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. 2026 Apr 6;27:497. doi: 10.1186/s12864-026-12795-8

Dynamic development of muscle during embryogenesis in Arbor Acres broilers and TaoYuan chickens revealed by RNA sequencing

Di Zhao 1,2,3, Yifan Shi 1,2,3, Qingyuan Ouyang 1,2,3, Haihan Zhang 1,2,3, Yuming Guo 4, Zehe Song 1,2,3,✉, Xi He 1,2,3,✉
PMCID: PMC13188739  PMID: 41942875

Abstract

Background

Embryonic muscle development is a highly dynamic and complex process, coordinated by numerous genes and transcriptional regulators such as small RNAs.

Results

Here, we profiled transcriptomic dynamics in breast muscle from Arbor Acres (AA) broilers and TaoYuan (TY) chickens at three embryonic time points (E9, E13, and E18). While developmentally regulated genes enriched similar biological pathways in both breeds, the architecture of microRNA (miRNA)-mRNA interaction networks was distinct. Integrative analysis combining weighted gene co-expression network analysis, differential expression analysis, and time-series clustering identified 161 candidate genes, including a subset of 39 showing progressively decreasing expression. Differential expression analysis of miRNA revealed that gga-miR-1744-3p was uniquely up regulated at E13 and E18 in TY chickens. By integrating predictions from TargetScan and miRDB, we further identified two core genes (USP8 and ZBTB38) from the set of 39 candidate genes, which are predicted targets of gga-miR-1744-3p. This gene was differentially expressed in the heart, breast muscle, and adipose tissue, and exhibited significantly higher expression in TY chickens. Functional assays confirmed that gga-miR-1744-3p promotes proliferation of chicken primary myoblasts.

Conclusions

This study provides a comprehensive understanding of the development patterns and molecular mechanisms of breast muscles in broilers during embryogenesis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-026-12795-8.

Keywords: Chicken, Embryonic muscle, Gga-miR-1744-3p, RNA sequencing

Background

From an evolutionary perspective, the two peaks of myofiber formation during embryonic muscle development in poultry represent significant phenotypic traits shaped by long-term adaptive evolution. The rapid proliferation of early myoblasts establishes a quantitative foundation for muscle fiber development [1]. This mechanism is evolutionarily conserved across species, as seen in the expansion of muscle progenitor cells in primitive chordates [2]. However, the subsequent differentiation of myoblasts and the formation of myotubes reflect lineage-specific adaptations of birds to ecological demands such as flight. The spatiotemporal transition between these phases is governed by a precisely coordinated molecular network. A central regulatory module consisting of MRFs family transcription factors including MYOD1, MYOG, and MYF5, exhibits strong conservation among amniotes [2, 3].

At the post-transcriptional level, microRNAs (miRNAs) fine-tune gene expression by targeting the 3′ untranslated regions of key genes involved in myogenesis, thereby forming a sophisticated regulatory layer [4, 5]. Conserved miRNA families such as miR-1 and miR-133 play critical roles across vertebrates and display dynamic expression patterns during the two major phases of embryonic muscle development [6]. For instance, miR-206 and miR-486 help maintain muscle satellite cell populations by suppressing PAX7 degradation during myoblast proliferation [7], whereas miR-27b promotes fiber maturation during myotube differentiation by targeting MDFI [8]. Notably, species-specific miRNAs in poultry may further contribute to adaptive regulation of muscle fiber type and quantity through lineage-specific target genes, offering molecular insights into the evolutionary specialization of muscle development.

In this study, we employed a multi-dimensional integration strategy based on RNA sequencing to decipher the dynamic regulatory mechanisms underlying embryonic muscle development in poultry. The TaoYuan (TY) chicken is a local breed native to Hunan Province, China, and represents a typical slow-growing yellow-feathered broiler known for its superior meat quality. In contrast, Arbor Acres (AA) broilers are a fast-growing white-feathered breed characterized by high meat yield. To elucidate the dynamic developmental patterns of slow-growing broilers, we used AA broilers as a reference and selected three embryonic time points: E9, E13, and E18. From an evolutionary standpoint, we compared gene expression profiles between two chicken breeds at key developmental stages to identify breed-specific regulatory networks and elucidate molecular drivers of fiber trait diversification. In the comparative analysis between varieties, we integrated weighted gene co-expression network analysis (WGCNA) [9], enrichment analysis of differentially expressed genes (DEGs), and miRNA-mRNA target prediction to delineate breed-specific regulatory modules. This approach provides a theoretical framework for understanding the molecular basis of breeds-specific muscle development during embryogenesis.

Materials and methods

Sample collection

We collected 180 fertilized eggs, which were respectively from Hunan Shuncheng Industrial Co., Ltd. (AA) and Hunan Xiangjia Husbandry Limited by Share Ltd. (TY), and incubated them in an incubator (YD-1000 incubator, Shandong Yuda Incubation Equipment Co., Ltd, China) with a temperature of 37.8 °C and a humidity of 55% [10, 11]. To obtain three biologically repeated female samples at 9, 13, and 18 embryonic ages (E9, E13, and E18), we used DNA as a template and identified them through primers of the chromodomain helicase DNA binding protein 1 (CHD1) gene (F:5′-GTTACTGATTCGTCTACGAGA-3′, R:5′-ATTGAAATGATCCAGTGCTTG-3′). And the annealing temperature of CHD1 is 55 °C. Left breast muscle tissues were collected at 4 °C for rapid freezing in liquid nitrogen and right breast muscle tissues were fixed in 4% paraformaldehyde, while leg muscle tissues were collected for DNA extraction. Additionally, twelve 1-day-old chicks through carotid artery bloodletting from each breed were sampled across ten tissues: heart, liver, spleen, lung, kidney, breast muscle, leg muscle, adipose, brain, and duodenum, the duodenum was carefully excised from the chick intestinal tract, immediately opened longitudinally, and flushed with PBS to remove digesta and debris. The cleaned tissue was then rapidly frozen in liquid nitrogen and stored at -80 °C until further analysis.

Morphological analysis of chicken breast muscles

Two 5-µm-thick sections per sample were prepared and subjected to simplified hematoxylin and eosin (HE) staining: stained with hematoxylin for 2 min, rinsed twice with water, immersed in 60%, 70%, 80%, and 90% ethanol (2 min each), stained with eosin for 2 s, treated with 95% ethanol (1 min), dehydrated in absolute ethanol (6 min), cleared in xylene (15 min), and finally mounted with neutral resin. The prepared sections were scanned under a light microscope. Using Image Pro Plus 6.0 (Media Cybernetics, USA), three fields of view (each containing ≥ 200 muscle fibers) were randomly selected per section to measure the cross-sectional area (CSA, µm²). Muscle fiber size (µm) was defined as the mean CSA of the counted fibers [11].

RNA extraction and library preparation

Total RNA was extracted from breast muscle tissues utilizing TRIzol reagent (Invitrogen, Carlsbad, CA, USA). RNA integrity was evaluated through agarose gel electrophoresis combined with Agilent 2100 Bioanalyzer analysis (Agilent Technologies, Waldbronn, Germany), and only high-integrity RNA samples (RIN value > 8) were subjected to subsequent procedures. Ribosomal RNA depletion was performed using the Ribominus Eukaryotic Kit (Invitrogen, Carlsbad, CA, USA) to enrich coding and non-coding RNA fractions. For mRNA sequencing, library construction was carried out by reverse transcription and cDNA synthesis as previously described [12], followed by paired-end sequencing on the Illumina Novaseq 6000 platform. For miRNA sequencing, small RNA libraries were prepared using the TruSeq Small RNA Sample Prep Kit (Invitrogen, Carlsbad, CA, USA), subjected to PCR amplification, and validated for insert size distribution (~ 140–150 bp) [13], using the Agilent High Sensitivity DNA Kit (5067 − 4626, Agilent Technologies, California, USA). Quantification of library concentrations was performed with the Quan-iT™ PicoGreen™ dsDNA Assay Kit (Q33120, Thermo Fisher Scientific, California, USA). Both mRNA (cDNA) and miRNA libraries were sequenced on the Illumina Hiseq platform by Shanghai Personal Biotechnology Co., Ltd [14, 15].

High-throughput sequencing

After quality control, qualified RNA libraries were constructed and sequenced on the Illumina PE150 platform, yielding > 10 G raw reads per sample [16]. Mapping rates exceeded 93.37%, with Q20 and Q30 values above 96.46% and 93.72%, respectively (Table S1). Raw reads were processed using Trimmomatic (v0.36) [17] with the default parameters, and clean reads were aligned to the chicken reference genome (Gallus_gallus.bGalGal1.mat.broiler.GRCg7b.dna.toplevel.fa 109) (http://asia.ensembl.org/Gallus_gallus/Info/Index) via HISAT2 (v2.2.1) [18] and assembled with StringTie (v2.1.6) [19]. Gene expression was quantified as fragments per kilobase of transcript per million mapped reads (FPKM), and miRNA expression was computed and normalized them to counts per million. We simultaneously used the counting tool featureCounts (v2.0.4) to obtain raw count values for subsequent differential expression analysis.

Real-time quantitative PCR assays

Total RNA from ten tissues and chicken primary myoblasts (CPMs) was extracted with TRIzol (Invitrogen, Carlsbad, CA, USA). The cDNA was synthesized by reverse transcription using miRNA 1st Strand cDNA Synthesis Kit (by tailing A) and HisyGo RT Red SuperMix for qPCR (+ gDNA Wiper) (Vazyme, Nanjing, China). RT-qPCR was performed on a LightCycler 480 System (Roche, Switzerland) with miRNA Unimodal SYBR qPCR Master Mix and ChamQ Blue Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China). Primers are listed in Table S2. U6 and   18S rRNA served as internal controls for miRNA, and RPL32 and β-ACTIN served as internal controls for mRNA (Table S2). Relative expression was calculated using the 2−ΔΔCt method [20].

Weighted gene co-expression network analysis (WGCNA)

A total of 16,314 genes were included in the analysis using WGCNA package (v1.73). The soft threshold power (β) was set to 5 to achieve a scale-free topology fit index of R² > 0.75. This approach transforms the gene correlation matrix into an adjacency matrix by raising pairwise correlation coefficients to the power β, emphasizing strong correlations while maintaining a scale-free network structure. Gene modules were identified using a dynamic tree-cutting algorithm with the following parameters: minModuleSize = 50 and mergeCutHeight = 0.25. For each embryonic time point (E9, E13, and E18) and each breed (AA broilers and TY chickens), the module displaying the highest correlation with the developmental stage was selected for further analysis. Hub genes within these key modules were defined based on gene significance (GS > 0.2) and module membership (MM > 0.8) [14].

Differential expression analysis, time-series clustering analysis, and pathway enrichment analysis

We identified differentially expressed genes (DEGs) and miRNAs (DEMs) using the DESeq2 package (v1.46.0) [21] (Benjamini-Hochberg) in R software (v4.4), with significance defined as an absolute fold change > 2 and a P value < 0.05. The results, including volcano plots and Venn diagrams, were visualized with Hiplot (ORG) (http://hiplot.cn). To explore temporal dynamics, we performed time-series clustering on 423 overlapping DEGs and hub genes using the Mfuzz package (v2.64.0) [22, 23] in R software (v4.4). Gene expression patterns were categorized via fuzzy c-means clustering after determining the optimal fuzzifier (2.0) [22, 24], nine clusters were obtained. Furthermore, KEGG pathway enrichment analysis was conducted with KOBAS 3.0, the analysis focused on the Gallus gallus species and employed the Benjamini-Hochberg method for multiple test correction. And the enriched pathways were also visualized using Hiplot (ORG) (http://hiplot.cn).

Network construction of miRNA-mRNA

The DEMs target genes were predicted using TargetScan (https://www.targetscan.org/vert_80/) and miRDB (https://mirdb.org/). The intersecting genes and DEGs were used to construct regulatory networks, which were visualized using Cytoscap (v3.10.1) [25]. We generated miRNA-mRNA network consisting of nodes and edges. Subsequently, we employed the MCC method from the Cytoscape plugin cytoHubba [26] to identify miRNA-mRNA network.

Cell culture, RNA oligonucleotides, and cell transfection

Chicken primary myoblasts (CPMs) were isolated from the breast muscle of less than 10-day-old chickens [27]. Briefly, tissue was washed, minced, and digested with collagenase, followed by erythrocyte removal. Cells were cultured in complete DMEM/F12 medium (15% Fetal bovine serum) (Gibco CA, USA) and differentiated in medium with 2% horse serum at high confluence. For differentiation, cells were seeded at 2 × 106 cells per well in six-well plates and induced at > 90% confluence with DMEM/F12 containing 2% horse serum and 1% penicillin-streptomycin. To probe its function, we transfected CPMs at 70% confluence in 96-well plates with 100 nM of gga-miR-1744-3p mimic, inhibitor, or their respective negative controls (synthesized by GenePharma, Shanghai, China) using TransIT-TKO (Mirus Bio, Germany) and Opti-MEM medium (Gibco, USA). The oligonucleotide sequences of gga-miR-1744-3p mimic, inhibitor, or their respective negative controls in the Table S2. RNA was isolated 24 h post-transfection for RT-qPCR including PAX7, CCNB1, CCND1, and MYOD1 in the Table S2.

5-ethynyl-2-deoxyuridine (EdU) proliferation assays

Cell proliferation was assessed using the Cell-Light™ EdU Apollo 488 In Vitro Kit (RiboBio Co., Ltd., Guangzhou, China) according to the manufacturer’s instructions [28]. At 24 h after transfection, cells were incubated with 50 µM EdU for 2 h, fixed with 4% paraformaldehyde, permeabilized with 0.5% Triton X-100. Subsequently, the cells were incubated in Apollo reaction solution for 1 h and stained with Hoechst 33,342 for 0.5 h. The percentage of EdU-positive cells was quantified from three randomly selected fields per well using an Axiovert 5 microscope (Carl Zeiss, Jena, Germany). Three biological replicates were performed per group.

Statistical analysis

Principal component analysis (PCA) was performed using R software (v4.4). All experimental data were presented as the mean ± standard deviation (SD). Statistical significance was determined by two-way ANOVA in SPSS (v18.0). We also performed a one-way ANOVA followed by multiple comparisons for different time points within each breed, and conducted independent samples t-tests at each time point to compare between breeds in SPSS (v18.0). The significance levels defined as no significant P > 0.05, * P < 0.05, ** P < 0.01, *** P < 0.001, and **** P < 0.0001, respectively. All graphs were generated and statistical analyses were conducted using GraphPad Prism 8 (GraphPad Software, Inc, San Diego, CA, US).

Results

Embryo weight and muscle fiber characteristics of two varieties

As previously outlined, the period from E9 to E13 corresponds to the proliferative stage of myoblasts, while E13 to E18 marks the phase of myocytes differentiation and fusion (Fig. 1A). Embryo weights were recorded for both varieties, revealing that AA broilers exhibited significantly higher embryo weights compared to TY chickens at E13 and E18 (P < 0.05) (Fig. 1B). As the embryos aged, the body weight of TY chickens increased significantly (P < 0.001), with AA broilers showing a consistent trend (P < 0.001) (Fig. 1B). A two-way ANOVA indicated a significant interaction between developmental time and breed. Due to the small size of E9 and E13 embryos, particularly in the breast muscles of TY chickens, it was currently not feasible to calculate the muscle fiber area. Thus, muscle fiber characteristic was presented only for E18. Myocytes were clearly observed within the muscle fibers (Fig. 1C). And there were significant differences in their CSA (Fig. 1D).

Fig. 1.

Fig. 1

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Embryo weight and histological morphology during embryogenesis. A Schematic diagram illustrating key time points during embryogenesis. B Comparison of embryo weights between two breeds at three developmental stages. No significant P > 0.05, *: P < 0.05, **: P < 0.01. C Histological morphology of embryos at E18. D Cross-sectional area (CSA) of muscle fibers at E18. **: P < 0.01. AA: Arbor Acres, TY: TaoYuan, E9: embryonic day 9, E13: embryonic day 13, E18: embryonic day 18

Transcriptome characteristics and miRNA regulatory networks in AA broilers

To investigate temporal expression dynamics in AA broilers, we performed differential expression analysis across three time points: E9 vs. E13 and E13 vs. E18. A total of 4,204 DEGs were identified between E9 and E13, comprising 2,026 down regulated and 2,178 up regulated genes (Fig. 2A). Between E13 and E18, 3,860 DEGs were found, including 1,793 down regulated and 2,067 up regulated genes (Fig. 2B). We combined the two groups of 8,010 DEGs, and finally obtained 6,530 unique DEGs. Time-series clustering of the 6,530 DEGs (Table S3) revealed that cluster 5 (600 genes) represented up regulated genes, cluster 6 (627 genes) represented down regulated genes, and clusters 3, 4, 7, and 8 exhibited bimodal expression patterns (Fig. 2C). KEGG enrichment analysis showed that up regulated genes from cluster 5 were significantly associated with the insulin signaling pathway (13/600), ECM-receptor interaction (10/600), and focal adhesion (14/600) (P < 0.05; Fig. 2D), including PPP1R3E, PPP1R3D, PRKAB2, LAMA2, COL1A2, and COL6A1, while down regulated genes were enriched in cell cycle (25/600), oocyte meiosis (15/600), and steroid biosynthesis (8/600) (P < 0.05; Fig. 2E).

Fig. 2.

Fig. 2

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Expression profiles of mRNAs and miRNAs in AA broilers. A and B Volcano plot of differentially expressed mRNAs (DEGs) between AAE9E13 and AAE13E18. Red dots: significantly up regulated DEGs; blue dots: significantly down regulated DEGs; gray dots: genes not meeting the significance threshold. C Time-series clustering analysis of DEGs. D KEGG enrichment analysis for genes in cluster 5 (P < 0.05). E KEGG enrichment analysis for genes in cluster 3 and 6 (P < 0.05). F and G Volcano plot of differentially expressed miRNAs (DEMs) between AAE9E13 and AAE13E18. Red dots: significantly up regulated DEMs; blue dots: significantly down regulated DEMs; gray dots: miRNAs not meeting the significance threshold. H Upset plot illustrating DEMs between AAE9E13 and AAE13E18. I Regulatory network constructed based on up regulated miRNAs. J Regulatory network constructed based on down regulated miRNAs. AA: Arbor Acres; E9: embryonic day 9; E13: embryonic day 13; E18: embryonic day 18

Differential expression analysis of miRNA identified 149 and 32 DEMs in the two comparisons, respectively (Table S4 and Fig. 2F-G). An UpSet plot showed that three miRNAs (gga-miR-155, gga-miR-449a, and gga-miR-1684a-3p) were up regulated and three (gga-miR-187-3p, gga-miR-499-3p, and gga-miR-449-5p) were down regulated in both comparisons (Fig. 2H). Using TargetScan and miRDB, we identified overlapping target genes that were inversely correlated with miRNA expression, leading to the construction of two miRNA-mRNA regulatory networks: one consisting of 3 miRNAs and 20 mRNAs (Fig. 2I), and the other of 3 miRNAs and 83 mRNAs (Fig. 2J).

Dynamic changes of coding and non-coding genes in TY chickens

We next examined temporal expression profiles in TY chickens. Between E9 and E13, 1,204 genes were down regulated and 1,519 genes were up regulated (Fig. 3A), while between E13 and E18, 2,538 genes were down regulated and 2,824 genes up regulated (Fig. 3B and Table S5). We combined the two groups of 8,085 DEGs, and finally obtained 6,800 unique DEGs. Time-series clustering of all 6,800 DEGs indicated that cluster 9 (1,190 genes) represented up regulated genes, and clusters 2 and 7 (964 and 648 genes) represented down regulated genes (Fig. 3C). Cluster 9 was significantly enriched in metabolic pathways (122/1,190), carbon metabolism (25/1,190), and oxidative phosphorylation (23/1,190) (P < 0.05; Fig. 3D), while clusters 2 and 7 were enriched in 22 pathways including cell cycle (27/1,612), fanconi anemia pathway (17/1,612), and oocyte meiosis (19/1,612) (P < 0.05; Fig. 3E).

Fig. 3.

Fig. 3

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Expression profiles of mRNAs and miRNAs in TY chickens. A and B Volcano plot of differentially expressed mRNAs (DEGs) between TYE9E13 and TYE13E18. Red dots: significantly up regulated DEGs; blue dots: significantly down regulated DEGs; gray dots: genes not meeting the significance threshold. C Time-series clustering analysis of DEGs. D KEGG enrichment analysis for genes in cluster 4 and 9 (P < 0.05). E KEGG enrichment analysis for genes in clusters 2 and 7 (P < 0.05). F and G Volcano plot of differentially expressed miRNAs (DEMs) between TYE9E13 and TYE13E18. Red dots: significantly up regulated DEMs; blue dots: significantly down regulated DEMs; gray dots: miRNAs not meeting the significance threshold. H Upset plot of DEMs between TYE9E13 and TYE13E18. I Venn diagram showing the distribution of gga-miR-302d. J Regulatory network constructed based on down regulated miRNAs. AA: Arbor Acres; TY: TaoYuan; E9: embryonic day 9; E13: embryonic day 13; E18: embryonic day 18

Analysis of non-coding RNAs identified 44 down regulated and 40 up regulated miRNAs between E9 and E13 (Fig. 3F), and 95 down regulated and 36 up regulated between E13 and E18 (Fig. 3G and Table S6). Only gga-miR-302d was up regulated in both comparisons, while seven miRNAs (gga-miR-1a-1-5p, gga-miR-1416-3p, gga-miR-1416-5p, gga-miR-2954, gga-miR-3528, gga-miR-499-3p, and gga-miR-499-5p) were down regulated in both (Fig. 3H). No overlapping target genes were identified for gga-miR-302d between TargetScan and miRDB predictions and the corresponding DEGs (Fig. 3I). For the seven down regulated miRNAs, a regulatory network comprising 7 miRNAs and 107 mRNAs was constructed (Fig. 3J).

Hub genes at different time points between varieties

PCA analysis of 17,007 genes showed clear separation by developmental stage and breed, with biological replicates clustering tightly at each time point (Fig. 4A). WGCNA analysis was applied to 16,314 non-zero genes across 18 samples. Twenty co-expression modules were identified after merging similar modules (eigengene similarity > 0.75; Fig. 4B). The orangered4, midnightblue, darkgreen, darkgrey, ivory, and cyan modules were identified as most relevant to time and breed (Fig. 4B). Among these 20 modules, the number and proportion of genes in each module were indicated. Among them, the module with the largest number of genes (8,461 genes) is the blue module, while the module with the smallest number of genes (55 genes) is the darkorange2 module (Fig. 4B and Table S7). Using criteria of GS > 0.2 and MM > 0.8, 2,112 hub genes were identified, including MYH7B, MYOD1, MYOG, USP8, and ZBTB38 (Table S8 and Fig. S1). From the orangered4, midnightblue, darkgreen, darkgrey, ivory, and cyan modules, 70, 774, 111, 193, 322, and 642 hub genes were identified, respectively (Table S8). KEGG enrichment analysis was then performed on the hub genes from each module, they were consistently enriched in focal adhesion, tight junction, and cardiac muscle contraction in both breeds (P < 0.05; Table S8 and Fig. 4C-D).

Fig. 4.

Fig. 4

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Weighted gene co-expression network analysis (WGCNA). A Principal component analysis (PCA) of mRNA expression profiles across all samples. B Module-trait correlations across developmental time. Red indicates a positive correlation, blue a negative correlation; darker shades represent stronger correlations. C KEGG enrichment analysis of modules significantly associated with developmental stages in the AA line (P < 0.05). D KEGG enrichment analysis of significant modules in the TY line (P < 0.05). AA: Arbor Acres; TY: TaoYuan; E9: embryonic day 9; E13: embryonic day 13; E18: embryonic day 18

Joint analysis across varieties and identification of candidate genes

Differential expression analysis between the two breeds revealed 75 down regulated and 228 up regulated genes at E9 (Fig. 5A and Table S9), 846 down regulated and 910 up regulated at E13 (Fig. 5B), and 1,055 down regulated and 712 up regulated at E18 (Fig. 5C). A total of 18 genes were differentially expressed across all three stages (Table S10 and Fig. 5D). Five genes, including ENSGALG00010007873, ENSGALG00010015339, KCNS3, MLPH, and EDNRB2, of these genes exhibited an increase in expression over time, while two genes (ENSGALG00010003339 and ENSGALG00010013569) showed a decrease in expression. These 7 genes are the differentially expressed genes during embryonic development (Fig. 5D; Table S10). Based on the UpSet plot, a comparison of hub genes and DEGs across the three time points indicated that E9 had the smallest set of candidate genes. In contrast, the highest numbers of DEGs were found at the E13 and E18 (Fig. 5E). Time-series clustering of the 432 overlapping genes from WGCNA and differential expression analyses identified clusters 1, 5, and 9 as up regulated (45, 47, and 30 genes), and cluster 3 (39 genes) as down regulated, which contained 39 genes including USP8 and ZBTB38 (Fig. 5F). These genes were significantly enriched in 27 KEGG pathways, such as oxidative phosphorylation (19/161), metabolic pathways (33/161), and carbon metabolism (8/161) (Fig. 5G and Table S11). Expression patterns of the 161 candidate genes displayed two distinct trends, as visualized in the heatmap (Fig. S2).

Fig. 5.

Fig. 5

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Expression profiles of mRNAs in AA broilers and TY chickens. A-C Volcano plots of differentially expressed mRNAs (DEGs) between AA and TY at E9 (A), E13 (B), and E18 (C). Red dots: DEGs significantly up regulated in TY; blue dots: DEGs significantly down regulated in AA; gray dots: genes not meeting the significance threshold. D Venn diagram showing overlapping DEGs between AA and TY at E9, E13, and E18. E Upset plot illustrating intersections among hub genes and DEGs across E9, E13, and E18. F Time-series clustering analysis of 432 overlapping genes from DEGs and hub genes at each time point. G Kyoto Encyclopedia of Genes and Genomes enrichment analysis in 161 candidate genes (P < 0.05). AA, Arbor Acres; TY, TaoYuan; E9, embryonic day 9; E13, embryonic day 13; E18, embryonic day 18

Differential expression of miRNAs across time points

A total of 1,131 miRNAs were analyzed. PCA revealed clear separation by breed and developmental stage (Fig. 6A), with over 300 miRNAs detected per sample (Fig. 6B). Differential expression analysis identified 56 DEMs across time points (Table S12). The largest number of DEMs (27) was found at E18, including 23 down regulated and 4 up regulated (Fig. 6C). The fewest were at E13 (7 down regulated, 3 up regulated; Fig. 6D), while E9 showed 10 down regulated and 14 up regulated DEMs (Fig. 6E). Only three miRNAs exhibited consistent expression patterns across stages: gga-miR-1684b-3p was up regulated at E9 and E18, gga-miR-1798-3p at E9 and E13, and gga-miR-1744-3p at E13 and E18 (Fig. 6F). Notably, gga-miR-1744-3p was significantly highly expressed in TY chickens specifically (E13 and E18). Integration of TargetScan and miRDB predictions with the 161 candidate genes identified two potential targets: USP8 and ZBTB38 (Fig. 6G and Table S13). Expression of three genes (gga-miR-1744-3p, USP8 and ZBTB38) increased significantly in TY chickens at later stages, particularly at E13, with statistically significant breed-by-stage interactions (Fig. 6H-J).

Fig. 6.

Fig. 6

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Expression profiles of miRNAs in AA broilers and TY chickens. A Principal component analysis (PCA) of miRNA expression across all samples. B Distribution of miRNA counts in all samples. C-E Volcano plots of differentially expressed mioRNAs (DEMs) between AA and TY at E9 (C), E13 (D), and E18 (E). Red dots: DEMs significantly up regulated in TY; blue dots: DEMs significantly down regulated in AA; gray dots: miRNAs not meeting the significance threshold. F Upset plot showing DEM overlaps across E9, E13, and E18. G Venn diagram of gga-miR-1744-3p occurrence. H-J Expression levels of gga-miR-1744-3p (H), USP8 (I), and ZBTB38 (J) in AA broilers and TY chickens at three time points. No significant P > 0.05, *P < 0.05, **P < 0.01, ***P < 0.001. AA, Arbor Acres; TY, TaoYuan; E9, embryonic day 9; E13, embryonic day 13; E18, embryonic day 18

The function of gga-miR-1744-3p in myoblasts

Multi-tissue expression profiling in 1-day-old chicks showed that gga-miR-1744-3p was differentially expressed in heart, breast muscle, and adipose tissue, with significantly higher expression in TY chickens (Fig. 7A). Compared with the differentiation stage, gga-miR-1744-3p was significantly highly expressed during the proliferation stage (Fig. 7B). Gain and loss of function experiments confirmed successful over expression and knockdown (Fig. 7C). EdU assays revealed that knockdown significantly reduced proliferation (Fig. 7D), while over expression enhanced it (Fig. S3). RT-qPCR analysis of proliferation-related genes supported these findings, showing concordant expression changes (Fig. 7E-F).

Fig. 7.

Fig. 7

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Tissue expression profile and functional analysis of gga-miR-1744-3p. A Tissue expression pattern of gga-miR-1744-3p. No significant P > 0.05, *P < 0.05. B Expression of gga-miR-1744-3p during proliferation (GM) and differentiation (DM) of chicken primary myoblasts (CPMs). No significant P > 0.05, *P < 0.05. C Over expression and knockdown efficiency of gga-miR-1744-3p in CPMs. *P < 0.05, **P < 0.01. D Proliferation of transfected CPMs evaluated by EdU assay. *P < 0.05. E-F Expression levels of proliferation-related genes after over expression or knockdown of gga-miR-1744-3p. *P < 0.05, **P < 0.01, ***P < 0.001. AA, Arbor Acres; TY, TaoYuan; mimics-NC, mimic negative control; mimics-1744, gga-miR-1744-3p mimic; inhibitor-NC, inhibitor negative control; inhibitor-1744, gga-miR-1744-3p inhibitor

Discussion

Notably, TY embryos are comparatively smaller than those of AA broilers, and E9 represents the earliest stage at which breast muscle samples can be reliably collected, a period during which myoblasts are predominantly proliferating [29]. E13 marks a transitional phase in muscle fiber development, characterized by the shift from peak myoblast proliferation to the onset of differentiation and fusion [30]. By E18, muscle fibers have largely completed fusion, forming multinucleated myotubes that establish the foundational population of muscle fibers [1]. Thus, investigation of these three critical stages provides valuable insight into the dynamic regulation of muscle fiber development during embryogenesis.

The significantly higher embryo weight observed in AA broilers compared to TY chickens at E13 and E18 is consistent with their respective growth characteristics. This phenotypic divergence likely reflects the cumulative effect of genetic selection for rapid growth traits in commercial breeds, which manifests as early as embryonic development [31, 32]. The significant interaction between time and breed further highlights that these developmental trajectories are not parallel but diverge progressively. AA broilers (20.84 μm²) exhibited a CSA twice as large as that of TY chickens (11.70 μm²) (Fig. 1D). The finer fibers characteristic of TY chickens were likely associated with better meat quality and enhanced flavor [33]. Embryological and morphological evidence set the stage for a mechanistic interpretation of molecular results, illustrating how genetic background influences embryonic muscle development and ultimately dictates post-natal phenotype.

Dynamic developmental pattern in AA broilers

To elucidate the dynamic regulatory patterns of gene expression during critical embryonic stages in AA broilers, we performed differential expression and time-series clustering analyses. As shown in Fig. 2A and B, the number of DEGs between E9 and E13 was slightly higher than that between E13 and E18, with up regulated genes outnumbering down regulated genes in both intervals. This suggests a higher level of transcriptional activity during early to mid-embryogenesis, potentially facilitating cell proliferation, differentiation, and tissue morphogenesis [34].

Up regulated genes in cluster 5 were significantly enriched in the insulin signaling pathway, ECM-receptor interaction, and focal adhesion (Fig. 2D). Activation of insulin signaling is likely linked to enhanced energy metabolism and embryonic growth [35, 36], while up regulation of ECM and adhesion-related genes supports tissue structuring and cellular migration [37, 38]. In contrast, continuously down regulated genes in cluster 6 (Fig. 2E) were enriched in pathways including cell cycle, oocyte meiosis, steroid biosynthesis, and fanconi anemia [39]. The decline in cell cycle gene expression may reflect a developmental shift from proliferation to differentiation [40]. Down regulation of oocyte meiosis-related genes could indicate stage-specific changes in germ cell development [41], while reduced expression in steroid biosynthesis may point to dynamic endocrine regulation during mid-to-late embryogenesis.

Among miRNAs, relatively few DEMs were identified across stages. This sparse yet complex expression pattern implies that these miRNAs may play multifunctional roles at different developmental periods, fine-tuning biological processes through context-specific target interactions [14, 42, 43]. Furthermore, we found that this regulatory network, including 4 miRNAs (gga-miR-155, gga-miR-449a, gga-miR-187-3p, and gga-miR-1684a-3p) is specific to the AA broiler breed.

Dynamic developmental pattern in TY chickens

The dynamic gene expression profile of TY chickens during embryonic development (E9 to E18) reveals both conserved and breed-specific regulatory features, offering new insights into the developmental biology of indigenous poultry breeds [42, 44]. In terms of mRNAs, the number of DEGs was higher between E13 and E18 (5,362) than between E9 and E13 (2,723) (Fig. 3A-B), with up regulated genes outnumbering down regulated ones in both intervals. This pronounced transcriptional remodeling during mid-to-late embryogenesis, approximately twice as many DEGs as in the earlier transition, suggests that TY chickens undergo a delayed but intense period of regulatory activation, potentially supporting the rapid differentiation and fusion of myoblasts [45, 46].

Genes in the consistently highly expressed clusters 9 were significantly enriched in energy metabolism-related pathways such as metabolic pathways, carbon metabolism, and oxidative phosphorylation. This pattern aligns with the elevated energy demands of late embryonic development. Oxidative phosphorylation, as a core ATP-producing process, likely supplies energy for muscle cell proliferation and bone calcification [47]. Similarly, activation of carbon metabolism may help maintain metabolic homeostasis through glycolysis and TCA cycle regulation [48]. In contrast, clusters 2 and 7, comprising continuously low expressed genes, were enriched in pathways including cell cycle [40], fanconi anemia pathway [39], and oocyte meiosis [47]. These show partial overlap with pathways down regulated in AA broilers but involve a broader set of significant terms. Sustained down regulation of cell cycle genes reinforces the developmental transition from proliferation to differentiation.

At the non-coding level, miRNA expression exhibited notable specificity. The number of DEMs was substantially higher between E13 and E18 (131) than between E9 and E13 (84). Notably, gga-miR-302d, previously implicated in spermatogonial stem cell differentiation [49], was up regulated across both stages. A particularly distinctive feature was the complex regulatory network formed by seven continuously down regulated miRNAs (including gga-miR-1a-1-5p and gga-miR-499-3p/5p), targeting 107 mRNAs. Overall, the gene expression dynamics in TY chicken embryos reflect breed-specific features, including enhanced transcriptional remodeling in late stages and intricate miRNA-mediated regulation (gga-miR-1a-1-5p, gga-miR-1416-3p, gga-miR-1416-5p, gga-miR-2954, and gga-miR-3528). These findings enhance our understanding of the molecular mechanisms underlying economically important traits in native chickens and provide a theoretical basis for targeted genetic improvement.

Differences in developmental regulation between breeds

Joint analysis of hub genes across developmental stages provides important insights into the molecular mechanisms [50–52], underlying breed-specific embryonic development in AA broilers and TY chickens. Functional enrichment analysis revealed consistent pathway activation between breeds despite temporal differences in module composition. Key conserved pathways included focal adhesion [53], tight junction [54], and cardiac muscle contraction [55]. These processes are known to regulate critical developmental events such as myoblast differentiation, muscle fiber formation, and mechanotransduction in response to physiological loads [56]. Their consistent enrichment underscores the existence of evolutionarily conserved regulatory networks underlying avian embryonic development [57–59]. Breed-specific traits may therefore arise not from fundamental pathway differences, but from temporal shifts or expression-level variations in key genes within these shared pathways.

Differential expression analysis further elucidated the molecular basis of phenotypic divergence. The number of DEGs was lowest at E9 (303 genes) and increased substantially at E13 (1,756 genes) and E18 (1,767 genes) (Fig. 6D), reinforcing the notion that genetic differences between breeds become more pronounced as development proceeds. We have discovered that these two miRNAs (gga-miR-499-3p and gga-miR-499-5p) are also specific genes during the embryonic development stage. The consistent suppression of the gga-miR-499 family is of interest due to its established role in muscle fiber-type specification [60]. It should be noted, however, that these observations are based on transcriptional profiling and require functional validation to establish causal relationships with phenotypic traits.

Proliferative function of gga-miR-1744-3p

The differential expression of non-coding RNAs during embryonic development adds a new layer of complexity to understanding the molecular basis of phenotypic divergence [59, 61] between AA broilers and TY chickens. This highlights the strong breed-specificity and temporal dynamics of non-coding RNA expression [62], suggesting their roles as fine-tuners of embryonic development. Interestingly, the number of DEMs between breeds followed a “medium-low-high” pattern over time (lowest at E13, highest at E18), contrasting with the steadily increasing divergence seen in coding genes. This implies that non-coding gene regulation may involve more complex temporal mechanisms. Of the three miRNAs showing consistent expression trends across stages-gga-miR-1684b-3p, gga-miR-1798-3p, and gga-miR-1744-3p-the latter stands out due to its functional relevance. It was significantly highly expressed during key myocytes differentiation stages (E13 and E18) in TY chickens and was computationally predicted to target two candidate genes, USP8 and ZBTB38, among the 39 identified. USP8 has been implicated in immune regulation and mitophagy [63], and was recently linked to osteogenic differentiation via the lncRNA XIST/miR-302a-3p/USP8 axis [64]. ZBTB38, a transcriptional repressor of terminal muscle differentiation [65], may suppress genes involved in myoblast maturation. The concurrent high expression of gga-miR-1744-3p, USP8, and ZBTB38 at E13, significantly more pronounced in TY chickens. However, three genes exhibited expression patterns consistent with expectations at E18. This indicates that the miRNA-mRNA regulatory network (ZBTB38-gga-miR-1744-3p-USP8) may play a significant role in the proliferation and differentiation process of myocytes at E18.

Multi-tissue expression profiling revealed high gga-miR-1744-3p levels in heart, breast muscle, and adipose tissue of TY chickens, may exert a broad regulatory influence on growth traits characteristic of indigenous breeds. Functional assays in CPMs confirmed its specific up regulation during proliferation. These findings hypothesize that gga-miR-1744-3p contributes to muscle development in TY chickens by expanding the myoblast pool, thereby providing a cellular foundation for subsequent fiber differentiation and influencing final meat quality traits.

Conclusions

We performed not only independent analyses of each breed to identify distinct miRNA-mRNA regulatory networks, but also integrated three analytical approaches (differential expression analysis, WGCNA, and time-series clustering) to pinpoint key genes and regulatory circuits shared between the two varieties. This integrated strategy revealed two protein-coding genes (USP8 and ZBTB38) and one non-coding gene (gga-miR-1744-3p) as central players. Furthermore, functional validation confirmed that gga-miR-1744-3p promotes the proliferation of chicken primary myoblasts (CPMs). Our findings provide novel candidate genes and regulatory networks active during embryonic muscle differentiation and fusion, offering new insights into the molecular mechanisms underlying muscle development and serving as a valuable resource for future research.

Supplementary Information

Additional file 1. (1.1MB, xlsx)
12864_2026_12795_MOESM2_ESM.docx (5.7MB, docx)

Additional file 2: Table S1. Summary of RNA-seq data. Table S2. PCR primer specifications. Table S3. Number of differentially expressed genes in AA broilers. Table S4. Number of differentially expressed microRNAs in AA broilers. Table S5. Number of differentially expressed genes in TaoYuan chickens. Table S6. Number of differentially expressed microRNAs in TaoYuan chickens. Table S7. Each module represents the quantity and proportion of a specific gene. Table S8. Kyoto Encyclopedia of Genes and Genomes pathways of hub genes were enriched. Table S9. Number of differentially expressed genes between AA broilers and TaoYuan chickens. Table S10. 18 overlapping DEGs between AA and TY at E9, E13, and E18. Red: consistently higher in AA broilers at all three time points; Blue: consistently higher in TY chickens at all three time points. AA, Arbor Acres; TY, TaoYuan. Table S11. Kyoto Encyclopedia of Genes and Genomes pathways of hub genes were enriched. Table S12. Number of differentially expressed microRNAs between AA broilers and TaoYuan chickens. Table S13. Identify the intersection gene that may bind to gga-miR-1744-3p.

Acknowledgements

We thank Miss. Zhuoga DEji, Miss.Xueying Ma, and other members of the He laboratory for RT-qPCR from Hunan Agricultural University. The authors also thank Shanghai Personalbio Technology Co.,Ltd (Shanghai, China) for sequencing.

Abbreviations

AA

Arbor Acres

CPMs

Chicken primary myoblasts

CSA

Cross-sectional area

DEGs

Differentially expressed genes

DEMs

Differentially expressed microRNAs

EdU

5-ethynyl-2-deoxyuridine

FPKM

Fragments per kilobase of transcript per million mapped reads

GS

Gene significance

KEGG

Kyoto Encyclopedia of Genes and Genomes

miRNA

microRNA

MM

Module membership

PCA

Principal component analysis

RT-qPCR

Real time-polymerase chain reaction

TY

TaoYuan

WGCNA

Weighted gene co-expression network analysis

Authors' contributions

DZ analyzed the data, performed the sample collection and drafted the manuscript. YFS completed the experimental verification. QYOY, HHZ, and YMG participated in revising the manuscript. ZHS contributed to the design of the study, assisted in interpreting the data, and was involved in drafting the manuscript. XH contributed to acquisition of financial support, interpretation of data, and drafting of the manuscript. All authors contributed to the article and approved the final manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (U21A20253), Hunan Poultry Industry Technology System and Hunan Agriculture Research System of Poultry Industry (HARS-06).

Data availability

The datasets analyzed during the current study are available from the corresponding author upon reasonable request, and we’ve uploaded them to Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsa) under the BioProject No. CRA028252 and CRA028249.

Declarations

Ethics approval and consent to participate

The animal experiments detailed in this paper were carried out in strict compliance with relevant national regulations regarding animal ethics and welfare, as well as the ethical guidelines and the animal experimental safety review system of Hunan Agricultural University (approval number: HAU ACC 2024089).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Zehe Song, Email: zehesong111@163.com.

Xi He, Email: hexi111@126.com.

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Supplementary Materials

Additional file 1. (1.1MB, xlsx)
12864_2026_12795_MOESM2_ESM.docx (5.7MB, docx)

Additional file 2: Table S1. Summary of RNA-seq data. Table S2. PCR primer specifications. Table S3. Number of differentially expressed genes in AA broilers. Table S4. Number of differentially expressed microRNAs in AA broilers. Table S5. Number of differentially expressed genes in TaoYuan chickens. Table S6. Number of differentially expressed microRNAs in TaoYuan chickens. Table S7. Each module represents the quantity and proportion of a specific gene. Table S8. Kyoto Encyclopedia of Genes and Genomes pathways of hub genes were enriched. Table S9. Number of differentially expressed genes between AA broilers and TaoYuan chickens. Table S10. 18 overlapping DEGs between AA and TY at E9, E13, and E18. Red: consistently higher in AA broilers at all three time points; Blue: consistently higher in TY chickens at all three time points. AA, Arbor Acres; TY, TaoYuan. Table S11. Kyoto Encyclopedia of Genes and Genomes pathways of hub genes were enriched. Table S12. Number of differentially expressed microRNAs between AA broilers and TaoYuan chickens. Table S13. Identify the intersection gene that may bind to gga-miR-1744-3p.

Data Availability Statement

The datasets analyzed during the current study are available from the corresponding author upon reasonable request, and we’ve uploaded them to Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsa) under the BioProject No. CRA028252 and CRA028249.

Articles from BMC Genomics are provided here courtesy of BMC

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