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Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics

Open ArchivePublished:March 01, 2019DOI:https://doi.org/10.3168/jds.2018-15601

      ABSTRACT

      The metabolic responses of cows undergo substantial changes during the transition from late pregnancy to early lactation. However, the molecular mechanisms associated with these changes in physiological metabolism have not been clearly elucidated. The objective of this study was to investigate metabolic changes in transition cows from the perspective of plasma metabolites. Plasma samples collected from 24 multiparous dairy cows on approximately d 21 prepartum and immediately postpartum were analyzed using ultra-high-performance liquid chromatography/time-of-flight mass spectrometry in positive and negative ion modes. In conjunction with multidimensional statistical methods (principal component analysis and orthogonal partial least squares discriminant analysis), differences in plasma metabolites were identified using the t-test and fold change analysis. Sixty-seven differential metabolites were identified consisting of AA, lipids, saccharides, and nucleotides. The levels of 32 plasma metabolites were significantly higher and those of 35 metabolites significantly lower after parturition than on d 21 prepartum. Pathway analysis indicated that the metabolites that increased from late pregnancy to early lactation were primarily involved in lipid metabolism and energy metabolism, whereas decreased metabolites were related to AA metabolism.

      Key words

      INTRODUCTION

      The transition period represents an important period in the production cycle of dairy cows, and generally refers to the period extending from 21 d before to 21 d after calving (
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      Biology of dairy cows during the transition period: The final frontier?.
      ). During the transition from late pregnancy to early lactation, the physiological conditions, nutritional status, and metabolic responses of cows undergo marked changes. During late pregnancy, DMI decreases, which can probably be attributed to physical obstruction of the rumen by the fetus and an increase in reproductive hormone levels (
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      Dry matter intake and energy balance in the transition period.
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      The role of estrogens in control of energy balance and glucose homeostasis.
      ). However, large amounts of glucose are required for the synthesis and secretion of milk after calving. Accordingly, the energy demand during this period is higher than the intake, resulting in a negative energy balance (
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      Interactions between negative energy balance, metabolic diseases, uterine health and immune response in transition dairy cows.
      ). The development of a negative energy balance will increase lipid mobilization and production of fatty acids that further exacerbate metabolic stress and inflammation response (
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      Regulation of organic nutrient metabolism during transition from late pregnancy to early lactation.
      ;
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      ). Moreover, fatty acid production is the main factor inducing the dysfunctional inflammatory response during liver lipid mobilization and can markedly increase the risk of metritis, fatty liver, ketosis, and mastitis in postpartum cows (
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      Lipid mobilization and inflammatory responses during the transition period of dairy cows.
      ). Additionally, elevated levels of reactive oxygen species generated during lipid peroxidation will aggravate the postpartum oxidative stress response (
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      Influence of body condition score on relationships between metabolic status and oxidative stress in periparturient dairy cows.
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      Oxidative stress.
      ). Presently, to augment the anti-stress and anti-inflammatory abilities of cows during calving, dietary supplementation strategies are used, including the provision of selenium, vitamin E, and n-3 PUFA, which might also reduce the risk of postpartum illness (
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      Supplementation of vitamin E, selenium and increased energy allowance mitigates the transition stress and improves postpartum reproductive performance in the crossbred cow.
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      Influence of conjugated linoleic acids and vitamin E on biochemical, hematological, and immunological variables of dairy cows during the transition period.
      ). Given that the physiological mechanisms associated with transition in dairy cows are not comprehensively understood, only limited measures are available to relieve transitional stress. Researchers have attempted to solve this problem using omics approaches, particularly metabolomics, from 2 perspectives: (1) the first involves gaining a better understanding of disease pathogenesis based on metabolomics to identify blood metabolites that differ between diseased and healthy cows, thereby providing information for initiating preventive measures. This method has been widely used to address ketosis, metritis, placenta retention, and fatty liver problems in dairy cows (
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      GC-MS metabolomics identifies metabolite alterations that precede subclinical mastitis in the blood of transition dairy vows.
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      • Ametaj B.N.
      043 Metabolomics uncovers serum biomarkers that can predict the risk of retained placenta in transition dairy cows.
      ). (2) The second perspective involves intensive study of the changes in metabolite levels in blood at different stages during the production cycle. Previous studies have assessed the dynamic changes in certain AA, biogenic amines, acylcarnitines, phosphatidylcholines, and sphingomyelins during the transition from nonlactating to lactating stages based on targeted metabolomics (−42, −10, 3, 21, and 100 d;
      • Kenéz Á.
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      A metabolomics approach to characterize phenotypes of metabolic transition from late pregnancy to early lactation in dairy cows.
      ), and it has accordingly been demonstrated that concentrations of glycerophospholipids and sphingolipids are significantly lower at 10 d before and 3 d after calving than at earlier or later stages in the transition period. However, few studies have shown changes in global metabolites and metabolic pathways during the transitional periods.
      Metabolomics was first systematically introduced by
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      • Holmes E.
      ‘Metabonomics': Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data.
      and has subsequently evolved into untargeted and targeted approaches. Targeted metabolomics is used to quantitatively detect metabolites in metabolic pathways of interest, whereas untargeted metabolomics examines metabolite differences between control and experimental groups, which also plays an important role in marker screening for disease (
      • Patti G.J.
      • Yanes O.
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      Innovation: Metabolomics: The apogee of the omics trilogy.
      ;
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      The use of “Omics” in lactation research in dairy cows.
      ). In this study, we used an untargeted metabolomics approach to analyze plasma metabolite profiles in transition cows and identify differential metabolites at 2 crucial time points (21 d before the due date and on the calving day) in the dairy cow production cycle. The identification of differential metabolites may provide novel information for elucidating physiological responses and characterizing new pathways that may be initiated under parturition stress.

      MATERIALS AND METHODS

      Sample Collection

      Sample collection was performed in strict accordance with the guidelines of the Care and Use of Laboratory Animals of China, and all procedures were approved by the Animal Care and Use Committee of Sichuan Agricultural University. For the experimental animals, we selected 24 healthy multiparous Holstein dairy cows with similar 305-d milk yields, parity, BCS, and due date from Puzhou Dairy Farm (Sichuan, China). The cows were housed in freestall barns and had free access to fresh water. A TMR diet was provided daily at 0600, 1200, and 1900 h. The ingredients and chemical composition of the prepartum TMR are shown in Supplemental Table S1 (https://doi.org/10.3168/jds.2018-15601). Blood was collected via the caudal vein at −21 d (21 d before the due date) before the morning feeding and on the day of calving within 6 h after the milking colostrum. Plasma was collected using EDTA as an anticoagulant, then centrifuged at 1,500 × g for 10 min at room temperature, and subsequently stored at −80°C. Samples were divided into 2 groups according to sampling time: 21 d before the due date (P-21 d) and after calving (P-0 d).

      Sample Pretreatment

      Forty-eight plasma samples were slowly thawed at 4°C, and from each sample a 100-μL aliquot was taken and added to 400 μL of a pre-cooled methanol/acetonitrile solution (1:1, vol/vol). Samples were then vortex mixed and maintained at −20°C for 60 min, followed by centrifugation at 14,000 × g and 4°C for 20 min. The supernatant fraction was collected and dried. The dried metabolites were dissolved by adding 100 μL of aqueous acetonitrile (acetonitrile:water = 1:1, vol/vol), vortex mixed, and centrifuged at 14,000 × g and 4°C for 15 min. The resulting supernatant was collected and analyzed. The 2 sets of treated samples were mixed in equal amounts for the preparation of quality control (QC) samples, and 6 replicates were set up to evaluate system stability over the entire experiment before testing. After the completion of sample pretreatment, the samples were sent to Shanghai Applied Protein Technology Co., Ltd. (Shanghai, China) for liquid chromatography-tandem mass spectrometry (MS/MS) analysis.

      Ultra-High-Performance Liquid Chromatography Time-of-Flight/MS Analysis

      Samples after pretreatment were separated using an ultra-high-performance liquid chromatography (UHPLC) system (1290 Infinity II, Agilent Technologies, Santa Clara, CA) incorporating an HILIC column (2.1 mm × 100 mm, 1.7 µm; Waters, Milford, MA). The column temperature was 25°C, and we used a flow rate of 0.3 mL/min. The mobile phase consisted of A (water + 25 mmol/L of ammonium acetate + 25 mmol/L of ammonia) and B (acetonitrile). The gradient elution procedure was as follows: 0–1 min, 95%B; 1–14 min, 95% to 65%B; 14–16 min, 65% to 40%B; 16–18 min, 40%B; 18–18.1 min, 40% to 95%B; and 18.1–23 min, 95%B. The autosampler was maintained at 4°C and the injection volume was 2 μL. The QC samples were inserted into the samples to monitor system stability and data quality.
      The samples were analyzed using a triple time-of-flight (TOF) 5600+ system (AB/SCIEX, Framingham, MA) equipped with an electrospray ionization source used in positive and negative ion modes. The mass spectrometry detection variables were as follows: gas 1, 0.4137 MPa; gas 2, 0.4137 MPa; curtain gas, 0.20685 MPa; ion source temperature, 600°C; ionization voltage, ±5,500 V; TOF-MS scan range, 60–1,000 m/z; precursor ion scan range, 25–1,000 m/z; scan accumulation time, 0.2 s/spectrum; precursor ion scan accumulation time, 0.05 s/spectrum; declustering potential, 60 V; and collision energy, 35 ± 15 eV. Tandem mass spectrometry data were acquired in the information-dependent acquisition mode and high sensitivity modes were used.

      Data Processing and Statistical Analyses

      The raw data were converted into the mzXML format using ProteoWizard (
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      A cross-platform toolkit for mass spectrometry and proteomics.
      ), and then peak alignment, retention time correction, and peak area extraction were performed using the R package XCMS (
      • Jia H.
      • Shen X.
      • Guan Y.
      • Xu M.
      • Tu J.
      • Mo M.
      • Xie L.
      • Yuan J.
      • Zhang Z.
      • Cai S.
      • Zhu J.
      • Zhu Z.
      Predicting the pathological response to neoadjuvant chemoradiation using untargeted metabolomics in locally advanced rectal cancer.
      ). For the data extracted using XCMS, ion peak data for which >50% of the data were missing within a group were deleted. After the data had been pre-processed by pareto-scaling, pattern recognition was performed using SIMCA-P software (version 14.1, Umetrics, Umea, Sweden), consisting of unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). Principal component analysis was used to determine intra-group aggregation and inter-group separation tendencies, whereas OPLS-DA was performed to further determine inter-group differences. The OPLS-DA models were validated based on interpretation of variation in Y (R2Y) and forecast ability based on the model (Q2) in cross-validation and permutation tests by applying 200 iterations. When 1 ≥ R2Y and Q2 ≥ 0.4, the models were determined to be stable and reliable (
      • Westerhuis J.A.
      • Hoefsloot H.C.
      • Smit S.
      • Vis D.J.
      • Smilde A.K.
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      Assessment of PLSDA cross validation.
      ). In addition, a Q2 intercept <0.05 from the permutation test was used to verify that there was no overfitting (
      • Liu H.
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      • Gu L.
      A 1 H NMR-based approach to investigate metabolomic differences in the plasma and urine of young women after cranberry juice or apple juice consumption.
      ), and univariate analysis was performed, including Student's t-test and fold change analysis.

      Metabolite Identification and Pathway Analysis

      Significantly differential metabolites were screened using variable importance in projection (VIP) scores (VIP >1) obtained from the OPLS-DA model and P-values (P < 0.05). Identification of differential metabolites was carried out by searching an in-house standard MS/MS library and the online database METLIN (http://metlin.scripps.edu/) using MS/MS spectra or exact mass data (
      • Jia H.
      • Shen X.
      • Guan Y.
      • Xu M.
      • Tu J.
      • Mo M.
      • Xie L.
      • Yuan J.
      • Zhang Z.
      • Cai S.
      • Zhu J.
      • Zhu Z.
      Predicting the pathological response to neoadjuvant chemoradiation using untargeted metabolomics in locally advanced rectal cancer.
      ). The in-house library contains MS/MS spectra of approximately 800 compounds, which were obtained from standards. The MS/MS spectra matching score was calculated using the dot-product algorithm and the score cutoff was set as 0.8 (
      • Stein S.E.
      • Scott D.R.
      Optimization and testing of mass spectral library search algorithms for compound identification.
      ;
      • Jia H.
      • Shen X.
      • Guan Y.
      • Xu M.
      • Tu J.
      • Mo M.
      • Xie L.
      • Yuan J.
      • Zhang Z.
      • Cai S.
      • Zhu J.
      • Zhu Z.
      Predicting the pathological response to neoadjuvant chemoradiation using untargeted metabolomics in locally advanced rectal cancer.
      ). The MS/MS spectra that could not be matched to any of those in the in-house library were searched in online databases. Mass error was set within 25 ppm. Moreover, clustering and pathway analyses data were processed and analyzed using MetaboAnalyst 4.0 (http://www.metaboanalyst.ca).

      RESULTS

      Metabolite Profiles of Plasma Samples and Data Analysis

      We compared the total ion chromatograms (TIC) of 6 QC samples in positive or negative ion modes, including the retention time (RT), peak, intensity, and degree of separation. Overlap of the TIC of QC samples was good, indicating that the method used was robust, with high repeatability and stability. The sample TIC showed that the peak shape was intact and that adjacent peaks were well separated from each other, indicating that the chromatographic and mass spectrometric conditions were suitable for sample identification (Supplemental Figure S1; https://doi.org/10.3168/jds.2018-15601).
      The PCA score plot showed that the model interpretation rates for the P-0 d and P-21 d groups under the positive and negative ion mode conditions were R2X = 0.525 and 0.565, respectively. The 2 groups of samples were well separated, and samples in the same group were well aggregated together (Figure 1A and 1B). A OPLS-DA supervised model was used to assess inter-group sample differences. In the positive ion mode of the OPLS-DA score plot, R2Y = 0.986 and Q2 = 0.930, whereas in the negative ion mode, R2Y = 0.982 and Q2 = 0.939. Both R2Y and Q2 values were greater than 0.4, indicating that the model was stable and reliable (Figure 2A and 2B). A Q2 value of approximately 1 indicated that the OPLS-DA model had good predictability. The Q2 intercept values were less than 0.05, indicating that there was no overfitting (Figure 2C and 2D).
      Figure thumbnail gr1
      Figure 1(A) Principal component analysis score plot for the P-0 d (sampling time after calving) and P-21 d group (sampling time 21 d before the due date) samples analyzed in the positive ion mode. (B) Principal component analysis score plot for the P-0 d and P-21 d group samples analyzed in the negative ion mode. t[1] = first principal component. t[2] = second principal component.
      Figure thumbnail gr2
      Figure 2(A) and (C) Orthogonal partial least square discriminant analysis (OPLS-DA) of scores and permutation test plots for the P-0 d (sampling time after calving) and P-21 d (sampling time 21 d before the due date) group samples analyzed in the positive ion mode, respectively. (B) and (D) Orthogonal partial least square discriminant analysis of scores and permutation test plots for the P-0 d and P-21 d group samples analyzed in the negative ion mode, respectively. t[1] = first principal component. to[2] = second orthogonal component. The intercept limit of Q2, calculated by regression line, is the plot of Q2 from permutation test in the OPLS-DA model.

      Differential Metabolite Analysis

      A VIP score >1 and P < 0.05 were used as criteria for differential metabolite screening. We identified a total of 67 differential metabolites, of which 37 were detected in the positive ion mode and 30 in the negative ion mode (Table 1, Table 2). Of these, the levels of 32 metabolites had increased, whereas those of 35 had decreased at P-0 d with respect to the levels at P-21 d. A generated cluster heat map showed that similar metabolites were located in close proximity, and a dendrogram indicated that the samples of the P-0 d and P-21 d groups can be separated (Figure 3A and 3B).
      Table 1Differential metabolites identified using positive-mode electrospray ionization
      Fold changes were calculated as the average levels in the P-0 d group (sampling time after calving) relative to those in the P-21 d group (sampling time 21 d before the due date). A fold change greater than 0 indicates relatively higher concentration in the P-0 d group, whereas a fold change of less than 0 indicates a concentration lower than that in the P-21 d group. RT = retention time; VIP = variable importance in projection.
      No.Metabolitem/zRT (s)VIPFold changeP-valueAdductPrimary pathway
      1l-Pro116.070574.9031.970−0.2780.002(M+H)+Arg and Pro metabolism
      2Nicotinamide123.05593.4982.422−0.957<0.001(M+H)+Nicotinate and nicotinamide metabolism
      3Thymine127.049175.0811.5160.771<0.001(M+H)+Pyrimidine metabolism
      45-Oxoproline130.049567.0511.2130.828<0.001(M+H)+Glutathione metabolism
      5Creatine132.076661.0662.1820.407<0.001(M+H)+Arg and Pro metabolism
      6Orn133.096936.2571.332−1.621<0.001(M+H)+Arg and Pro metabolism
      72-Methylglutaric acid146.06178.1191.194−0.5480.016M+
      8Acetylcholine146.117704.3532.114−0.548<0.001(M+H)+Glycerophospholipid metabolism
      9l-Pipecolic acid147.112972.6191.374−0.761<0.001(M+NH4)+Lys degradation
      10l-His156.076701.5121.338−0.385<0.001(M+H)+His metabolism
      11l-Carnitine162.112655.1263.725−0.3680.016(M+H)+Bile secretion
      121-MethylHis170.092650.5451.1310.5090.047(M+H)+His metabolism
      13l-Arg175.118948.9782.327−0.725<0.001(M+H)+Arg and Pro metabolism
      14l-Cit176.102732.0391.292−0.2400.01(M+H)+Arg and Pro metabolism
      15Phosphorylcholine184.072896.7221.132−0.749<0.001(M+H)+Glycerophospholipid metabolism
      165-Hydroxyindoleacetate192.064472.4871.200−0.831<0.001(M+H)+Trp metabolism
      17Phenylacetylglycine194.080361.6821.8630.6880.015(M+H)+Phe metabolism
      18d-Mannose198.096565.7614.1720.589<0.001(M+NH4)+Fructose and mannose metabolism
      19l-Kynurenine209.091472.7101.549−0.769<0.001(M+H)+Trp metabolism
      20l-Carnosine227.113791.4531.493−0.546<0.001(M+H)+His metabolism
      21Thymidine243.096175.9662.2980.683<0.001(M+H)+Pyrimidine metabolism
      22Uridine245.076287.9421.1250.508<0.001(M+H)+Pyrimidine metabolism
      23Glycerophosphocholine258.109725.4702.1450.621<0.001M+Glycerophospholipid metabolism
      24l-Norleucine263.196501.7081.138−0.747<0.001(2M+H)+
      25N-Oleoylethanolamine326.30460.9391.4551.126<0.001(M+H)+
      26l-Palmitoylcarnitine400.341293.9921.8041.967<0.001(M+H)+Fatty acid metabolism
      27Chenodeoxycholate410.325278.3422.7271.9790.01(M+NH4)+Secondary bile acid biosynthesis
      28Stearoylcarnitine428.372286.9562.6261.837<0.001(M+H)+
      29Vitamin E431.38651.7861.0590.811<0.001(M+H)+Vitamin digestion and absorption
      30Glycodeoxycholic acid450.320387.3071.652−0.7200.033(M+H)+Primary bile acid biosynthesis
      31LysoPC (14:0)468.307359.1941.757−1.381<0.001(M+H)+
      32LysoPC (16:0)496.338331.1852.479−0.744<0.001(M+H)+
      33Taurodeoxycholic acid517.329304.8001.225−0.7150.018(M+NH4)+Primary bile acid biosynthesis
      34LysoPC [18:1(9Z)]522.354324.5822.618−1.083<0.001(M+H)+
      35LysoPC (18:0)546.354339.3904.400−2.127<0.001(M+Na)+
      36Bilirubin585.26876.8763.9294.682<0.001(M+H)+Porphyrin metabolism
      37Phosphatidylcholine770.601254.6381.4580.926<0.001(M+H-H2O)+Glycerophospholipid metabolism
      1 Fold changes were calculated as the average levels in the P-0 d group (sampling time after calving) relative to those in the P-21 d group (sampling time 21 d before the due date). A fold change greater than 0 indicates relatively higher concentration in the P-0 d group, whereas a fold change of less than 0 indicates a concentration lower than that in the P-21 d group. RT = retention time; VIP = variable importance in projection.
      Table 2Identification results of differential metabolites using negative-mode electrospray ionization
      Fold changes were calculated as the average levels in the P-0 d group (sampling time after calving) relative to those in the P-21 d group (sampling time 21 d before the due date). A fold change greater than 0 indicates a relatively higher concentration in the P-0 d group, whereas a fold change of less than 0 indicates a concentration lower than that in the P-21 d group. RT = retention time; VIP = variable importance in projection.
      No.Metabolitem/zRT (s)VIPFold changeP-valueAdductPrimary pathway
      1l-Lactic acid89.026433.4363.7491.259<0.001(M−H)−Glycolysis or gluconeogenesis
      2Pyrocatechol109.03037.6181.285−1.023<0.001(M−H)−
      3l-Val116.073560.0592.387−0.813<0.001(M−H)−Val, Leu, and Ile degradation
      4Ketoleucine129.05774.7101.5070.321<0.001(M−H)−
      5l-Ile130.088507.0573.421−0.655<0.001(M−H)−Val, Leu, and Ile degradation
      6l-Leu130.088483.5573.668−0.398<0.001(M−H)−Val, Leu, and Ile degradation
      7l-Glutamate146.046756.9301.436−0.559<0.001(M−H)−d-Gln and d-glutamate metabolism
      83-Phenylpropanoic acid149.061173.8936.790−1.293<0.001(M−H)−Phe metabolism
      9Allantoin157.037332.5342.594−0.2280.006(M−H)−
      10Capric acid171.13978.3941.0710.863<0.001(M−H)−Fatty acid biosynthesis
      11Hippuric acid178.051377.4553.959−0.580<0.001(M−H)−Phe metabolism
      12Alpha-d-glucose179.056560.0291.1690.444<0.001(M−H)−Glycolysis or gluconeogenesis
      13d-Fructose179.057577.7152.2810.555<0.001(M−H)−Fructose and mannose metabolism
      14l-Tyr180.066557.4261.137−0.388<0.001(M−H)−Tyr metabolism
      15l-Trp203.083471.0482.669−1.676<0.001(M−H)−Trp metabolism
      16Palmitic acid255.23373.35611.8000.977<0.001(M−H)−Fatty acid biosynthesis
      1716-Hydroxypalmitic acid271.22778.7443.2832.073<0.001(M−H)−
      18γ-Linolenic acid277.21772.2453.7031.055<0.001(M−H)−Linoleic acid metabolism
      19Linoleic acid279.23271.6338.0120.985<0.001(M−H)−Linoleic acid metabolism
      20Oleic acid281.24870.40228.9571.835<0.001(M−H)−Biosynthesis of UFA
      21Arachidonic acid303.23269.2002.0310.562<0.001(M−H)−Arachidonic acid metabolism
      22Dihomo-γ-linolenic acid305.24868.5861.1880.519<0.001(M−H)−Linoleic acid metabolism
      23Lactose341.108763.2393.4793.435<0.001(M−H)−Galactose metabolism
      24Deoxycholic acid391.284269.5201.1111.4920.012(M−H)−Secondary bile acid biosynthesis
      25Glycochenodeoxycholate448.306391.0721.308−0.7320.024(M−H)−Primary bile acid biosynthesis
      26Myristic acid455.40975.3211.5502.629<0.001(2M−H)−Fatty acid biosynthesis
      27Glycocholic acid464.301475.3402.205−0.9600.016(M−H)−Primary bile acid biosynthesis
      28Taurolithocholic acid482.294137.6701.022−1.117<0.001(M−H)−
      29Taurochenodeoxycholate498.289306.2371.277−0.9380.006(M−H)−Primary bile acid biosynthesis
      30Palmitoleic acid507.44173.9803.2264.076<0.001(2M−H)−Fatty acid biosynthesis
      1 Fold changes were calculated as the average levels in the P-0 d group (sampling time after calving) relative to those in the P-21 d group (sampling time 21 d before the due date). A fold change greater than 0 indicates a relatively higher concentration in the P-0 d group, whereas a fold change of less than 0 indicates a concentration lower than that in the P-21 d group. RT = retention time; VIP = variable importance in projection.
      Figure thumbnail gr3
      Figure 3Heatmap of the 37 and 30 differential metabolites identified in serum samples analyzed in positive and negative ion modes, respectively. P-0 d = sampling time after calving; P-21 d = sampling time 21 d before the due date.
      We subsequently queried differential metabolites in the Kyoto Encyclopedia of Genes and Genomes pathway database and searched published articles for data relating to global metabolism (Figure 4). The global metabolic pathways identified are involved in lipid metabolism, energy metabolism, AA metabolism, and nucleotide metabolism, and 3 reaction pathways were analyzed: gluconeogenesis, urea cycle, and tricarboxylic acid (TCA) cycle. To further determine the biological significance of the differential metabolites, we performed a metabolic pathway analysis using MetaboAnalyst software. A total of 10 pathway impacts > 0.2 with P < 0.05 were observed for the main metabolic pathway, among which 3 pathways had a pathway impact value of 1: linoleic acid (LA) metabolism; Val-Leu and Ile biosynthesis; and d-Gln and d-glutamate metabolism (Figure 5).
      Figure thumbnail gr4
      Figure 4Metabolic pathway for differential metabolites, mainly for lipid metabolism, energy metabolism, amino metabolism, and nucleotide metabolism. (+) indicates higher concentrations during calving; (−) indicates lower concentrations than those observed at d 21 prepartum. ARA = arachidonic acid; GLA = gamma linolenic acid; LA = linoleic acid; NEFA = nonesterified fatty acids; PC = phosphatidylcholine; Gly = glycerophosphocholine; Ach = acetylcholine; ChoP = phosphorylcholine; TG = triglyceride; PEP = phosphoenolpyruvate; PRPP = 5-phosphoribosyl 1-pyrophosphate; UMP = uridine monophosphate; TCDC = taurochenodeoxycholate; GCDC = glycochenodeoxycholate; CDC = chenodeoxycholate; DC = deoxycholic acid; TCA = tricarboxylic acid.
      Figure thumbnail gr5
      Figure 5Metabolic pathway analysis using MetaboAnalyst 4.0 (http://www.metaboanalyst.ca). x-axis, pathway impact; y-axis, −log (P). Circles represent metabolic pathways. Darker circles indicate more significant changes in the metabolites in the corresponding pathway, whereas the size of the circle corresponds to the pathway impact score.

      DISCUSSION

      Several previous studies have indicated that inflammatory responses and oxidative stress are common occurrences around the time of calving in dairy cows (
      • Contreras G.A.
      • O'Boyle N.J.
      • Herdt T.H.
      • Sordillo L.M.
      Lipomobilization in periparturient dairy cows influences the composition of plasma nonesterified fatty acids and leukocyte phospholipid fatty acids.
      ;
      • Sharma N.
      • Singh N.K.
      • Singh O.P.
      • Pandey V.
      • Verma P.K.
      Oxidative stress and antioxidant status during transition period in dairy cows.
      ;
      • Trevisi E.
      • Amadori M.
      • Cogrossi S.
      • Razzuoli E.
      • Bertoni G.
      Metabolic stress and inflammatory response in high-yielding, periparturient dairy cows.
      ). Lipid mediators derived from lipid metabolism play an important role in the inflammatory responses of cows during calving (
      • Sordillo L.M.
      • Mavangira V.
      The nexus between nutrient metabolism, oxidative stress and inflammation in transition cows.
      ;
      • Mavangira V.
      • Sordillo L.M.
      Role of lipid mediators in the regulation of oxidative stress and inflammatory responses in dairy cattle.
      ). And among these, the oxylipid derived from the n-6 PUFA enzymatic pathway is involved in the initiation of inflammation and pro-inflammation (
      • Marion-Letellier R.
      • Savoye G.
      • Ghosh S.
      Polyunsaturated fatty acids and inflammation.
      ). Although we did not identify any oxylipids in the present study, we did identify several important precursors in the pathway, including LA, arachidonic acid, γ-linolenic acid, and phosphatidylcholine (PC). These lipids were upregulated postpartum, which is consistent with the findings of previous studies. Some previous studies have indicated that eicosanoids derived from arachidonic acid via the cyclooxygenase/lipoxygenase pathway, including leukotrienes, prostaglandins, and hydroxyeicosatetraenoic acids, may act as regulators of inflammation (
      • Raphael W.
      • Halbert L.
      • Contreras G.A.
      • Sordillo L.M.
      Association between polyunsaturated fatty acid-derived oxylipid biosynthesis and leukocyte inflammatory marker expression in periparturient dairy cows.
      ;
      • Mavangira V.
      • Gandy J.C.
      • Zhang C.
      • Ryman V.E.
      • Daniel Jones A.
      • Sordillo L.M.
      Polyunsaturated fatty acids influence differential biosynthesis of oxylipids and other lipid mediators during bovine coliform mastitis.
      ;
      • Ryman V.E.
      • Pighetti G.M.
      • Lippolis J.D.
      • Gandy J.C.
      • Applegate C.M.
      • Sordillo L.M.
      Quantification of bovine oxylipids during intramammary Streptococcus uberis infection.
      ). As a major n-6 PUFA, LA can be used as a precursor of arachidonic acid via dehydrogenase catalysis. Moreover, it can be derived from hydroperoxyoctadecadienoic acid to promote inflammation via the 15-LOX pathway (
      • Gulliver C.E.
      • Friend M.A.
      • King B.J.
      • Clayton E.H.
      The role of omega-3 polyunsaturated fatty acids in reproduction of sheep and cattle.
      ;
      • Ryman V.E.
      • Packiriswamy N.
      • Sordillo L.M.
      Apoptosis of endothelial cells by 13-HPODE contributes to impairment of endothelial barrier integrity.
      ). The PC metabolism has been shown to be enhanced under inflammatory conditions and increases the production of arachidonic acid and LA via the action of a phospholipase (
      • Dennis E.A.
      • Norris P.C.
      Eicosanoid storm in infection and inflammation.
      ;
      • Leng X.
      • Kinnun J.J.
      • Cavazos A.T.
      • Canner S.W.
      • Shaikh S.R.
      • Feller S.E.
      • Wassall S.R.
      All n-3 PUFA are not the same: MD simulations reveal differences in membrane organization for EPA, DHA and DPA.
      ). It has also been reported that LA, oleic acid, and palmitic acid levels are significantly elevated in the serum of cows with mastitis, which is consistent with the lipid changes identified in the present study (
      • Dervishi E.
      • Zhang G.
      • Dunn S.M.
      • Mandal R.
      • Wishart D.S.
      • Ametaj B.N.
      GC-MS metabolomics identifies metabolite alterations that precede subclinical mastitis in the blood of transition dairy vows.
      ). Furthermore, we found that several lysophosphatidylcholines (LysoPC) were reduced in the PC metabolic pathway via the phospholipase A2 (PLA2) pathway. This is consistent with the observed significant decreases in LysoPC, including LysoPC14:0, LysoPC16:0, LysoPC18:0, and LysoPC18:1, when cows enter the lactation stage, as reported by
      • Kenéz Á.
      • Dänicke S.
      • Rolle-Kampczyk U.
      • von Bergen M.
      • Huber K.
      A metabolomics approach to characterize phenotypes of metabolic transition from late pregnancy to early lactation in dairy cows.
      . Studies have also shown that, compared with 4 wk before calving, both LysoPC C20:4 and C28:0 decreased after calving, whereas cows with a retained placenta show a decreasing trend at 8 wk before calving (
      • Dervishi E.
      • Zhang G.
      • Mandal R.
      • Wishart D.S.
      • Ametaj B.N.
      043 Metabolomics uncovers serum biomarkers that can predict the risk of retained placenta in transition dairy cows.
      ). However, research on these types of LysoPC has indicated that pro-inflammatory cytokine vascular cell adhesion molecule-1 promotes the production of LysoPC16:0, LysoPC18:0, and LysoPC18:1, and that this activity is inhibited by the action of oleyloxyethyl phosphorylcholine, a PLA2-selective inhibitor (
      • Zhuge Y.
      • Yuan Y.
      • van Breemen R.
      • Degrand M.
      • Holian O.
      • Yoder M.
      • Lum H.
      Stimulated bronchial epithelial cells release bioactive lysophosphatidylcholine 16:0, 18:0, and 18:1.
      ). The observed decreases in levels of LysoPC after parturition may thus be related to the action of PLA2-selective inhibitors, and further research is needed in this regard.
      Previous studies have confirmed that the DMI of dairy cows is significantly reduced during the transition period, particularly immediately before and after parturition (
      • Bell A.W.
      Regulation of organic nutrient metabolism during transition from late pregnancy to early lactation.
      ;
      • Gandra J.R.
      • Freitas Junior., J.E.D.
      • Maturna Filho M.
      • Barletta R.V.
      • Verdurico L.C.
      • Rennó F.P.
      Soybean oil and calcium salts of fatty acids as fat sources for Holstein dairy cows in transition period.
      ;
      • Cheong S.H.
      • Sa Filho O.G.
      • Absalon-Medina V.A.
      • Pelton S.H.
      • Butler W.R.
      • Gilbert R.O.
      Metabolic and endocrine differences between dairy cows that do or do not ovulate first postpartum dominant follicles.
      ). However, when cows enter the lactation stage, energy intake is insufficient to meet the energy demand for lactation, and thus the body will increase the production of endogenous glucose to maintain glucose homeostasis (
      • Wankhade P.R.
      • Manimaran A.
      • Kumaresan A.
      • Jeyakumar S.
      • Ramesha K.P.
      • Sejian V.
      • Rajendran D.
      • Varghese M.R.
      Metabolic and immunological changes in transition dairy cows: A review.
      ). Scholars have found that blood glucose levels in cows increase significantly after parturition (
      • Salin S.
      • Vanhatalo A.
      • Elo K.
      • Taponen J.
      • Boston R.C.
      • Kokkonen T.
      Effects of dietary energy allowance and decline in dry matter intake during the dry period on responses to glucose and insulin in transition dairy cows.
      ). Consistently, in the present study, we observed that glucose levels in the plasma of dairy cows also increased significantly after parturition. Moreover, we found that levels of lactic acid, which is a TCA cycle precursor, also increased significantly in the gluconeogenesis pathway. We also suspect that uridine in the nucleotide metabolic pathway is involved in insulin resistance. Uridine is a pyrimidine nucleotide synthesized from pyrimidine and ribose, and can be phosphorylated to produce uridine triphosphate that acts in concert with glucose-1-phosphate to produce uridine diphosphate glucose, which is involved in glycogen synthesis (
      • Connolly G.P.
      • Duley J.A.
      Uridine and its nucleotides: Biological actions, therapeutic potentials.
      ;
      • Roach P.J.
      • Depaoliroach A.A.
      • Hurley T.D.
      • Tagliabracci V.S.
      Glycogen and its metabolism: Some new developments and old themes.
      ). Uridine plays a role in insulin resistance and promotes gluconeogenesis, largely through an increase in liver protein glycosylation and a reduction in protein phosphorylation in the hepatic insulin signaling pathway (
      • Park S.Y.
      • Ryu J.
      • Lee W.
      O-GlcNAc modification on IRS-1 and Akt2 by PUGNAc inhibits their phosphorylation and induces insulin resistance in rat primary adipocytes.
      ;
      • Urasaki Y.
      • Pizzorno G.
      • Le T.T.
      Uridine affects liver protein glycosylation, insulin signaling, and heme biosynthesis.
      ). Thus, we believe that nucleotide metabolism is involved in glucose homeostasis after parturition in dairy cows.
      Amino acids are important substances in the synthesis of tissue proteins and milk components. Previous studies have indicated that certain AA can affect cow performance and milk quality (
      • Robinson P.H.
      • Swanepoel N.
      • Evans E.
      Effects of feeding a ruminally protected lysine product, with or without isoleucine, valine and histidine, to lactating dairy cows on their productive performance and plasma amino acid profiles.
      ;
      • Lee C.
      • Giallongo F.
      • Hristov A.N.
      • Lapierre H.
      • Cassidy T.W.
      • Heyler K.S.
      • Varga G.A.
      • Parys C.
      Effect of dietary protein level and rumen-protected amino acid supplementation on amino acid utilization for milk protein in lactating dairy cows.
      ;
      • Giallongo F.
      • Harper M.T.
      • Oh J.
      • Lopes J.C.
      • Lapierre H.
      • Patton R.A.
      • Parys C.
      • Shinzato I.
      • Hristov A.N.
      Effects of rumen-protected methionine, lysine, and histidine on lactation performance of dairy cows.
      ). However, limited information is currently available regarding the AA metabolism of dairy cows, particularly during the transition period. Although 21 AA were detected in a study conducted by
      • Kenéz Á.
      • Dänicke S.
      • Rolle-Kampczyk U.
      • von Bergen M.
      • Huber K.
      A metabolomics approach to characterize phenotypes of metabolic transition from late pregnancy to early lactation in dairy cows.
      , none of these were observed to undergo significant changes during the prepartum and postpartum periods. In the present study, however, we found that certain AA in the plasma of cows, including Val, Ile, Leu, His, Trp, Glu, and Arg, underwent significant changes during parturition. Furthermore, we found that the urea cycle may be affected during parturition. Previous studies have indicated that the levels of Arg and Glu in the urea cycle are closely related to the synthesis of antioxidant and anti-inflammatory molecules (
      • Jiao N.
      • Wu Z.
      • Ji Y.
      • Wang B.
      • Dai Z.
      • Wu G.
      L-Glutamate enhances barrier and antioxidative functions in intestinal porcine epithelial cells.
      ;
      • Liang M.
      • Wang Z.
      • Li H.
      • Cai L.
      • Pan J.
      • He H.
      • Wu Q.
      • Tang Y.
      • Ma J.
      • Yang L.
      l-Arginine induces antioxidant response to prevent oxidative stress via stimulation of glutathione synthesis and activation of Nrf2 pathway.
      ;
      • Zhao F.F.
      • Wu T.Y.
      • Wang H.R.
      • Ding L.Y.
      • Ahmed G.
      • Li H.W.
      • Tian W.
      • Shen Y.Z.
      Jugular arginine infusion relieves lipopolysaccharide-triggered inflammatory stress and improves immunity status of lactating dairy cows.
      ). Moreover, we found that certain AA affect gluconeogenesis in dairy cows after calving by participating in the TCA cycle. Consistently, Arg, Pro, and Glu in the urea cycle have previously been shown to participate in the TCA cycle by affecting the formation of fumarate (
      • Sugiyama K.
      • Ebinuma H.
      • Nakamoto N.
      • Sakasegawa N.
      • Murakami Y.
      • Chu P.S.
      • Usui S.
      • Ishibashi Y.
      • Wakayama Y.
      • Taniki N.
      • Murata H.
      • Saito Y.
      • Fukasawa M.
      • Saito K.
      • Yamagishi Y.
      • Wakita T.
      • Takaku H.
      • Hibi T.
      • Saito H.
      • Kanai T.
      Prominent steatosis with hypermetabolism of the cell line permissive for years of infection with hepatitis C virus.
      ;
      • Yoshimi N.
      • Futamura T.
      • Kakumoto K.
      • Salehi A.M.
      • Sellgren C.M.
      • Holmen-Larsson J.
      • Jakobsson J.
      • Palsson E.
      • Landen M.
      • Hashimoto K.
      Blood metabolomics analysis identifies abnormalities in the citric acid cycle, urea cycle, and amino acid metabolism in bipolar disorder.
      ). We found that other AA involved in the TCA cycle, including Leu and Trp, also undergo similar changes after calving, whereas previous studies have found that levels of the AA His, Leu, Lys, Pro, and Trp are significantly increased in the serum of cows with ketosis (
      • Sun L.W.
      • Zhang H.Y.
      • Wu L.
      • Shu S.
      • Xia C.
      • Xu C.
      • Zheng J.S.
      (1)H-Nuclear magnetic resonance-based plasma metabolic profiling of dairy cows with clinical and subclinical ketosis.
      ;
      • Zhang G.
      • Dervishi E.
      • Dunn S.M.
      • Mandal R.
      • Liu P.
      • Han B.
      • Wishart D.S.
      • Ametaj B.N.
      Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease.
      ), thereby indicating that the urea cycle participates in the gluconeogenesis of cows in response to a negative energy balance. However, the specific mechanisms underlying the AA metabolism involved in energy metabolism during calving need further investigation.

      CONCLUSIONS

      In this study, a metabolomics approach based on UHPLC-TOF/MS analysis was used to study metabolic changes in transition dairy cows. We accordingly identified 67 metabolites that showed differential expression between d 21 before the due date and after calving. Furthermore, we found that lipid, glucose, and nucleotide metabolism increased after calving, whereas AA metabolism decreased. Further studies should use targeted metabolomics to verify metabolite levels and to analyze related regulatory enzymes in metabolic pathways of interest.

      ACKNOWLEDGMENTS

      This project was supported by the Double Subject Construction Plan of Sichuan Agricultural University (no. 03571537). We thank Shanghai Applied Protein Technology Co. Ltd. (Shanghai, China) for UHPLC-TOF/MS analysis.

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