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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2017, Vol. 11 Issue (1) : 97-109    https://doi.org/10.1007/s11684-016-0496-1
RESEARCH ARTICLE
iTRAQ-based quantitative proteomic analysis on differentially expressed proteins of rat mandibular condylar cartilage induced by reducing dietary loading
Liting Jiang1,2,Yinyin Xie3,Li Wei4,Qi Zhou4,Ning Li1,Xinquan Jiang2(),Yiming Gao1()
1. Department of Stomatology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
2. Department of Prosthodontics, Ninth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Stomatology, Shanghai 200011, China
3. State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
4. Shanghai Institute of Traumatology and Orthopedics, Shanghai 200025, China
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Abstract

As muscle activity during growth is considerably important for mandible quality and morphology, reducing dietary loading directly influences the development and metabolic activity of mandibular condylar cartilage (MCC). However, an overall investigation of changes in the protein composition of MCC has not been fully described in literature. To study the protein expression and putative signaling in vivo, we evaluated the structural changes of MCC and differentially expressed proteins induced by reducing functional loading in rat MCC at developmental stages. Isobaric tag for relative and absolute quantitation-based 2D nano-high performance liquid chromatography (HPLC) and matrix-assisted laser desorption/ionization time-of-flight/ time-of-flight (MALDI-TOF/TOF) technologies were used. Global protein profiling, KEGG and PANTHER pathways, and functional categories were analyzed. Consequently, histological and tartrate-resistant acid phosphatase staining indicated the altered histological structure of condylar cartilage and increased bone remodeling activity in hard-diet group. A total of 805 differentially expressed proteins were then identified. GO analysis revealed a significant number of proteins involved in the metabolic process, cellular process, biological regulation, localization, developmental process, and response to stimulus. KEGG pathway analysis also suggested that these proteins participated in various signaling pathways, including calcium signaling pathway, gap junction, ErbB signaling pathway, and mitogen-activated protein kinase signaling pathway. Collagen types I and II were further validated by immunohistochemical staining and Western blot analysis. Taken together, the present study provides an insight into the molecular mechanism of regulating condylar growth and remodeling induced by reducing dietary loading at the protein level.

Keywords condylar cartilage      mechanical loading      proteomic analysis      iTRAQ      bioinformatics analysis     
Corresponding Author(s): Xinquan Jiang,Yiming Gao   
Just Accepted Date: 19 December 2016   Online First Date: 23 January 2017    Issue Date: 20 March 2017
 Cite this article:   
Liting Jiang,Yinyin Xie,Li Wei, et al. iTRAQ-based quantitative proteomic analysis on differentially expressed proteins of rat mandibular condylar cartilage induced by reducing dietary loading[J]. Front. Med., 2017, 11(1): 97-109.
 URL:  
https://academic.hep.com.cn/fmd/EN/10.1007/s11684-016-0496-1
https://academic.hep.com.cn/fmd/EN/Y2017/V11/I1/97
Fig.1  Schematic flowchart of experimental procedures.
Fig.2  Sagittal section of the mandibular condyle. (A) Anterior, superior, and posterior regions of condylar cartilage stained with toluidine blue (original magnification, 4×; Bar, 500 μm). (B) Condylar cartilage consisted of four layers: fibrous (F), proliferative (P), maturing (M), and hypertrophic layers (H) (original magnification, 20×). (C) Representative images of superior and anterior regions of condylar cartilage both in soft-diet and hard-diet groups (original magnification, 20×; Bar, 100 mm).
Fig.3  Histologic analysis of the condylar cartilage. (A) Safranin O (original magnification, 20×), tartrate-resistant acid phosphatase (TRAP) staining, and immunostaining for Col I and Col II proteins (original magnification, 40×) and negative controls in the anterior region of condylar cartilage in soft-diet and hard-diet groups. Bars, 100 μm. Immunohistochemical staining-positive cells were indicated by arrows. (B) Comparison of the thickness of the anterior region between soft-diet and hard-diet groups. (C) TRAP-positive cells found in soft-diet and hard-diet groups. (D) Semiquantitative analysis of Col I and Col II-positive areas in soft-diet and hard-diet groups. Bar graph represents the mean±SE of three independent experiments, *P<0.05, **P<0.01, t-test.
Fig.4  PANTHER gene ontology annotation of the differentially expressed proteins identified in rat condylar cartilages. Results were obtained by a web-based browser (http://pantherdb.org). (A) Molecular function distribution of identified proteins. (B) Biological process distribution of identified proteins.
Gene symbol Protein name 116.1/114.1 117.1/114.1
Calcium binding protein (PC00060)
CAB45 5 kDa calcium binding protein precursor 0.53 0.61
PLCE1 Phospholipase C-epsilon-1 0.59 0.53
KPCZ Protein kinase C ζ type 0.57 0.77
CABP1 Ras GTPase-activating protein 1 0.91 1.02
KPCT Protein kinase C ι type 0.61 0.64
PLCG2 Phospholipase C-γ-2 0.70 0.74
CAN9 Calpain-9 0.69 0.73
PLCG1 Phospholipase C-γ-1 0.87 1.01
M2OM Mitochondrial 2-oxoglutarate/malate carrier protein 1.07 1.19
PLCB2 Phospholipase C-β-2 0.76 0.75
PLCB1 Phospholipase C-β-1 0.39 0.51
NUCB1 Nucleobindin-1 precursor 0.48 0.68
PKN2 Protein-kinase C-related kinase 2 2.39 2.96
EFCB3 EF-hand calcium binding domain-containing protein 3 0.69 0.81
CAN10 Calpain-10 1.44 1.22
KPCB Protein kinase C β type 0.60 0.51
KPCG Protein kinase C γ type 1.55 1.61
MAST1 Microtubule-associated serine/threonine-protein kinase 1 0.74 0.75
PKN1 Serine/threonine-protein kinase N1 0.29 0.53
FA10 Coagulation factor X precursor 0.47 0.50
Cytoskeletal protein (PC00085)
ACTN1 α-actinin-1 1.12 1.20
MYH10 Myosin-10 0.60 0.79
FBP1L Formin-binding protein 1-like 0.48 0.53
DREB Drebrin 0.34 0.34
CLIC6 Chloride intracellular channel 6 0.96 1.02
PDLI3 Actinin-associated LIM protein 0.83 0.95
ADDA α-adducin 1.11 0.93
DYN1 Dynamin-1 0.81 0.85
TBA3 Tubulin α-3 chain 0.76 0.81
LMNB1 Lamin-B1 0.64 0.66
K1C14 Keratin, type I cytoskeletal 14 0.53 0.57
SYN3 Synapsin-3 0.58 0.67
DYN2 Dynamin-2 0.29 0.53
NUDC Nuclear migration protein nudC 0.73 0.54
KIF2C Kinesin-like protein KIF2C 0.97 1.08
LHX1 LIM/homeobox protein Lhx1 1.08 1.29
MAP1B Microtubule-associated protein 1B 0.96 0.97
TBB2B Tubulin β-2B chain 1.06 1.06
K2C1B Keratin, type II cytoskeletal 1b 0.45 0.56
CNN1 Calponin-1 0.11 0.21
Extracellular matrix protein (PC00102)
MEGF8 Multiple epidermal growth factor-like domains 8 0.71 0.61
CO5A1 Collagen α-1(V) chain precursor 0.37 0.51
ITB4 Integrin β-4 precursor 1.34 1.40
MEGF6 Multiple epidermal growth factor-like domains 6 precursor 0.66 0.72
R4RL2 Reticulon-4 receptor-like 2 precursor 0.69 0.72
CSPG2 Versican core protein precursor 1.24 1.44
FETUA α-2-HS-glycoprotein precursor 2.39 2.96
CO1A1 Collagen α-1(I) chain precursor 0.61 0.76
CHRD Chordin (Fragment) 0.51 0.65
CO1A2 Collagen α-2(I) chain precursor 0.39 0.44
GAS6 Growth-arrest-specific protein 6 precursor 1.41 1.22
CO2A1 Collagen α-1(II) chain precursor 0.68 0.91
LAMB2 Laminin subunit β-2 precursor 0.59 0.77
AP3M1 AP-3 complex subunitμ-1 1.15 1.50
DSPP Dentin sialophosphoprotein precursor 0.40 0.60
Signaling molecule (PC00207)
CD151 CD151 antigen 1.36 1.41
PLCE1 Phospholipase C-epsilon-1 0.59 0.53
CD53 Cell surface glycoprotein CD53 0.37 0.34
GDF6 Growth/differentiation factor 6 precursor 1.05 1.15
FZD5 Frizzled-5 precursor 0.40 0.60
R4RL2 Reticulon-4 receptor-like 2 precursor 0.69 0.72
PLCG2 Phospholipase C-γ-2 0.70 0.74
CLIC6 Chloride intracellular channel 6 1.03 1.04
PLCG1 Phospholipase C-γ-1 0.87 1.01
SFRP4 Secreted frizzled-related protein 4 precursor 1.39 1.36
A1M α-1-macroglobulin precursor 0.47 0.63
WISP2 WNT1-inducible-signaling pathway protein 2 precursor 1.42 1.69
PLCB1 Phospholipase C-β-1 0.39 0.51
DSPP Dentin sialophosphoprotein precursor 0.40 0.60
RASA1 Ras GTPase-activating protein 1 0.91 1.02
BMP3 Bone morphogenetic protein 3 precursor 1.10 1.03
SYGP1 Ras GTPase-activating protein SynGAP 0.60 0.60
PDGFD Platelet-derived growth factor D precursor 0.94 0.99
VEGFD Vascular endothelial growth factor D precursor 0.44 0.54
FGF5 Fibroblast growth factor 5 precursor 0.49 0.47
CD40L CD40 ligand 0.70 0.64
FA10 Coagulation factor X precursor 0.47 0.50
DAB2P DAB2-interacting protein 0.86 0.98
UBF1 Nucleolar transcription factor 1 0.32 0.33
TNFL4 Tumor necrosis factor ligand superfamily member 4 1.37 1.44
RASA3 Ras GTPase-activating protein 3 1.37 1.43
Transcription factor (PC00218)
MAF Transcription factor Maf 0.63 0.67
ISL2 Insulin gene enhancer protein ISL-2 0.57 0.68
JUN Transcription factor AP-1 1.24 1.15
FOSL1 Fos-related antigen 1 0.92 0.96
1433B 14-3-3 protein β/α 0.44 0.38
Tab.1  List of selected differentially expressed proteins in rat condylar cartilage according to the PANTHER protein class
KEGG Gene symbol P value
rno04020: Calcium signaling pathway PDE1C, VDAC1, ERBB4, CAC1H, KCC2D, NOS1, GNAL, CLTR1, IP3KB, NMDE1, CAC1A, P2RX5, P2RX1, CAC1G, NOS3, ERBB2, KPCG, NMDE3, PLCB2, NMDZ1, CAC1I, PLCB1, TA2R, PLCG1, AT2B1, PLCG2, PLCE1, V1AR, ADCY3, ADRB2, ERBB3, AT2B2, ALEX, AA2AR, KPCB 6.73E-09
rno04540: Gap junction ADCY3, KPCG, PLCB2, GCYA3, PDGFD, ALEX, PLCB1, CXA1, RASN, TBA3, KPCB, TBB2B 0.013
rno04012: ErbB signaling pathway ERBB2, ERBB3, KPCG, NRG2, FRAP, ERBB4, JUN, RASN, KCC2D, KPCB, PLCG1, PLCG2 0.016
rno04010: MAPK signaling pathway FGFR1, JUN, RASA1, ARRB1, CAC1A, CAC1G, RASM, RASN, CAC1H, PA21B, CAC1I, CCG6, DAXX, KPCB, KPCG, DUS6, TGFR1, FGF1, FGF5, FGFR4, GA45A, M3K12 0.035
rno04310: Wnt signaling pathway AXN1, KPCG, SENP2, APC, PLCB2, JUN, KCC2D, PLCB1, FZD5, SMAD2, SFRP4, KPCB, FOSL1, 2AAB, ROCK1 0.075
Tab.2  Identification of differentially expressed proteins by isobaric tag for relative and absolute quantitation (iTRAQ) quantification according to KEGG pathway category
PANTHER Gene symbol P value
P00021: FGF signaling pathway RASA1, KPCG, FGF1, KPCZ, PLCG1, ARAF, PLCG2, PTN6, FGFR4, FGFR1, PP4R1, KPCT, RM38, FGF5, RASN, KPCB, SYGP1, 2AAB, 1433B 0.006
P00005: Angiogenesis RASA1, AXN1, FGF1, APC, KPCZ, EPHA5_RAT, NOS1, FGFR4, FGFR1, KPCT, VGFR2, NOS3, KPCG, JAG2, EPHA6, PDGFD, JUN, FZD5, PLCG1, EPHB1, ARAF, PLCG2, PTN6, GRB14, EPHA8, RASN, KPCB 0.006
P00027: Heterotrimeric G-protein ?signaling pathway KPCG, DRD3, GPSM1, SI1L1, SSR4, PLCB2, KPCZ, GPSM3, PLCB1, ACM4, ARHGB, RGS3, RGS14, GNRHR, KPCT, IRK9, CAC1A, RGS8, AA2AR, KPCB 0.007
Tab.3  Identification of differentially expressed proteins by iTRAQ quantification according to PANTHER pathway category
Fig.5  Representative results of Western blot. (A) The protein expression levels of Col I and Col II from condylar cartilage in the soft-diet and hard-diet groups analyzed by Western blot. Values were normalized to GAPDH. (B) Quantitation of relative Col I and Col II protein expression (Bar graph represents the mean±SE of three independent experiments, *P<0.05, **P<0.01, t-test).
1 Cianferotti L, Brandi ML. Muscle-bone interactions: basic and clinical aspects. Endocrine 2014; 45(2): 165–177
https://doi.org/10.1007/s12020-013-0026-8 pmid: 23990248
2 Vreeke M, Langenbach GE, Korfage JA, Zentner A, Grünheid T. The masticatory system under varying functional load. Part 1: Structural adaptation of rabbit jaw muscles to reduced masticatory load. Eur J Orthod 2011; 33(4): 359–364
https://doi.org/10.1093/ejo/cjq083 pmid: 20923937
3 Hichijo N, Kawai N, Mori H, Sano R, Ohnuki Y, Okumura S, Langenbach GE, Tanaka E. Effects of the masticatory demand on the rat mandibular development. J Oral Rehabil 2014; 41(8): 581–587
https://doi.org/10.1111/joor.12171 pmid: 24702545
4 Shimizu Y, Ishida T, Hosomichi J, Kaneko S, Hatano K, Ono T. Soft diet causes greater alveolar osteopenia in the mandible than in the maxilla. Arch Oral Biol 2013; 58(8): 907–911
https://doi.org/10.1016/j.archoralbio.2013.02.003 pmid: 23490352
5 Yonemitsu I, Muramoto T, Soma K. The influence of masseter activity on rat mandibular growth. Arch Oral Biol 2007; 52(5): 487–493
https://doi.org/10.1016/j.archoralbio.2006.10.019 pmid: 17126288
6 Ödman A, Mavropoulos A, Kiliaridis S. Do masticatory functional changes influence the mandibular morphology in adult rats. Arch Oral Biol 2008; 53(12): 1149–1154
https://doi.org/10.1016/j.archoralbio.2008.07.004 pmid: 18721914
7 Grünheid T, Langenbach GE, Korfage JA, Zentner A, van Eijden TM. The adaptive response of jaw muscles to varying functional demands. Eur J Orthod 2009; 31(6): 596–612
https://doi.org/10.1093/ejo/cjp093 pmid: 19656804
8 Grünheid T, Langenbach GE, Brugman P, Everts V, Zentner A. The masticatory system under varying functional load. Part 2: Effect of reduced masticatory load on the degree and distribution of mineralization in the rabbit mandible. Eur J Orthod 2011; 33(4): 365–371
https://doi.org/10.1093/ejo/cjq084 pmid: 20923936
9 Burn AK, Herring SW, Hubbard R, Zink K, Rafferty K, Lieberman DE. Dietary consistency and the midline sutures in growing pigs. Orthod Craniofac Res 2010; 13(2): 106–113
https://doi.org/10.1111/j.1601-6343.2010.01483.x pmid: 20477970
10 Kawai N, Sano R, Korfage JA, Nakamura S, Kinouchi N, Kawakami E, Tanne K, Langenbach GE, Tanaka E. Adaptation of rat jaw muscle fibers in postnatal development with a different food consistency: an immunohistochemical and electromyographic study. J Anat 2010; 216(6): 717–723
https://doi.org/10.1111/j.1469-7580.2010.01235.x pmid: 20579175
11 Rawlinson SC, Boyde A, Davis GR, Howell PG, Hughes FJ, Kingsmill VJ. Ovariectomy vs. hypofunction: their effects on rat mandibular bone. J Dent Res 2009; 88(7): 615–620
https://doi.org/10.1177/0022034509340132 pmid: 19641148
12 Kingsmill VJ, Boyde A, Davis GR, Howell PG, Rawlinson SC. Changes in bone mineral and matrix in response to a soft diet. J Dent Res 2010; 89(5): 510–514
https://doi.org/10.1177/0022034510362970 pmid: 20348483
13 Sakurai M, Yonemitsu I, Muramoto T, Soma K. Effects of masticatory muscle force on temporomandibular joint disc growth in rats. Arch Oral Biol 2007; 52(12): 1186–1193
https://doi.org/10.1016/j.archoralbio.2007.07.003 pmid: 17765198
14 Enomoto A, Watahiki J, Nampo T, Irie T, Ichikawa Y, Tachikawa T, Maki K. Mastication markedly affects mandibular condylar cartilage growth, gene expression, and morphology. Am J Orthod Dentofacial Orthop 2014; 146(3): 355–363
https://doi.org/10.1016/j.ajodo.2014.05.028 pmid: 25172258
15 Kuroda S, Tanimoto K, Izawa T, Fujihara S, Koolstra JH, Tanaka E. Biomechanical and biochemical characteristics of the mandibular condylar cartilage. Osteoarthritis Cartilage 2009; 17(11): 1408–1415
https://doi.org/10.1016/j.joca.2009.04.025 pmid: 19477310
16 Stanković S, Vlajković S, Bošković M, Radenković G, Antić V, Jevremović D. Morphological and biomechanical features of the temporomandibular joint disc: an overview of recent findings. Arch Oral Biol 2013; 58(10): 1475–1482
https://doi.org/10.1016/j.archoralbio.2013.06.014 pmid: 23871384
17 Papachristou D, Pirttiniemi P, Kantomaa T, Agnantis N, Basdra EK. Fos- and Jun-related transcription factors are involved in the signal transduction pathway of mechanical loading in condylar chondrocytes. Eur J Orthod 2006; 28(1): 20–26
https://doi.org/10.1093/ejo/cji101 pmid: 16373449
18 Von den Hoff JW, Delatte M. Interplay of mechanical loading and growth factors in the mandibular condyle. Arch Oral Biol 2008; 53(8): 709–715
https://doi.org/10.1016/j.archoralbio.2008.03.002 pmid: 18395696
19 Wilson R, Norris EL, Brachvogel B, Angelucci C, Zivkovic S, Gordon L, . Changes in the chondrocyte and extracellular matrix proteome during post-natal mouse cartilage development. Mol Cell Proteomics 2012;11(1):M111 014159
20 Kobayashi-Miura M, Miura T, Osago H, Yamaguchi Y, Aoyama T, Tanabe T, Matsumoto KI, Fujita Y. Rat articular cartilages change their tissue and protein compositions during perinatal period. Anat Histol Embryol 2016; 45(1): 9–18
https://doi.org/10.1111/ahe.12165 pmid: 25487350
21 Hsueh MF, Khabut A, Kjellström S, Önnerfjord P, Kraus VB. Elucidating the molecular composition of cartilage by proteomics. J Proteome Res 2016; 15(2): 374–388
https://doi.org/10.1021/acs.jproteome.5b00946 pmid: 26632656
22 Li H, Yang HS, Wu TJ, Zhang XY, Jiang WH, Ma QL, Chen YX, Xu Y, Li S, Hua ZC. Proteomic analysis of early-response to mechanical stress in neonatal rat mandibular condylar chondrocytes. J Cell Physiol 2010; 223(3): 610–622
pmid: 20127708
23 Evans C, Noirel J, Ow SY, Salim M, Pereira-Medrano AG, Couto N, Pandhal J, Smith D, Pham TK, Karunakaran E, Zou X, Biggs CA, Wright PC. An insight into iTRAQ: where do we stand now? Anal Bioanal Chem 2012; 404(4): 1011–1027
https://doi.org/10.1007/s00216-012-5918-6 pmid: 22451173
24 Basak T, Bhat A, Malakar D, Pillai M, Sengupta S. In-depth comparative proteomic analysis of yeast proteome using iTRAQ and SWATH based MS. Mol Biosyst 2015; 11(8): 2135–2143
https://doi.org/10.1039/C5MB00234F pmid: 26099114
25 Glibert P, Van Steendam K, Dhaenens M, Deforce D. iTRAQ as a method for optimization: enhancing peptide recovery after gel fractionation. Proteomics 2014; 14(6): 680–684
https://doi.org/10.1002/pmic.201300444 pmid: 24449435
26 Tabb DL, Wang X, Carr SA, Clauser KR, Mertins P, Chambers MC, Holman JD, Wang J, Zhang B, Zimmerman LJ, Chen X, Gunawardena HP, Davies SR, Ellis MJ, Li S, Townsend RR, Boja ES, Ketchum KA, Kinsinger CR, Mesri M, Rodriguez H, Liu T, Kim S, McDermott JE, Payne SH, Petyuk VA, Rodland KD, Smith RD, Yang F, Chan DW, Zhang B, Zhang H, Zhang Z, Zhou JY, Liebler DC. Reproducibility of differential proteomic technologies in CPTAC fractionated xenografts. J Proteome Res 2016; 15(3): 691–706
https://doi.org/10.1021/acs.jproteome.5b00859 pmid: 26653538
27 Wang H, Alvarez S, Hicks LM. Comprehensive comparison of iTRAQ and label-free LC-based quantitative proteomics approaches using two Chlamydomonas reinhardtii strains of interest for biofuels engineering. J Proteome Res 2012; 11(1): 487–501
https://doi.org/10.1021/pr2008225 pmid: 22059437
28 Aggarwal K, Choe LH, Lee KH. Shotgun proteomics using the iTRAQ isobaric tags. Brief Funct Genomics Proteomics 2006; 5(2): 112–120
https://doi.org/10.1093/bfgp/ell018 pmid: 16772272
29 Papadopoulou AK, Papachristou DJ, Chatzopoulos SA, Pirttiniemi P, Papavassiliou AG, Basdra EK. Load application induces changes in the expression levels of Sox-9, FGFR-3 and VEGF in condylar chondrocytes. FEBS Lett 2007; 581(10): 2041–2046
https://doi.org/10.1016/j.febslet.2007.04.037 pmid: 17467696
30 Lei Q, Chen J, Huang W, Wu D, Lin H, Lai Y. Proteomic analysis of the effect of extracellular calcium ions on human mesenchymal stem cells: implications for bone tissue engineering. Chem Biol Interact 2015; 233: 139–146
https://doi.org/10.1016/j.cbi.2015.03.021 pmid: 25824407
31 Huang W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 2009; 4(1): 44–57
https://doi.org/10.1038/nprot.2008.211 pmid: 19131956
32 Hashimoto K, Goto S, Kawano S, Aoki-Kinoshita KF, Ueda N, Hamajima M, Kawasaki T, Kanehisa M. KEGG as a glycome informatics resource. Glycobiology 2006; 16(5): 63R–70R PMID:16014746
https://doi.org/10.1093/glycob/cwj010
33 Jiao K, Dai J, Wang MQ, Niu LN, Yu SB, Liu XD. Age- and sex-related changes of mandibular condylar cartilage and subchondral bone: a histomorphometric and micro-CT study in rats. Arch Oral Biol 2010; 55(2): 155–163
https://doi.org/10.1016/j.archoralbio.2009.11.012 pmid: 20034609
34 Farias-Neto A, Martins AP, Sánchez-Ayala A, Rabie AB, Novaes PD, Rizzatti-Barbosa CM. The effect of posterior tooth loss on the expression of type II collagen, IL-1b and VEGF in the condylar cartilage of growing rats. Arch Oral Biol 2012; 57(11): 1551–1557
https://doi.org/10.1016/j.archoralbio.2012.05.002 pmid: 22658342
35 Li QF, Rabie AB. A new approach to control condylar growth by regulating angiogenesis. Arch Oral Biol 2007; 52(11): 1009–1017
https://doi.org/10.1016/j.archoralbio.2007.05.009 pmid: 17640614
36 Zhang M, Chen YJ, Ono T, Wang JJ. Crosstalk between integrin and G protein pathways involved in mechanotransduction in mandibular condylar chondrocytes under pressure. Arch Biochem Biophys 2008; 474(1): 102–108
https://doi.org/10.1016/j.abb.2008.03.010 pmid: 18375197
37 Singh M, Detamore MS. Biomechanical properties of the mandibular condylar cartilage and their relevance to the TMJ disc. J Biomech 2009; 42(4): 405–417
https://doi.org/10.1016/j.jbiomech.2008.12.012 pmid: 19200995
38 Pirttiniemi P, Kantomaa T, Sorsa T. Effect of decreased loading on the metabolic activity of the mandibular condylar cartilage in the rat. Eur J Orthod 2004; 26(1): 1–5
https://doi.org/10.1093/ejo/26.1.1 pmid: 14994876
39 Naveh GR, Lev-Tov Chattah N, Zaslansky P, Shahar R, Weiner S. Tooth-PDL-bone complex: response to compressive loads encountered during mastication—a review. Arch Oral Biol 2012; 57(12): 1575–1584
https://doi.org/10.1016/j.archoralbio.2012.07.006 pmid: 22877793
40 Willems NM, Langenbach GE, Everts V, Zentner A. The microstructural and biomechanical development of the condylar bone: a review. Eur J Orthod 2014; 36(4): 479–485
https://doi.org/10.1093/ejo/cjt093 pmid: 24375755
41 Kiliaridis S, Thilander B, Kjellberg H, Topouzelis N, Zafiriadis A. Effect of low masticatory function on condylar growth: a morphometric study in the rat. Am J Orthod Dentofacial Orthop 1999; 116(2): 121–125
https://doi.org/10.1016/S0889-5406(99)70207-6 pmid: 10434083
42 Liu Q, Gibson MP, Sun H, Qin C. Dentin sialophosphoprotein (DSPP) plays an essential role in the postnatal development and maintenance of mouse mandibular condylar cartilage. J Histochem Cytochem 2013; 61(10): 749–758
https://doi.org/10.1369/0022155413502056 pmid: 23900597
43 Gredes T, Mack H, Spassov A, Kunert-Keil C, Steele M, Proff P, Mack F, Gedrange T. Changes in condylar cartilage after anterior mandibular displacement in juvenile pigs. Arch Oral Biol 2012; 57(6): 594–598
https://doi.org/10.1016/j.archoralbio.2011.09.017 pmid: 22041020
44 Li XB, Zhou Z, Luo SJ. Expressions of IGF-1 and TGF-β 1 in the condylar cartilages of rapidly growing rats. Chin J Dent Res 1998; 1(2): 52–56
pmid: 10557195
45 Hinton RJ, Serrano M, So S. Differential gene expression in the perichondrium and cartilage of the neonatal mouse temporomandibular joint. Orthod Craniofac Res 2009; 12(3): 168–177
https://doi.org/10.1111/j.1601-6343.2009.01450.x pmid: 19627518
46 Priam S, Bougault C, Houard X, Gosset M, Salvat C, Berenbaum F, Jacques C. Identification of soluble 14-3-3∈ as a novel subchondral bone mediator involved in cartilage degradation in osteoarthritis. Arthritis Rheum 2013; 65(7): 1831–1842 PMID:23552998
https://doi.org/10.1002/art.37951
47 Sun Y, Gandhi V, Prasad M, Yu W, Wang X, Zhu Q, Feng JQ, Hinton RJ, Qin C. Distribution of small integrin-binding ligand, N-linked glycoproteins (SIBLING) in the condylar cartilage of rat mandible. Int J Oral Maxillofac Surg 2010; 39(3): 272–281
https://doi.org/10.1016/j.ijom.2009.12.017 pmid: 20097540
48 Papachristou DJ, Papachroni KK, Basdra EK, Papavassiliou AG. Signaling networks and transcription factors regulating mechanotransduction in bone. BioEssays 2009; 31(7): 794–804
https://doi.org/10.1002/bies.200800223 pmid: 19444851
49 Mariani E, Pulsatelli L, Facchini A. Signaling pathways in cartilage repair. Int J Mol Sci 2014; 15(5): 8667–8698
https://doi.org/10.3390/ijms15058667 pmid: 24837833
50 Hsueh MF, Önnerfjord P, Kraus VB. Biomarkers and proteomic analysis of osteoarthritis. Matrix Biol 2014; 39: 56–66
https://doi.org/10.1016/j.matbio.2014.08.012 pmid: 25179675
51 Sun H, Li M, Gong L, Liu M, Ding F, Gu X. iTRAQ-coupled 2D LC-MS/MS analysis on differentially expressed proteins in denervated tibialis anterior muscle of Rattus norvegicus. Mol Cell Biochem 2012; 364(1-2): 193–207
https://doi.org/10.1007/s11010-011-1218-2 pmid: 22227918
[1] Yiming Ma,Ting Xiao,Quan Xu,Xinxin Shao,Hongying Wang. iTRAQ-based quantitative analysis of cancer-derived secretory proteome reveals TPM2 as a potential diagnostic biomarker of colorectal cancer[J]. Front. Med., 2016, 10(3): 278-285.
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