Comparative Transcriptome Analysis Revealed Transcriptome Regulators Associated with Muscle Growth and Development

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Comparative transcriptome analysis revealed transcriptome regulators associated with muscle growth and development in three chicken breeds

Abbreviations:

DEGs: differently expressed genes; ECM: extracellular matrix ; GO:gene ontology; KEGG: Kyoto encyclopedia of genes and genomes; TNNT2: Cardiac muscle troponin T; MYBPC1: myosin binding protein C, MYH15:myosin heavy chain 15; NOV: nephroblastoma overexpressed; MYLK: myosin light chain kinase; JUN:Jun proto-oncogene, AP-1 transcription factor subunit, IGF2: insulin like growth factor 2, BCL-2:apoptosis regulator, FOS:Fos proto-oncogene, AP-1 transcription factor subunit, EDN1:endothelin 1, IL6: interleukin 6, TLR4:toll-like receptor 4; SRC: SRC proto-oncogene; qRT-PCR :quantitative reverse transcription PCR.

Abstract

Background: Poultry production has shown to be a very important to meet the protein nutrition needs of the increasing global population. Local chicken production is increasing but still limited with slow growth rates and low feed efficiency. The growth mechanisms for local chickens remains unknown. The broiler chicken is a large breed with fast growth, Daheng has a middle growth rate, and roman powder laying hens is a small breed with slowest growth. This study was aimed to investigate the mechanisms of muscle growth and development through performing microarray analyses in breast muscle of these three breeds under three development stages.

Results: The differently expressed genes (DEGs) between any two lines that unique for a development stage is markedly greater than those common for all stages. Functional analysis revealed that genes differently expressed across whole development process primarily involved in positive cell proliferation, growth, cell differentiation, and development processes, and the genes involved in the muscle regulation, muscle construction and muscle cell differentiation were up-regulated in the larger breed. Several DEGs, such as MYH15, MYOZ2, MYBPC3, IGF2 Bcl-2, JUN and FOS were directly regulated muscle growth or in the centrat of the Act-network.  Moreover, the pathway including ECM-receptor interaction, MAPK signaling pathway, and Focal adhesion are most enriched for DEGs between lines or within lines under different development stages.

Conclusions: We speculated that muscle growth and development are associated with regulation of genes involved in muscle construction and cell differentiation, and may via ECM-receptor interaction, Focal adhesion, and MAPK signaling pathway in chicken.

Keywords: Muscle growth, Muscle development, Chicken, Breast muscle, Microarray.

Background

The efficiency of animal production is important to meet the protein nutrition needs of the increasing global population. Over the past two decades, China’s chicken production has been growing at an average rate of 5% to 6% per year, and chicken has become the second largest meat consumer product. Moreover, the growth and development of local chicken is slow, while the genetically selected chicken breeds acquired high production efficiency via improving feed efficiency and increasing breast muscle size [1]. Therefore, it is of high research value and application value to study molecular mechanisms regulating chicken muscle growth and development.

The development of high-throughput profiling technology has improved the study of gene expression via interrogating thousands of genes synchronously [2]. Gene expression ananlysis of breast muscle in a single male broiler line demonstrate that signal transduction pathways, such as Jnk, G-coupled, and retinoic acid were associated with feed efficiency [3]. A report about gene expression analysis of breast muscles in Legacy and Modern Broiler Chickens found that the transcriptional profile of differentially enriched genes on day 6 and 21 were involved in regulation of myogenic growth and development [4]. Transcriptome analysis of muscle and liver between Wuding and Daweishan chicken showed that genes related to protein metabolism, ABC receptors, and IL6-related mechanism were more enrich in mini chicken breed [5]. Using microarray, Cogburn e al. showed changes in egg incubation temperatures altered immediate and long-term gene expression, in turn, affect broiler breast muscle traits [6]. To date, these studies have provided invaluable insight into global gene expression in muscle of chicken. However, the underlying mechanism(s)  of muscle growth and development in chicken remains unclear.

Broiler, Daheng, and Roman powder laying hens are three different lines with significantly different growth rate, which Broiler has a fastest growth rate, and Daheng has a middle growth rate, and roman powder laying hens growth slowest. This study aim to identify genes and pathways that differently regulated in breast muscle of Broiler, Daheng, and Roman powder laying hens under three development stages. The differentially expressed genes and their functional annotation to pathways and networks offers insight into the molecular mechanism that regulate muscle growth and development including those relevant to cell proliferation, cell differentiation, development and muscle process.

Materials and Methods

Ethics statement

All procedures conducted with the chickens were performed in accordance with relevant guidelines and regulations, and were approved by the Science and Technology Department of Sichuan Province and the Animal Care and Use Committee of the Sichuan Animal Science Academy. No associated permit number was required, for commercial animals sampling was approved. All efforts were made to minimize suffering.

Sample collection

One-day-old Broiler (lines B) were purchased from the Chengdu Yusen Agriculture and Animal Husbandry Science and Technology Co., Ltd., and Daheng (lines C)  were from Sichuan Daheng Poultry Breeding Co., Ltd., and Roman powder laying hens  (lines A) were from Chengdu Muxing Birds Industry Co.,Ltd. All chickens were fed under the same conditions, with caged and free access to feed and water. Two birds per breed were sacrificed at 2 weeks, 6 weeks and 10 weeks, respectively. Chicken body weight were measure before slaughter, and muscle tissues were collected in liquid nitrogen, and stored at −80 °C. Breast muscle weights were determined immediately after slaughter.

RNA isolation and microarray hybridization

Total RNA was isolated from breast muscle using TRIzol reagent (Invitrogen, Carlsbad, CA, U.S.) per the manufacturer’s instructions. The quantity and purity of RNA were determined using NanoDrop ND-1000 spectrophotometer at 260/280 nm (Nano Drop Technologies, Wilmington, Delaware). RNA integrity was measured via agarose gel electrophoresis. Sample labeling and array hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technology). Briefly, 1ug of total RNA was reverse transcribed into cDNA, then transcribed into cRNA and labelled with Cy3-UTP using Quick Amp Labeling Kit, One-Color (Agilent p/n 5190-0442). The labelled cRNA was purified with RNeasy Mini Kit (Qiagen p/n 74104) and the quality of each cRNA sample was verified using NanoDrop ND-1000(Nano Drop Technologies, Wilmington, Delaware). The labeled cRNAs were hybridized onto the Whole Chicken Genome Oligo Microarray (4 x 44K, Agilent Technologies). After having washed the slides, the arrays were scanned by the Agilent Scanner G2505C.

Bioinformatics analysis

Agilent Feature Extraction software (version 11.0.1.1) was used to analyze acquired array images. Quantile normalization and subsequent data processing were performed using the GeneSpring GX v11.5.1 software package (Agilent Technologies). After quantile normalization of the raw data, genes that at least 4 out of 18 samples have flags in Detected (“All Targets Value”) were chosen for further data analysis. To identify differentially expressed genes (DEGs) with statistical significance, we performed a Volcano filtering between the two compared groups from the experiment. The threshold is Fold Change >= 2.0 and P-value <= 0.05. Enrichment analysis using gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) were applied to determine the roles of these differentially expressed genes played in these biological pathways or GO terms, using DAVID software packages (https://david.ncifcrf.gov/summary.jsp). Finally, Hierarchical Clustering was performed to show the distinguishable gene expression profiling among samples. The gene network was generated using Cytoscape 311.

Quantitative real-time PCR

Seven genes from the DEGs were randomly selected for quantitative reverse transcription PCR (qRT-PCR). Each group was studied in triplicate and gene-specific primers (Table S1) were designed through Primer 5 software. RT-PCR was carried out utilizing HiScriptTM Q RT SuperMix (Vazyme, Jiangsu Sheng, China) according to the manufacturer’s instructions.The qRT-PCR was performed using SYBR Premix Ex Taq™ (TaKaRa, Tokyo, Japan) on an ABI one-step fast thermocycler. The conditions of qRT-PCR were 1 cycle of 95°C for 10 min, followed by 40 cycles of 95°C for 5 s, and annealing and extension at 55-58°C for 30 s. Transcript levels were calculated by the comparative ΔCT method and normalized against the level of actin mRNA. Fold changes in expression were determined by the 2-ΔΔCT method.

Results

Global analysis of the age and line effects on gene expression

The boxplot is a traditional method for visualizing the distribution of a dataset. Here, a boxplot view is used to look at, and compare, the distributions of expression values for the chickens of different lines at different development stages. After normalization, the distributions of log2-ratios among all samples are nearly the same (Fig S1), suggesting that age and line had a slight impact on gene expression globally. In addition, hierarchical clustering was performed based on “All Targets Value”. The result of hierarchical clustering shows a distinguishable gene expression profiling among samples. As shown in Fig 1, genens clearly self-segregated into 4 clusters of samples: one cluster for the high body weight samples (A10, B10, C10, B6, and C6), a nother for the low body weight samples (A2) and a third for the middle body weight samples (A6, B2, and C2). The six-week-old roman powder laying hens clustered together with the two-week-old broiler, while Daheng meat-type chickens and broiler were distinct from the other roman powder laying hens, indicating that body weight were significantly associated with gene expression.

Analysis of DEGs betweenlines

To compare genetic difference among these three lines, the significantly expressed genes between lines A and B, lines A and C, and lines B and C under different development stages were mined separately. Initially, we compared the gene expression profile between lines B and A, and found 4,920 genes were significantly different expressed at the two-week-old stage, including 2,135 up-regulated and 2,785 down-regulated genes in line B. While 4,201 genes were significantly different expressed between lines B and A of six-week old. Of the 4, 201 genes, 2,411 genes were more highly expressed in line B than line A, and 1,790 were down-regulated in line B compared to line A of ten-week old. Furthermore, 2,388 genes were differentially expressed with 1,201 had higher expression in line B than line A. Remarkably, among these DEGs between line B and line A, only 276 were comon at different growth stages, while 3,555, 2,687 and 1,123 were exclusively different expressed at the two-week-old, six-week-old and ten-week-old stage, respectively (Fig 2 A and B).

A total number of 2,471 DEGs were found between two-week-old lines B and C, including 1,390 up- and 1,081down-regulated in the line B compared to the line C. Moreover, there were 1,683 DEGs Between line B and line C of six-week old, and 764 had higher expression level in line B than line C. In addition, a total of 1953 DEGs were screened between ten-week-old line C and line B, and 932 genes were more highly expressed in line B than line C. Noteworthily, there were 117 genes that differently expressed between lines B and C at all three growth stages, while 2,182, 1,040 and 1,306 were exclusively different expressed at the two-week-old, six-week-old and ten-week-old stage, respectively (Fig 2 C and D).

In the comparison of gene expression between lines C and A, there were 3,296 DEGs with 1,471 had higher expression levl in line C than line A of two-week old. Compared to the six-week-old line A, 2,356 genes were up-regulated and 1,706 genes were down-regulated in the six-week-old line C. Additionaly, 3,011 genes were significantly different expressed between lines C and A of ten-week old, including 1,588 genes that higher expressed in line C than line A. Among these DEGs between lines C and A, 224 were shared at different growth stages, while 2,330, 2,417 and 1,498 were exclusively different expressed at the two-week-old, six-week-old and ten-week-old stage, respectively (Fig 2 E and F). Overall DEGs showed that the number of unique DEGs for each growth stage is markedly greater than that of common DEGs, indicating that a particular time-window of muscle development affects different gene-sets and pathways.

Functional analysisof DEGs

To gain insight into molecularmechanisms underlying the genetic effects on body weight, only the genes have long-term effect in the growth was selected to further analysis. Based on this, DEGs between specific two lines common at three stages were subjected to a enrichment analysis for significant functions and pathways. GO analysis performed on the DEGs between lines B and A indicated that several important biological processes, including cell proliferation (GO term “regulation of cell proliferation”), cell differentiation (GO terms “regulation of cell differentiation”, “negative regulation of cell differentiation”, “neuron differentiation”, “chondrocyte differentiation”), growth (GO terms “growth”, developmental growth, response to growth factor, regulation of cellular response to growth factor stimulus, cellular response to growth factor stimulus, and ), development (including GO term “cell development”) and metabolic processes, were common differentially regulated at three stages (P<0.05, Fig 3A ). Important cellular components such as the myosin complex, actin cytoskeleton and extracellular region were differentially regulated (lines B vs A) at all stages (P<0.05, Fig S1 A). Moreover, differences of line B and line A in molecular functions affected included receptor binding, carbohydrate derivative binding, ATP binding and motor activity (P<0.05, Fig S1 B). Similar to the DEGs of lines B and A, DEGs between lines B and C primarily enriched in biological processes of cell proliferation, muscle cell differentiation, growth, muscle structure development, tissue development and immune response (Fig 3B). While cellular components included extracellular space, plasma membrane receptor complex, and high-density lipoprotein particle, and molecular functions included CCR chemokine receptor binding, receptor binding, and chemokine activity (Fig S1 C and D). Nevertheless, the functions of DEGs from “lines C Vs A” were quite different with those of “lines B Vs A” and “lines B Vs C”. Function analysis of DEGs between lines C and A indicated that biological processes including cell proliferation, metabolic and immune response (Fig 3C), and cellular components including extracellular region and extracellular space, and molecular functions including receptor binding, antigen binding and chemokine activity (Fig S1 E and F) were differentially regulated at all stages. We selected ten GO terms (muscle structure development, regulation of muscle system process, regulation of muscle contraction, phasic smooth muscle contraction,  muscle structure development, vein smooth muscle contraction, regulation of muscle cell differentiation, muscle cell differentiation, artery smooth muscle contraction, tonic smooth muscle contraction, regulation of striated muscle cell differentiation ) that are directly involved in the biological process of muscle growth and development, and 22 DEGs associated with these GO terms were obtained (Table 1).

KEGG analysisof DEGs

KEGG analysis was performed on the genes that common differently expressed at three stages, between any two lines. KEGG classification of common genes differentially expressed under different stages revealed that the extracellular matrix (ECM)-receptor interaction, Progesterone-mediated oocyte maturation, Tight junction, and Focal adhesion were most enriched for the DEGs from lines B and A (Fig 4A). While Toll-like receptor signaling pathway, Neuroactive ligand-receptor interaction, and Focal adhesion were most enriched for the DEGs from lines B and C (Fig 4B). For the DEGs between lines B and C, the most enriched pathways were Phagosome, Cytokine-cytokine receptor interaction, MAPK signaling pathway and Toll-like receptor signaling pathway (Fig 4C).

Gene-act-network and cnaditate genes for muscle growth

After GO analysis and pathway analysis, 150 DEGs (Supplementary Table S2) that probably regulated muscle growth and development were selected from 34 significantly enriched GO terms(including terms related to muscle cell differentiation, muscle structure development, growth, cell proliferation, cell differentiation and tissue development) and 17 significantly enriched pathways. To further explore the interactions between these DEGs, the gene-act-network was established based on the relationships between these DEGs in terms of expression and  interaction. As a result, a network including 138 network nodes and 149 connections were built for these DEGs, and the average node degree in the PPI network was 3.57. As shown in Fig 5, several DEGs played a core role in the PPI network, including Jun proto-oncogene, AP-1 transcription factor subunit (JUN), insulin like growth factor 2 (IGF2), apoptosis regulator (BCL-2), Fos proto-oncogene, AP-1 transcription factor subunit (FOS), endothelin 1 (EDN1), interleukin 6 (IL6), toll-like receptor 4 (TLR4) and SRC proto-oncogene (SRC), indicating that these genes may play key roles in regulating muscle growth of chicken.

Analysis of DEGsacross the develpment stages withinlines

Within line A, altogether 4,118, 3,957 and 4,147 DEGs were found from A6 vs A2, A10 vs A2 and A10 vs A6, respectively. Moreover, there were 4,901, 5,289 and only 2,071 DEGs identified from B6 vs B2, B10 vs B2 and B10 vs B6, respectively. Within line C, a total of 3,430, 4,244 and 2,436 DEGs were identified from C6 vs C2, C10 vs C2 and C10 vs C6, repectively. The results showed that there were relatively few genes which were differentially expressed between ten-week-old and six-week -old stages.

In order to investigate the changes of age effects on gene expression level, we carried out the Series Tests of Cluster (STC) of these DEGs using short time-series expression miner. As a result, there was a set of genes including 520 genes that showed significant expression trend in line A. As shown in Fig 6A, these genes were down-regulated at six-week-old stage, then up-regulated at ten-week-old stage. Moreover, biological processes analysis of these genes indicated that with the increase of development, the functions of neuron development, multicellular organismal development, cell surface receptor signaling pathway, and protein ADP-ribosylation were enhanced (Fig 6B). Furthermore, the KEGG analysis showed that these genes may work pathway of via Neuroactive ligand-receptor interaction, Calcium signaling pathway, and Regulation of actin cytoskeleton.

In line B, we found eight sets of genes displayed significantly up- or down-regurealted during development, with different degrees. We performed GO analysis for the gene sets that steadily up- or down-regulated during development (Fig 6C Profile 3 and 12). Biological processes classification revealed that with the development of line B, the functions of intracellular signal transduction,programmed cell death,apoptotic process,and positive regulation of MAPK cascade were enhanced, while the functions of system process, neurological system process, and DNA recombination were suppressed. Moreover, the steadily upregulated genes were involved in MAPK signaling pathway, Herpes simplex infection, VEGF signaling pathway, and Insulin resistance, while the down-regulated genes may work through the pathway of Homologous recombination and Biosynthesis of antibiotics.

In line C, 125 genes were steadily up-regulated and 163 genes were steadily down-regulated during the development (Fig 6). Functional analysis indicated that the steadily up-regulated genes predominantly involved in regulation of transcription, RNA biosynthetic process, and nitrogen compound metabolic process, while the steadily down-regulated genes primarily participate in chemical synaptic transmission, cell-cell signaling, and neuromuscular synaptic transmission. Additionally, no significantly enriched pathway was found for the 125 steadily up-regulated genes, while the up-regulated genes were mainly involved in ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion.

Validation of the microarray data using qRT-PCR

To validate the microarray data, eight DEGs that may important to muscle growth and development selected for qRT-PCR verification. Fold changes from microarray and  qRT-PCR were compared. As shown in Fig 7, the Fold changes from qRT-PCR were in consistent with those from RNA-seq, with virtually identical trends in up- or down-regulation for each gene in the indicated sample, suggesting the important role of these genes and the reliability and accuracy of the microarray results.

Discussion

Meat performance is a key economic trait in poultry industry, and is also crucial reference index chicken breeding. The broiler (line B), Daheng(line C) and roman powder laying hens (line A) has a significantly different muscle growth and development performance. To investigate potential genes and pathways that are involved in the muscle growth and development, genome-wide expression analysis was performed in the muscle tissues of the three chicken breeds, using whole genome expression array.

Our study suggested that DEGs between two lines were vastly different in different development stages, while there were still several genes that persistently up- or down-regulated throughout the development process, and the essential growth difference between two lines may genetically driven by these persistent DEGs. By GO analysis of these DEGs, we found that several important biological processes, including cell proliferation, development, cell differentiation, growth, were differentially regulated between any two lines across all three development stages. Similar to the myogenic growth study of legacy and modern broiler chickens, which showed differentially regulated of cell proliferation, cell differentiation, growth, catabolic processes, and metabolic processes, between legacy and modern broiler chickens at Day 6 and Day 21 [4]. Consistent with the muscle regulation from Qianhua Mutton Merino and Small Tail Han sheep, the biological processes (GO terms: regulation of muscle system process, regulation of muscle contraction, phasic smooth muscle contraction,  muscle structure development, vein smooth muscle contraction, regulation of muscle cell differentiation, muscle cell differentiation, artery smooth muscle contraction, tonic smooth muscle contraction, regulation of striated muscle cell differentiation ) that are directly associated with muscle growth and development were differently regulated between lines B and A, and lines B and C [7]. Among the DEGs from these GO terms, Cardiac muscle troponin T (TNNT2), myosin binding protein C ( MYBPC1), myosin heavy chain 15 (MYH15), and nephroblastoma overexpressed (NOV) have been reported to paly important role in determining muscle growth and feed efficiency in broilers [8]. Furthermore, skeletal muscle genes, including MYH15 and myosin light chain kinase (MYLK), were significantly dysregulated s following administration of clenbuterol to  horses [9]. MYH15 belongs to myosin heavy chain family (MHC), MYLK is a kinase of myosin light chain, and MYBPC3 is a binding protein of myosin, and myosin is the primary component of myofibrillar thick filaments and is important in muscle growth and contraction [10]. Moreover, myosin belongs to actin-based motor proteins that are important in the generation of mechanical force and function in contraction of muscle cells [11]. Additionally, myozenin 2(MYOZ2) belongs to a family of sarcomeric proteins that tether calcineurin to alpha-actinin at the z-line of the sarcomere of cardiac and skeletal muscle cells, and may play a role in skeletal muscle differentiation, growth and homeostasis  [12]. The MYOZ2‑knockout mice will lead to the abnormal calcineurin activation and the excess of slow-twitch skeletal muscle fibres [13]. The protein encoded by troponin T2 (TNNT2) is the tropomyosin-binding subunit of the troponin complex, which is located on the thin filament of striated muscles and regulates muscle contraction [14]. Though previous studies may enhance the accuracy of our prediction of key genes, further studies are needed to determine the function of these DEGs in muscle growth and development.

DEGs was further studied from another perspective via pathway analysis. We found that ECM-receptor interaction, MAPK signaling pathway, and Focal adhesion are differently regulated both between and within lines under different development stages. Similarly, in the skeletal muscle study of pigs, the ECM-receptor interaction, focal adhesion and MAPK signaling pathway were enriched from DEGs between red and white Chinese Meishan pigs [15]. The different expression of some collagens of ECM were found in different chicken lines in this study, indicating the different composition of collagens in different types of muscles. The ECM plays a key role in tissue architecture [16]. In addition to function in the strength and form of tissues, each collagen type of ECM has specific sequences that provide specific features such as flexibility and the ability to interact with other substrates and cells [17]. The cells and ECM interact with each other through intermembrane molecules or other components of the cell surface, thereby directly or indirectly controlling cellular activity, such as adhesion and migration. Focal adhesions is a large dynamic protein complex by which the cytoskeleton of a cell is connected to the ECM. Moreover, focal adhesions and actin cytoskeleton play pivotal role in cell growth, shape, and movement, as well as vascular remodeling [18]. Under high tension, α-smooth muscle actin (α-SMA), which renders fibroblasts highly contractile and hallmarks myofibroblast differentiation, was recruited to stress fibers by focal adhesions [19]. While disruption of focal adhesions was involved in smooth muscle cell apoptosis [20]. In addition, the focal adhesion kinase functions in the cell migration and adhesion that is important for wound healing and regeneration of damaged muscle [21]. For the MAPK pathway, it acts as a global regulator of skeletal muscle differentiation [22]. The p38 MAPK signaling pathway integrates the environmental signal of chromatin and establishes the adaptive response of satellite cells in the process of muscle regeneration [23], and the sustained MAPK activation enhances muscle function via promoting muscle fiber regeneration [24]. Therefor, the the ECM-receptor interaction, Focal adhesion, and MAPK signaling pathway may play crucial role in muscle growth and develop ment of chicken.

Gene-act-network showed that MYH15, MYOZ2,TNNT2, and MYBPC3 were also in the network and were up-regulated in the high-weight chicken. Moreover, the TNNT2 was interacted with MYOZ2 and MYBPC3. Inaddition to interaction with TNNT2, MYBPC3 can also be interacte with MYH15and MYOZ2. Based on the network, we identified several core regulatory genes, including JUN, IGF2, BCL-2, FOS, EDN1, IL6, TLR4 and SRC, among which, JUN, BCL-2, and FOS were involved in the biological process that directly participate in muscle growth. It has been reported that C-jun can regulate vascular smooth muscle cell growth and proliferation after arterial injury [25], and it was up-regulated during in trout regenerating muscle after muscle injury [26]. Bcl-2 is an important anti-apoptotic protein that localizing to the outer membrane of mitochondria, where it plays key role in facilitating cellular survival and suppressing the actions of pro-apoptotic proteins [27]. The Bcl-2-overexpressing myoblasts can form the remarkably cell-dense and viable constructs of muscle tissue, and the combination of IGF-I and Bcl-2 gene can significantly enhance the force generation of the tissue-engineered skeletal muscle constructs [28]. Moreover, BCL-2 interacting cell death suppressor enhances the stabilization of myosin heavy chain in skeletal muscle [29]. FOS was first discovered in rat fibroblasts and encodes a 62 kDa protein that forms heterodimer with JUN. It play important role in momentous cellular events, including cell proliferation, differentiation and survival and angiogenesis [30]. Insulin-like growth factor 2 (IGF-2) is known as a major fetal growth factor, which exerts its effects via binding to the IGF-1 receptor or the IGF-2 receptor [31]. In bovine, protein intake during gestation has a great influence on the postnatal bovine skeletal muscle growth, may through regulating the expression level of IGF1, IGF1R, IGF2 and IGF2R [32]. Moreover, ZBED6 regulates muscle growth by abrogating a binding site for a repressor and up-regulated IGF2 [33]. Overall, these DEGs may play an impotant role in muscle growth and development process in chicken.

In conclusion, we established the transcriptome profiles of the breast muscle from three chicken breeds (broiler, Daheng and roman powder laying hens, which have different growth rate of the pectoralis muscle) under three development stages using Genome-wide expression microarray. Succeeding bioinformatic analyses indicated that some DEGs, such as MYH15, MYOZ2, MYBPC3, IGF2Bcl-2, JUN and FOS, and pathways such as ECM-receptor interaction, MAPK signaling pathway, and Focal adhesion, might be indispensable for the regulation of muscle growth and development in chicken. This comparative transcriptome analysis of broiler, Daheng, and roman powder laying hens muscle is reported for the first time, and reveals a role of gene expression regulation in promoting muscle growth in chicken. Our study will be helpfull to predict the function of new genes and to explore the mechanism involved in muscle growth and development process in chicken.

1. Le Bihan-Duval E, Mignon-Grasteau S, Millet N, Beaumont C (1998) Genetic analysis of a selection experiment on increased body weight and breast muscle weight as well as on limited abdominal fat weight. Br Poult Sci 39: 346-353.

2. Li X, Chiang HI, Zhu J, Dowd SE, Zhou H (2008) Characterization of a newly developed chicken 44K Agilent microarray. BMC Genomics 9: 60.

3. Bottje WG, Kong BW, Song JJ, Lee JY, Hargis BM, et al. (2012) Gene expression in breast muscle associated with feed efficiency in a single male broiler line using a chicken 44K microarray. II. Differentially expressed focus genes. Poult Sci 91: 2576-2587.

4. Davis RV, Lamont SJ, Rothschild MF, Persia ME, Ashwell CM, et al. (2015) Transcriptome analysis of post-hatch breast muscle in legacy and modern broiler chickens reveals enrichment of several regulators of myogenic growth. PLoS One 10: e0122525.

5. Dou T, Zhao S, Rong H, Gu D, Li Q, et al. (2017) Biological mechanisms discriminating growth rate and adult body weight phenotypes in two Chinese indigenous chicken breeds. BMC Genomics 18: 469.

6. Naraballobh W, Trakooljul N, Murani E, Brunner R, Krischek C, et al. (2016) Transient Shifts of Incubation Temperature Reveal Immediate and Long-Term Transcriptional Response in Chicken Breast Muscle Underpinning Resilience and Phenotypic Plasticity. PLoS One 11: e0162485.

7. Sun L, Bai M, Xiang L, Zhang G, Ma W, et al. (2016) Comparative transcriptome profiling of longissimus muscle tissues from Qianhua Mutton Merino and Small Tail Han sheep. Sci Rep 6: 33586.

8. Kong BW, Hudson N, Seo D, Lee S, Khatri B, et al. (2017) RNA sequencing for global gene expression associated with muscle growth in a single male modern broiler line compared to a foundational Barred Plymouth Rock chicken line. BMC Genomics 18: 82.

9. Knych HK, Harrison LM, Steinmetz SJ, Chouicha N, Kass PH (2016) Differential expression of skeletal muscle genes following administration of clenbuterol to exercised horses. BMC Genomics 17: 596.

10. Zhang SZ, Xu Y, Xie HQ, Li XQ, Wei YQ, et al. (2009) The possible role of myosin light chain in myoblast proliferation. Biol Res 42: 121-132.

11. Weiss A, McDonough D, Wertman B, Acakpo-Satchivi L, Montgomery K, et al. (1999) Organization of human and mouse skeletal myosin heavy chain gene clusters is highly conserved. Proc Natl Acad Sci U S A 96: 2958-2963.

12. Braun T, Gautel M (2011) Transcriptional mechanisms regulating skeletal muscle differentiation, growth and homeostasis. Nat Rev Mol Cell Biol 12: 349-361.

13. Schiaffino S, Sandri M, Murgia M (2007) Activity-dependent signaling pathways controlling muscle diversity and plasticity. Physiology (Bethesda) 22: 269-278.

14. Perry SV (1998) Troponin T: genetics, properties and function. J Muscle Res Cell Motil 19: 575-602.

15. Li Y, Xu Z, Li H, Xiong Y, Zuo B (2010) Differential transcriptional analysis between red and white skeletal muscle of Chinese Meishan pigs. Int J Biol Sci 6: 350-360.

16. Chrominski K, Tkacz M (2015) Comparison of High-Level Microarray Analysis Methods in the Context of Result Consistency. PLoS One 10: e0128845.

17. Uitto VJ, Larjava H (1991) Extracellular matrix molecules and their receptors: an overview with special emphasis on periodontal tissues. Crit Rev Oral Biol Med 2: 323-354.

18. Ishida T, Ishida M, Suero J, Takahashi M, Berk BC (1999) Agonist-stimulated cytoskeletal reorganization and signal transduction at focal adhesions in vascular smooth muscle cells require c-Src. J Clin Invest 103: 789-797.

19. Goffin JM, Pittet P, Csucs G, Lussi JW, Meister JJ, et al. (2006) Focal adhesion size controls tension-dependent recruitment of alpha-smooth muscle actin to stress fibers. J Cell Biol 172: 259-268.

20. Leskinen MJ, Lindstedt KA, Wang Y, Kovanen PT (2003) Mast cell chymase induces smooth muscle cell apoptosis by a mechanism involving fibronectin degradation and disruption of focal adhesions. Arterioscler Thromb Vasc Biol 23: 238-243.

21. Wang D, Gao CQ, Chen RQ, Jin CL, Li HC, et al. (2016) Focal adhesion kinase and paxillin promote migration and adhesion to fibronectin by swine skeletal muscle satellite cells. Oncotarget 7: 30845-30854.

22. Segales J, Islam AB, Kumar R, Liu QC, Sousa-Victor P, et al. (2016) Chromatin-wide and transcriptome profiling integration uncovers p38alpha MAPK as a global regulator of skeletal muscle differentiation. Skelet Muscle 6: 9.

23. Segales J, Perdiguero E, Munoz-Canoves P (2016) Regulation of Muscle Stem Cell Functions: A Focus on the p38 MAPK Signaling Pathway. Front Cell Dev Biol 4: 91.

24. Peralta S, Garcia S, Yin HY, Arguello T, Diaz F, et al. (2016) Sustained AMPK activation improves muscle function in a mitochondrial myopathy mouse model by promoting muscle fiber regeneration. Hum Mol Genet 25: 3178-3191.

25. Khachigian LM, Fahmy RG, Zhang G, Bobryshev YV, Kaniaros A (2002) c-Jun regulates vascular smooth muscle cell growth and neointima formation after arterial injury. Inhibition by a novel DNA enzyme targeting c-Jun. J Biol Chem 277: 22985-22991.

26. Montfort J, Le Cam A, Gabillard JC, Rescan PY (2016) Gene expression profiling of trout regenerating muscle reveals common transcriptional signatures with hyperplastic growth zones of the post-embryonic myotome. BMC Genomics 17: 810.

27. Cleary ML, Smith SD, Sklar J (1986) Cloning and structural analysis of cDNAs for bcl-2 and a hybrid bcl-2/immunoglobulin transcript resulting from the t(14;18) translocation. Cell 47: 19-28.

28. Kazushi Ikeda AI, Masanori Sato, Yoshinori Kawabe, Masamichi Kamihira (2016) Improved contractile force generation of tissue-engineered skeletal muscle constructs by IGF-I and Bcl-2 gene transfer with electrical pulse stimulation.

29. Hong J, Park JS, Lee H, Jeong J, Hyeon Yun H, et al. (2016) Myosin heavy chain is stabilized by BCL-2 interacting cell death suppressor (BIS) in skeletal muscle. Exp Mol Med 48: e225.

30. Tulchinsky E (2000) Fos family members: regulation, structure and role in oncogenic transformation. Histol Histopathol 15: 921-928.

31. Frasca F, Pandini G, Scalia P, Sciacca L, Mineo R, et al. (1999) Insulin receptor isoform A, a newly recognized, high-affinity insulin-like growth factor II receptor in fetal and cancer cells. Mol Cell Biol 19: 3278-3288.

32. Micke GC, Sullivan TM, McMillen IC, Gentili S, Perry VE (2011) Protein intake during gestation affects postnatal bovine skeletal muscle growth and relative expression of IGF1, IGF1R, IGF2 and IGF2R. Mol Cell Endocrinol 332: 234-241.

33. Markljung E, Jiang L, Jaffe JD, Mikkelsen TS, Wallerman O, et al. (2009) ZBED6, a novel transcription factor derived from a domesticated DNA transposon regulates IGF2 expression and muscle growth. PLoS Biol 7: e1000256.

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