A method for classifying a subject as high-risk for neurological or psychiatric disorder

Abstract

In the present invention provided is a method for classifying a subject as a high-risk for neurological or psychiatric disorder subject. The method is particularly useful when the neurological or psychiatric disorder is selected from the group containing schizophrenia, psychosis, mood disorder, depression (such as major depressive disorder), bipolar disorder, Alzheimer's disease, Parkinson disorder, and cognitive impairment. The present invention further relates to a biomarker kit comprising reagents for determining expression level of biomarkers for extracellular vesicles, brain-derived extracellular vesicles and biomarkers for mitochondrial and redox impairment, N-methyl-D-aspartate receptor pathway, and other pathways related to central nervous system disorders.

Description

[0001] A method for classifying a subject as high-risk for neurological or psychiatric disorder

[0002] Field of the invention

[0003] Present invention provides a method for classifying a subject as a high-risk for neurological or psychiatric disorder subject. The method is particularly useful when the neurological or psychiatric disorder is selected from the group comprising schizophrenia, psychosis, mood disorder, depression (such as major depressive disorder), bipolar disorder, Alzheimer's disease, Parkinson disorder, and cognitive impairment. The present invention further relates to a biomarker kit comprising reagents for determining expression level of biomarkers for extracellular vesicles, brain-derived extracellular vesicles and biomarkers for mitochondrial and redox impairment, N-methyl-D-aspartate receptor pathway, and other pathways related to central nervous system disorders.

[0004] Background of the invention

[0005] Neurological and psychiatric disorders, such as Alzheimer's disease, Parkinson's disease, depression, and schizophrenia, present significant challenges in diagnostics due to their complex and often overlapping symptoms. Early detection is critical, yet current diagnostic methods largely rely on subjective assessments, behavioral observations, invasive procedures like biopsies or expensive imaging techniques. The lack of definitive biomarkers and the brain's complexity make it difficult to accurately diagnose these conditions, often leading to delayed treatment or misdiagnosis. Advances in molecular diagnostics, such as the study of brain-derived extracellular vesicles, hold promise for more precise and non-invasive diagnostics of neurological and psychiatric disorders.

[0006] Currently, diagnosis and stratification of patients into low and high risk-patients requires a thorough psychological evaluation and a comprehensive medical exam to rule out other conditions, whereby the clinical high-risk period is a phase denoting a risk for overt psychosis. However, no single symptom is characteristic of the disease and / or of the disease state. The classification as high-risk psychotic disorder subject, for example schizophrenia, involves individual evaluation of the severity of the characteristic symptoms and is based on the patient’s self-reported experiences and observed behavior. The schizophrenia subjects are believed to be heterogeneous. The pathophysiology of schizophrenia remains unclear and there are no laboratory tests or biomarkers used as direct diagnostic and / or prognostic and / or stratification tool at present. Thus, there is a clear need to develop a laboratory-based test for classification of psychotic disorder subjects, which would be independent of often subjective clinical criteria. Indeed, mechanism-based molecular biomarkers, preferably biomarkers that can be obtained from peripheral blood samples, which would be evident for specific behavioral and cognitive alterations, are needed for classification of psychosis subjects, including schizophrenia subjects, in view of treatments and prevention.

[0007] Extracellular vesicles (EVs) are considered now to be crucial mediators of intercellular communication and potential biomarkers for various diseases, including neurological and psychiatric disorders. Brain- derived extracellular vesicles (EVBBM) offer a unique opportunity to non-invasively assess central nervous system conditions through peripheral measurements. Yet, the means and methods for detecting and quantifying EVBBM and their contents, including proteins, miRNAs, and other biomolecules, in particular for the classification / stratification of neurological and psychiatric disorder subjects, are still missing.

[0008] Document WO 2021 / 123428 discloses certain method for classifying a subject as a high risk schizophrenia subject.

[0009] Summary of the invention

[0010] Particularly desirable is an ability to classify a subject as high-risk or low-risk neurological or psychiatric disorder subject based on analysis of a single biological sample from the subject.

[0011] Accordingly, it was an objective technical problem of the present invention to provide means and methods for early detection of neurological or psychiatric disorder. In particular, it was an object of the present invention to provide means and methods for classifying a subject as a high-risk for neurological or psychiatric disorder subject.

[0012] The objective technical problem is solved by the embodiments described herein and as characterized by the claims.

[0013] The present invention allows for the targeted analysis of specific biological pathways associated with various neurological and psychiatric disorders, including but not limited to schizophrenia, psychosis, mood disorders, major depression, bipolar disorder, Alzheimer's disease, Parkinson disorder and cognitive impairment. By utilizing a combination of universal EV markers, brain-specific EV markers, and pathwayspecific biomarkers, the kit of the invention provides a comprehensive tool for disease stage-mapping, early detection, and monitoring of disease progression.

[0014] The invention further involves a method for sample preparation, EV isolation, biomarker quantification, and data analysis, as well as validation techniques using high-resolution confocal microscopy. This innovative approach offers potential applications in personalized medicine, drug target validation, and the development of novel therapeutics for central nervous system disorders.

[0015] Furthermore, the present inventors have demonstrated that with a panel of biomarkers for the detection of extracellular vesicles, brain-derived extracellular vesicles, mitochondrial and redox impairment and N- methyl-D-aspartate receptor pathway, it is possible to discriminate with high accuracy between clinical low-risk and high-risk schizophrenia, Alzheimer’s disease and cognitive impairment. These findings will allow individualized prognosis and stratification strategies, which has been up to date an unmet need in early detection and intervention for these diseases. Accordingly, the use of biomarkers identified by the present inventors allows generating individualized risk calculators that can be used in future clinical trials for treatment intervention and halt the progression of adverse outcomes related to schizophrenia, Alzheimer’s disease or cognitive impairment.

[0016] The present invention is summarized in the following embodiments.

[0017] In a first embodiment, the present invention provides an ex vivo method for classifying a subject as neurological or psychiatric disorder subject, the method comprising the steps of: a) providing a biological sample obtained from a subject and comprising extracellular vesicles, including brain-derived extracellular vesicles; b) determining in the sample of (a) the fraction of brain-derived extracellular vesicles within the extracellular vesicles; c) determining in the sample of (a) the expression level of at least one biomarker selected from:

[0018] (d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, SWOB and TIMPs, preferably selected from the group comprising miR-34a, miR-9 and miR-132,

[0019] (c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81 , NCAM1 , GLAST1 and L1CAM, preferably miR-137,

[0020] (c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941 , EAAT1 , EAAT2, EAAT3 and SHMTs, preferably selected from miR- 132 and miR-941 , (c4) myelination pathway biomarkers selected from the group comprising miR-9, oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs) and Fyn Kinase, preferably miR-9

[0021] (c5) excitatory-inhibitory balance pathway biomarkers selected from the group comprising miR-137, miR-138, PV, PNN, GAD65, VGAT, VGLUTs and PSD, preferably selected from miR-137 and miR-138; and

[0022] (d) classifying a subject as neurological or psychiatric disorder subject based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles determined in step (b) and the expression level determined in step (c).

[0023] In a second embodiment, the present invention provides a biomarker kit comprising:

[0024] (i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63;

[0025] (ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM119, preferably selected from L1CAM and NCAM1 ; and

[0026] (iii) reagents for determining the expression level of at least one biomarker selected from:

[0027] (d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, S100B and TIMPs,

[0028] (c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81, NCAM1, GLAST1 and L1CAM,

[0029] (c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941 , EAAT1, EAAT2, EAAT3 and SHMTs,

[0030] (c4) myelination pathway biomarkers selected from the group comprising miR-9, oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs) and Fyn Kinase,

[0031] (c5) excitatory-inhibitory balance pathway biomarkers selected from the group comprising miR-137, miR-138, PV, PNN, GAD65, VGAT, VGLUTs and PSD.

[0032] Brief description of figures

[0033] The invention is further illustrated in the following figures. It is to be understood that these serve merely illustrative purposes and are not to be construed as limiting.

[0034] Figure 1 shows mitochondrial marker expression in a mouse brain sample via confocal microscopy.

[0035] Figure 2 shows Model Performance. (A) Discrimination performance (measured by C-index) across fitted models incorporating miRNAs in CHR (CHR-T v CHR-NT) and general population (CHR-T v controls) samples in internal (EU-GEI) and external validation (NAPLS-3). (B-C) Decision curve analysis plot for models in B CHR-T v CHR-NT and C CHR-T v controls. The plot reports net benefit (y axis) of across a range of risk thresholds (x axis) compared with intervening in all individuals or in none using miRNA models in internal (EU-GEI) and external validation (NAPLS-3) datasets.

[0036] Figure 3 shows univariate group comparisons for miRNAs. Exosomal level of miR-9, miR-34a, miR-132, miR-137, and miR-941 in CHR-NT, CHR-T and controls across EU-GEI and NAPLS-3. Significant differences between groups are indicated by p < 0-05 (*); p<0 01 (**) and p < 0-001 (***). CHR-T individuals show distinct patterns of miRNA expression compared to CHR-NT and control groups.

[0037] Figure 4 shows an example of a diagnostic kit and obtained results.

[0038] Definitions

[0039] All publications, patent applications, patents, and other references mentioned herein are incorporated by reference in their entirety. The publications and applications discussed herein are provided solely for their disclosure prior to the filing date of the present application. Nothing herein is to be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. In addition, the materials, methods, and examples are illustrative only and are not intended to be limiting.

[0040] In the case of conflict, the present specification, including definitions, will control. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as is commonly understood by one of skill in art to which the subject matter herein belongs. As used herein, the following definitions are supplied in order to facilitate the understanding of the present invention.

[0041] The term “comprise” is generally used in the sense of include, permitting the presence of one or more features or components. Also as used in the specification and claims, the language "comprising" can include analogous embodiments described in terms of "consisting of “ and / or "consisting essentially of”. The terms "including," "comprising," or "having," and variations thereof, is meant to encompass the elements listed thereafter and equivalents thereof as well as additional elements. Embodiments recited as "including", "comprising" or "having" certain elements are also contemplated as "consisting essentially of' and "consisting of" those certain elements.

[0042] As used herein, the singular form "a", "an" and "the" include plural references unless the context clearly dictates otherwise. As used herein, the term "and / or" used in a phrase such as "A and / or B" herein is intended to include "A and B", "A or B", "A", and "B".

[0043] As used herein, "at least one" means "one or more", "two or more", "three or more", etc.

[0044] Unless indicated explicitly to the contrary, the following definitions apply throughout the present description. It is to be understood that in the RNA sequences referred to herein each U has been replaced by T.

[0045] As understood herein, miRNA-137 is preferably such that its precursor for DICER processing comprises an RNA sequence according to SEQ ID NO.: 1 :

[0046] GGTCCTCTGACTCTCTTCGGTGACGGGTATTCTTGGGTGGATAATACGGATTACGTTGTTATTGCTT AAGAATACGCGTAGTCGAGGAGAGTACCAGCGGCA

[0047] As it is further recognizable to the skilled person, further information on miRNA-137 can be found under following database codes: HGNC: 31523, NCBI Gene: 406928, Ensembl: ENSG00000284202, miRBase: hsa-mir-137, miRNA137.

[0048] As understood herein, miRNA-132 is preferably such that its precursor for DICER processing comprises an RNA sequence according to SEQ ID NO.: 2:

[0049] CCGCCCCCGCGTCTCCAGGGCAACCGTGGCTTTCGATTGTTACTGTGGGAACTGGAGGTAACAGT CTACAGCCATGGTCGCCCCGCAGCACGCCCACGCGC

[0050] As it is further recognizable to the skilled person, further information on miRNA-132 can be found under following database codes: HGNC: 31516, NCBI gene: 406921 , Ensembl: ENSG00000267200.1, miRBase: hsa-mir-132.

[0051] As understood herein, miRNA-34a is preferably such that its precursor for DICER processing comprises an RNA sequence according to SEQ ID NO.: 3:

[0052] GGCCAGCTGTGAGTGTTTCTTTGGCAGTGTCTTAGCTGGTTGTTGTGAGCAATAGTAAGGAAGCAA TCAGCAAGTATACTGCCCTAGAAGTGCTGCACGTTGTGGGGCCC As it is further recognizable to the skilled person, further information on miRNA-34 can be found under following database codes: HGNC: 31635, NCBI Gene: 407040, Ensembl: ENSG00000284357, miRBase: hsa-mir-34a, miRNA34A.

[0053] As understood herein, miRNA-9 is such that its precursor for DICER processing comprises an RNA sequence according to SEQ ID NO.: 4: GCGCAGTGTATGGGGTTATTTTTACTTTCGGTTATCTAGCTTTATGAAGACTCCACACCACTCATAC AGCTAGATAACCAAAGATAACAACCAACCCCGCCTCCTGGC

[0054] As it is further recognizable to the skilled person, further information on miRNA-9 can be found under following database codes: HGNC: 31641, NCBI Gene: 407046, Ensembl: ENSG00000207933, miRBase: hsa-mir-9-1 , miRNA9-1.

[0055] As it is further understood herein, miRNA-138 is preferably such that its precursor for DICER processing comprises an RNA sequence according to SEQ ID NO.: 5:

[0056] CCCTGGCATGGTGTGGTGGGGCAGCTGGTGTTGTGAATCAGGCCGTTGCCAATCAGAGAACGGCT ACTTCACAACACCAGGGCCACACCACACTACAGG

[0057] As it is further recognizable to the skilled person, further information on miRNA-138 can be found under following database codes: HGNC: 31524, NCBI Gene: 406929, Ensembl: ENSG00000207954, miRBase: hsa-mir-138-1 , miRNA138-1.

[0058] As it is further understood herein, miRNA-941 is preferably such that its precursor for DICER processing comprises an RNA sequence according to SEQ ID NO.: 41 :

[0059] TGTGCACATGTGCCCAGGGCCCGGGACAGCGCCACGGAAGAGGACGCACCCGGCTGTGTGCACA TGTGCCCA

[0060] As it is further recognizable to the skilled person, further information on miRNA-941 can be found under following database codes: NCBI_Gene:100126330, ENSEMBL:ENSG00000216195, miRBase: hsa-mir- 941-4, miRNA941-4.

[0061] The protein biomarkers referred to in the present description are defined in the following. As understood herein, COX6A2 is an important mitochondrial marker. It stands for cytochrome c oxidase subunit Via polypeptide 2, and it is a subunit of cytochrome c oxidase (COX), a multisubunit enzyme complex that couples the transfer of electrons from cytochrome c to molecular oxygen, and plays a role in creating a proton electrochemical gradient across the inner mitochondrial membrane, which in turns contributes to the ATP synthesis. COX is a terminal enzyme of the mitochondrial respiratory chain.

[0062] Preferably, COX6A2 is characterized by amino acid sequence according to SEQ ID NO.: 6: MALPLRPLTRGLASAAKGGHGGAGARTWRLLTFVLALPSVALCTFNSYLHSGHRPRPEFRPYQHLRIR TKPYPWGDGNHTLFHNSHVNPLPTGYEHP

[0063] As understood herein, ALIX (ALG-2-interacting protein X), also known as programmed cell death 6- interacting protein (PDCD6IP) is a scaffolding protein involved in various cellular processes, including membrane trafficking, exosome biogenesis, apoptosis, and autophagy. Alix has gained attention as a biomarker due to its role in maintaining cellular homeostasis, particularly in the nervous system.

[0064] Preferably, Alix is characterized by amino acid sequence according to SEQ ID NO.: 7:

[0065] MATFISVQLKKTSEVDLAKPLVKFIQQTYPSGGEEQAQYCRAAEELSKLRRAAVGRPLDKHEGALETLL RYYDQICSIEPKFPFSENQICLTFTWKDAFDKGSLFGGSVKLALASLGYEKSCVLFNCAALASQIAAEQN LDNDEGLKIAAKHYQFASGAFLHIKETVLSALSREPTVDISPDTVGTLSLIMLAQAQEVFFLKATRDKMKD AIIAKLANQAADYFGDAFKQCQYKDTLPKEVFPVLAAKHCIMQANAEYHQSILAKQQKKFGEEIARLQHA AELIKTVASRYDEYVNVKDFSDKINRALAAAKKDNDFIYHDRVPDLKDLDPIGKATLVKSTPVNVPISQKF TDLFEKMVPVSVQQSLAAYNQRKADLVNRSIAQMREATTLANGVLASLNLPAAIEDVSGDTVPQSILTKS RSVIEQGGIQTVDQLIKELPELLQRNREILDESLRLLDEEEATDNDLRAKFKERWQRTPSNELYKPLRAE GTNFRTVLDKAVQADGQVKECYQSHRDTIVLLCKPEPELNAAIPSANPAKTMQGSEWNVLKSLLSNLD EVKKEREGLENDLKSVNFDMTSKFLTALAQDGVINEEALSVTELDRVYGGLTTKVQESLKKQEGLLKNI QVSHQEFSKMKQSNNEANLREEVLKNLATAYDNFVELVANLKEGTKFYNELTEILVRFQNKCSDIVFAR KTERDELLKDLQQSIAREPSAPSIPTPAYQSSPAGGHAPTPPTPAPRTMPPTKPQPPARPPPPVLPANR APSATAPSPVGAGTAAPAPSQTPGSAPPPQAQGPPYPTYPGYPGYCQMPMPMGYNPYAYGQYNMPY PPVYHQSPGQAPYPGPQQPSYPFPQPPQQSYYPQQ

[0066] As understood herein, CD81 is a tetraspanin protein involved in various cellular functions, including cell signaling, adhesion, and exosome formation. CD81 has a role in neuronal communication and exosome- mediated processes. Preferably, CD81 is characterized by amino acid sequence according to SEQ ID NO.: 8: MGVEGCTKCIKYLLFVFNFVFWLAGGVILGVALWLRHDPQTTNLLYLELGDKPAPNTFYVGIYILIAVGAV MMFVGFLGCYGAIQESQCLLGTFFTCLVILFACEVAAGIWGFVNKDQIAKDVKQFYDQALQQAVVDDDA NNAKAVVKTFHETLDCCGSSTLTALTTSVLKNNLCPSGSNIISNLFKEDCHQKIDDLFSGKLYLIGIAAIW AVIMIFEMILSMVLCCGIRNSSVY

[0067] As understood herein, NCAM1 (Neural Cell Adhesion Molecule 1) is a cell adhesion protein involved in neuronal development, synaptic plasticity, and cell-cell communication. NCAM1 is critical for neural connectivity and brain function.

[0068] Preferably, NCAM1 is characterized by amino acid sequence according to SEQ ID NO.: 9: MLQTKDLIWTLFFLGTAVSLQVDIVPSQGEISVGESKFFLCQVAGDAKDKDISWFSPNGEKLTPNQQRIS WWNDDSSSTLTIYNANIDDAGIYKCWTGEDGSESEATVNVKIFQKLMFKNAPTPQEFREGEDAVIVCD WSSLPPTIIWKHKGRDVILKKDVRFIVLSNNYLQIRGIKKTDEGTYRCEGRILARGEINFKDIQVIVNVPPT IQARQNIVNATANLGQSVTLVCDAEGFPEPTMSWTKDGEQIEQEEDDEKYIFSDDSSQLTIKKVDKNDE AEYICIAENKAGEQDATIHLKVFAKPKITYVENQTAMELEEQVTLTCEASGDPIPSITWRTSTRNISSEEKA SWTRPEKQETLDGHMWRSHARVSSLTLKSIQYTDAGEYICTASNTIGQDSQSMYLEVQYAPKLQGPV AVYTWEGNQVNITCEVFAYPSATISWFRDGQLLPSSNYSNIKIYNTPSASYLEVTPDSENDFGNYNCTA VNRIGQESLEFILVQADTPSSPSIDQVEPYSSTAQVQFDEPEATGGVPILKYKAEWRAVGEEVWHSKWY DAKEASMEGIVTIVGLKPETTYAVRLAALNGKGLGEISAASEFKTQPVQGEPSAPKLEGQMGEDGNSIK VNLIKQDDGGSPIRHYLVRYRALSSEWKPEIRLPSGSDHVMLKSLDWNAEYEVYWAENQQGKSKAAH FVFRTSAQPTAIPANGSPTSGLSTGAIVGILIVIFVLLLVWDITCYFLNKCGLFMCIAVNLCGKAGPGAKG KDMEEGKAAFSKDESKEPIVEVRTEEERTPNHDGGKHTEPNETTPLTEPEKGPVEAKPECQETETKPA PAEVKTVPNDATQTKENESKA

[0069] As understood herein, GLAST1 (Glutamate Aspartate Transporter 1), also known as EAAT1 (Excitatory Amino Acid Transporter 1), is a glutamate transporter primarily expressed in astrocytes, responsible for regulating extracellular levels of the neurotransmitter glutamate in the brain. GLAST1 is crucial for maintaining glutamate homeostasis, which is critical for normal brain function.

[0070] Preferably, GLAST1 is characterized by amino acid sequence according to SEQ ID NO.: 10: MTKSNGEEPKMGGRMERFQQGVRKRTLLAKKKVQNITKEDVKSYLFRNAFVLLTVTAVIVGTILGFTLR PYRMSYREVKYFSFPGELLMRMLQMLVLPLIISSLVTGMAALDSKASGKMGMRAWYYMTTTIIAVVIGIII VIIIHPGKGTKENMHREGKIVRVTAADAFLDLIRNMFPPNLVEACFKQFKTNYEKRSFKVPIQANETLVGA VINNVSEAMETLTRITEELVPVPGSVNGVNALGLWFSMCFGFVIGNMKEQGQALREFFDSLNEAIMRLV AVIMWYAPVGILFLIAGKIVEMEDMGVIGGQLAMYTVTVIVGLLIHAVIVLPLLYFLVTRKNPWVFIGGLLQ ALITALGTSSSSATLPITFKCLEENNGVDKRVTRFVLPVGATINMDGTALYEALAAIFIAQVNNFELNFGQII TISITATAASIGAAGIPQAGLVTMVIVLTSVGLPTDDITLIIAVDWFLDRLRTTTNVLGDSLGAGIVEHLSRH ELKNRDVEMGNSVIEENEMKKPYQLIAQDNETEKPIDSETKM

[0071] As understood herein, EAAT2 (Excitatory Amino Acid Transporter 2), also known as GLT-1 (Glutamate Transporter 1), is a glutamate transporter predominantly found in astrocytes and is the primary transporter responsible for clearing excess glutamate from synaptic clefts in the brain. EAAT2 is critical in regulating glutamate homeostasis and preventing excitotoxicity.

[0072] Preferably, EAAT2 is characterized by amino acid sequence according to SEQ ID NO.: 11 : MASTEGANNMPKQVEVRMHDSHLGSEEPKHRHLGLRLCDKLGKNLLLTLTVFGVILGAVCGGLLRLAS PIHPDVVMLIAFPGDILMRMLKMLILPLIISSLITGLSGLDAKASGRLGTRAMVYYMSTTIIAAVLGVILVLAI HPGNPKLKKQLGPGKKNDEVSSLDAFLDLIRNLFPENLVQACFQQIQTVTKKVLVAPPPDEEANATSAV VSLLNETVTEVPEETKMVIKKGLEFKDGMNVLGLIGFFIAFGIAMGKMGDQAKLMVDFFNILNEIVMKLVI MIMWYSPLGIACLICGKIIAIKDLEWARQLGMYMVTVIIGLIIHGGIFLPLIYFVVTRKNPFSFFAGIFQAWIT ALGTASSAGTLPVTFRCLEENLGIDKRVTRFVLPVGATINMDGTALYEAVAAIFIAQMNGWLDGGQIVTV SLTATLASVGAASIPSAGLVTMLLILTAVGLPTEDISLLVAVDWLLDRMRTSVNVVGDSFGAGIVYHLSKS ELDTIDSQHRVHEDIEMTKTQSIYDDMKNHRESNSNQCVYAAHNSVIVDECKVTLAANGKSADCSVEEE PWKREK

[0073] As understood herein, EAAT3 (Excitatory Amino Acid Transporter 3), also known as SLC1A1 , is a glutamate transporter primarily expressed in neurons rather than astrocytes, where it plays a crucial role in reuptaking glutamate from the synaptic cleft. EAAT3 helps maintaining glutamate homeostasis in the brain and regulates excitatory neurotransmission.

[0074] Preferably, EAAT3 is characterized by amino acid sequence according to SEQ ID NO.: 12: MGKPARKGCEWKRFLKNNWVLLSTVAAWLGITTGVLVREHSNLSTLEKFYFAFPGEILMRMLKLIILPLII SSMITGVAALDSNVSGKIGLRAVVYYFCTTLIAVILGIVLWSIKPGVTQKVGEIARTGSTPEVSTVDAMLD LIRNMFPENLVQACFQQYKTKREEVKPPSDPEMNMTEESFTAVMTTAISKNKTKEYKIVGMYSDGINVL GLIVFCLVFGLVIGKMGEKGQILVDFFNALSDATMKIVQIIMCYMPLGILFLIAGKIIEVEDWEIFRKLGLYM ATVLTGLAIHSIVILPLIYFIWRKNPFRFAMGMAQALLTALMISSSSATLPVTFRCAEENNQVDKRITRFVL PVGATINMDGTALYEAVAAVFIAQLNDLDLGIGQIITISITATSASIGAAGVPQAGLVTMVIVLSAVGLPAED VTLIIAVDWLLDRFRTMVNVLGDAFGTGIVEKLSKKELEQMDVSSEVNIVNPFALESTILDNEDSDTKKSY

[0075] VNGGFAVDKSDTISFTQTSQF

[0076] As understood herein, LCAM1 (Liver Cell Adhesion Molecule 1), also known as CD146 or MUC18, is a cell adhesion molecule primarily involved in cell signaling, migration, and vascular integrity. LCAM1 is involved in maintaining BBB integrity, neural connectivity and vascular function.

[0077] Preferably, LCAM1 is characterized by amino acid sequence according to SEQ ID NO.: 13: MWALRYVWPLLLCSPCLLIQIPEEYEGHHVMEPPVITEQSPRRLWFPTDDISLKCEASGKPEVQFRWT RDGVHFKPKEELGVTVYQSPHSGSFTITGNNSNFAQRFQGIYRCFASNKLGTAMSHEIRLMAEGAPKW PKETVKPVEVEEGESWLPCNPPPSAEPLRIYWMNSKILHIKQDERVTMGQNGNLYFANVLTSDNHSDY ICHAHFPGTRTIIQKEPIDLRVKATNSMIDRKPRLLFPTNSSSHLVALQGQPLVLECIAEGFPTPTIKWLRP SGPMPADRVTYQNHNKTLQLLKVGEEDDGEYRCLAENSLGSARHAYYVTVEAAPYWLHKPQSHLYGP GETARLDCQVQGRPQPEVTWRINGIPVEELAKDQKYRIQRGALILSNVQPSDTMVTQCEARNRHGLLL ANAYIYVVQLPAKILTADNQTYMAVQGSTAYLLCKAFGAPVPSVQWLDEDGTTVLQDERFFPYANGTLG IRDLQANDTGRYFCLAANDQNNVTIMANLKVKDATQITQGPRSTIEKKGSRVTFTCQASFDPSLQPSITW RGDGRDLQELGDSDKYFIEDGRLVIHSLDYSDQGNYSCVASTELDWESRAQLLWGSPGPVPRLVLS DLHLLTQSQVRVSWSPAEDHNAPIEKYDIEFEDKEMAPEKWYSLGKVPGNQTSTTLKLSPYVHYTFRVT AINKYGPGEPSPVSETVVTPEAAPEKNPVDVKGEGNETTNMVITWKPLRWMDWNAPQVQYRVQWRP QGTRGPWQEQIVSDPFLWSNTSTFVPYEIKVQAVNSQGKGPEPQVTIGYSGEDYPQAIPELEGIEILNS SAVLVKWRPVDLAQVKGHLRGYNVTYWREGSQRKHSKRHIHKDHVWPANTTSVILSGLRPYSSYHLE VQAFNGRGSGPASEFTFSTPEGVPGHPEALHLECQSNTSLLLRWQPPLSHNGVLTGYVLSYHPLDEG GKGQLSFNLRDPELRTHNLTDLSPHLRYRFQLQATTKEGPGEAIVREGGTMALSGISDFGNISATAGEN YSWSWVPKEGQCNFRFHILFKALGEEKGGASLSPQYVSYNQSSYTQWDLQPDTDYEIHLFKERMFRH QMAVKTNGTGRVRLPPAGFATEGWFIGFVSAIILLLLVLLILCFIKRSKGGKYSVKDKEDTQVDSEARPM KDETFGEYRSLESDNEEKAFGSSQPSLNGDIKPLGSDDSLADYGGSVDVQFNEDGSFIGQYSGKKEKE AAGGNDSSGATSPINPAVALE

[0078] As understood herein, SHMTs (Serine Hydroxymethyltransferases) are a family of enzymes that play a crucial role in the metabolism of amino acids and the synthesis of one-carbon units. They catalyze the conversion of serine and tetrahydrofolate to glycine and 5,10-methylenetetrahydrofolate, facilitating important biochemical pathways related to folate metabolism and methylation. SHMTs include SHMT1 and SHMT2. Preferably, SHMT1 is characterized by amino acid sequence according to SEQ ID NO.: 14: MTMPVNGAHKDADLWSSHDKMLAQPLKDSDVEVYNIIKKESNRQRVGLEUASENFASRAVLEALGSCL NNKYSEGYPGQRYYGGTEFIDELETLCQKRALQAYKLDPQCWGVNVQPYSGSPANFAVYTALVEPHG RIMGLDLPDGGHLTHGFMTDKKKISATSIFFESMPYKVNPDTGYINYDQLEENARLFHPKLIIAGTSCYSR NLEYARLRKIADENGAYLMADMAHISGLVAAGVVPSPFEHCHWTTTTHKTLRGCRAGMIFYRKGVKSV DPKTGKEILYNLESLINSAVFPGLQGGPHNHAIAGVAVALKQAMTLEFKVYQHQVVANCRALSEALTELG YKIVTGGSDNHULVDLRSKGTDGGRAEKVLEACSIACNKNTCPGDRSALRPSGLRLGTPALTSRGLLEK DFQKVAHFIHRGIELTLQIQSDTGVRATLKEFKERLAGDKYQAAVQALREEVESFASLFPLPGLPDF

[0079] Preferably, SHMT2 is characterized by amino acid sequence according to SEQ ID NO.: 15: MLYFSLFWAARPLQRCGQLVRMAIRAQHSNAAQTQTGEANRGWTGQESLSDSDPEMWELLQREKDR QCRGLELIASENFCSRAALEALGSCLNNKYSEGYPGKRYYGGAEVVDEIELLCQRRALEAFDLDPAQW GVNVQPYSGSPANLAVYTALLQPHDRIMGLDLPDGGHLTHGYMSDVKRISATSIFFESMPYKLNPKTGLI DYNQLALTARLFRPRLIIAGTSAYARLIDYARMREVCDEVKAHLLADMAHISGLVAAKVIPSPFKHADIVTT TTHKTLRGARSGLIFYRKGVKAVDPKTGREIPYTFEDRINFAVFPSLQGGPHNHAIAAVAVALKQACTPM FREYSLQVLKNARAMADALLERGYSLVSGGTDNHLVLVDLRPKGLDGARAERVLELVSITANKNTCPGD RSAITPGGLRLGAPALTSRQFREDDFRRWDFIDEGVNIGLEVKSKTAKLQDFKSFLLKDSETSQRLANL RQRVEQFARAFPMPGFDEH

[0080] As understood herein, SWOB is a member of the S100 protein family, a group of calcium-binding proteins involved in various intracellular and extracellular functions. SWOB is primarily expressed in astrocytes and is found in the central nervous system (CNS). It plays roles in regulating cellular processes such as cell growth, differentiation, and apoptosis, as well as in mediating neuroinflammatory responses.

[0081] Preferably, SWOB is characterized by amino acid sequence according to SEQ ID NO.: 16: MSELEKAMVALIDVFHQYSGREGDKHKLKKSELKELINNELSHFLEEIKEQEWDKVMETLDNDGDGEC DFQEFMAFVAMVTTACHEFFEHE

[0082] As understood herein, TIMPs (Tissue Inhibitors of Metalloproteinases) are a family of proteins that specifically regulate metalloproteinases (MMPs), which are enzymes involved in the degradation of the extracellular matrix (ECM). TIMPs play a crucial role in regulating tissue remodeling, inflammation, and cellular processes such as proliferation and apoptosis. As understood herein, TIMPs include TIMP-1 , TIMP-2, TIMP-3 and TIMP-4. Preferably, TIMP-1 is characterized by amino acid sequence according to SEQ ID NO.: 37: MAPFEPLASGILLLLWLIAPSRACTCVPPHPQTAFCNSDLVIRAKFVGTPEVNQTTLYQRYEIKMTKMYK GFQALGDAADIRFVYTPAMESVCGYFHRSHNRSEEFLIAGKLQDGLLHITTCSFVAPWNSLSLAQRRGF TKTYTVGCEECTVFPCLSIPCKLQSGTHCLWTDQLLQGSEKGFQSRHLACLPREPGLCTWQSLRSQIA

[0083] Preferably, TIMP-2 is characterized by amino acid sequence according to SEQ ID NO.: 38: MGAAARTLRLALGLLLLATLLRPADACSCSPVHPQQAFCNADWIRAKAVSEKEVDSGNDIYGNPIKRIQ YEIKQIKMFKGPEKDIEFIYTAPSSAVCGVSLDVGGKKEYLIAGKAEGDGKMHITLCDFIVPWDTLSTTQK KSLNHRYQMGCECKITRCPMIPCYISSPDECLWMDWVTEKNINGHQAKFFACIKRSDGSCAWYRGAAP PKQEFLDIEDP

[0084] Preferably, TIMP-3 is characterized by amino acid sequence according to SEQ ID NO.: 39: MTPWLGLIVLLGSWSLGDWGAEACTCSPSHPQDAFCNSDIVIRAKWGKKLVKEGPFGTLVYTIKQMK MYRGFTKMPHVQYIHTEASESLCGLKLEVNKYQYLLTGRVYDGKMYTGLCNFVERWDQLTLSQRKGL NYRYHLGCNCKIKSCYYLPCFVTSKNECLWTDMLSNFGYPGYQSKHYACIRQKGGYCSWYRGWAPP DKSIINATDP

[0085] Preferably, TIMP-4 is characterized by amino acid sequence according to SEQ ID NO.: 40: MPGSPRPAPSWVLLLRLLALLRPPGLGEACSCAPAHPQQHICHSALVIRAKISSEKWPASADPADTEK MLRYEIKQIKMFKGFEKVKDVQYIYTPFDSSLCGVKLEANSQKQYLLTGQVLSDGKVFIHLCNYIEPWED LSLVQRESLNHHYHLNCGCQITTCYTVPCTISAPNECLWTDWLLERKLYGYQAQHYVCMKHVDGTCS WYRGHLPLRKEFVDIVQP

[0086] As understood herein, Oligodendrocyte-specific proteins (OSPs) are proteins that are primarily expressed in oligodendrocytes, the glial cells responsible for forming and maintaining the myelin sheath around neurons in the central nervous system (CNS). These proteins play crucial roles in oligodendrocyte development, myelination, and overall CNS function. Examples for OSPs are myelin basic protein (MBP) and proteolipid protein (PLP).

[0087] As understood herein, Myelin basic proteins (MBPs) are critical structural proteins that of the myelin sheath, which insulates nerve fibers in the central nervous system (CNS). It plays a key role in the formation, compaction, and maintenance of myelin, facilitating efficient electrical conduction along axons.

[0088] Preferably, MBP is characterized by amino acid sequence according to SEQ ID NO.: 17: MGNHAGKRELNAEKASTNSETNRGESEKKRNLGELSRTTSEDNEVFGEADANQNNGTSSQDTAVTDS KRTADPKNAWQDAHPADPGSRPHLIRLFSRDAPGREDNTFKDRPSESDELQTIQEDSAATSESLDVMA SQKRPSQRHGSKYLATASTMDHARHGFLPRHRDTGILDSIGRFFGGDRGAPKRGSGKDSHHPARTAH YGSLPQKSHGRTQDENPVVHFFKNIVTPRTPPPSQGKGRGLSLSRFSWGAEGQRPGFGYGGRASDY KSAHKGFKGVDAQGTLSKIFKLGGRDSRSGSPMARR

[0089] As understood herein, FYN kinase is a member of the Src family of non-receptor tyrosine kinases. It plays a crucial role in various cellular processes, including cell signaling, proliferation, differentiation, and survival. FYN is expressed in various tissues, including the brain, where it is particularly important in neuronal signaling.

[0090] Preferably, FYN kinase is characterized by amino acid sequence according to SEQ ID NO.: 18: MGCVQCKDKEATKLTEERDGSLNQSSGYRYGTDPTPQHYPSFGVTSIPNYNNFHAAGGQGLTVFGGV NSSSHTGTLRTRGGTGVTLFVALYDYEARTEDDLSFHKGEKFQILNSSEGDWWEARSLTTGETGYIPS NYVAPVDSIQAEEWYFGKLGRKDAERQLLSFGNPRGTFLIRESETTKGAYSLSIRDWDDMKGDHVKHY KIRKLDNGGYYITTRAQFETLQQLVQHYSERAAGLCCRLWPCHKGMPRLTDLSVKTKDVWEIPRESLQ LIKRLGNGQFGEVWMGTWNGNTKVAIKTLKPGTMSPESFLEEAQIMKKLKHDKLVQLYAWSEEPIYIVT EYMNKGSLLDFLKDGEGRALKLPNLVDMAAQVAAGMAYIERMNYIHRDLRSANILVGNGLICKIADFGLA RLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELVTKGRVPYPGMNNREVLEQVER GYRMPCPQDCPISLHELMIHCWKKDPEERPTFEYLQSFLEDYFTATEPQYQPGENL

[0091] As understood herein, PV (Parvalbumin) is a calcium-binding protein found predominantly in certain types of neurons, particularly GABAergic interneurons in the brain. It plays a crucial role in calcium signaling and contributes to various neuronal functions, including neurotransmitter release and synaptic plasticity.

[0092] Preferably, PV is characterized by amino acid sequence according to SEQ ID NO.: 19: MSMTDLLNAEDIKKAVGAFSATDSFDHKKFFQMVGLKKKSADDVKKVFHMLDKDKSGFIEEDELGFILK GFSPDARDLSAKETKMLMAAGDKDGDGKIGVDEFSTLVAES

[0093] As understood herein, PNN (Perineuronal Nets) are specialized extracellular matrix structures that surround certain types of neurons, particularly GABAergic interneurons. They are composed of various components, including proteoglycans (such as hyaluronan), glycoproteins, and collagen, and they play essential roles in neuronal function and synaptic regulation. PNNs are composed of several key components: 1 . Hyaluronan: The main structural component in terms of volume

[0094] 2. Chondroitin Sulfate Proteoglycans (CSPGs):

[0095] • Aggrecan

[0096] • Brevican

[0097] • Neurocan

[0098] • Versican

[0099] • Phosphacan

[0100] 3. Tenascins: Particularly Tenascin-R (Tn-R)

[0101] 4. Hyaluronan and Proteoglycan Binding Link Proteins (HAPLNs):

[0102] • HAPLN-1

[0103] • HAPLN-3

[0104] • HAPLN-4

[0105] PNNs form a lattice-like structure on the neuronal membrane, with regions of uncovered membrane mostly occupied by synapses. They surround the perikaryon and proximal dendrites of specific neurons, particularly GABAergic interneurons expressing parvalbumin (PV). PNNs serve several important functions:

[0106] 1 . Synaptic stabilization and plasticity regulation

[0107] 2. Closure of critical period plasticity

[0108] 3. Maintenance of excitatory / i n hi bitory balance and regulation of neuronal excitability

[0109] 4. Neuroprotection, including protection from oxidative stress.

[0110] PNNs develop postnatally and reach full maturity at the end of the critical period. Their formation and maintenance are regulated by various factors:

[0111] 1 . Neuronal activity

[0112] 2. Chondroitinase ABC (ChABC), an enzyme used experimentally to degrade PNNs

[0113] PNN abnormalities have been observed in several neurological and psychiatric conditions:

[0114] 1. Schizophrenia

[0115] 2. Alzheimer's disease

[0116] 3. Epilepsy

[0117] 4. Fragile X Syndrome

[0118] 5. Autism Spectrum Disorders

[0119] As understood herein, GAD65 (Glutamic Acid Decarboxylase 65) is one of the two isoforms of the enzyme glutamic acid decarboxylase, which catalyzes the conversion of glutamate, the primary excitatory neurotransmitter, into gamma-aminobutyric acid (GABA), the main inhibitory neurotransmitter in the central nervous system (CNS). GAD65 is primarily expressed in GABAergic neurons and is essential for maintaining GABA levels, thereby playing a crucial role in inhibitory neurotransmission.

[0120] Preferably, GAD65 is characterized by amino acid sequence according to SEQ ID NO.: 20: MASPGSGFWSFGSEDGSGDSENPGTARAWCQVAQKFTGGIGNKLCALLYGDAEKPAESGGSQPPRA AARKAACACDQKPCSCSKVDVNYAFLHATDLLPACDGERPTLAFLQDVMNILLQYWKSFDRSTKVIDF HYPNELLQEYNWELADQPQNLEEILMHCQTTLKYAIKTGHPRYFNQLSTGLDMVGLAADWLTSTANTN

[0121] MFTYEIAPVFVLLEYVTLKKMREIIGWPGGSGDGIFSPGGAISNMYAMMIARFKMFPEVKEKGMAALPRL IAFTSEHSHFSLKKGAAALGIGTDSVILIKCDERGKMIPSDLERRILEAKQKGFVPFLVSATAGTTVYGAFD PLLAVADICKKYKIWMHVDAAWGGGLLMSRKHKWKLSGVERANSVTWNPHKMMGVPLQCSALLVREE GLMQNCNQMHASYLFQQDKHYDLSYDTGDKALQCGRHVDVFKLWLMWRAKGTTGFEAHVDKCLELA EYLYNIIKNREGYEMVFDGKPQHTNVCFWYIPPSLRTLEDNEERMSRLSKVAPVIKARMMEYGTTMVSY QPLGDKVNFFRMVISNPAATHQDIDFLIEEIERLGQDL

[0122] As understood herein, VGAT (Vesicular GABA Transporter), also known as SLC32A1 , is a key protein responsible for the transport of gamma-aminobutyric acid (GABA) and glycine into synaptic vesicles in GABAergic and glycinergic neurons. This transport is essential for the storage and release of these neurotransmitters, facilitating inhibitory signaling in the central nervous system (CNS).

[0123] Preferably, VGAT is characterized by amino acid sequence according to SEQ ID NO.: 21 : MATLLRSKLSNVATSVSNKSQAKMSGMFARMGFQAATDEEAVGFAHCDDLDFEHRQGLQMDILKAEG EPCGDEGAEAPVEGDIHYQRGSGAPLPPSGSKDQVGGGGEFGGHDKPKITAWEAGWNVTNAIQGMF VLGLPYAILHGGYLGLFLIIFAAWCCYTGKILIACLYEENEDGEVVRVRDSYVAIANACCAPRFPTLGGRV

[0124] VNVAQIIELVMTCILYWVSGNLMYNSFPGLPVSQKSWSIIATAVLLPCAFLKNLKAVSKFSLLCTLAHFVI NILVIAYCLSRARDWAWEKVKFYIDVKKFPISIGIIVFSYTSQIFLPSLEGNMQQPSEFHCMMNWTHIAAC VLKGLFALVAYLTWADETKEVITDNLPGSIRAWNIFLVAKALLSYPLPFFAAVEVLEKSLFQEGSRAFFP ACYSGDGRLKSWGLTLRCALWFTLLMAIYVPHFALLMGLTGSLTGAGLCFLLPSLFHLRLLWRKLLWH QVFFDVAIFVIGGICSVSGFVHSLEGLIEAYRTNAED

[0125] As understood herein, VGLUTs (Vesicular Glutamate Transporters) are a family of proteins responsible for packaging glutamate, the primary excitatory neurotransmitter in the central nervous system (CNS), into synaptic vesicles. This process is crucial for facilitating glutamate release during neurotransmission and ensuring effective excitatory signaling. VGLUTs include VGLUT1 , VGLUT2 and VGLUT3. Preferably, VGLUT1 is characterized by amino acid sequence according to SEQ ID NO.: 22:

[0126] MEFRQEEFRKLAGRALGKLHRLLEKRQEGAETLELSADGRPVTTQTRDPPVVDCTCFGLPRRYIIAIMS

[0127] GLGFCISFGIRCNLGVAIVSMVNNSTTHRGGHVWQKAQFSWDPETVGLIHGSFFWGYIVTQIPGGFIC

[0128] QKFAANRVFGFAIVATSTLNMLIPSAARVHYGCVIFVRILQGLVEGVTYPACHGIWSKWAPPLERSRLAT

[0129] TAFCGSYAGAVVAMPLAGVLVQYSGWSSVFYVYGSFGIFWYLFWLLVSYESPALHPSISEEERKYIEDA

[0130] IGESAKLMNPLTKFSTPWRRFFTSMPVYAIIVANFCRSWTFYLLLISQPAYFEEVFGFEISKVGLVSALPH

[0131] LVMTIIVPIGGQIADFLRSRRIMSTTNVRKLMNCGGFGMEATLLLWGYSHSKGVAISFLVLAVGFSGFAI

[0132] SGFNVNHLDIAPRYASILMGISNGVGTLSGMVCPIIVGAMTKHKTREEWQYVFLIASLVHYGGVIFYGVF

[0133] ASGEKQPWAEPEEMSEEKCGFVGHDQLAGSDDSEMEDEAEPPGAPPAPPPSYGATHSTFQPPRPPP PVRDY

[0134] Preferably, VGLUT2 is characterized by amino acid sequence according to SEQ ID NO.: 23:

[0135] MESVKQRILAPGKEGLKNFAGKSLGQIYRVLEKKQDTGETIELTEDGKPLEVPERKAPLCDCTCFGLPR

[0136] RYIIAIMSGLGFCISFGIRCNLGVAIVDMVNNSTIHRGGKVIKEKAKFNWDPETVGMIHGSFFWGYIITQIP

[0137] GGYIASRLAANRVFGAAILLTSTLNMLIPSAARVHYGCVIFVRILQGLVEGVTYPACHGIWSKWAPPLERS

[0138] RLATTSFCGSYAGAVIAMPLAGILVQYTGWSSVFYVYGSFGMVWYMFWLLVSYESPAKHPTITDEERR

[0139] YIEESIGESANLLGAMEKFKTPWRKFFTSMPVYAIIVANFCRSWTFYLLLISQPAYFEEVFGFEISKVGML

[0140] SAVPHLVMTIIVPIGGQIADFLRSKQILSTTTVRKIMNCGGFGMEATLLLWGYSHTRGVAISFLVLAVGFS

[0141] GFAISGFNVNHLDIAPRYASILMGISNGVGTLSGMVCPIIVGAMTKNKSREEWQYVFLIAALVHYGGVIFY

[0142] AIFASGEKQPWADPEETSEEKCGFIHEDELDEETGDITQNYINYGTTKSYGATTQANGGWPSGWEKKE EFVQGEVQDSHSYKDRVDYS

[0143] Preferably, VGLUT3 is characterized by amino acid sequence according to SEQ ID NO.: 24:

[0144] MPFKAFDTFKEKILKPGKEGVKNAVGDSLGILQRKIDGTTEEEDNIELNEEGRPVQTSRPSPPLCDCHC

[0145] CGLPKRYIIAIMSGLGFCISFGIRCNLGVAIVEMVNNSTVYVDGKPEIQTAQFNWDPETVGLIHGSFFWG

[0146] YIMTQIPGGFISNKFAANRVFGAAIFLTSTLNMFIPSAARVHYGCVMCVRILQGLVEGVTYPACHGMWSK

[0147] WAPPLERSRLATTSFCGSYAGAWAMPLAGVLVQYIGWSSVFYIYGMFGIIWYMFWLLQAYECPAAHP

[0148] TISNEEKTYIETSIGEGANVVSLSKFSTPWKRFFTSLPVYAIIVANFCRSWTFYLLLISQPAYFEEVFGFAIS

[0149] KVGLLSAVPHMVMTIVVPIGGQLADYLRSRQILTTTAVRKIMNCGGFGMEATLLLWGFSHTKGVAISFL

[0150] VLAVGFSGFAISGFNVNHLDIAPRYASILMGISNGVGTLSGMVCPLIVGAMTRHKTREEWQNVFLIAALV

[0151] HYSGVIFYGVFASGEKQEWADPENLSEEKCGIIDQDELAEEIELNHESFASPKKKMSYGATSQNCEVQK

[0152] KEWKGQRGATLDEEELTSYQNEERNFSTIS As understood herein, PH and SEC7 domain-containing protein 1 (PSD) is a multifunctional protein involved in various cellular processes, including vesicle trafficking, signal transduction, and cytoskeletal organization. It contains a PH (pleckstrin homology) domain and a SEC7 domain, which are critical for its functions

[0153] Preferably, PSD is characterized by amino acid sequence according to SEQ ID NO.: 25: MAQGAMRFCSEGDCAISPPRCPRRWLPEGPVPQSPPASMYGSTGSLLRRVAGPGPRGRELGRVTAP CTPLRGPPSPRVAPSPWAPSSPTGQPPPGAQSSVVIFRFVEKASVRPLNGLPAPGGLSRSWDLGGVS PPRPTPALGPGSNRKLRLEASTSDPLPARGGSALPGSRNLVHGPPAPPQVGADGLYSSLPNGLGGPP

[0154] ERLATLFGGPADTGFLNQGDTWSSPREVSSHAQRIARAKWEFFYGSLDPPSSGAKPPEQAPPSPPGV GSRQGSGVAVGRAAKYSETDLDTVPLRCYRETDIDEVLAEREEADSAIESQPSSEGPPGTAYPPAPRP GPLPGPHPSLGSGNEDEDDDEAGGEEDVDDEVFEASEGARPGSRMPLKSPVPFLPGTSPSADGPDSF SCVFEAILESHRAKGTSYTSLASLEALASPGPTQSPFFTFELPPQPPAPRPDPPAPAPLAPLEPDSGTSS AADGPWTQRGEEEEAEARAKLAPGREPPSPCHSEDSLGLGAAPLGSEPPLSQLVSDSDSELDSTERL ALGSTDTLSNGQKADLEAAQRLAKRLYRLDGFRKADVARHLGKNNDFSKLVAGEYLKFFVFTGMTLDQ ALRVFLKELALMGETQERERVLAHFSQRYFQCNPEALSSEDGAHTLTCALMLLNTDLHGHNIGKRMTC GDFIGNLEGLNDGGDFPRELLKALYSSIKNEKLQWAIDEEELRRSLSELADPNPKVIKRISGGSGSGSSP FLDLTPEPGAAVYKHGALVRKVHADPDCRKTPRGKRGWKSFHGILKGMILYLQKEEYKPGKALSETELK NAISIHHALATRASDYSKRPHVFYLRTADWRVFLFQAPSLEQMQSWITRINWAAMFSAPPFPAAVSSQ KKFSRPLLPSAATRLSQEEQVRTHEAKLKAMASELREHRAAQLGKKGRGKEAEEQRQKEAYLEFEKSR YSTYAALLRVKLKAGSEELDAVEAALAQAGSTEDGLPPSHSSPSLQPKPSSQPRAQRHSSEPRPGAGS GRRKP

[0155] As understood herein, CD9 is a tetraspanin protein that plays a crucial role in the biogenesis, secretion, and function of extracellular vesicles (EVs), particularly exosomes. It is expressed on the surface of many cell types and serves as a marker for exosomes.

[0156] Preferably, CD9 is characterized by amino acid sequence according to SEQ ID NO.: 26: MPVKGGTKCIKYLLFGFNFIFWLAGIAVLAIGLWLRFDSQTKSIFEQETNNNNSSFYTGVYILIGAGALMM LVGFLGCCGAVQESQCMLGLFFGFLLVIFAIEIAAAIWGYSHKDEVIKEVQEFYKDTYNKLKTKDEPQRE TLKAIHYALNCCGLAGGVEQFISDICPKKDVLETFTVKSCPDAIKEVFDNKFHIIGAVGIGIAVVMIFGMIFS

[0157] MILCCAIRRNREMV As understood herein, CD63 is a member of the tetraspanin family of proteins and is commonly found on the surface of various cell types, including immune cells and endothelial cells. It is often used as a marker for exosomes and other extracellular vesicles (EVs).

[0158] Preferably, CD63 is characterized by amino acid sequence according to SEQ ID NO.: 27: MAVEGGMKCVKFLLYVLLLAFCACAVGLIAVGVGAQLVLSQTIIQGATPGSLLPVVIIAVGVFLFLVAFVG CCGACKENYCLMITFAIFLSLIMLVEVAAAIAGYVFRDKVMSEFNNNFRQQMENYPKNNHTASILDRMQ ADFKCCGAANYTDWEKIPSMSKNRVPDSCCINVTVGCGINFNEKAIHKEGCVEKIGGWLRKNVLWAAA ALGIAFVEVLGIVFACCLVKSIRSGYEVM

[0159] As understood herein, Hsp70 (Heat Shock Protein 70) is a highly conserved protein that functions as a molecular chaperone, playing critical roles in protein folding, assembly, translocation, and degradation. It is part of the heat shock protein family, which is upregulated in response to stress conditions such as heat, oxidative stress, and other cellular stresses.

[0160] Preferably, Hsp70 is characterized by amino acid sequence according to SEQ ID NO.: 28: MAKAAAIGIDLGTTYSCVGVFQHGKVEIIANDQGNRTTPSYVAFTDTERLIGDAAKNQVALNPQNTVFDA KRLIGRKFGDPWQSDMKHWPFQVINDGDKPKVQVSYKGETKAFYPEEISSMVLTKMKEIAEAYLGYPV TNAVITVPAYFNDSQRQATKDAGVIAGLNVLRIINEPTAAAIAYGLDRTGKGERNVLIFDLGGGTFDVSILT IDDGIFEVKATAGDTHLGGEDFDNRLVNHFVEEFKRKHKKDISQNKRAVRRLRTACERAKRTLSSSTQA SLEIDSLFEGIDFYTSITRARFEELCSDLFRSTLEPVEKALRDAKLDKAQIHDLVLVGGSTRIPKVQKLLQD FFNGRDLNKSINPDEAVAYGAAVQAAILMGDKSENVQDLLLLDVAPLSLGLETAGGVMTALIKRNSTIPT KQTQIFTTYSDNQPGVLIQVYEGERAMTKDNNLLGRFELSGIPPAPRGVPQIEVTFDIDANGILNVTATDK STGKANKITITNDKGRLSKEEIERMVQEAEKYKAEDEVQRERVSAKNALESYAFNMKSAVEDEGLKGKI SEADKKKVLDKCQEVISWLDANTLAEKDEFEHKRKELEQVCNPIISGLYQGAGGPGPGGFGAQGPKGG SGSGPTIEEVD

[0161] As understood herein, Synaptophysin is a synaptic vesicle protein that plays a crucial role in neurotransmitter release and synaptic function. It is a transmembrane protein predominantly found in the presynaptic terminals of neurons and is involved in the formation and maintenance of synaptic vesicles.

[0162] Preferably, synaptophysin is characterized by amino acid sequence according to SEQ ID NO.: 29: MLLLADMDWNQLVAGGQFRWKEPLGFVKVLQWVFAIFAFATCGSYSGELQLSVDCANKTESDLSIEV EFEYPFRLHQVYFDAPTCRGGTTKVFLVGDYSSSAEFFVTVAVFAFLYSMGALATYIFLQNKYRENNKG PMLDFLATAVFAFMWLVSSSAWAKGLSDVKMATDPENIIKEMPVCRQTGNTCKELRDPVTSGLNTSW FGFLNLVLWVGNLWFVFKETGWAAPFLRAPPGAPEKQPAPGDAYGDAGYGQGPGGYGPQDSYGPQ GGYQPDYGQPAGSGGSGYGPQGDYGQQGYGPQGAPTSFSNQM

[0163] As understood herein, C11 b (also known as CD11 b) is an integrin protein that is primarily expressed on the surface of myeloid cells, including monocytes, macrophages, and neutrophils. It forms a complex with CD18 (another integrin subunit) to create Mac-1 (also known as aM02), which plays essential roles in immune responses and inflammation.

[0164] Preferably, C11 b is characterized by amino acid sequence according to SEQ ID NO.: 30: MALRVLLLTALTLCHGFNLDTENAMTFQENARGFGQSWQLQGSRVWGAPQEIVAANQRGSLYQCDY STGSCEPIRLQVPVEAVNMSLGLSLAATTSPPQLLACGPTVHQTCSENTYVKGLCFLFGSNLRQQPQKF PEALRGCPQEDSDIAFLIDGSGSIIPHDFRRMKEFVSTVMEQLKKSKTLFSLMQYSEEFRIHFTFKEFQN NPNPRSLVKPITQLLGRTHTATGIRKVVRELFNITNGARKNAFKILVVITDGEKFGDPLGYEDVIPEADRE GVIRYVIGVGDAFRSEKSRQELNTIASKPPRDHVFQVNNFEALKTIQNQLREKIFAIEGTQTGSSSSFEHE MSQEGFSAAITSNGPLLSTVGSYDWAGGVFLYTSKEKSTFINMTRVDSDMNDAYLGYAAAIILRNRVQS LVLGAPRYQHIGLVAMFRQNTGMWESNANVKGTQIGAYFGASLCSVDVDSNGSTDLVLIGAPHYYEQT RGGQVSVCPLPRGRARWQCDAVLYGEQGQPWGRFGAALTVLGDVNGDKLTDVAIGAPGEEDNRGAV YLFHGTSGSGISPSHSQRIAGSKLSPRLQYFGQSLSGGQDLTMDGLVDLTVGAQGHVLLLRSQPVLRV KAIMEFNPREVARNVFECNDQWKGKEAGEVRVCLHVQKSTRDRLREGQIQSWTYDLALDSGRPHSR AVFNETKNSTRRQTQVLGLTQTCETLKLQLPNCIEDPVSPIVLRLNFSLVGTPLSAFGNLRPVLAEDAQR LFTALFPFEKNCGNDNICQDDLSITFSFMSLDCLVVGGPREFNVTVTVRNDGEDSYRTQVTFFFPLDLS YRKVSTLQNQRSQRSWRLACESASSTEVSGALKSTSCSINHPIFPENSEVTFNITFDVDSKASLGNKLLL KANVTSENNMPRTNKTEFQLELPVKYAVYMVVTSHGVSTKYLNFTASENTSRVMQHQYQVSNLGQRS LPISLVFLVPVRLNQTVIWDRPQVTFSENLSSTCHTKERLPSHSDFLAELRKAPWNCSIAVCQRIQCDIP FFGIQEEFNATLKGNLSFDWYIKTSHNHLLIVSTAEILFNDSVFTLLPGQGAFVRSQTETKVEPFEVPNPL PLIVGSSVGGLLLLALITAALYKLGFFKRQYKDMMSEGGPPGAEPQ

[0165] As understood herein, TMEM119 (Transmembrane Protein 119) is a protein that is primarily expressed in microglia, the resident immune cells of the central nervous system (CNS). It plays a critical role in the identification and characterization of microglia, distinguishing them from other cell types in the brain.

[0166] Preferably, TMEM119 is characterized by amino acid sequence according to SEQ ID NO.: 31 : MVSAAAPSLLILLLLLLGSVPATDARSVPLKATFLEDVAGSGEAEGSSASSPSLPPPWTPALSPTSMGP QPITLGGPSPPTNFLDGIVDFFRQYVMLIAWGSLAFLLMFIVCAAVITRQKQKASAYYPSSFPKKKYVDQ SDRAGGPRAFSEVPDRAPDSRPEEALDSSRQLQADILAATQNLKSPTRAALGGGDGARMVEGRGAEE EEKGSQEGDQEVQGHGVPVETPEAQEEPCSGVLEGAWAGEGQGELEGSLLLAQEAQGPVGPPESP

[0167] CACSSVHPSV

[0168] Detailed description of the invention

[0169] The invention is described in detail in the following. It is to be understood that, unless explicitly indicated to the contrary, all the disclosed features can be combined with each other.

[0170] In one embodiment, the present invention provides an ex vivo method for classifying a subject as neurological or psychiatric disorder subject, the method comprising the steps of: a) providing a biological sample obtained from a subject and comprising extracellular vesicles, including brain-derived extracellular vesicles; b) determining in the sample of (a) the fraction of brain-derived extracellular vesicles within the extracellular vesicles; c) determining in the sample of (a) the expression level of at least one biomarker selected from:

[0171] (d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, SWOB and TIMPs, preferably selected from the group comprising miR-34a, miR-9 and miR-132,

[0172] (c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81, NCAM1, GLAST1 and L1CAM, preferably miR-137,

[0173] (c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941 , EAAT1, EAAT2, EAAT3 and SHMTs, preferably selected from miR- 132 and miR-941,

[0174] (c4) myelination pathway biomarkers selected from the group comprising miR-9, oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs) and Fyn Kinase, preferably miR-9

[0175] (c5) excitatory-inhibitory balance pathway biomarkers selected from the group comprising miR-137, miR-138, PV, PNN, GAD65, VGAT, VGLUTs and PSD, preferably selected from miR-137 and miR-138; and d) classifying a subject as neurological or psychiatric disorder subject based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles determined in step (b) and the expression level determined in step (c). It is to be understood that the method of the invention is not to be performed on a living subject, but it is to be performed using a sample obtained from a living subject. Accordingly, said step of obtaining a sample, which can be understood as requiring invasive intervention at the subject, such as, for example, drawing the blood from the subject, is excluded from the method of the present invention. It is further embodied in the use of the wording “ex vivo” when referring to the method of the invention.

[0176] As it is to be understood herein, the term neurological or psychiatric disorder subject refers to a subject, patient or individual that likely to develop a neurological or psychiatric disorder in the future or are in the course of developing such disorder. The neurological or psychiatric disorder may show symptoms of neurological or psychiatric disorder. One such exemplary symptom is cognitive impairment.

[0177] As understood herein, a neurological disorder is any disorder of the nervous system. Such disorder may manifest itself in abnormalities in the brain, spinal cord and / or other nerves, which may in turn lead to a variety of symptom, such as, but not limited to, paralysis, muscle weakness, poor coordination, loss of sensation, seizures, confusion, pain, tauopathies, and altered levels of consciousness. Preferably, the term “neurological disorder” refers to a disease selected from the group comprising Alzheimer's disease, Parkinson disorder, and cognitive impairment. However, the term neurological disorder is not limited to these examples and may also include other neurological disorders, known to the skilled person.

[0178] As understood herein, psychiatric disorders are behavioural, emotional or cognitive disfunctions that are not readily controlled by the affected individual. Preferably, the term “psychiatric disorder” refers to a disease selected from the group comprising schizophrenia, epilepsy, mood disorder, psychosis, bipolar disorder (manic psychosis), cognitive impairment, and age-associated disorders. The term psychiatric disorder is not limited to these examples and may also include other psychiatric disorders. The psychiatric disorder may refer to early psychotic disorder, preferably it may refer to early schizophrenia, early symptoms of epilepsy, early bipolar disorder.

[0179] The method of the present invention has been demonstrated and exemplified explicitly for an embodiment wherein the psychotic disorder is schizophrenia. However, based on the mechanism proposed by the present inventors, it would be apparent that the method of the present invention is generalizable to other psychiatric or psychotic disorders, in particular selected from epilepsy, mood disorder, bipolar disorder, age-associated diseases, and cognitive impairment.

[0180] The term “subject,” as used herein refers to an individual organism, preferably a human that is affected by particular disorder. In some embodiments of this invention subjects may also be referred to as patients or as individuals. Therefore, the terms psychotic disorder subjects, psychotic disorder subjects and psychotic disorder individuals are herein used interchangeably.

[0181] The present invention concerns determination of biomarker level in certain extracellular vesicles. Accordingly, the skilled person is familiar with this term. Preferably, the term “extracellular vesicles” or “exosomes” relates to small vesicles that are released into the extracellular environment from a variety of different cells, for example, cells that originate from, or are derived from, the ectoderm, endoderm, or mesoderm, including any such cells that have undergone genetic, environmental, and / or any other variations or alterations. An exosome is typically created intracellularly when a segment of the cell membrane spontaneously invaginates and is ultimately exocytosed (see e.g., Keller et al. (2006), Immunol. Lett. 107: 102-8). Exosomes may also be referred to as microvesicles, tausomes, nanovesicles, vesicles, dexosomes, bleb, blebby, prostasomes, microparticles, intralumenal vesicles, endosomal-like vesicles, or exocytosed vehicles. Exosomes can also include any shed membrane-bound particle that is derived from either the plasma membrane or an internal membrane. Exosomes can also include cell- derived structures bounded by a lipid bilayer membrane arising from both herniated evagination separation and sealing of portions of the plasma membrane or from the export of any intracellular membrane-bound vesicular structure containing various membrane-associated proteins of tumor origin, including surface-bound molecules derived from the host circulation that bind selectively to the tumor- derived proteins together with molecules contained in the exosome lumen including tumor-derived microRNAs or intracellular proteins. Exosomes can also include membrane fragments.

[0182] Extracellular vesicles that originate in the central nervous system are referred to herein as brain-derived extracellular vesicles. The origin of the exosomes can be assessed by determining the presence and level of the neural markers in the samples containing the exosomes. It is known to that skilled in the art that the exosomes can be isolated from blood and other bodily fluids by the process of ultracentrifugation. Of particular interest are extracellular vesicles originating from the brain and present in the blood, as they allow diagnostic insight into the situation in the brain.

[0183] The method of the present invention necessarily comprises the step (a) of providing a biological sample obtained from a subject and comprising extracellular vesicles, including brain-derived extracellular vesicles. It is to be reiterated that providing said sample specifically excludes directly obtaining the sample from the subject as part of step (a) of the method of the present invention. As used herein, a term “biological sample” refers to a sample of biological material obtained from a subject, preferably a human subject, including but not limiting to a tissue, a tissue sample, cell sample, fluid sample. Non-limiting examples of such biological samples are selected from plasma, cerebrospinal fluid, serum, saliva, nasal mucus, and urine. It is further preferred that the biological sample is free, or substantially free, of cells. Accordingly, it is preferred that the biological sample is selected from plasma, cerebrospinal fluid, serum, saliva, nasal mucus, and urine. Alternatively, the biological sample as in the invention preferably comprises at least one of samples selected from selected from plasma, cerebrospinal fluid, serum, saliva, nasal mucus, and urine.

[0184] In the method of the present invention, biological sample preferably comprises bodily fluid. The term bodily fluid is known to the skilled person, and can be used interchangeably with the term body fluid and / or the term biofluid. Preferably, in the method of the invention, the bodily fluid is substantially free of cells, preferably wherein the bodily fluid is selected from plasma, cerebrospinal fluid, serum, saliva, nasal mucus, and urine, and / or preferably wherein no further isolation of extracellular vesicles was performed on the biological sample.

[0185] Preferably, as understood herein, the sample is considered to be preferably free of cells if no cells can be detected in the sample according to standard evaluation methods. It is to be understood that plasma, cerebrospinal fluid, serum, saliva, nasal mucus, and urine are free of cells, or substantially free of cells, according to the present invention.

[0186] As encompassed by the invention, the biological sample can be subjected to a variety of post-collection procedures prior to assessing the amount of marker in the sample. These procedures are in principle known to the skilled person and may include, but are not limited to, nucleic acid and / or protein extraction, fixation, storage, freezing, ultrafiltration, concentration, evaporation, centrifugation. The purpose of subjecting the sample to these procedures is to prepare it for assessing the amount or level of biomarker in the sample. Furthermore, these procedures may allow for long-term storage of the sample, without affecting the biomarker amount or level.

[0187] As disclosed herein, it is required in the method of the present invention that the biological sample contains extracellular vesicles. It is further to be understood that the sample must contain brain-derived extracellular vesicles. The extracellular vesicles and the brain-derived extracellular vesicles are as described herein. The method of the present invention further necessarily comprises the step (b) of determining in the sample of (a) the fraction of brain-derived extracellular vesicles within the extracellular vesicles. It is to be understood that the step (b) directly follows step (a). However, further optional steps may be included between steps (a) and (b). For example, optional further processing of the biological sample can be performed upon conclusion of step (a) and before commencement of step (b).

[0188] The term “fraction” as denoted herein, relates to a ratio of brain-derived extracellular vesicles over the total extracellular vesicles. Said ratio is to be defined as the ratio of amount of brain-derived, and can be determined according to the methods and protocols described herein, or according to any other method known to the skilled person. It is to be understood that the ratio used here is a number-based ratio. Accordingly, determination of the ratio preferably requires counting the number of brain-derived extracellular vesicles (based e.g. on their staining by relying on biomarkers, as described herein), counting the number of all extracellular vesicles (based on e.g. their staining by relying on biomarkers, as described herein), and determining said ratio.

[0189] It is to be understood, that the skilled person is capable of detecting extracellular vesicles and brain- derived extracellular vesicles. The skilled person can use a variety of techniques available for that purpose. For example, extracellular vesicles and brain-derived extracellular vesicles can be detected by standard methods of biochemistry and molecular biology, including the methods selected from by antibody staining, in situ hybridization and microscopy. This list of methods is however not meant to be seen as limiting, and skilled person can also use other methods for this purpose.

[0190] It further follows that the skilled person is capable of calculating the fraction of brain-derived extracellular vesicles within the extracellular vesicles. Preferably, as understood herein, the calculation is to be done based on the determination of amount of extracellular vesicles (to be understood as total extracellular vesicles in the sample, regardless of their origin) and - among these extracellular vesicles - an amount of brain-derived extracellular vesicles, followed by mathematically calculating the ratio of the two values.

[0191] Accordingly, the calculation is performed as in the following:

[0192] 1 . Determine the total amount of extracellular vesicles (EVs) in the sample.

[0193] 2. Determine the amount of brain-derived extracellular vesicles (Brain EVs) in the sample.

[0194] 3. Calculate the ratio using the following formula:

[0195] Fraction of Brain EVs = Amount of Brain EVs / Total amount ofEVs Preferably, the step (b) of the method of the present invention further includes detecting and quantifying brain-derived extracellular vesicles with respect to total population of extracellular vesicles in the sample of (a). In some embodiments, detecting and quantifying brain-derived extracellular vesicles with respect to total population of extracellular vesicles in the sample of (a) is performed by antibody staining, in situ hybridization and / or microscopy.

[0196] According to the method of the invention, the fraction of brain-derived extracellular vesicles within the extracellular vesicles indicative of a healthy subject is in the range of 10-20%. For example, said fraction of brain-derived extracellular vesicles within the extracellular vesicles indicative of a healthy subject may be 10, 11 , 12, 13, 14, 15, 16, 17, 18, 19 or 20%. Accordingly, and as apparent to the skilled person, in the healthy subjects the fraction of brain-derived extracellular preferably, the fraction of brain-derived extracellular vesicles within the extracellular vesicles indicative of a healthy subject is in the range of 10- 15%. For example, said fraction may be 10, 11 , 12, 13, 14, or 15%.

[0197] Further in line with that, according to the present invention, the fraction of brain-derived extracellular vesicles within the extracellular vesicles indicative of a subject at risk for neurological or psychiatric disorder is less than 10%, less than 9% or less than 8%, or in other words in the range of 0-10%, 0-9% or 0-8%. Preferably, the fraction of brain-derived extracellular vesicles within the extracellular vesicles indicative of a subject at risk for neurological or psychiatric disorder is in the range of 0-10%, 3-10%, 0- 9%, 3-9%, 0-8% or 3-8%. Thus, such fraction may be 3, 4, 5, 6, 7, 8, or 9%.

[0198] It is to be understood that these value ranges play a role in the steps of classifying a subject, as referred to herein. The determination is performed, for example, as described in the following, based on the biomarkers specific for extracellular vesicles and for brain-derived extracellular vesicles.

[0199] The term “biomarker”, as used herein refers to an indicator, e.g. predictive, diagnostic, and / or prognostic, which can be detected in a sample. The biomarker may serve as an indicator of a particular subtype of a disease or disorder, preferably psychosis, most preferably schizophrenia, characterized by certain molecular, pathological, histological, and / or clinical features. The biomarker, as referred to herein, may be a gene, polynucleotide (e.g. DNA and / or RNA) and / or protein (or functional variants thereof). For example, altered level of expression of certain biomarker in a sample may be associated with a disorder and / or disease state. Alternatively, level of said biomarker may also be indicative of, for instance, origin of the sample (such as seen for biomarkers allowing for identifying brain-derived extracellular vesicles. 1

[0200] According to the present invention it is preferred that the biomarker is a protein and / or a microRNA.

[0201] Preferably, the step (b) of the method of the present invention further includes determination of the expression of at least one biomarker selected from biomarkers for extracellular vesicles (such as CD9, CD63, CD81 , Hsp70, and ALIX); and at least one biomarker selected from biomarkers for brain-derived extracellular vesicles (such as L1CAM, NCAM1, GLAST1 , Synaptophysin, C11 b, SWOB and TMEMI ).

[0202] As it is to be understood herein, determination of the expression of the biomarker may also be done by visualizing the biomarker e.g., by using an antibody-based system, and observing the cells expressing said biomarkers by relying on imaging techniques, such as microscopy.

[0203] Accordingly, in the method of the present invention, preferably at least one biomarker selected from biomarkers for extracellular vesicles is determined in the biological sample during step (b). Preferably, said at least one biomarker is selected from CD9, CD63, CD81 , Hsp70, and ALIX, more preferably said at least one biomarker is selected from CD9 and CD63.

[0204] The at least one biomarkers is preferably one or two biomarkers, more preferably one biomarkers. However, in one embodiment of the invention the at least one biomarkers can be two biomarkers. This however does not exclude the embodiments of the invention where more biomarkers are measured, for example three, four or five biomarkers.

[0205] Thus, in one embodiment of the present invention, the biomarker for extracellular vesicles is one, two, three, four or five biomarkers selected from the group of CD9, CD63, CD81 , Hsp70, and ALIX. Preferably, the biomarkers for extracellular vesicles include CD9, CD63, CD81 , Hsp70, and ALIX. More preferably, the biomarkers for extracellular vesicles are CD9, CD63, CD81 , Hsp70, and ALIX or the biomarkers for extracellular vesicles are CD9 and CD63.

[0206] Further according to the present invention, in step (b) of the method of the present invention the biomarker for brain-derived extracellular vesicles is at least one biomarker selected from L1 CAM, NCAM1 , GLAST 1 , Synaptophysin, C11 b, SWOB and TMEM1 , preferably the biomarker for brain-derived extracellular vesicles is at least one biomarker selected from L1 CAM and NCAM1 .

[0207] Thus, the method of the present invention further relates to an embodiment, wherein the biomarker for extracellular vesicles is selected from CD9, CD63, CD81 , Hsp70, and ALIX, preferably selected from CD9 and CD63, and / or wherein the biomarker for brain-derived extracellular vesicles is selected from L1 CAM, NCAM1 , GLAST1, Synaptophysin, C11 b, SWOB and TMEM1 , preferably selected from L1 CAM and NCAM1.

[0208] In one embodiment of the invention, the biomarker for brain-derived extracellular vesicles is one, two, three, four, five, six or seven biomarkers selected from the group of L1 CAM, NCAM1 , GLAST1 , Synaptophysin, 011 b, SWOB and TMEM119. Preferably, the biomarkers for brain-derived extracellular vesicles includes L1 CAM, NCAM1 , GLAST1, Synaptophysin, 011 b, SWOB and TMEM119. More preferably, the biomarkers for brain-derived extracellular vesicles are L1CAM, NCAM1 , GLAST1, Synaptophysin, 011 b, SWOB and TMEM119 or the biomarkers for brain-derived extracellular vesicles are L1 CAM and NCAM1.

[0209] Biomarkers for extracellular vesicles and brain-derived extracellular vesicles can be detected separately, in different samples obtained from the same sample from the subject. Alternatively, as also provided by the present invention, the biomarkers as referred to herein can be detected simultaneously, i.e. in a single measurement in a single sample. For example, reagents necessary for detection of biomarkers referred to herein can be placed on a single plate (such as plate used in an ELISA assay).

[0210] The method of the present invention further necessarily comprises the step (c) of determining in the sample of (a) the expression level of at least one biomarker selected from (d) immunology and / or inflammation pathway biomarkers, (c2) mitochondrial and redox impairment pathway biomarkers, (c3) N- methyl-D-aspartate receptor pathway biomarkers, (c4) myelination pathway biomarkers, and (c5) excitatory-inhibitory balance pathway biomarkers. In a specific embodiment, the expression level of the at least one biomarker is determined in the brain-derived extracellular vesicles isolated from the sample of (a).

[0211] It is to be understood that step (c) is necessarily performed after step (a). Step (c) can be performed simultaneously with step (b), together with step (b), in parallel to step (b), following the step (b), or before step (b) is performed. The timing of performing steps (b) and (c) is not particularly limited and the invention is meant to encompass all possible relative configurations.

[0212] The term “expression level” of a biomarker, is a detectable level in a biological sample. It can be measured by methods known to one skilled in the art and are also disclosed herein. The expression level of biomarkers assessed can be used to stratify groups of patients. The term “expression” generally refers to the process by which information (e.g. gene-encoded and / or epigenetic) is converted into the biological molecules of a different type or function that are present and operate in the cells. Therefore, as used herein, "expression" may refer to transcription into a polynucleotide, translation into a polypeptide, or even polynucleotide and / or polypeptide modifications (e.g., posttranslational modification of a polypeptide). Fragments of the transcribed polynucleotide, the translated polypeptide, or polynucleotide and / or polypeptide modifications (e.g. posttranslational modification of a polypeptide) shall also be regarded as expressed whether they originate from a transcript generated by alternative splicing or a degraded transcript, or from a post-translational processing of the polypeptide, e.g., by proteolysis. "Expressed genes" include those that are transcribed into a polynucleotide as mRNA and then translated into a polypeptide, and also those that are transcribed into RNA but not translated into a polypeptide (e.g. microRNAs). As it is understood herein, wherever reference is made to quantification of the biomarkers, preferably determination of the expression level of said biomarker is meant.

[0213] As understood herein, the biomarker is selected from the group of mitochondrial and redox impairment pathway biomarkers. The mitochondrial and redox impairment pathway biomarkers are known to the skilled person. For example, the biomarker mitochondrial and redox impairment pathway can be selected from COX6A2, ALIX, CD81 , NCAM1 , GLAST1 , L1 CAM, and miR-137. In a preferred embodiment, the biomarker mitochondrial and redox impairment pathway is one, two, three, four, five, six or seven biomarkers (preferably one or two biomarkers, more preferably one biomarker) selected from COX6A2, ALIX, CD81 , NCAM1 , GLAST1 , L1CAM, and miR-137. Preferably, the biomarkers for mitochondrial and redox impairment pathway are two biomarkers selected from COX6A2, ALIX, CD81 , NCAM1 , GLAST1 , L1 CAM, and miR-137. Alternatively, the biomarkers for mitochondrial and redox impairment pathway are COX6A2, ALIX, CD81 , NCAM1 , GLAST1 , L1 CAM, and miR-137.

[0214] As provided herein, the biomarker in (c) can be selected from N-methyl-D-aspartate receptor pathway biomarkers. The N-methyl-D-aspartate receptor pathway biomarkers, referred to herein, are known to the skilled person. Preferably, the biomarker for N-methyl-D-aspartate receptor pathway is selected from the group comprising miR-132, EAAT1, EAAT2, EAAT3, SHMTs. In one embodiment, the biomarker for N- methyl-D-aspartate receptor pathway is one, two, three, four, or five biomarkers (preferably one or two biomarkers, more preferably one biomarker) selected from the group comprising miR-132, EAAT1 , EAAT2, EAAT3, SHMTs.

[0215] As provided herein, the biomarker may also be selected from immunology and / or inflammation pathway biomarkers. These biomarkers are well known to the skilled person. For example, the biomarker of immunology and / or inflammation pathway can be selected from miR-34a, miR-132, miR-9, S1 OOB, TIMPs. In a preferred embodiment, the biomarker for of immunology and / or inflammation pathway is one, two, three, four, five or six biomarkers (preferably one or two, more preferably one biomarker) selected from the group comprising miR-34a, miR-132, miR-9, SWOB, TIMPs.

[0216] As provided herein, the biomarker can also be selected from the group of myelination pathway biomarkers. As understood herein, myelination pathway biomarkers are known to the skilled person. For example, the biomarker for myelination pathway can be selected from the group comprising oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs), miR-9, Fyn Kinase. In a preferred embodiment, the biomarker for myelination pathway is one, two, three or four biomarkers (preferably one or two biomarkers, more preferably one biomarker) selected from oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs), miR-9, Fyn Kinase.

[0217] As provided in the present invention, the biomarker in step (c) can also be selected from the group of excitatory-inhibitory balance pathway biomarkers. The excitatory-inhibitory balance pathway biomarkers are known to the skilled person. For example, the biomarker for excitatory-inhibitory balance pathway can be selected from miR-137, PV, PNN, GAD65, VGAT, VGLUTs, PSD, miR-138. Preferably, the biomarker for excitatory-inhibitory balance pathway is one, two, three, four, five, six, seven or eight biomarker(s) (preferably one or two, more preferably one biomarker) selected from miR-137, PV, PNN, GAD65, VGAT, VGLUTs, PSD, miR-138.

[0218] In the method of the present invention, preferably the at least one biomarker determined in (c) is selected from mitochondrial and redox impairment pathway biomarkers and N-methyl-D-aspartate receptor pathway biomarkers. As understood herein, the at least one biomarker is preferably one, two, three or four biomarkers, more preferably one or two biomarkers, even more preferably one biomarker.

[0219] It accordingly follows that preferably the at least one biomarker determined in (c) is selected from mitochondrial and redox impairment pathway biomarkers. Preferably, the at least one biomarker determined in (c) is one, two three or four biomarkers, preferably one or two biomarkers, more preferably one biomarker selected from COX6A2, ALIX, CD81 , NCAM1 , GLAST1 , L1 CAM, and miR-137.

[0220] In one embodiment of the method of the present invention, the at least one biomarkers determined in (c), i.e., (c3), is selected from miR-132, EAAT1 , EAAT2, EAAT3, and SHMTs. In a particularly preferred embodiment of the invention, in step (c) the expression level of COX6A2 and miR-137 is determined.

[0221] In another particularly preferred embodiment of the invention, in step (c) the expression level of at least one biomarker selected from the group comprising or consisting of miR-34a, miR-9, miR-132, miR-137 and miR-941 is determined. As understood herein, the at least one biomarker is preferably one, two, three, four or five biomarkers, more preferably one or two biomarkers, even more preferably one biomarker. In a specific embodiment, in step (c) the expression levels of biomarkers miR-34a, miR-9, miR- 132, miR-137 and miR-941 are determined.

[0222] In another particularly preferred embodiment of the invention, in step (c) the expression level of at least one biomarker selected from the group comprising or consisting of miR-34a, miR-9, miR-132 and miR- 137 is determined. As understood herein, the at least one biomarker is preferably one, two, three or four biomarkers, more preferably one or two biomarkers, even more preferably one biomarker. In a specific embodiment, in step (c) the expression levels of biomarkers miR-34a, miR-9, miR-132 and miR-137 are determined.

[0223] In another particularly preferred embodiment of the invention, in step (c) the expression level(s) of miR- 34a and / or miR-9 is (are) determined.

[0224] In another particularly preferred embodiment of the invention, in step (c) the expression level(s) of miR- 34a and / or miR-941 is (are) determined.

[0225] In another particularly preferred embodiment of the invention, in step (c) the expression level(s) of miR- 137 and / or miR-132 is (are) determined. miR-34a modulates mitochondrial function and oxidative stress responses. Elevated levels are associated with psychosis and are thought to exacerbate oxidative damage by suppressing antioxidant defenses. miR-132 regulates neuronal differentiation, synaptic plasticity and blood-brain barrier integrity, processes disrupted in psychosis. Dysregulation is linked to impaired NMDA receptor signaling. miR-9 affects neurogenesis and neuronal differentiation. Reduced levels are associated to developmental disorders associated with psychosis. miR-941 , a human-specific miRNA, promotes antioxidant responses via the Nrf2 cascade. Its downregulation may impair cellular responses to oxidative stress.

[0226] Oxidative stress induces the upregulation of miR-137 in parvalbumin positive interneurons. Its blood concentration is altered in schizophrenia. miR-137 polymorphisms have been linked to schizophrenia in genome-wide association studies.

[0227] It is noted that according to the present invention, multiple biomarkers can be measured in the same biological sample. However, this feature is not limiting and in certain embodiments of this invention different biomarkers can be measured in different samples, said samples originating from the same subject. The samples can be collected at the same time, but they can also be collected on different times, for example on different occasions on the same day, or on different days. In the preferred embodiment of this invention, both biomarkers in (b) and biomarkers in (c) are determined using material coming from the same biological sample.

[0228] The method of the present invention further necessarily comprises the step (d) of classifying a subject as neurological or psychiatric disorder subject based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles determined in step (b) and / or the expression level determined in step (c). Accordingly, it is required that the step (d) takes place following the steps (b) and (c). Preferably, in step (d), classifying a subject as neurological or psychiatric disorder subject is based on the fraction of brain- derived extracellular vesicles within the extracellular vesicles determined in step (b) and the expression level determined in step (c). However, the present invention, in particular embodiments also provides that classifying a subject as neurological or psychiatric disorder subject can be based on the fraction of brain- derived extracellular vesicles within the extracellular vesicles determined in step (b) or the expression level determined in step (c).

[0229] As provided by the present invention, the classification is done based on values of biomarker level determined in (c) and the value of the ratio determined in (b). Classification is done preferably by comparing the determined values to certain threshold values. This is explained based on non-limiting examples provided below.

[0230] In one preferred embodiment, the subject is classified as neurological or psychiatric disorder subject based on the expression level of COX6A2 being lower than a threshold value. It is preferred that said threshold value is 2.0 ng / mL, 1.8 ng / mL, 1.6 ng / mL, 1.4 ng / mL, 1.2 ng / mL, 1.0 ng / mL, 0.8 ng / mL, 0.6 ng / mL, 0.4 ng / mL or 0.2 ng / mL. More preferably, said threshold value is 1.4 ng / mL, 1.2 ng / mL, or 1.0 ng / mL. Even more preferably, this threshold value is 1.2 ng / mL.

[0231] The method of the present invention further relates to a preferred embodiment, wherein the subject is classified as neurological or psychiatric disorder subject based on the expression level of miR-137. As apparent to the skilled person, the expression level of microRNA needs to be provided with respect to some other expression level. Herein, the expression level of miR-137 is normalized with respect to the average expression level of miR-16, snRNA-U1 and snRNA-U6. Accordingly, preferably as provided by the present invention, the subject is classified as neurological or psychiatric disorder subject based on the expression level of miR-137, normalized as disclosed herein, being higher than a threshold value. Preferably the threshold value is 3.0 a.u., 3.6 a.u., 4.8 a.u., 6.0 a.u. 12 a.u.. More preferably, said threshold value is 3.6 a.u., 4.8 a.u., 6.0 a.u,. Even more preferably, the threshold value is 4.8 a.u.. In some embodiments, the threshold is in the range from 3.0 a.u. to 12.0 a.u., preferably in the range from 3.6 to 6.0, more preferably in the range from 4.0 a.u. to 6.0 a.u.

[0232] In one exemplary embodiment of the present invention, the subject is classified as neurological or psychiatric disorder subject based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles being around 10%.

[0233] In one exemplary embodiment of the present invention, the subject is classified as neurological or psychiatric disorder subject based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles being not more than 10%.

[0234] In another exemplary embodiment of the present invention, the subject is classified as neurological or psychiatric disorder subject based on the higher expression level of miR-34a compared to the expression level of miR-34a in subjects not being affected with neurological or psychiatric disorder, and on the lower expression levels of miR-941 , miR-9, miR-132 and miR-137 compared to the expression levels of miR- 941 , miR-9, miR-132 and miR-137 in subjects not being affected with neurological or psychiatric disorder.

[0235] In one specific embodiment, higher expression level of miR-34a, compared to the expression level of miR- 34a in subjects not being affected with neurological or psychiatric disorder, is associated with increased neurological or psychiatric disorder risk, such as psychosis risk. In another specific embodiment, higher expression level of miR-941 , compared to the expression level of miR-941 in subjects not being affected with neurological or psychiatric disorder and / or subjects being affected with neurological or psychiatric disorder, is associated with decreased neurological or psychiatric disorder risk, such as psychosis risk.

[0236] In another specific embodiment, higher expression level of miR-9, compared to the expression level of miR-9 in subjects not being affected with neurological or psychiatric disorder and / or subjects being affected with neurological or psychiatric disorder, is associated with decreased neurological or psychiatric disorder risk, such as psychosis risk.

[0237] In another specific embodiment, higher expression level of miR-132, compared to the expression level of miR-132 in subjects not being affected with neurological or psychiatric disorder and / or subjects being affected with neurological or psychiatric disorder, is associated with decreased neurological or psychiatric disorder risk, such as psychosis risk.

[0238] In another specific embodiment, higher expression level of miR-137, compared to the expression level of miR-137 in subjects not being affected with neurological or psychiatric disorder and / or subjects being affected with neurological or psychiatric disorder, is associated with decreased neurological or psychiatric disorder risk, such as psychosis risk.

[0239] Further according to the present invention, classification of a subject as a neurological or psychiatric disorder subject in step (d) based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles (EVs) determined in step (b) and the expression level determined in step (c), can for example be performed as follows:

[0240] 1 . Determine the fraction of Brain EVs within total EVs

[0241] 2. Apply appropriate Brain EV fraction and individual biomarker scores based on their known relevance to specific neurological or psychiatric disorders.

[0242] 3. Establish threshold values that indicate the likelihood of a neurological or psychiatric disorder.

[0243] 4. Classify the subject based on the established thresholds.

[0244] The exact thresholds, weights, and decision criteria are determined through statistical analysis of data from both healthy individuals and those with confirmed neurological or psychiatric disorders.

[0245] The method of the present invention further relates to an embodiment, wherein classifying a subject as neurological or psychiatric disorder subject is stratifying said subject into disease stages of said neurological or psychiatric disorder based on determination in (b) and (c). Accordingly, the term “stratifying” is known to the skilled person and may refer to stratifying a particular subject into one of several groups of subjects based e.g. on stage or on the progress of the disease. Preferably, in the method of the present invention, the subject is classified as low-risk or high-risk neurological or psychiatric disorder subject.

[0246] In one embodiment, classification in step (d) may refer to differential classification between different diseases. For example, in one preferred embodiment, in step (d) differentiation between Alzheimer’s disease subject and cognitive impairment subject is made.

[0247] In the method of the present invention, the neurological or psychiatric disorder is preferably selected from schizophrenia, psychosis, mood disorder, depression (such as major depressive disorder), bipolar disorder, Alzheimer's disease, Parkinson disorder, and cognitive impairment. However, the term “psychiatric disorder” is not meant to be particularly limited, and in any case is not limited to these examples recited in the foregoing. Accordingly, the term “psychiatric disorder” may also include other psychiatric disorders.

[0248] In one preferred embodiment, the psychiatric disorder may refer to early psychiatric disorder, preferably selected from early schizophrenia, early epilepsy, and early bipolar disorder.

[0249] In particularly preferred embodiment of the present invention, the psychiatric disorder is a psychotic disorder, such as psychosis, preferably schizophrenia, preferably the early-stage schizophrenia, more preferably the early-stage schizophrenia with cognitive impairment.

[0250] However, in one embodiment, the neurological or psychiatric disorder is a neurological disorder. Preferably, the neurological disorder is Alzheimer’s disease or cognitive impairment.

[0251] In the method of the present invention, the expression levels of biomarkers are preferably determined by an in vitro assay. Such assay is well known to the skilled person. Accordingly and preferably, in the method of the present invention, the in vitro assay is selected from selected from an immunoassay, an ELISA- based assay, an aptamer-based assay, an mRNA expression level assay, in situ hybridization assay, a proteomics-based assay, an MS-based assay, a PCR-based assay, a real time PCR-based assay, next generation sequencing, an electrochemistry-based assay, a lateral-flow assay, a nanobead-based assay, a microfluidics-based assay, and an oligonucleotide-templated reaction. However, as it is to be understood herein, the method of determining the expression level of biomarkers is not meant to be limited and any determination method conceivable to the skilled person is deemed encompassed by the present invention.

[0252] To this end, particularly preferred are embodiments, wherein the expression level protein biomarkers is determined by using an immunoassay or an ELISA-based assay. Further particularly preferred are embodiments, wherein the expression level of miRNA is determined by using a real time PCR-based assay and validated by miREIA-miRNA enzyme immunoassay (Biovendor R&D). miREIA, as known to the skilled person, and as understood herein, is an immunoassay-based method of miRNA quantification which involves hybridization of miRNA isolated from a plasma sample to complementary biotinylated DNA oligonucleotide probe. The formed hybrids are then captured by a microtiter plate- immobilized monoclonal antibody specific to perfectly matched DNA / miRNA hybrids. Next steps are the same used on standard ELISA protocols. The miREIA assay does not require amplification steps.

[0253] The measurements of the biomarker level are to be performed with sufficient confidence, as it is understood by the one skilled in the art, to determine beyond any doubt that the subject can be classified as low-risk or high risk for a neurological or psychiatric disorder. Should the measurements of any one of the biomarkers provide a value about the threshold, as set forth in this application, it is known to that skilled in the art that these measurements can be repeated until suitable level of confidence is reached.

[0254] As the biomarkers of the present invention are preferably measured in brain-derived extracellular vesicles, it is likely that they reflect changes in central nervous system, rather than peripheral tissue. As they can be assessed with a single venous blood sample, their evaluation is feasible in most clinical settings. Finally, the results indicate that the biomarkers of the present invention can discriminate between people who will or will not go on to develop neurological or psychiatric disorders with predictive performance that is high enough for clinical implementation and generalizable to new settings.

[0255] The use of biomarkers of the present invention allows classifying a subject as neurological or psychiatric disorder subject, which can be used for treating neurological or psychiatric disorders and / or to halt / prevent the progression of adverse outcomes related to neurological or psychiatric disorders.

[0256] According to an embodiment, the ex vivo method for classifying a subject as neurological or psychiatric disorder subject of the present invention is useful to set up / establish a suitable treatment strategy for treating and / or preventing progression of neurological or psychiatric disorder in a subject that is classified as neurological or psychiatric disorder subject or that has a risk to develop a neurological or psychiatric disorder.

[0257] Thus, the present invention also provides a method for treating and / or preventing progression of neurological or psychiatric disorder in a subject that is classified as neurological or psychiatric disorder subject. The treatment for neurological or psychiatric disorders is personalized and typically includes one or more treatments selected from:

[0258] • treatment with a drug selected from the group comprising antidepressants, mood stabilizers, psychostimulants, antipsychotics, cholinergic medications and combinations thereof,

[0259] • various forms of therapy selected from the group comprising psychotherapy, cognitive behavioral therapy (CBT), physical therapy and combinations thereof, and

[0260] • other interventions such as neurostimulation (for example Vagal Nerve Stimulation (VNS)).

[0261] A key factor in successful treatment is a coordinated care approach with a healthcare provider to develop an individualized treatment plan based on the specific condition and its severity.

[0262] The present invention further relates to a kit of parts, comprising reagents for determining the expression level of at least one biomarker selected from biomarkers for (i) extracellular vesicles, (ii) brain-derived extracellular vesicles, (iii) (d) biomarkers for immunology and / or inflammation pathway, (c2) mitochondrial and redox impairment, (c3) biomarkers for N-methyl-D-aspartate receptor pathway, (c4) biomarkers for myelination pathway, and (c5) excitatory-inhibitory balance pathway.

[0263] In an embodiment, the present invention provides a biomarker kit comprising:

[0264] (i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63;

[0265] (ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM1 W, preferably selected from L1CAM and NCAM1 ; and

[0266] (iii) reagents for determining the expression level of at least one biomarker selected from:

[0267] (d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, S100B and TIMPs,

[0268] (c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81, NCAM1, GLAST1 and L1CAM, (c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941 , EAAT1, EAAT2, EAAT3 and SHMTs,

[0269] (c4) myelination pathway biomarkers selected from the group comprising miR-9, oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs) and Fyn Kinase,

[0270] (c5) excitatory-inhibitory balance pathway biomarkers selected from the group comprising miR-137, miR-138, PV, PNN, GAD65, VGAT, VGLUTs and PSD.

[0271] Preferably, the kit of the present invention further comprises reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles and brain-derived extracellular vesicles and (iii) mitochondrial and redox impairment.

[0272] Preferably, the kit of the present invention further comprises reagents for isolating extracellular vesicles including brain-derived extracellular vesicles from a biological sample.

[0273] The present invention further provides use of the kit of the invention in the method of the invention. Accordingly, it is to be understood that the kit of the invention is suitable for use in the method of the invention. For example, the kit of the invention may further comprise instructions with regard how the reagents comprised in the kit can be used in the method of the present invention.

[0274] The present invention further relates to the kit of the present invention for use in a diagnostic method of the present invention. In one embodiment, the present invention relates to kit of parts of the present invention for use in manufacture of a reagent for determining the expression level of biomarkers, as required in the method of the present invention.

[0275] Further embodiments of the present invention are disclosed in the following numbered items.

[0276] 1. An ex vivo method for classifying a subject as neurological or psychiatric disorder subject, the method comprising the steps of: a) providing a biological sample obtained from a subject and comprising extracellular vesicles, including brain-derived extracellular vesicles; b) determining in the sample of (a) the fraction of brain-derived extracellular vesicles within the extracellular vesicles; c) determining in the sample of (a) the expression level of at least one biomarker selected from: (d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, SWOB and TIMPs, preferably selected from the group comprising miR-34a, miR-9 and miR-132,

[0277] (c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81, NCAM1, GLAST1 and L1CAM, preferably miR-137,

[0278] (c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941 , EAAT1, EAAT2, EAAT3 and SHMTs, preferably selected from miR- 132 and miR-941,

[0279] (c4) myelination pathway biomarkers selected from the group comprising miR-9, oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs) and Fyn Kinase, preferably miR-9

[0280] (c5) excitatory-inhibitory balance pathway biomarkers selected from the group comprising miR-137, miR-138, PV, PNN, GAD65, VGAT, VGLUTs and PSD, preferably selected from miR-137 and miR-138; and d) classifying a subject as neurological or psychiatric disorder subject based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles determined in step (b) and the expression level determined in step (c).

[0281] 2. The method of item 1, wherein the neurological or psychiatric disorder is selected from schizophrenia, psychosis, mood disorder, depression (such as major depressive disorder), bipolar disorder, Alzheimer's disease, Parkinson disorder, and cognitive impairment; preferably the neurological or psychiatric disorder is selected from schizophrenia, psychosis, mood disorder, Alzheimer's disease, Parkinson disorder and cognitive impairment.

[0282] 3. The method of item 1 or 2, wherein the neurological or psychiatric disorder is a psychiatric disorder.

[0283] 4. The method of any one of items 1 to 3, wherein the psychiatric disorder is psychosis or schizophrenia.

[0284] 5. The method of item 1 or 2, wherein the neurological or psychiatric disorder is a neurological disorder.

[0285] 6. The method of item 5, wherein the neurological disorder is Alzheimer's disease, Parkinson's disease and / or cognitive impairment.

[0286] 7. The method of any one of items 1 to 6, wherein the biological sample comprises bodily fluid, preferably wherein the bodily fluid is substantially free of cells, preferably wherein the bodily fluid is selected from plasma, cerebrospinal fluid, serum, saliva, nasal mucus, and urine, and / or preferably wherein no further isolation of extracellular vesicles was performed on the biological sample.

[0287] 8. The method of any one of items 1 to 7, wherein step (b) includes detecting and quantifying brain-derived extracellular vesicles with respect to total population of extracellular vesicles in the sample of (a), by antibody staining, in situ hybridization and / or microscopy.

[0288] 9. The method of any one of items 1 to 8, wherein step (b) includes determination of the expression of at least one biomarker for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63; and at least one biomarker for brain-derived extracellular vesicles selected from the group comprising L1CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SIOOB and TMEM119, preferably selected from L1 CAM and NCAM1.

[0289] 10. The method of any one of items 1 to 9, wherein expression levels of biomarkers are determined by an in vitro assay.

[0290] 11. The method of item 10, wherein the in vitro assay is selected from an immunoassay, an ELISA-based assay, an aptamer-based assay, an mRNA expression level assay, in situ hybridization assay, a proteomics-based assay, an MS-based assay, a PCR-based assay, a real time PCR-based assay, next generation sequencing, an electrochemistry-based assay, a lateral-flow assay, a nanobead-based assay, a microfluidics-based assay, and an oligonucleotide-templated reaction.

[0291] 12. The method of any one of items 1 to 11, wherein at least one biomarker determined in (c) is selected from

[0292] (c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81 , NCAM1 , GLAST1 and L1CAM, preferably miR-137, and

[0293] (c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941 , EAAT1 , EAAT2, EAAT3 and SHMTs, preferably selected from miR- 132 and miR-941 , preferably wherein the at least one biomarker determined in (c) is selected from

[0294] (c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81 , NCAM1 , GLAST1 and L1CAM, preferably miR-137.

[0295] 13. The method of any one of items 1 to 12, wherein in step (c) the expression level of COX6A2 and miR-137 is determined. The method of item 13, wherein the subject is classified as neurological or psychiatric disorder subject based on: expression level of COX6A2 being lower than a threshold value, wherein preferably the threshold value is 1.2 ng / ml, and expression level of mi R- 137 normalised with respect to the average expression level of miR-16, snRNA-U1 and snRNA-U6 is higher than a threshold value, wherein preferably the threshold value is 4.8 a.u. The method of any one of items 1 to 11, wherein at least one biomarker determined in (c) is selected from the group comprising miR-132, miR-941 , EAAT1 , EAAT2, EAAT3, and SHMTs. The method of any one of items 1 to 11, wherein at least one biomarker determined in (c) is selected from

[0296] (d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, SWOB and TIMPs, preferably selected from the group comprising miR-34a, miR-9 and miR-132. The method of any one of items 1 to 11, wherein at least one biomarker determined in (c) is selected from the group comprising miR-34a, miR-9, miR-132, miR-137 and miR-941. The method of any one of items 1 to 11 , wherein at least one biomarker determined in (c) is miR- 34a and / or miR-9. The method of any one of items 1 to 11 , wherein at least one biomarker determined in (c) is miR- 34a and / or miR-941. The method of any one of items 1 to 11 , wherein at least one biomarker determined in (c) is miR- 137 and / or miR-132 is determined. The method of any one of items 1 to 11, wherein biomarkers determined in (c) are miR-34a, miR-9, miR- 132, miR-137 and miR-941. The method of any one of items 16 to 19 or 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on higher expression level of miR-34a, compared to the expression level of miR-34a in subjects not being affected with neurological or psychiatric disorder. 23. The method of any one of items 15, 17, 19 or 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on lower expression level of miR-941 , compared to the expression level of miR-941 in subjects not being affected with neurological or psychiatric disorder.

[0297] 24. The method of any one of items 16 to 18 or 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on lower expression level of miR-9, compared to the expression level of miR-9 in subjects not being affected with neurological or psychiatric disorder.

[0298] 25. The method of any one of items 15 to 17 or 20 to 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on lower expression level of miR-132, compared to the expression level of miR-132 in subjects not being affected with neurological or psychiatric disorder.

[0299] 26. The method of any one of items 17, 20 or 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on lower expression level of miR-137, compared to the expression level of miR-137 in subjects not being affected with neurological or psychiatric disorder.

[0300] 27. The method of any one of items 1 to 26, wherein the subject is classified as neurological or psychiatric disorder subject based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles being less than 10%.

[0301] 28. The method of any one of items 1 to 27, wherein classifying a subject as neurological or psychiatric disorder subject is stratifying said subject into disease stages of said neurological or psychiatric disorder based on determination in (b) and (c), preferably wherein the subject is classified as low-risk or high-risk neurological or psychiatric disorder subject.

[0302] 29. The method of item 28, wherein in step (d) differentiation between Alzheimer's disease subject and cognitive impairment subject is made.

[0303] 30. A biomarker kit comprising:

[0304] (i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63; (ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM1 W, preferably selected from L1CAM and NCAM1 ; and

[0305] (iii) reagents for determining the expression level of at least one biomarker selected from:

[0306] (d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, S100B and TIMPs,

[0307] (c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81, NCAM1, GLAST1 and L1CAM,

[0308] (c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941 , EAAT1, EAAT2, EAAT3 and SHMTs,

[0309] (c4) myelination pathway biomarkers selected from the group comprising miR-9, oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs) and Fyn Kinase,

[0310] (c5) excitatory-inhibitory balance pathway biomarkers selected from the group comprising miR-137, miR-138, PV, PNN, GAD65, VGAT, VGLUTs and PSD. The biomarker kit of item 30, comprising:

[0311] (i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63;

[0312] (ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM119, preferably selected from L1CAM and NCAM1 ; and

[0313] (iii) reagents for determining the expression level of at least one biomarker selected from biomarkers for immunology and / or inflammation pathway selected from the group comprising miR-34a, miR-9, miR-132, SWOB and TIMPs, preferably selected from the group comprising miR-34a, miR-9 and miR-132. The biomarker kit of item 30, comprising:

[0314] (i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63; and (ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM1 W, preferably selected from L1 CAM and NCAM1 ; and

[0315] (iii) reagents for determining the expression level of at least one biomarker selected from biomarkers for mitochondrial and redox impairment selected from the group comprising miR-137, COX6A2, ALIX, CD81 , NCAM1 , GLAST1 and L1 CAM, preferably selected from miR-137 and COX6A2..

[0316] 33. The biomarker kit of item 30, comprising:

[0317] (i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63;

[0318] (ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM1 W, preferably selected from L1CAM and NCAM1 ; and

[0319] (iii) reagents for determining the expression level of at least one biomarker selected from the group comprising miR-34a, miR-9, miR-W2, miR-W7 and miR-941.

[0320] 34. The biomarker kit of any one of items 30 to 33, further comprising the reagents for isolating extracellular vesicles including brain-derived extracellular vesicles from a biological sample.

[0321] 35. Use of the kit of any one of items 30 to 34 in the method of any one of items 1 to 29.

[0322] The invention is illustrated by relying on the following examples. These, however, are not meant to be construed as limiting the scope of protection of the present invention, which is determined by the hereto appended claims.

[0323] Those skilled in the art will appreciate that the invention described herein is susceptible to variations and modifications other than those specifically described. It is to be understood that the invention includes all such variations and modifications without departing from the spirit or essential characteristics thereof. The invention also includes all of the steps, features, compositions and compounds referred to or indicated in this specification, individually or collectively, and any and all combinations or any two or more of said steps or features. The present disclosure is therefore to be considered as in all aspects illustrated and not restrictive, the scope of the invention being indicated by the appended claims, and all changes which come within the meaning and range of equivalency are intended to be embraced therein.

[0324] The foregoing description will be more fully understood with reference to the following Examples. Such Examples, are, however, exemplary of methods of practising the present invention and are not intended to limit the application and the scope of the invention.

[0325] Examples

[0326] Example 1

[0327] Confocal microscopy image of mouse anterior cingulate cortex (ACC) stained with DAPI (blue) to highlight the nuclei of neurons and fluorescently labelled anti-TOMM20 (red), a marker for mitochondria (Figure 1). Abundance of mitochondria in neurons highlight the importance of mitochondrial pathways for healthy central nervous system function. Changes in mitochondrial pathways indicate onset of neurological or psychiatric disorders.

[0328] General experimental procedures

[0329] For mRNAs and miRNAs assays:

[0330] Key steps:

[0331] 1. Pre-analytical blood preparation: Centrifuge whole blood and collect plasma (could be another biofluids).

[0332] 2. Reagent preparation: Reconstitute standards, controls, and prepare working solutions.

[0333] 3. Sample preparation: Homogenize and dilute plasma samples.

[0334] 4. Hybridization: Mix samples / standards with DNA probe.

[0335] 5. Add samples to coated plate, incubate, wash, add detection antibody, substrate, and measure. Notable features:

[0336] • Uses RNA inhibitor in sample preparation.

[0337] • Includes a DNA probe hybridization step.

[0338] • Utilizes ready-to-use controls and a coated plate.

[0339] For antigenes detection and quantifications

[0340] This protocol outlines a general ELISA procedure for multiple biomarkers.

[0341] Key steps:

[0342] 1 . Pre-analytical blood preparation: Similar to mRNAs and miRNAs protocol. 2. Reagent preparation: Prepare blocking buffer, antibody buffer, and dilute antibodies.

[0343] 3. Coating: Add standards, samples, and blanks to plate and incubate overnight.

[0344] 4. ELISA procedure: Block, add primary and secondary antibodies, wash, add substrate, and measure.

[0345] Notable features:

[0346] • Requires coating the plate with samples (not pre-coated)

[0347] • Use a blocking step

[0348] • Includes multiple biomarker assays with slight variations in dilutions.

[0349] • Two-day procedure with overnight incubation

[0350] Both protocols involve similar principles but differ in specific steps, reagents, and the biomarkers being detected

[0351] Example 2

[0352] Brain-derived exosomal miRNAs quantification

[0353] Participants and clinical assessments

[0354] Model development and internal validation

[0355] Participants were recruited from 11 centers in Europe (Amsterdam, Barcelona, Basel, Cologne, Copenhagen, Den Haag, London, Paris, Vienna), South America (Sao Paulo), and Australia (Melbourne) between September 2010 and July 2015, as part of the European Union Gene-Environment Interactions (EU-GEI) study. The study received local ethical approval at each site and each participant provided written informed consent or assent, with a parent or guardian also consenting for minor participants.

[0356] Clinical High Risk (CHR) participants underwent baseline Comprehensive Assessment of At Risk Mental States (CAARMS) assessments and were invited for follow-up assessments at 12 and 24 months. The assessment of participants using the CAARMS was performed by trained raters, with inter-rater reliability assessed using online training videos (IRRseverity=0-78; IRRfrequency=0-90).

[0357] Transition to psychosis during the 24 month follow up period was defined psychometrically by CAARMS criteria. Additional outcome data were obtained from information recorded in their electronic health records up to seven years after baseline. In these participants, psychosis was defined using I CD- 10 / DSM- IV criteria or accepted referral to an early intervention team. External validation

[0358] Participants were recruited from nine study sites in the US and Canada between February 2015 and November 2018 as part of the North American Prodrome Longitudinal Study (NAPLS-3). The study received local ethical approval at each site, and each participant provided written informed consent or assent, with a parent or guardian also consenting for minor participants.

[0359] CHR criteria were based on the Structured Interview for Psychosis-Risk Syndromes (SIPS) and transition to psychosis up to 36 months by the Criteria of Psychosis-Risk State (COPS) by clinical raters trained to meet reliability standards for administration and scoring, as previously described. Controls did not meet CHR or DSM psychotic disorder criteria and had no first degree relative with a history of a psychotic disorder.

[0360] Inclusion and exclusion criteria for EU-GEI and NAPLS-3

[0361] In EU-GEI, CHR participants met Comprehensive Assessment for At Risk Mental States (CAARMS), were aged between 15 and 45 years. Exclusion criteria included (i) meeting criteria for current or lifetime psychotic disorder; (ii) IQ<60; (iii) CHR symptoms could be explained by a medical disorder or substance use. Control participants were recruited from four of the same sites as the CHR participants (Amsterdam, Den Haag, London and Melbourne). The exclusion criteria were the same as for the CHR group, except that they did not meet criteria for the CHR state.

[0362] In NAPLS-3, CHR participants met Structured Interview for Psychosis-Risk Syndromes (SIPS) criteria and were aged between 12 and 35 years. Exclusion criteria included (i) meeting criteria for current or lifetime psychotic disorder; (ii) IQ<70; (iii) history of a central nervous system disorder, or (iv) CHR symptoms were clearly caused by an Axis I disorder. Control participants (Cited in NAPLS-3 studies as UC: unaffected controls) did not meet criteria for psychosis high-risk and did not have a family history of a psychotic disorder in any first-degree relatives. CHR non-transitions and control participants were drawn from a larger pool of participants, formally matched with CHR transitions on the basis of age, gender and ethnicity.

[0363] Extracellular vesicle extraction and miRNA quantification

[0364] Blood was collected in EDTA (EU-GEI) or ACD (NAPLS-3) tubes after overnight fasting (from midnight until blood collection) and immediately centrifuged at 2,000g for 10 minutes (EU-GEI) and 3,000g for 5 minutes (NAPLS-3) at 4°C. All plasma samples were stored at -80°C until processing. Total extracellular vesicle isolation including those of brain origin, was performed on plasma samples using an ExoQuick™ kit (System Biosciences). The fraction of brain-derived extracellular vesicles within the total extracellular vesicle population was then determined by analyzing the isolated vesicles for the presence of both general extracellular vesicle markers and brain-specific markers. The fraction of brain- derived extracellular vesicles within the total extracellular vesicles population was determined by analyzing the isolated vesicles for both general and brain-specific markers. General markers included CD81, CD63, CD9, and ALIX, while brain-specific markers were NCAM1 , GLAST1 , Synaptophysin, and L1 CAM.

[0365] The total isolated brain-specific extracellular vesicles were used for miR quantification using a miREIA- miRNA enzyme immunoassay (Biovendor R&D), which involves hybridization of miRNA isolated from the plasma sample to complementary biotinylated DNA oligonucleotide probes. The formed hybrids were captured by a microtiter plate-immobilized monoclonal antibody specific to perfectly matched DNA / miRNA hybrids for five miRNAs: hsa-miR-9-5p (miR-9; UCUUUGGUUAUCUAGCUGUAUGA, SEQ ID NO:32), hsa-miR-34a-5p (miR-34a; UGGCAGUGUCUUAGCUGGUUGU, SEQ ID NO:33), hsa-miR-132-3p (miR- 132; UAACAGUCUACAGCCAUGGUCG, SEQ ID NO:34), hsa-miR-137-3p (miR-137; UUAUUGCUUAAGAAUACGCGUAG, SEQ ID NO:35) and hsa-miR-941 (miR-941 ; CACCCGGCUGUGUGCACAUGUGC, SEQ ID NO:36).

[0366] Statistical analysis

[0367] Prediction modelling analysis

[0368] Model development, internal validation and external validation followed gold standard methodological guidelines and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD+AI).

[0369] Descriptive analyses of baseline clinical and sociodemographic characteristics was first performed, obtaining means and frequencies for continuous and categorical variables, respectively, to characterise the similarities between internal and external validation samples. External validation only assesses true model generalisability if there are differences between cohorts.

[0370] Model development

[0371] The primary analysis was to develop a model to discriminate between CHR individuals who did (CHR-T) and CHR individuals who did not transition to psychosis (CHR-NT). As a second step, it was aimed to develop a model to discriminate between controls and CHR-T to evaluate potential for population-level screening. There were no missing data in participants who provided plasma samples. Models were fitted using logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO). All models initially outputted probabilities, with a threshold of 0.5 used for classification measures (e.g. sensitivity).

[0372] Internal validation

[0373] Internal validation was performed using repeated nested cross-validation with 5 folds and 5 repetitions, as implemented in caret (version 6.0-93). Lambda was tuned within the training folds with the optimal lambda value, minimizing the cross-validated partial likelihood deviance, being used in the final model.

[0374] External validation

[0375] The final model was fitted on all development data and extracted coefficients. These coefficients were than applied to generate an individualised prognostic index (PI) for each participant in the NAPLS-3 dataset. Due to expected exacerbation of miscalibration, logistic recalibration was performed within the external validation data, as recommended by methodological guidelines, re-estimating the intercept and slope to better align with observed risk in the new setting, while maintaining the relationships between coefficients. This results in improved calibration but will not affect discrimination performance.

[0376] Model performance

[0377] Model performance was primarily assessed through discrimination (Harrell’s C statistic [0-9-1 -0 is considered outstanding, 0- 8- 0 9 excellent and 0 7— 0 8 acceptable]) and calibration (calibration intercept [ideally close to 0] and slope [ideally close to 1]). The Brier score was also recorded, which is an overall measure of algorithm performance (ideally close to 0). Balanced accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (LR+) and negative likelihood ratio (LR-) were also calculated.

[0378] Assessment of clinical utility

[0379] The decision curve analysis quantifies the clinical “net benefit” for a clinical prediction model over a range of clinical preferences. Net benefit indexes the difference between the clinical benefit of accurate predictions (e.g., detecting psychosis cases early) and the clinical harm of inaccurate predictions (e.g., provision of unnecessary preventive care). As the definition of clinical preference is subjective, net benefit was plotted across a range of threshold probabilities (0-50%), where values closer to 0% represent greater concern for missing potential psychosis cases and values closer to 50% represent greater concern for providing unnecessary preventive care. The net benefits of using the model were plotted compared to default strategies of assessing all or no individuals at risk. Standardised net benefit was calculated as the net benefit divided by the incidence of the outcome, showing the percentage increase in net benefit at a given risk threshold. For our primary analysis, we used the meta-analytic incidence of psychosis onset in CHR individuals at 3 years (22%). For our secondary analysis, the meta-analytic incidence of psychosis in the general population (0-027%) was used.

[0380] Post-hoc univariate analyses miRNAs plasma concentrations were not normally distributed, therefore, Mann Whitney U test was employed for pairwise significance and Wilcoxon effect size (r) for effect sizes. Wilcoxon effect size was interpreted as follows: r<0 1 “negligible”, 0 1 <r<0 3 “small”, 0-3<r<0 5 “medium”, otherwise “large”.

[0381] A false discovery rate correction was used to address the issue of multiple comparisons and control the overall rate of false positives (type I error). All statistical tests set a significance threshold of 0-05, indicating that the results with p-values below that threshold are considered statistically significant.

[0382] Sensitivity analyses

[0383] To mitigate against potential batch effects, all prediction models were re-run using a mean offset correction to harmonise miRNA data across sites and computed relevant performance metrics. Sample size calculations were conducted using the pmsampsize package (version 1.1.3) post hoc to ensure parameters were accurate.

[0384] RESULTS

[0385] Participant characteristics

[0386] Data from one CHR participant in EU-GEI and one control participant in NAPLS-3 were excluded due to consistent extreme outliers in the levels of miRNA markers. In EU-GEI, 251 CHR individuals were included, 48 (19%) developed psychosis (CHR-T) during follow up and 203 did not (CHR-NT) (mean follow-up time = 650-9 [SD=344 9; range=11-2623] days), along with 56 controls. In NAPLS-3, a subset of 103 CHR individuals were included, as well as 48 CHR-T and 55 CHR-NT (mean follow-up time = 526-5 [SD=426-5; range=0-1568] days), along with 45 controls.

[0387] Prediction modelling

[0388] Discrimination between CHR-T and CHR-NT

[0389] Model development and internal validation

[0390] The model retained all miRNAs, with miR-34a associated with the greatest increase in transition risk and miR-941 with the greatest reduction in risk. The miRNAs had outstanding discrimination performance in EU-GEI (C=0- 99, 95%Cls:0 96-1 -00; Figure 2A). Calibration showed some evidence of under-estimation of risk at higher levels of observed risk (intercept=0 31 , slope=0 87; Figures not shown).

[0391] External validation

[0392] The outstanding discrimination performance was maintained in the NAPLS-3 dataset (C=1 00, 95%Cls:0 98-1 00; Figure 2A) but patterns of miscalibration were exacerbated (intercept=-2077, slope=8-65). However, recalibration improved this (intercept=-87- 1 , slope=1 -38) alongside classification metrics as a result (Table 2).

[0393] Assessment of clinical utility

[0394] For CHR-T vs CHR-NT, using the model led to a net benefit compared to the default strategies of assessing either all CHR subjects or none across all risk threshold probabilities (Figure 2B). If a preventive intervention were considered necessary at a risk threshold higher than 33% (i.e. missed detection of a psychosis case early is twice as harmful as an unnecessary intervention), according to the external validation, the model would provide a net benefit of 21 -3% in a CHR population over other strategies (assessing all or none), meaning that an additional 96-6% of psychosis cases could be identified at the CHR stage. This would translate into identifying 21 additional individuals who would subsequently develop psychosis among 100 people at CHR.

[0395] Discrimination between CHR-T and controls

[0396] Model development and internal validation

[0397] Models discriminating CHR-T from controls consistently retained miR-9 and miR-34a, with miR-132, miR- 137 and miR-941 consistently dropped by LASSO. MiR-34a was associated with increased and miR-9 with decreased psychosis risk. miR-9 and miR-34a had excellent performance in terms of discrimination (C=0'94, 95%Cls:0 84-1 00; Figure 2A). Calibration generally showed good agreement between observed and expected risk at most predicted probabilities with some evidence of under-estimation of risk at higher observed probabilities (intercept=O- 14, slope= 1 -30).

[0398] External validation

[0399] The excellent performance seen in internal validation was not retained but performance was still acceptable (C=0 73, 95%Cls:0-71-0 75). Miscalibration was more evident, showing over-estimation of risk at lower observed probabilities (intercept=0 47, slope=0 23). Recalibration was not able to entirely rectify these issues (intercept=-87- 1 , slope=1 -38), particularly in terms of classification metrics. Decision curve analysis

[0400] Decision curve analysis indicated that the model for CHR-T vs controls only showed a net benefit above risk thresholds of 48% in internal validation with no net benefit in external validation (Figure 2C), demonstrating that the model would be of no clinical benefit in the general population in its current form.

[0401] Post-hoc univariate analyses

[0402] Post-hoc univariate analyses (Figure 3) revealed similar patterns of group differences in the levels of individual miRNAs across the EU-GEI and NAPLS-3 cohorts. Levels of miR-34a were higher in the CHR- T than in the CHR-NT group in both studies (rEU-GEI=0 61 , 95%Cls:0 51 -0 69; rNAPLS-3=0 41 , 95%Cls:0-25-0-57; p<0 001 ), whereas miR-9 levels were lower (rEU-GEI =0-35, 95%Cls:0-26-0 45; rNAPLS-3=0-29, 95%Cls:0 10-0 46; p<0 001). In contrast, the group differences in miR-941 , miR-132 and miR-137 concentration reflected higher levels in the CHR-NT group than in the CHR-T group, with a range of effect sizes (p<0 001; miR-132: rEU-GEI=0-31 , 95%Cls:0 21 -0 42; rNAPLS-3=0'86, 95%Cls:0-82-0-86; miR-941 : rEU-GEI=0 43, 95%Cls:0-45-0-61 ; rNAPLS-3=0'86, 95%Cls:0-83-0 86). miR-137 concentrations were higher in CHR-NT compared to CHR-T in the EU-GEI dataset but showed no significant differences in NAPLS-3 (rEU-GEI=0 26, 95%Cls:0 15-0 36, p<0 001 ; rNAPLS-3=0 01 , 95%Cls:0 004-0 23, p=0-91).

[0403] Sensitivity analyses

[0404] Implementing a mean offset correction to mitigate against potential batch effects resulted in models with similarly excellent discrimination performance (C>0-92) to the original models. Calibration was generally improved, particularly in external validation. Classification metrics suggested there was an increase in false positives compared to the original models. CHR-T v CHR-NT models were adequately powered given the final model parameters (required n=237; sample n=251 ). However, CHR-T v controls models were underpowered (required n=382; sample n=104).

[0405] DISCUSSION of RESULTS miRNAs of extracellular vesicles as predictors of psychosis transition

[0406] These findings provide the first evidence that brain-derived miRNAs related to redox hub function are predictive of psychosis onset in people at clinical high risk. The results are biologically plausible, as these miRNAs are master regulators of redox activity, mitochondrial bioenergetics, immune / neuroinflammation, and NMDAR pathways, processes that have all been independently implicated in the pathophysiology of psychosis. The main finding was that a logistic regression model that included these five miRNAs predicted psychosis onset in CHR individuals with outstanding prognostic performance in internal and external validation.

[0407] Post-hoc univariate analyses indicated that these findings reflected clear group differences in miRNA concentration between CHR individuals who did and did not transition to psychosis and controls. Strikingly similar patterns of group differences were evident across the two cohorts. For two of the miRNAs, the concentrations in the CHR participants who later developed psychosis were distinct from both those in CHR subjects who did not progress to psychosis and in healthy controls, with higher levels of miR-34a and lower levels of miR-9. This suggests that the later onset of psychosis may be related to reduced expression of genes regulated by miR-34a, but greater expression of genes regulated by miR-9. miR-34a influences neurogenesis, neuronal differentiation, and the permeability of the BBB. Altered miR-34a levels have previously been described in brain tissue and peripheral blood from people with schizophrenia. miR- 9 regulates neurogenesis and neuronal differentiation. Reduced miR-9 levels have been reported in blood samples and in neuronal progenitor cells derived from people with schizophrenia.

[0408] A different pattern of group differences was evident for the levels of miR-941 , miR-132 and miR-137. For these miRNAs, it was the concentration in CHR participants who did not develop psychosis that distinguished the groups, with this group showing higher levels than both the CHR subjects that progressed to psychosis and healthy controls. These observations suggest that in people at CHR, unusually high suppression of certain redox hub genes may confer protection against progression to psychosis. This is supported by the previous mechanistic literature. Much of the preclinical literature supports this signature as being specific to increasing psychosis risk but this instead could be more reflective of general worsening of mental health. CHR individuals in the EU-GEI cohort with comorbid depression were significantly more likely to develop psychosis compared to those without, however this finding was not seen in the NAPLS-2 cohort or across the literature. Moreover, while there is some weak evidence implicating miR-9, miR-34a, miR-132 and miR-137 in the neurobiology of other psychiatric disorders, miR-941 is less commonly found in the literature for any psychiatric condition, making its specificity to psychosis even more plausible.

[0409] Because the concentrations of miR-132, miR-9 and miR-941 were higher in the subgroup of CHR subjects who did not progress to psychosis (CHR-NT) than in those who did (CHR-T), reduced expression of the target genes for these miRNAs might confer protection against progression to illness. Brain-derived extracellular vesicles

[0410] A key challenge in the interpretation of data on candidate blood biomarkers is that it is difficult to know if alterations reflect dysfunction in the central nervous system or in peripheral tissues. The present study sought to address this issue by analysing miRNAs in brain-derived extracellular vesicles. This reduces the likelihood that the findings might be related to redox hub dysfunction in peripheral rather than in central tissues.

[0411] Biological pathways implicated in psychosis risk

[0412] To strengthen the predictive model, blood levels of proteins targeted by the investigated miRNAs, including VCAM, MIF, SERPINE1 , and TIMP1 , along with the redox marker GPx activity were analysed. VCAM, a potential target of miR-34a and miR-941 , is crucial in CNS disorder development, with miR-34a overexpression increasing VCAM-1 and ICAM-1 protein levels. SERPINE1 , encoding plasminogen activator inhibitor-1 , is dysregulated in various CNS disorders, including schizophrenia. TIMP1 regulates the extracellular matrix, MIF is involved in immune response and inflammation, and GPx and GSH are linked to oxidative stress, a factor in schizophrenia pathophysiology. Significant variations in SERPINE1 , and VCAM activity were observed in CHR-Trans individuals, distinguishing them from CHR-NoTr and HC, while TIMP1 levels remained relatively stable. Correlations between miRNAs and their altered targets revealed a distinct profile in CHR-Trans compared to CHR-NoTr and controls, highlighting the potential for integrating these markers for stratification and diagnosis. Investigating these four miRNAs and their putative targets provides valuable insights into psychosis pathophysiology and biomarker development.

[0413] The observed alterations in the levels of proteins involved in redox, immune, and ECM pathways (SERPINE1 , VCAM) in CHR-Trans individuals align with the "redox hub" hypothesis of psychosis. Dysregulation of these pathways may contribute to oxidative stress, neuroinflammation, and impaired neural circuitry, ultimately leading to the emergence of psychotic symptoms. The distinct correlations between exosomal miRNAs and their target proteins in CHR-Trans further support their potential as biomarkers for stratification and personalized interventions.

[0414] Clinical implications

[0415] A number of promising candidate interventions for the prevention of psychosis are under evaluation. The clinical utility of a preventive intervention could be enhanced if a biomarker was available to identify the minority of individuals within CHR samples that are most likely to progress to psychosis. This would allow for stratification into clinical trials of putative preventive treatment and selective offering of more intensive preventive interventions. The findings from the present study suggest that exosomal markers of redox hub dysfunction have the potential to serve as such a biomarker. Further research is needed to directly map the neurobiological and clinical mechanisms underlying the relationship between this biomarker signature and psychosis risk.

[0416] CONCLUSION

[0417] This study demonstrated the potential of exosomal miRNAs as biomarkers for predicting the onset of psychosis in CHR individuals. Moreover, the findings were externally validated in an independent dataset. These findings uncover a new potential therapeutic target for clinical intervention in neurological or psychiatric disorder, such as psychosis.

[0418] Because miRNAs in brain-derived extracellular vesicles were measured, it is likely that the findings reflected alterations in central, as opposed to peripheral miRNA concentrations. Another strength is that since most of the participants were naive to antipsychotic medication, the findings are not attributable to the potential confounds of previous treatment. The size of the CHR sample provided adequate power for analysis of the CHR-T v CHR-NT model, the 95% Cis were all above C=0.80 and retained high performance in external validation, indicating that the findings are statistically robust. Relatively simple models (regularised logistic regression) were used and a small number of biologically-informed predictors selected a priori to eliminate potential biases that can be introduced with poorly applied prediction modelling methodology and data-driven feature selection approaches. Discrimination performance was excellent in external validation for the CHR-T v CHR-NT model despite evident differences in case-mix. Discrimination was reduced (but still adequate) in the CHR-T v controls model, which may be due to smaller sample size or differences in higher prevalences of mental disorders in NAPLS-3 controls compared to EU-GEI.

[0419] Example 3

[0420] A diagnostic kit was designed as a companion diagnostic tool to carry out the method for classifying subjects at risk for neurological and psychiatric disorders. This diagnostic kit is a specific embodiment designed for the management of conditions such as psychosis, schizophrenia, mood disorders, Alzheimer’s disease, and Parkinson’s disease.

[0421] This diagnostic kit involves the non-invasive analysis of liquid biopsy samples to provide biological insights for personalized diagnosis and therapeutic strategies. It integrates two key aspects of the method of the invention for classifying a subject as neurological or psychiatric disorder subject:

[0422] • The quantification of brain-derived extracellular vesicles (EVs).

[0423] • The determination of expression levels of specific biomarkers. This diagnostic kit precisely quantifies a panel of biomarkers for extracellular vesicles such as CD9, CD63, CD81 , Hsp70, and ALIX, preferably CD9 and CD63, and a panel of biomarkers previously identified as critical for brain disorders consisting of biomarkers for brain-derived extracellular vesicles such as L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB, and TMEM1 , preferably as L1 CAM and NCAM1. It also measures miR-137 and / or miR-132 profiles and correlates their expression with disease risk and stage based on validated thresholds (see Figure 4).

Claims

57CLAIMS1. An ex vivo method for classifying a subject as neurological or psychiatric disorder subject, the method comprising the steps of: a) providing a biological sample obtained from a subject and comprising extracellular vesicles, including brain-derived extracellular vesicles; b) determining in the sample of (a) the fraction of brain-derived extracellular vesicles within the extracellular vesicles; c) determining in the sample of (a) the expression level of at least one biomarker selected from:(d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, SWOB and TIMPs, preferably selected from the group comprising miR-34a, miR-9 and miR-132,(c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81 , NCAM1 , GLAST1 and L1CAM, preferably miR-137,(c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941 , EAAT1 , EAAT2, EAAT3 and SHMTs, preferably selected from miR- 132 and miR-941 ,(c4) myelination pathway biomarkers selected from the group comprising miR-9, oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs) and Fyn Kinase, preferably miR-9(c5) excitatory-inhibitory balance pathway biomarkers selected from the group comprising miR-137, miR-138, PV, PNN, GAD65, VGAT, VGLUTs and PSD, preferably selected from miR-137 and miR-138; and d) classifying a subject as neurological or psychiatric disorder subject based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles determined in step (b) and the expression level determined in step (c).

2. The method of claim 1 , wherein the neurological or psychiatric disorder is selected from schizophrenia, psychosis, mood disorder, depression (such as major depressive disorder), bipolar disorder, Alzheimer's disease, Parkinson disorder, and cognitive impairment; preferably the neurological or psychiatric disorder is selected from schizophrenia, psychosis, mood disorder, Alzheimer's disease, Parkinson disorder and cognitive impairment.

3. The method of claim 1 or 2, wherein the neurological or psychiatric disorder is a psychiatric disorder.

584. The method of any one of claims 1 to 3, wherein the psychiatric disorder is psychosis or schizophrenia.

5. The method of claim 1 or 2, wherein the neurological or psychiatric disorder is a neurological disorder.

6. The method of claim 5, wherein the neurological disorder is Alzheimer's disease, Parkinson's disease and / or cognitive impairment.

7. The method of any one of claims 1 to 6, wherein the biological sample comprises bodily fluid, preferably wherein the bodily fluid is substantially free of cells, preferably wherein the bodily fluid is selected from plasma, cerebrospinal fluid, serum, saliva, nasal mucus, and urine, and / or preferably wherein no further isolation of extracellular vesicles was performed on the biological sample.

8. The method of any one of clams 1 to 7, wherein step (b) includes detecting and quantifying brain-derived extracellular vesicles with respect to total population of extracellular vesicles in the sample of (a), by antibody staining, in situ hybridization and / or microscopy.

9. The method of any one of claims 1 to 8, wherein step (b) includes determination of the expression of at least one biomarker for extracellular vesicles selected from the group comprising CD9, CD63, CD81, Hsp70 and ALIX, preferably selected from CD9 and CD63; and at least one biomarker for brain-derived extracellular vesicles selected from the group comprising L1CAM, NCAM1, GLAST1, Synaptophysin, C11b, SIOOB and TMEM119, preferably selected from L1 CAM and NCAM1.

10. The method of any one of claims 1 to 9, wherein expression levels of biomarkers are determined by an in vitro assay.

11. The method of claim 10, wherein the in vitro assay is selected from an immunoassay, an ELISA-based assay, an aptamer-based assay, an mRNA expression level assay, in situ hybridization assay, a proteomics-based assay, an MS-based assay, a PCR-based assay, a real time PCR-based assay, next generation sequencing, an electrochemistry-based assay, a lateral-flow assay, a nanobead-based assay, a microfluidics-based assay, and an oligonucleotide-templated reaction.

12. The method of any one of claims 1 to 11, wherein at least one biomarker determined in (c) is selected from(c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81, NCAM1, GLAST1 and L1CAM, and(c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941, EAAT1, EAAT2, EAAT3 and SHMTs, preferably wherein the at least one biomarker determined in (c) is selected from59(c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81 , NCAM1 , GLAST1 and L1CAM.

13. The method of any one of claims 1 to 12, wherein in step (c) the expression level of COX6A2 and miR- 137 is determined.

14. The method of claim 13, wherein the subject is classified as neurological or psychiatric disorder subject based on: expression level of COX6A2 being lower than a threshold value, wherein preferably the threshold value is 1.2 ng / ml, and expression level of miR-137 normalised with respect to the average expression level of miR-16, snRNA-U1 and snRNA-U6 is higher than a threshold value, wherein preferably the threshold value is 4.8 a.u.

15. The method of any one of claims 1 to 11, wherein at least one biomarker determined in (c) is selected from the group comprising miR-132, miR-941 , EAAT1 , EAAT2, EAAT3, and SHMTs.

16. The method of any one of claims 1 to 11, wherein at least one biomarker determined in (c) is selected from(d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, SWOB and TIMPs,17. The method of any one of claims 1 to 11, wherein at least one biomarker determined in (c) is selected from the group comprising miR-34a, miR-9, miR-132, miR-137 and miR-941.

18. The method of any one of claims 1 to 11 , wherein at least one biomarker determined in (c) is miR- 34a and / or miR-9.

19. The method of any one of claims 1 to 11 , wherein at least one biomarker determined in (c) is miR- 34a and / or miR-941.

20. The method of any one of claims 1 to 11 , wherein at least one biomarker determined in (c) is miR- 137 and / or miR-132 is determined.21 . The method of any one of claims 1 to 11 , wherein biomarkers determined in (c) are miR-34a, miR-9, miR- 132, miR-137 and miR-941.6022. The method of any one of claims 16 to 19 or 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on higher expression level of miR-34a, compared to the expression level of miR-34a in subjects not being affected with neurological or psychiatric disorder.

23. The method of any one of claims 15, 17, 19 or 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on lower expression level of miR-941 , compared to the expression level of miR-941 in subjects not being affected with neurological or psychiatric disorder.

24. The method of any one of claims 16 to 18 or 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on lower expression level of miR-9, compared to the expression level of miR-9 in subjects not being affected with neurological or psychiatric disorder.

25. The method of any one of claims 15 to 17 or 20 to 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on lower expression level of miR-132, compared to the expression level of miR-132 in subjects not being affected with neurological or psychiatric disorder.

26. The method of any one of claims 17, 20 or 21 , wherein the subject is classified as neurological or psychiatric disorder subject based on lower expression level of miR-137, compared to the expression level of miR-137 in subjects not being affected with neurological or psychiatric disorder.

27. The method of any one of claims 1 to 26, wherein the subject is classified as neurological or psychiatric disorder subject based on the fraction of brain-derived extracellular vesicles within the extracellular vesicles being less than 10%.

28. The method of any one of claims 1 to 27, wherein classifying a subject as neurological or psychiatric disorder subject is stratifying said subject into disease stages of said neurological or psychiatric disorder based on determination in (b) and (c), preferably wherein the subject is classified as low-risk or high-risk neurological or psychiatric disorder subject.

29. The method of claim 28, wherein in step (d) differentiation between Alzheimer's disease subject and cognitive impairment subject is made.6130. A biomarker kit comprising:(i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63;(ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM1 W, preferably selected from L1CAM and NCAM1 ; and(iii) reagents for determining the expression level of at least one biomarker selected from:(d) immunology and / or inflammation pathway biomarkers selected from the group comprising miR-34a, miR-9, miR-132, S100B and TIMPs,(c2) mitochondrial and redox impairment pathway biomarkers selected from the group comprising miR-137, COX6A2, ALIX, CD81, NCAM1, GLAST1 and L1CAM,(c3) N-methyl-D-aspartate receptor pathway biomarkers selected from the group comprising miR-132, miR-941 , EAAT1, EAAT2, EAAT3 and SHMTs,(c4) myelination pathway biomarkers selected from the group comprising miR-9, oligodendrocyte-specific proteins (OSPs), myelin basic proteins (MBPs) and Fyn Kinase,(c5) excitatory-inhibitory balance pathway biomarkers selected from the group comprising miR-137, miR-138, PV, PNN, GAD65, VGAT, VGLUTs and PSD.31 . The biomarker kit of claim 30, comprising:(i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63;(ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM119, preferably selected from L1CAM and NCAM1 ; and(iii) reagents for determining the expression level of at least one biomarker selected from biomarkers for immunology and / or inflammation pathway selected from the group comprising miR-34a, miR-9, miR-132, SWOB and TIMPs, preferably selected from the group comprising miR-34a, miR-9 and miR-132.

32. The biomarker kit of claim 30, comprising:(i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63; and(ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM1 W, preferably selected from L1 CAM and NCAM1 ; and(iii) reagents for determining the expression level of at least one biomarker selected from biomarkers for mitochondrial and redox impairment selected from the group comprising miR-137, COX6A2, ALIX, CD81 , NCAM1 , GLAST1 and L1 CAM, preferably selected from miR-137 and COX6A2..

33. The biomarker kit of claim 30, comprising:(i) reagents for determining the expression level of at least one biomarker selected from biomarkers for extracellular vesicles selected from the group comprising CD9, CD63, CD81 , Hsp70 and ALIX, preferably selected from CD9 and CD63;(ii) reagents for determining the expression level of at least one biomarker selected from biomarkers for brain-derived extracellular vesicles selected from the group comprising L1 CAM, NCAM1 , GLAST1 , Synaptophysin, C11 b, SWOB and TMEM1 W, preferably selected from L1CAM and NCAM1 ; and(iii) reagents for determining the expression level of at least one biomarker selected from the group comprising miR-34a, miR-9, miR-W2, miR-W7 and miR-941.

34. The biomarker kit of any one of claims 30 to 33, further comprising the reagents for isolating extracellular vesicles including brain-derived extracellular vesicles from a biological sample.

35. Use of the kit of any one of claims 30 to 34 in the method of any one of claims 1 to 29.

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