BioMed Research International
Volume 2018 (2018), Article ID 5859013, 18 pages
https://doi.org/10.1155/2018/5859013

Research Article

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

Yong-Soo Lee, Ja-Yeon Kim, Hyo-Nam Kim, Dhong-Won Lee, and Seok Won Chung

Department of Orthopedic Surgery, Konkuk University School of Medicine, Seoul, Republic of Korea

Correspondence should be addressed to Seok Won Chung; smilecsw@gmail.com

Received 27 July 2018; Accepted 12 November 2018; Published 23 December 2018

Academic Editor: Antonio Salgado

Copyright © 2018 Yong-Soo Lee et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

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.

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 Figure 2 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.

(a)

(b)

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.

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) (Figure 3).

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 Table 2.

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 (Figure 4). 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-like

(a)

(b)

Figure 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.

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 (Figure 5 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)

(a)

(b)

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).

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 (Figure 6 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
myostatin

(a)

(b)

Figure 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).

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 superfamily
domain containing 2A

Downregulated

Pla2g1b
NM_011107
0.428
phospholipase A2, group IB,
pancreas

Slc27a5
NM_009512
0.456
solute carrier family 27 (fatty acid
transporter),
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 V

(a)

(b)

Figure 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).

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 (available here)’.

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 Figure 8.

(a)

(b)

(c)

(d)

(e)

(f)

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.

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.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

This study was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT [NRF-2017R1D1A1B03033758 to Y.-S. Lee and NRF-2017R1A2B4003343 to S. W. Chung].

Supplementary Materials

“Supplementary Figures” and “Supplementary Table” show gene expression patterns and the top 10 up- and downregulated genes in an additional muscle physiology-related category after rotator cuff tear, respectively. (Supplementary Materials)

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