Skip to main content
PMC home page
PLOS ONE logoLink to PLOS ONE
. 2016 Oct 19;11(10):e0164702. doi: 10.1371/journal.pone.0164702

Molecular Differences in Hepatic Metabolism between AA Broiler and Big Bone Chickens: A Proteomic Study

Aijuan Zheng 1,#, Wenhuan Chang 1,#, Guohua Liu 1,*, Ying Yue 1, Jianke Li 2, Shu Zhang 1, Huiyi Cai 1, Aijun Yang 3, Zhimin Chen 1
Editor: Sompop Bencharit4
PMCID: PMC5070854  PMID: 27760160

Abstract

Identifying the metabolic differences in the livers of modern broilers and local chicken breeds is important for understanding their biological characteristics, and many proteomic changes in their livers are not well characterized. We therefore analyzed the hepatic protein profiles of a commercial breed, Arbor Acres (AA) broilers, and a local dual purpose breed, Big Bone chickens, using two-dimensional electrophoresis combined with liquid chromatography-chip/electrospray ionization-quadruple time-of-flight/mass spectrometry (LC-MS/MS). A total of 145 proteins were identified as having differential abundance in the two breeds at three growth stages. Among them, 49, 63 and 54 belonged to 2, 4, and 6 weeks of age, respectively. The higher abundance proteins in AA broilers were related to the energy production pathways suggesting enhanced energy metabolism and lipid biosynthesis. In contrast, the higher abundance proteins in Big Bone chickens showed enhanced lipid degradation, resulting in a reduction in the abdominal fat percentage. Along with the decrease in fat deposition, flavor substance synthesis in the meat of the Big Bone chickens may be improved by enhanced abundance of proteins involved in glycine metabolism. In addition, the identified proteins in nucleotide metabolism, antioxidants, cell structure, protein folding and transporters may be critically important for immune defense, gene transcription and other biological processes in the two breeds. These results indicate that selection pressure may have shaped the two lines differently resulting in different hepatic metabolic capacities and extensive metabolic differences in the liver. The results from this study may help provide the theoretical basis for chicken breeding.

Introduction

Over the past few decades, China’s broiler industry has transformed from traditional farming to the intensive farming model. At the same time, the rapid development of the broiler industry is associated with poor meat quality and negative feedback. Commercial broilers are characterized by fast growth and a high feed-to-weight conversion rate; however, excessive abdominal fat deposition and poor meat quality are becoming the major concerns in the poultry industry [1]. Fortunately, indigenous chicken breeds produce better quality meat and the cross between indigenous chickens and broilers may provide a way to overcome these problems [2].

Broiler chickens demonstrate differences in fat deposition, indicating the importance of genetic factors in fat deposition [3]. Unlike mammals, de novo fatty acid synthesis (lipogenesis) in birds occurs mainly in the liver [4]. Previous studies have revealed that fat and lean lines of broilers differed in the metabolism of very low-density lipoprotein [5, 6] and fatty acid in the liver [7, 8]. Moreover, the liver is a vital organ in chickens which carries out multiple metabolic roles. Profiling of liver proteins abundance can aid in the understanding of variations in poultry liver metabolism.

The Arbor Acres (AA) broiler is a well-known commercial breed in the poultry industry. It is featured by a large size, rapid-growth rate, high feed-conversion rate and strong adaptability, but possesses less favorable meat quality and excessive abdominal fat [9]. Gene expression analysis demonstrated that the AA breed had dysregulated lipid metabolism and other cellular pathways compared to a Chinese breed with high meat quality [10]. In contrast, Big Bone Chickens, which originated in the Liaoning province of China, is a meat-and-egg dual-purpose local breed and famous for its big size and large eggs as well as delicious and nutritious meat [11]. Thus, these two breeds can offer suitable models to study the difference in the relationship between liver metabolism and meat quality. This study aims to identify the molecular mechanism of metabolic differences between modern broilers and local chicken breeds, in an attempt to provide the theoretical basis for improving meat quality in commercial chickens.

Materials and Methods

AA broilers and Zhuanghe Big Bone chickens were raised, sampled and slaughtered in the Nankou experimental farm of the Feed Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Beijing, China. The care and use of all birds in this experiment was approved by the Animal Care and Use Committee of the Feed Research Institute of CAAS. The proteomics analysis was conducted in the Feed Research Institute, CAAS.

Reagents

Tris-base, ammonium persulfate, sodium dodecyl sulfate (SDS), N,N,N’,N’-tetramethylethylenediamine (EDTA), sodium bicarbonate (NH4HCO3), glycine, agarose, urea trichloroacetic acid (TCA), and formic acid were purchased from Sigma (St. Louis, USA). Bio-lyte was purchased from Bio-Rad (Hercules, CA, USA). Acrylamide, N, N’-methylenebisacrylamide, bromophenol blue, coomassie brilliant blue G-250, thiourea, 3-[(3-cholamidopropyl) dimethylammonio]-1-propanesulfonate (CHAPS), glycerol, and bovine serum albumin were purchased from Amresco (Solon, OH). Dithiothreitol (DTT) and iodoacetamide were purchased from Merck (Darmstadt, Germany). Trypsin was purchased from Roche (Basel, Switzerland), and trifluoroacetic acid and acetonitrile were from J. T. Baker (Phillipsburg, NJ). Lastly, dipotassium phosphate (K2HPO4), monopotassium phosphate (KH2PO4), sodium chloride (NaCl), sodium hydroxide (NaOH), and acetone were purchased from Beijing Shiji Co. (Beijing, China).

Care and management of chickens

Seventy-two each of AA broilers and Zhuanghe Big Bone chickens (1-day-old) were purchased from the Xingwang Chicken Company. (Liaoning, China). Each group had 6 replicates and each replicate had 12 individuals. The chickens were reared from 0 to 42 days and fed with corn-soybean starter (days 0–21) and grower (days 22–42) diets. All chickens were subject to 23 h light and 1 h dark on days 0–7, and 20 h light and 4 h dark thereafter in accordance with the AA broiler and Zhuanghe Big Bone Chicken Management Guides. The room temperature was maintained at 33–35°C on days 0–3, at 32–34°C on days 4–7 and gradually reduced to the maintenance temperature of 20°C by day 42. The relative humidity was kept at 70% during the first week and thereafter at about 60%.

Liver protein extraction

At the age of 2 weeks, 4 weeks, and 6 weeks, twelve chickens from each group (two birds each replicate) were randomly selected, electrically stunned, and manually slaughtered within 5 min [12] Livers were collected and washed in cold saline solution (0.9% NaCl) to clear blood and other contaminants, and were frozen immediately in liquid nitrogen and stored at -80°C for further processing.

Protein preparation was performed as described previously with some modifications [13]. To avoid erroneous conclusions due to individual variations, the same quantity of proteins from the liver of four chickens were pooled as a biological replicate, and three biological replicates were acquired for each group. Briefly, the frozen liver tissues were manually ground into fine powders using a mortar and pestle in liquid nitrogen, and were then homogenized in PBS (pH 7.6) containing 32.5 mM K2HPO4, 2.6 mM KH2PO4, and 400 mM NaCl. The mixture was sonicated for 2 min and centrifuged at 14,000 g for 10 min at 4°C. The supernatant was stored for later use. The pellets were washed in PBS (pH 7.6) and were homogenized in lysis buffer (LB, 9 M urea, 2 M thiourea, 4% CHAPS, 20 mM Tris-base, 30 mM DTT, and 2% Bio-lyte, pH 3–10). The homogenate was then sonicated for 2 min and centrifuged at 14,000 g, 4°C for 10 min. The supernatant was transferred to a tube containing PBS protein extract. TCA was added to a final concentration of 10% and then the mixture was kept on ice for 10 min for protein precipitation and desalting. Subsequently, the mixture was centrifuged twice at 14,000 g for 10 min at 4°C and the pellets were suspended in loading buffer, sonicated for 1 min and adjusted to pH 7.0. About four-times volume of acetone was added into the protein mixture, the mixture stored at -20°C for 2 hours for protein precipitation and desalting. Next, the mixture was centrifuged and the protein pellets were resolved in LB. The protein extract was stored at -70°C for further use. Protein concentration was determined according to the Bradford method [14] against a bovine serum albumin standard curve, at 595 nm in a spectrophotometer DU800 (Beckman Coulter, Los Angeles, CA).

Two dimensional gel electrophoresis

Three gels were independently carried out with each biological replicate sample. Each 500 μg protein sample suspended in lysis buffer was mixed with rehydration buffer (8 M urea, 2% CHAPS, 0.001% bromophenol blue, 45 mM DTT, 0.2% Bio-lyte pH 3–10). The mixture was loaded onto a 17 cm IPG strip (pH 3–10, linear, Bio-Rad). Isoelectric focusing (IEF) was performed at 18°C according to manufacturer’s instructions (Protean IEF Cell, Bio-Rad). Before the second dimension of electrophoresis, the IPG strips were first equilibrated in equilibration buffer I [6 M urea, 0.375 M Tris-HCl (pH 8.8), 20% glycerol, 2% SDS, 2% DTT] for 15 minutes and then in equilibration buffer II [6 M urea, 0.375M Tris-HCl (pH 8.8), 20% glycerol, 2% SDS, 2.5% iodoacetoamide] for another 15 minutes. After equilibration, the strip was transferred to a SDS polyacrylamide gel, 12% T separating gel (1.00 mm). The second dimension of electrophoresis was performed in a Protean II Xi Cell (Bio-Rad) at 25 mA/gel for about 5 h.

Image acquisition and statistical analysis

Gels were fixed in 50% (v/v) ethanol and 10% (v/v) acetic acid solution overnight and then stained with coomassie brilliant blue G-250. Three technical replicate gels were scanned to acquire images and evaluate reproducibility. Images were imported into PD Quest V 8.0 (Bio-Rad) for further analysis. The abundance of each spot was expressed as %Vol (spot volume / total volumes of all spots resolved in the gel). The average values from three independent experiments were calculated and considered to be statistically significant with p<0.05 and at least a 2-fold change.

Mass spectrometry analysis

The protein spots were excised and digested with 10 ng/μl trypsin (Roche) for MS analysis as previously reported [15]. The peptides were analyzed by liquid chromatography-chip/electrospray ionization-quadruple time-of-flight/mass spectrometry (QTOF G6520, Agilent Technologies). Tandem mass spectra were retrieved using MassHunter software (Version B. 02. 01, Agilent Technologies). Before MS/MS data searching, a peak-list was generated by Mascot Distiller software (Version 3. 2. 1. 0, Matrix Science). MS/MS data were searched with Mascot 2.2 (Matrix Science) against NCBInr (release date on March, 2015). Carbamidomethylation (C) and oxidation (M) parameters were selected as fixed and variable modifications, respectively. The other parameters were: taxonomy, all entries; enzyme, trypsin; missed cleavages, 1; peptide tolerance, ± 20 ppm; MS/MS tolerance, ±0.02 Da. A total of 6,649,798 sequences and 2,279,950,795 residues in the database were searched. When the identified peptides matched to multiple members of a protein family, the match was determined based on a higher Mascot score, and differential patterns of protein spots on 2-DE gels. Protein identifications were accepted if they had a probability score greater than 95% and contained at least two identified peptides with maximum peptide coverage (S1 Table).

Bioinformatics analysis of differentially expressed proteins in the liver between AA broiler and Big Bone chickens

The ClueGo plug-in of Cytoscape software (http://cytoscape.org/) with the Gene Ontology (GO) database (released June 2015) and the Kyoto encyclopedia of genes and genomes (KEGG) database (released October 2015) were used for functional and pathway enrichment analysis of the identified differential abundance proteins. The significantly enriched GO terms were performed by the right-side hypergeometric statistical test, which compares the background set of GO annotations in the whole genome of Gallus gallus database. Its probability value was corrected by the Bonferroni method [16]. The protein-protein biological interaction network (BIN) of differential proteins was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) 10.0 (http://string-db.org/) [17]. The network nodes represent proteins, and the edges represent the predicted functional associations.

Validation of proteins with differential abundance by qPCR (Real-time Quantitative PCR Detecting System)

To further understand the relationship between proteins and their encoding genes, qPCR was run for differential abundance proteins at the mRNA level. Specific primers for target genes of the identified proteins were designed using the primer BLAST of NCBI and nucleotide information in GenBank (S2 Table). Total RNA was prepared from the liver of control and treated groups using SGTriEx (SinoGene Scientific Co., Beijing, China). RNA quality and concentration were detected using Biophotometer (Eppendorf, Hamburg, Germany) and agarose gel electrophoresis (S1 Fig). cDNA synthesis with 5 μg of RNA was performed using Thermo First cDNA Synthesis Kit (SinoGene). qPCR was conducted using the StepOnePLUS(Applied Biosystems, Massachusetts, USA). The PCR was performed in a 20-μL reaction system containing 1 μL of cDNA, 0.4 μL of each primer (10 μM), 10 μL of SG Green qPCR Mix (SinoGene) and 8.2 μL of water. The fold-change was calculated with the 2 −ΔΔCt method [18]. All operation for qPCR was followed by the MIQE guide-lines [19].

Results

Abdominal fat and liver weight comparison between AA broiler and Big Bone chickens

Average abdominal fat percentage was significantly higher in AA broilers than that in Big Bone chickens on day 28 (p < 0.05, Table 1). Abdominal fat percentage was not significantly different between female and male Big Bone chickens, but female AA broilers had higher abdominal fat than male AA broilers of the same age (day 28). Female AA broilers had significantly higher abdominal fat than male AA broilers and Big Bone chickens of the same body weight (3.0±0.13kg). The same weight Big Bone chickens and male AA broilers showed no significant difference in abdominal fat percentage.

Table 1. Carcass index contrast between AA broiler and Big Bone chickens.

Big Bone chickens AA broiler chickens
male female male female
Abdominal fat percentage, %
Same age (day 28) 5.53±1.03a 5.20±0.91a 18.25±2.93b 24.82±2.84c
Same body weight (3.0±0.13kg) 6.97±2.57a 7.12±1.21a 8.08±2.82a 14.03±1.81b
Liver percentage, %
Same age (day 28) 37.49±3.53a 34.45±1.22a 26.17±0.74b 26.23±1.36b
Same body weight (3.0±0.13kg) 23.77±2.19a 24.37±1.12a 28.59±1.56a 27.66±1.76a
Open in a new tab

Notes: There were significant difference between different letters of groups in the same row (P< = 0.05), but no significant difference with the same letter (p>0.05).

There was no significant difference in the liver weight between female and male chickens of these two breeds on the same day or the same body weight. Big Bone chickens and AA broilers of the same weight showed no significant difference in liver weight percentage. However, the liver weight was significantly higher in Big Bone chickens than that in AA broilers on the same day (day 28).

Identification of proteins with differential abundance

We compared the liver proteome between AA broiler and Big Bone chickens at three age stages using 2-DE and LC-MS/MS analysis. There were 264 to 263, 242 to 235 and 275 to 200 protein spots detected on 2-DE gels at 2 weeks, 4 weeks, and 6 weeks of age, in AA broiler to Big Bone chickens, respectively. Among them, 78, 84, and 87 protein spots showed significantly differential abundance (> 2 fold, p < 0.05) at 2, 4, and 6 weeks respectively. Subsequently, 49, 63, and 54 protein spots were identified after MS analysis at 2, 4, and 6 weeks respectively (Fig 1, Table 2). The remaining unidentified differential protein spots on 2-DE images could be due to their low abundance to produce enough spectra or their less than 95% search scores in the databases.

Fig 1. 2-DE hepatic protein profiles of AA broilers and Big Bone chickens.

Fig 1

Open in a new tab

Protein spots showing significant differences (2-fold, p < 0.05) were manually excised and identified by LC-Chip-ESI-QTOF-MS. Proteins of differential abundance with known identities were numbered and marked red or blue for increased or decreased abundance, respectively.

Table 2. Protein spots of differential abundance identified in the liver of AA broiler and Big Bone chickens.a.

Spot no. Protein name Accession no.(NCBInr) Symbol ID Theoretical Mr (kDa)/pI Sequence coverage (%) Matched/searched Mascot score Log 2ratio (treatment/control)
2 weeks 4 weeks 6 weeks
Carbohydrate metabolism and energy production
4 NADP-dependent malic enzyme gi|45383538 ME1 62.53/6.45 63 32/62 785 8.93
5 NADP-dependent malic enzyme gi|45383538 ME1 62.53/6.45 50 16/37 331 8.65 8.85 8.74
7 malic enzyme gi|45383538 ME1 62.53/6.45 33 10/28 142 9.86
10 sulfotransferase gi|45384226 CHST3 36.33/5.89 68 22/56 739 8.50 8.17
11 ubiquinol—cytochrome c reductase gi|50754375 UQCRFS1 53.41/6.58 6 2/5 58 9.38
14 alpha-enolase gi|46048768 ENO1 47.62/6.17 26 8/25 320 8.38 6.82 8.25
16 sulfotransferase gi|45384226 CHST3 36.33/5.89 30 3/10 115 7.65
17 sulfotransferase gi|118088279 CHST3 36.79/5.50 8 1/4 62 9.79 9.01
18 phosphoglycolate phosphatase gi|71894743 PGP 33.55/5.53 19 4/5 121 8.57
22 sepiapterin reductase gi|50767570 SPR 29.28/5.82 45 6/13 635 8.42 8.00 8.82
23 phosphoglycerate mutase 1 gi|71895985 PGAM1 29.05/7.03 72 11/30 396 9.42
29 electron transfer flavoprotein-ubiquinone oxidoreductase, mitochondrial gi|71895853 ETFDH 53.61/8.99 42 7/35 969 -8.19 -9.63
44 malate dehydrogenase, cytoplasmic gi|57530355 MDH1 36.75/6.92 54 6/32 724 1.10
45 fructose 1,6-bisphosphatase gi|50762393 FBP2 37.05/8.07 65 16/52 1132 1.02
47 voltage-dependent anion-selective channel protein 1 gi|76443696 VDAC1 30.74/6.85 32 2/15 295 -1.15
80 sorting and assembly machinery component 50 homolog gi|118083116 SAMM50 52.47/5.99 30 2/17 540 7.37 8.84
83 galactose mutarotase (aldose 1-epimerase) gi|118087781 GALM 38.18/6.19 18 1/6 166 7.53
84 alcohol dehydrogenase [NADP+] gi|57529654 ADH5 37.34/7.66 35 11/17 168 7.90
85 L-lactate dehydrogenase B chain gi|45383766 LDHB 36.69/7.07 40 10/13 249 8.73 9.06
87 ubiquinol-cytochrome c reductase hinge protein gi|118094600 UQCRH 9.45/5.17 43 1/6 125 8.30 8.15
88 phosphoglucomutase-1 gi|84619526 PGM1 67.06/8.98 65 43/61 1142 1.16
89 fumarate hydratase, mitochondrial gi|57530433 FH 54.49/9.20 49 27/31 1098 -1.08
98 NADP-dependent malic enzyme gi/45383538 ME1 62.53/6.45 43 27/68 657 1.73
102 pyruvate carboxylase gi|45383466 PCX 128.03/6.26 61 81/135 2199 -1.32
104 pyruvate carboxylase gi|45383466 PCX 128.03/6.26 53 49/99 1099 -1.09
120 carbonic anhydrase II gi|833606 CA2 28.82/6.51 41 8/12 218 7.31
121 phosphomannomutase 2 gi|71895479 PMM2 28.59/5.79 9 1/2 56 9.06
122 triosephosphate isomerase gi|45382061 TPI1 26.83/6.71 55 6/14 105 8.70
123 triosephosphate isomerase gi|45382061 TPI1 26.83/6.71 75 13/23 276 8.66
130 alpha-enolase gi|46048768 ENO1 47.62/6.17 39 9/22 943 -8.76
132 malate dehydrogenase, cytoplasmic gi|57530355 MDH1 36.75/6.92 27 1/11 237 -8.76
133 ubiquinol-cytochrome c reductase gi|50754375 UQCRH 53.41/6.58 26 2/16 430 -9.99
144 L-lactate dehydrogenase A chain gi|45384208 LDHA 36.78/7.75 36 6/15 271 1.14
145 alpha-enolase gi|46048769 ENO1 47.62/6.17 49 15/33 644 1.35
Protein and amino acid metabolism
1 elongation factor 2 gi|45382453 EEF2 96.34/6.40 39 24/53 387 8.34
2 glycine dehydrogenase [decarboxylating], mitochondrial precursor gi|45383510 GLDC 111.78/7.55 19 9/22 246 7.93
3 methylcrotonoyl-CoA carboxylase subunit alpha, mitochondrial gi|57529595 MCCC1 78.88/6.51 17 3/14 95 6.33 6.75 7.20
8 cytosol aminopeptidase gi|71897015 LAP3 56.92/8.38 27 7/14 239 10.50
21 guanidinoacetate N-methyltransferase gi|118103242 GAMT 17.46/6.59 48 13/23 551 9.36
26 SH3 domain-binding glutamic acid-rich-like protein gi|60302796 Q5F3C9 12.79/5.07 90 1/22 325 8.54 7.76 7.81
35 cathepsin B precursor gi|46195455 CTSB 38.48/5.74 27 6/21 530 -9.02
43 glutamine synthetase gi|45382781 GLUL 42.75/6.38 11 1/8 108 1.39
50 sarcosine dehydrogenase, mitochondrial gi|363740469 SARDH 102.26/6.42 27 4/30 633 8.34 -7.10
51 dimethylglycine dehydrogenase, mitochondrial gi|363744224 DMGDH 96.68/7.33 40 3/41 1091 7.93 -6.91
52 histidine ammonia-lyase gi|45383354 HAL 73.54/6.17 31 3/26 590 -7.52
53 elongation factor 2 gi|45382453 EEF2 96.34/6.40 25 2/27 499 -7.73
54 glycine dehydrogenase [decarboxylating], mitochondrial precursor gi|45383510 GLDC 113.26/7.55 23 7/21 122 -6.86
55 elongation factor 2 gi|45382453 EEF2 96.34/6.40 21 6/19 90 -7.63
56 ovoinhibitor precursor gi|71895337 SPINK5 54.39/6.16 18 6/9 137 -7.53
57 S-adenosylmethionine synthase isoform type-1 gi|313760551 MAT1A 44.24/6.28 30 7/14 137 -7.73
58 fumarylacetoacetase (Fumarylacetoacetate hydrolase) gi|50753071 FAA 46.80/7.31 43 18/49 259 -9.88
64 guanidinoacetate N-methyltransferase gi|118103242 GAMT 17.46/6.59 35 4/ 12 249 -8.71
69 glutamine synthetase gi|45382781 GLUL 42.75/6.38 29 6/15 171 -9.73
74 alanyl-tRNA synthetase, cytoplasmic gi|57524852 AARS 102.00/5.68 38 25/50 623 8.85
78 trifunctional purine biosynthetic protein adenosine-3 gi|47825387 GART 107.56/7.51 26 6/26 132 7.07
81 glutamine synthetase gi|45382781 GLUL 42.75/6.38 40 8/24 171 9.64
90 4-hydroxyphenylpyruvate dioxygenase gi|363739843 HPD 45.05/6.41 59 5/27 374 -1.43
93 eukaryotic translation initiation factor 3 subunit I gi|256419027 EIF3I 36.87/5.38 32 2/13 338 1.02
94 3-hydroxyanthranilate 3,4-dioxygenase gi|118087959 HAAO 33.74/6.27 18 1/4 131 -1.82
95 guanidinoacetate N-methyltransferase gi|118103242 GAMT 17.46/6.59 44 7/18 370 1.17
97 pterin-4-alpha-carbinolamine dehydratase gi|45382483 PCBD1 12.05/6.04 69 4/20 415 -1.36
103 S-adenosylmethionine synthase isoform type-1 gi|313760551 MAT1A 44.24/6.28 55 28/57 664 -2.67
105 S-adenosylmethionine synthase isoform type-1 gi|313760551 MAT1A 44.24/6.28 10 3/5 94 1.11
108 lysyl-tRNA synthetase gi|71895483 KARS 68.33/5.89 36 3/28 809 6.775
110 phenylalanine-4-hydroxylase gi|47604920 PAH 51.51/6.49 53 8/42 1247 8.80
139 cytosolic non-specific dipeptidase gi|57530409 CNDP2 53.39/5.71 39 4/21 690 1.66
140 aldehyde dehydrogenase 9 family, member A1 gi|118094103 ALDH9A1 57.00/7.81 65 34/53 1215 1.13
Nucleotide metabolism
19 adenosine 5-diphosphosugar pyrophosphatase gi|118081976 GMPS 35.43/6.36 28 9/15 312 8.78 8.38
39 adenosine kinase gi|57529848 ADK 40.46/6.06 20 3/8 96 -1.21
40 dihydropyrimidinase gi|118087274 DPYS 69.50/6.42 19 4/16 108 1.48
61 adenosine 5-diphosphosugar pyrophosphatase gi|118081976 GMPS 35.43/6.36 22 6/12 504 -9.03
62 ribokinase gi|118088003 RBKS 36.72/5.39 22 3/7 378 -8.01
99 ribose-phosphate pyrophosphokinase 2 gi|57525515 PRPS2 36.04/6.37 58 31/53 865 -1.35
125 nucleoside diphosphate kinase gi|45384260 NME5 17.45/7.72 79 16/36 264 8.59
128 nucleolar protein B23/No38 gi|212456 NPM1 10.77/4.38 57 1/6 205 -8.94
135 p32 subunit of splicing factor SF2 gi|5509946 C1QBP 23.78/4.41 23 2/5 238 1.14
141 thiosulfate sulfurtransferase gi|268370289 TST 33.09/6.80 52 22/34 503 -1.20
143 nucleoside diphosphate kinase gi|2827446 NME5 17.54/7.11 66 32/45 1004 -1.33
Fatty acid metabolism
27 fatty acid-binding protein gi|45384320 FABP7 15.03/5.61 56 2/6 85 8.68 8.52 9.24
30 long-chain specific acyl-CoA dehydrogenase, mitochondrial gi|57529797 ACADL 48.26/8.34 32 10/17 159 -10.58
33 dodecenoyl-Coenzyme A delta isomerase gi|118098151 ECI1 34.56/9.30 33 18/32 379 -8.52 -7.68
42 3-hydroxy-3-methylglutaryl-coenzyme A synthase gi|118094097 HMGCS1 52.98/6.57 31 11/26 195 1.69
48 fatty acid-binding protein gi|45384320 FABP7 15.03/5.61 71 9/16 276 2.16
65 sulfotransferase 1B gi|118090275 SULT1B1 32.01/6.00 48 1/16 351 -6.71
70 phosphatidylethanolamine-binding protein 1 gi|310772215 PEBP1 21.12/6.96 66 4/17 419 8.23
82 3-ketoacyl-CoA thiolase, mitochondrial gi|57529492 HADHA 42.17/8.02 18 3/7 89 10.03
111 3-hydroxy-3-methylglutaryl-coenzyme A synthase gi|118094097 HMGCS1 52.98/6.57 30 16/25 383 10.33
112 3-hydroxy-3-methylglutaryl-coenzyme A synthase gi|118094097 HMGCS1 52.98/6.57 32 15/27 462 9.48
114 SEC14-like 2 gi|50756739 SEC14L3 46.94/6.73 60 24/43 520 10.40
116 phosphatidylinositol transfer protein beta isoform gi|86129444 PITPNB 30.86/5.63 71 12/27 224 7.01
Antioxidants
6 epoxide hydrolase 2 gi|45384320 EPHX2 63.72/5.89 45 17/29 448 8.37
46 peroxiredoxin-6 gi|57529797 PRDX6 25.08/5.72 58 4/35 793 1.00
68 thioredoxin gi|118098151 TXNRD1 11.98/5.10 49 1/12 285 -8.97
76 serum albumin precursor gi|118094097 ALB 71.87/5.51 26 1/14 362 8.70
77 serum albumin precursor gi|45384320 ALB 71.87/5.51 48 18/31 455 9.23 9.39
86 glutathione S-transferase 2 gi|118090275 GSTM2 26.05/6.85 80 9/33 746 8.86
118 thioredoxin-like protein gi|310772215 TXNL1 32.73/4.94 40 5/18 131 8.55
124 peroxiredoxin-1 isoform 1 gi|57529492 PRDX1 22.53/8.24 40 2/15 390 8.19
126 thioredoxin gi|118094097 TXNRD1 11.98/5.10 49 2/6 61 8.82
127 thioredoxin gi|118094097 TXNRD1 11.98/5.10 32 1/3 67 8.22
136 protein disulfide-isomerase A3 precursor gi|50756739 PDIA3 56.55/5.76 32 2/16 507 2.25
142 retinal dehydrogenase 1 gi|86129444 ALDH1A1 56.40/7.49 51 24/35 615 1.30
Cell structure
12 alpha-tropomyosin 2 gi|27465053 TPM1 32.85/4.65 40 8/17 164 7.73
15 protein syndesmos gi|45382147 NUDT16L1 33.98/5.74 48 12/33 277 7.24
28 lamin-A gi|45384214 LMNA 73.35/6.50 14 1/11 373 -8.00
36 mitochondrial inner membrane protein gi|57530041 IMMT 79.54/5.72 31 2/31 745 1.24
37 mitochondrial inner membrane protein gi|57530041 IMMT 79.54/5.72 24 2/28 479 1.02
38 desmin gi|2959450 DES 51.69/5.30 53 12/35 272 1.46
63 F-actin-capping protein subunit alpha-1 gi|45382905 CAPZA1 33.11/5.43 53 3/15 499 -8.42
66 translationally-controlled tumor protein homolog gi|45382329 TPT1 19.69/4.90 90 12/23 372 -7.95
67 glia maturation factor beta gi|71894963 GMFB 16.88/5.19 28 1/4 144 -8.10
71 translationally-controlled tumor protein homolog gi|45382329 TPT1 19.69/4.90 50 8/24 190 8.19
75 mitochondrial inner membrane protein gi|57530041 IMMT 79.54/5.72 40 15/36 1360 6.91
79 lamin-A gi|45384214 LMNA 73.35/6.50 58 24/44 512 7.76
92 alpha-tropomyosin gi|211109 TPM1 32.81/4.75 15 2/4 177 1.61
109 coronin-1C gi|86129440 CORO1C 53.74/6.22 39 20/34 244 6.78
Protein folding
49 10 kDa heat shock protein, mitochondrial gi|45384204 HSPE1 11.13/8.68 82 1/20 438 -1.5892
59 cyclophilin gi|118089782 PPIA 39.80/5.61 27 4/16 95 -7.12
106 T-complex protein 1 subunit alpha gi|57530301 TCP1 61.06/5.66 34 9/18 181 8.83
107 T-complex protein 1 subunit alpha gi|57530301 TCP1 61.06/5.66 43 11/32 1071 9.28
131 hsc70-interacting protein gi|71896903 ST13 40.36/5.07 36 8/22 646 -6.97
134 peptidylprolyl isomerase A gi|261490820 PPIA 18.08/8.29 67 9/28 194 -9.24
Transporter
24 heme-binding protein 1 gi|71896913 HEBP1 21.26/5.76 34 3/7 88 7.85
31 chloride intracellular channel protein 2 gi|71895359 CLIC2 28.46/5.39 42 4/19 492 -8.06
119 coatomer subunit epsilon gi|57530593 COPE 34.52/4.99 35 4/31 371 9.29
129 clathrin light chain A gi|86129544 CLTA 23.86/4.42 40 6/12 189 -8.33
137 transitional endoplasmic reticulum ATPase gi|113206112 VCP 89.95/5.14 54 21/60 496 1.04
Unknown function
9 MGC82288 protein gi|50729534 50.12/6.35 44 14/23 264 10.66
13 protein PRRC1 gi|71894751 PRRC1 45.84/5.52 10 2/4 80 7.89
20 hypothetical protein RCJMB04_5n23 gi|53129586 TPM3 28.80/4.69 47 12/24 305 9.33
25 hypothetical protein gi|118083300 10.04/4.17 14 2/4 58 9.29
32 ES1 protein homolog, mitochondrial gi|71895261 C1H21orf33 27.87/8.54 46 17/45 213 -9.54
34 hypothetical protein gi|118098539 28.31/7.63 42 3/28 647 -8.71
41 hypothetical protein RCJMB04_1j22 gi|53127216 60.28/8.09 51 20/40 412 1.23
60 hypothetical protein gi|118087385 44.65/5.82 16 4/6 185 -8.23
72 sorcin gi|124249424 SRI 22.21/5.37 55 9/17 216 8.01
73 sorcin gi|124249425 SRI 22.21/5.37 24 2/5 51 8.76
91 hypothetical protein RCJMB04_1g3 gi|53126716 33.27/5.60 46 5/19 579 -1.06
96 hypothetical protein gi|50800573 11.84/4.42 66 8/11 348 1.06
100 LOC495096 protein isoform 3 gi|50734923 28.44/6.45 34 2/13 297 1.26
101 thyroid hormone responsive spot 14 beta 2 gi|45826439 THRSPB 14.52/5.10 20 1/3 98 1.13
113 hypothetical protein gi|50728520 48.21/6.29 42 15/30 430 10.66
115 LOC495029 protein gi|118098511 45.06/6.55 63 26/45 716 9.86
117 MGC83663 protein gi|118093845 51.57/6.30 30 6/18 730 8.70
138 MGC82230 protein gi|50756617 43.26/5.76 61 11/33 1036 1.03
Open in a new tab

a Spot no. corresponds to the number of protein spots in Fig 1. Protein name is given when proteins were identified by LC-Chip ESI-QTOF MS. Accession no. is the unique number given to mark the entry of a protein in the database NCBInr. Theoretical molecular weight (Mr) and isoelectric point (pI) of the identified proteins are retrieved from the protein database of NCBInr (S1 Table). Sequence coverage is the ratio of the number of amino acids in every peptide that matches with the mass spectrum divided by the total number of amino acids in the protein sequence. Matched peptide is the number of paring an experimental fragmentation spectrum to a theoretical segment of protein and searched peptide is the total searched peptide. Mascot scores are derived from ion scores as a non-probabilistic basis for ranking protein hits.

Qualitative comparisons of proteins with differential abundance

In general, the liver protein profiles between AA broiler and Big Bone chickens during three age stages were similar. However, some protein spots displayed obvious differences in abundance. As mentioned previously, a total of 49, 63, and 54 proteins were differentially expressed in AA broiler and Big Bone chickens at 2, 4, and 6 weeks, respectively. From the 49 identified proteins at 2 weeks, 38 (77.6%) and 11 (22.4%) had increased abundance in the AA broiler and the Big Bone chickens, respectively. Similarly at 4 weeks, 34 (54.0%) and 29 (46.0%) proteins had higher abundance in the AA broiler and the Big Bone chickens, respectively. Also at 6 weeks, the AA broiler and the Big Bone chickens differentially expressed 43 (79.6%) and 11(20.4%) proteins, respectively (Fig 2A and 2B). On average, the abundance for 115 (69.3%) proteins was higher in the AA broiler and that for 51 (30.7%) was higher in the Big Bone chickens.

Fig 2. Comparisons proteins with higher abundance in the livers between the AA broiler and Big Bone chickens at 2, 4 and 6 weeks, respectively.

Fig 2

Open in a new tab

A represents the percentage of proteins with increased abundance, B represents the numbers of proteins with increased abundance.

Classification of proteins with differential abundance

Based on biological processes in the database annotations, the identified differential proteins between the AA broiler and the Big Bone chickens were classified into nine main functional categories, including carbohydrate metabolism and energy production (23.4%), protein and amino acid metabolism (22.8%), cell structure (9.7%), fatty acid metabolism (8.3%), antioxidants (8.3%), nucleotide metabolism (7.6%), protein folding (4.1%), transporters (3.4%), and unknown function (12.4%) (Fig 3).

Fig 3. Functional classification of the proteins with differential abundance identified in the livers of AA broiler and Big Bone chickens at 2, 4 and 6 weeks.

Fig 3

Open in a new tab

Qualitative comparisons of differentially expressed proteins

Interestingly, the higher abundance proteins at the different stages in the two breeds showed distinct functional categories (Fig 4). The number of proteins with higher abundance in the AA broilers liver was greater than in the Big Bone chickens liver at three age stages, which were mainly involved in carbohydrate metabolism and energy production, fatty acid metabolism, antioxidants and cell structure.

Fig 4. Comparisons of functional classification of proteins with higher abundance in the livers between the AA broiler and Big Bone chickens at 2, 4 and 6 weeks.

Fig 4

Open in a new tab

The relative abundance between the two breeds was shown by Fig 5A, 5B and 5C. Enrichment analysis of the proteomes at 2 weeks showed that two major functional groups, namely carbohydrate metabolism and fatty acid metabolism, were significantly enriched (Fig 6A). Similarly, two main functional groups, including protein and amino acid metabolism and nucleotide metabolism, were enriched at 4 weeks (Fig 6B). At 6 weeks, the functional group carbohydrate metabolism was significantly enriched (Fig 6C).

Fig 5. Quantitative comparisons of differentially expressed proteins in the livers of AA broiler and Big Bone chickens at 2, 4 and 6 weeks.

Fig 5

Open in a new tab

The ratios of the protein abundance (AA broilers to Big Bone chickens) are transformed, and the protein spots with |log2 ratio|≥1 (p≤0.05) are selected as the differentially expressed proteins. A, B and C represent differentially expressed proteins at 2, 4 and 6 weeks, respectively.

Fig 6. Functional enrichment analysis of the proteins of differential abundance in the livers between AA broiler and Big Bone chickens at 2, 4 and 6 weeks using ClueGO software.

Fig 6

Open in a new tab

* and ** mean p < 0.05 and p < 0.01 levels of significance. A, B and C represent enrichment analysis of differentially expressed proteins at 2, 4 and 6 weeks, respectively.

Biological network analysis

Proteins are fundamental parts of living cells and cellular functions are mainly carried out by protein complexes. Using the online tool STRING 10.0, sixty proteins were recognized as nodes in biological interaction networks, in which eight clusters were enriched and connected by cytosolic non-specific dipeptidase (CNDP2), 3-ketoacyl-CoA thiolase (HADHA), electron transfer flavoprotein-ubiquinone oxidoreductase (ETFDH), ubiquinol—cytochrome c reductase (UQCRFS1), ubiquinol-cytochrome c reductase hinge protein (UQCRH), peroxiredoxin-6 (PRDX6), glycine dehydrogenase (GLDC), thioredoxin (TXNRD1), sorting and assembly machinery component 50 (SAMM50), fructose 1,6-bisphosphatase (FBP2) and aldehyde dehydrogenase 9 family (ALDH9A1) (Fig 7). The biggest cluster mainly involved in glycolysis/gluconeogenesis, carbon metabolism and amino acid metabolism pathway by the interaction between 37 proteins.

Fig 7. Biological interaction network of the proteins of differential abundance in the livers of AA broiler and Big Bone chickens at 2, 4 and 6 weeks.

Fig 7

Open in a new tab

Red lines indicate fusion evidence, green lines indicate neighborhood evidence, blue lines indicate co-occurrence evidence, purple lines indicate experimental evidence, yellow lines indicate text mining evidence, light blue lines indicate database evidence and black lines indicate co-expression evidence.

Validation of proteins of differential abundance by qPCR

Of the liver proteins with differential abundance at 2 weeks, 4 weeks and 6 weeks, proteins that played an important role in nutrient metabolism (amino acid and lipid metabolism) and antioxidants were selected to validate their expression at the level of mRNA (Fig 8). At 2 weeks (Fig 8A), GLDC (spot 2) and FABP (spots 27 and 48) at the protein levels were consistent with their mRNA expression levels; but HMSC1 (spot 42), ACADL (spot 30) and ECI1 (spot 33) showed an inconsistent pattern between the mRNA expression and protein abundance level. At 4 weeks (Fig 8B), GLDC (spot 54), FABP (spot 27), SARDH (spot 50), DMGDH (spot 51) and TXNRD1 (spot 168) were consistent with their mRNA expression levels. The similar expression pattern at the transcript level indicates a prospective opportunity for reverse genetic research through gene manipulation. At 6 weeks (Fig 8C), ECI1 (spot 33) at the protein level were consistent with their mRNA expression levels; however, FABP (spot 27), HMSC1 (spots 111 and 112) and TXNRD1 (spots 126 and 127) showed an inconsistent pattern between the mRNA expression and protein abundance level.

Fig 8. Validation using qPCR of proteins of differential abundance at the mRNA level in the livers between the AA broiler and Big Bone chickens at 2, 4 and 6 weeks.

Fig 8

Open in a new tab

Samples were normalized with the reference genes β-actin. A, B and C represent differentially expressed proteins at the mRNA level at 2, 4 and 6 weeks, respectively.

Discussion

This study compared differences in the proteome and the weight of the liver between AA broiler and Big Bone chickens at three different age stages. Average abdominal fat percentage and the liver weight showed significant differences between two breeds at four weeks of age. Moreover, AA broilers also showed the most differential proteome at four weeks compared to Big Bone chickens. Additionally, the number of proteins with increased abundance related to carbohydrate metabolism and energy production were higher in AA broilers than Big Bone chickens at three stages of age, which indicates breed differentiation of the two strains may be associated with differential abdominal fat deposition.

Malic enzyme (ME1, spot 4, 5, 7 and 98), ubiquinol-cytochrome C reductase (UQCRFS1, spot 11), enolase 1 (ENO1, spot 14), phosphoglycerate mutase 1 (PGAM1, spot 23), malate dehydrogenase (MDH1, spot 44), fructose-1, 6-bisphosphatase (FBP2, spot 45), phosphoglucomutase 1 (PGM1, spot 88), and triosephosphate isomerase (TPI1, spot 122 and 123) are key proteins which showed higher abundance in AA broiler chickens to mediate energy production. MDH1, FBP2, PGM1 and phosphoglycerate mutase 1 (PGAM1, spot 23) participate in the TCA cycle and UQCRFS1 (spot 11) is an electron transporter chain protein of the mitochondria to promote energy production. Although the TCA cycle is mainly involved in energy metabolism, it also produces intermediates to be converted to glucose, fatty acids, or non-essential amino acids [20]. Previously, malic enzyme gene expression in hepatic metabolism was found to be enhanced in fatter chicken varieties over lean chicken varieties [21], which was consistent with increased abundance for ME1 protein in AA broilers. Moreover, the higher abundance for pyruvate carboxylase (PCX, spot 102 and 104) and fumarate hydratase (FH, spot 89) in AA broilers at 4 weeks are the key enzymes involved in energy production and gluconeogenesis. Therefore, these differentially expressed proteins strongly suggest higher energy metabolism and enhanced lipid biosynthesis in AA broilers than that in Big Bone chicken.

The liver is a vital organ for dietary protein and amino acid metabolism. Protein synthesis is subject to regulation by eukaryotic initiation factor 2 and elongation factor 2 (EEF2, spot 1, 53 and 55) [22, 23]. The abundance of EEF2 was higher in the liver of AA broilers at two weeks of age and in Big Bone chickens at four weeks. The differential EEF2 abundance at different stages in the two breeds may affect the protein synthesis. At the same time, AA broilers had more proteins with increased abundance involved in amino acid metabolism than Big Bone chickens at two weeks, but less than Big Bone chicken at four weeks. Guanidinoacetate N-methyltransferase (GAMT, spot 21) was higher at -two weeks and GAMT (spot 64) was lower at four weeks in AA broilers, however the abundance of GAMT (spot 95) was significantly differential between Big Bone chickens and AA broilers at four weeks of age. These protein spots were differentially modified and played different roles in creatine biosynthesis between two breeds [24]. Furthermore at four weeks, Big Bone chickens had increased levels of glycine dehydrogenase (GLDC, spot 54), sarcosine dehydrogenase (SARDH, spot 50) and dimethylglycine dehydrogenase (DMGDH, spot 51), which are all involved in glycine metabolism [25, 26]. This enhanced glycine metabolism may contribute to the better meat quality in Big Bone chickens because glycine is one of most important flavor substances.

Previous studies demonstrated differential lipid metabolism between fat and lean broiler breeds [5, 9, 27]. Specifically, this study found that AA broilers had enhanced levels of fatty acid-binding protein (FABP7, spot 27 and 48), enzyme HMG-CoA synthase 1 (HMGCS1, spot 42, 111 and 112), phosphatidylinositol transfer protein (PITPNB, spot 116) and lipid binding protein SEC14-Like 2 (SEC14L3, spot 114). This fatty acid-binding protein is involved in intracellular fatty acid movement and its abundance has been associated with fat deposition in chickens [28–30]. HMGCS1 is a rate-limiting enzyme for ketone body formation from fatty acids in mitochondria [31]. PITPNB catalyzes the transfer of phosphatidylinositol and phosphatidylcholine between membranes for lipid delivery [32]. SEC14L3 stimulates squalene monooxygenase in the cholesterol biosynthetic pathway [33]. The increased abundance of these proteins suggest that AA broilers have enhanced lipid metabolism. On the contrary, Big Bone chickens had higher levels of acyl-CoA dehydrogenase long chain (ACADL, spot 30) and enoyl CoA isomerase 1 (ECI1, spot 33), both of which are involved in the beta-oxidation of fatty acids [34, 35]. Thus the enhanced lipid degradation in Big Bone chickens may be related to their reduced abdominal fat percentage.

Nucleotides are used in a wide variety of cellular metabolism and are fundamental for cellular functions [36]. The abundance for ribose-phosphate pyrophosphokinase 2 (PRPS2, spot 99) was increased in AA broilers liver at two weeks and four weeks of age. PRPS2 catalyzes the synthesis of phosphoribosyl pyrophosphate in purine nucleotide synthesis [37, 38]. In contrast, adenosine kinase (ADK, spot 39) was higher abundance in Big Bone chickens at two weeks of age and its function is known to be associated with liver disease [39]. Moreover, ribokinase (RBKS, spot 62) was higher abundance in Big Bone chickens at four weeks. RBKS belongs to the transferase family and participates in the pentose phosphate pathway. Nucleolar protein B23/No38 (NPM1, spot 128) and thiosulfate sulfurtransferase (TST, spot 141) were higher abundance in Big Bone chickens at six weeks. Thus, the differential nucleotide metabolism may contribute to variation in body weight between AA broiler and Big Bone chickens.

Additionally, differential abundance of liver proteins was observed, including those involved in the antioxidant system, protein folding, cytoskeleton, and transport proteins. These proteins may also play a role in regulating the size of the liver and the body weight of different breeds. Nevertheless, proteins function together in the context of networks through protein-protein interactions [40]. Protein associations involved in the development of the two breeds was analyzed by STRING. The majority of proteins identified were related to carbohydrate metabolism and energy production; approximately 31% of the biological interaction networks, followed by proteins in amino acid and protein metabolism (29%), antioxidants (13%) and nucleotide metabolism (13%). Moreover, proteins associated with protein folding, cell structure and fatty acid metabolism were nodes in the biological interaction networks, indicating that these proteins play a role in the construction and function of the liver of chickens. Some of the key node proteins that were highly linked in the BIN were validated at a gene level. GLDC, FABP, SARDH, DMGDH and TXNRD1 at different age stages were consistent or in consistent with mRNA levels may represent potential targets for genetic manipulation.

In conclusion, proteomic analysis of two unique chicken breeds was performed and differences in multiple metabolic pathways were identified between modern broilers and a local chicken breed. These findings can be used in the future to improve meat quality in commercial chicken supplies as the demand increases for not only greater amounts of food but better tasting meat.

Supporting Information

S1 Fig. The quality of RNA and qPCR reaction curve of the differentially expressed proteins at the mRNA level in the liver of AA broiler and Big Bone chickens.

(DOCX)

Click here for additional data file. (613.4KB, docx)
S1 Table. Peptides identified from the liver of AA broiler and Big Bone chickens by mass spectrometry.

(XLSX)

Click here for additional data file. (123.9KB, xlsx)
S2 Table. The primer sequences used for qPCR analysis of the differentially expressed proteins in the liver of AA broiler and Big Bone chickens.

(DOCX)

Click here for additional data file. (18.2KB, docx)

Acknowledgments

We thank Jiancheng Yang from Shenyang Agricultural University for sample analyses. This work was supported by China Agriculture Research System (CARS-42) and the National Natural Science Foundation (31101731) of China.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The funders are China Agriculture Research System (http://www.zgrj.org/, CARS-42) and the National Natural Science Foundation (http://www.nsfc.gov.cn/,31101731) of China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Griffiths L, Leeson S, Summers JD. Studies on Abdominal Fat with Four Commercial Strains of Male Broiler Chicken. Poultry Science. 1978;57(5):1198–203. 10.3382/ps.0571198 [DOI] [Google Scholar]
  • 2.Guan RF, Lyu F, Chen XQ, Ma JQ, Jiang H, CG X. Meat quality traits of four Chinese indigenous chicken breeds and one commercial broiler stock. Journal of Zhejiang University Science B. 2013;14(10):896–902. 10.1631/jzus.B1300163 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Tumova E, Teimouri A. Fat deposition in the broiler chicken: a review. Scientia Agriculturae Bohemica. 2010; 41(2):121–8. [Google Scholar]
  • 4.Hermier D. Lipoprotein metabolism and fattening in poultry. J Nutr. 1997;127(5 Suppl):805S–8S. Epub 1997/05/01. . [DOI] [PubMed] [Google Scholar]
  • 5.Leclercq B, Hermier D, G G. Metabolism of very low density lipoproteins in genetically lean or fat lines of chicken. Reproduction Nutrition Development. 1990;30(6): 701–15. [DOI] [PubMed] [Google Scholar]
  • 6.Saadoun A, Leclercq B. Comparison of in vivo fatty acid synthesis of the genetically lean and fat chickens. Comp Biochem Physiol B. 1983;75(4):641–4. Epub 1983/01/01. . [DOI] [PubMed] [Google Scholar]
  • 7.Hermier D, Chapman MJ. [Plasma lipoproteins and fattening: description of a model in the domestic chicken, Gallus domesticus]. Reprod Nutr Dev. 1985;25(1B):235–41. Epub 1985/01/01. . [PubMed] [Google Scholar]
  • 8.Legrand P, Hermier D. Hepatic delta 9 desaturation and plasma VLDL level in genetically lean and fat chickens. Int J Obes Relat Metab Disord. 1992;16(4):289–94. Epub 1992/04/01. . [PubMed] [Google Scholar]
  • 9.Huang J, Tang X, Ruan J, Ma H, Zou S. Use of comparative proteomics to identify key proteins related to hepatic lipid metabolism in broiler chickens: evidence accounting for differential fat deposition between strains. Lipids. 2010; 45(1):81–9. 10.1007/s11745-009-3373-8 [DOI] [PubMed] [Google Scholar]
  • 10.Cui HX, Liu RR, Zhao GP, Zheng MQ, Chen JL, Wen J. Identification of differentially expressed genes and pathways for intramuscular fat deposition in pectoralis major tissues of fast-and slow-growing chickens. BMC Genomics. 2012;13:213 Epub 2012/06/01. 1471-2164-13-213 [pii] 10.1186/1471-2164-13-213 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Li HF, Han W, Zhu YF, Shu JT, Zhang XY, Chen KW. Analysis of genetic structure and relationship among nine indigenous Chinese chicken populations by the Structure program. J Genet. 2009;88(2):197–203. Epub 2009/08/25. . [DOI] [PubMed] [Google Scholar]
  • 12.Zhang L, Yue HY, Wu SG, Xu L, Zhang HJ, Yan HJ, et al. Transport stress in broilers. II. Superoxide production, adenosine phosphate concentrations, and mRNA levels of avian uncoupling protein, avian adenine nucleotide translocator, and avian peroxisome proliferator-activated receptor-gamma coactivator-1alpha in skeletal muscles. Poult Sci. 2010;89(3):393–400. Epub 2010/02/26. 89/3/393 [pii] 10.3382/ps.2009-00281 . [DOI] [PubMed] [Google Scholar]
  • 13.Zheng A, Liu G, Zhang Y, Hou S, Chang W, Zhang S, et al. Proteomic analysis of liver development of lean Pekin duck (Anas platyrhynchos domestica). J Proteomics. 2012;75(17):5396–413. Epub 2012/07/10. S1874-3919(12)00485-X [pii] 10.1016/j.jprot.2012.06.019 . [DOI] [PubMed] [Google Scholar]
  • 14.Bradford MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem. 1976;72:248–54. Epub 1976/05/07. S0003269776699996 [pii]. . [DOI] [PubMed] [Google Scholar]
  • 15.Zheng A, Chang W, Hou S, Zhang S, Cai H, Chen G, et al. Unraveling molecular mechanistic differences in liver metabolism between lean and fat lines of Pekin duck (Anas platyrhynchos domestica): a proteomic study. J Proteomics. 2014;98:271–88. Epub 2014/01/15. S1874-3919(13)00656-8 [pii] 10.1016/j.jprot.2013.12.021 . [DOI] [PubMed] [Google Scholar]
  • 16.Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25(8):1091–3. Epub 2009/02/25. btp101 [pii] 10.1093/bioinformatics/btp101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Research. 2015;43(Database issue):D447–D52. 10.1093/nar/gku1003 PubMed PMID: PMC4383874. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-Time quantitative PCR and the 2-ΔΔCT method. Methods 2001;25(4):402–8. 10.1006/meth.2001.1262 [DOI] [PubMed] [Google Scholar]
  • 19.Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611–22. Epub 2009/02/28. clinchem.2008.112797 [pii] 10.1373/clinchem.2008.112797 . [DOI] [PubMed] [Google Scholar]
  • 20.Owen OE, Kalhan SC, Hanson RW. The key role of anaplerosis and cataplerosis for citric acid cycle function. J Biol Chem. 2002;277(34):30409–12. Epub 2002/06/28. 10.1074/jbc.R200006200 R200006200 [pii]. . [DOI] [PubMed] [Google Scholar]
  • 21.Daval S, Lagarrigue S, Douaire M. Messenger RNA levels and transcription rates of hepatic lipogenesis genes in genetically lean and fat chickens. Genet Sel Evol. 2000;32(5):521–31. Epub 2004/01/23. 10.1051/gse:2000134g320505 [pii]. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Browne GJ, Proud CG. Regulation of peptide-chain elongation in mammalian cells. Eur J Biochem. 2002;269(22):5360–8. Epub 2002/11/09. 3290 [pii]. . [DOI] [PubMed] [Google Scholar]
  • 23.Proud CG. eIF2 and the control of cell physiology. Semin Cell Dev Biol. 2005;16(1):3–12. Epub 2005/01/22. S1084-9521(04)00106-5 [pii] 10.1016/j.semcdb.2004.11.004 . [DOI] [PubMed] [Google Scholar]
  • 24.http://www.uniprot.org/uniprot/Q14353.
  • 25.Fujiwara K, Okamura-Ikeda K, Motokawa Y. Amino acid sequence of the phosphopyridoxyl peptide from P-protein of the chicken liver glycine cleavage system. Biochem Biophys Res Commun. 1987;149(2):621–7. Epub 1987/12/16. 0006-291X(87)90413-X [pii]. . [DOI] [PubMed] [Google Scholar]
  • 26.Porter DH, Cook RJ, Wagner C. Enzymatic properties of dimethylglycine dehydrogenase and sarcosine dehydrogenase from rat liver. Arch Biochem Biophys. 1985;243(2):396–407. Epub 1985/12/01. . [DOI] [PubMed] [Google Scholar]
  • 27.Whitehead CC, Griffin HD. Development of divergent lines of lean and fat broilers using plasma very low density lipoprotein concentration as selection criterion: the first three generations. Br Poult Sci. 1984;25(4):573–82. Epub 1984/10/01. 10.1080/00071668408454899 . [DOI] [PubMed] [Google Scholar]
  • 28.Besnard P, Niot I, Poirier H, Clement L, A B. New insights into the fatty acid-binding protein (FABP) family in the small intestine. Molecular and cellular biochemistry. 2002;239(1–2):139–47. [PubMed] [Google Scholar]
  • 29.Chmurzynska A. The multigene family of fatty acid-binding proteins (FABPs): function, structure and polymorphism. J Appl Genet. 2006;47(1):39–48. Epub 2006/01/21. 313 [pii] 10.1007/BF03194597 . [DOI] [PubMed] [Google Scholar]
  • 30.Wang Q, Li H, Li N, Leng L, Wang Y. Tissue expression and association with fatness traits of liver fatty acid-binding protein gene in chicken. Poult Sci. 2006;85(11):1890–5. Epub 2006/10/13. 85/11/1890 [pii]. . [DOI] [PubMed] [Google Scholar]
  • 31.Miziorko HM, Clinkenbeard KD, Reed WD, Lane MD. 3-Hydroxy-3-methylglutaryl coenzyme A synthase. Evidence for an acetyl-S-enzyme intermediate and identification of a cysteinyl sulfhydryl as the site of acetylation. J Biol Chem. 1975;250(15):5768–73. Epub 1975/08/10. . [PubMed] [Google Scholar]
  • 32.Cockcroft S, Carvou N. Biochemical and biological functions of class I phosphatidylinositol transfer proteins. Biochim Biophys Acta. 2007;1771(6):677–91. Epub 2007/05/11. S1388-1981(07)00076-5 [pii] 10.1016/j.bbalip.2007.03.009 . [DOI] [PubMed] [Google Scholar]
  • 33.Singh DK, Mokashi V, Elmore CL, Porter TD. Phosphorylation of supernatant protein factor enhances its ability to stimulate microsomal squalene monooxygenase. J Biol Chem. 2003;278(8):5646–51. Epub 2002/11/28. 10.1074/jbc.M211750200 M211750200 [pii]. . [DOI] [PubMed] [Google Scholar]
  • 34.Chegary M, Brinke H, Ruiter JP, Wijburg FA, Stoll MS, Minkler PE, et al. Mitochondrial long chain fatty acid beta-oxidation in man and mouse. Biochim Biophys Acta. 2009;1791(8):806–15. Epub 2009/05/26. S1388-1981(09)00131-0 [pii] 10.1016/j.bbalip.2009.05.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Janssen U, Fink T, Lichter P, Stoffel W. Human mitochondrial 3,2-trans-enoyl-CoA isomerase (DCI): gene structure and localization to chromosome 16p13.3. Genomics. 1994;23(1):223–8. Epub 1994/09/01. S0888-7543(84)71480-7 [pii] 10.1006/geno.1994.1480 . [DOI] [PubMed] [Google Scholar]
  • 36.Lane AN, Fan TW. Regulation of mammalian nucleotide metabolism and biosynthesis. Nucleic Acids Res. 2015;43(4):2466–85. Epub 2015/01/30. gkv047 [pii] 10.1093/nar/gkv047 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Banerjee D, Nandagopal K. Phylogenetic analysis and in silico characterization of the GARS-AIRS-GART gene which codes for a tri-functional enzyme protein involved in de novo purine biosynthesis. Mol Biotechnol. 2009;42(3):306–19. Epub 2009/03/21. 10.1007/s12033-009-9160-1 . [DOI] [PubMed] [Google Scholar]
  • 38.Fridman A, Saha A, Chan A, Casteel DE, Pilz RB, Boss GR. Cell cycle regulation of purine synthesis by phosphoribosyl pyrophosphate and inorganic phosphate. Biochem J. 2013;454(1):91–9. Epub 2013/06/06. BJ20130153 [pii] 10.1042/BJ20130153 . [DOI] [PubMed] [Google Scholar]
  • 39.Boison D, Scheurer L, Zumsteg V, Rulicke T, Litynski P, Fowler B, et al. Neonatal hepatic steatosis by disruption of the adenosine kinase gene. Proc Natl Acad Sci U S A. 2002;99(10):6985–90. Epub 2002/05/09. 10.1073/pnas.092642899 092642899 [pii]. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Phizicky EM, Fields S. Protein-protein interactions: methods for detection and analysis. Microbiol Rev. 1995;59(1):94–123. Epub 1995/03/01. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. The quality of RNA and qPCR reaction curve of the differentially expressed proteins at the mRNA level in the liver of AA broiler and Big Bone chickens.

(DOCX)

Click here for additional data file. (613.4KB, docx)
S1 Table. Peptides identified from the liver of AA broiler and Big Bone chickens by mass spectrometry.

(XLSX)

Click here for additional data file. (123.9KB, xlsx)
S2 Table. The primer sequences used for qPCR analysis of the differentially expressed proteins in the liver of AA broiler and Big Bone chickens.

(DOCX)

Click here for additional data file. (18.2KB, docx)

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES