A bootstrap-based regression method for comprehensive discovery of differential expressions: an application to the osteoporosis study

Yan Lu1,2, Yao-Zhong Liu3, Peng–Yuan Liu2, Volodymyr Dvornyk4, and Hong–Wen Deng1,3

1. College of Life Sciences and Bioengineering, Beijing Jiaotong University, Beijing 100044, P. R.

China

2. Department of Physiology and the Cancer Center, Medical College of Wisconsin, Milwaukee, WI

53226, USA

3. Department of Biostatistics and Bioinformatics & Center for Bioinformatics and Genomics, Tulane

University School of Public Health, New Orleans, LA 70112, USA

4. School of Biological Sciences, University of Hong Kong, Pokfulam Rd., Pokfulam, Hong Kong, P.R.

China

Running title: Bootstrap-based regression method for microarray analysis

Key words: microarray, bootstrap, regression, osteoporosis

Corresponding author:

Hong–Wen Deng, Ph. D.

Department of Biostatistics and Bioinformatics & Center for Bioinformatics and Genomics,

Tulane University School of Public Health, 1440 Canal Street, Suite 2001, New Orleans, LA

70112

Tel: 504-988-1310 Email: [email protected]

1

Abstract A common purpose of microarray experiments is to study the variation in across the categories of an experimental factor such as tissue types and drug treatments. However, it is not uncommon that the studied experimental factor is a quantitative variable rather than categorical variable. Loss of information would occur by comparing gene-expression levels between groups that are factitiously defined according to the quantitative threshold values of an experimental factor. Additionally, lack of control for some sensitive clinical factors may bring serious false positive or negative findings.

In the present study, we described a bootstrap-based regression method for analyzing gene expression data from the non-categorical microarray experiments. To illustrate the utility of this method, we applied it to our recent gene-expression study of circulating monocytes in subjects with a wide range of variations in bone mineral density (BMD). This method allows a comprehensive discovery of gene expressions associated with osteoporosis-related traits while controlling other common confounding factors such as height, weight and age. Several identified in our study are involved in osteoblast and osteoclast functions and bone remodeling and/or menopause-associated estrogen-dependent pathways, which provide important clues to understand the etiology of osteoporosis.

Availability: SAS code is available from the authors upon request.

2

Introduction

DNA microarrays are a powerful tool to provide a comprehensive picture of cell function as they can assay expression of tens of thousands of genes simultaneously. A typical microarray experiment may involve a comparison between disease and normal tissues, or a comparison between a strain grown under an experimental treatment and the same strain under a control condition. Various fold-change algorithms or t statistics were used for statistical analyses of this kind of two-sample experiment designs (1, 2). More complex experimental designs, in contrast, may comprise more than two samples as characterized by their genotypes, environments or developmental stages (3-5). A common purpose of these microarray experiments is to study the variation in gene expression across the categories of an experimental factor such as the above mentioned tissue types and drug treatments. However, an experimental factor is often a quantitative variable rather than a categorical variable. Loss of information would occur by comparing gene-expression levels between groups that are factitiously defined according to quantitative threshold values of an experimental factor. Additionally, lack of control for some sensitive clinical factors such as height, weight and age may yield serious false positive or negative findings.

In the present study, we described a bootstrap-based regression method for analyzing DNA expression data from the non-categorical microarray experiments. Bootstrap is an interesting method to select covariables in multivariable models. It allows increasing internal validity of models (6) and has been used widely in biology (7, 8). To illustrate the utility of this method, we applied it to our recent gene-expression study of circulating monocytes in subjects with a wide range of variations in bone mineral density (BMD).

BMD, a quantitative clinical , is a major risk factor for osteoporosis in the elderly especially in the postmenopausal women (9). BMD has a large genetic determinant while is significantly affected by height, weight, age, and life-style factors such as smoking, exercise and alcohol consumption (10-14). Menopause is a major physiological event associated with accelerated bone loss in females (15). Using the proposed

3 method, we attempted to identify differentially expressed genes associated with BMD variation and menopausal events while controlling other confounding factors such as height, weight and age.

Methods

Subjects and measurement

The study subjects came from an expanding database being created for genetic studies of osteoporosis, which are underway in the Osteoporosis Research Center of Creighton University. Since our major goal is to find genes related to osteoporosis, the exclusion criteria to exclude diseases or medications known to affect bone metabolism were used. Given that monocytes were used in this study, the additional exclusion criteria were also implemented to exclude those diseases/conditions, which may lead to gene expression changes of blood monocytes. All the exclusion criteria were detailed earlier by Liu et al. (16).

We analyzed gene expressions of blood monocytes using Affymetrix HG-U133A GeneChip®, containing probes for 14,500 genes. The raw fluorescence intensity data within CEL files were processed with Robust Multichip Average (RMA) algorithm (17), as implemented with R packages from Bioconductor

(www.bioconductor.org). Blood monocytes were obtained from 19 otherwise healthy women, each woman had total hip BMD, lumbar spine BMD (L1-L4), weight and height measurements, and completed a questionnaire with age, menopause status and years since menopause for postmenopausal women. Hip and spine BMD were measured with Hologic 4500 dual energy X ray absorptiometry (DXA) scanners (Hologic

Corporation, Waltham, Massachusetts, USA). The machine was calibrated daily. The coefficient of variation

(CV) values of the DXA measurements for BMD is 0.9%. Weight was measured using a calibrated balance beam scale; height was measured using a calibrated stadiometer. Table 1 presents the basic characteristics of the study subjects.

Statistical methods

4

Let yn be the expression level for gene n and x be a vector of experimental factors of interest (such as

BMD, menopause status and years since menopause) and potential clinical covariates (such as height, weight and age). Then, the model for a multiple linear regression takes the form,

yn    x   where  is regression intercept,  are regression coefficients and  is residual. Note that the years since menopause for premenopausal women were set to zero. The stepwise method was used for choosing independent variables in this multiple regression model. The selection p value for both inclusion and exclusion of a variable in the regression model was set as 0.05. In our preliminary data, there were 6363 genes for each of which at least one independent variable was selected in the model. To obtain a robust list of important genes in relation to the independent variables, 1000 bootstrap resampling was used to estimate bootstrap frequencies of each variable selected in the regression model for each gene. We observed that the random sampling errors are very low when using 1000 bootstrap samples. If a variable has strong bootstrap support (frequency greater than 80%), this variable is regarded to be related with the tested gene. Although

80% bootstrap frequency was an empirical threshold, we believe this threshold is very stringent for choosing

BMD-related genes. All statistical analyses were implemented in the SAS 8.0e (SAS Institute, Cary, N.C.,

USA).

Results and Discussion

Using the standard of bootstrap frequency greater than 80%, we found 75 hip BMD-related genes,

173 spine BMD-related genes, 221 menopause-related genes, and 139 years since menopause-related genes

(Supplemental Table 1).

Among BMD-related genes, 37.4% of them were associated with cellular metabolisms based on the inference of (GO). Particularly, BMPR1A, TNFRSF10C, TNF, FIP2, TGFBR1, CCL11,

PTHB1, CXCL3 and MTHFR are potentially involved in osteoblast and osteoclast functions and bone 5 remodeling according to the currently available literatures. BMPR1A is bone morphogenetic protein receptor

(BMP), type 1A. A recent mouse mutation study demonstrated essential and age-dependent roles for BMP signaling mediated by BMPR1A in osteoblasts for bone remodeling (18). BMPR1A is located on the human 10q22.3. Klein et al. reported two mouse QTLs for whole body BMD in the genomic regions homologous to human 10q21-24 and 10q23-26 (19, 20). TNFRSF10C, TNF and FIP2 are members of tumor necrosis factor (TNF) superfamily. TNF is a proinflammatory cytokine that promotes osteoclastic bone resorption and inhibits osteoblast differentiation (21-23). CCL11 is another proinflammatory cytokine important to osteoarthritis. The CCL11 expresses eotaxin-1 induced by treatment with interleukin-1β and

TNF- and plays an important role in cartilage degradation in osteoarthritis. TGFBR1 is a transforming growth factor (TGF) beta receptor whose expression is important in mediating 1, 25(OH) 2D3-associated changes in the growth rate of osteoblasts (24). PTHB1 (parathyroid hormone-responsive B1) is downregulated by parathyroid hormone in osteoblastic cells, and therefore, is thought to be involved in parathyroid hormone action in bones. CXCL3 is a chemokine (C-X-C motif) ligand. CXCL3 is potentially involved in chemokine activity based on the NCBI GO inference, which is known to participate in bone monocyte recruitment (25, 26). MTHFR (5,10-methylenetetrahydrofolate reductase) can affect the methylation of homocysteine to methionine and high serum homocysteine concentrations have adverse effects on bone (27, 28). A polymorphism of the MTHFR gene, C677T, which causes an alanine to valine substitution and gives rise to a thermolabile variant of the MTHFR protein with reduced activity (29), was associated with elevated levels of circulating homocysteine (30) and lumbar spine BMD (31). During the past two years, a number of association studies appeared with regard to its relevance to osteoporosis (32-35). It is worth noting that several osteoblast-related genes such as BMPR1A and PTHB1 were found in circulating monocytes which are early precursors of osteoclasts. Bone loss in osteoporosis is due to the persistent excess of bone resorption over bone formation. Osteoblasts play a central role in bone formation by synthesizing multiple bone matrix , while they also regulate osteoclast maturation by soluble factors and cognate interactions, resulting in bone resorption. For example, while FGF-2 induces RANKL expression by

6 osteoblasts, it also inhibits osteoclast differentiation directly by interfering with the action of M-CSF (36).

Thus, osteoclast differentiation, formation, and, to a lesser degree, activation depend upon the proximity and products of the osteoblast. Therefore, it is not unexpected to identify osteoblast-related genes differentially expressed in monocytes in the present study. These genes may regulate the balance between osteoblast and osteoclast activity that is important for bone remodeling and health.

Among genes that are related to menopausal status or years since menopause, several are upstream and downstream targets of estrogen receptors, including MAPK1, U29725, laminin 2, laminin β1, PLAU,

PRKD3, ALPP, ADAM2, ADAM21, ADAM22, ADAMTS2, ADAMTS6, TNFSF13, TNFRSF13B, TNFRSF25,

TRADD, TNFAIP6 and IL22R. The expression changes of these genes may be due to the fact that ovaries reduce and stop producing estrogen in postmenopausal women. In our data, the status of menopause had effects on MAPK1 gene expression (bootstrap frequency=83.5%) and years since menopause affected

U29725 (MAPK7) gene expression (bootstrap frequency=93.5%). Estrogen can activate the MAPK family member extracellular regulated kinase-1 (ERK-1). Increases in ERK activation coincided with increased ER-

 phosphorylation. Reduced availability of this pathway when estrogen levels are reduced could explain diminished effectiveness of mechanically related control of bone architecture after the menopause (37). Two laminin genes, laminin 2 and laminin β1, were detected to be associated with menopausal status and years since menopause, which were potentially involved in maintenance of estrogen receptor alpha expression (38).

PLAU (urokinase-type plasminogen activator) was shown to be downregulated by ovariectomy but restored with estrogen during fracture healing (39). PRKD3 is a member of protein kinase C (PKC) family. Recent data revealed a direct PKC-c-Src-ER interaction, which may be crucial in the modulation of estrogen responsiveness and the differentiation process in osteoblasts (40). ALPP (alkaline phosphatase) is a bone formation marker. Bone alkaline phosphatase was significantly increased in postmenopausal women (41).

Interestingly, we found three genes (ADAM2, ADAM21 and ADAM22) that encode a member of the ADAM

(a disintegrin and metalloprotease domain) family and two genes (ADAMTS2 and ADAMTS6) that encode a 7 member of the ADAM with thrombospondin motifs (ADAMTS) protein family. Both ADAM and ADAMTS gene families have been shown to play a role in bone osteoblast function in several recent studies (42-45). It has been suggested that one of the mechanisms by which estrogen protects against postmenopausal osteoporosis is by modulating the production of cytokines, such as tumor necrosis factors (TNF), interleukin-

1 (IL-1) and interleukin-6 (IL-6), in the bone microenvironment (46). In our data, five TNF superfamily genes (TNFSF13, TNFRSF13B, TNFRSF25, TRADD and TNFAIP6) and one interleukin cytokine (IL22R) were associated with menopausal female events.

Figure 1 shows hierarchical clustering for 19 samples using differential expression associated with

BMD variation, menopausal status, and years since menopause. According to the results of the cluster analysis, women with hip BMD value greater than 0.9 were classified into one group and those smaller than

0.9 were classified into another group. Pre- and postmenopausal women also had quite distinct patterns of gene expression and therefore were clearly clustered into two well-defined groups. Based on the expression data associated with years since menopause, those women who have not experienced or recently experienced menopause were classified into one group, while those women who have experienced menopause for a long time (at least more than 4 years) were classified into another group.

Conclusions

In summary, we described a bootstrap-based regression method for handling expression data from the non-categorical microarray experiments. This method allows a comprehensive discovery of gene expressions associated with osteoporosis-related traits while controlling other common confounding factors such as height, weight and age. We prioritized a small list of candidate genes for future confirmation studies in terms of their functional relevance to osteoblast and osteoclast functions and bone remodeling and/or menopause- associated estrogen-dependent pathways. These gene expression data provide important clues to understand the etiology of osteoporosis.

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Acknowledgments

Investigators of this work were partially supported by grants from NIH, State of Nebraska and CNSF.

The study was also benefited by grants from Huo Ying Dong Education Foundation, the Ministry of

Education of China, and Xi’an Jiaotong University.

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Figure legend

Figure 1 Hierarchical clustering for 19 samples using differential expression associated with (A) BMD variation, (B) menopausal status, and (C) years since menopause. Red indicates upregulated genes, and green represents down-regulated genes. At the top of panel A, the numbers represent hip BMD value of each woman. At the top of panel B, the letter “pre” represents premenopausal women and “post” represents postmenopausal women. At the top of panel C, the numbers represents years since menopause for each woman. Note that the years since menopause for premenopausal women were set to zero.

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Table 1 Basic characteristics of the study subjects

Trait Mean ± SD Range 2 Hip BMD (g/cm ) 0.932 ± 0.169 0.721-1.339 2 Spine BMD (g/cm ) 1.023 ± 0.234 0.748-1.428 Age (years) 51 ± 2.54 47-55 Height (cm) 167.0 ± 7.5 154.8-177.4 Weight (kg) 77.0 ± 21.7 48.7-126.1 Years since menopause (years) 6.4 ± 4.7 1-15

16

0 0 0 0 0 1 2 0 3 0 0 0 3 11 11 15 8 6 4

MAN1B1 LONP PRKCZ PSCD1 SEC23B --- SMARCA4 DOCK6 MCM2 KIAA1026 ANK2 MAPK7 HNRPD PHF15 PGPL GPR57 PRDM10 SULT2A1 FLJ12788 CRYL1 LOC160313 MAP2K7 IGKC DERP6 --- AKAP6 HP XPO6 PGRMC2 KCNV1 UBE2L3 FLJ21940 FLJ20489 PACS1 PRKCN BUB1 TIMM50 KCNJ4 TLL2 TRADD KPNB1 TNFRSF25 ZNF44 THAP4 IGHM MGC4809 IGHG1 IGLC1 TOSO IGHG1 ST7L STAR BHLHB3 DXS1283E SMAD6 RPL3L MUC5B C22orf19 SPDEF CHD3 MASP1 OSMR EAP30 SOX13 CAPN9 H2AFY ADAM21 ALDH6A1 TDGF1 TWIST1 SIRT5 CA6 G22P1 CTH ALPP DKFZp547G183 TDRD4 NOVA1 --- EIF4G2 --- AP4S1 FLJ11301 TED FLJ20477 FLJ10547 MBL2 AMPD3 XPO1 --- CLDN1 MTX1 --- GRIK5 --- HNRPM TNFAIP6 --- MASP1 GPR157 CADPS2 FARSLA RAP2B AQP6 APG12L TIMM17A GAGE5 LRRN5 KIAA0669 HBXAP SYNJ2 STAP2 HOMER2 GJB3 --- DTNA KCNJ8 EVC C11orf9 KIAA0643 TOM1L1 PLAU FZD6 LOC377064 RGS13 SEPP1 SEMA3D RNF14 FLJ23861 WARS2 SLC28A3 LAMB1 SLC14A2 PPL STIP1 DPP4 CES1 DIRAS2 KIAA0117 pre_3 pre_4 pre_5 pre_2 pre_1 pre_7 pre_6 pre_8 pre_9 post_3 post_4 post_2 post_5 post_1 post_6 post_8 post_7 post_9 post_10

IL22RA1 PLAC8 PPIA ADAM2 PMS2L2 MTAP GCG UGCG C4orf10 SSX1 CDIPT POLA2 CSNK2A1 CORO2A SLC7A5 ITSN1 LOC339229 C10orf7 FLJ21511 BRAP SBNO1 HBG1 FLJ22659 DAPK2 C9orf36 PTPRB PPAP2B ST7L BIRC1 MAGEE1 FCGR1A FCGR1A TTC3 LAMA2 --- ADRA1B NID2 SSRP1 SLC8A1 PRO2964 ACTN2 UPK3B ADAM22 FLJ12151 C1GALT2 NCR3 MRPS18B SNX1 METAP2 DDR1 DEFB4 NR3C2 HESX1 PHKB KIAA1069 UBB CACNA1G AIF1 ASCL2 MKRN4 LRRFIP1 NINJ2 RNASET2 EIF2S1 KIAA1361 ZNF16 THADA PGF POLR1B NDUFA9 KIAA0773 TCL6 LGALS13 MDH1 ALS2CR3 TM4SF13 IRF8 NQO2 FNDC3 GPC6 BCAS2 UBE2I MYO6 FLJ21369 BHLHB3 ARHGAP6 BBP C14orf32 MAPK1 LOC138046 FLJ23091 NF2 DLG7 PTPN11 TULP1 U2AF2 C21orf108 FSCN1 SRISNF2L ACADSB FLJ20509 PPP1R3D TRO DKFZp547I014 SLC27A6 RBM5 UPF3A PAICS MPPE1 CD44 CLDN15 HNRPDL PTPRR NF2 CBFA2T2 KIAA0514 TEB4 MTMR4 DCLRE1B PLK4 --- TNFRSF13B SCA7 TPM1 CORO1B RHOBTB3 POLRMT ZNF623 CRHR1 --- SPP2 KIAA0953 IF PSEN2 FGB PEX3 WDR8 PRDM13 NDRG2 MCL1 NEDD5 GTF2I MAST2 WDR1 SPN BCL2L1 BCL2L1 C7orf25 ABO ATRN DO KIAA0626 ADAMTS2 CGI-79 GFI1B GSN C9orf82 ALOX15B PERP HPGD --- FLJ12735 MAN1C1 SLC2A10 UTY --- RAB6C RPIP8 PHF7 ADAMTS6 C9orf77 RBP4 PSG3 FLJ22843 SGCD LYPLA3 SLC43A3 PASK SERPINB10 FLJ10902 CLC OR3A2 UBE2H OK/SW-cl.56 PTD008 --- EN2 DDX28 CG018 SLC2A1 FLJ20257 NICE-4 C6orf35 FLJ20898 TNFSF13 NFX1 ASTN2 SSTR5 G3BP --- CHN2 ELF2 FBLN2 FMO3 LAD1 COL4A4 LYZ SBNO1 HSPA2 KIAA0509 MGC13098 1.05 1.339 1.127 1.075 1.078 1.02 1.038 0.925 1.052 0.991 0.794 0.751 0.721 0.811 0.793 0.758 0.831 0.776 0.781

MYO1B SNRPA1 ZHX2 MERTK ZNF204 RFC1 TRIB2 NRP2 LY6G6E CCNT2 WDR4 RAB2 OPTN UBPH TPR FLJ10808 ZNF473 PFN2 RYK GFER SAP30 MS4A2 --- SAMSN1 BMPR1A EDNRB CAV2 SCIN FLJ30656 ZNF237 --- MRAS NET1 SLC27A2 RAB32 AR OSBPL3 U2AF2 C18orf10 TMLHE CDC25C SNIP1 TGFBR1 RGS16 LOC81558 RDX JAK2 PCTK2 EIF2AK3 SMC4L1 TRA1 PSMC2 SEC23B PPP1CC TRIM8 RBM5 NUP107 MEG3 RNASE3L NR3C1 BUB3 FLJ11011 RIMS3 PDE4B GATM EFNA1 KNS2 RBL1 HERC2 DDX27 ATRX HNRNPG-T DC50 CXCL3 ITPR1 LGALS8 RASSF3 FLJ10652 NCK1 BCLAF1 ALDH1A1 ABI1 MARCO FLJ21908 HNRPU ENDOG --- ABHD5 ALS2CR3 LMAN2L SLC11A2 NCBP1 ABCA1 EIF4A2 hnRNPA3 SIP CCT2 B1 HNRPA3 HNRPA3 KIAA0436 TOMM20 GRSF1 MRPS28 CCT7 ELOVL5 PAI-RBP1 MO25 FBXO11 FUSIP1 MGC21416 HMGN3 NR3C1 SMARCA4 ICSBP1 PPP2R5E PC4 ATP5F1 DDX39 TRAPPC3 H41 DLD HNRPA1 EIF2B1 C15orf15 RUNX1 NOTCH2 SMC4L1 SENP6 ZNF451 HHLA1 TNF NRP2 CASP4 SF3B2 MDC1 CCL11 TNFRSF10C TMEM24 RORC FLJ13544 PRKAG2 SLC2A8 SLC7A8 PDE4DIP CHEK1 CSPG5 BRPF1 KIAA0556 HCK ZG16 --- GPM6B ADAM19 SARM1 CENPA LZTR1 KIAA0676 FTL PRG-3 MMP24 SPUF --- HSPA6 HSPA6 ARSE C4BPA ZNF235 SHOX2 DTR PADI1 PCDHGC3 PPP1R15A LYPLA2 ANAPC13 EPHB4 PPT2 TSC1 ARMC4 LY6G5C CYLC2 SSB1 C19orf22 INHA ARF5 GGT1 POLQ CACNA1B NPM3 DOM3Z TIMP2 HMG20B PTPN18 GGA1 SDAD1 NUCB1 RXRA CLTB MTHFR --- MAN2C1 TNS ASAHL DDX11 ANGPT1 ICOSL BANF1 PPIA PAK2 CD99 MRPL52 B1 DOK1 BIRC5 HIST1H2BN NRL FLJ20477 TAGLN2 CYP2C8 KIAA0101 LOC55924 SERPINA1 MYL4 MAPK8IP3 GPLD1 NFASC UGT2B28 PURA PPIG ATP6V1F LYPLA2 PLVAP FLJ22318 RGS6 TBR1 WDR10 CPR8 FN1 KIAA0090 CLEC2 LOC55831 MAGEL2 SCN2A2 CSNK2B MID2 ODZ3 NCDN PSKH1