EP1395683A2 - Tools for the diagnosis, molecular definition and development of treatment of chronic inflammatory joint diseases - Google Patents

Abstract

The invention relates to tools for the diagnosis, molecular definition and development of treatment of chronic inflammatory joint diseases and other inflammatory, infectious or tumourous diseases. According to the invention, genome data (genomics), proteome data (proteomics) and immunome data (immunomics) are used in the analysis and development of treatment of chronic joint diseases. The invention is based on the use of gene sequences and derived mRNAs and proteins, in addition to antibodies having a specific nature for the derived proteins, for characterising inflammatory and non-inflammatory rheumatic joint diseases, auto-immune diseases and infectious diseases. Etiologically significant pathogenicity principles of chronic inflammatory joint diseases which have been unclear until now can be derived from the examinations carried out. Furthermore, interpretation algorithms can be created for the classification, prognosis evaluation and treatment optimisation of said joint diseases, and new strategies for treatment and points of attack for medicaments can be derived.

Description

 Tools for the diagnosis, molecular definition and therapy development of chronic inflammatory joint diseases

description

The invention relates to tools for diagnosis, molecular definition and therapy development of chronic inflammatory joint diseases and other inflammatory, infectious or tumorous diseases on the basis of genomics (genomics), proteomics (proteomics) and immunomas (immunomics) in the analysis and therapy development chronic joint diseases. The invention is based on the use of gene sequences and derived mRNAs and proteins as well as antibodies with specificity for the derived proteins for the characterization of inflammatory-rheumatic and non-inflammatory rheumatic joint diseases, autoimmune diseases and infectious diseases. Etiologically important pathogenicity principles of previously unexplained chronic inflammatory joint diseases can be derived from the studies. Furthermore, interpretation algorithms for the classification, prognosis assessment and therapy optimization of these joint diseases can be set up and new therapy strategies and targets for medicines can be derived.

Characteristics of the known problem

Chronic inflammatory joint diseases have not been aetiologically clarified. Rheumatoid arthritis (RA - list of abbreviations behind the examples) is the classic representative of these diseases. Significant disease processes take place in the inflammatory synovial membrane and lead to chronic joint damage. The clinical presentation is very heterogeneous and suggests that there are different entities with the common symptom of destructive synovitis. These diseases are also to be understood as systemic diseases, in which numerous changes in the blood are observed and sometimes serious organ manifestations can occur.

Excessive inflammatory activities due to incorrect regulation in the inflammatory cascade are discussed as the main pathomechanisms responsible. Furthermore, autoimmune reactions are described which suggest that the specific humoral and cellular immune system is involved in the disease process. However, other mechanisms such as enzymatic tissue destruction, cell and tissue proliferation or regeneration are also discussed, which could also play an important role in pathogenesis. So far, it has not been possible to conclusively clarify whether these pathomechanisms mentioned are the only and only relevant ones. It is also unknown which parameters can capture all these changes at the same time. As a result of the insufficient pathophysiological understanding, there are many medications, but the main representatives only pursue one essential therapy concept:

Based on the common symptom of excessive inflammation, the current therapy aims to suppress the inflammation. So-called basic therapies have an immunomodulatory and disease-modifying character. They intervene in the basic mechanisms of cell metabolism and cell activity (e.g. methotrexate, azathioprine). However, the comprehensive principles of the molecular mode of action of these therapies for joint diseases are not well known. Corresponding parameters are therefore also missing in order to control the therapy efficiency of individual basic therapies differentially and specifically in individual cases.

Previous tools

Patients with joint inflammation are assessed in everyday clinical practice according to the following criteria: reported course of the disease (anamnesis), clinical examination (involvement pattern of the joints, organ involvement), inflammation parameters (non-specific inflammation indications from serum electrophoresis, blood sedimentation and the C-reactive protein), autoimmune parameters (rheumatoid factor , antinuclear antibodies and a few special autoantibodies such as anti-Ro, -La, - U1RNP, -Sm, -Histon, -Scl70, -Centromer, -dsDNA, -phospholipid antibodies), genetic disposition via HLA markers (DR4, B27, DR3), imaging (destructive changes in the x-ray findings of the joints), extended organ diagnostics through routine parameters of laboratory diagnostics (liver enzymes, muscle enzymes, kidney retention values) and, if necessary, advanced sonographic, radiological and magnetic resonance imaging techniques. These provide only very limited statements about the prognostically expected aggressiveness of the disease or the concrete prospect of success of a basic therapeutic in the individual case of illness. In addition, the diagnostic criteria today are not designed to adequately classify the variety of manifestations within the most common arthritis disease, the RA (1 - references behind the examples). Especially in the early phase of the disease, the diagnosis is difficult and uncertain. But after a year of illness, the majority of patients experienced irreversible joint damage. It is known from early arthritis studies that earlier confirmation of the diagnosis, followed by adequate therapy, brings about decisive improvements in the long-term development of the disease. Therefore, new methods and criteria are urgently needed that integrate molecular features beyond the clinical picture. The monitoring of the success of the therapy has also been carried out to date using the methods of diagnostics mentioned above. Many of these parameters change very slowly. she require many weeks to months of follow-up to make a statement as to whether the chosen drug is effective. It is not uncommon to switch to another medication due to poor improvement and progression of the disease. In principle, it is not possible to cure the diseases with the medication currently available.

Experimental approaches

In addition to the established tools, there are numerous experimental approaches to improve diagnostics, particularly in RA.

This concerns the search for key proteins that 1.) maintain or prevent the inflammatory process in a central position, 2.) are significantly involved in the enzymatic destruction of the cartilage and bone matrix or inhibit these enzymes, or 3.) regenerative and induce reparative processes or inhibit their opponents. The role of the inflammation-mediating cytokine tumor necrosis factor (TNF-) alpha and interleukin (IL-) 1 beta has been shown to be essential and has brought appropriate therapeutic approaches into clinical use. Although inhibition of TNF-alpha can in many cases alleviate RA that cannot be influenced sufficiently by conventional means, the success does not lead to a cure for the disease. In some cases, the inhibition is so strong that infections or even septic complications arise and still insufficient control of the arthritis is achieved. This suggests that the TNF-alpha inflammation pathway at least cannot be the only central pathomechanism of the disease. In addition to the two cytokines mentioned, the role of numerous other messenger substances in the pathogenesis of arthritis is being researched. In addition, the connected intracellular signaling pathways increasingly come into the focus of therapeutic intervention options.

Matrix metalloproteinases and cathepsins are also at the center of the enzymatic destruction of cartilage and bone. Studies on regenerative mechanisms are just beginning to be explored. Messenger substances from the Transforming Growth Factor (TGF-) beta family are primarily to be named here. A large number of these representatives play a major role in the development of the musculoskeletal system. Initial studies on synovial tissue and cartilage have shown that representatives of this group of growth factors and morphogens are also produced in adult synovial tissue. In the case of inflammatory joint diseases, it was possible to show in our own studies that some of these factors are obviously relatively reduced. Furthermore, for bone morphogenetic protein (BMP-) 7 it could be shown that the cellular invasion into the forming artificial cartilage tissue is suppressed (2). Many of the factors and enzymes mentioned can also be found in other joint diseases such as osteoarthrosis or reactive arthritis and do not constitute a diagnostic parameter in themselves.

The experimental approaches also aim to cause autoreactive T and B cells to appear in RA and to count the disease as an autoimmune disease. This goes back to the discovery of the so-called rheumatoid factor, an autoantibody that is directed against immunoglobulin G. However, rheumatoid factors only occur in about two thirds of RA patients, but also in other rheumatic and non-rheumatic diseases and even in up to 5% of healthy people (significantly higher with increasing age). The appearance of rheumatoid factors appears under certain pathological conditions, e.g. bacterial endocarditis to be a physiological response of the body. Autoreactive B cells with specificity for IgG apparently exist in a large part of the population and can be activated by different mechanisms. However, the term "rheumatoid factor" has been retained because diagnostic and prognostic significance only arise for RA. The same characteristics apply, however, qualitatively to almost all previously known autoantibodies in RA: the frequency of positive patients is well below 100% and the disease specificity is also sometimes well below 100%. The clinically pronounced heterogeneity of RA in the pattern of infestation, the intensity of the inflammation and the batch-wise course contrasts with a heterogeneity of the immunologically incorrectly regulated processes. This clinical and immunological heterogeneity also supports the assumption that "rheumatoid arthritis" could be a collective term for different disease entities. The distinction between RF-positive and negative (RF - rheumatoid factors) RA serves as a prime example, whereby the former form is said to have a more severe course with higher destruction potential and systemic humoral activity. The term seronegative even incorrectly implies the absence of any autoantibodies. But neither the rheumatoid factor nor any of the known auto-reactivities has been proven to be the cause of the development of RA or one of its postulated subgroups or forms.

Autoantibodies are used as the main representative in other rheumatic autoimmune diseases such as collagenosis with systemic lupus erythematosus (SLE). A primary pathogenicity of these autoantibodies is repeatedly discussed. With a high autoantibody titer in connection with an uncontrolled excessive release of autoantigens in the disease flare, the resulting immune complex formation and complement activation is associated with organ damage, particularly of the kidney, and vasculitic features. However, the importance of autoreactive B and T cells for RA is unclear. Instead, new autoantigens are always described as targets for an auto-reactive immune response in RA. Some of these antigens are biochemical and well characterized in terms of their antigenicity, while only a few parameters are known from others. For their discoverers some autoantibodies are very promising because the B and / or T cell response appears to be highly RA specific. Interest in these antibodies quickly declined when the same auto-activity could also be demonstrated in other autoimmune diseases. In the meantime, numerous T-cellular autoreactivities have been characterized in RA, of which only a few are RA-specific.

Heatshock proteins

The RA was suspected early on of being an infectious disease. Various xenogeneic - mostly microbial and viral - antigen sources were therefore investigated in order to identify potential pathogens as triggers for auto-reactivity. One of the potentially RA-inducing agents was Mycobacterium tuberculosis because it induces adjuvant arthritis in the animal model, a disease that resembles human RA in certain aspects. This experimental disease could also be triggered by mycobacterial sweatshock protein 65 (mt-Hsp65) or with T cells that are specific for this antigen. Sweatshirt proteins help natural proteins to fold correctly and create tertiary and quaternary structures. mt-Hsp65 is homologous to the essential Hsp60 in mammals. Reports of mt-Hsp65-specific T cells and antibodies in synovial fluid from RA patients suggested that the highly homologous human Hsp60 would be recognized as an autoantigen in RA patients. However, these antibodies are not RA-specific: They also occur in patients with Reiter's syndrome, SLE and active tuberculosis, as well as in healthy people. Although the reactivity to mt-Hsp65 does not seem to play a dominant role in RA, human Hsp60 could nevertheless be important in the pathogenesis of RA: in its amino acid sequence, human Hsp60 has an identity across ranges from 11 to 22 amino acids with proteins such as cytokeratin and Hsp90 , It is therefore conceivable that autoreactive T cells or antibodies against these proteins originally result from naturally occurring - but strictly regulated - Hsp60 reactivity.

Dna J

Dna J, the bacterial stress protein with homology to the mammalian Hsp70, has the amino acid sequence QKRAA, better known under the name Shared Epitope, which predisposes to RA (3). This epitope also occurs in the Epstein-Barr virus (EBV) -coded protein gpl 10. Dna J is the target of autoreactive T cells in RA, but not in healthy people (4). Although it is still unknown how shared epitopes predispose for RA, a conceivable mechanism is the generation of the shared epitope peptide from non-MHC proteins and their subsequent presentation on MHC class II molecules with the triggering of an immune response against foreigners (EBV-gpl 10) and self (MHC class II). EBV-encoded nuclear antigen

Epstein-Barr virus (EBV) was suspected early on to trigger RA, although it has only recently been detected in the synovial fluid of RA patients. An antibody directed against the EBV-encoded nuclear antigen (EBNA-1) showed strong reactivity with a p62 protein from synovial cells in RA patients. EBNA-1 contains a glycine-alanine repeat sequence (IR-3) that is recognized by autoantibodies in patients with RA, SLE, systemic sclerosis (SSc) and infectious mononucleosis as well as in healthy individuals with comparable frequencies. EBNA-1 shows cross-reactivity with numerous human proteins, typically via the IR-3 sequence. Among them are essentially p62 and p542, the latter being recognized mainly by antibodies from patients with infectious mononucleosis, but also from RA patients. p542 was recently identified as a 71k component of hnRNPs due to its high sequence identity with the mouse hnRNP called Raly and similarities to the human hnRNP C2.

Sa antigen; Filaggrm, citrullinated peptides / proteins The Sa antigen (5), like Filaggrin, are two recently known antigens that do not occur in the inflamed joint, but which attracted attention due to the very RA-specific immune response. The Sa antigen is a 50k protein from human spleen and placenta. Sa-specific antibodies occur in 43% of RA patients and have a disease specificity of 78% to 99%. Filaggrin is a 42k protein that crosslinks intermediate filaments, especially cytokeratin, and occurs in the endothelium. Filaggrin-specific antibodies appear to be the same as the long-described "antiperinuclear factor" and the so-called antikeratin antibodies. The main determinant of the epitope (s) recognized by anti-filaggrin antibodies is citrulline, a post-translationally modified arginine (6, 7). The sensitivity of these antibodies is between 36% and 91% and the specificity between 66% and 100%). Although filaggrin is only extra-articular, citrulline has now also been detected in synovial cells.

Collagen II

Type II collagen is an essential component of the articular cartilage and therefore appears to be predisposed as an autoantigen for RA. Consequently, many studies have dealt with the role of the collagen-specific immune response. Mouse T cells that react with bovine type II collagen are specific for an epitope that also occurs in human collagen II and also overlaps with an important T cell epitope from mice with collagen-induced arthritis. Type II collagen is a component of the extracellular matrix that forms triple helices from identical tropocollagen subunits, which in turn are processed from the even larger procollagen. B cells with specificity for collagen appear to be inflamed Joints of patients with RA appear more often. Collagen II-specific T cells occur both in RA patients and in healthy people.

Of particular interest was the collagen reactivity in the context of the oral tolerance studies in RA. Oral tolerance can be created in the animal model by antigens that occur in the compartment of the (autoimmune) inflammation, but are not necessarily involved in the inflammatory process itself. If such an antigen is administered orally, T cells with specificity for the fed antigen are apparently tolerated and can then be suppressed at other locations, the inflamed joint, via suppressive factors, e.g. IL-10 and TGF-ß, which produce so-called bystander suppression. Such collagen II-specific T cells should modulate the inflammation in RA. However, three placebo-controlled, double-blind oral tolerance studies with Collagen II did not show a remarkable improvement in disease activity. The same applies to the clinical studies with peptides from Hsp65 (Subreum).

Chondrocyte Antigen 65 (CH65)

Chondrocyte membranes have been described as targeting autoreactive T cells in RA and osteoarthritis patients (8), whereas normal donor T cells did not respond. In addition, chondrocyte membranes are recognized by autoantibodies in 70% of RA patients. Antigen is the cartilage-specific CH65, which has sequence similarity to mycobacterial Hsp65 and certain cytokeratins. CH65 has a high proportion of glycine - similar to, but not identical to, Hsps. The sequences are similar to those of cytokeratins, but are still completely untypical for them. Such similarities tempt to assume molecular mimicry between mycobacterial and human Hsps with other proteins. However, there is no cross-reactivity between monoclonal antibodies with specificity for CH65, cytokeratin or Hsp65. T cell reactivity was only investigated against unpurified chondrocyte membranes.

HCgp39 There are many autoantigens in the synovial fluid that have only been tested in small patient and control cohorts. One example is the human cartilage glycoprotein (HC gp39), an important product that is secreted by articular chondrocytes, synovial cells, macrophages at later stages of differentiation and neutrophils. The gp39 level is increased in patients with degenerative joint disease compared to healthy people in serum and synovial fluid. It was later shown that an increased titer not only occurs in osteoarthritis but also in colorectal cancer, alcohol-related cirrhosis and breast cancer. gp39 is not only important for tissue remodeling and degradation of the extracellular matrix, it is also the target of autoreactive T cells in RA. Accordingly, peptides from the gp39 sequence have also been tested to bind HLA-DR4 (DRB1 * 0401) and to stimulate T cells. gp39-reactive T cells were detected in 8 of 18 RA patients and 3 of 11 healthy people become. In the animal model, immunization of Balb / c mice leads to chronic arthritis with relapses, which in turn could be cured with nasal application of the gp39.

rheumatoid factor

At the same time, the best known autoantigen of the RA is not tissue-specific, but can appear almost ubiquitously. It is immunoglobulin G (IgG) as the target for other antibodies, the so-called rheumatoid factors (RF). The rheumatoid factor remains the only serological parameter that is included in the American College of Rheumatology (ACR) criteria. The pathological relevance of RF for RA is still a matter of controversy, because RF also occurs in patients with SLE, Sjögren's syndrome, endocarditis, liver diseases and even in healthy people. The RF titer is not strict with RA clinical or serological activity. or correlated joint destruction.

hnRNP A2 protein (RA33)

The A2 protein from human nuclear ribonucleoproteins (hnRNPs) is a ubiquitous protein that was originally described as RA33 autoantigen. Subsequently, its identity with the A2 component was shown, as was the reactivity with sera from patients with SLE, mixed collagen diseases (Mixed Connective Tissue Disease; MCTD) and others. A2 is complexed with numerous other factors that make up the hnRNPs in the cell nucleus. The exact function of the A2 is unknown, however, a function is assumed for the splicing of human nuclear rbonucleic acid (hnRNA). Therefore, A2 also has two RNA binding domains and a core import / export signal. Antibodies in RA and SLE are directed against the region between the RNA binding domains, while those of MCTD patients (Mixed Connective Tissue Disease - Mischkollagenose) recognize a discontinuous epitope, which is composed of both RNA binding domains. It is not yet clear how the immune system comes into contact with A2. From the point of view of the homunculus, however, hnRNPs are good candidate antigens for RA. So far, however, it can only be speculated that A2 will reach the cell surface under certain circumstances, e.g. when cells decay due to inflammation.

Calpastatin

Calpastatin is a ubiquitous cytoplasmic protein with a molecular mass of 72k and four inhibitory domains for calpaine. Calpaine include a family of cysteine proteases suspected of being involved in joint destruction in rheumatic diseases. Calpains are cytoplasmic and are stringently regulated by calcium ions for activation and by calpastatin for inhibition. After activation of cells, calpastatin also occurs extracellularly and is thus accessible to antibodies. Calpastatin is recognized by autoantibodies in patients with RA, SLE, polymyositis dermatomyositis (PM DM), MCTD, activated arthrosis and venous thrombosis. In the animal model on calpastatin-deficient rats, none can be found Trigger symptoms of arthritis. Calpastatin, calpaine and calpastatin-specific antibodies are found in the inflamed joints of RA and OA patients and can therefore be involved in the pathogenesis of these diseases.

Calreticulin Calreticulin is a ubiquitous protein of the endoplasmic reticulum (ER), which under certain conditions also occurs in the cell nucleus, the cytoplasm and on the cell surface. It is a highly conserved Ca ⁺⁺ binding protein. Calreticulin is the target of autoantibodies in various diseases of autoimmune or inflammatory origin, mainly in SLE and onchocerciasis, but also in RA. Finally, the RA-associated haplotype DR4Dw4 / DR53 binds a peptide from calreticulin.

BiP (Heavy Chain Binding Protein)

Another promising target antigen for the RA homunculus is ubiquitous BiP (binding protein), which was originally described as heavy chain binding protein because it interacts with the heavy chains of immunoglobulins. BiP is itself a resident ER protein and has a peptide sequence that ensures that it is not normally exported. It is now known that BiP is a so-called molecular chaperone (chaperone) and as such interacts with most proteins that are transported into the endoplasmic reticulum (ER) and take the secretory path. In addition to this essential functionality, BiP is overexpressed by stress factors such as heavy metal ions or agents that influence the cell's calcium ion balance or the integrity of protein biosynthesis. Under these conditions it can even be detected in the cell nucleus, but also on the cell surface.

BiP is the target of autoreactive antibodies and T cells in 66% of RA patients and was originally described against the background of RA as p68. The disease specificity of these autoantibodies is extremely high at 99%. The antigen is O-glycosylated and it is speculated that this modification could have a regulatory function like Mono-O-GlcNAc on many other proteins. In these proteins, the switch from O-GlcNAc to O-phosphate modification is coupled with a change in the activation state or in the cellular compartment. Similarly, a stress-induced shift in BiP from ER to the nucleus or cell surface could be of pathogenetic importance. The rather unnatural presence of BiP on the cell surface could serve as an alarm and activation signal for other cells, including cells of the immune system. In RA, such activation could be due to local infection or other inflammatory tissue damage. BiP could reach the surface of damaged cells due to cell or tissue damage and then become the target of autoreactive T cells. There are indications that these BiP-reactive T cells also occur naturally, which are down-regulated again by regulatory T cells after the conditions causing them have subsided. These regulatory cells are antigen-specific and HLA-restricted. The HLA Restriction of the regulatory T cells differs from the HLA restriction of the effector T cells and can be specifically inhibited. Here again, the epitope O-GlcNAc could play a special role: It is conceivable that this epitope is not only the target of the autoantibody response, but also of the T cell response.

p205

Another protein that has been isolated from synovial fluid, but whose function extends far beyond this compartment, is the p205 antigen. It is the target of autoreactive T cells in RA patients. p205 is also expressed in the synovial membrane and is probably the antigen with the highest T-cell stimulatory capacity in RA and sometimes even reaches the proliferation rate that can be achieved with synovial fluid or even with the lectin phytohemagglutinin (PHA). The function of the p205 antigen is still unknown. However, it has an 11 amino acid sequence that is identical to an area of IgG, namely in the region between the constant domains C _H 2 and C _H 3, an area in which rheumatoid factors bind. This region of the p205 is both bound by monoclonal rheumatoid factors and recognized by autoreactive T cells. Furthermore, p205-specific T cells help stimulate with cognate antigen B-cell to secrete rheumatoid factors. It can therefore be assumed that this is the first time that an antigen has been found that has T cell reactivity and is also able to help B cells with specificity for IgG for affinity maturation. In contrast, a T cell reactivity against intact IgG or IgG fragments has not been found so far. The amino acid sequence of the p205 may be a peptide which apparently does not or does not arise in sufficient quantities in vivo when IgG is processed. Thus it appears likely that auto-reactivity against p205 induces the production of rheumatoid factors in RA.

This compilation of RA-associated auto-reactivities shows that many different autoantigens become targets of the immune system in the course of RA. These are to a greater or lesser extent also in other rheumatic and non-rheumatic diseases, up to a healthy state, the target of the immune system. It thus remains to be stated that, according to the prior art, no auto-reactivity alone is suitable for improving the diagnosis of RA, neither in the early stages nor in the course or for the monitoring of therapy.

The essence of the invention

The invention has for its object to make chronic inflammatory joint diseases more recognizable and treatable. This task is accomplished by providing the "tools for diagnostics, molecular definition and therapy development described below chronic inflammatory joint diseases "and other inflammatory, infectious or tumorous diseases solved.

High-throughput methods such as DNA array or protein array technology enable the simultaneous determination of a large number of different parameters (9). Gene expressions can be determined at the mRNA level using DNA arrays by hybridizing labeled RNA or cDNA samples and at the protein level using arrays of selected protein-specific antibodies (10). Furthermore, immunoreactivities can be determined using arrays of selected antigens (11).

First of all, it is necessary to define the genes and proteins that are relevant to the disease and are used for assessment.

The tools according to the invention for diagnosis and therapy development in inflammatory joint diseases are based on the use of such a defined selection of parameters (Tables 1 and 2). Using these genes mentioned here for gene expression analysis in the array method enables a fundamentally new diagnosis. For DNA arrays for determining specific mRNA expression patterns in arthritis, the genes named in table 1 can be used in their entirety, and those genes in their entirety which code for the proteins named in table 2; in addition, genes or partial sequences of individual or a selection of the genes mentioned in Table 1 can also be used, or partial sequences and genes or partial sequences of individual or a selection of genes or partial sequences which encode the proteins mentioned in Table 2.

For the characterization of the autoimmune reactivities, the proteins named in Table 2 can be used in their entirety, as well as those proteins which are encoded by the genes named in Table 1. Furthermore, a reduced selection of these proteins, selected parts of the proteins in the form of oligo- or polypeptides or modifications thereof can also be used. At the protein level, post-translational modifications (e.g. glycosylation, phosphorylation, etc.) must also be taken into account, which may be relevant for discrimination between rheumatic diseases. The proteins, partial protein sequences as well as modified proteins and partial protein sequences are applied together or individually or in groups to a carrier matrix which is suitable for determining the reactivity of patient antibodies to one or more of these components. This results in a profile of reactivities and non-reactivities for a patient. The major difference between the diagnostics according to the prior art and that shown here is the determination and analysis of individual auto-reactivities in the prior art and the determination and analysis of a large number of auto-reactivities according to the invention. The invention makes use of the unexpected discovery that the combination of several - in themselves unsuitable auto-reactivities - into one or more profiles, for example an RA in 100% of the cases can discriminate from a non-RA (i.e. other rheumatic and non-rheumatic diseases as well as from the healthy state). The assignment to discriminatory profiles takes place via a suitable algorithm, optimally via a self-learning one, which is able to incorporate knowledge gained at a later point in time. For the determination of protein expression patterns, array systems have been developed from protein-specific antibodies. By labeling the proteins from a protein extract of a sample, these can be detected quantitatively after specific binding to their antibody on the array (10). Accordingly, as a molecular tool in the sense of the invention, an array is defined which consists of different antibodies or molecules with comparable protein-specific binding behavior, which are used to detect all or a selection of the proteins derived from the genes of Table 1 or all or a selection of the proteins of the Table 2 can serve.

For diagnosis, biopsies from the synovial tissue, synovial fluid, blood cells and also serum or plasma are used for the different array examinations. The humoral autoreactivities in the liquid samples, the cellular ones with blood or synovial tissue cells can be examined. Protein expression can be examined in all of the samples mentioned, gene expression at the mRNA level in synovial tissue, in cells in the synovial fluid or in blood cells.

For the investigation by means of DNA arrays, the RNA is extracted from the tissue or the cell samples from blood or synovial fluid. Using established protocols for the amplification (12) and the labeling of the derived cDNA or cRNA (13), a sample for a DNA array hybridization is produced.

The genes named in the table with their known sequence (see Accession Number GeneBank - http://www.ncbi.nlm.nih.gov/) provide the template for deriving specific probes for each gene. These probes are assembled in an array, either by applying the finished probes using specific printing processes (14) or by site-specific synthesis as in the case of photolithography on a solid phase (15, 16).

The hybridization of the labeled sample on the array provides quantitative signals via the site-specific and gene-specific binding, which can be translated into an expression profile / pattern. The patterns are correlated with various established methods of assessment, including histological features and classification. Through an additional comparison with various joint diseases such as osteoarthritis, psoriatic arthritis, reactive arthritis or other, for example, undifferentiated arthritis, this allows patients to be divided into different groups according to the respective expression profile. Novelty of the approach

To define reliable parameters for the array examination that a grouping and

Comprehensive comparative examinations were carried out to allow an assessment of joint diseases. Various joint diseases were used and a combination of different, sometimes complementary methods, was used for the first time.

For example, synovial tissue from RA, osteoarthritis and normal joints was examined. The "representational difference analysis" (17, 18) was first carried out for differential gene expression analysis. It has the advantage that all mRNAs present in the sample are taken into account, even if their sequence is still unknown. The disadvantage is a strong selection of the most pronounced differences in expression. In addition, gene expression was therefore additionally carried out using two different methods of DNA array hybridization, firstly on cDNA filter arrays (19), secondly on oligonucleotide microarrays (US Pat. Nos. 5,445,934; 5,744,305; 5,700,637 and 5,945,334 and EP 619321 and 373203). According to the current state of knowledge, these make it possible to largely take into account all known human genes and to individually present the expression for each of these genes by comparing the tissue samples. Finally, the semi-quantitative polymerase chain reaction (PCR) (real-time PCR) was used to verify the differential gene expression for selected genes on a larger sample collective.

In addition, tissues were characterized histologically and compared with the corresponding differential gene expression pattern according to the histological classification. The genes listed in Table 1 were identified as differentially expressed genes both between the various chronic joint diseases and with respect to normal synovial tissue. These genes are therefore important for the characterization of chronic joint diseases. There is also a novelty in the approach chosen to identify the relevant genes. On the other hand, the list of genes found showed that most have not yet been linked to inflammatory rheumatic joint diseases and also provide new evaluation criteria for diagnostics, research into pathophysiology and the treatment of chronic joint diseases. The features of the invention emerge from the elements of the claims and from the description, both individual features and several, in the form of combinations, representing advantageous embodiments for which protection is requested with this document. These features consist of known elements - the genes or partial sequences named in Table 1 and those genes or partial sequences which code for the proteins named in Table 2 - and new elements - the new tools based on the use of a defined one Selection of parameters (Tables 1 and 2) are based - together, in their combination to the Tools according to the invention and using the genes mentioned for gene expression analysis in the array method enable fundamentally new diagnostics and therapy development for inflammatory joint diseases.

The tools according to the invention are based on the use of a high-throughput method of (micro) array hybridization and / or a high-throughput method with techniques of the polymerase chain reaction for (semi) quantification.

They are further characterized in that they are based on the use of a labeled patient sample and a second differently labeled control sample for comparative double hybridization to a (micro) array together with the patient sample (red / green comparison hybridization). The samples can also be examined on separate arrays and then compared with one another.

According to the invention, there are tools for diagnostic purposes that are based on use

- Individual, a selection or all of the proteins or peptides derived from the gene sequences in claims 1 to 3 - Individual proteins, a selection of proteins or all of the proteins mentioned in Table 2 and

- Based on partial sequences of individual proteins, a selection of proteins or all proteins that are listed in Table 1. They include proteins or partial protein sequences which are identical in sequence to the proteins derived in Table 1 or the proteins mentioned in Table 2 or have at least 80% sequence identity. Furthermore, they are characterized by the fact that they are based on the use of

- High-throughput methods in protein expression analysis (high-resolution, two-dimensional protein gel electrophoresis, MALDI techniques)

- High-throughput methods in the protein spotting technique (protein arrays) for the screening of autoantibodies as a diagnostic tool for inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans

- High-throughput methods in the protein spotting technique (protein arrays) for the screening of autoreactive T cells as a diagnostic tool for inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans as well

- Non-high-throughput method in the protein spotting technique for the screening of autoreactive T cells as a diagnostic tool for inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans based.

Furthermore, the tools according to the invention are based on their use

- of antibodies that are specific for proteins or partial sequences that are listed under claims 6 to 9 and - the corresponding homologous sequences of another species for analysis in animal experiments or for diagnosis in animals with inflammatory joint diseases and other inflammatory, infectious or tumorous diseases ,

The tools according to the invention are suitable as diagnostic tools for the detection of genetic changes (mutations) - in the genes mentioned in claims 1 to 3 or their regulatory sequences (promoter, enhancer, silencer, specific sequences for binding further regulatory factors) and

- In the genes or their regulatory sequences (promoter, enhancer, silencer, specific sequences for the binding of further regulatory factors) which code for the proteins mentioned in Table 2. They are also suitable as tools for the molecular definition of inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans using the genes, DNA sequences or proteins or peptides derived therefrom as well as the proteins and partial protein sequences from claim 6 to 9 or the gene sequences coding therefor. The tools according to the invention are also used

- For the therapy decision of inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans using the genes, DNA sequences or proteins or peptides derived therefrom mentioned in claims 1 to 3

- for follow-up / therapy control of inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans using the in

Claims 1 to 3 named genes, DNA sequences or proteins or peptides derived therefrom

- As molecular tools for the development of therapy concepts which include the direct or indirect influencing of the expression of the genes or gene sequences named in claims 1 to 3

- For the development of therapy concepts which have the direct or indirect influence on the expression of the proteins or partial protein sequences named in claims 6 to 9

- For the development of therapy concepts which include the direct or indirect influencing of autoreactive T cells, directed against the proteins or partial protein sequences named in claims 8 to 11 - To influence the biological effect of the proteins derived from the gene sequences named in claims 1 to 3

for influencing the immediate molecular control loops, into which the genes named in claims 1 to 3 and proteins derived therefrom are integrated, for the development of therapy concepts using the design and use of interpretation algorithms which use the genes and sequences mentioned and their regulatory mechanisms, in order to - Identify or predict effects, optimizations or disease prognoses, as well as

- for the development of biologically active drugs (biologicals) using genes, gene sequences, regulation of genes or gene sequences, or using

Proteins, protein sequences, fusion proteins according to claims 1 to 3 and 6 to 9 or using antibodies or autoreactive T cells according to claims 10 to 14.

The use of the claimed tools according to the invention lies in

- Examination of blood samples or tissue samples in medical diagnostics - Application in the analysis according to example 1 and in the

- Application for therapy concepts according to example 2.

material and methods

Patients and Tissue Reservation All patients were selected according to the ACR criteria for RA (1) and OA (20). Synovial tissue was in RPMI medium (RPMI - commercially available cell culture medium, dilution medium RPMI 1640; Moore, GE et al., J. Am. Assoc. 199, 519 - 524, 1967), with the addition of penicillin and streptomycin (each 100U / ml) , brought directly from the operating room to the laboratory. After the synovial membrane was prepared, the samples were immediately snap frozen in liquid nitrogen. The samples were stored at -80 ° C until further processing. It was for Representational Difference Analysis (RDA) that hybridizations to Unigene filter arrays

(http://www.ncbi.nlm.nih.gov/UniGene/) and hybridization to Affymetrix Arrays Normal Donors (ND), Osteoarthrosis (OA) and Rheumatoid Arthritis synovial tissue samples.

RNA isolation

The samples were homogenized to obtain RNA: tissue <50 mg were ground into powder with a mortar and pestle while cooling with liquid nitrogen and then in Solution containing guanidinium isothiocynate (RLT buffer from Qiagen, Hilden, Germany - www.qiagen.com/literature/handbooks/rna/ rny96 / 1019545_PREHB_RNY96_prot2.pdf). Larger amounts of tissue were made using a tissue homogenizer; IKA-Ultra-Turrax T 25 (Jahnke & Kunkel, Staufen) in ice-cold solution containing guanidinium isothiocynate (RLT buffer from Qiagen, Hilden, Germany). The RNA isolation was carried out using a modified protocol that uses the phenol-chloroform extraction according to Chomczynski (21) and then immediately the RNA from the aqueous phase using the QIAGEN-RNaesy kit (manual of the manufacturer http: //www.qiagen. eom / literature / rnalit.asp # mini) extracted. The kit was used according to the manufacturer's protocol. The RNA was eluted in 30-100 ul RNAse-free water. For quality control, the optical density (OD) was measured at 260 nm (OD260), the ratio OD260 / OD280nm was determined and gel electrophoresis was carried out in 1% agarose. DNA contamination could either be detected in the gel or, after first strand synthesis, detected in a PCR using an intron primer for glyceraldehyde-3-phosphate dehydrogenase (GAPDH). In these exceptional cases, DNAse was also digested in accordance with the QIAGEN protocol.

first strand synthesis

Superscript II Reverse Transcriptase (RT), including the 5-fold reaction buffer from Invitrogen / Life Technologies (Karlsruhe, Germany http://www.invitrogen.com), was used for the cDNA synthesis. The RNA quantities used were 3-5μg for the semi-quantitative PCR and 10-20μg for the RDA and the array hybridizations in a final volume of 20μl. The reaction for the description in cDNA contained the following components: 500ng of the respective oligonucleotide (01igo (dT) ι _2- i8; T7-OHgo (dT ₂ )) as a primer, 50 mM Tris, pH 8.3, 75 mM KC1, 3 mM MgCl ₂ , 10 mM dithiothreitol, deoxy-nucleotide triphosphate (dNTP) mixture with each nucleotide in final ImM concentration, 40U RNase inhibitor and 20U Superscript ™ II RT. The incubation period was 1 hour and 30 minutes, and the enzymes were then inactivated for 15 minutes. Heat to 72 ° C.

Second strand synthesis

90μl distilled water were added to the cDNA. pipetted, 30μl 5-fold second-strand buffer of the following composition: 500mM KC1, 50mM ammonium acetate, 25mM MgCl ₂ , 0.75mM beta-nicotinamide adenene dinucleotide (ß-NAD) and 0.25mg / ml bovine serum albumin (BSA) as well as 3μL of a lOmM dNTP- Solution and enzyme solution of the following activities and quantities: lμl E. coli ligase (lOU / μl), 4μl DNA polymerase I (lOU / μl) and lμl RNAseH (2U / μl) (Invitrogen / Life Technologies, Karlsruhe, Germany). The incubation was 2 hours at a temperature of 16 ° C. After adding 2μl of a T4 DNA polymerase (5U / μl), the mixture was kept for a further 30 min. incubated at 16 ° C. Subtractive hybridization and RDA

The PCR suppression subtractive hybridization (SSH) (22) was carried out according to the working instructions of the manufacturer of the PCR Select Kit (Clontech, Palto Alto, USA http://www.clontech.com/pcr-select/index.shtml). The double-stranded cDNA was digested with the restriction enzyme Rsal from Rhodopseudomonas sphaeroides. For the RDA (18), the double-stranded cDNA was cut with the restriction enzyme DPNII from Diplococcus pneumoniae (20U in 100 μl). Then it was ligated to adapter primer (RBgll2, RBgl24) and amplified according to published protocols (17, 18). After renewed restriction digestion with DPNII, the tester amplicon was obtained by ligation to a further adapter oligonucleotide (JBgll2 and JBgl24 or NBgl 12 and NBgl24 (l 8)) in the second round of subtraction).

After the hybridization, the sequences derived from the tester were selectively amplified by PCR in both methods and thereby enriched in the subtraction product.

Description of the subtraction approaches

The RDA protocols were varied so that it was possible to identify both less and more expressed genes in the samples from RA, OA and normal donor tissues.

It was:

1 OA (driver) subtracted from RA (tester) to obtain sequences that are more strongly expressed in RA than in OA tissues

2 RA (driver) subtracted from ND (tester) to obtain sequences that are less expressed in RA than in ND samples

3 ND (Driver) subtracted from OA (Tester) to obtain sequences that are more strongly expressed in OA than in ND tissues

Creation of subtraction bank cloning, sequencing and database comparisons

The subtraction products of the SSH approach were cloned into a pCRJI vector (TA cloning kit; Invitrogen, Heidelberg, Germany http://www.invitrogen.com). The subtraction products from the RDA were cloned into a pBluescript KS ⁺ II vector (Stratagene, La Jolla, USA http://www.stratagene.com/vectors/selection/plasmidl.htm), which had previously been cut with the BamHI restriction enzyme from Bacillus amyloliquefaciens and then dephosphorylated and purified. About 150 clones were isolated and sequenced using an ABI 377 sequencer (Applied Biosystems, Weiterstadt, Germany http://home.appliedbiosystems.com). Sequencing was carried out according to the manufacturer's Dye Terminator Chemistry protocol using a T7 primer.

The sequence comparisons were carried out after eliminating the vector sequences using the Genebank and NCBI databases (http://www.ncbi.nlm.nih.gov). Microarray hybridisation

Two different chip technologies were used: 1.) Filters were used on which PCR products from cDNA clones of the UNIGENE library

(http://www.ncbi.nlm.nih.gov/UniGene/) were spotted. The hybridization was carried out at 65 ° C with ³³ P-labeled cDNA samples after first strand synthesis with 01igo (dT ₍ i ₂ _i ₈₎ ) (23, 24). 2.) With MicroArrays (HU95A; HU95B, HU95C, HU95D and HU95E) from Affymetrix (Affymetrix Inc., Santa Clara, USA www.affymetrix.com) hybridizations were carried out, which are oligonucleotide arrays whose base sequence is derived from 12,000 known genes and 24,000 Expressed Sequence Tag (EST) entries. The labeled samples were synthesized in accordance with the manufacturer's technical manual (Affymetrix Inc., Santa Clara, USA).

The fluorescence-labeled sample is synthesized after transcription with a 01igo-dT ₂₄ primer, which has a T7 polymerase binding site. The labeling reaction was carried out using T7 RNA polymerase and biotinylated dNTPs in accordance with the manufacturer's protocol (ENZO-Biochem, New York, USA, http://www.enzo.com/entrance.html). In both chip analyzes, the sample to be tested and the reference sample were hybridized on separate filters. The signal intensities were compared after normalization.

Evaluation of the chip results decision matrix

The signal intensities were evaluated with the software developed for the corresponding array after normalization and determination of an intensity value for the corresponding sample according to the Tukey's Biweight Method (http://inathworld.wolfram.com/TukeysBiweight.html).

The algorithm for the evaluation of the Unigene filter arrays was developed at the Max Planck Institute for Molecular Genetics in Berlin-Dahlem (http://algorithmics.molgen.mpg.de/), the MicroArraySuite 5.0 software for the Affymetrix chips http://www.affVmetrix.com/products/software/specific/mas.aff) including the standard settings or specifications of the manufacturer.

For the evaluation of the Affymetrix arrays, the target intensity was set to 100 and the normalization factor to 1 for the normalization of the data, and the scaling factor was calculated for each sample. Chips with comparable scaling factors (factor <4) were included in the comparative analyzes. Decision criterion for the detection of a gene (detection p value) was set to <0.05. Using the DMT 3.0 software (http://www.affVmetrix.com/products/software/specific/dmt.affx ') from Affymetrix, the comparative analyzes were carried out for the corresponding arrays.

The Wilcoxon test (http://faculty.vassar.edu/lowry/wilcoxon.html) calculates the differences between the perfect matches and the perfect and mismatch intensities and uses the decision criterion cut-off (γ value < 0.04) compared. In the output of the results for the A change call (increased, marginally increased, no change or decreased) as well as the signal log ratio, a measure of the factor of the change (logarithmic factor) is given for comparison of the respective chips.

Decision criterion: The comparative analyzes were carried out for all samples (each sample against each of the other groups: ND, OA, RA).

For the Unigene filter hybridizations, a signal difference> 2 for at least 3 out of 4 comparisons and a detection signal with a p-value <0.01 was taken into account.

The procedure for the arrays from Affymetrix was as follows: Each RA sample was compared with each of the OA samples in the direction of both increased and decreased expression. Genes that showed a deflection in the sense of "increased" or "lowered" in 80% of these comparisons with a regulatory factor> 2 (signal log ratio> 1) were selected as candidate genes. In the U95A chip, the selection criterion was set to a regulation factor> 3.

Semiquantitative PCR Primers with a comparable annealing temperature and product length were selected from the detected sequence areas. The DNASTAR Primer Select software (DNASTAR Inc., Madison, USA http://www.dnastar.com/) was used for the primer search. The primer synthesis took place at Gibco-Life Technologies (Karslruhe, Germany). The real-time PCR system GeneAmp 5700 and the Sybr-Green-PCR-Core Kit (Applied Biosystem, Weiterstedt, Gennany; http://europe.appliedbiosystems.com/) were used for the semi-quantification of the PCR products.

The cDNA amounts were matched for all samples based on the real-time amplification results for GAPDH-specific primers. The PCR products of various other genes were quantified in relation to the GAPDH-specific product as an internal standard. As a control, ß-actin was amplified for all samples and determined as a second household gene.

immunohistochemistry

A sample of the synovial membrane was used for histopathological assessment. For this purpose, cryosections with a thickness of 6 μm were made, air dried and then fixed in a 1: 1 mixture of acetone and methanol. The hematoxilin staining was carried out according to standard protocols and divided according to histopathological evaluation criteria (25).

Methods and results for immunoma analysis

To determine autoreactivity patterns at the T and B cell level (inimunoma) that are specific for RA and that therefore discriminate the disease from other rheumatic and non-rheumatic diseases. Knowledge of the RA-specific immunoma is of paramount importance in order to develop diagnostic tools that diagnose arthritis much earlier and more reliably than RA or exclude it as RA than was previously possible. This in turn allows the RA to be controlled by medication before irreversible damage to the joints and bones has occurred. For this purpose, the techniques of proteomics were used to generate tissue-specific protein patterns using high-resolution 2D gel electrophoresis. These were screened using known immunomics techniques for known and unknown auto-reactivities. Protein spots with a useful sensitivity and specificity are identified by sequencing and MALDI-TOF (26). These proteins are then screened for T cell autoreactivity in the same cohort. According to the invention, auto-reactivity patterns have been established that are absolutely RA-specific. It is very important in this analysis that no single autoreactivity has achieved this specificity. This is only achieved by combining several auto-reactivities. Such patterns that clearly distinguish a patient with RA from another rheumatic or non-rheumatic disease include the autoantigens citrullinated peptides (Cit), IgG, BiP (Heavy Chain Binding Protein), Calpastatin (Calp), RA33 (hnRNP A2) ) and calreticulin (CaIr). The table shows all possible combinations of five of these auto-reactivities (RF, Cit, BiP, RA33 and Calp) as well as the two possible states "positive" and "negative". The highlighted patterns are (statistically significant, p <0.01, Whitney U Test, http://faculty.vassar.edu/lowry/utest.html) only expressed in the RA. Fig. 1 shows the sensitivities of all possible combinations for the RA and the control cohorts. The RA-specific patterns are highlighted analogously to Table 1 and essentially include those that are 4 and 5 times positive for the Individual parameters are. The combination of those autoreactivity profiles that occur exclusively in RA results in a sensitivity of 54%.

RA-exclusively expressed patterns of the three auto-reactivities, directed against IgG, Cit and BiP (RF + Cit + BiP + and RF-Cit + BiP +) result in an overall sensitivity of 43%. RA-exclusive samples of the four auto-reactivities, directed against IgG, Cit, BiP and RA33 (RF + Cit + Bip + RA33 +, RF + Cit + BiP- RA33 + and RF + Cit + BiP + RA33-) have an overall sensitivity of 40% , When analyzing six patterns, a sensitivity of 60% is achieved.

According to initial studies, these patterns are also relevant in patients with early RA. Other candidate antigens that have already been characterized include the Sa antigen (5), which is believed to consist of α-enolase and citrullinated vimentin.

The identification of the immunoma of RA is not only of diagnostic, but also of pathogenetic relevance. When those T-cell autoreactivities that drive early RA are identified, it appears possible to develop specifically effective therapy protocols.

Tab. 1 Autoreactivity patterns with RF, citrulline, BiP, calpastatin and RA33

All 32 possible five-fold combinations of the auto-activities against IgG (RF), citrulline, BiP, calpastatin and RA33 are shown. The RA-specific combinations are highlighted in color as in FIG. 1.

Use

Complex molecular patterns are recorded. These can be divided into groups and degrees of kinship using mathematical calculation models. The classification that can be derived from this and the knowledge of the association e.g. The following conclusions can be drawn from the array analysis with the duration of the disease, the clinical activity (disease activity score (Ref.)), the inflammatory activity measured by the increase in the C reactive protein or blood depression, the radiological joint destruction and the specific influence of medication: assignment the clinical picture for a defined diagnosis and a molecularly delimitable subgroup, assessment of the disease activity and the expected progression (prognosis assessment), chances of success of the different forms of therapy, recommendation for the therapeutic approach (e.g. methotrexate instead of leflunomide or combination of sulfasalazine with methotrexate instead of methotrexate alone) and finally Monitoring the success of therapy.

By using before and during defined drug treatment measures, it can be determined which of the genes used are influenced by the drug. It is used to measure how the drug influences the gene expression that is typically changed for the disease. This shows which disease-associated molecular changes are still active despite therapy. Knowledge of the function of these pathologically active genes can in principle reveal pathophysiological processes in joint diseases and derive new therapy concepts. Compilation of the genes

Table 1:

Accesion Unigene Gene Name Method Regulation

Number coding

X57809 Hs. 181125 RDA, RA> OA

Affymetrix

X58141 RDA RÄ> OA

X63527 Hs.252723 ribosomal protein L19 RDA RA> OA

U10362 Hs.75864 cl romosome 5 open RDA RA> OA reading frame 8

M80244 Hs.184601 NM_003486 RDA RA> OA

M24594 Hs.20315 interferon-induced protein RDA OA> RA with tetratricopeptide repeats 1

U01244 Hs.79732 fibulin 1 isoform C RDA RA> OA precursor NM_006485

X02761 Hs.287820 fϊbronectin 1, isoform 1 RDA RA> OA, preproprotein OA> NS

L01124 Hs.165590 ribosomal protein S13 RDA NS> RA

M65062 Hs. 107169 insulin-like growth factor RDA binding protein 5

M15330 Hs.126256 interleukin 1, beta

L13210 Hs. 79339 galectin 3 binding protein RDA

X05232 Hs.83326 matrix metalloproteinase; 5 preproprotein RA> NS, OA

M22490 Hs.68879 bone morphogenetic RDA, NS> RA protein 4 Affymetrix

AL034397 RDA OA> NS

M22806 RDA OA> NS

X06256 Hs.149609 integrin alpha 5 precursor Unigene NS> RA

L49169 Hs.75678 FBJ murine osteosarcoma Unigene NS> RA viral oncogene homolog B

AB002409 Hs.57907 small inducible cytokine Unigene RA> NS subfamily A (Cys-Cys), member 21

X03473 Hs.226117 Hl histone family, RDA OA> NS member 0

M92843 Hs. 343586 zinc finger protein 36, Unigene NS> RA C3H type, homolog (mouse)

M21121 Hs. 241392 small inducible cytokine Affymetrix RA> OA A5 (RANTES)

U05259 Affymetrix RA> OA

U80114 Hs. 247987 Affymetrix RA> OA

U81234 Hs.164021 small inducible cytokine Affymetrix RA> θA subfamily B (Cys-X-Cys), member 6 (granulocyte chemotac

D11086 Hs.84 interleukin 2 receptor, Affymetrix RA> 0A rromm <- * lιαιτn TM'o πrcπ gamma chain, precursor

X97267 Affymetrix RA> OA

U23852 Affymetrix RA> OA

AA522530 Hs.l 11244 RTP801 Affymetrix RA> OA

AF037335 Hs.5338 carbonic anhydrase XII Affymetrix RA> OA precursor

U97145 Hs.19317 GDNF family receptor Affymetrix RA> OA alpha 2

AA919102 Hs.95327 CD3D antigen, delta Affymetrix RA> OA polypeptide (TiT3 complex)

M63928 Hs.l 80841 CD27 antigen Affymetrix RA> OA

Z49194 Hs.2407 POU domain, class 2, Affymetrix RA> OA associating factor 1

AL031983 Affymetrix RA> OA

Dl 5050 Hs. 232068 Affymetrix RA> OA

X92997 Hs. 342651 Affymetrix RA> OA

J03910 Affymetrix RA> OA

J04132 Hs.97087 T-cell receptor zeta chain Affymetrix RA> OA precursor

M55153 Hs.8265 transglutaminase 2 (C Affymetrix RA> OA polypeptide, protein-glutamine-gamma-glutamyl transferase)

M12959 Hs.74647 Affymetrix RA> OA

AF031815 Hs.89230 potassium intermediate Affymetrix RA> OA

L31584 Affymetrix RA> OA

X54489 Affymetrix RA> OA

AF043129 Affymetrix RA> OA

X59871 Hs.l 69294 transcription factor 7 (T- Affymetrix RA> OA cell specific, HMG-box)

AI743134 Hs.21858 trinucleotide repeat Affymetrix RA> OA containing 3

Y13323 Hs.145296 disintegrin protease Affymetrix RA> OA

U77735 Hs.80205 pim-2 oncogene Affymetrix RA, OA> NS

U58515 Hs.154138 chitinase 3-like 2 Affymetrix RA, OA> NS

M17016 Hs.l 051 granzyme B precursor Affymetrix RA> OA

X03066 Hs.l 802 major histocompatibility Affymetrix RA, OA> NS complex, class II, DO beta

M28170 Hs.96023 CD 19 antigen Affymetrix RA, OA> NS

L24564 Hs.l 027 Ras-related associated Affymetrix NS> RA with diabetes

M68840 Hs.l 83109 monoamine oxidase A Affymetrix NS> RA

U76456 Hs.l 90787 tissue inhibitor of Affymetrix OA> RA metalloproteinase 4 precursor

D13814 Hs. 89472 angiotensin receptor 1 Affymetrix NS> RA NM_004835

AA420624 Hs.l 83109 monoamine oxidase A Affymetrix OA> RA

X51757 Hs.3268 heat shock 70kD protein 6 i Affymetrix NS> RA (HSP70B ') U29344 Hs.83190 fatty acid synthase Affymetrix NS> RA

L19871 Hs.460 activating transcription Affymetrix NS> RA factor 3 long isoform NM 304024

J02611 Hs.75736 apolipoprotein D Affymetrix NS> RA precursor

Ml 2272 Hs.2523 class I alcohol Affymetrix NS> RA dehydrogenase, gamma subunit

L34041 Hs.348601 glycerol-3-phosphate Affymetrix NS> RA dehydrogenase 1 (soluble)

L12760 Hs.l 872 phosphoenolpyruvate Affymetrix OA> RA carboxykinase 1 (soluble)

M63978 Affymetrix RA> OA

S95936 Hs.284176 transferrin precursor Affymetrix NS> RA

U42031 Hs.7557 FK506-binding protein 5 Affymetrix NS> RA

Z97171 Affymetrix NS> RA

S69790 Affymetrix NS> RA

U41843 Hs.295362 DRl-associated protein 1 Affymetrix OA, NS> RA (negative cofactor 2 alpha)

AL049653 Affymetrix NS> RA

M31682 Hs.1735 inhibin beta B subunit Affymetrix NS> RA precursor

AF009767 Hs.132898 fatty acid desaturase 1 Affymetrix NS> RA, OA

X02910 Hs. 241570 tumor necrosis factor (cachectin)

AB023152 Hs.12183 Affymetrix NS> RA, OA

U37283 Hs.300946 Microfibril-associated Affymetrix OA, NS> RA glycoprotein-2

X05451 Hs.l 58295 Affymetrix OA, NS> RA

W26480 Hs.132898 fatty acid desaturase 1 Affymetrix NS> RA

D14874 Hs.394 adrenomedullin Affymetrix RA> NS

M12174 Hs. 204354 ras homolog gene family, Affymetrix NS> RA member B

M60974 Hs.80409 growth arrest and DNA- Affymetrix NS> RA damage-inducible, alpha

S62138 Affymetrix NS> RA

XI 6706 Hs.301612 FOS-like antigen 2 Affymetrix NS> RA

X56667 Hs.106857 calbindin 2, fill length Affymetrix NS> RA protein isoform NM_007087

H15814 Affymetrix NS> RA

AL021977 Affymetrix NS> RA

U80055 Affymetrix NS> RA

U09564 Hs.75761 SFRS protein kinase 1 Affymetrix RA> OA

U14407 Hs.168132 interleukin 15 Affymetrix RA> OA

U27185 Hs.82547 retinoic acid receptor Affymetrix RA> OA responder (tazarotene induced) 1

Z35278 Hs.170019 runt-related transcription Affymetrix RA> OA factor 3 M12886 Hs.303157 Affymetrix RA> OA

L05424 Affymetrix RA> OA

L09230 Hs.301921 chemokine (C-C motif) Affymetrix RA> OA receptor 1

L22075 Hs.l 666 guanine nucleotide Affymetrix RA> OA binding protein (G protein), alpha 13

M28130 Affymetrix RA> OA

M29696 Hs.237868 interleukin 7 receptor Affymetrix RA> OA

M31165 Hs. 29352 tumor necrosis factor, Affymetrix RA> OA alpha-induced protein 6

M16038 Hs.80887 v-yes-1 Yamaguchi Affymetrix RA> OA sarcoma viral related oncogene homolog

X83490 Affymetrix RA> OA

D13666 Hs.136348 osteoblast specific factor 2 Affymetrix RA> OA

(fasciclin I-like)

L10717 Hs.211576 IL2-inducible T-cell Affymetrix RA> OA kinase

X04500 Hs.126256 interleukin 1, beta Affymetrix RA> OA

U24153 Hs.30692 p21 (CDKNlA) -activated Affymetrix RA> OA kinase 2

M32315 Hs.256278 tumor necrosis factor Affymetrix RA> OA receptor 2 (75kD)

U51903 Hs.78993 IQ motif containing Affymetrix RA> OA

GTPase activating protein

2

AF002700 Hs.19317 GDNF family receptor Affymetrix RA> OA alpha 2

U37518 Hs.83429 tumor necrosis factor Affymetrix RA> OA (ligand) superfamily, member 10

HG1103-HT1103 Affymetrix RA> OA

HG3521-HT3715 Affymetrix RA> OA

AF024710 Affymetrix RA> OA

U01134 Hs.l 38671 fms-related tyrosine Affymetrix RA> OA kinase 1 (vascular endothelial growth factor

U27467 Hs.227817 BCL2-related protein AI Affymetrix RA> OA

M79321 Hs.80887 v-yes-1 Yamaguchi Affymetrix RA> OA sarcoma viral related oncogene homolog

J04765 Hs.313 secreted phosphoprotein 1 Affymetrix RA> OA (osteopontin, bone sialoprotein I, early T-lymphocyte

M21154 Hs.262476 S-adenosylmethionine Affymetrix RA> OA decarboxylase 1 precursor

AF098641 Hs.306278 Affymetrix RA> OA

D63789 Hs.l 74228 small inducible cytokine Affymetrix RA> OA oπ -fαrviilir C ¹ subfamily C, member 2

S68134 Hs. 351252 cAMP responsive element t Affymetrix RA> OA modulator

AB014515 Hs.323712 KIAA0615 gene product Affymetrix RA> OA

AI800499 Hs.l 61002 Affymetrix RA> OA

Y13710 Hs.16530 small inducible cytokine Affymetrix RA> OA subfamily A (Cys-Cys), member 18, pulmonary and activat

AJ011915 Hs.l 84376 synaptosomal-associated Affymetrix RA> OA protein, 23kD

AF030339 Hs.286229 plexin Cl Affymetrix RA> OA

XI 7042 Hs.l 908 proteoglycan 1, secretory Affymetrix RA> OA granule

AF059214 Hs.l 94687 cholesterol 25- Affymetrix RA> OA hydroxylase

D42043 Hs. 79123 Affymetrix RA> OA

M24283 Hs.168383 intercellular adhesion Affymetrix RA> OA molecule 1 precursor

AF042729 Hs.171776 inositol (myo) -l (or 4) - Affymetrix RA> OA monophosphatase 1

M64595 Hs.l 73466 ras-related C3 botulinum Affymetrix RA> OA toxin substrates 2

AA868382 Hs. 198253 major histocompatibility Affymetrix RA> OA complex, class II, DQ alpha 1

AB006746 Hs.198282 phospholipid scramblase 1 Affymetrix RA> OA

X00437 Hs.303157 Affymetrix RA> OA

M59287 Affymetrix RA> OA

AA725102 Hs.51305 v-maf Affymetrix RA> OA musculoaponeurotic fibrosarcoma oncogene homolog F (avian)

M97935 Hs.21486 signal transducer and Affymetrix RA> OA activator of transcription l, 91kD

X54134 Hs.31137 protein tyrosine Affymetrix RA> OA phosphatase, receptor type, E

U89942 Hs. 83354 lysyl oxidase-like 2 Affymetrix RA> OA

AF099935 Hs.17839 TNF-induced protein Affymetrix RA> OA

M93056 Affymetrix RA> OA

M97936 Affymetrix RA> OA

AI887421 Hs.82547 retinoic acid receptor Affymetrix RA> OA responder (tazarotene induced) 1

D50532 Hs. 54403 macrophage lectin 2 Affymetrix RA> OA (calcium dependent)

AI813532 Hs.256278 tumor necrosis factor Affymetrix RA> OA receptor 2 (75kD) U02020 Hs.239138 pre-B-cell colony- Affymetrix RA> OA enhancing factor

X05276 Hs. 250641 tropomyosin 4 Affymetrix RA> OA

AF006516 Hs.24752 spectrin SH3 domain Affymetrix RA> OA binding protein 1

AB018301 Hs. 22039 Affymetrix RA> OA

AB010812 Hs.22900 nuclear factor (erythroid- Affymetrix RA> OA derived 2) -like 3

AF052124 Hs.313 secreted phosphoprotein 1 Affymetrix RA> OA (osteopontin, bone sialoprotein I, early T-lymphocyte

AB008775 Hs.l 04624 aquaporin 9 Affymetrix RA> OA

AF024714 Hs.105115 absent in melanoma 2 Affymetrix RA> OA

M28696 Hs.278443 Fc fragment of IgG, low Affymetrix RA> OA affmity Ilb, receptor for (CD32)

X62573 Affymetrix RA> OA

X07834 Hs.318885 Superoxide dismutase 2, Affymetrix RA> OA mitochondrial

AL050267 Hs.23889 DKPZP564A032 protein Affymetrix RA> OA

U83461 Hs. 24030 solute carrier family 31 Affymetrix RA> OA (copper transporters), member 2

AB018285 Hs.321707 Affymetrix RA> OA

AF007875 Hs.5085 dolichyl-phosphate Affymetrix RA> OA mannosyltransferase polypeptide 1

X78686 Hs.89714 small inducible cytokine Affymetrix RA> OA subfamily B (Cys-X-Cys). > member 5 (epithelial-derived n

AF053712 Hs.l 15770 Affymetrix RA> OA

AF006083 Hs.5321 ARP3 actin-related Affymetrix RA> OA protein 3 homolog

AL050025 H & .5344 adapter-related protein Affymetrix RA> OA complex 1, gamma 1 subunit

M17017 Hs.624 interleukin 8 Affymetrix RA> OA

AI651024 Hs.l 5780 Affymetrix RA> OA

AF038172 Affymetrix RA> OA

M55542 Hs.62661 guanylate binding protein Affymetrix RA> OA

1, interferon-inducible,

67kD

U11276 Hs.l 69824 killer cell lectin-like Affymetrix RA> OA receptor subfamily B, member 1

Z19585 Hs.75774 thrombospondin 4 Affymetrix OA> RA

L27560 Affymetrix OA> RA

M98539 Affymetrix OA> RA

J00153 Affymetrix OA> RA M25079 Hs.155376 hemoglobin, beta Affymetrix OA> RA

M80482 Hs.l 70414 paired basic amino acid Affymetrix OA> RA cleaving system 4

L48215 Hs.155376 hemoglobin, beta Affymetrix OA> RA

AA524547 Hs.l 60318 phospholemman, isoform Affymetrix OA> RA b precursor NM_005031

AL038340 Affymetrix OA> RA

AB 81790 Hs.74120 adipose specifϊc 2 Affymetrix OA> RA

X00129 Hs.76461 retinol-binding protein 4, Affymetrix OA> RA plasma precursor

U66619 Hs.71622 SWI Affymetrix OA> RA

M30038 Hs.334455 alpha tryptase I precursor Affymetrix OA> RA

U13666 Hs.l 84907 G protein-coupled Affymetrix OA> RA receptor 1

L05144 Hs.l 872 phosphoenolpyruvate Affymetrix OA> RA carboxykinase 1 (soluble)

U39447 Hs.l 98241 copper containing amine Affymetrix OA> RA oxidase 3 precursor

AL049313 Affymetrix OA> RA

AL050125 Affymetrix OA> RA

D12485 Affymetrix OA> RA

X78416 Hs.3155 casein, alpha Affymetrix OA> RA

AB028998 Hs.6147 Affymetrix OA> RA

AB020629 Hs.38095 ATP -binding cassette, Affymetrix OA> RA sub-family A member 8

X03350 Hs.4 class I alcohol Affymetrix OA> RA dehydrogenase, beta subunit

AJ224677 Hs.7122 scrapie responsive protein. Affymetrix OA> RA 1

AB018317 Hs. 22201 Affymetrix OA> RA

AF009314 Affymetrix OA> RA

L77730 Affymetrix OA> RA

D76435 Hs.41154 Zic family member 1 Affymetrix OA> RA (odd-paired homolog, Drosophila)

W28828 Hs. 133988 Affymetrix OA> RA

M73720 Affymetrix OA> RA

M55150 Hs.73875 fumarylacetoacetase Affymetrix OA> RA

U13616 Hs.75893 ankyrin 3, isoform 2 Affymetrix OA> RA NM_020987

AB005293 Hs.l 03253 perilipin Affymetrix OA> RA

L07765 Hs.76688 carboxylesterase 1 Affymetrix OA> RA (monocyte

X82209 Hs.268515 meningioma 1 Affymetrix OA> RA

J03507 Hs.78065 complement component 7 Affymetrix OA> RA precursor

AF013570 Hs.78344 smooth muscle myosin Affymetrix OA> RA heavy chain 11, isoform SM1 NM_022870

U70370 Hs. 84136 paired-like homeodomain Affymetrix OA> RA transcription factor 1

U75744 Hs.88646 deoxyribonuclease I-like 3 Affymetrix OA> RA M60278 Hs.799 diphtheria toxin receptor Affymetrix OA> RA (heparin-binding epidermal growth factor- like growth f

AF042166 Hs.81008 filamin B, beta (actin Affymetrix OA> RA binding protein 278)

J00123 Affymetrix OA> RA AI207842 Hs.8272 prostaglandin D2 synthase Affymetrix OA> RA (21kD, brain)

AA128249 Hs.83213 fatty acid binding protein Affymetrix OA> RA 4, adipocyte

AA152406 Hs.l14346 cytochrome c oxidase Affymetrix OA> RA subunit Vlla polypeptide 1 (muscle) precursor

AF093118 Hs.l1494 fibulin 5 Affymetrix OA> RA L38486 Hs.296049 Affymetrix OA> RA U66689 Affymetrix OA> RA AF049884 Hs.350266 Arg Affymetrix OA> RA ABO 11089 Hs.12372 tripartite motif protein Affymetrix OA> RA

TRIM2

AF060568 Affymetrix OA> RA AF059293 Hs.l14948 cytokine receptor-like Affymetrix OA> RA factor 1

AC003107 Hs.1584 cartilage oligomeric Affymetrix OA> RA matrix protein presursor

J05037 Hs.76751 serine dehydratase Affymetrix OA> RA D45371 Hs.80485 adipose most abundant Affymetrix OA> RA gene transcript 1

U78190 Affymetrix OA> RA U24578 Hs.444 serine Affymetrix OA> RA M15856 Hs.180878 lipoprotein lipase Affymetrix OA> RA precursor

AF055033 Hs.l07169 insulin-like growth factor Affymetrix OA> RA binding protein 5

AA976838 Hs.268571 apolipoprotein C-I Affymetrix OA> RA precursor

L13698 Hs.65029 growth arrest-specifϊc 1 Affymetrix OA> RA AB020316 Hs.l34015 uronyl-2-sulfotransferase Affymetrix OA> RA

U32324 Hs.64310 interleukin 11 receptor, Affymetrix OA> RA alpha

S67070 Hs.78846 heat shock 27kD protein 2 Affymetrix OA> RA

M12529 Hs.169401 apolipoprotein E Affymetrix OA> RA

D50495 Hs.80598 transcription elongation Affymetrix OA> RA factor A (SII), 2

D00632 Hs.336920 plasma glutathione Affymetrix OA> RA peroxidase 3 precursor

AI760613 Hs. 29283 Affymetrix RA> OA AO 14646 Hs.303157 Affymetrix RA> OA W74027 Hs.132906 19A24 protein Affymetrix RA> OA W72338 Hs. 23703 Affymetrix RA> OA

AI805006 Hs.8882 Affymetrix RA> OA 67655 Affymetrix RA> OA

AA631460 Hs. 285814 Affymetrix RA> OA

AI741321 Hs.l 0760 asporin (LRR class 1) Affymetrix RA> OA

AI983115 Hs.132781 class I cytokine receptor Affymetrix RA> OA

AI535730 Hs. 262958 Affymetrix RA> OA

AA977937 Hs.l 02308 potassium inwardly- Affymetrix RA> OA rectifying Channel, subfamily J, member 8

AA447232 Hs. 334838 Affymetrix RA> OA

AI720806 Hs.49943 Affymetrix RA> OA 23237 Hs.296162 Affymetrix RA> OA

AI762695 Hs.146381 RNA binding motif Affymetrix RA> OA protein, X chromosome

AI653211 Hs. 96657 Affymetrix RA> OA

AA633405 Hs.1101 POU domain, class 2, Affymetrix RA> OA transcription factor 2

N78018 Hs. 267566 hypothetical protein Affymetrix RA> OA FLJ20371

AI625959 Hs.l 12242 Affymetrix RA> OA

T66196 Hs.l 11554 ADP-ribosylation factor- Affymetrix RA> OA like 7

AI697841 Hs.20450 BCM-like membrane Affymetrix RA> OA protein precursor NM 014036

AA569128 Hs. 283021 chloride intracellular Affymetrix OA> RA Channel 5

R53594 Hs. 260164 Affymetrix OA> RA

AI970898 Hs. 234898 Affymetrix OA> RA

AI972390 Hs. 348493 Affymetrix OA> RA

N23769 Hs.26691 Affymetrix OA> RA

AI806324 Hs.28625 Affymetrix OA> RA

N28741 Hs. 75354 Affymetrix OA> RA

AL040912 Hs.31595 oligodendrocyte Affymetrix OA> RA transmembrane protein

AI681917 Hs.3321 Affymetrix OA> RA

AW006235 Hs.41502 hypothetical protein Affymetrix OA> RA FLJ21276

W73819 Hs. 352100 Affymetrix OA> RA

T77033 Hs.l 82364 Affymetrix OA> RA

AWO 15787 Hs.237731 Affymetrix OA> RA

N30858 Hs.44234 triggering receptor Affymetrix OA> RA expressed on myeloid cells 2

AI810669 Hs.44829 Affymetrix OA> RA

N49922 Hs.l 787 proteolipid proteinl Affymetrix OA> RA (Pelizaeus-Merzbacher disease, spastic paraplegia

2, uncomp

AA082546 Hs.48516 Affymetrix OA> RA AI694320 Hs.6295 Affymetrix OA> RA

AI632283 Hs.47448 Affymetrix OA> RA

AA039324 Hs.201925 Affymetrix OA> RA

AA877186 Hs.90250 Affymetrix OA> RA

R42166 ms.94000 Affymetrix OA> RA

AI631882 Hs.6510 thyrotropin-releasing Affymetrix OA> RA hormone degrading ectoenzyme

W68636 Hs.168640 ankylosis, progressive Affymetrix OA> RA homolog NM_054027 ankylosis, progressive homolog

AA700227 Hs.10119 Affymetrix OA> RA

AI948584 Hs. 350495 Affymetrix OA> RA

AI678080 Hs. 141693 Affymetrix OA> RA

AI732274 Hs.l 1006 Affymetrix OA> RA

AI341383 Hs.349764 Affymetrix OA> RA

Z99386 ms.173638 Affymetrix OA> RA 95023 ms.173933 Affymetrix OA> RA

AI860775 Hs. 98506 Affymetrix OA> RA

AA464846 Hs.l 03262 Affymetrix OA> RA

AI751698 Hs.l 84907 G protein-coupled Affymetrix OA> RA receptor 1

AA545730 Hs. 293821 Affymetrix OA> RA

AA181060 Hs. 349283 Affymetrix OA> RA

AA195184 Affymetrix OA> RA

AI680541 Hs.23767 hypothetical protein Affymetrix OA> RA FLJ12666

AI659533 Hs.348490 Affymetrix OA> RA

AI750575 Hs.173933 Affymetrix OA> RA

AI870335 Hs.32450 Affymetrix OA> RA

AA160945 Hs.14479 Affymetrix OA> RA

AI936699 Hs. 193784 Affymetrix OA> RA

AI130027 Hs. 293539 Affymetrix OA> RA

AA081093 Hs.68055 Affymetrix OA> RA

AA142913 Hs.71721 Affymetrix OA> RA

AI984000 Hs.37482 COPZ2 for nonclathrin Affymetrix OA> RA coat protein zeta-COP

AI864898 Hs. 43125 Affymetrix OA> RA

AI670876 Hs.44276 homeo box CIO Affymetrix OA> RA

AA541787 Hs. 23837 Affymetrix OA> RA

AA775711 Hs. 348392 Affymetrix OA> RA

AI659927 Hs. 6634 Affymetrix OA> RA

AI084224 Hs.53542 Affymetrix OA> RA

AI123555 Hs.81796 Affymetrix OA> RA 73230 Hs.7913 Affymetrix OA> RA 27376 Hs.8395 hypothetical protein Affymetrix OA> RA FLJ10781

A 022607 Hs.12482 glyceronephosphate O-Affymetrix OA> RA acyltransferase

W70242 Hs.58086 Affymetrix OA> RA W25528 Hs. 89319 Affymetrix OA> RA

AA947123 Hs. 8861 Affymetrix OA> RA

AA528821 Hs. 235857 Affymetrix OA> RA

AA131648 Hs.23767 hypothetical protein Affymetrix OA> RA FLJ12666

R12398 Hs.21075 GTF2I repeat domain- Affymetrix OA> RA containing 1, isoform 1 NM_005685 52683 Hs.l 07260 hypothetical protein Affymetrix OA> RA DKFZp586H0623 72194 Hs.l 08924 ponsinNM_015385 Affymetrix OA> RA

AA885516 Hs.l 04627 Affymetrix OA> RA

W68796 Hs.237731 Affymetrix OA> RA

AI879337 Hs. 323432 mammalian inositol Affymetrix OA> RA hexakisphosphate kinase 2

W45581 Hs. 23133 Affymetrix OA> RA

N98637 Hs.7759 Affymetrix OA> RA

AI809953 Hs. 123933 Affymetrix OA> RA

T68423 Hs.l 1873 Affymetrix OA> RA

AL044670 Hs.l 82364 Affymetrix OA> RA

AA779895 Hs.l 9339 - Affymetrix OA> RA

AI719167 Hs.12731 Affymetrix OA> RA

T99215 Hs.168640 ankylosis, progressive Affymetrix _. OA> RA homolog NM_054027 ankylosis, progressive homolog

AA534296 Hs. 20953 Affymetrix OA> RA

AI819043 Hs.21342 Affymetrix OA> RA

AI762879 Hs. 86437 Affymetrix RA> OA

W61000 Hs.238730 Affymetrix RA> OA

AL043192 Hs.103378 Affymetrix RA> OA

AI741313 Hs.103657 Affymetrix RA> OA

AI031674 Hs.236494 ras-related GTP-binding Affymetrix RA> OA protein

AA670193 Affymetrix RA> OA

A 005250 Hs. 238936 Affymetrix RA> OA

AA682496 Hs.270737 tumor necrosis factor Affymetrix RA> OA (ligand) superfamily, member 13b

AI128225 Hs.914 Affymetrix RA> OA

A 026543 Hs.238936 Affymetrix RA> OA

AI991095 Hs. 293441 Affymetrix RA> OA

AI872510 Hs.181125 Affymetrix RA> OA

AI828404 Hs. 300697 Affymetrix RA> OA

A1807353 Hs.237868 interleukin 7 receptor Affymetrix RA> OA

AL048481 Hs.l 1571 Affymetrix RA> OA

AW014626 Hs.l 0949 Affymetrix RA> OA

AI400414 Affymetrix RA> OA

AI655112 Hs.16179 hypothetical protein Affymetrix RA> OA FLJ23467

AI936345 Hs.95549 hypothetical protein Affymetrix RA> OA AI961907 Hs.179573 alpha 2 type I collagen Affymetrix RA> OA preproprotein

AI743730 Hs.30822 hypothetical protein Affymetrix RA> OA FLJ11110

AI990512 Hs. 34192 Affymetrix RA> OA

AI741715 Hs.l 466 glycerol kinase Affymetrix RA> OA

T66305 Hs.l 2920 hypothetical protein Affymetrix RA> OA FLJ20668

AA424160 Hs.165909 Affymetrix RA> OA

AI075407 Hs. 296083 Affymetrix RA> OA

AA811088 Hs. 24143 WASP-interacting protein t Affymetrix RA> OA

AI978918 Hs.l 79608 retinol dehydrogenase Affymetrix RA> OA homolog

AA740831 Hs. 193514 Affymetrix RA> OA

W84421 Hs. 349096 Affymetrix RA> OA

AA233208 Hs.91165 hypothetical protein Affymetrix RA> OA

AA886976 Hs.95821 osteoclast stimulating Affymetrix RA> OA factor 1

AA864400 Hs.71215 docking protein 2, 56kD Affymetrix RA> OA

AI073984 Hs. 14453 interferon consensus Affymetrix RA> OA sequence binding protein

1

AI983633 Hs.179573 alpha 2 type I collagen Affymetrix RA> OA preproprotein

AI564488 Hs. 300697 Affymetrix RA> OA

AI655781 Hs.237868 interleukin 7 receptor Affymetrix RA> OA

AA814195 Hs.l 84465 hypothetical protein Affymetrix RA> OA FLJ11259

AI916783 Hs.234149 hypothetical protein Affymetrix RA> OA FLJ20647

AA829355 Hs.267993 hypothetical protein Affymetrix RA> OA FLJ10143

N66595 Hs.24283 Affymetrix RA> OA

AA165400 Hs.l 0927 Affymetrix RA> OA

AI478759 Hs.234149 hypothetical protein Affymetrix RA> OA FLJ20647

AI655719 Hs.2157 Wiskott-Aldrich Affymetrix RA> OA syndrome protein

N63815 Hs.110121 SEC7 homolog Affymetrix RA> OA

AW001184 Hs.44672 hypothetical protein Affymetrix RA> OA FLJ10470

N21390 Hs. 5888 Affymetrix RA> OA

AA587944 Hs.259737 FN5 protein Affymetrix RA> OA

AI951459 Hs.7337 hypothetical protein Affymetrix RA> OA FLJ10936

AA464464 Hs.l 0949 Affymetrix RA> OA

AI692538 Hs.11135 Affymetrix RA> OA

AI817147 Hs.181301 cathepsin S Affymetrix RA> OA

AI263085 Hs.17914 CD20-like precusor Affymetrix RA> OA

W58252 Hs.l 82793 golgi phosphoprotein 2 Affymetrix RA> OA

AA056180 Hs.70704 Affymetrix RA> OA AA224174 Hs.l 11099 Affymetrix OA> RA

AI571452 Hs.l 1169 Gene 33 Affymetrix OA> RA

AA155952 Hs. 349303 Affymetrix OA> RA

W68504 Hs. 191098 Affymetrix OA> RA

AI200456 Hs.48516 Affymetrix OA> RA

AW003093 Hs.349764 Affymetrix OA> RA

All 90027 Hs.38034 Affymetrix OA> RA

R52934 Hs. 8562 hypothetical protein Affymetrix OA> RA FLJ20374

W44633 Hs.301296 Affymetrix OA> RA

AW024474 Hs.44276 homeo box C10 Affymetrix OA> RA

AI806502 Hs.334800 Affymetrix OA> RA

AI492370 Hs.105606 hypothetical protein Affymetrix OA> RA FLJ20512

AW021179 Hs.90443 NADH dehydrogenase Affymetrix OA> RA (ubiquinone) Fe-S protein 8 (23kD) (NADH-coenzyme Q reductase

AI679110 Hs. 323067 Affymetrix OA> RA

R85633 Affymetrix OA> RA

N91161 Hs.117176 poly (A) -binding protein, Affymetrix OA> RA nuclear 1

AW020657 Affymetrix OA> RA

AI871043 Hs.173233 hypothetical protein Affymetrix OA> RA FLJ10970

N39237 Hs.44977 Affymetrix OA> RA

AI949833 Hs. 21914 Affymetrix OA> RA

AA679297 Hs.l 09494 secreted protein of Affymetrix OA> RA unknown function

AI962647 Hs.l 82364 Affymetrix OA> RA

AL037611 Hs. 285902 Affymetrix OA> RA

AI871278 Hs.301804 Affymetrix OA> RA

AI357650 Hs.28847 AD026 protein Affymetrix OA> RA

AI149793 Hs.38034 Affymetrix OA> RA

AI797684 Hs.39619 hypothetical protein Affymetrix OA> RA LOC57333

R52250 Hs. 348297 Affymetrix OA> RA

AI669738 Hs.128856 CSR1 protein Affymetrix OA> RA

AA058770 Hs. 18987 Affymetrix OA> RA

AI039005 Hs.l 64680 Affymetrix OA> RA

AI936560 Hs.6136 Affymetrix OA> RA

AA521373 Hs.9469 pleckstrin homology Affymetrix OA> RA domain-containing, family

A (phosphoinositide binding specif

H15888 Hs.27621 sema domain, seven Affymetrix OA> RA thrombospondin repeats (type 1 and type 1-like), transmembrane

AI333793 Hs.337062 Affymetrix OA> RA

AA523172 Hs.103135 Affymetrix OA> RA AI860960 Hs. 352081 Affymetrix OA> RA

AI355848 Hs.35841 nuclear factor I Affymetrix OA> RA

AI982754 Hs.75106 clusterin (complement Affymetrix OA> RA lysis inl ibitor, SP-40.40, sulfated glycoprotein 2, testos

AI800218 Hs. 289019 latent transforming Affymetrix OA> RA growth factor beta binding protein 3

AW016356 Hs.126857 Affymetrix OA> RA

AA968552 Hs. 25523 Affymetrix OA> RA

AI634557 Hs. 28107 Affymetrix OA> RA

AW025494 Hs.95867 hypothetical protein Affymetrix OA> RA EST00098

AA628405 Hs. 339352 Affymetrix OA> RA

AI810399 Hs. 55940 Affymetrix OA> RA

AA029735 Hs.l 59993 Affymetrix OA> RA

AA723927 Hs. 209569 Affymetrix OA> RA

AI799784 Hs. 49696 Affymetrix OA> RA

AI817330 Hs.l 10477 dolichyl-phosphate Affymetrix OA> RA mannosyltransferase polypeptide 3

AI990803 Hs. 293782 Affymetrix OA> RA

AA034418 Hs.30627 Affymetrix OA> RA

AA115295 Hs.284208 DKFZP434N161 protein Affymetrix OA> RA

AJ673281 Hs.l 81444 hypothetical protein Affymetrix OA> RA

W63805 Hs.84344 CGI-135 protein Affymetrix OA> RA

AA427597 TGFß-induc early growth Unigene NS> RA response 2

AA806239 IG-ALPHA2-C REGION Unigene RA> NS

AB014518 KIAA0618 Unigene RA> NS

AB021871 AK1 RDA, Unigene RA> OA,

RA> NS

AF 000984 DB Y old transcript 2 Affymetrix NS> RA

AF 001691 cornified envelope Affymetrix NS> RA precursor

AF005058 CXC

AF0605668 leukemia zinc finger Affymetrix OA> RA PLZF

AF068293 HDCMB07P / PCM-1 Unigene RA> NS

AF105036 GKLF RDA OA> NS

AF182035 a Actin RDA OA> NS

AF182035 myosin light chain RDA OA> NS

AF216292 GDP

AF218004 CSNK1A1 Unigenic RA> NS

AJ000542 natural killer cell receptor RDA RA> OA p58

J05008 EDN1 Affymetrix NS> RA

L08187 cytokine receptor EBI 3 RDA RA> OA

L31581 EBI1 / CCR7 Affy RA> NS

L37036 ENA-78 = Affymetrix RA> OA M10988 TNFμ Ml 9997 elongation factor 2 RDA RA> OA M29469 lg rearranged k chain (VJ RDA, RA> OA regions) Affymetrix

M31164 TSG6 RDA, Unigene 5 RA> OA, RA> NS

M83248 OSTP (Osteopontin) RDA, RA> OA

Affymetrix

NM_002450 Metallomethionein Unigene NS> RA NM_003573 TGFB-BP4 Unigene RA> NS NM_000362 TIMP-3 RDA NS> RA NM_000396 Cathepsin K RDA RA> OA, OA> NS

NM_0006091 SDF1 RDA OA> NS

NM_001908 Cathepsin B RDA OA> NS

NM_002084 glutathione peroxidase 3 RDA NS> RA

NM_002229 Jun B Unigenous NS> RA

NM_002989 SLC Unigene RA> NS

NM_003966 SEMA5A RDA RA> OA

NM_004039 Annexin II RDA RA> OA, OA> NS

NM_005368 Myoglobin RDA OA> NS

NM_006472 VDUP1 RDA, Unigene NS> RA

NM_007016 Mysin light polypeptide2 RDA OA> NS

NM_015675 GADD45B / MYD118 RDA, Unigene; NS> RA

R75775 EGR1 Unigene NS> RA

U070136 megakaryocyte RDA, Unigene NS> RA stimulating factor

U34690 COROlA / p57 Unigene RA> NS U93569 Ll element RDA, Unigene RA> OA;

RA> NS

X03754 SCYA3 (MIP a) / GOS19 Unigene RA> NS

X0523 MMP1

X14723 Clustrin / SP40 RDA, Unigene NS> RA

X15332 collagen III al RDA, Unigene RA> OA

X54629 c-myc RDA, Unigene NS> RA

X54629 pHL-1 gene RDA NS> RA

X58122 Nebulin RDA OA> NS

X62996 mitochondrial mRNA RDA OA> NS

X63596 TRE-2 RDA RA> OA

X65968 PMP22 Unigene RA> NS

X88971 HLA DRB1 RDA RA> OA

X94771 EMP3 Unigene RA> NS

XM 008868 latent transforming RDA, Unigene NS> RA growth factor beta binding prot. LTBP4

XM 031289 interleukin 8 = Affymetrix RA> OA

XM012651 collagen I al RDA RA> OA Compilation of the proteins

Table 2:

Chitinase 3-like protein 2 precursor (YKL-39) (Chondrocyte protein 39) Q15749

Fructose bisphosphate aldolase A (muscle-type aldolase) (Lung cancer P04075 antigen NY-LU-1)

Proteoglycan link protein precursor (Cartilage link protein) (LP) P10915

Matrix metalloproteinase-19 precursor (MMP-19) (matrix Q99542 metalloproteinase RASI)

MMP-19 (matrix metalloproteinase) CAA63299

Aggrecan core protein precursor (Cartilage-specific proteoglycan core P16112 protein) (CSPCP) (Chondroitin sulfate proteoglycan core protein 1)

Ezrin (p81) (cytovillin) (Villin 2) P15311

Radixin P35241

Moesin (Membrane-organizing extension spike protein) P26038

The invention is to be explained in more detail on the basis of exemplary embodiments, without being restricted to these examples.

embodiments

Example 1: Application in clinical diagnostics

A patient with joint problems for 4 months has asymmetrical swelling and painfulness in 2 finger base and 1 finger middle joint as well as the right wrist. The morning stiffness is about 30 minutes. Radiologically, erosive changes begin to show in a basic toe joint. The C-reactive protein is in the normal range, the decrease is slightly increased, the rheumatoid factor and HLA-DR4 are negative. There is no family history of an inflammatory rheumatic disease.

There is an outpatient presentation for minimally invasive arthroscopic removal of a synovial biopsy on the right wrist. A small sample of 4 tissue samples, each with a weight of approx. 10 mg, is fixed in formalin for the subsequent histological assessment. The remaining pieces of tissue are taken up in RNA lysis buffer, crushed and the RNA extracted according to the standard protocol. After transcription into cDNA, the in vitro transcription into a biotin-labeled cRNA takes place as a copy of the cDNA. The cRNA is fragmented and used for hybridization on the DNA array.

The array is manufactured by a commercial provider for the production of DNA arrays such as Affymetrix. There, from the sequences in Table 1 and the gene sequences coding for the proteins in Table 2, suitable oligo-nucleotides are determined which enable a specific hybridization with the respective cRNA sequences. These sequences are either synthesized as oligonucleotides and printed on an array support or directly synthesized on the support, for example using photolithographic methods.

Hybridization takes place according to the manufacturer's specified protocol. The DNA array is read out with a scanner. The translation of the image information into expression signals is carried out using standard software such as "Micro-Array Suite" from Affymetrix. The signals for the RNA expression strength of the genes or proteins mentioned in Tables 1 and 2 are now available. On the basis of this newly defined selection of genes for the diagnostic assessment and therapy development in joint diseases In preliminary experiments, clinically and histologically characterized tissue samples according to cluster analysis were hierarchically divided by the comparative association with clinical findings, this division in particular depending on the form of the disease (arthrosis, reactive arthritis, rheumatoid arthritis, subgroups of rheumatoid arthritis), the activity of the disease and thus the prognosis as well as the influence of the pathologically changed gene expression on a given medication. The signal data of the above-mentioned patient are now compared with this database. This enables an assignment to one of these groups and to the associated groups engined clinical associations to draw conclusions. This results in statements about the diagnosis, the activity, the prognosis and the therapy options in the individual case of illness.

Example 2: Application for therapy assessment

A patient with chronic joint inflammation for 5 years and diagnosed with rheumatoid arthritis has progressive specific radiological changes in several finger joints, accompanied by pain and swelling in several finger joints, the left elbow joint and the right ankle joint despite current basic therapy with 15 mg methotrexate per week. There is an outpatient presentation for minimally invasive arthroscopic removal of a synovial biopsy on the left elbow joint. Several samples with a total weight of approx. 30 mg are taken up in lysis buffer, crushed and the RNA extracted. The sample is worked up in a manner comparable to the procedure in Example 1. The same DNA chip as in Example 1 is used for the analysis. After hybridization, reading out the hybridization result in an image file and translating it into signal information for each of the genes examined, an assignment to defined expression patterns takes place. These were determined in preliminary experiments using the newly defined selection of genes from Tables 1 and 2. The change in the expression profile of a sample depending on the associated joint disease was analyzed under the influence of defined drugs at defined concentrations. The profiles were classified hierarchically, taking into account the association with the medication used and the dose used. If the patient sample is compared with these defined expression patterns, the assignment to a specific pattern and the associated efficacy statements make it possible to estimate whether the given drug contains methotrexate a higher dose could be successful, or whether a switch should be made to a drug whose efficacy is best able to influence the pathological changes in this individual case.

Example 3: Autoreactivity profiles in RA The RA differs from other rheumatic and other inflammatory diseases by the formation of autoantibodies. Discrimination between RA and non-RA is not guaranteed by antibody reactivity, but by different profiles of different auto-reactivities. It is thus possible to create reliable diagnostics, to check therapy courses and to carry out preventive examinations by determining the RA-specific auto-activity profiles.

Antibodies are directed against antigens or more specifically against epitopes which are bound by the paratopes in a specific antibody-antigen reaction. By definition, epitope is the area of an antigen that specifically interacts with an antibody (i.e., its paratope). This is usually understood to mean a 16 to 20 amino acid peptide sequence of a protein. This sequence can be consecutive (continuous epitope) or interrupted (discontinuous epitope). Typically, however, only a few amino acids, in rare cases even a single amino acid, are necessary and sufficient for the specific interaction of antibody and antigen. It is now known that even nucleic acids can act as an antigen. Post-translational modifications, e.g. Phosphorylation, acylation, glycosylation, methylation, de-elimination etc. Since these modifications often have a regulatory function, they seem to be the target structure of antibodies in a special way, especially under pathological conditions. Since it has already been shown in some RA-associated autoantigens that certain post-translational modifications form epitopes for autoantibodies, special care must be taken to ensure that these structures are implemented for the test system.

The proteins listed in Table 2 are described as RA-associated autoantigens. However, the relevance of most of these individual components for the diagnosis of RA is low or not apparent. The same applies to the genes which are overexpressed at the mRNA level and are listed in Table 1. By themselves, these components are not suitable for significantly improving RA diagnostics. This can be seen from the fact that practically the majority of the proteins listed in Tables 1 and 2 are not registered for a patent for these purposes. Only a few proteins are so characteristic that a relevance for RA was assumed. This applies, for example, to BiP (Heavy Chain Binding Protein), which is the target of an immune response in RA. Here, for example, a post-translational modification in the form of glycosylation has to be taken into account, since this is part of epitopes, which is necessary both for the detection of autoantibodies from RA and for the differentiation between RA and non-RA autoantibodies. Furthermore was described the post-translational amino acid converted from arginine to citrulline as an essential epitope for RA-associated autoantibodies (6). The Sa antigen (5), the RA33 antigen and the calpastatin are of similar importance for RA diagnosis.

Nevertheless, these components were not in themselves suitable for enabling a clear diagnosis of RA or even therapy monitoring. The new approach presented here according to the invention relates to the immunoma of RA. The immunoma of RA encompasses all of the autoreactive antibodies that occur in RA and all of the autoantigens or auto-epitopes they recognize. It was unexpectedly found that it is possible for the first time to clearly define a disease as RA by considering the combination of RA-associated autoantibodies. It was shown for the first time that different patterns of autoantibodies exist that only occur in RA. These patterns also include autoantigens or auto-activities that in themselves appeared to be insignificant for RA. This is all the more surprising since first steps in this direction by other groups have not led to this result, although it is emphasized that the most important autoantigens of eight different autoimmune diseases in humans have been used (11). The same is true for an approach using autoantigens that are relevant to another rheumatic disease, systemic lupus erythematosus (SLE). Obviously, the main difference between previously published approaches and the one described here is the type of analysis (multivariate) and the composition of the autoantigens. Only a sufficiently high number of RA-relevant auto-activities enables a clear diagnosis. Thus, the totality of RA-associated autoantibodies and autoantigens is information that - together with other techniques (ProteinArray technology (27), data processing) - i.a. can be used as a tool for diagnosis and classification of RA. Even an expert in this area could not have reached this degree of utilization by concluding analogies. The immunoma of the RA or even subsets of the immunoma of the RA can be used to clearly differentiate the RA from other diseases or the healthy state. In addition, an economic use of the unexpected invention is only possible due to the possibilities of high-throughput technologies that are available today or are still in development. This relates in particular to the multi-parameter analysis of auto-reactivities, since it is necessary here to carry out a large number of examinations in parallel and using the smallest patient sample quantities.

Proteins or partial protein sequences of the components listed in Table 2 or proteins and partial protein sequences which are encoded by the genes listed in Table 1, including any post-translational modifications that may be necessary for the discrimination of RA and non-RA, are synthesized and provided for the creation of auto-activity profiles. The synthesis can be carried out by any molecular biological approach known per se or else by any protein chemical approach. In addition, the semi-artificial (in vitro translation) or artificial synthesis according to the prior art is suitable for producing the proteins or partial protein sequences mentioned.

ProteinArray / PeptidArray (28) Proteins or protein partial sequences according to Table 2 or 1, all or only a selection of these which is useful for the immunomic discrimination of clinical pictures is used to create a test possibility which is suitable for determining the autoreactivity status of an individual. This applies in particular to the selection of citrulline, BiP, p205, IgG, calpastatin, RA33, Sa antigen and calreticulin. For this purpose, the proteins are applied separately in spatially resolvable positions to a carrier matrix according to the known. The position and identity of each immobilized protein, peptide, modified protein or modified peptide are known. The microformat allows the parallel detection of thousands of different antigens and / or autoantigens (proteins / peptides) in the submicroliter range of human sera. It is preferred to create a protein array, a high-density filter or a high-density glass carrier or another matrix produced in the high-density process, which is coupled or uncoated to the proteins or partial protein sequences. For example, proteins or partial protein sequences can be stamped onto derivatized or coated / activated glass supports, or the application takes place in the ink jet process, capillary or by direct synthesis on the array using photolithographic masks or digital micromirrors. Instead of glass supports, membranes and filters, polystyrene matrices, nanowell plates and microparticles can also be used (29).

The ProteinArray is incubated with a suitable dilution of patient sera or patient articular effusions. During this incubation, any antibodies that are specific for one or more protein components can bind to these protein antigens. This is followed by a washing step in order to remove unbound antibodies and serum components. This is followed by incubation with a second antibody which, on the one hand, is suitable for marking an antigen-antibody reaction by binding the first antibody and, on the other hand, a suitable marking which allows visualization and quantification, suitably a covalently coupled fluorescent dye or a covalently coupled enzyme that can form a dye from a precursor. This is followed by a further washing step in order to remove excess secondary antibodies.

SuspensionsArray (30)

The suspension array uses plastic particles as a matrix, which are coated with the proteins mentioned. The optical properties of the particles that are coupled to a particular protein differ from those that are coupled to another protein. The immunomeasurement is carried out analogously by incubation with patient sera or others Body fluids. Another optical (fluorescent) signal is set directly or again indirectly by means of a suitable second antibody via the antibody binding. The analysis is then carried out in a multicolor fluorescence activated cell (FAC) scan.

Time-resolved protein arrays (31) A polystyrene surface is coupled with various proteins or partial protein sequences from Tables 1 and 2. The antibodies of the patient sera to be analyzed are biotinylated using an active biotin ester. Alternatively, in order to avoid inter-patient fluctuations due to different biotinylation efficiency, biotinylated secondary antibodies which are specific for human antibodies can also be used. Patient antibodies are then incubated with the protein-coupled polystyrene surfaces. After the subsequent washing step, detection is carried out using streptavidin, which is coupled with a fluorescent europium chelate. The detection is carried out after a washing and drying step by means of laser-excited, time-resolved solid-phase fluorescence analysis.

Data patterns and multifactorial analysis parameters (e.g. the autoreactivities resulting from the proteins / autoantigens listed in Tables 1 and 2; e.g. the autoreactivities RF / citrullin / BIP / Calpastatin / Calreticulin / RA33) are determined as completely as possible. Data samples from individual patients with more than two out of six missing values are excluded a priori from the analysis.

The evaluation of the immunodetection system delivers either a negative or a positive result for each patient and each auto-reactivity. Alternatively, continuous values (ProteinArray, ELISA) are also possible, which are either artificial (mathematical) or via a control group-related (analysis against a suitable control group, e.g. age- and sex-matched healthy controls or control patients with another disease) cut off in positive and be divided negatively. Each data pattern is analyzed and classified using the CLASSIF1 program system (32).

In a first step, triple matrix characters of each clinical diagnosis category are entered into the first reference classification mask. Each patient is then classified according to the highest positional match between the patient mask and a clinical reference mask. In a second step, those data columns that have the triple matrix character "0" for all reference masks are eliminated, since these are not able to discriminate between the disease entities.

In a third step, the CLASSIF1 algorithm temporarily eliminates either individual ones

Parameters or combinations of two parameters in all permutations from the classification process. The entire data set is then reclassified. Parameters by her temporary removals affect the classification result are informative, since apparently no essential information is lost. The information content of each parameter is in the meantime made available by the algorithm, used again after the operation, and the next parameter or the next pair of parameters is temporarily extracted and analyzed analogously. The temporary removal and reinsertion is carried out until the information content of all parameters, either individually or in combination, is known. Parameters that have proven to be uniform either individually or in combination with another parameter are eliminated. The remaining sequence of informative parameters forms the reference classification mask for the respective clinical prediction category. In a fourth step, the classification is optimized by classifying the percentile threshold values 10/90%, 15/85%, 20/80%, 25/75% and 30/70% with subsequent selection of the best discriminating pair. The best classification results are typically achieved between the 10/90% and 25/75% percentile pairs. Negative and positive predictive values in a confusion matrix provide information about how well reference and test samples are discriminated by the pattern or patterns used.

In addition, the data patterns of each patient are subjected to a multifactorial analysis. The multifactors for five parameter patterns were obtained by multiplying or dividing the various parameters in all possible combinations, followed by standardizing the five data columns against the mean values of the RA reference group. The mean values for each parameter of the other patient groups (e.g. OA, reA, PsoA, others) were then determined. Multifactors of all parameter permutations were determined either by multiplication if the parameter mean of the patient group in question was increased compared to the reference value (RA), or by division if the value was decreased.

The multifactorial database contains the measured parameters (RF / Citrullin / BiP / Calpastatin / Calreticulin / RA33). 26 multifators were classified by the CLASSIFl algorithm. For this purpose, all numbers of each database column were either transformed into - (smaller than the lower percentile of the value distribution of the reference patients [RA]), 0 (between lower and upper percentile) or + (larger than the upper percentile) triple matrix characters , Following the transformation of the database columns, a confusion matrix between clinical diagnosis and computer classification is established.

The diagonal values of this confusion matrix represent the specificity of the reference samples and the sensitivity for the samples to be tested. These are further optimized during the subsequent iterative learning process. An optimal classification is achieved if all samples are correctly classified, i.e. if all diagonal values of the Confusion Matrix reach 100% and the values of the non-diagonal fields are 0%. The learning process serves to eliminate non-informative parameters and thus to enrich the discriminatory parameters. Legend for the figure

Figure 1: Autoreactivity pattern with RA33, RF, Citrulline, BiP and Calpastatin

All 32 possible combinations of auto-reactivities against IgG (RF), citrulline, BiP, calpastatin, RA33 and calpastatin for the disease entities RA (rheumatoid arthritis), reA (reactive arthritis), OA (osteoarthritis), PsoA (psoriasis-associated arthritis) are shown and other.

List of abbreviations

ACR American College of Rheumatology

BiP binding protein, heavy chain binding protein

BSA bovine serum albumin (Bovine serum albumin)

Calp calpastatin

Calr calreticulin

Calp Calpastatin cDNA complementary DNA, copy DNA

CH Chondrocyte antigen

Cit citrullinated peptides

CrP C-reactive protein

DNA deoxyribonucleic acid

DPNII from Diplococcus pneumoniae dNTP deoxynucleotide triphosphate (equimolar mixture of dATP, dCTP, dGTP, dTTP) dNTP deoxynucleotide triphosphate

EBNA-1 Epstein Barr Virus Nuclear Antigen- 1

EBV Epstein-Barr virus

ER endoplasmic reticulum

FACS Fluorescence Activated Cell Sorting

GAPDH glyceraldehyde phosphate dehydrogenase

HC Human Cartilage HC gp39 human cartilage glycoprotein 39

HLA system histocompatibility antigens (HLA - human leucocyte antigen)

HLA-DR4 HLA trait that has an increased association with rheumatoid arthritis hnRNP heterogenous ribonucleoprotein (RA33)

Hsp Heatshock Protein

Ig immunoglobulin

IgG immunoglobulin G

IL- Interleukin IR-3 Infernal Repeat Region 3

MCTD Mixed Connective Tissue Disease

MHC major histocompatibility complex

MMP matrix metalloproteinase mRNA messenger ribonucleic acid NAD nicotinamide adenine dinucleotide

NCBI National Center for Biotechnology Information

ND Normal Donor

OA osteoarthritis

O-GlcNAc O-N-acetylglucosamine PCR polymerase chain reaction

PHA phytohemagglutinin

PM / DM polymyositis / dermatomyositis

PsoA Psoriasis-associated arthritis

RA Rheumatoid Arthritis RA-A47 Arthritis-related antigen

RA33 hnRNP A2

RDA Representational Difference Analysis reA reactive arthritis

RF Rheumatoid Factor RNA Ribonucleic Acid

RPMI commercial cell culture medium, dilution medium RPMI 1640; Moore, G.E. et al, J. Am. Assoc. 199, 519 - 524, 1967)

Rsal DNA restriction enzyme Rsal from Rhodopseudomonas sphaeroides

RT Reverse Transcriptase (RT Sa antigen 50k protein from human spleen and placenta

SLE systemic lupus erythematosus

SSH suppression subtractive hybridization

TGF transforming growth factor

UNIGENE UniGene is an experimental system for automatic

Partitioning of the GenBank sequences into a non-redundant set of gene-oriented clusters. YKL-39 human cartilage-related protein

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Claims

claims

1. Tools for the diagnosis, molecular definition and therapy development of chronic inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans using the sequences of individual genes, a selection of genes or all genes mentioned in Table 1 and the genes, which code for the proteins listed in Table 2.

2. Tools according to claim 1, characterized in that they include gene sequences which are identical in sequence to the genes mentioned in Table 1 or to the genes coding for the proteins mentioned in Table 2, or at least 80% > Have sequence identity in the protein coding sections.

3. Tools according to claims 1 and 2, characterized in that they include sequence sections or partial sequences which are identical in their sequence to the genes mentioned in Table 1 and covered by claim 2 or a sequence identity of at least 80%> to the corresponding Have sections of the genes mentioned.

4. Tools according to claims 1 to 3, characterized in that they are based on the use of a 4.1. High-throughput method of (micro) array hybridization

4.2. High-throughput process with techniques of polymerase chain reaction for (semi)

Quantification based.

5. Tools according to claims 1 to 3, characterized in that they are based on the use of a marked patient sample and a second differently labeled control sample for comparative double hybridization to a (micro) array with the patient sample (red / green comparison hybridization ) are based.

6. Tools according to claims 1 to 5 for diagnostic purposes, characterized in that they are based on the use of individual, a selection or all of the proteins or peptides derived from the gene sequences in claims 1 to 3.

7. Tools according to claim 6, characterized in that they are based on the use of individual proteins, a selection of proteins or all of the proteins mentioned in Table 2.

8. Tools according to claims 6 and 7, characterized in that they are based on the use of partial sequences of individual proteins, a selection of proteins or all of the proteins mentioned in Table 1.

9. Tools according to claims 6 to 8, characterized in that they include proteins or partial protein sequences which are identical in their sequence to the proteins derived in Table 1 or the proteins mentioned in Table 2 or have at least 80% sequence identity ,

10. Tools according to claims 6 to 9, characterized in that they are based on the use of

10.1. High-throughput methods in protein expression analysis (high-resolution, two-dimensional protein gel electrophoresis, MALDI techniques) 10.2. High-throughput method in the protein spotting technique (protein arrays) for the screening of autoantibodies as a diagnostic tool for inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans

10.3. High-throughput method in the protein spotting technique (protein arrays) for the screening of autoreactive T cells as a diagnostic tool for inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans

10.4. Non-high-throug put procedures based on the protein spotting technique for screening autoreactive T cells as a diagnostic tool for inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans.

11. Tools according to claims 6 to 9, characterized in that they are based on the use of antibodies which are specific for proteins or partial sequences which are listed under claims 6 to 9.

12. Tools according to claims 1 to 11, characterized in that they are based on the use of the corresponding homologous sequences of another species for analysis in animal experiments or for diagnosis in animals with inflammatory joint diseases and other inflammatory, infectious or tumorous diseases.

13. Tools according to claims 6 to 11 as diagnostic tools for the detection of genetic changes (mutations) in the genes mentioned in claims 1 to 3 or their Regulatory sequences (promoter, enhancer, silencer, specific sequences for the binding of other regulatory factors).

14. Tools according to claims 6 to 11 and 13 for the detection of genetic changes (mutations) in the genes or their regulatory sequences (promoter, enhancer, silencer, specific sequences for the binding of further regulatory factors), for the proteins mentioned in Table 2 encode.

15. Tools according to claims 1 to 5 for the molecular definition of inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans using the genes mentioned in claims 1-3, DNA sequences or proteins or peptides derived therefrom as well as the proteins and protein Partial sequences from claims 6 to 9 or the gene sequences coding therefor.

16. Tools according to claims 1 to 5 for the therapy decision of inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans using the genes, DNA sequences or proteins or peptides derived therefrom mentioned in claims 1-3.

17. Tools according to claims 1 to 5 for follow-up / therapy control of inflammatory joint diseases and other inflammatory, infectious or tumorous diseases in humans using the genes, DNA sequences or proteins or peptides derived therefrom mentioned in claims 1-3.

18. Tools according to claims 1 to 5 as molecular tools for the development of therapy concepts which include the direct or indirect influencing of the expression of the genes or gene sequences named in claims 1-3.

19. Tools according to claims 1 to 5 and 18 for the development of therapy concepts which include the direct or indirect influencing of the expression of the proteins or protein partial sequences named in claims 6 to 9.

20. Tools according to claims 1 to 5 and 18 to 19 for the development of therapy concepts which directly or indirectly influence the autoreactive T cells, directed against the proteins or partial protein sequences named in claims 8-11.

21. Tools according to claims 1 to 5 and 18 to 20 for influencing the biological effect of the proteins derived from the gene sequences named in claims 1-3.

22. Tools according to claims 1 to 5 and 18 to 21 for influencing the immediate molecular control loops in which the genes named in claims 1-3 and proteins derived therefrom are incorporated.

23. Tools according to claims 1 to 5 and 18 to 22 for the development of therapy concepts using the design and use of interpretation algorithms which use the said genes and sequences and their regulatory mechanisms in order to identify or predict therapy concepts, effects, optimizations or disease prognoses ,

24. Tools according to claims 1 to 5 and 18 to 22 for the development of biologically active drugs (biologicals) using genes, gene sequences, regulation of genes or gene sequences, or using proteins, protein sequences, fusion proteins according to claims 1 to 3 and 6 to 9 or using antibodies or autoreactive T cells according to claims 10-14.

25. Array as a molecular tool, consisting of various antibodies or molecules with comparable protein-specific binding behavior, which serve to detect all or a selection of the proteins derived from the genes in Table 1 or all or a selection of the proteins in Table 2.

26. Use of tools according to claims 1 to 24 for

26.1. Examination of blood samples or tissue samples in medical diagnostics

26.2. Application in analysis according to example 1

26.3. Application for therapy concepts according to example 2.