Analysis of metabolomic patterns in thoroughbreds before and after exercise.
Exercise affects metabolic responses throughout the body [']. During exercise, muscles generate ATP by using various intramuscular and extramuscular substrates such as creatine phosphate, muscle glycogen, blood-borne glucose, lactate, and free fatty acids. The various substrates for exercise metabolism are dependently determined by exercise intensity and duration as well as training status, dietary manipulation, and other environmental factors . Exercise of maximal intensity increases the amount of lactate derived from the degradation of muscle glycogen, products of adenine nucleotide catabolism, and tricarboxylic acid cycle intermediates related to aerobic energy production ; it also promotes glycogenolysis, lipolysis, and ammonia metabolism . Prolonged submaximal intensity exercise improves insulin sensitivity, arterial compliance, and endothelial function ; increases lipid catabolism ; decreases the catecholamine response ; and maintains bone density, skeletal muscle mass, and muscle metabolic capacity during ageing .
The equine skeletal muscle displays intrinsic metabolic adaptations based on myofiber structure and function, substrate and by-product transport across the sarcolemma, and coordinated integration of metabolic pathways to produce ATP in response to exercise . Equine muscles store a large amount of glycogen (300 to 650 mol/g dry weight) in fast fibers. The stored glycogen is used as the most important source of energy for muscle contraction during both submaximal (<85% V[O.sub.2max]) and maximal exercise (>85% V[O.sub.2max]) . During prolonged submaximal intensity exercise, lipids also contribute to produce muscle energy with glycogen . After exercise, supplementation of muscle glycogen can slowly take up to 72 h in horses . Previous studies have shown that muscle glycogen supplement after exercise was enhanced by certain processes such as intravenous glucose infusion, oral acetate administration, and rehydration with hypotonic electrolyte solutions in horses . In addition, the buffering capacity that prevents muscle acidosis by lactate is higher in horses than in other species, probably because of high carnosine content . Some studies have also suggested that equine adaptation to exercise could improve both aerobic and anaerobic capacities . However, the mechanism underlying equine metabolism in response to exercise is still unclear.
Recently, multivariate approaches of metabolomic analysis have been used to understand biological mechanisms . With respect to biological endpoints, quantifications of metabolomes could elucidate biological phenomena with other omics studies such as genomics, transcriptomics, and proteomics. For the acquisition of metabolic data, high-resolution [sup.1]H or [sup.13]C nuclear magnetic resonance (NMR) spectroscopy and mass spectroscopy have been used along with other spectroscopic methodologies [14,15]. The acquired data can be interpreted using multivariate statistical analysis, such as hierarchical cluster analysis, principal component analysis, different types of partial least square analysis, and subsequent modeling with new regression algorithms [16,17].
In this study, we analyzed the metabolic profiles of equine muscle, plasma, and urine before and after exercise by using [sup.1]H NMR spectroscopy. On the basis of the analysis results, commonly or specifically expressed metabolites were selected from the muscle, plasma, and urine, and they reflected the effects of exercise. Subsequently, we suggested metabolic pathways related to those metabolites. Our study could contribute to understanding fluctuations in equine metabolism because of exercise.
MATERIALS AND METHODS
Horses and ethical statement
Three Thoroughbred were used in this study. The Pusan National University-Institutional Animal Care and Use Committee approved the study design (Approval Number: PNU-2015-0864).
Blood, muscle, and urine samples were collected from each horse before and after exercise (30 min). Briefly, venous blood samples were collected using a-50 mL syringe and transferred to heparin-containing tubes and centrifuged at 5,000 rpm for 15 min to obtain plasma. The plasma samples were stored at -20[degrees]C until NMR sample preparation. For skeletal muscle biopsy, local anesthesia was administered to the gluteus medius, and a biopsy collection syringe was used to obtain the muscle samples before and after exercise. The samples were stored in liquid nitrogen until analysis. Urine was collected from the subjects before and after exercise and centrifuged to remove solids. An 600 [micro]L aliquot of the supernatant was added to a micro centrifuge tube containing 70 [micro]L of D2O solution with 5 mM dextran sulphate sodium (DSS) and 10 mM imidazole. The DSS was used as the qualitative standard for the chemical shift scale. In addition, 30 [micro]L of 0.42% sodium azide was added. The urine samples were stored at -70[degrees]C until analysis.
Nuclear magnetic resonance spectroscopy
The skeletal muscle and plasma samples were subjected to [sup.1]H NMR spectroscopy analysis. Briefly, 45 [micro]L of the samples was used with 5 [micro]L of deuterium oxide ([D.sub.2]O) containing 20 mM of the reference material trimethylsilylpropionate (TSP); 20 mg of the skeletal muscle samples was analyzed with 25 [micro]L of D2O containing 2 mM of TSP, and 630 [micro]L of the urine samples was mixed with 70 [micro]L of D2O containing 20 mM of TSP before NMR measurement.
We conducted high-resolution magic angle spinning NMR for the skeletal muscle and plasma samples. The spinning rate was 2,050 Hz. To analyze the skeletal muscle, plasma, and urine samples, the Carr-Purcell-Meiboom-Gill pulse sequence was used to remove the water peak and macromolecular peak signal. The acquisition time was 1.704 s, and the relaxation delay was 1.0 s. Each sample was scanned 128 times, and the total analysis time was 8 min and 13 s.
Chenomx NMR Suite 7.1 (Chenomx Inc., Edmonton, AB, Canada) and SIMCAp+12.0 (Umetrics, Umea, Sweden) software were used to minimize the errors of the measured spectrum and statistical analysis, respectively. In this study, we quantified 22 metabolites in the plasma, while 33 metabolites were investigated in the skeletal muscle in both groups. We used TSP as the standard and measured the absolute concentrations of the metabolites to normalize the samples; the relative concentration of each metabolite was measured. The multivariate statistical analysis method was used to calculate the amount of metabolites present in the samples.
Orthogonal partial least square discriminant analysis
All data were converted from the NMR software format to the Microsoft Excel format. One-dimensional NMR spectra data were imported into SIMCA-P (version 12.0, Umetrics Inc., Kinnelon, NJ, USA) for multivariate statistical analysis, to examine intrinsic variations in the data set. These data were scaled using centered scaling prior to the orthogonal partial least square discriminant analysis (OPLS-DA). For the scaling process, the average value of each variable was calculated and then subtracted from the data. OPLS-DA score plots were used to interpret intrinsic variations in the data.
Means and standard deviations of the metabolites were calculated using Microsoft Excel. The statistical significance (p<0.05, p<0.01, or p<0.001) of apparent differences in metabolite concentrations before and after exercise was assessed using analysis of variance, followed by the f-test (Prism 5.01, San Diego, CA, USA).
Differentially expressed metabolites and metabolic patterns before and after exercise in horses
The metabolite analyses before and after exercise showed that 35, 25, and 34 metabolites were detected in the muscle, plasma, and urine, respectively. Sixteen metabolites were commonly changed among the muscle, plasma, and urine after exercise, and 11, 3, and 14 metabolites were specifically changed in the muscle, plasma, and urine, respectively, after exercise (Figure 1, Table 1). The relative levels of the metabolites after exercise in the muscle, plasma, and urine were measured and compared with the corresponding levels of before exercise. The results showed that aspartate, betaine, choline, cysteine, ethanol, and threonine were increased over 2-fold in the muscle; propionate and trimethylamine were increased over 2-fold in the plasma; and alanine, glycerol, inosine, lactate, and pyruvate were increased over 2-fold and acetoacetate, arginine, citrulline, creatine, glutamine, glutarate, hippurate, lysine, methionine, phenylacetylglycine, taurine, trigonelline, trimethylamine, and trimethylamine N-oxide were decreased below 0.5-fold in the urine (Figure 1).
OPLS-DA and variable important plots of the metabolites before and after exercise
OPLS-DA showed clear separation of the metabolic patterns before and after exercise in the muscle, plasma, and urine (Figure 2A, 2B, 2C). Subsequently, when variable important plots (VIPs) were derived from OPLS-DA for the metabolic patterns before and after exercise, the detected metabolites that contributed to separating the clusters in the respective samples were scored to reflect their priorities (Figure 2D, 2E, 2F). Lactate, creatine, taurine, and cysteine had VIP scores >1 in the muscle; lactate, alanine, glycine, trimethylamine, acetate, and choline had VIP scores >1 in the plasma; and lactate and glycerol had VIP scores >1 in the urine (Table 2).
Metabolites that responded to exercise When the levels of the differentially expressed (fold change >2 or <0.5) and high-VIP-score (VIP score >1) metabolites were collectively analyzed in the muscle, plasma, and urine, the expressed levels were observed to be significantly changed in the urine after exercise, while no significant differences were detected in the muscle and plasma before and after exercise. After exercise, acetoacetate, arginine, glutamine, hippurate, phenylacetylglycine trimethylamine, trimethylamine N-oxide, and trigonelline were significantly decreased by 38.8%, 44.6%, 19.6%, 22.7%, 33.8%, 30.6%, 37.8%, and 30.8%, respectively, while alanine, glycerol, inosine, lactate, and pyruvate were significantly increased by 436.7%, 2,184.4%, 1,008.8%, 8,347.9%, and 726.5%, respectively, in the urine (p<0.05; Figure 3). With respect to the commonly detected metabolites, alanine, glutamine, lactate, and pyruvate showed significantly different expressions in the urine after exercise (p<0.05; Figure 4); the concentrations of alanine, lactate, and pyruvate in the plasma were significantly higher than in the muscle and urine, whereas the concentration of glutamine was not signifycantly different between the muscle and plasma (p<0.05; Figure 4).
Enrichment analyses of metabolic pathways that responded to exercise
Enrichment analyses for the differentially expressed (fold change >2 or <0.5) and high-VIP-score (VIP score >1) metabolites were performed using MetaboAnalyst 3.0 , and 36 pathways were predicted (Table 3).
Metabolic alteration reflects biological responses to various genetic, transcriptiomic, proteomic, and environmental influences [19-21]. Many metabolic studies have applied to characterize metabolic patterns derived from altered gene function in plants [22,23], explore microbial metabolism , assess drug toxicity  and diagnostic applications , and discover biomarkers for animal health and disease [21,27,28]. Therefore, metabolic biomarkers are regarded as a promising tool for improving animal health and welfare.
Since domestication, horses have been selected for superior athletic traits related to strength, endurance, and speed. In particular, racehorses have undergone artificial structural and functional adaptations for athletic performances. As a result, racehorses developed maximal aerobic capacity, intramuscular energy stores, mitochondrial volume in the muscle, and oxygen-carrying capacity in the blood . From the unique physiological properties, most of the metabolic studies on exercising horses focused on glycogen stores, whereas only a few studies have addressed muscle triglyceride or protein stores. During intensive short-term exercise, muscle glycogen stores may be depleted by 20% to 35%, and prolonged exercise results in a decline in muscle glycogen by 50% to 100% . However after cessation of exercise, the rate of glycogen repletion is much lower in horses than in other animal species and human athletes . In addition, exercise induces changes in the amino acid profile in the blood and muscle. An increase in branched-chain amino acids, such as leucine, isoleucine, and valine, has been observed during prolonged sub-maximal exercise in horses , and it may have been due to increased output by the liver in which proteolysis has been shown to accelerate during exercise . Furthermore, certain amino acids are believed to be oxidized for energy production in the muscle , although the contribution of proteins to energy expenditure in horses during exercise is still unknown. Recently, exercise in young horses was associated with lipid metabolism, including choline and glycerol; carbohydrate metabolism, including lactate, fumarate, and glucose; and amino acid metabolism, including creatine, creatinine, phenylalanine, tyrosine, and glutamate . Collectively, our results showed the consistency of the differentially expressed metabolites in relation with the enrichment analysis of the metabolic pathways. We also suggested additional metabolic changes during equine exercise.
Alanine, glutamine, lactate, and pyruvate, which were commonly detected among the muscle, plasma, and urine, showed significantly different expressions in the urine after exercise (p< 0.05). During exercise, muscle glycogen, which is a primary energy source, is sequentially processed to pyruvate and pyruvate and can be used to produce ATP aerobically or anaerobically through glycolysis . When the muscle cannot use enough oxygen for aerobic glycolysis at high-exercise intensities, anaerobic glycolysis produces ATP in the cytosol of the muscle by the incomplete breakdown of glucose into lactate . Subsequently, muscle lactate is excreted into the blood for the balance of production rate and removal . Once in the bloodstream, lactate can be taken up by exercising or non-exercising skeletal muscles, kidney, or liver, where it is converted to pyruvate for gluconeogenesis . Concurrently, when muscles degrade amino acids for energy needs, the resulting nitrogen is transaminated to pyruvate to produce alanine. This alanine is transported to the liver, where nitrogen enters the urea cycle and pyruvate is used to produce glucose . In addition, glutamine is primarily synthesized from glutamate and glutamic acid in the skeletal muscle. Glutamine is considered important for the maintenance of the renal tubules, contributing to the healthy functioning of the kidneys. Glutamine in the kidneys contributes to the elimination of acids from the blood, and it is lysed to glutamate, aspartate, pyruvate, lactate, alanine, and citrate through a series of metabolic reactions . Collectively, we suggest that the fluctuations in alanine, glutamine, lactate, and pyruvate are potentially associated with exercise in the muscle, blood, and urine of Thoroughbred horses (Figure 4). The balances of these metabolites in equine biofluid could be utilized as an effective indicator of feeding and management to maintain optimal racing performance.
In conclusion, we first tried to analyze the integrated metabolic patterns and enrichment of metabolic pathways in the muscle, plasma, and urine of racehorses before and after exercise. Our results could contribute to understanding metabolic regulation and development of metabolic markers for equine exercise.
CONFLICT OF INTEREST
We certify that there is no conflict of interest with any financial organization regarding the material discussed in the manuscript.
This study was supported by grants from the Next Generation Bio Green 21 Program (No. PJ01117301, PJ01104401), Rural Development Administration, Republic of Korea.
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Hyun-Jun Jang (1,2), (a), Duk-Moon Kim (3), (a), Kyu-Bong Kim (1), Jeong-Woong Park (4), Jae-Young Choi (4), Jin Hyeog Oh (4), Ki-Duk Song (2), Suhkmann Kim (5) *, Byung-Wook Cho (4) *
* Corresponding Authors: Suhkmann Kim
Tel: +82-51-510-2240, Fax: +82-51-516-7421, E-mail: firstname.lastname@example.org
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(1) College of Pharmacy, Dankook University, Cheonan 31116, Korea
(2) Department of Animal Biotechnology, Chonbuk National University, Jeonju 54896, Korea
(3) Department of Animal Biotechnology, College of Applied Life Sciences, Jeju National University, Jeju 63243, Korea
(4) Department of Animal Science, College of Natural Resources and Life Sciences, Pusan National University, Miryang 50463, Korea
(5) Department of Chemistry and Chemistry Institute for Functional Materials, Pusan National University, Busan 46241, Korea
(a) These authors contributed equally to this work.
Submitted Feb 3, 2017; Revised May 3, 2017; Accepted Jun 2, 2017
Caption: Figure 1. Metabolic clustering (left) and heatmap analysis of the differentially expressed metabolites (right) among the muscle, plasma, and urine.
Caption: Figure 2. Analysis of the metabolic patterns in equine muscle, plasma, and urine before and after exercise. Orthogonal partial least square discriminant analysis (OPLS-DA) (R2X: 0.977; R2Y: 0.852; [Q.sup.2]: -0.142) (A) and variable importance plots (VIPs) for the muscle (D). OPLS-DA (R2X: 0.889; R2Y: 0.883; [Q.sup.2]: -1.33) (B) and VIPs for the plasma (E). OPLS-DA (R2X: 0.987; R2Y: 1; [Q.sup.2]: 0.971) (C) and VIPs for the urine (F).
Caption: Figure 3. On the basis of the differentially expressed (fold change >2 or <0.5) or high-variable importance plots (VIPs)-score (VIP >1) metabolites, concentration of the metabolites in the urine before and after exercise. Error bars are expressed as standard deviation; * p<0.05; ** p<0.01; *** p<0.001.
Caption: Figure 4. The metabolic cycles for alanine, glutamine, lactate, and pyruvate from the muscle to the kidney, and the concentrations of alanine, glutamine, lactate, and pyruvate in the muscle, plasma, and urine before and after exercise.
Table 1. Metabolic clustering among the muscle, plasma, and urine Clustering Total Metabolites Muscle only 11 Anserine, aspartate, betaine, carnitine, cysteine, ethanol, fumarate, o-phosphocholine, o-phosphoethanolamine, serine, sn-glyce- ro-3-phosphocholine Plasma only 3 Formate, histidine, propionate Urine only 13 Acetoacetate, allantoin, benzoate, citrate, citrulline, glutarate, hippurate, homocitrulline, inosine, methylsuccinate, phenylac- etylglycine, trigonelline, trimethylamine n-oxide Muscle and plasma 5 Choline, glycine, myo-inositol, phenylalanine, proline Plasma and urine 1 Trimethylamine Urine and muscle 3 Arginine, glucose, glycerol Muscle, plasma, 16 Lactate, creatine, taurine, glutamine, and urine methionine, threonine, pyruvate, succinate, leucine, valine, isoleucine, glutamate, alanine, acetate, tyrosine, lysine Table 2. VIP scores show the list of metabolites that contributed to the separation of the clustering in the muscle ([R.sup.2]X: 0.977; [R.sup.2]Y: 0.852; [Q.sup.2]: -0.142), plasma ([R.sup.2]X: 0.889; [R.sup.2]Y: 0.883; [Q.sup.2]: -1.33), and urine ([R.sup.2]X: 0.987; [R.sup.2]Y: 1; [Q.sup.2]: 0.971) before and after exercise Muscle Var ID (Primary) VIP VIPcvSE Lactate 4.33964 4.90219 Creatine 2.86407 3.73581 Taurine 1.15696 0.677321 Cysteine 1.07798 1.11629 Proline 0.950802 1.58483 O- 0.768788 1.08083 Phosphoethanolamine Glutamine 0.757915 0.408429 Glucose 0.712887 5.76654 Choline 0.655007 0.984574 Carnitine 0.597443 1.45509 Betaine 0.560761 0.476186 Methionine 0.509355 0.76717 Threonine 0.490596 0.624621 Ethanol 0.489648 0.81646 Pyruvate 0.421163 1.43033 Succinate 0.419546 1.43253 Arginine 0.40325 0.711309 Leucine 0.340958 2.08214 Serine 0.339834 0.873955 Valine 0.274741 1.8082 Fumarate 0.221147 0.15998 Isoleucine 0.19769 1.37773 Glutamate 0.151078 1.67088 Anserine 0.147928 1.74206 Phenylalanine 0.147539 0.487178 O-Phosphocholine 0.129257 1.55877 Glycerol 0.117228 0.76393 myo-Inositol 0.113193 0.541967 Alanine 0.0848435 1.37882 sn-Glycero-3- 0.0792956 0.543817 phosphocholine Glycine 0.0652003 1.38621 Aspartate 0.0604699 1.36912 Acetate 0.0461708 0.938492 Tyrosine 0.032664 0.755125 Lysine 0.0203432 0.702034 Plasma Var ID VIP VIPcvSE (Primary) Lactate 3.75706 1.41472 Alanine 1.60664 1.205 Glycine 1.08877 1.57026 Trimethylamine 1.08766 1.87254 Acetate 1.05611 2.35361 Choline 1.05136 1.17688 Valine 0.927083 0.844553 Formate 0.788366 1.50607 Isoleucine 0.590047 0.434523 Pyruvate 0.588535 3.40277 Succinate 0.54452 1.40882 Tyrosine 0.485224 0.149815 Methionine 0.442662 0.236588 Glutamate 0.423751 0.939892 Propionate 0.422877 0.92142 Leucine 0.320238 0.485677 Proline 0.289082 0.731382 Phenylalanine 0.272284 0.387797 Taurine 0.235986 0.816538 Creatine 0.225572 0.492157 Lysine 0.217242 0.344466 myo-Inositol 0.151193 0.41876 Glutamine 0.109232 0.445521 Threonine 0.0471337 0.192853 Histidine 0.0349163 0.347014 Urine Var ID (Primary) VIP VIPcvSE Lactate 4.95072 0.185054 Glycerol 2.43861 0.961256 Hippurate 0.830247 0.844768 Benzoate 0.733784 0.860513 Pyruvate 0.64467 0.1 10574 Phenylacetylglycine 0.553367 0.73167 Alanine 0.483287 0.0354834 Glutamine 0.330252 0.337596 Acetate 0.225269 0.12487 Inosine 0.2025 0.0420699 Threonine 0.185985 0.128163 Taurine 0.133553 0.276668 Citrate 0.121972 0.0814875 Citrulline 0.1 17822 0.128122 Glutamate 0.1 12898 0.216093 Creatine 0.103247 0.271346 Methylsuccinate 0.101376 0.134033 Arginine 0.0948408 0.16968 Acetoacetate 0.0927904 0.284629 Trigonelline 0.0869787 0.0891645 Trimethylamine 0.0845139 0.127546 N-oxide Glucose 0.0585096 0.144095 Methionine 0.0564183 0.0567537 Trimethylamine 0.0424139 0.0581431 Isoleucine 0.0411947 0.0560735 Lysine 0.0279116 0.11095 Glutarate 0.0243009 0.0617341 Tyrosine 0.0228266 0.0955172 Valine 0.01 15851 0.0491691 Succinate 0.01 13337 0.0459536 Leucine 0.0106587 0.073076 Homocitrulline 0.00670114 0.133487 Allantoin 0.000348166 0.206484 VIP, variable important plots; VIPcvSE, variables associated with lower standard errors in relation to VIP Bold box indicates major metabolites that have a VIP score of more than 1 according to orthogonal partial least square discriminant analysis. Table 3. List of metabolic pathways obtained using enrichment analysis for the differentially expressed (fold change >2 or <0.5) and high-VIP-score (VIP score >1) metabolites Related metabolic pathway Total Expected Hits Raw p Protein biosynthesis 19 0.622 5 0.000217 Urea cycle 20 0.655 5 0.000283 Glycine, serine and 26 0.851 5 0.00105 threonine metabolism Ammonia recycling 18 0.589 4 0.00204 Arginine and proline 26 0.851 4 0.00832 metabolism Pyruvate metabolism 20 0.655 3 0.0246 Betaine metabolism 10 0.327 2 0.0395 Methionine metabolism 24 0.785 3 0.04 Aspartate metabolism 12 0.393 2 0.0556 Biotin metabolism 4 0.131 1 0.125 Alanine metabolism 6 0.196 1 0.181 Taurine and hypotaurine 7 0.229 1 0.208 metabolism Gluconeogenesis 27 0.884 2 0.22 Cysteine metabolism 8 0.262 1 0.235 Malate-aspartate shuttle 8 0.262 1 0.235 Butyrate metabolism 9 0.295 1 0.26 Glutathione metabolism 10 0.327 1 0.284 Ketone body metabolism 10 0.327 1 0.284 Glucose-alanine cycle 12 0.393 1 0.331 Beta-alanine metabolism 13 0.425 1 0.353 Phenylalanine and 13 0.425 1 0.353 tyrosine metabolism Lysine degradation 13 0.425 1 0.353 Glycerolipid metabolism 13 0.425 1 0.353 Purine metabolism 45 1.47 2 0.44 Propanoate metabolism 18 0.589 1 0.454 Glutamate metabolism 18 0.589 1 0.454 Phospholipid biosynthesis 19 0.622 1 0.472 Insulin signalling 19 0.622 1 0.472 Bile acid biosynthesis 49 1.6 2 0.485 Glycolysis 21 0.687 1 0.507 Porphyrin metabolism 22 0.72 1 0.524 Citric acid cycle 23 0.753 1 0.54 Galactose metabolism 25 0.818 1 0.57 Valine, leucine and 36 1.18 1 0.706 isoleucine degradation Pyrimidine metabolism 36 1.18 1 0.706 Tyrosine metabolism 38 1.24 1 0.726 Related metabolic pathway Holm p FDR Protein biosynthesis 0.0174 0.0113 Urea cycle 0.0224 0.0113 Glycine, serine and 0.0817 0.0279 threonine metabolism Ammonia recycling 0.157 0.0408 Arginine and proline 0.632 0.133 metabolism Pyruvate metabolism 1 0.328 Betaine metabolism 1 0.4 Methionine metabolism 1 0.4 Aspartate metabolism 1 0.495 Biotin metabolism 1 0.999 Alanine metabolism 1 1 Taurine and hypotaurine 1 1 metabolism Gluconeogenesis 1 1 Cysteine metabolism 1 1 Malate-aspartate shuttle 1 1 Butyrate metabolism 1 1 Glutathione metabolism 1 1 Ketone body metabolism 1 1 Glucose-alanine cycle 1 1 Beta-alanine metabolism 1 1 Phenylalanine and 1 1 tyrosine metabolism Lysine degradation 1 1 Glycerolipid metabolism 1 1 Purine metabolism 1 1 Propanoate metabolism 1 1 Glutamate metabolism 1 1 Phospholipid biosynthesis 1 1 Insulin signalling 1 1 Bile acid biosynthesis 1 1 Glycolysis 1 1 Porphyrin metabolism 1 1 Citric acid cycle 1 1 Galactose metabolism 1 1 Valine, leucine and 1 1 isoleucine degradation Pyrimidine metabolism 1 1 Tyrosine metabolism 1 1 Raw p, raw p value; FDR, false discovery rate.
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|Author:||Jang, Hyun-Jun; Kim, Duk-Moon; Kim, Kyu-Bong; Park, Jeong-Woong; Choi, Jae-Young; Hyeog Oh, Jin; Son|
|Publication:||Asian - Australasian Journal of Animal Sciences|
|Date:||Nov 1, 2017|
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