Genes and goals: An approach to microarray analysis in autoimmunity

Autoimmunity Reviews 4 (2005) 414 – 422 www.elsevier.com/locate/autrev

Genes and goals: An approach to microarray analysis in autoimmunity Sabine Oertelta,b, Carlo Selmia,b, Pietro Invernizzib, Mauro Poddab, M. Eric Gershwina,* a

Division of Rheumatology, Allergy and Clinical Immunology, University of California at Davis, School of Medicine, 451 E. Health Sciences Drive, Suite 6510, Davis, California, 95616, USA b Division of Internal Medicine, Department of Medicine, Surgery and Dentistry, San Paolo School of Medicine, University of Milan, Milan, Italy Received 17 March 2005; accepted 5 May 2005 Available online 31 May 2005

Abstract A significantly body of data on gene expression patterns in autoimmune diseases has been generated by microarray analysis. Although results are very promising, there are many factors that have detracted from the data. Indeed, no common methodological directions are available. Similarly, collection techniques, processing methods, and statistical approaches are often different. The impact of future studies will depend on the comparison of large datasets to validate results and must include rigorous statistical analysis. To better illustrate the issue we review herein the gene expression patterns observed in five representative autoimmune diseases, i.e. systemic lupus erythematosus, multiple sclerosis, rheumatoid arthritis, dermatomyositis, and primary biliary cirrhosis. We also emphasize how, once potential chromosome regions or pathways are identified, specific array design will be a powerful resource when used on large and representative populations. D 2005 Elsevier B.V. All rights reserved. Keywords: Microarray; Systemic lupus erythematosus; Multiple sclerosis; Rheumatoid arthritis; Dermatomyositis; Primary biliary cirrhosis

Contents 1. 2. 3.

Introduction . . . . . . . . . . . . . . . . . Methodology . . . . . . . . . . . . . . . . Tissue samples. . . . . . . . . . . . . . . . 3.1. Systemic Lupus Erythematosus (SLE) 3.2. Multiple sclerosis (MS). . . . . . . .

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* Corresponding author. Tel.: +1 530 752 2884; fax: +1 530 752 4669. E-mail address: [email protected] (M.E. Gershwin). 1568-9972/$ - see front matter D 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.autrev.2005.05.004

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3.3. Rheumatoid arthritis (RA) . . . 3.4. Inflammatory muscle diseases . 3.5. Primary biliary cirrhosis (PBC) 4. Bioinformatics . . . . . . . . . . . . 5. Future directions . . . . . . . . . . . Take-home messages. . . . . . . . . . . . References . . . . . . . . . . . . . . . . .

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1. Introduction One decade after the introduction of microarray analysis for large transcription detection among yeasts [1], the technology has experienced a vast distribution and an impressive range of applications in human medicine, from cancerogenesis to aging analysis and immunology. The possibility to identify and quantify gene transcripts in a tissue sample, using a single chip, represents dramatic progress in genetic analysis and now potentially allows one to unravel in weeks what would have been a lifetime’s work only 10 years ago. Briefly, the technique is based on DNA microarray chips made of different materials that contain oligonucleotides or purified cDNA samples designed to bind matching mRNA samples plotted on the chip. The chip is scanned and the image analyzed using the fluorescent intensity of the probe value as background [2,3]. In the case of autoimmune diseases, studies using microarrays have used three different approaches: i.e. a field restriction on known candidate cell processes, a broad-scale comparison of samples from healthy and affected individuals, and an analysis of samples from subjects with different disease phenotypes.

2. Methodology During the development of microarray technology two different methods have been used, cDNA and oligonucleotide arrays, reviewed in recent articles [4,5]. These approaches differ in the length of the spotted fragments, about 200 bp for cDNA arrays and 25–60 bp for oligonucleotide arrays, and thus in the quantity of sample needed, 5- to 10-fold lower for oligonucleotide arrays. Further, cDNA arrays consist of two differently marked probes hybridized on the same array and a subsequent ratio extrapolation, while

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oligonucleotide arrays are based on multiple 25-mer oligonucleotides of wild type and single base mutant strands of the same gene. The recent introduction of robotic technology has exponentially increased the potential of a single tissue sample, allowing the investigation of up to 38,000 genes with a single chip.

3. Tissue samples The selection of tissues to be analyzed remains the crucial issue in the design of microarray experiments as autoimmune diseases often present multiorgan involvement. The use of samples from a specific tissue (raising the concern of contamination by several cell types) could lead to data poorly applicable to other organs also involved in the autoimmune injury. On the other hand, however, obtaining tissue samples from all involved organs in the same patient is not feasible. Other major concerns in the collection of samples are due to the high risk of RNA degradation as well as of expression of stress induced molecules (cytokines, heat shock proteins) as consequences of the extraction process. These issues led to the development of media specifically designed for the conservation of RNA obtained from whole blood or other tissues [6,7]. Peripheral blood mononuclear cells (PBMCs) constitute the most commonly employed sample for microarray analysis, being gradient fractionated from whole blood. As mentioned above, new collection techniques involving tissue stabilization have been developed [7], but their use has not been validated. PBMCs offer several advantages over other tissues as they are easily obtained, provide defined amounts of RNA, and are potentially representative of multiorgan conditions. Organ specific autoimmune diseases allow the use of samples derived from involved tissues and potentially minimize confounding factors such as

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sample contamination with unrelated cells. Further, we note that the presence of different cell populations in a tissue sample is a frequently overlooked source of important bias. Laser capture microdissection can overcome this issue allowing the efficient and selective extraction only of target cells. The method, however, presents two major problems, i.e. the paucity of cells obtained, often resulting in insufficient amounts of RNA, and the limited organ applications in different tissues. Finally, intraindividual sampling variability cannot be ruled out; therefore we encourage a blind comparison of gene expression patterns among different samples from the same individual. 3.1. Systemic Lupus Erythematosus (SLE) SLE is a complex and heterogeneous autoimmune disease, the precise pathogenesis of which remains poorly understood. The disease is characterized by a high variability in clinical expression and organ involvement. In recent years our understanding has been advanced by the development of novel genetic and immunological techniques. Among the former, SLE has been widely studied using microarray technology and studies have concentrated on PBMCs rather than affected organs. Rus and colleagues [8,9] first studied the expression of 375 selected genes in PBMCs from 21 patients with SLE and 12 controls; the authors used a 2.5 ratio to define differential expression between groups. The obtained data were then analyzed in clusters and indicated that SLE was associated with a significant overexpression of tumor necrosis factor-a receptor (TNFR), TRAIL, PD-ECGF, IL-1 as well as IL-1 Receptor. Moreover, several growth factors and related receptors, including G-CSF Receptor and PBEF, metalloproteases, and CNTF Receptor were also upregulated in SLE. We also note that no differences in IL-10, CD40L, and IL-6 expression were reported. The same authors then performed a comparative analysis between patients with active and inactive disease, using the same 375 gene arrays. Results confirmed the importance of TNF, protease, cadherin, and neurotrophic factor expression based on a differential pattern in different clinical phenotypes. In 2003, three research groups independently identified a SLE specific gene signature, based on the

consistent overexpression of interferon (IFN), mostly a-related genes. Baechler et al. [10] studied PBMCs from 48 patients and 42 controls and used a 10,260 oligonucleotide array. The previously reported [8] overexpression of TNF-R and IL-1 was confirmed; similarly, the transcription enhancement of Fc receptors of IgG and IgA, MAPK, and the Drosophila analog Jagged 1 was also enhanced in SLE. A total of 161 genes were differentially expressed in patients and controls; among these, 23 were found to be IFN regulated by culture of lymphocytes of healthy donors in IFN-enriched media. The subsequent extrapolation of an bIFN scoreQ related to the gene expression, directly correlated to disease severity expressed by the SLEDAI (SLE disease activity index; [11]) score; accordingly, patients with higher IFN score presented a higher incidence of major SLE complications, such as kidney failure or neurological impairment. The importance of the SLE signature was also confirmed by Bennet et al. [12] who investigated pediatric patients with SLE, juvenile chronic arthritis, and healthy controls. Results demonstrated the presence of 15 upregulated genes, 14 of which were targets of INF. The upregulated genes included IFIT1, MX1, MX2, ISG 15, IRF 7B, C1 inhibitor, TAP1 and TRAIL. Other differences in gene expression were reported by Han et al. [13] who used a 3000 gene oligonucleotide array. The authors reported 61 differentially expressed genes (24 increased, 37 decreased). Interferon N, previously known as interferon a II, was upregulated in most analyzed subjects, up to a 150-fold increase in a single patient. Significantly overexpressed genes in SLE included TNF, OASL, IFIT1, IFIT2, IFIT4, MMP5, LY6E, and TLR5; however, data did not confirm the previously reported upregulation of IL-10 or IL-6 [14]. 3.2. Multiple sclerosis (MS) MS is a primary autoimmune disease of the central nervous system characterized by the presence of demyelinating lesions (plaques) and a wide range of neurological manifestations. Lock et al. [15] first compared acute and silent lesion patterns in early autopsy samples of 4 patients with MS and 2 controls and reported differences in the expression of about 90 genes related to the immune response, inflammation, and apoptosis. In particular, the VJC

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region within the immunoglobulin locus and MAP kinase were 125- and 72-fold over-expressed in active MS lesions, respectively. Moreover, MHC molecules, MCP, MCL1, G-CSF and FGF-2 as well as genes of complement factors were all enhanced in active MS lesions, while the expression of integrin a, histamine and IgE receptors, and TGF h was enhanced in chronic silent lesions, up to 18-fold. Particular importance has been given to the finding of increased IL-1 and IL-6 transcripts, as antagonists of both molecules have been demonstrated to inhibit the disease in animal models [16,17]. Tajouri et al. [18] used a similar experimental design and studied samples from 2 acute and 3 chronic active MS lesions with a 5000 gene cDNA array. Significant differences in expression patterns, up to 500- to 1000fold, were found for integrin a, XRCC9, CNN2, myosin VIIA, cytochrome B5, and ribonuclease 6 precursor. Upregulated genes included MBP, ubiquitin activating enzyme, transferrin, ankyrin, and ribonuclease 1. HLA-A and T-lymphocyte maturation associated protein were more expressed only in acute lesions and should be regarded as markers of immune activation. Further, Mycko and colleagues [19,20] compared the transcripts between different brain areas in 4 patients with MS using a 588 cDNA array. The marginal zone presented an expression pattern consistent with transcription rates from previous data [15,21], such as a 6-fold upregulation of MAPKK1 and a 5-fold upregulation of HSP90A. Other genes involved were IFN-g, ADORA1 receptor, and nerve specific genes, such as NT3 and NGF2. Relative overexpression of CCK4, IL-6, CASP9 and TNF was detected in chronic active lesions when compared to inactive ones. Chronic active lesions also express IFN-g over background levels, but less then active lesions, delineating a transcription gradient that might correlate with inflammation. Due to their similar natural histories characterized by the alternation of silent phases and clinical reactivation, SLE and MS were used for comparative analysis. Mandel et al. [21] therefore analyzed PBMCs from 5 patients with SLE, 13 with MS, and 18 healthy subjects. The authors reported a common pattern as well as specific pathological features, each made of hundreds of specifically expressed genes. IL-1 h, IL11 receptor, CD19, as well as caspase and TRAF were consistently upregulated in both diseases, thus con-

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firming the involvement of cell activation and degradation pathways. The MS-specific gene signature included cell interaction impairment and inflammatory upregulation, as represented by reduced NFkB and HSP and increased CD24 and IL-15 transcription. On the other hand, the SLE pattern demonstrated an upregulation of MSH3, POLS, MBD4, DNA damage inducible genes and impaired antiapoptotic function (represented by TIAL1 expression). Transcripts encoding SLE autoantigens such as NXP2 and TOPBP1, antinuclear matrix protein, and DNA topoisomerase II were also upregulated. In particular, these results could relate to the production of autoantibodies after externalization of intracellular structures during apoptosis. We also note that the increase in IL-19 expression might be of interest, given its promoter activity of spontaneous systemic autoimmunity in transgenic mice [22]. Aune et al. [23] analyzed the dynamics of the immune response in PBMCs from 9 healthy subjects using a 4400 cDNA array. Samples were obtained at baseline and after immunization with influenza virus and compared to the results of gene expression in MS, SLE, type-1 diabetes and rheumatoid arthritis (RA). Data indicated a common expression pattern in all autoimmune diseases and differentiated it from the immune response to infections, thus supporting the hypothesis of a diseasespecific gene expression pattern, whether genetically determined or not. Such overexpressed genes included HLA and MHC molecules, TGF-hR, CSF, NFkB genes, FASTK; on the other hand, pro-apoptotic and inhibitory factors such as caspases, TRAF, protein 53, and cyclin dependent kinase inhibitors were downregulated. In the same study, healthy first degree relatives of patients were also investigated and consistent results with affected individuals were observed in 70–80% of genes. 3.3. Rheumatoid arthritis (RA) Rheumatoid arthritis is a common complex autoimmune disease that presents a significant genetic element with HLA-DRB1 as the only associated locus although accounting for less than half the overall genetic susceptibility. For these reasons, the study of gene expression using microarray has been pursued. First, Devauchelle et al. [24] analyzed differences of gene expression in synovial specimens

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from 7 patients with RA and 12 controls with osteoarthritis (OA) using a 5760 gene cDNA arrays. The cluster analysis revealed 63 differentially expressed genes, partially clustered in susceptibility regions on different chromosomes. Among other genes, 2- to 4fold expression differences were identified for clusterin, cathepsin cyclin-dependent protein kinase 7, and TIMP2. The cluster regions mapped on chromosome 6, close to the MHC locus, on chromosome X, and on 4 other chromosomes. Some of the genes, such as cathepsin, might be linked to disease-specific pathology in cartilage and bone erosion. These data do not identify RA markers, but reveal potential generic joint impairment patterns. CTSL (Cathepsin L) was selected by the authors as an erosion marker and as a possible therapeutic target. Van der Pouw Kraan et al. [25] analyzed synovial tissue from 15 patients with RA on a 24,000 spot oligonucleotide array and used a tree statistic for the analysis. The patients were grouped in two major clusters with distinctive gene expression, RA I and RA II, with the former presenting overexpression of the HLA genes, immunoglobulins, MMP3, HSPA1A, IL-16, and T cell receptor a, and the latter RAII a transcription increase in troponin, collagen and creatine kinase genes. Both groups shared the upregulation of CXCR4, SDF1, TNFSF13h, and CD8. Interestingly, another commonly overexpressed gene was Wnt5, but so were the Wnt antagonists DKK3 and SFRP2. A relevant number of identified genes cluster on 6p21, the human MHC locus, the same region was also identified by Devauchelle. Finally, we also note that Ruschpler et al. [26] used synoviocytes from 20 patients with RA and 10 controls with OA and concentrated on the CXCR3 overexpression and the contemporary increase of its ligands CXCL9 and 10; results indicated CXCs as playing a role in RA pathogenesis. 3.4. Inflammatory muscle diseases The etiology of dermatomyositis (DM) and polymyositis (PM) remains unknown, but multiple steps of the disease pathogenesis have been identified and point towards alterations of the immune response. Moreover, genetic predisposition is multifactorial with MHC associations providing the strongest evidence [27]. Zhou et al. [28] compared diagnostic

muscle biopsies of 10 patients (6 with PM and 4 with DM) and 5 controls using a 4000 gene cDNA array. Cluster analysis and a Wilcoxon rank-sum test suggested a shared over-expression of MHC class I molecules and inflammatory protein genes, thus confirming previously reported genomic associations. In PM and DM, while genes preserving myofibril integrity and function were less transcribed, h2 microglobulin, HLA-C, immunoglobulin chain, NK cell protein 4, and IFN inducible proteins were to 2- to 5-fold upregulated. Similarly, while collagen III a was found overexpressed, tropomyosin a and h and actinin were 2-fold underexpressed. These findings indicate a high concordance between the genetic pattern and the clinical manifestations, although the overlap in gene expression patterns between PM and DM appears to contradict their apparently diverging immune assets, with the alterations of humoral and cellular immunity playing a role in PM and DM, respectively. 3.5. Primary biliary cirrhosis (PBC) The study of gene expression patterns in PBC has so far received limited attention. Shackel et al. [29] used two different cDNA chips, 268 cytokines and respective receptor genes and 588 commonly expressed genes, to compare liver biopsies from patients with PBC (n = 6), primary sclerosing cholangitis (n = 4), and healthy controls (n = 8). Regression plots noted a 70% expression similarity among both biliary diseases when compared to controls, but still revealed distinctive features. PBC samples presented a significant upregulation of SDF-1 receptor, CXCR4, CD135 connective tissue growth factor, and a high number of Drosophila analogs, such as Wnt-13, Jagged-1, PTC, also found in other autoimmune diseases. Cytokines such as IL-3, IL-5, and IL-15 as well as growth factors of the TGF and FGF families, were also overexpressed. Apoptotic impairment was associated with caspase and inhibitory protein downregulation. Of particular interest and difficult explanation is a single 80-fold transcription increase of transcription initiation factor 250, not previously identified. Wnt13 overexpression in biliary epithelial cells of PBC patients, associated with an increased TGF h receptor, CD24 signal transducer, and calmodulin expression were also consistent with previous reports [30].

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4. Bioinformatics A wide range of statistical tools are required to analyze data obtained from microarrays, including but not limited to the generalization of t-Test statistics to multiple groups such as ANOVA, the hierarchical clustering methods, and the centroid algorithms focusing on tree-like associations between expressed genes. The general approach suggests that the acquired data must be divided into subgroups according to expression similarities, thus providing a visual and intuitive classification. While these statistic methods study correlations, others such as class prediction focus on the integration of the acquired information in predefined structures, potentially correlated to disease progression or therapy response. Both approaches might be suitable but should be evaluated

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for the specific aims as they analyze data using different logical pathways. Thus, the selection of the analytical method is triggered mainly by the basic hypothesis and the desired predictive power of the study [31]. Ideally more than one method should be applied to confer multiple levels of interpretation to a dataset and allowing cross-sectional comparisons of different sources. Table 1 illustrates the major reported alterations in gene expression. Genes markedly overexpressed are indicated (over less significant differences) and only few disease specific aspects have been considered. This table is not exhaustive of all reported gene patterns and should therefore be considered as an example. However, the data presented share the potential to provide insight into common pathways of autoimmunity; importantly what remains to be clari-

Table 1 Altered gene expression patterns reported in autoimmune diseases Diseases

Cell receptors adhesion molecules

Inflammatory molecules growth factors

Apoptosis/cell remodeling

Disease specific gene expression

Ref.

SLE

CTNFR, IL-1RII, TNF-RII, CD64, TRAIL, CXCR-2, CCR-7, CDH3, EphA3, IL-1R, G-CSFR

PD-ECGF, PBEF,

MMP-3, TIMP

CNTFRa, FGF, ITGA7 INF IFIT1, MX1, MX2, ISG15, IRF7B, C1 Inh.

[8–10,12,13]

CD47, SULT, MC4R

LOC243461, RNASE3, IGF-1, G-CSF, FGF2, VJC, IL-1, IL-6, IL-8, TNF IL-17, MOX1, IGHD

MAPKK, CASP9

NEUROD, GJB2

[15–20]

TPS, MMP-1

COL9A3, TGF h3, TNC

HSPA1A, IL-16

TIMP2, MMP3, CDPK7

MS Active plaques

Chronic plaques RA

DM/PM

MS4A2, HRH2, PTAFR, c-erbA CXCR3, CXCR4, CXCL9, CXCL10, TNFSFR13B HLA-C, HLA-A, B2M, HLA-DRB1

NKAT-4, IGLC, CST, STAT, C1S

PBC

FGFR1, FGFR3, PDGFR, CD49, SDF-1, CXCR4, CD24

BDNF, flt3, IL-4, IL-5, IL-3, IL-13, VLA 5, VLA6, TAFII250, HSP70.1, HSP90A

STK1, CASP2, CASP6, CASP8, Bcl-2, IAP1, IAP3, BAK

MS, SLE, IDDM, RA

CSF3R, HLA-DMB, HLALS, TGFBR2, RARA, ESRRA

MSTP9, BDNF, ELA3, SPINK2, CYR61, MPP3

CASP6, CASP8, APAF1, TPS3, PIG11, MAP4K2, CDKN1B, CKN2A, p53, NFkB, FASTK

(Black=overexpressed, green=underexpressed, red=bINF-signatureQ).

[24–26]

COL3A1, COL1A2, TMSB4, TPM 1!, TPM1! CTGF, TGFh3, TGFh2, FGFR3, [Drosophila analogs: Jagged1, Wnt-2]

[28]

[29,30]

[21,23]

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fied is the origin of autoreactivities. Why certain individuals develop autoimmune diseases and firstdegree relatives do not (despite a familial clustering of autoimmune diseases and subclinical immunological disturbances)? Why are women predominantly affected? What mechanisms break tolerance? These are questions that refer to common characteristics of autoimmune diseases and might therefore be referred to a common background.

5. Future directions In 2001, King and Sinha [32] presented a complete review of the potential problems of microarrays; 4 years later, several issues remain unsolved. Some of the potential sources of differences in such studies are depicted in Fig. 1. First, the economic costs are still extremely high, translating into reduced analysis and eventually in insufficient statistical power. Second, no universal reference values for tissue specific gene expression are available, despite a preliminary attempt made using tumor cell lines [33]. Third, a major issue is the lack of standardized collecting procedures. RNA quality evaluation protocols are being developed [34] to ensure reliability of the presented data. Fourth, statistical comparisons often translate in transcriptional differences of 2- to 5-fold when compared to the bnormalQ expression, an extremely low and sometimes non-significant difference [35]. Twenty-fold or higher

increases are rare. Fifth, the number of differentially expressed genes covers hundreds of transcripts, making additional information sources, as linkage analysis, fundamental to avoid false positive findings that corrections for multiple testing fail to avoid. We welcome the creation of large databases that will make the micorarray information available to the scientific community in a format more agile than the peerreviewed publications. Sixth, different array typologies and different samples make comparison still difficult. Interestingly, a recent study compared the results obtained from 6 different methods [36] and demonstrated how the comparison of different platforms might be possible. Results, however, further emphasized fundamental differences based on biological variability, not necessarily on differences between techniques. Seventh, we note that other approaches for candidate gene identification are based on inference from animal data. To address this issue, Liu et al. [37] compared murine and human immune expression patterns and reported low correspondence rates between the two species, even when analog pathways were analyzed. Technology improvement will lead to the division of microarray analyses in two major branches. New types of arrays, specifically created for an application, such as the bLymphochipQ [38], used for analysis of leukocyte malignancies or specific autoantigen arrays, which might target overlap syndromes or clinical findings, have been designed. Similarly, Robinson et al. [39] designed an original

Fig. 1. Sources of potential biases in microarray analysis.

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chip using autoantibodies of eight autoimmune diseases, antibodies against human IgG and IgM, and other autoimmune-correlated proteins. The array was then validated using patient sera and showed a sensitivity 4- to 8-fold higher than conventional ELISA. The possibility of designing arrays based on specific needs, pathways or genes on particular chromosomal regions, identified either by linkage or clustering after array analysis, discloses novel possibilities in the study of autoimmunity or other complex diseases. From a clinical standpoint, the use of arrays will demand more accurate and powerful statistical analysis, once again emphasizing that the power of a study will depend on the dimensions of the study cohort, as well as on the number of genes and the threshold values used. Take-home messages ! Microarray analysis has been widely used for the study of autoimmune diseases; a particular gene expression pattern was detected in SLE. ! Uniform techniques in tissue collection and processing as well as statistical analysis remain critical issues for the comparison of data. ! Comparative analysis of large populations of patients is needed for data validation and extrapolation.

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Autoantibody activity in Waldnstrom’s macroglobulinemia. Some monoclonal proteins from patients with Waldenstrom’s macroglobulinemia (WM) or immunoglobulin (Ig) M monoclonal gammopathy of undetermined significance possess antigen-binding activity directed to autogenous or foreign antigens. In a review by Stone MJ. et al. (Clin Lymphoma 2005;5:225–9), this issue was summarized by showing that these monoclonal IgM autoantibodies include cold agglutinins, mixed cryoglobulins, and anti-neural components. Patients with these autoimmune syndromes often present with hemolytic anemia, mixed cryoglobulinemia, or peripheral neuropathy, respectively, at an earlier stage than patients with typical WM who do not have evident antibody activity. Monoclonal IgM antibodies display polyreactivity to antigens of microbial origin in addition to self-antigens and may arise through T-independent as well as Tdependent pathways. Waldenstrom’s proteins with antibody activity appear to provide a link between autoimmunity, infection, and lymphoproliferative disease. Study of the antigens reacting with monoclonal IgM’s may provide further insight into the pathogenesis of WM.