BioMed Research International

BioMed Research International / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 9478949 |

Han Wang, Zhonghao Shen, Xiaolong Zhou, Songbai Yang, Feifei Yan, Ke He, Ayong Zhao, "Identification of Differentially Expressed Genes in Different Types of Broiler Skeletal Muscle Fibers Using the RNA-seq Technique", BioMed Research International, vol. 2020, Article ID 9478949, 13 pages, 2020.

Identification of Differentially Expressed Genes in Different Types of Broiler Skeletal Muscle Fibers Using the RNA-seq Technique

Academic Editor: Hesham H. Ali
Received29 Mar 2020
Revised10 Jun 2020
Accepted27 Jun 2020
Published06 Jul 2020


The difference in muscle fiber types is very important to the muscle development and meat quality of broilers. At present, the molecular regulation mechanisms of skeletal muscle fiber-type transformation in broilers are still unclear. In this study, differentially expressed genes between breast and leg muscles in broilers were analyzed using RNA-seq. A total of 767 DEGs were identified. Compared with leg muscle, there were 429 upregulated genes and 338 downregulated genes in breast muscle. Gene Ontology (GO) enrichment indicated that these DEGs were mainly involved in cellular processes, single organism processes, cells, and cellular components, as well as binding and catalytic activity. KEGG analysis shows that a total of 230 DEGs were mapped to 126 KEGG pathways and significantly enriched in the four pathways of glycolysis/gluconeogenesis, starch and sucrose metabolism, insulin signalling pathways, and the biosynthesis of amino acids. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) was used to verify the differential expression of 7 selected DEGs, and the results were consistent with RNA-seq data. In addition, the expression profile of MyHC isoforms in chicken skeletal muscle cells showed that with the extension of differentiation time, the expression of fast fiber subunits (types IIA and IIB) gradually increased, while slow muscle fiber subunits (type I) showed a downward trend after 4 days of differentiation. The differential genes screened in this study will provide some new ideas for further understanding the molecular mechanism of skeletal muscle fiber transformation in broilers.

1. Introduction

Chicken is widely welcomed by consumers because it is low in fat, is low in cholesterol, and has no religious restrictions. Therefore, chicken has become the second-largest consumed meat product after pork [1, 2]. In recent years, with the continuous improvement in people’s living standards, their requirements for meat flavor have also become stricter. Age, sex, heredity, environment, nutrition, and intramuscular fat content (IMF) are all important factors affecting chicken quality [36]. In addition, studies have shown that muscle fiber properties were an essential factor affecting the meat quality of chicken [7, 8]. Skeletal muscle is one of the most important components of meat-producing animals, accounting for approximately 45%~60% of the whole animal body [9]. According to the characteristics of contraction and metabolism, skeletal muscle fibers can be roughly divided into two types: slow-twitch (type I) and fast-twitch (type II) muscles. Type II muscle fibers can be categorized into a fast-twitch oxidation type (IIA), a fast-twitch glycolytic type (IIB), and a super-fast-twitch type (IIX) [10, 11]. Previous studies showed that muscle with a high proportion of fast-twitch muscle fibers appeared paler, while the muscle with more slow-twitch muscle fibers tended to be redder [8]. Therefore, skeletal muscle was usually divided into two kinds of muscle, red muscle and white muscle [12]. Moreover, it was found that muscles with a higher proportion of oxidized muscle fibers had better meat quality [13].

Currently, it has become a research hotspot to study the molecular mechanism of muscle fiber-type transformation in order to improve meat quality. It has been shown that PGC-1 can promote the transformation of muscle into type I fibers in the skeletal muscle [14]. Cofilin2b (CFL2b) can affect the transformation pattern of myosin heavy chain (MyHC) subunits in the muscle of piglets [15]. Furthermore, noncoding RNA was also proven to play a role in slow-twitch muscle fiber formation and skeletal myogenesis [16]. At present, preliminary progress has been made on the molecular mechanism of chicken muscle fiber-type transformation. One study showed that the expression of PGC-1α might be closely related to the slow muscle fiber content in chicken skeletal muscle [17]. In addition, miR-1611 was discovered to be highly expressed in slow-twitch muscle fibers and could drive the transformation from fast-twitch to slow-twitch muscle fibers in chicken [18]. However, the regulatory network of muscle development is complex, and studies on the formation and transformation of chicken skeletal muscle fiber types are still incomplete. Therefore, more relevant, important genes need to be further explored.

At present, transcriptome sequencing technology has been widely used in biological research, medical diagnostics, and therapeutic studies [19]. Meanwhile, this technique also plays an important role in farm animal muscle development. Based on the transcriptome analysis of biceps between Small-tail Han sheep and Duper sheep, a total of 1300 differentially expressed genes were identified, which could help further elucidate the mechanisms of muscle development as they are affected by breed [20]. Forty-nine differentially expressed genes were screened from the longissimus dorsi of Nellore cattle among different grades of marbling by RNA-seq, in an effort to find strategies to select animals with greater marbling [21]. Moreover, another study used RNA-seq to identify genes related to pig muscle development and meat quality [22]. RNA-seq technology was also used in broiler chicken groups to uncover 68 genes that might participate in the tenderization process [23]. Thus, it can be seen that whole-genome sequencing technology exerts an immense influence on the discovery of new functional genes.

To better explore the molecular mechanism of skeletal muscle fiber transformation in broilers, this study used RNA-seq technology to screen differentially expressed genes between breast and leg muscles of Ross 308 broiler chickens. Subsequently, through GO and KEGG pathway analysis, genes and pathways related to skeletal muscle fiber-type conversion were discovered. In addition, to further explore the growth characteristics of broiler muscle fibers, a skeletal muscle cell differentiation model of broilers was constructed in vitro. Subsequently, the expression of MyHC subunits during the differentiation of chicken skeletal muscle satellite cells was studied. These results will provide a theoretical basis for the genetic improvement in muscle quality in broilers in the future.

2. Materials and Methods

2.1. Experimental Animals and Tissues

The chickens used in this study were Ross 308 broiler chickens from a farm in Fuyang District, Hangzhou, Zhejiang Province, China. Five 42-day-old Ross 308 broilers with similar body weights were selected for slaughter. The leg and breast muscles were collected and frozen at -80°C for RNA extraction.

At the same time, we bought 15 fertilized eggs of the same breed and incubated them for 10 days to collect chicken skeletal muscle satellite cells. All animal procedures used in this study were approved by the Ethics Committee for Animal Experiments of Zhejiang A&F University and were performed in accordance with the Guidelines for Animal Experimentation of University (Hangzhou, China).

2.2. Total RNA Extraction, Library Construction, and Sequencing

Total RNA was extracted from the gastrocnemius of legs and the pectoralis major of breasts using the TRIzol method [24]. The quality inspection was carried out through the Sample Testing Center of Biomarker Technologies Corporation (Beijing, China) to ensure that the samples were up to the standard, and the test quality report of the RNA samples is presented in Table S1. All RNA samples with an OD 260/280 1.8-2.0 qualified for further analysis. Sequencing libraries were generated using a NEBNext Ultra™ RNA Library Prep Kit (NEB, USA). Briefly, mRNA was purified from 1 μg of total RNA per sample using poly-T oligoattached magnetic beads. Then, first-strand cDNA was synthesized using a random hexamer primer and M-MuLV Reverse Transcriptase. Second-strand cDNA synthesis was subsequently performed using DNA polymerase I and RNase H. The cDNA fragments of 240 bp were preferentially selected with an AMPure XP system (Beckman Coulter, USA) for cDNA adaptor ligating. At last, polymerase chain reaction (PCR) amplification was carried out and the products were purified (AMPure XP system). The library quality was assessed on the Agilent Bioanalyzer 2100 system. The libraries were sequenced by paired-end (PE 150 bp) sequencing on an Illumina HiSeq™ platform (Illumina, USA), and the raw reads were obtained. The leg muscle and breast muscle tissues of broilers were numbered PT1-PT5 and PX1-PX5, respectively, for subsequent analysis.

2.3. Sequencing Quality Assessment and DEG Screening

Clean data (clean reads) was obtained by removing reads containing adapters or poly-N; low-quality reads were also removed from the raw data. At the same time, Q20, Q30, GC-content, and sequence duplication levels of the clean data were calculated. Ultimately, all downstream analyses were based on high-quality clean data. The chicken genome sequence (GRCg6a) was downloaded from the NCBI database ( Then, Hisat2 software was used to map these clean reads to the chicken reference genome [25]. Transcript assembly was performed using StringTie software [26]. StringTie is an algorithm based on optimization theory, which uses comparison information to construct a multivariable clipping graph and traffic network to assemble and evaluate the expression of reads according to the maximum flow algorithm. Compared with other software such as Cufflinks, it can build a more complete transcript and better evaluate expression [26]. Quantification of gene expression levels was estimated by the fragments per kilobase of transcript per million fragments (FPKM) method [27]. Based on raw counts, the reads mapped to the reference genome were quantified by StringTie software to obtain FPKM [28]. Then, DESeq R package (1.39.0), a statistical program for determining differential expression in digital gene expression data based on a model of negative binomial distribution, was used to analyze the differential gene expression between samples with biological repetition [29]. Differentially expressed genes (DEGs) were identified using the following filter criteria: and . Fold change indicates the ratio of expression quantity between two samples (groups). The false discovery rate (FDR) was obtained by correcting the significant difference value.

2.4. Gene Ontology (GO) and KEGG Pathway Enrichment Analysis

Gene Ontology (GO) enrichment analysis of the DEGs was implemented by GOseq R packages ( based on Wallenius noncentral hypergeometric distribution [30], which could adjust for gene length bias in DEGs. Pathway significant enrichment analysis was run based on the hypergeometric test in the KEGG database ( to uncover the most significantly enriched pathways among the DEGs. KOBAS software [31] was used to test the statistical enrichment of DEGs in KEGG pathways.

2.5. Quantitative Real-Time RT-PCR Verification

Utilizing the NCBI gene bank, primers of 7 randomly selected DEGs, three different isoforms of MyHC genes (MyHC I, IIA, and IIB) and the house-keeping gene GAPDH were designed by Primer Premier 5 software (Table S1). Primers were synthesized by Hangzhou Youkang Biotechnology Co., Ltd. (Zhejiang, China). Total RNA from cells or tissues was used to synthesize cDNA by a 5X All-In-One MasterMix Kit (abm, China). The cDNA was used as a template for quantitative PCR by using the EvaGreen 2X qPCR MasterMix Kit (abm, China). There were three replicates for each sample. The relative expression level of each related gene was calculated using the 2-△△CT statistical analysis method.

2.6. Isolation and Culture of Chicken Skeletal Muscle Satellite Cells In Vitro

The 10-day fertilized eggs were disinfected with ethanol. The eggshell on the upper part of the air chamber was removed using tweezers on the egg receptacle. A new pair of tweezers was used to clip out the chicken embryo. The leg muscle of the chicken embryo was isolated and placed in a new petri dish. Muscle samples were washed 3 times in phosphate buffer solution (PBS) (Logan, USA) containing penicillin-streptomycin liquid (Solarbio, China), and skin, blood vessels, adipose tissue, and connective tissue were removed. The sterile muscle tissue was cut into meat paste and digested with 0.25% trypsin-EDTA (Thermo Fisher, China) at 37°C for 20 min. DMEM/F12 (Logan, USA) containing 10% fetal bovine serum (FBS, Thermo Fisher, China) was added to terminate the digestion. The suspension was filtered through a 70 μm mesh sieve and centrifuged at 1200 r/min for 8 min at room temperature. As the supernatant was discarded, cells were resuspended with DMEM/F12 containing 15% FBS and cultured in a 5% CO2 incubator at 37°C. One hour later, the cell suspension was transferred into a new cell petri dish; this was repeated twice to enrich for muscle satellite cells and eliminate fibroblasts. Cell differentiation was induced by replacing 15% FBS with 2% equine serum (Logan, USA). Once the isolated skeletal muscle satellite cells reached 70%-80% fusion, the growth medium was replaced with differentiation medium and the cells were cultured for 2, 4, and 6 days. Cells at 70%-80% fusion indicated day 0.

2.7. Immunofluorescence Staining

The isolated skeletal muscle satellite cells at 70%-80% fusion and the cells that were induced to differentiate after transfection were used for immunofluorescence detection following the method of Luo et al. [32]. Briefly, cells grown in 12-well plates were washed three times with precooled PBS and fixed with 4% paraformaldehyde for 15 min. Thereafter, the cells were permeabilized with 0.25% Triton X-100 per well for 10 min and blocked at 4°C overnight. Afterwards, cells were incubated with 1 : 100 diluted primary anti-Desmin (Abcam, China), anti-Pax7 (Abcam, China), or anti-MyHC (Santa, USA) antibodies for 1 hour at room temperature. Then, 1 : 100 diluted fluorescent secondary antibody (Thermo Fisher, China) was incubated with the cells for 1 hour at room temperature. After being washed by PBS three times, DAPI (Invitrogen, USA) was added to the cells and then incubated for 15 min at room temperature to stain the cell nuclei. Lastly, samples were captured using a fluorescence microscope (OLYMPUS, Japan).

3. Results

3.1. Expression of MyHC Genes in Different Muscle Tissues of Broilers

In some previous studies, compared with histochemical methods, qRT-PCR was a fast and sensitive approach to distinguish different muscle types by identifying the expression of MyHC subunits [33, 34]. This method was also applied to identify the muscle types of chickens [35, 36]. Therefore, in order to compare the fiber types of different muscles in broilers, the expression of MYH3, a fast muscle fiber marker gene [37], and MYH1C, a slow muscle fiber marker gene [38], were detected by qRT-PCR between breast and leg muscles of 10 randomly selected broilers. The results showed that the expression of MYH3 in the breast muscle was significantly higher than that in the leg muscle, while the expression of MYH1C showed the opposite result (Figure 1). The results indicated that the breast and leg muscles of broilers were two different types of skeletal muscle. The former was mainly composed of slow muscle fibers, while the latter was mainly composed of fast muscle fibers.

3.2. Sequencing Quality Evaluation

To verify the authenticity of the sequencing data, samples were tested for quality. In Table 1, it could be found that the Q20 (a and ) and Q30 (a and ) levels were both above 90%. Meanwhile, the GC content was between 49% and 53%. The results showed that the sequencing data had a small probability of error.

Sample nameClean readsClean basesGC (%)Q20 (%)Q30 (%)


It was also found that more than 75% of reads aligned to the reference genome and that the number of reads aligned to a unique location of the reference genome was between 74% and 92%, which indicated that these samples were highly available (Table 2). To summarize, the sequencing data were good and the subsequent analysis could be carried out.

Sample nameTotal readsMapped readsMapped ratio (%)Uniquely mapUniquely mapped ratio (%)Reads mapped to “+” strandReads mapped to “+” strand ratio (%)Reads mapped to “-” strandReads mapped to “-” strand ratio (%)


3.3. Screening of DEGs

To show the distribution of gene expression, a volcano plot of the differentially expressed genes was constructed, with -log10(FDR) as the ordinate and log2(FC) as the abscissa. Because the differential expression analysis of transcriptome sequencing was an independent statistical hypothesis test for a large number of gene expression values, there was a false-positive problem. Therefore, in the process of differential expression analysis, the recognized Benjamini-Hochberg correction method was used to correct the significant values obtained by the original hypothesis test. Finally, FDR was used as the key index for differentially expressed gene screening. In our study, was used as a screening condition for DEGs. The results showed that there were a large number of differentially expressed genes. A total of 767 DEGs were identified. Compared with leg muscle, there were 429 upregulated genes and 338 downregulated genes in the breast muscle (Figure 2 and Table S2).

Meanwhile, in order to ensure the accuracy of the analysis, the screened differentially expressed genes were analyzed by hierarchical cluster analysis where genes with the same or similar expression patterns clustered. The abscissa represented the sample clustering. Among them, one column represents a sample, and clustering is based on the similarity of gene expression between samples. The more similar the gene expression between samples, the closer the distance. This result suggested that these DEGs could significantly separate the samples into the leg muscle group and breast muscle group (Figure 3), indicating the reliability of our analysis.

3.4. Gene Ontology (GO) Annotation and Enrichment Analysis

To further reveal the molecular characterization of the DEGs, GO annotation was used to analyze the DEGs. The GO database contains three main branches: biological process, molecular function, and cellular component. The results showed that a total of 468 DEGs were annotated into 61 GO terms, including 24 biological processes, 19 cellular components, and 18 molecular function annotations. In biological processes, the significantly enriched GO terms mainly contained cellular process, single organism process, biological regulation, metabolic process, response to stimulus, multicellular organismal process, localization, developmental process, cellular component organization, biogenesis signalling, etc. Cells, cellular parts, and organelles were significantly enriched in cellular components. Simultaneously, the main functional terms of molecular function were binding and catalytic activity, which were enriched with 273 and 183 genes, respectively (Figure 4).

In addition, some important genes () that may be involved in the transformation of muscle fiber types are enriched in these GO terms, such as mitotic cell cycle process (FHL1, PDS5A) and calcium ion binding (NKD1, RYR3) (Table S3).

3.5. KEGG Pathway Enrichment Analysis

To know the signal pathways where the genes were distributed, a KEGG pathway analysis was performed. A total of 230 DEGs were mapped to 126 KEGG pathways, of which 20 pathways were significantly enriched () (Table S4). There were several extremely significantly enriched pathways (Figure 5), including glycolysis/gluconeogenesis, starch and sucrose metabolism, insulin signalling, and biosynthesis of amino acids. Some important genes were found to be enriched in these pathways: PKM, LDHA, LDHA, PYGM, and HRAS () (Table S4).

3.6. Validation of DEGs by qRT-PCR

The expression of 7 randomly selected DEGs, including 4 downregulated genes (FHL1, MYBPC1, ADPRHL, and TPM2) and 3 upregulated genes (HOMER3, MYH3, and BPGM), was verified through quantitative real-time PCR. The results illustrated that the expression of FHL1, MYBPC1, ADPRHL, and TPM2 in the breast muscle was significantly lower than that in the leg muscle, while the expression of HOMER3, MYH3, and BPGM was exactly the opposite (Figure 6(a)). As shown in Figure 6, the expression trend of these 7 genes was consistent with the results of the RNA-seq experiment, which proved that the RNA-seq results were reliable (Figure 6(b)).

3.7. Identification of Chicken Skeletal Muscle Satellite Cells

To facilitate the study of muscle fiber growth characteristics in broilers, we isolated skeletal muscle satellite cells from the leg muscle of 15 chicken embryos which were incubated for 10 days in vitro. The cells in the process of proliferation and differentiation were identified by immunofluorescence with the skeletal muscle satellite cell-specific markers Pax7, Desmin, and MyHC [39]. The results showed that Pax7 was positive in the nucleus (Figure 7(a)), and Desmin and MyHC were positive in the cytoplasm (Figures 7(d) and 7(g)). Therefore, the results showed that the isolated cells are indeed skeletal muscle satellite cells and could be used in the following research.

3.8. Expression Profile of MyHC Genes

In order to further explore the growth of muscle fibers in broilers, the expression patterns of MyHC subunits during cell differentiation were analyzed by qRT-PCR. MyHC IIA is a genetic marker of fast oxidative muscle fibers [40]. MyHC IIB is a genetic marker of fast glycolytic muscle fibers [41]. MyHC I is a genetic marker of slow oxidative muscle fibers [42]. The results suggested that the expression of MyHC IIA was significantly upregulated during cell differentiation (Figure 8(a)). However, the expression of MyHC IIB was significantly downregulated on the second day of differentiation and then gradually upregulated from day 4 to day 6 (Figure 8(b)). Additionally, the expression of MyHC I was significantly upregulated at the beginning and decreased after the 4th day of differentiation (Figure 8(c)). The results indicated different development of muscle fiber types in broilers.

4. Discussion

The skeletal muscle of vertebrates is composed of muscle fibers with different morphological, contractile, and metabolic characteristics. Meanwhile, the properties of muscle fibers have an important effect on the meat quality of livestock [8]. In our study, we found that slow/fast muscle fiber marker genes were differentially expressed between the breast and leg muscles of broilers. The results were consistent with several previous studies demonstrating that the breast muscle of the chicken was a fast muscle, while the leg muscle was a slow muscle [43]. Therefore, RNA-seq was performed on these two skeletal muscles to screen candidate genes related to muscle fiber-type conversion in our research. A total of 767 DEGs were discovered between both investigated groups, of which 7 DEGs were randomly selected and verified by qRT-PCR analysis. Although the expression fold changes of the selected genes between the two methods were not the same, the expression trend was highly consistent, proving that the results obtained by chip analysis were credible.

The basal processes of muscle cell growth and muscle fiber formation in broilers include cellular processes, single biological processes, and biological regulation. GO functional analysis showed that 11 DEGs were enriched in GO term mitotic cell cycle processes (Table S3). Among these DEGs, the FHL1 gene was found to be highly abundant in oxidative muscle fibers [44]. Overexpression of FHL1 promoted the transformation of muscle fibers to the oxidative type, while knockdown of FHL1 could cause muscle fiber hypertrophy [45]. Slow muscle fibers are usually small in diameter, whereas the fast muscle fibers are usually large in diameter [46]. In this study, the PDS5A gene was also screened as an upregulated DEG in the breast muscle. PDS5A was found to be highly expressed in the skeletal muscle but low in other tissues and to have an effect on the proliferation of skeletal muscle cells [47]. These genes might play a key role in regulating muscle cell proliferation and muscle fiber transformation. In addition, intracellular calcium signalling is also an important factor that influences muscle fiber types [48]. Our study showed that 15 DEGs were enriched in the GO term calcium ion binding (Table S3). Among these genes, NKD1 was upregulated in leg muscles. Some studies have shown that Wnt/β-catenin signalling could transform slow muscle fibers into fast muscle fibers and that NKD1 has a strong antagonistic effect on Wnt/β-catenin signalling [49, 50]. Another gene, RYR3, controls calcium ion release channels in muscle cells [51]. RYR3 has a high calcium sensitivity and regulates rapid muscle contraction together with the RYR1 gene [52]. Some studies have shown that calcium sensitivity is related to the generation of fast myofibril subtypes, which was consistent with the results of our study [53]. KEGG pathway analysis also showed that the expression of calcium signalling pathway-related genes was altered significantly (Table S4). The function of these aforementioned DEGs in skeletal muscle fiber transformation needs further investigation.

One of the methods to classify fast or slow muscle is by their energy metabolism [54]. GO functional annotation analysis showed that a total of 198 DEGs were enriched in metabolic process-related GO terms (Table S3). Previous studies showed that slow-twitch muscles have lower glycolytic enzyme activity, while fast-twitch muscles act mainly through the glycolytic pathway to meet the energy requirements for rapid muscle contraction [55, 56]. In our study, the KEGG pathway enrichment analysis results indicated that the four most significant enrichment pathways were related to the energy metabolism of muscle cells. Most of the DEGs involved in the glycolysis/gluconeogenesis pathway were upregulated in the breast muscle (15 of 17) (Table S4). Protein synthesis during skeletal muscle development is accompanied by energy metabolism, and the glycolysis/gluconeogenesis pathway plays a significant role [57]. PKM is a gene that catalyses the last step of glycolysis [58] and was upregulated in the breast muscle. The muscle type of pyruvate kinase (PKM) is one of the key mediators of the Warburg effect and tumor metabolism [59] and was closely related to the development of PSE meat [60]. Another gene, LDHA, the predominant LDH isoform in the skeletal muscle, is also an important enzyme involved in glycolysis [61]. The mutation at the C.423A>G site in exon 6 of porcine LDHA had a significant effect on the muscle fiber type II ratio [62]. In addition, 13 DEGs were enriched in the biosynthesis of amino acid pathway, of which 12 DEGs were upregulated in the breast muscle (Table S4). Another study found that genes related to glycolysis/gluconeogenesis and the biosynthesis of amino acid pathway were gradually upregulated during the growth and development of chicken breast muscle [63]. Therefore, we inferred that these DEGs were activated during fast-twitch muscle fiber formation.

Our KEGG results further discovered that 22 DEGs were enriched in the insulin signalling pathway (Table S4). Insulin plays a key role in a series of biological reactions, such as glucose uptake, lipid and protein synthesis, cell growth, and glycogen synthesis [64]. In skeletal muscle cells, the insulin signalling pathway regulates glycogen synthesis and decreases the rate of glycogen breakdown [65]. It has been shown that the content of glycogen in type I skeletal muscle fibers is significantly lower than that in type II muscle fibers [66]. We found that 5 DEGs in the insulin signalling pathway were also enriched in the GO term glycogen metabolic process (Table S3), including GYS1 and PYGM; these were all upregulated in the breast muscle. The insulin signalling pathway stimulates the expression of GYS1 mRNA and promotes the synthesis of glycogen in the skeletal muscle [67]. Skeletal muscle fiber type IIB predominantly uses glycogen and glucose as fuel, and the upregulation of PYGM accelerates glycogen phosphorylation [68]. In addition, 3 DEGs in the insulin signalling pathway were upregulated in the leg muscle, including HRAS (Table S4). The expression of HRAS was negatively correlated with metabolic traits related to the insulin signalling pathway [69]. Thus, HRAS may be a candidate gene involved in slow muscle fiber formation.

Skeletal muscle fibers are generally divided into four types according to the myosin heavy chain (MyHC) isoforms, namely, types I, IIA, IIB, and IIX [70]. However, there are very few type IIX muscle fibers in poultry skeletal muscle [71]. A previous study showed that Pax7, Desmin, and MyHC were specific genetic markers of skeletal muscle satellite cells [72]. To further explore the mechanism of skeletal muscle fiber transformation in broilers, an in vitro cell model was successfully constructed in our study. Then, the expression profiles of MyHC isoforms (type I, type IIA, and type IIB) during broiler skeletal muscle satellite cell differentiation were detected. MyHC IIA mRNA expression increased throughout the time course. Expression of the MyHC IIB isoform was low on day 2 but rose until day 6. MyHC I expression first grew dramatically from day 0 to day 4 and then declined until day 6. Interestingly, these results coincided with those in a C2C12 mouse myoblast cell differentiation model [73]. Therefore, future studies will use these approximate time points of skeletal muscle fiber transformation in vitro to explore the function of the key DEGs in broilers.

5. Conclusion

In the current research, RNA-seq was carried out to compare the leg and breast muscles of broilers. A total of 767 DEGs were identified. Compared with leg muscle, there were 429 upregulated genes and 338 downregulated genes in the breast muscle. The expression profile of MyHC isoforms during broiler skeletal muscle cell differentiation was detected. Therefore, our study provides useful information for understanding the molecular mechanisms of muscle fiber-type transition in broilers. However, the transformation of skeletal muscle fiber in vivo is a dynamic process and these transcriptome data are preliminary, so the function of the DEGs still requires further investigation.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

Authors’ Contributions

Han Wang and Zhonghao Shen analyzed the data and drafted the manuscript. Han Wang, Xiaolong Zhou, Songbai Yang, Feifei Yan, and Ayong Zhao designed the experiments. Zhonghao Shen, Ke He, and Han Wang performed the experiments. All authors provided help for sample collection and reviewed the final version of the manuscript. Han Wang and Zhonghao Shen contributed equally to this work.


This work was supported by the Zhejiang A&F University Scientific Research Development Fund Project (W20180043), Key Laboratory Research Project of Zhejiang Province (KLGEH011), and the National Natural Science Foundation of China (31802030).

Supplementary Materials

Table S1: primer sequences for the genes selected for qRT-PCR. Table S2: DEGs identified under certain filter criteria. Table S3: GO enrichment analysis of DEGs. Table S4: KEGG pathway analysis of DEGs. (Supplementary Materials)


  1. Y. S. Bae, J. C. Lee, S. Jung et al., “Differentiation of deboned fresh chicken thigh meat from the frozen-thawed one processed with different deboning conditions,” Korean Journal for Food Science of Animal Resources, vol. 34, no. 1, pp. 73–79, 2014. View at: Publisher Site | Google Scholar
  2. D. Skunca, I. Tomasevic, N. Zdolec, R. Kolaj, G. Aleksiev, and I. Djekic, “Consumer-perceived quality characteristics of chicken meat and chicken meat products in Southeast Europe,” British Food Journal, vol. 119, no. 7, pp. 1525–1535, 2017. View at: Publisher Site | Google Scholar
  3. C. Franke, L. Holl, H. C. Langowski, H. Petermeier, and R. F. Vogel, “Sensory evaluation of chicken breast packed in two different modified atmospheres,” Food Packaging and Shelf Life, vol. 13, pp. 66–75, 2017. View at: Publisher Site | Google Scholar
  4. M. Hejdysz, S. A. Kaczmarek, A. Rogiewicz, and A. Rutkowski, “Influence of graded levels of meals from three lupin species on growth performance and nutrient digestibility in broiler chickens,” British Poultry Science, vol. 60, no. 3, pp. 288–296, 2019. View at: Publisher Site | Google Scholar
  5. N. A. Mir, A. Rafiq, F. Kumar, V. Singh, and V. Shukla, “Determinants of broiler chicken meat quality and factors affecting them: a review,” Journal of Food Science and Technology, vol. 54, no. 10, pp. 2997–3009, 2017. View at: Publisher Site | Google Scholar
  6. M. Zhang, F. Li, X.-f. Ma et al., “Identification of differentially expressed genes and pathways between intramuscular and abdominal fat-derived preadipocyte differentiation of chickens in vitro,” BMC Genomics, vol. 20, no. 1, p. 743, 2019. View at: Publisher Site | Google Scholar
  7. A. M. Felício, L. G. Gaya, J. B. S. Ferraz et al., “Heritability and genetic correlation estimates for performance, meat quality and quantitative skeletal muscle fiber traits in broiler,” Livestock Science, vol. 157, no. 1, pp. 81–87, 2013. View at: Publisher Site | Google Scholar
  8. S. H. Lee, S. T. Joo, and Y. C. Ryu, “Skeletal muscle fiber type and myofibrillar proteins in relation to meat quality,” Meat Science, vol. 86, no. 1, pp. 166–170, 2010. View at: Publisher Site | Google Scholar
  9. S. Han, C. Cui, Y. Wang et al., “FHL3 negatively regulates the differentiation of skeletal muscle satellite cells in chicken,” 3 Biotech, vol. 9, no. 6, p. 206, 2019. View at: Publisher Site | Google Scholar
  10. J. M. Stein and H. A. Padykula, “Histochemical classification of individual skeletal muscle fibers of the rat,” American Journal of Anatomy, vol. 110, no. 2, pp. 103–123, 1962. View at: Publisher Site | Google Scholar
  11. R. Bassel-Duby and E. N. Olson, “Signaling pathways in skeletal muscle remodeling,” Annual Review of Biochemistry, vol. 75, no. 1, pp. 19–37, 2006. View at: Publisher Site | Google Scholar
  12. M. Petracci, F. Sirri, M. Mazzoni, and A. Meluzzi, “Comparison of breast muscle traits and meat quality characteristics in 2 commercial chicken hybrids,” Poultry Science, vol. 92, no. 9, pp. 2438–2447, 2013. View at: Publisher Site | Google Scholar
  13. S. Raj, G. Skiba, D. Weremko et al., “The relationship between the chemical composition of the carcass and the fatty acid composition of intramuscular fat and backfat of several pig breeds slaughtered at different weights,” Meat Science, vol. 86, no. 2, pp. 324–330, 2010. View at: Publisher Site | Google Scholar
  14. Z. Q. Lu, Y. Ren, X. H. Zhou et al., “Maternal dietary linoleic acid supplementation promotes muscle fibre type transformation in suckling piglets,” Journal of Animal Physiology and Animal Nutrition, vol. 101, no. 6, pp. 1130–1136, 2017. View at: Publisher Site | Google Scholar
  15. H. Zhu, H. Yang, W. Zhao, Y. Su, and Y. Tian, “Associations of the expression levels of genes involved inCFL2bandMyHCisoform type changes in longissimus dorsi muscle of HeBao and Large White pigs (Sus scrofa) during postnatal growth,” Canadian Journal of Animal Science, vol. 99, no. 1, pp. 74–81, 2019. View at: Publisher Site | Google Scholar
  16. Y. Zhang, H. Yan, P. Zhou, Z. Zhang, J. Liu, and H. Zhang, “MicroRNA-152 promotes slow-twitch myofiber formation via targeting uncoupling protein-3 gene,” Animals, vol. 9, no. 9, p. 669, 2019. View at: Publisher Site | Google Scholar
  17. J. T. Shu, W. J. Xu, M. Zhang et al., “Transcriptional co-activator PGC-1α gene is associated with chicken skeletal muscle fiber types,” Genetics and Molecular Research, vol. 13, no. 1, pp. 895–905, 2014. View at: Publisher Site | Google Scholar
  18. M. Ma, B. Cai, L. Jiang et al., “lncRNA-Six1 is a target of miR-1611 that functions as a ceRNA to regulate Six1 protein expression and fiber type switching in chicken myogenesis,” Cells, vol. 7, no. 12, p. 16, 2018. View at: Publisher Site | Google Scholar
  19. M. Ghosh, N. Sharma, A. K. Singh, M. Gera, K. K. Pulicherla, and D. K. Jeong, “Transformation of animal genomics by next-generation sequencing technologies: a decade of challenges and their impact on genetic architecture,” Critical Reviews in Biotechnology, vol. 38, no. 8, pp. 1157–1175, 2018. View at: Publisher Site | Google Scholar
  20. C. Zhang, G. Wang, J. Wang et al., “Characterization and comparative analyses of muscle transcriptomes in Dorper and small-tailed Han sheep using RNA-Seq technique,” Plos One, vol. 8, no. 8, p. e72686, 2013. View at: Publisher Site | Google Scholar
  21. L. F. S. Fonseca, D. B. Dos Santos Silva, D. F. J. Gimenez et al., “Gene expression profiling and identification of hub genes in Nellore cattle with different marbling score levels,” Genomics, vol. 112, no. 1, pp. 873–879, 2020. View at: Publisher Site | Google Scholar
  22. S.-Q. Song, W.-w. Ma, S.-X. Zeng et al., “Transcriptome analysis of differential gene expression in the longissimus dorsi muscle from Debao and landrace pigs based on RNA-sequencing,” Bioscience Reports, vol. 39, no. 12, p. 23, 2019. View at: Publisher Site | Google Scholar
  23. K. Piórkowska, K. Żukowski, J. Nowak, K. Połtowicz, K. Ropka-Molik, and A. Gurgul, “Genome-wide RNA-Seq analysis of breast muscles of two broiler chicken groups differing in shear force,” Animal Genetics, vol. 47, no. 1, pp. 68–80, 2016. View at: Publisher Site | Google Scholar
  24. H. Dong, J. J. Wang, T. Y. Ying, L. I. Ting, and H. L. Wang, “Extraction of total RNA and protein by the method of TRIzol,” Letters in Biotechnology, vol. 16, no. 6, pp. 649-650, 2005. View at: Google Scholar
  25. D. Kim, B. Langmead, and S. L. Salzberg, “HISAT: a fast spliced aligner with low memory requirements,” Nature Methods, vol. 12, no. 4, pp. 357–360, 2015. View at: Publisher Site | Google Scholar
  26. M. Pertea, G. M. Pertea, C. M. Antonescu, T.-C. Chang, J. T. Mendell, and S. L. Salzberg, “StringTie enables improved reconstruction of a transcriptome from RNA-seq reads,” Nature Biotechnology, vol. 33, no. 3, pp. 290–295, 2015. View at: Publisher Site | Google Scholar
  27. L. Florea, L. Song, and S. L. Salzberg, “Thousands of exon skipping events differentiate among splicing patterns in sixteen human tissues,” F1000res, vol. 2, p. 188, 2013. View at: Publisher Site | Google Scholar
  28. M. S. I. Dona, L. A. Prendergast, S. Mathivanan, S. Keerthikumar, and A. Salim, “Powerful differential expression analysis incorporating network topology for next-generation sequencing data,” Bioinformatics, vol. 33, no. 10, pp. 1505–1513, 2017. View at: Publisher Site | Google Scholar
  29. S. Anders and W. Huber, “Differential expression analysis for sequence count data,” Genome Biology, vol. 11, no. 10, p. R106, 2010. View at: Publisher Site | Google Scholar
  30. M. D. Young, M. J. Wakefield, G. K. Smyth, and A. Oshlack, “Gene ontology analysis for RNA-seq: accounting for selection bias,” Genome biology, vol. 11, no. 2, p. R14, 2010. View at: Publisher Site | Google Scholar
  31. X. Mao, T. Cai, J. G. Olyarchuk, and L. Wei, “Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary,” Bioinformatics, vol. 21, no. 19, pp. 3787–3793, 2005. View at: Publisher Site | Google Scholar
  32. W. Luo, H. Wu, Y. Ye et al., “The transient expression of miR-203 and its inhibiting effects on skeletal muscle cell proliferation and differentiation,” Cell Death & Disease, vol. 5, no. 7, p. e1347, 2014. View at: Publisher Site | Google Scholar
  33. H. M. Hu, J. Y. Wang, R. S. Zhu, J. F. Guo, and Y. Wu, “Effect of myosin heavy chain composition of muscles on meat quality in Laiwu pigs and Duroc,” Science in China. Series C, Life Sciences, vol. 51, no. 2, pp. 127–132, 2008. View at: Publisher Site | Google Scholar
  34. H. Sanchez, R. Chapot, S. Banzet et al., “Quantification by real-time PCR of developmental and adult myosin mRNA in rat muscles,” Biochemical and biophysical research communications, vol. 340, no. 1, pp. 165–174, 2006. View at: Publisher Site | Google Scholar
  35. A. M. Ahhmed, C. Birisik, G. Kaneko et al., “Differences in gelling properties induced by transglutaminase in chicken muscles are explained by determining myosin heavy chain mRNA ratios using RT-PCR technique,” Fleischwirtschaft, vol. 95, no. 7, pp. 98–104, 2015. View at: Google Scholar
  36. Y. Li, L. Yuan, X. Yang et al., “Effect of early feed restriction on myofibre types and expression of growth-related genes in the gastrocnemius muscle of crossbred broiler chickens,” The British journal of nutrition, vol. 98, no. 2, pp. 310–319, 2007. View at: Publisher Site | Google Scholar
  37. R. Amann, S. Wyder, A. M. Slavotinek, and B. Trueb, “The FgfrL1 receptor is required for development of slow muscle fibers,” Developmental Biology, vol. 394, no. 2, pp. 228–241, 2014. View at: Publisher Site | Google Scholar
  38. S. Machida, S. Noda, A. Takao, M. Nakazawa, and R. Matsuoka, “Expression of slow skeletal myosin heavy chain 2 gene in Purkinje fiber cells in chick heart,” Biology of the cell, vol. 94, no. 6, pp. 389–399, 2002. View at: Publisher Site | Google Scholar
  39. L. I. Fang-Hua, L. L. Hou, M. A. Yue-Hui, Q. H. Pang, and W. J. Guan, “Isolation, culture, identification and muscle differentiation of skeletal muscle satellite cells in Beijing fatty chicken,” Scientia Agricultura Sinica, vol. 43, no. 22, pp. 4725–4731, 2010. View at: Google Scholar
  40. C. Jurie, B. Picard, Y. Heyman et al., “Comparison of cloned and non-cloned Holstein heifers in muscle contractile and metabolic characteristics,” Animal, vol. 3, no. 2, pp. 244–250, 2009. View at: Publisher Site | Google Scholar
  41. F. Dai, D. Feng, Q. Cao et al., “Developmental differences in carcass, meat quality and muscle fibre characteristics between the Landrace and a Chinese native pig,” South African Journal of Animal Science, vol. 39, no. 4, 2010. View at: Publisher Site | Google Scholar
  42. V. Taglietti, G. Maroli, S. Cermenati et al., “Nfix induces a switch in Sox6 transcriptional activity to regulate MyHC-I expression in fetal muscle,” Cell Reports, vol. 17, no. 9, pp. 2354–2366, 2016. View at: Publisher Site | Google Scholar
  43. L.-H. Yu, E.-S. Lee, H.-S. Chen et al., “Comparison of physicochemical characteristics of hot-boned chicken breast and leg muscles during storage at 20°C,” Korean Journal for Food Science of Animal Resources, vol. 31, no. 5, pp. 676–683, 2011. View at: Publisher Site | Google Scholar
  44. P. T. Loughna, P. Mason, S. Bayol, and C. Brownson, “The LIM-domain protein FHL1 (SLIM 1) exhibits functional regulation in skeletal muscle,” Molecular Cell Biology Research Communications, vol. 3, no. 3, pp. 136–140, 2000. View at: Publisher Site | Google Scholar
  45. B. S. Cowling, M. J. McGrath, M.-A. Nguyen et al., “Identification of FHL1 as a regulator of skeletal muscle mass: implications for human myopathy,” Journal of Cell Biology, vol. 183, no. 6, pp. 1033–1048, 2008. View at: Publisher Site | Google Scholar
  46. H. B. Hendricks, D. T. Lafferty, E. D. Aberle, M. D. Judge, and J. C. Forrest, “Relation of porcine muscle fiber type and size to postmortem shortening,” Journal of Animal Science, vol. 32, no. 1, pp. 57–61, 1971. View at: Publisher Site | Google Scholar
  47. G. Capalbo, T. Müller-Kuller, O. G. Ottmann, D. Hoelzer, and U. J. Scheuring, “HIV-1 infection suppresses expression of host cell cycle-associated GenePDS5A,” Intervirology, vol. 55, no. 4, pp. 79–91, 2012. View at: Publisher Site | Google Scholar
  48. K. Ropka-Molik, A. Bereta, K. Żukowski, K. Piórkowska, A. Gurgul, and G. Żak, “Transcriptomic gene profiling of porcine muscle tissue depending on histological properties,,” Animal Science Journal, vol. 88, no. 8, pp. 1178–1188, 2017. View at: Publisher Site | Google Scholar
  49. J.-M. Tee, C. van Rooijen, R. Boonen, and D. Zivkovic, “Regulation of slow and fast muscle myofibrillogenesis by Wnt/β-catenin and myostatin signaling,” Plos One, vol. 4, no. 6, p. e5880, 2009. View at: Publisher Site | Google Scholar
  50. D. Angonin and T. J. Van Raay, “Nkd1 functions as a passive antagonist of Wnt signaling,” PLoS One, vol. 8, no. 8, pp. e74666–e74666, 2013. View at: Publisher Site | Google Scholar
  51. A. Conti, L. Gorza, and V. Sorrentino, “Differential distribution of ryanodine receptor type 3 (RyR3) gene product in mammalian skeletal muscles,” Biochemical Journal, vol. 316, no. 1, pp. 19–23, 1996. View at: Publisher Site | Google Scholar
  52. C. F. Perez, J. R. López, and P. D. Allen, “Expression levels of RyR1 and RyR3 control resting free Ca2+ in skeletal muscle,” American Journal of Physiology. Cell Physiology, vol. 288, no. 3, pp. C640–C649, 2005. View at: Publisher Site | Google Scholar
  53. D. Danieli-Betto, R. Betto, and M. Midrio, “Calcium sensitivity and myofibrillar protein isoforms of rat skinned skeletal muscle fibres,” Pflügers Archiv European Journal of Physiology, vol. 417, no. 3, pp. 303–308, 1990. View at: Publisher Site | Google Scholar
  54. C. J. M. Nicol and I. A. Johnston, “Energy metabolism of fast- and slow-twitch skeletal muscle in the rat: thyroid hormone induced changes,” Journal of Comparative Physiology, vol. 142, no. 4, pp. 465–472, 1981. View at: Publisher Site | Google Scholar
  55. Y. M. Choi, Y. C. Ryu, and B. C. Kim, “Influence of myosin heavy- and light chain isoforms on early postmortem glycolytic rate and pork quality,” Meat Science, vol. 76, no. 2, pp. 281–288, 2007. View at: Publisher Site | Google Scholar
  56. N. M. Rizk, D. A. Meier, D. J. Pastorek, G. R. Krakower, and A. H. Kissebah, “Glucose utilization in muscle fiber types: use of the partial pancreatectomized rat model to distinguish effects of glucose and insulin on insulin resistance,” Molecular Genetics and Metabolism, vol. 65, no. 1, pp. 44–50, 1998. View at: Publisher Site | Google Scholar
  57. J. Liu, R. Fu, R. Liu et al., “Protein profiles for muscle development and intramuscular fat accumulation at different post-hatching ages in chickens,” Plos One, vol. 11, no. 8, p. e0159722, 2016. View at: Publisher Site | Google Scholar
  58. Y. Xiong, Q.-Y. Lei, S. Zhao, and K.-L. Guan, “Regulation of glycolysis and gluconeogenesis by acetylation of PKM and PEPCK,” Cold Spring Harbor Symposia on Quantitative Biology, vol. 76, pp. 285–289, 2012. View at: Publisher Site | Google Scholar
  59. C. Zhan, L. Yan, L. Wang et al., “Isoform switch of pyruvate kinase M1 indeed occurs but not to pyruvate kinase M2 in human tumorigenesis,” Plos One, vol. 10, no. 3, p. e0118663, 2015. View at: Publisher Site | Google Scholar
  60. F. Schwägele, C. Haschke, G. Krauss, and K. O. Honikel, “Comparative studies of pyruvate kinase from PSE and normal pig muscles,” Zeitschrift für Lebensmittel-Untersuchung und -Forschung, vol. 203, no. 1, pp. 14–20, 1996. View at: Publisher Site | Google Scholar
  61. G. Van Hall, “Lactate as a fuel for mitochondrial respiration,” Acta Physiologica Scandinavica, vol. 168, no. 4, pp. 643–656, 2000. View at: Publisher Site | Google Scholar
  62. H. Qiu, X. Xu, B. Fan, M. F. Rothschild, Y. Martin, and B. Liu, “Investigation of LDHA and COPB1 as candidate genes for muscle development in the MYOD1 region of pig chromosome 2,” Molecular Biology Reports, vol. 37, no. 1, pp. 629–636, 2010. View at: Publisher Site | Google Scholar
  63. J. Liu, Q. Lei, F. Li et al., “Dynamic transcriptomic analysis of breast muscle development from the embryonic to post-hatching periods in chickens,” Frontiers in Genetics, vol. 10, p. 12, 2020. View at: Publisher Site | Google Scholar
  64. C. Taha and A. Klip, “The insulin signaling pathway,” vol. 169, no. 1, pp. 1–12, 1999. View at: Google Scholar
  65. G. Dimitriadis, P. Mitrou, V. Lambadiari, E. Maratou, and S. A. Raptis, “Insulin effects in muscle and adipose tissue,” Diabetes Res Clin Pract, vol. 93, Supplement 1, pp. S52–S59, 2011. View at: Publisher Site | Google Scholar
  66. J. H. Choe, Y. M. Choi, S. H. Lee et al., “The relation between glycogen, lactate content and muscle fiber type composition, and their influence on postmortem glycolytic rate and pork quality,” Meat Science, vol. 80, no. 2, pp. 355–362, 2008. View at: Publisher Site | Google Scholar
  67. X. Huang, A. Vaag, M. Hansson, J. Weng, E. Laurila, and L. Groop, “Impaired insulin-stimulated expression of the glycogen synthase gene in skeletal muscle of type 2 diabetic patients is acquired rather than inherited,” Journal of Clinical Endocrinology & Metabolism, vol. 85, no. 4, pp. 1584–1590, 2000. View at: Publisher Site | Google Scholar
  68. G. Nogales-Gadea, E. Mormeneo, I. García-Consuegra et al., “Expression of glycogen phosphorylase isoforms in cultured muscle from patients with McArdle's disease carrying the p.R771PfsX33 PYGM mutation,” PloS one, vol. 5, no. 10, p. e13164, 2010. View at: Publisher Site | Google Scholar
  69. N. Xu, D. H. Geller, M. R. Jones, V. A. Funari, R. Azziz, and M. O. Goodarzi, “Comprehensive assessment of expression of insulin signaling pathway components in subcutaneous adipose tissue of women with and without polycystic ovary syndrome,” Journal of Clinical & Translational Endocrinology, vol. 2, no. 3, pp. 99–104, 2015. View at: Publisher Site | Google Scholar
  70. C. L. Francisco, A. M. Jorge, M. Dal-Pai-Silva, F. R. Carani, L. C. Cabeço, and S. R. Silva, “Muscle fiber type characterization and myosin heavy chain (MyHC) isoform expression in Mediterranean buffaloes,” Meat Science, vol. 88, no. 3, pp. 535–541, 2011. View at: Publisher Site | Google Scholar
  71. E. Bandman and B. W. C. Rosser, “Evolutionary significance of myosin heavy chain heterogeneity in birds,” Microscopy Research and Technique, vol. 50, no. 6, pp. 473–491, 2000. View at: Publisher Site | Google Scholar
  72. C. Bai, L. Hou, F. Li, X. He, M. Zhang, and W. Guan, “Isolation and biological characteristics of Beijing fatty chicken skeletal muscle satellite cells,” Cell Communication and Adhesion, vol. 19, no. 5-6, pp. 69–77, 2012. View at: Publisher Site | Google Scholar
  73. D. M. Brown, T. Parr, and J. M. Brameld, “Myosin heavy chain mRNA isoforms are expressed in two distinct cohorts during C2C12 myogenesis,” Journal of Muscle Research and Cell Motility, vol. 32, no. 6, pp. 383–390, 2012. View at: Publisher Site | Google Scholar

Copyright © 2020 Han Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder

Related articles

Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.