Molecular Medicine Reports Molecular Medicine Reports 1791-2997 1791-3004 D.A. Spandidos 10.3892/mmr.2017.6544 mmr-15-06-3969 Articles Identification of novel genes associated with fracture healing in osteoporosis induced by Krm2 overexpression or Lrp5 deficiency GaoFeng 1 XuFeng 2 WuDankai 1 ChengJieping 1 XiaPeng 1 Department of Orthopedics, The Second Hospital of Jilin University, Changchun, Jilin 130041, P.R. China Department of Spine Surgery, The First Hospital of Jilin University, Changchun, Jilin 130021, P.R. China Correspondence to: Dr Feng Xu, Department of Spine Surgery, The First Hospital of Jilin University, 71 Xinmin Street, Chaoyang, Changchun, Jilin 130021, P.R. China, E-mail: xufengtx1978@163.com Dr Dankai Wu, Department of Orthopedics, The Second Hospital of Jilin University, 218 Zi Qiang Street, Changchun, Jilin 130041, P.R. China, E-mail: wudankai@163.com 062017 03052017 15 6 3969 3976 20012016 30012017 Copyright: © Gao et al. 2017 This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

The aim of the present study was to screen potential key genes associated with osteoporotic fracture healing. The microarray data from the Gene Expression Omnibus database accession number GSE51686, were downloaded and used to identify differentially expressed genes (DEGs) in fracture callus tissue samples obtained from the femora of type I collagen (Col1a1)-kringle containing transmembrane protein 2 (Krm2) mice and low density lipoprotein receptor-related protein 5−/− (Lrp5−/−) transgenic mice of osteoporosis compared with those in wild-type (WT) mice. Enrichment analysis was performed to reveal the DEG function. In addition, protein-protein interactions (PPIs) of DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes database. The coexpression associations between hub genes in the PPI network were investigated, and a coexpression network was constructed. A total of 841 DEGs (335 upregulated and 506 downregulated) were identified in the Col1a1-Krm2 vs. the WT group, and 50 DEGs (16 upregulated and 34 downregulated) were identified in the Lrp5−/− vs. the WT group. The DEGs in Col1a1-Krm2 mice were primarily associated with immunity and cell adhesion (GO: 0007155) functions. By contrast, the DEGs in Lrp5−/− mice were significantly associated with muscle system process (GO: 0003012) and regulation of transcription (GO: 0006355). In addition, a series of DEGs demonstrated a higher score in the PPI network, and were observed to be coexpressed in the coexpression network, and included thrombospondin 2 (Thbs2), syndecan 2 (Sdc2), FK506 binding protein 10 (Fkbp10), 2–5-oligoadneylate synthase-like protein 2 (Oasl2), interferon induced protein with tetratricopeptide repeats (Ifit) 1 and Ifit2. Thbs2 and Sdc2 were significantly correlated with extracellular matrix-receptor interactions. The results suggest that Thbs2, Sdc2, Fkbp10, Oasl2, Ifit1 and Ifit2 may serve important roles during the fracture healing process in osteoporosis. In addition, this is the first study to demonstrate that Sdc2, Fkbp10, Oasl2, Ifit1 and Ifit2 may be associated with osteoporotic fracture healing.

 fracture healing osteoporosis gene network coexpression Introduction

Osteoporotic fracture is a common event in the elderly, resulting in substantial mortality, and the mortality rate of hip fracture for 6 months is ~10–20% (1). The prevalence of osteoporotic fractures, hip fractures in particular, is increasing in many regions of the world (2). Current therapies focus on the prevention and treatment of osteoporotic fractures; however, this may easily lead to complications, thus it remains a worldwide public health concern. Therefore, a greater understanding of the underlying molecular mechanisms of fracture healing in the osteoporotic bone is required, as well as identifying candidate biomarkers for osteoporotic fracture therapies.

Over the past few years, a number of remarkable achievements have been made in the genetic study of fracture healing in osteoporosis. One such study demonstrated that transgenesis of bone morphogenetic protein-2 promotes fracture healing in osteoporosis by inducing increased callus density and a larger cross-sectional callus area (3). During remodeling of fractured bone, parathyroid hormone (PTH) promotes the formation of osteoclasts to restore the mechanical strength and structure of bones, and polymorphisms in genes encoding PTH influence the genetic regulation of bone mineral density (4). Low density lipoprotein receptor-related protein 5 (LRP5) serves a significant functional role in skeletal homeostasis, and mutations in LRP5 induce a variety of bone density-associated diseases (5). Lrp5 deficiency results in decreased osteoblast proliferation and function, which induces a low bone mass phenotype (6). Kringle containing transmembrane protein 2 (KREMEN2), also known as KRM2, is a high-affinity transmembrane receptor of dickkopf homolog 1, and is thought to be a regulator of bone remodeling (7). It has been demonstrated that Krm2−/− mice develop a high bone mass phenotype and overexpression of Krm2 in type I collagen (Col1a1)-Krm2 transgenic mice induces severe osteoporosis with decreased levels of osteoblasts and elevated osteoclast differentiation (8). Using a model of fracture healing in Col1a1-Krm2 transgenic mice and Lrp5−/− mice, a previous study revealed that fracture healing is greatly damaged in Col1a1-Krm2 transgenic mice and Lrp5−/− mice; however, the Col1a1-Krm2 mice were more severely impaired than Lrp5−/− mice (9). In addition, this previous study identified a set of differentially expressed genes (DEGs) in the two mouse models using microarray analysis (9). However, DEG interactions and functions require further investigation in order to provide a more comprehensive understanding of the effect of osteoporosis on fracture healing.

In order to investigate the interactions and functions of DEGs in Col1a1-Krm2 transgenic mice and Lrp5−/− mice further, the microarray data obtained by Liedert et al (9) were analyzed in the present study. Following identification of DEGs, enrichment analysis was performed. In addition, protein-protein interactions (PPIs) of DEGs and hub genes in the PPI network were analyzed. Furthermore, coexpression associations between hub genes and additional DEGs were examined. These results may contribute to a greater understanding of the effect of osteoporosis on fracture healing, and provide novel information that facilitates the development of future clinical therapies for osteoporotic fractures.

 Materials and methods <sec> <title>Affymetrix microarray data

The raw gene expression profile dataset GSE51686 (9) was obtained from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The data was generated by the (Mouse430_2) Affymetrix Mouse Genome 430 2.0 Array platform (GEO accession, GPL1261; Affymetrix, Inc., Santa Clara, CA, USA). This dataset contained 9 fracture callus tissue samples obtained from the femora at 10 days following osteotomy in the wild-type (WT) mice (n=3), Col1a1-Krm2 transgenic mice with severe osteoporosis (n=3), and Lrp5−/− mice with low bone mass (n=3), respectively. All mice were female and 26 weeks of age.

The CEL and probe annotation files for this dataset were downloaded. The raw expression data were preprocessed by background correction, quantile normalization and probe summarization using the robust microarray analysis algorithm in the affy package (version 3.3.2) (10) of Bioconductor (version 3.4; http://www.bioconductor.org/). Subsequently, the org.Hs.eg.db (version 3.4.0) (11) and illuminaHumanv3.db (version 1.26.0) (12) packages of Bioconductor were used to translate probe identifications (IDs) to gene symbols. If one gene symbol was matched by multiple probe IDs, the mean expression value was selected as the expression level of this gene.

 Identification of DEGs

DEGs in Col1a1-Krm2 mice and Lrp5−/− mice compared with the WT controls were identified using the linear models for microarray data (LIMMA) package (version 3.30.3; http://www.bioconductor.org/packages/release/bioc/html/limma.html) (13), which is a commonly used tool for the identification of DEGs. The P-value for each gene was calculated using the unpaired t-test in LIMMA, which was then adjusted for the false discovery rate (FDR) using the Benjamini-Hochberg method (14). Only the genes with FDR values <0.05 and log2 fold change values ≥0.5 were selected as DEGs.

The Venny online tool (version 2.0; http://bioinfogp.cnb.csic.es/tools/venny/index.html) (15) was utilized to construct Venn diagrams for the upregulated and downregulated genes identified between the Col1a1-Krm2 vs. WT and Lrp5−/− vs. WT groups.

 Enrichment analysis of DEGs

Functional Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of upregulated and downregulated genes were performed using the Database for Annotation Visualization and Integrated Discovery (version 6.8; http://david.abcc.ncifcrf.gov/) database (16). The P-value was calculated using the modified Fishers exact test, and P<0.05 was considered to indicate a statistically significant difference. A gene count in each term ≥2 was set as the cut-off criteria. Additional parameters were set to the default values.

 Construction of PPI networks

PPIs of DEGs were obtained from the Search Tool for the Retrieval of Interacting Genes database (version 10.0; http://string-db.org/), which integrates a variety of known and predicted protein associations (17). The combined score for each PPI was calculated, and a score of >0.4 was set as the cut-off criterion. Additional parameters were set to the default values. The PPI network was visualized using the Cytoscape software (version 3.4.0; http://cytoscape.org/), which is an open access software for visualizing biomolecular networks (18). In the network, ‘node’ represents a gene or protein, and ‘line’ represents an interaction between the two nodes. The degree of each node (number of interactions with other proteins) is equal to the number of nodes that interacted with this node.

 Analysis of hub genes in the PPI network

Hub genes refer to the relatively key genes in the network. Hub genes were identified using three centricity methods in the PPI network, including the degree centrality (19), betweenness centrality (20) and subgraph centrality methods (21). The scores obtained from the degree, betweenness and subgraph methods were calculated using the CytoNCA plug-in (version 2.1.6) (22) in Cytoscape. High scores for the degree, betweenness and subgraph methods indicated that the nodes were more significant in the network. Hierarchical clustering of hub genes with higher scores was performed using the pvclust R package (version 1.3–2) (23).

 Coexpression associations of hub genes with DEGs

The Pearsons correlation coefficient (PCC) method (24) was used to identify the coexpression associations of hub genes with other DEGs. Only coexpression associations with PCC values of >0.9 were selected for analysis. A PCC value of >0 indicated that the two genes were positively correlated, and a PCC value of <0 indicated that the two genes were negatively correlated.

 Results <sec> <title>Statistical analysis

Based on the cut-off criteria, a total of 841 DEGs (335 upregulated and 506 downregulated) and 50 DEGs (16 upregulated and 34 downregulated) were identified in the Col1a1-Krm2 and Lrp5−/− mice when compared with WT mice, respectively. When compared with WT mice, 12 of these genes were upregulated and 25 were downregulated in the Col1a1-Krm2 and Lrp5−/− mice (Fig. 1).

 DEG function

To further reveal gene function in the two groups, GO and KEGG pathway enrichment analyses were performed. In the Col1a1-Krm2 vs. WT group, the upregulated genes were primarily associated with hemopoiesis (GO: 0030097), hemopoietic or lymphoid organ (GO: 0048534) and immune system development (GO: 0002520), as well as pathways associated with primary immunodeficiency (mmu05340) and nitrogen metabolism (mmu00910) (Fig. 2A). The downregulated genes were significantly associated with cell adhesion (GO: 0007155) and regulation of the smoothened signaling pathway, as well as the hedgehog signaling pathway (mmu04340) and cell adhesion molecules (mmu04514) (Fig. 2A).

In the Lrp5−/− vs. WT group, the upregulated genes were implicated in muscle contraction (GO: 0006936) and muscle system process (GO: 0003012) (Fig. 2B). The downregulated genes were markedly associated with the regulation of transcription (GO: 0006355) and RNA metabolic processes (GO: 0051252) (Fig. 2B). No significant pathways were enriched by the upregulated genes.

 Analysis of PPI network

In order to determine interactions between DEGs, a PPI network was constructed. The network was composed of 551 nodes and 1,608 PPIs (Fig. 3). Based on the centricity methods, the top 40 nodes with the highest scores in the PPI network were selected as hub genes for further analysis, including 2–5-oligoadneylate synthase-like protein 2 (Oasl2), thrombospondin 2 (Thbs2), syndecan 2 (Sdc2), FK506 binding protein 10 (Fkbp10), interferon induced protein with tetratricopeptide repeats (Ifit) 1 and Ifit2 (Table I). Following the removal of duplicates in Table I, a total of 66 genes remained, which were clustered into two groups and used to distinguish the WT, Col1a1-Krm2 and Lrp5−/− samples in a heat map (Fig. 4).

The 66 hub genes were significantly associated to the five signaling pathways (Table II). Matrix metalloproteinase (Mmp) 2 and Mmp9 were associated with the leukocyte transendothelial migration pathway, whereas Thbs2 and Sdc2 were associated with the extracellular matrix (ECM)-receptor interaction pathway. The protein tyrosine phosphatase receptor type C and Sdc2 were implicated in the cell adhesion molecule pathway (Table II).

 Analysis of the coexpression network

In order to investigate the coexpression associations between the selected hub genes and additional DEGs, a coexpression network was constructed. A total of 21 hub genes were determined to coexpress with additional DEGs (Fig. 5). A set of hub genes were observed to coexpress with each other, including Thbs2, Sdc2 and Fkbp10, as well as Oasl2, Ifit1 and Ifit2 (Fig. 5).

 Discussion

In the present study, a set of 841 DEGs (335 upregulated and 506 downregulated) and 50 DEGs (16 upregulated and 34 downregulated) were identified in the Col1a1-Krm2 vs. WT and Lrp5−/− vs. WT groups, respectively. A number of DEGs demonstrated a high score in the PPI network, and were coexpressed in the coexpression network. These genes included Thbs2, Sdc2 and Fkbp10, as well as Oasl2, Ifit1 and Ifit2. Thbs2 and Sdc2 were associated with the ECM-receptor interaction pathway.

Thbs2 is a part of the thrombospondin family and mediates cell-to-cell and cell-to-matrix interactions (25). A previous review reported that disrupted Thbs2 expression increases cortical bone density, accelerates fracture healing, induces resistance to ovariectomy-induced bone loss and alters the pattern of load-induced bone formation (26). In Thbs2-null mice, marrow-derived osteoprogenitor cells are increased, and endosteal bone formation is promoted, indicating that Thbs2 modulates the proliferation of osteoprogenitor cells and bone remodeling (27,28). Sdc2 functions as an integral membrane protein and mediates cell-to-matrix interactions via its ECM protein receptor (29). Sdc2 is a crucial determinant of chemosensitivity in osteoblasts, and it stimulates the mitogenic activity of granulocyte-macrophage colony-stimulating factor (30). Fkbp10 is a part of the FKBP-type peptidyl-prolyl cis/trans isomerase family and interacts with collagens (31). A homozygous splicing mutation in Fkbp10 leads to osteogenesis imperfecta with a mineralization defect via a reduction in bone collagen content (32,33). There is no direct evidence to implicate Sdc2 and Fkbp10 in osteoporotic fracture healing, however, they are thought to coexpress with Thbs2. Therefore, Sdc2 and Fkbp10, as well as Thbs2 may serve key roles during the fracture healing process in osteoporosis, via their coexpression associations with each other.

In the present study, Oasl2, Ifit1 and Ifit2 demonstrated high scores in the PPI network and coexpressed with each other. Ifit1 and Ifit2 were interferon-induced proteins containing tetratricopeptide repeats (34). Ifit1 is known to be an important innate immune bottleneck (35). During the response of osteoblasts to immune cytokine interferon-β, the expression of Ifit1 is induced (36). Ifit2 and Oasl2 are involved in innate immunity (37,38). Only a limited number of studies have investigated the association between the Ifit1, Ifit2 and Oasl2 genes and fracture repair; however they present potential novel candidates for osteoporotic fracture repair therapies.

In the present study, the number of identified DEGs in the Col1a1-Krm2 vs. WT group was markedly higher than that observed in the Lrp5−/− vs. WT group, which was consistent with previous findings (9). According to the DEGs enrichment analysis, the DEGs in the Col1a1-Krm2 vs. WT group were primarily associated with immunity and cell adhesion. By contrast, the DEGs in the Lrp5−/− vs. WT group were significantly associated with muscle system processes (GO: 0003012) and the regulation of transcription (GO: 0006355). These results suggest that during the fracture repair process in osteoporosis, the DEGs induced by Krm2 overexpression or Lrp5 deficiency, and their functions, may be distinctly different.

Compared with the findings presented by Liedert et al (9), the present study identified the interactions and coexpression patterns among a set of genes, which was not determined previously. However, these predictions require validation in further studies. In a future study, the DEGs and their interactions will be determined in patients.

In conclusion, a series of DEGs, including Thbs2, Sdc2 and Fkbp10, as well as Oasl2, Ifit1 and Ifit2, demonstrated a significant role in the PPI network and were observed to form co-expression patterns. The results suggest that these genes may serve crucial roles during the fracture repair process in osteoporosis. Sdc2, Fkbp10, Oasl2, Ifit1 and Ifit2 were demonstrated to be novel genes associated with osteoporotic fracture healing.

 Abbreviations DEGs

differentially expressed genes

 PPIs

protein-protein interactions

 BMP-2

bone morphogenetic protein-2

 PTH

parathyroid hormone

 LRP5

lipoprotein receptor-related protein 5

 KREMEN2

kringle containing transmembrane protein 2

 DKK1

dickkopf homolog 1

 GEO

Gene Expression Omnibus database

 RMA

robust microarray analysis

 FDR

false discovery rate

 GO

Gene Ontology

 KEGG

Kyoto Encyclopedia of Genes and Genomes

 DAVID

Database for Annotation, Visualization and Integrated Discovery

 STRING

Search Tool for the Retrieval of Interacting Genes

 PCC

pearson correlation coefficient

 CAMs

cell adhesion molecules

 Thbs2

thrombospondin 2

 Sdc2

syndecan 2

 Fkbp10

FK506 Binding Protein 10

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Venn diagrams of differentially expressed genes between Col1a1-Krm2 vs. WT and Lrp5−/− vs. WT groups. (A) Venn diagram of upregulated genes. (B) Venn diagram of downregulated genes. Col1a1, type I collagen; Krm2, kringle containing transmembrane protein 2; Lrp5, low density lipoprotein receptor-related protein 5; Col1a1-Krm2, Col1a1-Krm2 transgenic mice; Lrp5−/−, mice deficient in Lrp5; WT, wild-type mice.

GO and KEGG pathway terms enriched by differentially expressed genes between (A) Col1a1-Krm2 transgenic mice and wild-type mice and (B) Lrp5−/− mice and wild-type mice. ‘Up’ represents upregulated genes and ‘Down’ represents downregulated genes. Red bars indicate the P-values, and blue bars indicate the gene count. GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; Col1a1, type I collagen; Krm2, kringle containing transmembrane protein 2; Lrp5, low density lipoprotein receptor-related protein 5; BP, biological process; CC, cellular component; MF, molecular function.

Protein-protein interaction network of differentially expressed genes as determined using the Search Tool for the Retrieval of Interacting Genes database (http://string-db.org/). Each node represents a protein, and each line represents the interaction between the two proteins.

Heat map of hub genes in the protein-protein interaction network in WT, Lrp5−/− and Col1a1-Krm2 mice. Each row represents a single gene and each column represents a sample. The gradual color alteration from orange to blue represents the process from upregulation to downregulation of genes. WT, wild-type; Col1a1, type I collagen; Krm2, kringle containing transmembrane protein 2; Lrp5, low density lipoprotein receptor-related protein 5.

Coexpression network of hub genes and additional differentially expressed genes. Red-colored nodes represent hub genes in the protein-protein interaction network, and blue-colored genes represent differentially expressed genes that are not hub genes. Each node represents a protein, and each line represents the coexpression association between the two genes. Solid lines indicate the coexpression (pearson correlation coefficient) >0.9 (positive correlation), and the dotted lines indicate the coexpression (pearson correlation coefficient) <0.9 (negative correlation).

Top 40 nodes with a high score in the protein-protein interaction network.

A, Subgraph
Node Score
Mmp2 132118.94
Oasl2 115994.88
Ifit1 95969.49
Trim30a 92368.98
Mmp9 89980.91
Usp18 87137.86
Ifit2 83711.57
Rsad2 78389.78
Parp9 65845.10
Psmb9 62329.73
Ifi44 61890.17
Ptprc 60581.35
Bst2 52788.61
Lgals3bp 52307.49
Parp12 50636.56
Col5a1 42741.73
Ifi204 32780.95
Thbs2 32724.00
Tlr3 32063.93
Lgals9 30456.22
Pcolce 27954.06
Mmp14 27788.79
Ifi203 27352.81
Pcna 26264.86
Nmi 25586.03
Frk 25365.84
Loxl1 24301.15
Lox 23572.33
Isg20 21952.91
Rhoc 20744.50
Mdk 20643.03
Fstl1 20386.14
Ddx41 19990.48
Sdc1 18627.42
Serpinh1 17851.99
Gpx8 17771.15
Rcn3 17400.74
Cd68 17032.79
Sdc2 14213.72
Fkbp10 14067.95
B, Degree
Mmp2 47.00
Ptprc 41.00
Mmp9 40.00
Oasl2 34.00
Pcna 31.00
Frk 31.00
Col5a1 27.00
Rhoc 27.00
Ddx41 25.00
Prkar2b 24.00
Spna1 23.00
Mapk13 23.00
Ifit1 22.00
Usp18 22.00
Psmb9 22.00
Gpx8 22.00
Acacb 22.00
Slc4a1 22.00
Trim30a 21.00
Pcolce 21.00
Mmp14 20.00
Obscn 20.00
Ifit2 19.00
Rsad2 19.00
Thbs2 19.00
Myh4 19.00
Alas2 19.00
Slc2a4 18.00
Actn3 18.00
Klf1 17.00
Parp9 16.00
Tlr3 16.00
Loxl1 16.00
Lox 16.00
Mdk 16.00
Fgfr2 16.00
Smc2 16.00
Ifi44 15.00
Cd68 15.00
Fkbp10 15.00
C, Betweenness
Ptprc 30306.94
Mmp2 30018.82
Pcna 26498.73
Oasl2 24770.62
Frk 20782.62
Rhoc 17367.36
Acacb 16584.40
Mmp9 16291.33
Spna1 13323.41
Mapk13 13268.12
Prkar2b 11615.84
Gpx8 11494.19
Slc2a4 11248.73
Ddx41 10606.64
Obscn 10462.83
Pfas 9557.37
Psmb9 9042.65
Col5a1 8806.07
Rps6ka1 8625.42
Pcolce 7343.19
Mmp14 6612.19
Actn3 6597.27
Dlg3 6507.87
Msn 6370.79
Myh4 6325.04
Alas2 6084.76
Fgfr2 5320.19
Glrx5 5243.41
Mdk 5180.35
Slc4a1 5082.71
Atp8a1 5063.36
Copb2 5019.16
Ppargc1a 4905.46
Cd68 4728.94
H2-Aa 4708.25
Cxcr4 4565.29
Ncf4 4559.99
Rps3 4274.92
Hk2 4171.70
Thbs2 3997.35

Pathways enriched by the hub genes in the protein-protein interaction network.

KEGG entry term: pathway P-value Gene count Genes
mmu04670: Leukocyte transendothelial migration   1.82×10−5 7 Mapk13, Cxcr4, Mmp9, Ncf4, Msn, Actn3, Mmp2
mmu04910: Insulin signaling pathway 0.00420 5 Prkar2B, Slc2A4, Hk2, Acacb, Ppargc1A
mmu04512: ECM-receptor interaction 0.00740 4 Sdc1, Thbs2, Col5A1, Sdc2
mmu04514: Cell adhesion molecules 0.03803 4 Ptprc, Sdc1, H2-Aa, Sdc2
mmu04920: Adipocytokine signaling pathway 0.04178 3 Slc2A4, Acacb, Ppargc1A

KEGG, Kyoto Encyclopedia of Genes and Genomes; ECM, extracellular matrix.