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Article

Exploration of Potential Target Genes of miR-24-3p in Chicken Myoblasts by Transcriptome Sequencing Analysis

by
Xuanze Ling
1,2,
Qifan Wang
1,2,
Pengfei Wu
1,2,
Kaizhi Zhou
1,2,
Jin Zhang
1,2 and
Genxi Zhang
1,2,*
1
College of Animal Science and Technology, Yangzhou University, Yangzhou 225009, China
2
Joint International Research Laboratory of Agriculture & Agri-Product Safety, Yangzhou University, Yangzhou 225009, China
*
Author to whom correspondence should be addressed.
Genes 2023, 14(9), 1764; https://doi.org/10.3390/genes14091764
Submission received: 20 July 2023 / Revised: 2 September 2023 / Accepted: 4 September 2023 / Published: 5 September 2023
(This article belongs to the Special Issue Poultry Genetics and Genomics)
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Abstract

:
Broiler skeletal muscle growth is significantly influenced by miRNAs. Our earlier research demonstrated that miR-24-3p significantly suppressed the proliferation of chicken myoblasts while promoting their differentiation. The purpose of this study is to investigate miR-24-3p potential target genes in chickens. We collected myoblasts of Jinghai yellow chicken and transfected four samples with mimics of miR-24-3p and another four samples with mimic NC (negative control) for RNA-seq. We obtained 54.34 Gb of raw data in total and 50.79 Gb of clean data remained after filtering. Moreover, 11,635 genes were found to be co-expressed in these two groups. The mimic vs. NC comparison group contained 189 DEGs in total, 119 of which were significantly up-regulated and 70 of which were significantly down-regulated. Important biological process (BP) terminology such as nuclear chromosomal segregation, reproduction, and nuclear division were discovered by GO enrichment analysis for DEGs in the mimic vs. NC comparison group. KEGG pathway analysis showed that focal adhesion, cytokine–cytokine receptor interaction, the TGF-β signaling pathway, and the MAPK signaling pathway were enriched in the top 20. Variation site analysis illustrated the SNP (single nucleotide polymorphisms) and INDEL (insertion–deletion) in the tested samples. By comparing the target genes predicted by miRDB (MicroRNA target prediction database) and TargetScan with the 189 DEGs found by the transcriptome sequencing, we discovered two up-regulated DEGs (NEURL1 and IQSEC3) and two down-regulated DEGs (REEP1 and ST6GAL1). Finally, we carried out qPCR experiments on eight DEGs and discovered that the qPCR results matched the sequencing outcomes. These findings will aid in identifying potential miR-24-3p target genes in chicken skeletal muscle and offer some new directions for upcoming research on broiler breeding.

1. Introduction

Chicken is a globally ubiquitous food that provides nutrition and health for humans [1]. With the improvement in people’s life quality, the consumption of chicken rises constantly and becomes a symbol of a healthy and low-carbon lifestyle. Up to now, slow growth rates and low feed utilization rates are still severe issues in cultivating local broiler breeds in China. Molecular breeding is utilized to hasten the selection of fast-growth traits to address these issues. It is well known that chicken myofiber develops in the early stages of embryogenesis and the total number of muscle fibers is almost fully established before hatching [2]. As a result, the development of the chicken’s skeletal muscle during the embryonic stage is crucial.
miRNAs are one kind of evolutionarily conserved non-coding RNA that takes part in the regulation of eukaryotic genes after transcription [3]. Lin-4 and Let-7, the first two identified miRNAs, were initially found to regulate the differentiation and division of stem cells in the nematode [4]. The translation of messenger RNAs (mRNAs) can be inhibited or their degradation promoted by miRNAs to modulate gene expression [5]. This finding suggests that miRNAs play a significant role in human diseases like cancer and cardiovascular and metabolic disorders. It has also been demonstrated that Drosophila melanogaster (D. melanogaster) contains various miRNAs that are associated with human disease [6]. In summary, miRNAs can be found to be conserved across various species.
The role of miRNAs in the regulation of numerous biological processes has recently come to light [7], including skeletal muscle development [8]. Various studies have revealed that miRNAs target specific genes and have an impact on biological function. According to Liu et al. [9], miR-200c-5p targeted Adamts5 to influence myoblast migration and differentiation during the process of skeletal muscle regeneration. Wang et al. [10] identified that miR-34b-5p targeted IGFBP2 to regulate the proliferation and differentiation of myoblasts. In our previous study, we collected the leg muscles from two groups of Jinghai yellow chickens (slow- and fast-growing groups) at the age of 300 days for miRNA sequencing to search for new miRNAs related to skeletal development. Through bioinformatics analysis, the outcome revealed that novel_miR_133 and miR-24-3p were two miRNAs that are significantly related to muscle development [11]. We also demonstrated that miR-24-3p inhibited myoblast proliferation while promoting their differentiation.
The discovery and study of miRNAs offer fresh insights into the research of skeletal muscle growth and development in chickens [12]. Jinghai yellow chicken is a well-known indigenous Chinese breed with good flavor and disease resistance [13]. The purpose of this study was to look for new miR-24-3p target genes and prepare for future research into its role in regulating the proliferation and differentiation of Jinghai yellow chicken myoblasts. In the experiment, we collected eight samples of Jinghai yellow chicken myoblasts and transfected four of them with miR-24-3p mimic and the other four with mimic NC. Finally, the total RNA was extracted to perform RNA-seq. We look forward to discovering some key genes related to the muscle development of chickens.

2. Materials and Methods

2.1. Ethics Statement

This study was fully consistent with the codes made by the Chinese Ministry of Agriculture. The animal experiments performed in the study were all evaluated and approved by the Animal Ethics Committee of Yangzhou University (202103298).

2.2. Isolation and Culture of Chicken Primary Myoblasts (CPMs)

As mentioned above, we used Jinghai yellow chickens as the laboratory animals in this experiment. Fertilized eggs were collected after artificial insemination and then hatched at 37 °C and 60% humidity. The leg muscle tissues were collected at embryonic stage 14 (E14) and the chicken primary myoblasts (CPMs) were extracted from the leg tissues. We removed the bones and blood from the leg muscle and then cut it into meat paste. We digested the meat paste with collagenase type I (Gibco, Grand Island, NY, USA) at 37 °C. After digestion, we filtered out the impurities in the mixture and cultured the CPMs in a 10 cm cell culture dish [11]. The culture medium we used was DMEM/F12 medium (Solarbio, Beijing, China) supplemented with 20% fetal bovine serum (Gibco, Grand Island) and 1% penicillin/streptomycin solution (Sangon Biotech, Shanghai, China) [11].

2.3. Transfections, RNA Extraction, Quantification, and Qualification

When the density of CPMs reached 60%, we transfected four samples with mimic and the other four samples with mimic NC, respectively. The reagents used for transfection were jetPRIME reagent (Polyplus, Illkirch, France) and 50 nM miR-24-3p mimic and mimic NC (GenePharma, Shanghai, China) [11]. Trizol (TIANGEN, Beijing, China) was added to these eight samples and the total RNA was extracted by standard methods [14]. Afterward, the Agilent 2100 bioanalyzer (Agilent, Santa Clara, CA, USA) was employed to assess the RNA integrity, with stringent quality control being applied to the RNA samples [15].
Sequences of the mimic (5′→3′), Sense: UGGCUCAGUUCAGCAGGAACAG. Antisense: GUUCCUGCUGAACUGAGCCAUU. Sequences of the mimic NC (5′→3′). Sense: UUCUCCGAACGUGUCACGUTT. Antisense: ACGUGACACGUUCGGAGAATT.

2.4. cDNA Library Preparation, Quality Inspection, and Sequencing

The RNA samples prepared for sequencing were the total RNA contained from 8 samples in the previous step. Enriched mRNA was obtained from the total RNA utilizing poly-T oligo-attached magnetic beads. Then, mRNA was randomly broken into fragments in fragmentation buffer. The fragmented mRNA was used as a template to produce the first strand of cDNA. Following that, the second strand cDNA synthesis was carried out using DNA Polymerase I and RNase H. We screened the cDNA fragments of 370~420 bp and purified them with the AMPure XP system (Beckman Coulter, Beverly, CA, USA) [16]. Then, PCR was performed and the PCR products were purified again by AMPure XP system to obtain the cDNA library. When the library construction was completed, the quantification was performed by Qubit2.0 Fluorometer. The concentration of the library was adjusted to 1.5 ng/µL. Subsequently, we applied the Agilent 2100 bioanalyzer to measure the insert size of the library. Finally, RNA-seq was performed on a NovaSeq 6000 platform (Illumina, San Diego, CA, USA).

2.5. Analysis of the Sequencing Results

Sequencing fragments were converted into raw reads from image data measured by the sequencer. The format of the raw reads was FASTQ. The sequence quality was checked (Q20, Q30, and GC content) and any low-quality reads were removed. The error rate was confirmed as low enough to enable successful downstream analyses. Bowtie 2 (v2.2.8) was used to align the paired-end clean reads to the GRCg6a reference genome [17]. Differential expression analysis between mimic and NC was carried out using DESeq2 [18]. Among all the genes tested in this study, those whose expression level showed p-adjust ≤ 0.05 were assigned as differentially expressed genes (DEGs). Moreover, the gene ontology (GO) enrichment and KEGG pathway analysis of the DEGs were implemented with GO-seq (R-3.3.2) and KOBAS (v2.0) [19,20], respectively. We determined the significant degree of GO enrichment and KEGG pathways by the standard of p-value ≤ 0.05 in the comparison group mimic vs. NC. Since variation site analysis was of great significance for learning about the protein function or gene-related diseases, we used GATK software (4.1.1.0) to perform SNP and INDEL variation site analysis on the RNA-seq data of these 8 samples; the SNP and INDEL variation sites were annotated by using SnpEff software (4.3) with default parameters [21]. The parameters of GATK are RMSMappingQuality (MQ) < 40.0, QualByDepth (QD) < 2.0, FisherStrand (FS) > 30.0, depth (DP) < 10, and quality (QUAL) < 20.

2.6. Real-Time Quantitative PCR of Differentially Expressed Genes

To help confirm the sequencing results, we conducted real-time fluorescence quantification experiments on 8 differentially expressed genes (DEGs). Primers of DEGs were designed with Primer 5.0 (Table S1). The company Tsingke (Nanjing, China) produced the primers of these 8 DEGs. The PCR products were detected by agarose gel electrophoresis. On the Applied Biosystems 7500 (ABI, Los Angeles, CA, USA), we performed RT-qPCR with three technical repetitions for each sample using the reagent Taq Pro Universal SYBR qPCR Master Mix (Q712, Vazyme Biotech Co., Ltd., Nanjing, China). We employed β-actin as the housekeeping gene in the RT-qPCR experiment and calculated the relative expression level of these 8 DEGs by means of the 2−ΔΔCT method [22].

3. Results

3.1. Sequencing Data Quality Summary

We performed the RNA-seq experiment on eight samples of leg muscle from the Jinghai yellow chickens. The amount of raw data was 54.34 Gb and 50.79 Gb of clean data were obtained after quality control. The percentage of Q20 and Q30 reads was around 96% and 90%, respectively. The GC content in these eight samples was around 50% (Table 1).
To assess the inter-group differences and sample duplication, we performed PCA analysis on the gene expression values of these eight samples and the result is shown in Figure 1A. In Figure 1B, the main scatter points are concentrated on the diagonal of the scatter plot, which demonstrated the repeatability and comparability of the sequencing results. Depending on the expression value (FPKM) of all genes, the condition of gene expression level distribution was shown through the box diagram (Figure 1C). The development of a Venn diagram showed that there were 355 genes uniquely expressed in the mimic group, 227 genes uniquely expressed in the NC group, and 11,635 genes that are co-expressed in these two groups (Figure 1D).

3.2. Statistics of Differentially Expressed Genes

A total of 189 DEGs (Table S2) were identified with the standard of p-adjust ≤ 0.05 and |log2FoldChange| ≥ 1, including 119 up-regulated and 70 down-regulated genes (Figure 2A). The FPKM values of all DEGs were analyzed by the methods of hierarchical clustering and Z-Score normalization. The color in each square reflected the value obtained after homogenization in rows. According to the heatmap, we could clearly find that the DEGs and samples with similar expression patterns were grouped together (Figure 2B).

3.3. Functional Enrichment Analysis of the Differentially Expressed Genes

GO function enrichment analysis found 32 significantly enriched GO terms (p-value ≤ 0.05) in mimic vs. NC. The biological process (BP) terms of the mimic vs. NC groups included nuclear chromosome segregation, reproduction, nuclear division, reproductive process, and so on (Figure 3A). Additionally, Figure 3A also included terms for molecular function (MF) and cellular component (CC). In addition, the numbers of down-regulated genes and rich factors in different GO enrichment terms are illustrated in Figure 3B.
KEGG pathway analysis showed the top 20 KEGG pathways in the mimic vs. NC comparison groups, including focal adhesion, cytokine–cytokine receptor interaction, Salmonella infection, the TGF-β signaling pathway, thiamine metabolism, and the MAPK signaling pathway (Figure 4). Focal adhesion, cytokine–cytokine receptor interaction, and Salmonella infection pathways had p-values lower than 0.05. We could see the genes enriched in terms of pathways. For example, VAV3, FLNA, ENSGALG00000048077, and MYL10 were enriched in terms of focal adhesion. GDF11, GDF7, CCR2, IL18, and ENSGALG00000054572 were enriched in terms of the cytokine–cytokine receptor interaction. ENSGALG00000048475, FLNA, MYL10, and IL18 were enriched in terms of Salmonella infection. ENSGALG00000053285, ID4, and GDF7 were enriched in the TGF-β signaling pathway. Only ALPL was enriched in terms of thiamine metabolism. ENSGALG00000031572, FLNA, and ENSGALG00000050166 were enriched in the MAPK signaling pathway.

3.4. Variation Site Analysis

The variation sites included SNP (single nucleotide polymorphisms) and INDEL (insertion–deletion). Generally speaking, SNP refers to single nucleotide variations with a variation frequency greater than 1%. INDEL refers to the insertion–deletion of small segments of the tested samples compared to the reference genome, which might contain one or more bases.
After using GATK (genome analysis ToolKit) to detect variation sites, we made statistics on each variation site according to SnpEff annotation information, mainly including variation site function statistics, variation site area statistics, and variation site impact statistics. The INDEL impact was analyzed from four aspects: HIGH, LOW, MODERATE, and MODIFIER (Figure 5A). It was obvious that the MODIFIER level was highest in these four levels. The INDEL annotation included 12 descriptors, as detailed in Figure 5B. It was clear that the downstream variants made up the largest proportion of the INDELs. The SNP function was counted and mapped from three aspects: missense variant, nonsense variant, and silent variant (Figure 5C). We could see that silent variants were most common, with a few nonsense variants highlighted. The impact of the SNPs was calculated and mapped using the four levels of HIGH, LOW, MODERATE, and MODIFIER (Figure 5D).
Finally, different types of INDEL variants like the downstream_gene_variant, 3_prime_UTR_variant, frameshift_variant, splice_region_variant, and intron_variant were found in REEP1. The impact of the frameshift_variant in REEP1 was HIGH. The impacts of the splice_region_variant and intron_variant were LOW. The impacts of downstream_gene_variant and 3_prime_UTR_variant were MODIFIER. 3_prime_UTR_variant and intron_variant occurred in NEURL1. The impacts of 3_prime_UTR_variant and intron_variant were all MODIFIER. We also discovered 3_prime_UTR_variant and downstream_gene_variant in ST6GAL1. The impacts of 3_prime_UTR_variant and downstream_gene_variant were all MODIFIER. However, we did not find any kind of INDEL variant in IQSEQ3. More details are provided in Table S3.
For SNPs, the impact of downstream_gene_variant, 3_prime_UTR_variant, and intron_variant was MODIFIER in REEP1. The impact of the synonymous_variant was LOW in REEP1. The impact of the missense_variant was MODERATE in REEP1. The impact of 3_prime_UTR_variant, 5_prime_UTR_variant, upstream_gene_variant, downstream_gene_variant, and intron_variant was MODIFIER in ST6GAL1. While the impact of synonymous_variant in ST6GAL1 was LOW, the impact of 3_prime_UTR_variant, intron_variant, and downstream_gene_variant was MODIFIER in NEURL1. While the impact of synonymous_variant was LOW in NEURL1, we did not find any kind of SNP variant in IQSEQ3. More details are provided in Table S4.

3.5. Target Gene Prediction and RT-qPCR Experiment Results

Four of these target genes predicted by miRDB were the same as the DEGs detected by RNA-seq, including two down-regulated genes REEP1 and ST6GAL1, as well as two up-regulated genes NEURL1 and IQSEC3 (Figure 6A). The target genes predicted by Targetscan shared only REEP1 with the DEGs detected by RNA-seq (Figure 6B). Based on the agarose gel electrophoresis results, the PCR product sizes of the primers for the 8 DEGs were consistent with those designed by Primer 5.0 (Figure 6C). According to the RNA-seq result, these 8 DEGs and samples were clustered into two groups (Figure 6D). In addition, we calculated the relative expression level of RNA-seq and that of RT-qPCR for the 8 DEGs in groups of mimic and NC with the standard of log2FoldChange (Figure 6E,F). The results showed that the tendency of changes in the 8 DEGs in the sequencing and quantitative results remains consistent, which proved that RNA-seq results were reliable. From these studies, we identified that REEP1 and ST6GAL1 may be the target genes of miR-24-3p.

4. Discussion

Chicken supplies essential proteins to humans all over the world and has the benefits of being high in protein, low in fat, and low in calories [23]. In addition, chicken can be a substitute for many other kinds of meat. Growth traits have always been the focus of attention in the development of livestock products. Skeletal muscle development is particularly crucial for progress in the broiler industry as it directly contributes to chicken production [24]. Studies of miRNAs offer fresh perspectives on the investigation of skeletal muscle development. Extensive experiments have been performed on miRNAs and their target genes to illustrate their functions in chicken skeletal muscle growth. Zhang et al. [25] observed that miR-10a-5p targeted the BCL6 gene to regulate skeletal muscle growth, development, and autophagy. Zhang et al. [26] revealed that miR-21-5p targeted KLF3 to regulate the proliferation and differentiation of skeletal muscle satellite cells in chicken. Cao et al. [27] found miR-99a-5p targeted at MTMR3 to regulate the proliferation and differentiation of satellite cells in chicken.
In our earlier study, we discovered that miR-24-3p significantly inhibited the proliferation and promoted the differentiation of CPMs, which indicated that miR-24-3p correlated with muscle development. In this research, we overexpressed miR-24-3p and performed RNA-seq on CPM samples in order to look for potential target genes of miR-24-3p. According to the RNA-seq results, 119 up-regulated DEGs and 79 down-regulated DEGs were found in mimic vs. NC, including REEP1, ST6GAL1, and ASB15. We also predicted the possible target genes by miRDB and Targetscan. Using RNA-seq and target gene prediction, REEP1 and ST6GAL1 were the most likely genes targeted by miR-24-3p.
Moreover, we performed GO enrichment and KEGG pathway analysis to learn about the biological functions of these DEGs. We speculated that the target genes REEP1 and ST6GAL1 were involved in skeletal muscle development. For example, REEP1 was reported to have the function of endoplasmic reticulum antistress [28]. In another study, Qin et al. [29] discovered that the up-regulation of REEP1 can restore mitochondrial function to rescue motor neuron degeneration in SOD1G93A mice. Li et al. [30] found that REEP1 could be transcriptionally activated by p53, which leads to an increase in Ca2+ in skeletal muscle mitochondria. In the Golgi compartment, ST6GAL1 is a sialyl-transferase that mediates the glycosylation of proteins and lipids to generate functionally significant glycoproteins and glycolipids [31]. Extensive studies were performed to find that ST6GAL1 was related to cancers [32,33]. However, few studies have been found about the relationship between ST6GAL1 and skeletal muscle growth. It was reported that ST6GAL1 was principally expressed in the liver, placenta, and skeletal muscle [34]. In our study, ST6GAL1 was enriched in GO_BP entries like the glycoprotein biosynthetic process, macromolecule glycosylation, and glycoprotein metabolic process. These GO_BP entries were all related to ‘glycoprotein’. According to Osseni et al. [35], skeletal muscle fiber fragility and atrophy were caused by the disruption of the dystrophin-associated glycoprotein complex caused by the absence of dystrophin in Duchenne muscular dystrophy. This discovery illustrated that ‘glycoprotein’ had a relationship with skeletal muscle development. We therefore hypothesize that ST6GAL1 is involved in skeletal muscle development. We also found that ST6GAL1 was enriched in KEGG pathways such as N-glycan biosynthesis and other types of O-glycan biosynthesis. O-glycan was also shown to participate in the process of muscle development [36].
Apart from these two potential target genes, some other DEGs were also discovered to have relationships with skeletal muscle development. For instance, ASB15, which is primarily expressed in chicken skeletal muscle, is a member of the ankyrin repeat and suppressor of cytokine signaling box families [37]. Moreover, Wang et al. [38] discovered that the ASB15 gene was expressed in different tissues of Gushi chickens such as the heart, breast muscle, and leg muscle. In another experiment, single-nucleotide variations (SNPs) in the ASB15’s flanking region were detected in Gushi chickens and the results suggested that ASB15 had a remarkable impact on chicken performance traits [39]. In our study, ASB15 was enriched in the intracellular signal transduction pathway. Intracellular signal transduction, for instance, the Notch signaling pathway, is a crucial regulator of skeletal muscle growth and regeneration [40]. Our result indicated that ASB15 may function in skeletal muscle development. NEURL1 was found to be related to reproduction traits in Bama Xiang pigs [41]. Li et al. [42] identified NEURL1 as a biomarker of muscle growth in selective breeding programs for rice flower carp. As shown by reports, RBM15 is vital to the development of cancer [43,44,45]. RBM15 was discovered to be involved in the process of developing duck embryonic skeletal muscle in breast and leg muscle by Chen et al. [46] through qRT-PCR analysis.
Moreover, among all the KEGG pathways we found, the top six were focal adhesion, cytokine–cytokine receptor interaction, Salmonella infection, the TGF-β signaling pathway, thiamine metabolism, and the MAPK signaling pathway. Huang et al. [47] suggested that fat mass- and obesity-associated (FTO) genes promoted chicken myoblast differentiation via the focal adhesion pathway. Cui et al. [48] found that focal adhesion was closely associated with the growth of embryonic skeletal muscle in chickens. Metzger et al. [49] discovered that cytokine–cytokine receptor interaction was involved in myoblasts proliferating derived from piglets of different ages. We have found no previous studies on the relationship between Salmonella infection and skeletal muscle development. The TGF-β signaling pathway was proven to be a key regulator of skeletal muscle growth [50,51]. Studies provided evidence that the MAPK signaling pathway played a significant role in myoblast development. For example, Guan et al. [52] discovered that butyrate activated the ERK/MAPK pathway to promote C2C12 myoblast proliferation. Chen et al. [53] demonstrated that lncRNA Has2os regulated skeletal muscle differentiation by inhibiting the JNK/MAPK signaling pathway in mice. In our study, genes detected in these above-mentioned KEGG pathways have the potential to participate in regulating skeletal muscle proliferation and differentiation.
Through variation site analysis, we identified where mutations could be resulting in functional consequences in our identified candidate genes. REEP1 and ST6GAL1 are the two potential target genes of miR-24-3p. We identified an INDEL in REEP1 which causes a frameshift mutation with predicted HIGH impact, which indicates that this variant may cause protein truncation or functional loss. No high-impact SNP variants were identified in REEP1. The impacts of INDEL and SNP variants discovered in ST6GAL1 were MODIFIER, meaning that there was no evidence of the functional impact on ST6GAL1. A previous study showed that REEP1 variants were related to hereditary spastic paraplegia (HSP) [54]. We predict that variants in REEP1 may also be related to skeletal muscle development.

5. Conclusions

Through RNA-seq and bioinformatics analysis, we identified 189 DEGs in the miR-24-3p mimic vs. NC comparison group, 119 DEGs were significantly up-regulated and 70 others were significantly down-regulated. Based on the GO enrichment terms and KEGG pathways, we found some significant genes. BP terms of GO enrichment analysis found that ENSGALG00000045898 and ENSGALG00000002511 were included in nuclear chromosomal segregation, reproduction, and nuclear division in the mimic vs. NC comparison. group. Pathways including focal adhesion, cytokine–cytokine receptor interaction, and MAPK signaling were among the top 20 most enriched KEGG pathways in our analysis. In addition, the variation site analysis of the tested samples provided an important basis for genetic variation in chickens. Finally, we predicted that REEP1 and ST6GAL1 were potential target genes of miR-24-3p by combining sequencing results with software predictions as well as RT-qPCR validation. These findings will help to reveal how the predicted genes work with miR-24-3p in the development of chicken myoblasts.

Supplementary Materials

The following supplemental information in this study can be downloaded at: https://www.mdpi.com/article/10.3390/genes14091764/s1, Table S1: Primers of 8 DEGs. Table S2: The list of 189 DEGs. Table S3: INDEL mimic NC. Table S4: SNP mimic NC.

Author Contributions

X.L. wrote the original manuscript. P.W., K.Z., J.Z., Q.W. and G.Z. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was jointly supported by the National Natural Science Foundation of China (32272856), the China Agriculture Research System (CARS-41), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Institutional Review Board Statement

The Animal Ethics Committee of Yangzhou University (202103298) gave their approval and evaluation of the animal experiments conducted in the study.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data of the study have been uploaded to the sequence read archive (SRA) database; the accession number is PRJNA993809 (https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA993809) (accessed on 12 July 2023).

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Gilroy, R. Spotlight on the avian gut microbiome: Fresh opportunities in discovery. Avian. Pathol. 2021, 50, 291–294. [Google Scholar] [CrossRef] [PubMed]
  2. Wang, Y.H.; Lin, J.; Wang, J.; Wu, S.G.; Qiu, K.; Zhang, H.J.; Qi, G.H. The Role of Incubation Conditions on the Regulation of Muscle Development and Meat Quality in Poultry. Front. Physiol. 2022, 13, 883134. [Google Scholar] [CrossRef] [PubMed]
  3. Tafrihi, M.; Hasheminasab, E. MiRNAs: Biology, Biogenesis, their Web-based Tools, and Databases. Microrna 2019, 8, 4–27. [Google Scholar] [CrossRef] [PubMed]
  4. Roush, S.; Slack, F.J. The let-7 family of microRNAs. Trends Cell Biol. 2008, 18, 505–516. [Google Scholar] [CrossRef] [PubMed]
  5. Correia de Sousa, M.; Gjorgjieva, M.; Dolicka, D.; Sobolewski, C.; Foti, M. Deciphering miRNAs’ Action through miRNA Editing. Int. J. Mol. Sci. 2019, 20, 6249. [Google Scholar] [CrossRef] [PubMed]
  6. Wei, G.; Sun, L.; Qin, S.; Li, R.; Chen, L.; Jin, P.; Ma, F. Dme-Hsa Disease Database (DHDD): Conserved Human Disease-Related miRNA and Their Targeting Genes in Drosophila melanogaster. Int. J. Mol. Sci. 2018, 19, 2642. [Google Scholar] [CrossRef]
  7. Stavast, C.J.; Erkeland, S.J. The Non-Canonical Aspects of MicroRNAs: Many Roads to Gene Regulation. Cells 2019, 8, 1465. [Google Scholar] [CrossRef] [PubMed]
  8. Yue, B.; Yang, H.; Wang, J.; Ru, W.; Wu, J.; Huang, Y.; Lan, X.; Lei, C.; Chen, H. Exosome biogenesis, secretion and function of exosomal miRNAs in skeletal muscle myogenesis. Cell Prolif. 2020, 53, e12857. [Google Scholar] [CrossRef]
  9. Liu, Y.; Yao, Y.; Zhang, Y.; Yan, C.; Yang, M.; Wang, Z.; Li, W.; Li, F.; Wang, W.; Yang, Y.; et al. MicroRNA-200c-5p Regulates Migration and Differentiation of Myoblasts via Targeting Adamts5 in Skeletal Muscle Regeneration and Myogenesis. Int. J. Mol. Sci. 2023, 24, 4995. [Google Scholar] [CrossRef]
  10. Wang, Z.; Zhang, X.; Li, Z.; Abdalla, B.A.; Chen, Y.; Nie, Q. MiR-34b-5p Mediates the Proliferation and Differentiation of Myoblasts by Targeting IGFBP2. Cells 2019, 8, 360. [Google Scholar] [CrossRef]
  11. Wu, P.; He, M.; Zhang, X.; Zhou, K.; Zhang, T.; Xie, K.; Dai, G.; Wang, J.; Wang, X.; Zhang, G. miRNA-seq analysis in skeletal muscle of chicken and function exploration of miR-24-3p. Poult. Sci. 2022, 101, 102120. [Google Scholar] [CrossRef]
  12. Shi, H.; He, Y.; Li, X.; Du, Y.; Zhao, J.; Ge, C. Regulation of Non-Coding RNA in the Growth and Development of Skeletal Muscle in Domestic Chickens. Genes 2022, 13, 1033. [Google Scholar] [CrossRef] [PubMed]
  13. Wu, P.; Zhang, X.; Zhang, G.; Chen, F.; He, M.; Zhang, T.; Wang, J.; Xie, K.; Dai, G. Transcriptome for the breast muscle of Jinghai yellow chicken at early growth stages. PeerJ 2020, 8, e8950. [Google Scholar] [CrossRef] [PubMed]
  14. Takács, R.; Póliska, S.; Juhász, T.; Barna, K.B.; Matta, C. Isolation of High-Quality Total RNA from Small Animal Articular Cartilage for Next-Generation Sequencing. Curr. Protoc. 2023, 3, e692. [Google Scholar] [CrossRef]
  15. Gaines, D.; Brodsky, E.; Kaur, H.; Nestorova, G.G. RNA capture pin technology: Investigating long-term stability and mRNA purification specificity of oligonucleotide immobilization on gold and streptavidin surfaces. Anal. Bioanal. Chem. 2023; Epub ahead of print. [Google Scholar] [CrossRef]
  16. Liu, C.; Chen, Z.; Chen, J.; Wang, S.; Li, J.; Mao, X. Transcriptome analysis reveals the potential mechanism of carotenoids change in hepatopancreas under low-temperature storage from swimming crab (Portunus trituberculatus). Food Chem. 2023, 408, 135241. [Google Scholar] [CrossRef] [PubMed]
  17. Wilton, R.; Szalay, A.S. Performance optimization in DNA short-read alignment. Bioinformatics 2022, 38, 2081–2087. [Google Scholar] [CrossRef] [PubMed]
  18. Liu, S.; Wang, Z.; Zhu, R.; Wang, F.; Cheng, Y.; Liu, Y. Three Differential Expression Analysis Methods for RNA Sequencing: Limma, EdgeR, DESeq2. J. Vis. Exp. 2021, 175, 62528. [Google Scholar] [CrossRef]
  19. Tan, L.P.; Gao, R.F.; Yin, X.; Chen, X.M.; Li, F.F.; Yuan, L.; He, J.L. The expression of lincRNA AC027700.1 in mouse decidualization. Yi Chuan 2022, 44, 168–177. [Google Scholar] [CrossRef]
  20. Przybyła, W.; Gjersvoll Paulsen, K.M.; Mishra, C.K.; Nygård, S.; Engebretsen, S.; Ruud, E.; Trøen, G.; Beiske, K.; Baumbusch, L.O. Whole exome sequencing of high-risk neuroblastoma identifies novel non-synonymous variants. PLoS ONE 2022, 17, e0273280. [Google Scholar] [CrossRef] [PubMed]
  21. McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M.; et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20, 1297–1303. [Google Scholar] [CrossRef] [PubMed]
  22. Kumar, S.; Nadda, N.; Paul, S.; Gamanagatti, S.; Dash, N.R.; Vanamail, P.; Saraya, A.; Shalimar; Nayak, B. Evaluation of the cell-free DNA integrity index as a liquid biopsy marker to differentiate hepatocellular carcinoma from chronic liver disease. Front. Mol. Biosci. 2022, 9, 1024193. [Google Scholar] [CrossRef]
  23. Zhang, G.; Zhang, J.; Wu, P.; Ling, X.; Wang, Q.; Zhou, K.; Li, P.; Zhang, L.; Ye, H.; Zhang, Q.; et al. Transcriptome Sequencing Analysis of circRNA in Skeletal Muscle between Fast- and Slow-Growing Chickens at Embryonic Stages. Animals 2022, 12, 3166. [Google Scholar] [CrossRef]
  24. Soglia, F.; Petracci, M.; Davoli, R.; Zappaterra, M. A critical review of the mechanisms involved in the occurrence of growth-related abnormalities affecting broiler chicken breast muscles. Poult. Sci. 2021, 100, 101180. [Google Scholar] [CrossRef]
  25. Zhang, G.; Zhang, X.; Zhou, K.; Ling, X.; Zhang, J.; Wu, P.; Zhang, T.; Xie, K.; Dai, G. miRNA-10a-5p Targeting the BCL6 Gene Regulates Proliferation, Differentiation and Apoptosis of Chicken Myoblasts. Int. J. Mol. Sci. 2022, 23, 79545. [Google Scholar] [CrossRef] [PubMed]
  26. Zhang, D.; Ran, J.; Li, J.; Yu, C.; Cui, Z.; Amevor, F.K.; Wang, Y.; Jiang, X.; Qiu, M.; Du, H.; et al. miR-21-5p Regulates the Proliferation and Differentiation of Skeletal Muscle Satellite Cells by Targeting KLF3 in Chicken. Genes 2021, 12, 814. [Google Scholar] [CrossRef]
  27. Cao, X.; Tang, S.; Du, F.; Li, H.; Shen, X.; Li, D.; Wang, Y.; Zhang, Z.; Xia, L.; Zhu, Q.; et al. miR-99a-5p Regulates the Proliferation and Differentiation of Skeletal Muscle Satellite Cells by Targeting MTMR3 in Chicken. Genes 2020, 11, 369. [Google Scholar] [CrossRef]
  28. Guglielmi, A. A complete overview of REEP1: Old and new insights on its role in hereditary spastic paraplegia and neurodegeneration. Rev. Neurosci. 2020, 31, 351–362. [Google Scholar] [CrossRef]
  29. Qin, S.; You, P.; Yu, H.; Su, B. REEP1 Preserves Motor Function in SOD1(G93A) Mice by Improving Mitochondrial Function via Interaction with NDUFA4. Neurosci. Bull. 2022, 39, 929–946. [Google Scholar] [CrossRef]
  30. Li, J.; Zhao, B.; Chen, S.; Wang, Z.; Shi, K.; Lei, B.; Cao, C.; Ke, Z.; Wang, R. Downhill running induced DNA damage enhances mitochondrial membrane permeability by facilitating ER-mitochondria signaling. J. Muscle Res. Cell Motil. 2022, 43, 185–193. [Google Scholar] [CrossRef] [PubMed]
  31. Kramer, G.; Steiner, G.E.; Neumayer, C.; Prinz-Kashani, M.; Hohenfellner, M.; Gomha, M.; Ghoneim, M.; Newman, M.; Marberger, M. Over-expression of anti-CD75 reactive proteins on distal and collecting renal tubular epithelial cells in calcium-oxalate stone-forming kidneys in Egypt. BJU Int. 2004, 93, 822–826. [Google Scholar] [CrossRef] [PubMed]
  32. Hait, N.C.; Maiti, A.; Wu, R.; Andersen, V.L.; Hsu, C.C.; Wu, Y.; Chapla, D.G.; Takabe, K.; Rusiniak, M.E.; Bshara, W.; et al. Extracellular sialyltransferase st6gal1 in breast tumor cell growth and invasiveness. Cancer Gene Ther. 2022, 29, 1662–1675. [Google Scholar] [CrossRef]
  33. Gc, S.; Bellis, S.L.; Hjelmeland, A.B. ST6Gal1: Oncogenic signaling pathways and targets. Front. Mol. Biosci. 2022, 9, 962908. [Google Scholar] [CrossRef] [PubMed]
  34. Dorsett, K.A.; Marciel, M.P.; Hwang, J.; Ankenbauer, K.E.; Bhalerao, N.; Bellis, S.L. Regulation of ST6GAL1 sialyltransferase expression in cancer cells. Glycobiology 2021, 31, 530–539. [Google Scholar] [CrossRef]
  35. Osseni, A.; Ravel-Chapuis, A.; Belotti, E.; Scionti, I.; Gangloff, Y.G.; Moncollin, V.; Mazelin, L.; Mounier, R.; Leblanc, P.; Jasmin, B.J.; et al. Pharmacological inhibition of HDAC6 improves muscle phenotypes in dystrophin-deficient mice by downregulating TGF-β via Smad3 acetylation. Nat. Commun. 2022, 13, 7108. [Google Scholar] [CrossRef]
  36. Endo, T. Mammalian O-mannosyl glycans: Biochemistry and glycopathology. Proc. Jpn. Acad. Ser. B Phys. Biol. Sci. 2019, 95, 39–51. [Google Scholar] [CrossRef]
  37. Wang, Y.C.; Jiang, R.R.; Kang, X.T.; Li, Z.J.; Han, R.L.; Geng, J.; Fu, J.X.; Wang, J.F.; Wu, J.P. Identification of single nucleotide polymorphisms in the ASB15 gene and their associations with chicken growth and carcass traits. Genet. Mol. Res. 2015, 14, 11377–11388. [Google Scholar] [CrossRef] [PubMed]
  38. Wang, Y.C.; Li, Z.J.; Han, R.L.; Xu, C.L.; Wang, S.H.; Sun, G.R.; Wang, S.H.; Wu, J.P.; Kang, X.T. Promoter analysis and tissue expression of the chicken ASB15 gene. Br. Poult. Sci. 2017, 58, 26–31. [Google Scholar] [CrossRef] [PubMed]
  39. Wang, Y.C.; Han, R.L.; Li, Z.J.; Geng, J.; Tian, Y.D.; Jiang, R.R.; Wu, J.P.; Kang, X.T. Polymorphisms of Flanking Region of the ASB15 Gene and Their Associations with Performance Traits in Chicken. Anim. Biotechnol. 2017, 28, 53–60. [Google Scholar] [CrossRef]
  40. 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] [PubMed]
  41. Mo, J.; Lu, Y.; Zhu, S.; Feng, L.; Qi, W.; Chen, X.; Xie, B.; Chen, B.; Lan, G.; Liang, J. Genome-Wide Association Studies, Runs of Homozygosity Analysis, and Copy Number Variation Detection to Identify Reproduction-Related Genes in Bama Xiang Pigs. Front. Vet. Sci. 2022, 9, 892815. [Google Scholar] [CrossRef] [PubMed]
  42. Li, Z.; Du, X.; Wen, L.; Li, Y.; Qin, J.; Chen, Z.; Huang, Y.; Wu, X.; Luo, H.; Lin, Y.; et al. Transcriptome analysis reveals the involvement of ubiquitin-proteasome pathway in the regulation of muscle growth of rice flower carp. Comp. Biochem. Physiol. Part D Genom. Proteom. 2022, 41, 100948. [Google Scholar] [CrossRef] [PubMed]
  43. Zhang, Z.; Mei, Y.; Hou, M. Knockdown RBM15 Inhibits Colorectal Cancer Cell Proliferation and Metastasis Via N6-Methyladenosine (m6A) Modification of MyD88 mRNA. Cancer Biother. Radiopharm. 2022, 37, 976–986. [Google Scholar] [CrossRef]
  44. Zeng, X.; Chen, K.; Li, L.; Tian, J.; Ruan, W.; Hu, Z.; Peng, D.; Chen, Z. Epigenetic activation of RBM15 promotes clear cell renal cell carcinoma growth, metastasis and macrophage infiltration by regulating the m6A modification of CXCL11. Free Radic. Biol. Med. 2022, 184, 135–147. [Google Scholar] [CrossRef]
  45. Ma, M.; Wang, W.; Wang, B.; Yang, Y.; Huang, Y.; Zhao, G.; Ye, L. The prognostic value of N6-methyladenosine RBM15 regulators in lung adenocarcinoma. Cell Mol. Biol. 2022, 68, 130–139. [Google Scholar] [CrossRef]
  46. Chen, B.; Liu, S.; Zhang, W.; Xiong, T.; Zhou, M.; Hu, X.; Mao, H.; Liu, S. Profiling Analysis of N6-Methyladenosine mRNA Methylation Reveals Differential m6A Patterns during the Embryonic Skeletal Muscle Development of Ducks. Animals 2022, 12, 2593. [Google Scholar] [CrossRef] [PubMed]
  47. Huang, H.; Liu, L.; Li, C.; Liang, Z.; Huang, Z.; Wang, Q.; Li, S.; Zhao, Z. Fat mass- and obesity-associated (FTO) gene promoted myoblast differentiation through the focal adhesion pathway in chicken. 3 Biotech 2020, 10, 403. [Google Scholar] [CrossRef]
  48. Cui, C.; Yin, H.; Han, S.; Zhang, Y.; Zhang, Y.; Zhu, Q. Quantitative proteomic and phosphoproteomic analysis of chicken skeletal muscle during embryonic development. Anim. Biotechnol. 2023, 34, 122–133. [Google Scholar] [CrossRef]
  49. Metzger, K.; Kalbe, C.; Siengdee, P.; Ponsuksili, S. The effects of temperature and donor piglet age on the transcriptomic profile and energy metabolism of myoblasts. Front. Physiol. 2022, 13, 979283. [Google Scholar] [CrossRef] [PubMed]
  50. Liao, Z.; Lan, H.; Jian, X.; Huang, J.; Wang, H.; Hu, J.; Liao, H. Myofiber directs macrophages IL-10-Vav1-Rac1 efferocytosis pathway in inflamed muscle following CTX myoinjury by activating the intrinsic TGF-β signaling. Cell Commun. Signal. 2023, 21, 168. [Google Scholar] [CrossRef] [PubMed]
  51. Xiao, J.; Huang, J.; Jian, X.; Wang, H.; Lan, H.; Liao, Z.; Gu, R.; Hu, J.; Liao, H. IRE1α arm of unfolded protein response in muscle-specific TGF-β signaling-mediated regulation of muscle cell immunological properties. Cell Mol. Biol. Lett. 2023, 28, 15. [Google Scholar] [CrossRef] [PubMed]
  52. Guan, L.; Cao, Z.; Pan, Z.; Zhao, C.; Xue, M.; Yang, F.; Chen, J. Butyrate promotes C2C12 myoblast proliferation by activating ERK/MAPK pathway. Mol. Omics 2023, 19, 552–559. [Google Scholar] [CrossRef] [PubMed]
  53. Chen, W.; Chen, W.; Liu, P.; Qian, S.; Tao, S.; Huang, M.; Xu, W.; Li, C.; Chen, X.; Lin, H.; et al. Role of lncRNA Has2os in Skeletal Muscle Differentiation and Regeneration. Cells 2022, 11, 3497. [Google Scholar] [CrossRef] [PubMed]
  54. McCorquodale, D.S., 3rd; Ozomaro, U.; Huang, J.; Montenegro, G.; Kushman, A.; Citrigno, L.; Price, J.; Speziani, F.; Pericak-Vance, M.A.; Züchner, S. Variant screening of spastin, atlastin, and REEP1 in hereditary spastic paraplegia. Clin. Genet. 2011, 79, 523–530. [Google Scholar] [CrossRef] [PubMed]
Genes 14 01764 g001
Figure 1. Sequencing data analysis. (A) The principal component analysis (PCA) of these 8 samples. (B) The correlation scatterplot with density. The colors from yellow to purple represent the intensity of FPKM values from high to low. (C) Boxplot of gene expression distribution of all examples. (D) Venn diagram of mimic vs. NC.
Figure 1. Sequencing data analysis. (A) The principal component analysis (PCA) of these 8 samples. (B) The correlation scatterplot with density. The colors from yellow to purple represent the intensity of FPKM values from high to low. (C) Boxplot of gene expression distribution of all examples. (D) Venn diagram of mimic vs. NC.
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Figure 2. Statistics of DEGs in mimic vs. NC. (A) The Volcano plot of mimic vs. NC. (B) Clustering analysis of differentially expressed genes.
Figure 2. Statistics of DEGs in mimic vs. NC. (A) The Volcano plot of mimic vs. NC. (B) Clustering analysis of differentially expressed genes.
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Figure 3. Gene ontology enrichment analysis. (A) The top 30 GO terms in mimic vs. NC. (B) The numbers of down-regulated genes and rich factors in GO enrichment terms.
Figure 3. Gene ontology enrichment analysis. (A) The top 30 GO terms in mimic vs. NC. (B) The numbers of down-regulated genes and rich factors in GO enrichment terms.
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Figure 4. KEGG pathway analysis. The figure displays the top 20 pathways and their corresponding predicted target genes in the mimic vs. NC comparison group.
Figure 4. KEGG pathway analysis. The figure displays the top 20 pathways and their corresponding predicted target genes in the mimic vs. NC comparison group.
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Figure 5. Variation site analysis. (A) INDEL (insertion–deletion) impact analysis. (B) INDEL region analysis. The downstream_gene_variant occurred in DOWNSTREAM. The frameshift_variant occurred in EXON. The intergenic_region occurred in INTERGENIC. The intron_variant occurred in INTRON. The splice_acceptor_variant occurred in SPLICE_SITE_ACCEPTOR. The splice_donor_variant occurred in SPLICE_SITE_DONOR. The splice_region_variant occurred in SPLICE_SITE_REGION. The non_coding_transcript_exon_variant occurred in TRANSCRIPT. The upstream_gene_variant occurred in UPSTREAM. The 3_prime_UTR_variant occurred in UTR_3_PRIME. The 5_prime_UTR_variant occurred in UTR_5_PRIME. Few variation sites were found in GENE. (C) SNP function analysis. (D) SNP impact analysis.
Figure 5. Variation site analysis. (A) INDEL (insertion–deletion) impact analysis. (B) INDEL region analysis. The downstream_gene_variant occurred in DOWNSTREAM. The frameshift_variant occurred in EXON. The intergenic_region occurred in INTERGENIC. The intron_variant occurred in INTRON. The splice_acceptor_variant occurred in SPLICE_SITE_ACCEPTOR. The splice_donor_variant occurred in SPLICE_SITE_DONOR. The splice_region_variant occurred in SPLICE_SITE_REGION. The non_coding_transcript_exon_variant occurred in TRANSCRIPT. The upstream_gene_variant occurred in UPSTREAM. The 3_prime_UTR_variant occurred in UTR_3_PRIME. The 5_prime_UTR_variant occurred in UTR_5_PRIME. Few variation sites were found in GENE. (C) SNP function analysis. (D) SNP impact analysis.
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Figure 6. Target gene prediction and verification of the sequencing results. (A) Venn diagram showed the common genes between the target genes predicted by miRDB and the DEGs detected by sequencing. (B) The Venn diagram showed common genes between the target genes predicted by Targetscan and DEGs detected by sequencing. (C) Agarose gel results of 8 DEGs primers. Character “M” represented the marker. (D) Expression level of 8 DEGs in RNA-seq results. The colors from red to blue represent a level from high to low. (E) RT-qPCR and RNA-seq results of 4 down-regulated DEGs. (F) RT-qPCR and RNA-seq results of 4 up-regulated DEGs.
Figure 6. Target gene prediction and verification of the sequencing results. (A) Venn diagram showed the common genes between the target genes predicted by miRDB and the DEGs detected by sequencing. (B) The Venn diagram showed common genes between the target genes predicted by Targetscan and DEGs detected by sequencing. (C) Agarose gel results of 8 DEGs primers. Character “M” represented the marker. (D) Expression level of 8 DEGs in RNA-seq results. The colors from red to blue represent a level from high to low. (E) RT-qPCR and RNA-seq results of 4 down-regulated DEGs. (F) RT-qPCR and RNA-seq results of 4 up-regulated DEGs.
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Table 1. Quality assessment of myoblast transcriptome sequencing data.
Table 1. Quality assessment of myoblast transcriptome sequencing data.
SampleRaw ReadsClean ReadsError RateQ20Q30GC_ pct
mimic_146,980,61444,438,9560.0396.08%90.12%51.65%
mimic_243,638,87041,261,9880.0396.34%90.68%50.73%
mimic_346,386,29044,033,6540.0396.41%90.85%51.03%
mimic_447,859,39245,430,2480.0396.14%90.30%51.36%
NC_141,796,29437,958,9900.0396.28%90.42%48.71%
NC_247,161,43245,087,5400.0396.62%91.18%49.07%
NC_341,438,59638,035,4540.0396.02%89.94%49.64%
NC_446,961,06242,302,2240.0396.54%90.92%48.09%
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Ling, X.; Wang, Q.; Wu, P.; Zhou, K.; Zhang, J.; Zhang, G. Exploration of Potential Target Genes of miR-24-3p in Chicken Myoblasts by Transcriptome Sequencing Analysis. Genes 2023, 14, 1764. https://doi.org/10.3390/genes14091764

AMA Style

Ling X, Wang Q, Wu P, Zhou K, Zhang J, Zhang G. Exploration of Potential Target Genes of miR-24-3p in Chicken Myoblasts by Transcriptome Sequencing Analysis. Genes. 2023; 14(9):1764. https://doi.org/10.3390/genes14091764

Chicago/Turabian Style

Ling, Xuanze, Qifan Wang, Pengfei Wu, Kaizhi Zhou, Jin Zhang, and Genxi Zhang. 2023. "Exploration of Potential Target Genes of miR-24-3p in Chicken Myoblasts by Transcriptome Sequencing Analysis" Genes 14, no. 9: 1764. https://doi.org/10.3390/genes14091764

APA Style

Ling, X., Wang, Q., Wu, P., Zhou, K., Zhang, J., & Zhang, G. (2023). Exploration of Potential Target Genes of miR-24-3p in Chicken Myoblasts by Transcriptome Sequencing Analysis. Genes, 14(9), 1764. https://doi.org/10.3390/genes14091764

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