Integrated analysis of physiological and metabolic data uncovers essential dynamic mechanisms involved in the maturation of cigar tobacco leaves

Integrated analysis of physiological and metabolic data uncovers essential dynamic mechanisms involved in the maturation of cigar tobacco leaves | 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 Integrated analysis of physiological and metabolic data uncovers essential dynamic mechanisms involved in the maturation of cigar tobacco leaves Haiying Liu, Xinwen Chi, Jinpeng Yang, Guangda Ding, Fangsen Xu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4467753/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The timing of maturity significantly impacts the quality of cigar tobacco leaves, with both premature and delayed maturation leading to quality degradation. Despite the known association between maturity and physiological metabolic activities, there is a paucity of concrete evidence detailing the physiological behavior of cigar leaves harvested at varying times. This research involved a comprehensive physiological and metabolomic examination of the cigar tobacco variety CX-014, cultivated in Danjiangkou City, Hubei Province. The study focused on leaves picked at 35 (T1), 42 (T2), 49 (T3), and 56 (T4) days following the removal of the apical inflorescence. As the harvest period progressed, the leaves’ hue transitioned from green to yellow, displaying white mature spots. Between T1 and T2, there was an uptick in pigment indices (like chlorophyll a and b) and photosynthetic traits (such as stomatal conductance), which then diminished in the T3 and T4 samples. Optimal levels of sugar-to-nicotine and potassium-to-chlorine ratios—key indicators of smoking quality and tobacco combustibility—were observed at T3, suggesting a more balanced chemical composition in the leaves harvested at this stage. Metabolomic analysis revealed 2,153 distinct metabolites, with the most significant changes occurring between T2 and T3, highlighting critical physiological transformations during this interval. Pathway enrichment analysis via KEGG pinpointed notable shifts in amino acid synthesis pathways, particularly those involving tryptophan, alanine, and aspartate. Tryptophan metabolism and zeatin biosynthesis were substantially altered, with compounds like indolepyruvic acid, N-formylpurine nucleotide, isopentenyladenine nucleotide, and dihydrozeatin showing marked reductions at T3. This study also explored how the timing of lower leaf harvest influences the physiological processes of middle leaves, finding that a plethora of metabolites associated with the breakdown of arachidonic acid—a primitive metazoan signaler implicated in plant stress and defense networks—were abundant in T3 leaves when lower leaves were harvested 43 to 38 days prior. These findings suggest that the harvest timing of lower leaves may sway the maturation physiology and environmental adaptability of middle leaves. Overall, this investigation sheds light on the intricate physiological dynamics of cigar leaves throughout maturation and pinpoints crucial metabolites that signify pivotal metabolic pathways. Biological sciences/Biological techniques/Metabolomics Biological sciences/Plant sciences/Plant physiology Cigar Harvestingtime Maturation physiology Metabolism Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Plant leaf maturation is a complex process in which leaf cell structure, metabolism, and gene expression undergo orderly changes [ 1 ]. Leaf maturation senescence is manifested by a gradual decrease in leaf chlorophyll content[ 2 ] and a decrease in photosynthetic rate [ 3 ] leading to a gradual yellowing of the leaf. It also leads to changes at the cellular level of the leaf, and studies have shown that senescence leads to a decrease in the orderliness of the palisade cell, enlargement of cells, vesicles, and cell gaps; increase in leaf area and thickness, etc. [ 4 ]. This process cannot be separated from the regulatory effects of various chemicals, for example, gibberellins (GA) are involved in processes such as cell elongation and leaf expansion, and exogenous application of GA can delay leaf senescence[ 5 ]. The opposite is true for abscisic acid (ABA), where increased levels of ABA promote leaf senescence [ 6 ]. Nowadays, the advent of histology has changed the strategy for studying leaf maturation, offering the possibility of systematically assessing the dynamics of the leaf maturation process on a large scale, both at the molecular level and at the metabolic level. Through transcriptomics studies, it was found that the NAC family genes NAC4 and NAC48 positively regulate leaf maturation, and ORE1 can control the expression of many senescence-associated genes (SAGs) by directly binding to their promoters [ 7 ]. At the same time, metabolomics studies are showing that many metabolites change during leaf maturation, such as glutamic acid (Glu) and aspartic acid (Asp) significantly decrease with leaf senescence[ 8 ]. At present, the wide application of omics technology has made it possible to study the regulation mechanism of leaf maturation, and great progress has been made. At the molecular level, it has been identified that many transcription factors TFs (such as NAC) play a key role in regulating leaf maturation and senescence by interacting with other transcription factors, hormones, and environmental signals to alter transcription levels of related genes (such as chlorophyll catabolism genes) [ 9 ]. As the end products of genetic expression, many metabolites (such as sugars, amino acids, etc.) have also been shown to play a crucial role in leaf maturation and senescence. It has been reported that the accumulation of sugars in early maturation of arabidopsis can induce the expression of age-related genes (SAGs), while the expression of SAGs in late maturation is inhibited by sugars [ 10 ]. Glycine (Gly) and serine (Ser) can change leaf photosynthesis by affecting the photorespiration rate during the aging process of tobacco leaves [ 11 ]. Polyamines can delay the aging and ripening of tobacco leaves by delaying membrane degradation and chlorophyll loss[ 12 ]. IAA (indole-3-acetic acid) can accelerate leaf senescence by promoting biosynthesis of related hormones (ethylene, abscisic acid, etc.) that inhibit plant growth [ 13 ]. Studies have shown that with the maturity of leaves, anabolic activities in leaves decrease and catabolic activities increase [ 14 ]. As a rapidly developing technology in the post-genomics era, metabolomics technology can intuitively and quickly enable us to understand the metabolic differences between samples and determine the physiological functions of certain metabolites[ 11 ]. Tobacco is a good tool for research, such as the study of transgenic technology, protein localization studies and protein-protein interactions, protein-gene interactions, etc. Most of these types of experiments are currently being carried out in Ben's tobacco. Tobacco leaves are also a very suitable material for histological studies of real environments and plant leaf maturation processes due to their high biomass and ease of cultivation in the field. It was found through transcriptomics studies that genes such as AP2/EREBP, bHLH, and WRKY are involved in nicotine biosynthesis in tobacco leaves[ 15 ]. It was found through metabolomics studies that tobacco topping favors the accumulation of secondary metabolites such as chlorogenic acid and rutin in the upper leaves[ 16 ]. The expression of proteins related to photosynthesis and glycolysis was found to decrease during tobacco leaf senescence through proteomics studies [ 17 ]. However, there are not many studies on histology in cigar tobacco, and the main studies focus on the material changes of tobacco during fermentation and drying such as Li, N et al. [ 18 ] revealed the mechanism of reduction of green spot in the drying process of cigar tobacco through metabolomics analysis, and none of these studies specifically elaborated on the dynamic process of metabolite alteration in the process of tobacco maturation. Therefore, in the present study, the dynamic analysis of physiological metabolites in tobacco leaves at different growth time points was carried out using the tools of metabolomics, and it was found that the amino acid levels were significantly responded to by the maturation and senescence of tobacco leaves. The effect of lower leaf position on the maturation process of the cutter leaf was also assessed, revealing that the signaling between the lower leaf influence and the cutter leaf leads to changes in the synthesis of relevant compounds and thus affects the maturation process of the cutter leaf. Results Physiological changes in tobacco leaves at different harvesting times To record the appearance changes of leaves (middle position of seedling), we compared samples harvested at 35 d (T1), 42 d (T2), 49 (T3) and 56 d (T4) after tobacco topping. T1 samples showed a green color which gradually declined along with the yellow color increasing, especially in T3 and T4 samples (Fig. 2 a). The visible white color appeared in the main branch vein from T1 samples to T4 samples (Fig. 2 a). These appearance changes were more obvious in pseudo-color processing (Fig. 2 b). It is noted that yellow-white mature spots appeared in T3 and T4 samples. To understand the physiological dynamics in these samples, pigment contents and photosynthesis parameters were measured. Contents of chlorophyll a and chlorophyll b increased slightly in the T2 samples followed by a decrease in the T3 and T4 samples compared to the T1 samples (Fig. 2 c-d). For the content of carotenoid, only a significant reduction was observed in the T4 samples relative to other samples (Fig. 2 e). Compared with T1 samples, T2 samples had an increase in the stomatal conductance (Gs) and transpiration rate (Tr) by 18.65% and 11.06% (Fig. 2 g-h). In contrast, Gs were sharply reduced by 78.32% and 75.51% in the T3 and T4 samples, respectively and Tr were largely reduced by 86.60% and 74.20% in the T3 and T4 samples, respectively (Fig. 2 g-h). The Net photosynthetic (Pn) level showed a decline of 26.92% compared with T1 samples, and a further decline of 65.80% and 54.32% in the T3 and T4 samples, respectively (Fig. 2 f). These results suggest that tobacco leaves might undergo a physiological mature transition from the T2 to the T3 stage. Differences in the nutritional quality of tobacco samples Yang et al. [ 19 ] reported that chemical composition indexes are tightly correlated with tobacco leaf nutritional quality. We thus determined the chemical composition indexes in our samples (Table 1 ). Compared with T1, the contents of nicotine, reducing sugar, total sugar, and total nitrogen in tobacco leaves increased in T2 and decreased in T3 and T4. Among them, reducing sugar content and total sugar content had a significant increase of 2.5-fold and 1-fold respectively, in comparison with T1. The contents of potassium and chlorine in tobacco leaves showed an increasing trend. The contents of potassium and chlorine in T4 samples were the highest, increasing by 126.67% and 86.05% compared with T1 respectively. The ratio of potassium to chlorine was positively correlated with tobacco leaf flammability. However, there was no significant difference in the potassium-chlorine ratio among all samples. Sugar-nicotine ratio is related to the aroma and taste of tobacco leaves. Among samples, the sugar-nicotine ratio reached the maximum value in the T2 sample. As a result of harvesting tobacco leaves too late, their quality will decline. Table 1 Nutritional quality of cigar tobacco leaves after curing under different treatments Groups Nicotine (% DW) Reducing sugar (% DW) Total Sugar (% DW) Total Nitrogen (% DW) Potassium (% DW) Chlorine (% DW) Sugar-nicotine ratio K/Cl T1 5.39 ± 0.22a 0.32 ± 0.12bc 0.78 ± 0.15bc 3.50 ± 0.15ab 1.65 ± 0.32b 0.43 ± 0.03b 0.14 ± 0.02b 3.79 ± 0.49a T2 6.29 ± 0.17a 1.12 ± 0.41a 1.56 ± 0.38a 3.94 ± 0.33a 2.03 ± 0.17b 0.47 ± 0.05b 0.25 ± 0.06a 4.35 ± 0.10a T3 6.93 ± 0.84a 0.93 ± 0.57ab 1.31 ± 0.57ab 3.54 ± 0.27ab 2.63 ± 1.25ab 0.75 ± 0.04a 0.19 ± 0.06ab 3.48 ± 1.50a T4 3.20 ± 1.65b 0.18 ± 0.04c 0.42 ± 0.06c 3.42 ± 0.26b 3.74 ± 0.50a 0.80 ± 0.17a 0.15 ± 0.05b 4.77 ± 0.42a Metabolomics analysis of tobacco leaves after different treatments Principal component analysis To investigate the changes in cigar tobacco metabolic processes under varying harvesting times, we analyzed the metabolites of the above samples using LC-MS. Eventually, 1078 valid peaks were detected in cation mode and 1075 valid peaks in anion mode for different treatment groups (Fig. 3 c). PCA analysis of metabolic profiles showed T1 and T2 were clustered and T3 was close to T4 samples either in positive ion modes (POS) or negative ion modes (NEG) (Fig. 3 a-b). According to this result, a distinct physiological metabolic process occurred between T2 and T3 samples. In T3 and T4 samples, this process slowly changed. Screening of different metabolites and KEGG analysis of functional pathways in samples Based on the metabolic data of T2 vs T1, T3 vs T2 and T4 vs T3, we set the VIP > 1.0 and P < 0.05 to identify differential metabolites (DMs). A total of 2016 significant DMs were obtained, among which the T2 vs T1 group screened out 180 down-DMs and 95 up-DMs; the T3 vs T2 group screened out 914 down-DMs and 562 up-DMs; and the T4 vs T3 group checked out 571 down-DMs and 168 up-DMs (Fig. 4 a-c). Therefore, the T3 vs T2 group represents the key physiological changes since its DMs account for 73.21% of all DMs. In addition, there were more down-DMs than up-DMs in all groups. We next focused on the T3 vs T2 group’s DMs to obtain the hierarchical rank of physiological functions involved in metabolites. According to annotation in the KEGG compound database, the T3 vs T2 group’s DMs were mainly clustered in three categories: compounds with biological roles, phytochemical compounds, and lipids, respectively (Fig. S1 a-c). The most prominent three metabolites are amino acids、fatty acids、monosaccharides and phospholipids in the category of “compounds with biological roles” (Fig. S1 a). The most prominent three metabolites are alkaloids derived from tryptophan and anthranilic acid、monoterpenoids (C10)、diterpenoids (C20)、flavonoids and alkaloids derived from ornithine in the category of “phytochemical compounds” (Fig. S1 c). The top three metabolites are FA01 fatty acids and conjugates, PR01 isoprenoids, and PK12 flavonoids in the category of “lipid metabolites” (Fig. S1 b). The metabolic pathways for these DMs were established using annotation of the KEGG pathway database. Most DMs involved three pathways, metabolism, environmental information processing and genetic information processing (Fig. 4 d). The metabolism pathway had the most abundant secondary metabolic pathways such as biosynthesis of other secondary metabolites (67 DMs), amino acid metabolism (67 DMs) and metabolism of cofactors and vitamins (34 DMs). KEGG pathway enrichment analysis of differential metabolites We further screened the significantly enriched metabolic pathways from the pathways involved in T3vsT2 DMs using P -value ≤ 0.05 as the threshold. There were 27 enriched metabolic pathways (the top 20 were displayed in Fig. 5 ), of which 6 were highly significant enriched metabolic pathways with P < 0.001. Among them are tryptophan metabolism, secondary metabolism in plants, alanine, aspartate and glutamate metabolism, phenylpropanoid biosynthesis, zeatin biosynthesis, and aminoacyl-tRNA biosynthesis (Fig. 5 ). In the top 20 pathways, a large portion of DMs were enriched in amino acid synthesis and metabolism pathways including alanine, aspartate, lysine glutamate, tyrosine and phenylalanine. In addition, the enrichment ratios of betalain biosynthesis, cyanoamino acid metabolism, linoleic acid metabolism, and plant hormone signal transduction were over 0.15. Key differential metabolites in samples Combined analyses of KEGG enrichment in DMs of T3vsT2, T3vsT1, T4vsT1 and T4vsT2 found tryptophan metabolism pathway and zeatin biosynthesis pathway were the most significantly enriched pathways shared in all groups with P < 0.001 (Fig. 5 and Fig. S2). In zeatin biosynthesis pathway, 4-up DMs and 5-down DMs were found from T1 to T4 (Fig. 6 a). In particular, isopentenyl adenosine was the most significant with upregulation of 128.17% in T3 and T4 samples compared with T1 and T2 samples (Fig. 6 b). In tryptophan metabolic pathway, 5-up DMs and 11-down DMs were found (Fig. 6 c). Among them, indolepyruvic acid was the most significantly differentially expressed, which was down-regulated by 34.29% in T4 compared with T1(Fig. 6 d). Effect of lower leaf's growth on metabolism of cutter leaf In production practice, lower leaves are usually harvested one week earlier than T2 cutter leaves. However, whether removal time of lower leaf affects T2 cutter leave’s physiological maturity remains unclear. Here, we removed lower leaves from seedlings at 3 weeks, 2 weeks or 1 week before T2 harvest and named these T2 samples as F1, F2 and F3, respectively. Samples were then analyzed with LC-MS for metabolite changes. PCA analysis PCA analyses of POS modes and NEG modes clearly concentrated each sample point (Fig. 7 a-b), indicating that metabolites within each sample are highly consistent. F1 and F2 had very similar distribution patterns with 207 (differential ion peaks) DIPs in POS modes and 220 DIPs in NEG modes (Fig. 7 c), suggesting that their metabolites are not much different. F3 samples, on the other hand, differed fromF1 and F2 samples. F3 vs F2 samples had 390 DIPs in POS modes and 358 DIPs in NEG modes, and F3 vs F1 samples had 528 DIPs in POS modes and 486 DIPs in NEG modes (Fig. 7 c). This result suggests that F3 samples had differential physiological processes from F1 and F2. It supports the concept that lower leaf growth can affect cutter leaves' physiological maturity. Different metabolites analysis and functional pathways of KEGG compounds A total of 1072 DMs were screened out in F2vsF1 and F3vsF2 groups, including 222 down-DMs and 205 up-DMs in F2vsF1 group(Fig. 8 a), and 486 down-DMs and 262 up-DMs in F3vsF2 group (Fig. 8 b).This result confirmed that harvesting time of lower leaf can affect the physiological process of cutter leaves. Based on annotation of the KEGG compound database, DMs from the F3vsF2 groups grouped into three categories: compounds with biological roles, phytochemical compounds, and lipids (Fig.S3), which is consistent with T1-T4 results (Fig. S1 ). In the category of “compounds with biological roles”, lipids were the largest compounds and the number of lipids was 10 (Fig.S3a). Ranked second were steroids followed by peptides and antibiotics, nucleic acids and organic acids, steroid hormones, carbohydrates and vitamins in order of numbers. Category of “lipids” included 37 fatty acyls, 21 sterol lipids, 19 prenol lipids, 14 polyketides, 6 glycerophospholipids and 2 sphingolipids (Fig. S3b). Category of “phytochemical compounds” included 19 terpenoids, 16 alkaloids, 14 phenylpropanoids, 9 flavonoids, 6 amino acid related compounds, 1 polyketide and 1 fatty acid (Fig. S3c). In the KEGG PATHWAY database, DMs of the F3 vs F2 groups were primarily involved in "metabolism, environmental information processing, and genetic information processing" (Fig. 8 c). There was a high amount of metabolism, including amino acid metabolism, secondary metabolite biosynthesis, and lipid metabolism. KEGG pathway enrichment of differential metabolites Significantly enriched metabolic pathways were further screened in F3 vs F2 group using P -value ≤ 0.05. as the threshold. Arachidonic acid metabolism was only one enriched metabolic pathway with P < 0.001 (Fig. 9 ). In addition, amino acid metabolic pathways (e.g., tyrosine metabolism) as well as tricarboxylic acid cycle-related pathways (e.g., citrate cycle) were also significantly enriched. These results indicate that DMs of F3 vs F2 group mainly enriched in the amino acid metabolic pathway and lipid metabolic pathway. Key differential metabolites in enriched pathways We also found that arachidonic acid metabolic was also the highest enriched pathway in all F3vsF1 KEGG enriched pathways (Fig. S4). Subsequently, the DMs of arachidonic acid metabolic pathways in the F2vs F1 and F3 vs F1 groups were extracted. No up-DMs were found and total 15 metabolites showed down regulation from F1 to F3 samples (Fig. 10 a). The most down-regulated was (19S)-Hydroxyeicosatetraenoic acid (19(S)-HETE), with a 15.07% decrease in F3 over F1 (Fig. 10 b), followed by 20-Hydroxyeicosatetraenoic acid (20-HETE) and Prostaglandin A2 (PGA2), with a 7.89% decrease and 7.65% decrease, respectively (Fig. 10 c-d). Discussion Leaf maturation and senescence is an important process in plants, especially for the quality of cigar tobacco leaves [ 20 ]. So far in China, cigar harvesting time depends on the farmer’s experience which easily causes uneven in cigar quality. Study of cigar maturation mechanism is helpful to provide theoretical and technical support for the establishment of technical standard system of tobacco production and harvesting technology in tobacco field. This study investigates the nutrient changes and metabolic processes of cigar tobacco during its maturation from physiological and molecular aspects. Harvesting time affected physiological process of tobacco leaves According to the results of appearance changes of tobacco leaves, the color of tobacco leaves gradually turns yellow, which may be due to the degradation of chlorophyll during leaf maturation and aging. Studies have shown that chlorophyll loss is a significant feature of leaf aging and a marker of chloroplast degradation [ 21 ]. In this study, with the improvement of harvest maturity, the contents of chlorophyll a, chlorophyll b and carotenoid in leaves showed a trend of first increasing and then decreasing(Fig. 2 c-e), suggesting that T1 to T4 tobacco leaves may be undertaken in the pre-maturation to maturation periods. At the early stage, assimilation is the main process, thus the chloroplasts did not degrade. Then at late stage, chloroplasts degraded accompanied by color changes. Photosynthetic parameters such as net photosynthetic rate, stomatal conductance, and transpiration rate are used as indicators to consider the strength of photosynthesis in studies [ 17 ]. In this study, both stomatal conductance and transpiration rate in tobacco leaves consistently decrease with increased maturity(Fig. 2 f-h), aligning with the findings by He et al. that photosynthetic parameters decline as the leaves age. This suggests that maturation significantly impacts the light and water use efficiency of tobacco, potentially accelerating the weakening of the net photosynthetic rate in the later stages of maturity. Chemical components such as sugar-nicotine ratios, potassium-chlorine ratios are closely related to the quality of tobacco [ 22 ]. Research suggests that high nicotine levels in tobacco increase its harshness and spiciness. The sugars, particularly reducing sugars, in the leaves can neutralize alkaline components in smoke by producing an acidic reaction during combustion, maintaining the smoke's pH within a suitable range [ 20 ]. Apropriate sugar-nicotine ratios enable tobacco to maintain good taste as well as flavor [20、23]. Additionally, the nitrogen content in tobacco leaves is related to the formation of nicotine and is a fundamental element for high tobacco yield and quality, reflecting the leaf's nitrogen-supplying capacity [20、24]. In this study, the levels of nicotine, total nitrogen, reducing sugars, total sugars, and the nicotine ratio in tobacco leaves initially increased and then decreased over time, reaching their lowest values with the T4 treatment (Table 1 ) . This may be due to robust growth and strong photosynthesis in the early maturation stage of the leaves, which leads to better absorption and conversion of nutrients, resulting in a significant accumulation of sugars and nitrogen. Subsequently, as the leaves mature and age, their chemical components gradually transform and decompose, with sugars breaking down more rapidly, leading to a decrease in sugars and nitrogen content. Additionally, an optimal potassium content in the leaves can enhance their combustibility [ 20 ]. In contrast, chlorine can suppress the burning of tobacco leaves [ 25 ]. Therefore, the potassium-to-chlorine ratio is commonly used to compare the combustion properties of tobacco leaves. Therefore, chemical components dynamics are consistent with the alteration of photosynthesis parameters from T1 to T4 leaves, confirming that leaves harvested at different times undertaken distinct physiological processes. Key metabolic pathways and metabolites of tobacco leaves under different harvesting times Metabolomic analysis of different samples revealed that significant metabolic differences began to appear between T2 and T3 samples, suggesting that harvested at 42d (T2)-49d (T3) is likely the transition stage from vigorous growth to senescence and maturity of tobacco leaves. According to the results of KEGG enrichment analysis of T3vsT2 differential metabolites (Fig. 5 ), pathways related to plant cell homeostasis and plant growth and development, such as amino acid metabolism, zeatin biosynthesis, and plant hormone signal transduction, were significantly affected in cigar tobacco leaves. The pathways involved in amino acid synthesis and metabolism were most affected, including tryptophan, alanine, aspartate, lysine glutamate, tyrosine, and phenylalanine. Studies have shown that alanine, aspartic acid, and glutamic acid have very important roles in plants, glutamic acid influences nitrogen metabolism and intervenes in nitrogen assimilation in plants [ 26 ]. Aspartic acid metabolism is involved in the synthesis of several important amino acids such as alanine, threonine, and lysine [ 25 ], Tyrosine, phenylalanine, and tryptophan are the three aromatic amino acids in plants, and the compounds derived from these aromatic amino acids play as a constituent of several phenolic substances including pigments and cell walls, as well as hormones such as growth hormone and salicylic acid in plants important roles [ 27 ]. It can be seen that changes in these amino acid metabolic pathways can affect the biosynthesis of related substances and thus affect plant development. Further, to find the key metabolic pathways regulating leaf growth to maturity, we also analyzed the KEGG enrichment of other comparison groups with large metabolic differences (including T1vsT3, T1vsT4, T2vsT4), and found that the zeatin biosynthesis and tryptophan metabolism were both significantly enriched ( P < 0.0001) in these groups(Fig. S2), indicating that these two metabolic pathways are probably the most critical ones ( P < 0.0001), indicating that these two metabolic pathways are probably the most critical metabolic pathways. Zeatin is a natural cytokinin, and several of the most common derivatives are isopentenyladenine, trans-zeatin, cis-zeatin, and dihydrozeatin, which play an important role in the regulation of plant senescence [ 28 ], one manifestation of which is the retardation of leaf degradation of chlorophyll content [ 29 ]. In this study, by analyzing the metabolic pathways of zeatin, it was found that trans-Zeatin riboside, cis-Zeatin riboside, and dihydrozeatin contents were significantly decreased in T3 and T4 samples(Fig. 6 a), which is highly consistent with the results of the study that showed a significant decrease in chlorophyll content in T3 and T4 samples(Fig. 2 c-e), suggesting that possibly zeatin, especially the decrease in dihydrozeatin content, may lead to the decrease in chlorophyll content and thus promote the maturation and senescence of leaves, which is the important reason for the transition from T2 to T3. The decrease in dihydrozeatin content may be due to the inhibition of Isopentenyl adenosine catabolism. Tryptophan metabolism plays an important role in plant defense [ 30 ], and past studies have demonstrated that the metabolites of tryptophan, such as growth hormones like indole-3-acetic Acid, Melatonin, Serotonin, and Cinnabarinic acid, as well as organic acids can protect cellular growth activity by enhancing the antioxidant system, regulating stomatal closure, reducing ROS production and protecting cell growth activity thereby delaying plant senescence [31、32、33、34], in the present study the tryptophan metabolic pathway was analyzed and it was found that most of the metabolites of tryptophan were down-regulated and expressed in the T3 and T4 samples. It is noteworthy that growth hormones such as indole-3-acetic Acid, Melatonin, Serotonin, etc. were not significantly different among the samples however their related synthetic precursors or catabolic products showed significant changes in T3 and T4 samples, especially Indolepyruvic acid was significantly down-regulated in expression (Fig. 6 ). This suggests that the differential expression of tryptophan metabolites, especially the down-regulation of growth-related hormones and organic acid synthesis precursors and catabolites, may have led to a decline in the antioxidant defense system of the leaves in this study, resulting in a decrease in the physiological function of the leaves and the gradual maturation and senescence of the leaves. Effects of lower leaf harvesting time on metabolism of cutter tobacco leaves Tobacco plant as a whole, the growth and development of different parts of the plant must have mutual influence and constraints. Studies have shown that the lower leaves senesce first during the development of tobacco leaves, which triggers nutrient redistribution from the lower leaves to the upper leaves and leads to relevant changes in leaf metabolism [ 8 ]. This study investigated the effect of lower leaf harvesting time on the metabolism of central leaves aiming to find the optimal harvesting time point. According to the results of this study, lipid metabolism, amino acid metabolism, and Citrate cycle (TCA cycle) metabolism of central tobacco leaves changed significantly with the lower leaf harvesting time (Fig. 9 ). Among them, the Arachidonic acid metabolism pathway was most significantly enriched, which affects the catabolism of arachidonic acid. Arachidonic acid is a polyunsaturated fatty acid that is an important component of membrane lipids influencing membrane function [ 35 ], and is also a major contributor to many eicosanoids (arachidonic-like acids) including hydroxy eicosatetraenoic acids (HETEs), leukotrienes (LTs), the prostaglandins (PGs), thromboxanes (TXs), isoprostanes (IsoPs), etc. [ 36 ], and studies have shown that arachidonic acid and arachidonic acid-like acids are particularly potent signaling molecules in plants and animals and are involved in cellular signaling and thus trigger the plants and animals to respond to stress [37、38] In the present study, the arachidonic acid catabolism process was significantly influenced by the down-regulation of the content of many classes of arachidonic acids, especially 19(S)-HETE as well as 20-HETE in hydroxy eicosatetraenoic (Fig. 10 ), which can be seen as a possible reason for the harvesting of the lower leaf. As a result, the time of harvesting the lower leaves affects the certain physiological processes in the middle leaves. Conclusion In this study, significant changes in the physiology and metabolism of tobacco leaves occurred with the passage of harvesting time. In terms of physiology, tobacco leaves gradually turned yellow, chlorophyll content and photosynthesis efficiency decreased, and overripe harvested tobacco leaves resulted in more disjointed chemical nutrients related to tobacco quality. Metabolically, significant metabolic changes occurred in tobacco leaves harvested at 42d (T2)-49d (T3), which we hypothesized to be the turning point of tobacco leaves from vigorous growth to maturity, mainly by affecting tryptophan metabolism and zeatin biosynthesis metabolism pathway, thereby affecting the biosynthesis and catabolism of tobacco cytokinin and growth hormone, which then hindered the growth and development of plants leading to Leaves grow vigorously to maturity and senescence. Delayed harvesting of the lower leaves also affected the metabolism of the central leaves, mainly affecting leaf arachidonic acid metabolism and thus the content of arachidonic acid in the leaves, which led to changes in leaf physiology due to the inhibition of leaf signaling, and the related key metabolites showed significant changes in the F3 treatment (harvested 38 days after the lower leaves were harvested). In summary, we hypothesized that the lower leaves were harvested 33–38 days before the central leaves were harvested, and the central leaves were harvested 42–49 days after topping, which indicated that the maturity was better and suitable for harvesting. Materials and methods Plant culture and experimental design The experiment was conducted in Xijiadian Town (111°18′N, 32°75′E), Danjiangkou City, Hubei Province in 2022. During the growing period (April to August), the monthly mean temperature and rainfall are shown in Fig. 1 . The soil was yellow brown soil and the basic physical and chemical properties are: pH7.5, organic matter 8.4 g/kg, available phosphorus 8.1 mg/kg, available potassium 161.7 mg/kg, alkali hydrolyzed nitrogen 88.7 mg/kg. The experimental material was Chuxue No. 14 (CX-014) provided by Hubei Tobacco Research Institute, China. Seedlings were planted in early February using the intensive floating garden method and transplanted on 20 April. Field experiments were conducted in different zones in a completely randomized block design. Experiment 1: The plant topping was completed on June 13. Cutter leaves are study materials. The lower leaves were uniformly harvested 38 days before the collection of the cutter leaves, and 4 treatments were set according to different harvesting times of the cutter leaves, which were recorded as T1, T2, T3 and T4, representing 35 days, 42 days, 49 days and 56 days after topping, respectively. Each treatment had 3 area replicates. Experiment 2: The plant topping was completed on June 13. The cutter leaves were uniformly harvested 42 days after topping, and three treatments were set according to different harvesting times of the lower leaves, which were recorded as F1, F2 and F3, representing that the cutter leaves were harvested 48, 43 and 38 days after the lower part of the leaf was removed, respectively. Each treatment was repeated 3 times, and 30 plants were planted in each replicate plot. Samples were collected from bottom to top according to the order of leaf position. On the day of harvest, six representative tobacco plants were selected, and fresh cutter leaves were collected for the determination of relevant physiological indicators. The remaining cutter leaves after harvest were placed in a drying room for drying. After drying, six-leaf samples were randomly selected from each treatment for the determination of tobacco leaf nutrient content and metabolomics, with mixed samples used to ensure uniformity during analysis. Determination of photosynthetic index and pigment content The net photosynthetic rate (Pn), transpiration rate (Tr) and stomatal conductance (Gs) of the cutter leaves were measured by a Li-6400 portable photosynthetic apparatus. After the mature fresh samples were crushed and extracted with 95% ethanol, the absorbance at 665 nm, 649 nm and 470 nm was determined. The contents of chlorophyll a, chlorophyll b and carotenoid were calculated according to the formula. Chla=(13.95A 665 -6.88A 649 )×V×N/M Chlb=(24.96A 649 -7.32A 665 )×V×N/M carotenoid=( \(\frac{1000{A}_{470}-2.05{C}_{a}-114.8{C}_{b}}{245}\) )×V×N/M V: extraction liquid volume (ml) N: dilution ratio M: sample fresh weight (g) Determination of nutrient content The nutrient content index included nicotine, reducing sugar, total sugar, chlorine, total nitrogen and potassium in cigar leaves. The contents of total nitrogen, chlorine, total sugar, reducing sugar and nicotine were measured using a mobile injection analyzer (AA3, Seal Aultical). The potassium content was measured by flame spectrophotometry (M410). Metabolomics analysis 50 mg tobacco sample was added to a 2 mL centrifuge tube and a 6 mm diameter grinding bead was added. 400 µL of extraction solution (methanol: water = 4:1 (v:v)) containing 0.02 mg/mL of internal standard (L-2-chlorophenylalanine) was used for metabolite extraction. Samples were ground by the Wonbio-96c (Shanghai Wanbo Biotechnology Co., LTD) frozen tissue grinder for 6 min (-10°C, 50 Hz), followed by low-temperature ultrasonic extraction for 30 min (5°C, 40 kHz). The samples were left at -20°C for 30 min, centrifuged for 15 min (4°C, 13000 g), and the supernatant was transferred to the injection vial for LC-MS/MS analysis. The LC-MS/MS analysis of sample was conducted on a SCIEX UPLC-Triple TOF 5600 system equipped with an ACQUITY HSS T3 column (100 mm × 2.1 mm i.d., 1.8 µm; Waters, USA) at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The mobile phases consisted of 0.1% formic acid in water:acetonitrile (95:5, v/v) (solvent A) and 0.1% formic acid in acetonitrile:isopropanol:water (47.5:47.5, v/v) (solvent B). Positive ion mode separation gradient: 0–3 min, mobile phase B was increased from 0–20%; 3-4.5 min, mobile phase B was increased from 20–35%; 4.5-5 min, mobile phase B was increased from 35–100%; 5-6.3 min, mobile phase B was maintained at 100%; 6.3–6.4 min, mobile phase B was decreased from 100–0%; 6.4-8 min, mobile phase B was maintained at 0%. Separation gradient in negative ion mode: 0-1.5 min, mobile phase B rises from 0 to 5%; 1.5-2 min, mobile phase B rises from 5–10%; 2-4.5 min, mobile phase B rises from 10–30%; 4.5-5 min, mobile phase B rises from 30–100%; 5-6.3 min, mobile phase B linearly maintains 100%; 6.3–6.4 min, the mobile phase B decreased from 100–0%; 6.4-8 min, the mobile phase B was linearly maintained at 0%. The flow rate was 0.40 mL/min and the column temperature was 40°C. The pretreatment of LC/MS raw data was performed by Progenesis QI (Waters Corporation, Milford, USA) software, and a three-dimensional data matrix in CSV format was exported. The information in this three-dimensional matrix included sample information, metabolite name and mass spectral response intensity. Internal standard peaks, as well as any known false positive peaks (including noise, column bleed, and derivatized reagent peaks), were removed from the data matrix, deredundant and peaks pooled. At the same time, the metabolites were identified by searching databases, and the main databases were HMDB ( http://www.hmdb.ca/ ), Metlin ( https://metlin.scripps.edu/ ) and Majorbio Database. Perform variance analysis on the matrix file after data preprocessing. The R package “ropls” (Version 1.6.2) was used to perform principal component analysis (PCA) and orthogonal least partial squares discriminant analysis (OPLS-DA), and 7-cycle interactive validation to evaluate the stability of the model. The metabolites with VIP > 1, p < 0.05 were determined as significantly different metabolites based on the variable importance in the projection (VIP) obtained by the OPLS-DA model and the p-value generated by the student’s t test. Differential metabolites among two groups were mapped into their biochemical pathways through metabolic enrichment and pathway analysis based on the KEGG database ( http://www.genome.jp/kegg/ ). These metabolites could be classified according to the pathways they involve or the functions they perform. Enrichment analysis was used to analyze a group of metabolites in a function node whether they appears or not. The principle was that the annotation analysis of a single metabolite developed into an annotation analysis of a group of metabolites. Python package “scipy.stats” ( https://docs.scipy.org/doc/scipy/ ) was used to perform enrichment analysis to obtain the most relevant biological pathways for experimental treatments. Statistical Analysis WPS Office 2021 was used for data sorting and statistical analysis, GraphPad Prism8 was utilized for mapping, IBM SPASS Statistics 27 was used for variance analysis, and Duncan's test was performed to compare the significance of differences between treatments (P < 0.05). Declarations Acknowledgements The data were analyzed through the free online platform of majorbio choud platform (cloud.majorbio.com). Author contributions statement Jinpeng Yang, Guangda Ding, Fangsen Xu, Chunlei Yang and Sheliang Wang provided research ideas and designed experiments. Haiying Liu, Xinwen Chi, and Jinpeng Yang performed the experiments. Haiying Liu performed metabolomics analyses. Haiying Liu, Xinwen Chi and Sheliang Wang wrote the manuscript. Haiying Liu and Xinwen Chi prepared figures and supplementary data. All authors commented on previous versions of the manuscript. All authors reviewed and approved the manuscript prior to submission. Data availability Sequence data that support the findings of this study have been deposited in the European Nucleotide Archive with the primary accession code MTBLS10189. Funding This study was funded by the China National Tobacco Corporation Science and Technology Major Project (110202001039, XJ-01). Competing interests Te authors declare no competing interests. References Uzelac, B. et al. Characterization of natural leaf senescence in tobacco (Nicotiana tabacum) plants grown in vitro. Protoplasma 253, 259-275 (2016). Wang, N. et al. Exogenous Melatonin Alleviated Leaf Yellowing via Inhibiting Respiration and Ethylene Biosynthesis during Shelf Life in Pakchoi. Plants 11, 2102; https://doi.org/10.3390/plants11162102 (2022). Yamamoto, T. et al. Characterization of a genomic region that maintains chlorophyll and nitrogen contents during ripening in a high-yielding stay-green rice cultivar, Field Crops Res . 206, 54-64 (2017). Romanova, A. K. et al. Physiological, biochemical, and fluorescence parameters of senescing sugar beet leaves in the vegetative phase of growth. Russ . J . Plant Physiol . 58, 271-282 (2011). Ritonga, F. N. et al . The Roles of Gibberellins in Regulating Leaf Development. Plants 12, 1243 (2023). Song, Y. et al . Abscisic Acid as an Internal Integrator of Multiple Physiological Processes Modulates Leaf Senescence Onset in Arabidopsis thaliana. Front. Plant Sci. 7 (2016). Ma, X., Balazadeh, S., Mueller-Roeber, B. Tomato fruit ripening factor NOR controls leaf senescence. J . E xp. B ot. 70, 2727-2740 (2019). Li, W. et al. Intergrative metabolomic and transcriptomic analyses unveil nutrient remobilization events in leaf senescence of tobacco. Sci . Rep . 7, 12126 (2017). https://doi.org/10.1038/s41598-017-11615-0. D’Incà, E. et al. The transcription factor VviNAC60 regulates senescence- and ripening-related processes in grapevine, Plant Physiol . , 192, 1928-1946 (2023). Pourtau, N. et al. Effect of sugar-induced senescence on gene expression and implications for the regulation of senescence in Arabidopsis. Planta 224, 556-568 (2006). Li, L. et al. Comprehensive investigation of tobacco leaves during natural early senescence via multi-platform metabolomics analyses. Sci . Rep . 6, 37976 (2016). Nölke, G. et al . Polyamines delay leaf maturation in low-alkaloid tobacco varieties. Plant Direct . 2, 1-12 (2018). Hansen, H., Grossmann, K. Auxin-Induced Ethylene Triggers Abscisic Acid Biosynthesis and Growth Inhibition, Plant Physiol . 124, 1437-1448 (2000). Bovet, L. et al . Asparagine Synthesis during Tobacco Leaf Curing. Plants 8, 492 (2019). Qin, Y. et al. Transcriptome analysis reveals key genes involved in the regulation of nicotine biosynthesis at early time points after topping in tobacco (Nicotiana tabacum L.). BMC Plant Biol . 20, 30 (2020). Zhao, J. et al. Metabolic changes in primary, secondary, and lipid metabolism in tobacco leaf in response to topping. Anal . Bioanal . Chem . 410, 839-851 (2018). He, C. et al. Photosynthesis-Related Proteins Play Crucial Role During Senescence Stage in Flue-Cured Tobacco Plants: A Combine iTRAQ-PRM Study. J . Plant Growth Regul . 41, 1013-1031 (2022). Li, N. et al . Metabolomic analysis reveals key metabolites alleviating green spots under exogenous sucrose spraying in air-curing cigar tobacco leaves. Sci . Rep . 13, 1311 (2023). Yang, R. et al . Effects of different nitrogen application rates on the quality and metabolomics of cigar tobacco. Agron . J . 114, 1155-1167 (2022). Chen, Y. et al. Effects of enzymatic browning reaction on the usability of tobacco leaves and identification of components of reaction products. Sci . Rep . 9, 17850 (2019). Gomez, F. M. Extra-plastidial degradation of chlorophyll and photosystem I in tobacco leaves involving ‘senescence-associated vacuoles’. Plant J . 99, 465-477 (2019) . Gršić, K., Čavlek, M. Effect of topping height and maturity on the quality of flue-cured tobacco cultivars. J. Cent. Eur. Agric. 20, 841-851 (2019). Gong, Y. et al. Application of starch degrading bacteria from tobacco leaves in improving the flavor of flue-cured tobacco. Front . Microbiol . 14, 1211936 (2023). Wang, M. et al. Soil fungal communities affect the chemical quality of flue-cured tobacco leaves in Bijie, Southwest China. Sci . Rep . 12, 2815 (2022). Tiecher, T. et al. Flue‐cured tobacco and Cl rates: Implications on yield, quality, and nutrient concentration. Agron . J. 115, 896-908 (2022). Qiu, X. M., Sun, Y. Y., Ye X. Y., Li Z. G. Signaling Role of Glutamate in Plants. Frontiers Plant Sci . 10 (2020). Lim, S. et al. Biosynthetic pathway of shikimate and aromatic amino acid and its metabolic engineering in plants. J . Plant Biotechnol . 42, 135-153 (2015). Hernández, I. et al. Zeatin modulates flower bud development and tocopherol levels in Cistus albidus (L.) plants as they age. Plant biol. 17, 90-96 (2014). Hallmark, H., Cerny, M., Brzobohatý, B., Rashotte, A. trans-Zeatin-N-glucosides have biological activity in Arabidopsis thaliana. PLOS ONE 15, e0232762 (2020). Xia, S. et al. The ell1 mutation disrupts tryptophan metabolism and induces cell death. Plant Signal. Behav. (2021) 10.1080/15592324.2021.1905336. Xu, Y. et al. Effect of exogenous plant hormones on agronomic and physiological performance of a leaf early-senescent rice mutant osled. Plant Growth Regul . 92, 517-533 (2020). Khan, D. The role of phytomelatonin receptor 1-mediated signaling in plant growth and stress response. Front. Plant Sci. 14,1142753 (2023). Kang, K., Kim, Y., Park, S., Back, K. Senescence-Induced Serotonin Biosynthesis and Its Role in Delaying Senescence in Rice Leaves. Plant physiol . 150, 1380-1393 (2009). Sharma, S., Charan, B., Rai, V. Influence of Abscisic Acid and Trans Cinnamic Acid on Senescence of Detached Tropaeolum majus Leaves in Relation to Stomatal Movements. J . Plant Physiol. 146, 751-753. (1995). Horn, T. et al. Evolutionary aspects of lipoxygenases and genetic diversity of human leukotriene signaling. Progress in lipid research. 57, 13-39 (2014). Panagiotopoulos, A., Kalyvianaki, K., Castanas, E., Kampa, M. Eicosanoids in prostate cancer. Cancer Metast . Rev. 37, 237-243 (2018). Savchenko, T. et al. Arachidonic Acid: An Evolutionarily Conserved Signaling Molecule Modulates Plant Stress Signaling Networks. Plant Cell 22, 3193-3205 (2010). Tallima, H., Ridi, R. E. Arachidonic Acid: Physiological Roles and Potential Health Benefits. A Review. J Adv . Res. 11, 33-41 (2017). Additional Declarations No competing interests reported. Supplementary Files Additionalpicture.pdf Cite Share Download PDF Status: Published Journal Publication published 28 Dec, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Aug, 2024 Reviews received at journal 14 Aug, 2024 Reviews received at journal 10 Aug, 2024 Reviewers agreed at journal 31 Jul, 2024 Reviewers agreed at journal 30 Jul, 2024 Reviewers invited by journal 30 Jul, 2024 Editor assigned by journal 24 Jul, 2024 Editor invited by journal 24 May, 2024 Submission checks completed at journal 24 May, 2024 First submitted to journal 23 May, 2024 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4467753","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":310098351,"identity":"15c6963a-3a20-4727-863b-3b4952ffa175","order_by":0,"name":"Haiying Liu","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Haiying","middleName":"","lastName":"Liu","suffix":""},{"id":310098352,"identity":"18d0bea4-ca9f-40b0-9d00-1a07204e763a","order_by":1,"name":"Xinwen Chi","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Xinwen","middleName":"","lastName":"Chi","suffix":""},{"id":310098353,"identity":"4520ab87-0fa6-4977-9f3d-4409ada9db99","order_by":2,"name":"Jinpeng Yang","email":"","orcid":"","institution":"Tobacco Research Institute of Hubei Province","correspondingAuthor":false,"prefix":"","firstName":"Jinpeng","middleName":"","lastName":"Yang","suffix":""},{"id":310098354,"identity":"4c223dd0-65b2-4f49-9f4f-443d49571f28","order_by":3,"name":"Guangda Ding","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Guangda","middleName":"","lastName":"Ding","suffix":""},{"id":310098355,"identity":"a5c71e2e-d0a0-4e3c-94e8-e24cc47344f8","order_by":4,"name":"Fangsen Xu","email":"","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Fangsen","middleName":"","lastName":"Xu","suffix":""},{"id":310098356,"identity":"ecb3397d-7a76-4f0b-ba58-ccdff278d9e8","order_by":5,"name":"Chunlei Yang","email":"","orcid":"","institution":"Tobacco Research Institute of Hubei Province","correspondingAuthor":false,"prefix":"","firstName":"Chunlei","middleName":"","lastName":"Yang","suffix":""},{"id":310098358,"identity":"0b348967-26d3-46bf-b46d-93d2174c2c7b","order_by":6,"name":"Sheliang Wang","email":"data:image/png;base64,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","orcid":"","institution":"Huazhong Agricultural University","correspondingAuthor":true,"prefix":"","firstName":"Sheliang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-05-23 15:08:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4467753/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4467753/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-82859-w","type":"published","date":"2024-12-28T15:57:18+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57796556,"identity":"a6636958-b075-4a6b-a715-9ee356aebf83","added_by":"auto","created_at":"2024-06-05 19:33:21","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27268,"visible":true,"origin":"","legend":"\u003cp\u003eClimatic condition in Danjiangkou City.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/c662fedb1f9054a5b2111eaf.jpg"},{"id":57795915,"identity":"06664c97-4b10-4c96-a365-d977b68a633c","added_by":"auto","created_at":"2024-06-05 19:17:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":92483,"visible":true,"origin":"","legend":"\u003cp\u003eChanges of phenotype and physiological characteristics of cigar tobacco under different harvesting time. (a-b) tobacco leaf phenotype,bar=5cm; (c) Chlorophyll a content; (d) chlorophyll b content; (e) Carotenoid content; (f) Net photosynthetic rate; (g) stomatal conductance; (h) Transpiration rate. n=6, the letter above the bar indicates a significant difference (ANOVA, p\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/5bd36aa0ec1499983a1613f0.jpg"},{"id":57796289,"identity":"3fd42830-6558-49a3-9fa9-f4b88a8b2661","added_by":"auto","created_at":"2024-06-05 19:25:21","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37225,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of metabolites in tested samples. (a-b) Cationic model and anionic mode of PCA analysis;(c) The number of ion peaks in different groups.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/474cefcb73172ad3f1edc507.jpg"},{"id":57795922,"identity":"e0451e4b-524a-4f87-a813-9b0a936ac1a6","added_by":"auto","created_at":"2024-06-05 19:17:21","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75407,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano maps and KEGG functional pathway maps of tested samples (a-c) Volcano maps of metabolites in tested samples. The horizontal coordinate is the multiple change value of the difference in metabolite expression between the two groups, that is, log2FC; the vertical coordinate is the statistical test value of the difference in metabolite expression, that is, the -log10 (\u003cem\u003ep\u003c/em\u003e-value) value; the higher the value, the more significant the difference in expression. Both horizontal and vertical values were logized. Each point in the figures. represents a specific metabolite, and the size of the point represents the VIP value. (d) Differential metabolite KEGG functional pathway in T2vsT3 group. The ordinate is the secondary classification of KEGG metabolic pathway, and the abscissa is the number of metabolites annotated to this pathway.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/33c9e58aeaa254ae36c873d2.jpg"},{"id":57795919,"identity":"d7fd4f35-a6be-431a-93ce-dd3ef78213d4","added_by":"auto","created_at":"2024-06-05 19:17:21","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":102083,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway enrichment analysis of different metabolites in T3vsT2 group. The horizontal coordinate represents the pathway name, and the vertical coordinate represents enrichment rate This is the ratio of the metabolite number enriched in this pathway to the Background number annotated in the pathway. The larger the ratio, the greater the enrichment degree. The column color gradient indicates enrichment significance. The darker the default color, the greater the enrichment of the KEGG term. This is where \u003cem\u003eP\u003c/em\u003e-value or FDR \u0026lt; 0.001 is marked ***,\u003cem\u003e P\u003c/em\u003e-value or FDR \u0026lt; 0.01 is marked **, and \u003cem\u003eP\u003c/em\u003e-value or FDR \u0026lt; 0.05 is indicated by *. The\u003cem\u003e P\u003c/em\u003e-value sampling in this study was calibrated by the BH method.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/744a46c8042797c57f5ef2a6.jpg"},{"id":57795925,"identity":"ce961d4f-3ba2-40b5-9f19-109c6f5bab2e","added_by":"auto","created_at":"2024-06-05 19:17:21","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":85771,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential metabolites in zeatin biosynthesis and tryptophan metabolism pathways in tested samples (a) Differential metabolites in zeatin biosynthesis; (b)Isopentenyl adenosine contents in tested samples; (c) Differential metabolites in tryptophan metabolic pathways; (d) Indolepyruvic acid contents in tested samples. Pathway maps are based on the differential metabolites enriched in this study (reference from KEGG; http //www.kegg.jp). The corresponding values on the color scale are values for metabolite abundance; substances in the boxes were not screened for differential metabolites in this study. The average of six independent measurements was analyzed for each sample and depicted on a heat map. All differences in the gradient in the bar graph are significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), and the vertical coordinate is the mass spectral intensity value (mass spectral intensity after data preprocessing).\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/aaeffab15a74c3841a5c5a17.jpg"},{"id":57795918,"identity":"0995c82a-385d-4a79-a4ec-d6a534ce85fc","added_by":"auto","created_at":"2024-06-05 19:17:21","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":34211,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis of tested samples (a) Cationic model; (b) Anionic mode; (c) The number of ion peaks in different groups.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/79c01bce26723fd677325a36.jpg"},{"id":57795921,"identity":"9eec004a-6e85-4df1-a112-aab79faaf2bb","added_by":"auto","created_at":"2024-06-05 19:17:21","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":78658,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcanic maps of metabolites and KEGG functional pathway analysis in tested samples \u003c/strong\u003e(a-b) Volcanic maps of metabolites in tested samples. The horizontal coordinate is the multiple change value of the difference in metabolite expression between group samples, that is, log2FC; the vertical coordinate is the statistical test value of the difference in metabolite expression, that is, the -log10 (\u003cem\u003ep\u003c/em\u003e-value) value; the higher the value, the more significant the difference in expression. Both horizontal and vertical values were logized. Each point in the figure represents a specific metabolite, and the size of the point represents the VIP value; (c) Differential metabolite KEGG functional pathway. The ordinate is the secondary classification of KEGG metabolic pathway, and the abscissa is the number of metabolites annotated to this pathway.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/ddef552ac72982eeb0fb29ee.jpg"},{"id":57795920,"identity":"b655745b-36e6-46d2-a076-c4571ac2f145","added_by":"auto","created_at":"2024-06-05 19:17:21","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":61494,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis of KEGG pathway of differential metabolites in F3vsF2 group. The horizontal coordinate represents the pathway name, and the vertical coordinate represents enrichment rate. This is the ratio of the metabolite number enriched in this pathway to the Background number annotated in the pathway. The larger the ratio, the greater the degree of enrichment. The column color gradient indicates enrichment significance. The darker the default color, the more significantly enriched the KEGG term, where \u003cem\u003eP-\u003c/em\u003evalue or FDR \u0026lt; 0.001 is marked ***, \u003cem\u003eP-\u003c/em\u003evalue or FDR \u0026lt; 0.01 is marked **, and\u003cem\u003e P-\u003c/em\u003evalue or FDR \u0026lt; 0.05 is marked *.The \u003cem\u003eP\u003c/em\u003e-value sampling in this study was calibrated by BH method.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/aff22060e85fad6f87e2a013.jpg"},{"id":57795923,"identity":"ff0d49c4-9241-4353-bca2-24aef5d4cd1b","added_by":"auto","created_at":"2024-06-05 19:17:21","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":46537,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential metabolites in arachidonic acid metabolism pathway in tested samples (a) Abundance comparison of DMs in arachidonic acid metabolism pathway; (b-d) Content changes of (19S)-Hydroxyeicosatetraenoic acid , 20-Hydroxyeicosatetraenoic acid and Prostaglandin A2 in tested samples; Pathway maps are based on the DMs enriched in this study (reference from KEGG; http //www.kegg.jp). The corresponding values on the color scale are values for metabolite abundance; substances in the boxes were not screened for differential metabolites in this study. The average of six independent measurements was analyzed for each sample and depicted as a heat map. All differences in the gradient in the bar graph are significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05), and the vertical coordinate is the mass spectral intensity value (mass spectral intensity after data preprocessing).\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/fb2c80dbe15c44c21e547ab9.jpg"},{"id":72640546,"identity":"31b5bdfd-0be5-49a1-9521-65a58d733e2b","added_by":"auto","created_at":"2024-12-30 16:06:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1384975,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/68be2557-523b-4ecf-acb1-548d6ad2d237.pdf"},{"id":57795924,"identity":"f3857e6f-3987-44e9-942a-c6bb48fd2359","added_by":"auto","created_at":"2024-06-05 19:17:21","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1705074,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalpicture.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4467753/v1/22e0461aa24a50ce4a7e68ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated analysis of physiological and metabolic data uncovers essential dynamic mechanisms involved in the maturation of cigar tobacco leaves","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlant leaf maturation is a complex process in which leaf cell structure, metabolism, and gene expression undergo orderly changes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Leaf maturation senescence is manifested by a gradual decrease in leaf chlorophyll content[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and a decrease in photosynthetic rate [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] leading to a gradual yellowing of the leaf. It also leads to changes at the cellular level of the leaf, and studies have shown that senescence leads to a decrease in the orderliness of the palisade cell, enlargement of cells, vesicles, and cell gaps; increase in leaf area and thickness, etc. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This process cannot be separated from the regulatory effects of various chemicals, for example, gibberellins (GA) are involved in processes such as cell elongation and leaf expansion, and exogenous application of GA can delay leaf senescence[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The opposite is true for abscisic acid (ABA), where increased levels of ABA promote leaf senescence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Nowadays, the advent of histology has changed the strategy for studying leaf maturation, offering the possibility of systematically assessing the dynamics of the leaf maturation process on a large scale, both at the molecular level and at the metabolic level. Through transcriptomics studies, it was found that the NAC family genes NAC4 and NAC48 positively regulate leaf maturation, and ORE1 can control the expression of many senescence-associated genes (SAGs) by directly binding to their promoters [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. At the same time, metabolomics studies are showing that many metabolites change during leaf maturation, such as glutamic acid (Glu) and aspartic acid (Asp) significantly decrease with leaf senescence[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt present, the wide application of omics technology has made it possible to study the regulation mechanism of leaf maturation, and great progress has been made. At the molecular level, it has been identified that many transcription factors TFs (such as NAC) play a key role in regulating leaf maturation and senescence by interacting with other transcription factors, hormones, and environmental signals to alter transcription levels of related genes (such as chlorophyll catabolism genes) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As the end products of genetic expression, many metabolites (such as sugars, amino acids, etc.) have also been shown to play a crucial role in leaf maturation and senescence. It has been reported that the accumulation of sugars in early maturation of arabidopsis can induce the expression of age-related genes (SAGs), while the expression of SAGs in late maturation is inhibited by sugars [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Glycine (Gly) and serine (Ser) can change leaf photosynthesis by affecting the photorespiration rate during the aging process of tobacco leaves [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Polyamines can delay the aging and ripening of tobacco leaves by delaying membrane degradation and chlorophyll loss[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. IAA (indole-3-acetic acid) can accelerate leaf senescence by promoting biosynthesis of related hormones (ethylene, abscisic acid, etc.) that inhibit plant growth [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Studies have shown that with the maturity of leaves, anabolic activities in leaves decrease and catabolic activities increase [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As a rapidly developing technology in the post-genomics era, metabolomics technology can intuitively and quickly enable us to understand the metabolic differences between samples and determine the physiological functions of certain metabolites[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTobacco is a good tool for research, such as the study of transgenic technology, protein localization studies and protein-protein interactions, protein-gene interactions, etc. Most of these types of experiments are currently being carried out in Ben's tobacco. Tobacco leaves are also a very suitable material for histological studies of real environments and plant leaf maturation processes due to their high biomass and ease of cultivation in the field. It was found through transcriptomics studies that genes such as AP2/EREBP, bHLH, and WRKY are involved in nicotine biosynthesis in tobacco leaves[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It was found through metabolomics studies that tobacco topping favors the accumulation of secondary metabolites such as chlorogenic acid and rutin in the upper leaves[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The expression of proteins related to photosynthesis and glycolysis was found to decrease during tobacco leaf senescence through proteomics studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, there are not many studies on histology in cigar tobacco, and the main studies focus on the material changes of tobacco during fermentation and drying such as Li, N et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] revealed the mechanism of reduction of green spot in the drying process of cigar tobacco through metabolomics analysis, and none of these studies specifically elaborated on the dynamic process of metabolite alteration in the process of tobacco maturation. Therefore, in the present study, the dynamic analysis of physiological metabolites in tobacco leaves at different growth time points was carried out using the tools of metabolomics, and it was found that the amino acid levels were significantly responded to by the maturation and senescence of tobacco leaves. The effect of lower leaf position on the maturation process of the cutter leaf was also assessed, revealing that the signaling between the lower leaf influence and the cutter leaf leads to changes in the synthesis of relevant compounds and thus affects the maturation process of the cutter leaf.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePhysiological changes in tobacco leaves at different harvesting times\u003c/h2\u003e \u003cp\u003eTo record the appearance changes of leaves (middle position of seedling), we compared samples harvested at 35 d (T1), 42 d (T2), 49 (T3) and 56 d (T4) after tobacco topping. T1 samples showed a green color which gradually declined along with the yellow color increasing, especially in T3 and T4 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The visible white color appeared in the main branch vein from T1 samples to T4 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). These appearance changes were more obvious in pseudo-color processing (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). It is noted that yellow-white mature spots appeared in T3 and T4 samples. To understand the physiological dynamics in these samples, pigment contents and photosynthesis parameters were measured. Contents of chlorophyll a and chlorophyll b increased slightly in the T2 samples followed by a decrease in the T3 and T4 samples compared to the T1 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-d). For the content of carotenoid, only a significant reduction was observed in the T4 samples relative to other samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCompared with T1 samples, T2 samples had an increase in the stomatal conductance (Gs) and transpiration rate (Tr) by 18.65% and 11.06% (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eg-h). In contrast, Gs were sharply reduced by 78.32% and 75.51% in the T3 and T4 samples, respectively and Tr were largely reduced by 86.60% and 74.20% in the T3 and T4 samples, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003eg-h). The Net photosynthetic (Pn) level showed a decline of 26.92% compared with T1 samples, and a further decline of 65.80% and 54.32% in the T3 and T4 samples, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). These results suggest that tobacco leaves might undergo a physiological mature transition from the T2 to the T3 stage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in the nutritional quality of tobacco samples\u003c/h2\u003e \u003cp\u003eYang et al. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] reported that chemical composition indexes are tightly correlated with tobacco leaf nutritional quality. We thus determined the chemical composition indexes in our samples (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared with T1, the contents of nicotine, reducing sugar, total sugar, and total nitrogen in tobacco leaves increased in T2 and decreased in T3 and T4. Among them, reducing sugar content and total sugar content had a significant increase of 2.5-fold and 1-fold respectively, in comparison with T1. The contents of potassium and chlorine in tobacco leaves showed an increasing trend. The contents of potassium and chlorine in T4 samples were the highest, increasing by 126.67% and 86.05% compared with T1 respectively. The ratio of potassium to chlorine was positively correlated with tobacco leaf flammability. However, there was no significant difference in the potassium-chlorine ratio among all samples. Sugar-nicotine ratio is related to the aroma and taste of tobacco leaves. Among samples, the sugar-nicotine ratio reached the maximum value in the T2 sample. As a result of harvesting tobacco leaves too late, their quality will decline.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutritional quality of cigar tobacco leaves after curing under different treatments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNicotine\u003c/p\u003e \u003cp\u003e(% DW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReducing sugar\u003c/p\u003e \u003cp\u003e(% DW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eSugar\u003c/p\u003e \u003cp\u003e(% DW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eNitrogen\u003c/p\u003e \u003cp\u003e(% DW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003cp\u003e(% DW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChlorine\u003c/p\u003e \u003cp\u003e(% DW)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSugar-nicotine ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eK/Cl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.39\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.22a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.12bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.15bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.50\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.15ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.65\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.32b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.03b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.02b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.79\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.49a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.29\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.17a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.41a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.38a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.94\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.33a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.03\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.17b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.47\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.05b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.06a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.35\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.10a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.93\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.84a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.57ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.31\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.57ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.54\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.27ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.63\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.25ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.75\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.04a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.06ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.48\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.50a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.20\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.65b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.04c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.42\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.06c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.42\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.26b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.74\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.50a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.80\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.17a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.05b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.77\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.42a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics analysis of tobacco leaves after different treatments\u003c/h2\u003e \u003cp\u003ePrincipal component analysis\u003c/p\u003e \u003cp\u003eTo investigate the changes in cigar tobacco metabolic processes under varying harvesting times, we analyzed the metabolites of the above samples using LC-MS. Eventually, 1078 valid peaks were detected in cation mode and 1075 valid peaks in anion mode for different treatment groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). PCA analysis of metabolic profiles showed T1 and T2 were clustered and T3 was close to T4 samples either in positive ion modes (POS) or negative ion modes (NEG) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-b). According to this result, a distinct physiological metabolic process occurred between T2 and T3 samples. In T3 and T4 samples, this process slowly changed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eScreening of different metabolites and KEGG analysis of functional pathways in samples\u003c/p\u003e \u003cp\u003eBased on the metabolic data of T2 vs T1, T3 vs T2 and T4 vs T3, we set the VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 to identify differential metabolites (DMs). A total of 2016 significant DMs were obtained, among which the T2 vs T1 group screened out 180 down-DMs and 95 up-DMs; the T3 vs T2 group screened out 914 down-DMs and 562 up-DMs; and the T4 vs T3 group checked out 571 down-DMs and 168 up-DMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ea-c). Therefore, the T3 vs T2 group represents the key physiological changes since its DMs account for 73.21% of all DMs. In addition, there were more down-DMs than up-DMs in all groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe next focused on the T3 vs T2 group\u0026rsquo;s DMs to obtain the hierarchical rank of physiological functions involved in metabolites. According to annotation in the KEGG compound database, the T3 vs T2 group\u0026rsquo;s DMs were mainly clustered in three categories: compounds with biological roles, phytochemical compounds, and lipids, respectively (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea-c). The most prominent three metabolites are amino acids、fatty acids、monosaccharides and phospholipids in the category of \u0026ldquo;compounds with biological roles\u0026rdquo; (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea). The most prominent three metabolites are alkaloids derived from tryptophan and anthranilic acid、monoterpenoids (C10)、diterpenoids (C20)、flavonoids and alkaloids derived from ornithine in the category of \u0026ldquo;phytochemical compounds\u0026rdquo; (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec). The top three metabolites are FA01 fatty acids and conjugates, PR01 isoprenoids, and PK12 flavonoids in the category of \u0026ldquo;lipid metabolites\u0026rdquo; (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb). The metabolic pathways for these DMs were established using annotation of the KEGG pathway database. Most DMs involved three pathways, metabolism, environmental information processing and genetic information processing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The metabolism pathway had the most abundant secondary metabolic pathways such as biosynthesis of other secondary metabolites (67 DMs), amino acid metabolism (67 DMs) and metabolism of cofactors and vitamins (34 DMs).\u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis of differential metabolites\u003c/p\u003e \u003cp\u003eWe further screened the significantly enriched metabolic pathways from the pathways involved in T3vsT2 DMs using \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 as the threshold. There were 27 enriched metabolic pathways (the top 20 were displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), of which 6 were highly significant enriched metabolic pathways with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Among them are tryptophan metabolism, secondary metabolism in plants, alanine, aspartate and glutamate metabolism, phenylpropanoid biosynthesis, zeatin biosynthesis, and aminoacyl-tRNA biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In the top 20 pathways, a large portion of DMs were enriched in amino acid synthesis and metabolism pathways including alanine, aspartate, lysine glutamate, tyrosine and phenylalanine. In addition, the enrichment ratios of betalain biosynthesis, cyanoamino acid metabolism, linoleic acid metabolism, and plant hormone signal transduction were over 0.15.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKey differential metabolites in samples\u003c/p\u003e \u003cp\u003eCombined analyses of KEGG enrichment in DMs of T3vsT2, T3vsT1, T4vsT1 and T4vsT2 found tryptophan metabolism pathway and zeatin biosynthesis pathway were the most significantly enriched pathways shared in all groups with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig. S2). In zeatin biosynthesis pathway, 4-up DMs and 5-down DMs were found from T1 to T4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). In particular, isopentenyl adenosine was the most significant with upregulation of 128.17% in T3 and T4 samples compared with T1 and T2 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). In tryptophan metabolic pathway, 5-up DMs and 11-down DMs were found (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). Among them, indolepyruvic acid was the most significantly differentially expressed, which was down-regulated by 34.29% in T4 compared with T1(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffect of lower leaf's growth on metabolism of cutter leaf\u003c/h3\u003e\n\u003cp\u003eIn production practice, lower leaves are usually harvested one week earlier than T2 cutter leaves. However, whether removal time of lower leaf affects T2 cutter leave\u0026rsquo;s physiological maturity remains unclear. Here, we removed lower leaves from seedlings at 3 weeks, 2 weeks or 1 week before T2 harvest and named these T2 samples as F1, F2 and F3, respectively. Samples were then analyzed with LC-MS for metabolite changes.\u003c/p\u003e \u003cp\u003ePCA analysis\u003c/p\u003e \u003cp\u003ePCA analyses of POS modes and NEG modes clearly concentrated each sample point (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ea-b), indicating that metabolites within each sample are highly consistent. F1 and F2 had very similar distribution patterns with 207 (differential ion peaks) DIPs in POS modes and 220 DIPs in NEG modes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ec), suggesting that their metabolites are not much different. F3 samples, on the other hand, differed fromF1 and F2 samples. F3 vs F2 samples had 390 DIPs in POS modes and 358 DIPs in NEG modes, and F3 vs F1 samples had 528 DIPs in POS modes and 486 DIPs in NEG modes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). This result suggests that F3 samples had differential physiological processes from F1 and F2. It supports the concept that lower leaf growth can affect cutter leaves' physiological maturity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDifferent metabolites analysis and functional pathways of KEGG compounds\u003c/p\u003e \u003cp\u003eA total of 1072 DMs were screened out in F2vsF1 and F3vsF2 groups, including 222 down-DMs and 205 up-DMs in F2vsF1 group(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003ea), and 486 down-DMs and 262 up-DMs in F3vsF2 group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eb).This result confirmed that harvesting time of lower leaf can affect the physiological process of cutter leaves. Based on annotation of the KEGG compound database, DMs from the F3vsF2 groups grouped into three categories: compounds with biological roles, phytochemical compounds, and lipids (Fig.S3), which is consistent with T1-T4 results (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In the category of \u0026ldquo;compounds with biological roles\u0026rdquo;, lipids were the largest compounds and the number of lipids was 10 (Fig.S3a). Ranked second were steroids followed by peptides and antibiotics, nucleic acids and organic acids, steroid hormones, carbohydrates and vitamins in order of numbers. Category of \u0026ldquo;lipids\u0026rdquo; included 37 fatty acyls, 21 sterol lipids, 19 prenol lipids, 14 polyketides, 6 glycerophospholipids and 2 sphingolipids (Fig. S3b). Category of \u0026ldquo;phytochemical compounds\u0026rdquo; included 19 terpenoids, 16 alkaloids, 14 phenylpropanoids, 9 flavonoids, 6 amino acid related compounds, 1 polyketide and 1 fatty acid (Fig. S3c). In the KEGG PATHWAY database, DMs of the F3 vs F2 groups were primarily involved in \"metabolism, environmental information processing, and genetic information processing\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). There was a high amount of metabolism, including amino acid metabolism, secondary metabolite biosynthesis, and lipid metabolism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKEGG pathway enrichment of differential metabolites\u003c/p\u003e \u003cp\u003eSignificantly enriched metabolic pathways were further screened in F3 vs F2 group using \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05. as the threshold. Arachidonic acid metabolism was only one enriched metabolic pathway with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In addition, amino acid metabolic pathways (e.g., tyrosine metabolism) as well as tricarboxylic acid cycle-related pathways (e.g., citrate cycle) were also significantly enriched. These results indicate that DMs of F3 vs F2 group mainly enriched in the amino acid metabolic pathway and lipid metabolic pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKey differential metabolites in enriched pathways\u003c/p\u003e \u003cp\u003eWe also found that arachidonic acid metabolic was also the highest enriched pathway in all F3vsF1 KEGG enriched pathways (Fig. S4). Subsequently, the DMs of arachidonic acid metabolic pathways in the F2vs F1 and F3 vs F1 groups were extracted. No up-DMs were found and total 15 metabolites showed down regulation from F1 to F3 samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003ea). The most down-regulated was (19S)-Hydroxyeicosatetraenoic acid (19(S)-HETE), with a 15.07% decrease in F3 over F1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eb), followed by 20-Hydroxyeicosatetraenoic acid (20-HETE) and Prostaglandin A2 (PGA2), with a 7.89% decrease and 7.65% decrease, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003ec-d).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLeaf maturation and senescence is an important process in plants, especially for the quality of cigar tobacco leaves [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. So far in China, cigar harvesting time depends on the farmer\u0026rsquo;s experience which easily causes uneven in cigar quality. Study of cigar maturation mechanism is helpful to provide theoretical and technical support for the establishment of technical standard system of tobacco production and harvesting technology in tobacco field. This study investigates the nutrient changes and metabolic processes of cigar tobacco during its maturation from physiological and molecular aspects.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHarvesting time affected physiological process of tobacco leaves\u003c/h2\u003e \u003cp\u003eAccording to the results of appearance changes of tobacco leaves, the color of tobacco leaves gradually turns yellow, which may be due to the degradation of chlorophyll during leaf maturation and aging. Studies have shown that chlorophyll loss is a significant feature of leaf aging and a marker of chloroplast degradation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, with the improvement of harvest maturity, the contents of chlorophyll a, chlorophyll b and carotenoid in leaves showed a trend of first increasing and then decreasing(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-e), suggesting that T1 to T4 tobacco leaves may be undertaken in the pre-maturation to maturation periods. At the early stage, assimilation is the main process, thus the chloroplasts did not degrade. Then at late stage, chloroplasts degraded accompanied by color changes. Photosynthetic parameters such as net photosynthetic rate, stomatal conductance, and transpiration rate are used as indicators to consider the strength of photosynthesis in studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, both stomatal conductance and transpiration rate in tobacco leaves consistently decrease with increased maturity(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ef-h), aligning with the findings by He et al. that photosynthetic parameters decline as the leaves age. This suggests that maturation significantly impacts the light and water use efficiency of tobacco, potentially accelerating the weakening of the net photosynthetic rate in the later stages of maturity.\u003c/p\u003e \u003cp\u003eChemical components such as sugar-nicotine ratios, potassium-chlorine ratios are closely related to the quality of tobacco [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Research suggests that high nicotine levels in tobacco increase its harshness and spiciness. The sugars, particularly reducing sugars, in the leaves can neutralize alkaline components in smoke by producing an acidic reaction during combustion, maintaining the smoke's pH within a suitable range [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Apropriate sugar-nicotine ratios enable tobacco to maintain good taste as well as flavor [20、23]. Additionally, the nitrogen content in tobacco leaves is related to the formation of nicotine and is a fundamental element for high tobacco yield and quality, reflecting the leaf's nitrogen-supplying capacity [20、24]. In this study, the levels of nicotine, total nitrogen, reducing sugars, total sugars, and the nicotine ratio in tobacco leaves initially increased and then decreased over time, reaching their lowest values with the T4 treatment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. This may be due to robust growth and strong photosynthesis in the early maturation stage of the leaves, which leads to better absorption and conversion of nutrients, resulting in a significant accumulation of sugars and nitrogen. Subsequently, as the leaves mature and age, their chemical components gradually transform and decompose, with sugars breaking down more rapidly, leading to a decrease in sugars and nitrogen content. Additionally, an optimal potassium content in the leaves can enhance their combustibility [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In contrast, chlorine can suppress the burning of tobacco leaves [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, the potassium-to-chlorine ratio is commonly used to compare the combustion properties of tobacco leaves. Therefore, chemical components dynamics are consistent with the alteration of photosynthesis parameters from T1 to T4 leaves, confirming that leaves harvested at different times undertaken distinct physiological processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eKey metabolic pathways and metabolites of tobacco leaves under different harvesting times\u003c/h2\u003e \u003cp\u003eMetabolomic analysis of different samples revealed that significant metabolic differences began to appear between T2 and T3 samples, suggesting that harvested at 42d (T2)-49d (T3) is likely the transition stage from vigorous growth to senescence and maturity of tobacco leaves. According to the results of KEGG enrichment analysis of T3vsT2 differential metabolites (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e), pathways related to plant cell homeostasis and plant growth and development, such as amino acid metabolism, zeatin biosynthesis, and plant hormone signal transduction, were significantly affected in cigar tobacco leaves. The pathways involved in amino acid synthesis and metabolism were most affected, including tryptophan, alanine, aspartate, lysine glutamate, tyrosine, and phenylalanine. Studies have shown that alanine, aspartic acid, and glutamic acid have very important roles in plants, glutamic acid influences nitrogen metabolism and intervenes in nitrogen assimilation in plants [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Aspartic acid metabolism is involved in the synthesis of several important amino acids such as alanine, threonine, and lysine [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], Tyrosine, phenylalanine, and tryptophan are the three aromatic amino acids in plants, and the compounds derived from these aromatic amino acids play as a constituent of several phenolic substances including pigments and cell walls, as well as hormones such as growth hormone and salicylic acid in plants important roles [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It can be seen that changes in these amino acid metabolic pathways can affect the biosynthesis of related substances and thus affect plant development. Further, to find the key metabolic pathways regulating leaf growth to maturity, we also analyzed the KEGG enrichment of other comparison groups with large metabolic differences (including T1vsT3, T1vsT4, T2vsT4), and found that the zeatin biosynthesis and tryptophan metabolism were both significantly enriched (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) in these groups(Fig. S2), indicating that these two metabolic pathways are probably the most critical ones (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that these two metabolic pathways are probably the most critical metabolic pathways.\u003c/p\u003e \u003cp\u003eZeatin is a natural cytokinin, and several of the most common derivatives are isopentenyladenine, trans-zeatin, cis-zeatin, and dihydrozeatin, which play an important role in the regulation of plant senescence [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], one manifestation of which is the retardation of leaf degradation of chlorophyll content [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In this study, by analyzing the metabolic pathways of zeatin, it was found that trans-Zeatin riboside, cis-Zeatin riboside, and dihydrozeatin contents were significantly decreased in T3 and T4 samples(Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), which is highly consistent with the results of the study that showed a significant decrease in chlorophyll content in T3 and T4 samples(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003ec-e), suggesting that possibly zeatin, especially the decrease in dihydrozeatin content, may lead to the decrease in chlorophyll content and thus promote the maturation and senescence of leaves, which is the important reason for the transition from T2 to T3. The decrease in dihydrozeatin content may be due to the inhibition of Isopentenyl adenosine catabolism. Tryptophan metabolism plays an important role in plant defense [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and past studies have demonstrated that the metabolites of tryptophan, such as growth hormones like indole-3-acetic Acid, Melatonin, Serotonin, and Cinnabarinic acid, as well as organic acids can protect cellular growth activity by enhancing the antioxidant system, regulating stomatal closure, reducing ROS production and protecting cell growth activity thereby delaying plant senescence [31、32、33、34], in the present study the tryptophan metabolic pathway was analyzed and it was found that most of the metabolites of tryptophan were down-regulated and expressed in the T3 and T4 samples. It is noteworthy that growth hormones such as indole-3-acetic Acid, Melatonin, Serotonin, etc. were not significantly different among the samples however their related synthetic precursors or catabolic products showed significant changes in T3 and T4 samples, especially Indolepyruvic acid was significantly down-regulated in expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This suggests that the differential expression of tryptophan metabolites, especially the down-regulation of growth-related hormones and organic acid synthesis precursors and catabolites, may have led to a decline in the antioxidant defense system of the leaves in this study, resulting in a decrease in the physiological function of the leaves and the gradual maturation and senescence of the leaves.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eEffects of lower leaf harvesting time on metabolism of cutter tobacco leaves\u003c/h2\u003e \u003cp\u003eTobacco plant as a whole, the growth and development of different parts of the plant must have mutual influence and constraints. Studies have shown that the lower leaves senesce first during the development of tobacco leaves, which triggers nutrient redistribution from the lower leaves to the upper leaves and leads to relevant changes in leaf metabolism [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This study investigated the effect of lower leaf harvesting time on the metabolism of central leaves aiming to find the optimal harvesting time point. According to the results of this study, lipid metabolism, amino acid metabolism, and Citrate cycle (TCA cycle) metabolism of central tobacco leaves changed significantly with the lower leaf harvesting time (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong them, the Arachidonic acid metabolism pathway was most significantly enriched, which affects the catabolism of arachidonic acid. Arachidonic acid is a polyunsaturated fatty acid that is an important component of membrane lipids influencing membrane function [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and is also a major contributor to many eicosanoids (arachidonic-like acids) including hydroxy eicosatetraenoic acids (HETEs), leukotrienes (LTs), the prostaglandins (PGs), thromboxanes (TXs), isoprostanes (IsoPs), etc. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and studies have shown that arachidonic acid and arachidonic acid-like acids are particularly potent signaling molecules in plants and animals and are involved in cellular signaling and thus trigger the plants and animals to respond to stress [37、38] In the present study, the arachidonic acid catabolism process was significantly influenced by the down-regulation of the content of many classes of arachidonic acids, especially 19(S)-HETE as well as 20-HETE in hydroxy eicosatetraenoic (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e), which can be seen as a possible reason for the harvesting of the lower leaf. As a result, the time of harvesting the lower leaves affects the certain physiological processes in the middle leaves.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, significant changes in the physiology and metabolism of tobacco leaves occurred with the passage of harvesting time. In terms of physiology, tobacco leaves gradually turned yellow, chlorophyll content and photosynthesis efficiency decreased, and overripe harvested tobacco leaves resulted in more disjointed chemical nutrients related to tobacco quality. Metabolically, significant metabolic changes occurred in tobacco leaves harvested at 42d (T2)-49d (T3), which we hypothesized to be the turning point of tobacco leaves from vigorous growth to maturity, mainly by affecting tryptophan metabolism and zeatin biosynthesis metabolism pathway, thereby affecting the biosynthesis and catabolism of tobacco cytokinin and growth hormone, which then hindered the growth and development of plants leading to Leaves grow vigorously to maturity and senescence. Delayed harvesting of the lower leaves also affected the metabolism of the central leaves, mainly affecting leaf arachidonic acid metabolism and thus the content of arachidonic acid in the leaves, which led to changes in leaf physiology due to the inhibition of leaf signaling, and the related key metabolites showed significant changes in the F3 treatment (harvested 38 days after the lower leaves were harvested). In summary, we hypothesized that the lower leaves were harvested 33\u0026ndash;38 days before the central leaves were harvested, and the central leaves were harvested 42\u0026ndash;49 days after topping, which indicated that the maturity was better and suitable for harvesting.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePlant culture and experimental design\u003c/h2\u003e \u003cp\u003eThe experiment was conducted in Xijiadian Town (111\u0026deg;18\u0026prime;N, 32\u0026deg;75\u0026prime;E), Danjiangkou City, Hubei Province in 2022. During the growing period (April to August), the monthly mean temperature and rainfall are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The soil was yellow brown soil and the basic physical and chemical properties are: pH7.5, organic matter 8.4 g/kg, available phosphorus 8.1 mg/kg, available potassium 161.7 mg/kg, alkali hydrolyzed nitrogen 88.7 mg/kg. The experimental material was Chuxue No. 14 (CX-014) provided by Hubei Tobacco Research Institute, China.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeedlings were planted in early February using the intensive floating garden method and transplanted on 20 April. Field experiments were conducted in different zones in a completely randomized block design.\u003c/p\u003e \u003cp\u003eExperiment 1: The plant topping was completed on June 13. Cutter leaves are study materials. The lower leaves were uniformly harvested 38 days before the collection of the cutter leaves, and 4 treatments were set according to different harvesting times of the cutter leaves, which were recorded as T1, T2, T3 and T4, representing 35 days, 42 days, 49 days and 56 days after topping, respectively. Each treatment had 3 area replicates.\u003c/p\u003e \u003cp\u003eExperiment 2: The plant topping was completed on June 13. The cutter leaves were uniformly harvested 42 days after topping, and three treatments were set according to different harvesting times of the lower leaves, which were recorded as F1, F2 and F3, representing that the cutter leaves were harvested 48, 43 and 38 days after the lower part of the leaf was removed, respectively. Each treatment was repeated 3 times, and 30 plants were planted in each replicate plot.\u003c/p\u003e \u003cp\u003eSamples were collected from bottom to top according to the order of leaf position. On the day of harvest, six representative tobacco plants were selected, and fresh cutter leaves were collected for the determination of relevant physiological indicators. The remaining cutter leaves after harvest were placed in a drying room for drying. After drying, six-leaf samples were randomly selected from each treatment for the determination of tobacco leaf nutrient content and metabolomics, with mixed samples used to ensure uniformity during analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of photosynthetic index and pigment content\u003c/h2\u003e \u003cp\u003eThe net photosynthetic rate (Pn), transpiration rate (Tr) and stomatal conductance (Gs) of the cutter leaves were measured by a Li-6400 portable photosynthetic apparatus. After the mature fresh samples were crushed and extracted with 95% ethanol, the absorbance at 665 nm, 649 nm and 470 nm was determined. The contents of chlorophyll a, chlorophyll b and carotenoid were calculated according to the formula.\u003c/p\u003e \u003cp\u003eChla=(13.95A\u003csub\u003e665\u003c/sub\u003e-6.88A\u003csub\u003e649\u003c/sub\u003e)\u0026times;V\u0026times;N/M\u003c/p\u003e \u003cp\u003eChlb=(24.96A\u003csub\u003e649\u003c/sub\u003e-7.32A\u003csub\u003e665\u003c/sub\u003e)\u0026times;V\u0026times;N/M\u003c/p\u003e \u003cp\u003ecarotenoid=(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1000{A}_{470}-2.05{C}_{a}-114.8{C}_{b}}{245}\\)\u003c/span\u003e\u003c/span\u003e)\u0026times;V\u0026times;N/M\u003c/p\u003e \u003cp\u003eV: extraction liquid volume (ml) N: dilution ratio M: sample fresh weight (g)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of nutrient content\u003c/h2\u003e \u003cp\u003eThe nutrient content index included nicotine, reducing sugar, total sugar, chlorine, total nitrogen and potassium in cigar leaves. The contents of total nitrogen, chlorine, total sugar, reducing sugar and nicotine were measured using a mobile injection analyzer (AA3, Seal Aultical). The potassium content was measured by flame spectrophotometry (M410).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMetabolomics analysis\u003c/h2\u003e \u003cp\u003e50 mg tobacco sample was added to a 2 mL centrifuge tube and a 6 mm diameter grinding bead was added. 400 \u0026micro;L of extraction solution (methanol: water\u0026thinsp;=\u0026thinsp;4:1 (v:v)) containing 0.02 mg/mL of internal standard (L-2-chlorophenylalanine) was used for metabolite extraction. Samples were ground by the Wonbio-96c (Shanghai Wanbo Biotechnology Co., LTD) frozen tissue grinder for 6 min (-10\u0026deg;C, 50 Hz), followed by low-temperature ultrasonic extraction for 30 min (5\u0026deg;C, 40 kHz). The samples were left at -20\u0026deg;C for 30 min, centrifuged for 15 min (4\u0026deg;C, 13000 g), and the supernatant was transferred to the injection vial for LC-MS/MS analysis.\u003c/p\u003e \u003cp\u003eThe LC-MS/MS analysis of sample was conducted on a SCIEX UPLC-Triple TOF 5600 system equipped with an ACQUITY HSS T3 column (100 mm \u0026times; 2.1 mm i.d., 1.8 \u0026micro;m; Waters, USA) at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The mobile phases consisted of 0.1% formic acid in water:acetonitrile (95:5, v/v) (solvent A) and 0.1% formic acid in acetonitrile:isopropanol:water (47.5:47.5, v/v) (solvent B). Positive ion mode separation gradient: 0\u0026ndash;3 min, mobile phase B was increased from 0\u0026ndash;20%; 3-4.5 min, mobile phase B was increased from 20\u0026ndash;35%; 4.5-5 min, mobile phase B was increased from 35\u0026ndash;100%; 5-6.3 min, mobile phase B was maintained at 100%; 6.3\u0026ndash;6.4 min, mobile phase B was decreased from 100\u0026ndash;0%; 6.4-8 min, mobile phase B was maintained at 0%. Separation gradient in negative ion mode: 0-1.5 min, mobile phase B rises from 0 to 5%; 1.5-2 min, mobile phase B rises from 5\u0026ndash;10%; 2-4.5 min, mobile phase B rises from 10\u0026ndash;30%; 4.5-5 min, mobile phase B rises from 30\u0026ndash;100%; 5-6.3 min, mobile phase B linearly maintains 100%; 6.3\u0026ndash;6.4 min, the mobile phase B decreased from 100\u0026ndash;0%; 6.4-8 min, the mobile phase B was linearly maintained at 0%. The flow rate was 0.40 mL/min and the column temperature was 40\u0026deg;C.\u003c/p\u003e \u003cp\u003eThe pretreatment of LC/MS raw data was performed by Progenesis QI (Waters Corporation, Milford, USA) software, and a three-dimensional data matrix in CSV format was exported. The information in this three-dimensional matrix included sample information, metabolite name and mass spectral response intensity. Internal standard peaks, as well as any known false positive peaks (including noise, column bleed, and derivatized reagent peaks), were removed from the data matrix, deredundant and peaks pooled. At the same time, the metabolites were identified by searching databases, and the main databases were HMDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hmdb.ca/\u003c/span\u003e\u003cspan address=\"http://www.hmdb.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Metlin (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metlin.scripps.edu/\u003c/span\u003e\u003cspan address=\"https://metlin.scripps.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Majorbio Database. Perform variance analysis on the matrix file after data preprocessing. The R package \u0026ldquo;ropls\u0026rdquo; (Version 1.6.2) was used to perform principal component analysis (PCA) and orthogonal least partial squares discriminant analysis (OPLS-DA), and 7-cycle interactive validation to evaluate the stability of the model. The metabolites with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were determined as significantly different metabolites based on the variable importance in the projection (VIP) obtained by the OPLS-DA model and the p-value generated by the student\u0026rsquo;s t test.\u003c/p\u003e \u003cp\u003eDifferential metabolites among two groups were mapped into their biochemical pathways through metabolic enrichment and pathway analysis based on the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These metabolites could be classified according to the pathways they involve or the functions they perform. Enrichment analysis was used to analyze a group of metabolites in a function node whether they appears or not. The principle was that the annotation analysis of a single metabolite developed into an annotation analysis of a group of metabolites. Python package \u0026ldquo;scipy.stats\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://docs.scipy.org/doc/scipy/\u003c/span\u003e\u003cspan address=\"https://docs.scipy.org/doc/scipy/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) was used to perform enrichment analysis to obtain the most relevant biological pathways for experimental treatments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eWPS Office 2021 was used for data sorting and statistical analysis, GraphPad Prism8 was utilized for mapping, IBM SPASS Statistics 27 was used for variance analysis, and Duncan's test was performed to compare the significance of differences between treatments (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were analyzed through the free online platform of majorbio choud platform (cloud.majorbio.com).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Jinpeng Yang, Guangda Ding, Fangsen Xu, Chunlei Yang and Sheliang Wang provided research ideas and designed experiments. Haiying Liu, Xinwen Chi, and Jinpeng Yang performed the experiments. Haiying Liu performed metabolomics analyses. Haiying Liu, Xinwen Chi and Sheliang Wang wrote the manuscript. Haiying Liu and Xinwen Chi prepared figures and supplementary data. All authors commented on previous versions of the manuscript. All authors reviewed and approved the manuscript prior to submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequence data that support the findings of this study have been deposited in the European Nucleotide Archive with the primary accession code MTBLS10189.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the China National Tobacco Corporation Science and Technology Major Project (110202001039, XJ-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eUzelac, B. \u003cem\u003eet al.\u003c/em\u003e Characterization of natural leaf senescence in tobacco (Nicotiana tabacum) plants grown in vitro. \u003cem\u003eProtoplasma\u003c/em\u003e 253, 259-275 (2016).\u003c/li\u003e\n\u003cli\u003eWang, N. \u003cem\u003eet al.\u003c/em\u003e Exogenous Melatonin Alleviated Leaf Yellowing via Inhibiting Respiration and Ethylene Biosynthesis during Shelf Life in Pakchoi. \u003cem\u003ePlants\u003c/em\u003e 11, 2102; https://doi.org/10.3390/plants11162102 (2022).\u003c/li\u003e\n\u003cli\u003eYamamoto, T. \u003cem\u003eet\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003eal.\u003c/em\u003e\u003cem\u003e \u003c/em\u003eCharacterization of a genomic region that maintains chlorophyll and nitrogen contents during ripening in a high-yielding stay-green rice cultivar, \u003cem\u003eField Crops Res\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 206, 54-64 (2017).\u003c/li\u003e\n\u003cli\u003eRomanova, A. K. \u003cem\u003eet al.\u003c/em\u003e Physiological, biochemical, and fluorescence parameters of senescing sugar beet leaves in the vegetative phase of growth. \u003cem\u003eRuss\u003c/em\u003e\u003cem\u003e. \u003c/em\u003e\u003cem\u003eJ\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Plant Physiol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 58, 271-282 (2011). \u003c/li\u003e\n\u003cli\u003eRitonga, F. N. \u003cem\u003eet al\u003c/em\u003e. The Roles of Gibberellins in Regulating Leaf Development. \u003cem\u003ePlants\u003c/em\u003e 12, 1243 (2023). \u003c/li\u003e\n\u003cli\u003eSong, Y. \u003cem\u003eet al\u003c/em\u003e. Abscisic Acid as an Internal Integrator of Multiple Physiological Processes Modulates Leaf Senescence Onset in Arabidopsis thaliana. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e\u003cem\u003e \u003c/em\u003e7 (2016). \u003c/li\u003e\n\u003cli\u003eMa, X., Balazadeh, S., Mueller-Roeber, B. Tomato fruit ripening factor NOR controls leaf senescence. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e E\u003c/em\u003e\u003cem\u003exp.\u003c/em\u003e\u003cem\u003e B\u003c/em\u003e\u003cem\u003eot. \u003c/em\u003e70, 2727-2740 (2019).\u003c/li\u003e\n\u003cli\u003eLi, W. \u003cem\u003eet al.\u003c/em\u003e Intergrative metabolomic and transcriptomic analyses unveil nutrient remobilization events in leaf senescence of tobacco. \u003cem\u003eSci\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Rep\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 7, 12126 (2017). https://doi.org/10.1038/s41598-017-11615-0.\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Inc\u0026agrave;, E. \u003cem\u003eet al.\u003c/em\u003e The transcription factor VviNAC60 regulates senescence- and ripening-related processes in grapevine, \u003cem\u003ePlant Physiol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e, 192, 1928-1946 (2023).\u003c/li\u003e\n\u003cli\u003ePourtau, N. \u003cem\u003eet al.\u003c/em\u003e Effect of sugar-induced senescence on gene expression and implications for the regulation of senescence in Arabidopsis. Planta 224, 556-568 (2006).\u003c/li\u003e\n\u003cli\u003eLi, L. \u003cem\u003eet al.\u003c/em\u003e Comprehensive investigation of tobacco leaves during natural early senescence via multi-platform metabolomics analyses. \u003cem\u003eSci\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Rep\u003c/em\u003e. 6, 37976 (2016).\u003c/li\u003e\n\u003cli\u003eN\u0026ouml;lke, G. \u003cem\u003eet al\u003c/em\u003e. Polyamines delay leaf maturation in low-alkaloid tobacco varieties. \u003cem\u003ePlant Direct\u003c/em\u003e. 2, 1-12 (2018).\u003c/li\u003e\n\u003cli\u003eHansen, H., Grossmann, K. Auxin-Induced Ethylene Triggers Abscisic Acid Biosynthesis and Growth Inhibition, \u003cem\u003ePlant Physiol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 124, 1437-1448 (2000).\u003c/li\u003e\n\u003cli\u003eBovet, L. \u003cem\u003eet al\u003c/em\u003e. Asparagine Synthesis during Tobacco Leaf Curing. \u003cem\u003ePlants\u003c/em\u003e 8, 492 (2019).\u003c/li\u003e\n\u003cli\u003eQin, Y. \u003cem\u003eet al.\u003c/em\u003e Transcriptome analysis reveals key genes involved in the regulation of nicotine biosynthesis at early time points after topping in tobacco (Nicotiana tabacum L.).\u003cem\u003e BMC Plant Biol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 20, 30 (2020). \u003c/li\u003e\n\u003cli\u003eZhao, J. \u003cem\u003eet al. \u003c/em\u003eMetabolic changes in primary, secondary, and lipid metabolism in tobacco leaf in response to topping. \u003cem\u003eAnal\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Bioanal\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Chem\u003c/em\u003e. 410, 839-851 (2018). \u003c/li\u003e\n\u003cli\u003eHe, C. \u003cem\u003eet al.\u003c/em\u003e Photosynthesis-Related Proteins Play Crucial Role During Senescence Stage in Flue-Cured Tobacco Plants: A Combine iTRAQ-PRM Study. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Plant Growth Regul\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 41, 1013-1031 (2022).\u003c/li\u003e\n\u003cli\u003eLi, N. \u003cem\u003eet al\u003c/em\u003e. Metabolomic analysis reveals key metabolites alleviating green spots under exogenous sucrose spraying in air-curing cigar tobacco leaves. \u003cem\u003eSci\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Rep\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 13, 1311 (2023). \u003c/li\u003e\n\u003cli\u003eYang, R. \u003cem\u003eet al\u003c/em\u003e. Effects of different nitrogen application rates on the quality and metabolomics of cigar tobacco. \u003cem\u003eAgron\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e J\u003c/em\u003e. 114, 1155-1167 (2022). \u003c/li\u003e\n\u003cli\u003eChen, Y. \u003cem\u003eet al.\u003c/em\u003e Effects of enzymatic browning reaction on the usability of tobacco leaves and identification of components of reaction products. \u003cem\u003eSci\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Rep\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 9, 17850 (2019).\u003c/li\u003e\n\u003cli\u003eGomez, F. M. Extra-plastidial degradation of chlorophyll and photosystem I in tobacco leaves involving \u0026lsquo;senescence-associated vacuoles\u0026rsquo;. \u003cem\u003ePlant J\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 99, 465-477 (2019) .\u003c/li\u003e\n\u003cli\u003eGr\u0026scaron;ić, K., Čavlek, M. Effect of topping height and maturity on the quality of flue-cured tobacco cultivars.\u003cem\u003e J. Cent. Eur. Agric.\u003c/em\u003e 20, 841-851 (2019). \u003c/li\u003e\n\u003cli\u003eGong, Y. \u003cem\u003eet al.\u003c/em\u003e Application of starch degrading bacteria from tobacco leaves in improving the flavor of flue-cured tobacco.\u003cem\u003e Front\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Microbiol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 14, 1211936 (2023).\u003c/li\u003e\n\u003cli\u003eWang, M. \u003cem\u003eet al.\u003c/em\u003e Soil fungal communities affect the chemical quality of flue-cured tobacco leaves in Bijie, Southwest China. \u003cem\u003eSci\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Rep\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 12, 2815 (2022).\u003c/li\u003e\n\u003cli\u003eTiecher, T. \u003cem\u003eet al.\u003c/em\u003e Flue‐cured tobacco and Cl rates: Implications on yield, quality, and nutrient concentration. \u003cem\u003eAgron\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e J.\u003c/em\u003e 115, 896-908 (2022). \u003c/li\u003e\n\u003cli\u003eQiu, X. M., Sun, Y. Y., Ye X. Y., Li Z. G. Signaling Role of Glutamate in Plants.\u003cem\u003e \u003c/em\u003e\u003cem\u003eFrontiers Plant Sci\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 10 (2020).\u003c/li\u003e\n\u003cli\u003eLim, S. \u003cem\u003eet al.\u003c/em\u003e Biosynthetic pathway of shikimate and aromatic amino acid and its metabolic engineering in plants. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Plant Biotechnol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 42, 135-153 (2015). \u003c/li\u003e\n\u003cli\u003eHern\u0026aacute;ndez, I. \u003cem\u003eet al.\u003c/em\u003e Zeatin modulates flower bud development and tocopherol levels in Cistus albidus (L.) plants as they age. \u003cem\u003ePlant biol. \u003c/em\u003e17, 90-96 (2014).\u003c/li\u003e\n\u003cli\u003eHallmark, H., Cerny, M., Brzobohat\u0026yacute;, B., Rashotte, A. trans-Zeatin-N-glucosides have biological activity in Arabidopsis thaliana.\u003cem\u003e PLOS ONE \u003c/em\u003e15, e0232762 (2020). \u003c/li\u003e\n\u003cli\u003eXia, S. \u003cem\u003eet al.\u003c/em\u003e\u003cem\u003e \u003c/em\u003eThe ell1 mutation disrupts tryptophan metabolism and induces cell death. \u003cem\u003ePlant Signal. Behav. \u003c/em\u003e (2021) 10.1080/15592324.2021.1905336.\u003c/li\u003e\n\u003cli\u003eXu, Y. \u003cem\u003eet al.\u003c/em\u003e\u003cem\u003e \u003c/em\u003eEffect of exogenous plant hormones on agronomic and physiological performance of a leaf early-senescent rice mutant osled. \u003cem\u003ePlant Growth Regul\u003c/em\u003e. 92, 517-533 (2020).\u003c/li\u003e\n\u003cli\u003eKhan, D. The role of phytomelatonin receptor 1-mediated signaling in plant growth and stress response. \u003cem\u003eFront. Plant Sci.\u003c/em\u003e 14,1142753 (2023).\u003c/li\u003e\n\u003cli\u003eKang, K., Kim, Y., Park, S., Back, K. Senescence-Induced Serotonin Biosynthesis and Its Role in Delaying Senescence in Rice Leaves. \u003cem\u003ePlant physiol\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e 150, 1380-1393 (2009).\u003c/li\u003e\n\u003cli\u003eSharma, S., Charan, B., Rai, V. Influence of Abscisic Acid and Trans Cinnamic Acid on Senescence of Detached Tropaeolum majus Leaves in Relation to Stomatal Movements. \u003cem\u003eJ\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Plant Physiol.\u003c/em\u003e 146, 751-753. (1995).\u003c/li\u003e\n\u003cli\u003eHorn, T. \u003cem\u003eet al.\u003c/em\u003e\u003cem\u003e \u003c/em\u003eEvolutionary aspects of lipoxygenases and genetic diversity of human leukotriene signaling. Progress in lipid research. 57, 13-39 (2014).\u003c/li\u003e\n\u003cli\u003ePanagiotopoulos, A., Kalyvianaki, K., Castanas, E., Kampa, M. Eicosanoids in prostate cancer. \u003cem\u003eCancer\u003c/em\u003e\u003cem\u003e \u003c/em\u003e\u003cem\u003eMetast\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Rev.\u003c/em\u003e 37, 237-243 (2018).\u003c/li\u003e\n\u003cli\u003eSavchenko, T. \u003cem\u003eet al.\u003c/em\u003e Arachidonic Acid: An Evolutionarily Conserved Signaling Molecule Modulates Plant Stress Signaling Networks. \u003cem\u003ePlant Cell\u003c/em\u003e 22, 3193-3205 (2010).\u003c/li\u003e\n\u003cli\u003eTallima, H., Ridi, R. E. Arachidonic Acid: Physiological Roles and Potential Health Benefits. A Review. \u003cem\u003eJ Adv\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e Res.\u003c/em\u003e 11, 33-41 (2017).\u003c/li\u003e\n\u003c/ol\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cigar, Harvestingtime, Maturation physiology, Metabolism","lastPublishedDoi":"10.21203/rs.3.rs-4467753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4467753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe timing of maturity significantly impacts the quality of cigar tobacco leaves, with both premature and delayed maturation leading to quality degradation. Despite the known association between maturity and physiological metabolic activities, there is a paucity of concrete evidence detailing the physiological behavior of cigar leaves harvested at varying times. This research involved a comprehensive physiological and metabolomic examination of the cigar tobacco variety CX-014, cultivated in Danjiangkou City, Hubei Province. The study focused on leaves picked at 35 (T1), 42 (T2), 49 (T3), and 56 (T4) days following the removal of the apical inflorescence. As the harvest period progressed, the leaves\u0026rsquo; hue transitioned from green to yellow, displaying white mature spots. Between T1 and T2, there was an uptick in pigment indices (like chlorophyll a and b) and photosynthetic traits (such as stomatal conductance), which then diminished in the T3 and T4 samples. Optimal levels of sugar-to-nicotine and potassium-to-chlorine ratios\u0026mdash;key indicators of smoking quality and tobacco combustibility\u0026mdash;were observed at T3, suggesting a more balanced chemical composition in the leaves harvested at this stage. Metabolomic analysis revealed 2,153 distinct metabolites, with the most significant changes occurring between T2 and T3, highlighting critical physiological transformations during this interval. Pathway enrichment analysis via KEGG pinpointed notable shifts in amino acid synthesis pathways, particularly those involving tryptophan, alanine, and aspartate. Tryptophan metabolism and zeatin biosynthesis were substantially altered, with compounds like indolepyruvic acid, N-formylpurine nucleotide, isopentenyladenine nucleotide, and dihydrozeatin showing marked reductions at T3. This study also explored how the timing of lower leaf harvest influences the physiological processes of middle leaves, finding that a plethora of metabolites associated with the breakdown of arachidonic acid\u0026mdash;a primitive metazoan signaler implicated in plant stress and defense networks\u0026mdash;were abundant in T3 leaves when lower leaves were harvested 43 to 38 days prior. These findings suggest that the harvest timing of lower leaves may sway the maturation physiology and environmental adaptability of middle leaves. Overall, this investigation sheds light on the intricate physiological dynamics of cigar leaves throughout maturation and pinpoints crucial metabolites that signify pivotal metabolic pathways.\u003c/p\u003e","manuscriptTitle":"Integrated analysis of physiological and metabolic data uncovers essential dynamic mechanisms involved in the maturation of cigar tobacco leaves","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-05 19:17:16","doi":"10.21203/rs.3.rs-4467753/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-20T06:16:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-14T20:58:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-10T12:43:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117533569966620169529347624527708105152","date":"2024-07-31T10:47:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1280730810266015547172731325131846736","date":"2024-07-30T15:36:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-30T09:53:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-24T10:36:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-24T12:09:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-24T12:02:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-23T15:07:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e68ac30f-f7cd-4f55-8235-20a19776c9e0","owner":[],"postedDate":"June 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32768209,"name":"Biological sciences/Biological techniques/Metabolomics"},{"id":32768210,"name":"Biological sciences/Plant sciences/Plant physiology"}],"tags":[],"updatedAt":"2024-12-30T16:00:30+00:00","versionOfRecord":{"articleIdentity":"rs-4467753","link":"https://doi.org/10.1038/s41598-024-82859-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-12-28 15:57:18","publishedOnDateReadable":"December 28th, 2024"},"versionCreatedAt":"2024-06-05 19:17:16","video":"","vorDoi":"10.1038/s41598-024-82859-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-82859-w","workflowStages":[]},"version":"v1","identity":"rs-4467753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4467753","identity":"rs-4467753","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}