TMT-Based Proteomic Analysis of Mature Milk versus Late Lactation Milk in Dairy Goats

TMT-Based Proteomic Analysis of Mature Milk versus Late Lactation Milk in Dairy Goats | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article TMT-Based Proteomic Analysis of Mature Milk versus Late Lactation Milk in Dairy Goats Xinyang Ren, Lu Chen, Yingxin Qu, Shari Akang, Guang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6158030/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The nutritional value of goat milk varies depending on the lactation stage. This study employed Tandem Mass Tag (TMT) proteomics to explore differential protein profiles between mature milk and late-lactation milk in Guanzhong dairy goats. Using differential screening criteria (fold change > 1.2, P-value < 0.05), we identified 48 differentially expressed proteins, with 39 significantly upregulated and 9 significantly downregulated. Proteins in mature milk appear to play more substantial roles in immune functions and lactation-related processes. Conversely, late-lactation milk may demonstrate greater suitability for cheese production and related dairy processing applications. These findings provide valuable insights for breeding improved dairy goat varieties and developing nutrient-rich functional dairy products. Dairy goat goat milk mature milk late lactation milk proteomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Goat milk is renowned for its rich nutrient profile, ease of digestion, and lower allergenic potential compared to other animal milks. Notably, it is considered the closest animal milk to human breast milk in composition(Park, 1994 ). Proteins constitute a primary component of goat milk(Morton et al., 2016 ), with five major protein groups: 70% casein (CN), 25% whey proteins (WP), and 5% milk fat globule membrane (MFGM) proteins(Clark and Garcia, 2017 ). This foundational work spurred subsequent research into goat milk proteomics(Chen et al., 2016 ). The protein composition and functional properties of goat milk vary significantly across lactation stages, conferring distinct nutritional benefits. Colostrum, for instance, plays a critical role in immune system development for newborn lambs, promotes beneficial gut microbiota, and aids in alleviating indigestion(Li et al., 2020b ). Mature milk, meanwhile, exhibits antioxidant and pharmacological properties(Lugonja et al., 2013 ). Despite these findings, limited comparative data exist on the nutritional distinctions between mature and late lactation goat milk. Advances in proteomics technologies, particularly tandem mass spectrometry tagging (TMT), have enabled comprehensive analysis of regulatory mechanisms in animal production traits(Beccaria et al., 2014 ) TMT facilitates cross-species and cross-lactational comparisons of milk proteins. For instance, Zhang et al. employed data-independent acquisition (DIA) proteomics to profile human breast milk proteins across lactation stages(Zhang et al., 2022b ), while Li et al. compared whey protein variations in donkey colostrum and mature milk(Li et al., 2020a ). However, no studies to date have explored proteomic differences between mature and late lactation milk in goats, particularly within the Guanzhong dairy goat breed. This study aims to analyze the proteomic differences between mature milk and late-lactation milk of Guanzhong dairy goats using TMT technology, with the goal of identifying key proteins and associated enriched pathways. The findings will provide guidance for establishing a dynamic milk protein database, developing specialized functional dairy products (such as immune-enriched milk(Verruck et al., 2019 ) and hypoallergenic formulas), discovering precursors of novel bioactive peptides, and expanding the research scope of functional milk components in future studies. 2 Materials and Methods 2.1 Sample Collection The mammary gland samples from mature lactation (120 d after parturition, classified as the B group) and late lactation (180 d after parturition, classified as the C group) of six Guanzhong dairy goats (n = 3) were obtained from the Heshi Dairy Goat Farm (Long County, Shaanxi Province, China). All these dairy goats were raised under the same environmental conditions with natural light and free access to food and water. The collected samples were immediately frozen in liquid nitrogen and then stored at − 80 ℃ until further use. All experimental procedures were approved by Shaanxi province, P.R. China Biological Studies Animal Care and Use Committee. All the experimental procedures used in this study was approved by the Animal Ethical and Welfare Committee of the College of Animal Science and Technology, Northwest A&F University, Yangling, China (protocol number DK2022008). 2.2 Protein Extraction and Digestion 200 µL of frozen sample was mixed with six volumes of acetone in a 1.5 mL tube, incubated at -40°C overnight, and centrifuged (12,000 rpm, 10 min, 4°C). The pellet was air-dried, resuspended in lysis buffer for 3 h, and centrifuged twice (12,000 rpm, 10 min each) to obtain supernatant. Total protein was quantified via BCA assay (Beyotime, P0012), aliquoted, and stored at -80°C. 50 µg of protein was adjusted to uniform concentration with lysis buffer. After sequential treatment with 5 mM DTT (55°C, 30 min) and 10 mM iodoacetamide (dark, 15 min), proteins were precipitated with six acetone volumes (-20°C, 4 h). The pellet was centrifuged (8000×g, 10 min, 4°C), dried briefly, redissolved in 200 mM TEAB, and digested with trypsin (1:50, w/w) at 37°C overnight. Digested samples were lyophilized and stored at -80°C. 2.3 TMT labeling and mass spectrometry Lyophilized samples were mixed with 50 µL of 100 mM TEAB buffer in 1.5 mL EP tubes and vortexed for labeling. TMT reagents, equilibrated to room temperature, were dissolved in 88 µL anhydrous acetonitrile, vortexed for 5 minutes, and centrifuged. Forty-one microliters of TMT reagent were added to the samples, followed by vortexing and incubation at room temperature for 1 hour. The reaction was terminated with 8 µL of 5% hydroxylamine for 15 minutes, after which the samples were lyophilized and stored at -80°C. 2.4 LC-MS/MS analysis Mobile phases A (100% water with 0.1% formic acid) and B (80% acetonitrile with 0.1% formic acid) were prepared. The lyophilized powder was dissolved in 10 µL of mobile phase A, centrifuged at 14,000×g for 20 min at 4°C, and 1 µg of the supernatant was injected into a homemade C18 Nano-Trap column (4.5 cm × 75 µm, 3 µm). A homemade analytical column (25 cm × 150 µm, 1.9 µm) was utilized, with the column oven temperature set at 55°C, and a linear gradient elution was applied. The separated peptides were analyzed using an Orbitrap Exploris 480 mass spectrometer coupled with FAIMS (Thermo Fisher) equipped with a Nanospray Flex ESI ion source (spray voltage: 2.1 kV; ion transport capillary temperature: 320°C). Data-dependent acquisition mode was employed for mass spectrometry analysis. The FAIMS compensation voltages were set at − 45 V and − 65 V. For full MS scans (m/z 350–1,500), a resolution of 60,000 (at m/z 200) was applied, with an automatic gain control (AGC) target value of 3×10⁶ and maximum ion injection time set to Auto. In MS/MS mode, a 1s scan cycle was implemented. Precursor ions were fragmented via higher-energy collisional dissociation (HCD) at 30% normalized collision energy, with MS/MS parameters including: resolution of 15,000 (at m/z 200), AGC target of 7.5×10⁴, maximum injection time of 22 ms, intensity threshold of 5.0×10³, and dynamic exclusion duration of 40 s. 2.5 Data analysis All resulting spectra were searched against the UniProt-taxonomy_9922.fasta database using Proteome Discoverer 2.4 (PD 2.4; Thermo). Only peptide spectrum matches (PSMs) with confidence levels greater than 99% and proteins containing at least one unique peptide were retained and subjected to a false discovery rate (FDR) threshold of ≤ 1.0%. Protein quantification results were statistically analyzed using Student's t-test. Proteins demonstrating significantly different expression levels between experimental and control groups (P 1.2) were defined as differentially expressed proteins (DEPs). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral.proteomexchange.org ) via the iProX partner repository(J et al., 2019 , T et al., 2022 ) with the dataset identifier PXD047737. 2.6 Bioinformatics analysis Principal component analysis (PCA) was executed through the R software platform (version 4.4.0) employing three key packages: ggplot2 (v3.5.1), ggpubr (v0.6.0), and ggthemes (v5.1.0). Functional annotation processing was carried out via the InterProScan tool with integrated analysis across six specialized databases: Pfam, PRINTS, ProDom, SMART, ProSite, and PANTHER(Jones et al., 2014 ). Metabolic pathway investigation was implemented in R using reference pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database ( http://www.genome.jp/kegg/ ). Pathway enrichment significance was determined through Fisher's exact testing protocol, setting the significance threshold at P < 0.05. 3 Results 3.1 Overview of Proteomic Analyses. Principal component analysis (PCA) indicated that while the groups (mature and late lactation milk) were distinct, the differences were not large at the principal component level (Fig. 1 ). Cluster analysis further confirmed that the two groups formed independent clusters, suggesting significant proteomic differences (Fig. 2 ). 3.2 Screening and identification of differentially expressed proteins (DEPs) Using a threshold of 1.2-fold change and P -value < 0.05, 48 differentially expressed proteins (DEPs; 39 up-regulated and 9 down-regulated) were detected between the mature milk (Group B) and late lactation milk (Group C) in proteomic analysis (Fig. 3 and Table S1 ). Compared with late lactation milk, mature milk exhibited up-regulation of C3, C9, THBS1, LAMC1, EFNA1, LGB, and ANXA2 proteins, whereas CSN1S2 and RNASE1 were down-regulated (Table 1 ). Table 1 significantly up-regulated and down-regulated differentially expressed proteins Accession Gene Name P -value FC Regulation A0A452EX53 LOC106503728 0.0287 2.5083 up A0A452FLJ4 LOC102168428 0.0336 2.2199 up A0A8C2RY13 MYBPH 0.0362 2.1659 up A0A452FLV4 PLXNA3 0.0365 1.8620 up A0A8C2QT22 THBS1 0.0059 1.8010 up A0A452DX18 C3 0.0249 1.7286 up F8R0Y9 MSTN 0.0201 1.6516 up A0A452E7W7 LOC108636060 0.0174 1.5141 up A0A452EK27 STAG1 0.0194 1.5088 up A0A8C2NBA2 MGP 0.0322 1.5075 up A0A452FFG9 DSC2 0.0131 1.5030 up A0A452DRA2 EFNA1 0.0383 1.4567 up A0A8C2QXC9 PAEP 0.0168 1.4348 up P02756 LGB 0.0270 1.3615 up A0A452ENB6 C9 0.0274 1.3196 up A0A452E5U5 PRG4 0.0425 1.3194 up A0A452ERB9 GAS6 0.0489 1.3090 up A0A452EL69 LAMC1 0.0307 1.2813 up A0A452F333 CDH1 0.0144 1.2774 up A0A452DSB1 ANXA2 0.0103 1.2530 up A0A452DTS9 AGRN 0.0390 1.2507 up A0A452G457 PCMT1 0.0405 1.2138 up A0A8C2QYX7 FST 0.0014 0.8167 down A0A452FNS1 PPIA 0.0443 0.7890 down A0A452DYZ1 LOC108634682 0.0163 0.7834 down A0A452G5W1 GRN 0.0033 0.6924 down A0A452DRK5 HPX 0.0185 0.5460 down A0A452E500 STC1 0.0028 0.3357 down P67926 RNASE1 0.0107 0.2690 down A0A6H0DWX2 CSN1S2 0.0444 0.2620 down 3.3 Gene Ontology (GO) Analysis of DEPs GO analysis of all the DEPs in the mature milk (Group B) versus the late lactation milk (Group C) is shown in Fig. 4 and Table S2. In the cellular component category, most of the DEPs were mainly assigned to the extracellular space, extracellular region, plasma membrane, integral component of membrane, high-density lipoprotein particle and cytoplasm. In the biological process category, a large number of DEPs were involved in the acute-phase response, complement activation, alternative pathway and complement activation, classical pathway. For the molecular function category, the GO terms including calcium ion binding and identical protein binding were the predominant functions of the DEPs. 3.4 KEGG Pathway Analysis of DEPs Figure 5 and Table S3 shows the KEGG pathway enrichment analysis of DEPs. In the analysis, a total of 24 up-regulated DEPs were significantly enriched in the pathways of complement and coagulation cascades, PI3K-Akt signaling pathway, ECM-receptor interaction, Phagosome and Rap1 signaling pathway. 4 Discussion Protein is the main component of goat milk and plays an important role in it(Y et al., 2019). The protein components of dairy products play different roles at different stages of production(SM et al., 2024). Many studies have explored the differences in protein composition between colostrum and mature milk from a proteomic perspective(Y et al., 2020, Zhang et al., 2024), but there is almost no research on the protein differences between mature milk and late lactation milk, especially goat milk. In this experiment, a comparative proteomic analysis of mature and late lactation milk was conducted using TMT proteomic marker technology. Principal component analysis (PCA) revealed that while there is overlapped between mature milk and late lactation milk, there were still differences. Cluster analysis showed that the two groups formed distinct clusters, with within-group expression levels being similar and significant differences between groups. Overall, the results indicate reliable differences between mature and late lactation milk, supporting the construction of a model for further analysis. Using differential screening criteria (fold change > 1.2 and P -value < 0.05), 48 differential proteins were identified, with 39 proteins significantly up-regulated and 9 proteins significantly down-regulated. In mature milk, C3 and C9 are upregulated and enriched in the extracellular space and the Complement and coagulation cascades pathways. C3 upregulation has been observed in donkey milk(Zhang et al., 2022a) , where this complement protein contributes to establishing natural immunity in newborns(S and S, 2001) . Meanwhile, yak and bovine milk exosomes can alleviate LPS-induced intestinal inflammation development and improve IEC-6 cell survival by inhibiting the PI3K-AKT/C3 pathway(Gao et al., 2021). The complement and coagulation cascades pathway has also been identified in both colostrum and mature milk of dairy goats(Sun et al., 2020) . These findings suggest that mature milk may be more conducive to generating immune responses. Moreover, our study supplements the understanding of pathways associated with mature milk and late-lactation milk. In mature milk, the upregulation of THBS1, LAMC1, and EFNA1 is enriched in the ECM-receptor interaction, PI3K-Akt signaling pathway, and Rap1 signaling pathway. Compared to bovine milk, human milk proteins show greater enrichment in the PI3K-Akt signaling pathway and Rap1 signaling pathway( Tong et al., 2023) . This demonstrates the association between goat milk and human milk. Studies have shown that ceRNAs are upregulated during early lactation and function in the PI3K-AKT pathway or ECM-receptor interactions(Yu et al., 2017, Zhang et al., 2024). Both the PI3K-AKT pathway and ECM-receptor interactions promote lactation(Fu et al., 2021), which aligns with the functional requirements of this lactation stage – preparing mammary glands for full lactation. These findings are consistent with our results, indicating that the enrichment of pathway-related proteins during the mature stage enhances milk production in dairy goats during this phase. In mature milk, LGB is upregulated while CSN1S2 and RNASE1 are downregulated. These proteins are enriched in the extracellular region pathway. CSN1S2 primarily influences protein content and cheese texture, with its gene playing a significant role in goat milk yield(ML et al., 2023) . This suggests that milk from late lactation stages is more suitable for cheese production and related dairy processing. RNASE1 is associated with mammary gland remodeling in cattle and participates in gastrointestinal tract remodeling(Boutinaud et al., 2013, L et al., 2015) . LGB demonstrates notable antioxidant activity in milk(Kazimierska and Kalinowska-Lis, 2021, KT et al., 2025) . In buffalo milk, most proteins originate from the extracellular region pathway, revealing potential health benefits of buffalo extracellular vesicles as therapeutic agents and drug delivery vehicles(Joshi et al., 2024) . These findings may guide the development of functional dairy products using goat milk. The protein content of mature milk and late lactation goat milk significantly influences their functional properties and utilization value. The primary limitation of this study was the small sample size. In our experimental design, three biological replicates were employed for proteomic analysis. Increasing the number of biological replicates might reveal more pronounced inter-individual variations among animals and identify a greater number of proteins. In conclusion, proteins in mature milk appear to play a more prominent role in immune functions and lactation processes. However, late lactation milk may be more suitable for cheese production and related dairy processing applications. These dynamic changes in milk proteins could guide future investigations into protein regulation mechanisms. These findings provide valuable insights for breeding new varieties of dairy goats and developing functional dairy products with enhanced nutritional profiles. Abbreviations C3 Complement C3 C9 Complement component C9 THBS1 Thrombospondin 1 LAMC1 Laminin subunit gamma 1 EFNA1 Ephrin A1 LGB Beta-lactoglobulin ANXA2 Annexin CSN1S2 Alpha-S2-casein RNASE1 Ribonuclease pancreatic Declarations Funding This research was supported by the Major Science and Technology Project of Shaanxi Agricultural Collaborative Innovation and Extension Alliance (LMZD202002), Shaanxi Provincial Science and Technology Co-ordination Innovation Project (2018ZDCXL-NY-01-04), and Shaanxi Agricultural Science and Technology Innovation and Extension Project (NYKJ-2019-YL16) Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Ren Xinyang proposed the study design. Qu Yingxin, Shari Akang, and Chen Lu conducted the experiments and wrote the manuscript. Li Guang revised the manuscript. All authors read and approved the final manuscript. 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Shi. 2024. Proteomic and Transcriptomic Profiling Revealed Vital Molecular Events in the Transition from Goat Colostrum to Mature Milk. Journal of Agricultural and Food Chemistry. Zhang, Y. F., X. X. Zhang, L. J. Mi, C. G. Li, Y. R. Zhang, R. Bi, J. Z. Pang, and Y. X. Li. 2022b. Comparative Proteomic Analysis of Proteins in Breast Milk during Different Lactation Periods. Nutrients 14(17):15. Supplementary Files SupplementaryTable.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 31 Jul, 2025 Reviewers invited by journal 02 Apr, 2025 Editor assigned by journal 06 Mar, 2025 First submitted to journal 04 Mar, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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02:24:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6158030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6158030/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81204430,"identity":"2584d791-3af2-48fb-889b-60d47c085066","added_by":"auto","created_at":"2025-04-23 11:57:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32458,"visible":true,"origin":"","legend":"\u003cp\u003ePCA scores of the mature milk (Group B) and late lactation milk (Group C)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6158030/v1/9f53b462e65c8e7e4a4ed07b.png"},{"id":81204425,"identity":"a34d5e7c-256a-4ee7-bd57-6dab1c3eac1d","added_by":"auto","created_at":"2025-04-23 11:57:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105261,"visible":true,"origin":"","legend":"\u003cp\u003eCluster level analysis of the mature milk (Group B) and late lactation milk (Group C)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6158030/v1/218511f05b96e0f0383496b4.png"},{"id":81204426,"identity":"42b81768-bcc2-4390-9163-797462663a5e","added_by":"auto","created_at":"2025-04-23 11:57:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":47478,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano map of the mature milk (Group B) and late lactation milk (Group C)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6158030/v1/ef64276b22cd0edce5f0df03.png"},{"id":81204429,"identity":"3a610a24-86d1-48ab-8063-c6d23157d8fb","added_by":"auto","created_at":"2025-04-23 11:57:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":82162,"visible":true,"origin":"","legend":"\u003cp\u003eGO Analysis of DEPs in the mature milk (Group B) versus the late lactation milk (Group C)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6158030/v1/3711d737e0df5455903648df.png"},{"id":81204820,"identity":"6c871712-3a55-4f10-9831-0444501069c8","added_by":"auto","created_at":"2025-04-23 12:05:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":100465,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathways of DEPs in the mature milk (Group B) versus the late lactation milk (Group C)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6158030/v1/3a79f5604a320f96ae6b2c6c.png"},{"id":81207283,"identity":"d243fbb8-c333-4de8-8185-8d5a451fb032","added_by":"auto","created_at":"2025-04-23 12:29:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":987651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6158030/v1/09ccee13-7fd2-4f7a-9083-54d8b068695e.pdf"},{"id":81206381,"identity":"7b480808-de0a-403c-b07c-bce0c734050f","added_by":"auto","created_at":"2025-04-23 12:21:11","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":46556,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6158030/v1/f96adbe13723303aa9ca480e.xlsx"}],"financialInterests":"","formattedTitle":"TMT-Based Proteomic Analysis of Mature Milk versus Late Lactation Milk in Dairy Goats","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGoat milk is renowned for its rich nutrient profile, ease of digestion, and lower allergenic potential compared to other animal milks. Notably, it is considered the closest animal milk to human breast milk in composition(Park, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Proteins constitute a primary component of goat milk(Morton et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with five major protein groups: 70% casein (CN), 25% whey proteins (WP), and 5% milk fat globule membrane (MFGM) proteins(Clark and Garcia, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This foundational work spurred subsequent research into goat milk proteomics(Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe protein composition and functional properties of goat milk vary significantly across lactation stages, conferring distinct nutritional benefits. Colostrum, for instance, plays a critical role in immune system development for newborn lambs, promotes beneficial gut microbiota, and aids in alleviating indigestion(Li et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Mature milk, meanwhile, exhibits antioxidant and pharmacological properties(Lugonja et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Despite these findings, limited comparative data exist on the nutritional distinctions between mature and late lactation goat milk.\u003c/p\u003e \u003cp\u003eAdvances in proteomics technologies, particularly tandem mass spectrometry tagging (TMT), have enabled comprehensive analysis of regulatory mechanisms in animal production traits(Beccaria et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) TMT facilitates cross-species and cross-lactational comparisons of milk proteins. For instance, Zhang et al. employed data-independent acquisition (DIA) proteomics to profile human breast milk proteins across lactation stages(Zhang et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e), while Li et al. compared whey protein variations in donkey colostrum and mature milk(Li et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). However, no studies to date have explored proteomic differences between mature and late lactation milk in goats, particularly within the Guanzhong dairy goat breed.\u003c/p\u003e \u003cp\u003eThis study aims to analyze the proteomic differences between mature milk and late-lactation milk of Guanzhong dairy goats using TMT technology, with the goal of identifying key proteins and associated enriched pathways. The findings will provide guidance for establishing a dynamic milk protein database, developing specialized functional dairy products (such as immune-enriched milk(Verruck et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and hypoallergenic formulas), discovering precursors of novel bioactive peptides, and expanding the research scope of functional milk components in future studies.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample Collection\u003c/h2\u003e \u003cp\u003eThe mammary gland samples from mature lactation (120 d after parturition, classified as the B group) and late lactation (180 d after parturition, classified as the C group) of six Guanzhong dairy goats (n\u0026thinsp;=\u0026thinsp;3) were obtained from the Heshi Dairy Goat Farm (Long County, Shaanxi Province, China). All these dairy goats were raised under the same environmental conditions with natural light and free access to food and water.\u003c/p\u003e \u003cp\u003eThe collected samples were immediately frozen in liquid nitrogen and then stored at \u0026minus;\u0026thinsp;80 ℃ until further use. All experimental procedures were approved by Shaanxi province, P.R. China Biological Studies Animal Care and Use Committee.\u003c/p\u003e \u003cp\u003eAll the experimental procedures used in this study was approved by the Animal Ethical and Welfare Committee of the College of Animal Science and Technology, Northwest A\u0026amp;F University, Yangling, China (protocol number DK2022008).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Protein Extraction and Digestion\u003c/h2\u003e \u003cp\u003e200 \u0026micro;L of frozen sample was mixed with six volumes of acetone in a 1.5 mL tube, incubated at -40\u0026deg;C overnight, and centrifuged (12,000 rpm, 10 min, 4\u0026deg;C). The pellet was air-dried, resuspended in lysis buffer for 3 h, and centrifuged twice (12,000 rpm, 10 min each) to obtain supernatant. Total protein was quantified via BCA assay (Beyotime, P0012), aliquoted, and stored at -80\u0026deg;C.\u003c/p\u003e \u003cp\u003e50 \u0026micro;g of protein was adjusted to uniform concentration with lysis buffer. After sequential treatment with 5 mM DTT (55\u0026deg;C, 30 min) and 10 mM iodoacetamide (dark, 15 min), proteins were precipitated with six acetone volumes (-20\u0026deg;C, 4 h). The pellet was centrifuged (8000\u0026times;g, 10 min, 4\u0026deg;C), dried briefly, redissolved in 200 mM TEAB, and digested with trypsin (1:50, w/w) at 37\u0026deg;C overnight. Digested samples were lyophilized and stored at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 TMT labeling and mass spectrometry\u003c/h2\u003e \u003cp\u003eLyophilized samples were mixed with 50 \u0026micro;L of 100 mM TEAB buffer in 1.5 mL EP tubes and vortexed for labeling. TMT reagents, equilibrated to room temperature, were dissolved in 88 \u0026micro;L anhydrous acetonitrile, vortexed for 5 minutes, and centrifuged. Forty-one microliters of TMT reagent were added to the samples, followed by vortexing and incubation at room temperature for 1 hour. The reaction was terminated with 8 \u0026micro;L of 5% hydroxylamine for 15 minutes, after which the samples were lyophilized and stored at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 LC-MS/MS analysis\u003c/h2\u003e \u003cp\u003eMobile phases A (100% water with 0.1% formic acid) and B (80% acetonitrile with 0.1% formic acid) were prepared. The lyophilized powder was dissolved in 10 \u0026micro;L of mobile phase A, centrifuged at 14,000\u0026times;g for 20 min at 4\u0026deg;C, and 1 \u0026micro;g of the supernatant was injected into a homemade C18 Nano-Trap column (4.5 cm \u0026times; 75 \u0026micro;m, 3 \u0026micro;m). A homemade analytical column (25 cm \u0026times; 150 \u0026micro;m, 1.9 \u0026micro;m) was utilized, with the column oven temperature set at 55\u0026deg;C, and a linear gradient elution was applied. The separated peptides were analyzed using an Orbitrap Exploris 480 mass spectrometer coupled with FAIMS (Thermo Fisher) equipped with a Nanospray Flex ESI ion source (spray voltage: 2.1 kV; ion transport capillary temperature: 320\u0026deg;C). Data-dependent acquisition mode was employed for mass spectrometry analysis. The FAIMS compensation voltages were set at \u0026minus;\u0026thinsp;45 V and \u0026minus;\u0026thinsp;65 V. For full MS scans (m/z 350\u0026ndash;1,500), a resolution of 60,000 (at m/z 200) was applied, with an automatic gain control (AGC) target value of 3\u0026times;10⁶ and maximum ion injection time set to Auto. In MS/MS mode, a 1s scan cycle was implemented. Precursor ions were fragmented via higher-energy collisional dissociation (HCD) at 30% normalized collision energy, with MS/MS parameters including: resolution of 15,000 (at m/z 200), AGC target of 7.5\u0026times;10⁴, maximum injection time of 22 ms, intensity threshold of 5.0\u0026times;10\u0026sup3;, and dynamic exclusion duration of 40 s.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis\u003c/h2\u003e \u003cp\u003eAll resulting spectra were searched against the UniProt-taxonomy_9922.fasta database using Proteome Discoverer 2.4 (PD 2.4; Thermo). Only peptide spectrum matches (PSMs) with confidence levels greater than 99% and proteins containing at least one unique peptide were retained and subjected to a false discovery rate (FDR) threshold of \u0026le;\u0026thinsp;1.0%. Protein quantification results were statistically analyzed using Student's t-test. Proteins demonstrating significantly different expression levels between experimental and control groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.2) were defined as differentially expressed proteins (DEPs).\u003c/p\u003e \u003cp\u003eThe mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://proteomecentral.proteomexchange.org\u003c/span\u003e\u003cspan address=\"http://proteomecentral.proteomexchange.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) via the iProX partner repository(J et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, T et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with the dataset identifier PXD047737.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Bioinformatics analysis\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) was executed through the R software platform (version 4.4.0) employing three key packages: ggplot2 (v3.5.1), ggpubr (v0.6.0), and ggthemes (v5.1.0). Functional annotation processing was carried out via the InterProScan tool with integrated analysis across six specialized databases: Pfam, PRINTS, ProDom, SMART, ProSite, and PANTHER(Jones et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Metabolic pathway investigation was implemented in R using reference pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pathway enrichment significance was determined through Fisher's exact testing protocol, setting the significance threshold at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Overview of Proteomic Analyses.\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) indicated that while the groups (mature and late lactation milk) were distinct, the differences were not large at the principal component level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Cluster analysis further confirmed that the two groups formed independent clusters, suggesting significant proteomic differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Screening and identification of differentially expressed proteins (DEPs)\u003c/h2\u003e \u003cp\u003eUsing a threshold of 1.2-fold change and \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, 48 differentially expressed proteins (DEPs; 39 up-regulated and 9 down-regulated) were detected between the mature milk (Group B) and late lactation milk (Group C) in proteomic analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Compared with late lactation milk, mature milk exhibited up-regulation of C3, C9, THBS1, LAMC1, EFNA1, LGB, and ANXA2 proteins, whereas CSN1S2 and RNASE1 were down-regulated (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esignificantly up-regulated and down-regulated differentially expressed proteins\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRegulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452EX53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLOC106503728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452FLJ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLOC102168428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.2199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A8C2RY13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMYBPH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452FLV4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLXNA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A8C2QT22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTHBS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452DX18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.7286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF8R0Y9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMSTN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.6516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452E7W7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLOC108636060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452EK27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTAG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A8C2NBA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMGP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452FFG9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDSC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.5030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452DRA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEFNA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A8C2QXC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.4348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP02756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452ENB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452E5U5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452ERB9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.3090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452EL69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLAMC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452F333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452DSB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eANXA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452DTS9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGRN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452G457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCMT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.2138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eup\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A8C2QYX7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452FNS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePPIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452DYZ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLOC108634682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452G5W1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGRN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452DRK5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHPX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A452E500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSTC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP67926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNASE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA0A6H0DWX2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSN1S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003edown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Gene Ontology (GO) Analysis of DEPs\u003c/h2\u003e \u003cp\u003eGO analysis of all the DEPs in the mature milk (Group B) versus the late lactation milk (Group C) is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table S2. In the cellular component category, most of the DEPs were mainly assigned to the extracellular space, extracellular region, plasma membrane, integral component of membrane, high-density lipoprotein particle and cytoplasm. In the biological process category, a large number of DEPs were involved in the acute-phase response, complement activation, alternative pathway and complement activation, classical pathway. For the molecular function category, the GO terms including calcium ion binding and identical protein binding were the predominant functions of the DEPs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 KEGG Pathway Analysis of DEPs\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Table S3 shows the KEGG pathway enrichment analysis of DEPs. In the analysis, a total of 24 up-regulated DEPs were significantly enriched in the pathways of complement and coagulation cascades, PI3K-Akt signaling pathway, ECM-receptor interaction, Phagosome and Rap1 signaling pathway.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eProtein is the main component of goat milk and plays an important role in it(Y et al., 2019). The protein components of dairy products play different roles at different stages of production(SM et al., 2024). Many studies have explored the differences in protein composition between colostrum and mature milk from a proteomic perspective(Y et al., 2020, Zhang et al., 2024), but there is almost no research on the protein differences between mature milk and late lactation milk, especially goat milk. In this experiment, a comparative proteomic analysis of mature and late lactation milk was conducted using TMT proteomic marker technology. Principal component analysis (PCA) revealed that while there is overlapped between mature milk and late lactation milk, there were still differences. Cluster analysis showed that the two groups formed distinct clusters, with within-group expression levels being similar and significant differences between groups. Overall, the results indicate reliable differences between mature and late lactation milk, supporting the construction of a model for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing differential screening criteria (fold change \u0026gt; 1.2 and \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.05), 48 differential proteins were identified, with 39 proteins significantly up-regulated and 9 proteins significantly down-regulated. In mature milk, C3 and C9 are upregulated and enriched in the extracellular space and the Complement and coagulation cascades pathways. C3 upregulation has been observed in donkey milk(Zhang et al., 2022a) , where this complement protein contributes to establishing natural immunity in newborns(S and S, 2001) . Meanwhile, yak and bovine milk exosomes can alleviate LPS-induced intestinal inflammation development and improve IEC-6 cell survival by inhibiting the PI3K-AKT/C3 pathway(Gao et al., 2021). The complement and coagulation cascades pathway has also been identified in both colostrum and mature milk of dairy goats(Sun et al., 2020) . These findings suggest that mature milk may be more conducive to generating immune responses. Moreover, our study supplements the understanding of pathways associated with mature milk and late-lactation milk.\u003c/p\u003e\n\u003cp\u003eIn mature milk, the upregulation of THBS1, LAMC1, and EFNA1 is enriched in the ECM-receptor interaction, PI3K-Akt signaling pathway, and Rap1 signaling pathway. Compared to bovine milk, human milk proteins show greater enrichment in the PI3K-Akt signaling pathway and Rap1 signaling pathway( Tong et al., 2023) . This demonstrates the association between goat milk and human milk. Studies have shown that ceRNAs are upregulated during early lactation and function in the PI3K-AKT pathway or ECM-receptor interactions(Yu et al., 2017, Zhang et al., 2024). Both the PI3K-AKT pathway and ECM-receptor interactions promote lactation(Fu et al., 2021), which aligns with the functional requirements of this lactation stage \u0026ndash; preparing mammary glands for full lactation. These findings are consistent with our results, indicating that the enrichment of pathway-related proteins during the mature stage enhances milk production in dairy goats during this phase.\u003c/p\u003e\n\u003cp\u003eIn mature milk, LGB is upregulated while CSN1S2 and RNASE1 are downregulated. These proteins are enriched in the extracellular region pathway. CSN1S2 primarily influences protein content and cheese texture, with its gene playing a significant role in goat milk yield(ML et al., 2023) . This suggests that milk from late lactation stages is more suitable for cheese production and related dairy processing. RNASE1 is associated with mammary gland remodeling in cattle and participates in gastrointestinal tract remodeling(Boutinaud et al., 2013, L et al., 2015) . LGB demonstrates notable antioxidant activity in milk(Kazimierska and Kalinowska-Lis, 2021, KT et al., 2025) . In buffalo milk, most proteins originate from the extracellular region pathway, revealing potential health benefits of buffalo extracellular vesicles as therapeutic agents and drug delivery vehicles(Joshi et al., 2024) . These findings may guide the development of functional dairy products using goat milk.\u003c/p\u003e\n\u003cp\u003eThe protein content of mature milk and late lactation goat milk significantly influences their functional properties and utilization value. The primary limitation of this study was the small sample size. In our experimental design, three biological replicates were employed for proteomic analysis. Increasing the number of biological replicates might reveal more pronounced inter-individual variations among animals and identify a greater number of proteins.\u003c/p\u003e\n\u003cp\u003eIn conclusion, proteins in mature milk appear to play a more prominent role in immune functions and lactation processes. However, late lactation milk may be more suitable for cheese production and related dairy processing applications. These dynamic changes in milk proteins could guide future investigations into protein regulation mechanisms. These findings provide valuable insights for breeding new varieties of dairy goats and developing functional dairy products with enhanced nutritional profiles.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eComplement C3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eC9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eComplement component C9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eTHBS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eThrombospondin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eLAMC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eLaminin subunit gamma 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eEFNA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eEphrin A1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eLGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eBeta-lactoglobulin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eANXA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eAnnexin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eCSN1S2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eAlpha-S2-casein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eRNASE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003eRibonuclease pancreatic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Major Science and Technology Project of Shaanxi Agricultural Collaborative Innovation and Extension Alliance (LMZD202002), Shaanxi Provincial Science and Technology Co-ordination Innovation Project (2018ZDCXL-NY-01-04), and Shaanxi Agricultural Science and Technology Innovation and Extension Project (NYKJ-2019-YL16)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRen Xinyang proposed the study design. Qu Yingxin, Shari Akang, and Chen Lu conducted the experiments and wrote the manuscript. Li Guang revised the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available in the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository, reference number [PXD047737].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe procedures of this study were approved by the Animal Ethical and Welfare Committee of Northwest A\u0026amp;F University (Yangling, P.R.China, Approval No. DK2022008) and were in accordance with the university\u0026rsquo;s guidelines for animal research.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e Tong, L., S. \u0026nbsp;Zhang, Q. \u0026nbsp;Liu, C. \u0026nbsp;Huang, H. \u0026nbsp;Hao, M. 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Nutrients 14(17):15.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dairy goat, goat milk, mature milk, late lactation milk, proteomics","lastPublishedDoi":"10.21203/rs.3.rs-6158030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6158030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe nutritional value of goat milk varies depending on the lactation stage. This study employed Tandem Mass Tag (TMT) proteomics to explore differential protein profiles between mature milk and late-lactation milk in Guanzhong dairy goats. Using differential screening criteria (fold change\u0026thinsp;\u0026gt;\u0026thinsp;1.2, P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we identified 48 differentially expressed proteins, with 39 significantly upregulated and 9 significantly downregulated. Proteins in mature milk appear to play more substantial roles in immune functions and lactation-related processes. Conversely, late-lactation milk may demonstrate greater suitability for cheese production and related dairy processing applications. These findings provide valuable insights for breeding improved dairy goat varieties and developing nutrient-rich functional dairy products.\u003c/p\u003e","manuscriptTitle":"TMT-Based Proteomic Analysis of Mature Milk versus Late Lactation Milk in Dairy Goats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 11:57:06","doi":"10.21203/rs.3.rs-6158030/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-01T02:17:30+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T12:41:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-06T07:30:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2025-03-04T21:24:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2cf183fa-cafb-47d1-8b44-e9c3ff56aff1","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-23T11:57:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-23 11:57:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6158030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6158030","identity":"rs-6158030","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}