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. 2024 May 23;103(8):103882. doi: 10.1016/j.psj.2024.103882

Transcriptomic and epigenomic insights into pectoral muscle fiber formation at the late embryonic development in pure chicken lines

Shuang Gu *,†, Qiang Huang *,†, Congjiao Sun *,†,‡, Chaoliang Wen *,†,‡, Ning Yang *,†,‡,1
PMCID: PMC11190745  PMID: 38833745

Abstract

Long-term intensive genetic selection has led to significant differences between broiler and layer chickens, which are evident during the embryonic period. Despite this, there is a paucity of research on the genetic regulation of the initial formation of muscle fiber morphology in chick embryos. Embryonic d 17 (E17) is the key time point for myoblast fusion completion and muscle fiber morphology formation in chickens. This study aimed to explore the genetic regulatory mechanisms underlying the early muscle fiber morphology establishment in broiler chickens of Cornish (CC) and White Plymouth Rock (RR) and layer chickens of White Leghorn (WW) at E17 using the transcriptomic and chromatin accessibility sequencing of pectoral major muscles. The results showed that broiler chickens exhibited significant higher embryo weight and pectoral major muscle weight at E17 compared to layer chickens (P = 0.000). A total of 1,278, 1,248, and 892 differentially expressed genes (DEGs) of RNA-seq data were identified between CC vs. WW, RR vs. WW, and CC vs. RR, separately. All DEGs were combined for cluster analysis and they were divided into 6 clusters, including cluster 1 with higher expression in broilers and cluster 6 with higher expression in layers. DEGs in cluster 1 were enriched in terms related to macrophage activation (P = 0.002) and defense response to bacteria (P = 0.002), while DEGs in cluster 6 showed enrichment in protein-DNA complex (P = 0.003) and monooxygenase activity (P = 0.000). ATAC-seq data analysis identified a total of 38,603 peaks, with 13,051 peaks for CC, 18,780 peaks for RR, and 6,772 peaks for WW. Integrative analysis of transcriptomic and chromatin accessibility data revealed GOLM1, ISLR2, and TOPAZ1 were commonly upregulated genes in CC and RR. Furthermore, screening of all upregulated DEGs in cluster 1 from CC and RR identified GOLM1, ISLR2, and HNMT genes associated with neuroimmune functions and MYOM3 linked to muscle morphology development, showing significantly elevated expression in broiler chickens compared to layer chickens. These findings suggest active neural system connectivity during the initial formation of muscle fiber morphology in embryonic period, highlighting the early interaction between muscle fiber formation morphology and the nervous system. This study provides novel insights into late chick embryo development and lays a deeper foundation for further research.

Key words: chicken, embryonic period, muscle development, RNA-seq, ATAC-seq

INTRODUCTION

Skeletal muscle, the largest tissue in animal body, constitutes about 40% of adult body weight (Zhang et al., 2020). It serves as the primary protein reservoir in animals and plays a crucial role in regulating metabolism and maintaining equilibrium (Zhang et al., 2022). The fundamental component of skeletal muscle is the multinucleated cylindrical muscle fiber (Uezumi et al., 2016). Skeletal muscle fibers are formed during embryonic myogenesis, progressing through various cell stages such as pluripotent preimplantation embryos, somatic cells, skeletal muscle stem cells, proliferating myoblasts, and multinucleated myotubes (Choi et al., 2020). The development of myofibers predominantly hinges on the 2-stage fusion of myoblasts. Initially, the fusion of myoblasts gives rise to new multinucleated myotubes, followed by the subsequent fusion of mononucleated myoblasts and multinucleated myotubes, leading to the initial formation of muscle fibers (Redelsperger et al., 2016; Petkov et al., 2022).

Poultry industry plays a pivotal role in the livestock industry by providing abundant and cost-effective sources of eggs and meat, thereby significantly meeting people's demand for high-quality animal protein (Lin et al., 2020; Moussa et al., 2021). Consequently, understanding the regulatory mechanisms underlying the growth and development of chicken skeletal muscles is crucial for enhancing human health and the livestock farming (Han et al., 2022). Over the past few decades, substantial improvements in the growth rate and feed conversion efficiency of broiler chickens have been achieved through intensive genetic selection and continuous optimization of feed nutrition (Richards et al., 2020; Wang et al., 2022a). Through prolonged genetic breeding, broilers and laying hens have exhibited significant differences in the growth rapid, metabolism and weight gain (Sączyńska et al., 2019). For instance, even under the optimal feeding conditions, broiler chickens can reach five times the weight of layer chickens by the age of 6 wk (Shen et al., 2019). There was study found that broiler myoblasts actively proliferated and promptly differentiated into myotubes compared to layer myoblasts (Nihashi et al., 2019), which corresponded with the muscle phenotype of broilers. Broiler chickens are primarily bred to provide humans with a high-quality source of protein. This necessitates a rapid growth rate, enabling them to reach market weight by 42 d of age. Muscle fibers are established during the embryonic stage, and their number remains constant after hatching. Consequently, the rapid hypertrophy of muscle fibers post-hatching contributes significantly to the rapid growth of broiler chickens (Shen et al., 2020). It is worth noting that once the muscle fibers are formed during the embryonic period, the number remains almost unchanged after hatching (Liu et al., 2019; Li et al., 2022a). Our previous research has indicated that broiler chickens might form more muscle fibers than layer chickens during the embryonic period (Gu et al., 2022), highlighting the early onset of differences between broilers and layers, making them suitable models for studying skeletal muscle growth and development.

High-throughput sequencing technologies have revolutionized the study of animal genetic development mechanisms (Wang et al., 2019). With the rapid advancement of high-throughput DNA and RNA sequencing technologies, sequencing time and costs have been significantly reduced (Cao et al., 2020). There are studies use RNA-seq to identify circRNAs affecting muscle development by comparing fast-growing and slow-growing chickens (Wu et al., 2022). In addition to comparative transcriptomic studies of chickens with different growth rates, time-series transcriptomic analyses of muscles at various developmental stages have been conducted to identify important candidate genes regulating muscle development at key time points (Yin et al., 2020; Liu et al., 2021). With the application of multi-omics approaches, identifying key genes based solely on gene expression levels at the transcriptional level is no longer reliable. Integration with other omics disciplines is necessary. ATAC-seq has been used to map chromatin activity states at high resolution, serving as a measure of DNA accessibility changes (Buenrostro et al., 2013). Combining this technique with RNA-seq greatly enhances our understanding of gene regulation. Researches using both omics technologies to investigate mechanisms such as sex differentiation (Li et al., 2022b; Zhang et al., 2023a) and the fate specification of feathers/scales (Lai et al., 2018) has been conducted, while studies on muscle development during the embryonic period remain scarce.

The process of myogenesis during chicken embryogenesis includes primary myogenesis from embryonic d 3 (E3) to E7 and secondary myogenesis from E8 onwards, primarily involving the fusion of myoblasts to form myotubes, which then merge with additional myoblasts to create muscle fibers (Chal and Pourquié, 2017). The muscle-specific membrane protein, named Myomaker, that controls the fusion of myoblasts and it is considered a marker gene for this fusion process (Millay et al., 2013; Millay et al., 2016). Previous studies have found that the gene expression of Myomaker remains high in chickens from E10 to E16 (Luo et al., 2015).The morphological analysis of broiler, native and layer chicken pectoral muscle in our previous study has found that myoblast fusion is still ongoing at E15, with the preliminary formation of muscle fiber morphology not yet evident and the muscle fibers are largely complete at E17 with the distinct boundaries (Gu et al., 2022). This indicates that E17 is the crucial stage for the completion of myoblast fusion and initial formation of muscle fiber morphology in chickens. While considerable research has focused on myoblast fusion (Mo et al., 2015; Redelsperger et al., 2016; Hamoud et al., 2018; Youm et al., 2019), there are still relatively few studies on the regulatory mechanisms about the early stage of muscle fiber formation. Therefore, in this study, we conducted multi-omics sequencing on muscle samples from E17 of fast-growing broilers and slow-growing layer chickens to elucidate the regulatory networks during early formation of muscle fiber morphology, laying the groundwork for further investigations into muscle fiber development.

MATERIALS AND METHODS

Ethics Statement

All experimental protocols associated with the chickens used in this study were conducted with the guidelines established by the Ministry of Science and Technology (Beijing, China). Ethical approval was granted by the Animal Welfare Committee of China Agricultural University (AW71802202-1-2) and performed in accordance with the procedures outlined in the “Guide for Care and Use of Laboratory Animals” (China Agricultural University).

Experimental Animals and Sample Collection

Cornish (CC) and White Plymouth Rock (RR) are used as the paternal and maternal lines of commercial broilers, respectively, due to their rapid growth rate. White Leghorn (WW) is an egg-type chicken breed with the good laying performance and the slow growth rate. The 33, 40, 17 fertile eggs of CC, RR, and WW used in this study were obtained from Beijing Huadu Yukou Poultry Breeding Co. Ltd. All fertile eggs were sterilized with Benzalkonium bromide solution before incubation. Subsequently, they were incubated at 37.8°C and 60% relative humidity for hatching until E17. The viable chicken embryos were removed from the eggs to weighted the body weight (BW) and sacrificed by cervical dislocation. After dissection, chicken embryos were sexed by examining their gonad development using the previously described methods (Gu et al., 2022). The right pectoral major muscles (PM) of 15, 10, and 10 males of CC, RR, and WW were collected, weighed, and rapidly put in liquid nitrogen for quick freezing and transferred to an ultra-low-temperature refrigerator at -80°C for preservation. For the BW and pectoral major muscle weight (PMW) determination, the number of birds used was 6 per experimental group. Statistical analysis was performed using R software (v.4.2.3), with results presented as mean ± sem. One-way analysis of variance (one-way ANOVA) was employed to assess the growth performance differences among the CC, RR, and WW groups in our experiment. In this model, breed was considered as the fixed effect, and no random effects were included. To determine significant differences between the means, we employed the least significant difference (LSD) method for posthoc comparisons. The experimental unit in our study was defined as the individual measurement taken from each sample. The differences were considered to be statistically significant at a P value < 0.05.

RNA and ATAC Library Preparation and Sequencing

Total RNAs were extracted from PM using the Eastep Super Total RNA Extraction Kit (LS1040, Shanghai Promega, Shanghai, China) and the assessment of RNA integrity was performed with the Agient2100 Bioanalyzer (LabChip GX). RNA samples with the RNA integrity number greater than six were used for library preparations. The libraries were generated using Hieff NGS Ultima Dual-mode mRNA Library Prep Kit (13533ES96, Shanghai, China) along with Hieff NGS DNA selection Beads (Superior Ampure XP alternative) (12601ES56, Shanghai, China) for PCR products purification. Subsequently, the RNA libraries underwent sequencing on the Illumina NovaSeq 6000 platform with six biological replicate per group.

Three biological replicates of PM were flash-frozen in liquid nitrogen and pulverized using tissue solution. The powdered tissue was then lysed with cell lysis buffer, and the nuclei were isolated through centrifugation at 2,000 g for minutes. The cell pellets were resuspended in 50 µL of transposition solution and incubated at 37 °C for 30 min. Subsequently, DNA purification was performed using 2☓ DNA clean beads, followed by PCR amplification of transposed fragments with 15 cycles. Transposition and high-throughput DNA sequencing library construction were carried out using TruePrep DNA Library Prep Kit V2 for Illumina (Catalog NO. TD501, Vazyme). The library products were enriched, quantified and finally sequenced on a Novaseq 6000 sequencer (Illumina, San Diego, CA) with the PE150 model.

RNA-seq Data Analysis

Sequencing adapters and low-quality nucleotides were removed from the raw RNA-seq data to obtain high-quality reads. Reads were then aligned to the chicken reference genome Galgal7 using HISAT2 (v.2.2.1) (Kim et al., 2015) with default parameters. The sequencing statistics of reads and bases obtained in each sample and mapping statistics of clean reads to the reference genome for each sample are summarized. (Table S1). Read counts were obtained with featureCounts (SUBREAD package; v.1.6.3) (Liao et al., 2014). To quantify the gene expression, the raw read counts were normalized using transcripts per million (TPM) procedure to compute the TPM values for subsequent analysis. Principal component analysis (PCA) was performed using FactoMineR package in R (v.2.9.0) (Lê et al., 2008). Differentially expressed genes (DEGs) between different groups were analyzed by DESeq2 package (v.1.36.0) (Love et al., 2014) in the R program. The fixed effects of the DESeq2 model were the different experimental groups. Genes with P value less than 0.05 and log2 Fold Change greater than |1| were considered significant DEGs. The clustering of DEGs was performed using the Mfuzz package (v.2.56.0) in R (Kumar and E, 2007). Subsequently, gene ontology (GO) function and Kyoto Encyclopedia of genes and genomes (KEGG) pathway enrichment analysis were performed using the clusterProfiler package (v.4.7.1.3) in R (Yu et al., 2012).

ATAC-seq Data Processing

Trimming and filtering of the raw sequencing data were performed using fastp (v.0.23.1) (Chen et al., 2018) and then the clean reads were aligned to the chicken reference genome Galgal7 assembly using Bowtie2 (v.2.2.6) (Langmead et al., 2009) with default parameters. Our sequencing data showed high quality, evidenced by both robust mapping rates and peak calling results (Table S2). Duplicate reads were marked and removed using Sambamba (v.0.7.1) (Tarasov et al., 2015). The insert length was calculated from the aligned BAM file with Samtools (v.0.1.11) (Li et al., 2009). MACS2 (v.2.2.7.1) (Zhang et al., 2008) was used for peak calling. IDR (v.2.0.3) (Landt et al., 2012) was used to obtain high confidence peaks with an IDR threshold of 0.05. PCA was conducted by FactoMineR package in R (v.2.9.0) (Lê et al., 2008). To analyze the correlation between ATAC-seq and RNA-seq changes, we assigned ATAC-seq peaks to the nearest genes. Peak annotation and analysis were done with ChIPseeker (v.1.32.1) (Yu et al., 2015). Promoter regions were defined as peaks overlapping a region that was ± 3 kb from the transcriptional start site (TSS). Differential peak analysis was performed using the diffBind package in R (v.3.6.5) (Stark and Brown, 2012) with a 0.05 false discovery rates (FDR) cutoff in 2 comparison groups. The Bedtools (v.2.30.0) (Quinlan and Hall, 2010) suite was employed to calculate overlap and enrichment between different intervals. Enriched motifs in differential peaks were predicted by HOMER (v.4.11) (Heinz et al., 2010).

Integrative Analysis of RNA-seq and ATAC-seq Data

Since chromatin accessibility is closely related to gene regulation, the VennDiagram package (v.1.7.3) (Chen and Boutros, 2011) in R was used to obtain the overlapping genes of DEGs derived from RNA-seq and genes associated with differentially accessible peaks identified in ATAC-seq. UpSet plots were generated using the UpSetR package (v.1.4.0) (Conway et al., 2017). Subsequently, the clusterProfiler (v.4.7.1.3) (Yu et al., 2012) was utilized for GO enrichment function analysis and KEGG pathway analysis of these overlapping genes. One-way ANOVA was used to analyze the gene expression differences among CC, RR, and WW groups with the post-hoc comparisons using LSD methods. The differences were considered to be statistically significant at a P value < 0.05.

RESULTS

Embryonic Growth Rate of Broilers Exceeds Than That of Layers

There was a significant difference in BW between broilers and layers at E17 (P < 0.01) (Figure 1A). The BW of the 2 broiler breeds, CC and RR, were 22.82 ± 0.18 g and 22.62 ± 0.65 g, respectively, which were 18.85% and 18.13% higher than that of WW (18.52 ± 0.24 g). Similarly, the PMW of CC was significantly higher than that of WW (P < 0.01), and the PMW of RR was significantly higher than that of WW (P < 0.05), too (Figure 1B). The PMW of CC and RR were 0.5535 ± 0.0315 g and 0.4503 ± 0.0191 g, respectively, which were 33.91% and 18.77% higher than that of WW (0.3658 ± 0.0110 g) (Table S3), indicating that the faster growth of broilers during the embryonic period is mainly due to the faster muscle development than that of layers. The BW and PMW between CC and RR were not significantly different (P > 0.05). To further explore the gene regulatory network during the early formation of muscle fibers, PM of the 3 experimental groups were collected at E17 for RNA-seq and ATAC-seq sequencing and analysis (Figure 1C).

Figure 1.

Figure 1

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Embryonic growth patterns of broiler and layer chickens. (A) The body weight (BW) of individuals with Cornish (CC), White Plymouth Rock (RR) and White Leghorn (WW) at the embryonic d 17 (E17). (B) The pectoral major muscle weight (PMW) of individuals with CC, RR, and WW at E17. (C) Pectoral major muscles were collected at D21 and D42 for RNA-seq and ATAC-seq analysis. ⁎⁎ and * indicate P-value less than 0.01 and 0.05, respectively.

Transcriptional Analysis of Muscle Development in Broilers and Layers

The PCA results showed a clear separation between broilers and layers (Figure 2A). Prior to differential analysis, genes with low expression were filtered out, leading to the identification of DEGs between each pair of the 3 groups. Between CC and WW, a total of 448 and 830 up- and down-regulated DEGs were identified (Figure 2B, Table S3). Similarly, we found 621 and 627 up- and down- DEGs between RR and WW (Figure 2C, Table S4). Comparison between the 2 broiler breeds revealed 288 and 604 up- and down-regulated DEGs between CC and RR (Figure S1, Table S5). Subsequently, all the DEGs were merged for cluster analysis and they were divided into 6 clusters based on the expression patterns among the 3 groups (Figure 2D). Cluster 2 represents DEGs that were highly expressed in both the CC and WW groups but lowly expressed in the RR group. Cluster 3 indicated DEGs that were highly expressed in the CC group but lowly expressed in both the RR and WW groups. Cluster 4 was characterized by DEGs that were highly expressed in the RR group and lowly expressed in the CC and WW groups. Cluster 5 included DEGs that showed a linear increase in expression across the CC, RR, and WW groups. Notably, DEGs in cluster 1 exhibited higher expression levels in both broiler groups, while expression levels decreased in layers (Figure 2E). Conversely, DEGs in cluster 6 showed the opposite expression trend between broilers and layers (Figure 2E).

Figure 2.

Figure 2

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Transcriptomic analysis of pectoral major muscle at E17. (A) PCA plot of RNA-seq data of CC, RR. and WW groups at E17. Each point represents a muscle sample. (B) Volcano plot of differential expressed genes (DEGs) obtained between CC and WW. (C) Volcano plot of differential expressed genes (DEGs) obtained between RR and WW. (D) Different expression patterns among the 3 experimental groups of all DEGs. (E) Heatmaps of intergroup expression of DEGs contained in cluster 1 and cluster 6. (F) GO enrichment results of DEGs of cluster 1 and cluster 6.

There were 334 and 495 DEGs, which were subjected to functional enrichment analysis, were identified in cluster 1 and cluster6 (Table S6). The results revealed that DEGs in cluster 1 were primarily enriched in the following terms: macrophage activation, defense response to bacterium, extracellular region, and extracellular space (Figure 2F). Additionally, DEGs in cluster 6 were mainly enriched in extracellular region, extracellular space, protein-DNA complex, nucleosome, chromatin, and monooxygenase activity categories (Figure 2F).

Chromatin Accessibility Analysis of Broilers and Layers

The insert size distributions for the ATAC-seq experiments were showed in Fig S2A. To further confirm the quality of ATAC-seq data, we performed PCA on all samples. The Results showed a good similarity of the biological replicates and overall separation between the 3 groups (Figure 3A), reflecting the high-quality data. For the 9 samples, a total of 38,603 peaks were identified, including 13,051 peaks for CC, 18,780 peaks for RR, and 6,772 peaks for WW (Figure S2B). The number of overlapping ATAC-seq peaks between groups is showed in Figure 3B. Subsequently, annotation of the distribution of these peaks in functional regions revealed annotation across promoter, exon, intron, and intergenic regions, with a higher abundance observed in the promoter region (Figure 3C).

Figure 3.

Figure 3

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Analysis of chromatin accessibility from the pectoral muscle. (A) PCA plot of ATAC-seq data of CC, RR and WW groups at E17. Each point represents a muscle sample. (B) Overlap peaks between 2 groups. (C) The functional region distributions of ATAC-seq peak dataset. (D) The predicted motifs for the upregulated peaks of CC between CC and WW. (E) The predicted motifs for the upregulated peaks of RR between RR and WW.

Considering that the motifs binding with various transcription factors mark the initiation sites of RNA methylation and demethylation, we performed differential analysis on the peaks to identify intergroup differential peaks. Subsequently, a comprehensive motif scan analysis was performed on the upregulated peaks in CC and RR compared to WW. The results revealed that the motif of UME6 and ETV3 was identified as highly enriched motifs in the upregulated peaks in CC (Figure 3D), while the motifs of MYOG, ETV5, and BATF were identified as highly enriched motifs in the upregulated peaks in RR (Figure 3E). Annotation of the genes associated with differential peaks revealed that the number of upregulated peaks associated genes in CC and RR compared to WW were 84 and 7,814, respectively, while the number of downregulated peaks associated genes were 9 and 106, respectively (Figure S2C).

Identification of Candidate Genes for Embryonic Muscle Development Through Integration Analysis

To further investigate the correlation between gene expression and open chromatin accessibility, we combined the RNA-seq and ATAC-seq data of PM at E17. As shown in the Venn diagram (Figure 4A), we identified 6 upregulated DEGs in CC compared to WW, including GOLM1, DNAH17, and ISLR2, carrying the genes associated with upregulated peaks (Figure 4A). Additionally, downregulated BG8 and PRLR in CC were also identified. Moreover, 62 upregulated DEGs in RR, such as GOLM1, GAA, BATF, HNMT, and TOPAZ1, carrying the genes associated with upregulated peaks, and 5 downregulated DEGs, such as DUSP29 and GLRX2, were identified in RR compared to WW (Figure 4B). In the CC and RR groups, we identified a DEG NIM1K, upregulated in CC, carrying the genes associated with upregulated peaks. Additionally, 76 DEGs, including LRCH2 and CBLN1, downregulated in CC, were also identified (Figure S3A). By assigning open chromatin regions to the nearest DEGs and calculating the fold change, we found a positive correlation between proximity features and gene expression patterns (Figure 4C, Figure S3B).

Figure 4.

Figure 4

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Integration of RNA-seq and ATAC-seq for candidate gene identification. (A)Venn diagrams showed the overlap of DEGs and the associated genes of different peaks between CC and WW. (B) Venn diagrams showed the overlap of DEGs and the associated genes of different peaks between RR and WW. (C) The relationship between gene expression and open chromatin regions. (D) UpSet plot of 2 classes of the upregulated DEGs. (E) GO enrichment analysis of DEGs filtered from cluster 1.

In the comparison between CC and RR against WW, the upregulated DEGs in CC and RR were subjected to upset analysis. The results revealed that 3 genes were commonly upregulated in both broiler breeds, namely GOLM1, ISLR2, and TOPAZ1 (Figure 4D). Among the upregulated DEGs in CC and RR, belonging to cluster 1, a total of 39 genes were identified, including GOLM1, ISR2, ETV7, and BATF3. Functional enrichment analysis of these genes showed enrichment in various terms such as muscle cell cellular homeostasis and vitamin D biosynthetic process (Figure 4E), as well as pathways like histidine metabolism, linoleic acid metabolism, and galactose metabolism (Figure S3C). Furthermore, among the downregulated DEGs in CC and RR, belonging to cluster 6, a total of 7 genes were identified, namely BG8, PRLR, DUSP29, GLRX2, STK32A, HCK, and EPHA6, which were enriched in terms such as peptidyl-tyrosine phosphorylation and response to redox state (Figure S3D).

Subsequently, we performed the differential expression analysis of the above key genes in the PM across the 3 experimental groups (Figure 5). The results revealed that the expression levels of ISLR2, MYOM3, GOLM1, HNMT, GAA, MYLK4, and BATF3 were significantly higher in broilers CC and RR compared to egg-type chicken WW (P < 0.05). Additionally, ISLR2, MYOM3, GOLM1, and HNMT exhibited high expression levels in the PM of broilers at E17, indicating their critical regulatory roles during the initial formation of muscle fibers in embryonic period.

Figure 5.

Figure 5

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Expression analysis of candidate genes. Differential expression analysis of ISLR2, MYOM3, GOLM1, HNMT, GAA, MYLK4, BATF3, and FGF19 genes between broiler chickens (CC and RR) compared to layer chicken (WW).

DISCUSSION

As the typical oviparous animal, chickens have become one of the commonly used model organisms for studying muscle development due to the simple fertilization process and ease of handling (Nurislamov et al., 2022). Numerous studies have directly demonstrated that broiler chickens have thicker muscle fiber diameters compared to egg-type chickens (Tian et al., 2021), with indirect evidence suggesting that broilers may form a greater number of muscle fibers during embryonic development (Gu et al., 2022). Skeletal muscles in vertebrates originate from myoblasts in the somites, a transient embryonic structure derived from the paraxial mesoderm (Chal and Pourquié, 2017). In chickens, the first stage of myogenesis occurs between E3 and E7, resulting in the formation of primary muscle fibers through the fusion of myoblasts (Tejeda et al., 2021). The second stage of myogenesis occurs between E8 and E12, leading to the formation of secondary muscle fibers (Tejeda et al., 2021). While the fusion of myoblasts continues during E12 to E16, and the morphology of muscle fibers remains unclear until E17 (Luo et al., 2015; Gu et al., 2022). In recent years, several studies have identified genes involved in regulating myoblast fusion, such as Myomaker (Millay et al., 2016; Zhang et al., 2017), Myomixer (Bi et al., 2017), BAI3 (Hamoud et al., 2014; Hamoud et al., 2018), SYISL (Jin et al., 2018), and EPHA7 (Nie et al., 2020). However, studies on the stages of muscle fiber morphology formation rarely reported. Therefore, we choose E17, a crucial period for muscle fiber morphology formation, to analyze chromatin accessibility and transcript expression, aiming to explore the differences in the molecular regulatory mechanisms between broilers and layers during the initial formation of muscle fiber morphology.

The high-strength genetic selection for growth traits in the past few decades has led the growth rate of broiler chickens far surpassed that of egg-laying or indigenous breeds (Yin et al., 2020; Tan et al., 2021). The pectoral muscle weight of broiler chickens at 42 d of age is 6 times greater than that of egg-laying chickens, with muscle fiber diameter approximately twice as large (An et al., 2010). The developmental differences between broiler and layer chickens have emerged during the embryonic period, as confirmed in this study. At E17, broiler chicken embryos weigh 18% more than laying chicken embryos, primarily due to the rapid development of pectoral muscles. Here we used multiomics analysis techniques to screen for regulatory genes associated with the early formation of muscle fibers in embryonic period. Expression clustering is a simple and commonly used clustering method to identify genes with similar expression patterns (Smith et al., 2016). In this study, we performed the clustering analysis on all DEGs obtained from differential expression analysis of transcriptome, resulting in 6 clusters. CC and RR were grouped together as the representatives of broiler chickens. Thus, the subsequent analysis on the highly expressed genes in cluster 1 for broiler chickens and cluster 6 for layers was focused. There are studies have shown that macrophages can promote muscle fiber growth by delivering nutrient factors to growing skeletal muscle precursors and young fibers (Linard et al., 2018). We noticed that DEGs in cluster 1 were enriched in the macrophage activation term, indicating a potential coordinated role of the immune system in promoting muscle fiber metabolism development during this period in broiler chickens.

ATAC-seq in CC, RR, WW groups identified 6,700 to 18,800 open chromatin peaks in this study, predominantly annotated in the promoter regions, consistent with previous studies (Zhang et al., 2023b). MYOG was predicted within the upregulated peaks in RR, serving as a myogenic regulatory factor controlling myogenic differentiation. It can bind to the promoter region of Myomaker, facilitating myoblast fusion and maintaining the balance of muscle fiber numbers (Ganassi et al., 2018). The transcription factor BATF (basic leucine zipper activating transcription factor-like transcription factor) is involved in the development, differentiation, and function of CD8+ T cells, dendritic cells, and B cells (Itahashi et al., 2022; Tsao et al., 2022). In this study, BATF was predicted in the upregulated peaks in RR compared to WW, consistent with the results of transcriptome analysis that the coordinated regulation of immune cells with muscle fiber development in broilers. Subsequently, we performed integrated analysis of RNA-seq and ATAC-seq data, overlapping the DEGs with the genes associated with differential peaks and found several candidate genes.

The correlation analysis confirmed a positive correlation between the differential peaks identified by ATAC-seq and their associated genes, consistent with previous findings (Bhattacharyya et al., 2017; Emming et al., 2020). Subsequently, we identified 6 and 62 upregulated DEGs in CC and RR compared to WW, respectively. Among these, GOLM1, ISLR2, and TOPAZ1 were upregulated in both broiler breeds. GOLM1, known as Golgi membrane protein 1, initially localizes to the Golgi apparatus within cells, cycling between membrane compartments such as the sorting endosome and the plasma membrane (Chen et al., 2021). GOLM1 has been extensively studied in hepatocellular carcinoma, where it influences mitochondrial function, cell proliferation, and migration (Xie et al., 2021; Nagaraj et al., 2022). Additionally, studies have found that GOLM1 can interact with key regulators of anti-apoptosis to inhibit apoptosis (Zi et al., 2023). While direct research on the role of GOLM1 in muscle development is relatively lacking, previous studies suggest that GOLM1 may play a role in myocyte migration and autophagy. ISLR2 (immunoglobulin superfamily containing leucine-rich repeat 2) belongs to the leucine-rich repeat and immunoglobulin (LIG) family of membrane proteins (Mandai et al., 2009). It regulates the immune response by reducing the expression of inflammatory factors (Bi et al., 2022). Moreover, cis-Mendelian Randomization analysis has identified a causal relationship between ISLR2 and muscle wasting syndrome (Bi et al., 2022). Additionally, the LIG family protein encoded by ISLR2 is expressed in a subset of developing motor neurons and interacts with RTK to control axonal extension and branching (Mandai et al., 2009). These findings suggest that ISLR2 may plays a crucial role in neuromuscular junctions during the early stages of muscle fiber formation.

We screened for genes differentially expressed between broiler chickens and layer chickens in cluster 1 and cluster 6. As a result, we identified ISLR2, MYOM3, GOLM1, and HNMT genes highly expressed in broiler chickens. MYOM3 has been reported to be associated with muscle quality and developmental morphology in sheep (Chen et al., 2022; Kizilaslan et al., 2022). Additionally, MYOM3 has been identified as a potential target gene of MEF2C, influencing muscle development in cattle (Wang et al., 2022b). Our study also found MYOM3 to be relatively highly expressed in broiler chickens compared to layer chickens, indicating its potential significant impact on embryonic muscle fiber development. And the specific regulatory mechanism still needs to be further explored. HNMT, the histamine N-methyltransferase (Schwelberger et al., 2017), an enzyme widely present in the central nervous system (Apolloni et al., 2019). While extensive research has been conducted on its role in the brain's neural system, there have been no reports regarding its presence in chicken muscles. Our study reported for the first time that the gene expression levels of HNMT in broiler chickens was significantly higher than that in layers, suggesting its potential involvement in muscle fiber development.

Overall, this study provides a high-resolution exploration of the transcriptome and chromatin accessibility at the initial muscle fiber morphology formation stage of the pectoral muscle. Through integrative analysis of the genomics data from broiler and layer chicken embryos at E17, we found that broiler chickens exhibited a higher expression of genes associated with neuromuscular junctions compared to layer chickens, such as GOLM1, ISLR2, and HNMT. This suggests that during the initial formation of muscle fibers, there is an active establishment of connections with the nervous system. Additionally, MYOM3, which is associated with muscle development morphology, was also identified. These findings indicate that the early establishment of muscle fiber morphology is accompanied by close interactions with the nervous system, providing new insights into the study of the late chicken embryonic stage and laying a deeper foundation for future research in the formation of muscle fiber morphology.

DISCLOSURES

The authors declare no conflicts of interest.

ACKNOWLEDGMENTS

We thank Beijing Huadu Yukou Poultry Industry Co., Ltd. for providing the experimental fertile eggs of CC, RR and WW. This work was supported by the National Key Research and Development Program of China (2022YFF1000204), the STI2030-Major Projects (2023ZD04052), the National Natural Science Foundation of China (32102535) and the Key Research and Development Program of Hainan province (ZDYF2023XDNY036).

Footnotes

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2024.103882.

Appendix. Supplementary materials

mmc1.pdf (1.4MB, pdf)
mmc2.pdf (34.5MB, pdf)
mmc3.pdf (521.2KB, pdf)
mmc4.pdf (1.1MB, pdf)
mmc5.xlsx (718KB, xlsx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

mmc1.pdf (1.4MB, pdf)
mmc2.pdf (34.5MB, pdf)
mmc3.pdf (521.2KB, pdf)
mmc4.pdf (1.1MB, pdf)
mmc5.xlsx (718KB, xlsx)

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