Changes in The Liver Transcriptome and Physiological Parameters of Japanese Black Steers During The Fattening Period

Changes in The Liver Transcriptome and Physiological Parameters of Japanese Black Steers During The Fattening Period | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Changes in The Liver Transcriptome and Physiological Parameters of Japanese Black Steers During The Fattening Period Minji Kim, Tatsunori Masaki, Kentaro Ikuta, Eiji Iwamoto, Yoshinobu Uemoto, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-882318/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Mar, 2022 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract We investigated the physiological changes during the fattening period and production characteristics in Japanese Black steers bred and raised using the typical feeding system in Japan. Here, 21 Japanese black steers aged 12 months were used, and the experimental period was divided into early (12–14 months of age), middle (15–22 months), and late fattening phases (23–30 months). The liver transcriptome, blood metabolites, hormones, and rumen fermentation characteristics were analyzed. The blood triglyceride and non-esterified fatty acid concentrations increased, and blood ketone levels decreased, with fattening phases. Blood insulin increased with fattening phases and was higher in groups with high carcass weight and marbling. Rumen fermentation characteristics showed high propionate levels and low butyrate levels in late fattening phases, likely due to increased energy intake. Genes related to glucose metabolism, such as SESN3, INSR, LEPR, and FOXO3, were down-regulated in late fattening phases. Genes related to lipid metabolism, such as FABP4, were up-regulated, whereas FADS1 and FADS2 were down-regulated. These findings suggest that the physiological changes resulted from changes in the energy content and composition of diets. Liver metabolism changed with changes in fat metabolism. Insulin was strongly associated with physiological changes and productivity in Japanese Black cattle. Physiology Animal Physiology liver transcriptome physiological Black steers FOXO3 Figures Figure 1 Figure 2 Introduction Japanese Black cattle is a breed of Japanese beef cattle well-known for producing premium beef with high intramuscular fat (marbling), and has been improved by genetic selection in the last half a century. It has genetic characteristics that allow more intramuscular fat accumulation compared to other beef cattle. Therefore, Japan has a special feeding management system that includes using a large amount of concentrate and controlling the vitamin A levels in the feed and cattle blood to improve the marbling in beef 1 . Based on these differences, the Japanese Black is likely to have differences in physiology compared to other beef cattle. However, previous studies on the Japanese Black cattle have focused on its genetic features and improvement of management strategies for the production of high-quality beef. Therefore, there is a lack of research on the physiological features of this cattle breed. Several studies have investigated the physiological characteristics in crossbred black cattle raised outside Japan. Samantha et al. 2 reported the relationship between blood metabolome and fat accumulation in Wagyu crossbred steers and suggested that the relationship is influenced by days in a feedlot and the growth development status. Another study reported that Japanese Black × Angus steers had higher triacylglycerol content and enzymes activities involved in de novo lipogenesis than Limousin steers 3 . This study also suggested that physiological characteristics differ according to breed, even in beef cattle produced for meat production. However, even if the genetic background is partially similar, the physiological features of the Japanese Black cattle are likely to be different from those of crossbred cattle because the rearing environment and feeding management are different between the 2 cattle breeds. Therefore, it is necessary to investigate the physiological features of Japanese Black cattle raised by feeding management developed according to the livestock practices in Japan. The present study focused on the liver transcriptome to investigate the physiological features of Japanese Black cattle. In ruminants, the liver regulates the overall energy balance by synthesizing glucose with propionic acid absorbed from the rumen 4 and regulates fat metabolism through fat oxidation and accumulation 5 . Recent studies have shown that liver function is related to livestock productivity, such as meat and milk production. Brown et al. 6 reported that the carcass weight and rib eye area were lower in cattle with liver abnormalities than in those with normal liver function. The study also reported that liver function abnormalities caused a significant economic liability to the cattle feeding and beef processing industries. Therefore, this study aimed to investigate the physiological features associated with the phases of the fattening period (early, middle, and late fattening phase) and production characteristics in Japanese Black steers bred and raised in Japan. To comprehensively examine the metabolic status of black steers according to the fattening period, we collected various physiological parameters, including the liver transcriptome, blood metabolites, hormones, and rumen fermentations. Results Blood metabolome and rumen fermentation profiles Table 1 shows the concentrations of blood metabolites and hormones in the early (T1), middle (T2), and late (T3) fattening phases in Japanese Black cattle. The concentrations of several blood metabolites increased significantly ( P < 0.05) during the middle and late fattening phases, including blood urea nitrogen (BUN), creatinine, triglyceride, total cholesterol, triglyceride, non-esterified fatty acids (NEFA), phospholipid, alanine aminotransferase (ALT), gamma-glutamyl transferase (γ-GTP), and lactate dehydrogenase (LD), whereas that of blood glucose, alkaline phosphatase (ALP), and β-hydroxybutyric acid (BHB) decreased during the late fattening phase ( P < 0.05). The concentrations of insulin and cortisol increased significantly ( P < 0.05) as fattening progressed, whereas that of the insulin-like growth factor 1 (IGF-I) decreased ( P < 0.05). Supplementary Table S1 online lists the concentrations of blood amino acids during each fattening period in Japanese Black Steers. Threonine, glycine, alanine, valine, methionine, isoleucine, arginine, and hydroxyproline levels were higher in T1 than in T2 and T3, whereas glutamine, cysteine, tyrosine, and 1-methylhistidine levels were higher in T3 ( P < 0.05). Taurine, α-amino adipic acid, and ornithine concentrations increased in T2 and then sharply decreased in T3 ( P < 0.05). In rumen fermentation samples, the pH and NH3 were significantly lower ( P < 0.01) in T3 than in T1 and T2 (Supplementary Table S2 online). The percent of propionate increased significantly ( P < 0.01) with fattening periods, but the acetate and butyrate levels decreased ( P < 0.01 and P < 0.1, respectively). Table 1 Concentrations of blood metabolites and hormones during the early, middle, and late fattening phases cattle in the early (T1; 13 months of age), middle (T2; 20 months of age), and late fattening phases (T3; 28 months of age). Values indicate mean ± standard error and means in the same row with different superscripts differ significantly ( P < 0.05). BUN: blood urea nitrogen, NEFA: non-esterified fatty acid, ALP: alkaline phosphatase, AST: aspartate aminotransferase, ALT: alanine aminotransferase, γ-GTP: gamma(γ)-glutamyl transferase, LD: lactate dehydrogenase, CK: creatine kinase, BHB: β-hydroxybutyric acid, IGF-I: insulin-like growth factor 1. Variable Period P-value T1 T2 T3 Metabolites Total protein (g/dL) 6.48 ± 0.09 6.59 ± 0.08 6.61 ± 0.09 0.49 Albumin (g/dL) 3.53 ± 0.05 3.67 ± 0.03 3.57 ± 0.03 0.04 BUN (mg/dL) 14.68 ± 0.58a 15.36 ± 0.48a 9.85 ± 0.43b < 0.01 Creatinine (mg/dL) 1.01 ± 0.02b 1.21 ± 0.02a 1.29 ± 0.03a < 0.01 Total cholesterol (mg/dL) 96.24 ± 2.87b 130.67 ± 5.35a 99.52 ± 3.61b < 0.01 Triglyceride (mg/dL) 12.48 ± 0.46b 14.57 ± 0.82ab 16.05 ± 0.95a < 0.01 NEFA (mEq/l) 0.08 ± 0.00b 0.09 ± 0.00b 0.11 ± 0.01a < 0.01 Phospholipid (mg/dL) 90.9 ± 2.22b 117.24 ± 4.27a 91.57 ± 2.78b < 0.01 Glucose (mg/dL) 71.62 ± 4.91a 66.33 ± 4.28b 67.9 ± 3.81b < 0.01 ALP (U/l) 319.81 ± 16.85a 221.43 ± 7.90b 210.14 ± 6.42b < 0.01 AST (U/l) 71.62 ± 1.07 66.33 ± 0.93 67.9 ± 0.83 0.86 ALT (U/l) 19.14 ± 0.47ab 17 ± 0.51b 21.48 ± 1.09a < 0.01 γ-GTP (U/l) 20.86 ± 0.57b 29.43 ± 1.79a 22.33 ± 0.81b < 0.01 LD (U/l) 943.43 ± 22.99c 1114.19 ± 40.12b 1297.86 ± 35a < 0.01 CK (U/l) 144.71 ± 4.85 137.33 ± 7.31 147.71 ± 15.28 0.76 Acetate (µmol/l) 21.62 ± 5.46 8.62 ± 2.24 8.52 ± 2.62 0.38 BHB (µmol/l) 717.52 ± 40.09a 531.71 ± 25.25b 500.33 ± 42.12b < 0.01 Total ketone body (µmol/l) 753.1 ± 44.08a 540.1 ± 26.61b 508.38 ± 43.88b < 0.01 Hormone Insulin (ng/ml) 2.22 ± 1.13b 3.82 ± 2.45a 4.08 ± 2.36a < 0.01 IGF-I (ng/ml) 206.36 ± 57.45a 139.18 ± 26.46b 149.48 ± 38.99b < 0.01 Cortisol (ng/ml) 4.70 ± 6.18b 9.62 ± 12.58b 21.61 ± 13.88a < 0.01 Figure 1 a shows the principal component analysis (PCA) score plot for the 18 blood metabolites and 3 hormones of Japanese Black cattle, with each data point colored according to the fattening phase. PC1 and PC2 explained 20.72% and 13.17% of the total variance, respectively, and the clustering patterns suggested differences in blood metabolite profiles among the fattening phases. Figure 1 b shows the PCA score plot for the 29 blood amino acids of Japanese black cattle, where PC1 and PC2 explained 30.54% and 15.43% of the total variance, respectively. Blood amino acids in T1 were different from those in T2 and T3. However, in the PCA plot of rumen fermentation characteristics (Fig. 1 c), there was no distinct clustering according to fattening period compared to the blood composition. Gene expression analysis by RNA-Seq A total of 2,965,384,118 reads and 299,503,795,918 base pairs were generated from RNA sequencing of liver tissue during the entire experimental period. The average reads were 49.60, 49.86, and 51.37 million in T1, T2, and T3, respectively, and the clean read rate was 95.39%, 95.83%, and 95.84% in each period, respectively (Supplementary Tables S3 and S4 online). In total, 19,063, 19,649, and 19,343 expressed genes were detected in the liver of black steers in T1, T2, and T3, respectively (Supplementary Fig. S1 online). Of these, 18,317 genes were expressed in all fattening phases, 298, 197, and 482 genes were commonly identified between pairs of groups (T1 vs T2, T1 vs T3, and T2 vs T3, respectively), and 251, 552, and 347 genes were discovered exclusively in T1, T2, and T3, respectively (Supplementary Fig. S1 online). Figure 2 a shows the PCA plot (with 2 outliers removed) of gene expressions in individual samples. PC1 and PC2 explained 51.0% and 4.5% of the variance within the dataset of annotated genes, and the plot showed clustering within each of the fattening phases, especially in T1 and T3. To investigate differentially expressed genes (DEGs) in liver tissues between pair of fattening phases (T1 vs T2, T1 vs T3, and T2 vs T3), we defined hepatic genes as DEGs according to the following criteria: a ≥ 1.5 log 2 fold change in expression level, a base mean > 30, and a false discovery rate (FDR) of < 0.05. Volcano plots were prepared to visualize the DEGs between 2 phases, with red points marking the genes with significantly increased or decreased expression (Supplementary Fig. S2 online). In total, 509 DEGs were identified between any 2 periods, with 58 (30 upregulated and 28 downregulated), 370 (341 upregulated and 29 downregulated), and 81 (68 upregulated and 13 downregulated) DEGs between T1 vs T2, T1 vs T3, and T2 vs T3, respectively (Fig. 2 b). There were 3 up-regulated DEGs in comparisons across all periods, including patatin-like phospholipase domain-containing 3 (PNPLA3), neuronal cell adhesion molecule (NRCAM), and an unknown gene. Additionally, 4 Japanese Black cattle with high vs low carcass weight and beef marbling score (BMS) were identified and their hepatic transcriptomes were analyzed (Table 2 , Supplementary Tables 5 and 6 online). In total, 14 and 5 DEGs were found in comparisons of groups with different carcass weights (high vs low) and BMS (high vs low), respectively. Table 2 Mean carcass traits in carcass weight and BMS groups. Values indicate the mean ± standard error. BMS: beef marbling score. Variable Carcass weight group BMS group High (n = 4) Low (n = 4) P-value High (n = 4) Low (n = 4) P-value Live weight 782.50 ± 6.95 698.00 ± 18.02 < 0.01 770.50 ± 7.23 747.50 ± 22.56 0.39 Growth rate 0.84 ± 0.01 0.75 ± 0.02 0.02 0.83 ± 0.01 0.81 ± 0.03 0.62 Carcass weight 491.50 ± 5.33 417.25 ± 9.31 < 0.01 482.00 ± 8.44 452.75 ± 12.13 0.10 Eye muscle area 59.25 ± 3.33 50.00 ± 2.45 0.07 57.25 ± 5.11 58.50 ± 2.22 0.83 Rib thickness 8.38 ± 0.30 6.98 ± 0.30 0.02 8.23 ± 0.40 7.70 ± 0.18 0.30 Subcutaneous fat 3.50 ± 0.21 2.28 ± 0.15 < 0.01 3.38 ± 0.14 2.33 ± 0.36 0.05 BMS 9.50 ± 0.87 7.50 ± 0.65 0.12 10.00 ± 0.41 6.50 ± 0.29 < 0.01 Gene ontology analysis of DEGs Gene ontology (GO) analysis was conducted by running queries for each DEG against the GO database, which provided information on relevant biological processes (BP), cellular components (CC), and molecular functions (MF) associated with the DEGs. GO and pathway analysis of the DEGs in T1 and T3 showed the greatest difference in gene expression. The DEGs between T1 and T3 were mainly involved in energy and lipid metabolism processes, such as glucose homeostasis, cholesterol biosynthetic process, phospholipid homeostasis, triglyceride biosynthetic process, and fatty acid metabolic process (Tables 3 and 4 , Supplementary Tables 7 and 8 online). The CC- and MF-related genes were enriched in substance transport processes, including calcium ion binding, transporter activity, and voltage-gated chloride channel activity (Table 3 , Supplementary Tables 7 and 8 online). Table 3 Gene ontology of differentially expressed genes in early fattening phases (13 months of age) vs late fattening phases (28 months of age). GO: gene ontology, DEGs: differentially expressed genes. Ontology GO Term ID Description No. of DEGs P-value Biological processes GO:0008152 Metabolic Process 7 0.01 GO:0001525 Angiogenesis 7 0.03 GO:0042593 Glucose Homeostasis 6 0.02 GO:0030335 Positive Regulation of Cell Migration 6 0.07 GO:0006695 Cholesterol Biosynthetic Process 5 < 0.01 GO:0032922 Circadian Regulation of Gene Expression 5 0.01 GO:0060395 Smad Protein Signal Transduction 5 0.02 GO:0006469 Negative Regulation of Protein Kinase Activity 5 0.04 GO:0035264 Multicellular Organism Growth 5 0.04 GO:0006491 N-Glycan Processing 4 0.01 GO:0018107 Peptidyl-Threonine Phosphorylation 4 0.01 GO:0006629 Lipid Metabolic Process 4 0.07 GO:0051897 Positive Regulation of Protein Kinase B Signaling 4 0.09 GO:0006874 Cellular Calcium Ion Homeostasis 4 0.10 GO:0055091 Phospholipid Homeostasis 3 < 0.01 GO:0019432 Triglyceride Biosynthetic Process 3 0.01 GO:0003222 Ventricular Trabecula Myocardium Morphogenesis 3 0.02 GO:0006631 Fatty Acid Metabolic Process 3 0.07 GO:0001568 Blood Vessel Development 3 0.09 GO:0006814 Sodium Ion Transport 3 0.09 Cellular composition GO:0016021 Integral Component of Membrane 107 < 0.01 GO:0070062 Extracellular Exosome 57 < 0.01 GO:0005887 Integral Component of Plasma Membrane 26 0.01 GO:0005783 Endoplasmic Reticulum 20 < 0.01 GO:0005794 Golgi Apparatus 17 0.03 Molecular functions GO:0005509 Calcium Ion Binding 23 < 0.01 GO:0004571 Mannosyl-Oligosaccharide 1,2-Alpha-Mannosidase Activity 3 0.01 GO:0015269 Calcium-Activated Potassium Channel Activity 3 0.02 GO:0005215 Transporter Activity 6 0.03 GO:0004008 Copper-Exporting Atpase Activity 2 0.03 Table 4 Gene ontology of differentially expressed genes related to lipid and energy metabolism in early fattening phases (13 months of age) vs late fattening phases (28 months of age). GO: gene ontology, DEGs: differentially expressed genes. GO Term ID Description No. of DEGs P-value Up-regulated genes Down-regulated genes GO:0008152 Metabolic Process 7 0.01 LPGAT1, FASN, EDEM1, IDS, DIP2C, PIGG GSTA3 GO:0042593 Glucose Homeostasis 6 0.02 SESN3, HNF4A, INSR, LEPR, PTPN11, FOXO3 GO:0006695 Cholesterol Biosynthetic Process 5 0.00 NPC1L1, DHCR24, HMGCR, DHCR7, HSD17B7 GO:0006629 Lipid Metabolic Process 4 0.07 FADS2, ACOX1, HNF4A, FADS1 GO:0055091 Phospholipid Homeostasis 3 0.00 ABCA1, GPAM, HNF4A GO:0019432 Triglyceride Biosynthetic Process 3 0.01 GPAM, LPIN1, LPIN2 GO:0006631 Fatty Acid Metabolic Process 3 0.07 GPAM FABP4, SNCA Pathway analysis of DEGs We used the KEGG database ( www.kegg.jp/kegg/kegg1.html ) to determine significant pathways involving the DEGs between early and late fattening phases. In total, 30 pathways were significantly enriched for the identified DEGs ( P < 0.05 and P < 0.1; Supplementary Table 9 online). Among these, the top 10 DEGs were mainly involved in lipid metabolism and signaling pathways, including metabolic pathways, glycerolipid metabolism, bile secretion, glycerophospholipid metabolism, AMPK signaling pathway, cGMP-PKG signaling pathway, calcium signaling pathway, fatty acid metabolism, PPAR signaling pathway, and vascular smooth muscle contraction. Moreover, 41 genes were involved in the metabolic pathway, including 40 up-regulated genes and 1 down-regulated gene. Association between physiological parameters and carcass traits Supplementary Table S10 online summarizes the carcass traits after slaughter. The average carcass weight and eye muscle area were 458.8 kg and 55.9 cm 2 , respectively. The BMS was 6–11 (average, 8.3). The Japan black cattle used here had lower carcass weight and eye muscle area, and similar in rib thickness and subcutaneous fat thickness, compared to the national average in Japan in 2019 7 which may be considered characteristic of the Tajigyu cattle (i.e., the wagyu breed produced in Hyogo Prefecture, Japan) used in this experiment. Table 5 lists the feed composition during the T1, T2, and T3. Table 6 lists the correlation coefficients between carcass traits and physiological parameters in T1. Blood triglyceride was positively ( P < 0.05) correlated with rib eye area and BMS. ALT levels were positively correlated ( P < 0.05) with live weight, growth rate, carcass weight, rib thickness, and subcutaneous fat. Other metabolites (ALP and γ-GTP) related to liver function were not significantly correlated with carcass trait. IGF-1 had a positive trend with carcass trait in T1. Taurine, leucine, and phenylalanine levels were negatively correlated ( P < 0.05) with BMS. Aspartic acid level was positively correlated with rib thickness and BMS ( P < 0.05), and 1-methylhistidine and hydroxyproline levels were positively correlated with rib thickness and subcutaneous fat ( P < 0.05). In rumen fermentation samples, pH was negatively correlated with BMS and NH 3 was positively correlated with eye muscle area ( P < 0.05) (Table 6 ). Table 5 Mean growth performance and nutrient composition in Japanese black cattle during the early (T1; 13 months of age), middle (T2; 20 months of age), and late fattening phases (T3; 28 months of age). The cattle were fed concentrates as follows: T1: steam-flaked corn 42%, wheat bran 27%, corn starch 10%, soybean meal 12%, soybean hull 6%, salt 1% (TDN 71.2%, CP 15.9%); T2: steam-flacked corn 42%, barley 14%, wheat bran 21%, corn starch 5%, soybean meal 10%, soybean hull 6%, salt 1% (TDN 72.5%, CP 14.4%); T3: steam-flacked corn 44%, barley 25%, wheat bran 14%, soybean meal 5%, soybean hull 10%, salt 1% (TDN 72.8%, CP 12.0%). The rice straw intakes were as follows: DM 87.8%, TDN 37.7%, CP 4.7%. The kraft pulp feed intakes were as follows: DM 76.9%, TDN 51.1%, CP 0.3%. Variable Period T1 T2 T3 Growth performance Average body weight (kg) 369.8 (337.7–400.7) 522.6 (478.9–584.3) 686.9 (607.0–757.0) Average dairy gain (kg/day) 0.86 (0.45–1.07) 0.83 (0.61–1.04) 0.61 (0.49–0.80) Feed intake Concentrate (kg/day) 5.41 (4.13–5.61) 7.31 (5.96–8.30) 7.44 (4.67–8.96) Rice straw (kg/day) 1.93 (1.21–2.70) 1.13 (0.72–1.61) 0.74 (0.49–0.99) Kraft pulp feed (kg/day) 0.72 (0–1.54) 0.45 (0–0.92) 0.29 (0–0.60) Feed intake Dry matter (kg/day) 6.98 (5.67–7.27) 7.72 (6.52–8.65) 7.36 (4.89–8.69) Total digestible nutrients (kg/day) 4.95 (3.94–5.28) 5.95 (5.00–6.69) 5.84 (3.84–6.89) Crude protein (kg/day) 0.95 (0.74–1.02) 0.93 (0.75–1.07) 0.93 (0.59–1.12) Table 6 Heat map illustrating the correlations between physiological parameters and carcass traits in the early fattening period (13 months of age). BUN: blood urea nitrogen, NEFA: non-esterified fatty acid, ALP: alkaline phosphatase, AST: aspartate aminotransferase, ALT: alanine aminotransferase, γ-GTP: gamma(γ)-glutamyl transferase, LD: lactate dehydrogenase, CK: creatine kinase, BHB: β-hydroxybutyric acid, IGF-I: insulin-like growth factor 1. ** and * indicate P < 0.01 and P < 0.05, respectively. Live weight Growth rate Carcass weight Rib eye area Rib thickness Subcutaneous fat BMS Live weight 0.973** 0.953** 0.499* 0.508* 0.319 0.38 Growth rate 0.914** 0.535* 0.524* 0.258 0.348 Carcass weight 0.499* 0.617** 0.408 0.474* Eye muscle area 0.61** -0.083 0.383 Rib thickness 0.526* 0.393 Subcutaneous fat 0.272 Blood metabolites and hormones Total protein -0.03 -0.026 -0.097 0.02 -0.064 -0.028 -0.102 Albumin -0.064 -0.057 -0.054 -0.111 0.194 0.113 -0.199 BUN 0.094 0.19 0.087 0.155 0.215 -0.14 -0.202 Total cholesterol 0.201 0.262 0.29 0.071 0.261 0.103 -0.042 Triglyceride 0.124 0.132 0.226 0.458* 0.392 0.035 0.499* NEFA 0.064 0.058 0.003 -0.255 -0.005 0.19 -0.181 Glucose -0.395 -0.369 -0.325 -0.269 0.033 0.257 0.009 ALP 0.299 0.293 0.289 0.367 0.312 0.106 0.187 ALT 0.468* 0.515* 0.531* 0.108 0.492* 0.553** 0.187 γ-GTP 0.042 -0.075 0.065 0.174 0.079 0.199 0.192 BHB 0.086 0.063 0.051 0.258 0.08 -0.103 0.176 Insulin 0.165 0.192 0.181 0.119 0.267 0.084 0.112 IGF-I 0.321 0.341 0.253 0.104 0.331 0.393 -0.103 Cortisol 0.063 0.012 0.107 -0.156 0.083 0.17 0.195 Blood amino acids Taurine 0.018 -0.024 0.027 -0.241 0.011 0.209 -0.477* Urea -0.151 -0.083 -0.162 -0.108 0.103 0.061 -0.287 Aspartic acid 0.333 0.34 0.324 0.418 0.465* 0.064 0.514* Threonine 0.106 0.062 0.193 -0.219 -0.018 -0.032 -0.273 Serine -0.185 -0.229 -0.082 -0.297 -0.069 0.058 -0.249 Glutamic acid 0.127 0.04 0.065 0.024 0.156 0.169 0.114 Glutamine -0.041 -0.069 0.029 -0.125 0.198 0.241 -0.341 α-Amino adipic acid 0.061 0.108 0.044 0.166 0.449* 0.236 0.005 Glycine -0.037 -0.088 0.033 -0.333 -0.046 0.085 -0.08 Alanine 0.221 0.206 0.139 0.157 0.258 0.207 0.092 Citrulline 0.138 0.15 0.204 0.178 0.207 0.185 0.016 α-Aminobutyric acid -0.086 -0.029 -0.164 0.085 -0.242 -0.4 0.123 Valine -0.351 -0.33 -0.26 -0.336 -0.102 -0.097 -0.415 Methionine 0.22 0.218 0.22 0.217 0.263 0.04 -0.141 Cysteine 0.215 0.247 0.149 0.051 0.098 0.058 -0.101 Isoleucine -0.309 -0.331 -0.232 -0.23 -0.112 -0.208 -0.33 Leucine -0.38 -0.377 -0.277 -0.319 -0.14 -0.205 -0.452* Tyrosine -0.201 -0.179 -0.156 -0.057 -0.115 -0.205 0.026 Phenylalanine -0.326 -0.32 -0.302 -0.407 -0.293 -0.292 -0.523* NH3 -0.214 -0.274 -0.16 -0.18 -0.108 0.09 -0.302 Ornithine 0.015 -0.06 0.125 -0.142 -0.109 -0.069 -0.007 1-methylhistidine 0.352 0.298 0.324 0.38 0.588* 0.426 0.257 Histidine -0.162 -0.192 -0.069 -0.062 0.246 0.043 -0.17 3-methylhistidine -0.166 -0.185 -0.133 -0.112 -0.363 -0.267 0.095 Lysine -0.094 -0.121 -0.07 -0.057 -0.111 -0.211 -0.216 Tryptophan -0.373 -0.39 -0.228 -0.421 -0.03 0.055 -0.309 Arginine -0.096 -0.066 -0.107 0.145 -0.072 -0.403 -0.125 Hydroxyproline 0.314 0.339 0.37 0.331 0.68* 0.531* 0.044 Proline -0.051 -0.144 0.042 -0.301 0.035 0.418 -0.1 Rumen fractions pH -0.123 -0.098 -0.165 -0.133 -0.087 0.254 -0.403 NH3 0.103 0.125 0.011 0.48* 0.201 -0.119 0.061 C2(Acetic)% -0.075 -0.033 -0.011 0.095 0.102 -0.027 -0.194 C3(Propionic)% -0.244 -0.238 -0.21 -0.006 0.062 0.018 -0.122 C4(Butyric)% 0.15 0.095 0.051 -0.029 -0.147 -0.11 0.148 C5(Other)% 0.133 0.126 0.123 -0.13 -0.039 0.186 0.254 Table 7 shows the correlation coefficients between carcass traits and physiological parameters in T3. Blood albumin was negatively correlated with carcass weight and BMS ( P < 0.05), but BUN exhibited a positive trend with carcass weight. Blood glucose was positively correlated with subcutaneous fat ( P < 0.05). ALP was significantly negatively correlated with growth rate ( P < 0.05) and also with carcass weight, rib thickness, and subcutaneous fat. Ketones were positively correlated with all carcass traits. Unlike in the T1 group, blood insulin in T3 was positively correlated with carcass traits ( P < 0.05). IGF-I was negatively correlated with BMS, and cortisol level showed no correlation with carcass traits. Blood taurine and urea showed positive correlations with growth rate and carcass weight. Rib thickness was positively correlated with α-amino adipic acid and negatively correlated with glycine, arginine, and hydroxyproline levels ( P < 0.05). BMS was negatively correlated with cysteine and proline levels ( P < 0.05). In rumen fermentation samples, there was no significant correlation in the T3 group (Table 7 ). Table 7 Heat map illustrating the correlations between physiological parameters and carcass traits in the late fattening period (28 months of age). BUN: blood urea nitrogen, NEFA: non-esterified fatty acid, ALP: alkaline phosphatase, AST: aspartate aminotransferase, ALT: alanine aminotransferase, γ-GTP: gamma(γ)-glutamyl transferase, LD: lactate dehydrogenase, CK: creatine kinase, BHB: β-hydroxybutyric acid, IGF-I: insulin-like growth factor 1. ** and * indicate P < 0.01 and P < 0.05, respectively. Live weight Growth rate Carcass weight Rib eye area Rib thickness Subcutaneous fat BMS Live weight 0.973** 0.953** 0.499* 0.508* 0.319 0.38 Growth rate 0.914** 0.535* 0.524* 0.258 0.348 Carcass weight 0.499* 0.617** 0.408 0.474* Eye muscle area 0.61** -0.083 0.383 Rib fat 0.526* 0.393 Subcutaneous fat 0.272 Blood metabolites and hormones Total protein -0.232 -0.211 -0.346 0.031 -0.154 -0.128 -0.463* Albumin -0.349 -0.339 -0.437* -0.241 -0.352 -0.205 -0.478* BUN 0.361 0.429 0.31 0.326 0.274 0.096 0.119 Total cholesterol 0.201 0.234 0.284 0.03 -0.053 -0.146 0.12 Triglyceride 0.182 0.19 0.168 0.05 0.04 -0.13 0.164 NEFA -0.085 -0.144 -0.105 -0.091 -0.222 -0.085 0.184 Glucose 0.178 0.123 0.243 -0.31 0.153 0.639** 0.186 ALP -0.414 -0.437* -0.38 -0.127 -0.297 -0.274 -0.152 ALT 0.04 0.068 0.017 0.101 0.08 0.235 -0.085 γ-GTP -0.141 -0.215 -0.119 -0.217 -0.268 0.173 -0.087 BHB 0.377 0.363 0.344 0.452* 0.409 0.13 0.467* Insulin 0.582** 0.573** 0.656** 0.416 0.498* 0.273 0.539* IGF-I 0.152 0.213 0.037 0.041 0.07 0.299 -0.466* Cortisol -0.128 -0.139 -0.042 -0.21 -0.002 0.157 -0.068 Blood amino acids Taurine 0.368 0.395 0.387 0.22 0.076 0.069 0.096 Urea 0.402 0.467* 0.345 0.319 0.255 0.091 0.154 Aspartic acid -0.01 0.044 0.027 -0.004 0.15 -0.031 0.248 Threonine -0.043 -0.065 0.089 -0.233 -0.07 0.018 0.112 Serine -0.336 -0.36 -0.166 -0.375 -0.209 -0.065 -0.101 Glutamic acid -0.205 -0.135 -0.334 -0.063 -0.112 -0.275 -0.219 Glutamine 0.16 0.21 0.116 0.08 -0.15 -0.177 -0.188 α-Amino adipic acid -0.013 -0.005 0.028 0.447* 0.614** 0.219 0.236 Glycine -0.191 -0.208 -0.251 -0.142 -0.567** -0.413 -0.212 Alanine -0.173 -0.139 -0.206 0.113 -0.015 -0.357 0.088 Citrulline -0.239 -0.17 -0.185 -0.187 -0.287 -0.102 -0.351 α-Aminobutyric acid -0.306 -0.41 -0.256 0.183 -0.077 -0.163 0.086 Valine -0.021 -0.067 0.126 -0.164 -0.018 0.118 0.11 Methionine -0.047 -0.055 0.021 -0.119 -0.081 -0.023 -0.064 Cysteine -0.09 -0.054 -0.238 -0.079 -0.228 -0.233 -0.502* Isoleucine 0.071 0.02 0.185 -0.067 0.125 0.169 0.294 Leucine 0.093 0.059 0.238 -0.072 0.082 0.093 0.238 Tyrosine -0.011 -0.026 0.125 -0.198 0.117 0.231 0.33 Phenylalanine 0.253 0.206 0.294 -0.229 -0.124 0.009 -0.097 NH3 0.147 0.141 0.171 0.027 0.049 0.425 -0.125 Ornithine -0.184 -0.215 -0.084 -0.202 -0.147 0.021 0.066 1-methylhistidine -0.006 -0.062 -0.126 0.243 -0.239 -0.252 -0.051 Histidine -0.239 -0.31 -0.117 -0.134 -0.306 -0.396 -0.077 3-methylhistidine 0.038 0.105 -0.065 0.424 0.178 -0.234 -0.064 Lysine 0.109 0.058 0.226 -0.102 0.069 0.109 0.14 Tryptophan 0.087 0.019 0.22 -0.236 0.079 0.304 -0.009 Arginine -0.371 -0.34 -0.346 -0.372 -0.416 -0.178 -0.188 Hydroxyproline -0.053 0.004 -0.232 -0.192 -0.468* -0.285 -0.131 Proline -0.117 -0.121 -0.106 -0.017 0.132 0.195 -0.55** Rumen fractions pH 0.026 0.059 0.035 0.191 -0.068 -0.285 0.074 NH3 0.074 0.127 -0.05 0.077 0.051 0.013 -0.07 C2(Acetic)% -0.012 -0.07 -0.135 0.04 -0.124 -0.3 -0.19 C3(Propionic)% -0.136 -0.107 0.049 -0.121 0.073 0.229 -0.034 C4(Butyric)% 0.213 0.234 0.135 0.116 0.09 0.048 0.271 C5(Other)% -0.159 -0.104 -0.302 -0.025 -0.288 -0.028 -0.08 Discussion This study comprehensively analyzed the metabolic status of Japanese Black steers according to fattening phase, including various physiological parameters such as the liver transcriptome, blood metabolites, hormones, and rumen fermentation characteristics. The major physiological changes associated with fattening period and productivity are discussed below. Changes in physiological parameters in Japanese Black steer according to fattening phase Here, we reported the changes in metabolite profiles, hormones, and rumen fermentation characteristics during the fattening period in Japanese Black steer bred and raised in Japan. The triglyceride and NEFA concentrations (associated with lipid metabolism) increased with fattening phase due to the changes in the feed, including higher amounts of concentrated diet and increased dry matter intake. Increasing amounts of the concentrated diet from T1 to T2 elevated the total cholesterol, phospholipids, glucose, and γ-GTP levels in T2; however, the levels of these metabolites in T3 were similar to those in T1. Japanese Black cattle are fed large amounts of high-energy concentrate diets in the middle and late fattening phases to improve their growth performance and productivity. A study on Yellow breed × Simmental cattle showed that lipid metabolism-related blood metabolites (triglycerides, cholesterol, and high-density and low-density lipoprotein) were significantly higher in the high-energy group (6.64 MJ/kg) than in the low-energy group (5.98 MJ/kg), and that blood insulin levels increased with increasing energy levels 8 . Blood glucose and insulin levels also increase significantly with high-starch diets in Angus and Angus × Simmental cattle 9 . In ruminants, concentrate diets are a major source of glucose, either through increased production of rumen propionate (a gluconeogenic precursor), or—to a smaller extent—through glucose absorption from rumen-bypassed diets 10 . Glucose synthesized via gluconeogenesis is the primary energy source in ruminants 11 , and excess glucose is converted into fatty acids that circulate to other parts of the body and are stored as fat in adipose tissue 12 . Accordingly, the high-energy feeding boosts rumen propionate production and liver gluconeogenesis, and may explain the increased concentrations of blood lipid metabolites during the middle and late fattening phases. In cattle, NEFAs—indicators of fat mobilization—increase due to energy deficiency 13 or pathological problems such as liver dysfunction, ketosis, and fatty liver 14 . In a state of negative energy balance, NEFAs are produced via lipolysis from triglycerides stored in adipose tissue and transported to organs and tissues 15 . In the present study, high blood levels of NEFAs in the middle and late fattening phases were likely due to relative increases in body fat reserves as fattening progressed, rather than energy deficiency. Samra et al. 16 reported that cortisol—an indicator of stress response—promotes lipolysis, thus increasing blood NEFA concentrations. The increased cortisol levels during the fattening period in this study may have promoted lipolysis, resulting in increased concentrations of NEFA in the blood. The blood insulin level depends on age, weight, diets, and feeding management 17 , and is generally known to increase as fattening progresses. Ruminants are less sensitive to insulin than monogastric animals since insulin secretion in ruminants is mainly stimulated by VFAs 18 , 19 . Propionate is a strong stimulator of insulin secretion in sheep 20 . The increase in insulin secretion from T1 to T3 may be related to the increase in rumen propionate during the fattening period, and may have facilitated the intracellular influx of glucose, thus reducing blood glucose levels. Generally, BHB and other ketone bodies increase during energy deficit and are produced as metabolites in the lipid oxidation process of NEFAs in the liver. Here, the ketone concentrations decreased significantly in the middle and late fattening phases. Particularly, the decrease in BHB levels was likely due to the decreased levels of its precursor, rumen butyrate, during the middle and late fattening phases. To evaluate the liver function in Japanese Black steers, we analyzed the blood levels of ALP, AST, ALT, and γ-GTP. These metabolites are present in hepatocytes and released into the blood stream when hepatocytes are damaged by stimulation such as mold toxins and long-term high-energy feeding. Blood ALP—mainly released from the liver and bones—is related to liver dysfunction and bone development in beef cattle 21 , 22 . In growing animals, blood ALP is mainly released from bone tissue; the main tissue that releases the ALP into the blood stream transforms into liver tissue and the overall release decreases with growth 23 , 24 . In Japanese Black calves, blood ALP concentrations are significantly lower in the late growing stage than in the early growing stage, likely due to the slower rate of bone growth with increasing age 23 . In accordance with the findings of the previous study, our results showed that the blood ALP concentration decreased with fattening phases, suggesting that blood ALP concentrations decrease with age unless there is a severe abnormality in liver function. The blood γ-GTP is mainly released from the liver and bile duct and reflects chronic liver function because changes in its blood concentration occur at a slower rate than those of AST and ALT 25 . Blood γ-GTP concentration is positively correlated with BUN levels in Japanese Black cattle during the fattening period. Similar to the results of the previous study, our results showed that the blood γ-GTP levels were comparatively higher in T2 and T3 than in T1, and the reduction was likely caused by increased feed intake in T2 and T3. The above 4 enzymes increased or decreased slightly with the fattening period, but all were within the normal range. Amino acids are not only the organic components of proteins, but are also involved in various physiological mechanisms, such as glucose synthesis via gluconeogenesis, fatty acid synthesis, and hormone regulation 26 . Amino acid metabolism is closely related to liver function because the absorption and transformation of amino acids occur in the liver. Thus, the blood concentrations of amino acids can be used to determine the nutritional and metabolic conditions of animals. Here, 22 of the 29 blood amino acids showed differences in concentration between fattening phases, and 13 decreased during the late fattening phase. Samantha and Connolly 2 also reported that the progression of the feeding period in the feedlot was associated with decreasing blood concentrations of amino acids such as proline, leucine, isoleucine, histidine, whereas aspartate, and glutamic acid (a typical gluconeogenic acid) increased in wagyu crossbred steers. Liver transcriptome changes in Japanese Black cattle according to fattening period We conducted the GO pathway analysis for enriched DEGs to define the functional roles of DEGs according to fattening period. Here, we focused on comparisons between early and late fattening phases, which showed the greatest differences in gene expression in the DEG analysis. The DEGs were significantly enriched in metabolic processes such as glucose and lipid metabolic process. The expression of genes related to glucose metabolism—such as SESN3, insulin receptor (INSR), leptin receptor (LEPR), forkhead box O1 (FOXO3)—was decreased in late fattening phases. LEPR is a liver-specific leptin receptor and regulates energy balance. Hepatic LEPR expression is positively correlated with plasma leptin concentration 27 ; serum leptin concentration gradually increases from 9–23 months of age (with slight changes until 30 months) in Japanese Black steer 28 , 29 . Additionally, leptin level changes with the body fat mass and stimulation of insulin 30 . Thus, LEPR regulation in the late fattening phases may be attributed to fat accumulation and increased insulin secretion, and the increased leptin levels may lead to a decrease in leptin receptor levels. The insulin receptor plays a key role in glucose homeostasis and is closely regulated by hormones such as insulin, IGF-I, and IGF-II. Fisher et al. 31 , 32 reported that plasma insulin levels were elevated in liver insulin receptor knockout mice, and the insulin regulation of receptors was related to the inhibition of glycogenolysis and gluconeogenesis. Previous studies in cattle have reported that the mRNA expression of insulin receptor in hepatocytes decreased with increasing insulin concentration in a dose-dependent manner 32 , and that dairy cattle with ketosis and fatty liver showed low mRNA expression of hepatic insulin receptor 33 . FOXO3 is also inactivated by the binding of insulin and its receptor, regulates the expression of gluconeogenesis-related genes, and promotes gluconeogenesis. The hepatic suppression of FOXO1 and FOXO3 causes hypoglycemia and hyperlipidemia in FOXO1 and FOXO3 knockout mice; moreover, the expression of hepatic FOXOs affects the reduction of gluconeogenic gene expression and insulin resistance 34 . Additionally, FOXO3 also activates the SESN3, which seems to be related the reactive oxygen species rescue pathway 35 . We also found that the expression of genes related to lipid metabolism such as fatty acid-binding protein 4 (FABP4) was increased in late fattening phases, whereas fatty acid desaturase 1 (FADS1) and FADS2 expressions were decreased. FABP4 encodes the fatty acid binding protein, which is considered to be related to the uptake and metabolism of fatty acids. The FABP4 genotype significantly affects the marbling score in Wagyu and Limousin crossbred cattle 36 ; FABP4 may be associated with the fatty acid composition of intramuscular fat tissue 37 . Here, FABP4 expression was higher in the late fattening phases than in the early phases. This increase in gene expression related to fat absorption and metabolism is thought to reflect the active accumulation of fat or other energy source in the liver during the late fattening phases. FADS1 and FADS2 are associated with the composition of fatty acids such as arachidonic (C20:4n-6), linoleic (C18:2n-6), alpha-linolenic (C18:3n-3) and eicosadienoic acids 38 . Supplementation of dietary fat in lambs results in increased expression levels of hepatic FADS1, along with increased intramuscular docosahexaenoic acid (DHA) levels in the DHA supplemented group 39 . Therefore, here, the changes in FADS1 and FADS2 expression may be due to changes in fat composition with changes in feed. To identify the significant pathways involving the DEGs between the early and late fattening phases, we performed the KEGG pathway analysis. In total, 33 pathways were significantly enriched for the identified DEGs. The pathway analysis suggested that the main physiological changes associated with fattening period were related to energy and lipid metabolism in Japanese black steers. Blood metabolic profiles and liver transcriptomes related with carcass traits in Japanese Black cattle Here, blood triglyceride contents were significantly positively correlation with rib eye area and BMS in T1. Blood triglyceride levels are negatively correlated with the level of intramuscular fat accumulation (BMS) 40 which may explain the inconsistent results in prior studies. In our study, the blood ALT level was positively correlated with carcass traits in T1, and the ALT concentration was higher in steers with high carcass weight than in steers with relatively low carcass weights in all fattening periods (Supplementary Table 12 online). This indicates that lipid accumulation with changing fattening diets may contribute to carcass traits such as BMS and carcass weight. The blood aspartic acid had a positive correlation with rib thickness and BMS, and α-amino adipic acid, 1-methylhistidine, and hydroxyproline showed a strong positive correlation with rib thickness in T1. A study on the wagyu crossbred reported that aspartic acid showed positive correlation with growth rate, no significant correlation with other properties, and that methyl histidine showed a negative correlation with the rump fat 41 . The α-amino adipic acid is potentially correlated with insulin resistance. Lee et al. 42 reported that α-amino adipic acid is correlated with adipogenic differentiation, and that the α-amino adipic acid levels were increased in cell and mouse models of obesity-related insulin resistance. Here, the blood α-amino adipic acid sharply increased between T1 and T2, corresponding to the changes in insulin levels. Thus, the correlation with carcass traits appears to be similar to that with insulin, likely because the blood α-amino adipic acid level reflects changes in insulin levels. Interestingly, our results showed that ketogenic amino acids such as isoleucine, leucine, tyrosine, and phenylalanine were negatively correlated with most carcass traits in early fattening phases. The ketones were used as an energy source in early fattening phases and more amino acids were consumed, which may have led to a negative relationship between the blood amino acids and the carcass traits of Japanese Black steers. This result suggested that Japanese Black steers in the early fattening phases consumed a lot of amino acids to replenish the considerable amount of energy needed through ketones, resulting in a negative correlation between amino acids and productivity. In the late fattening phases, glucose had a strong positive correlation with subcutaneous fat. Similar to the results of the present study, some previous studies have reported that glucose level is positively correlated to rump fat and glucose in wagyu crossbred steers 41 . Glucose is known as the precursor to fatty acid biosynthesis in ruminants, and blood glucose level is deeply related to the accumulation of intramuscular and subcutaneous fat 10 . Here, the decreased glucose level in late fattening phases may have been to be due to the increase in intracellular influx of glucose by insulin. The positive correlation between blood glucose and subcutaneous fat suggested that more fat is accumulated in Japanese Black cattle with high blood glucose levels. Blood insulin showed a strong positive relationship with carcass weight, growth rates, rib thickness, and BMS. The steers with high carcass weight and BMS had higher concentrations of insulin compared to those with low carcass weight and BMS (Supplementary Tables 12 and 15 online). As noted in an earlier study, insulin promotes the intracellular influx of glucose for fat accumulation, and blood insulin concentrations are associated with carcass traits such as carcass weight, growth rate, rib thickness, and BMS 10 , 40 . This suggests that insulin is a more important regulator of lipid metabolism in the T3 phase than in the T1 phase in Japanese Black steers. We also investigate hepatic DEGs according to carcass weight (High vs Low) and BMS (High vs Low) to investigate the physiological changes with productivity in Japanese Black cattle. In total, 14 genes were differentially expressed when compared between carcass weights (High vs Low) and were mainly involved in organ development, cell growth, and muscle contraction. In comparison between BMS (High vs Low), 5 genes related to calcium channel and cytoskeleton components were found. This study revealed slight changes in the liver transcriptome according to carcass characteristics. No differences in energy- and fat metabolism-related gene were found in each fattening period, likely due to long-term adaptation and small metabolic changes into 2 different carcass weight and BMS categories. However, we found differences in the candidate genes and pathways responsible with large metabolic changes between the T1 and T3 phases. The results suggest that the main physiological difference in Japanese Black cattle is that the high-production steers show high blood insulin concentration. As with high insulin levels in the late fattening phases, the steers that consumed a lot of high-energy diets had high energy intake (Supplementary Tables 12 and 15 online) compared to other steers with relatively low intake, resulting in the secretion of insulin to store excess energy. Conclusion Our study showed that the physiological changes associated with fattening phases were typically characterized by high-energy diets for growth and carcass traits in Japanese Black steers. The increased glucose, total cholesterol, and NEFA and decreased ketones during the fattening period were related to lipid metabolism, fat accumulation, and fatty acid synthesis, and strongly associated to insulin regulation. Additionally, the DEGs—such as PNPLA3, INSR, LEPR, FOXO3, FABP4, FADS1, and FADS2—enriched in relation to lipid metabolism were regulated to accumulate energy in the liver; insulin may have affected this process. Insulin also showed significant differences depending on the productivity of cattle; blood insulin level was higher in the high productive steers. Therefore, the physiological characteristics of Japanese Black cattle are as follows: the physiological changes during the fattening periods result from changes in energy content and composition of diets; the changes in liver metabolism according to fattening period are strongly related to aspects of fat metabolism, such as fat accumulation and transport; and insulin regulates physiological changes according to fattening periods and productivity. Our results also suggest that the above blood metabolites and liver transcriptome could be used as parameters to determine the metabolic status of Japanese Black steer bred and raised in accordance with the typical feeding systems in Japan. Additionally, the parameters could be used to evaluate metabolic status in other cattle breeds for beef production. Methods Animals and experimental design The animal experiments were performed at the Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries (Hyogo Prefecture, Japan) and conducted by the Guideline for the Institute of Livestock and Grassland Science 43 and the Ethical guidance of the Hyogo Prefectural Institute of Agriculture of Forestry and Fisheries Animal Care and Use Committee. Experiment protocol was evaluated and approved by Hyogo Prefectural Institute of Agriculture of Forestry and Fisheries Animal Care and Use Committee. All animal experiments were carried out in accordance with ARRIVE guidelines ( https://arriveguidelines.org ). Twenty-one Japanese black steers aged 12 months (initial body weight, 335.6 ± 19.8 kg) were reared until 30 months of age (final body weight, 742.1 ± 49.9 kg). The experimental period was divided into early fattening (12–14 months of age), middle fattening (15–22 months of age), and late fattening phases (23–30 months of age). Experimental animals were fed concentrate and roughage (rice straw and kraft pulp feed) twice daily (at 09:30 and 15:00). Water was always freely available, and other feeding management was conducted in accordance with the practices of the Hyogo Prefectural Technology Center. The steers were fed with specific amounts of formula diet that were changed each fattening period as shown in Table 5 . The chemical composition of the formula diet was analyzed according to standard tables of feed composition in Japan 44 and provisional values for official specification set by the Ministry of Agriculture, Forestry and Fisheries 45 . The adequacy rate of the diet was calculated based on the nutrient requirement of Japanese Feeding Standard for Beef cattle 46 . Sample preparation and carcass trait assessment Experimental samples—including blood, rumen fluid, and liver tissue—were collected at the early fattening (13 months of age), middle fattening (20 months of age), and late fattening phases (28 months of age) in 21 Japanese black steers. Blood samples were collected at 5 h 30 min after morning feeding (15:00 h) from the jugular vein using heparin-sodium tubes (Venoject II VP-H100K; Terumo, Tokyo, Japan). All samples were transferred to the laboratory on ice and stored until centrifugation at 3,000 × g for 15 min. Plasma samples were stored at − 30°C until metabolic profiling. Rumen fluid was collected using a suitable catheter. Approximately 200 ml of rumen juice was collected in a sterilized and dried flask and immediately filtered through 4 layers of cheesecloth to separate the rumen fluid from diet particles. The pH of rumen samples was measured using a digital electric pH meter. The filtrates were stored at − 80°C until component analysis. Liver tissue was biopsied as described previously 47 . The biopsy site was at the intersection of the right 11th to 12th intercostal space and the line from the right point of the acromion to the tuber coxa. The biopsy site was anesthetized with xylazine. A 0.5 cm long stab incision was made in the skin and the biopsy was performed using an automatic biopsy gun with 150 mm biopsy needles (ACECUT ACE-141502, DELTA surgical, Staffordshire, UK). After the biopsy, the spot was disinfected with 10% isodine gel. All the tissues were rapidly separated, immediately frozen in liquid nitrogen, and stored at − 80°C until RNA extraction All steers were slaughtered at a commercial meat abattoir after a 24 h rest period. Carcasses were chilled for 24 h at 0°C, after which the left side was opened between the 6th and 7th ribs to evaluate the yield and quality of the carcass according to the standard criteria of the Japan Meat Grading Association 7 . The traits measured during carcass evaluation were carcass weight, beef marbling standard (BMS) 5, rib eye area, rib thickness, and subcutaneous fat thickness. Blood metabolome and rumen fermentation profiling Blood metabolites were analyzed using an Automatic Biochemical Analyzer (HITACHI 7070, Hitachi, Ltd., Tokyo, Japan). The analyses included measurements of total protein, albumin, blood urea nitrogen (BUN), creatinine, total cholesterol, triglycerides, NEFA, glucose, ALP, AST, ALT, LD, γ-GTP, creatine kinase, acetate, BHB, and total ketone bodies. Plasma insulin, IGH-1, and cortisol were assayed by enzyme immunoassay according to manufacturer instructions using a multi-species insulin ELISA kit (Mercodia bovine insulin ELISA, Mercodia AB, Uppsala, Sweden), human IGF-1 ELISA kit (Human IGF-I, R&D system, Minneapolis, USA), and cortisol ELISA kit (Cortisol ELISA kit, Enzo Life Sciences Inc., Budapest, Hungary), respectively. After adding trichloroacetic acid to the plasma and filtering the proteins through a membrane filter, the blood amino acid concentrations were determined by a high-speed amino acid spectrometer (L-8900, Hitachi High Tech, Tokyo, Japan). Total VFA and VFA components (acetic acid, propionic acid, butyric acid, and valeric acid) were separated and quantified by gas chromatography (GC2014; Shimadzu, Kyoto, Japan) using a packed glass column (Thermon-3000 [3%]) on a Shimalite TPA 60–80 support (Shinwa Chemical Industries Ltd., Kyoto, Japan). The operating conditions of gas chromatography were as follows: career gas, N2; flow volume, 30 mL/min; temperature of the column injection and FID detection, 220°C; and temperature of the column oven, 140°C. The ammonium nitrogen concentration in the rumen fluid was quantified by the steam distillation method using an automatic nitrogen analyzer (Kjeltec Auto 1,035; Tecator, Sweden) RNA purification Liver tissues were soaked in 200 µL RNAiso Plus (TAKARA Bio Inc., Shiga, Japan) in liquid nitrogen and homogenized using a Multibeads shocker (YASUIKIKAI Inc., Osaka, Japan) following manufacturer instructions. Homogenization was carried out twice at 2,000 rpm for 10–15 s, and 800 µL RNAiso was added at 25°C. The homogenate was collected in 1.5 mL tubes, 200 µL chloroform was added to it, and the solution was thoroughly mixed. After settling for 5 min at room temperature (25°C), the solution was centrifuged at 12,000 × g for 15 min at 4°C. The supernatant was collected and mixed with 500 µL isoprophanol, and the mixture was centrifuged at 12,000 × g for 15 min at 4°C to precipitate the RNA. The supernatant was carefully removed. The pellet was washed twice with 75% cold ethanol and dissolved it with RNase-free water. Purified total RNA was quantified in a Nano Drop ND-1000 Spectrophotometer V3.7.1 (THERMO FISHER SCIENTIFIC, Waltham, MA, USA) at wavelengths of 230, 260, and 280 nm. The purity of total RNA was determined as the A 260 /A 280 ratio (expected values > 1.8) and verified by 1.0 % agarose gels electrophoresis. All samples were stored at -80°C. RNA sequencing and data analyses To check whether the purified total RNA could be used in RNA-seq, the RNA integrity number (RIN) was confirmed in a Tape Station 4200 using an RNA Screen Tape kit (AGILENT, Palo Alto, CA, USA). The average RIN and mRNA rate were 7.1 (range, 6.9–7.9) and 0.8 (range, 0.7–1.2), respectively. RNA-seq libraries were prepared using the TruSeq stranded mRNA Kit (Illumina) and sequenced using a NovaSeq 6000 platform at Macrogen Japan Corp. The quality of the sequencing reads was evaluated by the FastQC software ( http://www.bioinformatics.babraham.ac.uk/projects/fastqc , version 0.11.8). Sequencing reads were trimmed using the Trim Galore software ( http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/ , version 0.5.0) with reassessing by the FastQC software. Sequencing reads were mapped to the ARS-UCD1.2 bovine reference genome ( ftp://ftp.ensembl.org/pub/release-101/fasta/bos_taurus/dna/ ) using the HISAT2 software 48 . The String Tie software 48 were used to calculate the read counts for expressed transcripts with the GTF Bovine gene annotation file ( ftp://ftp.ensembl.org/pub/release-101/gtf/bos_taurus ). Normalization of counts and PCA were performed using the DESseq2 package 49 in R statistical software to determine the relationship among individual samples. Two outliers to all samples were identified and removed. The DESeq2 package 49 was also used to detect the differentially expressed genes between 2 groups (T1 vs T2, T2 vs T3, and T1 vs T3) using normalization of counts and the Wald test. FDR was calculated to account for multiple testing. Significant differentially expressed genes were defined with FDR 1.5 in this study. Functional annotation enrichment analyses using the GO and KEGG databases were performed by DAVID (Database for Annotation, Visualization, and Integrated Discovery) ( https://david.ncifcrf.gov/summary.jsp ). GO terms and KEGG pathways with P -values ≤ 0.05 were considered significantly enriched. Statistical analysis Statistical analyses were performed using SAS software (2012, release 9.4; SAS Institute Inc., Cary, NC) and R software (2020, release v4.0.3; R Foundation for Statistical Computing, Vienna, Austria). All physiological parameters were first assessed for normality and variance homogeneity using the Shapiro-Wilk test and Levene’s test. Significance of difference among the fattening phases was calculated by One-way analysis of variance (ANOVA) and Kruskal Wallis test for data meted normal distribution and for those did not, respectively. Following the ANOVA and Kruskal Wallis test, post-hoc pairwise comparisons among the fattening periods were performed using Tukey’s Honestly Significant Difference test and Bonferroni correction, respectively. PCAs were performed using the relative concentrations of identified blood metabolites and rumen fermentation characteristics as variables in the model with all the possible PCs. PC1 and PC2 score plots were prepared and the data points were colored to visualize the potential clustering of Japanese Black steers according to their fattening phases. From the data of each fattening period, correlation coefficients between the blood parameters (or rumen fluid parameters) and carcass traits were analyzed using Pearson’s correlation. The associations between physiological differences and productivity in Japanese Black cattle were analyzed using general model analysis with fixed effects of fattening period (T1, T2, and T3) and productivity (carcass weight, High vs Low; BMS, High vs Low). In the general model test, post-hoc pairwise comparisons were performed between groups using Bonferroni correction. In the present study, statistical differences were considered significant at P < 0.05 and statistical tendencies were indicated by 0.05 < P ≤ 0.10. Declarations Acknowledgements We would like to acknowledge Hyogo Prefectural Technology Center of Agriculture employees for their assistance in the care of experimental animals and sample collection. We also would like to thank Dr. Itoko Nonaka and Dr. Akane Ashihara for performing the amino acid analysis. Author contributions M.K. contributed to sample collection, sample analysis, data analysis, and writing; T.M. and E.I. contributed to experiment design, sample collection and data analyses; K.I. contributed to sample collection and sample analysis; U.Y. contributed to data analysis, data interpretation, and manuscript revision; F.T. and S.R. contributed to experiment design, data interpretation, writing and revision; All authors read and approved the final manuscript. Competing interests The authors declare no competing interests. Data availability RNA sequence data generated from the present study has been submitted to NCBI BioProject database with project number PRJNA760255. The data will be available with the following link on May 31, 2022: https://www.ncbi.nlm.nih.gov/sra/PRJNA760255. References Smith, S. B., Gotoh, T. & Greenwood, P. L. 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Supplementary Files Supplementarytableandfigurelegens.docx SupplementaryFigure.1.pdf SupplementaryFigure.2.pdf SupplementaryTable.1.xlsx SupplementaryTable.3.xlsx SupplementaryTable.2.xlsx SupplementaryTable.4.xlsx SupplementaryTable.5.xlsx SupplementaryTable.6.xlsx SupplementaryTable.9.xlsx SupplementaryTable.8.xlsx SupplementaryTable.7.xlsx SupplementaryTable.10.xlsx SupplementaryTable.11.xlsx SupplementaryTable.12.xlsx SupplementaryTable.13.xlsx SupplementaryTable.14.xlsx SupplementaryTable.15.xlsx SupplementaryTable.16.xlsx SupplementaryTable.17.xlsx Cite Share Download PDF Status: Published Journal Publication published 07 Mar, 2022 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 17 Dec, 2021 Reviews received at journal 03 Dec, 2021 Reviews received at journal 14 Oct, 2021 Reviewers agreed at journal 30 Sep, 2021 Reviewers agreed at journal 18 Sep, 2021 Reviewers invited by journal 15 Sep, 2021 Editor assigned by journal 13 Sep, 2021 Editor invited by journal 13 Sep, 2021 Submission checks completed at journal 13 Sep, 2021 First submitted to journal 07 Sep, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-882318","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":51599009,"identity":"3df1101b-62e5-4159-8c78-6502d170da43","order_by":0,"name":"Minji Kim","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Minji","middleName":"","lastName":"Kim","suffix":""},{"id":51599010,"identity":"30a8aa5d-305a-4b70-acb4-b58dd24a2ff0","order_by":1,"name":"Tatsunori Masaki","email":"","orcid":"","institution":"Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tatsunori","middleName":"","lastName":"Masaki","suffix":""},{"id":51599011,"identity":"a1aca137-ab73-44fc-a147-ba128eb7c5f2","order_by":2,"name":"Kentaro Ikuta","email":"","orcid":"","institution":"Awaji Agricultural Technology Center","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kentaro","middleName":"","lastName":"Ikuta","suffix":""},{"id":51599012,"identity":"062b8c63-7a35-4aaf-9444-7fd41c5a7567","order_by":3,"name":"Eiji Iwamoto","email":"","orcid":"","institution":"Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eiji","middleName":"","lastName":"Iwamoto","suffix":""},{"id":51599013,"identity":"e8de4a6b-6807-4fd9-830c-0c82180c0184","order_by":4,"name":"Yoshinobu Uemoto","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yoshinobu","middleName":"","lastName":"Uemoto","suffix":""},{"id":51599014,"identity":"aa7a5144-71dd-4005-bbf2-23b75fa51407","order_by":5,"name":"Fuminori Terada","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fuminori","middleName":"","lastName":"Terada","suffix":""},{"id":51599015,"identity":"06cfc634-91bb-4b06-8212-bd80942ca0a3","order_by":6,"name":"Sanggun Roh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYHADHsYHDAwHGCQgPDaitDAbHCBVC5sEkhbcQL797OGXX9vuMJi39x6r/lBxR05yRgLjhx8MfHm4tBicyUuzlm17xiBz5lzajQNnnhlLSyQwS/YwsBXj1MKQY2Ys2XaYQUIix+zGwbbDifMkEhikgX5JbMDlsP43CC0FUC3Mv/FpYbiRY/zwI1QLA0jLbIkENry2GNx4Y8bMcO4wjwTPGWOJM2cOG0v2PGyz7DHA7Rf5/hzjjz/KDstJsPcYfqioADKOJx++8aPiGM4QAwI2aR5gpCAJMAKdZHAsAY8W5o8/sIjW4NMyCkbBKBgFIwsAANAEWEE9bfNuAAAAAElFTkSuQmCC","orcid":"","institution":"Tohoku University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sanggun","middleName":"","lastName":"Roh","suffix":""}],"badges":[],"createdAt":"2021-09-07 04:44:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-882318/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-882318/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-022-08057-8","type":"published","date":"2022-03-07T19:53:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":13406051,"identity":"365d0e27-18db-44d7-9edb-b78dc4a0b3dd","added_by":"auto","created_at":"2021-09-15 14:36:47","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40299,"visible":true,"origin":"","legend":"Principal component analysis of physiological parameter of 21 Japanese black cattle, showing the PC1 vs PC2 values. 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14:36:47","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":17710,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.10.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-882318/v1/363ccbf10609df513c627296.xlsx"},{"id":13406569,"identity":"ace1d588-76cd-465f-8db4-8bacade262ba","added_by":"auto","created_at":"2021-09-15 14:39:51","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":30562,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.11.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-882318/v1/8acb2ca399be61a5d473e16a.xlsx"},{"id":13406050,"identity":"b140ea32-b162-4467-8982-59ca7022a5f5","added_by":"auto","created_at":"2021-09-15 14:36:47","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":19410,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.12.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-882318/v1/452a304fc9eb4cb5be0fe9f8.xlsx"},{"id":13406099,"identity":"503ef2a6-17f5-4e6d-89ee-1812302d31f0","added_by":"auto","created_at":"2021-09-15 14:36:48","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":19211,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.13.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-882318/v1/006f1709edf1ee2db8ed4aed.xlsx"},{"id":13406043,"identity":"08687d68-5ed2-4876-bdb6-6d3707b03b86","added_by":"auto","created_at":"2021-09-15 14:36:45","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":16910,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.14.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-882318/v1/a8790770660fbaf0dc444ab2.xlsx"},{"id":13406122,"identity":"5ebffa49-652d-4589-aeba-4e8afb84b64f","added_by":"auto","created_at":"2021-09-15 14:36:50","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":18024,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.15.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-882318/v1/a37f46fa5440605d3b88770e.xlsx"},{"id":13406123,"identity":"9f8e3ed8-4caa-4397-9d5b-25b940425240","added_by":"auto","created_at":"2021-09-15 14:36:50","extension":"xlsx","order_by":19,"title":"","display":"","copyAsset":false,"role":"supplement","size":18369,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.16.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-882318/v1/4d63952d45df1c8d548a18e5.xlsx"},{"id":13406074,"identity":"3184ada4-b42f-4933-9958-c07396021245","added_by":"auto","created_at":"2021-09-15 14:36:48","extension":"xlsx","order_by":20,"title":"","display":"","copyAsset":false,"role":"supplement","size":16885,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.17.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-882318/v1/9fcf70b4e5c2436f5bccc5e8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eChanges in The Liver Transcriptome and Physiological Parameters of Japanese Black Steers During The Fattening Period\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eJapanese Black cattle is a breed of Japanese beef cattle well-known for producing premium beef with high intramuscular fat (marbling), and has been improved by genetic selection in the last half a century. It has genetic characteristics that allow more intramuscular fat accumulation compared to other beef cattle. Therefore, Japan has a special feeding management system that includes using a large amount of concentrate and controlling the vitamin A levels in the feed and cattle blood to improve the marbling in beef\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Based on these differences, the Japanese Black is likely to have differences in physiology compared to other beef cattle. However, previous studies on the Japanese Black cattle have focused on its genetic features and improvement of management strategies for the production of high-quality beef. Therefore, there is a lack of research on the physiological features of this cattle breed. Several studies have investigated the physiological characteristics in crossbred black cattle raised outside Japan. Samantha et al.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e reported the relationship between blood metabolome and fat accumulation in Wagyu crossbred steers and suggested that the relationship is influenced by days in a feedlot and the growth development status. Another study reported that Japanese Black \u0026times; Angus steers had higher triacylglycerol content and enzymes activities involved in de novo lipogenesis than Limousin steers\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. This study also suggested that physiological characteristics differ according to breed, even in beef cattle produced for meat production. However, even if the genetic background is partially similar, the physiological features of the Japanese Black cattle are likely to be different from those of crossbred cattle because the rearing environment and feeding management are different between the 2 cattle breeds. Therefore, it is necessary to investigate the physiological features of Japanese Black cattle raised by feeding management developed according to the livestock practices in Japan.\u003c/p\u003e \u003cp\u003eThe present study focused on the liver transcriptome to investigate the physiological features of Japanese Black cattle. In ruminants, the liver regulates the overall energy balance by synthesizing glucose with propionic acid absorbed from the rumen\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and regulates fat metabolism through fat oxidation and accumulation\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Recent studies have shown that liver function is related to livestock productivity, such as meat and milk production. Brown et al.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e reported that the carcass weight and rib eye area were lower in cattle with liver abnormalities than in those with normal liver function. The study also reported that liver function abnormalities caused a significant economic liability to the cattle feeding and beef processing industries. Therefore, this study aimed to investigate the physiological features associated with the phases of the fattening period (early, middle, and late fattening phase) and production characteristics in Japanese Black steers bred and raised in Japan. To comprehensively examine the metabolic status of black steers according to the fattening period, we collected various physiological parameters, including the liver transcriptome, blood metabolites, hormones, and rumen fermentations.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eBlood metabolome and rumen fermentation profiles\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the concentrations of blood metabolites and hormones in the early (T1), middle (T2), and late (T3) fattening phases in Japanese Black cattle. The concentrations of several blood metabolites increased significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) during the middle and late fattening phases, including blood urea nitrogen (BUN), creatinine, triglyceride, total cholesterol, triglyceride, non-esterified fatty acids (NEFA), phospholipid, alanine aminotransferase (ALT), gamma-glutamyl transferase (\u0026gamma;-GTP), and lactate dehydrogenase (LD), whereas that of blood glucose, alkaline phosphatase (ALP), and \u0026beta;-hydroxybutyric acid (BHB) decreased during the late fattening phase (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The concentrations of insulin and cortisol increased significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as fattening progressed, whereas that of the insulin-like growth factor 1 (IGF-I) decreased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Supplementary Table S1 online lists the concentrations of blood amino acids during each fattening period in Japanese Black Steers. Threonine, glycine, alanine, valine, methionine, isoleucine, arginine, and hydroxyproline levels were higher in T1 than in T2 and T3, whereas glutamine, cysteine, tyrosine, and 1-methylhistidine levels were higher in T3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Taurine, \u0026alpha;-amino adipic acid, and ornithine concentrations increased in T2 and then sharply decreased in T3 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In rumen fermentation samples, the pH and NH3 were significantly lower (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in T3 than in T1 and T2 (Supplementary Table S2 online). The percent of propionate increased significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) with fattening periods, but the acetate and butyrate levels decreased (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1, respectively).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eConcentrations of blood metabolites and hormones during the early, middle, and late fattening phases cattle in the early (T1; 13 months of age), middle (T2; 20 months of age), and late fattening phases (T3; 28 months of age).\u003c/strong\u003e Values indicate mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error and means in the same row with different superscripts differ significantly (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). BUN: blood urea nitrogen, NEFA: non-esterified fatty acid, ALP: alkaline phosphatase, AST: aspartate aminotransferase, ALT: alanine aminotransferase, \u0026gamma;-GTP: gamma(\u0026gamma;)-glutamyl transferase, LD: lactate dehydrogenase, CK: creatine kinase, BHB: \u0026beta;-hydroxybutyric acid, IGF-I: insulin-like growth factor 1.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal protein (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.87b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.67\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.52\u0026thinsp;\u0026plusmn;\u0026thinsp;3.61b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.82ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEFA (mEq/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhospholipid (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.24\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.57\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.62\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.28b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALP (U/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319.81\u0026thinsp;\u0026plusmn;\u0026thinsp;16.85a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.43\u0026thinsp;\u0026plusmn;\u0026thinsp;7.90b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210.14\u0026thinsp;\u0026plusmn;\u0026thinsp;6.42b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST (U/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT (U/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma;-GTP (U/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLD (U/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e943.43\u0026thinsp;\u0026plusmn;\u0026thinsp;22.99c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1114.19\u0026thinsp;\u0026plusmn;\u0026thinsp;40.12b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1297.86\u0026thinsp;\u0026plusmn;\u0026thinsp;35a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCK (U/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.33\u0026thinsp;\u0026plusmn;\u0026thinsp;7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.71\u0026thinsp;\u0026plusmn;\u0026thinsp;15.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcetate (\u0026micro;mol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.62\u0026thinsp;\u0026plusmn;\u0026thinsp;5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.52\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBHB (\u0026micro;mol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e717.52\u0026thinsp;\u0026plusmn;\u0026thinsp;40.09a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e531.71\u0026thinsp;\u0026plusmn;\u0026thinsp;25.25b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500.33\u0026thinsp;\u0026plusmn;\u0026thinsp;42.12b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal ketone body (\u0026micro;mol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e753.1\u0026thinsp;\u0026plusmn;\u0026thinsp;44.08a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e540.1\u0026thinsp;\u0026plusmn;\u0026thinsp;26.61b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e508.38\u0026thinsp;\u0026plusmn;\u0026thinsp;43.88b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHormone\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIGF-I (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e206.36\u0026thinsp;\u0026plusmn;\u0026thinsp;57.45a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.18\u0026thinsp;\u0026plusmn;\u0026thinsp;26.46b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149.48\u0026thinsp;\u0026plusmn;\u0026thinsp;38.99b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCortisol (ng/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.58b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.61\u0026thinsp;\u0026plusmn;\u0026thinsp;13.88a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea shows the principal component analysis (PCA) score plot for the 18 blood metabolites and 3 hormones of Japanese Black cattle, with each data point colored according to the fattening phase. PC1 and PC2 explained 20.72% and 13.17% of the total variance, respectively, and the clustering patterns suggested differences in blood metabolite profiles among the fattening phases. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb shows the PCA score plot for the 29 blood amino acids of Japanese black cattle, where PC1 and PC2 explained 30.54% and 15.43% of the total variance, respectively. Blood amino acids in T1 were different from those in T2 and T3. However, in the PCA plot of rumen fermentation characteristics (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ec), there was no distinct clustering according to fattening period compared to the blood composition.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003eGene expression analysis by RNA-Seq\u003c/h2\u003e\n \u003cp\u003eA total of 2,965,384,118 reads and 299,503,795,918 base pairs were generated from RNA sequencing of liver tissue during the entire experimental period. The average reads were 49.60, 49.86, and 51.37\u0026nbsp;million in T1, T2, and T3, respectively, and the clean read rate was 95.39%, 95.83%, and 95.84% in each period, respectively (Supplementary Tables S3 and S4 online). In total, 19,063, 19,649, and 19,343 expressed genes were detected in the liver of black steers in T1, T2, and T3, respectively (Supplementary Fig. S1 online). Of these, 18,317 genes were expressed in all fattening phases, 298, 197, and 482 genes were commonly identified between pairs of groups (T1 vs T2, T1 vs T3, and T2 vs T3, respectively), and 251, 552, and 347 genes were discovered exclusively in T1, T2, and T3, respectively (Supplementary Fig. S1 online).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea shows the PCA plot (with 2 outliers removed) of gene expressions in individual samples. PC1 and PC2 explained 51.0% and 4.5% of the variance within the dataset of annotated genes, and the plot showed clustering within each of the fattening phases, especially in T1 and T3.\u003c/p\u003e\n \u003cp\u003eTo investigate differentially expressed genes (DEGs) in liver tissues between pair of fattening phases (T1 vs T2, T1 vs T3, and T2 vs T3), we defined hepatic genes as DEGs according to the following criteria: a\u0026thinsp;\u0026ge;\u0026thinsp;1.5 log\u003csub\u003e2\u003c/sub\u003e fold change in expression level, a base mean\u0026thinsp;\u0026gt;\u0026thinsp;30, and a false discovery rate (FDR) of \u0026lt;\u0026thinsp;0.05. Volcano plots were prepared to visualize the DEGs between 2 phases, with red points marking the genes with significantly increased or decreased expression (Supplementary Fig. S2 online). In total, 509 DEGs were identified between any 2 periods, with 58 (30 upregulated and 28 downregulated), 370 (341 upregulated and 29 downregulated), and 81 (68 upregulated and 13 downregulated) DEGs between T1 vs T2, T1 vs T3, and T2 vs T3, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). There were 3 up-regulated DEGs in comparisons across all periods, including patatin-like phospholipase domain-containing 3 (PNPLA3), neuronal cell adhesion molecule (NRCAM), and an unknown gene. Additionally, 4 Japanese Black cattle with high vs low carcass weight and beef marbling score (BMS) were identified and their hepatic transcriptomes were analyzed (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Tables 5 and 6 online). In total, 14 and 5 DEGs were found in comparisons of groups with different carcass weights (high vs low) and BMS (high vs low), respectively.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean carcass traits in carcass weight and BMS groups.\u003c/strong\u003e Values indicate the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error. BMS: beef marbling score.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eCarcass weight group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBMS group\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLive weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e782.50\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e698.00\u0026thinsp;\u0026plusmn;\u0026thinsp;18.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e770.50\u0026thinsp;\u0026plusmn;\u0026thinsp;7.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e747.50\u0026thinsp;\u0026plusmn;\u0026thinsp;22.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrowth rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarcass weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e491.50\u0026thinsp;\u0026plusmn;\u0026thinsp;5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e417.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e482.00\u0026thinsp;\u0026plusmn;\u0026thinsp;8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e452.75\u0026thinsp;\u0026plusmn;\u0026thinsp;12.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEye muscle area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58.50\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRib thickness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSubcutaneous\u0026nbsp;fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eGene ontology analysis of DEGs\u003c/h2\u003e\n \u003cp\u003eGene ontology (GO) analysis was conducted by running queries for each DEG against the GO database, which provided information on relevant biological processes (BP), cellular components (CC), and molecular functions (MF) associated with the DEGs. GO and pathway analysis of the DEGs in T1 and T3 showed the greatest difference in gene expression. The DEGs between T1 and T3 were mainly involved in energy and lipid metabolism processes, such as glucose homeostasis, cholesterol biosynthetic process, phospholipid homeostasis, triglyceride biosynthetic process, and fatty acid metabolic process (Tables \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, Supplementary Tables 7 and 8 online). The CC- and MF-related genes were enriched in substance transport processes, including calcium ion binding, transporter activity, and voltage-gated chloride channel activity (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Tables 7 and 8 online).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene ontology of differentially expressed genes in early fattening phases (13 months of age) vs late fattening phases (28 months of age).\u003c/strong\u003e GO: gene ontology, DEGs: differentially expressed genes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOntology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Term ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of DEGs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"20\"\u003e\n \u003cp\u003eBiological\u003c/p\u003e\n \u003cp\u003eprocesses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0008152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0001525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAngiogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0042593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose Homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0030335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive Regulation of Cell Migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCholesterol Biosynthetic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0032922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCircadian Regulation of Gene Expression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0060395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmad Protein Signal Transduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative Regulation of Protein Kinase Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0035264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMulticellular Organism Growth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN-Glycan Processing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0018107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePeptidyl-Threonine Phosphorylation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipid Metabolic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0051897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive Regulation of Protein Kinase B Signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCellular Calcium Ion Homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0055091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhospholipid Homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0019432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglyceride Biosynthetic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0003222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVentricular Trabecula Myocardium Morphogenesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFatty Acid Metabolic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0001568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood Vessel Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006814\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium Ion Transport\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eCellular\u003c/p\u003e\n \u003cp\u003ecomposition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0016021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegral Component of Membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0070062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtracellular Exosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntegral Component of Plasma Membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEndoplasmic Reticulum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGolgi Apparatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eMolecular\u003c/p\u003e\n \u003cp\u003efunctions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium Ion Binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0004571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMannosyl-Oligosaccharide 1,2-Alpha-Mannosidase Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0015269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium-Activated Potassium Channel Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0005215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransporter Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0004008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopper-Exporting Atpase Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab4\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene ontology of differentially expressed genes related to lipid and energy metabolism in early fattening phases (13 months of age) vs late fattening phases (28 months of age).\u003c/strong\u003e GO: gene ontology, DEGs: differentially expressed genes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGO Term ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of DEGs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUp-regulated genes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDown-regulated genes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0008152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMetabolic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLPGAT1, FASN, EDEM1, IDS, DIP2C, PIGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSTA3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0042593\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose Homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSESN3, HNF4A, INSR, LEPR, PTPN11, FOXO3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCholesterol Biosynthetic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNPC1L1, DHCR24, HMGCR, DHCR7, HSD17B7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipid Metabolic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFADS2, ACOX1, HNF4A, FADS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0055091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhospholipid Homeostasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eABCA1, GPAM, HNF4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0019432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglyceride Biosynthetic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPAM, LPIN1, LPIN2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGO:0006631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFatty Acid Metabolic Process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFABP4, SNCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003ePathway analysis of DEGs\u003c/h2\u003e\n \u003cp\u003eWe used the KEGG database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.kegg.jp/kegg/kegg1.html\" target=\"_blank\"\u003ewww.kegg.jp/kegg/kegg1.html\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e) to determine significant pathways involving the DEGs between early and late fattening phases. In total, 30 pathways were significantly enriched for the identified DEGs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1; Supplementary Table\u0026nbsp;9 online). Among these, the top 10 DEGs were mainly involved in lipid metabolism and signaling pathways, including metabolic pathways, glycerolipid metabolism, bile secretion, glycerophospholipid metabolism, AMPK signaling pathway, cGMP-PKG signaling pathway, calcium signaling pathway, fatty acid metabolism, PPAR signaling pathway, and vascular smooth muscle contraction. Moreover, 41 genes were involved in the metabolic pathway, including 40 up-regulated genes and 1 down-regulated gene.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003eAssociation between physiological parameters and carcass traits\u003c/h2\u003e\n \u003cp\u003eSupplementary Table S10 online summarizes the carcass traits after slaughter. The average carcass weight and eye muscle area were 458.8 kg and 55.9 cm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, respectively. The BMS was 6\u0026ndash;11 (average, 8.3). The Japan black cattle used here had lower carcass weight and eye muscle area, and similar in rib thickness and subcutaneous fat thickness, compared to the national average in Japan in 2019\u003csup\u003e7\u003c/sup\u003e which may be considered characteristic of the Tajigyu cattle (i.e., the wagyu breed produced in Hyogo Prefecture, Japan) used in this experiment.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e lists the feed composition during the T1, T2, and T3. Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e lists the correlation coefficients between carcass traits and physiological parameters in T1. Blood triglyceride was positively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) correlated with rib eye area and BMS. ALT levels were positively correlated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with live weight, growth rate, carcass weight, rib thickness, and subcutaneous fat. Other metabolites (ALP and \u0026gamma;-GTP) related to liver function were not significantly correlated with carcass trait. IGF-1 had a positive trend with carcass trait in T1. Taurine, leucine, and phenylalanine levels were negatively correlated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with BMS. Aspartic acid level was positively correlated with rib thickness and BMS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and 1-methylhistidine and hydroxyproline levels were positively correlated with rib thickness and subcutaneous fat (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In rumen fermentation samples, pH was negatively correlated with BMS and NH\u003csub\u003e3\u003c/sub\u003e was positively correlated with eye muscle area (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab5\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean growth performance and nutrient composition in Japanese black cattle during the early (T1; 13 months of age), middle (T2; 20 months of age), and late fattening phases (T3; 28 months of age).\u003c/strong\u003e The cattle were fed concentrates as follows: T1: steam-flaked corn 42%, wheat bran 27%, corn starch 10%, soybean meal 12%, soybean hull 6%, salt 1% (TDN 71.2%, CP 15.9%); T2: steam-flacked corn 42%, barley 14%, wheat bran 21%, corn starch 5%, soybean meal 10%, soybean hull 6%, salt 1% (TDN 72.5%, CP 14.4%); T3: steam-flacked corn 44%, barley 25%, wheat bran 14%, soybean meal 5%, soybean hull 10%, salt 1% (TDN 72.8%, CP 12.0%). The rice straw intakes were as follows: DM 87.8%, TDN 37.7%, CP 4.7%. The kraft pulp feed intakes were as follows: DM 76.9%, TDN 51.1%, CP 0.3%.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage body weight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e369.8 (337.7\u0026ndash;400.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e522.6 (478.9\u0026ndash;584.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e686.9 (607.0\u0026ndash;757.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage dairy gain (kg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.86 (0.45\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83 (0.61\u0026ndash;1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61 (0.49\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConcentrate (kg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.41 (4.13\u0026ndash;5.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.31 (5.96\u0026ndash;8.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.44 (4.67\u0026ndash;8.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRice straw (kg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.93 (1.21\u0026ndash;2.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.13 (0.72\u0026ndash;1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74 (0.49\u0026ndash;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKraft pulp feed (kg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72 (0\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.45 (0\u0026ndash;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29 (0\u0026ndash;0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFeed intake\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry matter (kg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.98 (5.67\u0026ndash;7.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.72 (6.52\u0026ndash;8.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.36 (4.89\u0026ndash;8.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal digestible nutrients (kg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.95 (3.94\u0026ndash;5.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.95 (5.00\u0026ndash;6.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.84 (3.84\u0026ndash;6.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrude protein (kg/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95 (0.74\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93 (0.75\u0026ndash;1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93 (0.59\u0026ndash;1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab6\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeat map illustrating the correlations between physiological parameters and carcass traits in the early fattening period (13 months of age).\u003c/strong\u003e BUN: blood urea nitrogen, NEFA: non-esterified fatty acid, ALP: alkaline phosphatase, AST: aspartate aminotransferase, ALT: alanine aminotransferase, \u0026gamma;-GTP: gamma(\u0026gamma;)-glutamyl transferase, LD: lactate dehydrogenase, CK: creatine kinase, BHB: \u0026beta;-hydroxybutyric acid, IGF-I: insulin-like growth factor 1. ** and * indicate \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLive weight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGrowth rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCarcass weight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRib eye area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRib thickness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubcutaneous\u0026nbsp;fat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBMS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLive weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.973**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.953**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.499*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.508*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.914**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.535*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.524*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarcass weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.499*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.617**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.474*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEye muscle area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRib thickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.526*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubcutaneous\u0026nbsp;fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood metabolites and hormones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.458*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.499*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.468*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.515*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.531*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.492*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.553**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma;-GTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIGF-I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCortisol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood amino acids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaurine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.477*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspartic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.465*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.514*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThreonine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlutamic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlutamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;-Amino adipic\u0026nbsp;acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.449*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCitrulline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;-Aminobutyric\u0026nbsp;acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethionine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsoleucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.452*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenylalanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.523*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrnithine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-methylhistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.588*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-methylhistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLysine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTryptophan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydroxyproline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.531*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRumen fractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2(Acetic)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3(Propionic)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC4(Butyric)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC5(Other)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e shows the correlation coefficients between carcass traits and physiological parameters in T3. Blood albumin was negatively correlated with carcass weight and BMS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but BUN exhibited a positive trend with carcass weight. Blood glucose was positively correlated with subcutaneous fat (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ALP was significantly negatively correlated with growth rate (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and also with carcass weight, rib thickness, and subcutaneous fat. Ketones were positively correlated with all carcass traits. Unlike in the T1 group, blood insulin in T3 was positively correlated with carcass traits (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). IGF-I was negatively correlated with BMS, and cortisol level showed no correlation with carcass traits. Blood taurine and urea showed positive correlations with growth rate and carcass weight. Rib thickness was positively correlated with \u0026alpha;-amino adipic acid and negatively correlated with glycine, arginine, and hydroxyproline levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). BMS was negatively correlated with cysteine and proline levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In rumen fermentation samples, there was no significant correlation in the T3 group (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab7\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeat map illustrating the correlations between physiological parameters and carcass traits in the late fattening period (28 months of age).\u003c/strong\u003e BUN: blood urea nitrogen, NEFA: non-esterified fatty acid, ALP: alkaline phosphatase, AST: aspartate aminotransferase, ALT: alanine aminotransferase, \u0026gamma;-GTP: gamma(\u0026gamma;)-glutamyl transferase, LD: lactate dehydrogenase, CK: creatine kinase, BHB: \u0026beta;-hydroxybutyric acid, IGF-I: insulin-like growth factor 1. ** and * indicate \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"13\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLive weight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGrowth rate\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCarcass weight\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRib eye area\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRib thickness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSubcutaneous\u0026nbsp;fat\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBMS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLive weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.973**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.953**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.499*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.508*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrowth rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.535*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.524*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCarcass weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.499*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.617**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.474*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEye muscle area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.61**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.383\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRib fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.526*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubcutaneous\u0026nbsp;fat\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood metabolites and hormones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.463*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.437*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.478*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal cholesterol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglyceride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.639**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.437*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gamma;-GTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.452*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.467*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.582**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.573**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.656**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.498*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.539*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIGF-I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.466*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCortisol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood amino acids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaurine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.467*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAspartic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThreonine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlutamic acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlutamine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;-Amino adipic\u0026nbsp;acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.447*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.614**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlycine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.567**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCitrulline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;-Aminobutyric\u0026nbsp;acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eValine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMethionine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCysteine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.502*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsoleucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeucine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTyrosine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhenylalanine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrnithine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-methylhistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-methylhistidine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLysine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTryptophan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArginine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHydroxyproline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.468*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.55**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRumen fractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC2(Acetic)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3(Propionic)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC4(Butyric)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.271\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC5(Other)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study comprehensively analyzed the metabolic status of Japanese Black steers according to fattening phase, including various physiological parameters such as the liver transcriptome, blood metabolites, hormones, and rumen fermentation characteristics. The major physiological changes associated with fattening period and productivity are discussed below.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eChanges in physiological parameters in Japanese Black steer according to fattening phase\u003c/h2\u003e \u003cp\u003eHere, we reported the changes in metabolite profiles, hormones, and rumen fermentation characteristics during the fattening period in Japanese Black steer bred and raised in Japan. The triglyceride and NEFA concentrations (associated with lipid metabolism) increased with fattening phase due to the changes in the feed, including higher amounts of concentrated diet and increased dry matter intake. Increasing amounts of the concentrated diet from T1 to T2 elevated the total cholesterol, phospholipids, glucose, and γ-GTP levels in T2; however, the levels of these metabolites in T3 were similar to those in T1. Japanese Black cattle are fed large amounts of high-energy concentrate diets in the middle and late fattening phases to improve their growth performance and productivity. A study on Yellow breed \u0026times; Simmental cattle showed that lipid metabolism-related blood metabolites (triglycerides, cholesterol, and high-density and low-density lipoprotein) were significantly higher in the high-energy group (6.64 MJ/kg) than in the low-energy group (5.98 MJ/kg), and that blood insulin levels increased with increasing energy levels\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Blood glucose and insulin levels also increase significantly with high-starch diets in Angus and Angus \u0026times; Simmental cattle\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. In ruminants, concentrate diets are a major source of glucose, either through increased production of rumen propionate (a gluconeogenic precursor), or\u0026mdash;to a smaller extent\u0026mdash;through glucose absorption from rumen-bypassed diets\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Glucose synthesized via gluconeogenesis is the primary energy source in ruminants\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and excess glucose is converted into fatty acids that circulate to other parts of the body and are stored as fat in adipose tissue\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Accordingly, the high-energy feeding boosts rumen propionate production and liver gluconeogenesis, and may explain the increased concentrations of blood lipid metabolites during the middle and late fattening phases. In cattle, NEFAs\u0026mdash;indicators of fat mobilization\u0026mdash;increase due to energy deficiency\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e or pathological problems such as liver dysfunction, ketosis, and fatty liver\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In a state of negative energy balance, NEFAs are produced via lipolysis from triglycerides stored in adipose tissue and transported to organs and tissues\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In the present study, high blood levels of NEFAs in the middle and late fattening phases were likely due to relative increases in body fat reserves as fattening progressed, rather than energy deficiency. Samra et al.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e reported that cortisol\u0026mdash;an indicator of stress response\u0026mdash;promotes lipolysis, thus increasing blood NEFA concentrations. The increased cortisol levels during the fattening period in this study may have promoted lipolysis, resulting in increased concentrations of NEFA in the blood. The blood insulin level depends on age, weight, diets, and feeding management\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and is generally known to increase as fattening progresses. Ruminants are less sensitive to insulin than monogastric animals since insulin secretion in ruminants is mainly stimulated by VFAs\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Propionate is a strong stimulator of insulin secretion in sheep\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The increase in insulin secretion from T1 to T3 may be related to the increase in rumen propionate during the fattening period, and may have facilitated the intracellular influx of glucose, thus reducing blood glucose levels. Generally, BHB and other ketone bodies increase during energy deficit and are produced as metabolites in the lipid oxidation process of NEFAs in the liver. Here, the ketone concentrations decreased significantly in the middle and late fattening phases. Particularly, the decrease in BHB levels was likely due to the decreased levels of its precursor, rumen butyrate, during the middle and late fattening phases. To evaluate the liver function in Japanese Black steers, we analyzed the blood levels of ALP, AST, ALT, and γ-GTP. These metabolites are present in hepatocytes and released into the blood stream when hepatocytes are damaged by stimulation such as mold toxins and long-term high-energy feeding. Blood ALP\u0026mdash;mainly released from the liver and bones\u0026mdash;is related to liver dysfunction and bone development in beef cattle\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In growing animals, blood ALP is mainly released from bone tissue; the main tissue that releases the ALP into the blood stream transforms into liver tissue and the overall release decreases with growth\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In Japanese Black calves, blood ALP concentrations are significantly lower in the late growing stage than in the early growing stage, likely due to the slower rate of bone growth with increasing age\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In accordance with the findings of the previous study, our results showed that the blood ALP concentration decreased with fattening phases, suggesting that blood ALP concentrations decrease with age unless there is a severe abnormality in liver function. The blood γ-GTP is mainly released from the liver and bile duct and reflects chronic liver function because changes in its blood concentration occur at a slower rate than those of AST and ALT\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Blood γ-GTP concentration is positively correlated with BUN levels in Japanese Black cattle during the fattening period. Similar to the results of the previous study, our results showed that the blood γ-GTP levels were comparatively higher in T2 and T3 than in T1, and the reduction was likely caused by increased feed intake in T2 and T3. The above 4 enzymes increased or decreased slightly with the fattening period, but all were within the normal range.\u003c/p\u003e \u003cp\u003eAmino acids are not only the organic components of proteins, but are also involved in various physiological mechanisms, such as glucose synthesis via gluconeogenesis, fatty acid synthesis, and hormone regulation\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Amino acid metabolism is closely related to liver function because the absorption and transformation of amino acids occur in the liver. Thus, the blood concentrations of amino acids can be used to determine the nutritional and metabolic conditions of animals. Here, 22 of the 29 blood amino acids showed differences in concentration between fattening phases, and 13 decreased during the late fattening phase. Samantha and Connolly\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e also reported that the progression of the feeding period in the feedlot was associated with decreasing blood concentrations of amino acids such as proline, leucine, isoleucine, histidine, whereas aspartate, and glutamic acid (a typical gluconeogenic acid) increased in wagyu crossbred steers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLiver transcriptome changes in Japanese Black cattle according to fattening period\u003c/h2\u003e \u003cp\u003eWe conducted the GO pathway analysis for enriched DEGs to define the functional roles of DEGs according to fattening period. Here, we focused on comparisons between early and late fattening phases, which showed the greatest differences in gene expression in the DEG analysis. The DEGs were significantly enriched in metabolic processes such as glucose and lipid metabolic process.\u003c/p\u003e \u003cp\u003eThe expression of genes related to glucose metabolism\u0026mdash;such as SESN3, insulin receptor (INSR), leptin receptor (LEPR), forkhead box O1 (FOXO3)\u0026mdash;was decreased in late fattening phases. LEPR is a liver-specific leptin receptor and regulates energy balance. Hepatic LEPR expression is positively correlated with plasma leptin concentration\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e; serum leptin concentration gradually increases from 9\u0026ndash;23 months of age (with slight changes until 30 months) in Japanese Black steer\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Additionally, leptin level changes with the body fat mass and stimulation of insulin\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Thus, LEPR regulation in the late fattening phases may be attributed to fat accumulation and increased insulin secretion, and the increased leptin levels may lead to a decrease in leptin receptor levels. The insulin receptor plays a key role in glucose homeostasis and is closely regulated by hormones such as insulin, IGF-I, and IGF-II. Fisher et al.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e reported that plasma insulin levels were elevated in liver insulin receptor knockout mice, and the insulin regulation of receptors was related to the inhibition of glycogenolysis and gluconeogenesis. Previous studies in cattle have reported that the mRNA expression of insulin receptor in hepatocytes decreased with increasing insulin concentration in a dose-dependent manner\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and that dairy cattle with ketosis and fatty liver showed low mRNA expression of hepatic insulin receptor\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. FOXO3 is also inactivated by the binding of insulin and its receptor, regulates the expression of gluconeogenesis-related genes, and promotes gluconeogenesis. The hepatic suppression of FOXO1 and FOXO3 causes hypoglycemia and hyperlipidemia in FOXO1 and FOXO3 knockout mice; moreover, the expression of hepatic FOXOs affects the reduction of gluconeogenic gene expression and insulin resistance\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Additionally, FOXO3 also activates the SESN3, which seems to be related the reactive oxygen species rescue pathway\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe also found that the expression of genes related to lipid metabolism such as fatty acid-binding protein 4 (FABP4) was increased in late fattening phases, whereas fatty acid desaturase 1 (FADS1) and FADS2 expressions were decreased. FABP4 encodes the fatty acid binding protein, which is considered to be related to the uptake and metabolism of fatty acids. The FABP4 genotype significantly affects the marbling score in Wagyu and Limousin crossbred cattle\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e; FABP4 may be associated with the fatty acid composition of intramuscular fat tissue\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Here, FABP4 expression was higher in the late fattening phases than in the early phases. This increase in gene expression related to fat absorption and metabolism is thought to reflect the active accumulation of fat or other energy source in the liver during the late fattening phases. FADS1 and FADS2 are associated with the composition of fatty acids such as arachidonic (C20:4n-6), linoleic (C18:2n-6), alpha-linolenic (C18:3n-3) and eicosadienoic acids\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Supplementation of dietary fat in lambs results in increased expression levels of hepatic FADS1, along with increased intramuscular docosahexaenoic acid (DHA) levels in the DHA supplemented group\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Therefore, here, the changes in FADS1 and FADS2 expression may be due to changes in fat composition with changes in feed.\u003c/p\u003e \u003cp\u003eTo identify the significant pathways involving the DEGs between the early and late fattening phases, we performed the KEGG pathway analysis. In total, 33 pathways were significantly enriched for the identified DEGs. The pathway analysis suggested that the main physiological changes associated with fattening period were related to energy and lipid metabolism in Japanese black steers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eBlood metabolic profiles and liver transcriptomes related with carcass traits in Japanese Black cattle\u003c/h2\u003e \u003cp\u003eHere, blood triglyceride contents were significantly positively correlation with rib eye area and BMS in T1. Blood triglyceride levels are negatively correlated with the level of intramuscular fat accumulation (BMS)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e which may explain the inconsistent results in prior studies. In our study, the blood ALT level was positively correlated with carcass traits in T1, and the ALT concentration was higher in steers with high carcass weight than in steers with relatively low carcass weights in all fattening periods (Supplementary Table\u0026nbsp;12 online). This indicates that lipid accumulation with changing fattening diets may contribute to carcass traits such as BMS and carcass weight.\u003c/p\u003e \u003cp\u003eThe blood aspartic acid had a positive correlation with rib thickness and BMS, and α-amino adipic acid, 1-methylhistidine, and hydroxyproline showed a strong positive correlation with rib thickness in T1. A study on the wagyu crossbred reported that aspartic acid showed positive correlation with growth rate, no significant correlation with other properties, and that methyl histidine showed a negative correlation with the rump fat\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The α-amino adipic acid is potentially correlated with insulin resistance. Lee et al.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e reported that α-amino adipic acid is correlated with adipogenic differentiation, and that the α-amino adipic acid levels were increased in cell and mouse models of obesity-related insulin resistance. Here, the blood α-amino adipic acid sharply increased between T1 and T2, corresponding to the changes in insulin levels. Thus, the correlation with carcass traits appears to be similar to that with insulin, likely because the blood α-amino adipic acid level reflects changes in insulin levels.\u003c/p\u003e \u003cp\u003eInterestingly, our results showed that ketogenic amino acids such as isoleucine, leucine, tyrosine, and phenylalanine were negatively correlated with most carcass traits in early fattening phases. The ketones were used as an energy source in early fattening phases and more amino acids were consumed, which may have led to a negative relationship between the blood amino acids and the carcass traits of Japanese Black steers. This result suggested that Japanese Black steers in the early fattening phases consumed a lot of amino acids to replenish the considerable amount of energy needed through ketones, resulting in a negative correlation between amino acids and productivity.\u003c/p\u003e \u003cp\u003eIn the late fattening phases, glucose had a strong positive correlation with subcutaneous fat. Similar to the results of the present study, some previous studies have reported that glucose level is positively correlated to rump fat and glucose in wagyu crossbred steers\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Glucose is known as the precursor to fatty acid biosynthesis in ruminants, and blood glucose level is deeply related to the accumulation of intramuscular and subcutaneous fat\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Here, the decreased glucose level in late fattening phases may have been to be due to the increase in intracellular influx of glucose by insulin. The positive correlation between blood glucose and subcutaneous fat suggested that more fat is accumulated in Japanese Black cattle with high blood glucose levels. Blood insulin showed a strong positive relationship with carcass weight, growth rates, rib thickness, and BMS. The steers with high carcass weight and BMS had higher concentrations of insulin compared to those with low carcass weight and BMS (Supplementary Tables\u0026nbsp;12 and 15 online). As noted in an earlier study, insulin promotes the intracellular influx of glucose for fat accumulation, and blood insulin concentrations are associated with carcass traits such as carcass weight, growth rate, rib thickness, and BMS\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. This suggests that insulin is a more important regulator of lipid metabolism in the T3 phase than in the T1 phase in Japanese Black steers. We also investigate hepatic DEGs according to carcass weight (High vs Low) and BMS (High vs Low) to investigate the physiological changes with productivity in Japanese Black cattle. In total, 14 genes were differentially expressed when compared between carcass weights (High vs Low) and were mainly involved in organ development, cell growth, and muscle contraction. In comparison between BMS (High vs Low), 5 genes related to calcium channel and cytoskeleton components were found. This study revealed slight changes in the liver transcriptome according to carcass characteristics. No differences in energy- and fat metabolism-related gene were found in each fattening period, likely due to long-term adaptation and small metabolic changes into 2 different carcass weight and BMS categories. However, we found differences in the candidate genes and pathways responsible with large metabolic changes between the T1 and T3 phases.\u003c/p\u003e \u003cp\u003eThe results suggest that the main physiological difference in Japanese Black cattle is that the high-production steers show high blood insulin concentration. As with high insulin levels in the late fattening phases, the steers that consumed a lot of high-energy diets had high energy intake (Supplementary Tables\u0026nbsp;12 and 15 online) compared to other steers with relatively low intake, resulting in the secretion of insulin to store excess energy.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study showed that the physiological changes associated with fattening phases were typically characterized by high-energy diets for growth and carcass traits in Japanese Black steers. The increased glucose, total cholesterol, and NEFA and decreased ketones during the fattening period were related to lipid metabolism, fat accumulation, and fatty acid synthesis, and strongly associated to insulin regulation. Additionally, the DEGs\u0026mdash;such as PNPLA3, INSR, LEPR, FOXO3, FABP4, FADS1, and FADS2\u0026mdash;enriched in relation to lipid metabolism were regulated to accumulate energy in the liver; insulin may have affected this process. Insulin also showed significant differences depending on the productivity of cattle; blood insulin level was higher in the high productive steers. Therefore, the physiological characteristics of Japanese Black cattle are as follows: the physiological changes during the fattening periods result from changes in energy content and composition of diets; the changes in liver metabolism according to fattening period are strongly related to aspects of fat metabolism, such as fat accumulation and transport; and insulin regulates physiological changes according to fattening periods and productivity. Our results also suggest that the above blood metabolites and liver transcriptome could be used as parameters to determine the metabolic status of Japanese Black steer bred and raised in accordance with the typical feeding systems in Japan. Additionally, the parameters could be used to evaluate metabolic status in other cattle breeds for beef production.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAnimals and experimental design\u003c/h2\u003e \u003cp\u003eThe animal experiments were performed at the Hyogo Prefectural Technology Center of Agriculture, Forestry and Fisheries (Hyogo Prefecture, Japan) and conducted by the Guideline for the Institute of Livestock and Grassland Science\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and the Ethical guidance of the Hyogo Prefectural Institute of Agriculture of Forestry and Fisheries Animal Care and Use Committee. Experiment protocol was evaluated and approved by Hyogo Prefectural Institute of Agriculture of Forestry and Fisheries Animal Care and Use Committee. All animal experiments were carried out in accordance with ARRIVE guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://arriveguidelines.org\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTwenty-one Japanese black steers aged 12 months (initial body weight, 335.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.8 kg) were reared until 30 months of age (final body weight, 742.1\u0026thinsp;\u0026plusmn;\u0026thinsp;49.9 kg). The experimental period was divided into early fattening (12\u0026ndash;14 months of age), middle fattening (15\u0026ndash;22 months of age), and late fattening phases (23\u0026ndash;30 months of age). Experimental animals were fed concentrate and roughage (rice straw and kraft pulp feed) twice daily (at 09:30 and 15:00). Water was always freely available, and other feeding management was conducted in accordance with the practices of the Hyogo Prefectural Technology Center. The steers were fed with specific amounts of formula diet that were changed each fattening period as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The chemical composition of the formula diet was analyzed according to standard tables of feed composition in Japan\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e and provisional values for official specification set by the Ministry of Agriculture, Forestry and Fisheries\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. The adequacy rate of the diet was calculated based on the nutrient requirement of Japanese Feeding Standard for Beef cattle\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSample preparation and carcass trait assessment\u003c/h2\u003e \u003cp\u003eExperimental samples\u0026mdash;including blood, rumen fluid, and liver tissue\u0026mdash;were collected at the early fattening (13 months of age), middle fattening (20 months of age), and late fattening phases (28 months of age) in 21 Japanese black steers. Blood samples were collected at 5 h 30 min after morning feeding (15:00 h) from the jugular vein using heparin-sodium tubes (Venoject II VP-H100K; Terumo, Tokyo, Japan). All samples were transferred to the laboratory on ice and stored until centrifugation at 3,000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 15 min. Plasma samples were stored at \u0026minus;\u0026thinsp;30\u0026deg;C until metabolic profiling.\u003c/p\u003e \u003cp\u003eRumen fluid was collected using a suitable catheter. Approximately 200 ml of rumen juice was collected in a sterilized and dried flask and immediately filtered through 4 layers of cheesecloth to separate the rumen fluid from diet particles. The pH of rumen samples was measured using a digital electric pH meter. The filtrates were stored at \u0026minus;\u0026thinsp;80\u0026deg;C until component analysis.\u003c/p\u003e \u003cp\u003eLiver tissue was biopsied as described previously\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The biopsy site was at the intersection of the right 11th to 12th intercostal space and the line from the right point of the acromion to the tuber coxa. The biopsy site was anesthetized with xylazine. A 0.5 cm long stab incision was made in the skin and the biopsy was performed using an automatic biopsy gun with 150 mm biopsy needles (ACECUT ACE-141502, DELTA surgical, Staffordshire, UK). After the biopsy, the spot was disinfected with 10% isodine gel. All the tissues were rapidly separated, immediately frozen in liquid nitrogen, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA extraction\u003c/p\u003e \u003cp\u003eAll steers were slaughtered at a commercial meat abattoir after a 24 h rest period. Carcasses were chilled for 24 h at 0\u0026deg;C, after which the left side was opened between the 6th and 7th ribs to evaluate the yield and quality of the carcass according to the standard criteria of the Japan Meat Grading Association\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The traits measured during carcass evaluation were carcass weight, beef marbling standard (BMS) 5, rib eye area, rib thickness, and subcutaneous fat thickness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBlood metabolome and rumen fermentation profiling\u003c/h2\u003e \u003cp\u003eBlood metabolites were analyzed using an Automatic Biochemical Analyzer (HITACHI 7070, Hitachi, Ltd., Tokyo, Japan). The analyses included measurements of total protein, albumin, blood urea nitrogen (BUN), creatinine, total cholesterol, triglycerides, NEFA, glucose, ALP, AST, ALT, LD, γ-GTP, creatine kinase, acetate, BHB, and total ketone bodies. Plasma insulin, IGH-1, and cortisol were assayed by enzyme immunoassay according to manufacturer instructions using a multi-species insulin ELISA kit (Mercodia bovine insulin ELISA, Mercodia AB, Uppsala, Sweden), human IGF-1 ELISA kit (Human IGF-I, R\u0026amp;D system, Minneapolis, USA), and cortisol ELISA kit (Cortisol ELISA kit, Enzo Life Sciences Inc., Budapest, Hungary), respectively.\u003c/p\u003e \u003cp\u003eAfter adding trichloroacetic acid to the plasma and filtering the proteins through a membrane filter, the blood amino acid concentrations were determined by a high-speed amino acid spectrometer (L-8900, Hitachi High Tech, Tokyo, Japan). Total VFA and VFA components (acetic acid, propionic acid, butyric acid, and valeric acid) were separated and quantified by gas chromatography (GC2014; Shimadzu, Kyoto, Japan) using a packed glass column (Thermon-3000 [3%]) on a Shimalite TPA 60\u0026ndash;80 support (Shinwa Chemical Industries Ltd., Kyoto, Japan). The operating conditions of gas chromatography were as follows: career gas, N2; flow volume, 30 mL/min; temperature of the column injection and FID detection, 220\u0026deg;C; and temperature of the column oven, 140\u0026deg;C. The ammonium nitrogen concentration in the rumen fluid was quantified by the steam distillation method using an automatic nitrogen analyzer (Kjeltec Auto 1,035; Tecator, Sweden)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRNA purification\u003c/h2\u003e \u003cp\u003eLiver tissues were soaked in 200 \u0026micro;L RNAiso Plus (TAKARA Bio Inc., Shiga, Japan) in liquid nitrogen and homogenized using a Multibeads shocker (YASUIKIKAI Inc., Osaka, Japan) following manufacturer instructions. Homogenization was carried out twice at 2,000 rpm for 10\u0026ndash;15 s, and 800 \u0026micro;L RNAiso was added at 25\u0026deg;C. The homogenate was collected in 1.5 mL tubes, 200 \u0026micro;L chloroform was added to it, and the solution was thoroughly mixed. After settling for 5 min at room temperature (25\u0026deg;C), the solution was centrifuged at 12,000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 15 min at 4\u0026deg;C. The supernatant was collected and mixed with 500 \u0026micro;L isoprophanol, and the mixture was centrifuged at 12,000 \u0026times; \u003cem\u003eg\u003c/em\u003e for 15 min at 4\u0026deg;C to precipitate the RNA. The supernatant was carefully removed. The pellet was washed twice with 75% cold ethanol and dissolved it with RNase-free water. Purified total RNA was quantified in a Nano Drop ND-1000 Spectrophotometer V3.7.1 (THERMO FISHER SCIENTIFIC, Waltham, MA, USA) at wavelengths of 230, 260, and 280 nm. The purity of total RNA was determined as the A\u003csub\u003e260\u003c/sub\u003e/A\u003csub\u003e280\u003c/sub\u003e ratio (expected values\u0026thinsp;\u0026gt;\u0026thinsp;1.8) and verified by 1.0 % agarose gels electrophoresis. All samples were stored at -80\u0026deg;C.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRNA sequencing and data analyses\u003c/h2\u003e \u003cp\u003eTo check whether the purified total RNA could be used in RNA-seq, the RNA integrity number (RIN) was confirmed in a Tape Station 4200 using an RNA Screen Tape kit (AGILENT, Palo Alto, CA, USA). The average RIN and mRNA rate were 7.1 (range, 6.9\u0026ndash;7.9) and 0.8 (range, 0.7\u0026ndash;1.2), respectively. RNA-seq libraries were prepared using the TruSeq stranded mRNA Kit (Illumina) and sequenced using a NovaSeq 6000 platform at Macrogen Japan Corp. The quality of the sequencing reads was evaluated by the FastQC software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.babraham.ac.uk/projects/fastqc\u003c/span\u003e\u003c/span\u003e, version 0.11.8). Sequencing reads were trimmed using the Trim Galore software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioinformatics.babraham.ac.uk/projects/trim_galore/\u003c/span\u003e\u003c/span\u003e, version 0.5.0) with reassessing by the FastQC software. Sequencing reads were mapped to the ARS-UCD1.2 bovine reference genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.ensembl.org/pub/release-101/fasta/bos_taurus/dna/\u003c/span\u003e\u003c/span\u003e) using the HISAT2 software\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The String Tie software\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e were used to calculate the read counts for expressed transcripts with the GTF Bovine gene annotation file (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://ftp.ensembl.org/pub/release-101/gtf/bos_taurus\u003c/span\u003e\u003c/span\u003e). Normalization of counts and PCA were performed using the DESseq2 package\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e in R statistical software to determine the relationship among individual samples. Two outliers to all samples were identified and removed. The DESeq2 package\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e was also used to detect the differentially expressed genes between 2 groups (T1 vs T2, T2 vs T3, and T1 vs T3) using normalization of counts and the Wald test. FDR was calculated to account for multiple testing. Significant differentially expressed genes were defined with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e Fold Change|\u0026gt;1.5 in this study. Functional annotation enrichment analyses using the GO and KEGG databases were performed by DAVID (Database for Annotation, Visualization, and Integrated Discovery) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/summary.jsp\u003c/span\u003e\u003c/span\u003e). GO terms and KEGG pathways with \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026le;\u0026thinsp;0.05 were considered significantly enriched.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using SAS software (2012, release 9.4; SAS Institute Inc., Cary, NC) and R software (2020, release v4.0.3; R Foundation for Statistical Computing, Vienna, Austria). All physiological parameters were first assessed for normality and variance homogeneity using the Shapiro-Wilk test and Levene\u0026rsquo;s test. Significance of difference among the fattening phases was calculated by One-way analysis of variance (ANOVA) and Kruskal Wallis test for data meted normal distribution and for those did not, respectively. Following the ANOVA and Kruskal Wallis test, post-hoc pairwise comparisons among the fattening periods were performed using Tukey\u0026rsquo;s Honestly Significant Difference test and Bonferroni correction, respectively. PCAs were performed using the relative concentrations of identified blood metabolites and rumen fermentation characteristics as variables in the model with all the possible PCs. PC1 and PC2 score plots were prepared and the data points were colored to visualize the potential clustering of Japanese Black steers according to their fattening phases. From the data of each fattening period, correlation coefficients between the blood parameters (or rumen fluid parameters) and carcass traits were analyzed using Pearson\u0026rsquo;s correlation. The associations between physiological differences and productivity in Japanese Black cattle were analyzed using general model analysis with fixed effects of fattening period (T1, T2, and T3) and productivity (carcass weight, High vs Low; BMS, High vs Low). In the general model test, post-hoc pairwise comparisons were performed between groups using Bonferroni correction. In the present study, statistical differences were considered significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and statistical tendencies were indicated by 0.05\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.10.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Hyogo Prefectural Technology Center of Agriculture employees for their assistance in the care of experimental animals and sample collection. We also would like to thank Dr. Itoko Nonaka and Dr. Akane Ashihara for performing the amino acid analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.K. contributed to sample collection, sample analysis, data analysis, and writing; T.M. and E.I. contributed to experiment design, sample collection and data analyses; K.I. contributed to sample collection and sample analysis; U.Y. contributed to data analysis, data interpretation, and manuscript revision; F.T. and S.R. contributed to experiment design, data interpretation, writing and revision; All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA sequence data generated from the present study has been submitted to NCBI BioProject database with project number PRJNA760255. The data will be available with the following link on May 31, 2022: https://www.ncbi.nlm.nih.gov/sra/PRJNA760255.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmith, S. B., Gotoh, T. \u0026amp; Greenwood, P. L. Current situation and future prospects for global beef production: overview of special issue. \u003cem\u003eAsian-Australas J. Anim. Sci\u003c/em\u003e, \u003cb\u003e31\u003c/b\u003e, 927\u0026ndash;932 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5713/ajas.18.0405\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConnolly, S., Dona, A., Hamblin, D., D\u0026rsquo;Occhio, M. J. \u0026amp; Gonz\u0026aacute;lez, L. A. Changes in the blood metabolome of Wagyu crossbred steers with time in the feedlot and relationships with marbling. \u003cem\u003eSci. 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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. \u003cem\u003eGenome Biol\u003c/em\u003e, \u003cb\u003e15\u003c/b\u003e, 1\u0026ndash;21 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-014-0550-8\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"liver transcriptome, physiological, Black steers, FOXO3","lastPublishedDoi":"10.21203/rs.3.rs-882318/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-882318/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe investigated the physiological changes during the fattening period and production characteristics in Japanese Black steers bred and raised using the typical feeding system in Japan. Here, 21 Japanese black steers aged 12 months were used, and the experimental period was divided into early (12\u0026ndash;14 months of age), middle (15\u0026ndash;22 months), and late fattening phases (23\u0026ndash;30 months). The liver transcriptome, blood metabolites, hormones, and rumen fermentation characteristics were analyzed. The blood triglyceride and non-esterified fatty acid concentrations increased, and blood ketone levels decreased, with fattening phases. Blood insulin increased with fattening phases and was higher in groups with high carcass weight and marbling. Rumen fermentation characteristics showed high propionate levels and low butyrate levels in late fattening phases, likely due to increased energy intake. Genes related to glucose metabolism, such as SESN3, INSR, LEPR, and FOXO3, were down-regulated in late fattening phases. Genes related to lipid metabolism, such as FABP4, were up-regulated, whereas FADS1 and FADS2 were down-regulated. These findings suggest that the physiological changes resulted from changes in the energy content and composition of diets. Liver metabolism changed with changes in fat metabolism. Insulin was strongly associated with physiological changes and productivity in Japanese Black cattle.\u003c/p\u003e","manuscriptTitle":"Changes in The Liver Transcriptome and Physiological Parameters of Japanese Black Steers During The Fattening Period","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-09-15 14:36:24","doi":"10.21203/rs.3.rs-882318/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-12-17T11:33:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-12-03T20:02:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-10-14T20:47:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a80b51c4-087e-4319-8b62-1317126a69ee","date":"2021-09-30T23:43:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"91cd5977-4d7c-45ef-9ece-a348580e7e0d","date":"2021-09-18T19:45:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-09-15T16:10:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-09-13T12:21:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-09-13T08:41:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-09-13T07:00:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2021-09-07T04:31:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37fc8347-8cb7-4fc9-af96-494c4b7cb9d6","owner":[],"postedDate":"September 15th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":7207030,"name":"Physiology"},{"id":7207031,"name":"Animal Physiology"}],"tags":[],"updatedAt":"2022-03-07T19:53:05+00:00","versionOfRecord":{"articleIdentity":"rs-882318","link":"https://doi.org/10.1038/s41598-022-08057-8","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2022-03-07 19:53:05","publishedOnDateReadable":"March 7th, 2022"},"versionCreatedAt":"2021-09-15 14:36:24","video":"","vorDoi":"10.1038/s41598-022-08057-8","vorDoiUrl":"https://doi.org/10.1038/s41598-022-08057-8","workflowStages":[]},"version":"v1","identity":"rs-882318","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-882318","identity":"rs-882318","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}