Integrative transcriptomics and metabolomics analyses reveals Maillard reaction and antioxidant capacity-mediated leaf Browning during the flue-curing process in Nicotiana tabacum L

Nan Xu; Jingtao Lei; Xiangli Xu; Risheng Hu; Mengchen Liu; Bingyan Jia; Xiaoxu Li; Lixin Zhou; Xiaonian Yang; Zhi Liu

doi:10.1186/s12870-025-07150-0

. 2025 Aug 25;25:1126. doi: 10.1186/s12870-025-07150-0

Integrative transcriptomics and metabolomics analyses reveals Maillard reaction and antioxidant capacity-mediated leaf Browning during the flue-curing process in Nicotiana tabacum L

Nan Xu ^1,^#, Jingtao Lei ^1,^#, Xiangli Xu ^2,^✉, Risheng Hu ³, Mengchen Liu ¹, Bingyan Jia ¹, Xiaoxu Li ², Lixin Zhou ², Xiaonian Yang ², Zhi Liu ^1,^✉

PMCID: PMC12379460 PMID: 40855466

Abstract

The browning reaction during the flue-curing process of tobacco (Nicotiana tabacum L.) dramatically affects leaf quality and value, leading to significant economic losses. However, the molecular regulatory mode of the browning in flue-cured tobacco is still unclear. The two tobacco cultivars, Dabaijin and Yunyan87, with browning differences in upper, middle, and lower leaves during the flue-curing process, were characterized through transcriptomic and metabolomic analyses. The results showed that the significant browning in Dabaijin was mainly due to the preferential accumulation of products of amino acid metabolism (L-arginine, L-ornithine, L-leucine, and L-valine) and amino sugar and nucleotide sugar metabolism (L-fucose) which promoted the Maillard reaction. Meanwhile, riboflavin metabolism (downregulation of PYRP and RIBF expression) impaired the function of the antioxidant system, and imbalanced inositol phosphate metabolism affected membrane homeostasis through PIP2. However, the consumption rate of reduced hydrogen by oxidative phosphorylation, as well as enhanced sulfur metabolism, and taurine and hypotaurine metabolism for scavenging ROS was mainly increased in Yunyan87, ultimately reducing the accumulation of ROS and slowing membrane damage during the flue-curing process. This research revealed that the difference in browning between Yunyan87 and Dabaijin cultivars was mainly regulated by the accumulation of Maillard substrates and the efficiency of the antioxidant system, which provides a theoretical basis for improving tobacco cultivars resistant to browning and optimizing processing techniques.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-07150-0.

Keywords: Leaf browning, Metabolome, Transcriptome, Maillard reaction, Antioxidant system, Tobacco

Introduction

Browning is a phenomenon that occurs when a series of reactions produce brown polymers during the storage and processing of flue-cured tobacco and the value of the flue-cured tobacco is closely related to its degree of browning, which increases dramatically, especially during intense flue-curing. Flue-cured tobacco is a valuable commodity because of its unique aroma [1–3]. The flue-curing process includes stages of yellowing, color-fixing, and stem-drying. Browning takes place during the yellowing and color-fixing stages, gradually transforming the leaf’s color from green to brown while imparting a distinctive aroma [4]. However, excessive browning can lead to black spots, reducing the value and industrial usability of tobacco leaves [5, 6].

Browning reactions can be categorized into non-enzymatic and enzymatic. Non-enzymatic browning is a reaction of carbonyl compounds (reducing sugars) and amino compounds (amino acids) that does not involve enzymes during heating or storage, of which the Maillard reaction is the most important non-enzymatic browning [7]. In contrast, enzymatic browning is the oxidation of polyphenols to brown polymers by the action of polyphenol oxidase (PPO) [8]. Under high-temperature flue-curing stress, the chloroplast and mitochondrial electron transport chains accumulate excess reactive oxygen species (ROS) such as superoxide anion, hydrogen peroxide, and hydroxyl radicals [9, 10]. ROS peroxidize lipid membranes and disrupt cellular compartmentalization that causes the localization of PPOs and substrates in the plastid, ultimately leading to enzymatic browning [11]. In addition, the integrity of the plant biofilm system under stress directly determines the occurrence of non-enzymatic and enzymatic browning [12]. The plant biofilm system acts as a vital structural barrier, compartmentalizing various substances within living organisms. The stress process induces injuries, disrupting the stability of the membrane and resulting in the leakage of cellular contents. This is conducive to the occurrence of browning reaction [13–16].

With the application of analysis methods of the transcriptomics and metabolomics, the identification and analysis of key genes and metabolites in plants under stress are increasing, making it possible to elucidate the molecular mechanisms of browning [11]. These approaches have been successfully applied to investigate the molecular mechanisms of browning in crops such as Malus domestica [17], Prunus salicina [18], Glycyrrhiza uralensis [19], and Lactuca sativa [20]. In tobacco, metabolomics reveals that the accumulation of amino acids mainly causes the post-harvest browning of cigar tobacco [5]. In addition, transcriptomic and metabolomic analyses of tobacco leaf with stem show that inhibiting membrane lipid degradation and increasing the supply of reduced hydrogen reduce browning [4]. However, the molecular mechanisms of browning in flue-cured tobacco are still not fully understood.

In this study, the transcriptome and metabolome of the two tobacco cultivars Yunyan87 and Dabaijin were analyzed to determine their changes during the browning process and to dissect the molecular mechanism underpinning the difference in the degree of browning of the upper, middle, and lower leaves during the flue-curing process (yellowing stage and color-fixing stages). The results of this study will provide a more hierarchical insight into the molecular mechanisms of browning and provide theoretical basis for the further optimization of the flue-curing process.

Materials and methods

Plant materials and experimental treatments

The tobacco cultivars Yunyan87 (Y) and Dabaijin (D) were grown according to the National Standard of China Tobacco Industry (GB/T 23221–2008). Mature tobacco plants in uniform developmental stage and of high-quality were selected for analysis. Each tobacco leaf of uniform quality was randomly collected from the upper (U), middle (M), and lower (L) positions in the stem. After harvesting, the different leaf sets were separately flue-cured following the technical specification for flue-cured tobacco [21]. Three special periods were set: the mature stage (M), the yellowing stage (Y), and the color-fixing stage (C). There were 18 treatment groups in total, and each treatment group was subjected to three biological replicates. The collected tobacco leaves were frozen in liquid nitrogen and kept at −80 °C for the downstream analyses.

Determination of browning index

The browning index is measured by assessing the degree of browning on the leaf surface. The browning range index of tobacco leaves was obtained by calculating the proportion of the browning area of the leaf to the total leaf area using the Photoshop pixel method. The methodology was adapted from a previous study with minor adjustments [22]. An average of five tobacco leaves was taken for each set of results.

Quasi-targeted metabolomics analysis

Each sample was ground thoroughly in liquid nitrogen and 100 mg of the resulting powder was extracted in 80% methanol and centrifuged at 15,000 g for 20 min at 4 °C. Subsequently, the supernatant was diluted with ddH₂O to a final concentration of 53% methanol. Subsequently, the sample was centrifuged at 15,000 g for 20 min at 4 °C. The supernatant was filtered (0.22 μm, ANPEL, China) and then analyzed by LC-MS/MS (Novogene Co. Ltd., Beijing, China). Each biological replicate was subjected to two technical replicates in LC-MS/MS. The detection of the experimental samples using MRM (Multiple Reaction Monitoring) were based on novogene in-house database. The Q3 were used to the metabolite quantification. The Q1, Q3, RT (retention time), DP (declustering potential) and CE (collision energy) were used to the metabolite identification. These metabolites were annotated using the KEGG (http://www.genome.jp/kegg/), HMDB (http://www.hmdb.ca/) and Lipidmaps (http://www.lipidmaps.org/).LC-MS/MS analyses were performed on an ExionLC™ AD system (SCIEX, US) and a QTRAP^® 6500 + mass spectrometer (SCIEX, US). MRM and QQQ-MS were used for metabolite quantification. Subsequently, accumulated metabolites in the same leaf positions of the same tobacco cultivar were compared across different flue-curing treatments (e.g. DUY vs. DUM, DUC vs. DUM; YUY vs. YUM, YUC vs. YUM). Metabolites with |log₂ (fold change)| > 1, p-value < 0.05, and VIP > 1 were considered as differentially accumulated metabolites (DAMs).

RNA extraction, library construction, and transcriptome analysis

The total RNA from tobacco leaves of 18 treatment groups was extracted using StarSpin Plant RNA Kit (GenStar, Beijing, China) according to the manufacturer’s protocol. Genomic DNA contamination was removed using DNase I (Takara, Beijing, China). Subsequently, the 2100 Bioanalyzer (Agilent Technologies Ltd, California, USA) and ND-2000 (NanoDrop Technologies, Thermo Fisher Corporation, Madison, USA) were used to assess RNA integrity and quality. High-quality RNA transcriptome libraries were prepared using the TruSeqTM RNA Sample Prep Kit (Illumina, Inc., San Diego, California, USA). Subsequently, RNA-seq sequencing was performed using the Illumina NovaSeq 6000 system (Novogene Co.). Clean reads were mapped to the Nicotiana tabacum L. genome using HISAT2 (https://solgenomics.net/ftp/tobacco_genome/edwards_et_al_2017/annotation/). NR (https://www.ncbi.nlm.nih.gov/protein/), KEGG, GO (http://geneontology.org/), and other free public databases were used for gene function annotation. Furthermore, expressed genes in the same leaf positions of the same tobacco cultivar were compared across different flue-curing treatments (e.g. DUY vs. DUM, DUC vs. DUM; YUY vs. YUM, YUC vs. YUM). Differentially expressed genes (DEGs) between two groups were screened using DESeq2 (http://bioconductor.org/packages/stats/bioc/DESeq2/) based on |log2 (fold change)| >1 and p_adj <0.05.

Quantitative real-time PCR (qRT-PCR) validation

Eight DEGs were randomly selected, and their RNA-seq data was verified using qRT-PCR. Based on the sequences of the screened transcripts, Primer Premier 6.0 software was used to design primers to amplify the screened genes (Table S1). The cDNA was synthesized using FastKing gDNA dispelling RT SuperMix (TianGen, Beijing, China). qRT-PCR validation was performed using Talent qPCR PreMix (TianGen, Beijing, China) on Roche LightCycler 480. Actin was used as an internal reference gene. Relative gene expression levels were calculated using the 2^-ΔΔCt method.

Identification of hub genes using WGCNA

WGCNA package (version 1.71) based on R (version 4.2.2) was used to analyze the DEGs and corresponding DAMs in the yellowing and color-fixing stages for each leaf position obtained from Yunyan87 and Dabaijin cultivars. The DAMs significantly correlated with the up-regulation module in each site’s yellowing and color-fixing stages were extracted, and the first ten were taken from each site. Finally, core DAMs were identified based on the correlation and significance based on literature. The core DAMs were analyzed using the WGCNA package for co-expression network analysis of unique DEGs in Dabaijin and Yunyan87 to screen for genes associated with browning in flue-cured tobacco. The correlation between DEGs and DAMs was calculated using Pearson’s weighted correlation coefficient values. Hierarchical clustering trees were constructed using TOM-based dissimilarity degrees. ‘minModuleSize’ was selected as 250 for Dabaijin and Yunyan87, and the rest of the parameters were set to default.

Accession numbers

The raw metabolome and transcriptome data have been submitted to the China National Center for Bioinformation (https://www.cncb.ac.cn/), with accession numbers OMIX009385 and CRA023683, respectively. The raw transcriptome data has also been submitted to the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/), with accession number PRJNA1293734.

Statistical analysis

The means and standard deviations of the browning index and qRT-PCR values were analyzed using Excel 2016. Statistical differences between sample groups were analyzed by ANOVA using SPSS 25 and their significance was defined as * P < 0.05, ** P < 0.01, and *** P < 0.001. The R language online platform was used for graphics (https://www.chiplot.online/).

Results

Phenotypic characteristics of browning

After the Yunyan87 and Dabaijin tobacco leaves had undergone the mature, yellowing, and color-fixing stages of flue-curing, gradual changes in tobacco leaves were observed. The leaves shrank and shriveled, their color turned green to yellow, and browning occurred in some areas (Fig. 1A). Among them, the degree of browning of the leaves in all parts of Dabaijin was more significant than that of Yunyan87 (Fig. 1A, B). Moreover, the degree of browning in leaves of Dabaijin at the color-fixing stage was higher than that at the yellowing stage (Fig. 1B). The browning index (BI) of the upper and middle leaves of Dabaijin at color-fixing stage was higher than that of Yunyan87, reaching 36.84% and 34.95%, respectively (Fig. 1B).

Fig. 1 — Browning phenotypes (A) and browning indexes (B) of the upper, middle, and lower leaves of Yunyan87 and Dabaijin tobacco cultivars at the mature, yellowing, and color-fixing stages

Metabolomic analysis during the flue-curing process

A quasi-targeted metabolomics analysis was performed for the 18 samples of Yunyan87 and Dabaijin to profile the metabolites during the flue-curing process. A total of 1207 metabolites were identified by LC-MS/MS, which were divided into 20 categories. The top eight categories were: amino acid and its derivatives, flavonoids, organic acid and its derivatives, carbohydrates and its derivatives, lipids, organoheterocyclic compounds, nucleotide and its derivatives, and phenolic acids, each accounting for more than 5% of the total metabolites (Fig. 2A). In addition, 767 of the identified metabolites were classified into 10 super-classes according to the HMDB 4.0 database, of which organic acids and derivatives were the most abundant (Fig. 2B). Principal component analysis (PCA) showed that the 18 flue-cured tobacco samples had good intra-group repeatability (Fig. 2C). The metabolomic profiles of the yellowing stage and the color-fixing stage were overlapping but they appeared different from that of the mature stage, indicating that significant metabolite changes occurred during the curing process.

DAMs were screened during the flue-curing process, and most of the DAMs were significantly upregulated at the yellowing and color-fixing stages compared to the mature stage (Fig. 2D). In the upper leaves, the number of DAMs identified at yellowing stage was 219 (203 up and 16 down) in Dabaijin (DUY vs. DUM) and 350 (331 up and 19 down) in Yunyan87 (YUY vs. YUM); at color-fixing stage, the DAMs were 361 (325 up and 36 down) in Dabaijin (DUC vs. DUM) and 461 (439 up and 22 down) in Yunyan87 (YUC vs. YUM). In the middle leaves, the DAMs at yellowing stage were 378 (352 up and 26 down) in Dabaijin (DMY vs. DMM) and 523 (33 up and 490 down) in Yunyan87 (YMY vs. YMM); the DAMs at color-fixing stage were 452 (26 up and 426 down) in Dabaijin (DMC vs. DMM) and 546 (41 up and 505 down) in Yunyan87 (YMC vs. YMM). In the lower leaves, there were 251 (229 up and 22 down) DAMs in Dabaijin (DLY vs. DLM) and 349 (335 up and 14 down) DAMs in Yunyan87 (YLY vs. YLM) at yellowing stage, while there were 387 (369 up and 18 down) DAMs in Dabaijin (DLC vs. DLM) and 543 (528 up and 15 down) DAMs in Yunyan87 (YLC vs. YLM) at color-fixing stage (Fig. 2D).

The DAMs unique to Dabaijin and to Yunyan87 across leaf positions and flue-curing stages (294 and 528, respectively) were determined (Fig. 2E). K-means clustering of those unique DAMs resulted to five and seven clusters in Dabaijin and Yunyan87, respectively, each cluster exhibited a different expression pattern (Fig. S2A, B). To better understand the affected metabolic pathways in which the unique DAMs were involved, KEGG enrichment analysis was performed. The top 30 KEGG pathways significantly enriched in Dabaijin-specific DAMs include amino sugar and nucleotide sugar metabolism; glutathione metabolism; alanine, aspartate and glutamate metabolism; arginine and proline metabolism; and ascorbate and aldarate metabolism among others (Fig. 2F). On the other hand, the enriched pathways in Yunyan87-specific DAMs include flavone and flavonol biosynthesis, phenylpropanoid biosynthesis, folate biosynthesis, lysine biosynthesis, and nicotinate and nicotinamide metabolism (Fig. 2G).

Transcriptomic analysis during the flue-curing process

We performed transcriptome sequencing on 18 flue-cured tobacco samples to comprehensively analyze the potential molecular mechanisms of browning in Yunyan87 and Dabaijin. A total of 2.48 billion clean reads were obtained from 54 cDNA libraries, with an average of 45.96 million reads per cDNA library. The number of clean reads per library ranged from 39.04 million to 55.23 million, and the Q30 value ranged from 91.98 to 94.98% (Table S2). These results indicated that the quality and quantity of the sequencing data were suitable for further analysis. PCA showed that the 18 samples had good intra-group repeatability, with more significant differences between the mature stage and the yellowing stage and color-fixing stage, indicating the changes in gene expression during the flue-curing process (Fig. 3A).

DEGs were identified by comparing the transcriptome data. In the upper leaves, the number of DEGs identified at yellowing stage was 9152 (3999 up and 5153 down) in Dabaijin (DUY vs. DUM) and 11,552 (5176 up and 6376 down) in Yunyan87 (YUY vs. YUM); at color-fixing stage, the DEGs were 12,189 (5642 up and 6547 down) in Dabaijin (DUC vs. DUM) and 12,869 (6170 up and 6699 down) in Yunyan87 (YUC vs. YUM). In the middle leaves, the DEGs at yellowing stage were 14,504 (7228 up and 7276 down) in Dabaijin (DMY vs. DMM) and 10,515 (7361 up and 7805 down) in Yunyan87 (YMY vs. YMM); at color-fixing stage, the DEGs were 15,166 (7361 up and 7805 down) in Dabaijin (DMC vs. DMM) and 11,534 (6241 up and 5293 down) in Yunyan87 (YMC vs. YMM). In the lower leaves, the DEGs at yellowing stage were 9335 (4574 up and 4761 down) in Dabaijin (DLY vs. DLM) and 10,196 (5621 up and 4575 down) in Yunyan87 (YLY vs. YLM); at color-fixing stage, the DEGs were 13,560 (7116 up and 6444 down) in Dabaijin (DLC vs. DLM) and 12,537 (6245 up and 6292 down) in Yunyan87 (YLC vs. YLM) (Fig. 3B).

The unique DEGs in Dabaijin and Yunyan87 were collected (10792 and 9088, respectively) (Fig. 3C) and analyzed by K-means clustering which generated two clusters for each dataset (Fig. S2C). GO and KEGG enrichment analyses showed significant differences between Dabaijin and Yunyan87 (Fig. 3D-G). The unique DEGs in Dabaijin were significantly enriched in GO terms such as tubulin binding, ion channel activity, DNA repair, cellular response to stress, and oxoacid metabolic process, etc. (Fig. 3D), as well as in KEGG pathways such as valine, leucine and isoleucine biosynthesis; pantothenate and CoA biosynthesis; starch and sucrose metabolism; and biosynthesis of cofactors and arginine biosynthesis (Fig. 3F). However, in Yunyan87 that has lower browning degree, the unique DEGs were significantly enriched in GO terms such as coenzyme biosynthetic process, intracellular transport, intracellular protein transport, secretion, and oxidoreduction coenzyme metabolic process (Fig. 3E), and in KEGG pathways such as phosphatidylinositol signaling system, N-glycan biosynthesis, mismatch repair, pentose phosphate pathway, and MAPK signaling pathway (Fig. 3G).

qRT–PCR validation

Eight DEGs were randomly selected (including Nitab4.5_0003858g0010, Nitab4.5_0000694g0080, Nitab4.5_0001223g0010, Nitab4.5_0000356g0010, Nitab4.5_0000263g0190, Nitab4.5_0000273g0080, and Nitab4.5_0000249g0160, Nitab4.5_0000407g0130), and their expression patterns were verified by qRT–PCR. The expression data obtained by RNA-Seq and qRT–PCR were compared (Fig. 4A). These genes had the same trends as the transcriptome results. Significant positive correlations were observed between the qRT–PCR and transcriptome data of the eight DEGs (R = 0.43), which indicated that the RNA-Seq data were reliable (Fig. 4B).

Fig. 4 — qRT-PCR validation of the eight genes in the transcriptome. A The relative expression levels of eight DEGs were analysed by qRT-PCR and Vactin was used as reference gene. The primary coordinate axis represents the relative expression, and the secondary coordinate axis represents the value of LOG₂ (FPKM + 1). B Correlation plot of the RNA-Seq results and qRT-PCR results. Results were calculated using log₂FC variation measurements. The R² value represents the correlation between the RNA-seq and qRT-PCR results

Combined metabolomic and transcriptomic analyses

In this study, the unique DEGs and DAMs in Dabaijin and Yunyan87 were enriched in several KEGG pathways (Fig. 5). In the two omics data of Daibaijin, the valine, leucine and isoleucine biosynthesis (ko00290) and degradation (ko00280), arginine biosynthesis (ko00220), riboflavin metabolism (ko00740), inositol phosphate metabolism (ko00562), and amino sugar and nucleotide sugar metabolism (ko00520) were enriched altogether. In the two omics data of Yunyan87, the galactose metabolism (ko00052), lysine biosynthesis (ko00300), taurine and hypotaurine metabolism (ko00430), and sulfur metabolism (ko00920) were enriched. Comprehensively, these distinct pathways between Dabaijin and Yunyan87 might underlie the significant difference in the browning reaction during the flue-curing process.

DAMs and DEGs related to amino acid metabolism in Dabaijin

By comparing the transcriptome and metabolome in Dabaijin leaves during the flue-curing process, three common KEGG pathways related to amino acid metabolism were identified, including the valine, leucine and isoleucine biosynthesis and degradation, and arginine biosynthesis (Fig. 5). Metabolome analysis found that L-leucine, L-valine, L-arginine, and L-ornithine, which contain free amino groups, were upregulated in Dabaijin during the flue-curing process at all leaf positions; however, L-glutamate was downregulated during the flue-curing process at all leaf positions. Transcriptome analysis revealed 27 DEGs in Dabaijin during the flue-curing process, including acetolactate synthase small subunit 1 (LOC107770361), ketol-acid reductoisomerase (LOC107759104), 3-isopropylmalate dehydrogenase (IMDH3), branched-chain-amino-acid aminotransferase 2 (BCAT2), 2-oxoisovalerate dehydrogenase subunit beta 1 (BCDH), methylcrotonoyl-CoA carboxylase beta chain (MCCB), hydroxymethylglutaryl-CoA lyase (HMGCL), probable enoyl-CoA hydratase 1 (ECHIA), 3-hydroxyisobutyryl-CoA hydrolase-like protein 5 (CHY5), aspartate aminotransferase, chloroplastic (ASP), glutamate dehydrogenase B-like (GDH), glutamine synthetase-like (GLN), amino-acid N-acetyltransferase (NAGS), N-acetyl-gamma-glutamyl-phosphate reductase (LOC107766214), acetylornithine deacetylase-like (LOC107780733), glutamate N-acetyltransferase (LOC107783830), arginase 1, mitochondrial-like (ARGAH), and ornithine carbamoyltransferase (OTC) genes (Fig. 6A). While in various parts of Dabaijin, the remaining DEGs showed a downregulated trend in all leaf positions during the flue-curing process, except for Nitab4.5_0001646g0050 (BCAT2), Nitab4.5_0000174g0040 (BCDH), Nitab4.5_0000252g0120 (MCCB), Nitab4.5_0000177g0080 (ECHIA), and novel.3761 (CHY5) which were upregulated in the lower and upper leaf.

Fig. 6 — Integrated analysis of the transcriptome and metabolome in Dabaijin leaves during the flue-curing process. A Amino acid metabolism-related (valine, leucine, and isoleucine biosynthesis and degradation; arginine biosynthesis) DAMs and DEGs. B Riboflavin metabolism-related DAMs and DEGs. C Amino sugar and nucleotide sugar metabolism-related DAMs and DEGs. D Inositol phosphate metabolism-related DAMs and DEGs. The numbers in each box represent the log₂FC values of DAMs or DEGs. Samples were labeled with three letters pertaining to cultivar (Yunyan87, Y; Dabaijin, D), leaf position (lower, L; middle, M; upper, U), and curing stage (mature stage, M; yellowing stage, Y; color-fixing stage, C), respectively. Red stars represent the core DAMs of combined analysis in the figure

DAMs and DEGs related to riboflavin metabolism in Dabaijin

Riboflavin metabolism was an enriched KEGG pathway shared by both transcriptome and metabolome of Dabaijin leaves (Fig. 5). Ribitol and vitamin B2 were upregulated in various Dabaijin leaf positions, and six related DEGs encoding riboflavin-biosynthesis protein pyrimidine reductase PYRR (PYRR), 5-amino-6-(5-phospho-D-ribitylamino) uracil phosphatase (PYRP), 6,7-dimethyl-8-ribityllumazine synthase (LOC107759548), riboflavin synthase (LOC107796682), purple acid phosphatase (PAP), and FAD synthetase (RIBF) were identified (Fig. 6B). PYRR, PYRP, LOC107759548, LOC107796682, and RIBF were downregulated in all leaf positions, with FHY being downregulated to a greater extent. In addition, PAP was upregulated in the lower and middle leaves.

DAMs and DEGs related to amino sugar and nucleotide sugar metabolism in Dabaijin

The amino sugar and nucleotide sugar metabolism KEGG pathway appeared in both the transcriptome and metabolome of Dabaijin leaves (Fig. 5). UDP-D-glucose, L-fucose, and D-glucose 1-phosphate were implicated in the browning of Dabaijin during the flue-curing process. Among them, L-fucose was mainly upregulated in middle leaves and D-glucose 1-phosphate was mainly downregulated in upper leaves (Fig. 6C). Meanwhile, UDP-D-glucose was downregulated in the middle and lower leaves. On the other hand, there were 34 related DEGs in Dabaijin during the flue-curing process including endochitinase A (CHN-A), beta-hexosaminidase 3 (HEXO3), hexokinase-2 (HXK2), L-arabinokinase (ARA), glucosamine-phosphate N-acetyltransferase (GNA), glutamine fructose-6-phosphate aminotransferase (GFAT), mannose-6-phosphate isomerase 2 (PMI2), galacturonokinase (GALAK), bifunctional fucokinase (FKGP), UDP-N-acetylglucosamine diphosphorylase 1 (GLCNAC1PUT1), alpha-L-arabinofuranosidase 1 (ASD1), UTP-glucose-1-phosphate uridylyltransferase (UGP), mannose-1-phosphate guanylyltransferase 1 (CYT1), glucose-1-phosphate adenylyltransferase (ADG2), UDP-arabinose 4-epimerase (MUR4), UDP-glucuronate decarboxylase (UXS1), UDP-glucose 6-dehydrogenase (EC:1.1.1.22), UDP-glucuronate 4-epimerase 1 (GAE1), GDP-L-fucose synthase (GER2), beta-xylosidase (BXL2), UDP-glucose 4,6-dehydratase (RHM), alpha-1,4-galacturonosyltransferase (GAUT), and cytochrome-b5 reductase (CBR1). Among them, ARA (Nitab4.5_0000970g0150 and Nitab4.5_0001942g0140), ASD1, EC:1.1.1.22 (Nitab4.5_0000154g0320), GAUT, HXK2, MUR4, PMI2, and RHM showed an increasing trend from yellowing to color-fixing stage in the lower and upper leaves of Dabaijin. In contrast, GAE1 and UXS1 (novel.4196) were upregulated in the middle and upper leaves. The remaining genes showed a decreasing trend in all leaf positions. It is worth noting that except for ASD1, GAE1, PMI2, and UXS1 (novel.4196), all genes were downregulated in the middle leaf at yellowing stage compared to the mature stage (Fig. 6C).

DAMs and DEGs related to inositol phosphate metabolism in Dabaijin

The KEGG pathway inositol phosphate metabolism was also shared by the transcriptome and metabolome of Dabaijin leaves. The dihydroxyacetone phosphate was downregulated in the upper leaves at the color-fixing stage compared to the mature stage. During the flue-curing process, 34 DEGs related to inositol phosphate metabolism were identified in Dabaijin leaves which include multiple inositol polyphosphate phosphatase 1 (EC:3.1.3.62), 1-phosphatidylinositol-3-phosphate 5-kinase (FAB1C), myo-inositol-1(or 4)-monophosphatase (IMPL1), myo-inositol-1-phosphate synthase (IPS), inositol-tetrakisphosphate 1-kinase 3 (ITPK), phosphatidylinositol-3-phosphatase myotubularin-1 (MTM1), phosphatidylinositol 3-kinase (PI3K), phosphatidylinositol 4-phosphate 5- kinase 6 (PIP5K6), phosphoinositide phospholipase C 2-like (PLC2), non-specific phospholipase C4 (PLC4), phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase, dual-specificity protein phosphatase PTEN (PTEN), and triosesephosphate isomerase (TIM). Of these, EC:3.1.3.62, IMPL1 (Nitab4.5_0003234g0080), MTM1, PI3K, PIP5K6 (Nitab4.5_0000494g0010), PLC4, and PTEN were found to be upregulated, while the FAB1C, IMPL1 (Nitab4.5_0002056g0080), IPS, PLC2 (Nitab4.5_0000119g0150), and TIM were found to be downregulated in various leaf positions of Dabaijin during the flue-curing process (Fig. 6D).

DAMs and DEGs related to amino acid metabolism in Yunyan87

In this study, lysine biosynthesis, as a branch of amino acid metabolism, was also shared in transcriptome and metabolome profiles of Yunyan87. Metabolomics analysis revealed that L-saccharopine, L-2-aminoadipic acid, and 2-oxoadipic acid were upregulated, while L-aspartic acid was downregulated in various parts of the leaves during the flue-curing process. L-homoserine showed varying trends in various parts. Transcriptome analysis revealed four related DEGs, including the genes encoding for diaminopimelate decarboxylase 1 (LYSA1), 4-hydroxy-tetrahydrodipicolinate reductase 1 (DAPB1), aspartate-semialdehyde dehydrogenase (EC:1.2.1.11), and diaminopimelate epimerase (DAPF). LYSA1 and EC:1.2.1.11 were upregulated in the lower and middle leaves, respectively, while DAPB1 and DAPF were downregulated in all leaf positions (Fig. 7A). The above results indicated that the DAMs and DEGs related to amino acid metabolism were markedly different between Dabaijin and Yunyan87.

Fig. 7 — Integrated analysis of the transcriptome and metabolome in Yunyan87 leaves during the flue-curing process. A Amino acid metabolism-related (lysine biosynthesis) DAMs and DEGs. B Sulfur metabolism-related DAMs and DEGs. C Taurine and hypotaurine metabolism-related DAMs and DEGs. D Galactose metabolism-related DAMs and DEGs. Samples were labeled with three letters pertaining to cultivar (Yunyan87, Y; Dabaijin, D), leaf position (lower, L; middle, M; upper, U), and curing stage (mature stage, M; yellowing stage, Y; color-fixing stage, C), respectively

DAMs and DEGs related to sulfur metabolism and taurine and hypotaurine metabolism in Yunyan87

In this study, the sulfur metabolism was shared in transcriptome and metabolome profiles of Yunyan87. Succinic acid, 2-aminoethanesulfonic acid, L-homoserine, and L-cysteine involved in sulfur metabolism were especially changed during browning in Yunyan87. Among them, L-cysteine was downregulated in the lower and upper leaves. Meanwhile, 2-aminoethanesulfonic acid was upregulated during the flue-curing process. In addition, L-homoserine and succinic acid were upregulated in the lower leaves and downregulated in the upper leaves. Relevant to these metabolites were the 11 relevant DEGs during the flue-curing process in Yunyan87 leaves, including ATP sulfurylase 2 (APS2), histidine triad nucleotide-binding protein 3 (HINT3), 3’-phosphoadenosine 5’-phosphosulfate synthase (APK), 5’-adenylylsulfate reductase 3 (APR3), PAP-specific phosphatase HAL2 (AHL), serine acetyltransferase 1 (SAT1), and cysteine synthase (CYS). Among them, HINT3 and APK (Nitab4.5_0002119g0120 and Nitab4.5_0000394g0020) were upregulated during the flue-curing in all leaf positions. Conversely, APK (Nitab4.5_0001079g0180), AHL, and CYS were downregulated in all leaf positions during the flue-curing process. Furthermore, APR3 and SAT1 exhibited different expression trends during the flue-curing of different leaves (Fig. 7B).

Taurine and hypotaurine metabolism, an important branch of sulfur metabolism, was also notable in Yunyan87. The metabolites L-alanine, L-cysteinesulfinic acid, guanidinoethyl sulfonate, and 2-aminoethanesulfonic acid as well as the genes GGT4 and GAD1 were upregulated during the flue-curing process. Conversely, Nitab4.5_0000783g0240 was downregulated in the lower and upper leaves and upregulated in the middle leaves (Fig. 7C).

DAMs and DEGs related to galactose metabolism in Yunyan87

During the flue-curing process in Yunyan87 leaves, the accumulation of raffinose, stachyose, dulcitol, D-fructose 6-phosphate, lactose, galactinol, sucrose, D-glucose, and D-sorbitol was observed. Among them, the non-reducing sugars are raffinose, stachyose, galactinol, sucrose, D-sorbitol, and dulcitol. The DEGs associated with these metabolites include alpha-galactosidase 3 (AGAL3), aldose 1-epimerase (GAL), UTP-glucose-1-phosphate uridylyltransferase (UGP), phosphoglucomutase (PGMP), phosphoglucomutase (HXK2), beta-fructofuranosidase (CWINV1), alpha-galactosidase 3 (AGAL3), ATP-dependent 6- phosphofructokinase 6 (PFK6). AGAL3, PGMP, HXK2, and AGAL3 were downregulated in the upper leaves while GAL was downregulated in all leaf positions. In contrast, PFK6, UGP, and CWINV1 (Nitab4.5_0001780g0120) were upregulated in the lower leaf (Fig. 7D).

Identification of key KEGG pathways related to Browning by WGCNA

The WGCNA screened eight core DAMs associated with the browning process in Dabaijin, namely, L-glutamine (Com_412_neg), O-acetylserine (Com_7_neg), homocysteic acid (Com_592_pos), valine (Com_ 686_pos), naringerin (Com_796_pos), Vitamin A (Com_1029_pos), D-xylonic acid (Com_466_neg), and vanillic acid (Com_276_neg) (Fig. S4, Table S3). The eight core DAMs were then correlated with the unique DEGs in Dabaijin and Yunyan87, generating six and five gene modules, respectively (Fig. S5). The turquoise module eigenvalues of both Dabaijin and Yunyan87 were significantly and positively correlated with all eight core DAMs (Fig. 8A, B). The turquoise modules of Dabaijin and Yunyan87 contained 2869 and 2255 DEGs, respectively (Fig. 8A-D). The DEGs in the turquoise module of Dabaijin were enriched in nucleotide excision repair, inositol phosphate metabolism, arginine and proline metabolism, cysteine and methionine metabolism, and oxidative phosphorylation, among other KEGG pathways (Fig. 8E). Conversely, the DEGs in the turquoise module of Yunyan87 were enriched in oxidative phosphorylation, MAPK signaling pathway, phosphatidylinositol signaling system, ABC transporters, and plant hormone signal transduction, among other KEGG pathways (Fig. 8F).

Fig. 8 — WCGNA analysis of unique DEGs in Dabaijin and Yunyan87 with eight core DAMs. A Module–trait relationships of Dabaijin. B Module–trait relationships in Yunyan87. The color and size of circles in A and B represent the correlation value and P-value. Significantly upregulated turquoise module in Dabaijin (C) and in Yunyan87 (D). KEGG pathway enrichment analysis of turquoise module genes in Dabaijin (E) and in Yunyan87 (F). Samples were labeled with three letters pertaining to cultivar (Yunyan87, Y; Dabaijin, D), leaf position (lower, L; middle, M; upper, U), and curing stage (mature stage, M; yellowing stage, Y; color-fixing stage, C), respectively

Identification of hub genes associated with oxidative phosphorylation by WGCNA

Based on the above WGCNA results, the oxidative phosphorylation pathway shared by Dabaijin and Yunyan87 was further analyzed to screen for potential key hub genes that might be implicated for the browning differences between the two tobacco cultivars. The results showed that 14 DEGs in Dabaijin were notable during the flue-curing process, five of which encode for NADH dehydrogenases (ND5, Ndufa1, Ndufa9, Ndufab1 and Nitab4.5_0001170g0160), four encode for cytochrome c oxidases (COX10, COX17, Nitab4.5_0000999g0020, and Nitab4.5_0002546g0050) and five encode for ATP synthases (ATPB, Nitab4.5_0001584g0020, VHA-A, VHA-E1, PPA). Except for ND5 which was upregulated higher in Dabaijin than in Yunyan87 middle leaf, the rest of the DEGs in Dabaijin were not significantly different from those in all leaf positions in Yunyan87 (Fig. 9).

Fig. 9 — WGCNA identifies DAMs and DEGs related to oxidative phosphorylation. Red stars represent the selected key genes in oxidative phosphorylation based on Log₂FC values. Samples were labeled with three letters pertaining to cultivar (Yunyan87, Y; Dabaijin, D), leaf position (lower, L; middle, M; upper, U), and curing stage (mature stage, M; yellowing stage, Y; color-fixing stage, C), respectively

In Yunyan87 leaves, 27 relevant DEGs were identified, comprising of 13 DEGs that encode for NADH dehydrogenases (ND1, ND2, ND5, ND6, Ndufa9, Nitab4.5_0000127g0050, and Nitab4.5_0001034g0060), one DEG that encodes for a cytochrome c reductase (Nitab4.5_0000120g0110), six DEGs that encode for cytochrome c oxidase (COX1, COX6B, Nitab4.5_0001057g0180, Nitab4.5_0003120g0030 and Nitab4.5_0003013g0040), and seven DEGs that encode for ATP synthases (ATPA, ATPB, VHA-E1, AHA, Nitab4.5_0000435g0090 and Nitab4.5_0000172g1000). The upregulation of the above DEGs in all Yunyan87 leaf positions during the flue-curing process was generally much higher than those of Dabaijin (Fig. 9). Meanwhile, the number of DEGs enriched in oxidative phosphorylation was also higher in Yunyan87 than in Dabaijin, indicating that oxidative phosphorylation was the most important factor influencing the browning differences between the two tobacco cultivars during the flue-curing process. Based on the kWithin values of Yunyan87 genes and their expression within the module (Table S4), 12 hub genes were identified for subsequent studies, including ND5 (Nitab4.5_0000085g0160 and Nitab4.5_0001152g0250), ND6 (Nitab4.5_0001152g0030, Nitab4.5_0001127g0280, and Nitab4.5_0000172g0300), E 7.1.1.8 (Nitab4.5_0000120g0110), COX1 (Nitab4.5_0000161g0030), E 7.1.2.2 (Nitab4.5_ 0000435g0090 and Nitab4.5_0000172g1000), and the genes encoding for the unknown enzymes E 7.1.1.2 (Nitab4.5_0000127g0050 and Nitab4.5_0000134g0060) and E 7.1.1.9 (Nitab4.5_ 0003013g0040) (Fig. 9, Table S4).

Discussion

Accumulation of Maillard reaction substrates promoted Browning

Browning affects the quality of flue-cured tobacco leaves which causes significant economic losses [2, 23]. Studies have shown that the accumulation of melanoidins, a product of the Maillard reaction, is one of the significant causes of browning formation during flue-curing and processing [24]. In this study, Dabaijin and Yunyan87 tobacco cultivars had different accumulation patterns of amino acid metabolites during the flue-curing process, such that the amino acids were more abundant in Dabaijin than in Yunyan87. In addition, the amino acids accumulated in Dabaijin due to the downregulation of genes involved in amino acid metabolism (Figs. 6A and 7A). L-arginine, L-ornithine, L-leucine, and L-valine accumulated in Dabaijin, but L-saccharopine and L-2-aminoadipic acid accumulated in Yunyan87. Previous studies have shown that the type of amino acids in the Maillard reaction influences the degree of browning [25, 26]. In cigars, L-glutamate, L-arginine, and L-ornithine are the primary amino acids that promote the browning reaction in tobacco [5]. Consistently, amino sugar and nucleotide sugar metabolism were enriched in Dabaijin. Our results showed that L-fucose accumulated, while UDP-glucose and its D-glucose 1-phosphate were depleted in the lower leaves during the flue-curing process. Notably, most of the genes in amino sugar and nucleotide sugar metabolism, such as CBR1, CHN-A, and HEXO3, were downregulated in overall, indicating that the accumulation of DAMs may be a combination of the downregulation of related genes and the occurrence of flue-curing stress (Fig. 6C). Studies have shown that L-fucose and glucose can be involved in the Maillard reaction to promote melanoidin production [27, 28]. Likewise, UDP-glucose and the glucose product of its D-glucose 1-phosphate hydrolysis were also involved in the Maillard reaction. In addition, we speculated that the accumulation of Maillard substrates was also a manifestation of the imbalance of sugar and amino acid metabolism. This metabolic imbalance may indirectly weaken the ability of cells to maintain energy and redox homeostasis. Hence, these types of amino and nucleotide sugars and amino acids accumulated in Dabaijin leaves and provided abundant Maillard substrates, promoting the browning reaction during the flue-curing process.

In addition, our results showed that galactose metabolism was enriched in Yunyan87 leaves, with the accumulation of raffinose, stachyose, dulcitol, D-fructose 6-phosphate, lactose, galactinol, sucrose, D-glucose, and D-sorbitol in each leaf position during the flue-curing process. The occurrence of more non-reducing sugars and derivatives (i.e., raffinose, stachyose, galactinol, sucrose, D-sorbitol, dulcitol) (Fig. 7D) might have caused the lower degree of browning in Yunyan87, as non-reducing sugars have high stability and unique properties that buffer the Maillard reaction [29, 30].

The antioxidant system plays an important role in Browning

Plants have developed several effective antioxidant defense mechanisms to scavenge ROS that are induced by adverse environmental conditions. These include enzymatic and non-enzymatic systems such as the ascorbate-glutathione (AsA-GSH) cycle and thioredoxin reductase (Trx) [31]. Riboflavin metabolism was enriched in Dabaijin leaves, and ribitol and vitamin B2 were upregulated. Conversely, the FAD and FMN-related synthesis genes PYRP and RIBF have decreased expression, while the FMN metabolic gene PAP showed induced expression (Fig. 6B). Concurrently, the AsA-GSH precursor D-glutamate showed decreased expression (Fig. 6A). Studies have shown that FAD is a cofactor for antioxidant enzymes such as glutathione reductase (GR) and TrxR which remove ROS by maintaining the reduced state of GSH and Trx [32, 33]. The reduced levels of GSH and Trx in Dabaijin leaves resulted in the accumulation of ROS during the flue-curing process. In Yunyan87, however, sulfur metabolism as well as taurine and hypotaurine metabolism were enhanced, in conjunction with a decrease in L-cysteine level and accumulation of L-cysteine derivatives L-cysteinesulfinic acid, guanidinoethyl sulfonate, and 2-aminoethanesulfonic acid (taurine). Concurrently, the key taurine synthesis gene GAD1 was upregulated in all leaf positions (Fig. 7C). Taurine has been shown to have potent antioxidant activity, which can reduce the accumulation of ROS by directly scavenging hydroxyl radicals and inhibiting PPO-mediated enzymatic browning [34]. Therefore, the accumulation of taurine in Yunyan87 leaves during the flue-curing process likely contributed to the modulation of browning by scavenging ROS.

ROS is released along with hydrogen carriers through the electron transport chain, and oxidative phosphorylation is an important energy production pathway that consumes reduced hydrogen and e^-, and produces ATP [35, 36]. In this study, the number and upregulation of NADH dehydrogenase, cytochrome c reductase, cytochrome c oxidase, and ATP synthase-related DEGs were higher in Yunyan87 than in Dabaijin (Fig. 9). Studies have shown that GR24 treatment accelerates the browning of the pericarp of Litchi chinensis Sonn, and it is positively correlated with the downregulation of oxidative phosphorylation-related genes [37]. Exogenous AT-MAP treatment affects the expression of specific genes in the ETS pathway, especially upregulating the expression of ATP synthase, thereby reducing postharvest browning of Agaricus bisporus [38]. In addition, browning in Lactuca sativa L [39]., Flammulina filiformis [40], and Vitis vinifera [41] was correlated with the expression of genes/proteins related to oxidative phosphorylation. The upregulation of oxidative phosphorylation expression helps to consume more reduced hydrogen through the respiratory chain and reduces the chance of electron leakage to produce ROS [35, 42, 43]. The excess reduced hydrogen and e^- consumed through oxidative phosphorylation in Yunyan87 during the flue-curing process, might decrease ROS accumulation and thus, reduce browning.

We also observed that the MAPK signaling pathway and phosphatidylinositol signaling system were enriched in unique DEGs (Fig. 3G) and in WGCNA gene modules (Fig. 8F) in Yunyan87. MAPK signaling pathway can regulate growth and development, hormonal response, and stress responses by an extremely conserved network [44, 45]. Studies have shown that after being activated by stress, the MAPK signaling pathway activates downstream antioxidant defense systems (GSH, Trx, superoxide dismutase, etc.) through a cascade reaction that reduces ROS accumulation, thereby inhibiting cell damage and browning caused by oxidative stress [46–48]. Concretely, MEKK1, MPK4, and MPK6 can regulate the expression of CAT1 and CAT2 to overcome oxidative stress, indicating that MAPKs could control ROS and arbitrate oxidative stress in plants [49, 50]. Phosphatidylinositol signaling system can be involved in membrane lipid metabolism and damage repair, maintaining cell membrane integrity and reducing browning [51, 52]. In addition, phosphatidylinositol signaling may also stabilize the membrane structure by affecting calcium signaling or specific membrane lipid remodeling [53–56]. Thus, the MAPK signaling pathway and phosphatidylinositol signaling system in Yunyan87 were firstly activated during the flue-curing process which then triggers the antioxidant defense system that maintains membrane homeostasis, thereby slowing browning.

ROS accumulation can lead to biofilm peroxidation, change the localization of the Maillard reaction substrates, PPO, and oxidizing enzymes in the plastid and other organelles, and cause non-enzymatic and enzymatic browning reactions [57–59]. In this study, inositol phosphate metabolism was enriched in Dabaijin leaves. Among these, PI3K, MTM1, and PIP5K6 which were involved in the synthesis of PI/PIP2, were upregulated, whereas, PLC2 which was involved in the decomposition of PIP2, was downregulated (Fig. 6D). Studies have shown that PI/PIP2 is an essential component for maintaining the integrity of the biofilm, i.e., its metabolism affects the stability of the biofilm [60, 61]. Furthermore, PIP2 can be hydrolyzed by PLC to generate IP₃ and DAG, with IP₃ activating calcium channels to release intracellular Ca²⁺, leading to an increase in Ca²⁺ concentration and inhibiting PPO to accelerate enzymatic browning [62–65]. We speculated that the accumulation of ROS during the flue-curing process accelerated the destruction of the membrane stability in Dabaijin leaves, and that the downregulation of PLC2 reduced the Ca²⁺ concentration which also accelerated the enzymatic browning.

Conclusions

This study investigates the transcriptional and metabolomic mechanisms underlying the contrasting browning responses observed in two flue-cured tobacco cultivars, Yunyan87 and Dabaijin. The significant accumulation of Maillard reaction substrates (L-arginine, L-ornithine, L-leucine, L-valine, and L-fucose) in Dabaijin leaves during the flue-curing process promoted non-enzymatic browning. Concurrently, membrane homeostasis and antioxidant system function were impaired, further weakening the ability of the leaf to remove ROS which, in turn, promoted enzymatic browning. Conversely, the MAPK signaling pathway and phosphatidylinositol signaling were activated in Yunyan87 to regulate the antioxidant system and slow down membrane damage. Moreover, the efficiency of oxidative phosphorylation was enhanced in Yunyan87, which likely consumed the reduced hydrogen and electrons thereby decreasing ROS accumulation. Taken together, the accumulation of Maillard substrates and the efficacy of the antioxidant system and membrane homeostasis underlie the differential regulation of the browning process between Yunyan87 and Dabaijin tobacco cultivars (Fig. 10). These findings provide a key theoretical basis for the targeted improvement of tobacco processing technology and for breeding tobacco cultivars with improved resistance to browning.

Fig. 10 — Model of browning mechanisms during the flue-curing process of Yunyan87 and Dabaijin tobacco leaves. In Dabaijin, Maillard reaction substrate accumulation promotes non-enzymatic browning. Concurrently, impaired membrane homeostasis and antioxidant function reduce ROS -scavenging capacity, exacerbating enzymatic browning. Conversely, Yunyan87 activates the MAPK and phosphatidylinositol signaling pathways, enhancing antioxidant defense and mitigating membrane damage. Further, upregulated oxidative phosphorylation consumes reducing equivalents (hydrogen/electrons), limiting ROS accumulation. Therefore, the combined effects of Maillard substrate levels and the efficacy of antioxidant systems determine the distinct browning outcomes between Dabaijin and Yunyan87 cultivars

Supplementary Information

Supplementary Material 1.^{(31.2KB, docx)}

Supplementary Material 2.^{(827.4KB, docx)}

Acknowledgements

Not applicable.

Authors' contributions

N X: Conceptualization, data curation, methodology, writing–original draft. JT L: Formal analysis, verification, data curation, methodology. XL X: Funding, supervision, conceptualization, writing: reviewing and editing. RS H: Supervision, resource, project management. MC L: Verification, data curation. BY J: Verification, data curation. XX L: Supervision, project management. LX Z: Supervision, project management. XN Y: Supervision, methodology, project management. Z L: Conceptualization, methodology, funding, writing: reviewing and editing. All authors have approved the submitted version.

Funding

This work was supported by the Molecular Mechanism Dissection of Enzymatic Browning in Flue-cured Tobacco Leaf and Germplasm Innovation by China Tobacco Hunan Industrial Co. Ltd. (grant number KY2023YC0019).

Data availability

Data will be made available on request. Metabolome and transcriptome data that support the findings of this study have been deposited in the the China National Center for Bioinformation (https://www.cncb.ac.cn/), with accession numbers OMIX009385 and CRA023683, respectively. The raw transcriptome data has also been submitted to the National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/), with accession number PRJNA1293734.

Declarations

Ethics approval and consent to participate

The authors have respected the relevant institutional, national and international guidelines in collecting biological materials for this work. This research contributes to facilitating future studies in promoting tobacco breeding and process technology improvement.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Nan Xu and Jingtao Lei are joint first authorship-the two authors contributed equally to this manuscript.

Contributor Information

Xiangli Xu, Email: xuxlhnzy@163.com.

Zhi Liu, Email: zhiliu@hunau.edu.cn.

References

1.Chen YJ, Zhou JF, Ren K, Zou CM, Liu JJ, Yao GM, He JS, Zhao GK, Huang W, Hu BB, et al. Effects of enzymatic Browning reaction on the usability of tobacco leaves and identification of components of reaction products. Sci Rep. 2019;9:17580. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Meng L, Song WJ, Chen SW, Hu FQ, Pang BW, Cheng JJ, He B, Sun FS. Widely targeted metabolomics analysis reveals the mechanism of quality improvement of flue-cured tobacco. Front Plant Sci. 2022;13:1074029. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Wang P, Chen ZG, Xu CS, Song JY, Liu JW, Li JC, Duan WD, Sun GW. Effects of enzymatic Browning and leaf Browning inhibitors on tobacco. J Biobased Mater Bioenergy. 2021;15(6):766–73. [Google Scholar]
4.Wang CK, Ding YL, He B, Qiu MS, Shen DM, Chen SW, Song WJ, Qi WC, Lv YD, Meng L. Integrating transcriptome and metabolome analysis unveils the Browning mechanism of leaf response to high temperature stress in Nicotiana tabacum. Agronomy-Basel. 2024;14(8):1722. [Google Scholar]
5.Zhao GK, Zhang Q, Kong GH, Yao H, Wu YP, Cai B, Liu T, Zhang GH. Identification of physiological and metabolic networks involved in postharvest Browning of cigar tobacco leaves. Chem Biol Technol Agric. 2023;10(1):135. [Google Scholar]
6.Chen YJ, Ren K, He X, Gong JSQ, Hu XD, Su JE, Jin Y, Zhao ZX, Zhu YM, Zou CM. Dynamic changes in physiological and biochemical properties of flue-cured tobacco of different leaf ages during flue-curing and their effects on yield and quality. BMC Plant Biol. 2019;19(1):555. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Han ZS, Zhu MT, Wan XC, Zhai XT, Ho CT, Zhang L. Food polyphenols and Maillard reaction: regulation effect and chemical mechanism. Crit Rev Food Sci Nutr. 2024;64(15):4904–20. [DOI] [PubMed] [Google Scholar]
8.Tilley A, McHenry MP, McHenry JA, Solah V, Bayliss K. Enzymatic Browning: the role of substrates in polyphenol oxidase mediated Browning. Curr Res Food Sci. 2023;7:100623. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Suzuki N. Fine tuning of ROS, redox and energy regulatory systems associated with the functions of chloroplasts and mitochondria in plants under heat stress. Int J Mol Sci. 2023;24(2):1356. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Mittler R, Zandalinas SI, Fichman Y, Van Breusegem F. Reactive oxygen species signalling in plant stress responses. Nat Rev Mol Cell Biol. 2022;23(10):663–79. [DOI] [PubMed] [Google Scholar]
11.Wang CK, Meng L, Zhang GC, Yang XJ, Pang BW, Cheng JJ, He B, Sun FS. Unraveling crop enzymatic Browning through integrated omics. Front Plant Sci. 2024;15:1342639. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Ma WL, Li JX, Murtaza A, Iqbal A, Zhang J, Zhu LJ, Xu XY, Pan SY, Hu WF. High-pressure carbon dioxide treatment alleviates Browning development by regulating membrane lipid metabolism in fresh-cut lettuce. Food Control. 2022;134:108749. [Google Scholar]
13.Gao JJ, Guo WB, Liu QW, Liu MG, Shang C, Song YQ, Hao RJ, Li LL, Feng XX. The physiological response of apricot flowers to Low-Temperature stress. Plants-Basel. 2024;13(7):1002. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Sun J, Li CB, Prasad KN, You XR, Li L, Liao F, Peng HX, He XM, Li ZC, Zhang YY. Membrane deterioration, enzymatic Browning and oxidative stress in fresh fruits of three Litchi cultivars during six-day storage. Sci Hort. 2012;148:97–103. [Google Scholar]
15.Sangsoy K, Sanongkiet S, Srisamlee S, Beckles DM, Luengwilai K. Role of enzymatic Browning and calcium transporters in internal Browning of pineapple fruit. Postharvest Biol Technol. 2024;218:113174. [Google Scholar]
16.Yuan X, Zhan ZX, Lin W, Zhang C, Wang B. The membrane May be a key factor influencing Browning: a mini review on Browning mechanisms of fresh-cut fruit and vegetables from a multi-omics perspective. Front Nutr. 2025;12:1534594. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Zuo WF, Lu L, Su MY, Zhang J, Li YY, Huang SL, Li CX, Wang N, Zhang ZY, Chen XS. Analysis of differentially expressed genes and differentially abundant metabolites associated with the browning of Meihong red-fleshed apple fruit. Postharvest Biol Technol. 2021;174:111437.
18.Peng C, Deng LP, Tan HJ, Meng WC, Luo JL, Zhang ZW, Chen HQ, Qiu JS, Chang XX, Lu YS. Transcriptome and metabolome analysis of preharvest internal Browning in Nane Plum fruit caused by high temperature. Hortic Plant J. 2024;10(5):1099–111. [Google Scholar]
19.Zhao XY, Li XS, Bao AD, Zhang XL, Xu YB, Li YL. Metabolome and transcriptome integration explored the mechanism of Browning in Glycyrrhiza uralensis fisch cells. Front Plant Sci. 2024;15:1305871. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Liu YF, Liao LY, Yin FL, Song MB, Shang FF, Shuai L, Cai JH. Integration of metabolome and transcriptome profiling reveals the effect of 6-Benzylaminopurine on the Browning of fresh-cut lettuce during storage. Postharvest Biol Technol. 2022;192:112015. [Google Scholar]
21.Chen J, Li Y, He X, Jiao FC, Xu ML, Hu BB, Jin Y, Zou CM. Influences of different curing methods on chemical compositions in different types of tobaccos. Ind Crops Prod. 2021;167:113534. [Google Scholar]
22.Jiang Q, Wang HL, Wang HG. Two new methods for severity assessment of wheat Stripe rust caused by Puccinia striiformis f. Sp. tritici. Front Plant Sci. 2022;13:1002627. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Zhao SC, Wu ZY, Lai M, Zhao MQ, Lin BS. Determination of optimum humidity for air-curing of cigar tobacco leaves during the Browning period. Ind Crops Prod. 2022;183:114939. [Google Scholar]
24.Poojary MM, Lund MN. Chemical stability of proteins in foods: oxidation and the Maillard reaction. Annual Rev Food Sci Technol. 2022;13:35–58. [DOI] [PubMed] [Google Scholar]
25.Ataç B, Yildirim A, Gökmen V. Inhibition of enzymatic Browning in actual food systems by the Maillard reaction products. J Sci Food Agric. 2010;90(15):2556–62. [DOI] [PubMed] [Google Scholar]
26.Liu PL, Lu XM, Li NY, Zheng ZJ, Zhao RX, Tang XZ, Qiao XG. Effects and mechanism of free amino acids on Browning in the processing of black Garlic. J Sci Food Agric. 2019;99(10):4670–6. [DOI] [PubMed] [Google Scholar]
27.Yin XC, Wei YM, Li TH, Zhang JX, Zou L, Cui QQ, Lu CY, Ning JM. Heterocyclic compounds formation in large-leaf yellow tea induced by the Maillard reaction at different roasting temperatures. Lwt-Food Sci Technol. 2023;182:114856. [Google Scholar]
28.Yang H, Xie Y, Li XM, Wu DH, Cai GL, Lu J. Key compounds and metabolic pathway responsible for the Browning in Dangshan Pear (Pyrus spp.) wine. J Agric Food Chem. 2021;69(35):10311–20. [DOI] [PubMed] [Google Scholar]
29.Huang X, Jiang B, Chen JJ, Zhang T, Zheng LH. Enzymatic Preparation of non-reducing oligosaccharides from maltodextrins and nigerooligosaccharides. Starch-Starke. 2021;73(9–10):2100028. [Google Scholar]
30.Zhou P, Guo MF, Liu DS, Liu XM, Labuza TP. Maillard-reaction-induced modification and aggregation of proteins and hardening of texture in protein bar model systems. J Food Sci. 2013;78(3):C437–44. [DOI] [PubMed] [Google Scholar]
31.Gill SS, Tuteja N. Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiol Biochem. 2010;48(12):909–30. [DOI] [PubMed] [Google Scholar]
32.Lu J, Holmgren A. The thioredoxin antioxidant system. Free Radic Biol Med. 2014;66:75–87. [DOI] [PubMed] [Google Scholar]
33.Pinto JT, Cooper AJL. From cholesterogenesis to steroidogenesis: role of riboflavin and flavoenzymes in the biosynthesis of vitamin D. Adv Nutr. 2014;5(2):144–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Surai PF, Earle-Payne K, Kidd MT. Taurine as a natural antioxidant: from direct antioxidant effects to protective action in various toxicological models. Antioxidants. 2021;10(12):1876. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Ghifari AS, Saha S, Murcha MW. The biogenesis and regulation of the plant oxidative phosphorylation system. Plant Physiol. 2023;192(2):728–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Meyer EH, Letts JA, Maldonado M. Structural insights into the assembly and the function of the plant oxidative phosphorylation system. New Phytol. 2022;235(4):1315–29. [DOI] [PubMed] [Google Scholar]
37.Liu JL, Bao YM, Meng LH, Xu XB, Zhu LS, Zhang BZ. Physiological and transcriptomic analyses provide new insights into the regulatory mechanisms of accelerated senescence of Litchi fruit in response to Strigolactones. Food Bioscience. 2024;59:104213. [Google Scholar]
38.Lei M, Guo LL, Zhang YH, Yan XR, Jiang FL, Sun BX. Effectiveness of anaerobic treatment combined with microperforated film packaging in reducing Agaricus bisporus postharvest Browning. Postharvest Biol Technol. 2024;211:112833. [Google Scholar]
39.Zhang L, Wang ZQ, Zeng SX, Yuan SZ, Yue XZ, Tian T, Zhu XQ, Zheng SF, Xu XB, Zuo JH, et al. Browning mechanism in stems of fresh-cut lettuce. Food Chem. 2023;405:134575. [Google Scholar]
40.Fu Y, Tan H, Wang B, Peng WH, Sun Q, Yu Y. Integrated multi-omic analyses on yellow Flammulina filiformis cultivar reveal postharvest oxidative damage responses. Postharvest Biol Technol. 2023;195:112111. [Google Scholar]
41.Liu FJ, Huang WS, Feng ZS, Tao YX, Fan YY, He WZ, Li XL, Fang XT, Wang C, Bai YJ. Proteomic analyses on the Browning of shade-dried Thompson seedless grape. Appl Biol Chem. 2021;64(1):41. [Google Scholar]
42.Ghifari AS, Murcha MW. Proteolytic regulation of mitochondrial oxidative phosphorylation components in plants. Biochem Soc Trans. 2022;50(3):1119–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Lee KP, Kim C. Photosynthetic ROS and retrograde signaling pathways. New Phytol. 2024;244(4):1183–98. [DOI] [PubMed] [Google Scholar]
44.Majeed Y, Zhu X, Zhang N, ul-Ain N, Raza A, Haider FU, Si HJ. Harnessing the role of mitogen-activated protein kinases against abiotic stresses in plants. Front Plant Sci. 2023;14:932923. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Zhang MM, Zhang SQ. Mitogen-activated protein kinase cascades in plant signaling. J Integr Plant Biol. 2022;64(2):301–41. [DOI] [PubMed] [Google Scholar]
46.Liu YK, He CZ. A review of redox signaling and the control of MAP kinase pathway in plants. Redox Biol. 2017;11:192–204. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Wang XX, Wei YY, Jiang S, Ye JF, Chen Y, Xu F, Shao XF. Transcriptome analysis reveals that Trehalose alleviates chilling injury of Peach fruit by regulating ROS signaling pathway and enhancing antioxidant capacity. Food Res Int. 2024;186:114331. [DOI] [PubMed] [Google Scholar]
48.Liang BB, Wan SG, Ma QL, Yang L, Hu W, Kuang LQ, Xie JH, Liu DC, Liu Y. Transcriptome and physiological analyses of a navel orange mutant with improved drought tolerance and water use efficiency caused by increases of cuticular wax accumulation and ROS scavenging capacity. Int J Mol Sci. 2022;23(10):5660. [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Ning J, Li XH, Hicks LM, Xiong LZ. A Raf-like MAPKKK gene DSM1 mediates drought resistance through reactive oxygen species scavenging in rice. Plant Physiol. 2010;152(2):876–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Xu J, Xie J, Yan CF, Zou XQ, Ren DT, Zhang SQ. A chemical genetic approach demonstrates that MPK3/MPK6 activation and NADPH oxidase-mediated oxidative burst are two independent signaling events in plant immunity. Plant J. 2014;77(2):222–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Tan JXJ, Finkel T. A phosphoinositide signalling pathway mediates rapid lysosomal repair. Nature. 2022;609(7928):815–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Wen TM, Thapa N, Cryns VL, Anderson RA. Regulation of phosphoinositide signaling by scaffolds at cytoplasmic membranes. Biomolecules. 2023;13(9):1297. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Laxalt AM, van Hooren M, Munnik T. Plant PI-PLC signaling in stress and development. Plant Physiol. 2025;197(2):kiae534. [DOI] [PMC free article] [PubMed]
54.Li Z, Cheng BZ, Zhao Y, Luo L, Zhang Y, Feng GY, Han LB, Peng Y, Zhang XQ. Metabolic regulation and lipidomic remodeling in relation to Spermidine-induced stress tolerance to high temperature in plants. Int J Mol Sci. 2022;23(20):12247. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Zhao XC, Wei YL, Zhang JJ, Yang L, Liu XY, Zhang HY, Shao WJ, He L, Li ZT, Zhang YF, et al. Membrane lipids’ metabolism and transcriptional regulation in maize roots under cold stress. Front Plant Sci. 2021;12:639132. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Vrana I, Alempijevic SB, Novosel N, DeNardis NI, Zigon D, Ogrinc N, Gasparovic B. Hyposalinity induces significant Polar lipid remodeling in the marine microalga Dunaliella tertiolecta (Chlorophyceae). J Appl Phycol. 2022;34(3):1457–70. [Google Scholar]
57.Mostofa MG, Rahman MM, Ansary MMU, Keya SS, Abdelrahman M, Miah MG, Tran LSP. Silicon in mitigation of abiotic stress-induced oxidative damage in plants. Crit Rev Biotechnol. 2021;41(6):918–34. [DOI] [PubMed] [Google Scholar]
58.Sies H, Belousov VV, Chandel NS, Davies MJ, Jones DP, Mann GE, Murphy MP, Yamamoto M, Winterbourn C. Defining roles of specific reactive oxygen species (ROS) in cell biology and physiology. Nat Rev Mol Cell Biol. 2022;23(7):499–515. [DOI] [PubMed] [Google Scholar]
59.Sun H, Luo ML, Zhou X, Zhou Q, Sun YY, Ge WY, Wei BD, Cheng SC, Ji SJ. Exogenous Glycine betaine treatment alleviates low temperature-induced pericarp Browning of ‘nanguo’ Pears by regulating antioxidant enzymes and proline metabolism. Food Chem. 2020;306:125626. [DOI] [PubMed] [Google Scholar]
60.Suh BC, Hille B. PIP₂ is a necessary cofactor for ion channel function: how and why. Annual Rev Biophys. 2008;37:175–95. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Horvath SE, Daum G. Lipids of mitochondria. Prog Lipid Res. 2013;52(4):590–614. [DOI] [PubMed] [Google Scholar]
62.Taylor CW. Controlling calcium entry. Cell. 2002;111(6):767–9. [DOI] [PubMed] [Google Scholar]
63.Li LY, Shi XS, Guo XD, Li H, Xu CQ. Ionic protein lipid interaction at the plasma membrane: what can the charge do? Trends Biochem Sci. 2014;39(3):130–40. [DOI] [PubMed] [Google Scholar]
64.Tao YM, Yao LY, Qin QY, Shen W. Purification and characterization of polyphenol oxidase from jackfruit (Artocarpus heterophyllus) bulbs. J Agric Food Chem. 2013;61(51):12662–9. [DOI] [PubMed] [Google Scholar]
65.Zhi HH, Liu QQ, Dong Y, Liu MP, Zong W. Effect of calcium dissolved in slightly acidic electrolyzed water on antioxidant system, calcium distribution, and cell wall metabolism of Peach in relation to fruit Browning. J Hortic Sci Biotechnol. 2017;92(6):621–9. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1.^{(31.2KB, docx)}

Supplementary Material 2.^{(827.4KB, docx)}

Data Availability Statement

[CR1] 1.Chen YJ, Zhou JF, Ren K, Zou CM, Liu JJ, Yao GM, He JS, Zhao GK, Huang W, Hu BB, et al. Effects of enzymatic Browning reaction on the usability of tobacco leaves and identification of components of reaction products. Sci Rep. 2019;9:17580. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR2] 2.Meng L, Song WJ, Chen SW, Hu FQ, Pang BW, Cheng JJ, He B, Sun FS. Widely targeted metabolomics analysis reveals the mechanism of quality improvement of flue-cured tobacco. Front Plant Sci. 2022;13:1074029. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Wang P, Chen ZG, Xu CS, Song JY, Liu JW, Li JC, Duan WD, Sun GW. Effects of enzymatic Browning and leaf Browning inhibitors on tobacco. J Biobased Mater Bioenergy. 2021;15(6):766–73. [Google Scholar]

[CR4] 4.Wang CK, Ding YL, He B, Qiu MS, Shen DM, Chen SW, Song WJ, Qi WC, Lv YD, Meng L. Integrating transcriptome and metabolome analysis unveils the Browning mechanism of leaf response to high temperature stress in Nicotiana tabacum. Agronomy-Basel. 2024;14(8):1722. [Google Scholar]

[CR5] 5.Zhao GK, Zhang Q, Kong GH, Yao H, Wu YP, Cai B, Liu T, Zhang GH. Identification of physiological and metabolic networks involved in postharvest Browning of cigar tobacco leaves. Chem Biol Technol Agric. 2023;10(1):135. [Google Scholar]

[CR6] 6.Chen YJ, Ren K, He X, Gong JSQ, Hu XD, Su JE, Jin Y, Zhao ZX, Zhu YM, Zou CM. Dynamic changes in physiological and biochemical properties of flue-cured tobacco of different leaf ages during flue-curing and their effects on yield and quality. BMC Plant Biol. 2019;19(1):555. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR7] 7.Han ZS, Zhu MT, Wan XC, Zhai XT, Ho CT, Zhang L. Food polyphenols and Maillard reaction: regulation effect and chemical mechanism. Crit Rev Food Sci Nutr. 2024;64(15):4904–20. [DOI] [PubMed] [Google Scholar]

[CR8] 8.Tilley A, McHenry MP, McHenry JA, Solah V, Bayliss K. Enzymatic Browning: the role of substrates in polyphenol oxidase mediated Browning. Curr Res Food Sci. 2023;7:100623. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR9] 9.Suzuki N. Fine tuning of ROS, redox and energy regulatory systems associated with the functions of chloroplasts and mitochondria in plants under heat stress. Int J Mol Sci. 2023;24(2):1356. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR10] 10.Mittler R, Zandalinas SI, Fichman Y, Van Breusegem F. Reactive oxygen species signalling in plant stress responses. Nat Rev Mol Cell Biol. 2022;23(10):663–79. [DOI] [PubMed] [Google Scholar]

[CR11] 11.Wang CK, Meng L, Zhang GC, Yang XJ, Pang BW, Cheng JJ, He B, Sun FS. Unraveling crop enzymatic Browning through integrated omics. Front Plant Sci. 2024;15:1342639. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR12] 12.Ma WL, Li JX, Murtaza A, Iqbal A, Zhang J, Zhu LJ, Xu XY, Pan SY, Hu WF. High-pressure carbon dioxide treatment alleviates Browning development by regulating membrane lipid metabolism in fresh-cut lettuce. Food Control. 2022;134:108749. [Google Scholar]

[CR13] 13.Gao JJ, Guo WB, Liu QW, Liu MG, Shang C, Song YQ, Hao RJ, Li LL, Feng XX. The physiological response of apricot flowers to Low-Temperature stress. Plants-Basel. 2024;13(7):1002. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR14] 14.Sun J, Li CB, Prasad KN, You XR, Li L, Liao F, Peng HX, He XM, Li ZC, Zhang YY. Membrane deterioration, enzymatic Browning and oxidative stress in fresh fruits of three Litchi cultivars during six-day storage. Sci Hort. 2012;148:97–103. [Google Scholar]

[CR15] 15.Sangsoy K, Sanongkiet S, Srisamlee S, Beckles DM, Luengwilai K. Role of enzymatic Browning and calcium transporters in internal Browning of pineapple fruit. Postharvest Biol Technol. 2024;218:113174. [Google Scholar]

[CR16] 16.Yuan X, Zhan ZX, Lin W, Zhang C, Wang B. The membrane May be a key factor influencing Browning: a mini review on Browning mechanisms of fresh-cut fruit and vegetables from a multi-omics perspective. Front Nutr. 2025;12:1534594. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR17] 17.Zuo WF, Lu L, Su MY, Zhang J, Li YY, Huang SL, Li CX, Wang N, Zhang ZY, Chen XS. Analysis of differentially expressed genes and differentially abundant metabolites associated with the browning of Meihong red-fleshed apple fruit. Postharvest Biol Technol. 2021;174:111437.

[CR18] 18.Peng C, Deng LP, Tan HJ, Meng WC, Luo JL, Zhang ZW, Chen HQ, Qiu JS, Chang XX, Lu YS. Transcriptome and metabolome analysis of preharvest internal Browning in Nane Plum fruit caused by high temperature. Hortic Plant J. 2024;10(5):1099–111. [Google Scholar]

[CR19] 19.Zhao XY, Li XS, Bao AD, Zhang XL, Xu YB, Li YL. Metabolome and transcriptome integration explored the mechanism of Browning in Glycyrrhiza uralensis fisch cells. Front Plant Sci. 2024;15:1305871. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR20] 20.Liu YF, Liao LY, Yin FL, Song MB, Shang FF, Shuai L, Cai JH. Integration of metabolome and transcriptome profiling reveals the effect of 6-Benzylaminopurine on the Browning of fresh-cut lettuce during storage. Postharvest Biol Technol. 2022;192:112015. [Google Scholar]

[CR21] 21.Chen J, Li Y, He X, Jiao FC, Xu ML, Hu BB, Jin Y, Zou CM. Influences of different curing methods on chemical compositions in different types of tobaccos. Ind Crops Prod. 2021;167:113534. [Google Scholar]

[CR22] 22.Jiang Q, Wang HL, Wang HG. Two new methods for severity assessment of wheat Stripe rust caused by Puccinia striiformis f. Sp. tritici. Front Plant Sci. 2022;13:1002627. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR23] 23.Zhao SC, Wu ZY, Lai M, Zhao MQ, Lin BS. Determination of optimum humidity for air-curing of cigar tobacco leaves during the Browning period. Ind Crops Prod. 2022;183:114939. [Google Scholar]

[CR24] 24.Poojary MM, Lund MN. Chemical stability of proteins in foods: oxidation and the Maillard reaction. Annual Rev Food Sci Technol. 2022;13:35–58. [DOI] [PubMed] [Google Scholar]

[CR25] 25.Ataç B, Yildirim A, Gökmen V. Inhibition of enzymatic Browning in actual food systems by the Maillard reaction products. J Sci Food Agric. 2010;90(15):2556–62. [DOI] [PubMed] [Google Scholar]

[CR26] 26.Liu PL, Lu XM, Li NY, Zheng ZJ, Zhao RX, Tang XZ, Qiao XG. Effects and mechanism of free amino acids on Browning in the processing of black Garlic. J Sci Food Agric. 2019;99(10):4670–6. [DOI] [PubMed] [Google Scholar]

[CR27] 27.Yin XC, Wei YM, Li TH, Zhang JX, Zou L, Cui QQ, Lu CY, Ning JM. Heterocyclic compounds formation in large-leaf yellow tea induced by the Maillard reaction at different roasting temperatures. Lwt-Food Sci Technol. 2023;182:114856. [Google Scholar]

[CR28] 28.Yang H, Xie Y, Li XM, Wu DH, Cai GL, Lu J. Key compounds and metabolic pathway responsible for the Browning in Dangshan Pear (Pyrus spp.) wine. J Agric Food Chem. 2021;69(35):10311–20. [DOI] [PubMed] [Google Scholar]

[CR29] 29.Huang X, Jiang B, Chen JJ, Zhang T, Zheng LH. Enzymatic Preparation of non-reducing oligosaccharides from maltodextrins and nigerooligosaccharides. Starch-Starke. 2021;73(9–10):2100028. [Google Scholar]

[CR30] 30.Zhou P, Guo MF, Liu DS, Liu XM, Labuza TP. Maillard-reaction-induced modification and aggregation of proteins and hardening of texture in protein bar model systems. J Food Sci. 2013;78(3):C437–44. [DOI] [PubMed] [Google Scholar]

[CR31] 31.Gill SS, Tuteja N. Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiol Biochem. 2010;48(12):909–30. [DOI] [PubMed] [Google Scholar]

[CR32] 32.Lu J, Holmgren A. The thioredoxin antioxidant system. Free Radic Biol Med. 2014;66:75–87. [DOI] [PubMed] [Google Scholar]

[CR33] 33.Pinto JT, Cooper AJL. From cholesterogenesis to steroidogenesis: role of riboflavin and flavoenzymes in the biosynthesis of vitamin D. Adv Nutr. 2014;5(2):144–63. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR34] 34.Surai PF, Earle-Payne K, Kidd MT. Taurine as a natural antioxidant: from direct antioxidant effects to protective action in various toxicological models. Antioxidants. 2021;10(12):1876. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR35] 35.Ghifari AS, Saha S, Murcha MW. The biogenesis and regulation of the plant oxidative phosphorylation system. Plant Physiol. 2023;192(2):728–47. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR36] 36.Meyer EH, Letts JA, Maldonado M. Structural insights into the assembly and the function of the plant oxidative phosphorylation system. New Phytol. 2022;235(4):1315–29. [DOI] [PubMed] [Google Scholar]

[CR37] 37.Liu JL, Bao YM, Meng LH, Xu XB, Zhu LS, Zhang BZ. Physiological and transcriptomic analyses provide new insights into the regulatory mechanisms of accelerated senescence of Litchi fruit in response to Strigolactones. Food Bioscience. 2024;59:104213. [Google Scholar]

[CR38] 38.Lei M, Guo LL, Zhang YH, Yan XR, Jiang FL, Sun BX. Effectiveness of anaerobic treatment combined with microperforated film packaging in reducing Agaricus bisporus postharvest Browning. Postharvest Biol Technol. 2024;211:112833. [Google Scholar]

[CR39] 39.Zhang L, Wang ZQ, Zeng SX, Yuan SZ, Yue XZ, Tian T, Zhu XQ, Zheng SF, Xu XB, Zuo JH, et al. Browning mechanism in stems of fresh-cut lettuce. Food Chem. 2023;405:134575. [Google Scholar]

[CR40] 40.Fu Y, Tan H, Wang B, Peng WH, Sun Q, Yu Y. Integrated multi-omic analyses on yellow Flammulina filiformis cultivar reveal postharvest oxidative damage responses. Postharvest Biol Technol. 2023;195:112111. [Google Scholar]

[CR41] 41.Liu FJ, Huang WS, Feng ZS, Tao YX, Fan YY, He WZ, Li XL, Fang XT, Wang C, Bai YJ. Proteomic analyses on the Browning of shade-dried Thompson seedless grape. Appl Biol Chem. 2021;64(1):41. [Google Scholar]

[CR42] 42.Ghifari AS, Murcha MW. Proteolytic regulation of mitochondrial oxidative phosphorylation components in plants. Biochem Soc Trans. 2022;50(3):1119–32. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR43] 43.Lee KP, Kim C. Photosynthetic ROS and retrograde signaling pathways. New Phytol. 2024;244(4):1183–98. [DOI] [PubMed] [Google Scholar]

[CR44] 44.Majeed Y, Zhu X, Zhang N, ul-Ain N, Raza A, Haider FU, Si HJ. Harnessing the role of mitogen-activated protein kinases against abiotic stresses in plants. Front Plant Sci. 2023;14:932923. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR45] 45.Zhang MM, Zhang SQ. Mitogen-activated protein kinase cascades in plant signaling. J Integr Plant Biol. 2022;64(2):301–41. [DOI] [PubMed] [Google Scholar]

[CR46] 46.Liu YK, He CZ. A review of redox signaling and the control of MAP kinase pathway in plants. Redox Biol. 2017;11:192–204. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR47] 47.Wang XX, Wei YY, Jiang S, Ye JF, Chen Y, Xu F, Shao XF. Transcriptome analysis reveals that Trehalose alleviates chilling injury of Peach fruit by regulating ROS signaling pathway and enhancing antioxidant capacity. Food Res Int. 2024;186:114331. [DOI] [PubMed] [Google Scholar]

[CR48] 48.Liang BB, Wan SG, Ma QL, Yang L, Hu W, Kuang LQ, Xie JH, Liu DC, Liu Y. Transcriptome and physiological analyses of a navel orange mutant with improved drought tolerance and water use efficiency caused by increases of cuticular wax accumulation and ROS scavenging capacity. Int J Mol Sci. 2022;23(10):5660. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR49] 49.Ning J, Li XH, Hicks LM, Xiong LZ. A Raf-like MAPKKK gene DSM1 mediates drought resistance through reactive oxygen species scavenging in rice. Plant Physiol. 2010;152(2):876–90. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR50] 50.Xu J, Xie J, Yan CF, Zou XQ, Ren DT, Zhang SQ. A chemical genetic approach demonstrates that MPK3/MPK6 activation and NADPH oxidase-mediated oxidative burst are two independent signaling events in plant immunity. Plant J. 2014;77(2):222–34. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR51] 51.Tan JXJ, Finkel T. A phosphoinositide signalling pathway mediates rapid lysosomal repair. Nature. 2022;609(7928):815–24. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR52] 52.Wen TM, Thapa N, Cryns VL, Anderson RA. Regulation of phosphoinositide signaling by scaffolds at cytoplasmic membranes. Biomolecules. 2023;13(9):1297. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR53] 53.Laxalt AM, van Hooren M, Munnik T. Plant PI-PLC signaling in stress and development. Plant Physiol. 2025;197(2):kiae534. [DOI] [PMC free article] [PubMed]

[CR54] 54.Li Z, Cheng BZ, Zhao Y, Luo L, Zhang Y, Feng GY, Han LB, Peng Y, Zhang XQ. Metabolic regulation and lipidomic remodeling in relation to Spermidine-induced stress tolerance to high temperature in plants. Int J Mol Sci. 2022;23(20):12247. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR55] 55.Zhao XC, Wei YL, Zhang JJ, Yang L, Liu XY, Zhang HY, Shao WJ, He L, Li ZT, Zhang YF, et al. Membrane lipids’ metabolism and transcriptional regulation in maize roots under cold stress. Front Plant Sci. 2021;12:639132. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR56] 56.Vrana I, Alempijevic SB, Novosel N, DeNardis NI, Zigon D, Ogrinc N, Gasparovic B. Hyposalinity induces significant Polar lipid remodeling in the marine microalga Dunaliella tertiolecta (Chlorophyceae). J Appl Phycol. 2022;34(3):1457–70. [Google Scholar]

[CR57] 57.Mostofa MG, Rahman MM, Ansary MMU, Keya SS, Abdelrahman M, Miah MG, Tran LSP. Silicon in mitigation of abiotic stress-induced oxidative damage in plants. Crit Rev Biotechnol. 2021;41(6):918–34. [DOI] [PubMed] [Google Scholar]

[CR58] 58.Sies H, Belousov VV, Chandel NS, Davies MJ, Jones DP, Mann GE, Murphy MP, Yamamoto M, Winterbourn C. Defining roles of specific reactive oxygen species (ROS) in cell biology and physiology. Nat Rev Mol Cell Biol. 2022;23(7):499–515. [DOI] [PubMed] [Google Scholar]

[CR59] 59.Sun H, Luo ML, Zhou X, Zhou Q, Sun YY, Ge WY, Wei BD, Cheng SC, Ji SJ. Exogenous Glycine betaine treatment alleviates low temperature-induced pericarp Browning of ‘nanguo’ Pears by regulating antioxidant enzymes and proline metabolism. Food Chem. 2020;306:125626. [DOI] [PubMed] [Google Scholar]

[CR60] 60.Suh BC, Hille B. PIP₂ is a necessary cofactor for ion channel function: how and why. Annual Rev Biophys. 2008;37:175–95. [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR61] 61.Horvath SE, Daum G. Lipids of mitochondria. Prog Lipid Res. 2013;52(4):590–614. [DOI] [PubMed] [Google Scholar]

[CR62] 62.Taylor CW. Controlling calcium entry. Cell. 2002;111(6):767–9. [DOI] [PubMed] [Google Scholar]

[CR63] 63.Li LY, Shi XS, Guo XD, Li H, Xu CQ. Ionic protein lipid interaction at the plasma membrane: what can the charge do? Trends Biochem Sci. 2014;39(3):130–40. [DOI] [PubMed] [Google Scholar]

[CR64] 64.Tao YM, Yao LY, Qin QY, Shen W. Purification and characterization of polyphenol oxidase from jackfruit (Artocarpus heterophyllus) bulbs. J Agric Food Chem. 2013;61(51):12662–9. [DOI] [PubMed] [Google Scholar]

[CR65] 65.Zhi HH, Liu QQ, Dong Y, Liu MP, Zong W. Effect of calcium dissolved in slightly acidic electrolyzed water on antioxidant system, calcium distribution, and cell wall metabolism of Peach in relation to fruit Browning. J Hortic Sci Biotechnol. 2017;92(6):621–9. [Google Scholar]

PERMALINK

Integrative transcriptomics and metabolomics analyses reveals Maillard reaction and antioxidant capacity-mediated leaf Browning during the flue-curing process in Nicotiana tabacum L

Nan Xu

Jingtao Lei

Xiangli Xu

Risheng Hu

Mengchen Liu

Bingyan Jia

Xiaoxu Li

Lixin Zhou

Xiaonian Yang

Zhi Liu

Abstract

Supplementary Information

Introduction

Materials and methods

Plant materials and experimental treatments

Determination of browning index

Quasi-targeted metabolomics analysis

RNA extraction, library construction, and transcriptome analysis

Quantitative real-time PCR (qRT-PCR) validation

Identification of hub genes using WGCNA

Accession numbers

Statistical analysis

Results

Phenotypic characteristics of browning

Fig. 1.

Metabolomic analysis during the flue-curing process

Fig. 2.

Transcriptomic analysis during the flue-curing process

Fig. 3.

qRT–PCR validation

Fig. 4.

Combined metabolomic and transcriptomic analyses

Fig. 5.

DAMs and DEGs related to amino acid metabolism in Dabaijin

Fig. 6.

DAMs and DEGs related to riboflavin metabolism in Dabaijin

DAMs and DEGs related to amino sugar and nucleotide sugar metabolism in Dabaijin

DAMs and DEGs related to inositol phosphate metabolism in Dabaijin

DAMs and DEGs related to amino acid metabolism in Yunyan87

Fig. 7.

DAMs and DEGs related to sulfur metabolism and taurine and hypotaurine metabolism in Yunyan87

DAMs and DEGs related to galactose metabolism in Yunyan87

Identification of key KEGG pathways related to Browning by WGCNA

Fig. 8.

Identification of hub genes associated with oxidative phosphorylation by WGCNA

Fig. 9.

Discussion

Accumulation of Maillard reaction substrates promoted Browning

The antioxidant system plays an important role in Browning

Conclusions

Fig. 10.

Supplementary Information

Acknowledgements

Authors' contributions

Funding

Data availability

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

Contributor Information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases