BioMed Research International

BioMed Research International / 2018 / Article

Research Article | Open Access

Volume 2018 |Article ID 5859013 | 18 pages | https://doi.org/10.1155/2018/5859013

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

Academic Editor: Antonio Salgado
Received27 Jul 2018
Accepted12 Nov 2018
Published23 Dec 2018

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 [1113]. 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 [1419]; 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, 2022].

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, 2125]; 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).

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.

(a)

Total10 categories associated with muscle physiology

1 weekTotalAgingAngio.Apop.Diff.Migr.Prolif.ECMInfl.FA tr.Atro

Total gene number39,4293704431,1004,7019947886835326619

% of Total1000.941.122.7911.922.5221.731.350.170.05

Up significant (n)8,6621271743781,422386262264249103

% of Up significant2234.339.334.430.238.833.238.746.815.215.8

Down significant (n)3,516403011343675455431139

% of Down significant8.910.86.810.39.37.55.77.95.819.747.4

Total significant (n)12,1781842035251,8674633002672782311

% of Total significant30.945.14644.639.546.43946.652.634.863.2

(b)

Total10 categories associated with muscle physiology

4 weeksTotalAgingAngio.Apop.Diff.Migr.Prolif.ECMInfl.FA tr.Atro

Total gene number39,4293704431,1004,7019947886835326619

% of Total1000.941.122.7911.922.5221.731.350.170.05

Up Significant (n)1,2332120522035935314621

% of Up Significant3.15.74.54.74.35.94.44.58.635.3

Down Significant (n)1,13789341503132121504

% of Down Significant2.92.223.13.23.14.11.82.8021.1

Total Significant (n)2,3702929863539067436125

% of Total Significant67.86.57.87.59.18.56.311.5326.3

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.

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).

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.

(a)

Gene symbolGenbank accessionFold changeGene name

Upregulated

Krt18NM_010664217.799keratin 18

Sirpb1bNM_001173460150.129signal-regulatory protein beta 1B

Cd300lfNM_001169153128.186CD300 antigen like family member F

Ms4a4cNM_029499122.342membrane-spanning 4-domains, subfamily A, member 4C

Plekhh1NM_181073119.491pleckstrin homology domain containing, family H (with MyTH4 domain) member 1

Cd5lNM_009690104.575CD5 antigen-like

Krt8AK166854104.209keratin 8

Myh8AK08148295.091myosin, heavy polypeptide 8, skeletal muscle, perinatal

LOC102642410XM_00654450494.351tyrosine-protein phosphatase non-receptor type substrate 1-like

Myh3NM_00109963592.621myosin, heavy polypeptide 3, skeletal muscle, embryonic

Downregulated

Pitx1NM_0110970.025paired-like homeodomain transcription factor 1

Adam28NM_1833660.025a disintegrin and metallopeptidase domain 28

Rnf180NM_0279340.027ring finger protein 180

Gm5860NR_0406590.029predicted gene 5860

H2-M9NM_0082050.03histocompatibility 2, M region locus 9

Smco1NM_1832830.03single-pass membrane protein with coiled-coil domains 1

AU042410BB2096730.031expressed sequence AU042410

Kcnc1NM_0011127390.031potassium voltage gated channel, Shaw-related subfamily, member 1

8430426J06RikNR_0772290.032RIKEN cDNA 8430426J06 gene

Gm10822AK1448600.033predicted gene 10822

(b)

Gene symbolGenbank accessionFold changeGene name

Upregulated

Gm7325NM_00117747045.121predicted gene 7325

Plekhh1NM_18107342.051pleckstrin homology domain containing, family H (with MyTH4 domain) member 1

Ces2bNM_19817128.227carboxylesterase 2B

Ces2cNM_14560325.457carboxylesterase 2C

SlnNM_02554024.155sarcolipin

Krt18NM_01066422.114keratin 18

Myh3NM_00109963517.984myosin, heavy polypeptide 3, skeletal muscle, embryonic

Krt8AK16685416.550keratin 8

Zfp735NM_00112648915.836zinc finger protein 735

Epha1NM_02358015.534Eph receptor A1

Downregulated

Dok6NM_0010391730.063docking protein 6

Snhg11NM_1756920.072small nucleolar RNA host gene 11

Olfr981NM_1462860.087olfactory receptor 981

Pitx1NM_0110970.087paired-like homeodomain transcription factor 1

Gm12NM_0011955440.093predicted gene 12

Olfr586NM_1471110.098olfactory receptor 586

Samd11XR_8814290.101sterile alpha motif domain containing 11

Dnase1NM_0100610.111deoxyribonuclease I

1190003K10RikNM_0011954350.114RIKEN cDNA 1190003K10 gene

Gpr176NM_2013670.117G 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.

(a)

Gene symbolGenbank accessionFold changeGene name

Upregulated

Igfbp2NM_00834255.685insulin-like growth factor binding protein 2

Arg1NM_00748254.973arginase, liver

Cdk1NM_00765934.242cyclin-dependent kinase 1

Cdkn2aNM_00987730.112cyclin-dependent kinase inhibitor 2A

Aldh3a1NM_00743619.548aldehyde dehydrogenase family 3, subfamily A1

DdcNM_00119044817.423dopa decarboxylase

Ccl2NM_01133316.814chemokine (C-C motif) ligand 2

Inpp5dNM_01056615.961inositol polyphosphate-5-phosphatase D

MpoNM_01082414.836myeloperoxidase

Rad54lNM_00901513.26RAD54 like (S. cerevisiae)

Downregulated

Tfcp2l1NM_0237550.125transcription factor CP2-like 1

Nat1NM_0086730.146N-acetyl transferase 1

Ppargc1aNM_0089040.153peroxisome proliferative activated receptor, gamma, coactivator 1 alpha

Atp8a2NM_0158030.157ATPase, aminophospholipid transporter-like, class I, type 8A, member 2

Pde4dNM_0110560.183phosphodiesterase 4D, cAMP specific

EndogNM_0079310.195endonuclease G

Cyp1a1NM_0099920.199cytochrome P450, family 1, subfamily a, polypeptide 1

KlNM_0138230.2klotho

CryabNM_0012897820.21crystallin, alpha B

P2ry1NM_0087720.216purinergic receptor P2Y, G-protein coupled 1

(b)

Gene symbolGenbank accessionFold changeGene name

Upregulated

Slc1a2NM_00107751410.851solute carrier family 1 (glial high affinity glutamate transporter), member 2

Krtap4-16NM_0010138239.991keratin associated protein 4-16

GhrhrNM_0010036859.285growth hormone releasing hormone receptor

DdcNM_0011904488.068dopa decarboxylase

Aldh3a1NM_0074367.897aldehyde dehydrogenase family 3, subfamily A1

Cdkn2aNM_0098776.885cyclin-dependent kinase inhibitor 2A

Igfbp2NM_0083425.164insulin-like growth factor binding protein 2

RetnNM_0012049593.729resistin

Adra1aNM_0134613.523adrenergic receptor, alpha 1a

Cnr1NM_0077263.51cannabinoid receptor 1 (brain)

Downregulated

Il10NM_0105480.195interleukin 10

Cdk1NM_0076590.227cyclin-dependent kinase 1

Glrx2NM_0010385920.325glutaredoxin 2 (thioltransferase)

Tfcp2l1NM_0237550.391transcription factor CP2-like 1

FosNM_0102340.438FBJ osteosarcoma oncogene

Atp8a2NM_0158030.459ATPase, aminophospholipid transporter-like, class I, type 8A, member 2

Hmga1NM_0011665460.462high mobility group AT-hook 1

Bcl2NM_1774100.464B-cell leukemia/lymphoma 2

SrfNM_0204930.482serum response factor

ArntlNM_0074890.519aryl hydrocarbon receptor nuclear translocator-like

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.

(a)

Gene symbolGenbank accessionFold changeGene name

Upregulated

Krt18NM_010664217.799keratin 18

Cd5lNM_009690104.575CD5 antigen-like

Krt8AK166854104.209keratin 8

Serpina3gNM_00925185.473serine (or cysteine) peptidase inhibitor, clade A, member 3G

MelkNM_01079072.784maternal embryonic leucine zipper kinase

Birc5NM_00968955.603baculoviral IAP repeat-containing 5

Gpr65NM_00815254.323G-protein coupled receptor 65

GzmaNM_01037052.943granzyme A

Top2aNM_01162344.234topoisomerase (DNA) II alpha

Bcl2a1dNM_00753643.608B-cell leukemia/lymphoma 2-related protein A1d

Downregulated

Dnase1NM_0100610.051deoxyribonuclease I

Casp14NM_0098090.099caspase 14

Robo2BC0553330.104roundabout homolog 2 (Drosophila)

Camk2aNM_0097920.107calcium/calmodulin-dependent protein kinase II alpha

Il24NM_0530950.112interleukin 24

Chac1NM_0269290.119ChaC, cation transport regulator 1

Nr4a1NM_0104440.122nuclear receptor subfamily 4, group A, member 1

Mtfp1NM_0264430.124mitochondrial fission process 1

Rps6ka2NM_0112990.156ribosomal protein S6 kinase, polypeptide 2

CasrNM_0138030.158calcium-sensing receptor

(b)

Gene symbolGenbank accessionFold changeGene name

Upregulated

Krt18NM_01066422.114keratin 18

Krt8AK16685416.55keratin 8

Bcl2l14NM_02577814.994BCL2-like 14 (apoptosis facilitator)

Tox3NM_17291310.794TOX high mobility group box family member 3

FaslNM_01017710.487Fas ligand (TNF superfamily, member 6)

Pak7NM_1728589.86p21 protein (Cdc42/Rac)-activated kinase 7

Fgf21NM_0200137.87fibroblast growth factor 21

AvpNM_0097327.557arginine vasopressin

Nlrc4NM_0010333677.471NLR family, CARD domain containing 4

Cdkn2aNM_0098776.885cyclin-dependent kinase inhibitor 2A

Downregulated

Dnase1NM_0100610.111deoxyribonuclease I

Cd27NM_0010331260.117CD27 antigen

Fgf8NM_0102050.162fibroblast growth factor 8

AY074887NM_1452290.168cDNA sequence AY074887

TnfNM_0136930.205tumor necrosis factor

Naip1NM_0086700.217NLR family, apoptosis inhibitory protein 1

Cdk1NM_0076590.227cyclin-dependent kinase 1

Epha7NM_0101410.239Eph receptor A7

Tiam1NM_0093840.253T-cell lymphoma invasion and metastasis 1

Bub1NM_0097720.284budding uninhibited by benzimidazoles 1 homolog (S. cerevisiae)

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.

(a)

Gene symbolGenbank accessionFold changeGene name

Upregulated

MyogNM_03118938.535myogenin

GatmNM_0259618.862glycine amidinotransferase (L-arginine:glycine amidinotransferase):energy metabolism of muscle

CflarNM_0098052.442CASP8 and FADD-like apoptosis regulator

Rps6kb1NM_0282591.287ribosomal protein S6 kinase, polypeptide 1

Downregulated

Il15NM_0083570.727interleukin 15

TbceNM_1783370.648tubulin-specific chaperone E

GsnNM_1461200.439Gelsolin:actin-binding protein

Trim63NM_0010390480.435tripartite motif-containing 63

Actn3NM_0134560.279actinin alpha 3:alpha-actinin skeletal muscle isoform 3

Ppargc1aNM_0089040.216peroxisome proliferative activated receptor, gamma, coactivator 1 alpha

MstnNM_0108340.108myostatin

(b)

Gene symbolGenbank accessionFold changeGene name

Upregulated

MyogNM_03118911.945myogenin

GatmNM_0259611.712glycine amidinotransferase (L-arginine:glycine amidinotransferase)

GsnNM_1461201.249gelsolin

Rps6kb1NM_0282591.245ribosomal protein S6 kinase, polypeptide 1

Downregulated

Il15NM_0083570.98interleukin 15

CflarNM_2076530.935CASP8 and FADD-like apoptosis regulator

Ppargc1aNM_0089040.763peroxisome proliferative activated receptor, gamma, coactivator 1 alpha

TbceNM_1783370.712tubulin-specific chaperone E

Actn3NM_0134560.494actinin alpha 3

Trim63NM_0010390480.387tripartite motif-containing 63

MstnNM_0108340.378myostatin

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).

(a)

Gene symbolGenbank accessionFold changeGene name

Upregulated

Mfsd2aNM_02966212.606major facilitator superfamily domain containing 2A

ApoeNM_00969612.229apolipoprotein E

Drd4NM_00787810.607dopamine receptor D4

Slc27a6NM_0010810727.557solute carrier family 27 (fatty acid transporter), member 6

Pla2g4aNM_0088696.457phospholipase A2, group IVA (cytosolic, calcium-dependent)

Anxa1NM_0107305.517annexin A1

Plin2NM_0074085.018perilipin 2

Pla2g2eNM_0120443.407phospholipase A2, group IIE

Pla2g1bNM_0111073.384phospholipase A2, group IB, pancreas

Hnf1aM579662.029HNF1 homeobox A

Downregulated

Fabp3NM_0101740.131fatty acid binding protein 3, muscle and heart

Pla2g5NM_0011229540.15phospholipase A2, group V

Cpt1bNM_0099480.186carnitine palmitoyltransferase 1b, muscle

Pla2g12aNM_1834230.262phospholipase A2, group XIIA

Got2NM_0103250.289glutamic-oxaloacetic transaminase 2, mitochondrial

Slc27a2NM_0119780.299solute carrier family 27 (fatty acid transporter), member 2

Slc27a1NM_0119770.32solute carrier family 27 (fatty acid transporter), member 1

Acsl1NM_0079810.354acyl-CoA synthetase long-chain family member 1

Pnpla8NM_0261640.372patatin-like phospholipase domain containing 8

Pla2g2cNM_0088680.408phospholipase A2, group IIC

(b)

Gene symbolGenbank accessionFold changeGene name

Upregulated

Nmur2NM_1530793.742neuromedin U receptor 2

ApoeNM_0096962.729apolipoprotein E

Pla2g2dNM_0111092.704phospholipase A2, group IID

PpargNM_0111461.56peroxisome proliferator activated receptor gamma

Proca1XM_0065329631.548protein interacting with cyclin A1

Pla2g2eNM_0120441.539phospholipase A2, group IIE

Pla2g4aNM_0088691.485phospholipase A2, group IVA
(cytosolic, calcium-dependent)

Anxa1NM_0107301.377annexin A1

Pla2g2aNM_0010825311.286phospholipase A2,
group IIA (platelets, synovial fluid)

Mfsd2aNM_0296621.268major facilitator superfamily
domain containing 2A

Downregulated

Pla2g1bNM_0111070.428phospholipase A2, group IB,
pancreas

Slc27a5NM_0095120.456solute carrier family 27 (fatty acid
transporter),
member 5

Pla2g3NM_1727910.511phospholipase A2, group III

Hnf1aM579660.529HNF1 homeobox A

Fabp3NM_0101740.587fatty acid-binding protein 3, muscle and heart

Pla2g2fNM_0120450.592phospholipase A2, group IIF

Abcc4NM_0010333360.592ATP-binding cassette, sub-family C (CFTR/MRP), member 4

Pnpla8NM_0261640.614patatin-like phospholipase domain containing 8

PpardNM_0111450.628peroxisome proliferator activator receptor delta

Pla2g5NM_0011229540.661phospholipase A2, group V

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.

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)

References

  1. A. Djahangiri and A. Farron, “When to operate a rotator cuff tear?” Revue Médicale Suisse, vol. 5, no. 230, pp. 2551–2554, 2009. View at: Google Scholar
  2. L. M. Galatz, S. Griggs, B. D. Cameron, and J. P. Iannotti, “Prospective longitudinal analysis of postoperative shoulder function: A ten-year follow-up study of full-thickness rotator cuff tears,” The Journal of Bone & Joint Surgery, vol. 83, no. 7, pp. 1052–1056, 2001. View at: Publisher Site | Google Scholar
  3. J. N. Gladstone, J. Y. Bishop, I. K. Y. Lo, and E. L. Flatow, “Fatty infiltration and atrophy of the rotator cuff do not improve after rotator cuff repair and correlate with poor functional outcome,” The American Journal of Sports Medicine, vol. 35, no. 5, pp. 719–728, 2007. View at: Publisher Site | Google Scholar
  4. J.-Y. Park, S.-H. Lhee, J.-H. Choi, H.-K. Park, J.-W. Yu, and J.-B. Seo, “Comparison of the clinical outcomes of single- and double-row repairs in rotator cuff tears,” The American Journal of Sports Medicine, vol. 36, no. 7, pp. 1310–1316, 2008. View at: Publisher Site | Google Scholar
  5. T. Teunis, B. Lubberts, B. T. Reilly, and D. Ring, “A systematic review and pooled analysis of the prevalence of rotator cuff disease with increasing age,” Journal of Shoulder and Elbow Surgery, vol. 23, no. 12, pp. 1913–1921, 2014. View at: Publisher Site | Google Scholar
  6. L. Osti, M. Buda, A. Del Buono, R. Osti, L. Massari, and N. Maffulli, “Apoptosis and rotator cuff tears: Scientific evidence from basic science to clinical findings,” British Medical Bulletin, vol. 122, no. 1, pp. 123–133, 2017. View at: Publisher Site | Google Scholar
  7. M. C. Gibbons, A. Singh, O. Anakwenze et al., “Histological evidence of muscle degeneration in advanced human rotator cuff disease,” Journal of Bone and Joint Surgery - American Volume, vol. 99, no. 3, pp. 190–199, 2017. View at: Publisher Site | Google Scholar
  8. S. W. Chung, J. P. Yoon, K.-S. Oh et al., “Rotator cuff tear and sarcopenia: are these related?” Journal of Shoulder and Elbow Surgery, vol. 25, no. 9, pp. e249–e255, 2016. View at: Publisher Site | Google Scholar
  9. J. Fabis, M. Danilewicz, J. T. Zwierzchowski, and K. Niedzielski, “Atrophy of type I and II muscle fibers is reversible in the case of grade >2 fatty degeneration of the supraspinatus muscle: An experimental study in rabbits,” Journal of Shoulder and Elbow Surgery, vol. 25, no. 3, pp. 487–492, 2016. View at: Publisher Site | Google Scholar
  10. Y.-S. Lee, J.-Y. Kim, K.-S. Oh, and S. W. Chung, “Fatty acid-binding protein 4 regulates fatty infiltration after rotator cuff tear by hypoxia-inducible factor 1 in mice,” Journal of Cachexia, Sarcopenia and Muscle, vol. 8, no. 5, pp. 839–850, 2017. View at: Publisher Site | Google Scholar
  11. S. W. Chung, J. H. Oh, H. S. Gong, J. Y. Kim, and S. H. Kim, “Factors affecting rotator cuff healing after arthroscopic repair: Osteoporosis as one of the independent risk factors,” The American Journal of Sports Medicine, vol. 39, no. 10, pp. 2099–2107, 2011. View at: Publisher Site | Google Scholar
  12. H. Yamaguchi, N. Suenaga, N. Oizumi, Y. Hosokawa, and F. Kanaya, “Will Preoperative Atrophy and Fatty Degeneration of the Shoulder Muscles Improve after Rotator Cuff Repair in Patients with Massive Rotator Cuff Tears?” Advances in Orthopedics, vol. 2012, Article ID 195876, 7 pages, 2012. View at: Publisher Site | Google Scholar
  13. H. Ohzono, M. Gotoh, H. Nakamura et al., “Effect of Preoperative Fatty Degeneration of the Rotator Cuff Muscles on the Clinical Outcome of Patients With Intact Tendons After Arthroscopic Rotator Cuff Repair of Large/Massive Cuff Tears,” The American Journal of Sports Medicine, vol. 45, no. 13, pp. 2975–2981, 2017. View at: Publisher Site | Google Scholar
  14. J. H. Oh, S. W. Chung, S. H. Kim, J. Y. Chung, and J. Y. Kim, “2013 Neer Award: Effect of the adipose-derived stem cell for the improvement of fatty degeneration and rotator cuff healing in rabbit model,” Journal of Shoulder and Elbow Surgery, vol. 23, no. 4, pp. 445–455, 2014. View at: Publisher Site | Google Scholar
  15. S. W. Chung, H. Park, J. Kwon, G. Y. Choe, S. H. Kim, and J. H. Oh, “Effect of Hypercholesterolemia on Fatty Infiltration and Quality of Tendon-to-Bone Healing in a Rabbit Model of a Chronic Rotator Cuff Tear: Electrophysiological, Biomechanical, and Histological Analyses,” The American Journal of Sports Medicine, vol. 44, no. 5, pp. 1153–1164, 2016. View at: Google Scholar
  16. C. Jung, G. Spreiter, L. Audigé, S. J. Ferguson, and M. Flury, “Patch-augmented rotator cuff repair: influence of the patch fixation technique on primary biomechanical stability,” Archives of Orthopaedic and Trauma Surgery, vol. 136, no. 5, pp. 609–616, 2016. View at: Publisher Site | Google Scholar
  17. T. Tokunaga, T. Karasugi, H. Arimura et al., “Enhancement of rotator cuff tendon–bone healing with fibroblast growth factor 2 impregnated in gelatin hydrogel sheets in a rabbit model,” Journal of Shoulder and Elbow Surgery, vol. 26, no. 10, pp. 1708–1717, 2017. View at: Publisher Site | Google Scholar
  18. N. Sevivas, F. G. Teixeira, R. Portugal et al., “Mesenchymal Stem Cell Secretome: A Potential Tool for the Prevention of Muscle Degenerative Changes Associated With Chronic Rotator Cuff Tears,” The American Journal of Sports Medicine, vol. 45, no. 1, pp. 179–188, 2016. View at: Publisher Site | Google Scholar
  19. C. Gerber, D. C. Meyer, M. Flück, M. C. Benn, B. Von Rechenberg, and K. Wieser, “Anabolic Steroids Reduce Muscle Degeneration Associated with Rotator Cuff Tendon Release in Sheep,” The American Journal of Sports Medicine, vol. 43, no. 10, pp. 2393–2400, 2015. View at: Publisher Site | Google Scholar
  20. E. Frey, F. Regenfelder, P. Sussmann et al., “Adipogenic and myogenic gene expression in rotator cuff muscle of the sheep after tendon tear,” Journal of Orthopaedic Research, vol. 27, no. 4, pp. 504–509, 2009. View at: Publisher Site | Google Scholar
  21. S. K. Joshi, X. Liu, S. P. Samagh et al., “MTOR regulates fatty infiltration through SREBP-1 and PPARγ after a combined massive rotator cuff tear and suprascapular nerve injury in rats,” Journal of Orthopaedic Research, vol. 31, no. 5, pp. 724–730, 2013. View at: Publisher Site | Google Scholar
  22. N. Maffulli, U. G. Longo, A. Berton, M. Loppini, and V. Denaro, “Biological factors in the pathogenesis of rotator cuff tears,” Sports Medicine and Arthroscopy Review, vol. 19, no. 3, pp. 194–201, 2011. View at: Publisher Site | Google Scholar
  23. H. Lee, Y. Kim, J. Ok, and H. Song, “Apoptosis Occurs Throughout the Diseased Rotator Cuff,” The American Journal of Sports Medicine, vol. 41, no. 10, pp. 2249–2255, 2013. View at: Publisher Site | Google Scholar
  24. X. Liu, B. Ravishankar, A. Ning, M. Liu, H. T. Kim, and B. T. Feeley, “Knocking-out matrix metalloproteinase-13 exacerbates rotator cuff muscle fatty infiltration,” Muscle, Ligaments and Tendons Journal, vol. 7, no. 2, pp. 202–207, 2017. View at: Publisher Site | Google Scholar
  25. Y. Raz, J. F. Henseler, A. Kolk et al., “Molecular signatures of age-associated chronic degeneration of shoulder muscles,” Oncotarget , vol. 7, no. 8, pp. 8513–8523, 2016. View at: Publisher Site | Google Scholar
  26. H. Yin, F. Price, and M. A. Rudnicki, “Satellite cells and the muscle stem cell niche,” Physiological Reviews, vol. 93, no. 1, pp. 23–67, 2013. View at: Publisher Site | Google Scholar
  27. A. Choo, M. McCarthy, R. Pichika et al., “Muscle gene expression patterns in human rotator cuff pathology,” Journal of Bone and Joint Surgery - American Volume, vol. 96, no. 18, pp. 1558–1565, 2014. View at: Publisher Site | Google Scholar
  28. L. J. Jensen, M. Kuhn, M. Stark et al., “STRING 8—a global view on proteins and their functional interactions in 630 organisms,” Nucleic Acids Research, vol. 37, supplement 1, pp. D412–D416, 2009. View at: Publisher Site | Google Scholar
  29. D. Szklarczyk, A. Franceschini, M. Kuhn et al., “The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored,” Nucleic Acids Research, vol. 39, no. 1, pp. D561–D568, 2011. View at: Publisher Site | Google Scholar
  30. C. Caulín, G. S. Salvesen, and R. G. Oshima, “Caspase cleavage of keratin 18 and reorganization of intermediate filaments during epithelial cell apoptosis,” The Journal of Cell Biology, vol. 138, no. 6, pp. 1379–1394, 1997. View at: Publisher Site | Google Scholar
  31. Y. O. Mannery, C. J. Mcclain, and M. B. Vos, “Keratin 18, apoptosis, and liver disease in children,” Current Pediatric Reviews, vol. 7, no. 4, pp. 310–315, 2011. View at: Publisher Site | Google Scholar
  32. A. R. Davalos, J.-P. Coppe, J. Campisi, and P.-Y. Desprez, “Senescent cells as a source of inflammatory factors for tumor progression,” Cancer and Metastasis Reviews, vol. 29, no. 2, pp. 273–283, 2010. View at: Publisher Site | Google Scholar
  33. S. K. Chiou, M. K. Jones, and A. S. Tarnawski, “Survivin - an anti-apoptosis protein: its biological roles and implications for cancer and beyond,” Medical Science Monitor, vol. 9, pp. PI25–PI29, 2003. View at: Google Scholar
  34. S. Sridharan and A. Basu, “S6 kinase 2 promotes breast cancer cell survival via Akt,” Cancer Research, vol. 71, no. 7, pp. 2590–2599, 2011. View at: Publisher Site | Google Scholar
  35. H.-Y. Fang, C.-Y. Chen, M.-F. Hung et al., “Caspase-14 is an anti-apoptotic protein targeting apoptosis-inducing factor in lung adenocarcinomas,” Oncology Reports, vol. 26, no. 2, pp. 359–369, 2011. View at: Publisher Site | Google Scholar
  36. M. Castedo, J.-L. Perfettini, T. Roumier, and G. Kroemer, “Cyclin-dependent kinase-1: Linking apoptosis to cell cycle and mitotic catastrophe,” Cell Death & Differentiation, vol. 9, no. 12, pp. 1287–1293, 2002. View at: Publisher Site | Google Scholar
  37. P. Jiang and D. Zhang, “Maternal embryonic leucine zipper kinase (MELK): A novel regulator in cell cycle control, embryonic development, and cancer,” International Journal of Molecular Sciences, vol. 14, no. 11, pp. 21551–21560, 2013. View at: Publisher Site | Google Scholar
  38. L. Kazak, E. T. Chouchani, G. Z. Lu et al., “Genetic Depletion of Adipocyte Creatine Metabolism Inhibits Diet-Induced Thermogenesis and Drives Obesity,” Cell Metabolism, vol. 26, no. 4, pp. 660–671, 2017. View at: Publisher Site | Google Scholar
  39. R. Chouinard-Watkins and M. Plourde, “Fatty acid metabolism in carriers of apolipoprotein E epsilon 4 allele: is it contributing to higher risk of cognitive decline and coronary heart disease?” Nutrients, vol. 6, no. 10, pp. 4452–4471, 2014. View at: Publisher Site | Google Scholar
  40. S. H. Um, D. D'Alessio, and G. Thomas, “Nutrient overload, insulin resistance, and ribosomal protein S6 kinase 1, S6K1,” Cell Metabolism, vol. 3, no. 6, pp. 393–402, 2006. View at: Publisher Site | Google Scholar
  41. S. Kothakota, T. Azuma, C. Reinhard et al., “Caspase-3-generated fragment of gelsolin: Effector of morphological change in apoptosis,” Science, vol. 278, no. 5336, pp. 294–298, 1997. View at: Publisher Site | Google Scholar

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.

1053 Views | 273 Downloads | 0 Citations
 PDF  Download Citation  Citation
 Download other formatsMore
 Order printed copiesOrder