Specification

The present invention relates to new markers for

Neuromyelitis Optica (NMO) , a method for identifying markers for NMO, the use of markers for NMO identified by the method, diagnostic devices, panels of markers, assays, protein arrays comprising the markers for NMO and a method for identifying NMO.

Protein arrays are gaining increasing industrial importance in analysis and diagnosis as well as in pharmaceutical development. Protein arrays are widely used for example in high throughput screening. The rapid and highly parallel detection of a multiplicity of specifically binding molecules in a single experiment is rendered possible hereby. To produce protein arrays, it is necessary to have the required proteins available.

Another method for screening, e.g. high-throughput

screening, is the well established Luminex® or xMAP®

Technology. This method is a bead-based multiplex assay for the analysis of hundreds of analytes per well. This technology combines advanced fluidics, optics, and digital signal processing with microsphere technology to deliver multiplexed assay capabilities.

xMAP® Technology uses colour-coded tiny beads

("microspheres", "microsphere particle") . Each bead can be coated with a reagent specific to a particular bioassay, allowing the capture and detection of specific analytes from a sample. Inside an analyzer, e.g. a flow-cytometer like the Luminex® analyzer or Bio-Rad® Bio-Plex® analyzer, a light source excites the internal dyes that identify each bead, and also any reporter dye captured during the assay.

The colour-coded beads are pre-coated with analyte-specific capture antibody for the molecule of interest, than the analyte can be bound to the antibody. Analyte bound to the antibody immobilized to the bead can be quantitatively detected by a fluorescence-labelled detection antibody.

The beads are than read on a dual-laser flow-based

detection instrument. One laser classifies the bead and determines the analyte that is being detected. The second laser determines the magnitude of the fluorescence signal, which is in direct proportion to the amount of bound analyte .

Because each bead serves as an individual test, a large number of "different bioassays" can be performed and analyzed simultaneously. And since many readings can be made on each bead set results can be validated.

Different types of beads can be used in this technology, for example MicroPlex® Microspheres. MicroPlex®

Microspheres are carboxylated polystyrene micro-particles that have been dyed into spectrally distinct sets (or regions) allowing them to be individually identified by a flow cytometer, e.g. an xMAP® Instrument. MicroPlex®

Microspheres are in addition magnetic which allows them to be separated from a solution quickly.

Neuromyelitis optica (NMO) or Devic's disease is an inflammatory demyelinating disease of the CNS with severe optic neuritis and myelitis. In that it is very similar to Multiple Sclerosis (MS), but the relationship has been controversial. The NMO-patients suffer from deterioration of the motor functions and as well from deterioration of visual and sensory function. The identification of NMO is very crucial, as the course of the untreated NMO is worse than the course of MS (Kuhle and Petzold, 2011) and

requires therefore early diagnosis and therapy.

Clinical, epidemiological and pathological data have meanwhile demonstrated that MS and NMO are distinct

entities. In addition, in 2004 (Lennon et al, 2004) a serum autoantibody has been identified specific for NMO (NMO- IgG) , that has been identified as antibody directed against Aquaporin-4 (AQP-4), a high abundant water channel of brain tissue, but as well other tissues.

The revised diagnostic criteria for NMO have been revised in 2006 (Wingerchuk, 2006) . These criteria consider optic neuritis and acute myelitis as absolute criteria and two of the following three criteria as supportive for the

diagnosis of NMO: negative brain-MRI at disease onset, contiguous signal abnormalities in spinal cord MRI three or more segment in length and positive NMO- (AQP-4) IgG status .

These diagnostic criteria have been established using the final clinical diagnosis, but NMO can be confused with e.g. MS early in the course of the disease, but untreated NMO leads faster to disability than MS. Therefore many groups have tried to develop assays to detect AQP-4 antibodies in patient samples to facilitate early diagnosis and

treatment. Today, several assays for the detection of AQP-4 antibodies have been developed, showing a high specificity (95%-100% comparing NMO with MS or healthy control groups), but a much lower sensitivity (30% - 47%, 54%-91%, (Fazio et al (2009), Waters (2008)).

Cross Shelly Ann (Journal of Neuro-Ophthalmology, Vol.

27(1), 2007, 57-60) relates to NMO-IgG that targets AQP-4. This antibody was identified using indirect

immunofluorescence on a substrate of mouse central nervous system tissue and identified in the sera of patients with NMO and Japanese opticospinal Multiple Sclerosis, a distinctive IgG staining pattern localizing to the blood- brain barrier and partly colocalizing with laminin.

Benavente, E. and Paira, S. (Curr. Rheumatol. Rep. 13,

2011, 496-505) also refers to the NMO-IgG which targets AQP-4 for diagnosis of NMO.

Satoh J. -I et al. (Neurobiology of Disease 18, 2005, 537- 550) relates to microarry analysis for an aberrant

expression of apoptosis and DNA-regulatory genes in

Multiple Sklerosis.

Reynolds et al . (Clinical Chemistry Vol. 49(10), 2003, 1733-1739) refers to early biomarkers in stroke and data analysis by univariate analysis and multivariable

regression .

Herges et al. (Multiple Sclerosis Journal, Vol. 18(4),

2012, 398-408) assessed the blood of NMO patients for NMO markers by using Luminx and Elisa.

The known marker AQP-4 has several additional

disadvantages. For example it is expected, that AQP-4 might be serving as a better marker in women than in men. In this respect there is further need to increase the sensitivity of NMO-specific assays and existing demand for indication-specific diagnostic means for the detection of NMO, in particular for differentiating between NMO and MS and for early diagnosis of NMO. There is therefore a need to identify markers for NMO that are directed to different, more specific targets than AQP-4.

The goal of the present invention was to identify

additional markers or auto-antibody signatures that can be used to identify NMO-patients with higher sensitivity and / or an earlier stage of disease, either as stand-alone markers or in combination with AQP-4-antibodies .

The present invention provides new markers for NMO and for differentiating between NMO and MS. In addition, these markers can be used for early diagnosis of NMO. The identified NMO specific markers of the invention are suitable and can be used for early recognition, detection, diagnosis, prognosis, surveillance of treatment and stratification of patients with NMO and for monitoring of progression or regression of the NMO disease respectively.

The present invention further provides means comprising these new markers for NMO and the use of the new NMO makers, for example the use of one or more of the new markers in panels of markers, diagnostic devices, text kits or protein arrays .

The term "Neuromyelitis Optica" (NMO) and Multiple

Sclerosis (MS) are defined e.g., according to Pschyrembel, de Gruyter, 263st edition (2012), Berlin.

According to the invention Neuromyelitis optica (NMO) , also known as Devic's disease or Devic's syndrome, is an autoimmune, inflammatory disorder in which a person's own immune system attacks the optic nerves and spinal cord. This produces an inflammation of the optic nerve (optic neuritis) and the spinal cord (myelitis) . Although

inflammation may also affect the brain, the lesions are different from those observed in the related condition, Multiple Sclerosis. Spinal cord lesions lead to varying degrees of weakness or paralysis in the legs or arms, loss of sensation (including blindness), and/or bladder and bowel dysfunction.

Immunological mechanisms have been implicated as major contributors to the pathological process in NMO. Thus, antibodies may play a critical role in the destructive cascade involved in the pathological process in NMO.

Assuming immunological mechanisms involved in NMO and the requirements to specific markers, antibodies are candidates for markers in NMO. 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 the ability to monitor disease progression.

With this invention a novel bead-based screening strategy for the discovery of NMO-specific markers and autoantibodies was developed. According to the invention, sera samples, clinical and other data of NMO patients, MS- diseased and healthy controls were collected and colour- coded beads displaying different human proteins were used for the detection of NMO-specific markers and auto-antibody signatures in human blood.

The Data for auto-antibody signatures of NMO patients, MS patients and healthy persons (this means persons without MS) was collected and processed by statistical procession analysis thereby leading to the identification of NMO specific marker sequences SEQ ID No. 1 to 261. The new NMO specific markers of the present invention can be used to detect characteristics in the immune profile of NMO patients. They are also suitable for the use to detect differences in the immune profile of different NMO patients and for use in individualized medicine. Due to the novel approach of identification used in this invention, NMO markers with a specificity different to the already known AQP-4 and AQP-4 antibodies were provided. It is the general inventive concept of the invention to identify NMO markers with specific properties and specificity by using the method according to the invention.

With this invention it was shown that these novel NMO specific markers can discriminate NMO patients from reference groups, in particular between persons with NMO and persons with MS and between persons with NMO and persons without MS. The identified markers SEQ ID No. 1 to 261, partial sequences and homologous thereof can therefore be used to separate between patients with NMO, patients with MS, and healthy controls or persons without MS, respectively .

All NMO markers according to the invention were identified by a new statistical approach. This common statistical approach comprises at least two steps: univariate analysis and multivariate analysis. In a preferred embodiment of the invention two different approaches of univariate analysis and one approach of multivariate analysis are applied in order to identify the NMO markers. Finally the results of univariant and multivariant analysis are combined leading to the identification of markers that can differentiate between NMO and MS and markers that can differentiate between NMO and healthy or persons without MS,

respectively .

The statistical analysis plan (SAP) underlying the present invention provides a comprehensive and detailed description of strategy and statistical techniques to be used for the analysis of data. The details of the SAP are described in detail below and individual data that illustrate the SAP can be obtained from the examples and tables . A man of skill in the art easily can use and apply this information to identify further suitable markers for NMO.

The purpose of the SAP underlying the present invention was to ensure the credibility of results by pre-specifying the statistical approaches prior to the analysis of data. The SAP follows the principles of the International Conference on Harmonization (ICH) E3, E6, and E9 and the relevant Standard Operating Procedures (SOPs) .

In a preferred embodiment, the present invention relates to a method for identifying markers for Neuromelitis Optica (NMO) comprising the steps a) Expose a marker candidate for NMO to sample (s) of NMO patient (s), measure the bonding of the marker candidate by immunofluorescent assay and determine the median fluorescence intensity (MFI) for the marker candidate; b) Expose the same marker candidate to control sample (s), measure the bonding of the marker candidate by

immunofluorescent assay and determine the median

fluorescence intensity (MFI) for the marker candidate; c) Process MFI data from steps a) and b) by univariate analysis ; d) Process MFI data from steps a) and b) by multivariate analysis ; e) Combine the data obtained by univariate analysis and multivariate analysis and identify thereby markers for NMO.

Another preferred embodiment of the method for identifying markers for Neuromelitis Optica (NMO) comprising the steps a) Expose a marker candidate for NMO to sample (s) of NMO patient (s), measure the bonding of the marker candidate by immunofluorescent assay and determine the median

fluorescence intensity (MFI) for the marker candidate; b) Expose the same marker candidate to control sample (s), measure the bonding of the marker candidate by

immunofluorescent assay and determine the median

fluorescence intensity (MFI) for the marker candidate; c) Process MFI data from steps a) and b) by univariate analysis ; d) Process MFI data from steps a) and b) by multivariate analysis ; e) Combine the data obtained by univariate analysis and multivariate analysis and identify thereby marker (s) for NMO, f) Select the marker from the group of markers comprising SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences) .

In another embodiment of the method, the marker in step f) of the method is selected from the group comprising SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785.

Univariate analysis is the simplest form of quantitative (statistical) analysis. The analysis is carried out with the description of a single variable and its attributes of the applicable unit of analysis. A basic way of presenting univariate data is to create a frequency distribution of the individual cases, which involves presenting the number of attributes of the variable studied for each case

observed in the sample. This can be done for example in a table format, with a bar chart or a similar form of

graphical representation. Multivariate analysis relates to the analysis of multiple variables simultaneously.

In a preferred embodiment of the invention the following statistical approaches are used:

1) Univariate analysis based on exploratory statistics and testing :

The easiest approach for univariate analysis is to check for each antigen separately the discriminating power between two groups. Ranking lists of antigens are provided taking into account the p-value of the univariate Mann- Whitney U test, and exploratory summary statistics such as the absolute median fluorescence intensity (MFI) value within groups, the effect size and the fold-change.

Univariate TOP candidates for the separation between NMO patients and healthy controls as well as between NMO patients and MS patients can be identified by the

univariate analysis .

2) Volcano plot

The volcano plot arranges antigens along dimensions of biological relevance and statistical significance. The "edge candidates" in the areas outside the reference lines in the left and right upper corner of the graph show antigens with a high fold-change in either direction and at the same time a low p-value (dots can be marked with numbers) . Interesting candidates are than picked up from this type of graph for both comparisons NMO patients vs. healthy controls and NMO patients vs. MS patients.

Multivariate analysis - PLS-DA (also called "PPLS The aim of the partial least squares discriminant analysis ("PLS-DA" or "PPLS-DA") is to extract relevant linear combinations of the antigens for the discrimination between the predefined groups of NMO patients and healthy controls as well as between NMO patients and MS patients. The PPLS- DA starts with all antigens und results in a TOP-list of antigens. This procedure has been run 200 times in order to identify multivariate candidates. With these candidates, the statistical model was run again and TOP antigens can be picked up according to their importance within the set.

4) Combination of analyses

As all of these procedures produce independent ranking and/or TOP lists, the overlap and the union of respective candidates were built for group comparisons and as well for inclusion and exclusion of AQP-4.

NMO specific markers were identified by collecting MFI data obtained upon binding of specific markers (e.g. antibodies) to NMO specific substances (e.g. NMO auto-antibodies) in body fluids of NMO patients and processing the obtained MFI data by statistical analysis comprising at least one method of univariate analysis and at least one method of

multivariate analysis.

In one embodiment of the method according to the invention procession of MFI data is performed by univariate analysis based on EST (exploratory statistics and testing) and / or volcano plot.

In another embodiment of the method according to the invention univariate analysis of MFI data of a marker candidate comprises one or more parameters selected from p- value, fold change, effect size, Fisher ^' s ration, area under the curve (AUC) , median absolute MFI within the group, the univariate Mann-Whitney U test.

In statistical hypothesis testing, the p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true. When the null hypothesis is rejected, the result is said to be statistically significant .

In statistics, the Mann-Whitney U test (also called the Mann-Whitney-Wilcoxon (MWW) or Wilcoxon rank-sum test) is a non-parametric statistical hypothesis test for assessing whether one of two samples of independent observations tends to have larger values than the other.

In another embodiment of the method according to the invention procession of MFI data by multivariate analysis is performed by partial least squares discriminant analysis (PLS-DA) and/or powered PLS-DA.

Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression. It finds a linear regression model by projecting the predicted variables and the observable variables to a new space.

In another embodiment of the method according to the invention control samples are selected from healthy persons .

Healthy persons in the context of the invention are individuals that have no diagnosis of NMO (individuals without NMO) . Healthy persons in the context of the

invention are individuals that have no diagnosis of MS (individuals without MS) . Healthy persons are in the healthy state. In a preferred embodiment of the invention healthy persons are individuals that have no diagnosis of an infection or illness at all. In another embodiment of the invention healthy persons might have an infection or illness, however, this other infection or illness must be different from MS. In a preferred embodiment healthy persons have no diagnosis of MS or NMO.

In another embodiment of the method according to the invention control samples are selected from persons that have the diagnosis MS.

Within the scope of this invention, "patient" or "person" means any test subject - human or mammal - with the proviso that the test subject is tested for NMO. The term "patient" means a person that has NMO or is tested positive for NMO. The patients are therefore a subgroup of the persons.

In another aspect, the present invention relates to markers for NMO identified by the method according to the

invention. In a preferred embodiment the invention relates to markers for MNO identified by a method according to the invention and selected from the group comprising SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785. In a preferred embodiment the invention relates to TOP markers SEQ ID No. 1 to 16 (clone sequence), SEQ ID No. 45 to 63 (clone sequence), SEQ ID No. 262 to 279 (clone sequence), SEQ ID No. 360 to 375 (clone sequence) and the corresponding RNA and/or protein sequences thereof.

In another embodiment the invention relates to the markers identified by SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785.

In another embodiment the invention relates to markers for discriminating MNO from multiple sclerosis and wherein the markers are identified by a method according to the

invention and are selected from the group comprising SEQ ID No. 1 to 44 (clone sequences), SEQ ID No. 88 to 131 (RNA sequences), SEQ ID No. 175 to 218 (protein sequences), SEQ ID No. 262 to 359 (clone sequences), SEQ ID No. 465 to 562 (RNA sequences), SEQ ID No. 668 to 765 (protein sequences) partial sequences and homologous thereof, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 88 to 103, SEQ ID No. 175 to 190, SEQ ID No. 262 to 279, SEQ ID No. 465 to 562, SEQ ID NO. 668 to 765. In a

preferred embodiment the invention relates to TOP markers SEQ ID No. 1 to 16, SEQ ID No. 262 to 359 the corresponding RNA sequences SEQ ID No. 88 to 103 and 465 to 562, and the corresponding protein sequences SEQ ID No. 175 to 190 and 668 to 765, partial sequences and homologous thereof.

In another embodiment the invention relates to markers for discriminating MNO from healthy state and wherein the markers are identified by a method according to the

invention and are selected from the group comprising SEQ ID No. 45 to 87 (clone sequences), SEQ ID No. 132 to 174 (RNA sequence), SEQ ID No. 219 to 261 (protein sequence), SEQ ID No. 360 to 464 (clone sequences), SEQ ID No. 563 to 667 (RNA sequence), SEQ ID No. 766 to 870 (protein sequence partial sequences and homologous thereof, preferably selected from the group of SEQ ID No. 45 - 63, SEQ ID No. 132 to 150, SEQ ID No. 219 to 237, SEQ ID No. 360 to 375, SEQ ID No. 563 to 578, SEQ ID NO. 766 to 785. In a preferred embodiment the invention relates to TOP markers SEQ ID No. 45 to 63, SEQ ID No. 360 to 375 the corresponding RNA sequences SEQ ID No. 132 to 150, SEQ ID No. 563 to 578 and the corresponding protein sequences SEQ ID No. 766 to 785, partial sequences and homologous thereof.

IN another embodiment the invention relates to the use of the marker sequences selected from the group comprising SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785 for

discrimination of NMO from both, the healthy status and MS, at the same time. Preferably this discrimination can be achieved by using one or more sequences selected from SEQ ID No. 357 to 464, SEQ ID No. 660 to 667, SEQ ID No. 863 to 870, homologous or derivatives thereof.

In another embodiment the invention relates to markers for diagnosis of MNO selected form the group comprising SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785, partial

sequences and homologous thereof.

In another embodiment the invention relates to the use of one or more marker (s) selected from the group comprising sequence SEQ ID No. 1 to 87 and 262 to 464 (clone

sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785 as diagnostic agent, for use in diagnosis of MNO, for prognosis in NMO, for determination of treatment of NMO, for surveillance of treatment of MNO, for

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

In a further embodiment of the invention, the markers for NMO according to the invention can likewise be combined, supplemented, fused or expanded likewise with known

biomarkers for this indication. In a preferred embodiment of the invention one or more NMO markers of the invention are combined or used together with AQP-4.

Therefore the present invention also relates to the use of at least one preferably at least two, three or more of the new NMO markers optionally together with other markers, preferably other markers for NMO. The present invention relates for example to the use of combinations of one or more of the new markers SEQ ID No. 1 to 261, partial sequences and/or homologous thereof with AQP-4 as a marker. In another embodiment the invention relates to a diagnostic agent or test kit comprising one or more marker (s) for NMO selected from the group comprising sequence SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785 and optionally further substances and/or additives. In a preferred embodiment AQP- 4 is used as additional marker in this connection.

In another embodiment the invention relates to a panel of markers comprising one or more marker (s) for NMO selected from the group comprising sequence SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785. In a preferred embodiment AQP-4 is used as additional marker in this connection. In another embodiment the invention relates to an assay or protein array comprising a panel of marker (s) according to the invention, characterized in that the marker (s) is/are applied to a solid support, in particular a filter, a membrane, a bead or microsphere like for example a magnetic or fluorophore-labelled bead, a silica wafer, glass, metal, ceramics, plastics, a chip, a target for mass spectrometry or a matrix.

In another embodiment the invention relates to the use of a panel of markers according to the invention or an assay or protein array according to the invention for the

identification and/or validation of an active agent for the prevention or treatment of NMO wherein the panel or the assay or protein array contains means for detecting a binding success, characterized in that the panel or assay or protein array a.) is brought into contact with at least one substance to be tested and b.) a binding success is detected .

In another aspect the invention relates to a method for detecting MNO comprising the steps a. providing at least one marker for NMO selected from the group comprising sequence SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785, b. bringing it into contact with body fluid or tissue extract of a person, for example a patient and c. detecting an interaction of the body fluid or tissue extract with the marker (s) from a.).

In another embodiment the invention relates to a target for the treatment and/or therapy of NMO selected from the group comprising sequence SEQ ID No. 1 to 87 and 262 to 464

(clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785.

In a further preferred embodiment of the invention, the invention relates to the diagnosis of NMO, wherein at least one marker is selected from the group of sequences SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785 and the one or more marker (s) is/are used for detecting one or more autoantibodies on or from a patient to be examined.

In a further embodiment at least 2 to 5 or 10, preferably 30 to 50 markers or 50 to 100 or more markers are used to determined NMO specific auto-antibodies / NMO specific auto-antibody profiles on or from a patient to be examined, in particular such NMO markers are selected from the group comprising SEQ ID No. 1 to 87 and 262 to 464 (clone

sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785.

In a preferred embodiment, the determination of binding partners (e.g. auto-antibodies) of the NMO specific

marker (s) according to the invention is carried out outside the body and the determination is carried out in an ex vivo / in vitro diagnosis. The detection of an interaction of this type can for example be carried out with 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 array, which permits a diagnosis or examination for NMO.

Furthermore, the invention relates to a method for the stratification, in particular risk stratification and/or therapy control and/or of a patient with NMO wherein at least one binding partner to a marker for NMO is determined on a patient to be examined.

Furthermore, the stratification of the patients with NMO in new or established subgroups of NMO or MS 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. The present

invention therefore also relates to the use of the markers according to the invention for individualized medicine.

"Diagnosis" for the purposes of this invention means the positive determination of NMO by means of the marker (s) according to the invention as well as the assignment of the patients to NMO. 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 NMO by means of the marker (s) according to the invention and the prognosis of NMO.

"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.

The term "marker" 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 NMO . For example, the cDNA or the polypeptide or protein that can be

respectively obtained thereof can exhibit an interaction with substances from the body fluid or tissue extract of a patient with NMO (e.g. antigen (epitope) / antibody

(paratope) interaction, preferably interaction with an auto-antibody) . For the purposes of the invention "wherein at least one marker is selected from SEQ ID No. 1 to 87, SEQ ID No. 88 to 174, SEQ ID No. 175 to 261, partial sequences of SEQ ID No. 1 to 261 and homologous of SEQ ID No. 1 to 261 is determined on a patient to be examined" means that an interaction between the body fluid or tissue extract of a patient and the marker 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 (binding partners, e.g. auto-antibodies and/or auto-antibody profiles) 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 .

In a further embodiment of the invention, however, the binding partners of the markers according to the invention can be represented in a significantly higher or lower amount or concentration in the body fluid or tissue extract of an NMO patient in comparison to for example the healthy state. The difference in concentration or amount can be determined by the markers according to the invention and indicate NMO. The relative sick/healthy expression rates of the binding partners of the NMO markers according to the invention can hereby determined.

Auto-antibodies that are significant for NMO are either expressed only in case of NMO or the levels of these autoantibodies vary significantly in case of NMO, e.g. they are more or less expressed in case of NMO in comparison to the levels of the respective autoantibody levels in healthy persons or in comparison to the respective levels in MS. According to the invention the marker can especially be used to determine one or more auto-antibodies or autoantibody profiles that are specific for NMO, preferably that are specific for early detection of NMO and/or

diagnosis of NMO and/or surveillance of the treatment of NMO and/or prognosis of NMO.

Auto-antibody profiles in this respect relate to the amount of one or more auto-antibodies that are specifically expressed, e.g. up- or down-regulated in NMO. The autoantibody profiles relate therefore in one aspect to the composition (one or more auto-antibodies) of the profile and in another aspect to the amount or concentration of a particular auto-antibody in NMO.

In one embodiment of the invention the marker binds to / recognizes one or more auto-antibodies that are more or less expressed during development, establishment, therapy and/or progression of NMO. In order to characterize theses specific auto-antibody profiles one or more markers according to the invention can be used / are necessary.

The invention comprises the use of at least one NMO marker. 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 markers selected from the group comprising SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785 are used together or sequentially.

The markers according to the invention are the subject matter of sequence listing and can be clearly identified by the sequences SEQ ID No. 1 - 261 and SEQ ID No. 262 to 870 in the sequence listing and from the data in table 1 and table 2.

According to the invention, the markers 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.

In a further embodiment of the invention, the marker has a recognition signal that is addressed to the substance to be bound (e.g., antibody, autoantibody, 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 recognizes (e.g. hybridizes, binds) to an autoantibody which is significant for NMO .

Homologous according to the invention are homologous protein / peptide or nucleic acid sequences, in particular homologous of SEQ ID No. 1-261 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 sequence.

Partial sequences according to the invention are parts of the respective protein / peptide sequences, in particular partial sequences of those determined by SEQ ID No. 175-261 and SEQ ID No. 668 to 870 and the nucleic acids encoding theses partial proteins, peptides like for example SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences) . Partial sequences miss one or more amino acids or nucleotides respectively in comparison to the respective complete sequences. 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 a further embodiment of the invention, partial sequences of the markers 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 markers according to the invention. Another object of the invention relates to an arrangement of markers (panel) containing at least one marker selected from the group of sequences SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870

(protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785. Preferably, the arrangement contains at least 2 to 5 or 10, preferably 30 to 50 markers or 50 to 100 or more markers.

In a further embodiment, the respective marker can be represented in different quantities in one more regions in a panel e.g. on a solid support. This permits a variation of the sensitivity. The regions can have respectively a totality of markers, i.e. a sufficient number of different markers, in particular 2 to 5 or 10 or more and optionally additional nucleic acids and/or proteins, preferably AQP-4. However, at least 96 to 25,000 (numerical) or more from different or identical markers and further nucleic acids and/or proteins, in particular AQP-4 is preferred.

Furthermore preferred are more than 2,500, in particular preferred 10,000 or more different or identical markers and optionally further nucleic acids and/or proteins, in particular AQP-4. Within the scope of this invention, "arrangement" is synonymous with "panel" and "array" and if this "array" is used to identify substances or binding partners for the marker (s), this is to be understood to be an "assay" or diagnostic device.

In a preferred embodiment, the arrangement is designed such that the marker (s) 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 markers 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.

As used herein, the word "array" or „panel" shall be taken to mean any ordered arrangement of a plurality of specified integers, including both linear and non-liner arrangements of a plurality of proteins and/or nucleic acids. In the present context, the word "array" or „panel" includes any elements derived e.g. from a complex mixture of proteins/ nucleic acids resolved by 1-dimensional or 2-dimensional gel electrophoresis or chromatography, or peptide or protein expression libraries and the ordered arrangement of the proteins or nucleic acids on a grid, such as in microtitre wells or on a membrane support or silicon chip or on a grid comprising a plurality of polymeric pins and / or on beads, e.g. magnetic beads.

The solid support or matrix is typically glass or a polymer, the most commonly used polymers being cellulose, polyacrylamide , nylon, polystyrene, polyvinyl chloride or polypropylene. The solid supports may be in the form of tubes, beads, discs, silicon chips, microplates,

polyvinylidene difluoride (PVDF) membrane, nitrocellulose membrane, nylon membrane, other porous membrane, non-porous membrane (eg. plastic, polymer, perspex, silicon, amongst others), a plurality of polymeric pins, or a plurality of microtitre wells, or any other surface suitable for immobilising proteins and/or nucleic acids and/or

conducting an assay. The binding processes are well-known in the art and generally consist of cross-linking,

covalently binding or physically adsorbing the protein or nucleic acid molecule to the solid support.

In all of the embodiments, the term "solid support" covers embodiments such as a filter, a membrane, a bead,

preferably a magnetic or fluorophore-labeled bead, a silica wafer, glass, metal, ceramics, plastics, a chip, a target for mass spectrometry or a matrix. However, a filter is preferred according to the invention.

As a filter, furthermore PVDF, nitrocellulose or nylon is preferred (e.g., Immobilon P Millipore, Protran Whatman, Hybond N+ Amersham) .

In another preferred 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.

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 array in accordance with the invention is a systematic arrangement of proteins on a solid support or a matrix.

The markers of the arrangement are preferably fixed on a solid support, for example spotted or immobilized or printed on, i.e. applied in a reproducible manner. One or more markers can be present multiple times in the totality of all markers and present in different quantities based on one spot. Furthermore, the markers can be standardized on the solid support.

The invention therefore further relates to an assay or a protein array comprising an arrangement containing markers according to the invention.

In a further embodiment, the markers are represented 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 arrays (protein microarrays, 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 markers are present as clones.

Additionally, the markers 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 a further embodiment, the invention relates to an assay or a protein array for identifying and characterizing a substance for NMO, characterized in that an arrangement or 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, 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, as

antigen/antibody, e.g. autoantibody) 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 NMO and obtainable through the use of the assay or protein array according to the invention.

In a further embodiment, the invention likewise relates to the use of the marker 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 NMO, such as blood or plasma, bind to the markers according to the invention and consequently can be selectively withdrawn from the body fluid.

The invention further relates to the use of one or more markers for NMO selected from the group comprising SEQ ID No. 1 to 87 and 262 to 464 (clone sequences), SEQ ID No. 88 to 174 and 465 to 667 (RNA sequences), SEQ ID No. 175 to 261 and 668 to 870 (protein sequences), partial sequences of SEQ ID No. 1 to 261 and 262 to 870 and homologous of SEQ ID No. 1 to 261 and 262 to 870, preferably selected from the group of SEQ ID No. 1 to 16, SEQ ID No. 262 to 279, SEQ ID No. 88 to 103, SEQ ID No. 465 to 482, SEQ ID No. 175 to 190, SEQ ID No. 668 to 685, SEQ ID No. 45 to 63, SEQ ID No. 360 to 375, SEQ ID No. 132 to 150, SEQ ID No. 563 to 578, SEQ ID No. 219 to 237, SEQ ID No. 766-785 for screening of drugs and active compounds for treatment of NMO.

The invention is further described in the following

examples and tables, however, without restricting the invention to these examples and tables. All of them are generated according to the SAP of the invention. All tables and data listings are presented in Landscape Orientation.

Examples

Example 1 : Measurement principle

In this study, the bead-based Luminex xMAP® Technology is used for the detection of auto-antibodies in serum samples derived from endometriosis patients.

In a bead-based assay, a set of different fluorescent colour-coded magnetic polystyrene microspheres (MagPlex ^® ) is used, allowing the simultaneous analysis of up to 500 analytes in a single approach by the Luminex FlexMap3D device. Each colour-coded bead is covalently linked to a specific antigen. During serum sample incubation,

autoantibodies present in the serum sample bind to the coupled antigens on the beads and can be quantitatively detected by a fluorescence-labelled (e.g. phycoerythrin) detection antibody.

The Luminex FlexMap3D device is based on the principle of flow cytometry using a dual-laser system to identify the specific bead colour with a 635 nm red laser and the signal strength of reporter molecules with a 532 nm green laser.

1.1. Coupling Procedure

Histidine-tagged antigens are purified under denaturing conditions and cross-linked to magnetic microspheres

(MagPlex ^® ) using standard bead coupling procedure (WI 3-17- 1.03 VerOl) . The coupling reaction is performed in a semi- automated fashion with a Freedom Evo ^® 150 liquid handling roboter (Tecan) . The carboxylated beads are activated with EDC and Sulfo-NHS and up to 12.5 g of protein is subjected to the activated beads forming covalent peptide bonds between the carboxyl groups of the bead and primary amines of the protein.

Proteins, which will be coupled, have to fulfil following conditions to enable successful bead coupling and quality control .

Requirements of protein conditions amine-free buffer (w/o Tris, lysine,

Buffer conditions:

glycine, ethanolamine ) ; pH < 8.0 Protein amount: 100 g

Protein

> 0.25 g/μΐ

concentration : Protein

HIS-tagged proteins

conditions : Protein detection

Penta-HIS antibody

(QC) :

Coupled beads of 400 different colour-codes are combined to a bead mix. The bead mixes are stored at 4°C in the dark. The coupling efficiency is controlled according to the working instruction (WI 3-17-2.02 Ver02) using an anti- penta-His antibody (Qiagen) and a secondary PE-con ugated anti-mouse antibody (Dianova) . Internal control beads are used to monitor the assay performance of each well. Hence, beads coupled with human IgG (Sigma) and mouse IgG (Sigma) are added to each bead mix controlling the accuracy of goat-anti-human-PE or goat- anti-mouse-PE detection antibodies, respectively.

1.2. Antigen-Autoantibody Assay

The antigen-autoantibody assay is performed according to the working instructions (WI 3-17-3.03 VerOl, WI 3-17-3.04 Ver02) in a semi-automated fashion using the liquid handling workstations MICROLAB ^® STARlet (Hamilton) and Freedom Evo ^® 150 (Tecan) .

Serum samples are thawed and re-arrayed in the appropriate format for the bead-based assay using the MICROLAB ^® STARlet followed by a 1:100 dilution in assay buffer using the Freedom Evo ^® 150.

The bead mix is analyzed with all serum samples. After distributing the bead mixes in the microtiter plates the 1:100 diluted serum samples are applied for 22 hours at 4°C. Unbound human auto-antibodies of the serum samples are removed by washing. Bound human auto-antibodies are quantitatively labelled by a PE-labelled goat-anti-human IgG antibody (Dianova) followed by washing cycles of the beads. The median fluorescence intensities (MFI) of the detection antibody is analyzed for each bead using the FlexMAP 3D instrument.

For the calculation of intra- and inter-plate CVs three replicates of selected serum samples are measured on each assay plate. Example 2: Study Design

2.1. Analysis Groups

There are three different analysis groups within the study. The following groups will be considered (n = number of different samples, each sample belongs to a different patient/person ) :

NMO (n=12)

MS (n=18) healthy controls (n=12)

2.2. Randomization and Blinding

This is an open study, so no blinding on a patient level is possible. Nevertheless, allocation of serum samples on plates followed a block randomization scheme in which matched pairs (patient and control, if applicable) were randomly arranged.

2.3. Measurement Quality

The entire processes including bead coupling, coupling control and the antigen-autoantibody assay are performed according to Protagen's working instructions.

Coefficients of variation (CV) will be determined and will be shown for inter- and intra-plate variation in form of histograms and boxplots. Besides, the bead count

distribution will be presented analogously. 2.4. Analysis Population

All samples available according to the setting described above will be included, no further definition of analysis populations is necessary for this exploratory approach.

Example 3: Collection and Procession of data

3.1. Data Base

Laboratory raw data are available in CSV-format showing relevant MFI values by sample number. Additionally, demographic data are available such as age and gender. All data will be transferred to the R environment for

statistical evaluation. Analysis data sets will be

generated in the context of data management.

In total, the data base consists of 42 samples: 12 NMO samples, 12 healthy controls, and 18 MS samples coming from one screen.

No course over time will be monitored, i.e. one observation per patient or healthy control is available.

In total, 384 antigens were documented, thereof 247 will be considered for the analysis.

3.2. Analysis Variables

The MFI of the detection antibody is the target variable for all analyses. Values are considered for 247 antigens in total. Demographic data will only be used to check

homogeneity within the analysis population. No further variables will be considered. The aim of this study is to answer the question whether the immune profile is able to separate between patients with NMO, patients with MS, and healthy controls. Special interest is on AQP-4 as a marker and potentially

accompanying additional or alternative markers.

Therefore, the following objectives will be addressed:

The immune profile for different indications will be investigated and compared. Possible discrimination between the following groups will be analyzed:

NMO vs healthy controls and NMO vs MS

3.3. Data Pre-processing

3.3.1. Replicates

If replicates are present, the first measurement values will be used for statistical analyses.

3.3.2. Transformation

For MFI values are log ₂ transformed before normalization. As long as the statistical method requires log-transformed values, the transformed values are maintained. All other analyses are based on the back-transformed MFI values.

3.3.3. Handling of Missing Values

Total amount of employed beads is optimized, targeting at ≥ 100 beads counted for each measured antigen. Observations from previous studies show that MFI values are unreliable for a specific antigen if less than 10 beads are counted. These cases are rare, and the corresponding MFI value is set to missing. The numbers of missing values will be reported for each antigen and sample. Samples or antigens are discarded from further analysis if

1) there are more than 20% missing values for a sample,

2) there are more than 20% missing values for an antigen.

Samples or antigens excluded from further analysis will be reported .

Missing values for an antigen will be replaced after normalization and back-transformation by median imputation, i.e. by the median MFI value measured in all samples for this antigen.

3.3.4. Normalization

Normalization is applied after log ₂ transformation. On each Luminex plate, three reference sera are measured serving as quality control and normalization reference for plate- specific measurement differences. To this end, the median of each antigen is calculated from all reference samples on a plate, yielding the median reference for this individual plate. An overall median reference based on all measured plates is calculated analogously. Quantile normalization is used to normalize all measured samples on the individual plate by BBA set.

3.3.4. Statistical Analysis

The complete statistical analysis will be carried out for two group comparisons :

• NMO vs. healthy controls

• NMO vs . MS Multivariate methods will only be applied as long as the number of samples is sufficient.

Example 4: Statistical Analysis

4.1. Univariate Analysis

Summary statistics will be determined for all MFI results for all antigens separately for all three groups: NMO, MS and healthy controls. (Note: not log-transformed values but original values to be used)

The median will be determined as a representative parameter for location.

Group comparison will be performed between results derived from NMO patients in comparison to healthy controls, and additionally in comparison to MS patients. The following system of hypotheses will be investigated for the log ₂ transformed MFI values for each antigen j , j = 1, J:

H _{0 j} : The medians of log ₂ transformed MFI values are

identical in the two groups.

Hi _j : The medians of log ₂ transformed MFI values differ between the two groups .

Besides, the fold change (=ratio) between the two groups will be determined.

The effect size will be calculated as the ratio of the absolute value of mean difference between the two groups and the respective standard deviation.

A receiver operating characteristic (ROC) curve will be constructed. Sensitivity, specificity and the area under the curve (AUC) will be estimated together with the respective bootstrapped 95% confidence intervals.

The TOP candidates will be identified for further

investigation taking into account the following

characteristics as decision criteria:

P-value (TOP 30 rank)

- Fold change (at least 2-fold) Effect size (TOP 30 rank)

- Fisher's ratio (TOP 30 rank)

- AUC resulting from ROC analysis (at least 0.75)

- Median absolute MFI value in at least one treatment group >500

- Median absolute MFI value in at least one treatment group >1000

A scoring system will be implemented: The criteria

mentioned above will be treated as binary variables, so that one scoring point will be given if the respective criterion is fulfilled. The score will present the sum of all scoring points.

TOP candidates will be ranked according to this scoring system. In case of ties the second variable for ranking is the p-value, so that a clear cut-off for the univariate TOP 30 candidates within the list is possible. The ranking list will be provided for the comparison between NMO and healthy controls and additionally for the comparison between NMO and MS.

4.2. Graphical Display

The volcano plot will visualize a part the univariate results for all antigens at a glance. A volcano plot arranges antigens along dimensions of biological relevance and statistical significance. The horizontal dimension is the fold change between the two groups on a log ₂ scale, so that up and down regulation appear symmetric, and the vertical axis represents the p-value for a Mann-Whitney U test of differences between samples on a negative logio scale, so that smaller p-values appear higher up. The horizontal axis indicates biological relevance of the difference, the vertical axis indicates the statistical significance. Judgment about promising candidates is possible by trading off both these criteria by eye.

Reference lines are implemented at -1 and 1 (reflecting a 2-fold change in either direction) on the horizontal axis and at 1.3 (reflecting a p-value of 0.05) on the vertical axis .

The "edge candidates" coming from the areas outside the reference lines (left and right upper corner) will be listed with gene-ID, gene name and log ₂ (ratio) and -logio(p- value )

Graphs and listings will be provided for the comparison between NMO patients and healthy controls, and the same visualizations will be given for NMO patients in comparison to MS patients. 4.3. Multivariate Analysis

Partial least squares discriminant analysis (PLS-DA) is partial least squares regression adapted to classification tasks. The aim is to extract relevant components (linear combination of the variables) for the discrimination between the predefined groups. This technique is especially suited to deal with a much larger number of predictors than observations and with multicollinearity, two of the main problems encountered when analyzing expression data.

Powered partial least squares discriminant analysis is a specialized version of the method PLS-DA. One aspect different from PLS-DA is, that a so called power parameter is fitted in order to maximize the correlation between the latent components and the response matrix (dummy coded group memberships) . For the final classification a linear discriminant analysis is applied with the latent components as predictors .

The PLS-DA will start with all antigens und result in a TOP-list of 30 antigens. Cross validation will be

implemented with a number of 200 runs.

An evaluation over these 200 runs will summarize the ranking based on the frequency of TOP 30 ranks for each antigen and the median value for the rank. An antigen is qualified for the multivariate panel if the frequency of TOP 30 ranks is at least 100, or the frequency is at least 100 and the median rank is not higher than 16.

For this panel a PLS-DA (also "PPLS-DA") will be applied with focus on the first component and the results will be shown in form of a score plot, an importance plot, and a ranking list based on the loading weights.

As the number of samples available is very small, this analysis will be carried out only as supportive. On the one hand all antigens will be used within the PPLS-DA, on the other hand AQP-4 will be left out to investigate a possible difference. Graphical display of results will be provided.

The two groups for comparison will be NMO and healthy controls. An analysis for NMO patients in comparison to MS patients will be carried out analogously.

4.4. Combination of Results

In order to get an overview of the candidate lists based on different statistical analyses the results will be pooled. The overlap of panels from univariate (Scoring and "Edge candidates" resulting from Volcano plot) and multivariate analysis will be presented for the two comparisons NMO patients versus healthy controls and NMO patients versus MS patients. Analogously, the respective union of panels will be presented.

4.5. Software

In-house developed software is used to perform Luminex raw data file parsing to produce an easy readable CSV file that is suitable as input for further statistical analysis software. All statistical analyses are performed within the R project for statistical computing, http://www.r- project.org/ version R-2.14.0 (2011-10-31). Table 1: Markers for NMO identified by statistical analysis of MFI data from NMO vs MS. The sequence of the markers can be obtained from the enclosed sequence listening.

SEQ ID Marker Gene No . Classification GenelD Gene Name Symbol

1 nuclear factor of activated

T-cells, cytoplasmic,

TOP Marker 4775 calcineurin-dependent 3 NFATC3

2 replication factor C

TOP Marker 5982 (activator 1) 2, 40kDa RFC2

3 Homo sapiens cDNA clone

IMAGE : 30377818 5'mRNA

TOP Marker sequence

4 tubulointerstitial nephritis

TOP Marker 64129 antigen-like 1 TINAGL1

5 EMG1 nucleolar protein

TOP Marker 10436 homolog (S. cerevisiae) EMG1

6 transformer 2 beta homolog

TOP Marker 6434 (Drosophila) TRA2B

7 UPF3 regulator of nonsense

TOP Marker 65109 transcripts homolog B (yeast) UPF3B

8 TOP Marker 6152 ribosomal protein L24 RPL24

9 TOP Marker 6838 surfeit 6 SURF6

10 TOP Marker 23608 makorin ring finger protein 1 MKRN1

11 TOP Marker 4155 myelin basic protein MBP

12 eukaryotic translation

TOP Marker 1938 elongation factor 2 EEF2

13 proprotein convertase

subtilisin/kexin type 1

TOP Marker 27344 inhibitor PCSK1N

14 TOP Marker - OOF4155 -

15 TOP Marker - OOF1938 -

16 TOP Marker - OOF27344 -

17 Marker 58506 SR-related CTD-associated SCAF1 factor 1

Marker 324 adenomatous polyposis coli APC

hematological and

Marker 90861 neurological expressed 1-like HN1L chromosome 10 open reading

Marker 119032 frame 32 C10orf32

Marker 84893 F-box protein, helicase, 18 FBX018

SLU7 splicing factor homolog

Marker 10569 (S. cerevisiae) SLU7

REX1, RNA exonuclease 1

Marker 57455 homolog (S. cerevisiae) REXOl

Src homology 2 domain

Marker 6461 containing adaptor protein B SHB

Marker 79155 TNFAIP3 interacting protein 2 TNIP2 coiled-coil domain containing

Marker 339230 137 CCDC137 phospholipase D family,

Marker 23646 member 3 PLD3

Marker 11019 lipoic acid synthetase LIAS

Marker 6130 ribosomal protein L7a RPL7A pyrrol ine-5-carboxylate

Marker 5831 reductase 1 PYCR1 mannosidase, alpha, class 2A,

Marker 4122 member 2 MAN2A2

Marker 51510 chromatin modifying protein 5 CHMP5

WAS protein family homolog 5

Marker 375690 pseudogene WASH5P hepatoma-derived growth

Marker 3068 factor HDGF chromatin modifying protein

Marker 128866 4B CHMP4B voltage-dependent anion

Marker 7416 channel 1 VDAC1 erythrocyte membrane protein

Marker 2037 band 4.1-like 2 EPB41L2

Marker 4924 nucleobindin 1 NUCB1

Marker 7431 vimentin VIM Marker 10081 programmed cell death 7 PDCD7

Marker - OOF2037 -

Marker - OOF4924 -

Marker - OOF7431 -

Marker - OOF10081 -

TOP Marker 80152 centromere protein T CENPT

TOP Marker 6895 TAR (HIV-1) RNA binding TARBP2 protein 2

TOP Marker 23080 AVL9 homolog (S. cerevisiase) AVL9

TOP Marker 6834 surfeit 1 SURF1

TOP Marker 2114 v-ets erythroblastosis virus ETS2

E26 oncogene homolog 2

( avian )

TOP Marker 23404 exosome component 2 EXOSC2

TOP Marker 10155 tripartite motif-containing TRIM28

28

TOP Marker 31 acetyl-Coenzyme A carboxylase ACACA alpha

TOP Marker 8897 myotubularin related protein MTMR3

3

TOP Marker 151313 fumarylacetoacetate hydrolase FAHD2B domain containing 2B

TOP Marker 53343 nudix (nucleoside diphosphate NUDT9 linked moiety X)-type motif 9

TOP Marker 10807 serologically defined colon SDCCAG3 cancer antigen 3

TOP Marker 92922 Homo sapiens coiled-coil CCDC102A domain containing 102A, mRNA

(cDNA clone MGC: 10992

IMAGE:3637387) , complete cds

TOP Marker 3707 inositol 1, 4, 5-trisphosphate ITPKB

3-kinase B

TOP Marker 51019 coiled-coil domain containing CCDC53

53

TOP Marker 51780 lysine (K)-specific KDM3B demethylase 3B

TOP Marker 26146 TNF receptor-associated TRAF3IP1 factor 3 interacting protein

1

TOP Marker 1410 crystallin, alpha B CRYAB

Marker 3329 heat shock 60kDa protein 1 HSPD1

(chaperonin)

Marker 64946 centromere protein H CENPH

Marker 11237 ring finger protein 24 RNF24

Marker 728621 coiled-coil domain containing CCDC30

30

Marker 7917 Homo sapiens BCL2-associated BAG6 athanogene 6 (BAG6),

transcript variant 5, mRNA Marker 5827 peroxisomal membrane protein PXMP2 2, 22kDa

Marker 79921 transcription elongation TCEAL4 factor A (Sll)-like 4

Marker 80263 tripartite motif-containing TRIM45

45

Marker 23170 tubulin tyrosine ligase-like TTLL12 family, member 12

Marker 26088 golgi associated, gamma GGA1 adaptin ear containing, ARF

BP1

Marker 23743 betaine-homocysteine BHMT2 methyltransferase 2

Marker 55689 YEATS domain containing 2 YEATS2

Marker 6814 syntaxin binding protein 3 STXBP3

Marker 2159 coagulation factor X F10

Marker 28987 NIN1/RPN12 binding protein 1 NOB1 homolog (S. cerevisiae)

Marker 4597 mevalonate (diphospho) MVD

decarboxylase

Marker 2803 golgi autoantigen, golgin GOLGA4 subfamily a, 4

Marker 23268 dynamin binding protein DNMBP

Marker 6730 signal recognition particle SRP68

68kDa

Marker 140465 myosin, light chain 6B, MYL6B alkali, smooth muscle and

non-muscle

Marker 5912 RAP2B, member of RAS oncogene RAP2B family

Marker 784 calcium channel, voltage- CACNB3 dependent, beta 3 subunit

Marker 79791 F-box protein 31 FBX031

Marker 10180 RNA binding motif protein 6 RBM6

Marker 2173 fatty acid binding protein 7, FABP7 brain

Marker 6426 splicing factor, SFRS1 arginine/ serine-rich 1

Marker 6429 splicing factor, SFRS4 arginine/ serine-rich 4

Marker 7316 ubiguitin C UBC

Marker 5590 protein kinase C, zeta PRKCZ

Marker 1155 tubulin folding cofactor B TBCB

Marker 27445 piccolo (presynaptic PCLO cytomatrix protein)

Marker 60673 chromosome 12 open reading C12orf44 frame 44

Marker 6612 SMT3 suppressor of mif two 3 SUM03 homolog 3 (S. cerevisiae)

Marker 10969 EBNA1 binding protein 2 EBNA1BP2 Marker 51093 chromosome 1 open reading Clorf66 frame 66

Marker 7448 vitronect in VTN

Marker 6830 suppressor of Ty 6 homolog SUPT6H

(S. cerevisiae)

Marker 51367 processing of precursor 5, POP5 ribonuclease P/MRP subunit

(S. cerevisiae)

Marker 10483 Sec23 homolog B (S. SEC23B cerevisiae)

Marker 11332 acyl-CoA thioesterase 7 ACOT7

Marker 6949 Treacher Collins- TCOF1

Franceschetti syndrome 1

Marker 9131 apoptosis-inducing factor, AIFM1 mitochondrion-associated, 1

Marker 2040 stomatin STOM

Marker 8636 Sjogren syndrome nuclear SSNA1 autoantigen 1

Marker 5223 phosphoglycerate mutase 1 PGAM1

(brain)

Marker 2197 Finkel-Biskis-Reilly murine FAU

sarcoma virus (FBR-MuSV)

ubiguitously expressed

Marker 4591 tripartite motif-containing TRIM37

37

Marker 6903 tubulin folding cofactor C TBCC

SERPINEl mRNA binding protein

Marker 26135 1 SERBP1

Marker 3728 junction plakoglobin JUP

family with seguence

Marker 283991 similarity 100, member B FAM100B

Marker 124930 ankyrin repeat domain 13B ANKRD13B

Homo sapiens protein

phosphatase 1, regulatory

subunit 10 (PPP1R10),

Marker 5514 transcript variant 1, mRNA PPP1R10

Homo sapiens 6- phosphogluconolactonase

Marker 25796 (PGLS), mRNA PGLS

Marker 83933 histone deacetylase 10 HDAC10

SAP domain containing

Marker 84324 ribonucleoprotein SARNP

CCAAT/enhancer binding

Marker 1051 protein (C/EBP), beta CEBPB eomesodermin homolog (Xenopus

Marker 8320 laevis ) EOMES dual specificity phosphatase

Marker 1844 2 DUSP2

Marker 7276 transthyretin TTR

protein arginine

Marker 55170 methyltransferase 6 PRMT6 341 ubiquitin specific peptidase

Marker 57646 28 USP28

342 SH3 domain binding glutamic

M3rker 6451 acid-rich protein like SH3BGRL

343 hexaribonucleotide binding

Marker 146713 protein 3 hCG_1776007

344 CD2 (cytoplasmic tail)

Marker 10421 binding protein 2 CD2BP2

345

Marker 1949 ephrin-B3 EFNB3

346 glioblastoma amplified

Marker 2631 sequence GBAS

347

Marker 9440 mediator complex subunit 17 MED17

348 interferon stimulated

Marker 81875 exonuclease gene 20kDa-like 2 ISG20L2

349 ubiquitin-conjugating enzyme

Marker 140739 E2F (putative) UBE2F

350 baculoviral IAP repeat-

Marker 329 containing 2 BIRC2

351 transcription elongation

Marker 85012 factor A (Sll)-like 3 TCEAL3

352 ADP-ribosylation-like factor

Marker 51329 6 interacting protein 4 ARL6IP4

353

Marker 5174 PDZ domain containing 1 PDZK1

354

Marker 79637 armadillo repeat containing 7 ARMC7

355 naked cuticle homolog 1

Marker 85407 (Drosophila) NKD1

356 amyloid beta (A4) precursor

protein-binding, family B,

Marker 54518 member 1 interacting protein APBB1IP

357 advanced glycosylation end

Marker 177 product-specific receptor AGER

358

Marker 7561 zinc finger protein 14 ZNF14

359 chromodomain protein, Y-like

Marker 124359 2 CDYL2

Table 2: Markers for NMO identified by statistical analysis of MFI data from NMO vs. Healthy. The sequence of the markers can be obtained from the enclosed sequence

listening .

SEQ ID Marker Gene No . Classification GenelD Gene name Symbol

45 ankyrin repeat and BTB (POZ)

TOP Marker 25841 domain containing 2 ABTB2

46 brain-derived neurotrophic

TOP Marker 627 factor BDNF SR-related CTD-associated

TOP Marker 58506 factor 1 SCAF1 replication factor C

TOP Marker 5982 (activator 1) 2, 40kDa RFC2 laminin, gamma 1 (formerly

TOP Marker 3915 LAMB2 ) LAMC1

TOP Marker 324 adenomatous polyposis coli APC

poliovirus receptor-related 2

TOP Marker 5819 (herpesvirus entry mediator B) PVRL2

REX1, RNA exonuclease 1

TOP Marker 57455 homolog (S. cerevisiae) REXOl

EMGl nucleolar protein homolog

TOP Marker 10436 (S. cerevisiae) EMGl

DDB1 and CUL4 associated

TOP Marker 55827 factor 6 DCAF6 pyrroline-5-carboxylate

TOP Marker 5831 reductase 1 PYCR1

TOP Marker 10445 microspherule protein 1 MCRS1

TOP Marker 128866 chromatin modifying protein 4B CHMP4B heat shock protein 90kDa alpha

TOP Marker 3320 (cytosolic) , class A member 1 HSP90AA1

TOP Marker 23608 makorin ring finger protein 1 MKRN1 pro-melanin-concentrating

TOP Marker 5370 hormone-like 2, pseudogene PMCHL2

TOP Marker 11054 opioid growth factor receptor OGFR

TOP Marker - OOF5370 -

TOP Marker - OOF11054 - chromosome 20 open reading

Marker 57136 frame 3 C20orf3

HECT, UBA and WWE domain

Marker 10075 containing 1 HUWE1

Homo sapiens cDNA clone

Marker - IMAGE : 30377818 5 ' mRNA seguence - neurofilament, heavy

Marker 4744 polypeptide NEFH triple functional domain

Marker 7204 (PTPRF interacting) TRIO Marker 2935 Gl to S phase transition 1 GSPT1 tubulointerstitial nephritis

Marker 64129 antigen-like 1 TINAGL1

Marker 10539 glutaredoxin 3 GLRX3 mitogen-activated protein

Marker 5595 kinase 3 MAPK3

Rho GTPase activating protein

Marker 80728 39 ARHGAP39

Marker 1270 ciliary neurotrophic factor CNTF membrane protein,

Marker 4354 palmitoylated 1, 55kDa MPP1

Marker 23114 neurofascin NFASC

Rho guanine nucleotide

Marker 8874 exchange factor (GEF) 7 ARHGEF7

UPF3 regulator of nonsense

Marker 65109 transcripts homolog B (yeast) UPF3B

Marker 7316 ubiguitin C UBC

RAB3A, member RAS oncogene

Marker 5864 family RAB3A

Marker 79582 sperm associated antigen 16 SPAG16 erythrocyte membrane protein

Marker 2037 band 4.1-like 2 EPB41L2

Homo sapiens REST corepressor

Marker 283248 2 (RCOR2), mRNA RCOR2

Marker - OOF5864 -

Marker - OOF79582 -

Marker - OOF2037 -

Marker - OOF283248 - chaperonin containing TCP1,

Top Marker 10576 subunit 2 (beta) CCT2

Top Marker 794 calbindin 2 CALB2

Top Marker 90592 zinc finger protein 700 ZNF700

Top Marker 90075 zinc finger protein 30 ZNF30

Top Marker 55552 zinc finger protein 823 ZNF823

Top Marker 80184 centrosomal protein 290kDa CEP290

Top Marker 311 annexin All ANXA11 armadillo repeat gene deletes

Top Marker 421 in velocardiofacial syndrome ARVCF Top Marker 9124 PDZ and LIM domain 1 PDLIM1 chromobox homolog 5 (HP1 alpha

Top Marker 23468 homolog, Drosophila) CBX5 minichromosome maintenance

Top Marker 4173 complex component 4 MCM4 spectrin, beta, non-

Top Marker 6711 erythrocytic 1 SPTBN1

Top Marker 89953 kinesin light chain 4 KLC4 retinol dehydrogenase 16 (all-

Top Marker 8608 trans ) RDH16 inositol polyphosphate-5-

Top Marker 3632 phosphatase, 40kDa INPP5A

V-set and immunoglobulin

Top Marker 11326 domain containing 4 VSIG4

Marker 55082 arginine and glutamate rich 1 ARGLU1 nuclear factor of kappa light

polypeptide gene enhancer in

Marker 4796 B-cells inhibitor-like 2 NFKBIL2

Marker 23521 ribosomal protein L13a RPL13A alpha-2-glycoprotein 1, zinc-

Marker 563 binding AZGP1 family with seguence

Marker 11170 similarity 107, member A FAM107A

Marker 84622 zinc finger protein 594 ZNF594

PC 4 and SFRS1 interacting

Marker 11168 protein 1 PSIP1

Marker 203068 tubulin, beta TUBB

Homo sapiens NHS-like 1

(NHSL1), transcript variant 1,

Marker 57224 mRNA NHSL1 glucosamine-phosphate N-

Marker 64841 acetyltransferase 1 GNPNAT1

Marker 6138 ribosomal protein L15 RPL15 chromosome 5 open reading

Marker 9315 frame 13 C5orf13 mitochondrial ribosomal

Marker 84311 protein L45 MRPL45 immunoglobulin heavy constant

Marker 3507 mu IGHM immunoglobulin heavy variable

Marker 28396 4-31 IGHV4-31

NLR family, pyrin domain

Marker 126206 containing 5 NLRP5

Marker 10524 K(lysine) acetyltransferase 5 KAT5 methylmalonic aciduria

(cobalamin deficiency) cblB

Marker 326625 type MMAB

Marker 23636 nucleoporin 62kDa NUP62 fermitin family homolog 3

Marker 83706 (Drosophila) FERMT3 Marker 7390 uroporphyrinogen III synthase UROS ecto-NOX disulfide-thiol

Marker 55068 exchanger 1 ENOX1 ankyrin repeat and SOCS box-

Marker 140459 containing 6 ASB6 ga1actose-1-phosphate

Marker 2592 uridylyltransferase GALT radial spoke head 9 homolog

Marker 221421 (Chlamydomonas ) RSPH9 polypyrimidine tract binding

Marker 5725 protein 1 PTBP1

Marker 84062 dystrobrevin binding protein 1 DTNBP1

Marker 27101 calcyclin binding protein CACYBP cytoskeleton-associated

Marker 10970 protein 4 CKAP4

Marker 9326 zinc finger, HIT type 3 ZNHIT3 adaptor-related protein

Marker 8943 complex 3, delta 1 subunit AP3D1 coagulation factor XII

Marker 2161 (Hageman factor) F12

Marker 50626 cysteine/histidine-rich 1 CYHR1

RNA binding protein,

autoantigenic (hnRNP- associated with lethal yellow

Marker 22913 homolog (mouse)) RALY

TBC1 domain family, member 9B

Marker 23061 (with GRAM domain) TBC1D9B signal-induced proliferation-

Marker 6494 associated 1 SIPA1 family with seguence

Marker 56975 similarity 20, member C FAM20C

Marker 6667 Spl transcription factor SP1

Src homology 2 domain

Marker 6461 containing adaptor protein B SHB

Marker 118 adducin 1 (alpha) ADD1 tumor suppressing

Marker 10078 subtransferable candidate 4 TSSC4

Marker 4287 ataxin 3 ATXN3 casein kinase 2, beta

Marker 1460 polypeptide CSNK2B polymerase (DNA directed),

Marker 5428 gamma POLG metastasis associated 1

Marker 9219 family, member 2 MTA2 golgi reassembly stacking

Marker 64689 protein 1, 65kDa GORASPl zinc finger protein 14 homolog

Marker 57677 (mouse ) ZFP14

Marker 283899 INO80 complex subunit E INO80E

Marker 8565 tyrosyl-tRNA synthetase YARS mitochondrial ribosomal

Marker 65993 protein S34 MRPS34

RNA binding motif, single

Marker 5937 stranded interacting protein 1 RBMS1

Marker 29094 galectin-related protein HSPC159

Marker 3642 insulinoma-associated 1 INSM1

Marker 7568 zinc finger protein 20 ZNF20 mitochondrial ribosomal

Marker 65003 protein Lll MRPL11 immunoglobulin heavy constant

Marker 3503 gamma 4 (G4m marker) IGHG4

Homo sapiens cysteinyl-tRNA

synthetase (CARS), transcript

Marker 833 variant 2, mRNA CARS

Marker 4638 myosin light chain kinase MYLK

ClpX caseinolytic peptidase X

Marker 10845 homolog (E. coli) CLPX

DnaJ (Hsp40) homolog,

Marker 5611 subfamily C, member 3 DNAJC3

Marker 5917 arginyl-tRNA synthetase RARS

Marker 147837 zinc finger protein 563 ZNF563 protein phosphatase 2,

regulatory subunit B', alpha

Marker 5525 isoform PPP2R5A cytochrome P450, family 2,

Marker 1562 subfamily C, polypeptide 18 CYP2C18 cytoplasmic linker associated

Marker 23122 protein 2 CLASP2

DIP2 disco-interacting protein

Marker 22982 2 homolog C (Drosophila) DIP2C elastin microfibril interfacer

Marker 11117 1 EMILIN1

Marker 283373 ankyrin repeat domain 52 ANKRD52 pleckstrin homology-like

Marker 90102 domain, family B, member 2 PHLDB2

Marker 84527 zinc finger protein 559 ZNF559

Marker 338440 anoctamin 9 AN09

Rac/Cdc42 guanine nucleotide

Marker 9459 exchange factor (GEF) 6 ARHGEF6 runt-related transcription

Marker 864 factor 3 RUNX3

FXYD domain containing ion

Marker 53827 transport regulator 5 FXYD5 family with seguence

Marker 165215 similarity 171, member B FAM171B

Marker 2810 stratifin SFN

chromosome 6 open reading

Marker 135398 frame 141 C6orf141 mitochondrial ribosomal

Marker 64981 protein L34 MRPL34 heterogeneous nuclear

Marker 3183 ribonucleoprotein C (C1/C2) HNRNPC

Homo sapiens ribosomal protein

Marker 6125 L5 ( RPL5 ) , mRNA RPL5

Homo sapiens chemokine (C-X-C motif) ligand 1 (melanoma

growth stimulating activity, alpha) (CXCL1), transcript

Marker 2919 variant 1, mRNA CXCL1

Homo sapiens small nuclear

ribonucleoprotein D3

polypeptide 18kDa ( SNRPD3 ) ,

Marker 6634 transcript variant 1, mRNA SNRPD3 transcription elongation

Top Marker 85012 factor A (Sll)-like 3 TCEAL3

Marker 2159 coagulation factor X F10

Marker 7561 zinc finger protein 14 ZNF14

Marker 7448 vitronectin VTN

SERPINEl mRNA binding protein

Marker 26135 1 SERBP1

Marker 3728 junction plakoglobin JUP advanced glycosylation end

Marker 177 product-specific receptor AGER

Univariate Analysis:

Table 3.1 NMO vs. healthy controls

Scoring and identification of univariate TOP 30 candidates based on score and p-value ranking .

Table 3.2 NMO vs. MS

Scoring and identification of univariate TOP 30 candidates based on score and p-value ranking .

Table 3.3 NMO vs. healthy controls

"Edge candidates" resulting from Volcano plot, fold-change at least 2 in either direction and p-value <0.05.

Table 3.4 NMO vs . MS

"Edge candidates" resulting from Volcano plot, fold-change at least 2 in either direction and p-value <0.05.

Multivariate Analysis:

Table 4.1 NMO vs. healthy controls

Antigens with freq >/= 100 or (freq >/=80 and median rank </= 16) obtained from a ranking list and panel definition according to PPLS-DA TOP 30 (200 runs) based on all antigens.

Table 4.2 NMO vs healthy controls

Ranking list of PPLS-DA results based on antigens with freq >/= 100 or (freq >/= 80 and median rank </= 16) obtained from a ranking list and panel definition according to PPLS- DA TOP 30 (200 runs) based on all antigens.

Table 4.3. NMO vs. healthy controls

Antigens from ranking list and panel definition according to PPLS-DA TOP 30 (200 runs) based on all antigens without AQP-4 and with freq >/= 100 or (freq >/= 80 and median rank

</= 16) .

ProteinID GenelD Gene.Name freq median rank

Table 4.4 NMO vs. healthy controls

Ranking list of PPLS-DA results (TOP 30 antigens based on 200 runs) based on antigens without AQP-4 and with freq > / = 100 or (freq >/= 80 and median rank </= 16) .

Table 4.5 NMO vs MS

Ranking list and panel definition according to PPLS-DA TOP 30 (200 runs) based on all antigens (NMO vs. MS) . Data is shown only for antigens with freq >/= 100 or (freq >/= and median rank </= 16) .

Table 4.6 NMO vs MS

Ranking list of PPLS-DA results based on antigens with freq >/= 100 or (freq >/= 80 and median rank </= 16) . This ranking was obtained from a ranking list and panel definition according to PPLS-DA TOP 30 (200 runs) based on all antigens.

Table 4.7 NMO vs MS

Antigens (without AQP-4) with freq >/= 100 or (freq >/= 80 and median rank </= 16. This data was obtained from a ranking list and panel definition according to PPLS-DA TOP 30 (200 runs) based on all antigens without AQP-4.

Table 4.8 NMO vs MS

Ranking list of PPLS-DA results based on antigens (without AQP-4) with freq >/= 100 or (freq >/= 80 and median rank </= 16) . The data was obtained from ranking list and panel definition according to PPLS-DA TOP 30 (200 runs) based on all antigens without AQP-4.

Combined Analysis Results:

Table 5.1 NMO vs healthy controls

Overlap of panels from univariate (Ranking based on scoring and "edge candidates" resulting from volcano plot) and multivariate analysis (Ranking based on PPLS-DA) AQP-4.

Table 5.2 NMO vs. MS

Overlap of panels from univariate (Ranking based on scoring and "edge candidates" resulting from volcano plot) and multivariate analysis (Ranking based on PPLS-DA) w AQP-4.

Table 5.3 NMO vs. healthy controls

Overlap of panels from univariate (Ranking based on scoring and "edge candidates" resulting from volcano plot) and multivariate analysis (Ranking based on PPLS-DA) AQP-4.

Table 5.4 NMO vs . MS

Overlap of panels from univariate (Ranking based on scoring and "edge candidates" resulting from volcano plot) and multivariate analysis (Ranking based on PPLS-DA) without AQP-4.

Table 5.5 NMO vs. healthy controls

Union of panels from univariate and multivariate analysis.

Table 5.6 NMO vs. MS

Union of panels from univariate and multivariate analysis.

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