1. Introduction
Lycoris radiata (L’ Herit.) Herb, a perennial bulbous plant within the Amaryllidaceae family, predominantly thrives in East Asia’s warm temperate and subtropical regions. This species is esteemed for its substantial ornamental and medicinal virtues. Alkaloids extracted from the bulbs of Lycoris, such as galanthamine, lycoramine, and lycorine, hold potential for pharmaceutical development, targeting an array of diseases [1,2,3]. Additionally, due to its distinctive and vibrant flowers, coupled with robust stress and shade tolerance, L. radiata frequently serves in landscaping as a ground cover in shaded gardens, as well as in containers and as fresh-cut flowers. Currently, the integration of large-scale cultivation with tourism and the production of medicinal substances yields considerable economic benefits. Nonetheless, the commercial scalability of L. radiata is constrained by challenges in effectively regulating its flowering period. Investigating its biological traits and flowering mechanisms is pivotal for advancing flowering regulation technologies.
L. radiata exhibits the unique biological characteristic of “flowers and leaves not meeting”; it enters dormancy in summer, blooms in autumn without leaves, and subsequently produces leaves that remain through autumn, winter, and spring [4]. Simplifying, the life cycle of L. radiata encompasses a leaf stage and a leafless stage. The leaf stage extends from October to May of the subsequent year and is subdivided into four periods: leaf emergence (October), rapid leaf growth (November), leaf maturity (December to February), and leaf senescence (March to May). The leafless stage comprises a dormancy period (June to July) and a flowering period (August to September) [5]. Flower development can be further categorized into the flower bud differentiation period (April to May), flower organ development period (June to July), scape formation (August), and flowering period (September) [6]. Flower bud differentiation occurs as the leaves begin to yellow, while flower organ development coincides with the dormancy period [6].
Recent research on the development and flowering of Lycoris primarily investigates the sink-source transition of carbohydrates within bulbs. Key areas of focus include the analysis of biomass composition across eight developmental stages [6], carbohydrate content, starch synthesis, and metabolic enzyme activity during the vegetative growth stage [7], the distribution pattern of non-structural carbohydrates during dormancy, and alterations in the levels of soluble sugar, soluble protein, starch, glucose, fructose, and sucrose throughout the flowering phase [8] in bulbs. Furthermore, studies have examined the variation in hormone levels across different growth stages [9] and the hormone-related genes in bulbs of varying ages [10]. Physiologically, these investigations reveal that the flowering process in L. radiata is energy-intensive, with the bulb acting primarily as a “source” during the peak flowering period and as a “sink” throughout the extended leaf growth phase. The sink-source conversion involves a series of metabolic activities within the bulb, such as starch synthesis and breakdown, sucrose utilization, glucose accumulation, and hormone synthesis and regulation [8,9,10]. However, these metabolic processes have yet to be confirmed at the metabolic level, leaving the metabolic regulation mechanisms of development and flowering in Lycoris plants largely undefined.
Cellular signaling, energy transfer, and intercellular communication, essential to plant growth and development, are modulated by metabolites [11,12]. Metabolomics, through both qualitative and quantitative analysis of all low molecular weight metabolites, has emerged as a formidable tool for delving into the cellular biology of plants. While transcriptomic and proteomic technologies have been employed to probe the molecular underpinnings of Lycoris development and flowering [10,13,14,15], metabolomics is essential to bridge the genotype-phenotype gap. Recently, metabolomics has been applied to study the metabolic regulation mechanisms behind bulblet formation [16], petal color development [14], and galanthamine biosynthesis [17] in Lycoris. However, the metabolic regulation mechanisms across the different developmental stages of Lycoris remain unexplored. This study pioneers a comparative metabolome analysis to chart the dynamic shifts in metabolite accumulation at the bulb tips across six developmental stages of L. radiata, utilizing non-targeted metabolomics based on ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS/MS). This analysis aims to lay the groundwork for elucidating the regulatory mechanisms underpinning the development and flowering of L. radiata.
2. Materials and Methods
2.1. Plant Materials
From December 2018 to November 2019, a total of 48 underground bulbs of L. radiata, representing six distinct developmental stages, were systematically collected from Jiangxi Agricultural University Flower Gardens, Nanchang, Jiangxi Province, China. The stages included the stage of leafing out (LO, collected on 9 October), rapid leaf extension (RLE, collected on 27 October), leaf maturity (LMa, collected on 30 December), leaf withering (LWi, collected on 13 April), dormancy (Dor, collected on 28 June), and flowering (Flo, collected on 3 September). Nanchang’s subtropical monsoon climate features an annual mean rainfall of 1600–1700 mm, an annual average minimum temperature of 3.9 °C, and an annual average maximum temperature of 38.7 °C. The soil is predominantly red clay with a pH value of 6.43. Bulb tips were immediately placed in liquid nitrogen upon collection and stored at −80 °C until analysis. Each developmental stage comprised eight independent biological replicates.
2.2. Sample Extraction
Freeze-dried samples were pulverized to a fine powder in liquid nitrogen. Approximately 0.06 g of the powder was dissolved in a 70% methanol aqueous solution. Subsequently, 1 mL of a pre-chilled methanol-acetonitrile solution (2:2:1, v/v) was added to 60 mg of the lyophilized powder, vortexed for 60 s, and subjected to low-temperature ultrasonication twice, each for 30 min. The sample was then centrifuged at 14,000 rcf for 20 min at 4 °C. The supernatant was collected and concentrated to dryness in preparation for UPLC-MS/MS analysis performed by Shanghai Applied Protein Technology Co., Ltd. (Shanghai, China). A quality control (QC) sample, created by combining equal volumes of extract from each developmental stage, was used to evaluate the system’s stability throughout the experiment.
2.3. LC-MS Analysis
The chromatographic conditions were set with a column temperature of 25 °C and a flow rate of 0.3 mL/min. The mobile phase A consisted of water with 25 mM ammonium acetate and 25 mM ammonia, while mobile phase B was acetonitrile. A linear gradient of mobile phase B was employed as follows by volume ratio: 0–0.5 min, 95% B; 0.5–7 min, 95–65% B; 7–8 min, 65–40% B; 8–9 min, 40% B; 9–9.1 min, 40–95% B; and 9.1–12 min, 95% B. To mitigate the impact of instrument detection signal fluctuations, samples were analyzed in a randomized sequence. Mix all samples in equal volumes to form Quality Control (QC) samples. The QC samples were interspersed within the sample queue to monitor and assess the system’s stability and the reliability of the experimental data.
Electrospray ionization (ESI) in both positive and negative ion modes was utilized to detect the mass spectrum signals of the samples. Following detection, metabolites were identified using an AB Triple TOF 6600 mass spectrometer, and primary and secondary mass spectra of the QC samples were collected.
2.4. Statistical Analysis
The original data were converted into mzXML format using ProteoWizard, followed by peak alignment, retention time correction, and peak area extraction via the XCMS program. Metabolite structures were identified by matching the accurate mass number (<25 ppm) and secondary spectrum. The R software package ropls (Version 1.6.2) facilitated principal component analysis (PCA) and orthogonal least squares discriminant analysis (OPLS-DA), employing seven cycles of interactive validation to assess model stability. The variable importance for projection (VIP) score derived from the OPLS-DA model identified metabolites as differential (DAMs) if they exhibited a VIP score ≥1 and a t-test p-value ≤ 0.05. Pathway enrichment analysis of the DAMs was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, with a significance threshold of q-value ≤ 0.05 (p-value < 0.05 after false discovery rate correction).
3. Results
3.1. PCA of the Metabolites in Lycoris Bulb Samples
XCMS software (released in 2006) was used to extract the ion peaks of metabolites. The number of ion peaks in positive ion mode was 7460, and the number of ion peaks in negative ion mode was 8074. Principal component analysis (PCA) scores showed that the QC samples were closely clustered together, and the metabolomic data of the same sample, with at least five biological repetitions, were also clustered together (Figure 1). This indicates that the experiment had good repeatability. In addition, the samples of Dor, RLE, LMa, and LWi at four developmental stages were clearly separated from each other and were distinct from Flo and LO (Figure 1). However, there was no obvious separation between Flo and LO, indicating that the metabolites of Dor, RLE, LMa, and LWi at four developmental stages of L. radiata were different, whereas the differences between the metabolites of Flo and LO were small (Figure 1).
3.2. Orthogonal Projections to Latent Structures-Discrimination Analysis (OPLS-DA)
The OPLS-DA model was used to analyze the data and further explore the differences between adjacent developmental stages. The samples from two adjacent developmental stages were located in different quadrants on the left and right sides of the Y-axis (Figure 2). In the positive mode, except for the RLE vs. LO group, model evaluation parameters (R2X, R2Y) and prediction ability (Q2) were all greater than 0.5 (Figure 2a1–f1). In the negative mode, R2X, R2Y, and Q2 were all greater than 0.35 (Figure 2a2–f2). This indicated that the OPLS-DA model was stable and highly reliable for the dataset and could be used to describe the differences between different developmental stages of L. radiata. The permutation test was used to further verify the reliability of the OPLS-DA model. All Q2 points from left to right are lower than the original Q2 points on the rightmost side, indicating that the model is robust and reliable without overfitting (Figure S1).
3.3. Differentially Accumulated Metabolites (DAMs) Analysis
Based on the OPLS-DA model, DAMs between two adjacent developmental stages of L. radiata were identified with a VIP score ≥ 1 and a t-test p-value < 0.05. In the positive ion mode, 195 DAMs were identified, comprising 101 over-accumulated and 94 down-accumulated metabolites. Conversely, in the negative ion mode, 315 DAMs were identified, including 146 over-accumulated and 169 down-accumulated metabolites (Figure 3, Table S1). In the positive ion mode, the comparison group with the highest number of DAMs was LMa vs. RLE (41), followed by LWi vs. LMa (39), with the lowest being Flo vs. Dor (24) (Figure 3a). The most over-accumulated DAMs were observed in the LO vs. Flo comparison group (24), with LWi vs. LMa (21) following. The fewest over-accumulated DAMs occurred in the RLE vs. LO comparison (only 7). For down-accumulated DAMs, LMa vs. RLE had the most (23), followed by RLE vs. LO (21), with the fewest in LO vs. Flo (7). In the negative ion mode, the highest number of DAMs was identified in the RLE vs. LO (68) and LMa vs. RLE (68) comparison groups, with the fewest in the Flo vs. Dor group (36) (Figure 3b). The most over-accumulated metabolites were found in LO vs. Flo (36), followed by LMa vs. RLE (34), and the fewest over-accumulated metabolites were in the Dor vs. LWi comparison (10). Regarding down-accumulated DAMs, RLE vs. LO showed the most (47), followed by LMa vs. RLE (34), and the fewest were in LO vs. Flo (10).
We further analyzed the proportion of DAMs within each subclass relative to the total DAMs in each comparison group. Figure 4a reveals that the majority of DAMs across all six comparison groups belonged to the subclasses “carbohydrates and carbohydrate conjugates” and “amino acids, peptides, and analogs”. Additionally, the proportion of DAMs categorized as “fatty acids and conjugates”, “pyridine carboxaldehydes”, “pyrimidine nucleotide sugars”, “quaternary ammonium salts”, and “tricarboxylic acids and derivatives” in the Flo vs. Dor comparison was higher than in other groups. The DAMs in the subclasses “beta hydroxy acids and derivatives”, “linoleic acids and derivatives”, and “pyrimidines and pyrimidine derivatives” were more prevalent in the LO vs. Flo comparison compared to others (Figure 4a). A radar chart analysis (Figure 4b) showed that the number of over-accumulated DAMs in the “carbohydrates and carbohydrate conjugates” subclass was notably higher in the LMa vs. RLE and RLE vs. LO comparisons than in the other four groups.
Additionally, the number of down-accumulated DAMs was highest in the LWi vs. LMa comparison. Meanwhile, the “amino acids, peptides, and analogs” subclass exhibited the most over-accumulated DAMs in LWi vs. LMa, followed by the LO vs. Flo and Flo vs. Dor comparisons. This subclass also exhibited the most down-accumulated DAMs in LMa vs. RLE and RLE vs. LO comparisons (Figure 4c).
3.4. The KEGG Annotation Analysis of DAMs
KEGG pathway enrichment analysis was performed based on the DAMs identified under positive and negative ion modes. The DAMs of the six comparison groups were significantly enriched in 59 (Flo vs. Dor), 45 (LO vs. Flo), 63 (RLE vs. LO), 56 (LMa vs. RLE), 55 (LWi vs. LMa), and 47 (Dor vs. LWi) metabolic pathways (Table S2) (p-value < 0.05). The top 20 KEGG pathways for each of the six comparison groups are illustrated in Figure 5, with the significance of key pathways determined by a q-value. For all comparison groups, DAMs were primarily associated with pathways such as “ABC transporters”, “protein digestion and absorption”, “aminoacyl-tRNA biosynthesis”, “alanine, aspartate and glutamate metabolism”, and “galactose metabolism”, among others.
The enriched pathways of DAMs across the six comparison groups were mainly categorized into five types: amino acid metabolism, carbohydrate metabolism, signal transduction, energy metabolism, and biosynthesis of other secondary metabolites (Figure S2). A significant proportion of the metabolic pathways were related to amino acid and carbohydrate metabolism. Specifically, the percentage of pathways enriched by DAMs related to carbohydrate metabolism in the LO vs. Flo comparison was the highest among all groups, followed by RLE vs. LO. The proportion of pathways related to amino acid metabolism enriched by DAMs in LMa vs. RLE was highest, followed by Dor vs. LWi. Additionally, the proportion of pathways associated with signal transduction and the biosynthesis of other secondary metabolites enriched by DAMs in the Flo vs. Dor comparison exceeded those in other groups, followed by LWi vs. LMa.
To elucidate developmental stage differences in L. radiata, we conducted a statistical analysis of KEGG pathways involved in amino acid and carbohydrate metabolism across each comparison group. The pathways associated with carbohydrate metabolism enriched by DAMs across the groups included “galactose metabolism”, “glyoxylate and dicarboxylate metabolism”, “the citrate cycle”, and “starch and sucrose metabolism”. Notably, “C5-branched dibasic acid metabolism” was uniquely enriched by DAMs in the Flo vs. Dor comparison, “glycolysis/gluconeogenesis” was specific to the LO vs. Flo comparison, and the “pentose phosphate pathway” was distinctive to the LMa vs. RLE comparison (Figure 6a). In terms of amino acid metabolism, the pathways enriched by DAMs across the groups included “alanine, aspartate, and glutamate metabolism”, “arginine biosynthesis”, and “glycine, serine, and threonine metabolism”. “Lysine biosynthesis” was specifically enriched by DAMs in the Flo vs. Dor comparison (Figure 6b).
4. Discussion
4.1. Stress Responses during Dormancy and Flowering Stages
The dormancy stage (Dor) of L. radiata, spanning from May to July, is characterized by the absence of aboveground leaves while the underground bulbs undergo complex flower bud differentiation, involving the development of stages, ovaries, pistils, anthers, and pollen [6]. Following this period, the plant transitions into the late flower development stage from mid to late July. During these critical phases, our study has identified significant metabolic changes that underscore the plant’s adaptation to its growth and environmental conditions.
Our results indicate a notable increase in the proportion of DAMs associated with “tricarboxylic acids and derivatives” and “quaternary ammonium salt” during the Flo vs. Dor comparison. The tricarboxylic acid (TCA) cycle is essential not only for energy production but also for integrating various biochemical processes [18]. It plays a significant role in the oxidative defense mechanism, maintaining a balance with reactive oxygen species (ROS) and contributing to cellular immunity under stress [19]. Tricarboxylic acids and their derivatives play a core role in this process [20]. Quaternary ammonium compounds (QACs) are vital for osmotic adjustment and response to stress in plants [21], with studies demonstrating their effectiveness in alleviating drought stress in maize, which results in higher grain yields [22]. Moreover, this study highlights the unique enrichment of “C5 branched chain dicarboxylic acid metabolism” in the Flo vs. Dor comparison. This pathway is implicated in various plant stress responses, including drought stress in Phaseolus vulgaris and maize and cadmium stress tolerance in Brassica juncea [23,24,25]. It also plays a critical role in reprogramming protoplasts to stem cells during cell division in Physcomitrella patens [26]. Lycoris species are sensitive to environmental changes, particularly temperature variations during dormancy, which significantly affect flower bud differentiation, flowering period, and flowering rate [5]. L. radiata experienced temperature stimuli, stress responses, and immune reactions during its dormancy and flowering stages [13]. The metabolomic data from this study indicate specialized roles for tricarboxylic acids and derivatives, QACs and C5 branched chain dicarboxylic acid metabolism in managing stress responses during crucial developmental transitions such as flower bud differentiation and flowering of L. radiata.
4.2. Active Carbohydrate Metabolism during the Vegetative Growth Stages
Carbohydrate metabolism is crucial for the energy supply and biosynthetic processes in plants. In L. radiata, non-structural carbohydrates (NSCs), including starch and soluble sugars, play pivotal roles in bulb enlargement, leaf growth, and flower bud differentiation [27]. These carbohydrates are the bulbs’ key storage, metabolic, and regulatory agents [28,29]. During the nutritional growth stages—leafing out (LO), rapid leaf extension (RLE), and leaf maturity (LMa)—the bulb functions as a repository where carbohydrates synthesized by the leaves are transported and stored [5,6]. Investigations into NSC fluctuations throughout L. radiata’s annual growth cycle revealed that the aggregate content of NSCs, including soluble sugars, fructose, and reducing sugars, peaked during the LMa stage.
In contrast, the accumulation of starch and sucrose was delayed, reaching peak levels during the leaf withering (LWi) stage [27]. Previous transcriptomic analyses corroborate these findings, showing significant upregulation of genes associated with starch and sucrose metabolism, and overall carbohydrate metabolism during leaf development in L. radiata [15]. During the RLE and LMa stage of L. radiata, differentially expressed genes (DEGs) and DAMs were both enriched in several carbohydrate metabolism pathways, including glyoxylate and dicarboxylate metabolism, citrate cycle (TCA cycle), starch and sucrose metabolism, and butanoate metabolism (Figure S3). Consistently, the present study observed an elevated proportion of over-accumulated metabolites in the carbohydrate and carbohydrate conjugate subclasses during the RLE vs. LO and LMa vs. RLE comparisons, indicating a more active carbohydrate metabolism at the bulb tips during the vegetative growth stages compared to the reproductive stage.
Moreover, this study identified specific enhancements in pathways associated with carbohydrate degradation: glycolysis/gluconeogenesis and the pentose phosphate pathway were notably enriched during the LO vs. Flo and LMa vs. RLE stages, respectively. Glycolysis is a central metabolic pathway converting glucose into pyruvate, which generates energy in the form of ATP and NADH, whereas gluconeogenesis involves the formation of glucose from non-carbohydrate sources [30]. The pronounced activity of glycolysis/gluconeogenesis during the LO vs. Flo comparison may reflect a shift in energy requirements as L. radiata transitions from reproductive to nutritional growth. Conversely, the pentose phosphate pathway, crucial for producing NADPH and ribose 5-phosphate—essential for biosynthetic reactions and maintaining cellular redox balance—was specifically enriched during the LMa vs. RLE stage [31]. Ribose 5-phosphate also serves as a precursor for synthesizing nucleotides and nucleic acids [32]. It suggests the importance of the glycolysis/gluconeogenesis pathway in supporting rapid cell proliferation and tissue expansion during the leaf growth period of L. radiata.
4.3. Robust Amino Acid Metabolism during the Reproductive Stages
Amino acid metabolism is crucial in plants, supporting many biological processes such as protein biosynthesis, nitrogen assimilation and transportation, synthesis of secondary metabolites, stress responses, growth regulation, and signal transduction [33]. This study observed that the proportion of upregulated metabolites identified as amino acids, peptides, and analogs was highest in the LWi vs. LMa stage, followed by the LO vs. Flo and Flo vs. Dor stage. The LWi stage marks the end of vegetative growth for L. radiata, transitioning into the developmental phases of inflorescence and floret primordial formation, including the development of both inner and outer perianths [6]. Conversely, the Dor stage is crucial for the development of stamens, ovaries, pistils, and the formation of anthers and pollen [6]. Plants typically accumulate soluble proteins prior to the morphological differentiation of flower buds [34]. Upon commencement of differentiation, these proteins are degraded and converted into various amino acids [35]. These amino acids are then utilized to construct structural proteins for new cells or synthesize diverse enzymes that aid cellular metabolism [35]. The findings of this study suggest robust amino acid metabolism during the reproductive stages of L. radiata, which is intricately linked to flower bud differentiation.
Lycoriaceae alkaloids have high medicinal value, primarily including lycorine, galanthamine, and lycoamine [1]. Among them, lycorine is the most representative compound, confirmed to have significant pharmacological activity and exhibiting notable anti-tumor activity in cancer cells [36,37]. In this non-targeted metabolomic study, two stereoisomers of lycorine were identified: one with higher accumulation levels in the Dor, Flo, and LWi stages, and the other with higher accumulation levels in the LWa and LWi stages (Table S3). This indicated that the synthesis of lycorine in the bulb tips of L. radiata may mainly occur during reproductive stages. The enrichment of amino acid metabolism pathways at the bulb tips of L. radiata may also be linked to the biosynthesis of lycorine [38].
Additionally, our analysis highlighted the unique enrichment of lysine biosynthesis in the Flo vs. Dor comparison group, underscoring its importance during the flowering stage of L. radiata. Notably, two differential metabolites (DAMs) in this pathway, aspartate and lysine, were upregulated. This aligns with findings from Park et al. [39], who observed an increase in most amino acids, including aspartate, during flower development in L. radiata. The activation of lysine biosynthesis by aspartate and lysine represents a critical biochemical pathway essential for the development of L. radiata flowers, indicating a specific metabolic focus during pivotal reproductive phases.
5. Conclusions
This study comprehensively analyzed the metabolic adaptations of L. radiata across various developmental stages, elucidating significant shifts in stress response mechanisms carbohydrate, and amino acid metabolism that are crucial for its growth and flowering. During the dormancy and flowering stages, L. radiata demonstrates significant metabolic responses to environmental stress, notably through the activation of the metabolites involved in tricarboxylic acids and derivatives, QACs and the pathway of C5 branched chain dicarboxylic acids metabolism. In the vegetative stages, there is a pronounced emphasis on carbohydrate metabolism. The increased activity in carbohydrate-related pathways, particularly glycolysis and the pentose phosphate pathway, underscores the metabolic adjustments needed during periods of rapid growth and development. Furthermore, the study highlighted a robust amino acid metabolism during reproductive phases. The specific enrichment of lysine biosynthesis during the flowering stage accentuates its importance in reproductive success. Investigating the genetic regulation underlying these metabolic pathways may offer deeper insights into the physiological and molecular mechanisms driving the growth cycles of L. radiata. Our comprehensive metabolomic analysis of L. radiata across various developmental stages provides significant insights that can be leveraged to optimize cultivation practices. The metabolic shifts observed throughout the plant’s growth and dormancy stages reveal critical periods where targeted interventions can enhance growth, stress resilience, and flowering success.
Conceptualization, J.C. and X.J.; data curation, X.Y.; validation, X.W. and H.C.; writing—original draft preparation, X.J.; writing—review and editing, X.W., H.C. and J.C.; funding acquisition, J.C. and X.J. All authors have read and agreed to the published version of the manuscript.
Data are contained within the article and
The authors declare no competing interests.
Footnotes
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Enlarge this image.Figure 1. Principal component analysis (PCA) scores for Lycoris bulb samples: (a) PCA scores in positive ion mode (POS); (b) PCA scores in negative (NEG) ion mode. Dor—dormancy; Flo—lowering; LO—leafing out; RLE—rapid leaf extension; LMa—leaf maturity; LWi—leaf withering; QC—quality control.
Enlarge this image.Figure 2. OPLS-DA score plots for six comparison groups in positive (a1–f1) and negative (a2–f2) ion modes. (a1,a2) Flo vs. Dor; (b1,b2) LO vs. Flo; (c1,c2) RLE vs. LO; (d1,d2) LMa vs. RLE; (e1,e2) LWi vs. LMa; (f1,f2) Dor vs. LWi. Dor—dormancy; Flo—flowering; LO—leafing out; RLE—rapid leaf extension; LMa—leaf maturity; LWi—leaf withering; POS—positive; NEG—negative.
Enlarge this image.Figure 3. Number of differentially accumulated metabolites (DAMs) identified in Lycoris bulb Samples. (a) in positive (POS) ion mode and (b) in negative (NEG) ion mode. Dor—dormancy; Flo—flowering; LO—leafing out; RLE—rapid leaf extension; LMa—leaf maturity; LWi—leaf withering.
Enlarge this image.Figure 4. Subclass distribution of DAMs identified under positive and negative ion modes. (a) The ratio of the number of DAMs per subclass to the total DAMs within each comparison group. (b) Number of DAMs in the carbohydrates and carbohydrate conjugates subclasses across comparison groups. (c) Number of DAMs in the amino acids, peptides, and analogs subclasses across comparison groups. Dor—dormancy; Flo—flowering; LO—leafing out; RLE—rapid leaf extension; LMa—leaf maturity; LWi—leaf withering.
Enlarge this image.Figure 5. Top 20 KEGG enrichment of DAMs for six comparison groups. (a) Flo vs. Dor, (b) LO vs. Flo, (c) RLE vs. LO, (d) LMa vs. RLE, (e) LWi vs. LMa, (f) Dor vs. LWi.
Enlarge this image.Figure 6. KEGG pathways associated with carbohydrate (a) and amino acid (b) metabolism across comparison groups, displaying DEM counts in each box. The color changes from yellow to red, with a closer proximity to red indicating a higher number of DAMs, a red box signifies a higher number of DAMs, while a blue box indicates the absence of DAMs. Dor—dormancy; Flo—flowering; LO—leafing out; RLE—rapid leaf extension; LMa—leaf maturity; LWi—leaf withering.
Supplementary Materials
The following supporting information can be downloaded at:
References
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; Wei, Xuying 2 ; Cheng, Hua 1 ; You, Xin 1 ; Cai, Junhuo 1
1 Jiangxi Provincial Key Laboratory of Conservation Biology, Jiangxi Provincial Key Laboratory of Subtropical Forest Resources Cultivation, College of Forestry/College of Art and Landscape, Jiangxi Agricultural University, Nanchang 330045, China;
2 College of Art, Jiangxi Finance and Economics University, Nanchang 330032, China;