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. 2023 Sep 13;102(12):103105. doi: 10.1016/j.psj.2023.103105

Exploring the role of miRNAs in early chicken embryonic development and their significance

Liqin Liao *,†,‡,§, Ziqi Yao *,†, Jie Kong *,†,‡, Xinheng Zhang *,†,‡,§, Hongxin Li *,†,§, Weiguo Chen *,‡, Qingmei Xie *,†,‡,§,1
PMCID: PMC10587638  PMID: 37852050

Abstract

In the early stages of embryonic development, a precise and strictly controlled hierarchy of gene expression is essential to ensure proper development of all cell types and organs. To better understand this gene control process, we constructed a small RNA library from 1- to 5-day-old chick embryos, and identified 2,459 miRNAs including 827 existing, 695 known, and 937 novel miRNAs with bioinformatic analysis. There was absolute high expression of a number of miRNAs in each stage, including gga-miR-363-3p (Em1d), gga-miR-26a-5p (Em2d and Em3d), gga-miR-10a-5p (Em4d), and gga-miR-199-5p (Em5d). We evaluated enriched miRNA profiles, identifying VEGF, Insulin, ErbB, MAPK, Hedgehog, TLR and Hippo signaling pathways as primary regulatory mechanisms enabling complex morphogenetic transformations within tight temporal constraints. Pathway analysis revealed miRNAs as pivotal nodes of interaction, coordinating cascades of gene expression critical for cell fate determination, proliferation, migration, and differentiation across germ layers and developing organ systems. Weighted Gene Co-Expression Network Analysis (WGCNA) generated hub miRNAs whose modular connections spanned regulatory networks, including: gga-miR-181a-3p (blue module), coordinating immunegenesis and myogenesis; gga-miR-126-3p (brown module), regulating vasculogenesis and angiogenesis; gga-miR-302c-5p (turquoise module), enabling pluripotency and self-renew; and gga-miR-429-3p (yellow module), modulating neurogenesis and osteogenesis. The findings of this study extend the knowledge of miRNA expression in early embryonic development of chickens, providing insights into the intricate gene control process that helps ensure proper development.

Key words: chicken, miRNA, embryo, development, small RNA-seq

INTRODUCTION

The chicken embryo serves as an ideal model system that bridges the gap between mammals and vertebrates and provides valuable insights into developmental biology, virology, immunology, genetics, and cell biology (Wang and White, 2021). Embryonic development is a complex process that involves the coordinated expression of multiple genes, which regulate cell fate decisions and tissue patterning (Shahbazi, 2020). These patterns of gene expression are established during cell development and are maintained even during mitotic cell division, through epigenetic mechanisms that control gene expression (Bednarczyk et al., 2021). The epigenetic processes include DNA methylation or hydroxy-methylation of CG dinucleotides, chemical modifications of histones, interaction of DNA with small RNAs, and different states of chromatin condensation (Guerrero-Bosagna et al., 2018). In this study, we focus on small RNAs, specifically, miRNAs that regulating development of early embryos. A key feature of miRNA activity is the modulation of gene expression throughout various developmental stages.

MicroRNAs (miRNAs) are short noncoding RNA species that have a role in regulation of wide range of biological processes (Laurent, 2008). After transportation to the cytoplasm, single-stranded miRNAs bind to their complementary sequences in mRNA and inhibit translation (Pillai, 2005; Valencia-Sanchez et al., 2006). However, this binding is nonhomologous and enables individual miRNAs to regulate hundreds of target genes (Taganov et al., 2007). Majority of known miRNAs are expressed during embryogenesis, in a wide variety of temporally and spatially distinct patterns that implicate miRNAs in important developmental processes (Darnell et al., 2006). For examples, gga-miR-30a-5p played a regulatory role in the development of chicken follicles by targeting Beclin1to prevent granulosa cell apoptosis (He et al., 2022); gga-miR-126-5p is involved in the negative regulation of chicken innate immunity, contributing to maintain immune balance (Wang et al., 2022); gga-miR-499-5p promoting slow-twitch muscle fiber formation by repressing SOX6 and gga-miRNA-24-3p promoting differentiation of primary myoblasts indicates that such miRNA play critical roles in skeletal development (Liu et al., 2022; Wu et al., 2022); by directly binding to TGF, the gga-miR-301a-5p regulates chicken germline cell differentiation and spermatogenesis (Guo et al., 2021).

Furthermore, emerging evidences suggest that miRNAs regulate critical pathways involved in stem cell function (Mens and Ghanbari, 2018) and have also been identified as important regulators of pluripotency. Since miRNAs can repress the translation of many mRNA targets, they are good candidates to regulate cell fates (Mathieu and Ruohola-Baker, 2013). Some miRNAs such as miR-290-295, miR-302/367, and miR-92 are able to activate the promoters of stem cell core regulators (Marson et al., 2008) or interact with epigenetic regulatory mechanisms to stabilize pluripotency or to switch to a differentiated state (Lakshmipathy et al., 2010). For examples, gga-miR-21-5p regulates the growth and development of skeletal muscle, specifically for myogenesis by promoting proliferation and differentiation of satellite cells (Zhang et al., 2021); gga-miR-375 and gga-miR-26a induced insulin-producing cells from stem cells showing promise for advancing the development of cell therapy (Bai et al., 2017); gga-let-7 as well as Wnt/Lin28 being a regulatory circuit controls neural crest multipotency (Bhattacharya et al., 2018); gga-miR-375 regulates chondrogenic differentiation of limb mesenchymal cells by targeting cadherin-7 (Song et al., 2013); gga-miR-302-367 cluster has been shown to regulate cell growth, metabolism, transcription, and chromatin modification, demonstrating the abilities of these miRNAs in maintaining the stemness phenotype (Ren et al., 2009). Thus, it is essential to compare the expression of miRNAs that are well-established to have regulatory roles during early embryonic development.

Collectively, we aimed to examine the miRNA expression pattern in early chicken embryos and develop enrichment functions to gain further insights into their potential regulatory mechanisms during embryonic development.

MATERIALS AND METHODS

Ethics Approval and Consent to Participate

All of the animals used in this investigation were raised in conformity with the Administration of Affairs Concerning Experimental Animals Regulations (Ministry of Science and Technology, China, revised in 2004). Collection and experiments on chicken embryos were approved by the Animal Ethics Committee at the South China Agricultural University, China (approval ID: SYXK-2022-0136). We have confirmed that all works were followed in strict accordance with the South China Agricultural University Laboratory Animal Welfare and Ethics guidelines and IACUC Ethical guidelines.

Collection of Chicken Embryos

This study used the specific pathogen free (SPF) as the experimental animal. As previous study described (Liao et al., 2022), fertilized eggs from White Leghorns were purchased from Guangdong Wen's DaHuaNong Biotechnology Co., Ltd. The eggs were incubated at 37.5°C and 65% relative humidity in an automated egg incubator, rotating every 6 h. Fifteen embryos were collected at the following times point: 24, 48, 72, 96, and 120 h, with 3 biological duplicates for each embryonic stage, labeled Em1d-Em5d. In a concise manner, the entire embryo was carefully extracted and promptly subjected to rapid freezing using liquid nitrogen, followed by preservation at a temperature of −80°C until further analysis.

Sequencing Library Preparation and Construction

After total RNA was extracted by Trizol reagent kit (Invitrogen, Carlsbad, CA), the RNA molecules in a size range of 18 to 30 nt were enriched by polyacrylamide gel electrophoresis (PAGE). Then the 3′ adapters were added and the 36 to 48 nt RNAs were enriched. The 5′ adapters were then ligated to the RNAs as well. The ligation products were reverse transcribed by PCR amplification and the 140 to 160 bp size PCR products were enriched to generate a cDNA library and sequenced using Illumina HiSeq Xten by Gene Denovo Biotechnology Co. (Guangzhou, China). Reads obtained from the sequencing machines included dirty reads containing adapters or low-quality bases which would affect the following assembly and analysis. Thus, to get clean tags, raw reads were further filtered according to the following rules. All of the clean tags were aligned with small RNAs in GeneBank database (Release 209.0) and Rfam database (Release 11.0) to identify and remove rRNA, scRNA, snoRNA, snRNA, and tRNA. Meanwhile, the chicken reference genome reads were aligned. Those mapped to exons or introns might be fragments from mRNA degradation, so these tags were removed. The tags mapped to repeat sequences were also removed. Then, the reads were used for subsequent transcriptome analysis. The raw data are available at genome sequence archive (GSA) of China national center for bioinformation (CNCB) with PRJCA010236.

Identification of miRNAs

Total clean tags were compared to the miRbase database (Release 22) to identify known miRNAs and were aligned with other species to find existing miRNAs. All of the unannotated tags were aligned with reference genome. According to their genome positions and hairpin structures predicted by software mirdeep2, the novel miRNA candidates were identified. After tags were annotated as mentioned previously, the results of the annotation were arranged in this priority order: rRNA etc. > exist miRNA > exist miRNA edit > known miRNA > repeat > exon > novel miRNA > intron. Unannotated tags were recorded for those that cannot be associated with any of the mentioned compounds.

MiRNA Expression Profiles and Differential Analysis

Based on their expression in each sample, total miRNA is composed of known, novel, and existing miRNA, and their expression level was calculated and normalized to transcripts per million (TPM). MiRNAs differential expression analysis was performed by edgeR software between 2 different groups or samples. We identified miRNAs with a fold change ≥2 and P value <0.05 in a comparison as significant DE miRNA. In order to perform hierarchical clustering and correlation analysis, we applied k-means clustering and calculated distances using either correlation or Euclidean methods. Besides, we employed Pearson's correlation for the purpose of correlation analysis. Additionally, software including RNAhybrid (Version 2.1.2) + svm_light (Version 6.01), Miranda (Version 3.3a) and TargetScan (Version 7.0) were used to predict targets of miRNAs. It was more credible that using the intersection of the results from 3 software when choosing the predicted miRNA target genes. The Mfuzz package (http://www.bioconductor.org/packages/release/bioc/html/Mfuzz.html; version 2.42.0) was utilized to cluster the expression patterns of DE-miRNAs across various time point samples. The identification of hub miRNAs at different developmental stages was carried out using the WGCNA package (http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.).

MiRNA-mRNA Function Enrichment Analysis

The target gene function was examined and the mechanism of miRNA involvement in chicken embryo development was clarified using the miRNA-mRNA regulatory interaction. Enrichment analysis for the target genes of hub miRNA in different module were performed using Metascape (http://metascape.org/gp/index.html) and Omicshare tool (https://www.omicshare.com/tools/) with the default parameters set. Construction of the miRNA interacting with mRNA regulatory network was conducted by Cyotoscape (v3.8.2).

Validation and Statistical Analysis

Generally, 12 miRNAs and 4 mRNAs were employed to confirm the sequencing findings and the correlation between the expression levels of miRNAs and mRNAs by using RT-qPCR. The primers were designed by Primer Premier (Table S7). RNA reverse transcription and real-time fluorescence quantitative PCR were performed by Bio-Rad with SYBR qPCR Master Mix (Selleck, Houston, TX). The U6 and GAPDH were used as the internal control. The relative miRNA and mRNA expression were calculated using the 2−ΔΔCt method. Graphics were plotted using GraphPad Prism 7. Statistically significant and extremely significant were determined to be P < 0.05 and P < 0.01, respectively.

RESULTS

Summary of Small-RNA Sequencing Data

To identify miRNAs during the early development of chicken embryos, we constructed 15 cDNA libraries (Em1d-1, Em1d-2, Em1d-3, Em2d-1, Em2d-2, Em2d-3, Em3d-1, Em3d-2, Em3d-3, Em4d-1, Em4d-2, Em4d-3, Em5d-1, Em5d-3, Em5d-4) from embryos to obtain complete miRNA transcripts of the chicken. Fifteen cDNA libraries yielded a total of 266,996,594 clean reads after excluding low-quality readings. Each duplicate still had over 99% high-quality reads, while the percentage of clean tags was higher than 85% (Table S1). According to TPM data, miRNAs exhibited various expressions (Figure 1A), and heatmap of sample correlations revealed remarkable levels of consistency across all samples (Figure 1B). The length distribution analysis in different samples was approximately 22 bp, consistent with conventional animal samples (Figure S1). Nearly 15% of the tags aligned to noncoding RNAs (including rRNA, scRNA, snRNA, snoRNA, and tRNA) based on the GeneBank (Release 209.0, Table S2) and Rfam (version 11.0) database. The additional 85% of the tags were used for following analysis. Moreover, over 80% of the transcripts had a high genome match (Figure 1C). The repeat alignment results are shown in Table S3. About 5% of the miRNA was base-edited for each sample (Tables S4 and S5), and 695 known miRNAs were identified (Table S6). The first nucleotide bias within the existing and known miRNA tag sequences was U (Figures S2–S3). Taken together, 2,459 miRNAs were identified including 827 existing miRNAs, 695 known miRNAs, and 937 novel miRNAs (Figure 1D). Besides, Figure S4 shows the tag annotations for different samples.

Figure 1.

Figure 1

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Overview of sequencing data analysis. (A) Violin plots depicting the expression levels of miRNAs across different samples. (B) Heatmap illustrating the correlation between samples based on their miRNA expression profiles. Color intensity reflects the degree of correlation, with warmer colors indicating stronger correlations. (C) Alignment of sequencing reads to the reference genome, showing the distribution of mapped reads across genomic regions. (D) Distribution of miRNA species, categorized through alignment of miRBase, providing the diversity of miRNA populations.

MiRNA Differential Expression Analysis

The PCA analysis of all the miRNA revealed 15 samples sorted into 5 distinct groups by time point (Figure 2A), demonstrating the reliability of the sequencing data. Meanwhile, the cluster analysis demonstrated that most miRNAs from different comparison groups exhibited high expression levels in Em1d, Em2d, and Em3d, emphasizing the significance of miRNA during the initial 3 d of embryonic development (Figure 2B). The edgeR program uncovered a total of 1,706 miRNAs that were differentially expressed (Figure 2C). In order to pinpoint key miRNAs in embryonic development, we generated an upset plot on miRNAs from different stages and found that the greater the number of differentially expressed genes across a broader time range (Figure 2D). Also, we used the Mfuzz combined with heat-map to cluster differentially expressed miRNA for showing the dynamic changes of miRNAs expression during early 5 d of development (Figure 3). Six patterns of expression model were generated, which were similar with the expression accessed by the whole mount in situ hybridization (Darnell et al., 2006; Hicks, 2009; Jeong, 2014).

Figure 2.

Figure 2

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Analysis of differential miRNA expression. (A) Principal component analysis (PCA) plot illustrating the distribution of 15 samples based on their miRNA expression profiles. Each point represents a sample, and the axes capture the major sources of variation in the data. (B) Cluster analysis highlighting differentially expressed miRNAs across samples. The heatmap displays the relative expression levels, with clustering revealing patterns of upregulation and downregulation. (C) Statistical analysis of differential miRNA expression at 5 time points, revealing the number of miRNAs with altered expression levels. (D) Upset plot depicting the intersection of miRNA expression changes at different time points, providing insights into miRNAs with consistent or unique expression alterations.

Figure 3.

Figure 3

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The dynamic change and expression of miRNAs. Differentially expressed miRNA sequencing data at 5 developmental stages were analyzed by Mfuzz and heatmap, presenting 6 expression patterns. Representative miRNAs of each stage were selected and shown in the plot. The selected miRNAs (as detected by in situ hybridization) are cited by A Chicken Embryo Gene Expression Database (GEISHA) and Jeong et al. (2014). According to Hamburger and Hamilton Stages, Em1d refers to HH1-7, Em2d to HH7-13, Em3d to HH12-19, Em4d to HH19-24, Em5d to HH24-27.

Identification of Key miRNAs During Early Embryonic Development

To investigate the primary modules of miRNAs associated with their regulatory functions during the various stages of embryonic development, we performed the “WGCNA” R program (Langfelder and Horvath, 2008) to analyze. To verify that the module conforms to scale-free distribution, the seventh soft threshold was employed (Figure 4A). Therefore, 5 modules (excluding unclassified miRNAs) were identified in the different module colors (Figure 4B). The Pearson correlation was stronger when the intersection of the row and column was brighter because it indicated a closer gene relationship between the relevant row and column (Figure 4C). In the analysis performed, a noteworthy correlation was observed between Em1d and brown (r = −0.92, P = 1e−06), Em2d and yellow (r = 0.62, P = 0.01) (as shown in Figure 4D), Em3d and yellow (r = 0.51, P = 0.05), Em4d and turquoise (r = −0.5, P = 0.06), and Em5d and turquoise (r = −0.64, P = 0.01), shown as Figure 4D. Additionally, the correlations between modules and genes were displayed in Figure S5. Hub miRNAs with the greatest linkages were those with high KME (eigengene connectivity) values. As hub miRNAs of the relevant modules, the top 5 miRNAs in each module with the highest KME were shown (Table 1).

Figure 4.

Figure 4

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Weighted Gene Co-Expression Network Analysis of miRNAs. (A) Power value curve depicting the selection of an appropriate soft thresholding power for constructing the gene coexpression network. (B) Clustering of module eigenvalues, identifying distinct modules of coexpressed miRNAs. (C) Gene correlation analysis within modules, illustrating the interconnectivity and relationships among miRNAs within each module. (D) Correlation analysis of module traits, showcasing the associations between miRNA modules and developmental stages.

Table 1.

Expression of hub miRNAs in different modules.

Module id Em1d_1 Em1d_2 Em1d_3 Em2d_1 Em2d_2 Em2d_3 Em3d_1 Em3d_2 Em3d_3 Em4d_1 Em4d_2 Em4d_3 Em5d_1 Em5d_2 Em5d_3
Blue gga-miR-181a-3p 17.4811 12.4595 18.866 100.9336 131.6359 124.2048 555.0338 906.1067 476.3619 1055.8996 1130.3312 901.1901 1161.9032 1087.9731 978.5976
Blue gga-miR-100-5p 3903.2057 1600.17 3243.8272 10239.423 11279.7106 11727.9511 32564.3677 36774.778 32423.6227 51638.8073 50767.3969 32320.3571 53782.0296 49738.8598 49563.3763
Blue gga-miR-125b-3p 1850.1107 5028.2693 5278.1524 4133.3982 4204.3261 2033.6276 17346.595 22951.0141 15077.5445 16222.7578 38218.4103 33520.9876 59655.1205 52006.91 53484.511
Blue gga-miR-202-5p 3347.2051 2269.8667 2913.1055 458.3178 300.4318 371.8212 33.491 52.1951 79.8961 3.6882 7.0631 3.5631 2.7889 4.1248 2.6858
Blue gga-miR-129-1-3p 233.8733 256.5405 517.1182 120.7544 68.8748 56.0962 38.0709 31.1083 25.6271 9.9889 9.7566 10.2266 4.944 5.2894 5.9685
Brown gga-miR-126-3p 334.5168 204.9583 408.0725 3922.8912 3873.5168 4586.1729 4668.4095 4893.6024 5415.0637 2161.1288 2516.7287 1930.5135 2378.6975 1971.7935 2160.8408
Brown gga-miR-205a 380.3411 312.4836 435.2396 1757.7488 2541.6819 2767.4107 2591.9706 2302.6384 2557.3228 1496.9463 1475.5239 1333.4375 1499.7432 1175.3215 1117.4252
Brown gga-miR-218-5p 18.16 12.4595 21.3186 88.0247 98.5837 112.5323 133.6776 127.9823 144.9329 138.6915 96.4888 94.8158 120.1772 97.2479 115.8489
Brown gga-miR-10b-3p 12.2198 2.6165 11.131 131.2238 313.9966 309.9455 284.8162 521.7421 462.5793 386.0311 390.983 300.8749 236.7415 204.9291 266.4942
Brown gga-let-7f-5p 4896.0639 3338.1419 4098.6475 1547.75 1138.9662 1386.3118 697.0125 606.5069 808.437 1244.6123 1384.4821 1243.9432 2333.8212 2065.2078 2243.8629
Turquoise gga-miR-302c-5p 5369.7506 5319.4472 5359.6537 2454.526 1228.1882 1190.0319 222.128 91.0282 335.3054 10.6036 12.9889 4.6274 2.7255 3.4454 2.7455
Turquoise gga-miR-302c-3p 10515.6413 10214.7739 12101.4336 5025.5373 3278.4802 2888.2158 739.3771 283.9413 754.3833 37.5735 40.5827 24.6179 10.5852 9.2201 11.3999
Turquoise gga-miR-302d 14307.6808 14594.4031 16948.497 7554.8741 5557.8455 4231.0106 1236.3027 494.3918 1406.4737 96.7383 74.3418 34.8908 20.1563 19.9446 20.8301
Turquoise gga-miR-302a 5434.9228 6005.5903 7463.5947 2650.5994 2057.361 1582.1384 442.8248 164.9365 464.0868 30.5813 21.3089 12.2164 7.2892 5.8232 5.9685
Turquoise gga-miR-9-5p 314.49 277.7216 377.3208 510.5634 245.886 211.3522 963.2226 2101.1653 816.4051 11688.1984 9900.8129 9567.0993 19065.773 20228.9237 16624.6991
Yellow gga-miR-429-3p 200.778 131.1982 254.1256 333.091 269.0035 278.8943 228.1392 197.715 256.486 78.0668 92.0594 74.5477 66.9341 51.39 67.2055
Yellow gga-miR-22-3p 6647.907 5288.9215 6760.2686 15016.6413 10709.5111 10889.0018 10087.9324 5934.79 11564.9136 2074.9172 2412.6382 1855.3642 1921.3776 1728.3341 1876.8589
Yellow gga-miR-460b-5p 23.591 7.7249 26.2238 41.6745 36.0136 48.8433 30.6285 17.7463 38.979 8.99 5.3272 7.3576 8.6837 8.0555 6.1476
Yellow gga-miR-375 88.2541 26.0403 101.122 442.3595 290.1149 354.2558 250.1803 103.9726 347.3652 45.5646 62.4304 35.9551 32.3261 20.5754 32.5881
Yellow gga-miR-200b-3p 1025.7838 671.6902 1484.1915 1720.2418 1795.1399 1681.8649 1897.8205 1666.2759 1704.7383 630.5277 794.3563 755.7966 488.9489 444.652 502.907
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Functional Enrichment Analysis of miRNAs

The thought that miRNAs might be effectively employed as tools to drive cellular differentiation arises from the possibility that one of the main roles of miRNAs is to affect changes in cell state (Laurent, 2008). For further study of miRNAs, the top 1 hub miRNA in 4 modules with different embryonic development time points was chosen. Therefore, target genes were predicted by tools mentioned in materials (total predicted miRNA target genes were listed in Table S8). Subsequently, we performed a meta-enrichment analysis based on 4 clusters of target genes. As shown in Figure 5, target genes from 4 clusters coregulated in cell junction (GO:0000902), behavior (GO:0007610), cell morphogenesis (GO:0000902), neuron migration (GO:0001764), skeletal development (GO:0001501), respiratory development (GO:0060541), and cell projection organization (GO:0031344), which are from multicellular organismal process and developmental process (Figure 5A–C). The Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/kegg/) revealed the miRNA enriched pathways. Consequently, the insulin signaling pathway and VEGF signaling pathway were the most significantly enriched in gga-miR-126 while MAPK signaling pathway, NOD-like receptor signaling pathway and Hippo signaling pathway in gga-miR-302 (Figure 6A and B). The most significantly enriched route for gga-miR-181a was the Toll and Imd signaling pathway (Figure 6C). In gga-miR-429, Hedgehog signaling pathway, MAPK signaling pathway, autophagy, GnRH signaling, and VEGF signaling pathway were significantly enriched (Figure 6D).

Figure 5.

Figure 5

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Meta-enrichment analysis summary for target genes of miRNA in 4 clusters (cluster1: target genes of gga-miR-126-3p; cluster2: target genes of gga-miR-302c-5p; cluster3: target genes of gga-miR-181a-3p; cluster4: target genes of gga-miR-429-3p). (A) Overlap among target gene lists, where purple curves link identical genes, and blue curves link genes belong to the same enriched ontology term. The inner circle represents gene lists, where hits are arranged along the arc. Genes hit multiple lists are colored in dark orange, and genes unique to a list are shown in light orange. (B) Network of enriched terms: circles represent enrichment to cluster ID, colored by cluster ID, where nodes that share the same cluster ID are typically close to each other. (C) Heatmap of top 100 enriched terms across 4 target gene clusters, colored by P value.

Figure 6.

Figure 6

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Analysis of enriched KEGG pathway in 4 clusters of target genes respectively. (A) Target genes of gga-miR-126-3p. (B) Target genes of gga-miR-302c-5p. (C) Target genes of gga-miR-181a-3p. (D) Target genes of gga-miR-429-3p.

Interaction Network Construction and Analysis

Protein-protein interaction network of target genes from hub miRNAs was generated using Metascape and visualized with Cytoscape 3.82 (Figure 7A), producing a network with 348 nodes and 1,097 edges. The genes containing MAPK1, KRAS, RHOJ, PRKACB, FGFR2, MAPK13, PTPN13, USP7, and GNA12 have the highest degree and betweenness centrality scores (Table S9). Furthermore, the MCODE method was applied to identify closely related proteins from the PPI network. The MCODE algorithm subclustered PPI network into 13 subclusters. Therefore, the genes that FMR1, KCNS2, HS6ST3, ACKR3, CNKSR2, AHCTF1, AP2A2, MAP4K4, ERG, EPB41, and EPHA5 were identified as seed genes (Figure 7B, Supplementary Table S10). The GO enrichment analysis was applied to each MCODE network to assign annotation to the network component. Specially, seed genes targeted from gga-miR-302s were enriched into MAPK signaling, GPCRs rhodopsin-like receptors, RIG-I-like receptor signaling and protein catabolic process (GO:0030163). The enriched categories for seeds of cluster4 (target genes of gga-miR-429-3p) included membrane trafficking, PED-EPHA-FWD pathway, TLR4 signaling, and RHO GTPase cycle. Cluster 3 was enriched into regulation of translation, voltage gated potassium channels and clathrin-mediated endocytosis. Heparan sulfate/heparin (HS-GAG) metabolism, Glycosaminoglycan metabolism and cell-cell adhesion were important processes for cluster 1 (Supplementary Table S9).

Figure 7.

Figure 7

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Network analysis of protein-protein interaction (PPI) enrichment. (A) Construction of PPI Enrichment Analysis Network. Four clusters of target genes were analyzed in this network. Each circle represents a target protein, with the same color indicating an MCODE (molecular complex detection) component, representing a subset of interacting proteins within the network. (B) MCODE components identified in the cluster of genes. The MCODE algorithm identifies densely connected regions within the PPI network, revealing distinct functional components or complexes among the genes.

Based on functional enrichment analysis, we focused on some pathways related to embryonic development. The key miRNA-mRNA-pathway regulatory networks for different embryo development stages were built by Cytoscape 3.82. Thus, VEGF signaling pathway and ErbB signaling pathway are involved in Em1d, and key genes included MAPKAPK2, PAK3, and SPHK (Figure 8A). MAPK signaling was the most significant pathway for gga-miR-302c-5p at Em1d, and the important genes included PKA, FGF, ERK, MAP2K2, JNK, P38, AKT, DUSP, PDGFRB, RAP1B, and PLA2G4 (Figure 8B). Similarly, the most important pathway for Em2d and Em3d was Toll and Imd signaling pathway whose key genes are UBEZV and ANK (Figure 8C). In contrast, FGF, NTRK2, IGF2, EGF, MAP3K1, CDC42, CRK, and PDGFRA were key for MAPK signaling pathway in Em4d and Em5d. Additionally, Hedgehog signaling was the most significant pathway in Em4d and Em5d, including key genes such as PTCHI1, SMO, KIF3A, HHIP, BTRC, SMURF, and CCND2 (Figure 8D).

Figure 8.

Figure 8

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Interaction network of miRNA, mRNA, and key signal pathway. (A) Target genes of gga-miR-126-3p and pathways. (B) Target genes of gga-miR-302c-5p and pathways. (C) Target genes of gga-miR-181a-3p and pathways. (D) Target genes of gga-miR-429-3p and pathways.

Validation of Candidate miRNAs and miRNA-mRNA Relationship

The miRNAs with high KME values and high expression in the key modules corresponding to different developmental stages of chicken embryos were selected for RT-qPCR. They included gga-miR-181a-3p, gga-miR-100-5p, gga-miR-126-3p, gga-miR-205a, gga-miR-302c-5p, gga-miR-302d, gga-miR-429-3p, and gga-miR-375. The RT-qPCR results and small RNA-seq results were highly correlated (Figure 9), confirming the accuracy of the sequencing results. Four pairs of miRNA-mRNA were randomly selected for RT-qPCR based on candidate miRNAs and target gene predication results, and found that there was a significant negative correlation of miRNA-mRNA expression relationship (Figure 10).

Figure 9.

Figure 9

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The validation of candidate miRNAs. (A) gga-miR-181a-3p; (B) gga-miR-100-5p; (C) gga-miR-302c-5p; (D) gga-miR-302d; (E) gga-miR-126-3p; (F) gga-miR-205a; (G) gga-miR-429-3p; (H) gga-miR-375. Blue means RNA-seq, red means RT-qPCR, and r means correlation coefficient, U6 was used as the reference gene for RT-qPCR, RNA-seq relative expression was represent by TPM.

Figure 10.

Figure 10

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The validation of correlation miRNA-mRNA. (A) gga-miR-126-3p—SLC35A3. (B) gga-miR-302c-5p—AKT1. (C) gga-miR-181a-3p—UBE2V2. (D) gga-miR-429-3p—SMO.

DISCUSSION

MiRNA is known to be involved in controlling the timing of cell differentiation, cell proliferation, and apoptosis during the development of chicken embryos. It is also known to be involved in regulating the levels of homeobox proteins, substances that play key roles in cell fate determination. Herein, the main purpose of this study was to identify miRNAs that play roles in chicken embryonic development to improve our current understanding of gene regulation during vertebrate development.

Using small RNA libraries of Em1d, Em2d, Em3d, Em4d, and Em5d embryonic chickens, we identified 2,459 miRNAs across 5 different developmental time points from a total of 266,996,594 clean reads. The differential analysis identified 1,761 differentially expressed miRNAs, indicating that significance of these miRNAs in the development of the chicken embryo. In our profile of miRNA expression, the most highly expressed miRNA in each developmental stage was gga-miR-363-3p (Em1d), gga-miR-26a-5p (Em2d and Em3d), gga-miR-10a-5p (Em4d), and gga-miR-199-5p (Em5d). Previous studies demonstrated that gga-miR-363 was highly expressed in chicken early embryo and had extensive function in early development of embryo including that controlling central nervous system development, determining development of gonads and sexual differentiation (Darnell et al., 2006; Hicks et al., 2008; Shao et al., 2008; Huang et al., 2010). MiR-26 were an important player mostly in regulating neural differentiation along with neural progenitor development and maturing as well as expansion of B cells (Zhang et al., 2018; Sauer et al., 2021; Hutter et al., 2022). It is reported that gga-miR-10a is highly expressed during spleen development in chicken embryos (Hicks et al., 2009), which is associated with immune system (immune cell trafficking) and hematopoesis targeting ITGB1 and Rab and Rho families (Trakooljul et al., 2012). According to findings of Richbourg et al. discovered, it appears that gga-miR-199 influences SHH expression in the FEZ and the capacity of cells to respond to SHH signaling both contributing directly or indirectly to the regulation of face morphogenesis (Richbourg et al., 2020).

The WGCNA package is a comprehensive collection of R functions performing such as the general network structures in Bioconductor, gene network enrichment analysis, functional analysis of gene coexpression networks, and others (Langfelder and Horvath, 2008). The presence of miRNAs in chickens has not yet been fully characterized; presumably they exist in low abundance and may be obscured by miRNAs that are substantially expressed. In this study, therefore, the WGCNA technique revealed key modules and hub miRNAs related to embryos at 5 different developmental stages. The WGCNA analysis identified 302 miRNAs in the blue module, correlated with Em4d and Em5d, 141 miRNAs in the brown module for Em3d, 112 miRNAs in the yellow module for Em2d, and 295 miRNAs in the turquoise module for Em1d. Most miRNAs in each module account for growth and developmental process, indicating that such miRNAs are important players in promoting embryogenesis. For example, the expression of gga-miR-205a was detected in the caudal non-neural ectoderm at HH stage 10 (Em2d-Em3d) and at stage 20 in the lateral and ventral ectoderm including the limb buds (according to GEISHA). Concerning gga-miR-205a, Wang et al. demonstrated that miR-205a can directly binding CDH11 to promote the differentiation of myoblasts (Wang et al., 2018). According to Lateral view, miR-125b was mostly located in forebrain, gonad, limb buds, neural tube and spinal cord at stage 21 (Em3d) (GEISHA), while complex neural expression pattern of miR-9 in forebrain or telencephalic vesicles and diencephalon at HH stage 22. Both of these miRNAs displayed high expression levels in our study. This observation suggests that these miRNAs are critical players in regulating embryogenesis.

Furthermore, the hub miRNAs from different modules were identified and used to predict target genes. Therefore, through GO and KEGG hub gene target analysis, intriguing miRNAs for biological processes and signaling pathways associated with embryonic development were discovered. Of note, cell division, cell migration, intercellular communication, differentiation, and apoptosis are 4 cellular processes that have been demonstrated to be regulated by miRNAs and are crucial for coordinating growth and pattern formation during embryonic development. In this study, we identified 4 hub miRNA which were, gga-miR-181a-3p, gga-miR-126-3p, gga-miR-429-3p, and gga-miR-302c-5p regulating core developmental process in chicken embryo. These “core” events are building the foundation for embryogenesis and delivering the proper progenitors to the right place at the right time (Alberti and Cochella, 2017).

The miR-181 family is one of the most abundant miRNAs in lymphoid tissue considered as vital immune organ (Neilson et al., 2007). The role of miR-181a in regulating the function of innate immune cells like macrophages and dendritic cells as well as B cell growth in the bone marrow has been documented (Fragoso et al., 2012; Xie et al., 2013; Lim et al., 2020). In the enrichment analysis from this study, gga-miR-181a was strongly associated with immune response which was Toll and Imd signaling pathway. However, recent studies have revealed that Toll-like receptor signaling carry out unanticipated functions in development including regulation of cell fate, neural circuit connectivity and synaptogenesis (Anthoney et al., 2018).

Mir-126 highly conserved in vertebrates has an increased expression in vascular system like blood islands, blood vessels, and heart tube (Darnell et al., 2006; Nikolic et al., 2010), which is accord with our results. MiR-126 is located in intron7 of egf7 that plays undisputed role in angiogenesis. Additionally, MiR-126 functionality was studied in numerous developments and brought evidence that this miRNA is implicated in several biological processes such as modulating lymphatic development, regulating gland development, proliferating glycogen trophoblast, and promoting retinal endothelial cells (Cui et al., 2011; Kontarakis et al., 2018; Villain et al., 2018; Sharma et al., 2019).

MiR-429 is one of members of miR-200 family sharing the seed sequence (AAUACU), which is involved in epithelial to mesenchymal transition (EMT) that facilitating tissue remodeling during embryonic development (Gregory et al., 2008). It is known that EMT was first observed during embryonic development which mainly happening where gastrulation, neural tube formation and embryonic palatal fusion (Lachat et al., 2021), additionally, EMT or MET also control somite formation (Takahashi et al., 2005) and heart valve formation (Snider et al., 2009). From our enrichment results, Hedgehog signaling, MAPK signaling, VEGF signaling, and TGF-β signaling pathway were enriched in target genes of mir-429, which can activate an EMT or MET to ensure that embryo develop successfully.

The evolutionarily conserved cluster of miR-302s are essential for maintaining stemness of pluripotent stem cells and function as self-renewal (Kuo et al., 2012), inhibition of ESC differentiation, inhibition of apoptosis, promotion of somatic cell reprogramming (Greve et al., 2013), cell cycle regulation(Wang et al., 2008), and epigenetic modification (Lee et al., 2013). However, miR-302s are also a crucial player in early embryonic development, since it was expressed in epiblast, primitive streak and more strongly in neural fold or neural tube during HH4-HH8 through whole mount in situ hybridization (Jeong et al., 2014). Similarly with our result, gga-miR-302s were highly expressed in Em1d chick embryos (Figure 3). In current enrichment analysis, target genes of miR-302 were enriched into neuronal system development process and relative pathway including MAPK signaling pathway, ErbB signaling pathway, and hippo signaling pathway. Key neural progenitor marker expression requires MAPK activation, since Layden et al. suggested that MAPK is key early regulator of neurogenesis in the embryonic ectoderm of Nematostella (Layden et al., 2016), while MAPK also operate in the lateral avian epiblast fated to become NC cells (Stuhlmiller and Garcia-Castro, 2012). ErbB receptors being a key developmental regulator, regulate a wide range of developmental processes and are widely used for cell communication during the formation of the nervous system (Birchmeier, 2009). According to mounting evidences, the Hippo pathway regulates the proliferation, migration, apoptosis, and differentiation of nerve cells as well as the formation of synapses, the corpus callosum, and the cortex, all of which are important for the development of the nervous system (Li et al., 2021; Sahu and Mondal, 2021). In general, gga-miR-302 participates in neurulation, which is a distinguishing characteristic of vertebrate development and is necessary for healthy CNS development and embryo survival (Parchem et al., 2015).

Importantly, due to conservation of function of miRNAs (Hicks et al., 2008), chicken embryos are considerably simpler to collect and manipulate than those of most other vertebrate species, making them a particularly useful model organism for research on miRNA function during vertebrate development.

In summary, we constructed a comprehensive miRNA sequencing library of chicken embryos throughout the first 5 d of development, resulting in the identification of hub miRNAs including gga-miR-181a-3p, gga-miR-126-3p, gga-miR-302c-5p, and gga-miR-429-3p. Our analysis revealed significant enrichment of several signaling pathways, including VEGF, Insulin, ErbB, MAPK, Hedgehog, TLR, and Hippo, among others, particularly during the development of the embryonic immune, hemopoietic, and nervous systems. This study is quite compatible with earlier mRNA sequencing findings (Liao et al., 2022). Collectively, proper timing of the transition from fertilized egg to embryo is carefully orchestrated by miRNAs as fine-tuner during embryonic development. Although scientists have made some progress understanding the role of miRNA expression in chicken embryo development, much research is still needed to fully understand the precise mechanisms of miRNA action. Indubitably, the applied implications of understanding the effects of miRNA in embryo development would be powerful. Knowledge of this type might lead to improved and increase the production of poultry products, and providing reference for other vertebrate development. Combining current findings serve as an inspired resource for further researches.

ACKNOWLEDGMENTS

We thank the researchers in our laboratory for their assistance in samples collection. We are grateful to Guangzhou Gene-de-novo Biotechnology Co., Ltd for assisting in sequencing and bioinformatics analysis.

Funding: This study was supported by the Heyuan Branch, Guangdong Laboratory for Lingnan Modern Agriculture Project (DT20220003), the Guangdong Provincial Key R&D Program (2020B020222001, 2019B1515210034, 2019B020218004, 2019A1515012006), the Guangdong Basic and Applied Basic Research Foundation (2023A1515010584), the Chief expert Project of modern Agricultural Industry Technology innovation alliance in Guangdong Province (2023KJ128, 2022KJ128, 2021KJ128, 2020KJ128), the Natural Science Foundation of Guangzhou (2023A04J1461), the Science and Technology Program of Guangdong province, China (2020B1212060060), the China Agriculture Research System of MOF and MARA (CARS-42-13), the Special Project of National Modern Agricultural Industrial Technology System (CARS-41) and Provincial Science and Technology Special Fund Project for Zhongshan City (major special project + Task list management mode) (2021sdr003).

DISCLOSURES

The authors declare that there are no financial or relationships that might lead to a conflict of interest of the present article.

Footnotes

Supplementary material associated with this article can be found in the online version at doi:10.1016/j.psj.2023.103105.

Appendix. Supplementary materials

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Supplementary Materials

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