Comparative transcriptomic and proteomic analyses provide insights into the key genes involved in muscle growth in the large Diqing Tibetan pig
Publication: Canadian Journal of Animal Science
20 September 2023
Abstract
Pig growth involves multiple genes and coordinated regulatory mechanisms. The large Diqing Tibetan pigs (TPs) are a unique plateau pig breed in China. Data on the mechanisms of muscle growth and development in TPs are limited, and its transcriptional regulation mechanism remains unclear. This study identifies important genes and proteins involved in muscle growth in TPs. We obtained transcriptomic and proteomic data from the longissimus dorsi muscle tissues of TPs and Duroc pigs (DPs) via RNA-seq and isobaric tags for relative and absolute quantitation analyses, respectively. Comparative analyses of TPs and DPs yielded 557 differentially expressed genes (DEGs) and 56 differentially abundant proteins (DAPs). Functional annotation of these DEGs and DAPs was enriched in metabolic processes, metabolic pathways, cytoskeletal protein binding, AMPK signaling pathway, insulin signaling pathway, PPAR signaling pathway, and other related pathways. Ten genes were identified as key candidate regulators (FASN, PPARG, PCK1, ACTA2, TXN, SNU13, APOA1, ATP8, ALDH2, and IGFN1) that may play important roles in the muscle growth traits of TPs. This study provides a reference for analyzing the genetic regulation mechanism underlying muscle growth in pigs and improving the meat yield of TPs via molecular marker-assisted selection.
Introduction
Pork is an important source of animal protein. Growth traits are among the most important economic characteristics that attract the attention of pig breeders. Muscle growth is influenced by a series of genes and factors regulated via complex pathways, such as temporal and spatial expression, signal cascades, transcriptional regulation, and feedback mechanisms (Hettmer and Wagers 2010).
The growth and development of mammalian skeletal muscles are divided into two stages, namely pre- and post-birth. During the pre-birth stage, myoblasts proliferate and coalesce to form myotubes, which in turn fuse to form muscle fibers, while in the post-birth stage, muscle fibers do not increase in number, mainly due to hypertrophy of muscle fibers and transformation of muscle fiber types (Rahman et al. 2014), and the growth and regeneration of skeletal muscles mainly depend on muscle satellite cells. Under normal circumstances, muscle satellite cells are in static and dormant states. When subjected to physical stimulation or muscle damage, muscle satellite cells participate in the damage repair process (Wagers and Conboy 2005). Studies have shown that the activation of muscle satellite cells can promote muscle growth. Following activation, muscle satellite cells exit the static state, proliferate, and differentiate into myoblasts, which further differentiate and fuse into multinucleated myotubes (Feige et al. 2018).
With the rapid development of high-throughput sequencing technology, several transcription factors affecting pig muscle growth have been identified. For example, TNF-α plays an important role in muscle repair and myogenesis, while NF-κB is an important transcription factor involved in skeletal muscle atrophy caused by various catabolic stimuli, including TNF-α (Dogra et al. 2007). When differentiating into muscle cells, TNF-α-induced NF-κB activation decreases the expression of myogenic proteins and myosin heavy chains. In differentiated myotubes, transfected mutant I-κBa induces NF-κB activation and decreases the levels of total protein and rapid myosin heavy chains (Li and Reid 2000). Shang et al. (2019) sequenced the transcriptome and proteome of the embryonic muscle tissues of Tibetan, Wujin, and large white pigs and found that 20 genes, including CRYAB, FSCN1, and MAPK12, which are associated with myoblast differentiation and muscle fiber formation, may play an important role in determining the postnatal growth rate and theoretical weight of pigs. Xu et al. (2019) compared the 90 kg Min pig with the Changbai Mountain wild boar and identified 4522 differential genes enriched in the development, differentiation, and growth of muscle fibers in pig skeletal muscle. Zhang et al. (2019) sequenced the longissimus dorsi muscle (LD) muscle of Landrace and Wuzhishan miniature pigs at 18, 21, and 28 days after birth via whole genome bisulfite sequencing. Tet1 is involved in the demethylation of the myogenin promoter and promotes the immortalization of the mouse myoblast cell line C2C12 and the differentiation of porcine embryonic myoblasts. Meanwhile, Zhao et al. (2015) compared the LD muscle of Tongcheng and Yorkshire pigs from 30 days to 5 weeks after birth and found that CXCL10, EIF2B5, PSMA6, FBXO32, and Loc100622249 play an important role in the muscle regulatory network of Tongcheng pigs, while SGCD, ENG, THBD, AQP4, and BTG2 play a major role in large white pigs, showing various specific and development-dependent differential expression patterns.
However, the genetic mechanisms underlying muscle growth, which is a complex trait involving multi-gene expression, are not fully understood. Developing transcriptomic and proteomic profiles allows for the identification of genes involved in the regulation of muscle growth at the mRNA and protein levels. RNA-Seq provides a more precise measurement of transcript levels and isoforms compared with alternative approaches; proteomics can also quantify protein levels accurately (Wang et al. 2017; Zhang et al. 2017). The main links of gene expression include transcription and protein synthesis. mRNA is an intermediate of gene expression, determining the identity of the expressed gene and its transcription level (Costa et al. 2010), and protein is a direct functional executor (Berry 2004). Joint analysis of two omics can achieve complementarity between the two, providing comprehensive analysis of mRNA and protein expression levels in specific states of organisms. In addition, we can obtain an in-depth analysis of differential expression, dig out differential genes or differential proteins regulated by post-transcription, find and verify some important regulatory pathways. Both RNA-seq and isobaric tags for relative and absolute quantitation (iTRAQ) have been widely used to screen functional genes involved in muscle growth and lipid deposition in pigs and other domestic species (Wang et al. 2013; Wang et al. 2016). Integrating transcriptome and proteome analyses provides insights into the key functional genes and their regulatory mechanisms underlying muscle growth in TPs.
TPs are a unique plateau pig breed capable of adapting to the cold and low-oxygen conditions at high altitudes and containing high fat deposition and good meat quality. The current research on TPs mainly focuses on their origin and domestication (Yang et al. 2011), genetic diversity (Xiang-Yun et al. 2000; Ge et al. 2020), hypoxia adaptation (Jia et al. 2016; Wu et al. 2019), meat quality (Gan et al. 2019), reproductive performance, and microbiota (Yang et al. 2021). Relatively little research has been conducted on the mechanisms underlying muscle growth and development in TPs, and the analysis of transcriptional regulation of muscle growth and development in TPs is limited and needs to be explored. The DP is a typical lean-type modern breed with a higher growth rate, lean meat percentage, and high food conversion efficiency (Pan et al. 2003), whereas TPs have slow growth and low fecundity. These differences may be caused by gene expression in muscle tissue. Therefore, comparing the differentially expressed genes (DEGs) in the LD muscle tissue of TPs and DPs can be used to understand the molecular basis of pig muscle growth and development.
In this study, we performed a comparative analysis of the transcriptomic and proteomic profiles of LD muscle tissues obtained from TPs and DPs, using RNA-seq and iTRAQ technologies to better understand muscle growth and developmental regulation in pigs and other agricultural animals, and provide a reference for genetic improvement in TPs.
Materials and methods
Animals and samples
Domestic pigs that are neither endangered nor protected were used in this study. Animal care was conducted in strict accordance with the Guide for the Care and Use of Laboratory Animals of China. All experimentation protocols were approved by the Committee on the Ethics of Animal Experiments of Yunnan Agricultural University (Approval number: 2007-0081).
Twelve individuals each of TPs and DPs with the same parity, age, and weight were selected for fattening tests at the Tibetan pig breeding farm of the Lvyuan Ecological Breeding Professional Cooperative in Shangri-La City, Yunnan Province, China. The experimental pigs were raised in the same semi-closed cement floor pigsty, with a small herd of eight pigs per herd. The pigs were fed three times a day on a free-choice basis. The remaining feed was weighed, and the daily feed intake was calculated. The pigs were allowed to drink water freely, and were immunized and dewormed according to the formal requirements of the pig farm. A dietary formula was designed based on the Chinese pig feeding standard NY/T 65-2004. The formula composition and nutritional level are shown in Table 1. The animals were slaughtered when they reached a weight of ∼120 kg. We selected three pigs of average weight from each group to collect LD muscle tissue. The extracted tissues were frozen in liquid nitrogen, transported back to the laboratory, and stored at −80 °C for the extraction of total RNA and proteins.
Table 1.
Library preparation, sequencing, and data analysis in RNA-seq
RNA isolation, library preparation, and sequencing
Total RNA was isolated using TRIzol® reagent (Invitrogen, Waltham, MA, USA). The integrity, concentration, and purity of each sample were evaluated via 1% agarose gel electrophoresis and a NanoDrop™ 2000 Biophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The samples were then reverse-transcribed into cDNA using Superscript II reverse transcriptase (Invitrogen) and random hexamer primers. RNA-seq libraries were constructed according to the manuals provided by Illumina Inc. (San Diego, CA, USA) and sequenced using the HiSeq 2000 platform.
Quality control and alignment of sequencing reads
Raw RNA-seq reads were processed using the CLC Genomics Workbench 4.8 software (CLC Bio, Aarhus, Denmark) (https://digitalinsights.qiagen.com/products-overview). After removing the adapters, the remaining clean reads were aligned to the whole pig genome (Suscrofa11.1) (ftp://ftp.ensembl.orgpub/release-94/fasta/sus_scrofa/dna/Sus_scrofa.Sscrofa11.1.dna.toplevel.fa.gz) using TopHat software (version 2.0.9) (Trapnell et al. 2009). Finally, BAM files generated using SAMtools (Li et al. 2009) were used for subsequent analysis. Quality control and read statistics were determined using the FastQC software (http://www.bioinfor-matics.babraham.ac.uk/projects/fastqc/).
Quantification and comparison of gene expression
FPKM values obtained using Cufflink software (version 2.1.1) (Trapnell et al. 2010) were used to normalize gene expression. Differential expression analyses of the comparison group (large Diqing Tibetan pigs (TPs) vs. Duroc pigs (DPs)) were performed using the DESeq R package (1.10.1) (Anders and Huber 2010). The resulting P values were adjusted using the Benjamini–Hochberg method. The results were expressed as the fold change (FC) of the average expression of case groups to that of the respective control group. In the comparison group, DEGs were identified as genes for which |log2(FC)| > 1 and P < 0.01.
Protein preparation, mass spectrometry, and peptide analysis in iTRAQ
Protein isolation, enzymolysis, and iTRAQ labeling
LD muscle tissues in liquid nitrogen were ground into powder using lysis buffer (Roche, Basel, Switzerland). The resulting samples were ultrasonically disrupted for extraction of total protein, and protein concentrations were determined using the BCA protein assay (Beyotime Institute of Biotechnology, Shanghai, China). Protein samples (200 µg) were mixed with dl-dithiothreitol, alkylated with iodoacetamide, and then treated with trypsin overnight at a trypsin:protein ratio of 1:100. Peptides (15 µg) from each group were labelled using an 8plex iTRAQ reagent multiplex kit (SCIEX, Framingham, MA, USA). The retained peptides were eluted with Buffer A [10 mmol·L−1 KH2PO4 in 25% acetonitrile (ACN), pH 3.0] and Buffer B (10 mmol·L−1 KH2PO4 and 500 mmol·L−1 KCl in 25% ACN, pH 3.0) at a flow rate of 1.0 mL·min−1.
LC-MS/MS analysis
Eluted fractions were lyophilized using a centrifugal speed vacuum concentrator (CentriVap® Complete Vacuum Concentrator; Labconco, Kansas City, MO, USA) and dissolved in 0.1% formic acid. Equivalent amounts of peptides from each fraction were subjected to reversed-phase nanoflow liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis using a high-performance liquid chromatography system (EASY-nLC™; Thermo Fisher Scientific) connected to a hybrid quadrupole/time-of-flight mass spectrometer equipped with a nanoelectrospray ion source (Michalski et al. 2011). The peptides were separated on a C18 analytical reverse-phase column using mixtures of solution A (0.1% formic acid in water) and solution B (0.1% formic acid in ACN). A full MS scan was conducted using a Q Exactive™ mass spectrometer (Thermo Fisher Scientific).
Database search and protein identification and quantification
For peptide identification and quantification, MS/MS data were searched against the “X101SC19080936-Z01-Sus_scrofa-Ensemble.fasta(45898 sequences)” file using Mascot 2.2 and Proteome Discoverer™ 1.4 software (Thermo Fisher Scientific). Unique proteins with at least two unique peptides that had FDR < 0.01 (Sandberg et al. 2012) were used for further analysis. The final protein ratios were normalized to the median average protein content of the 8plex samples. FC > 1.2 or FC < 0.83 and P < 0.05 were set as the thresholds for identifying differentially abundant proteins (DAPs).
Functional annotation of DEGs and DAPs
DEGs and DAPs were classified via gene ontology (GO) and Kyoto Encylopaedia of Genes and Genomes (KEGG) using DAVID online software (https://david.ncifcrf.gov/). For these analyses, official gene symbols for each DEG or DAP were uploaded, and the species with the maximum number of annotations was used. The GO terms used were biological process (BP), cellular component, and molecular function. The KEGG pathways with corrected P values <0.05 were considered significantly enriched. We used Cytoscape 3.9.0 software (https://apps.cytoscape.org/) to mine the interaction relationships between different genes and construct the interaction network diagram of different genes. A protein–protein interaction network diagram was also constructed, and the interaction relationship was analyzed using the STRING online website (https://string-db.org/).
Verification of important candidate genes
Ten candidate genes identified using RNA-seq and iTRAQ were selected for verification using quantitative real-time PCR (qPCR). The qPCR primers designed for these ten genes are listed in Table 2. qPCR analysis was performed using an SYBR® Green I PCR Master Mix Kit (FP204; Tiangen Biotech Co. Ltd., Beijing, China) on a CFX96™ Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA), according to the manufacturer's instructions. The gene expression levels were calculated using the 2−△△Ct method.
Table 2.
Statistical analysis
The data were analyzed with SAS 9.0 software for one-way analysis of variance, and the mathematical model used was
where Yij is the observed value of traits, 
is the population mean, ai is the variety (combination) effect, and eij is the random residual. According to the above model, the general linear model process of SAS 9.0 and Microsoft Excel are used for data statistical analysis, and the results are expressed as mean ± standard error of mean (SEM).
is the population mean, ai is the variety (combination) effect, and eij is the random residual. According to the above model, the general linear model process of SAS 9.0 and Microsoft Excel are used for data statistical analysis, and the results are expressed as mean ± standard error of mean (SEM).Results
Analysis of phenotypic data
The average daily gain and loin eye area of DPs (691 ± 24 g and 38.58 ± 4.12 cm2, respectively) were 75.38% and 82.33% higher than those of TPs (394 ± 32 g and 21.16 ± 4.41 cm2, respectively) (P < 0.01), respectively. The lean meat ratio of DPs (64.37 ± 0.79%) was 11.01% higher than that of TPs (53.36 ± 0.70%) (P < 0.01; Table 3).
Table 3.
Summary of RNA-seq data
After removing low-quality reads, an average of 46.56 million paired-end reads were obtained for each sample, with approximately 96.04% being mapped to the Sus scrofa 11.1 genome sequence (Table 4). By calculating the FPKM values for the gene expression levels, 19 008 genes were identified in the LD muscle tissue, of which 16,529 had identical expression in both groups (Fig. 1a). The distributions of FPKM values were similar among the six samples, and there were no outliers (Fig. 1b).
Fig. 1.
Table 4.
Identification and functional analysis of DEGs
In total, 557 DEGs (398 upregulated and 159 downregulated) were identified between the TP and DP groups (Fig. 2a). The DEGs were enriched in critical GO terms that included development process, tissue development, muscle cell differentiation, response to transforming growth factor beta, muscle tissue development, myotube differentiation, and other related processes (Fig. 2b). We also observed representative KEGG pathways that included pyruvate metabolism, AMPK signaling pathway, Wnt signaling pathway, ECM-receptor interaction, PPAR signaling pathway, TNF signaling pathway, MAPK signaling pathway, and other related processes (Fig. 2c).
Fig. 2.
Interaction network analysis of DEGs
We selected the genes in the significantly enriched pathway during functional enrichment analysis to construct a gene network diagram (Fig. 3). PPARG (peroxisome proliferator activated receptor gamma), FASN (fatty acid synthase), SCD (stearoyl-CoA desaturase), ACTA2 (actin alpha 2), PCK1 (phosphoenolpyruvate carboxykinase 1), ENG (endoglin), PLIN1 (perilipin 1), SOX9 (SRY-box transcription factor 9), GATA2 (GATA binding protein 2), and ACSS1 (acyl-CoA synthetase short-chain family member 1) interacted with each other. PPARG, FASN, SCD, and ACTA2 interacted strongly in this network. Except for ACSS1, which was downregulated, other genes were upregulated. PPARG, ACTA2, and GATA2 are primarily associated with developmental processes, cell differentiation, multicellular organism development, cellular developmental processes, and cell development. ENG and SOX9 are associated with cellular response to growth factor stimulus, growth factors, and transforming growth factor beta. SCD and PCK1 are associated with the AMPK and PPAR signaling pathways, while FASN is involved in the AMPK signaling pathway. PLIN1 is involved in the PPAR signaling pathway, while ACSS1 is associated with pyruvate, glyoxylate, and dicarboxylate metabolism (Table 5). Based on the functional annotation, these ten DEGs may play important roles in the muscle growth of TPs.
Fig. 3.
Table 5.
Protein identification and quantification
A total of 575 693 spectra were obtained from 8PLEX LC-MS/MS analysis along with 13 362 peptides, of which 9421 unique peptides corresponded to 1576 proteins identified (Fig. 4a). The lengths of the peptides were mainly distributed between 7 and 25 amino acid residues, with peptides of length 10–14 being most abundant (Fig. 4b). In addition, identified proteins showed high peptide coverage; 60.29% of the proteins had more than 10% sequence coverage, and 33.70% of the proteins had more than 20% sequence coverage (Fig. 4c). The molecular weight of proteins was mainly distributed between 10 and 70 kDa (Fig. 4d). The proportion of proteins with a variation coefficient <20% accounted for >90%, demonstrating good biological reproducibility within the group (Fig. 4e).
Fig. 4.
Identification and functional analysis of daps
Comparing TP and DP, we detected 56 DAPs, including 40 upregulated and 16 downregulated (Fig. 5a). The DAPs could be classified into two groups with good uniformity within the groups and distinct diversity between the two groups, suggesting that the selected DAPs were relatively accurate (Fig. 5b).
Fig. 5.
Functional annotation of the 56 DAPs revealed GO terms primarily associated with metabolic processes, regulation of protein kinase activity, single-organism metabolic processes, oxidation–reduction processes, organic substance metabolic processes, skeletal muscle tissue development, and other related BPs (Fig. 5c). The enriched representative KEGG pathways mainly included circadian rhythm, the renin–angiotensin system, PPAR signaling pathway, fatty acid degradation, the peroxisome, and the NOD-like receptor signaling pathway (Fig. 5d).
Protein–protein interaction network analysis
Fifty-six DAPs were used to construct a protein–protein interaction network (Fig. 6). TXN (thioredoxin), SNU13 (small nuclear ribonucleoprotein 13), PRDX5 (peroxiredoxin 5), SOD1 (superoxide dismutase 1), RPL28 (ribosomal protein L28), RPS6 (ribosomal protein S6), and TNXL4A (thioredoxin-like 4 A) proteins interact with each other. TXN and SNU13 proteins interacted strongly in the network. PRDX5 and TXNL4A were downregulated, whereas the other proteins were upregulated. TXN is mainly associated with the glycerol ether metabolic process, protein disulfide oxidoreductase activity, and cell redox homeostasis. SNU13 is involved in RNA binding. PRDX5 is associated with oxidoreductase activity and cell redox homeostasis. SOD1 is related to the superoxide metabolic process, metal ion binding, and oxidation-reduction processes. RPL28 and RPS6 are associated with translation. TXNL4A belongs to the DIM1 family (Table 6). These seven DAPs may play important roles in TP muscle growth.
Fig. 6.
Table 6.
Integrated analysis of iTRAQ and RNA-seq data
Integrating the 19 008 detected genes via RNA-seq and the 1576 proteins detected via iTRAQ, the Pearson correlation coefficient of the fold changes of TP/DP between the mRNA and protein expression levels was 0.122 (Fig. 7a). Among the 557 DEGs identified using RNA-seq and 56 DAPs via iTRAQ, four genes (APOA1, IGFN1, ALDH2, and ATP8) overlapped (Fig. 7b). APOA1 (apolipoprotein A1) was associated with lipid-binding and cholesterol metabolism. ALDH2 (aldehyde dehydrogenase 2 family member) is involved in metabolic processes and pathways. ATP8 (ATP synthase F0 subunit 8) is related to metabolic processes and Huntington's disease (Figs. 7c and 7d). The DEGs and DAPs mainly included GO terms for metabolic process, single-organism process, structural molecule activity, lipid binding, metal ion binding, macromolecular complex, cytoskeletal protein binding, cell part, extracellular region, and transport (Fig. 7e). The enriched KEGG pathways for the DEGs and DAPs were mainly associated with metabolic pathways, cholesterol metabolism, spliceosome, longevity regulating pathway—multiple species, phagosome, arrhythmogenic right ventricular cardiomyopathy, base excision repair, NOD-like receptor signaling pathway, peroxisome, Huntington's disease, terpenoid backbone biosynthesis, PI3K–Akt signaling pathway, protein processing in the endoplasmic reticulum, insulin signaling pathway, aminoacyl-tRNA biosynthesis, Jak-STAT signaling pathway, and renin secretion (Fig. 7f).
Fig. 7.
Validation of degs
Ten DEGs (ACTA2, APOA1, FASN, MYF6, NR4A2, PLIN1, PPARG, ALDH2, ATP8, and IGFN1) were selected to validate the expression differences via RNA-Seq using quantitative PCR (qPCR). The expression of the genes in the LD vs. DP group was consistent with the results of transcriptome sequencing (Fig. 8). These results indicated that the DEGs identified using RNA-seq were reliable.
Fig. 8.
Discussion
Local Chinese pig breeds have good meat quality but slow growth. TPs are a unique plateau pig breed with good meat quality and high fat deposition. Muscle growth is controlled by a series of genes or factors that are regulated in a complex manner (Hettmer and Wagers 2010). In the current study, we screened for key genes and proteins associated with muscle growth by comparing TPs and DPs raised under the same feeding conditions using RNA-seq and iTRAQ protein sequencing analyses. Subsequent functional enrichment analysis of each DEG and DAP identified via these analyses using GO and KEGG analysis identified 10 DEGs (PPARG, FASN, SCD, ACTA2, PCK1, ENG, PLIN1, SOX9, GATA2, and ACSS1) and 7 DAPs (TXN, SNU13, PRDX5, SOD1, RPL28, RPS6, and TNXL4A) that appear to be associated with muscle growth in the large Diqing Tibetan pig. APOA1, IGFN1, ALDH2, and ATP8 were identified as both DEGs and DAPs.
The genesis of skeletal muscle is divided into three stages: the formation and proliferation of myoblasts, the fusion of myoblasts to form myotubes, and the formation of muscle fibers. The growth and regeneration of skeletal muscle after birth mainly depend on muscle satellite cells, which participate in the repair of skeletal muscle injuries.
In this study, we identified a pair of DEG and DAP (FASN and TXN) associated with myoblast proliferation, among which FASN preferentially produces palmitic acid (16:0) and, to a lesser extent, stearic acid (18:0) (Balgoma et al. 2019). These fatty acids are utilized for de novo synthesis of phosphatidic acid (PA) for cell proliferation, followed by conversion to diacylglycerol (DG) by PA phosphohydrolase and cytidinediphosphate-diacylglycerol (CDP-DG) by CDP-DG synthetase to yield phospholipids and triacylglycerols. Diacylglycerol kinase η (DGKη) is highly expressed in C2C12 myoblasts and regulates myoblast proliferation. C30−C36-PA species generated by DGKη control FASN expression through the mTOR signaling pathway to regulate myoblast proliferation. FASN catalyzes the de novo synthesis of fatty acids for myoblast proliferation (Sakai et al. 2020; Solsona et al. 2021). In addition, FASN showed the strongest correlation with intramuscular fat (IMF) content (Wang et al. 2020a), and FASN is a lipogenic gene known to influence fat deposition and fatty acid metabolism in meat and adipose tissues (Otto et al. 2022). It is a multifunctional enzyme that catalyzes the conversion of acetyl-CoA and malonyl-CoA to palmitate, and the FASN gene has been mapped to BTA19. Of particular interest was the SNP g.17924A > G polymorphism, which causes the replacement of the threonine to alanine, which affects the fat content in carcasses and the proportion of FA and SFA/MUFA/PUFA in beef meat (Pećina et al. 2023). TXN is a growth factor secreted by virus-transformed leukemic cell lines and exerts its effects on cell growth and proliferation via its reducing activity. Several studies have revealed that the thioredoxin-interacting protein (TXNIP) binds to TXN and inhibits its function (Yamanaka et al. 2000). In addition, growth control and transcription factor modulation by TXN may be regulated by TXNIP as part of a nuclear transcriptional coactivation complex in the nucleus, while TXNIP, a negative regulator of TXN, may regulate diverse cellular processes, including growth signaling. TRX is the major component of the TXN system. Several independent reports revealed that TXNIP binds to reduced TRX to form a stable disulfide-linked complex that decreases TRX-reducing activity and inhibits the interaction of TRX with other proteins, indicating that TXNIP acts as an endogenous inhibitor of TRX. Yu et al. (2007) found that both TXNIP and TRX played a crucial role in the redox-mediated control of cell proliferation and growth traits in a large number of pig samples. Furthermore, the increased expression of TXNIP and decreased expression of TRX might lead to atrophy in rats (Matsushima et al. 2006). FASN and TXN were upregulated in our study, which may have promoted myoblast proliferation and led to skeletal muscle hypertrophy.
In addition, APOA1 is involved in cholesterol transport and lipid metabolism. Further, APOA1 has been shown to play an important role in the early stages of muscle development in Thai indigenous chickens. Picard et al. (2010) observed a high abundance of APOA1 during the early stages of myogenesis and a decrease during the later stages in both bovine and chicken muscles. This progressive decrease was confirmed using Western blotting and immunohistochemical analyses of bovine muscle. However, its expression in muscle fibers has not been previously described. Our study suggests that APOA1 is involved in muscle development and growth and plays a role in the early stages of muscle growth and development.
During skeletal myogenesis, mononuclear myoblasts fuse to form primary myotubes, which further differentiate under the control of motor neurons, and the number of myofibrils in the cells continues to increase. Simultaneously, the nucleus gradually moves from the center of the cell to the edge of the cell, completing the maturation process of myotubes (muscle fibers). A set of DEGs and DAPs (ACTA2, SNU13, IGFN1, and SOX9) were involved in these processes. Of these, ACTA2, one of the six actin subtypes and a marker of myofibroblast formation, is a reliable marker protein of the myofibroblast phenotype. The process of transformation of stellate cells into myofibroblasts, also known as “activation”—in which ACTA2 is an integral component—appears to be analogous to the process occurring in fibroblasts after injury and wound healing in pathological settings (Barnes and Gorin 2011). Myofibroblasts, which share the unique property of expressing ACTA2 during wound repair, appear to be central to this process. SNU13 is an RNA-binding protein that binds noncoding RNAs associated with the spliceosome and regulates myogenesis in vertebrates (Johnson et al. 2013). Morpholino knockdown experiments in zebrafish indicated that SNU13 is a conserved essential regulator of myogenesis, which disrupts skeletal muscle development. SNU13 causes myotube elongation and significantly restores MHC expression in somatic muscle tissue during myogenesis. IGFN1 is a large skeletal muscle protein localized to both the z-disc and the nucleus. The domain composition of IGFN1 consists of immunoglobulin and fibronectin-like domains, similar to those of sarcomeric proteins involved in maintaining the structural integrity of the muscle fiber. Evidence has implicated IGFN1 in both atrophies and myoblast fusion (Li et al. 2017). IGFN1 expression is positively correlated with atrophic conditions and myostatin signaling, a negative regulator of skeletal muscle mass (Rahimov et al. 2011). When myostatin signaling is increased via injection of adenoviral vectors in mice, muscle mass decreases, while IGFN1 mRNA expression increases (Chen et al. 2014). Knockdown of IGFN1 in C2C12 cells has previously been shown to decrease myoblast fusion and aberrant cell morphology (Li et al. 2017). Therefore, the upregulation of SNU13 in our study may indicate skeletal muscle development, while the downregulation of IGFN1, which inhibits myostatin, may indicate an increase in muscle mass. SOX9 is necessary for chondrocyte differentiation and cartilage development. Schmidt et al. (2003) found that in the process of adult skeletal muscle myogenic differentiation, the downregulation of SOX8 and SOX9 in satellite cells occurs at the same time. In contrast, the overexpression of SOX9 attenuates the ability of myoblasts to form myotubes. At the same time, the expression of MyoD and MyoG is reduced, as the MyoG activity induced by MyoD is strongly reduced by SOX8. SOX8 and SOX9 are specific negative regulators of myoprotein expression. This may be responsible for maintaining the undifferentiated state of myoblasts and preventing premature differentiation into myotubes. Co-transfection of SOX9 with MyoD prevented the emergence of myogenin, supporting the role of SOX9 in preventing myocyte differentiation.
The repair of skeletal muscle injury and the transformation of muscle fiber types promote skeletal muscle development. In this study, two DEGs (PCK1 and PPARG) were associated with these processes and highly expressed in TPs. When PCK1 was specifically overexpressed in skeletal muscle by generating transgenic mice, an enhanced exercise capacity relative to the control was observed, while their muscles had higher mitochondrial numbers as well as elevated triglyceride content. This indicated a re-patterning of energy metabolism toward a more oxidative metabolism, with increased use of fats as a substrate. Muscles from muscle-specific PCK1 transgenic mice had increased mitochondrial density and high succinate dehydrogenase enzyme activities (Hakimi et al. 2007), which suggests that their muscle fibers had become oxidative-type fibers. Varga et al. (2016) found that macrophage PPARG is a metabolic sensor and regulator of skeletal muscle regeneration, and in macrophages, it modulates an unknown signaling system that could influence myoblast proliferation in a paracrine manner. GDF3, a direct PPARG target, is a regulator of myoblast proliferation, differentiation, and muscle regeneration (Patsalos et al. 2018). The PPARG–GDF3 regulatory axis identifies a sensory-regulatory-effector mechanism by which macrophages regulate the tissue progenitor compartment, namely myogenic precursor cells. This axis orchestrates tissue regeneration, possibly in unison with other members of the TGF-β family, leading to synchronous regeneration (Hakimi et al. 2007). During skeletal muscle regeneration, PPARG is strongly upregulated 3 days post-injury (Lukjanenko et al. 2013) when fibro/adipogenic progenitors expand to support myogenesis.
PPARG deletion in skeletal muscle induces insulin resistance and decreases carbohydrate oxidation and glucose uptake. In this study, PPARG and PCK1 were upregulated, which suggests that energy metabolism had probably transformed into oxidative metabolism and that muscle fiber hypertrophy during skeletal muscle regeneration had occurred.
We identified a set of associated DEGs (PPARG, FASN, SCD, PCK1, and PLIN1) with the PPAR, AMPK, and insulin signaling pathways. The PPAR signaling pathway is involved in the regulation of lipid and energy metabolism, inflammation, and diabetes. Its physiological functions mainly involve fatty acid metabolism, glucose metabolism, cell proliferation, and differentiation (Chinetti et al. 2000). And PPARG was the core regulator gene of the PPAR signaling pathway. PPARG regulates lipid metabolism and glucose homeostasis and promotes adipocyte differentiation and fat deposition (Wang et al. 2013). The AMPK signaling pathway can restore intracellular energy homeostasis by inhibiting the biosynthetic process of ATP consumption, including gluconeogenesis, lipid and protein synthesis, and the activation of metabolic-related pathways (Xiao et al. 2007). Following PPARG activation, AMPK is activated by increased adiponectin expression. During adiponectin resistance, the stimulatory effect of adiponectin on AMPK is weakened, and consequently, the effect of fatty acid oxidation in skeletal muscles is also weakened. This promotes insulin resistance, which affects the absorption and utilization of glucose in muscle tissue and the sensitivity of adipose tissue to insulin (Wang et al. 2020b), regulating energy metabolism.
According to GO and KEGG pathway analyses, few of the identified DEGs and DAPs (ALDH2, ATP8, SOD1, and ACSS1) were primarily involved in energy metabolism pathways, such as the metabolic pathway, which participate in lipid metabolism, energy regulation, and other processes (Shi et al. 2013) and affect the absorption and utilization of glucose in muscle tissue and the sensitivity of adipose tissue to insulin (Wang et al. 2020b). When the sensitivity of insulin is reduced, the efficiency of glucose uptake and utilization is reduced, excessive energy intake occurs and muscle fibers become thicker (Chien et al. 2020), lipolysis is reduced (Kim et al. 2017), and lipid deposition is increased (Banerjee et al. 2010).
Conclusions
We identified crucial genes and proteins involved in muscle growth in TPs by combining RNA-seq and iTRAQ data obtained from pig LD muscle tissues and elucidated the regulatory relationship between DEGs and DAPs. A combination of transcriptomic and proteomic data revealed several key candidate regulators (PPARG, APOA1, and IGFN1) and pathways (metabolic processes, AMPK signaling pathway, and PPAR signaling pathway) that might play high-priority roles in the muscle growth traits of TPs. This study provides a reference for analyzing the genetic regulatory mechanism underlying muscle growth in pigs for improving the lean meat yield of TPs via molecular marker-assisted selection.
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Information & Authors
Information
Published In
Canadian Journal of Animal Science
Volume 103 • Number 4 • December 2023
Pages: 373 - 387
History
Received: 4 June 2022
Accepted: 14 June 2023
Version of record online: 20 September 2023
Copyright
© 2023 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
Data Availability Statement
The raw data of the transcriptome were submitted to the Sequence Read Archive (SRA) at the National Center for Biotechnology Information (NCBI) database (reference number PRJNA955421). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the iProX partner repository with the dataset identifier PXD042529.
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Authors
Author Contributions
Data curation: JN, BZ, YB
Formal analysis: JN, BZ, LM
Funding acquisition: DY
Methodology: BZ, HZ, XD
Project administration: DY, XD
Software: YB
Supervision: HZ
Validation: SL
Visualization: YB
Writing – original draft: JN
Writing – review & editing: DY, XD
Competing Interests
The authors declare there are no competing interests.
Funding Information
Yunnan Agricultural Basic Research Joint Project: 202101BD070001-103
Yunnan Shangri La Tibetan Pig Industry Science and Technology Mission: 202104BI090021
National Transgenic Major Project of China: 2018ZX0800928B
This study was funded by the Yunnan Shangri-La Tibetan Pig Industry Science and Technology Mission (202104BI090021), Yunnan Agricultural Basic Research Joint Project (202101BD070001-103), and the National Transgenic Major Project of China (2018ZX0800928B).
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Jingru Nie, Bo Zhang, Li Ma, Dawei Yan, Hao Zhang, Ying Bai, Shiyi Liu, and Xinxing Dong. 2023. Comparative transcriptomic and proteomic analyses provide insights into the key genes involved in muscle growth in the large Diqing Tibetan pig. Canadian Journal of Animal Science.
103(4): 373-387. https://doi.org/10.1139/cjas-2022-0073
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