TMT-labeled quantitative proteomic analyses on the longissimus dorsi to identify the proteins underlying intramuscular fat content in pigs

Journal Pre-proof TMT-labeled quantitative proteomic analyses on the longissimus dorsi to identify the proteins underlying intramuscular fat content in pigs

Cai Ma, Wenwen Wang, Yuding Wang, Yi Sun, Li Kang, Qin Zhang, Yunliang Jiang PII:

S1874-3919(19)30402-6

DOI:

https://doi.org/10.1016/j.jprot.2019.103630

Reference:

JPROT 103630

To appear in:

Journal of Proteomics

Received date:

27 March 2019

Revised date:

11 November 2019

Accepted date:

22 December 2019

Please cite this article as: C. Ma, W. Wang, Y. Wang, et al., TMT-labeled quantitative proteomic analyses on the longissimus dorsi to identify the proteins underlying intramuscular fat content in pigs, Journal of Proteomics (2019), https://doi.org/10.1016/ j.jprot.2019.103630

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

© 2019 Published by Elsevier.

Journal Pre-proof TMT-labeled quantitative proteomic analyses on the longissimus dorsi to identify the proteins underlying intramuscular fat content in pigs

Cai Ma1 , Wenwen Wang1 , Yuding Wang2 , Yi Sun1 , Li Kang1 , Qin Zhang*1 and Yunliang Jiang*1

Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control

oo

f

1

and Prevention, Shandong Agricultural University, No. 61 Daizong Street, Taian 2

Department of Biology Science and Technology, Taishan

pr

271018, P. R. China.

Pr

*

e-

271018, P. R. China

Corresponding author: [email protected]

Jo u

Email address:

rn

al

[email protected]

CM: [email protected];

WW: [email protected]; YW: [email protected]; YS: [email protected]; LK: [email protected]; QZ: [email protected]; YJ: [email protected]

Journal Pre-proof Abstract The Laiwu pig is famous for its excessively extremely high level of intramuscular fat content (IMF), however, the exact regulatory mechanism underlying intramuscular fat deposition in skeletal muscle is still unknown. As an economically important trait in pigs, IMF is controlled by multiple genes and biological pathways. In this study, we performed an integrated transcriptome-assisted TMT- labeled quantitative proteomic analysis of the longissimus dorsi (LD) muscle in Laiwu pigs at the fastest IMF deposition stage and identified 5074 unique proteins and 52 differentially abundant

oo

f

proteins (DAPs) (> 1.5- fold cutoff, p < 0.05). These DAPs were hierarchically clustered in the LD muscle over two developmental stages from 120 d to 240 d. A

pr

comparison between transcriptomic (mRNA) and proteomic data revealed two

e-

differentially expressed genes corresponding to the DAPs. Changes in the levels of the nine proteins were further analyzed using RT-qPCR and parallel reaction

Pr

monitoring (PRM). The proteins identified in this study could serve as candidates for

rn

Significance

al

elucidating the molecular mechanism of IMF deposition in pigs.

The intramuscular fat content (IMF) refers to the amount of fat within muscles and

Jo u

plays an important role in meat quality by affecting meat quality-related traits, such as tenderness, juiciness and flavor. Using the integrated transcriptome-assisted TMT- labeled quantitative proteomic approach to characterize changes in the proteomic profile of the longissimus dorsi muscle, we identified differentially abundant proteins, such as ALDH1B1, OTX2, AnxA6 and Zfp512, that are associated with intramuscular fat deposition and fat biosynthesis in pigs. These proteins could serve as candidates for elucidating the molecular mechanism of IMF deposition in pigs.

Keywords: Pig; longissimus dorsi muscle; Intramuscular fat content; Proteomics; TMT; Protein

Journal Pre-proof 1 Introduction Pork is a major source of meat for human consumption. In many countries, consumers are becoming increasingly aware of meat quality. Intramuscular fat (IMF) content positively influences sensory quality traits, for instance, taste and flavor [1]. IMF, also known as marbling, refers to the amount of fat within muscles. The triglycerides in mammalian muscles are of particular importance to human health and the meat industry [2, 3]. IMF is considered to be a late developing fat storage, which is determined both by

oo

f

hyperplasia and hypertrophy of adipocyte during the development of pigs [4]. Cellular and molecular networks involved in adipogenesis and myogenesis regulate the

pr

dynamic balance between the number and size of adipocytes and myocytes [5, 6].

e-

However, the proportion of fat and muscle is highly variable and influenced by many factors, including breed, nutrition, and post-slaughter handling, among which the

Pr

breed is the most important factor [7, 8]. The Laiwu pig is an indigenous fatty pig breed distributed in North China, characterized by an extremely high level of IMF

rn

variation [9, 10].

al

content (9%~12%) and suitable for the identification of proteins controlling IMF

In our previous study on Laiwu pigs, we identified 127 differentially expressed

Jo u

genes (DEGs) that are related to lipid biosynthesis in the longissimus dorsi muscle between 120 d and 240 d [9]. However, the major limit of transcriptomics is that the mRNA expression levels and actual protein abundance cannot be directly correlated; hence, information on protein abundance is also needed to fully understand the biological processes and pathways [11, 12, 13]. Over the past decade, proteomic approaches based on two-dimensional gel electrophoresis (2-DE) and mass spectrometry (MS) have been used to study skeletal muscle, but studies at the proteome level to understand the mechanism and regulation of adipogenesis are limited [14-15]. Due to its high technical reproducibility, improved proteome coverage, and more confident peptide identification and quantification, proteome analysis based on the Tandem Mass Tag (TMT) is suitable for analyzing the abundance of thousands of proteins in complex biological samples [16, 17].

Journal Pre-proof The genetic basis of IMF in different breeds of pigs and cattle has been investigated, and some proteins associated with IMF were reported [18-21]; however, due to the complexity of IMF, critical genes controlling IMF variation were not identified. Proteomic analysis on the molecular mechanisms of IMF deposition in Laiwu pigs is still lacking. In this study, based on the transcriptome results and by using TMT-based quantitative proteome analysis, we further investigated the differences in protein profiles of LD muscle samples from 120 d to 240 d of development. The results of this study could help complement existing transcriptome knowledge and understand

oo

f

complex biological processes controlling intramuscular fat deposition in the skeletal

pr

muscle of pigs.

e-

2 Materials and methods 2.1. Animal sampling

Pr

All animals used in this study were treated according to the International Guiding Principles for Biomedical Research Involving Animals. A total of six castrated ma le

al

Laiwu pigs reared under similar environmental and feeding conditions were randomly

rn

selected from the Conservation Center of Laiwu (Laiwu, Shandong, China) at 120 and 240 d to cover two developmental stages (n = 3). Each stage includes three

Jo u

individuals of similar body weights. These pigs were weighed and slaughtered. Immediately after slaughter, about 3 g sample of the LD muscle at the third lumbar vertebra of each pig was collected in a 2- mL cryogenic vial (Corning, USA) and frozen in liquid nitrogen, approximately 0.5 g of each frozen muscle sample was selected for protein extraction. The IMF content (%), live weight, and muscle cross-sectional area data used herein were previously reported [9]. We used the same samples in this study as those used in the transcriptome study, including the samples used in the proteomics, PRM and RT-qPCR assays. 2.2. Muscle protein extraction Each sample was crushed in a mortar containing liquid nitrogen, and then transferred to a 5- mL centrifuge tube. After that, four volumes of lysis buffer containing 8 M urea (Sigma, USA) and 1% Protease Inhibitor Cocktail (Sigma-Aldrich) were added to the

Journal Pre-proof cell powder, followed by sonication three times on ice using a high intensity ultrasonic processor (Scientz, China). The remaining debris was removed by centrifugation at 12,000 g at 4 °C for 10 min. Finally, the supernatant was collected, and the protein concentration was determined with a BCA kit (Beyotime, China) according to the manufacturer’s instructions. 2.3. Trypsin Digestion and TMT Labeling For digestion, the extracted protein solution was reduced with 5 mM dithiothreitol (Sigma, USA) for 30 min at 56 °C and alkylated with 11 mM iodoacetamide (Sigma,

oo

f

USA) for 15 min at room temperature in darkness. The protein sample was then diluted, by adding 100 mM TEAB to urea, to a concentration of less than 2M. Finally,

pr

trypsin (Promega, USA) was added at 1:50 trypsin- to-protein mass ratio for the first

e-

digestion overnight and 1:100 trypsin-to-protein mass ratios for a second 4 h-digestion. After trypsin digestion, the peptides were desalted using a Strata X C18

Pr

SPE column (Phenomenex, USA) and vacuum-dried. Peptides were reconstituted in 0.5 M TEAB and processed according to the manufacturer’s protocol for the TMT kit

al

(Thermo Scientific, USA). Briefly, one unit of TMT reagent were thawed at room

rn

temperature and reconstituted in anhydrous acetonitrile (suited for about 100 mg protein). Carefully added 41 μL of the TMT Label Reagent to each 100 μL sample, six

Jo u

samples were differentially labeled with six TMT tags. The peptide mixtures were then incubated for 2 h at room temperature. In order to stop trypsin, reactions were treated with 8 µL 5% hydroxylamine for 15 minutes. Finally, the six labeled peptides were combined in a new microcentrifuge tube and 1 mL mixed peptides were dried by vacuum concentrator. 2.4. HPLC Fractionation The labeled peptides were fractionated into 60 fractions by high pH reverse-phase high-performance liquid chromatography (HPLC) using Agilent 300Extend C-18 column (5 μm particle size, 4.6 mm ID, 250 mm length) with a gradient of 8% to 32% acetonitrile (pH 9.0) over 60 min. Then, the 60 fractions were combined into 18 fractions and each fraction (volume of 800μL) was dried by vacuum centrifuging pending for MS analysis.

Journal Pre-proof 2.5. LC-MS/MS Analysis Peptides were dissolved in solvent A (0.1% FA in 2% ACN, 98% H2 O) and directly loaded onto a reversed-phase pre-column (Acclaim PepMap100 C18 column, 3 μm, 75μm× 2 mm, 100 Å Thermo Scientific). Peptide separation was performed using a reversed-phase analytical column (Acclaim PepMap RSLC C18 column, 2 μm, 50μm× 15 mm, 100 Å, Thermo Scientific). The gradient was comprised of an increase from 6% to 23% solvent B (0.1% FA in 98% ACN) for 24 min, 23% to 35% for 10 min, a climb to 80% in 3 min and holding at 80% for the last 3 min, at a constant flow

oo

f

rate of 400 nl/min on an EASY-nLC 1000 UPLC system. The resulting peptides were analyzed by a Q ExactiveT M Plus Hybrid Quadrupole-Orbitrap Mass Spectrometer

pr

(Thermo Scientific, USA).

The peptides were subjected to nano electrospray ionization (NSI) source followed

e-

by tandem mass spectrometry (MS/MS) in the Q Exactive T M Plus coupled online to

Pr

the UPLC. Intact peptides were detected in the Orbitrap at a resolution of 70,000. Peptides were selected for MS/MS using a normalized collision energy (NCE) setting

al

of 28; ion fragments were detected in the Orbitrap at a resolution of 17,500. A

rn

data-dependent procedure that alternated between one MS scan followed by 20 MS/MS scans was applied for the top 20 precursor ions above a threshold ion count of

Jo u

1.5E4 in the MS survey scan with 15.0s dynamic exclusion. An electrospray voltage of 2.0 kV was applied. Automatic gain control (AGC) was used to prevent overfilling of the ion trap; 5E4 ions were accumulated for generation of the MS/MS spectra. For MS scans, the m/z scan range was 350 to 1600. 2.6. Database Search and Data Analysis Protein identification and quantification were performed through MaxQuant with an integrated Andromeda search engine (version 1.5.2.8). Tandem mass spectra were searched against a transcriptome database. Trypsin/P was specified as the cleavage enzyme, allowing up to 2 missing cleavages. Mass error was set to 5 ppm for precursor ions and to 0.02 Da for fragment ions. The false discovery rate (FDR) was < 1%, and the minimum score for the peptides was > 40. WoLF PSORT, an updated version of the PSORT/PSORT II software for the prediction of eukaryotic sequences,

Journal Pre-proof was used to predict subcellular localization. Biological processes, cellular components, molecular functions, and KEGG pathways analysis were conducted using DAVID (Database for Annotation, Visualization, and Integrated Discovery) version 6.7 [22]. Functional descriptions of identified protein domains were annotated by InterProScan (a sequence analysis application) based on protein sequence the InterPro (http://www.ebi.ac.uk/interpro/) domain database.

DAPs

were

identified

by

enrichment analysis of GO, KEGG and protein structure domains. For TMT quantification, the ratios of the TMT reporter ion intensities in the

oo

f

MS/MS spectra (m/z 126-131) from rawdatasets were used to calculate fold changes between samples. Only peptides unique for a given protein were considered for

pr

relative quantification. For each sample, the quantification was normalized using the

e-

average ratio of all the unique peptides. The two-tailed Fisher’s exact test was employed to test the enrichment of the differentially abundant proteins versus all

Pr

identified proteins, and a p-value < 0.05 was considered significant. Protein-protein interactions were analyzed by STRING version 10 (http://string-db.org) [23] against

al

the Sus scrofa database and considering a medium confidence score of 0.7 for

rn

interactions. The mass spectrometry proteomics data have been deposited into the ProteomeXchange Consortium via the PRIDE [24] partner repository with the dataset

Jo u

identifier PXD011718.

2.7. Transcriptome database assembly The LD muscle RNA-Sequencing data [9] from the six animals were used to build a Sus scrofa database for this study. The transcriptome data have been deposited with the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo) under accession number GSE90135. Low quality reads were removed by Perl script, and the clean reads were filtered from the raw reads and mapped to the Sus scrofa genome (Sscrofa10.2) using Tophat2 software [25]. Gene expression levels were estimated based on the FPKM values obtained using Cufflinks software [26]. Only genes with an absolute value of log2 (FoldChange) ≥ 1 and an FDR < 0.01 were used for subsequent analysis. 2.8. RT-qPCR

Journal Pre-proof Total RNA was isolated from the 120 d and 240 d LD muscles in a reagent (Takara Biotechnology, Inc., Japan), according to the manufacturer ’s protocol. First-strand cDNA was synthesized using the Primescript RT reagent Kit (Takara Bio Inc., Otsu, Japan) in a total volume of 20 μl, containing 1 μL of total RNA, 1 μL of gDNA Eraser, 2 μL of 5 × gDNAEraser Buffer, 4 μL of 5 × Prime Script Buffer 2, 1 μL of Prime Script RT Enzyme Mix, 1 μL of RT Primer Mix4 and RNase-Free dH2 O. The expression levels of seven genes were quantified using the SYBR Premix Ex Taq T M II kit (TaKaRa, Dalian, China) on a LightCycler 480 real-time PCR system (Roche) in a

oo

f

total volume of 25 μL containing 12.5 μL of 2× Premix Ex Taq, 0.5 μL of ROX II, 2 μL of cDNA, 0.5 μL each of the forward and reverse primers (10 μM) and dH 2 O.

pr

Primers for the genes were designed by DNAMAN (version 7.0.2; Lynnon Biosoft,

e-

San Ramon, USA). Sus scrofa glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was selected as the endogenous control [9]; all the primer sequences, the melting

Pr

curves and the efficiencies are listed in Supplementary Table S1. The 2 -ΔΔCT method was used to calculate the relative expression levels of each mRNA [27].

al

2.9. Parallel Reaction Monitoring Analysis

rn

The protein abundance levels obtained using TMT analysis were confirmed by quantifying the expression levels of two selected proteins by a PRM-MS analysis

Jo u

carried out at the Jingjie PTM BioLab Co., Ltd. (Hangzhou, China). The PRM quantification used was the relative quantification method. Signature peptides for the target proteins were defined according to the TMT data. We set the minimum peptide count for quantification to 2, including unique and razor peptides. The protein extraction and trypsin digestion were performed as described above. Next, peptides were dissolved in 0.1% formic acid (solvent A) and directly loaded onto an in- house packed reversed-phase analytical column (150 mm length, 75 μm i.d.). The gradient steps are as follows: from 6% to 23% solvent B (0.1% formic acid in 98% acetonitrile) over 38 min, from 23% to 35% solvent B over 14 min, from 35% to 80% solvent B over 4 min, and 80% solvent B for 4 min; a constant flow rate of 400 nL/min was maintained using an EASY-nLC 1000 UPLC system. The peptides were subjected to an NSI source followed by tandem mass spectrometry (MS/MS)

Journal Pre-proof using a Q ExactiveT M Plus spectrometer (Thermo) coupled online to the UPLC. The electrospray voltage was 2.0 kV. The m/z scan range was 350 to 1000 for the full scan, and intact peptides were detected using an Orbitrap at a resolution of 35,000. Then, the peptides were selected for MS/MS using NCE set at 27, and the fragments were detected using an Orbitrap at a resolution of 17,500. A data-dependent procedure that alternated between one MS scan followed by 20 MS/MS scans was applied for the top 20 precursor ions above a threshold ion count of 1.5E4 in the MS survey scan with 15.0 s dynamic exclusion. Automatic gain control (AGC) was used to prevent

oo

f

overfilling of the ion trap; 5E4 ions were accumulated for generation of MS/MS spectra. The maximal IT was set at 20 ms for the full MS and switched to automatic

pr

mode for MS/MS. The isolation window for MS/MS was set to 2.0 m/z. The resulting

e-

MS/MS data were processed using Skyline (v.3.6). The following peptide settings were used: the enzyme was set as trypsin [KR/P]; the maximal missed cleavage was

Pr

set as 2; the peptide length was set as 8-25; variable modifications were set as carbamidomethylation of Cys and oxidation of Met; and the maximal variable

al

modification was set as 3. The following transition settings were used: precursor

rn

charges were set as 2, 3; ion charges were set as 1, 2; and ion types were set as b, y, p. The product ions were derived from ion 3 to the last ion, and the ion match tolerance

Jo u

was set to 0.02 Da.

2.10. Statistical analysis

The statistical analysis was performed using SPSS v 16.0 software. The data were shown as the means ± SD and analyzed by one-way ANOVA followed by Duncan's multiple comparison, p values < 0.05 or 0.01 were considered to be significantly different.

3 Results 3.1. The background behind the selection of the two stages from 120 d to 240 d The intramuscular fat (IMF), live weight, and muscle cross-sectional area for the selected animals are shown in Fig. 1. Along with the growth of the pigs, from 120 d to 240 d, the IMF content of porcine LD muscle increased from 3.59% to 9.88%,

Journal Pre-proof representing the fastest fat-deposition stage in the LD muscle of Laiwu pigs (Fig. 1a, b, c) [9]. 3.2. Protein identification and quantification based on TMT Our experimental workflow is shown in Fig. 2. To examine the proteome of LD muscle samples of Laiwu pigs across two postnatal developmental stages of 120 d and 240 d, six samples were initially analyzed using TMT proteomics to identify the differentially abundant proteins (DAPs). As shown in Fig. 3a, 5074 proteins were identified with an average of 7 peptides

oo

f

per protein, among which 4770 proteins were quantified (Supplementary material S2). The normality test strongly fitted the frequency distribution of the normalized

pr

log-transformed median values of the expression ratios (240 d/120 d) of the 4770

e-

quantitative proteins (Fig. 3b). For comparison between 240 day (240 d) LD muscle samples and 120 day (120 d) LD muscle samples, a protein exhibiting a fold change

Pr

of > 1.5 or < 0.67 and a p value of < 0.05 was regarded as a DAP. Based on the two criteria, 52 DAPs were identified, of which 14 were upregulation proteins a nd 38

al

were downregulation proteins in 240 d compared with 120 d (Fig. 3c, Supplementary

rn

material S3). Also, the DAPs of each group were analyzed and displayed in the form of a hierarchical clustering heat map (Fig. 3d). From 120 d to 240 d, the top 10

Jo u

upregulated or downregulated proteins in Laiwu pigs were found to be involved in biological activities such as metabolic processes (CA13, NDUFA4 and CA2), regulation of cellular processes (Dclk1, SORBS1), muscle development (CGN) and lipid binding (AnxA6) (Table 1). 3.3. Annotation analysis of the differentially abundant proteins The aforementioned 52 DAPs performed significantly different molecular functions, in diverse biological processes and at distinct subcellular locations (p < 0.05) (Fig. 4). They were mainly clustered into 19 GO functional categories, which account for 10 biological processes, 4 cellular components, and 5 molecular functions (Fig. 4a–c, Supplementary material S4). Biological process analysis showed the altered proteins were mostly involved in cellular process (22%), metabolic process (21%) and single-organism process (21%)

Journal Pre-proof (Fig. 4a, Supplementary material S4). According to cellular component annotation, the majority of the DAPs originated from cell (45%) and organelle (30%) (Fig. 4b, Supplementary material S4). Molecular function analysis revealed that 68% of the DAPs were involved in binding and 26% participated in catalytic activity (Fig. 4c, Supplementary material S4). Prediction of the subcellular localization of the 52 DAPs by WoLF PSORT indicated that the largest subcellular fraction was in the nucleus, accounting for 44% of the DAPs; another significant subcellular location site was the cytoplasm (27%) (Fig. 4d, Supplementary material S5).

oo

f

Gene Ontology (GO) analysis (Fig. 5a, Supplementary material S6) of the 52 DAPs revealed the most significantly enriched biological processes as the mismatch repair

pr

and coenzyme metabolic process, the main cellular components as the nucleosome,

e-

DNA packaging complexes and protein-DNA complexes, and the main molecular functions as carbonate dehydratase activity and mismatched DNA binding. Functional

Pr

domains related to α carbonic anhydrase and DNA mismatch repair protein MutS were conspicuously enriched in these DAPs (Fig. 5b, Supplementary material S7).

al

KEGG analysis was performed to investigate the enriched pathways participated by

rn

the DAPs. By KEGG enrichment analysis, we found that DAPs were mainly mapped to 11 signaling pathway. Three proteins (i.e. CA2, CA13 and GLUL) were involved in

Jo u

nitrogen metabolism which showed the lowest p value among the identified pathways (Fig. 5c, Supplementary material S8). 3.4. Protein-protein interaction network analysis A network of physical and functional protein-protein interactions (PPI) was built using STRING v.10.0 online software against the Sus scrofa database. A total of 52 known or predicted interactions (PPI enrichment p- value ＜ 1.0e−16) were formed among DAPs in the PPI network (Fig. 6). The prediction of the protein interaction network of DAPs showed that CARNS1, ALDH1B1, GLUL and AMT had a pivotal role in the network. The highest number of interactions was observed for glutamate-ammonia ligase (GLUL). 3.5. Comparative analysis of proteomic and transcriptomic data Next, we compared the present proteomic results with the transcriptome data on the

Journal Pre-proof same biological samples [9]. In our previous RNA-seq study, out of 22,524 genes identified in the LD muscle, 1158 were differentially expressed genes (DEGs, p < 0.05) between 120 d and 240 d. In this study, 4770 proteins were quantified and 52 were DAPs. By comparison, only TAGLN2 and CA2 were considered as differentially expressed at both the mRNA and protein levels between 120 d and 240 d, 13 upregulated and 37 downregulated proteins were not differentially expressed at the mRNA level (Supplementary material S9). 3.6. Validation of proteins identified in the TMT results by PRM and RT-qPCR

oo

f

Based on the results of transcriptome and proteome analyses, seven differentially expressed genes were selected and quantified using RT-qPCR to validate the

pr

sequencing data: Zfp512, OGA, CA2, ACOT7, PSME4, TAGLN2 and CAP2B (Fig.

e-

7). The results from the real-time PCR analysis were in accordance with the proteomics for most of the genes, suggesting that the TMT proteomic analysis method

Pr

was reliable. In addition, two fat biosynthesis relevant proteins, HSP90B and ACAT1, were selected and quantified using PRM and the results indicated that the TMT data

rn

4 Discussion

Jo u

material S10.

al

were reliable (Fig. 7). The peptides used in PRM were submitted as Supplement

The Laiwu pig was chosen because this pig breed has much higher fat deposition capability than other pig breeds. In our previous studies we measured the dynamics in IMF content in Laiwu pigs across four developmental stages, the IMF content of Laiwu pigs is increased from 3.59% to 9.88% from 120 d to 240 d, representing the fastest fat deposition stage of the LD muscle in Laiwu pigs (p < 0.01). By analyzing transcriptome changes in the LD muscle of Laiwu pigs from 120 d to 240 d, 127 DEGs that are related to lipid biosynthesis in the longissimus dorsi muscle were identified. However, because the changes in the mRNA level do not consistently reflect the functions of the genes, the protein abundance is the ultimate trait and functional phenotype so proteomic analysis was performed at the fastest IMF deposition stage. Global analysis of changes in both mRNA and protein levels allow

Journal Pre-proof us to fully understand the function of these genes. In this study, we identified more than five thousand proteins (5074) in the LD muscle using TMT Labeling coupled with a protein database compared to previous studies [28-32], which is clearly beneficial for the identification of several distinct proteins in a single experiment and results in a higher quality of genome functional annotation [18, 33]. The most prominent upregulated proteins among the 52 DAPs identified between 120 d and 240 d in Laiwu pigs were CA13 and AnxA6. Carbonic anhydrases (CAs) are ubiquitous enzymes that catalyze the reversible hydration/dehydration of CO 2 and

oo

f

water, which is involved in the oxidation of glucose and fatty acids [34]. AnxA6 is the largest member of the annexin family and may also contribute to whole body control

pr

of lipid and glucose metabolism [35]. Studies have shown that AnxA6 is associated

e-

with lipid droplets in adipocytes and that depletion of AnxA6 reduces lipolysis [35-38]. Previous studies reported that AnxA6 is upregulated in obese humans and

Pr

AnxA6-KO mice gain less weight on a high- fat diet, which correlates with reduced adiposity [35, 39-41]. These findings indicate AnxA6 positively influences IMF

al

deposition.

rn

The most prominent downregulated proteins were OTX2 and Zfp512. OTX2 protein is a homeodomain-containing transcription factor, which may participate in

Jo u

the regulation of adipose tissue functions [42-45]. Zfp512 is a member of the Zinc finger protein (ZFP) family, the largest transcription factor family in mammals containing one or more zinc finger motif(s) that can regulate the differentiation of mesenchymal stem cells into adipogenic cells [46-50]. Zinc finger protein 512 (Zfp512) and PR domain zinc finger protein 10 (PRDM 10) were identified as having a lower abundance in the LD of the 240 d Laiwu pigs. Proteins from glycolysis/ gluconeogenesis provide intermediates from glucose to fat deposition [18, 51-52]. The three glycolysis/ gluconeogenesis related proteins, aldehyde dehydrogenase X (ALDH1B1), aminomethyltransferase (AMT) and pyruvate dehydrogenase kinase isozyme 1 (PDK1) exhibited lower abundance in 240 d compared with 120 d pigs (Supplementary material S3). These results were consistent with other studies, which also demonstrated that proteins related to

Journal Pre-proof glycolysis/ gluconeogenesis were downregulated in the high- fat group [18, 52-53]. ALDH1B1 plays a role in acetaldehyde metabolism and maintaining glucose homeostasis [54]. AMT was involved in glyoxylate and dicarboxylate metabolism. PDK1 is a mitochondrial multi- enzyme complex that catalyzes the oxidative decarboxylation of pyruvate and is one of the major enzymes responsible for the regulation of homeostasis of carbohydrate fuels in mammals. Although it appears that a decreased level of glycolytic enzymes is associated with intramuscular fat accumulation, the role of glycolytic metabolism in marbling synthesis requires further

oo

f

investigation.

To better understand the potential protein-protein interaction (PPI) of the DAPs, we

pr

subsequently performed PPI proteomics network analysis using string software.

e-

Glutamate-ammonia ligase (GLUL) was shown as the central core of network predicted and downregulated proteins in the LD muscle of animals with high

Pr

intramuscular fat content. GLUL is also called GLNS (glutamine synthetase), is the enzyme that catalyzes the synthesis of glutamine from glutamate and ammonia in an

al

ATP-dependent reaction [55]. This protein plays a role in ammonia and glutamate

rn

detoxification, acid-base homeostasis, cell signaling, and cell proliferation [56]. Studies indicated that high- fat mice had a lower abundance of GLUL [57], suggesting

Jo u

that the decreased abundance of GLUL may indirectly lead to greater fat deposition, but this requires further investigation. Additionally, through PPI proteomics network analysis we found that proteins linked with glycolysis were gathered. A comparative analysis between transcriptomic (mRNA) and proteomic data allowed us to investigate the relationship between mRNA expression level and protein abundance. The relation of mRNA expression with protein abundance is influenced by several posttranscriptional regulatory mechanisms [58]. Previous studies also demonstrated divergent results between transcriptomic and proteomic data. In an integrative analysis of transcriptomic and proteomic data of skeletal muscle from pigs, only three genes were found to be differentially expressed at the protein and mRNA levels [59]. In this study, only TAGLN2 and CA2 were considered as differentially expressed at both the mRNA and protein levels between 120 d and 240 d. Using

Journal Pre-proof RT-qPCR, the expression of genes was in accordance with the proteomics. A number of studies reported the analysis of intramuscular fat deposition mechanisms in pig and cattle [18-21]. There are some obvious differences between their work and the present study. For one thing, the types of DAPs involved in the similar biological process were different. The proteins associated with glycolysis in cattle were reported as ENO3, PGK1 and PKM, while in our studies, ALDH1B1, AMT and PDK1 were found to be associated with glycolysis/ gluconeogenesis in pig. This difference is likely due to that the main energetic source of ruminant is volatile fatty acids (mainly

oo

f

propionic) produced by bacteria in the rumen, whereas in the monogastric the glycolysis is the main one. However, evidence also indicates that intramuscular

pr

adipocytes use glucose/lactate as a source of acetyl units for lipogenesis [60]. For

e-

another, through TMT-based proteomic analysis in the LD tissue from the same pig breed between two stages, we identified new types of DAPs related to fat deposition

Pr

that have not reported in either cattle or pigs, the results of which may complement the proteomic analysis results of animals with different genetic backgrounds.

al

To summarize, by proteomic comparison of LD muscle during the fastest IMF

rn

deposition stage in Laiwu pigs, we identified a series of DAPs related to lipid biosynthesis between 120 d and 240 d, including AnxA6, ALDHB1, OTX2 and

Jo u

Zfp512. Only TAGLN2 and CA2 were considered as differentially expressed at both the mRNA and protein levels between 120 d and 240 d. The proteins identified in this study could serve as candidates for elucidating the molecular mechanism of IMF deposition in pigs.

Funding This research was financially supported by the Agricultural Breed Project of Shandong Province (2019LZGC019) , the Application of Agricultural Technology of Shandong

Province

(2013)

and

the

Shandong

“Double

Tops”Program

(SYL2017YSTD12). The funding body had no role in the design of the study and collection, analysis, interpretation of data or writing the manuscript.

Journal Pre-proof Authors' contributions CM and YJ designed and drafted the manuscript. YW and CM carried out animal care, prepared samples and performed the experiments. CM and LK performed the data processing and biological information analysis. YJ, QZ, YS and WW conceived the study and the experimental design and helped draft the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank Mr. Yanxiao Sun for providing the Laiwu pigs. The

oo

f

authors would also like to thank Jingjie PTM Biolab (Hangzhou) for their

Ethics approval and consent to participate

pr

bioinformatics support.

The animal experiments were carried out in accordance with the protocols of the

e-

‘Guidelines for Experimental Animals’ of the Ministry of Science and Technology

Pr

(Beijing, China) and all efforts were made to minimize suffering. The animal experiments were approved by the Institutional Animal Care and Use Ethics

5 References

rn

al

Committee of Shandong Agricultural University (Permit Number: 2007005).

Jo u

[1] J.F. Hocquette, F. Gondret, E. Baéza, F. Médale, C. Jurie, D.W. Pethick, Intramuscular fat content in meat-producing animals: development, genetic and nutritional control, and identification of putative markers, Animal 4 (2) (2010) 303–319

[2] M.V. Dodson, Z. Jiang, J. Chen, G.J. Hausman, L.L. Guan, J. Novakofski, D.P. Thompson, C.L. Lorenzen, M.E. Fernyhough, P.S. Mir, J.M. Reecy, Allied industry approaches to alter intramuscular fat content and composition in beef animals, J Food Sci. 75(1) (2010) R1-8. [3] J.D. Wood, M. Enser, A.V. Fisher, G.R. Nute, P.R. Sheard, R.I. Richardson, S.I. Hughes, F.M. Whittington, Fat deposition, fatty acid composition and meat quality: A review, Meat Sci. 78(4) (2008) 343-58.

Journal Pre-proof [4] C. Ovilo, R. Benitez, A. Fernandez, Y. Nunez, M. Ayuso, A.I. Fernandez, C. Rodriguez, B. Isabel, A.I. Rey, C. Lopez-Bote, L. Silio, Longissimus dorsi transcriptome analysis of purebred and crossbred Iberian pigs differing in muscle characteristics, BMC Genomics 15 (2014) 413. [5] J.M. Argiles, J. Lopez-Soriano, V. Almendro, S. Busquets, F.J. Lopez-Soriano, Cross-talk between skeletal muscle and adipose tissue: a link with obesity? Med Res Rev 25 (2005) 49-65. [6] M. Bonnet, I. Cassar-Malek, Y. Chilliard, B. Picard, Ontogenesis of muscle and

oo

f

adipose tissues and their interactions in ruminant and other species, Animal 4 (7) (2010) 1093–1109

pr

[7] J.F. Hocquette, F. Gondret, E. Baeza, F. Medale, C. Jurie, D.W. Pethick,

e-

Intramuscular fat content in meat-producing animals: development, genetic and nutritional control, and identification of putative markers, Animal 4 (2010) 303-319.

Meat Sci 64(3) (2003) 219-37.

Pr

[8] K. Rosenvold, H.J. Andersen, Factors of significance for pork quality-a review,

al

[9] Y. Wang, C. Ma, Y. Sun, Y. Li, L. Kang, Y. Jiang, Dynamic transcriptome and

rn

DNA methylome analyses on longissimus dorsi to identify genes underlying intramuscular fat content in pigs, BMC Genomics 18(1) (2017) 780.

Jo u

[10] H. Yang, X. Huang, Z. Zeng, W. Zhang, C. Liu, S. Fang, L. Huang, C. Chen, Genome-Wide Association Analysis for Blood Lipid Traits Measured in Three Pig Populations Reveals a Substantial Level of Genetic Heterogeneity, PLoS One 10(6) (2015) e0131667.

[11] B. Schwanhäusser, D. Busse, N. Li, G. Dittmar, J. Schuchhardt, J. Wolf, W. Chen, M. Selbach, Global quantification of mammalian gene expression control, Nature 473 (7347) (2011) 337–342. [12] C. Vogel, R.S. Abreu, D. Ko, S.-Y. Le, B.A. Shapiro, S.C. Burns, D. Sandhu, D.R. Boutz, E.M. Marcotte, L.O. Penalva, Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line, Mol. Syst. Biol 6 (2010) 400 [13] H. Sauerwein, E. Bendixen, L. Restelli, F. Ceciliani, The Adipose Tissue in Farm

Journal Pre-proof Animals: A Proteomic Approach, Curr Protein Pept Sci 15 (2014) 146-155. [14] N.K. Kim, H.R. Park, H.C. Lee, D. Yoon, E.S. Son, Y.S. Kim, S.R. Kim, O.H. Kim, C.S. Lee, Comparative studies of skeletal muscle proteome and transcriptome profilings between pig breeds, Mamm Genome. 21 (2010) 307-319. [15] S. Liu, W. Han, S. Jiang, C. Zhao, C. Wu, Integrative transcriptomics and proteomics analysis of longissimus dorsi muscles of Canadian double- muscled Large White pigs, Gene. 577 (2016) 14-23. [16] C. Ramus, A. Hovasse, M. Marcellin, A.M. Hesse, E. Mouton-Barbosa, D.

oo

f

Bouyssie, S. Vaca, C. Carapito, K. Chaoui, C. Bruley, J. Garin, S. Cianferani, M. Ferro, A. Van Dorssaeler, O. Burlet-Schiltz, C. Schaeffer, Y. Coute, A. Gonzalez de

pr

Peredo, Benchmarking quantitative label- free LC-MS data processing workflows

e-

using a complex spiked proteomic standard dataset, J Proteomics 132 (2016) 51-62. [17] N.J. Bond, P.V. Shliaha, K.S. Lilley, L. Gatto, Improving qualitative and

Pr

quantitative performance for MS(E)-based label- free proteomics, J Proteome Res 12(6) (2013) 2340-53.

al

[18] M.D. Poleti, L.C.A. Regitano, G. Souza, A.S.M. Cesar, R.C. Simas, B.

rn

Silva-Vignato, G.B. Oliveira, S.C.S. Andrade, L.C. Cameron, L.L. Coutinho, Longissimus dorsi muscle label- free quantitative proteomic reveals biological

30-41.

Jo u

mechanisms associated with intramuscular fat deposition, J Proteomics 179 (2018)

[19] Z. Wang, P. Shang, Q. Li, L. Wang, Y. Chamba, B. Zhang, H. Zhang, C. Wu, iTRAQ-based proteomic analysis reveals key proteins affecting muscle growth and lipid deposition in pigs, Sci Rep 7 (2017) 46717. [20] Y.N. Shen, S.H. Kim, D.H. Yoon, H.G. Lee, H.S. Kang, K.S. Seo, Proteome analysis of bovine longissimus dorsi muscle associated with the marbling score, Asian Australas. J. Anim. Sci. 25 (8) (2012) 1083–1088. [21] L. Murgiano, A. D'Alessandro, M.G. Egidi, A. Crisa, G. Prosperini, A.M. Timperio, A. Valentini, L. Zolla, Proteomics and transcriptomics investigation on longissimus muscles in Large White and Casertana pig breeds, J Proteome Res. 9 (2010) 6450-6466.

Journal Pre-proof [22] W. Huang da, B.T. Sherman, R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat. Protoc. 4 (1) (2009) 44–57 [23] D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, K.P. Tsafou, M. Kuhn, P. Bork, L.J. Jensen, C. von Mering, STRING v10: protein-protein interaction networks, integrated over the tree of life, Nucleic Acids Res. 43 (2015) D447–52 [24] E.W. Deutsch, A. Csordas, Z. Sun, A. Jarnuczak, Y. Perez-Riverol, T. Ternent,

oo

f

D.S. Campbell, et al., The ProteomeXchange Consortium in 2017: supporting the cultural change in proteomics public data deposition, Nucleic Acids Res. 45 (2017)

pr

D1100–D1106.

e-

[25] D. Kim, G. Pertea, C. Trapnell, H. Pimentel, R. Kelley, S.L. Salzberg, accurate

Genome Biol. 14 (2013) R36.

Pr

alignment of transcriptomes in the presence of insertions, deletions and gene fusions.

[26] C. Trapnell, B.A. Williams, G. Pertea, A. Mortazavi, G. Kwan, M.J. van Baren,

al

S.L. Salzberg, B.J. Wold, L. Pachter, Transcript assembly and quantification by

rn

RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation, Nat Biotechnol. 28 (2010) 511-515.

Jo u

[27] K.J. Livak, T.D. Schmittgen, Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method, Methods 25(4) (2001) 402-408

[28] J. Bouley, B. Meunier, C. Chambon, S. De Smet, J.F. Hocquette, B. Picard, Proteomic analysis of bovine skeletal muscle hypertrophy, Proteomics 5(2) (2005) 490-500. [29] M.E. Carvalho, G. Gasparin, M.D. Poleti, A.F. Rosa, J.C. Balieiro, C.A. Labate, R.T. Nassu, R.R. Tullio, L.C. Regitano, G.B. Mourao, L.L. Coutinho, Heat shock and structural proteins associated with meat tenderness in Nellore beef cattle, a Bos indicus breed, Meat Sci 96(3) (2014) 1318-24. [30] X. Jia, K.I. Hildrum, F. Westad, E. Kummen, L. Aass, K. Hollung, Changes in enzymes associated with energy metabolism during the early post mortem period in

Journal Pre-proof longissimus thoracis bovine muscle analyzed by proteomics, J Proteome Res 5(7) (2006) 1763-9. [31] R.T. Rodrigues, M.L. Chizzotti, C.E. Vital, M.C. Baracat-Pereira, E. Barros, K.C. Busato, R.A. Gomes, M.M. Ladeira, T.D. Martins, Differences in Beef Quality between Angus (Bos taurus taurus) and Nellore (Bos taurus indicus) Cattle through a Proteomic and Phosphoproteomic Approach, PLoS One 12(1) (2017) e0170294. [32] A.M. Almeida, P. Nanni, A.M. Ferreira, C. Fortes, J. Grossmann, R.J.B. Bessa, P. Costa, The longissimus thoracis muscle proteome in Alentejana bulls as affected by

oo

f

growth path, J Proteomics 152 (2017) 206-215.

[33] V.C. Evans, G. Barker, K.J. Heesom, J. Fan, C. Bessant, D.A. Matthews, De novo

pr

derivation of proteomes from transcriptomes for transcript and protein identification,

e-

Nat Methods 9(12) (2012) 1207-11.

[34] J.B. Moynihan, Carbonic anhydrase activity in mammalian skeletal and cardiac

Pr

muscle, Biochem J. 168 (1977) 567-569.

[35] S. Locate, J. Colyer, D.J. Gawler, J.H. Walker, Annexin A6 at the cardiac

rn

32(11) (2008) 1388-96.

al

myocyte sarcolemma--evidence for self-association and binding to actin, Cell Biol Int

[36] S. Turro, M. Ingelmo-Torres, J.M. Estanyol, F. Tebar, M.A. Fernandez, C.V.

Jo u

Albor, K. Gaus, T. Grewal, C. Enrich, A. Pol, Identification and characterization of associated with lipid droplet protein 1: A novel membrane-associated protein that resides on hepatic lipid droplets, Traffic 7(9) (2006) 1254-69. [37] Y. Ding, Y. Wu, R. Zeng, K. Liao, Proteomic profiling of lipid droplet-associated proteins in primary adipocytes of normal and obese mouse, Acta Biochim Biophys Sin (Shanghai) 44(5) (2012) 394-406. [38] S. Krautbauer, E.M. Haberl, K. Eisinger, R. Pohl, L. Rein-Fischboeck, C. Rentero, A. Alvarez-Guaita, C. Enrich, T. Grewal, C. Buechler, M. Neumeier, Annexin A6 regulates adipocyte lipid storage and adiponectin release, Mol Cell Endocrinol 439 (2017) 419-430. [39] H. Misu, T. Takamura, H. Takayama, H. Hayashi, N. Matsuzawa-Nagata, S. Kurita, K. Ishikura, H. Ando, Y. Takeshita, T. Ota, M. Sakurai, T. Yamashita, E.

Journal Pre-proof Mizukoshi, T. Yamashita, M. Honda, K. Miyamoto, T. Kubota, N. Kubota, T. Kadowaki, H.J. Kim, I.K. Lee, Y. Minokoshi, Y. Saito, K. Taka hashi, Y. Yamada, N. Takakura, S. Kaneko, A liver-derived secretory protein, selenoprotein P, causes insulin resistance, Cell Metab 12(5) (2010) 483-95. [40] J. Pihlajamaki, T. Boes, E.Y. Kim, F. Dearie, B.W. Kim, J. Schroeder, E. Mun, I. Nasser, P.J. Park, A.C. Bianco, A.B. Goldfine, M.E. Patti, Thyroid hormone-related regulation of gene expression in human fatty liver, J Clin Endocrinol Metab 94(9) (2009) 3521-9.

oo

f

[41] R. Cairns, A.W. Fischer, P. Blanco-Munoz, A. Alvarez-Guaita, E. Meneses-Salas, A. Egert, C. Buechler, A.J. Hoy, J. Heeren, C. Enrich, C. Rentero, T. Grewal, Altered

pr

hepatic glucose homeostasis in AnxA6-KO mice fed a high- fat diet, PLoS One 13(8)

e-

(2018) e0201310.

[42] S.N. Dankel, D.J. Fadnes, A.K. Stavrum, C. Stansberg, R. Holdhus, T. Hoang,

Pr

V.L. Veum, B.J. Christensen, V. Vage, J.V. Sagen, V.M. Steen, G. Mellgren, Switch from stress response to homeobox transcription factors in adipose tissue after

al

profound fat loss, PLoS One 5 (2010) e11033.

rn

[43] R.M. Cowherd, R.E. Lyle, C.P. Miller, R.E. McGehee, Jr., Developmental profile of homeobox gene expression during 3 T3-L1 adipogenesis, Biochem Biophys Res

Jo u

Commun. 237 (1997) 470-475.

[44] M. Cantile, A. Procino, M. D'Armiento, L. Cindolo, C. Cillo, HOX gene network is involved in the transcriptional regulation of in vivo human adipogenesis, J Cell Physiol. 194 (2003) 225-236. [45] K. Karastergiou, S.K. Fried, H. Xie, M.J. Lee, A. Divoux, M.A. Rosencrantz, R.J. Chang, S.R. Smith, Distinct developmental signatures of human abdominal and gluteal subcutaneous adipose tissue depots, J Clin Endocrinol Metab. 98 (2013) 362-371. [46] M.M. Ladeira, J.P. Schoonmaker, M.P. Gionbelli, J.C.O. Dias, T.R.S. Gionbelli, J.R.R. Carvalho, P.D. Teixeira, Nutrigenomics and beef quality: a review about lipogenesis, Int. J. Mol. Sci. 17 (2016) 918–938.

Journal Pre-proof [47] B. Ganss, A. Jheon, Zinc finger transcription factors in skeletal development. Crit Rev Oral Biol Med. 15 (2004) 282-297. [48] O. Leon, M. Roth, Zinc fingers: DNA binding and protein-protein interactions, Biol Res. 33 (2000) 21-30. [49] S. Wei, L. Zhang, X. Zhou, M. Du, Z. Jiang, G.J. Hausman, W.G. Bergen, L. Zan, M.V. Dodson, Emerging roles of zinc finger proteins in regulating adipogenesis, Cell Mol Life Sci. 70 (2013) 4569-4584. [50] H. Gao, N. Mejhert, J.A. Fretz, E. Arner, S. Lorente-Cebrian, A. Ehrlund, K.

oo

f

Dahlman-Wright, X. Gong, S. Stromblad, I. Douagi, J. Laurencikiene, I. Dahlman, C.O. Daub, M. Ryden, M.C. Horowitz, P. Arner, Early B cell factor 1 regulates

pr

adipocyte morphology and lipolysis in white adipose tissue. Cell Metab. 19 (2014)

e-

981-992.

[51] A. Kwasiborski, T. Sayd, C. Chambon, V. Sante-Lhoutellier, D. Rocha, C.

Pr

Terlouw, Pig Longissimus lumborum proteome: Part II: Relationships between protein content and meat quality, Meat Sci. 80 (2008) 982-996.

al

[52] A. D'Alessandro, C. Marrocco, V. Zolla, M. D'Andrea, L. Zolla, Meat quality of

rn

the longissimus lumborum muscle of Casertana and Large White pigs: metabolomics and proteomics intertwined, J Proteomics, 75 (2011) 610-627.

Jo u

[53] Y. Mao, D.L. Hopkins, Y. Zhang, P. Li, L. Zhu, P. Dong, R. Liang, J. Dai, X. Wang, X. Luo, Beef quality with different intramuscular fat content and proteomic analysis using isobaric tag for relative and absolute quantitation of differentially expressed proteins, Meat Sci. 118 (2016) 96–102 [54] S. Singh, Y. Chen, A. Matsumoto, D.J. Orlicky, H. Dong, D.C. Thompson, V. Vasiliou, Biochem Biophys, ALDH1B1 links Alcohol Consumption and Diabetes, Res Commun. 463 (2015) 768-773. [55] N.M. Joseph, L.D. Ferrell, D. Jain, M.S. Torbenson, T.T. Wu, M.M. Yeh, S. Kakar, Diagnostic utility and limitations of glutamine synthetase and serum amyloid-associated protein immunohistochemistry in the distinction of focal nodular hyperplasia and inflammatory hepatocellular adenoma, Mod Pathol. 27 (2014) 62-72. [56] Q. Zhou, W.W. Souba, C.M. Croce, G.N. Verne, MicroRNA-29a regulates

Journal Pre-proof intestinal membrane permeability in patients with irritable bowel syndrome, Gut. 59 (2010) 775-784. [57] F. Ying, H. Gu, Y. Xiong, B. Zuo, Analysis of Differentially Expressed Genes in Gastrocnemius Muscle between DGAT1 Transgenic Mice and Wild-Type Mice, Biomed Res Int, 2017 (2017) 5404682. [58] Y. Liu, A. Beyer, R. Aebersold, On the dependency of cellular protein levels on mRNA abundance, Cell 165 (3) (2016) 535–550 [59] H. Yang, X.L. Xu, H.M. Ma, J. Jiang, Integrative analysis of transcriptomics and

oo

f

proteomics of skeletal muscles of the Chinese indigenous Shaziling pig compared with the Yorkshire breed, BMC Genet. 17 (1) (2016) 80

pr

[60] D.W. Pethick, G.S. Harper, V.H. Oddy, Growth, development and nutritional

Jo u

rn

al

Pr

e-

manipulation of marbling in cattle: a review, Aust. J. Exp. Agric. 44 (2004) 705–715.

Journal Pre-proof Table 1 The top 10 up- and down-regulated proteins of 120 d vs 240 d Laiwu pigs Regulate

Gene

Protein Description

Ratio

p value

up

CA13

carbonic anhydrase 13 isoform X1

2.358

0.0287

GO:0008152 metabolic process

Dclk1

serine/threonine-protein kinase DCLK1 isoform

1.845

0.0398

GO:0050794 regulation of cellular

X6 TM SB4X

Function

process

PREDICTED: thymosin beta-4

1.683

0.0349

GO:0016043 cellular component organization

SDC2

syndecan-2 precursor

1.635

0.0386

SORBS1

sorbin and SH3 domain-containing protein 2

1.632

0.00438

KEGG pathway：Proteoglycans in cancer

TAGLN2

transgelin-2

ANKRD52

serine/threonine-protein phosphatase 6 regulatory

0.0382

GO:0005515 protein binding

1.607

0.0299

GO:0005515 protein binding

1.601

0.00478

KEGG pathway：M etabolic pathways

1.569

0.0372

GO:0008289 lipid binding

1.557

0.0445

GO:0005488 binding

cytochrome c oxidase subunit NDUFA4

ANXA6

LOW QUALITY PROTEIN: annexin A6

ZYX

zyxin

OTX2

homeobox protein OTX2 isoform X2

0.246

0.0257

GO:0006355 regulation of transcription

Zfp512

zinc finger protein 512 isoform X1

0.300

0.00118

GO:0005488 binding

M DK

midkine isoform X2

0.326

0.0197

GO:0008083 growth factor activity

Sall4

sal-like protein 4

0.371

0.0303

GO:0003676 nucleic acid binding

CA2

carbonic anhydrase 2

0.424

0.00808

GO:0008152 metabolic process

GRB7

growth factor receptor-bound protein 7 isoform X3

0.455

0.00952

GO:0007165 signal transduction

CARNS1

carnosine synthase 1 isoform X1

0.464

0.0246

GO:0005524 ATP binding

CGN

cingulin

0.465

0.0422

GO:0016459 myosin complex

PREDICTED: histone H3.1

0.472

0.00678

GO:0000786 nucleosome

0.489

0.0311

GO:0005488 binding

NPAT

Pr

al

rn

H3.1

e-

NDUFA4

Jo u

down

process

1.629

pr

ankyrin repeat subunit C isoform X1

oo

f

isoform X20

GO:0050794 regulation of cellular

protein NPAT isoform X1

Journal Pre-proof Fig.1. Measurement of the meat characteristics for the longissimus dorsi (LD) muscle. (A) The 2

pr

oo

f

live weight of Laiwu pigs. (B) Histogram of the cross-sectional area (μm ) of the muscle fibers during developmental progression. (C) The intramuscular fat (IMF) content of the LD muscle over the two development stages. The data show the means ± SD analyzed by one-way ANOVA followed by Duncan's multiple comparison.

Jo u

rn

al

Pr

e-

Fig.2. The experimental design of the quantitative proteome analysis. Flowchart detailing the experiments including the sample preparation, data acquisition, and data analys is performed in this study.

Journal Pre-proof Fig.3. Results of the identified proteins in 120 d and 240 d. (a) Results of the LC-MS/MS for the proteins. After data filtering to eliminate low -scoring spectra, 46865 spectra were matched to 24066 peptides in the database with an error ＜ 5 ppm, of which 22879 were unique peptides. (b) The quantitative ratio histogram of protein quantification. (c) The volcano plot shows the up- (red) or down-regulated (blue) proteins between 240 d and 120 d groups. (d) Hierarchical clustering of differentially abundant proteins. The colour scale bar locates in the right, and blue and red indicate decreased and increased levels of the identified poteins, respectively. 120-1,120-2 and 120-3, 3

Jo u

rn

al

Pr

e-

pr

oo

f

replicate of 120 d groups; 240-1,240-2 and 240-3, 3 replicate of 240 d groups.

Journal Pre-proof Fig.4. Annotations of the differentially abundant proteins. (a) Biological processes, (b) cellular

Jo u

rn

al

Pr

e-

pr

oo

f

components and (c) molecular functions of GO annotation based function classification. (d) Wolfpsort-based subcellular localization prediction.

Journal Pre-proof Fig.5 Enrichment analysis of the differentially abundant proteins. (a) GO enrichment analysis on

e-

pr

oo

f

the ontology of biological process, cellular component and molecular function. (b) Enrichment analysis of protein domain. (c) KEGG pathway enrichment analysis of the DAPs.

Pr

Fig.6. Network of predicted protein-protein interactions against Sus scrofa database with the

al

threshold change fold > 1.5 or < 0.67, all the differentially changed proteins were used for PPI analys is by STRING 10.0 online software based on the 0.7 confidence. The network nodes are proteins and the edges represent the predicted functional associations. Each colored lines represent different evidences for each interaction: red, gene fusions; green, gene neighborhood; blue, gene

Jo u

rn

co-occurrence; purple, experimentally determined; yellow, textmining; light blue, from curated databases.

Journal Pre-proof Fig.7. Nine proteins were analyzed using RT-qPCR and PRM to confirm the reliability of the TMT

Jo u

rn

al

Pr

e-

pr

oo

f

proteomic analysis. (a) Zfp512, (b) OGA, (c) CA2, (d) ACOT7, (e) PSME4, (f) TAGLN2, and (g) CAP2B were validated by RT-qPCR, (h) HSP90B1 and ACAT1 were validated by PRM.

Pr

e-

pr

oo

f

Journal Pre-proof

Jo u

rn

al

Graphical abstract

Journal Pre-proof

Jo u

rn

al

Pr

e-

pr

oo

f

Highlights  TMT-labeled quantitative proteomic analysis on LD muscle in Laiwu pigs  Totally 5074 proteins and 52 DAPs were identified in LD muscle  Fat relevant proteins were identified, such as ALDH1B1, OTX2, AnxA6 and Zfp512  Two DEGs corresponding to DAPs were identified