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Article

Metabolomic Investigations Reveal Properties of Natural Low-Temperature Adaptation Strategies in Five Evergreen Trees

by
Bin Liu
1,
Tao Li
2,
Xuting Zhang
3,
Yanxia Zhang
4,
Zhenping He
5,
Xiaorui Shang
1,
Guojing Li
1,2 and
Ruigang Wang
1,2,*
1
Key Laboratory of Plants Adversity Adaptation and Genetic Improvement in Cold and Arid Regions of Inner Mongolia, Inner Mongolia Agricultural University, Hohhot 010018, China
2
College of Life Science, Inner Mongolia Agricultural University, Hohhot 010018, China
3
Inner Mongolia Academy of Agricultural & Animal Husbandry Sciences, Hohhot 010018, China
4
Department of Medicine, Hetao College, Bayannur 015000, China
5
Ordos Afforestation Field, Ordos 014300, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(6), 886; https://doi.org/10.3390/f16060886 (registering DOI)
Submission received: 24 April 2025 / Revised: 20 May 2025 / Accepted: 22 May 2025 / Published: 24 May 2025
(This article belongs to the Section Genetics and Molecular Biology)
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Abstract

:
In northern China’s arid and semi-arid regions, evergreen trees demonstrate significant cold tolerance to natural low-temperature stress during winter. However, the metabolic strategies and their associated properties underlying their overwintering adaptation remain incompletely elucidated. This study aims to reveal the metabolic properties of natural low-temperature adaptation strategies in five evergreen trees through metabolomic analysis and to identify key metabolites and their dynamic variation patterns. The GC-TOF-MS platform was used to investigate seasonal differential metabolites in five evergreen trees across January, April, July, and October and further explore core differentially expressed metabolites responsive to low-temperature stress. The results demonstrated that the seasonal changes in the chlorophyll content of five evergreens exhibited distinct patterns, that significant differences were observed between Juniperus sabina L. and Picea meyeri R., Ammopiptanthus mongolicus M., Buxus sinica var. parvifolia M.Cheng, and Pinus tabuliformis C., and that no significant differences were found among the other tree species. A total of 427 metabolites were detected in the metabolome; when assessing seasonal dynamics, it was found that the types of differentially expressed metabolites in the five evergreens underwent significant changes. In spring, the differentially expressed metabolites included some carbohydrates, alcohols, organic acids, and lipids. During summer and autumn, the largest number of differentially expressed metabolites accumulated, mainly including carbohydrates, organic acids, and amino acid compounds. In winter, while Picea meyeri primarily accumulated carbohydrates, the remaining four species mainly accumulated organic acids, along with a small number of alcohols, phenylpropanoids, and polyketides. Three shared carbohydrate metabolites, L-threose, galactinol, and gluconic lactone, were commonly downregulated across all species. Additionally, coniferous trees collectively accumulated 3,6-anhydro-D-galactose, showing downregulation. The KEGG enrichment analysis of winter-accumulated metabolites revealed significant associations with the pentose phosphate pathway, amino acid metabolism, phenylpropanoid biosynthesis, the tricarboxylic acid cycle, and ascorbate–aldarate metabolism pathways. Through comparative analysis with the summer growth season, we ultimately identified the core differentially expressed metabolites of the five evergreens, providing potential metabolic markers for the breeding of cold-tolerant species. In summary, these findings provide critical metabolomic insights into how plants adapt to low temperatures, significantly enhancing our understanding of the metabolic foundations of cold tolerance in evergreen species.

1. Introduction

Evergreen plants are characterized by their capacity to retain green leaves throughout the year or during the growing season. Although small numbers of older leaves may shed during growth, new leaves develop continuously, preventing any significant leafless period and maintaining an overall evergreen state. This characteristic represents one of the most common ancestral traits in vascular plants, originating from warm and humid climates that prevailed before the Late Cretaceous period [1,2]. Under diverse geographical and climatic conditions, evergreens have gradually evolved distinct adaptive traits and features. Nevertheless, when facing low temperatures—an omnipresent and highly challenging environmental stressor—whether these plants adopt some common adaptive strategies and metabolic properties has attracted significant scientific attention.
In the arid and semi-arid regions of northern China, where winter freezing temperatures and drought combine to form synergistic stress, the harsh climatic conditions pose severe challenges to plant survival. However, evergreens, including Picea meyeri R., Juniperus sabina L., Pinus tabuliformis C., Buxus sinica var. parvifolia M.Cheng, and Ammopiptanthus mongolicus M., have adapted well to these environments, which renders them excellent species for ecological restoration and landscape construction, possessing significant ecological–economic value (Figure 1A–T). Studies indicate that overwintered plants can sense a decline in ambient temperature and enhance their freezing tolerance through physiological and molecular adjustments before winter’s arrival. This process activates frost-resistant defense mechanisms to improve survival efficiency in low-temperature environments [3,4].
In recent years, metabolomics has emerged as a crucial tool for deciphering the adaptive mechanisms of plants under stress, given its ability to identify the end products of gene transcription and protein modifications [5]. In contrast to genomics, transcriptomics, or proteomics, metabolomics offers a better approximation of the genuine state of biological systems [6]. This approach enables the identification of cold-responsive metabolic markers and pathways. Moreover, it can be integrated other omics data to create a more comprehensive and profound model of the mechanisms underlying cold resistance in plants. Current analytical platforms include liquid chromatography (LC), gas chromatography (GC), and capillary electrophoresis (CE), all of which can be coupled with mass spectrometry (MS). Among these, gas chromatography–mass spectrometry (GC-MS) stands out as one of the first analytical techniques in metabolomics research. Owing to its high sensitivity and broad coverage, GC-MS has become the main platform for plant metabolomics [7], enabling the simultaneous detection of carbohydrates, organic acids, amino acids, fatty acids, and various secondary metabolites [8,9]. Metabolomic analyses have revealed that plants respond to low-temperature stress by accumulating osmotic regulators (e.g., glucose, sucrose, proline, and betaine) [10,11,12], antioxidant molecules (e.g., ascorbic acid and flavonoids) [13,14], and membrane lipid modifications (e.g., lipid compounds) [15,16].
Despite these advances, current research predominantly focuses on single-timepoint analyses or artificial short-term stress conditions within laboratory settings to investigate plant cold tolerance, while there is a lack of studies on the properties of metabolic strategies throughout the year in natural habitats. In this study, we focus on five evergreens and utilize the gas chromatography–time-of-flight mass spectrometry (GC-TOF-MS) platform for non-targeted metabolite detection and the identification of leaf samples, aiming to characterize the metabolic properties of natural low-temperature adaptation strategies through the following strategies: Using multivariate model recognition analysis, specifically Partial Least Squares Discriminant Analysis (PLS-DA), we identified differential metabolites. Following this, Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation analysis was performed on the differentially accumulated metabolites observed during winter to identify the relevant metabolic pathways. Through the comparison of the results with those from the summer growing season, we finally identified the core differential metabolites. These findings deepen our understanding of the natural low-temperature adaptation strategy properties of the five evergreens and provide valuable insights for further cold tolerance research on other plant species.

2. Results

2.1. Seasonal Variation and Interspecific Differences in the Chlorophyll Content of Five Evergreens

Chlorophyll, the core pigment in photosynthesis, is closely associated with plant stress adaptation, and fluctuations in its content directly determine light absorption and utilization efficiency [17], thereby affecting various metabolic pathways, including crucial physiological processes such as material synthesis and energy conversion. Consequently, chlorophyll content serves as a vital indicator for evaluating plants’ responses to environmental stress [18].
The chlorophyll content of the five evergreens exhibited distinct seasonal variation patterns (Figure 1U). Picea meyeri exhibited an “M-shaped” pattern in its chlorophyll content, which reached a significant peak in July (the highest value across seasons), declined substantially in October, and increased again during winter as temperatures dropped. In contrast, Juniperus sabina reached its maximum chlorophyll content in April, followed by significant reductions in July and January, continuing to decline during winter as temperatures fell. The chlorophyll content of Pinus tabuliformis exhibited a peak in April, with no significant differences observed in July, October, and January. Further, Buxus sinica var. parvifolia displayed its maximum chlorophyll content in July and decreased as temperatures lowered, declining to the lowest level in January. For Ammopiptanthus mongolicus, the chlorophyll content peaked in October and decreased during winter as temperatures declined, showing no significant difference compared to April. The analysis of variance for the chlorophyll content of the five evergreens indicated significant differences across different seasons and among species (p < 0.05; Supplementary File Table S1). The results of a further least-significant-difference (LSD) multiple-comparison analysis revealed that Juniperus sabina exhibited significant differences in terms of chlorophyll content from Picea meyeri, Ammopiptanthus mongolicus, Buxus sinica var. parvifolia, and Pinus tabuliformis, but no significant differences were observed among the other comparisons (Supplementary File Table S2).

2.2. GC-TOF-MS Analysis and Identification of Differentially Expressed Metabolites

2.2.1. GC-TOF-MS Analysis Results

Metabolomic analysis was conducted using GC-TOF-MS, which revealed that the total ion chromatograms (TICs) of the quality control (QC) samples exhibited good overlap in both peak retention times and peak areas (Supplementary File Figure S1A). Strong linear correlations (with most QC correlation coefficients > 0.850) confirmed robust instrument stability and reliable results (Supplementary File Figure S1B). A total of 427 metabolites were identified in the five evergreens. Chemical classification analysis was conducted on all these metabolites (Figure 1V), revealing that 63.70% (272 metabolites) could not be classified into specific categories. The remaining metabolites were categorized as follows: 8.20% (35 metabolites) as organic acids and derivatives, 7.49% (32 metabolites) as carbohydrates, 6.09% (26 metabolites) as amino acids and derivatives, 3.28% (14 metabolites) as lipids and lipid-like molecules, 0.47% (2 metabolites) as vitamins, 1.87% (8 metabolites) as phenylpropanoids and polyketides, 2.81% (12 metabolites) as others, 0.23% (1 metabolite) as nucleosides, nucleotides, and analogs, 0.47% (2 metabolites) as ketone compounds, 3.28% (14 metabolites) as benzene and substituted derivatives, and 2.11% (9 metabolites) as alcohols and polyols.

2.2.2. Identification of Differentially Expressed Metabolites

In this study, PLS-DA was utilized to conduct multivariate comparisons, aiming to comprehensively reveal the differentially expressed metabolites (DEMs) closely associated with the natural low temperatures during January. The specific operational steps are as follows: First, metabolites with significant seasonal variations (one-way analysis of variance, p < 0.05) were screened, and subsequently, their expression states were determined via multiple comparisons. PLS-DA was then used to elucidate the differences both between and within sample groups, and the variable importance for the projection (VIP) values were calculated. Generally, metabolites with VIP > 1 are considered to significantly contribute to the model explanation. The model in this research was validated by 200 permutation tests, and the results indicated that the differences between the five evergreens sample groups were significant, suggesting that the data are suitable for further analysis (Figure 2).
In the present study, the screening criteria for DEMs were set as VIP > 1 and p value < 0.05. The results revealed 19 DEMs in Picea meyeri (7 upregulated, 12 downregulated), 15 in Juniperus sabina (2 upregulated, 13 downregulated), 24 in Pinus tabuliformis (7 upregulated, 17 downregulated), 29 in Buxus sinica var. parvifolia (12 upregulated, 17 downregulated), and 14 in Ammopiptanthus mongolicus (3 upregulated, 11 downregulated). Further classification and statistical analysis showed that the DEMs in the five evergreens were predominantly categorized into carbohydrates and organic acids and their derivatives. This finding suggests that these tree species may help to maintain the stability of their intracellular environment by regulating metabolic pathways related to energy supply and cellular osmotic adjustment, thereby enhancing their tolerance to low temperatures (Table 1, Supplementary File Figure S2).

2.3. Analysis of Differentially Expressed Metabolites

2.3.1. Seasonal Response Patterns of Differentially Expressed Metabolites in Coniferous Species

The divergent metabolic profiles of the three coniferous species (Picea meyeri, Juniperus sabina, Pinus tabuliformis) revealed their distinct physiological adaptation strategies under seasonal cycles. During spring, the metabolite alterations in the three species displayed significant species-specific characteristics. Within Picea meyeri, the relative contents of DL-threitol and 1,5-anhydroglucitol peaked. Juniperus sabina displayed a marked accumulation of DL-threitol, indanone, and fumaric acid, while Pinus tabuliformis showed that the relative content of glucoheptonic acid reached its peak. In summer, the accumulation of carbohydrates became the predominant aspect of metabolic activities across all three tree species. Picea meyeri accumulated L-threose, arbutin, and D-glucoheptose; Juniperus sabina accumulated sorbitol, L-threose, and N-acetyl-D-galactosamine; and Pinus tabuliformis showed the accumulation of sorbitol, arbutin, and L-threose. As autumn approached, carbohydrate accumulation intensified further. Picea meyeri and Juniperus sabina continued to accumulate carbohydrates, whereas Pinus tabuliformis additionally accumulated organic acids. Specifically, Picea meyeri accumulated galactinol, gluconic lactone, 3,6-anhydro-D-galactose, L-gulonolactone, and alpha-D-glucosamine 1-phosphate; Juniperus sabina accumulated maltotriose, galactinol, gluconic lactone, and 3,6-anhydro-D-galactose; and Pinus tabuliformis accumulated galactinol, lactose, gluconic lactone, 3,6-anhydro-D-galactose, alpha-D-glucosamine 1-phosphate, D-glucoheptose, and ribose, as well as organic acids such as D-galacturonic acid, lactobionic acid, and galactonic acid. In winter, Picea meyeri showed a significant increase in the relative content of six metabolites, namely threitol, 1,5-anhydroglucitol, glucose, D-glyceric acid, butyraldehyde, and (2R,3S)-2-hydroxy-3-isopropylbutanedioic acid; Juniperus sabina had four notably elevated metabolites, namely fumaric acid, glutaric acid, citraconic acid, and indanone, which had low relative contents in July and October but accumulated during natural overwintering, with glutaric acid and citraconic acid reaching their peak levels in winter; and Pinus tabuliformis maintained high relative contents of seven compounds under low-temperature stress, namely sarcosine, glucoheptonic acid, hexenedioic acid, oxamic acid, 4-hydroxy-3-methoxycinnamaldehyde, adenine, and (R)-(-)-carvone (Figure 3A–C).
These results suggest that carbohydrate accumulation during the growing season serves as a critical strategy for conifers to provide the necessary energy and carbon sources, which, as environmental temperatures decline, is essential to maintain a certain level of energy metabolism to sustain basic life activities. Concurrently, the accumulation of organic acids is closely related to the plant’s stress resistance mechanisms, enhancing its tolerance to low-temperature stress.

2.3.2. Seasonal Response Patterns of Differentially Expressed Metabolites in Broad-Leaved Species

To characterize the seasonal metabolic adaptation mechanisms of two broad-leaved species (Buxus sinica var. parvifolia and Ammopiptanthus mongolicus), an analysis of their DEMs was conducted. The results revealed that in spring, the relative contents of 2,4-diaminobutyric acid, 5-aminovaleric acid, and 4-hydroxy-3-methoxycinnamaldehyde reached their maximum levels in Buxus sinica var. parvifolia. Meanwhile, Ammopiptanthus mongolicus showed significant accumulation of 1,5-anhydroglucitol, 2-amino-2-methylpropane-1,3-diol, D-erythro-sphingosine, and diglycerol. During summer, Buxus sinica var. parvifolia accumulated amino acid compounds, including cycloleucine and O-,ethylthreonine, with asparagine peaking in summer and re-accumulating again in winter. Similar seasonal patterns were observed for acetol, Cis-1,2-dihydronaphthalene-1,2-diol, phenyllactic acid, and 1-hydroxy-2-naphthoic acid, and the relative content of galactinol reached its maximum value in summer. In Ammopiptanthus mongolicus, the relative contents of biuret, 4-hydroxy-3-methoxycinnamaldehyde, and aspartic acid peaked in summer and decreased with decreasing temperatures, maintaining low levels in January. Simultaneously, lactobionic acid, L-threose, and galactinol also accumulated significantly in summer. As autumn approached, carbohydrate accumulation predominated in Buxus sinica var. parvifolia, including 3,6-anhydro-D-galactose, alpha-D-glucosamine 1-phosphate, gluconic lactone, L-threose, L-gulonolactone, and so on, while sorbitol maintained high relative levels from April to October, peaking in October. Ammopiptanthus mongolicus displayed significant D-glucoheptose and gluconic lactone accumulation during autumn. In winter, Buxus sinica var. parvifolia mainly accumulated organic acid compounds, including Bis2-hydroxypropyl amine, lactic acid, phenylphosphoric acid, fumaric acid, and isocitric acid, while amino acid compounds such as valine and 4-aminobutyric acid reached their maximum relative content in January as temperatures dropped, and scopoletin also accumulated significantly in winter. In contrast Ammopiptanthus mongolicus only exhibited significant accumulation of 4-hydroxybenzaldehyde and 2-amino-2-methylpropane-1,3-diol (Figure 3D,E).
Regarding seasonal dynamics, the types of DEMs in the five evergreens underwent significant changes. The DEMs accumulated in spring were complex and could not be classified simply, including some carbohydrates, alcohols, organic acids, and lipids. The greatest number of differential metabolites accumulated during summer and autumn, mainly carbohydrates, organic acids, and amino acid compounds. During winter, while Picea meyeri primarily accumulated carbohydrates, the remaining four species mainly accumulated organic acids, along with a small number of alcohols, phenylpropanoids, and polyketides. These results indicate that seasonal transitions profoundly influence the metabolism of the five evergreens, with carbohydrates, organic acids, and amino acid compounds playing important roles and potentially exhibiting conservation in core metabolic pathways.

2.3.3. Venn Analysis of Differentially Expressed Metabolites

Further, Venn analysis of the DEMs identified in the five evergreens revealed three carbohydrates commonly accumulated by all species: L-threose, galactinol, and gluconic lactone, all exhibiting unimodal accumulation patterns with peak relative levels in summer or autumn. Specifically, L-threose showed consistent seasonal dynamics in three conifers (Picea meyeri, Juniperus sabina, Pinus tabuliformis) and one broad-leaved species (Ammopiptanthus mongolicus), peaking in summer and declining as temperatures decreased. Galactinol reached its maximum levels in autumn for the three conifers but peaked in summer for the two broad-leaved species (Buxus sinica var. parvifolia, and Ammopiptanthus mongolicus). Gluconic lactone reached its peak levels in autumn across all five evergreens. Additionally, the three conifers shared a fourth carbohydrate, 3,6-anhydro-D-galactose, which displayed a unimodal pattern with peak levels in autumn and a significant reduction during overwintering (Figure 4).

2.3.4. Identification of Core Differentially Expressed Metabolites in Five Evergreens

After cold acclimation in autumn, the low-temperature tolerance of overwintered plants is significantly enhanced, enabling them to withstand freezing stress during winter [19]. This process involves plants adapting to external environmental changes by altering their internal homeostasis. Low-temperature stress inhibits the activity of metabolic enzymes, severely affecting plant metabolism and leading to the reprogramming of primary metabolic pathways, particularly those related to sugar and nitrogen metabolism. This results in the accumulation of soluble sugars, sugar alcohols, organic acids, amino acids, polyamines, and substrates for secondary metabolites [20,21], which ultimately allows plants to endure lower temperatures [22].
To further identify core DEMs associated with cold tolerance, we statistically analyzed the DEMs with significantly increased levels during winter conditions. Through KEGG annotation analysis of these DEMs, we found that the DEMs of Picea meyeri were mapped to the pentose phosphate pathway and glycine, serine, and threonine metabolism. The DEMs of Juniperus sabina were mapped to valine, leucine, and isoleucine biosynthesis and fatty acid degradation pathways. The DEMs of Pinus tabuliformis were mapped to the biosynthesis of phenylpropanoids and purine metabolism pathways. The DEMs of Buxus sinica var. parvifolia were mapped to the biosynthesis of phenylpropanoids; the tricarboxylic acid cycle; ascorbate and aldarate metabolism; and the alanine, aspartate, and glutamate metabolism pathways. Notably, the DEMs of Ammopiptanthus mongolicus could not be annotated, possibly because its metabolic pathways in the KEGG database are not fully covered for this species.
Furthermore, by comparing these DEMs with those observed during the growing season in summer, we focused on identifying metabolites with a (Fold Change > 1.5 or <0.67) to determine the core DEMs of the five evergreens. Specifically, the core DEMs of Picea meyeri were mainly carbohydrates, including threitol, 1,5-anhydroglucitol, D-glucose, and an organic acid, D-glyceric acid. The core DEMs in Juniperus sabina were primarily organic acid compounds, such as citraconic acid, glutaric acid, and fumaric acid. The core DEMs in Pinus tabuliformis were hexenedioic acid, 4-hydroxy-3-methoxycinnamaldehyde, adenine, and (R)-(-)-carvone. The core DEMs in Buxus sinica var. parvifolia included scopoletin, lactic acid, digitoxose, mucic acid, fumaric acid, phenyllactic, 4-aminobutyric acid, acetol, Cis-1,2-dihydronaphthalene-1,2-diol, 2,4-diaminobutyric acid, and cycloleucine. Only one core DEM was screened in Ammopiptanthus mongolicus, which was 2-amino-2-methylpropane-1,3-diol (Table 2). These findings provide novel insights into understanding the low-temperature tolerance of evergreens and offer potential metabolic markers for the selective breeding of cold-resistant cultivars.

3. Discussion

Plant adaptation to low-temperature stress is intrinsically linked to the synthesis and turnover of specific compounds, particularly small-molecule metabolites that regulate physiological homeostasis [23]. Studies demonstrate that cold tolerance arises from the integrated response of multiple pathways, including carbohydrate metabolism, amino acid metabolism, and the coordinated modulation of secondary metabolites [24]. Among primary metabolites, sugars, sugar alcohols, and amino acids are most critically impacted by stress, often as downstream consequences of impaired CO2 assimilation [25,26]. In this study, the winter-induced accumulation of carbohydrates, organic acids, and amino acids aligns with observations in other species. For instance, the DEMs of Poa pratensis L.revealed predominant accumulation in sugars, sugar alcohols, amino acids, and organic acids [27]. Similarly, cold-treated winter wheat (Triticum aestivum L.) showed a high proportion (51.8%) of primary metabolism-related DEMs, particularly enriched in carbohydrate and amino acid pathways [28]. Cold-stressed Magnolia liliiflora D. and Magnolia denudate D. had predominant concentrations of metabolites in amino acid metabolism pathways [29], while tobacco (Nicotiana tabacum L.) cultivars with strong cold tolerance displayed pronounced fluctuations in amino acids, carbohydrates, and organic acids [30]. These research results indicate that changes in primary metabolism are more pronounced in plants responding to low-temperature stress, which reflects a universal response pattern to cold stress and plays a critical role in maintaining the basic physiological functions of plants, further suggesting that there may be certain commonalities in the mechanisms of low-temperature adaptation across different species [31].
Carbohydrates, the primary products of photosynthesis, undergo adaptive changes under adverse stress conditions [32,33]. Researchers have found that the carbohydrate pathways in tomatoes are significantly altered under low-temperature conditions [34]. In this study, carbohydrate accumulation peaked in the summer and autumn seasons across most species, with the core differential metabolites dominated by sugars, primarily in Picea meyeri during winter. D-glucose, an important soluble monosaccharide, is metabolized through metabolic pathways such as glycolysis, the tricarboxylic acid cycle, and oxidative phosphorylation to provide energy support for cells. In this study, significant accumulation of D-glucose was observed in Pinus tabuliformis and Ammopiptanthus mongolicus during autumn, with a decrease in relative content as temperatures dropped, suggesting that these sugar metabolism-related metabolites accumulated in autumn may be utilized to cope with low-temperature stress in winter. In Picea meyeri, winter-enriched carbohydrates included D-glucose, threitol (DL-threitol), and 1,5-anhydroglucitol. Notably, D-glucose exhibited a lower relative content in October, which increased as temperatures further decreased in January, consistent with observations of upregulated contents of glucose, inositol galactoside, and raffinose in tobacco under cold treatment conditions [30]. In addition, sugar alcohols such as threitol and 1,5-anhydro-D-sorbitol, which have not been widely reported to accumulate significantly under low-temperature stress, could be considered potential metabolic markers. These findings highlight carbohydrate metabolism as a pivotal strategy for plants to cope with low-temperature stress and its crucial role in maintaining cellular osmotic balance and energy homeostasis in winter. Different tree species show species-specific responses when resisting low-temperature stress, which may be due to differences in their energy metabolism utilization strategies.
Amino acid compounds play crucial roles in plants’ responses to low-temperature stress. For instance, proline has been widely confirmed to act as an osmoprotectant, while other amino acids, including branched-chain amino acids (BCAAs), accumulate under various stress conditions as compatible solutes [9,35]. The research indicates that BCAAs can directly donate electrons to the mitochondrial electron transport chain via the electron-transferring flavoprotein complex and indirectly enter the tricarboxylic acid cycle through their catabolic intermediates [36]. In the present study, differential accumulation of certain amino acid compounds was observed exclusively in Buxus sinica var. parvifolia, such as branched-chain valine reaching peak levels during winter, which aligns with the significant increase in valine concentration in Picea obovata L. during the full acclimation period (5 November to 2 January) [37], further supporting the association between BCAAs and plants’ cold tolerance. In a study on the key regulatory network of the common bean (Phaseolus vulgaris L.) responding to cold stress, the accumulation of different amino acids was significantly upregulated during the cold stress period [38]. Previous research has shown that the content of 4-aminobutyric acid accumulation was observed in cold-hardy apple cultivars under freezing stress, suggesting its critical role in enhancing plants’ cold resistance [39]. Its accumulation might be an adaptive strategy for plants to respond to freezing stress, protecting cells from low-temperature damage through osmotic regulation and radical scavenging activities [40]. Similarly, our study detected that significant 4-aminobutyric acid also accumulates in BS during winter, implying its essential function in maintaining cellular integrity and improving low-temperature tolerance.
In addition to accumulating amino acids and carbohydrates, plants undergoing low-temperature stress exhibit dynamic changes in organic acids to maintain cellular charge balance, stabilize pH, and regulate osmotic potential [41]. While previous studies have reported a decline in fumarate, which is a key intermediate in the tricarboxylic acid cycle under cold stress [42], our findings revealed contrasting patterns in Juniperus sabina and Buxus sinica var. parvifolia, where fumaric acid levels significantly increased in January. Notably, Juniperus sabina displayed winter accumulations of citraconic acid and glutaric acid at levels exceeding threefold those of the warm growth season, suggesting active organic acid synthesis as a cold adaptation strategy. Furthermore, the relative content of hexenedioic acid in Pinus tabuliformis and mucic acid in Buxus sinica var. parvifoli exhibited winter increases of over 10-fold and 3-fold, respectively, further underscoring the critical role of organic acid metabolism in plant cold tolerance. Meanwhile, existing research has shown that the phenylpropanoid metabolic pathway is one of the key defense mechanisms in plants, playing an important antioxidant role in resistance to low temperatures [43]. In this study, the contents of 4-hydroxy-3-methoxycinnamaldehyde and (R)-(-)-carvone in Pinus tabuliformis during the overwintering period significantly increased, being more than threefold higher compared to the warm season. Additionally, scopoletin, a coumarin linked to phenylpropanoid biosynthesis, significantly accumulated in Buxus sinica var. parvifolia during winter, reinforcing the importance of this pathway in low-temperature adaptation. While GC-TOF-MS analysis provided insights into primary metabolites, its limitations in detecting secondary metabolites necessitate complementary UPLC-MS profiling, which will allow for more accurate identification and quantification of other secondary metabolites (e.g., flavonoids, alkaloids) in plant leaves related to low-temperature response, thereby fully revealing the potential metabolic mechanisms of cold tolerance in these five evergreens.
Plant leaf color is primarily determined by three types of pigments: chlorophyll, carotenoids, and flavonoids [44,45]. Chlorophyll is the dominant functional pigment in the process of photosynthesis in plants and is commonly used to measure photosynthetic capacity. Under non-lethal low-temperature stress conditions, the decreasing chlorophyll content typically reflects degradation or synthesis inhibition rather than chloroplast damage [46,47]. In this study, we observed significant differences in chlorophyll content among the five evergreens. Previous studies have reported that the variation patterns of chlorophyll content in different pine species during winter are not consistent; for example, Pinus tabuliformis var. mukdensis U.has higher chlorophyll content in winter than in summer [48], which is similar to the chlorophyll content variation pattern observed in our study for PT. Both Juniperus sabina and Pinus tabuliformis displayed peak chlorophyll levels in April, but they declined during the growth season (July), aligning with the findings of Lin et al. for the chlorophyll content changes in two fruiting Pinus koraiensis Sieb et Zucc. species [49]. This phenomenon may arise from accelerated photosynthetic activity depleting chlorophyll reserves [50]. While cold-tolerant species generally show minimal chlorophyll fluctuations under cooling temperatures [51,52], Picea meyeri showed an upward trend in chlorophyll content during winter, which may imply that Picea meyeri does not reduce chlorophyll content as a strategy to decrease light energy absorption under low-temperature stress. In winter, Juniperus sabina, Buxus sinica var. parvifoli, and Ammopiptanthus mongolicus reduced the chlorophyll content in their leaves to decrease potential light-induced damage due to low temperatures, which is consistent with the observed significant downregulation of chlorophyll content in Abies species after entering winter [53] and aligns with the reported results of leaf color changes in Juniperus sabina during winter [54]. Additionally, anthocyanins, a type of flavonoid, are primarily responsible for the red, blue, and purple hues in leaves [55] and have free radical scavenging and antioxidant functions [56], and they can protect chloroplasts, which is of great significance for plants responding to low-temperature stress. Plants that turn red during winter exhibit lower photoinhibition stress, as anthocyanins intercept a significant proportion of incident photosynthetically active radiation [57]. In this study, we also observed the phenomenon of winter leaf reddening in Buxus sinica var. parvifolia, which may be due to the reduction in light absorption, leading to the accumulation of anthocyanins or carotenoids in the outer cell layers [58].

4. Materials and Methods

4.1. Plant Materials

Plant materials were collected from two-year-old leaves of Picea meyeri, Juniperus sabina, Pinus tabuliformis, Buxus sinica var. parvifolia, and Ammopiptanthus mongolicus. Sampling locations included Hohhot, Inner Mongolia (40°48′38″ N, 111°40′13″ E), for Picea meyeri, Juniperus sabina, Pinus tabuliformis, and Buxus sinica var. parvifolia and the natural habitat of Ammopiptanthus mongolicus in Wuhai, Inner Mongolia (39°39′15″ N, 106°47′17″ E). All materials were collected from healthy, vigorously growing individuals. Sampling was conducted on 14 April 2020 (Hohhot: 20 °C/6 °C, Wuhai: 23 °C/6 °C), 14 July 2020 (Hohhot: 31 °C/18 °C, Wuhai: 33 °C/21 °C), 16 October 2020 (Hohhot: 12 °C/−2 °C, Wuhai: 15 °C/−1 °C), and 16 January 2021 (Hohhot: −9 °C/−18 °C, Wuhai: −5 °C/−16 °C). The parentheses indicate the daily maximum and minimum temperatures, and sampling was performed at 10:00 AM under clear weather conditions. Sampling was completed within 2 h, and the collected leaf samples were immediately immersed in liquid nitrogen for quick freezing. The frozen samples were then transported back to the laboratory and stored in a −80 °C ultra-low temperature freezer for subsequent metabolomics research. To ensure clarity, convenience, and consistency in data recording and organization, the following are the abbreviations corresponding to each species (abbreviations are based on the Latin names of the species): Picea meyeri (PM), Juniperus sabina (JS), Pinus tabuliformis (PT), Buxus sinica var. parvifolia (BS), and Ammopiptanthus mongolicus (AM).

4.2. Chlorophyll Content Determination

Chlorophyll quantification was performed on fresh leaves of Picea meyeri, Juniperus sabina, Pinus tabuliformis, Buxus sinica var. parvifolia, and Ammopiptanthus mongolicus using an acetone–ethanol extraction protocol. Approximately 0.1 g of leaf tissue was finely chopped into 0.2 mm segments using a scalpel and then incubated in darkness at room temperature for 24 h with an extraction mixture (acetone/ethanol: 1:1 v/v) until complete decolorization. Absorbance values at 663 nm and 645 nm were measured using a UV-Vis spectrophotometer [59]. Three biological replicates were performed. Total chlorophyll content (mg/g or mg/dm2) was calculated using the following formula: Total Chlorophyll = (20.2 × OD645 + 8.02 × OD663) × V/(1000 × w), where V represents extraction volume (mL) and w denotes fresh weight (g).

4.3. GC-TOF-MS Metabolomic Analysis

A total of 20 groups of leaf samples from 5 evergreen tree species were analyzed. The samples collected in April included PM-4, JS-4, PT-4, BS-4, and AM-4 groups; the July samples included PM-7, JS-7, PT-7, BS-7, and AM-7 groups; the October samples included PM-10, JS-10, PT-10, BS-10, and AM-10 groups; and the January samples included PM-1, JS-1, PT-1, BS-1, and AM-1 groups. Each group consisted of 6 biological replicates. GC-TOF-MS metabolomic profiling was performed by Shanghai Biotree Biotechnology Co., Ltd. (Shanghai, China) following standardized protocols.

4.3.1. Metabolite Extraction

Approximately 20 ± 1 mg of sample was transferred into a 2 mL tube, and 500 μL of pre-cold extraction mixture (methanol/dH2O (v:v) = 3:1) with 10 μL of internal standard (L-2-Chlorophenylalanine, 1 mg/mL stock) was added. Vortex mixing was carried out for 30 s. A steel ball was added, and the mixture was processed with a 35 Hz grinding instrument for 4 min and then underwent ultrasonic treatment in an ice water bath for 5 min (repeat three times). After centrifugation at 4 °C for 15 min at 12,000 rpm (RCF = 13,800× g, R = 8.6 cm), 100 μL of supernatant was transferred to a fresh tube. To prepare the QC (quality control) sample, 20 μL of each sample was taken out and combined with one another. After evaporation in a vacuum concentrator, 120 μL of methoxyamination hydrochloride (20 mg/mL in pyridine) was added and then incubated at 80 °C for 30 min, then derivatized by 120 μL of BSTFA regent (1% TMCS, v/v) at 70 °C for 1.5 h. After gradually cooling samples to room temperature, 10 μL of FAMEs (in chloroform) was added to the QC sample. All samples were then analyzed by a gas chromatograph coupled with aGC-TOF-MS (Agilent Technologies, Santa Clara, CA, USA).

4.3.2. GC-TOF-MS Analysis

GC-TOF-MS analysis was performed using an Agilent 7890 gas chromatograph coupled with a time-of-flight mass spectrometer. The system utilized a DB-5MS capillary column (30 m × 250 μm × 0.25 μm, J&W Scientific, Folsom, CA, USA), with the specific analysis conditions required for GC-TOF-MS (Supplementary File Table S3).

4.4. Data Analysis

Raw data analysis, including peak extraction, baseline adjustment, deconvolution, alignment, and integration, was completed with Chroma TOF (V 4.3x, LECO) software, and the LECO-Fiehn Rtx5 database was used for metabolite identification by matching the mass spectrum and retention index. Finally, the peaks detected in less than half of the QC samples or with an RSD > 30% in the QC samples were removed [60,61]. Data were exported to Microsoft Excel for initial organization. One-way analysis of variance was performed using R software (version 3.5.1). Multivariate statistical analysis was conducted with SIMCA-P 14.1 software and Metware Cloud (https://cloud.metware.cn, accessed on 10 July 2024) to identify metabolite patterns associated with cold stress responses.

5. Conclusions

This study investigated five evergreens—Picea meyeri, Juniperus sabina, Pinus tabuliformis, Buxus sinica var. parvifolia, and Ammopiptanthus mongolicus—representing both coniferous and broadleaf trees, aiming to characterize the properties of their low-temperature adaptation strategies and elucidate their adaptive responses to winter low-temperature stress using the GC-TOF-MS metabolomics analysis platform. Our goal was to highlight the differences and commonalities in the metabolic pathways adopted by these trees, underpinning their cold adaptation. The results revealed that DEMs across the five evergreens were primarily enriched in the carbohydrate, organic acid, and derivative categories, with amino acid-related DEMs being most abundant in Buxus sinica var. parvifolia. By screening for core DEMs in winter, we found distinct accumulation patterns: Picea meyeri primarily accumulated carbohydrates; Juniperus sabina showed a predominance of organic acids; Pinus tabuliformis displayed a combined accumulation of organic acids and phenylpropanoids; Buxus sinica var. parvifolia accumulated both organic acids and amino acids; and Ammopiptanthus mongolicus contained only one core DEM, an alcohol compound. Notably, three shared carbohydrate metabolites, L-threose, galactinol, and gluconic lactone, were commonly downregulated across all species. Additionally, coniferous trees (Picea meyeri, Juniperus sabina, Pinus tabuliformis) collectively accumulated 3,6-anhydro-D-galactose, showing downregulation. In summary, through GC-TOF-MS metabolomics analysis, we speculate that these five evergreens exhibit a certain degree of conservation in their metabolic strategy properties in response to low-temperature stress. We have preliminarily identified candidate metabolites potentially involved in cold tolerance and characterized their accumulation dynamics during overwintering, which reflect species-specific material bases for cold adaptation. These findings provide important metabolomic information for further exploring the metabolic mechanisms of plants’ cold tolerance, laying a foundation for future functional validation studies.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/f16060886/s1: Table S1: Variance analysis of Chl content in five evergreens; Table S2: LSD multiple comparison of Chl content for five evergreens; Table S3: Instrument parameters of this experiment; Figure S1: TIC Plot of QC sample: Mass Spectrometry Detection (A) and heatmap of correlation between QC samples (B); Figure S2: Statistics chart of DEMs in five evergreens.

Author Contributions

B.L.: first author, writing—original draft preparation; R.W.: corresponding author; T.L. and X.Z.: co-processing of the data; G.L., Y.Z., Z.H. and X.S.: review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Hangjin Qi Science and Technology Plant Project (2024HJG03); Basic Scientific Research Business Expenses Project of Directly Affiliated Institutions of Higher Learning in Inner Mongolia Autonomous Region (BR221021 and BR22-13-05); and Hohhot Major Science and Technology Special Project (2022-She-Zhong-1-3).

Data Availability Statement

The datasets used during the current study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that they have no competing interests.

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Figure 1. Comparison of leaf morphologies of five evergreens (A–T) in spring, summer, autumn, and winter, (A–D) for Picea meyeri R. (PM), (E–H) Juniperus sabina L. (JS), (I–L) Pinus tabuliformis C. (PT), (M–P) Buxus sinica var. parvifolia M.Cheng. (BS), and (Q–T) Ammopiptanthus mongolicus M. (AM). Chlorophyll content indices of five evergreens (U), different letters indicate significant differences (p < 0.05), Duncan’s analysis. Proportion of metabolites in five evergreen leaves by chemical category (V).
Figure 1. Comparison of leaf morphologies of five evergreens (A–T) in spring, summer, autumn, and winter, (A–D) for Picea meyeri R. (PM), (E–H) Juniperus sabina L. (JS), (I–L) Pinus tabuliformis C. (PT), (M–P) Buxus sinica var. parvifolia M.Cheng. (BS), and (Q–T) Ammopiptanthus mongolicus M. (AM). Chlorophyll content indices of five evergreens (U), different letters indicate significant differences (p < 0.05), Duncan’s analysis. Proportion of metabolites in five evergreen leaves by chemical category (V).
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Figure 2. PLS-DA score and model test between different comparison groups of five evergreens. PLS-DA score plots (left) and permutation test results (right) (A,B) for Picea meyeri (PM); (C,D) for Juniperus sabina (JS); (E,F) Pinus tabuliformis (PT); (G,H) Buxus sinica var. parvifolia (BS); (I,J) and Ammopiptanthus mongolicus (AM).
Figure 2. PLS-DA score and model test between different comparison groups of five evergreens. PLS-DA score plots (left) and permutation test results (right) (A,B) for Picea meyeri (PM); (C,D) for Juniperus sabina (JS); (E,F) Pinus tabuliformis (PT); (G,H) Buxus sinica var. parvifolia (BS); (I,J) and Ammopiptanthus mongolicus (AM).
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Figure 3. The relative content change patterns of DEMs (A) for Picea meyeri (PM); (B) Juniperus sabina (JS); (C) Pinus tabuliformis (PT); (D) Buxus sinica var. parvifolia (BS); and (E) Ammopiptanthus mongolicus (AM).
Figure 3. The relative content change patterns of DEMs (A) for Picea meyeri (PM); (B) Juniperus sabina (JS); (C) Pinus tabuliformis (PT); (D) Buxus sinica var. parvifolia (BS); and (E) Ammopiptanthus mongolicus (AM).
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Figure 4. Venn analysis of DEMs (A); pattern of relative content changes in common DEMs (B).
Figure 4. Venn analysis of DEMs (A); pattern of relative content changes in common DEMs (B).
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Table 1. Classification and statistics of DEMs in five evergreens.
Table 1. Classification and statistics of DEMs in five evergreens.
ClassificationPicea meyeriJuniperus sabinaPinus tabuliformisBuxus sinica var. parvifoliaAmmopiptanthus mongolicus
Vitamins00200
Phenylpropanoids and polyketides00120
Others10111
Organic acids and derivatives24752
Lipids32011
Carbohydrates1181095
Benzene and substituted derivatives11212
Amino acids and derivatives00171
Alcohols and polyols10032
Total1915242914
Table 2. Statistical analysis of core DEMs.
Table 2. Statistical analysis of core DEMs.
Fold Change (January vs. July)
DEMsPicea meyeriJuniperus sabinaPinus tabuliformisBuxus sinica var. parvifoliaAmmopiptanthus mongolicus
Threitol8.251----
D-Glyceric acid8.565----
1,5-Anhydroglucitol4.033----
D-Glucose0.523----
Butyraldehyde0.896----
(2R,3S)-2-hydroxy-3-isopropylbutanedioic acid1.031----
Indanone-8.836---
Citraconic acid-3.018---
Fumaric acid-0.196-1.957-
Glutaric Acid-4.044---
Sarcosine--1.491--
Glucoheptonic acid--245,813.318--
Hexenedioic acid--10.077--
Oxamic acid--1.207--
4-Hydroxy-3-methoxycinnamaldehyde--3.943--
Adenine--4.393--
(R)-(-)-carvone--6.506--
Bis2-hydroxypropyl amine---117,241.384-
Scopoletin---4.337-
Phenyllactic---0.372-
Phenylphosphoric acid---19.296-
Lactic acid---10.505-
Acetol---0.326-
Cis-1,2-Dihydronaphthalene-1,2-diol---0.459-
Isocitric acid---1.221-
1-Hydroxy-2-naphthoic acid---0.811-
Digitoxose---7.587-
Mucic acid---3.207-
Valine---283,766.217-
4-Aminobutyric acid---6.087-
2,4-Diaminobutyric acid---0.404-
Asparagine---0.868-
Cycloleucine---0.060-
4-hydroxybenzaldehyde----471,021.059
2-Amino-2-methylpropane-1,3-diol----4.341
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MDPI and ACS Style

Liu, B.; Li, T.; Zhang, X.; Zhang, Y.; He, Z.; Shang, X.; Li, G.; Wang, R. Metabolomic Investigations Reveal Properties of Natural Low-Temperature Adaptation Strategies in Five Evergreen Trees. Forests 2025, 16, 886. https://doi.org/10.3390/f16060886

AMA Style

Liu B, Li T, Zhang X, Zhang Y, He Z, Shang X, Li G, Wang R. Metabolomic Investigations Reveal Properties of Natural Low-Temperature Adaptation Strategies in Five Evergreen Trees. Forests. 2025; 16(6):886. https://doi.org/10.3390/f16060886

Chicago/Turabian Style

Liu, Bin, Tao Li, Xuting Zhang, Yanxia Zhang, Zhenping He, Xiaorui Shang, Guojing Li, and Ruigang Wang. 2025. "Metabolomic Investigations Reveal Properties of Natural Low-Temperature Adaptation Strategies in Five Evergreen Trees" Forests 16, no. 6: 886. https://doi.org/10.3390/f16060886

APA Style

Liu, B., Li, T., Zhang, X., Zhang, Y., He, Z., Shang, X., Li, G., & Wang, R. (2025). Metabolomic Investigations Reveal Properties of Natural Low-Temperature Adaptation Strategies in Five Evergreen Trees. Forests, 16(6), 886. https://doi.org/10.3390/f16060886

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