EP2867678A2 - Marker sequences for parkinson's disease and use thereof - Google Patents

Abstract

The present invention relates to new marker sequences for Parkinson 's disease and the diagnostic use thereof together with a method for Screening of potential active substances for Parkinson' s disease by means of these marker sequences. Furthermore, the invention relates to a diagnostic device containing such marker sequences for Parkinson's disease, in particular a protein biochip and the use thereof.

Description

Marker sequences for Parkinson' s disease and use thereof

Specification

The present invention relates to new marker sequences for Parkinson's disease and the diagnostic use thereof together with a method for screening potential active substances for Parkinson's disease by means of these marker sequences. Furthermore, the invention relates to a diagnostic device containing marker sequences of this type for Parkinson's disease, in particular a protein biochip and the use thereof .

Protein biochips are gaining increasing industrial

importance in analysis and diagnosis as well as in

pharmaceutical development. Protein biochips have become established as screening instruments.

The rapid and highly parallel detection of a multiplicity of specifically binding analysis molecules in a single experiment is rendered possible hereby. To produce protein biochips, it is necessary to have the required proteins available. For this purpose, in particular protein

expression libraries have become established. The high throughput cloning of defined open reading frames is one possibility (Heyman, J. A., Cornthwaite, J., Foncerrada, L . , Gilmore, J.R., Gontang, E., Hartman, K.J., Hernandez, C.L., Hood, R., Hull, H.M., Lee, W.Y., Marcil, R., Marsh, E.J., Mudd, K.M., Patino, M.J., Purcell, T.J., Rowland, J.J., Sindici, M.L. and Hoeffler, J. P., (1999) Genome-scale cloning and expression of individual open reading frames using topoisomerase I-mediated ligation. Genome Res, 9, 383-392; Kersten, B., Feilner, T., Kramer, A., Wehrmeyer, S., Possling, A., Witt, I., Zanor, M.I., Stracke, R., Lueking, A., Kreutzberger , J., Lehrach, H. and Cahill, D.J. (2003) Generation of Arabidopsis protein chip for antibody and serum screening. Plant Molecular Biology, 52, 999-1010; Reboul, J., Reboul, J., Vaglio, P., Rual, J. F., Lamesch, P., Martinez, M., Armstrong, CM., Li, S., Jacotot, L . , Bertin, N., Janky, R., Moore, T . , Hudson, J. R., Jr., Hartley, J. L . , Brasch, M. A., Vandenhaute, J., Boulton, S., Endress, G. A., Jenna, S., Chevet, E.,

Papasotiropoulos , V., Tolias, P.P., Ptacek, J., Snyder, M., Huang, R. , Chance, M. R. , Lee, H., Doucette-Stamm, L., Hill, D.E. and Vidal, M. (2003) C. elegans ORFeome Version 1.1: experimental verification of the genome annotation and resource for proteome-scale protein expression. Nat Genet, 34, 35-41.; Walhout, A. J., Temple, G. F., Brasch, M. A., Hartley, J. L., Lorson, M. A. , van den Heuvel, S. and Vidal, M. (2000) GATEWAY recombinational cloning:

application to the cloning of large numbers of open reading frames or ORFeomes. Methods Enzymol, 328, 575-592) .

However, an approach of this type is strongly connected to the progress of the genome sequencing projects and the annotation of these gene sequences. Furthermore, the determination of the expressed sequence can be ambiguous due to differential splicing processes. This problem may be circumvented by the application of cDNA expression

libraries (Biissow, K., Cahill, D., Nietfeld, W., Bancroft, D., Scherzinger, E., Lehrach, H. and Walter, G. (1998) A method for global protein expression and antibody screening on high-density filters of an arrayed cDNA library. Nucleic Acids Research, 26, 5007-5008; Biissow, K., Nordhoff, E., Lubbert, C, Lehrach, H. and Walter, G. (2000) A human cDNA library for high-throughput protein expression screening. Genomics, 65, 1-8; Holz, C, Lueking, A., Bovekamp, L . , Gutjahr, C, Bolotina, N., Lehrach, H. and Cahill, D. J. (2001) A human cDNA expression library in yeast enriched for open reading frames. Genome Res, 11, 1730-1735;

Lueking, A., Holz, C, Gotthold, C, Lehrach, H. and Cahill, D. (2000) A system for dual protein expression in Pichia pastoris and Escherichia coli, Protein Expr . Purif., 20, 372-378) . The cDNA of a particular tissue is hereby cloned into a bacterial or an eukaryotic expression vector, such as, e.g., yeast. The vectors used for the expression are generally characterized in that they carry inducible promoters that may be used to control the time of protein expression. Furthermore, expression vectors have sequences for so-called affinity epitopes or affinity proteins, which on the one hand permit the specific detection of the recombinant fusion proteins by means of an antibody directed against the affinity epitope, and on the other hand the specific purification via affinity chromatography (IMAC) is rendered possible.

For example, the gene products of a cDNA expression library from human fetal brain tissue in the bacterial expression system Escherichia coli were arranged in high-density format on a membrane and could be successfully screened with different antibodies. It was possible to show that the proportion of full-length proteins is at least 66%.

Additionally, the recombinant proteins from the library could be expressed and purified in a high-throughput manner (Braun P., Hu, Y., Shen, B., Halleck, A., Koundinya, M., Harlow, E. and LaBaer, J. (2002) Proteome-scale

purification of human proteins from bacteria. Proc Natl Acad Sci U S A, 99, 2654-2659; Bussow (2000) supra; Lueking, A., Horn, M., Eickhoff, H., Biissow, K., Lehrach, H. and Walter, G. (1999) Protein microarrays for gene expression and antibody screening. Analytical Biochemistry, 270, 103-111) . Protein biochips of this type based on cDNA expression libraries are in particular the subject matter of WO 99/57311 and WO 99/57312.

Furthermore, in addition to antigen-presenting protein biochips, antibody-presenting arrangements are likewise described (Lai et al (2002) Antibody arrays: An embryonic but rapidly growing technology, DDT, 7, 143-149; Kusnezow et al. (2003), Antibody microarrays: An evaluation of production parameters, Proteomics, 3, 254-264).

Parkinson's disease (PD) also called primary or idiopathic parkinsonism is the second most common neurodegenerative disorder in the elderly. With no available biomarker the diagnosis of PD is still based on clinical criteria (Gibb and Lees, 1988) . Not all cases of PD are diagnosed

correctly, especially in early stages as several conditions can mimic PD (Tolosa et al. , 2006) . The trigger for the neurodegeneration in idiopathic PD is still unknown; and age is the only strong risk factor (Schapira and Jenner, 2011) . The loss of dopaminergic neurons in the substantia nigra pars compacta (SN) is crucial for the development of the specific clinical symptoms, but the pathology of PD affects a variety of other neuronal populations. The pathological process leading to alpha-synuclein inclusions and finally the presence of Lewy bodies remains unclear. Immunological mechanisms have been implicated as major contributors to this pathological process. Besides general mechanisms activating and amplifying neuroinflammation , such as generation of proinflammatory cytokines by local glial cells, a neuronal labelling in the substantia nigra (SN) with PD IgG was described (Rowe et al. , 1998).

Parkinson's disease is a common neurodegenerative disorder for which no biomarker is available to aid diagnosis or to monitor disease progression. Extracellular fluid markers of proteins involved in the pathology of Parkinson's disease are thought to be promising candidates, but no specific biomarker could be identified yet. Marker sequences and the diagnostic use thereof for Parkinson's disease, in

particular in the embodiment of a protein biochip, as well as tests in this regard for the screening of active substances have not been described in the prior art.

Therefore, there is a great need to provide indication- specific diagnostic devices, such as biomarkers for diagnosis of Parkinson's disease.

The present invention provides biomarkers for Parkinson's disease, in particular marker sequences and the use thereof for protein biochips and diagnosis.

The provision of specific marker sequences permits the reliable diagnosis and stratification of patients with Parkinson's disease, in particular by means of a protein biochip and test kits. The identified Parkinson's disease specific marker sequences are suitable for early

recognition, diagnosis, prognosis and surveillance of treatment of patients with Parkinson ^'s disease and of progression or regression of the disease respectively.

Immunological mechanisms have been implicated as major contributors to the pathological process in Parkinson's disease. Besides general mechanisms activating and

amplifying neuroinflammation, such as generation of

proinflammatory cytokines by local glial cells, a neuronal labelling in the substantia nigra (SN) with PD IgG was described (Rowe et al. , 1998) .

Thus, antibodies may play a critical role in the

destructive cascade involved in the pathological process. Assuming immunological mechanisms involved in PD and the requirements to an optimal biomarker, antibodies are candidates for biomarkers in neurodegenerative disorders. Antibodies are highly stable proteins, which are easily accessible e.g. in blood or also saliva and can be easily measured with protein microarrays, ELISA, or other methods. For these reasons they could be seen as a good starting point to find candidates for an early diagnosis, with a high sensitivity and specificity and perhaps the ability to monitor disease progression. Following the hypothesis, a protein array-based screening strategy for the discovery of PD disorder-specific autoantibodies was developed. Sera samples, clinical and other data of PD patients, diseased and healthy controls are collected in an ongoing clinical study called ParkChip. Protein arrays displaying more than 9,500 different human proteins were incubated with serum and used for the detection of PD-specific autoantibody signatures in human blood. The performance of this protein array allowed the identification of marker sequences SEQ ID No. 1 - 37 that discriminate PD patients from reference groups .

The invention therefore relates to a marker sequence for Parkinson's disease, wherein the marker sequence is selected from the group of protein sequences SEQ ID No. 1 -

36, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for

homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37.

The invention further relates to a method for identifying marker sequences for Parkinson's disease by means of differential screening of samples from healthy test subjects (healthy persons), samples from patients with other neurodegenerative diseases (DC) and samples from patients with Parkinson's disease. With this method the marker sequences according to the invention were

identified. These 37 marker sequences are highly specific for Parkinson's disease and discriminate PD from other neurodegenerative diseases (DC) and healthy controls (HC) . The rough data obtained from differential screening was processed by a three-step feature selection approach

(Examples 1 to 3) . An average classification accuracy of 73.5% has been achieved, and no obvious over fitting has been detected. The method according to the invention is shown in Figure 1.

The invention relates to a marker sequence for Parkinson's disease identified by means of differential screening of samples from healthy persons and samples from patients with Parkinson's disease and wherein the marker sequence is selected from the group of protein sequences SEQ ID No. 1 -

37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for

homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37.

The invention further relates to the use of marker

sequences for the diagnosis of Parkinson's disease, wherein at least one marker sequence is selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37 and is determined on or from a patient to be examined.

The term "marker sequence (s) according to the invention" or "marker sequence (s) for Parkinson's disease" or "marker sequence (s) " relates to one or more sequences selected from the group of protein sequences SEQ ID No. 1 - 37,

homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37.

The term "Parkinson's disease" (PD) is defined e.g., according to Pschyrembel, de Gruyter, 263st edition (2012), Berlin) . According to the invention Parkinsonism is also enclosed in „Parkinson's disease". The term "Parkinsonism" is used for a motor syndrome whose main symptoms are tremor at rest, stiffness, slowing of movement and postural instability. Parkinson's disease (Parkinsonian syndromes) can be divided into four subtypes according to their origin: primary or idiopathic, secondary or acquired, hereditary parkinsonism, and Parkinson plus syndromes or multiple system degeneration. Parkinson's disease is the most common form of parkinsonism and is usually defined as "primary" parkinsonism, meaning parkinsonism with no external identifiable cause. According to the invention Parkinson's disease encloses also "familial Parkinson's disease" and "sporadic Parkinson's disease" and „movement disorder". It can be recognized for example by several non- motor types of symptoms such as sensory deficits, cognitive difficulties or sleep problems. Parkinson plus diseases are primary Parkinsonism's which present additional features. They include multiple system atrophy, progressive

supranuclear palsy, corticobasal degeneration and dementia with Lewy bodies. In terms of pathophysiology, PD is considered a synucleinopathy due to an abnormal

accumulation of alpha-synuclein protein in the brain in the form of Lewy bodies, as opposed to other diseases such as Alzheimer's disease where the brain accumulates tau protein in the form of neurofibrillary tangles. In a preferred embodiment Parkinson's disease relates to Morbus Parkinson.

In a further preferred embodiment of the invention, the invention relates to the diagnosis of Parkinson's disease, wherein at least one marker sequence is selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37 and is determined on or from a patient to be examined. In this respect, the invention relates to the use of the marker sequences according to the invention as biomarker for PD.

In a further embodiment at least 2 to 5 or 10, preferably 30 to 50 marker sequences or 50 to 100 or more marker sequences are determined on or from a patient to be examined, in particular such respectively from the group SEQ ID No. 1 - 37.

In a further embodiment of the invention, the marker sequences according to the invention can likewise be combined, supplemented, fused or expanded likewise with known biomarkers for this indication.

In a preferred embodiment, the determination of the marker sequences is carried out outside the human body and the determination is carried out in an ex vivo / in vitro diagnosis .

In a further embodiment of the invention, the invention relates to the use of marker sequences as diagnostic agents, wherein at least one marker sequence is selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37.

Furthermore, the invention relates to a method for the diagnosis of Parkinson's disease, wherein a. ) at least one marker sequence is selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37 and is applied to a solid support and b. ) is brought into contact with body fluid or tissue extract of a patient and c. ) the detection of an interaction of the body fluid or tissue extract with the marker sequences from a.) is carried out.

The invention therefore likewise relates to diagnostic agents for the diagnosis of Parkinson's disease comprising at least one marker sequence selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37.

The detection of an interaction of this type can be carried out, for example, by a probe, in particular by an antibody.

The invention therefore likewise relates to the object of providing a diagnostic device or an assay, in particular a protein biochip, which permits a diagnosis or examination for Parkinson's disease.

Furthermore, the invention relates to a method for the stratification, in particular risk stratification and/or therapy control of a patient with Parkinson's disease, wherein at least one marker sequence selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37 is determined on a patient to be examined.

Furthermore, the stratification of the patients with

Parkinson's disease in new or established subgroups of Parkinson's disease is also covered, as well as the

expedient selection of patient groups for the clinical development of new therapeutic agents. The term therapy control likewise covers the allocation of patients to responders and non-responders regarding a therapy or the therapy course thereof. "Diagnosis" for the purposes of this invention means the positive determination of Parkinson's disease by means of the marker sequences according to the invention as well as the assignment of the patients to Parkinson's disease. The term diagnosis covers medical diagnostics and examinations in this regard, in particular in-vitro diagnostics and laboratory diagnostics, likewise proteomics and nucleic acid blotting. Further tests can be necessary to be sure and to exclude other diseases. The term diagnosis therefore likewise covers the differential diagnosis of Parkinson's disease by means of the marker sequences according to the invention and the prognosis of Parkinson's disease.

"Stratification or therapy control" for the purposes of this invention means that the method according to the invention renders possible decisions for the treatment and therapy of the patient, whether it is the hospitalization of the patient, the use, effect and/or dosage of one or more drugs, a therapeutic measure or the monitoring of a course of the disease and the course of therapy or etiology or classification of a disease, e.g., into a new or existing subtype or the differentiation of diseases and the patients thereof.

In a further embodiment of the invention, the term

"stratification" covers in particular the risk

stratification with the prognosis of an outcome of a negative health event.

Within the scope of this invention, "patient" means any test subject - human or mammal - with the proviso that the test subject is tested for Parkinson's disease. The term "marker sequences" for the purposes of this invention means that the protein (polypeptide, peptide) and / or the nucleic acid, e.g. RNA / cDNA / DNA encoding for the polypeptide or protein is significant for Parkinson's disease. For example, the cDNA or the polypeptide or protein that can be obtained thereof can exhibit an interaction with substances from the body fluid or tissue extract of a patient with Parkinson's disease (e.g., antigen (epitope) / antibody (paratope) interaction) .

For the purposes of the invention "wherein at least one marker sequence is selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37 and is determined on a patient to be examined" means that an interaction between the body fluid or tissue extract of that patient and the marker sequence according to the invention is detected. An interaction of this type is, e.g., a bond, in particular a binding substance on at least one marker sequence according to the invention or in the case of a cDNA the hybridization with a suitable substance under selected conditions, in particular stringent

conditions (e.g., such as usually defined in J. Sambrook, E. F. Fritsch, T. Maniatis (1989), Molecular cloning: A laboratory manual, 2nd Edition, Cold Spring Harbor

Laboratory Press, Cold Spring Harbor, USA or Ausubel, "Current Protocols in Molecular Biology, " Green Publishing Associates and Wiley Interscience , N. Y. (1989)). One example of stringent hybridization conditions is: hybridization in 4 x SSC at 65°C (alternatively in 50% formamide and 4 x SSC at 42°C), followed by several washing steps in 0.1 x SSC at 65°C for a total of approximately one hour. An example of less stringent hybridization conditions is hybridization in 4 x SSC at 37°C, followed by several washing steps in 1 x SSC at room temperature .

According to the invention, substances of this type are constituents of a body fluid, in particular blood, whole blood, blood plasma, blood serum, patient serum, urine, cerebrospinal fluid, synovial fluid or of a tissue extract of the patient.

In a further embodiment of the invention, however, the marker sequences according to the invention can be present in a significantly higher or lower expression rate or concentration that indicates Parkinson's disease. The relative sick/healthy expression rates of the marker sequences for Parkinson's disease according to the

invention are hereby determined by means methods wherein at least one marker sequence is selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37 and is determined on a patient to be examined in comparison to a control, e.g. a person without Parkinson's disease like for example a healthy person. The relative sick/healthy expression rates of the marker sequences for Parkinson's disease according to the invention are hereby determined for example by means of proteomics or nucleic acid

blotting .

In a further embodiment of the invention, the marker sequences have a recognition signal that is addressed to the substance to be bound (e.g., antibody, nucleic acid) . It is preferred according to the invention for a protein the recognition signal is an epitope and/or a paratope and/or a hapten and for a cDNA is a hybridization or binding region. In a preferred embodiment of the invention the marker sequence recognizes (e.g. hybridizes, binds, ..) to an autoantibody which is significant for Parkinson's disease .

Autoantibodies that are significant for Parkinson's disease are either expressed only in case of Parkinson's disease or the levels of these autoantibodies vary significantly in case of Parkinson's disease, e.g. they are more or less expressed in case of Parkinson's disease in comparison to the levels of the respective autoantibody levels in a control like for example healthy persons. According to the invention the marker sequences can especially be used to determine autoantibody profiles that are specific for early detection of Parkinson's disease and/or diagnosis of

Parkinson's disease and/or surveillance of the treatment of Parkinson's disease and/or prognosis of Parkinson's disease . Autoantibody profiles in this respect relate to the amount of one or more autoantibodies that are specifically expressed, e.g. up- or down regulated in Parkinson's disease. The autoantibody profiles relate therefore in one aspect to the composition (one or more autoantibodies) of the profile and in another aspect to the amount or

concentration of a particular autoantibody.

In one embodiment of the invention the marker sequence binds to / recognizes one or more autoantibodies that are more or less expressed during development, establishment, therapy and/or progression of Parkinson's disease. In order to characterize theses specific autoantibody profiles one or more marker sequence can be used.

The invention comprises the use of at least one marker sequence. In preferred embodiments of the invention two, three, four, five, six seven, eight, nine or ten or more, e.g. 15 or 20 or more marker sequences are used together or sequentially .

The marker sequences according to the invention are the subject matter of sequence listing and can be clearly identified by the sequences SEQ ID No. 1 - 37.

According to the invention, the marker sequences also cover those modifications of the cDNA sequence and the

corresponding amino acid sequence as chemical modification, such as citrullination, acetylation, phosphorylation, glycosylation or poly (A) strand and other modifications known to one skilled in the art.

Homologous sequences according to the invention are homologous protein / peptide or nucleic acid sequences, in particular of SEQ ID No. 1 - 37 that display an identity of at least 70 % or 80 %, preferred 90 % or 95 %, most preferred 96 % or 98 % or more, e.g. 98 % or 99 % homology with the respective protein, peptide or nucleic acid sequences or the respective partial sequences.

Partial sequences according to the invention are parts of the respective protein / peptide sequences, in particular of SEQ ID No. 1 - 37, and the nucleic acids encoding theses partial proteins or peptides. These parts are missing one or more amino acids or nucleotides respectively in

comparison to the respective complete sequences, in particular in comparison with SEQ ID No. 1 - 37. The/these missing part(s) could be located at the beginning, the end or within the sequence. Enclosed are also sequences that contain additional sequence parts at the beginning, the end or within the sequence in comparison to the respective complete sequences, in particular in comparison with SEQ ID No. 1 - 37.

In a further embodiment of the invention, partial sequences or fragments of the marker sequences according to the invention are likewise covered. In particular those partial sequences that have an identity of 95%, 90%, in particular 80% or 70% with the nucleic acid or amino acid sequence of the marker sequences according to the invention.

Another object of the invention relates to an arrangement of marker sequences containing at least one marker sequence selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37. Preferably, the arrangement contains at least 2 to 5 or 10, for example 2, 3, 4 or 5, preferably 30 to 50 different marker sequences or 50 to 100 or more different marker sequences.

Within the scope of this invention, "arrangement" is synonymous with "array, " and if this "array" is used to identify substances on marker sequences, this is to be understood to be an "assay" or diagnostic device. In a preferred embodiment, the arrangement is designed such that the marker sequences represented on the arrangement are present in the form of a grid on a solid support.

Furthermore, those arrangements are preferred that permit a high-density arrangement of protein binders and the marker sequences are spotted. Such high-density spotted

arrangements are disclosed, for example, in WO 99/57311 and WO 99/57312 and can be used advantageously in a robot- supported automated high-throughput method.

Within the scope of this invention, however, the term "assay" or diagnostic device likewise comprises those embodiments of a device, such as ELISA, bead-based assay, line assay, Western Blot, immunochromatographic methods (e.g., lateral flow immunoassays) or similar immunological single or multiplex detection measures. A protein biochip in accordance with the invention is a systematic

arrangement of proteins on a solid support.

The marker sequences of the arrangement are fixed on a solid support, but preferably spotted or immobilized even printed on, i.e. applied in a reproducible manner. One or more marker sequences can be present multiple times in the totality of all marker sequences and present in different quantities based on one spot. Furthermore, the marker sequences can be standardized on the solid support (i.e., by means of serial dilution series of, e.g., human

globulins as internal calibrators for data normalization and quantitative evaluation) .

In a further embodiment, the respective marker sequence can be represented in different quantities in one more regions on a solid support. This permits a variation of the sensitivity. The regions can have a totality of marker sequences, i.e., a sufficient number of different marker sequences, in particular 2 to 5 or 10 or more and

optionally more nucleic acids and/or proteins, in

particular biomarkers. However, at least 96 to 25,000 (numerical) or more from different or identical marker sequences and further nucleic acids and/or proteins, in particular biomarkers are preferred. Furthermore preferred are more than 2,500, in particular preferred 10,000 or more different or identical marker sequences and optionally further nucleic acids and/or proteins, in particular biomarkers .

The invention therefore relates to an assay or a protein biochip comprising an arrangement containing marker sequences according to the invention.

In a further embodiment, the marker sequences are present as clones. Clones of this type can be obtained, for example, by means of a cDNA expression library according to the invention (Biissow et al. 1998 (supra)) . In a preferred embodiment, such expression libraries containing clones are obtained using expression vectors from a cDNA expression library comprising the cDNA marker sequences. These expression vectors preferably contain inducible promoters. The induction of the expression can be carried out, e.g., by means of an inductor, such as IPTG. Suitable expression vectors are described in Terpe et al. (Terpe T Appl

Microbiol Biotechnol. 2003 Jan; 60(5): 523-33).

One skilled in the art is familiar with expression

libraries; they can be produced according to standard works, such as Sambrook et al, "Molecular Cloning, A laboratory handbook, 2nd edition (1989), CSH press, Cold Spring Harbor, New York. Expression libraries are also preferred which are tissue-specific (e.g., human tissue, in particular human organs) . Furthermore included according to the invention are expression libraries that can be obtained by exon-trapping . A synonym for expression library is expression bank.

Also preferred are protein biochips or corresponding expression libraries that do not exhibit any redundancy (so-called: Uniclone® library) and that may be produced, for example, according to the teachings of WO 99/57311 and WO 99/57312. These preferred Uniclone libraries have a high portion of non-defective fully expressed proteins of a cDNA expression library.

Within the context of this invention, the clones can also be, but not limited to, transformed bacteria, recombinant phages or transformed cells from mammals, insects, fungi, yeasts or plants. The clones are fixed, spotted or immobilized on a solid support .

The invention therefore relates to an arrangement wherein the marker sequences are present as clones.

Additionally, the marker sequences can be present in the respective form of a fusion protein, which contains, for example, at least one affinity epitope or tag. The tag may be one such as contains c-myc, his tag, arg tag, FLAG, alkaline phosphatase, VS tag, T7 tag or strep tag, HAT tag, NusA, S tag, SBP tag, thioredoxin, DsbA, a fusion protein, preferably a cellulose-binding domain, green fluorescent protein, maltose-binding protein, calmodulin-binding protein, glutathione S-transferase or lacZ .

In all of the embodiments, the term "solid support" covers embodiments such as a filter, a membrane, a bead, for example a magnetic or fluorophore-labeled bead, a silica wafer, glass, metal, ceramics, plastics, a chip, a target for mass spectrometry or a matrix. As a filter for example PVDF, nitrocellulose or nylon (e.g., Immobilon P Millipore, Protran Whatman, Hybond N+ Amersham) can be used.

In another embodiment of the arrangement according to the invention, the arrangement corresponds to a grid with the dimensions of a microtiter plate (8 - 12 wells strips, 96 wells, 384 wells or more), a silica wafer, a chip, a target for mass spectrometry, or a matrix.

Furthermore, the invention relates to the use of at least one marker sequence for example an arrangement, a protein biochip or an assay according to the invention for

identifying and characterizing a substance for Parkinson's disease. The invention therefore likewise relates to the use of an arrangement according to the invention or an assay for screening active substances for Parkinson's disease .

The invention relates to a method for identifying and characterizing a substance for Parkinson's disease, characterized in that at least one marker sequence for example an arrangement, a protein biochip or an assay according to the invention is a.) brought into contact with at least one substance to be tested and b.) a binding success is detected.

The substance to be tested can be any native or non-native biomolecule, a synthetic chemical molecule, a mixture or a substance library.

After the substance to be tested contacts a marker

sequence, the binding success is evaluated, which, for example, is carried out using commercially available image analyzing software (GenePix Pro (Axon Laboratories), Aida (Ray test), ScanArray (Packard Bioscience).

The visualization of protein-protein interactions according to the invention (e.g., protein on marker sequence, as antigen/antibody) or corresponding means for detecting the binding success can be performed, for example, using fluorescence labelling, biotinylation, radioisotope labelling or colloid gold or latex particle labelling in the usual way. A detection of bound antibodies is carried out with the aid of secondary antibodies, which are labelled with commercially available reporter molecules (e.g., Cy, Alexa, Dyomics, FITC or similar fluorescent dyes, colloidal gold or latex particles), or with reporter enzymes, such as alkaline phosphatase, horseradish

peroxidase, etc., and the corresponding colorimetric, fluorescent or chemiluminescent substrates. Readout is conducted, e.g., using a microarray laser scanner, a CCD camera or visually.

In a further embodiment, the invention relates to a drug/active substance or prodrug developed for Parkinson's disease and obtainable through the use of the assay or protein biochip according to the invention.

In a further embodiment, the invention therefore likewise relates to a target for the treatment and therapy of

Parkinson's disease wherein the target is selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37.

In a further embodiment, the invention likewise relates to the use of the marker sequences according to the invention, preferably in the form of an arrangement, as an affinity material for carrying out an apheresis or in the broadest sense a blood lavage, wherein substances from body fluids of a patient with Parkinson's disease, such as blood or plasma, bind to the marker sequences according to the invention and consequently can be selectively withdrawn from the body fluid. The diagnosis of PD is still a challenge. Especially at early stages the clinical signs and symptoms of PD can overlap with other neurodegenerative diseases (Tolosa et al. , 2006) . Up to now no suitable biomarker for PD could be identified. Most studies have focused on the detection of a single protein known to be involved in the

pathological sequel (Mollenhauer et al. , 2011) . This approach can fail as the process of neurodegeneration is complex, and a hypothesis is needed to find markers in the blood that might reflect the changes in the brain. In conclusion it seems reasonable to argue for a large set of biomarkers at this first stage of marker identification instead of a single marker. In the present approach a large array of proteins was taken and extensive bioinformatics was applied in search for putative markers capable of classifying PD in contrast not only to healthy subjects but also to patients with other neurodegenerative diseases . Antibodies seem to fulfil the criteria of biomarkers. These markers are known to be biological stable and can be related to neurodegeneration. 37 markers were identified that discriminated PD from other neurodegenerative diseases (DC) and healthy controls (HC) . An average classification accuracy of 73.5% has been achieved, and no obvious over fitting has been detected.

This was the first extensive clinical study of immune response profiling using protein microarrays. Serum is an easily accessible body fluid and already 10 are enough to perform one protein microarray experiment. All three groups consisted of 72 subjects, which were well matched regarding age and gender. This is crucial to avoid bias and to detect disease-related biomarkers. Neglecting this best practice is the most serious shortcoming in recent studies (Han et al. , 2012, Nagele et al. , 2011). There, the difference in age between Parkinson and control samples was about 33 years. Furthermore, they had 55% males in the Parkinson group and 89% in the control group. Moreover, the serum preparation protocol was the same for all subjects. Additionally, subjects taking immune modulating drugs were excluded, which is a critical point when analyzing

autoantibodies .

The screening platform allows a simultaneous analysis in a high-throughput manner. In a three step approach

autoantibodies were narrowed down to 37 which can

distinguish PD patients from HC and DC. These

autoantibodies can be divided into three subgroups: 1) autoantibodies to proteins involved in the pathological cascade of the disease, 2) autoantibodies possibly related to therapeutic interventions and 3) autoantibodies to proteins with unknown biological significance.

Several molecular mechanisms are involved in the

pathological process in neurodegenerative disorders like PD, with neuroinflammation probably contributing to PD (McGeer and McGeer, 2004). McGeer et al . (McGeer et al . , 1988) first described activated microglia in the SN of PD patients and these results were confirmed by other authors (Imamura et al . , 2003, Banati et al. , 1998) . PET findings support the hypothesis of an inflammatory response (Ouchi et al. , 2005, Gerhard et al. , 2006) . Studies

investigating mediators like interleukin (IL)-6 (Chen et al. , 2008) demonstrated an elevated risk for PD in those individuals with higher serum levels. This observation has been further supported by genetic studies, demonstrating an association with defined mutations in genes encoding immunological proteins like IL-6, IFN-β and IFN-a (Mattila et al. , 2002, Schulte et al. , 2002, Kruger et al. , 2000, McGeer et al. , 2002) . Furthermore, epidemiological studies indicate a disease-slowing effect of anti-inflammatory drugs (Hakansson et al. , 2005, Chen et al. , 2003, Chen et al. , 2005, Becker et al . , 2011, Gao et al. , 2011). The role of the immune system is strengthened by the finding of autoantibodies recognizing proteins from dopaminergic neurons and neuromelanin (Double et al. , 2009, Farkas et al. , 2000, Mogi et al. , 1995, Mogi et al. , 1994).

In the present invention autoantibodies to antigenes were identified that are involved in the pathological cascade of PD . These comprise in the first line Septin 4 (SEPT4), Recoverin and BCL2-associated athanogene 5 (BAG5) . Various markers have been described as related to a-synuclein as one of the hallmarks of PD . SEPT4 is a GTP-binding protein that coordinates changes in the cytoskeleton and in

membranes, where it has a role as a scaffold (Hall et al. , 2005, Hall and Russell, 2004, Hall and Russell, 2012, Weirich et al. , 2008) . SEPT4 has been localized also in Lewy bodies in the SN of PD patients (Ihara et al . , 2007) . SEPT4 could be further detected together with β-synuclein in the presynaptic terminals of dopaminergic neurons, suggesting a physiological association (Ihara et al. , 2003) . Additionally, in a-synuclein ^A53T transgenic mice the loss of septin4 enhanced neuropathology (Ihara et al. , 2007) . Recoverin is known to be a neuronal calcium sensor (Nagao and Hayashi, 2009) . The intracellular Ca²⁺-concentration is essential for neurotransmitter release and other neuronal processes. Another member of that protein family,

hippocalcin, was recently localized in Lewy bodies (Nagao and Hayashi, 2009) .

BAG5 is another candidate marker known to regulate the E3 ubiquitin ligase CHIP. A well-known function of E3

ubiquitin ligases is to target substrate proteins for degradation where a-synuclein is a prominent substrate (Kalia et al. , 2011) . BAG5 is also shown to inhibit also Parkin E3 ligase activity (Kalia et al. , 2004). In 1998, mutations in the parkin gene were identified as a cause of autosomal recessive PD (Kitada et al. , 1998) . Since then, more than one hundred different mutations of that candidate gene have been described (West and Maidment, 2004) . Genetic and biochemical studies revealed that mutations induce a loss of parkin function, leading to the hypothesis that the accumulation of parkin substrates causes neurotoxicity and results in the death of dopaminergic neurons .

Some autoantibodies found in PD patients are related to metabolic enzymes involved in the degradation of levodopa, like 5 , 10-methylentetrahydrofolate reductase (MTHF). The clinical significance of this observation has to be further elucidated. In the present invention additional

autoantibodies to antigenes as part of the 37-marker panel were identified but with unknown biological significance.

The aim and complexity of the biomarker candidate selection procedure was to find a suitable panel of proteins that discriminates the main groups HC, DC and PD . The M score provided by Prospector has been used to rate the discriminating power of particular proteins for biomarker candidate pre-selection . The M score has revealed proteins with sub-group differences. Other pre-selection methods (e.g., t-test or Mann-Whitney U test) had been not able to detect these sub-groups (Love, 2007, Sboner et al. , 2009) .

The list assembling approach (see methods section) has solved two tasks. On the one hand, the multiclass problem of finding biomarker candidates to distinguish PD patients from HC and DC has been solved. On the other hand, the aim to exclude biomarker candidates discriminating

manufacturing batches has been attained. Because

classifying ProtoArrays from different lots (ignoring clinical classes) yield extremely high classification accuracies (about 100%, data not shown) , the latter aspect is crucial. This "batch problem" is a well known issue of all microarray platforms . It is caused by concentration differences in protein spots and a few cases of additional spots or spot absence when manufacturing batches are compared. Consequently, these differences have to be adjusted when microarrays from different lots are included into the same study. Conventional normalization approaches are not sufficient to handle this kind of bias (Johnson et al. , 2007) . The solution was to distribute all clinical classes equally through manufacturing batches and to exclude proteins showing extremely low M scores when these lots are compared. Alternative approaches (not investigated in this study) can be found, e.g., in (Johnson et al. , 2007, Chen et al. , 2011, Rudy and Valafar, 2011) . However, in other ProtoArray studies (Han et al. , 2012, Nagele et al . , 2011) there is no solution for the batch problem.

Although the corresponding data (published in Gene

Expression Omnibus under GSE29654 and GSE29676) is composed of microarrays from different lots neither clinical class distribution nor protein exclusion or other batch

adjustment has been described. In contrast to the present invention there is a series bias concerning the

distribution of clinical classes through the batches.

Accordingly, among the ten biomarker candidates proposed in Han et al., 2012, four proteins have been excluded in the present study due to batch discrimination and two further proteins have shown low M scores during batch

discrimination. Furthermore, four proteins have been excluded due to M scores — 0.05 in the HC vs. PD and DC vs. PD comparisons.

The univariante pre-selection method that has been used in this study (M score approach) has been combined with two other approaches (manual and wrapper approach) to enhance biomarker candidate detection. The wrapper approach that has been used in this study is a widely used multivariate feature selection method (Saeys et al . , 2007, Kohavi R. and John, 1997) for optimal variable set detection when the number samples is very much smaller than the number of variables (so called "n << p" problems; this is

characteristic for microarray data (Saeys et al . , 2007, Guyon and Elisseeff, 2003) . The manual approach has been conducted to confirm and extend the biomarker candidate panel revealed by the automatic wrapper approach by human inspection . The invention is further described in the following figures and examples, however, without restricting the invention to these examples and figures.

Figure 1: The biomarker candidate selection procedure that has been performed in this study. As the first step, four two group comparisons (arrays incubated with HC sera vs. arrays incubated with PD sera, arrays incubated with DC sera vs. arrays incubated with PD sera, arrays incubated with HC sera vs. arrays incubated with DC sera and all arrays from one of the ProtoArray lots vs. all arrays from the other lot ("lot 1 vs. lot 2")) have been performed with the software Prospector. For the corresponding results that have been sorted by means of M score thresholds have been set to identify the respective most discriminative proteins for the four comparisons and to discard all proteins that not match these thresholds. The four reduced lists have been assembled by restoring the proteins in the HC vs. PD list that are contained in the DC vs. PD list and that have been discarded previously ("+") . Then, all proteins that are contained in the HC vs. DC and in the lot 1 vs. lot 2 lists are discarded from the HC vs. PD list ("-") . The resulting list has contained 284 preliminary biomarker candidates. This list has been used for manual and wrapper biomarker selection ("manual" and "wrapper") as described in the text. Finally, the resulting lists containing 22 and 14 proteins, respectively, have been assembled to the final biomarker candidate list containing 37 proteins.

Figure 2: Exemplary Single Feature Scatter Plot. Here, an exemplary plot for manual feature selection and general feature inspection is shown. All 144 data points are intensity values of a particular protein measured on 72 protein arrays incubated with HC sera (left) and 72 protein arrays incubated with PD sera (right) . As can be seen, there is a notable subgroup of PD samples with higher intensity values than all HC samples (besides two

outliers ) .

Figure 3 Raw Data Normalization. In diagram a), b) , c) and d) box plots of the 216 arrays (from two lots) of the

ParkCHIP data (in log scale) are shown where 117 arrays from one lot ("batch 1") are displayed in the lower part and the 99 arrays from the other lot ("batch 2") are displayed in the upper part of each diagram. In a) raw data, in b) data after cyclic loess normalization in c) data after vsn normalization and in d) data after quantile normalization is shown.

Figure 4 Exemplary two-microarray plots ("same-same plots") of ProtoArray pairs are shown; left: raw intensities, right: log intensities; details: In a) raw data (intensity values) of two technical replicates (different microarrays incubated with the same serum) from the same lot is drawn (average coefficient of variation (CV) : 7.9%) . b) shows the corresponding plot with log-scale data of the same

microarrays like in a) (CV: 1.2%) . In c) raw data of two technical replicates from different lots is drawn (CV:

10.1%) . d) shows the corresponding plot with log-scale data of the same microarrays like in c) (CV: 1.6%) . In e) raw data of two microarrays incubated with different sera from the same lot is drawn (CV: 17.2%) . f) shows the

corresponding plot with log-scale data of the same

microarrays like in e) (CV: 2.9%) . In g) raw data of two microarrays incubated with different sera from different lots is drawn (CV: 47.2%) . h) shows the corresponding plot with log-scale data of the same microarrays like in g) (CV: 8.1%) .

Example 1 : Material and methods Subjects and samples

ParkCHIP is an on-going cross-sectional study of cases with PD, diseased controls with other neurodegenerative diseases (DC), and healthy controls (HC) . All together over 2,500 participants were screened for inclusion criteria. This analysis was conducted with a subset of 72 triplets where a PD patient was matched 1:1:1 by gender and age to two controls (DC and HC) . Non-eligible were subjects with severe cognitive impairment, drug addiction, and being HIV positive. Socio-demographic characteristics of the triplets and of the diseases of DC are shown in Tables 1 and 2. The diseases of DC comprised neurodegenerative other than PD and autoimmune disorders. Subjects with neurodegenerative disorders other than PD were diagnosed according to the guidelines for the specific disease. Eligible were PD cases with validated diagnosis according to the diagnostic criteria of the Parkinson^'s UK Brain Bank (Hughes et al. , 1992) . Furthermore, patients had to fulfil at least four supportive criteria, with an excellent response to

dopaminergic medication as obligate inclusion criteria. Patients not on therapy underwent functional imaging via Single Photon Emission Computed Tomography (SPECT) . HC were unrelated to cases and comprised subjects without

neurodegenerative disorders like spouses of patients. All subjects were recruited at St. Josef Hospital in Bochum, Germany. A physician enrolled and diagnosed eligible subjects with written informed consent. All participants were investigated with a standardized protocol including neurological examination, socio-demographic questions, and disease-related questionnaires. Details are given in supplementary material. For all PD cases, the severity of the disease was assessed using the Unified Parkinson's Disease Rating Scale (UPDRS) (Gallagher et al. , 2012), the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) (Goetz et al. , 2007), and the Hoehn and Yahr scale (H&Y) (Hoehn and Yahr, 1967) . Movement disorders were documented in the "on" state by video recording with permission of the patient. The study was approved by the ethics committee of the Ruhr- University Bochum.

Blood collection

Venous blood samples were taken from an antecubital vein through an indwelling catheter for the protein microarray analysis and for the determination of routine laboratory parameters. Blood samples for routine laboratory parameters were collected in EDTA tubes containing 100 L of 0.5% sodium disulfite solution and in 5 mL tubes for serum preparation (Kabevette® V serum Gel S831 V, Kabe

Laborstechnik GmbH, Niimbrecht-Elsenroth Germany) .

Determination of routine variables was done according to the established routine protocols (data shown in

Supplementary Table SI) . For the protein microarray analysis the serum preparation was done according to the manufactures protocol (Kabe Laborstechnik GmbH) . The sera were stored at -80°C in aliquots till usage. Example 2: Protein microarray probing, data acquisition and pre-processing

The protein array slides stored at -20°C were equilibrated at 4°C for 15 minutes and another 15 minutes at room temperature. Blocking, serum incubation and washing steps were done as described in the manufacturer's protocol (http : //tools . invitrogen . com/content/sfs /manuals /protoarray IRBP_short_protocol.pdf) . Image acquisition of the

processed protein microarrays was done with an Array- Scanner FR202 using CCD-Technology (Strix Diagnostics GmbH, Berlin, Germany) . In the present study fluorescent image analysis was performed with the software StrixAluco 3.0 (Strix Diagnostics GmbH) . For raw data pre-processing and further analysis the acquired data have been imported into R (http://www.r-pro ect.org/) (Team, 2011) using the

Bioconductor (http://www.bioconductor.org/) (Gentleman et al. , 2004) package limma

(http : / /www . bioconductor . org/packages /2.9 /bioc/html/limma . h tml) (Smyth, 2005) . Because cyclic loess normalized data showed the best central tendency and range adjustment, this method has been used in this study.

Example 3: Statistical analysis of protein microarray data and biomarker candidate selection ("feature selection")

To identify discriminative proteins a three-step feature selection approach has been performed. The whole feature selection procedure is shown in Figure 1.

Step 1: For the first step the software Prospector provided by the ProtoArray vendor has been used (current version 5.2 can be downloaded from Life Technologies' web site (http://www.lifetechnologies.com)). Due to its sensitivity for unknown biological subgroups Prospector's minimal M Statistic ("M score", proposed in Love B., 2007) is an appropriate measure to score the proteins concerning two group discrimination. After importing the normalized data into Prospector four comparisons with two groups each have been computed. These comparisons are: HC vs. PD, DC vs. PD, HC vs. DC and lot 1 vs. lot 2. Thus, Prospector has computed altogether four comparison result lists each containing about 9,500 proteins and a corresponding M score. Subsequently all lists have been sorted by means of the M score increasingly. All proteins with an M score — 0.00001 from the lot 1 vs. lot 2 list and all proteins with an M score - 0.05 from the remaining lists have been discarded (i.e. lot differences and non-significant differences between clinical groups) .

Step 2: In the second step the four lists from step 1 have been assembled by the following procedure: First, all proteins that had been discarded from the original HC vs. PD list but not from the DC vs. PD list during step 1 have been restored in the HC vs. PD list (including their corresponding HC vs. PD M score values) . Thus, proteins that discriminate HC and PD as well as DC and PD have been finally included. Then, all proteins contained in the HC vs. DC list and the lot 1 vs. lot 2 list have been deleted from the HC vs. PD list. Thus, proteins that discriminate HC and DC as well as the two different ProtoArray

manufacturing batches used in this study have been finally excluded. The latter exclusion has been performed to solve the manufacturing batch problem that arises when protein microarrays from different manufacturing batches are compared. The resulting HC vs. PD list containing 284 proteins has been used for the following selection steps.

Step 3: In step 3 the remaining 284 biomarker candidates have been further narrowed down by a manual and an

automatic selection approach (wrapper selection) which have been used in parallel. The manual selection approach is based on 284 plots that have been drawn for the 284 biomarker candidates (one plot for each protein) . An exemplary plot is shown in Figure 2. Five persons have inspected these plots, rated the corresponding proteins and selected the 22 most promising features. As an automatic method a multivariate feature selection wrapper approach (Saeys et al. , 2007) has been implemented. Therefore, as wrapping procedure an evolutionary algorithm (Goldberg, 1989) and as wrapped machine learning approach a random forest (R package: randomForest ) (Breiman, 2001, Liaw A, 2002) have been used. After six runs with 100,000

iterations each 14 additional proteins (distinct to the 22 received from manual selection) have been returned. The results of both selection approaches have been combined to a final biomarker candidate set containing 37 proteins.

Example 4: Computational validation of the potential distinguishing autoantibodies

To validate the 37 candidate proteins computationally two methods have been applied. As a first validation method, the following procedure has been performed: First, all samples of the reduced PD vs. "HC+DC" (combined group consisting of HC and DC) data set (containing 37 proteins) have been split randomly into a training subset (2/3 - of the samples; data set for classifier training) and a test subset (1/3 - of the samples; data set for classifier validation) . The respective experimental group proportions in the sets have been conserved. Then, a random forest classifier has been trained on the 37 biomarker candidates of the training set. Subsequently, this classifier has been applied to the test set to determine the actual

classification accuracy. The whole procedure including split has been repeated ten times to avoid a selection bias caused by the splitting step. Finally, the average accuracy of the ten sub-run accuracy values has been computed to assess the overall performance of the biomarker candidates. As a second validation method, classification results for the group comparisons HC vs. PD, DC vs. PD and HC vs. DC (all ParkCHIP samples) have been compared to analogous classification results based on all ParkCHIP samples with randomly mixed group assignments (hereafter referred to as groups "A", "B" and "C") .

Table 1: Sociodemographic characteristics of the study groups

Parkinson Diseased Healthy

Total patients controls controls

(N=72) (N=72) (N=72)

P

N N N N value⁸

Gender Male 115 53 2 38 52.8 39 54 2 38 52.8 0.98

Female 101 46 8 34 47.2 33 45 8 34 47.2

Smoking Never 95 44 0 36 50.0 27 37 5 32 44.4 0.20 status

Former 98 45 4 33 45.8 35 48 6 30 41.7

Current 23 10 7 3 4.2 10 13 9 10 13.9

Education" Low 111 51 4 35 48.6 40 55 6 36 50.0 0.28

Medium 54 25 0 14 19.4 19 26 4 21 29.17

High 51 23 6 23 31.9 13 18 1 15 20.8

Mother ' s German 195 90 3 63 87.5 65 90 3 67 93.1 0.53 language

Non- 21 9. 7 9 12.5 7 9. 7 5 6.9

German

N Median N Median N Median N Median P

(IQR) (IQR) (IQR) (IQR) value⁸

Age 216 65 72 67 72 66 72 64 0.79 years 56- 70 57-71 54- 70 53-70 Parkinson Diseased Healthy

Total patients controls controls

(N=72) (N=72) (N=72)

P

N % N % N % N % value⁸

Odors correctly 212 10 70 7 70 11 72 11 <0.001 identified with 16 7-12 5-9 8-13 10-13

Sniffin ' sticks

Minimental score 215 29 71 28 72 28 72 29 <0.001 maximum 29 (27-29 27-29 26-29 28-30

aP values of Kruskal Wallis test for continuous variables and χ² tests for categorical variables.

bLow: elementary school, medium: high-school diploma, high: university degree

Table 2: Diseases among hospital controls

Disease N

Apoplexia 8 11 .11

Rheumatic disorder 7 9. .72

Multiple sclerosis 6 8. .33

Essential tremor 6 8. .33

Progressive supranuclear

6 8. .33 palsy

Alzheimer's disease 4 5. .56

Sarcoidosis 4 5. .56

Multiple system atrophy 4 5. .56

Epilepsy 2 2. .78

Amyotrophic lateral

2 4. .17 sclerosis

Pure freezing of gait 2 2. .78

Diffuse Lewy body disease 2 2. .78

Corticobasal degeneration 2 2. .78

Bechterew's disease 2 2. .78

Polyneuropathy 3 4, 17

Huntington's disease 1 1. .39

Drug-induced Tremor 2 2, 78

Dystonia 1 1. .39

Facial palsy 1 1. .39

Tourette's syndrome 1 1. .39

Focal Seizure after cerebral

_L _L , .39 ischaemia

Systemic lupus erythematosus 1 1. .39

Normal pressure _ .39 hydrocephalus

Restless legs syndrome 1 1. .39

Trigeminal neuralgia 1 1. .39

Table 3: Biomarker Candidates

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Claims

Claims

1. Method for identifying marker sequences for

Parkinson's disease (PD) by differential screening of samples from healthy persons (HD) , samples from patients with other neurodegenerative diseases (DC) and samples from patients with Parkinson's disease and processing the data obtained from the differential screening by a three-step feature selection approach.

2. Marker sequence for Parkinson's disease identified by a method according to claim 1 and selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37.

3. Use of a marker sequence for Parkinson's disease selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for

homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37 for diagnosis, prognosis,

stratification, in particular risk stratification and therapy control.

4. Use according to claims 3, wherein at least one marker sequence is determined on or from a patient to be examined .

5. Use according to claims 3 or 4, wherein in that at least 2 to 5 marker sequences are determined on or from a patient to be examined.

6. Use according to claims 3 to 5, characterized in that the marker sequence (s) is/are applied onto a solid support, in particular a filter, a membrane, a bead, a silica wafer, glass, metal, ceramics, plastics, a chip, a target for mass spectrometry or a matrix.

7. Arrangement of marker sequences comprising at least one marker sequence selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37.

8. Arrangement according to claim 7 wherein the marker sequences are attached to a solid support.

9. Diagnostic agent, test kit, assay or protein biochip comprising an arrangement according to claim 7 or 8 and optionally further substances and/or additives.

10. Method for diagnosis of Parkinson's disease, wherein a. ) at least one marker sequence is selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37 and b. ) brought into contact with body fluid or tissue extract of a patient and c. ) the detection of an interaction of the body fluid or tissue extract with the marker sequences from a.) is carried out.

11. Method for the stratification, in particular risk stratification or therapy control or prediction of

prognosis of a patient with Parkinson's disease, wherein at least one marker sequence selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37 is determined on or from a patient to be examined.

12. Method according to claim 11, wherein the

stratification or the therapy control or prediction of prognosis covers decisions for the treatment and therapy of the patient, in particular the hospitalization of the patient, the use, effect and/or dosage of one or more drugs, a therapeutic measure or the monitoring of a course of the disease and the course of therapy, etiology or classification of a disease together with prognosis.

13. Use of an arrangement according to claim 7 or 8 or an assay according to claim 9 for the identification and / or characterization of a substance for PD .

14. Method for identifying or characterizing a substance for PD, wherein at least one marker sequence selected from the group of protein sequences SEQ ID No. 1 - 37,

homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No.

1 - 37 is brought into contact with at least one substance to be tested and a binding success is detected.

15. Target for the treatment and / or therapy of PD selected from the group of protein sequences SEQ ID No. 1 - 37, homologous of SEQ ID No. 1 - 37, partial sequences of SEQ ID No. 1 - 37, nucleic acid sequences encoding for SEQ ID No. 1 - 37, nucleic acid sequences encoding for

homologous of SEQ ID No. 1 - 37, nucleic acid sequences encoding for partial sequences of SEQ ID No. 1 - 37 and nucleic acids encoding for partial sequences of homologous of SEQ ID No. 1 - 37.