Next Article in Journal
Antimicrobial Tolerance in Salmonella: Contributions to Survival and Persistence in Processing Environments
Previous Article in Journal
Treatment of Medial Instability of the Carpometacarpal and Tarsometatarsal Joints Using the Isolock® System in Two Dogs
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Animals
Volume 14
Issue 4
10.3390/ani14040576
animals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Transcriptome Data Revealed the circRNA–miRNA–mRNA Regulatory Network during the Proliferation and Differentiation of Myoblasts in Shitou Goose

by
Rongqin Huang
1,2,
Jiahui Chen
1,2,
Xu Dong
1,2,
Xiquan Zhang
1,2 and
Wen Luo
1,2,*
1
College of Animal Science, South China Agricultural University, Guangzhou 510642, China
2
Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, China
*
Author to whom correspondence should be addressed.
Animals 2024, 14(4), 576; https://doi.org/10.3390/ani14040576
Submission received: 5 January 2024 / Revised: 5 February 2024 / Accepted: 6 February 2024 / Published: 8 February 2024
(This article belongs to the Section Animal Genetics and Genomics)
Browse Figures
Review Reports Versions Notes

Abstract

:

Simple Summary

The Shitou goose, derived from the swan goose (Anser cygnoides), is an excellent large goose breed in China and has high economic value. Therefore, it is imperative to delve into the study of its muscle characteristics. Circular RNA (CircRNA), acting as a molecular sponge, binds to miRNA, thereby intricately modulating targeted gene expression and influencing muscle growth and development. To unravel these complexities, we employed transcriptome sequencing to scrutinize the myoblasts and myotubes of Shitou geese, generating comprehensive circRNA and mRNA maps for two distinct developmental stages. In the subsequent analysis, a circRNA–miRNA–mRNA interaction network emerged, delineating a regulatory framework for muscle growth and development. This network is pivotal in understanding the underlying mechanisms of goose myogenesis and contributes new ideas and perspectives concerning the role of non-coding RNAs in the regulation of growth in geese.

Abstract

CircRNA, a recently characterized non-coding RNA (ncRNA) variant, functions as a molecular sponge, exerting regulatory control by binding to microRNA (miRNA) and modulating the expression of downstream proteins, either promoting or inhibiting their expression. Among poultry species, geese hold significant importance, prized by consumers for their delectable taste and rich nutritional content. Despite the prominence of geese, research on the growth and development of goose muscle, particularly the regulatory role of circRNAs in goose muscle formation, remains insufficiently explored. In this study, we constructed comprehensive expression profiles of circRNAs and messenger RNAs (mRNAs) within the myoblasts and myotubes of Shitou geese. We identified a total of 96 differentially expressed circRNAs (DEcircRNAs) and 880 differentially expressed mRNAs (DEmRNAs). Notably, the parental genes of DEcircRNAs and DEmRNAs exhibited enrichment in the Wnt signaling pathway, highlighting its potential impact on the proliferation and differentiation of goose myoblasts. Employing RNAhybrid and miRDB, we identified circRNA-miRNA pairs and mRNA-miRNA pairs that may play a role in regulating myogenic differentiation or muscle growth. Subsequently, utilizing Cytoscape, we constructed a circRNA–miRNA–mRNA interaction network aimed at unraveling the intricate regulatory mechanisms involved in goose muscle growth and development, which comprises 93 circRNAs, 351 miRNAs, and 305 mRNAs. Moreover, the identification of 10 hub genes (ACTB, ACTN1, BDNF, PDGFRA, MYL1, EFNA5, MYSM1, THBS1, ITGA8, and ELN) potentially linked to myogenesis, along with the exploration of their circRNA–miRNA–hub gene regulatory axis, was also conducted. These competitive endogenous RNA (ceRNA) regulatory networks elucidate the molecular regulatory mechanisms associated with muscle growth in Shitou geese, providing deeper insights into the reciprocal regulation of circRNA, miRNA, and mRNA in the context of goose muscle formation.

1. Introduction

Poultry meat enjoys worldwide popularity owing to its delectable taste and nutritional richness. Among the various poultry varieties, geese stand out as a prominent category. Renowned for its palatable taste and notable richness in protein, fat, and essential amino acids, goose meat boasts distinct nutritional and culinary significance, underlining its considerable economic value [1,2]. The Shitou goose, originating from the swan goose (Anser cygnoides), holds the distinction of being the world’s largest body-size goose breed, with its roots tracing back to Raoping County, Guangdong Province, China [3]. Distinguished by its efficiency in grain utilization, substantial body size, enhanced meat production, and rapid growth, this breed presents notable advantages [4]. Despite these merits, research on goose muscle remains limited in comparison to more extensively studied poultry varieties such as chickens and ducks [4,5].
Skeletal muscle holds a pivotal role in the animal organism, performing essential functions such as locomotion, supporting respiration, and maintaining posture [6]. Comprising muscle fibers, muscle bundles, tendons, and muscle membranes, skeletal muscle is intricately structured. Recognized as the fundamental functional unit of skeletal muscle, muscle fibers contribute significantly to its overall functionality [7]. Myogenesis, a complex and orderly process, involves the fusion of myoblasts to form multinucleated myofibrils, meticulously regulated by endogenous factors [8]. In most species, the determination of the number of muscle fibers occurs during the embryonic period. Subsequently, post birth, skeletal muscle growth is confined to the expansion of muscle fiber volume [9,10]. Consequently, the proliferation and differentiation of myoblasts emerge as critical processes influencing the growth and development of skeletal muscle. Furthermore, in livestock and poultry, skeletal muscle stands as a key component contributing to meat consumption by consumers. The yield and quality of meat products exhibit a robust correlation with the growth and development of skeletal muscle [11,12], directly influencing the supply of goose meat and the economic outcomes for breeders. However, diverse goose breeds exhibit varying growth rates and muscle weights. For instance, two native goose breeds in Guangdong Province, the Shitou goose and the Wuzong goose, have attracted considerable attention due to substantial differences in body size. Specifically, at the marketable age, the weight of the Shitou goose can surpass four times that of the Wuzong goose [13,14]. It is noteworthy that recent studies have demonstrated the regulatory role of non-coding RNAs in various stages of skeletal muscle development [15,16].
NcRNA defies the conventional understanding of RNA solely serving as a messenger for protein coding, as it does not participate in such coding processes. Based on their size, ncRNAs can be broadly categorized into various types, with circRNA, miRNA, and long noncoding RNA (lncRNA) emerging as the most extensively researched among them [17,18]. A plethora of studies have illustrated the profound impacts of ncRNAs on organismal development and tissue [17,19]. CircRNA, a type of endogenous ncRNA, is generated through atypical reverse-splicing events. Distinguished by the absence of a 5′-terminal cap and a 3′-terminal poly(A) tail, circRNA forms a closed circular RNA molecule through covalent bonds. This unique structure imparts higher stability to circRNA compared to linear RNA [20,21,22]. The ceRNA hypothesis posits that circRNA contains miRNA response elements (MREs) capable of binding to specific miRNAs, thereby regulating the expression of miRNA target genes. Consequently, circRNA is often referred to as an ‘miRNA sponge’ [23,24,25,26]. Notably, an increasing body of literature underscores the substantial impact of this intricate regulatory network on skeletal muscle growth and development [27,28,29,30,31]. Despite this, the influence of circRNA on goose skeletal muscle still needs to be more adequately explored.
The aim of this study was to investigate the regulatory impact of the ceRNA regulatory network involving circRNA–miRNA–mRNA on the growth and development of skeletal muscle in Shitou goose. Utilizing transcriptome sequencing technology and bioinformatics methods, we analyzed the DEcircRNAs and DEmRNAs during the myogenesis stage of Shitou goose, the largest goose breed in eastern Guangdong, China. Comprehensive analyses of functions and associated regulatory networks were conducted, with the identification of hub genes and subnetworks. This study contributes to the understanding of the molecular regulatory mechanisms underlying myoblast proliferation and differentiation in Shitou goose, providing a theoretical foundation for a more profound comprehension of the molecular regulatory processes governing goose muscle growth and development.

2. Materials and Methods

2.1. Isolation and Culture of Goose Myoblasts

Fertilized goose eggs were uniformly placed in the same incubator and maintained at 37 °C with 70% humidity. Following 15 days of incubation, the thigh muscles from 10 Shitou goose embryos were meticulously isolated and sectioned using sterile surgical instruments. The minced muscle tissue underwent digestion with 10 mL of 0.25% trypsin for 20 min in a constant temperature incubator, and digestion was halted with 10 mL of DMEM medium containing 20% fetal bovine serum (FBS) (Gibco, Grand Island, NE, USA). Subsequently, the digested suspension was transferred to a 50 mL centrifuge tube and centrifuged at 1200× g for 5 min, and the supernatant was discarded. Resuspension of the cells took place in DMEM medium containing 15% FBS, and they were evenly seeded in a petri dish. This continuous plating method aimed to enrich myoblasts while eliminating fibroblasts. The mixed primary myoblasts were then seeded across six culture dishes. Of these, three cultures were designated for the collection of growth myoblasts (GM), while the remaining three cultures underwent induction for differentiation into myoblasts using 2% horse serum (HS) (Biosharp, Hefei, China) in lieu of the 20% fetal bovine serum and were used to collect differentiated myotubes (DMs) [11].

2.2. RNA Isolation, Library Construction, and Sequencing

Once the myoblasts achieved a proliferation density of 70%, they underwent three washes with PBS and were directly added to the Trizol reagent (Invitrogen, Carlsbad, CA, USA). For the differentiated myoblasts, when their density reached 80%, they were cultured in DMEM containing 2% HS. Following three days of differentiation, the samples were collected using Trizol. Subsequently, total RNA extraction was conducted according to the manufacturer’s instructions. The specific steps were as follows: after washing the cells three times with PBS, 1 mL of Trizol reagent was added directly to the culture dish and placed on ice for 5 min to facilitate complete lysis. After repeated aspiration, the solution was transferred into a 1.5 mL RNase-free centrifuge tube, and 200 μL of chloroform was added. The solution was thoroughly mixed and left on ice for 5 min before centrifugation at 4 °C, 1200× g for 15 min. In a new RNase-free centrifuge tube, 500 μL isopropanol was added, and the supernatant obtained by means of centrifugation was aspirated. The solution was shaken up and down again to mix it well and centrifuged at 4 °C, 1200× g for 10 min. The supernatant was discarded, 1 mL of 75% ethanol was added, and the tube was centrifuged at 4 °C, 7500× g for 5 min. The supernatant was discarded, and the tube was left on ice for 5 min to dry the tube wall. Finally, 30 μL DEPC solution was added to dissolve the RNA. The Nanodrop ND-1000 (NanoDrop, Wilmington, DE, USA) was utilized to quantify the amount and purity of RNA in each sample. We employed the Agilent 2100 system to assess RNA integrity, where the RNA integrity number (RIN) of ≥7.0 is commonly regarded as optimal for subsequent high-throughput applications. In our experiment, we strictly adhered to the criterion of utilizing RNA with RIN > 7.0 to ensure the integrity of the RNA samples [32].
Ribosomal RNA was eliminated by utilizing 5 μg of total RNA in accordance with the instructions provided by the Ribo-Zero™ rRNA Removal Kit (Illumina, San Diego, CA, USA). Following the removal of ribosomal RNAs, the remaining RNAs were fragmented into small pieces using divalent cations under high temperatures. A volume of 8.5 μL cleaved RNA fragments was reverse-transcribed with random primers to generate cDNA, subsequently used to synthesize U-labeled second-stranded DNAs employing E. coli DNA polymerase I, RNase H, and dUTP. Blunt ends were introduced to each strand by adding an A-base, preparing them for ligation to the indexed adapters. Each adapter, featuring a T-base overhang, facilitated ligation to the A-tailed fragmented DNA. Fragments were ligated with single- or dual-index adapters, and size selection was executed using AMPureXP beads. The U-labeled second-stranded DNAs underwent treatment with the heat-labile UDG enzyme.
Ligated products were then amplified via PCR under the following conditions: initial denaturation at 95 °C for 3 min; 8 cycles of denaturation at 98 °C for 15 s, annealing at 60 °C for 15 s, and extension at 72 °C for 30 s; and a final extension at 72 °C for 5 min. The resulting cDNA library exhibited an average insert size of 300 bp (±50 bp). Finally, double-end sequencing was conducted on an Illumina NovaseqTM 6000 (LC Bio, Hangzhou, China), following the protocol recommended by the supplier.

2.3. Read Mapping and Transcriptome Assembly

Cutadapt [33] was employed to eliminate reads containing adaptor contamination, low-quality bases, and undetermined bases. Subsequently, sequence quality was assessed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ accessed on 7 May 2021). Read mapping to the species genome was carried out utilizing both Bowtie2 [34] and Hisat2 [35]. For any remaining reads (unmapped reads), a supplementary mapping step to the genome was performed using tophat-fusion [36].

2.4. Identification of circRNAs

CIRCExplorer2 [37,38] and CIRI [39] were employed for de novo assembly of the mapped reads into circular RNAs initially. Subsequently, back-splicing reads were identified within the unmapped reads using tophat-fusion. This process ensured the generation of unique circular RNAs for all samples.

2.5. Differential Expression Analysis of circRNAs and mRNAs

To identify DEcircRNAs and DEmRNAs between myoblasts and myotubes, edgeR was employed for differential expression analysis. A threshold of |log2FC| ≥ 1 and p-value < 0.05 was applied for the analysis.

2.6. Prediction of Targeting Relationship

To identify the target miRNAs of DEcircRNAs, we utilized RNAhybrid (https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid accessed on 16 October 2023) to predict the interacting miRNAs. Additionally, we used miRDB (https://mirdb.org/ accessed on 16 October 2023) to predict the target miRNA that interacts with mRNA, with a threshold set to ‘Target Score > 90′. The final miRNA (FmiRNA) was obtained by intersecting the two predicted miRNAs.

2.7. Functional Enrichment Analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis along with the visualization of DEcircRNA host genes and DEmRNAs were conducted using the R package clusterProfiler.
For a deeper insight into gene functions, gene set enrichment analysis (GSEA) and its visualization for mRNAs were carried out using the R packages clusterProfiler (version 4.10.0), enrichplot (version 1.22.0), and ggstatsplot (version 0.12.1). The clusterProfiler package’s gseGO and gseKEGG functions were utilized for the GO and KEGG enrichment analyses of gene sets, respectively. Visualization of the results from the GSEA was carried out using Enrichplot and ggstatsplot. The set threshold was established at |NES| > 1, NOM p-value < 0.05, and FDR (padj) < 0.25.

2.8. Establishment of the circRNA–miRNA–mRNA/hub Gene Network

The circRNA–miRNA and mRNA–miRNA pairs were integrated to eliminate nodes that could not contribute to the formation of the circRNA–miRNA–mRNA axis. The resulting circRNA–miRNA–mRNA interaction network was visually represented using Cytoscape (version 3.10.0).
After identifying 10 hub genes, we integrated them into the original circRNA–miRNA–mRNA network to explore the relationships between circRNAs, miRNAs, and hub genes. Subsequently, relevant circRNAs and mRNAs were extracted, and the circRNA–miRNA–hub gene subnetwork was reconstructed using Cytoscape.

2.9. Protein–Protein Interaction (PPI) Network Analysis

The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://string-db.org/ accessed on 19 October 2023) was utilized to discern the associations among mRNAs. The interaction network of PPI was analyzed using the CytoHubba application in Cytoscape. The top 10 hub genes in the interaction network were determined based on degree.

3. Results

3.1. Analysis of DEcircRNAs in the Proliferation and Differentiation Stages of Myoblasts in Shitou Goose

To discern variations in the proliferation and differentiation of myoblasts, transcriptome sequencing of cells at GM and DM phases was conducted using the Illumina HiSeq platform. The sequencing quality across all samples remained consistently high, as confirmed by the box plot (Figure 1A). A total of 5612 circRNAs were identified and distributed across different chromosomes during the transcriptome sequencing process. The classification results indicated that 81.27% of circRNAs were located in exons, 15.41% were located in introns, and 3.32% were located in intergenic regions (Figure 1B, Table S1). In total, 96 DEcircRNAs were identified in the proliferation and differentiation stages of myoblasts, applying a threshold of |log2FC| ≥ 1 and a p-value < 0.05. Among these, 76 were upregulated, and 20 were downregulated (Figure 1C,D, Table S2).
After mapping the DEcircRNAs to their parental genes, the subsequent GO and KEGG enrichment analysis revealed significant enrichment. The results of the enrichment analysis showed that the genes in the process of myoblast proliferation and differentiation were predominantly enriched in metal ion binding, cation binding, endosome membranes, endosomes, cytoplasmic vesicle membranes, vesicle membranes, phagosomes, and Wnt signaling pathway (Figure 1E,F).

3.2. Analysis of DEmRNAs between Myoblasts and Myotubes in Shitou Goose

Following the circRNA analysis, we identified 880 DEmRNAs from a pool of 20,208 mRNAs (Table S3), with 536 mRNAs exhibiting upregulation and 344 mRNAs showing downregulation (Figure 2A,B, Table S4). The subsequent GO and KEGG analyses aimed to unveil critical genes and pathways associated with goose muscle development. The analysis of GO revealed enrichment in biological pathways associated with cell development and differentiation, including cell differentiation, cellular developmental processes, animal organ development, and tissue development. The KEGG results also pointed towards the involvement of these genes in related biological signaling pathways, such as the MAPK signaling pathway, the calcium signaling pathway, and focal adhesion (Figure 2C,D). Subsequently, we ranked mRNAs using log2FC and conducted GSEA to elucidate gene functions. The GSEA results validated the earlier enrichment content (Figure 2E–G).

3.3. Construction of circRNA–miRNA Pairs and miRNA–mRNA Pairs

Our objective was to unveil miRNAs interacting with DEcircRNAs. The mature sequences of miRNAs were obtained from miRbase (https://mirbase.org accessed on 16 October 2023). Utilizing RNAhybrid prediction, we identified 692 target miRNAs, establishing a total of 4076 circRNA–miRNA interaction pairs (Table S5). These pairs were determined based on the perfect pairing of the second to eighth nucleotides of DEcircRNAs at the 5′ end of the miRNA sequence. Simultaneously, miRDB was employed to pinpoint miRNAs interacting with DEmRNAs. We acquired 681 miRNAs, resulting in 2602 mRNA–miRNA pairs (Table S6) based on a target score > 90. To enhance prediction accuracy, we intersected the two sets of predicted target miRNAs, yielding 351 FmiRNAs (Figure 3A, Table S7).
Studies have elucidated that miR-204 can impede the proliferation and differentiation of chicken skeletal muscle satellite cells [40]. Moreover, miR-204 orchestrates myogenesis and skeletal muscle regeneration in mice by binding to specific genes [41]. Our prediction highlights that miR-204 forms circRNA–miRNA pairs with circRNA1188, circRNA4337, and ciRNA781 (Figure 3B). Additionally, miR-204 establishes mRNA–miRNA pairs with TM6SF1, BICC1, MGAT3, PIK3IP1, RASA3 and EFNA5. Furthermore, we have identified several pertinent genes, such as myosin light polypeptide 1 (MYL1) and miR-3532-3p, EFNA5 and miR-1575, miR-10585-5p, miR-204, miR-211, and miR-12280-3p, forming mRNA–miRNA pairs (Table S6). These genes have been demonstrated to exert regulatory effects on livestock and poultry myogenesis [42,43,44,45].

3.4. Construction of the circRNA–miRNA–mRNA Interaction Network

Studies have emphasized the capacity of circRNA to act as a ceRNA, functioning as a molecular sponge to competitively bind miRNA and regulate the expression of miRNA target genes [46,47]. Our objective was to identify the ceRNA interaction network linked to myogenesis. By filtering circRNAs and mRNAs regulated by the same FmiRNA expression, we integrated circRNA-miRNA pairs and miRNA-mRNA pairs using Cytoscape software, thereby constructing a preliminary circRNA–miRNA–mRNA interaction network consisting of 749 nodes and 2867 edges (Figure 4, Table S8).

3.5. Establishment of the PPI Network and Identification of Hub Genes

To further identify the interaction network among mRNAs and enhance our understanding of gene–gene interactions, we utilized the STRING database and Cytoscape. The analysis revealed 125 genes with interaction relationships, forming a PPI interaction network of 125 nodes and 192 edges (Figure 5A). Additionally, we utilized the cytoHubba plug-in within Cytoscape software to rank mRNAs based on their Degree. The Degree value indicates the gene’s importance, with higher values indicating greater significance. The top 10 genes, identified as hub genes, are crucial nodes in the regulatory network, including ACTB (actin beta), ACTN1 (actinin alpha 1), BDNF (brain-derived neurotrophic factor), PDGFRA (platelet-derived growth factor receptor alpha), MYL1 (myosin light chain 1), EFNA5 (ephrin-A5), MYSM1 (Myb-like, SWIRM, and MPN domains 1), THBS1 (thrombospondin 1), ITGA8 (integrin subunit alpha 8), and ELN (elastin) (Figure 5B, Table 1).

3.6. Reconstructing the circRNA–miRNA–Hub Gene Interaction Network

Incorporating the 10 hub genes from the initial circRNA–miRNA–mRNA interaction network, we examined associated circRNAs and miRNAs, by constructing a circRNA–miRNA–hub gene interaction network. This network, further categorized as a circRNA–miRNA–mRNA subnetwork, comprised 44 circRNAs, 30 miRNAs, 10 mRNAs, and 156 edges (Figure 6). In total, 126 circRNA–miRNA–mRNA regulatory axes were identified within the subnetwork (Table S9).

4. Discussion

As an endogenous ncRNA, circRNA is produced through the reverse splicing of mRNA precursors (pre-mRNA). It is a recent addition to the family of ncRNAs, following miRNA and lncRNA, and has great research potential. CircRNA possesses features such as high stability, universality, and tissue cell specificity [20]. Research has revealed that circRNA usually contains abundant miRNA binding sites and functions as a ceRNA. It competes for the binding of shared miRNAs through partial complementary sequences, also referred to as MREs, which leads to the degeneration of target genes or the inhibition of translation at the post-transcriptional level, thereby controlling biological processes [48,49,50,51,52]. In recent years, studies have shown that the ceRNA network has a significant impact on myogenesis. For instance, Chen et al. demonstrated that circMYBPC1 effectively binds with miR-23a, reducing its inhibition of myosin heavy chain (MYHC), and promotes myoblast differentiation [53]. Similarly, Shen et al. found that muscle-specific circGPD2 releases the inhibitory effect of miR-203a on c-JUN and MEF2C [54]. The objective of our study was to investigate the existence of a ceRNA network during the process of goose muscle formation. To achieve this, we utilized myoblasts from the Shitou goose as research materials to identify DEcircRNAs and DEmRNAs during the GM and DM periods. We then employed various bioinformatics prediction methods to identify miRNAs that interact with each other, resulting in the formation of a circRNA–miRNA–mRNA interaction network that may play a role in regulating goose myogenesis. However, compared to model animals such as chickens and mice, research on the regulation of circRNA in goose skeletal muscle is still at an early stage. Currently, only a limited number of circRNA and miRNA identifications have been made in geese. The existing circRNA and miRNA databases are predominantly focused on chickens, mice, and other model animals. Consequently, there is currently no database for geese that incorporates tissue-specific expression patterns for circRNAs and miRNAs. This limitation may lead to the omission of certain circRNAs and miRNAs with spatiotemporal expression patterns or those that are unannotated.
Livestock and poultry have various signaling pathways that regulate muscle growth, such as the P53 signaling pathway [55], the Notch signaling pathway [56], the mTOR signaling pathway [57], and the p38 MAPK signaling pathways [58]. In this study, we conducted enrichment analysis on the parental genes of DEcircRNAs and DEmRNAs, revealing enrichment in the Wnt signaling pathway. The Wnt protein functions as a cell signaling molecule, primarily stimulating cell proliferation, differentiation, and migration [59]. Numerous studies have demonstrated the significant role of the Wnt signaling pathway in both development and disease [60,61,62]. Additionally, some researchers have identified a close relationship between the Wnt signaling pathway and myogenesis. For instance, knockout mice of core components of the Wnt signal transduction pathway typically exhibit inadequate muscle development and high lethality [63]. The miR-136-5p/FZD4 axis regulates myogenesis and muscle regeneration through the Wnt signaling pathway [64]. Our results also support this notion, highlighting the crucial role of Wnt signaling in Shitou goose myogenesis.
The formation of skeletal muscle in livestock and poultry is a complex biological process. It involves coding genes, signaling pathways, and ncRNAs [65,66]. Myostatin (MSTN), a member of the transforming growth factor β superfamily, regulates skeletal muscle growth and development through autocrine or paracrine signal transduction [54]. H19 lncRNA increases muscle insulin sensitivity by activating AMPK [67]. In this study, 10 hub genes were extracted using the PPI interaction network. One of these genes, ACTB, encodes β-actin, which is associated with cell shape, migration, proliferation, and gene expression, and can therefore affect organ development [68]. Another related gene for cytoskeletal actin is ACTN1, which is commonly expressed in most tissues and cell types. In muscle cells, actin connects adjacent muscle segments and coordinates muscle contraction [69,70]. MYL1 plays a crucial role in embryonic skeletal muscle development, and its mutation can result in the loss or malnutrition of type II muscle fibers. It is a key gene in ensuring skeletal muscle integrity [42,43]. EFNA5 is a member of the ephrin-A family of protein genes. Previous studies have shown that this gene can directly or indirectly affect the growth of motor neurons adjacent to it [71]. Researchers have found that interactions between motor neurons and muscle stem cells contribute to the establishment of functional muscle during development and regeneration [72]. Additionally, He et al. discovered a negative correlation between the expression level of EFNA5 and the weight of chicken pectoral muscles. EFNA5 has the potential to regulate muscle development [73]. ITGA8 mediates various cellular processes, including cytoskeleton rearrangement and cell adhesion. Studies have shown that this gene helps regulate cell proliferation and the apoptosis of glomerular cells [74]. Here, we found that it is differentially expressed between myoblasts and myotubes and forms mRNA-miRNA pairs with multiple miRNAs, indicating that its regulatory influence on other cells may also be established in myoblasts.
Although we constructed a ceRNA regulatory network that regulates the proliferation and differentiation of goose myoblasts and screened potential key genes and the related circRNA–miRNA–hub gene regulatory axis, there are still some limitations. Firstly, this experiment only used the transcriptome data of the proliferation and differentiation stages of myoblasts of the Shitou goose as the data source. Additional prospective studies, involving multiple goose breeds and a larger sample size, are necessary to validate our findings. Furthermore, while we have implemented rigorous standards in our bioinformatics prediction process to enhance the dependability of our regulatory network, the potential for false positives cannot be completely eliminated. Further experimental studies are required to confirm the potential biological regulatory mechanisms of these ceRNAs in myogenesis.

5. Conclusions

In this study, we leveraged transcriptome data and employed bioinformatics analysis methods to assemble the expression profiles of circRNAs and mRNAs in the proliferation and differentiation of myoblasts in Shitou goose. We identified 96 DEcircRNAs and 880 DEmRNAs during the two periods. The functional annotation results indicate that the parental genes of DEcircRNAs and DEmRNAs were enriched in the Wnt signaling pathway, suggesting a crucial role of the Wnt signaling pathway in regulating the proliferation and differentiation of adult myoblasts. Additionally, we constructed a circRNA–miRNA–mRNA ceRNA regulatory network that could potentially participate in myogenesis. We identified 10 highly related hub genes (ACTB, ACTN1, BDNF, PDGFRA, MYL1, EFNA5, MYSM1, THBS1, ITGA8, and ELN) involved in goose muscle growth and development, along with their related circRNA–miRNA–hub gene regulatory axis. In conclusion, our analysis of transcriptome data from the myoblasts and myotubes of the Shitou goose enabled us to construct a comprehensive ceRNA regulatory network. This elucidated how circRNA regulates the mechanism underlying myoblast proliferation and differentiation. These findings establish a robust foundation for comprehending the mechanism and regulatory network of goose muscle formation, contributing valuable insights to further research and development in this field.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani14040576/s1. Table S1: The levels of expression for all circRNAs identified in GM and DM; Table S2: DEcircRNAs identified in GM and DM; Table S3: The levels of expression for all mRNAs identified in GM and DM; Table S4: DEmRNAs identified in GM and DM; Table S5: CircRNA-miRNA pairs predicted using RNAhybrid; Table S6: MRNA–miRNA pairs predicted using miRDB; Table S7: List of all FmiRNAs; Table S8: Node information for the circRNA–miRNA–mRNA regulatory network; Table S9: Regulatory axis of the circRNA–miRNA–hub gene network.

Author Contributions

Conceptualization, W.L. and X.Z.; methodology, R.H. and W.L.; software, R.H. and X.D.; resources, J.C.; data curation, J.C.; writing—original draft preparation, R.H.; writing—review and editing, W.L. and X.Z.; visualization, R.H., J.C. and X.D.; supervision, X.Z. and W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by Key-Area Research and Development Program of Guangdong Province (grant No. 2020B020222003), the Natural Scientific Foundation of China (grant No. 32272861), the Guangdong Special Branch Plans of Young Talent with Scientific and Technological Innovation (grant No. 2019TQ05N470), and the Natural Science Foundation of Guangdong Province, China (grant No. 2019A1515010923).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by South China Agricultural University Institutional Animal Care and Use Committee (approval number: SCAU#2020C030, approval date: 11 August 2020).

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets supporting the conclusions of this article are available in GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE213147, accessed on 15 September 2022.

Acknowledgments

Thanks to all participants for their advice and support during the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wereńska, M. Comparative study on the effects of sous-vide, microwave cooking, and stewing on functional properties and sensory quality of goose meat. Poult. Sci. 2023, 102, 103064. [Google Scholar] [CrossRef] [PubMed]
  2. Zhang, Z.; Shi, W.; Wang, Y.; Meng, X.; Dabbour, M.; Kumah Mintah, B.; Chen, X.; Chen, X.; He, R.; Ma, H. Mono-frequency ultrasonic-assisted thawing of frozen goose meat: Influence on thawing efficiency, product quality and microstructure. Ultrason. Sonochem. 2023, 98, 106489. [Google Scholar] [CrossRef]
  3. Hong, L.; Sun, Z.; Xu, D.; Li, W.; Cao, N.; Fu, X.; Huang, Y.; Tian, Y.; Li, B. Transcriptome and lipidome integration unveils mechanisms of fatty liver formation in Shitou geese. Poult. Sci. 2023, 103, 103280. [Google Scholar] [CrossRef] [PubMed]
  4. Tang, J.; Ouyang, H.; Chen, X.; Jiang, D.; Tian, Y.; Huang, Y.; Shen, X. Comparative Transcriptome Analyses of Leg Muscle during Early Growth between Geese (Anser cygnoides) Breeds Differing in Body Size Characteristics. Genes 2023, 14, 1048. [Google Scholar] [CrossRef] [PubMed]
  5. Wang, S.; Jin, J.; Xu, Z.; Zuo, B. Functions and Regulatory Mechanisms of lncRNAs in Skeletal Myogenesis, Muscle Disease and Meat Production. Cells 2019, 8, 1107. [Google Scholar] [CrossRef]
  6. Shen, L.; Liao, T.; Chen, Q.; Lei, Y.; Wang, L.; Gu, H.; Qiu, Y.; Zheng, T.; Yang, Y.; Wei, C.; et al. tRNA-derived small RNA, 5’tiRNA-Gly-CCC, promotes skeletal muscle regeneration through the inflammatory response. J. Cachexia Sarcopenia Muscle 2023, 14, 1033–1045. [Google Scholar] [CrossRef]
  7. Smith, J.A.B.; Murach, K.A.; Dyar, K.A.; Zierath, J.R. Exercise metabolism and adaptation in skeletal muscle. Nat. Rev. Mol. Cell Biol. 2023, 24, 607–632. [Google Scholar] [CrossRef]
  8. Sweeney, H.L.; Hammers, D.W. Muscle Contraction. Cold Spring Harb. Perspect. Biol. 2018, 10, a023200. [Google Scholar] [CrossRef]
  9. Huo, W.; Weng, K.; Li, Y.; Zhang, Y.; Zhang, Y.; Xu, Q.; Chen, G. Comparison of muscle fiber characteristics and glycolytic potential between slow- and fast-growing broilers. Poult. Sci. 2022, 101, 101649. [Google Scholar] [CrossRef]
  10. Yin, H.; Zhang, S.; Gilbert, E.R.; Siegel, P.B.; Zhu, Q.; Wong, E.A. Expression profiles of muscle genes in postnatal skeletal muscle in lines of chickens divergently selected for high and low body weight. Poult. Sci. 2014, 93, 147–154. [Google Scholar] [CrossRef]
  11. Wang, H.; He, K.; Zeng, X.; Zhou, X.; Yan, F.; Yang, S.; Zhao, A. Isolation and identification of goose skeletal muscle satellite cells and preliminary study on the function of C1q and tumor necrosis factor-related protein 3 gene. Anim. Biosci. 2021, 34, 1078–1087. [Google Scholar] [CrossRef] [PubMed]
  12. Fu, Y.; Shang, P.; Zhang, B.; Tian, X.; Nie, R.; Zhang, R.; Zhang, H. Function of the Porcine TRPC1 Gene in Myogenesis and Muscle Growth. Cells 2021, 10, 147. [Google Scholar] [CrossRef] [PubMed]
  13. Zhang, X.; Li, Y.; Zhu, C.; Li, F.; Liu, Z.; Li, X.; Shen, X.; Wu, Z.; Fu, M.; Xu, D.; et al. DNA Demethylation of Myogenic Genes May Contribute to Embryonic Leg Muscle Development Differences between Wuzong and Shitou Geese. Int. J. Mol. Sci. 2023, 24, 7188. [Google Scholar] [CrossRef]
  14. Chen, G.; Qi, L.; Zhang, S.; Peng, H.; Lin, Z.; Zhang, X.; Nie, Q.; Luo, W. Metabolomic, lipidomic, and proteomic profiles provide insights on meat quality differences between Shitou and Wuzong geese. Food Chem. 2024, 438, 137967. [Google Scholar] [CrossRef] [PubMed]
  15. Dey, B.K.; Pfeifer, K.; Dutta, A. The H19 long noncoding RNA gives rise to microRNAs miR-675-3p and miR-675-5p to promote skeletal muscle differentiation and regeneration. Genes Dev. 2014, 28, 491–501. [Google Scholar] [CrossRef] [PubMed]
  16. Lv, W.; Jiang, W.; Luo, H.; Tong, Q.; Niu, X.; Liu, X.; Miao, Y.; Wang, J.; Guo, Y.; Li, J.; et al. Long noncoding RNA lncMREF promotes myogenic differentiation and muscle regeneration by interacting with the Smarca5/p300 complex. Nucleic Acids Res. 2022, 50, 10733–10755. [Google Scholar] [CrossRef] [PubMed]
  17. Slack, F.J.; Chinnaiyan, A.M. The Role of Non-coding RNAs in Oncology. Cell 2019, 179, 1033–1055. [Google Scholar] [CrossRef] [PubMed]
  18. Zhang, Y.; Zhang, J.; Xu, Z.; Zhang, D.; Xia, P.; Ling, J.; Tang, X.; Liu, X.; Xuan, R.; Zhang, M.; et al. Regulation of NcRNA-protein binding in diabetic foot. Biomed. Pharmacother. 2023, 160, 114361. [Google Scholar] [CrossRef]
  19. Beermann, J.; Piccoli, M.T.; Viereck, J.; Thum, T. Non-coding RNAs in Development and Disease: Background, Mechanisms, and Therapeutic Approaches. Physiol. Rev. 2016, 96, 1297–1325. [Google Scholar] [CrossRef]
  20. Kristensen, L.S.; Andersen, M.S.; Stagsted, L.V.W.; Ebbesen, K.K.; Hansen, T.B.; Kjems, J. The biogenesis, biology and characterization of circular RNAs. Nat. Rev. Genet. 2019, 20, 675–691. [Google Scholar] [CrossRef]
  21. Liu, C.-X.; Chen, L.-L. Circular RNAs: Characterization, cellular roles, and applications. Cell 2022, 185, 2016–2034. [Google Scholar] [CrossRef]
  22. Qu, S.; Yang, X.; Li, X.; Wang, J.; Gao, Y.; Shang, R.; Sun, W.; Dou, K.; Li, H. Circular RNA: A new star of noncoding RNAs. Cancer Lett. 2015, 365, 141–148. [Google Scholar] [CrossRef] [PubMed]
  23. Salmena, L.; Poliseno, L.; Tay, Y.; Kats, L.; Pandolfi, P.P. A ceRNA hypothesis: The Rosetta Stone of a hidden RNA language? Cell 2011, 146, 353–358. [Google Scholar] [CrossRef] [PubMed]
  24. Tay, Y.; Rinn, J.; Pandolfi, P.P. The multilayered complexity of ceRNA crosstalk and competition. Nature 2014, 505, 344–352. [Google Scholar] [CrossRef] [PubMed]
  25. Hansen, T.B.; Jensen, T.I.; Clausen, B.H.; Bramsen, J.B.; Finsen, B.; Damgaard, C.K.; Kjems, J. Natural RNA circles function as efficient microRNA sponges. Nature 2013, 495, 384–388. [Google Scholar] [CrossRef] [PubMed]
  26. Thomson, D.; Dinger, M. Endogenous microRNA sponges: Evidence and controversy. Nat. Rev. Genet. 2016, 17, 272–283. [Google Scholar] [CrossRef] [PubMed]
  27. Li, M.; Zhang, N.; Zhang, W.; Hei, W.; Cai, C.; Yang, Y.; Lu, C.; Gao, P.; Guo, X.; Cao, G.; et al. Comprehensive analysis of differentially expressed circRNAs and ceRNA regulatory network in porcine skeletal muscle. BMC Genom. 2021, 22, 320. [Google Scholar] [CrossRef]
  28. Yu, Z.; Xu, X.; Ai, N.; Wang, K.; Zhang, P.; Li, X.; LiuFu, S.; Liu, X.; Jiang, J.; Gu, J.; et al. Integrated analysis of circRNA, lncRNA, miRNA and mRNA to reveal the ceRNA regulatory network of postnatal skeletal muscle development in Ningxiang pig. Front. Cell Dev. Biol. 2023, 11, 1185823. [Google Scholar] [CrossRef]
  29. Lei, Q.; Hu, X.; Han, H.; Wang, J.; Liu, W.; Zhou, Y.; Cao, D.; Li, F.; Liu, J. Integrative analysis of circRNA, miRNA, and mRNA profiles to reveal ceRNA regulation in chicken muscle development from the embryonic to post-hatching periods. BMC Genom. 2022, 23, 342. [Google Scholar] [CrossRef]
  30. Dong, X.; Xing, J.; Liu, Q.; Ye, M.; Zhou, Z.; Li, Y.; Huang, R.; Li, Z.; Nie, Q. CircPLXNA2 Affects the Proliferation and Apoptosis of Myoblast through circPLXNA2/gga-miR-12207-5P/MDM4 Axis. Int. J. Mol. Sci. 2023, 24, 5459. [Google Scholar] [CrossRef]
  31. Wang, J.; Ren, Q.; Hua, L.; Chen, J.; Zhang, J.; Bai, H.; Li, H.; Xu, B.; Shi, Z.; Cao, H.; et al. Comprehensive Analysis of Differentially Expressed mRNA, lncRNA and circRNA and Their ceRNA Networks in the Longissimus Dorsi Muscle of Two Different Pig Breeds. Int. J. Mol. Sci. 2019, 20, 1107. [Google Scholar] [CrossRef]
  32. Tang, R.; Zhu, L.; Zhu, P.; Yin, R.; Zheng, C. The Effect of Blood Clots on the Quality of RNA Extracted from PAXgene Blood RNA Tubes. Biopreserv. Biobank. 2023, ahead of print. [Google Scholar] [CrossRef]
  33. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 2011, 17, 10–12. [Google Scholar] [CrossRef]
  34. Langmead, B.; Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 2012, 9, 357–359. [Google Scholar] [CrossRef]
  35. Kim, D.; Langmead, B.; Salzberg, S.L. HISAT: A fast spliced aligner with low memory requirements. Nat. Methods 2015, 12, 357–460. [Google Scholar] [CrossRef]
  36. Kim, D.; Salzberg, S.L. TopHat-Fusion: An algorithm for discovery of novel fusion transcripts. Genome Biol. 2011, 12, R72. [Google Scholar] [CrossRef]
  37. Zhang, X.O.; Dong, R.; Zhang, Y.; Zhang, J.L.; Luo, Z.; Zhang, J.; Chen, L.L.; Yang, L. Diverse alternative back-splicing and alternative splicing landscape of circular RNAs. Genome Res. 2016, 26, 1277–1287. [Google Scholar] [CrossRef] [PubMed]
  38. Zhang, X.O.; Wang, H.B.; Zhang, Y.; Lu, X.; Chen, L.L.; Yang, L. Complementary sequence-mediated exon circularization. Cell 2014, 159, 134–147. [Google Scholar] [CrossRef] [PubMed]
  39. Gao, Y.; Wang, J.; Zhao, F. CIRI: An efficient and unbiased algorithm for de novo circular RNA identification. Genome Biol. 2015, 16, 4. [Google Scholar] [CrossRef]
  40. Wei, Y.; Tian, Y.; Li, X.; Amevor, F.K.; Shen, X.; Zhao, J.; Zhao, X.; Zhang, X.; Huang, W.; Hu, J.; et al. Circular RNA circFNDC3AL Upregulates BCL9 Expression to Promote Chicken Skeletal Muscle Satellite Cells Proliferation and Differentiation by Binding to miR-204. Front. Cell Dev. Biol. 2021, 9, 736749. [Google Scholar] [CrossRef] [PubMed]
  41. Cheng, X.; Du, J.; Shen, L.; Tan, Z.; Jiang, D.; Jiang, A.; Li, Q.; Tang, G.; Jiang, Y.; Wang, J.; et al. MiR-204-5p regulates C2C12 myoblast differentiation by targeting MEF2C and ERRγ. Biomed. Pharmacother. 2018, 101, 528–535. [Google Scholar] [CrossRef] [PubMed]
  42. Ravenscroft, G.; Zaharieva, I.T.; Bortolotti, C.A.; Lambrughi, M.; Pignataro, M.; Borsari, M.; Sewry, C.A.; Phadke, R.; Haliloglu, G.; Ong, R.; et al. Bi-allelic mutations in MYL1 cause a severe congenital myopathy. Hum. Mol. Genet. 2018, 27, 4263–4272. [Google Scholar] [CrossRef] [PubMed]
  43. Ling, F.; Fang, W.; Chen, Y.; Li, J.; Liu, X.; Wang, L.; Zhang, H.; Chen, S.; Mei, Y.; Du, H.; et al. Identification of novel transcripts from the porcine MYL1 gene and initial characterization of its promoters. Mol. Cell. Biochem. 2010, 343, 239–247. [Google Scholar] [CrossRef] [PubMed]
  44. Alonso-Martin, S.; Rochat, A.; Mademtzoglou, D.; Morais, J.; de Reyniès, A.; Auradé, F.; Chang, T.H.; Zammit, P.S.; Relaix, F. Gene Expression Profiling of Muscle Stem Cells Identifies Novel Regulators of Postnatal Myogenesis. Front. Cell Dev. Biol. 2016, 4, 58. [Google Scholar] [CrossRef] [PubMed]
  45. Swartz, M.E.; Eberhart, J.; Pasquale, E.B.; Krull, C.E. EphA4/ephrin-A5 interactions in muscle precursor cell migration in the avian forelimb. Development 2001, 128, 4669–4680. [Google Scholar] [CrossRef] [PubMed]
  46. Meng, L.; Zhang, Y.; Wu, P.; Li, D.; Lu, Y.; Shen, P.; Yang, T.; Shi, G.; Chen, Q.; Yuan, H.; et al. CircSTX6 promotes pancreatic ductal adenocarcinoma progression by sponging miR-449b-5p and interacting with CUL2. Mol. Cancer 2022, 21, 121. [Google Scholar] [CrossRef]
  47. Memczak, S.; Jens, M.; Elefsinioti, A.; Torti, F.; Krueger, J.; Rybak, A.; Maier, L.; Mackowiak, S.D.; Gregersen, L.H.; Munschauer, M.; et al. Circular RNAs are a large class of animal RNAs with regulatory potency. Nature 2013, 495, 333–338. [Google Scholar] [CrossRef]
  48. Zhao, C.; Qian, S.; Tai, Y.; Guo, Y.; Tang, C.; Huang, Z.; Gao, J. Proangiogenic role of circRNA-007371 in liver fibrosis. Cell Prolif. 2023, 56, e13432. [Google Scholar] [CrossRef]
  49. Wang, K.; Long, B.; Liu, F.; Wang, J.X.; Liu, C.Y.; Zhao, B.; Zhou, L.Y.; Sun, T.; Wang, M.; Yu, T.; et al. A circular RNA protects the heart from pathological hypertrophy and heart failure by targeting miR-223. Eur. Heart J. 2016, 37, 2602–2611. [Google Scholar] [CrossRef]
  50. Yao, Z.; Xu, R.; Yuan, L.; Xu, M.; Zhuang, H.; Li, Y.; Zhang, Y.; Lin, N. Circ_0001955 facilitates hepatocellular carcinoma (HCC) tumorigenesis by sponging miR-516a-5p to release TRAF6 and MAPK11. Cell Death Dis. 2019, 10, 945. [Google Scholar] [CrossRef]
  51. Chen, Q.; Wang, H.; Li, Z.; Li, F.; Liang, L.; Zou, Y.; Shen, H.; Li, J.; Xia, Y.; Cheng, Z.; et al. Circular RNA ACTN4 promotes intrahepatic cholangiocarcinoma progression by recruiting YBX1 to initiate FZD7 transcription. J. Hepatol. 2022, 76, 135–147. [Google Scholar] [CrossRef] [PubMed]
  52. Yue, B.; Wang, J.; Song, C.; Wu, J.; Cao, X.; Huang, Y.; Lan, X.; Lei, C.; Huang, B.; Chen, H. Biogenesis and ceRNA role of circular RNAs in skeletal muscle myogenesis. Int. J. Biochem. Cell Biol. 2019, 117, 105621. [Google Scholar] [CrossRef] [PubMed]
  53. Shen, X.; Cui, C.; Tang, S.; Han, S.; Zhang, Y.; Xia, L.; Tan, B.; Ma, M.; Kang, H.; Yu, J.; et al. MyoG-enhanced circGPD2 regulates chicken skeletal muscle development by targeting miR-203a. Int. J. Biol. Macromol. 2022, 222 Pt B, 2212–2224. [Google Scholar] [CrossRef]
  54. Chen, M.M.; Zhao, Y.P.; Zhao, Y.; Deng, S.L.; Yu, K. Regulation of Myostatin on the Growth and Development of Skeletal Muscle. Front. Cell Dev. Biol. 2021, 9, 785712. [Google Scholar] [CrossRef]
  55. Park, J.W.; Lee, J.H.; Han, J.S.; Shin, S.P.; Park, T.S. Muscle differentiation induced by p53 signaling pathway-related genes in myostatin-knockout quail myoblasts. Mol. Biol. Rep. 2020, 47, 9531–9540. [Google Scholar] [CrossRef] [PubMed]
  56. Vargas-Franco, D.; Kalra, R.; Draper, I.; Pacak, C.A.; Asakura, A.; Kang, P.B. The Notch signaling pathway in skeletal muscle health and disease. Muscle Nerve 2022, 66, 530–544. [Google Scholar] [CrossRef]
  57. Ilha, J.; do Espírito-Santo, C.C.; de Freitas, G.R. mTOR Signaling Pathway and Protein Synthesis: From Training to Aging and Muscle Autophagy. Adv. Exp. Med. Biol. 2018, 1088, 139–151. [Google Scholar] [CrossRef]
  58. Keren, A.; Tamir, Y.; Bengal, E. The p38 MAPK signaling pathway: A major regulator of skeletal muscle development. Mol. Cell. Endocrinol. 2006, 252, 224–230. [Google Scholar] [CrossRef]
  59. Tao, H.; Yang, J.J.; Shi, K.H.; Li, J. Wnt signaling pathway in cardiac fibrosis: New insights and directions. Metabolism 2016, 65, 30–40. [Google Scholar] [CrossRef]
  60. Rudnicki, M.A.; Williams, B.O. Wnt signaling in bone and muscle. Bone 2015, 80, 60–66. [Google Scholar] [CrossRef]
  61. Zhou, Y.; Xu, J.; Luo, H.; Meng, X.; Chen, M.; Zhu, D. Wnt signaling pathway in cancer immunotherapy. Cancer Lett. 2022, 525, 84–96. [Google Scholar] [CrossRef] [PubMed]
  62. Gao, Y.; Chen, N.; Fu, Z.; Zhang, Q. Progress of Wnt Signaling Pathway in Osteoporosis. Biomolecules 2023, 13, 483. [Google Scholar] [CrossRef] [PubMed]
  63. van Amerongen, R.; Berns, A. Knockout mouse models to study Wnt signal transduction. Trends Genet. 2006, 22, 678–689. [Google Scholar] [CrossRef] [PubMed]
  64. Zhang, D.; Yin, L.; Lin, Z.; Yu, C.; Li, J.; Ren, P.; Yang, C.; Qiu, M.; Liu, Y. miR-136-5p/FZD4 axis is critical for Wnt signaling-mediated myogenesis and skeletal muscle regeneration. J. Cell. Physiol. 2023, ahead of print. [Google Scholar] [CrossRef]
  65. Chen, M.; Wei, X.; Song, M.; Jiang, R.; Huang, K.; Deng, Y.; Liu, Q.; Shi, D.; Li, H. Circular RNA circMYBPC1 promotes skeletal muscle differentiation by targeting MyHC. Mol. Ther. Nucleic Acids 2021, 24, 352–368. [Google Scholar] [CrossRef] [PubMed]
  66. Chen, B.; Yu, J.; Guo, L.; Byers, M.S.; Wang, Z.; Chen, X.; Xu, H.; Nie, Q. Circular RNA circHIPK3 Promotes the Proliferation and Differentiation of Chicken Myoblast Cells by Sponging miR-30a-3p. Cells 2019, 8, 177. [Google Scholar] [CrossRef] [PubMed]
  67. Geng, T.; Liu, Y.; Xu, Y.; Jiang, Y.; Zhang, N.; Wang, Z.; Carmichael, G.G.; Taylor, H.S.; Li, D.; Huang, Y. H19 lncRNA Promotes Skeletal Muscle Insulin Sensitivity in Part by Targeting AMPK. Diabetes 2018, 67, 2183–2198. [Google Scholar] [CrossRef]
  68. Cuvertino, S.; Stuart, H.M.; Chandler, K.E.; Roberts, N.A.; Armstrong, R.; Bernardini, L.; Bhaskar, S.; Callewaert, B.; Clayton-Smith, J.; Davalillo, C.H.; et al. ACTB Loss-of-Function Mutations Result in a Pleiotropic Developmental Disorder. Am. J. Hum. Genet. 2017, 101, 1021–1033. [Google Scholar] [CrossRef]
  69. Yang, X.; Pang, Y.; Zhang, J.; Shi, J.; Zhang, X.; Zhang, G.; Yang, S.; Wang, J.; Hu, K.; Wang, J.; et al. High Expression Levels of ACTN1 and ACTN3 Indicate Unfavorable Prognosis in Acute Myeloid Leukemia. J. Cancer 2019, 10, 4286–4292. [Google Scholar] [CrossRef]
  70. Matsumoto, M.; Tsuru, H.; Suginobe, H.; Narita, J.; Ishii, R.; Hirose, M.; Hashimoto, K.; Wang, R.; Yoshihara, C.; Ueyama, A.; et al. Atomic force microscopy identifies the alteration of rheological properties of the cardiac fibroblasts in idiopathic restrictive cardiomyopathy. PLoS ONE 2022, 17, e0275296. [Google Scholar] [CrossRef]
  71. Muñoz, L.M.; Zayachkivsky, A.; Kunz, R.B.; Hunt, J.M.; Wang, G.; Scott, S.A. Ephrin-A5 inhibits growth of embryonic sensory neurons. Dev. Biol. 2005, 283, 397–408. [Google Scholar] [CrossRef]
  72. Zmojdzian, M.; Jagla, K. The relationship between muscle stem cells and motor neurons. Cell. Mol. Life Sci. 2021, 78, 5043–5049. [Google Scholar] [CrossRef] [PubMed]
  73. He, Y.; Shi, H.; Li, Z.; Kang, J.; Li, M.; Liu, M.; Liu, Y.; Zhao, J.; Dou, T.; Jia, J.; et al. Identification of New Genes and Genetic Variant Loci Associated with Breast Muscle Development in the Mini-Cobb F2 Chicken Population Using a Genome-Wide Association Study. Genes 2022, 13, 2153. [Google Scholar] [CrossRef] [PubMed]
  74. Marek, I.; Lichtneger, T.; Cordasic, N.; Hilgers, K.F.; Volkert, G.; Fahlbusch, F.; Rascher, W.; Hartner, A.; Menendez-Castro, C. Alpha8 Integrin (Itga8) Signalling Attenuates Chronic Renal Interstitial Fibrosis by Reducing Fibroblast Activation, Not by Interfering with Regulation of Cell Turnover. PLoS ONE 2016, 11, e0150471. [Google Scholar] [CrossRef] [PubMed]
Animals 14 00576 g001
Figure 1. Analysis of circRNAs in myoblasts and myotubes of the Shitou goose. (A) Transcript expression levels for each sample showcase consistently high sequencing quality, confirmed by a box plot. (B) CircRNAs were identified and classified based on their gene source, revealing their distribution across exons, introns, and intergenic regions. (C,D) Volcano maps (C) and heat maps (D) visually represent the DEcircRNAs in myoblasts and myotubes. Upregulated circRNAs are marked in red, while downregulated ones are shown in blue. (E,F) GO enrichment (E) and KEGG pathway (F) analysis of DEcircRNAs parental genes in myoblasts and myotubes.
Figure 1. Analysis of circRNAs in myoblasts and myotubes of the Shitou goose. (A) Transcript expression levels for each sample showcase consistently high sequencing quality, confirmed by a box plot. (B) CircRNAs were identified and classified based on their gene source, revealing their distribution across exons, introns, and intergenic regions. (C,D) Volcano maps (C) and heat maps (D) visually represent the DEcircRNAs in myoblasts and myotubes. Upregulated circRNAs are marked in red, while downregulated ones are shown in blue. (E,F) GO enrichment (E) and KEGG pathway (F) analysis of DEcircRNAs parental genes in myoblasts and myotubes.
Animals 14 00576 g001
Animals 14 00576 g002
Figure 2. Analysis of mRNAs between myoblasts and myotubes in Shitou goose. (A,B) Volcano plots (A) and heat maps (B) visually representing the DEmRNAs in myoblasts and myotubes. The upregulated mRNAs are depicted in yellow, while the downregulated ones are depicted in blue. (C,D) GO enrichment analysis (C) and KEGG pathway enrichment analysis (D) of DEmRNAs in myoblasts and myotubes. (E,F) GSEA analysis revealed significantly enriched GO terms for upregulated (E) and downregulated (F) mRNAs. (G) The GSEA analysis highlighted significantly enriched upregulated KEGG pathways.
Figure 2. Analysis of mRNAs between myoblasts and myotubes in Shitou goose. (A,B) Volcano plots (A) and heat maps (B) visually representing the DEmRNAs in myoblasts and myotubes. The upregulated mRNAs are depicted in yellow, while the downregulated ones are depicted in blue. (C,D) GO enrichment analysis (C) and KEGG pathway enrichment analysis (D) of DEmRNAs in myoblasts and myotubes. (E,F) GSEA analysis revealed significantly enriched GO terms for upregulated (E) and downregulated (F) mRNAs. (G) The GSEA analysis highlighted significantly enriched upregulated KEGG pathways.
Animals 14 00576 g002
Animals 14 00576 g003
Figure 3. Construction of circRNA–miRNA pairs and mRNA–miRNA pairs. (A) Venn diagram illustrating target miRNAs. The blue circle denotes the miRNAs predicted by circRNA, the red circle represents miRNAs predicted by mRNA, and the overlap signifies FmiRNAs. (B) Schematic depiction of binding sites between miR-204 and circRNA1188, circRNA4337, and ciRNA781. Green lines signify miRNA, and red lines signify circRNA.
Figure 3. Construction of circRNA–miRNA pairs and mRNA–miRNA pairs. (A) Venn diagram illustrating target miRNAs. The blue circle denotes the miRNAs predicted by circRNA, the red circle represents miRNAs predicted by mRNA, and the overlap signifies FmiRNAs. (B) Schematic depiction of binding sites between miR-204 and circRNA1188, circRNA4337, and ciRNA781. Green lines signify miRNA, and red lines signify circRNA.
Animals 14 00576 g003
Animals 14 00576 g004
Figure 4. circRNA–miRNA–mRNA interaction network diagram. The blue diamond represents circRNAs, the green triangle represents miRNAs, and the yellow oval represents mRNAs.
Figure 4. circRNA–miRNA–mRNA interaction network diagram. The blue diamond represents circRNAs, the green triangle represents miRNAs, and the yellow oval represents mRNAs.
Animals 14 00576 g004
Animals 14 00576 g005
Figure 5. PPI interaction network. (A) PPI interaction network diagram of mRNAs. (B) Hub genes interaction network diagram. The size and color of the circles correspond to the degree score.
Figure 5. PPI interaction network. (A) PPI interaction network diagram of mRNAs. (B) Hub genes interaction network diagram. The size and color of the circles correspond to the degree score.
Animals 14 00576 g005
Animals 14 00576 g006
Figure 6. The circRNA–miRNA–hub gene interaction network diagram. The blue diamond represents circRNAs, the red circle represents miRNAs, and the green triangle represents mRNAs.
Figure 6. The circRNA–miRNA–hub gene interaction network diagram. The blue diamond represents circRNAs, the red circle represents miRNAs, and the green triangle represents mRNAs.
Animals 14 00576 g006
Table 1. Ten hub genes obtained by degree.
Table 1. Ten hub genes obtained by degree.
NameScoreGene Description
ACTB6actin beta
ACTN13actinin alpha 1
BDNF3brain-derived neurotrophic factor
PDGFRA3platelet-derived growth factor receptor alpha
MYL12myosin light chain 1
EFNA52ephrin-A5
MYSM12Myb-like, SWIRM, and MPN domains 1
THBS12thrombospondin 1
ITGA82integrin subunit alpha 8
ELN1elastin
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Huang, R.; Chen, J.; Dong, X.; Zhang, X.; Luo, W. Transcriptome Data Revealed the circRNA–miRNA–mRNA Regulatory Network during the Proliferation and Differentiation of Myoblasts in Shitou Goose. Animals 2024, 14, 576. https://doi.org/10.3390/ani14040576

AMA Style

Huang R, Chen J, Dong X, Zhang X, Luo W. Transcriptome Data Revealed the circRNA–miRNA–mRNA Regulatory Network during the Proliferation and Differentiation of Myoblasts in Shitou Goose. Animals. 2024; 14(4):576. https://doi.org/10.3390/ani14040576

Chicago/Turabian Style

Huang, Rongqin, Jiahui Chen, Xu Dong, Xiquan Zhang, and Wen Luo. 2024. "Transcriptome Data Revealed the circRNA–miRNA–mRNA Regulatory Network during the Proliferation and Differentiation of Myoblasts in Shitou Goose" Animals 14, no. 4: 576. https://doi.org/10.3390/ani14040576

APA Style

Huang, R., Chen, J., Dong, X., Zhang, X., & Luo, W. (2024). Transcriptome Data Revealed the circRNA–miRNA–mRNA Regulatory Network during the Proliferation and Differentiation of Myoblasts in Shitou Goose. Animals, 14(4), 576. https://doi.org/10.3390/ani14040576

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop