José A. Guerrero-Analco, and et al. 2024. "Integrating Proteomics and Metabolomics Approaches to Elucidate the Mechanism of Responses to Combined Stress in the Bell Pepper (Capsicum annuum)" Plants 13, no. 13: 1861. https://doi.org/10.3390/plants13131861

APA Style

Morales-Merida, B. E., Grimaldi-Olivas, J. C., Cruz-Mendívil, A., Villicaña, C., Valdez-Torres, J. B., Heredia, J. B., León-Chan, R. G., Lightbourn-Rojas, L. A., Monribot-Villanueva, J. L., Guerrero-Analco, J. A., Ruiz-May, E., & León-Félix, J. (2024). Integrating Proteomics and Metabolomics Approaches to Elucidate the Mechanism of Responses to Combined Stress in the Bell Pepper (Capsicum annuum). Plants, 13(13), 1861. https://doi.org/10.3390/plants13131861

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