Biochemical and transcriptomic analyses reveal the mechanisms underlying SNP and melatonin effects on antioxidant capacity and chlorophyll metabolism in postharvest okra

Biochemical and transcriptomic analyses reveal the mechanisms underlying SNP and melatonin effects on antioxidant capacity and chlorophyll metabolism in postharvest okra | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Biochemical and transcriptomic analyses reveal the mechanisms underlying SNP and melatonin effects on antioxidant capacity and chlorophyll metabolism in postharvest okra Xianjun Chen, Yao Jiang, Jianwei Zhang, Xiaocheng Liu, Lulu Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6832753/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Aug, 2025 Read the published version in npj Science of Food → Version 1 posted 12 You are reading this latest preprint version Abstract Okra fruit undergo rapid chemical deterioration after harvest. This study investigated how sodium nitroprusside (SNP) and melatonin (MT), alone or combined (MT+SNP), affect chemical stability, antioxidant capacity, and chlorophyll metabolism in okra stored at 20°C and 80-90% humidity. MT+SNP treatment most effectively preserved fruit quality by reducing weight loss, maintaining color parameters, decreasing oxidative stress markers (H 2 O 2 , MDA), enhancing antioxidant capacity, and regulating antioxidant enzymes (SOD, CAT, POD, APX). MT+SNP stabilized chlorophyll content by modulating chlorophyll-degrading enzymes (CLH, PPH, MDcase). Transcriptome analysis revealed differential expression of genes involved in antioxidant defense and chlorophyll metabolism, with synergistic effects from combined treatment. Weighted gene co-expression network analysis identified transcription factors ( NAC86 , ERF4 , MYB24 ) connecting antioxidant and chlorophyll metabolism pathways. This combined treatment effectively preserves okra’s phytochemical integrity and nutritional quality by stabilizing redox homeostasis and pigment metabolism. Biological sciences/Biochemistry Biological sciences/Plant sciences SNP MT antioxidant enzymes chlorophyll degradation postharvest quality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Okra ( Abelmoschus esculentus L.) is a widely cultivated vegetable in tropical and subtropical regions, valued for its distinctive chemical composition including dietary fiber (primarily pectins and hemicelluloses), phenolic compounds (quercetin derivatives and hydroxycinnamic acids), vitamins (A, B complex, C, E, and K), and essential minerals (Ca, K, Mg) (Agregán et al., 2023 ). The fruit also contains bioactive compounds with antioxidant, anti-inflammatory, and anti-diabetic properties, notably flavonoids, isoquercitrin, and unique mucilaginous polysaccharides that contribute to its health-promoting effects (Agregán et al., 2023 ). Despite these nutritional and functional attributes, postharvest okra fruit have a short shelf life and undergo rapid quality deterioration characterized by biochemical changes including chlorophyll degradation, cell wall polysaccharide breakdown, and oxidation of phenolic compounds (Palumbo et al., 2022 ). These chemical transformations manifest as weight loss, color changes, and textural softening, substantially reducing consumer acceptability, nutritional value, and market potential. Various postharvest strategies have been investigated to maintain the chemical stability of produce, including biological methods employing antagonistic microorganisms or natural compounds (Hosseini et al., 2024 ), physical techniques such as low-temperature storage and modified atmosphere packaging (Palumbo et al., 2022 ), and chemical approaches using phytohormones and antioxidants (Zhou et al., 2023a ). Nevertheless, there remains a need for alternative or synergistic chemical interventions that can more effectively maintain the molecular integrity of postharvest produce. Postharvest deterioration in horticultural commodities is intricately linked to oxidative stress, primarily driven by the excessive accumulation of reactive oxygen species (ROS) (Zhou et al., 2022 ). At the molecular level, ROS cause lipid peroxidation of membrane phospholipids, protein oxidation, and DNA damage, resulting in disrupted cellular compartmentalization, increased membrane permeability, and compromised metabolic regulation (Meitha et al., 2020 ). Plants have evolved a sophisticated antioxidant system, encompassing both enzymatic [superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), ascorbate peroxidase (APX)] and non-enzymatic defenses (ascorbic acid, glutathione, phenolic compounds), to neutralize ROS and maintain redox homeostasis (Zhou et al., 2023a ). Recent biochemical and molecular evidence has demonstrated that antioxidant-related genes not only scavenge excess ROS but also modulate key aspects of fruit ripening and quality retention through complex signaling networks (Huan et al., 2016 ; Zhou et al., 2023a ). For instance, overexpression of SOD , CAT , or APX in tomato and apple has been reported to inhibit ethylene biosynthesis and cell wall-degrading enzymes, thereby preserving fruit firmness and extending shelf life (Huan et al., 2016 ; Lv et al., 2020 ). Similarly, enhanced activity of SOD, CAT, APX, and MDHAR in horticultural product was correlated with delayed softening and reduced decay incidence by maintaining membrane integrity and limiting oxidative damage to structural polysaccharides (Zhou et al., 2023a ). The efficacy of these defense networks during postharvest storage can be enhanced by exogenous signaling molecules, such as nitric oxide (NO) donors and melatonin (MT), both of which have been reported to improve antioxidant capacity and delay senescence in various perishable horticultural crops (Zhang et al., 2020 ). Notably, NO (frequently supplied via sodium nitroprusside, SNP) alleviates oxidative stress through direct chemical quenching of ROS and by inducing antioxidant enzymes, whereas melatonin functions as a potent antioxidant while also maintaining membrane phospholipid integrity, modulating gene expression, and preserving important physiological parameters (Nabaei and Amooaghaie, 2019 ). Chlorophyll metabolism represents another critical determinant of postharvest quality in green vegetables, with direct implications for visual appeal and nutritional value. Chlorophyll degradation leads to visible color shifts and potential loss of associated antioxidant compounds, often diminishing consumer acceptance and nutritional quality (Liu et al., 2024 ). The chemical pathway of chlorophyll catabolism is orchestrated by several key enzymes, including chlorophyllase (CLH), which hydrolyzes the phytol chain; pheophytin pheophorbide hydrolase (PPH), which removes the central Mg 2+ ion; and pheophorbide a oxygenase (PAO), which opens the tetrapyrrole ring (Hörtensteiner, 2013a , 2013b ; Nguyen et al., 2021 ; Schelbert et al., 2009 ). Conversely, protochlorophyllide oxidoreductase (POR) and chlorophyllide a oxygenase (CAO) are pivotal for chlorophyll biosynthesis and interconversion between chlorophyll a and b (Oster et al., 2000 ). Recent molecular research highlights the importance of transcription factors (TFs) that directly regulate these enzymes during postharvest senescence (Cao et al., 2023 ; Liu et al., 2024 ). For instance, AP2/ERF-, MYB-, and NAC-family TFs bind to specific promoter elements of chlorophyll catabolic genes, thereby orchestrating pigment breakdown and color evolution in various horticultural produce (Dai et al., 2024 ; Lu et al., 2022 ; Zhu et al., 2015 ). Moreover, crosstalk among different TF families can finely tune the biosynthesis and degradation pathways of chlorophyll, as evidenced in citrus, where MYB-based regulatory networks simultaneously manage anthocyanin accumulation and chlorophyll breakdown (Tian et al., 2024 ). The interplay of these TFs underscores the complex regulation of chlorophyll metabolism and suggests that exogenous regulators, such as MT and NO, may delay color loss in green produce by modulating both chlorophyll-related enzymes and their upstream transcriptional activators (Kim et al., 2020 ; Liu et al., 2025 ; Zhang et al., 2020 ). However, investigations into how SNP and MT might jointly influence the chemical stability of chlorophyll and related pigments in postharvest okra fruit have been limited. Accordingly, this study aimed to evaluate the efficacy of SNP, MT, and their combined application (MT + SNP) in preserving the chemical composition and postharvest quality of okra fruit, focusing on weight loss, color parameters, antioxidant capacity, and chlorophyll metabolism. We employed a combination of targeted biochemical analyses and untargeted transcriptomic profiling to elucidate the underlying molecular mechanisms, with an emphasis on identifying differentially expressed genes (DEGs) involved in antioxidant defense, chlorophyll metabolism, transcription factor regulation, and protein kinase signaling. The findings from this work not only shed light on the biochemical and genetic basis of exogenous NO and MT action but also provide valuable insights for developing novel postharvest strategies to maintain the phytochemical stability, nutritional value, and sensory attributes of okra fruit. This integrated approach addresses the critical need for effective, sustainable postharvest technologies that preserve both the commercial quality and functional food properties of fresh produce. 2. Materials and methods 2.1 Plant materials and treatments ‘Lvba’ okra ( Abelmoschus esculentus L.) fruit at green and mature stages were harvested from an experimental station in Zhanjiang, China (Fig. 1 A). Uniform, defect-free fruit were sanitized with 0.01% sodium hypochlorite for 5 min, air-dried, and immersed for 5 min in one of four solutions: (1) distilled water (Control), (2) 100 µM melatonin (MT), (3) 0.5 mM sodium nitroprusside (SNP), or (4) MT + SNP at these same concentrations. The concentration used in the study was determined based on previous experiments (Fig. S1 ; S2). Tween-80 (1:1000, v/v) was added to each solution. After treatment, fruit were air-dried at ambient temperature, then stored at 20°C and 80–90% relative humidity (RH) for up to four days. Each treatment included 180 fruit, with three biological replicates per parameter. 2.2 Measurement of weight loss and color Weight loss (%) was determined by comparing fruit weight at 0 d to that on each sampling day. Color parameters (L * , a * , b * , and hue angle) were measured using a Konica Minolta Chroma meter CR400 (Japan). Chroma (C) was calculated as \(\:\sqrt{{\text{a}}^{2}+{\text{b}}^{2}}\) . Whiteness index (WI), color index (CI), and browning index (BI) were computed following the previous study (Zhou et al., 2022 ). 2.3 Soluble protein, MDA, H 2 O 2 and Total antioxidant capacity Soluble protein was quantified by the Coomassie Brilliant Blue G-250 assay. Malondialdehyde (MDA), hydrogen peroxide (H 2 O 2 ) and total antioxidant capacity were measured according to Zhou, Huang, et al. (2023). 2.4 SOD, POD, CAT and APX activities Superoxide dismutase (SOD; EC 1.15.1.1) activity was determined by the inhibition of nitro-blue tetrazolium photoreduction. Peroxidase (POD; EC 1.11.1.7) activity, catalase (CAT; EC 1.11.1.6), and ascorbate peroxidase (APX; EC 1.11.1.11) was measured according to Zhou, Huang, et al. (2023). 2.5 Chlorophyll Content and Metabolism-Related Enzymes Chlorophyll content was determined spectrophotometrically, with chlorophyll a and b calculated using Q. Liu et al. ( 2024 ). Activities of key chlorophyll degradation enzymes, chlorophyllase (CLH), pheophytinase (PPH), magnesium-dechelatase (MDCase), pheophorbide a oxygenase (PAO), red chlorophyll catabolite reductase (RCCR), and 7-hydroxymethyl chlorophyll a reductase (HCAR) were assayed as described by previous study (Cao et al., 2023 ; Hörtensteiner, 2013b ; Keawmanee et al., 2022 ; Liu et al., 2022 , 2025 ; Sun et al., 2021 ). 2.6 RNA Extraction, Library Construction, Sequencing, and Annotation Total RNA was isolated from fruit tissues using TRIzol® (Invitrogen, USA) and quantified spectrophotometrically. Libraries were prepared with the Illumina TruSeq™ RNA Sample Preparation Kit (Illumina, USA) by enriching poly(A) mRNA, synthesizing double-stranded cDNA, and performing end-repair before PCR amplification. A total of 15 RNA-seq libraries, including fresh harvest (FH), control, SNP, MT, and SNP + MT-treated fruit aftrer 4 d of storage, were sequenced on an Illumina NovaSeq 6000. Raw data were deposited in the NCBI database (accession number PRJNA1250921). Clean reads were assembled using Trinity. Unigenes were annotated by BLASTX searches (E-value < 1.0 × 10 − 5 ) against NR, COG, KEGG, and NCBI databases. Gene Ontology (GO) terms were assigned via BLAST2GO, and KEGG analysis was used to identify relevant metabolic pathways. 2.7 Identification of Differentially Expressed Genes and Functional Enrichment Transcript abundance was normalized as fragments per kilobase of transcript per million mapped reads (FPKM), and gene-level FPKM was computed with RSEM Differentially expressed genes (DEGs) were identified via EdgeR using |log 2 FC| > 1 and Q-value ≤ 0.05. GO and KEGG enrichment analyses were considered significant at a Bonferroni-corrected P ≤ 0.05. 2.8 Network Analysis A gene co-expression network was constructed using WGCNA (v1.68) for 1867 genes related to antioxidant capacity, chlorophyll metabolism, transcription factors, and protein kinases. After correcting for batch effects, a signed network was generated (soft-thresholding power β = 9) based on topological overlap. Modules were identified with the Dynamic Tree Cut algorithm, setting a minimum module size of 30 and a merge cut height of 0.25. The module eigen-gene (ME) was derived to investigate associations with antioxidant and chlorophyll metabolic processes. Gene significance (GS), module membership (MM), and intra-modular connectivity (Kin) were used to rank genes. Modules (Blue, Yellow, Brown, Turquoise) most strongly linked to antioxidant and chlorophyll pathways were visualized in Cytoscape by selecting the top 30 genes and edges with weight ≥ 0.02. 2.9 Quantitative Real-Time PCR Analysis Real-time PCR was performed following the method described by Zhou, Huang, et al. (2023). Primers were designed using Primer Premier version 5.0 (Table S1 ), yielding amplicons between 73 and 232 bp. The relative expression of the target genes was computed using the 2 −ΔΔCt method, with actin serving as the internal control. 2.10 Statistical Analysis All data were analyzed using SPSS (v19.0). Tukey’s test determined significant differences among treatment means ( P < 0.05). Different lowercase letters above bars in figures indicate statistically significant differences. 3. Results 3.1 Effects of SNP and MT on the weight loss and color of okra fruit Postharvest okra fruit exhibited an increasing trend in weight loss during storage (Fig. 1 B). Compared with the control, treatments with MT, SNP, and the combined MT + SNP resulted in lower weight loss rates throughout the storage period, except for the SNP treatment at 2 d and 4 d of storage. Notably, the MT + SNP treatment demonstrated the most effective mitigation, maintaining the lowest weight loss rate among all treatments. This combined intervention resulted in a reduction in weight loss of 14–28% compared with the control group during the entire experimental period. Over extended storage durations, the L * values of all okra fruit displayed an increasing trend. Throughout the storage period, the MT, SNP, and MT + SNP treatments maintained lower L * values compared with the control group (Fig. 1 C). The MT + SNP treatment consistently yielded the lowest L * values among all groups. The MT, SNP, and MT + SNP treatments reduced the a * and CI values of okra fruit compared with the control throughout the entire storage period. Conversely, these treatments increased the b * values (except for the SNP treatment at 2 d), chroma values (except for the SNP treatment at 3 d), h values (except for the SNP treatment at 3 d and 4 d), BI (except for the SNP treatment at 1 d), and WI values (except for the MT and MT + SNP treatments at 1 d, and the SNP treatment at 2 d) relative to the control. Notably, the MT + SNP treatment demonstrated enhanced effects by increasing the L * values by 8–13%, b * values by 22–62%, chroma values by 18–64%, h values by 3–5%, and BI by 21–47%, while decreasing the a * values by 17–78% and CI values by 15–22%, respectively, compared with the control throughout the storage period (Fig. 1 D–J). 3.2 Effects of SNP and MT on soluble protein, MDA, H 2 O 2 content, and total antioxidant capacity in okra fruit Compared with the control, the MT treatment decreased soluble protein content by 4–27% on 1 d–3 d and increased it by 19% on 4 d, whereas the SNP treatment reduced soluble protein content both by 17% on 1 d and 3 d, but increased it by 10% on 2 d and by 9% on 4 d (Fig. 2 A). The combined MT + SNP treatment enhanced soluble protein content by 15–41% on 1 d–3 d. For MDA content, the MT treatment increased values by 2% on 1 d and decreased them by 9% and 23% on 2 d and 3 d (Fig. 2 B). In contrast, the SNP treatment lowered MDA by 12–20% on 1 d–3 d and raised it by 23% on 4 d, while the MT + SNP treatment reduced MDA by 13%, 4%, and 15% on 1 d, 2 d, and 4 d, yet increased it by 2% on 3 d. Regarding H 2 O 2 , the MT treatment elevated its content by 31% and 29% on 2 d and 3 d and reduced it by 28% on 4 d; the SNP treatment consistently decreased H 2 O 2 levels by 8–48% throughout the storage period, and the MT + SNP treatment increased H 2 O 2 by 6% on 1 d but decreased it by 13% and 19% on 2 d and 4 d (Fig. 2 C). Finally, for total antioxidant capacity, the MT treatment decreased it by 34% on 1 d and increased it by 47% on 4 d; the SNP treatment decreased capacity by 9% on 1 d and increased it by 14–181% on 2 d–4 d; and the MT + SNP treatment reduced total antioxidant capacity by 17% on 1 d and increased it by 13–131% on 2 d–4 d (Fig. 2 D). 3.3 Effects of SNP and MT on SOD, CAT, POD, and APX activities in okra fruit Compared with the control, the MT treatment decreased SOD activity by 50% on 3 d and increased it by 2% on 4 d. The SNP treatment reduced SOD activity by 13%, 31%, and 65% on 1 d, 3 d and 4 d, whereas the combined MT + SNP treatment increased SOD activity by 12–60% on 1 d–3 d and decreased it by 24% on 4 d (Fig. 2 E). For CAT activity, MT treatment increased activity by 63% on 1 d but decreased it by 21–25% on 2 d–4 d. Conversely, the SNP treatment enhanced CAT activity by 62%, 75%, and 90% on 1 d, 3 d and 4 d, respectively, while reducing it by 30% on 2 d. The MT + SNP treatment elevated CAT activity by 32% on 2 d and 30% on 3 d, but decreased it by 18% on 4 d (Fig. 2 F). Regarding POD activity, MT treatment increased POD activity by 20% on 2 d and 27% on 4 d, while reducing it by 18% on 2 d. The SNP treatment boosted POD activity by 34% on 2 d but decreased it by 42% on 3 d and 11% on 4 d. The MT + SNP treatment consistently increased POD activity by 6–73% throughout the entire period (Fig. 2 G). Concerning APX activity, MT treatment decreased activity by 12–42% on 1 d–3 d and increased it by 9% on 4 d. The SNP treatment raised APX activity by 4% on 1 d and 55% on 4 d. In contrast, the MT + SNP treatment increased APX activity by 39%, 20%, and 46% on 1 d, 2 d and 4 d, respectively, while decreasing it by 37% on 3 d (Fig. 2 H). 3.4 Effects of SNP and MT on chlorophyll content and chlorophyll metabolism and stability enzyme activity in okra fruit Compared with the control, the chlorophyll a, chlorophyll b, and total chlorophyll content of MT-treated fruit increased by 5%, 8%, and 7% on 1 d and decreased by 13–38%, 7–21%, and 11–31% on 2 d–4 d, respectively (Fig. 3 A–C). In SNP-treated fruit, chlorophyll a, chlorophyll b, and total chlorophyll content decreased by 9% and 12%, 4% and 4%, and 6% and 9% on 1 d and 2 d, while they increased by 24% and 10%, 14% and 5%, and 19% and 9% on 3 d and 4 d, respectively. The SNP + MT treatment increased chlorophyll a and chlorophyll b content by 10% and 7% on 3 d and by 5% and 4% on 4 d, respectively, and increased total chlorophyll content by 3%, 9%, and 5% on 1 d, 3 d and 4 d, respectively. In okra fruit during storage, treatments with MT, SNP, and their combination (MT + SNP) produced distinct effects on enzyme activities relative to the control (Figs. 3 D– 3 J). For NYC1 activity, the MT treatment increased activity by 33% and 36% on 2 d and 3 d of storage, respectively, while both SNP and MT + SNP treatments enhanced activity by 8–43% and 12–79% during 2 d–4 d. For CLH activity, the MT treatment elevated activity by 14–23% during storage 2 d–4 d, and the SNP treatment increased activity by 23% and 13% on 2 d and 3 d; the MT + SNP treatment consistently enhanced CLH activity by 10–16% throughout storage. In contrast, PPH activity was boosted by the MT and SNP treatments by 10–71% and 19–59%, respectively, during 1 d–3 d, with the MT + SNP treatment further increasing activity by 65% on 4 d. Regarding MDcase, the MT treatment reduced activity by 15% and 13% on storage 2 d and 3 d, and the SNP treatment decreased activity by 8% and 10% on 1 d and 3 d; the MT + SNP treatment lowered MDcase activity by 10%, 12%, and 6% on storage 1 d, 2 d and 4 d, respectively. In addition, PAO activity was increased by the SNP treatment by 10% and 13% on storage 3 d and 4 d, and by the MT + SNP treatment by 24–29% over the entire storage period. For RRCR activity, the MT treatment decreased it by 5% on 4 d, whereas the SNP treatment increased it by 9% and 10% on 3 d and 4 d; notably, the MT + SNP treatment reduced activity by 9% on 1 d but increased it by 22% and 10% on 2 d and 3 d, respectively. Finally, HCAR activity was decreased by the MT treatment on the final storage day by 14%, and by the SNP treatment on 3 d by 11%, while the MT + SNP treatment enhanced HCAR activity by 9–17% throughout storage. 3.5 Transcriptome Profiling and Differential Gene Expression Analysis of okra Fruit Under MT, SNP, and MT + SNP Treatment On the fourth day of storage, postharvest okra fruit subjected to MT, SNP, and especially the combined MT + SNP treatment exhibited a suite of improved quality indices, including lower weight loss, reduced L * , a * , and CI values with concomitant increases in b * , chroma, hue, and BI, enhanced soluble protein content, reduced H 2 O 2 levels, modified MDA content, improved total antioxidant capacity, differential enzyme activities including reduced SOD, variable CAT and POD responses, elevated APX activity, and altered chlorophyll parameters—with MT reducing chlorophyll a, b, and total chlorophyll, while SNP and MT + SNP treatments increased these chlorophyll components—and modifications in chlorophyll metabolism enzymes, increase in PPH and decrease in MDcase activity. To explore the underlying molecular responses, we conducted RNA sequencing on fruit tissues from freshly harvested (FH) and both control, MT, SNP and MT + SNP-treated fruit on 4 d of storage. Fifteen cDNA libraries yielded 128.93 Gb of raw sequence data. Following removal of adapters and low-quality reads, we retained 429,092,998 high-quality reads (Q30 > 96%; Table S2 ). Assembly produced 60,127 transcripts and 20,111 unigenes, with an N50 of 1,928 bp and a mean length of 1,743 bp (Table S3 ). Unigenes were distributed as follows by length: 300–500 bp (21%), 501-1,000 bp (11%), 1,001–2,000 bp (42%), and over 2,000 bp (27%) (Fig. S3 ), with 13,801 unigenes exceeding 1,000 bp. Out of the total 19,097 annotated unigenes, 5,522 (29%) were between 300 and 1,000 bp, while 13,575 (71%) were ≥ 1,000 bp. Notably, the NR database provided the most comprehensive annotation with 18,954 unigenes (5,444 in the 300–1,000 bp range and 13,510 ≥ 1,000 bp), followed by TrEMBL (18,907 unigenes; 5,414 and 13,493 in the respective length categories) and eggNOG (16,540 unigenes; 4,631 and 11,909). The GO database contributed annotations for 15,436 unigenes (4,416 for 300–1,000 bp and 11,020 for ≥ 1,000 bp), whereas KEGG annotated 13,693 unigenes (3,709 and 9,984, respectively). In addition, the COG, KOG, Pfam, and SwissProt databases annotated 6,167 (1,316 and 4,851), 11,563 (3,253 and 8,310), 14,877 (3,367 and 11,510), and 14,541 (4,095 and 10,446) unigenes, respectively (Table S4 ). Using FH samples as a baseline, pairwise comparisons were performed to identify DEGs between control and MT-, SNP-, and MT + SNP-treated fruit at four days of storage (Table S5 ; Fig. S4 ). A total of 6,225 DEGs (3,288 upregulated and 2,937 downregulated) were identified in the Control vs. FH comparison; 2,426 DEGs (1,187 upregulated and 1,239 downregulated) in the SNP vs. Control comparison; 527 DEGs (262 upregulated and 265 downregulated) in the MT vs. Control comparison; and 256 DEGs (125 upregulated and 131 downregulated) in the MT + SNP vs. Control comparison. In addition, 4,365 postharvest senescence-related DEGs were detected, of which 2,175 were upregulated and 2,190 were downregulated; 754 SNP-specific DEGs were identified (313 upregulated and 451 downregulated); 140 MT-specific DEGs were found (65 upregulated and 75 downregulated); and finally, 44 MT + SNP-specific DEGs were identified (13 upregulated and 31 downregulated). To validate the transcriptomic profiling, the expression of 16 DEGs associated with four specific comparison pathways was analyzed via qRT-PCR (Fig. S5 ). The gene expression patterns observed in the qRT-PCR experiments for FH, Control, MT, SNP, and MT + SNP fruit (collected on 4 d) corresponded closely with the RNA-seq data, confirming the high reproducibility and reliability of the transcriptome analysis. 3.6 Functional Analysis of four comparison specific DEGs: GO and KEGG Enrichment We conducted a Gene Ontology (GO) enrichment analysis to elucidate the functions of specific DEGs in okra fruit on 4 d of storage (Fig. 4 ; Table S6 ). The predominant GO categories identified were “response to organic substance”, “cell wall”, and “DNA-binding transcription factor activity” in the Control vs. FH group (Fig. 4 ); “1,3 − β − D − glucan biosynthetic process”, “organic substance biosynthetic process”, and “oxidoreductase activity” in the MT vs. Control group; “oxidation − reduction process”, “chloroplast thylakoid membrane”, and “oxidoreductase activity” in the SNP vs. Control group; and “positive regulation of biological process” in the SNP + MT vs. Control group, all of which are indicative of roles in antioxidant defense and chlorophyll metabolism. In addition, the DEGs specific to each of the four comparisons were mapped to pathways in the KEGG database. In the Control vs. FH group, the most enriched pathways were plantpathogen interaction, plant hormone signal transduction, and MAPK signaling pathway–plant (Fig. 5 ; Table S7 ). In the MT vs. Control group, oxidative phosphorylation, protein processing in the endoplasmic reticulum, and plant hormone signal transduction were most enriched. For the SNP vs. Control group, the top pathways were plantpathogen interaction, carbon metabolism, and biosynthesis of amino acids. Finally, in the MT + SNP vs. Control group, endocytosis, plantpathogen interaction, and propanoate metabolism were the most enriched pathways. 3.7 Differential Expression of Antioxidant Defense Related DEGs Although antioxidant pathways did not rank among the top ten KEGG-enriched pathways for DEGs specific to the four comparison groups, thirtysix DEGs associated with redox homeostasis were selected to investigate the molecular mechanisms underlying postharvest senescence and reactive oxygen species (ROS) mitigation elicited by exogenous MT and SNP. On 4 d of storage, okra fruit exhibited upregulation of eight PODs , one APX1 , three monodehydroascorbate reductases ( MDHARs ), and five glutathione Stransferases ( GSTs ), whereas two CAT2 , nine PODs , one MDHAR, four GSTs, and two ferredoxins ( Frxs ) were downregulated. Under MT treatment alone, only one MDHAR was downregulated in postharvest okra fruit. Under SNP treatment, two PODs , three MDHARs , two GSTs , and one Frx2 were upregulated, while eight PODs and one GSTF9 were downregulated. Similarly, under combined MT and SNP treatment, one POD42 was upregulated and one MDHAR was downregulated (Table 1). 3.8 Differential Expression of Chlorophyll Metabolism Related DEGs We identified 24 candidate DEGs related to chlorophyll metabolism across four comparison groups. At the end of storage, one each of ChlB , SGR , and HO1 was upregulated, whereas five chlorophyll a‑b binding proteins ( Lhcbs ), one HO1 , one HY2 , one GGDR , and two PORs ( POR‑like and POR ) were downregulated. Under MT treatment of okra fruit, Lhcb7 and Lhcb5 were downregulated. Under SNP treatment, 12 Lhcbs , two PORs , and one HCAR were upregulated, while one PAO was downregulated. 3.9 Differential Expression of Transcription factors and protein kinases We identified 1867 DEGs associated with transcription factors and protein kinases (Fig. S6 ). Specifically, 483 DEGs were found in the Control vs. FH group, 11 in the MT vs. Control group, 69 in the SNP vs. Control group, and 6 in the MT + SNP vs. Control group (Table S8 ). Of these, 237 were transcription factors (TFs), 51 were transcription regulators (TRs), and 195 were protein kinases (PKs). In the Control vs. FH group, the majority of differentially expressed genes belonged to the AP2/ERF-ERF gene family, with 17 genes upregulated and four downregulated (Table 1). Moreover, WRKY4 and RAX3 , members of the MYB family, were downregulated by MT treatment. Similarly, in the Control vs FH group, an additional analysis of the AP2/ERF-ERF gene family revealed five upregulated genes and one downregulated gene ( ERF27 ). Finally, four PKs and two TFs were differentially expressed; specifically, bHLH106 , SRF6 , SRF7 , and PMEI were upregulated, while NAC90 and PTI1-3 were downregulated. 3.10 Gene Module Analysis in MT and SNP-Treated Okra Fruit Using WGCNA, we examined how genes induced by MT and SNP are regulated during okra fruit postharvest senescence. Our study focused on genes involved in antioxidant defense and chlorophyll metabolism (Table 1), as well as their TFs and PKs (Table S8 ). We identified seven distinct co‑expression modules (Fig. 6 A), which were organized into two meta‑modules based on their correlation patterns (Fig. 6 B). Meta1 comprised the blue and yellow modules, while Meta2 included the grey, brown, turquoise, green, and red modules. Within each meta‑module, constituent modules exhibited positive correlations; however, Meta1 showed negative associations with H 2 O 2 , SOD, chlorophyll a, chlorophyll b, and total chlorophyll, while correlating positively with NYC1, CLH, PPH, MDCase, PAO, and HCCR activities. Moreover, the grey, brown, turquoise, green, and red modules demonstrated both negative and positive relationships with antioxidant defense and chlorophyll metabolism (Fig. 6 C). These modules exhibited strong positive and negative correlations with total antioxidant capacity and CLH activity. To further explore the relationships among total antioxidant capacity, CLH, and these modules, we filtered transcripts from the yellow, turquoise, blue, and brown modules that simultaneously displayed the highest gene significance (GS) and module membership (MM) (Fig. 7 A, B, E, F). Figure 7 B, C, G, H illustrates the interactions between transcription factors and genes involved in antioxidant defense and chlorophyll metabolism within those four modules (Table S9 ). In the yellow module, the three highest‑degree genes were NAC86 , FER , and ERF4 ; these were co‑expressed with POD25 (Table S10 ). Conversely, in the turquoise module, the top three highest‑degree genes were zf_CCCH20 , HAT5 , and APL , which were co‑expressed with GSTZ . In the blue module, the three highest‑degree genes were MYB24 , GT-3B , and FAM135B ; these were co‑expressed with POD11 , POD25 , and GST7 . In the brown module, the top three highest‑degree genes were COL16 , AUX28 , and CEPR2 ; these were co‑expressed with POD73 , NECT3 , Fd2 , CAB3 , CAB7 , and POR . These results suggest that MT and SNP modulate okra fruit postharvest senescence by promoting the co‑expression of transcription factors and genes associated with antioxidant defense and chlorophyll metabolism—a relationship that warrants further investigation. 4. Discussion Postharvest okra fruit, like many other horticultural commodities, are prone to rapid quality deterioration characterized by weight loss, color changes, and loss of nutritional and antioxidant properties (Agregán et al., 2023 ). In the present study, SNP and MT treatments, alone or in combination, effectively suppressed weight loss, mitigated color degradation, and enhanced antioxidant capacity during storage. Notably, the combined MT + SNP treatment exerted the most pronounced effects, underscoring the potential of these two signaling molecules to synergistically preserve the chemical stability and nutritional quality of okra fruit. Weight loss in postharvest fruit is predominantly driven by water evaporation and metabolic processes that alter cell wall polysaccharides and membrane phospholipids (Agregán et al., 2023 ; Hosseini et al., 2024 ). Consistent with earlier findings in mango (Zhou et al., 2023a ) and papaya (Zhou et al., 2022 ), exogenous application of MT and NO effectively reduced weight loss in okra fruit, with the combined application of MT + SNP showing even stronger effect. This observation suggests that simultaneous enhancement of antioxidative pathways and maintenance of membrane lipid integrity could help preserve cellular compartmentalization and reduce water loss during storage (Chang et al., 2023 ). The mechanisms likely involve protection of membrane phospholipids against peroxidation and preservation of cell wall polysaccharide integrity, which collectively maintain tissue structure and water retention capacity. The visual appearance of okra fruit, particularly its color parameters, significantly influences consumer acceptance and market value. In the present study, all treatments (MT, SNP, and especially MT + SNP) contributed to lower L * , a * , and CI values and higher b * , chroma, hue, and BI values, preserving desirable color attributes compared with controls. These color metrics directly reflect the chemical stability of chlorophyll molecules and related pigments, which are susceptible to oxidative degradation during postharvest storage. Similar effects on maintaining color-related phytochemicals have been reported in other perishable produce, such as mango treated with phytohormones or antioxidants (Zhou et al., 2023a , 2023b ). The improved color retention in MT + SNP-treated okra fruit likely results from reduced chlorophyll degradation and oxidative stress, reflecting protective mechanisms that maintain pigment molecular stability and preserve the visual quality attributes valued by consumers (Shi et al., 2024 ; Sun et al., 2021 ). Postharvest senescence is biochemically characterized by the overproduction of ROS, which leads to lipid peroxidation of membrane phospholipids and subsequent cellular damage (Huan et al., 2016 ). Our results indicated that NO and MT treatments modulated oxidative stress indicators, specifically MDA and H 2 O 2 contents, while enhancing total antioxidant capacity. Particularly on 4 d, combined MT + SNP treatment maintained comparatively lower levels of H 2 O 2 and MDA, highlighting its stronger ability to scavenge ROS and mitigate lipid peroxidation of membrane components. The chemical basis for this protection likely involves both direct scavenging of free radicals and the modulation of enzymatic antioxidant systems. Similar protective effects on cellular redox homeostasis have been noted when exogenous salicylic acid, auxin, glutathione, and ascorbic acid were used to strengthen the enzymatic antioxidant system in winter jujube, mango, tomato, and papaya (Yang et al., 2022 ; Zhou et al., 2023a , 2022 ). Enzymatic antioxidants (SOD, CAT, POD, and APX) constitute a critical line of defense against ROS by catalyzing specific redox reactions that neutralize potentially harmful oxidative species (Li et al., 2021 ). In okra fruit, SNP and MT treatments differentially influenced the activities of these enzymes through complex regulatory mechanisms. Although some individual treatments led to transient declines in specific enzymes (e.g., reduced SOD and CAT activity in certain storage stages), the combined MT + SNP treatment generally enhanced POD and APX activities while maintaining balanced SOD and CAT levels. Such cooperative regulation of multiple enzymatic systems might explain the superior ROS scavenging capacity observed in MT + SNP-treated fruit. Previous work in mango suggested that co-expression of TFs, such as bZIP and ERFs , with antioxidant enzyme genes contributed to the regulation of ROS homeostasis (Lei et al., 2024 ; Zhou et al., 2023a ). Similarly, in okra, our transcriptome data revealed that genes encoding PODs , GSTs , and MDHARs were differentially expressed in response to MT and SNP, thereby reinforcing enzymatic and non-enzymatic antioxidative networks that protect valuable bioactive compounds from oxidative degradation (Li et al., 2023 ). Chlorophyll degradation is a hallmark of postharvest senescence in green vegetables, leading to color changes that often reduce market value and nutritional quality (Zhang et al., 2020 ). At the molecular level, this process involves a cascade of enzymatic reactions that transform chlorophyll into colorless catabolites. In this study, exogenous MT and SNP delayed chlorophyll breakdown by modulating key enzymes involved in chlorophyll catabolism, such as PPH, CLH, and PAO. While MT alone sometimes showed reduced chlorophyll contents on certain days, SNP consistently increased chlorophyll a, chlorophyll b, and total chlorophyll, particularly at later storage stages. These findings align with reports that exogenous MT and SNP can retard chlorophyll degradation in cabbage and other horticultural produce by interfering with the activity of chlorophyll-degrading enzymes (Liu et al., 2025 ; Peng et al., 2023 ). Furthermore, the increase in CLH, PPH, PAO, and related genes under combined treatment suggests a complex regulatory mechanism that accelerates pigment turnover while simultaneously conserving overall chlorophyll content to retain greener coloration (Keawmanee et al., 2022 ). Interestingly, reduced MDcase activity under MT + SNP treatment may also contribute to stabilizing chlorophyll, as MDcase (mesophyll-derived cell death-related enzyme) has been associated with tissue senescence and chlorophyll degradation (Liu et al., 2022 ). These data collectively imply that molecular crosstalk between NO and MT fine-tunes chlorophyll metabolism, slows down color loss, and maintains both the visual appeal and nutritional quality of okra fruit (Zhang et al., 2020 ). The preservation of chlorophyll is particularly important from a food chemistry perspective, as these pigments not only contribute to color but also possess antioxidant properties and are associated with other bioactive compounds that enhance the nutritional value of okra. RNA-seq analysis confirmed the biochemical trends by identifying DEGs associated with redox homeostasis, chlorophyll metabolism, and transcriptional regulation at the molecular level. Under combined MT + SNP treatment, the number of uniquely regulated DEGs (44 in total) was lower than that observed for either treatment alone, suggesting that NO and MT share overlapping downstream targets while also generating synergistic effects on gene expression networks (Feng et al., 2021 ; Imran et al., 2022 ). Notable changes included the upregulation of POD- and GST-encoding genes, consistent with increased enzymatic activities that promote ROS scavenging and detoxification of oxidation products (Zhou et al., 2023a ). In contrast, genes encoding certain Frxs and CATs were downregulated, supporting the observed transient decreases in CAT activity at specific time points. These differential expression patterns reflect the complex biochemical coordination required to maintain cellular redox balance during extended storage. With respect to chlorophyll metabolism, the present transcriptome data highlighted the differential expression of Lhcbs , POR , PAO , and HCAR , underscoring their pivotal roles in regulating chlorophyll biosynthesis and degradation pathways (Kim et al., 2020 ; Nguyen et al., 2021 ). The upregulation of POR-like in SNP-treated fruit suggests an enhancement of chlorophyll synthesis pathways, whereas the downregulation of a PAO in the same group indicates attenuated chlorophyll breakdown. Meanwhile, the MT + SNP treatment led to selective overexpression of PAO , implying a more dynamic modulation of pigment turnover. These seemingly contradictory patterns point to a coordinated molecular mechanism allowing precise control of chlorophyll homeostasis through balanced regulation of both biosynthetic and catabolic pathways (Kim et al., 2020 ; Nguyen et al., 2021 ; Pang et al., 2008 ). TFs such as AP2 / ERF , MYB , and NAC families often function as global regulators of postharvest physiological processes and biochemical pathways (Dai et al., 2024 ). Our weighted gene co-expression network analysis revealed that key TFs—such as NAC86 , ERF4 , MYB24 , and GT-3B —were co-expressed with antioxidant- and chlorophyll-related genes, suggesting their role as master regulators of multiple quality-related pathways. Similar findings in other fruit systems have shown that TFs directly or indirectly modulate the expression of antioxidative enzymes, cell wall-modifying enzymes, and senescence-associated genes that collectively determine postharvest quality retention (Li et al., 2022 ; Lira et al., 2017 ). Here, co-expression in the blue, yellow, turquoise, and brown modules suggests that NO and MT may converge on shared regulatory nodes, enhancing the transcription of genes that bolster antioxidant capacity and chlorophyll retention (Quesada et al., 2009; Upadhyay et al., 2023). Moreover, protein kinases, integral to numerous signal transduction cascades, were differentially expressed under MT and SNP treatments, further implying that multi-level regulation underpins the synergistic effects observed (Keawmanee et al., 2022 ; Pardo-Hernández et al., 2020 ). These protein kinases likely facilitate the phosphorylation-dependent activation of transcription factors and metabolic enzymes, thereby connecting external chemical signals (SNP and MT) to specific biochemical responses that preserve food quality attributes. The identification of these regulatory hubs provides potential targets for future postharvest interventions aimed at enhancing the chemical stability, nutritional value, and shelf life of okra and similar perishable produce. 5. Conclusion This study demonstrates that MT and SNP treatments effectively preserve the chemical stability and quality attributes of postharvest okra fruit (Fig. 8 ). These treatments reduced the weight loss while maintaining desirable color parameters, including lower L * and a * values and higher b * , chroma, hue, and BI values, which directly reflect the preservation of pigment molecules and tissue integrity. Biochemical analysis revealed that MT and SNP enhanced the antioxidant defense system by modulating SOD, CAT, POD, and APX enzyme activities, thereby reducing MDA and H 2 O 2 . Notably, the combined MT + SNP treatment exhibited synergistic effects in preserving chlorophyll a, chlorophyll b, and total chlorophyll content through selective regulation of key enzymes in chlorophyll metabolism (particularly PPH and MDcase). Transcriptome analysis further elucidated the molecular mechanisms underlying these biochemical changes, revealing complex gene expression networks involving antioxidant-related genes ( PODs , GSTs , MDHARs ), chlorophyll metabolism genes ( POR , PAO , Lhcbs ), and their upstream regulators ( NAC86, ERF4 , MYB24 ). The co-expression patterns identified through WGCNA highlighted the integrated nature of redox homeostasis and pigment metabolism pathways in maintaining postharvest quality. Our findings establish the molecular basis for how SNP and MT treatments preserve the phytochemical composition, nutritional value, and visual quality of okra fruit, providing valuable insights for developing innovative postharvest technologies aimed at extending shelf life while maintaining the functional food properties of fresh produce. Future research should explore the practical applications of these treatments in commercial settings and investigate their effects on specific bioactive compounds and nutritional components that contribute to the health-promoting properties of okra. Declarations Author Contribution Xianjun Chen: Formal analysis, Writing – original draft, Methodology. Yan Zhou: Formal analysis, Writing – original draft, Writing – review & editing. Yao Jiang, Xiaocheng Liu, Lulu Wang, Jingtong Zheng, and Jianyu Zeng: Investigation, Formal analysis. Jianwei Zhang: Investigation, Conceptualization, Writing – review & editing. Qin Yang: Writing – review & editing, Supervision. Acknowledgement This research was funded by the Growth of Young Scientific and Techno-logical Talents of Guizhou Educational Commission (No. Qian Jiaoji [2024]232), the Specialized Fund for the Doctoral of Kaili University (No. BS20240218), the Provincial famous teacher Yang Qin studio (No. MSGZS-SJ-2024002), the Specialized Fund for the Doctoral Development of Kaili University (No. BSFZ202206) and the Key Laboratory of the Department of Education of Guizhou Province (No. Qianjiaoji [2022] 053). Data Availability PRJNA1250921 References Agregán, R., Pateiro, M., Bohrer, B.M., Shariati, M.A., Nawaz, A., Gohari, G., Lorenzo, J.M., 2023. Biological activity and development of functional foods fortified with okra ( Abelmoschus esculentus ). Crit. Rev. Food Sci. Nutr. 63, 6018–6033. https://doi.org/10.1080/10408398.2022.2026874 Cao, J., Liu, H., Tan, S., Li, Z., 2023. 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Plant J. 84, 597–610. https://doi.org/10.1111/tpj.13030 Table Table 1. List of selected genes that may be responsible for SNP- and MT-mediated postharvest quality of okra fruit. Gene id Gene name Log2 Fold Change Gene description Control vs FH MT vs Control SNP vs Control MT+SNP vs Control Antioxidant defense-related DEGs TRINITY_DN5801_c0_g3 CAT2 -0.74 - - - Catalase isozyme 2 TRINITY_DN7862_c0_g1 CAT2 -0.67 - - - Catalase isozyme 2 TRINITY_DN9274_c0_g1 POD5 -2.55 - - - Peroxidase 5 TRINITY_DN8559_c0_g1 POD11 11.26 - -2.80 - Peroxidase 11 TRINITY_DN3291_c0_g1 POD17 -1.72 - 1.37 - Peroxidase 17 TRINITY_DN8861_c0_g1 POD21 -2.87 - 2.97 - Peroxidase 21 TRINITY_DN15109_c0_g1 POD25 2.66 - -1.73 - Peroxidase 25 TRINITY_DN71851_c0_g1 POD25 3.19 - - - peroxidase 25 TRINITY_DN5145_c0_g1 POD31 1.89 - -1.55 - peroxidase 31-like TRINITY_DN34030_c0_g1 POD40 3.96 - - - peroxidase 40 precursor TRINITY_DN22974_c0_g1 POD42 -1.13 - -1.53 0.72 Peroxidase 42 TRINITY_DN26603_c0_g1 POD54 -2.26 - - - Peroxidase 54 TRINITY_DN216153_c0_g1 POD54 4.31 - -4.58 - Peroxidase 54 TRINITY_DN105024_c0_g1 POD55 2.29 - -2.83 - Peroxidase 55 TRINITY_DN8076_c0_g1 POD64 -3.71 - - - PREDICTED: peroxidase 64-like TRINITY_DN9372_c0_g1 POD64 -1.57 - - - peroxidase 64-like TRINITY_DN1208_c1_g3 POD66 -5.16 - - - Peroxidase 66 TRINITY_DN10147_c1_g1 POD73 -2.71 - - - Peroxidase 73 TRINITY_DN14032_c0_g1 PODP7 1.31 - -2.07 - PREDICTED: peroxidase P7-like isoform X2 TRINITY_DN7047_c0_g1 PODP7 - - -2.00 - PREDICTED: peroxidase P7-like TRINITY_DN3376_c0_g1 APX1 0.68 - - - L-ascorbate peroxidase 1, cytosolic TRINITY_DN147219_c0_g1 MDHAR -1.42 - 2.27 -0.76 Monodehydroascorbate reductase TRINITY_DN1764_c0_g1 MDHAR -0.86 - - - Monodehydroascorbate reductase TRINITY_DN465_c0_g1 MDHAR 0.10 -0.59 0.96 - Monodehydroascorbate reductase TRINITY_DN2663_c0_g1 MDHAR3 -1.38 - 1.35 - bifunctional monodehydroascorbate reductase and carbonic anhydrase nectarin-3-like TRINITY_DN25863_c0_g2 GST -2.54 - 1.59 - Glutathione s-transferase-like protein TRINITY_DN40077_c0_g1 GST 4.10 - - - Glutathione S-transferase family protein isoform 1 TRINITY_DN6347_c0_g2 GST -2.02 - - - PREDICTED: glutathione S-transferase zeta class-like TRINITY_DN297_c0_g1 GST1 2.13 - - - Glutathione S-transferase family protein isoform 1 TRINITY_DN29445_c0_g2 GST7 2.17 - - - putative Glutathione S-transferase tau 7 TRINITY_DN2034_c0_g1 GSTF9 0.85 - -0.78 - glutathione S-transferase F9-like TRINITY_DN4089_c0_g1 GST-DHAR4 1.69 - - - putative glutathione S-transferase DHAR4 TRINITY_DN6588_c1_g1 GST-PARB -1.74 - - - Glutathione S-transferase PARB TRINITY_DN86442_c0_g1 GSTU7 -2.00 - 3.13 - Glutathione S-transferase U7 TRINITY_DN5794_c0_g1 Frx2 -2.08 - 1.41 - Ferredoxin-2 TRINITY_DN18174_c0_g1 Frx3 -1.80 - - - Ferredoxin-3 Chlorophyll metabolism-related DEGs TRINITY_DN32450_c0_g1 Lhcb -3.01 - 2.78 - chlorophyll a-b binding protein, chloroplastic-like TRINITY_DN13078_c0_g1 Lhcb3 -2.12 - 2.15 - Chlorophyll a-b binding protein 3 TRINITY_DN34119_c0_g1 Lhcb4.2 - - 2.19 - Chlorophyll a-b binding protein CP29.2 TRINITY_DN16265_c0_g1 Lhcb5 -1.56 - 2.45 - PREDICTED: chlorophyll a-b binding protein CP26, chloroplastic TRINITY_DN16878_c1_g1 Lhcb5 - - 3.94 - PREDICTED: chlorophyll a-b binding protein of LHCII type 1-like TRINITY_DN48304_c0_g1 Lhcb5 - -1.65 2.02 - chlorophyll a-b binding protein of LHCII type 1-like TRINITY_DN12532_c0_g1 Lhcb5 - - 2.60 - PREDICTED: chlorophyll a-b binding protein of LHCII type 1-like TRINITY_DN1071_c0_g1 Lhcb6 - - 1.74 - chlorophyll a-b binding protein 6, chloroplastic TRINITY_DN14406_c0_g1 Lhcb6 - - 2.36 - PREDICTED: chlorophyll a-b binding protein 6, chloroplastic TRINITY_DN54967_c0_g1 Lhcb6 -2.68 - 2.33 - Chlorophyll a-b binding protein TRINITY_DN127_c0_g1 Lhcb7 -1.90 -1 2.02 - PREDICTED: chlorophyll a-b binding protein 7, chloroplastic TRINITY_DN10806_c0_g1 Lhcb151 - - 1.76 - PREDICTED: chlorophyll a-b binding protein 151, chloroplastic TRINITY_DN9711_c1_g1 ChlB 1.28 - - - Light-independent protochlorophyllide reductase subunit B TRINITY_DN9711_c1_g1 ChlB 1.28 - - - Light-independent protochlorophyllide reductase subunit B TRINITY_DN11720_c0_g2 SGR 8.32 - - - Protein STAY-GREEN TRINITY_DN1344_c0_g1 HO1 3.12 - - - Heme oxygenase 1 TRINITY_DN1484_c0_g1 HO1 -1.26 - - - Heme oxygenase-like, multi-helical isoform 1 TRINITY_DN90537_c0_g1 HY2 -1.27 - - - Phytochromobilin:ferredoxin oxidoreductase TRINITY_DN5896_c0_g1 GGDR -1.92 - - - Geranylgeranyl diphosphate reductase TRINITY_DN11844_c0_g1 POR - - 2.30 - Protochlorophyllide reductase TRINITY_DN13487_c0_g1 POR -2.27 - - - Protochlorophyllide reductase TRINITY_DN10593_c0_g1 POR-like -2.76 - 2.13 - PREDICTED: protochlorophyllide reductase-like TRINITY_DN4391_c0_g1 PAO - - -0.97 - Pheophorbide a oxygenase TRINITY_DN8481_c0_g1 HCAR - - 1.56 - 7-hydroxymethyl chlorophyll a reductase Transcription factors and protein kinases TRINITY_DN1096_c0_g1 RAP2-4 -0.73 - - - Ethylene-responsive transcription factor RAP2-4 TRINITY_DN11501_c1_g1 DREB1A 4.69 - - - Dehydration-responsive element-binding protein 1A TRINITY_DN6395_c0_g1 ERF3 - - 0.98 - Ethylene-responsive transcription factor 3 TRINITY_DN8242_c0_g1 ERF4 1.27 - - - Ethylene-responsive transcription factor 4 TRINITY_DN3772_c0_g1 ERF4 2.13 - - - PREDICTED: ethylene-responsive transcription factor 4-like TRINITY_DN3772_c0_g2 ERF4 1.18 - - - PREDICTED: ethylene-responsive transcription factor 4-like TRINITY_DN8058_c0_g1 ERF5 - - 2.13 - Ethylene-responsive transcription factor 5 TRINITY_DN19693_c0_g1 ERF9 2.74 - - - PREDICTED: ethylene-responsive transcription factor 9 TRINITY_DN12966_c0_g1 ERF011 1.13 - - - ERF011 protein TRINITY_DN3805_c0_g1 ERF017 11.41 - - - ERF017 protein TRINITY_DN8459_c0_g1 ERF017 2.77 - - - ERF017 protein TRINITY_DN9173_c0_g1 ERF025 3.29 - - - ERF025 protein TRINITY_DN114837_c0_g1 ERF27 - - -3.88 - ERF027 protein TRINITY_DN214995_c0_g1 ERF061 1.15 - - - ethylene-responsive transcription factor ERF061-like TRINITY_DN152802_c0_g1 ERF112 8.38 - - - ERF112 protein TRINITY_DN6479_c0_g1 ERF112 1.96 - - - ERF112 protein TRINITY_DN2798_c0_g1 ERF113 1.12 - - - ERF113 protein TRINITY_DN8359_c0_g1 ERF1A 3.78 - - - Ethylene-responsive transcription factor 1A TRINITY_DN6812_c0_g1 CRF1 -4.76 - - - PREDICTED: ethylene-responsive transcription factor CRF1 TRINITY_DN66856_c0_g1 CRF2 1.50 - - - Ethylene-responsive transcription factor CRF2 TRINITY_DN6812_c0_g2 CRF3 3.01 - - - Ethylene-responsive transcription factor CRF3 TRINITY_DN8882_c0_g1 TOE3 -1.47 - - - AP2-like ethylene-responsive transcription factor TOE3 TRINITY_DN9903_c0_g1 DREB1 -1.21 - - - PREDICTED: ethylene-responsive transcription factor RAP2-1-like TRINITY_DN9237_c0_g1 DREB2E 1.90 - - - Dehydration-responsive element-binding protein 2E TRINITY_DN5597_c0_g1 WRKY4 - -1.54 - - PREDICTED: LOW QUALITY PROTEIN: probable WRKY transcription factor 4 TRINITY_DN8258_c0_g1 RAX3 - -1.20 - - Transcription factor RAX3 TRINITY_DN13605_c0_g1 RAP2-3 - - 1.18 - PREDICTED: ethylene-responsive transcription factor RAP2-3 TRINITY_DN6318_c0_g1 RAP2-10 - - 0.93 - ethylene-responsive transcription factor RAP2-10-like TRINITY_DN25001_c0_g1 bHLH106 - - - 3.25 bHLH106 protein TRINITY_DN3188_c0_g1 NAC90 - - - -1.88 PREDICTED: NAC domain-containing protein 90-like TRINITY_DN1740_c0_g1 SRF6 - - - 0.93 Protein STRUBBELIG-RECEPTOR FAMILY 6 TRINITY_DN16448_c0_g1 SRF7 - - - 0.89 Protein STRUBBELIG-RECEPTOR FAMILY 7 TRINITY_DN21988_c0_g1 PMEI - - - 1.76 invertase/pectin methylesterase inhibitor family protein TRINITY_DN41399_c0_g1 PTI1-3 - - - -1.05 PTI1-like tyrosine-protein kinase 3 Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Figure S1. Effects of different concentrations of SNP on phenotype (A), weight loss (B), L (lightness) value (C), a (greenness; positive values) (D), b * (yellowness) (E), chroma (F), color index (positive values) (G), h (hue angle; positive values) (H), browning index (I), and whiteness index (J) in okra fruits after 4 d of storage. Error bars represent SD (n = 3). Bars with different letters within a sampling date are significantly different ( P < 0.05). FigureS2.tif Figure S2. Effects of different concentrations of MT on phenotype (A), weight loss (B), L (lightness) value (C), a (greenness; positive values) (D), b * (yellowness) (E), chroma (F), color index (positive values) (G), h (hue angle; positive values) (H), browning index (I), and whiteness index (J) in okra fruits after 4 d of storage. Error bars represent SD (n = 3). Bars with different letters within a sampling date are significantly different ( P < 0.05). FigureS3.tif Figure S3. Length distribution of unigenes. FigureS4.tif Figure S4. Venn diagrams depicting differentially expressed genes between fresh harvest (FH) and four treatments at the end of storage. FigureS5.tif Figure S5. Comparison of relative gene expression by RNA-seq and qRT-PCR. FigureS6.tif Figure S6. The number of transcription factors and protein kinases. TableS1.docx Table S1. The primer sequences used for DEGs in qRT-PCR analysis. TableS2.xlsx Table S2. Quality control of sequencing data. TableS3.xlsx Table S3. Statistical table of assembly results. TableS4.docx Table S4. Functional annotation analysis. TableS5.docx Table S5. Up- and downregulated treatment-related DEGs in okra fruit after 4 d of storage. TableS6.xlsx Table S6. GO terms significantly enriched in the specific DEGs of okra fruit in Control vs. FH (A), MT vs. Control (B), SNP vs. Control (C), and MT+SNP vs. Control (D) comparison groups. TableS7.xlsx Table S7. KEGG analysis of specific DEGs of okra fruit in Control vs. FH (A), MT vs. Control (B), SNP vs. Control (C), and MT+SNP vs. Control (D) comparison groups. TableS8.xlsx Table S8. Specific DEG-related TFs of okra fruit in Control vs. FH (A), MT vs. Control (B), SNP vs. Control (C), and MT+SNP vs. Control (D) comparison groups. TableS9.xlsx Table S9. Correlation between four modules and physiological indices. TableS10.xlsx Table S10. Details of 30 genes in the network diagram of four modules. Cite Share Download PDF Status: Published Journal Publication published 25 Aug, 2025 Read the published version in npj Science of Food → Version 1 posted Editorial decision: Revision requested 12 Jul, 2025 Reviews received at journal 12 Jul, 2025 Reviews received at journal 06 Jul, 2025 Reviewers agreed at journal 30 Jun, 2025 Reviewers agreed at journal 26 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers agreed at journal 24 Jun, 2025 Reviewers invited by journal 23 Jun, 2025 Editor assigned by journal 23 Jun, 2025 Submission checks completed at journal 10 Jun, 2025 First submitted to journal 05 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6832753","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":475598899,"identity":"b5da6d0b-bb7e-4939-a8d7-42698ddfdb00","order_by":0,"name":"Xianjun Chen","email":"","orcid":"","institution":"Kaili University","correspondingAuthor":false,"prefix":"","firstName":"Xianjun","middleName":"","lastName":"Chen","suffix":""},{"id":475598900,"identity":"56daf019-56d8-414c-960e-71dd96a26222","order_by":1,"name":"Yao Jiang","email":"","orcid":"","institution":"Kaili University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Jiang","suffix":""},{"id":475598901,"identity":"acf7e884-ff60-4f26-8108-74910d1aba6b","order_by":2,"name":"Jianwei Zhang","email":"","orcid":"","institution":"Kaili University","correspondingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Zhang","suffix":""},{"id":475598902,"identity":"557f9406-f563-47cf-a368-87c4b4a1f8e5","order_by":3,"name":"Xiaocheng Liu","email":"","orcid":"","institution":"Lingnan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Xiaocheng","middleName":"","lastName":"Liu","suffix":""},{"id":475598903,"identity":"b807c052-e61a-4c1f-be7c-ce62ab6c1584","order_by":4,"name":"Lulu Wang","email":"","orcid":"","institution":"Lingnan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Lulu","middleName":"","lastName":"Wang","suffix":""},{"id":475598904,"identity":"43cad29f-e592-4a7a-bcb7-f26e93ca0147","order_by":5,"name":"Jintong Zheng","email":"","orcid":"","institution":"Lingnan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jintong","middleName":"","lastName":"Zheng","suffix":""},{"id":475598905,"identity":"2029d743-c9ee-46c9-8a99-6db015881f62","order_by":6,"name":"Jiayu Zeng","email":"","orcid":"","institution":"Lingnan Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiayu","middleName":"","lastName":"Zeng","suffix":""},{"id":475598906,"identity":"d1c23fa6-5f29-48e5-883f-e26674bd6650","order_by":7,"name":"Qin Yang","email":"","orcid":"","institution":"Kaili University","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Yang","suffix":""},{"id":475598907,"identity":"8171b5be-0ace-46f1-9fea-7f4211bab8eb","order_by":8,"name":"Yan Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACCQglx8cMF0ogTosxG8laEtsQQgS0yM9uPibNU3MnvY2d9/Drgpo7DPzsOQYMP3fg1sI451iaNM+xZ7ltzHxp1jOOPWOQ7HljwNh7BrcWZokcM2ketsNALTxmxkAGg8GNHANmxjbcWtjAWv4dTmcDa/l3mMGekBYekBbetsMJQC3Gj4EMBgMJAlokJNKSLef2HTYEOYyZt+8wj8SZZwUHe/FokZ+RfPDGm2+H5fn5zxh/5vl2WI6/PXnjg594tIAAEw/MXyCXglgH8GsABvQPCM38gZDKUTAKRsEoGJkAAPZORd34XyX9AAAAAElFTkSuQmCC","orcid":"","institution":"Lingnan Normal University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2025-06-06 02:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6832753/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6832753/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41538-025-00553-1","type":"published","date":"2025-08-25T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85575177,"identity":"5c72072b-ffe5-4d6a-aac2-02ba2322608a","added_by":"auto","created_at":"2025-06-27 18:14:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19099577,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of SNP and MT on phenotype (A), weight loss (B), L\u003csup\u003e*\u003c/sup\u003e (lightness) value (C), a\u003csup\u003e*\u003c/sup\u003e (greenness; positive values) (D), b\u003csup\u003e*\u003c/sup\u003e (yellowness) (E), chroma (F), color index (positive values) (G), h (hue angle; positive values) (H), browning index (I), and whiteness index (J) in okra fruit after 4 d of storage. Error bars represent SD (n = 3). Bars with different letters within a sampling date are significantly different (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/ba7b623f53dd17634be4b532.png"},{"id":85575170,"identity":"3240b790-3b6b-4876-8e9f-438976936790","added_by":"auto","created_at":"2025-06-27 18:14:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"placeholderimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/0494f520ce2871c8da67b916.png"},{"id":85575178,"identity":"ec946f2f-fa6a-4f73-aedc-ed3d92be3c0f","added_by":"auto","created_at":"2025-06-27 18:14:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13243178,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of SNP and MT on chlorophyll content and chlorophyll metabolism- and stability-related enzyme activities in okra fruit after 4 d of storage. Chlorophyll a content (A), chlorophyll b content (B), total chlorophyll content (C), NYC1 (D), CLH (E), PPH (F), MDCase (G), PAO (H), RCCR (I), and HCAR (J) activities. Error bars represent SD (n = 3). Bars with different letters within a sampling date are significantly different (\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/57262e773c05107f5b255c40.png"},{"id":85575656,"identity":"88489fb6-4f97-416c-a390-9689ae946ba6","added_by":"auto","created_at":"2025-06-27 18:30:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":13087049,"visible":true,"origin":"","legend":"\u003cp\u003eGene Ontology (GO) terms significantly enriched in the specific DEGs of okra fruit in Control vs. FH (A), MT vs. Control (B), SNP vs. Control (C), and MT+SNP vs. Control (D) comparison groups.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/1882b1940abb3e53cb58e341.png"},{"id":85575184,"identity":"9d5d6673-4944-45df-84f1-bf1c301c7d09","added_by":"auto","created_at":"2025-06-27 18:14:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":21583940,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG analysis of specific DEGs of okra fruit in Control vs. FH (A), MT vs. Control (B), SNP vs. Control (C), and MT+SNP vs. Control (D) comparison groups.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/d7ad29dac385c7efc05c40c5.png"},{"id":85575654,"identity":"607fc6c0-ebd3-4c44-a315-38965691fa78","added_by":"auto","created_at":"2025-06-27 18:30:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":10850870,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA and identification of antioxidant defense- and chlorophyll metabolism-related genes, transcription factors, and protein kinases. (A) The number of co-expressed genes in different modules (by color). (B) Hierarchical clustering dendrogram (upper panel) and correlation heatmap (lower panel) of module eigengenes (ME) to examine higher-order relationships between the modules. (C) Heatmap of correlations with antioxidant defense- and chlorophyll metabolism-related physiological indicators.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/d19a5ec1018905590616f3ed.png"},{"id":85575181,"identity":"69d22444-1c7a-4be1-80f1-8cff37fd1760","added_by":"auto","created_at":"2025-06-27 18:14:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":24700928,"visible":true,"origin":"","legend":"\u003cp\u003eScatterplots of module membership vs. gene significance in the yellow (A), turquoise (B), blue (E), and brown (F) modules. Cytoscape representation of the module of genes of interest (MGI) within the yellow (C), turquoise (D), blue (G), and brown (H) modules. Edges with a weight above 0.02 are shown.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/813a98d9cac95148dff63961.png"},{"id":85575275,"identity":"8eee78dd-44c9-4445-a1dd-112bded51683","added_by":"auto","created_at":"2025-06-27 18:22:47","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1522753,"visible":true,"origin":"","legend":"\u003cp\u003eA model of okra fruit postharvest senescence as mediated by SNP and MT.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/b7cbc8c682363a8d2bb2bdf5.png"},{"id":93060520,"identity":"45c7e324-5329-4869-9582-a8b49034618f","added_by":"auto","created_at":"2025-10-08 15:57:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":101644147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/175a8a0c-48a9-4e2f-ad0c-a342b14b2c11.pdf"},{"id":85575267,"identity":"e6b06c94-c3f6-46ca-a13a-0ebadc8a9210","added_by":"auto","created_at":"2025-06-27 18:22:46","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10763660,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1. \u003c/strong\u003eEffects of different concentrations of SNP on phenotype (A), weight loss (B), L\u003csup\u003e*\u003c/sup\u003e (lightness) value (C), a\u003csup\u003e*\u003c/sup\u003e (greenness; positive values) (D), b\u003csup\u003e*\u003c/sup\u003e (yellowness) (E), chroma (F), color index (positive values) (G), h (hue angle; positive values) (H), browning index (I), and whiteness index (J) in okra fruits after 4 d of storage. Error bars represent SD (n = 3). Bars with different letters within a sampling date are significantly different (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/cf5f35a9699dfade4e68dc87.tif"},{"id":85575179,"identity":"78cea1fb-24ec-4304-b173-c7c84bcb0571","added_by":"auto","created_at":"2025-06-27 18:14:46","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10769292,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S2. \u003c/strong\u003eEffects of different concentrations of MT on phenotype (A), weight loss (B), L\u003csup\u003e*\u003c/sup\u003e (lightness) value (C), a\u003csup\u003e*\u003c/sup\u003e (greenness; positive values) (D), b\u003csup\u003e*\u003c/sup\u003e (yellowness) (E), chroma (F), color index (positive values) (G), h (hue angle; positive values) (H), browning index (I), and whiteness index (J) in okra fruits after 4 d of storage. Error bars represent SD (n = 3). Bars with different letters within a sampling date are significantly different (\u003cem\u003eP \u003c/em\u003e\u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/971741cf95099f72414dc023.tif"},{"id":85575180,"identity":"5de22e3b-4753-4891-b0db-79f067db4702","added_by":"auto","created_at":"2025-06-27 18:14:46","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1581712,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S3. \u003c/strong\u003eLength distribution of unigenes.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/0a54160c8b8cfe5ceff58a55.tif"},{"id":85575264,"identity":"2f69a027-2997-452d-9804-0a0c976fcc92","added_by":"auto","created_at":"2025-06-27 18:22:46","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":924088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S4.\u003c/strong\u003e Venn diagrams depicting differentially expressed genes between fresh harvest (FH) and four treatments at the end of storage.\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/45f1195184cc625db7021a48.tif"},{"id":85575176,"identity":"c9a4ae34-ea32-4d16-b77a-67e677951340","added_by":"auto","created_at":"2025-06-27 18:14:46","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":3850788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S5. \u003c/strong\u003eComparison of relative gene expression by RNA-seq and qRT-PCR.\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/db879b6b4f76c70dc9428fc5.tif"},{"id":85575171,"identity":"0d307ad5-3e00-435f-8f63-2710e19b8afe","added_by":"auto","created_at":"2025-06-27 18:14:46","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":821532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S6. \u003c/strong\u003eThe number of transcription factors and protein kinases.\u003c/p\u003e","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/958047b14a235ea20c53626b.tif"},{"id":85575268,"identity":"901cfda3-ba6c-41d5-a37f-722af967e691","added_by":"auto","created_at":"2025-06-27 18:22:47","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":15972,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S1. \u003c/strong\u003eThe primer sequences used for DEGs in qRT-PCR analysis.\u003c/p\u003e","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/da906146ac66670c2cfd755e.docx"},{"id":85575655,"identity":"71550900-1f78-445d-b555-36d636296a55","added_by":"auto","created_at":"2025-06-27 18:30:47","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":10912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S2.\u003c/strong\u003e Quality control of sequencing data.\u003c/p\u003e","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/241c6233398f363bcc7318a6.xlsx"},{"id":85575183,"identity":"2d5018ff-70d2-4242-b476-62a7a28e43e8","added_by":"auto","created_at":"2025-06-27 18:14:47","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":10072,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S3. \u003c/strong\u003eStatistical table of assembly results.\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/f28b10b580e9c093296ceab3.xlsx"},{"id":85575168,"identity":"e4b6baa9-d58c-45b3-a5ad-70b1f9fb84ad","added_by":"auto","created_at":"2025-06-27 18:14:46","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":11961,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S4. \u003c/strong\u003eFunctional annotation analysis.\u003c/p\u003e","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/618c5a538bf2c77ed0c03e30.docx"},{"id":85575278,"identity":"c07d9bad-ac45-4f5a-bcf8-eb3d8c4dcab4","added_by":"auto","created_at":"2025-06-27 18:22:47","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":12317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S5. \u003c/strong\u003eUp- and downregulated treatment-related DEGs in okra fruit after 4 d of storage.\u003c/p\u003e","description":"","filename":"TableS5.docx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/18294a69706cb7347489e0fb.docx"},{"id":85575200,"identity":"d074360b-d92c-4bf7-a0b3-bfaf6ef93bc4","added_by":"auto","created_at":"2025-06-27 18:14:47","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":12549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S6.\u003c/strong\u003e GO terms significantly enriched in the specific DEGs of okra fruit in Control vs. FH (A), MT vs. Control (B), SNP vs. Control (C), and MT+SNP vs. Control (D) comparison groups.\u003c/p\u003e","description":"","filename":"TableS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/c635dac85c47df70ff49dbd6.xlsx"},{"id":85575659,"identity":"ab5c561a-2a88-417d-aa27-9a8589d57e87","added_by":"auto","created_at":"2025-06-27 18:30:47","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":11210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S7.\u003c/strong\u003e KEGG analysis of specific DEGs of okra fruit in Control vs. FH (A), MT vs. Control (B), SNP vs. Control (C), and MT+SNP vs. Control (D) comparison groups.\u003c/p\u003e","description":"","filename":"TableS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/b723635d145350f777054ef7.xlsx"},{"id":85575191,"identity":"f2f67f23-2dea-4b11-b112-d5c1b554cb09","added_by":"auto","created_at":"2025-06-27 18:14:47","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":508187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S8. \u003c/strong\u003eSpecific DEG-related TFs of okra fruit in Control vs. FH (A), MT vs. Control (B), SNP vs. Control (C), and MT+SNP vs. Control (D) comparison groups.\u003c/p\u003e","description":"","filename":"TableS8.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/6fd6dced521b2c91c333b217.xlsx"},{"id":85575272,"identity":"5ff000ff-fe91-4618-a911-d2d892f70faf","added_by":"auto","created_at":"2025-06-27 18:22:47","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":27997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S9. \u003c/strong\u003eCorrelation between four modules and physiological indices.\u003c/p\u003e","description":"","filename":"TableS9.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/6b0ee5f1aae9f3061316dde6.xlsx"},{"id":85575212,"identity":"ecd74e29-2527-4a82-8896-497d8891823d","added_by":"auto","created_at":"2025-06-27 18:14:47","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":138042,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable S10. \u003c/strong\u003eDetails of 30 genes in the network diagram of four modules.\u003c/p\u003e","description":"","filename":"TableS10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6832753/v1/5895f3d249b919588ae2cb2d.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biochemical and transcriptomic analyses reveal the mechanisms underlying SNP and melatonin effects on antioxidant capacity and chlorophyll metabolism in postharvest okra","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOkra (\u003cem\u003eAbelmoschus esculentus\u003c/em\u003e L.) is a widely cultivated vegetable in tropical and subtropical regions, valued for its distinctive chemical composition including dietary fiber (primarily pectins and hemicelluloses), phenolic compounds (quercetin derivatives and hydroxycinnamic acids), vitamins (A, B complex, C, E, and K), and essential minerals (Ca, K, Mg) (Agreg\u0026aacute;n et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The fruit also contains bioactive compounds with antioxidant, anti-inflammatory, and anti-diabetic properties, notably flavonoids, isoquercitrin, and unique mucilaginous polysaccharides that contribute to its health-promoting effects (Agreg\u0026aacute;n et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite these nutritional and functional attributes, postharvest okra fruit have a short shelf life and undergo rapid quality deterioration characterized by biochemical changes including chlorophyll degradation, cell wall polysaccharide breakdown, and oxidation of phenolic compounds (Palumbo et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These chemical transformations manifest as weight loss, color changes, and textural softening, substantially reducing consumer acceptability, nutritional value, and market potential. Various postharvest strategies have been investigated to maintain the chemical stability of produce, including biological methods employing antagonistic microorganisms or natural compounds (Hosseini et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), physical techniques such as low-temperature storage and modified atmosphere packaging (Palumbo et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and chemical approaches using phytohormones and antioxidants (Zhou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, there remains a need for alternative or synergistic chemical interventions that can more effectively maintain the molecular integrity of postharvest produce. Postharvest deterioration in horticultural commodities is intricately linked to oxidative stress, primarily driven by the excessive accumulation of reactive oxygen species (ROS) (Zhou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the molecular level, ROS cause lipid peroxidation of membrane phospholipids, protein oxidation, and DNA damage, resulting in disrupted cellular compartmentalization, increased membrane permeability, and compromised metabolic regulation (Meitha et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Plants have evolved a sophisticated antioxidant system, encompassing both enzymatic [superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), ascorbate peroxidase (APX)] and non-enzymatic defenses (ascorbic acid, glutathione, phenolic compounds), to neutralize ROS and maintain redox homeostasis (Zhou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Recent biochemical and molecular evidence has demonstrated that antioxidant-related genes not only scavenge excess ROS but also modulate key aspects of fruit ripening and quality retention through complex signaling networks (Huan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). For instance, overexpression of \u003cem\u003eSOD\u003c/em\u003e, \u003cem\u003eCAT\u003c/em\u003e, or \u003cem\u003eAPX\u003c/em\u003e in tomato and apple has been reported to inhibit ethylene biosynthesis and cell wall-degrading enzymes, thereby preserving fruit firmness and extending shelf life (Huan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lv et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similarly, enhanced activity of SOD, CAT, APX, and MDHAR in horticultural product was correlated with delayed softening and reduced decay incidence by maintaining membrane integrity and limiting oxidative damage to structural polysaccharides (Zhou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). The efficacy of these defense networks during postharvest storage can be enhanced by exogenous signaling molecules, such as nitric oxide (NO) donors and melatonin (MT), both of which have been reported to improve antioxidant capacity and delay senescence in various perishable horticultural crops (Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Notably, NO (frequently supplied via sodium nitroprusside, SNP) alleviates oxidative stress through direct chemical quenching of ROS and by inducing antioxidant enzymes, whereas melatonin functions as a potent antioxidant while also maintaining membrane phospholipid integrity, modulating gene expression, and preserving important physiological parameters (Nabaei and Amooaghaie, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChlorophyll metabolism represents another critical determinant of postharvest quality in green vegetables, with direct implications for visual appeal and nutritional value. Chlorophyll degradation leads to visible color shifts and potential loss of associated antioxidant compounds, often diminishing consumer acceptance and nutritional quality (Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The chemical pathway of chlorophyll catabolism is orchestrated by several key enzymes, including chlorophyllase (CLH), which hydrolyzes the phytol chain; pheophytin pheophorbide hydrolase (PPH), which removes the central Mg\u003csup\u003e2+\u003c/sup\u003e ion; and pheophorbide a oxygenase (PAO), which opens the tetrapyrrole ring (H\u0026ouml;rtensteiner, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013a\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schelbert et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Conversely, protochlorophyllide oxidoreductase (POR) and chlorophyllide a oxygenase (CAO) are pivotal for chlorophyll biosynthesis and interconversion between chlorophyll a and b (Oster et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Recent molecular research highlights the importance of transcription factors (TFs) that directly regulate these enzymes during postharvest senescence (Cao et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For instance, AP2/ERF-, MYB-, and NAC-family TFs bind to specific promoter elements of chlorophyll catabolic genes, thereby orchestrating pigment breakdown and color evolution in various horticultural produce (Dai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Moreover, crosstalk among different TF families can finely tune the biosynthesis and degradation pathways of chlorophyll, as evidenced in citrus, where MYB-based regulatory networks simultaneously manage anthocyanin accumulation and chlorophyll breakdown (Tian et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The interplay of these TFs underscores the complex regulation of chlorophyll metabolism and suggests that exogenous regulators, such as MT and NO, may delay color loss in green produce by modulating both chlorophyll-related enzymes and their upstream transcriptional activators (Kim et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, investigations into how SNP and MT might jointly influence the chemical stability of chlorophyll and related pigments in postharvest okra fruit have been limited.\u003c/p\u003e \u003cp\u003eAccordingly, this study aimed to evaluate the efficacy of SNP, MT, and their combined application (MT\u0026thinsp;+\u0026thinsp;SNP) in preserving the chemical composition and postharvest quality of okra fruit, focusing on weight loss, color parameters, antioxidant capacity, and chlorophyll metabolism. We employed a combination of targeted biochemical analyses and untargeted transcriptomic profiling to elucidate the underlying molecular mechanisms, with an emphasis on identifying differentially expressed genes (DEGs) involved in antioxidant defense, chlorophyll metabolism, transcription factor regulation, and protein kinase signaling. The findings from this work not only shed light on the biochemical and genetic basis of exogenous NO and MT action but also provide valuable insights for developing novel postharvest strategies to maintain the phytochemical stability, nutritional value, and sensory attributes of okra fruit. This integrated approach addresses the critical need for effective, sustainable postharvest technologies that preserve both the commercial quality and functional food properties of fresh produce.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Plant materials and treatments\u003c/h2\u003e \u003cp\u003e\u0026lsquo;Lvba\u0026rsquo; okra (\u003cem\u003eAbelmoschus esculentus\u003c/em\u003e L.) fruit at green and mature stages were harvested from an experimental station in Zhanjiang, China (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Uniform, defect-free fruit were sanitized with 0.01% sodium hypochlorite for 5 min, air-dried, and immersed for 5 min in one of four solutions: (1) distilled water (Control), (2) 100 \u0026micro;M melatonin (MT), (3) 0.5 mM sodium nitroprusside (SNP), or (4) MT\u0026thinsp;+\u0026thinsp;SNP at these same concentrations. The concentration used in the study was determined based on previous experiments (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e; S2). Tween-80 (1:1000, v/v) was added to each solution. After treatment, fruit were air-dried at ambient temperature, then stored at 20\u0026deg;C and 80\u0026ndash;90% relative humidity (RH) for up to four days. Each treatment included 180 fruit, with three biological replicates per parameter.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement of weight loss and color\u003c/h2\u003e \u003cp\u003eWeight loss (%) was determined by comparing fruit weight at 0 d to that on each sampling day. Color parameters (L\u003csup\u003e*\u003c/sup\u003e, a\u003csup\u003e*\u003c/sup\u003e, b\u003csup\u003e*\u003c/sup\u003e, and hue angle) were measured using a Konica Minolta Chroma meter CR400 (Japan). Chroma (C) was calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{{\\text{a}}^{2}+{\\text{b}}^{2}}\\)\u003c/span\u003e\u003c/span\u003e. Whiteness index (WI), color index (CI), and browning index (BI) were computed following the previous study (Zhou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Soluble protein, MDA, H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and Total antioxidant capacity\u003c/h2\u003e \u003cp\u003eSoluble protein was quantified by the Coomassie Brilliant Blue G-250 assay. Malondialdehyde (MDA), hydrogen peroxide (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e) and total antioxidant capacity were measured according to Zhou, Huang, et al. (2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 SOD, POD, CAT and APX activities\u003c/h2\u003e \u003cp\u003eSuperoxide dismutase (SOD; EC 1.15.1.1) activity was determined by the inhibition of nitro-blue tetrazolium photoreduction. Peroxidase (POD; EC 1.11.1.7) activity, catalase (CAT; EC 1.11.1.6), and ascorbate peroxidase (APX; EC 1.11.1.11) was measured according to Zhou, Huang, et al. (2023).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Chlorophyll Content and Metabolism-Related Enzymes\u003c/h2\u003e \u003cp\u003eChlorophyll content was determined spectrophotometrically, with chlorophyll a and b calculated using Q. Liu et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Activities of key chlorophyll degradation enzymes, chlorophyllase (CLH), pheophytinase (PPH), magnesium-dechelatase (MDCase), pheophorbide a oxygenase (PAO), red chlorophyll catabolite reductase (RCCR), and 7-hydroxymethyl chlorophyll a reductase (HCAR) were assayed as described by previous study (Cao et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; H\u0026ouml;rtensteiner, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013b\u003c/span\u003e; Keawmanee et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 RNA Extraction, Library Construction, Sequencing, and Annotation\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from fruit tissues using TRIzol\u0026reg; (Invitrogen, USA) and quantified spectrophotometrically. Libraries were prepared with the Illumina TruSeq\u0026trade; RNA Sample Preparation Kit (Illumina, USA) by enriching poly(A) mRNA, synthesizing double-stranded cDNA, and performing end-repair before PCR amplification. A total of 15 RNA-seq libraries, including fresh harvest (FH), control, SNP, MT, and SNP\u0026thinsp;+\u0026thinsp;MT-treated fruit aftrer 4 d of storage, were sequenced on an Illumina NovaSeq 6000. Raw data were deposited in the NCBI database (accession number PRJNA1250921). Clean reads were assembled using Trinity. Unigenes were annotated by BLASTX searches (E-value\u0026thinsp;\u0026lt;\u0026thinsp;1.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) against NR, COG, KEGG, and NCBI databases. Gene Ontology (GO) terms were assigned via BLAST2GO, and KEGG analysis was used to identify relevant metabolic pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Identification of Differentially Expressed Genes and Functional Enrichment\u003c/h2\u003e \u003cp\u003eTranscript abundance was normalized as fragments per kilobase of transcript per million mapped reads (FPKM), and gene-level FPKM was computed with RSEM Differentially expressed genes (DEGs) were identified via EdgeR using |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 1 and Q-value\u0026thinsp;\u0026le;\u0026thinsp;0.05. GO and KEGG enrichment analyses were considered significant at a Bonferroni-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Network Analysis\u003c/h2\u003e \u003cp\u003eA gene co-expression network was constructed using WGCNA (v1.68) for 1867 genes related to antioxidant capacity, chlorophyll metabolism, transcription factors, and protein kinases. After correcting for batch effects, a signed network was generated (soft-thresholding power β\u0026thinsp;=\u0026thinsp;9) based on topological overlap. Modules were identified with the Dynamic Tree Cut algorithm, setting a minimum module size of 30 and a merge cut height of 0.25. The module eigen-gene (ME) was derived to investigate associations with antioxidant and chlorophyll metabolic processes. Gene significance (GS), module membership (MM), and intra-modular connectivity (Kin) were used to rank genes. Modules (Blue, Yellow, Brown, Turquoise) most strongly linked to antioxidant and chlorophyll pathways were visualized in Cytoscape by selecting the top 30 genes and edges with weight\u0026thinsp;\u0026ge;\u0026thinsp;0.02.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Quantitative Real-Time PCR Analysis\u003c/h2\u003e \u003cp\u003eReal-time PCR was performed following the method described by Zhou, Huang, et al. (2023). Primers were designed using Primer Premier version 5.0 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), yielding amplicons between 73 and 232 bp. The relative expression of the target genes was computed using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method, with actin serving as the internal control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll data were analyzed using SPSS (v19.0). Tukey\u0026rsquo;s test determined significant differences among treatment means (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Different lowercase letters above bars in figures indicate statistically significant differences.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Effects of SNP and MT on the weight loss and color of okra fruit\u003c/h2\u003e \u003cp\u003ePostharvest okra fruit exhibited an increasing trend in weight loss during storage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Compared with the control, treatments with MT, SNP, and the combined MT\u0026thinsp;+\u0026thinsp;SNP resulted in lower weight loss rates throughout the storage period, except for the SNP treatment at 2 d and 4 d of storage. Notably, the MT\u0026thinsp;+\u0026thinsp;SNP treatment demonstrated the most effective mitigation, maintaining the lowest weight loss rate among all treatments. This combined intervention resulted in a reduction in weight loss of 14\u0026ndash;28% compared with the control group during the entire experimental period.\u003c/p\u003e \u003cp\u003eOver extended storage durations, the L\u003csup\u003e*\u003c/sup\u003e values of all okra fruit displayed an increasing trend. Throughout the storage period, the MT, SNP, and MT\u0026thinsp;+\u0026thinsp;SNP treatments maintained lower L\u003csup\u003e*\u003c/sup\u003e values compared with the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The MT\u0026thinsp;+\u0026thinsp;SNP treatment consistently yielded the lowest L\u003csup\u003e*\u003c/sup\u003e values among all groups. The MT, SNP, and MT\u0026thinsp;+\u0026thinsp;SNP treatments reduced the a\u003csup\u003e*\u003c/sup\u003e and CI values of okra fruit compared with the control throughout the entire storage period. Conversely, these treatments increased the b\u003csup\u003e*\u003c/sup\u003e values (except for the SNP treatment at 2 d), chroma values (except for the SNP treatment at 3 d), h values (except for the SNP treatment at 3 d and 4 d), BI (except for the SNP treatment at 1 d), and WI values (except for the MT and MT\u0026thinsp;+\u0026thinsp;SNP treatments at 1 d, and the SNP treatment at 2 d) relative to the control. Notably, the MT\u0026thinsp;+\u0026thinsp;SNP treatment demonstrated enhanced effects by increasing the L\u003csup\u003e*\u003c/sup\u003e values by 8\u0026ndash;13%, b\u003csup\u003e*\u003c/sup\u003e values by 22\u0026ndash;62%, chroma values by 18\u0026ndash;64%, h values by 3\u0026ndash;5%, and BI by 21\u0026ndash;47%, while decreasing the a\u003csup\u003e*\u003c/sup\u003e values by 17\u0026ndash;78% and CI values by 15\u0026ndash;22%, respectively, compared with the control throughout the storage period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u0026ndash;J).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Effects of SNP and MT on soluble protein, MDA, H\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e \u003cb\u003eO\u003c/b\u003e \u003csub\u003e \u003cb\u003e2\u003c/b\u003e \u003c/sub\u003e \u003cb\u003econtent, and total antioxidant capacity in okra fruit\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCompared with the control, the MT treatment decreased soluble protein content by 4\u0026ndash;27% on 1 d\u0026ndash;3 d and increased it by 19% on 4 d, whereas the SNP treatment reduced soluble protein content both by 17% on 1 d and 3 d, but increased it by 10% on 2 d and by 9% on 4 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The combined MT\u0026thinsp;+\u0026thinsp;SNP treatment enhanced soluble protein content by 15\u0026ndash;41% on 1 d\u0026ndash;3 d. For MDA content, the MT treatment increased values by 2% on 1 d and decreased them by 9% and 23% on 2 d and 3 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In contrast, the SNP treatment lowered MDA by 12\u0026ndash;20% on 1 d\u0026ndash;3 d and raised it by 23% on 4 d, while the MT\u0026thinsp;+\u0026thinsp;SNP treatment reduced MDA by 13%, 4%, and 15% on 1 d, 2 d, and 4 d, yet increased it by 2% on 3 d. Regarding H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, the MT treatment elevated its content by 31% and 29% on 2 d and 3 d and reduced it by 28% on 4 d; the SNP treatment consistently decreased H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e levels by 8\u0026ndash;48% throughout the storage period, and the MT\u0026thinsp;+\u0026thinsp;SNP treatment increased H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e by 6% on 1 d but decreased it by 13% and 19% on 2 d and 4 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Finally, for total antioxidant capacity, the MT treatment decreased it by 34% on 1 d and increased it by 47% on 4 d; the SNP treatment decreased capacity by 9% on 1 d and increased it by 14\u0026ndash;181% on 2 d\u0026ndash;4 d; and the MT\u0026thinsp;+\u0026thinsp;SNP treatment reduced total antioxidant capacity by 17% on 1 d and increased it by 13\u0026ndash;131% on 2 d\u0026ndash;4 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Effects of SNP and MT on SOD, CAT, POD, and APX activities in okra fruit\u003c/h2\u003e \u003cp\u003eCompared with the control, the MT treatment decreased SOD activity by 50% on 3 d and increased it by 2% on 4 d. The SNP treatment reduced SOD activity by 13%, 31%, and 65% on 1 d, 3 d and 4 d, whereas the combined MT\u0026thinsp;+\u0026thinsp;SNP treatment increased SOD activity by 12\u0026ndash;60% on 1 d\u0026ndash;3 d and decreased it by 24% on 4 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). For CAT activity, MT treatment increased activity by 63% on 1 d but decreased it by 21\u0026ndash;25% on 2 d\u0026ndash;4 d. Conversely, the SNP treatment enhanced CAT activity by 62%, 75%, and 90% on 1 d, 3 d and 4 d, respectively, while reducing it by 30% on 2 d. The MT\u0026thinsp;+\u0026thinsp;SNP treatment elevated CAT activity by 32% on 2 d and 30% on 3 d, but decreased it by 18% on 4 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Regarding POD activity, MT treatment increased POD activity by 20% on 2 d and 27% on 4 d, while reducing it by 18% on 2 d. The SNP treatment boosted POD activity by 34% on 2 d but decreased it by 42% on 3 d and 11% on 4 d. The MT\u0026thinsp;+\u0026thinsp;SNP treatment consistently increased POD activity by 6\u0026ndash;73% throughout the entire period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eG). Concerning APX activity, MT treatment decreased activity by 12\u0026ndash;42% on 1 d\u0026ndash;3 d and increased it by 9% on 4 d. The SNP treatment raised APX activity by 4% on 1 d and 55% on 4 d. In contrast, the MT\u0026thinsp;+\u0026thinsp;SNP treatment increased APX activity by 39%, 20%, and 46% on 1 d, 2 d and 4 d, respectively, while decreasing it by 37% on 3 d (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.4 Effects of SNP and MT on chlorophyll content and chlorophyll metabolism and stability enzyme activity in okra fruit\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCompared with the control, the chlorophyll a, chlorophyll b, and total chlorophyll content of MT-treated fruit increased by 5%, 8%, and 7% on 1 d and decreased by 13\u0026ndash;38%, 7\u0026ndash;21%, and 11\u0026ndash;31% on 2 d\u0026ndash;4 d, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;C). In SNP-treated fruit, chlorophyll a, chlorophyll b, and total chlorophyll content decreased by 9% and 12%, 4% and 4%, and 6% and 9% on 1 d and 2 d, while they increased by 24% and 10%, 14% and 5%, and 19% and 9% on 3 d and 4 d, respectively. The SNP\u0026thinsp;+\u0026thinsp;MT treatment increased chlorophyll a and chlorophyll b content by 10% and 7% on 3 d and by 5% and 4% on 4 d, respectively, and increased total chlorophyll content by 3%, 9%, and 5% on 1 d, 3 d and 4 d, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn okra fruit during storage, treatments with MT, SNP, and their combination (MT\u0026thinsp;+\u0026thinsp;SNP) produced distinct effects on enzyme activities relative to the control (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ). For NYC1 activity, the MT treatment increased activity by 33% and 36% on 2 d and 3 d of storage, respectively, while both SNP and MT\u0026thinsp;+\u0026thinsp;SNP treatments enhanced activity by 8\u0026ndash;43% and 12\u0026ndash;79% during 2 d\u0026ndash;4 d. For CLH activity, the MT treatment elevated activity by 14\u0026ndash;23% during storage 2 d\u0026ndash;4 d, and the SNP treatment increased activity by 23% and 13% on 2 d and 3 d; the MT\u0026thinsp;+\u0026thinsp;SNP treatment consistently enhanced CLH activity by 10\u0026ndash;16% throughout storage. In contrast, PPH activity was boosted by the MT and SNP treatments by 10\u0026ndash;71% and 19\u0026ndash;59%, respectively, during 1 d\u0026ndash;3 d, with the MT\u0026thinsp;+\u0026thinsp;SNP treatment further increasing activity by 65% on 4 d. Regarding MDcase, the MT treatment reduced activity by 15% and 13% on storage 2 d and 3 d, and the SNP treatment decreased activity by 8% and 10% on 1 d and 3 d; the MT\u0026thinsp;+\u0026thinsp;SNP treatment lowered MDcase activity by 10%, 12%, and 6% on storage 1 d, 2 d and 4 d, respectively. In addition, PAO activity was increased by the SNP treatment by 10% and 13% on storage 3 d and 4 d, and by the MT\u0026thinsp;+\u0026thinsp;SNP treatment by 24\u0026ndash;29% over the entire storage period. For RRCR activity, the MT treatment decreased it by 5% on 4 d, whereas the SNP treatment increased it by 9% and 10% on 3 d and 4 d; notably, the MT\u0026thinsp;+\u0026thinsp;SNP treatment reduced activity by 9% on 1 d but increased it by 22% and 10% on 2 d and 3 d, respectively. Finally, HCAR activity was decreased by the MT treatment on the final storage day by 14%, and by the SNP treatment on 3 d by 11%, while the MT\u0026thinsp;+\u0026thinsp;SNP treatment enhanced HCAR activity by 9\u0026ndash;17% throughout storage.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.5 Transcriptome Profiling and Differential Gene Expression Analysis of okra Fruit Under MT, SNP, and MT\u0026thinsp;+\u0026thinsp;SNP Treatment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOn the fourth day of storage, postharvest okra fruit subjected to MT, SNP, and especially the combined MT\u0026thinsp;+\u0026thinsp;SNP treatment exhibited a suite of improved quality indices, including lower weight loss, reduced L\u003csup\u003e*\u003c/sup\u003e, a\u003csup\u003e*\u003c/sup\u003e, and CI values with concomitant increases in b\u003csup\u003e*\u003c/sup\u003e, chroma, hue, and BI, enhanced soluble protein content, reduced H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e levels, modified MDA content, improved total antioxidant capacity, differential enzyme activities including reduced SOD, variable CAT and POD responses, elevated APX activity, and altered chlorophyll parameters\u0026mdash;with MT reducing chlorophyll a, b, and total chlorophyll, while SNP and MT\u0026thinsp;+\u0026thinsp;SNP treatments increased these chlorophyll components\u0026mdash;and modifications in chlorophyll metabolism enzymes, increase in PPH and decrease in MDcase activity. To explore the underlying molecular responses, we conducted RNA sequencing on fruit tissues from freshly harvested (FH) and both control, MT, SNP and MT\u0026thinsp;+\u0026thinsp;SNP-treated fruit on 4 d of storage. Fifteen cDNA libraries yielded 128.93 Gb of raw sequence data. Following removal of adapters and low-quality reads, we retained 429,092,998 high-quality reads (Q30\u0026thinsp;\u0026gt;\u0026thinsp;96%; Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Assembly produced 60,127 transcripts and 20,111 unigenes, with an N50 of 1,928 bp and a mean length of 1,743 bp (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Unigenes were distributed as follows by length: 300\u0026ndash;500 bp (21%), 501-1,000 bp (11%), 1,001\u0026ndash;2,000 bp (42%), and over 2,000 bp (27%) (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), with 13,801 unigenes exceeding 1,000 bp.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOut of the total 19,097 annotated unigenes, 5,522 (29%) were between 300 and 1,000 bp, while 13,575 (71%) were \u0026ge;\u0026thinsp;1,000 bp. Notably, the NR database provided the most comprehensive annotation with 18,954 unigenes (5,444 in the 300\u0026ndash;1,000 bp range and 13,510\u0026thinsp;\u0026ge;\u0026thinsp;1,000 bp), followed by TrEMBL (18,907 unigenes; 5,414 and 13,493 in the respective length categories) and eggNOG (16,540 unigenes; 4,631 and 11,909). The GO database contributed annotations for 15,436 unigenes (4,416 for 300\u0026ndash;1,000 bp and 11,020 for \u0026ge;\u0026thinsp;1,000 bp), whereas KEGG annotated 13,693 unigenes (3,709 and 9,984, respectively). In addition, the COG, KOG, Pfam, and SwissProt databases annotated 6,167 (1,316 and 4,851), 11,563 (3,253 and 8,310), 14,877 (3,367 and 11,510), and 14,541 (4,095 and 10,446) unigenes, respectively (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUsing FH samples as a baseline, pairwise comparisons were performed to identify DEGs between control and MT-, SNP-, and MT\u0026thinsp;+\u0026thinsp;SNP-treated fruit at four days of storage (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e; Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e). A total of 6,225 DEGs (3,288 upregulated and 2,937 downregulated) were identified in the Control vs. FH comparison; 2,426 DEGs (1,187 upregulated and 1,239 downregulated) in the SNP vs. Control comparison; 527 DEGs (262 upregulated and 265 downregulated) in the MT vs. Control comparison; and 256 DEGs (125 upregulated and 131 downregulated) in the MT\u0026thinsp;+\u0026thinsp;SNP vs. Control comparison. In addition, 4,365 postharvest senescence-related DEGs were detected, of which 2,175 were upregulated and 2,190 were downregulated; 754 SNP-specific DEGs were identified (313 upregulated and 451 downregulated); 140 MT-specific DEGs were found (65 upregulated and 75 downregulated); and finally, 44 MT\u0026thinsp;+\u0026thinsp;SNP-specific DEGs were identified (13 upregulated and 31 downregulated).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate the transcriptomic profiling, the expression of 16 DEGs associated with four specific comparison pathways was analyzed via qRT-PCR (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). The gene expression patterns observed in the qRT-PCR experiments for FH, Control, MT, SNP, and MT\u0026thinsp;+\u0026thinsp;SNP fruit (collected on 4 d) corresponded closely with the RNA-seq data, confirming the high reproducibility and reliability of the transcriptome analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Functional Analysis of four comparison specific DEGs: GO and KEGG Enrichment\u003c/h2\u003e \u003cp\u003eWe conducted a Gene Ontology (GO) enrichment analysis to elucidate the functions of specific DEGs in okra fruit on 4 d of storage (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e; Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). The predominant GO categories identified were \u0026ldquo;response to organic substance\u0026rdquo;, \u0026ldquo;cell wall\u0026rdquo;, and \u0026ldquo;DNA-binding transcription factor activity\u0026rdquo; in the Control vs. FH group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e4\u003c/span\u003e); \u0026ldquo;1,3\u0026thinsp;\u0026minus;\u0026thinsp;β\u0026thinsp;\u0026minus;\u0026thinsp;D\u0026thinsp;\u0026minus;\u0026thinsp;glucan biosynthetic process\u0026rdquo;, \u0026ldquo;organic substance biosynthetic process\u0026rdquo;, and \u0026ldquo;oxidoreductase activity\u0026rdquo; in the MT vs. Control group; \u0026ldquo;oxidation\u0026thinsp;\u0026minus;\u0026thinsp;reduction process\u0026rdquo;, \u0026ldquo;chloroplast thylakoid membrane\u0026rdquo;, and \u0026ldquo;oxidoreductase activity\u0026rdquo; in the SNP vs. Control group; and \u0026ldquo;positive regulation of biological process\u0026rdquo; in the SNP\u0026thinsp;+\u0026thinsp;MT vs. Control group, all of which are indicative of roles in antioxidant defense and chlorophyll metabolism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition, the DEGs specific to each of the four comparisons were mapped to pathways in the KEGG database. In the Control vs. FH group, the most enriched pathways were plantpathogen interaction, plant hormone signal transduction, and MAPK signaling pathway\u0026ndash;plant (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e; Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). In the MT vs. Control group, oxidative phosphorylation, protein processing in the endoplasmic reticulum, and plant hormone signal transduction were most enriched. For the SNP vs. Control group, the top pathways were plantpathogen interaction, carbon metabolism, and biosynthesis of amino acids. Finally, in the MT\u0026thinsp;+\u0026thinsp;SNP vs. Control group, endocytosis, plantpathogen interaction, and propanoate metabolism were the most enriched pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Differential Expression of Antioxidant Defense Related DEGs\u003c/h2\u003e \u003cp\u003eAlthough antioxidant pathways did not rank among the top ten KEGG-enriched pathways for DEGs specific to the four comparison groups, thirtysix DEGs associated with redox homeostasis were selected to investigate the molecular mechanisms underlying postharvest senescence and reactive oxygen species (ROS) mitigation elicited by exogenous MT and SNP. On 4 d of storage, okra fruit exhibited upregulation of eight \u003cem\u003ePODs\u003c/em\u003e, one \u003cem\u003eAPX1\u003c/em\u003e, three monodehydroascorbate reductases (\u003cem\u003eMDHARs\u003c/em\u003e), and five glutathione Stransferases (\u003cem\u003eGSTs\u003c/em\u003e), whereas two \u003cem\u003eCAT2\u003c/em\u003e, nine \u003cem\u003ePODs\u003c/em\u003e, one MDHAR, four GSTs, and two ferredoxins (\u003cem\u003eFrxs\u003c/em\u003e) were downregulated. Under MT treatment alone, only one \u003cem\u003eMDHAR\u003c/em\u003e was downregulated in postharvest okra fruit. Under SNP treatment, two \u003cem\u003ePODs\u003c/em\u003e, three \u003cem\u003eMDHARs\u003c/em\u003e, two \u003cem\u003eGSTs\u003c/em\u003e, and one \u003cem\u003eFrx2\u003c/em\u003e were upregulated, while eight \u003cem\u003ePODs\u003c/em\u003e and one \u003cem\u003eGSTF9\u003c/em\u003e were downregulated. Similarly, under combined MT and SNP treatment, one \u003cem\u003ePOD42\u003c/em\u003e was upregulated and one \u003cem\u003eMDHAR\u003c/em\u003e was downregulated (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Differential Expression of Chlorophyll Metabolism Related DEGs\u003c/h2\u003e \u003cp\u003eWe identified 24 candidate DEGs related to chlorophyll metabolism across four comparison groups. At the end of storage, one each of \u003cem\u003eChlB\u003c/em\u003e, \u003cem\u003eSGR\u003c/em\u003e, and \u003cem\u003eHO1\u003c/em\u003e was upregulated, whereas five chlorophyll a‑b binding proteins (\u003cem\u003eLhcbs\u003c/em\u003e), one \u003cem\u003eHO1\u003c/em\u003e, one \u003cem\u003eHY2\u003c/em\u003e, one \u003cem\u003eGGDR\u003c/em\u003e, and two \u003cem\u003ePORs\u003c/em\u003e (\u003cem\u003ePOR‑like\u003c/em\u003e and \u003cem\u003ePOR\u003c/em\u003e) were downregulated. Under MT treatment of okra fruit, \u003cem\u003eLhcb7\u003c/em\u003e and \u003cem\u003eLhcb5\u003c/em\u003e were downregulated. Under SNP treatment, 12 \u003cem\u003eLhcbs\u003c/em\u003e, two \u003cem\u003ePORs\u003c/em\u003e, and one \u003cem\u003eHCAR\u003c/em\u003e were upregulated, while one \u003cem\u003ePAO\u003c/em\u003e was downregulated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Differential Expression of Transcription factors and protein kinases\u003c/h2\u003e \u003cp\u003eWe identified 1867 DEGs associated with transcription factors and protein kinases (Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Specifically, 483 DEGs were found in the Control vs. FH group, 11 in the MT vs. Control group, 69 in the SNP vs. Control group, and 6 in the MT\u0026thinsp;+\u0026thinsp;SNP vs. Control group (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). Of these, 237 were transcription factors (TFs), 51 were transcription regulators (TRs), and 195 were protein kinases (PKs). In the Control vs. FH group, the majority of differentially expressed genes belonged to the AP2/ERF-ERF gene family, with 17 genes upregulated and four downregulated (Table\u0026nbsp;1). Moreover, \u003cem\u003eWRKY4\u003c/em\u003e and \u003cem\u003eRAX3\u003c/em\u003e, members of the MYB family, were downregulated by MT treatment. Similarly, in the Control vs FH group, an additional analysis of the AP2/ERF-ERF gene family revealed five upregulated genes and one downregulated gene (\u003cem\u003eERF27\u003c/em\u003e). Finally, four PKs and two TFs were differentially expressed; specifically, \u003cem\u003ebHLH106\u003c/em\u003e, \u003cem\u003eSRF6\u003c/em\u003e, \u003cem\u003eSRF7\u003c/em\u003e, and \u003cem\u003ePMEI\u003c/em\u003e were upregulated, while \u003cem\u003eNAC90\u003c/em\u003e and \u003cem\u003ePTI1-3\u003c/em\u003e were downregulated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Gene Module Analysis in MT and SNP-Treated Okra Fruit\u003c/h2\u003e \u003cp\u003eUsing WGCNA, we examined how genes induced by MT and SNP are regulated during okra fruit postharvest senescence. Our study focused on genes involved in antioxidant defense and chlorophyll metabolism (Table\u0026nbsp;1), as well as their TFs and PKs (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e). We identified seven distinct co‑expression modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), which were organized into two meta‑modules based on their correlation patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Meta1 comprised the blue and yellow modules, while Meta2 included the grey, brown, turquoise, green, and red modules. Within each meta‑module, constituent modules exhibited positive correlations; however, Meta1 showed negative associations with H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, SOD, chlorophyll a, chlorophyll b, and total chlorophyll, while correlating positively with NYC1, CLH, PPH, MDCase, PAO, and HCCR activities. Moreover, the grey, brown, turquoise, green, and red modules demonstrated both negative and positive relationships with antioxidant defense and chlorophyll metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). These modules exhibited strong positive and negative correlations with total antioxidant capacity and CLH activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the relationships among total antioxidant capacity, CLH, and these modules, we filtered transcripts from the yellow, turquoise, blue, and brown modules that simultaneously displayed the highest gene significance (GS) and module membership (MM) (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B, E, F). Figure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e7\u003c/span\u003eB, C, G, H illustrates the interactions between transcription factors and genes involved in antioxidant defense and chlorophyll metabolism within those four modules (Table \u003cspan refid=\"MOESM9\" class=\"InternalRef\"\u003eS9\u003c/span\u003e). In the yellow module, the three highest‑degree genes were \u003cem\u003eNAC86\u003c/em\u003e, \u003cem\u003eFER\u003c/em\u003e, and \u003cem\u003eERF4\u003c/em\u003e; these were co‑expressed with \u003cem\u003ePOD25\u003c/em\u003e (Table \u003cspan refid=\"MOESM10\" class=\"InternalRef\"\u003eS10\u003c/span\u003e). Conversely, in the turquoise module, the top three highest‑degree genes were \u003cem\u003ezf_CCCH20\u003c/em\u003e, \u003cem\u003eHAT5\u003c/em\u003e, and \u003cem\u003eAPL\u003c/em\u003e, which were co‑expressed with \u003cem\u003eGSTZ\u003c/em\u003e. In the blue module, the three highest‑degree genes were \u003cem\u003eMYB24\u003c/em\u003e, \u003cem\u003eGT-3B\u003c/em\u003e, and \u003cem\u003eFAM135B\u003c/em\u003e; these were co‑expressed with \u003cem\u003ePOD11\u003c/em\u003e, \u003cem\u003ePOD25\u003c/em\u003e, and \u003cem\u003eGST7\u003c/em\u003e. In the brown module, the top three highest‑degree genes were \u003cem\u003eCOL16\u003c/em\u003e, \u003cem\u003eAUX28\u003c/em\u003e, and \u003cem\u003eCEPR2\u003c/em\u003e; these were co‑expressed with \u003cem\u003ePOD73\u003c/em\u003e, \u003cem\u003eNECT3\u003c/em\u003e, \u003cem\u003eFd2\u003c/em\u003e, \u003cem\u003eCAB3\u003c/em\u003e, \u003cem\u003eCAB7\u003c/em\u003e, and \u003cem\u003ePOR\u003c/em\u003e. These results suggest that MT and SNP modulate okra fruit postharvest senescence by promoting the co‑expression of transcription factors and genes associated with antioxidant defense and chlorophyll metabolism\u0026mdash;a relationship that warrants further investigation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePostharvest okra fruit, like many other horticultural commodities, are prone to rapid quality deterioration characterized by weight loss, color changes, and loss of nutritional and antioxidant properties (Agreg\u0026aacute;n et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the present study, SNP and MT treatments, alone or in combination, effectively suppressed weight loss, mitigated color degradation, and enhanced antioxidant capacity during storage. Notably, the combined MT\u0026thinsp;+\u0026thinsp;SNP treatment exerted the most pronounced effects, underscoring the potential of these two signaling molecules to synergistically preserve the chemical stability and nutritional quality of okra fruit. Weight loss in postharvest fruit is predominantly driven by water evaporation and metabolic processes that alter cell wall polysaccharides and membrane phospholipids (Agreg\u0026aacute;n et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hosseini et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consistent with earlier findings in mango (Zhou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e) and papaya (Zhou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), exogenous application of MT and NO effectively reduced weight loss in okra fruit, with the combined application of MT\u0026thinsp;+\u0026thinsp;SNP showing even stronger effect. This observation suggests that simultaneous enhancement of antioxidative pathways and maintenance of membrane lipid integrity could help preserve cellular compartmentalization and reduce water loss during storage (Chang et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The mechanisms likely involve protection of membrane phospholipids against peroxidation and preservation of cell wall polysaccharide integrity, which collectively maintain tissue structure and water retention capacity. The visual appearance of okra fruit, particularly its color parameters, significantly influences consumer acceptance and market value. In the present study, all treatments (MT, SNP, and especially MT\u0026thinsp;+\u0026thinsp;SNP) contributed to lower L\u003csup\u003e*\u003c/sup\u003e, a\u003csup\u003e*\u003c/sup\u003e, and CI values and higher b\u003csup\u003e*\u003c/sup\u003e, chroma, hue, and BI values, preserving desirable color attributes compared with controls. These color metrics directly reflect the chemical stability of chlorophyll molecules and related pigments, which are susceptible to oxidative degradation during postharvest storage. Similar effects on maintaining color-related phytochemicals have been reported in other perishable produce, such as mango treated with phytohormones or antioxidants (Zhou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). The improved color retention in MT\u0026thinsp;+\u0026thinsp;SNP-treated okra fruit likely results from reduced chlorophyll degradation and oxidative stress, reflecting protective mechanisms that maintain pigment molecular stability and preserve the visual quality attributes valued by consumers (Shi et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePostharvest senescence is biochemically characterized by the overproduction of ROS, which leads to lipid peroxidation of membrane phospholipids and subsequent cellular damage (Huan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our results indicated that NO and MT treatments modulated oxidative stress indicators, specifically MDA and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e contents, while enhancing total antioxidant capacity. Particularly on 4 d, combined MT\u0026thinsp;+\u0026thinsp;SNP treatment maintained comparatively lower levels of H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e and MDA, highlighting its stronger ability to scavenge ROS and mitigate lipid peroxidation of membrane components. The chemical basis for this protection likely involves both direct scavenging of free radicals and the modulation of enzymatic antioxidant systems. Similar protective effects on cellular redox homeostasis have been noted when exogenous salicylic acid, auxin, glutathione, and ascorbic acid were used to strengthen the enzymatic antioxidant system in winter jujube, mango, tomato, and papaya (Yang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Enzymatic antioxidants (SOD, CAT, POD, and APX) constitute a critical line of defense against ROS by catalyzing specific redox reactions that neutralize potentially harmful oxidative species (Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In okra fruit, SNP and MT treatments differentially influenced the activities of these enzymes through complex regulatory mechanisms. Although some individual treatments led to transient declines in specific enzymes (e.g., reduced SOD and CAT activity in certain storage stages), the combined MT\u0026thinsp;+\u0026thinsp;SNP treatment generally enhanced POD and APX activities while maintaining balanced SOD and CAT levels. Such cooperative regulation of multiple enzymatic systems might explain the superior ROS scavenging capacity observed in MT\u0026thinsp;+\u0026thinsp;SNP-treated fruit. Previous work in mango suggested that co-expression of TFs, such as \u003cem\u003ebZIP\u003c/em\u003e and \u003cem\u003eERFs\u003c/em\u003e, with antioxidant enzyme genes contributed to the regulation of ROS homeostasis (Lei et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Similarly, in okra, our transcriptome data revealed that genes encoding \u003cem\u003ePODs\u003c/em\u003e, \u003cem\u003eGSTs\u003c/em\u003e, and \u003cem\u003eMDHARs\u003c/em\u003e were differentially expressed in response to MT and SNP, thereby reinforcing enzymatic and non-enzymatic antioxidative networks that protect valuable bioactive compounds from oxidative degradation (Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChlorophyll degradation is a hallmark of postharvest senescence in green vegetables, leading to color changes that often reduce market value and nutritional quality (Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the molecular level, this process involves a cascade of enzymatic reactions that transform chlorophyll into colorless catabolites. In this study, exogenous MT and SNP delayed chlorophyll breakdown by modulating key enzymes involved in chlorophyll catabolism, such as PPH, CLH, and PAO. While MT alone sometimes showed reduced chlorophyll contents on certain days, SNP consistently increased chlorophyll a, chlorophyll b, and total chlorophyll, particularly at later storage stages. These findings align with reports that exogenous MT and SNP can retard chlorophyll degradation in cabbage and other horticultural produce by interfering with the activity of chlorophyll-degrading enzymes (Liu et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Peng et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the increase in CLH, PPH, PAO, and related genes under combined treatment suggests a complex regulatory mechanism that accelerates pigment turnover while simultaneously conserving overall chlorophyll content to retain greener coloration (Keawmanee et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Interestingly, reduced MDcase activity under MT\u0026thinsp;+\u0026thinsp;SNP treatment may also contribute to stabilizing chlorophyll, as MDcase (mesophyll-derived cell death-related enzyme) has been associated with tissue senescence and chlorophyll degradation (Liu et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These data collectively imply that molecular crosstalk between NO and MT fine-tunes chlorophyll metabolism, slows down color loss, and maintains both the visual appeal and nutritional quality of okra fruit (Zhang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The preservation of chlorophyll is particularly important from a food chemistry perspective, as these pigments not only contribute to color but also possess antioxidant properties and are associated with other bioactive compounds that enhance the nutritional value of okra.\u003c/p\u003e \u003cp\u003eRNA-seq analysis confirmed the biochemical trends by identifying DEGs associated with redox homeostasis, chlorophyll metabolism, and transcriptional regulation at the molecular level. Under combined MT\u0026thinsp;+\u0026thinsp;SNP treatment, the number of uniquely regulated DEGs (44 in total) was lower than that observed for either treatment alone, suggesting that NO and MT share overlapping downstream targets while also generating synergistic effects on gene expression networks (Feng et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Imran et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notable changes included the upregulation of POD- and GST-encoding genes, consistent with increased enzymatic activities that promote ROS scavenging and detoxification of oxidation products (Zhou et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). In contrast, genes encoding certain \u003cem\u003eFrxs\u003c/em\u003e and \u003cem\u003eCATs\u003c/em\u003e were downregulated, supporting the observed transient decreases in CAT activity at specific time points. These differential expression patterns reflect the complex biochemical coordination required to maintain cellular redox balance during extended storage. With respect to chlorophyll metabolism, the present transcriptome data highlighted the differential expression of \u003cem\u003eLhcbs\u003c/em\u003e, \u003cem\u003ePOR\u003c/em\u003e, \u003cem\u003ePAO\u003c/em\u003e, and \u003cem\u003eHCAR\u003c/em\u003e, underscoring their pivotal roles in regulating chlorophyll biosynthesis and degradation pathways (Kim et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The upregulation of \u003cem\u003ePOR-like\u003c/em\u003e in SNP-treated fruit suggests an enhancement of chlorophyll synthesis pathways, whereas the downregulation of a \u003cem\u003ePAO\u003c/em\u003e in the same group indicates attenuated chlorophyll breakdown. Meanwhile, the MT\u0026thinsp;+\u0026thinsp;SNP treatment led to selective overexpression of \u003cem\u003ePAO\u003c/em\u003e, implying a more dynamic modulation of pigment turnover. These seemingly contradictory patterns point to a coordinated molecular mechanism allowing precise control of chlorophyll homeostasis through balanced regulation of both biosynthetic and catabolic pathways (Kim et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTFs such as \u003cem\u003eAP2\u003c/em\u003e/\u003cem\u003eERF\u003c/em\u003e, \u003cem\u003eMYB\u003c/em\u003e, and \u003cem\u003eNAC\u003c/em\u003e families often function as global regulators of postharvest physiological processes and biochemical pathways (Dai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our weighted gene co-expression network analysis revealed that key TFs\u0026mdash;such as \u003cem\u003eNAC86\u003c/em\u003e, \u003cem\u003eERF4\u003c/em\u003e, \u003cem\u003eMYB24\u003c/em\u003e, and \u003cem\u003eGT-3B\u003c/em\u003e\u0026mdash;were co-expressed with antioxidant- and chlorophyll-related genes, suggesting their role as master regulators of multiple quality-related pathways. Similar findings in other fruit systems have shown that TFs directly or indirectly modulate the expression of antioxidative enzymes, cell wall-modifying enzymes, and senescence-associated genes that collectively determine postharvest quality retention (Li et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lira et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Here, co-expression in the blue, yellow, turquoise, and brown modules suggests that NO and MT may converge on shared regulatory nodes, enhancing the transcription of genes that bolster antioxidant capacity and chlorophyll retention (Quesada et al., 2009; Upadhyay et al., 2023). Moreover, protein kinases, integral to numerous signal transduction cascades, were differentially expressed under MT and SNP treatments, further implying that multi-level regulation underpins the synergistic effects observed (Keawmanee et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pardo-Hern\u0026aacute;ndez et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These protein kinases likely facilitate the phosphorylation-dependent activation of transcription factors and metabolic enzymes, thereby connecting external chemical signals (SNP and MT) to specific biochemical responses that preserve food quality attributes. The identification of these regulatory hubs provides potential targets for future postharvest interventions aimed at enhancing the chemical stability, nutritional value, and shelf life of okra and similar perishable produce.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that MT and SNP treatments effectively preserve the chemical stability and quality attributes of postharvest okra fruit (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These treatments reduced the weight loss while maintaining desirable color parameters, including lower L\u003csup\u003e*\u003c/sup\u003e and a\u003csup\u003e*\u003c/sup\u003e values and higher b\u003csup\u003e*\u003c/sup\u003e, chroma, hue, and BI values, which directly reflect the preservation of pigment molecules and tissue integrity. Biochemical analysis revealed that MT and SNP enhanced the antioxidant defense system by modulating SOD, CAT, POD, and APX enzyme activities, thereby reducing MDA and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e. Notably, the combined MT\u0026thinsp;+\u0026thinsp;SNP treatment exhibited synergistic effects in preserving chlorophyll a, chlorophyll b, and total chlorophyll content through selective regulation of key enzymes in chlorophyll metabolism (particularly PPH and MDcase). Transcriptome analysis further elucidated the molecular mechanisms underlying these biochemical changes, revealing complex gene expression networks involving antioxidant-related genes (\u003cem\u003ePODs\u003c/em\u003e, \u003cem\u003eGSTs\u003c/em\u003e, \u003cem\u003eMDHARs\u003c/em\u003e), chlorophyll metabolism genes (\u003cem\u003ePOR\u003c/em\u003e, \u003cem\u003ePAO\u003c/em\u003e, \u003cem\u003eLhcbs\u003c/em\u003e), and their upstream regulators (\u003cem\u003eNAC86, ERF4\u003c/em\u003e, \u003cem\u003eMYB24\u003c/em\u003e). The co-expression patterns identified through WGCNA highlighted the integrated nature of redox homeostasis and pigment metabolism pathways in maintaining postharvest quality. Our findings establish the molecular basis for how SNP and MT treatments preserve the phytochemical composition, nutritional value, and visual quality of okra fruit, providing valuable insights for developing innovative postharvest technologies aimed at extending shelf life while maintaining the functional food properties of fresh produce. Future research should explore the practical applications of these treatments in commercial settings and investigate their effects on specific bioactive compounds and nutritional components that contribute to the health-promoting properties of okra.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXianjun Chen: Formal analysis, Writing \u0026ndash; original draft, Methodology. Yan Zhou: Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing. Yao Jiang, Xiaocheng Liu, Lulu Wang, Jingtong Zheng, and Jianyu Zeng: Investigation, Formal analysis. Jianwei Zhang: Investigation, Conceptualization, Writing \u0026ndash; review \u0026amp; editing. Qin Yang: Writing \u0026ndash; review \u0026amp; editing, Supervision.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was funded by the Growth of Young Scientific and Techno-logical Talents of Guizhou Educational Commission (No. Qian Jiaoji [2024]232), the Specialized Fund for the Doctoral of Kaili University (No. BS20240218), the Provincial famous teacher Yang Qin studio (No. MSGZS-SJ-2024002), the Specialized Fund for the Doctoral Development of Kaili University (No. BSFZ202206) and the Key Laboratory of the Department of Education of Guizhou Province (No. Qianjiaoji [2022] 053).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePRJNA1250921\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgreg\u0026aacute;n, R., Pateiro, M., Bohrer, B.M., Shariati, M.A., Nawaz, A., Gohari, G., Lorenzo, J.M., 2023. 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PeerJ 11, e15902. https://doi.org/10.7717/peerj.15902\u003c/li\u003e\n\u003cli\u003eZhu, X., Chen, J., Xie, Z., Gao, J., Ren, G., Gao, S., Zhou, X., Kuai, B., 2015. Jasmonic acid promotes degreening via MYC 2/3/4‐ and ANAC 019/055/072‐mediated regulation of major chlorophyll catabolic genes. Plant J. 84, 597\u0026ndash;610. https://doi.org/10.1111/tpj.13030\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e List of selected genes that may be responsible for SNP- and MT-mediated postharvest quality of okra fruit.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"947\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eGene id\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 278px;\"\u003e\n \u003cp\u003eLog2 Fold Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 389px;\"\u003e\n \u003cp\u003eGene description\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eControl vs FH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eMT vs Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eSNP vs Control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eMT+SNP vs Control\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 947px;\"\u003e\n \u003cp\u003eAntioxidant defense-related DEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN5801_c0_g3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eCAT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eCatalase isozyme 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN7862_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eCAT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eCatalase isozyme 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN9274_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8559_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD11\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN3291_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8861_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD21\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN15109_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD25\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN71851_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD25\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eperoxidase 25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN5145_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD31\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eperoxidase 31-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN34030_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD40\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eperoxidase 40 precursor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN22974_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD42\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN26603_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD54\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN216153_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD54\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN105024_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD55\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8076_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD64\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-3.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: peroxidase 64-like\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN9372_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD64\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eperoxidase 64-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN1208_c1_g3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD66\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN10147_c1_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOD73\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePeroxidase 73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN14032_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePODP7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: peroxidase P7-like isoform X2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN7047_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePODP7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: peroxidase P7-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN3376_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eAPX1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eL-ascorbate peroxidase 1, cytosolic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN147219_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eMDHAR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eMonodehydroascorbate reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN1764_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eMDHAR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eMonodehydroascorbate reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN465_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eMDHAR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eMonodehydroascorbate reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN2663_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eMDHAR3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ebifunctional monodehydroascorbate reductase and carbonic anhydrase nectarin-3-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN25863_c0_g2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGST\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eGlutathione s-transferase-like protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN40077_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGST\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eGlutathione S-transferase family protein isoform 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN6347_c0_g2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGST\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: glutathione S-transferase zeta class-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN297_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGST1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eGlutathione S-transferase family protein isoform 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN29445_c0_g2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGST7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eputative Glutathione S-transferase tau 7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN2034_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGSTF9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eglutathione S-transferase F9-like\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN4089_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGST-DHAR4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eputative glutathione S-transferase DHAR4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN6588_c1_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGST-PARB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eGlutathione S-transferase PARB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN86442_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGSTU7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eGlutathione S-transferase U7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN5794_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eFrx2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eFerredoxin-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN18174_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eFrx3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eFerredoxin-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 947px;\"\u003e\n \u003cp\u003eChlorophyll metabolism-related DEGs \u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN32450_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003echlorophyll a-b binding protein, chloroplastic-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN13078_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eChlorophyll a-b binding protein 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN34119_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb4.2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eChlorophyll a-b binding protein CP29.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN16265_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: chlorophyll a-b binding protein CP26, chloroplastic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN16878_c1_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: chlorophyll a-b binding protein of LHCII type 1-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN48304_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003echlorophyll a-b binding protein of LHCII type 1-like\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN12532_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: chlorophyll a-b binding protein of LHCII type 1-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN1071_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003echlorophyll a-b binding protein 6, chloroplastic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN14406_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: chlorophyll a-b binding protein 6, chloroplastic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN54967_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eChlorophyll a-b binding protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN127_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: chlorophyll a-b binding protein 7, chloroplastic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN10806_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eLhcb151\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: chlorophyll a-b binding protein 151, chloroplastic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN9711_c1_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eChlB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eLight-independent protochlorophyllide reductase subunit B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN9711_c1_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eChlB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eLight-independent protochlorophyllide reductase subunit B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN11720_c0_g2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eSGR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e8.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eProtein STAY-GREEN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN1344_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eHO1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eHeme oxygenase 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN1484_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eHO1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eHeme oxygenase-like, multi-helical isoform 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN90537_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eHY2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePhytochromobilin:ferredoxin oxidoreductase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN5896_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eGGDR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eGeranylgeranyl diphosphate reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN11844_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eProtochlorophyllide reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN13487_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eProtochlorophyllide reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN10593_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePOR-like\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: protochlorophyllide reductase-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN4391_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePAO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePheophorbide a oxygenase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8481_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eHCAR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003e7-hydroxymethyl chlorophyll a reductase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" style=\"width: 947px;\"\u003e\n \u003cp\u003eTranscription factors and protein kinases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN1096_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eRAP2-4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eEthylene-responsive transcription factor RAP2-4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN11501_c1_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eDREB1A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eDehydration-responsive element-binding protein 1A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN6395_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eEthylene-responsive transcription factor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8242_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eEthylene-responsive transcription factor 4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN3772_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: ethylene-responsive transcription factor 4-like\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN3772_c0_g2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: ethylene-responsive transcription factor 4-like\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8058_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eEthylene-responsive transcription factor 5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN19693_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: ethylene-responsive transcription factor 9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN12966_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF011\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eERF011 protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN3805_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF017\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eERF017 protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8459_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF017\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eERF017 protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN9173_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF025\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eERF025 protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN114837_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF27\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-3.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eERF027 protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN214995_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF061\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eethylene-responsive transcription factor ERF061-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN152802_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF112\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e8.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eERF112 protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN6479_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF112\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eERF112 protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN2798_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF113\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eERF113 protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8359_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eERF1A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eEthylene-responsive transcription factor 1A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN6812_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eCRF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-4.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: ethylene-responsive transcription factor CRF1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN66856_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eCRF2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eEthylene-responsive transcription factor CRF2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN6812_c0_g2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eCRF3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eEthylene-responsive transcription factor CRF3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8882_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eTOE3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eAP2-like ethylene-responsive transcription factor TOE3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN9903_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eDREB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: ethylene-responsive transcription factor RAP2-1-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN9237_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eDREB2E\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eDehydration-responsive element-binding protein 2E\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN5597_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eWRKY4\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: LOW QUALITY PROTEIN: probable WRKY transcription factor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN8258_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eRAX3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eTranscription factor RAX3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN13605_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eRAP2-3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: ethylene-responsive transcription factor RAP2-3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN6318_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eRAP2-10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eethylene-responsive transcription factor RAP2-10-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN25001_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ebHLH106\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ebHLH106 protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN3188_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eNAC90\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePREDICTED: NAC domain-containing protein 90-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN1740_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eSRF6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eProtein STRUBBELIG-RECEPTOR FAMILY 6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN16448_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003eSRF7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003eProtein STRUBBELIG-RECEPTOR FAMILY 7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN21988_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePMEI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003einvertase/pectin methylesterase inhibitor family protein\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 207px;\"\u003e\n \u003cp\u003eTRINITY_DN41399_c0_g1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cem\u003ePTI1-3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 389px;\"\u003e\n \u003cp\u003ePTI1-like tyrosine-protein kinase 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-science-of-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjscifood","sideBox":"Learn more about [npj Science of Food](http://www.nature.com/npjscifood/)","snPcode":"41538","submissionUrl":"https://submission.springernature.com/new-submission/41538/3","title":"npj Science of Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"SNP, MT, antioxidant enzymes, chlorophyll degradation, postharvest quality","lastPublishedDoi":"10.21203/rs.3.rs-6832753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6832753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOkra fruit undergo rapid chemical deterioration after harvest. This study investigated how sodium nitroprusside (SNP) and melatonin (MT), alone or combined (MT+SNP), affect chemical stability, antioxidant capacity, and chlorophyll metabolism in okra stored at 20°C and 80-90% humidity. MT+SNP treatment most effectively preserved fruit quality by reducing weight loss, maintaining color parameters, decreasing oxidative stress markers (H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e, MDA), enhancing antioxidant capacity, and regulating antioxidant enzymes (SOD, CAT, POD, APX). MT+SNP stabilized chlorophyll content by modulating chlorophyll-degrading enzymes (CLH, PPH, MDcase). Transcriptome analysis revealed differential expression of genes involved in antioxidant defense and chlorophyll metabolism, with synergistic effects from combined treatment. Weighted gene co-expression network analysis identified transcription factors (\u003cem\u003eNAC86\u003c/em\u003e, \u003cem\u003eERF4\u003c/em\u003e, \u003cem\u003eMYB24\u003c/em\u003e) connecting antioxidant and chlorophyll metabolism pathways. This combined treatment effectively preserves okra’s phytochemical integrity and nutritional quality by stabilizing redox homeostasis and pigment metabolism.\u003c/p\u003e","manuscriptTitle":"Biochemical and transcriptomic analyses reveal the mechanisms underlying SNP and melatonin effects on antioxidant capacity and chlorophyll metabolism in postharvest okra","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-27 18:14:41","doi":"10.21203/rs.3.rs-6832753/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-13T00:16:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-12T16:00:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-06T06:46:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95936336944551817549662380335776487306","date":"2025-06-30T06:37:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"121719749027722655942536298897635614920","date":"2025-06-26T07:31:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181872391394905957204577837592950514995","date":"2025-06-25T22:56:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50215626292776231752021614744954483017","date":"2025-06-24T06:39:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303107846041483033500122568617487414262","date":"2025-06-24T05:42:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-23T19:00:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-23T18:57:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-10T17:38:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Science of Food","date":"2025-06-06T01:52:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-science-of-food","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjscifood","sideBox":"Learn more about [npj Science of Food](http://www.nature.com/npjscifood/)","snPcode":"41538","submissionUrl":"https://submission.springernature.com/new-submission/41538/3","title":"npj Science of Food","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1e5d415-6aea-4957-9cbc-3fdfb019a2ad","owner":[],"postedDate":"June 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50501338,"name":"Biological sciences/Biochemistry"},{"id":50501339,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-09-01T16:01:10+00:00","versionOfRecord":{"articleIdentity":"rs-6832753","link":"https://doi.org/10.1038/s41538-025-00553-1","journal":{"identity":"npj-science-of-food","isVorOnly":false,"title":"npj Science of Food"},"publishedOn":"2025-08-25 15:57:31","publishedOnDateReadable":"August 25th, 2025"},"versionCreatedAt":"2025-06-27 18:14:41","video":"","vorDoi":"10.1038/s41538-025-00553-1","vorDoiUrl":"https://doi.org/10.1038/s41538-025-00553-1","workflowStages":[]},"version":"v1","identity":"rs-6832753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6832753","identity":"rs-6832753","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}