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Microarray-based bioinformatics analysis of osteoblasts on TiO2 nanotube layers
Colloids and Surfaces B: Biointerfaces 93 (2012) 135–142
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Colloids and Surfaces B: Biointerfaces journal homepage: www.elsevier.com/locate/colsurfb
Microarray-based bioinformatics analysis of osteoblasts on TiO2 nanotube layers Weiqiang Yu, Yilin Zhang, Ling Xu, Shengjun Sun, Xingquan Jiang, Fuqiang Zhang ∗ Department of Prosthodontics, School of Stomatology and Affiliated Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Research Institute of Stomatology, Shanghai 200011, China
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Article history: Received 22 August 2011 Received in revised form 22 December 2011 Accepted 22 December 2011 Available online 30 December 2011 Keywords: Nanotubes Osteoblast Gene expression microarray Molecular mechanism
a b s t r a c t The TiO2 nanotube layers fabricated by electrochemical anodization have received considerable attention in dentistry and orthopedic medicine due to their increased osseointegration compared with the unanodized titanium. The molecular mechanisms underlying the interactions between nanotubes and osteoblasts is unknown. To examine this, the mRNA expression profile of MG-63 osteoblast-like cells cultured on the TiO2 nanotubes was explored by DNA microarray. The differentially expressed genes were identified by bioinformatics analysis. Gene ontology (GO) and Go-map network analysis indicated that the TiO2 nanotubes enhanced osteoblast proliferation and differentiation and decreased osteoblast adhesion and immunization. The expressions of genes were mainly increased in pathways influencing cell proliferation and differentiation (Cell cycle, Terpenoid backbone biosynthesis, and TGF-beta signaling) and were decreased in pathways controlling cell immunization (Cell adhesion molecules (CAMs), Allograft rejection, and Graft-versus-host disease). Signal network analysis generated from differentially expressed genes suggested that CTNNB1 (beta-catenin) was the central gene for increasing osteoblast proliferation and differentiation, and IKBKG (inhibitor of kappa light polypeptide gene enhancer in Bcells, kinase gamma) was the central gene for repressing osteoblast immunization on nanotube layers. These two genes were further confirmed by quantitative PCR. The identified signal pathways and central genes in the study are well correlated with osteoblast phenotype. Furthermore, microarray-based bioinformatics analysis is a powerful tool in efficiently understanding molecular mechanisms underlying the interactions between osteoblasts and the nanotube layers. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Titanium (Ti) and its alloys are widely used as implanting materials in orthopedics and dental sphere due to their good biocompatibility and adequate mechanical properties [1]. The morphology or composition of Ti implant surface is often modified to increase biocompatibility [2]. Recent researches were focused on modification of Ti implant at nanometer scale [3]. Many reports have shown that compared with conventional micro-implant, the nano-implant could increase in vitro function of osteobalst and improve osseointegration in vivo [4–9]. There are various nanometer morphologies of Ti implant produced by physical or chemical treatments [10]. The TiO2 nanotubes by anodization have aroused interest in biomedical material researchers because of easy preparation and excellent bioactivity [11]. Preliminary study reported that the adhesion and function were enhanced in osteoblast cultured on anodized TiO2 nanotubes compared with that on unanodized Ti [12,13]. Modification
∗ Corresponding author. Tel.: +86 21 23271699x5694; fax: +86 21 63136856. E-mail address: [email protected] (F. Zhang). 0927-7765/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.colsurfb.2011.12.025
of Ti implant with nanotubes by anodic oxidation process resulted in increased alkaline phosphatase activity of in vitro cultivated marrow stroma cells [14] and improved osseointegration in vivo [15,16]. Furthermore, some literatures reported that the diameter of nanotubes could play an important role on cell behaviors [17–19]. Cells are prone to adhere on TiO2 nanotubes with small diameter while exhibit better differentiation on TiO2 nanotubes with large diameter [17,18]. However, the exact molecular mechanism by which the TiO2 nanotubes promote osteoblast growth and differentiation is largely unknown. The DNA microarray is a powerful technology for large scale analysis on the molecular mechanisms of cellular responses to physiological and pathological stimuli [20,21]. Furthermore, the cell–biomaterial interactions could also be studied by DNA microarray in some reports [22–24]. Scarano and co-workers [24] performed DNA microarray to explore the molecular mechanisms underlying the effect of anatase coating on osteoblast-like cells and generated the genetic portrait of anatase coating effects. Recently, bioinformatics have been effectively applied in integration and interpretation of microarray gene data from biomaterials [25–27], including gene clustering, gene ontology, pathway analysis, signal network and so on. Vlacic-Zischke et al. used the microarray to
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study the differentially expressed genes of osteoblasts in response to sand-blasted acid-etched and hydrophilic SLA titanium surfaces. The bioinformatics analysis suggested that the superior outcomes of hydrophilic SLA titanium surfaces might be partly caused by successful osseointegration through TGF/BMP signaling [26]. The microarray-based bioinformatics analysis has enabled researchers to assess biocompatibility on materials at the molecular level [27,28]. In this study, we investigated the gene expression profile of osteoblasts cultured on TiO2 nanotubes by DNA microarray containing 46,000 different oligonucleotides. Furthermore, the gene expression data were processed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology (KO) and Signal network, which are effective bioinformatics analytical methods [29]. 2. Materials and methods 2.1. Preparation and characterization of materials The nanotube layers were prepared using Ti thin foils (0.25 mm thick, 99.5%, Alfa Aesar, British) by anodization. Firstly, the Ti foils were immersed in a mixture (2 mL 48% HF, 3 mL 70% HNO3 (both reagent grade chemicals) and 100 mL deionized water) for 5 min to remove the natural oxide layer, followed by rinsing in deionized water and then drying in nitrogen stream. The TiO2 nanotubes were fabricated in an electrolyte containing 0.5 wt% HF and 1 M H3 PO4 with a potentiostat under voltages of 15 V for 3 h [19]. After anodization, the samples were rinsed with deionized water and dried in a nitrogen stream. To crystallize the amporphous-strutured TiO2 nanotubes, the samples were then sintered at 500 ◦ C for 2 h. All the nanotubes (3 cm × 3 cm) used in this study were sterilized in a steam autoclave at 120 ◦ C for 30 min. The Ti foil polished by SiC emery paper (no. 1200 grit size) served as a control group. The microstructures of the nanotube layers were observed by scanning electron microscopy (SEM, FEI SIRION 200, Hillsboro, USA operated at 5 KV) at 100,000×. 2.2. Cell culture MG-63 osteoblast-like cells (maintained in the current research lab) were cultured in Eagle’s minimum essential medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (PS) at 37 ◦ C, 5% CO2 environment. The logarithmic cells were seeded at 1 × 104 cells/cm2 density onto the experimental substrates, which were then placed on a 60 mm polystyrene plate. Culture media were changed every 2 or 3 days. Finally, after 1-week of cultivation, the proliferation and early differentiation of osteoblasts were examined [23,30], and then cells were processed for RNA extraction.
raise degrees of freedom effectively in the cases of small samples. The significance analysis and false discovery rate (FDR) analysis were performed to select the differentially expressed genes (p < 0.05 and FDR < 0.1). 2.4. Gene ontology (GO) and Go-map analysis Functional analysis of differentially expressed genes was carried out by the Gene Ontology project (http://www.geneontology.org) on the basis of biological process [31]. The Fisher’s exact test and 2 test were used to classify the GO category, and the FDR was calculated to correct the p-value. p Value < 0.05 and FDR < 0.1 were used as a threshold to select significant GO categories. The GOmap was the interaction network of the significant GOs of the differentially expressed genes, and it was built according to the interactions of GOs in the Gene Ontology. This method aims to find the direct interactions among the significant GOs systemically and it could summarize the functional interactions among differentially expressed genes on different samples [32]. 2.5. Pathway analysis Pathway analysis was used to find out the significant pathway of the differential genes according to KEGG, Biocarta and Reatome. The Fisher’s exact test and 2 test was applied to select the significant pathway, and the threshold of significance was defined by p-value (p < 0.05) and FDR (FDR < 0.1) [33]. 2.6. Signal network analysis Gene–gene interaction network was constructed based on the data of differentially expressed genes. The matrix of genes expression values was built up on the data of the interaction database from KEGG. Networks are stored and presented as graphs, where nodes are mainly genes and edges represent relation types between the nodes, e.g. activation or phosphorylation. The “degree” is defined as the number of interactions of a gene with other genes in the gene network. The gene with the highest degree is the most identical central gene in the network [34]. 2.7. Real time PCR Quantitative real-time polymerase chain reaction (PCR) was used to validate the central genes acquired from signal network
2.3. Gene expression profiling by DNA microarray Whole genome expression analysis was carried out after 1week culture of the osteoblasts on the surfaces. The samples were hybridized onto human U133 plus 2.0 array (Affymetrix) containing 46,000 probes. The total RNA was extracted using Trizol reagent (Sigma, USA) from homogenized cells according to the manufacturer’s protocol, and was purified by RNeasy mini kit (QIAGEN, P/N 74104). The RNA of each sample was amplified and labeled following manufacturer’s protocols (http://www.affymetrix.com). The samples were hybridized and then analyzed using GeneChip® Scanner 3000 (Affymetrix, P/N 00-00212) to generate the raw gene expression data. The random variance model (RVM) t-test was performed to filter the differentially expressed genes for cells between smooth Ti group and the TiO2 nanotubes group. The RVM t-test can
Fig. 1. SEM of the TiO2 nanotube layers fabricated by anodization (×100,000).
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Fig. 2. The significant GO categories of the differential genes. (A and B) show significant GO categories targeted by upregulated and downregulated categories respectively. The vertical axis is the Gene ontology category, and the horizontal axis is the −lg p of GO categories (only shown when lg p > 2).
analysis. CTNNB1 (beta-catenin) and IKBKG (inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma) were selected for validation because of their important role in osteoblast behaviors. In brief, total RNA of cell was extracted using Trizol reagent (Sigma, USA) to synthesize cDNA using 20 L of reverse transcription reaction solution (1 g of total RNA, 10 mM Tris–HCl buffer (pH 8.3), 50 mM KCl, 5 mM MgCl2, Moloney murine leukemia virus reverse transcriptase (50 units), deoxynucleoside triphosphates (1 mM), random hexamers (2.5 M), RNase inhibitor (20 units), all from TaKaRa, Japan). To evaluate the expression of CTNNB1 and IKBKG, quantitative real-time PCR was performed using SYBR Premix Ex Taq Kit (TaKaRa, Japan) and the Sequence Detection System. A housekeeping gene GAPDH was used as internal control. Sequences of the primers used were listed as follows: (forward and reverse) (1) GAPDH, 5 -ACC CAG AAG ACT GTG GAT GG-3 and 5 CAG TGA GCT TCC CGT TCA G-3 (2) CTNNB1, 5 -TAC CTC CCA AGT CCT GTA TGA G-3 and 5 -TGA GCA GCA TCA AAC TGT GTA G-3 (3) IKBKG, 5 -CGT ACT GGG CGA AGA GTC TC-3 and 5 -GGC TGG CTT GGA AAT GCA G-3 . The expression of the tested genes was expressed as TiO2 nanotubes relative to smooth-Ti using the 2−C method.
3. Results 3.1. The features of nanotubes The diameter of nanotube layers anodized at 15 V was approximately 70 nm (Fig. 1). It is shown that the TiO2 nanotube layer is uniform over the substrate. 3.2. DNA microarrays data analysis Triplicate microarray results exhibited that 781 genes were identified as significantly (RVM t-test p < 0.05 and FDR p < 0.1) differentially regulated between the two surfaces (Supplementary Table 1). Of these, 290 genes were upregulated and 491 genes were downregulated (the TiO2 nanotubes VS smooth Ti). Supplementary material related to this article found, in the online version, at doi:10.1016/j.colsurfb.2011.12.025. 3.3. Gene ontology (GO) and Go-map analysis The main GO categories for upregulated genes were related to functions such as transcription, transport, transmembrane
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Fig. 3. The Go-map results from the significant GOs of the differential expression genes. Red cycle nodes represent over-expression functions, blue cycle nodes represent under-expression functions, and yellow cycle nodes represent both over-expression and under-expression functions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
transport, signal transduction, G-protein coupled receptor protein signaling pathway, response to estradiol stimulus, cell division, spindle organization, mitosis, mitotic chromosome movement towards spindle pole, protein complex localization, response to protein stimulus, etc. (Fig. 2). The main GO categories for downregulated genes were related to functions such as viral response, transmembrane transport, transport, antigen processing and presentation of peptide antigen via MHC class I, response to organic cyclic substance, innate immune response, cell adhesion, extracellular matrix organization, response to bacterium, response to cytokine stimulus, G-protein coupled receptor protein signaling pathway, etc. (Fig. 2). The differential genes within the significant GO categories are showed in Supplementary Table 2. The GO-map of differential expression genes suggests that osteoblast exposed to nanotubes are more proliferative (5 GOs promote the process versus 3 GOs inhibit it) and differentiated (3 GOs promote the process versus 1 GOs inhibit it). Furthermore, the osteoblasts on nanotubes seemed more resistant to cell immunoreaction (9 GOs inhibit the process versus 2 promote it) and adhesion (5 GOs inhibit the process) (Fig. 3). Supplementary material related to this article found, in the online version, at doi:10.1016/j.colsurfb.2011.12.025. 3.4. Pathway analysis In this study, 20 local KEGG biological pathways were upregulated and 19 local KEGG biological pathways were downregulated in MG 63 osteoblasts cultured on TiO2 nanotubes (Fig. 4). The first three upregulated biological pathways include Cell cycle, Terpenoid backbone biosynthesis, and TGF-beta signaling pathway. The first three downregulated biological pathways include Cell adhesion molecules (CAMs), Allograft rejection, and
Graft-versus-host disease. The differentially expressed genes were shown in Supplementary Table 3. Supplementary material related to this article found, in the online version, at doi:10.1016/j.colsurfb.2011.12.025. 3.5. Signal network analysis Construction of the gene–gene interaction network was based on the data of differentially expressed genes (Fig. 5). Four genes (CTNNB1, HSP90AAI, FYN, IKBKG) belong to the most significant central genes. The CTNNB1 and HSP90AAI genes are upregulated, but FYN and IKBKG genes are downregulated. Therefore they might be of great importance to construction of the gene–gene interaction network in osteoblasts cultured on the TiO2 nanotubes (Fig. 6). 3.6. Real time PCR The two central genes acquired from signal network analysis, CTNNB1 and IKBKG, was further confirmed by real-time quantitative PCR. The relative expression of CTNNB1 and IKBKG showed significant difference between two surfaces (p < 0.05). Compared to cells cultured on smooth Ti, osteoblast cultured on the TiO2 nanotubes exhibited significantly higher expression for CTNNB1 and significantly lower expression for IKBKG, which was in accordance with microarray results. 4. Discussion The TiO2 nanotubes exhibit good biocompatibility with bone tissue and therefore received more and more considerable attention in orthopedics and dental sphere [12–17]. But the exact mechanism by which the TiO2 nanotubes stimulate osteoblast activity remains
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Fig. 4. The significant pathways of the differential genes. (A and B) show significant pathways targeted by upregulated and downregulated pathways respectively. The vertical axis is the pathway category, and the horizontal axis is the −lg p of pathways.
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Fig. 5. The gene–gene interaction network of differential genes. Red cycle nodes represent upregulated genes, blue cycle nodes represent downregulated genes, and bluish green cycle nodes represent genes belonging to significant GO categories and significant pathways. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
unknown. Increasing researches have confirmed DNA microarray to be a powerful tool in studying biocompatibility with materials at the molecular level [20,21]. Therefore, we evaluated gene expression profile of osteoblast cultured on the TiO2 nanotubes by microarray-based bioinformatics analysis. We found 290 genes were upregulated and 491 genes were downregulated on the
Fig. 6. Adherent cell mRNA expression of Catenin (cadherin-associated protein), beta 1 (CTNNB1) and inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma (IKBKG) cultured on the smooth Ti and TiO2 nanotubes. The results were normalized by GAPDH and shown as fold change (baseline = cells on smooth Ti; *p < 0.05 compared to the smooth Ti, n = 4).
nanotubes. More importantly, our results indicate that particular biological pathways might control the distinctive characteristics of osteoblast grown on the nanotubes. Furthermore, the most significant central genes were found by signal network analysis, which was further confirmed by real-time PCR. Our GO and GO-map analysis are consistent with previous reports about the effects of TiO2 nanotubes on osteoblast or preosteoblast [17–19]. It has been verified that the diameter of nanotubes plays an important role on behavior of cells [17,18]. More adhesive cells were found on smaller nanotubes due to absorbed special proteins by TiO2 nanotubes. However, more proliferative and differentiated cells were found on larger nanotubes due to extraordinary elongation effect on cell morphology [17,18]. The TiO2 nanotubes with 70 nm diameter used in this study could decrease cell adhesion and increase proliferation and differentiation of osteoblast, which is consistent with previous reports [17,19]. More importantly, the anti-immunoreaction effect of the nanotubes is an original finding in this study. Sollazzo et al. [24] reported that the TiO2 anatase coating on Ti implant could reduce the immunity of osteoblast-like cell lines (MG-63) through DNA microarray analysis. The TiO2 nanotubes using in this study were also anatase, which may be a reason for the anti-immunoreaction effect. Furthermore, Carinci et al. [22] studied the gene expression profiles in MG63 osteoblastic-cell response to a new nanoporous and found the immunity of osteoblastic-cell was reduced by the nanopore surface. In addition to diameter of nanotubes, their nanopore structure may be another reason for the anti-immunoreaction. The antiimmunoreaction might make the implant with nanotubes more similar to the ‘self’ after grafting process, which may be conducive to implant osseointegration [22,24]. The significant pathways were acquired from the differential genes according to KEGG in this study. Results from pathway analysis demonstrate that the pathways influencing cell proliferation
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and differentiation were mainly enhanced and the pathways controlling cell immunization were repressed, which is consistent with the results of Go-map analysis. For example, cell cycle pathway is the most important upregulated pathway in our study. This pathway plays an important role in cell growth, cell proliferation, and cell development. Six genes were upregulated in this pathway. Cyclin A2 (CCNA2) can promotes both cell cycle G1/S and G2/M transitions by activating CDC2 or CDK2 kinases [35]. Transcription factor Dp-1(TFDP1) could control the transcriptional activity of numerous genes involved in cell cycle progression from G1 to S phase [36]. TGF-beta signaling pathway is another important upregulated pathway and regulates cell proliferation, apoptosis, differentiation and migration. Inhibitor of DNA binding 1 (ID-1) inactivates basic Helix-Loop-Helix (bHLH) proteins and controls cell growth, differentiation [37]. Furthermore, cell adhesion molecule pathway is the most important downregulated pathway and plays a critical role in hemostasis, immune response, inflammation, embryogenesis, and development of neuronal tissue. Allograft rejection pathway is the response of the recipient’s alloimmune response to nonself antigens expressed by donor tissues. In addition, we found that the human leukocyte antigen (HLA) gene family was the most related downregulated pathways. The HLA complex could help the immune system distinguish the body’s own proteins from proteins made by foreigners [38]. So the immune system may recognize the nanotubes as ‘self’ which might be related to the osseointegration process [24]. The signal network analysis could construct the gene–gene interaction network from differential genes, and could find the central genes with the highest degree. CTNNB1 (beta 1 catenin) is among the genes showing the greatest degree in differential expression. Osteoblasts cultured on the TiO2 nanotubes produced nearly 1.5 times more CTNNB1 than cells on smooth Ti. This change in CTNNB1 expression was also confirmed by quantitative PCR, with fold change of 1.8 times compared to smooth Ti. CTNNB1 could increase osteoblast proliferation and differentiation [39], furthermore it is indispensable to the conversion from an early osteoblast cell to a mature osteoblast [40]. In addition, Wnt/catenin signaling (an upregulated pathway in our study) increases bone density and also enhances healing [41]. Some studies have shown that CTNNB1 expression is regulated via mechanical cues [40,42]. The gene in this network suggests that osteoblast may be sensing nanotube cues as mechano-sensitive elements. Similar phenomenon was also found in vascular cells by Peng et al. [25]. So in our study, CTNNB1 serves as a marker for osteoblast proliferation and differentiation on nanotubes. IKBKG gene locates in the Xq28 chromosomal region and results in activation of genes involved in inflammation and immunity [43]. IKBKG showed a decrease in expression by quantitative PCR (fold change = 0.29) or by microarray (fold change = 0.72), which suggests that TiO2 nanotubes have even more profound anti-immune effects than what was predicted by microarray.
5. Conclusion In summary, the TiO2 nanotubes have extensive biological effects on osteoblasts and underlying mechanisms can be explained by GO functional groups. It enhances osteoblast proliferation and differentiation and decreases osteoblast adhesion and immunization. In addition, this study identified possible pathways enhancing cell proliferation and differentiation (Cell cycle, Terpenoid backbone biosynthesis, and TGF-beta signaling, etc.) and controlling cell immunization (CAMs, Allograft rejection, and Graft-versus-host disease, etc.). Gene–gene interaction network identified CTNNB1 and IKBKG as the central genes influencing osteoblast behavior on the nanotubes. Our data showed that
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microarray-based bioinformatics analysis could provide an effective method to comprehend the implant–bone interaction at the molecular level. Acknowledgments The authors would like to thank Xiu-li Zhang (Oral Bioengineering Lab, Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong University) for helpful assistance in experiments. This work was supported by Shanghai Leading Academic Discipline Project (project number: S30206) and Science and Technology Committee of Shanghai (08DZ2271100, 1052nm04300, and 10JC1408600) and Shanghai Leadind Academic Discipline Project (T0202), and National Natural Science Foundation of China (81070866). Genminix Company provides us with technical assistance. References [1] T. Albrektsson, P.I. Branemark, H.A. Hansson, J. Lindstrom, Acta Orthop. Scand. 52 (1981) 155. [2] L. Le Guéhennec, A. Soueidan, P. Layrolle, Y. Amouriq, Dent. Mater. 23 (2007) 844. [3] T.J. Webster, R.W. Siegel, R. Bizios, Biomaterials 20 (1999) 1222. [4] T.J. Webster, E.L. Hellenmeyer, R.L. Price, Biomaterials 26 (2005) 953. [5] E.E. Swan, K.C. Popat, C.A. Grimes, T.A. Desai, J. Biomed. Mater. Res. A 72 (2005) 288. [6] A. Bigi, N. Nicoli-Aldini, B. Bracci, J. Biomed. Mater. Res. A 82 (2007) 213. [7] M. Sato, A. Aslani, M.A. Sambito, N.M. Kalkhoran, E.B. Slamovich, T.J. Webster, J. Biomed. Mater. Res. A 84 (2008) 265. [8] M. Kalbacova, B. Rezek, V. Baresova, C. Wolf-Brandstetter, A. Kromka, Acta Biomater. 5 (2009) 3076. [9] E. Lamers, X.F. Walboomers, M. Domanski, J. Riet, F.C. van Delft, R. Luttge, Biomaterials 31 (2010) 3307. [10] G. Mendonc¸a, D.B. Mendonc¸a, F.J. Aragão, L.F. Cooper, Biomaterials 29 (2008) 3822. [11] S.H. Oh, R.R. Finõnes, C. Daraio, L.H. Chen, S. Jin, Biomaterials 26 (2005) 4938. ˜ [12] S. Oh, C. Daraio, L.H. Chen, T.R. Pisanic, R.R. Finones, S. Jin, J. Biomed. Mater. Res. A 78 (2006) 97. [13] C. Yao, E.B. Slamovich, T.J. Webster, J. Biomed. Mater. Res. A 85 (2008) 157. [14] K.C. Popat, L. Leoni, C.A. Grimes, T.A. Desai, Biomaterials 28 (2007) 3188. [15] C. Von Wilmowsky, S. Bauer, R. Lutz, M. Meisel, F.W. Neukam, T. Toyoshima, J. Biomed. Mater. Res. B: Appl. Biomater. 89 (2009) 165. [16] L.M. Bjursten, L. Rasmusson, S. Oh, G.C. Smith, K.S. Brammer, S. Jin, J. Biomed. Mater. Res. A 92 (2010) 1218. [17] K.S. Brammer, S. Oh, C.J. Cobb, L.M. Bjursten, H. van der Heyde, S. Jin, Acta Biomater. 5 (2009) 3215. [18] S. Oh, K.S. Brammer, Y.S. Li, D. Teng, A.J. Engler, S. Chien, Proc. Natl. Acad. Sci. 106 (2009) 2130. [19] W.Q. Yu, X.Q. Jiang, F.Q. Zhang, L. Xu, J. Biomed. Mater. Res. A 94 (2010) 1012. [20] F. Yang, Y. Mei, R. Langer, D.G. Anderson, Comb. Chem. High Throughput Screen. 12 (2009) 554. [21] H.D. Choi, W.C. Noh, J.W. Park, J.M. Lee, J.Y. Suh, J. Periodontal Implant Sci. 41 (2011) 30. [22] F. Carinci, F. Pezzetti, S. Volinia, F. Francioso, D. Arcelli, J. Marchesini, Clin. Oral Implants Res. 15 (2004) 180. [23] K.F. Bombonato-Prado, L.S. Bellesini, C.M. Junta, M.M. Marques, G.A. Passos, A.L. Rosa, J. Biomed. Mater. Res. A 88 (2009) 401. [24] V. Sollazzo, A. Palmieri, F. Pezzetti, A. Scarano, M. Martinelli, L. Scapoli, J. Biomed. Mater. Res. B: Appl. Biomater. 85 (2008) 29. [25] L. Peng, A.J. Barczak, R.A. Barbeau, Y. Xiao, T.J. LaTempa, C.A. Grimes, Nano Lett. 10 (2010) 143. [26] J. Vlacic-Zischke, S.M. Hamlet, T. Friis, M.S. Tonetti, S. Ivanovski, Biomaterials 32 (2011) 665. [27] X. Lü, H. Lu, L. Zhao, Y. Yang, Z. Lu, Biomaterials 31 (2010) 1965. [28] K.A. Power, K.T. Fitzgerald, W.M. Gallagher, Biomaterials 31 (2010) 6667. [29] X. Mao, T. Cai, J.G. Olyarchuk, L. Wei, Bioinformatics 21 (2005) 3787. [30] M. Van der Zande, X.F. Walboomers, M. Brännvall, B. Olalde, M.J. Jurado, J.I. Alava, Acta Biomater. 6 (2010) 4352. [31] Wright, G.W.R.M. Simon, Bioinformatics 19 (2003) 2448. [32] M. shburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, Nat. Genet. 25 (2000) 25. [33] M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno, M. Hattori, Nucleic Acids Res. 32 (2000) 277. [34] Li, C.H. Li, Bioinformatics 24 (2008) 1175. [35] M. Pagano, R. Pepperkok, F. Verde, W. Ansorge, G. Draetta, EMBO J. 11 (1992) 961. [36] K. Helin, C.L. Wu, A.R. Fattaey, J.A. Lees, B.D. Dynlacht, C. Ngwu, Genes Dev. 7 (1993) 1850.
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Colloids and Surfaces B: Biointerfaces journal homepage: www.elsevier.com/locate/colsurfb
Microarray-based bioinformatics analysis of osteoblasts on TiO2 nanotube layers Weiqiang Yu, Yilin Zhang, Ling Xu, Shengjun Sun, Xingquan Jiang, Fuqiang Zhang ∗ Department of Prosthodontics, School of Stomatology and Affiliated Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Research Institute of Stomatology, Shanghai 200011, China
a r t i c l e
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Article history: Received 22 August 2011 Received in revised form 22 December 2011 Accepted 22 December 2011 Available online 30 December 2011 Keywords: Nanotubes Osteoblast Gene expression microarray Molecular mechanism
a b s t r a c t The TiO2 nanotube layers fabricated by electrochemical anodization have received considerable attention in dentistry and orthopedic medicine due to their increased osseointegration compared with the unanodized titanium. The molecular mechanisms underlying the interactions between nanotubes and osteoblasts is unknown. To examine this, the mRNA expression profile of MG-63 osteoblast-like cells cultured on the TiO2 nanotubes was explored by DNA microarray. The differentially expressed genes were identified by bioinformatics analysis. Gene ontology (GO) and Go-map network analysis indicated that the TiO2 nanotubes enhanced osteoblast proliferation and differentiation and decreased osteoblast adhesion and immunization. The expressions of genes were mainly increased in pathways influencing cell proliferation and differentiation (Cell cycle, Terpenoid backbone biosynthesis, and TGF-beta signaling) and were decreased in pathways controlling cell immunization (Cell adhesion molecules (CAMs), Allograft rejection, and Graft-versus-host disease). Signal network analysis generated from differentially expressed genes suggested that CTNNB1 (beta-catenin) was the central gene for increasing osteoblast proliferation and differentiation, and IKBKG (inhibitor of kappa light polypeptide gene enhancer in Bcells, kinase gamma) was the central gene for repressing osteoblast immunization on nanotube layers. These two genes were further confirmed by quantitative PCR. The identified signal pathways and central genes in the study are well correlated with osteoblast phenotype. Furthermore, microarray-based bioinformatics analysis is a powerful tool in efficiently understanding molecular mechanisms underlying the interactions between osteoblasts and the nanotube layers. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Titanium (Ti) and its alloys are widely used as implanting materials in orthopedics and dental sphere due to their good biocompatibility and adequate mechanical properties [1]. The morphology or composition of Ti implant surface is often modified to increase biocompatibility [2]. Recent researches were focused on modification of Ti implant at nanometer scale [3]. Many reports have shown that compared with conventional micro-implant, the nano-implant could increase in vitro function of osteobalst and improve osseointegration in vivo [4–9]. There are various nanometer morphologies of Ti implant produced by physical or chemical treatments [10]. The TiO2 nanotubes by anodization have aroused interest in biomedical material researchers because of easy preparation and excellent bioactivity [11]. Preliminary study reported that the adhesion and function were enhanced in osteoblast cultured on anodized TiO2 nanotubes compared with that on unanodized Ti [12,13]. Modification
∗ Corresponding author. Tel.: +86 21 23271699x5694; fax: +86 21 63136856. E-mail address: [email protected] (F. Zhang). 0927-7765/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.colsurfb.2011.12.025
of Ti implant with nanotubes by anodic oxidation process resulted in increased alkaline phosphatase activity of in vitro cultivated marrow stroma cells [14] and improved osseointegration in vivo [15,16]. Furthermore, some literatures reported that the diameter of nanotubes could play an important role on cell behaviors [17–19]. Cells are prone to adhere on TiO2 nanotubes with small diameter while exhibit better differentiation on TiO2 nanotubes with large diameter [17,18]. However, the exact molecular mechanism by which the TiO2 nanotubes promote osteoblast growth and differentiation is largely unknown. The DNA microarray is a powerful technology for large scale analysis on the molecular mechanisms of cellular responses to physiological and pathological stimuli [20,21]. Furthermore, the cell–biomaterial interactions could also be studied by DNA microarray in some reports [22–24]. Scarano and co-workers [24] performed DNA microarray to explore the molecular mechanisms underlying the effect of anatase coating on osteoblast-like cells and generated the genetic portrait of anatase coating effects. Recently, bioinformatics have been effectively applied in integration and interpretation of microarray gene data from biomaterials [25–27], including gene clustering, gene ontology, pathway analysis, signal network and so on. Vlacic-Zischke et al. used the microarray to
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study the differentially expressed genes of osteoblasts in response to sand-blasted acid-etched and hydrophilic SLA titanium surfaces. The bioinformatics analysis suggested that the superior outcomes of hydrophilic SLA titanium surfaces might be partly caused by successful osseointegration through TGF/BMP signaling [26]. The microarray-based bioinformatics analysis has enabled researchers to assess biocompatibility on materials at the molecular level [27,28]. In this study, we investigated the gene expression profile of osteoblasts cultured on TiO2 nanotubes by DNA microarray containing 46,000 different oligonucleotides. Furthermore, the gene expression data were processed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology (KO) and Signal network, which are effective bioinformatics analytical methods [29]. 2. Materials and methods 2.1. Preparation and characterization of materials The nanotube layers were prepared using Ti thin foils (0.25 mm thick, 99.5%, Alfa Aesar, British) by anodization. Firstly, the Ti foils were immersed in a mixture (2 mL 48% HF, 3 mL 70% HNO3 (both reagent grade chemicals) and 100 mL deionized water) for 5 min to remove the natural oxide layer, followed by rinsing in deionized water and then drying in nitrogen stream. The TiO2 nanotubes were fabricated in an electrolyte containing 0.5 wt% HF and 1 M H3 PO4 with a potentiostat under voltages of 15 V for 3 h [19]. After anodization, the samples were rinsed with deionized water and dried in a nitrogen stream. To crystallize the amporphous-strutured TiO2 nanotubes, the samples were then sintered at 500 ◦ C for 2 h. All the nanotubes (3 cm × 3 cm) used in this study were sterilized in a steam autoclave at 120 ◦ C for 30 min. The Ti foil polished by SiC emery paper (no. 1200 grit size) served as a control group. The microstructures of the nanotube layers were observed by scanning electron microscopy (SEM, FEI SIRION 200, Hillsboro, USA operated at 5 KV) at 100,000×. 2.2. Cell culture MG-63 osteoblast-like cells (maintained in the current research lab) were cultured in Eagle’s minimum essential medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin (PS) at 37 ◦ C, 5% CO2 environment. The logarithmic cells were seeded at 1 × 104 cells/cm2 density onto the experimental substrates, which were then placed on a 60 mm polystyrene plate. Culture media were changed every 2 or 3 days. Finally, after 1-week of cultivation, the proliferation and early differentiation of osteoblasts were examined [23,30], and then cells were processed for RNA extraction.
raise degrees of freedom effectively in the cases of small samples. The significance analysis and false discovery rate (FDR) analysis were performed to select the differentially expressed genes (p < 0.05 and FDR < 0.1). 2.4. Gene ontology (GO) and Go-map analysis Functional analysis of differentially expressed genes was carried out by the Gene Ontology project (http://www.geneontology.org) on the basis of biological process [31]. The Fisher’s exact test and 2 test were used to classify the GO category, and the FDR was calculated to correct the p-value. p Value < 0.05 and FDR < 0.1 were used as a threshold to select significant GO categories. The GOmap was the interaction network of the significant GOs of the differentially expressed genes, and it was built according to the interactions of GOs in the Gene Ontology. This method aims to find the direct interactions among the significant GOs systemically and it could summarize the functional interactions among differentially expressed genes on different samples [32]. 2.5. Pathway analysis Pathway analysis was used to find out the significant pathway of the differential genes according to KEGG, Biocarta and Reatome. The Fisher’s exact test and 2 test was applied to select the significant pathway, and the threshold of significance was defined by p-value (p < 0.05) and FDR (FDR < 0.1) [33]. 2.6. Signal network analysis Gene–gene interaction network was constructed based on the data of differentially expressed genes. The matrix of genes expression values was built up on the data of the interaction database from KEGG. Networks are stored and presented as graphs, where nodes are mainly genes and edges represent relation types between the nodes, e.g. activation or phosphorylation. The “degree” is defined as the number of interactions of a gene with other genes in the gene network. The gene with the highest degree is the most identical central gene in the network [34]. 2.7. Real time PCR Quantitative real-time polymerase chain reaction (PCR) was used to validate the central genes acquired from signal network
2.3. Gene expression profiling by DNA microarray Whole genome expression analysis was carried out after 1week culture of the osteoblasts on the surfaces. The samples were hybridized onto human U133 plus 2.0 array (Affymetrix) containing 46,000 probes. The total RNA was extracted using Trizol reagent (Sigma, USA) from homogenized cells according to the manufacturer’s protocol, and was purified by RNeasy mini kit (QIAGEN, P/N 74104). The RNA of each sample was amplified and labeled following manufacturer’s protocols (http://www.affymetrix.com). The samples were hybridized and then analyzed using GeneChip® Scanner 3000 (Affymetrix, P/N 00-00212) to generate the raw gene expression data. The random variance model (RVM) t-test was performed to filter the differentially expressed genes for cells between smooth Ti group and the TiO2 nanotubes group. The RVM t-test can
Fig. 1. SEM of the TiO2 nanotube layers fabricated by anodization (×100,000).
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Fig. 2. The significant GO categories of the differential genes. (A and B) show significant GO categories targeted by upregulated and downregulated categories respectively. The vertical axis is the Gene ontology category, and the horizontal axis is the −lg p of GO categories (only shown when lg p > 2).
analysis. CTNNB1 (beta-catenin) and IKBKG (inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma) were selected for validation because of their important role in osteoblast behaviors. In brief, total RNA of cell was extracted using Trizol reagent (Sigma, USA) to synthesize cDNA using 20 L of reverse transcription reaction solution (1 g of total RNA, 10 mM Tris–HCl buffer (pH 8.3), 50 mM KCl, 5 mM MgCl2, Moloney murine leukemia virus reverse transcriptase (50 units), deoxynucleoside triphosphates (1 mM), random hexamers (2.5 M), RNase inhibitor (20 units), all from TaKaRa, Japan). To evaluate the expression of CTNNB1 and IKBKG, quantitative real-time PCR was performed using SYBR Premix Ex Taq Kit (TaKaRa, Japan) and the Sequence Detection System. A housekeeping gene GAPDH was used as internal control. Sequences of the primers used were listed as follows: (forward and reverse) (1) GAPDH, 5 -ACC CAG AAG ACT GTG GAT GG-3 and 5 CAG TGA GCT TCC CGT TCA G-3 (2) CTNNB1, 5 -TAC CTC CCA AGT CCT GTA TGA G-3 and 5 -TGA GCA GCA TCA AAC TGT GTA G-3 (3) IKBKG, 5 -CGT ACT GGG CGA AGA GTC TC-3 and 5 -GGC TGG CTT GGA AAT GCA G-3 . The expression of the tested genes was expressed as TiO2 nanotubes relative to smooth-Ti using the 2−C method.
3. Results 3.1. The features of nanotubes The diameter of nanotube layers anodized at 15 V was approximately 70 nm (Fig. 1). It is shown that the TiO2 nanotube layer is uniform over the substrate. 3.2. DNA microarrays data analysis Triplicate microarray results exhibited that 781 genes were identified as significantly (RVM t-test p < 0.05 and FDR p < 0.1) differentially regulated between the two surfaces (Supplementary Table 1). Of these, 290 genes were upregulated and 491 genes were downregulated (the TiO2 nanotubes VS smooth Ti). Supplementary material related to this article found, in the online version, at doi:10.1016/j.colsurfb.2011.12.025. 3.3. Gene ontology (GO) and Go-map analysis The main GO categories for upregulated genes were related to functions such as transcription, transport, transmembrane
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Fig. 3. The Go-map results from the significant GOs of the differential expression genes. Red cycle nodes represent over-expression functions, blue cycle nodes represent under-expression functions, and yellow cycle nodes represent both over-expression and under-expression functions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
transport, signal transduction, G-protein coupled receptor protein signaling pathway, response to estradiol stimulus, cell division, spindle organization, mitosis, mitotic chromosome movement towards spindle pole, protein complex localization, response to protein stimulus, etc. (Fig. 2). The main GO categories for downregulated genes were related to functions such as viral response, transmembrane transport, transport, antigen processing and presentation of peptide antigen via MHC class I, response to organic cyclic substance, innate immune response, cell adhesion, extracellular matrix organization, response to bacterium, response to cytokine stimulus, G-protein coupled receptor protein signaling pathway, etc. (Fig. 2). The differential genes within the significant GO categories are showed in Supplementary Table 2. The GO-map of differential expression genes suggests that osteoblast exposed to nanotubes are more proliferative (5 GOs promote the process versus 3 GOs inhibit it) and differentiated (3 GOs promote the process versus 1 GOs inhibit it). Furthermore, the osteoblasts on nanotubes seemed more resistant to cell immunoreaction (9 GOs inhibit the process versus 2 promote it) and adhesion (5 GOs inhibit the process) (Fig. 3). Supplementary material related to this article found, in the online version, at doi:10.1016/j.colsurfb.2011.12.025. 3.4. Pathway analysis In this study, 20 local KEGG biological pathways were upregulated and 19 local KEGG biological pathways were downregulated in MG 63 osteoblasts cultured on TiO2 nanotubes (Fig. 4). The first three upregulated biological pathways include Cell cycle, Terpenoid backbone biosynthesis, and TGF-beta signaling pathway. The first three downregulated biological pathways include Cell adhesion molecules (CAMs), Allograft rejection, and
Graft-versus-host disease. The differentially expressed genes were shown in Supplementary Table 3. Supplementary material related to this article found, in the online version, at doi:10.1016/j.colsurfb.2011.12.025. 3.5. Signal network analysis Construction of the gene–gene interaction network was based on the data of differentially expressed genes (Fig. 5). Four genes (CTNNB1, HSP90AAI, FYN, IKBKG) belong to the most significant central genes. The CTNNB1 and HSP90AAI genes are upregulated, but FYN and IKBKG genes are downregulated. Therefore they might be of great importance to construction of the gene–gene interaction network in osteoblasts cultured on the TiO2 nanotubes (Fig. 6). 3.6. Real time PCR The two central genes acquired from signal network analysis, CTNNB1 and IKBKG, was further confirmed by real-time quantitative PCR. The relative expression of CTNNB1 and IKBKG showed significant difference between two surfaces (p < 0.05). Compared to cells cultured on smooth Ti, osteoblast cultured on the TiO2 nanotubes exhibited significantly higher expression for CTNNB1 and significantly lower expression for IKBKG, which was in accordance with microarray results. 4. Discussion The TiO2 nanotubes exhibit good biocompatibility with bone tissue and therefore received more and more considerable attention in orthopedics and dental sphere [12–17]. But the exact mechanism by which the TiO2 nanotubes stimulate osteoblast activity remains
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Fig. 4. The significant pathways of the differential genes. (A and B) show significant pathways targeted by upregulated and downregulated pathways respectively. The vertical axis is the pathway category, and the horizontal axis is the −lg p of pathways.
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Fig. 5. The gene–gene interaction network of differential genes. Red cycle nodes represent upregulated genes, blue cycle nodes represent downregulated genes, and bluish green cycle nodes represent genes belonging to significant GO categories and significant pathways. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
unknown. Increasing researches have confirmed DNA microarray to be a powerful tool in studying biocompatibility with materials at the molecular level [20,21]. Therefore, we evaluated gene expression profile of osteoblast cultured on the TiO2 nanotubes by microarray-based bioinformatics analysis. We found 290 genes were upregulated and 491 genes were downregulated on the
Fig. 6. Adherent cell mRNA expression of Catenin (cadherin-associated protein), beta 1 (CTNNB1) and inhibitor of kappa light polypeptide gene enhancer in B-cells, kinase gamma (IKBKG) cultured on the smooth Ti and TiO2 nanotubes. The results were normalized by GAPDH and shown as fold change (baseline = cells on smooth Ti; *p < 0.05 compared to the smooth Ti, n = 4).
nanotubes. More importantly, our results indicate that particular biological pathways might control the distinctive characteristics of osteoblast grown on the nanotubes. Furthermore, the most significant central genes were found by signal network analysis, which was further confirmed by real-time PCR. Our GO and GO-map analysis are consistent with previous reports about the effects of TiO2 nanotubes on osteoblast or preosteoblast [17–19]. It has been verified that the diameter of nanotubes plays an important role on behavior of cells [17,18]. More adhesive cells were found on smaller nanotubes due to absorbed special proteins by TiO2 nanotubes. However, more proliferative and differentiated cells were found on larger nanotubes due to extraordinary elongation effect on cell morphology [17,18]. The TiO2 nanotubes with 70 nm diameter used in this study could decrease cell adhesion and increase proliferation and differentiation of osteoblast, which is consistent with previous reports [17,19]. More importantly, the anti-immunoreaction effect of the nanotubes is an original finding in this study. Sollazzo et al. [24] reported that the TiO2 anatase coating on Ti implant could reduce the immunity of osteoblast-like cell lines (MG-63) through DNA microarray analysis. The TiO2 nanotubes using in this study were also anatase, which may be a reason for the anti-immunoreaction effect. Furthermore, Carinci et al. [22] studied the gene expression profiles in MG63 osteoblastic-cell response to a new nanoporous and found the immunity of osteoblastic-cell was reduced by the nanopore surface. In addition to diameter of nanotubes, their nanopore structure may be another reason for the anti-immunoreaction. The antiimmunoreaction might make the implant with nanotubes more similar to the ‘self’ after grafting process, which may be conducive to implant osseointegration [22,24]. The significant pathways were acquired from the differential genes according to KEGG in this study. Results from pathway analysis demonstrate that the pathways influencing cell proliferation
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and differentiation were mainly enhanced and the pathways controlling cell immunization were repressed, which is consistent with the results of Go-map analysis. For example, cell cycle pathway is the most important upregulated pathway in our study. This pathway plays an important role in cell growth, cell proliferation, and cell development. Six genes were upregulated in this pathway. Cyclin A2 (CCNA2) can promotes both cell cycle G1/S and G2/M transitions by activating CDC2 or CDK2 kinases [35]. Transcription factor Dp-1(TFDP1) could control the transcriptional activity of numerous genes involved in cell cycle progression from G1 to S phase [36]. TGF-beta signaling pathway is another important upregulated pathway and regulates cell proliferation, apoptosis, differentiation and migration. Inhibitor of DNA binding 1 (ID-1) inactivates basic Helix-Loop-Helix (bHLH) proteins and controls cell growth, differentiation [37]. Furthermore, cell adhesion molecule pathway is the most important downregulated pathway and plays a critical role in hemostasis, immune response, inflammation, embryogenesis, and development of neuronal tissue. Allograft rejection pathway is the response of the recipient’s alloimmune response to nonself antigens expressed by donor tissues. In addition, we found that the human leukocyte antigen (HLA) gene family was the most related downregulated pathways. The HLA complex could help the immune system distinguish the body’s own proteins from proteins made by foreigners [38]. So the immune system may recognize the nanotubes as ‘self’ which might be related to the osseointegration process [24]. The signal network analysis could construct the gene–gene interaction network from differential genes, and could find the central genes with the highest degree. CTNNB1 (beta 1 catenin) is among the genes showing the greatest degree in differential expression. Osteoblasts cultured on the TiO2 nanotubes produced nearly 1.5 times more CTNNB1 than cells on smooth Ti. This change in CTNNB1 expression was also confirmed by quantitative PCR, with fold change of 1.8 times compared to smooth Ti. CTNNB1 could increase osteoblast proliferation and differentiation [39], furthermore it is indispensable to the conversion from an early osteoblast cell to a mature osteoblast [40]. In addition, Wnt/catenin signaling (an upregulated pathway in our study) increases bone density and also enhances healing [41]. Some studies have shown that CTNNB1 expression is regulated via mechanical cues [40,42]. The gene in this network suggests that osteoblast may be sensing nanotube cues as mechano-sensitive elements. Similar phenomenon was also found in vascular cells by Peng et al. [25]. So in our study, CTNNB1 serves as a marker for osteoblast proliferation and differentiation on nanotubes. IKBKG gene locates in the Xq28 chromosomal region and results in activation of genes involved in inflammation and immunity [43]. IKBKG showed a decrease in expression by quantitative PCR (fold change = 0.29) or by microarray (fold change = 0.72), which suggests that TiO2 nanotubes have even more profound anti-immune effects than what was predicted by microarray.
5. Conclusion In summary, the TiO2 nanotubes have extensive biological effects on osteoblasts and underlying mechanisms can be explained by GO functional groups. It enhances osteoblast proliferation and differentiation and decreases osteoblast adhesion and immunization. In addition, this study identified possible pathways enhancing cell proliferation and differentiation (Cell cycle, Terpenoid backbone biosynthesis, and TGF-beta signaling, etc.) and controlling cell immunization (CAMs, Allograft rejection, and Graft-versus-host disease, etc.). Gene–gene interaction network identified CTNNB1 and IKBKG as the central genes influencing osteoblast behavior on the nanotubes. Our data showed that
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microarray-based bioinformatics analysis could provide an effective method to comprehend the implant–bone interaction at the molecular level. Acknowledgments The authors would like to thank Xiu-li Zhang (Oral Bioengineering Lab, Ninth People’s Hospital, School of Medicine, Shanghai Jiao Tong University) for helpful assistance in experiments. This work was supported by Shanghai Leading Academic Discipline Project (project number: S30206) and Science and Technology Committee of Shanghai (08DZ2271100, 1052nm04300, and 10JC1408600) and Shanghai Leadind Academic Discipline Project (T0202), and National Natural Science Foundation of China (81070866). Genminix Company provides us with technical assistance. References [1] T. Albrektsson, P.I. Branemark, H.A. Hansson, J. Lindstrom, Acta Orthop. Scand. 52 (1981) 155. [2] L. Le Guéhennec, A. Soueidan, P. Layrolle, Y. Amouriq, Dent. Mater. 23 (2007) 844. [3] T.J. Webster, R.W. Siegel, R. Bizios, Biomaterials 20 (1999) 1222. [4] T.J. Webster, E.L. Hellenmeyer, R.L. Price, Biomaterials 26 (2005) 953. [5] E.E. Swan, K.C. Popat, C.A. Grimes, T.A. Desai, J. Biomed. Mater. Res. A 72 (2005) 288. [6] A. Bigi, N. Nicoli-Aldini, B. Bracci, J. Biomed. Mater. Res. A 82 (2007) 213. [7] M. Sato, A. Aslani, M.A. Sambito, N.M. Kalkhoran, E.B. Slamovich, T.J. Webster, J. Biomed. Mater. Res. A 84 (2008) 265. [8] M. Kalbacova, B. Rezek, V. Baresova, C. Wolf-Brandstetter, A. Kromka, Acta Biomater. 5 (2009) 3076. [9] E. Lamers, X.F. Walboomers, M. Domanski, J. Riet, F.C. van Delft, R. Luttge, Biomaterials 31 (2010) 3307. [10] G. Mendonc¸a, D.B. Mendonc¸a, F.J. Aragão, L.F. Cooper, Biomaterials 29 (2008) 3822. [11] S.H. Oh, R.R. Finõnes, C. Daraio, L.H. Chen, S. Jin, Biomaterials 26 (2005) 4938. ˜ [12] S. Oh, C. Daraio, L.H. Chen, T.R. Pisanic, R.R. Finones, S. Jin, J. Biomed. Mater. Res. A 78 (2006) 97. [13] C. Yao, E.B. Slamovich, T.J. Webster, J. Biomed. Mater. Res. A 85 (2008) 157. [14] K.C. Popat, L. Leoni, C.A. Grimes, T.A. Desai, Biomaterials 28 (2007) 3188. [15] C. Von Wilmowsky, S. Bauer, R. Lutz, M. Meisel, F.W. Neukam, T. Toyoshima, J. Biomed. Mater. Res. B: Appl. Biomater. 89 (2009) 165. [16] L.M. Bjursten, L. Rasmusson, S. Oh, G.C. Smith, K.S. Brammer, S. Jin, J. Biomed. Mater. Res. A 92 (2010) 1218. [17] K.S. Brammer, S. Oh, C.J. Cobb, L.M. Bjursten, H. van der Heyde, S. Jin, Acta Biomater. 5 (2009) 3215. [18] S. Oh, K.S. Brammer, Y.S. Li, D. Teng, A.J. Engler, S. Chien, Proc. Natl. Acad. Sci. 106 (2009) 2130. [19] W.Q. Yu, X.Q. Jiang, F.Q. Zhang, L. Xu, J. Biomed. Mater. Res. A 94 (2010) 1012. [20] F. Yang, Y. Mei, R. Langer, D.G. Anderson, Comb. Chem. High Throughput Screen. 12 (2009) 554. [21] H.D. Choi, W.C. Noh, J.W. Park, J.M. Lee, J.Y. Suh, J. Periodontal Implant Sci. 41 (2011) 30. [22] F. Carinci, F. Pezzetti, S. Volinia, F. Francioso, D. Arcelli, J. Marchesini, Clin. Oral Implants Res. 15 (2004) 180. [23] K.F. Bombonato-Prado, L.S. Bellesini, C.M. Junta, M.M. Marques, G.A. Passos, A.L. Rosa, J. Biomed. Mater. Res. A 88 (2009) 401. [24] V. Sollazzo, A. Palmieri, F. Pezzetti, A. Scarano, M. Martinelli, L. Scapoli, J. Biomed. Mater. Res. B: Appl. Biomater. 85 (2008) 29. [25] L. Peng, A.J. Barczak, R.A. Barbeau, Y. Xiao, T.J. LaTempa, C.A. Grimes, Nano Lett. 10 (2010) 143. [26] J. Vlacic-Zischke, S.M. Hamlet, T. Friis, M.S. Tonetti, S. Ivanovski, Biomaterials 32 (2011) 665. [27] X. Lü, H. Lu, L. Zhao, Y. Yang, Z. Lu, Biomaterials 31 (2010) 1965. [28] K.A. Power, K.T. Fitzgerald, W.M. Gallagher, Biomaterials 31 (2010) 6667. [29] X. Mao, T. Cai, J.G. Olyarchuk, L. Wei, Bioinformatics 21 (2005) 3787. [30] M. Van der Zande, X.F. Walboomers, M. Brännvall, B. Olalde, M.J. Jurado, J.I. Alava, Acta Biomater. 6 (2010) 4352. [31] Wright, G.W.R.M. Simon, Bioinformatics 19 (2003) 2448. [32] M. shburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, Nat. Genet. 25 (2000) 25. [33] M. Kanehisa, S. Goto, S. Kawashima, Y. Okuno, M. Hattori, Nucleic Acids Res. 32 (2000) 277. [34] Li, C.H. Li, Bioinformatics 24 (2008) 1175. [35] M. Pagano, R. Pepperkok, F. Verde, W. Ansorge, G. Draetta, EMBO J. 11 (1992) 961. [36] K. Helin, C.L. Wu, A.R. Fattaey, J.A. Lees, B.D. Dynlacht, C. Ngwu, Genes Dev. 7 (1993) 1850.
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