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. 2017 Dec 11;12(12):e0187966. doi: 10.1371/journal.pone.0187966

RNA-seq reveals transcriptome changes in goats following myostatin gene knockout

Lamei Wang 1, Bei Cai 1, Shiwei Zhou 1, Haijing Zhu 2,3, Lei Qu 2,3, Xiaolong Wang 1, Yulin Chen 1,*
Editor: Meijia Zhang4
PMCID: PMC5724853  PMID: 29228005

Abstract

Myostatin (MSTN) is a powerful negative regulator of skeletal muscle mass in mammalian species that is primarily expressed in skeletal muscles, and mutations of its encoding gene can result in the double-muscling trait. In this study, the CRISPR/Cas9 technique was used to edit MSTN in Shaanbei Cashmere goats and generate knockout animals. RNA sequencing was used to determine and compare the transcriptome profiles of the muscles from three wild-type (WT) goats, three fibroblast growth factor 5 (FGF5) knockout goats (FGF5+/- group) and three goats with disrupted expression of both the FGF5 and MSTN genes (FM+/- group). The sequence reads were obtained using the Illumina HiSeq 2000 system and mapped to the Capra hircus reference genome using TopHat (v2.0.9). In total, 68.93, 62.04 and 66.26 million clean sequencing reads were obtained from the WT, FM+/- and FGF5+/- groups, respectively. There were 201 differentially expressed genes (DEGs) between the WT and FGF5+/- groups, with 86 down- and 115 up-regulated genes in the FGF5+/- group. Between the WT and FM+/- groups, 121 DEGs were identified, including 81 down- and 40 up-regulated genes in the FM+/- group. A total of 198 DEGs were detected between the FGF5+/- group and FM+/- group, with 128 down- and 70 up-regulated genes in the FM+/- group. At the transcriptome level, we found substantial changes in genes involved in fatty acid metabolism and the biosynthesis of unsaturated fatty acids, such as stearoyl-CoA dehydrogenase, 3-hydroxyacyl-CoA dehydratase 2, ELOVL fatty acid elongase 6 and fatty acid synthase, suggesting that the expression levels of these genes may be directly regulated by MSTN and that these genes are likely downstream targets of MSTN with potential roles in lipid metabolism in goats. Moreover, five randomly selected DEGs were further validated with qRT-PCR, and the results were consistent with the transcriptome analysis. The present study provides insight into the unique transcriptome profile of the MSTN knockout goat, which is a valuable resource for studying goat genomics.

Introduction

Myostatin (MSTN) is a secreted growth factor and a member of the TGF-β superfamily that functions as a critical autocrine/paracrine inhibitor and negatively regulates skeletal muscle growth and development through the regulation of anabolic and catabolic pathways in skeletal muscles [1]. Myostatin is expressed almost exclusively in skeletal muscle [2]. Mutations in the coding region of MSTN result in a double-muscling phenotype in many species, including cattle [3, 4], mice [5–7] and humans [8]. Myostatin both regulates lean tissue mass and affects body fat content in mice [9]. In MSTN knockout mice (MSTN-/-), the absence of MSTN results in increased skeletal muscle mass, reduced fat tissue, increased insulin sensitivity, enhanced fatty acid oxidation, and an elevated resistance to obesity [6, 10], whereas overexpression of myostatin induces muscle atrophy [11]. Consequently, a number of strategies to block the effects of myostatin have been developed and tested in various models of neuromuscular disorders, muscle-wasting conditions or metabolic disturbances [12–14]. Mice with Lewis Lung carcinoma treated with ActRIIB-Fc, a soluble myostatin receptor that binds myostatin, showed increased body weight and muscle weight as well as significantly increased grip strength [15]. In mdx mice, a model for Duchenne muscular dystrophy, an antibody-mediated myostatin blockade was found to ameliorate the pathophysiology and muscle weakness associated with this condition [16]. However, these strategies have less efficiency in blocking the effects of myostatin.

Natural myostatin gene mutations occur in cattle breeds such as the Belgian Blue, which exhibits obviously increased muscle mass [17]. Muscular hypertrophy, or the double-muscle phenotype, is a heritable condition in cattle [3]. The Shaanbei Cashmere goat is a local breed in China that produces both fiber and meat products. The meat is renowned for its lower fat content compared to that of beef and lamb [18], and thus, it is recommended as a healthy food for consumers. To increase meat production in goats, efforts aimed at the development of strategies to significantly increase muscle growth by manipulating MSTN gene expression have been intensively undertaken [19].

High-throughput mRNA sequencing (RNA-seq) offers the ability to discover new genes and transcripts and to measure both transcript abundance and expression in a single experiment [20, 21]. Based on RNA-seq technology, we measured the integrated global gene expression and signaling pathway activities in goats after MSTN gene knockout. Microarray technology enables a broad overview of the impact of treatments on the expression of all known genes, and this technology has been already been used to examine the effects of constitutive MSTN deficiency in mice and cattle [22–25]. However, high-throughput mRNA sequencing (RNA-Seq) offers the ability to discover new genes and transcripts and to measure transcript expression levels in a single assay [26–28]. Many studies have shown that RNA-Seq is more accurate over a greater dynamic range of gene expression than expression microarrays [29, 30]. Recently, genome-wide gene expression profiling has gained ground due to the development and application of large-scale sequencing techniques to obtain gene sequences and develop molecular markers, especially in less researched species [31–34].

Wang et al. recently reported the successful application of the CRISPR/Cas9 system to manipulate the goat genome [35]. They also demonstrated the utility of this approach by disrupting MSTN, which resulted in enhanced body weight and larger muscle fibers in goats with Cas9-mediated genetic modifications. They further characterized the effects of these genome modifications by hematoxylin and eosin (H&E) staining, quantitative PCR, Western blotting, and immunofluorescence staining (Animal Genetics, under review). However, a transcriptomic analysis of the changes in either gene expression or lipid metabolism in MSTN knockout goats has not been reported.

MSTN was initially characterized as a potent inhibitor of skeletal muscle growth and development [36]. However, its role in the regulation of glucose and lipid metabolism has gained significant attention [9, 37–40]. The goals of this study were to identify important candidate genes related to muscle, glucose and lipid metabolism and to further determine the transcriptomic changes using RNA-Seq. These results will provide valuable information about key genes in this species and improve our understanding of the molecular mechanisms regulating muscle, glucose and lipid metabolism in goats.

Materials and methods

Ethics statement

All animal experiments and procedures were carried out in strict accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals by the National Institutes of Health. The protocol was approved by the Committee on the Ethics of Animal Experiments of Northwest A&F University (Approval ID: 2014ZX08008-002).

Animal samples

The animals used in the present study were previously generated by Wang et al. [35]. The genotypes of three MSTN- and FGF5-disrupted goats and three FGF5-disrupted goats are summarized in S1 Table. All animals were raised in the same way under natural light with free access to water and food at the Shaanbei Cashmere Goat Farm at Yulin University. Longissimus dorsi muscles were obtained from nine one-year-old Shaanbei Cashmere goats: three wild-type (WT) goats (each was determined to possess an unedited genome), three goats with only the fibroblast growth factor 5 (FGF5) gene knocked out (FGF5+/- group) and three goats with disruptions in both the FGF5 and MSTN genes (FM+/- group). The transgenic goats were generated using the CRISPR/Cas9 system. Fresh longissimus dorsi muscle samples were harvested immediately from goats after surgery under local anesthesia using 3 mL of procaine (1%) injected into the skin/muscle tissues. All efforts were made to minimize animal suffering and to reduce the number of animals used. Obtained fresh longissimus dorsi muscle samples were washed with sterile normal saline three times, frozen in RNAlater (Takara, Dalian, China) using liquid nitrogen, and sent to Novogene Bioinformatics Technology Co. Ltd. (Beijing, China).

RNA extraction and preparation

Genome-wide transcriptome libraries were constructed from the three sets of muscle samples from the WT, FGF5+/- and FM+/- goats. Longissimus dorsi muscle tissue blocks containing approximately 80–100 mg of tissue were dissolved in TRIzol reagent (Invitrogen, USA) for total RNA extraction. RNA degradation and contamination were monitored by electrophoresis in 1% agarose gels. The quality and quantity of RNA were checked using a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA) and a Qubit® RNA Assay Kit in a Qubit® 2.0 Fluorometer (Life Technologies, CA, USA). RNA integrity was assessed using an RNA Nano 6000 Assay Kit and a Bioanalyzer 2100 system (Agilent Technologies, CA, USA).

mRNA-Seq library preparation and sequencing

Illumina® (NEB, USA) mRNA-Seq libraries were prepared with 3 μg of total RNA using the NEBNext® Ultra™ RNA Library Prep Kit according to the manufacturer’s instructions, and index codes were added to attribute the sequences to each sample. Briefly, RNA was purified from total RNA using Poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature conditions in NEBNext First Strand Synthesis Reaction Buffer (5x). First-strand cDNA was synthesized using random hexamer primers and M-MLV Reverse Transcriptase (RNase H minus). Second-strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of the 3’ ends of cDNA fragments, a NEBNext Adaptor with a hairpin loop structure was ligated to prepare the cDNA for hybridization. To preferentially select cDNA fragments of 150~200 bp in length, the library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA). Then, 3 μL of USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95°C before PCR. Then, PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. Finally, the PCR products were purified (AMPure XP system), and the library quality was assessed using the Agilent Bioanalyzer 2100 system.

Clustering of the index-coded samples was performed on a cBot Cluster Generation System using the TruSeq PE Cluster Kit v3-cBot-HS (Illumina) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina HiSeq 2000 platform, and 100-bp paired-end reads were generated.

Quality control and read mapping to the reference genome

Raw data (raw reads) in the FASTQ format were first processed using in-house Perl scripts and the Q20, Q30 and GC contents of the clean data were calculated.

For mapping, an index of the reference genome was built using Bowtie v2.0.6, and paired-end clean reads were aligned to the reference genome using TopHat v2.0.9.

Gene expression

The counts of the read numbers mapped to each gene were processed by HTSeq v0.6.1, and the FPKM (expected number of fragments per kilobase sequence per million base pairs sequenced) of each gene was calculated based on the length of the gene and the reads count mapped to that gene [41]. The FPKM considers both the effect of sequencing depth and gene length for the read count and is currently the most commonly used method for estimating gene expression levels [42].

DEGs were identified using the R package “DESeq” (1.10.1) with raw gene counts as the input, and quantile normalization was applied for variable library sizes. DESeq provides statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate [43]. Differentially expressed genes (DEGs) were determined by DEGseq with a cutoff threshold of P-value < 0.05 [44].

Differential gene functional annotation

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes databases (KEGG, http://www.genome.jp/kegg) enrichment was analyzed using differential genes as the foreground and all genes as the background for the identified differential transcripts. GO enrichment analysis of the DEGs was performed using the GOseq R package [45], in which gene length bias was corrected. GO terms with corrected P-values < 0.05 were considered significantly enriched by DEGs.

The KEGG is a database resource for understanding high-level functions and utilities of the biological system [46]. KEGG Orthology Based Annotation System (KOBAS, 2.0) software was used to test the statistical enrichment of differentially expression genes in the KEGG pathways.

qRT-PCR analysis for the validation of RNA-Seq data

Real-time quantitative reverse transcription PCR (qRT-PCR) was used to validate the RNA-Seq data. The total RNA isolated for RNA sequencing was used to perform qRT-PCR. Reverse transcription was performed with 1 μg of total RNA using the PrimeScript RT Reagent Kit (Invitrogen, USA). Real-time quantitative PCR analyses were then performed with a Bio-Rad IQ5 Optical System; individual reactions were prepared with 5 ng of cDNA and SYBR Green PCR master mix (Takara, Dalian, China) in a final volume of 20 μL. All reactions were performed in triplicate. Cycle threshold (Ct) values were normalized to glyceraldehyde 3-phosphate dehydrogenase (GAPDH), and comparative quantification of mRNA was performed using the 2-ΔΔCt method. The primer sequences used in the qPCR assay are provided in S2 Table [47–49].

Statistical analysis

All data are presented as the means ± standard deviation (SD), and comparisons were performed by analysis of variance (ANOVA) (SAS). A probability of less than 0.05 was considered to be statistically significant.

Results

Summary of the raw sequence reads and DEGs

To understand the gene expression profiles and differences between the three samples at transcript resolution, we performed RNA-Seq on three cDNA sequencing libraries constructed using muscle tissues from WT, FGF5+/- and FM+/- goats. We then mapped the filtered clean reads to the reference goat genome CHIR-1.0 (Table 1). In total, we obtained 68.93, 62.04 and 66.26 million reads for the sequencing libraries constructed from the WT, FGF5+/- and FM+/- goat muscle tissues, respectively. We found that in each sample, ~73% of reads could be mapped to the reference genome, which is comparable to the reports for two non-model organisms, swine [50] and bovine [51], in which 61.4–75.0% of reads are mapped to the reference genome. Thus, our mapping percentage suggests good sequence quality.

Table 1. Statistics of the Illumina RNA-Seq reads in the normal and transgenic goat muscle libraries that were mapped to the goat reference genome CHIR-1.0.

Summary statistics Number Percentage Number Percentage Number Percentage
WT FM+/- FGF5+/-
Total raw reads 70553433 63677463 67796573
Total mapped 51626789 74.99% 45847205 73.90% 48958298 73.90%
Total clean reads 68933544 62036347 66257588
Total clean base pairs (Gb) 10.34 Gb 9.30 Gb 9.94 Gb
Read map to '+' 25346942 36.81% 22469249 36.22% 24050354 36.30%
Read map to '-' 25053573 36.39% 22207247 35.80% 23732089 35.83%
Multiply mapped 1226274 1.79% 1170709 1.89% 1175855 1.77%
Uniquely mapped 50400515 73.00% 44676496 72.01% 47782443 72.13%
Non-splice reads 25925041 37.59% 22021886 35.52% 24005700 36.24%
Splice reads 24475474 35.61% 22654610 36.49% 23776743 35.88%
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Identification of DEGs

The read count data obtained from the transcriptome were used to analyze differences in gene expression. We obtained 201 DEGs, including 115 up-regulated genes and 86 down-regulated genes, between the FGF5+/- group and the WT group. Moreover, 121 DEGs, including 40 up-regulated genes and 81 down-regulated genes, were identified between the FM+/- group and the WT group. A total of 198 DEGs, including 70 up-regulated genes and 128 down-regulated genes, were identified between the FM+/- group and the FGF5+/- group. We used a false discovery rate (FDR) ≤ 0.001 and an absolute value of the log2 ratio ≥ 1 as the threshold to judge the significance of differences in gene expression. Library size-normalized counts for the three samples were generated, and volcano plots for the DEGs are shown in Fig 1A, 1B and 1C. A Venn diagram showing the number of DEGs among the three pairwise comparisons is shown in Fig 1D. Hierarchical clustering showed the expression profiles of the top 40 DEGs (Fig 2). The full names of the top 20 DEGs in the WT, FM+/- and FGF5+/- goats and their functional characteristics are shown in Table 2 [52–70].

Fig 1. Differentially expressed genes in the RNA-Seq data.

Fig 1

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Volcano plot of statistically significant differentially expressed genes at P ≤ 0.05 identified from the RNA-Seq libraries of normal and transgenic goat muscle (1A, 1B, 1C). A Venn diagram showing the DEGs identified from comparisons of FGF5+/- vs WT goats, FM+/- vs WT goats and FM+/- vs FGF5+/- goats (1D).

Fig 2. A hierarchically clustered heatmap showing the expression patterns of the 40 most DEGs.

Fig 2

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The red blocks represent the overexpressed genes, and the blue blocks represent genes with the lowest expression levels. Colored bars indicate the expression levels.

Table 2. Biological functions of the 20 most differentially expressed genes in the longissimus dorsi muscle identified by pairwise comparisons of FGF5+/- vs WT, FM+/- vs WT and FM+/- vs FGF5+/-.

Gene ID Symbol Summary
100861382 KRT27 This gene encodes a member of the type I (acidic) keratin family, which belongs to the superfamily of intermediate filament (IF) proteins that is essential for the proper assembly of type I and type II keratin protein complexes and the formation of keratin intermediate filaments in the inner root sheath (IRS) [52].
102168539 ROR2 The protein encoded by this gene is a receptor protein tyrosine kinase and type I transmembrane protein that belongs to the ROR subfamily of cell surface receptors. The protein may be involved in the early formation of the chondrocytes and may be required for cartilage and growth plate development [53].
102169758 NUDT2 This gene encodes a member of the MutT family of nucleotide pyrophosphatases, a subset of the larger NUDIX hydrolase family. The gene may be a candidate tumor suppressor gene [54].
102168410 GJC3 This gene encodes a gap junction protein. The encoded protein is a connexin that plays a role in the formation of gap junctions, which provide direct connections between adjacent cells [55].
102168261 EPHA7 Activation of the protein encoded by this gene results in activating phosphorylation of components of the ERK signaling pathway, including MAP2K1, MAP2K2, MAPK1 and MAPK3 [56].
102168984 TNMD The gene tenomodulin (TNMD) is a tendon-specific marker known to be important for tendon maturation, with key implications for the residing tendon stem/progenitor cells and for the regulation of endothelial cell migration in chordae tendineae cordis in the heart and in experimental tumor models [57]. Myostatin has a potential role in the induction of tenogenic differentiation of C2C12 cells [58].
102169639 MIB1 This gene encodes a protein that contains multiple ankyrin repeats and RING finger domains and functions as an E3 ubiquitin ligase. The encoded protein positively regulates Notch signaling by ubiquitinating Notch receptors, thereby facilitating Notch receptor endocytosis. This protein may also promote the ubiquitination and degradation of death-associated protein kinase 1 (DAPK1) [59].
102169348 CDH19 This gene is one of three related type II cadherin genes situated in a cluster on chromosome 18.
The encoded protein is a calcium-dependent cell-cell adhesion glycoprotein containing five extracellular cadherin repeats.
CDH19 plays important roles in cell adhesion and in the formation of adherens junctions, which bind cells together within tissues [60].
102169909 ARL15 The function of this gene has yet to be established.
102168680 LOC102168680 The function of this gene has yet to be established.
102169025 SLC24A3 Plasma membrane sodium/calcium exchangers play important roles in intracellular calcium homeostasis and electrical conduction. Potassium-dependent sodium/calcium exchangers such as SLC24A3 are believed to exchange 1 intracellular calcium ion and 1 potassium ion for 4 extracellular sodium ions [61]. 
102169863 EGFL6 This gene encodes a member of the epidermal growth factor (EGF) repeat superfamily.
Members of this superfamily are characterized by the presence of EGF-like repeats and are often involved in the regulation of the cell cycle, proliferation, and developmental processes [62].
100860763 SCD Stearoyl-CoA desaturase (Δ-9-desaturase) is an endoplasmic reticulum enzyme that catalyzes the rate-limiting step in the formation of monounsaturated fatty acids (MUFAs), specifically oleate and palmitoleate, from stearoyl-CoA and palmitoyl-CoA [63].
102169918 HECW2 This gene encodes a member of a family of E3 ubiquitin ligases that plays an important role in the proliferation, migration and differentiation of neural crest cells via the regulation of glial cell line-derived neurotrophic factor (GDNF)/Ret signaling. This gene also plays an important role in angiogenesis by stabilizing endothelial cell-to-cell junctions via the regulation of angiomotin-like 1 stability [64].
100861286 FASN Fatty acid synthase is a multi-enzyme protein that catalyzes fatty acid synthesis.
Its main function is to catalyze the synthesis of palmitate (C16:0), a long-chain saturated fatty acid, from acetyl-CoA and malonyl-CoA in the presence of NADPH [65].
102168503 DOK6 DOK6 is a member of the DOK (see DOK1; MIM 602919) family of intracellular adaptors that plays a role in the RET (MIM 164761) signaling cascade [66].
102168351 TIAM2 This gene encodes a guanine nucleotide exchange factor. A highly similar mouse protein specifically activates ras-related C3 botulinum substrate 1, converting this Rho-like guanosine triphosphatase (GTPase) from an inactive, guanosine diphosphate-bound state to an active, guanosine triphosphate-bound state. The encoded protein may also play a role in neural cell development [67].
102170054 MERTK This gene encodes a member of the MER/AXL/TYRO3 receptor kinase family. The encoded transmembrane protein contains two fibronectin type-III domains, two Ig-like C2-type (immunoglobulin-like) domains, and one tyrosine kinase domain. Mutations in this gene have been associated with disruption of the retinal pigment epithelium (RPE) phagocytosis pathway and the onset of autosomal recessive retinitis pigmentosa [68].
100860988 TPMT Thiopurine methyltransferase methylates thiopurine compounds. The methyl donor is S-adenosyl-L-methionine, which is converted to S-adenosyl-L-homocysteine. This enzyme uses S-adenosyl-L-methionine as an S-methyl donor to metabolize thiopurine drugs, producing S-adenosyl-L-homocysteine as a byproduct [69].
102169914 KLHL23 The protein encoded by this gene is a member of the kelch family of proteins, which is characterized by a 44–56 amino acid repeat motif. The kelch motif appears in many different polypeptide contexts and contains multiple potential protein-protein contact sites. Members of this family have diverse activities and are present both throughout the cell and extracellularly [70].
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Functional enrichment of DEGs

The identified DEGs were further analyzed using GO and KEGG enrichment to determine their potential functions and metabolic pathways.

GO analysis based on biological process (BP) enrichment was performed for sets of DEGs with significant cluster profiles. Only significant GO categories with P-values < 0.05 were chosen for analysis. The results of the GO enrichment analysis of DEGs were classified into three categories: BP, cellular component (CC) and molecular function (MF). Fig 3 shows the GO classifications of the DEGs from the comparisons between FGF5+/- and WT goats, FM+/- and WT goats, and FM+/- and FGF5+/- goats.

Fig 3. Gene ontology analysis summary.

Fig 3

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Classification of the annotated amino-acid sequences. Amino-acid sequences were grouped into different functional subcategories: cellular component (CC), molecular function (MF) and biological process (BF). 3A: FGF5+/- vs WT; 3B: FM+/- vs WT; and 3C: FM+/- vs FGF5+/-.

The top 20 up- and down-regulated gene-enriched GO terms are shown in Tables 3 and 4. By comparing the gene list of FGF5+/- goats with that of WT goats, two major GO terms relevant to muscle contraction [(actin cytoskeleton and myosin heavy chain (MHC) protein complex)] were identified (Fig 3A). The machinery that powers cell migration is built from the actin cytoskeleton, which is larger than any organelle, and the assembly or disassembly of the actin cytoskeleton can easily change cell morphology [71]. The distribution of enriched GO terms between gene lists of FM+/- and WT goats is shown in Fig 3B. Moreover, the myosin complex and actin cytoskeleton were significantly enriched in the CC. Myosin comprises a superfamily of ATP-dependent motor proteins and is best known for its roles in muscle contraction and its involvement in a wide range of other motility processes in eukaryotes [72]. In terms of BPs, several processes in the fatty acid metabolic system, including peptide metabolism, protein processing and cellular amide metabolism, were found to be enriched in the genes down-regulated in FM+/- goats compared with their expression in WT goats. There were several major enriched GO terms in the comparison of gene lists from FM+/- and FGF5+/- goats (Fig 3C).

Table 3. The top 20 GO terms of significantly up-regulated genes.

Treatments GO ID Category GO description P-Value
FGF5+/- vs WT GO:0015629 cellular component actin cytoskeleton 4.29E-06
GO:0042611 cellular component MHC protein complex 4.45E-05
GO:0019882 biological process antigen processing and presentation 7.64E-05
GO:0016459 cellular component myosin complex 1.45x10-4
GO:0042613 cellular component MHC class II protein complex 2.97 x10-4
GO:0003774 molecular function motor activity 3.38 x10-4
GO:0005861 cellular component troponin complex 6.65 x10-4
GO:0005865 cellular component striated muscle thin filament 6.65 x10-4
GO:0030016 cellular component myofibril 6.65 x10-4
GO:0030017 cellular component sarcomere 6.65 x10-4
GO:0036379
GO:0043292
GO:0044449		 cellular component
cellular component
cellular component		 myofilament
contractile fiber
contractile fiber part		 6.65 x10-4
6.65 x10-4
6.65 x10-4
GO:0005886 cellular component plasma membrane 1.31x10-3
GO:0044281 biological process small molecule metabolic process 1.44x10-3
GO:0032182 molecular function mall conjugating protein bindings 1.72 x10-3
GO:0043130 molecular function ubiquitin binding 1.72 x10-3
GO:0017119 cellular component Golgi transport complex 1.96 x10-3
GO:0006206 biological process pyrimidine nucleobase metabolic process 2.78 x10-3
GO:0019856 biological process pyrimidine nucleobase biosynthetic process 2.78 x10-3
FM+/- vs WT GO:0016459 cellular component myosin complex 2.68E-06
GO:0015629 cellular component actin cytoskeleton 1.46E-05
GO:0003774 molecular function motor activity 6.82E-05
GO:0030131 cellular component clathrin adaptor complex 1.14 x10-3
GO:0030119 cellular component AP-type membrane coat adaptor complex 1.39 x10-3
GO:0030118 cellular component clathrin coat 1.85 x10-3
GO:0016192 biological process vesicle-mediated transport 3.12 x10-3
GO:0044430 cellular component cytoskeletal part 3.62 x10-3
GO:0009755 biological process hormone-mediated signaling pathway 4.8 x10-3
GO:0032870 biological process cellular response to hormone stimulus 4.8 x10-3
GO:0071495 biological process cellular response to endogenous stimulus 4.97 x10-3
GO:0005575 cellular component cellular component 5.13 x10-3
GO:0005388 molecular function calcium-transporting ATPase activity 5.18 x10-3
GO:0071310 biological process cellular response to organic substance 5.29 x10-3
GO:0009725 biological process response to hormone stimulus 5.29 x10-3
GO:0009719 biological process response to endogenous stimulus 5.45 x10-3
GO:0005856 cellular component cytoskeleton 5.95 x10-3
GO:0070887 biological process cellular response to chemical stimulus 6.49 x10-3
GO:0010033 biological process response to organic substance 6.67 x10-3
GO:0009399 biological process nitrogen fixation 6.89 x10-3
FM+/- vs FGF5+/- GO:0004853 molecular function uroporphyrinogen decarboxylase activity 4.70E-05
GO:0034220 biological process ion transmembrane transport 1.72 x10-3
GO:0031090 cellular component organelle membrane 2.2 x10-3
GO:0065008 biological process regulation of biological quality 2.72 x10-3
GO:0035435 biological process phosphate ion transmembrane transport 3.82 x10-3
GO:0019725 biological process cellular homeostasis 4.22 x10-3
GO:0015672 biological process monovalent inorganic cation transport 5.14 x10-3
GO:0006779 biological process porphyrin-containing compound biosynthetic process 5.56 x10-3
GO:0042592 biological process homeostatic process 7.41 x10-3
GO:0006778 biological process porphyrin-containing compound metabolic process 7.66 x10-3
GO:0006811 biological process ion transport 8.43 x10-3
GO:0051701 biological process interaction with host 9.33 x10-3
GO:0004427 molecular function inorganic diphosphatase activity 9.39 x10-3
GO:0033014 biological process tetrapyrrole biosynthetic process 0.01
GO:0005388 molecular function calcium-transporting ATPase activity 0.01
GO:0006812 biological process cation transport 0.01
GO:0015114 molecular function phosphate ion transmembrane transporter activity 0.01
GO:0033013 biological process tetrapyrrole metabolic process 0.01
GO:0045454 biological process cell redox homeostasis 0.01
GO:0019048 biological process modulation by virus of host morphology or physiology 0.01
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Table 4. The top 20 GO terms of significantly down-regulated genes.

Treatments GO ID Category GO description P-Value
FGF5+/- vs WT GO:0009055 molecular function electron carrier activity 1.95 x10-3
GO:0019911 molecular function structural constituent of myelin sheath 3.11 x10-3
GO:0015109 molecular function chromate transmembrane transporter activity 3.89 x10-3
GO:0015703 biological process chromate transport 3.89 x10-3
GO:0005198 molecular function structural molecule activity 7.78 x10-3
GO:0045502 molecular function dynein binding 9.59 x10-3
GO:0008813 molecular function chorismate lyase activity 0.01
GO:0005515 molecular function protein binding 0.01
GO:0004620 molecular function phospholipase activity 0.01
GO:0042803 molecular function protein homodimerization activity 0.01
GO:0019015 cellular component viral genome 0.01
GO:0007156 biological process homophilic cell adhesion 0.01
GO:0016833 molecular function oxo-acid-lyase activity 0.02
GO:0016337 biological process cell-cell adhesion 0.02
GO:0003913 molecular function DNA photolyase activity 0.02
GO:0005521 molecular function lamin binding 0.02
GO:0046983 molecular function protein dimerization activity 0.02
GO:0015099 molecular function nickel cation transmembrane transporter activity 0.03
GO:0015675 biological process nickel cation transport 0.03
GO:0044699 biological process single-organism process 0.03
FM+/- vs WT GO:0016485 biological process protein processing 7.3 x10-4
GO:0051604 biological process protein maturation 7.3 x10-4
GO:0016491 molecular function oxidoreductase activity 2.62 x10-3
GO:0005787 cellular component signal peptidase complex 2.94 x10-3
GO:0006465 biological process signal peptide processing 2.94 x10-3
GO:0055114 biological process oxidation-reduction process 4.44 x10-3
GO:0004126 molecular function cytidine deaminase activity 7.06 x10-3
GO:0006216 biological process cytidine catabolic process 7.06 x10-3
GO:0009972 biological process cytidine deamination 7.06 x10-3
GO:0046087 biological process cytidine metabolic process 7.06 x10-3
GO:0046133 biological process pyrimidine ribonucleoside catabolic process 7.06 x10-3
GO:0046135 biological process pyrimidine nucleoside catabolic process 7.06 x10-3
GO:0072529 biological process pyrimidine-containing compound catabolic process 7.06 x10-3
GO:0003690 molecular function double-stranded DNA binding 7.11 x10-3
GO:0019080 biological process viral genome expression 7.86 x10-3
GO:0006518 biological process peptide metabolic process 9.37 x10-3
GO:004353 molecular function ADP binding 0.01
GO:0004506 molecular function squalene monooxygenase activity 0.01
GO:0005135 molecular function interleukin-3 receptor binding 0.02
GO:0043566 molecular function structure-specific DNA binding 0.02
FM+/- vs FGF5+/- GO:0007217 biological process tachykinin receptor signaling pathway 1.27 x10-3
GO:0071973 biological process bacterial-type flagellar cell motility 4.24 x10-3
GO:0070838 biological process divalent metal ion transport 4.79 x10-3
GO:0070588 biological process calcium ion transmembrane transport 5.67 x10-3
GO:0072511 biological process divalent inorganic cation transport 8.87 x10-3
GO:0051607 biological process defense response to virus 9.86 x10-3
GO:0032501 biological process multicellular organismal process 0.01
GO:0042168 biological process heme metabolic process 0.01
GO:0072509 molecular function divalent inorganic cation transmembrane transporter activity 0.01
GO:0007601 biological process visual perception 0.01
GO:0030553 molecular function cGMP binding 0.01
GO:0050953 biological process sensory perception of light stimulus 0.01
GO:0003856 molecular function 3-dehydroquinate synthase activity 0.01
GO:0044707 biological process single-multicellular organism process 0.01
GO:0030416 biological process methylamine metabolic process 0.01
GO:1901160 biological process primary amino compound metabolic process 0.01
GO:0006816 biological process calcium ion transport 0.01
GO:0050877 biological process neurological system process 0.01
GO:0008113 molecular function peptide-methionine (S)-S-oxide reductase activity 0.01
GO:0030551 molecular function cyclic nucleotide binding 0.01
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Pathway analysis

As shown in Fig 4, the top 20 pathways were identified by pairwise comparisons of complete DEGs lists from every group. The results from the KEGG analysis revealed many genes related to fatty acid metabolism, the peroxisome proliferator-activated receptor (PPAR) signaling pathway, the mitogen-activated protein kinase (MAPK) signaling pathway and cell adhesion molecules. Additionally, the KEGG analysis revealed several genes involved in Notch signaling, AMP-activated protein kinase (AMPK) signaling, mTOR-signaling, oxidative phosphorylation, and Jak-STAT signaling. Many genes significantly down-regulated in the FM+/- goats compared to their expression in WT and FGF5+/- goats were associated with fatty acid biosynthesis (stearoyl-CoA desaturase, fatty acid synthase, ELOVL fatty acid elongase 6 and 3-hydroxyacyl-CoA dehydratase 2) and the biosynthesis of unsaturated fatty acids (stearoyl-CoA desaturase, fatty acid synthase and 3-hydroxyacyl-CoA dehydratase 2). Genes involved in the PPAR signaling pathway (stearoyl-CoA desaturase), MAPK signaling pathway [(mitogen-activated protein kinase 3, MAPK3 or ERK1), (ras-related C3 botulism toxin substrate 2, RAC2), cell division cycle 25B (CDC25B), phospholipase A2 group IVE (PLA2G4E)], biosynthesis of unsaturated fatty acids, Notch signaling (hes family bHLH transcription factor 1 and HES 1), AMPK signaling (calcium binding protein 39-like and fructose-bisphosphatase 1) and mTOR-signaling (calcium binding protein 39-like) were also down-regulated. On the other hand, along with an increased percentage of differentially expressed gene sets between FGF5+/- goats and WT goats, the DEGs clustered in the pathways of viral myocarditis, cell adhesion molecules (CAMs), antigen processing and presentation, graft-versus-host disease, leishmaniosis, allograft rejection, staphylococcus aureus infection and type I diabetes mellitus were up-regulated. Our KEGG pathway enrichment analysis of the DEGs showed that genes involved in oxidative phosphorylation (pyro phosphatase and ATPase H+ transporting V0 subunit a4) and Jak-STAT signaling (colony stimulating factor 3) were up-regulated after knockout of the MSTN gene (Fig 5).

Fig 4. KEGG pathway enrichment analysis.

Fig 4

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4A: FM+/- vs WT; 4B: FGF5+/- vs WT. 4C: FM+/- vs FGF5+/-.

Fig 5. Cluster analysis of DEGs.

Fig 5

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Heat map of the expression levels of DEGs related to lipid, glucose, protein and oxidative phosphorylation metabolism pathways in the muscles of the goats.

Verification of DEGs by qRT-PCR

To further confirm and validate the transcriptome analysis results, the RNA samples isolated for RNA-Seq were used in the qRT-PCR analysis. The DEGs were selected based on the expression profiles and the following criteria: DEGs with a fold-change (log2) ≥ 1 or fold-change (log2) ≤ -1 and a P-value < 0.05. A subset of 5 genes selected from the list of DEGs was selected for qRT-PCR analysis. These genes were associated with muscle structure [myogenic actor 5 (Mfy5)], muscle fat content [stearoyl coenzyme A dehydrogenase (SCD), CCAAT-enhancer-binding protein α (C/EBPα) and sterol-regulatory element binding proteins (SREBP)] and muscle growth/differentiation (MSTN). Overall, the qPCR results showed good correspondence with the transcriptome analysis results, indicating that the RNA-Seq data were reliable and accurate (Fig 6). The discrepancies with respect to the ratio should be attributed to the different algorithms and sensitivity of the two techniques [73, 74].

Fig 6. Relative expression levels of MSTN, SCD, C/EBPα, SREBP and Mfy5 determined by qRT-PCR and RNA-Seq.

Fig 6

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A: qRT-PCR; and B: RNA-Seq.

Discussion

Myostatin is a powerful negative regulator of skeletal muscle growth and development. Although myostatin inhibition in skeletal muscles is valuable for agricultural applications, the molecules downstream of myostatin in skeletal muscle have not been fully identified [75]. Transcriptome analysis is an important method of exploring functional genes and is both the foundation and starting point for studying gene functions and structures [76]. Previous studies have not explored the transcriptome profile of the MSTN knockout goat. To our knowledge, this is the first study to investigate the effects of MSTN knockout on muscle hypertrophy and functionality as well as whole-body lipid metabolism in a goat model.

FGF5 resides on chromosome 4q21.21, directly within the region of homozygosis [77]. FGF5, which is a member of the FGF family and has 23 related genes, regulates hair length in humans and a variety of other animals. The FGF5 family is involved in the control of numerous physiological processes, including embryonic development, neuronal survival, wound repair and angiogenesis, but is also involved in a number of pathological responses [78–80]. In postnatal muscles, satellite cells are the myogenic precursors and are situated beneath the myofiber basement membrane. FGF family proteins can maintain the proliferative state of satellite cells in rat myofiber cultures; specifically, FGF1, FGF4, and FGF6 enhance satellite cell proliferation, whereas FGF5 and FGF7 are ineffective [81]. Mice that are homozygous null for FGF5 display no obvious defects in limb or axial muscle development but are characterized by substantial hair overgrowth. This finding, which is an important demonstration that FGF5 is not required for normal limb development [82], provides evidence that FGF5 is not involved in muscle development. Recent studies have suggested that the FGF5 gene is associated with hair length and in controls the cessation of the anagen stage. FGF5 signaling pathway is binding of canonical FGFs to FGFR with heparin sulfate as a cofactor induces the formation of ternary FGF—FGFR-HS complex, which activates the FGFR intracellular tyrosine kinase domain by phosphorylation of specific tyrosine residues. The activated receptor is coupled to intracellular RAS-MAPK signaling pathway [83, 84]. However, MSTN is a negative regulator of skeletal muscle development and growth, and it is expressed mainly in muscle. The myostatin-Smad pathway alters the activity of protein kinase AKT, thereby inhibiting mTOR pathway and protein synthesis [85]. Therefore, hitherto, no works have yet addressed the possible interaction between MSTN and FGF5. Taken together, our results support that FGF5 has not affect the function of MSTN. We assessed the function and molecular mechanism of introducing myostatin dysfunction in FGF5 knockout goats in order to compare the differences between FGF5/MSTN dual knockout and FGF5 single knockout goats.

Myostatin dysfunction results in a dramatic increase of animal muscle mass due to increases in both the numbers and cross-sectional areas of myofibrils [7, 86]. In the present study, we were the first to use transcriptome analysis to identify genes expression changes caused by the knockout of MSTN in goats. In total, 68.93 Gb of clean data were obtained. Several DEGs involved in muscle development, fatty acid metabolism, glucose metabolism and oxidative phosphorylation were identified following MSTN and FGF5 gene knockout; these results may benefit studies of the function and molecular mechanism of myostatin in goats. Changes in the downstream molecules of MSTN, including the increased expression levels of MYH15 (myosin heavy chain 15), MAPKAPK3 (mitogen-activated protein kinase-active protein kinase) and MYOZ3 (myozenin 3) [87–89], indicated that functional MSTN activity or expression was enhanced in FM +/- goats compared to that in the FGF5 +/- and WT goats. Myostatin expression is restricted to developing skeletal muscles, but myostatin protein is still expressed and secreted by skeletal muscles in adulthood [90, 91]. Though the effects of MSTN knockout varies by age, no differences were found in body size and weight at birth among homozygous KO lambs and their WT counterparts. However, KO lambs were 20–30% heavier than WT lambs 60 days later despite having a similar body size [92, 93]. The causes of these results are not entirely understood, but it is clear that these results are dependent on the genetic background.

This study showed that genes related to the immune system were up-regulated in the FGF5+/- group compared with the WT and FM+/- groups. Overexpression of KRT27 has been reported in the inner root sheath [52], and KRT27 has been shown to regulate innate immune functions [94]. Furthermore, cell adhesion pathway enrichment analysis of the DEGs revealed changes in the intercellular adhesion molecule 3 (ICAM-3), human leukocyte antigen (HLA) class II histocompatibility antigen, BOLA class I histocompatibility antigen and HLA class II histocompatibility antigen genes. ICAM-3, a member of the ICAM immunoglobulin family of adhesion molecules, binds to leukocyte function antigen and mediates the initial localization of neutrophils to sites of tissue injury and inflammation in autoimmune diseases [95]. CAMs of the immunoglobulin superfamily nucleate and maintain groups of cells at key sites during early development and in the adult stage. In addition to their adhesive properties, the binding of CAMs can affect intracellular signaling and developmental events, including cell migration, proliferation, and differentiation [96].

Our KEGG pathway enrichment analysis of the DEGs showed that the skeletal muscle growth, fatty acid metabolism, glucose metabolism and oxidative phosphorylation pathways were enriched in DEGs after MSTN knockout. The DEG HES1, which is involved in Notch signaling, may act as a regulator of myogenesis by inhibiting the function of MyoD1 and ASH1. It is well known that AMPK acts as a key energy sensor that balances anabolism and catabolism by monitoring either cellular ATP levels or glycogen content.

The MAPK signaling pathway was significantly enriched in DEGs (MAPKAPK3, RAC2, CDC25B and PLA2G4E) after knockout of the MSTN and FGF5 genes. Myostatin activates Erk1/2 MAPK in both proliferating and differentiating C2C12 cells [97]; thus, MSTN may affect muscle structures in goats. The MAPK/ERK pathway is reported to be associated with cell proliferation, differentiation, migration, senescence and apoptosis [98, 99]. A wealth of data has revealed a cross-talk between myostatin and the intracellular AKT/mTOR signaling pathway, strongly supporting the notion that myostatin affects the muscle protein balance by regulating protein synthesis and degradation [100]. Adult muscle growth is primarily due to the increase of protein content through activation of the AKT/mTOR pathway, which regulates protein synthesis [101]. Inhibition of myostatin, a negative growth modulator in muscles, functionally enhances muscle mass and improves glucose and fat metabolism in myostatin propeptide transgenic mice [102]. Published studies have clearly shown that adipogenesis is decreased in MSTN KO mice. Moreover, the absence of MSTN results in enhanced peripheral tissue fatty acid oxidation and increased thermogenesis, culminating in increased fat utilization and reduced adipose tissue mass [103]. Consistent with previous studies, our study showed that fatty acid synthesis is decreased in heterozygous MSTN goats, and our KEGG pathway enrichment analysis of the DEGs showed that the fatty acid metabolism pathway was significantly enriched in DEGs after MSTN knockout in goats.

Both adipocytes and myocytes are derived from the same mesodermal precursor cells during development. C3H10T(1/2) cells, a mesenchymal fibroblast-like cell line of embryonic origin, have the capacity to undergo differentiation into multiple cell lineages, such as myoblasts, chondrocytes, and adipocytes, after incubation in different media in vitro [104–106]. Later, differentiation controlled by different transcriptional pathways yields individual tissues, suggesting that the myostatin gene is involved in regulating both adiposity and muscularity [92]. In a previous study, we used CRISPR/Cas9 technology and single cell embryo injection and showed that these two methods differ in terms of gene editing efficiency. Specifically, single cell embryo injection could result in abnormal fetus development and high fetal mortality, whereas CRISPR/Cas9 technology did not affect the health of the animals [107]. More importantly, the present study identified the response of some important genes, including TNMD, SCD and FANS, to MSTN disruption. These results will be beneficial for breeding purposes.

Conclusions

In this study, we assembled, characterized, and evaluated the muscle tissue transcriptome and quantified the gene expression levels in MSTN and FGF5 gene-modified goats. Through the development of a rigorous multistep bioinformatics approach, the sequencing reads obtained from MSTN knockout goats were successfully cleaned of host sequences and were found to contain high numbers of important genes. The results presented here and the associated assembly will help improve our understanding of the MSTN gene expression profile and molecular mechanisms. These data are valuable resources for future studies of goat genomics and will be beneficial for breeding applications that target multiple genes with strong effects on economically important traits in animals. Finally, the results of this study suggest that myostatin disruption might be a promising strategy for the treatment of type 2 diabetes and related metabolic diseases.

Supporting information

S1 Checklist. NC3Rs ARRIVE guidelines checklist.

(DOCX)

Click here for additional data file. (56.9KB, docx)
S1 Table. Genotypes of six live MSTN- and FGF5-knockout goats.

(DOCX)

Click here for additional data file. (17.7KB, docx)
S2 Table. Primers for quantitative PCR analysis.

(DOCX)

Click here for additional data file. (21.5KB, docx)

Acknowledgments

This study was carried out using the animals from the Shaanbei Cashmere Goat Farm at Yulin University. The authors would like to thank all participants who were involved in our study.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work is supported by National Natural Science Foundation of China (31372279, 31171377, 31572369) to YC, and China Agriculture Research System (CARS-39-12) to YC, as well as the Major Projects for New Varieties of Genetically Modified Organisms of China (2014ZX08008-002) to YC, National Natural Science Foundation of China (31402038) and the key Research Program of Shaanxi Province (2017NY-072) to XW, and the Special Fund for Agro-scientific Research in the Public Interest (201303059) to YC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Associated Data

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Supplementary Materials

S1 Checklist. NC3Rs ARRIVE guidelines checklist.

(DOCX)

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S1 Table. Genotypes of six live MSTN- and FGF5-knockout goats.

(DOCX)

Click here for additional data file. (17.7KB, docx)
S2 Table. Primers for quantitative PCR analysis.

(DOCX)

Click here for additional data file. (21.5KB, docx)

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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