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International Journal of Clinical and Experimental Medicine logoLink to International Journal of Clinical and Experimental Medicine
. 2015 Jan 15;8(1):1027–1034.

Identification of B cells participated in the mechanism of postmenopausal women osteoporosis using microarray analysis

Bing Yan 1,*, Jie Li 1,*, Li Zhang 1
PMCID: PMC4358544  PMID: 25785089

Abstract

To further understand the molecular mechanism of lymphocytes B cells in postmenopausal women osteoporosis. Microarray data (GSE7429) were downloaded from Gene Expression Omnibus, in which B cells were separated from the whole blood of postmenopausal women, including 10 with high bone mineral density (BMD) and 10 with low BMD. Differentially expressed genes (DEGs) between high and low BMD women were identified by Student’s t-test, and P < 0.01 was used as the significant criterion. Functional enrichment analysis was performed for up- and down-regulated DEGs using KEGG, REACTOME, and Gene Ontology (GO) databases. Protein-protein interaction network (PPI) of up- and down-regulated DEGs was respectively constructed by Cytoscape software using the STRING data. Total of 169 up-regulated and 69 down-regulated DEGs were identified. Functional enrichment analysis indicated that the genes (ITPA, ATIC, UMPS, HPRT1, COX10 and COX15) might participate in metabolic pathways, MAP3K10 and MAP3K9 might participate in the activation of JNKK activity, COX10 and COX15 might involve in mitochondrial electron transport, and ATIC, UMPS and HPRT1 might involve in transferase activity. MAPK3, ITPA, ATIC, UMPS and HPRT1 with a higher degree in PPI network were identified. MAPK3, MAP3K10, MAP3K9, COX10, COX15, ATIC, UMPS and HPRT1 might participate in the pathogenesis of osteoporosis.

Keywords: Osteoporosis, function enrichment, pathway enrichment, PPI network

Introduction

Osteoporosis is a common disease for postmenopausal women and characterized by the reduction in the density of bone tissue, and subsequently the increase of bone fragility [1]. The old bone is replaced by new bone tissue through continuous remodeling dynamics, including bone resorption and bone formation [2]. The imbalance between bone resorption and formation may result in the decrease of bone density [3]. The activation of hematopoietic precursors to become osteoclast (OC) contributes to the bone remodeling in OC, osteoblast (OB) and osteocytes [4]. T cells, acted as a regulator of OC and OB formation, participate in the development of rheumatoid arthritis [5], bone metastasis [6] and osteoporosis [7]. T cells may contribute to osteoclastogenesis by the overexpression of receptor activator of nuclear factor-kappa B ligand (RANKL) and tumor necrosis factor-alpha (TNF-alpha) [8]. The expression of TNF increases the numbers of T cells, and induces the bone loss due to estrogen deficiency [9]. Furthermore, Onal et al. suggested that the expression of RANKL can be increased in T cells and B cells and may finally lead to the bone loss [10]. However, few studies showed that B cells participate in the development of osteoporosis. So it is an interesting study to identify the correlation of B cells and osteoporosis.

In 2008, Xiao et al. [11] analyzed the gene expression profile in B cells of postmenopausal osteoporosis patients and identified that down-regulation of estrogen receptor 1 (ESR1) and mitogen activated protein kinase 3 (MAPK3) in B cells regulates secretion of factors, resulting in increased osteoclastogenesis or decreased osteoblastogenesis. To obtain the more genes in B cells correlated with the osteoporosis, differentially expressed genes (DEGs) between high BMD (normal) and low BMD (osteoporosis) were screened by the cut-off point of P < 0.01, but not the Benjamini and Hochberg (BH) adjusted P ≤ 0.05 as the work of Xiao et al. [11]. Furthermore, we also explored the underlying function of DEGs by KEGG, REACTOME pathway and Gene Ontology (GO) enrichment analyses. Protein-protein interaction network (PPI) was also constructed to obtain the crucial genes that are involved in osteoporosis by regulating and influencing the other genes.

Methods

Microarray data

Microarray data (GSE7429) [11] were downloaded from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). A total of 20 samples were available. B cells were isolated from the whole blood of 20 unrelated postmenopausal women 54 to 60 years of age, including 10 with high BMD and 10 with low BMD. The microarray platform of GSE7429 was GPL96 [HG-U133A] Affymetrix Human Genome U133A Array.

Data preprocessing and identification of DEGs

The data were preprocessed by Affy package [12] in Bioconductor and Affymetrix annotation files from Brain Array Lab. The background correction, quartile data normalization and probe summarization were performed by the Robust Multiarray Average (RMA) algorithm [13] to obtain the gene expression matrix. DEGs were identified by Student’s t-test. P < 0.01 was used as the significant criterion.

Functional enrichment of DEGs

Pathway enrichment analysis in KEGG database based on conserved sub-pathways information [14]. However, REACTOME database based on open-source open-data resource of human pathways and reactions [15]. So we performed the pathway enrichment analysis for up- and down-regulated DEGs using the two databases with the threshold of P < 0.01. In addition, DEGs were also enriched in the biological process (BP), cellular component (CC) and molecular function (MF) categories of GO [16] database, and P < 0.01 was chosen as cut-off criteria.

Construction of PPI network

PPI network of up- and down-regulated DEGs was respectively constructed using the STRING (Search Tool for the Retrieval of Interacting Genes) data [17], and the confidence score > 0.4 was used as cut-off criterion. The PPI network was visualized by Cytoscape software [18].

Results

DEGs analysis

Total of 235 DEGs between the high BMD group and low BMD group were identified, of which 169 DEGs were up-regulated and 69 DEGs were down-regulated.

Pathway enrichment analysis

The two enriched KEGG pathway of up-regulated DEGs were prion diseases pathway (P = 0.006874007) and p53 signaling pathway (P = 0.006924881) (Table 1). MAPK3 involved in the prion diseases pathway. The six enriched KEGG pathway of down-regulated DEGs included metabolic pathways (P = 0.000226044), nucleotide excision repair (P = 0.001525797), drug metabolism-other enzymes (P = 0.002474417), Glycosylphosphatidylinositol (GPI)-anchor biosynthesis (P = 0.007508316), mRNA surveillance pathway (P = 0.009212606) and purine metabolism (P = 0.009837667) (Table 1). The genes (ITPA, ATIC, UMPS, HPRT1, COX10 and COX15) participated in metabolic pathways.

Table 1.

Enriched KEGG pathways for up- and down-regulated DEGs

DEGs KEGG pathway Gene counts P-value Gene
Up-regulated Prion diseases 3 0.006874007 C1QB, C7, MAPK3
p53 signaling pathway 4 0.006924881 CHEK1, PIDD, TNFRSF10B, TP53I3
Down-regulated Metabolic pathways 15 0.000226044 ATIC, CMAS, COX10, COX15, DPM1, GBE1, HPRT1, ITPA, ODC1, PIGC, PIGK, PIP5K1B, POLE3, SUCLG2, UMPS
Nucleotide excision repair 3 0.001525797 GTF2H1, POLE3, RFC4
Drug Metabolism-other enzymes 3 0.002474417 HPRT1, ITPA, UMPS
Glycosylphosphatidylinositol (GPI)-anchor biosynthesis 2 0.007508316 PIGC, PIGK
mRNA surveillance pathway 3 0.009212606 CPSF6, CSTF2T, PPP2CA
Purine metabolism 4 0.009837667 ATIC, HPRT1, ITPA, POLE3
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KEGG: Kyoto Encyclopedia of Genes and Genomes; DEGs: differentially expressed genes.

The two enriched REACTOME pathway of up-regulated DEGs were complement cascade (P = 0.00757477) and synthesis of PE (P = 0.00772870) (Table 2). The REACTOME pathway enrichment of down-regulated DEGs showed that the genes (ATIC, HPRT1, ITPA and UMPS) involved in metabolism of nucleotides (P = 0.00059424). The genes (COX10 and COX15) participated in the two pathways, including heme biosynthesis (P = 0.00124899) and metabolism of porphyrins (P = 0.00602279) (Table 2).

Table 2.

Enriched REACTOME pathways for up- and down-regulated DEGs

DEGs REACTOME pathway Gene counts P-value Adjust p-value Gene
Up-regulated Complement cascade 3 0.00757477 1 C1QB, C7, GZMM
Synthesis of PE 2 0.00772870 1 CHKA, PISD
Down-regulated Post-translational modification 3 0.00025911 0.39591249 DPM1, PIGC, PIGK
Metabolism of nucleotides 4 0.00058424 0.89271608 ATIC, HPRT1, ITPA, UMPS
Heme biosynthesis 2 0.00124899 1 COX10, COX15
Metabolism of porphyrins 2 0.00602279 1 COX10, COX15
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DEGs: differentially expressed genes.

GO enrichment analysis

GO enrichment analysis of up-regulated DEGs showed that 44, 6 and 32 terms were respectively enriched in BP, CC and MF category, and the top five terms in BP, CC and MF category were listed (Table 3). The top three enriched GO terms in BP category of up-regulated DEGs were the activation of JNKK activity (P = 0.000916844), humoral immune response (P = 0.001738278) and response to stilbenoid (P = 0.001900954) (Table 3). MAP3K10 and MAP3K9 involved in activation of JNKK activity. The top three enriched GO terms in MF category were alcohol dehydrogenase (NADP+) activity (P = 0.000150784), JUN kinase kinase kinase activity (P = 0.000243013) and aldo-keto reductase (NADP) activity (P = 0.000537427) (Table 3). MAP3K10 and MAP3K9 involved in JUN kinase kinase kinase activity.

Table 3.

Top five GO terms were respectively enriched in BP, CC and MF category for up-regulated DEGs

GO ID Cate gory Term Gene counts P-value Genes
GO: 0007256 BP activation of JNKK activity 2 0.000916844 MAP3K10, MAP3K9
GO: 0006959 BP humoral immune response 6 0.001738278 C1QB, C7, FOXJ1, IFNG, TREM2, ZP3
GO: 0035634 BP response to stilbenoid 2 0.001900954 FGL1, SLC22A7
GO: 0048609 BP multicellular organismal reproductive process 15 0.002142066 DAZ1, DAZ2, DAZ3, DAZ4, EIF2B4
GO: 0046683 BP response to organophosphorus 5 0.002641967 AKR1C1, ALDH3A1, AQP8, DMTN, VGF
GO: 0034703 CC cation channel complex 6 0.001553268 CNGB1, KCNA6, KCNG1, KCNJ5, SCN4A, SCNN1B
GO: 0005887 CC integral to plasma membrane 21 0.003272558 AGTR2, ABCC1, AQP8, ART3, C7, CNGB1
GO: 0044459 CC plasma membrane part 29 0.003560869 MAPK3, ABCC1, AGTR2, AQP8, ART3
GO: 0031226 CC intrinsic to plasma membrane 21 0.005042495 AGTR2, ABCC1, AQP8, ART3, C7
GO: 0005796 CC Golgi lumen 4 0.006999886 MUC3A, MUC3B, MUC5AC, WNT7A
GO: 0008106 MF alcohol dehydrogenase (NADP +) activity 3 0.000150784 AKR1C1, ALDH3A1, DHRS3
GO: 0004706 MF JUN kinase kinase kinase activity 2 0.000243013 MAP3K10, MAP3K9
GO: 0004033 MF aldo-keto reductase (NADP) activity 3 0.000537427 AKR1C1, ALDH3A1, DHRS3
GO: 0022838 MF substrate-specific channel activity 11 0.000700403 AQP8, CNGB1, FXYD7, KCNA6, KCNG1
GO: 0005261 MF cation channel activity 9 0.000949791 CNGB1, KCNA6, KCNG1, KCNJ5, KCNK7
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GO: Gene Ontology; BP: biological process; CC: cellular component; MF: molecular function; DEGs: differentially expressed genes.

GO enrichment analysis of down-regulated DEGs showed that 67, 8 and 14 terms were respectively enriched in BP, CC and MF category, and the top five terms in BP, CC and MF category were listed (Table 4). The top three enriched GO terms in BP category showed that the genes (COX10 and COX15) involved in the three GO terms, such as mitochondrial electron transport (P = 1.66E-05), heme a biosynthetic process (P = 1.66E-05) and heme a metabolic process (P = 1.66E-05) (Table 4). The enriched GO terms in MP category was transferase activity (P = 0.000211892) involving in ATIC, HPRT1 and UMPS, cytochrome-c oxidase activity (P = 0.004120099) including in COX10 and COX15, and heme-copper terminal oxidase activity (P = 0.004120099) involving in COX10 and COX15 (Table 4).

Table 4.

Top five GO terms were respectively enriched in BP, CC and MF category for down-regulated DEGs

GO ID Cate gory Term Gene counts P-value Genes
GO: 0006123 BP mitochondrial electron transport, cytochrome c to oxygen 2 1.66E-05 COX10, COX15
GO: 0006784 BP heme a biosynthetic process 2 1.66E-05 COX10, COX15
GO: 0046160 BP heme a metabolic process 2 1.66E-05 COX10, COX15
GO: 0006501 BP C-terminal protein lipidation 3 0.000142394 DPM1, PIGC, PIGK
GO: 0018410 BP C-terminal protein amino acid modification 3 0.000247707 DPM1, PIGC, PIGK
GO: 0005622 CC intracellular 57 0.000964678 ATIC, CPSF6, HPRT1, ITPA, POLE3, RFC4, UMPS, ADORA2A, ASH2L, ATP5S
GO: 0044464 CC cell part 62 0.001191597 ATIC, CPSF6, HPRT1, ITPA, POLE3, RFC4, UMPS, ADORA2A, ASH2L, CCNE1
GO: 0005623 CC cell 62 0.001196158 ATIC, CPSF6, HPRT1, ITPA, POLE3, RFC4, UMPS, ADORA2A, ASH2L, CCNE1
GO: 0005671 CC Ada2/Gcn5/Ada3 transcription activator complex 2 0.001560069 POLE3, MBIP
GO: 0044424 CC intracellular part 56 0.001932115 ATIC, CPSF6, HPRT1, ITPA, POLE3, RFC4, UMPS, ADORA2A, ASH2L, CD1A
GO: 0016740 MF transferase activity 17 0.000211892 ATIC, HPRT1, POLE3, UMPS, ASH2L, CCNE1, CMAS, COX10, DPM1, EIF2B3
GO: 0001664 MF G-protein coupled receptor binding 5 0.000925208 ADORA2A, CREB3, NPFF, ROR2, YARS
GO: 0016757 MF transferase activity, transferring glycosyl groups 5 0.003521176 UMPS, DPM1, GBE1, HPRT1, PIGC
GO: 0004129 MF cytochrome-c oxidase activity 2 0.004120099 COX10, COX15
GO: 0015002 MF heme-copper terminal oxidase activity 2 0.004120099 COX10, COX15
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GO: Gene Ontology; BP: biological process; CC: cellular component; MF: molecular function; DEGs: differentially expressed genes.

PPI network analysis

The genes/proteins with the degree in PPI network of up-regulated DEGs were MAPK3 (degree = 3) and AGTR2 (degree = 3) (Figure 1). The genes/proteins with the degree in PPI network of down-regulated DEGs were ITPA (degree = 4), RFC4 (degree = 3), ATIC (degree = 3), UMPS (degree = 2) and HPRT1 (degree = 2) (Figure 2).

Figure 1.

Figure 1

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Protein-protein interaction network of up-regulated differentially expressed genes. The nodes represented up-regulated differentially expressed genes.

Figure 2.

Figure 2

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Protein-protein interaction network of down-regulated differentially expressed genes. The nodes represented down-regulated differentially expressed genes.

Discussion

In this study, 235 DEGs between the high BMD group and low BMD group were identified, including 169 up-regulated DEGs and 69 down-regulated DEGs. Functional enrichment analysis showed that MAPK3 involved in the prion diseases pathway, MAP3K10 and MAP3K9 involved in the activation of JNKK activity, COX15 and COX10 involved in mitochondrial electron transport and heme a biosynthetic process, and ATIC, UMPS and HPRT1 involved in transferase activity.

MAPK3 belongs to the Mitogen-Activated Protein Kinase (MAPK) family, which also known as the extracellular signal-regulated kinase (ERK) [19]. According to the work of Park et al., hydrogen peroxide (H2O2)-induced cell apoptosis of osteoblasts may be mediated by the ERK signaling pathway [20]. Osteocyte apoptosis could be inhibited by estrogens via the activation of the ERKs signaling pathway [21]. ERK activity played an important role in the serum-induced osteoblast proliferation, and the up-regulated expression of MKP-1, acted as dual-specificity MAPK phosphatases, could be induced by the glucocorticoid which decreased the numbers of osteoblasts [22]. MAPK3 may participate in the etiology of osteoporosis via the ERK/MAPK signaling pathway [11]. Although our findings were consistent with the previous results that MAPK3 involved in the development of osteoporosis, MAPK3 participated in the prion diseases in this study. Prion diseases and Alzheimer disease (AD) share similar pathogenic mechanisms, including generation of oxidative stress molecules and complement activation [23]. Reactive oxygen species (ROS) involve in the pathogenesis of osteoarthritis which are induced by pro-inflammatory cytokines, such as interleukin-1 (IL-1) and tumour necrosis factor alpha (TNFalpha) [24,25]. The results implied that MAPK3 also involved in the development of osteoporosis via the prion diseases pathway, which was a new identified pathway in this study.

MAP3K10 (Mitogen-Activated Protein Kinase Kinase Kinase 10), MAP3K9 (Mitogen-Activated Protein Kinase Kinase Kinase 9) and MAP3K11 (Mitogen-Activated Protein Kinase Kinase Kinase 11) activate the JNK signaling cascade [26]. In addition, miR-155, targeting MAP3K10 (Mitogen-Activated Protein Kinase Kinase Kinase 10) [27,28], involves in the regulation of MAPK pathway, which included extracellular signal-regulated kinases (ERKs) pathway, c-Jun N-terminal kinase (JNK) pathway, p38 MAPK pathway and ERK5 pathway [29]. miR-155 also regulates the release of IL-6 and TNF-α [30]. The production of cytokines, including IL-1β, IL-6 and TNF-α, are higher in osteoporotic postmenopausal women than in healthy women [31]. Based on these results, we could speculate that MAPK3, MAP3K10 and MAP3K9 participated in the etiology of osteoporosis through the MAPK pathway.

According to the above reports, ROS involves in the development of osteoporosis. The impairment of mitochondrial electron transport chain causes the increase of intracellular ROS, and finally results in the diseases related to mitochondria [32]. Guo et al. report that mitochondrial DNA participated in the pathogenesis of osteoporosis, age-related diseases [33]. According to the work of Petruzzella et al., COX15 and COX10 involved in the formation of mitochondrial respiratory chain [34]. COX15, along with SCO1, involve in COX deficiency, and the expression of COX15 increases heme A products and activates the COX [35]. SCO1 and COX10 are involved in the Cytochrome c oxidase (COX) assembly, and COX defects are determined in mitochondrial disorders [36]. Our results, which COX15 and COX10 involved in mitochondrial electron transport and heme a biosynthetic process, were consistent with the previous reports. So we could speculate that COX15 and COX10 involved in the pathology of osteoarthritis via mitochondrial respiratory chain.

In this study, the genes (ATIC, UMPS and HPRT1) involved in transferase activity. Gamma-glutamyl transferase contributes to the generation of free radical species, and oxidative stress has harmful effects on bone metabolism [37]. Isomura et al. also report that oxidative stress participated in the pathogenesis of osteoporosis by analysis of the correlation of oxidative stress and bone metabolism using bone metabolic markers and cytokines, including serum osteopontin and TGF-β1 [38]. The distinct metabolic changes, including lipid, energy and amino acid metabolism changed, in osteoporotic ovariectomized rats have been determined, and altered metabolites participate in the oxidative defense system [39]. Based on these findings, we could speculate that ATIC, UMPS and HPRT1 might regulate the bone metabolism to involve in the development of osteoporosis via the process of the transferase activity.

Conclusion

In this study, 235 DEGs between the high and low BMD group were identified, of which 169 DEGs were up-regulated and 69 DEGs were down-regulated. MAPK3 and the genes (MAP3K10 and MAP3K9) might involve in the pathogenesis of osteoporosis via the prion diseases pathway (or the MAPK signaling pathway) and the MAPK signaling pathway, respectively. COX10 and COX15 might participate in the pathogenesis of osteoporosis via the mitochondrial electron transport chain. ATIC, UMPS and HPRT1 might involve in the development of osteoporosis via the process of the transferase activity. The results implied that B cells may be participated in the mechanism of postmenopausal women osteoarthritis. However, the results need to be further confirmed by experiments.

Disclosure of conflict of interest

None.

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