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.
Citation: Wang L, Cai B, Zhou S, Zhu H, Qu L, Wang X, et al. (2017) RNA-seq reveals transcriptome changes in goats following myostatin gene knockout. PLoS ONE 12(12): e0187966. https://doi.org/10.1371/journal.pone.0187966
Editor: Meijia Zhang, China Agricultural University, CHINA
Received: June 4, 2017; Accepted: October 30, 2017; Published: December 11, 2017
Copyright: © 2017 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: 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.
Competing interests: The authors have declared that no competing interests exist.
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 . Myostatin is expressed almost exclusively in skeletal muscle . 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 . Myostatin both regulates lean tissue mass and affects body fat content in mice . 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 . 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 . 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 . 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 . Muscular hypertrophy, or the double-muscle phenotype, is a heritable condition in cattle . 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 , 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 .
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 . 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 . 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
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).
The animals used in the present study were previously generated by Wang et al. . 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.
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 . 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 .
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 . Differentially expressed genes (DEGs) were determined by DEGseq with a cutoff threshold of P-value < 0.05 .
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 , 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 . 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].
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  and bovine , in which 61.4–75.0% of reads are mapped to the reference genome. Thus, our mapping percentage suggests good sequence quality.
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].
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).
The red blocks represent the overexpressed genes, and the blue blocks represent genes with the lowest expression levels. Colored bars indicate the expression levels.
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.
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 . 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 . 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).
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).
4A: FM+/- vs WT; 4B: FGF5+/- vs WT. 4C: FM+/- vs FGF5+/-.
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].
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 . Transcriptome analysis is an important method of exploring functional genes and is both the foundation and starting point for studying gene functions and structures . 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 . 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 . 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 , 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 . 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 , and KRT27 has been shown to regulate innate immune functions . 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 . 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 .
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 ; 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 . Adult muscle growth is primarily due to the increase of protein content through activation of the AKT/mTOR pathway, which regulates protein synthesis . Inhibition of myostatin, a negative growth modulator in muscles, functionally enhances muscle mass and improves glucose and fat metabolism in myostatin propeptide transgenic mice . 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 . 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 . 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 . 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.
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.
S1 Checklist. NC3Rs ARRIVE guidelines checklist.
S1 Table. Genotypes of six live MSTN- and FGF5-knockout goats.
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.
- 1. Lee S. Regulation of muscle mass by myostatin. Annual Review of Cell and Developmental Biology. 2004;20(1):61–86.
- 2. Mcpherron AC, Lawler AM, Lee SJ. Regulation of skeletal muscle mass in mice by a new TGF-beta superfamily member. Nature. 1997;387(6628):83. pmid:9139826
- 3. Kambadur R, Sharma M, Smith TP, Bass JJ. Mutations in myostatin (GDF8) in double-muscled Belgian Blue and Piedmontese cattle. Genome Research. 1997;7(9):910. pmid:9314496
- 4. Mcpherron AC, Lee SJ. Double muscling in cattle due to mutations in the myostatin gene. Proceedings of the National Academy of Sciences. 1997;94(23):12457–61.
- 5. Nakatani M, Takehara Y, Sugino H, Matsumoto M, Hashimoto O, Hasegawa Y, et al. Transgenic expression of a myostatin inhibitor derived from follistatin increases skeletal muscle mass and ameliorates dystrophic pathology in mdx mice. Faseb Journal Official Publication of the Federation of American Societies for Experimental Biology. 2008;22(2):477–87. pmid:17893249
- 6. Mcpherron AC, Lee SJ. McPherron A.C. & Lee S.J. Suppression of body fat accumulation in myostatin-deficient mice. J. Clin. Invest. 109, 595–601. Journal of Clinical Investigation. 2002;109(5):595–601. pmid:11877467
- 7. Lee SJ. Quadrupling Muscle Mass in Mice by Targeting TGF-ß Signaling Pathways. Plos One. 2007;2(8):: e789. pmid:17726519
- 8. Schuelke M, Wagner KR, Stolz LE, Hübner C, Riebel T, Kömen W, et al. Myostatin mutation associated with gross muscle hypertrophy in a child. New England Journal of Medicine. 2004;350(26):2682–8. pmid:15215484
- 9. Zhang C, Mcfarlane C, Lokireddy S, Masuda S, Ge X, Gluckman PD, et al. Inhibition of myostatin protects against diet-induced obesity by enhancing fatty acid oxidation and promoting a brown adipose phenotype in mice. Diabetologia. 2012;55(1):183–93. pmid:21927895
- 10. Guo T, Jou W, Chanturiya T, Portas J, Gavrilova O, Mcpherron AC. Myostatin inhibition in muscle, but not adipose tissue, decreases fat mass and improves insulin sensitivity. Plos One. 2009;4(3):e4937. pmid:19295913
- 11. Zimmers TA, Davies MV, Koniaris LG, Haynes P, Esquela AF, Tomkinson KN, et al. Induction of cachexia in mice by systemically administered myostatin. Science. 2002;296(5572):1486–8. pmid:12029139
- 12. Breitbart A, Auger-Messier M, Molkentin JD, Heineke J. Myostatin from the heart: local and systemic actions in cardiac failure and muscle wasting. American Journal of Physiology Heart & Circulatory Physiology. 2011;300(6):1973–82.
- 13. Kainulainen H, Papaioannou K, Silvennoinen M, Autio R, Saarela J, Oliveira BM, et al. Myostatin/activin blocking combined with exercise reconditions skeletal muscle expression profile of mdx mice. Molecular and Cellular Endocrinology. 2015;399:131–42. pmid:25304272
- 14. Heineke J, Augermessier M, Xu J, Sargent MA, York AJ, Welle S, et al. Genetic Deletion of Myostatin From the Heart Prevents Skeletal Muscle Atrophy in Heart Failure. Circulation. 2010;121(3):419–25. pmid:20065166
- 15. Sílvia B, Míriam T, Marcel O, David M, Maria P, Eva C, et al. Myostatin blockage using actRIIB antagonism in mice bearing the Lewis lung carcinoma results in the improvement of muscle wasting and physical performance. Journal of Cachexia Sarcopenia & Muscle. 2012;3(1):37–43.
- 16. Bogdanovich S, Krag TO, Barton ER, Morris LD, Whittemore LA, Ahima RS, et al. Functional improvement of dystrophic muscle by myostatin blockade. Nature. 2002;420(6914):418–21. pmid:12459784
- 17. Dominique JE, Gérard C. Myostatin regulation of muscle development: Molecular basis, natural mutations, physiopathological aspects. Experimental Cell Research. 2006;312(13):2401–14. pmid:16793037
- 18. James NA, Berry BW. Use of chevon in the development of low-fat meat products. Journal of Animal Science. 1997;75(2):571–7. pmid:9051482
- 19. Patel UA, Patel AK, Joshi CG. Stable suppression of myostatin gene expression in goat fetal fibroblast cells by lentiviral vector-mediated RNAi. Biotechnology Progress. 2015;31:452–9. pmid:25395261
- 20. Mortazavi A, Williams BA, Mccue K, Schaeffer L, Wold BJ. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods. 2008;5(7):621–8. pmid:18516045
- 21. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The Transcriptional Landscape of the Yeast Genome Defined by RNA Sequencing. Science. 2008;320(5881):1344–9. pmid:18451266
- 22. Cassarmalek I, Passelaigue F, Bernard C, Léger J, Hocquette JF. Target genes of myostatin loss-of-function in muscles of late bovine fetuses. BMC Genomics. 2007;8(1):423–32.
- 23. Sadkowski T, Jank M, Zwierzchowski L, Siadkowska E, Oprządek J, Motyl T. Gene expression profiling in skeletal muscle of Holstein-Friesian bulls with single-nucleotide polymorphism in the myostatin gene 5’-flanking region. Journal of Applied Genetics. 2008;49(3):237–50. pmid:18670060
- 24. Steelman CA, Recknor JC, Nettleton D, Reecy JM. Transcriptional profiling of myostatin-knockout mice implicates Wnt signaling in postnatal skeletal muscle growth and hypertrophy. Faseb Journal Official Publication of the Federation of American Societies for Experimental Biology. 2006;20(3):580–2. pmid:16423875
- 25. Welle S, Cardillo A, Zanche M, Tawil R. Skeletal muscle gene expression after myostatin knockout in mature mice. Physiological Genomics. 2009;38(3):342–50. pmid:19509079
- 26. Mortazavi A, Williams BA, Mccue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods. 2008;5(7):621–8. pmid:18516045
- 27. Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nature Methods. 2008;5(7):613–9. pmid:18516046
- 28. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320(5881): 1344–9. pmid:18451266
- 29. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Research. 2008;18(9):1509–17. pmid:18550803
- 30. Fu X, Fu N, Guo S, Yan Z, Xu Y, Hu H, et al. Estimating accuracy of RNA-Seq and microarrays with proteomics. BMC Genomics. 2009;10(1):161.
- 31. Strickler SR, Bombarely A, Mueller LA. Designing a transcriptome next-generation sequencing project for a nonmodel plant species. American Journal of Botany. 2012;99(2):257–66. pmid:22268224
- 32. Calduchginer JA, Bermejonogales A, Beneditopalos L, Estensoro I, Ballesterlozano G, Sitjàbobadilla A, et al. Deep sequencing for de novo construction of a marine fish (Sparus aurata) transcriptome database with a large coverage of protein-coding transcripts. BMC Genomics. 2013;14(1):178.
- 33. Panserat S. Differential gene expression after total replacement of dietary fish meal and fish oil by plant products in rainbow trout (Oncorhynchus mykiss) liver. Aquaculture. 2009;294(1–2):123–31.
- 34. Chen QL, Luo Z, Huang C, Pan YX, Wu K. De novo characterization of the liver transcriptome of javelin goby Synechogobius hasta and analysis of its transcriptomic profile following waterborne copper exposure. Fish Physiology and Biochemistry. 2016;42(3):979–94. pmid:26719065
- 35. Wang X, Yu H, Lei A, Zhou J, Zeng W, Zhu H, et al. Generation of gene-modified goats targeting MSTN and FGF5 via zygote injection of CRISPR/Cas9 system. Scientific Reports. 2015;5:13878. pmid:26354037
- 36. Kirk SP, Oldham JM, Kambadur R, Sharma M, Dobbie PM, Bass JJ. Myostatin regulation during skeletal muscle regeneration. Journal of Cellular Physiology. 2000;184(3):356–63. pmid:10911367
- 37. Zhang C, Mcfarlane C, Lokireddy S, Bonala S, Ge X, Masuda S, et al. Myostatin-deficient mice exhibit reduced insulin resistance through activating the AMP-activated protein kinase signalling pathway. Diabetologia. 2011;54(6):1491–501. pmid:21347623
- 38. Mcpherron AC, Lee SJ. Suppression of body fat accumulation in myostatin-deficient mice. Journal of Clinical Investigation. 2002;109(5):595–601. pmid:11877467
- 39. Guo T, William J, Tatyana C, Jennifer P, Oksana G, Mcpherron AC. Myostatin Inhibition in Muscle, but Not Adipose Tissue, Decreases Fat Mass and Improves Insulin Sensitivity. Plos One. 2009;4(3):e4937. pmid:19295913
- 40. Zhao B, Wall RJ, Yang J. Transgenic expression of myostatin propeptide prevents diet-induced obesity and insulin resistance. Biochemical & Biophysical Research Communications. 2005;337(1):248–55.
- 41. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology. 2010;28(5):511–5. pmid:20436464
- 42. Leng N, Dawson JA, Thomson JA, Ruotti V, Rissman AI, Smits BM, et al. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics. 2013;29(8):1035. pmid:23428641
- 43. Benjamini Y, Hochberg Y. Controlling The False Discovery Rate—A Practical And Powerful Approach To Multiple Testing. Journal of the Royal Statistical Society. 1995;57(57):289–300.
- 44. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biology. 2010;11(10):1–12.
- 45. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biology. 2010;11(2):1–12.
- 46. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Research. 2008;36(Database issue):D480–D4. pmid:18077471
- 47. Tripathi AK, Ramani UV, Patel AK, Rank DN, Joshi CG. Short Hairpin RNA-Induced Myostatin Gene Silencing in Caprine Myoblast Cells In Vitro. Applied Biochemistry & Biotechnology. 2013;169(2):688–94.
- 48. Patel UA, Patel AK, Joshi CG. Stable suppression of myostatin gene expression in goat fetal fibroblast cells by lentiviral vector-mediated RNAi. Biotechnology Progress. 2015;31(2):452. pmid:25395261
- 49. Ohsaki H, Sawa T, Sasazaki S, Kano K, Taniguchi M, Mukai F, et al. Stearoyl-CoA desaturase mRNA expression during bovine adipocyte differentiation in primary culture derived from Japanese Black and Holstein cattle. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology. 2007;148(3):629–34.
- 50. Chen C, Ai H, Ren J, Li W, Li P, Qiao R, et al. A global view of porcine transcriptome in three tissues from a full-sib pair with extreme phenotypes in growth and fat deposition by paired-end RNA sequencing. Bmc Genomics. 2011;12(1):448–.
- 51. Wickramasinghe S, Rincon G, Islas-Trejo A, Medrano JF. Transcriptional profiling of bovine milk using RNA sequencing. Bmc Genomics. 2012;13(1):1–14.
- 52. Langbein L, Rogers MA, Praetzelwunder S, Helmke B, Schirmacher P, Schweizer J. K25 (K25irs1), K26 (K25irs2), K27 (K25irs3), and K28 (K25irs4) represent the type I inner root sheath keratins of the human hair follicle. Journal of Investigative Dermatology. 2006;126(11):2377–86. pmid:16874310
- 53. Ma SSQ, Henry C, Llamosas E, Higgins R, Daniels B, Hesson LB, et al. Validation of specificity of antibodies for immunohistochemistry: the case of ROR2. Virchows Archiv. 2016:1–10.
- 54. Ogawa T, Ueda Y, Yoshimura K, Shigeoka S. Comprehensive Analysis of Cytosolic Nudix Hydrolases in Arabidopsis thaliana. Journal of Biological Chemistry. 2005;280(26):25277–83. pmid:15878881
- 55. Marsh A, Caseygreen K, Probert F, Withall DM, Mitchell DA, Dilly SJ, et al. Simvastatin Sodium Salt and Fluvastatin Interact with Human Gap Junction Gamma-3 Protein. PLOS ONE. 2016;11(2).
- 56. Oricchio E, Nanjangud G, Wolfe AL, Schatz JH, Mavrakis KJ, Jiang M, et al. The Eph-Receptor A7 Is a Soluble Tumor Suppressor for Follicular Lymphoma. Cell. 2011;147(3):554–64. pmid:22036564
- 57. Shukunami C, Takimoto A, Oro M, Hiraki Y. Scleraxis positively regulates the expression of tenomodulin, a differentiation marker of tenocytes. Developmental Biology. 2006;298(1):234–47. pmid:16876153
- 58. Uemura K, Hayashi M, Itsubo T, Oishi A, Iwakawa H, Komatsu M, et al. Myostatin promotes tenogenic differentiation of C2C12 myoblast cells through Smad3. Febs Open Bio. 2017;7(4):522–32. pmid:28396837
- 59. Kwon DY, Dimitriadi M, Terzic B, Cable C, Hart AC, Chitnis AB, et al. The E3 ubiquitin ligase mind bomb 1 ubiquitinates and promotes the degradation of survival of motor neuron protein. Molecular Biology of the Cell. 2013;24(12):1863–71. pmid:23615451
- 60. Lin J, Luo J, Redies C. Cadherin-19 expression is restricted to myelin-forming cells in the chicken embryo. Neuroscience. 2010;165(1):168–78. pmid:19850111
- 61. Kraev A, Quednau BD, Leach SA, Li X, Dong H, Winkfein R, et al. Molecular Cloning of a Third Member of the Potassium-dependent Sodium-Calcium Exchanger Gene Family,NCKX3. Journal of Biological Chemistry. 2001;276(25):23161–72. pmid:11294880
- 62. Wouters MA, Rigoutsos I, Chu CK, Feng LL, Sparrow DB, Dunwoodie SL. Evolution of distinct EGF domains with specific functions. Protein Science. 2005;14(4):1091–103. pmid:15772310
- 63. Heinemann FS, Ozols J. Stearoyl-CoA desaturase, a short-lived protein of endoplasmic reticulum with multiple control mechanisms. Prostaglandins Leukotrienes and Essential Fatty Acids. 2003;68(2):123–33.
- 64. Choi K, Choi H, Lee J, Im S, Zhang H, Jeong Y, et al. The endothelial E3 ligase HECW2 promotes endothelial cell junctions by increasing AMOTL1 protein stability via K63-linked ubiquitination. Cellular Signalling. 2016;28(11):1642–51. pmid:27498087
- 65. Loftus TM, Jaworsky DE, Frehywot GL, Townsend CA, Ronnett GV, Lane MD, et al. Reduced Food Intake and Body Weight in Mice Treated with Fatty Acid Synthase Inhibitors. Science. 2000;288(5475):2379–81. pmid:10875926
- 66. Crowder RJ, Enomoto H, Yang M, Johnson EM, Milbrandt J. Dok-6, a novel p62 Dok family member, promotes Ret-mediated neurite outgrowth. Journal of Biological Chemistry. 2004;279(40):42072–81. pmid:15286081
- 67. Chiu C, Leng S, Martin KA, Kim EHJ, Gorman SE, Duhl D. Cloning and Characterization of T-Cell Lymphoma Invasion and Metastasis 2 (TIAM2), a Novel Guanine Nucleotide Exchange Factor Related to TIAM1 ☆. Genomics. 1999;61(1):66–73. pmid:10512681
- 68. Gal A, Li Y, Thompson DA, Weir J, Orth U, Jacobson SG, et al. Mutations in MERTK, the human orthologue of the RCS rat retinal dystrophy gene, cause retinitis pigmentosa. Nature Genetics. 2000;26(3):270–1. pmid:11062461
- 69. Wang L, Pelleymounter LL, Weinshilboum RM, Johnson JA, Hebert JM, Altman RB, et al. Very important pharmacogene summary: thiopurine S-methyltransferase. Pharmacogenetics and Genomics. 2010;20(6):401–5. pmid:20154640
- 70. Dhanoa BS, Cogliati T, Satish AG, Bruford EA, Friedman JS. Update on the Kelch-like (KLHL) gene family. Human Genomics. 2013;7(1):13.
- 71. Uzman A. Molecular Cell Biology (4th edition). Biochemistry & Molecular Biology Education. 2001;29(3):126–8.
- 72. Pollard TD, Korn ED. Acanthamoeba myosin. I. Isolation from Acanthamoeba castellanii of an enzyme similar to muscle myosin. Journal of Biological Chemistry. 1973;248(13):4682–90. pmid:4268863
- 73. Li PH, Ponnala L, Gandotra N, Wang L, Si YQ, Tausta SL, et al. The developmental dynamics of the maize leaf transcriptome. Nature Genetics. 2010;42(12):1060–7. pmid:21037569
- 74. Shi T, Gao Z, Wang L, Zhang Z, Zhuang W, Sun H, et al. Identification of differentially-expressed genes associated with pistil abortion in Japanese apricot by genome-wide transcriptional analysis. Plos One. 2012;7(10):17302–7.
- 75. Egerman MA, Cadena SM, Gilbert JA, Meyer A, Nelson HN, Swalley SE, et al. GDF11 Increases with Age and Inhibits Skeletal Muscle Regeneration. Cell Metabolism. 2015;22(1):164–74. pmid:26001423
- 76. Glenn TC. Field guide to nextextA, Meyer A, Nelson HN, Swalley SE, et al. GDF11 Increases with Age and I
- 77. Higgins CA, Petukhova L, Harel S, Ho YY, Drill E, Shapiro L, et al. FGF5 is a crucial regulator of hair length in humans. Proceedings of the National Academy of Sciences. 2014;111(29):10648–53.
- 78. Gerwins P, Sköldenberg E, Claesson-Welsh L. Function of fibroblast growth factors and vascular endothelial growth factors and their receptors in angiogenesis. Critical Reviews in Oncology/hematology. 2000;34(3):185–94. pmid:10838264
- 79. Braun S, Auf dKU, Beer HD, Krampert M, Müller M, S, et al. Meeting report: growth factors in development, repair and disease. European Journal of Cell Biology. 2002;81(7):375–82. pmid:12160145
- 80. Giavazzi R, Sennino B, Coltrini D, Garofalo A, Dossi R, Ronca R, et al. Distinct role of fibroblast growth factor-2 and vascular endothelial growth factor on tumor growth and angiogenesis. American Journal of Pathology. 2003;162(6):1913. pmid:12759248
- 81. Kästner S, Elias MC, Rivera AJ, Yablonkareuveni Z. Gene expression patterns of the fibroblast growth factors and their receptors during myogenesis of rat satellite cells. Journal of Histochemistry & Cytochemistry Official Journal of the Histochemistry Society. 2000;126(8):1079.
- 82. Hébert JM, Rosenquist T, Götz J, Martin GR. FGF5 as a regulator of the hair growth cycle: Evidence from targeted and spontaneous mutations. Cell. 1994;78(6):1017. pmid:7923352
- 83. Ornitz DM, Itoh N. The Fibroblast Growth Factor signaling pathway. Wiley Interdisciplinary Reviews-Developmental Biology. 2015;4(3):215–66. pmid:25772309
- 84. Belov AA, Mohammadi M. Molecular Mechanisms of Fibroblast Growth Factor Signaling in Physiology and Pathology. Cold Spring Harbor Perspectives in Biology. 2013;5(6).
- 85. Rodriguez J, Vernus B, Chelh I, Cassarmalek I, Gabillard J, Sassi AH, et al. Myostatin and the skeletal muscle atrophy and hypertrophy signaling pathways. Cellular and Molecular Life Sciences. 2014;71(22):4361–71. pmid:25080109
- 86. Lee SJ, Mcpherron AC. Regulation of myostatin activity and muscle growth. Proceedings of the National Academy of Sciences of the United States of America. 2001;98(16):9306–11. pmid:11459935
- 87. Nakamura S, Pourkheirandish M, Morishige H, Kubo Y, Nakamura M, Ichimura K, et al. Mitogen-Activated Protein Kinase Kinase 3 Regulates Seed Dormancy in Barley. Current Biology. 2016;26(6):775–81. pmid:26948880
- 88. Rossi AC, Mammucari C, Argentini C, Reggiani C, Schiaffino S. Two novel/ancient myosins in mammalian skeletal muscles: MYH14/7b and MYH15 are expressed in extraocular muscles and muscle spindles. The Journal of Physiology. 2010;588(2):353–64.
- 89. Takada F, Woude DLV, Tong H, Thompson TG, Watkins SC, Kunkel LM, et al. Myozenin: An α-actinin- and γ-filamin-binding protein of skeletal muscle Z lines. Proceedings of the National Academy of Sciences of the United States of America. 2001;98(4):1595–600. pmid:11171996
- 90. Mcpherron AC, Lawler AM, Lee SJ. Regulation of skeletal muscle mass in mice by a new TGF-beta superfamily member. Nature. 1997;387(6628):83–90. pmid:9139826
- 91. Elkasrawy MN, Hamrick MW. Myostatin (GDF-8) as a key factor linking muscle mass and bone structure. Journal of Musculoskeletal & Neuronal Interactions. 2010;10(1):56–63.
- 92. Lin J, Arnold HB, Della-Fera MA, Azain MJ, Hartzell DL, Baile CA. Myostatin knockout in mice increases myogenesis and decreases adipogenesis. Biochemical & Biophysical Research Communications. 2002;291(3):701–6.
- 93. Crispo M, Mulet AP, Tesson L, Barrera N, Cuadro F, Santosneto PC, et al. Efficient Generation of Myostatin Knock-Out Sheep Using CRISPR/Cas9 Technology and Microinjection into Zygotes. Plos One. 2014;10(8):e0136690.
- 94. Lessard JC, Pinapaz S, Rotty JD, Hickerson RP, Kaspar RL, Balmain A, et al. Keratin 16 regulates innate immunity in response to epidermal barrier breach. Proceedings of the National Academy of Sciences of the United States of America. 2013;110(48):19537–42. pmid:24218583
- 95. Wang XL , YN , Zhou JK, Zhu HJ, Ma BH, Chen YL, et al. CRISPR/Cas9-mediated MSTN disruption and heritable mutagenesis in goats causes increased body mass 2017;under review.
- 96. Crossin KL, Krushel LA. Cellular signaling by neural cell adhesion molecules of the immunoglobulin superfamily. Developmental Dynamics. 2000;218(2):260–79. pmid:10842356
- 97. Rodriguez JB, Vernus B, Chelh I, Cassarmalek I, Gabillard JC, Sassi AH, et al. Myostatin and the skeletal muscle atrophy and hypertrophy signaling pathways. Cellular and Molecular Life Sciences. 2014;71(22):4361–71. pmid:25080109
- 98. Liu Q, Hofmann PA. Protein phosphatase 2A-mediated cross-talk between p38 MAPK and ERK in apoptosis of cardiac myocytes. American Journal of Physiology-Heart and Circulatory Physiology. 2004;286(6):H2204–H12. pmid:14962831
- 99. Sun Y, Liu W-Z, Liu T, Feng X, Yang N, Zhou H-F. Signaling pathway of MAPK/ERK in cell proliferation, differentiation, migration, senescence and apoptosis. Journal of Receptors and Signal Transduction. 2015;35(6):600–4. pmid:26096166
- 100. Elliott B, Renshaw D, Getting SJ, Mackenzie R. The central role of myostatin in skeletal muscle and whole body homeostasis. Acta Physiologica. 2012;205(3):324–40. pmid:22340904
- 101. Rodriguez J, Vernus B, Chelh I, Cassarmalek I, Gabillard JC, Hadj SA, et al. Myostatin and the skeletal muscle atrophy and hypertrophy signaling pathways. Cellular & Molecular Life Sciences Cmls. 2014;71(22):4361–71.
- 102. Qiao C, Li J, Zhu X, Wang B, Li J, Xiao X. Myostatin propeptide gene delivery by adeno-associated virus serotype 8 vectors enhances muscle growth and ameliorates dystrophic phenotypes in mdx mice. Human Gene Therapy. 2008;19(3):241–54. pmid:18288893
- 103. Mcpherron AC, Lee S. Suppression of body fat accumulation in myostatin-deficient mice. Journal of Clinical Investigation. 2002;109(5):595–601. pmid:11877467
- 104. Feldman BJ, Streeper RS, Farese RV, Yamamoto KR. Myostatin modulates adipogenesis to generate adipocytes with favorable metabolic effects. Proceedings of the National Academy of Sciences of the United States of America. 2006;103(42):15675–80. pmid:17030820
- 105. Artaza JN, Bhasin S, Magee TR, Reiszporszasz S, Shen R, Groome NP, et al. Myostatin Inhibits Myogenesis and Promotes Adipogenesis in C3H 10T(1/2) Mesenchymal Multipotent Cells. Endocrinology. 2011;146(8):3547–57.
- 106. Muruganandan S, Roman A, Sinal CJ. Adipocyte differentiation of bone marrow-derived mesenchymal stem cells: Cross talk with the osteoblastogenic program. Cellular and Molecular Life Sciences. 2009;66(2):236–53. pmid:18854943
- 107. Li D, Qiu Z, Shao Y, Chen Y, Guan Y, Liu M, et al. Heritable gene targeting in the mouse and rat using a CRISPR-Cas system. Nature Biotechnology. 2013;31(8):681. pmid:23929336