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Biomaterials 26 (2005) 2933–2945

www.elsevier.com/locate/biomaterials

Gene expression clustering using self-organizing maps: analysis of themacrophage response to particulate biomaterials

Grant E. Garriguesa, David R. Chob, Harry E. Rubasha, Steven R. Goldringb,James H. Herndona, Arun S. Shanbhaga,�

aBiomaterials Laboratory, Massachusetts General Hospital, Harvard Medical School, GRJ 1115, 55 Fruit Street, Boston, MA 02114, USAbBone and Joint Institute, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA

Received 27 January 2004; accepted 1 June 2004

Available online 27 August 2004

Abstract

The most common cause of total joint replacement failure is peri-implant bone loss causing pain and prosthesis loosening. This

process, known as osteolysis or aseptic loosening, is characterized by macrophage phagocytosis of particulate implant wear debris.

In an incompletely defined step, particulate biomaterial debris induces macrophages to release a variety of inflammatory mediators

and signaling proteins that lead to bone loss. In an in vitro model of this process, we used microarray technology and data analysis

techniques, including the use of self-organizing maps (SOMs), to understand the mRNA gene expression changes occurring in

macrophages exposed to clinically relevant particles of ultra-high molecular weight polyethylene and TiAlV alloy. Earlier studies

have been limited by technology that only allowed analysis of a few genes at a time, but the microarray techniques used in this paper

generate the quantitative analysis of over a thousand genes simultaneously.

Our microarray analysis utilized an SOM clustering to elucidate general patterns in the data, lists of top up- and down-regulated

genes for each time point and genes with differential expression under different biomaterial exposures. The expression levels of the

majority of genes ð495%Þ did not vary over time or with exposure to different biomaterials, but a few important genes, such as

TNF-a, IL-1b, IL-6, and MIP1a, proved to be highly regulated in response to biomaterial exposure. We also uncovered a novel set

of genes, which not only validates and logically extends the current model of the pathogenesis of osteolysis and aseptic loosening,

but also provides new targets for further research and therapeutics.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Osteolysis; Aseptic loosening; Gene expression; Self-organizing maps; Macrophage; Total joint replacement; Microarrays; Ti-alloy;

UHMWPE; Wear debris

1. Introduction

A key dividend from the genomics revolution isthe development of associated technology permittingsmall laboratories to study large numbers of genesusing cDNA (complementary DNA) microarrays. Each

e front matter r 2004 Elsevier Ltd. All rights reserved.

omaterials.2004.06.034

ing author. Tel.: +1-617-724-1923; fax: +1-617-726-

ess: shanbhag@helix.mgh.harvard.edu

).

www.Osteolysis.org/ShanbhagLab.

microarray is a matrix of probes for each gene placed atparticular locations on a solid substrate. After purifying,amplifying, and labeling the messenger RNA, we canhybridize this sample to complementary nucleotideprobes on the microarray. The signal emitted by eachlabeled mRNA (messenger RNA) as it binds to itscorresponding probe allows a quantitative measure ofthat gene’s expression. Using a microarray that hasnucleotide probe-sets for thousands of genes, we canquantitatively measure gene expression levels for each ofthe genes, even the entire genome, by measuring the levelof each gene’s mRNA transcript simultaneously [1].

ARTICLE IN PRESSG.E. Garrigues et al. / Biomaterials 26 (2005) 2933–29452934

Microarrays thus afford us the ability to detect subtlechanges in gene expression that are associated withdisease.

Because microarrays allow thousands of simultaneousmeasurement using only a relatively small RNA sample,they offer distinct advantages over earlier techniquessuch as Northern blots and RNase protection assays.Analysis of a few microarray experiments can unravelmany important biological phenomena, such as patternsof gene expression over time, groups of genes regulatedby the same processes, highly responsive genes, andcomparisons between experimental conditions. Thisdata analysis is often the limiting factor in a microarrayexperiment. Fortunately, microarray data analysisrepresents an emerging field in which cell biology,informatics, computer science, and mathematics arecombining to forge new research frontiers and solveclinical problems.

In this study, we used microarrays to investigatealterations in the phenotype of macrophages as theyinteract with particulate wear debris. Macrophagephagocytosis of wear debris from joint replacementcomponents is the crucial step in the pathogenesis ofosteolysis and aseptic loosening [2–4]. These two relatedconditions of pathologic bone resorption around aprosthetic joint are the most important clinical problemswith total hip replacement (THR) and total kneereplacement (TKR), easily outstripping infection andmechanical dysfunction as the major cause of jointreplacement failure.

Osteolysis and aseptic loosening cause peri-implantbone loss and compromise bony anchors stabilizing theimplant, leading to implant loosening, pain, andnecessitating a surgical procedure to replace thecomponents. Approximately 500,000 hip and kneereplacements are performed each year in the UnitedStates [5]. While the failure rates vary tremendouslyacross different implant designs, recent epidemiologicaldata from the Swedish Hip Registry estimates that8–9% of hip replacements in that country are revisions.The vast majority of these are for aseptic loosening withand without osteolysis [6]. These figures are comparableto the statistics in the United States. Osteolysis andaseptic loosening thus represent a significant clinicalproblem facing all joint replacements, especially THRand TKR [5,7,8].

Over the last two decades, numerous investigatorshave studied the etiology of osteolysis and asepticloosening [2,9–12]. A synthesis of these studies suggeststhat osteolysis and aseptic loosening are radiographicmanifestations of the same biological process, which isprimarily initiated by a macrophage response toparticulate wear debris from the prosthetic components[13]. This debris consists predominantly of submicronultra-high molecular weight polyethylene (UHMWPE)from the acetabular liner in THR, smaller amounts of

titanium–aluminum–vanadium alloy (TiAlV) and co-balt–chrome alloy particles from the components,fragments from the polymethylmethacrylate (PMMA)bone cement, as well as other debris from cables used forsecondary fixation [14–17]. Clinical histopathology, aswell as in vitro and in vivo models have demonstratedthat macrophages phagocytize the particulate weardebris, are stimulated to release a variety of inflamma-tory mediators such as tumor necrosis factor a ðTNFaÞ,interleukin (IL)-1a, IL-1b, prostaglandin E2 ðPGE2Þ andIL-6, and participate in the formation of a granuloma-tous tissue [12,18–22]. These mediators initiate differ-entiation, maturation, formation, and stimulation ofosteoclasts to resorb bone [2,23–25]. Fibroblasts asfacultative phagocytes are also capable of ingesting weardebris, releasing mediators, and contributing further tothe inflammatory milieu [26]. Macrophage interactionwith particles can also down-regulate collagen synthesisand inhibit osteoblast bone formation activities [27]. Toalleviate this problem, investigative teams have im-proved implant designs to enhance metaphyseal fill,adopted circumferential porous coatings to preventmigration of wear debris, and developed wear resistantUHMWPE to minimize the generation of wear debris.

In the investigations carried out in our laboratory, wehave focused our efforts on understanding the macro-phage response to wear debris as the critical step in thepathogenesis of osteolysis and aseptic loosening, and aplausible juncture for therapeutic intervention. Earlierstudies into the biology of this process by severalinvestigators, including our laboratory, have focused ona few key cytokines and mediators such as TNFa, IL-1a,IL-1b, PGE2 and IL-6, [9,12,19,21]. This focus waspartly due to the large sample required for cytokineELISAs or mRNA Northern Blot analyses and theimpracticality of running many individual ReverseTranscriptase–Polymerase Chain Reactions (RT-PCR).The advent of nucleotide microarrays has now made itfeasible to investigate a large number of genes simulta-neously. In these preliminary studies, we investigatednearly 1200 genes using a first generation nylonmembrane based cDNA microarray. The importantchallenges in these studies are to develop methodologiesto sift through the large amount of data, identifypatterns in gene expression, and make meaningfulconclusions about the underlying biology. For exactlysuch a purpose, several analysis techniques, includingcluster analysis and the use of self-organizing maps,were developed [28]. In this report, we have outlinedour efforts using such tools to gain a deeper under-standing of the gene expression changes that occur inmacrophages, consequent to their interaction withclinically relevant wear particles of UHMWPE andTiAlV.

The macrophage responses to both types of particleswere generally similar, and broadly resembled the

ARTICLE IN PRESSG.E. Garrigues et al. / Biomaterials 26 (2005) 2933–2945 2935

macrophage activation by lipopolysaccharide (LPS)endotoxin. Using gene expression profiling that includedcluster analysis and self organizing maps, we were ableto uncover key inflammatory genes involved in themacrophage response to particulate biomaterials. Ourfindings further validate the important roles of TNF-a,IL-1b, IL-1a, IL-6, Macrophage inflammatory proteinðMIPÞ1a and MIP1b. Using this powerful tool, we alsohad the opportunity to identify genes that have hithertonot been studied in the context of aseptic looseningand osteolysis, but are known to be involved inmacrophage activation in other contexts, includingTNF-receptors 1 and 2, TNF inducible protein andTNFa inducible protein 2, and members of the Januskinase/Signal transducer and activator of transcrip-tion (JAK/STAT) pathway. In addition we delineate anovel set of genes, including the angiogenesis-inducingPlacenta growth factors 1 and 2 (PGF 1 and 2), withputative roles in osteolysis and aseptic loosening,representing a new set of potential targets for researchand therapeutics.

2. Materials and methods

2.1. Cell culture

Monocytes were separated from peripheral blood ofhealthy adult male volunteers ðn ¼ 4Þ by sequentialdiscontinuous Percoll gradients (Sigma, St Louis, MO)using established protocols which were previouslyreported [24,29,30]. The purified cells were washed andresuspended in macrophage-serum-free medium. Cellcount and viability were determined, and 2:0� 106

nucleated cells per milliliter were plated in each well of a24-well tissue culture plate (Costar, Cambridge, MA).Viability measured by trypan blue dye exclusion, wasalways greater than 97%. Monocytes remained adherentduring the over night incubation, while nonadherentcontaminating red cells, lymphocytes, and dead or dyingcells, were removed by vigorous pipetting and washingwith PBS. From each subject, adherent cells in onerandomly selected well were trypsinized and counted.Approximately 50% ð�5%Þ of the cells remainedadherent to the tissue culture polystyrene dishes [29]and yielded 1� 106 cells per well. Adherent layers ofmonocytes representing cells differentiating toward themacrophage phenotype, were cultured with eitherUHMWPE or TiAlV challenge particles in 1.0ml ofmacrophage-serum-free medium. The UHMWPE parti-cles were fabricated by cryogenic attrition [31] and had amean diameter of 2:3� 0:2mm, with 60% of particleshaving a diameter less than 1mm. TiAlV particles werealso fabricated [32] and had a mean diameter of 1:1�0:8mm and 62% of particles were less than 1mm in size.Biomaterials were added to obtain a particle dosage

representing 2 times the surface area of the cells,equivalent to approximately 40 particles per cell. Inadditional wells, lipopolysaccharide (LPS) was added asa positive control, and medium only was added as anegative, non-stimulated (NS) control. Particles wereconfirmed to be free of endotoxin (LPS) by limulusassay (E-Toxate; Sigma Chemical).

Cells with the various additives were cultured (37 1C,5% CO2 in air) and the incubation terminated at30 min, 4, 8, and 24 h after addition of particles.Supernatants were harvested, aliquoted and stored at�80 1C for later determination of inflammatory media-tors. RNA was purified from the cell extract (TRIzol,Gibco BRL) and converted into radiolabeled cDNAusing RT-PCR with 32P-labeled primers specific forevery gene on the array as prescribed in the manufac-turer protocols. The cDNA was then hybridized to anylon membrane with specifically arrayed probes for1176 genes (Atlas Human Array 1.2, Clontech, PaloAlto, CA; www.clontech.com) and analyzed by auto-radiography. Since the exposure of each spot on the filmwas proportional to the amount of radiolabeled mRNAbound to the specific probe for each gene, the geneexpression level for each of the 1176 genes on thearray can be quantified (Fig. 1). Microarrays wereperformed using the RNA of macrophages isolated from3 of the human volunteers, and the RNA from the 4thhuman sample was used for confirmatory PCR. The firstsample was used to evaluate hybridization as well asimage and data analysis techniques, while the data fromthe second and third samples were used for the moredetailed treatment with the analytical tools describedbelow.

2.2. Image acquisition

Autoradiographs of each microarray were digitizedusing a radiograph scanner (UMAX Mirage II, RASBioMedical Innovations, UmeA, Sweden). The resultingimages are grids of spots with the radiographic intensityof each spot representing the mRNA expression level ofa specific gene. These images were cropped and rotated,and overall brightness and contrast were standardized(Adobe Photoshop 5.0). Using array-specific software, agrid template was applied to each microarray, and anydistortions in location and background were adjusted(Atlas Image, Clontech).

2.3. Normalization

An adjusted signal intensity for each gene wascalculated by subtracting the background from the spotintensity and bringing all values less than 1000 up to thelow value of 1000. Each treatment condition,UHMWPE, TiAlV, and LPS, was compared to thenon-stimulated condition (NS) using a normalized ratio.

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Fig. 1. Methods Schema. Macrophages were cultured with various particle additives (UHMWPE, TiAlV) as well as positive (LPS) and negative (NS)

controls. Cultures were interrupted at four time-points. mRNA was harvested and converted to radiolabeled cDNA. cDNA was then hybridized to a

gene array where complementary nucleotide sequences for each gene are attached to a solid substrate in a known pattern. Radiographic signal at each

spot on the array gives a measure of gene activity that can be used to analyze both overall patterns in gene expression (SOMs) and the quantitatively

most important genes.

G.E. Garrigues et al. / Biomaterials 26 (2005) 2933–29452936

To avoid biases inherent in two common normalizedratio methods, the simple ratio (condition/NS) and thelog2 transform of the ratio (log2(condition/NS)), wecalculated an adjusted ratio where genes with simpleratios greater than 1, i.e. up-regulated with respect tothe NS case, were left unchanged while for genes withsimple ratios less than 1, the negative inverse wasused. This shows the fold change for responses greaterin the condition than NS with a positive sign,representing an up-regulation of mRNA transcriptlevels under the condition relative to NS, and NSgreater than condition with a negative sign, representinga down-regulation of mRNA levels under the conditionrelative to NS.

Calculating adjusted, normalized ratios for each time-point, each condition, and each experimental replicate,yields nearly 40,000 distinct gene expression ratios. Toseek out unique traits within such a large data set, weused a powerful pattern finding tool.

2.4. Cluster analysis

Clustering algorithms are designed to find patterns inlarge sets of data by grouping similar data elementstogether. In order to understand complicated gene-expression time-courses for nearly 1200 genes, thesepowerful and objective pattern-finding and groupingmethods are essential. The adjusted ratio time-courses,representing the sequential gene expression changesfor each gene relative to the non-stimulated condition,were ordered and clustered with the program Cluster(M. Eisen, 1999 http://rana.lbl.gov/EisenSoftware.htm)using a self-organizing map (SOM) with 1,000,000iterations. The SOM algorithm begins by laying downa pre-specified geometry of interconnected nodes. Toselect the optimal number of nodes, we first clustered thedata with the number of nodes ranging from 2 to 12 andcalculated each intra-cluster group variance. This groupvariance is a measure of the degree of dissimilarity

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Start

End

1 million

iterations

Fig. 2. Five SOM iterations and the final solution. Small points ð�Þ

represent expression patterns for a particular gene. The larger circles

( ) represent the nodes of the SOM moving toward the randomly

selected points ( ) according to the learning rule.

G.E. Garrigues et al. / Biomaterials 26 (2005) 2933–2945 2937

among the gene expressions clustered together. Eventhough the variance decreased as the number of nodesincreased, the benefit was marginal after adding morethan 7 nodes, and the pattern generalization provided bythe clustering was progressively diminished. We thuschose a uni-dimensional string of 7 nodes, whichoptimally extracted the most salient data patterns froma large database of gene expression time-courses.

The gene-expression data can be represented by aseries of points in 4-dimensional space. Each point in thedata set represents a gene, and the coordinates of thatpoint are the gene expression levels for each of the fourtime-points. Each SOM iteration consists of randomlyselecting a point from the data set and moving the nodestoward that point according to the learning rule. Thelearning rule moves the nodes such that the closer anode is to the selected point, the nearer that node ismoved toward the selected point, and the amount ofmovement decreases with each iteration (Fig. 2) [33].This procedure results in nodes spreading out as ifattracted to clusters of points, hence the term ‘‘self-organizing’’ [33]. The actual points can then be collapsedonto the array of nodes to yield the one-dimensional listof genes ordered by gene expression. In this ordering,the topological relationships between the points are notchanged, but the scale is lost and the four dimensions foreach gene are collapsed into the more comprehensible 1-dimensional list, hence the ‘‘map’’ in the term ‘‘self-organizing map’’.

Clustering genes with SOMs produces not only agrouping of genes into seven rough baskets, but also anordering of each gene on the array that can be visualized(Fig. 3) (TreeView, M. Eisen) (http://rana.lbl.gov/EisenSoftware.htm). Average responses over time foreach cluster were calculated using a custom-designedprogram (Microsoft Visual Basic 6.3). Atlas micro-array’s annotated biochemical classification groups foreach gene on the array were further grouped andmodified into five functional classifications: ‘‘cell cycle’’,‘‘signal transduction’’, ‘‘apoptosis’’, ‘‘inflammation’’,and ‘‘other’’ which included genes associated withprotein turnover, neurons specifically, tumor suppres-sors & oncogenes, transporters & channels, hormones &hormone receptors (Table 1). We wrote another soft-ware program to read the microarray coordinate andreturn the classification data and gene/protein names.Using this program, we compared the frequency of eachgene class within each cluster to the expected distribu-tion based on class-size alone.

The custom software also tabulated the genes thatwere among the top 25 up- and down-regulated genes,i.e. the top and bottom 2% of adjusted ratio responses,under at least one condition across both replicates of theexperiment. This software then related the conditions byclassifying the response of each gene as ‘‘highly up-regulated’’ (at least 2-fold), ‘‘highly down-regulated’’ (at

least 2-fold), or ‘‘unchanged’’, and then searching forgenes that differentiated each condition under bothtrials.

3. Results

Creation of the SOM produces an ordering by geneexpression response of all genes measured. Any gene ofinterest can be indexed and its response profile over timeaccessed for each condition. Complicated response time-courses can be compared simply by indexing the desiredgenes and comparing their locations in the clustereddata set (Fig. 3).

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Table 1

Selection of the types of gene functions grouped into the five classes. Ligands and their receptors were always grouped together

Cell cycle Signal transduction Apoptosis Inflammation Other

Cyclins Receptors Caspases Cytokines Neuron-specific genes

CDKs G-proteins Bcl family Interleukins Tumor suppressors

DNA replication Kinases DNA fragmentation Signal transductionb Oncogenes

Transcription factorsa Transporters

Hormones

aNot specifically related to other categories.bSpecific for inflammation.

7 Clusters of Genes

24 h

30 min

4 h

8 h

24 h

30 min

4 h

8 h

Fig. 3. Gene response clustering with self organizing maps (SOMs) for

UHMWPE (top) and TiAlV (bottom). Each column represents the

response time-course of a particular gene. Red represents up-

regulation, green represents down-regulation, and black represents

no change in gene expression of the biomaterial-containing culture

relative to NS. Vertical white bars delineate the seven large clusters

defined by the 7 nodes. Note the smooth transition between clusters

and within clusters as both genes and clusters are ordered.

G.E. Garrigues et al. / Biomaterials 26 (2005) 2933–29452938

3.1. Top up- and down-regulated genes

The compilation of the top 25 up- and down-regulated genes (top and bottom 2%) under at leastone condition in both replicates was used to extract themost salient response features. After 30min of culturingmacrophages with UHMWPE, TiAlV, or LPS, 20 geneswere included in the highly up-regulated compilation,while 30 genes were on the highly down-regulated list.The top 5 within this listing are presented in Table 2. Asexpected, inflammation-related genes were commonamong these highly up-regulated genes, with 9/20 genespre-classified as inflammation associated (Fig. 4).

After 4 h of macrophage culture with particles or LPS,16 highly up-regulated genes were identified, including12 from the inflammation category (Table 2). This up-regulation of inflammation-related genes at both 30minand 4 h is consistent with previous RT-PCR studies [34].Though similar pathways were implicated in manycases, none of the highly down-regulated genes were incommon between the two trials.

At the 8 h mark, 15 highly up-regulated and 6 highlydown-regulated genes were common to both arrays.Again, the vast majority (13) of highly up-regulated

genes were associated with inflammation. Many of thesegenes were also present on the list of genes upregulatedat 4 h, which indicated similarities in the short-termresponse at 4 and 8 h (Table 2).

The 24 h culture was meant to emulate the response ofmacrophages chronically exposed to particulate bioma-terials. Ten genes were in common between the highlyup-regulated lists, and all were associated with inflam-mation (Table 2). Six highly down-regulated genes werein common to both trials at the 24 h time-point, and allsix down-regulated genes were also associated withknown markers of inflammation.

This analysis generated a compacted list of genes withsuspected important roles in macrophage mediated peri-implant bone loss.

3.2. Biomaterial induced responses

In our experimental design, we also compared specificbiomaterial induced macrophage responses. Geneswhich appear on the top 25 lists in response to both ofthe biomaterials tested, as well as LPS, may highlightgenetic elements involved in a generic macrophageresponse to all foreign stimuli, while those genes whichappear to be regulated in response to only one material,would yield potential clues to understanding theconsequences of macrophage interaction with thespecific biomaterial tested.

To arrive at a global view of the similarities amongthe macrophage response to LPS and each of theparticulate biomaterials, the ‘‘up’’ (greater than 2-fold),‘‘down’’ (greater than 2-fold), or ‘‘unchanged’’ (less than2-fold in either direction) categorizations were em-ployed. The response category was the same on eacharray trial for over 70% of the genes, with the vastmajority of the disparities resulting from expressionlevels falling close to, but on opposite sides of the 2-foldup- or down-regulation cut-off in the trials. Theresponse category of each gene was compared undereach condition to estimate the similarity of the macro-phage gene expression response when cultured withdifferent biomaterials. The average response similarityfor each comparison pair (LPS:UHMWPE, LPS:TiAlV,UHMWPE:TiAlV) exceeded 95% in all cases.

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Fibrosis/Matrix Remodeling

AngiogenesisVascular Permeability

Recruitment/Activation oflymphocytes, macrophages

Formation/Activation ofosteoclasts

Formation/Degradation ofextracellular matrix

Granulation Tissue

Granuloma Formation

PathologyBiology Histology

InterfacialMembrane

Bone ResorptionOsteolysis & Aseptic Loosening

Presentation

Peri-implant boneresorption

Fig. 4. The effects indicated by the macrophage gene functions identified in our experiments validate and logically extend the current model of

osteolysis and aseptic loosening.

Table 2

Top up- and down-regulated genes and their function

Up-regulated (fold increase) Function Down-regulated (fold decrease) Function

30min TIMP-1 (26.4) ECM turnover MAD (4.1) Gene inhibition

MMP-14 (20.9) ECM turnover MAD receptor 4 (4.1) TGF-b signaling

Monocyte ARG-serpin (20.1) ECM turnover MARK3 (3.8) Protein kinase

IEX-IL (20.1) Anti-apoptosis Fos-related antigen (3.8) Osteoclastogenesis

RANTES (20.0) Cytokine Anaplastic lymphoma kinase (3.7) Protein kinase

4 h IL-6 (46.3) Cytokine None of top 2% down-regulated

genes present in both trials

MIP-1a (36.3) Cytokine

PGF-1,2 (31.2) Vascular permeability

TNF-inducible Protein (28.2) Cytokine related

IL-5 (18.6) Cytokine

8 h Small inducible cytokine A1 (25.2) Cytokine IL-1b CE 2 (12.2) Cytokine related

TNF-inducible protein 2 (22.6) Cytokine related Ezrin (11.4) Cytoskeleton/motility

PGF-1,2 (21.2) Vascular permeability MMP-9 (11.3) ECM turnover

TNF-inducible protein (19.5) Cytokine related Proteasome inhibitor (10.8) Protein turnover

MIP–1a (16.5) Cytokine Cytoplasmic beta-actin (7.3) Cytoskeleton/motility

24 h 1L-1b (41.0) Cytokine Integrin b2 (19.1) Cell adhesion

TNF-inducible protein (39.6) Cytokine related MRP 8 (14.4) Cytokine

TNFa-UHMWPE Culture (21.1) Cytokine TNF a-LPS Culture (14.1) Cytokine

Corticotropin RFR 1 (20.2) Stress response MRP 14 (13.7) Cytokine

PGF-1,2 (19.7) Vascular permeability MCSF-1R (11.9) Macrophageogenesis

Abbreviations: TIMP: Tissue inhibitor of metalloproteinase; MMP: Matrix metalloproteinase; ARG: Arginine; IEX: Immediate early stress

inducible; RANTES: Regulated upon activation in normal T-cell expressed and secreted; MAD: Mothers against decapentaplegic; MARK:

Microtubule Affinity Regulating Kinase; IL: Interleukin; MIP: Macrophage inflammatory protein; PGF: Placental growth factor; TNF: Tumor

necrosis factor; CE: Converting enzyme; RFR: Releasing factor receptor; MRP: Migration inhibitory factor-related protein; MCSF-1R: Macrophage

colony stimulating factor-1 receptor.

G.E. Garrigues et al. / Biomaterials 26 (2005) 2933–2945 2939

3.3. Discriminator analysis:

Using the same response categories of ‘‘up’’, ‘‘down’’,and ‘‘unchanged’’, we identified a small subset of genestermed ‘‘discriminators’’, which responded uniquely to aspecific particle or LPS treatment, while the other twotreatments produced a markedly different, yet consis-

tent, response. Each condition evokes a unique geneexpression signature in the macrophage, and these‘discriminating’ differences could be used to assay forthe biocompatibility of a new material based on the geneexpression levels of a few key genes. Stimulation withLPS elicited only eleven discriminators—the leastnumber in our analyses. This suggests that LPS

ARTICLE IN PRESSG.E. Garrigues et al. / Biomaterials 26 (2005) 2933–29452940

stimulated very few genes which were not also similarlyaffected by other biomaterials (Table 3). Macrophagescultured with UHMWPE diverged most from theother biomaterial treatments and had 27 discriminators(Table 3). This list included predominantly cytokinesand inflammatory mediators, including many membersof the interleukin family (IL-3,5,9,15) (Table 3). TiAlV

Table 3

Discriminator analysis

LPS UHMW

Up-regulated GTP binding protein b3 Acidic fi

TATA-box factor Acidic fi

ADP-rib

Apopto

Aquapor

CACCC

c-cbl pr

CD19

c-rel (24

GAP-as

Glucago

Glycoge

lL-1alL-3

IL-5 (24

lL-9

lL-15

Integrin

MAP ki

MAP ki

NF-kap

Protein

Ski onco

Uromod

Unchanged Alpha-1 antitrypsin Cdc25

Delta-like protein precursor (24 h) Cyclin d

IEX-1L anti-death protein Seroton

Down-regulated c-myc

GABAA receptor b 1 subunit

IGF binding protein 2

lni 1

Orphanin FQ receptor

Prostaglandin E2 receptor EP3

Gene responses were divided into three rough categories of up-regulated by

response was in the same category for two conditions, but differed for the

response was generally similar under all conditions, these genes could potenti

biologic activity of different biomaterials. Genes represented in ‘bold’ discrim

had 23 discriminators, including a group of 7 cell-cycleregulators, which were down-regulated or unchangedwith TiAlV and up-regulated after LPS and UHMWPEexposure (Table 3). While the discriminators at eachtime point give us information about the differences inbiologic activity between the different biomaterials, the24 h culture, presumably more representative of chronic

PE TiAlV

broblast growth factor (4 h) Acyl-CoA-binding protein

broblast growth factor (8 h) Cadherin 2 (24 h)

osylation factor 1

sis regular bcl-x

in 9 (24 h)

box DNA binding protein

oto-oncogene

h)

sociated protein

n precursor

n synthase kinase 3 b

h)

alpha 7B

nase 2

nase kinase kinase 5 (24 h)

pa B

phosphatase 2Cagene

ulin

calpain (0.5 h)

ependent kinase 2 Cdc2-related protein

in receptor channel c-src

Cyclin K

DNA excision repair protein

GTP binding protein a-SHMG-1

IGF ll receptor

IL-6

Ink adapter protein

integrin alpha X

integrin alpha 5

Leukocyte janus kinase

Placenta growth factors 1,2

aurora-related kinase 1

C enhancer binding prot a (24 h)

calpain (24 h)

cyclin D3

cyclin-dependent kinase4inhib

MAP kinase assoc. protein

MAP kinase kinase 5 (24 h)

42-fold, down-regulated by 42 fold, or unchanged. Genes where the

third condition were termed ‘‘discriminators’’. While the macrophage

ally be used to investigate key differences and help explain variations in

inate at 24 h, thus representing more chronic differences.

ARTICLE IN PRESSG.E. Garrigues et al. / Biomaterials 26 (2005) 2933–2945 2941

stimulation, has the potential of determining whichbiomaterial, or LPS, is potentially driving the macro-phage response in the clinical case (Table 3).

4. Discussion

We used cDNA microarrays, analyzed with self-organizing maps (SOMs), to provide a comprehensive,quantitative, yet lucid picture of the gene expressionchanges in macrophages exposed to clinically relevant,joint replacement wear debris. More traditional techni-ques are limited because they require a larger amount ofsample mRNA and only measure the response of a fewgenes at a time. Using microarrays we measured thegene expression time-course for each of 1176 genes aftermacrophage exposure to UHMWPE, TiAlV, and LPS.SOMs were used to organize this prodigious amount ofdata by ordering each gene based on its expression time-course. In addition we categorized the response of eachgene at each time-point as ‘‘up’’, ‘‘down’’, or ‘‘un-changed’’, and compared these responses under eachcondition. The greater than 95% similarity for eachcomparison indicated that macrophages may have afairly limited repertoire of responses to foreign materialsincluding such diverse materials as particulate metalalloys (TiAlV) or polymers (UHMWPE), or evenphylogenetically ancient bacterial products (LPS).

To delve deeper than global trends, we created foreach time point, a compilation of the top 25 up- anddown-regulated genes (top and bottom 2%) under atleast one condition in both replicates of the experiment.In an analysis of 1176 genes, extracting a small set ofgenes with the most extreme expression responses to theparticulate biomaterials was crucial to ascertaining theplausibility of the data and identifying the mostpromising targets for research and therapy aimed atimproving the long-term stability of total joint replace-ments.

The highly up- and down-regulated genes identified inour top 2% compilations validate and logically extendthe proposed etiology of aseptic loosening and osteo-lysis. The macrophage phagocytosis of particulatebiomaterials is hypothesized to be the central event inboth the bone resorption and the formation of the peri-implant granulomatous interfacial membrane tissue(Fig. 4) [2,19,24,35]. The macrophage genes mostdramatically regulated by exposure to UHMWPE,TiAlV, and LPS often directly indicate biologicalprocesses consistent with the histology and pathologyobserved in osteolysis and aseptic loosening [2,10].These macrophage orchestrated processes include in-duction of angiogenesis and increased vascular perme-ability; recruitment and activation of other leukocytes;increased matrix turnover; and stimulation of boneresorption. These cellular processes are consistent with

the tissue changes of vascular granulation tissue forma-tion, granuloma formation, fibrosis, and bone resorp-tion consistently seen histologically. The top five up-and down-regulated genes, representing the 0.4% mostresponsive to the three conditions, reflected most of themacrophage functions identified in the entire top 25compilations (Table 2). Thus the macrophage geneexpression in response to particulate biomaterialsindicates cellular actions consistent with the histologyof osteolysis and aseptic loosening.

Within 30min of macrophage interaction with parti-cles, 20 genes were highly up-regulated and showed thewide range of activated macrophage functions. Extra-cellular matrix turnover is exemplified by the over 20-fold up-regulation of Matrix metalloproteinase 4(MMP4) and Tissue inhibitor of metalloproteinase 1(TIMP1). As sentinels of the immune response, macro-phages also provide the initial danger signal to both theinnate and adaptive immune responses [36,37]. Manycytokines and chemokines are highly up-regulated after30min: RANTES (Regulated upon activation in normalT-cell expressed and secreted), MIP1a, and IL-6. Thesemediators activate leukocytes and provide a chemicalsignaling gradient to direct their movement out of thebloodstream and to the site of inflammation. In additionto initiating cell–cell communication with other leuko-cytes through cytokine production, the macrophage alsoup-regulates its cytokine receptors such as the TNFreceptors 1 and 2, and the alpha subunit of the IL-2receptor. The macrophage not only recruits otherleukocytes, but it also promotes its own survival withanti-apoptotic proteins such as Immediate early stressinducible (IEX-1L) anti-death protein. The initialresponse to particle stimulation involves genes favoringleukocyte activation and migration, an increase inextracellular matrix turnover, and macrophage long-evity.

After 30min, the up-regulation of IL-6, believed to bein response to IL-1 and TNF, is particularly interesting,considering its importance in the formation of multi-nucleated osteoclasts and bone resorption [38,39].Serum levels of IL-6 have also been shown to increasewith age and Paget’s Disease, and to be predictive ofosteopenia, osteoporosis, and systemic juvenile rheuma-toid arthritis [38–41].

The down-regulated genes at 30min tell a quitedifferent story. While the IL-6 receptor is down-regulated in an apparent negative feedback from thesimultaneous increase in IL-6 production, the inflam-mation-related genes are generally not the centralplayers. Instead, there is a down-regulation of signaltransduction proteins as the macrophage reorganizes itsregulatory cascades. A major theme of these transcrip-tional changes is the down-regulation of two majorRNA polymerase II (Pol II) promoter transcriptionfactors along with the down-regulation of a variety of

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transcripts controlled through the Pol II promoter, aregulatory sequence in the DNA that controls geneexpression. Fos is a Pol II transcription factor with animportant role, both in the inflammatory response aswell as the skeletal development, possibly by regulatingthe differentiation of the macrophage-osteoclast lineage[42]. The fos-related antigen (FRA1), another Pol IItranscription factor, has a role in osteoclastogenesis withtranscription induced by Receptor activator of NF-KappaB ligand (RANKL), also called osteoclast differ-entiation factor (ODF) [43,44]. Fos and FRA1 are bothamong the most highly down-regulated genes withapproximately 3.5 times lower mRNA levels in thestimulated cases than the non-stimulated case. While theup-regulation of IL-6 indicates otherwise, the acutedown-regulation of these important bone-resorptiongenes may indicate that inflammation outweighs boneresorption as a cellular priority at this early time-point.

As expected, a variety of proteins regulated by the PolII promoter are simultaneously down-regulated. Manyof these are involved in the Transforming growth factorb ðTGFbÞ pathway, which blocks the activation ofmacrophages and counters the proinflammatory effectsof many cytokines [45]. The down-regulated genesinclude STAT3 and Myelocytomatosis-related oncogene(N-myc). STAT3 is a transcription factor involved in theinflammatory response and the TGFb pathway [46].Two of the most highly down-regulated genes, theMothers against decapentaplegic (MAD) receptors 1and 4, are both involved in TFGb signaling as well. Theother Pol II regulated protein, N-myc, is also atranscription factor, which can be activated by theMAD protein, the most highly down-regulated gene at30min. After 30min, the Ski-related novel oncogene(snoN) transcriptional repressor of TGFb target genes isone of the most highly up-regulated genes by approxi-mately 4-fold [47]. An early, coordinated down-regula-tion of key components of the TGFb signaling pathwayis achieved through a cascade of specific transcriptionalchanges allowing inflammation and macrophage activa-tion to proceed, and inhibiting the fibrosis andangiogenesis effects of this protein.

Other major signal transduction changes that occurafter 30min include the down-regulation of a number ofkinases and phosphatases, including multiple elementsof the Mitogen activated protein (MAP) kinase path-way. Many of these kinases, as well as other highlydown-regulated proteins, including cadherins, integrins,and small G-proteins, regulate cell adhesion andmotility [48–50]. The down-regulation of specific cellmotility and adhesion genes may not necessarily indicatea decrease in these two processes, but certainlyrepresents intracellular changes with regulatory con-sequences to chemotaxis and phagocytosis.

The highly up-regulated genes at 4 h resemble the listafter 30min both generally and specifically. The

majority of these genes are inflammation-related, withcytokines and cytokine receptors that are highly up-regulated both at 30min and 4 h (IL-6, MIP1a, and theTNF receptors 1 and 2) and new transcripts up-regulated at 4 hours (IL-1a, IL-3, IL-5, and IL-2Rg).The matrix turnover function of activated macrophagesis continued with TIMP1 and MMP14, and extended toinclude MMP9, a gelatinase which not only cleavesdenatured collagen, but also IL-8 and TGFb. One of themost highly up-regulated genes, placenta growth factors1 and 2 (PGF1 and 2), had 31-fold increased expressionunder LPS and UHMWPE conditions. PGF1 and 2 areVascular endothelial growth factor (VEGF) relatedproteins which promote angiogenesis—the sprouting ofnew blood vessels, and vascular permeability throughendothelial receptor tyrosine kinases [51]. In thesecapacities they could initiate granulation tissue forma-tion and inflammation. The TNF inducible protein(TSG6) is rapidly up-regulated by TNFa, IL-1, or LPSin peripheral blood mononuclear cells and was alsohighly up-regulated in response to UHMWPE exposure.TSG6 has been implicated in the pathogenesis ofrheumatoid arthritis because of its elevated levels insynovial fluid of Rheumatoid Arthritis patients and maybe important in other orthopaedic maladies withunderlying inflammatory processes [52].

The highly up-regulated genes at 8 h are dominated bycytokines and cytokine related factors; many of themare the same, or similar to those seen at 30min and 4 h.For example, small inducible cytokine A1 is seen insteadof the small inducible cytokine A5 (RANTES) seen at30min. As another example, in addition to the MIP1aup-regulation seen at 30min and 4 h, MIP1a, MIP1b,and MIP2a are all highly up-regulated after 8 h. Thepotent bone resorption mediator IL-6 is again up-regulated, as is TSG-6. Like TSG-6, TNFa-induced-protein-2 (TNFAIP2) is known to be rapidly induced byTNFa, IL-1b, or LPS exposure, and we discovereddramatic up-regulation in response to UHMWPE aswell. TNFAIP2 is thought to have a role in angiogenesis[53]. Other highly up-regulated angiogenesis factors at8 h again include PGF1 and PGF2, as well as b-endothelial cell growth factor. As the inflammatoryresponse continues to smolder, angiogenesis appears tobe another key function of the macrophage response.

At 8 h, the highly down-regulated genes include theapoptotic protease FADD-like IL-1b converting enzyme2 (FLICE2), which would serve to protect the macro-phages from apoptosis as the presence of this caspasestarts an apoptotic cascade and can also directly bind toTNF Receptor 1 through its paired death effectordomains (DEDs) [54]. This expression change mayincrease the longevity of macrophages in vivo or mayserve to balance the apoptotic effects of cytokines suchas TNFa. Ezrin, a cell adhesion and migration proteininvolved in creating the immunological synapse, the

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intimate connection between antigen presenting cells,such as macrophages, and T-cells, was highly down-regulated at 30min and again at 8 h [55]. Macrophagesnot only provide the immunological ‘‘danger signal’’,they also provide the link to the specific, adaptiveimmune response by presenting antigen in the presenceof costimulatory signals to T cells which carry out amore targeted response to invaders.

After 24 h of macrophage culture, the highly up-regulated genes are consistent with current views of theimportant mediators in osteolysis and aseptic loosening[56]. IL-1b and TNFa are both highly up-regulated afterUHMWPE particle exposure. Other up-regulated in-flammatory cytokines and related proteins include TNF-inducible-protein (TSG6), TGFb3, the IL-6 relatedcytokine oncostatin M, and the IL-6 receptor a-subunit.PGF1 and PGF2 are again highly up-regulated, indicat-ing prolonged increases in angiogenesis and vascularpermeability. In addition to the cytokine communication,integrin b8 and the integrin associated protein CD47 maymediate the cell adhesion and mobility changes in theactivated macrophages. CD47, as the ligand for themacrophage fusion receptor (MFR), is known to beinvolved in the fusion of macrophages to form osteo-clasts, representing another transcriptional change favor-ing bone resorption [57]. Thus, while the 30min geneexpression profile indicates a decrease in osteoclastformation and activity, by 24h this trend is reversedand osteoclast formation and stimulation is favored. Thissuggests that macrophages first interact with foreignbiomaterials as sentinels of the immune response. Oncethe inflammatory process is set in motion, the macro-phages begin to modulate components of the boneresorption mechanisms by initiating and stimulating theosteoclast formation and maturation cascade.

The highly down-regulated genes after 24 h includeTNFa in the LPS culture, but TNFa remains highly up-regulated in the UHMWPE culture. While the macro-phage responses to LPS and UHMWPE are similar in somany other ways, the difference in the gene expressiontime-course of this very important pro-inflammatorymediator may shed light on a key characteristic ofUHMWPE—its non-degradability. LPS-induced TNFadecrease in gene expression is probably due to thephagocytic degradation and clearance of LPS within24 h. In sharp contrast, UHMWPE particles are non-degradable even within the acidic environment of thephagolysosome and lead to a prolonged stimulation andrelease of inflammatory cytokines such as TNFa. Thiscould be the primary reason why the chronic stimula-tory action of UHMWPE particulate wear debris isstrongly implicated in aseptic loosening of joint replace-ment components.

Migration inhibitory factor related proteins 8 and 14(MRP8,14) are the light and heavy subunits of acomplex expressed in infiltrating granulocytes with an

over-expression in multiple types of arthritis [58]. Bothof these proteins are highly down-regulated after 24 h,possibly representing coordinated transcriptional regu-lation of both complex subunits. This decrease in pro-granulocytic cytokines at 24 h is consistent with thebiologically observed decrease in the granulocyteresponse in the absence of bacterial products.

Some down-regulation of inflammation-related genesat 24 h may be a result of the in vitro system notcontaining the diversity of cell types found in an in vivosetting such as a foreign body granuloma, asepticloosening, or osteolytic lesions. If the initial burst oflymphocyte stimulatory cytokines from the macro-phages is not followed by T-cells reciprocally stimulat-ing with IFNg, the macrophages do not become fullyactivated [59].

5. Conclusion

The macrophage response to modern syntheticbiomaterials such as UHMWPE and TiAlV alloy hasmuch in common with its response to a much olderfoe—the gram-negative bacterium. This response in-cludes recruitment and activation of other leukocytes,increased matrix turnover, angiogenesis, promotion ofcell survival, and stimulation of bone resorption. Thus,the most prominent macrophage gene expressionchanges that occur after culture with particulatebiomaterials explain both the formation of the peri-implant granulomatous interfacial tissue and the lyticbone resorption observed in osteolysis and asepticloosening after total joint replacement. Biomaterialshowever, differ from gram-negative bacteria in impor-tant ways as orthopaedic biomaterials are generally notbiodegradable and are quite new in an evolutionarysense, to the macrophage. While there is some specificmacrophage response to LPS via the Toll-like receptor 4(TLR4), biomaterials generally stimulate the same,stereotyped expression changes in macrophages withadditional biomaterial specific responses. The subtletiesof the macrophage–material interaction continue to beexplored, and gene expression analysis using micro-arrays is already an invaluable tool for teasing out thesediverse intracellular responses. This study not onlyunderscores the extensive interplay between man-madeimplant components and the patients they reside in, butit also points to exciting new methods of gene-expression analysis and creates a vastly expanded listof potentially important genes in aseptic loosening.

Acknowledgements

The authors are grateful to the technical expertise ofKoen Kas and Bob Choy while at the Beth Israel

ARTICLE IN PRESSG.E. Garrigues et al. / Biomaterials 26 (2005) 2933–29452944

Deaconess Medical Center’s Bone and Joint Institute.This study was supported by grants from Zimmer Inc,Sulzer Inc and the NIH AR47465 A03.

References

[1] Wodicka L, Dong H, Mittmann M, Ho MH, Lockhart DJ.

Genome-wide expression monitoring in Saccharomyces cerevisiae.

Nat Biotechnol 1997;15(13):1359–67.

[2] Willert HG, Semlitsch M. Reactions of the articular capsule to

wear products of artificial joint prostheses. J Biomed Mater Res

1977;11:157–64.

[3] Friedman RJ, Black J, Galante JO, Jacobs JJ, Skinner HB.

Current concepts in orthopaedic biomaterials and implant

fixation. J Bone Joint Surg 1993;75-A:1086–109.

[4] Jacobs JJ, Shanbhag AS, Giant TT, Black J, Galante JO. Wear

debris in total joint replacements. J Am Acad Orthop Surg

1994;2(4):212–20.

[5] Wright TM, Goodman SB. Implant wear: the future of total joint

replacement. Rosemont, IL: American Academy of Orthopaedic

Surgeons; 1996.

[6] Herberts P, Malchau H. Long-term registration has improved

the quality of hip replacement: a review of the Swedish THR

Register comparing 160,000 cases. Acta Orthop Scand 2000;71(2):

111–21.

[7] Goldring SR, Clark CR, Wright TM. Editorial: the problem in

total joint arthroplasty: aseptic loosening. J Bone Jt Surg 1993;75-

A:799–801.

[8] Harris WH. The problem is osteolysis. Clin Orthop 1995;(311):

46–53.

[9] Goldring SR, Schiller AL, Roelke M, Rourke CM, O’Neill DA,

Harris WH. The synovial-like membrane at the bone-cement

interface in loose total hip replacements and its proposed role in

bone lysis. J Bone Jt Surg 1983;65A:575–84.

[10] Shanbhag AS, Jacobs JJ, Black J, Galante JO, Glant TT. Cellular

mediators secreted by interfacial membranes obtained at revision

total hip arthroplasty. J Arthroplasty 1995;10(4):498–506.

[11] Goodman SB, Chin RC, Chiou SS, Schurman DJ, Woolson ST,

Masada MP. A clinical-pathologic-biochemical study of the

membrane surrounding loosened and nonloosened total hip

arthroplasties. Clin Orthop 1989;(244):182–7.

[12] Horowitz SM, Doty SB, Lane JM, Burstein AH. Studies of the

mechanism by which the mechanical failure of polymethylmetha-

crylate leads to bone resorption. J Bone Jt Surg 1993;75-A:

802–13.

[13] Sinha RK, Shanbhag AS, Maloney WJ, Hasselman CT, Rubash

HE. Osteolysis: cause and effect. Instr Course Lect 1998;47:

307–20.

[14] Shanbhag AS, Jacobs JJ, Giant TT, Gilbert JL, Black J, Galante

JO. Composition and morphology of wear debris in failed

uncemented total hip replacement. J Bone Jt Surg Br 1994;76(1):

60–7.

[15] Shanbhag AS, Bailey HO, Hwang DS, Cha CW, Eror NG,

Rubash HE. Quantitative analysis of ultrahigh molecular weight

polyethylene (UHMWPE) wear debris associated with total knee

replacements. J Biomed Mater Res 2000;53(1):100–10.

[16] Lee J-M, Salvati EA, Betts F, DiCarlo EF, Doty SB, Bullough

PG. Size of metallic and polyethylene debris in failed cemented

total hip replacements. J Bone Jt Surg 1992;74-B:380–4.

[17] Campbell P, Ma S, Schmalzried TP, Amstutz HC. Technical note:

tissue digestion for wear debris particle isolation. J Biomed Mater

Res 1994;28:523–6.

[18] Shanbhag AS, Jacobs JJ, Black J, Galante JO, Glant TT.

Macrophage/particle interactions: effect of size composition and

surface area. J Biomed Mater Res 1994;28(1):81–90.

[19] Shanbhag AS, Jacobs JJ, Black J, Galante JO, Giant TT. Human

monocyte response to particulate biomaterials generated in vivo

and in vitro. J Orthop Res 1995;13(5):792–801.

[20] Shanbhag AS, Macaulay W, Stefanovic-Racic M, Rubash HE.

Nitric oxide release by macrophages in response to particulate

wear debris. J Biomed Mater Res 1998;41(3):497–503.

[21] Jiranek WA, Machado M, Jasty M, Jevsevar D, Wolfe HJ,

Goldring SR, et al. Production of cytokines around loosened

cemented acetabular components. Analysis with immunohisto-

chemical techniques and in situ hybridization. J Bone Jt Surg Am

1993;75(6):863–79.

[22] Haynes DR, Rogers SD, Hay S, App B, Pearcy MJ, Howie DW.

The differences in toxicity and release of bone-resorbing

mediators induced by titanium and cobalt-chromium-alloy wear

particles. J Bone Jt Surg 1993;75-A:825–34.

[23] Glant TT, Jacobs JJ, Molnar G, Shanbhag AS, Valyon M,

Galante JO. Bone resorption activity of particulate-stimulated

macrophages. J Bone Miner Res 1993;8(9):1071–9.

[24] Shanbhag AS, Jacobs JJ, Black J, Galante JO, Glant TT. Effects

of particles on fibroblast proliferation and bone resorption in

vitro. Clin Orthop 1997;342:205–17.

[25] Schwarz EM, Benz EB, Lu AP, Goater JJ, Mollano AV, Rosier

RN, et al. Quantitative small-animal surrogate to evaluate drug

efficacy in preventing wear debris-induced osteolysis. J Orthop

Res 2000;18(6):849–55.

[26] Maloney WJ, Smith RL, Castro F, Schurman DJ. Fibro-

blast response to metallic debris in vitro. Enzyme induc-

tion, cell proliferation, and toxicity. J Bone Jt Surg 1993;75-A:

835–44.

[27] Vermes C, Glant TT, Hallab NJ, Fritz EA, Roebuck KA, Jacobs

JJ. The potential role of the osteoblast in the development of

periprosthetic osteolysis: review of in vitro osteoblast responses to

wear debris, corrosion products, and cytokines and growth

factors. J Arthroplasty 2001;16(8 Suppl. 1):95–100.

[28] Bassett Jr DE, Eisen MB, Boguski MS. Gene expression

informatics—it’s all in your mine. Nat Genet 1999;21(1 Suppl):

51–5.

[29] Sethi RK, Neavyn MJ, Rubash HE, Shanbhag AS. Macrophage

response to cross-linked and conventional UHMWPE. Biomater-

ials 2003;24(15):2561–73.

[30] Absolom DR. Basic methods for the study of phagocytosis. Meth

Enzymol 1986;132:95–180.

[31] Shanbhag AS, Hasselman CT, Rubash HE. Technique for

generating submicrometer ultra high molecular weight polyethy-

lene particles. J Orthop Res 1996;14(6):1000–4.

[32] Yang IH, Kim SY, Rubash HE, Shanbhag AS. Fabrication of

submicron titanium-alloy particles for biological response studies.

J Biomed Mater Res 1999;48(3):220–3.

[33] Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S,

Dmitrovsky E, et al. Interpreting patterns of gene expression

with self-organizing maps: methods and application to hemato-

poietic differentiation. Proc Natl Acad Sci USA 1999;96(6):

2907–12.

[34] Anderson JM. Multinucleated giant cell. Curr Opin Hematol

2000;7(1):40–7.

[35] Maloney WJ, Smith RL. Periprosthetic osteolysis in total hip

arthroplasty: the role of particulate wear debris. Instr Course Lect

1996;45:171–82.

[36] Solbach W, Moll H, Rollinghoff M. Lymphocytes play the music

but the macrophage calls the tune. Immunol Today 1991;12(1):

4–6.

[37] Morrissette N, Gold E, Aderem A. The macrophage—a cell for all

seasons. Trends Cell Biol 1999;9(5):199–201.

[38] Makhluf HA, Mueller SM, Mizuno S, Glowacki J. Age-related

decline in osteoprotegerin expression by human bone marrow

cells cultured in three-dimensional collagen sponges. Biochem

Biophys Res Commun 2000;268(3):669–72.

ARTICLE IN PRESSG.E. Garrigues et al. / Biomaterials 26 (2005) 2933–2945 2945

[39] Roodman GD, Kurihara N, Ohsaki Y, Kukita A, Hosking D,

Demulder A, et al. Interleukin 6. A potential autocrine/paracrine

factor in Paget’s disease of bone. J Clin Invest 1992;89(1):46–52.

[40] Haden ST, Glowacki J, Hurwitz S, Rosen C, LeBoff MS. Effects

of age on serum dehydroepiandrosterone sulfate, IGF-I, and IL-6

levels in women. Calcif Tissue Int 2000;66(6):414–8.

[41] Papadopoulos NG, Georganas K, Skoutellas V, Konstantellos E,

Lyritis GP. Correlation of interleukin-6 serum levels with bone

density in postmenopausal women. Clin Rheumatol

1997;16(2):162–5.

[42] Grigoriadis AE, Wang ZQ, Cecchim MG, Hofstetter W, Felix R,

Fleisch HA, et al. c-Fos: a key regulator of osteoclast-macro-

phage lineage determination and bone remodeling. Science

1994;266(5184):443–8.

[43] Jones DH, Kong YY, Penninger JM. Role of RANKL and

RANK in bone loss and arthritis. Ann Rheum Dis 2002;61

(Suppl 2):ii32–9.

[44] Yasuda H, Shima N, Nakagawa N, Yamaguchi K, Kinosaki M,

Goto M, et al. A novel molecular mechanism modulating

osteoclast differentiation. Bone 1999;25(1):109–13.

[45] Letterio JJ, Roberts AB. TGF-beta: a critical modulator of

immune cell function. Clin Immunol Immunopathol 1997;84(3):

244–50.

[46] Massague J. How cells read TGF-beta signals. Nature Reviews

Molecular Cell Biology 2000;1(3):169–78.

[47] Stroschein SL, Wang W, Zhou S, Zhou Q, Luo K. Negative

feedback regulation of TGF-beta signaling by the SnoN

oncoprotein. Science 1999;286(5440):771–4.

[48] Peng CY, Graves PR, Ogg S, Thoma RS, Byrnes III MJ, Wu Z,

et al. C-TAK1 protein kinase phosphorylates human Cdc25C on

serine 216 and promotes 14-3-3 protein binding. Cell Growth

Differ 1998;9(3):197–208.

[49] Knox AL, Brown NH. Rap1 GTPase regulation of adherens

junction positioning and cell adhesion. Science 2002;295(5558):

1285–8.

[50] Takai Y, Sasaki T, Matozaki T. Small GTP-binding proteins.

Physiol Rev 2001;81(1):153–208.

[51] Mattei MG, Borg JP, Rosnet O, Marme D, Birnbaum D.

Assignment of vascular endothelial growth factor (VEGF) and

placenta growth factor (PLGF) genes to human chromosome

6p12-p21 and 14q24-q31 regions, respectively. Genomics

1996;32(1):168–9.

[52] Klampfer L, Lee TH, Hsu W, Vilcek J, Chen-Kiang S. NF-IL6

and AP-1 cooperatively modulate the activation of the TSG-6

gene by tumor necrosis factor alpha and interleukin-1. Mol Cell

Biol 1994;14(10):6561–9.

[53] Sarma V, Wolf FW, Marks RM, Shows TB, Dixit VM.

Cloning of a novel tumor necrosis factor-alpha-inducible primary

response gene that is differentially expressed in development and

capillary tube-like formation in vitro. J Immunol 1992;148(10):

3302–12.

[54] Vincenz C, Dixit VM. Fas-associated death domain protein

interleukin-1beta-converting enzyme 2 (FLICE2), an ICE/Ced-3

homologue, is proximally involved in. J Biol Chem 1997;272(10):

6578–83.

[55] Roumier A, Olivo-Marin JC, Arpin M, Michel F,

Martin M, Mangeat P, et al. The membrane-microfila-

ment linker ezrin is involved in the formation of the immunolo-

gical synapse and in T cell activation. Immunity 2001;15(5):

715–28.

[56] Chiba J, Rubash HE, Kim KJ, Iwaki Y. The characterization of

cytokines in the interface tissue obtained from failed cementless

total hip arthroplasty with and without femoral osteolysis. Clin

Orthop 1994;300:304–12.

[57] Han X, Sterling H, Chen Y, Saginario C, Brown EJ, Frazier WA,

et al. CD47, a ligand for the macrophage fusion receptor,

participates in macrophage multinucleation. J Biol Chem

2000;275(48):37984–92.

[58] Youssef P, Roth J, Frosch M, Costello P, Fitzgerald O, Sorg C,

et al. Expression of myeloid related proteins (MRP) 8 and 14 and

the MRP8/14 heterodimer in rheumatoid arthritis synovial

membrane. J Rheumatol 1999;26(12):2523–8.

[59] Held TK, Weihua X, Yuan L, Kalvakolanu DV, Cross AS.

Gamma interferon augments macrophage activation by lipopoly-

saccharide by two distinct mechanisms, at the signal transduction

level and via an autocrine mechanism involving tumor necrosis

factor alpha and interleukin-1. Infection Immunity 1999;67(1):

206–12.