ava i l ab l e a t www.sc i enced i r ec t . com

C l i n i ca l Immuno logy

www.e l sev i e r. com/ loca te /yc l im

Clinical Immunology (2009) 131, 354–359

Transcriptional profiling of immune system-relatedgenes in postmenopausal osteoporotic versusnon-osteoporotic human bone tissue☆

Bernadett Balla a ,⁎, János P. Kósa a, János Kiss b, János Podani c,István Takács a, Áron Lazáry a, Zsolt Nagy a, Krisztián Bácsi a,Gábor Speer a, Péter Lakatos a

a 1st Department of Internal Medicine, Semmelweis University, Korányi S. u. 2/a, Budapest H-1083, Hungaryb Department of Orthopedics, Semmelweis University, Karolina út 27, Budapest H-1113, Hungaryc Department of Plant Taxonomy and Ecology, Eötvös Loránd University, Pázmány Péter sétány 1/c,Budapest H-1117, Hungary

Received 27 August 2008; accepted with revision 6 January 2009Available online 20 February 2009

☆ All the authors hereby state thatinterests and they have no conflicts of⁎ Corresponding author. Fax: +36 1 2E-mail address: betti@bel1.sote.hu

1521-6616/$ - see front matter © 200doi:10.1016/j.clim.2009.01.004

KEYWORDSImmune-related genes;Expression pattern;Osteoporosis;Human bone tissue

Abstract The functional interaction between the immune system and bone metabolism hasbeen established at both molecular and cellular levels. We have used non-parametric andmultidimensional expression pattern analyses to determine significantly changed mRNA profileof immune system-associated genes in postmenopausal osteoporotic (OP) vs. non-osteoporotic(NOP) bone tissue. Seven bone tissue samples from OP patients and ten bone tissue samples fromNOP women were examined in our study. The transcription differences of selected 44 genes were

analyzed in Taqman probe-based quantitative real-time RT-PCR system. Mann–Whitney testindicated significantly down-regulated transcription activity of 3 genes (FCGR2A, NFKB1 andSCARA3) in OP bone tissue which have prominent role in (antibody) clearance, phagocytosis,pathogen recognition and inflammatory response. According to the canonical variates analysisresults, the groups of postmenopausal OP and NOP women are separable by genes coding forcytokines, co-stimulators and cell surface receptors affected in innate immunity which have highdiscriminatory power. Based on the complex gene expression patterns in human bone cells, wecould distinguish OP and NOP states from an immunological aspect. Our data may provide furtherinsights into the changes of the intersystem crosstalk between the immune and skeletal systems,as well as into the local immune response in the altered microenvironment of OP bone.© 2009 Elsevier Inc. All rights reserved.

they do not possess financialinterest.10 4874.(B. Balla).

9 Elsevier Inc. All rights reserv

ed.

Introduction

Numerous connections including cellular and molecularmechanisms have been discovered between bone and theimmune system (i.e. using same progenitors as well as

355Transcriptional profiling of immune system-related genes in human bone tissue

parallel overlapping signaling networks and cytokine panelsin the common bone marrow niche). The regulatory role of Tlymphocytes and the importance of RANK/RANKL pathway inosteoclast (OC) functions are the most extensively investi-gated interplay between bone and immune system. ActivatedT cells produce receptor activator of nuclear factor kappa Bligand (RANKL) which is capable of inducing osteoclastoge-nesis and promoting bone resorption via receptor activator ofnuclear factor kappa B (RANK) presented on the surface ofOCs [1,2,3]. Moreover, Tcells secrete a number of factors thatstimulate OC differentiation, principally the pro-inflamma-tory cytokines (tumor necrosis factor alpha, interleukin-1,interleukin-6) and cytokines which inhibit OC development(granulocyte macrophage colony-stimulating factor, inter-leukin-4, interleukin-10, interferon gamma) [2]. Besides theT cell-OC interaction, osteoblast (OB) also has immune-competent properties. OBs have been found to express MHCtype II molecules [4,5] and ligands for T cell co-stimulation(CD80, CD86, CD54) [6]. Hence, OBs are able to come intocontact with Tcells and to trigger the T lymphocyte-specificimmune response through antigen presentation and cytokineproduction [7]. Furthermore, OB cells participate in patho-gen pattern recognition by plasma membrane bound toll-likereceptors (TLR-4, -5) [3].

Several prominent crosstalks between bone cells and Blymphocyte-mediated immune reactions have been published.Various osteoblastic cell lines have been found to secretesoluble factors including RANKL which directly induce early Bcells maturation and augment immunoglobulin secretion of Bcells [8,9,10]. In addition, B cells can regulate OC formationboth positively and negatively [11,12].

There are a few studies reporting the immunologicalaspects of osteoporosis and bone loss. The CD4+/CD8+ T cellratio is changed in osteoporotic patients [2,3,9]. Increasednumber of another lymphocyte subpopulation, CD3+ andCD56+ natural killer Tcells is associated with decreased bonemineral density (BMD) in osteoporotic women [2]. Thymec-tomy or immune-suppressant drug administrations thatinhibit T cell function resulted in rapid bone loss anddemineralized bone production [9]. There is increasingevidence that the immune system modifies bone resorptionand formation, nevertheless only limited data are availableon the influence of osteoblastic and osteoclastic cells onimmune functions in osteoporotic bone tissue.

The objectives of the present study were 1) to identifydifferences in the multiple transcription profiles of immune-related genes in osteoporotic versus non-osteoporotic humanbone samples, and 2) to search for gene clusters whichseparate the healthy state from the pathologic one in animmunological approach.

Materials and methods

Human bone tissue samples

Gene expression profile in bone samples was determined in 7postmenopausal, unrelated, consecutive, Hungarian, Cauca-sian osteoporotic women (OP group). The control groupincluded 10 bone tissue samples from postmenopausal non-osteoporotic women (NOP group). Subjects on steroid orhormone replacement therapy as well as any other medica-

tion thatmight affect the immune systemor bonemetabolismwere excluded. Menopause was defined according to therecent WHO criteria (at least one year of amenorrhea andserum estradiol level less than 30 pg/ml). BMD was measuredat the total femur and at the lumbar spine (L2–L4) by dualX-ray absorptiometry (DPX-L, Lunar Corp. Madison, WI, USA).Osteoporosis was determined by WHO criteria as T-score lessthan −2.5 SD at any measured side. All subjects participatingin the study had undergone surgery due to primary osteoar-thritis. Bone tissue samples were collected from thespongious substance of femoral head during total hiparthroplasty before resection. Osteoarthritic patients wereclassified as grade III, according to the American Academy ofOrthopedic Surgeon (AAOS) using the Kellgren–LawrenceGrading System for Osteoarthritis [13]. There was no diffe-rence in the degree of arthrosis among subjects by X-rayexamination. Surgically removed bone samples were exten-sively washed in PBS for eliminating blood, fat and marrowcontamination and then placed immediately into liquidnitrogen. The study was approved by the Regional Committeeof Science and Research Ethics, Semmelweis University(6392-1/2004-1018EKU), and all patients gave writteninformed consent.

mRNA isolation, quantitative real-time RT-PCR

Human bone samples (approximately 500 mg) were cryo-grinded under liquid nitrogen using a freezer-mill 6750 (SPEXCertiprep Inc. NJ, USA). Direct mRNA isolation using DynabeadsOligo (dt)25 kit (Dynal Biotech ASA, Oslo, Norway), DNasetreatment, quality controlling and first strand cDNA synthesiswere made according to our previously published method [14].For quantitative real-time RT-PCR comparison of the geneexpression pattern of the bone tissue of postmenopausal OPandNOP women, we selected 44 immune-related genes which aretranscribed in bone cells based on recent literature (www.pubmed.com), OMIM (Online Mendelian Inheritance in Man)onlinedatabase, aswell as our data concerning genetic pathwayanalysis [14]. These genes are involved in T and B lymphocytematuration, survival, activation, co-stimulation, antigen pre-sentation, phagocytosis, clearance, inflammatory processes,and complement system functions (Supplementary Table).

Predesigned and validated gene-specific TaqMan GeneExpression Assays from Applied Biosystems (Foster City, CA,USA) were used in triplicate for quantitative real-time PCRaccording to the manufacturer's protocol [14]. Relativequantification (RQ) studies were made from collected data(threshold cycle numbers, referred to as Ct) with 7500System SDS software 1.3 (Applied Biosystems). Generallyapplied housekeeping genes (GAPDH and ACTB) wereanalyzed in every sample, and for further statisticalevaluations the level of GAPDH was used as endogenouscontrol for data normalization. The use of GAPDH is justifiedby the fact that other genes, such as ACTB, commonlyutilized for housekeeping purposes might be affected byestrogenic hormones in osteoblasts [15].

Data analysis

For univariate statistical analysis, we selected a non-parametric (i.e., distribution-free) method, the Mann–

Table 1 Clinical and biochemical characteristics of OP and NOP women

Median (range)

OP group NOP group p values(n=7) (n=10)

Age (years) 69.00 (56–75) 60.50 (55–77) 0.31T-score L1–L4 (SD) −2.5 (−3.2 to −0.8) −0.8 (−1.5–3.9) 0.002Z-score L1–L4 (SD) −0.7 (−0.9–0.8) −0.2 (−1.3–3.5) 0.81BMD L1–L4 (g/cm2) 0.882 (0.612–1.099) 1.089 (1.016–1.652) 0.002T-score total femur (SD) −2.5 (−4.0 to −1.3) 0.1 (−1.8–1.8) 0.0004Z-score total femur (SD) −0.8 (−3.3–0.5) 0.7 (−1.0–3.3) 0.02BMD total femur (g/cm2) 0.695 (0.524–0.838) 1.008 (0.760–1.221) 0.0004BMI (kg/m2) 26.37 (21.30–28.95) 26.42 (23.53–33.69) 0.47Beta-CrossLaps (pg/ml) 424.80 (183.00–707.00) 335.00 (126.00–626.00) 0.19Osteocalcin (ng/ml) 22.29 (12.89–36.77) 16.70 (6.12–29.96) 0.31Parathyroid hormone (pg/ml) 33.80 (23.00–47.00) 26.50 (15.00–74.00) 0.07TSH (μIU/l) 1.04 (0.43–1.92) 1.35 (0.37–9.64) 0.47

Probabilities (p values) in the right column refer to the results of the Mann–Whitney U test for comparing the two samples.

356 B. Balla et al.

Whitney U test. Computations were performed using SPSS forWindows, release 15.1 (SPSS Inc.). Results with a p valueof ≤0.05 were considered statistically significant. However,postmenopausal osteoporosis is a multifactorial disease,which is caused by additive/cumulative effects of minimalchanges in the expression of numerous genes. In our case,each of the 17 patients was described in terms of 44 geneexpression values, and thus the Mann–Whitney U test cannotfully recover the information hidden in the data. A moreexhaustive multivariate procedure, Canonical variates ana-lysis (CVA, alias discriminant function analysis) was thereforeused, which maximizes separation of a priori defined groupsof observations. The results of CVA are canonical scoresobtained from the canonical functions derived througheigenanalysis, which serve as coordinates of observations inthe canonical space. Since the number of canonical axes isone less than the number of groups, in our case CVA producedonly one variate. Furthermore, CVA is useful especially whenthe genes best discriminating between the groups of patientsare to be identified. A partial limitation of CVA is that thenumber of variables (genes) cannot exceed the number ofobservations (patients). Therefore, five CVA runs were doneusing different subsets of genes, each subset defined on alogical basis (see Results for more). Computations wereperformed by the SYN-TAX 2000 program package [16].

Table 2 Summary of quantitative real-time RT-PCR data for threwomen

ABI assay ID a Gene symbol b Gene name b

Hs00234969_m1 FCGR2A Fc fragment of IgG, lowHs00231653_m1 NFKB1 nuclear factor of kappa

enhancer in B-cells 1 (p1Hs00212206_m1 SCARA3 scavenger receptor clasa Applied Biosystems TaqMan Gene Expression Assay Identification/Ob Symbols and names for human genes are used according to the stac Changes of relative gene expression in OP women compared to NOd p values of the Mann–Whitney U test.

Results

Study population

The clinical and biochemical parameters of OP and NOPsubjects are shown in Table 1. There were no significantdifferences between the postmenopausal osteoporotic andnon-osteoporotic subjects in age, smoking habits, calciumintake, alcohol, caffeine consumption and physical activity.Remarkable differences between the two study groups wereobserved especially in T-score and BMD at the total femur(p=0.0004) as well as in T-score and BMD at the lumbar spine(p=0.002).

Comparison of gene expression in osteoporotic vs.non-osteoporotic women by Mann–Whitney U test

Table 2 summarizes data for the fold changes (RQ OP/RQNOP) of three significantly altered genes (p≤0.05) in the 17examined patients. All the three genes (FCGR2A, NFKB1 andSCARA3) showed two-fold or more down-regulated expres-sion panel in the bone of osteoporotic patients. The observeddecrease in mRNA expression was 0.49-fold in case ofFCGR2A, 0.44-fold for NFKB1 and 0.27-fold for SCARA3.

e significantly (p≤0.05) changed genes in 7 OP and in 10 NOP

Fold change c p value d

affinity IIa, receptor (CD32) 0.49 0.04light polypeptide gene05)

0.44 0.02

s A, member 3 0.27 0.02

rdering Numbers.ndard “Gene Cards” (www.genecards.org).P control.

357Transcriptional profiling of immune system-related genes in human bone tissue

These genes are involved mainly in pathogen recognition,phagocytosis or acute phase response.

The quantitative real-time RT-PCR data of all theexamined 44 genes in the present study (including the non-significantly altered genes) are listed in the SupplementaryTable. The function of genes and their physiological roles inbonemetabolism aswell as in immune biology are also shown.

Canonical variates analysis

The selection of gene subsets was based on searching factorswhich are presented by bone cells and are involved in bothinnate and adaptive immune processes; including T and Blymphocyte activation (14 and 5 genes) [17,18], antigenpresentation and co-stimulation (11 genes), phagocytosis (9genes) and complement functions (10 genes) [19,20,21]. Thegene subset of T cell activating molecules exhibited the bestdiscriminatory power, with unambiguous separation of theOP and NOP subjects (Fig. 1). Genes involved in antigenpresentation also showed strong correlation with the singlecanonical variate, hence achieving clear separation of thetwo groups of examined subjects. Segregation was especiallysharp based on marker genes of phagocytosis (Fig. 1).

Canonical correlation measures the strength between thecanonical variate and the group membership variable. The

Figure 1 Canonical variates analysis of immune-related gene expbars) and 10 NOP (black bars) women. Symbols and names for humastandard “Gene Cards” (www.genecards.org). Gene symbols and cosummarized in the table pertaining to each subset of genes.

stronger the separation of groups along the CVA axis, thehigher the correlation. Canonical correlation values were0.918 in case of the T cell-specific gene cluster, 0.857 forthe subset of phagocytosis marker genes, 0.851 for genes ofthe complement cascade, 0.766 for the gene subset ofantigen presentation and 0.580 for B cell gene group,respectively.

Discussion

Numerous studies have reported the immune phenotype, invivo cytokine transcription and surface antigen expression ofperipheral blood mononuclear cells from postmenopausalosteoporotic patients [22,23,24,25]. In the present study, weextended these earlier results with mRNA expression data inhuman OP bone tissue samples. We have disclosed differ-ences in the gene transcription profiles of human postme-nopausal osteoporotic and non-osteoporotic bone tissue froman immunological aspect for the first time. We could sharplyseparate the immunological phenotypes of OP and non-OPbone based on genetic information. In addition, we haveidentified novel immune-associated genes showing markedvariations in their expression levels that have not yet beenrelated to involutional osteoporosis.

ression patterns of bone tissue in 7 postmenopausal OP (whiten genes belonging to the five subsets are used according to therrelations of genes with the single canonical variates (CV) are

358 B. Balla et al.

We have demonstrated significantly diminished expres-sion of a novel gene, SCARA3 (SR-AIII, class A scavengerreceptor) in human OP bone tissue. Primarily, it is secreted inmyeloid cells and it plays a role in microbial patternrecognition of innate immunity [26,27]. Moreover, SCARA3can bind endogenous ligands, e.g. extracellular matrixproteins (biglycan, decorin, denatured collagens) and mo-dified LDLs (AcLDL, OxLDL) [28]. We have previouslyreported the increased transcriptional activity of another,class B scavenger receptor, the CD36 in the bone tissue of OPindividuals [14]. Both receptors control lipid homeostasiswhich is critical in the osteoblast/adipocyte differentiationbalance [28,29]. The shifted bone/fat marrow ratio towardsfat might lead to decreased bone mineral density. Our resultsraise the possibility that the altered gene expression of classA and B scavenger receptors could be involved in this shiftand consequently in the development of osteoporosis.

In this study, canonical variates analysis (CVA) was used tocheck whether the groups of OP and NOP patients areseparable in the multidimensional space spanned by thegenetic variables, and if so, which gene subsets have the bestdiscriminatory power [30]. Moreover, this method provided amore profound evaluation of the relationship between thestudied gene subsets. CVA revealed several relevant genesubsets coding for new immunological mechanisms that havefirm impact on the process of osteoporosis (Fig. 1). Perfectdiscrimination of healthy and pathologic states was seen incase of genes affected in T cell-mediated immune response.It is generally established that Tcell and bone cell actions aretightly related, e.g. (i) osteoblasts can stimulate naive Tcells through antigen-presentation and co-stimulation [7],(ii) effector T cells regulate osteoclast maturation via theRANK/RANKL/OPG network, involving a number of differentcytokines [1]. Furthermore, we observed arrested NFKB1expression in OP individuals compared to NOP ones [14].Activation of NF-κB transcription factor complex is essentialin RANK/RANKL signaling cascade [31,32], which is princi-pally examined in connection with osteoporosis [33]. How-ever, NF-κB can be activated in many signal cascades: e.g.NF-κB activation through Fas/FasL system leads to enhancedosteoclastgenesis [34], and contrarily, it mediates apoptoticcell death in osteoblasts [35]. On the other hand, NF-κBactivation through TRAIL/TRAIL-receptor system protectsosteoblasts from apoptosis [36], but induces apoptosis inosteoclasts [37]. The balance of survival and death signalsthrough NF-κB activation cascades results in normal bonehomeostasis and healthy remodeling. In this complexinteraction, the uncoupled and impaired bone cell functionsmodify the Tcell-transmitted immunological events in osteo-porotic bone tissue.

Very clear segregation of subjects could be achieved bythe gene subset of phagocytosis markers. This findingcorroborates the results obtained by the Mann–Whitney Utest confirming the marked changes in the expression ofSCARA3, FCGR2A and ITGAM. The transcribed mRNA levelsof FCGR2A and ITGAM genes were also lower in OP patients.Additionally, we noted that numerous components (adhe-sion molecules, co-stimulators and cytokines) of antigen-presenting system are modulated in osteoporotic women.Our data suggest that the altered bone metabolism andremodeling have important regulating influence on mole-cular constituents of the non-specific defense system. CVA

results underline the significance of innate immunity inosteoporosis.

The distinct expression profiles of certain immune-associatedgenes may lead to imbalance in immune homeostasis. Therecognized immunological changes might contribute to thedeterioration of bone mass. It is recently reported that manyof the immunological disorders (e.g. chronic inflammatorybowel diseases, autoimmune diabetes mellitus, allergicdiseases, periodontal disease and some metastatic cancers)are accompanied with decreased bone mass [1,2]. Inaddition, osteoporosis as a systemic skeletal disease mightinfluence the immune reactions in bone tissue.

Limitations of our study include that the measurement ofgene expression is not carried out exactly on homogeneousbone cell population because occasionally other bone mar-row cells (e.g. immune cell progenitors, mesenchymal cells,stromal cells, fibroblasts) might have remained in thesamples in spite of the extensive washing procedure. How-ever, only this experimental strategy could determine thetranscriptional interplay of immune and bone system inosteoporotic microenvironment. Also, bone samples fromosteoarthritic patients do not serve as perfect controls,however, healthy bone tissue cannot be collected for ethicalreasons. Moreover, arthrosis affects mostly chondral andsubchondral regions, while the deep intact spongious bonesubstance of femoral head from which we acquired oursamples remains unaffected.

In conclusion, we have found significant differences in theexpression pattern of immune-competent genes in osteo-porotic versus non-osteoporotic bone tissue. The separationof the two groups by CVA suggests the involvement of novelgene subsets that might be useful for a deeper understandingof the immunological aspects of osteoporosis. Our findingsprovide further insight into the process of the altered boneand immune homeostasis in osteoporotic microenvironment,and may promote wider application of an otherwise well-known statistical method (CVA) for the evaluation of batchedgenetic data.

Acknowledgments

This work was supported by grants NKFP-1A/007/2004,NKFP-1A/002/2004 from National Research and Technologi-cal Office (NKTH) of Hungary, as well as by research grantETT 022/2006 from the Ministry of Health, Hungary. Podani J.was supported by a Hungarian Scientific Research Fund(OTKA) grant no. NI 68218. Kiss J. was supported by OTKAgrant no. T-037436.

Appendix A. Supplementary data

Supplementary data associatedwith this article can be found,in the online version, at doi:10.1016/j.clim.2009.01.004.

References

[1] M.C. Walsh, N. Kim, Y. Kadono, J. Rho, S.Y. Lee, J. Lorenzo, Y.Choi, Osteoimmunology: interplay between the immune systemand bone metabolism, Annu. Rev. Immunol. 24 (2006) 33–63.

359Transcriptional profiling of immune system-related genes in human bone tissue

[2] J.A. Clowes, B.L. Riggs, S. Khosla, The role of the immunesystem in the pathophysiology of osteoporosis, Immunol. Rev.208 (2005) 207–227.

[3] M. Rauner, W. Sipos, P. Pietschmann, Osteoimmunology, Int.Arch. Allergy Immunol. 143 (2007) 31–48.

[4] H. Skjodt, D.E. Hughes, P.R. Dobson, R.G. Russell, Constitutiveand inducible expression of HLA class II determinants by humanosteoblast-like cells in vitro, J. Clin. Invest. 85 (1990) 1421–1426.

[5] H. Skjodt, T. Moller, S.F. Freiesleben, Human osteoblast-likecells expressing MHC class II determinants stimulate allogeneicand autologous peripheral bloodmononuclear cells and functionas antigen-presenting cells, Immunology 68 (1989) 416–420.

[6] O. Garcia-Martinez, C. Reyes-Botella, O. Aguilera-Castillo, M.F.Vallecillo-Capilla, C. Ruiz, Antigenic profile of osteoblasts pre-sent in human bone tissue sections, Biosci. Rep. 26 (2006) 39–43.

[7] K.T. Stanley, C. VanDort, C. Motyl, J. Endres, D.A. Fox,Immunocompetent properties of human osteoblasts: interac-tions with T lymphocytes, J. Bone Miner. Res. 21 (2006) 29–36.

[8] D. Benayahu, I. Altboum, D. Zipori, S. Wientroub, I. Zan-Bar,Osteoblasts release a soluble factor that modulates immuno-globulin secretion by mature B cells, Biochem. Biophys. Res.Commun. 194 (1993) 391–398.

[9] D. Grcevic, V. Katavic, I.K. Lukic, N. Kovacic, J.A. Lorenzo, A.Marusic, Cellular and molecular interactions between immunesystem and bone, Croat. Med. J. 42 (2001) 384–392.

[10] M.C. Horowitz, A.L. Bothwell, D.G. Hesslein, D.L. Pflugh, D.G.Schatz, B cells and osteoblast and osteoclast development,Immunol. Rev. 208 (2005) 141–153.

[11] M.N. Weitzmann, S. Cenci, J. Haug, C. Brown, J. DiPersio, R.Pacifici, B lymphocytes inhibit human osteoclastogenesis by se-cretion of TGFbeta, J. Cell. Biochem. 78 (2000) 318–324.

[12] Y. Choi, K.M. Woo, S.H. Ko, Y.J. Lee, S.J. Park, H.M. Kim, B.S.Kwon, Osteoclastogenesis is enhanced by activated B cells butsuppressed by activated CD8(+) T cells, Eur. J. Immunol. 31(2001) 2179–2188.

[13] J.H. Kellgren, J.S. Lawrence, Radiological assessment of osteo-arthrosis, Ann. Rheum. Dis. 16 (1957) 494–502.

[14] B. Balla, J.P. Kosa, J. Kiss, A. Borsy, J. Podani, I. Takacs, A.Lazary, Z. Nagy, K. Bacsi, G. Speer, L. Orosz, P. Lakatos,Different gene expression patterns in the bone tissue of agingpostmenopausal osteoporotic and non-osteoporotic women,Calcif. Tissue Int. 82 (2008) 12–26.

[15] D. Benayahu, Estrogen effects on protein expressed by marrowstromal osteoblasts, Biochem. Biophys. Res. Commun. 233(1997) 30–35.

[16] J. Podani, SYN-TAX 2000. User's Manual, Scientia, Budapest,2001.

[17] S. Romagnani, Regulation of the Tcell response, Clin. Exp. Allergy36 (2006) 1357–1366.

[18] A. Corthay, A three-cell model for activation of naive T helpercells, Scand. J. Immunol. 64 (2006) 93–96.

[19] R.M. Steinman, H. Hemmi, Dendritic cells: translating innate toadaptive immunity, Curr. Top. Microbiol. Immunol. 311 (2006)17–58.

[20] D.H. Schuurhuis, N. Fu, F. Ossendorp, C.J. Melief, Ins and outsof dendritic cells, Int. Arch. Allergy Immunol. 140 (2006) 53–72.

[21] M.W. Hornef, M.J. Wick, M. Rhen, S. Normark, Bacterialstrategies for overcoming host innate and adaptive immuneresponses, Nat. Immunol. 3 (2002) 1033–1040.

[22] F.G. Hustmyer, E. Walker, X.P. Yu, G. Girasole, Y. Sakagami, M.Peacock, S.C. Manolagas, Cytokine production and surfaceantigen expression by peripheral blood mononuclear cells inpostmenopausal osteoporosis, J. BoneMiner. Res. 8 (1993) 51–59.

[23] Y.Z. Liu, V. Dvornyk, Y. Lu, H. Shen, J.M. Lappe, R.R. Recker,H.W. Deng, A novel pathophysiological mechanism for osteo-porosis suggested by an in vivo gene expression study ofcirculating monocytes, J. Biol. Chem. 280 (2005) 29011–29016.

[24] S.X. Zheng, Y. Vrindts, M. Lopez, D. De Groote, P.F. Zangerle, J.Collette, N. Franchimont, V. Geenen, A. Albert, J.Y. Reginster,Increase in cytokine production (IL-1 beta, IL-6, TNF-alpha butnot IFN-gamma, GM-CSF or LIF) by stimulated whole blood cellsin postmenopausal osteoporosis, Maturitas 26 (1997) 63–71.

[25] P. Pietschmann, J. Grisar, R. Thien, M. Willheim, K. Kerschan-Schindl, E. Preisinger, M. Peterlik, Immune phenotype andintracellular cytokine production of peripheral blood mono-nuclear cells from postmenopausal patients with osteoporoticfractures, Exp. Gerontol. 36 (2001) 749–759.

[26] L. Peiser, S. Gordon, The function of scavenger receptorsexpressed by macrophages and their role in the regulation ofinflammation, Microbes Infect. 3 (2001) 149–159.

[27] L. Peiser, S. Mukhopadhyay, S. Gordon, Scavenger receptors ininnate immunity, Curr. Opin. Immunol. 14 (2002) 123–128.

[28] A. Pluddemann, C. Neyen, S. Gordon, Macrophage scavengerreceptors and host-derived ligands, Methods 43 (2007) 207–217.

[29] D. Calvo, D. Gomez-Coronado, Y. Suarez, M.A. Lasuncion, M.A.Vega, Human CD36 is a high affinity receptor for the nativelipoproteins HDL, LDL, and VLDL, J. Lipid Res. 39 (1998) 777–788.

[30] J. Podani, Introduction to the Exploration of MultivariateBiological Data, Backhuys, Leiden, 2000.

[31] X. Li, G.R. Stark, NFkappaB-dependent signaling pathways,Exp. Hematol. 30 (2002) 285–296.

[32] Z.H. Lee, H.H. Kim, Signal transduction by receptor activator ofnuclear factor kappa B in osteoclasts, Biochem. Biophys. Res.Commun. 305 (2003) 211–214.

[33] T. Wada, T. Nakashima, N. Hiroshi, J.M. Penninger, RANKL-RANKsignaling in osteoclastogenesis and bone disease, Trends Mol.Med. 12 (2006) 17–25.

[34] H. Park, Y.K. Jung, O.J. Park, Y.J. Lee, J.Y. Choi, Y. Choi, Inte-raction of Fas ligand and Fas expressed on osteoclast precursorsincreases osteoclastogenesis, J. Immunol. 175 (2005) 7193–7201.

[35] N. Ozeki, M. Mogi, H. Nakamura, A. Togari, Differential expres-sion of the Fas–Fas ligand system on cytokine-inducedapoptotic cell death in mouse osteoblastic cells, Arch. OralBiol. 47 (2002) 511–517.

[36] G.J. Atkins, S. Bouralexis, A. Evdokiou, S. Hay, A. Labrinidis, A.C.Zannettino, D.R. Haynes, D.M. Findlay, Human osteoblasts areresistant to Apo2L/TRAIL-mediated apoptosis, Bone 31 (2002)448–456.

[37] S. Roux, P. Lambert-Comeau, C. Saint-Pierre, M. Lepine, B.Sawan, J.L. Parent, Death receptors, Fas and TRAIL receptors,are involved in human osteoclast apoptosis, Biochem. Biophys.Res. Commun. 333 (2005) 42–50.