Multi-omics analysis of the characteristic components of Apis cerana honey from Changbai Mountain

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In this study, UPLC-MS/MS metabolomics and ultra-fast proteomics technologies were employed to analyze the differences in metabolites and proteins among Apis cerana honey samples collected from various Chinese regions, including Jilin, Sichuan, Hainan, Hubei, and Tibet. Through an integrated multi-omics analysis, we identified potential characteristic biomarkers that differentiated CBS honey from Apis cerana honeys produced in other regions. These findings provide a theoretical foundation for the authentication and geographical origin traceability of CBS honey. Apis cerana honey Changbai Mountain metabolomics proteomics biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Honey is valued not only for its unique flavor but also for its wide range of pharmacological activities and biological functions, particularly as a natural dietary antioxidant. Apis cerana honey has been used for thousands of years in China as a unique honey variety and a form of traditional medicine[ 1 , 2 ]. The Apis cerana population in the Changbai Mountain region represents the only ecological type of this species within the local ecosystem. However, this population has been in a continuous decline, placing it in an endangered-sustained state [ 3 , 4 ]. Apis cerana honey from Changbai Mountain (CBS honey) is produced exclusively by bees foraging on wild mountain flowers. Traditionally, it is believed to tonify the spleen and kidneys, moisten the lungs and intestines, calm the major organs, harmonize medicinal compounds, and neutralize toxins [ 5 ]. As a high-quality characteristic honey with limited production, it has been widely exchanged as tribute, gifts, and commodities among government institutions and the general population since the Tang Dynasty. During the Qing dynasty, a specialized institution was established in Jilin to collect and supply tribute honey for the imperial household [ 6 ]. In China, honey is primarily produced by Apis cerana and Apis mellifera . Due to its limited production and strong local consumer preferences, Apis cerana honey is priced at least ten times higher than Apis mellifera honey, and sometimes significantly more. Given these economic incentives, the adulteration and counterfeiting of Apis ceran a honey have become increasingly severe. The nutritional composition of honey varies with its botanical and geographical origins, and specific chemical constituents can serve as reliable markers for traceability and authentication [ 7 ]. However, characterization techniques specific to CBS honey have not been established, making it difficult to differentiate this variety from others based on functional components. Metabolomics and proteomics, which can provide comprehensive profiles of proteins and small-molecule metabolites, have emerged as powerful tools for addressing honey adulteration and elucidating the mechanisms underlying its biological activities [ 8 ]. In this study, we selected representative Apis cerana honey samples from various geographically indicated regions across China. The primary focus was CBS honey, with Apis cerana honeys from Sichuan, Hainan, Tibet, and Hubei Provinces serving as controls. Metabolomic analysis using ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS), combined with ultra-fast proteomic techniques, was employed to investigate regional variations in metabolite and protein profiles among these honey samples. This integrated approach enabled the identification of potential characteristic markers unique to CBS honey, thereby establishing a scientific foundation for its geographical origin authentication and quality evaluation. Materials and methods Honey sampling CBS honey samples were collected from apiaries within the Changbai Mountain Nature Reserve in Jilin Province. These samples were provided by local beekeepers and produced exclusively by purebred Apis cerana colonies native to the Changbai Mountain region. The honey was harvested during mid-to-late October. Following simple filtration, it was bottled and stored at 4°C for subsequent analysis. The control group consisted of honey samples recognized as China National Geographical Indication (GI) products, including Tibetan honey from Apis cerana in Tibet (XZ), Qiongzhong honey from Apis cerana hainana in Hainan Province (QZ), Shennong Baihua honey from Apis cerana in Hubei Province (SN), and Aba honey from Apis cerana abanisis in Sichuan Province (AB). All samples were natural, ripe honeys produced by purebred ecological types of Apis cerana , with colonies maintained in traditional cylindrical log hives (Table S1 ). From 2022 to 2024, three production batches were collected annually from each geographical origin. Each batch was divided into three biological replicates, with three technical replicates for each analysis. Instruments and reagents Dithiothreitol (DL), bovine serum albumin, ethylenediaminetetraacetic acid, xylene brilliant cyanin G, iodoacetamide, phenylmethanesulfonyl fluoride, sodium dodecyl sulfate, tetraethylammonium bromide, thiourea, trypsin, and urea were sourced from Shanghai Aladdin Biochemical Technology Co., Ltd. (China) and Sinopharm Chemical Reagent Co., Ltd. (China), with all purities exceeding 97.0%. Untargeted metabolomics and data-independent acquisition (DIA) proteomics analyses were conducted using an ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) system coupled with an Orbitrap Astral high-resolution mass spectrometer (Thermo Fisher Scientific, USA) to screen and identify unknown compounds in the honey samples. HPLC-grade methanol, acetonitrile, and formic acid were obtained from Shanghai CINC High Purity Solvent Co., Ltd. (China). Metabolomics profiling After thawing, samples were vortexed for 1 min or manually mixed for 30 s. Proportionally add 70% methanolic water internalstandard extract pre-chilled at -20℃ and vortex for 15 min. Centrifuge for 3 min, take the supernatant and filter it by a microporous membrane, then save it in the injection bottle. UPLC conditions: column: Agilent SB-C18 (1.8 µm, 2.1 mm * 100 mm); Mobile phase: A = ultrapure water with 0.1% formic acid (v/v), B = acetonitrile with 0.1% formic acid (v/v). Elution gradient: 0.00 min, 5% B; 0.00–9.00 min, linear increase to 95% B; 9.00–10.00 min, hold at 95% B; 10.00-11.10 min, linear decrease to 5% B; 11.10–14.00 min, hold at 5% B (equilibration). The flow rate was set at 0.35 mL/min, the column temperature was maintained at 40°C, and the injection volume was 2 µL. Mass spectrometry conditions: Electrospray ionization (ESI) source temperature: 500°C; Ion spray voltage (IS): 5500 V (positive ion mode) / -4500 V (negative ion mode); Ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were set to 50, 60, and 25 psi, respectively; Collision-induced dissociation parameter: high. MRM scans were performed with collision gas set to medium. PCA analysis Unsupervised PCA (principal component analysis) was performed by statistics function prcomp within R ( www.r-project.org ). The data was unit variance scaled before unsupervised PCA. Identification of differential metabolites Z-score normalization was applied for data standardization during the analytical process. This method utilized the mean (µ) and standard deviation (σ) of the raw data, with the transformation defined as: $$\:{\:x}^{{\prime\:}}=\frac{x-\mu\:}{\sigma\:}$$ , where x is the original value, µ is the arithmetic mean, and σ is the population standard deviation. KEGG annotation and enrichment analysis Identified metabolites were annotated using KEGG Compound database ( http://www.kegg.jp/kegg/compound/ ), annotated metabolites were then mapped to KEGG Pathway database ( http://www.kegg.jp/kegg/pathway.html ). Proteomics profiling Sample handling: Begin by retrieving the samples and gently thawing them on ice. Then, add PMSF to the samples to achieve a final concentration of 1mM thoroughly mix. Centrifuge at 4°C, 4500 g for 10 minutes, and determine the protein concentration using a BCA assay kit. Finally, protein samples were proteolytic desalted according to standard protocols. Liquid Chromatography Detection: Mobile phases: Phase A consisted of 0.1% formic acid in water (v/v), phase B consisted of 0.1% formic acid in acetonitrile (v/v; 100% acetonitrile). Separations were performed on an Easy-Spray™ PepMap™ Neo UHPLC column (150 µm × 15 cm, 2 µm) maintained at 55°C. Samples (200 ng) were loaded at a flow rate of 250 nL/min with an effective gradient duration of 6.9 min and total run time of 8 min. Mass spectrometry was performed on an Orbitrap Astral mass spectrometer operating in positive ion mode. Full MS scans: m/z range 380–980, resolution 240,000 (at m/z 200), normalized AGC target 500%, maximum injection time 5 ms. MS/MS: Isolation window 2 Th, HCD collision energy 25%, normalized AGC target 500%, maximum injection time 3 ms. Identification of differentially expressed proteins (DEPs): The criteria for identifying DEPs were defined as follows: (1) For replicated experiments (biological replicates ≥ 2), in pairwise comparisons, proteins were classified as DEPs when meeting both a fold change (FC) ≥ 1.5 or ≤ 0.6667 and a P -value ≤ 0.05, while in multi-group comparisons, proteins with a P -value ≤ 0.05 were considered significant, regardless of the FC. (2) For non-replicated experiments (biological replicate = 1), in pairwise comparisons, DEPs were identified based solely on the FC threshold of ≥ 1.5 or ≤ 0.6667. Bioinformatics analysis of DEPs: Functional annotation of proteins was performed using the KOG, GO, KEGG, and InterPro databases. Subcellular localization prediction was conducted using WoLF PSORT (version 1.0) with default parameters for eukaryotes. Combined analysis of the metabolome and proteome KEGG analysis: Based on KEGG annotation and enrichment analysis results of differential metabolites and differentially expressed proteins, KEGG pathways annotated by both omics datasets were identified. Expression correlation analysis: In each differential comparison group, Pearson correlation analysis was performed to identify protein–metabolite pairs with an absolute correlation coefficient greater than 0.8 and a P -value less than 0.05. The fold changes of the correlated proteins and metabolites were visualized using a nine-quadrant diagram. This diagram was segmented into quadrants 1 through 9, arranged from left to right and top to bottom, using black dotted lines. Orthogonal Projections to Latent Structures analysis(O2PLS) was performed using the OmicsPLS R package (v2.0.2). All differential metabolites (the X dataset) and differentially expressed proteins (the Y dataset) were included to establish an O2PLS model. The model was cross-validated using the crossval_o2m and crossval_o2m_adjR2 parameters to obtain the optimal model configuration. A loading plot was generated to identify key variables with significant influence across omics datasets. Results Metabolite composition of CBS honey A total of 890 metabolites were identified in CBS honey using the UPLC-MS/MS detection platform and public metabolomic databases. The distribution of metabolite categories, from the highest to lowest proportion, was as follows: amino acids and their derivatives (35.96%), lipids (22.58%), organic acids (18.54%), others (11.80%) and nucleotides and their derivatives (11.12%) (Fig. 1 A). The stability of the analytical system was validated using quality control (QC) samples. Over 75% of the detected metabolites exhibited coefficients of variation (CVs) below 0.3, confirming both methodological robustness and data reliability (Fig. S1 ). PCA results showed the separation of all honey samples into five distinct groups (Fig. 1 B), indicating significant metabolic differences between CBS honey and Apis cerana honeys from other regions (XZ, SN, AB, and QZ honeys). Differential metabolites between CBS honey and Apis cerana honeys from other regions A Venn diagram was constructed to visualize the relationships among differential metabolites across comparison groups. The results showed the following: 51 unique differential metabolites between CBS and XZ honeys, 71 unique differential metabolites between CBS and QZ honeys, 28 unique differential metabolites between CBS and SN honeys, and 33 unique differential metabolites between CBS and AB honeys. A total of 112 differential metabolites were shared across all four comparison groups (Fig. 2 ). Based on the established screening criteria, 393 differential metabolites were identified between CBS and XZ honeys. Among them, CBS honey exhibited significantly higher relative abundances of the following metabolites: 3-nitro-L-tyrosine, barbiturate, 1,7-dimethylxanthine, 4-guanidinobutanoate, (4-acetylphenyl)-L-alanine, (2-carboxyethyl)-L-phenylalanine, Gly-Gly-Phe, hydroxythreonine xyloside, maltitol, N-acetyl-L-phenylalanine, 2-{4-amino-1H-imidazo[4,5-d]pyridazin-1-yl}-5-(hydroxymethyl)oxolane-3,4-diol, adenosine, 3-methylxanthine, methyl 2-furoate, 9-arabinosyladenine, 2-decenedioic acid, N-lactoylphenylalanine, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, pyridoxine, AMP, mevalonic acid glucoside, (6Z,9Z,12Z)-octadecatrienoic acid, cyclo(Pro-Leu), cyclo(D-Leu-L-Pro), 2-oxopentanoic acid, D-ribulose 5-phosphate, perseitol-1-O-xyloside, D-sorbitol, allitol, gamma-resorcylate, aminomalonate, 9-alpha-ribofuranosyladenine*(* indicates an isomer, the same applies hereafter), and hydroxy-o-tolyl-acetic acid (Fig. 3 A). Venn diagram analysis further revealed that among the 51 unique differential metabolites between CBS and XZ honeys, cyclo(Pro-Leu)* and D-ribulose 5-phosphate exhibited significantly higher relative abundances in CBS honey than in XZ honey (Table S2 ). There were 373 differential metabolites between CBS and QZ honeys. Compared to QZ honey, CBS honey exhibited significantly higher relative abundances of the following metabolites: 9,12-octadecadien-6-ynoic acid, Lys-Asn, sphinganine, 9-oxoODE*, lysoPC 17:2 (2n isomer)*, perseitol-1-O-xyloside, 4-guanidinobutanoate, rabdosia acid A*, barbiturate, N-benzoyl-(2R,3S)-3-phenylisoserine, dehydroascorbate, gorlic acid, cis-4-hydroxy-D-proline*, hydroxyproline*, cis-3-hydroxy-L-proline*, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, 10,16-dihydroxyhexadecanoic acid, Lys-Thr, 12(13)-EpOME, Ser-Val-Leu, maltitol, 9,16-dihydroxypalmitic acid, D-sorbitol, benzocyclobutyl-1-carboxylic acid, AMP, N-lactoylphenylalanine, 3-methylxanthine, mevalonic acid glucoside, lysoPC 18:3 (2n isomer), nicotinate, lysoPC 16:2 (2n isomer)*, 3-bromo-tyrosine, phosphatidylcholine lyso 18:3, pyridoxine phosphate, Lys-Ala, lysoPE 18:2, lysoPC 15:0 (2n isomer)*, 3,4,5-trihydroxy-1-{[(2E)-3-(4-hydroxyphenyl)prop-2-enoyl]oxy}cyclohexane-1-carboxylic acid (Fig. 3 B). Venn diagram analysis revealed that among the 71 unique differential metabolites between CBS and QZ honeys, cis-4-hydroxy-D-proline*, hydroxyproline*, cis-3-hydroxy-L-proline*, lysoPC 17:2 (2n isomer)*, Lys-Ala, pyridoxine phosphate, Lys-Asn, and 12(13)-EpOME exhibited significantly higher relative abundances in CBS honey than in QZ honey (Table S2 ). A total of 324 differential metabolites were identified between CBS and SN honeys. Compared to SN honey, CBS honey exhibited significantly higher relative abundances of the following metabolites: 3-nitro-L-tyrosine, barbiturate, hydroxythreonine xyloside, adenosine*, dehydroascorbate, 9-alpha-ribofuranosyladenine*, 9-arabinosyladenine*, 3-methylsuberic acid, kestose, {4-amino-1H-imidazo[4,5-d]pyridazin-1-yl}-5-(hydroxymethyl)oxolane-3,4-diol, allitol, 4-guanidinobutanoate, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, perseitol-1-O-xyloside, 2-oxopentanoic acid, (6Z,9Z,12Z)-octadecatrienoic acid, methyl 2-furoate, and D-sorbitol (Fig. 3 C). Venn diagram analysis revealed that among the 28 unique differential metabolites between CBS and SN honeys, 3-methylsuberic acid exhibited a significantly higher relative abundance in CBS honey than in SNZ honey (Table S2 ). There were 383 differential metabolites between CBS and AB honeys. Compared to AB honey, CBS honey exhibited significantly higher relative abundances of the following metabolites: benzocyclobutyl-1-carboxylic acid, lysoPC 15:0*, sphinganine, 9-oxoODE*, sepiapterin, glycerol 9,11,13-octadecatrienoyl ester*, rabdosia acid A*, hibiscus acid, (2-carboxyethyl)-L-phenylalanine, 2-hydroxyisobutyric acid*, 2-hydroxybutanoic acid*, lysoPC 18:2, Gly-Gly-Phe, (4-acetylphenyl)-L-alanine, Lys-Thr, Ser-Val-Leu, hexosylLPE 16:0, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, 1-α-linolenoyl-glycerol*, cis-4-hydroxy-D-proline*, succinate*, lysoPC 15:0 (2n isomer)*, γ-resorcylate, N-lactoylphenylalanine, 2-oxopentanoic acid, and mevalonic acid glucoside (Fig. 3 D). Venn diagram analysis revealed that among 33 unique differential metabolites between CBS and AB honeys, none showed a higher relative abundance in CBS honey than in AB honey. Notably, across all four comparison groups, the relative abundance of (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid was significantly higher in CBS honey than in the corresponding control honey. Based on these findings, the following metabolites were suggested as the potential characteristic metabolites of CBS honey: cyclo(Pro-Leu)*, D-ribulose 5-phosphate, cis-4-hydroxy-D-proline*, hydroxyproline*, cis-3-hydroxy-L-proline*, lysoPC 17:2 (2n isomer)*, Lys-Ala, pyridoxine phosphate, Lys-Asn, 12(13)-EpOME, 3-methylsuberic acid, and (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid (Table S2 ). Qualitative and quantitative analyses of proteins in CBS honey Proteomic analysis was conducted using a reference database containing 9932 protein sequences. A total of 1425 peptides and 202 proteins were identified (Fig. 4 A). Functional annotation of these identified proteins revealed the following distributions across various databases: GO (165 proteins), KOG (161 proteins), KEGG (189 proteins), Subcellular localization (202 proteins), SignalP (63 proteins), and InterPro (194 proteins) (Fig. 4 B). Qualitative quality assessment of proteomic data Analysis of peptide length distribution showed that most peptides consisted of 7–20 amino acids, aligning with expectations based on enzymatic digestion and mass spectrometry fragmentation, thereby meeting quality control requirements (Fig. S2 A). The distribution of peptide counts per protein group suggested that the protein groups contained a relatively large number of peptides, supporting the reliability of the identified proteins (Fig. S2 B). Moreover, the distribution of missed cleavage sites among peptides reflected the thoroughness of enzymatic digestion, with 84.28% of peptides exhibiting 0 missed cleavage sites, indicating efficient digestion that was beneficial for accurate protein identification (Fig. S2 C). Quantitative quality assessment of proteomic data PCA of the protein samples revealed significant differences between CBS honey and Apis cerana honeys from other regions (XZ, SN, AB, and QZ honeys) (Fig. S3 A). Correlation analysis demonstrated that all inter-group correlation coefficients exceeded 0.9, indicating the high reproducibility of the experimental data (Fig. S3 B). Collectively, these qualitative and quantitative quality assessments confirmed the reliability of the generated proteomic data. Differentially expressed proteins between CBS honey and Apis cerana honeys from other regions Based on the established screening criteria, 99 differentially expressed proteins (DEPs), including 17 upregulated and 82 downregulated proteins, were obtained between CBS and XZ honeys; 69 DEPs, including 19 upregulated and 50 downregulated proteins, were found between CBS and AB honeys; 58 DEPs, including 17 upregulated and 41 downregulated proteins, were identified between CBS and QZ honeys; and 78 DEPs, including 15 upregulated and 63 downregulated proteins, were detected between CBS and AB honeys (Fig. 5 A). Venn diagram analysis identified 15 shared DEPs across all four comparison groups and 23 unique DEPs between CBS and XZ honeys. Among them, the following DEPs showed significantly higher relative abundances in CBS honey than in XZ honey: APICC_01685 (mannose-6-phosphate isomerase), APICC_04696 (solute carrier family 2, facilitated glucose transporter member), APICC_00562 (alpha-galactosidase), APICC_08137 (S-adenosylmethionine synthase), APICC_07862 (glutamate receptor U1), APICC_09184 (glucosylceramidase), and APICC_08085 (chymotrypsin inhibitor). There were 8 unique DEPs between CBS and QZ honeys, among which APICC_04206 (peroxidasin) exhibited a significantly higher relative abundance in CBS honey than in QZ honey. There were 10 unique DEPs between CBS and SN honeys, among which APICC_05792 (MD-2-related lipid recognition domain-containing protein) and APICC_01096 (serine protein bubble) demonstrated significantly higher relative abundances in CBS honey than in SN honey. There were 12 unique DEPs between CBS and AB honeys, among which APICC_03450 (hymenoptaecin) and APICC_06442 (vacuolar proton pump subunit B) showed significantly higher relative abundances in CBS honey than in AB honey (Fig. 5 B and Table S2 ). Among the identified DEPs, APICC_10171 (hyaluronidase) consistently exhibited a significantly higher relative abundance in CBS honey than in all control honeys across all four comparison groups. Based on these findings, we proposed that the following proteins represented potential signature proteins of CBS honey: APICC_01685, APICC_04696, APICC_00562, APICC_08137, APICC_07862, APICC_09184, APICC_08085, APICC_04206, APICC_05792, APICC_01096, APICC_03450, APICC_06442, and APICC_10171 (Table S3 ). Functional annotation and enrichment analysis of DEPs GO annotation and enrichment analysis of DEPs in each comparison group showed that, in the Biological process category, DEPs were mainly annotated to translation, carbohydrate metabolic process, and the glycolytic process. In the Cellular component category, annotations were mainly associated with the ribosome, lysosome, extracellular region, and nucleus. For the Molecular function category, DEPs were mainly annotated to metal ion binding and GTP binding (Fig. 6 ). Further KOG functional classification analysis revealed that the top three functional categories of DEPs were General function prediction only; Posttranslational modification, protein turnover, chaperones; and Carbohydrate transport and metabolism (Fig. S4 ). Annotation and enrichment analysis of the structural domain functions of DEPs across all four comparison groups showed that DEPs were annotated to Hemocyanin, C-terminal domain superfamily; Hemocyanin, N-terminal domain superfamily; Hemocyanin/hexamerin; Di-copper center-containing domain superfamily; Hemocyanin, N-terminal; Hemocyanin, C-terminal; Hemocyanin/hexamerin middle domain; and Immunoglobulin E-set (Fig. S5 ). Subcellular localization analysis of DEPs showed that the cytoplasm and extracellular space were the subcellular structures with the highest number of annotated DEPs across all comparison groups (Fig. S6 ). Combined analysis of metabolomics and proteomics KEGG pathway enrichment analysis Based on the identified differential metabolites and the KEGG annotation and enrichment analysis of DEPs, the KEGG pathways annotated by both the metabolomic and proteomic datasets were determined. The results showed that DEPs in each comparison group were consistently associated with the β-alanine metabolism pathway (Fig. 7 ). Expression correlation analysis To explore functional relationships between proteins and metabolites, we conducted an expression correlation analysis focusing on the previously identified potential signature proteins. The results revealed discordant regulatory trends between several protein–metabolite pairs: A0A2A3EDE3 (APICC_03450) and Sasp001236 (7-hydroxy-tryptophan), A0A2A3EDR1 (APICC_06442) and pme0124 (L-glycyl-L-proline), A0A2A3EPZ0 (APICC_04206) and Lmbn001288 ((S)-2-acetolactate), A0A2A3E4S5 (APICC_01096) and Lcfn121066 (Glu-Glu-Phe), A0A2A3EHG0 (APICC_10171) and MWS201471 (Lys-Tyr), A0A2A3EIU8 (APICC_07862) and PD0280915 (Leu-Ser-Ile*), A0A2A3EMA9 (APICC_09184) and PD0280915 (Leu-Ser-Ile*), A0A2A3EPJ1 (APICC_08085) and pme0008 (L-citrulline), and A0A2A3E771 (APICC_04696) and Lmqn000432 (sn-glycero-3-phospho-1-inositol). In contrast, positive correlations were observed between A0A2A3EHG0 (APICC_10171) and PDP228396 ((5E,10R)-10-methyl-7,8,9,10-tetrahydro-3H-oxecine-2,4-dione), as well as between A0A2A3E771 (APICC_04696) and pma6455 (D-ribulose 5-phosphate) (Table S4 ). O2PLS analysis O2PLS analysis was performed to identify the top 10 DEPs with the strongest influence on metabolic profiles: A0A2A3EHG0 (hyaluronidase), A0A2A3E5C0 (acetylcholinesterase), A0A2A3EKU2 (glucosylceramidase), A0A2A3E8C9 (alpha-glucosidase), A0A2A3ED74 (chitinase protein Idgf4), A0A2A3ERC7 (glutathione peroxidase), A0A2A3ESV4 (arylsulfatase J), A0A2A3E889 (hexamerin), A0A2A3EMG3 (transcription elongation factor spt6), and A0A2A3EM77(histone H2B) (Fig. S7 ). Discussion Apis cerana exhibits heightened olfactory sensitivity, which enables the efficient detection and collection of dispersed, sporadic floral resources. Consequently, honey produced by Apis cerana is classified as polyfloral honey[ 5 ]. During the process of nectar collection and its subsequent storage in the hive until it maturesmaturation, bees enzymatically convert sugars in the nectar through secretions from their salivary glands [ 9 ]. Among the amino acids present in honey, proline is the most abundant. The proline content in honey not only serves as a benchmark for assessing free amino acid levels but also functions as a critical physicochemical parameter for evaluating honey maturity and detecting adulteration [ 10 ]. Cis-4-hydroxy-D-proline*, hydroxyproline*, cis-3-hydroxy-L-proline*, and cyclo(Pro-Leu)* are derivatives formed by the hydroxylation or cyclization of the proline core structure. The first three derivatives possess antioxidant properties, capable of scavenging superoxide anions and hydroxyl radicals, and can inhibit the generation of inhibiting hydroxyl radical formation through the Fenton reaction, and regulating the cellular redox state, thereby protecting cells from oxidative damage and delaying the aging process [ 11 – 13 ]. These hydroxylated proline derivatives may result from microbial metabolism in nectar-producing plants and enter honey through bee foraging, thus indirectly participating in long-term honey preservation by inhibiting microbial spoilage. Cyclo(Pro-Leu), a cyclic dipeptide composed of proline and leucine, has been reported to inhibit bacterial biofilm formation, participate in intercellular signaling, and exhibit antitumor activity [ 14 – 16 ]. This compound may synergize with other bioactive compounds in honey to suppress the proliferation of spoilage bacteria and pathogenic microorganisms. Additionally, it likely helps delay the oxidative degradation of bioactive compounds in honey, thereby preserving honey freshness and extending its shelf life. Lys-Ala and Lys-Asn are lysine-containing dipeptides, and honey, being rich in free amino acids, provides precursors for the synthesis of these dipeptides [ 17 ]. Enzymatic hydrolysis or microbial metabolism in honey may catalyze the condensation of amino acids into small peptides [ 18 ], which may act as amino acid metabolites and influence the functional properties of honey. LysoPC 17:2 (2n isomer)*, an intermediate product of membrane phospholipid metabolism, participates in cellular signal transduction, enhances membrane fluidity, and acts as a fatty acid transporter [ 19 ]. Free fatty acids such as 3-methylsuberic acid, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, and 12(13)-EpOME are organic acids present in honey. Collectively, they account for approximately 0.5% of total honey composition, contributing to pH regulation and a characteristic flavor [ 20 ]. Compared to immature honey, Apis cerana honey is a mature honey that is rich in fatty acids, especially unsaturated fatty acids, which enhance its nutritional value [ 7 ]. CBS honey is amber-colored, and studies have shown that darker honeys are richer in functional minerals and vitamins than their lighter counterparts [ 21 ]. The vitamins found in honey are primarily derived from the pollen it contains. Among them, pyridoxine phosphate exhibits several physiological functions, including anti-inflammatory, antioxidant, neuroregulatory, and antitumor activities [ 20 , 22 ]. The pentose phosphate pathway, which contributes to carbohydrate metabolism in honey, not only supplies ribose-5-phosphate (R5P) but also generates NADPH, which plays pivotal roles in scavenging reactive oxygen species (ROS) and supporting reductive biosynthetic processes [ 23 ]. In this pathway, D-ribulose 5-phosphate serves as a key intermediate that may influence NADPH production, thereby indirectly contributing to antioxidant defense and the biosynthesis of flavor compounds. Protein is an important endogenous component of honey, accounting for approximately 0.2–1.0% of its total mass. Variations in protein composition can be used to trace the plant and bee species origins of honey and to detect potential adulteration [ 17 ]. Mannose-6-phosphate isomerase catalyzes the interconversion of mannose-6-phosphate and fructose-6-phosphate, thereby linking glycolysis with mannose metabolism [ 24 ]. The solute carrier family 2, facilitated glucose transporter member, mediates the transmembrane transport of glucose [ 25 ]. S-adenosylmethionine synthase is an essential metabolic enzyme. In addition to serving as a methyl donor, it participates in aminopropyl transfer and transsulfuration reactions, plays a vital role in maintaining normal metabolic activities, and holds significant pharmaceutical importance [ 26 , 27 ]. Hymenoptaecin is a natural antimicrobial peptide in bees and an important component of their innate humoral immune system. It plays a crucial role in inhibiting and eliminating harmful agents, including microorganisms and parasites [ 28 , 29 ]. MD-2-related lipid-recognition family proteins are characterized by the presence of ML domains, which mediate antibacterial immunity and lipid metabolism by recognizing specific lipids from endogenous and exogenous sources [ 30 ]. It is hypothesized that these proteins can recognize pathogen-associated lipid molecules and help bees defend against bacterial infections. Serine proteases represent the most ancient and diverse family of proteolytic enzymes. They are ubiquitously expressed across various tissues, organs, and body fluids, playing essential roles in organismal development, tissue repair, and related biological processes [ 31 , 32 ]. The vacuolar proton pump primarily facilitates the transport of intracellular substances into organelles, regulates ion exchange across the vacuolar membrane, maintains osmotic balance between the cytoplasm and vacuole, and modulates vacuolar morphology [ 33 , 34 ]. Glutamate receptors serve multifunctional roles in plants, not only regulating growth and development but also mediating responses to various biotic and abiotic stresses. Studies have reported that glutamate receptor genes can mediate neurotransmitter transmission in bees and influence long-term learning and memory functions [ 35 – 37 ]. Peroxidasin, a member of the peroxidase family, is functionally linked to insect hemocytes and plasma cells. It is involved in several critical biological processes, including extracellular matrix biosynthesis, basement membrane formation, tissue morphogenesis, and innate immune defense [ 38 , 39 ]. Both hyaluronidase and alpha-galactosidase are glycoproteins with distinct roles. The former is a major allergen in bee venom, capable of catalyzing the decomposition of hyaluronic acid in nectar. It also serves as a diffusion factor in venom, and its level in the venom sacs of bees is maintained relatively constant from the late pupal stage through adulthood, unaffected by the aging of worker bees. Meanwhile, the latter specifically cleaves α-1,6-linked galactoside residues and is ubiquitously distributed in nature [ 40 – 43 ]. Both enzymes may be involved in the decomposition and transformation of sugars in honey, affecting its flavor and nutritional value. Chymotrypsin inhibitors are small polypeptides that suppress the hydrolytic activity of chymotrypsin. They are widely distributed in insect tissues, including the hemolymph, midgut, and fat body, and play essential roles in regulating developmental processes and innate immune responses [ 44 , 45 ]. In bees, three chymotrypsin inhibitors (AMCI-1, AMCI-2, and AMCI-3) have been molecularly characterized [ 46 , 47 ]. Glucosylceramidase is a key enzyme in the sphingolipid metabolic pathway, regulating the breakdown and remodeling of sphingolipids. Therefore, it plays a crucial role in insect physiological activities. Disruptions to its activity may lead to imbalances in sphingolipid metabolism, thereby reducing colony resistance to parasites such as Varroa mites [ 48 – 50 ]. Hemocyanin is a copper-containing multifunctional protein responsible for oxygen transport and storage in insects. It also exhibits diverse physiological roles, including immune responses, hormone regulation, apoptosis modulation, antimicrobial activity, and environmental adaptation [ 51 – 54 ]. DEPs were predominantly annotated to hemocyanin-associated domains, suggesting their involvement in critical biological processes such as oxygen metabolism regulation, immune defense, and developmental adaptation. Further investigations incorporating functional experiments are required to elucidate the molecular mechanisms and regulatory networks of these proteins under specific physiological and pathological conditions. The DEPs identified in each comparison group were annotated in the GO and KOG databases to categories associated with energy-related processes, such as carbohydrate metabolism, glycolysis, and carbohydrate transport. Apis cerana from Changbai Mountain is a bee species with superior cold resistance. By mid-to-late October, these bees enter the overwintering period, during which the colony actively engages in essential physiological and metabolic activities, including the substantial accumulation of cryoprotectants and energy reserves, to enhance its resistance to cold stress [ 55 , 56 ]. This period also coincides with the annual honey harvest, during which bee-derived proteins are enriched in honey through energy metabolism pathways [ 56 ]. As the sole naturally occurring β-amino acid ubiquitously distributed across living organisms, β-alanine plays a critical role in pantothenate biosynthesis. This essential metabolic precursor is utilized in the production of coenzyme A (CoA), a fundamental cofactor in cellular biochemical processes [ 57 – 59 ]. Due to multiple factors such as nectar plant species, local climate and environmental conditions, and honey-gathering preferences, the types and activities of digestive enzymes secreted by different bee species may vary. Higher protease activity promotes the decomposition and recombination of amino acids, thereby affecting the synthesis and accumulation of β-alanine. As an essential precursor for carnosine synthesis, β-alanine contributes to antioxidant activity. Additionally, β-alanine supplementation has demonstrated physiological and pharmacological benefits, including enhanced physical performance and reduced fatigue [ 60 – 62 ]. In this study, differential metabolites and DEPs identified across all four comparison groups were consistently enriched in this metabolic pathway, suggesting a higher abundance of β-alanine-related β-alanine–related metabolites in CBS honey. Therefore, compared to honeys produced by other bee species, CBS honey may offer greater potential in supporting energy metabolism, physical endurance, and metabolic regulation. These findings provide valuable insights for tracing the origin of honey, developing honey-based functional products, and investigating the physiological adaptation mechanisms of bees to their local environments. To evaluate the nutritional quality of CBS honey, we measured various physicochemical and nutritional parameters (encompassing the diastase number and the contents of fructose, glucose, maltose, sucrose, fat, protein, proline, sodium (Na), and vitamin C) in samples collected from different regions within Changbai Mountain, including Tonghua, Jilin, Baishan, and Yanbian. Volatile components were analyzed using gas chromatography–ion mobility spectrometry (GC-IMS), and the results demonstrated that CBS honey exhibited comparative advantages in nutritional value, with higher proline, protein, and vitamin C levels, compared to Apis cerana honeys from other regions. The following flavor compounds were identified as the characteristic constituents of CBS honey: linalool oxide, furfural, ethyl acetate, acetic acid, 2-methyl-1-propanol, and 3-methyl-1-butanol(unpublished data). Moreover, integrated multi-omics analysis provided a more comprehensive mechanistic perspective by unraveling dynamic interrelationships between proteins and metabolites. Notably, hyaluronidase emerged as a potential characteristic protein with significant metabolomic influence. It exhibited a divergent regulatory trend with Lys-Tyr and demonstrated a positive correlation with (5E,10R)-10-methyl-7,8,9,10-tetrahydro-3H-oxecine-2,4-dione. Future studies should focus on validating the regulatory mechanisms of hyaluronidase on these critical metabolites to elucidate its role in shaping the bioactive profile of honey. Conclusion This study provides the first comprehensive characterization of the metabolome and proteome of CBS honey. Using a structured multi-omics approach, we detected 890 metabolites and quantitatively profiled 202 proteins. This integrated multi-omics analysis identified 12 potential characteristic metabolites and 13 characteristic proteins specific to CBS honey. These compounds can serve as biomarkers in future efforts to authenticate honey and distinguish products from different bee species. Future studies should include honey samples from a broader range of geographical regions and incorporate larger sample sizes to comprehensively evaluate the quality characteristics of Apis cerana honey. Declarations Acknowledgements The authors are grateful to the beekeepers who provided samples for this study and Metware Biotechnologies Inc. (Wuhan) for technical assistance. We also thank TopEdit (www.topeditsci.com) for editing this manuscript. Funding This work was financially supported by Jilin Province science and technology development Project(20240601087RC) and the Doctoral Scientific Research Foundation of Jilin Agricultural Science and Technology College (Protocol No. (2022)751). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript. Author contributions LN, SG and QS designed and coordinated the research. RQ and LL wrote the main manuscript text, WL analyzed the data. All authors have read and agreed to the published version of the manuscript. Conflict of Interest The authors have no competing interests to disclose. Ethical approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Author Contribution LN, SG and QS designed and coordinated the research. RQ and LL wrote the main manuscript text, WL analyzed the data. All authors have read and agreed to the published version of the manuscript. References Zhao H, Cheng N, He L, Peng G, Xue X, Wu L, Cao W (2017) Antioxidant and hepatoprotective effects of A. cerana honey against acute alcohol-induced liver damage in mice. 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Supplementary Files Fig.S1.png Fig.S2.jpg Fig.S3.jpg Fig.S4.jpg Fig.S5.jpg Fig.S6.jpg Fig.S7.png TableS1.xlsx TableS2.xlsx TableS3.xlsx TableS4.xlsx Supplementarydata.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Jun, 2025 Reviews received at journal 26 Jun, 2025 Reviews received at journal 19 Jun, 2025 Reviews received at journal 19 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviews received at journal 10 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers invited by journal 05 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Submission checks completed at journal 04 Jun, 2025 First submitted to journal 30 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6787940","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468017921,"identity":"610afcf4-2368-45d6-ae54-119eab91783c","order_by":0,"name":"Nan-nan Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACPmYYi7354IMPPBYMEiAODx4tbHAtPMeSDWfwSBChBc6SyDGT5mEgRgs7j9mDnzvuyJsDbTG2kZHIk5yRwPjgbRuDvDlOh/GYG/aeeWa4s7354OMcHoliaYkEZsO5bQyGOxtwajGT4G07zLjhDNAWoJbEeRIJbNK8bQwJBgdwa5H823bYfsMNoF8sIFrYfxPSAjTzcCJYCwNQy2ygLcz4tbCVScu2HU4GOcywB6hlZs/DZsk55yQMN+DQws9/eJvk27bDthuOA6PyZ49N4ozjyQc/vCmzkcdlCypg7AGTDUBCghj1IPCDWIWjYBSMglEwkgAAdhJUYYR5K/QAAAAASUVORK5CYII=","orcid":"","institution":"Jilin Agricultural Science and Technology College","correspondingAuthor":true,"prefix":"","firstName":"Nan-nan","middleName":"","lastName":"Liu","suffix":""},{"id":468017922,"identity":"97c295c2-d1c6-489c-8df6-f26a7d9fe028","order_by":1,"name":"Qin-dan Ren","email":"","orcid":"","institution":"Jilin Provincial Animal Husbandry General Station","correspondingAuthor":false,"prefix":"","firstName":"Qin-dan","middleName":"","lastName":"Ren","suffix":""},{"id":468017923,"identity":"23cb65d5-2f3c-441e-acc1-1fe7f8ee1b7d","order_by":2,"name":"Long Li","email":"","orcid":"","institution":"Jilin Agricultural Science and Technology College","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Li","suffix":""},{"id":468017924,"identity":"29c0a910-5ede-4f37-b8ce-b68f66947b4a","order_by":3,"name":"Li-na Wang","email":"","orcid":"","institution":"Jilin Agricultural Science and Technology College","correspondingAuthor":false,"prefix":"","firstName":"Li-na","middleName":"","lastName":"Wang","suffix":""},{"id":468017925,"identity":"721cc6a7-ca9f-4ed3-86fc-3eaf8ddc5a64","order_by":4,"name":"Guang-liang Shi","email":"","orcid":"","institution":"Northeast Agricultural University","correspondingAuthor":false,"prefix":"","firstName":"Guang-liang","middleName":"","lastName":"Shi","suffix":""},{"id":468017926,"identity":"38a2c95f-da9b-4ff6-8faf-5b12e75969f6","order_by":5,"name":"Shou-tao Qin","email":"","orcid":"","institution":"Jilin City Animal Disease Control Center","correspondingAuthor":false,"prefix":"","firstName":"Shou-tao","middleName":"","lastName":"Qin","suffix":""}],"badges":[],"createdAt":"2025-05-31 00:23:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6787940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6787940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84210469,"identity":"1a0109a6-94eb-48c4-ab92-8bd1bad3307c","added_by":"auto","created_at":"2025-06-09 09:54:16","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":314669,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolite composition and PCA analysis of CBS honey. (A) Metabolite category distribution of CBS honey. (B) A PCA score plot showing the separation between CBS honey and \u003cem\u003eApis cerana \u003c/em\u003ehoneys from other regions\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/7990a6b17b06fbf6ab1b1da5.jpg"},{"id":84209458,"identity":"995d2d36-f985-466c-9139-a3b7fa0ccedc","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":319497,"visible":true,"origin":"","legend":"\u003cp\u003eA Venn diagram of differential metabolites in honey samples from each comparison group\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/cb53e61f82069396c67cb0fa.png"},{"id":84209459,"identity":"bfd03c3d-05fa-40fc-b914-1fbe19b19230","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2203174,"visible":true,"origin":"","legend":"\u003cp\u003eZ-score plots of differential metabolites between CBS honey and \u003cem\u003eApis cerana\u003c/em\u003e honeys from other regions. (A) CBS vs. XZ, (B) CBS vs. QZ, (C) CBS vs. SN, and (D) CBS vs. AB\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/89fa38362fc49182bbc0747b.jpg"},{"id":84210473,"identity":"f8cf2fd3-25bc-4b38-b807-3eab2817fce3","added_by":"auto","created_at":"2025-06-09 09:54:16","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":306084,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and functional annotation of proteins in CBS honey. (A) Protein identification results and (B) functional annotation results for identified proteins\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/06b6af4faa05d6fa5943a5e3.jpg"},{"id":84210847,"identity":"3b23a4d3-6479-4212-84fa-7d12475ea93b","added_by":"auto","created_at":"2025-06-09 10:02:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":454860,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential protein expression between CBS honey and \u003cem\u003eApis cerana\u003c/em\u003e honeys from other regions. (A) The number of DEPs in each comparison group. (B) A Venn diagram showing shared and unique DEPs among the four comparison groups\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/2a28a9f5d5c240f3d12caf09.jpg"},{"id":84210475,"identity":"755687c2-77ed-4c26-9cc0-5e1c77761885","added_by":"auto","created_at":"2025-06-09 09:54:16","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3759567,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment bar chart of GO Terms for DEPs in each comparison group: (A) CBS vs. XZ, (B) CBS vs. QZ, (C) CBS vs. SN, (D) CBS vs. AB\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/4e5e73919d9a23c48d656901.jpg"},{"id":84210849,"identity":"465950c2-1d45-4182-9a03-b0b1456d9857","added_by":"auto","created_at":"2025-06-09 10:02:16","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1823381,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathway enrichment analysis of DEPs in each comparison group: (A) CBS vs. XZ, (B) CBS vs. QZ, (C) CBS vs. SN, and (D) CBS vs. AB\u003c/p\u003e","description":"","filename":"Fig.7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/1647fc554c95ad9a009e900c.jpg"},{"id":84212378,"identity":"051abb83-d16a-421c-9060-2a750f3dba67","added_by":"auto","created_at":"2025-06-09 10:18:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":10052397,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/8e7a9684-7c3f-4b6d-a196-f63b5c4e002b.pdf"},{"id":84209455,"identity":"607064bc-c316-43d1-b078-c6e684fe4ba0","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":178469,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S1.png","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/2d5554f5886671b2e2630afb.png"},{"id":84209456,"identity":"6a66707a-6a71-4141-81d6-d23d3c208e1d","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":376347,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/f16f13377a1e5eee50664b3e.jpg"},{"id":84209460,"identity":"51ce91d7-156d-4e42-b617-aa1311509cf7","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":915248,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/51ea03cdcdf18c34a8a0b754.jpg"},{"id":84209462,"identity":"6c9cddc9-5d91-4d73-9675-43dfc1459468","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1316583,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/59bc2f3c90020bd6832f0b9b.jpg"},{"id":84209465,"identity":"c50973e1-3c89-478e-a917-8524a3891bc6","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1785418,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/b6b76afe9b734fa1e927cd5e.jpg"},{"id":84209471,"identity":"3adf4f32-d612-4005-b36d-2929a1e7f69c","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1018283,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/e6aea2de5317c012ce9ee16c.jpg"},{"id":84210476,"identity":"ae047dab-c5de-46a1-a7cd-9bbe4611864b","added_by":"auto","created_at":"2025-06-09 09:54:16","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":110334,"visible":true,"origin":"","legend":"","description":"","filename":"Fig.S7.png","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/dad629aab1887ef1ffb1571a.png"},{"id":84210851,"identity":"4e97cd5c-e6bf-42a8-b693-2abd0c9add0d","added_by":"auto","created_at":"2025-06-09 10:02:16","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":9856,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/6d935eba5bcfc9c99b4acc78.xlsx"},{"id":84211888,"identity":"20d19b68-d27b-4c69-88c7-13d7fee9cbda","added_by":"auto","created_at":"2025-06-09 10:10:16","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":12526,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/04b0bc2165f9156e9574f0fd.xlsx"},{"id":84209481,"identity":"20e95290-9a8f-4cfd-a523-465d781f4eaa","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":15507,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/082dd12bb73244b3723f2e2c.xlsx"},{"id":84209479,"identity":"7f84f968-c4e4-4638-8979-26603e0a807b","added_by":"auto","created_at":"2025-06-09 09:46:16","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":11069,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/6391bbc240a93fb2b2b61882.xlsx"},{"id":84210479,"identity":"6f28e57c-d697-4e8e-8e71-f5e163154094","added_by":"auto","created_at":"2025-06-09 09:54:16","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":14423,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata.docx","url":"https://assets-eu.researchsquare.com/files/rs-6787940/v1/c92f5579d3c457f80ad5a5db.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-omics analysis of the characteristic components of Apis cerana honey from Changbai Mountain","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHoney is valued not only for its unique flavor but also for its wide range of pharmacological activities and biological functions, particularly as a natural dietary antioxidant. \u003cem\u003eApis cerana\u003c/em\u003e honey has been used for thousands of years in China as a unique honey variety and a form of traditional medicine[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The \u003cem\u003eApis cerana\u003c/em\u003e population in the Changbai Mountain region represents the only ecological type of this species within the local ecosystem. However, this population has been in a continuous decline, placing it in an endangered-sustained state [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. \u003cem\u003eApis cerana\u003c/em\u003e honey from Changbai Mountain (CBS honey) is produced exclusively by bees foraging on wild mountain flowers. Traditionally, it is believed to tonify the spleen and kidneys, moisten the lungs and intestines, calm the major organs, harmonize medicinal compounds, and neutralize toxins [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. As a high-quality characteristic honey with limited production, it has been widely exchanged as tribute, gifts, and commodities among government institutions and the general population since the Tang Dynasty. During the Qing dynasty, a specialized institution was established in Jilin to collect and supply tribute honey for the imperial household [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In China, honey is primarily produced by \u003cem\u003eApis cerana\u003c/em\u003e and \u003cem\u003eApis mellifera\u003c/em\u003e. Due to its limited production and strong local consumer preferences, \u003cem\u003eApis cerana\u003c/em\u003e honey is priced at least ten times higher than \u003cem\u003eApis mellifera\u003c/em\u003e honey, and sometimes significantly more. Given these economic incentives, the adulteration and counterfeiting of \u003cem\u003eApis ceran\u003c/em\u003ea honey have become increasingly severe.\u003c/p\u003e \u003cp\u003eThe nutritional composition of honey varies with its botanical and geographical origins, and specific chemical constituents can serve as reliable markers for traceability and authentication [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, characterization techniques specific to CBS honey have not been established, making it difficult to differentiate this variety from others based on functional components. Metabolomics and proteomics, which can provide comprehensive profiles of proteins and small-molecule metabolites, have emerged as powerful tools for addressing honey adulteration and elucidating the mechanisms underlying its biological activities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this study, we selected representative \u003cem\u003eApis cerana\u003c/em\u003e honey samples from various geographically indicated regions across China. The primary focus was CBS honey, with \u003cem\u003eApis cerana\u003c/em\u003e honeys from Sichuan, Hainan, Tibet, and Hubei Provinces serving as controls. Metabolomic analysis using ultra-performance liquid chromatography\u0026ndash;tandem mass spectrometry (UPLC-MS/MS), combined with ultra-fast proteomic techniques, was employed to investigate regional variations in metabolite and protein profiles among these honey samples. This integrated approach enabled the identification of potential characteristic markers unique to CBS honey, thereby establishing a scientific foundation for its geographical origin authentication and quality evaluation.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHoney sampling\u003c/h2\u003e \u003cp\u003eCBS honey samples were collected from apiaries within the Changbai Mountain Nature Reserve in Jilin Province. These samples were provided by local beekeepers and produced exclusively by purebred \u003cem\u003eApis cerana\u003c/em\u003e colonies native to the Changbai Mountain region. The honey was harvested during mid-to-late October. Following simple filtration, it was bottled and stored at 4\u0026deg;C for subsequent analysis. The control group consisted of honey samples recognized as China National Geographical Indication (GI) products, including Tibetan honey from \u003cem\u003eApis cerana\u003c/em\u003e in Tibet (XZ), Qiongzhong honey from \u003cem\u003eApis cerana hainana\u003c/em\u003e in Hainan Province (QZ), Shennong Baihua honey from \u003cem\u003eApis cerana\u003c/em\u003e in Hubei Province (SN), and Aba honey from \u003cem\u003eApis cerana abanisis\u003c/em\u003e in Sichuan Province (AB). All samples were natural, ripe honeys produced by purebred ecological types of \u003cem\u003eApis cerana\u003c/em\u003e, with colonies maintained in traditional cylindrical log hives (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). From 2022 to 2024, three production batches were collected annually from each geographical origin. Each batch was divided into three biological replicates, with three technical replicates for each analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInstruments and reagents\u003c/h3\u003e\n\u003cp\u003eDithiothreitol (DL), bovine serum albumin, ethylenediaminetetraacetic acid, xylene brilliant cyanin G, iodoacetamide, phenylmethanesulfonyl fluoride, sodium dodecyl sulfate, tetraethylammonium bromide, thiourea, trypsin, and urea were sourced from Shanghai Aladdin Biochemical Technology Co., Ltd. (China) and Sinopharm Chemical Reagent Co., Ltd. (China), with all purities exceeding 97.0%. Untargeted metabolomics and data-independent acquisition (DIA) proteomics analyses were conducted using an ultra-high-performance liquid chromatography\u0026ndash;tandem mass spectrometry (UHPLC-MS/MS) system coupled with an Orbitrap Astral high-resolution mass spectrometer (Thermo Fisher Scientific, USA) to screen and identify unknown compounds in the honey samples. HPLC-grade methanol, acetonitrile, and formic acid were obtained from Shanghai CINC High Purity Solvent Co., Ltd. (China).\u003c/p\u003e\n\u003ch3\u003eMetabolomics profiling\u003c/h3\u003e\n\u003cp\u003eAfter thawing, samples were vortexed for 1 min or manually mixed for 30 s. Proportionally add 70% methanolic water internalstandard extract pre-chilled at -20℃ and vortex for 15 min. Centrifuge for 3 min, take the supernatant and filter it by a microporous membrane, then save it in the injection bottle.\u003c/p\u003e \u003cp\u003eUPLC conditions: column: Agilent\u0026ensp;SB-C18 (1.8 \u0026micro;m, 2.1 mm * 100 mm); Mobile phase: A\u0026thinsp;=\u0026thinsp;ultrapure water with 0.1% formic acid (v/v), B\u0026thinsp;=\u0026thinsp;acetonitrile with 0.1% formic acid (v/v). Elution gradient: 0.00 min, 5% B; 0.00\u0026ndash;9.00 min, linear increase to 95% B; 9.00\u0026ndash;10.00 min, hold at 95% B; 10.00-11.10 min, linear decrease to 5% B; 11.10\u0026ndash;14.00 min, hold at 5% B (equilibration). The flow rate was set at 0.35 mL/min, the column temperature was maintained at 40\u0026deg;C, and the injection volume was 2 \u0026micro;L.\u003c/p\u003e \u003cp\u003eMass spectrometry conditions: Electrospray ionization (ESI) source temperature: 500\u0026deg;C; Ion spray voltage (IS): 5500 V (positive ion mode) / -4500 V (negative ion mode); Ion source gas I (GSI), gas II (GSII), and curtain gas (CUR) were set to 50, 60, and 25 psi, respectively; Collision-induced dissociation parameter: high. MRM scans were performed with collision gas set to medium.\u003c/p\u003e\n\u003ch3\u003ePCA analysis\u003c/h3\u003e\n\u003cp\u003eUnsupervised PCA (principal component analysis) was performed by statistics function prcomp within R (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The data was unit variance scaled before unsupervised PCA.\u003c/p\u003e\n\u003ch3\u003eIdentification of differential metabolites\u003c/h3\u003e\n\u003cp\u003eZ-score normalization was applied for data standardization during the analytical process. This method utilized the mean (\u0026micro;) and standard deviation (σ) of the raw data, with the transformation defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\:x}^{{\\prime\\:}}=\\frac{x-\\mu\\:}{\\sigma\\:}$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ex\u003c/em\u003e is the original value, \u003cem\u003e\u0026micro;\u003c/em\u003e is the arithmetic mean, and σ is the population standard deviation.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eKEGG annotation and enrichment analysis\u003c/h2\u003e \u003cp\u003eIdentified metabolites were annotated using KEGG Compound database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp/kegg/compound/\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp/kegg/compound/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), annotated metabolites were then mapped to KEGG Pathway database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.kegg.jp/kegg/pathway.html\u003c/span\u003e\u003cspan address=\"http://www.kegg.jp/kegg/pathway.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProteomics profiling\u003c/h3\u003e\n\u003cp\u003eSample handling: Begin by retrieving the samples and gently thawing them on ice. Then, add PMSF to the samples to achieve a final concentration of 1mM thoroughly mix. Centrifuge at 4\u0026deg;C, 4500 g for 10 minutes, and determine the protein concentration using a BCA assay kit. Finally, protein samples were proteolytic desalted according to standard protocols.\u003c/p\u003e \u003cp\u003eLiquid Chromatography Detection: Mobile phases: Phase A consisted of 0.1% formic acid in water (v/v), phase B consisted of 0.1% formic acid in acetonitrile (v/v; 100% acetonitrile). Separations were performed on an Easy-Spray\u0026trade; PepMap\u0026trade; Neo UHPLC column (150 \u0026micro;m \u0026times; 15 cm, 2 \u0026micro;m) maintained at 55\u0026deg;C. Samples (200 ng) were loaded at a flow rate of 250 nL/min with an effective gradient duration of 6.9 min and total run time of 8 min.\u003c/p\u003e \u003cp\u003eMass spectrometry was performed on an Orbitrap Astral mass spectrometer operating in positive ion mode. Full MS scans: m/z range 380\u0026ndash;980, resolution 240,000 (at m/z 200), normalized AGC target 500%, maximum injection time 5 ms. MS/MS: Isolation window 2 Th, HCD collision energy 25%, normalized AGC target 500%, maximum injection time 3 ms.\u003c/p\u003e \u003cp\u003eIdentification of differentially expressed proteins (DEPs): The criteria for identifying DEPs were defined as follows: (1) For replicated experiments (biological replicates\u0026thinsp;\u0026ge;\u0026thinsp;2), in pairwise comparisons, proteins were classified as DEPs when meeting both a fold change (FC)\u0026thinsp;\u0026ge;\u0026thinsp;1.5 or \u0026le;\u0026thinsp;0.6667 and a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05, while in multi-group comparisons, proteins with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered significant, regardless of the FC. (2) For non-replicated experiments (biological replicate\u0026thinsp;=\u0026thinsp;1), in pairwise comparisons, DEPs were identified based solely on the FC threshold of \u0026ge;\u0026thinsp;1.5 or \u0026le;\u0026thinsp;0.6667.\u003c/p\u003e \u003cp\u003eBioinformatics analysis of DEPs: Functional annotation of proteins was performed using the KOG, GO, KEGG, and InterPro databases. Subcellular localization prediction was conducted using WoLF PSORT (version 1.0) with default parameters for eukaryotes.\u003c/p\u003e\n\u003ch3\u003eCombined analysis of the metabolome and proteome\u003c/h3\u003e\n\u003cp\u003eKEGG analysis: Based on KEGG annotation and enrichment analysis results of differential metabolites and differentially expressed proteins, KEGG pathways annotated by both omics datasets were identified.\u003c/p\u003e \u003cp\u003eExpression correlation analysis: In each differential comparison group, Pearson correlation analysis was performed to identify protein\u0026ndash;metabolite pairs with an absolute correlation coefficient greater than 0.8 and a \u003cem\u003eP\u003c/em\u003e-value less than 0.05. The fold changes of the correlated proteins and metabolites were visualized using a nine-quadrant diagram. This diagram was segmented into quadrants 1 through 9, arranged from left to right and top to bottom, using black dotted lines.\u003c/p\u003e \u003cp\u003eOrthogonal Projections to Latent Structures analysis(O2PLS) was performed using the OmicsPLS R package (v2.0.2). All differential metabolites (the X dataset) and differentially expressed proteins (the Y dataset) were included to establish an O2PLS model. The model was cross-validated using the crossval_o2m and crossval_o2m_adjR2 parameters to obtain the optimal model configuration. A loading plot was generated to identify key variables with significant influence across omics datasets.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetabolite composition of CBS honey\u003c/h2\u003e \u003cp\u003eA total of 890 metabolites were identified in CBS honey using the UPLC-MS/MS detection platform and public metabolomic databases. The distribution of metabolite categories, from the highest to lowest proportion, was as follows: amino acids and their derivatives (35.96%), lipids (22.58%), organic acids (18.54%), others (11.80%) and nucleotides and their derivatives (11.12%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The stability of the analytical system was validated using quality control (QC) samples. Over 75% of the detected metabolites exhibited coefficients of variation (CVs) below 0.3, confirming both methodological robustness and data reliability (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). PCA results showed the separation of all honey samples into five distinct groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), indicating significant metabolic differences between CBS honey and \u003cem\u003eApis cerana\u003c/em\u003e honeys from other regions (XZ, SN, AB, and QZ honeys).\u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferential metabolites between CBS honey and\u003c/b\u003e \u003cb\u003eApis cerana\u003c/b\u003e \u003cb\u003ehoneys from other regions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA Venn diagram was constructed to visualize the relationships among differential metabolites across comparison groups. The results showed the following: 51 unique differential metabolites between CBS and XZ honeys, 71 unique differential metabolites between CBS and QZ honeys, 28 unique differential metabolites between CBS and SN honeys, and 33 unique differential metabolites between CBS and AB honeys. A total of 112 differential metabolites were shared across all four comparison groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the established screening criteria, 393 differential metabolites were identified between CBS and XZ honeys. Among them, CBS honey exhibited significantly higher relative abundances of the following metabolites: 3-nitro-L-tyrosine, barbiturate, 1,7-dimethylxanthine, 4-guanidinobutanoate, (4-acetylphenyl)-L-alanine, (2-carboxyethyl)-L-phenylalanine, Gly-Gly-Phe, hydroxythreonine xyloside, maltitol, N-acetyl-L-phenylalanine, 2-{4-amino-1H-imidazo[4,5-d]pyridazin-1-yl}-5-(hydroxymethyl)oxolane-3,4-diol, adenosine, 3-methylxanthine, methyl 2-furoate, 9-arabinosyladenine, 2-decenedioic acid, N-lactoylphenylalanine, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, pyridoxine, AMP, mevalonic acid glucoside, (6Z,9Z,12Z)-octadecatrienoic acid, cyclo(Pro-Leu), cyclo(D-Leu-L-Pro), 2-oxopentanoic acid, D-ribulose 5-phosphate, perseitol-1-O-xyloside, D-sorbitol, allitol, gamma-resorcylate, aminomalonate, 9-alpha-ribofuranosyladenine*(* indicates an isomer, the same applies hereafter), and hydroxy-o-tolyl-acetic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Venn diagram analysis further revealed that among the 51 unique differential metabolites between CBS and XZ honeys, cyclo(Pro-Leu)* and D-ribulose 5-phosphate exhibited significantly higher relative abundances in CBS honey than in XZ honey (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThere were 373 differential metabolites between CBS and QZ honeys. Compared to QZ honey, CBS honey exhibited significantly higher relative abundances of the following metabolites: 9,12-octadecadien-6-ynoic acid, Lys-Asn, sphinganine, 9-oxoODE*, lysoPC 17:2 (2n isomer)*, perseitol-1-O-xyloside, 4-guanidinobutanoate, rabdosia acid A*, barbiturate, N-benzoyl-(2R,3S)-3-phenylisoserine, dehydroascorbate, gorlic acid, cis-4-hydroxy-D-proline*, hydroxyproline*, cis-3-hydroxy-L-proline*, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, 10,16-dihydroxyhexadecanoic acid, Lys-Thr, 12(13)-EpOME, Ser-Val-Leu, maltitol, 9,16-dihydroxypalmitic acid, D-sorbitol, benzocyclobutyl-1-carboxylic acid, AMP, N-lactoylphenylalanine, 3-methylxanthine, mevalonic acid glucoside, lysoPC 18:3 (2n isomer), nicotinate, lysoPC 16:2 (2n isomer)*, 3-bromo-tyrosine, phosphatidylcholine lyso 18:3, pyridoxine phosphate, Lys-Ala, lysoPE 18:2, lysoPC 15:0 (2n isomer)*, 3,4,5-trihydroxy-1-{[(2E)-3-(4-hydroxyphenyl)prop-2-enoyl]oxy}cyclohexane-1-carboxylic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Venn diagram analysis revealed that among the 71 unique differential metabolites between CBS and QZ honeys, cis-4-hydroxy-D-proline*, hydroxyproline*, cis-3-hydroxy-L-proline*, lysoPC 17:2 (2n isomer)*, Lys-Ala, pyridoxine phosphate, Lys-Asn, and 12(13)-EpOME exhibited significantly higher relative abundances in CBS honey than in QZ honey (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA total of 324 differential metabolites were identified between CBS and SN honeys. Compared to SN honey, CBS honey exhibited significantly higher relative abundances of the following metabolites: 3-nitro-L-tyrosine, barbiturate, hydroxythreonine xyloside, adenosine*, dehydroascorbate, 9-alpha-ribofuranosyladenine*, 9-arabinosyladenine*, 3-methylsuberic acid, kestose, {4-amino-1H-imidazo[4,5-d]pyridazin-1-yl}-5-(hydroxymethyl)oxolane-3,4-diol, allitol, 4-guanidinobutanoate, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, perseitol-1-O-xyloside, 2-oxopentanoic acid, (6Z,9Z,12Z)-octadecatrienoic acid, methyl 2-furoate, and D-sorbitol (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Venn diagram analysis revealed that among the 28 unique differential metabolites between CBS and SN honeys, 3-methylsuberic acid exhibited a significantly higher relative abundance in CBS honey than in SNZ honey (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere were 383 differential metabolites between CBS and AB honeys. Compared to AB honey, CBS honey exhibited significantly higher relative abundances of the following metabolites: benzocyclobutyl-1-carboxylic acid, lysoPC 15:0*, sphinganine, 9-oxoODE*, sepiapterin, glycerol 9,11,13-octadecatrienoyl ester*, rabdosia acid A*, hibiscus acid, (2-carboxyethyl)-L-phenylalanine, 2-hydroxyisobutyric acid*, 2-hydroxybutanoic acid*, lysoPC 18:2, Gly-Gly-Phe, (4-acetylphenyl)-L-alanine, Lys-Thr, Ser-Val-Leu, hexosylLPE 16:0, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, 1-α-linolenoyl-glycerol*, cis-4-hydroxy-D-proline*, succinate*, lysoPC 15:0 (2n isomer)*, γ-resorcylate, N-lactoylphenylalanine, 2-oxopentanoic acid, and mevalonic acid glucoside (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Venn diagram analysis revealed that among 33 unique differential metabolites between CBS and AB honeys, none showed a higher relative abundance in CBS honey than in AB honey.\u003c/p\u003e\u003cp\u003eNotably, across all four comparison groups, the relative abundance of (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid was significantly higher in CBS honey than in the corresponding control honey. Based on these findings, the following metabolites were suggested as the potential characteristic metabolites of CBS honey: cyclo(Pro-Leu)*, D-ribulose 5-phosphate, cis-4-hydroxy-D-proline*, hydroxyproline*, cis-3-hydroxy-L-proline*, lysoPC 17:2 (2n isomer)*, Lys-Ala, pyridoxine phosphate, Lys-Asn, 12(13)-EpOME, 3-methylsuberic acid, and (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQualitative and quantitative analyses of proteins in CBS honey\u003c/h2\u003e \u003cp\u003eProteomic analysis was conducted using a reference database containing 9932 protein sequences. A total of 1425 peptides and 202 proteins were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Functional annotation of these identified proteins revealed the following distributions across various databases: GO (165 proteins), KOG (161 proteins), KEGG (189 proteins), Subcellular localization (202 proteins), SignalP (63 proteins), and InterPro (194 proteins) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQualitative quality assessment of proteomic data\u003c/h2\u003e \u003cp\u003eAnalysis of peptide length distribution showed that most peptides consisted of 7\u0026ndash;20 amino acids, aligning with expectations based on enzymatic digestion and mass spectrometry fragmentation, thereby meeting quality control requirements (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eA). The distribution of peptide counts per protein group suggested that the protein groups contained a relatively large number of peptides, supporting the reliability of the identified proteins (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eB). Moreover, the distribution of missed cleavage sites among peptides reflected the thoroughness of enzymatic digestion, with 84.28% of peptides exhibiting 0 missed cleavage sites, indicating efficient digestion that was beneficial for accurate protein identification (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative quality assessment of proteomic data\u003c/h2\u003e \u003cp\u003ePCA of the protein samples revealed significant differences between CBS honey and \u003cem\u003eApis cerana\u003c/em\u003e honeys from other regions (XZ, SN, AB, and QZ honeys) (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA). Correlation analysis demonstrated that all inter-group correlation coefficients exceeded 0.9, indicating the high reproducibility of the experimental data (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB). Collectively, these qualitative and quantitative quality assessments confirmed the reliability of the generated proteomic data.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDifferentially expressed proteins between CBS honey and\u003c/b\u003e \u003cb\u003eApis cerana\u003c/b\u003e \u003cb\u003ehoneys from other regions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBased on the established screening criteria, 99 differentially expressed proteins (DEPs), including 17 upregulated and 82 downregulated proteins, were obtained between CBS and XZ honeys; 69 DEPs, including 19 upregulated and 50 downregulated proteins, were found between CBS and AB honeys; 58 DEPs, including 17 upregulated and 41 downregulated proteins, were identified between CBS and QZ honeys; and 78 DEPs, including 15 upregulated and 63 downregulated proteins, were detected between CBS and AB honeys (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVenn diagram analysis identified 15 shared DEPs across all four comparison groups and 23 unique DEPs between CBS and XZ honeys. Among them, the following DEPs showed significantly higher relative abundances in CBS honey than in XZ honey: APICC_01685 (mannose-6-phosphate isomerase), APICC_04696 (solute carrier family 2, facilitated glucose transporter member), APICC_00562 (alpha-galactosidase), APICC_08137 (S-adenosylmethionine synthase), APICC_07862 (glutamate receptor U1), APICC_09184 (glucosylceramidase), and APICC_08085 (chymotrypsin inhibitor). There were 8 unique DEPs between CBS and QZ honeys, among which APICC_04206 (peroxidasin) exhibited a significantly higher relative abundance in CBS honey than in QZ honey. There were 10 unique DEPs between CBS and SN honeys, among which APICC_05792 (MD-2-related lipid recognition domain-containing protein) and APICC_01096 (serine protein bubble) demonstrated significantly higher relative abundances in CBS honey than in SN honey. There were 12 unique DEPs between CBS and AB honeys, among which APICC_03450 (hymenoptaecin) and APICC_06442 (vacuolar proton pump subunit B) showed significantly higher relative abundances in CBS honey than in AB honey (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eB and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the identified DEPs, APICC_10171 (hyaluronidase) consistently exhibited a significantly higher relative abundance in CBS honey than in all control honeys across all four comparison groups. Based on these findings, we proposed that the following proteins represented potential signature proteins of CBS honey: APICC_01685, APICC_04696, APICC_00562, APICC_08137, APICC_07862, APICC_09184, APICC_08085, APICC_04206, APICC_05792, APICC_01096, APICC_03450, APICC_06442, and APICC_10171 (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation and enrichment analysis of DEPs\u003c/h2\u003e \u003cp\u003eGO annotation and enrichment analysis of DEPs in each comparison group showed that, in the Biological process category, DEPs were mainly annotated to translation, carbohydrate metabolic process, and the glycolytic process. In the Cellular component category, annotations were mainly associated with the ribosome, lysosome, extracellular region, and nucleus. For the Molecular function category, DEPs were mainly annotated to metal ion binding and GTP binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Further KOG functional classification analysis revealed that the top three functional categories of DEPs were General function prediction only; Posttranslational modification, protein turnover, chaperones; and Carbohydrate transport and metabolism (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnnotation and enrichment analysis of the structural domain functions of DEPs across all four comparison groups showed that DEPs were annotated to Hemocyanin, C-terminal domain superfamily; Hemocyanin, N-terminal domain superfamily; Hemocyanin/hexamerin; Di-copper center-containing domain superfamily; Hemocyanin, N-terminal; Hemocyanin, C-terminal; Hemocyanin/hexamerin middle domain; and Immunoglobulin E-set (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Subcellular localization analysis of DEPs showed that the cytoplasm and extracellular space were the subcellular structures with the highest number of annotated DEPs across all comparison groups (Fig. \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCombined analysis of metabolomics and proteomics\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003eKEGG pathway enrichment analysis\u003c/h2\u003e \u003cp\u003eBased on the identified differential metabolites and the KEGG annotation and enrichment analysis of DEPs, the KEGG pathways annotated by both the metabolomic and proteomic datasets were determined. The results showed that DEPs in each comparison group were consistently associated with the β-alanine metabolism pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eExpression correlation analysis\u003c/h2\u003e \u003cp\u003eTo explore functional relationships between proteins and metabolites, we conducted an expression correlation analysis focusing on the previously identified potential signature proteins. The results revealed discordant regulatory trends between several protein\u0026ndash;metabolite pairs: A0A2A3EDE3 (APICC_03450) and Sasp001236 (7-hydroxy-tryptophan), A0A2A3EDR1 (APICC_06442) and pme0124 (L-glycyl-L-proline), A0A2A3EPZ0 (APICC_04206) and Lmbn001288 ((S)-2-acetolactate), A0A2A3E4S5 (APICC_01096) and Lcfn121066 (Glu-Glu-Phe), A0A2A3EHG0 (APICC_10171) and MWS201471 (Lys-Tyr), A0A2A3EIU8 (APICC_07862) and PD0280915 (Leu-Ser-Ile*), A0A2A3EMA9 (APICC_09184) and PD0280915 (Leu-Ser-Ile*), A0A2A3EPJ1 (APICC_08085) and pme0008 (L-citrulline), and A0A2A3E771 (APICC_04696) and Lmqn000432 (sn-glycero-3-phospho-1-inositol). In contrast, positive correlations were observed between A0A2A3EHG0 (APICC_10171) and PDP228396 ((5E,10R)-10-methyl-7,8,9,10-tetrahydro-3H-oxecine-2,4-dione), as well as between A0A2A3E771 (APICC_04696) and pma6455 (D-ribulose 5-phosphate) (Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eO2PLS analysis\u003c/h2\u003e \u003cp\u003eO2PLS analysis was performed to identify the top 10 DEPs with the strongest influence on metabolic profiles: A0A2A3EHG0 (hyaluronidase), A0A2A3E5C0 (acetylcholinesterase), A0A2A3EKU2 (glucosylceramidase), A0A2A3E8C9 (alpha-glucosidase), A0A2A3ED74 (chitinase protein Idgf4), A0A2A3ERC7 (glutathione peroxidase), A0A2A3ESV4 (arylsulfatase J), A0A2A3E889 (hexamerin), A0A2A3EMG3 (transcription elongation factor spt6), and A0A2A3EM77(histone H2B) (Fig. \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cem\u003eApis cerana\u003c/em\u003e exhibits heightened olfactory sensitivity, which enables the efficient detection and collection of dispersed, sporadic floral resources. Consequently, honey produced by \u003cem\u003eApis cerana\u003c/em\u003e is classified as polyfloral honey[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. During the process of nectar collection and its subsequent storage in the hive until it maturesmaturation, bees enzymatically convert sugars in the nectar through secretions from their salivary glands [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Among the amino acids present in honey, proline is the most abundant. The proline content in honey not only serves as a benchmark for assessing free amino acid levels but also functions as a critical physicochemical parameter for evaluating honey maturity and detecting adulteration [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Cis-4-hydroxy-D-proline*, hydroxyproline*, cis-3-hydroxy-L-proline*, and cyclo(Pro-Leu)* are derivatives formed by the hydroxylation or cyclization of the proline core structure. The first three derivatives possess antioxidant properties, capable of scavenging superoxide anions and hydroxyl radicals, and can inhibit the generation of inhibiting hydroxyl radical formation through the Fenton reaction, and regulating the cellular redox state, thereby protecting cells from oxidative damage and delaying the aging process [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These hydroxylated proline derivatives may result from microbial metabolism in nectar-producing plants and enter honey through bee foraging, thus indirectly participating in long-term honey preservation by inhibiting microbial spoilage. Cyclo(Pro-Leu), a cyclic dipeptide composed of proline and leucine, has been reported to inhibit bacterial biofilm formation, participate in intercellular signaling, and exhibit antitumor activity [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This compound may synergize with other bioactive compounds in honey to suppress the proliferation of spoilage bacteria and pathogenic microorganisms. Additionally, it likely helps delay the oxidative degradation of bioactive compounds in honey, thereby preserving honey freshness and extending its shelf life. Lys-Ala and Lys-Asn are lysine-containing dipeptides, and honey, being rich in free amino acids, provides precursors for the synthesis of these dipeptides [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Enzymatic hydrolysis or microbial metabolism in honey may catalyze the condensation of amino acids into small peptides [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which may act as amino acid metabolites and influence the functional properties of honey. LysoPC 17:2 (2n isomer)*, an intermediate product of membrane phospholipid metabolism, participates in cellular signal transduction, enhances membrane fluidity, and acts as a fatty acid transporter [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Free fatty acids such as 3-methylsuberic acid, (9Z,11Z)-13-hydroxyhexadeca-9,11-dienoic acid, and 12(13)-EpOME are organic acids present in honey. Collectively, they account for approximately 0.5% of total honey composition, contributing to pH regulation and a characteristic flavor [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Compared to immature honey, \u003cem\u003eApis cerana\u003c/em\u003e honey is a mature honey that is rich in fatty acids, especially unsaturated fatty acids, which enhance its nutritional value [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. CBS honey is amber-colored, and studies have shown that darker honeys are richer in functional minerals and vitamins than their lighter counterparts [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The vitamins found in honey are primarily derived from the pollen it contains. Among them, pyridoxine phosphate exhibits several physiological functions, including anti-inflammatory, antioxidant, neuroregulatory, and antitumor activities [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The pentose phosphate pathway, which contributes to carbohydrate metabolism in honey, not only supplies ribose-5-phosphate (R5P) but also generates NADPH, which plays pivotal roles in scavenging reactive oxygen species (ROS) and supporting reductive biosynthetic processes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In this pathway, D-ribulose 5-phosphate serves as a key intermediate that may influence NADPH production, thereby indirectly contributing to antioxidant defense and the biosynthesis of flavor compounds.\u003c/p\u003e \u003cp\u003eProtein is an important endogenous component of honey, accounting for approximately 0.2\u0026ndash;1.0% of its total mass. Variations in protein composition can be used to trace the plant and bee species origins of honey and to detect potential adulteration [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Mannose-6-phosphate isomerase catalyzes the interconversion of mannose-6-phosphate and fructose-6-phosphate, thereby linking glycolysis with mannose metabolism [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The solute carrier family 2, facilitated glucose transporter member, mediates the transmembrane transport of glucose [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. S-adenosylmethionine synthase is an essential metabolic enzyme. In addition to serving as a methyl donor, it participates in aminopropyl transfer and transsulfuration reactions, plays a vital role in maintaining normal metabolic activities, and holds significant pharmaceutical importance [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Hymenoptaecin is a natural antimicrobial peptide in bees and an important component of their innate humoral immune system. It plays a crucial role in inhibiting and eliminating harmful agents, including microorganisms and parasites [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. MD-2-related lipid-recognition family proteins are characterized by the presence of ML domains, which mediate antibacterial immunity and lipid metabolism by recognizing specific lipids from endogenous and exogenous sources [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It is hypothesized that these proteins can recognize pathogen-associated lipid molecules and help bees defend against bacterial infections. Serine proteases represent the most ancient and diverse family of proteolytic enzymes. They are ubiquitously expressed across various tissues, organs, and body fluids, playing essential roles in organismal development, tissue repair, and related biological processes [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The vacuolar proton pump primarily facilitates the transport of intracellular substances into organelles, regulates ion exchange across the vacuolar membrane, maintains osmotic balance between the cytoplasm and vacuole, and modulates vacuolar morphology [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Glutamate receptors serve multifunctional roles in plants, not only regulating growth and development but also mediating responses to various biotic and abiotic stresses. Studies have reported that glutamate receptor genes can mediate neurotransmitter transmission in bees and influence long-term learning and memory functions [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Peroxidasin, a member of the peroxidase family, is functionally linked to insect hemocytes and plasma cells. It is involved in several critical biological processes, including extracellular matrix biosynthesis, basement membrane formation, tissue morphogenesis, and innate immune defense [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Both hyaluronidase and alpha-galactosidase are glycoproteins with distinct roles. The former is a major allergen in bee venom, capable of catalyzing the decomposition of hyaluronic acid in nectar. It also serves as a diffusion factor in venom, and its level in the venom sacs of bees is maintained relatively constant from the late pupal stage through adulthood, unaffected by the aging of worker bees. Meanwhile, the latter specifically cleaves α-1,6-linked galactoside residues and is ubiquitously distributed in nature [\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Both enzymes may be involved in the decomposition and transformation of sugars in honey, affecting its flavor and nutritional value. Chymotrypsin inhibitors are small polypeptides that suppress the hydrolytic activity of chymotrypsin. They are widely distributed in insect tissues, including the hemolymph, midgut, and fat body, and play essential roles in regulating developmental processes and innate immune responses [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In bees, three chymotrypsin inhibitors (AMCI-1, AMCI-2, and AMCI-3) have been molecularly characterized [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Glucosylceramidase is a key enzyme in the sphingolipid metabolic pathway, regulating the breakdown and remodeling of sphingolipids. Therefore, it plays a crucial role in insect physiological activities. Disruptions to its activity may lead to imbalances in sphingolipid metabolism, thereby reducing colony resistance to parasites such as \u003cem\u003eVarroa\u003c/em\u003e mites [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHemocyanin is a copper-containing multifunctional protein responsible for oxygen transport and storage in insects. It also exhibits diverse physiological roles, including immune responses, hormone regulation, apoptosis modulation, antimicrobial activity, and environmental adaptation [\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. DEPs were predominantly annotated to hemocyanin-associated domains, suggesting their involvement in critical biological processes such as oxygen metabolism regulation, immune defense, and developmental adaptation. Further investigations incorporating functional experiments are required to elucidate the molecular mechanisms and regulatory networks of these proteins under specific physiological and pathological conditions. The DEPs identified in each comparison group were annotated in the GO and KOG databases to categories associated with energy-related processes, such as carbohydrate metabolism, glycolysis, and carbohydrate transport. \u003cem\u003eApis cerana\u003c/em\u003e from Changbai Mountain is a bee species with superior cold resistance. By mid-to-late October, these bees enter the overwintering period, during which the colony actively engages in essential physiological and metabolic activities, including the substantial accumulation of cryoprotectants and energy reserves, to enhance its resistance to cold stress [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. This period also coincides with the annual honey harvest, during which bee-derived proteins are enriched in honey through energy metabolism pathways [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs the sole naturally occurring β-amino acid ubiquitously distributed across living organisms, β-alanine plays a critical role in pantothenate biosynthesis. This essential metabolic precursor is utilized in the production of coenzyme A (CoA), a fundamental cofactor in cellular biochemical processes [\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Due to multiple factors such as nectar plant species, local climate and environmental conditions, and honey-gathering preferences, the types and activities of digestive enzymes secreted by different bee species may vary. Higher protease activity promotes the decomposition and recombination of amino acids, thereby affecting the synthesis and accumulation of β-alanine. As an essential precursor for carnosine synthesis, β-alanine contributes to antioxidant activity. Additionally, β-alanine supplementation has demonstrated physiological and pharmacological benefits, including enhanced physical performance and reduced fatigue [\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. In this study, differential metabolites and DEPs identified across all four comparison groups were consistently enriched in this metabolic pathway, suggesting a higher abundance of β-alanine-related β-alanine\u0026ndash;related metabolites in CBS honey. Therefore, compared to honeys produced by other bee species, CBS honey may offer greater potential in supporting energy metabolism, physical endurance, and metabolic regulation. These findings provide valuable insights for tracing the origin of honey, developing honey-based functional products, and investigating the physiological adaptation mechanisms of bees to their local environments.\u003c/p\u003e \u003cp\u003eTo evaluate the nutritional quality of CBS honey, we measured various physicochemical and nutritional parameters (encompassing the diastase number and the contents of fructose, glucose, maltose, sucrose, fat, protein, proline, sodium (Na), and vitamin C) in samples collected from different regions within Changbai Mountain, including Tonghua, Jilin, Baishan, and Yanbian. Volatile components were analyzed using gas chromatography\u0026ndash;ion mobility spectrometry (GC-IMS), and the results demonstrated that CBS honey exhibited comparative advantages in nutritional value, with higher proline, protein, and vitamin C levels, compared to \u003cem\u003eApis cerana\u003c/em\u003e honeys from other regions. The following flavor compounds were identified as the characteristic constituents of CBS honey: linalool oxide, furfural, ethyl acetate, acetic acid, 2-methyl-1-propanol, and 3-methyl-1-butanol(unpublished data). Moreover, integrated multi-omics analysis provided a more comprehensive mechanistic perspective by unraveling dynamic interrelationships between proteins and metabolites. Notably, hyaluronidase emerged as a potential characteristic protein with significant metabolomic influence. It exhibited a divergent regulatory trend with Lys-Tyr and demonstrated a positive correlation with (5E,10R)-10-methyl-7,8,9,10-tetrahydro-3H-oxecine-2,4-dione. Future studies should focus on validating the regulatory mechanisms of hyaluronidase on these critical metabolites to elucidate its role in shaping the bioactive profile of honey.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides the first comprehensive characterization of the metabolome and proteome of CBS honey. Using a structured multi-omics approach, we detected 890 metabolites and quantitatively profiled 202 proteins. This integrated multi-omics analysis identified 12 potential characteristic metabolites and 13 characteristic proteins specific to CBS honey. These compounds can serve as biomarkers in future efforts to authenticate honey and distinguish products from different bee species. Future studies should include honey samples from a broader range of geographical regions and incorporate larger sample sizes to comprehensively evaluate the quality characteristics of \u003cem\u003eApis cerana\u003c/em\u003e honey.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the beekeepers who provided samples for this study and Metware Biotechnologies Inc. (Wuhan) for technical assistance. We also thank TopEdit (www.topeditsci.com) for editing this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was financially supported by Jilin Province science and technology development Project(20240601087RC) and the Doctoral Scientific Research Foundation of Jilin Agricultural Science and Technology College (Protocol No. (2022)751). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLN, SG and QS designed and coordinated the research. RQ and LL wrote the main manuscript text, WL analyzed the data. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLN, SG and QS designed and coordinated the research. RQ and LL wrote the main manuscript text, WL analyzed the data. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhao H, Cheng N, He L, Peng G, Xue X, Wu L, Cao W (2017) Antioxidant and hepatoprotective effects of \u003cem\u003eA. cerana\u003c/em\u003e honey against acute alcohol-induced liver damage in mice. Food research international (Ottawa, Ont.) 101: 35\u0026ndash;44 https://doi.org/10.1016/j.foodres.2017.08.014\u003c/li\u003e\n\u003cli\u003eWu J, Xu XT, Xing C, Hao XB, Fang X Y, Xie ZH, Zhao S, Gao JL, Xu L, Wang SJ (2025) Metabolic profiling and evaluation of antioxidant and anti-inflammatory properties of \u003cem\u003eApis cerana cerana\u003c/em\u003e Honey from Sansha City, Hainan Province, China. Food chemistry 475: 143256 https://doi.org/10.1016/j.foodchem.2025.143256\u003c/li\u003e\n\u003cli\u003eThe National Animal Genetic Resources Committee(2011) Animal genetic resources in China bees. 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ACS applied materials \u0026amp; interfaces 12(14):16104-16113 https://doi.org/10.1021/acsami.0c01669\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-food-research-and-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [European Food Research and Technology](https://link.springer.com/journal/217)","snPcode":"217","submissionUrl":"https://submission.springernature.com/new-submission/217/3","title":"European Food Research and Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Apis cerana honey, Changbai Mountain, metabolomics, proteomics, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-6787940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6787940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eApis cerana\u003c/em\u003e honey from Changbai Mountain (CBS honey), produced in northeastern China, is a high-quality specialty honey that has long been recognized as a natural health product with medicinal properties. In this study, UPLC-MS/MS metabolomics and ultra-fast proteomics technologies were employed to analyze the differences in metabolites and proteins among \u003cem\u003eApis cerana\u003c/em\u003e honey samples collected from various Chinese regions, including Jilin, Sichuan, Hainan, Hubei, and Tibet. Through an integrated multi-omics analysis, we identified potential characteristic biomarkers that differentiated CBS honey from \u003cem\u003eApis cerana\u003c/em\u003e honeys produced in other regions. These findings provide a theoretical foundation for the authentication and geographical origin traceability of CBS honey.\u003c/p\u003e","manuscriptTitle":"Multi-omics analysis of the characteristic components of Apis cerana honey from Changbai Mountain","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 09:46:11","doi":"10.21203/rs.3.rs-6787940/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-27T03:53:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-26T06:35:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-19T22:03:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-19T08:06:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144017085000836390597603173549193536501","date":"2025-06-11T07:35:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-10T20:57:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"531756290445478899785193935466013314","date":"2025-06-10T07:40:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334419907552796806124605685089345662858","date":"2025-06-09T14:29:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333620210773181812778836725607323885674","date":"2025-06-09T14:28:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109386770877515743014991069960421466515","date":"2025-06-07T10:57:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111983274213208994868901143252953868159","date":"2025-06-07T07:24:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62370577409741771936900596872522170999","date":"2025-06-06T03:30:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207144412005756280623629602172089310090","date":"2025-06-05T19:13:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30349040336570606430067529437063053723","date":"2025-06-05T08:40:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-05T05:44:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T22:50:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-04T22:49:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Food Research and Technology","date":"2025-05-31T00:15:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-food-research-and-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [European Food Research and Technology](https://link.springer.com/journal/217)","snPcode":"217","submissionUrl":"https://submission.springernature.com/new-submission/217/3","title":"European Food Research and Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"34b95b53-4ca4-4abd-98f2-8583449439da","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-05T15:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-09 09:46:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6787940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6787940","identity":"rs-6787940","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}