Comparative Metabolomic Analysis of Antler-Derived Medicinal Materials from Red Deer and Sika Deer

YUZhibiao1

HUZheng1

YANGXuzhong2

YuanmiaoHUANG2,3

DaiquanJIANG4

DINGZhangfeng1

LeiLIANG1

SHUYu1

YihaoJIANG1✉Emailjiangyihao@ncu.edu.cn

1School of Chemistry and Chemical EngineeringNanchang UniversityNanchangChina

2Gansu Qilian Mountain Biotechnology Development Co., LtdZhangyeChina

3Gansu Jie Tao Pharmaceutical Technology Co., LtdLanzhouChina

4Jiangxi University of Traditional Chinese MedicineNanchangChina

YU Zhibiao¹,HU Zheng¹,YANG Xuzhong²,HUANG Yuanmiao^2,3,JIANG Daiquan⁴,DING Zhangfeng¹,LIANG Lei¹,SHU Yu¹,JIANG Yihao^1*

¹ Affiliation 1; School of Chemistry and Chemical Engineering, Nanchang University, Nanchang, China

² Affiliation 2; Gansu Qilian Mountain Biotechnology Development Co., Ltd.,Zhangye,China

³ Affiliation 3; Gansu Jie Tao Pharmaceutical Technology Co., Ltd,Lanzhou, China.

⁴ Affiliation 4; Jiangxi University of Traditional Chinese Medicine,Nanchang, China.

* Correspondence: jiangyihao@ncu.edu.cn

Abstract

This study employed a TM widely-targeted metabolomics approach to systematically characterize endogenous metabolites in the antlers, antler tips, and pedicles of sika deer and red deer. A total of eighteen samples were selected and divided into six groups for metabolomic profiling. Based on ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), metabolic features were comprehensively identified by combining untargeted metabolite screening with a self-constructed database from MWE, leading to the detection of 2,361 metabolites. Multivariate statistical analyses, including PCA and OPLS-DA, were applied to investigate metabolic differences among tissues and between species. The results revealed significant tissue-specific metabolic profiles, particularly in antler tips, as well as species-specific differences between sika and red deer. Multiple differentially abundant metabolites were identified and predominantly enriched in pathways related to ABC transporters, glycerophospholipid metabolism, and nucleotide metabolism. These findings elucidate the metabolic basis underlying the medicinal properties of antler-derived materials and provide a scientific foundation for quality assessment, species discrimination, and biocompound discovery.

Key words:

Sika deer

Red deer

Tissue specificity

TM wide-target metabolomics

Differential metabolites

1 Introduction

Antler-derived medicinal materials, including velvet antler, antler base (Luhuapan), and mature antler, have been traditionally used as tonic herbs, with a long history of therapeutic application and notable clinical efficacy. Their therapeutic value has been substantiated by millennia of traditional practice as well as modern scientific research. Contemporary pharmacological studies have isolated diverse bioactive constituents, including peptides, growth factors (e.g., IGF-1, TGF-β), polysaccharides, and abundant trace elements [1–6]. Furthermore, studies using cellular and animal models have revealed multiple mechanisms, including stimulation of bone formation, facilitation of cartilage repair, inhibition of neuronal apoptosis, and modulation of immune responses. These properties underscore their therapeutic potential in anti-osteoporosis and arthritis prevention, neuroprotection, and as adjuncts in anti-tumor therapy [7–9].

The antler base (also referred to as the antler cap or Luhuapan), retained after velvet antler harvesting, is rich in collagen, peptides, and calcium-phosphorus minerals [10–12]. Studies have demonstrated that its extracts possess significant anti-inflammatory, antioxidant, and wound-healing activities[13]. Notably, it has shown distinctive efficacy in treating gynecological disorders such as mammary gland hyperplasia and mastitis, with its capacity to promote blood circulation, resolve stasis, and relieve mammary duct obstruction further supported by modern clinical practice [14].

Deer antler is utilized in both raw and processed forms, each with distinct applications: the raw form is used to clear heat and alleviate swelling, whereas the processed form (e.g., Cervi Cornus Colla) is used to warm and tonify the liver and kidney, and to strengthen bones and tendons. Its abundant protein matrix and osteoinductive components render it an effective natural material for bone defect repair and fracture healing [15, 16]. Moreover, its role in regulating endocrine function and improving physical debilitation [17, 18] has recently regained research attention.

Metabolomics focuses on the qualitative and quantitative analysis of endogenous small-molecule metabolites with molecular weights below 1000, such as amino acids, lipids, and nucleotides, examining their composition, dynamics, and alterations in response to internal and external factors [19]. This approach has been widely applied in traditional Chinese medicine research, clinical diagnostics, chemical ecology, environmental science, toxicology, and related fields [20, 21]. Recent advances in mass spectrometry have further facilitated the application of metabolomics to antler-derived materials, particularly velvet antler. For instance, Zhang et al. [22] compared the chemical profiles of velvet antler before and after thermal processing, while Su et al. [23] applied metabolomics to investigate metabolic differences between sika and red deer velvet antlers. Thus, metabolomics has emerged as a crucial tool not only for profiling the composition of antler-derived medicines but also for exploring their growth processes and pharmacological properties.

In this study, we utilized UPLC-MS/MS coupled with multivariate statistical analyses to investigate the metabolomic differences between antler-derived medicinal materials from red deer and sika deer.

2. Materials and Methods

2.1 Materials and Instruments

Sika deer antler, velvet antler, and antler base (Luhuapan) were obtained from Lihaixia Deer Farm (Shuangyang District, Changchun, China), while red deer antler, velvet antler, and antler base were provided by Gansu Qilian Mountains Biotechnology Development Co., Ltd.

Methanol (AR) was purchased from Merck; acetonitrile (AR) was obtained from Shanghai Xingke High Purity Solvent Co., Ltd.; acetic acid was supplied by Shanghai Eno Chemical Technology Co., Ltd.; ammonium formate (AR), ammonia solution (AR), and formic acid (AR) were purchased from Shanghai Aladdin Biochemical Technology Co., Ltd.

Chromatographic columns included a Waters ACQUITY UPLC BEH HILIC Column (1.7 µm, 1.0 mm × 100 mm) and a Waters ACQUITY UPLC HSS T3 C18 Column (1.8 µm, 2.1 mm × 100 mm), both from Waters Corporation. The UPLC system (ExionLC AD) and quadrupole time-of-flight mass spectrometer (TripleTOF 6600) were obtained from AB SCIEX.

2.2 Sample preparation and Metabolite extraction

Samples stored at -80℃ were thawed on ice. A 400 µL methanol/water solution (7:3, V/V) containing the internal standard was added to 20 mg of sample, followed by vortexing for 3 min. The mixture was sonicated in an ice bath for 10 min, vortexed for 1 min, and then incubated at -20℃ for 30 min. Subsequently, the sample was centrifuged at 12,000 rpm for 10 min at 4 ℃. The sediment was removed, and the supernatant was centrifuged again at 12,000 rpm for 3 min at 4 ℃. A 200 µL aliquot of the supernatant was transferred for LC-MS analysis.

2.3 LC-MS collection conditions

2.3.1 UPLC Conditions

All samples were analyzed using three LC/MS methods. One aliquot was analyzed under positive ionization conditions using a Waters ACQUITY UPLC HSS T3 C18 column (1.8 µm, 2.1 mm * 100 mm). The mobile phases were 0.1% formic acid in water (solvent A) and 0.1% formic acid in acetonitrile (solvent B). The gradient program was as follows: 5–20% B over 2 min, 20–60% B over the next 3 min, 60–99% B in 1 min and held for 1.5 min, then returned to 5% B within 0.1 min and maintained for 2.4 min. Analytical conditions were as follows: column temperature, 40°C; flow rate, 0.4 mL/min; injection volume, 2 or 5 µL; Another aliquot was analyzed under negative ionization conditions using the same gradient as in the positive mode. The third aliquot was analyzed under negative ionization conditions using a Waters ACQUITY UPLC BEH HILIC Column (1.7 µm, 1.0 mm * 150 mm). The mobile phases were solvent A (80% ACN, 10% water, and 10% MeOH with 20 mM ammonium formate, pH 10.6) and solvent B (40% ACN and 60% water with 20 mM ammonium formate). The gradient program was as follows: 5–20% B over 2 min, 20–70% B over 1.5 min, 70–95% B over 3 min, held at 95% B for 1 min, and then rapidly returned to the initial conditions.

2.3.2 QTOF-MS/MS

Data acquisition was carried out in information-dependent acquisition (IDA) mode with Analyst TF 1.7.1 Software (Sciex, Concord, ON, Canada). Source settings were as follows: ion source gas 1 (GAS1), 50 psi; ion source gas 2 (GAS2), 50 psi; curtain gas (CUR), 25 psi; temperature (TEM), 550°C; declustering potential (DP), ± 60 V for both positive and negative modes; and ion spray voltage floating (ISVF), ་5000 V or − 4000 V in positive and negative modes, respectively. TOF-MS settings included: mass range of 50–1000 Da; accumulation time, 200 ms; and dynamic background subtraction turned on. For product ion scanning, parameters were: mass range, 25–1000 Da; accumulation time, 40 ms; collision energy, ± 30 V in both ion modes; collision energy spread, 15; resolution, UNIT; charge state, 1; intensity threshold, 100 cps; isotope exclusion within 4 Da; mass tolerance, 50 ppm; and up to 18 candidate ions per cycle.

2.3.3 ESI-Q TRAP-MS/MS

LIT and QQQ scans were conducted using a QTRAP® LC-MS/MS System equipped with an ESI Turbo Ion-Spray interface, operating in positive and negative ion modes under Analyst 1.6.3 software (Sciex). The ESI source conditions were: source temperature, 500°C; ion spray voltage (IS), ་5500 V (positive) and − 4500 V (negative); ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) at 50, 50, and 25 psi, respectively; collision gas (CAD) set to high. Tuning and calibration were done using 10 and 100 µmol/L polypropylene glycol solutions in QQQ and LIT modes, respectively. MRM transitions were defined for each chromatographic interval based on eluting metabolites.

2.4Data preprocessing

For all initial samples, missing values were imputed with 1 / 5 of the minimum value of each metabolite. CVs were then calculated for the QC samples, and only metabolites with CV values less than 0.3 were retained to generate the final dataset.

2.5Data analyses

2.5.1 PCA

Unsupervised principal component analysis (PCA) was implemented with the prcomp function in R (www.r-project.org). Data were scaled to unit variance before analysis.

2.5.2 Hierarchical Cluster Analysis and Pearson Correlation Coefficients

Hierarchical cluster analysis (HCA) of samples and metabolites was visualized as heatmaps with dendrograms, while pearson correlation coefficients (PCCs) between samples were calculated by the cor function in R and displayed as heatmaps. Both HCA and PCC analyses were performed with the R package ComplexHeatmap. For HCA, normalized metabolite signal intensities (unit variance scaling) were represented as a color spectrum.

2.5.3 Differential metabolites selection

Differential metabolites between two groups were identified using VIP values (VIP > 1) and P-values (P < 0.05) from Student’s t-test. VIP scores were derived from OPLS-DA models generated via the MetaboAnalystR package in R. Before OPLS-DA, data underwent log2 transformation and mean-centering. Overfitting was assessed through permutation tests (200 permutations).

2.5.4 KEGG annotation and enrichment analysis

Metabolite identification involved annotation against the KEGG Compound database (http://www.kegg.jp/kegg/compound/), followed by mapping to the KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html).

3.Results

3.1.Untargeted metabonomics results

Untargeted metabonomics results are as follows:

Fig. 1

Untargeted metabonomics plot(note:RT (min) is the retention time; Precursor (DA) is the mass of parent ion)

3.2. Sample quality control

3.2.1. Total ion flow diagram

Comparison of the total ion chromatograms of three QC samples under positive and negative ion detection modes showed that the peak response intensities and retention times were largely overlapping, with stable baselines. This indicates minimal variation due to instrument error throughout the entire analytical process (Fig. 2).Metabolite ion peaks from pooled QC samples were extracted using Analyst 1.6.3 software, yielding 881 retention peaks in T3 positive ion mode, 1014 in T3 negative ion mode, and 466 in HILIC negative ion mode.

3.2.2. QC Sample Correlation Analysis

C. Overlaid TIC spectra of QC samples in negative ion mode using the HILIC column.

QC sample correlation analysis. (Note: The diagonal panels represent QC sample names; the lower-left panels show correlation scatter plots of the corresponding QC samples, with the x and y axes representing log-transformed metabolite abundances (each point represents one metabolite); the upper-right panels present the Pearson correlation coefficients for the corresponding QC pairs.).

a b

c d

Fig. 2

Sample quality control.

a.b. Overlaid TIC spectra of QC samples in positive and negative ion modes using the T3 column, respectively.

c. Overlaid TIC spectra of QC samples in negative ion mode using the HILIC column.

d. QC sample correlation analysis. (Note: The diagonal panels represent QC sample names; the lower-left panels show correlation scatter plots of the corresponding QC samples, with the x and y axes representing log-transformed metabolite abundances (each point represents one metabolite); the upper-right panels present the Pearson correlation coefficients for the corresponding QC pairs.)

3.2.3. Stability of internal standards in QC samples.

Internal standards at known concentrations were spiked into the QC samples. The variations in their responses were minimal (CV ≤ 15%), demonstrating a stable detection process and high data quality (Supplementary Table 1).

Table 1
Stability of internal standards in QC samples.
MetaboliteID	Q1(Da)	RT(min)	CV	Purity(%)
L-leucine-d7	139.1464	1.14	0.026377791	98
Adenine-2-d1	135.0532	1.34	0.045343284	98
L-Tryptophan-d5	210.1291	2.4	0.029075434	98
Cytidine-5,6-d2	244.0903	2	0.037518393	98
benzoic acid-d5	126.0614	1.02	0.008719586	98
benzoic acid-d5	126.0614	4.43	0.028459972	98
CAR(16:0)-d3	403.3621	6.87	0.038034561	98
Cholic Acid-d4	411.3049	4.613	0.078976806	98
Cholic Acid-d4	411.3127	6.31	0.018792434	98
D-Luciferin	278.9898	4.34	0.029027956	98
L-2-chlorophenylalanine	198.0322	2.48	0.017790481	98
L-2-chlorophenylalanine	200.0478	2.48	0.023773807	98

3.3. Principal component analysis (PCA)

3.3.1. Principal component analysis of population samples

For red deer and sika deer velvet antler, antler, and antler base, the contribution rate of the first (PC1), second (PC2), and third (PC3) principal components was 61.79%, 12.19%, and 6.25%, respectively (Fig. 3a,b). Together, these three components accounted for 80.23% of the total variance, reflecting metabolic differences across all samples. Based on PC1, PC2, and PC3, CCAD_PA samples were clearly separated from CCAD_SDAB and CCAD_ANT samples; similarly, CENI_PA was separated from CENI_SDAB and CENI_ANT samples, and CCAD_PA samples were further distinguished from CENI_PA samples. However, CCAD_SDAB, CCAD_ANT, CENI_SDAB, and CENI_ANT clustered closely in PC1 and PC2, indicating that their metabolic differences were primarily captured by PC3.

When PCA was applied to all metabolites in CCAD and CENI, substantial overlap and cross-distribution were observed between SDAB and ANT samples within each species, indicating minimal metabolic differences between antler base and antler samples of the same species. In contrast, PA samples showed limited overlap with SDAB and ANT samples of the same species, indicating pronounced metabolic differences between velvet antler and antler base/antler samples. Additionally, SDAB and ANT samples displayed considerable overlap between species, suggesting minor metabolic differences in antler base and antler across species. By contrast, PA samples from different species showed little overlap, highlighting distinct metabolic differences in velvet antler between species.

Overall, the samples were divided into three distinct groups, each with characteristic metabolic profiles: Group1 comprised sika deer velvet antler, Group2 comprised red deer velvet antler, and Group3 comprised antler and antler base samples from both species. Within each group, metabolic profiles were relatively consistent.

3.3.2. Principal component univariate statistical process control

Using the established PCA model, quality control (QC) samples were monitored based on detected ion peaks to evaluate instrument stability. In the control chart, each point represents a sample, with the horizontal axis indicating the injection sequence. Point-to-point fluctuations may occur due to variations in instrument status. Normally, PC1 scores of QC samples are expected to fall within ± 3 standard deviations (SD). As shown in Fig. 3c, all QC sample PC1 scores fell within the ± 3 SD range, indicating minimal instrument fluctuations during the experiment and low instrument-induced error.

a b

Fig. 3

PCA

a.2D PCA plot

b.3D PCA plot

c.PC1 Scroes

3.4. Metabolite expression profile

To clarify the metabolite variation patterns in velvet antler, antler, and antler base of the two deer species, primary and secondary metabolites were identified using widely targeted metabolomics based on the UPLC-MS platform. A total of 25 metabolite classes were identified (Fig. 4), comprising 540 amino acids and derivatives, 279 organic acids and derivatives, 265 fatty acyls, 255 benzenes and derivatives, 195 heterocyclic compounds, 118 nucleotides and derivatives, 118 aldehydes, ketones, and esters, 104 glycerophospholipids, 102 alcohols and amines, 84 carbohydrates and derivatives, 83 hormones and hormone-related compounds, 33 flavonoids, 26 coenzymes and vitamins, 23 bile acids, 13 alkaloids, 13 terpenoids, 12 glycerides, 11 sphingolipids, 8 tryptamines, cholines, and pigments, 4 lignans and coumarins, 2 phenolic acids, 2 quinones, 1 sterol ester, 1 steroid, and 69 other metabolites.

Fig. 4

metabolic profiling

3.5 Cluster heatmap

Differences in metabolite accumulation patterns among samples were assessed using a clustering heatmap (Fig. 5). The results showed clear intergroup variations, which were classified into four clusters. Cluster 1: Metabolite levels were most abundant in the CCAD_ANT group, followed by CENI_ANT, lowest in CCAD_PA, and moderate in the remaining groups. Cluster 2: Metabolite levels were highest in CCAD_PA, followed by CENI_PA, and relatively low in other groups. Cluster 3: Metabolite levels were most abundant in CENI_PA, followed by CCAD_PA, and lowest in other groups. Cluster 4: Metabolite levels were highest in CENI_PA, followed by CCAD_SDAB, and CCAD_PA, and lowest in the other groups. Biological replicates clustered tightly, indicating high data homogeneity and reliability.

In summary, both cluster analysis and principal component analysis demonstrated significant metabolic differences among the groups. Among them, CCAD_PA and CENI_PA exhibited the most distinct differences compared to other groups.

Fig. 5

clustering heatmap

3.6 Differential Metabolites Analysis

3.6.1 OPLS-DA Analysis

To identify significantly different metabolites in velvet antler, antler, and antler base of the two deer species, Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was first performed. OPLS-DA is a supervised statistical method that models the relationship between metabolite expression and sample categories through partial least squares regression, thereby enabling sample classification. The Variable Importance in Projection (VIP) score was calculated to evaluate the contribution and explanatory power of each metabolite to sample classification.

Pairwise OPLS-DA was conducted for the CCAD_PA, CCAD_SDAB, CCAD_ANT, CENI_PA, CENI_SDAB, and CENI_ANT groups, and the corresponding score plots were generated (Fig. 6). In this model, R²X and R²Y represent the model’s explanatory and predictive performance. All Q² values exceeded 0.9, and the p-values for R²Y were below 0.005, confirming the robustness of the constructed models. The OPLS-DA score plots revealed clear separation among the comparison groups.

a b

c d

e f

g h

i j

k l

m n

o p

q r

Fig. 6

OPLS-DA permutation and score

a. CCAD_ANT_VS_CCAD_SDAB OPLS-DA permutation

b. CCAD_PA_vs_CCAD_ANT OPLS-DA permutation

c. C).CCAD_SDAB_vs_CCAD_PA OPLS-DA permutation

d. CENI_ANT_vs_CCAD_ANT OPLS-DA permutation

e. CENI_ANT_vs_CENI_SDAB OPLS-DA permutation

f. CENI_PA_vs_CCAD_PA OPLS-DA permutation

g. CENI_PA_vs_CENI_ANT OPLS-DA permutation

h. CENI_SDAB_vs_CCAD_SDAB OPLS-DA permutation

i. CENI_SDAB_vs_CENI_PA OPLS-DA permutation

j. CCAD_ANT_VS_CCAD_SDAB OPLS-DA score

k. CCAD_PA_vs_CCAD_ANT OPLS-DA score

l. CCAD_SDAB_vs_CCAD_PA OPLS-DA score

m. CENI_ANT_vs_CCAD_ANT OPLS-DA score

n. CENI_ANT_vs_CENI_SDAB OPLS-DA score

o. CENI_PA_vs_CCAD_PA OPLS-DA score

p. CENI_PA_vs_CENI_ANT OPLS-DA score

q. CENI_SDAB_vs_CCAD_SDAB OPLS-DA score

r. CENI_SDAB_vs_CENI_PA OPLS-DA score

3.6.2 Screening of Differential Metabolites

Differential metabolites were identified based on P-value and VIP thresholds. Metabolites simultaneously satisfying P < 0.05 and VIP > 1 were considered significantly differential (Fig. 7).

The results were as follows:

Between CCAD_ANT and CCAD_SDAB: 657 differential metabolites were identified, including 577 up-regulated and 80 down-regulated;

Between CCAD_PA and CCAD_ANT: 1720 differential metabolites were identified, including 1581 up-regulated and 139 down-regulated;

Between CCAD_PA and CCAD_SDAB: 1667 differential metabolites were identified, including 66 up-regulated and 1601 down-regulated;

Between CENI_ANT and CCAD_ANT: 891 differential metabolites were identified, including 381 up-regulated and 510 down-regulated;

Between CENI_ANT and CENI_SDAB: 846 differential metabolites were identified, including 447 up-regulated and 399 down-regulated;

Between CENI_PA and CCAD_PA: 1506 differential metabolites were identified, including 165 up-regulated and 1341 down-regulated;

Between CENI_PA and CENI_ANT: 1311 differential metabolites were identified, including 1110 up-regulated and 201 down-regulated;

Between CENI_SDAB and CCAD_SDAB: 561 differential metabolites were identified, including 469 up-regulated and 92 down-regulated;

Between CENI_SDAB and CENI_PA: 1242 differential metabolites were identified, including 160 up-regulated and 1082 down-regulated.

3.5.3 Enrichment Analysis of Differential Metabolites

Differential metabolites from each comparison were mapped to the KEGG database to identify associated pathways. Pathway enrichment analysis was then performed to identify significantly enriched pathways.

The main annotated and enriched pathways for each comparison were as follows^[24–26]:

CCAD_ANT vs. CCAD_SDAB: Metabolic pathways, Nucleotide metabolism, Amino sugar and nucleotide sugar metabolism, Biosynthesis of nucleotide sugars;

CCAD_PA vs. CCAD_ANT: ABC transporters, Glycerophospholipid metabolism, Choline metabolism in cancer;

CCAD_PA vs. CCAD_SDAB: ABC transporters, Thermogenesis, Pyrimidine metabolism;

CENI_ANT vs. CCAD_ANT: Glycerophospholipid metabolism, Choline metabolism in cancer, Efferocytosis;

CENI_ANT vs. CENI_SDAB: Metabolic pathways, Biosynthesis of cofactors, Nucleotide metabolism;

CENI_PA vs. CCAD_PA: ABC transporters, D-Amino acid metabolism, Purine metabolism;

CENI_PA vs. CENI_ANT: Metabolic pathways, Glycerophospholipid metabolism, Choline metabolism in cancer;

CENI_SDAB vs. CCAD_SDAB: Metabolic pathways, Arachidonic acid metabolism, Nucleotide metabolism;

CENI_SDAB vs. CENI_PA: Metabolic pathways, Thermogenesis, Biosynthesis of amino acids.

Notably, certain pathways, such as "Metabolic pathways," recurred across multiple comparisons. Nucleotide metabolism, which generates upstream metabolites, serves a foundational role. These metabolic pathways are closely related to the focus of this study.

a b

c d

e f

g h

Fig. 7

KEGG enrichment P-value

a. CCAD_ANT_vs_CCAD_SDAB_KEGG_Enrichment_P-value

b. CCAD_PA_vs_CCAD_ANT_KEGG_Enrichment_P-value

c. CCAD_SDAB_vs_CCAD_PA_KEGG_Enrichment_P-value

d. CENI_ANT_vs_CCAD_ANT_KEGG_Enrichment_P-value

e. CENI_ANT_vs_CENI_SDAB_KEGG_Enrichment_P-value

f. CENI_PA_vs_CCAD_PA_KEGG_Enrichment_P-value

g. CENI_PA_vs_CENI_ANT_KEGG_Enrichment_P-value

aa.

h. CENI_SDAB_vs_CCAD_SDAB_KEGG_Enrichment_P-value

i. CENI_SDAB_vs_CENI_PA_KEGG_Enrichment_P-value

4 Discussion

Antler-type medicinal materials, including antler, velvet antler, and antler base from red deer or sika deer, are key deer-derived products long used in traditional Chinese medicine. Previous studies have shown that different tissues (antler, velvet antler, antler base) from the same species exhibit distinct therapeutic effects, while the same tissue can also vary in efficacy between species. These differences largely stem from variations in chemical composition, yet the underlying pharmacodynamic material basis remains poorly understood. In recent years, molecular biology, particularly omics technologies, has provided powerful tools for elucidating the active components of antler-based medicines. Among these, metabolomics, encompassing both targeted and untargeted approaches, enables comprehensive assessment of metabolic alterations and systematic exploration of small-molecule metabolites. In this study, untargeted metabolomics was applied to compare the metabolic profiles of velvet antler, antler, and antler base between sika deer and red deer, aiming to uncover tissue- and species-specific metabolic characteristics.

Principal component analysis (PCA) and clustering revealed significant differences in metabolite composition among antler-derived tissues (velvet antler, antler, and antler base), with velvet antler showing the greatest divergence. This suggests that velvet antler may have unique properties related to its developmental stage, physiological functions, or bioactive compound accumulation. Furthermore, while the metabolic profiles of antler and antler base were relatively conserved between species (sika deer vs. red deer), velvet antler exhibited notable tissue specificity, indicating its potential as a key tissue for species identification and systematic quality assessment.

OPLS-DA analysis further confirmed intergroup metabolic differences and identified numerous differentially expressed metabolites. These metabolites were enriched in key pathways, including ABC transporters, glycerophospholipid metabolism, nucleotide metabolism, and amino acid biosynthesis, which are closely associated with energy metabolism, cell membrane structure, signal transduction, and other physiological processes potentially affecting the bioactivity and medicinal value of velvet antler. Notably, “Metabolic pathways” and “Nucleotide metabolism” were consistently enriched across comparisons, underscoring the central role of energy metabolism and nucleic acid synthesis in the metabolic network of antler-derived medicines. The recurrent enrichment of “Glycerophospholipid metabolism” and “Choline metabolism” further suggests that lipid metabolism may contribute to the rapid growth and cellular proliferation of velvet antler.

Furthermore, several known bioactive metabolites were identified among the differentially expressed compounds in this study. For example, specific lipids and amino acids significantly upregulated in velvet antler have been reported to exhibit anti-inflammatory, antioxidant, and tissue-regenerative properties. The enrichment of these metabolites may underlie the traditional medicinal effects of velvet antler, including kidney reinforcement and the strengthening of bones and tendons. Moreover, species-specific differential metabolites between sika and red deer velvet antlers may serve as molecular markers for distinguishing these two valuable medicines, which is crucial for quality standardization and preventing misidentification.

The untargeted metabolomics approach used in this study, incorporating cross-species and multi-tissue comparisons, enabled comprehensive profiling of the complex metabolic landscape of antler-derived materials, overcoming the limitations of traditional targeted analyses, including narrow coverage and high selectivity. These findings demonstrate the effectiveness of this approach in deciphering the material basis of traditional medicines. However, this study has certain limitations. For instance, all samples were collected from a single farm, and future studies should include specimens from diverse geographical origins to enhance the generalizability of the conclusions. Additionally, the identified differential metabolites and pathways require further validation through targeted quantification and pharmacological experiments to confirm their functional significance.

5. Conclusion

This untargeted metabolomic study revealed pronounced differences in the metabolic profiles of velvet antler, antler, and antler base from sika deer and red deer. Velvet antler exhibited distinct metabolic characteristics compared with other tissues, and interspecies metabolic differences were more pronounced in velvet antler than in antler or antler base, suggesting that velvet antler is a highly species- and tissue-specific medicinal component. These differential metabolites were enriched in key pathways, including ABC transporters, glycerophospholipid metabolism, and nucleotide metabolism, which are likely associated with the rapid growth and bioactive potential of velvet antler. This study systematically elucidates the metabolic basis of antler-derived medicinal materials and provides theoretical and empirical support for quality evaluation, species identification, and the further development of bioactive components.

Supplementary Materials:Table S1:Information sheet of all sample metabolites;Table S2:Untargeted metabolites;Table S3:Clustering heat map of all metabolite contents, showing the English names of substances (clustering both metabolites and samples);Table S4:PCA data of total samples (including QC samples);Table S5:Interpretation rate table of PCA principal components of total samples (including QC samples).

Author Contribution

Methodology, Z.Y., Z.H., Y.S. and Y.J.;validation, Z.Y.; formal analysis, Z.Y.; investigation, Z.Y., Z.H.,Y.S., L.L.,D.J., Z.D. and Y.J.; resources, Y.J.,X.Y.,Y.H.;Data curation, Z.H. and Y.J.;writing—original draft, Z.H. and Y.J.;project administration, Z.Y. and Y.J.;All authors have read and agreed to the published version of the manuscript.

Funding:

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent

Statement: Not applicable.

Data Availability

The datasets generated during and/or analysed during the current study are available in the figshare repository, https://doi.org/10.6084/m9.figshare.30178057.v1.

Competing interests:

The authors declare no competing interests.

Abbreviations:

The following abbreviations are used in this manuscript:

CCAD	Red deer
CENI	Sika deer
PA	Velvet antler
SDAB	Antler base
ANT	Antler

Electronic Supplementary Material

Below is the link to the electronic supplementary material

Supplementary Material 1

Acknowledgement

The authors thank Wuhan Metware Biotechnology Co., Ltd. for the testing services.

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