Comparative proteomic analysis of ovaries from Huoyan geese between pre-laying and laying periods using an iTRAQ-based approach

Comparative proteomic analysis of ovaries from Huoyan geese between pre-laying and laying periods... ABSTRACT To explore the key regulatory genes and their translated products involved in Huoyan goose egg laying, we first applied the iTRAQ technology integrated with LC-MS/MS analysis to identify differentially abundant proteins in the ovarian tissue of Huoyan geese between the pre-laying and laying periods. Then, Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses of the proteins were conducted. As observed, 403 proteins, which included 255 up-regulated and 148 down-regulated proteins, were identified. Some differentially abundant proteins, such as apolipoprotein B (apoB), retinol-binding protein 4 (RBP4), vitellogenin-2 (VTG-2), apolipoprotein II (apoVLDL-II), and fatty-acid binding protein 4 (FABP4), were found to be involved in lipid transportation and metabolic process, follicle development and final egg production, ovarian steroidogenesis, and steroid hormone biosynthesis. Subsequently, the modification of the abundance of 3 proteins, adiponectin, FABP4, and RBP4, was verified using Western blotting. Our findings might provide comprehensive protein expression information that can facilitate the understanding of the reproductive biology and improve the egg-laying performance of Huoyan geese. INTRODUCTION The Liaoning Huoyan goose is considered a national treasure by the Chinese goose industry and was listed as one of the nationally protected domestic animals by the Chinese government (Chen et al., 2004). Due to the lack of systematic breeding programs to improve egg productivity, the egg production rate of this breed has gradually decreased. Laying performance in poultry is closely related to the development of ovarian follicles and the establishment of the follicle hierarchy. Identifying differences in abundance of proteins between distinct follicular stages could help us to understand follicular development and investigate the molecular mechanisms underlying the reproductive biology of Huoyan geese. The ovary performs numerous roles critical for oocyte development and ovulation (Nelson and Bulun, 2001; Padmanabhan et al., 2002). The number of eggs laid by a bird is determined by the number of follicles destined for ovulation and the capacity of the oviduct to transform the ova into a hard-shelled egg. The dynamic and highly regulated process of follicle development requires the coordinated actions of a great number of genes and their translated products, which is orderly orchestrated at the translational and posttranslational levels. Several studies have reported identified genes associated with the reproductive traits in the HPG (hypothalamus-pituitary-gonadal) of birds using genomic and transcriptome approaches (Shiue et al., 2006; Yen et al., 2006; Kang et al., 2009; Habig et al., 2012; Luan et al., 2013; Luan et al., 2014a; Luan et al., 2014b; Gao et al., 2015). Selecting for an individual gene in birds is an effective alternative for improving economically important traits, but this approach may not guarantee success if multiple factors and their dynamic interactions are involved in the phenotypic expression of the target trait (Emsley, 1997). Proteomics offers a new platform for studies of complex biological functions involving large numbers and networks of proteins (Grant and Blackstock, 2001) and allows us to obtain substantial information regarding protein posttranslational modification, proteolytic processing, and mRNA alternative splicing, which could not be achieved by DNA sequence analysis alone (Lewis et al., 2000). To improve egg production, the selection of birds for increased egg number or laying rate using proteomic approaches has become a possible alternative. To date, several research groups have reported the application of conventional proteomics approaches in the study of bird reproduction. Kuo et al. analyzed the abundance of hypothalamic proteins between high and low egg-producing strains of chickens and found differences in the abundance between both groups, revealing that protein levels may serve as molecular markers to select for egg productivity (Kuo et al., 2005). Huang et al. investigated serum protein profiles during the development of chickens and found that the levels of 13 proteins differed during the developmental stages (Huang et al., 2006). In addition, Nam Soo Kim et al. used the 2-DE (two-dimensional gel electrophoresis) method to show that anterior gradient-2 (AGR-2) may be an oviduct-specific protein involved in egg formation and epithelial cell differentiation during the egg-laying period of hens (Kim et al., 2007). The results of these studies imply that protein levels may serve as valuable parameters to improve egg production. In general, 2-DE provides a visual representation of the proteome, in which distinct protein isoforms resulting from the changes in Mr and/or pI can be observed. However, 2-DE suffers from low throughput non-quantitative information, coupled with difficulties in separating and/or detecting low-abundance proteins and post-translationally modified proteins (Gygi et al., 2000; Luche et al., 2003), as well as those proteins with a pI value lower than 4 or higher than 9 (Kameshita et al., 1998). Recently, a new technique known as iTRAQ followed by LC-MS/MS has been applied for proteomic quantitation. This method overcomes some of the limitations of the other conventional proteomics techniques and improves the throughput of proteomic studies. This is one of the most highly sensitive proteomic technologies because it can detect and quantitatively analyze low-abundance proteins in complex biological samples (Zieske, 2006). As from the bibliography, no information is available with respect to the proteomic analysis of geese ovaries during different egg-laying stages. To explore the key regulatory genes and their translated products involved in goose egg laying, we applied the iTRAQ technology integrated with LC-MS/MS analysis to identify differentially abundant proteins in the ovarian tissue of Huoyan geese between the pre-laying and laying periods. The possible biological significances of these differentially expressed proteins were further evaluated using various bioinformatics programs. Several selected differentially abundant proteins were then validated using Western blotting. MATERIALS AND METHODS Ethics Statement Experimental procedures were approved by the animal welfare committee of the College of Animal Science and Veterinary Medicine of Shenyang Agricultural University (No. 2,013,025) and performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals (State Science and Technology Commission in China, 1988) and EU Directive 2010/63/EU for animal experiments. All of the surgery was performed according to recommendations proposed by the European Commission (Close, et al., 1997), and all efforts were made to minimize the suffering of the animals. Animal and Tissue Collection The Huoyan geese were selected and raised on the Liaoning Huoyan Goose Stock Breeding Farm according to the farm program. During the experiment, geese were fed ad libitum with rice grain and supplemented with green grass or water plants whenever possible. Feed was provided during the daytime when the geese were released into an open area outside the building. Huoyan geese become sexually mature at approximately 7 mo of age and reach the peak egg-laying stage in the following year. In the current study, goslings were purchased in the fall of the yr and became sexually mature during the summer of the following year. Nine pre-laying-period geese were sampled at 6 mo of age, and 9 laying-period geese were sampled at 12 mo of age. The health and egg-laying status of the birds were monitored daily, and only reproductively active geese were chosen for the study. Geese were killed by exsanguination to obtain ovary samples. All of the samples were quickly dissected, frozen in liquid nitrogen, and stored at −80°C until protein was prepared. Protein Preparation All ovarian tissues were homogenized in 0.5 ml STD lysis buffer (4% SDS, 150 mM Tris–HCl, 1 mM DTT, pH 8.0, and protease inhibitor). Then, the samples were sonicated and boiled at 100°C for 15 min, followed by centrifugation at 14,000 × g for 45 min at 25°C. The supernatant was precipitated overnight with cold acetone. After discarding the acetone and air drying, the resulting pellet was dissolved in 30 μl STD lysis buffer. Protein concentrations were determined using the BCA (Bicinchoninic Acid) protein assay reagent (Beyotime Institute of Biotechnology, Shanghai, China). Protein samples were stored at −80°C until needed. Protein Digestion and iTRAQ Labeling Protein digestion was performed according to the procedure described by Wisniewski et al. (Wisniewski et al., 2009). To reduce overall variability by minimizing individual heterogeneity (Karp and Lilley, 2009), the protein samples from 3 randomly selected individuals within each group were pooled in equal amounts to generate one biological sample pool, and there were 3 biological protein pools for both the pre-laying group and the laying group. Protein (300 μg) from 3 equally pooled biological replicates was diluted with 200 μl UA buffer (8 M Urea and 150 mM Tris-HCl, pH 8.0) and loaded onto an ultrafiltration filter (30 kDa cut-off, Sartorius, Germany). Samples were centrifuged at 14,000 × g for 15 min; 200 μl UA buffer was then added, followed by centrifugation for an additional 15 minutes. After discarding the supernatant, 100 μl of 50 mM iodoacetamide in UA buffer was subsequently added to the filter with oscillation at 600 rpm for 1 minute. The samples were incubated for 30 min in darkness and then centrifuged at 14,000 × g for 10 min. The filters were washed twice with 100 μl UA buffer, and 100 μl dissolution buffer (50 mM triethylammonium bicarbonate at pH 8.5) were added to the filters, followed by centrifugation for 10 minutes. This step was repeated twice, and 40 μl trypsin buffer (2 μg trypsin in 40 μl dissolution buffer) were then added to each filter. The samples were oscillated at 600 rpm for 1 min and incubated at 37°C for 16 to 18 hours. Finally, the filter unit was transferred to a new tube and centrifuged at 14,000 × g for 10 minutes. The resulting peptides were collected as a filtrate, and the peptide concentration was analyzed at OD280 (Wisniewski et al., 2009). The resulting peptide mixture was labeled using the 8-plex iTRAQ reagent according to the manufacturer's instructions (Applied Biosystems, Foster City, CA). Three samples from the pre-laying group were labeled with mass 113, 116, and 117 isobaric iTRAQ tags, while the other 3 samples from the laying group were labeled with mass 114, 115, and 118 isobaric iTRAQ tags. The labeling solution reaction was then incubated at room temperature for 1 h prior to further analysis. Peptide Fractionation with Strong Cation Exchange Chromatography The iTRAQ-labeled peptides were subjected to strong cation exchange (SCX) fractionation in an AKTA Purifier 100 (GE Healthcare, Uppsala, Sweden). The dried peptide mixture was reconstituted and acidified with 2 mL buffer A (10 mM KH2PO4 in 25% of ACN, pH 3.0) and loaded onto a polysulphoethyl (PolyLC Inc., Columbia, Maryland) column (4.6 mm × 100 mm, 5 μm, 200 Å). The peptides were eluted at a flow rate of 1 mL/min with a gradient of 100% Buffer A for 25 min, 0 to 10% Buffer B (500 mM KCl, 10 mM KH2PO4 in 25% of ACN, pH 3.0) for 7 min, 10 to 20% Buffer B for 10 min, 20 to 45% Buffer B for 5 min, 45 to 100% Buffer B for 5 min, 100% Buffer B for 8 min, and finally 100% Buffer A for 15 minutes. The elution process was monitored by absorbance at 214 nm, and fractions were collected every 1 minute. The collected fractions (approximately 30) were finally combined into 6 pools and desalted on C18 cartridges (Sigma, Steinheim, Germany). Each fraction was concentrated via vacuum centrifugation and reconstituted in 40 μl of 0.1% (v/v) trifluoroacetic acid. All samples were stored at –80°C until LC-MS/MS analysis. LC-MS/MS The iTRAQ-labeled samples were analyzed using an Easy-nLC nanoflow HPLC system connected to a Q-Exactive mass spectrometer (Thermo Fisher, San Jose, CA). A total of 5 μg of each sample was loaded onto a Thermo Scientific EASY column (2 cm × 100 μm, 5 μm-C18) using an auto sampler at a flow rate of 250 nl/min. The peptides were separated on a Thermo Scientific EASY column (100 mm × 75 μm, 3 μm-C18) using a segmented 2-h gradient from Solvent A (0.1% formic acid in water) to 35% Solvent B (84% acetonitrile in 0.1% formic acid) for 100 min, followed by 35 to 100% Solvent B for 8 min and then 100% Solvent B for 12 minutes. The column was re-equilibrated to its initial highly aqueous solvent composition before each analysis. The peptides were subjected to the Q-Exactive mass spectrometer. MS data were acquired using a data-dependent top 10 method, dynamically choosing the most abundant precursor ions from the survey scan (300 to 1,800 m/z) for subsequent high-energy collisional dissociation (HCD) fragmentation in the positive ion mode. The determination of the target value is based on predictive automatic gain control (pAGC). Dynamic exclusion was used with a 40.0-second duration. The resolving power of the MS scan and the MS/MS scan at 200 m/z were set at 70,000 and 17,500, respectively. The top 10 most intense signals in the acquired MS spectra were selected for further MS/MS analysis. The isolation window was 2 m/z, the normalized collision energy was 30 eV, and the underfill ratio, which specifies the minimum percentage of the target value likely to be reached at maximum fill time, was defined as 0.1%. The maximum ion injection times were set at 10 ms for the survey scan and 60 ms for the MS/MS scans, and the automatic gain control target values for both scan modes were set to 3.0 × 10−6. The instrument was run with peptide recognition mode enabled. Sequence Database Searching and Data Analysis The MASCOT 2.2 (Matrix Science, London, UK) and Proteome Discoverer 1.4 software (Thermo Scientific, San Jose, CA) were used for identification and quantitative analysis. The raw files were analyzed using the Proteome Discoverer software. Protein identifications were performed using the MASCOT search engine embedded into Proteome Discoverer, searching against the Uniprot database of Anatidae protein sequences (08–15-2014, 34,815 entries, downloaded from http://www.uniprot.org/). Search parameters were set as follows: monoisotopic mass, trypsin as the cleavage enzyme, 2 missed cleavages, peptide mass tolerance at ±20 ppm, and MS/MS tolerance at 0.1 Da. Variable modifications were defined as the oxidation of methionine and iTRAQ 8-plex labeled tyrosine, while lysine and the N-term of peptides labeled by iTRAQ 8-plex and carbamidomethylation on cysteine were specified as fixed modifications, with the Decoy database pattern = Reverse. The results were filtered based on the false discovery rate (FDR), and the score threshold for peptide identification was set at a 1% FDR (Chen et al., 2017; Pu et al., 2017). All reported data were based on 99% confidence for protein identification as determined by an FDR of ≤ 1%. FDR = N(decoy)*2/((N(decoy)+ N(target)) (Sandberg et al., 2012). The relative quantitative analysis of the proteins in the samples based on the ratios of iTRAQ reporter ions from all unique peptides representing each protein was performed using Proteome Discoverer. The relative peak intensities of the iTRAQ reporter ions released in each of the MS/MS spectra were used. The sum of the intensities in all channels was employed as a reference for calculating the iTRAQ ratios of all reporter ions. The iTRAQ ratio of every group of proteins was obtained by using the intensity of each channel normalized to the reference (Unwin et al., 2010). For statistical analysis, Student's t test was employed to identify significant changes between the pre-laying and laying group samples. The FDR value was further computed by using the p.adjust function in R (version 3.1.3, R Foundation for Statistical Computing, http://www.r-project.org). Proteins with a statistically significant iTRAQ ratio of > 1.2 or < 0.83 (FDR < 0.05) were considered differentially abundant proteins (Chen et al., 2017; Pu et al., 2017). Bioinformatics Analysis The sequence data of the selected differentially abundant proteins were retrieved in batches in FASTA format from the UniProtKB database (Release 2015_03). The retrieved sequences were locally searched against the non-redundant protein database (NCBI nr) using the NCBI BLAST+ client software (ncbi-blast-2.2.28 + -win32.exe) to find homologous sequences from which the functional annotation was transferred to the studied sequences. In this study, the top 10 blast hits with an E-value of ≤ 1e - 3 for each query sequence were retrieved and loaded into Blast2GO (Version 2.8.0) for Gene Ontology (GO) mapping and annotation (Gotz et al., 2008). Un-annotated sequences were then re-annotated with more permissive parameters. The sequences without BLAST hits and the un-annotated sequences were then selected to go through InterProScan (Quevillon et al., 2005) against EBI databases to retrieve functional annotations of protein motifs and merged the InterProScan GO terms to the annotation set. The GO mapping described the roles of proteins according to the following 3 domains: biological process, molecular function, and cellular component (Ashburner et al., 2000). Following annotation and annotation augmentation steps, the studied proteins were blasted against Kyoto Encyclopaedia of Genes and Genomes (KEGG) genes (Birds) to retrieve their KEGG orthology identifications and were subsequently mapped to pathways in the KEGG (http://www.genome.jp/kegg/) using the online KEGG Automatic Annotation Server (KAAS) (http://www.genome.jp/kegg/kaas/) (Moriya et al., 2007; Kanehisa et al., 2012). Western Blot Analysis The protein samples of ovarian tissues from the pre-laying and laying groups were extracted and identified using kits according to the manufacturer's instructions (Applygen Co., LTD, Beijing, China). Equivalent amounts of total protein were subjected to 12% SDS-PAGE and then transferred to a nitrocellulose membrane. After blocking with 5% skim milk in PBS containing 0.1% Tween 20 (PBST) at 4°C overnight, the membranes were incubated separately with rabbit anti-adiponectin antibody (bs-0471R, Beijing Biosynthesis Biotechnology Co., LTD, Beijing, China), rabbit anti-adipocyte fatty acid-binding protein (FABP4) antibody (12,802–1-AP, Proteintech Group, Inc. Chicago, IL), and rabbit anti-Retinol-binding protein 4 (RBP4) antibody (11,774–1-Ap, Proteintech Group, Inc. Chicago, IL) overnight at 4°C. The membranes were subsequently incubated with goat anti-rabbit IgG/HRP antibody (bs-0295G-HRP, Beijing Biosynthesis Biotechnology Co., LTD, Beijing, China) for 1 h at 37°C. Finally, the bands were captured using a MicroChemi4.2 imaging system (DNR Bio-imaging Systems, Jerusalem, Israel), and densitometry analysis of the protein bands was performed using GelQuant software (DNR Bio-imaging Systems, Jerusalem, Israel). GAPDH (sc-20,357, Santa Cruz Biotechnology Inc., Santa Cruz, CA) was used as a reference protein to ensure equal loading. Triplicate experiments were performed for each sample. All data were analyzed using SPSS 16.0 for Windows (SPSS Inc. Chicago, IL). The data were analyzed by Student's t test. The results are expressed as the mean ± SEM. P < 0.05 was considered statistically significant. RESULTS Global Profiling of the Proteins in Ovarian Tissue We employed the iTRAQ labeling technology in combination with LC-MS/MS to investigate differentially abundant proteins in the ovarian tissue of Huoyan geese between the pre-laying period and laying period. A schematic workflow illustrating the steps applied in this study is shown in Figure 1. A total of 4,281 proteins was identified according to the standard of protein identification (Supplemental Tables S1 and S2), and 4,262 of those were quantified (Supplemental Table S3). Of these quantified proteins, 36.67% (1,570) of the proteins were inferred from one peptide, and 45.41% (1,944) were proteins with more than 3 unique peptides (Figure 2). The molecular weight and the predicted pI of the identified proteins were in the ranges of 1.90 to 3750.2 kDa and 3.97 to 12.26, respectively. The mass spectrometry proteomics data have been deposited to the Proteome Xchange Consortium via the PRIDE partner repository with the dataset identifier PXD004735 (username: reviewer22912@ebi.ac.uk; password: N1EOAwwE). Figure 1. View largeDownload slide Experimental design and schematic diagram of the workflow used in this study. Figure 1. View largeDownload slide Experimental design and schematic diagram of the workflow used in this study. Figure 2. View largeDownload slide Unique peptides for the identified proteins. The number of proteins in each category is presented at the top of each bar. Figure 2. View largeDownload slide Unique peptides for the identified proteins. The number of proteins in each category is presented at the top of each bar. Differentially Abundant Proteins Between the Pre-laying Period and Laying Period To identify differentially abundant proteins between the pre-laying period and the laying period, the ratios of the iTRAQ reporter ions between the pre-laying and laying samples were determined. Student's t test and the FDR value corresponding to each P-value of differentially abundant proteins were computed. The differentially abundant proteins were selected according to the following criteria: FDR < 0.05 and a iTRAQ ratio of > 1.2 or < 0.83. As listed in Supplemental Table S4, 403 proteins (255 up-regulated and 148 down-regulated) were extracted. Bioinformatics Analysis of Differentially Abundant Proteins The Gene Ontology database is an internationally standardized gene functional classification system that comprehensively describes the characteristics of different genes and their products. To better understand the differentially abundant proteins, Gene Ontology category enrichment analysis was conducted using all 403 identified proteins. A total of 354 differentially abundant proteins were annotated to 1,547 GO function entries. Second-level GO terms were applied to classify the proteins in terms of their involvement in 3 main categories (cellular component, molecular function, and biological process), and each protein was assigned to at least one term. As summarized in Supplemental Table S5, in the cellular component category (Figure 3), the differentially abundant proteins were mainly distributed in the cell, organelles, extracellular region, and membrane. In the molecular function category (Figure 4), the differentially abundant proteins were mostly related to binding, such as catalytic activity, transporter activity, structural molecule activity, and enzyme regulator activity. In the biological process category (Figure 5), the differentially abundant proteins were mainly associated with the cellular process, the single-organism process, the metabolic process, and biological regulation. According to these GO classifications, some proteins such as apoB, RBP4, vitellogenin-2 (VTG II), apolipoprotein II (apoVLDL-II), and FBP4 were found to be involved in reproduction and the reproductive process, lipid transport, and the metabolic process (Table 1). Particularly, the abundance of these proteins was increased during the egg-laying period compared with the pre-laying period. Figure 3. View largeDownload slide Gene Ontology (GO) cellular component analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 3. View largeDownload slide Gene Ontology (GO) cellular component analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 4. View largeDownload slide Gene Ontology (GO) molecular function analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 4. View largeDownload slide Gene Ontology (GO) molecular function analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 5. View largeDownload slide Gene Ontology (GO) biological process analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 5. View largeDownload slide Gene Ontology (GO) biological process analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Table 1. List of some interesting differentially abundant proteins that are involved in important GO biological processes associated with egg-laying regulation. GO biological process  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Regulation of MAPK cascade  Tyrosine-protein kinase Lyn  R0K926  Lyn  4.80    Caveolin  D7RF71    2.61    Uncharacterized protein  U3J1Z5  RAP1A  2.31    Caveolin  D7RF70    1.53    Macrophage migration inhibitory factor  R0JLZ8    1.52  Lipid metabolic process  Uncharacterized protein  U3J6E8  Apo-VLDL-II  3.76    Adipocyte fatty acid-binding protein  F8SKC8  FABP4  2.97    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3J2D6  PLTP  2.36    Uncharacterized protein  U3IWA4  APOB  2.23    Serine incorporator 5  R0M708    2.22    Retinol-binding protein 4  R0K679  RBP4  1.88    Fatty acid-binding protein, epidermal  R0LD44  FABP5  1.82    Apolipoprotein AI  Q9PRR6    1.76    Proactivator polypeptide  R0J9J6    1.73  Lipid transport  Adipocyte fatty acid-binding protein  F8SKC8    2.97    Uncharacterized protein  U3JA11    2.89    Uncharacterized protein  U3I8J0    2.83    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3IWA4  APOB  2.23    Vitellogenin-2  R0JQ97    2.20    Putative phospholipid-transporting ATPase IH  R0LH43  ATP11A  1.96    Epididymal secretory protein E1  R0L315    1.87    Macrophage migration inhibitory factor  R0JLZ8    1.52  Sexual reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Reproductive process  Putative apolipoprotein B  A7WPP0  APOB  2.39    Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Cholesterol transport  Putative apolipoprotein B  A7WPP0  APOB  2.39    Epididymal secretory protein E1  R0L315    1.87  Sex differentiation  Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88  Female genitalia development  Retinol-binding protein 4  R0K679  RBP4  1.88  Down-regulated proteins  Lipid transport  Uncharacterized protein  U3ITT6    0.66  Lipid metabolic process  85 kDa calcium-independent phospholipase A2  R0LQB6    0.65    Synaptic glycoprotein SC2  R0JFF3    0.60  GO biological process  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Regulation of MAPK cascade  Tyrosine-protein kinase Lyn  R0K926  Lyn  4.80    Caveolin  D7RF71    2.61    Uncharacterized protein  U3J1Z5  RAP1A  2.31    Caveolin  D7RF70    1.53    Macrophage migration inhibitory factor  R0JLZ8    1.52  Lipid metabolic process  Uncharacterized protein  U3J6E8  Apo-VLDL-II  3.76    Adipocyte fatty acid-binding protein  F8SKC8  FABP4  2.97    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3J2D6  PLTP  2.36    Uncharacterized protein  U3IWA4  APOB  2.23    Serine incorporator 5  R0M708    2.22    Retinol-binding protein 4  R0K679  RBP4  1.88    Fatty acid-binding protein, epidermal  R0LD44  FABP5  1.82    Apolipoprotein AI  Q9PRR6    1.76    Proactivator polypeptide  R0J9J6    1.73  Lipid transport  Adipocyte fatty acid-binding protein  F8SKC8    2.97    Uncharacterized protein  U3JA11    2.89    Uncharacterized protein  U3I8J0    2.83    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3IWA4  APOB  2.23    Vitellogenin-2  R0JQ97    2.20    Putative phospholipid-transporting ATPase IH  R0LH43  ATP11A  1.96    Epididymal secretory protein E1  R0L315    1.87    Macrophage migration inhibitory factor  R0JLZ8    1.52  Sexual reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Reproductive process  Putative apolipoprotein B  A7WPP0  APOB  2.39    Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Cholesterol transport  Putative apolipoprotein B  A7WPP0  APOB  2.39    Epididymal secretory protein E1  R0L315    1.87  Sex differentiation  Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88  Female genitalia development  Retinol-binding protein 4  R0K679  RBP4  1.88  Down-regulated proteins  Lipid transport  Uncharacterized protein  U3ITT6    0.66  Lipid metabolic process  85 kDa calcium-independent phospholipase A2  R0LQB6    0.65    Synaptic glycoprotein SC2  R0JFF3    0.60  View Large Table 1. List of some interesting differentially abundant proteins that are involved in important GO biological processes associated with egg-laying regulation. GO biological process  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Regulation of MAPK cascade  Tyrosine-protein kinase Lyn  R0K926  Lyn  4.80    Caveolin  D7RF71    2.61    Uncharacterized protein  U3J1Z5  RAP1A  2.31    Caveolin  D7RF70    1.53    Macrophage migration inhibitory factor  R0JLZ8    1.52  Lipid metabolic process  Uncharacterized protein  U3J6E8  Apo-VLDL-II  3.76    Adipocyte fatty acid-binding protein  F8SKC8  FABP4  2.97    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3J2D6  PLTP  2.36    Uncharacterized protein  U3IWA4  APOB  2.23    Serine incorporator 5  R0M708    2.22    Retinol-binding protein 4  R0K679  RBP4  1.88    Fatty acid-binding protein, epidermal  R0LD44  FABP5  1.82    Apolipoprotein AI  Q9PRR6    1.76    Proactivator polypeptide  R0J9J6    1.73  Lipid transport  Adipocyte fatty acid-binding protein  F8SKC8    2.97    Uncharacterized protein  U3JA11    2.89    Uncharacterized protein  U3I8J0    2.83    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3IWA4  APOB  2.23    Vitellogenin-2  R0JQ97    2.20    Putative phospholipid-transporting ATPase IH  R0LH43  ATP11A  1.96    Epididymal secretory protein E1  R0L315    1.87    Macrophage migration inhibitory factor  R0JLZ8    1.52  Sexual reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Reproductive process  Putative apolipoprotein B  A7WPP0  APOB  2.39    Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Cholesterol transport  Putative apolipoprotein B  A7WPP0  APOB  2.39    Epididymal secretory protein E1  R0L315    1.87  Sex differentiation  Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88  Female genitalia development  Retinol-binding protein 4  R0K679  RBP4  1.88  Down-regulated proteins  Lipid transport  Uncharacterized protein  U3ITT6    0.66  Lipid metabolic process  85 kDa calcium-independent phospholipase A2  R0LQB6    0.65    Synaptic glycoprotein SC2  R0JFF3    0.60  GO biological process  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Regulation of MAPK cascade  Tyrosine-protein kinase Lyn  R0K926  Lyn  4.80    Caveolin  D7RF71    2.61    Uncharacterized protein  U3J1Z5  RAP1A  2.31    Caveolin  D7RF70    1.53    Macrophage migration inhibitory factor  R0JLZ8    1.52  Lipid metabolic process  Uncharacterized protein  U3J6E8  Apo-VLDL-II  3.76    Adipocyte fatty acid-binding protein  F8SKC8  FABP4  2.97    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3J2D6  PLTP  2.36    Uncharacterized protein  U3IWA4  APOB  2.23    Serine incorporator 5  R0M708    2.22    Retinol-binding protein 4  R0K679  RBP4  1.88    Fatty acid-binding protein, epidermal  R0LD44  FABP5  1.82    Apolipoprotein AI  Q9PRR6    1.76    Proactivator polypeptide  R0J9J6    1.73  Lipid transport  Adipocyte fatty acid-binding protein  F8SKC8    2.97    Uncharacterized protein  U3JA11    2.89    Uncharacterized protein  U3I8J0    2.83    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3IWA4  APOB  2.23    Vitellogenin-2  R0JQ97    2.20    Putative phospholipid-transporting ATPase IH  R0LH43  ATP11A  1.96    Epididymal secretory protein E1  R0L315    1.87    Macrophage migration inhibitory factor  R0JLZ8    1.52  Sexual reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Reproductive process  Putative apolipoprotein B  A7WPP0  APOB  2.39    Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Cholesterol transport  Putative apolipoprotein B  A7WPP0  APOB  2.39    Epididymal secretory protein E1  R0L315    1.87  Sex differentiation  Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88  Female genitalia development  Retinol-binding protein 4  R0K679  RBP4  1.88  Down-regulated proteins  Lipid transport  Uncharacterized protein  U3ITT6    0.66  Lipid metabolic process  85 kDa calcium-independent phospholipase A2  R0LQB6    0.65    Synaptic glycoprotein SC2  R0JFF3    0.60  View Large Because different proteins interact and cooperate to complete biochemical reactions, following annotation and annotation augmentation, a KEGG pathway-based analysis was performed to identify pathways that would be potentially affected by the modification of the abundance of the proteins in ovarian tissue. A total of 168 differentially abundant proteins were mapped to 246 KEGG pathway entries. All KEGG analysis results are shown in Figure 6 and Supplemental Table S6. The KEGG pathway analysis for the identified proteins showed that the top 3 pathways identified were focal adhesion, the PI3K-Akt signaling pathway, and ECM-receptor interaction. It is worth mentioning that some proteins such as CYP19A1, CYP17A1, serine/threonine-protein kinase PRKX, HSD11B2, cell division cycle protein 27-like protein, MAPK11, and secreted frizzled-related protein 2 (SFRP2) were mapped for pathways involved in ovarian steroidogenesis, steroid hormone biosynthesis, the estrogen signaling pathway, oocyte meiosis, progesterone-mediated oocyte maturation, the prolactin signaling pathway, the MAPK signaling pathway, ECM-receptor interaction, and the Wnt signaling pathway (Table 2). Figure 6. View largeDownload slide Distribution of the KEGG pathways in the ovary proteome. Figure 6. View largeDownload slide Distribution of the KEGG pathways in the ovary proteome. Table 2. List of some interesting differentially abundant proteins that are involved in important pathways associated with egg-laying regulation. Pathway  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Progesterone-mediated oocyte maturation  Cell division cycle protein 27-like protein  R0JBZ8  CDC27  3.89    Uncharacterized protein  U3I9V4  MAPK11  1.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oocyte meiosis  Cell division cycle protein 27-like protein  R0JBZ8    3.89    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Calcium signaling pathway  Uncharacterized protein  U3I953    2.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Oestrogen signaling pathway  Uncharacterized protein  U3J395  MMP9  2.53    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  MAPK signaling pathway  Uncharacterized protein  U3J1Z5  RAP1A  2.31    Uncharacterized protein  U3I9 × 9    1.97    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9B6    1.71    Uncharacterized protein  U3I9V4  MAPK11  1.62    Inhibitor of nuclear factor kappa-B kinase subunit alpha  R0JZA4    1.56    Serine/threonine-protein phosphatase  R0K3B0    1.55  ECM-receptor interaction  Uncharacterized protein  U3IFG6  TNXB  2.21    Uncharacterized protein  U3IYP2  ITGA8  2.13    Fibronectin  R0LH70    2.06    Collagen alpha-4(IV) chain  R0JKV2    2.00    Uncharacterized protein  U3IQ97  COL4A2  1.55    Integrin beta  U3IPD4  ITGB1  1.53    Collagen alpha-2(VI) chain  R0JJW5    1.52    Collagen alpha-1(IV) chain  R0KF46    1.61    Uncharacterized protein  U3ID88  COL6A3  1.58    Laminin subunit alpha-4  R0KXM7    1.56    Uncharacterized protein  U3I7L8  ITGA11  1.89    Collagen alpha-1(VI) chain  R0JKK6    1.85    Collagen alpha-2(V) chain  R0LNH9    1.65  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Wnt signaling pathway  Uncharacterized protein  U3IX89  DAAM2  1.51  Serotonergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oxytocin signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Ovarian steroidogenesis  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  GnRH signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9V4  MAPK11  1.62  Glutamatergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  GABAergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Dopaminergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55    Uncharacterized protein  U3I9V4  MAPK11  1.62  Prolactin signaling pathway  Uncharacterized protein  U3I9V4  MAPK11  1.62  Down-regulated proteins  Steroid hormone biosynthesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IJB2  CYP19A1  0.61    Uncharacterized protein  U3HZB8  UGT1A8  0.40  Prolactin signaling pathway  Uncharacterized protein  U3IXW3  CYP17A1  0.66  Ovarian steroidogenesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IRR4    0.66    3-keto-steroid reductase  R0JFP8    0.55    Uncharacterized protein  U3IJB2  CYP19A1  0.61  Steroid hormone biosynthesis  Uncharacterized protein  U3IRR4    0.66    Uncharacterized protein  U3IJ20  HSD11B2  0.59    3-keto-steroid reductase  R0JFP8    0.55  Steroid biosynthesis  Uncharacterized protein  U3IUF3  FDFT1  0.65    3-keto-steroid reductase  R0JFP8    0.55  MAPK signaling pathway  Microtubule-associated protein  U3IKL9  MAPT  0.64  Serotonergic synapse  Uncharacterized protein  U3I684  DDC  0.63  Dopaminergic synapse  Uncharacterized protein  U3I684  DDC  0.63    Kinesin heavy chain isoform 5C  R0M2A6    0.52  Glutamatergic synapse  Excitatory amino acid transporter 1  R0L916    0.61    Excitatory amino acid transporter 2  R0M3W2    0.58  Oxytocin signaling pathway  Uncharacterized protein  U3IJV2    0.45  Calcium signaling pathway  Uncharacterized protein  U3IJV2    0.45  ECM-receptor interaction  Uncharacterized protein  U3I9H2  SV2A  0.33  Pathway  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Progesterone-mediated oocyte maturation  Cell division cycle protein 27-like protein  R0JBZ8  CDC27  3.89    Uncharacterized protein  U3I9V4  MAPK11  1.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oocyte meiosis  Cell division cycle protein 27-like protein  R0JBZ8    3.89    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Calcium signaling pathway  Uncharacterized protein  U3I953    2.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Oestrogen signaling pathway  Uncharacterized protein  U3J395  MMP9  2.53    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  MAPK signaling pathway  Uncharacterized protein  U3J1Z5  RAP1A  2.31    Uncharacterized protein  U3I9 × 9    1.97    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9B6    1.71    Uncharacterized protein  U3I9V4  MAPK11  1.62    Inhibitor of nuclear factor kappa-B kinase subunit alpha  R0JZA4    1.56    Serine/threonine-protein phosphatase  R0K3B0    1.55  ECM-receptor interaction  Uncharacterized protein  U3IFG6  TNXB  2.21    Uncharacterized protein  U3IYP2  ITGA8  2.13    Fibronectin  R0LH70    2.06    Collagen alpha-4(IV) chain  R0JKV2    2.00    Uncharacterized protein  U3IQ97  COL4A2  1.55    Integrin beta  U3IPD4  ITGB1  1.53    Collagen alpha-2(VI) chain  R0JJW5    1.52    Collagen alpha-1(IV) chain  R0KF46    1.61    Uncharacterized protein  U3ID88  COL6A3  1.58    Laminin subunit alpha-4  R0KXM7    1.56    Uncharacterized protein  U3I7L8  ITGA11  1.89    Collagen alpha-1(VI) chain  R0JKK6    1.85    Collagen alpha-2(V) chain  R0LNH9    1.65  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Wnt signaling pathway  Uncharacterized protein  U3IX89  DAAM2  1.51  Serotonergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oxytocin signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Ovarian steroidogenesis  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  GnRH signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9V4  MAPK11  1.62  Glutamatergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  GABAergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Dopaminergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55    Uncharacterized protein  U3I9V4  MAPK11  1.62  Prolactin signaling pathway  Uncharacterized protein  U3I9V4  MAPK11  1.62  Down-regulated proteins  Steroid hormone biosynthesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IJB2  CYP19A1  0.61    Uncharacterized protein  U3HZB8  UGT1A8  0.40  Prolactin signaling pathway  Uncharacterized protein  U3IXW3  CYP17A1  0.66  Ovarian steroidogenesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IRR4    0.66    3-keto-steroid reductase  R0JFP8    0.55    Uncharacterized protein  U3IJB2  CYP19A1  0.61  Steroid hormone biosynthesis  Uncharacterized protein  U3IRR4    0.66    Uncharacterized protein  U3IJ20  HSD11B2  0.59    3-keto-steroid reductase  R0JFP8    0.55  Steroid biosynthesis  Uncharacterized protein  U3IUF3  FDFT1  0.65    3-keto-steroid reductase  R0JFP8    0.55  MAPK signaling pathway  Microtubule-associated protein  U3IKL9  MAPT  0.64  Serotonergic synapse  Uncharacterized protein  U3I684  DDC  0.63  Dopaminergic synapse  Uncharacterized protein  U3I684  DDC  0.63    Kinesin heavy chain isoform 5C  R0M2A6    0.52  Glutamatergic synapse  Excitatory amino acid transporter 1  R0L916    0.61    Excitatory amino acid transporter 2  R0M3W2    0.58  Oxytocin signaling pathway  Uncharacterized protein  U3IJV2    0.45  Calcium signaling pathway  Uncharacterized protein  U3IJV2    0.45  ECM-receptor interaction  Uncharacterized protein  U3I9H2  SV2A  0.33  View Large Table 2. List of some interesting differentially abundant proteins that are involved in important pathways associated with egg-laying regulation. Pathway  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Progesterone-mediated oocyte maturation  Cell division cycle protein 27-like protein  R0JBZ8  CDC27  3.89    Uncharacterized protein  U3I9V4  MAPK11  1.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oocyte meiosis  Cell division cycle protein 27-like protein  R0JBZ8    3.89    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Calcium signaling pathway  Uncharacterized protein  U3I953    2.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Oestrogen signaling pathway  Uncharacterized protein  U3J395  MMP9  2.53    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  MAPK signaling pathway  Uncharacterized protein  U3J1Z5  RAP1A  2.31    Uncharacterized protein  U3I9 × 9    1.97    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9B6    1.71    Uncharacterized protein  U3I9V4  MAPK11  1.62    Inhibitor of nuclear factor kappa-B kinase subunit alpha  R0JZA4    1.56    Serine/threonine-protein phosphatase  R0K3B0    1.55  ECM-receptor interaction  Uncharacterized protein  U3IFG6  TNXB  2.21    Uncharacterized protein  U3IYP2  ITGA8  2.13    Fibronectin  R0LH70    2.06    Collagen alpha-4(IV) chain  R0JKV2    2.00    Uncharacterized protein  U3IQ97  COL4A2  1.55    Integrin beta  U3IPD4  ITGB1  1.53    Collagen alpha-2(VI) chain  R0JJW5    1.52    Collagen alpha-1(IV) chain  R0KF46    1.61    Uncharacterized protein  U3ID88  COL6A3  1.58    Laminin subunit alpha-4  R0KXM7    1.56    Uncharacterized protein  U3I7L8  ITGA11  1.89    Collagen alpha-1(VI) chain  R0JKK6    1.85    Collagen alpha-2(V) chain  R0LNH9    1.65  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Wnt signaling pathway  Uncharacterized protein  U3IX89  DAAM2  1.51  Serotonergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oxytocin signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Ovarian steroidogenesis  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  GnRH signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9V4  MAPK11  1.62  Glutamatergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  GABAergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Dopaminergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55    Uncharacterized protein  U3I9V4  MAPK11  1.62  Prolactin signaling pathway  Uncharacterized protein  U3I9V4  MAPK11  1.62  Down-regulated proteins  Steroid hormone biosynthesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IJB2  CYP19A1  0.61    Uncharacterized protein  U3HZB8  UGT1A8  0.40  Prolactin signaling pathway  Uncharacterized protein  U3IXW3  CYP17A1  0.66  Ovarian steroidogenesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IRR4    0.66    3-keto-steroid reductase  R0JFP8    0.55    Uncharacterized protein  U3IJB2  CYP19A1  0.61  Steroid hormone biosynthesis  Uncharacterized protein  U3IRR4    0.66    Uncharacterized protein  U3IJ20  HSD11B2  0.59    3-keto-steroid reductase  R0JFP8    0.55  Steroid biosynthesis  Uncharacterized protein  U3IUF3  FDFT1  0.65    3-keto-steroid reductase  R0JFP8    0.55  MAPK signaling pathway  Microtubule-associated protein  U3IKL9  MAPT  0.64  Serotonergic synapse  Uncharacterized protein  U3I684  DDC  0.63  Dopaminergic synapse  Uncharacterized protein  U3I684  DDC  0.63    Kinesin heavy chain isoform 5C  R0M2A6    0.52  Glutamatergic synapse  Excitatory amino acid transporter 1  R0L916    0.61    Excitatory amino acid transporter 2  R0M3W2    0.58  Oxytocin signaling pathway  Uncharacterized protein  U3IJV2    0.45  Calcium signaling pathway  Uncharacterized protein  U3IJV2    0.45  ECM-receptor interaction  Uncharacterized protein  U3I9H2  SV2A  0.33  Pathway  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Progesterone-mediated oocyte maturation  Cell division cycle protein 27-like protein  R0JBZ8  CDC27  3.89    Uncharacterized protein  U3I9V4  MAPK11  1.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oocyte meiosis  Cell division cycle protein 27-like protein  R0JBZ8    3.89    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Calcium signaling pathway  Uncharacterized protein  U3I953    2.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Oestrogen signaling pathway  Uncharacterized protein  U3J395  MMP9  2.53    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  MAPK signaling pathway  Uncharacterized protein  U3J1Z5  RAP1A  2.31    Uncharacterized protein  U3I9 × 9    1.97    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9B6    1.71    Uncharacterized protein  U3I9V4  MAPK11  1.62    Inhibitor of nuclear factor kappa-B kinase subunit alpha  R0JZA4    1.56    Serine/threonine-protein phosphatase  R0K3B0    1.55  ECM-receptor interaction  Uncharacterized protein  U3IFG6  TNXB  2.21    Uncharacterized protein  U3IYP2  ITGA8  2.13    Fibronectin  R0LH70    2.06    Collagen alpha-4(IV) chain  R0JKV2    2.00    Uncharacterized protein  U3IQ97  COL4A2  1.55    Integrin beta  U3IPD4  ITGB1  1.53    Collagen alpha-2(VI) chain  R0JJW5    1.52    Collagen alpha-1(IV) chain  R0KF46    1.61    Uncharacterized protein  U3ID88  COL6A3  1.58    Laminin subunit alpha-4  R0KXM7    1.56    Uncharacterized protein  U3I7L8  ITGA11  1.89    Collagen alpha-1(VI) chain  R0JKK6    1.85    Collagen alpha-2(V) chain  R0LNH9    1.65  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Wnt signaling pathway  Uncharacterized protein  U3IX89  DAAM2  1.51  Serotonergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oxytocin signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Ovarian steroidogenesis  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  GnRH signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9V4  MAPK11  1.62  Glutamatergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  GABAergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Dopaminergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55    Uncharacterized protein  U3I9V4  MAPK11  1.62  Prolactin signaling pathway  Uncharacterized protein  U3I9V4  MAPK11  1.62  Down-regulated proteins  Steroid hormone biosynthesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IJB2  CYP19A1  0.61    Uncharacterized protein  U3HZB8  UGT1A8  0.40  Prolactin signaling pathway  Uncharacterized protein  U3IXW3  CYP17A1  0.66  Ovarian steroidogenesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IRR4    0.66    3-keto-steroid reductase  R0JFP8    0.55    Uncharacterized protein  U3IJB2  CYP19A1  0.61  Steroid hormone biosynthesis  Uncharacterized protein  U3IRR4    0.66    Uncharacterized protein  U3IJ20  HSD11B2  0.59    3-keto-steroid reductase  R0JFP8    0.55  Steroid biosynthesis  Uncharacterized protein  U3IUF3  FDFT1  0.65    3-keto-steroid reductase  R0JFP8    0.55  MAPK signaling pathway  Microtubule-associated protein  U3IKL9  MAPT  0.64  Serotonergic synapse  Uncharacterized protein  U3I684  DDC  0.63  Dopaminergic synapse  Uncharacterized protein  U3I684  DDC  0.63    Kinesin heavy chain isoform 5C  R0M2A6    0.52  Glutamatergic synapse  Excitatory amino acid transporter 1  R0L916    0.61    Excitatory amino acid transporter 2  R0M3W2    0.58  Oxytocin signaling pathway  Uncharacterized protein  U3IJV2    0.45  Calcium signaling pathway  Uncharacterized protein  U3IJV2    0.45  ECM-receptor interaction  Uncharacterized protein  U3I9H2  SV2A  0.33  View Large Western Blot Validation To further validate the results obtained by iTRAQ and LC-MS/MS, we examined the modification of the abundance of adiponectin, FABP4, and RBP4 during the pre-laying and laying periods in the ovarian tissues using Western blotting. As shown in Figure 7, the abundance of these 3 proteins was up-regulated during the laying period compared with the pre-laying period (Adiponectin, 0.2056 ± 0.0421 vs. 0.1277 ± 0.0018; FABP4, 0.2065 ± 0.0348 vs. 0.1248 ± 0.0416; RBP4, 0.2037 ± 0.0536 vs. 0.1295 ± 0.0188). In comparison with the results obtained using proteomics, the changes in the trends in the abundance of these 3 proteins were consistent with the observations made by iTRAQ, although the differences were not statistically significant (P > 0.05) through statistical analysis. Figure 7. View largeDownload slide Western blotting analysis of adiponectin, FABP4, and RBP4 in the ovarian tissues of Huoyan geese between the pre-laying and laying periods. GAPDH was used as the internal control. The protein band density was analyzed using the GelQuant software. (A) Representative immunoblots. (B) Densitometric analysis of adiponectin, FABP4, and RBP4 protein relative to GAPDH protein. Values are expressed as the means±SEM of the mean in arbitrary optical density units (AU). Figure 7. View largeDownload slide Western blotting analysis of adiponectin, FABP4, and RBP4 in the ovarian tissues of Huoyan geese between the pre-laying and laying periods. GAPDH was used as the internal control. The protein band density was analyzed using the GelQuant software. (A) Representative immunoblots. (B) Densitometric analysis of adiponectin, FABP4, and RBP4 protein relative to GAPDH protein. Values are expressed as the means±SEM of the mean in arbitrary optical density units (AU). DISCUSSION The current study is the first report, to the best of our knowledge, of global differently proteomic profiles in Huoyan geese ovaries between the pre-laying and laying periods using the iTRAQ-based approach. Some proteins, such as very low-density apoVLDL-II, apoB, vitellogenin II, FABP4, and RBP4, which are associated with lipid transportation, the metabolic process, follicular activation, and development, have been identified to be up-regulated expressed in the laying group. The abundance of proteins encoded by CYP19A1 and CYP17A1 was found to be altered, and these proteins were mainly involved of ovarian steroidogenesis and steroid hormone biosynthesis. ApoVLDL-II, apoB, vitellogenins (VTG), and the fatty acid-binding protein are essential for follicle development and final egg production. Yolk lipid content is critical in avian species, as it comprises nearly 60% of the yolk dry mass. Approximately 67% of the yolk lipids are triglycerides (TG) (Noble and Cocchi, 1990; Bujo et al., 1997). In poultry, there is a large quantity of lipids in egg yolk, and thus, the initiation and maintenance of egg production is an energy-intensive process and requires a large quantity of lipids (especially TG) for egg yolk formation. Birds have the ability to store large quantities of excess energy (in the form of TG) in the liver, adipose tissue, and in the yolk of developing oocytes (Hermier, 1997). The avian liver is responsible for more than two-thirds of de novo lipogenesis and the synthesis of key proteins involved in lipid metabolism and egg formation, such as apoVLDL-II, apoB, VTG, and the fatty acid-binding protein (Yang et al., 2013). Apolipoprotein B is a major protein component of plasma very low-density and low-density lipoproteins (VLDL and LDL, respectively) and serves as a recognition signal for the cellular binding and internalization of LDL by the apoB/E receptor. As one of the components of specialized VLDL particles, avian apoB is produced by the liver in response to estrogen. It helps to transport cholesterol and triglycerides from the liver to the ovary for deposition in the egg yolk (Kirchgessner et al., 1987). A previous study showed that plasma VLDL and apoB levels were dramatically elevated in the laying hen or estrogen-stimulated rooster as a result of increased hepatic apoB synthesis and VLDL production (Capony and Williams, 1980). In addition to apoB, laying birds VLDL particles also contain large amounts of apolipoprotein apoVLDL-II, which is induced by estrogens at the onset of egg laying when yolk synthesis has just been initiated. ApoVLDL-II is critical to lipid transport during the laying period due to its inhibitory effect on lipoprotein lipase (LPL), which protects apoVLDL-II featured VLDL from being digested by LPL in circulation, so it can successfully deliver TG to the developing oocyte for subsequent use as an energy source (Schneider et al., 1990). The evidence shows that the expression of apoVLDL-II is associated with egg laying. The hepatic apoVLDL-II mRNA concentration in high egg production Tsaiya ducks was found to be significantly greater in laying ducks compared with pre-laying ducks (Yen et al., 2005). Similarly, the ApoVLDL-II mRNA in the liver tissues also was found to be more than 2-fold higher in laying geese compared with pre-laying geese (Ding et al., 2007). VTG are yolk precursor proteins produced by the liver under estrogenic induction and circulate within the bloodstream until the follicle enters a stage of vitellogenesis, where the endocytosis of vitellogenins transports them into the yolk. As follicles are recruited and enter the preovulatory hierarchy, they increase in size via the accumulation of vitellogenin (Nepomuceno et al., 2015). In birds including geese, the timing of VTG gene expression parallels that of egg laying (Ding et al., 2007), and the positive correlations between E2 and VTG mRNA expression, as well as estrogen receptors with VTG mRNA expression, were observed (Wu et al., 2013; Li et al., 2014). In line with previous reports, our current study also indicated that the abundance of these proteins was significantly increased during the laying period. It was suggested that high egg production requires a high lipid generation machinery to support egg yolk lipid accumulation. Accordingly, egg laying may affect particular aspects of lipid metabolism, and related biochemical pathways of these proteins also are involved. As another kind of important protein related to lipid metabolism, fatty acid-binding proteins (FABP) play specific roles in the transportation of fatty acids (FA), even in ovary development and ovarian hormone synthesis. FABP are small intracellular proteins that bind non-covalently to hydrophobic ligands, primarily FA (Esteves and Ehrlich, 2006). To date, 12 structural and functional distinct types of FABP have been discovered. In waterfowl, FABP4 is found to be expressed in many tissues: liver, spleen, gizzard, proventriculus, ovary, brain, and intestine (He et al., 2012). Physiological roles of FABP include the uptake and utilization of FA, the intracellular targeting of FA to specific organelles and metabolic pathways, and the protection of cellular structures from the detergent effects of FA (Storch et al., 2002); some reports have investigated the roles of FABP in respect to reproduction. In female Chinese mitten crab, lipid nutrients, especially FA, are transported from the hepatopancreas to the ovary in great supply via FABP expressing haemocytes during the stage of rapid ovarian development. FABP transported lipids, especially hormone precursor substances, are then stored in the ovaries in preparation for the significant and physiological function required during reproduction (Gong et al., 2010). In addition, 2 types of FABP are found to be expressed in distinct steroid-producing cell types of a rat ovary, and their expressions seem to be regulated in coincidence with the expressions of their respective steroid hormones (Iseki et al., 1995). Moreover, the laying duck hepatic FABP mRNA was determined to be approximately 30% higher than that in pre-laying ducks, owing to the layers needing to assemble yolk lipids for egg yolk lipid accumulation (Ko et al., 2004). Ovary development needs to consume a large amount of FA in order to meet its energy consumption and for the synthesis of steroid hormone precursor substances that are required for the reproduction. In the present study, the increase of ovarian FABP4 expression during the laying period compared to the pre-laying period mirrors lipid nutritional requirements. That is, the ovary requires substantial accumulation of lipids, especially FA, during the egg-laying stage. Retinoid-binding proteins (RBP) might play an important role in reproduction traits in egg-laying avian species. Ovulation is a complex physiological trait, which is not only influenced by hereditary factors and environment, but also by nutritional factors. Vitamin A (retinol) is known to be involved in ovarian follicular growth and oocyte maturation, and affects the steroidogenesis of ovarian follicles. Particularly, the vitamin A-active retinoids are essential to several aspects of avian reproduction and development (Blomhoff and Blomhoff, 2006). It was reported that a deficiency in vitamin A can lead to irregular ovulation, a decrease in egg production, and poor egg quality in the laying hen (Lin et al., 2002). Retinol and its derivatives must be bound to specific proteins called RBP because of its chemical instability and low solubility in an aqueous medium (Newcomer, 1995). In our present study, the abundance of RBP 4 (RBP4) was shown to be increased in the egg-laying group compared with the pre-laying group. RBP4 is one of the necessary intracellular factors in the absorption, transport, metabolism, and homeostasis of retinol and its derivatives. The expression of the RBP4 gene was identified to be associated with retinoid metabolism in a trout ovarian follicle (Levi et al., 2008). This gene also was investigated as a candidate gene for increased litter size in pigs (Rothschild et al., 2000). In poultry, RBP4 may be a candidate gene to affect egg production and reproductive traits. The expression of RBP4 mRNA in the liver, ovary, small intestine, oviduct, and hypothalamus of Erlang mountainous chickens was detected to be increased from 12 wk to 32 wk, with a decrease to 45 weeks. This could be because chickens need more retinol to modulate follicular development and maturation during the laying period. To transport more retinol to meet the requirements of the tissue and cells, greater expression of RBP4 is required (Yin et al., 2013). We noticed that the expression of 2 key enzymes for steroid hormone synthesis encoded by steroidogenic genes CYP19A1 and CYP17A1 was down-regulated in the laying group compared to the pre-laying group. Ovarian steroidogenesis occurs both in granulosa and theca layers of follicles, and is essential for follicular recruitment, oocyte maturation, and ovulation. The granulosa produces mainly progesterone (P4) while the theca interna and externa produce testosterone (T) and estradiol (E2), respectively. During the transition of the yellowish non-hierarchical follicles to a preovulatory hierarchy, the cells of the granulosa layer, stimulated initially by FSH (follicle stimulating hormone) and next by LH (luteinizing hormone), start to express a steroidogenic acute regulatory protein (STAR) and a cytochrome P450 cholesterol side-chain cleavage (P450scc/CYP11A1) enzyme and begin to produce P4 predominantly via the Δ4-ketosteroid pathway. The process of P4 synthesis form pregnenolone (P5) is catalyzed by a 3-hydroxysteroid dehydrogenase (3-HSD/HSD3B) whose expression steadily increases in granulosa cells of growing hierarchical follicles. P4 synthesized in the granulosa layer is transported to the theca interna where it is initially converted T, which is eventually metabolized to E2 by cytochrome P450 aromatase (P450arom/CYP19A1) in the theca externa cells (Sechman et al., 2014). The ability of ovarian follicles to produce steroid hormones is different according to their developmental status. Researchers found that the gene expression of STAR, CYP11A1 and HSD3B reduced significantly immediately after ovulation (Zhu et al., 2015). In chickens, the concentration of plasma oestradiol increased from 7 wk of age, reached a peak at 2 to 3 wk before laying, and then dropped to a stable level similar to that in birds one to 2 wk before laying (Senior, 1974). In house sparrow, the mRNA expression of follicular steroidogenic enzyme (including CYP19A1 and CYP17A1) decreased with follicle maturation, and exhibited a significant negative relationship with yolk T concentrations (Egbert et al., 2013). In our study, mechanisms, or factors responsible for the abundance alteration of these steroidogenic enzymes need to be further investigated. Furthermore, it is worth mentioning that the expression of adiponectin in the laying group was identified to be up-regulated compared to the pre-laying group in both our previous and current studies. Apart from a dominant role in lipid and carbohydrate metabolism, adiponectin also seems to be an important factor linking the regulation of reproductive processes. The evidence indicated that adiponectin may affect reproductive functions by exerting central or peripheral effects on the hypothalamus-pituitary-gonadal (HPG) axis, and the expression of adiponectin is dependent on the endocrine status of the animals (Cao et al., 2015). In summary, we investigated the proteome profiles of ovarian tissues from Huoyan geese during the laying period and the pre-laying period. Some proteins were involved in lipid transportation and the metabolic process, follicle development and final egg production, ovarian steroidogenesis, and steroid hormone biosynthesis; they showed different changes in abundance in coincidence with the different egg-laying stages. However, the functional significance of these differently abundant proteins needs further investigation. Our findings might provide comprehensive protein expression information that can facilitate the understanding of the reproductive biology and improve the egg-laying performance of Huoyan geese. SUPPLEMENTARY DATA Supplementary data are available at Poultry Science online. Supplemental Table S1. All identified proteins in the ovary between the pre-laying period and the laying period by iTRAQ analysis. Supplemental Table S2. Raw data of peptide quantification of the ovary between the pre-laying period and the laying period. Supplemental Table S3. Significance analysis of all quantified proteins in the ovary between the pre-laying period and the laying period. Supplemental Table S4. Significant differentially abundant proteins in the ovary between the pre-laying period and the laying period. Supplemental Table S5. GO analysis results for differentially abundant proteins in the ovary. Supplemental Table S6. KEGG analysis results for differentially abundant proteins in the ovary. ACKNOWLEDGMENTS We would like to thank the staff of the Liaoning Huoyan Goose Stock Breeding Farm for assisting in the collection of ovarian samples. This study was supported by the National Natural Science Foundation of China (Grant No. 31372395). REFERENCES Ashburner M., Ball C. A., Blake J. A., Botstein D., Butler H., Cherry J. M., Davis A. P., Dolinski K., Dwight S. S., Eppig J. T., Harris M. A., Hill D. P., Issel-Tarver L., Kasarskis A., Lewis S., Matese J. C., Richardson J. E., Ringwald M., Rubin G. M., Sherlock G.. 2000. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet.  25: 25– 29. Google Scholar CrossRef Search ADS PubMed  Blomhoff R., Blomhoff H. K.. 2006. Overview of retinoid metabolism and function. J. Neurobiol.  66: 606– 630. Google Scholar CrossRef Search ADS PubMed  Bujo H., Hermann M., Lindstedt K. A., Nimpf J., Schneider W. J.. 1997. Low density lipoprotein receptor gene family members mediate yolk deposition. J. Nutr.  127: 801S– 804S. Google Scholar CrossRef Search ADS PubMed  Cao Z., Li J., Luo L., Li X., Liu M., Gao M., Yin Y., Luan X.. 2015. Molecular cloning and expression analysis of adiponectin and its receptors (AdipoR1 and AdipoR2) in the hypothalamus of the Huoyan goose during different stages of the egg-laying cycle. Reprod. Biol Endocrinol.  13: 87. Google Scholar CrossRef Search ADS PubMed  Capony F., Williams D. L.. 1980. Apolipoprotein B of avian very low density lipoprotein: Characteristics of its regulation in nonstimulated and estrogen-stimulated rooster. Biochemistry.  19: 2219– 2226. Google Scholar CrossRef Search ADS PubMed  Chen G.-H., Wang K.-H., Wang J.-Y., Ding C., Yang N.. 2004. Poultry genetic resources in China . Shanghai Scientific and Technological Press, Shanghai, China. Chen Y., Fu X., Mei X., Zhou Y., Cheng S., Zeng L., Dong F., Yang Z.. 2017. Proteolysis of chloroplast proteins is responsible for accumulation of free amino acids in dark-treated tea (Camellia sinensis) leaves. J. Proteomics.  157: 10– 17. Google Scholar CrossRef Search ADS PubMed  Close B., Banister K., Baumans V., Bernoth E. M., Bromage N., Bunyan J., Erhardt W., Flecknell P., Gregory N., Hackbarth H., Morton D., Warwick C.. 1997. Recommendations for euthanasia of experimental animals: Part 2. DGXT of the European Commission. Lab. Anim.  31: 1– 32. Google Scholar CrossRef Search ADS PubMed  Ding S. T., Yen C. F., Wang P. H., Lin H. W., Hsu J. C., Shen T. F.. 2007. The differential expression of hepatic genes between prelaying and laying geese. Poult. Sci.  86: 1206– 1212. Google Scholar CrossRef Search ADS PubMed  Egbert J. R., Jackson M. F., Rodgers B. D., Schwabl H.. 2013. Between-female variation in house sparrow yolk testosterone concentration is negatively associated with CYP19A1 (aromatase) mRNA expression in ovarian follicles. Gen. Comp. Endocrinol.  183: 53– 62. Google Scholar CrossRef Search ADS PubMed  Emsley A. 1997. Integration of classical and molecular approaches of genetic selection: Egg production. Poult Sci.  76: 1127– 1130. Google Scholar CrossRef Search ADS PubMed  Esteves A., Ehrlich R.. 2006. Invertebrate intracellular fatty acid binding proteins. Comp. Biochem. Physiol. C Toxicol. Pharmacol.  142: 262– 274. Google Scholar CrossRef Search ADS PubMed  Gao G., Li Q., Zhao X., Ding N., Han Q., Su J., Wang Q.. 2015. Transcriptome profiling of the hypothalamus during prelaying and laying periods in Sichuan white geese (Anser cygnoides). Anim. Sci. J.  86: 800– 805. Google Scholar CrossRef Search ADS PubMed  Gong Y. N., Li W. W., Sun J. L., Ren F., He L., Jiang H., Wang Q.. 2010. Molecular cloning and tissue expression of the fatty acid-binding protein (Es-FABP) gene in female Chinese mitten crab (Eriocheir sinensis). BMC Mol. Biol.  11: 71. Google Scholar CrossRef Search ADS PubMed  Gotz S., Garcia-Gomez J. M., Terol J., Williams T. D., Nagaraj S. H., Nueda M. J., Robles M., Talon M., Dopazo J., Conesa A.. 2008. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic. Acids. Res.  36: 3420– 3435. Google Scholar CrossRef Search ADS PubMed  Grant S. G., Blackstock W. P.. 2001. Proteomics in neuroscience: From protein to network. J. Neurosci.  21: 8315– 8318. Google Scholar CrossRef Search ADS PubMed  Gygi S. P., Corthals G. L., Zhang Y., Rochon Y., Aebersold R.. 2000. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc. Natl. Acad. Sci. U S A.  97: 9390– 9395. Google Scholar CrossRef Search ADS PubMed  Habig C., Geffers R., Distl O.. 2012. Differential gene expression from genome-wide microarray analyses distinguishes Lohmann Selected Leghorn and Lohmann Brown layers. PLoS One.  7: e46787. Google Scholar CrossRef Search ADS PubMed  He J., Tian Y., Li J. J., Shen J. D., Tao Z. R., Fu Y., Niu D., Lu L. Z.. 2012. Expression pattern of adipocyte fatty acid-binding protein gene in different tissues and its regulation of genes related to adipocyte differentiation in duck. Poult. Sci.  91: 2270– 2274. Google Scholar CrossRef Search ADS PubMed  Hermier D. 1997. Lipoprotein metabolism and fattening in poultry. J. Nutr.  127: 805S– 808S. Google Scholar CrossRef Search ADS PubMed  Huang S. Y., Lin J. H., Chen Y. H., Chuang C. K., Chiu Y. F., Chen M. Y., Chen H. H., Lee W. C.. 2006. Analysis of chicken serum proteome and differential protein expression during development in single-comb White Leghorn hens. Proteomics.  6: 2217– 2224. Google Scholar CrossRef Search ADS PubMed  Iseki S., Amano O., Fujii H., Kanda T., Ono T.. 1995. Immunohiostochemical localization of two types of fatty acid‐binding proteins in rat ovaries during postnatal development and in immature rat ovaries treated with gonadotropins. The Anatomical Record.  241: 235– 243. Google Scholar CrossRef Search ADS PubMed  Kameshita I., Ishida A., Fujisawa H.. 1998. Analysis of protein-protein interaction by two-dimensional affinity electrophoresis. Anal. Biochem.  262: 90– 92. Google Scholar CrossRef Search ADS PubMed  Kanehisa M., Goto S., Sato Y., Furumichi M., Tanabe M.. 2012. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic. Acids Res.  40: D109– D114. Google Scholar CrossRef Search ADS PubMed  Kang B., Guo J. R., Yang H. M., Zhou R. J., Liu J. X., Li S. Z., Dong C. Y.. 2009. Differential expression profiling of ovarian genes in prelaying and laying geese. Poult. Sci.  88: 1975– 1983. Google Scholar CrossRef Search ADS PubMed  Karp N. A., Lilley K. S.. 2009. Investigating sample pooling strategies for DIGE experiments to address biological variability. Proteomics.  9: 388– 397. Google Scholar CrossRef Search ADS PubMed  Kim N. S., Shen Y. N., Kim T. Y., Byun S. J., Jeon I. S., Kim S. H.. 2007. Expression of AGR-2 in chicken oviduct during laying period. J. Biochem. Mol. Biol.  40: 212– 217. Google Scholar PubMed  Kirchgessner T. G., Heinzmann C., Svenson K. L., Gordon D. A., Nicosia M., Lebherz H. G., Lusis A. J., Williams D. L.. 1987. Regulation of chicken apolipoprotein B: Cloning, tissue distribution, and estrogen induction of mRNA. Gene.  59: 241– 251. Google Scholar CrossRef Search ADS PubMed  Ko Y. H., Cheng C. H., Shen T. F., Ding S. T.. 2004. Cloning and expression of Tsaiya duck liver fatty acid binding protein. Poult. Sci.  83: 1832– 1838. Google Scholar CrossRef Search ADS PubMed  Kuo Y. M., Shiue Y. L., Chen C. F., Tang P. C., Lee Y. P.. 2005. Proteomic analysis of hypothalamic proteins of high and low egg production strains of chickens. Theriogenology.  64: 1490– 1502. Google Scholar CrossRef Search ADS PubMed  Levi L., Levavi-Sivan B., Lubzens E.. 2008. Expression of genes associated with retinoid metabolism in the trout ovarian follicle. Biol. Reprod.  79: 570– 577. Google Scholar CrossRef Search ADS PubMed  Lewis T. S., Hunt J. B., Aveline L. D., Jonscher K. R., Louie D. F., Yeh J. M., Nahreini T. S., Resing K. A., Ahn N. G.. 2000. Identification of novel MAP kinase pathway signaling targets by functional proteomics and mass spectrometry. Mol. Cell.  6: 1343– 1354. Google Scholar CrossRef Search ADS PubMed  Li J., Leghari I. H., He B., Zeng W., Mi Y., Zhang C.. 2014. Estrogen stimulates expression of chicken hepatic vitellogenin II and very low-density apolipoprotein II through ER-alpha. Theriogenology.  82: 517– 524. Google Scholar CrossRef Search ADS PubMed  Lin H., Wang L. F., Song J. L., Xie Y. M., Yang Q. M.. 2002. Effect of dietary supplemental levels of vitamin A on the egg production and immune responses of heat-stressed laying hens. Poult. Sci.  81: 458– 465. Google Scholar CrossRef Search ADS PubMed  Luan X., Cao Z., Li R., Liu M., Hu J.. 2014a. Differential expression profiling of hypothalamus genes in laying period and ceased period Huoyan geese. Mol. Biol Rep.  41: 3401– 3411. Google Scholar CrossRef Search ADS   Luan X., Cao Z., Xu W., Gao M., Wang L., Zhang S.. 2013. Gene expression profiling in the pituitary gland of laying period and ceased period Huoyan geese. Asian-Australasian Journal of Animal Sciences (AJAS).  26: 921– 929. Google Scholar CrossRef Search ADS   Luan X., Liu D., Cao Z., Luo L., Liu M., Gao M., Zhang X.. 2014b. Transcriptome profiling identifies differentially expressed genes in Huoyan goose ovaries between the laying period and ceased period. PLoS One.  9: e113211. Google Scholar CrossRef Search ADS   Luche S., Santoni V., Rabilloud T.. 2003. Evaluation of nonionic and zwitterionic detergents as membrane protein solubilizers in two-dimensional electrophoresis. Proteomics.  3: 249– 253. Google Scholar CrossRef Search ADS PubMed  Moriya Y., Itoh M., Okuda S., Yoshizawa A. C., Kanehisa M.. 2007. KAAS: An automatic genome annotation and pathway reconstruction server. Nucleic Acids Res.  35: W182– W185. Google Scholar CrossRef Search ADS PubMed  Nelson L. R., Bulun S. E.. 2001. Estrogen production and action. J. Am. Acad. Dermatol.  45: S116– S124. Google Scholar CrossRef Search ADS PubMed  Nepomuceno A. I., Muddiman D. C., Petitte J. N.. 2015. Global proteomic analysis of functional compartments in immature avian follicles using laser microdissection coupled to LC-MS/MS. J. Proteome. Res.  14: 3912– 3923. Google Scholar CrossRef Search ADS PubMed  Newcomer M. E. 1995. Retinoid-binding proteins: structural determinants important for function. FASEB J.  9: 229– 239. Google Scholar CrossRef Search ADS PubMed  Noble R. C., Cocchi M.. 1990. Lipid metabolism and the neonatal chicken. Prog. Lipid. Res.  29: 107– 140. Google Scholar CrossRef Search ADS PubMed  Padmanabhan V., Karsch F. J., Lee J. S.. 2002. Hypothalamic, pituitary and gonadal regulation of FSH. Reprod. Suppl.  59: 67– 82. Google Scholar PubMed  Pu Y. Z., Wan Q. L., Ding A. J., Luo H. R., Wu G. S.. 2017. Quantitative proteomics analysis of Caenorhabditis elegans upon germ cell loss. J. Proteomics.  156: 85– 93. Google Scholar CrossRef Search ADS PubMed  Quevillon E., Silventoinen V., Pillai S., Harte N., Mulder N., Apweiler R., Lopez R.. 2005. InterProScan: Protein domains identifier. Nucleic Acids Res.  33: W116– W120. Google Scholar CrossRef Search ADS PubMed  Rothschild M. F., Messer L., Day A., Wales R., Short T., Southwood O., Plastow G.. 2000. Investigation of the retinol-binding protein 4 (RBP4) gene as a candidate gene for increased litter size in pigs. Mamm. Genome.  11: 75– 77. Google Scholar CrossRef Search ADS PubMed  Sandberg A., Lindell G., Kallstrom B. N., Branca R. M., Danielsson K. G., Dahlberg M., Larson B., Forshed J., Lehtio J.. 2012. Tumor proteomics by multivariate analysis on individual pathway data for characterization of vulvar cancer phenotypes. Mol. Cell. Proteomics.  11: M112 016998. Google Scholar CrossRef Search ADS PubMed  Schneider W. J., Carroll R., Severson D. L., Nimpf J.. 1990. Apolipoprotein VLDL-II inhibits lipolysis of triglyceride-rich lipoproteins in the laying hen. J. Lipid. Res.  31: 507– 513. Google Scholar PubMed  Sechman A., Antos P., Katarzynska D., Grzegorzewska A., Wojtysiak D., Hrabia A.. 2014. Effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin on secretion of steroids and STAR, HSD3B and CYP19A1 mRNA expression in chicken ovarian follicles. Toxicol. Lett.  225: 264– 274. Senior B. E. 1974. Oestradiol concentration in the peripheral plasma of the domestic hen from 7 weeks of age until the time of sexual maturity. J. Reprod. Fertil.  41: 107– 112. Google Scholar CrossRef Search ADS PubMed  Shiue Y. L., Chen L. R., Chen C. F., Chen Y. L., Ju J. P., Chao C. H., Lin Y. P., Kuo Y. M., Tang P. C., Lee Y. P.. 2006. Identification of transcripts related to high egg production in the chicken hypothalamus and pituitary gland. Theriogenology.  66: 1274– 1283. Google Scholar CrossRef Search ADS PubMed  State Science and Technology Commission in China. 1988. Regulation for the Administration of Affairs Concerning Experimental Animals. The Ministry of Science and Technology of the People's Republic of China, Beijing. Storch J., Veerkamp J. H., Hsu K. T.. 2002. Similar mechanisms of fatty acid transfer from human anal rodent fatty acid-binding proteins to membranes: Liver, intestine, heart muscle, and adipose tissue FABPs. Mol. Cell. Biochem.  239: 25– 33. Google Scholar CrossRef Search ADS PubMed  Unwin R. D., Griffiths J. R., Whetton A. D.. 2010. Simultaneous analysis of relative protein expression levels across multiple samples using iTRAQ isobaric tags with 2D nano LC-MS/MS. Nat. Protoc.  5: 1574– 1582. Google Scholar CrossRef Search ADS PubMed  Wisniewski J. R., Zougman A., Nagaraj N., Mann M.. 2009. Universal sample preparation method for proteome analysis. Nat. Methods.  6: 359– 362. Google Scholar CrossRef Search ADS PubMed  Wu J., Fu W., Huang Y., Ni Y., Zhao R.. 2013. Kisspeptin-10 enhanced egg production in quails associated with the increase of triglyceride synthesis in liver. Asian-Australas J. Anim. Sci.  26: 1080– 1088. Google Scholar CrossRef Search ADS PubMed  Yang S., Suh Y., Choi Y. M., Shin S., Han J. Y., Lee K.. 2013. Loss of fat with increased adipose triglyceride lipase-mediated lipolysis in adipose tissue during laying stages in quail. Lipids.  48: 13– 21. Google Scholar CrossRef Search ADS PubMed  Yen C. F., Jiang Y. N., Shen T. F., Wong I. M., Chen C. C., Chen K. C., Chang W. C., Tsao Y. K., Ding S. T.. 2005. Cloning and expression of the genes associated with lipid metabolism in Tsaiya ducks. Poult. Sci.  84: 67– 74. Google Scholar CrossRef Search ADS PubMed  Yen C. F., Lin H. W., Hsu J. C., Lin C., Shen T. F., Ding S. T.. 2006. The expression of pituitary gland genes in laying geese. Poult. Sci.  85: 2265– 2269. Google Scholar CrossRef Search ADS PubMed  Yin H. D., Gilbert E. R., Chen S. Y., Li D. Y., Zhang Z. C., Wang Y., Liu Y. P., Zhu Q.. 2013. Ontogenic expression pattern and genetic polymorphisms of the retinol-binding protein 4 (RBP4) gene in Erlang mountainous chickens. Gene.  526: 170– 175. Google Scholar CrossRef Search ADS PubMed  Zhu G., Mao Y., Zhou W., Jiang Y.. 2015. Dynamic changes in the follicular transcriptome and promoter DNA methylation pattern of steroidogenic genes in chicken follicles throughout the ovulation cycle. PLoS One.  10: e0146028. Google Scholar CrossRef Search ADS PubMed  Zieske L. R. 2006. A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies. J. Exp. Bot.  57: 1501– 1508. Google Scholar CrossRef Search ADS PubMed  © 2018 Poultry Science Association Inc. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Poultry Science Oxford University Press

Comparative proteomic analysis of ovaries from Huoyan geese between pre-laying and laying periods using an iTRAQ-based approach

Poultry Science , Volume Advance Article (6) – Mar 1, 2018

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© 2018 Poultry Science Association Inc.
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0032-5791
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Abstract

ABSTRACT To explore the key regulatory genes and their translated products involved in Huoyan goose egg laying, we first applied the iTRAQ technology integrated with LC-MS/MS analysis to identify differentially abundant proteins in the ovarian tissue of Huoyan geese between the pre-laying and laying periods. Then, Gene Ontology (GO) enrichment and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses of the proteins were conducted. As observed, 403 proteins, which included 255 up-regulated and 148 down-regulated proteins, were identified. Some differentially abundant proteins, such as apolipoprotein B (apoB), retinol-binding protein 4 (RBP4), vitellogenin-2 (VTG-2), apolipoprotein II (apoVLDL-II), and fatty-acid binding protein 4 (FABP4), were found to be involved in lipid transportation and metabolic process, follicle development and final egg production, ovarian steroidogenesis, and steroid hormone biosynthesis. Subsequently, the modification of the abundance of 3 proteins, adiponectin, FABP4, and RBP4, was verified using Western blotting. Our findings might provide comprehensive protein expression information that can facilitate the understanding of the reproductive biology and improve the egg-laying performance of Huoyan geese. INTRODUCTION The Liaoning Huoyan goose is considered a national treasure by the Chinese goose industry and was listed as one of the nationally protected domestic animals by the Chinese government (Chen et al., 2004). Due to the lack of systematic breeding programs to improve egg productivity, the egg production rate of this breed has gradually decreased. Laying performance in poultry is closely related to the development of ovarian follicles and the establishment of the follicle hierarchy. Identifying differences in abundance of proteins between distinct follicular stages could help us to understand follicular development and investigate the molecular mechanisms underlying the reproductive biology of Huoyan geese. The ovary performs numerous roles critical for oocyte development and ovulation (Nelson and Bulun, 2001; Padmanabhan et al., 2002). The number of eggs laid by a bird is determined by the number of follicles destined for ovulation and the capacity of the oviduct to transform the ova into a hard-shelled egg. The dynamic and highly regulated process of follicle development requires the coordinated actions of a great number of genes and their translated products, which is orderly orchestrated at the translational and posttranslational levels. Several studies have reported identified genes associated with the reproductive traits in the HPG (hypothalamus-pituitary-gonadal) of birds using genomic and transcriptome approaches (Shiue et al., 2006; Yen et al., 2006; Kang et al., 2009; Habig et al., 2012; Luan et al., 2013; Luan et al., 2014a; Luan et al., 2014b; Gao et al., 2015). Selecting for an individual gene in birds is an effective alternative for improving economically important traits, but this approach may not guarantee success if multiple factors and their dynamic interactions are involved in the phenotypic expression of the target trait (Emsley, 1997). Proteomics offers a new platform for studies of complex biological functions involving large numbers and networks of proteins (Grant and Blackstock, 2001) and allows us to obtain substantial information regarding protein posttranslational modification, proteolytic processing, and mRNA alternative splicing, which could not be achieved by DNA sequence analysis alone (Lewis et al., 2000). To improve egg production, the selection of birds for increased egg number or laying rate using proteomic approaches has become a possible alternative. To date, several research groups have reported the application of conventional proteomics approaches in the study of bird reproduction. Kuo et al. analyzed the abundance of hypothalamic proteins between high and low egg-producing strains of chickens and found differences in the abundance between both groups, revealing that protein levels may serve as molecular markers to select for egg productivity (Kuo et al., 2005). Huang et al. investigated serum protein profiles during the development of chickens and found that the levels of 13 proteins differed during the developmental stages (Huang et al., 2006). In addition, Nam Soo Kim et al. used the 2-DE (two-dimensional gel electrophoresis) method to show that anterior gradient-2 (AGR-2) may be an oviduct-specific protein involved in egg formation and epithelial cell differentiation during the egg-laying period of hens (Kim et al., 2007). The results of these studies imply that protein levels may serve as valuable parameters to improve egg production. In general, 2-DE provides a visual representation of the proteome, in which distinct protein isoforms resulting from the changes in Mr and/or pI can be observed. However, 2-DE suffers from low throughput non-quantitative information, coupled with difficulties in separating and/or detecting low-abundance proteins and post-translationally modified proteins (Gygi et al., 2000; Luche et al., 2003), as well as those proteins with a pI value lower than 4 or higher than 9 (Kameshita et al., 1998). Recently, a new technique known as iTRAQ followed by LC-MS/MS has been applied for proteomic quantitation. This method overcomes some of the limitations of the other conventional proteomics techniques and improves the throughput of proteomic studies. This is one of the most highly sensitive proteomic technologies because it can detect and quantitatively analyze low-abundance proteins in complex biological samples (Zieske, 2006). As from the bibliography, no information is available with respect to the proteomic analysis of geese ovaries during different egg-laying stages. To explore the key regulatory genes and their translated products involved in goose egg laying, we applied the iTRAQ technology integrated with LC-MS/MS analysis to identify differentially abundant proteins in the ovarian tissue of Huoyan geese between the pre-laying and laying periods. The possible biological significances of these differentially expressed proteins were further evaluated using various bioinformatics programs. Several selected differentially abundant proteins were then validated using Western blotting. MATERIALS AND METHODS Ethics Statement Experimental procedures were approved by the animal welfare committee of the College of Animal Science and Veterinary Medicine of Shenyang Agricultural University (No. 2,013,025) and performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals (State Science and Technology Commission in China, 1988) and EU Directive 2010/63/EU for animal experiments. All of the surgery was performed according to recommendations proposed by the European Commission (Close, et al., 1997), and all efforts were made to minimize the suffering of the animals. Animal and Tissue Collection The Huoyan geese were selected and raised on the Liaoning Huoyan Goose Stock Breeding Farm according to the farm program. During the experiment, geese were fed ad libitum with rice grain and supplemented with green grass or water plants whenever possible. Feed was provided during the daytime when the geese were released into an open area outside the building. Huoyan geese become sexually mature at approximately 7 mo of age and reach the peak egg-laying stage in the following year. In the current study, goslings were purchased in the fall of the yr and became sexually mature during the summer of the following year. Nine pre-laying-period geese were sampled at 6 mo of age, and 9 laying-period geese were sampled at 12 mo of age. The health and egg-laying status of the birds were monitored daily, and only reproductively active geese were chosen for the study. Geese were killed by exsanguination to obtain ovary samples. All of the samples were quickly dissected, frozen in liquid nitrogen, and stored at −80°C until protein was prepared. Protein Preparation All ovarian tissues were homogenized in 0.5 ml STD lysis buffer (4% SDS, 150 mM Tris–HCl, 1 mM DTT, pH 8.0, and protease inhibitor). Then, the samples were sonicated and boiled at 100°C for 15 min, followed by centrifugation at 14,000 × g for 45 min at 25°C. The supernatant was precipitated overnight with cold acetone. After discarding the acetone and air drying, the resulting pellet was dissolved in 30 μl STD lysis buffer. Protein concentrations were determined using the BCA (Bicinchoninic Acid) protein assay reagent (Beyotime Institute of Biotechnology, Shanghai, China). Protein samples were stored at −80°C until needed. Protein Digestion and iTRAQ Labeling Protein digestion was performed according to the procedure described by Wisniewski et al. (Wisniewski et al., 2009). To reduce overall variability by minimizing individual heterogeneity (Karp and Lilley, 2009), the protein samples from 3 randomly selected individuals within each group were pooled in equal amounts to generate one biological sample pool, and there were 3 biological protein pools for both the pre-laying group and the laying group. Protein (300 μg) from 3 equally pooled biological replicates was diluted with 200 μl UA buffer (8 M Urea and 150 mM Tris-HCl, pH 8.0) and loaded onto an ultrafiltration filter (30 kDa cut-off, Sartorius, Germany). Samples were centrifuged at 14,000 × g for 15 min; 200 μl UA buffer was then added, followed by centrifugation for an additional 15 minutes. After discarding the supernatant, 100 μl of 50 mM iodoacetamide in UA buffer was subsequently added to the filter with oscillation at 600 rpm for 1 minute. The samples were incubated for 30 min in darkness and then centrifuged at 14,000 × g for 10 min. The filters were washed twice with 100 μl UA buffer, and 100 μl dissolution buffer (50 mM triethylammonium bicarbonate at pH 8.5) were added to the filters, followed by centrifugation for 10 minutes. This step was repeated twice, and 40 μl trypsin buffer (2 μg trypsin in 40 μl dissolution buffer) were then added to each filter. The samples were oscillated at 600 rpm for 1 min and incubated at 37°C for 16 to 18 hours. Finally, the filter unit was transferred to a new tube and centrifuged at 14,000 × g for 10 minutes. The resulting peptides were collected as a filtrate, and the peptide concentration was analyzed at OD280 (Wisniewski et al., 2009). The resulting peptide mixture was labeled using the 8-plex iTRAQ reagent according to the manufacturer's instructions (Applied Biosystems, Foster City, CA). Three samples from the pre-laying group were labeled with mass 113, 116, and 117 isobaric iTRAQ tags, while the other 3 samples from the laying group were labeled with mass 114, 115, and 118 isobaric iTRAQ tags. The labeling solution reaction was then incubated at room temperature for 1 h prior to further analysis. Peptide Fractionation with Strong Cation Exchange Chromatography The iTRAQ-labeled peptides were subjected to strong cation exchange (SCX) fractionation in an AKTA Purifier 100 (GE Healthcare, Uppsala, Sweden). The dried peptide mixture was reconstituted and acidified with 2 mL buffer A (10 mM KH2PO4 in 25% of ACN, pH 3.0) and loaded onto a polysulphoethyl (PolyLC Inc., Columbia, Maryland) column (4.6 mm × 100 mm, 5 μm, 200 Å). The peptides were eluted at a flow rate of 1 mL/min with a gradient of 100% Buffer A for 25 min, 0 to 10% Buffer B (500 mM KCl, 10 mM KH2PO4 in 25% of ACN, pH 3.0) for 7 min, 10 to 20% Buffer B for 10 min, 20 to 45% Buffer B for 5 min, 45 to 100% Buffer B for 5 min, 100% Buffer B for 8 min, and finally 100% Buffer A for 15 minutes. The elution process was monitored by absorbance at 214 nm, and fractions were collected every 1 minute. The collected fractions (approximately 30) were finally combined into 6 pools and desalted on C18 cartridges (Sigma, Steinheim, Germany). Each fraction was concentrated via vacuum centrifugation and reconstituted in 40 μl of 0.1% (v/v) trifluoroacetic acid. All samples were stored at –80°C until LC-MS/MS analysis. LC-MS/MS The iTRAQ-labeled samples were analyzed using an Easy-nLC nanoflow HPLC system connected to a Q-Exactive mass spectrometer (Thermo Fisher, San Jose, CA). A total of 5 μg of each sample was loaded onto a Thermo Scientific EASY column (2 cm × 100 μm, 5 μm-C18) using an auto sampler at a flow rate of 250 nl/min. The peptides were separated on a Thermo Scientific EASY column (100 mm × 75 μm, 3 μm-C18) using a segmented 2-h gradient from Solvent A (0.1% formic acid in water) to 35% Solvent B (84% acetonitrile in 0.1% formic acid) for 100 min, followed by 35 to 100% Solvent B for 8 min and then 100% Solvent B for 12 minutes. The column was re-equilibrated to its initial highly aqueous solvent composition before each analysis. The peptides were subjected to the Q-Exactive mass spectrometer. MS data were acquired using a data-dependent top 10 method, dynamically choosing the most abundant precursor ions from the survey scan (300 to 1,800 m/z) for subsequent high-energy collisional dissociation (HCD) fragmentation in the positive ion mode. The determination of the target value is based on predictive automatic gain control (pAGC). Dynamic exclusion was used with a 40.0-second duration. The resolving power of the MS scan and the MS/MS scan at 200 m/z were set at 70,000 and 17,500, respectively. The top 10 most intense signals in the acquired MS spectra were selected for further MS/MS analysis. The isolation window was 2 m/z, the normalized collision energy was 30 eV, and the underfill ratio, which specifies the minimum percentage of the target value likely to be reached at maximum fill time, was defined as 0.1%. The maximum ion injection times were set at 10 ms for the survey scan and 60 ms for the MS/MS scans, and the automatic gain control target values for both scan modes were set to 3.0 × 10−6. The instrument was run with peptide recognition mode enabled. Sequence Database Searching and Data Analysis The MASCOT 2.2 (Matrix Science, London, UK) and Proteome Discoverer 1.4 software (Thermo Scientific, San Jose, CA) were used for identification and quantitative analysis. The raw files were analyzed using the Proteome Discoverer software. Protein identifications were performed using the MASCOT search engine embedded into Proteome Discoverer, searching against the Uniprot database of Anatidae protein sequences (08–15-2014, 34,815 entries, downloaded from http://www.uniprot.org/). Search parameters were set as follows: monoisotopic mass, trypsin as the cleavage enzyme, 2 missed cleavages, peptide mass tolerance at ±20 ppm, and MS/MS tolerance at 0.1 Da. Variable modifications were defined as the oxidation of methionine and iTRAQ 8-plex labeled tyrosine, while lysine and the N-term of peptides labeled by iTRAQ 8-plex and carbamidomethylation on cysteine were specified as fixed modifications, with the Decoy database pattern = Reverse. The results were filtered based on the false discovery rate (FDR), and the score threshold for peptide identification was set at a 1% FDR (Chen et al., 2017; Pu et al., 2017). All reported data were based on 99% confidence for protein identification as determined by an FDR of ≤ 1%. FDR = N(decoy)*2/((N(decoy)+ N(target)) (Sandberg et al., 2012). The relative quantitative analysis of the proteins in the samples based on the ratios of iTRAQ reporter ions from all unique peptides representing each protein was performed using Proteome Discoverer. The relative peak intensities of the iTRAQ reporter ions released in each of the MS/MS spectra were used. The sum of the intensities in all channels was employed as a reference for calculating the iTRAQ ratios of all reporter ions. The iTRAQ ratio of every group of proteins was obtained by using the intensity of each channel normalized to the reference (Unwin et al., 2010). For statistical analysis, Student's t test was employed to identify significant changes between the pre-laying and laying group samples. The FDR value was further computed by using the p.adjust function in R (version 3.1.3, R Foundation for Statistical Computing, http://www.r-project.org). Proteins with a statistically significant iTRAQ ratio of > 1.2 or < 0.83 (FDR < 0.05) were considered differentially abundant proteins (Chen et al., 2017; Pu et al., 2017). Bioinformatics Analysis The sequence data of the selected differentially abundant proteins were retrieved in batches in FASTA format from the UniProtKB database (Release 2015_03). The retrieved sequences were locally searched against the non-redundant protein database (NCBI nr) using the NCBI BLAST+ client software (ncbi-blast-2.2.28 + -win32.exe) to find homologous sequences from which the functional annotation was transferred to the studied sequences. In this study, the top 10 blast hits with an E-value of ≤ 1e - 3 for each query sequence were retrieved and loaded into Blast2GO (Version 2.8.0) for Gene Ontology (GO) mapping and annotation (Gotz et al., 2008). Un-annotated sequences were then re-annotated with more permissive parameters. The sequences without BLAST hits and the un-annotated sequences were then selected to go through InterProScan (Quevillon et al., 2005) against EBI databases to retrieve functional annotations of protein motifs and merged the InterProScan GO terms to the annotation set. The GO mapping described the roles of proteins according to the following 3 domains: biological process, molecular function, and cellular component (Ashburner et al., 2000). Following annotation and annotation augmentation steps, the studied proteins were blasted against Kyoto Encyclopaedia of Genes and Genomes (KEGG) genes (Birds) to retrieve their KEGG orthology identifications and were subsequently mapped to pathways in the KEGG (http://www.genome.jp/kegg/) using the online KEGG Automatic Annotation Server (KAAS) (http://www.genome.jp/kegg/kaas/) (Moriya et al., 2007; Kanehisa et al., 2012). Western Blot Analysis The protein samples of ovarian tissues from the pre-laying and laying groups were extracted and identified using kits according to the manufacturer's instructions (Applygen Co., LTD, Beijing, China). Equivalent amounts of total protein were subjected to 12% SDS-PAGE and then transferred to a nitrocellulose membrane. After blocking with 5% skim milk in PBS containing 0.1% Tween 20 (PBST) at 4°C overnight, the membranes were incubated separately with rabbit anti-adiponectin antibody (bs-0471R, Beijing Biosynthesis Biotechnology Co., LTD, Beijing, China), rabbit anti-adipocyte fatty acid-binding protein (FABP4) antibody (12,802–1-AP, Proteintech Group, Inc. Chicago, IL), and rabbit anti-Retinol-binding protein 4 (RBP4) antibody (11,774–1-Ap, Proteintech Group, Inc. Chicago, IL) overnight at 4°C. The membranes were subsequently incubated with goat anti-rabbit IgG/HRP antibody (bs-0295G-HRP, Beijing Biosynthesis Biotechnology Co., LTD, Beijing, China) for 1 h at 37°C. Finally, the bands were captured using a MicroChemi4.2 imaging system (DNR Bio-imaging Systems, Jerusalem, Israel), and densitometry analysis of the protein bands was performed using GelQuant software (DNR Bio-imaging Systems, Jerusalem, Israel). GAPDH (sc-20,357, Santa Cruz Biotechnology Inc., Santa Cruz, CA) was used as a reference protein to ensure equal loading. Triplicate experiments were performed for each sample. All data were analyzed using SPSS 16.0 for Windows (SPSS Inc. Chicago, IL). The data were analyzed by Student's t test. The results are expressed as the mean ± SEM. P < 0.05 was considered statistically significant. RESULTS Global Profiling of the Proteins in Ovarian Tissue We employed the iTRAQ labeling technology in combination with LC-MS/MS to investigate differentially abundant proteins in the ovarian tissue of Huoyan geese between the pre-laying period and laying period. A schematic workflow illustrating the steps applied in this study is shown in Figure 1. A total of 4,281 proteins was identified according to the standard of protein identification (Supplemental Tables S1 and S2), and 4,262 of those were quantified (Supplemental Table S3). Of these quantified proteins, 36.67% (1,570) of the proteins were inferred from one peptide, and 45.41% (1,944) were proteins with more than 3 unique peptides (Figure 2). The molecular weight and the predicted pI of the identified proteins were in the ranges of 1.90 to 3750.2 kDa and 3.97 to 12.26, respectively. The mass spectrometry proteomics data have been deposited to the Proteome Xchange Consortium via the PRIDE partner repository with the dataset identifier PXD004735 (username: reviewer22912@ebi.ac.uk; password: N1EOAwwE). Figure 1. View largeDownload slide Experimental design and schematic diagram of the workflow used in this study. Figure 1. View largeDownload slide Experimental design and schematic diagram of the workflow used in this study. Figure 2. View largeDownload slide Unique peptides for the identified proteins. The number of proteins in each category is presented at the top of each bar. Figure 2. View largeDownload slide Unique peptides for the identified proteins. The number of proteins in each category is presented at the top of each bar. Differentially Abundant Proteins Between the Pre-laying Period and Laying Period To identify differentially abundant proteins between the pre-laying period and the laying period, the ratios of the iTRAQ reporter ions between the pre-laying and laying samples were determined. Student's t test and the FDR value corresponding to each P-value of differentially abundant proteins were computed. The differentially abundant proteins were selected according to the following criteria: FDR < 0.05 and a iTRAQ ratio of > 1.2 or < 0.83. As listed in Supplemental Table S4, 403 proteins (255 up-regulated and 148 down-regulated) were extracted. Bioinformatics Analysis of Differentially Abundant Proteins The Gene Ontology database is an internationally standardized gene functional classification system that comprehensively describes the characteristics of different genes and their products. To better understand the differentially abundant proteins, Gene Ontology category enrichment analysis was conducted using all 403 identified proteins. A total of 354 differentially abundant proteins were annotated to 1,547 GO function entries. Second-level GO terms were applied to classify the proteins in terms of their involvement in 3 main categories (cellular component, molecular function, and biological process), and each protein was assigned to at least one term. As summarized in Supplemental Table S5, in the cellular component category (Figure 3), the differentially abundant proteins were mainly distributed in the cell, organelles, extracellular region, and membrane. In the molecular function category (Figure 4), the differentially abundant proteins were mostly related to binding, such as catalytic activity, transporter activity, structural molecule activity, and enzyme regulator activity. In the biological process category (Figure 5), the differentially abundant proteins were mainly associated with the cellular process, the single-organism process, the metabolic process, and biological regulation. According to these GO classifications, some proteins such as apoB, RBP4, vitellogenin-2 (VTG II), apolipoprotein II (apoVLDL-II), and FBP4 were found to be involved in reproduction and the reproductive process, lipid transport, and the metabolic process (Table 1). Particularly, the abundance of these proteins was increased during the egg-laying period compared with the pre-laying period. Figure 3. View largeDownload slide Gene Ontology (GO) cellular component analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 3. View largeDownload slide Gene Ontology (GO) cellular component analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 4. View largeDownload slide Gene Ontology (GO) molecular function analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 4. View largeDownload slide Gene Ontology (GO) molecular function analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 5. View largeDownload slide Gene Ontology (GO) biological process analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Figure 5. View largeDownload slide Gene Ontology (GO) biological process analysis of the differentially abundant proteins in ovarian tissue. All data are presented based on GO second-level terms. Numbers refer to assigned proteins in each category. Table 1. List of some interesting differentially abundant proteins that are involved in important GO biological processes associated with egg-laying regulation. GO biological process  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Regulation of MAPK cascade  Tyrosine-protein kinase Lyn  R0K926  Lyn  4.80    Caveolin  D7RF71    2.61    Uncharacterized protein  U3J1Z5  RAP1A  2.31    Caveolin  D7RF70    1.53    Macrophage migration inhibitory factor  R0JLZ8    1.52  Lipid metabolic process  Uncharacterized protein  U3J6E8  Apo-VLDL-II  3.76    Adipocyte fatty acid-binding protein  F8SKC8  FABP4  2.97    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3J2D6  PLTP  2.36    Uncharacterized protein  U3IWA4  APOB  2.23    Serine incorporator 5  R0M708    2.22    Retinol-binding protein 4  R0K679  RBP4  1.88    Fatty acid-binding protein, epidermal  R0LD44  FABP5  1.82    Apolipoprotein AI  Q9PRR6    1.76    Proactivator polypeptide  R0J9J6    1.73  Lipid transport  Adipocyte fatty acid-binding protein  F8SKC8    2.97    Uncharacterized protein  U3JA11    2.89    Uncharacterized protein  U3I8J0    2.83    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3IWA4  APOB  2.23    Vitellogenin-2  R0JQ97    2.20    Putative phospholipid-transporting ATPase IH  R0LH43  ATP11A  1.96    Epididymal secretory protein E1  R0L315    1.87    Macrophage migration inhibitory factor  R0JLZ8    1.52  Sexual reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Reproductive process  Putative apolipoprotein B  A7WPP0  APOB  2.39    Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Cholesterol transport  Putative apolipoprotein B  A7WPP0  APOB  2.39    Epididymal secretory protein E1  R0L315    1.87  Sex differentiation  Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88  Female genitalia development  Retinol-binding protein 4  R0K679  RBP4  1.88  Down-regulated proteins  Lipid transport  Uncharacterized protein  U3ITT6    0.66  Lipid metabolic process  85 kDa calcium-independent phospholipase A2  R0LQB6    0.65    Synaptic glycoprotein SC2  R0JFF3    0.60  GO biological process  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Regulation of MAPK cascade  Tyrosine-protein kinase Lyn  R0K926  Lyn  4.80    Caveolin  D7RF71    2.61    Uncharacterized protein  U3J1Z5  RAP1A  2.31    Caveolin  D7RF70    1.53    Macrophage migration inhibitory factor  R0JLZ8    1.52  Lipid metabolic process  Uncharacterized protein  U3J6E8  Apo-VLDL-II  3.76    Adipocyte fatty acid-binding protein  F8SKC8  FABP4  2.97    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3J2D6  PLTP  2.36    Uncharacterized protein  U3IWA4  APOB  2.23    Serine incorporator 5  R0M708    2.22    Retinol-binding protein 4  R0K679  RBP4  1.88    Fatty acid-binding protein, epidermal  R0LD44  FABP5  1.82    Apolipoprotein AI  Q9PRR6    1.76    Proactivator polypeptide  R0J9J6    1.73  Lipid transport  Adipocyte fatty acid-binding protein  F8SKC8    2.97    Uncharacterized protein  U3JA11    2.89    Uncharacterized protein  U3I8J0    2.83    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3IWA4  APOB  2.23    Vitellogenin-2  R0JQ97    2.20    Putative phospholipid-transporting ATPase IH  R0LH43  ATP11A  1.96    Epididymal secretory protein E1  R0L315    1.87    Macrophage migration inhibitory factor  R0JLZ8    1.52  Sexual reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Reproductive process  Putative apolipoprotein B  A7WPP0  APOB  2.39    Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Cholesterol transport  Putative apolipoprotein B  A7WPP0  APOB  2.39    Epididymal secretory protein E1  R0L315    1.87  Sex differentiation  Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88  Female genitalia development  Retinol-binding protein 4  R0K679  RBP4  1.88  Down-regulated proteins  Lipid transport  Uncharacterized protein  U3ITT6    0.66  Lipid metabolic process  85 kDa calcium-independent phospholipase A2  R0LQB6    0.65    Synaptic glycoprotein SC2  R0JFF3    0.60  View Large Table 1. List of some interesting differentially abundant proteins that are involved in important GO biological processes associated with egg-laying regulation. GO biological process  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Regulation of MAPK cascade  Tyrosine-protein kinase Lyn  R0K926  Lyn  4.80    Caveolin  D7RF71    2.61    Uncharacterized protein  U3J1Z5  RAP1A  2.31    Caveolin  D7RF70    1.53    Macrophage migration inhibitory factor  R0JLZ8    1.52  Lipid metabolic process  Uncharacterized protein  U3J6E8  Apo-VLDL-II  3.76    Adipocyte fatty acid-binding protein  F8SKC8  FABP4  2.97    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3J2D6  PLTP  2.36    Uncharacterized protein  U3IWA4  APOB  2.23    Serine incorporator 5  R0M708    2.22    Retinol-binding protein 4  R0K679  RBP4  1.88    Fatty acid-binding protein, epidermal  R0LD44  FABP5  1.82    Apolipoprotein AI  Q9PRR6    1.76    Proactivator polypeptide  R0J9J6    1.73  Lipid transport  Adipocyte fatty acid-binding protein  F8SKC8    2.97    Uncharacterized protein  U3JA11    2.89    Uncharacterized protein  U3I8J0    2.83    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3IWA4  APOB  2.23    Vitellogenin-2  R0JQ97    2.20    Putative phospholipid-transporting ATPase IH  R0LH43  ATP11A  1.96    Epididymal secretory protein E1  R0L315    1.87    Macrophage migration inhibitory factor  R0JLZ8    1.52  Sexual reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Reproductive process  Putative apolipoprotein B  A7WPP0  APOB  2.39    Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Cholesterol transport  Putative apolipoprotein B  A7WPP0  APOB  2.39    Epididymal secretory protein E1  R0L315    1.87  Sex differentiation  Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88  Female genitalia development  Retinol-binding protein 4  R0K679  RBP4  1.88  Down-regulated proteins  Lipid transport  Uncharacterized protein  U3ITT6    0.66  Lipid metabolic process  85 kDa calcium-independent phospholipase A2  R0LQB6    0.65    Synaptic glycoprotein SC2  R0JFF3    0.60  GO biological process  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Regulation of MAPK cascade  Tyrosine-protein kinase Lyn  R0K926  Lyn  4.80    Caveolin  D7RF71    2.61    Uncharacterized protein  U3J1Z5  RAP1A  2.31    Caveolin  D7RF70    1.53    Macrophage migration inhibitory factor  R0JLZ8    1.52  Lipid metabolic process  Uncharacterized protein  U3J6E8  Apo-VLDL-II  3.76    Adipocyte fatty acid-binding protein  F8SKC8  FABP4  2.97    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3J2D6  PLTP  2.36    Uncharacterized protein  U3IWA4  APOB  2.23    Serine incorporator 5  R0M708    2.22    Retinol-binding protein 4  R0K679  RBP4  1.88    Fatty acid-binding protein, epidermal  R0LD44  FABP5  1.82    Apolipoprotein AI  Q9PRR6    1.76    Proactivator polypeptide  R0J9J6    1.73  Lipid transport  Adipocyte fatty acid-binding protein  F8SKC8    2.97    Uncharacterized protein  U3JA11    2.89    Uncharacterized protein  U3I8J0    2.83    Putative apolipoprotein B  A7WPP0  APOB  2.39    Uncharacterized protein  U3IWA4  APOB  2.23    Vitellogenin-2  R0JQ97    2.20    Putative phospholipid-transporting ATPase IH  R0LH43  ATP11A  1.96    Epididymal secretory protein E1  R0L315    1.87    Macrophage migration inhibitory factor  R0JLZ8    1.52  Sexual reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Reproductive process  Putative apolipoprotein B  A7WPP0  APOB  2.39    Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Reproduction  Putative apolipoprotein B  A7WPP0  APOB  2.39  Cholesterol transport  Putative apolipoprotein B  A7WPP0  APOB  2.39    Epididymal secretory protein E1  R0L315    1.87  Sex differentiation  Mitochondrial import inner membrane translocase subunit TIM14  R0KWC9  TIM14  1.98    Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Retinol-binding protein 4  R0K679  RBP4  1.88  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88  Female genitalia development  Retinol-binding protein 4  R0K679  RBP4  1.88  Down-regulated proteins  Lipid transport  Uncharacterized protein  U3ITT6    0.66  Lipid metabolic process  85 kDa calcium-independent phospholipase A2  R0LQB6    0.65    Synaptic glycoprotein SC2  R0JFF3    0.60  View Large Because different proteins interact and cooperate to complete biochemical reactions, following annotation and annotation augmentation, a KEGG pathway-based analysis was performed to identify pathways that would be potentially affected by the modification of the abundance of the proteins in ovarian tissue. A total of 168 differentially abundant proteins were mapped to 246 KEGG pathway entries. All KEGG analysis results are shown in Figure 6 and Supplemental Table S6. The KEGG pathway analysis for the identified proteins showed that the top 3 pathways identified were focal adhesion, the PI3K-Akt signaling pathway, and ECM-receptor interaction. It is worth mentioning that some proteins such as CYP19A1, CYP17A1, serine/threonine-protein kinase PRKX, HSD11B2, cell division cycle protein 27-like protein, MAPK11, and secreted frizzled-related protein 2 (SFRP2) were mapped for pathways involved in ovarian steroidogenesis, steroid hormone biosynthesis, the estrogen signaling pathway, oocyte meiosis, progesterone-mediated oocyte maturation, the prolactin signaling pathway, the MAPK signaling pathway, ECM-receptor interaction, and the Wnt signaling pathway (Table 2). Figure 6. View largeDownload slide Distribution of the KEGG pathways in the ovary proteome. Figure 6. View largeDownload slide Distribution of the KEGG pathways in the ovary proteome. Table 2. List of some interesting differentially abundant proteins that are involved in important pathways associated with egg-laying regulation. Pathway  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Progesterone-mediated oocyte maturation  Cell division cycle protein 27-like protein  R0JBZ8  CDC27  3.89    Uncharacterized protein  U3I9V4  MAPK11  1.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oocyte meiosis  Cell division cycle protein 27-like protein  R0JBZ8    3.89    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Calcium signaling pathway  Uncharacterized protein  U3I953    2.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Oestrogen signaling pathway  Uncharacterized protein  U3J395  MMP9  2.53    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  MAPK signaling pathway  Uncharacterized protein  U3J1Z5  RAP1A  2.31    Uncharacterized protein  U3I9 × 9    1.97    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9B6    1.71    Uncharacterized protein  U3I9V4  MAPK11  1.62    Inhibitor of nuclear factor kappa-B kinase subunit alpha  R0JZA4    1.56    Serine/threonine-protein phosphatase  R0K3B0    1.55  ECM-receptor interaction  Uncharacterized protein  U3IFG6  TNXB  2.21    Uncharacterized protein  U3IYP2  ITGA8  2.13    Fibronectin  R0LH70    2.06    Collagen alpha-4(IV) chain  R0JKV2    2.00    Uncharacterized protein  U3IQ97  COL4A2  1.55    Integrin beta  U3IPD4  ITGB1  1.53    Collagen alpha-2(VI) chain  R0JJW5    1.52    Collagen alpha-1(IV) chain  R0KF46    1.61    Uncharacterized protein  U3ID88  COL6A3  1.58    Laminin subunit alpha-4  R0KXM7    1.56    Uncharacterized protein  U3I7L8  ITGA11  1.89    Collagen alpha-1(VI) chain  R0JKK6    1.85    Collagen alpha-2(V) chain  R0LNH9    1.65  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Wnt signaling pathway  Uncharacterized protein  U3IX89  DAAM2  1.51  Serotonergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oxytocin signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Ovarian steroidogenesis  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  GnRH signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9V4  MAPK11  1.62  Glutamatergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  GABAergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Dopaminergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55    Uncharacterized protein  U3I9V4  MAPK11  1.62  Prolactin signaling pathway  Uncharacterized protein  U3I9V4  MAPK11  1.62  Down-regulated proteins  Steroid hormone biosynthesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IJB2  CYP19A1  0.61    Uncharacterized protein  U3HZB8  UGT1A8  0.40  Prolactin signaling pathway  Uncharacterized protein  U3IXW3  CYP17A1  0.66  Ovarian steroidogenesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IRR4    0.66    3-keto-steroid reductase  R0JFP8    0.55    Uncharacterized protein  U3IJB2  CYP19A1  0.61  Steroid hormone biosynthesis  Uncharacterized protein  U3IRR4    0.66    Uncharacterized protein  U3IJ20  HSD11B2  0.59    3-keto-steroid reductase  R0JFP8    0.55  Steroid biosynthesis  Uncharacterized protein  U3IUF3  FDFT1  0.65    3-keto-steroid reductase  R0JFP8    0.55  MAPK signaling pathway  Microtubule-associated protein  U3IKL9  MAPT  0.64  Serotonergic synapse  Uncharacterized protein  U3I684  DDC  0.63  Dopaminergic synapse  Uncharacterized protein  U3I684  DDC  0.63    Kinesin heavy chain isoform 5C  R0M2A6    0.52  Glutamatergic synapse  Excitatory amino acid transporter 1  R0L916    0.61    Excitatory amino acid transporter 2  R0M3W2    0.58  Oxytocin signaling pathway  Uncharacterized protein  U3IJV2    0.45  Calcium signaling pathway  Uncharacterized protein  U3IJV2    0.45  ECM-receptor interaction  Uncharacterized protein  U3I9H2  SV2A  0.33  Pathway  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Progesterone-mediated oocyte maturation  Cell division cycle protein 27-like protein  R0JBZ8  CDC27  3.89    Uncharacterized protein  U3I9V4  MAPK11  1.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oocyte meiosis  Cell division cycle protein 27-like protein  R0JBZ8    3.89    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Calcium signaling pathway  Uncharacterized protein  U3I953    2.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Oestrogen signaling pathway  Uncharacterized protein  U3J395  MMP9  2.53    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  MAPK signaling pathway  Uncharacterized protein  U3J1Z5  RAP1A  2.31    Uncharacterized protein  U3I9 × 9    1.97    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9B6    1.71    Uncharacterized protein  U3I9V4  MAPK11  1.62    Inhibitor of nuclear factor kappa-B kinase subunit alpha  R0JZA4    1.56    Serine/threonine-protein phosphatase  R0K3B0    1.55  ECM-receptor interaction  Uncharacterized protein  U3IFG6  TNXB  2.21    Uncharacterized protein  U3IYP2  ITGA8  2.13    Fibronectin  R0LH70    2.06    Collagen alpha-4(IV) chain  R0JKV2    2.00    Uncharacterized protein  U3IQ97  COL4A2  1.55    Integrin beta  U3IPD4  ITGB1  1.53    Collagen alpha-2(VI) chain  R0JJW5    1.52    Collagen alpha-1(IV) chain  R0KF46    1.61    Uncharacterized protein  U3ID88  COL6A3  1.58    Laminin subunit alpha-4  R0KXM7    1.56    Uncharacterized protein  U3I7L8  ITGA11  1.89    Collagen alpha-1(VI) chain  R0JKK6    1.85    Collagen alpha-2(V) chain  R0LNH9    1.65  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Wnt signaling pathway  Uncharacterized protein  U3IX89  DAAM2  1.51  Serotonergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oxytocin signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Ovarian steroidogenesis  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  GnRH signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9V4  MAPK11  1.62  Glutamatergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  GABAergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Dopaminergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55    Uncharacterized protein  U3I9V4  MAPK11  1.62  Prolactin signaling pathway  Uncharacterized protein  U3I9V4  MAPK11  1.62  Down-regulated proteins  Steroid hormone biosynthesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IJB2  CYP19A1  0.61    Uncharacterized protein  U3HZB8  UGT1A8  0.40  Prolactin signaling pathway  Uncharacterized protein  U3IXW3  CYP17A1  0.66  Ovarian steroidogenesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IRR4    0.66    3-keto-steroid reductase  R0JFP8    0.55    Uncharacterized protein  U3IJB2  CYP19A1  0.61  Steroid hormone biosynthesis  Uncharacterized protein  U3IRR4    0.66    Uncharacterized protein  U3IJ20  HSD11B2  0.59    3-keto-steroid reductase  R0JFP8    0.55  Steroid biosynthesis  Uncharacterized protein  U3IUF3  FDFT1  0.65    3-keto-steroid reductase  R0JFP8    0.55  MAPK signaling pathway  Microtubule-associated protein  U3IKL9  MAPT  0.64  Serotonergic synapse  Uncharacterized protein  U3I684  DDC  0.63  Dopaminergic synapse  Uncharacterized protein  U3I684  DDC  0.63    Kinesin heavy chain isoform 5C  R0M2A6    0.52  Glutamatergic synapse  Excitatory amino acid transporter 1  R0L916    0.61    Excitatory amino acid transporter 2  R0M3W2    0.58  Oxytocin signaling pathway  Uncharacterized protein  U3IJV2    0.45  Calcium signaling pathway  Uncharacterized protein  U3IJV2    0.45  ECM-receptor interaction  Uncharacterized protein  U3I9H2  SV2A  0.33  View Large Table 2. List of some interesting differentially abundant proteins that are involved in important pathways associated with egg-laying regulation. Pathway  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Progesterone-mediated oocyte maturation  Cell division cycle protein 27-like protein  R0JBZ8  CDC27  3.89    Uncharacterized protein  U3I9V4  MAPK11  1.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oocyte meiosis  Cell division cycle protein 27-like protein  R0JBZ8    3.89    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Calcium signaling pathway  Uncharacterized protein  U3I953    2.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Oestrogen signaling pathway  Uncharacterized protein  U3J395  MMP9  2.53    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  MAPK signaling pathway  Uncharacterized protein  U3J1Z5  RAP1A  2.31    Uncharacterized protein  U3I9 × 9    1.97    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9B6    1.71    Uncharacterized protein  U3I9V4  MAPK11  1.62    Inhibitor of nuclear factor kappa-B kinase subunit alpha  R0JZA4    1.56    Serine/threonine-protein phosphatase  R0K3B0    1.55  ECM-receptor interaction  Uncharacterized protein  U3IFG6  TNXB  2.21    Uncharacterized protein  U3IYP2  ITGA8  2.13    Fibronectin  R0LH70    2.06    Collagen alpha-4(IV) chain  R0JKV2    2.00    Uncharacterized protein  U3IQ97  COL4A2  1.55    Integrin beta  U3IPD4  ITGB1  1.53    Collagen alpha-2(VI) chain  R0JJW5    1.52    Collagen alpha-1(IV) chain  R0KF46    1.61    Uncharacterized protein  U3ID88  COL6A3  1.58    Laminin subunit alpha-4  R0KXM7    1.56    Uncharacterized protein  U3I7L8  ITGA11  1.89    Collagen alpha-1(VI) chain  R0JKK6    1.85    Collagen alpha-2(V) chain  R0LNH9    1.65  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Wnt signaling pathway  Uncharacterized protein  U3IX89  DAAM2  1.51  Serotonergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oxytocin signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Ovarian steroidogenesis  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  GnRH signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9V4  MAPK11  1.62  Glutamatergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  GABAergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Dopaminergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55    Uncharacterized protein  U3I9V4  MAPK11  1.62  Prolactin signaling pathway  Uncharacterized protein  U3I9V4  MAPK11  1.62  Down-regulated proteins  Steroid hormone biosynthesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IJB2  CYP19A1  0.61    Uncharacterized protein  U3HZB8  UGT1A8  0.40  Prolactin signaling pathway  Uncharacterized protein  U3IXW3  CYP17A1  0.66  Ovarian steroidogenesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IRR4    0.66    3-keto-steroid reductase  R0JFP8    0.55    Uncharacterized protein  U3IJB2  CYP19A1  0.61  Steroid hormone biosynthesis  Uncharacterized protein  U3IRR4    0.66    Uncharacterized protein  U3IJ20  HSD11B2  0.59    3-keto-steroid reductase  R0JFP8    0.55  Steroid biosynthesis  Uncharacterized protein  U3IUF3  FDFT1  0.65    3-keto-steroid reductase  R0JFP8    0.55  MAPK signaling pathway  Microtubule-associated protein  U3IKL9  MAPT  0.64  Serotonergic synapse  Uncharacterized protein  U3I684  DDC  0.63  Dopaminergic synapse  Uncharacterized protein  U3I684  DDC  0.63    Kinesin heavy chain isoform 5C  R0M2A6    0.52  Glutamatergic synapse  Excitatory amino acid transporter 1  R0L916    0.61    Excitatory amino acid transporter 2  R0M3W2    0.58  Oxytocin signaling pathway  Uncharacterized protein  U3IJV2    0.45  Calcium signaling pathway  Uncharacterized protein  U3IJV2    0.45  ECM-receptor interaction  Uncharacterized protein  U3I9H2  SV2A  0.33  Pathway  Associated proteins  Protein ID (UniProt accession)  Gene symbol  Fold change (laying/pre-laying)  Up-regulated proteins  Progesterone-mediated oocyte maturation  Cell division cycle protein 27-like protein  R0JBZ8  CDC27  3.89    Uncharacterized protein  U3I9V4  MAPK11  1.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oocyte meiosis  Cell division cycle protein 27-like protein  R0JBZ8    3.89    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Calcium signaling pathway  Uncharacterized protein  U3I953    2.62    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Oestrogen signaling pathway  Uncharacterized protein  U3J395  MMP9  2.53    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  MAPK signaling pathway  Uncharacterized protein  U3J1Z5  RAP1A  2.31    Uncharacterized protein  U3I9 × 9    1.97    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9B6    1.71    Uncharacterized protein  U3I9V4  MAPK11  1.62    Inhibitor of nuclear factor kappa-B kinase subunit alpha  R0JZA4    1.56    Serine/threonine-protein phosphatase  R0K3B0    1.55  ECM-receptor interaction  Uncharacterized protein  U3IFG6  TNXB  2.21    Uncharacterized protein  U3IYP2  ITGA8  2.13    Fibronectin  R0LH70    2.06    Collagen alpha-4(IV) chain  R0JKV2    2.00    Uncharacterized protein  U3IQ97  COL4A2  1.55    Integrin beta  U3IPD4  ITGB1  1.53    Collagen alpha-2(VI) chain  R0JJW5    1.52    Collagen alpha-1(IV) chain  R0KF46    1.61    Uncharacterized protein  U3ID88  COL6A3  1.58    Laminin subunit alpha-4  R0KXM7    1.56    Uncharacterized protein  U3I7L8  ITGA11  1.89    Collagen alpha-1(VI) chain  R0JKK6    1.85    Collagen alpha-2(V) chain  R0LNH9    1.65  Wnt signaling pathway  Secreted frizzled-related protein 2  R0L3 × 7  SFRP2  1.88    Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  Wnt signaling pathway  Uncharacterized protein  U3IX89  DAAM2  1.51  Serotonergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Oxytocin signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Myosin light chain kinase  R0LM85  MYLK  1.66    Serine/threonine-protein phosphatase  R0K3B0    1.55  Ovarian steroidogenesis  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  GnRH signaling pathway  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Uncharacterized protein  U3I9V4  MAPK11  1.62  Glutamatergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55  GABAergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88  Dopaminergic synapse  Serine/threonine-protein kinase PRKX  R0LMM9  PRKX  1.88    Serine/threonine-protein phosphatase  R0K3B0    1.55    Uncharacterized protein  U3I9V4  MAPK11  1.62  Prolactin signaling pathway  Uncharacterized protein  U3I9V4  MAPK11  1.62  Down-regulated proteins  Steroid hormone biosynthesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IJB2  CYP19A1  0.61    Uncharacterized protein  U3HZB8  UGT1A8  0.40  Prolactin signaling pathway  Uncharacterized protein  U3IXW3  CYP17A1  0.66  Ovarian steroidogenesis  Uncharacterized protein  U3IXW3  CYP17A1  0.66    Uncharacterized protein  U3IRR4    0.66    3-keto-steroid reductase  R0JFP8    0.55    Uncharacterized protein  U3IJB2  CYP19A1  0.61  Steroid hormone biosynthesis  Uncharacterized protein  U3IRR4    0.66    Uncharacterized protein  U3IJ20  HSD11B2  0.59    3-keto-steroid reductase  R0JFP8    0.55  Steroid biosynthesis  Uncharacterized protein  U3IUF3  FDFT1  0.65    3-keto-steroid reductase  R0JFP8    0.55  MAPK signaling pathway  Microtubule-associated protein  U3IKL9  MAPT  0.64  Serotonergic synapse  Uncharacterized protein  U3I684  DDC  0.63  Dopaminergic synapse  Uncharacterized protein  U3I684  DDC  0.63    Kinesin heavy chain isoform 5C  R0M2A6    0.52  Glutamatergic synapse  Excitatory amino acid transporter 1  R0L916    0.61    Excitatory amino acid transporter 2  R0M3W2    0.58  Oxytocin signaling pathway  Uncharacterized protein  U3IJV2    0.45  Calcium signaling pathway  Uncharacterized protein  U3IJV2    0.45  ECM-receptor interaction  Uncharacterized protein  U3I9H2  SV2A  0.33  View Large Western Blot Validation To further validate the results obtained by iTRAQ and LC-MS/MS, we examined the modification of the abundance of adiponectin, FABP4, and RBP4 during the pre-laying and laying periods in the ovarian tissues using Western blotting. As shown in Figure 7, the abundance of these 3 proteins was up-regulated during the laying period compared with the pre-laying period (Adiponectin, 0.2056 ± 0.0421 vs. 0.1277 ± 0.0018; FABP4, 0.2065 ± 0.0348 vs. 0.1248 ± 0.0416; RBP4, 0.2037 ± 0.0536 vs. 0.1295 ± 0.0188). In comparison with the results obtained using proteomics, the changes in the trends in the abundance of these 3 proteins were consistent with the observations made by iTRAQ, although the differences were not statistically significant (P > 0.05) through statistical analysis. Figure 7. View largeDownload slide Western blotting analysis of adiponectin, FABP4, and RBP4 in the ovarian tissues of Huoyan geese between the pre-laying and laying periods. GAPDH was used as the internal control. The protein band density was analyzed using the GelQuant software. (A) Representative immunoblots. (B) Densitometric analysis of adiponectin, FABP4, and RBP4 protein relative to GAPDH protein. Values are expressed as the means±SEM of the mean in arbitrary optical density units (AU). Figure 7. View largeDownload slide Western blotting analysis of adiponectin, FABP4, and RBP4 in the ovarian tissues of Huoyan geese between the pre-laying and laying periods. GAPDH was used as the internal control. The protein band density was analyzed using the GelQuant software. (A) Representative immunoblots. (B) Densitometric analysis of adiponectin, FABP4, and RBP4 protein relative to GAPDH protein. Values are expressed as the means±SEM of the mean in arbitrary optical density units (AU). DISCUSSION The current study is the first report, to the best of our knowledge, of global differently proteomic profiles in Huoyan geese ovaries between the pre-laying and laying periods using the iTRAQ-based approach. Some proteins, such as very low-density apoVLDL-II, apoB, vitellogenin II, FABP4, and RBP4, which are associated with lipid transportation, the metabolic process, follicular activation, and development, have been identified to be up-regulated expressed in the laying group. The abundance of proteins encoded by CYP19A1 and CYP17A1 was found to be altered, and these proteins were mainly involved of ovarian steroidogenesis and steroid hormone biosynthesis. ApoVLDL-II, apoB, vitellogenins (VTG), and the fatty acid-binding protein are essential for follicle development and final egg production. Yolk lipid content is critical in avian species, as it comprises nearly 60% of the yolk dry mass. Approximately 67% of the yolk lipids are triglycerides (TG) (Noble and Cocchi, 1990; Bujo et al., 1997). In poultry, there is a large quantity of lipids in egg yolk, and thus, the initiation and maintenance of egg production is an energy-intensive process and requires a large quantity of lipids (especially TG) for egg yolk formation. Birds have the ability to store large quantities of excess energy (in the form of TG) in the liver, adipose tissue, and in the yolk of developing oocytes (Hermier, 1997). The avian liver is responsible for more than two-thirds of de novo lipogenesis and the synthesis of key proteins involved in lipid metabolism and egg formation, such as apoVLDL-II, apoB, VTG, and the fatty acid-binding protein (Yang et al., 2013). Apolipoprotein B is a major protein component of plasma very low-density and low-density lipoproteins (VLDL and LDL, respectively) and serves as a recognition signal for the cellular binding and internalization of LDL by the apoB/E receptor. As one of the components of specialized VLDL particles, avian apoB is produced by the liver in response to estrogen. It helps to transport cholesterol and triglycerides from the liver to the ovary for deposition in the egg yolk (Kirchgessner et al., 1987). A previous study showed that plasma VLDL and apoB levels were dramatically elevated in the laying hen or estrogen-stimulated rooster as a result of increased hepatic apoB synthesis and VLDL production (Capony and Williams, 1980). In addition to apoB, laying birds VLDL particles also contain large amounts of apolipoprotein apoVLDL-II, which is induced by estrogens at the onset of egg laying when yolk synthesis has just been initiated. ApoVLDL-II is critical to lipid transport during the laying period due to its inhibitory effect on lipoprotein lipase (LPL), which protects apoVLDL-II featured VLDL from being digested by LPL in circulation, so it can successfully deliver TG to the developing oocyte for subsequent use as an energy source (Schneider et al., 1990). The evidence shows that the expression of apoVLDL-II is associated with egg laying. The hepatic apoVLDL-II mRNA concentration in high egg production Tsaiya ducks was found to be significantly greater in laying ducks compared with pre-laying ducks (Yen et al., 2005). Similarly, the ApoVLDL-II mRNA in the liver tissues also was found to be more than 2-fold higher in laying geese compared with pre-laying geese (Ding et al., 2007). VTG are yolk precursor proteins produced by the liver under estrogenic induction and circulate within the bloodstream until the follicle enters a stage of vitellogenesis, where the endocytosis of vitellogenins transports them into the yolk. As follicles are recruited and enter the preovulatory hierarchy, they increase in size via the accumulation of vitellogenin (Nepomuceno et al., 2015). In birds including geese, the timing of VTG gene expression parallels that of egg laying (Ding et al., 2007), and the positive correlations between E2 and VTG mRNA expression, as well as estrogen receptors with VTG mRNA expression, were observed (Wu et al., 2013; Li et al., 2014). In line with previous reports, our current study also indicated that the abundance of these proteins was significantly increased during the laying period. It was suggested that high egg production requires a high lipid generation machinery to support egg yolk lipid accumulation. Accordingly, egg laying may affect particular aspects of lipid metabolism, and related biochemical pathways of these proteins also are involved. As another kind of important protein related to lipid metabolism, fatty acid-binding proteins (FABP) play specific roles in the transportation of fatty acids (FA), even in ovary development and ovarian hormone synthesis. FABP are small intracellular proteins that bind non-covalently to hydrophobic ligands, primarily FA (Esteves and Ehrlich, 2006). To date, 12 structural and functional distinct types of FABP have been discovered. In waterfowl, FABP4 is found to be expressed in many tissues: liver, spleen, gizzard, proventriculus, ovary, brain, and intestine (He et al., 2012). Physiological roles of FABP include the uptake and utilization of FA, the intracellular targeting of FA to specific organelles and metabolic pathways, and the protection of cellular structures from the detergent effects of FA (Storch et al., 2002); some reports have investigated the roles of FABP in respect to reproduction. In female Chinese mitten crab, lipid nutrients, especially FA, are transported from the hepatopancreas to the ovary in great supply via FABP expressing haemocytes during the stage of rapid ovarian development. FABP transported lipids, especially hormone precursor substances, are then stored in the ovaries in preparation for the significant and physiological function required during reproduction (Gong et al., 2010). In addition, 2 types of FABP are found to be expressed in distinct steroid-producing cell types of a rat ovary, and their expressions seem to be regulated in coincidence with the expressions of their respective steroid hormones (Iseki et al., 1995). Moreover, the laying duck hepatic FABP mRNA was determined to be approximately 30% higher than that in pre-laying ducks, owing to the layers needing to assemble yolk lipids for egg yolk lipid accumulation (Ko et al., 2004). Ovary development needs to consume a large amount of FA in order to meet its energy consumption and for the synthesis of steroid hormone precursor substances that are required for the reproduction. In the present study, the increase of ovarian FABP4 expression during the laying period compared to the pre-laying period mirrors lipid nutritional requirements. That is, the ovary requires substantial accumulation of lipids, especially FA, during the egg-laying stage. Retinoid-binding proteins (RBP) might play an important role in reproduction traits in egg-laying avian species. Ovulation is a complex physiological trait, which is not only influenced by hereditary factors and environment, but also by nutritional factors. Vitamin A (retinol) is known to be involved in ovarian follicular growth and oocyte maturation, and affects the steroidogenesis of ovarian follicles. Particularly, the vitamin A-active retinoids are essential to several aspects of avian reproduction and development (Blomhoff and Blomhoff, 2006). It was reported that a deficiency in vitamin A can lead to irregular ovulation, a decrease in egg production, and poor egg quality in the laying hen (Lin et al., 2002). Retinol and its derivatives must be bound to specific proteins called RBP because of its chemical instability and low solubility in an aqueous medium (Newcomer, 1995). In our present study, the abundance of RBP 4 (RBP4) was shown to be increased in the egg-laying group compared with the pre-laying group. RBP4 is one of the necessary intracellular factors in the absorption, transport, metabolism, and homeostasis of retinol and its derivatives. The expression of the RBP4 gene was identified to be associated with retinoid metabolism in a trout ovarian follicle (Levi et al., 2008). This gene also was investigated as a candidate gene for increased litter size in pigs (Rothschild et al., 2000). In poultry, RBP4 may be a candidate gene to affect egg production and reproductive traits. The expression of RBP4 mRNA in the liver, ovary, small intestine, oviduct, and hypothalamus of Erlang mountainous chickens was detected to be increased from 12 wk to 32 wk, with a decrease to 45 weeks. This could be because chickens need more retinol to modulate follicular development and maturation during the laying period. To transport more retinol to meet the requirements of the tissue and cells, greater expression of RBP4 is required (Yin et al., 2013). We noticed that the expression of 2 key enzymes for steroid hormone synthesis encoded by steroidogenic genes CYP19A1 and CYP17A1 was down-regulated in the laying group compared to the pre-laying group. Ovarian steroidogenesis occurs both in granulosa and theca layers of follicles, and is essential for follicular recruitment, oocyte maturation, and ovulation. The granulosa produces mainly progesterone (P4) while the theca interna and externa produce testosterone (T) and estradiol (E2), respectively. During the transition of the yellowish non-hierarchical follicles to a preovulatory hierarchy, the cells of the granulosa layer, stimulated initially by FSH (follicle stimulating hormone) and next by LH (luteinizing hormone), start to express a steroidogenic acute regulatory protein (STAR) and a cytochrome P450 cholesterol side-chain cleavage (P450scc/CYP11A1) enzyme and begin to produce P4 predominantly via the Δ4-ketosteroid pathway. The process of P4 synthesis form pregnenolone (P5) is catalyzed by a 3-hydroxysteroid dehydrogenase (3-HSD/HSD3B) whose expression steadily increases in granulosa cells of growing hierarchical follicles. P4 synthesized in the granulosa layer is transported to the theca interna where it is initially converted T, which is eventually metabolized to E2 by cytochrome P450 aromatase (P450arom/CYP19A1) in the theca externa cells (Sechman et al., 2014). The ability of ovarian follicles to produce steroid hormones is different according to their developmental status. Researchers found that the gene expression of STAR, CYP11A1 and HSD3B reduced significantly immediately after ovulation (Zhu et al., 2015). In chickens, the concentration of plasma oestradiol increased from 7 wk of age, reached a peak at 2 to 3 wk before laying, and then dropped to a stable level similar to that in birds one to 2 wk before laying (Senior, 1974). In house sparrow, the mRNA expression of follicular steroidogenic enzyme (including CYP19A1 and CYP17A1) decreased with follicle maturation, and exhibited a significant negative relationship with yolk T concentrations (Egbert et al., 2013). In our study, mechanisms, or factors responsible for the abundance alteration of these steroidogenic enzymes need to be further investigated. Furthermore, it is worth mentioning that the expression of adiponectin in the laying group was identified to be up-regulated compared to the pre-laying group in both our previous and current studies. Apart from a dominant role in lipid and carbohydrate metabolism, adiponectin also seems to be an important factor linking the regulation of reproductive processes. The evidence indicated that adiponectin may affect reproductive functions by exerting central or peripheral effects on the hypothalamus-pituitary-gonadal (HPG) axis, and the expression of adiponectin is dependent on the endocrine status of the animals (Cao et al., 2015). In summary, we investigated the proteome profiles of ovarian tissues from Huoyan geese during the laying period and the pre-laying period. Some proteins were involved in lipid transportation and the metabolic process, follicle development and final egg production, ovarian steroidogenesis, and steroid hormone biosynthesis; they showed different changes in abundance in coincidence with the different egg-laying stages. However, the functional significance of these differently abundant proteins needs further investigation. Our findings might provide comprehensive protein expression information that can facilitate the understanding of the reproductive biology and improve the egg-laying performance of Huoyan geese. SUPPLEMENTARY DATA Supplementary data are available at Poultry Science online. Supplemental Table S1. All identified proteins in the ovary between the pre-laying period and the laying period by iTRAQ analysis. Supplemental Table S2. Raw data of peptide quantification of the ovary between the pre-laying period and the laying period. Supplemental Table S3. Significance analysis of all quantified proteins in the ovary between the pre-laying period and the laying period. Supplemental Table S4. Significant differentially abundant proteins in the ovary between the pre-laying period and the laying period. Supplemental Table S5. GO analysis results for differentially abundant proteins in the ovary. Supplemental Table S6. KEGG analysis results for differentially abundant proteins in the ovary. ACKNOWLEDGMENTS We would like to thank the staff of the Liaoning Huoyan Goose Stock Breeding Farm for assisting in the collection of ovarian samples. This study was supported by the National Natural Science Foundation of China (Grant No. 31372395). REFERENCES Ashburner M., Ball C. A., Blake J. A., Botstein D., Butler H., Cherry J. M., Davis A. P., Dolinski K., Dwight S. S., Eppig J. T., Harris M. A., Hill D. P., Issel-Tarver L., Kasarskis A., Lewis S., Matese J. C., Richardson J. E., Ringwald M., Rubin G. M., Sherlock G.. 2000. Gene ontology: Tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet.  25: 25– 29. Google Scholar CrossRef Search ADS PubMed  Blomhoff R., Blomhoff H. K.. 2006. Overview of retinoid metabolism and function. J. Neurobiol.  66: 606– 630. Google Scholar CrossRef Search ADS PubMed  Bujo H., Hermann M., Lindstedt K. A., Nimpf J., Schneider W. J.. 1997. Low density lipoprotein receptor gene family members mediate yolk deposition. J. Nutr.  127: 801S– 804S. Google Scholar CrossRef Search ADS PubMed  Cao Z., Li J., Luo L., Li X., Liu M., Gao M., Yin Y., Luan X.. 2015. Molecular cloning and expression analysis of adiponectin and its receptors (AdipoR1 and AdipoR2) in the hypothalamus of the Huoyan goose during different stages of the egg-laying cycle. Reprod. Biol Endocrinol.  13: 87. Google Scholar CrossRef Search ADS PubMed  Capony F., Williams D. L.. 1980. Apolipoprotein B of avian very low density lipoprotein: Characteristics of its regulation in nonstimulated and estrogen-stimulated rooster. Biochemistry.  19: 2219– 2226. Google Scholar CrossRef Search ADS PubMed  Chen G.-H., Wang K.-H., Wang J.-Y., Ding C., Yang N.. 2004. Poultry genetic resources in China . Shanghai Scientific and Technological Press, Shanghai, China. Chen Y., Fu X., Mei X., Zhou Y., Cheng S., Zeng L., Dong F., Yang Z.. 2017. Proteolysis of chloroplast proteins is responsible for accumulation of free amino acids in dark-treated tea (Camellia sinensis) leaves. J. Proteomics.  157: 10– 17. Google Scholar CrossRef Search ADS PubMed  Close B., Banister K., Baumans V., Bernoth E. M., Bromage N., Bunyan J., Erhardt W., Flecknell P., Gregory N., Hackbarth H., Morton D., Warwick C.. 1997. Recommendations for euthanasia of experimental animals: Part 2. DGXT of the European Commission. Lab. Anim.  31: 1– 32. Google Scholar CrossRef Search ADS PubMed  Ding S. T., Yen C. F., Wang P. H., Lin H. W., Hsu J. C., Shen T. F.. 2007. The differential expression of hepatic genes between prelaying and laying geese. Poult. Sci.  86: 1206– 1212. Google Scholar CrossRef Search ADS PubMed  Egbert J. R., Jackson M. F., Rodgers B. D., Schwabl H.. 2013. Between-female variation in house sparrow yolk testosterone concentration is negatively associated with CYP19A1 (aromatase) mRNA expression in ovarian follicles. Gen. Comp. Endocrinol.  183: 53– 62. Google Scholar CrossRef Search ADS PubMed  Emsley A. 1997. Integration of classical and molecular approaches of genetic selection: Egg production. Poult Sci.  76: 1127– 1130. Google Scholar CrossRef Search ADS PubMed  Esteves A., Ehrlich R.. 2006. Invertebrate intracellular fatty acid binding proteins. Comp. Biochem. Physiol. C Toxicol. Pharmacol.  142: 262– 274. Google Scholar CrossRef Search ADS PubMed  Gao G., Li Q., Zhao X., Ding N., Han Q., Su J., Wang Q.. 2015. Transcriptome profiling of the hypothalamus during prelaying and laying periods in Sichuan white geese (Anser cygnoides). Anim. Sci. J.  86: 800– 805. Google Scholar CrossRef Search ADS PubMed  Gong Y. N., Li W. W., Sun J. L., Ren F., He L., Jiang H., Wang Q.. 2010. Molecular cloning and tissue expression of the fatty acid-binding protein (Es-FABP) gene in female Chinese mitten crab (Eriocheir sinensis). BMC Mol. Biol.  11: 71. Google Scholar CrossRef Search ADS PubMed  Gotz S., Garcia-Gomez J. M., Terol J., Williams T. D., Nagaraj S. H., Nueda M. J., Robles M., Talon M., Dopazo J., Conesa A.. 2008. High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic. Acids. Res.  36: 3420– 3435. Google Scholar CrossRef Search ADS PubMed  Grant S. G., Blackstock W. P.. 2001. Proteomics in neuroscience: From protein to network. J. Neurosci.  21: 8315– 8318. Google Scholar CrossRef Search ADS PubMed  Gygi S. P., Corthals G. L., Zhang Y., Rochon Y., Aebersold R.. 2000. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc. Natl. Acad. Sci. U S A.  97: 9390– 9395. Google Scholar CrossRef Search ADS PubMed  Habig C., Geffers R., Distl O.. 2012. Differential gene expression from genome-wide microarray analyses distinguishes Lohmann Selected Leghorn and Lohmann Brown layers. PLoS One.  7: e46787. Google Scholar CrossRef Search ADS PubMed  He J., Tian Y., Li J. J., Shen J. D., Tao Z. R., Fu Y., Niu D., Lu L. Z.. 2012. Expression pattern of adipocyte fatty acid-binding protein gene in different tissues and its regulation of genes related to adipocyte differentiation in duck. Poult. Sci.  91: 2270– 2274. Google Scholar CrossRef Search ADS PubMed  Hermier D. 1997. Lipoprotein metabolism and fattening in poultry. J. Nutr.  127: 805S– 808S. Google Scholar CrossRef Search ADS PubMed  Huang S. Y., Lin J. H., Chen Y. H., Chuang C. K., Chiu Y. F., Chen M. Y., Chen H. H., Lee W. C.. 2006. Analysis of chicken serum proteome and differential protein expression during development in single-comb White Leghorn hens. Proteomics.  6: 2217– 2224. Google Scholar CrossRef Search ADS PubMed  Iseki S., Amano O., Fujii H., Kanda T., Ono T.. 1995. Immunohiostochemical localization of two types of fatty acid‐binding proteins in rat ovaries during postnatal development and in immature rat ovaries treated with gonadotropins. The Anatomical Record.  241: 235– 243. Google Scholar CrossRef Search ADS PubMed  Kameshita I., Ishida A., Fujisawa H.. 1998. Analysis of protein-protein interaction by two-dimensional affinity electrophoresis. Anal. Biochem.  262: 90– 92. Google Scholar CrossRef Search ADS PubMed  Kanehisa M., Goto S., Sato Y., Furumichi M., Tanabe M.. 2012. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic. Acids Res.  40: D109– D114. Google Scholar CrossRef Search ADS PubMed  Kang B., Guo J. R., Yang H. M., Zhou R. J., Liu J. X., Li S. Z., Dong C. Y.. 2009. Differential expression profiling of ovarian genes in prelaying and laying geese. Poult. Sci.  88: 1975– 1983. Google Scholar CrossRef Search ADS PubMed  Karp N. A., Lilley K. S.. 2009. Investigating sample pooling strategies for DIGE experiments to address biological variability. Proteomics.  9: 388– 397. Google Scholar CrossRef Search ADS PubMed  Kim N. S., Shen Y. N., Kim T. Y., Byun S. J., Jeon I. S., Kim S. H.. 2007. Expression of AGR-2 in chicken oviduct during laying period. J. Biochem. Mol. Biol.  40: 212– 217. Google Scholar PubMed  Kirchgessner T. G., Heinzmann C., Svenson K. L., Gordon D. A., Nicosia M., Lebherz H. G., Lusis A. J., Williams D. L.. 1987. Regulation of chicken apolipoprotein B: Cloning, tissue distribution, and estrogen induction of mRNA. Gene.  59: 241– 251. Google Scholar CrossRef Search ADS PubMed  Ko Y. H., Cheng C. H., Shen T. F., Ding S. T.. 2004. Cloning and expression of Tsaiya duck liver fatty acid binding protein. Poult. Sci.  83: 1832– 1838. Google Scholar CrossRef Search ADS PubMed  Kuo Y. M., Shiue Y. L., Chen C. F., Tang P. C., Lee Y. P.. 2005. Proteomic analysis of hypothalamic proteins of high and low egg production strains of chickens. Theriogenology.  64: 1490– 1502. Google Scholar CrossRef Search ADS PubMed  Levi L., Levavi-Sivan B., Lubzens E.. 2008. Expression of genes associated with retinoid metabolism in the trout ovarian follicle. Biol. Reprod.  79: 570– 577. Google Scholar CrossRef Search ADS PubMed  Lewis T. S., Hunt J. B., Aveline L. D., Jonscher K. R., Louie D. F., Yeh J. M., Nahreini T. S., Resing K. A., Ahn N. G.. 2000. Identification of novel MAP kinase pathway signaling targets by functional proteomics and mass spectrometry. Mol. Cell.  6: 1343– 1354. Google Scholar CrossRef Search ADS PubMed  Li J., Leghari I. H., He B., Zeng W., Mi Y., Zhang C.. 2014. Estrogen stimulates expression of chicken hepatic vitellogenin II and very low-density apolipoprotein II through ER-alpha. Theriogenology.  82: 517– 524. Google Scholar CrossRef Search ADS PubMed  Lin H., Wang L. F., Song J. L., Xie Y. M., Yang Q. M.. 2002. Effect of dietary supplemental levels of vitamin A on the egg production and immune responses of heat-stressed laying hens. Poult. Sci.  81: 458– 465. Google Scholar CrossRef Search ADS PubMed  Luan X., Cao Z., Li R., Liu M., Hu J.. 2014a. Differential expression profiling of hypothalamus genes in laying period and ceased period Huoyan geese. Mol. Biol Rep.  41: 3401– 3411. Google Scholar CrossRef Search ADS   Luan X., Cao Z., Xu W., Gao M., Wang L., Zhang S.. 2013. Gene expression profiling in the pituitary gland of laying period and ceased period Huoyan geese. Asian-Australasian Journal of Animal Sciences (AJAS).  26: 921– 929. Google Scholar CrossRef Search ADS   Luan X., Liu D., Cao Z., Luo L., Liu M., Gao M., Zhang X.. 2014b. Transcriptome profiling identifies differentially expressed genes in Huoyan goose ovaries between the laying period and ceased period. PLoS One.  9: e113211. Google Scholar CrossRef Search ADS   Luche S., Santoni V., Rabilloud T.. 2003. Evaluation of nonionic and zwitterionic detergents as membrane protein solubilizers in two-dimensional electrophoresis. Proteomics.  3: 249– 253. Google Scholar CrossRef Search ADS PubMed  Moriya Y., Itoh M., Okuda S., Yoshizawa A. C., Kanehisa M.. 2007. KAAS: An automatic genome annotation and pathway reconstruction server. Nucleic Acids Res.  35: W182– W185. Google Scholar CrossRef Search ADS PubMed  Nelson L. R., Bulun S. E.. 2001. Estrogen production and action. J. Am. Acad. Dermatol.  45: S116– S124. Google Scholar CrossRef Search ADS PubMed  Nepomuceno A. I., Muddiman D. C., Petitte J. N.. 2015. Global proteomic analysis of functional compartments in immature avian follicles using laser microdissection coupled to LC-MS/MS. J. Proteome. Res.  14: 3912– 3923. Google Scholar CrossRef Search ADS PubMed  Newcomer M. E. 1995. Retinoid-binding proteins: structural determinants important for function. FASEB J.  9: 229– 239. Google Scholar CrossRef Search ADS PubMed  Noble R. C., Cocchi M.. 1990. Lipid metabolism and the neonatal chicken. Prog. Lipid. Res.  29: 107– 140. Google Scholar CrossRef Search ADS PubMed  Padmanabhan V., Karsch F. J., Lee J. S.. 2002. Hypothalamic, pituitary and gonadal regulation of FSH. Reprod. Suppl.  59: 67– 82. Google Scholar PubMed  Pu Y. Z., Wan Q. L., Ding A. J., Luo H. R., Wu G. S.. 2017. Quantitative proteomics analysis of Caenorhabditis elegans upon germ cell loss. J. Proteomics.  156: 85– 93. Google Scholar CrossRef Search ADS PubMed  Quevillon E., Silventoinen V., Pillai S., Harte N., Mulder N., Apweiler R., Lopez R.. 2005. InterProScan: Protein domains identifier. Nucleic Acids Res.  33: W116– W120. Google Scholar CrossRef Search ADS PubMed  Rothschild M. F., Messer L., Day A., Wales R., Short T., Southwood O., Plastow G.. 2000. Investigation of the retinol-binding protein 4 (RBP4) gene as a candidate gene for increased litter size in pigs. Mamm. Genome.  11: 75– 77. Google Scholar CrossRef Search ADS PubMed  Sandberg A., Lindell G., Kallstrom B. N., Branca R. M., Danielsson K. G., Dahlberg M., Larson B., Forshed J., Lehtio J.. 2012. Tumor proteomics by multivariate analysis on individual pathway data for characterization of vulvar cancer phenotypes. Mol. Cell. Proteomics.  11: M112 016998. Google Scholar CrossRef Search ADS PubMed  Schneider W. J., Carroll R., Severson D. L., Nimpf J.. 1990. Apolipoprotein VLDL-II inhibits lipolysis of triglyceride-rich lipoproteins in the laying hen. J. Lipid. Res.  31: 507– 513. Google Scholar PubMed  Sechman A., Antos P., Katarzynska D., Grzegorzewska A., Wojtysiak D., Hrabia A.. 2014. Effects of 2,3,7,8-tetrachlorodibenzo-p-dioxin on secretion of steroids and STAR, HSD3B and CYP19A1 mRNA expression in chicken ovarian follicles. Toxicol. Lett.  225: 264– 274. Senior B. E. 1974. Oestradiol concentration in the peripheral plasma of the domestic hen from 7 weeks of age until the time of sexual maturity. J. Reprod. Fertil.  41: 107– 112. Google Scholar CrossRef Search ADS PubMed  Shiue Y. L., Chen L. R., Chen C. F., Chen Y. L., Ju J. P., Chao C. H., Lin Y. P., Kuo Y. M., Tang P. C., Lee Y. P.. 2006. Identification of transcripts related to high egg production in the chicken hypothalamus and pituitary gland. Theriogenology.  66: 1274– 1283. Google Scholar CrossRef Search ADS PubMed  State Science and Technology Commission in China. 1988. Regulation for the Administration of Affairs Concerning Experimental Animals. The Ministry of Science and Technology of the People's Republic of China, Beijing. Storch J., Veerkamp J. H., Hsu K. T.. 2002. Similar mechanisms of fatty acid transfer from human anal rodent fatty acid-binding proteins to membranes: Liver, intestine, heart muscle, and adipose tissue FABPs. Mol. Cell. Biochem.  239: 25– 33. Google Scholar CrossRef Search ADS PubMed  Unwin R. D., Griffiths J. R., Whetton A. D.. 2010. Simultaneous analysis of relative protein expression levels across multiple samples using iTRAQ isobaric tags with 2D nano LC-MS/MS. Nat. Protoc.  5: 1574– 1582. Google Scholar CrossRef Search ADS PubMed  Wisniewski J. R., Zougman A., Nagaraj N., Mann M.. 2009. Universal sample preparation method for proteome analysis. Nat. Methods.  6: 359– 362. Google Scholar CrossRef Search ADS PubMed  Wu J., Fu W., Huang Y., Ni Y., Zhao R.. 2013. Kisspeptin-10 enhanced egg production in quails associated with the increase of triglyceride synthesis in liver. Asian-Australas J. Anim. Sci.  26: 1080– 1088. Google Scholar CrossRef Search ADS PubMed  Yang S., Suh Y., Choi Y. M., Shin S., Han J. Y., Lee K.. 2013. Loss of fat with increased adipose triglyceride lipase-mediated lipolysis in adipose tissue during laying stages in quail. Lipids.  48: 13– 21. Google Scholar CrossRef Search ADS PubMed  Yen C. F., Jiang Y. N., Shen T. F., Wong I. M., Chen C. C., Chen K. C., Chang W. C., Tsao Y. K., Ding S. T.. 2005. Cloning and expression of the genes associated with lipid metabolism in Tsaiya ducks. Poult. Sci.  84: 67– 74. Google Scholar CrossRef Search ADS PubMed  Yen C. F., Lin H. W., Hsu J. C., Lin C., Shen T. F., Ding S. T.. 2006. The expression of pituitary gland genes in laying geese. Poult. Sci.  85: 2265– 2269. Google Scholar CrossRef Search ADS PubMed  Yin H. D., Gilbert E. R., Chen S. Y., Li D. Y., Zhang Z. C., Wang Y., Liu Y. P., Zhu Q.. 2013. Ontogenic expression pattern and genetic polymorphisms of the retinol-binding protein 4 (RBP4) gene in Erlang mountainous chickens. Gene.  526: 170– 175. Google Scholar CrossRef Search ADS PubMed  Zhu G., Mao Y., Zhou W., Jiang Y.. 2015. Dynamic changes in the follicular transcriptome and promoter DNA methylation pattern of steroidogenic genes in chicken follicles throughout the ovulation cycle. PLoS One.  10: e0146028. Google Scholar CrossRef Search ADS PubMed  Zieske L. R. 2006. A perspective on the use of iTRAQ reagent technology for protein complex and profiling studies. J. Exp. Bot.  57: 1501– 1508. Google Scholar CrossRef Search ADS PubMed  © 2018 Poultry Science Association Inc. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

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Poultry ScienceOxford University Press

Published: Mar 1, 2018

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