# Gene Expression Patterns Analysis in the Supraspinatus Muscle after a Rotator Cuff Tear in a Mouse Model

Authors: Yong-Soo Lee; Ja-Yeon Kim; Hyo-Nam Kim; Dhong-Won Lee; Seok Won Chung
Journal: BioMed Research International
 (2018)

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## Abstract

Rotator cuff tear is a muscle-tendinous injury representative of various musculoskeletal disorders. In general, rotator cuff tear occurs in the tendon, but it causes unloading of the muscle resulting in muscle degeneration including fatty infiltration. These muscle degenerations lead to muscle weakness, pain, and loss of shoulder function and are well known as important factors for poor functional outcome after rotator cuff repair. Given that rotator cuff tear in various animal species results in similar pathological changes seen in humans, the animal model can be considered a good approach to understand the many aspects of the molecular changes in injured muscle. To comprehensively analyze changes in gene expression with time following a rotator cuff tear, we established a rotator cuff tear in mouse supraspinatus tendon of shoulder. At weeks 1 and 4 after the tear, the injured muscles were harvested for RNA isolation, and microarray analysis was performed. Expression patterns of genes belonging to 10 muscle physiology-related categories, including aging, apoptosis, atrophy, and fatty acid transport, were analyzed and further validated using real-time PCR. A total of 39,429 genes were analyzed, and significant changes in expression were observed for 12,178 genes at 1 week and 2,370 genes at 4 weeks after the tear. From the list of top 10 significantly up- and downregulated genes at the 2 time periods and the network evaluation of relevant genes according to the 10 categories, several important genes in each category were observed. In this study, we found that various genes are significantly altered after rotator cuff tear, and these genes may play key roles in controlling muscle degeneration after a rotator cuff tear.


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## Body

## 1. Introduction

Rotator cuff tear is a common condition that causes pain and functional disability, and it has been reported that more than 50% of patients over the age of 60 years have a rotator cuff tear [1]. Surgical repair of rotator cuff tears is widely practiced and has been a commonly accepted treatment for full-thickness rotator cuff tears, especially when conservative treatment fails [2]. However, failure in rotator cuff healing remains one of the most common and well-known complications of surgical repair [3, 4]. Several factors associated with rotator cuff muscle changes, such as aging [5], apoptosis [6], muscle degeneration [7], sarcopenia [8], muscle atrophy [9], and muscle fatty infiltration [10], have been demonstrated to be associated with rotator cuff tear. Further, these anatomical and physiological rotator cuff muscle changes reportedly result in healing failure and poor functional outcomes after rotator cuff repair [11–13]. Various trials have been performed to improve the quality of rotator cuff muscles by using growth factors, platelet-rich plasma, stem cells or its secretome, and anabolic steroids [14–19]; however, only a few studies to identify the molecular mechanisms underlying changes in the rotator cuff muscle after a rotator cuff tear have been performed [10, 20–22].Identification of gene expression patterns of rotator cuff muscle cells after a rotator cuff tear would be the first step and an important determinant in understanding the rotator cuff tear-related muscle changes and improving outcomes of rotator cuff repair surgery. Several studies have suggested various causes and mechanisms for muscle changes, such as atrophy and degeneration [9, 10, 21–25]; however, despite this, much remains unknown. Progression of muscle degeneration, atrophy, and fatty infiltration is usually caused by abnormal signaling processes in muscle cells [25]. Abnormal muscle cell activities are the results of external stimuli, such as physical damage or aging, which lead to differentiation into fat cells or fibrous tissue and ultimately myocyte destruction (instead of normal myocyte differentiation) [26]. Most of these cellular activities are directly related to intracellular gene regulation. Therefore, if we know how genes are regulated in muscle cells in response to external stimuli, it would be possible to understand the mechanism(s) underlying pathologic rotator cuff muscle changes after a rotator cuff tear. Further, this may also provide important information for the treatment of rotator cuff tears by controlling the expression of relevant genes. To date, only a few genes, such as those coding for peroxisome proliferator-activated receptors gamma (PPARγ), CCAAT-enhancer-binding proteins alpha (CEBPα), myogenin, myostatin, and matrix metallopeptidases (MMPs), have been identified in relation to muscle changes after rotator cuff tears [20, 21, 24, 27]. However, to the best of our knowledge, a comprehensive analysis of the time-dependent changes in gene expression patterns after rotator cuff tears has not been reported.Therefore, this study aimed to comprehensively analyze the patterns of gene expression in rotator cuff muscles with time after a rotator cuff tear (acute or chronic) by categorizing muscle physiology-related genes in a mouse model. In this study, we hypothesized that rotator cuff injury may cause alterations in gene expression regarding pathophysiology of rotator cuff muscle. Our study may improve understanding of molecular events after rotator cuff injury and may help the identification of novel regulation or control way to overcome the poor functional outcome after rotator cuff repair.


## 2. Materials and Methods

### 2.1. Animal Experiment

Eight-week-old male C57BL/6 mice (Orient Bio Inc., Seongnam, Korea) were used in this study. Before beginning the experiments, the mice were acclimatized to a 12:12-h light/dark cycle at 22 ± 2°C for 1 week and allowed unlimited access to food and water.We generated the rotator cuff tear model in mice as described previously [10]. Briefly, the supraspinatus tendon of the right shoulder of each mouse was fully exposed and completely transected from the greater tuberosity of the humerus under anesthesia using Zoletile (30 mg/kg; Virbac, Carros, France) and Rompun (10 mg/kg; Bayer Korea Ltd., Seoul, Korea). The supraspinatus of the left shoulder served as a control. A 3-0 nylon suture was used to close the skin, and the mice were allowed unrestricted cage activity. At weeks 1 and 4 after the surgery, mice (n = 4 per time interval) were sacrificed by cervical dislocation, and the supraspinatus muscles of both shoulders were completely harvested from the scapular fossa. The muscle tissue samples were used for total RNA extraction. All animal experiments were approved by the Institutional Animal Care and Use Committee of the Konkuk University (IACUC: KU17122) and were performed in accordance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health.


### 2.2. RNA and Gene Expression Profiling

RNA quality was assessed using Agilent 2100 Bioanalyzer (Agilent Technologies, USA), and its quantity was determined using the ND-1000 spectrophotometer (NanoDrop Technologies, USA). The total RNAs from each supraspinatus muscle at different phase (1 or 4 weeks) after injury were pooled and used for microarray. Gene expression analyses were performed using the global Affymetrix GeneChip® Human Gene 2.0 ST oligonucleotide arrays. About 300 ng of each RNA sample was used for the Affymetrix procedure, as recommended by the manufacturer (http://www.affymetrix.com). Briefly, 300 ng total RNA from each sample was converted to double-strand cDNA. Using a random hexamer incorporating a T7 promoter, amplified RNA (cRNA) was generated from the double-stranded cDNA template though an IVT (in vitro transcription) reaction and purified with the Affymetrix Sample Cleanup Module. cDNA was synthesized by random-primed reverse transcription using a dNTP mix containing dUTP. Next, cDNA was digested using the UDG and APE 1 restriction endonucleases, and end-labeled using the terminal transferase reaction incorporating a biotinylated dideoxynucleotide. The fragmented end-labeled cDNA was hybridized to the GeneChip® Human Gene 2.0 ST arrays for 16 h at 45°C and 60 rpm, as described in the Gene Chip Whole Transcript (WT) Sense Target Labeling Assay manual (Affymetrix). After hybridization, the chips were stained using Streptavidin Phycoerythrin (SAPE), washed in Genechip Fluidics Station 450 (Affymetrix), and scanned using Genechip Array Scanner 3000 7G (Affymetrix).Ten gene categories that have direct or indirect effects on muscle physiology in relation to rotator cuff tears were selected. The categories were as follows: (1) aging, (2) inflammation, (3) apoptosis, (4) neovascularization, (5) extracellular matrix composition, (6) myocyte differentiation, (7) myocyte proliferation, (8) cellular migration, (9) fatty acid transport, and (10) muscle atrophy. Every gene expression values were measured, and among them, those with the fold change of rotator cuff tear group/control group were more than 2 or less than 1/2 with the raw values (log 2) of more than 4 were defined as significant genes and further analyzed. Especially, the aging-, apoptosis-, fatty acid transport-, and muscle atrophy-related categories were analyzed in detail because these categories are known to be closely associated with muscle changes, such as degeneration, fatty infiltration, and atrophy, after rotator cuff tear [10, 22, 23].


### 2.3. STRING Network

The genes showing significant changes in expression were selected and used as the input for STRING (Search Tool for the Retrieval of Interacting Genes/Proteins;https://string-db.org/). Protein network analyses were performed. The database and web-tool STRING is a meta-resource that integrates most of the available information on protein–protein associations, and scores, weighs, and augments it with predicted interactions, as well as with the results of automatic literature mining searches [28]. Using this, we obtained the protein interaction network images associated with functional enrichment [29]. Information regarding the size of each node and edges between nodes is shown in Figure 1 (EBIOGEN Inc., Korea).Figure 1
Information for interpreting the STRING network.


### 2.4. Data Analysis

After the final washing and staining step, the Affymetrix GeneChip® Human Gene 2.0 ST oligonucleotide array was scanned using Affymetrix Model 3000 G7 Scanner and the image data was extracted using the Affymetrix Commnad Console software v1.1. The raw.cel file generated after the above procedure showed the expression intensity data and was used for the next step. Expression data were generated by the Affymetrix Expression Console software version 1.1. For normalization, the Robust Multi-Average (RMA) algorithm implemented in the Affymetrix Expression Console software was used. In order to find the coexpressing gene groups (which had similar expression patterns), we performed hierarchical clustering in the MultiExperiment Viewer software v4.4 (MEV;www.tm4.org). The web-based tool DAVID (Database for Annotation, Visualization, and Integrated Discovery; http://david.abcc.ncifcrf.gov/home.jsp) was used to perform biological interpretation for the differentially expressed genes. Next, these genes were classified based on the information about their functions in Gene Ontology in the KEGG Pathway database.


### 2.5. Quantitative Reverse Transcription (qRT) PCR Analysis

To validate the gene expression analysis results obtained using the microarray process described above, we performed qRT-PCR analysis for several representative genes. Total RNA was extracted from the supraspinatus muscles using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s instructions, and used for cDNA synthesis using the Maxime RT PreMix kit (iNtRON Biotechnology, Korea). The qRT-PCR analysis was carried out using Light Cycler 480 System (Roche Diagnostics, Swiss) with 2 × qPCR BIO SyGreen Mix Lo-ROX (PCR Biosystems, London, UK). All expression data were normalized to actin expression.


### 2.6. Statistical Analysis

Descriptive statistics were used to present the analyzed data in this study.


## 2.1. Animal Experiment

Eight-week-old male C57BL/6 mice (Orient Bio Inc., Seongnam, Korea) were used in this study. Before beginning the experiments, the mice were acclimatized to a 12:12-h light/dark cycle at 22 ± 2°C for 1 week and allowed unlimited access to food and water.We generated the rotator cuff tear model in mice as described previously [10]. Briefly, the supraspinatus tendon of the right shoulder of each mouse was fully exposed and completely transected from the greater tuberosity of the humerus under anesthesia using Zoletile (30 mg/kg; Virbac, Carros, France) and Rompun (10 mg/kg; Bayer Korea Ltd., Seoul, Korea). The supraspinatus of the left shoulder served as a control. A 3-0 nylon suture was used to close the skin, and the mice were allowed unrestricted cage activity. At weeks 1 and 4 after the surgery, mice (n = 4 per time interval) were sacrificed by cervical dislocation, and the supraspinatus muscles of both shoulders were completely harvested from the scapular fossa. The muscle tissue samples were used for total RNA extraction. All animal experiments were approved by the Institutional Animal Care and Use Committee of the Konkuk University (IACUC: KU17122) and were performed in accordance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health.


## 2.2. RNA and Gene Expression Profiling

RNA quality was assessed using Agilent 2100 Bioanalyzer (Agilent Technologies, USA), and its quantity was determined using the ND-1000 spectrophotometer (NanoDrop Technologies, USA). The total RNAs from each supraspinatus muscle at different phase (1 or 4 weeks) after injury were pooled and used for microarray. Gene expression analyses were performed using the global Affymetrix GeneChip® Human Gene 2.0 ST oligonucleotide arrays. About 300 ng of each RNA sample was used for the Affymetrix procedure, as recommended by the manufacturer (http://www.affymetrix.com). Briefly, 300 ng total RNA from each sample was converted to double-strand cDNA. Using a random hexamer incorporating a T7 promoter, amplified RNA (cRNA) was generated from the double-stranded cDNA template though an IVT (in vitro transcription) reaction and purified with the Affymetrix Sample Cleanup Module. cDNA was synthesized by random-primed reverse transcription using a dNTP mix containing dUTP. Next, cDNA was digested using the UDG and APE 1 restriction endonucleases, and end-labeled using the terminal transferase reaction incorporating a biotinylated dideoxynucleotide. The fragmented end-labeled cDNA was hybridized to the GeneChip® Human Gene 2.0 ST arrays for 16 h at 45°C and 60 rpm, as described in the Gene Chip Whole Transcript (WT) Sense Target Labeling Assay manual (Affymetrix). After hybridization, the chips were stained using Streptavidin Phycoerythrin (SAPE), washed in Genechip Fluidics Station 450 (Affymetrix), and scanned using Genechip Array Scanner 3000 7G (Affymetrix).Ten gene categories that have direct or indirect effects on muscle physiology in relation to rotator cuff tears were selected. The categories were as follows: (1) aging, (2) inflammation, (3) apoptosis, (4) neovascularization, (5) extracellular matrix composition, (6) myocyte differentiation, (7) myocyte proliferation, (8) cellular migration, (9) fatty acid transport, and (10) muscle atrophy. Every gene expression values were measured, and among them, those with the fold change of rotator cuff tear group/control group were more than 2 or less than 1/2 with the raw values (log 2) of more than 4 were defined as significant genes and further analyzed. Especially, the aging-, apoptosis-, fatty acid transport-, and muscle atrophy-related categories were analyzed in detail because these categories are known to be closely associated with muscle changes, such as degeneration, fatty infiltration, and atrophy, after rotator cuff tear [10, 22, 23].


## 2.3. STRING Network

The genes showing significant changes in expression were selected and used as the input for STRING (Search Tool for the Retrieval of Interacting Genes/Proteins;https://string-db.org/). Protein network analyses were performed. The database and web-tool STRING is a meta-resource that integrates most of the available information on protein–protein associations, and scores, weighs, and augments it with predicted interactions, as well as with the results of automatic literature mining searches [28]. Using this, we obtained the protein interaction network images associated with functional enrichment [29]. Information regarding the size of each node and edges between nodes is shown in Figure 1 (EBIOGEN Inc., Korea).Figure 1
Information for interpreting the STRING network.


## 2.4. Data Analysis

After the final washing and staining step, the Affymetrix GeneChip® Human Gene 2.0 ST oligonucleotide array was scanned using Affymetrix Model 3000 G7 Scanner and the image data was extracted using the Affymetrix Commnad Console software v1.1. The raw.cel file generated after the above procedure showed the expression intensity data and was used for the next step. Expression data were generated by the Affymetrix Expression Console software version 1.1. For normalization, the Robust Multi-Average (RMA) algorithm implemented in the Affymetrix Expression Console software was used. In order to find the coexpressing gene groups (which had similar expression patterns), we performed hierarchical clustering in the MultiExperiment Viewer software v4.4 (MEV;www.tm4.org). The web-based tool DAVID (Database for Annotation, Visualization, and Integrated Discovery; http://david.abcc.ncifcrf.gov/home.jsp) was used to perform biological interpretation for the differentially expressed genes. Next, these genes were classified based on the information about their functions in Gene Ontology in the KEGG Pathway database.


## 2.5. Quantitative Reverse Transcription (qRT) PCR Analysis

To validate the gene expression analysis results obtained using the microarray process described above, we performed qRT-PCR analysis for several representative genes. Total RNA was extracted from the supraspinatus muscles using the TRIzol reagent (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s instructions, and used for cDNA synthesis using the Maxime RT PreMix kit (iNtRON Biotechnology, Korea). The qRT-PCR analysis was carried out using Light Cycler 480 System (Roche Diagnostics, Swiss) with 2 × qPCR BIO SyGreen Mix Lo-ROX (PCR Biosystems, London, UK). All expression data were normalized to actin expression.


## 2.6. Statistical Analysis

Descriptive statistics were used to present the analyzed data in this study.


## 3. Results

A total of 39,429 genes were analyzed. Among these, 9,696 genes were associated with muscle physiology. Significant changes in expression were observed for 12,178 genes during the acute phase and 2,370 genes during the chronic phase. The number and distribution of genes expressed per category are shown in Figure2 and Table 1.Table 1
Summary of the rotator cuff tear-induced expression for genes belonging to 10 categories at (a) 1 and (b) 4 weeks.
(a) Total 10 categories associated with muscle physiology 1 week Total Aging Angio. Apop. Diff. Migr. Prolif. ECM Infl. FA tr. Atro  Total gene number 39,429 370 443 1,100 4,701 994 788 683 532 66 19  % of Total 100 0.94 1.12 2.79 11.92 2.52 2 1.73 1.35 0.17 0.05  Up significant (n) 8,662 127 174 378 1,422 386 262 264 249 10 3  % of Up significant 22 34.3 39.3 34.4 30.2 38.8 33.2 38.7 46.8 15.2 15.8  Down significant (n) 3,516 40 30 113 436 75 45 54 31 13 9  % of Down significant 8.9 10.8 6.8 10.3 9.3 7.5 5.7 7.9 5.8 19.7 47.4  Total significant (n) 12,178 184 203 525 1,867 463 300 267 278 23 11  % of Total significant 30.9 45.1 46 44.6 39.5 46.4 39 46.6 52.6 34.8 63.2  (b) Total 10 categories associated with muscle physiology 4 weeks Total Aging Angio. Apop. Diff. Migr. Prolif. ECM Infl. FA tr. Atro  Total gene number 39,429 370 443 1,100 4,701 994 788 683 532 66 19  % of Total 100 0.94 1.12 2.79 11.92 2.52 2 1.73 1.35 0.17 0.05  Up Significant (n) 1,233 21 20 52 203 59 35 31 46 2 1  % of Up Significant 3.1 5.7 4.5 4.7 4.3 5.9 4.4 4.5 8.6 3 5.3  Down Significant (n) 1,137 8 9 34 150 31 32 12 15 0 4  % of Down Significant 2.9 2.2 2 3.1 3.2 3.1 4.1 1.8 2.8 0 21.1  Total Significant (n) 2,370 29 29 86 353 90 67 43 61 2 5  % of Total Significant 6 7.8 6.5 7.8 7.5 9.1 8.5 6.3 11.5 3 26.3 Angio., angiogenesis; Apop., apoptosis; Diff., cell differentiation; Migr., cell migration; Prolif., cell proliferation; ECM, extracellular matrix; Infl., inflammation; FA tr., fatty acid transport; Atro., muscle atrophy. Up significant means the genes with the fold change of rotator cuff tear/control of more than 2 the raw values (log2) of more than 4. Down significant means the genes with the fold change of rotator cuff tear/control of less than 1/2 with the raw values (log2) of more than 4.Figure 2
Percentages of total significant genes and up and down significant genes according to 10 categories after rotator cuff tear at (a) 1 and (b) 4 weeks.
(a) (b)From the Venn diagram of genes showing significantly different expression at weeks 1 and 4 after the rotator cuff tear, we could identify 115 genes which showed a reverse expression pattern (genes that increased during the acute phase but decreased during the chronic phase or genes that decreased during the acute phase but increased during the chronic phase) (Figure3).Figure 3
Venn diagram depicting the number of genes up-, down-, and contraregulated among the significant genes for weeks 1 and 4.Overall, the most highly expressed gene at week 1 was keratin 18, which increased 217.8 times compared to the control. This gene decreased 22.1 times compared to the control at week 4. Signal-regulatory protein beta 1B (Sirpb1b) and keratin 8 also displayed higher expression (more than 100 times) than the control at week 1, then subsequently decreased (expression: 4 and 16 times higher than the control, respectively) at week 4. The overall up- and downregulated genes and fold-change values of rotator cuff tear/control at weeks 1 and 4 are listed in Table2.Table 2
The top 10 up- and downregulated genes after rotator cuff tear (at weeks 1 and 4).
(a) Gene symbol Genbank accession Fold change Gene name Upregulated  Krt18 NM_010664 217.799 keratin 18  Sirpb1b NM_001173460 150.129 signal-regulatory protein beta 1B  Cd300lf NM_001169153 128.186 CD300 antigen like family member F  Ms4a4c NM_029499 122.342 membrane-spanning 4-domains, subfamily A, member 4C  Plekhh1 NM_181073 119.491 pleckstrin homology domain containing, family H (with MyTH4 domain) member 1  Cd5l NM_009690 104.575 CD5 antigen-like  Krt8 AK166854 104.209 keratin 8  Myh8 AK081482 95.091 myosin, heavy polypeptide 8, skeletal muscle, perinatal  LOC102642410 XM_006544504 94.351 tyrosine-protein phosphatase non-receptor type substrate 1-like  Myh3 NM_001099635 92.621 myosin, heavy polypeptide 3, skeletal muscle, embryonic  Downregulated  Pitx1 NM_011097 0.025 paired-like homeodomain transcription factor 1  Adam28 NM_183366 0.025 a disintegrin and metallopeptidase domain 28  Rnf180 NM_027934 0.027 ring finger protein 180  Gm5860 NR_040659 0.029 predicted gene 5860  H2-M9 NM_008205 0.03 histocompatibility 2, M region locus 9  Smco1 NM_183283 0.03 single-pass membrane protein with coiled-coil domains 1  AU042410 BB209673 0.031 expressed sequence AU042410  Kcnc1 NM_001112739 0.031 potassium voltage gated channel, Shaw-related subfamily, member 1  8430426J06Rik NR_077229 0.032 RIKEN cDNA 8430426J06 gene  Gm10822 AK144860 0.033 predicted gene 10822  (b) Gene symbol Genbank accession Fold change Gene name Upregulated  Gm7325 NM_001177470 45.121 predicted gene 7325  Plekhh1 NM_181073 42.051 pleckstrin homology domain containing, family H (with MyTH4 domain) member 1  Ces2b NM_198171 28.227 carboxylesterase 2B  Ces2c NM_145603 25.457 carboxylesterase 2C  Sln NM_025540 24.155 sarcolipin  Krt18 NM_010664 22.114 keratin 18  Myh3 NM_001099635 17.984 myosin, heavy polypeptide 3, skeletal muscle, embryonic  Krt8 AK166854 16.550 keratin 8  Zfp735 NM_001126489 15.836 zinc finger protein 735  Epha1 NM_023580 15.534 Eph receptor A1  Downregulated  Dok6 NM_001039173 0.063 docking protein 6  Snhg11 NM_175692 0.072 small nucleolar RNA host gene 11  Olfr981 NM_146286 0.087 olfactory receptor 981  Pitx1 NM_011097 0.087 paired-like homeodomain transcription factor 1  Gm12 NM_001195544 0.093 predicted gene 12  Olfr586 NM_147111 0.098 olfactory receptor 586  Samd11 XR_881429 0.101 sterile alpha motif domain containing 11  Dnase1 NM_010061 0.111 deoxyribonuclease I  1190003K10Rik NM_001195435 0.114 RIKEN cDNA 1190003K10 gene  Gpr176 NM_201367 0.117 G protein-coupled receptor 176


### 3.1. Gene Expression Patterns in the Aging Category

Analysis of the up- and downregulated genes in the 10 categories showed that insulin-like growth factor binding protein 2 (Igfbp2) was the most highly expressed gene in the aging category. It displayed 55.7 times higher expression in the RC tear side than in the control at week 1, and subsequently showed a decreasing trend (expressed 5.2 times higher than control) at week 4. In the string network which depicts the interactions between genes, we observed that IL6, Ccl2, and Vcam1, which are known to be related to inflammation or cell adhesion, actively interacted with each other showing high expression at week 1 after the RC tear. In addition, RAD54L (a DNA repair-related gene) and cyclin-dependent kinase 1 (Cdk1; a cell cycle regulation-related gene) also interacted closely (Figure4). The top 10 up- and downregulated genes in the aging category at weeks 1 and 4 after the RC tear are listed in Table 3.Table 3
Gene expression patterns after rotator cuff tear in the aging category ((a) week 1 after tear; (b) week 4 after tear).
(a) Gene symbol Genbank accession Fold change Gene name Upregulated  Igfbp2 NM_008342 55.685 insulin-like growth factor binding protein 2  Arg1 NM_007482 54.973 arginase, liver  Cdk1 NM_007659 34.242 cyclin-dependent kinase 1  Cdkn2a NM_009877 30.112 cyclin-dependent kinase inhibitor 2A  Aldh3a1 NM_007436 19.548 aldehyde dehydrogenase family 3, subfamily A1  Ddc NM_001190448 17.423 dopa decarboxylase  Ccl2 NM_011333 16.814 chemokine (C-C motif) ligand 2  Inpp5d NM_010566 15.961 inositol polyphosphate-5-phosphatase D  Mpo NM_010824 14.836 myeloperoxidase  Rad54l NM_009015 13.26 RAD54 like (S. cerevisiae)  Downregulated  Tfcp2l1 NM_023755 0.125 transcription factor CP2-like 1  Nat1 NM_008673 0.146 N-acetyl transferase 1  Ppargc1a NM_008904 0.153 peroxisome proliferative activated receptor, gamma, coactivator 1 alpha  Atp8a2 NM_015803 0.157 ATPase, aminophospholipid transporter-like, class I, type 8A, member 2  Pde4d NM_011056 0.183 phosphodiesterase 4D, cAMP specific  Endog NM_007931 0.195 endonuclease G  Cyp1a1 NM_009992 0.199 cytochrome P450, family 1, subfamily a, polypeptide 1  Kl NM_013823 0.2 klotho  Cryab NM_001289782 0.21 crystallin, alpha B  P2ry1 NM_008772 0.216 purinergic receptor P2Y, G-protein coupled 1  (b) Gene symbol Genbank accession Fold change Gene name Upregulated  Slc1a2 NM_001077514 10.851 solute carrier family 1 (glial high affinity glutamate transporter), member 2  Krtap4-16 NM_001013823 9.991 keratin associated protein 4-16  Ghrhr NM_001003685 9.285 growth hormone releasing hormone receptor  Ddc NM_001190448 8.068 dopa decarboxylase  Aldh3a1 NM_007436 7.897 aldehyde dehydrogenase family 3, subfamily A1  Cdkn2a NM_009877 6.885 cyclin-dependent kinase inhibitor 2A  Igfbp2 NM_008342 5.164 insulin-like growth factor binding protein 2  Retn NM_001204959 3.729 resistin  Adra1a NM_013461 3.523 adrenergic receptor, alpha 1a  Cnr1 NM_007726 3.51 cannabinoid receptor 1 (brain)  Downregulated  Il10 NM_010548 0.195 interleukin 10  Cdk1 NM_007659 0.227 cyclin-dependent kinase 1  Glrx2 NM_001038592 0.325 glutaredoxin 2 (thioltransferase)  Tfcp2l1 NM_023755 0.391 transcription factor CP2-like 1  Fos NM_010234 0.438 FBJ osteosarcoma oncogene  Atp8a2 NM_015803 0.459 ATPase, aminophospholipid transporter-like, class I, type 8A, member 2  Hmga1 NM_001166546 0.462 high mobility group AT-hook 1  Bcl2 NM_177410 0.464 B-cell leukemia/lymphoma 2  Srf NM_020493 0.482 serum response factor  Arntl NM_007489 0.519 aryl hydrocarbon receptor nuclear translocator-likeFigure 4
Differential expression patterns in hierarchical clustering and the STRING network for the aging category ((a) week 1 after tear; (b) week 4 after tear). Left side shows differential expression patterns and hierarchical clustering, and right side shows the STRING network for the aging category.
(a) (b)


### 3.2. Gene Expression Patterns in the Apoptosis Category

In the apoptosis category, keratin 18 showed the highest expression (217.8 times higher expression than the control group) at week 1. This expression subsequently decreased (22 times higher expression than the control group) at week 4 (Figure5 and Table 4). Interestingly, Birc5 (55.6 and 1.6 times higher expression than control at weeks 1 and 4, respectively), Rps6ka2 (0.2 times and 0.5 times higher expression than control at weeks 1 and 4, respectively), and Bub1 (37.5 and 0.3 times higher expression than control at weeks 1 and 4, respectively), which also showed reverse expression patterns, presented active interactions with each other revealed by the STRING network analysis results of week 1. These genes are known to be involved in apoptotic processes, such as cell growth, cell cycle, and cell differentiation.Table 4
Gene expression patterns in the apoptosis category after rotator cuff tear ((a) week 1 after tear; (b) week 4 after tear).
(a) Gene symbol Genbank accession Fold change Gene name Upregulated  Krt18 NM_010664 217.799 keratin 18  Cd5l NM_009690 104.575 CD5 antigen-like  Krt8 AK166854 104.209 keratin 8  Serpina3g NM_009251 85.473 serine (or cysteine) peptidase inhibitor, clade A, member 3G  Melk NM_010790 72.784 maternal embryonic leucine zipper kinase  Birc5 NM_009689 55.603 baculoviral IAP repeat-containing 5  Gpr65 NM_008152 54.323 G-protein coupled receptor 65  Gzma NM_010370 52.943 granzyme A  Top2a NM_011623 44.234 topoisomerase (DNA) II alpha  Bcl2a1d NM_007536 43.608 B-cell leukemia/lymphoma 2-related protein A1d  Downregulated  Dnase1 NM_010061 0.051 deoxyribonuclease I  Casp14 NM_009809 0.099 caspase 14  Robo2 BC055333 0.104 roundabout homolog 2 (Drosophila)  Camk2a NM_009792 0.107 calcium/calmodulin-dependent protein kinase II alpha  Il24 NM_053095 0.112 interleukin 24  Chac1 NM_026929 0.119 ChaC, cation transport regulator 1  Nr4a1 NM_010444 0.122 nuclear receptor subfamily 4, group A, member 1  Mtfp1 NM_026443 0.124 mitochondrial fission process 1  Rps6ka2 NM_011299 0.156 ribosomal protein S6 kinase, polypeptide 2  Casr NM_013803 0.158 calcium-sensing receptor  (b) Gene symbol Genbank accession Fold change Gene name Upregulated  Krt18 NM_010664 22.114 keratin 18  Krt8 AK166854 16.55 keratin 8  Bcl2l14 NM_025778 14.994 BCL2-like 14 (apoptosis facilitator)  Tox3 NM_172913 10.794 TOX high mobility group box family member 3  Fasl NM_010177 10.487 Fas ligand (TNF superfamily, member 6)  Pak7 NM_172858 9.86 p21 protein (Cdc42/Rac)-activated kinase 7  Fgf21 NM_020013 7.87 fibroblast growth factor 21  Avp NM_009732 7.557 arginine vasopressin  Nlrc4 NM_001033367 7.471 NLR family, CARD domain containing 4  Cdkn2a NM_009877 6.885 cyclin-dependent kinase inhibitor 2A  Downregulated  Dnase1 NM_010061 0.111 deoxyribonuclease I  Cd27 NM_001033126 0.117 CD27 antigen  Fgf8 NM_010205 0.162 fibroblast growth factor 8  AY074887 NM_145229 0.168 cDNA sequence AY074887  Tnf NM_013693 0.205 tumor necrosis factor  Naip1 NM_008670 0.217 NLR family, apoptosis inhibitory protein 1  Cdk1 NM_007659 0.227 cyclin-dependent kinase 1  Epha7 NM_010141 0.239 Eph receptor A7  Tiam1 NM_009384 0.253 T-cell lymphoma invasion and metastasis 1  Bub1 NM_009772 0.284 budding uninhibited by benzimidazoles 1 homolog (S. cerevisiae)Figure 5
Differential expression patterns in hierarchical clustering and the STRING network for the apoptosis category ((a) week 1 after tear; (b) week 4 after tear).
(a) (b)


### 3.3. Gene Expression Patterns in the Muscle Atrophy Category

Among the 39,429 genes analyzed, a total of 11 genes belonged to the muscle atrophy category (Figure6 and Table 5). Myog (myogenin), which is associated with myogenesis, showed the highest expression at week 1. In contrast, Mstn (myostatin), which is to inhibit myogenesis, showed the lowest expression at week 1. Further, Actin 3 (actin alpha 3), which is coexpressed with myostatin, also showed low expression at week 1 but a high expression at week 4 together with myostatin. This result was in contrast to that of myogenin, which showed the highest expression at week 1 but low expression at week 4.Table 5
Gene expression patterns in the muscle atrophy category after rotator cuff tear ((a) week 1 after tear; (b) week 4 after tear).
(a) Gene symbol Genbank accession Fold change Gene name Upregulated  Myog NM_031189 38.535 myogenin  Gatm NM_025961 8.862 glycine amidinotransferase (L-arginine:glycine amidinotransferase):energy metabolism of muscle  Cflar NM_009805 2.442 CASP8 and FADD-like apoptosis regulator  Rps6kb1 NM_028259 1.287 ribosomal protein S6 kinase, polypeptide 1  Downregulated  Il15 NM_008357 0.727 interleukin 15  Tbce NM_178337 0.648 tubulin-specific chaperone E  Gsn NM_146120 0.439 Gelsolin:actin-binding protein  Trim63 NM_001039048 0.435 tripartite motif-containing 63  Actn3 NM_013456 0.279 actinin alpha 3:alpha-actinin skeletal muscle isoform 3  Ppargc1a NM_008904 0.216 peroxisome proliferative activated receptor, gamma, coactivator 1 alpha  Mstn NM_010834 0.108 myostatin  (b) Gene symbol Genbank accession Fold change Gene name Upregulated  Myog NM_031189 11.945 myogenin  Gatm NM_025961 1.712 glycine amidinotransferase (L-arginine:glycine amidinotransferase)  Gsn NM_146120 1.249 gelsolin  Rps6kb1 NM_028259 1.245 ribosomal protein S6 kinase, polypeptide 1  Downregulated  Il15 NM_008357 0.98 interleukin 15  Cflar NM_207653 0.935 CASP8 and FADD-like apoptosis regulator  Ppargc1a NM_008904 0.763 peroxisome proliferative activated receptor, gamma, coactivator 1 alpha  Tbce NM_178337 0.712 tubulin-specific chaperone E  Actn3 NM_013456 0.494 actinin alpha 3  Trim63 NM_001039048 0.387 tripartite motif-containing 63  Mstn NM_010834 0.378 myostatinFigure 6
Differential expression patterns in hierarchical clustering and the STRING network for the muscle atrophy category ((a) week 1 after tear; (b) weeks 4 after tear).
(a) (b)


### 3.4. Gene Expression Patterns in the Fatty Acid Transport Category

In the fatty acid transport category, the expression of apolipoprotein E (ApoE; expressed 12.229 times higher than control at week 1), annexin A1 (expressed 5.517 times higher than control at week 1) which is a phospholipid-binding protein, and perilipin 2 (expressed 5.018 times higher than control at week 1) which is an adipose differentiation-related gene was notable. The expression of all these genes decreased at week 4 (ApoE, annexin A1, and perilipin 2 were expressed 2.729, 1.377, and 1.196 times higher than control, respectively). In addition, these genes interacted with the phospholipase groups regulating PPARγ expression, which regulates fatty acid storage and glucose metabolism, at weeks 1 and 4 as shown in the STRING network (Figure 7 and Table 6).Table 6
Gene expression patterns in the fatty acid transport category after rotator cuff tear ((a) week 1 after tear; (b) week 4 after tear).
(a) Gene symbol Genbank accession Fold change Gene name Upregulated  Mfsd2a NM_029662 12.606 major facilitator superfamily domain containing 2A  Apoe NM_009696 12.229 apolipoprotein E  Drd4 NM_007878 10.607 dopamine receptor D4  Slc27a6 NM_001081072 7.557 solute carrier family 27 (fatty acid transporter), member 6  Pla2g4a NM_008869 6.457 phospholipase A2, group IVA (cytosolic, calcium-dependent)  Anxa1 NM_010730 5.517 annexin A1  Plin2 NM_007408 5.018 perilipin 2  Pla2g2e NM_012044 3.407 phospholipase A2, group IIE  Pla2g1b NM_011107 3.384 phospholipase A2, group IB, pancreas  Hnf1a M57966 2.029 HNF1 homeobox A  Downregulated  Fabp3 NM_010174 0.131 fatty acid binding protein 3, muscle and heart  Pla2g5 NM_001122954 0.15 phospholipase A2, group V  Cpt1b NM_009948 0.186 carnitine palmitoyltransferase 1b, muscle  Pla2g12a NM_183423 0.262 phospholipase A2, group XIIA  Got2 NM_010325 0.289 glutamic-oxaloacetic transaminase 2, mitochondrial  Slc27a2 NM_011978 0.299 solute carrier family 27 (fatty acid transporter), member 2  Slc27a1 NM_011977 0.32 solute carrier family 27 (fatty acid transporter), member 1  Acsl1 NM_007981 0.354 acyl-CoA synthetase long-chain family member 1  Pnpla8 NM_026164 0.372 patatin-like phospholipase domain containing 8  Pla2g2c NM_008868 0.408 phospholipase A2, group IIC  (b) Gene symbol Genbank accession Fold change Gene name Upregulated  Nmur2 NM_153079 3.742 neuromedin U receptor 2  Apoe NM_009696 2.729 apolipoprotein E  Pla2g2d NM_011109 2.704 phospholipase A2, group IID  Pparg NM_011146 1.56 peroxisome proliferator activated receptor gamma  Proca1 XM_006532963 1.548 protein interacting with cyclin A1  Pla2g2e NM_012044 1.539 phospholipase A2, group IIE  Pla2g4a NM_008869 1.485 phospholipase A2, group IVA(cytosolic, calcium-dependent)  Anxa1 NM_010730 1.377 annexin A1  Pla2g2a NM_001082531 1.286 phospholipase A2,group IIA (platelets, synovial fluid)  Mfsd2a NM_029662 1.268 major facilitator superfamilydomain containing 2A  Downregulated  Pla2g1b NM_011107 0.428 phospholipase A2, group IB,pancreas  Slc27a5 NM_009512 0.456 solute carrier family 27 (fatty acidtransporter), member 5  Pla2g3 NM_172791 0.511 phospholipase A2, group III  Hnf1a M57966 0.529 HNF1 homeobox A  Fabp3 NM_010174 0.587 fatty acid-binding protein 3, muscle and heart  Pla2g2f NM_012045 0.592 phospholipase A2, group IIF  Abcc4 NM_001033336 0.592 ATP-binding cassette, sub-family C (CFTR/MRP), member 4  Pnpla8 NM_026164 0.614 patatin-like phospholipase domain containing 8  Ppard NM_011145 0.628 peroxisome proliferator activator receptor delta  Pla2g5 NM_001122954 0.661 phospholipase A2, group VFigure 7
Differential expression patterns in hierarchical clustering and the STRING network for the fatty acid transport category ((a) week 1 after tear; (b) week 4 after tear).
(a) (b)


### 3.5. Gene Expression Patterns in Other Categories Related to Muscle Physiology

Gene expression patterns and STRING networks for other categories, such as angiogenesis, inflammation, cell migration, cell proliferation, extracellular matrix, and cell differentiation, are described in ‘Supplementary Materials (availablehere)’.


### 3.6. Validation of Gene Expression Using Real-Time PCR Analysis

Cdk1 from the aging category, keratin 8 and 18 from the apoptosis category, and myogenin, myostatin, and actn3 from the muscle atrophy category were selected as representative genes for the validation of gene expression using quantitative real-time PCR analysis. The result of qRT-PCR analyses for all these selected genes was similar to the result of microarray analysis discussed above. The mRNA levels from both microarray and qRT-PCR analyses are depicted in Figure8.Figure 8
Validation of microarray gene expression patterns using qRT-PCR analysis. (a) Cdk1, (b) keratin 8, (c) keratin 18, (d) myogenin, (e) myostatin, and (f) actn3.
(a) (b) (c) (d) (e) (f)


### 3.7. Commonly Expressed Genes

There were 2 common genes, namely, Rps6kb1 and gelsolin, which displayed commonly regulated expressions in the 3 categories (aging, apoptotic process, and muscle atrophy) of our interest that are known to be associated with muscle degeneration and atrophy after a rotator cuff tear. Rps6kb1 in the rotator cuff tear side was downregulated (expressed 0.48 times of the control) during the acute phase, and showed increased expression during the chronic phase (expressed 0.806 times of the control). In addition, Gelsolin in the rotator cuff tear side was also downregulated (0.40 times of the control) during the acute phase and showed increased expression during the chronic phase (expressed 1.17 times of the control).


## 3.1. Gene Expression Patterns in the Aging Category

Analysis of the up- and downregulated genes in the 10 categories showed that insulin-like growth factor binding protein 2 (Igfbp2) was the most highly expressed gene in the aging category. It displayed 55.7 times higher expression in the RC tear side than in the control at week 1, and subsequently showed a decreasing trend (expressed 5.2 times higher than control) at week 4. In the string network which depicts the interactions between genes, we observed that IL6, Ccl2, and Vcam1, which are known to be related to inflammation or cell adhesion, actively interacted with each other showing high expression at week 1 after the RC tear. In addition, RAD54L (a DNA repair-related gene) and cyclin-dependent kinase 1 (Cdk1; a cell cycle regulation-related gene) also interacted closely (Figure4). The top 10 up- and downregulated genes in the aging category at weeks 1 and 4 after the RC tear are listed in Table 3.Table 3
Gene expression patterns after rotator cuff tear in the aging category ((a) week 1 after tear; (b) week 4 after tear).
(a) Gene symbol Genbank accession Fold change Gene name Upregulated  Igfbp2 NM_008342 55.685 insulin-like growth factor binding protein 2  Arg1 NM_007482 54.973 arginase, liver  Cdk1 NM_007659 34.242 cyclin-dependent kinase 1  Cdkn2a NM_009877 30.112 cyclin-dependent kinase inhibitor 2A  Aldh3a1 NM_007436 19.548 aldehyde dehydrogenase family 3, subfamily A1  Ddc NM_001190448 17.423 dopa decarboxylase  Ccl2 NM_011333 16.814 chemokine (C-C motif) ligand 2  Inpp5d NM_010566 15.961 inositol polyphosphate-5-phosphatase D  Mpo NM_010824 14.836 myeloperoxidase  Rad54l NM_009015 13.26 RAD54 like (S. cerevisiae)  Downregulated  Tfcp2l1 NM_023755 0.125 transcription factor CP2-like 1  Nat1 NM_008673 0.146 N-acetyl transferase 1  Ppargc1a NM_008904 0.153 peroxisome proliferative activated receptor, gamma, coactivator 1 alpha  Atp8a2 NM_015803 0.157 ATPase, aminophospholipid transporter-like, class I, type 8A, member 2  Pde4d NM_011056 0.183 phosphodiesterase 4D, cAMP specific  Endog NM_007931 0.195 endonuclease G  Cyp1a1 NM_009992 0.199 cytochrome P450, family 1, subfamily a, polypeptide 1  Kl NM_013823 0.2 klotho  Cryab NM_001289782 0.21 crystallin, alpha B  P2ry1 NM_008772 0.216 purinergic receptor P2Y, G-protein coupled 1  (b) Gene symbol Genbank accession Fold change Gene name Upregulated  Slc1a2 NM_001077514 10.851 solute carrier family 1 (glial high affinity glutamate transporter), member 2  Krtap4-16 NM_001013823 9.991 keratin associated protein 4-16  Ghrhr NM_001003685 9.285 growth hormone releasing hormone receptor  Ddc NM_001190448 8.068 dopa decarboxylase  Aldh3a1 NM_007436 7.897 aldehyde dehydrogenase family 3, subfamily A1  Cdkn2a NM_009877 6.885 cyclin-dependent kinase inhibitor 2A  Igfbp2 NM_008342 5.164 insulin-like growth factor binding protein 2  Retn NM_001204959 3.729 resistin  Adra1a NM_013461 3.523 adrenergic receptor, alpha 1a  Cnr1 NM_007726 3.51 cannabinoid receptor 1 (brain)  Downregulated  Il10 NM_010548 0.195 interleukin 10  Cdk1 NM_007659 0.227 cyclin-dependent kinase 1  Glrx2 NM_001038592 0.325 glutaredoxin 2 (thioltransferase)  Tfcp2l1 NM_023755 0.391 transcription factor CP2-like 1  Fos NM_010234 0.438 FBJ osteosarcoma oncogene  Atp8a2 NM_015803 0.459 ATPase, aminophospholipid transporter-like, class I, type 8A, member 2  Hmga1 NM_001166546 0.462 high mobility group AT-hook 1  Bcl2 NM_177410 0.464 B-cell leukemia/lymphoma 2  Srf NM_020493 0.482 serum response factor  Arntl NM_007489 0.519 aryl hydrocarbon receptor nuclear translocator-likeFigure 4
Differential expression patterns in hierarchical clustering and the STRING network for the aging category ((a) week 1 after tear; (b) week 4 after tear). Left side shows differential expression patterns and hierarchical clustering, and right side shows the STRING network for the aging category.
(a) (b)


## 3.2. Gene Expression Patterns in the Apoptosis Category

In the apoptosis category, keratin 18 showed the highest expression (217.8 times higher expression than the control group) at week 1. This expression subsequently decreased (22 times higher expression than the control group) at week 4 (Figure5 and Table 4). Interestingly, Birc5 (55.6 and 1.6 times higher expression than control at weeks 1 and 4, respectively), Rps6ka2 (0.2 times and 0.5 times higher expression than control at weeks 1 and 4, respectively), and Bub1 (37.5 and 0.3 times higher expression than control at weeks 1 and 4, respectively), which also showed reverse expression patterns, presented active interactions with each other revealed by the STRING network analysis results of week 1. These genes are known to be involved in apoptotic processes, such as cell growth, cell cycle, and cell differentiation.Table 4
Gene expression patterns in the apoptosis category after rotator cuff tear ((a) week 1 after tear; (b) week 4 after tear).
(a) Gene symbol Genbank accession Fold change Gene name Upregulated  Krt18 NM_010664 217.799 keratin 18  Cd5l NM_009690 104.575 CD5 antigen-like  Krt8 AK166854 104.209 keratin 8  Serpina3g NM_009251 85.473 serine (or cysteine) peptidase inhibitor, clade A, member 3G  Melk NM_010790 72.784 maternal embryonic leucine zipper kinase  Birc5 NM_009689 55.603 baculoviral IAP repeat-containing 5  Gpr65 NM_008152 54.323 G-protein coupled receptor 65  Gzma NM_010370 52.943 granzyme A  Top2a NM_011623 44.234 topoisomerase (DNA) II alpha  Bcl2a1d NM_007536 43.608 B-cell leukemia/lymphoma 2-related protein A1d  Downregulated  Dnase1 NM_010061 0.051 deoxyribonuclease I  Casp14 NM_009809 0.099 caspase 14  Robo2 BC055333 0.104 roundabout homolog 2 (Drosophila)  Camk2a NM_009792 0.107 calcium/calmodulin-dependent protein kinase II alpha  Il24 NM_053095 0.112 interleukin 24  Chac1 NM_026929 0.119 ChaC, cation transport regulator 1  Nr4a1 NM_010444 0.122 nuclear receptor subfamily 4, group A, member 1  Mtfp1 NM_026443 0.124 mitochondrial fission process 1  Rps6ka2 NM_011299 0.156 ribosomal protein S6 kinase, polypeptide 2  Casr NM_013803 0.158 calcium-sensing receptor  (b) Gene symbol Genbank accession Fold change Gene name Upregulated  Krt18 NM_010664 22.114 keratin 18  Krt8 AK166854 16.55 keratin 8  Bcl2l14 NM_025778 14.994 BCL2-like 14 (apoptosis facilitator)  Tox3 NM_172913 10.794 TOX high mobility group box family member 3  Fasl NM_010177 10.487 Fas ligand (TNF superfamily, member 6)  Pak7 NM_172858 9.86 p21 protein (Cdc42/Rac)-activated kinase 7  Fgf21 NM_020013 7.87 fibroblast growth factor 21  Avp NM_009732 7.557 arginine vasopressin  Nlrc4 NM_001033367 7.471 NLR family, CARD domain containing 4  Cdkn2a NM_009877 6.885 cyclin-dependent kinase inhibitor 2A  Downregulated  Dnase1 NM_010061 0.111 deoxyribonuclease I  Cd27 NM_001033126 0.117 CD27 antigen  Fgf8 NM_010205 0.162 fibroblast growth factor 8  AY074887 NM_145229 0.168 cDNA sequence AY074887  Tnf NM_013693 0.205 tumor necrosis factor  Naip1 NM_008670 0.217 NLR family, apoptosis inhibitory protein 1  Cdk1 NM_007659 0.227 cyclin-dependent kinase 1  Epha7 NM_010141 0.239 Eph receptor A7  Tiam1 NM_009384 0.253 T-cell lymphoma invasion and metastasis 1  Bub1 NM_009772 0.284 budding uninhibited by benzimidazoles 1 homolog (S. cerevisiae)Figure 5
Differential expression patterns in hierarchical clustering and the STRING network for the apoptosis category ((a) week 1 after tear; (b) week 4 after tear).
(a) (b)


## 3.3. Gene Expression Patterns in the Muscle Atrophy Category

Among the 39,429 genes analyzed, a total of 11 genes belonged to the muscle atrophy category (Figure6 and Table 5). Myog (myogenin), which is associated with myogenesis, showed the highest expression at week 1. In contrast, Mstn (myostatin), which is to inhibit myogenesis, showed the lowest expression at week 1. Further, Actin 3 (actin alpha 3), which is coexpressed with myostatin, also showed low expression at week 1 but a high expression at week 4 together with myostatin. This result was in contrast to that of myogenin, which showed the highest expression at week 1 but low expression at week 4.Table 5
Gene expression patterns in the muscle atrophy category after rotator cuff tear ((a) week 1 after tear; (b) week 4 after tear).
(a) Gene symbol Genbank accession Fold change Gene name Upregulated  Myog NM_031189 38.535 myogenin  Gatm NM_025961 8.862 glycine amidinotransferase (L-arginine:glycine amidinotransferase):energy metabolism of muscle  Cflar NM_009805 2.442 CASP8 and FADD-like apoptosis regulator  Rps6kb1 NM_028259 1.287 ribosomal protein S6 kinase, polypeptide 1  Downregulated  Il15 NM_008357 0.727 interleukin 15  Tbce NM_178337 0.648 tubulin-specific chaperone E  Gsn NM_146120 0.439 Gelsolin:actin-binding protein  Trim63 NM_001039048 0.435 tripartite motif-containing 63  Actn3 NM_013456 0.279 actinin alpha 3:alpha-actinin skeletal muscle isoform 3  Ppargc1a NM_008904 0.216 peroxisome proliferative activated receptor, gamma, coactivator 1 alpha  Mstn NM_010834 0.108 myostatin  (b) Gene symbol Genbank accession Fold change Gene name Upregulated  Myog NM_031189 11.945 myogenin  Gatm NM_025961 1.712 glycine amidinotransferase (L-arginine:glycine amidinotransferase)  Gsn NM_146120 1.249 gelsolin  Rps6kb1 NM_028259 1.245 ribosomal protein S6 kinase, polypeptide 1  Downregulated  Il15 NM_008357 0.98 interleukin 15  Cflar NM_207653 0.935 CASP8 and FADD-like apoptosis regulator  Ppargc1a NM_008904 0.763 peroxisome proliferative activated receptor, gamma, coactivator 1 alpha  Tbce NM_178337 0.712 tubulin-specific chaperone E  Actn3 NM_013456 0.494 actinin alpha 3  Trim63 NM_001039048 0.387 tripartite motif-containing 63  Mstn NM_010834 0.378 myostatinFigure 6
Differential expression patterns in hierarchical clustering and the STRING network for the muscle atrophy category ((a) week 1 after tear; (b) weeks 4 after tear).
(a) (b)


## 3.4. Gene Expression Patterns in the Fatty Acid Transport Category

In the fatty acid transport category, the expression of apolipoprotein E (ApoE; expressed 12.229 times higher than control at week 1), annexin A1 (expressed 5.517 times higher than control at week 1) which is a phospholipid-binding protein, and perilipin 2 (expressed 5.018 times higher than control at week 1) which is an adipose differentiation-related gene was notable. The expression of all these genes decreased at week 4 (ApoE, annexin A1, and perilipin 2 were expressed 2.729, 1.377, and 1.196 times higher than control, respectively). In addition, these genes interacted with the phospholipase groups regulating PPARγ expression, which regulates fatty acid storage and glucose metabolism, at weeks 1 and 4 as shown in the STRING network (Figure 7 and Table 6).Table 6
Gene expression patterns in the fatty acid transport category after rotator cuff tear ((a) week 1 after tear; (b) week 4 after tear).
(a) Gene symbol Genbank accession Fold change Gene name Upregulated  Mfsd2a NM_029662 12.606 major facilitator superfamily domain containing 2A  Apoe NM_009696 12.229 apolipoprotein E  Drd4 NM_007878 10.607 dopamine receptor D4  Slc27a6 NM_001081072 7.557 solute carrier family 27 (fatty acid transporter), member 6  Pla2g4a NM_008869 6.457 phospholipase A2, group IVA (cytosolic, calcium-dependent)  Anxa1 NM_010730 5.517 annexin A1  Plin2 NM_007408 5.018 perilipin 2  Pla2g2e NM_012044 3.407 phospholipase A2, group IIE  Pla2g1b NM_011107 3.384 phospholipase A2, group IB, pancreas  Hnf1a M57966 2.029 HNF1 homeobox A  Downregulated  Fabp3 NM_010174 0.131 fatty acid binding protein 3, muscle and heart  Pla2g5 NM_001122954 0.15 phospholipase A2, group V  Cpt1b NM_009948 0.186 carnitine palmitoyltransferase 1b, muscle  Pla2g12a NM_183423 0.262 phospholipase A2, group XIIA  Got2 NM_010325 0.289 glutamic-oxaloacetic transaminase 2, mitochondrial  Slc27a2 NM_011978 0.299 solute carrier family 27 (fatty acid transporter), member 2  Slc27a1 NM_011977 0.32 solute carrier family 27 (fatty acid transporter), member 1  Acsl1 NM_007981 0.354 acyl-CoA synthetase long-chain family member 1  Pnpla8 NM_026164 0.372 patatin-like phospholipase domain containing 8  Pla2g2c NM_008868 0.408 phospholipase A2, group IIC  (b) Gene symbol Genbank accession Fold change Gene name Upregulated  Nmur2 NM_153079 3.742 neuromedin U receptor 2  Apoe NM_009696 2.729 apolipoprotein E  Pla2g2d NM_011109 2.704 phospholipase A2, group IID  Pparg NM_011146 1.56 peroxisome proliferator activated receptor gamma  Proca1 XM_006532963 1.548 protein interacting with cyclin A1  Pla2g2e NM_012044 1.539 phospholipase A2, group IIE  Pla2g4a NM_008869 1.485 phospholipase A2, group IVA(cytosolic, calcium-dependent)  Anxa1 NM_010730 1.377 annexin A1  Pla2g2a NM_001082531 1.286 phospholipase A2,group IIA (platelets, synovial fluid)  Mfsd2a NM_029662 1.268 major facilitator superfamilydomain containing 2A  Downregulated  Pla2g1b NM_011107 0.428 phospholipase A2, group IB,pancreas  Slc27a5 NM_009512 0.456 solute carrier family 27 (fatty acidtransporter), member 5  Pla2g3 NM_172791 0.511 phospholipase A2, group III  Hnf1a M57966 0.529 HNF1 homeobox A  Fabp3 NM_010174 0.587 fatty acid-binding protein 3, muscle and heart  Pla2g2f NM_012045 0.592 phospholipase A2, group IIF  Abcc4 NM_001033336 0.592 ATP-binding cassette, sub-family C (CFTR/MRP), member 4  Pnpla8 NM_026164 0.614 patatin-like phospholipase domain containing 8  Ppard NM_011145 0.628 peroxisome proliferator activator receptor delta  Pla2g5 NM_001122954 0.661 phospholipase A2, group VFigure 7
Differential expression patterns in hierarchical clustering and the STRING network for the fatty acid transport category ((a) week 1 after tear; (b) week 4 after tear).
(a) (b)


## 3.5. Gene Expression Patterns in Other Categories Related to Muscle Physiology

Gene expression patterns and STRING networks for other categories, such as angiogenesis, inflammation, cell migration, cell proliferation, extracellular matrix, and cell differentiation, are described in ‘Supplementary Materials (availablehere)’.


## 3.6. Validation of Gene Expression Using Real-Time PCR Analysis

Cdk1 from the aging category, keratin 8 and 18 from the apoptosis category, and myogenin, myostatin, and actn3 from the muscle atrophy category were selected as representative genes for the validation of gene expression using quantitative real-time PCR analysis. The result of qRT-PCR analyses for all these selected genes was similar to the result of microarray analysis discussed above. The mRNA levels from both microarray and qRT-PCR analyses are depicted in Figure8.Figure 8
Validation of microarray gene expression patterns using qRT-PCR analysis. (a) Cdk1, (b) keratin 8, (c) keratin 18, (d) myogenin, (e) myostatin, and (f) actn3.
(a) (b) (c) (d) (e) (f)


## 3.7. Commonly Expressed Genes

There were 2 common genes, namely, Rps6kb1 and gelsolin, which displayed commonly regulated expressions in the 3 categories (aging, apoptotic process, and muscle atrophy) of our interest that are known to be associated with muscle degeneration and atrophy after a rotator cuff tear. Rps6kb1 in the rotator cuff tear side was downregulated (expressed 0.48 times of the control) during the acute phase, and showed increased expression during the chronic phase (expressed 0.806 times of the control). In addition, Gelsolin in the rotator cuff tear side was also downregulated (0.40 times of the control) during the acute phase and showed increased expression during the chronic phase (expressed 1.17 times of the control).


## 4. Discussion

The major findings of this study were comprehensive analysis of genetic changes with time following a rotator cuff tear and categorization of those genes associated with 10 muscle physiology, including aging, apoptosis, atrophy, and fatty acid transport.We regarded week 1 as the acute phase and week 4 as the chronic phase, based on our previous study using the same animal model [10]. In our previous study, we detected acute inflammatory reactions in the rotator cuff muscle with a rapid increase in inflammatory cytokines at week 1; however, the inflammatory reactions almost disappeared 4 weeks after inducing the rotator cuff tear. Conversely, fat deposition with degenerative changes in the rotator cuff muscle started increasing during week 2 and a noticeable increase was observed 4 weeks after the rotator cuff tear [10]. Based on these findings, we defined week 1 after the rotator cuff tear as the acute phase and week 4 as the chronic phase.Among a total of 39,429 genes, the gene that showed the highest increase in expression (expressed 217.7 times higher than the control) at week 1 after the rotator cuff tear was Krt18, which plays an important role in maintaining cell structure and is a major component of the intermediate filaments of epithelial cells [30]. It is also well-known to be an indicator of the progression of chronic liver diseases because it is related to apoptosis [31]. In the present study, a rapid increase in Krt18 expression was found during the acute phase after inducing the rotator cuff tear. We considered that the natural apoptotic process in muscle cells started considerably early after the rotator cuff tear, and if the apoptotic process progressed faster than the restoration process of damaged myocytes, a permanent and irreversible damage to the rotator cuff muscle may occur and the outcome may be worse even after a successful rotator cuff repair.In the aging category, among all genes, the expression of lgfbp2 (insulin-like growth factor binding protein 2) was the highest at week 1 after the rotator cuff tear. This suggested that the muscle damage induced by the rotator cuff tear affected the aging process of myocytes. Davalos et al. showed that the IGF-binding proteins were highly expressed in aged fibroblast cells, which supported our results [32]. Particularly, IL-6 and Ccl2, which are adhesion molecules known to be secreted from aged cells, and Vcam1 interacted with each other and showed high expression as shown in the STRING network. In the apoptosis category, Birc5 (baculoviral IAP repeat-containing 5), which is known to be a survivin like krt 18, significantly increased (expressed 55.6 times of the control) during the acute phase. This survivin is known to influence cell division and cause cell death inhibition. Hence, it is related to tissue injury and healing [33]. In addition, this gene interacts with Rps6ka2 [34], which is related to cell survival; caspase 14 [35] and Cdk1 [36], which play important roles in cell growth and apoptosis; and MELK [37], which is related to cell proliferation, apoptosis, RNA processing, and embryonic development. Birc5, which is known to play an important role in apoptosis, seemed to play a similar role in the rotator cuff muscle after the rotator cuff tear. Only 11 among the 39,429 genes analyzed belonged to the muscle atrophy category. Among these, Myog (myogenin), which is known to play an important role in the differentiation of myocytes, showed the highest expression during the acute phase after rotator cuff tear as expected. On the contrary, Mstn (myostatin), which is known to play an inhibitory role during myogenesis, exhibited the lowest expression. These results suggested that myogenin rapidly increases during the early phase after the rotator cuff tear in order to regenerate damaged muscles (with low myostatin expression) and decreases as muscle atrophy and degeneration progressed with time. It was interesting that Gatm (L-arginine: glycine amidinotransferase), which is known to be related to obesity associated with creatine metabolism [38], showed the highest expression after myogenin. In addition, two new genes, namely, Cflar and Ppargc1a, which showed reverse patterns of expression between the acute and chronic phases (Cflar: CASP8 and FADD-like apoptosis regulator, expression decreased from 2.442 times during the acute phase to 0.935 times during the chronic phase; Ppargc1a, expression changed from 0.216 times during the acute phase to 0.763 times during the chronic phase), although not significant, can be studied as novel targets associated with changes in muscle atrophy after rotator cuff tear or repair. In the fatty acid transport category, the expression of ApoE (Apolipoprotein E) was noticeable. ApoE is known to play a pivotal role in lipid homeostasis [39], and was also highly expressed among the cell proliferation category genes. This gene can be analyzed in future studies for understanding its role in the reversal of fatty infiltration after rotator cuff repair. In this study, we observed two genes (Rps6kb1 and gelsolin) which showed common expression patterns across 3 categories, namely aging, apoptosis, and muscle atrophy. Rps6kb1 is known to play an important role in anabolic signaling by increasing lipid accumulation in the adipose tissue and inducing skeletal muscle hypertrophy [40], whereas gelsolin is known to be a physiological effector of apoptotic morphological changes after being cleaved by caspase 3 [41]. In this study, low expression of Rps6kb1 during the acute phase (expressed 0.48 times of the control) and high expression during the chronic phase (expressed 0.81 times of the control) suggested muscle damage recovery over time. Conversely, low expression of gelsolin during the acute phase (expressed 0.40 times of the control) and increased expression during the chronic phase (expressed 1.17 times of the control) potentially indicates ongoing apoptotic processes in rotator cuff muscles against the force to restore the damaged muscle. These two key genes may link and control muscle degeneration and sarcopenia after the rotator cuff tear.The strength of our study is that this is the first study which comprehensively analyzed time-dependent expression of genes belonging to different categories associated with muscle physiology after making a rotator cuff tear. This finding may help to understand the rotator cuff tear-related muscle changes and, further it may improve various detrimental outcomes of rotator cuff repair surgery by regulation of the potent key molecule or its signal pathway.Nevertheless, this study has several limitations that require consideration. First, this study was an animal-based study. Differences in anatomic features, different injury and healing reactions, and genetic variations between humans and rats limit generalization of the results. In this study, we established rotator cuff tear (RCT) model by injuring supraspinatus to make the most similar condition to the real clinical situation because most tears occur in the supraspinatus tendon in clinical situation. However, we humbly admit that 2 tendons (supraspinatus + infraspinatus) tear model may be better to mimic the muscle degenerative changes compared to the 1 tendon model, and there could exist a likelihood of self-healing in a mouse supraspinatus tear model. Thus, we have to be cautious while interpretation of results. Second, we investigated gene expression patterns in the rotator cuff muscles up to 4 weeks after making the tear, based on the results of our previous study [10]; however, there can be more biological and genetic changes during later time points (more than 4 weeks). Finally, molecular pathways underlying changes in muscle physiology after a rotator cuff tear were not identified in this study. This should be the next step of the study.


## 5. Conclusions

(i) Rotator cuff tear induces specific genes associated with changes in muscle physiology such as aging, apoptosis, muscle atrophy, and fatty acid transport.(ii) Several genes which are significantly altered after rotator cuff tear may play key roles in controlling muscle degeneration after a rotator cuff tear.(iii) Mouse rotator cuff tear model could be a good approach to understand the many aspects of the molecular changes in injured muscle.



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