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College of Animal Science and Technology, Northwest A&F University, Yangling 712100 ChinaState 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
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
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
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
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
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
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
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.
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 (
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.
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 (
Maternal metabolic responses, nutritional status, and physiological conditions undergo marked changes in the transition from late pregnancy to early lactation (
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.
). As a central metabolic organ within the whole body, the activities of the liver are intensively influenced by many nutritional and physiological factors (
Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
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.
Effects of a wide range of dietary forage-to-concentrate ratios on nutrient utilization and hepatic transcriptional profiles in limit-fed Holstein heifers.
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 (
). 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 (
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 (
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 (
). 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 (
). However, the changes in transcriptome cannot guarantee the subsequent phenotypic variation due to translation efficiency, post-transcriptional regulation, and protein half-life (
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.
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.
Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
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.
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 (
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, 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;
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
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 (
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 (
Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
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 (
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 (
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, after being reduced with 20 mMdl-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) (
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 (
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.
). 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 (
). 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 (
). 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 (
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 (
). 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 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;
). 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;
). 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;
). 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.
P-values were determined utilizing the Student's t-test between the PREP and POSP groups.
2-Oxobutanoate
7.725
89
1.356
0.786
−
0.027
Alanine
8.184
116
1.347
0.737
−
0.024
2-Hydroxybutyrate
8.565
147
1.832
7.827
+
0.001
3-Hydroxypropanoate
8.733
220
1.557
1.259
+
0.030
Sulfate
9.119
147
1.987
1.330
+
<0.001
N-Methyl-dl-alanine
9.174
130
2.206
5.071
+
0.004
Alpha-ketoisocaproic acid
9.293
89
1.710
2.048
+
0.010
Valine
9.794
144
1.465
0.665
−
0.030
2-Ketoadipate
10.243
96
2.027
1.322
+
<0.001
Ethanolamine
10.516
174
1.087
1.797
+
0.025
Glycerol
10.624
218
1.323
2.017
+
0.030
Phosphate
10.650
211
1.173
2.396
+
0.032
3-Hydroxypyruvate
10.821
150
1.596
0.729
−
0.007
(-)-Dihydrocarveol
10.849
181
1.711
0.750
−
0.003
Isoleucine
10.896
158
1.420
0.677
−
0.014
Proline
10.981
142
2.055
0.495
−
0.004
Beta-hydroxypyruvate
11.263
130
1.877
1.478
+
0.002
Uracil
11.502
99
1.442
3.876
+
<0.001
Fumarate
11.683
245
2.163
2.031
+
<0.001
1-Indanol
11.977
159
2.183
0.418
−
<0.001
3-Hydroxybenzaldehyde
13.215
71
1.732
0.626
−
0.011
L-Malate
13.443
73
2.033
1.924
+
0.001
Fluorene
15.298
326
1.948
1.714
+
0.002
Levoglucosan
15.992
57
1.052
0.441
−
0.004
β-Glycerophosphoric acid
16.219
243
1.839
0.658
−
0.002
Ciliatine
16.422
398
1.453
0.456
−
0.015
O-phosphonothreonine
16.687
73
1.847
0.684
−
0.002
N-Acetyl-l-glutamate
16.913
84
1.896
4.229
+
0.001
2,6-Diaminopimelate
17.656
174
1.603
0.620
−
0.009
Fructose
17.763
103
1.815
2.883
+
0.022
Galactose
18.372
81
1.726
5.288
+
0.001
Gluconic acid
18.480
87
1.431
7.229
+
<0.001
Tyrosine
18.536
218
1.381
1.763
+
0.034
Spermidine
20.951
188
1.113
1.965
+
0.018
Fructose 2,6-biphosphate
21.063
73
1.319
1.928
+
0.043
Fructose-6-phosphate
21.808
217
1.174
2.138
+
0.043
Glucose-6-phosphate
21.919
387
1.039
2.382
+
0.017
Arachidonate
22.451
80
1.179
2.276
+
0.001
Purine riboside
22.481
59
1.674
2.923
+
0.014
D-erythro-sphingosine
23.146
204
1.557
3.682
+
0.015
1-Monopalmitin
24.143
371
1.082
1.780
+
0.025
Adenosine
24.493
236
1.340
2.020
+
0.033
Monoolein
25.308
218
1.595
6.375
+
0.013
Zymosterol
29.195
129
1.133
2.307
+
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 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.
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;
). 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;
). 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 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 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.
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.
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 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 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 (
) 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 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 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.
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 (
), 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 (
). 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 (
), 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 (
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 (
). 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 (
). 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 (
). 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 (
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 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 (
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 (
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 (
). 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 (
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 (
). Peroxisome proliferator-activated receptors also contribute to metabolism pathways such as lipid transport, FA transport, FA oxidation, cholesterol metabolism, adipocyte differentiation, and gluconeogenesis (
). 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 (
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 (
). 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 (
). In POSP dairy cows, elevated NEFA, especially LCFA, might active PPARs and lead to increased oxidation and decreased esterification of FA in the liver (
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 (
). 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 (
Carnitine palmitoyltransferase I in liver of periparturient dairy cows: Effects of prepartum intake, postpartum induction of ketosis, and periparturient disorders.
). 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 (
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 (
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 (
). 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) (
). 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 (
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 (
Increased anaplerosis, TCA cycling, and oxidative phosphorylation in the liver of dairy cows with intensive body fat mobilization during early lactation.
). 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 (
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 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 (
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.
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 (
). 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 (
). 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 (
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.
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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Carnitine palmitoyltransferase I in liver of periparturient dairy cows: Effects of prepartum intake, postpartum induction of ketosis, and periparturient disorders.
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.
Differential amino acid, carbohydrate and lipid metabolism perpetuations involved in a subtype of rheumatoid arthritis with Chinese medicine cold pattern.
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.
Increased anaplerosis, TCA cycling, and oxidative phosphorylation in the liver of dairy cows with intensive body fat mobilization during early lactation.
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.
Choline and methionine regulate lipid metabolism via the AMPK signaling pathway in hepatocytes exposed to high concentrations of nonesterified fatty acids.
Effects of a wide range of dietary forage-to-concentrate ratios on nutrient utilization and hepatic transcriptional profiles in limit-fed Holstein heifers.
Integrative hepatic metabolomics and proteomics reveal insights into the mechanism of different feed efficiency with high or low dietary forage levels in Holstein heifers.
Proteome analysis identified proteins associated with mitochondrial function and inflammation activation crucially regulating the pathogenesis of fatty liver disease.
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.
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