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Article

Developmental Stage-Dependent Distribution and Interrelationships of Leaf Nutrients and Flavonoids in Lithocarpus litseifolius (Hance) Chun

by
Yan-Fen Huang
,
Shao-Fen Jian
,
Yang Lin
and
Chu Zhong
*
Guangxi Key Laboratory of Medicinal Resource Protection and Genetic Improvement, Guangxi Botanical Garden of Medicinal Plants, Nanning 530023, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(9), 2029; https://doi.org/10.3390/agronomy15092029
Submission received: 11 July 2025 / Revised: 12 August 2025 / Accepted: 22 August 2025 / Published: 25 August 2025
(This article belongs to the Section Soil and Plant Nutrition)
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Abstract

Lithocarpus litseifolius, a traditional sweet tea rich in dihydrochalcones, relies on plant nutrients for secondary metabolite accumulation. However, nutrient distribution patterns during leaf development and its relationship with secondary metabolites remain inadequately characterized. This study examined mineral elements, carbon and nitrogen metabolites, and primary dihydrochalcones in L. litseifolius leaves at various developmental stages, and analyzed their interrelationships. Mineral nutrients such as phosphate (P), potassium (K), magnesium (Mg), zinc (Zn), boron (B), and copper (Cu), along with trilobatin, were most abundant in the youngest leaves. Conversely, calcium (Ca), iron (Fe), sulfur (S), manganese (Mn), selenium (Se), sugars, soluble protein, amino acids, chlorophyll, and carotenoids predominantly accumulated in old leaves, paralleling the distribution of phlorizin. Nitrogen (N) and molybdenum (Mo) concentrations were higher in mature leaves. In young leaves, P, K, Mg, S, Mn, Zn, and B positively correlated with phlorizin and trilobatin, while N, chlorophyll, carotenoids, and fructose correlated negatively. Trilobatin was the primary contributor to hydroxyl radical (·OH) scavenging capacity. Redundancy analysis highlighted N, P, Mg, B, Zn, Cu, Fe, Mo, and Se as key mineral nutrients influencing phlorizin and trilobatin accumulation. These findings offer insights for mineral nutrient management and effective utilization of L. litseifolius.

1. Introduction

Mineral nutrients, along with primary carbon and nitrogen metabolites, constitute essential nutrient sources for both humans and animals. Furthermore, plants synthesize a diverse array of secondary metabolites, among which flavonoids are notably abundant. Flavonoids, plant-derived compounds, have been demonstrated to promote human health and mitigate disease risk [1,2]. The biosynthesis of flavonoids in plants is modulated by mineral nutrients [3,4,5]. Several mechanisms underlie the involvement of mineral nutrients in flavonoid biosynthesis: (1) mineral nutrients indirectly influence the biosynthesis and accumulation of flavonoids in medicinal plants by regulating plant carbon and nitrogen metabolism [6], (2) mineral nutrients affect the activity of key enzymes within the flavonoid biosynthetic pathway [7], and (3) mineral nutrients modulate the biosynthesis of flavonoids through the regulation of endogenous plant hormone metabolism [8]. Moreover, interactions among mineral nutrients represent a significant factor influencing flavonoid accumulation [9].
Lithocarpus litseifolius (Hance) Chun, an evergreen tree of the Fagaceae family, has been traditionally consumed as a sweet tea in South China for more than 1600 years [10]. This species is well recognized in traditional Chinese medicine and is utilized in multiple forms, including as a beverage, medicinal agent, and natural sweetener. The dihydrochalcones present in L. litseifolius sweet tea were incorporated as supplementary materials in the China National Health Ministry’s “Hygienic Standards for the Use of Food Additives” in 1997 [11]. Furthermore, in 2017, the tender leaves and buds of L. litseifolius were approved as novel food raw materials by the China National Health and Family Planning Commission [12]. L. litseifolius is particularly rich in dihydrochalcones, primarily phlorizin and trilobatin [13]. These compounds have demonstrated diverse pharmacological activities, including anti-inflammatory, antioxidant, hypoglycemic, hypolipidemic, hepatoprotective, and cardioprotective effects [14,15,16].
The developmental stage of leaves plays a pivotal role in plant physiology by influencing nutrient partitioning, photosynthetic efficiency, and the accumulation of primary and secondary metabolites [17,18,19]. Young leaves generally function as sink tissues, necessitating substantial nutrient import and exhibiting limited photosynthetic capacity, whereas mature and senescing leaves serve as source tissues, facilitating the export of assimilates. These functional distinctions are manifested in metabolic profiles and can affect the biosynthesis of compounds such as flavonoids, sugars, amino acids, and phenolic acids [20]. Among the various factors influencing the dihydrochalcone content in the leaves of L. litseifolius, in addition to harvesting time [21], geographic location [22], and germplasm [23,24], leaf age represents a significant intrinsic determinant [21,25,26]. Although it is well known that phlorizin is predominantly abundant in older leaves, while trilobatin is more concentrated in younger leaves [21,26], the dynamic changes of these compounds throughout leaf development remain unclear. Variations in dihydrochalcone content in L. litseifolius leaves have been primarily attributed to differences in soil mineral nutrients, including organic matter and total nitrogen and phosphorus contents [22], as well as plant inorganic and organic constituents such as carbon, nitrogen, phosphate, and potassium [24]. However, these findings were derived from studies conducted across different geographic locations and germplasms. Consequently, the nutritional characteristics and profiles of active ingredients in leaves at distinct developmental stages, along with the relationship between nutrient status and flavonoid accumulation in L. litseifolius leaves, remain inadequately understood.
The current utilization of L. litseifolius primarily emphasizes tender leaves and buds, with comparatively limited attention directed toward mature and older leaves. In this study, we quantified mineral nutrients, carbon and nitrogen metabolites, and flavonoids in both young and old leaves. The objective was to characterize the nutrient distribution across different leaf developmental stages of L. litseifolius and to identify factors influencing flavonoid accumulation in its leaves. The findings of this research may offer valuable insights for the comprehensive development and utilization of L. litseifolius raw materials, as well as for optimizing nutrient management to ensure the high-quality production of sweet tea.

2. Materials and Methods

2.1. Overview of Sampling Site and Sample Collection

The samples were collected from the L. litseifolius cultivation base of Guangxi Ganliangjian Biotechnology Co., Ltd., situated in Nanyang Town, Nanning City, China. The site is located at an altitude of 500 m, with coordinates of 22°44′44″ N latitude and 108°47′48″ E longitude. It lies within a subtropical monsoon climate zone, characterized by humid and hot conditions, rainy summers, and dry winters. Monthly average temperatures range from 9.9 °C in February to 29.2 °C in July. The annual average monthly sunshine duration is 123.2 h. The L. litseifolius trees were transplanted in 2016 using 2-year-old seedlings propagated through cuttings, and they are pruned biannually to maintain an approximate height of 1 m.
Sampling was performed at 10:00–12:00 on 3 April 2024, the day before the Qingming solar term. Leaves were collected from new branches of the current year, referred to as young leaves, and from the uppermost leaves of the previous year’s old branches, referred to as old leaves.
The leaves were classified into six distinct developmental stages according to their sequential emergence on branches. The two uppermost leaves on the previous year’s branches were designated as L6, whereas the leaves on new branches were grouped into five developmental stages, with every two adjacent leaves from the bottom to the top labeled as L5, L4, L3, L2, and L1 [26]. Three biological replicates were conducted, each comprising a composite of leaf samples collected from three individual trees. The leaf samples were placed in 50 mL containers and immediately frozen in liquid nitrogen before being transported to the laboratory on dry ice. Subsequently, the samples were pulverized in liquid nitrogen and stored at −80 °C for future analyses.

2.2. Nitrogen Content Measurement

Approximately 0.1 g of each sample was digested using a H2SO4-H2O2 mixture at 260 °C. The concentrations of NH4+ and NO3− in the resulting digestion solution were determined by the indophenol blue colorimetric method at 625 nm [27] and ultraviolet spectrophotometry at 210 nm [28], respectively, employing (NH4)2SO4 and KNO3 as standards. The total nitrogen content was calculated as the sum of the NH4+ and NO3− concentrations.

2.3. Mineral Nutrient Quantification by ICP-MS

The concentrations of mineral nutrients, excluding N, were measured using inductively coupled plasma mass spectrometry (ICP-MS). A 0.1 g fresh leaf sample was placed in a Teflon digestion vessel and soaked overnight in 5 mL of HNO3. After sealing, the container was subjected to a controlled digestion process in a constant-temperature drying oven temperature program, maintained at 80 °C for 1–2 h, then increased to 120 °C for 1–2 h, and finally raised to 160 °C for 4 h. Upon natural cooling to room temperature within the oven, residual acid in the vessel was evaporated by heating. The digestion solution was transferred to a 25 mL volumetric flask, and the vessel and lid were rinsed three times with 1% HNO3. The rinsates were combined in a volumetric flask and diluted to volume with 1% HNO3. Qualitative analysis of phosphate (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), molybdenum (Mo), boron (B), and selenium (Se) was conducted based on their specific mass-to-charge ratios (m/z) using an inductively coupled plasma emission spectrometer (iCAP 7400, Thermo Fisher Scientific, Waltham, MA, USA). Quantitative analysis was performed using an inductively coupled plasma mass spectrometer (RQ, Thermo Fisher Scientific, Waltham, MA, USA) and an atomic fluorescence spectrometer (AFS-8800, Haiguang Instrument, Beijing, China). External standards were employed for quantification by comparing the intensity ratio of the element’s mass spectrometry signal with that of an internal standard element, which is proportional to the element’s concentration.

2.4. Chlorophyll and Carotenoid

Chlorophyll and carotenoids were extracted from leaf samples (~0.1 g) by immersing them in a 1:1 (v/v) ethanol–acetone mixture for 24 h in the dark at room temperature. The absorbance of the resulting extract was measured spectrophotometrically at wavelengths of 663 nm, 645 nm, and 470 nm. The concentrations of chlorophyll and carotenoids in the extract were calculated using the following equations [29]:
Ca = 12.21 × OD663 − 2.81 × OD645
Cb = 20.13 × OD645 − 5.03 × OD663
Cc = (1000 × OD470 − 3.27 × Ca − 104 × Cb)/229
where Ca, Cb, and Cc denote the concentrations (mg·L−1) of chlorophyll a, chlorophyll b, and carotenoids, respectively; OD663, OD645, and OD470 represent the absorbance of the extract measured at 663 nm, 645 nm, and 470 nm, respectively. The concentration of chlorophyll is the sum of the concentrations of chlorophyll a and chlorophyll b.

2.5. Soluble Protein and Free Amino Acids

The soluble protein content was determined using the Coomassie Brilliant Blue G-250 assay [30]. Soluble proteins were extracted from leaf samples by homogenizing approximately 0.1 g of previously pulverized frozen tissue in a pre-cooled mortar and pestle with 100 mmol·L−1 sodium phosphate buffer (pH 7.5). The homogenates were subsequently centrifuged at 10,000× g for 10 min at 4 °C, and the supernatant was analyzed spectrophotometrically at 595 nm. Bovine serum albumin (BSA) served as the standard, and protein concentration was calculated based on a linear calibration curve relating BSA concentrations (0–100 μg·mL−1) to absorbance.
The free amino acid content was determined using the ninhydrin method as described in reference [31]. Frozen leaf samples were homogenized in 2 mL of 10% acetic acid and subsequently centrifuged at 10,000× g and 4 °C for 10 min. The resulting supernatant was then reacted with a ninhydrin solution (pH 5.4) containing 0.3% ascorbic acid in a boiling water bath for 15 min. Following cooling, the absorbance of the reaction mixture was measured spectrophotometrically at 580 nm. Leucine served as the standard, and the amino acid content was calculated using a linear calibration curve correlating leucine concentrations (0–5 μg·mL−1) with absorbance.

2.6. Sugar Content Measurement

Sugars were extracted from approximately 0.1 g of frozen leaf samples using 3 mL of deionized water in a water bath maintained at 80 °C for 30 min; this procedure was repeated three times. The extracts were collected by centrifugation. The residual material was oven-dried at 70 °C, after which 2 mL of deionized water was added, and the mixture was incubated in a boiling water bath for 10 min. Starch in the residues was subsequently extracted using 0.5 mol·L−1 perchloric acid. Sucrose and fructose concentrations were quantified spectrophotometrically at 480 nm employing the dioxybenzene method, with sucrose and fructose serving as standards, respectively [32]. Soluble sugars and starch were measured using the sulfuric acid–anthrone method, with glucose as the standard [33]. Concentrations of sugars and starch were calculated based on the linear relationship between standard concentrations (0–100 μg·mL−1) and absorbance. Glucose content was determined using a commercial assay kit (M1501B, Suzhou Michy Biomedical Technology Co., Ltd., Suzhou, Jiangsu, China).

2.7. Dihydrochalcone Components

Phloretin, phlorizin, trilobatin, and 3-hydroxyphlorizin (3-hydroxyphloretin-4′-glucoside) were quantified using high-performance liquid chromatography (HPLC). Frozen leaf samples (0.1 g) were immersed in 3 mL of methanol and subjected to ultrasonic extraction for 20 min. Subsequently, the extracts were filtered through a 0.22 μm Millipore membrane and analyzed using an HPLC system (2030C-Plus, Shimadzu Corporation, Kyoto, Japan) at a detection wavelength of 280 nm. Separation was achieved on an Agilent C18 column (250 mm × 4.6 mm, 5 μm; Agilent Technologies Inc., Santa Clara, CA, USA) with octadecylsilane-bonded silica gel as the stationary phase. The mobile phase comprised acetonitrile (Phase A) and 0.1% phosphoric acid aqueous solution (Phase B). The elution gradient was programmed as follows: 0–10 min, 28% A; 10–15 min, 28–40% A; 15–20 min, 40% A; and 20–26 min, 40–28% A. The injection volume was 10 μL, the column temperature was maintained at 40 °C, and the flow rate was set at 1 mL·min−1. Standard substances of phloretin (CAS: 60-82-2), phlorizin (CAS: 60-81-1), and trilobatin (CAS: 4192-90-9) were procured from Shanghai Acmec Biochemical Technology Co., Ltd. (Shanghai, China), whereas 3-hydrophlorizin (CAS: 18777-73-6) was sourced from Chengdu Alfa Biotechnology Co., Ltd. (Chengdu, China). Phloretin was not detectable in the analyzed samples. Quantification of phlorizin and trilobatin was performed using linear regression equations derived from the corrected peak areas and corresponding standard concentrations.

2.8. Antioxidant Capacity

The hydroxyl radical (·OH) scavenging capacity was quantitatively assessed spectrophotometrically at 510 nm using the Fenton method with a commercial kit (M0116B, Suzhou Michy Biomedical Technology Co., Ltd.). The percentage of radical scavenging was calculated based on the reduction in Fe2+ concentration, which is oxidized by hydroxyl radicals.

2.9. Statistical Analysis

Three biological replicates were used for each leaf developmental stage. Data are expressed as means ± standard deviation (SD). Statistical differences among the different leaf developmental stages were assessed using one-way ANOVA, followed by Duncan’s new multiple range test. Differences were considered statistically significant at p < 0.05. Pearson correlation coefficients were calculated to analyze correlations, with significance determined at p < 0.05. Visualization of correlations, canonical correlation analysis, and redundancy analysis were performed using Metware Cloud, a free online data analysis platform (https://cloud.metware.cn, 12 August 2025).

3. Results

3.1. Distribution Characteristics of Mineral Nutrient Elements

3.1.1. Primary Macronutrient Elements

N content increased significantly in leaves at stages L1–L4 as the leaves matured, followed by relatively minor fluctuations (Figure 1A). P levels exhibited a pattern similar to that of K, attaining their highest concentrations in leaves at stage L1 and decreasing markedly with leaf maturation (Figure 1B,C). Differences in the concentrations of these two nutrients were minimal among leaves at stages L3 to L6.

3.1.2. Secondary Macronutrient Elements

Ca content was low and remained stable in young leaves (L1–L5), whereas it was approximately six-fold higher in old leaves (L6) (Figure 1D). Mg content was relatively higher in both tender and old leaves but decreased as the young leaves matured (Figure 1E). Specifically, Mg levels in leaves at stages L4 and L5 were significantly lower than those observed in leaves at stages L1, L2, and L6 (Figure 1E). S content exhibited a pattern similar to that of Ca in leaves at stage L6, being nearly twice as high as in young leaves (Figure 1F). Among young leaves, S content in leaves at stages L3 to L5 was substantially lower than that in leaves at stage L1 (Figure 1F).

3.1.3. Micronutrient Elements

The concentrations of Fe, Mn, and Se were significantly greater in old leaves, whereas Zn, B, and Cu levels were notably higher in leaves at stage L1 (Table 1). In contrast, Mo content was comparatively elevated in leaves at stage L3; however, no statistically significant differences were observed among the leaves. The mean micronutrient concentrations were ranked as follows: Mn > B > Fe > Zn > Cu > Se > Mo. The coefficient of variation (C.V.) indicated considerable variability in the concentrations of Mn, Mo, and Se across different stages of leaf development.

3.2. Distribution Characteristics of Carbon and Nitrogen Metabolites

The distribution of glucose, sucrose, and starch was consistent across different leaf developmental stages. These compounds were significantly lower in young leaves (L1–L5) compared with old leaves (L6), with no significant differences observed among the young leaves (Figure 2A,B,D). Fructose content increased considerably with leaf maturity, exhibiting no significant difference between L4 and L5; however, it was markedly higher in L6 than in the young leaves (Figure 2C).
Soluble protein and free amino acid contents were low in young leaves but exhibited significant accumulation in old leaves (Figure 3A,B). Furthermore, chlorophyll and carotenoid levels were significantly higher in old leaves and increased progressively as young leaves (Figure 3C,D).

3.3. Distribution Characteristics of Dihydrochalcone Bioactive Ingredients and Hydroxyl Radical (·OH) Scavenging Capacity

Phlorizin content was markedly low in young leaves relative to old leaves and decreased further with the maturation of young leaves (Figure 4A). In contrast, trilobatin content was notably low in old leaves and also decreased during the maturation of young leaves (Figure 4B). No significant differences were observed in the content of 3-hydroxyphlorizin across the different leaf developmental stages (Figure 4C). Additionally, the hydroxyl radical (·OH) scavenging capacity was remarkably higher in young leaves and diminished as the leaves matured (Figure 4D).

3.4. Correlation Analysis Between Nutrients and Bioactive Ingredients and Redundancy Analysis

Correlation analysis was conducted using parameters from all leaf developmental stages (Figure 5A). Phlorizin content showed a strong positive correlation with the levels of Ca, S, Mn, and Se and a weaker positive correlation with Fe content. In contrast, trilobatin content was negatively correlated with Ca, S, Fe, Mn, and Se levels, while showing a positive correlation with P content. The content of 3-hydroxyphlorizin did not display a significant correlation with any of the mineral nutrients analyzed (Figure 5A).
The correlation analysis between secondary metabolites and organic nutrients indicated that phlorizin content exhibited a strong positive correlation with soluble protein, amino acids, sucrose, glucose, and starch levels, while exhibiting a weak positive correlation with fructose content. Conversely, trilobatin content was negatively correlated with these biochemical parameters. Additionally, 3-hydroxyphlorizin content did not display any significant correlation with the measured biochemical parameters (Figure 5A).
Given the substantial differences observed in various physiological and biochemical parameters between young and old leaves, a correlation analysis was conducted using data exclusively from young leaves to elucidate the relationships among nutrients and secondary metabolites within these tissues. The results of this analysis, based on young leaves (L1–L5), differed from those obtained when considering all leaf ages (Figure 5B). Regarding organic nutrients, glucose and fructose exhibited negative correlations with secondary metabolites. Phlorizin content was positively correlated with P, K, Mg, and B contents, while trilobatin content was significantly positively correlated only with Mg content. Additionally, hydroxyl radical (·OH) scavenging capacity was positively correlated with K, Mg, and B contents.
Canonical correlation analysis (Figure 5C) revealed a positive correlation between ·OH scavenging capacity and trilobatin. Fe, Ca, Mn, S, and Se were closely interrelated and identified as the primary factors influencing glucose, sucrose, starch, protein, and amino acid levels. The effects of Cu, Zn, B, and Mg on active compounds were similar, whereas K, P, and Mo exhibited distinct influences on these compounds. Additionally, N was positively correlated with fructose content.
Redundancy analysis (Figure 5D) indicated that the first and second redundancy analysis axes (RDA1 and RDA2) explained 88.48% and 5.71% of the variance in flavonoid levels across different leaf developmental stages, respectively. Regarding mineral nutrients, N, P, Mg, Fe, Se, B, Zn, Cu, and Mo were identified as the principal explanatory variables. In contrast, amino acids and starch were the primary explanatory variables for carbon and nitrogen metabolites.

4. Discussion

4.1. Developmental Stage-Dependent Characteristics of Nutrient and Secondary Metabolite Accumulation in L. litseifolius Leaves

Leaf age significantly influences both the content and distribution of mineral nutrients within plant tissues [17,34]. The spatial distribution of these nutrients is governed by their uptake and subsequent remobilization processes. Conversely, the distribution patterns of mineral nutrients reflect the specific physiological requirements of leaves at different developmental stages. In the present study, young leaves accumulated relatively higher concentrations of K, Mg, Zn, B, and Cu, among which K, Mg, and Zn were characterized by high mobility. These nutrients are predominantly required in young, developing leaves for cell wall synthesis, the assembly of the photosynthetic apparatus, and the transport and utilization of photo-assimilates [35,36,37,38]. In contrast, nutrient elements such as Ca, S, Fe, Mn, and Se, which exhibit lower mobility, were found to accumulate significantly in older leaves. The observed differential accumulation patterns of mineral nutrients may result from their mutual interactions and competitive uptake. For example, B plays a critical role in facilitating the uptake and translocation of K, Zn, and Cu [37]; Cu deficiency has been correlated with increased concentrations of Ca, S, Fe, and Mn [17]; and the uptake of Cu and Zn can reduce the bioavailability of Fe and Ca. Therefore, the complex interactions among mineral nutrients, coupled with the developmental stage of leaves, should be carefully considered when formulating mineral fertilizer applications during the cultivation of L. litseifolius.
Developing leaves function as sink organs in plants, as their growth necessitates the import of assimilates predominantly derived from mature leaves and stem and root storage. Soluble sugars and starch constitute essential energy sources for plant metabolic processes. During early leaf expansion, young leaves exhibit low photosynthetic capacity, attributable to delayed chloroplast development, limited starch accumulation, and diminished activity of photosynthetic enzymes [39]. Consequently, the production and accumulation of carbohydrate in young leaves remain low [18]. In L. litseifolius, the concentrations of chlorophyll and carotenoids in young leaves were significantly lower than those in old leaves, further supporting the role of young, developing leaves as sinks relative to mature leaves. Thus, young leaves primarily consume carbohydrates and accumulate them to a lesser extent.
The protein and amino acid contents in leaves of different ages vary among species [40,41]. Rapidly developing leaves require substantial N to support the development of their morphological and physiological functions. L. litseifolius, an evergreen tree species, exhibits an N utilization strategy distinct from that of deciduous plants and herbaceous species, in which N is readily mobilized from older to younger leaves. In evergreen leaves, N is predominantly allocated to structural proteins, such as cell-wall binding proteins, to enhance leaf longevity [42,43]. In the present study, soluble protein, amino acid, and chlorophyll levels remained relatively low in young leaves, despite an increase in N content with leaf maturation, indicating that N allocation constitutes a primary limiting factor for N assimilation in these leaves.
Leaf senescence is commonly associated with elevated levels of total phenolics and flavonoids, alongside an enhanced capacity for free radical scavenging in plants [44,45]. Contrarily, ·OH scavenging capacity decreased with the maturation of leaf in this study. Additionally, trilobatin content showed a positive correlation with ·OH scavenging capacity. Flavonoids serve as crucial antioxidants in plants, conferring protection against both biotic and abiotic stresses [46]. Since physical protective structures, such as the cuticle layer, are not yet fully developed in young leaves, flavonoids may act as antioxidative agent by scavenging reactive oxygen species (ROS) generated in response to high light intensity and other environmental stimuli [47]. Contemporary pharmacological studies have demonstrated that trilobatin mitigates oxidative damage to biological macromolecules [48]. In cucumber (Cucumis sativus), trilobatin serves as a marker metabolite associated with trypsin-activated flavonoid accumulation [49]. High accumulation of trilobatin in young leaves may function as effective protectant. Furthermore, the canonical correspondence analysis (CCA) conducted in this study further indicated that trilobatin significantly contributes to the ·OH scavenging capacity of flavonoids, thereby enhancing leaf protection against stress.
Trilobatin and phlorizin accumulate in differing quantities within the leaves of apple (Malus domestica) and L. polystachyus, with trilobatin exhibiting a high concentration in young leaves and phlorizin predominating in older leaves [50,51]. Our findings align with these previous studies. Both phlorizin and trilobatin are biosynthesized from phloretin via the enzymatic activities of PGT1 and PGT2, respectively [51]. The distinct promoter activities of PGT1 and PGT2 are responsible for the accumulation patterns of phlorizin and trilobatin [52]. Considering the distribution of these compounds across leaf developmental stages, it is hypothesized that PGT2 expression is elevated in young leaves, whereas PGT1 expression is more prominent in older leaves. Nevertheless, the regulatory mechanisms governing the differential expression of PGT1 and PGT2 warrant further investigation.

4.2. Correlation Between Nutrients and Secondary Metabolites

Trilobatin and phlorizin serve as marker metabolites of flavonoids in L. litseifolius. Correlation analyses across leaves of all stages indicated the accumulation patterns of phlorizin and trilobatin in relation to nutrient indices. Notably, these correlations differed in young leaves.
A substantial body of evidence indicates that an optimal N level promotes the accumulation of carbon-rich secondary metabolites, including terpenes and flavonoids, whereas excessive N application decreases their accumulation [6,53,54]. Appropriate N fertilization enhances phlorizin metabolism in L. polystachyus by modulating the expression of genes such as PAL, PGT1, C3′H, C4H, and HCT [55]. Flavonoid content is generally positively correlated with carbohydrate levels and the carbon-to-nitrogen (C/N) ratio, but negatively correlated with protein and amino acid content [4,6]. In the present study, N content exhibited a significant negative correlation with phlorizin and trilobatin levels. These findings align with previous reports in Labisia pumila and tea plant roots, where N deficiency promotes flavonoid accumulation [56,57]. Conversely, protein and amino acid contents were positively correlated with flavonoid, phlorizin, and trilobatin levels, and negatively correlated with glucose and fructose content in young leaves. This observation contradicts the Protein Competition Model (PCM), which predicts an inverse relationship due to competition for phenylalanine [58], suggesting that PCM alone may not fully explain the observed metabolic patterns. A plausible explanation involves sink-source dynamics, wherein young leaves function as metabolic sinks that simultaneously support protein and flavonoid synthesis through imported nitrogen and carbon resources. Moreover, limited nitrogen assimilation in young leaves may redirect carbohydrate metabolism toward flavonoid biosynthesis. These results indicate that relatively low N content in young leaves favors the biosynthesis of flavonoids and trilobatin in L. litseifolius. Consequently, N fertilizers should be applied cautiously during the development of young, tender leaves. Our findings are corroborated by Ye et al. [59], who reported that low N application enhances phlorizin content.
P deficiency is a major limiting factor for plant growth and yield formation, especially in acidic soils [60]. In the present study, P content exhibited a significant positive correlation with phlorizin and trilobatin contents in young leaves. This observation contrasts with previous research on other plant species, where P deficiency was reported to induce flavonoid accumulation by upregulating the expression of biosynthetic pathway genes, including CHS, F3H, DFR, and FLS [61,62]. A plausible explanation for the positive correlation between P content and trilobatin accumulation is that P functions as a co-factor or activator of key enzymes involved in flavonoid biosynthesis, such as PAL and CHS, and contributes to trilobatin biosynthesis by supplying energy in the form of ATP. The effects of K on secondary metabolite levels were analogous to those of P, and canonical correspondence analysis (CCA) revealed a strong correlation between these two elements, suggesting their cooperative role in mediating trilobatin biosynthesis in young leaves. These findings indicate that foliar application of P and K fertilizers to young leaves is critical for enhancing trilobatin content in L. litseifolius.
Notably, the leaves of L. litseifolius exhibited enrichment in Se, an element known for its beneficial effects on human health. Se serves as a critical component of glutathione peroxidase, an enzyme that effectively scavenges free radicals and mitigates oxidative stress-induced cellular damage. The application of Se has been shown to enhance the concentrations of phenolic acids (16.9–94.2%), total phenols (15.7%), total flavonoids (29.5%), and betacyanins (34.1%) in fruit pulp, concomitantly increasing the activity of antioxidant enzymes [63]. Although numerous studies have documented the advantageous effects of Se on plants, including the accumulation of secondary metabolites, the underlying mechanisms at the level of plant secondary metabolism remain incompletely understood [63]. Foliar application of Se not only augments tea leaf yield but also improves antioxidant capacity and sensory quality [64]. Furthermore, it reduces the accumulation of toxic heavy metals while elevating the concentrations of macronutrients and micronutrients [64]. In this study, the contents of Se, Ca, S, Mn, and Fe were significantly higher in mature leaves, correlating with the accumulation of flavonoids and phlorizin. Redundancy analysis (RDA) indicated that Se and Fe are principal factors influencing phlorizin content and nutrient homeostasis in mature leaves, suggesting that these elements interactively modulate phlorizin accumulation. Additionally, Se has been reported to interact with cadmium (Cd), reducing its accumulation and alleviating Cd stress in tea plants [65]. The synergistic interaction between Se and S has been demonstrated to enhance sulforaphane metabolism in broccoli sprouts [66] and to increase glucosinolate content in Chinese cabbage [67]. Collectively, these findings imply that the combined application of Se with other nutrients may represent an effective strategy to enhance phlorizin yield in L. litseifolius. Nevertheless, further research is warranted to elucidate the interactions between Se and Ca, Fe, and Mn and their effects on plant performance.

5. Conclusions

Our findings have advanced the understanding of nutrient distribution patterns across leaves of varying developmental stages in L. litseifolius and their association with the principal dihydrochalcone compounds, trilobatin and phlorizin. The results demonstrated that Mg, B, Cu, Zn, and P predominantly accumulate in young leaves, whereas Fe, Ca, Mn, S, and Se are more concentrated in old leaves. Trilobatin was primarily found in young leaves, while phlorizin was more abundant in old leaves. Furthermore, trilobatin was identified as the main contributor to the leaf ·OH scavenging capacity. N functions as a negative regulator of both trilobatin and phlorizin, whereas P, K, Mg, S, Mn, Zn, and Cu serve as positive regulators of these compounds. Additionally, N, P, Mg, B, Zn, Cu, Fe, Mo, and Se constitute the main mineral elements, while amino acids and starch are the major biochemical constituents influencing the accumulation of trilobatin and phlorizin in L. litseifolius. These findings provide valuable insights for the management of mineral nutrients and the effective utilization of L. litseifolius.

Author Contributions

Conceptualization, C.Z.; methodology, S.-F.J.; validation, C.Z. and S.-F.J.; investigation, Y.-F.H., S.-F.J. and Y.L.; resources, Y.L.; data curation, C.Z.; writing—original draft preparation, Y.-F.H.; writing—review and editing, C.Z., S.-F.J. and Y.L.; visualization, Y.-F.H.; supervision, C.Z.; project administration, C.Z.; funding acquisition, S.-F.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Guangxi Natural Science Foundation of China, grant number 2019GXNSFBA245073, and the Guangxi Qihuang Scholars Training Program, grant number GXQH202402.

Data Availability Statement

The original contributions presented in this study are included in the article material. Further inquiries can be directed to the corresponding author.

Acknowledgments

We thank Fu-Cai Zhou, from Guangxi Ganliangjian Biotechnology Co., Ltd., for providing plant materials for this research.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. Contents of primary macronutrients and secondary macronutrients in different leaf developmental stages of L. litseifolius: (A) nitrogen (N) content, (B) phosphorus (P) content, (C) potassium (K) content, (D) calcium (Ca) content, (E) magnesium (Mg) content, and (F) sulfur (S) content. Data represent means ± SD (n = 3). Different letters on the bars denote statistically significant differences at p < 0.05, as determined by Duncan’s new multiple range test.
Figure 1. Contents of primary macronutrients and secondary macronutrients in different leaf developmental stages of L. litseifolius: (A) nitrogen (N) content, (B) phosphorus (P) content, (C) potassium (K) content, (D) calcium (Ca) content, (E) magnesium (Mg) content, and (F) sulfur (S) content. Data represent means ± SD (n = 3). Different letters on the bars denote statistically significant differences at p < 0.05, as determined by Duncan’s new multiple range test.
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Figure 2. Contents of carbohydrates in different leaf developmental stages of L. litseifolius: (A) sucrose content, (B) fructose content, (C) glucose content, and (D) starch content. Data represent means ± SD (n = 3). Different letters on the bars denote statistically significant differences at p < 0.05, as determined by Duncan’s new multiple range test.
Figure 2. Contents of carbohydrates in different leaf developmental stages of L. litseifolius: (A) sucrose content, (B) fructose content, (C) glucose content, and (D) starch content. Data represent means ± SD (n = 3). Different letters on the bars denote statistically significant differences at p < 0.05, as determined by Duncan’s new multiple range test.
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Figure 3. Contents of N metabolites in different leaf developmental stages of L. litseifolius: (A) soluble protein content, (B) amino acid content, (C) chlorophyll content, and (D) carotenoid content. Data represent means ± SD (n = 3). Different letters on the bars denote statistically significant differences at p < 0.05, as determined by Duncan’s new multiple range test.
Figure 3. Contents of N metabolites in different leaf developmental stages of L. litseifolius: (A) soluble protein content, (B) amino acid content, (C) chlorophyll content, and (D) carotenoid content. Data represent means ± SD (n = 3). Different letters on the bars denote statistically significant differences at p < 0.05, as determined by Duncan’s new multiple range test.
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Figure 4. Contents of secondary metabolites and antioxidative capacity in different leaf developmental stages of L. litseifolius: (A) phlorizin content, (B) trilobatin content, (C) 3-hydroxyphlorizin content, and (D) ·OH scavenging capacity. Data represent means ± SD (n = 3). Different letters on the bars denote statistically significant differences at p < 0.05, as determined by Duncan’s new multiple range test.
Figure 4. Contents of secondary metabolites and antioxidative capacity in different leaf developmental stages of L. litseifolius: (A) phlorizin content, (B) trilobatin content, (C) 3-hydroxyphlorizin content, and (D) ·OH scavenging capacity. Data represent means ± SD (n = 3). Different letters on the bars denote statistically significant differences at p < 0.05, as determined by Duncan’s new multiple range test.
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Figure 5. Correlation analysis (A,B), canonical correlation analysis (C), and redundancy analysis (D) of nutrients and bioactive ingredients. (A) Correlation analysis based on the parameters from all leaf developmental stages (L1–L6). (B) Correlation analysis based on the parameters of young leaves (L1–L5). Asterisk indicates significant difference at p < 0.05 level.
Figure 5. Correlation analysis (A,B), canonical correlation analysis (C), and redundancy analysis (D) of nutrients and bioactive ingredients. (A) Correlation analysis based on the parameters from all leaf developmental stages (L1–L6). (B) Correlation analysis based on the parameters of young leaves (L1–L5). Asterisk indicates significant difference at p < 0.05 level.
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Table 1. Micronutrient element contents in different leaf developmental stages. Data represent means ± SD (n = 3). Different letters among leaf stages indicate statistically significant differences at p < 0.05, as determined using Duncan’s new multiple range test. C.V. (%) = Standard Deviation/Mean × 100%.
Table 1. Micronutrient element contents in different leaf developmental stages. Data represent means ± SD (n = 3). Different letters among leaf stages indicate statistically significant differences at p < 0.05, as determined using Duncan’s new multiple range test. C.V. (%) = Standard Deviation/Mean × 100%.
StagesFe
(μg·g−1)		Mn
(mg·g−1)		Zn
(μg·g−1)		Mo
(μg·g−1)		B
(μg·g−1)		Cu
(μg·g−1)		Se
(μg·g−1)
L137.40 ± 3.43 c0.13 ± 0.02 b10.42 ± 0.90 a0.30 ± 0.10 a72.18 ± 0.54 a6.51 ± 0.85 a0.40 ± 0.11 b
L251.20 ± 5.46 bc0.10 ± 0.01 bc8.45 ± 1.41 ab0.33 ± 0.09 a68.03 ± 1.68 ab5.13 ± 0.76 ab0.32 ± 0.01 b
L342.01 ± 5.79 c0.08 ± 0.01 c6.53 ± 0.51 b0.49 ± 0.24 a64.18 ± 1.83 bc4.63 ± 0.48 b0.31 ± 0.01 b
L463.05 ± 4.49 ab0.08 ± 0.01 c7.88 ± 0.74 ab0.33 ± 0.07 a59.99 ± 2.66 c4.68 ± 0.13 ab0.30 ± 0.00 b
L545.06 ± 8.61 bc0.09 ± 0.01 bc7.48 ± 0.37 b0.14 ± 0.02 a59.37 ± 4.22 c5.40 ± 0.16 ab0.35 ± 0.01 b
L674.58 ± 5.25 a0.77 ± 0.01 a8.77 ± 0.24 ab0.17 ± 0.06 a64.40 ± 1.87 bc5.39 ± 0.45 ab0.85 ± 0.11 a
Average52.51 ± 5.510.21 ± 0.018.26 ± 0.070.29 ± 0.1064.69 ± 2.135.29 ± 0.470.42 ± 0.04
C.V. (%)29.96124.6420.5571.488.9019.2752.38
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Huang, Y.-F.; Jian, S.-F.; Lin, Y.; Zhong, C. Developmental Stage-Dependent Distribution and Interrelationships of Leaf Nutrients and Flavonoids in Lithocarpus litseifolius (Hance) Chun. Agronomy 2025, 15, 2029. https://doi.org/10.3390/agronomy15092029

AMA Style

Huang Y-F, Jian S-F, Lin Y, Zhong C. Developmental Stage-Dependent Distribution and Interrelationships of Leaf Nutrients and Flavonoids in Lithocarpus litseifolius (Hance) Chun. Agronomy. 2025; 15(9):2029. https://doi.org/10.3390/agronomy15092029

Chicago/Turabian Style

Huang, Yan-Fen, Shao-Fen Jian, Yang Lin, and Chu Zhong. 2025. "Developmental Stage-Dependent Distribution and Interrelationships of Leaf Nutrients and Flavonoids in Lithocarpus litseifolius (Hance) Chun" Agronomy 15, no. 9: 2029. https://doi.org/10.3390/agronomy15092029

APA Style

Huang, Y.-F., Jian, S.-F., Lin, Y., & Zhong, C. (2025). Developmental Stage-Dependent Distribution and Interrelationships of Leaf Nutrients and Flavonoids in Lithocarpus litseifolius (Hance) Chun. Agronomy, 15(9), 2029. https://doi.org/10.3390/agronomy15092029

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