Metabolomics Comparison Between Ovine and Bovine Serum at Mid-lactation

Metabolomics Comparison Between Ovine and Bovine Serum at Mid-lactation | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Metabolomics Comparison Between Ovine and Bovine Serum at Mid-lactation Xiaohu Su, Zhong Zheng, Liguo Zhang, Urhan Bai, Guanghua Su, Yunxi Wu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-274458/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The ovine milk owns higher lactoprotein, fat and other solids than bovine milk. However, the mechanism was not fully clear. To discover the specific mechanism, an untargeted metabolomics analyze of serum at mid-lactation by liquid chromagraphy-mass spectrometry (LC-MS) was performed. Then multivariate statistical analysis was carried out to find the specific differences. Final the different abundant metabolites were functionally enrichment by KEGG. In total, 1615 metabolites were detected in serum and 486 were annotated. The the largest metabolic category was lipids and lipid-like molecules (188 metabolites). 412 metabolites were identified as differential metabolites between two groups. KEGG pathway enrichment showed that 18 and 10 functional pathways of differential metabolites were enriched at positive and negative ion mode, separately. Notably, hernandezine, which is a novel AMPK activator, may play a role in the formation of lactoprotein of ovine milk. The results indicated that there may be different biological effects between two species serum. The serum metabolites could make help for the formation of milk. Endocrinology & Metabolism Paleozoology serum metabolomics lactation ovine bovine Figures Figure 1 Figure 2 Figure 3 Introduction Milk is a nutritious food which contains multiple components such as proteins, fat, lactose, bioactive peptides and micronutrients 1 . Although the bovine milk occupies the major marker, small ruminants’ milk, such as caprine and ovine, are emphasized nowadays. Compared with bovine milk, the ovine milk owns higher percentages of lactoprotein, milk fat and so on 2 . The molecular composition of milk is influenced by various genetic and environmental factors. However, the specific regulation mechanism of milk composition differences is unclear. Metabolites are effectively the end products of complex interactions occurring inside the cell (the genome) and outside the cell or organism (the environment). The advanced analytical chemistry techniques were used to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids, named metabolomics 3 . The blood metabolomics study was largely used to explain or identify the economic traits of livestock 4-6 . For a part of milk metabolites were from blood and the blood metabolites could regulate the biological function of mammary cells, the serum metabolomics would be one of useful strategy to explain the mechanism of milk composition differences between different species 7-9 . In dairy cows, Hippuric acid, nicotinamide and pelargonic acid of serum could be milk protein biomarkers 10 . However, little research to discover milk traits differences between species through blood metabolomics. One cause may be that the metabolomics is affected by various factors, such as the genome and the environment, and it is hard to compare at ideal research conditions. In this study, we fed the ewes and cows at same place to maximize eliminate the impact of environment. We analyzed the milk composition and the blood serum metabolome of ovine and bovine at mid-lactation. The aim of this study was to partly explain the mechanism of milk composition differences between two species through serum metabolomics analysis. It would be helpful for dairy stock’s feeding and understanding of milk composition formation basis. Materials And Methods Ethical statement Animal manipulations in this study including welfare, husbandry and experimental sampling were approved by the Animal Ethics Committee of Inner Mongolia University (Permit number: IMU-IACUC-2018-B78C). All procedures involving animals were approved by the Ethical Principles for the Use of Animals for Scientific Purposes of the Inner Mongolia University of China. All experiments were performed according to Chinese laws and institutional guidelines. Animals and samples preparation The breeds of this research were Holstein cow and F1 cross-breed from Small-Tailed Han and DairyMeade sheep. All were 2~4 years and parous. The animals which were used for this study were fed at the standard conditions and the same region of Mengtianran Dairy Co. Ltd. (Ulanqab, Inner Mongolia autonomous region, China). The blood samples were collected from similar physical individuals and 6 of each group. The time of samples collection was D90 after parturition. Blood samples were collected in vacuum blood collection tubes. Then, the samples were centrifuged at 3000×g 4 °C for 15 min to obtain the corresponding serum within 30 min of collection. All samples were stored in liquid nitrogen until analysis. LC-MS analysis conditions The LC-MS/MS analysis was processed by Novogene Co. LTD. (Beijing, China). The detailed processes of metabolies annotation and identification analysis were followed as Wang et al. 11 Metabolites Extraction The samples (100 μL) and prechilled methanol (400 μL) were mixed by well vortexing. The samples were incubated on ice for 5 min and then were centrifuged at 15000 rpm, 4°C for 5 min. A some of supernatant was diluted to final concentration containing 60% methanol by LC-MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube with 0.22 μm filter and then were centrifuged at 15000 g, 4°C for 10 min. Finally, the filtrate was injected into the LC-MS/MS system analysis. UHPLC-MS/MS Analysis LC-MS/MS analyses were performed using a Vanquish UHPLC system (Thermo Fisher) coupled with an Orbitrap Q Exactive series mass spectrometer (Thermo Fisher). Samples were injected onto an Hyperil Gold column (100×2.1 mm, 1.9μm) using a 16-min linear gradient at a flow rate of 0.2mL/min. The eluents for the positive polarity mode were eluent A (0.1% FA in Water) and eluent B (Methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH 9.0) and eluent B (Methanol).The solvent gradient was set as follows: 2% B, 1.5 min; 2-100% B, 12.0 min; 100% B, 14.0 min；100-2% B, 14.1 min；2% B, 16 min. Q Exactive mass series spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320°C, sheath gas flow rate of 35 arb and aux gasflow rate of 10 arb. Metabolomics data processing Database search The raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, Thermo Fisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The normalized data was used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. And then peaks were matched with the mzCloud (https://www.mzcloud.org/) and ChemSpider (http://www.chemspider.com/) database to obtained the accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version) and CentOS (CentOS release 6.6), When data were not normally distributed, normal transformations were attempted using of area normalization method. Data Analysis These metabolites were annotated using the HMDB database ( http://www.hmdb.ca/). Principal components analysis (PCA) and Partial least squares discriminant analysis (PLS-DA) were performed at metaX (a flexible and comprehensive software for processing metabolomics data). Volcano plots were used to filter metabolites of interest which based on Log2 (FC) and -log10 (P-value) of metabolites. The functions of these metabolites and metabolic pathways were studied using the KEGG database. The metabolic pathway enrichment of differential metabolites were performed, when ratio were satisfied by x/n > y/N, metabolic pathway were considered as enrichment, when P-value of metabolic pathway < 0.05, metabolic pathway were considered as statistically significant enrichment. Results Untargeted metabolic profiling of ovine and bovine serum at mid-lactation To detect the metabolic differences between cow, goat and sheep milk, an untargeted metabolomics analysis was performed and Human Metabolome Database (HMDB) was used to annotation. In total, 313 annotated metabolites from 1050 positive-ion feature and 173 annotated metabolites from 565 negative-ion feature were identified (Table S1). The results showed that the largest metabolic category was lipids and lipid-like molecules (188 metabolites), followed by organic acids and derivatives (98 metabolites) and organoheterocyclic compounds (55 metabolites) (Table S2). In the positive-ion mode, the top 3 metabolites of ovine serum were Platelet-activating factor, Betaine and callystatin A and the top 3 metabolites of bovine serum were Hippuric acid, callystatin A and Platelet-activating factor. In the negative-ion mode, the top 3 metabolites of ovine serum were Oleic acid, Stearic acid and Ethyl myristate and the top 3 metabolites of bovine serum were Stearic acid, Ethyl myristate, Cholic acid. The results showed that high level of long-chain fatty acid at serum which could supply the formation of butterfat. Multivariate Statistical Analysis Principal Components Analysis (PCA) was used to determine the sample separation and aggregation between three milks. Each point on the PCA score graph represents a single sample. Aggregation of points indicates that the observed variables are highly similar, and discrete points represent significant differences (VIP ≥ 1; ratio ≥ 2 or ratio ≤ 1/2; q ≤ 0.05) in the observed variables. In the positive-ion mode, the PCA scores illustrated that PC1 and PC2 were responsible for 53.25 and 17.79% of the variation, respectively (Figure 1A). In the negative-ion mode, the PCA scores revealed that PC1 and PC2 were responsible for 54.35 and 18.51% of the variation, respectively (Figure 1B). The results demonstrated that serum from different species had different metabolic characteristics. To identify specific differences between groups, partial least squares discrimination analysis (PLS-DA) was used. Higher values for PLS-DA model parameters (R2 and Q2) denote greater reliability for the PLS-DA model. In the positive-ion mode, R2 of the PLS-DA model was 1.00, and Q2 was 0.99 (Figure 2A). Coincidentally, R2 of the PLS-DA model was 1.00 and Q2 was 0.99 in the negative-ion mode (Figure 2B). The results indicated that both R2 and Q2 were high and subsequent analyses were credible. Differential metabolites analysis Next, we subjected the metabolomics data to univariate analysis of fold changes and T statistical testing to perform Benjamini-Hochberg correction and obtain the P-value. This was combined with multivariate statistical analysis of the VIP obtained via PLS-DA to screen for differential metabolites. Differential ions were defined as follows: VIP ≥ 1; ratio ≥ 2 or ratio ≤1/2; P ≤ 0.05. 269 and 143 metabolites were identified as differential metabolites in positive-ion and negative-ion modes, separately (Figure 3). In the positive-ion mode, 113 metabolites present higher level in ovine serum while 156 metabolites present higher level in bovine serum (Table S3). And 38 metabolites present higher level in ovine serum while 105 metabolites present higher level in bovine serum in the negative-ion mode (Table S3). The top 5 significant abundant metabolites of ovine serum were LAPPAOL C, 2-ETHYL-4,5-DIMETHYLOXAZOLE, N-C18:0 Phytoceramide, (2S)-2-Amino-8-hydroxyoctanoic acid and carisoprodol (Table 1). The top 5 significant abundant metabolites of bovine serum were 4-Ethyl-2,6-dihydroxyphenyl hydrogen sulfate, (2R)-1-(Nonadecanoyloxy)-3-(phosphonooxy)-2-propanyl docosanoate, Epinephrine, DG(16:1(9Z)/22:0/0:0) and tak-475 (Table 1). Interestingly, much of metabolites which present higher level in ovine serum were associated with anti-microbico, antiviral or anticancer, such as Prunin, etravirine and Luteolin. While some of metabolites which present higher level in bovine serum were associated with contraception, such as gemeprost and Loxoprofen, which indicated that it may not suitable for pregnancy at this period. Notably, hernandezine, which is a novel AMPK activator, may play a role in the formation of lactoprotein of ovine milk. Pathway enrichment of differential abundant metabolites KEGG pathway enrichment showed that 18 and 10 functional pathways of differential metabolites were enriched at positive and negative ion mode, separately (Table 2). The most five enriched pathways of differential metabolites at positive-ion mode were Steroid hormone biosynthesis, Pathways in cancer, Prostate cancer, Purine metabolism and Oxidative phosphorylation (Table 2). The most five enriched pathways of differential metabolites at negative-ion mode were Carbohydrate digestion and absorption, Prion diseases, Insect hormone biosynthesis, Regulation of lipolysis in adipocytes and Aldosterone synthesis and secretion (Table 2). The results indicated that there may be different biological effects between two species serum. Discussion The blood metabolomics is one of an effective approach to discover the mechanism and prediction of livestock economic traits. The ovine milk owns higher percentages of lactoprotein and milk fat 2 . The blood serum metabolome of ovine and bovine at mid-lactation were analyzed to discover the mechanism. Among the metabolites, lappaol C and hernandezine were identified as high level at ovine serum. Lappaol C has antioxidant and antiaging properties, it may promote the C. elegans longevity and stress resistance through a JNK-1-DAF-16 cascade 12 . Phospho-JNK play a role of phospho-AKT, and the phosphatidylinositol-3-kinase (PI3K)/Akt could activate the mTOR pathway 13,14 . Hernandezine is a noval activator of AMPK, which is one of the upstream targets of mTOR 15-17 。And the mTOR signaling is crucial for the synthesis of lactoprotein and milk fat 18 . In addition, the hernandezine also could inhibit the Ca 2+ intake of calcium-depletion cells 19,20 , which may helpful for high calcium level of ovine milk. Based on these, we surmised that lappaol C and hernandezine may helpful for milk traits. Thromboxane B2 is associated with arachidonic acid metabolism. Arachidonic acid and esterified arachidonate are ubiquitous components of every mammalian cell. This polyunsaturated fatty acid serves very important biochemical roles, including being the direct precursor of bioactive lipid mediators such as prostaglandin and leukotrienes 21 . High level of thromboxane B2 in ovine serum may be a marker of high polyunsaturated fatty acid in milk. Another research showed that arachidonic acid metabolites can promote angiogenesis in metastatic breast cancer 22 . Thus we surmise that it may contribute to angiogenesis of mammary gland during lactation. Flavin mononucleotide (FMN) is a metabolite from vitamin B2. Without an adequate amount of vitamin B2, macronutrients like carbohydrates, fats, and proteins cannot be digested and maintain the body 23 . Vitamin B2 could improve the intake of protein and may helpful for milk protein biosynthesis. And the apoenzyme of lactate oxidase is specifically activated by FMN. FMN in serum may play a role for the biosynthesis of milk protein and fat. Conclusion In this study, the results showed that there are different metabolome profiles of ovine and bovine serum during lactation and distinct biological function. The metabolites of serum would affect the milk traits. Declarations Author contributions Xiaohu Su contributed in design of experiments, analyzed the data and manuscript writing. Zhong Zheng obtained the samples, contributed to planning and design of the study. Liguo Zhang obtained the samples. Urhan Bai contributed to LC–MS analysis of samples and data collection. Guanghua Su contributed to experimental part and data analysis. Yunxi Wu obtained the samples. Guangpeng Li contributed to planning of the study and experiments. Li Zhang contributed to planning of the study and experiments, data collection and execution of experiments. All authors reviewed the manuscript. Ethics declarations Competing Interest The authors declare no competing interests. Acknowledgements This work was funded by the Major Science and Technology project of Inner Mongolia Autonomous Region of China (30900-5173910), the Independent project of The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock of Inner Mongolia University of China (30500-518390205) and the Science and Technology Innovation Guidance project of Inner Mongolia Autonomous Region of China (30500-5173203). We owe many thanks to Mengtianran Dairy Co. Ltd. (Ulanqab, Inner Mongolia autonomous region, China) for the supply of place and animals. We owe many thanks to Novogene Co. LTD. (Beijing, China) for the metabolomics profiles processing. References Punia, H. et al. Identification and Detection of Bioactive Peptides in Milk and Dairy Products: Remarks about Agro-Foods. Molecules (Basel, Switzerland). 25 , https://doi.org/10.3390/molecules25153328 (2020). Nguyen, H. T. 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Tables Table 1 The top 5 significant differential abundant metabolites of blood serum during lactation of ovine and bovine. Name_des Formula Molecular Weight Ovine average Bovine average Fold Change P value ROC VIP Up.Down LAPPAOL C C30 H34 O10 554.21 912086.22 3435.83 265.463 1.67E-09 1.00 4.78 up 2-ETHYL-4,5-DIMETHYLOXAZOLE C7 H11 N O 125.08 6457567.62 34758.38 185.785 3.53E-07 1.00 4.36 up N-C18:0 Phytoceramide C36 H73 N O4 583.55 460466.51 2500.33 184.162 4.61E-08 1.00 4.52 up (2S)-2-Amino-8-hydroxyoctanoic acid C8 H17 N O3 175.12 6461861.25 35733.12 180.837 5.62E-07 1.00 4.33 up carisoprodol C12 H24 N2 O4 260.17 332164.28 6878.27 48.292 2.64E-05 1.00 3.09 up 4-Ethyl-2,6-dihydroxyphenyl hydrogen sulfate C8 H10 O6 S 234.02 1226.86 105015.18 0.012 2.58E-03 1.00 2.75 down (2R)-1-(Nonadecanoyloxy)-3-(phosphonooxy)-2-propanyl docosanoate C44 H87 O8 P 774.62 2960.50 288444.10 0.010 1.12E-08 1.00 3.96 down Epinephrine C9 H13 N O3 183.09 1911.49 251523.56 0.008 1.33E-04 1.00 3.44 down DG(16:1(9Z)/22:0/0:0) C41 H78 O5 650.58 1534.23 300351.92 0.005 3.02E-04 1.00 4.03 down tak-475 C33 H41 Cl N2 O9 644.25 1242.80 342837.71 0.004 2.27E-06 1.00 4.64 down Table 2 KEGG enrichment of significant differential abundant metabolites of blood serum during lactation of ovine and bovine. MapTitle P value Metabolites Positive ion mode Steroid hormone biosynthesis 0.0027 Androstanolone, Testosterone, tetrahydrocortisol, Cortisone Pathways in cancer 0.0043 Androstanolone, Testosterone, Cortisone Prostate cancer 0.0043 Androstanolone, Testosterone, Cortisone Purine metabolism 0.0757 Xanthine Oxidative phosphorylation 0.1728 Flavin mononucleotide Caffeine metabolism 0.1728 Xanthine Arachidonic acid metabolism 0.1728 Thromboxane B2 Endocrine resistance 0.1728 Testosterone Serotonergic synapse 0.1728 Thromboxane B2 Ovarian steroidogenesis 0.1728 Testosterone Aldosterone-regulated sodium reabsorption 0.1728 Cortisone alpha-Linolenic acid metabolism 0.3176 Jasmonic acid Insect hormone biosynthesis 0.3176 Juvenile hormone III Riboflavin metabolism 0.4385 Flavin mononucleotide Neomycin, kanamycin and gentamicin biosynthesis 0.5393 Paromamine Porphyrin and chlorophyll metabolism 0.5393 pyropheophorbide a Bile secretion 0.6500 Thromboxane B2, Aspirin Vitamin digestion and absorption 1.0000 Flavin mononucleotide Negative ion mode Carbohydrate digestion and absorption 0.1277 Sucralose Prion diseases 0.1277 Corticosterone Insect hormone biosynthesis 0.2414 Ecdysterone Regulation of lipolysis in adipocytes 0.2414 Corticosterone Aldosterone synthesis and secretion 0.2414 Corticosterone Steroid hormone biosynthesis 0.3426 Corticosterone Phenylalanine metabolism 0.3426 Salicylic acid Biosynthesis of unsaturated fatty acids 1.0000 Nervonic acid Metabolic pathways 1.0000 Corticosterone, Luteolin, Salicylic acid Bile secretion 1.0000 Salicylic acid Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx Table S1 The blood serum metabolome profiles during lactation of ovine and bovine. TableS2.xlsx Table S2 The annotated metabolites. TableS3.xls Table S3 Significantly differential abundant metabolites between ovine and bovine serum during lactation. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies 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-274458","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":13648640,"identity":"956a08ae-196c-4112-9aab-fa1c3ef3cfac","order_by":0,"name":"Xiaohu Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYBACNv7mA8Z/DNiY+eUfNj5I+GFDWAufxLGEAp4CPnbJhuTDBg970ghrkWPIMfjA80GO36AhLU3yAdthIhzGcMZwg4SBmbQBwxmzigSewwz87d0J+LUwtxUbGBikGZsz9pjdSLBIZ5A4c3YDAVsObzNIMDiWbNnMA9TCY81gIJFLSEuC+Y8DBv/rNxzjMStIYGMmRkuKgWEDMJANzrClMSSwOROhBRjIxgxALZIzmA9LJPak8RD0i3w/MCoZ/gCjUoKx8eOPHzZy/O29+LVgAB7SlI+CUTAKRsEowAoAX35FEA8Q8jwAAAAASUVORK5CYII=","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiaohu","middleName":"","lastName":"Su","suffix":""},{"id":13648641,"identity":"bbba9426-1770-4b18-b983-aafe2b457098","order_by":1,"name":"Zhong Zheng","email":"","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhong","middleName":"","lastName":"Zheng","suffix":""},{"id":13648642,"identity":"fa126547-d015-4b3f-9ecf-d181675da450","order_by":2,"name":"Liguo Zhang","email":"","orcid":"","institution":"Ulanqab Agriculture and Animal Husbandry Bureau","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liguo","middleName":"","lastName":"Zhang","suffix":""},{"id":13648643,"identity":"af53952c-0c8c-4adf-bd49-eee7bb7fa801","order_by":3,"name":"Urhan Bai","email":"","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Urhan","middleName":"","lastName":"Bai","suffix":""},{"id":13648644,"identity":"bc33ee35-01fb-471b-b190-fe37692d7247","order_by":4,"name":"Guanghua Su","email":"","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guanghua","middleName":"","lastName":"Su","suffix":""},{"id":13648645,"identity":"5f3219a1-664c-48a0-85ec-301e7c64f738","order_by":5,"name":"Yunxi Wu","email":"","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yunxi","middleName":"","lastName":"Wu","suffix":""},{"id":13648646,"identity":"72a72171-adb1-4fd9-b860-ba4f9c66e15d","order_by":6,"name":"Guangpeng Li","email":"","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guangpeng","middleName":"","lastName":"Li","suffix":""},{"id":13648647,"identity":"3f916505-a7c3-4380-9602-e43a9c972665","order_by":7,"name":"Li Zhang","email":"","orcid":"","institution":"Inner Mongolia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2021-02-24 14:14:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-274458/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-274458/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":6711723,"identity":"e48978d7-beaa-4946-95ab-d774fcfbcaca","added_by":"auto","created_at":"2021-03-08 14:29:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15328,"visible":true,"origin":"","legend":"Principal components analysis (PCA) scores plot of metabolites identified in blood serum during lactation of ovine and bovine.\nA: positive-ion mode; B: negative-ion mode.","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-274458/v1/296f5fc5c203d30a991e8cb0.png"},{"id":6712104,"identity":"58b4475c-e584-4161-bb7d-298e7d57ed8a","added_by":"auto","created_at":"2021-03-08 14:32:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13907,"visible":true,"origin":"","legend":"Partial least squares discrimination analysis (PLS-DA) scores plot of metabolites identified in blood serum during lactation of ovine and bovine.\nA: positive-ion mode; B: negative-ion mode.","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-274458/v1/7a19610ce312af385f47de77.png"},{"id":6712105,"identity":"25b9406d-664a-43d3-b1f2-a6ff1e1fca70","added_by":"auto","created_at":"2021-03-08 14:32:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":27294,"visible":true,"origin":"","legend":"Volcano plot of metabolites identified in blood serum during lactation of ovine and bovine.\nA: positive-ion mode; B: negative-ion mode.","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-274458/v1/d1261d4aa599fe1a7ab40d7d.png"},{"id":13675550,"identity":"1cb56a45-4f24-400f-8472-8833856494fe","added_by":"auto","created_at":"2021-09-17 11:27:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":409107,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-274458/v1/88f73e91-9bb8-4927-8bf1-69cdc2d201dc.pdf"},{"id":6712106,"identity":"1585d997-f393-44aa-8e37-6df25cb34133","added_by":"auto","created_at":"2021-03-08 14:32:53","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":485226,"visible":true,"origin":"","legend":"Table S1 \nThe blood serum metabolome profiles during lactation of ovine and bovine.\n","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-274458/v1/15b0b6fa885f8bceca546e98.xlsx"},{"id":6712593,"identity":"8ac2b36f-04d3-4672-ad95-92138d9cff58","added_by":"auto","created_at":"2021-03-08 14:35:53","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":51709,"visible":true,"origin":"","legend":"Table S2 \nThe annotated metabolites.\n\n","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-274458/v1/9a71d981de5627d542363ad6.xlsx"},{"id":6712108,"identity":"7ab4b859-b2ab-47d8-bcd0-df9b47837300","added_by":"auto","created_at":"2021-03-08 14:32:53","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":235008,"visible":true,"origin":"","legend":"Table S3 \nSignificantly differential abundant metabolites between ovine and bovine serum during lactation.\n","description":"","filename":"TableS3.xls","url":"https://assets-eu.researchsquare.com/files/rs-274458/v1/69be9d20e544fc675a41dd59.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMetabolomics Comparison Between Ovine and Bovine Serum at Mid-lactation\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMilk is a nutritious food which contains multiple components such as proteins, fat, lactose, bioactive peptides and micronutrients\u003csup\u003e1\u003c/sup\u003e. Although the bovine milk occupies the major marker, small ruminants\u0026rsquo; milk, such as caprine and ovine, are emphasized nowadays. Compared with bovine milk, the ovine milk owns higher percentages of lactoprotein, milk fat and so on\u003csup\u003e2\u003c/sup\u003e. The molecular composition of milk is influenced by various genetic and environmental factors. However, the specific regulation mechanism of milk composition differences is unclear.\u003c/p\u003e\n\u003cp\u003eMetabolites are effectively the end products of complex interactions occurring inside the cell (the genome) and outside the cell or organism (the environment). The advanced analytical chemistry techniques were used to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids, named metabolomics\u003csup\u003e3\u003c/sup\u003e. The blood metabolomics study was largely used to explain or identify the economic traits of livestock\u003csup\u003e4-6\u003c/sup\u003e. For a part of milk metabolites were from blood and the blood metabolites could regulate the biological function of mammary cells, the serum metabolomics would be one of useful strategy to explain the mechanism of milk composition differences between different species\u003csup\u003e7-9\u003c/sup\u003e. In dairy cows, Hippuric acid, nicotinamide and pelargonic acid of serum could be milk protein biomarkers\u003csup\u003e10\u003c/sup\u003e. However, little research to discover milk traits differences between species through blood metabolomics. One cause may be that the metabolomics is affected by various factors, such as the genome and the environment, and it is hard to compare at ideal research conditions.\u003c/p\u003e\n\u003cp\u003eIn this study, we fed the ewes and cows at same place to maximize eliminate the impact of environment. We analyzed the milk composition and the blood serum metabolome of ovine and bovine at mid-lactation. The aim of this study was to partly explain the mechanism of milk composition differences between two species through serum metabolomics analysis. It would be helpful for dairy stock\u0026rsquo;s feeding and understanding of milk composition formation basis.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimal manipulations in this study including welfare, husbandry and experimental sampling were approved by the Animal Ethics Committee of Inner Mongolia University (Permit number: IMU-IACUC-2018-B78C). All procedures involving animals were approved by the Ethical Principles for the Use of Animals for Scientific Purposes of the Inner Mongolia University of China. All experiments were performed according to Chinese laws and institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimals and samples preparation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe breeds of this research were Holstein cow and F1 cross-breed from Small-Tailed Han and DairyMeade sheep. All were 2~4 years and parous. The animals which were used for this study were fed at the standard conditions and the same region of Mengtianran Dairy Co. Ltd. (Ulanqab, Inner Mongolia autonomous region, China). The blood samples were collected from similar physical individuals and 6 of each group. The time of samples collection was D90 after parturition. Blood samples were collected in vacuum blood collection tubes. Then, the samples were centrifuged at 3000\u0026times;g 4 \u0026deg;C for 15 min to obtain the corresponding serum within 30 min of collection. All samples were stored in liquid nitrogen until analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC-MS analysis conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LC-MS/MS analysis was processed by Novogene Co. LTD. (Beijing, China). The detailed processes of metabolies annotation and identification analysis were followed as Wang et al.\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eMetabolites Extraction\u003c/p\u003e\n\u003cp\u003eThe samples (100 \u0026mu;L) and prechilled methanol (400 \u0026mu;L) were mixed by well vortexing. The samples were incubated on ice for 5 min and then were centrifuged at 15000 rpm, 4\u0026deg;C for 5 min. A some of supernatant was diluted to final concentration containing 60% methanol by LC-MS grade water. The samples were subsequently transferred to a fresh Eppendorf tube with 0.22 \u0026mu;m filter and then were centrifuged at 15000 g, 4\u0026deg;C for 10 min. Finally, the filtrate was injected into the LC-MS/MS system analysis.\u003c/p\u003e\n\u003cp\u003eUHPLC-MS/MS Analysis\u003c/p\u003e\n\u003cp\u003eLC-MS/MS analyses were performed using a Vanquish UHPLC system (Thermo Fisher) coupled with an Orbitrap Q Exactive series mass spectrometer (Thermo Fisher). Samples were injected onto an Hyperil Gold column (100\u0026times;2.1 mm, 1.9\u0026mu;m) using a 16-min linear gradient at a flow rate of 0.2mL/min. The eluents for the positive polarity mode were eluent A (0.1% FA in Water) and eluent B (Methanol). The eluents for the negative polarity mode were eluent A (5 mM ammonium acetate, pH 9.0) and eluent B (Methanol).The solvent gradient was set as follows: 2% B, 1.5 min; 2-100% B, 12.0 min; 100% B, 14.0 min；100-2% B, 14.1 min；2% B, 16 min. Q Exactive mass series spectrometer was operated in positive/negative polarity mode with spray voltage of 3.2 kV, capillary temperature of 320\u0026deg;C, sheath gas flow rate of 35 arb and aux gasflow rate of 10 arb.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolomics data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatabase search\u003c/p\u003e\n\u003cp\u003eThe raw data files generated by UHPLC-MS/MS were processed using the Compound Discoverer 3.1 (CD3.1, Thermo Fisher) to perform peak alignment, peak picking, and quantitation for each metabolite. The normalized data was used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. And then peaks were matched with the mzCloud (https://www.mzcloud.org/) and ChemSpider (http://www.chemspider.com/) database to obtained the accurate qualitative and relative quantitative results. Statistical analyses were performed using the statistical software R (R version R-3.4.3), Python (Python 2.7.6 version) and CentOS (CentOS release 6.6), When data were not normally distributed, normal transformations were attempted using of area normalization method.\u003c/p\u003e\n\u003cp\u003eData Analysis\u003c/p\u003e\n\u003cp\u003eThese metabolites were annotated using the HMDB database ( http://www.hmdb.ca/). Principal components analysis (PCA) and Partial least squares discriminant analysis (PLS-DA) were performed at metaX (a flexible and comprehensive software for processing metabolomics data). Volcano plots were used to filter metabolites of interest which based on Log2 (FC) and -log10 (P-value) of metabolites.\u003c/p\u003e\n\u003cp\u003eThe functions of these metabolites and metabolic pathways were studied using the KEGG database. The metabolic pathway enrichment of differential metabolites were performed, when ratio were satisfied by x/n \u0026gt; y/N, metabolic pathway were considered as enrichment, when P-value of metabolic pathway \u0026lt; 0.05, metabolic pathway were considered as statistically significant enrichment.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eUntargeted metabolic profiling of\u003c/strong\u003e\u003cstrong\u003e ovine and bovine serum at mid-lactation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo detect the metabolic differences between cow, goat and sheep milk, an untargeted metabolomics analysis was performed and Human Metabolome Database (HMDB) was used to annotation. In total, 313 annotated metabolites from 1050 positive-ion feature and 173 annotated metabolites from 565 negative-ion feature were identified (Table S1). The results showed that the largest metabolic category was lipids and lipid-like molecules (188 metabolites), followed by organic acids and derivatives (98 metabolites) and organoheterocyclic compounds (55 metabolites) (Table S2).\u003c/p\u003e\n\u003cp\u003eIn the positive-ion mode, the top 3 metabolites of ovine serum were Platelet-activating factor, Betaine and callystatin A and the top 3 metabolites of bovine serum were Hippuric acid, callystatin A and Platelet-activating factor. In the negative-ion mode, the top 3 metabolites of ovine serum were Oleic acid, Stearic acid and Ethyl myristate and the top 3 metabolites of bovine serum were Stearic acid, Ethyl myristate, Cholic acid. The results showed that high level of long-chain fatty acid at serum which could supply the formation of butterfat.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal Components Analysis (PCA) was used to determine the sample separation and aggregation between three milks. Each point on the PCA score graph represents a single sample. Aggregation of points indicates that the observed variables are highly similar, and discrete points represent significant differences (VIP \u0026ge; 1; ratio \u0026ge; 2 or ratio \u0026le; 1/2; q \u0026le; 0.05) in the observed variables. In the positive-ion mode, the PCA scores illustrated that PC1 and PC2 were responsible for 53.25 and 17.79% of the variation, respectively (Figure 1A). In the negative-ion mode, the PCA scores revealed that PC1 and PC2 were responsible for 54.35 and 18.51% of the variation, respectively (Figure 1B). The results demonstrated that serum from different species had different metabolic characteristics.\u003c/p\u003e\n\u003cp\u003eTo identify specific differences between groups, partial least squares discrimination analysis (PLS-DA) was used. Higher values for PLS-DA model parameters (R2 and Q2) denote greater reliability for the PLS-DA model. In the positive-ion mode, R2 of the PLS-DA model was 1.00, and Q2 was 0.99 (Figure 2A). Coincidentally, R2 of the PLS-DA model was 1.00 and Q2 was 0.99 in the negative-ion mode (Figure 2B). The results indicated that both R2 and Q2 were high and subsequent analyses were credible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential metabolites analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we subjected the metabolomics data to univariate analysis of fold changes and T statistical testing to perform Benjamini-Hochberg correction and obtain the P-value. This was combined with multivariate statistical analysis of the VIP obtained via PLS-DA to screen for differential metabolites. Differential ions were defined as follows: VIP \u0026ge; 1; ratio \u0026ge; 2 or ratio \u0026le;1/2; P \u0026le; 0.05. 269 and 143 metabolites were identified as differential metabolites in positive-ion and negative-ion modes, separately (Figure 3). In the positive-ion mode, 113 metabolites present higher level in ovine serum while 156 metabolites present higher level in bovine serum (Table S3). And 38 metabolites present higher level in ovine serum while 105 metabolites present higher level in bovine serum in the negative-ion mode (Table S3). The top 5 significant abundant metabolites of ovine serum were LAPPAOL C, 2-ETHYL-4,5-DIMETHYLOXAZOLE, N-C18:0 Phytoceramide, (2S)-2-Amino-8-hydroxyoctanoic acid and carisoprodol (Table 1). The top 5 significant abundant metabolites of bovine serum were 4-Ethyl-2,6-dihydroxyphenyl hydrogen sulfate, (2R)-1-(Nonadecanoyloxy)-3-(phosphonooxy)-2-propanyl docosanoate, Epinephrine, DG(16:1(9Z)/22:0/0:0) and tak-475 (Table 1).\u003c/p\u003e\n\u003cp\u003eInterestingly, much of metabolites which present higher level in ovine serum were associated with anti-microbico, antiviral or anticancer, such as Prunin, etravirine and Luteolin. While some of metabolites which present higher level in bovine serum were associated with contraception, such as gemeprost and Loxoprofen, which indicated that it may not suitable for pregnancy at this period. Notably, hernandezine, which is a novel AMPK activator, may play a role in the formation of lactoprotein of ovine milk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway enrichment of differential abundant metabolites\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKEGG pathway enrichment showed that 18 and 10 functional pathways of differential metabolites were enriched at positive and negative ion mode, separately (Table 2). The most five enriched pathways of differential metabolites at positive-ion mode were Steroid hormone biosynthesis, Pathways in cancer, Prostate cancer, Purine metabolism and Oxidative phosphorylation (Table 2). The most five enriched pathways of differential metabolites at negative-ion mode were Carbohydrate digestion and absorption, Prion diseases, Insect hormone biosynthesis, Regulation of lipolysis in adipocytes and Aldosterone synthesis and secretion (Table 2). The results indicated that there may be different biological effects between two species serum.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe blood metabolomics is one of an effective approach to discover the mechanism and prediction of livestock economic traits. The ovine milk owns higher percentages of lactoprotein and milk fat\u003csup\u003e2\u003c/sup\u003e. The blood serum metabolome of ovine and bovine at mid-lactation were analyzed to discover the mechanism. Among the metabolites, lappaol C and hernandezine were identified as high level at ovine serum. Lappaol C has antioxidant and antiaging properties, it may promote the C. elegans longevity and stress resistance through a JNK-1-DAF-16 cascade\u003csup\u003e12\u003c/sup\u003e. Phospho-JNK play a role of phospho-AKT, and the phosphatidylinositol-3-kinase (PI3K)/Akt could activate the mTOR pathway\u003csup\u003e13,14\u003c/sup\u003e. Hernandezine is a noval activator of AMPK, which is one of the upstream targets of mTOR\u003csup\u003e15-17\u003c/sup\u003e。And the mTOR signaling is crucial for the synthesis of lactoprotein and milk fat\u003csup\u003e18\u003c/sup\u003e. In addition, the hernandezine also could inhibit the Ca\u003csup\u003e2+\u003c/sup\u003e intake of calcium-depletion cells\u003csup\u003e19,20\u003c/sup\u003e, which may helpful for high calcium level of ovine milk. Based on these, we surmised that lappaol C and hernandezine may helpful for milk traits.\u003c/p\u003e\n\u003cp\u003eThromboxane B2 is associated with arachidonic acid metabolism. Arachidonic acid and esterified arachidonate are ubiquitous components of every mammalian cell. This polyunsaturated fatty acid serves very important biochemical roles, including being the direct precursor of bioactive lipid mediators such as prostaglandin and leukotrienes\u003csup\u003e21\u003c/sup\u003e. High level of thromboxane B2 in ovine serum may be a marker of high polyunsaturated fatty acid in milk. Another research showed that arachidonic acid metabolites can promote angiogenesis in metastatic breast cancer\u003csup\u003e22\u003c/sup\u003e. Thus we surmise that it may contribute to angiogenesis of mammary gland during lactation. Flavin mononucleotide (FMN) is a metabolite from vitamin B2. Without an adequate amount of vitamin B2, macronutrients like carbohydrates, fats, and proteins cannot be digested and maintain the body\u003csup\u003e23\u003c/sup\u003e. Vitamin B2 could improve the intake of protein and may helpful for milk protein biosynthesis. And the apoenzyme of lactate oxidase is specifically activated by FMN. FMN in serum may play a role for the biosynthesis of milk protein and fat.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, the results showed that there are different metabolome profiles of ovine and bovine serum during lactation and distinct biological function. The metabolites of serum would affect the milk traits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaohu Su contributed in design of experiments, analyzed the data and manuscript writing. Zhong Zheng obtained the samples, contributed to planning and design of the study. Liguo Zhang obtained the samples. Urhan Bai contributed to LC\u0026ndash;MS analysis of samples and data collection. Guanghua Su contributed to experimental part and data analysis. Yunxi Wu obtained the samples. Guangpeng Li contributed to planning of the study and experiments. Li Zhang contributed to planning of the study and experiments, data collection and execution of experiments. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompeting Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Major Science and Technology project of Inner Mongolia Autonomous Region of China (30900-5173910), the Independent project of The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock of Inner Mongolia University of China (30500-518390205) and the Science and Technology Innovation Guidance project of Inner Mongolia Autonomous Region of China (30500-5173203). We owe many thanks to Mengtianran Dairy Co. Ltd. (Ulanqab, Inner Mongolia autonomous region, China) for the supply of place and animals. We owe many thanks to Novogene Co. LTD. (Beijing, China) for the metabolomics profiles processing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePunia, H. \u003cem\u003eet al.\u003c/em\u003e Identification and Detection of Bioactive Peptides in Milk and Dairy Products: Remarks about Agro-Foods. \u003cem\u003eMolecules (Basel, Switzerland).\u003c/em\u003e\u003cb\u003e25\u003c/b\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules25153328\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen, H. T. H., Afsar, S. \u0026amp; Day, L. 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W. in StatPearls (StatPearls Publishing Copyright \u0026copy; 2020, StatPearls Publishing LLC 2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cp\u003e\u003cstrong\u003eTable 1 \u003c/strong\u003e\u003cstrong\u003eThe top 5 \u003c/strong\u003e\u003cstrong\u003esignificant differential abundant metabolites of blood serum during lactation of ovine and bovine.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eName_des\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eFormula\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003eMolecular Weight\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003eOvine average\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003eBovine average\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003eFold Change\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003eROC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003eVIP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003eUp.Down\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eLAPPAOL C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC30 H34 O10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e554.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e912086.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e3435.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e265.463\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e1.67E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e4.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e2-ETHYL-4,5-DIMETHYLOXAZOLE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC7 H11 N O\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e125.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e6457567.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e34758.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e185.785\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e3.53E-07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e4.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eN-C18:0 Phytoceramide\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC36 H73 N O4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e583.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e460466.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e2500.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e184.162\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e4.61E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e4.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e(2S)-2-Amino-8-hydroxyoctanoic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC8 H17 N O3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e175.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e6461861.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e35733.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e180.837\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e5.62E-07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e4.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003ecarisoprodol\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC12 H24 N2 O4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e260.17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e332164.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e6878.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e48.292\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e2.64E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e3.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003eup\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e4-Ethyl-2,6-dihydroxyphenyl hydrogen sulfate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC8 H10 O6 S\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e234.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e1226.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e105015.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e2.58E-03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e2.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e(2R)-1-(Nonadecanoyloxy)-3-(phosphonooxy)-2-propanyl docosanoate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC44 H87 O8 P\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e774.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e2960.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e288444.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e1.12E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e3.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eEpinephrine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC9 H13 N O3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e183.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e1911.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e251523.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e0.008\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e1.33E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e3.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eDG(16:1(9Z)/22:0/0:0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC41 H78 O5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e650.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e1534.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e300351.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e0.005\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e3.02E-04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e4.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003etak-475\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003eC33 H41 Cl N2 O9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e644.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"78\"\u003e\n\u003cp\u003e1242.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e342837.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"58\"\u003e\n\u003cp\u003e0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e2.27E-06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"42\"\u003e\n\u003cp\u003e1.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"38\"\u003e\n\u003cp\u003e4.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"67\"\u003e\n\u003cp\u003edown\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003eTable 2 KEGG enrichment of significant differential abundant metabolites of blood serum during lactation of ovine and bovine.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"72\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eMapTitle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003eP value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eMetabolites\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"18\" width=\"72\"\u003e\n\u003cp\u003ePositive ion mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eSteroid hormone biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.0027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eAndrostanolone, Testosterone, tetrahydrocortisol, Cortisone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003ePathways in cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.0043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eAndrostanolone, Testosterone, Cortisone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eProstate cancer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.0043\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eAndrostanolone, Testosterone, Cortisone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003ePurine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.0757\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eXanthine\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eOxidative phosphorylation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.1728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eFlavin mononucleotide\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eCaffeine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.1728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eXanthine\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eArachidonic acid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.1728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eThromboxane B2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eEndocrine resistance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.1728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eTestosterone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eSerotonergic synapse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.1728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eThromboxane B2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eOvarian steroidogenesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.1728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eTestosterone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eAldosterone-regulated sodium reabsorption\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.1728\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eCortisone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003ealpha-Linolenic acid metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.3176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eJasmonic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eInsect hormone biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.3176\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eJuvenile hormone III\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eRiboflavin metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.4385\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eFlavin mononucleotide\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eNeomycin, kanamycin and gentamicin biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.5393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eParomamine\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003ePorphyrin and chlorophyll metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.5393\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003epyropheophorbide a\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eBile secretion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.6500\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eThromboxane B2, Aspirin\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eVitamin digestion and absorption\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eFlavin mononucleotide\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"10\" width=\"72\"\u003e\n\u003cp\u003eNegative ion mode\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eCarbohydrate digestion and absorption\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.1277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eSucralose\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003ePrion diseases\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.1277\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eCorticosterone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eInsect hormone biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.2414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eEcdysterone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eRegulation of lipolysis in adipocytes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.2414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eCorticosterone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eAldosterone synthesis and secretion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.2414\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eCorticosterone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eSteroid hormone biosynthesis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.3426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eCorticosterone\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003ePhenylalanine metabolism\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e0.3426\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eSalicylic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eBiosynthesis of unsaturated fatty acids\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eNervonic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eMetabolic pathways\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eCorticosterone, Luteolin, Salicylic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"222\"\u003e\n\u003cp\u003eBile secretion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"55\"\u003e\n\u003cp\u003e1.0000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"201\"\u003e\n\u003cp\u003eSalicylic acid\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"serum metabolomics, lactation, ovine, bovine","lastPublishedDoi":"10.21203/rs.3.rs-274458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-274458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe ovine milk owns higher lactoprotein, fat and other solids than bovine milk. However, the mechanism was not fully clear. To discover the specific mechanism, an untargeted metabolomics analyze of serum at mid-lactation by liquid chromagraphy-mass spectrometry (LC-MS) was performed. Then multivariate statistical analysis was carried out to find the specific differences. Final the different abundant metabolites were functionally enrichment by KEGG. In total, 1615 metabolites were detected in serum and 486 were annotated. The the largest metabolic category was lipids and lipid-like molecules (188 metabolites). 412 metabolites were identified as differential metabolites between two groups. KEGG pathway enrichment showed that 18 and 10 functional pathways of differential metabolites were enriched at positive and negative ion mode, separately. Notably, hernandezine, which is a novel AMPK activator, may play a role in the formation of lactoprotein of ovine milk. The results indicated that there may be different biological effects between two species serum. The serum metabolites could make help for the formation of milk.\u003c/p\u003e","manuscriptTitle":"Metabolomics Comparison Between Ovine and Bovine Serum at Mid-lactation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-08 14:29:51","doi":"10.21203/rs.3.rs-274458/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"750314f2-263a-4596-a681-d4e52e4c2c05","owner":[],"postedDate":"March 8th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2811239,"name":"Endocrinology \u0026 Metabolism"},{"id":2811240,"name":"Paleozoology"}],"tags":[],"updatedAt":"2021-07-07T07:29:17+00:00","versionOfRecord":[],"versionCreatedAt":"2021-03-08 14:29:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-274458","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-274458","identity":"rs-274458","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}