Identification of genes related to skeletal muscle growth and development by integrated analysis of transcriptome and proteome in myostatin-edited Meishan pigs

Journal Pre-proof Identification of genes related to skeletal muscle growth and development by integrated analysis of transcriptome and proteome in myostatin-edited Meishan pigs

Xiang Li, Shanshan Xie, Lili Qian, Chunbo Cai, Hanfang Bi, Wentao Cui PII:

S1874-3919(19)30400-2

DOI:

https://doi.org/10.1016/j.jprot.2019.103628

Reference:

JPROT 103628

To appear in:

Journal of Proteomics

Received date:

16 May 2019

Revised date:

8 November 2019

Accepted date:

22 December 2019

Please cite this article as: X. Li, S. Xie, L. Qian, et al., Identification of genes related to skeletal muscle growth and development by integrated analysis of transcriptome and proteome in myostatin-edited Meishan pigs, Journal of Proteomics (2019), https://doi.org/ 10.1016/j.jprot.2019.103628

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© 2019 Published by Elsevier.

Journal Pre-proof Identification of genes related to skeletal muscle growth and development by integrated analysis of transcriptome and proteome in myostatin-edited Meishan pigs

Xiang Li# , Shanshan Xie # , Lili Qian, Chunbo Cai, Hanfang Bi, Wentao Cui*

Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, P.

These two authors have contributed equally to this work

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#

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R. China.

*Correspondence to: Wentao Cui, email: [email protected]

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Address for correspondence:

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Wentao Cui, Associate Professor, Institute of Animal Sciences, Chinese Academy of

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Agricultural Sciences, Beijing 100193, P. R. China. Tel: +86-010-62819480.

Journal Pre-proof ABSTRACT Embryonic development of skeletal muscle is a complex process that is important to the growth of skeletal muscle after birth. However, the mechanisms by which skeletal muscle growth and development in embryonic phase remain unclear. We have previously produced myostatin-knockout (MKO) Meishan pigs with double-muscle (DM) phenotype via zinc finger nucleases (ZFN) technology. To further investigate the molecular mechanisms involved in skeletal muscle growth and development, in this study, we performed an integrated analysis of transcriptome and proteome in longissimus dorsi muscle from myostatin wild type (MWT)

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and MKO pigs on 65 days post coitus. Results showed that, compared with MWT group, there were 438 differentially expressed genes (DEGs) and 66 differentially expressed proteins

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(DEPs) in MKO group. These DEGs and DEPs were mainly enriched in signaling pathways

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that are involved in skeletal muscle growth and development, glucose metabolism and apoptosis. Furthermore, we identified two key genes, Troponin T 1 (TNNT1) and Myosin

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regulatory light chain 9 (MYL9), which showed significant changes in both mRNA and protein levels with the similar changing trends in MKO group. It is thus speculated that

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TNNT1 and MYL9 may play an important role in skeletal muscle growth and development.

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Significance: Our study analyzed some important regulatory genes and proteins during skeletal muscle growth and development, our results provided (1) a new insight to further

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understanding of the molecular mechanisms by which growth and development are regulated in porcine skeletal muscle, and (2) some possible molecular makers for improvement of meat quality in the animal husbandry and diagnosis of human muscle diseases in medicine. Key words: Skeletal muscle; Myostatin; Transcriptome; Proteome; Meishan pigs

Journal Pre-proof 1. Introduction Pork is one of the important sources of proteins in human diet. Pigs and human have higher similarity and homology in physiology, pathology and genomics, so pigs are ideal disease models [1-3]. Hence, studies on the mechanisms of skeletal muscle growth and development in pigs can help to improve pork quality and make contributions to biomedical research. The growth and development in skeletal muscle is a complicated process, including myogenic progenitor cells proliferation, migration, differentiation, fusion to form myotubes and eventually form muscle tissue during embryonic development and muscle regeneration

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and repair after birth [4-6]. Skeletal myogenisis occurs mainly at embryonic stage [6]. Furthermore, the number of myofibers in porcine skeletal muscle has been determined before

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birth [4], so the prenatal skeletal muscle development plays a critical role in postnatal skeletal

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muscle growth and meat quality [7].

Many biological pathways and regulatory factors are involved in the growth and

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development process. Myostatin (MSTN) is a member of the transcription growth factor-β (TGF-β) super family, it negatively regulates skeletal muscle development [8, 9]. Previous

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reports showed that natural mutations in MSTN gene could lead to faster growth of skeletal

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muscle and the subsequent weight gains [10-12]. At the embryonic development stage, the expression of MSTN gene limited skeletal muscle development, the inhibition of MSTN

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expression might enhance both muscle mass and bone strength [13]. Marie et al. [14] pointed out that MSTN is an important factor in balancing the proliferation and differentiation of embryonic progenitor cells and in controlling the continuous muscle growth during the entire embryonic period. MSTN also plays a regulatory role in the number, size and type of myofibers. Schiaffino et al. [15] demonstrated an increase in the number of fast muscle fibers and a decrease in slow muscle fibers in double-muscle (DM) cattle containing a natural loss-of-function MSTN mutation. Similarly, the MSTN-induced changes in muscle fiber type were also observed in MSTN-knockout mice [16, 17]. Since MSTN plays an important role in skeletal muscle growth and development, establishing a MSTN mutant animal model can help researchers better study the mechanisms of skeletal myogenesis and muscle diseases. In recent years, transcriptomics and/or proteomics have been widely used in the research field of skeletal muscle growth and development and meat quality [18-20]. Li et al. [21]

Journal Pre-proof identified 150 differentially expressed genes (DEGs) in LDM using transcriptomic sequencing and they identified two critical candidate genes, Triadin (TRDN) and MSTN, which were involved in muscle growth and pork quality. Murani et al. [22] performed the transcriptomic sequencing analysis for embryonic muscle tissue collected from two different pig breeds and identified 37 breed-associated DEGs and 48 development-associated DEGs. Zhang et al. [23] employed Isobaric Tag for Relative and Absolute Quantification (iTRAQ) technology to analyze the protein expression profiles of the LDM collected from the early embryonic development stage in Wuzhishan and Landrace pigs, and identified several

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candidate proteins related to myofibers components and myogenesis. It has been known that management conditions such as housing, weaning, transport, heat and nutritional stress can

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have an impact on pork quality. In a recent comprehensive literature review, Marco-Ramel et

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al. [24] pointed out that proteomics analysis could play a critical role in identifying new biomarkers that could objectively and accurately predict the effects of non-genetic factors

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such as stress and animal welfare on animal economic characters. Kim et al. [25] compared the transcriptome and proteome of the LDM from the Korean native pigs and the Western

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meat-producing Landrace pigs and found that six genes involved in energy and lipid

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metabolism are more highly expressed at the protein or mRNA levels in the Korean native pig breed. Xu et al. [26] analyzed transcriptomic and proteomic data of porcine skeletal muscle

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from various growth stages and found that there were many DEPs and DEGs during different development stages from embryos to mature pigs. However, a single omics analysis is not usually a good method for DEGs prediction due to the low correlations between transcription and translation level [27]. Proteomics can be used for qualitative and quantitative analysis of proteins, which are the direct effectors of genes and the material basis of life [24]. On the other hand, transcriptomic data can help map proteins to certain pathways or regulatory processes to explain life phenomenon [28, 29]. Therefore, the integrated analysis of transcriptome and proteome has become one of the hotspots in animal husbandry as well as in all life science research. Our lab recently successfully generated MKO Meishan pigs containing the loss-of-function MSTN mutations by zinc finger nucleases (ZFN) and somatic cell nucleus transfer (SCNT) techniques [30]. Similar to previous studies, the MSTN-edited pigs showed

Journal Pre-proof obvious skeletal muscle hypertrophy and an increased number of fast muscle fibers compared to the MSTN wide type (MWT) [30]. In this study, we performed RNA-seq and iTRAQ to produce a comprehensive analysis of LDM from MKO and MWT Meishan pigs on 65 days post coitus (dpc). By combing transcriptomic and proteomic analysis of DEGs and DEPs, we expected to identify key candidate genes involved in skeletal muscle growth and development, and to provide new insights into the molecular mechanisms of skeletal myogenesis. Additionally, we were able to establish proteomic and transcriptomic reference database for MSTN gene editing Meishan pigs to further investigate muscle growth and development,

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including myoblast proliferation and differentiation, myofibers type, muscle regeneration,

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muscle atrophy and hypertrophy.

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2. Materials and Methods

All experimental protocols related to animal work described in this study were reviewed

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and approved by the Institutional Animal Care and Use Committee (IACUC) at Institute of

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Animal Sciences, Chinese Academy of Agricultural Sciences.

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2.1. Animal production, care and sample collection

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Both MKO and MWT Meishan pigs were produced using the same method as previously described[30]. All pigs were maintained in Qingdao animal facility, fed with the same standard diet, and raised under the same conditions. LDM samples were collected on 65 dpc from 3 MKO and 3 MWT Meishan sows, respectively. Each muscle sample was snap frozen in liquid nitrogen and then stored in a -80 ℃ freezer until used for transcriptome sequencing or iTRAQ analysis.

2.2. Transcriptome analysis

2.2.1. Transcriptome sequencing and data analysis The Illumina High-Seq 2000 platform was used for transcriptome sequencing. Clean reads with high quality were aligned to the reference genome using TopHat v2.0.12. FPKM

Journal Pre-proof (expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) of each gene was used to estimate gene expression levels. Differential expression analysis of two conditions/groups was performed using the DESeq R package (1.8.3). The Database for Annotation, Visualization and Integrated Discovery (DAVID) online software (http://david.abcc.ncifcrf.gov/home.jsp) was used for function annotation and pathway analysis of the DEGs. Other detailed methods of transcriptome sequencing and data analysis were performed as described in our previous study [31].

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2.2.2. Real-time quantitative PCR (RT-qPCR) analysis

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Total RNA was extracted from muscle samples using Trizol (Invitrogen) by following

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the manufacturer’s instructions. Each sample (1 μg) was reverse-transcribed into cDNA by using the Revert Aid TM First Strand cDNA Synthesis Kit (Fermentas). RT-qPCR of ten

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DEGs was performed in the Applied Biosystems 7500 Real-time PCR system using 10

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pmol/L of each specific primer (Table1) and SYBR Premix Ex Tag TM (Takara) according to the manufacturer’s protocols. Porcine TATA-binding protein 1 (TBP1) was used as an internal control, and the 2-ΔΔCt method was employed to calculate the relative expression levels of

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2.3. Proteome analysis

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mRNAs.

2.3.1. Protein extraction

For sample preparation, each muscle sample was ground in liquid nitrogen. Two mL of lysis buffer (8 mol/L urea, 2% SDS, 1 x Protease Inhibitor Cocktail (Roche Ltd. Basel, Switzerland)) was added to each sample, followed by sonication on ice and centrifugation at 13,000 rpm for 10 min at 4 °C. The supernatant was then transferred to a fresh tube. For each sample, proteins were precipitated with ice-cold acetone at -20 °C overnight, and the precipitations were cleaned with 50% ethanol and 50% acetone three times. 2.3.2. Protein digestion and Tandem Mass Tag (TMT) labeling Add 8 mol/L urea in 0.1 mol/L Tris-HCl, pH = 8.0 to dilute 50 μg of protein to a final volume of 100 μL, then 11 μL of 1 mol/L DL-Dithiothreitol (DTT) was added and were

Journal Pre-proof incubated at 37 °C for one hour. Each sample was then transferred to a 10 kDa ultrafiltration tube (Millipore, MA, USA) and centrifuged at 14000 g for 15 min. Then 100 μL of 55 mmol/L iodoacetamide was added to each ultrafiltration tube, followed by incubation for 20 minutes in the dark at room temperature. After the incubation, each sample was then buffer-exchanged into 50 mmol/L triethylammonium bicarbonate (TEAB). Proteins were digested with sequence-grade modified trypsin (Promega, WI, USA), and the peptide mixture from each group was labeled with chemicals from the TMT reagent kit (Pierce Biotechnology,

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IL, USA) by following the manufacturer’s instructions. Samples were then dried in vacuum.

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2.3.3. High pH reverse phase separation

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The vacuum dried peptides mixture from each group described in the above step was redissovled in buffer A (20 mmol/L ammonium formate in water, pH 10.0, adjusted with

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ammonium hydroxide), and then fractionated by high pH separation using a reverse phase

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column (XBridge C18 column, 4.6 mm x 250 mm, 5 μm, from Waters Corporation, MA, USA) connected to a Ultimate 3000 system (Thermo Fisher Scientific, MA, USA). A linear gradient of 5% to 45% buffer B (20 mmol/L ammonium formate in 80% acetonitrile, pH 10.0, adjusted

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with ammonium hydroxide) in 40 min was used to achieve high pH separation. The flow ra te

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was 1 mL/min and column temperature was maintained at 30°C. Twelve fractions were

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collected during the separation run. Each fraction was dried in a vacuum concentrator for the next step (see below).

2.3.4. High performance liquid chromatography (HPLC)-MS/MS analysis Each fraction from the above step was resuspended with 30 μL of solvent C (0.1% formic acid in water), separated by nano LC and analyzed by online electrospray tandem mass spectrometry. The experiments were performed on a Nano Aquity UPLC system (Waters Corporation, Milford, MA) connected to a Quadrupole-Orbitrap mass spectrometer (Q-Exactives, Thermo Fisher Scientific, Bremen, Germany) equipped with an online nano-electrospray ion source. Five μL of peptide sample was loaded onto the trap column (Thermo Fisher Scientific Acclaim Pep Map C18, 100 μm x 2 cm), with a flow of 10 μL/min for 3 min and subsequently separated on an analytical column (Acclaim Pep Map C18, 75 μm x 15cm) with a linear gradient of 2% to 40% solvent D (0.1% formic acid in acetonitrile) in

Journal Pre-proof 100 min. The flow rate was 300 nL/min and column temperature was maintained at 40°C. The electrospray voltage of 1.9 kV versus the inlet of the mass spectrometer was used. 2.3.5. Database searching Tandem mass spectra were extracted, charge state deconvoluted and deisotoped by Mascot Distiller version 2.6. All MS/MS samples were analyzed using Mascot (Matrix Science, London, UK; version 2.5.1). Mascot was set up to search the NCBI non-redundant (nr) Sus Scrofa database (Accession number: GCF_000003025.6. 201610, 69930 entries)

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assuming the digestion enzyme being trypsin. Mascot was searched with a fragment ion mass

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tolerance of 0.050 Da and a parent ion tolerance of 10.0 PPM. Carbamidomethyl of cysteine,

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TMT6 plex of lysine, and the N-terminus were specified in Mascot as fixed modifications. Deamidation of asparagine and glutamine, oxidation of methionine, and acetyl of the

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2.3.6. Criteria for protein identification

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N-terminus were specified in Mascot as variable modifications.

Scaffold (version Scaffold_4.7.1, Proteome Software Inc., Portland, OR) was used to

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validate MS/MS based peptide and protein identifications. Peptide identifications were

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accepted if they achieved a False Discovery Rate (FDR) less than 1.0% by the Scaffold Local FDR algorithm. Protein identifications were accepted if they contain at least 2 identified

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peptides. Proteins that contain similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. 2.3.7. Quantitative data analysis Scaffold Q+ (version Scaffold_4.7.1, Proteome Software Inc., Portland, OR) was used to quantitate peptide and protein identifications. Normalization was performed iteratively (across samples and spectra) on intensities, as described in Statistical Analysis of Relative Labeled Mass Spectrometry Data from complex samples using analysis of variance (ANOVA) [32]. Medians were used for averaging. Spectra data were log-transformed, pruned of those matched to multiple proteins and those missing a reference value, and weighted by an adaptive intensity weighting algorithm. DEPs were determined by applying Mann-Whitney Test with significance level p < 0.05 and fold change over 1.2.

Journal Pre-proof 2.3.8. Gene Ontology (GO) Analysis Blast2 GO version 4 was used for functional annotation. Whole protein sequence database was analyzed by Blast P using whole NCBI nr database (Accession number: GCF_000003025.6. 201605), mapped, and annotated with gene ontology database. Statistically altered functions of DEPs were calculated by Fisher’s exact test in BLAST2 GO [33]. 2.3.9. Kyoto Encyclopedia of Genes and Genomes (KEGG) Analysis

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Pathway analysis was processed by KOBAS (http://kobas.cbi.pku.edu.cn/). Pathways

2.3.10. Protein-protein interaction network

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with p value < 0.05 were recognized as significantly changed [34].

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Protein-protein interaction network was constructed by using STRING v10

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(www.string-db.org) [35].

2.4.1. Data analysis

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2.4. Integrated analysis of proteome and transcriptome

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The Poisson distribution was used to assess the relationship between mRNA and protein

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expression levels in muscle samples from MWT and MKO Meishan pigs. The average fold change of the obtained transcriptome and proteome data was converted to log 2 (fold change). The transformed data was calculated using the R program and displayed as a scatter plot, screening criteria (for mRNA, fold change > 2; for protein, fold change > 1.2). 2.4.2. RT-qPCR and Western blot analysis RT-qPCR was performed using the methods described above, and the primer sequences of TNNT1, MYL9 and the internal reference gene TBP1 are shown in Table 1. SPSS14.0 was used for statistical analysis, the Student’s unpaired t-test was conducted to identify genes differing in expression, p < 0.05 was considered as significant. Extracting protein from using the methods as described above, total protein concentration was determined using a BCA Quantitative Test Kit (Beyotime). Each protein sample was loaded in equal amount and then separated by 12.5% SDS PAGE. Following

Journal Pre-proof transfer of protein from gel to nitrocellulose (NC) filter membrane and blocked with 5% skimmed milk for 2 h. Western blot was performed using standard method for the following proteins with corresponding detection antibodies (in brackets): troponin T1, slow skeletal type (TNNT1) (Rabbit polyclonal antibody, Abcam), myosin light chain 9 (MYL9) (Rabbit polyclonal antibody, Abcam), Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (Rabbit polyclonal antibody, Abcam) and beta-tublin (Rabbit polyclonal antibody, Cohesion) were used as an internal reference in Western blot. Super Signal West Pico chemiluminescent substrate (Thermo Fisher Scientific) was used to develop color band.

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3.1. Analysis of DEGs using transcriptomic data

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3. Results

DEGs in MKO and MWT pigs were analyzed using cuffdiff. The q value < 0.05 and fold

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change > 2 were set as the threshold value for DEGs. A total of 438 genes were identified as

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DEGs in LDM from MKO pigs when compared to MWT pigs. Among these 438 DEGs, 56 genes are newly predicted. Two hundred genes are up-regulated and 238 are down-regulated

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in the MKO group (see in Supplementary Table S1). Compared with MWT group, the expression levels of 5-hydroxytryptamine receptor 1D (HTR1D), Neurotrimin (NTM), solute

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carrier family 26 member 7 (SLC26A7), C-X-C motif chemokine ligand 13

(CXCL13),

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polypeptide N-acetylgalactosaminyltransferase 3 (GALNT3), gap junction protein, delta 2 (GJD2) were significantly increased in MKO group, and the expression levels of myosin heavy chain 7 (MYH7), F-box protein 40 (FBXO40), tropomyosin 3 (TPM3), troponin T1, TNNT1 and myosin light chain 3 (MYL3) were significantly decreased in MKO group. To further explore these DEGs, DAVID software was used to predict the potential function of each differentially expressed mRNA in all possible KEGG pathways (see in Supplementary Table S2). The hyper geometric distribution relationship between these genes and pathways was calculated according to the selected DEGs. The corresponding p-values were obtained. The differentially expressed mRNAs involved in signal pathways were screened by p-value < 0.05. The enrichment results in Fig. 1 show that these DEGs are mainly enriched in the hypertrophic cardiomyopathy (HCM), extracellular matrix (ECM)-receptor interaction, phosphatidylinositol-3-kinase/

protein

kinase

B

(PI3K-Akt)

signaling

pathway,

Journal Pre-proof Glycolysis/Gluconeogenesis and pyruvate metabolism signaling pathways, indicating that these differentially expressed mRNAs are involved in signaling pathways of skeletal muscle growth and glucose metabolism. Although most of DEGs we identified did not overlap the DEGs identified by Chelh et al. [36] in myostatin-null mice, the pathways enriched by these DEGs were similar, suggesting that the same functional changes may occur in different species after loss-of-function mutation of the MSTN gene. Results of RT-qPCR analysis of these genes (See in Fig. 2) showed that the expression trends of these genes were consistent

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with the RNA-seq results, indicating the reliability of transcriptome data analysis.

3.2. Proteomic analysis

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3.2.1. Identification results from protein quality and quantitative analysis The labeled peptide mixture was separated by a strong cation column (SCX). Thirty-four

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fractions between 7-40 mins were collected according to the peak time. The collected 34

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fractions were combined into 10 fractions and then analyzed by mass spectrometry and Mascot. Scaffold is used to convert the analytical results into a unified Peptide Prophet, which

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is then plotted against the corresponding number of identified spectra. Of the 170023 spectra in the experiment at the given thresholds, 135735 (80%) were included in quantitation. The

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frequencies of incorrect peptide spectrum matches (PSMs) were estimated using the

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“Target-Decoy” search strategy based on a reverse database generated automatically in Mascot [37]. As shown in Fig. 3A, the majority of PSMs in the Target library (red part) have high scores, while fewer PSMs have high scores in the reverse library (green part), indicating the high correct rate of PSMs in this library search and the effective identification. All analytic results were combined and filtered by Peptide FDR ≤ 0.01 [38]. A total of 27,518 different peptides were obtained by statistical analysis, from which 2446 proteins were identified. Please refer to Supplementary Table S3 for the quantitative results of peptides identification and Supplementary Table S4 for the quantitative results of proteins identification. Since each mass spectrometer has its own measurement range, so the identified peptides also have a length limit. Peptides that are too long or too short cannot be detected by the mass spectrometer. Fig. 3B shows the distribution of peptide lengths. The average length of the identified peptides is 12 amino acids, indicating that the peptide length is reasonable. In

Journal Pre-proof addition, the identified proteins have higher coverage (see Fig. 3C), about 11% of proteins has a coverage rate of >50%. As seen in Fig. 3D, most proteins have a molecular mass in the range of 10-60 kDa. Among these proteins, 253 (11%) proteins have a molecular weight in the range of 10-20 kDa, 341 (14%) proteins have a molecular weight in the range of 20-30 kDa, 324 (13%) proteins have a molecular weight in the range of 30-40 kDa, 297 (12%) proteins have a molecular weight in the range of 40-50 kDa, and 234 (10%) proteins have a molecular weight in the range of 50-60 kDa. To identify DEPs, at least 1.2 fold changes in protein expression levels and a p value of <

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0.05 are set as criteria. Based on these criteria, a total of 66 DEPs were identified in MKO group when compared to MWT group (see in Supplementary Table S5). Of these 66 DEPs, 54

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proteins were up-regulated in MKO group, which account for 82% of the total number of

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DEPs; and 12 proteins were down regulated in MKO group, which account for 18% of the total number of DEPs. These differential proteins were analyzed based on their classifications,

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then R language software package was used to make a heat map with the quantitative ratio data of the DEPs (Fig.4). A significant difference in the amount of protein expression in the

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LDM tissue was observed between MKO group (MKO1, MKO2, MKO3) and MWT (MWT1,

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MWT2, MWT3) group. On the other hand, the difference between the three replicates in each group is relatively small and can be clustered together.

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3.2.2. GO analysis of DEPs

To further study the functions of DEPs, we performed annotation of GO functional enrichment. The enrichment terms can provide useful information on gene functions using GO analysis of DEGs or DEPs. The results of enrichment analysis of DEPs using GO analysis are shown in Supplementary Table S6 and Fig. 5A. The functional terms enriched in biological processes include muscle system process (GO: 0003012), transition between fast and slow fiber (GO: 0014883), muscle tissue development (GO: 0060537), muscle structure development (GO: 0061061), glycolytic process (GO: 0006096), dormancy process (GO: 0022611), muscle fiber development (GO: 0048747), brown fat cell differentiation (GO: 0050873), glycolytic process through glucose-6-phosphate (GO: 0061620). Functional terms enriched in the molecular function category include actin binding (GO: 0003779), troponin T binding

(GO:

0031014),

structural

constituent

of

muscle

(GO:

0008307),

Journal Pre-proof 6-phosphofructokinase activity (GO: 0003872), pyruvate kinase activity (GO: 0004743), actin-dependent ATPase activity (GO: 0030898). The main functional terms related to cell components include myofibril (GO: 0030016), contractile fiber (GO: 0043292) sarcomere (GO: 0030017), myofilament (GO: 0036379), muscle myosin complex (GO: 0005859), and actin filament (GO: 0005884). In summary, the differential GO terms obtained by enrichment analysis indicated that the functions of DEPs identified in the MKO group and the MWT group were mainly related to skeletal muscle growth and development and glucose metabolism. Caroline et al. [39] performed proteomics analysis of skeletal muscle from the

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Follistatin-induced Skeletal Muscle Hypertrophy mice, whose phenotype consistent with MSTN-knockout models, and the results showed that most of DEPs are also enriched in the

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above GO terms. Bouley et al. [40] analyzed the changes in protein expression profiles

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induced by bovine skeletal muscle hypertrophy and identified DEPs involved in muscle contraction, energy conversion in MSTN gene-deficient cattle, which was consistent with the

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results of our study.

3.2.3. Annotation results from KEGG pathway analysis

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The DEPs sequences identified in LDM samples of MKO and MWT pigs were aligned

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with sheep protein sequences from the KEGG GENES database and annotated to the relevant KEGG pathway by the KEGG Orthologs (KO) number of the homologous/similar proteins. In

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this study, the related signaling pathways involved in DEPs are shown in Supplementary Table S7 and Fig. 5B. It is clear that that the DEPs are mainly enriched in the following pathways: cardiac muscle contraction (CMC), hypertrophic cardiomyopathy (HCM), tight junction, biosynthesis of amino acids (BAA), peroxisome proliferator-activated receptor (PPAR)

signaling

pathway,

glycolysis/gluconeogenesis,

adrenergic

signaling

in

cardiomyocytes (ASIC), and FoxO signaling pathway. These enriched pathways are mainly related to apoptosis, adipocyte differentiation, glucose metabolism, and dynamics, growth and development, metabolism of skeletal muscle or cardiac muscle. Fig. 6 shows that the KEGG signaling pathway enriched by DEPs associated with skeletal muscle growth and development (p < 0.05), and the columns represent the MKO and MWT samples, and the rows represent DEPs. Red color indicates the DEPs whose expression levels are significant ly increased in the corresponding group, while green indicates the DEPs whose expression levels are

Journal Pre-proof significantly decreased. It can be seen from this figure that some myofibrillar proteins, such as myosin, myosin light chain (myosin family), TnC (troponin family), TPM (tropomyosin family) are enriched in signal pathways such as CMC, HCM, PPAR, and ASIC. Additionally, these DEPs are involved in the regulation of network interaction that modulating a series of biological processes such as muscle growth and development, contraction movement. 3.2.4. STRING-Database Protein Interaction Network The STRING database contains information on direct and indirect interactions of known and predicted proteins. The interaction of all related proteins can be visualized in the

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STRING-Database Protein Interaction Network diagram and each node represents a protein in this map. The interaction protein map for DEPs related to muscle growth and development is

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shown Fig. 7. These proteins have strong interactions and are closely related to each other.

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Among them, TNNT1, myosin heavy chain 1 (MYH1), myosin heavy chain (MYH7), MYL9, tropomyosin 1 (TPM1), troponin I 3 (TNNI3), troponin I1 (TNNI1), troponin C1 (TNNC1)

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and troponin T2 (TNNT2) are important nodes in the network map. They all belong to members of the protein family related to myofibers components. Therefore, these proteins are

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likely important factors regulating muscle growth and development in pigs.

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3.3. Integrated analysis of proteome and transcriptome

Although the research methods for proteome and the transcriptome are different, their ultimate goals are to obtain specific information on different gene expression. The integrated analysis of transcriptome and proteome can provide a whole picture of gene expression profile and more information related to the status of post-transcriptional modification and regulation. Analysis of these two sets of data can make the transcriptome and proteome approach complementary to each other. To more accurately and comprehensively explore the molecular mechanisms of porcine skeletal muscle growth and development, we performed integrated analysis of transcriptome and proteome data generated from LDM collected on 65 dpc from MKO and MWT pigs, and then correlated mRNA expression with protein expression. The results showed that there was no linear relationship between transcriptome and proteome expression with Pearson’s correlation coefficient of -0.1513. All integrated

Journal Pre-proof analysis data points were divided into nine quadrants according to significant differential threshold values (Fig. 8). The genes of the third and the seventh quadrants showed the same trend for both transcriptome and protein expression. A gene is considered to play an important role in regulating muscle growth and development which has same expression trend at both of mRNA and protein levels. Based on data points from these two quadrants, we identified two DEGs, TNNT1 and MYL9. TNNT1 was significantly down-regulated while MYL9 was significantly up-regulated in MKO group at both transcriptome and proteome expression level. These two members of myofibrillar proteins family are likely to be key genes involved in

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muscle growth and development during porcine embryonic development. In particular, TNNT1 is down-regulated as the marker gene of slow myofibers in MKO, which is consistent

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with our previous study that showed a decrease in the proportion of slow muscle fibers [30].

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The fifth quadrant (gray dots) indicates that either mRNAs or proteins are not differentially expressed. For the second and fourth quadrants, the expression levels of proteins are lower

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than that of mRNAs. For the sixth, eighth, and ninth quadrants, the expression level of proteins are greater than that of mRNAs. These genes had lower or higher protein abundance

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than mRNA, indicating that they may undergo post-transcriptional regulation, such as

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microRNA-induced gene silencing or DNA methylation, and post-translational modification such as protein phosphorylation, or other biological processes, which will provide more

development.

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evidence for studying specific mechanisms of related proteins in skeletal muscle growth and

3.4. RT-qPCR and Western blot analysis TNNT1 and MYL9

To further verify TNNT1 and MYL9 as two key genes, the RT-qPCR and Western blot were performed. As shown in Fig. 9, compared with MWT group, the transcriptomic and proteomic expression level of TNNT1 gene were significantly down-regulated while MYL9 gene was significantly up-regulated in MKO group. This result is consistent with the RNA-seq and iTRAQ results described above. 4. Discussion Skeletal muscle development is a very complex process regulated by many key genes,

Journal Pre-proof proteins and signaling pathways. The growth and development of skeletal muscle and exercise capacity are related to the regulation of a series of myofibrillar proteins including myosin, tropomyosin, and troponin, all of which are each regulated by different genes [41]. Myosin, one of the major functional structural proteins in muscle tissue, is found in myofibrils, and is the most expressed protein in muscle cells [42]. It releases energy from ATP and binds to actin [43]. Troponin is present in the filaments of myofibrils and is composed of three different subtypes: troponin C (TnC), troponin I (TnI) and troponin T (TnT). Troponin is not only a key regulatory protein for skeletal muscle and myocardial contraction but also

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affects meat quality [44, 45]. Tropomyosin, a complex protein composed of two α-helix fibrin chains, is widely found in various muscle cells and is an indispensable regulatory protein in

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muscle contraction [46, 47].

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In this study, we have identified key DEGs such as MYH7, TPM3, TNNT1, and MYL3. It is very interesting to note that these genes encode myofibrillar proteins and participate in the

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regulation of muscle growth and development. Among them, MYH7 encodes β-myosin heavy chain, which is a key component of type I slow muscle fiber [48]. MYH7’s intron encodes

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miR-208b. Van Rooij et al. have previously showed that miR-208b interacts with its target

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genes to affect the transformation of mouse skeletal muscle fiber type [49]. TPM3 gene encodes the slow-shrinking skeletal muscle α-TM protein, which has an effect on the binding

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of thick and thin filaments, thereby affecting the state of muscle fibers, indirectly leading to the hydraulic, tenderness and flesh color of pork. Changes in production traits affect the quality of pork [50]. Our current study confirmed that slow muscle fibers are converted into fast muscle fibers when MSTN is knocked out in Meishan pig muscles, and those genes in slow muscle fibers or acting on slow muscle fibers are significantly reduced [30]. We performed an interaction analysis of DEPs related to muscle growth and development and found that all of the important nodes in the interaction network are myofibrillar proteins, implying that these proteins are related to each other and can regulate muscles through interaction. After GO function annotation, we found that the DEPs are mainly enriched in GO entries such as fast and slow muscle fibers turnover, muscle tissue development, actin binding, troponin T binding, and myofibrils. Our present findings are consistent with previous studies [51-53]. Among them, TNNT1 (slow muscle subtype), TNNT2 (myocardial subtype), TNNI3

Journal Pre-proof (myocardial type), TNNI1 (slow muscle type) and TNNC1 (slow muscle and cardiac troponin) belong to the troponin family; TPM1 belongs to the tropomyosin family; and MYH1, MYH7 belong to muscle globulin family, which is essential for the development of fast and slow muscle fibers [54]. Muthuchamy et al. [55] found that TPM1 was not expressed in mouse skeletal muscle at 4.5 d of embryonic stage, and at low expression level on 7.5 d. The expression of TPM1 gene can be detected at 8.5 d and during subsequent development, which is consistent with muscle development [55]. In addition, we also identified DEGs such as HTR1D, NTM, SLC26A7, CXCL13, GALNT3, GJD2, which are lowly expressed in skeletal

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muscle, but are significantly up-regulated in LDM of MKO Meishan pigs, indicating that they may be involved directly or indirectly in the regulation of skeletal muscle growth and

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development. At present, there are few studies on the regulatory mechanisms of these genes in

direction for the function of these genes.

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skeletal muscle growth and development, and our results may open up a new research

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RNA sequencing can provide quantitative mRNA expression levels for a sample, while iTRAQ measures and quantifies amount of expressed proteins at translational level, and can

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obtain information on protein functions. The integrated analysis of transcriptome and

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proteome can provide a whole picture of gene expression profile and more information related to the status of post-transcriptional modification and regulation. By combing transcriptomic

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and proteomic analysis in this study, we identified two key genes, TNNT1 and MYL9, which showed the same expression trends at mRNA and protein levels. Similar to TNNI1 which is identified from gastrocnemius (GAS) of MSTN-knockout mice in previous study[56], TNNT1 gene is also specifically expressed in skeletal slow muscle and regulates the contraction and relaxation of this type of muscle. TNNT1 encodes a protein called slow skeletal muscle Troponin T (ssTnT), which is a member of the troponin T family and is expressed in skeletal slow muscles. TNNT1 mutations are involved in the development of many diseases. Johnston et al. [57] reported that mutations in the TNNT1 gene resulted in complete loss of ssTnT in slow skeletal muscle, leading to severe nemaline myopathy, which may be fatal in infants.. In addition, TNNT1 also plays very important roles in livestock production. Wang et al. [51] performed a network interaction analysis of the differentially expressed proteins in Chinese and foreign pig group, and found that TNNT1 is at the key node of the network, indicating that

Journal Pre-proof it plays an important regulatory role in pig growth traits. Based on data from 100 pork samples and meat quality traits, Pierzchala et al. [53] observed that the expression level of TNNT1 was closely related to pork quality. MYL9 is a myosin-regulated light chain, and it is phosphorylated and dephosphorylated by various regulatory factors such as myosin light chain kinase (MLCK) to regulate ATP kinase activity and control muscle cells, protein contraction, cell migration, mitosis, and signal transduction [58, 59]. Currently, it is known that MLY9 is involved in many diseases. The expression of MLY9 mRNA was up-regulated in breast cancer, liver cancer, non-small cell lung cancer and other tumor cells, indicating that

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MYL9 gene may become a potential tumor therapeutic target [60-62]. There are very limited reports on the direct effect of MYL9 on skeletal muscle growth and development. In our

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current study, MYL9 was significantly up-regulated at both mRNA and protein levels in MKO

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pigs, indicating MYL9 is important in skeletal muscle growth and development. We suspect that MYL9 may regulate muscle energy metabolism through the control of ATPase activity, or

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MYL9 may be indirectly involved in the regulation of skeletal muscle growth and development by interacting with myosin.

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Some genes in the ninth quadrant had a negative correlation of mRNA expression and

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protein abundances, such as such as MYL3、 MYH7、 PDZ and LIM domain 1 (PDLDM1)、 Collagen type IX alpha 1 chain (COL9A1)、 Collagen type VI alpha 5 chain (COL6A5)、

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Sulfotransferase family 1A member 3 (SULT1A3)、 Keratin, type I cytoskeletal 42. PDLIM1 is a cytoskeletal protein that interacts with actin stress fibers, it also can target proteins to specific subcellular locations and facilitate cell migration [63, 64]. It is reported that COL9A1 is involved in the length, width and lean rate of body in pigs [65, 66]. COL6A5 belongs to the collagen protein family in extracellular matrix and has the function of regulating cellular metabolic process [67]. These genes were significantly up-regulated at the transcriptome level and significantly down-regulated at the protein level. In our previously generated gene-edited Meishan pigs, although total MSTN mRNA expression was increased, the functionally active MSTN protein expression was reduced [30]. These genes are consistent with the expression pattern of MSTN, suggesting that they may be regulated by MSTN. However, further study is and required to confirm this hypothesis. 5. Conclusion

Journal Pre-proof In this study, we analyzed transcriptomic and proteomic data of LDM collected from MKO and MWT Meishan pigs, and identified the following DEGs: HTR1D, NTM, SLC26A7, CXCL13, GALNT3, GJD2, RAH16, MYH7, FBXO40, TPM3, TNNT1 and MYL3; and a few differentially expressed key proteins such as TNNT1, MYH1, MYH7, MYL9, TPM1, TNNI3, TNNI1, TNNC1 and TNNT2. Based on results of KEGG pathway annotation, these genes and proteins were found to be involved in pathways of skeletal muscle growth and development. The integrated analysis of transcriptomic and proteomic data further indicated that MYL9 and TNNT1 showed strong correlations at both transcriptome and proteome levels and showed the

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same trends in levels of mRNA expression and protein expression, implying that they may be important genes regulated by MSTN during skeletal muscle growth and development. Our

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results provide a new approach to investigate the specific mechanisms by which MSTN

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regulates skeletal muscle growth and development. It is noted that the specific mechanisms of these DEGs and DEPs identified in this study are not known and it deserves further study in

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the future.

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Supplementary Tables Supplementary Table S1: Identification analysis results of differentially expressed genes between MKO and MWT groups Supplementary Table S2: KEGG pathway analysis for differentially expressed genes between MKO and MWT groups Supplementary Table S3: Quantitative results of peptide identification in MKO and MWT groups Supplementary Table S4: Quantitative results of protein identification in MKO and MWT groups Supplementary Table S5: Identification analysis results of differentially expressed proteins between MKO and MWT groups Supplementary Table S6: GO analysis of differentially expressed proteins between MKO and MWT groups. Supplementary Table S7: KEGG pathway analysis for differentially expressed proteins between MKO and MWT groups.

Research data: Transcriptome sequencing data was uploaded to NCBI and the proteomics data has not been uploaded yet. The accession numbers were SRR8606200, SRR8606196, SRR8606198, SRR8606197, SRR8606202, SRR8606201, SRR8606195, SRR8606199, a study number was SRP186508, a bioproject accession was PRJNA516765.

Journal Pre-proof Author Contributions: X.L. conducted most experiments and performed data analysis. S.X., L.Q., and C.C. contributed to the preparation of the gene-editing Meishan pig and performed some data analysis. H. B. conducted part of molecular biology experiments. W. C. designed this study and wrote the manuscript. Acknowledgements We thank the following: Dr. Shulin Yang, Dr. Yanfang Wang, Dr. Zhonglin Tang, Dr. Yulian Mu, and Dr. Hong Ao. for their guidance and suggestions.

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Funding: This study was supported by National Basic Research Project (2015CB943100), National Transgenic Project of China (2014ZX08006-003,2016ZX08006-001) and The

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Agricultural Science and Technology Innovation Program (ASTIP-IAS05).

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Conflicts of Interest. No potential conflicts of interest relevant to this article were reported. References：

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Fig. 1. Signaling pathway enrichment of differentially expressed genes between MKO and MWT groups. The p value was calculated by using hyper geometric test based on the number of differential genes enriched into each pathway. p value ≤ 0.05 as the threshold. The – log10 of p value (x axis) are plotted against signal pathways (y axis)

Fig. 2. Real-time quantitative PCR validations of RNA-seq results. The x axis is differentially expressed genes, and the y axis is the fold change of each gene between MKO and MWT groups. A fold change greater than 0 indicates the gene is up-regulated in MKO group, while less than 0 indicates the gene is down-regulated in MKO group.

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Fig. 3. Results of peptides and proteins identification by mass spectrometry in MWT and MKO groups. (A) The distribution of mass spectral scores (x axis) is plotted against the corresponding number of identified mass spectra(y axis). Red color represents the distribution scores of the correct peptide spectrum matches (PSMs) (the high scores and reliable results) in the Target spectrum library, while blue represents the distribution scores of the mismatches (the low scores and unreliable results). Green color represents the distribution scores of correct PSMs in the Decoy spectrum library (the reverse database). FDR ≤ 0.01. (B) Length distribution of identified peptides. The unit of peptide length is the number of amino acids. (C) Coverage of successfully identified peptides relative to the entire protein sequence. (D) Distribution of molecular weight range of identified proteins. The unit of Molecular Weight is kDa.

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Fig. 4. The hierarchical cluster analysis results of differentially expressed proteins (DEPs) between MKO and MWT groups. MKO-1, MKO-2 and MKO-3 are three individual pigs in the MKO group. MWT-1, MWT-2 and MWT-3 are three individual pigs in the MWT group. Red color represents up-regulated proteins, while blue color represents the down-regulated proteins. The color intensity changes with the protein abundance as commented on the key bar on the upper right. The dendrogram above represents clustering analysis results of different samples from MKO and MWT groups, while the dendrogram on the left represents clustering analysis results of DEPs from different samples.

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Fig. 5. Functional annotation of differentially expressed proteins (DEPs) between MKO and MWT groups. (A) GO analysis of DEPs between MKO and MWT groups. Red bar represents biological processes; green bar represents molecular function; blue bar represents cellular components. (B) The top terms of KEGG signaling pathway enrichment for DEPs between MKO and MWT groups. The abscissa is the signal pathway for main enrichment of DEPs; the left vertical axis is the –log10 of p value, and the right vertical axis is the ratio of the number of proteins enriched to the pathway to the total number of DEPs.

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Fig. 6. KEGG signaling pathways of the differentially expressed proteins (DEPs) related to skeletal muscle growth and development at p < 0.05. In the signal pathway diagram on the left, the red box represents up-regulated proteins in the MKO group and the green box represents down-regulated proteins in the MKO group. The heat map on the right is the differential protein cluster analysis of the corresponding pathways on the left. The columns represent MKO and MWT samples, and the rows represent DEPs. The logarithm value of expression of DEPs in various samples is shown in different colors. Red color indicates the significant increase in the expression of DEPs in the corresponding group, while green color

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represents a significant decrease in the expression of DEPs. The color intensity changes with the protein abundance as commented on the key bar on the bottom left. ASIC: adrenergic signaling in cardiomyocytes; CMC ： cardiac muscle contraction; HCM: hypertrophic cardiomyopathy; PPAR: peroxisome proliferator-activated receptor signaling pathway.

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Fig. 7. The interaction network map of differentially expressed proteins between MKO and MWT groups. Network nodes represent proteins; empty nodes: proteins with unknown three-dimensional structures; filled nodes: proteins with some three-dimensional structures known or predicted; colored nodes: query proteins and first shell of interactors; white nodes: second shell of interactors. Edges represent protein-protein associations. Known interactions: :from curated databases; interactions:

fusions. Others:

: experimentally determined. Predicted

: gene neighborhood;

: gene co-occurrence;

: protein homology;

co-expression.(Legend quoted from http://string-db.org/)

: text-mining ;

: gene :

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Fig. 8. Integrated analysis of transcriptomic and proteomic data of longissimus dorsi muscle from MKO and MWT Meishan pigs on 65 days post coitus. Each point in this figure represents one gene. The x axis is the log 2 value of the fold change in protein expression level, and the y axis is the log 2 value of the fold change in the level of transcripts. The four dotted lines represent the threshold values of significantly differentially expressed mRNAs or proteins (threshold: protein fold change > 1.2, mRNA fold change > 2). Genes that differ in both transcriptomic and proteomic are marked in red; genes only differ in proteomics are marked in blue; genes only differ in transcriptomics are marked in green. Genes with no differences in both transcriptomic and proteomic are marked in gray.

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Fig. 9. Changes in transcriptome and proteome expression levels of MYL9 and TNNT1 in MKO and MWT Meishan pigs. Total RNA and protein were extracted from longissimus dorsi muscle in MKO and MWT Meishan pigs on 65 days post coitus, and RT-qPCR and Western blot were performed for each sample. (A) Changes in mRNA expression level of MYL9 and TNNT1. (B) Western blot results of TNNT1 and MYL9 (C) Protein expression level of MYL9 and TNNT1 (detected by the results of Western blot). MYL9: Myosin regulatory light chain 9 , TNNT1: Troponin T 1; Student’s unpaired t-test, n = 3 pigs, *P<0.05, **P<0.01.

Table 1. Primer sequences for quantitative real-time PCR Gene Primer sequence (5’–3’) TBP1 HTR1D SLC26A7 FBXO40 CXCL13 GJD2

Forward: AACAGTTCAGTAGTTATGAGCCAGA Reverse: AGATGTTCTCAAACGCTTCG Forward: CGCCCGGAACCGCATCTTG Reverse: TGAGCGAGCACAGCGAGGAG Forward: TGGAATGGGGCGTCATGTTGC Reverse: CCGGCTGCTCAAAGGGACAAG Forward: ACCCACTGAGGAGGAACCAACC Reverse: CAGCATCCGCTCCACCAACAG Forward: CTGCTTCTCGTGCTGCTGCTG Reverse: GAGGCGGATAGGGACCCAGTTC Forward: GGGCAGTGGTGGTGGCAAAC

Product size (bp) 153 101 94 80 123 102

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120 88 99 80

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159

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TNNT1

148

Abbreviation TBP1 HTR1D SLC26A7 FBXO40 CXCL13 GJD2 MYH7 GALNT3 TPM3 MYL3 NTM MYL9 TNNT1

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MYL9

163

Full name TATA-binding protein 1 5-hydroxytryptamine receptor 1D solute carrier family 26 member 7 F-box protein 40 C-X-C motif chemokine ligand 13 gap junction protein, delta 2 myosin heavy chain 7 polypeptide N-acetylgalactosaminyltransferase 3 tropomyosin 3 myosin light chain 3 Neurotrimin Myosin regulatory light chain 9 Troponin T 1, slow skeletal type

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NTM

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MYL3

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TPM3

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GALNT3

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MYH7

Reverse: TCTGGCTCCGTCTCCTTGCTG Forward: GGCAAGACGGTGACTGTGAAGG Reverse: AGATCATCCAGGAGGCGTAGCG Forward: TGGCAATGTGGTGGGCAGTTG Reverse: GCGAACTTGGTTGCGAGCAATC Forward: AGGCCGATGATGCAGAGGAGAG Reverse: ACCTCAGCCTCAGCCTGTTCC Forward: GGGAAGCCAAAGCAGGAAGAGC Reverse: GGTGCCCGTGTCCTTGTTCTTG Forward: CACACCAACGCCAGCCTCAC Reverse: CAGCCCGCCCTCCTCCAC Forward: TGTTTGGGGAGAAGCTGAAC Reverse: TCTCGTCCACTTCCTCGTCT Forward: TTGCGGTGGATGTCATCGAAGTC Reverse: AAGAAGAGGAGGCTGCTGAGGAG

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Graphical abstract

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Highlights 

This study identified 438 differentially expressed genes, 66 differentially expressed proteins and potential signaling pathways which are involved in regulation of skeletal muscle growth and development.



This study indicated that TNNT1 and MYL9 are the key factor of skeletal muscle growth

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and development according to the integrated analysis of transcriptomic and proteomic

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The result revealed possible maker genes for improvement of meat quality in the animal

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husbandry and diagnosis of human muscle diseases in medicine.

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data.