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Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period

  • Jun Zhang
    Affiliations
    College of Animal Science and Technology, Northwest A&F University, Yangling 712100 China

    State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
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  • Naren Gaowa
    Affiliations
    State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
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  • Yajing Wang
    Affiliations
    State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
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  • Huanxu Li
    Affiliations
    Beijing Oriental Kingherd Biotechnology Company, Beijing 100193, China
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  • Zhijun Cao
    Affiliations
    State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
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  • Hongjian Yang
    Affiliations
    State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
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  • Xiaoming Zhang
    Affiliations
    State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
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  • Shengli Li
    Correspondence
    Corresponding author
    Affiliations
    State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing 100193 China
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Open AccessPublished:December 23, 2022DOI:https://doi.org/10.3168/jds.2022-22224

      ABSTRACT

      The transition period from late pregnancy to early lactation is a vital time of the lifecycle of dairy cows due to the marked metabolic challenges. Besides, the liver is the pivot point of metabolism in cattle. Nevertheless, the hepatic physiological molecular adaptation during the transition period has not been elucidated, especially from the metabolomics and proteomics view. Therefore, the present study aims to investigate the hepatic metabolic alterations in transition cows by using integrative metabolomics and proteomics methods. Gas chromatography quadrupole-time-of-flight mass spectrometry-based metabolomics and data-independent acquisition-based quantitative proteomics methods were used to analyze liver tissues collected from 8 healthy multiparous Holstein dairy cows 21 d before and after calving. In total, 44 metabolites and 250 proteins were identified as differentially expressed from 233 metabolites and 3,539 proteins detected from the liver biopsies during the transition period. Complementary functional analysis of different metabolites and proteins indicated the upregulated gluconeogenesis, TCA cycles, AA degradation, fatty acid oxidation, AMP-activated protein kinase signaling pathway, peroxisome proliferator-activated receptor signaling pathway, and ribosome proteins in postpartum dairy cows. In terms of the metabolites and proteins, glucose-6-phosphate, fructose-6-phosphate, carnitine palmitoyltransferase 1A, and phosphoenolpyruvate carboxykinase played a significant role in these pathways. The upregulated oxidative status may be accompanied by the pathways mentioned above. In addition, the upregulated glucagon and insulin signaling pathways also indicated the significant requirement for glucose in postpartum dairy cows. These outcomes, from the view of global metabolites and proteins, may present a better comprehension of the biology of the transition period, which can be helpful in further developing nutritional regulation strategies targeting the liver to help cows overcome this metabolically challenging time.

      Key words

      INTRODUCTION

      The transition period from late gestation to early lactation is the most physically, physiologically, and metabolically challenging time during the whole lifetime of dairy cows (
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      Impact of changes in organic nutrient metabolism on feeding the transition dairy cow.
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      Physiological changes at parturition and their relationship to metabolic disorders.
      ;
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      ADSA foundation scholar award. Biology of dairy cows during the transition period: The final frontier?.
      ). Maternal cows experience changes such as DMI decrease, fetal development, parturition, and the onset of lactation (
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      Dry matter intake and energy balance in the transition period.
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      Effects of energy density in close-up diets and postpartum supplementation of extruded full-fat soybean on lactation performance and metabolic and hormonal status of dairy cows.
      ). Moreover, cows are the most susceptible to diseases over the transition period (
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      Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows.
      ). Thus, the transition period is critical to the future production, health, and sustainable profitability of cows and is seen as the final frontier of the biology of dairy cows (
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      ADSA foundation scholar award. Biology of dairy cows during the transition period: The final frontier?.
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      Maternal metabolic responses, nutritional status, and physiological conditions undergo marked changes in the transition from late pregnancy to early lactation (
      • Ceciliani F.
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      Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows.
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      Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics.
      ). As a central metabolic organ within the whole body, the activities of the liver are intensively influenced by many nutritional and physiological factors (
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      • Cao S.Z.
      Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics.
      ;
      • Zhang J.
      • Shi H.
      • Li S.
      • Cao Z.
      • Yang H.
      • Wang Y.
      Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
      ). The associated processes are mainly regulated by variations of genes, proteins, enzymes, and metabolites (
      • Ceciliani F.
      • Lecchi C.
      • Urh C.
      • Sauerwein H.
      Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows.
      ;
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      • Ji S.
      • Cao Z.
      • Zhang H.
      • Wang Y.
      Effects of a wide range of dietary forage-to-concentrate ratios on nutrient utilization and hepatic transcriptional profiles in limit-fed Holstein heifers.
      ;
      • Zhang J.
      • Shi H.
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      • Cao Z.
      • Yang H.
      • Wang Y.
      Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
      ). Adapting the critical metabolic pathways in the liver, to a certain extent, determines whether the cows can survive the transition period smoothly (
      • Drackley J.K.
      • Overton T.R.
      • Douglas G.N.
      Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period.
      ). The physical compression of the rumen by the fetus and increased reproduction hormone levels during the late pregnancy period were 2 of the factors causing decreased DMI (
      • Grummer R.R.
      • Mashek D.G.
      • Hayirli A.
      Dry matter intake and energy balance in the transition period.
      ;
      • Loor J.J.
      Genomics of metabolic adaptations in the peripartal cow.
      ), which then induces negative energy balance (NEB) in transition dairy cows (
      • Huang W.
      • Tian Y.
      • Wang Y.
      • Simayi A.
      • Yasheng A.
      • Wu Z.
      • Li S.
      • Cao Z.
      Effect of reduced energy density of close-up diets on dry matter intake, lactation performance and energy balance in multiparous holstein cows.
      ;
      • Zhang Q.
      • Su H.
      • Wang F.
      • Cao Z.
      • Li S.
      Effects of energy density in close-up diets and postpartum supplementation of extruded full-fat soybean on lactation performance and metabolic and hormonal status of dairy cows.
      ). Furthermore, the imbalance between large amounts of energy required for milk synthesis and secretion and lower DMI after calving exacerbates the NEB. The intensified NEB facilitates body lipid as well as protein mobilization and mainly results in higher uptake of nonesterified fatty acids (NEFA) by the liver (
      • Kuhla B.
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      • Albrecht D.
      • Gors S.
      • Hammon H.M.
      • Metges C.C.
      Involvement of skeletal muscle protein, glycogen, and fat metabolism in the adaptation on early lactation of dairy cows.
      ;
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      • Waters S.
      • Morris D.
      • Kenny D.
      • Lynn D.
      • Creevey C.
      RNA-seq analysis of differential gene expression in liver from lactating dairy cows divergent in negative energy balance.
      ;
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      • Borner S.
      • Hacke S.
      • Kautzsch U.
      • Albrecht D.
      • Hammon H.M.
      • Rontgen M.
      • Kuhla B.
      Increased anaplerosis, TCA cycling, and oxidative phosphorylation in the liver of dairy cows with intensive body fat mobilization during early lactation.
      ). The excessive NEFA in the liver will be transformed into ketone bodies like BHB or reesterified triglycerides (TG) and further increase the risk of ketosis and fatty liver, respectively, both of which are, in turn, potentially detrimental to the health of the cows (
      • Goff J.P.
      • Horst R.L.
      Physiological changes at parturition and their relationship to metabolic disorders.
      ;
      • Drackley J.K.
      ADSA foundation scholar award. Biology of dairy cows during the transition period: The final frontier?.
      ;
      • McCabe M.
      • Waters S.
      • Morris D.
      • Kenny D.
      • Lynn D.
      • Creevey C.
      RNA-seq analysis of differential gene expression in liver from lactating dairy cows divergent in negative energy balance.
      ). Given the complexity of metabolic changes during the transition period, we still lack a complete explanation of the physiological mechanisms. Therefore, a better understanding of the global hepatic metabolites and protein profiles during the transition period may be beneficial in reducing the risk of metabolic disease and increasing the profitability of cows.
      To further investigate the complex changes during the transition period, the omics approaches, which feature high-throughput and large-scale data, appear to be ideal tools. In most previous studies, quantitative PCR, microarray, or RNA-seq-based transcriptomics methods were used to investigate the hepatic physiological mechanisms associated with the transition (
      • Loor J.J.
      • Dann H.M.
      • Everts R.E.
      • Oliveira R.
      • Green C.A.
      • Guretzky N.A.
      • Rodriguez-Zas S.L.
      • Lewin H.A.
      • Drackley J.K.
      Temporal gene expression profiling of liver from periparturient dairy cows reveals complex adaptive mechanisms in hepatic function.
      ;
      • Loor J.J.
      Genomics of metabolic adaptations in the peripartal cow.
      ;
      • Ha N.T.
      • Drogemuller C.
      • Reimer C.
      • Schmitz-Hsu F.
      • Bruckmaier R.M.
      • Simianer H.
      • Gross J.J.
      Liver transcriptome analysis reveals important factors involved in the metabolic adaptation of the transition cow.
      ). However, the changes in transcriptome cannot guarantee the subsequent phenotypic variation due to translation efficiency, post-transcriptional regulation, and protein half-life (
      • Gatto L.
      • Hansen K.D.
      • Hoopmann M.R.
      • Hermjakob H.
      • Kohlbacher O.
      • Beyer A.
      Testing and validation of computational methods for mass spectrometry.
      ;
      • Ceciliani F.
      • Lecchi C.
      • Urh C.
      • Sauerwein H.
      Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows.
      ;
      • Schatton D.
      • Rugarli E.I.
      Post-transcriptional regulation of mitochondrial function.
      ). With the fast development in bioinformatics tools and technology, using metabolomics and proteomics in livestock science is widely accepted (
      • Ceciliani F.
      • Lecchi C.
      • Urh C.
      • Sauerwein H.
      Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows.
      ;
      • Luo Z.Z.
      • Shen L.H.
      • Jiang J.
      • Huang Y.X.
      • Bai L.P.
      • Yu S.M.
      • Yao X.P.
      • Ren Z.H.
      • Yang Y.X.
      • Cao S.Z.
      Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics.
      ;
      • Zhang J.
      • Shi H.
      • Li S.
      • Cao Z.
      • Yang H.
      • Wang Y.
      Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
      ). Owing to the high sensitivity in measuring small molecular metabolites, metabolomics is used in diverse biological systems (
      • Zhang J.
      • Shi H.
      • Wang Y.
      • Li S.
      • Cao Z.
      • Ji S.
      • He Y.
      • Zhang H.
      Effect of dietary forage to concentrate ratios on dynamic profile changes and interactions of ruminal microbiota and metabolites in Holstein heifers.
      ;
      • Ceciliani F.
      • Lecchi C.
      • Urh C.
      • Sauerwein H.
      Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows.
      ;
      • Zhang J.
      • Shi H.
      • Li S.
      • Cao Z.
      • Yang H.
      • Wang Y.
      Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
      ). Quantitative proteomics, which achieves high accuracy and precision of the quantification and includes a description of post-translational protein modifications, has been accepted in most functional proteome studies (
      • Ceciliani F.
      • Lecchi C.
      • Urh C.
      • Sauerwein H.
      Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows.
      ;
      • Muntel J.
      • Kirkpatrick J.
      • Bruderer R.
      • Huang T.
      • Vitek O.
      • Ori A.
      • Reiter L.
      Comparison of protein quantification in a complex background by DIA and TMT workflows with fixed instrument time.
      ). Thus, in this exploratory and hypothesis-generating work, we aimed to perform a complementary bioinformatic analysis of the global hepatic metabolites and proteins by using untargeted metabolomics and data-independent acquisition (DIA)-based quantitative proteomic on liver samples from transition dairy cows. From this work, we expected to provide a comprehensive view of the hepatic adaptation to the transition period in dairy cows.

      MATERIALS AND METHODS

      Animals and Diets

      In the present study, requirements and regulations of Instructive Notions with Respect to Caring for Experimental Animals, Ministry of Science and Technology of China were followed in detail. This protocol was passed by the Institutional Animal Care and Use Committee of China Agricultural University (Beijing, P. R. China, permit no. AW03039102–2). For the experimental animals, 12 healthy multiparous Holstein dairy cows with similar 305 d milk yields (total milk yield of 9,210 to 10,870 kg of the last lactation period), age (57.42 ± 6.79 mo), BCS, and calving date (difference less than 2 wk) from Sunlon Livestock Jinyindao Farm (Daxing County, Beijing, China) were kept in a free-stall barn with free access to fresh water. During the far-off and early lactation period, the dry cow and lactating cow TMR was delivered twice daily at 0730 and 1400 h. During the close-up period, the prepartum (PREP) TMR was delivered once daily at 1400 h. The detailed ingredients and chemical composition of the TMR can be found in Supplemental Table S1 (https://doi.org/10.5281/zenodo.6792970;
      • Zhang J.
      • Gaowa N.
      • Wang Y.
      • Li H.
      • Cao Z.
      • Yang H.
      • Zhang X.
      • Li S.
      Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period [Data set]. Zenodo.
      ).

      Feed Intake, Blood, and Milk Sample Collection and Measurement

      Individual feed intake was recorded daily by the Roughage Intake Control System (RFID, Zhenghong Company), which can identify the cow ID before opening the trough and measure the feed weight before and after cow eating, as described by
      • Gaowa N.
      • Zhang X.
      • Li H.
      • Wang Y.
      • Zhang J.
      • Hao Y.
      • Cao Z.
      • Li S.
      Effects of rumen-protected niacin on dry matter intake, milk production, apparent total tract digestibility, and faecal bacterial community in multiparous Holstein dairy cow during the postpartum period.
      . Blood samples of all the cows were collected from the coccygeal vein into evacuated serum tubes on d −21, −7, 7, and 21 (7 and 21 d both before and after calving) at 0600 h. All the tubes were centrifuged at 3,500 × g at 4°C for 15 min to obtain serum and stored at −20°C for further analysis. Serum total cholesterol (TC), TG, BHB, and NEFA concentrations were analyzed on a Hitachi 7600 automated biochemistry analyzer (Hitachi Co. Ltd.) using kits from Nanjing Jiancheng Bioengineering Institute (Nanjing, China). Cows were milked 3 times each day after calving at 0700, 1300, and 2100 h, and milk production was recorded daily.

      Liver Sample Collection

      Eight cows were randomly selected to have liver biopsies on d −21 and 21 just after the second feeding. According to our previous protocol (
      • Shi H.
      • Zhang J.
      • Li S.
      • Ji S.
      • Cao Z.
      • Zhang H.
      • Wang Y.
      Effects of a wide range of dietary forage-to-concentrate ratios on nutrient utilization and hepatic transcriptional profiles in limit-fed Holstein heifers.
      ), 500–1,000 mg liver samples were obtained by biopsy, rinsed, and frozen in liquid nitrogen for further determination. Samples were divided into 2 groups according to sampling day: 21 d PREP and 21 d postpartum (POSP). The sample size was decided based on previous studies with similar designs and methods on dairy cows (
      • Skibiel A.L.
      • Zachut M.
      • do Amaral B.C.
      • Levin Y.
      • Dahl G.E.
      Liver proteomic analysis of postpartum Holstein cows exposed to heat stress or cooling conditions during the dry period.
      ;
      • Zhang J.
      • Shi H.
      • Li S.
      • Cao Z.
      • Yang H.
      • Wang Y.
      Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
      ), and a formal a priori sample size calculation was not performed for this study.

      Metabolomics Analysis

      The GC quadrupole TOF MS analysis and metabolites extraction were similar to a previous study (
      • Zhang J.
      • Shi H.
      • Li S.
      • Cao Z.
      • Yang H.
      • Wang Y.
      Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
      ). Briefly, 450 μL of methanol/chloroform (volumetric ratio = 3:1) was added to 50 mg samples from PREP and POSP to extract metabolites. Equal aliquots of extract liquid from all experimental samples were pooled as quality control (QC) specimens. Adonitol was utilized as an internal standard. To perform the following GC TOF MS analysis of all samples, an Agilent 7890 GC system was used along with a Pegasus HT TOF mass spectrometer in splitless mode (LECO Corporation). For 1 min, the initial temperatures were maintained at 50°C, then incremented to 310°C at a rate of 10°C min−1 and kept at 310°C for 8 min. The ion source, injection, and transfer line temperatures were 250, 280, and 280°C, respectively. The MS data were obtained in a full-scan mode after a solvent delay of 6.33 min with the m/z range of 50–500 at a rate of 12.5 spectra per second. The Chroma TOF 4.3X software built-in with the LECO-Fiehn Rtx5 database (LECO Corporation) was used to preprocess and annotate the metabolomics data. The peaks detected less than 50% of QC specimens or relative standard metabolomics data deviation of more than 30% in QC specimens were eliminated (
      • Dunn W.B.
      • Broadhurst D.
      • Begley P.
      • Zelena E.
      • Francis-McIntyre S.
      • Anderson N.
      • Brown M.
      • Knowles J.D.
      • Halsall A.
      • Haselden J.N.
      • Nicholls A.W.
      • Wilson I.D.
      • Kell D.B.
      • Goodacre R.
      The Human Serum Metabolome (HUSERMET) Consortium
      Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.
      ).

      Preparing Sample and Data-Independent Acquisition Mass Spectrometry for Proteomic Analysis

      The total protein in liver tissues from PREP and POSP were extracted by grinding method in the presence of extraction buffer, which contained 8 M urea, 2 M thiourea, 2% 3-[(3-cholamidopropyl)dimethylammonio]-1-propane sulfonate, and a proteasome inhibitor, as described in a previous study (
      • Zhang J.
      • Shi H.
      • Li S.
      • Cao Z.
      • Yang H.
      • Wang Y.
      Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
      ). The protein concentrations were determined using a bicinchoninic acid Assay Kit (Dingguo Changsheng) according to the manufacturer's instructions.
      The protein digestion was performed following the filter-aided sample preparation protocol (
      • Wiśniewski J.R.
      • Zougman A.
      • Nagaraj N.
      • Mann M.
      Universal sample preparation method for proteome analysis.
      ). Briefly, after being reduced with 20 mM dl-dithiothreitol, protein samples were alkylated with 50 mM iodoacetamide. Then, samples were transferred onto filters and digested by 2% trypsin at 37°C for 12 h. The peptide samples were collected for the following MS analysis.
      The tryptic samples were resuspended with buffer 1 (water, 0.1% formic acid) and pressure-loaded onto a fused silica capillary 3-μm Dionex C18 column (0.1 × 120 mm; Thermo Scientific). After desalting, they were separated by a C18 column (1.9 μm, 0.15 × 120 mm; Thermo Scientific) on an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific) coupled with an EASY-nLC system (Thermo Scientific) at a flow rate of 0.5 μL/min. The gradient elution profiles of buffers 1 and 2 (80% acetonitrile, 0.1% formic acid) were as follows: 95–90% of 1 and 5–10% of 2 for 15 min, 90–70% of 1 and 10–30% of 2 for 56 min, 70–55% of 1 and 30–45% of 2 for 8 min, and 55–5% of 1 and 45–95% of 2 for 7 min with the flow rate at 0.6 μL/min.
      The DIA MS technique utilized 20 m/z isolation windows from 400 to 800, 30 m/z isolation windows from 800 to 1,000, and 50 m/z isolation windows from 800 to 1,000. First, a full scan at 30,000 full widths at half maximum (FWHM) resolving power (at 200 m/z) was conducted after sequential high-energy collisional dissociation-MS/MS scans at a normalized collision energy of 30 and resolution of 15,000 FWHM. The ranges between 400 and 1,200 m/z were measured by such tests, with the highest injection times of 55 min for MS and the auto setting for MS/MS. Acquired at variable resolutions, values were adjusted to 3 × 106 for MS and 1 × 106 for MS/MS. The MS/MS scan range was adjusted to 400–1,200 m/z.

      Western Blot Analysis

      The protein specimens utilized in the western blot were the same as those in proteomic analysis. Protein samples were boiled for 10 min at 95°C. Then, by resolving 20 μg of total protein per lane via SDS PAGE, they were conveyed to a polyvinylidene fluoride membrane (0.45 μm, Merck Millipore) through the semidry transfer assembly (Bio-Rad Laboratories). Blocking of the membranes was performed in Tris-buffered saline-tween (TBST; 150 mM NaCl, 50 mM Tris, pH 7.6, and 0.1% Tween 20) with 5.0% skim milk powder at room temperature for 1 h with gentle agitation. After rinsing 3 times in TBST, the membranes were then incubated in TBST containing primary antibodies (1:10,000 dilutions) for rabbit anti-carnitine palmitoyltransferase 1A (CPT1A; catalog number 15184–1-AP; Proteintech Group) (
      • Li Y.
      • Zou S.
      • Ding H.
      • Hao N.
      • Huang Y.
      • Tang J.
      • Cheng J.
      • Feng S.
      • Li J.
      • Wang X.
      • Wu J.
      Low expression of sirtuin 1 in the dairy cows with mild fatty liver alters hepatic lipid metabolism.
      ), rabbit anti-carnitine O-palmitoyltransferase 2 (CPT2; catalog number 26555–1-AP; Proteintech Group) (
      • Li Y.
      • Zou S.
      • Ding H.
      • Hao N.
      • Huang Y.
      • Tang J.
      • Cheng J.
      • Feng S.
      • Li J.
      • Wang X.
      • Wu J.
      Low expression of sirtuin 1 in the dairy cows with mild fatty liver alters hepatic lipid metabolism.
      ), rabbit anti-glutathione S-transferase Mu 3 (GSTM3; catalog number 15214–1-AP; Proteintech Group), rabbit anti-pyruvate carboxylase (PC; catalog number 16588–1-AP; Proteintech Group) (
      • Zhang L.
      • Liu T.
      • Hu C.
      • Zhang X.
      • Zhang Q.
      • Shi K.
      Proteome analysis identified proteins associated with mitochondrial function and inflammation activation crucially regulating the pathogenesis of fatty liver disease.
      ), rabbit anti-phosphoenolpyruvate carboxykinase (PCK1; catalog number 16754–1-AP; Proteintech Group) with gentle agitation at 4°C overnight. By incubation with the primary antibody and washing the membranes, incubation with horseradish peroxidase-conjugated anti-rabbit secondary antibody (Beyotime Biotechnology) was performed in 5% milk in TBST for 1 h at room temperature. The membranes were rinsed and then incubated with an ECL reagent (Merck Millipore). β-Actin (1:5,000; Immunoway) was used as the internal control. Finally, the images were taken and measured in the ChemiDoc XRS+ system (Bio-Rad Laboratories) with Total Lab Quant software (V11.5; TotalLab Ltd.). Before using, the AA sequence homologies between these primary antibodies and bovine were tested using the COBALT tool (https://www.ncbi.nlm.nih.gov/tools/cobalt/) and average similarity of 87.9% with the lowest being 73.3% were obtained.

      Data Processing, Bioinformatics, and Statistical Analysis

      The DMI, milk production, and serum metabolites data were first checked for normality and analyzed using the PROC MIXED procedure of SAS version 9.4 (SAS Institute Inc.) with sampling time (week or day) as fixed effect and cows within time as a random effect. Results were reported as least squares means. Significant differences were declared at P ≤ 0.05, and trends were reported at 0.05 < P < 0.10.
      The metabolomics analysis process was similar to previous studies (
      • Dunn W.B.
      • Broadhurst D.
      • Begley P.
      • Zelena E.
      • Francis-McIntyre S.
      • Anderson N.
      • Brown M.
      • Knowles J.D.
      • Halsall A.
      • Haselden J.N.
      • Nicholls A.W.
      • Wilson I.D.
      • Kell D.B.
      • Goodacre R.
      The Human Serum Metabolome (HUSERMET) Consortium
      Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry.
      ;
      • Zhang J.
      • Shi H.T.
      • Wang Y.C.
      • Li S.L.
      • Cao Z.J.
      • Yang H.J.
      • Wang Y.J.
      Carbohydrate and amino acid metabolism and oxidative status in Holstein heifers precision-fed diets with different forage to concentrate ratios.
      ). Briefly, the raw data were converted into Chroma TOF4.3X software (LECO) with a built-in LECO-FiehnRtx5 database. Then peaks extraction, peak alignment, peak identification, deconvolution analysis, and integration of the peak area were performed. The missing value (metabolites that were not detected in some samples) in the original data was simulated by using a numerical simulation method that fills half of the minimum value. The limit of detection (LOD) was determined by the signal-to-noise ratio (S/N) of the corresponding peaks, and the peaks with S/N less than 3.0 were considered noise. The peaks detected in less than 50% of original and QC samples, less than 400 similarities, relative standard deviation greater than 30% in QC samples, or beyond the interquartile range to filter data were removed. Data were standardized by peak area normalization methods. The unit variance scaling was selected as the data scale conversion mode. The maximal covariance between response variables and measured data was obtained for metabolomics analysis using principal component analysis (PCA) and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) in SIMCA. Significantly differently produced metabolites (DPM) between treatments were recognized using variable importance in projection (VIP) scores (VIP no less than 1.0) obtained from the OPLS-DA model and P-values (P value less than 0.05). The metabolic pathways analysis of 44 DPM were processed using MetaboAnalyst 4.0 with default parameters and selecting Bos taurus as a pathway library (
      • Chong J.
      • Soufan O.
      • Li C.
      • Caraus I.
      • Li S.
      • Bourque G.
      • Wishart D.S.
      • Xia J.
      Metaboanalyst 4.0: Towards more transparent and integrative metabolomics analysis.
      ).
      The proteomics analysis process was adapted from a previously published protocol (
      • Egertson J.D.
      • MacLean B.
      • Johnson R.
      • Xuan Y.
      • MacCoss M.J.
      Multiplexed peptide analysis using data-independent acquisition and skyline.
      ). Briefly, all the data-dependent acquisition MS data were thoroughly searched against the database of the UniProtKB (Bos taurus; data of access 01.05.2021) for peptide identification and quantification by using Proteome Discoverer Version 2.2 (Thermo Scientific). A file for the results was created using raw data for each experimental set searched in a single batch. The Proteome Discoverer's outputs provide a set of files utilized as the reference spectra library containing peptide sequences, modifications, charge states, confidence scores, retention times, and the equivalent fragment ions intensity and m/z. Then, DIA data processing spectral and library generation were conducted utilizing Skyline Version 3.5 (
      • MacLean B.
      • Tomazela D.M.
      • Shulman N.
      • Chambers M.
      • Finney G.L.
      • Frewen B.
      • Kern R.
      • Tabb D.L.
      • Liebler D.C.
      • MacCoss M.J.
      Skyline: An open source document editor for creating and analyzing targeted proteomics experiments.
      ;
      • Egertson J.D.
      • MacLean B.
      • Johnson R.
      • Xuan Y.
      • MacCoss M.J.
      Multiplexed peptide analysis using data-independent acquisition and skyline.
      ). No statistical analysis or calculation was performed using the missing values. The raw data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository (
      • Du X.
      • Chen L.
      • Huang D.
      • Peng Z.
      • Zhao C.
      • Zhang Y.
      • Zhu Y.
      • Wang Z.
      • Li X.
      • Liu G.
      Elevated apoptosis in the liver of dairy cows with ketosis.
      ) with the data set identifier PXD025564.
      Protein differences between treatments were compared, and P values were determined utilizing the Student's t-test. A fold change (FC) of 1.5 and false discovery rates (FDR)-adjusted P-value <0.05 (q ≤ 0.05) were used as the threshold for identifying differently synthesized proteins (DSP). To increase the robustness of our study, the DSP presented in at least half of the samples were used in the following analysis. The overall DSP were examined to enrich Gene Ontology (GO) terms, cellular component (CC), molecular function (MF), and biological process (BP), as well as Kyoto Encyclopedia of Genes and Genomes database (KEGG) pathways. Considerably enriched GO terms and KEGG pathways were identified as q ≤ 0.05. Furthermore, the protein-protein interaction (PPI) networks of upregulated and downregulated DSP were built and graphically visualized utilizing the searching device for the Retrieval of Interacting Genes (STRING) V.11.0 with the default parameters (
      • Szklarczyk D.
      • Gable A.L.
      • Lyon D.
      • Junge A.
      • Wyder S.
      • Huerta-Cepas J.
      • Simonovic M.
      • Doncheva N.T.
      • Morris J.H.
      • Bork P.
      • Jensen L.J.
      • Mering C.V.
      String v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets.
      ). For western blot data, using a t-test, parameter differences between treatments were compared with calculate P values.

      RESULTS

      Dry Matter Intake, Milk Production, and Serum Metabolites

      From PREP to POSP, the DMI and serum TC content increased 1.63- and 1.87-fold (P ≤ 0.01), respectively, and TG content decreased 1.34-fold (P < 0.01; Figure 1). Milk production increased (P < 0.01) with the time after calving. Serum NEFA and BHB contents were similar from PREP to POSP.
      Figure thumbnail gr1
      Figure 1Comparison of DMI, milk production, and serum metabolites of cows during the transition period (n = 12). Total cholesterol (TC), triglycerides (TG), nonesterified fatty acids (NEFA), and BHB were measured from the serum samples. P-values were determined using the PROC MIXED procedure of SAS with sampling time (week or day) as a fixed effect and cows within time as a random effect. Error bars represent SEM.

      Metabolite Profiles of Liver Samples and Data Analysis

      In total, 553 valid peaks and 233 metabolites were identified in these 2 groups, and both were in the same metabolite classes (Supplemental Table S2; https://doi.org/10.5281/zenodo.6792970;
      • Zhang J.
      • Gaowa N.
      • Wang Y.
      • Li H.
      • Cao Z.
      • Yang H.
      • Zhang X.
      • Li S.
      Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period [Data set]. Zenodo.
      ). The 2 groups of specimens were well separated, and samples in the same group were well aggregated in the PCA score plot (Supplemental Figure S1A; https://doi.org/10.5281/zenodo.6792970;
      • Zhang J.
      • Gaowa N.
      • Wang Y.
      • Li H.
      • Cao Z.
      • Yang H.
      • Zhang X.
      • Li S.
      Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period [Data set]. Zenodo.
      ). The QC specimens were well overlapped in the PCA score plot, indicating that the metabolomics method used was robust, highly repeatable, and stable. The equivalent R2Y value of the OPLS-DA model was 0.995 in POSP vs. PREP, and the intercept of permutation tests was 0.91, revealing the good effectiveness of the model for identifying the difference between the 2 treatments (Supplemental Figure S1B). Besides, the Q2 value and intercept were 0.75 and −0.48, respectively, indicating that the OPLS-DA model had good predictability and no overfitting. The specimens in the OPLS-DA score plots were within Hotelling's T2 ellipse of 95% (Supplemental Figure S1C).

      Differential Metabolite and Pathway Analysis

      Under the criterion of VIP > 1.0 and P < 0.05, we identified 44 DPM from PREP to POSP, of which 30 increased and 14 decreased in the POSP group (Table 1; Supplemental Figure S2A, S3A, https://doi.org/10.5281/zenodo.6792970;
      • Zhang J.
      • Gaowa N.
      • Wang Y.
      • Li H.
      • Cao Z.
      • Yang H.
      • Zhang X.
      • Li S.
      Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period [Data set]. Zenodo.
      ). According to the pathway analysis of DPM, 15 pathways with q < 0.05 and impact ≥0.04 profoundly changed from PREP to POSP (Figure 2). The AA, lipid, energy, and nucleotide metabolism were mainly involved in these pathways.
      Table 1Differentially produced metabolite screening for pathway assessment recognized by GC-TOF/MS in the liver biopsies of dairy cows during the transition period (n = 8)
      FC = fold change; GC-TOF/MS = gas chromatography quadrupole-time-of-flight mass spectrometry; RT = retention time; m/z = mass to charge ratio; VIP = variable importance projection. PREP and POSP represent the prepartum and postpartum periods, respectively.
      MetaboliteRT (min)m/zVIPFC
      Determined as the normalized peak area of metabolites in the POSP group/PREP group.
      Trend
      + and −: abundance increased and decreased in the POSP group, respectively.
      P-value
      P-values were determined utilizing the Student's t-test between the PREP and POSP groups.
      2-Oxobutanoate7.725891.3560.7860.027
      Alanine8.1841161.3470.7370.024
      2-Hydroxybutyrate8.5651471.8327.827+0.001
      3-Hydroxypropanoate8.7332201.5571.259+0.030
      Sulfate9.1191471.9871.330+<0.001
      N-Methyl-dl-alanine9.1741302.2065.071+0.004
      Alpha-ketoisocaproic acid9.293891.7102.048+0.010
      Valine9.7941441.4650.6650.030
      2-Ketoadipate10.243962.0271.322+<0.001
      Ethanolamine10.5161741.0871.797+0.025
      Glycerol10.6242181.3232.017+0.030
      Phosphate10.6502111.1732.396+0.032
      3-Hydroxypyruvate10.8211501.5960.7290.007
      (-)-Dihydrocarveol10.8491811.7110.7500.003
      Isoleucine10.8961581.4200.6770.014
      Proline10.9811422.0550.4950.004
      Beta-hydroxypyruvate11.2631301.8771.478+0.002
      Uracil11.502991.4423.876+<0.001
      Fumarate11.6832452.1632.031+<0.001
      1-Indanol11.9771592.1830.418<0.001
      3-Hydroxybenzaldehyde13.215711.7320.6260.011
      L-Malate13.443732.0331.924+0.001
      Fluorene15.2983261.9481.714+0.002
      Levoglucosan15.992571.0520.4410.004
      β-Glycerophosphoric acid16.2192431.8390.6580.002
      Ciliatine16.4223981.4530.4560.015
      O-phosphonothreonine16.687731.8470.6840.002
      N-Acetyl-l-glutamate16.913841.8964.229+0.001
      2,6-Diaminopimelate17.6561741.6030.6200.009
      Fructose17.7631031.8152.883+0.022
      Galactose18.372811.7265.288+0.001
      Gluconic acid18.480871.4317.229+<0.001
      Tyrosine18.5362181.3811.763+0.034
      Spermidine20.9511881.1131.965+0.018
      Fructose 2,6-biphosphate21.063731.3191.928+0.043
      Fructose-6-phosphate21.8082171.1742.138+0.043
      Glucose-6-phosphate21.9193871.0392.382+0.017
      Arachidonate22.451801.1792.276+0.001
      Purine riboside22.481591.6742.923+0.014
      D-erythro-sphingosine23.1462041.5573.682+0.015
      1-Monopalmitin24.1433711.0821.780+0.025
      Adenosine24.4932361.3402.020+0.033
      Monoolein25.3082181.5956.375+0.013
      Zymosterol29.1951291.1332.307+0.007
      1 FC = fold change; GC-TOF/MS = gas chromatography quadrupole-time-of-flight mass spectrometry; RT = retention time; m/z = mass to charge ratio; VIP = variable importance projection. PREP and POSP represent the prepartum and postpartum periods, respectively.
      2 Determined as the normalized peak area of metabolites in the POSP group/PREP group.
      3 + and −: abundance increased and decreased in the POSP group, respectively.
      4 P-values were determined utilizing the Student's t-test between the PREP and POSP groups.
      Figure thumbnail gr2
      Figure 2The pathway analysis of differentially created metabolites recognized in the liver biopsies of dairy cows during the transition period utilizing MetaboAnalyst 4.0 (n = 8). The circles' color from white to yellow to red represents incremental fold change [−log(P)]. P-values were determined utilizing the built-in statistical method of MetaboAnalyst 4.0. The circle size from small to large denotes an increase in the pathway impact.

      Identifying and Quantifying Protein

      After removing the low-scoring spectra, 3,539 unique proteins were created in the DIA analysis (Supplemental Table S3; https://doi.org/10.5281/zenodo.6792970;
      • Zhang J.
      • Gaowa N.
      • Wang Y.
      • Li H.
      • Cao Z.
      • Yang H.
      • Zhang X.
      • Li S.
      Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period [Data set]. Zenodo.
      ). Accounting for 98.4% of total unique proteins, 3,483 were recognized as common proteins from both PREP and POSP groups (Supplemental Figure S4A and Table S3; https://doi.org/10.5281/zenodo.6792970;
      • Zhang J.
      • Gaowa N.
      • Wang Y.
      • Li H.
      • Cao Z.
      • Yang H.
      • Zhang X.
      • Li S.
      Complementary hepatic metabolomics and proteomics reveal the adaptive mechanisms of dairy cows to the transition period [Data set]. Zenodo.
      ). Most identified proteins (81.8%) possessed molecular weights of 10 to 90 kD (81.8% and 81.9% for the PREP and POSP groups, respectively; Supplemental Figure S4B).
      In total, 250 DSP were identified from PREP to POSP, of which 169 were upregulated and 81 were downregulated in the POSP group compared with the PREP group (Supplemental Figure S2B). Based on the protein abundance data of the 250 DSP, the 2 groups' clusters were well separated (Supplemental Figure S3B).
      For the 5 selected proteins, the FC among treatments in WB were in line with those in the DIA data, and 3 proteins possessed the same significance in the WB platform as in the proteomic platform (Figure 3).
      Figure thumbnail gr3
      Figure 3Expression patterns of selected protein candidates in the liver biopsies of dairy cows during the transition period (n = 8). A, B, C, and D = western blot of selected protein candidate levels in the liver of dairy cows during the transition period. E = relative selected protein candidates; β-actin protein levels were calculated by a grayscale scan. Data are expressed as mean ± standard error of means. AU = arbitrary unit; CPT1A = carnitine palmitoyltransferase 1A; CPT2 = carnitine O-palmitoyltransferase 2; GSTM3 = glutathione S-transferase Mu 3; PCK1 = phosphoenolpyruvate carboxykinase. Prepartum (PREP) and postpartum (POSP) represent the prepartum and postpartum periods, respectively.

      Functional Annotations and Interaction Network of the Upregulated Differently Synthesized Proteins (Postpartum Versus Prepartum)

      By enriching the 169 upregulated DSP from POSP to PREP into 1,482 GO terms, they were categorized based on their BP (70.7%), CC (15.0%), and MF (14.3%), and among which 88 GO terms were recognized as significant (q < 0.05; Figure 4). Such proteins were enriched into 86 pathways through KEGG pathway analysis, among which 4 pathways were denoted as significant (q ≤ 0.05), namely peroxisome proliferator-activated receptor (PPAR) signaling pathway, ribosome, peroxisome, and citrate cycle (TCA cycle). In addition, 11, 14, 9, and 6 DSP were mapped into these 4 pathways, respectively (Table 2).
      Figure thumbnail gr4
      Figure 4The top-most 20 Gene Ontology (GO) terms of differentially synthesized proteins in the liver biopsies of dairy cows during the transition period (n = 8). Green bars represent biological process terms; blue bars represent cellular component terms; red bars represent molecular function terms.
      Table 2Important differentially synthesized proteins in the liver biopsies of dairy cows during the transition period (n = 8)
      Only the differentially synthesized proteins mapping in the significant Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathways are displayed in the table. FC = fold change; PREP and POSP represent the prepartum and postpartum periods, respectively.
      Gene nameProteinAccessionFC
      Determined as the ratios for the tags in the POSP group/PREP group.
      Trend
      + and −: abundance increased and decreased in the AC group, respectively.
      q-value
      P-values were determined utilizing the Student's t-test between the PREP and POSP groups.
      AA metabolism
      LOC540544UDP-glucuronosyltransferaseF1MRL50.597<0.001
      MRPS928S ribosomal protein S9, mitochondrialQ58DQ51.919+0.013
      NADSYN1Glutamine-dependent NAD(+) synthetaseQ3ZBF00.6580.030
      PSAT1Phosphoserine aminotransferaseA6QR280.639<0.001
      RPL1360S ribosomal protein L13Q3SZG72.111+0.036
      RPL1960S ribosomal protein L19Q3T0W91.763+0.024
      RPL21Similar to ribosomal protein L21 (fragment)Q861S41.612+0.027
      RPL26L1Ribosomal protein L26 like 1E1BCF51.690+0.036
      RPL2860S ribosomal protein L28Q3T0L71.866+0.049
      RPL3560S ribosomal protein L35Q3MHM71.881+0.042
      RPL760S ribosomal protein L7Q58DT12.761+0.035
      RPL7A60S ribosomal protein L7aQ2TBQ52.483+0.024
      RPL860S ribosomal protein L8Q3T0S62.373+0.004
      RPS1140S ribosomal protein S11Q3T0V41.659+0.010
      RPS1340S ribosomal protein S13Q56JX81.678+<0.001
      RPS640S ribosomal protein S6F1MKZ51.891+0.005
      RPS940S ribosomal protein S9A6QLG51.593+0.013
      Carbohydrate metabolism
      GKGlycerol kinaseQ0IID92.163+0.010
      IDH2Isocitrate dehydrogenase [NADP], mitochondrialQ044671.850+<0.001
      IDH3AIsocitrate dehydrogenase [NAD] subunit, mitochondrialF1MN742.353+0.009
      IDH3GIsocitrate dehydrogenase [NAD] subunit gamma, mitochondrialQ58CP01.907+<0.001
      PCPyruvate carboxylaseQ29RK23.035+<0.001
      PCK1 (PEPCK)Phosphoenolpyruvate carboxykinase, cytosolic [GTP]F1N1Z71.584+0.001
      Lipid metabolism
      ACOT8Acyl-CoA thioesterase 8F6RWU62.161+<0.001
      AOX1Aldehyde oxidase 1P480340.542<0.001
      APOA1Apolipoprotein A-IP154971.714+0.001
      APOA5Apolipoprotein A-VA4FUZ92.830+0.002
      CDACytidine deaminaseE1BNY10.408<0.001
      CES1Carboxylic ester hydrolaseQ0VCI30.665<0.001
      CPT1ACarnitine palmitoyltransferase 1AE1BKY31.661+<0.001
      CPT2Carnitine O-palmitoyltransferase 2, mitochondrialF1N1M71.626+0.046
      CYP7A1Cholesterol 7-α-monooxygenaseE1BM292.405+0.002
      HAO2Hydroxyacid oxidaseQ3ZBW21.573+<0.001
      HSD17B217-β hydroxysteroid dehydrogenase 2Q0PHW60.5360.003
      MLYCDMalonyl-CoA decarboxylaseA5PJC51.818+<0.001
      MMP1Matrix metalloproteinase 1F1MT972.251+0.001
      NUDT12Nudix hydrolase 12Q29RH30.5920.001
      NUDT19Nudix hydrolase 19E1BDS71.758+0.039
      PEX11GPeroxisomal biogenesis factor 11 gammaF1MTQ52.366+0.001
      PHYHPhytanoyl-CoA dioxygenaseO187781.703+<0.001
      PLIN4Perilipin 4F1MNM72.353+0.024
      SLC27A2Solute carrier family 27 member 2F1MQP21.566+<0.001
      Cell redox homeostasis
      CYP2C18Uncharacterized proteinF1MRH20.5870.034
      CYP2D14Cytochrome P450 2D14Q013610.6030.001
      GSTM1Glutathione S-transferase Mu 1Q9N0V40.372<0.001
      GSTM2Glutathione S-transferase Mu 2A5PKM00.278<0.001
      GSTM3Glutathione S-transferase Mu 3Q2KIV80.6220.003
      GSTM4Glutathione S-transferase Mu 4A1A4L70.5280.004
      Other/unknown metabolic process
      AKR1C3Uncharacterized proteinM0QW210.6040.002
      GNG10Guanine nucleotide-binding protein subunit gammaQ3SZ980.6340.044
      LOC511161Nicotinamide N-methyltransferase-likeV6F9B50.480<0.001
      MGC127055Uncharacterized protein MGC127055Q2KJJ20.6270.003
      PDXPPyridoxal phosphate phosphataseF1MW600.551<0.001
      SMSSpermine synthaseB0JYM70.5790.001
      STSSteroid sulfataseQ19AM00.6280.032
      1 Only the differentially synthesized proteins mapping in the significant Kyoto Encyclopedia of Genes and Genomes (KEGG) database pathways are displayed in the table. FC = fold change; PREP and POSP represent the prepartum and postpartum periods, respectively.
      2 Determined as the ratios for the tags in the POSP group/PREP group.
      3 + and −: abundance increased and decreased in the AC group, respectively.
      4 P-values were determined utilizing the Student's t-test between the PREP and POSP groups.
      There were 2 tensive networks of upregulated DSP in the PPI network (Figure 5A). The first network featured lipid and carbohydrate metabolism, including acetyl-CoA acetyltransferase (ACAT1), ATP citrate synthase (ACLY), acyl-CoA thioesterase 8 (ACOT8), apolipoprotein A-I (APOA1), apolipoprotein A-V (APOA5), coenzyme A synthase (COASY), CPT1A, CPT2, glycerol kinase (GK), hydroxy acid oxidase 2 (HAO2), 3-hydroxy-3-methylglutaryl coenzyme A synthase (HMGCS1), hydroxysteroid dehydrogenase-like protein 2 (HSDL2), isocitrate dehydrogenase NADP (IDH2), isocitrate dehydrogenase NAD subunit (IDH3A), isocitrate dehydrogenase NAD subunit gamma (IDH3G), L-lactate dehydrogenase A (LDHA), LDHB, membrane-bound O-acyltransferase domain containing 2 (MBOAT2), malonyl-CoA decarboxylase (MLYCD), nudix hydrolase 8 (NUDT8), nudix hydrolase 19 (NUDT19), PC, PCK1, and solute carrier family 27 member 2 (SLC27A2), having more interactions than other DSP. The second network was several ribosomal proteins comprising 40S ribosomal protein S6 (RPS6), 40S ribosomal protein S9 (RPS9), 40S ribosomal protein S11 (RPS11), 40S ribosomal protein S13 (RPS13), 60S ribosomal protein L7 (RPL7), 60S ribosomal protein L7a (RPL7A), 60S ribosomal protein L8 (RPL8), 60S ribosomal protein L13 (RPL13), 60S ribosomal protein L19 (RPL19), 60S ribosomal protein L21 (RPL21), 60S ribosomal protein L26-like 1 (RPL26L1), 60S ribosomal protein L28 (RPL28), and 60S ribosomal protein L35 (RPL35), having more interactions than other DSP.
      Figure thumbnail gr5
      Figure 5Protein-protein interaction (PPI) network analysis of the upregulated (A) and downregulated (B) differentially synthesized proteins in the liver biopsies (n = 8). Protein-protein interaction network was visualized and analyzed utilizing STRING V.11.0. The nodes in the cluster denote the proteins, and the lines between the nodes represent direct or indirect PPI modes. A purple line shows experimental evidence, a blue line suggests database evidence, and a yellow line shows text mining.

      Functional Annotations and Interaction Network of the Downregulated Differently Synthesized Proteins (Postpartum Versus Prepartum)

      By enrichment of 81 downregulated DSP from POSP to PREP into 710 GO terms, they were categorized in terms of their BP (70.3%), MF (15.8%), and CC (13.9%), but none of them were as significant. These proteins were enriched into 39 pathways through KEGG pathway analysis, of which 10 paths were significant (q < 0.05; Figure 6), namely vitamin B6 metabolism, nicotinate and nicotinamide metabolism, platinum drug resistance, glutathione metabolism, metabolism of xenobiotics by cytochrome P450 (CYP), drug metabolism-other enzymes, drug metabolism-CYP, steroid hormone biosynthesis, serotonergic synapse, and chemical carcinogenesis. In addition, 21 DSP were mapped into these 10 pathways (Table 2).
      Figure thumbnail gr6
      Figure 6The Kyoto Encyclopedia of Genes and Genomes (KEGG) path improvement analysis of downregulated proteins in the liver biopsies of dairy cows during the transition period (n = 8). Only the top 20 paths are presented based on P-value.
      There was no clear network of downregulated DSP from POSP to PREP in the PPI network (Figure 5B). Only several glutathione S-transferase Mu (GSTM) and CYP, including GSTM1, GSTM2, GSTM3, GSTM4, cytochrome P450 2C18 (CYP2C18), CYP2C19, and CYP2D14 have more interactions than other DSP and may have critical roles in oxidative status regulation.

      Integrating Metabolomics and Proteomics Analyses

      The DSP and DPM in significantly changed KEGG pathways were mapped together using the KEGG Mapper tool (
      • Kanehisa M.
      • Furumichi M.
      • Tanabe M.
      • Sato Y.
      • Morishima K.
      KEGG: New perspectives on genomes, pathways, diseases and drugs.
      ) to the KEGG pathway. The mapped pathways included metabolic pathways, biosynthesis of AA, carbon metabolism, PPAR signaling pathway, peroxisome, ribosome, fatty acid (FA) degradation, glutathione metabolism, tyrosine metabolism, valine, leucine and isoleucine degradation, glycine, serine and threonine metabolism, cysteine and methionine metabolism, arginine and proline metabolism, alanine metabolism, AMP-activated protein kinase (AMPK) signaling pathway, pyruvate metabolism, TCA cycles, glycolysis/gluconeogenesis, tryptophan metabolism, and glycerolipid metabolism. These critical pathways mapped with DSP and DPM were mainly clustered into AA metabolism, lipid metabolism, carbohydrate metabolism, and oxidative status. Ten DPM and 34 DSP were primarily involved in these pathways and identified as key components. These crucial DPM and DSP with mapped pathways were manually linked together (Figure 7, Figure 8).
      Figure thumbnail gr7
      Figure 7Schematic sketch of AA and carbohydrate metabolism changed by proteins and differential metabolites in the liver biopsies of dairy cows during the transition period (n = 8). Please note that this was a hypothesized relationship based on the current data. The black rectangles encircle proteins. The red arrows denote upregulation in the after calving group. However, the blue arrows show the downregulation in the after calving group. ACLY = ATP citrate synthase; F6P = fructose-6-phosphate; G6P = glucose-6-phosphate; GK = glycerol kinase; IDH2 = isocitrate dehydrogenase [NADP], mitochondrial; IDH3A = isocitrate dehydrogenase [NAD] subunit, mitochondrial; IDH3G = isocitrate dehydrogenase [NAD] subunit gamma, mitochondrial; PC = pyruvate carboxylase; PCK1 = phosphoenolpyruvate carboxykinase, cytosolic [GTP]; PEP = phosphoenolpyruvate.
      Figure thumbnail gr8
      Figure 8Schematic sketch of the peroxisome proliferator-activated receptor (PPAR) signaling pathway (A) and fatty acid oxidation (B), changed by proteins and differential metabolites in the liver biopsies of dairy cows over the transition period (n = 8). Please note that this was a hypothesized relationship based on the current data. The black rectangles enclose proteins. The red arrows denote up-regulation in the after calving group. ACSL1 = Acyl-CoA synthetase long-chain family member 1; APOA1 = apolipoprotein A-I; APOA5 = apolipoprotein A-V; CPT1A = carnitine palmitoyltransferase 1A; CPT2 = carnitine O-palmitoyltransferase 2, mitochondrial; CYP7A1 = cholesterol 7-alpha-monooxygenase; GK = glycerol kinase; MMP1 = matrix metalloproteinase 1; PCK1 = phosphoenolpyruvate carboxykinase, cytosolic [GTP]; RXR = retinoid-X-receptor; ROS = reactive oxidative species; SLC27A2 = solute carrier family 27 members 2; VLDL = very-low-density lipoprotein.

      DISCUSSION

      Due to the dramatic changes from late pregnancy to early lactation, the transition period is critical in a dairy cow's lifecycle. The imbalance between energy requirement and energy intake may induce severe NEB in dairy cows, which increases the susceptibility to both metabolic and infectious diseases. To cope with the challenges, comprehensive adaptive mechanisms, including the metabolic, endocrine, and immune system, should be accomplished. Thus, this study used metabolomics and proteomics procedures to reveal an overview of physiological alterations in the liver of dairy cows during the transition period, which should provide a better understanding of the adaptation mechanism and further benefit cows to overcome this challenging time.
      To our knowledge, this study was one of the only studies that have investigated the liver samples of transition dairy cows by using metabolomics or proteomics methods. We also recognized that more sampling time points using the same dairy cows might be helpful to capture a data set on the whole dynamic adaptation of the liver to lactation. Moreover, further studies involving the regulation and coordination of metabolic interaction among other sections, such as the nervous system, adipose tissue, skeletal muscle, gut, and mammary gland, are also crucial components for adaptations to lactation. Part of our results revealed by the metabolomics and proteomics methods are in accordance with previous works based on transcriptomic analysis (
      • Ha N.T.
      • Drogemuller C.
      • Reimer C.
      • Schmitz-Hsu F.
      • Bruckmaier R.M.
      • Simianer H.
      • Gross J.J.
      Liver transcriptome analysis reveals important factors involved in the metabolic adaptation of the transition cow.
      ;
      • Gao S.T.
      • Girma D.D.
      • Bionaz M.
      • Ma L.
      • Bu D.P.
      Hepatic transcriptomic adaptation from prepartum to postpartum in dairy cows.
      ), which strengthens the importance of the findings presented in this work and also confirms that some necessary adaptions simultaneously occur in the mRNA, protein, and metabolite levels. On the other hand, the complementary results from multi-omics can help us to get a more comprehensive understanding of this adaptation process.

      Carbohydrate Metabolism

      The most prominent feature of the transition period in dairy cows is the imbalance between energy requirement and energy intake, which can induce NEB. During the POSP period, requirements for glucose and metabolizable energy increase 2- to 3-fold more than in the PREP period (
      • Drackley J.K.
      • Overton T.R.
      • Douglas G.N.
      Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period.
      ). To meet the energy requirement for maintenance and lactation, the body has to accelerate the carbohydrate metabolism, especially gluconeogenesis, to produce more energy and glucose. A discrepancy of nearly 500 g/d of glucose exists between predicted glucose from digestible energy intake and estimated glucose in POSP dairy cows, which must be made up by increased gluconeogenesis (
      • Drackley J.K.
      • Overton T.R.
      • Douglas G.N.
      Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period.
      ). Consistent with former studies (
      • Drackley J.K.
      • Overton T.R.
      • Douglas G.N.
      Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period.
      ;
      • Loor J.J.
      Genomics of metabolic adaptations in the peripartal cow.
      ;
      • Laguna J.G.
      • Cardoso M.S.
      • Lima J.A.
      • Reis R.B.
      • Carvalho A.U.
      • Saturnino H.M.
      • Teixeira S.M.R.
      Expression of hepatic genes related to energy metabolism during the transition period of Holstein and F1 Holstein-Gir cows.
      ), the upregulated rate-limiting enzymes, PCK1 and GK, and increased important intermediates, glucose-6-phosphate (G6P) and fructose-6-phosphate (F6P), indicated upregulated gluconeogenesis in the liver of dairy cows after calving in our study. By using the transcriptomic method, a previous study also identified increased hepatic PCK1 and G6P gene expression and gluconeogenesis to adapt to the transition period in dairy cows (
      • Gao S.T.
      • Girma D.D.
      • Bionaz M.
      • Ma L.
      • Bu D.P.
      Hepatic transcriptomic adaptation from prepartum to postpartum in dairy cows.
      ).
      By isocitrate dehydrogenase (IDH), the oxidative decarboxylation of isocitrate is catalyzed along with the production of α-ketoglutarate and CO2. Three isoforms of IDH exist, namely IDH1, IDH2, and IDH3, all of which localize to the mitochondrion and peroxisome as well as cytosol (
      • Corpas F.J.
      • Barroso J.B.
      • Sandalio L.M.
      • Palma J.M.
      • Lupianez J.A.
      • del Rio L.A.
      Peroxisomal NADP-dependent isocitrate dehydrogenase. Characterization and activity regulation during natural senescence.
      ). All of the IDH identified in our study were located in mitochondrion. ATP citrate synthase catalyzes the reversible reaction from phosphate, ADP, acetyl-CoA, and oxaloacetate to ATP, citrate, and CoA (
      • Lill U.
      • Schreil A.
      • Eggerer H.
      Isolation of enzymically active fragments formed by limited proteolysis of ATP citrate lyase.
      ). The upregulated IDH and ACLY, as well as increased essential substrates, fumarate and malate, indicated an increased TCA cycle in the liver after calving, which was in line with previous studies (
      • Da Poian A.T.
      • Castanho M.A.R.B.
      Integrative Human Biochemistry: A Textbook for Medical Biochemistry.
      ;
      • Luo Z.Z.
      • Shen L.H.
      • Jiang J.
      • Huang Y.X.
      • Bai L.P.
      • Yu S.M.
      • Yao X.P.
      • Ren Z.H.
      • Yang Y.X.
      • Cao S.Z.
      Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics.
      ). Upregulated TCA cycles in the liver and plasma were found in dairy cows immediately to 28 d POSP based on the transcriptomic and metabolomics methods (
      • Luo Z.Z.
      • Shen L.H.
      • Jiang J.
      • Huang Y.X.
      • Bai L.P.
      • Yu S.M.
      • Yao X.P.
      • Ren Z.H.
      • Yang Y.X.
      • Cao S.Z.
      Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics.
      ;
      • Gao S.T.
      • Girma D.D.
      • Bionaz M.
      • Ma L.
      • Bu D.P.
      Hepatic transcriptomic adaptation from prepartum to postpartum in dairy cows.
      ;
      • Schären M.
      • Riefke B.
      • Slopianka M.
      • Keck M.
      • Gruendemann S.
      • Wichard J.
      • Brunner N.
      • Klein S.
      • Snedec T.
      • Theinert K.B.
      • Pietsch F.
      • Rachidi F.
      • Koller G.
      • Bannert E.
      • Spilke J.
      • Starke A.
      Aspects of transition cow metabolomics-part III: Alterations in the metabolome of liver and blood throughout the transition period in cows with different liver metabotypes.
      ).
      Pyruvate carboxylase catalyzes the physiologically irreversible carboxylation of pyruvate to create oxaloacetate (
      • Da Poian A.T.
      • Castanho M.A.R.B.
      Integrative Human Biochemistry: A Textbook for Medical Biochemistry.
      ). Pyruvate can be generated from AA metabolism and then be converted to acetyl-CoA, which is also an end product of lipid metabolism. Thus, pyruvate and acetyl-CoA are critical intermediates in carbohydrate, lipid, and AA metabolism (
      • Guo H.
      • Niu X.
      • Gu Y.
      • Lu C.
      • Xiao C.
      • Yue K.
      • Zhang G.
      • Pan X.
      • Jiang M.
      • Tan Y.
      • Kong H.
      • Liu Z.
      • Xu G.
      • Lu A.
      Differential amino acid, carbohydrate and lipid metabolism perpetuations involved in a subtype of rheumatoid arthritis with Chinese medicine cold pattern.
      ). The upregulated PC, fumarate, and malate were consistent with the augmented pyruvate metabolism and TCA cycle and might indicate the flux of substrates from AA metabolism into the TCA cycle in our study. In accordance, previous studies reported an increased abundance of mRNA for PC around calving (
      • Drackley J.K.
      • Overton T.R.
      • Douglas G.N.
      Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period.
      ;
      • Loor J.J.
      • Dann H.M.
      • Guretzky N.A.
      • Everts R.E.
      • Oliveira R.
      • Green C.A.
      • Litherland N.B.
      • Rodriguez-Zas S.L.
      • Lewin H.A.
      • Drackley J.K.
      Plane of nutrition prepartum alters hepatic gene expression and function in dairy cows as assessed by longitudinal transcript and metabolic profiling.
      ;
      • Gao S.T.
      • Girma D.D.
      • Bionaz M.
      • Ma L.
      • Bu D.P.
      Hepatic transcriptomic adaptation from prepartum to postpartum in dairy cows.
      ).
      • Reynolds C.K.
      • Aikman P.C.
      • Lupoli B.
      • Humphries D.J.
      • Beever D.E.
      Splanchnic metabolism of dairy cows during the transition from late gestation through early lactation.
      also emphasized the important role of PC in converting alanine and lactate to glucose in early lactation dairy cows. Consistent with our study,
      • Luo Z.Z.
      • Shen L.H.
      • Jiang J.
      • Huang Y.X.
      • Bai L.P.
      • Yu S.M.
      • Yao X.P.
      • Ren Z.H.
      • Yang Y.X.
      • Cao S.Z.
      Plasma metabolite changes in dairy cows during parturition identified using untargeted metabolomics.
      also found decreased valine, proline, and isoleucine in dairy cows after calving, which served as precursors of the TCA cycle. Amino acids are also substrates for gluconeogenesis, such as alanine, valine, proline, and isoleucine, which can contribute up to 60% glucose in ruminants (
      • Seal C.J.
      • Reynolds C.K.
      Nutritional implications of gastrointestinal and liver metabolism in ruminants.
      ;
      • Da Poian A.T.
      • Castanho M.A.R.B.
      Integrative Human Biochemistry: A Textbook for Medical Biochemistry.
      ). The increased amplitude of converting alanine to glucose was even greater than converting propionate to glucose in the liver tissue isolated from early lactation dairy cows (
      • Drackley J.K.
      • Overton T.R.
      • Douglas G.N.
      Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period.
      ).

      Lipid Metabolism

      The PPAR signaling pathway is one of the most significant changed paths in our study with 11 DSP, including acyl-CoA synthetase long-chain family member 1 (ACSL1), APOA1, APOA5, CPT1A, CPT2, CYP7A1, GK, matrix metalloproteinase 1 (MMP1), PCK1, perilipin 4 (PLIN4), and SLC27A2, which were mapped in this pathway. Peroxisome proliferator-activated receptors are identified initially as novel members of the nuclear receptors involved in activating the acyl-CoA oxidase gene (ACOX1) promoter encoding the main enzymes of peroxisomal long-chain fatty acids (LCFA: 10–18 carbons long) β-oxidation in ruminants (
      • Bionaz M.
      • Chen S.
      • Khan M.J.
      • Loor J.J.
      Functional role of PPARs in ruminants: Potential targets for fine-tuning metabolism during growth and lactation.
      ). Peroxisome proliferator-activated receptors also contribute to metabolism pathways such as lipid transport, FA transport, FA oxidation, cholesterol metabolism, adipocyte differentiation, and gluconeogenesis (
      • Bionaz M.
      • Chen S.
      • Khan M.J.
      • Loor J.J.
      Functional role of PPARs in ruminants: Potential targets for fine-tuning metabolism during growth and lactation.
      ;
      • Hong F.
      • Pan S.
      • Guo Y.
      • Xu P.
      • Zhai Y.
      PPARs as nuclear receptors for nutrient and energy metabolism.
      ). Specifically, the abovementioned 11 DSP, which were all upregulated in our study, were involved in cholesterol transport, bile acids synthesis, FA β-oxidation, extracellular matrix breakdown, gluconeogenesis, and lipid storage (
      • Wolins N.E.
      • Skinner J.R.
      • Schoenfish M.J.
      • Tzekov A.
      • Bensch K.G.
      • Bickel P.E.
      Adipocyte protein s3–12 coats nascent lipid droplets.
      ;
      • McCabe M.
      • Waters S.
      • Morris D.
      • Kenny D.
      • Lynn D.
      • Creevey C.
      RNA-seq analysis of differential gene expression in liver from lactating dairy cows divergent in negative energy balance.
      ;
      • Shi H.
      • Zhang J.
      • Li S.
      • Ji S.
      • Cao Z.
      • Zhang H.
      • Wang Y.
      Effects of a wide range of dietary forage-to-concentrate ratios on nutrient utilization and hepatic transcriptional profiles in limit-fed Holstein heifers.
      ), indicating upregulated lipid metabolism, especially PPAR signaling pathway. A previous study also confirmed the pivotal role of the PPAR signaling pathway in hepatic adaptation to the early POSP period in dairy cows by using the transcriptomic method (
      • Gao S.T.
      • Girma D.D.
      • Bionaz M.
      • Ma L.
      • Bu D.P.
      Hepatic transcriptomic adaptation from prepartum to postpartum in dairy cows.
      ). Among the 3 isotypes of PPAR, the mRNA expression of PPARA (PPARα) is predominant in the liver of ruminants (
      • Bionaz M.
      • Chen S.
      • Khan M.J.
      • Loor J.J.
      Functional role of PPARs in ruminants: Potential targets for fine-tuning metabolism during growth and lactation.
      ). Previous studies showed that activating PPARα controls the catabolism of FA, and the expression of PPARA in the liver of dairy cows increases during the transition period (
      • Loor J.J.
      • Dann H.M.
      • Everts R.E.
      • Oliveira R.
      • Green C.A.
      • Guretzky N.A.
      • Rodriguez-Zas S.L.
      • Lewin H.A.
      • Drackley J.K.
      Temporal gene expression profiling of liver from periparturient dairy cows reveals complex adaptive mechanisms in hepatic function.
      ;
      • Schlegel G.
      • Keller J.
      • Hirche F.
      • Geissler S.
      • Schwarz F.J.
      • Ringseis R.
      • Stangl G.I.
      • Eder K.
      Expression of genes involved in hepatic carnitine synthesis and uptake in dairy cows in the transition period and at different stages of lactation.
      ). In POSP dairy cows, elevated NEFA, especially LCFA, might active PPARs and lead to increased oxidation and decreased esterification of FA in the liver (
      • Grummer R.R.
      Impact of changes in organic nutrient metabolism on feeding the transition dairy cow.
      ;
      • Drackley J.K.
      ADSA foundation scholar award. Biology of dairy cows during the transition period: The final frontier?.
      ). In addition, it is also reported that PPARs can be activated by glucose in the ruminants (
      • Bionaz M.
      • Chen S.
      • Khan M.J.
      • Loor J.J.
      Functional role of PPARs in ruminants: Potential targets for fine-tuning metabolism during growth and lactation.
      ).
      In this study, 9 upregulated DSP, including ACSL1, ACOT8, HAO2, MLYCD, NUDT12, NUDT19, peroxisomal biogenesis factor 11 gamma (PEX11G), phytanoyl-CoA dioxygenase (PHYH), and SLC27A2, were mapped in peroxisomes, which were involved in the α-, β-, and other-oxidation processes of FA. During the transition period, LCFA was the most affected FA in the plasma of dairy cows and was the energy source of the cells (
      • 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.
      ;
      • Contreras G.A.
      • Sordillo L.M.
      Lipid mobilization and inflammatory responses during the transition period of dairy cows.
      ;
      • Da Poian A.T.
      • Castanho M.A.R.B.
      Integrative Human Biochemistry: A Textbook for Medical Biochemistry.
      ). The upregulated ACSL in our result was in agreement with the important role of ACSL in the oxidation of LCFA in both peroxisomes and mitochondria. With the amount of NEFA entering the liver increased by multiple times, the peroxisomal pathway is induced as an auxiliary pathway to mitochondrial β-oxidation. Being critical players in the carnitine shuttle system, CPT1A and CPT2 were also upregulated in this study, which was similar to results from previous studies showing that hepatic CPT1 mRNA expression or protein activity increased after calving relative to late pregnancy in dairy cows (
      • Dann H.M.
      • Drackley J.K.
      Carnitine palmitoyltransferase I in liver of periparturient dairy cows: Effects of prepartum intake, postpartum induction of ketosis, and periparturient disorders.
      ;
      • Loor J.J.
      • Dann H.M.
      • Everts R.E.
      • Oliveira R.
      • Green C.A.
      • Guretzky N.A.
      • Rodriguez-Zas S.L.
      • Lewin H.A.
      • Drackley J.K.
      Temporal gene expression profiling of liver from periparturient dairy cows reveals complex adaptive mechanisms in hepatic function.
      ;
      • Loor J.J.
      • Dann H.M.
      • Guretzky N.A.
      • Everts R.E.
      • Oliveira R.
      • Green C.A.
      • Litherland N.B.
      • Rodriguez-Zas S.L.
      • Lewin H.A.
      • Drackley J.K.
      Plane of nutrition prepartum alters hepatic gene expression and function in dairy cows as assessed by longitudinal transcript and metabolic profiling.
      ). This indicated the increased oxidation of FA after calving in both peroxisome and mitochondria in our study. Similarly, a previous study also found that the active expression of PPARA in the liver of transition dairy cows resulted in downstream activation of genes, such as ACSL1, ACOX1, CPT1A, and PCK1, which have key functions in FA oxidation and gluconeogenesis (
      • Loor J.J.
      • Dann H.M.
      • Everts R.E.
      • Oliveira R.
      • Green C.A.
      • Guretzky N.A.
      • Rodriguez-Zas S.L.
      • Lewin H.A.
      • Drackley J.K.
      Temporal gene expression profiling of liver from periparturient dairy cows reveals complex adaptive mechanisms in hepatic function.
      ).
      As a sensor and regulator of energy, the AMPK signaling pathway can increase hepatic lipid oxidation by regulating the expression of PPARα and sterol regulatory element-binding protein 1c (SREBP-1c) and then help relieve the NEB in transition dairy cows (
      • Li X.
      • Li X.
      • Chen H.
      • Lei L.
      • Liu J.
      • Guan Y.
      • Liu Z.
      • Zhang L.
      • Yang W.
      • Zhao C.
      • Fu S.
      • Li P.
      • Liu G.
      • Wang Z.
      Non-esterified fatty acids activate the AMP-activated protein kinase signaling pathway to regulate lipid metabolism in bovine hepatocytes.
      ;
      • Shen J.
      • Sun B.
      • Yu C.
      • Cao Y.
      • Cai C.
      • Yao J.
      Choline and methionine regulate lipid metabolism via the AMPK signaling pathway in hepatocytes exposed to high concentrations of nonesterified fatty acids.
      ). In this work, CPT1A, PCK1, G6P, and F6P were also mapped in the AMPK signaling pathway, indicating the upregulated lipid oxidation, which was consistent with the former results. In accordance with our study, previous studies also reported the upregulated AMPK signaling pathway and suggested its activation effect on the PPAR signaling pathway in transition dairy cows (
      • Li X.
      • Li X.
      • Chen H.
      • Lei L.
      • Liu J.
      • Guan Y.
      • Liu Z.
      • Zhang L.
      • Yang W.
      • Zhao C.
      • Fu S.
      • Li P.
      • Liu G.
      • Wang Z.
      Non-esterified fatty acids activate the AMP-activated protein kinase signaling pathway to regulate lipid metabolism in bovine hepatocytes.
      ;
      • Ha N.T.
      • Drogemuller C.
      • Reimer C.
      • Schmitz-Hsu F.
      • Bruckmaier R.M.
      • Simianer H.
      • Gross J.J.
      Liver transcriptome analysis reveals important factors involved in the metabolic adaptation of the transition cow.
      ;
      • Gao S.T.
      • Girma D.D.
      • Bionaz M.
      • Ma L.
      • Bu D.P.
      Hepatic transcriptomic adaptation from prepartum to postpartum in dairy cows.
      ). However, the activators of the AMPK signaling pathway still needed further investigation in our study and previous transcriptomic studies.

      Ribosome Proteins

      Another significantly changed pathway was ribosome with 13 DSP, including RPL7, RPL7A, RPL8, RPL13, RPL19, RPL21, RPL21L1), RPL28, PRL35, RPS6, RPS9, PRS11, and RPS13, which were mapped in this pathway. Ribosomes consist of 2 major components, the small and large ribosomal subunit, and are often associated with the endoplasmic reticulum serving as the site of biological protein synthesis (translation) (
      • Da Poian A.T.
      • Castanho M.A.R.B.
      Integrative Human Biochemistry: A Textbook for Medical Biochemistry.
      ). All of these 13 DSP were upregulated, which indicated upregulated protein synthesis in this study. As mentioned above, most metabolic processes were upregulated after calving, which requires large amounts of enzymes to participate in these reactions. Given that most enzymes are proteins, there is no wonder that protein synthesis was upregulated after calving. In addition, the number of upregulated DSP after calving was about 2-fold that of upregulated DSP before calving, which was in line with the upregulated protein synthesis after calving. Similarly, previous studies also reported a substantially increased fractional protein synthetic rate in the liver of POSP dairy cows compared with PREP ones (
      • Bell A.W.
      Regulation of organic nutrient metabolism during transition from late pregnancy to early lactation.
      ;
      • Gao S.T.
      • Girma D.D.
      • Bionaz M.
      • Ma L.
      • Bu D.P.
      Hepatic transcriptomic adaptation from prepartum to postpartum in dairy cows.
      ). Meanwhile, the increased liver mass (about 9%) might also be a reason for upregulated protein synthesis in POSP dairy cows (
      • Drackley J.K.
      • Overton T.R.
      • Douglas G.N.
      Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period.
      ).

      Oxidative Status

      Even though the lipid and protein mobilization can provide energy-generated substrates to transiently meet the energy requirement of lactation and maintain that requirement in POSP dairy cows, this process may simultaneously produce some reactive oxidative species (ROS). The ROS, including superoxide (O2-) and hydrogen peroxide (H2O2), are mainly produced during oxidative phosphorylation, the TCA cycle, or intracellular FA oxidation; particularly, peroxisomal β-oxidation leads to considerable quantities of ROS (
      • Schäff C.
      • Borner S.
      • Hacke S.
      • Kautzsch U.
      • Albrecht D.
      • Hammon H.M.
      • Rontgen M.
      • Kuhla B.
      Increased anaplerosis, TCA cycling, and oxidative phosphorylation in the liver of dairy cows with intensive body fat mobilization during early lactation.
      ;
      • Surai P.F.
      • Kochish I.I.
      • Fisinin V.I.
      • Juniper D.T.
      Revisiting oxidative stress and the use of organic selenium in dairy cow nutrition.
      ). The β-oxidation of certain types of FA can produce a significant amount of ROS, especially valid for palmitic acid (
      • Contreras G.A.
      • Sordillo L.M.
      Lipid mobilization and inflammatory responses during the transition period of dairy cows.
      ). It was found that GST family members can remove ROS from the liver and that the CYP family, especially the CYP1–3 family enzymes, account for up to 80% of oxidative metabolism (
      • Uehara S.
      • Murayama N.
      • Nakanishi Y.
      • Zeldin D.C.
      • Yamazaki H.
      • Uno Y.
      Immunochemical detection of cytochrome p450 enzymes in liver microsomes of 27 cynomolgus monkeys.
      ;
      • Zhang J.
      • Shi H.
      • Li S.
      • Cao Z.
      • Yang H.
      • Wang Y.
      Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
      ). According to other studies (
      • Sharma N.
      • Singh N.K.
      • Singh O.P.
      • Pandey V.
      • Verma P.K.
      Oxidative stress and antioxidant status during transition period in dairy cows.
      ;
      • Abuelo A.
      • Hernandez J.
      • Benedito J.L.
      • Castillo C.
      Oxidative stress index (OSi) as a new tool to assess redox status in dairy cattle during the transition period.
      ), the downregulated CYP2C18, CYP2D14, GSTM1, GSTM2, GSTM3, and GSTM4 in our study might indicate increased oxidative status and decreased antioxidative defense ability in POSP dairy cows. The increased arachidonate and upregulated ACSL1 were probably involved in ROS generation in this study. Previous studies even showed that severely imbalanced redox would cause oxidative stress in the transition dairy cows, which is related to impaired immune function and subsequently increased susceptibility to production diseases and other health problems (
      • Sordillo L.M.
      • Mavangira V.
      The nexus between nutrient metabolism, oxidative stress and inflammation in transition cows.
      ;
      • Abuelo A.
      • Hernandez J.
      • Benedito J.L.
      • Castillo C.
      Redox biology in transition periods of dairy cattle: Role in the health of periparturient and neonatal animals.
      ;
      • Liang Y.
      • Batistel F.
      • Parys C.
      • Loor J.J.
      Glutathione metabolism and nuclear factor erythroid 2-like 2 (NFE2l2)-related proteins in adipose tissue are altered by supply of ethyl-cellulose rumen-protected methionine in peripartal Holstein cows.
      ).

      Other Important Metabolic Pathways

      Except for mapping to the AMPK signaling pathway, CPT1A, PCK1, and F6P were also mapped to both glucagon signaling and insulin signaling pathways in this study. The glucagon signaling pathway mainly helps glucagon to exert its contribution to increasing blood glucose by the conversion of liver glycogen into glucose (
      • De Koster J.D.
      • Opsomer G.
      Insulin resistance in dairy cows.
      ;
      • Cardoso F.C.
      • Kalscheur K.F.
      • Drackley J.K.
      Symposium review: Nutrition strategies for improved health, production, and fertility during the transition period.
      ). Insulin can elicit different effects on the carbohydrate, lipid, and protein metabolism in dairy cows (
      • De Koster J.D.
      • Opsomer G.
      Insulin resistance in dairy cows.
      ;
      • Zhang F.
      • Li D.
      • Wu Q.
      • Sun J.
      • Guan W.
      • Hou Y.
      • Zhu Y.
      • Wang J.
      Prepartum body conditions affect insulin signaling pathways in postpartum adipose tissues in transition dairy cows.
      ;
      • Wu J.
      • Liu J.
      • Wang D.
      Effects of body condition on the insulin resistance, lipid metabolism and oxidative stress of lactating dairy cows.
      ). In the liver, insulin has a suppression effect on ketogenesis, gluconeogenesis, glycogenolysis, and protein degradation (
      • De Koster J.D.
      • Opsomer G.
      Insulin resistance in dairy cows.
      ). In early lactating dairy cows, a transient state of insulin resistance can guarantee glucose enters the mammary gland by limiting glucose used by peripheral tissues such as skeletal muscles and adipose tissue to support lactation (
      • De Koster J.D.
      • Opsomer G.
      Insulin resistance in dairy cows.
      ;
      • Wu J.
      • Liu J.
      • Wang D.
      Effects of body condition on the insulin resistance, lipid metabolism and oxidative stress of lactating dairy cows.
      ). The simultaneously upregulated glucagon signaling and insulin signaling pathways indicated the significant demand for glucose in POSP dairy cows, which was consistent with former works (
      • Drackley J.K.
      • Overton T.R.
      • Douglas G.N.
      Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period.
      ;
      • Zhang Q.
      • Su H.
      • Wang F.
      • Cao Z.
      • Li S.
      Effects of energy density in close-up diets and postpartum supplementation of extruded full-fat soybean on lactation performance and metabolic and hormonal status of dairy cows.
      ;
      • Huang W.
      • Tian Y.
      • Li S.
      • Wu Z.
      • Cao Z.
      Reduced energy density of close-up diets decrease ruminal pH and increase concentration of volatile fatty acids postpartum in Holstein cows.
      ).

      CONCLUSIONS

      In this work, integrative proteomics and metabolomics techniques were utilized to assess the hepatic adaptation over the transition period in dairy cows. The omics data showed enhanced AA degradation, FA oxidation, AMPK signaling pathway, and PPAR signaling pathway in POSP cows to provide energetic substrates for the TCA cycle and gluconeogenesis. The upregulated glucagon and insulin signaling pathways also indicated the large requirement for energy in POSP dairy cows. As a consequence of increased lipid mobilization and AA and carbohydrate metabolism, oxidative status was elevated, which was highly associated with metabolic and infectious diseases. In addition, the G6P, F6P, CPT1A, and PCK1 might be the critical players participating in carbohydrate and lipid metabolism in that period. Such data, from the view of metabolites and proteins, which is different from the view of previous transcripts, present an integrative comprehension of the physiological metabolics in the liver during the transition period in dairy cows. This should help develop nutritional regulation strategies to further help cows overcome this challenging time.

      ACKNOWLEDGMENTS

      This work was supported by the National Natural Science Foundation of China (grant nos. 32102570 and 32130100), the fellowship of China Postdoctoral Science Foundation (grant number 2021M702691), the National Dairy Industry and Technology System of China (grant no. CARS-36), and the “Double First-Class” Funding for Animal Husbandry in China (grant no. Z1010222001). The authors thank the Beijing Sanyuan Lvhe Dairy Group for providing the trial site and animals and members of the Li laboratory for their assistance in keeping the animals and sampling. The authors have not stated any conflicts of interest.

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